[ { "path": ".github/config/check_env.sh", "content": "#!/usr/bin/env bash\n\n# pwd\n# ls -al\n\npython --version\npython -c \"import numpy; print('numpy %s' % numpy.__version__)\"\npython -c \"import scipy; print('scipy %s' % scipy.__version__)\"\npython -c \"import pandas; print('pandas %s' % pandas.__version__)\"\npython -c \"import torch; print('pytorch %s' % torch.__version__)\"\npython -c \"import pymatgen; print('pymatgen %s' % pymatgen.__version__)\"\npython -c \"import rdkit; print('rdkit %s' % rdkit.__version__)\"\npython -c \"from rdkit import Chem; print(Chem)\"\n" }, { "path": ".github/config/linux_win_env.yml", "content": "name: test\ndependencies:\n - pandas>=1.1.3\n - seaborn\n - numpy\n - scipy\n - requests\n - rdkit=2020.09\n - scikit-learn\n - scipy\n - pytorch >=1.7.0,<2.0.0\n - cpuonly\n - pytest\n - pytest-cov\n - pymatgen>=2020.10.9\n - ruamel.yaml\n - tqdm\n - mordred\n - pip\n - pip:\n - OpenNMT-py==1.2\n - Deprecated\n" }, { "path": ".github/config/macos_env.yml", "content": "name: test\ndependencies:\n - pandas>=1.1.3\n - seaborn\n - numpy\n - scipy\n - requests\n - rdkit=2020.09\n - scikit-learn\n - scipy\n - pytorch >=1.7.0,<2.0.0\n - pytest\n - pytest-cov\n - pymatgen>=2020.10.9\n - ruamel.yaml\n - tqdm\n - mordred\n - pip\n - pip:\n - OpenNMT-py==1.2\n - Deprecated\n" }, { "path": ".github/config/matplotlibrc_agg", "content": "backend: Agg\n" }, { "path": ".github/config/matplotlibrc_qtagg", "content": "backend: Qt5Agg\n" }, { "path": ".github/fetch_test.txt", "content": "Test xenonpy.utils.Loader._fetch_data" }, { "path": ".github/workflows/deploy_pypi.yml", "content": "name: Deploy PiPy\n\non:\n push:\n tags:\n - v*\n\nenv:\n MPLBACKEN: \"Agg\"\n\njobs:\n test_ubuntu:\n runs-on: ${{ matrix.os }}\n strategy:\n fail-fast: false\n max-parallel: 4\n matrix:\n python-version: [3.7]\n os: [\"ubuntu-20.04\"]\n\n steps:\n - uses: actions/checkout@v2\n\n - name: Install Python ${{ matrix.python-version }} on ${{ matrix.os }}\n uses: conda-incubator/setup-miniconda@v2\n with:\n auto-update-conda: true\n auto-activate-base: false\n activate-environment: test\n mamba-version: \"*\"\n channels: pytorch,conda-forge,defaults\n environment-file: .github/config/linux_win_env.yml\n python-version: ${{ matrix.python-version }}\n\n - name: Check conda env\n shell: bash -l {0}\n run: .github/config/check_env.sh\n\n - name: Build a binary wheel and a source tarball\n shell: bash -l {0}\n run: |\n python setup.py bdist_wheel\n\n - name: Publish distribution 📦 to PyPI\n if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags')\n uses: pypa/gh-action-pypi-publish@master\n with:\n user: __token__\n password: ${{ secrets.PYPI_API_TOKEN }}\n" }, { "path": ".github/workflows/mac.yml", "content": "name: MacOS\n\non:\n push:\n branches:\n - master\n paths-ignore:\n - \"conda_env/**\"\n - \"devtools/**\"\n - \"docs/**\"\n - \"samples/**\"\n - \"licenses/**\"\n - \"hooks/**\"\n - \".**\"\n - \"!.github/**\"\n - \"**.md\"\n - \"**.yml\"\n - \"**.txt\"\n - \"**.in\"\n - \"**.cfg\"\n pull_request:\n branches:\n - master\n paths-ignore:\n - \"conda_env/**\"\n - \"devtools/**\"\n - \"docs/**\"\n - \"samples/**\"\n - \"licenses/**\"\n - \"hooks/**\"\n - \".**\"\n - \"!.github/**\"\n - \"**.md\"\n - \"**.yml\"\n - \"**.txt\"\n - \"**.in\"\n - \"**.cfg\"\n\nenv:\n MPLBACKEN: \"Agg\"\n\njobs:\n test_macos:\n runs-on: ${{ matrix.os }}\n strategy:\n fail-fast: false\n max-parallel: 6\n matrix:\n python-version: [3.7, 3.8, 3.9]\n os: [\"macos-11.0\", \"macos-10.15\"]\n\n steps:\n - uses: actions/checkout@v2\n with:\n ref: ${{github.event.pull_request.head.ref}}\n repository: ${{github.event.pull_request.head.repo.full_name}}\n\n - name: Install Python ${{ matrix.python-version }} on ${{ matrix.os }}\n uses: conda-incubator/setup-miniconda@v2\n with:\n auto-update-conda: true\n auto-activate-base: false\n activate-environment: test\n mamba-version: \"*\"\n channels: pytorch,conda-forge,defaults\n environment-file: .github/config/macos_env.yml\n python-version: ${{ matrix.python-version }}\n\n - name: Check conda env\n shell: bash -l {0}\n run: .github/config/check_env.sh\n\n - name: Install XenonPy\n shell: bash -l {0}\n run: |\n pip install .\n\n - name: Test XenonPy\n shell: bash -l {0}\n env:\n api_key: ${{ secrets.api_key }}\n run: |\n pytest --cov=./ --cov-report=xml tests\n\n - name: Upload coverage to Codecov\n uses: codecov/codecov-action@v1\n env:\n OS: ${{ matrix.os }}\n PYTHON: ${{ matrix.python-version }}\n with:\n env_vars: OS,PYTHON\n fail_ci_if_error: false\n" }, { "path": ".github/workflows/ubuntu.yml", "content": "name: Ubuntu\n\non:\n push:\n branches:\n - master\n paths-ignore:\n - \"conda_env/**\"\n - \"devtools/**\"\n - \"docs/**\"\n - \"samples/**\"\n - \"licenses/**\"\n - \"hooks/**\"\n - \".**\"\n - \"!.github/**\"\n - \"**.md\"\n - \"**.yml\"\n - \"**.txt\"\n - \"**.in\"\n - \"**.cfg\"\n pull_request:\n branches:\n - master\n paths-ignore:\n - \"conda_env/**\"\n - \"devtools/**\"\n - \"docs/**\"\n - \"samples/**\"\n - \"licenses/**\"\n - \"hooks/**\"\n - \".**\"\n - \"!.github/**\"\n - \"**.md\"\n - \"**.yml\"\n - \"**.txt\"\n - \"**.in\"\n - \"**.cfg\"\n\nenv:\n MPLBACKEN: \"Agg\"\n\njobs:\n test_ubuntu:\n runs-on: ${{ matrix.os }}\n strategy:\n fail-fast: false\n max-parallel: 6\n matrix:\n python-version: [3.7, 3.8, 3.9]\n os: [\"ubuntu-18.04\", \"ubuntu-latest\"]\n\n steps:\n - uses: actions/checkout@v2\n with:\n ref: ${{github.event.pull_request.head.ref}}\n repository: ${{github.event.pull_request.head.repo.full_name}}\n\n - name: Install Python ${{ matrix.python-version }} on ${{ matrix.os }}\n uses: conda-incubator/setup-miniconda@v2\n with:\n auto-update-conda: true\n auto-activate-base: false\n activate-environment: test\n mamba-version: \"*\"\n channels: pytorch,conda-forge,defaults\n environment-file: .github/config/linux_win_env.yml\n python-version: ${{ matrix.python-version }}\n\n - name: Check conda env\n shell: bash -l {0}\n run: .github/config/check_env.sh\n\n - name: Install XenonPy\n shell: bash -l {0}\n run: |\n pip install .\n\n - name: Test XenonPy\n shell: bash -l {0}\n env:\n api_key: ${{ secrets.api_key }}\n run: |\n pytest --cov=./ --cov-report=xml tests\n\n - name: Upload coverage to Codecov\n uses: codecov/codecov-action@v1\n env:\n OS: ${{ matrix.os }}\n PYTHON: ${{ matrix.python-version }}\n with:\n env_vars: OS,PYTHON\n fail_ci_if_error: false\n" }, { "path": ".github/workflows/win.yml", "content": "name: Windows\n\non:\n push:\n branches:\n - master\n paths-ignore:\n - \"conda_env/**\"\n - \"devtools/**\"\n - \"docs/**\"\n - \"samples/**\"\n - \"licenses/**\"\n - \"hooks/**\"\n - \".**\"\n - \"!.github/**\"\n - \"**.md\"\n - \"**.yml\"\n - \"**.txt\"\n - \"**.in\"\n - \"**.cfg\"\n pull_request:\n branches:\n - master\n paths-ignore:\n - \"conda_env/**\"\n - \"devtools/**\"\n - \"docs/**\"\n - \"samples/**\"\n - \"licenses/**\"\n - \"hooks/**\"\n - \".**\"\n - \"!.github/**\"\n - \"**.md\"\n - \"**.yml\"\n - \"**.txt\"\n - \"**.in\"\n - \"**.cfg\"\n\nenv:\n MPLBACKEN: \"Agg\"\n\njobs:\n test_win:\n runs-on: ${{ matrix.os }}\n strategy:\n fail-fast: false\n max-parallel: 3\n matrix:\n python-version: [3.7, 3.8, 3.9]\n os: [\"windows-latest\"]\n\n steps:\n - uses: actions/checkout@v2\n with:\n ref: ${{github.event.pull_request.head.ref}}\n repository: ${{github.event.pull_request.head.repo.full_name}}\n\n - name: Install Python ${{ matrix.python-version }} on ${{ matrix.os }}\n uses: conda-incubator/setup-miniconda@v2\n with:\n auto-update-conda: true\n auto-activate-base: false\n activate-environment: test\n mamba-version: \"*\"\n channels: pytorch,conda-forge,defaults\n environment-file: .github/config/linux_win_env.yml\n python-version: ${{ matrix.python-version }}\n\n - name: Check conda env\n shell: bash -l {0}\n run: .github/config/check_env.sh\n\n - name: Install XenonPy\n shell: bash -l {0}\n run: |\n pip install .\n\n - name: Test XenonPy\n shell: bash -l {0}\n env:\n api_key: ${{ secrets.api_key }}\n run: |\n pytest --cov=./ --cov-report=xml tests\n\n - name: Upload coverage to Codecov\n uses: codecov/codecov-action@v1\n env:\n OS: ${{ matrix.os }}\n PYTHON: ${{ matrix.python-version }}\n with:\n env_vars: OS,PYTHON\n fail_ci_if_error: false\n" }, { "path": ".gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\n_build/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n.hypothesis/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# pyenv\n.python-version\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n\n# custom\n# *.csv\ntags\n.idea/\n.vscode/\npyinstrument\n\n.pytest_cache/\n/playground/\n.DS_Store\n" }, { "path": ".pylintrc", "content": "# Enable the message, report, category or checker with the given id(s). You can\n# either give multiple identifier separated by comma (,) or put this option\n# multiple time.\n#enable=\n\n# Disable the message, report, category or checker with the given id(s). You\n# can either give multiple identifier separated by comma (,) or put this option\n# multiple time (only on the command line, not in the configuration file where\n# it should appear only once).\n#disable=\n\n[DESIGN]\n# custom\nmax-attributes=15\nmax-line-length=120" }, { "path": ".readthedocs.yml", "content": "# Required\nversion: 2\n\n# Build documentation in the docs/ directory with Sphinx\nsphinx:\n configuration: docs/source/conf.py\n\nconda:\n environment: conda_env/devtools/rtd_env.yml\n\nbuild:\n os: \"ubuntu-20.04\"\n tools:\n python: \"mambaforge-4.10\"\n" }, { "path": ".style.yapf", "content": "[style]\nbased_on_style = google\n\n# The column limit.\ncolumn_limit=120\n" }, { "path": "Dockerfile", "content": "FROM yoshidalab/base:cuda11\n\nARG key\nENV api_key=$key\n\n# install xenonpy locally\nWORKDIR /opt/xenonpy\nCOPY . .\nRUN sudo chown -R user:user /opt && find . | grep -E \"(__pycache__|\\.pyc|\\.pyo$)\" | xargs rm -rf && \\\n pip install --user -v . && pytest tests -v && export api_key=\"\"\n\nEXPOSE 8888\n\nWORKDIR /workspace\nCMD [ \"jupyter\" , \"lab\", \"--ip=0.0.0.0\", \"--no-browser\", \"--port=8888\", \"--allow-root\"]\n" }, { "path": "LICENSE", "content": "Copyright (c) 2021 TsumiNa. All rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are\nmet:\n\n * Redistributions of source code must retain the above copyright\nnotice, this list of conditions and the following disclaimer.\n * Redistributions in binary form must reproduce the above\ncopyright notice, this list of conditions and the following disclaimer\nin the documentation and/or other materials provided with the\ndistribution.\n * Neither the name of Google Inc. nor the names of its\ncontributors may be used to endorse or promote products derived from\nthis software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n\"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\nLIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR\nA PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT\nOWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\nSPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT\nLIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,\nDATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY\nTHEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE." }, { "path": "MANIFEST.in", "content": "include *.rst LICENSE.rst docs/source/changes.rst requirement*.txt\nrecursive-include xenonpy *.py *.json *.yml *.txt\nrecursive-exclude tests/*.*\nrecursive-include licences *\n" }, { "path": "Makefile", "content": "export SHELL := /bin/bash\n\ndoctest:\n\tpytest --doctest-modules xenonpy\n\nunittest:\n\tpytest tests\n\nlint:\n\tpylint xenonpy\n" }, { "path": "README.md", "content": "

\n \"xenonpy\"\n

\n\n# NOTES\n\n> **To all those who have purchased the book [マテリアルズインフォマティクス](https://www.kyoritsu-pub.co.jp/book/b10013510.html) published by [KYORITSU SHUPPAN](https://www.kyoritsu-pub.co.jp/): The link to the exercises has changed to https://github.com/yoshida-lab/XenonPy/tree/master/mi_book. Please follow the new link to access all these exercises.**\n\n> **Our XenonPy.MDL is under indefinite technical maintenance due to some security issues found during a server upgrade. Our current plan is to completely re-structure the model library server, but the completion time is unclear. In the mean time, if you would like to get access to the pretrained models, please contact us directly with your purpose of using the models and your affiliation. We will try to provide necessary aid to access part of the model library based on specific needs. Sorry for all the inconvenience. We will make further announcement here when a more concrete recovery schedule is available.**\n\n\n**We apologize for the inconvenience** 🥺🙏🙇\n\n# XenonPy project\n\n[![MacOS](https://github.com/yoshida-lab/XenonPy/workflows/MacOS/badge.svg)](https://github.com/yoshida-lab/XenonPy/actions?query=workflow%3AMacOS)\n[![Windows](https://github.com/yoshida-lab/XenonPy/workflows/Windows/badge.svg)](https://github.com/yoshida-lab/XenonPy/actions?query=workflow%3AWindows)\n[![Ubuntu](https://github.com/yoshida-lab/XenonPy/workflows/Ubuntu/badge.svg)](https://github.com/yoshida-lab/XenonPy/actions?query=workflow%3AUbuntu)\n[![Documentation Status](https://readthedocs.org/projects/xenonpy/badge/?version=latest)](https://xenonpy.readthedocs.io/en/latest/?badge=latest)\n[![codecov](https://codecov.io/gh/yoshida-lab/XenonPy/branch/master/graph/badge.svg)](https://codecov.io/gh/yoshida-lab/XenonPy)\n[![Version](https://img.shields.io/github/tag/yoshida-lab/XenonPy.svg?maxAge=360)](https://github.com/yoshida-lab/XenonPy/releases/latest)\n[![Python Versions](https://img.shields.io/pypi/pyversions/xenonpy.svg)](https://pypi.org/project/xenonpy/)\n[![Downloads](https://pepy.tech/badge/xenonpy)](https://pepy.tech/project/xenonpy)\n![PyPI - Downloads](https://img.shields.io/pypi/dm/xenonpy.svg?label=PiPy%20downloads)\n\n**XenonPy** is a Python library that implements a comprehensive set of machine learning tools\nfor materials informatics. Its functionalities partially depend on PyTorch and R.\nThe current release provides some limited modules:\n\n- Interface to public materials database\n- Library of materials descriptors (compositional/structural descriptors)\n- Pre-trained model library **XenonPy.MDL** (v0.1.0.beta, 2019/8/7: more than 140,000 models (include private models) in 35 properties of small molecules, polymers, and inorganic compounds)\n- Machine learning tools.\n- Transfer learning using the pre-trained models in XenonPy.MDL\n\nXenonPy inspired by matminer: https://hackingmaterials.github.io/matminer/.\n\nXenonPy is a open source project https://github.com/yoshida-lab/XenonPy.\n\nSee our documents for details: http://xenonpy.readthedocs.io\n\n## Publications\n\n1. H. Ikebata, K. Hongo, T. Isomura, R. Maezono, and R. Yoshida, “Bayesian molecular design with a chemical language model,” J Comput Aided Mol Des, vol. 31, no. 4, pp. 379–391, Apr. 2017, doi: 10/ggpx8b.\n2. S. Wu et al., “Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm,” npj Computational Materials, vol. 5, no. 1, pp. 66–66, Dec. 2019, doi: 10.1038/s41524-019-0203-2.\n3. S. Wu, G. Lambard, C. Liu, H. Yamada, and R. Yoshida, “iQSPR in XenonPy: A Bayesian Molecular Design Algorithm,” Mol. Inform., vol. 39, no. 1–2, p. 1900107, Jan. 2020, doi: 10.1002/minf.201900107.\n4. H. Yamada et al., “Predicting Materials Properties with Little Data Using Shotgun Transfer Learning,” ACS Cent. Sci., vol. 5, no. 10, pp. 1717–1730, Oct. 2019, doi: 10.1021/acscentsci.9b00804.\n5. S. Ju et al., “Exploring diamondlike lattice thermal conductivity crystals via feature-based transfer learning,” Phys. Rev. Mater., vol. 5, no. 5, p. 053801, May 2021, doi: 10.1103/physrevmaterials.5.053801.\n6. C. Liu et al., “Machine Learning to Predict Quasicrystals from Chemical Compositions,” Adv. Mater., vol. 33, no. 36, p. 2102507, Sep. 2021, doi: 10.1002/adma.202102507.\n\n## XenonPy images (deprecated)\n\n> Docker has introduced a new [Subscription Service Agreement](https://www.docker.com/legal/docker-subscription-service-agreement) which requires organizations with more than 250 employees or more than $10 million in revenue to buy a paid subscription.\n> Since the fact that Docker company has been changed their policy to business first mode, we decided to drop the prebuilt Docker images service.\n\n[XenonPy base images](https://hub.docker.com/repository/docker/yoshidalab/base) packed a lot of useful packages for materials informatics using.\nThe following table list some core packages in XenonPy images.\n\n| Package | Version |\n| -------------- | --------- |\n| `PyTorch` | 1.7.1 |\n| `tensorly` | 0.5.0 |\n| `pymatgen` | 2021.2.16 |\n| `matminer` | 0.6.2 |\n| `mordred` | 1.2.0 |\n| `scipy` | 1.6.0 |\n| `scikit-learn` | 0.24.1 |\n| `xgboost` | 1.3.0 |\n| `ngboost` | 0.3.7 |\n| `fastcluster` | 1.1.26 |\n| `pandas` | 1.2.2 |\n| `rdkit` | 2020.09.4 |\n| `jupyter` | 1.0.0 |\n| `seaborn` | 0.11.1 |\n| `matplotlib` | 3.3.4 |\n| `OpenNMT-py` | 1.2.0 |\n| `Optuna` | 2.3.0 |\n| `plotly` | 4.11.0 |\n| `ipympl` | 0.5.8 |\n\n## Requirements\n\nIn order to use this image you must have Docker Engine installed. Instructions\nfor setting up Docker Engine are\n[available on the Docker website](https://docs.docker.com/engine/installation/).\n\n### CUDA requirements\n\nIf you have a CUDA-compatible NVIDIA graphics card, you can use a CUDA-enabled\nversion of the PyTorch image to enable hardware acceleration. This **only** can be\nused in Ubuntu Linux.\n\nFirstly, ensure that you install the appropriate NVIDIA drivers and libraries.\nIf you are running Ubuntu, you can install proprietary NVIDIA drivers\n[from the PPA](https://launchpad.net/~graphics-drivers/+archive/ubuntu/ppa)\nand CUDA [from the NVIDIA website](https://developer.nvidia.com/cuda-downloads).\n\nYou will also need to install `nvidia-docker2` to enable GPU device access\nwithin Docker containers. This can be found at\n[NVIDIA/nvidia-docker](https://github.com/NVIDIA/nvidia-docker).\n\n## Usage\n\nPre-built xenonpy images are available on Docker Hub under the name\n[yoshidalab/xenonpy](https://hub.docker.com/r/yoshidalab/xenonpy/). For example,\nyou can pull the CUDA 10.1 version with:\n\n```bash\ndocker pull yoshidalab/xenonpy:cuda10\n```\n\nThe table below lists software versions for each of the currently supported\nDocker image tags .\n\n| Image tag | CUDA | PyTorch |\n| --------- | ---- | ------- |\n| `latest` | 11.0 | 1.7.1 |\n| `cpu` | None | 1.7.1 |\n| `cuda11` | 11.0 | 1.7.1 |\n| `cuda10` | 10.2 | 1.7.1 |\n| `cuda9` | 9.2 | 1.7.1 |\n\n### Running XenonPy\n\nIt is possible to run XenonPy inside a container.\nUsing xenonpy with jupyter is very easy, you could run it with\nthe following command:\n\n```sh\ndocker run --rm -it \\\n --runtime=nvidia \\\n --ipc=host \\\n --publish=\"8888:8888\" \\\n --volume=$HOME/.xenonpy:/home/user/.xenonpy \\\n --volume=:/workspace \\\n -e NVIDIA_VISIBLE_DEVICES=0 \\\n yoshidalab/xenonpy\n```\n\nHere's a description of the Docker command-line options shown above:\n\n- `--runtime=nvidia`: Required if using CUDA, optional otherwise. Passes the\n graphics card from the host to the container. **Optional, based on your usage**.\n- `--ipc=host`: Required if using multiprocessing, as explained at\n **Optional**\n- `--publish=\"8888:8888\"`: Publish container's port 8888 to the host. **Needed**\n- `--volume=$Home/.xenonpy:/home/user/.xenonpy`: Mounts\n the XenonPy root directory into the container. **Optional, but highly recommended**.\n- `--volume=:/workspace`: Mounts\n the your working directory into the container. **Optional, but highly recommended**.\n- `-e NVIDIA_VISIBLE_DEVICES=0`: Sets an environment variable to restrict which\n graphics cards are seen by programs running inside the container. Set to `all`\n to enable all cards. Optional, defaults to all.\n\nYou may wish to consider using [Docker Compose](https://docs.docker.com/compose/)\nto make running containers with many options easier. At the time of writing,\nonly version 2.3 of Docker Compose configuration files supports the `runtime`\noption.\n\n## Copyright and license\n\n©Copyright 2021 The XenonPy project, all rights reserved.\nReleased under the `BSD-3 license`.\n" }, { "path": "conda_env/cpu.yml", "content": "channels:\n - pytorch\n - conda-forge\ndependencies:\n # Number\n - numpy\n - scipy\n - pandas\n\n # Mol/Crystall\n - pymatgen\n - ase\n - rdkit\n - matminer\n - matid\n\n # Plotting\n - seaborn\n - matplotlib\n - plotly\n\n # Machine larning\n - scikit-learn\n - imbalanced-learn\n - optuna\n - umap-learn\n - pytorch >=2.0.0,<3.0.0\n - cpuonly\n - torchvision\n - torchaudio\n - transformers\n - diffusers\n\n # Others\n - nodejs\n - jupyterlab\n - jupyterlab-lsp\n - ipywidgets\n - python-lsp-server\n - shapely\n - requests\n - ruamel.yaml\n - joblib\n - tqdm\n - Deprecated\n - fsspec\n - emmet-core\n - peewee\n - pip\n - pip:\n - xenonpy\n" }, { "path": "conda_env/cuda118.yml", "content": "channels:\n - pytorch\n - nvidia\n - conda-forge\ndependencies:\n # Number\n - numpy\n - scipy\n - pandas\n\n # Mol/Crystall\n - pymatgen\n - ase\n - rdkit\n - matminer\n - matid\n\n # Plotting\n - seaborn\n - matplotlib\n - plotly\n\n # Machine larning\n - scikit-learn\n - imbalanced-learn\n - optuna\n - umap-learn\n - pytorch >=2.0.0,<3.0.0\n - pytorch-cuda=11.8\n - torchvision\n - torchaudio\n - transformers\n - diffusers\n\n # Others\n - nodejs\n - jupyterlab\n - jupyterlab-lsp\n - ipywidgets\n - python-lsp-server\n - shapely\n - requests\n - ruamel.yaml\n - joblib\n - tqdm\n - Deprecated\n - fsspec\n - emmet-core\n - peewee\n - pip\n - pip:\n - xenonpy\n" }, { "path": "conda_env/cuda121.yml", "content": "channels:\n - pytorch\n - nvidia\n - conda-forge\ndependencies:\n # Number\n - numpy\n - scipy\n - pandas\n\n # Mol/Crystall\n - pymatgen\n - ase\n - rdkit\n - matminer\n - matid\n\n # Plotting\n - seaborn\n - matplotlib\n - plotly\n\n # Machine larning\n - scikit-learn\n - imbalanced-learn\n - optuna\n - umap-learn\n - pytorch >=2.0.0,<3.0.0\n - pytorch-cuda=12.1\n - torchvision\n - torchaudio\n - transformers\n - diffusers\n\n # Others\n - nodejs\n - jupyterlab\n - jupyterlab-lsp\n - ipywidgets\n - python-lsp-server\n - shapely\n - requests\n - ruamel.yaml\n - joblib\n - tqdm\n - Deprecated\n - fsspec\n - emmet-core\n - peewee\n - pip\n - pip:\n - xenonpy\n" }, { "path": "conda_env/devtools/extra_env.yml", "content": "channels:\n - conda-forge\ndependencies:\n - yapf\n - pytest\n - pytest-cov\n - pylint\n - sphinx\n - sphinx_rtd_theme\n - nbconvert!=5.4\n - nbsphinx\n - sphinx-autodoc-typehints\n - pip:\n - Deprecated\n - OpenNMT-py==1.2\n" }, { "path": "conda_env/devtools/rtd_env.yml", "content": "channels:\n - pytorch\n - conda-forge\ndependencies:\n - python=3.8\n - rdkit=2020.09\n - pytorch >=1.7.0,<2.0.0\n - cpuonly\n - pandas>=1.1.3\n - scikit-learn>=0.23.0\n - scipy>=1.5.2\n - pymatgen>=2020.10.9\n - sphinx-autodoc-typehints\n - tqdm\n - numpy\n - seaborn\n - mordred\n - ruamel.yaml\n - nbsphinx\n - joblib\n - requests\n - pip\n - pip:\n - Deprecated\n # - --requirement rtd_requirements.txt\n" }, { "path": "conda_env/osx.yml", "content": "channels:\n - pytorch\n - conda-forge\ndependencies:\n # Number\n - numpy\n - scipy\n - pandas\n\n # Mol/Crystall\n - pymatgen\n - ase\n - rdkit\n - matminer\n # - mordred\n - matid\n\n # Plotting\n - seaborn\n - matplotlib\n - plotly\n\n # Machine larning\n - scikit-learn\n - imbalanced-learn\n - optuna\n - umap-learn\n - pytorch::pytorch >=2.0.0,<3.0.0\n - torchvision\n - torchaudio\n - transformers\n - diffusers\n\n # Others\n - nodejs\n - jupyterlab\n - jupyterlab-lsp\n - ipywidgets\n - python-lsp-server\n - shapely\n - requests\n - ruamel.yaml\n - joblib\n - tqdm\n - Deprecated\n - fsspec\n - emmet-core\n - peewee\n - pip\n - pip:\n - xenonpy\n" }, { "path": "docker/cpu/Dockerfile", "content": "FROM yoshidalab/base:cpu\n\n# install xenonpy locally\nWORKDIR /opt/xenonpy\nCOPY . .\nRUN sudo chown -R user:user /opt && find . | grep -E \"(__pycache__|\\.pyc|\\.pyo$)\" | xargs rm -rf && \\\n pip install --user -v .\n\nEXPOSE 8888\n\nWORKDIR /workspace\nCMD [ \"jupyter\" , \"lab\", \"--ip=0.0.0.0\", \"--no-browser\", \"--port=8888\", \"--allow-root\"]" }, { "path": "docker/cuda10/Dockerfile", "content": "FROM yoshidalab/base:cuda10\n\n# install xenonpy locally\nWORKDIR /opt/xenonpy\nCOPY . .\nRUN sudo chown -R user:user /opt && find . | grep -E \"(__pycache__|\\.pyc|\\.pyo$)\" | xargs rm -rf && \\\n pip install --user -v .\n\nEXPOSE 8888\n\nWORKDIR /workspace\nCMD [ \"jupyter\" , \"lab\", \"--ip=0.0.0.0\", \"--no-browser\", \"--port=8888\", \"--allow-root\"]" }, { "path": "docker/cuda11/Dockerfile", "content": "FROM yoshidalab/base:cuda11\n\n# install xenonpy locally\nWORKDIR /opt/xenonpy\nCOPY . .\nRUN sudo chown -R user:user /opt && find . | grep -E \"(__pycache__|\\.pyc|\\.pyo$)\" | xargs rm -rf && \\\n pip install --user -v .\n\nEXPOSE 8888\n\nWORKDIR /workspace\nCMD [ \"jupyter\" , \"lab\", \"--ip=0.0.0.0\", \"--no-browser\", \"--port=8888\", \"--allow-root\"]" }, { "path": "docker/cuda9/Dockerfile", "content": "FROM yoshidalab/base:cuda9\n\n# install xenonpy locally\nWORKDIR /opt/xenonpy\nCOPY . .\nRUN sudo chown -R user:user /opt && find . | grep -E \"(__pycache__|\\.pyc|\\.pyo$)\" | xargs rm -rf && \\\n pip install --user -v .\n\nEXPOSE 8888\n\nWORKDIR /workspace\nCMD [ \"jupyter\" , \"lab\", \"--ip=0.0.0.0\", \"--no-browser\", \"--port=8888\", \"--allow-root\"]" }, { "path": "docs/Makefile", "content": "# Minimal makefile for Sphinx documentation\n#\n\n# You can set these variables from the command line.\nSPHINXOPTS =\nSPHINXBUILD = sphinx-build\nSPHINXPROJ = XenonPy\nSOURCEDIR = source\nBUILDDIR = build\n\n# Put it first so that \"make\" without argument is like \"make help\".\nhelp:\n\t@$(SPHINXBUILD) -M help \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n\n.PHONY: help Makefile\n\n# Catch-all target: route all unknown targets to Sphinx using the new\n# \"make mode\" option. $(O) is meant as a shortcut for $(SPHINXOPTS).\n%: Makefile\n\t@$(SPHINXBUILD) -M $@ \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)" }, { "path": "docs/make.bat", "content": "@ECHO OFF\n\npushd %~dp0\n\nREM Command file for Sphinx documentation\n\nif \"%SPHINXBUILD%\" == \"\" (\n\tset SPHINXBUILD=sphinx-build\n)\nset SOURCEDIR=source\nset BUILDDIR=build\nset SPHINXPROJ=XenonPy\n\nif \"%1\" == \"\" goto help\n\n%SPHINXBUILD% >NUL 2>NUL\nif errorlevel 9009 (\n\techo.\n\techo.The 'sphinx-build' command was not found. Make sure you have Sphinx\n\techo.installed, then set the SPHINXBUILD environment variable to point\n\techo.to the full path of the 'sphinx-build' executable. Alternatively you\n\techo.may add the Sphinx directory to PATH.\n\techo.\n\techo.If you don't have Sphinx installed, grab it from\n\techo.http://sphinx-doc.org/\n\texit /b 1\n)\n\n%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%\ngoto end\n\n:help\n%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%\n\n:end\npopd\n" }, { "path": "docs/source/_static/css/container.css", "content": "#container {\n /* min-height: 100vh; */\n display: flex;\n}\n.left {\n width: 30vw;\n /* flex: 1; */\n padding: .5rem;\n overflow: auto;\n}\n.right {\n width: 70vw;\n /* flex: 1; */\n padding: 0.5rem;\n overflow: auto;\n}" }, { "path": "docs/source/_static/placehoder", "content": "" }, { "path": "docs/source/_templates/layout.html", "content": "{% extends \"!layout.html\" %}\n{% set css_files = css_files + [ \"_static/css/hatnotes.css\" ] %}" }, { "path": "docs/source/_templates/placehoder", "content": "" }, { "path": "docs/source/api.rst", "content": "------------------\nIndices and tables\n------------------\n* :ref:`genindex`\n* :ref:`modindex`\n* :ref:`search`\n" }, { "path": "docs/source/books.rst", "content": "========================================================================\nExercises in \"Materials Informatics\" (KYORITSU SHUPPAN, in Japanese)\n========================================================================\n\n.. raw:: html\n\n
\n

Note

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\n

\n
\n \n \"マテリアルズインフォマティクス\"\n \n
\n
\n

共立出版発行の書籍「マテリアルズインフォマティクス」をご購入いただいた皆様へ:

\n

演習問題のリンクが https://github.com/yoshida-lab/XenonPy/tree/master/mi_book に変更になりました。\n 新しいリンクから練習問題にアクセスしてください。

\n

ご不便をおかけして申し訳ありません。

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\n
\n
\n\n\nマテリアルズインフォマティクス: https://www.kyoritsu-pub.co.jp/book/b10013510.html\n\n * 著者: 伊藤 聡 編・ 吉田 亮 著・ 劉 暢 著・ Stephen Wu 著・ 野口 瑶 著・ 山田 寛尚 著・ 赤木 和人 著・ 大林 一平 著・ 山下 智樹 著 \n * 出版社: 共立出版\n * 発売日: 2022/08/22\n * ISBN: 9784320072022\n\n\n.. _マテリアルズインフォマティクス: https://www.kyoritsu-pub.co.jp/book/b10013510.html\n.. _KYORITSU SHUPPAN: https://www.kyoritsu-pub.co.jp/" }, { "path": "docs/source/changes.rst", "content": ".. role:: raw-html(raw)\n :format: html\n\n=======\nChanges\n=======\n\nv0.6.2 ~ v0.6.3\n===============\n**Bug fix**\n\n* some minor fixes.\n\nv0.6.1\n======\n\n**Breaking change**\n\n* ``GaussianNLLLoss`` has been removed from ``xenonpy.model.training.loss``. ( `#249`_ )\n\n.. _#249: https://github.com/yoshida-lab/XenonPy/pull/249\n\nv0.6.0\n======\n\n**Bug fix**\n\n* Fix a bug in the ``Scaler`` class. ( `#243`_ )\n\n**New features**\n\n* Add ``average`` option to the ``classification_metrics`` function. Now users can decide how to calculate scores for multilabel tasks. ( `#240`_ )\n* Add ``only_best_states`` option to the ``Persist`` class. If ``True``, ``Persist`` will only save the best state to reduce the storage space. ( `#233`_ )\n* Add ``warming_up`` option to the ``Validator`` class. ( `#238`_ )\n\n.. _#243: https://github.com/yoshida-lab/XenonPy/pull/243\n.. _#240: https://github.com/yoshida-lab/XenonPy/pull/240\n.. _#233: https://github.com/yoshida-lab/XenonPy/pull/233\n.. _#238: https://github.com/yoshida-lab/XenonPy/pull/238\n\nv0.5.2\n======\n\n**Bug fix**\n\n* some minor fixes.\n\nv0.5.1\n======\n\n**Bug fix**\n\n* update the pip install dependencies in ``requirements.txt``. ( `#226`_ )\n\n**Enhance**\n\n* Add ``rdkit v2020.09`` support for fingerprint descriptors. ( `#228`_ )\n* Add return probability support for ``model.extension.TensorConverter``. ( `#229`_ )\n\n.. _#226: https://github.com/yoshida-lab/XenonPy/pull/226\n.. _#228: https://github.com/yoshida-lab/XenonPy/pull/228\n.. _#229: https://github.com/yoshida-lab/XenonPy/pull/229\n\nv0.5.0\n======\n\n**Breaking change**\n\n* Replace ``xenonpy.datatools.BoxCox`` with ``xenonpy.datatools.PowerTransformer``. ( `#222`_ )\n\n**New features**\n\n* Add ``xenonpy.datatools.PowerTransformer`` to provide *yeo-johnson* and *box-cox* transformation through the ``sklearn.preprocessing.PowerTransformer``. ( `#222`_ )\n* Add new contribution ``ISMD`` by `Qi`_, a new class of ``BaseProposal`` that allows generation of molecules based on virtual reaction of reactants in a predefined reactant pool. ( `#208`_ )\n* Add classifier training support for the ``xenonpy.model.Trainer``. ( `#184`_ )\n* Add ``IQSPR4DF`` to support ``pandas.DataFrame`` input to iQSPR.\n* Add ``LayeredFP``, ``PatternFP``, and ``MHFP`` (new rdkit fingerprints).\n\n**Enhance**\n\n* ``BaseFeaturizer.transform`` now supports ``pandas.DataFrame`` as input where relevant columns for descriptor calculation can be specified through ``target_col``.\n* ``BaseDescriptor.transform`` and ``BaseLogLikelihoodSet.log_likelihood`` now automatically check if any group names occur in the input ``pandas.DataFrame`` column names. If not, the entire ``pandas.DataFrame`` will be passed to the corresponding ``BaseFeaturizer`` and ``BaseLogLikelihood``, respectively.\n* Allow using custom elemental information matrix in ``xenonpy.descriptor.Compositions`` descriptor. ( `#221`_ )\n* Use ``joblib.parallel`` as default parallel backend. ( `#191`_, `#220`_ )\n* ``Splliter.split`` method now support python list as input. ( `#194`_ )\n* Allow user specific index for ``DescriptorHeatmap``. ( `#44`_ )\n* Allow control of number of layers to be extracted in ``FrozenFeaturizer``. ( `#174`_ )\n* ``bit_per_entry`` option is added to ``RDKitFP`` and ``AtomPairFP`` to allow control of number of bits to represent one fingerprint entry.\n* ``counting`` option is added to ``RDKitFP``, ``AtomPairFP``, ``TopologicalTorsionFP``, ``FCFP`` and ``ECFP`` to support returning counts of each fingerprint entry.\n* Column names of ``DescriptorFeature`` is updated to be consistent with the rdkit naming.\n\n\n**Infrastructure improve**\n\n* Move CI to github action. ( `#195`_ )\n* Move to readthedocs version 2. ( `#206`_ )\n\n.. _Qi: https://github.com/qi-zh\n.. _#222: https://github.com/yoshida-lab/XenonPy/pull/222\n.. _#208: https://github.com/yoshida-lab/XenonPy/pull/208\n.. _#221: https://github.com/yoshida-lab/XenonPy/pull/221\n.. _#184: https://github.com/yoshida-lab/XenonPy/pull/184\n.. _#195: https://github.com/yoshida-lab/XenonPy/pull/195\n.. _#206: https://github.com/yoshida-lab/XenonPy/pull/206\n.. _#191: https://github.com/yoshida-lab/XenonPy/pull/191\n.. _#220: https://github.com/yoshida-lab/XenonPy/pull/220\n.. _#194: https://github.com/yoshida-lab/XenonPy/pull/194\n.. _#44: https://github.com/yoshida-lab/XenonPy/pull/44\n.. _#174: https://github.com/yoshida-lab/XenonPy/pull/174\n\n\nv0.4.2\n======\n\n**Bug fix**\n\n* fix ``set_param`` method dose not set the children in ``BaseDescriptor`` and ``NGram``. ( `#163`_, `#159`_ )\n\n**Enhance**\n\n* Setting ``optimizer``, ``loss_func``, and etc. can be done in ``Trainer.load``. ( `#158`_ )\n* Improve docs. ( `#155`_ )\n\n.. _#163: https://github.com/yoshida-lab/XenonPy/issues/163\n.. _#159: https://github.com/yoshida-lab/XenonPy/issues/159\n.. _#158: https://github.com/yoshida-lab/XenonPy/issues/159\n.. _#155: https://github.com/yoshida-lab/XenonPy/issues/159\n\n\nv0.4.0\n======\n\n**Breaking change**\n\n* Remove ``xenonpy.datatools.MDL``.\n* Remove ``xenonpy.model.nn`` modules. Part of them will be kept until v1.0.0 for compatible.\n\n**New features**\n\n* Add ``xenonpy.mdl`` modules for XenonPy.MDL access.\n* Add ``xenonpy.model.training`` modules for model training.\n\n\nv0.3.6\n======\n\n**Breaking change**\n\n* Renamed ``BayesianRidgeEstimator`` to ``GaussianLogLikelihood``.\n* Removed the ``estimators`` property from ``BayesianRidgeEstimator``.\n* Added ``predict`` method into ``GaussianLogLikelihood``.\n\n\nv0.3.5\n======\n\n**Enhanced**\n\n* Added version specifiers to the *requirements.txt* file.\n\nv0.3.4\n======\n\n**Bug fix**\n\n* Fixed a critical error in ``BayesianRidgeEstimator`` when calculating the loglikelihood. ( `#124`_ )\n\n.. _#124: https://github.com/yoshida-lab/XenonPy/issues/124\n\nv0.3.3\n======\n\n**Bug fix**\n\n* fix *mp_ids.txt* not exist error when trying to build the sample data using ``preset.build``.\n\nv0.3.2\n======\n\n**Enhanced**\n\n* Updated sample codes.\n* Added progress bar for ngram training. ( `#93`_ )\n* Added error handling to NGram when generating new SMILES. ( `#97`_ )\n\n**CI**\n\n* Removed python 3.5 support. ( `#95`_ )\n* Added Appveyor CI for windows tests. ( `#90`_ )\n\n.. _#93: https://github.com/yoshida-lab/XenonPy/issues/93\n.. _#97: https://github.com/yoshida-lab/XenonPy/issues/97\n.. _#95: https://github.com/yoshida-lab/XenonPy/issues/95\n.. _#90: https://github.com/yoshida-lab/XenonPy/issues/90\n\n\nv0.3.1\n======\n\n**Enhanced**\n\n* Added tutorials for main modules. ( `#79`_ )\n\n.. _#79: https://github.com/yoshida-lab/XenonPy/issues/79\n\n\nv0.3.0\n======\n\n**Breaking changes**:\n\n* Removed Built-in data ``mp_inorganic``, ``mp_structure``, ``oqmd_inorganic`` and ``oqmd_structure``. ( `#12`_, `#20`_ )\n* Renamed ``LocalStorage`` to ``Storage``.\n\n**Enhanced**\n\n* Added error handling for ``NGram`` training. ( `#75`_, `#86`_ )\n* Added error handling for ``IQSPR``. ( `#69`_ )\n* Added error handling for ``BaseDescriptor`` and ``BaseFeaturizer``. ( `#73`_ )\n* Added featurizer selection function. ( `#47`_ )\n\n**New Features**\n\n* Added sample data building function for ``preset``. ( `#81`_, `#84`_ )\n\n\n.. _#12: https://github.com/yoshida-lab/XenonPy/issues/12\n.. _#20: https://github.com/yoshida-lab/XenonPy/issues/20\n.. _#75: https://github.com/yoshida-lab/XenonPy/issues/75\n.. _#73: https://github.com/yoshida-lab/XenonPy/issues/73\n.. _#86: https://github.com/yoshida-lab/XenonPy/issues/86\n.. _#69: https://github.com/yoshida-lab/XenonPy/issues/69\n.. _#81: https://github.com/yoshida-lab/XenonPy/issues/81\n.. _#84: https://github.com/yoshida-lab/XenonPy/issues/84\n.. _#47: https://github.com/yoshida-lab/XenonPy/issues/47\n\n\n\n\nv0.2.0\n======\n\n**Descriptor Generator**:\n\n* Added ``xenonpy.descriptor.Fingerprint`` descriptor generator. ( `#21`_ )\n* Added ``xenonpy.descriptor.OrbitalFieldMatrix`` descriptor generator. ( `#22`_ )\n\n\n**API Changes**:\n\n* Allowed ``BaseDescriptor`` class to use anonymous/renamed input. ( `#10`_ )\n\n.. _#10: https://github.com/yoshida-lab/XenonPy/issues/10\n.. _#21: https://github.com/yoshida-lab/XenonPy/issues/21\n.. _#22: https://github.com/yoshida-lab/XenonPy/issues/22\n" }, { "path": "docs/source/conf.py", "content": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\n#\n# XenonPy documentation build configuration file, created by\n# sphinx-quickstart on Mon Jan 15 16:41:55 2018.\n#\n# This file is execfile()d with the current directory set to its\n# containing dir.\n#\n# Note that not all possible configuration values are present in this\n# autogenerated file.\n#\n# All configuration values have a default; values that are commented out\n# serve to show the default.\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\nimport os\nimport sys\n\nsys.path.insert(0, os.path.abspath('../../'))\n\nimport xenonpy as package\n\nlanguage = 'en'\n# -- General configuration ------------------------------------------------\n\n# If your documentation needs a minimal Sphinx version, state it here.\n#\n# needs_sphinx = '1.0'\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n 'nbsphinx', 'sphinx.ext.autodoc', 'sphinx.ext.doctest', 'sphinx.ext.napoleon', 'sphinx_autodoc_typehints',\n 'sphinx.ext.intersphinx', 'sphinx.ext.todo', 'sphinx.ext.coverage', 'sphinx.ext.mathjax', 'sphinx.ext.viewcode',\n 'sphinx.ext.githubpages', 'sphinx.ext.autosectionlabel'\n]\n\nexclude_patterns = ['_build', '**.ipynb_checkpoints']\nnbsphinx_execute = 'never'\n\n# config autosectionlabel\nautosectionlabel_prefix_document = True\n\n# config autodoc\nautoclass_content = 'both'\nautodoc_member_order = 'groupwise'\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\n# The suffix(es) of source filenames.\n# You can specify multiple suffix as a list of string:\n#\n# source_suffix = ['.rst', '.md']\nsource_suffix = '.rst'\n\n# The master toctree document.\nmaster_doc = 'index'\n\n# General information about the project.\nproject = package.__name__\ncopyright = '2022, yoshida-lab'\nauthor = 'TsumiNa'\n\n# The version info for the project you're documenting, acts as replacement for\n# |version| and |release|, also used in various other places throughout the\n# built documents.\n#\n# The short X.Y version.\nversion = package.__version__\n# The full version, including alpha/beta/rc tags.\nrelease = package.__release__\n\n# The language for content autogenerated by Sphinx. Refer to documentation\n# for a list of supported languages.\n#\n# This is also used if you do content translation via gettext catalogs.\n# Usually you set \"language\" from the command line for these cases.\nlanguage = None\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This patterns also effect to html_static_path and html_extra_path\nexclude_patterns = []\n\n# The name of the Pygments (syntax highlighting) style to use.\npygments_style = 'sphinx'\n\n# If true, todo and todoList produce output, else they produce nothing.\ntodo_include_todos = True\n\n# -- Options for HTML output ----------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#\n# html_theme = 'alabaster'\nhtml_theme = \"sphinx_rtd_theme\"\n\n# Theme options are theme-specific and customize the look and feel of a theme\n# further. For a list of options available for each theme, see the\n# documentation.\n#\nhtml_theme_options = {\n 'logo_only': True,\n}\n\n# favicon\nhtml_favicon = 'favicon.ico'\n\n# read the docs banner\nhtml_logo = '_static/logo_readthedocs.png'\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = ['_static']\n\n# Custom sidebar templates, must be a dictionary that maps document names\n# to template names.\n#\n# This is required for the alabaster theme\n# refs: http://alabaster.readthedocs.io/en/latest/installation.html#sidebars\nhtml_sidebars = {\n '**': [\n 'relations.html', # needs 'show_related': True theme option to display\n 'searchbox.html',\n ]\n}\n\n# -- Options for HTMLHelp output ------------------------------------------\n\n# Output file base name for HTML help builder.\nhtmlhelp_basename = 'XenonPydoc'\n\n# -- Options for LaTeX output ---------------------------------------------\n\nlatex_elements = {\n # The paper size ('letterpaper' or 'a4paper').\n #\n # 'papersize': 'letterpaper',\n\n # The font size ('10pt', '11pt' or '12pt').\n #\n # 'pointsize': '10pt',\n\n # Additional stuff for the LaTeX preamble.\n #\n # 'preamble': '',\n\n # Latex figure (float) alignment\n #\n # 'figure_align': 'htbp',\n}\n\n# Grouping the document tree into LaTeX files. List of tuples\n# (source start file, target name, title,\n# author, documentclass [howto, manual, or own class]).\nlatex_documents = [\n (master_doc, 'XenonPy.tex', 'XenonPy Documentation', 'TsumiNa', 'manual'),\n]\n\n# -- Options for manual page output ---------------------------------------\n\n# One entry per manual page. List of tuples\n# (source start file, name, description, authors, manual section).\nman_pages = [(master_doc, 'XenonPy', 'XenonPy Documentation', [author], 1)]\n\n# -- Options for Texinfo output -------------------------------------------\n\n# Grouping the document tree into Texinfo files. List of tuples\n# (source start file, target name, title, author,\n# dir menu entry, description, category)\ntexinfo_documents = [\n (master_doc, 'XenonPy', 'XenonPy Documentation', author, 'XenonPy', 'One line description of project.',\n 'Miscellaneous'),\n]\n\n# Example configuration for intersphinx: refer to the Python standard library.\nintersphinx_mapping = {\n 'https://docs.python.org/3': None,\n 'pandas': ('https://pandas.pydata.org/pandas-docs/stable', None),\n 'numpy': ('https://numpy.org/doc/stable', None),\n 'scipy': ('https://docs.scipy.org/doc/scipy/reference', None),\n 'matplotlib': ('https://matplotlib.org', None),\n 'torch': ('https://pytorch.org/docs/stable/', None)\n}\n\nnbsphinx_execute = 'never'\n\n\ndef skip(app, what, name, obj, skip, options):\n if name == \"__call__\":\n return False\n return skip\n\n\ndef setup(app):\n app.add_css_file(\"css/container.css\")\n app.connect(\"autodoc-skip-member\", skip)\n" }, { "path": "docs/source/contact.rst", "content": "========\nContact\n========\n\n* For issues_\n* For Gitter_\n\n.. _issues: https://github.com/yoshida-lab/XenonPy/issues\n.. _Gitter: https://gitter.im/yoshida-lab/XenonPy\n" }, { "path": "docs/source/contribution.rst", "content": "=======================\nContribution guidelines\n=======================\n\n1. Fork it ( https://github.com/yoshida-lab/XenonPy/fork )\n2. Extend your python environment to support XenonPy development (See: :ref:`installation:installing in development mode`)\n3. Create your feature branch (git checkout -b my-new-feature)\n4. Commit your changes (git commit -am 'Add some feature')\n5. Push to the branch (git push origin my-new-feature)\n6. Create a new Pull Request\n\nWhen contribute your codes, please do the following\n\n* Discuss with others\n* Use `Numpy style`_ for Docstring.\n* Check codes with Pylint_ based on `.pylintrc`_.\n* Format codes with YAPF_ based on `.style.yapf`_.\n* Write tests if possible\n\n\n.. _Numpy style: https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt\n.. _Pylint: https://pylint.readthedocs.io/\n.. _YAPF: https://github.com/google/yapf\n.. _.pylintrc: https://github.com/yoshida-lab/XenonPy/blob/master/.pylintrc\n.. _.style.yapf: https://github.com/yoshida-lab/XenonPy/blob/master/.style.yapf" }, { "path": "docs/source/copyright.rst", "content": "=========\nCopyright\n=========\n\nCopyright © 2021 The XenonPy task force, all rights reserved.\nReleased under the `BSD-3 license`_.\n\n.. _BSD-3 license: https://opensource.org/licenses/BSD-3-Clause\n\n" }, { "path": "docs/source/features.rst", "content": ".. role:: raw-html(raw)\n :format: html\n\n========\nFeatures\n========\n\n\n-----------\nData access\n-----------\n.. _data-access:\n\n**Dataset** is an abstraction of the local file system.\nUsers can add their local dirs into this system then load data that under these dirs in a convenient way.\n\nXenonPy also uses this system to provide some built-in data.\nCurrently, two sets of element-level property data are available out-of-the-box (``elements`` and ``elements_completed`` (imputed version of ``elements``)).\nThese data were collected from `mendeleev`_, `pymatgen`_, `CRC Hand Book`_ and `Magpie`_.\n\n.. _CRC Hand Book: http://hbcponline.com/faces/contents/ContentsSearch.xhtml\n.. _Magpie: https://bitbucket.org/wolverton/magpie\n.. _mendeleev: https://mendeleev.readthedocs.io\n.. _pymatgen: http://pymatgen.org/\n\n``elements`` contains 74 element-level properties of 118 elements. Their missing values\nwere statistically imputed by performing the multiple imputation method [1]_ and stored as ``elements_completed``.\nBecause of the statistical unreliability of the imputation for a subset of properties and heavier atoms that contains many missing values in elements,\nthe ``elements_completed`` data set provides only 58 properties of 94 elements (from **H** to **Pu**). The following table shows the currently available elemental information.\n\n.. table:: Element-level properties\n\n ================================= ===================================================================================\n feature description\n --------------------------------- -----------------------------------------------------------------------------------\n ``period`` Period in the periodic table\n ``atomic_number`` Number of protons found in the nucleus of an atom\n ``mendeleev_number`` Atom number in mendeleev's periodic table\n ``atomic_radius`` Atomic radius\n ``atomic_radius_rahm`` Atomic radius by Rahm et al\n ``atomic_volume`` Atomic volume\n ``atomic_weight`` The mass of an atom\n ``icsd_volume`` Atom volume in ICSD database\n ``lattice_constant`` Physical dimension of unit cells in a crystal lattice\n ``vdw_radius`` Van der Waals radius\n ``vdw_radius_alvarez`` Van der Waals radius according to Alvarez\n ``vdw_radius_batsanov`` Van der Waals radius according to Batsanov\n ``vdw_radius_bondi`` Van der Waals radius according to Bondi\n ``vdw_radius_dreiding`` Van der Waals radius from the DREIDING FF\n ``vdw_radius_mm3`` Van der Waals radius from the MM3 FF\n ``vdw_radius_rt`` Van der Waals radius according to Rowland and Taylor\n ``vdw_radius_truhlar`` Van der Waals radius according to Truhlar\n ``vdw_radius_uff`` Van der Waals radius from the UFF\n ``covalent_radius_bragg`` Covalent radius by Bragg\n ``covalent_radius_cordero`` Covalent radius by Cerdero et al\n ``covalent_radius_pyykko`` Single bond covalent radius by Pyykko et al\n ``covalent_radius_pyykko_double`` Double bond covalent radius by Pyykko et al\n ``covalent_radius_pyykko_triple`` Triple bond covalent radius by Pyykko et al\n ``covalent_radius_slater`` Covalent radius by Slater\n ``c6`` C_6 dispersion coefficient in a.u\n ``c6_gb`` C_6 dispersion coefficient in a.u\n ``density`` Density at 295K\n ``proton_affinity`` Proton affinity\n ``dipole_polarizability`` Dipole polarizability\n ``electron_affinity`` Electron affinity\n ``electron_negativity`` Tendency of an atom to attract a shared pair of electrons\n ``en_allen`` Allen's scale of electronegativity\n ``en_ghosh`` Ghosh's scale of electronegativity\n ``en_pauling`` Pauling's scale of electronegativity\n ``gs_bandgap`` DFT bandgap energy of T=0K ground state\n ``gs_energy`` DFT energy per atom (raw VASP value) of T=0K ground state\n ``gs_est_bcc_latcnt`` Estimated BCC lattice parameter based on the DFT volume\n ``gs_est_fcc_latcnt`` Estimated FCC lattice parameter based on the DFT volume\n ``gs_mag_moment`` DFT magnetic momenet of T=0K ground state\n ``gs_volume_per`` DFT volume per atom of T=0K ground state\n ``hhi_p`` Herfindahl−Hirschman Index (HHI) production values\n ``hhi_r`` Herfindahl−Hirschman Index (HHI) reserves values\n ``specific_heat`` Specific heat at 20oC\n ``gas_basicity`` Gas basicity\n ``first_ion_en`` First ionisation energy\n ``fusion_enthalpy`` Fusion heat\n ``heat_of_formation`` Heat of formation\n ``heat_capacity_mass`` Mass specific heat capacity\n ``heat_capacity_molar`` Molar specific heat capacity\n ``evaporation_heat`` Evaporation heat\n ``linear_expansion_coefficient`` Coefficient of linear expansion\n ``boiling_point`` Boiling temperature\n ``brinell_hardness`` Brinell Hardness Number\n ``bulk_modulus`` Bulk modulus\n ``melting_point`` Melting point\n ``metallic_radius`` Single-bond metallic radius\n ``metallic_radius_c12`` Metallic radius with 12 nearest neighbors\n ``thermal_conductivity`` Thermal conductivity at 25 C\n ``sound_velocity`` Speed of sound\n ``vickers_hardness`` Value of Vickers hardness test\n ``Polarizability`` Ability to form instantaneous dipoles\n ``youngs_modulus`` Young's modulus\n ``poissons_ratio`` Poisson's ratio\n ``molar_volume`` Molar volume\n ``num_unfilled`` Total unfilled electron\n ``num_valance`` Total valance electron\n ``num_d_unfilled`` Unfilled electron in d shell\n ``num_d_valence`` Valance electron in d shell\n ``num_f_unfilled`` Unfilled electron in f shell\n ``num_f_valence`` Valance electron in f shell\n ``num_p_unfilled`` Unfilled electron in p shell\n ``num_p_valence`` Valance electron in p shell\n ``num_s_unfilled`` Unfilled electron in s shell\n ``num_s_valence`` Valance electron in s shell\n ================================= ===================================================================================\n\nFor more details on this system, see :doc:`tutorials/1-dataset`.\n\nAccess https://github.com/yoshida-lab/XenonPy/blob/master/samples/dataset_and_preset.ipynb to get a runnable script.\n\n\n----------------------\nDescriptor calculation\n----------------------\n\nCompositional descriptors\n-------------------------\n\nXenonPy can calculate 290 compositional features for a given chemical composition.\nThis calculation uses the information of the 58 element-level property data recorded in ``elements_completed``.\nFor example, let us consider a binary compound, :math:`A_{w_A}B_{w_B}`, whose element-level features are denoted by :math:`f_{A,i}` and :math:`f_{B,i} (i = 1, …, 58)`. Then, the 290 compositional descriptors are calculated: for :math:`i = 1, …, 58`,\n\n* Weighted average (abbr: ave): :math:`f_{ave, i} = w_{A}^* f_{A,i} + w_{B}^* f_{B,i}`,\n* Weighted variance (abbr: var): :math:`f_{var, i} = w_{A}^* (f_{A,i} - f_{ave, i})^2 + w_{B}^* (f_{B,i} - f_{ave, i})^2`,\n* Geometric mean (abbr: gmean): :math:`f_{gmean, i} = \\sqrt[w_A + w_B]{f_{A,i}^{w_A} * f_{V,i}^{w_B}}`,\n* Harmonic mean (abbr: hmean): :math:`f_{hmean, i} = \\frac{w_A +w_B}{\\frac{1}{f_{A,i}}*w_A + \\frac{1}{f_{B,i}}*w_B}`,\n* Max-pooling (abbr: max): :math:`f_{max, i} = max{f_{A,i}, f_{B,i}}`,\n* Min-pooling (abbr: min): :math:`f_{min, i} = min{f_{A,i}, f_{B,i}}`,\n* Weighted sum (abbr: sum): :math:`f_{sum, i} = w_{A} f_{A,i} + w_{B} f_{B,i}`,\n\nwhere :math:`w_{A}^*` and :math:`w_{B}^*` denote the normalized composition summing up to one.\n\nBy using compositional descriptors, we have succeeded in predicting the composition of quasicrystals [2]_.\n\nStructural descriptors\n----------------------\nCurrently, XenonPy implements RDF (radial distribution function) and OFM (orbital field matrix [3]_) descriptors of crystalline structures.\nWe also provide a compatible API to use the structural descriptors of `matminer `_.\nYou may check the summary table of featurizers in matminer `here `_.\n\n\n\nRDKit descriptors\n-----------------\nXenonPy also supports molecular descriptors available in the `RDKit`_ python package, including 10 sets of fingerprints, each contains corresponding options.\n\n.. _RDKit: https://www.rdkit.org/\n\n\nThe tutorial at :doc:`tutorials/2-descriptor` demonstrates how to calculate descriptors using ``XenonPy.descriptor`` classes.\n\nAccess https://github.com/yoshida-lab/XenonPy/blob/master/samples/calculate_descriptors.ipynb to get a runnable script.\n\n\n--------------------------------------------------\nVisualization of descriptor-property relationships\n--------------------------------------------------\n\nDescriptors on a set of given materials could be displayed on a heatmap plot in order to facilitate the understanding of\noverall patterns in relation to their properties. The following figure shows an example:\n\n.. figure:: _static/heatmap.jpg\n\n Heatmap of 290 compositional descriptors of 69,640 compounds in Materials Project (upper: volume Å\\ :sup:`3`\\ , lower: density g/cm\\ :sup:`3`\\ ).\n\nIn the heatmap of the descriptor matrix, the 69,640 materials are arranged from the top to bottom by the increasing order\nof formation energies. Plotting the descriptor-property relationships in this way, we could visually recognize which\ndescriptors are relevant or irrelevant to the prediction of formation energies. Relevant descriptors, which are linearly\nor nonlinearly dependent to formation energies, might exhibit certain patterns from top to bottom in the heatmap. For example,\na monotonically decrease or increase pattern would appear in a linearly dependent descriptor. On the other hand,\nirrelevant descriptors might exhibit no specific patterns.\n\nSee the tutorial for visualization of descriptor-property relationships at :doc:`tutorials/3-visualization`.\n\nAccess https://github.com/yoshida-lab/XenonPy/blob/master/samples/visualization.ipynb to get a runnable script.\n\n\n-----------\nXenonPy.MDL\n-----------\n\n**WARNING: This subsection's information is out-dated and the details are no long true. Currently XenonPy.MDL is undergoing indefinite maintenance. Please refer to the github README page for the latest update.**\n\nXenonPy.MDL is a library of pre-trained models that were obtained by feeding diverse materials data on structure-property relationships into neural networks and some other supervised learning algorithms.\nThe current release (version 0.1.0.beta) contains more than 140,000 models (include private models) on physical, chemical, electronic, thermodynamic, or mechanical properties of small organic molecules (15 properties), polymers/polymer composites (18), and inorganic compounds (12).\nPre-trained neural networks are distributed as either the R (MXNet) or Python (PyTorch) model objects.\nDetailed information about XenonPy.MDL, such as a list of models, properties, source data used for training, and so on, are prepared in this paper [3]_.\n\nThe following lists contain the information of current available pre-trained models and properties.\n\n.. table:: Information on model sets\n\n +----------+-----------------------------------+-------------------------------------------------------------------+\n | id | name | description |\n +==========+===================================+===================================================================+\n | | | Stable inorganic compounds | | Models in this set are trained on ~20,000 stable inorganic |\n | ``1`` | | in materials project (MP) | | compounds selected from the materials project. |\n | | | |\n +----------+-----------------------------------+-------------------------------------------------------------------+\n | | | All inorganic compounds | | Models in this set are trained on ~70,000 inorganic compounds |\n | ``2`` | | in materials project (MP) | | selected from the materials project. |\n | | | |\n +----------+-----------------------------------+-------------------------------------------------------------------+\n | | | QM9 Dataset from | | Quantum-Machine project can be access |\n | ``3`` | | Quantum-Machine website | | from http://quantum-machine.org/. |\n | | | |\n +----------+-----------------------------------+-------------------------------------------------------------------+\n | | PHYSPROP Dataset | | PHYSPROP database contains chemical structures, |\n | ``4`` | | | names and physical properties for over 41,000 chemicals. |\n | | | |\n +----------+-----------------------------------+-------------------------------------------------------------------+\n | | | Jean-Claude Bradley Open | | Jean-Claude Bradley's dataset of Open Melting Points. |\n | ``5`` | | Melting Point Dataset | |\n | | | |\n +----------+-----------------------------------+-------------------------------------------------------------------+\n | | | Polymer Genome Dataset (PG) | | Polymer Genome is an informatics platform for polymer property |\n | ``6`` | | | prediction and design using machine learning. |\n | | | | It can be accessed via https://www.polymergenome.org/. |\n +----------+-----------------------------------+-------------------------------------------------------------------+\n\n\n.. table:: Information of properties\n\n ================================ =================== ================================================\n name system querying name\n -------------------------------- ------------------- ------------------------------------------------\n Melting Temperature Organic Polymer organic.polymer.melting_temperature\n Ionization Energy Organic Polymer organic.polymer.ionization_energy\n Ionic Dielectric Constant Organic Polymer organic.polymer.ionic_dielectric_constant\n Hildebrand Solubility Parameter Organic Polymer organic.polymer.hildebrand_solubility_parameter\n Glass Transition Temperature Organic Polymer organic.polymer.glass_transition_temperature\n Molar Volume Organic Polymer organic.polymer.molar_volume\n Electron Affinity Organic Polymer organic.polymer.electron_affinity\n Dielectric Constant Organic Polymer organic.polymer.dielectric_constant\n Density Organic Polymer organic.polymer.density\n Cohesive Energy Organic Polymer organic.polymer.cohesive_energy\n Bandgap Organic Polymer organic.polymer.bandgap\n Atomization Energy Organic Polymer organic.polymer.atomization_energy\n Refractive Index Organic Polymer organic.polymer.refractive_index\n Molar Heat Capacity Organic Polymer organic.polymer.molar_heat_capacity\n Electronic Dielectric Constant Organic Polymer organic.polymer.electronic_dielectric_constant\n U0 Hartree Organic Nonpolymer organic.nonpolymer.u0_hartree\n R2 Bohr2 Organic Nonpolymer organic.nonpolymer.r2_bohr2\n Mu Debye Organic Nonpolymer organic.nonpolymer.mu_debye\n Lumo Hartree Organic Nonpolymer organic.nonpolymer.lumo_hartree\n Homo Hartree Organic Nonpolymer organic.nonpolymer.homo_hartree\n Gap Hartree Organic Nonpolymer organic.nonpolymer.gap_hartree\n Alpha Bohr3 Organic Nonpolymer organic.nonpolymer.alpha_bohr3\n U Hartree Organic Nonpolymer organic.nonpolymer.u_hartree\n Zpve Hartree Organic Nonpolymer organic.nonpolymer.zpve_hartree\n Bp Organic Nonpolymer organic.nonpolymer.bp\n Cv Calmol-1K-1 Organic Nonpolymer organic.nonpolymer.cv_calmol-1k-1\n Tm Organic Nonpolymer organic.nonpolymer.tm\n G Hartree Organic Nonpolymer organic.nonpolymer.g_hartree\n H Hartree Organic Nonpolymer organic.nonpolymer.h_hartree\n Density Inorganic Crystal inorganic.crystal.density\n Volume Inorganic Crystal inorganic.crystal.volume\n Refractive Index Inorganic Crystal inorganic.crystal.refractive_index\n Band Gap Inorganic Crystal inorganic.crystal.band_gap\n Dielectric Const Electron Inorganic Crystal inorganic.crystal.dielectric_const_elec\n Fermi Energy Inorganic Crystal inorganic.crystal.efermi\n Total Magnetization Inorganic Crystal inorganic.crystal.total_magnetization\n Dielectric Const Total Inorganic Crystal inorganic.crystal.dielectric_const_total\n Final Energy Per Atom Inorganic Crystal inorganic.crystal.final_energy_per_atom\n Formation Energy Per Atom Inorganic Crystal inorganic.crystal.formation_energy_per_atom\n ================================ =================== ================================================\n\nXenonPy.MDL provides a rich set of APIs to give users the abilities to interact with the pre-trained model database.\nThrough the APIs, users can search for a specific subset of models by keywords and download them via HTTP.\nThe tutorial at :doc:`tutorials/5-pre-trained_model_library` illustrates how to interact with the database in XenonPy (via the API querying).\n\nAccess https://github.com/yoshida-lab/XenonPy/blob/master/samples/pre-trained_model_library.ipynb to get a runnable script.\n\n\n-----------------\nTransfer learning\n-----------------\n\nTransfer learning has become one of the basic techniques in machine learning that covers a broad range of algorithms for\nwhich a model trained for one task is re-purposed to another related task [4]_ [5]_.\nIn general, the need of transfer learning occurs when there is a limited supply of training data for a specific task, \nyet data supply for related task is sufficient. This situation occurs in many materials science applications as described in [6]_ [7]_.\n\nXenonPy offers a simple-to-use toolchain to seamlessly perform transfer learning with the given pre-trained models.\nGiven a target property, by using the transfer learning module of XenonPy, a source model can be treated as a generator of machine learning acquired descriptors, so-called the neural descriptors, as demonstrated in [3]_.\n\nSee tutorial at :doc:`tutorials/6-transfer_learning` to learn how to perform frozen feature transfer learning in XenonPy.\n\nAccess https://github.com/yoshida-lab/XenonPy/blob/master/samples/transfer_learning.ipynb to get a runnable script.\n\n--------------\nInverse design\n--------------\n\nInverse molecular design is an important research subject in materials science that aims to create new chemical structures with desired properties computationally.\nXenonPy offers a Bayesian molecular design algorithm based on [8]_ that includes a SMILES generator based on N-gram model, likelihood calculator, and a sequential Monte Carlo algorithm for sampling the posterior distribution of molecules with properties specified by the likelihood function. Details of this algorithm, which is called iQSPR, can be found in [9]_. An example of using iQSPR to search for high thermal conductivity polymer can be found in [10]_. \n\nSee tutorial at :doc:`tutorials/7-inverse-design` to learn how to perform inverse molecular design using iQSPR in XenonPy.\n\nAccess https://github.com/yoshida-lab/XenonPy/blob/master/samples/iQSPR.ipynb to get a runnable script.\n\n**Reference**\n\n.. [1] D. B. Rubin, Ed., Multiple Imputation for Nonresponse in Surveys. Hoboken, NJ, USA: John Wiley & Sons, Inc., 1987. doi: 10.1002/9780470316696.\n.. [2] C. Liu et al., “Machine Learning to Predict Quasicrystals from Chemical Compositions,” Adv. Mater., vol. 33, no. 36, p. 2102507, Sep. 2021, doi: 10.1002/adma.202102507.\n.. [3] T. Lam Pham et al., “Machine learning reveals orbital interaction in materials,” Science and Technology of Advanced Materials, vol. 18, no. 1, pp. 756–765, Dec. 2017, doi: 10/gm4znv.\n.. [4] K. Weiss, T. M. Khoshgoftaar, and D. Wang, “A survey of transfer learning,” J Big Data, vol. 3, no. 1, p. 9, Dec. 2016, doi: 10/gfkr2w.\n.. [5] C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu, “A survey on deep transfer learning,” in Artificial neural networks and machine learning – ICANN 2018, Cham, 2018, pp. 270–279.\n.. [6] H. Yamada et al., “Predicting Materials Properties with Little Data Using Shotgun Transfer Learning,” ACS Cent. Sci., vol. 5, no. 10, pp. 1717–1730, Oct. 2019, doi: 10.1021/acscentsci.9b00804.\n.. [7] S. Ju et al., “Exploring diamondlike lattice thermal conductivity crystals via feature-based transfer learning,” Phys. Rev. Mater., vol. 5, no. 5, p. 053801, May 2021, doi: 10.1103/physrevmaterials.5.053801.\n.. [8] H. Ikebata, K. Hongo, T. Isomura, R. Maezono, and R. Yoshida, “Bayesian molecular design with a chemical language model,” J Comput Aided Mol Des, vol. 31, no. 4, pp. 379–391, Apr. 2017, doi: 10/ggpx8b.\n.. [9] S. Wu, G. Lambard, C. Liu, H. Yamada, and R. Yoshida, “iQSPR in XenonPy: A Bayesian Molecular Design Algorithm,” Mol. Inform., vol. 39, no. 1–2, p. 1900107, Jan. 2020, doi: 10.1002/minf.201900107.\n.. [10] S. Wu et al., “Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm,” npj Computational Materials, vol. 5, no. 1, pp. 66–66, Dec. 2019, doi: 10.1038/s41524-019-0203-2.\n\n" }, { "path": "docs/source/index.rst", "content": ".. Copyright 2019 TsumiNa. All rights reserved.\n\n\n.. role:: raw-html(raw)\n :format: html\n\n========================\nWhat is XenonPy project\n========================\n\n|MacOS| |Windows| |Ubuntu| |readthedocs| |codecov| |version| |python| |total-dl| |per-dl|\n\n.. raw:: html\n\n
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Note

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\n \n \"マテリアルズインフォマティクス\"\n \n
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To all those who have purchased the book Materials Informatics published by KYORITSU SHUPPAN:

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The link to the exercises has changed to https://github.com/yoshida-lab/XenonPy/tree/master/mi_book.\n Please follow the new link to access all these exercises.

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We apologize for the inconvenience.

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\n\n--------\nOverview\n--------\n**XenonPy** is a Python library that implements a comprehensive set of machine learning tools\nfor materials informatics. Its functionalities partially depend on Python (PyTorch) and R (MXNet).\nThis package is still under development. The current released version provides some limited features:\n\n* Interface to the public materials database\n* Library of materials descriptors (compositional/structural descriptors)\n* pre-trained model library **XenonPy.MDL** (v0.1.0.beta, 2019/8/9: more than 140,000 models (include private models) in 35 properties of small molecules, polymers, and inorganic compounds) [Currently under major maintenance, expected to be recovered in v0.7]\n* Machine learning tools.\n* Transfer learning using the pre-trained models in XenonPy.MDL\n\n\n.. image:: _static/xenonpy.png\n\n\n--------\nCitation\n--------\nXenonPy is an on-going research project that covers multiple important topics in materials informatics.\nWe recommend users to cite the papers that are relevant to their specific use of XenonPy.\nPlease refer to :doc:`features` for details of each feature in XenonPy with its corresponding citation.\nUser can also check the publication list below to pick the relevant citations.\n\n\n--------\nFeatures\n--------\nXenonPy has a rich set of tools for various materials informatics applications.\nThe descriptor generator class can calculate several types of numeric descriptors from ``compositional``, ``structure``.\nBy using XenonPy's built-in visualization functions, the relationships between descriptors and target properties can be easily shown in a heatmap.\nXenonPy also supports an interface to use the ``rdkit`` descriptors and provides the ``iQSPR`` algorithm for molecular design.\n\nTransfer learning is an important tool for the efficient application of machine learning methods to materials informatics.\nTo facilitate the widespread use of transfer learning,\nwe have developed a comprehensive library of pre-trained models, called **XenonPy.MDL**.\nThis library provides a simple API that allows users to fetch the models via an HTTP request.\nFor the ease of using the pre-trained models, some useful functions are also provided.\n\nSee :doc:`features` for details.\n\n\n------\nSample\n------\nSample codes of different features in XenonPy are available here: https://github.com/yoshida-lab/XenonPy/tree/master/samples\n\n\n.. _user-doc:\n.. toctree::\n :maxdepth: 2\n :caption: User Documentation\n\n books\n copyright\n installation\n features\n tutorial\n api\n contribution\n changes\n contact\n\n\n------------\nPublications\n------------\n\n.. [1] H. Ikebata, K. Hongo, T. Isomura, R. Maezono, and R. Yoshida, “Bayesian molecular design with a chemical language model,” J Comput Aided Mol Des, vol. 31, no. 4, pp. 379–391, Apr. 2017, doi: 10/ggpx8b.\n.. [2] S. Wu et al., “Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm,” npj Computational Materials, vol. 5, no. 1, pp. 66–66, Dec. 2019, doi: 10.1038/s41524-019-0203-2.\n.. [3] S. Wu, G. Lambard, C. Liu, H. Yamada, and R. Yoshida, “iQSPR in XenonPy: A Bayesian Molecular Design Algorithm,” Mol. Inform., vol. 39, no. 1–2, p. 1900107, Jan. 2020, doi: 10.1002/minf.201900107.\n.. [4] H. Yamada et al., “Predicting Materials Properties with Little Data Using Shotgun Transfer Learning,” ACS Cent. Sci., vol. 5, no. 10, pp. 1717–1730, Oct. 2019, doi: 10.1021/acscentsci.9b00804.\n.. [5] S. Ju et al., “Exploring diamondlike lattice thermal conductivity crystals via feature-based transfer learning,” Phys. Rev. Mater., vol. 5, no. 5, p. 053801, May 2021, doi: 10.1103/physrevmaterials.5.053801.\n.. [6] C. Liu et al., “Machine Learning to Predict Quasicrystals from Chemical Compositions,” Adv. Mater., vol. 33, no. 36, p. 2102507, Sep. 2021, doi: 10.1002/adma.202102507.\n\n\n------------\nContributing\n------------\n\nXenonPy is an `open source project `_ inspired by `matminer `_.\n:raw-html:`
`\nThis project is under continuous development. We would appreciate any feedback from the users.\n:raw-html:`
`\nCode contributions are also very welcomed. See :doc:`contribution` for more details.\n\n\n.. _pandas: https://pandas.pydata.org\n.. _PyTorch: http://pytorch.org/\n.. _Xenon: https://en.wikipedia.org/wiki/Xenon\n\n.. |MacOS| image:: https://github.com/yoshida-lab/XenonPy/workflows/MacOS/badge.svg\n :alt: MacOS Building Status\n :target: https://github.com/yoshida-lab/XenonPy/actions?query=workflow%3AMacOS\n\n.. |Windows| image:: https://github.com/yoshida-lab/XenonPy/workflows/Windows/badge.svg\n :alt: Windows Building Status\n :target: https://github.com/yoshida-lab/XenonPy/actions?query=workflow%3AWindows\n\n.. |Ubuntu| image:: https://github.com/yoshida-lab/XenonPy/workflows/Ubuntu/badge.svg\n :alt: Ubuntu Building Status\n :target: https://github.com/yoshida-lab/XenonPy/actions?query=workflow%3AUbuntu\n\n.. |readthedocs| image:: https://readthedocs.org/projects/xenonpy/badge/?version=latest\n :alt: Documentation Status\n :target: https://xenonpy.readthedocs.io/en/latest/?badge=latest\n\n.. |codecov| image:: https://codecov.io/gh/yoshida-lab/XenonPy/branch/master/graph/badge.svg\n :target: https://codecov.io/gh/yoshida-lab/XenonPy\n\n.. |version| image:: https://img.shields.io/github/tag/yoshida-lab/XenonPy.svg?maxAge=360\n :alt: Version\n :target: https://github.com/yoshida-lab/XenonPy/releases/latest\n\n.. |python| image:: https://img.shields.io/pypi/pyversions/xenonpy.svg\n :alt: Python Versions\n :target: https://pypi.org/project/xenonpy/\n\n.. |total-dl| image:: https://pepy.tech/badge/xenonpy\n :alt: Downloads\n :target: https://pepy.tech/badge/xenonpy\n\n.. |per-dl| image:: https://img.shields.io/pypi/dm/xenonpy.svg?label=PiPy%20downloads\n :alt: PyPI - Downloads\n\n" }, { "path": "docs/source/installation.rst", "content": ".. role:: raw-html(raw)\n :format: html\n\n.. note::\n\n To all those who have purchased the book `Materials Informatics`_ published by `KYORITSU SHUPPAN`_:\n\n The link to the exercises has changed to https://github.com/yoshida-lab/XenonPy/tree/master/mi_book.\n Please follow the new link to access all these exercises.\n\n We apologize for the inconvenience.\n\n============\nInstallation\n============\n\nXenonPy can be installed using pip_ in 3.6 and 3.7 on Mac, Linux, and Windows.\nAlternatively, we recommend using the `Docker Image`_ if you have no installation preference.\nWe have no plan to support Python 2.x. One of the main reasons is that the ``pymatgen`` library will not support Python 2 from 2019.\nSee `this link `_ for details.\n\n\n\n.. _install_xenonpy:\n\n-------------------\nUsing conda and pip\n-------------------\n\npip_ is a package management system for installing and updating Python packages, which comes with any Python distribution.\nConda_ is an open source package management system and environment management system that runs on Windows, macOS, and Linux.\nConda easily creates, saves, loads and switches between environments on your local computer.\n\nXenonPy has 3 peer dependencies, which are PyTorch, pymatgen and rdkit_. Before you install XenonPy, you have to install them.\nThe easiest way to install all 3 packages is to use Conda_. The following official tutorials will guide you through a successful installation.\n\nPyTorch: https://pytorch.org/get-started/locally/\n:raw-html:`
`\npymatgen: http://pymatgen.org/index.html#getting-pymatgen\n:raw-html:`
`\nrdkit: https://www.rdkit.org/docs/Install.html\n\nFor convenience, several environments preset are available at `conda_env`_.\nYou can use these files to create a runnable environment on your local machine.\n\n.. code-block:: bash\n\n $ conda create -n python={3.6 or 3.7} # use `conda create` command to create a fresh environment with specific name and python version\n $ conda env update -n -f # use `conda env update` command to sync packages with the preset environment\n\n.. note::\n\n For Unix-like (Linux, Mac, FreeBSD, etc.) system, the above command will be enough. For windows, additional tools are needed.\n We highly recommend you to install the `Visual C++ Build Tools `_ before creating your environment.\n Also, confirm that you have checked the **Windows 10 SDK** (assuming the computer is Windows 10) on when installing the build tools.\n\nThe following example shows how to create an environment named ``xenonpy`` in ``python3.7`` and ``cuda10.1`` supports step-by-step.\n\n1. **Chose an environment preset and download it**.\n\n Because we want to have cuda10.1 supports, The **cuda101.yml** preset will surely satisfy this work.\n If `curl `_ has been installed, the following command will download the environment file to your local machine.\n\n .. code-block:: bash\n\n $ curl -O https://raw.githubusercontent.com/yoshida-lab/XenonPy/master/conda_env/cuda101.yml\n\n2. **Create the environment and install packages using environment file**.\n\n The following commands will rebuild a python3.7 environment, and install the packages which are listed in **cuda101.yml**.\n\n .. code-block:: bash\n\n $ conda create -n xenonpy python=3.7\n $ conda env update -n xenonpy -f cuda101.yml\n\n\n3. **Enter the environment by name**.\n\n In this example, it's ``xenonpy``.\n\n .. code-block:: bash\n\n $ source activate xenonpy\n\n # or\n\n $ activate xenonpy\n\n # or\n\n $ conda activate xenonpy\n\n .. note::\n Which command should be used is based on your system and your conda configuration.\n\n4. **Update XenonPy**\n\n When you reached here, XenonPy has been installed successfully.\n If you want to update your old installation of XenonPy, ssing ``pip install -U xenonpy``.\n\n .. code-block:: bash\n\n $ pip install -U xenonpy\n\n\n------------\nUsing docker\n------------\n\n.. image:: _static/docker.png\n\n\n**Docker** is a tool designed to easily create, deploy, and run applications across multiple platforms using containers.\nContainers allow a developer to pack up an application with all of the parts it needs, such as libraries and other dependencies, into a single package.\nWe provide the `official docker images`_ via the `Docker hub `_.\n\nUsing docker needs you to have a docker installation on your local machine. If you have not installed it yet, follow the `official installation tutorial `_ to install docker CE on your machine.\nOnce you have done this, the following command will boot up a jupyterlab_ for you with XenonPy inside. See `here `_ to know what other packages are available.\n\n.. code-block:: bash\n\n $ docker run --rm -it -v $HOME/.xenonpy:/home/user/.xenonpy -v :/workspace -p 8888:8888 yoshidalab/xenonpy\n\nIf you have a GPU server/PC running Linux and want to bring the GPU acceleration to docker. Just adding ``--runtime=nvidia`` to ``docker run`` command.\n\n.. code-block:: bash\n\n $ docker run --runtime=nvidia --rm -it -v $HOME/.xenonpy:/home/user/.xenonpy -v :/workspace -p 8888:8888 yoshidalab/xenonpy\n\nFor more information about **using GPU acceleration in docker**, see `nvidia docker `_.\n.. note::\n \n If you get **docker: Error response from daemon: Unknown runtime specified nvidia.** when runing docker with ``--runtime=nvidia``.\n Please update your ``nvidia-docker`` to ``nvidia-docker2`` and repleace ``--runtime=nvidia`` with ``--gpus all``.\n see `this issue `_ for details.\n\n\nPermission failed\n-----------------\n\nYou may have a permission problem when you try to open/save jupyter files. This is because docker is a container system running like a virtual machine.\nFiles will have different permission when be mounted onto a docker container.\nThe simplest way to resolve this problem is changing the permission of failed files.\nYou can open a terminal in jupyter notebook and type:\n\n.. code-block:: bash\n\n $ sudo chmod 666 permission_failed_file\n\nThis will change file permission to ``r+w`` for all users.\n\n\n------------------------------\nInstalling in development mode\n------------------------------\n\nThe user who plans to contribute to XenonPy has to extend the python environment to support pytest and other development tools.\nThe simplest way to extend your environment is using `extra_env.yml`_.\n\n.. code-block:: bash\n\n $ git clone https://github.com/yoshida-lab/XenonPy.git\n\nunder the cloned folder, run the following to install XenonPy in development mode:\n\n.. code-block:: bash\n\n $ cd XenonPy\n $ conda env update -n -f devtools/extra_env.yml\n $ pip install -e .\n\n\n\n----------------------\nTroubleshooting/issues\n----------------------\n\nContact us at issues_ and Gitter_ when you have trouble.\n\nPlease provide detailed information (system specification, Python version, and input/output log, and so on).\n\n-----------------------------------------------------------------------------------------------------------\n\n.. _Materials Informatics: https://www.kyoritsu-pub.co.jp/book/b10013510.html\n.. _KYORITSU SHUPPAN: https://www.kyoritsu-pub.co.jp/\n.. _Conda: https://conda.io/en/latest/\n.. _official docker images: https://cloud.docker.com/u/yoshidalab/repository/docker/yoshidalab/xenonpy\n.. _yoshida-lab channel: https://anaconda.org/yoshida\n.. _pip: https://pip.pypa.io\n.. _docker image: https://docs.docker.com\n.. _extra_env.yml: https://github.com/yoshida-lab/XenonPy/blob/master/devtools/extra_env.yml\n.. _issues: https://github.com/yoshida-lab/XenonPy/issues\n.. _Gitter: https://gitter.im/yoshida-lab/XenonPy\n.. _PyTorch: http://pytorch.org/\n.. _rdkit: https://www.rdkit.org/\n.. _jupyterlab: https://jupyterlab.readthedocs.io/en/stable/\n.. _conda_env: https://github.com/yoshida-lab/XenonPy/tree/master/conda_env\n" }, { "path": "docs/source/modules.rst", "content": "xenonpy\n=======\n\n.. toctree::\n :maxdepth: 4\n\n xenonpy\n" }, { "path": "docs/source/setup.rst", "content": "setup module\n============\n\n.. automodule:: setup\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/source/tutorial.rst", "content": "========\nTutorial\n========\n\nThese tutorials will demonstrate the most foundational usages of XenonPy and XenonPy.MDL and\nwe assume that you can use `Jupyter notebook `_.\n\n.. toctree::\n :maxdepth: 1\n :glob:\n\n tutorials/*\n\n\n-----------\nSample data\n-----------\n\nBefore starting these tutorials, you need to prepare some sample data for the benchmark/test.\n\nWe selected 1,000 inorganic compounds randomly from the `Materials Project `_ database for this using.\nYou can check these **MP ids** at:\n\n https://github.com/yoshida-lab/XenonPy/blob/master/samples/mp_ids.txt.\n\nTo build the sample data, you also have to create an API key at the materials project.\nPlease see `The Materials API `_ and follow the official documents.\n\n\nYou can use the following codes to build your sample data. These data will be saved at ``~/.xenonpy/userdata``.\n\n >>> from xenonpy.datatools import preset\n >>> preset.build('mp_samples', api_key='your api key')\n\nIf you want to know what exactly the code did, please check:\n\n https://github.com/yoshida-lab/XenonPy/blob/master/samples/sample_data_building.ipynb\n\n" }, { "path": "docs/source/tutorials/1-dataset.rst", "content": "===========\nData access\n===========\n\n**Dataset** is an abstraction of the local file system.\nUsers can add their local paths into this system to easily access the data inside.\nThe basic concept is to treat a data file as a property of a ``Dataset`` object.\nThe following docs show how easy it is to interact with the data in this system.\n\n\n-------\nDataset\n-------\n\nAssuming that you have some data files ``data1.csv``, ``data1.pd.xz``, ``data1.pkl.z`` under dir `/set1`\nand ``data2.csv``, ``data2.pd.xz``, ``data2.pkl.z`` under dir `/set2`.\n\nThe following codes will create a ``Dataset`` object containing all available files under `/set1` and `/set2`.\n\n >>> from xenonpy.datatools import Dataset\n >>> dataset = Dataset('/set1', '/set2')\n >>> dataset\n includes:\n \"data1\": /set1/data1.pd.xz\n \"data2\": /set2/data2.pd.xz\n\nNow, you can retrieve data by their name like this:\n\n >>> dataset.dataframe.data1\n\nWhat the code did is that, the ``dataset`` loaded a file with name ``data1.pd.xz`` from `/set1` or `/set2`.\nIn this case, the `/set1/data1.pd.xz` was loaded.\n\nIt is important to note that we called a property named ``dataframe`` before we load ``data1`` in order to let ``dataset`` know that it is loading a ``pandas.DataFrame`` object file using the ``pd.read_pickle`` function.\n\nCurrently, 4 loaders are available out-of-the-box. The information of built-in loaders is summarised as below.\n\n.. table:: built-in loaders\n\n ============== ================== =============================\n file extension loader description\n -------------- ------------------ -----------------------------\n ``pd(.*)`` pd.read_pickle pandas.DataFrame object file\n ``csv`` pd.read_csv csv file\n ``xlsx|xls`` pd.read_excel excel file\n ``pkl(.*)`` joblib.load common pickled files\n ============== ================== =============================\n\nThe default loader is ``dataframe``. This means that if you want to load a pandas.DataFrame object, you can omit the ``dataframe``.\nThe following code exactly does the same work as explained above:\n\n >>> dataset.data1\n\nYou can also specify the default loader by setting the ``backend`` parameter:\n\n >>> dataset = Dataset('set1', 'set2', backend='csv')\n >>> dataset.data1 # this will load '/set1/data1.csv'\n\n\n\n------\npreset\n------\n\nXenonPy also uses this system to provide some built-in data.\nCurrently, two sets of element-level property data are available out-of-the-box (``elements`` and ``elements_completed`` (imputed version of ``elements``)).\nThese data were collected from `mendeleev`_, `pymatgen`_, `CRC Hand Book`_ and `Magpie`_.\nTo know the details of ``elements_completed``, see :ref:`features:Data access`\n\n.. _CRC Hand Book: http://hbcponline.com/faces/contents/ContentsSearch.xhtml\n.. _Magpie: https://bitbucket.org/wolverton/magpie\n.. _mendeleev: https://mendeleev.readthedocs.io\n.. _pymatgen: http://pymatgen.org/\n\nUse the following codes to load ``elements`` and ``elements_completed``.\n\n >>> from xenonpy.datatools import preset\n >>> preset.elements\n >>> preset.elements_completed\n\nIf you will get a ``file not exist`` error, please run the following code to sync your local dataset.\n\n >>> from xenonpy.datatools import preset\n >>> preset.sync('elements')\n >>> preset.sync('elements_completed')\n\nThese are still some advanced uses of ``Dataset`` and ``preset``. For more details, see :ref:`tutorials/1-dataset:Advance`.\n\nAlso see the jupyter files at:\n\n https://github.com/yoshida-lab/XenonPy/tree/master/samples/dataset_and_preset.ipynb\n\n\n-------\nStorage\n-------\n\nFor implementation details, you can check out our sample codes:\n\n https://github.com/yoshida-lab/XenonPy/tree/master/samples/storage.ipynb\n\n\n\n\n-------\nAdvance\n-------\n\nComing soon!\n" }, { "path": "docs/source/tutorials/2-descriptor.rst", "content": "======================\nDescriptor calculation\n======================\n\n**XenonPy** comes with a general interface for descriptor calculation.\nBy using this interface, users can implement their descriptor calculator with only a few lines of codes and run it smoothly.\n\nWe also use this system to provide built-in calculators. Currently, 15 featurizers in 4 types are available out-of-the-box.\nThe following list shows a summary.\n\n.. csv-table:: Summary of built-in featurizers\n :header: \"Featurizer\", \"Type\", \"Description\"\n\n \"Counting\", \"Composition\", \"Encoding number of compounds elements in to vector: :math:`f_{min, i} = min{f_{A,i}, f_{B,i}}`\"\n \"WeightedAverage\", \"Composition\", \"Weighted average (abbr: ave): :math:`f_{ave, i} = w_{A}^* f_{A,i} + w_{B}^* f_{B,i}`\"\n \"WeightedVariance\", \"Composition\", \"Weighted variance (abbr: var): :math:`f_{var, i} = w_{A}^* (f_{A,i} - f_{ave, i})^2 + w_{B}^* (f_{B,i} - f_{ave, i})^2`\"\n \"WeightedSum\", \"Composition\", \"Weighted sum (abbr: sum): :math:`f_{sum, i} = w_{A} f_{A,i} + w_{B} f_{B,i}`\"\n \"GeometricMean\", \"Composition\", \"Geometric mean (abbr: gmean): :math:`f_{gmean, i} = \\sqrt[w_A + w_B]{f_{A,i}^{w_A} * f_{V,i}^{w_B}}`\"\n \"HarmonicMean\", \"Composition\", \"Harmonic mean (abbr: hmean): :math:`f_{hmean, i} = \\frac{w_A +w_B}{\\frac{1}{f_{A,i}}*w_A + \\frac{1}{f_{B,i}}*w_B}`\"\n \"MaxPooling\", \"Composition\", \"Max-pooling (abbr: max): :math:`f_{max, i} = max{f_{A,i}, f_{B,i}}`\"\n \"MinPooling\", \"Composition\", \"Min-pooling (abbr: min): :math:`f_{min, i} = min{f_{A,i}, f_{B,i}}`\"\n \"RDKitFP\", \"Fingerprint\", \"RDKit fingerprint\"\n \"AtomPairFP\", \"Fingerprint\", \"Atom Pair fingerprints\"\n \"MACCS\", \"Fingerprint\", \"The MACCS keys for a molecule\"\n \"ECFP\", \"Fingerprint\", \"Morgan (Circular) fingerprints (ECFP)\"\n \"FCFP\", \"Fingerprint\", \"Morgan (Circular) fingerprints + feature-based (FCFP)\"\n \"TopologicalTorsionFP\", \"Fingerprint\", \"Topological Torsion fingerprints\"\n \"OrbitalFieldMatrix\", \"Structure\", \"Representation based on the valence shell electrons of neighboring atoms\"\n \"RadialDistributionFunction\", \"Structure\", \"Radial distribution in crystal\"\n \"FrozenFeaturizer\", \"NN \", \"Neural Network Extracted \"\n\n\n-------------------------\nCompositional descriptors\n-------------------------\n\nXenonPy can calculate 290 compositional features for a given chemical composition.\nThis calculation uses the information of the 58 element-level property data recorded in ``elements_completed``.\nSee :ref:`features:Data access` for details.\n\n >>> from xenonpy.descriptor import Compositions\n >>> cal = Compositions()\n >>> cal\n Compositions:\n |- composition:\n | |- Counting\n | |- WeightedAverage\n | |- WeightedSum\n | |- WeightedVariance\n | |- GeometricMean\n | |- HarmonicMean\n | |- MaxPooling\n | |- MinPooling\n\nThe structure information of the calculator ``Cal`` is shown above.\nThis information tells us ``Cal`` has one featurizer group called **composition** with featurizers\n``WeightedAvgFeature``, ``WeightedSumFeature``, ``WeightedVarFeature``, ``MaxFeature`` and ``MinFeature`` in it.\n\nTo use this calculator, users have to structure an iterable object that contains the information of compounds' composition, then feed it to the method ``transform`` or ``fit_transform`` in ``cal``.\nThese methods accept two types of input, the ``pymatgen.Structure`` objects, or dicts which have the structure like `{'H': 2, 'O': 1}`.\n\nUsing our sample data, users will obtain a pandas.DataFrame object that contains all the compositional descriptors.\n\n >>> from xenonpy.datatools import preset\n >>> samples = preset.mp_samples\n >>> comps = samples['composition']\n >>> descriptor = cal.transform(comps)\n >>> descriptor\n ave:atomic_number ... min:Polarizability\n 0 24.666667 ... 0.802000\n 1 33.000000 ... 1.100000\n 2 21.600000 ... 0.802000\n ... ... ... ...\n 928 44.500000 ... 5.500000\n 929 24.250000 ... 25.000000\n 930 26.750000 ... 4.800000\n 931 36.000000 ... 6.600000\n 932 16.500000 ... 0.802000\n [933 rows x 290 columns]\n\nwhere\n\n >>> comps.__class__\n pandas.core.series.Series\n >>> comps[0].__class__\n dict\n\n\nIf the input is a pandas.DataFrame object, the calculator will first try to read the data columns that have the same name as the featurizer groups.\nFor example, the name of the featurizer group in the example above is **composition**.\nTherefore, the whole object entry can be fed into the calculator's methods without explicitly extracting the **composition** column in the ``samples``:\n\n >>> descriptor = cal.transform(samples)\n >>> descriptor\n ave:atomic_number ... min:Polarizability\n 0 24.666667 ... 0.802000\n 1 33.000000 ... 1.100000\n 2 21.600000 ... 0.802000\n ... ... ... ...\n 928 44.500000 ... 5.500000\n 929 24.250000 ... 25.000000\n 930 26.750000 ... 4.800000\n 931 36.000000 ... 6.600000\n 932 16.500000 ... 0.802000\n [933 rows x 290 columns]\n\nThis does the same work as the previous one.\n\n\n----------------------\nStructural descriptors\n----------------------\n\nSimilar to the ``Compositions`` calculator, ``Structures`` accepts ``pymatgen.Structure`` objects as its input, and then return calculated results as a pandas.DataFrame.\n\n >>> from xenonpy.descriptor import Structures\n >>> cal = Structures()\n >>> cal\n Structures:\n |- structure:\n | |- RadialDistributionFunction\n | |- OrbitalFieldMatrix\n\n``Structures`` contains one featurizer group called **structure** with ``RadialDistributionFunction`` and ``OrbitalFieldMatrix`` in it.\n``samples`` also has the structure information. We can use these to calculate structural descriptors.\n\n >>> descriptor = cal.transform(samples)\n\nThis will take 3 ~ 5 min to run and finally, you will get:\n\n >>> descriptor.head(5)\n 0.1 0.2 0.30000000000000004 ... f14_f12 f14_f13 f14_f14\n mp-1008807 0.0 0.0 0.0 ... 0.0 0.0 0.0000\n mp-1009640 0.0 0.0 0.0 ... 0.0 0.0 0.0000\n mp-1016825 0.0 0.0 0.0 ... 0.0 0.0 0.0000\n mp-1017582 0.0 0.0 0.0 ... 0.0 0.0 0.3851\n mp-1021511 0.0 0.0 0.0 ... 0.0 0.0 0.0000\n [5 rows x 1224 columns]\n\n\n-------\nAdvance\n-------\n\nThere are more details of the descriptor calculator system that are not yet included in this tutorial.\nBefore we complete this document, you can check out https://github.com/yoshida-lab/XenonPy/blob/master/samples/custom_descriptor_calculator.ipynb for more information.\n" }, { "path": "docs/source/tutorials/3-visualization.rst", "content": "=============\nVisualization\n=============\n\nDescriptors on a set of given materials could be displayed as a heatmap, which helps you to understand the descriptor-property relationships.\nThis tutorial will show you how to draw a heatmap.\n\n\n------------------\nDescriptor heatmap\n------------------\n\nWe will use ``Composition`` and ``density`` as the descriptors and target property, respectively, for a step-by-step demonstration of how to draw a heatmap.\n\n1. calculate descriptors\n\n .. code-block:: python\n\n from xenonpy.datatools import preset\n from xenonpy.descriptor import Compositions\n\n samples = preset.mp_samples\n cal = Compositions()\n\n descriptor = cal.transform(samples['composition'])\n\n2. sort descriptors by property values\n\n .. code-block:: python\n\n # use formation energy as our target\n property_ = 'density'\n\n # --- sort property\n prop = samples[property_].dropna()\n sorted_prop = prop.sort_values()\n\n # --- sort descriptors\n sorted_desc = descriptor.loc[sorted_prop.index]\n\n3. draw the heatmap\n\n .. code-block:: python\n\n # --- import necessary libraries\n\n from xenonpy.visualization import DescriptorHeatmap\n\n heatmap = DescriptorHeatmap(\n bc=True, # use box-cox transform\n save=dict(fname='heatmap_density', dpi=200, bbox_inches='tight'), # save figure to file\n figsize=(70, 10))\n heatmap.fit(sorted_desc)\n heatmap.draw(sorted_prop)\n\n\nHere, we explain how to *read* the descriptor heatmap.\n\n.. figure:: ../_static/heatmap_density.png\n\n Heatmap of ~70,000 compounds with 290 compositional descriptors sorted by their property's value.\n\nIn the heatmap of the descriptor matrix, the materials are arranged from the top to bottom in the increasing order\nof density. Plotting the descriptor-property relationships in this way, we could visually recognize which\ndescriptors are relevant or irrelevant to the prediction of formation energies. Relevant descriptors, which are linearly\nor nonlinearly dependent to formation energies, might exhibit certain patterns from top to bottom in the heatmap. For example,\na monotonic decrease or increase pattern would appear in a linearly dependent descriptor. On the other hand,\nirrelevant descriptors might exhibit no specific patterns.\n\nYou can test run using this sample code:\n\n https://github.com/yoshida-lab/XenonPy/blob/master/samples/visualization.ipynb\n" }, { "path": "docs/source/tutorials/4-random_nn_model_and_training.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"source\": [\n \"# Random NN models\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This tutorial shows how to build neural network models.\\n\",\n \"We will learn how to calculate compositional descriptors using `xenonpy.descriptor.Compositions` calculator and train our model using `xenonpy.model.training` modules.\\n\",\n \"\\n\",\n \"In this tutorial, we will use some inorganic sample data from materials project. \\n\",\n \"If you don't have it, please see https://github.com/yoshida-lab/XenonPy/blob/master/samples/build_sample_data.ipynb.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### useful functions\\n\",\n \"\\n\",\n \"Running the following cell will load some commonly used packages, such as `numpy`, `pandas`, and so on. It will also import some in-house functions used in this tutorial. See `samples/tools.ipynb` to check what will be imported.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%run tools.ipynb\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Sequential linear model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We will use `xenonpy.model.SequentialLinear` to build a sequential linear model. The basic layer in `SequentialLinear` model is `xenonpy.model.LinearLayer`\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\\u001b[0;31mInit signature:\\u001b[0m\\n\",\n \"\\u001b[0mSequentialLinear\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0min_features\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mint\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mout_features\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mint\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mbias\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mbool\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mh_neurons\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mTuple\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mfloat\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m...\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mTuple\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mint\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m...\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mh_bias\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mbool\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mTuple\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mbool\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m...\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mh_dropouts\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mfloat\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mTuple\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mfloat\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m...\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;36m0.1\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mh_normalizers\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mfloat\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mNoneType\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mTuple\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mfloat\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mNoneType\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m...\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;36m0.1\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mh_activation_funcs\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mCallable\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mNoneType\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mTuple\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mCallable\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mNoneType\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m...\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mReLU\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mDocstring:\\u001b[0m \\n\",\n \"Sequential model with linear layers and configurable other hype-parameters.\\n\",\n \"e.g. ``dropout``, ``hidden layers``\\n\",\n \"\\u001b[0;31mInit docstring:\\u001b[0m\\n\",\n \"Parameters\\n\",\n \"----------\\n\",\n \"in_features\\n\",\n \" Size of input.\\n\",\n \"out_features\\n\",\n \" Size of output.\\n\",\n \"bias\\n\",\n \" Enable ``bias`` in input layer.\\n\",\n \"h_neurons\\n\",\n \" Number of neurons in hidden layers.\\n\",\n \" Can be a tuple of floats. In that case,\\n\",\n \" all these numbers will be used to calculate the neuron numbers.\\n\",\n \" e.g. (0.5, 0.4, ...) will be expanded as (in_features * 0.5, in_features * 0.4, ...)\\n\",\n \"h_bias\\n\",\n \" ``bias`` in hidden layers.\\n\",\n \"h_dropouts\\n\",\n \" Probabilities of dropout in hidden layers.\\n\",\n \"h_normalizers\\n\",\n \" Momentum of batched normalizers in hidden layers.\\n\",\n \"h_activation_funcs\\n\",\n \" Activation functions in hidden layers.\\n\",\n \"\\u001b[0;31mFile:\\u001b[0m ~/projects/XenonPy/xenonpy/model/sequential.py\\n\",\n \"\\u001b[0;31mType:\\u001b[0m type\\n\",\n \"\\u001b[0;31mSubclasses:\\u001b[0m \\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from xenonpy.model import SequentialLinear, LinearLayer\\n\",\n \"SequentialLinear?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\\u001b[0;31mInit signature:\\u001b[0m\\n\",\n \"\\u001b[0mLinearLayer\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0min_features\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mint\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mout_features\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mint\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mbias\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mbool\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mdropout\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mfloat\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;36m0.0\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mactivation_func\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mCallable\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mReLU\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mnormalizer\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mfloat\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mNoneType\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;36m0.1\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mDocstring:\\u001b[0m \\n\",\n \"Base NN layer. This is a wrap around PyTorch.\\n\",\n \"See here for details: http://pytorch.org/docs/master/nn.html#\\n\",\n \"\\u001b[0;31mInit docstring:\\u001b[0m\\n\",\n \"Parameters\\n\",\n \"----------\\n\",\n \"in_features:\\n\",\n \" Size of each input sample.\\n\",\n \"out_features:\\n\",\n \" Size of each output sample\\n\",\n \"dropout: float\\n\",\n \" Probability of an element to be zeroed. Default: 0.5\\n\",\n \"activation_func: func\\n\",\n \" Activation function.\\n\",\n \"normalizer: func\\n\",\n \" Normalization layers\\n\",\n \"\\u001b[0;31mFile:\\u001b[0m ~/projects/XenonPy/xenonpy/model/sequential.py\\n\",\n \"\\u001b[0;31mType:\\u001b[0m type\\n\",\n \"\\u001b[0;31mSubclasses:\\u001b[0m \\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"LinearLayer?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Following the official suggestion from PyTorch, `SequentialLinear` is a python class that inherit the `torch.nn.Module` class. Users can specify the hyperparameters to get a fully customized model object. For example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"SequentialLinear(\\n\",\n \" (layer_0): LinearLayer(\\n\",\n \" (linear): Linear(in_features=290, out_features=232, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(232, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_1): LinearLayer(\\n\",\n \" (linear): Linear(in_features=232, out_features=203, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(203, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_2): LinearLayer(\\n\",\n \" (linear): Linear(in_features=203, out_features=174, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(174, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (output): Linear(in_features=174, out_features=1, bias=True)\\n\",\n \")\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model = SequentialLinear(290, 1, h_neurons=(0.8, 0.7, 0.6))\\n\",\n \"model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### fully randomized model generation\\n\",\n \"\\n\",\n \"Process of random model generation:\\n\",\n \"\\n\",\n \"1. using a random parameter generator to generate a set of parameter.\\n\",\n \"2. using the generated parameter set to setup a model object.\\n\",\n \"3. loop step 1 and 2 as many times as needed.\\n\",\n \"\\n\",\n \"We provided a general parameter generator `xenonpy.utils.ParameterGenerator` to do all the 3 steps for any callable object.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\\u001b[0;31mInit signature:\\u001b[0m\\n\",\n \"\\u001b[0mParameterGenerator\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mseed\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mint\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mNoneType\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mAny\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mSequence\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mCallable\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mDict\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mDocstring:\\u001b[0m Generator for parameter set generating.\\n\",\n \"\\u001b[0;31mInit docstring:\\u001b[0m\\n\",\n \"Parameters\\n\",\n \"----------\\n\",\n \"seed\\n\",\n \" Numpy random seed.\\n\",\n \"kwargs\\n\",\n \" Parameter candidate.\\n\",\n \"\\u001b[0;31mFile:\\u001b[0m ~/projects/XenonPy/xenonpy/utils/parameter_gen.py\\n\",\n \"\\u001b[0;31mType:\\u001b[0m type\\n\",\n \"\\u001b[0;31mSubclasses:\\u001b[0m \\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from xenonpy.utils import ParameterGenerator\\n\",\n \"ParameterGenerator?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Calling an instance of `ParameterGenerator` will return a generator.\\n\",\n \"This generator can randomly select parameters from parameter candidates and yield them as a dict. This is what we want in step 1.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from math import ceil\\n\",\n \"from random import uniform\\n\",\n \"\\n\",\n \"generator = ParameterGenerator(\\n\",\n \" in_features=290,\\n\",\n \" out_features=1,\\n\",\n \" h_neurons=dict(\\n\",\n \" data=[ceil(uniform(0.1, 1.2) * 290) for _ in range(100)], \\n\",\n \" repeat=(2, 3)\\n\",\n \" )\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Because `generator` is a generator, `for ... in ...` statement can be applied. \\n\",\n \"For example, we can use `for parameters in generator(num_of_models)` to loop all these generated parameter sets.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{'in_features': 290, 'out_features': 1, 'h_neurons': (70, 109)}\\n\",\n \"{'in_features': 290, 'out_features': 1, 'h_neurons': (135, 45)}\\n\",\n \"{'in_features': 290, 'out_features': 1, 'h_neurons': (216, 261, 79)}\\n\",\n \"{'in_features': 290, 'out_features': 1, 'h_neurons': (111, 88, 49)}\\n\",\n \"{'in_features': 290, 'out_features': 1, 'h_neurons': (216, 79, 47)}\\n\",\n \"{'in_features': 290, 'out_features': 1, 'h_neurons': (161, 45)}\\n\",\n \"{'in_features': 290, 'out_features': 1, 'h_neurons': (193, 161, 78)}\\n\",\n \"{'in_features': 290, 'out_features': 1, 'h_neurons': (90, 36, 234)}\\n\",\n \"{'in_features': 290, 'out_features': 1, 'h_neurons': (230, 83, 295)}\\n\",\n \"{'in_features': 290, 'out_features': 1, 'h_neurons': (171, 247)}\\n\"\n ]\n }\n ],\n \"source\": [\n \"for parameters in generator(num=10):\\n\",\n \" print(parameters)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For step 2, we can give a model class to the `factory` parameter as a factory function.\\n\",\n \"If `factory` parameter is given, generator will feed generated parameters to the factory function automatically and yield the result as a second return in each loop. For example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"parameters: {'in_features': 290, 'out_features': 1, 'h_neurons': (182, 335, 204)}\\n\",\n \"SequentialLinear(\\n\",\n \" (layer_0): LinearLayer(\\n\",\n \" (linear): Linear(in_features=290, out_features=182, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(182, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_1): LinearLayer(\\n\",\n \" (linear): Linear(in_features=182, out_features=335, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(335, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_2): LinearLayer(\\n\",\n \" (linear): Linear(in_features=335, out_features=204, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(204, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (output): Linear(in_features=204, out_features=1, bias=True)\\n\",\n \") \\n\",\n \"\\n\",\n \"parameters: {'in_features': 290, 'out_features': 1, 'h_neurons': (108, 255)}\\n\",\n \"SequentialLinear(\\n\",\n \" (layer_0): LinearLayer(\\n\",\n \" (linear): Linear(in_features=290, out_features=108, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(108, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_1): LinearLayer(\\n\",\n \" (linear): Linear(in_features=108, out_features=255, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(255, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (output): Linear(in_features=255, out_features=1, bias=True)\\n\",\n \") \\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"for parameters, model in generator(2, factory=SequentialLinear):\\n\",\n \" print('parameters: ', parameters)\\n\",\n \" print(model, '\\\\n')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### scheduled random model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We also enable users to generate parameters in a functional way by given a function as candidate parameters. In this case, the function accept an int number as vector length and a vector of generated parameters. For example, generate a `n` length vector with float numbers sorted in ascending.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"generator = ParameterGenerator(\\n\",\n \" in_features=290,\\n\",\n \" out_features=1,\\n\",\n \" h_neurons=dict(\\n\",\n \" data=lambda n: sorted(np.random.uniform(0.2, 0.8, size=n), reverse=True), \\n\",\n \" repeat=(2, 3)\\n\",\n \" )\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"parameters: {'in_features': 290, 'out_features': 1, 'h_neurons': (0.7954011465554711, 0.6993610045591458)}\\n\",\n \"SequentialLinear(\\n\",\n \" (layer_0): LinearLayer(\\n\",\n \" (linear): Linear(in_features=290, out_features=231, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(231, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_1): LinearLayer(\\n\",\n \" (linear): Linear(in_features=231, out_features=203, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(203, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (output): Linear(in_features=203, out_features=1, bias=True)\\n\",\n \") \\n\",\n \"\\n\",\n \"parameters: {'in_features': 290, 'out_features': 1, 'h_neurons': (0.798810246990777, 0.38584535382368323, 0.29569841893337045)}\\n\",\n \"SequentialLinear(\\n\",\n \" (layer_0): LinearLayer(\\n\",\n \" (linear): Linear(in_features=290, out_features=232, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(232, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_1): LinearLayer(\\n\",\n \" (linear): Linear(in_features=232, out_features=112, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(112, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_2): LinearLayer(\\n\",\n \" (linear): Linear(in_features=112, out_features=86, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(86, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (output): Linear(in_features=86, out_features=1, bias=True)\\n\",\n \") \\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"for parameters, model in generator(2, factory=SequentialLinear):\\n\",\n \" print('parameters: ', parameters)\\n\",\n \" print(model, '\\\\n')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Model training\\n\",\n \"\\n\",\n \"We provide a general and extendable model training system for neural network models.\\n\",\n \"By customizing the extensions, users can fully control their model training process, and save the training results following the *XenonPy.MDL* format automatically.\\n\",\n \"\\n\",\n \"All these modules and extensions are under the `xenonpy.model.training`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import torch\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from pymatgen import Structure\\n\",\n \"\\n\",\n \"from xenonpy.model.training import Trainer, SGD, MSELoss, Adam, ReduceLROnPlateau, ExponentialLR, ClipValue\\n\",\n \"\\n\",\n \"from xenonpy.datatools import preset, Splitter\\n\",\n \"from xenonpy.descriptor import Compositions\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### prepare training/testing data\\n\",\n \"\\n\",\n \"If you followed the tutorial in https://github.com/yoshida-lab/XenonPy/blob/master/samples/build_sample_data.ipynb,\\n\",\n \"the sample data should be save at `~/.xenonpy/userdata` with name `mp_samples.pd.xz`.\\n\",\n \"You can use `pd.read_pickle` to load this file but we suggest that you use our `xenonpy.datatools.preset` module.\\n\",\n \"\\n\",\n \"We chose `volume` as the example to demonstrate how to use our training system. We only use the data which `volume` are smaller than 2,500 to avoid the disparate data.\\n\",\n \"we select data in `pandas.DataFrame` and calculate the descriptors using `xenonpy.descriptor.Compositions` calculator.\\n\",\n \"It is noticed that the input for a neuron network model in pytorch must has shape (N x M x ...), which mean if the input is a 1-D vector, it should be reshaped to 2-D, e.g. (N) -> (N x 1).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
band_gapcompositiondensitye_above_hullefermielementsfinal_energy_per_atomformation_energy_per_atompretty_formulastructurevolume
mp-10088070.0000{'Rb': 1.0, 'Cu': 1.0, 'O': 1.0}4.7846340.9963721.100617[Rb, Cu, O]-3.302762-0.186408RbCuO[[-3.05935361 -3.05935361 -3.05935361] Rb, [0....57.268924
mp-10096400.0000{'Pr': 1.0, 'N': 1.0}8.1457770.7593935.213442[Pr, N]-7.082624-0.714336PrN[[0. 0. 0.] Pr, [1.57925232 1.57925232 1.58276...31.579717
mp-10168250.7745{'Hf': 1.0, 'Mg': 1.0, 'O': 3.0}6.1658880.5895502.424570[Hf, Mg, O]-7.911723-3.060060HfMgO3[[2.03622802 2.03622802 2.03622802] Hf, [0. 0....67.541269
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" band_gap composition density \\\\\\n\",\n \"mp-1008807 0.0000 {'Rb': 1.0, 'Cu': 1.0, 'O': 1.0} 4.784634 \\n\",\n \"mp-1009640 0.0000 {'Pr': 1.0, 'N': 1.0} 8.145777 \\n\",\n \"mp-1016825 0.7745 {'Hf': 1.0, 'Mg': 1.0, 'O': 3.0} 6.165888 \\n\",\n \"\\n\",\n \" e_above_hull efermi elements final_energy_per_atom \\\\\\n\",\n \"mp-1008807 0.996372 1.100617 [Rb, Cu, O] -3.302762 \\n\",\n \"mp-1009640 0.759393 5.213442 [Pr, N] -7.082624 \\n\",\n \"mp-1016825 0.589550 2.424570 [Hf, Mg, O] -7.911723 \\n\",\n \"\\n\",\n \" formation_energy_per_atom pretty_formula \\\\\\n\",\n \"mp-1008807 -0.186408 RbCuO \\n\",\n \"mp-1009640 -0.714336 PrN \\n\",\n \"mp-1016825 -3.060060 HfMgO3 \\n\",\n \"\\n\",\n \" structure volume \\n\",\n \"mp-1008807 [[-3.05935361 -3.05935361 -3.05935361] Rb, [0.... 57.268924 \\n\",\n \"mp-1009640 [[0. 0. 0.] Pr, [1.57925232 1.57925232 1.58276... 31.579717 \\n\",\n \"mp-1016825 [[2.03622802 2.03622802 2.03622802] Hf, [0. 0.... 67.541269 \"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# if you have not have the samples data\\n\",\n \"# preset.build('mp_samples', api_key=)\\n\",\n \"\\n\",\n \"from xenonpy.datatools import preset\\n\",\n \"\\n\",\n \"data = preset.mp_samples\\n\",\n \"data.head(3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In case of the system did not automatically download some descriptor data in XenonPy, please run the following code to sync the data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"fetching dataset `elements` from https://github.com/yoshida-lab/dataset/releases/download/v0.1.3/elements.pd.xz.\\n\",\n \"fetching dataset `elements_completed` from https://github.com/yoshida-lab/dataset/releases/download/v0.1.3/elements_completed.pd.xz.\\n\"\n ]\n }\n ],\n \"source\": [\n \"from xenonpy.datatools import preset\\n\",\n \"preset.sync('elements')\\n\",\n \"preset.sync('elements_completed')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
ave:atomic_numberave:atomic_radiusave:atomic_radius_rahmave:atomic_volumeave:atomic_weightave:boiling_pointave:bulk_modulusave:c6_gbave:covalent_radius_corderoave:covalent_radius_pyykko...min:num_s_valencemin:periodmin:specific_heatmin:thermal_conductivitymin:vdw_radiusmin:vdw_radius_alvarezmin:vdw_radius_mm3min:vdw_radius_uffmin:sound_velocitymin:Polarizability
mp-100880724.666667174.067140209.33333325.66666755.0042671297.06333372.8686801646.90139.333333128.333333...1.02.00.3600.02658152.0150.0182.0349.5317.50.802
mp-100964033.000000137.000000232.50000019.05000077.4573301931.20000043.1824411892.85137.000000123.500000...2.02.00.1920.02583155.0166.0193.0360.6333.61.100
mp-101682521.600000153.120852203.40000013.92000050.1584001420.71400076.663625343.82102.80000096.000000...2.02.00.1460.02658152.0150.0182.0302.1317.50.802
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3 rows × 290 columns

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\"\n ],\n \"text/plain\": [\n \" ave:atomic_number ave:atomic_radius ave:atomic_radius_rahm \\\\\\n\",\n \"mp-1008807 24.666667 174.067140 209.333333 \\n\",\n \"mp-1009640 33.000000 137.000000 232.500000 \\n\",\n \"mp-1016825 21.600000 153.120852 203.400000 \\n\",\n \"\\n\",\n \" ave:atomic_volume ave:atomic_weight ave:boiling_point \\\\\\n\",\n \"mp-1008807 25.666667 55.004267 1297.063333 \\n\",\n \"mp-1009640 19.050000 77.457330 1931.200000 \\n\",\n \"mp-1016825 13.920000 50.158400 1420.714000 \\n\",\n \"\\n\",\n \" ave:bulk_modulus ave:c6_gb ave:covalent_radius_cordero \\\\\\n\",\n \"mp-1008807 72.868680 1646.90 139.333333 \\n\",\n \"mp-1009640 43.182441 1892.85 137.000000 \\n\",\n \"mp-1016825 76.663625 343.82 102.800000 \\n\",\n \"\\n\",\n \" ave:covalent_radius_pyykko ... min:num_s_valence min:period \\\\\\n\",\n \"mp-1008807 128.333333 ... 1.0 2.0 \\n\",\n \"mp-1009640 123.500000 ... 2.0 2.0 \\n\",\n \"mp-1016825 96.000000 ... 2.0 2.0 \\n\",\n \"\\n\",\n \" min:specific_heat min:thermal_conductivity min:vdw_radius \\\\\\n\",\n \"mp-1008807 0.360 0.02658 152.0 \\n\",\n \"mp-1009640 0.192 0.02583 155.0 \\n\",\n \"mp-1016825 0.146 0.02658 152.0 \\n\",\n \"\\n\",\n \" min:vdw_radius_alvarez min:vdw_radius_mm3 min:vdw_radius_uff \\\\\\n\",\n \"mp-1008807 150.0 182.0 349.5 \\n\",\n \"mp-1009640 166.0 193.0 360.6 \\n\",\n \"mp-1016825 150.0 182.0 302.1 \\n\",\n \"\\n\",\n \" min:sound_velocity min:Polarizability \\n\",\n \"mp-1008807 317.5 0.802 \\n\",\n \"mp-1009640 333.6 1.100 \\n\",\n \"mp-1016825 317.5 0.802 \\n\",\n \"\\n\",\n \"[3 rows x 290 columns]\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
volume
mp-100880757.268924
mp-100964031.579717
mp-101682567.541269
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\"\n ],\n \"text/plain\": [\n \" volume\\n\",\n \"mp-1008807 57.268924\\n\",\n \"mp-1009640 31.579717\\n\",\n \"mp-1016825 67.541269\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"prop = data[data.volume <= 2500]['volume'].to_frame() # reshape to 2-D\\n\",\n \"desc = Compositions(featurizers='classic').transform(data.loc[prop.index]['composition'])\\n\",\n \"\\n\",\n \"desc.head(3)\\n\",\n \"prop.head(3)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from xenonpy.datatools import preset\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Use the `xenonpy.datatools.Splitter` to split data into training and test sets.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\\u001b[0;31mInit signature:\\u001b[0m\\n\",\n \"\\u001b[0mSplitter\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0msize\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mint\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mtest_size\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mfloat\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mint\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;36m0.2\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mk_fold\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mint\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mIterable\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mNoneType\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mrandom_state\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mint\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mNoneType\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mshuffle\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mbool\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mDocstring:\\u001b[0m Data splitter for train and test\\n\",\n \"\\u001b[0;31mInit docstring:\\u001b[0m\\n\",\n \"Parameters\\n\",\n \"----------\\n\",\n \"size\\n\",\n \" Total sample size.\\n\",\n \" All data must have same length of their first dim,\\n\",\n \"test_size\\n\",\n \" If float, should be between ``0.0`` and ``1.0`` and represent the proportion\\n\",\n \" of the dataset to include in the test split. If int, represents the\\n\",\n \" absolute number of test samples. Can be ``0`` if cv is ``None``.\\n\",\n \" In this case, :meth:`~Splitter.cv` will yield a tuple only contains ``training`` and ``validation``\\n\",\n \" on each step. By default, the value is set to 0.2.\\n\",\n \"k_fold\\n\",\n \" Number of k-folds.\\n\",\n \" If ``int``, Must be at least 2.\\n\",\n \" If ``Iterable``, it should provide label for each element which will be used for group cv.\\n\",\n \" In this case, the input of :meth:`~Splitter.cv` must be a :class:`pandas.DataFrame` object.\\n\",\n \" Default value is None to specify no cv.\\n\",\n \"random_state\\n\",\n \" If int, random_state is the seed used by the random number generator;\\n\",\n \" Default is None.\\n\",\n \"shuffle\\n\",\n \" Whether or not to shuffle the data before splitting.\\n\",\n \"\\u001b[0;31mFile:\\u001b[0m ~/projects/XenonPy/xenonpy/datatools/splitter.py\\n\",\n \"\\u001b[0;31mType:\\u001b[0m type\\n\",\n \"\\u001b[0;31mSubclasses:\\u001b[0m \\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"Splitter?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(738, 290)\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"(738, 1)\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"(185, 290)\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"(185, 1)\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sp = Splitter(prop.shape[0])\\n\",\n \"x_train, x_val, y_train, y_val = sp.split(desc, prop)\\n\",\n \"\\n\",\n \"x_train.shape\\n\",\n \"y_train.shape\\n\",\n \"x_val.shape\\n\",\n \"y_val.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### model training\\n\",\n \"\\n\",\n \"Model training in pytorch is very flexible.\\n\",\n \"The [official document](https://pytorch.org/tutorials/beginner/pytorch_with_examples.html) explains the concept with examples.\\n\",\n \"In short, training a neuron network model includes:\\n\",\n \"1. calculating loss of training data for the model.\\n\",\n \"2. executing the backpropagation to update the weights between each neuron.\\n\",\n \"3. looping over step 1 and 2 until convergence.\\n\",\n \"\\n\",\n \"In step 1, a calculator, often called `loss function`, is needed to calculate the loss.\\n\",\n \"In step 2, an optimizer will be used.\\n\",\n \"`xenonpy.model.training.Trainer` covers step 3.\\n\",\n \"\\n\",\n \"Here is how you will do it in XenonPy:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"SequentialLinear(\\n\",\n \" (layer_0): LinearLayer(\\n\",\n \" (linear): Linear(in_features=290, out_features=232, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(232, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_1): LinearLayer(\\n\",\n \" (linear): Linear(in_features=232, out_features=174, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(174, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_2): LinearLayer(\\n\",\n \" (linear): Linear(in_features=174, out_features=116, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(116, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_3): LinearLayer(\\n\",\n \" (linear): Linear(in_features=116, out_features=58, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(58, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (output): Linear(in_features=58, out_features=1, bias=True)\\n\",\n \")\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model = SequentialLinear(290, 1, h_neurons=(0.8, 0.6, 0.4, 0.2))\\n\",\n \"model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Trainer(clip_grad=None, cuda=None, epochs=200, loss_func=MSELoss(),\\n\",\n \" lr_scheduler=None,\\n\",\n \" model=SequentialLinear(\\n\",\n \" (layer_0): LinearLayer(\\n\",\n \" (linear): Linear(in_features=290, out_features=232, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(232, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_1): LinearLayer(\\n\",\n \" (linear): Li...\\n\",\n \" (linear): Linear(in_features=116, out_features=58, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(58, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (output): Linear(in_features=58, out_features=1, bias=True)\\n\",\n \"),\\n\",\n \" non_blocking=False,\\n\",\n \" optimizer=Adam (\\n\",\n \"Parameter Group 0\\n\",\n \" amsgrad: False\\n\",\n \" betas: (0.9, 0.999)\\n\",\n \" eps: 1e-08\\n\",\n \" lr: 0.01\\n\",\n \" weight_decay: 0\\n\",\n \"))\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"trainer = Trainer(\\n\",\n \" model=model,\\n\",\n \" optimizer=Adam(lr=0.01),\\n\",\n \" loss_func=MSELoss()\\n\",\n \")\\n\",\n \"trainer\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 100%|██████████| 400/400 [00:06<00:00, 60.86it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"trainer.fit(\\n\",\n \" x_train=torch.tensor(x_train.values, dtype=torch.float),\\n\",\n \" y_train=torch.tensor(y_train.values, dtype=torch.float),\\n\",\n \" epochs=400\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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total_itersi_epochi_batchtrain_mse_loss
39739739813684.717041
39839839913058.761719
39939940013039.683350
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\"\n ],\n \"text/plain\": [\n \" total_iters i_epoch i_batch train_mse_loss\\n\",\n \"397 397 398 1 3684.717041\\n\",\n \"398 398 399 1 3058.761719\\n\",\n \"399 399 400 1 3039.683350\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"y_pred = trainer.predict(x_in=torch.tensor(x_val.values, dtype=torch.float)).detach().numpy().flatten()\\n\",\n \"y_true = y_val.values.flatten()\\n\",\n \"\\n\",\n \"y_fit_pred = trainer.predict(x_in=torch.tensor(x_train.values, dtype=torch.float)).detach().numpy().flatten()\\n\",\n \"y_fit_true = y_train.values.flatten()\\n\",\n \"\\n\",\n \"draw(y_true, y_pred, y_fit_true, y_fit_pred, prop_name='Volume ($\\\\AA^3$)')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can see that although `trainer` can keep training info automatically for us, we still need to convert `DataFrame` to `torch.Tensor` and convert dtype from `np.double` to `torch.float` during training, and eventually convert them back during prediction ourselves. Also, overfitting could be observed in the **prediction vs observation** plot. One possible caused is using all training data in each epoch of the training process. We can use a technique called mini-batch to avoid overfitting, but that will require more coding.\\n\",\n \"\\n\",\n \"To skip these tedious coding steps, we provide an extension system.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, we use `ArrayDataset` to wrap our data, and use `torch.utils.data.DataLoader` to a build mini-batch loader.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from xenonpy.model.training.dataset import ArrayDataset\\n\",\n \"from torch.utils.data import DataLoader\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"train_dataset = DataLoader(ArrayDataset(x_train, y_train), shuffle=True, batch_size=100)\\n\",\n \"val_dataset = DataLoader(ArrayDataset(x_val, y_val), batch_size=1000)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Second, we use `TensorConverter` extension to automatically convert data between `numpy` and `torch.Tensor`.\\n\",\n \"Additionally, we want to trace some stopping criterion and use early stopping when these criterion stop improving.\\n\",\n \"This can be done by using `Validator`.\\n\",\n \"\\n\",\n \"At last, to save our model for latter use, just add `Persist` to the trainer and saving will be done in slient.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from xenonpy.model.training.extension import Validator, TensorConverter, Persist\\n\",\n \"from xenonpy.model.training.dataset import ArrayDataset\\n\",\n \"from xenonpy.model.utils import regression_metrics\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We use `trainer.extend` method to stack up extensions. Note that extensions will be executed in the order of insertion, so `TensorConverter` should always go first and `Persist` be the last.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"trainer = Trainer(\\n\",\n \" optimizer=Adam(lr=0.01),\\n\",\n \" loss_func=MSELoss(),\\n\",\n \").extend(\\n\",\n \" TensorConverter(),\\n\",\n \" Validator(metrics_func=regression_metrics, early_stopping=30, trace_order=5, mae=0.0, pearsonr=1.0),\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\\u001b[0;31mInit signature:\\u001b[0m\\n\",\n \"\\u001b[0mPersist\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mpath\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mpathlib\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mPath\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mstr\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;34m'.'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mmodel_class\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mCallable\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mmodel_params\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mtuple\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mdict\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m<\\u001b[0m\\u001b[0mbuilt\\u001b[0m\\u001b[0;34m-\\u001b[0m\\u001b[0;32min\\u001b[0m \\u001b[0mfunction\\u001b[0m \\u001b[0many\\u001b[0m\\u001b[0;34m>\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mincrement\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mFalse\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0msync_training_step\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mFalse\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mdescribe\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mAny\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mDocstring:\\u001b[0m Trainer extension for data persistence\\n\",\n \"\\u001b[0;31mInit docstring:\\u001b[0m\\n\",\n \"Parameters\\n\",\n \"----------\\n\",\n \"path\\n\",\n \" Path for model saving.\\n\",\n \"model_class\\n\",\n \" A factory function for model reconstructing.\\n\",\n \" In most case this is the model class inherits from :class:`torch.nn.Module`\\n\",\n \"model_params\\n\",\n \" The parameters for model reconstructing.\\n\",\n \" This can be anything but in general this is a dict which can be used as kwargs parameters.\\n\",\n \"increment\\n\",\n \" If ``True``, dir name of path will be decorated with a auto increment number,\\n\",\n \" e.g. use ``model_dir@1`` for ``model_dir``.\\n\",\n \"sync_training_step\\n\",\n \" If ``True``, will save ``trainer.training_info`` at each iteration.\\n\",\n \" Default is ``False``, only save ``trainer.training_info`` at each epoch.\\n\",\n \"describe:\\n\",\n \" Any other information to describe this model.\\n\",\n \" These information will be saved under model dir by name ``describe.pkl.z``.\\n\",\n \"\\u001b[0;31mFile:\\u001b[0m ~/projects/XenonPy/xenonpy/model/training/extension/persist.py\\n\",\n \"\\u001b[0;31mType:\\u001b[0m type\\n\",\n \"\\u001b[0;31mSubclasses:\\u001b[0m \\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"Persist?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"`Persist` need a path to save model. For convenience, we prepare the path name by concatenating the number of neurons in a model.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def make_name(model):\\n\",\n \" name = []\\n\",\n \" for n, m in model.named_children():\\n\",\n \" if 'layer_' in n:\\n\",\n \" name.append(str(m.linear.in_features))\\n\",\n \" else:\\n\",\n \" name.append(str(m.in_features))\\n\",\n \" name.append(str(m.out_features))\\n\",\n \" return '-'.join(name)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"SequentialLinear(\\n\",\n \" (layer_0): LinearLayer(\\n\",\n \" (linear): Linear(in_features=290, out_features=232, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(232, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_1): LinearLayer(\\n\",\n \" (linear): Linear(in_features=232, out_features=174, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(174, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_2): LinearLayer(\\n\",\n \" (linear): Linear(in_features=174, out_features=116, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(116, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_3): LinearLayer(\\n\",\n \" (linear): Linear(in_features=116, out_features=58, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(58, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (output): Linear(in_features=58, out_features=1, bias=True)\\n\",\n \")\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": []\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 12%|█▏ | 47/400 [00:15<02:06, 2.78it/s]\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 31 iterations, finish training at iteration 372\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"model = SequentialLinear(290, 1, h_neurons=(0.8, 0.6, 0.4, 0.2))\\n\",\n \"model\\n\",\n \"\\n\",\n \"model_name = make_name(model)\\n\",\n \"persist = Persist(\\n\",\n \" f'trained_models/{model_name}', \\n\",\n \" # -^- required -^-\\n\",\n \"\\n\",\n \" # -v- optional -v-\\n\",\n \" increment=False, \\n\",\n \" sync_training_step=True,\\n\",\n \" author='Chang Liu',\\n\",\n \" email='liu.chang.1865@gmail.com',\\n\",\n \" dataset='materials project',\\n\",\n \")\\n\",\n \"_ = trainer.extend(persist)\\n\",\n \"trainer.reset(to=model)\\n\",\n \"\\n\",\n \"trainer.fit(training_dataset=train_dataset, validation_dataset=val_dataset, epochs=400)\\n\",\n \"persist(splitter=sp, data_indices=prop.index.tolist()) # <-- calling of this method only after the model training\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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yuyWzsOoYABxwwnL333pfXX3+VN99MVv8tWDCfJ574G08++XcuvvhSjj32+NpQs6SkhEMOOXyD59xss+J2Mk4k1l87/zHJrKJDh/WdjM8++1y+/e0TeeWVlxg//g0mTZrIhx/O4MMPZ/Doow9x002311Za7rrr7jz22FOMH/8mr7/+KhMmjGfOnNmMHv0Mo0c/w/e+dzpnn31uUZ+rGJpsDcIgCA4ErgaWASPCMKxJffRzoAz4bTpQAwjDcHYQBOcB/04dkxmqXZ7aXpAOB1NjXgiC4CbgUuCC1DiCINgaGJG69kXpcDA15rogCI4G9gEOB0anPjoJ2BZ4IR0Opo5fEwTB6cAnwFlBEFwZhuHSjXiWdi0Wt4JQkiRJklq6jhVl0MigrTwVENY0Q0DY3G688QYWL17EoYcewSWXXFGvynDlyhV5RrYNnTp1qjPNevbsT/jrXx/kmWf+wa233sxRRx1D9+49qKiooLq6mgsu+DE9e/aK7P769EmuFThv3ty8x8yd+ykAPXtuWmf/ppv25rjjTuC4404gHo/z3ntTue22PzB16hT+/OfbufHGP9QeW15ezr777se++yanIC9YMJ+nnnqC+++/mwcfvJfjj/8OvXvnm4zbPHJH3hspCIIOJNfgKwV+FoZh5k/+6NT2iRxDnweWA3sFQbB56lyDgUHAQuC1HGPS4duxGfuOJBncjQ3DcGWBY47Jd19hGH5OcvpwB5KhYqOeRXWrBl2DUJIkSZLUcjW8fG/q1CkAnHrq9+uFg/Pnz2fWrGQ7hLb278NPPPE3vvOdY3nggXvr7N9yy624+OJLKCsrY/XqVaxcuZLy8nJ23nkXAF577T85z3f77X/k9NNP5fHHHynqfaYborz22n9qpzlnWrRoIVOmTKK0tJShQ5PH/vrXv+SYYw6r/d1CsoJ0yJBdOeusHwLrp0Inu04fz+9+d32d8262WT/OPjvZ9CQej7No0cKiPlcxNElACPwE2IHkGn53pXcGQbAZycYf1cD07EGpSr/0/l1S2/RKmdPyNPx4D0gA2wVBUJk1Zmqe+3svtd01Y1+DxjTyWdq9zH8IJmhb/0CUJEmSJLUdHTt2AGDVqlUFr/XXo0dyvb5XXhlXZ/+8eXO5/PKf1q5FWFW1roh32vy23nobPvtsLo8++hCzZ39S57Pnnx9DLBajf/8B9OzZE4CTTx4JwG23/YGJE9+pc/zLL4/j0UcfYsaM6QRB3QYpG2vXXXdn8OCdWbp0Cf/7v1exZs36Ji9Lly7lqqsupbq6mmHDDqqt8OvbdzOWLPmcO+64hVWrvqg9Ph6PM3bsGAAGD94JSDau+fTTOTz77DN1AkWAiRPfYeHCBVRWdmGrrbYu6nMVQ9GnGAdB0I3klF+Aq7JCvXQl3fz02n85zMs6Nr3NWf8ZhuHaIAiWAT2BfsDHXzYmxzW+9DobuK+GPEu7l/kfSYq0lqokSZIkSUXXt28/OnToyMqVKxg16nQGDtyitmIsnxEjRnLTTb/jL3+5g1deGcfmmw/k888X8/7704BkRd3s2Z+wZMnnUTxCZIYO3ZOjj/4WzzzzJCNHnsiQIbvSo0dP5s2by4wZ0ykvL+fiiy+pPX7ffffj+98/k3vv/QsXXjiKHXYI6N9/c+bNm8uHH84AYNSo8xkyZNd8l2y0a665josu+iEvvfQikyZNYJdddicWq2HSpImsWbOanXfehZ/+9PLa408+eSSvvvoyU6ZM4oQTjmGnnXamoqIDH300g88+m0efPn05/fRzABgwYCBnnjmKP//5ds4990x22mkIvXv34fPPFzFt2lQSiQQXXvhjKiu7FP25NlZTrEF4DtAdmBCG4b+zPtsktV29gfHp+HaTrO2XjenZgDHZ12jMmMY8S9FVVJTRp0/Xpjp90VVWdqj9OgGt6t6V5O9MUfOdU5R83xQ13zlFzXeufYvH4yxZUkZNTZzy8tK8jTaKJb0WYWu1ySaVXH31/3LHHbcwY8Z0PvtsLiNGnFL7ea7nO+mkEWy22WY8/PCDfPLJLD79dA59+vTloIMOYcSIkSxYMJ9LLrmYV199mdNO+wGwvmFGWVlJ7TlLS0tqtxv6OZaVlRbl51xenv86G7pG5meXXvoLgiDg3/8ezYwZIVVV6+jZsxeHH34kI0d+n0GDtqszdtSoc9ljjz147LFHmDbtXf7734/ZdNNN2W+//Tn55FPZY489G/Ecue8z8/m22GIg9933MI888iDjxr3I+PFvUFFRwXbbbcfhhx/FscceV6dpS7dum3DrrX/igQfu5T//eZlJkyYA0K9ff045ZSQjR36fHj161h5/xhlnMXDgQJ566h989NEMPvjgfbp3784BBwzjpJNOYbfddi/oWZJFVcn77tOna9P/vRbzZEEQlAEXpb69Psch6WYhhcwtTU/2b8oxmQsKxEhOuS50TGPuq92r08W4ja25IEmSJElqW4YPP4jhww+qs+/NNzfchXjYsIMYNuygnJ/tuONX6o2/6qpruOqqa+rsO+usUZx11qi813jyyX9t8B4KtaFnef75Vxr0WVlZGSec8F1OOOG7BV9/r732Zq+99i74+IbK93xdu3blrLN++KUVoWndunXjvPMu5LzzLizo+MMOO4LDDjui4PtsCYpdQXgAMABYCjyd4/N0w5DKHJ+ldU5tVzXhmOzj02N6NWBMY+6r6KqrYyxbtqEixpZl+Yr1i4AmEgkWLcrVQ0YtUfq/Nvs7U1R85xQl3zdFzXdOUfOdEyQrCGtqkrUuNTVxmqogqT13MVbzaIvvXHLdywQ1NTEWLVpZUAVhjx6VVDS2+3ijRuV3Qmr7eBiGVTk+T6/vt1kQBCV5mo6k1+ublzWmf64LBkHQCehBspLvs0LG5LhGekyv1Jj/NuC+GvIs7Z5djCVJkiRJKp6nnnqCKVMmNWjMVlttzWmnndFEd1RchTxfapY26cihNT1fS1HsgPCo1PZvuT4Mw3BJEARzSVYZbgd8mPl5aoryjqlv383a7pTnmjuRnMI7MwzDdKVeIWMyj0t/PST12etfNqaRz9Lu1e1inKwiTK+3IEmSJEmSGmbatHd57rlnGzRmt92GtpoAra0/X0tRtIAwCIJ+wJZADbkDtrTRwFnAt6m/TuEhJBucTAnD8FOAMAw/CoJgBrBDEAT7hGH4ZtaY76S2/8zY9ywQBw4LgqBLRnCYdkKOMaOBU1L39eesZ9sUGAZUAWMb+yyqXzWYSKxP+iVJkiRJUsP84hdX84tfXN3ct9FkCnm+tjjFOGrFXHHgq6nt1ByBXKZbSTb4uCwIgn3SO4Mg2BK4JfXtdVlj/pDa/ikIgs0yxgwn2RSlCrgxvT8Mw7nAE8CmwB1BEHTIGHMZsA/wPvBUxjX+AcwmGSqem3F8Z+AuoAtwVxiGCzbyWdq1zCnGub6XJEmSJElStIo5xXjb1Hbmhg4Kw3BKEAS/IFlx92oQBC8Bq4HhJEO4v4Rh+FjWsNuBI0hOYZ4RBME4ktV5+5OcXnxaGIZzssZcCAwFTgX2D4JgPBCQnEa8DDgpc93AMAzXBEFwGsmqwFuDIDgD+BjYl+RagpOAS4vwLO1arF4FoQGhJEmSJElScypmBWGf1DY7qKsnDMPfAMcCr5KsPDyAZEXf6cA5OY6PA8cBFwOfAIcBg0lO9x0WhuEDOcZ8BuxNsvowARwDdAPuA/YIw3BqjjEvpe7ncZLTpb8JLAd+BRwYhuGKjX2W9q5eBaHVv5IkSZIkSc2qxAquVmsCMLS6OsayZaub+14K9uiLHzJm/PoM+dYf70/njsXulaOm0KdPVwAWLVrZzHei9sJ3TlHyfVPUfOcUNd85AcTjcRYuTP77WN++W1BaWsyaofVcD05Ra4vvXGP+Xnv0qKSiogxgIrBHQ67XNP80kPLIrhg0oJYkSZIkSWpeBoSKVHYX47j5oCRJkiRJUrMyIFSk7GIsSZIkSZLUshgQKlL1uhhbQihJkiRJktSsDAgVqfoVhM10I5IkSZIkSQIMCBWx7DUIbVIiSZIkSZLUvAwIFSnXIJQkSZIktXcTJ77DfvvtyciRJzb3rUiAAaEiZhdjSZIkSZKklsWAUJGq16TECkJJkiRJkqRmZUCoSNWrILSEUJIkSZIkqVmVN/cNqH2p36SkmW5EkiRJkqQ8/vSn27j//rs59tjj+dnPLq/3+fLlyzj22MOpqOjA00+PoXPnznz88Uz+9re/MmnSRBYvXkg8Hqdnz17stttQTj31+2yzzbZFv8/Ro5/huuuu4cwzR7HPPl/n7rvv5N13pxCLxdhuu+0588xR7LHHXnzyySz+9KdbmThxAolEgu22255Ro85n5513qXO+2bNncf/99/Dee1NZsGABHTt2ZNCg7TjiiG9y5JFHU1JSUuf46upqnnzy7zz33GhmzZpFIhFnq6224YgjjuJb3zqB8nJjp9bCCkJFKrtg0CYlkiRJkqSW5sgjjwZg3LgXqK6urvf5888/R01NDcOGHUTnzp159dWXOeOMU3nmmSeprOzMPvvsyy677MYXX6xkzJjRnH32acyePavJ7nfKlEn88IenM3PmRwwduid9+vRh6tQp/OQn5zN69DOceeZIpk//gN12G0qvXr2YPHki559/Nh999GHtOWbPnsVZZ53Gv//9LyoqKth336+zww47MnXqFH7961/yf/93Q51rrl69igsvPIebb/4dc+bMZqeddmaPPfZi7tw53HTT77j44gupqqpqsmdWcRnlKlLxeDzrewNCSZIkSWppEjXrIFbTqLHxWGnqHPEvObIJlJVTUt5xo08zcOAWDBmyK1OnTuGtt15nv/0OqPP5c889C8ARR3yTmpoabrjhOqqrq7n66ms5+ODDao9bsWIFP/nJ+Uyf/j7PPPMU55130UbfWy5vv/0WRx55NJdccgVlZWXU1NTwox+dy+TJE7nuums47LAjufTSK6moqCAWi/Gzn13E+PFv8s9/PsmPfvQzAB5++AFWrVrFyJE/4Jxzzqs994cfhowadTpPPvl3vve9M+jduzcAN930O6ZOfZevfnUfrrrqf+nRowcAK1eu5IorLmHChPHceeetXHDBj5vkmVVcBoSKVHYeaAGhJEmSJLUsa19/iOr3nm+d/8JWUkLFTgfTad9TNvpURx11NFOnTuG55/5dJyCcO/dT3ntvKv37D2C33YayaNFC9tprb8rKyuqEgwDdunXj0EOPYPr09/nss7kbfU/5dOjQkR/96GeUlZUBUF5ezsEHH8bkyRPp2DH5WUVFBQBlZWUMH34w48e/yezZs2vPsXjxIgD69t2szrm33z7gssuuIh5P0KFDRerYxYwZM5rKyi51wkGArl27csUVV3Piicfy5JOPc/rpZ9GlyyZN9uwqDqcYK1LZXYydYixJkiRJLUv1ey+0znAQIJFI3n8RDB9+CJ06deK1115h9epVtfvHjBkNwBFHHEVJSQl9+27GlVf+kssv/58645cuXcLbb7/Ju+9OBqCqqv5U5WLZdttBVFZW1tnXs2cvADbffABdu3at81nXrt1T97Sudt/QoXsC8Mc//p7rrruGceOeZ8WKFQAcfPBhHHro4XTrlhw3efIEYrEY22+/Q51wMK1Pn75st932rFu3jvfem1akp1RTsoJQkcoOBFvr/+dIkiRJUltVsdNBrbiCsJSKnQ4qyqkqK7twwAHDGDPmWV5+eRxHHPFNIDm9uKSkhMMPP6rO8RMmvM3o0U8zc+ZM5s2bWxsqrm/s0XQ/z27dutXbl75u9+71A7zsZiMAJ544gk8+mcXo0c/U/q+0tJTBg3fmgAOGc8wx36qtBFywYD6QXPtwv/323OC9pY9Vy2ZAqEhlrzloBaEkSZIktSyd9j2Fjl89odFrEJaVJycrxlrxGoRpRx55DGPGPMvYsf/miCO+ybRpU/n00znsvvsebL75ACC51v7//M/ljBv3PCUlJQwatD37738gW221DTvu+BU+/XQON954fdHuKZdidAsuLy/nssuu4rTTzuCVV8YxfvxbTJ06mWnT3mXatHd57LGHuf32u+jXr3/tv9sPHLgFgwfvvMHzZk9ZVstkQKhIZQeECQNCSZIkSWpxSso7QiODttJUQBgva4aAsGgIfSgAACAASURBVMiGDt2Tfv36M2HC2yxbtoyxY5PNSdJdjgHGjv0348Y9T9++m/G73/2BbbcdVOccM2d+SGuy+eYDOOmkUznppFOpqalh0qR3uPnm3zNr1sc8+OC9/PSnl9U2Khk4cAuuuupXzXzHKgbXIFSk6lUQ2sVYkiRJktRCpacSx2Ix/vOflxg37gU6d67kwAPXT2OeOnUKAAcddGi9cBDgrbfeAJKVhi3ZxRdfyFFHHcTChQtq95WXl7PXXvswYsRIYP104d12G0pJSQnvvTeN5cuX1TvX2rVrOf30UzjvvLP45JNZkdy/No4BoSLlGoSSJEmSpNbkiCO+SUlJCffc82eWLPmcYcMOonPnzrWfp9f4Gz/+TdauXVu7v7q6mttv/yNvv/0WAFVVVdHeeAP17NmT5cuXc8stN9W516qqKl58cSwAX/nKTgD07785BxwwnJUrV3DNNVeydOnSOsffcMO1zJgRsnTpErbccqtoH0SN4hRjRcouxpIkSZKk1mTAgIHsuuvuTJ48EaC2WUna0Ucfx9///hgzZ37Id75zDDvvPISamhref38ay5cvZ9ttB/HxxzNZsuTz5rj9go0adQETJ77Diy+OZcqUiey442AApk9/n88//5xttx3EiSeOqD3+5z+/nLlz5zB+/Bt897vfYscdv0JlZSXvvTeNpUuX0KNHD/73f2/I2RBFLY8VhIpU9oxiA0JJkiRJUkuXXnOwf/8B7Lbb0Dqf9evXj7vueoBDDz2Cjh078tZbb/DxxzPZZptBXHrpFdx990N069adWbP+y+zZnzTH7Rekd+/e3HnnPRx33Hfo1Kkz48e/yYQJb9OjRy/OPHMUd955L5tssknt8d26deeOO+7m3HMvYsstt+KDD95nwoS36d69ByeddCr33vvXnFOu1TKV2CSi1ZoADK2ujrFs2ermvpeCXXLH6yxatr7k+scn7sqQbTdtxjtSofr06QrAokUrm/lO1F74zilKvm+Kmu+couY7J0iugbdw4RwA+vbdgtLSpqkZKk81Kalpji7Gapfa4jvXmL/XHj0qqagoA5gI7NGQ61lBqEhlr8lqkxJJkiRJkqTm5RqEipRNSiRJkiRJquuXv7yywWOOPfZ4dt119ya4G7VHBoSKlE1KJEmSJEmq67nnnm3wmD33/KoBoYrGgFCRyp5S7BqYkiRJkqT27tVX32nuW1A75xqEilR2QOgShJIkSZIkSc3LgFCRqr8GoQmhJEmSJElSczIgVKTqVRBaQihJkiRJktSsDAgVKbsYS5IkSZIktSwGhIqUXYwlSZIkqXmUlJTUfu1yT1LLlvk3mvm321QMCBWZRCJRr2LQKcaSJEmSFI2SkhLKyioAqKpa28x3I2lD0n+jZWUVkQSE5U1+BSklV7Wg8aAkSZIkRadTp0pWrVrOihVLAOjQoVPRw4d4PL2NF/W8Uj5t6Z1LJBJUVa2t/Rvt1KkykusaECoyuf5OrSCUJEmSpOh06dKNdevWUFNTxfLli5voKunA0X/fU1Ta5jtXXt6BLl26RXOtSK4ikTsMdN0LSZIkSYpOaWkpvXptxqpVK1i7djWxWHXRr1FenlzNrKYmVvRzS7m0tXeurKyCTp0q6dKlG6Wl0awOaECoyOSaYmwBoSRJkiRFq7S0lK5de9C1a4/UWvHF/RezPn26ArBo0cqinlfKpy29cyUlJZGsOZjNgFCRye5gDHYxliRJkqTm1BRhRLriKarKJ8l3buP5k1NkcjYpsYRQkiRJkiSpWRkQKjK51iA0H5QkSZIkSWpeBoSKjE1KJEmSJEmSWh4DQkUmdwWhAaEkSZIkSVJzMiBUZGJ2MZYkSZIkSWpxDAgVmZxTjE0IJUmSJEmSmpUBoSLjFGNJkiRJkqSWx4BQkclVLGgBoSRJkiRJUvMyIFRk7GIsSZIkSZLU8hgQKjK5phM7xViSJEmSJKl5GRAqMrGcFYTNcCOSJEmSJEmqZUCoyORsUuIihJIkSZIkSc3KgFCRsYuxJEmSJElSy2NAqMjkCgPNByVJkiRJkpqXAaEiYwWhJEmSJElSy2NAqMhYQShJkiRJktTyGBAqMrm6GNukRJIkSZIkqXkZECoy8Xj9fQlLCCVJkiRJkppVeTFPFgRBb+BS4BhgS2ANMB74TRiGL+Y4/mDgEmBXoBL4ALgTuCsMw3rJURAE5cDpwDnA9kBN6vzXh2H4Up576gn8HDgO2ApYCjwHXBOG4X/zjNkBuAr4BtAX+BR4DLguDMNVecY06Fnao1xTjF2DUJIkSZIkqXkVrYIwCIIAmAJcDFQAo4FZwKHA80EQHJ91/ChgLHAAMAkYBwwG/gzcneP8JcD9JEO3bYDnganAIcCLQRD8IMeYXsCrJEPLcuCfwOfAacDkIAh2yTFmL2ACcAowH/gX0AW4HHg9CIJuOcY06Fnaq1iOEkLzQUmSJEmSpOZVlIAwVdn3CLA5cDOwXRiGx4dhuDvJMK4EuDcIgk6p43cAbgFWAHuHYXhYGIZHA18BZgLfD4LghKzL/AA4GZiccf4DSAaQ64DbgiDYImvMTSSDunuAIAzD74RhOAT4BdANeDAIgtqfQcZzbAKcEYbh3mEYngAMAp4GdgGuy3r2xjxLu5TIMcXYCkJJkiRJkqTmVawKwuOA3UhW6/04DMNY+oMwDO8HngUWpY6B5JTfMuC3YRhOyjh2NnBexjGZLk9tLwjDcEnGmBdIBoGdgAvS+4Mg2BoYASwDLsq6p+uAN4EhwOEZ1zgJ2BZ4IQzDuzOOX0NyavMq4KzUtOW0xjxLu5SrSYn5oCRJkiRJUvMqVkB4Ymr721zr7YVheGQYhoPCMHwztevo1PaJHOd6HlgO7BUEweYAQRAMJlnFtxB4LceYx1PbYzP2HUkyuBsbhuHKAscck+++wjD8nOTU4Q7UDRUb9CztWc41CO1iLEmSJEmS1KyKFRDuldq+EQRBryAIfhgEwR1BENwSBMF3gyAoSx8YBMFmJBt/VAPTs0+UqvRL70+vETgktZ2Wp+HHe0AC2C4IgsqsMVPz3PN7qe2uGfsaNKaRz9Ju5QoDnWIsSZIkSZLUvDY6IAyCoAPJ7sA1JEOwELiNZKfh80iu6fd2EAQDUkPSlXTzwzDMsSodAPOyjk1v5+Y6OAzDtSSnEpcC/QoZk+MajRnTmGdpt3KFgeaDkiRJkiRJzau8COdId/UtAZ4E3gZ+RjIoHEKyaclewNNBEHyVZAMQgNUbOOea1HaTrO2XjenZgDHZ12jMmMY8S1FVVJTRp0/Xpjh10XWu7FBvX2u6fyX5+1LUfOcUJd83Rc13TlHznVOUfN8UNd+5xitGQNgptS0DZgGHhWFYndr3RhAEh5CcZjsUOJ711XmF1I6VpLbpBiNNMaYk4+sYySrEQsc05r7arbhdjCVJkiRJklqcYgSEmdVzt2WEgwCEYbg8CIIHgZ8CBwO3pD6qJL/Oqe2q1DbdZKSYY7KPT4/p1YAxjbmvoqqujrFs2YYKGFuOFSvX1Nu3dm0Nixbl6iGjlib9X2L8fSkqvnOKku+bouY7p6j5zilKvm+Kmu9cUo8elVRUlH35gTkUo0nJcmBd6uv/5jkmvb836ysINwuCIF9VXXq9vvT6fekx/XMdHARBJ6AHyUq+zwoZk+MajRnTmGdpt3I1KUlYQShJkiRJktSsNjogTHXqTXf3HZDnsHTjkIVhGC4hGax1BLbLPjDV8XjH1LfvZm13ynP+nUhO4f04DMN0pV4hYzKPa/CYRj5Lu2UXY0mSJEmSpJanGBWEAP9MbU/N/iBVWXdE6tuXUtvRqe23c5zrEKA7MCUMw08BwjD8CJgBbB4EwT45xnwn6z4AngXiwGFBEHTJMeaEHGPy3lcQBJsCw4AqYGwhY3I9S3uWIx+0i7EkSZIkSVIzK1ZAeAewDDgwCIKr0tNtU9trgD2Bj0l2OQa4lWSDj8syA78gCLZk/RqF12Vd4w+p7Z+CINgsY8xw4CKSwd2N6f1hGM4FngA2Be4IgqBDxpjLgH2A94GnMq7xD2A2yVDx3IzjOwN3AV2Au8IwXJAxpjHP0i7FclUQ5koNJUmSJEmSFJliNCkhDMPPgiA4mWQgdw3wvSAI3gWGkJx6uxQ4OQzDdanjpwRB8AvgeuDVIAheItnsZDjJEO4vYRg+lnWZ20lWIh4FzAiCYBzJ6rz9SU4vPi0MwzlZYy4k2T35VGD/IAjGA0HqvpYBJ4VhWJtQhWG4JgiC00hWBd4aBMEZJIPNfUmuJTgJuDTr2RvzLO1SrvUGnWIsSZIkSZLUvIpVQUgYhv8GdgHuIbkm31Gp7V+APcIwHJ91/G+AY4FXga8CB5Cs6DsdOCfH+ePAccDFwCfAYcBgktN9h4Vh+ECOMZ8Be5OsPkwAxwDdgPtS9zQ1x5iXUvfzOLAl8E2SjVh+BRwYhuGKHGMa9CztVa4KQvNBSZIkSZKk5lWUCsK01FqBpzfg+KeBpxtwfDXw+9T/Ch2zmOQU5IsaMGYa69c1LHRMg56lPWrKJiWJRIIXJ85l7uJVHLXPVmzavVNRzitJkiRJktTWFTUglDYkV0CYa9pxY3w8bwUPjZ0BwBerqzj3uCFFOa8kSZIkSVJbV7QpxtKXieVagzBenHMvWr4m4+u1xTmpJEmSJElSO2BAqMgkmrCCMLM6MRZzYUNJkiRJkqRCGRAqMrmalBRrDcLMc9sZWZIkSZIkqXAGhIpMruCuWFlenQrCHEGkJEmSJEmScjMgVGSasotx5rnjxVrYUJIkSZIkqR0wIFRkchX25QoNG6POFGMrCCVJkiRJkgpmQKjI5Jr62xRTjGsMCCVJkiRJkgpmQKjI5OpiXLQmJQkrCCVJkiRJkhrDgFCRyV1BWKSAMGZAKEmSJEmS1BgGhIpMrmrBYmV5djGWJEmSJElqHANCRaYpuxjbpESSJEmSJKlxDAgVmVxhYK51CTf23FYQSpIkSZIkFc6AUJHJXUFYnHPHsqYYF2ttQ0mSJEmSpLbOgFCRyRUQFivIyz63+aAkSZIkSVJhDAgVmVgTNinJnlbsNGNJkiRJkqTCGBAqMk3ZpCT73DYqkSRJkiRJKowBoSITj9ffV6wmJfUrCHNcTJIkSZIkSfUYECoyuab9FqvQL7ti0CnGrV9NLM6K1VXNfRuSJEmSJLV55c19A2o/cjUkKVaTkuxA0CnGrdvK1VVcedd4Vq2p5qyjB/PVr2zW3LckSZIkSVKbZQWhIpO7grBIaxAmrCBsSyZ9uJgVq6qIxRO8Pm1+c9+OJEmSJEltmgFhGzBv8SrGjJ/NkhVrm/tWNihXGJhIFKeK0ArCtmVtVaz266rq2AaOlCRJkiRJG8spxq1cArjhr5NYsaqKt6cv5Irv7dnct5RXvtAuAZQU+dxWELZu1TXrQ0HDXkmSJEmSmpYVhK1cTSzOilXJRg4fz1vRosOUfNOJi3HPBoRtS3XN+i7UsSJNQ5ckSZIkSbkZELZ2WdlJZrDS0uQL7YqR/zjFuG3JfI/jLfeVliRJkiSpTTAgbOWy1+9bV9Ny12tL5AntitGoJJ6VIllB2LrVDQj9XUqSJEmS1JQMCFu57OikurrlllvlC+2KEQDVqyB0WmqrVh3LmGJsQChJkiRJUpMyIGzlsnOwqhZcQZgv52lolrdiVRVfrKmue+7sNQhjhkqtWZ0KQsNeSZIkSZKalF2MW7264UlVC64gzFcp2JAA6P1ZS7jpb1MoKy3lklN2Z+t+3YD6jSxiLlzXqtVpUmIFoSRJkiRJTcoKwlYuO1tryU1K8gWB2esobsg70xdSE0uwrjrGxBmL15/bJiVtSuZ7nG/tSkmSJEmSVBwGhK1cdrbWkpuU5K8gLPwc6zIqJGs2UGWWXVGo1sU1CCVJkiRJio4BYSuXyJpi3JKblOQLCBtSQZg5dbgmnj9EsoKwdauuXh90uwahJEmSJElNy4CwlWstTUriiewoM+OzBoR5mc1HMkNBm5S0LVYQSpIkSZIUHQPCNqalNinZUAjYkAKxmszgKE9YmOt7tS51uhj7u5QkSZIkqUkZELZy2dNzq1tqBeEGQp6GTCGtyVM1WK9JidNSWzUDQkmSJEmSomNA2MrVa1LSQisIN1TR15AwL1Zn6ukGmpQYKrVqdaYYG/ZKkiRJktSkDAhbuezopKVWEG6oEUnDphgXtgahVWetmxWEkiRJkiRFx4CwlcsO3qpqWmEFYQMCoJo8zSusIGxbDAglSZIkSYqOAWEb02KblGwg49lQdWG2OqFgLH8FoQFh65YZEPq7lCRJkiSpaRkQtnLZ2VpVC51inB3glZeVrP+ssV2MM8PChFOM24p4PFEvFLTpjCRJkiRJTceAsJWrN8W4pVYQZgU+ZWXrX70GVRDWWYMw/zRUq85ar+oc0+QNfCVJkiRJajoGhK1ca2lSkl3hV16aWUHYgDUI84SCNilpOzI7GKcZ+EqSJEmS1HQMCFu7elOMW2YFYSIj4CktKaGkZH1A2NguxjWpcyYS9aekZlYXqnWxglCSJEmSpGgZELZyCbKnGLfQCsLMgLC0hNLMCsIGhD+xzDUIY+mAcMPXU+uSqwrW36ckSZIkSU3HgLCVq9+kpGVWzmVOIy4thYwCwkZXEKarBHOFR1actV45KwhtUiJJkiRJUpMxIGzl6gWEraBJSVlpCaUljVyDMFZ/DcJcYaAVZ61XrjUIDXwlSZIkSWo6BoStXPYU45bapKROBWFJCRkzjAsOf7LXGkx/bQVh2+IahJIkSZIkRcuAsLVrJVOMs9cgrNukpLDwp14jktR041wViFYQtl653mF/n5IkSZIkNR0DwlYuOzZpqU1K4vHsCsLMKcaFnSMWywoIN1BBaKDUellBKEmSJElStAwIW7ns6ruWWkEYz64gLG14BWFNvO6zpZuU5AqPDJRarxqblEiSJEmSFCkDwlYuOzaprokXHLhFqX4FYcZnhQaEeSsInZLaluSqIPT3KUmSJElS0zEgbO1y5Ca5Apbmlpnv1O9iXNg5YlndbWvXILSCsE2xi7EkSZIkSdEyIGzlclULtsRpxhtsUlJg+FOTHRAmNrQGYcv7GagwVhBKkiRJkhQtA8JWLlds0hIblWROIy4tzZ5iXNg58nYxtklJm2KTEkmSJEmSomVA2MrlWr6vJVYQZq9B2KgmJfXWIIyntk4xbkuqauoH3DYpkSRJkiSp6RgQtno5phi3xArCOlOMaWSTkrrBZyKRHJt7irGBUmtlBaEkSZIkSdEqL8ZJgiDYAQi/5LA+YRguzhhzMHAJsCtQCXwA3AncFYZhvTQgCIJy4HTgHGB7oAYYD1wfhuFLee6rJ/Bz4DhgK2Ap8BxwTRiG/93As1wFfAPoC3wKPAZcF4bhqjxjGvQsxdRqKggzbrR+k5LCfkSxWO5KQacYty2uQShJkiRJUrSKVUE4NLV9H3goz//Wpg8OgmAUMBY4AJgEjAMGA38G7s4+eRAEJcD9JEO3bYDnganAIcCLQRD8IMeYXsCrwKUkg9B/Ap8DpwGTgyDYJceYvYAJwCnAfOBfQBfgcuD1IAi65RjToGcptlzZWnVLryAsyW5SUtg5sisIIRkaOsW4bbGLsSRJkiRJ0SpKBSGwR2p7cxiGf9rQgakKvVuAFcCBYRhOSu3fEngR+H4QBP8Kw/DxjGE/AE4GJgMHhWG4JDXmIJLB321BEDwfhuGcjDE3kQzq7gHOCsMwlhpzOXAt8GAQBLuFYRhP7S8HHgE2Ac4Iw/Du1P7Oqf3HANcB52/kszS5llhBWK+LccZnBU8xztGZOBaPW0HYxlhBKEmSJElStIpdQfh2Acf+HCgDfpsO1ADCMJwNnJdxTKbLU9sL0uFgaswLJIPATsAF6f1BEGwNjACWARelw8HUmOuAN4EhwOEZ1zgJ2BZ4IR0Opo5fQ3Jq8yrgrNS05Y15libXEgPCOl2MsyoIC1+DMHcQGMsx3oqz1qsm1xqENimRJEmSJKnJFDMgXAtMK+DYo1PbJ3J89jywHNgrCILNAYIgGAwMAhYCr+UYk67OOzZj35Ekg7uxYRiuLHDMMfnuKwzDz0lOHe5A3VCxQc8SlZbfpKSE0jpdjAs7RyzXFON8axAaKLVaNimRJEmSJClaGx0QBkGwLdADmAH8MAiCCUEQrAyC4PMgCJ5MreuXPnYzko0/qoHp2edKVfql96fXCByS2k7L0/DjPZKtfLcLgqAya8zUPLf9Xmq7a8a+Bo1p5LNEomVWEK7/urS0hJJGdDHO2a04zxqEuRqaqHXItQahU4wlSZIkSWo6xaggTK8/uAtwI8n1+F4EviBZofd6EASnpI5JV9LNT6/9l8O8rGPT27m5Dg7DcC3JqcSlQL9CxuS4RmPGNOZZItHSm5RkdzEutNgvZ5OSPGsQWnHWeuWqgPX3KUmSJElS0ylGk5LMDsbHhmH4EUAQBKXAJSQbe9wVBMFbJBuAAKzewPnWpLabZG2/bEzPBozJvkZjxjTmWSJR0bGCPn26RnnJL1VZ2aH2604dK+pUDXap7FDQ/XauXFJvX/celSxbUz9QKiktaXE/g7aiyX+umeWlKV026eTvsx3zd68o+b4par5ziprvnKLk+6ao+c41XjEqCK8EtgMOSIeDAGEYxsMw/DXwDNAR+CGQTnIKKQdKpwRNOSYziWjomMbcVyTWtcAKwswpomVldacYF7peYM4KwliCWM7uxlacFWrpyrWMeXMWC5ZsKOuOTq4p8vEcv2NJkiRJklQcG11BGIZhDTBzA4c8RbKZx17Aval9lXmPhs6p7arUNt1kpJhjso9Pj+nVgDGNua9ILFu+hkWLcvVmKb54PEE4eyn9Nu1Cz64d8x63YuXa2q+rq2rqBHgrV64r6H6XLVtTb9+ixV/k3F9VHYvsZ9DaXfvAO8ycu4Je3Trym1Ffo6w09383SP+XmKb+ua5ZW11vX5TvtFqOqN45CXzfFD3fOUXNd05R8n1T1Hznknr0qKSioqxRY4vVxXhD5qS2XVi/vt9mQRDkq6pLr9eXXr8vPaZ/roODIOhEsklKAviskDE5rtGYMY15lkhE2aTkiVc+5rePTOayO99gxeqqvMdldzEuySghLHR9uZocVWTxRL4mJVacFSIWjzNz7goAlqxYx+fL137JiKZnF2NJkiRJkqJVjC7GNwVB8EQQBDvlOWSL1HZOGIZLSAZrHUlOS84+VxmwY+rbd7O2+c6/E8kpvB+HYZiu1CtkTOZxDR7TyGeJRJRNSt6d+TmQDCWnf7I073H1m5Ss/yxR0CxtqMnRmTgWS+TsglxoZ+T2LjuMawlTs+1iLEmSJElStIrVxfg44Lt5Pv9eavuv1HZ0avvtHMceAnQHpoRh+ClAal3DGcDmQRDsk2PMd1Lbf2bsexaIA4cFQdAlx5gTcozJe19BEGwKDAOqgLGFjMn1LFFZF2EFYWbH2TXravIelxnYlZY0roIwV1VgLB7PXUFooFSQegFhjhA2ajW5Kgib/7YkSZIkSWqzihEQ3pra/jwIgmHpnUEQlAVBcAOwP/Ah8EDG8THgsszALwiCLYFbUt9el3WNP6S2fwqCYLOMMcOBi0gGdzem94dhOBd4AtgUuCMIgg4ZYy4D9iHZdfmpjGv8A5hNMlQ8N+P4zsBdJKdI3xWG4YKsZ2/oszS5KCsI19Wsv9baqvzXjWVNMS7NKCEstNgvV+hXE0/kDBidklqY7IAw1zTuqDnFWJIkSZKkaBWjSckjQRAcAIwCXgiC4C2SU2/3BLYC5gPHhGG4NnX8lCAIfgFcD7waBMFLwGpgOMkQ7i9hGD6WdZnbgSOAo4AZQRCMI1mdtz/J6cWnhWE4J2vMhcBQ4FRg/yAIxgMBMARYBpwUhmFt6hCG4ZogCE4jWRV4axAEZwAfA/uSXEtwEnBp1rM35lmaXJRrEFZVr79WwRWEpXW7GBc6HThXF+N4PPcahAZKhWlpFYSJRCLn+5urU7UkSZIkSSqOojQpCcPwh8CJwMvAYOCbQDXwe2BIGIbTs47/DXAs8CrwVeAAkhV9pwPn5Dh/nOQ05ouBT4DDUtcZCwwLw/CBHGM+A/YmWX2YAI4BugH3AXuEYTg1x5iXUvfzOLBl6jmWA78CDgzDcEWOMQ16lihU1URXQZg5xXhDFYTZTUpKSxpeQZh3DcI8lYWqa9ykudz59Ht8uvCL2n0tbQ3CXL9jMPCVJEmSJKkpbXQFYVoYhn8D/taA458Gnm7A8enA8fcNGLOY5BTkixowZhrr1zUsdEyDnqWpVVdHU21VE6u7/t/aqg1UEGbcUmlJ3YBw49cgdErql5m/ZDUPjAkBWLGqip+dvDtQvyFIrirNKOWaXgzNH1xKkiRJktSWFaWCUC1LVE1KssOcQisIyxo9xTh3MxLXIPxyC5euzvl1S6sgzNXBGGxSIkmSJElSUzIgbCM6VpTVfl0d0RTjqqxmKBsMCLO6GDemSUmuBhqxPGsQxuIJEoWeuB3IDAIz1/ir16Sk2SsIc79DBr6SJEmSJDUdA8I2orLT+tniVRFNMc6uVNxQk5LsLsYlmVOMN6aCMJbIO958cL3MyrwNBYTN3aQk3xRjA0JJkiRJkpqOAWEb0bljRkDY0isISyGjgLDgSr+8axDmCbXsfLteTU0i4+uMgDDWwqYYuwahJEmSJEmRMyBsIyozAsLq6ngk02uzKxU3VEFYp4txSVYFYYE5Xq6QKJ5ninG+49urzCAwOS07+X32lN5mn2Kcbw1Cf5eSJEmSJDUZA8I2InOKcYJogp51ja4grNvFuNAwM9cz1eRpUgKGSplqsirz0uFui2tSkmd6fKHT0CVJkiRJUsMZELYRmRWEUHeduaZSf4pxYRWERe1iHEsQyzO+ucOuliS7Mi/9ff01CFtmBaG/S0mSJEmSmo4BYRvRuVNWQBhBo5LsELImlihoDbnGdjHOFV7FE1YQFiK7gjBdHpem9QAAIABJREFUqZcdyNU0dwWhTUokSZIkSYqcAWEb0bGirE5VXhSNSrIrCCF/FWG8XhfjjM82qoIw7hqEBcgOAtPvR2vpYuzvUpIkSZKkpmNA2EaUl5XSoaKs9vt8a7kVU+6AMHcwmd2kpFFrEOboZhLbwBqEhkrrZQdv6e/rr0HYzFOM81UQugahJEmSJElNxoCwjSgvK6FD+fpf57oIKgjX5Qgh83UyzszqspuUFJpJ5awgtElJQbIbvOQNCJu7gtAuxpIkSZIkRc6AsI2oKCulQ3nEFYQ5Qsj8FYTr76exTUpyrUEYiyecYlyAvBWE9dYgbKEVhP4uJUmSJElqMgaEbURZWSkdKtb/OqNZg7B+mJN3DcLsCsLShk8xzhX4xWJWEBYiu4KwqqVWEOZ5bw17JUmSJElqOgaEbUR5WQkVGVOMI+li3IA1CLO7GJdkTjEuMPvJDrmS5y1Ok5JYPM6UjxYzc97ygse0JtVZwV86iMvubtzcQZxrEEqSJEmSFL3y5r4BFUe9JiV5gpZiatAU44yAp7QUMgoIC29Skm8NwjzjGxJ2jZs4l4ef/xCAX3xvDwZt3r3gsa1BdhCYbw3CXCFslOxiLEmSJElS9KwgbCNafJOSzArCek1KmmYNwoZMMZ723yW1X0//ZGnB41qL7LUGq/KsQdjcQVxmQNgxI/B2urgkSZIkSU3HgLCNKG+OJiUNmGIc38AU4/9n772jJLvra999UqUO0z1RmpFmUCwUUUISEhlsgWUbDNjG4Pswxtj3Ya7xura5Ds9+z/Z1wNi+gHHANsYYX8wFTEZIiCChiAQaZalGGmmkyTM907G6wknvj1On6vf7nd+p0F3V0129P2uxpqvqhN85dWZYvbX3d3ebHtU6CNvMIPR7KNwoV1zpmMPGmnEQCufPZigQEkIIIYQQQgghKwEFwiHBPiUlJclzdOMgtExDihh3M18uCPVR4nYzCHsRlRaqrXWrrrphQL2mNIFwNTkIc4KD0OcMQkIIIYQQQgghZGBQIBwSoohxS1BZiZKSmmZeXFczCA0DhtmbgzDN1RcE7RyE3YtK681BGAvIqpB8qq9dihjTQUgIIYQQQgghhKwIFAiHBNsy4QgOwhUpKdFGjPUOQr/dDMIuFMK06Gu7GYTdCoRBGKJcbQmEa91B6HpB4rvp1kF4yiPGKQLhqXY2EkIIIYQQQgghwwxbjIeEaAbhypaU6FyK6Q7C1s+mGjHuQvxpJwKm7d+t66xS8yQXo64MpR/4QYAflo7DsUy86LzNkkjaLw5NlfHn//sBBEGI33jrZTjr9HEASSFw9UaMW8+PGDEOKRASQgghhBBCCCEDgw7CIcFSIsYrUlKiESHTZxC21mOaasR4GQ5CP30GYbdilxgvBgbnILzz4cP4hy8/hr/5wiPYvWdqIOe49/GjWKi4WKx5uPvRI8331fuX2mK8Wh2EnEFICCGEEEIIIYQMDAqEQ4JzSkpK0h2ErufjC997Bp+77WlUah7EQmHTUCPGnc/VLmKcFlHuJroMAAsVWdQc1By+0vMzws/TAzlHVRBoRbE24SBsRJDV2YSn3EHo60tKOIOQEEIIIYQQQggZHIwYDwmWZa54SUm7FuM7Hj6Mr929DwAwmnMksc4yDYjp2m4chGmiXdsZhI19bv3Bfty2+yBefcUZeM2VZyS2E+cPAoNzEC4ITsV5xbXYL8S1i9+Pp9y/eLvEDMJTLRByBiEhhBBCCCGEELLi0EE4JNiWIZWUDNpBGIRhM6YqEjsI9x2eb773/LEFuaREdRB2If60dRAG6Z/VXB+f++5eHD6xiE/fugcn56qJ7RYUsW5QDkJJIFysD+QcosAmfj+qEFh3A4RhmJxBuJoixnQQEkIIIYQQQgghKwIFwiHBsUxkRQfhgFuM02Ycxi3G0wu15nvzi3VJ4Em0GHdxPtUBF6OWlFim3I68WPWa4mII4PuPH00cY6VmEMoC4WAchKKQGottgSaG7Xo+/CCEelcHJY52i3jvRQdht3FxQgghhBBCCCGE9A4FwiHBskw4QovxoEtK0lqSPT9ypc0IAuFc2VUEQsgR4+W0GPuhFIu1hXvgB2FC7LvnsaRAmHQQDubelautmYAr4yBszIPUXI/rBQn3ILC6Isa5TGsCAiPGhBBCCCGEEELI4KBAOCQ4lrGiJSW6+YMx1bqHmXnBQVipSw4w0zBgmv0qKQkk8dGxWvcgCMJmGUfMgeMLOHBsQXpPFO6AwTgI/SCQSkPmF92uZi/2iiiwxSKxTgispwiEqylinMswYkwIIYQQQgghhKwEFAiHhJUuKRGPb1uG9NncoiuJbguLruQAi0pK5ChwJ9KEKzViLIqkvh9oxb57Hj8ivVYjxmlx5uVQVpuSgxCVWv9FXFFIrTXENp24muYgPNVOPc4gJIQQQgghhBBCVh4KhENCryUlQRguy8EmHj/rWJKYc+REWdrWD+QyjGgGYevzbiLGadHXQGkxFh2EfhhqhdLvP35UEiUXqqpA2H9xVY0xR+/1P2YsOQjjiLFGCHQ9vXiadu1TsxX85ke+h9/5uzsxWx5MPNoPAum7FJ+pUy1cEkIIIYQQQgghwwwFwiHAMADL7L6k5ODxBfzux+7F73/8Pm2rbzeIwlvGsZDLts59+MRi230TJSXLihjLDkJxDmOgmUEIACfnanhq/0zz9VIdhI89exL/dksJ+47MddxWJxAOoqhEmkHopjsI657fk4Pwlvv2o/TcNB7dewJ3PXK4T6uV8Tz53CwpWdv4QYD7njiKB/Yc5/dHCCGEEEIIIascCoRDgN1wzYkOQtcNUh2C33voMI7NVHBoqoy7Hz2i3aYT4gzCjGNJhRIdBUJDjhh342RMa9f1fdl15qglJSlR63sea123Kt55XTRAV+sePvrFR3Db7oP4uy8+2vEaVBESGJBAqGkxTnUQ6kpKUu6zOLdRLKDpJ6qYyxmEa5u7HzmCf/jyY/joFx7Bg09NnerlEEIIIYQQQghpAwXCISCeASjOIAzCMNUNVhYitTpnWzfUBOEta5vIC2LOkZNl3S5NLNOAKTx53biLRBecJeSTEw5CtaQkxXl4/5PHmwKZOh/QCzoLhNPzNdTqkUg6NVvFybn2opneQTjYiHHd8xGG+nsQCYTJGLqfcu1Hp1ui76DmW6qCJSPGa5s9B1ou3b0HZ0/hSgghhBBCCCGEdIIC4RAQOwjFgg4gXcgRxbZ2UeR2iDMIIwdh9xFjw1BLSjqfL202neeHEHd3FFFJdDru3DbadBhWah72H1uAHwRYrCkCYRf3RHU0HpxaSNkyQp1zCADzSxRn2yGKgWEY3R/d9aS3GCe/jGrdw8xCS8xs12C9HETB0jCAjOAGDUPGjNca4n9EoMBLCCGEEEIIIasbCoRDQFMgtBWBMKWoRCqyWKLYU3PFkhIT+WwrYlyttz+mpcwg7CZiLIqamTZlLFJJieIgHMs7OG1jofl6araCxaosDgLphSjyNrK4dnCqvWtypRyEqhjoen5qbLii+Z78IFlec2y6Ir2uDUwgbK3dsU2YptyOvZpjxrW6j//41lP41C0lyaG7nhGFZAqEhBBCCCGEELK6oUA4BMSRW9sypfhtpZYUvwD5l/UlOwjVkhLBQdgJ0zQg6INdCT+iyCU6CFVtMVFS4oqik4XNG3LN1ydmq1rhrhsHoSq6HTzeXiDsNIPw5FwVTz43vWyXnBonTnMKAkgVslQx58hJ2RE6MAehsHZHeZaB7oTkU8Vdjx7GrT/Yj+/uPojvPnDwVC9nVUCBkBBCCCGEEELWDhQIh4BYFDMMA2MFp/l+WgmG5CBcskCoRIwFB2EnTEN1EHbex/dlQTKNjFpS4suutE2CQDg1W0VZ4yDUuejarQfoxkGYPE/8/UzP1/C7/3gv/uI/duNLdzzb9jidUL/PuhekNkDr3JNAMmZ8VHUQLvGZ6UTdbe8gXM0ikxirF+c1rmdEp2nQxVxPQgghhBBCCCGnDgqEQ4AlNH6MFzLNn+fK+girL80gXJobTJpBaJs9OgghOwi7iRinzCBUUR2EkpBpm9i8Id98PZXiIATS23xjVKfe4aly2+toFzF+eO9U08n58NNLb3v1gyAhtrqunyoCpwqEiphzVHUQdoiQLxVVzFUdhKs5Ylytt+7loEpc1hrSDMIOf58IIYQQQgghhJxaKBAOAXGLMQCMjbQEwrQZd67f/4hxPtO9gzBqMe7PDEIVxxZLSoLEXLvNkoOwoo3+qufTfy6vue4FmJqppGzdPmJ8fKbafK/T/MZ26ITAuhekNjkvpkSM1RmMqkA4KAeh/F1Za8pBKH5vS3XlDhs14Z74qzgeTgghhBBCCCGEAuFQYNuig7AVMZ5LiRiLDkJX43bqxtEnR4x7cxAupcU4bQahiqNGjBXRqZsZhNH52os8asQYaB8z1joIK5GAe0wQFit1vauvG3Sux7rrp16LLl4NdI4YD2oGoaeWlBhrx0EoimGDKnFZa4j3gQ5CQgghhBBCCFndUCAcAmzBaTUmRoxTHYSB9mfPD/AXn34Av/6RO/HgU+2jrmJ8MGtbUotxc12WkXgPiEpKRPFHJ/zsPTSLD33uIdz8/ecByIJcTxHjNg7CuhdIs+NEOkWMdZ+nFZWEYagVCOtugJrr47ggwPXbQei2KSlJcxCK93qh4ibWPrCSkk4txqtYYxK/t6XG9ocNlpQQQgghhBBCyNqBAuEQIDoIpZKS1BmEQsRY+CX+0WdO4snnZ7BQcfHN+59ve05pBmFKi/H2zSOJ9ww0SkqEJ08XMf4/33kaD+89gc9+92kcmipLAkNaxNiALEqqDsKMbaKQcyQx87kj89pjdY4YJz8/lOIgrLl+qkCysOjiuOAgdNuUinTC1QhT7UpK0hyEYsRYV7hRG9CMPbXFOBkxXr3RXXEGoc6Vu94IQlmcX83uT0IIIYQQQgghFAiHAjulpCS1xVh0EAq/xMeRV0DfuisizyA0kdPMINy+aQS2JT9isehjoH3EWJznd3R6UZlBqHcQmspswyAIJdEsdheKLsIDxxe0x1qKQHggxUGoOvBEB+SR6UUs1uR7vVQXoauLGHtLKCkRrk2dPwhE1z4IwSfhIDQMucxmFYtM4nc2qBmNawnVZeqtYnGXEEIIIYQQQgjQfbMEWZWcZs3gjbP/hvJ/jgCmiWI9xHvHKqiHNsxyFpXbdsNwsjCcLGBHf16G/ZjJGKiHNhDk4B97BnByCMszcODBhdVRIJPbgS3ksknRbmIsi7GCg+n5WvO9WMDrVFIiikWVmidFesUYsYhlGlKjsx+EkqgkCoT7jy00t9GxlIjxkZNl+EEgrQEAyoLYms9aGCtkcKwRK37m4GziONW6h9G8k3i/E56upMRtU1JSS3EQCtd29KS+eKXm+tpY+XJQBUIg+k7j9azmmKpcUsKIseoyXc3iLiGEEEIIIYQQCoRrHsfwMelPITgRzQzMAzhP0Ja8Pc8k9nmjA0DYZvFL3wAAXA7g8o2AHxqohRksfOYLMDJ5GJkCjEweaPxpZAq4sDqF8QywEOYwXh1H3t2KDFzUYQMNd+DkaBbjhYxWIJScYTqBUBC1Fque5GpzLBOWaSQEI9M0YJnpEeOWQJhPnE9lKSUlnh/i2HQFp2+So9Wig3Ak52Cs4LQEwkNzieNUa0t0EKbMIPQ8vTiT6iDsEDEGIoG43wJhXeP2jGZVRutZzSKTGDGuM2KcKGphSQkhhBBCCCGErG4oEJIElhGigBrCuWNI+7X+lQAw2njx4LcBAB/cCNRDC+Ugi4Uwh437NmMSNvbksjjmj+OIP4EFYyMASCUlqj4YhrKwt1jzpLl4VopAaOkixmI02Y5cjmLEOI2OEeMUserg8XJbgXA072As34qB79UJhEuOGGschJ6f6iBMa6sWZ/2lOggHEKOVxNxGNF36PlepxuT5geS6HFSJy1pCvQd+F83ohBBCCCGEEEJOHRQI1zjH/THcs/nNeNVlpwFhALfu4lPfeAwZw0fWcPHjLz4dGcMDvCpCt47QreKJvUeQMTxk4SJjeNhUMAC3AgTLFzYyho+MtYhJLALTJ1EEUCy0Pp8JRlC9fS/sifOQhYsanIQzzA9CSTSMIsYt8ci2DFiWASgGOK2D0NU5CLsRCDtFjPUC2cGpMq5S3lMFwlGhSEbXblypt5//mIbWQegG2uhxO+JrD8MQR9IchMtoW06jJhwzntNome3brlcDqqBb9wKEYQjD0Ld4rwfoICSEEEIIIYSQtQUFwjVONcxgaqwI5+zzAUTJ4d03eajXI1HoZeddjTO3jja394MAf/sXt0nH+PA7XorRvIPP3PI47n1oH/JGHeMZH+9/ywsR1hcR1itAbbH5c1iv4NEn9yMTVDBqVrE558F09U4zlQmzDLf0PeTxPfzJpIkn3e14xN2FoHIlzPw4gKTQFUWMWwKDbZqNOX+yCGGahuRMVB2EsUC4KUUgdGyzee6llJQAkUCoUlYdhIX28wWX7CDUzSBcQity7CCcK9cl0S5jm81m2toA5uyJwmgcXzYVwXc1UtUIuq4XpJbprAdUAXk1N1ATQgghhBBCCKFAOBSoTcHjhQymZqsAgPnFuvSZzhnnegEMw0AlMDEf5jEf5nGibsA+4+LUc/7L7tub4tEfvflq7Nicw/s/dCsy/iJGjBpGzSp++UfOxLNPPYvpQ89jqzWHbZYcp3WMAJdkDuCSzAGUP3UXzE27YJ95MdwtF8FAiLAxy7BS86QWVNuSo8QxltlwFjbwg1CKOnZyEG4Yad23zgJh6z6O5GyUG/P8DmkEQmkGoRIx1lFNKQ/phG7NdTe9xTj9ONG1HREajEfzDgp5B8ca7w3CQVgRZi82BUJj7TkIgUiYXc8CIUtKCCGEEEIIIWRtQYFwCBBFMQAYEwTCuYRAqHeZAfLcMD8IEYShJNDEhKEsvGUcE4Zpw8uM4cRCFgAwXnCQu+RlmAoP4RN7ngQAjBoVXDk+hZ8pVuE+/xCMuhxfDU48h/qJ52Dg6/ijiRweqe/E4+4OeNU8fLREvXgGoYppaCLGmhmEhZyDQtaWWnxty8BIzhEEwu4jxrtOG8Pj+6YBAEdPLsLzA0m0Xaj25iCs9NlBmDaDMI3YrXl0uuUKPW1jAXXhOIOYQVipyW3PgOogXJ0uNK1A6PrAEpqoh4VExJgCISGEEEIIIYSsaigQDgFOwkHYEibmy/KMuzQHIZBsX/X9AKaddEF5vjwjMHZK5TI2ZhEJkhNjkVA4Vmi55RbCPB7Deci/+jpUZsr4p3/+Il6UeR4XZQ5gwpTFwnGziutze3B9bg/C+e9iytiMxwub8bS7DRl/p1YgtHQRY80MQiByET5/bKH5eiTvwLZb+3ZuMW7dgB2bR5sCoR+EmJ6vYctEqyk5UVJS6OAgXOoMQs2aXU92EGYcs2PLbizEHRUchNs25nFsptp8PYgiDlkgjP5pstZASYnu+6oPQEBdS1AgJIQQQgghhJC1BQXCISDhIBxpCVAJB6HWZeZLfza39UM4midE3S525uUzLTFxcjR2EspiWCz4GJaNkrcdJW87sBji7991PozDj8Pb/xC8Q0/CCFvrNABsCafwitwUXpF7EvjB7dhkbcZd2bPwg/pZWAwjd6GupEQUakSBcJMiEI7mHElo7VTsIQqI2YwJ2zKb76lOPnEG4UjOHtgMQv13K88gLGRt1N16YjsRnYNw22QBc4stIUwVgPqB6JzUzSBcrTHVWpqDcB3DkhJCCCGEEEIIWVtQIBwC1BmEogCVmEGoiWnGLjvVWeb6AfKJrZPbZZzo/DlRIBxrRY1FYsFHniFowJjYjsyWM5G59AY8u+8wbv7C13BxZj/OtY9i1Kwl1nAapvDmkSm8ofBDPFzfiXtr56JsnJuIpHqakhIA2LxBvrKRvANLFAg7iFFyq7IJxzYQ66aq+zDpIOwgEC5xBqHeQagIhDkHMwvtBcL4GRHXPTGaRVb4fju5EJeC5CDMJB2Eq9WFljaDcD2jCqRBuDq/O0IIIYQQQgghERQIhwBdSUnMnBoxTnGZAclf6tNcdKI7yDKN5vlj1xegjxgDLWFQHW0oCgh1M4f76+fg/vo5AELscObworGT2FLbj3Oco9hgtpxtthHgiuw+XJHdh9ng+wj3XYecMYlqmEm4mDJKxFhkJGdLsenODkKhVdkyG/cgOp/qIFyotISvka4ixn2cQaiUlBSy+r/yWcdq3q/Y7eV68pzJrCMKhIOOGDdmEK7VkhI6CKXXfo9zMAkhhBBCCCGErCxm503IasdWIsaiQDhf6abFOPplXi2eSJvDpxaUxFxV3AogEnXin7MZS9omFnzU8hNRnJOdcAYOuhtwj/tCfLL8cvzBzFtw6CW/hfvNyzEfyCLfBsxj4plb8P7xr2GXdRzVmixSqDMIRUbzjhTV1jktRSQHoWlIxxY/84NAEr5G8w6yjnxPAGDDaOs7q/SzxdgL4ArfeSGnFwhz2Zb4Fzv11Hi26CDsd8TY8wNJyNRFjFevg5AzCFVqdWWe6Sr97gghhBBCCCGERAzMQVgsFj8H4C0A3lkqlf5V8/lrAfwPAC8CUADwBICPAfh4qVRK/DZZLBZtAL8I4FcAnAfAA3AfgD8vlUq3paxhEsD7AfwUgF0ApgF8E8AflkqlZ1P2OR/AHwB4GYCtAA4A+CyAPy2VSuWUfXq6ln6TiBiPtCsp6d5B6KbMDRPjpRnBVfaSi0/Dzm2jGMk7mGjMIASAsXwGJ9yo4KIZMW7jINQ54VpRaQPh+Hbck7ken566CBc5B3Bt9mlc6ByEaUTH2GQt4H3jN+OmyhX4Ni5EiOhkjlC4skl1EOYd6bydHISi4GE1HYSN9Qv3uFyVxaPRRrOteE8AYOfWMTyycAJAvx2EATzBCZjmIBQLZmK3l1jwkrGtniPGJ+eqeOSZE3jRuZul50GHKorqBMJwlcZU6SBMos4ppUBICCGEEEIIIaubgTgIi8XiLyESB9M+/68AbgXwCgC7AXwXwIUA/gnAv2i2NwD8GyLR7SwA3wLwCIAfAfCdYrH4Ts0+GwHcCeC3EQmhXwNwAsA7ADxYLBYv1ezzYgA/BPB2AEcAfB3ACIDfBXB3sVgcX+61DIK2EWN1BmHKnDogKWqkxQJrwi//WaXleMeW0YQYNC4IlrFz0GjnINQIXWqk1zINBDDxiLsT/7Twavy/M2/GD52rmmKgZYT4icIP8RvjX8fFzn4YCCWnpTqDUHUQpomjrfWIMwhlB6G4frGgxDKN5pxGdQ7hzm2jzZ/7KRC6ni9dSz7FQSgWzHhNB2FrHY5tIpdp7Vvz2q8xCEL81f95EJ+8uYS//MyDHcU9saDEAJpi5JqdQTiAGY1rCbYYE0IIIYQQQsjaou8CYcOB96EOn38UwByAa0ql0g2lUuknAFwAYC+AXygWi6q4+E4APwfgQQDnlkqlN5VKpVcA+FEANQB/VywWz1T2+RAioe4TAIqlUumnS6XSJQB+D8A4gH8vFovN6284FD8DYBTAu0ql0jWlUuktAM4B8BUAlwL40z5cS99RI8bijLtq3ZeEP13EOP5cjUXqSi/E7QEkorI6xPVYpj5iLM6X0wldIpZlSIUiADAXFnB/7nrMXPdrmPYLzffPtE/i3WPfxW9uuAn13V+Fu++HCGaOIJ81MSKIZSM5W2ox7jQzTXQY2oqDULzHYtHHSN5pCqPqHMIzt7YEwoomstoNuu+r7gXSWtMdhELEWNPGnJhB2EHEPDlXxeETiwCAQ1NlLHaITVcEp2Uua7Wi6GugxVgXMXY7CKjDjtrsvFq/O0IIIYQQQgghEX0VCIvFYgbAfyBqa9idstn7AVgAPlgqlZrblEql5wH8qrCNyO82/vxvpVLppLDPtxEJgTkA/01YxwsAvA3ADID3lUolX9jnTwHcC+ASAK8TzvFWAGcD+HapVPoXYfsKomhzGcC7G7Hl5VxL32nXYgwA84stkSqt6TYIw4QwlxazTYsYpyGuJ9Z7TOXJkyLGHcQ5yzQkZ1nz2KaBcPO5+Iu5n8ADtV3SZ2dYJ1D/wRdQ/ebfoPzZ30b5M/8Drx8voWBEDckbx3OS6NhpDWLLsW2Zkrgo3je1wThGvCcjORsbx1qR56U6CLUFNK4vXUvaDEKxYCYWOOUZhJYkIqrzKlVOzsvN034HR6ZcUNJaiygkr1YXmu77qq1zB2HCjRyEqzYiTgghhBBCCCGk/w7CPwFwBSJx7PmUbX6i8ecXNJ99C8AsgBcXi8XtAFAsFi9E5OI7BuAuzT6fb/z5BuG9H0Mk3N1aKpXmu9znJ9PWVSqVTiCKDmcgi4o9XcugUAVC2zIlp5gYM9Y54+peIM2bi9G5DQHFQWh3foTEyHOrxbi3iLGIbZmSsywmFg4Xwyw+WX4FPjh7Ix6uq8bSxvnmj+Nl3l34o4nP41d2PIoLzxxXHITLiBj7KQKhIM6JAuHWybwkvukcad2QJv6KwuFIzklsA0CKD8dijuQgVEpKOs3Ym1YEwrTCmxjRNSkKhKIQHKxSgUkbMV7vDkLNvyer9fsjhBBCCCGEENJHgbBR1PEbAD5TKpX+PWWbbYiKP1wAT6qfN5x+8fvxjMBLGn8+mlL48RiAEMC5xWKxoOzzSMpyH2v8+SLhvZ72WeK1DAQ1YgwAYyNCk3EXDkLdTLnUiLHXq4MwKRC2ixh3KgixrRQHoWFIwuEBfxM+vvAq/MXsjbgrvBz2rsthbNgGoLWNY/i4sPIAard+GBmj/X0SEQXEREmJNIOwJXyNCA5CcQbi9k0jUotw3Q3gd2hR1qEtKfECiH9p8t1EjIMgcayMbUrb9CoQdnL/SQ5CQaxcuxHj9e0g1LVcdxLdCSGEEEIIIYScOvrSYlwsFjcjKhE5AOD/brNp7KQ7UiqV0n6DPqRsG/95ULdxqVSqFovFGQCTAE4D8EynfTTn6HgCGts5AAAgAElEQVSeNuvq5VoGguogBIDxgoOjjTD2vOQg1Mwg9Hyt4JMeMRZnEHYWCIs7J5o/n719AwBA0Qel+KE6C1HFtszEDEIgPXp80N+Eu6ydeN0N1wAAgoUTcB//Ltwnb0dYjQym/oFHcVV+Gt8yrsdimOs8g7DLkpK0iPF1F5+Gh/ZOoVzx8GMv2SU5+IBohlsh15t+340opZtBqJuhqH4Hjm0h6wglJR0EwpPzVel1Z4Gwdbw0B+HaihivdwehRiBcpd8fIYQQQgghhJA+CYQAPg5gG4DXlEqlmTbbxU0Mi222qSjbdrvPZA/7qOdYyj5LuZaBsHnzKLZsGZPfmyzgqQOzAIDAMJqf5/InEvtbtoXRsXzi/cJINnFcALAFMWt8VL+NyJYtY/jDX3ZwfLqCV1yxoymGGUYrWjwxOYItm0cAAE6m/WO5desYRvKZxPv5vIPNm/W3Op93WuvcMgac9U4EN7wdx7/6UZQfj5LrGyoH8Wtjt+Dv538Epm21vS5RPtu8cRSjgksym2udS5RJtm4akY75Z7/6subPagQ3P5rDlskCesHQiKMqO05PFHEj65gYE5qnMxkb40rL8/bTN+Co4Ar0Q7S9P4uKaDa+Id92e1Now54YzzW3zQuR6EIh0/FZWyp+EOJvP/cgfvjkUfyX11+I1169s+t9dY3XVofnZ9jRRconN44kynm6ZT3fS7Ly8HkjKw2fObLS8JkjKwmfN7LS8JlbOsuOGBeLxV9FNL/vg6VS6bYOm8eqQTdWkljtGOQ+oqLS6z5LWddAcDRuuglB8JlZaDkIdb+4110/0ToKpDvSRMeUOJeuHVcUt+KGa2WnnBgzlh2E7d1XjmXC0sSqLdOEpbafNNDNSjTtDLa+4X0Yu+JHm++dbs/ifePfQL461XYNnlTgYcJOcRDOlVv3vp04YlumtMZKh9ZfHZ2cl4B+BqHjWNL6PT+QHGCmEbkkxRbjTkUqJ2YUB2GHeOliteW0FItUzBVyEN5897O49b7ncXKuhk9+/fGe9hUbmGPWu4NQ93x0mkNJCCGEEEIIIeTUsSwHYbFYvAjABwE8AOD3u9glLgxpZ42KrUvlAe6jbh/vs7GHfZayroEwP1fBcVsWzMSXR6cWcPx4tNzpmQpU5hdqOHo82eUyPbPY3E9kZq51jMDztdt0gxgznjqxAKehtc4p8+sS65ouw9XNfXM9zM7ob7URhqnrDK/8OWSQQ/2BrwAANlllvHbqf+PIE2fC2rxLu48Ys56fryAQRM3ZuUrzXDNzLaEs9Nvfq2zGaop8h4/MoaARQdtREUQ2HQaAxXLy3lqGgZqw70K5jqNHW+t0bAtTUwuSGFytum2v5di0bKydOrGAsUz6f484ITyXRtD6rlzpPleX/Ky1Y2ahhk/e9Lj0+tnnT0qR8HZUasn7Pj9fG8ha1wJhGKJaSwqEx48vwOvwjKrE//Vvvd5LsrLweSMrDZ85stLwmSMrCZ83stLwmYuYmCjA6WIUnI7lRow/gEgEWwTwiWKxKH52ZePPX24UmHwPrfbgbcVi0UgpHYnn9cXz++KZgKfrFlAsFnMAJhA5+Q53s4/mHPE+Gxv7PNvDunq5loGgc9ONCyUlUouxpvyi7gXaGYSpJSXCttklPnhA7CCMbptoDnM7OAjTZhCapqFtNwYikSsNwzCQvepNeOaEjzOe+zoAIB9WsPjVP0Phxt+CtfWcxD6esGDbVEpKhPvWy73KZ+xmoUxlCU3GHctdbFPrpHRsU5n1F0huxHi+ouj+rLU5lx8EmFlQSko6OAilkhKhsGUlZhB+5ttPSTMQAeD4TKUrgdDzA23bdzduzmHF80NtY3GnuZ6EEEIIIYQQQk4dy40YxwPfXgrg7cr/zmh89pLG6+tKpdJJRMJaFsC56sGKxaIF4IWNlw8rf16UsoaLEJmjnimVSrF9rJt9xO163meJ1zIQdCUlY4WWuCG1GGuEC9cLUHOT76dFAuvCtt2UlKRhiBFjQfzpVLZhmQYsteUE6SUlAKQSkTSmd7wUn1p4KfywcQy3isotH0Ywn4wbi2KHZRlyRFdYv6sR2tIQW4J1DqxOdGpedixTuwbHlgVO3w8lkTbjRJ+JAme97kuxcJHZhTrUjzrFS2WBUB9DH0SL8WPPnsR9TxxLvH9sOum01ZEWte7U8jzMpI0I8FOeF0IIIYQQQgghp55lCYSlUumVpVLJ0P0PwJcbm72z8d4vNF7f1PjzzZpD/giADQAeKpVKBxrneBrAHgDbi8XitZp9frrx59eE976BqEfihmKxOKLZ5y2afVLXVSwWNwF4FYA6gFu72Ud3LYNC32LcchBKLcYakaXu+dpf6nXOKEBpMe5CeEtDHBcoOo7aCYSWacAwDK1rsr2DsPM6bcvED+pn4+MLr4Lf+KsRVuZw4LN/jj/7xF147khkVQ7DULo3tmVKcyBFoU78uZ2LEQBygjC2FAdhJ2HVbjgFVW016SAMFQdhtG5RwAyRLvpNayLinVuM9QLhIB2ErhfgU98saT87poni66imfE+d5mgOM7p5pkBnFykhhBBCCCGEkFPHsktKlsDfIir4+B1R8CsWizsBfLTx8k+VfT7S+PMfi8XiNmGfVwN4HyLh7q/i90ul0kEAXwCwCcA/FIvFjLDP7wC4FsDjaImYAPBFAM8jEhXfI2yfR9TSPALg46VS6egyr6Xv2BqxbEyMGJfdpttL6yB09RHjtMiqKB4ty0EIsaREWE8bt1kshuqcgpaR7iDsRsiMj/2Yewa+abyi+f6kP4VXlW/CzfdGyXNVqLItUyn5EApX3CU6CDuUgOjo5NJzrEhczShCpWOZ0jPk+YHsEm2sWy2k0blOgTSBsJODsHW9koMwRUTuB6X9002noGEAF521sfmZOkMxjVQH4TqOGKcVtAyyZIYQQgghhBBCyPJYcYGwVCo9BOD3AIwDuLNYLH6rWCx+BZFgdw6Afy6VSp9Vdvt7AF8HcAmAPcVi8UvFYvG7iNx8WQC/VCqV9iv7/BqAZwD8PICnisXi54rF4sOIBLsZAG8V5waWSqUKgHcAqAD422Kx+MNisfg5AE8DeAOA3QB+uw/X0nf0DsJWxNjzg6aQoXPxRDMIe4kYJ+OnS0F0+4niT7tZerGQleYgTGsxtrsSCFvHfAhFOJe+vvn6osxBnD99R7Q+5b7YliE7CD29g7CTSLlcgVA8r+5cdkMYVIVKx5ZnOvqBEjG2kxFjID1GqxUIe5lBKNwHU/g++x0xXhCi9y84bRxXnr+l+fo4I8ZLRvdvCTCYiDghhBBCCCGEkP5wKhyEKJVKH0Akut0J4GoAr0Akqv0igF/RbB8A+CkAvwHgOQA3ALgQkUD4qlKp9CnNPocBXIPIfRgC+ElEQt4nAVxZKpUe0exzW2M9nwewE8CPA5gF8McAXlkqleaWey2DQOeaG8k7UpQ0LirRufNcL9BGItOcfKJzLNshNtsOcdldR4wbQpaZMoNQ8zYAJFxzOmxF5Mte/dM4PtYq3rnC/QH8o08note2MttPFBBFoa2Tg1B0zlVrvUWMAyX2XMgl+4echgCqFQjFKK+vLymxLHlWYZpTbNkRY2Ht4qzJfjvQxOM5loEtk/nm62VHjFNEsvVA2nPhdXCREkIIIYQQQgg5dSy3xTiVUqn0xg6ffwXAV3o4ngvgrxv/63afKUQR5Pf1sM+jaM017Hafnq6lnxiGXPYRYxoGxvIO5houqfmyi22Teleg6/kpJSUpMwilAot+lZSI62k/gxBAaoux0YgZq2JStzMIY/wghGGaeHDrG3DZzN9jizUPEyEqt/0z/Bt+T9nPkNyHbj9KSnp0EKoNsYWcg5mFuvRefH2quzByEAoR4yCU3YjCd5zLWFioRJ+liWAn56uJ99qJQ2EYSjMX5Yjx4EpKxGfENA1snWgJhDMLddRdv+PzLZbJGEYrKs+IcRI6CAkhhBBCCCFk9XJKHISkf6Q55gAgn2vFjGOnk04gjCLGPcwgHHDEuP0MwmgfWzeDsPGerqiklxmEQEvYW/Qt/Hv5egSNZuNw9gjC3V9Uzis7COP1B4Hs6uvoIMwIDsIeS0pUUVXrIGycP+EgtEzYQpTX90NJBBa3F+cQ9uQgbBMxrrm+NINSvA+Ss7HPMwhFgdCyTGwcz0rnO96Fi1AUcsfyrb9v6zlizJISQgghhBBCCFl7UCBc86QrhKIoFjuadK7AKGKscRCmuL5qUoHFchyErZ9DQfxpF8+MRTydCGi2EQh7nUEYl2rU6j72eVvxneqFzc+sp76Dc+0j0bmMqDlZFBdjYVUVOju2GAvim1ja0Q2qQDiSTQqE8RrVdSQdhAFcTUmJusaeZhC2cY+p1yo6CA2xpKTfDkLh+7Ea8ys3b8g13zvWxRxCUSQVi4H8IOxYzDKspJaU9FngJYQQQgghhBDSPygQrnHaOQhFd18s5ugchH4QamfepTkIpQKL5TgIDdFBKBy/jYMwLiHRthib6e7C3h2E0YJiseMblctwNJhofv7O0dtxmjUD207O9YvXr4p2HUtKsstwECYixukOQnUdtm1K5S6Rg1CMRrdEwazTOq5OCArCMMVBmP6divMHbcuQ7qU1wIixeLz4PL3OIRS/p/FCRvpsvc4hTBOO6SAkhBBCCCGEkNXLwGYQkpWhjT4ouftaDkK9aLFQcRPv6dyGvcZm2yGKm2klJQaihpmYVouxfgah+KdIdzMI5aKOMAxRjYVVWPiPysvw62M3AYGPUbOG94zdin+s3hgd3zKRgYsAJryGgKoKhHZtFvW9DyGcn0LoVhHWKzAyeVg7LoS94yLZQdjjDELxXLZlJBqH4zUCaS3GsnuyrmkxBuSIsU4Am190tW5Br62DUD9/EFBE5AHOIIwFQnEOYXcCYes+bRhRBEIvQD673FWuPXTzTAHOICSEEEIIIYSQ1QwFwjWOrRHKYkSRqNZ0EOp/SdcJhGmNxyLLiRiL4k+YIhCOFhzML7bWFl+v1kFotJtB2FuLcYhIQKoLAtC++iSyr3w3qt/5GAyE2GBW8Ev5m1H51vPYcegpfHDjNAAgCA3Mf+LfYOYn8c5RB1P+GM6yj6P6mU9Bljsb1/v4dwDTwq6Jc7HNLOJoMNFzSYksEJraOLPddgahEDH25ZISx+l+BuG0pqAEaO8ekwpKMvI/SYOcQegpMwgBYOtkofne8S4ixmJJyWjBkT5z1+kcwlQH4TqNXBNCCCGEEELIWoAR4zXOxvFc6meiENQuYgykOAg1EWPRWaaeo1cMyR0W/RmGobRG1ZXVdBDqBMI24mF3MwjlbXy/5SCM1gaYZ12N2Yt/tvnepLEA75n7YFenm++ZRgi4VZhzh3FZ5nm8Nv8YznGOQScONgl8FE6W8Gvjt2CbOdNzxFh1deqi37GDUG3mjRyEcoNzPWXOpCg664Sg6blkvDg6ZruIces4CQfhIFuMlRmEgOIg7EYgVMRN0W1ZW6dNxqkzCOkgJIQQQgghhJBVCwXCNU47gU4UidwOEeNyVSMQakSdhIOwzy3G6vrGFYEwFnJ0ImAzYqwZzNjrDEIgclCqcUnXCzC3/Wp8ofzijsfTYYxtgVN8GZxLbkDmijfAPudaIDvS/HzUrOE947ciX5/p6biu0jqsu96mg9BSI8ZWIl7tpkSMc5n2MwhPauYPAp1KSsSIsSxeis9IvwUmMdZuamYQnpirdnS9iU7PXMbSivLrDQqEhBBCCCGEELL2YMR4iMk4yXlxaRFjXWuu5yW3Fcsr4ubXpaJrMVYFSNVB2HQJaqLVTfFQ81mvMwiBSCirKU6+uhfA9wPcXrsAldDB1aMHceGlF2AuvwMfuGUafmgibwf4n++4FAefeRb33v0gNlvzqNmj+NE3vxHmlrMk5yQAhIEP76m7Ubn9X2AgxIRZwbvzN8NbuA726MaO6wbkOHgcMc7AxRWZfRg1q3ig/oLWDEJHFQjlkhIvUEtKUmYQahxyuoISoEPEuM0MQmuQMwiFNcUR6y1Ci7EfhDgxV5NchSqiwzSXsZBxLJSr0fWoz/J6IVUgZEkJIYQQQgghhKxaKBAOMVkhGlrz2keMdei2FV1Ry4kXA2oBRfRnUiCUWx7azSCMj6f7rNcZhEDsIJTFDtf1myLrffVzcdi+DFdcezXmZiqYDu4BAMy5gLlpF+bmx3BzNdp262Qer996tva8hmnBKb4M1VoduPdTAIDN1gIq3/wbjL7x92F0IcKKYu5GaxFnH70VfzjxfRTMOgDgxvxuHD/6GLxDPwXHVCLGlinP+lNnEEoOwiXOIFxqSckgI8ZSSUkrfj05lm0KncenK+0FQkFAzmVsRZRfrw7ClJKSPs+QJIQQQgghhBDSPxgxHmJ6mUGoQ1dSIrrGuonttkNXUqIKhGrEOHb56YpIrDYR425mEJqmIe3r+SFqdSVi7AfSPYzXo8Z2/SBdZEsjf9Gr8MXFq1pvTD0L98nbOu4XrwsALnP24d3Bp3H6kTua4iAAmAawrbwHla99AFcf/RxGjdZ8Pcc2ExFjucVYmEGY6TCDUHAQisds99xJMwhXsKREFAjF56mXJmM1YizNIEwRyoad1JKSHv7tIYQQQgghhBCyslAgHGK0Mwh7iD3qRB2xmVUtu+gVUceL3UVqVLaQUwWj6JrsNjMI9Q7C7h51227tW617CddT3Q2kqGQcZ1YFQNcLJIGw2xmId7oX4Qe1s5rv1e77HILF2Y77el6AazJP4x2jd8BG6zuqhjam/FFp2y3lp/DbG76KC5yDzbWLEeMQQK2ud4rKAmH7iPGmcTmum4bkIMzJz5SxUg5CQcwU5xAem15sewyxxTiKGIt/59apgzClgZszCAkhhBBCCCFk9cKI8RDTaQahYUTNvGnoW4x7c8W1Q3RtxetQXXcFJXLatsXYTHcXdrtW2zRRR7SGciXZJOx6gSxiNs6lzi90fVkgVB2GaeQyNr60eBUucg4gb7pAvYLa9z+L/Kve3Xa/DQfvwttG726+9u0CvjJ7Ie6tn4ta6OCFziH83Ol7sWFhHwBgzKziv459G7XQhn1XBqaTxdtGNuLOahHP+5slV58oemWd9JKSMAwlgXDzRB5HG03AbQVCpQlYZLAzCJMtxkBvTcZSxDhrS25L3YzG9QBLSgghhBBCCCFk7UEH4RCTdZLz4kRXoCrGqOgKTWRX3AAchIpAmM+pAmH0yOpEwKaD0FqOg7C1na7Z2fV8SViKt1fnF3peIIupXbotcxkL82EeX69c3jrWU3fBO/Rk6j61B7+GM5/7avP1olHAsRe/F7fVLkQ1zCCEgSfcHSid/y5kr/t5BEbrnmYND5a7CGNxGtdk9+I3NtyE/z7+dZzv74GB6DtxhO851yZiXK560jVLhR9tI8anZgZhIM0gFARCwUF4vEPEWBTDso4cMV6/Mwj14jIFQkIIIYQQQghZvVAgHGKkGYTNkpLWL+lqfFdFW1IixCbVNtxe0c0grCuuO9VBGIt/uqbipkComUHodClmik7AciUpENa9QLqHttkqTRFPu1QHYSyQ3Vk7H9XRHc33q3f8K8JaWdo2DEPU7v9P1O/7fPO9k/4Ibpl4KzCxPXFsx7GQufi12HPJr2K/l96OvMs+gbdmb8N/H78J59hHJdErK5WUyM+H6B50bFOaH9k+YizMIFS/7xWaQZgmEB6bqTSfTRXPl5+FuMU4Zr06CEVhVPz7S4GQEEIIIYQQQlYvFAiHGCnu6AYIglCaqaeKMSqDLikxOrQYayPGgiCnkhYxNpCMAKchOgEXqvqIsReIcxKj4xqGIYmAnhdIM+gyXYqpsUMvhIn9L/jJxuqBcPYIKrd+FKEfrSkMQ9Tu+TTqu1vOwWP+GD4yfwOq2U3a88Xr88dPx1/O3Yg/nHkT/nz2JzD38t+C84p3Y6+7Vdp+p30CvzZ+CyZ2fwKhW5PWByQdcmKD8eRYVrqX3bcYy0LuIB2EXpCcJQkAWycKzZ/rboBDJ/RzCKvKrL1cxqaDELJwLP4bw5ISQgghhBBCCFm9UCAcYrKO6CAMEo5AVXxTCUPAD+R9XLd/EWOzm4hxmoOwh5ISxzYlMbIdoqilcxC6qoNQ2F50bC5nBmHMdHYHMlf8ZPO1f+gJVG//OLznHsTil/8Y7qO3Nj+bz27FR+Zeh+lgFLZlar+beH3RZwZOBqM47E/C3PICZM67Dh+Zfx0+MPvjeLC+U9ovc+hBVL79dwgDv+0MwpOCg3DjWFaKei85Yix8b/12oKXNICzkbOzc1ip2efzZk9r9qzVZQKaDMCLNQagW/hBCCCGEEEIIWT1QIBxi5JISPyEQdnIQAoDnKS2+nr7ddikYmoix68vHV9cYC3JaB6GhdxD2sk4pYqyZQVj3fKm8RRTBbMlBGMoCYZczCEUHXaXmIXPlG2Gfd13ruE/fg8otH0Jw7Jnme+aWs/D9HT+P+TCKxjq2qXV3xutTP3MsE2YjIn3I34hPLLwS/2vudXjW3dLcxn/+IUx94x8TorPI9FxLIJwcy0rNyLp5ls3rbFdSskIzCNVn5qKzWhHsx/alCIR12SFqmobk3FyPDsIgDKXnQnYQUiAkhBBCCCGEkNUKBcIhxlHijqpIo5tBqEZxPdVB2MeIsRQf1bUYWyYc25Suw+5mBuGyBELRQdhNxNjU/ux6fmKeYjeIEd5q3YdhGMi9/Bdhbb9Av95dl6Nw4/uxGLYKQRzb1AqStm00PxeJtxUFvX3eVnx4/gY8JLgJ5x/8FrJP3tR8XaurEWNRIMzJ8wNTxD3PD5oN20CHkpI+60vimmzlmbn4BS2B8Mnnp6XnMkYUCHONe+jYyebw9YQqior/xvTqAH3iuWn8wcfuxtfufKbzxoQQQgghhBBClgUFwiFGjTt24yAs5BzptacII0tp5k1DV1LiKRFjQI4pWl3MIOybQKhtMQ4kJ1R6xFh2EHY/g7B1rdWGs86wbOR/5L0wJ89onXfX5Si88Q+Qv+F9MDJ56VxRxDh9BqFa2BK/r4rDIUx8auGlMLae23zPfPDLeJHzHADZTQokZxBKEeNAL5apc/zalpT0O2KcMoMQAM49Y6J5D+tugKcPzib2r7otATn+3mSH5fpzEKrFNflllJR8+tY92L3nOP7pS4/gxGy18w6EEEIIIYQQQpYMBcIhJisKVp48E88wZLdazIjiKlSLSuQZhMuNGLd+juOe6gxCQBYZ7C5mEKpx0V5mJcoR4xQHoR9ot5cixr5cUrIUB6HY7mtkR1D4qd9H7jXvQeGn/wT5G94Ha+vZ0vma51Jcl8312Wbzc5H4te6eurCRv+F9MCdOb7730yPfR8GowfNDSfg7Oa9GjMUZhHpxqKKZ4ycil5T015EnClamkRSVizsnm68fffZEYv+q8P3E61aLgdYb6lxKMTLeq0A4NReJgkEIHJ3WF8UQQgghhBBCCOkPFAiHmIzi8FsUxJg4vquSdSxJ2FFjyf2cQSg7CKM/XV/jIMyJAmHvDkJ7iQ7CBU1JSb3bkhJFkHWW4SCMMewsnHOuhjW5I7Gf7CA0YFtm0knZWKsowpmG0Ta2bVsGrPwY8jf8Ogw7AwAYM6t4Y+EHAGQRbFoRCG0hspwmDokCYS5jJcRdU2q67reDUD9LMuZicQ6hpqhEihg37qmzzh2EdWUuo3hfexV4xRIZ3d9FQgghhBBCCCH9gwLhEKMKeKJAaKcIhBnHkn6pVyPG/ZxBKDkIU1qMAWDn1laj7OmbCgDazyBMOgiXOoMwpaQkpf3WEe+bHyxpBqFYUqLGb9sh37d4Hp7eKbh5Qw7nn7EBAHDdJac1y2LUiLF4LHPDNky+4q3N96/J7kXRPtScOVepedJ6Ey3GKeJQuwZjQP4u+99iLESMNYKzWFTy/NEFzJXr0ueigJtrrD1rr+8WY9FBqP7Hhl5KSsIwlLafX6RASAghhBBCCCGDpHONLVmzqHPvFquiQGhoo7cZx4RjmU1nmBoxrmuEqKUiij+hrqSkcfw3vPQsZDMWtkzkcf6ZEwA6OQj1wlg32FZ7QcpLRIyFkpI2DkLVzZlGOwdhO9SIMRAJo6JoF6/VMAy8/+1X4Ph0BVsn883PdfdUFFc3XP3jWHjsTtSPRKURPzNyL2qVG4HRrBQvtkwDYyMZ6XtIjxi31qcTCK0BlpSIjkT1mQEiMXpyLNt0Rj6+7ySuvei05uc6B+F6bzFuKxCG3X+BQRhC3Hp+sZ66LSGEEEIIIYSQ5UMH4RBjmaYkeC0KpRt2ypy6rG1Jopcq7IiiR7fFG2kYYnxU5yBsrGPDaBY/++rz8Oorzmjuo7oExfdUoau3GYTtr6nerqREbDH2A+21dKIfDsL4O0+UkQjft2kY2LaxIH0HumsX9zFMC1tufA/8MNpns7UA85GvApALSiZGszANQ3aipkWMBRE0r5mJKc8gHGBJieZ5MgxDchGqMWPx+8lqWox1zcfDTkIgbPNvSTvU0QaMGBNCCCGEEELIYKFAOOSI4pgUMTb1TbcZx5SEovYOwuXOIGz93Cwp0TjhdGgdhCni4VJnEOpQ26ClkhLhPJ4qEHa5BtFBqBZ4tEN331QBt9O1aR2EivMxe9pZuNO/tPX53tvgn9gvzx8cz0bnk+KlS4sYD7LF2JNajJPXDshzCB/dd7LZtg0ANclBqGkxXucOwoxjLXmGpComUiAkhBBCCCGEkMFCgXDIEUsTpIixbWojwhnHSghdImIzby/OPB1ySUkkCIilF70KhOkOwqVFjHV4XiAJS+1KSpZS6JIVBDm1EbbTuprnspKNutH7nQTC9g7CmLuNKzHlR3MhjTBA9c5PYnqu5SDcOJZtHK+zuNdxBuEgS0pSZkmKXPiCjYg/mV2o48jJVpuuNIOwGTEWv7/15yAU//5mlZKSXgReT5lZyRmEhBBCCCGEEDJYKBAOOWJpgiQQmoY2IpyxLblsw0t3EC6/pCQ5X65bBxd7JtkAACAASURBVKFhGAlRJ44zqg7C3mYQdnIQ+tI9EQUQcV/VQdjtvcoKMdtaPZAca+2Q71sjYqw6CO324qdOHNWt23Ky+NziNc3XwdGnseHIfc3Xk7FAKMZLUwVCcQZh+4hxvx2EQdg+YgwAo3kHWyZacxqPz1SaP5erSXFTFYnXG6qDUGpE76HFWHUQUiAkhBBCCCGEkMFCgXDIEUVAKWJsp0eMrTYRY9ftvXgjDVGTiYUwr4e5faoQaKU4CHsRCDtt63qBJHSkzSD0vFBbuNIJ0UEYhGFiFlu7danryCgzB3UOQZFOJSXN9xwTT7o78EBtV/O9i6a/i1EjEs8mx3KJ4/UjYtytWNotcotx+r3Z2IhMA8DJuVaU+oTommxsI/6dqLt+39e82qm3KSnpLWIsPy8LFZaUEEIIIYQQQsggoUA45IiCheogTIsYO+1KSpYQm03DkBpq45KS7o+vClpxHHU5AmGakywmmkHYXcR4KTMIs4rrr9uYsewgTEaMuzm/pS0pST4jsYj5xcUXwzcjYSwb1vALo99D3qi1IsZdxEvlkhJNxFhxEPZTcPO7mEEIAJvGc82fRVHwpPBzvI0oqIZIRvSHnWSL8RJLSoLkDML1JrYSQgghhBBCyEpCgXDIEQULtcVY5w7L2nLzccJBKMZml9liLM8gTB6/V4Ewfq06C3uZldiNg9BPKylRnJdLKXRRXZndFl2Izss4Six+P51mKwJ6kUwbQ2+scS4s4MCO1zbfP885il8fvxmbrPnonII4lNZiLBd9tK49dGtw99wFZ8+3cIZ1ApHc1npOYhYqLu594CmcnF1Er3RqMY7ZKAiEsShYqXlSxHhjUyBUvr91FjOu1cUZhLKD0O9B4FMdhJ4f9tTqTQghhBBCCCGkN5KWHTJUSA5CMWJsmYkZdfH27UpK6kuIzaaxnBZjQOMg7EPEuFMM1/V8aRtRFJSEVc+X7l23Mwhty4RlGk3xqmsHoSZiLF53N03OdpclJeIz9fzEVTgrcwLB3nsBAKdZswjv/hDq/ltgjb+wuV2ae6x1fSHyZg3+1HNwn74H7pO3A/UKLAC/tQE44Y/iwfpOuDMXILvxdABAMHcM+z/7YVwUHMTJ+8dRePmbkS1eD8Ps7p81X4iKq6KyiBgxPtGIGIvuQcs0sGE0AyApqNbdACM5rBukGYQZpaSkFwehZtv5iquNoRNCCCGEEEIIWT78bWvIEYUp0fFkW6bWWZdxTGWWXktECcNwScUbaRhmBwdhp9Zd5fM0B2E/ZxDWvQCiyc8WziXuK5Zv9LqGrGM1xdx6F024YRhKwmosBkoR4w73EkhxEOoixsK11L0QC5f9F9z+6CJen38YAGDU5lH73icwAgPvHduKJ93t2OttQ+h7MKzWPznB7BFcvng33rBhDzab87AfCLD4gH5tm6wFvCb/OOr/+bsIzroK5qadqO/+Ks4Iotl0G405uHd8At6DX0PuurfB3nV5x+uVZxB2FzGOhUF1/mDshrUtE6ZhNCPz3TpAV4KFios7Hj6EM7eM4uKzNw3kHOoMwqW2UOsKTRYWXWwVCmMIIYQQQgghhPQPCoRDTuoMQsvQu8NsKxGVbf6sxCWXKxCa0M0gFATCDhFmVdSJtQjVBdiTg7BDFNd1A4hbWCklJaJbM1pD927LbKYlEHbjIIxm8wnn0jgIu5pBqBHJtM+I2LTs+ji5UMfNlcsw5Y/hbSP3wDKi79BAiPOcozjPOQoAWPjXb8MobIBhZwGECKYP4WoASLs1Tg4YPx3B1LMtt2kYwnvmfuCZ+7W7hPPHUbn1oyi86f+DtfHM1GsNw1CZQdiupKQlEE7P1xAEYdNJCMgCIhCJ7HEcdjVFjD/7nadx5yOHYQD4n+++BqdvGun7ORIzCKU5lEtvMQZYVEIIIYQQQgghg4QC4ZCTSYkL25aZGh8Vo7Ji1E8VO5YbMTaE0zcFQr8HB6EgaFmmASOlpKQXIbPTOV0/kByKaSUlohirftYJUdTtRiBUY+DxucTj2F04CHXb6GYQZm2xqTfA9HzkpvtB/RyYG8/EL5x7HN6+HyJcnJF39F2E81No6yMzbZgTp8N54cvhnP9SVEMHv/Ohm/GizHN4Wa6EbdactPlCkMVXK1fgLPs4rsk9AyMMgMCH++QdsK57W+pp1HF4ltFdxNgPQsyW6zgxmywoick4VksgXEUOwqcORN9HCGDf4fmBCITinMDEDMJltBgDwPyiq9mSEEIIIYQQQkg/oEA45KilFzFpAmHWMSWhyGvnIOxnSUmQPEcnAVIU6tJ+7uY4Ih0dhF4giR5pJSWVWkvMMNBdSUiM2GRc66KYQf1e4nVk+uEg7CAa1lwf0/MtN52/YQdyL30dwuvfjoXn9+DmL92Ec+yj2GVPIWMkr+VoOIl7K2fhCXcHfuFN1+Lcs7c3hV4AMOs+5sIC7qhdgDtrRfyvN4zCKn0TwfF9qJz2Ivzp4+ehHOZwb+08nPHCi3DGs18GAHhP34Pw2p9JnUeoutnafe+5jI2RnN2M6J+cq8oNxhsUgVCKYK8eB+HcYsuBV64ORmwTjzuSd+QW454ixpoZhEMoEFZqHoIwxEjOOdVLIYQQQgghhKxzKBAOOWkinm0ZMA0DtmXKZRptSkrqXkvgMYz2c9u6QZpPposYdywpaX1uKm5CkZ5mEHbhtBNFO6mkRHQQChFjxzYl0asTvToIVYGw6SAUS0qW6CDUiatjhUzz5yeem5b2mxyL3HaGYcLadi6+UbkMAGDBx1///FnIG3XAqyH0XJgTp+PDn3oG5Wp0jc7IWOI+id9rCBP+GZdj/IJrEQY+nt57EuXHHml+fmjkQpxhfh0IPITVefj7H4W96zLttaolGJ2e5Y3jOZSrCwCi+YNT0gzCpIMwZrU4COuuL83FVCPw/WKh0hLxRvOOFMfvpaREHzEeLoHwwLEF/Mm//xBBEOI333oZzjtj4lQviRBCCCGEELKOWZ4FjKx6dCUTgN5lFr+WS0pav6i7rlhQYvUkeukQdw+XIhAKri8xImoaS48Y62bRqccTpQvRGehIDsKWGNOLQAlE0cyYbgQmUcQVhduRfMuVVOii/VUnkukE5iuLW5rbTs/XcN8TR5ufbRxriWXS/DlYCCZ3wt5xIexdl8M552pYm85EzW3dTd2zqq4pLrowTAvliixyzXqOJAi6T92lv1C0BOnWedp/R3JRSU2OGK8BB+FcWZ7fp0bg+8WC4PIbKziSwKve83aosXkAmF8crhmE9z15FLW6D9cLcO9jRzvvQAghhBBCCCEDhALhkJMmjsUCoVoEEs0g1JeU1Lyli146VPEgCOTiiF5mEEoOQqu/DsLRfLq4luYgXOr5AVkgrKW0GC9UXHzj3ufw6DMnEs3PsXB7+XlbsH3zCApZG6++YkfH8+pitrq1T4xm8eILtjZfi3PnYgchkIxVq3PlgiCUhKCsJg6vatCiyLSoxGTLVRfO+dc3X3vP7UZYKyeOGa2lVwdh67qOz1Qws9CmpEQUCFeJg3BWEdcG4SCs1X1JEB3LO7JIrBH90tC2GA+Zg7Aq/EeEahejBAghhBBCCCFkkDBiPOSkzyCMfnFPOAgTJSXCDELRQbjM+YOALP4EYXpUNg21pET3M5DuotShmxVYyDmYX3S15RpWSkmJSO8CoTzjT8dnvv0U7n70CAwD+MUfu0B7rkLOxh+/62p4ftjVGrQlJSn37rVXnql1PYkCoeq8VOfKqdeme6YMw4BlGk3hOBCOUVZccOWKC+vMS2DkxhBW5wHfg/vM/chc8MrEcdV5eJ1mT4oi4N6Ds1LJyUbhmqPrEBygq8VBuCALhJUBOAjnlZbhkbyDaUFI7a2kRDODcMgEQrkhngIhIYQQQggh5NRCB+GQkz6DMI4YywJQxk4vKan3UCDSDXJJSSj9whydY2kOQlWYSnP26dCJZNmMlS7+pUSMRXoRKAHVQagXDp4+OAsgauN9YM/x5vvq+g3D6Fqg1EaMU/Y9e/s4ztk+nnhfFAhjcS9GdZCp4lmamC1G2X1JIFQchBUXhmnDPvfa5nveHn3MWC0pUYttVMQ5g/uPLzR/Hi84iXWvxhmEK+EgFB1++awN2zKXXFKi23bYSkrE/yCi/scRQgghhBBCCFlpKBAOOZ1mEKriUSbRYizMIBRcLr3M9UtDLilZgoNQWGd7B+HyBMKcky4QimtIKwLpRaAEuispEcWSpw7MNn9eTvRbN39RjaCLvPaqMxPvTYzKbjopYtrJQZh2j8UoeiBGjGWRa6Hx2jmvFTP2jz6F6UP78YXv7cVX73q2+QyLazGMpKisIkaMRfegOn9QvY5V4yBUZhAOosVYmj/YmH8pCq+9OQg1EeMhm0EoCYQ9xK8JIYQQQgghZBAwYjzktGsxBtSmWwOWaUoiU5qDsB8CoVpSIgqQBjrPhUt1EC6jxVgXMc5mLGQcKxFpVbdPO0+v96pTSYnnB6gIDjDRubUcgdDWOgjT3Y9XFrdgYjSDmUZ8dbzgJM4fOcii50aNjYrXlnHSm57TRKYFjYMQAMzNu2BO7kAwfRAA8M3//BJuKV8EANgwmsXLX7RdWkunghIgOWew3fvi37lV4yBcgZISMQI8WogEQjtF3O2E2jINRGv2g6Cr72stQAchIYQQQgghZDUxHL9pkVRSHYR2XFJiJbYVRa+0X2IHUVKiHr9TS3L3MwiXGTF2LG182IDsPNOJi8ASZhBm2peUtItaprkYu0EnyLZbu22ZeNUVZzRfb9qQb3tM1UFWV1qxu1mXXFKizCBsCIaGYaC2vdVmfIH5fPPnIycWE2vpNH8QADaMZrQuw40agVCM39dTSmZWGtVBWBlExFjjIFS//7DLJmNdSUmI5NzJtYw8g3B1PCeEEEIIIYSQ9QsFwiEntaTEjGcQmsK20c+pMwglx9fyZxAaasTY702ATG0xXpaDMGUGocaJaVmyiJnuIFz6DEKdA22+TdSy3xHjTuLqa688A2dvH0fWsfD6a3ZqjikKRMoMQuHadA3GMWaKC62slFZUan7zeb318Kbm+7vs4xgzKgBaz7N4HKuDEA1ELsPJsUzifW3EWHhWVkv5hCoQLtY8SWztB2JJSewgVN283Z5TV1ICDNccQo8OQkIIIYQQQsgqghHjISc1YmxHv7g7kkAYOwj1AqHb54hxoqSkR4eiNIPQ0IuFQG+uOm3E2LG016tum1ZS0vsMQqHFuJ4UmObaCYTLcRBqrt3pIATnszb+n//rqtTopy2WVPjpMwjbtWKLX6ffpsUYiISv8UIGT8yO4CV+AZPWIkwDuMg5gHvr5zWfZ9Gh1o2DEAAmx3M4MVeT3tNFjLN2ewfoqUCNGIdh9Gzls/37vwDZQRiJqaro7PshunlEvZSZfNEcwpElr3E1QQchIYQQQgghZDVBgXDISRPyYuFGEghtnUDYEmT63WIsGrd0EeNOmCmioChUdRNVFtGJebmMPmKsCo9pQuByZhDqSkraRoyXNYOwdwdhTNpcOFF8U2Oj0kzLNkKkrqQkDEPtHL1yxcV4IYOZch2P4Ey83CoBAC7O7G8IhNH+8gzC7p6PzWMZbMuWcH12D8phFk+427EVOxCGm2Un6RpwEAJRRLufAqFuBqF6b7stKknbbqEyPA5ClpQQQgghhBBCVhMUCIec1IixHUeMW59nnVg0FEQdqaSkO8dXt0gOwlCdcdhZgBTFJ1GIsIX1t4uu6tCKZI6lddJ16yDsNfbbqcV4XiP2dFpDN+icdMt1ikoz6NqUlGTbnEcqKWlEVKt1XxtXLVc8eH6A+UUXj9pn4uW5SCAsOofhwNNHjLsovfD2P4wfn/43jI1MNd873zkC3PYAFnefhuy1Pwtr52UwDEP6O3V8poogDDu2JA+SmuujqnGiLtY8bNJsv1REB+GoZgYh0INAuA4ixiwpIYQQQgghhKwmKBAOOaklJVZ3EWPpl1i3N4dfJ9o6CLsQuuyUuYNnbh3F1ok8js1UcNULt/a0JlFcjMmllJR06yDsuaSk0wzCNi6qZc0g1JaULM8pKopvqjgkRYwz7WYQto4RNo5RrurvwULVxVy5jhDA0942VEMHOcNFxvBRdA7D809PrCXNQRjMHoW79/vw9n4fwfRBjKWsL5g9gsotH4Z15qXIXfc2nLGlFYF97ug8vnnffrxOM59xpdC5BwFgMeUeLhXR3ReXlKhx/24FwrSIcbtnf61BgZAQQgghhBCymqBAOOSkOf1iwUv8PHaLpZaUSDMIlx8xTrQY91hSYqaWlJj4o3ddjUMnyti1LU3WSTmmYcBA1Jgak81Y2vuozlczDQOWaSREkOUIhLoZdmmCD7C8FmN1XwPpzczdIpeUpLcYZ7tsMY6PUa7o22zLFRczC9H98WFhj38GLrWfBQBc7OzHk/7ljeOkzyAMwwC1Oz8F94nvJo4fhMD99XMwExRwaf4ITsfx1tr2P4zy5x/H2Tf8Oq44fwse2BN99p+378UFuyax67TensV+kS4Q9rcRWBTvxgqNGYRqSclyI8bD5CDkDEJCCCGEEELIKoItxkOObZnaeGMsBuUyLY0415hHJgpa4gxCcZ5a30tKEhHjblqMhZISRYjIOBZecNp4T/MHgahZWXUCZh1Lux6deKZzEfbeYiyUlPQ4g7CfDkLH6W1+ow5bihintxh3W1ISC0xp7rdy1cPsQqtI5Dnn7ObPF2cOwGs8w+1mENYfulkrDrqbz8dfzt2IT5evx02Vy/G1ibej8JY/gbX9gtZGvofabf+Md7xmJybHstFbQYiPfeUxbeHMSpAqENb6JxCGYShHjNNmEHY5ay+1pKSSLo6vNcR/74IwTL1mQgghhBBCCFkJKBCuA3TiSywQXlXcglzGgmUaeMlFpwGQf6lPcxA6fZhBaCjCT+8txqKDsH+Psir8RQKhbgZh8py6KPJyHISuFyRcV/OCSLJ1Mt/x/N2iXk8/XKI691+MKH62mxVp6hyEKe63hYqLGUEgnBo9F2Hjn7kxs4pN7qHEWsTje4eeRP3+zzdfG+PbkLnqTRj52Q8g+2O/hYN+a2rfpvEcrI07kL/x/ci95j2AGQns4eIM7N2fxy/deAHiIx85uYjP37439RoHidpgHNNPB+FizZNmQsYzCA3DkP5DgK+ZG6lD/A8T4yOZ5s/DOoNQ95oQQgghhBBCVhIKhOsAXVFJLIJtnSzgr997Pf7qvdfj0nMi8UN2EAoCodvniLHkIOx9BqGVMoNwuahCWTZjaR2TOgehTgzsuaREmcdXV5pw58stkeTSs+Waib46CPvgEhVj2O0ixl23GIftZxCWqy6mF1qCWH50A8rjL2i+Pr/+eGItsRM1WJxB9dt/D4TRuozRTRh54+8je8VPwtywDYWsjazw3Wwaz0XbGQacc65G5qo3Nj/z9tyB88wD0uzB2x88tKwW3jAM8di+k/j+40d7cputhINQdA8aBlDItZzJUsw8pXxERYyAb9qQa/48LDMIQ41jkE3GhBBCCCGEkFMJBcJ1gF7car2Xy9gYL2S0n3l+iLAhyogR4/6UlIjCT+/Ht1JmEC6XhECYFjHWuBa7FQ3bobrp1DmEooPwknNkgTCtKKUb1Fl8/YiRi8dUBZFal63Y0qzKZsQ4fQahGDGeGMtgbtvlzdcX+E8gmD+emEEYBj6q3/4HhJXZxkkt5F/7qzByo83tDMPA5vGWWCUKVwCQufT1MLec1XxdveMTeMM1pzUdcJ4f4M6HD6deZyf27J/BX33mQXzsK4/hOz880PV+s4uDdxCKwt1o3pHEf9331wlRSJwU7vmwzCDUCbweHYSEEEIIIYSQUwgFwnWA3kGY/tWrn8VxP7mkpA8zCIVDqCUl3QhdgxMIlYhxpocZhJr72uu9ytgmxCOLUVzXC1CptV7v2DyCTePZ5mtnGaUi6tqX22AMyCKq6h6TZhC2KykxkhHjharsWIspV71mSQkATIxmUT79Kpzwo2ZhCwHqu78qOQht00D9B1+Ef/jJ5nvZl7wN1tbW/MKYl1+2HQCwYTSDF52zWfrMMC3kXvEuwIyuJSxPI3jwy3jZpac3t7lt90EpitsLTzw33fz5ceHnTogOQvE7Xqz1T2yT5g824sXNc7aJmachCmibxkUH4XDMINTFiRkxJoQQQgghhJxKKBCuA7qNx8aoYlj8y7oYCe2HeGS0ixh3IaptmWjN39uiuLmWQ9JBaGoFLLXFGEiLGPd2rwzDkETdulBuMa+4wcYKDs7ZsaH5esNoFkslWfTSBwdhG3FIajHucgahzkG4cUxwmCkzCCdGs7AdB9+sXtp8zy3dBatyovn6Bf8/e+cdJslZXvtfVefpSbs7s1lhFbZWu8o5oAgSIIRERiZYBPuCwZhr+xpjHMA2BgwYh+uAAxmMsHVBgMBCIAFCEkJCAcUtZWnzzobJHavq/lEdvqqu6jTd07077+95eKZDfRW6a8eeo3PeYz1L/sGbKs+jx55LbPNlgedy+ZlH8KnfOp9Pvvt8T4y2TGT5euKnX6Mc62dcvGVZRcTcO5nhsecOhF5rPdR4ciuz+NQZhKuWV//NdNZBWD3GkOJGhuAZko1Qt1uu/NvOF+zA4p5DjUJA1DovAqEgCIIgCIIgCD1EBMIlQKCDsI4A5xcPy86+QpOR0GZRY4i27XXQNOO6O3PTSl514QYuP/MIrjzvqAWfTxm/QJiMR4OFvy6VlEB4k7EqDLnOxgivvvAYjl8/wqnHjXHWppUtH6uMX/DsdMRYjfWC97qajRhbTm1JiVrUUhMxHowTjejckzuW/VYpLuxYrN7uthQv12e4LHtL9Vija0le9La67c0rRpJ1v9P4yS+F+ID7pJBleM8DHrfhj+/fEbq2Huo1+4XieqgOwjXLByqPOykQquLlkM9B6BWJW28xXj7sFf/nDoM5hIVircgpDkJBEARBEARBEHpJrQWmTQzDeD3w28DpuMLj08A3gM+YppkJ2P4lwB8CpwADwOPAvwKfM02zxl5hGEYUeAfwLuB4oAjcA3zCNM2fhJzTMuADwKuBo4CDwC3An5um+WzImo3AnwEXAiuB7cB/AR8zTXMuZE1L17LYBLYY12n99QtkVpcixqoG046DMBrRufqCDQ23a5XaFmM98Hz8M/ugMyUlUBZ1XSHEKxBWxZ7hAVeIWbV8gD96yxktH8NPtKakpMMtxv6IsUdwbs5B6JRbjBWRaOWyVCV+OzNf8Hxeo4MJDs7ksNG5JXMSvzb4cwDG9j/Ie4Z2sjoyRcIpCYrRBMnL34sWW5gbVYsmiBkXUnj4BwAUHruNS0//XR58ah8ADz61jwPT2RrhqxHtOghVgXD1ijQwAXS2pEQ9n8GBOgJh0yUl1e2S8SjJeIRsyUk7M19o+bPrN4Ijxoe+M1IQBEEQBEEQhEOXjjgIDcP4CK6IdgHwS+BHuOLaR4F7S0Kduv27gR8CFwMPAD8GNgP/Dnw+YP8a8GVc0W1Daf8PA5cDtxmG8faANcuBO4AP4gqhNwH7geuABw3DODlgzVnAfcCbgd3A94A08CHgLsMwhgPWtHQtvSAoHhuNhjuk/FHTqoOwsxFjj4PQN4OwmRbjbqEKpLqmEY3oDYte6r3WjpiqRm5VwWt6PjzKuVC6UlKiCNFFX7w0l28uYhw0g3A+xEHoj5+WHYQA9+SPZb89BICGjRHbzYhe/W8XyYveRmTZusYX1QTxEy6tPLYP7mBTah/jo66o5Thuo3GrqAJhrmA1FbXNFayKsAZddBDWmUGo3gNWk/MXi4qQGI1onn0eDnMIAwVCaTEWBEEQBEEQBKGHLFgBMAzjQuDDwCRwummal5qmeQ1wLHAzsAVXKCxvvxH4R2AaOMc0zZeapvlK4ARc1+HbDMN4ne8wbwd+DXgQOM40zdeYpnkxcAWQA/7ZMIwjfGv+Dleo+wJgmKb5etM0TwL+GBgGvmoYRuX6Sw7F64FB4J2maZ5jmubrStfxHeBk4GO+a2/nWhYdv4NQwyvO+dFKoliZcrumx/HV4RZjx+m8ANku6rUn4jqapi2opKR9B6FLWMR4uNMCoc9VGuvEDMI6EeP8AlqM55SSkpWjAzVrANLJKLFopPI92ejckj8tcNvYiVcQO+680HNoFX10NZF1WyrPi4/dxiWnVsXHnz20s9IO3iz+aG0zMWPVPQiu27RMJx2EdSPGkTYchGphUUT3CISzh0PEOEAMlIixIAiCIAiCIAi9pBM2retKPz9hmuZD5RdLcdwPl55epWz/ASACfMo0zQeU7V8A3qtso/Kh0s/3maZ5QFlzK64QmATeV37dMIyjgTfhipbvN03TUtZ8DLgbOAl4mXKMa4FjgFtN0/y8sn0GN9o8B/ymzw3ZzrUsOn4HYTSq152xBhBTHIZFy3YjwGpJSSdmEPpbjFuMGHcLj0BYEuqCBMtulZS4x63uRy3zqBflXCjdcRDWiRg32WLcaAbhipFEjesVqoUt6vf5y9wxJK/4HZ4Zv4xvzJ3L52Yu5gdjv07ivF9r9pKaJrblxZXHxWfv4/zjquLc5Gze07bcDKooCs3FjFWBcDAVY0i5Z7K5YtuNyn6aLSmx2ygpiUZ1UonqNAzVEXmoUpQWY0EQBEEQBEEQ+oxOqDDvxnXM/WvAe+W/+lWryitLP78ZsP2PgCngLMMw1gIYhrEZ18W3F7gzYM0NpZ/XKK9dWTr2D03TnGlyzdVh52Wa5n7c6HAcr6jY0rX0Cr87q16DcXWb6pqCZVO0HNQ/7esJOs2i13UQ9lIgrJ5XWSAMjBgHzHHslIPQEzHOB0eMO+0g9F9PLLLw71j9PPwCYa7JFuOIT2CybYeM4n4bTMZIBzQKjw7GS+egipQ20aNO45kVL+Ku3EYeKhzFdGJNQ8G8HaJHnoKWXu4+cSySL/y8ck4AO/bNNr2vomWTyXmFsWYchGqD8XA67mledsDzOS6E2WZnELZRGPpgmwAAIABJREFUUhLVdZLx6v2RzR36AmHwDEIRCAVBEARBEARB6B0LVmFM0yyaprnVNM1J9fVS5PdvSk+/UHptFe5swgKwNWBflvJ6eUbgSaWfj4QUfjyK+7fucYZhDPjWPBxy2o+Wfp6ivNbSmjavpSfUOAibmO/nF3b8A/Q70WKsaV7hpx9nECbiZQdhgBgYMMcxaLu2ZhDGgyPGqhAz1GUHYUcixnXEoXwbLca27dREYweSMdKp2s9iNMBB6OC601SHmh7gPuwEmh4hdsIlleeFx3/C+hXVeYk7JgI7jwIJmhfYqoNwJB0nFfcKqZ2aQ6jGfmtnENbOkGyEZwZhVPM5CDsXje4VIhAKgiAIgiAIgtBvdFyFMQzjbwzDuAN4FjgL+DTV2X1lJ91u0zTD/hra6du2/HNH0MamaWZxo8Q6sLqZNQHHaGdNO9fSE2odhM0IhNU/6guW7Wkwhs7ET1XDWk2Eue8ixs05CIOEzYXOIFRn9XXVQdiNiLGyT7WkpGjZHrGolZISNWqraZBMREgnawXCkYqDsLaVWxUrg9qoO0Vs00Wgu9fmzB3gtIFqOUkrAmHQ3L3pFmcQDqfj6LpGKlH9rDshEBYt2xP5rplBqJaUNB0x9s4g9DgID4OIscwgFARBEARBEASh36jN5S2cdwCjpcc5YA2wCtiFWwACMF9nfblWdND3s9GaZS2s8R+jnTXtXEvHicUijI8P1d1mua/EIRFvvCapOHbS6QRDw1Xnk6bBmtUjC45lLjtYbZDVdA31z+MVK9INz7FbDKarwtvQYILx8SHyBMy4G07WnOPQUKJmu7WrRzyCXzOMDicrj/Vo9ftS3XPr14x09DPyl2aMjqQC99/KMYcHq9eh3qvzvnl6a1aPMJwOFjwHFCE0mYoTT1afD6birFo5zLKRJOyY8qxbv3qY8fEhUoPeY40uGyCRqIpYQ+lEF++1IfZsOpe5x9zpCJsLDwPnArBnMtP0cffO1IqBlqM1XJ9TBLlVY+6/qcGBOJmc+28vnoot+NoPzmQ9z48+crnH8ZdQHg8MNPdZqzpiNKKzYln1d5ijNb7ufif53MGa1+LJhX8XQmeQ70FYbOSeExYbueeExUTuN2GxkXuufbph0zoZGADOxp0Z+GbgTsMw0kDZ+tGMjaSsyHRzjar6tLqmnfPqCWpcFZpztKnz5wpF2xNzjUUjHZnZ5o+OFjrcktwu6ueTrBcxDnALBgmB7TgIk0oUVJ1BODWbqzweHuysg9Btr66dv7gQIiEzCP0usGS8yZISyw6cd+cvxgBYURK1/Z9/oWh7Ztx1K2JcZviM6tjSgQNPsFJ3hcxte2aaLu2YDXALTir3wjd//CQf/Kc7uN/c691mprrNsiFXrFXdlv5m5HZQXYqxqF7zXXpmQDYdMfY6CFXBsZPty73C78gGKBQOfWekIAiCIAiCIAiHLh13EJqmua308F7DMK4EfgmcCPwmcGvpvYGgtSXKVrVy/q5cMtLJNf7ty2uWt7CmnfPqOIWCxeRkPRMj5PwigAMTE0HdLb6NSuw/OEdUeR6LaE2sb8z0VNVBWLRs7EL1GHOzuY4cox0Kinil2Q4TEzOBhRDZbKHmHPM572cdi+rsa6GMooxVqIogU9NZJiZmKBQtT1GFlas9/kKJ6DpFyz1Gzrf/8n+JaeWY2Wz1c5vL5Ctr9x6s3rMaMHlwLlR0zisz52bncuzcM115noxFmJiYIWAcJJptMTFRK8Lt2TvDrCJq5XPFrt5rTnI9+rL12Ae3A3BB8gm+NX8W2bzF1qcnGB9NNdgD7FCuucy+g/NMTMyw5+A8X7jpMQD+7uv38+n3nF/5LCeUzzniuPdyQhFMd+2dWfC1b9tRHT87mIrV3O+WIvZNTc03dTy15TcS0bAU8WxqJtuz3w2d4mDA7+yD05lD/roOddr5HScIC0HuOWGxkXtOWEzkfhMWG7nnXEZHB4i1afbpqk3LNM0c8I3S09OpzvdbZRhGmG2nPK+vPKyrvGZN0MaGYSRxI80Oboy54ZqAY7Szpp1r6QntzSCsblMsOr5CiYU7yyCgpEQRBTpRgtIuY6PVWOz4Mle8CXI0xgJm1/lnELZbtqJ+xmX3pr+UIsg1t1DUQomOzCDUgx2E+YL6Xdd3pEY89wmeGYTl9uLgFmM37q3rGuru3fmHigDVZQehpmnEtlxWeX5u4mniuNfQ7BzCuUxQSUm+Zh8HZ3JMTFaF9+lZpaSk5DhVm4wzHZhBqN6X/vmDUDtDshnUkpJYRPfMTTwcWoyLUlIiCIIgCIIgCEKfsWAFwDCMPzcM4xuGYawP2aSccYuZpnkAV1hLAMcF7CsCbCo9fcj3c0vI/rfgmpCeMU2z/JdyM2vU7Vpe0+a19AS/oOcvowgi5imXsD1/vHaqQERXhAPHoW9ajC84cQ0XnbKWC05czaWnrXPPJ+CaIwHnGPVt124TcCJAIFRLKVKJSFeKXNR7w99+vdD9FRVRTo2sJxp8Rv4oulqIURa7gluMqwKqej8VLdvjKuy2QAgQO+48iLnCc1LLc07iacBhR5Pu0rlsQEnJnPvavqksy/RZTojtQMfm6Z2u2zBfsDigzAccKc14HFDiunMdEAg9DcYBzdqemHkTAqHtONjKPEy3pOTwbzEOEg0FQRAEQRAEQRAWi04oDFcAbwCuDXn/ytLPe0s/v1/6+dqAbS8HRoBfmaa5HcA0zaeAJ4C1hmGcG7Dm9aWfNymv/Q9gAy8tzT7087qANaHnZRjGCuBSIA/8sJk1QdfSKxLR1h2EEY+D0Nti3Kn5gJqym0KxOyJkOwwko7zt5Zt451WbKy69iK7XCElBQmunHIRBAqHXqdV59yB4v/dOfAcRz/xA1UHYvCPVM4PQcTxz88rC4KBPIEwno8QUgdNzP1uOR6jqZotxGS2eInb8+ZXnr0vfw18vu57NW/+VuW9/lLkb/pS5b3yQ7J1fxbFrHXJBLcYzmTx2ZprVT9/In458i3cP3cr7hn7Ac9sOAPD0zumKEy8Ri7B2zP1VmEp2dp7fjCoQBgi1foG3Eep9Au73k1LmGmYOAwehtBgLgiAIgiAIgtBvdEKF+cfSzw8bhnF2+UXDMGKGYXwCuATYC3y+9NY/4RZ8/JEq+BmGcaSyr4/5jvEPpZ//ZhjGKmXNZcD7cYW7vym/bprmDuCbwArgs4ZhxJU1f4RbI/oY8G3lGN8CXsAVFd+jbJ8CPgekgc+ZprlHWdPOtSw6/vx5MwJhzCeo5IvekpJOoDoI53NFyqahaERjJF3bBtxr/O7AoM/RL6q1G8f2RIzzrnCglkEMpWuFmE6gRkTDWoVbwRMxVsShXNEbMa6/D1VgsplXXG/ViLH38yjHi8t4HLGW7RGh1HPsJrHNL/Y8T2oFxgq7sPc8hX1gG/bUbgqP/ojcPf9ds9YvEGrYXBB5mLnr/5AN0/cR0dzrOSY2wZHb3f/uYb5Qbco9bv1I5X5VHYTzHYkYK/dlgHAd1VUXaWOBsOgTz6IR3dOqfrg6CINEQ0EQBEEQBEEQhMViwSUlpml+zTCMC4F3AT83DOMu4CBwGrAe2A9cbZrmZGn7XxmG8cfAJ4A7DMP4CTAPXIYrwv2HaZr/5TvMvwAvB14BPGEYxo9x3XkX4caLr1PKUcr8Du7cw7cAFxmGcQ9gACcBk8C1pmlW/lo1TTNjGMZ1uK7AfzIM453AM8D5uLMEHwA+6Lv2dq5l0fE7/pqJGKtiWMGyKRQ67yDUQ+bOnbt5dU3zcj8Qj+qeRuEgYckvGrbtIIxX1+UX0UH4ygs2cP2tT7LpqFGOXr3weviIp8G2eg95HIQN7ifdN8POKxC6wmA65f1VNuJreI74IsbWIkeMASLL15E4/83MP3gzkfn9odsVHrqZyIojPY5D1TU5rM3z1sE72BjbDQElxCfbj5F57KeYL1S/v01HjlYeDyhiaqYDDsJGEWN/C3Uj/DFkfzNyNm/hOE5HmtR7RZBAqM7lFARBEARBEARBWGw6ovSYpvlu4I3Az4BTgJcCWeDvgJNN0/yFb/u/Bq4B7gDOBi7GdfS9A1do9O/fBl4N/D7wfGn/m3HjvpeapvmVgDW7gHNw3YcOcDUwDHwJOMM0zYcD1vykdD43AEcCVwFTwF8Cl5imWVMl2uq19IJEGw5Cz+w4X8S43bl6fsL+vr/i7CM6sv9O04zQWiMQdnAGoerUGu6Sg/AMY5xPved83vmKzR0RYMIixt4ZhK04CAmeQdjQQRgeMdYXSSAEiJ94OQNv/CR/ePBa/nb6ZXxt9nyyZ11H8or3oS+v3vfZ279AcfeT2NN7sfY8xYr5Z9kc28458af4wMhNrjhYYt6Jc+P8GTxXHKu8VrjrK2R3P1t5bhyxrPLY6yAMUBhbZFYVroNmEKrfn9NMxNgrlEUiOinlnC1foVEz3PyLF/jw5+/h7kd3N954ERAHoSAIgiAIgiAI/caCHYRlSk65pt1ypml+B/hOC9sXgM+U/tfsmn24EeT3t7DmEapzDZtd09K1LDa1JSWtRoy98wE7UV4BwQ7CLRuWs358sCP77zRR33X7I8dQGzHuxAzCcrzb4yDsQoNxNwiLGPtbjOuh+wQmVdQaTAbPIPQLhBF/xFhtMV6EGYQq8ViEkWWjPHcgznOs5MzUFsaPXklk+ZHMfesjkJsDq0DmO39VWfMGgABD5+SKk/jrp05g3knyQP5o/mD4Jgb1HJpd5F3pH/Dt+TN5wDmeo9cM4RSyWBPPMsziziD0lJRYzUSMvdu4JSXeeySbt5qO70/P57nhJ09jOw5f++ETnL15Vah7ebGQGYSCIAiCIAiCIPQbHRMIhf7FL1o1EzH2lzqoMwg7V1JSex4v7VP3IAQ4CAPOP+b7bNst+kj4ZhA6juNpMR4KEGL6EW/EWBEI1fuphRZjK6TFOBmPoGtaxaHmjxhHe9xi7GfdWJo9B+YB2LFvjjMBfXic1EveS+b7nwanvlhUcHS+OX82qaMuZd7ZAcCkneZLsxfxW0M/Qtcc0nqeNw3excWRF8j/8EGsHY+CVeSoSJxjo5fwdHF1RwTCuUYCoRZ8D4Shire65n4/iVgEDdcKDpDJF5uekXlwOle5L+ayReazxcDzXEwCHYQiEAqCIAiCIAiC0EN6VxUrLBoJnwAT5HzzU89B2KmGYb8us248zZajl3dk392gNmIc5CD0upraFVNVd5TtOBQtx+sg7ECByGLgKahQXFPqLMdGEWPV7WXbDnOKg7AcLdY0zTOHcJnPQRj1Cd7FPhAIy+zYN1d5HF23mcQFb1Xy9xokh5iwhthtjbC9uIyn9GP4zPQruCu3kWd2z3j2+0RxDV+du4CsUxXA1lnbsV74FViuGKhbeX5j8Cesjkx6xNZ2ySrfpRoFLhMmEoehOgjL/6FC0zRvUUkLTcb+GLUa1e8VIhAKgiAIgiAIgtBviINwCRCN6B73TbSJ1tZotPpHfcGyvZHQLkWMrzjriL4uHqh1YgaUlEQ77yAEd2afpy02YNZbPxIWL8232WKcL1ieezGtOMGOWjXEI88eQNPgSF/BSrQPWoxV1o0rAuHErOe9hyNb+PLktYymdf7PdRdSsOCj/3xX5f0XnbSGnft2AbBtb1UgPHr1EM/tnuG+/DE8XVzF6wd+wYnx7YHHH9DzvHvwR/zt9MuZmcvxjVseJWtHeetLDUYGW2sQV4tO/FFg8EXEm3IQVrdR/40l45HKsVppMva7JGfmC6xZ0fTyruBvagYoFJsXPQVBEARBEARBEDqNCIRLAE3TiMcilWIIv4gVhD+Sqf7x2qmSkmVDCVKJCJmcxYrhBOduXt2R/XYLvzswaHZdTUlJm2KqP3aby1tMzSklJYfMDMIOtBgr+5j2ub/KEWOAt1yxkdvu38Fx60ZYOZrybOe/nz0zCHvsINxzIEOhaFfE5J8+uJMZK8bMNPzi8QmMI6sFI4lYhGVDVQFPddsdu3aE2UyBfVNZJu00/z57KWcmX+C6jfuJDK8iuuEMnOws2Vv/BXBYFpnnQ6Pfpvi1G3kjFlN2ioduvYILr3l109dRKHoboZPxAAdhyD0QhiqeqcJuKhHl4EwOgEwLDkK/S1J14vYKKSkRBEEQBEEQBKHfEIFwiRCL6hWBsJniDE/EuGiTV5x9nZpBGI9F+D/XnsYDT+7j/BNXdyy63C381x30OdaUlLR5TdGITkTXKuLL7oPzHlFh3CeA9Sth8VK1xbgVB+GOiWocN52Mer6TlcsGuPbFxwfuwx8x9swgXOSSEoBVywcqrl7bcTg4k2XlsgEAT4R65/551imlPelUNHT23thokmPWDrNvKlt6RWN+9amkrzzNs52dmSZ/11cBSGpV8WxEz3Dqnm+TvTtH4uw3oDXhrPQ7+YIchGFN1mGo2/gdhGHHrce8TyCczUjEWBAEQRAEQRAEwU9/KzJCx1DnEEaaEAj9gkorkdBW2LBmmNdcdAyrlw90bJ/dwi/2BQlLftFwIWKqGjPetqcaQx0eiAXOeutH1Di7J2KsxIT9MzL9+EtKypx63FjTkfSaiHGPZxBGIzoJj+BlBT7etW/OWwKSjIXGy8dGkhy7dsTzmnHEaM12iRNfwm35U0LPrfDQzWRu/gxOdjZ0m6BzBTzXVMYjEDoOjuNw/a1P8qmvP8Bzu6drtlddhuq81JSy70y+FQehfwZhHzgIpcVYEARBEARBEIQ+49BQGYQFo4p6zTgI/YJKuQUUOldScqjR3AzCzjgIwRVbyvPTtitz6spOs0OBSIi4l2/BQeifVVnmjE0rmz6Pmohxj2cQgvv9lgU2VWhT3ZW79s8xq5aypGKhDdZjIylGh7zzA9V4ssqdkXO4b2o9A1qeGSeJhsNb0neyLnoQAGv7I8zd8CckL3oH0SNPDr0G9bwTpSZpP/45lOYLk9xy7zYAvvnTZ/i9N57q2V6NTasCs7ekZGEzCHtN0LxBEQgFQRAEQRAEQeglIhAuEdRikWYilX5BRbM7HzE+1GhGIPSLrwsRCFXhbNteVSA8NOLF4I8YKy3GheZbjIMcfqlEpKXGa1Xwtiyn5w5CcOf1TeHGXdXPQ23onZ4vsPdApvJ8MBULbbAeG01WZhQenMmxbCjBhjXDgdsOJKK8MOVt6vi76ZfxpsG7OC3+PADO/CSZmz9DbNNF6OPHgOOgRWNEjzwVLenGnhsVlEBtC/XEVPV69hycr9neM4NQmZeabNNB6I8Yz7QYMc4VLD7/vceZzRS47mVGRwT6IDHQst3ou96j+1EQBEEQBEEQhKWNCIRLBLVYpCkHoSJs+eNwnWoxPtTwX3eQsNSpkhLwRm937qvO3jukBELf/DnHcdA0zRdZbz5iXObU48ZaEl/V76XgKynplSCTjNVGjB3H8YiFAE/umKw8TqdiDAUU1KQSUdJJ11n4e284hfufmOD0jeOhn5Fa7gJubH16Hr44exHDm/dw7N7bwHaFtcLW22Hr7ZVttaFxBq7+EHp6mcdBGFRQArVzKNWCkdlMrRMwrMU4FW/PQbjQiPGdD+/i3q17Abjprud5xytOaGl9EGFuwULRDoxpC4IgCIIgCIIgdJulaQVbgqitqWtWNHbAeEtKHArKzDiJGAc/B69TDTo3g1AVTQ4lgVAVeMqFHOCNGCcaiKhBQuyZRvPxYv951DgIe1BSAvhmELqCV9F3bgDP7ZqpPB5MRRlMRfGf8dhIsvJ43fggr7xgg6fcxE9ZTAQ4dt0wF56ytvRM427nZAZe8xH0FUcErnVmJsh875PYmWlPWUiYg9AfM1fFvUyu6HEMgtdBqMaTPRHjBTgIZ1sUCJ/fXf38n98zU2fL5glrLJYmY0EQBEEQBEEQeoU4CJcIV1+wgaiuMz6aZGNAcYGfmpltimjRyPF1uFJTUhIgXGmaRjSiV0SOBc0gDInerjpEZxCCK85FdG9JSaMZhP4ikkQ8wpYNzceLwSsCFizb02Ic7dEMQlVQy1VmEdZ31A0mY0R0nXQqxqxSXqIKhM1w5qaV3P/EBLGYzrUvPp79leZjN84eWX4iA6/6MIVHbqG44zFwHLAKWLufAMCe3EXm+5+icPR1lXWppgRCm4zvGucyBUYGE8o2yrxTj4NQjRi332LcasR494F5z+OFxoAdx6nrIBQEQRAEQRAEQegFIhAuEZYNJXjzFRub3l6d/VW0bI+rZyGx2UMZ1Q0Y0bXQBt1YVKPcQdANgfBQchD6G7PL4k/OU1JS/zPyi4ynHjfWcpN2zOMgtCn2xQzC2ohxroEzLl0qKBka8AuErd0T52xexdFrhkjEIowOJjzx3d0H5ikULWLRKPFTriR+ypWV93L33Uj+vhsBsPdvY0PmS0S4CItIaMRY98XMM7548KxPIPQ6CJUZhO2WlAREjMtR92bYtb8qEBaKNvuns4yPtv9v0LIdHCf4vaDyEkEQBEEQBEEQhMVgaVrBhIaorqqiZXtnxi3ViLEiSgUVlFS2U97rVElJmXQy6omH9js1DsKSMJcvNl9S4ndrnWmMt34eHkes42kx7tkMQtVBWBJMs4VmBULvHMJWHYTgOlFHS8LcquWpyj3tOLBzX215CED89GuInfzyyvOh+W1cM3AfAMlE8Peo/i6xHO8MQsAjdAKe70b9dxYkqDbCcRzmfA7CQtGumfMYxmymUHN+u/bPhWzdHPVcguIgFARBEARBEAShVyxNpUdoiFpSMjNf8Lh+luwMQkWs8M8a9GynfD4LKXRJBDjrOtGgupjURoxdAaSViLEqSMdjOices6LO1sHEIl5HrFpS0rMZhDHVEdecg3CwJBAOD3hF4rHR1gVClYiue+aUqq3ZKpqmkTjnDcSMiyqvXZzcysmx55t3EPriwf7SkGZKSpptMc4X7JqZjkHHDGP3/lqhNEw8bRb/zEX1d0leBEJBEARBEARBEHrE0lR6hIasXTFQcXblizZFS51BuEQjxopg54/OqpTn46USEY5ZO7yA49V+zqsOoXgx1Ipvlu3UzrRsIDgfu26kct1XnnNUQ8dhEJ4W46LtiXj2RcS44IpmjZxxg6EOwoXfF+tXVgXC7RPBAiGURMIL3uIpMfm19F0s06YDt1c/X9sOiBj7IsCqgKaKZ6lEbalLI+ZDosh+V2AYuw7UugV3B7zWCn6X4IASnRYHoSAIgiAIgiAIvUJmEAqBDCRjvOWKjXz+e4/j998s2Yixct2xOq6zt1xhcOpx4xy1eohUov1/YkFC2KE0fxBA9815K9qOp8EYvG2+QcSiOn/xznOYzRQYHYzX3TYMVSDM+ea8RfqgpCRbKSlpEDFOuvfTkN9B2EbE2M8RSutxkIOwaNlsn5jlyFVD6NE4qRe/l8n//lNiToEBvcDZO68n/+g00WPOQk9VhXFVJLZsm2LOu19/q7BXIFQjxoqDMNecg3AuGywEzsw3V1QS6CAMeK0V1KZiXdPc+7/0GUiLsSAIgiAIgiAIvWJpKj1CU1xw0hp+45Wb8c/yX6oRY9Xpk6wj/EUjOqceP8ayoUToNs0QJJwdagKhpmneFlvLJlfwiiDNxLBjUZ1lQ4mmiyX8qE40f4y3HxyE5XPKFcKdcRpU5k+qDsJ0MrogIbrM+pVVgXCHz0HoOA4f+8p9/MUXf8lnv/0oAProau4cuLyyzUB+P7k7v8LcV99P5of/iJNznXbeFuPaiHHNDEI1Yqz8rkn6HIROWNOHgr/BuEzTEeMDtWJgkGjYCqpLMBbVPfe/OAgFQRAEQRAEQegVS1PpEZrmvC2redfVWypOsBXDiUOqJKOTHL9+lI3rR4hFdS4/84jGCxZIsIPw0JpBCF4XmGV5HYSaVn+eY6dQI+H+goqezSBsscV4IBmtzPNbMVx1DK5e3pl7QhUIp+cLTM1VXXZ7JzM8t3sGgF9u3Vtx+T2uHcetmS3eHTkOxWd/yfxNn8DOTHscmlZQxDjjdxAqAqEe7CB0HO8cyzDCHYRNRowDxMDZTIHpJh2IQagiYDSieUTQogiEgiAIgiAIgiD0CIkYCw05+4RVrBhO8ktzL+dsXtWz1tdeo+saH3zLGeQL1qLMYYwHlpQcWg5CqHWQ2QVvg3G7rsBWiHkEQq8I449BLxaq4BUUMY7omsdNpwrzJx6znBOPWc62vbO88oKjO3I+wwNxRtLxijC4fe8sI6V5mn4n3ny2yHA6TiZn8Z3MGTyQP4q3b5ll7OAjOHMHALD3byPznY8RO/VdlXWWZVcKWcrUOggVAS2qlpR4/81l8sWG8fRwB2Fjga9o2UxMZgLf27VvjuEj24u7+x2EqiM7X2wuOi0IgiAIgiAIgtBpxEEoNMWx60Z442XHc/Tq9ks3DhcWq6TF7yBMJSIMpQ4996bq0CvatqepdbE+S/Uc/DMQF8PBGITXQVhbUqI6+gDSyncfjej83htO5TPvvYCTjx3r2Dmpx1TnEPqFtrIzr3ze26wxZjZdQ/pNnyZ+ypWV7eyp3Yzf838Z0dy48XyuWDPTtKbF2OMgrH43sajuEZsbzWsMOu/KMZsoKZmYzFQEWl3TOHr1UOW9XQHR42ZR5wzGorpHvJaIsSAIgiAIgiAIvUIEQkHoU/wC4crRgUVx23Ua7wxCb8R4sQpvPA7CmhmEPSopUb7fcuxZjT8ftWrIM/9zMEAc7vT9EFZUEhYLVkW6ZDyCpunEz3498TNfU3k9mjnAtemfA05NucgyfZaXZb/H7FfeT/bOr2JnZ7wlJcr9oWmaZ26j/5yCCIsY+4tRglBnDY6NJjlCEU937VuAQOhxEEY8DkIRCAVBEARBEARB6BUiEApCn1IjEB6C8WKgZgZdzhcxXpRzCJlBqEHPIvONWowHUzHGR1PK8+5PhFi9ojrPcP90tvJ43ifGzWVqHY/lyLSmaSROv5rEOW+ovLc5vpMz488qe3A4P/EEHxz5Lpu0Z3EyUxQe/RFzX/8Ax0z+nCjuftX5leoxALJNCIRBpBL9AAAgAElEQVSqg7DcAA3NRYzVgpI1ywdYsyJdeb7rwFzD9WGocwZjEd0jkkuLsSAIgiAIgiAIvUIEQkHoU/zz1Q5ZgTCiziC0PeUSixUxjka8cxDL9HKepvr9Foo2lm1XIrvl99cqotRilAOpx5hX3HdBEWPb8Yq9/hmBsZNfTmT9iZXnrx64l7SWZaU+xXuHfsgb03eT1HxOvkKGkyZ/wvuHbyal5WsKZLxNxk1EjBURcZVS5tJMSYlaULJ6xQBrFPG0cw5CPdBBOJspsPX5g9hNNDULgiAIgiAIgiB0AhEIBaFP8cdvD1mBUBHhir6IcSKgiKUb+J1oZXrVYAxeNxxALm974s/JWISTjl1Reb7xiNGun5PqslPFtVoHYaEmqp1MeK9H0zSSF74NJ+KWeQzqOX576Bb+cOS7bIztru7bjuNsvAzi1fv7yOh+3jX0IxJ4hbyU8pll8q05CFcpDeAzmRYdhCvSrBmrirX7p7N1G6frUTOD0CcQZnJFPvRvd/PJrz/A13/0ZFvHEARBEARBEARBaBURCAWhT/HHb1WB41BCFecsy+s6WzwHYYhA2KP5g+CNGINb+KF+Nsl4hItOWcP/euVm3vOqEzndGO/6OQ0oAuGcIq7VzCDMFmpe818PgD40RuHEqyvP10YniWpVgeyh/BF8fOpqZra8hsFrP0V044WV9zZE97HxiS9jF3LVY7ToIFRnEK5aXhUgMznLM+swiF37qzHi1csHGBtOesS83W0WldQ4CCNeJ+kT2yYrMx4ffHJfW8cQBEEQBEEQBEFoFREIBaFP8TuyDgcHodWjFuOwpuJIDyPG0YjuOa9cwfKIXol4hIiuc+6W1Zy5aSX6IhTUqAJhLl8V0Woixpmi51yjES1UhLWPv5Tnit6m5f1Wmn+fuYTPzV7CtDPAbKaAlhwkefE72Jo4pbLd0Myz7LnhUziOex6q67KZkpIwByHUjxnPzOc9AunqFQPousZqJaasCoitUPDNIPQ7CKeV+Yj5YnsuRUEQBEEQBEEQhFYRgVAQ+pTBVIyTSxHTU48bY3Qw0eMzao+Ib/6fJ2K8SC3G4Q7C3rZCqy7RbN6qaQVebPxzDsvRYr8YN5ctBBaUBBGJRvjq7IvYaw2RsWPckjmJj09dwyOFI3FrYqpinaZp3JG8lLtzx1bWZ555gOJz9wPeOYetziAcHYx7Pu96RSXq/MF0MspQqUHaM4dwf5sOwgYRY7VhWZ3XKQiCIAiCIAiC0E26X4spCELb/M7rTmbPgXlPwcKhhqfFuGcR4xAHYQ9nEIIrrJWdatm85YsYL/6v52Q8gq5plXKMTLbI8EA8cAahWqhST8yM6BoT9jAfn7oGG42yKKgyq0SBLcfh+rnzGNYybI7vBCD/wE1Ejz6DVEJtMW4tYpxOxhgaiJGbctfNZMIdhGp8ePWKAbSSe9PTZNwBB2HU7yC0fA7CgoXjOJXjC4IgCIIgCIIgdAtxEApCH6NrGmtWpBclXtotaiLGnhbjpe0gTHoccd7Yrn8G5WKgaVrgHMLaGYTF5h2Epc/eRidIHAQ8rrmi5eCgc1PmtMpr9r7nsHY86vm8GpWUFC3vvTaQjDKYqjok6zkIPQUly6uioDrHcGIyW/f4oefVoMV4eq76WTjQcFaiIAiCIAiCIAhCJxCBUBCErlI3YtzjkhK9hyUl4M4ZLJPNW94W4x5EjAEGFJfefMmBVzuD0FtSopaH+GlGhJ1V3HyW7QpiO6wVZMZPqLyef+C7HiGyUcR4znfOroMwXnlebwbh1GxVPFw+XI32qxHsbKHNFuM6AmG+aNUIlzmJGQuCIAiCIAiCsAiIQCgIQleJKiJc0XLI9aSkJPhXXbSPHISz84VKtBe84uFiojoIm59BGH6ueshnrApjqkBYtKqfwdxxV1QeW7tMVuS3V543KimZV+LFEV0jHtMZGqgKfLN1BEJ136ooqArauQYOxjAKVvVzc1uMlX8fRbtGuMy3KUQKgiAIgiAIgiC0ggiEgiB0ldqIsTKDcNFKSvqvxRi8gtPkXM7zXq8chGlfxNh2nBoxLpOzPAJc3YhxyGc8NpKsPPY4CJVIrb3yeJJHVF2E63bfXnncyEGouh4HklE0TfMIhPVmEKrXpgqmHoGwTWdfKy3GgKf1WxAEQRAEQRAEoVuIQCgIQlfxRIytfosY99pBWBWf1FgrLJ670s+A4pibzxbI5S2cgO32T1cFzUYlJUGMj1bn+amuOcuuHi0a0Rk9/zWV50MHt7I2cgDAU5ISxJxHIHSvyRsxDp9BqJayqJFr9TpzebdApFVqI8bVfeaLds15iYNQEARBEARBEITFQARCQRC6iqfF2O5Ni7Gua4FFL71vMa5e/9RcVRhKxCI9K6bxl5T45w+W2T9dLemoJxBqWvBnrwqEc56Isdryq5E69jT0FUdWXrsk+TjQWsS47Ioc8pSU1HEQ5rzuwzLq/Wo7jicO3SyeFmPfDMKZ+XzNPvMyg1AQBEEQBEEQhEVABEJBELqKt6TE9sRJVfGl2wTFjCM9LinxCISzzTnyuo1nBmG2GCrE7ZuqCoSpOhFjCHZqehyEITMIoxEdTdOIn/LyymtnxJ9lWJtvHDEOcAEODjTXYqyKoqkQByHgEbubRRVA/SUlfhcpQK54+DsIdx+YZy4bLtgKgiAIgiAIgtB9RCAUBKGrqEUgluUwPVcVAkbS8aAl3TmPgJhxz2cQKoLTpCIO9aqgBLylHPPZgkdoU9mvCIT1Wowh2Kk5PlqdQZjJFSvCmSdiXBLPosechZZe5j7WbF6UNMnmLU+pi5+5bK0LUI0Yz4bMILRs2yM+hs0ghMYx5yDqzSAMuprDPWJ8+6928qF/u5vf/6c7OTiTa7xAEARBEARBEISuIAKhIAhdRXXpzeeKHtfV8KIKhEEOwl5HjKvikypYJXs0fxACIsYhAqHqhKtXUgLBbdGqg7B8LPCWlJQbsDU9SmzL5ZXXX5QwiVEkV8dF6I0Yl2cQKi3GmUKgwJjJefc5kKiu0XXNI+i1U1RS8DsIQ+ZjljncI8b3PL4HcK/zV0/v6/HZCIIgCIIgCMLSRQRCQRC6iuoeO6AUW2h4BZtuEw1oTO69QBgsBPbSQaiWcsznimRCZhCqNIpEB0WMR9Jxj9g2W4r8FgMchADxEy6GaAKAtJ7n7MTTdWPGgQ7CVFWQdhzv7MMyqiCqUeuO9DQZN4g5B+F3EMZjjQTCw9tBqH6Hh7sYKgiCIAiCIAj9jAiEgiB0FVWEOzhTjaUODsQWdQZgNOBYkQburW4TJqw1cuR1k2YjxiqNBMIgITaViDKY8jr6wOsgVNdpiTQx48LK84uTj5PJhs8RzAQIhKlExLPPoKKSjG/+oL9gxdtkvMCIcTMOwuLhLZqpjmLVlSoIgiAIgiAIwuIiAqEgCF0lzEG4mPFi/3mUCXK2LSb+mXaV1/uopKQ5gbC+oOkXCONRnWhErxEIHV8zcMzn+oyfdAVlg+GqyDT29odDjzkXEDHWNM1TVBI0h1CNJgeV6HgchJ2IGAc4W1UOdweh6sIsHOZiqCAIgiAIgiD0MyIQCoLQVaK+GYRlFrOgBAh0agXNxltMQh2EPZxBmPYLhE20y6YalZT43JvJcquwIhDOBMwE9BfL6MMreYKjK8/jz94Reky1iViNTavHbBQxVteVUcXbbGFhDsJoEwJhO03JhxKqACoCoSAIgiAIgiD0DhEIBUHoKkHOPeiFg7AfZxAGO+8aRXa7yYASMXbwuj7DhMBGDkK/UzNVuj6Pg3C+4HEPQvC981DkpOpx9z2OPXsg8JjqDEJV9BzyiZJ+5gOiySqqg7CdmXn1WoyDOPwjxtXrE4FQEARBEARBEHqHCISCIHSVsDmDwwOL7SDsw4hxn5eUAOybqs6NHB9J+TcHmphB6PvsU2UHoS/ua/lm0AW5PicSR7HPGgRAw6Fg/izwmB4noCJ6Bs09DFuXCnIQKgJhvZKUMPwzCP0uST+Hc8TYcRzP9ckMQkEQBEEQBEHoHSIQCoLQVcJceiODfeAg7NuSkt4JhLqueZyC+6cylcdjo20KhH4HYaLcKuwV69QGY6iNGAMkkzF+nju+8rxg3o5je4Ul23bI5IKdgA0FwgYOwoWUlNi2g2V7ZyxqmlbXRXg4N/vmizbqNy4OQkEQBEEQBEHoHSIQCoLQVUIjxovsIAwSm3odMQ4tKenhDELwuginlabf8dFkzbYajc/X/zmXRbZ0yu8g9EeMa7+zdDLKPbljsRx3n87sfqwdj3i28RerqBFjj2sxoMXYO4MwVvN+fAElJQW/QzLq7qtek3G+ePg6CP3zFf2fjyAIgiAIgiAIi4cIhIIgdJVQB+EizyCMBgiVPRcIQx2E9Wf6dRs1kqsyHuAgTMQjaFr9z9EfMx8IcRD6I8ZB39mKkRTTzgCPFtZXXis8/lMAnEIWx/IWq2hUS1EABlPV+27hDsLWxDt/hLYce1+yDkLf5ycOQkEQBEEQBEHoHb39K1QQhMOe0BmEiy4Q9p+DUNc0ErFIjZOqlxFj8DruVIIEwqA5fX78sx6TQTMI570R44iuBQqPZRfjXbmNnBzfBkDx+QeYu+FPsA/sAE1HO+Is0tqRzDlJUokourKfwVT1fIMEwkyjFmOPg7A1gdAvgJWFwXoCYbdajHMFi+0Ts2xYM+z5fBaTGgehCISCIAiCIAiC0DPEQSgIQlcJcoFBLxyEQTMIeysQQrAY2MuSEgh3EC4bSoTGhetRO4OwtsV4JlPwOOzCvpuVJZFya2ENk45bVoJjYx/YDjjgWCRfuJs/GbmRFyW2MpTyfu+qgzC4xbj6WmCLsXK92QUKhOV7sq6DMCRibDsOT26fZC5bew2NsGybP/vcL/irL9/HF/9na8vrO4U/oi0lJYIgCIIgCILQO0QgFAShqwTNkdM0GFr0GYRBEePe/woMEgN77SAMcs6VX/e7C9sTCEsOQkUgzOSKnjhtNOS7GSs1KTvo/CxzfOA2AAN6nten7+H90evJPXATdnYGgCGPazFfs26+FQdhixFjVSCMRvSKQ7KdiPH1tz7Jx796P3/0r3eHnofjONz1yC5uuecFskqhygt7ZpmYdNup7zcnWrqGTiIOQkEQBEEQBEHoHyRiLAhCVwmK8Q6lYjWx024TDRBhFvscgkgGFHz0vKQkJGKcSkRJp2Ke4pJm5iXWCITx8gxCr0g8NZerrglxEJZdjJbtcGt2C5efuZ4h5tDHjiKy8jisnY8x8/P/Ju644l/aniV/7w3k7/828VOvIn3MSyr7ms8WsW3Hcx/Mh7Qfl+lUxFgVBf0CoaaBU0pb50OOcV9J2JvNFHhy+yQnHrOiZptHnj3Af9z0eOVcX3nBBgCyiqDYyxIUEQgFQRAEQRAEoX8QgVAQhK4SJBAu9vxBCHakRftBIOxDB2HQDEJNc88r7YsfN3OufiG27CCMx3SiEb0SLZ2arTr6giLh5X2NjSTZczCDg86OVRdz6vFjlfcjy9fxtz9xuMi+h7MSzxDVSqKTVSB/37cY2L+NGBspEMUB5rIFj5t1TikpCZqvuJCSErWl1yMQ+q512VCCA9OuWJoPEM0cx2FGcT9mQ87jmZ3TlcfP7poJPO+i5dSIpIuFX/yUFmNBEARBEARB6B29z9cJgnBYE+QEW+z5g9CfLcYAiQAHXtBri0nQDMKBRBRN0zyxYGgyYuwTwMozCDVN80R+J2cVB2Gd70YtS5mYzHjeOziTY+dslOvnz+cjk6/FOekqtNRw5X3nuV/yvuFbGNLcdWpRiWXbHvGsmzMIVVEwFvV+hmPDycrjIAdhNm9RtKqFLhklPqyiFq6obr1swbt9r5x7foG1KA5CQRAEQRAEQegZIhAKgtBVgub89cRBGFhS0vtfgf3oIAwSxspuupoZhE20GPudmmosWRUcJxUHYb3vpp5A+NzuqmsuNjTK8HmvI/3GTxBZt6Xy+lHRffzu8PdZEznoEQgzOa9gNZCoFUrViHFY/DeMsIhx3BcxXjFSvT5/kQfUlquEOQjVuLS6jV+Y65VzryZiLA5CQRAEQRAEQegZvf/rWBCEw5ogJ9hIOrHo5xHkIOyHGYT+khKNWsFosQmKGJcLO9JtOAj9n7Na/qEKhFOKgzCs/RpgbLTqsKsRCJUo7dGrXeegFh8g9fLfJXbCpZX3VkTm+N/DN2Nve7jymtpgrAHJRP35kGHCXBjNziBcMVK9vqJlY9uO5/3Z+eYEwoxHIFTchL7tWxU6O4XMIBQEQRAEQRCE/kEEQkEQukpQxLgnDsIA0a0fIsZ+gS0Rj1TabXtFUMQ41EHYRkmJKrx5HIRz+dA1KuOKw27fVNbz3nO7VYFwqPJY06MkXvTrJM77NcpyW1IrsObhz5N/7DbA67hLJaLoAd+D+n3lCxaO49RsE0bBqgpiqqPVf2+OKQIh1BaJzPjal7MtR4z7Q5jzuyNFIBQEQRAEQRCE3iECoSAIXSWoHGQ4XStA9eI8+lEg7HW8GEIchMn2HYQ1LcaqgzBkBmFYSQnURozLIp3jOJ6I8dFrhjzrNE0jftJL+fHwq8g57jloOOTu+DLWnqeYz9ZvMAaIKw5Cy3Y8swAb0Y6DECDvE9JmfRHjsLIUj0BYJ2IcVISyGNSUlIhAKAiCIAiCIAg9o6OT8A3DeAvwG8ApQBrYA9wGfMI0zccDtn8D8H7gBCAC/Ar4O9M0vxmy/xTwO8BbgGOAOeB24C9N0/xVyJp1wJ8AlwPrS+f0HeAvTNOcCFlzJvCnwJnAMuAZ4EulcyuErGnpWgRhqRBcUtIfEeP+EAi9v4Z7XVAC3ghwmbKo11ZJiU+cTakzCBW34rTiIKzXMK0KhPmizfRcnpFBt/l3RonfliPGfqZGN/H324r8r6HbGNXnAcjd/23mN7y1sk3QZwC115srWDUCXxiqmFivxVgtKYFaIW2m6YixFbhN/zgIvedhOw6WbQfOLRUEQRAEQRAEobt05P8LNwxDMwzja8BXgAuAx4HvA0Xg14H7DMO4wrfmr4Fv4IqJdwK/AM4F/p9hGB8JOEYS+B/gE8CK0uNngdcC9xqGcXnAmg3AvcC7gQzw3dI5/TZwv2EY6wPWXAX8HLgKeBL4AbAW+CTwPcMwaqxPrV6LICwlgkQ4KSmpos60A0jGeu8gDGsxBkj73ks1IWiqMwgTsYjnueogVNO69b6bgWTU43KcmHRjxqp7cGwkWSNmqsfcYS3n63PnVV6ztj0M+5/1HCMI//cV5t4LIrzF2Hutw+m457WcT8Cbyfgjxo0dhK7b0Q4850KxP2YQAhSLzTsyBUEQBEEQBEHoHJ366/jNwJuAXcCZpmmeb5rmq4DjgL8CUsBXDMMYBDAM4zLgA8A2YItpmq80TfMKXMfePuDPDMM4y3eMDwEX4wp2x5qm+TrTNM8B3orrhKzsX+FLwBpct+BJpmm+HtgIfBbXTfhZdWPDMJYBXys9fblpmpeYpvnq0nX8AteF+H7fmnauRRCWDEFCT98IhH3hIKydQdhrYlG9piilMoMw5W8xbsJBqLg3/dsPhYh4Qc5TlbGAJuOw+YN+yq7FrYW1TERWV14ff+HWyuNUiINQ1zWPeOd349VDFeLCWoyjEZ1kPOJ5rbGDsPEMQnc7dz/9EjEOElelyVgQBEEQBEEQekOnBMLfKP38IzXqa5qmhRvVfRRYCZRdhB8q/zRN83ll+4dw48Aa8Afl1w3DSOMKczbwLtM0M8qar+K691bhuhXLa14EXIjrAvxz3zn9DvAC8ArDMDYr1/FeYBj4kmmatyhrDgBvKz39fcMw1L9wW7oWQVhq+KOimhYuCnX1PAJioP3QYtyPMwih1kFXmUGY9EeMWysp8Ud3w1x+QTMjVfxzCAGe26XOHwyOF4PqWtT4eaT632+WzzzBEZF97nmGOAjB6yJs20HomUFY3d9wOubOSox5y1BUmmkxLlp2jfBXFhL9oqZ/xuFiEdSeLHMIBUEQBEEQBKE3dEogPIgbK77d/4Zpmg5glp6uMwxjCLgEsHBnAfr5f4ADXKUIcRfjCnf3qSKcwg2ln9cor11d+nmjaZqevzhKcwS/U2dNzdxA0zS3Ao8Aq4GzAdq8FkFYUvhdekMD8Z4Ic0Ez7erNuVss/I7B/hEIfVHiBc0gVByEPkFRjRh71jRwEI6PVuf0TUxlSgUlTToIU1UH6yP5tejjGyrPX5p6CICBRLiI7REIW3EQWo1LSoYG3HPzCIQNIsZBIqXfPahu5z9ntV15MfG3GEPv4s6CIAiCIAiCsNTpiEBomuarTdPcbJrms/73SsLYGaWn24DNuCUez5umOe3f3jTNfcBe3Fjy8aWXTyr9fDjkFB4t/TxFea2dNSe2uKadaxGEJYU/YjzSg3gxBDsI+yNi7Csp6YMZhBDgICwJhMl4pPIdRiM6y4YaF86on3PKFzEOdRA2mA/pdRBmmZjKMqe0EB9VVyCsHnMuUyRx+tWV5yfFt7MucqCug1AVRTsxg1C9lnVjaQASC4wYBwmEZedgzQzCHjkIg8TVQgut0IIgCIIgCIIgdI7FmND/HuAo4ADwI9zCD4AdddbsLP1c6/sZtqa8/SrDMMrX1OyatVCZP1j+K62pNU0cI2iNICwp/CJcL+YPQrBbsB9KSvylJP0wgxAg7YsCp0qCmaZpvP3KTZy4YTnXvcyoiRwHoUZo/c7EMIGwkXg7PuKNGD/yzP7K85XLUnXPS3UtzmUKaOtPQV9xVOW18xNPhLYYg/c7asVBWAyJGJ94zHKuOv8ozt28imte5LoZvRFjr4DXTMRYbTD2b9fJGYS79s/xlR+Y/PONj7D34HxLa4NLSiRiLAiCIAiCIAi9oPHwqAVQKvD4VOnpB0zTnFWKROr9JVGeMTjo+xm2JqM8HgSmW1jjP0bOH0lewHkFrekosViE8fFwp4wgdJpW7ze/+2nl8oGe3LN7Z/I1r61Ynu75vx874hUEV4z25vPxs0xxtQGsWz1cOa8Xjw/x4nM3BC0L5PLzjua7dz1HrmDx8gs2eK7PcRxiUb1m9txguupMDPo8DK0qsE3O5rjxZ1UD+2nGyrqfYXqoGk92gNRgkoFzX8G+7/0zAKfHn2Pv8kToPgYHqiJ3LBFr+vvS1VmDw0nPune99tTQY8ST1WMULZv5gPKRsbFBNK0qqu6eytUcP5GMMz4+RN5XBJJINn8NZbbtmeEbP3yCnz24Hbtk+htKx/m9N51Rf6FCMaCQJD2Y7Pn93+vjC0sPueeExUbuOWExkftNWGzknmufrgmEhmFcBfwXkAD+xTTNz5XeKqsFzeSIyn/tNFqjvt7smna3X+gaQVhS+F16yxRxZjGJBUWMG8y5WwxqSkrqONcWE7+zrxmnYBirV6T54p9dQa5g1Xz/mqYxnI6zfyrreT3aaAbhshS6BrYDjgOzGddVl0pEufZyo+7aVCJKRNewSsrWzHyetSecz67v/RsxigzoeZZNPQ4cG7q+TDne+8QLB7n9gR1cdNo6Nh65LHBdWElJEGEuxZm5WqHbsh0KRdvjOpzLFmq2q5SU+ATGoLKQejz6zH7+5LN3UvTFgfcezISsCCbI+ZiXGYSCIAiCIAiC0BO68peoYRjvA/4Wdz7fPwHvU94uT5EfqLOLsnVlrsk16utlN1+jNWHHSBqGoYe4CFs9r6A1HaVQsJicbC3WJQjtUP4vMRMTMw22rEWjqqJHtfb2sVBmpmvFi+mpDBN1Zs0tBn7nXDFf7Mnn40d3vOJPZj7HxMTC58NNBAhXqYAm5LwyVy/s81g+nGSfT1h89YUbcAqNP8PBVIypktj2wo5JEtooj9obOFV/EoDEcz9nYuLiwLWa8tnsPzjPrt1T/OXn7mZyNs9P79/GJ3/r/MAZigenqvegU7TrnqNjV++LAwfnK9tu3zsbuP32nZOVghOA3Xtr9z2xf449e6drhLmDU5mW7rkbfmTWiIMA85lC0/txHIdsQAx63/45JkZ78x8RFvI7ThDaQe45YbGRe05YTOR+ExYbuedcRkcHiLU5176jA7gMw4gahvFZ4B9K+/5j0zR/u9RkXKY8r29NnV2V5/WV5/c1WlPefqLUUNzKmp0ApmnO4EaTwW0q7sR5Ba0RhCWH6tQbGezNDMJAB2EflJREI5qv5bc/ZhD6ZwXWm8m3UIYCmowblZSAt9wD3Obiy05f39Qx1TmE5Zl+d+eqjsHEPhN77mDgWtWpl8tbHJjJMTnrio2Ts3kmZ2rjveB19aVT9T9Pb4ux4iDM1AqsUOvGC2sxDnIL+kXqemTzRR56ujrv8ZRjV7S1n6LlYDu1ImMr+xAEQRAEQRAEoXN0TCA0DCMFfA94F66L7w2maX4sYNPHcKO5RxuGUeO8MwxjDFgJZIEnSy8/VPq5JeTwW3zbtbvmYd97jda0cy2CsOSI6NVfNb0qKQkqJOmHkhJN0zyiYN8IhIogGI/qTQl27RJUVNIoYgwwNlJ1mmkaXPeyTehNir6DigA6mylg2TZbM+Pst9xxsRoOhSfvDFzrbzGemvUKglPztTFgcBuTg44fRCIaXFIyE7LvZgTCbL4Y2Lrciij30NP7K6UmqUSUi09bp+yn+XhwWLlLwZKIsSAIgiAIgiD0go78xWcYRgS4EbgC2AtcYprmDUHbmqaZBW4DYsDVAZu8DjeR+APFDXgHrrvvbMMwguwhry/9vEl57fuln69Wmo3L5xsDrqmz5rX+AxiGsQk4EZgAfrGAaxGEJcd4KTKoaxprltdL5HePwBbjPnAQAj6BsD9mEKaV6HWqy3MRgwRCVVQOY+MRo5XHLz3rSI5a3fxAYo+DMFMgk7Nw0LhHcREWzDtwAlxuHgdhwaq4B8tMB8wJBL+DsL5AGI9Vr191/c2GOgi9gmBYi3E2QJhrZe7fvY/vrTw+/fgxUsq9WwgoHQkjbO6hOAgFQRAEQRAEoTd0yhLyx7ji4PU+1ckAACAASURBVCxwmWma9zbY/u9LPz9tGMbx5RcNwzgZ+MvS04+XXy8Jcf+GOzPxi4ZhDClr3owrEO4D/l1ZcxdwL3AC8DHDMLTS9pHS8Y8A/sc0zQeV8/oCrhD5TsMwKoKfYRjLS+8BfNon9rV0LYKwFHnrSw1OO36M615msHy4N/PFon0aMQY4w1gJwPBAjOPWj/T4bFzWrxykXIp7xKqulLBXaNdBeN6W1fzGVSfw9pdv4nWXBheKhDGkHHMmU2C+JN7dk6/ux5najb336Zq1qqCbLVhM+hyExYkXyN71nxSeva/ymm07zGerIl66wexLrwipOgibixj7m47d/VgLchBm80UeeqYaLz7rhJXEFKdjK+JemIOwKAKhIAiCIAiCIPSEBdtCDMNYBvxB6elO4I8MI7RB8j9N0/y+aZrfK80qfDfwkGEYt+EWmlwKxIE/NU3zF761HwEuAV4MPG0Yxu3AeuAcIAdca5qmvwjk7cBPgT8ErjEM4xHgNNxqyueB31Q3Nk1zl2EYvwV8BbjRMIy7cB2RFwPLgZtxy1fUNe1ciyAsKY5fP8rx60cbb9hFogGOtH4RCN942XGcdcJK1q5Ik2hzoGynGR9N8VvXnMgT2yd5yRnNzfVrl8GAGYTNOAh1XeP8E+uNgA1HdfDNZgoVQe2APcjT1hqOjewCoLD1diKrjvOsVb+jfMFiSnEQnhl/mo2P3k3BsSg8cgvWpotInP8W5guap+4+SBRV8TgIFYffbIhA6Bf+/I7C8jZBzcHNCnu/emp/ZduBRJTNRy9n1/5qSVYnBMJCQPmJIAiCIAiCIAjdpxMOwkuAsr1kI/DmOv/brKx7D/AO3Ll/lwBn4UZ3X22a5kf9BymJf5fguvIOAlfhCoTfBM4xTfPWgDWPAmfguv9GgFeW3vq/pTU7Atb8J64geDPuzMErgO3A7wGvCokKt3QtgiAsPrFoQMS4D2YQgjuH8Ni1I12P8rbKmZtW8qaXbGTlsu7GwgMjxk04CBeC6iCcnS943H0Pa5sqjwtP/wIn723AVgXCbN51EGrYXJ26j7cO3onuVMWvwtbbmf/uJ5jfv6fymqZBssF3HQ+bQZgJji9nfIJgkIMwG1JSkm9S2Lt3qxIv3jhONKITV5y5LQmEAUKluw+ZQSgIgiAIgiAIvWDBf42apvkt3Dl7ra5zcIW7LzTaVlkzB/xZ6X/NrnkeV7xr5dzuAK5sYfuWr0UQhMUlyJHWLw7Cpc5QYMS4u+KtfwahKhA+HzseovdAbg6KOQpP3U1886WV92tKSmay/Hr6Dk5PPKccQYOSZ9CeeIbEjz7O5tg5PFZYTzoZQ9fq33thMwibjRiHlZQEOghD3Hz+/antxWed4Mbi1XZwy3awbLsp96cam/acSxMi41M7pnjs2QOcf+JqxnxN1oIgCIIgCIIgtEd/2GcEQRC6jK5rNaJMs423QncJjhh397sZDIkYA8RTSWLHn195Xtj6E8/aRNxbUrJydqtHHNynj5F+w8eJnVAVFfX8HO8auo3XDvyCkSbGcHpizMVggVAV52oFwlrRL1ewAqO9zTgIf/XUPoqlEpJ0MsoJRy0Damd7FovNRYRDS0oaFJ3MZwv8zfUPcuMdz/IfNz3W1LEEQRAEQRAEQWiMCISCICwZor6YsTgI+4N2S0oWdsx45bHfQZhKRIlturjy3N73PNbEc5XnqnhnF7JcYt1Zef5UYRVfsq9BH11N8sLrSF70DohUj3VR0uRd+n+Tvft6itsfwSkGR4bDIsazSsR4TFEaa1uMgyPGgQ7CJtqHH37mQOXxaaV4MUDM5/Rstsk4dAZhA7Fy14H5ytqnd04HtkwLgiAIgiAIgtA6IhAKgrBk8BeViEDYHwQLhIsXMZ7LFJjLVp15A8kokeXr0ZVyEtVFqAqE59r3Maq7/VhFR+f6ufPYP18VrWKbLmLgNR9mLrW68tqIM03hoZvJfP/TzH7td7ECmpLViHFZEHMcx+MgHBupxmv9M/1CW4wDhLlCSNxX5eBMtvL4mDXDlccxn4MwzBkYdC5BFBuUlKhiqWU7gdcpCIIgCIIgCELriEAoCMKSQY1DRnQNrcEcOGFxSMQiNYJg1yPGyapA6AD7pqoC2EDCfS+uuAgLT92NU3C3KUeMx/VpLo4/Wtnmx9nNTNjDzGWLlTguQGTZOn5x5Dv4SfYEbL/+lZsjd88NNecX90SM3X1lchaWsoOxUdVBWBXcbNsJLAEJazHON1EMogqTw+mqIzIa0T3R/YU7COufi//96blgB6YgCIIgCIIgCK0hAqEgCEsGNbYq8wf7B03TGPLNIex2w3QqEfGIkE9sm6w8Tifd/q7oMWdDrOTSK2TJ/ODvyW/9KYnpF7gwsZW3Df6UqOYKYgetAW7JnFTZh1+4ms3Dt+bP4qNTr+bhFVcQOfKUynvWzsex9m/zbK+2A5ddeWq8WAPGhoMFQjVuHMEipeUr2/ijyNBcMciUcj2qQAheF2GzTcbhLcb11+d9bsew0paFcufDu/ibbzzIr57a15X9C4IgCIIgCEK/IQKhIAhLBtWlJvHi/iKd9AqE3Z5BqGmaJ9p8cCZXeZwqCYRaLEHs+PMqr1s7Hyd3+xeI3vIJXpe+h/XRg5X3bpw/kzzV/U3PewXCcoR5vz3EvlXnkXrp/0YfO7ryfuGRWzzbqzHmsmimimHpVIxUIlp5rgp/5djtSn2Kvxz9bz46+l9siW3Hsh3mMq0LhJZtM5dRHIQ+MbcdgdAv9DW73u927IZAOJ8t8uUfmDz67AG++D9bZc6hIAiCIAiCsCQQgVAQhCWDCIT9S42DUO/+/3k6/ojRwNePWVudsRc/5Uq0wRV19/Nofh0PFo7yvFbjIMx4xT1N04ifdEXltcJTP8fOTFePqwiElu1QtGxmlH0MDcRIKm3KqoOw3GD8yoH7Set5oprN5cmHS+dVFULL5It2XRFsdr6A+m5HHIRKxFh1SzacQVj0Owg7HzGenM1VrmNqLh8ahxYEQRAEQRCEw4lo400EQRAOD6KKKCgCYX/hLyrptoMQ4K1XbGTDmiEmZ/LYjgMOnHD0Mo5dO1LZRh8aI33tX2PteoLis/dRfP4BnPkpthVHeaqwkqcKq3issB439FtlyicQqs49NcKs3f0NnMwUWEUKj/+YxOnXAN6SEnAdd6oYNpSKkYxX/094ziMQFlkTOcjJ8WpseUNsgmFtnqm5dOBnUbRsYkpzcti1xGO657iwcIEwnYqRLzk4G80grI0Yd14g9AuCmZxVc82CIAiCIAiCcLgh/x+vIAhLBk9JSZdn3Amt4RcIF8NBODQQ5+XnHNVwO02PEl23mei6zfCit+I4Dp/9hzs8rkA/fgeh2pKcLl2rFokS23IZ+V9+C4DCo7cRP+UVaJEocZ9Yly9anuMNDsQrZSlQGzEuOwZVTopv46G5kZrXwRX2wgRCNS49PBCveX/BAmEyVol4Nyo5qSkp6ULE2N/EnMkVWTaU6PhxBEEQBEEQBKGfkL+QBUFYMoiDsH/phYOwXTRN88wILKPeU9NzXuFKneGnXmvshEsh4v63uv/P3nuHx3Hfd/6vaduARSUIdlISxWVXL7RltViSbRX3xPH5zmmO051c8kuc8ktyl4svdzknPid24sR2nsRx4iSynbjJkqwuq1NiU1n2AhIk0YHtuzNzf8zu7MzsLLAgQRIiPq/n0YMp3+/MdwYrCXjj/fm87fwElYMvAM7ze0O2S2XT129vuhJjc2yQqyKHG9Z3ReQomSaCWrB018tUNjzB2H0Gj9h+JinG7fH63ypnH1JyLhyE/nvki419GwVBEARBEAThYkMEQkEQFgxeB6GkGM8v2s9zivHZ4hXnaixbVC/f9bruLNsmV2gsMQZQ4x0Ya+tBKKXdD2LbttOj0CNClsqWT9wLEwhrfQR7jj5K7eNdpC7ordVPElcaexDC9AKhL8F4Rgdha/36SiWvQFj/3s+HkJIwB6EgCIIgCIIgXOzM79/ABEEQ5hAJKZm/NDgI5/n3JxLiIFzV3+5ue0uMc4WKL+SjLfCsxuZ6WIk1fATz1D4Aoh7hrVgxfW659njE1xevFmRiTQ3RO7LDPb49eTsZEgBois0mYyD0ecrTBHH4SoxDHITekJHphEYvXpee93tfmcGBeD5CShp6EJYkpEQQBEEQBEG4+BGBUBCEBYPmKzGW//zNJ5LBHoTzuMQYwh2EqxYn3W2vQOjtP6goEI/62/9qvSvRlm1w98u7HwJodBBOk2IMjouw9Mq3Uapy5LDZzsnOzRzSLnXHXBE5Evo805UGe5+lo81oOO/tXXimISWtzg+6+86Fg7AxpEQchIIgCIIgCMLFj/yGLAjCgsHwhZTMbwFqoREsMdbneYlxWA/ClYvrDkJvWa4/wdhAVRo/exGPi7ByeDvW1HBAIAz0IIwbvpASgOLwccrpp9z9HxQ2E4tFORq93D2WMgaJ0CiqBXv7eZkppMRbul85w5CSGjMJhMHzmXzZSaCeQ4LvQgRCQRAEQRAEYSEwv38DEwRBmEO8rkEpMZ5ftMeCKcbz+/sTFOfa4wY9HfWk22y+jGk5QpMvwTjmdw/W0FZdgZLsc3Zsm9KrP2go3Z3yOQgjqIGwFGXXt6Aqlg2ZSZ4vriUe1RlNrCZnOcJeRDHZYJwAWk8f9jsIZwgpaVEgLJWb9CCcqcQ4IN6Zlr+/41wgDkJBEARBEARhISICoSAICwZDlxTj+Upne9QVz9rjRkMZ7nwj6CDsbI/4xDObevmrtzQ42H+whqKqRDbf4e6X33iSNr0uVI1NFnxCVe1etTLjVdowkROvuOe/m78SC5V4VCcaibCnvMI9VyszTiZac+5NziKkpPUehHMTUgJz34ewUSC8+HoQ5osVHtk+wI79wxd6KYIgCIIgCMI8QQRCQRAWDJqElMxbDF3lY/du4rr1i/nYvRvnfYlxsP9fV3uUWEQnYtTXXRPWsl6BMBYuEAIYqbeBEXN2Sjk22mn33L7jE+52e9ygq90vEN6TqIuDp5U+dpTWAJCI6kQjGrtLq9zz640TKFg+YS5MeAOwbdtX2jxTSEkrDkLTsqiY9bJg7zpsG9d5GUaYADnXfQgXgoPwO88e5qsP7+Wz9+/i0ODkhV6OIAiCIAiCMA+Y37+BCYIgzCG6lBjPa7Ze1svPv2czWy7tvdBLmZFginFXVTjzOuxqvfuynhLYtnhzZ6QSiTsiYZUriy9h4MzdP1AXCFf1t6NU+xjGIjobjOOkjEH3/BPKDdg45+NVgTBdXoJpO8fa1BJrIuPEjJnDRbKFCqZVF/OCAqFtVtg0/jgfbXuC9cbxGUuEAYol/5jgO5lOZCyH9EqcawdhMAjlYhQIDx6vi4IHT4hAKAiCIAiCIMD8ruESBEGYQ3RvifE8d6gJ85sGB2HS6T/Y2RZheKIAhDsIg70Wg0Q230H51UfBNmm3prgjvpvv5a/yhZ6s6q+nJW9R93NL++PuvrZsA68fXQoUAYhHNGIRnSIRDlX6WGucBmBTdJBDxlp3XrPSYG95saYqJAI9FEvbv8mlY89AFK6OHuH4qcOYIx/FLmaxho9g5yfR+i9HW7UFRXXmBh16wbLrcsUi1mhUrK4zrMS4dQdhuWLyrR8e5tRojlhEJxbRWLaojW2bl7hjisGQktLFJxB6vwdBQVQQBEEQBEFYmIhAKAjCgkEchMJc0dCDsOYg9DjsJrOOcOULKWnSg7CG2rGYyNa7KO38HgA/EnuVF4uXMWR1uGNW9bdjWxalF+/njuKDVM2CWIpG4oYfJb/vmDs2HtNdp2C6vMwVCNfpJzjeQmmw152XTPgTmM3hw5R2PuAbv7xwgNzXf7/hOkosib52G5Gt76BUjtWPK04ZtJfpHIRhacuTs3AQPrHjBN999kjD8eGJAj//wSur97j4HYReQTgo2AqCIAiCIAgLE7HQCIKwYNB1EQiFuSEoEHa1Ow5Cv0DoCFeZvKfEuEmKsZfI1fehtHUDoCsW7297Hif2xGF1t0b+wc+4IiJAxoqya/VHUBet8TneaiXGAG+Ul7nHVyiniKv1ceUmPQgnmiQY21aFwhNfBtsRmir29D9O2IUpynseIvfNP6Q0NeYejxoamqrg0R2pTFOmfLYOwlOj+dDj+wfG3e1i6eIPKfE+Y6vBMoIgCIIgCMLFjQiEgiAsGC5ZWi/NXLO0Y5qRgjA9YSEl4O9BWBPXZuMgBFCMGNFtP+7ubzAGeVs0TYQyyyMZkk9+GvPYLvf8iUoXn558FycjKymUTOy6lkg8qrtrPWb2kLWc9WlYLDMH3HFhzjxonmBc2vl9rJGj7v4Xpn6Ev8vczJRS/fcqEkdbtgF99VWg1UVROz9JbOe/uvtRQ0NRFAyttaCT8JCS1h2E2WL9e1ELegEoeAQzr6MurpRYXBqgvP85rGxd2Hyz431GcRAKgiAIgiAIICXGgiAsIDat6eHn3r2JTL7MW7csvdDLEd7ENISUtIeUGOdCehC2IBAC6Jdcx2jbpfRkDwLwgbYXeG/iRSxFw56oO/9Otq3jM8euoYhBoWQ2lMMmorrrdrRR2VteylVRp8R2efkIsAloLspNhiQYW+MnKb387+7xkcXXsXfU+fcpH7+C3/zgeogk3CAVu5iltPN7lHZ8F4DYyZ1cYXSxs7zaXZuhq6741yzoxLbtJiElrTsIsx4355olHezYP+ys2+O6LFUsbou9ytuib9CrZQEoPApKvIPE+/4batXdebYUShXeODpOamUX8ej5/XHM68SUHoSCIAiCIAgCiINQEIQFhKIoXL+hn9uvXtFQIioIsyHoIOysCoSdISXGvhTjGUJKaiiKwpGV91D2lO5qiu2mGgNErn43r678MYo41ywGBEJDV9E11bfWNyr1MuPFhUPudlOBMKTEuLT7QTCd+yiJLoYuvbt+HctGiba54iCAEm0jct370Zam3GMfbHuehFJwhVa9hX6IpmVjee2RVWbjIMx53Jy9nfVeiF4H4eryAd6T2O6KgzXs/CSFx7+IbZ99Sa5t2/zpP+/gs/fv4n//8yvYIc91rrBs2+cYbeYeFQRBEARBEBYWIhAKgiAIwizp9JSndiejGLojdAV7EFq2HSgxbt0pZib7+czkO9lVWknO8giLeoTY23+R6LXvJRatHy+UTPIeoSteFQZjkfo90+W6c7a9PEqPmgHCe/vVnqFGRyKCbVYoH3zBPRa59r3osTZ3v5nYpCgqsZt/CjTn/STVAu9LvEQ04vwY0kqJcbNrz8ZBmPGItb0ddYGw1mfQzE1xt/qUb07Brr8/8/irlHc/1PL9mpEtVDg0OAnAkZNTjGdaFznPlqALUxyEgiAIgiAIAkiJsSAIgiDMmv7uBHddv5JX9g7z3psvdY97BcKpXJlcoeLrCdiqgxAgoqsMmL18KXMbKhYrtRE+fnMnizdcjdqxGPA7GQulis9BWCtbjXrGjFntnDI76NcccSplnODZ4rppSoy9DkIDc2APFKvOOk3HuPR6jIGcO6ZZeTCA2tlP9Lr3UXzuawBcFz1IwdoDXIvhcRA2CylpFqQylStj2bYvYbkZzRyEFdOiYlqMPPRFkooTZFKyNT47eRfHzF7+bMtutOM7ACi+cD/asg1oi1bPeL9mBINQpnIlupPRhnEDQxn+9tuv0RbT+aX3bSXRQsjNjPcOvEcJKREEQRAEQRBAHISCIAiCcEb82O2X8yc/t40bNva7xzoSdQHQsm1OjdbFMwVmJfB4y+AtVAbsxSy69k5XHISgQGiGC4SBcnpvmnHKGARaLzEu73/O3ddXXYkSifvFvSYiXg1j851MJVa4+zcVH6d88MWWHITFJsct2yZXqISe82IHxnkdhADje35I9tWn3f3v5q/imLkIUJja+iGURFf1hhUKj34B22zduRgkGAzSzAX5g5eOcex0hjeOjvPsqyfP+H5eSgFxUhyEgiAIgiAIAohAKAiCIAhzRjyqo2t1J9vhk1PudiKmt+RyqxEx/P+LXt7Xhq75j3nLhwslk1yIQBjsl5j2CoT6CTTMpi4yn4MwYlM58rK7r1++zfnapH/gUztP8Kf//Ao79g27xxRVZdeKDzJmJpx9oPDYF1itngy9hpeyR8gydBVNrb/LVvoQFkomplW3c/Z21B17CaXA5A++5O4fLPfxRGG9u5+zo8Ru+9nqisEaP0HlcP1dzJagQDjZZP3e0uOxqeIZ32+6exelB6EgCIIgCIKACISCIAiCMGcoisKq/qS7/+jLA+52W4sJxjUiul/Y8163hlf8KwZKjBNNHIT7yv2YirOWhFomZQyGlu8WShVf37/OsdehUhWsInH0lVsb1lkTGrOFMl95KM3rR8b40ndf85UNT9HGX2feTs6qlmObFe4tfZcu1SldbtqD0HM8oqu0e9yarfQhDLoM2+KGK8LeHd8BBUfMLdkaX82+FdvzI1K+aKIv34i+9kb3WOXAC5wpQdfeVDZcIPSGp+SKM7skW7p34P026z8pCIIgCIIgLCxEIBQEQRCEOeSmLfUgkMGReonxbPoPAm7Cb43VIQJhtIUSY1VVfG7EEgYTXevc/SsjR0IdhJMB0U0/9qK7bVxyHYrmPI8R4iAcmypSMR23XrZQ4cRwPRG4VLI4aXbxt5nbMBVn/VGKvDW6F2jeg9ArqkUMjWTc2+9xZgehNywmFtHQNZV4RGeZNspbovvccw/kr2DY6vDNrb1XwysQHtuFXcrPeN8wGh2E4QKnt1eht3/i2RDsfyglxoIgCIIgCAKIQCgIgiAIc8oNG/sbyoMB2mfrIAxcI0wgDPYgnMx6RLBo/VwsIDZm+ra621uMY5jlRvHJ239wcbyCNbDH3fc66bz9A03LxrJsCkW/6OQtta6JYwcr/RzovdU9fk3kIAp28xLjgIOwo63+PidzZQ6cmOBz39jNkztPhM7PehyEbbFa+bXK+xIvoirV0uOOJTxR2NAwN19y5mrLN0G0mtpslqkc3RF6r5kIlvU2EzgLpfqa58pBGBQnm6VDC4IgCIIgCAsLEQgFQRAEYQ6JR3Wu39DfcLwtPrsEWq/jUFFgxeK2hjHeHoSmZbM9fdrdX9pbHx8N9CEsLt6EpTpzE2qJ5ebRhmt7BcJrE8fAdoQkJdGFtrTen8/rIAQnydgrbEG4QAgw0nul83BAr5blEv100yRkr6hm6BrJRN1BODpZ4C/u38X2vUP8/QNvhPbry+brImjt3W4xjnC5cco9bl77o5hoDXNrDkJF0zHWXO0eP9My48YU43B3YMHzrvJSYiwIgiAIgiCcQ0QgFARBEIQ55pYrljUcm22JcXcyyrZN/aiKwrtuXO0TA2sEA0hqLjldU7l+gzft2D83Gk9Q7Nvo7q+zDjRcux6cYXO18oZ7XL/sBhS1/uNDg0BYscgHBLAjJyfdba9AqCS6HFdelWsjB6dxENbnRQ2VpMeR+fgrx90yXRsYGm8s/fU68BIxHbtS5FbzGfdYtncDec87aTZXv/R6d7tybDd2KRc2ZVpaDSnxlxjPkYMw8L2pmDamJS5CQRAEQRCEhY4IhIIgCIIwx1y6rIPlfX7HX62sdTZ87N5N/NWv38z7b7ks9HzQGVjj2vV9PkEyOC4a0Sgvu8rdX68cxjb9AlTNQbhKG6HPGnKPG6mbfOPCBMJCwO127HTG7S3o7XkXjWgYa7e5+1dFjlAphYtlXueboask2+oOwmxAPAvrq+dzEMYNKvufJ2k7zsaKrTKw5h5fKIgXb8m0tnwDSrTd2bEqVA6/Ejqnhm3bDY7K4PomQ0JKbNv2C4TnqMTYWY8IhIIgCIIgCAsdEQgFQRAEYY5RFIWbAy7C2fYgrGHo4SIggKooDSnFADdv9d872IMwZmhYy7dSsp3jcaWEefw135iaaHVTLO0e05asQ+tZ6RunqUqtShhwnH5BB2HFtDk+5ASVFH1hIyr6JddQcVOVS/Rk9hFGQ0hJovn7DBPBgj0IK0fqwt7zxbVMaN0Ui+ECobe8V1F19EuucffLB5uXGZfKJn/w5Rf4lf/7NM/sGWy6vql8Y4lxqWxhe9cwRw7CsJJiCSoRBEEQBEEQRCAUBEEQhHPAtk1L0D0BHm1nKBDORLDMeHF3nNSqLt+xMAdhJBbn9fJy91j54Iu+MZO5MgmlwFWRw+4xY+PtDfdXFKUhyTjomAM4XC0z9rrVooaGYsQ43V5PVV42ubthbu26NSK66ksxDhIuENZFuGQEKgOvuvs7S6vIFSqh64bG/n/6ZTe42+bAHuxiNjgFgB37hxkYylIxLR7Zfrzp+ools+FYIRgmUrGall/PhmCJMUBxDq4rCIIgCIIgvLkRgVAQBEEQzgHtcYPbr3YEOENX2bC6+5zcJygQ3nzFMhSvpS9kTDSiYegqO0qr3WOVw9uxywV3f2SiwA3RA0QUR1BS4h3ol1wbugZvknGpYjWkGIMTVDKVK3FqrN6zr+aqPN1VT1Vekj8QKrh5BTRD13wpxo1jGwUvr4NwWeUomI5DsmAb7K/0ky9WQoVFaBQItaUplFg1VdoyqRzbFTrP2wvRW+Ictr5gknExRKyci6CSsHJicRAKgiAIgiAIIhAKgiAIwjnig7ddxv/3oSv544/dQFd79JzcwxtAoioKb928pHGMoQf2NSKGxqulFRTt6rlSjtLuhwA4NDjJ4cEJ3hrd684xUjejaOF9FCOeEuamDsLBKZ7dc5KK6RTOdrRFuGRpBwCTnWuZsmIAaJiU9z/XMN/rnosaqi/FOEiYS84r0PXn6mXMr5eXYaKRK5R9PQg1tS6yBkumFVVDW7HZ3TeHj4SuY2SiLrh630nY+oJJxmH9EOeiD2ExtMRYHISCIAiCIAgLHREIBUEQBOEcoakqG9b0sKgzfs7u4XUHXrG2l84QIdJbYqzgOBoNTaWIwZOF9e650s4HsAsZ/uPpQ6SME/RpU9VJCsaGW5uuCO9+ewAAIABJREFUwesgLJuNKcYAA0MZHttxwt2/actStwTb0A22ly7xrON7DaEp/pASjc62iK/3YX9Pwt0OcwLWUoAVbLom6n0V95RWAI47zyvidbbXBcgw5562aJW7bY0cazgPMDzpFQjrawpz7AUdhKEC4Rz0ISyFXFcchIIgCIIgCIIIhIIgCILwJmbtik4AFAXuvG5l6BivQBiNaE7fQMP5EeCRwiZyVlUMK+c5/cNvcOjgAO+Jb3fnaCuvQE0uarqGhh6EIYKaadmcGq2XF998xVLf/CcKG6jYznXszAjl9JO++aVAuEk8qnPX9avQNZW3X7OCDZ6+i9P1IFyhjWCUHOHTRuG1ah/GfLHic/Z5HZ9hAqHqCWuxRsMFQq+DsFSxMC2r6foms34HYajIWWwMM5ktoSnGIa5CQRAEQRAEYWERXiskCIIgCMKbgnffdAnLetvo6YiSWhXe59CbYlwTC1VFQdcU8maUHxQ2c1/iZQAiB57gVzviLNIy7pzI5rdPuwbdIxBWKuEOQi8bVnezuLvu+NM1lVGrneeKa7kp5pQ1l175Dsa6m1B0R7wMhpQA/Ohta/nArZehKgr/8mi9bHi6FOPNkQH3WL5jNblRp7TZCSmpz+ts8zoITWzb9vV29AqEdn4SKzeBmuisH7Ntn0AITmlxIqaGru+8OQhDAkmkxFgQBEEQBEEQB6EgCIIgvInRNZVtm5c0FQfBX4bsFQtrzr8nC+uxok4/QB3TLw5eeQ+6p99eGBHdG1Ji+kp1OxKNYSK3XLnMt19bx8OFLZjVH03s7CiP/OvXXJEtWGJcQ62KdlHPcwXLaC3Ldl2Am42626+4uP5cjoOwvm6vg9Cy7QYRTU10osQ76mMCLsKpfLlBjKuJfqEOwgaBsFEMnJMehGEpxlJiLAiCIAiCsOARgVAQBEEQLnISMT10uya0ldHJXP6OhnmR6z5A9PoPzHj9xhLjuuAUFC7b4wZXXd4XOn/camOXssk9vn7yWR58dj/gLzGOGo0/vngFwkJA8KoJa91qhhX6mHvcXF5PT84HHIRd7f4QlHyIYOcrMw70IQy6B51r1ATCRsdeQ4lxiJCXnwMHYXiJsTgIBUEQBEEQFjoiEAqCIAjCRc7mS3rp64qhKgq3XLncPe51/k0suY5TplMia9lw+vL3Eb3qnpau7wspCaQYb1jtFwjfsnmJT1AEv8D4Q/tKTByxr1PN03vqBaC5g9B9Fo9AGBTBagnGXveg0tmP0V3vg5gL9CBsjxv+JOOwPoS9dYHQHJ1ZIKy9l9CQkrzfQRjeg/BclRiLg1AQBEEQBGGhIz0IBUEQBOEiJxrR+J8/u41MoUxHou6M8wpzw1Nl/n3q7VwfOcD+Sj//ddu7Wr6+z0EYSDG+dFkHbTHd7QF48xXLGud7BMYxM87ria1sLr4CwKbiK9iW2RBS0vCM05QYO/e2eUu03qdQX30VsWj9x6BS2XSDTJx7aMSjOpmquBgmzmk9K6nNCDoIh0MFwqqDMMQdOBVwEJ6rHoRh9xaBUBAEQRAEQRAHoSAIgiAsAFRV8YmD4Bf2TgxnGbfaeKiwlVP6cl8p8kx4HX2FoukLFGmLGfzUuzawflUX/+WuFMsWtTXM1wMC4xPFTZi2497rZIrK4ZcDISWNDkJvn8VgCW+2UOYy/TTL9PH6NdbfQjzqf8bxqaK7HTU04lH/cwVRe1e529b4CWyzLuCNTIYIhMXZ9CA8Nw7CsHuHlTwLgiAIgiAICwtxEAqCIAjCAsUrtJ0Yzrrbi7ris7qOV2gMpvHGohpXrevjqnV9wWmh80tli4N5nR2x1VwTPQxAec/DlCq31seHOAgj0/QgzBbK3BRNu/va8k2oXUuJWX5hbMwjENYchDXyxQqWZXNocJIlvQnaYgZq11JQNLBNsEys8UG0atlxsxLjimlhWnbDualcyZeUHOb0OxMHYTB9uVQRB6EgCIIgCILQiDgIBUEQBGGB4nMQjtQFwr6zEgj9pbJeZ1/T+Z4S43zREdGeKGxwj5kn97Kocsrdj4Y4CL3BJUHBqzw5xhWRI/X7bbodAE1VA+KkPwglHvELhF97ZB9//JXt/M7fPEeuUEbRdFRPH0NvknGog7BkNhXjKqbtcw2GpxiXG45Nx8MvHeMTn32af3nUKa0OS2OGcNFQEARBEARBWFiIQCgIgiAICxRvSMnoZN0919cVm9V1vCKbt1Q2oqto6sw/agRDSwCOmH0cqixy969X90w7PuotMQ647zoHn0dTHNdeVk2ir7rSPRdvImBGI34HYSZf5qldg4Ajgu4+OAr4k4zNkaPudngPwsq05bzedxd0QcLsHISmZfH1xw+QyZd58IVjjE0VKTe5d5hoKAiCIAiCICwsRCAUBEEQhAVKmNAG0Nc5Sweh5hUI6y63WLS1TiZhqcSAz0W4VTtEh5IDZg4p8fbZs60KS0dfcvePdFyFotbHxiLhawz2IHztyJjvukdOTQG4JcUA1ugA4Ah5YanHhZLpu4aqKCQThrvvDSoJLTGeRQ/C0cmiL7F4PFOk2MQpGNaXUBAEQRAEQVhYiEAoCIIgCAuUZsLcbEuMvYLdVLbugmulvBhA15TQ4ztLqxkzE84YxeJtMaePYFhIiVcgNC2biumIY5XDrxAzM862rTK06BrfvGZrjOh+B+EbR8Z854+cdARCX1BJ1UEYVl4MVYHQI/xFIyodbfXgGK+DMEwgzM/CQTg8nvftZ/LlhnTnGl4hURAEQRAEQViYiEAoCIIgCAuUMCcewKLZlhh7HIRel1u8iTuvYX4TJ6OFytPFlLt/e+xVlmpjoSEl0YDQVyyb2LZNadf33WM7S6sw2rt945q5HIMlxsFgkaOnprBtG7VnhXvMzk9i5SYYnvCLczWcEuO6SBcxNJJxj4PQW2IcIuaVKpYrfM7EUKDEOZsvN3UKSkiJIAiCIAiCIAKhIAiCICxQvMJeDUWB3o4z70HopVUHYbP5AD8spijqScBxEX6k7WkMpVHQ8joIwXHgmYNvYJ0+4B57orCBRMwvCDZbY9RQfQJhkGyhwvBEASXeiRJLuset0YHQBGNoDCmJGlrAQVgvMQ7rQQitlxkPhTgIm/U/lB6EgiAIgiAIggiEgiAIgrBACXPi9SRj6CHC4XToTQS+6QQ2L5qqoirhZcZ5O8Iri+5291foYyg7v924Bk1FU+vXKJZNSq/Ux6XLSzhi9tEeM3zzwtaoKM71Zlr/kZNTKIrSUGbsLTH2PlewB2HU0Egm6gKhtzw7rMQYWi8zDoakZAuVpk5BSTEWBEEQBEEQRCAUBEEQhAVKWC+/2SYYN7sOtO4gBND1cIEQYK+5nKcK9VLj8q4HqJx4o2Gcrw/h6YOYx19z9x/ObwFoyUEYNTQURWmacFyjFlTiFQjNk3t9DsL+nno/x2CJcdTQ6PCElNR6EFq23bQcuFUHYVgPQikxFgRBEARBEJohAqEgCIIgLFAiIc6/RbMMKIFpSoxbdBBCeLlzjbGpIt/KXc0ps6N6xCb/3T+l8Ny/YJfqQpi3D2E0/aC7faTSx77KEgDa4n4HYZhAGKkKjWEOwnbP/JpAqC+vpy1XTrzByHjO3V/e1+5uOw7Cejlv1FD9DsJqiXHQPVhbxyptGHNwb8OawmjoQVjwC4ReObZZ6bEgCIIgCIKwcGj9J/dZkEqlLgd2AH+XTqd/qcmYtwO/BVwBJIDXgS8AX0qn03bIeB34KeDjwOVABXgB+JN0Ov14k3t0A78JvBdYDYwBDwH/LZ1OH2oyZx3w+8DbgMXAAPCvwKfS6XR2Lp5FEARBEOYDYaXBs00whrPvQTjdNcARCEsY/GPmJn614wE0xQbbdJyE+58lcvW7MS7f5gp7K7QRYqf3uPMfzG+hJom1BUuMQ4JUotXS6zCB8NarlvOdZw4DTomxbdtoS1KgamCZUM4TKw4AThjKikVtvFSdWyj6U4wjwRLjqoMw6PTraY+wVX2Jd8R3wUtQKL2D6A0/htKkLLtYMpn0lCtDNcXYIwS2xQ0yeUeQlBJjQRAEQRAEYc4dhKlUqh/4DxyhrNmYnwMeBm4BXgEeAzYCfwt8OWS8AvwDjuh2CfADYDdwB/BoKpX6yZA5PcDTwCdxhNDvACPAR4EdqVRqa8ic64DtwH8CTgLfBdqA3wGeSaVSHSFzZvUsgiAIgjBfCHMQ9nXOvsS4mftvphJd3zWmEQjHM0UAjpqL+Bf7nSjtve45OzdO8em/J/OPv8Y9yhP8cvJBfr3ju/XzXSt4tbzc3W9rscQYGgVCQ1e57ar6taZyZcYzJRQjita/1j2+wjzmbi/va3O3CyV/H8BoRKOjzVtiXK6Oq4/RVLhTf8ERB6uUd32f0vZvNqy7xvB4jsXqBDdG93FvfDtr9CGyeX95c9JT2lwqW1i2/D1TEARBEARhITOnAmEqlboSR5TbMM2YdcBfApPADel0+q50On1vdc4B4CdSqdQHAtN+EvhxHFfi2nQ6/b50On0LcCdQBD6fSqVWBuZ8Bkeo+zsglU6nP5hOp7cAvwt0AP+YSqXc5686FL8GtAM/nU6nb0in0x8ALgO+BWwFPjUHzyIIgiAI84LwHoRn4CAMCTsBiIW485peI7AWbymvV7saMFbT9qOfInL1faB5rl/Os9V+nbXGKTxZJeTX3UnNPahrqusydNcY4hKsC4T+sWuXd9KdjNLTEXWPHTnplBlryze6x1LGoHNMVVjSU/976YwhJbmS03+wKhAq2Hyw7UWuLL/csMbSy9+i+NI3qRzdSem1Rym+cD/5R79A7lufou27n+R3u/6DH297lrfHX+WXkg8SzQ/5xMlkoNS6XJEyY0EQBEEQhIXMnAiEqVSqO5VK/S/gOWAtEFq+W+U3AQ3403Q6/UrtYDqdPgr8omeMl9+pfv3ldDo96pnzCI4QGAN+2bOeNcCHgXHgE+l02vTM+VR1nVuAd3ju8SHgUuCRdDr9Zc/4PE5pcxb4WLVs+WyeRRAEQRDmBWGuvTPqQdjEQRiLziKkRPOXy65ZkgwdF9FVFD1K9Nr30fbBT2FsejsYjWsuRjqJXPd+Jnq2uMfa4o1i4Gx6EK5f1QXA6v762tw+hMvqAuEl+hAGFbqTURKekmbTssl6UoiDISW2Ddl8mUKpgorFh9t+yDbjdff8QKWbTKTP3S+9/B/kv//nFJ/+B0o7vkNl/7OYJ/eilf0dUQzF4g77SYql+r29wiRIUIkgCIIgCMJCZ64chJ/AEcKGgPtwyoGbcW/16zdCzv0AmACuS6VSywBSqdRGHBffaeCHIXPur359t+fYu3CEu4fT6fRUi3Pua7audDo9glM6HMEvKs7qWQRBEARhPhEUCCOG6hOszvQ6NcL6+7V6jTVLmwmEdUFP7VhM7K0fof0jf070bT/BgegGfpDfxKcn3sWL63+N6FX3kvWk/gb7D0K4y9F1EEZ0vG3+1q92/kboEwirDkJ18SVgOOXZumJxqX6aRZ2xBgHS2xswYmjEozqax/I4mStTKuT4mfbHuD560D1+uLKIv5y6k6cWfwilsz/03QQ5bdbXuVYbpGd0t7vfHvg+lySohPFMUYRSQRAEQRAWLHMlEA4AvwGsS6fT3242qNqfcDFQBt4Inq86/WrHaz0Ca3/639Mk8ONVwAbWplKpRGDO7pDxtTnghIrUmNWcM3wWQRAEQZg3BHsQ9nXFmwZfTEfzFONZ9CAMuBDXLGlo++uMCylnVowYkQ238lLvvXw7fw1HzUVuMm+u4BUIW3UQOvdQVYUbNjhi3CVLO7h0mbOmVUsaHYSKqqMtXe8eTxmD9HTEfMnKABPZorsdNVQURaGjre7myw6dZPH2L7Apctw9NpZYw+cn307ejjJeiZG4+zdR+y4BzUDpWIy2bAP6urcSufo+ojf/JA8m389vj/0YfzzxHl4r1Xsmrh9+mJhScp9b97zzhR5U8sj2Af7rX/6QT37hWd9nRhAEQRAEYaEwJynG6XT6iy0OrTnpTqbT6WZ/qj4RGFv7ejxkLOl0upBKpcZx4gKXAAdnmhNyjxnvM826ZvMsgiAIgjBvMAL9+Po6Z19eDOG9DOHsehBesjRcIGx2L6g7/6CeBJytJvVCuIMwLKnYe52P3buRd21bzZKeBJrqCGpeB+HYVJHJbImOtgj68o2YR3cAsM4YxExGURWFqKG565nwOAhr9+lqj9KeO85tsddY8tRRFOo/VhzQ1zKy7sMUBw4DkCtWUNt7aXvvHzR9D7t++AI5OwPA/bnr+aTxLSKKSdzK8uG2Z3i1tIIV+QJ9EYXBvNNPcSE7CG3b5nvPHQFgPFNi98ERbtjYmktTEARBEAThYmFOBMJZ0F79mptmTD4wttU53bOYE7zHmcw5k2eZcwxDo68vvAxLEM4F8nkTzjfymTt3TBb9rrFVSzvO6H23Jcuhx5ctaf167R4XXTJhcPklveiaQsX0Fw90tEebXrPLI3CqmvP/R9vjkuvpijfMNdVGR2JnR8w3bvFiv1i5aFE7Xcko41OOG3CiYHLZmiSlLdcx8Ow/AbBcG6WwSKevL0kiprsCobfEeFFPG92xMu9XHmJl596GdTxdWMexNfexrb8HOAxA2bSnfae2bTMyWXD3R6wkD+W3cE/CES6viBzlishROA4bYhqfLr2LQbObeFvz93qxc3hwkrGpurMTTT2v72KhvnfhwiGfOeF8Ip834Xwjn7kzZ05TjFug9ptIWKlwkFqN07mc462jmu2cM1mXIAiCIMwbgom+/b2JJiOnJ+j+qxHmzmt6DY+Q19+TQFGUhiANaFyz736ect5CNZAjk6uLl8G+e4AvRKTGTM5HRVF8Dsej1TJjY9FKMjjvUFWgv3TUWZfnPdQETwWbnpPPc+wLn2BlwS8OFiPdfC17I/+Wu4FoNOJLdM7kw8XYGlO5MvmiP4zk0cImTpqdDWMNxeSu2C7nnlUBc2Qiz5GTk9Pe42Jj++unfPtTuenfsSAIgiAIwsXI+XYQ1gJDpvsNpPbn/1oE37mYExxfm9Mzizlnsq45p1w2GR+fzsQoCHND7S8xQ0NhuT+CMPfIZ+7ck5nK+/bjmnpG79u2bRQa/2KWzxQYarG3nWnWx3W1RxkamiIe1f3OLsCqmE3XWPEETExMFRkammJkrP7/SNWyG+ZWzMbSWrNcmfE9dHocj0dOjLvj95aXcLXhhIu0776fwUVdDf0VE0qBj7Y/RfeeQd87O1Lp5XDPWyj0b+HZkwPOQduiXKwLVlPZ4rRrOzRYF/eiEY3FXTEO5Ep8Yep2bo+9Rqeao0PNs0YfBhxHYW9+iqHhDDssiz/40gtYts3H7tnIts1Lpn0HFwvP7T7h2z89kjkv/92R/8YJ5xv5zAnnE/m8Cecb+cw5dHUlGtoItcr5dhDW+vv1p1KpZq66Wr++2k9rtTlLwwanUqkY0IXze8lgK3NC7nEmc87kWQRBEARh3hDs59fXFTuj6yiKEhpUEgzomI7Otqi7vazqZAwLFQkLKXHv5zlXc8RN5eolvWEOQl1T0TX//8ajLfxQ1dtRX+/IhFPSmy9WeLGwxj2uFibIP/Bp7rCfpE+dBGyWaaP8Rsf3WG8MuuNMLcbXsjfy55PvYld5DYVyXTaMRTQSHgdirjh9gMbQeF307euM0VZ1H45aSe7P3cCXMrfxmcl3Uowvctao2NwWe41S2WT7G6exbOfezwdcdRcr+WKFfQMTvmPZvISUCIIgCIKw8DivAmE6nR7FEdaiwNrg+VQqpQG1CMBdga+bmlx2E04J78F0Ol1z6rUyxztu1nPO8FkEQRAEYd6QiOmuCJeI6vR1nVlICTQmGUcM1Q31aIVbr1zG2uWdrF/VxY9cswLAV1rrXneakJJISEiJt1y0I6RkGRpLiiMtCJu9nXUxtdbzb3SqyGvlFXwjey0Vu/7sWyq7+b2uf+cPO7/Or3U8QK+Wcc/pq69i8K2/xbPFddgojGeKFEt1J2TU0Eh4hNJS2Qp1PdYYnqj3H+zrioe+QxuFiVW3uvs3RPdj5ScZy9TdmqOTxYZ5FyOvHxnDtPze12xhbkuMS2UT226lI40gCIIgCMKF43w7CAG+V/36/pBzdwCdwM50Oj0AkE6n9wN7gWWpVOrGkDkfrH79jufYA4AF3JVKpdpC5nwgZE7TdaVSqV7gNqAEPHymzyIIgiAI8wldU/nYvRu5NtXHx+7dOG1/v5kICoSzSTAGWNQV53f+8zX85oevdnsPhqUOR6ZxEMY8wl6pKrJNehyEyRAHYXAeQHQaEdJdb0ddTK05CGuhJU8UN/Il632ovSt9c7q1HBHFWZdlKxQ2vZvYnb9CsrfPHTOeKZL3CISxiN7Qy3E6F+Gwx0G4qDMe+g4BSiuvJ6c4Ts2IYtJ94hl3/QBjU4XQeRcbew6ONBybS4HwyZ0n+KXPPMX//OrLmNbCTYoWBEEQBGH+cyEEws/hBHz8tlfwS6VSq4C/rO5+KjDns9Wvf5NKpfo9c24HPoEj3H26djydTh8HvgH0An+dSqUinjm/DdwIvAb8h+ce3wSO4oiKv+AZHwe+BLQBX0qn096amzN5FkEQBEGYN2y9bBG/8N4tXLF20VldJygQxmdRXtyM2ToIowEHoW3bPgdhe6sOwlZKjD0OwmyhQr5YYdQjqpWTS0m85/eJXPd+Jow+39ysFeGvp34EZdM7UBSFrvZ6uXLF9KcQxyIasYiG4qmCzheaC4RDHgfhoq4Y7fFwoTYai7G37Wp3v3/oeaam6s7GbKHiujAvVmzbZneYQDiHJcYPPHeEimmxf2CCvccmZp4gCIIgCIJwgTjfISWk0+mdqVTqd4E/AZ5OpVKPAzngdhwR7ovpdPpfA9P+CngncDewN5VKPYbjzrsZp7z4o+l0+lhgzq8AVwMfAW5OpVIvAClgCzAOfCidTrv1Hul0Op9KpT6K4wr8XCqV+mngIPAWnF6CrwCfnINnEQRBEISLjmCS8WwdhGG0hYhbkZBeh+45j7BXKJvki6avfLSjiYMwHg04CCMz//20sz2Cpiru9UcmC75Ale5kFEUziF51Lz8cW88zL+5lnTFIl5pje+kSxq02t5S5Laaja6pbOnxqtB6sEo1oKIpCIqqTrQqDrToI+zrjPtHSS8RQOdxxDRumniOqVDDMPJeXd3GQlDtmfKpIf8+ZJVu/GTgxkmMkpJR6rhyEtu0Xe739MAVBEARBEOYbF8JBSDqd/l/Au4GngeuBW3AcfT8FfDxkvAW8F/h14AhwF7ARp9z3tnQ6/ZWQOYPADTjuQxu4D+gA/h64Jp1O7w6Z83h1PfcDq4B7gAngj4Bb0+n0ZMicWT2LIAiCIFyMNDgIo2fvIAwrj50+pMRTYlw2fYKMouAGdgQJipmthJSoikJPIKhkPCAQeq8/ZcfZXrqURwqbGbfafPdRFIXuZN3dWPCWGFfHePsQ5po4CC3LDvQgjIW6MGv3ViNtPFO83D12s7aDKPV3Njo1uz6E5YMvkPmX3yL/0F9gV+Z/D0NvebE3ECebr8xJz8CpfJmKWb/OTAEzgiAIgiAIF5Jz4iBMp9N/CPzhDGO+BXxrFtcsA39W/afVOcM4JcifmMWcPdT7GrY6Z1bPIgiCIAgXG2fbgzCMWZcYe8qaiyXLX14cN1AVJWxaQw/C6e7hZVFnnKFxR5AbmSz4BLXuZL0EOXh9cMofvG7Irvaoe62wtSWiBuCcbyY0/XD3oM8xuagzzngm3LUWNTQihsoj+c28JbqPqFKhXS1ye/w1HshfCcyuD2Fpz8MUn/kqAJWJU5Re/jbR6z8ww6wLi7e8+Nr1i3lixwkALNumUDIbej/OlvGAwJoXgVAQBEEQhHnMBXEQCoIgCIJwcWFoAYFwThyEISXGLToILdv2ldc2SzCGsBLj1tbe2+FJMp5oLDGuESYQRgyndLiGtw+hfy3VlGnPuwgTmo6cnOIfH97r7m++pIdoRAst03burxIxNKbsOI8WNrrHb4u9RlJxypTHWnAQ2rZN8aVvuOJgjdKu72NNnGoya35w5OSUu33d+sW+c9n82ZcZBx2YzZyfgiAIgiAI8wERCAVBEARBOGvOhYMwrCS41ZASwFdu2yzBGM6sxBj8QSVhPQibXd+5h/99NRcIaw7C5iXGuUKZz//7bsoVp4dhMmHwk+/aAEB7kxTjSNVBCPBYfhNTlvMsUaXCO+I7gdZKjEvbv0np5ZAiCqtC4dl/mnH+haJiWm5PR4DF3XGfYzA7B2LehXYQ2rbNrgMjbE+flgRlQRAEQRBmRARCQRAEQRDOmmB4yLlKMQ4KkV6C4SJegbBZgjE0OvyC4l0zvA7CkyM5Mh7XWXf7zA5CL13J8PXV3mPc24OwWL+Pbdt86buvu+XJigIfv2+TK1CGi6wqqqK4YmsRgwfzW9zz26L76FMnGQsJ8PBiFzKUdnzP3T9pdvHN7LXuvnl0J5WjO6e9xoUiKAAm4xGfYzUzB0ElQQfm+RYId+4f4TP/tpPPfXMPf/Xvr7ohOIIgCIIgCGGIQCgIgiAIwlnT6CA8NyElQWHNi6aq6Fq9bNeb6NsswRgaHX7T3cOL10F4bCjjbis4Kcf16zdeL1jGPBsHoVfcGhjK8sq+YXf/PW+7lI1renz31lR/78Xa83nLtZ8prmPYbAdAU2zujO+ascS4vP85sJy12NEk/3fyLh4vbmBfud8dU3jmn7DNuUkFng2WbZObRuTLeAJsdE0lYqg+MXUuSozHMhe2xPi1I6Pu9st7h/jLb+ymXDGnmSEIgiAIwkJGBEJBEARBEM6aBoHwLAMewBGw9EBvw6BTMYi3PNhfYjyNg9DTg1BTlYZ7NsMrEHpDbzvaI75rhJcYtygQVsc1E69Oj+Xc7f7uOHdvW+2bryhKQy/HmkPSuwYTzQ0nAbg6cpjS1CjTUd77lLs92X81OTsKKHw9dz0WjihpT56i/OoPpr3OXFNX3DDfAAAgAElEQVQqm/z/X3yeT3z2aZ7adSJ0jNftmUwYKIpCuy/J+OwFwgtdYpzJ+Z9h14ERPvv13RTLIhIKgiAIgtCICISCIAiCIJw1huYXvObCQagoSkPIxkzuPq8zr9UehHGPgNeqexCgJxklLBe5OyD2hQW2NAqEjQKmrtXFyqRHIPSmM3u3ezpioUnNwTLjuoPQv4aXS2sYt+LOvRWLK6zXmjrOzJGjWMNH3P1D8XqJ8qDZzeuRre5+8ZXvYJdyzJZaT8XZsuvACIMjOUzL5pHtA6FjvAJhrZTd+54yc+D2a3AQFs+vMDcVInK+emiU7z9/9LyuQxAEQRCENwciEAqCIAiCcNYEHYTxOQgpgcY+hLNxEHp7rk2XYuwVM1vtPwhOaWpXstH51x04dqYOQu88r8DpEwgDTrgwggJh7d7RwLu0UHm6kHL33xpLMzYRLuyV03X3oNp3KQfz7b7zT5hXg1F1WBazlHY+EHqdZnztkX18/P88zue+sRvLa89sAW9p9ES2FDpmKkwgjM1xifFk0EF4fkutpzxl1D0d9c/Xq4end4YKgiAIgrAwEYFQEARBEISzprHE+OwdhNDYh3C6kBJo7gCczkG4vK+NWpu+FYvbm44Lw1tmXKMn6T8WHlISEFSjekhYSn3fK5Rm8nXhxysCJePhImgwybiZgxCcXoRl21lbh1qgsO+5hjG2WaGy71l330i9jcHhrG/MyZxGZOs73P3S7gexcuOh6wuyc/8wD714DIDte4d4bZaC1ni2LsxlcmXsEIHRW35b+2x43arZswwpKZZNcoGS4vPtIPS6JK9NLa6v4zz3QhQEQRAE4c2BCISCIAiCIJw158pB6O2f10p/wKAzr8Z0KcaLOuN88qPX8Z5bLuMjd6aajgud29EoEAYTiSO6SrDyN2ydQRehVzD09lCc8ohevlLZpg7CYA/C5gJh1o6xx15bX8PBJxoEtsqRV7CL1VAWzcBYewODo36n4WSuhL7pTpRYsjqpROnlb4Wuz0upbPLVh/f6jj29a3DGeb57Z+qiqWnZFEqNwlxoibHPQXh2Ilqw/yBAoViZtRuyFUYnC/zTD/by1M56v0Xbtn1O0/6ehLt9tuKnIAiCIAgXJyIQCoIgCIJw1pyLFGPwO+dmcg9Cc4FwuhRjgG1blvHT921mcVd8VutrxUGoKEpDmXG4QOgXFr3v0Cv+eUWvMCdckGCZdq2MOuhiBFAVhaNd17v78cwA1ukD7r5tmZRff8zd1y+5lqxp+MQocEJbMqZO5Or73GPl15/AmjgVusYa33vuiK93JDgJvJlZlPyOB8qKvS7LGjMKhGcpooUlQNtA4Ry4CP/1sf384KUB/u6BNzg0OAlAqWz5ejj2d9c/12crfgqCIAiCcHEiAqEgCIIgCGdNg4NwDlKMwd8/r5UAkWiIMKkojX345oreUAdhWD/BQPlwyDqD87xj2mOGLxClJnp5hbmgEFgjWKbtlhjrjWvobI9Az0r2l+slqfkffJ7y/uewJofIf/tPMI+/5p4zUm9jcCTbcB2AyWwJY8OtKMlFzgHbJP/MP/H5b+7mD//uBb7x5AFOeEqTT4/l+N5zjQEaFdPm2VdPht4jjIlMQCAMERfDQ0q8JcZnJ6IFA0pqnIsk4yMnp9ztw9XtoCi62CMQVkyL0gJIMi6WzdDyckEQBEEQwpmbn94FQRAEQVjQnA8H4UwBJRAeMpKMG6HpvnNBuINwZoEwTOwMlhh7XYaqqtAWN1xhaypfZnG3vx9hsplA2CykJORddSej9HTEeKKwkbXGaQDs7CiFR//aUVo9govavxZt2XoGd4WLdxPZIisXtxO99n0UHvsbAKxjOylNdXG0vJqjpzJ855kj9PckUBVH2KsFy3S1R7jy8j4ef+U4AE/tHOTt16xAaeH7OJn1i3OZXKNA6BNWE3MfUhJWYgyQK1boPasrNzLpEQMnqsKkVxRNRPWGkJ5soTKrxO43G0/sOM5XH97L6v4kv/Wfrp6xNYEgCIIgCCIQCoIgCIIwBzQKhHPfg/BMS4yT0/QfPFtadxC2UmI8ffpxMuERCHP+r875JiElTQTCZiJldzLKrvJKvpe7grsSu9Golqp6xEF97TZiN/0XFEVt6iCsOfn0tdvQ0k9hnngdgPe3vUB6fClFnPWeCvQvVLH4yE2LWd5eYWjPAB1qjvapAqefPERXewwUBW1pCn1pY79I07Iayp2D+xAUVp11eIXUbMHp89iKIBnGaBOBcK4dhOWKSd5TtjyeCXGWJgwihoauqa4AmyuUG9K2LyYefOEYFdPmwIlJXj8yxpZL51qWFQRBEISLDxEIBUEQBEE4awytLjZFDBVVnRvH3qLOemlkUEALI1wgPDflxdAoELbF9NA1NCYUN4qd0/UghECSca5MsWxS8vSZaxpSEvP/uFfrPaipCqqi+IIzutujVQekwoOFK9irr+PXLz+AeeRlAEwtStstP4Gxdps7Z3DEL/DVmKz2AlQUhdhNHyX79d8Ds0KXmue+xMsc09dwubmP5dooumKhY2IoJu1qEV5wrvGzSc8F0zvwFs4am+8geuOHUFTNc88ywaLSsP6FYSXG3vdbMW1KZSu0FLwVpnMQziUTgX6LroPQm27tOiR1d/zZllDPd8Y9Jd7D4/kLuBJBEARBePMgAqEgCIIgCGeN1903VwnGABvWdPOWzUs4emqK+966Zsbx59tBGI1oJBP1kI5mrqxGgbBxncG5QXHKl2ScLzX0mWvWg7CZg1BRFCKG6kv57UpGfOs4nInzxiU/znd2L2a1NsIB/TL+u0ccBDjpEQi7k1E3oMMrXqldSzA3vBNtz7cBuCm2F9h7Vj+Jlvc8jDVxiviP/DxKJF69Z6MwN5X3v6eKaflcd/WQEv9isoXyGQuETXsQzrEwF3RH1gJavAJozSGZ8AiEuYtYICyVTd9nemQy/HtxMWFXitiFLEpb9xm7XgVBEARBBEJBEARBEM6aJT11p9+yRW1zdl1VUfiZeza2PD5M0DmXDkJwXIR1gbCx5BjOtMR4egehVwSKR/WmfdaahZTU1uETCKslxjVs4BtPHuREZTGHK4uhCIVSxX2ecsVkaKLu0Fq/qotnX3WSioPutnTHDfSYT7JEmwhdZwOROMQ6OTAGE5UYFVQ2rummo3ASa8zpTWge20XuP/6Y+N2/gZroaggogUYRLdhfsOa81DWVaESjWEuIzpfpCSkhbwVvirGhq26i8PlzEM7QY/EsU5rnM8Hv98hkocnIiwNz9Dj5B/4PdnaM6A0/SuSKd13oJQmCIAhvUkQgFARBEAThrFncneAn37me9LFx3nHDqgu2jvPtIAQnqKSWHtudDL9XLBoIKQlLMQ6WGBtBB2Fd4JnKlX3hG9OJoM0chFAvN3bXkIxi6BrtnkAUb9IwOOLX0l7nR8hTo3m3NaGmKqxd3lkXCAMuur3HsxzObuMXkg8TUUzQI+irrkRfczVKLAmqhqLpKPFOlEQHiu4IlQ//8yu8fmQMgB9fejlvv2Ixhcf+hsrh7QBYYwPkv//nJO797QbBDBpDSrwBHhFd9b2P9pjuCoRnWoZrWbZPqFzam+DoqQww9wLhZOB5J7NlLMv291isfjYSHofkxewgDDpGL2aB0MqMkH/g09hZ59+P4svfwth4O4pxZsK2IAiCsLCRSC9BEARBEOaEt12xjJ+5ZyMr+tov2BrCBMKOc+wgXLeiy92+bHln6JhWSowNXfOVuTaUGHsdhPmyP6CkSXkxOCKgrim+/fq2/x7dVRdjWBJzDa877oQnoKS/J+FzUAbFur0D4xyqLOZ/T9zL/vU/Rft//gvib/8FjLU3oq/YhL5sPVr/WtSOPlccBFjSm3C3T47kUIwosTt+kciVd7vHreEj5B/5KyanGvvNBQWjTIi7rsZcJBlP5kq+vo5eR+1ch5QEBULLtplq+GxUQ1g8n62L2UE4mfU/2+hFKhDaxSz5B/4MOztaP1guUDnwwoVb1JuATL7M9vTQRS2SC4IgnCniIBQEQRAE4aIhLJn3XDsIb71qGbZto2kq2zYtCR3TSokxOA6+mnMtHg2mGHt6EOZKPidcs/6D4PQabIsbrqvN5yDUAwJhVRjsTkY5ejoTej2vQOjtP7i0J0GnxwXpFa8y+TLHhxwxccjqYMmma1CM1lJ0l/R4BMJq4rGiqESv/yB2pUx5z0MAmEd3sjKpAZuAuiAadBCGBZTUCCYZnwne9xOPaq7oCmcnENq2zdB4nr6uuNtnbjLX6JicyBR9n426g7D+bOdKHLHtswt3mQuCvTnHpoqYloWmXjy+CNssk3/os26pvZdS+kmM9TdfgFXNfyzL5o+/sp1ToznWrejkkx+55kIvSRAEYV4hAqEgCIIgCBcNQacenPsehIaucef105dVt5JiDLBt0xLuf/wA8ajOxjU9vnNet5vjEvOWkU4vgq5d3sn29BCaqrCqvx4N7F1H1NDcdXZP03tv1COADY7WBcIlvQk62+rryBYqlCsWhq6yf6Ded7AjYfhEv5lYGiIQumu+8UPYmWEqh52U5UunXuaOmMXDha3umGCKcZiwats21tAh1iuHaI+MEFPKxE8WsAudKLFGR6xtW5jHdmOe3IddKUKlBLaN0rmEYqGTNqVA1o7RnYz5hN6zEeY+/809bN87xJVrF/ErH3CeL+ggBMe56etBGBLCci5SjMsVk//xD9sZHMnx0XekeOuWpXN+j1YI9iC0bUck9Caiv9kpv/445mDa3dcvvZ7KQcc5aJ3ajzl6HK1n+YVa3rzl1FiOU9X/huwdmCCTL0/7xxVBEISFhgiEgiAIgiBcNFyIHoSt0EqJMcA7b1jF1st66UnGfD3jIKQHYb55qWyQj9yxjjVLkly2rNMXQuJ1XHYlo64zLVhiHNFVStWgDa9DbtDTn3BZb1vDu57MlujtjLF3YNw9dvmKrlklrXrFxLGpoi8kRVFVYrd/nNy3/wRr6BAA9yR2ALgiYbZQoWJabohLxiOs1sSB4jP/SPnVR7gVoKYHHn2BzFf+DbXvUvRlG1B7V6J2L8caOUppx/ewxgZC17sc+FQ3pMtL2R2/jUSsLtR4HYTlioXa4muYyJbYvncIgB37hxmbKtKdjIYKhOOZou8Za98Tv4Nw7kuMd+4f4VjVdfrgC8cuoEDY+E5GJy8egdC2bcqvP+bu62tvJHbbx8lNnsIaPgJA+Y0n0N7y4Qu1xHnLeCDEaGg8LwKhIAiCh4vHay8IgiAIwoInGLoB595B2ArxQIlxWCk0OOXAK/raG8RB8JfD5osVxj1C3UzP2Nke5e5ta1i/utu/Dr3+vro95cFeEVFTFW6/eoW7P1bt6WbbNifH/A5CQ1d9TrVaH8J9x7wCYXifxmb0dMYwPOs8NervM6joUeJ3/SpKZ7977J7EDu6I7XL3vY65TL6+nYxHKL3xBOVXHwm/uW1jnT5Aacd3KDzyV+Tu/z0Kj/1NU3HQS8oY5H3Zf+bSgW/TpjjvrCYQHj01xU/89wf5iT96qMEVGcbQmP+Zh6vJ0ZO5RqFvbKroe95kIsRBOMe9EMEfBjI0kcf29GFsBcuy2XNwhIMnJs9qHWFl1/MxqGRwJMtDLx5jZGJ2azNP7ccaO+HuR695L4qiYKy/xT1W3vdD7Erje1joBIOThsYbe5YKgiAsZEQgFARBEAThoiHY+0xR/H3lLhStOginI+jO85b3nqkLJuggrLFxTY/ruLv1yuVcsqzDPVcrMR7PlCiVLfd4f7fj9OvwlBlPZIsUy6ab8gxw+cp6qEsrqIriXhtgcDTbOCbRSfzu3+K0WV/nPYkd3BN/GQXb56jzJvwuVU5TfPor7n5RT3KgvJjXy0vJKzOXQWsrt2JsvoPIlXdjbH0H2orNFNT6PBWb3pPP81ud36ZHnXJTjB9/5TiT2RLjU0We2NHYRy7I6XG/iDhcFZXCHIS1Xo816iXGZx/AMh1eZ2mxZM66jPmJHcf5s3/dyf/4h5fY6xGUZ0uwxBjmX1BJuWLyp//8Cl97ZB+f/fquWYmp5dcfd7e1ZRtQq8K4sfZG0Kv/7hWzbtm9UCfMQSgIgiDUkRJjQRAEQRAuGoLCWzJuoM6inPVc4Q0pMXQVtdXaUg9RQ/OV+np/uT3TMuoOz7w+TwlmdzLKpz52AydHc2xY082hwbrAVxOCTnkEymTCcF2PnW0RBqvhJRPZEgdPTGJajgASjWis6p99yvWS3gQDQ075qjcYxUtR7+AvJu/klzoeol9zXGh3xPfQp02SmdwE1XTtWg/CdiXP1oF/B8sRspR4B/tSP8/fPjoIwPqVnfz6XYuoDOzBGj6MNXYca/wkKAr6ZTcQueLu0D5vn/unl1EG9/CexEvuOjrVPB9rf4wvF+8D/N+702MzixTBMcPjeUzLChX6au8JQNdUV5z2ulLPRUiJtzclwMhEYVbC9Y79I+72zv3DrJulkFwjrMR4ti69c83gSM4Vq46dzpAtVFp6V3Yx6/YaBDA23OpuK5GE04tw79OAU2ZsrL1xbhf+JmciKw5CQRCE6RCBUBAEQRCEi4YGgXAe9B8E6O+Jo2sqFdNiRd/sBbIayYTByKTzS67XdJQ8QwfhzVcuY9fBEXRV4dar/GLXoq44i7oc0dDbkzCTL1Mqm5zylBd7HX6dntTeyUzJVwq9dlnHGaXJhiUZB5nIFpm0E/zl5J38YsfDLNGcYJQrI0cpPP8XmD2/iNa9jEyuzBr9NB9ue4ZouVrOqqjEfuQXiEz1Ao5AmCmYaItWoy1a7d7DNiugKChqcwfoWKbEyfIK0hNL+d1rRug99H0AlunjvLf8GLZ9K8OT9Xcy3IJ4NTTuHzM8UWAqV6b+EbCpJTefDAi3tX6P5zqkZGzKv8bRyQKrlySbjG6kVjYNMHQWgt5ktlE0HZkshoy8cJypmFre9wyYzvMpsST6mqt95431t7gCoXnidcyhQ2h9l8zRqt/8TDQ4COeXcCwIgnChEYFQEARBEISLhkaB8MKXF4MjVP7Cezez5+AIt1115umi7fFIqNgxU0hJM5b0JPijn75+xtCQzvYIilIXJccyRU55XG393XX3YaevxLjEgRP1BONgD8RWmS7J2L1X9Zf/STvBl81385HIE6wyndCGWOY4uX/7HbT+y7mtoLA1udcXEBK98cfQl62n/eiYeywbEuShaNP/6GzbNmPVPmcmGmx5F2bMRnv9QQA2GcfIP/ZFNuRNLo/aJJQSi/J5cg88hfL/2HvvKMft89z/A4CdHHJ6rzvbe69a9bKSJatZrnFs6zqxkziJ05N7fmn3OLFTrp1y7cRxYjuSLHfZKlZZrdruanvvs7Oz03vjkMNOAL8/MAQBkrNNK1lS8Dlnzww5IPgl8AW4ePC87+MqwrHsLpMgmSHX6TQ2FdfLizc5z7PNfZxR2c8PI5sYVbJl1kbh2BhSkpYVkil51l6Y18Jkjug1dhVlvaqqmlx+Y2/B2RWOFQopeXcJQbnjmQhfXkxVVZXUuTf0x7b5WxAk83EvVc1FrGjRA3uSR5/DfedvX6dRv/cJWj0ILSwsLC6JJRBaWFhYWFhYvG8QRQG7TSQ1U4b7bnEQAqycW87KueVvaR2zCZ5F7mv/nFeSKCyJIsU+py4CTYYSprLXylkEws7BED3D2ZLXVfMqrmmM1WVmgVBR1bzS8SlDPz6Xr4g3Sz7Exe7nudl1Vn9eHm5npcHAqCLgXPkB7EvvBHL69F1D0m80kSaRlPXHpUUubGsf5tjx0yxzaMEm8oU9PJgTqCv3aj/TF/bhWL4Nx5oHEGzZ7ZgvEMYIhWM85DnATa5zABSLMf4w8AuenN7M8ZQmMhqFY29O8E0knr5uAqGiqATDZmHuasp6w9GUXjoPV+aqLEQiKZv6YurrC8VRVfWq0rPfTiZyRP7cx4VQRjpQJrLhOI6FN+ctIwgCjlX3Ed/+LwCkuw4jT/QXLIX/n8hUTs/O8VDclHBuYWFh8T8dSyC0sLCwsLCweF/htEsGgfDd4SC8XhRyCkqigNt5/Zxgs1FSZBAIwwlzibHB4WcMKTGGk1SWuKkpu3zwRyGMJcbJlEIwnKDU7zItY7z4D3gdeD1OfhZdR0+6jAdK2vCnRk3LD8kByu/6HM45i/XnjIE2yZRCKi1jt2nbVlYU9p0eZjKc4JbVdSYxMYNRFHPYRIo8dlTgiemt/K7/BWptlwnfUBWSx58n1XUY962fR6poIZGU84SNVDhIyeH/pMl13vS8S0jxaNEbvBmfz4HkHPzuClRVRZ0ahqE2tnlOElckEqqN9KkIsfiQ1l8xGkQqb8bWtApb8ypEX9mlx5nDVCSJkhO0cTXJwbmC4HQsRSyRxu28ukuVQv0HQRMOo4l0wX32TqCqCmp0CiU8BskI6vgEXiFORHUCwhVtq+TZrHtQqlmIWFxdcDlb00rEkjqUSS38JnnsOdy3fu4axqzy0oFehiej3Le5Oe94ey+SW2Ksqto8NbZIeLegqgpy/xlS53aihEexL7gR+6KbEARLzLT4n0M8mTb1cLZ4+7G2toWFhYWFhcX7CqddZHrGcOV/FzkIrweFnII+Q5+5t5MSQx/C8VDc5CA09yAsvM1XzSu/5nG6nTYCXoculA1ORPMFQkP5YLHPoZfXHk7OwVa6kc9u9jF98jX6z53kVLKBl+NL+XrDAtM6Crnsin0SPcNhvvPCObpnBM+xqTifvnth3jiNAmGp34UgCAiA6HTzb+Hbudt9nLnlAiMTUURUEtiYlL0sW9xM5dhB1LAmYqpTw0Sf/QruO3+HUceMG1CIscbRxXJHN3NsI4jZym0mpTJc6RBuQXM9bnGdZ4vrPKmR14g84USNab0W7zZuspNg7EQo959G7j9NYs8TiGVN2JpXYWtahVjWeNn9lltenLstLoex/2D2uTgNlbP361Qik8gjF1FjU0g1C5BK6gjNJBi7hQQrPUM4lDiimsaGQmTfCDa3iCqnIJVATUZRU3FEXymO5ffMKri9FdT4NPFd3yXdfRSUrLN0G7CtBGKKnZfjy5iYurSzVk1GSXfs1x8bw0lyEQQRx6p7ib/6TQDSHftR1j6I6K+8qrGf7prgR69dACCVVvjsvYsv84p3N8mUrKeIGxkNxt5VAqGqqqTOvkby2C9Qp7PBPYnRTlLtb+La+mmk0vpf4gjfOicvjvPywV7WLqzkxhW1v+zhWLwLUVWVbz17hv1nhrljXQMfvW3eL3tI/2OwBEILCwsLCwuL9xVOhw3QBIv/CQ7Caw0ouVqMAuHFgZDu0oTcEmMnhXir5dU1ZR5dIBwaj7KkudT0d6PLzu91mvb9dCyFVNFMaOkj/OMBTXBz2qW8EluHXTKVqIciSV470s/z+7r1JGbQUnYLlawa++6VBbJqnNtpYzzh4YfRTSy3l3HCcOEP4ArMpeWW+0kc+hmpky9p1qZ0gtiLXyXRej8Pe06z0XkBhyCTy7FkI10tj9DT1cvDwsvU2bJ9FO1qAjV29QEdyng3yfFukod/juAOINUuRKpZqLnTvPl9JAsKhFfhICwkJo4GY3kCoRIeI3n8BdLdR1EjE6a/SfVLUf3L+Zh3P6sdXfnb6jwU8hfKQKrjAK6bHsU+Z/0Vj/lyqMkY0Re+ijJ6cdZl3GKKD3qO8OaEE1g263Kp9r0ga6MXnL68cJJcbHPWIxz6GWpoRHOlHnse142fvqrxd/SH9N87B0OXWPK9Qa4LN8O7Lagk1baTxO7HCv5NGb5A9Kd/iXPTR3EsveMdHtn1QVYUvvXsGaZjKc52T7K8tYxiX+HvDIv/uQxNRNl3ZhiAlw/18uDWOTgdb3+lhIUlEFpYWFhYWFi8zygpcjIwFgGgoth9maXfWxQSPN+pPoulRVnBq603Wyrr9zpMpaDGEuMMPredufWBt/T+1aUezvVo71soqCTXQegzbJdwTHOWTRtKUH3uwv8N9rpsBGdKEb/5zGkGxwu8VyTJ6FScypz5ZRS6ygwOR4/LxviMxtI3Ok0uY1NxBJsT18aPYqtfSmz7v0A6CYpMZftTVBao7pxUvOyML+C1+BI+UORF8VXwtcG72eI6zxJ7H3NsI9iErIgrltRxLuQhHovjFNLUlnsobZiDVN6M4AmQ7julCW9Tw6b3UWNTpDv2k+7YT2Lv93Bu+SSOhTeZxxLOF1mi0TjxqQmcPv9lw10K9Rw0BpUooVGSx54l1fYmqPkiKYDcd4pKTlF5LVpDKk58xzeQl7bj3PCRy473cqhyitjL/y9PHBScPnB6tO0iZN1sW9J7SbUtxr5ga/66VJXUudf1x7b5W0z9KQshiBKOFfeQ2PVdAFJtb2BrXgkV+eufjWHDMTY+9e7q4Xgt5JYXZxidfPcElSixEIn9P8o+IYhIDcsRJBvpzkPac6pMYs+TdMWLeLrdybpFldy88r3TY7J/NML0zPlYVlR6hqctgfB9ysWBEE/uOE9tmZdP370QUbzy88fFgexNCVXVXOZ1FbM7yn9ZKKpKMiW/r8qg3z+fxMLCwsLCwsIC+OCWZsLRJI2VRSxuKb38C95DFHIL+n4JDsKYoVTPmGAM2hiNiccAy1vLkMS31jur+jJJxrk9CI2pveGZ0tOMUAhaInQhvG67LhAaxcEyv4u0rOjvc6EvmCcQTlzCQZhdJt9tZxTDbPVL8dz7J0Rf+CokIqblwoqL/YlWjieb6JHLAEH/vAGvk05svB5fzOvxxThI8Ts3eZlfV4RU2Yrg8rHrx8c5Maq5Fz+2fh53rGswva+64SMowUHS3UdIdx1FGbkIGHaknCax8zsoY904N31cF9ImwwkqxBBrnRdpsY1QLk5TIkZI/fB7pNCEMcFTjOAJILj92k+bEyQbCAILB8+x0j9EtTTFlOJmZ3wh45PVqMkoiSPPkDr1sqlEN4PgLUWwu1CCA3l/iwtuJp01jITSpFWRqnI/LfVlINkQbMM700YAACAASURBVE76gjL7zwxxu+sUblGbF6lTLyOPdOC+/beuug9jBlVRiL/6TeT+0/pz9sW34tzwYQS7i8lwgj/++m6KxShfKNpOhaSVrcd3fmfGHbjKtD5ltBNlvDe7rkVmcXY27PO3aGWq4VFQVWI7vkGipgZn7dwrer3xGEumFcKx1Hu6ZUNugnGGd1OScWL/j7LHvN2F9+H/o5eGp3uOE9/1XdTIJKDiOvQ4nVP30dYTZEVruen8/G6mo3/K9HhwPMLy1ms71ize3Ty1s4OLAyEuDoRYOa+c1fOvPKTMKBCCdhPp3SYQtvcF+dazZwhOJ/n1+xazduHVtXF4t2IJhBYWFhYWFhbvK+bVF/NXn7l+pYLvJgq5BQuVHb8dzHYBmtu/SxQF/B6HSbBbNe+tlRdDTpJxAVdfcNooEDpxGYJbpmMpVFUlYhQIZ9luhYIsVs0r59fuW8yTL7ez++QgABf6pti8tMa0nLGsttzoILxM2MZYTjmuVNmK54P/m9gLX0WdHmdc9vFKfAnHlPlEUvkuDL/XQXFO78ckdqS6JdgasyXBxh6LhVKaBUFAKqlFKqnFufJelHgYebANeeAc6Y79qHFNzEqdeRV58BxiaQOC08earrNsK84X6TKoiWnUxDRM9hX8+0LQr0pcUopHvAeI9Jwh8gNBf099jL4yHCvuwda8GtFbgqqqyIPnSJ3cTqLnJF3JUt5MzCeweBNFPg9P7+4EYH1jJYu2LtXXs+uFs+yMV3A02cyjvtepnynNVkYuEvnpX+C+9XPYGpbP+pkKfs50kvir3yTddVh/zta6EeeWX9HDJbQ5IhBUvHwjfDtf9L9IQIyBqhB7+V9xrH0Qx8oP6Msb3YNSzQKk4kv3bBuaiHK+N8jKeeV47/xtos/8LaTikE4y9KO/pfbTXwYu3XNPVVVTCBFoLsL3skA4e4nxu0MgTA+2kT6/W3/sXPuQqW+krXEF7jt/h+jPvwSqTIkY4SHPQZ6MbKFnOJx3flbTSVLn3yTVtguSUcSSOsTSeqTKVqSGpVcddqKqKspIB/JoF0poGCU0glhUgWPZXYj+Kxd+LvSbhZ9CDm2L9wd9o9kbXN1D4asTCAfzBcJ3E3tPDfGdF86SlrUbaC8e6LEEQgsLCwsLCwsLi3eWQm7Bd6oHYelsAmFpfhm3MVDEJoksuQ5OTqODcCIUJ5mS9R6CaVnRy9ZAC0px2LIXwKm0QjKlmByEs2233KCSG1fU8Mm7FiCJInPrA7pA2J7jhIGcEuNZHISFGAvml3BKJXV4P/xlvvbtHZyZcqAgsmxOGScvjue93u+xEyhQppc7X4yuymg8P7AhF9FVhNiyFnvLWpQVd8+UzWqCmzI5gDKpiYJXftl35XjVaVTDNaHgLcWx5n7s87aYSoAFQcBWuwhb7SKeeOY0e2f6Vt3ncZvKvHN7InYPaaXe40oR/xS6m4c8B9js0kI5SESIvfA1bHPWYZ+3WRNUxOx7qoqMGplEiUwgOL2IgWqtZ+RL/4w82KYvJzUsx3XLZ01ijNFlOqEU8c3wbfx20Uuai1FVSB78KfLAOZybPopgd5O6YAgnWXhp92AskeZvHz/MdCzFwtPF/PHHV+O+4wvEXvgaqDJyZIr+b/8JUst67PM2IVa2FiwbDkVTxBJmx+bYVJyWGv8l3//djNFBWFvu1dtQjARjv/TyaWVq2NR3UCxrxL7ktrzlpIoWHGs+SPLQzwDY4OzgdLKegbFWVsz0eFXiYVKnXyF1+hWTuK5MDcGMcC3VLMR1y69dsUtWVWTir/8n6Qt7Tc/LQOrs69iX3oFz1b0ITu9l19UxkO8gfLtQFQVlahBltAt5ohfB5kQsrtH+ldQiSNfvu3MynODp3Z2U+p3cu7kZ8T1cjn8l7DjUy5Hzo9yzqYmlLfnzKJZIEzKI8lezn5Mpmb4RcyuOsWAUeaIflDSC06v9c8zeQkZRVbYf6CUUSXLPpqbrVmmhqio/23WR5/Z0m54fGIv80s8j1wtLILSwsLCwsLCweI/wy+xBWDyLQFhZIAHU73PAiPb74uaS69KfpzzgxiYJpGUVFRiezIZYhHLcQQGvI6/fUTiaZDpqLDEufMGwrLWMo+1jANy7uYkHt87R/9M/z9BHcWA0QjSe0kW3ZErWU3QhpwfhZQTCREpmOpbK25eqaOds0I0yU+a7qKmksEDodRRMj85d3+UchJdC9JXhue/PiO9+nPT5XQWXGUgXc0qdy8V4gDG5iE3r5vPBtRWo0SmU6BRyJMhzr57ARwybILO4oQifU2RXe4R+uZRRuYiVjm42O9uz/RNtDhwrP4Bj+d2X7b0XNnwmv9dBmd+QvG0Qb9OyYuoFmcLGD6ObuZiu4uP+A4hKClBJXzxA+uIBcHoRXUWoqgJyGjUaBDXb3xHJjuBw62nRAFLjCty3/6ZJWIT8EvN+uZRvhO/gtyv34EhoPTbl/tNEf/Ln5g/n9GJrWXvJz39xIKQL5ed6goQiSfz1S3Hd+Gnib/wXAEosjHLmFVJnXgGnF6m0HrG0HjFQg1hUjlBUxnAo/1gvlDR9vVGmJ1AmekAQtW1qcyCW1CHYCzThzEFVVU53TpBIKayaV553/BsdhHPrArpAGE8WPvbeTlQ5jTx0nnTPcdI9x1Gnhgx/FXDd8KsIYuFAhvi8O+nZt4tmm3aOerToDQbbekhVbEMebCPVtlsPtJkNefAckZ/8f7hu+FXsczddeqyKTPy1b5Hu2Fd4ASVN6sQLpNp2Yp9/A/aFNyKVFO6JGIomGcnp+XilDkI1nQA5fUUipDzRT+rUdlId+zX3bAEEpw/npo9hm7f5uog6T+3s4M2T2n6sK/eyZsH7w01WiJFgjO/vaEdF23//9wtb8gTRXGeucT8rU0PEXv5/qMkY9vk34Fh6B4IrWz7cMzJtCgWrFKdY3/0S0Q5zf1yxpBb74lu1m0Y5YuGRtlE9hT2RlvnknQve0mfOcLhtNE8cBO08MhlOUOq//Lnq3Y4lEFpYWFhYWFhYvEfwuuwImLrCvWNJzTZJxO915IlxuT0IAebU+Dl1UUuZ3bik6rq8vygKVJZ49Av7Z/d08eg9C3E5bKaLf7fTpjsLPU4b0Zl+ieFYyuQynE0gvGlFLVUlHrwuG41VRaa/VZd68LntWskyWrlcpn+W0aEmCgLFRVnBwe3Kv9iXRAGHXdSdWmNT8TyRYiIcN10oLW7OTxCGGYGwQDiMNyeIxeggjFyBgzAXwebAddOjKItvQR7rRo2HUeNhnj80zNFEA/1yKavmVXB2RmAdmVYRiyqgqAIJGBwO83w0eyF5d3Eja+ZX8tPjWgCDTRLoiFazI7aMLa427lxTi2/VtoLJyYUIRwwOUY+dUoOLc2o6SVpWsEki/aMRfbvaJIGqUg/9oxEOJltJC/U8GnhDc1xlSERQEpdwwMgpVMPcss3bguumz+SJg2B2EGbokcs52Po5boq9kg2jyME+b/NlBdLMsZHhQv8Uq+dXYF+wFTUVJ7n/R6iyQRhORLQScoPrEaBSdHC3eyGvxJaQRJsz11ripyZjKJEJ1MgkgjuAWFpvEmSU8BipjgOkOw8VTnwWRMSKZmw1C7HN3URfqphn3uykodLH/Te06Os6dmGMf/3pSQB+dduCvOAOY0hJXYUXt1PSj73RYP6x91ZRVQXSSdRUApJR5OAAykQ/ylg36f7TswpX9kU3I1XN3ifyQNsYO6Zv4I8Cz+lBNzWJTuKv/Fv+wpID+4KtSLULUCYHkYfOZ3tjJmPEX/0mqfa9ODd+pKCopzkHzeKgWFKHVD0PwR0g1bZzpicikIiQOvkSqZMvIQSqQZGJpGOoyYT2fSUIyIKDhz11PB9bSUzVROjpWIpwNFlw+yuRSdJdR0h3H0UeOAuKrL1//VJtGwmC1ptUTqHGp1Hj08ijnab+n7OhJqaJv/4tpI79uG78zBWfY2ajrScb3HW+d+p9LRB2DYb0/4NMRZIMjkepKzcLt7lC8NBEFFlREFWF2I5/Q5nQ2k0kjzxN8uRLOBbfimPVvQgOD516/0GVjc4LPOQ5iDOd/32lTA6QePMJEgd+ogmNK+6GCu07+7whSO1UgZtq18qBs1mRsqbMQyiS1L9LB8ejBQVCNRlDnhxAKq1HsL/7e4VaAqGFhYWFhYWFxXsEURTwzghUGd6pkBLQ+hDmCoSVBQTCbRsacTokfC47GxZdH4EQoKW6SBdBDp0boX90ml+5Yz49hnIko1Dm89h1gXA6ljKHlMwirAqCwKKmwheLgiAwty7AsQuaAHahP1hQICwpcppCWTzO/PcqL3bjdtromum1VKiEczSYXWfA66CmLN89Y5MEPE5bXhKo12XLC4YxOgivpMS4EIIgIFXOQaqcA2iuoOd2Znunzasv1h2Y4zmiUm9O2VhHf4gmgwhbXeplPBRnKuHh+dgqVs1Zg9975enX4Vh2bhZ5HKayeJWZMJViN93D2dLLugofn962kL/+7kEAjo05kT7x1ziGTpNu30O6+xgohbeV4CpCTURMbkL78m1aIMksPd5yhWRlJs1nNCLiuvO3SJ17g+Tx51HDY/p6BU8xjuV3X/bz55bxtfcF9b5fjqV3UL3pLiLn9jJ59HXkgXOYbzVkkZQk29wn2Oi8wLPR1RxJNufty8uR7jtN/M3H8lKxxco5OJZvQ/SUkDy1XRNE1cLjAEBVUEYukhy5SOLEixxNLOf49FKOto8xty7A0jna8ZeZcwDH2scKCITGlHMnFQG3ft4YDcaYU3t9yqeVqWESB39KuuvIrPOmEGJJPba5GzWR4xLsOz3EqOLnP8K38oh3P9VSfqsDwe3HvuR2HItvNTmzVFUl3b6H+JuP6wKl3HuCaN8p7AtvxNa0GjFQBZKddMd+Uud36W0EAKS6Jbjv+l1dqHasvIfkiZdIHn/eJHhmHJG5e9VGkhtdbaxydPNMdDUHk62oCAyOR3WBUE0nSXcdJtW2G7n/TN5alMl+lMl+UidfuvQG1TeGgFhch1jeBHISJTiEEhzU943ce4LID/8UW9MqbC2rsTUsvyLHqpFoPGUS0Asl1V8JSiykCaKdh5BHOxF95dhaN2Cfu+GaQ5OulMHxCB6XveCNplyMvQVBC57JEwhzHISyojIajFNy4XmU8RwHXipO8vjzpDr247rl17k4mKZaCvJB92GWOPrNy9pd+eJ6Kk7q9A5SZ15jdPlNFG9+iH7DzZLRYJyp6UTBNhxXg6KqnDMIwR+6uZUdh/o4262J5APjEZa0lGoO4cE20n0ntZ+jXYioxEUvga0fwz5/81X3AH0nsQRCCwsLCwsLC4v3EEUes0D4TpbGlRY56R7KiisBn6Ng+bDLYePuDU3X/f0fuWUuo8EY5/u0i+LB8Sj/8INjpmWMYR1FbrvuZLjSEuPLMbc+KxC292YvzmfrPwjgduY7CCtLPDkCYX4Jp7FMq6LYjd0mUuxzmAJZijwOBEHIu7DzFZgXnrdQYjwbk4aSWadd0su+Id8t1zNsvnDuGgwxPJntT1lR7EIUMAk3rbVXJhCqqkoox0Fot0mmfpgTobgmEBrmcFNVEQ2VPhx2kWRKQVWhbzzO3ObV2JtXoyYiyKOdmlgnSCCKiJ5iBF8Zgs2Bmk5qosVEH4K3FFv9kkuOcyKc3V6NVT66ZsYyHoojCAKORTfjWHQzqqpCKoaaiCJ4S2YtOTUykFOueaHPLB5JLi/+lbeTqNugua0melEm+jRnW3gUZXoMNTyuiyfFYpRP+nZzn3KYs5MLUaaqtH6LlyE9dJ7YS/8Ecv4cU0YuEt/xjVlfG1S8OD1uPDZVE18NYoCgKtzmOMY8fy9PTG+hvW9KFwiNPctyhWiAYE7KeUVJViDMFTOuBSUWInnkGVJnXgM1P3E7D8mOVLsIW+MKbI0rEIsuH+Q0OB6hc1CbLxfS1Xx56oPMsw2x1dXGcmcfUqAa+7I7Z3WbCoKAff4WDgeLsR98nPn2GZesqpA6+zqps6/PPtwccRBAsDlxrv4gjiW3kerYT+rcGyhj+eWXuRSJcT7h28O9ylE60xXIp0aI92uJ3fJoF6QLJ05fDWJZI45ld2JrWZfn2lKmx4nv/A5y3yntiXSCdMc+zSkp2ZDqlmJvWYPUtBLRVVRg7WZy51vvyPQV96NTlTTp7mOkzu1E7jtpEsuVRITkeDfJAz9CqpqHbe4GbHPWI7qvXy/QVFrh8e1t7D4xiNMh8WefWJ3nnM+lP0cAvdA3xY0rzOFJI5P5peOTF07hPf4L/bFUNQ85OKAnd6vT48Se/Qqb1Fo+6h9AFLLbIqI4KLr11/DOX4eqpFFCI6TOvqGH8GgrkAkff5XwyZ2UJrYCDdkx9odYs2D2brmqqgLqjB6tavtBVVDjYZTpcdTpCSYmpliudCI4VTxiknn9XfjS/dxeNE1EdeI/f4JY0EG67yQks+eUjBToUiIk3vhPUudex7XhI4hVc9+VPQstgdDCwsLCwsLC4j1EkdvOoOHxO+0gNJKbYPx24/c6+KOPr+Lnuzr5xd7CF6K1BieDUTydjppLjK813GVuXVaw6hwM6WWrRmdYWU6ZkadAMnJFiRuvYQxjwXyHVq5ACFovRqNAmEmW9XsdpvLzQqXn3gIhJaFIkrbeIIubSwomOF+OSYPgVVLkNImjk+EksqLoTsbeEXMicTKtcKx9VH9cFnAhCIIu3BTaJrMRT8qk5ayTL7NdSv0uXSAcnoyxoLHE5CBsqi5CFAUaq4p0Qa17KKzvZ8HpxVafTT/ORbA5kCpakCparmicRtF0bn1AFwhzxVRBEMDhQXBc+TGW6yDsGgqbwnwyqKpK75RKaclcfLWLzH9Lxtjx3f9grXpc7wNZLMbYxFEiPzyqlU9v+pjJmWZEHu8l9uLXzOKgKGluy2iw4GsEfyWR6tX860E7A3IxzoiNf/3iViQBlIkeQhdPETr6EqWCtq0abeP8UeA5TnWNoG5+FFW0mxxDk+EE07GUfm6UFYWwQSAs9jn04wmuLclYVVV+vquTvqEJPlLTjfvCDkjNth4Boahc7/coVbUi1S5CsF2do2nf6awbs77CRyyRoj1UQ/t0Db975xJWzL8yt/Yb7Qnawnew3N7LBz2HqZDCl1zeNmcdrps/O2uJu+D0aiWii2/VROepYQS7i5KqCkSHi4mJCIoi89STP+M2xwm9NDogxljp6IHeHlK9hd9bKKrA1rxaSy33BEj3n0HuO4UyNQKiyFgoRSguEygtpbK6AsHtR6pfhlSzYFbxRfSV4b77D0i37SK+7wdZgQlATiP3HEPuOQYICIEqpLJGxPImxOJqRH+1ltosiFpps5xivKuNlfYuSqVpysVpyqQw4e8/i6imtW1md2kipc2p/RQlSCW0mwsTvabepbMhD7cjD7eT2PMkUmUrYlkDlNQhBOpwVDRcUW/GXKYiSb7+1EkuzARuJZIyP3ilnT/62KpLCle5DskLBQK7ckuM3UKC8tPP6AKoEKjCfc8fasFMJ14kefS5GWFdpUUwuwbbUtV8b/oGfi+wEC8giDak4lqkTR/DufYhUud3a67r6ZlSYiXNw7bXCdtv4kSqEdDCcTICoRILIfccRx7pQB7pQJnoN/eUnQUP8BHDZlbPQzVA5mtzuof0FZhHleELRJ/5GwRfGbY567A1rkSqbLnq88HbhSUQWlhYWFhYWFi8h8h1hr1TPQihkEA4e4rg24Ukijx8UyvzG4r58WsdjExGcbtseJw2Gip93LMx61w0iqd5PQiv0XnZUlOkh6Uk0wo9w9PMqfVftYOwosSNz50dw9hUnFRa4b9+cYae4Wke2NpiusjKlHKXF7tMF2T+GeegTRLxeeyEZ1yShQRQc0hJmlRa4UuPHWJsKk59hY8//9Ra7LarK32aDJtLq43BIIqqMhlOUB5wo6pqnoMQ0B1RoImfxmb3VxOMEY5mBSCB7L6vr/DSOePSPNY+xpZl1SbHT3O15pZpNgiEXUOXv2C/FpIpWd8/oJVj7zik9eLKDS+5WsLRpGndoJX1dQ6GWNBoLpn/2a5OntvThdsp8aXPbjQd16rdxY+nVrBdbeYD7qOsdHQjGZw86fY3ifSdxLnlk9jnrDO/30Q/sef/MeuekWy47/oiUt1iBEFEHu0ieeJFLfhFVZCq52Nfvg1b40r2HelnQG4HtNCe3pFpWmr8SOXNfGv7BJ2T9/KA56CeNG0XFFbF9xH5yUVii+7FJUdIkT0f9Y+EmF9XBJKNUCRlKlQNeJ0mgXBsMooSnUKdHkMJj6GEx/Xf1fAYyvQ4gt2JVLsYW/0SxEA1He2dJI+c5gFXO+6g2S0l+MpwrnsYqX6pJghJjrfsFFJUlb2ns30xNy2t4lx3kPGZeTMwGWfFFaxnLBijrTcICJxINXJ6qo4/XT9NTayd1OQQRCYQBZiUPdgX3EDVmtu0suMrRCptQCrVnFuumX5wkhqmbyjMS5Gl7I+18KDnECsdl3Aa2pzY5qxHbd2EVDUfp8Gl7ghUw+JbAa209W8e15KZbdMi/3T/DSaX9KUQBEErq27dQLrvFOmuw1o7AaNYiIo6NUR6agguHph1XcuAZbmmu2ntZs0liucLj8tbgq15DbaGpcgjnaQ69pnL9FVFFwszJNFS3sXiGu2ntxjBW4LoKUGY+V1w+REMLScGxyN89YfH9PmT4VxPkFOdEyybU7ikOZGUGQ3GcAtJPEKSCcXL0EQ0r4/k8Mx3l4jCZud5trlP4Mq4gQUR9y2f052dzrUPYmtcQey1/zAF9owqAXaJG3kjXAkIjE3F89yNgt2JY8lt2BfdRLp9L8kDP0KJhZEElU/5dvJf0zdzJlXPhf4pVCVN6tTLJA4/PWsP0OuFKto5k6jiXLKGjnQVMdXOA57DrHD0ZJeZHid14kVSJ14EQUIsa0Aqb0YMVCEEqrS+mKJNu8Ei2RGcHrC7Tfsx730TEeSRi6jOhWAvnXW5S2EJhBYWFhYWFhYW7yGMgqDbKWGT3rleNrkNuKtK31kHoZFlc8pmvYjJYOwzeKZrgkQqW/p3rc5Lu02iqbqIjn5NRLrQF8wTCMvzBML8/3JXlnhM+3JsKsZrR/s5cFaLf/73p0+bhMWKYlfBdfu92XUEvM6sQFhAODY6GdOywrmeSb13Vt/oNM/v6+b+G67MCZfBWDJbWuTEbpNMYTbjU3HKA24mQgm9H+RslAdcSIb02dGrcBAaxTGv266n2K5fVMWuE5rn9uTFcS70TZFKa24RURCor9AsIU3V2QtPYwny1ZJKy/x8dyeplMIDW1tM29zothQEaDX0vYsm0sQSadNcSaUV3jw5yHQshddlw+OyI4kCybRMMq1Q5nexpLkUURRmTYO90D9lEgh7h8M8P+O+jSVkDpwd5q71jfrfJ0Jx0rLCOEU8FrmRZ5JJVooX2ORq1/vdqbEQ8R1fJ1nZitC6iV61mtqxfdCxJ+vEEQRct/2myX0pVTTjvu3zKJs/Dqk4oj8b5HBxwCzKtvcGaanxE4okZ/p+2flhdDPu1jW09D5DsagJEGpoBNf+b/OlEggqbqYVF0VinKKXnmAabSySIPLlYhujShGjagmcmGLB8Aif93VTKk1TOh0h8sSly4JVYxkqUAPU5J7+nF6cq+7DvuQ2BOn63rg51j6mH6sCsHFxNaFIUk81Hxi9RIiOgb1nzD0hZSQOKEv52Ace5MX9PTz1WhseIUFYdbNNaOKRqxAHL0XmpkZQ8fJ60b0Uryvn5RfeoMU2SotrigXz6pEqWhArWpDKm+gcSfD33z+CwCh/+onVpuMzw+nOCf33tKxw5PwoNyyvuapxCXYn9pY12FvW0DUwSbT7NM2pDpSeo7M6Xq87og1by1rsi26acT5q3+m2xpU41jyAMtZNqmMf6Y4DqJGJgqtQIxPIs/wNAEFAcAcQPAFQZJTJKX5PTNHrK+PV+GIGbPVE4tox8OPXOljSUqrfrFFVReuld2Efsf52vlI8ilvUzrdx1UZPupzJXf24WuchFleTdhZTEetik3uIlY5uqiTzse1Y/UG9h20GqXIO3of+mtPPPEZiqIMjyWaidRtQEGFCm+OXCkoSRBv2BVspm7uInv/+c6R0DJug8L98r9OdLmd4spjIT36KGhyYdR2zrttVhOAro304QUoBVRVoqCujpLqWhN3PD3b24RUT+IQEt62uwVazkL94PkTmlJzpNfvt6ZtZ5Rvm0cYulKHzmORjVUYZ60IZ67r8gJxerQ9wzSKkyhaU4JDmhBzp0PprAuqjfw9+SyC0sLCwsLCwsHjfYxS23snyYsAU+gBQWfzOOwivBqNIZnSqVRa7CfiuvXfjvLpiXSBs75/iTrh0iXFBgdCN35vdnmNTcV460GNaJpOyqo1ZUyPKA+Zt7jf0Hiz2Oeibqdgt1JvSm+OuMQY7APxibxcbFldRfRXCb9BYYjzjHizzu7IC4cx26Rm5vOiWKxBejYMwFDX2Zczu94VNxbpgKSsqT+3MJuXWlnux2zQRttkgQPSPRUikZJx2ia6hEPvPDLNuYZUpyCIST/HGsQFqy7ysnJftH/fDVy/w6hGtRG5oIsoXP7xCv9A2lhEX+5wUFzmRREFPVJ4IxamryJbuPrnjPG8cu/QF7Udunctd6xsZGC8sELXn9CH871+c0YNRQEtfNQqEwxPZbV5S5MTj8vL66GJ2JhbyZ2smqezZrpcPKyMdMNKBufuYhuvGR7E3rwa0ctx9p4cRRE2wFd1+yOmjlicQ9k1x53r0AADQ5vfWe27hD//ZwU3CYW50nTW5G4vFmC4cGhFUBY+YpEkcp4lxkkcuUAQsuk7tW9OqyEX/WlY/9KlZSz0VVeVY+xiH20aQJJEFDcUsaCim/ArOoaqq8uybXfrjlfPKKSlymtopzLb/c9ez99RQ3vOZxNfTneOkkQip2vF/pH2MR26ZPVH5augYyM7D1toAVTVVnEo1cirVUb2bDwAAIABJREFUiBCDb2y9yVQKv/1gO8mUJvA+t7eL33pwWd46T3eZBbH9Z4auWiDM0DMc5kuPH0NRVe5ct5mPfOKTqFMjyOPdKGPdyBN9pINDKKFR05wDSKg2xmUf44qPcaWIMdlHRX0j226YP1NKHNd+phKo6QQoaa2c1ObUWhjULZ61ZF8QBKSKZqSKZtQNH9YCe4Y72b3rEJVMUCMFdbHukqgqajSoi55+ABEWO/pZ7OgnVdLEc/0VpFURJSRy4eVumgIqSmwKuf+MnlZtA4wmc5eQ1npZdg8R735Ff/63CrRJTKo2fOs+iGPVvYU/q93Jy/J6ToZbAXigrtiUPj52Ba0AnNUtHGr4BCsu/jcuIYVNUGi1j9DKCKpR73V6sbesQaxsRapoQXC4AUG7cwNaCTla+bxgc9AzHOZfvqMFWYmCwL/esxWX04ZTVTm5a5fesmNx82riyTTj0RPa9pJEvvDQUv7px9rjo9NVhG+4n0pXknTnYdJdR5CHL1xd381EBLn3JHLvySt/zVVgCYQWFhYWFhYWFu8hjMLPOxlQAgVKjH+JDsIroZCAKokCj35gkamU9WqZWx+AmYqzc92TJFMyk+HshUxuiXEhgbCixGNKVUylFZPDLG/5WRyEAcMcmFPr59SMq6a5gOPGYdccp5lefcb+fwBpWeWxF89dsgeVrCi8fLAPSRS4bU29yUFYUqSNrSzg0st6M87K3mFzWW9XAZdeecCFZHDEToQSKIqquwEvhdFB6DdsE0kUWb+wkh2HtVJeo2DWVJ29KK8p85qCSnpHpqmv8PLVHx5nOpZix6E+fvvh5SxvLSMUTfL3Tx7VE7UfvWcRNyyvoXMwxGtHsv2zTnVO8OrhPm5fq5VcGsv5Sv1OREGg1O/UnZLjoYQuEI5MRtl13NhttDC7TgxqAqGhB195wKW7bS70TaGoKqIgcPLCGPtPmwWi871B0zYemsg6EatK3DjtEv2jERRE2v0baPnQVuK7/ht54GzB8Qj+SpwbP4K9eY3+3NO7O3lmRuDqHAjzsdvnmV4TiibzgkLa+4KoqsrZ7qwItLi5FEEQqK4q5efdazmQnMNnGy/iDnXjUa/MQXdJHB7EonLEonIEX5n2s6gc0VeGEh5H7jtFuv80qUiYwYSbccXHsBxgX2IekUiArwlOcvNvZUXhzZNDvLi/x7Rtd8+4WqtLPdy5roEty6p1sTqXkxfHTX0z79vSDJj7rQ6MRfX9PBudg2HTGDL0jISZiiRp6zWLycMTUQbHI9SUeZmaTvDc3m5qy73cvLL2qkumOwxtEVrrApQH3Pq5SJ15r0z5qKqqM2XQGic6xvPctbFEOk9UPtM9ec1ptXtODenC+RvHB3hw6xycxdWIxdXQugFFVfnaj45zdnIMvxhDVQUeumU+LXWl/MXjx9F8nVkawz7urVlwyfecDCfoGQ6z2ObhSm71CYKIVDWX05N+vh/OzBWVYjHKr6zzsrRCRo0GUSKTmhgY0X6fvTdmFvtkNw96DGXfXVrp8vVAUWF/Yi7Px1byF3Nvx5mT4CsrCkMTMaajSf17A2BOjZ9eQ7/DSzkIjZya8rM3fBuP+l7HL+a+Ristd6z/0GUDaGRF0ZyXaN/zGZqqi/S5KAgCNeUe/Ybh4HhE76ELsKK1jOWt5dSVe/U+qed7g1SvqMWx5DYcS25DVWSUyX7k4Q6U4ACjvd0kJwbxCgkckopdVEG+8kR0wekDx7XfvLUEQgsLCwsLCwuL9xCtddlb80ZH0ztBqd+F32MnFNUCAH4ZPQivhiJ3voD6sdvnMb+h+C2td1FTiX5xG4mn2XN6yOTKMvbhg8IlxhXFbhx2CY/Tlld6u2Z+BQPjEb1s1O2UdKdgrkBYZHAQ3rW+EUkU8LjsrJpfOLHR67LpoR3GsJMM53qC7Dk1xJZlhZ04T+/u4rk9XTOvT+SFlACUGxyUWQdh9qJpeWsZ4WjSJJi5nTMltAaBUFZUJsLxPNdkIcKzOAgBNiyu0gVCI02GflaFgkqGxqN630pZUfn6z07y6/ct4endnSZB7rGX2qgt9/L49ra8nmM/eq2DRU0l1FX4mDD0ayzNiKl+ly4QGh2Gz+3t1ueU32OnodJHJJ5GUVXskkjHjDgyMBZhIhQ3lRhvXlrNs3u6UFWtdHlwLEJNuZf/evZU3jaIJtL0jkzrJZzDBgGputSjB8yAJvaKgUbcH/hjlPEe2t54kcDoUfxinKDi5vX0aj70wCexu7L7f3giyvP7ssLDy4d6aaj0mZxenQP5PR9D0RQjwRhnurIX5oubtVLphkofZ7snGZBL2eFdzPmJIMnQBPW2CRykCasu4pKHv/zsjYgovHKwiz2HO6iWplhZmWR5jYTgKWZfd5oj/TITio8pivi9T2ya9ZwqlTdjb9FEz7/+zkG6QzkCd1Lm4NkRthrSXBVV5etPndJTzwsxNBHlsZfa+PnuTratb+TO9Q0mkS/XPbi8tYzmam2MtWVZgTCRkpmYil/SkWh0D86vD9A/FiEST6Oq8PzeblPIT4aj7WNUlXr4l5+e0F3Y0XiKD2xqnvV9ctl1YsDULqC1zo8oClSXuumbKY0eGI/oAuHIZMzkHEulFY61j7FpaTZBu60nqDtvM6gqHDw3ogvyGaYiSXYc6kUSBZqqi2iu9ufd7Dpu2EeJpMyR9lE2Lcm+3/YDvTMlzSJBRdvu+y9GkDxFZMTBzA2GzOfJBFjloigq2w/28rNdF0mlFRoqffzJx1cVDLMqxD5TmbhAUPHy8kAxq29ZXXB5NRXXxMLoJGosxPbDg5zoieAQZO4vv0hVouvybyqISPVL2DFaxZFhB+OKj2Xzqhm92EaTbZRm+wQrq9Jav0Q5RVy1cTFVxbS/hX2hajqi2s3EwfGoqVXJ1HSCv3n8cEHxr7nGTyyZddFfqaO8ZzhMMF3J/wk+xLqKGEJogBopSHmJhzV33ZdX3lyIA2eH+c/nzlJV6uY3H1g60+ZAY2GT+f8PtWVeXSAcGItysmNc/9vyVq0NyryGYl0gbO8NmlKfBVFCKmtEKtOc3I8/cZgLU9r3kN/r4Gtf2KJN7plUeyU0gjx4DnngHPJkP6K/AqmyVftX1Yrgr0L0X31oTQZLILSwsLCwsLCweA/RWhvgNx9Yymgwxk0rCxX3vX3YJJHfeGApe04NsXFJdV466ruNXKHohuU13LKq7i2v1+20sWxOqV6i+9KBbASn3+vIcwLZbaIebJJZJrPtygMuk3gmCPDILa24nDa+9cxp2nqD3Le5RXfslPpdCIIeBmkqMXY7bdy35dI9BD0GgTCDTRJY2Fiiuw9/8Eo7TdVF1FeYy96SKZnXjmSFtu0HzdGjmRJ0o4My4yDsMTigGiqLaK2LMh4a0Z/LCJ9Ou0TA69DH2NEf4s2TQ4SiSe7b3EzxLO6gUMSQUO01C8Nzav0mV12G3L5muUEluT0QU2mFr/8sv6wrLSv83ZNH9N6GoO2LWCJNWlb45jNn+PNPrTUJgJkydOPFckZAHA3GTGLOwze3snW5+Vj/k3/fo4/vdOeEKcF4Tm2AhgqfPq/a+6Zo75uiw+CeNG7jtp5JfVsMTRochKUeDLq3foEuCAJSeRM/Dq2mN9hKhRhiTClCRqL82DB3zwQFqarK93ac1+d9hsdeOkd1mUdPiu4oIBAC7Dk5ZNpni5q0nlqNVdl5eb43OLOMhzMpg6M5DeMpF1WlHoZS43TLFXTLFQRqGthwm+ZgXLcqxXPfPqAHxHzzmVP81WfWFxT0M/SNTpvcfIuaSvQy6F0nBk0C4fN7u03ioACsXlCB12WnrTdoEmNDkSQ/eu0C0USah27MChhnuidN2+e+zc36726njTK/MxtUMh6ZVSBMywr7z2aFpc3LajjWPqaP77WjWeerMQ39aPsoAa/D1KLhqZ0Xaa72s6Tl8j3ODp0d5r9faNMft9QU6XO/usyrC4RDBoHb6B7MsP/ssEkgNJYXG8e7/8ywSSCcjqX4+yeP5PXorK/w8XsfXkFJkZPhiageqpFhz6khXSDsGgrx0zc68sZ0rjtoOh+tnFvOoXOjKKpKWlYZnoiaWgaAJuh/5/mzpn3aOzLNv/z0JH/wkRWzukgzROIpTnTkC87ne4NMhhN5wieAYHchzLghFUXlped2M53WROabN26jpWSa1LmdqLEp+kdCBENRUqrEtOpm5bIWAtV12JpWIbr9vPovuwjNtBjYsLyRr3dM0RGvgjj8+UNraa728eMXjrH9xAQKIrctqMdum4ZpbZ8OjEdM8+b5fT0FxcHGKh8+t910Q2x0Ko6qqpd0r4YiSb3tRQobravW8Ph27dxWLDpYdwVp8/Fkmie2nyctK/SPRvjK98zn9kU5oU81BqH+aPuo6fMsmxEI59cHeH3mGDvfN3tvy/GpuP4dlPk8QxNR7T2cXtpHUpzudLN52d1UrXv4sp/lWnjnulpbWFhYWFhYWFhcF9YurOTujU1X7Di4nixoLOEz9yxiUVPJ5Rf+JdNY5dN7Dc6tC/DJO+e/5TTRDOsWZgMWjBf6uf0HQRNUjKKDcZncC/oNi6uoLPHg9zj4g4+u4hu/fxPbNmR7xNkkkZYav/57ffnVOQW8BeZMS42fT21biHNGtIzE0/zjD47llSMeODtCJJ51O8qKanLxZC5OjZ9vNBgnGk+bLpoaq3y01gZM6zZeCJYXZ3//5jOneXp3J68d6eexF9uYjXDM4CDMKS0XBIGNS8xhCwKaE82IUTA8eXFC780GmHojZli7sFJ3exkvIDctqeJzH1ysP+4bnebHr10wJRVn+jUaBcLxKe3vv9jbrW/X8oDL5GTKsKQlG9Bz+Pyoad215R6tDH6GH79+gcdeym67tQsrTQ5RoztmxNCDsKrUY9ovYwaBcywYo3s4jILIsFKMjDZ3XjzQQ2LG9XOsfYxTF7NCjmOmeVlaVvn6Uyd192mnoT+dcTvvOJwVoGvKPPr8aqjM7ifjvHI5JJOrOZNWHZzObhtj71Gvy86v37dEbzs2GozzxPbZ5xhowlGG5uoiPmzo0Xehf8pURvizXdl+l0uaS/jbX9/Ibz24jE/fvZAv//pGvvy5jdyyqs6UHP7i/h5T4JHRPbi4uYTWOvNxU2M4/vvHZi+zPto+prth7TaRtQsqTU5qo3tw64rs3LjYH+LHr5vFMVXVjstCPeFCkSQ9w2EuDoTYe3KArzx2MOuE9Tr4/P1L9XNwbVlW0B0wCoQ9k+RyunPClEJ/xiAQ3rw6e9OnYyCkl6snUjL//JPjBQN8+kaneXbGCX3c4Pgyrn8ynCCeTPPNp0/rx2NJkZPAzA0IZaa3ZoY5NX5qDJ/JWB47Gozx7efP8hf/daCgIH6+N8h/PHMGRbl07vHhtlFdcC/yZAU0Fc09eTm6hsL6dpREgUVNpUiVc3Dd+Gncd/0utY/8Kd/nXr41fSvfj2zi6xfnIs7biujWAoNChlYOzTV+0znzQv8UgiDSGxa1cBG0XrvGOWrcF9F4ip0nsj1WM07p1fMrePSeRYD5eyGRlE3fP4XoNYj3HqeN1Quy39PB6WReWvzhthH+8QdH2XMq287htaP9prkWjqaIz5zTJFEwnVtBO99mMJ6PmqqKdAHZeKyNBuOzthMptA/bZs7Pk+EEX/3RMZ7d08U//fjEZefKtWIJhBYWFhYWFhYWFu9L7DaJv/rMer74yAr+6GOrLuvOuBpWzC03XdhnyO0/mMFjEgizLo/ckuF7ZtxXGQqVqP2vDyzi9rX1fOGhpVfdb8vjyndHzW8opizg4tEPLNLFklAkyT98/yijBhHgtaP5ZbrGcWZ6PlaVZkWakWCMn7x+QX/sdkqUB1x5Qodxu1XMUlJ8/MKYaTxGwgZXpN+bX1q+YbFZZKsu8+BymLeFsW9jyLC+mjIPv/Oh5Sbx6o61DfzG/Ut45JZW0zrcThsfvnUey1vLudUgXOw43GdyRmVEVONcmAjFGZuK8ebJ7MXqvZubC86BpQYXjrGkzWEXKfW7mFefvSA1ht24nRIfurmVBY3Zv2f6EKZlhVFDGV91qSfPDarOiD1Hzmf7V5b5XTjs2hjD0RQvHuihd2Sa77/Sri+zqKmE3//ISn0bTkWSPPnyeRRV5aKh75hRyDWOe3FT9vPWlHmwSfmCbX2lzyT69s0INEbHbLHXfLzMbyg2ufL2nh7mcJu5N2cGWVHYa+jhuHlpNU3VRSZH4/d3nGfPqUG++cxp3X1ZHnDxGw8szevZWlXi4ZN3LeDvf2MzxTPCZVpWeGqnJsi9cazfJFIbx5mhztiHcJYk41RaNh2Dq+aV43HZZm21cPfGJn1eqmSPBZsk6Oe86ViKf/jBUb7+s5P8+9On+LvvHeF3/nkXX/zX3fzVdw7ypccO8bffPaiLxU6HxO89soIKww0Ro/NqaMYBm9t/MIOsqPqcyy2p37a+0dSPcdfxASZCcb759Gm99BO0m0TFBoF4z0xC+PECJeCqCvvODPHky+26u1AAPnvvYtYaRCdja4mGqiLqDfOvf1QrM37y5fP87//Yx+4Tg6blNy2pYpNhvh8+P8oPXs0eM4XYZ5h/6xdWsWFx9vUHzg4XeomJTOo1aL0gc78PPC47n7lnof64Z2RaF6n7DYJnkcdOwOvQXcCQ7TM5YnAhVxa7TaXwgwYRe+fxQX1+eJw2vvL5Tfz1o+v5wkPL9HJzn9uO05H93p7t/K+Pdyi7v2srvAS8Dr1/L2TTtEGbR//+9GnOdE3yn8+d5Wj7KImUzIv7zWFhRlpq/HnfG8Z5bCTjHgTtRpDxxtn5AnMcMLl8M5ybEcwPnRvRS9iHJ6Kc6swXtq8HlkBoYWFhYWFhYWHxviXgdbC8taygmPdWcDttLJ9Tlvd8eQEHYWb5DEbRxdgHb9W88ryy3kLUlHn5+O3zWd5aftllc8lNMgZYMCMUrFtYqTs3QHMsZETCzsGQqcww131XWuTUnUHVpR69XxzA64Yk3obKIgRBoLHKZ9onxj6DNbO4IlUwpfruPTXEP/34OD969YKpRNBfILynrtxrGnNueTFkg0pyuWFZDcvmlPHFD69gaUspj9zcykdvm4sgCNy5roH1i7KCwSM3t+oOow/fMpdGw3saXYalBRyE/WMR/u3np3S3Upnfyeal+e5B0AS3jHvR6COpKfUiCgLzclwuAEvmlPHl37yBymI3c+sC+uszfQhHgzFd1BIFgfKAyyRgxw0OnkMGgXDjkipuXVWvP356dyd/+e0DuptGEgU+ccd85jcU8/E75uvLHT4/yv4zw7oQKKAJr4UwziebJJoEoQwNFT6TQJNxEE4ZHIT+Aunl921pNrmCntxxnlgi36m08/ig3htPEgVdnDH2E8uIDRmHkCQKfP7+pZd0ewe8Dh7cmi0r3nt6mJcP9fLE9vP6c4uaSljQmO/aNoovA+MRYok0e08PmVx42w/26uXooiDobQgaq3y6azhDRbGLqhIPK+fm9zC9c10jn9qWDd4YDcY53DbKgbMjtPUGTa4rI5Io8IUHl+Udc0a33dBEDEVRGZuKm1xeaxdkx5ERwIzlxRXFLiqK3WwwHIO/2NvNH35jj6m8++aVtfzZr6zmK5/bpN/ISKYVth/sNYk1xnn2iz3d7DaI9fdsamJRUwlrFxbu79pQ6aO+Irs/ekem+ekbHew43GdyWlcUu/idh5fza/ct4TP3LNL71AHsONRncpCOBWN869kz/PvTp3hqZ4fuJgPYsKSKDYuyAuHFgdBlBbRTBoFw2ZzCJeJLW8q42dCK4xd7u7k4ENLLwQH9e8ooELb3TZGWFZOLrrLEbdrPmVYIaVnh5UNZh/DNq+ryhDfQ3N/lBVpWzEaPIfwqI54bx2gUCLcf7DXtl/967iw/faNDD7xy2EQ+tW2B6WbEwgKVE2UBl+6ONrKi1fz/g/kN2XEUKjMenojSXSC8q61HC2w62GZ2F75+9NIJ99eKJRBaWFhYWFhYWFhYXAPrDBelGWZzEPoM/RCNYti6RZXcs7GJrctr+IxBnHu7yBUqBAGTm2/Lsho+eVdWBBibivOV7x3hqZ3Zcsm59QF+68GlJmebsfeVIAh8+u6FuBz5js2MYGaTRF2YND4PcNOKWhY1ldBS4+dXty3g4Zuy4smuEwOk0gptPZN867kznOgY58UD5j5Wub0nM9y+JitirSkQ4pIJKjEiCOi9z5Y0l/L7H1nJ3RubdDFUEAR+7b7FfOaehXzhoWWmC2uHXeILDy0rKMqWFuhBOB1LmUTYezYVdg+CJjgbA4sy1MyUu5X6XWyZGXd9hY/f/dByvvybW2idcRa6nTaaa7Kfta1n0lRSXl7s0l2hRhFpfCpOcDph6me4ZkEF2zY0FrxIBk30ywh6N62sNe3rxw2lz3UVmogbyHGACgImxyNAY2W+wFtf6aOhwuwgVFU1x0GYLxBKoshn7l6ouxsnwwl+vqvTtMzO4wM8YRjrirnleor8xsXVeWPO8MjNrVcUJrVlWY1JXPr+jnZdvAh4HXz23sUFX1ebI0j90Tf28K1nz/B3Tx7lsRfPMTIZ1UtpAW5bU68LJzZJzJtDS2dK11fON9988HvsfGBTE5uX1nCb4TgqhMsh4XPbKfW7aKgq4vP3LynYr7C61KNn/2bcq0YBrKrUwx3rsoLx2e5JpiJJU3DNkmZtvUYnXS6r5pXzK3cuQBAEHHYpR/zq0rez12XjEwYB2xgeNafWz/03aMLqvPpi/DnnmDK/E5/bbrrB09YTZLuhP215wMVn7l7I3/zaRlbO07avTRL5jfuXmkSwI4Z0+W8/f5a9p4c4cHaE5/Z06zcDygMuWmv91FV4TWJ5Jh27ENOxlCn5eVmBG1wZPnxLqz4mRVX5t5+f1J1skBXfjN8dk+EErx/t128yCIL2XWd02IWiKaZjKQ6dGzGJ6JeaU0ZH+ehlgkqMvW5rC4zxcNsI0XiaSDzFG8fNAls0kWbHoaxL/uZVddy0so4vPrKCmjIPc2r93Lku/waGKAhUl5ndwT63XW8FkmGe4fuuvYCD0OgANc6HqUiScz1BU29CgOMdY6a+ttcLK6TEwsLCwsLCwsLC4hpY3lqGwyaSNDjDCvUgBLhlZR3ne4L4PHbTxaxNEvnQza0FX/N2kCtWNVYV5YUy3LKqjnRa0UtEJ8PmtOJbV9dRWeLhno2NPDNTfpbrKCwPuPnobfP47gvnTM83GMox///27jw8jurK+/i3ulu7LMuSLUvIluVN1zte8IZtvBADxsbGIWwxA2ELhOXlmTdv9slkJ+QNJJkkzEIGMiGThBCTZDIhJBP2hCRgwGaNL5gltrHxBvJuLFmaP251q6rVLam1Wu7f53l4CnVVd5es09VVp86954NL6/jlH16nenBRKAFUUpTLxy6elvj5wOEGfvXEmzQ0NrH/UAN/eelt7v/z30hnQIoKQnBNagaV5BHxvLRzaAYblYC7iE7XGCUuGom0aiISN7i0gGvPncQ3frohceGcE4sk5kksS9FUAFzl1IIpqTtJx00aWcarSReNwYqyK1dM4MLTx1KUH8PzvFbzb5qa0kTC4MU33iG2uSXBV+kPh41X8MTnt9u99zCvb9+XSFSUl+QzYqirCr1wyRh+7Ce2cmMRigpyMDWlrFrQ0hgg4nmcu2AU377veYDE3F7gkjCeX/34dGCY78iqklaJ7eR4A1dBGJxjcFf9EfbsPRJqkpJuSH5VeRHL545IxPODz2zh1EmVDKso4rEN20LVfAOLcrkgMLS8MD/G5y+fybqNO3lj+37e2L6PPfuOMH9yVSjB1ZZIxOOCxWP4xr3PhR6PRT2uf//klM0nIPz3bjzWTOOxlqTWoxu28cSLbycqVwcU5rBqfm3o+XXDS0MJt/jQdTO8NNRhffVpoxLHiYvfN5ZxNaXs2fceTU3NHGtqIj83xkmDi6geXJQY4j9kiEvi7trVuioKXAJ9SGlBYs7A3z25mYbAXIhmeCmjqwdSVpLHO/veo7kZbr1nfSgpMsFPEFYMKmTZnBr+56mWqjDPc41Drlk5kUhgeoDF06p54C9uns9gE57Jo8upKi9iTPXAUKVZfm6UD6+cmEjWRyIe0+uGtKqMdsuWuHyvoSW2q8oL+ccPzWxVsQlu+PXMcRU84A9tfdbuYukpw9lVfzg0P2jQnImVic/zrPEViYT2r//0JqXFuSye3jrh9uIbexKf24FFuSk/Qy2/c4yrVkzgaz9+luZm2LPvvVDX+XilbmlxXqhRz72PtMxXWV6ST04sQmlxLgV50USl8NMbd/LohpamOLMnDE0b3xC+6Zbc0KShsYkNm3YzsCiXuuGloQRhPIk5ZVQ50YjHsaZm6g8c5d5HXmXwwILE8OacWCRU3Q3uezk+9++E2jK+cvWctPsH7nO4eUfLEOxJo8pCMQdQF5j24a1dB9l/6Gjou+qpv7ZUCC6cehJ/eXkHb/lVmz95sPXQ8+Zmd+Pi3AWjaGpuZsuOA1QMKmizyVJHKEEoIiIiItIJ+bkxpowuDyUz0lUQTqsbwjdvnE9eTrTVhUNvSk60mDTzkC2dOZxYLBKq8AKXvIvPwXXOvFoinse+Q0dZkWJ+tAVTqnh6485Ed2QIV35VlhVy7apJ7e5zcUEOs8ZV8ITfIOLu39lEEiDieYyvHcQrW+ppaGyitnJAq2qOOM/zEhVS6SQPg5w/ue0kXUdMrC3jA4tG8zP/4vmkwUWJi/uCvBi1lQN40x9aNn7EIFbOq005nLTV644s5xdJlW7J82EVF6Qf2jquZhAP/MUlJYJ/IwhX7JWHEoRHQvOYzTBDEr/L4unDmD+liuZm2uxwfvKYckZWlfDG9nCzhlF+45oxw0pDn6lUydzgvH9x1UOKyMuNkp8bTSQeH362JRERi0ZSVnPGLZ87gidf3sGOd91Q66//ZD1HG5tCDTwGFufy8YunUTEoHGMDi/NC3XM7Y9KocibWDuJIfvgzAAAeD0lEQVSlQMLu7840oSGSyZI7GUO4q28w8XHewtFtfv6jES8xhDIWjbBmaR33Pf4aE2vLQgnwiOcxw7Sunu6MeZMrEzH86IZtofnmTE0pEc9j1rih/PYpF6dvBYa5eh6MDwwJPn/RGFYvGEVzs/tdPI+UTakGDchj5viKUIMRIDHU99RJlaEE4aVnGiqSmknNGFcRShDG43HQgLxQYhXcvlx9zoSUycG46WZIIkH4ytZ69h06GprvctCAPEadVMKOdw4xtMzdnIlbOLWah5/Zyr5DDTQDP/yfVzj0XiPL59aG3iPYMGjSqLJ2G3bVDS/l/EVjuPeRTa3WVQcqVy9cMoYvfH8dzYSb3cTnm/Q8j6ryosTNiLuTvlNSVeUFDQkmCAOd5fcdOsp373sh8bc6dVJlooMxtCQIB5cWsHJebSLOHn9ueyjOls2uYf/hBh4JHCsWTj2p3RtDQVVJ3zlTRrf+nqkqL6S4IIcDh93f6Tv3vcDfX3AyBXkxNu/YH2oyNHP8UOr3H03E+9bA/I8lhTmJZjGPP7eN+VOquONXL7Pprb0U5EU559SRXHTmODpLQ4xFRERERDpp5vjw0LZ0FYTgLub7MjkIrSsI0zUqAFdpc2WgcQm4+dbilTTRSISV80dyyRkmZWOQ+FDjeIOWgUW5KeeO64hgp9LgvFFnza7hoxdO5Ts3LeCLV8ziU5dMT8yt1xljhw1MDHssLsjh5DGZz/OYylmzajh/8WimjR3MpYEh3AA3nX8ya5bW8ZlLZ/Cxi6d1KDkIrqlK8t8z2FGzPcF5CIMmjSzj9EAFUnC42wNPbg5VnE1PGqqdE4u2mRwEFxerTxvZ6vH4UNzk+RPjVWJBrStW893ny/NCwzzjiSVww0DbSorkxKKh4fWH3msMJTwGDcjjkx+cnrYpQXe46PSxic/Lsjk1aStTg86aPQLPc5+vDywazTdvnM/iwOcFXOI7VbJ77LBS6vx/76WnDA9VH82dVMmt183j8rPH99hxa9mcEaFk73uBitJ48vJ9pwwLNfMJrk/uyh6LRsiJRYhEWlfMBiXPdRkJ3DyYM3FoIgF2xszhzEnRRdwMLw0l3+NTE3ieFxoqDrByXi21lW0PMx9ZVZKoomtudh3A/xzomL14WjXXr57MF6+czfWrJ4fm6xtYlMsn1kwPVeHd99jr3PPQq4lOt69t2xtqvtPW8OKgM2eF51iNCzbHqRk6gPkpqp2DHcVHVaX+/SfUDmo1rUOywYHk7G5/iPH2PQf5yt1PhxK5wQ7jRfmx0HdSujjLjUVYMmMYFy0Zy2j/+FNckMOy2S0J2I4Ifq95HilvRHmeF2pctemtvdx6zwbu//Ob3PKjZxOPj6wqoaK0oNW0CnFXLB+fmA6h/sBR/uF7Tyb+HQ6/d4x7H9nEtjY6mrdHFYQiIiIiIp00ZXQ5Q0rz2VV/hDEpukIeb5L3L1Uzi6B5k6vIz41xz0OvUj4wn7NmZXbhVFaSz6cumc66jTuZYSo63SxmVFUJNUOLQ8O4qsoLE0Mmc3OioQYVnVUxqJAPLq1jw6bdnD27ptua23iex7LZI2B263UDi3LbndctlUjEY0JtGes2uqFp0YgX6hLbnvg8hMF5yeZNquSyZeNCcx8Gq2KTuzu3Vd3Wlom1ZYwdNjAxRDo/N5oYLlsztDjxmSoryWNMirkWC/NzKC/JZ48/3DSYFBxWURxKHID7t1k5r3VSMtmE2jLmT64KNaeIeB6mppTLzjKtKge7W/WQYr7y4TkcOHSU6g40LAI3r+CpkyrJy40mEr5/d4arPPzJg68S8eDyZeNSJvkiEY9PrJnO/sMNKZv79LRYNMJVyyfwxR+sCw0FH1KaH5qn86vXzGXrrgO8sW0fr2/fR8TzWD53RLqXbdfIqpLQUOIx1SWJhF9+bozPfWgm+w81pB36GotGOG/hKH70+1cYUTkgVDE2orKEV/y4Hn1SCWd3YD8jnse0sYMTFa+//tOboeG0we7eqVSVF/GpS6Zz2z0bEg2bXHOaw5w5q4Z/Wvt8YshzQV4s1AW9LZ7ncfmy8WzbfShRxTakNL9VQ5H3nzaKpzbuDCV4g5+V5XNHcPBIA1t2HuDgkUYOHWmkrCSPD76vjvYEb1Dsqj/Md3/+Ahv/9m6oSjNZsEob3N/ryuUT+OJ/rAvdYJo/pSoR9x//4HSef203NUMHhOaG7QhTMygxjHqGqUhbub1y/kjqDxzlcX/+Q9f8K1xJHU8i1qVIEA4emM/kUeXMMEMSQ5KPJg2PBloNmc7E8X0GIyIiIiJyHMvLifLJNTPY9NbeDl909aXqwUWJIYijTypJO19f0AwzhBkmdefODr3nkOIOJzvS8TyPRdOqufu3bniaB1x+9nhyYm1Xq3XG6TOGdSph1xcmjWxJEFaWF6ZtapLOvMlViQTh8rkjeP9po1pVXgWb6sSNrBrAh5PmdsuE53mct3B0Yo6zKaPLE68VjUT46IVTWf/qbk4eMzjt37hmaHEiQRisKEyuLhxeUcyVy8e3W6kUd+lZBlNTyrGmZmqGFlM9uKhH4iydgUW5aZuepJNq3rG5EyuZOa6CSMRrs6rW87w+SQ7GDaso5twFo1j7aMv8dWZ4uIo2Fo1QW1lCbWUJi7vpfVctGJmYG3RRUsVlLBppc148cEN7T51URSwarlZcOnMYb7ztkphXrRhPNNKxz+SMuiGJBGEwOVg3vDTlZzDZ4IEFfPKSGXzzpxvY7HfwXv/qbta/2tLNORrxuGblhDa7aifLy41yw3mT+cY9G9hZf5gzZra+STSwOI/lc0aEmllVBCoIBxbncfU5Ezv8nkHBBGHjsWaeDXRQB5ec3PHuIZ54oaWCMNX3zfCKYs45tZZf/tENNfY8ODNwwysnFun00Pnighw+d/ks3ty+L+Xw4riI53Gp3xk5OP0BuKrH1aeNYp5f6VtSmEv14KLQ0ONTxlW478Kp1aE5C2PRCBcuGcP2PQd5ZH34dTOlBKGIiIiISBcMGpDHzHHdMydXT6sYVMhVKybwytb6dud+Ot6cOrGSp17ewStb9nL+4tGdrl47kcyaMJTHntvG1p0HWD4n84qqhVNPorKskMK8WKv5F+NGVg0g4nk0NTcTjXismj+SZXNqOpz4SKdueCn/94KpbNl5oNUQxYpBhaGL91TOnFXDxs315OdGWTi1ZSju7PFDefiZrezed4T3zRjGqvkjM0qcxqKRxEV6f5dpwrivnDWrhvWv7OI1P1kd7/LbkybWlvHlq2bT0NjU4eRxslQVxoMHFvDpS2Zk/Fp1NaUU5cc4eCRcGXfqpNZDnNOJDzf+t1+9xPOv7Qmti0Y8rjt3ElNGZ/5vW1FawJeums3ho41pk8lnzBzO489tY/feI0Q8j9o0x5NMFebnMLyimC07D4Qez4lFuHrFBE4ZV0FzczO1lSWsfew1Ih4smpp6aP7Zc0ewddcBNmzaw+oFIzOquG5PRWlBq7kqU4l4HmuW1pEbi/LbpzYTjXgsmT6Mc+bVtqo8NDWl4bkJ/fMMU1PKuJpSNm6uZ/DAfK5fPTlx/F40rTrllB8d5TUHW/dIpxlj6oB/BBYAFcBW4F7gZmtt5weBp/cMML2h4Rj19Yd64OVFwtrrRCbS3RRz0psUb9LbFHOd13isqd8kPnpLQ2NTu8OhuxJzz23azStb65k7sTI0nLevNTQ2EY14rSoZm5ubOdbUrDjpQ5nG28EjDdz/579RXpLPkunV7TbROBHddf9fQ8PbY9EI37pxXkYVfwBNTc3c89CrPPjMVsBVy127alKP38ja8e4hHnp6KxNqy7o1ybt9z0H++MJ2mpqaKSnMZUBhLpNGlbVqJDKgxCXo9u87nPa14vmv4yG+dtYfpjAvlnZI8jN2J7f/4kXAVVJ+7dq5if1uaGxi664DDK8obnWcKy0tJMfNBfsskFG2WgnCbmCMmQk8DBQDTwFbgFOBKuB5YIG1dl/6V+gUJQilV+lCRnqbYk56k+JNeptiTnqbYk56k+Itcxte3c2373s+8fMpZgjXrZ7c6dd76q872LBpN/MnV6Vs9nOiOdFirqmpmTv++yXe2L6PS84wHW4u05UEoYYYd5ExJgbcg0sOXmmtvct/vMB/fCVwM3BDn+2kiIiIiIiIiBy3Jo4cRF5uNNHsY24Gw4tTmTV+KLPGt93gRI5fkYjHtasm9e579uq7nZguAkYBD8WTgwDW2sPAFcBB4GpjzKA0zxcRERERERGRLJYTi3LB4jEU5EU5ZVwFJ3divkCRrlAFYdet9Jc/T15hrd1jjHkEWAGcBfykN3dMRERERERERPqHxdOqWTT1pONijjzJPqog7Lr4pAAvpFn/kr88uRf2RURERERERET6KSUHpa8oQdh18R7ab6VZvy1pOxERERERERERkeOGhhh3XbG/TNdK+HDSdt0qJyea6NYj0hsUb9LbFHPSmxRv0tsUc9LbFHPSmxRv0tsUc52nCsKuO+Yvm9vZTnXCIiIiIiIiIiJy3FEFYdftB8qAwjTrC/zlwZ5484aGY9TXpyteFOk+8Tsxu3bt7+M9kWyhmJPepHiT3qaYk96mmJPepHiT3qaYc0pLC8nJiXbquaog7Lr43INVadbH5x7clma9iIiIiIiIiIhIn1GCsOue95cT06yfmLSdiIiIiIiIiIjIcUMJwq77jb88L3mFMaYcWAwcBX7fmzslIiIiIiIiIiLSEUoQdt0vgM3AmcaY6+IPGmMKgDuBIuBOa+2OPto/ERERERERERGRtJQg7CJr7WHgMuAwcLsx5hljzM+ATcAqYD3wyT7cRRERERERERERkbSUIOwG1tpHgVnAWqAGWAHsBb4ELLLW7uu7vRMREREREREREUkv1tc7cKKw1r4InN/X+yEiIiIiIiIiIpIJVRCKiIiIiIiIiIhkMSUIRUREREREREREspgShCIiIiIiIiIiIllMCUIREREREREREZEspgShiIiIiIiIiIhIFlOCUEREREREREREJIspQSgiIiIiIiIiIpLFlCAUERERERERERHJYkoQioiIiIiIiIiIZDElCEVERERERERERLKY19zc3Nf7IJ2zByhrbm6msbGpr/dFskBOThSAhoZjfbwnki0Uc9KbFG/S2xRz0tsUc9KbFG/S2xRzTiwWwfM8gHeA8kyeqwRh/3UQKOzrnRARERERERERkePKIaAokyfEemhHpOftBCqAI8CbfbsrIiIiIiIiIiLSx2qBfFzOKCOqIBQREREREREREclialIiIiIiIiIiIiKSxZQgFBERERERERERyWJKEIqIiIiIiIiIiGQxJQhFRERERERERESymBKEIiIiIiIiIiIiWUwJQhERERERERERkSymBKGIiIiIiIiIiEgWU4JQREREREREREQkiylBKCIiIiIiIiIiksWUIBQREREREREREcliShCKiIiIiIiIiIhkMSUIRUREREREREREspgShCIiIiIiIiIiIllMCUIREREREREREZEspgShiIiIiIiIiIhIFlOCUEREREREREREJIspQSgiIiIiIiIiIpLFYn29A5IZY0wd8I/AAqAC2ArcC9xsrT3Yl/sm/ZMfU7adzYZYa3cHnvM+4BPAyUAh8Ffg34A7rbXNPbWv0r8ZY8YCG4DvW2tvSLNNRrFljIkBVwDXAGOBRuAp4BZr7aM98GtIP9JezBljPoyLr3RestZOSnqOYk5CjDGXAFfhjltFwA7gYVxM/DXF9hcANwHjgSjwHPAta+3P07x+AfB/gEuAUcBB4HHgS9ba57r9F5LjWibxZoy5GfhUGy93v7V2RdJzFG+SYIw5H7gBmI4rLnoN+CnwDWvt4RTb6zxOuiSTmNN5XPdTBWE/YoyZCTwDrAHeBu7HnRh8GviTMaakD3dP+q/p/vJl4Edp/jsS39gYcy3we2AhsB54BJgAfA+4q9f2WvoVY8xQ4L9wJ4vptskotowxHnA37sRgJPAg8AKwFHjYGHN59/4W0p90JOZoOf49Qupj338nvaZiThKMMZ4x5kfAD4F5uAvh3+AuNi4FnjHGnJH0nK/hLnROBp4AngTmAPcZYz6f4j3ygQeAW4By///fAM4D1hljlvbILyfHnc7EGy3HuF+T+hj3SNJ7KN4kwT8m3YuLt6dx33kVwJdx8TAoaXudx0mXZBpz6Dyu23nNzSr26Q/8TLfF3cm70lp7l/94AXAPsBK4PV1Vjkg6xpivA/8PuMZae0c729bhEokHgUXW2vX+4zW4u9ejgfOttWt7dq+lPzHGTAV+BozxH2p1rOpMbBljrgDuxFWInW6tfcd//HTcxRBAnbV2S0/9bnJ86kjM+dutA04BjLX2lQ68rmJOEvxKrh8C24Fl8eoqY0wU+ALwGWAnMNpae8AYswR4CNgCLLDW/s3ffor/eDkw21q7LvAeXwQ+C/wOWB2vnvDf+27/9cdYaw/0wq8sfSjTePPX7QTKgBJr7aEOvIfiTQAwxizAVY7WAwuttc/7jxcBa4GzgH+21l7vP67zOOmSTGPOX6fzuG6mCsL+4yJccvCheHIQwP/ivgJ3ML46RVZdpD3xOy/r2tzK+ThuONTX41/8ANbazcD1gW1EMMYM8qtl/oJL1LzRxuadia1P+8sb41/w/nMeAr4F5AM3dumXkH4lk5gzxuQAk4G9wKsdfAvFnARd5S8/FRx6aa09hkuyvISrfIhXdcXj59Px5KC//fPAPwAe8LH44/5F0U1AE+4m3uHAc/4TV4k4FFc9Jie+jOLNGDMcGIIbYteR5KDiTYIu85e3xBM1AP6UVp/zfwwOT9d5nHRVRjGn87ieoQRh/7HSX7aan8ZauwdXVpuLy6yLZGI6bgjxix3Y9hx/mWqepAdxB+iZxpiTumnfpH+7CXcyuAt3DLu7jW0zii1jzATc3eiduGF6yeJ3qFdlvtvSj2UScxOBPODpjsydqpiTFN7FDfN8PHmFH1Px+X2rjTEDgEXAMeBXKV7rPqAZWOFXhIEbplcCPBNMKAYo5rJLh+PNX87wlx25AQyKNwm7FjdPaqr53eLHqMbAYzqPk67KNOZ0HtcD1KSk/5jsL19Is/4lXEb9ZOAnvbJH0u8ZY0YBpcDzwEeMMZcBdcBR4A/AV+JDnfz5vCqABmBj8mtZa48ZYzYCs4EpwLZe+SXkeLYVN3z9n621h40xM1Jt1MnYih8TX0xzUvAS7mJ7jDGmsCPVE3JC6FDM+eLV028ZY27FfYeOAHbjhpl8yVobPI4p5iTEWrs63To/yRePvy24ebiiwOvW2n0pXmu3Pxx0KG7S9I107NwP3LmfnOAyjDdoOcbVG2PuAE7HJQ+34hLSX7XW1gdeRvEmCdbaRlKck/mVqbf5P37ff0zncdJlmcScT+dxPUAVhP1HvCLrrTTrtyVtJ9IR8ZPJKbgD7z7cPCEHcHdP/mSMWeNvE4+tt621TWleT3EoCdbaf7fW3paqy12SzsRWm8dEa+0R3BwmEaCyg7ss/VwGMQctx79LgauBTcAfcc2/rgXWG2MmB7ZXzEkmrsNdqLyDq55p7zwOMjzOBbYfaozROX12S443aDnGfRR3TvcCbvqFobhK63XGmOrAayjeJC1jzG3GmD/ipu6YCdwK3Oyv1nmcdLt2Yg50HtcjdHDvP4r9ZbpM9uGk7UQ6ItjBeLy1drG1dhWuq9OncVXGdxpjxtB+DILiUDqnM7GleJSuih//1gLDrLUrrLVLcce/+3DVEGv9JmGgmJMO8puRfN3/8eN+Q4eeOM4FE+GKuSyVJt6g5Rj3HWC4tfZca+0iwOBGiYwhPA2D4k3acgWus2wUN1VCFS7ZDDqPk57RVsyBzuN6hBKE/ccxf9ne+Hqvp3dETiifxZ0gLrTWboo/aK1tstZ+FdcaPg/4CB2PQVAcSmY6E1uKR+mqJcAkYI21dn/8QWvtXuBDuGqHOmCZv0oxJ+0yxqzADW3KA/7FWnunv6onjnPBxxVzWaiNeAN3/JoM3GStPRp/0B9ytwZ3kbwkUGGjeJO2TAEKgVm4+dvWAE/4zW10Hic9oa2YA53H9QglCPuPeNAXpllf4C8P9sK+yAnCWttorX3NWrs7zSb/5S9n0n4MguJQOqczsaV4lC6x1h621r4UvHAOrDuAm24B3PEPFHPSDmPMjcAvcXFwOy2dO6FnjnPBx7NyrqRs1k68Ya3db61NOdeWtXYL8Kz/Y0ePcYq3LGat3eJ/b64DzsY1NxyJG9qp8zjpdu3EnM7jeogShP1HfKx8VZr18TH1agwh3Sk+0XURLTE41BiT7q6K4lA6ozOx1eYx0RiTj2vA0wxs746dlKwTPP6BYk7SMMbEjDH/Cnwbd279GWvtDUmJmfbO4yDD41xg+13W2oYMd1v6qQ7GW0dkdIxD8SY+a+17wE/9H6ej8zjpYSliriN0HtcJShD2H8/7y4lp1k9M2k6kXcaYbxljfm6MSRdXw/3lFmvtO7gDax5uWHLya0WBcf6PikPpsE7GVkeOiR6uY2jW3gWU1Iwx1caYu4wxa/34SiVx/POXijlpxRhTANwPXIOrqrrAWntzik1fxg1vqjXGtKpeMMYMxs2XdAR41X9Y534S0tF4M8ZMNsb8wO9enE5njnHB7eQEZoz5gjHmp8aYYWk2ec9f5ug8TrpDJjGn87ieowRh//Ebf3le8gpjTDmwGDgK/L43d0r6vRnAauDCNOsv9Zf3+8u0cQgsBQYCz1lrt3bbHkq2yCi2/DkzXwFOMsbMSfGc8/3lr7t7R+WEUA9cjIu3hckrjTFlwDm4u8gPgGJOWvMvSn4JnAHsBBZZa9em2tbvjvgwkAOsTLHJB3AXJr8LVGf9EdgHzEpzwaSYyyKZxBsueXgpcLUxZnSK16oD5vrbPeo/rHiToDOAC4CL0qw/21+u85c6j5OuyiTmdB7XQ5Qg7D9+AWwGzjTGXBd/0L+TeCeudPZOa+2OPto/6Z9u95cfN8Ysjj9ojIkaY/4/cBqukuGHge2PAZ8KHliNMTXAd/0fU1VOiLSnM7H1bX95hzFmaOA5S4CbcDdNbuuxPZZ+y78z/AP/x381xoyIrzPGDAJ+hruY+U9rrQ08VTEnQZ/BXdAcAJb48yS15Z/85a3GmLHxB40xU4Av+T9+Nf64n1S8A4gB/2GMGRB4zhrcxcxu4Htd/D2kf+hwvFlrXwN+5/94t1+hCoCf/PsZrjPobdbaev85ijcJip97fc4YMyv+oDEmxxhzC7AIl6i+y1+l8zjpqg7HnM7jeo7X3JzpdBXSV4wxi3B3ZwpwEwu/DpyKm9NhPe5O4r4+20Hpl4wx/wJci7vD8iRuiMApwAjgbWCxtXZjYPtPALfgTgIexe+Ch0tS/7u19ure3H/pP4wxnwc+B9xurb0hxfqMYssYEwF+BSzHVT08gjsZOA1XiXOZtfaHSNZqK+aMMSW4qvtZwGFch7xDuDvRA3HVNMv8ia7jz1HMCZC4ANkMFOMqEtpKDv7YWvsb/3nx79x4RWEUNwokF/istfbLSe9ThDsengLsAh4HhgGzccOtlltrH+q2X0yOS52JN2NMNfAYMBpXbfMn3HFqEe5aYi1wsbW2MfA+ijdJ8Oe6vAZowsXPu8A0XEzswcXDk4HtdR4nXZJJzOk8rmeogrAfsdY+ivsArAVqgBXAXtxdZyUHpVOstR/BlXM/BkzAxVUD8A1gcjA56G//NWAV7qA7C3cQfhm4AndAF+mUTGPLWtuEGyL/UeBvwJm4GP49LrGd1V/w0jb/O/M04BO4C+75wOnAJuDvcRU6B5Keo5iTuEW4ZA1AHbCmjf8mBJ53He6Y9oL/GjNxN+dWJycHIVHtugh3rvcu7jt6GPBzYLaSNVljERnGm7X2LdxUMjfjbvieDszDFRlcjpu/MJEc9J+jeJMEa+21uGmI/gCcjPvOOwJ8C5gSTA762+s8Trokk5jTeVzPUAWhiIiIiIiIiIhIFlMFoYiIiIiIiIiISBZTglBERERERERERCSLKUEoIiIiIiIiIiKSxZQgFBERERERERERyWJKEIqIiIiIiIiIiGQxJQhFRERERERERESymBKEIiIiIiIiIiIiWUwJQhERERERERERkSymBKGIiIiIiIiIiEgWU4JQREREREREREQkiylBKCIiIiIiIiIiksWUIBQREREREREREcliShCKiIiIiIiIiIhkMSUIRUREREREREREspgShCIiIiIiIiIiIllMCUIREREREREREZEspgShiIiIiIiIiIhIFlOCUEREREREREREJIv9LxRgjMSgOTTHAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"_, ax = plt.subplots(figsize=(10, 5), dpi=150)\\n\",\n \"trainer.training_info.plot(y=['train_mse_loss', 'val_mse'], ax=ax)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Missing directory and/or file name information!\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"y_pred, y_true = trainer.predict(dataset=val_dataset, checkpoint='pearsonr_1')\\n\",\n \"y_fit_pred, y_fit_true = trainer.predict(dataset=train_dataset, checkpoint='pearsonr_1')\\n\",\n \"draw(y_true, y_pred, y_fit_true, y_fit_pred, prop_name='Volume ($\\\\AA^3$)')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Combine random model generating and training\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"generator = ParameterGenerator(\\n\",\n \" in_features=290,\\n\",\n \" out_features=1,\\n\",\n \" h_neurons=dict(\\n\",\n \" data=lambda n: sorted(np.random.uniform(0.2, 0.8, size=n), reverse=True), \\n\",\n \" repeat=(3, 4)\\n\",\n \" )\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 0%| | 0/400 [00:00\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
modelsetmethodpropertydescriptormeanAbsErrormeanSquareErrorpValuepearsonCorrid
0Stable inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositions0.4229600.418109None0.6310212335
1Stable inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositions0.5453811.641444None0.5786462338
2Stable inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositions0.7080273.087893None0.4390892339
3Stable inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositions0.5857782.238217None0.5311372341
4Stable inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositions0.5428112.174997None0.5585262342
..............................
3595Polymer Genome Datasetpytorch.nn.neural_networkorganic.polymer.refractive_indexxenonpy.compositions0.0823310.019165None0.82468431191
3596Polymer Genome Datasetpytorch.nn.neural_networkorganic.polymer.refractive_indexxenonpy.compositions0.0945220.023909None0.66740431192
3597Polymer Genome Datasetpytorch.nn.neural_networkorganic.polymer.refractive_indexxenonpy.compositions0.1285090.039972None0.68013631193
3598Polymer Genome Datasetpytorch.nn.neural_networkorganic.polymer.refractive_indexxenonpy.compositions0.0966440.025381None0.70492031194
3599Polymer Genome Datasetpytorch.nn.neural_networkorganic.polymer.refractive_indexxenonpy.compositions0.1220350.040172None0.67964431195
\\n\",\n \"

3600 rows × 9 columns

\\n\",\n \"\"\n ],\n \"text/plain\": [\n \" modelset \\\\\\n\",\n \"0 Stable inorganic compounds in materials project \\n\",\n \"1 Stable inorganic compounds in materials project \\n\",\n \"2 Stable inorganic compounds in materials project \\n\",\n \"3 Stable inorganic compounds in materials project \\n\",\n \"4 Stable inorganic compounds in materials project \\n\",\n \"... ... \\n\",\n \"3595 Polymer Genome Dataset \\n\",\n \"3596 Polymer Genome Dataset \\n\",\n \"3597 Polymer Genome Dataset \\n\",\n \"3598 Polymer Genome Dataset \\n\",\n \"3599 Polymer Genome Dataset \\n\",\n \"\\n\",\n \" method property \\\\\\n\",\n \"0 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"1 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"2 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"3 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"4 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"... ... ... \\n\",\n \"3595 pytorch.nn.neural_network organic.polymer.refractive_index \\n\",\n \"3596 pytorch.nn.neural_network organic.polymer.refractive_index \\n\",\n \"3597 pytorch.nn.neural_network organic.polymer.refractive_index \\n\",\n \"3598 pytorch.nn.neural_network organic.polymer.refractive_index \\n\",\n \"3599 pytorch.nn.neural_network organic.polymer.refractive_index \\n\",\n \"\\n\",\n \" descriptor meanAbsError meanSquareError pValue pearsonCorr \\\\\\n\",\n \"0 xenonpy.compositions 0.422960 0.418109 None 0.631021 \\n\",\n \"1 xenonpy.compositions 0.545381 1.641444 None 0.578646 \\n\",\n \"2 xenonpy.compositions 0.708027 3.087893 None 0.439089 \\n\",\n \"3 xenonpy.compositions 0.585778 2.238217 None 0.531137 \\n\",\n \"4 xenonpy.compositions 0.542811 2.174997 None 0.558526 \\n\",\n \"... ... ... ... ... ... \\n\",\n \"3595 xenonpy.compositions 0.082331 0.019165 None 0.824684 \\n\",\n \"3596 xenonpy.compositions 0.094522 0.023909 None 0.667404 \\n\",\n \"3597 xenonpy.compositions 0.128509 0.039972 None 0.680136 \\n\",\n \"3598 xenonpy.compositions 0.096644 0.025381 None 0.704920 \\n\",\n \"3599 xenonpy.compositions 0.122035 0.040172 None 0.679644 \\n\",\n \"\\n\",\n \" id \\n\",\n \"0 2335 \\n\",\n \"1 2338 \\n\",\n \"2 2339 \\n\",\n \"3 2341 \\n\",\n \"4 2342 \\n\",\n \"... ... \\n\",\n \"3595 31191 \\n\",\n \"3596 31192 \\n\",\n \"3597 31193 \\n\",\n \"3598 31194 \\n\",\n \"3599 31195 \\n\",\n \"\\n\",\n \"[3600 rows x 9 columns]\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"query(\\n\",\n \" 'modelset',\\n\",\n \" 'method',\\n\",\n \" 'property',\\n\",\n \" 'descriptor',\\n\",\n \" 'meanAbsError', \\n\",\n \" 'meanSquareError',\\n\",\n \" 'pValue',\\n\",\n \" 'pearsonCorr' \\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If everything goes right, you will get a pandas DataFrame in return.\\n\",\n \"You can see that 3600 models matched the keyword and these models are contained in two sets of models.\\n\",\n \"Note that all variables in column `pValue` are `None`. This is not a querying error, all variables are `None` indeed, because they were not recorded during training.\\n\",\n \"\\n\",\n \"You can also retrieve the last querying result from the query object via the `results` property.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
modelsetmethodpropertydescriptormeanAbsErrormeanSquareErrorpValuepearsonCorrid
0Stable inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositions0.4229600.418109None0.6310212335
1Stable inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositions0.5453811.641444None0.5786462338
2Stable inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositions0.7080273.087893None0.4390892339
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" modelset method \\\\\\n\",\n \"0 Stable inorganic compounds in materials project pytorch.nn.neural_network \\n\",\n \"1 Stable inorganic compounds in materials project pytorch.nn.neural_network \\n\",\n \"2 Stable inorganic compounds in materials project pytorch.nn.neural_network \\n\",\n \"\\n\",\n \" property descriptor meanAbsError \\\\\\n\",\n \"0 inorganic.crystal.refractive_index xenonpy.compositions 0.422960 \\n\",\n \"1 inorganic.crystal.refractive_index xenonpy.compositions 0.545381 \\n\",\n \"2 inorganic.crystal.refractive_index xenonpy.compositions 0.708027 \\n\",\n \"\\n\",\n \" meanSquareError pValue pearsonCorr id \\n\",\n \"0 0.418109 None 0.631021 2335 \\n\",\n \"1 1.641444 None 0.578646 2338 \\n\",\n \"2 3.087893 None 0.439089 2339 \"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"query.results.head(3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Querying with `mdl` is simple but not efficient enough. In most cases, we may know exactly what we want.\\n\",\n \"\\n\",\n \"Let's say we want to retrieve some models that were trained in the inorganic modelset and can predict the property of refractive index.\\n\",\n \"In this case, we need to feed the parameter `modelset_has` with **inorganic** and the `property_has` with **refractive**, respectively.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
idtransferredsucceedisRegressiondeprecatedmodelsetmethodpropertydescriptorlangmeanAbsErrormaxAbsErrormeanSquareErrorrootMeanSquareErrorr2pValuespearmanCorrpearsonCorr
02335FalseTrueTrueFalseStable inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositionspython0.4229601.7823200.4181090.6466130.226642None0.8051470.631021
12338FalseTrueTrueFalseStable inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositionspython0.5453818.9489271.6414441.2811880.330978None0.8081880.578646
22339FalseTrueTrueFalseStable inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositionspython0.70802710.1422183.0878931.7572400.173911None0.8398000.439089
32341FalseTrueTrueFalseStable inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositionspython0.58577811.8358472.2382171.4960670.241867None0.6909570.531137
42342FalseTrueTrueFalseStable inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositionspython0.54281111.0458352.1749971.4747870.275737None0.8864050.558526
.........................................................
23954863FalseTrueTrueFalseAll inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositionspython0.4296422.2048490.4340430.6588190.441885None0.8082800.712245
23964865FalseTrueTrueFalseAll inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositionspython0.5695797.6221731.6561911.2869310.215002None0.8295440.475225
23974867FalseTrueTrueFalseAll inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositionspython0.4361522.6351260.5590990.7477290.426260None0.8342260.655400
23984868FalseTrueTrueFalseAll inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositionspython0.3835062.4732030.3396530.5827970.599966None0.8400160.798442
23994871FalseTrueTrueFalseAll inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositionspython0.3948622.4901650.3974130.6304070.674495None0.8006930.839631
\\n\",\n \"

2400 rows × 18 columns

\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" id transferred succeed isRegression deprecated \\\\\\n\",\n \"0 2335 False True True False \\n\",\n \"1 2338 False True True False \\n\",\n \"2 2339 False True True False \\n\",\n \"3 2341 False True True False \\n\",\n \"4 2342 False True True False \\n\",\n \"... ... ... ... ... ... \\n\",\n \"2395 4863 False True True False \\n\",\n \"2396 4865 False True True False \\n\",\n \"2397 4867 False True True False \\n\",\n \"2398 4868 False True True False \\n\",\n \"2399 4871 False True True False \\n\",\n \"\\n\",\n \" modelset \\\\\\n\",\n \"0 Stable inorganic compounds in materials project \\n\",\n \"1 Stable inorganic compounds in materials project \\n\",\n \"2 Stable inorganic compounds in materials project \\n\",\n \"3 Stable inorganic compounds in materials project \\n\",\n \"4 Stable inorganic compounds in materials project \\n\",\n \"... ... \\n\",\n \"2395 All inorganic compounds in materials project \\n\",\n \"2396 All inorganic compounds in materials project \\n\",\n \"2397 All inorganic compounds in materials project \\n\",\n \"2398 All inorganic compounds in materials project \\n\",\n \"2399 All inorganic compounds in materials project \\n\",\n \"\\n\",\n \" method property \\\\\\n\",\n \"0 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"1 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"2 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"3 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"4 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"... ... ... \\n\",\n \"2395 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"2396 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"2397 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"2398 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"2399 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"\\n\",\n \" descriptor lang meanAbsError maxAbsError \\\\\\n\",\n \"0 xenonpy.compositions python 0.422960 1.782320 \\n\",\n \"1 xenonpy.compositions python 0.545381 8.948927 \\n\",\n \"2 xenonpy.compositions python 0.708027 10.142218 \\n\",\n \"3 xenonpy.compositions python 0.585778 11.835847 \\n\",\n \"4 xenonpy.compositions python 0.542811 11.045835 \\n\",\n \"... ... ... ... ... \\n\",\n \"2395 xenonpy.compositions python 0.429642 2.204849 \\n\",\n \"2396 xenonpy.compositions python 0.569579 7.622173 \\n\",\n \"2397 xenonpy.compositions python 0.436152 2.635126 \\n\",\n \"2398 xenonpy.compositions python 0.383506 2.473203 \\n\",\n \"2399 xenonpy.compositions python 0.394862 2.490165 \\n\",\n \"\\n\",\n \" meanSquareError rootMeanSquareError r2 pValue spearmanCorr \\\\\\n\",\n \"0 0.418109 0.646613 0.226642 None 0.805147 \\n\",\n \"1 1.641444 1.281188 0.330978 None 0.808188 \\n\",\n \"2 3.087893 1.757240 0.173911 None 0.839800 \\n\",\n \"3 2.238217 1.496067 0.241867 None 0.690957 \\n\",\n \"4 2.174997 1.474787 0.275737 None 0.886405 \\n\",\n \"... ... ... ... ... ... \\n\",\n \"2395 0.434043 0.658819 0.441885 None 0.808280 \\n\",\n \"2396 1.656191 1.286931 0.215002 None 0.829544 \\n\",\n \"2397 0.559099 0.747729 0.426260 None 0.834226 \\n\",\n \"2398 0.339653 0.582797 0.599966 None 0.840016 \\n\",\n \"2399 0.397413 0.630407 0.674495 None 0.800693 \\n\",\n \"\\n\",\n \" pearsonCorr \\n\",\n \"0 0.631021 \\n\",\n \"1 0.578646 \\n\",\n \"2 0.439089 \\n\",\n \"3 0.531137 \\n\",\n \"4 0.558526 \\n\",\n \"... ... \\n\",\n \"2395 0.712245 \\n\",\n \"2396 0.475225 \\n\",\n \"2397 0.655400 \\n\",\n \"2398 0.798442 \\n\",\n \"2399 0.839631 \\n\",\n \"\\n\",\n \"[2400 rows x 18 columns]\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# --- query data\\n\",\n \"\\n\",\n \"mdl(modelset_has='inorganic', property_has='refractive')()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can see that only the models that belong to **All inorganic compounds in materials project** modelset were returned. If you call a query object without parameters, all queryable variables will be returned.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### list/get_detail variables\\n\",\n \"\\n\",\n \"You can check some meta info of the database. To do so, we use `mdl.list_*` and `mdl.get_*_detail` methods. For example, `mdl.list_properties` will return:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
namefullNamesymbolunitdescribe
0inorganic.crystal.efermi
1inorganic.crystal.refractive_index
2inorganic.crystal.band_gap
3inorganic.crystal.density
4inorganic.crystal.total_magnetization
5inorganic.crystal.dielectric_const_elec
6inorganic.crystal.dielectric_const_total
7inorganic.crystal.final_energy_per_atom
8inorganic.crystal.formation_energy_per_atom
9inorganic.crystal.volume
10organic.polymer.band_gap_pbe
11organic.polymer.ionization_energy
12organic.polymer.electron_affinity
13organic.polymer.atomization_energy
14organic.polymer.cohesive_energy
15organic.polymer.refractive_index
16organic.polymer.density
17organic.polymer.dielectric_constant
18organic.polymer.volume_of_cell
19organic.polymer.dielectric_constant_electronic
20organic.polymer.dielectric_constant_ionic
21organic.polymer.band_gap_hse06
22organic.small_molecule
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" name fullName symbol unit \\\\\\n\",\n \"0 inorganic.crystal.efermi \\n\",\n \"1 inorganic.crystal.refractive_index \\n\",\n \"2 inorganic.crystal.band_gap \\n\",\n \"3 inorganic.crystal.density \\n\",\n \"4 inorganic.crystal.total_magnetization \\n\",\n \"5 inorganic.crystal.dielectric_const_elec \\n\",\n \"6 inorganic.crystal.dielectric_const_total \\n\",\n \"7 inorganic.crystal.final_energy_per_atom \\n\",\n \"8 inorganic.crystal.formation_energy_per_atom \\n\",\n \"9 inorganic.crystal.volume \\n\",\n \"10 organic.polymer.band_gap_pbe \\n\",\n \"11 organic.polymer.ionization_energy \\n\",\n \"12 organic.polymer.electron_affinity \\n\",\n \"13 organic.polymer.atomization_energy \\n\",\n \"14 organic.polymer.cohesive_energy \\n\",\n \"15 organic.polymer.refractive_index \\n\",\n \"16 organic.polymer.density \\n\",\n \"17 organic.polymer.dielectric_constant \\n\",\n \"18 organic.polymer.volume_of_cell \\n\",\n \"19 organic.polymer.dielectric_constant_electronic \\n\",\n \"20 organic.polymer.dielectric_constant_ionic \\n\",\n \"21 organic.polymer.band_gap_hse06 \\n\",\n \"22 organic.small_molecule \\n\",\n \"\\n\",\n \" describe \\n\",\n \"0 \\n\",\n \"1 \\n\",\n \"2 \\n\",\n \"3 \\n\",\n \"4 \\n\",\n \"5 \\n\",\n \"6 \\n\",\n \"7 \\n\",\n \"8 \\n\",\n \"9 \\n\",\n \"10 \\n\",\n \"11 \\n\",\n \"12 \\n\",\n \"13 \\n\",\n \"14 \\n\",\n \"15 \\n\",\n \"16 \\n\",\n \"17 \\n\",\n \"18 \\n\",\n \"19 \\n\",\n \"20 \\n\",\n \"21 \\n\",\n \"22 \"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"mdl.list_properties()()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"and `mdl.get_property_detail` will return the property information including how many models are relevant to this property:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'name': 'inorganic.crystal.efermi',\\n\",\n \" 'fullName': '',\\n\",\n \" 'symbol': '',\\n\",\n \" 'unit': '',\\n\",\n \" 'describe': '',\\n\",\n \" 'count': 2481}\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"mdl.get_property_detail('inorganic.crystal.efermi')()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"These querying statements are very useful when you want to know what is in the database.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### get training info/env and download url by model ID\\n\",\n \"\\n\",\n \"Note that all models have their unique IDs. We can use model ID to get more information about a particular model. The following shows how to get training info/env by model ID.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
total_itersi_epochi_batchtrain_mse_lossval_maeval_mseval_rmseval_r2val_pearsonrval_spearmanrval_p_valueval_max_ae
69469422232.0609051.0988122.1067171.4514530.7013760.8504550.8470060.012.112278
69569522242.0498121.1620042.3317021.5269910.6694850.8444440.8399160.012.192457
69669622252.1561361.0924422.1665511.4719210.6928950.8375540.8283050.012.244514
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" total_iters i_epoch i_batch train_mse_loss val_mae val_mse \\\\\\n\",\n \"694 694 22 23 2.060905 1.098812 2.106717 \\n\",\n \"695 695 22 24 2.049812 1.162004 2.331702 \\n\",\n \"696 696 22 25 2.156136 1.092442 2.166551 \\n\",\n \"\\n\",\n \" val_rmse val_r2 val_pearsonr val_spearmanr val_p_value val_max_ae \\n\",\n \"694 1.451453 0.701376 0.850455 0.847006 0.0 12.112278 \\n\",\n \"695 1.526991 0.669485 0.844444 0.839916 0.0 12.192457 \\n\",\n \"696 1.471921 0.692895 0.837554 0.828305 0.0 12.244514 \"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"info = mdl.get_training_info(model_id=1234)()\\n\",\n \"_, ax = plt.subplots(figsize=(10, 5), dpi=100)\\n\",\n \"info.tail(3)\\n\",\n \"info.plot(y=['train_mse_loss', 'val_mse'], ax=ax)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'python': '3.7.4 (default, Aug 13 2019, 20:35:49) \\\\n[GCC 7.3.0]',\\n\",\n \" 'system': '#60-Ubuntu SMP Tue Jul 2 18:22:20 UTC 2019',\\n\",\n \" 'numpy': '1.16.4',\\n\",\n \" 'torch': '1.1.0',\\n\",\n \" 'xenonpy': '0.4.0.beta4',\\n\",\n \" 'device': 'cuda:2',\\n\",\n \" 'start': '2019/09/17 20:55:56',\\n\",\n \" 'finish': '2019/09/17 21:03:04',\\n\",\n \" 'time_elapsed': '5 days, 15:33:23.552799',\\n\",\n \" 'author': 'Chang Liu',\\n\",\n \" 'email': 'liu.chang@ism.ac.jp',\\n\",\n \" 'dataset': 'materials project'}\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"mdl.get_training_env(model_id=1234)()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"nbsphinx\": \"hidden\"\n },\n \"source\": [\n \"Getting download url can be done in a similar way.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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idetagurl
012349274418b5ee2026ea8714b7edc7d012e-1http://xenon.ism.ac.jp/mdl/inorganic.crystal.e...
15678c5a962fce76a773b208cc59631999a25-1http://xenon.ism.ac.jp/mdl/inorganic.crystal.b...
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" id etag \\\\\\n\",\n \"0 1234 9274418b5ee2026ea8714b7edc7d012e-1 \\n\",\n \"1 5678 c5a962fce76a773b208cc59631999a25-1 \\n\",\n \"\\n\",\n \" url \\n\",\n \"0 http://xenon.ism.ac.jp/mdl/inorganic.crystal.e... \\n\",\n \"1 http://xenon.ism.ac.jp/mdl/inorganic.crystal.b... \"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"mdl.get_model_urls(1234, 5678)()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The output dataframe contains the column named `url`. If you only want to get the string of `url`, just use `queryable` specification.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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url
0http://xenon.ism.ac.jp/mdl/inorganic.crystal.e...
1http://xenon.ism.ac.jp/mdl/inorganic.crystal.b...
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" url\\n\",\n \"0 http://xenon.ism.ac.jp/mdl/inorganic.crystal.e...\\n\",\n \"1 http://xenon.ism.ac.jp/mdl/inorganic.crystal.b...\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"mdl.get_model_urls(1234, 5678)('url')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Also, if you don't want to get a dataframe or you want to control the output type yourself, you can set `return_json` to `True`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[{'url': 'http://xenon.ism.ac.jp/mdl/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3.tar.gz'},\\n\",\n \" {'url': 'http://xenon.ism.ac.jp/mdl/inorganic.crystal.band_gap/xenonpy.compositions/pytorch.nn.neural_network/290-168-132-111-1-$Nbe3TKMYM.tar.gz'}]\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"mdl.get_model_urls(1234, 5678)('url', return_json=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can use these urls to download models yourself, but we suggest you to use `mdl.pull`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\\u001b[0;31mSignature:\\u001b[0m\\n\",\n \"\\u001b[0mmdl\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mpull\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0mmodel_ids\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mint\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mpandas\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mcore\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mseries\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mSeries\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mpandas\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mcore\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mframe\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mDataFrame\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0msave_to\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mstr\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;34m'.'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;34m->\\u001b[0m \\u001b[0mpandas\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mcore\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mframe\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mDataFrame\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mDocstring:\\u001b[0m\\n\",\n \"Download model(s) from XenonPy.MDL server.\\n\",\n \"\\n\",\n \"Parameters\\n\",\n \"----------\\n\",\n \"model_ids\\n\",\n \" Model ids.\\n\",\n \" It can be given by a dataframe.\\n\",\n \" In this case, the column with name ``id`` will be used.\\n\",\n \"save_to\\n\",\n \" Path to save models.\\n\",\n \"\\n\",\n \"Returns\\n\",\n \"-------\\n\",\n \"\\u001b[0;31mFile:\\u001b[0m ~/projects/XenonPy/xenonpy/mdl/mdl.py\\n\",\n \"\\u001b[0;31mType:\\u001b[0m method\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"mdl.pull?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"nbsphinx\": \"hidden\"\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"100%|██████████| 2/2 [00:01<00:00, 1.49it/s]\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
idmodel
01234/Users/liuchang/Google 云端硬盘/postdoctoral/tutor...
15678/Users/liuchang/Google 云端硬盘/postdoctoral/tutor...
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" id model\\n\",\n \"0 1234 /Users/liuchang/Google 云端硬盘/postdoctoral/tutor...\\n\",\n \"1 5678 /Users/liuchang/Google 云端硬盘/postdoctoral/tutor...\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ret = mdl.pull(1234, 5678)\\n\",\n \"ret\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The column named `model` contains the local path of the downloaded models.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### upload model\\n\",\n \"\\n\",\n \"Uploading models is not yet availiable until we open the public registration. If you try to upload models, you will get ***'operation needs a logged in user and the corresponded api_key'*** error.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"ValueError\",\n \"evalue\": \"operation needs a logged in user and the corresponded api_key\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mValueError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m\\u001b[0m\\n\\u001b[1;32m 13\\u001b[0m \\u001b[0mtraining_info\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mdict\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0ma\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mb\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 14\\u001b[0m \\u001b[0msupplementary\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mdict\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtrue\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;36m3\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;36m4\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mpred\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;36m4\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 15\\u001b[0;31m )(file='data')\\n\\u001b[0m\",\n \"\\u001b[0;32m~/projects/XenonPy/xenonpy/mdl/base.py\\u001b[0m in \\u001b[0;36m__call__\\u001b[0;34m(self, file, return_json, *querying_vars)\\u001b[0m\\n\\u001b[1;32m 104\\u001b[0m \\u001b[0mret\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mret\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mjson\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 105\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0;34m'errors'\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0mret\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 106\\u001b[0;31m \\u001b[0;32mraise\\u001b[0m \\u001b[0mValueError\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mret\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m'errors'\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m0\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m'message'\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 107\\u001b[0m \\u001b[0mquery_name\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m__class__\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m__name__\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 108\\u001b[0m \\u001b[0mret\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mret\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m'data'\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mquery_name\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m0\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mlower\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;34m+\\u001b[0m \\u001b[0mquery_name\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mValueError\\u001b[0m: operation needs a logged in user and the corresponded api_key\"\n ]\n }\n ],\n \"source\": [\n \"mdl.upload_model(\\n\",\n \" modelset_id=2, \\n\",\n \" describe=dict(\\n\",\n \" property='test2',\\n\",\n \" descriptor='test2',\\n\",\n \" method='test',\\n\",\n \" lang='test',\\n\",\n \" deprecated=True,\\n\",\n \" isRegression=True,\\n\",\n \" meanAbsError=1.11,\\n\",\n \" pearsonCorr=0.85,\\n\",\n \" ),\\n\",\n \" training_info=dict(a=1, b=2),\\n\",\n \" supplementary=dict(true=[1,2,3,4], pred=[1,2,2,4])\\n\",\n \")(file='data')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### retrieve model\\n\",\n \"\\n\",\n \"You can use `xenonpy.model.training.Checker` to load the downloaded models. For example, to load the model with id **1234**:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from xenonpy.model.training import Checker\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/usr/local/miniconda3/envs/xepy37/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: `item` has been deprecated and will be removed in a future version\\n\",\n \" \\\"\\\"\\\"Entry point for launching an IPython kernel.\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \" includes:\\n\",\n \"\\\"final_state\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/final_state.pth.s\\n\",\n \"\\\"training_info\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/training_info.pd.xz\\n\",\n \"\\\"model_class\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/model_class.pkl.z\\n\",\n \"\\\"init_state\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/init_state.pth.s\\n\",\n \"\\\"model\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/model.pth.m\\n\",\n \"\\\"splitter\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/splitter.pkl.z\\n\",\n \"\\\"model_structure\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/model_structure.pkl.z\\n\",\n \"\\\"describe\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/describe.pkl.z\\n\",\n \"\\\"data_indices\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/data_indices.pkl.z\\n\",\n \"\\\"model_params\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/model_params.pkl.z\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"checker = Checker(ret[ret.id == 1234].model.item())\\n\",\n \"checker\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note that the random string *$WEnkZ6e3* in the file name is a magic number to guarantee that each model has a unique name.\\n\",\n \"\\n\",\n \"To load a model into python, call `checker.model` property.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"SequentialLinear(\\n\",\n \" (layer_0): LinearLayer(\\n\",\n \" (linear): Linear(in_features=290, out_features=180, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(180, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_1): LinearLayer(\\n\",\n \" (linear): Linear(in_features=180, out_features=177, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(177, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_2): LinearLayer(\\n\",\n \" (linear): Linear(in_features=177, out_features=162, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(162, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_3): LinearLayer(\\n\",\n \" (linear): Linear(in_features=162, out_features=46, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(46, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_4): LinearLayer(\\n\",\n \" (linear): Linear(in_features=46, out_features=32, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(32, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (output): Linear(in_features=32, out_features=1, bias=True)\\n\",\n \")\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"checker.model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Use `checker.checkpoints` to list checkpoints.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \" includes:\\n\",\n \"\\\"mse_3\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/mse_3.pth.s\\n\",\n \"\\\"mse_1\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/mse_1.pth.s\\n\",\n \"\\\"mae_2\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/mae_2.pth.s\\n\",\n \"\\\"r2_5\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/r2_5.pth.s\\n\",\n \"\\\"mse_5\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/mse_5.pth.s\\n\",\n \"\\\"r2_1\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/r2_1.pth.s\\n\",\n \"\\\"mae_4\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/mae_4.pth.s\\n\",\n \"\\\"r2_3\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/r2_3.pth.s\\n\",\n \"\\\"mae_3\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/mae_3.pth.s\\n\",\n \"\\\"r2_4\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/r2_4.pth.s\\n\",\n \"\\\"mse_2\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/mse_2.pth.s\\n\",\n \"\\\"mae_1\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/mae_1.pth.s\\n\",\n \"\\\"mae_5\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/mae_5.pth.s\\n\",\n \"\\\"r2_2\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/r2_2.pth.s\\n\",\n \"\\\"mse_4\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/mse_4.pth.s\\n\",\n \"\\\"pearsonr_5\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/pearsonr_5.pth.s\\n\",\n \"\\\"pearsonr_1\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/pearsonr_1.pth.s\\n\",\n \"\\\"pearsonr_3\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/pearsonr_3.pth.s\\n\",\n \"\\\"pearsonr_4\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/pearsonr_4.pth.s\\n\",\n \"\\\"pearsonr_2\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/pearsonr_2.pth.s\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"checker.checkpoints\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"and `checker.checkpoints[]` to load information from a specific checkpoint.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"OrderedDict([('id', 'mse_3'),\\n\",\n \" ('iterations', 670),\\n\",\n \" ('model_state',\\n\",\n \" OrderedDict([('layer_0.linear.weight',\\n\",\n \" tensor([[-2.7413, 2.2444, -0.6347, ..., -0.4093, 1.7569, 0.3331],\\n\",\n \" [-3.4710, 3.1230, -1.5549, ..., 0.7105, 0.1097, -0.5686],\\n\",\n \" [-1.0452, -0.0773, -1.8657, ..., -2.4842, -2.4716, 0.0133],\\n\",\n \" ...,\\n\",\n \" [-2.8635, 2.0810, -1.8306, ..., -0.7263, 0.1966, 0.6690],\\n\",\n \" [-1.4911, 0.3969, -2.4763, ..., -2.1228, -2.3481, -0.2280],\\n\",\n \" [-4.5753, 3.2695, -3.0195, ..., -1.5596, -0.2794, 0.9001]])),\\n\",\n \" ('layer_0.linear.bias',\\n\",\n \" tensor([ 1.8674e-01, 1.2594e+00, 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0.1193,\\n\",\n \" 0.2693, -0.2330, -0.2963, 0.0207, 0.0744, 0.3772, -0.0594, 0.2716,\\n\",\n \" -0.5286, -0.0988, 0.0071, 0.0785, -0.4082, -0.0996, -0.2286, -0.1072,\\n\",\n \" -0.0199, -0.0759])),\\n\",\n \" ('layer_2.normalizer.running_mean',\\n\",\n \" tensor([-0.4771, 0.3641, 0.8236, -0.5117, -0.0686, -0.1060, -0.2271, 1.1366,\\n\",\n \" -0.2673, -0.6526, -0.0598, -0.3061, -0.8754, -0.5231, -0.1757, 0.4500,\\n\",\n \" -0.8126, -0.2425, -0.5052, -0.7121, -0.4026, -0.9407, -0.2459, -0.9544,\\n\",\n \" 0.4757, -0.5780, -0.4919, 0.7358, -0.7519, -1.2301, -0.5611, -0.1652,\\n\",\n \" -0.2985, -1.5551, -0.4821, 0.0284, 0.7578, -0.6171, 1.1162, -0.2456,\\n\",\n \" 0.0706, 0.7641, -0.7250, -0.0534, 0.3452, -1.3787, -0.1682, -0.4565,\\n\",\n \" 0.0713, -0.7465, -0.1761, -0.3235, -0.1148, -1.7422, -0.4193, -0.4965,\\n\",\n \" 1.6195, -0.7968, -0.8410, -0.0312, -0.3054, -1.1875, -0.0566, -0.3492,\\n\",\n \" 0.0022, -0.2289, 0.0654, -0.3188, 1.0440, -0.6859, 0.0535, -0.2057,\\n\",\n \" -0.9382, -1.3899, 0.4321, 0.1345, -0.8927, 0.0757, -0.8157, -0.7032,\\n\",\n \" -0.0305, -0.7336, 0.8059, -0.1560, 0.6057, 0.1573, 1.3014, -0.1614,\\n\",\n \" 0.6065, -0.0084, -1.0559, -0.9398, -1.2521, 0.4449, -0.5810, -1.4539,\\n\",\n \" -0.2939, 0.2501, -0.4125, 0.1147, -0.6748, 0.5822, -1.2044, -0.1167,\\n\",\n \" 0.0862, 0.5174, -0.8685, -1.8570, 0.0200, -0.6124, -0.3150, 0.5020,\\n\",\n \" -0.7448, -0.5465, -1.0633, -0.2995, 0.4695, -0.5782, -0.0652, -0.5671,\\n\",\n \" -1.0913, -0.6757, 0.6314, -0.3749, -0.8141, -0.3002, 0.3342, -0.5968,\\n\",\n \" -0.5119, 0.0035, 0.1476, 0.0465, 0.4401, 0.0067, 0.2266, -0.7001,\\n\",\n \" -0.3087, -0.8820, -0.2069, 0.8888, -0.2508, 1.0863, -0.4786, 0.1945,\\n\",\n \" 0.5957, -0.1170, -0.3074, 0.1786, -0.8405, 0.0903, 0.1077, 0.4824,\\n\",\n \" -0.3188, 0.6018, 0.2859, -1.1954, -0.3282, -0.7685, -0.7977, -0.6385,\\n\",\n \" 0.9811, 0.7143])),\\n\",\n \" ('layer_2.normalizer.running_var',\\n\",\n \" tensor([1.2081, 0.7104, 0.2791, 2.3922, 1.3909, 2.2632, 2.7923, 3.4170, 0.2371,\\n\",\n \" 0.8223, 1.3578, 1.3718, 3.6416, 1.1920, 1.7282, 2.5161, 1.9402, 0.8729,\\n\",\n \" 1.1787, 1.5293, 0.9767, 3.6726, 1.8250, 1.4192, 3.7494, 1.0485, 2.5048,\\n\",\n \" 2.8385, 2.6879, 1.5259, 0.3320, 5.0883, 2.3942, 2.9072, 2.1876, 4.2835,\\n\",\n \" 2.2496, 1.4994, 1.1825, 3.0025, 1.3385, 4.1513, 1.3618, 1.3812, 3.3829,\\n\",\n \" 2.7717, 2.4212, 2.5637, 0.7795, 2.8931, 1.4690, 1.9977, 3.8444, 2.4258,\\n\",\n \" 0.9577, 3.2737, 2.3147, 2.9155, 1.6753, 1.4580, 1.7667, 2.2222, 1.0000,\\n\",\n \" 3.4903, 4.8764, 1.9582, 2.8803, 1.6848, 3.7632, 2.2867, 2.3507, 2.0570,\\n\",\n \" 1.8109, 2.8613, 3.5178, 2.9047, 1.7047, 0.8481, 2.2359, 1.6086, 1.2468,\\n\",\n \" 1.9544, 4.4804, 1.5151, 2.4366, 5.1584, 4.9039, 7.4413, 3.6987, 1.0244,\\n\",\n \" 2.5796, 1.8977, 1.5950, 2.9663, 2.5948, 1.5380, 4.0055, 1.2963, 1.2724,\\n\",\n \" 1.0664, 1.9278, 1.7581, 1.5861, 3.4465, 1.5621, 5.3650, 1.7151, 5.2662,\\n\",\n \" 1.2776, 1.0102, 0.9393, 3.4590, 2.4199, 1.5162, 2.2081, 1.1188, 3.0619,\\n\",\n \" 2.5459, 1.2963, 0.4084, 2.9916, 1.2197, 1.8776, 2.3511, 2.1200, 1.6047,\\n\",\n \" 2.1336, 2.0964, 1.9538, 3.3240, 2.2276, 1.0014, 0.3187, 2.3750, 2.5523,\\n\",\n \" 1.7299, 1.6644, 0.9426, 1.8765, 4.0248, 2.9807, 2.4890, 1.6497, 4.3925,\\n\",\n \" 3.5190, 3.1066, 2.3513, 1.8139, 2.6495, 4.8435, 3.8340, 3.7585, 1.8870,\\n\",\n \" 2.6590, 2.8758, 2.4845, 1.1878, 1.7869, 1.4710, 2.2284, 2.6282, 3.5244])),\\n\",\n \" ('layer_2.normalizer.num_batches_tracked',\\n\",\n \" tensor(670)),\\n\",\n \" ('layer_3.linear.weight',\\n\",\n \" tensor([[ 0.1380, -0.1854, 0.0619, ..., -0.2357, 0.0291, -0.0855],\\n\",\n \" [ 0.1408, 0.0109, 0.0273, ..., -0.1300, -0.2806, 0.0283],\\n\",\n \" [-0.0817, 0.0103, -0.1799, ..., 0.0643, -0.0872, -0.2488],\\n\",\n \" ...,\\n\",\n \" [-0.1365, 0.2386, 0.0441, ..., 0.1613, 0.0506, 0.0980],\\n\",\n \" [ 0.0804, 0.0271, -0.0292, ..., 0.1756, -0.0669, 0.1731],\\n\",\n \" [ 0.0381, 0.2557, -0.1221, ..., 0.2586, 0.0182, -0.0256]])),\\n\",\n \" ('layer_3.linear.bias',\\n\",\n \" tensor([ 1.7908e-02, -1.6474e-02, -1.1066e-01, 2.7437e-01, 7.9325e-01,\\n\",\n \" 7.8987e-02, 3.3952e-01, -8.7965e-02, 1.5753e-01, -1.2900e-01,\\n\",\n \" 7.6526e-02, 7.7629e-03, 5.0898e-01, -3.8382e-01, -6.2505e-02,\\n\",\n \" 7.2312e-01, 4.5212e-02, 9.1312e-01, 4.5915e-02, 1.3952e-02,\\n\",\n \" 2.2207e-01, -5.3636e-04, 1.4154e-01, 1.2470e-01, 1.3919e-01,\\n\",\n \" 1.1014e-01, 1.1680e-01, -3.1510e-02, 4.0542e-02, -1.7219e-04,\\n\",\n \" -1.0441e-01, 5.2226e-01, -5.1104e-02, 3.9572e-01, -2.4648e-01,\\n\",\n \" -3.9327e-01, -1.4421e-01, -6.7385e-02, 2.2774e-02, -1.5080e-01,\\n\",\n \" -5.9285e-03, 1.3464e-01, 1.1147e-01, -2.1358e-01, -3.9889e-01,\\n\",\n \" 1.2666e-01])),\\n\",\n \" ('layer_3.normalizer.weight',\\n\",\n \" tensor([ 0.4035, 0.9273, 0.3436, 0.8313, -0.5483, 0.5186, 0.3613, 0.3362,\\n\",\n \" 0.7600, 0.4670, 1.1825, 0.7006, 0.4275, 1.0126, 0.9773, 0.3357,\\n\",\n \" 0.2955, 0.3329, 0.3705, -0.0120, 0.8153, 0.9983, 0.7633, 0.8837,\\n\",\n \" 0.3240, 0.8691, 0.9471, 0.8034, 0.6000, 0.8582, 0.3787, 0.6470,\\n\",\n \" 1.0252, 0.7739, 0.3396, 0.8057, 0.8804, 0.7036, 0.8939, 0.5345,\\n\",\n \" 0.9509, 0.7852, 0.8444, 0.6482, 0.7647, 0.9864])),\\n\",\n \" ('layer_3.normalizer.bias',\\n\",\n \" tensor([ 0.3733, 0.2051, -0.0269, 0.2724, -0.4801, -0.1726, 0.4889, 0.4670,\\n\",\n \" 0.0054, 0.3607, -0.0175, 0.0667, 0.3025, -0.1931, 0.0393, 0.4796,\\n\",\n \" -0.4264, 0.4658, 0.4557, -0.0588, 0.1771, 0.1360, -0.1883, 0.3348,\\n\",\n \" -0.0160, 0.1065, 0.2562, -0.0518, 0.2925, 0.1273, 0.6136, 0.2823,\\n\",\n \" -0.1507, 0.1460, 0.0915, -0.2639, 0.0656, 0.1196, -0.1569, 0.1028,\\n\",\n \" 0.0275, 0.4489, 0.1869, -0.4018, -0.2103, -0.0286])),\\n\",\n \" ('layer_3.normalizer.running_mean',\\n\",\n \" tensor([-0.9715, -2.2032, -1.8011, 0.9685, 0.6200, -0.9597, 0.7768, -2.1247,\\n\",\n \" -2.0955, -2.6104, 1.6368, -1.4368, 1.0332, 1.4593, 0.8677, 1.6211,\\n\",\n \" -1.0680, 1.0735, -0.7232, 1.2941, -0.8071, -1.6285, -2.1765, -1.8546,\\n\",\n \" -1.0937, 0.5600, 0.9602, 0.9767, -0.7245, -1.1330, -2.3668, 0.4982,\\n\",\n \" 0.3226, -1.4879, -3.2258, -0.0994, -2.7357, -1.0780, -2.0709, -1.0271,\\n\",\n \" -1.8341, 1.0092, -0.7226, -0.3674, 3.3634, -0.3263])),\\n\",\n \" ('layer_3.normalizer.running_var',\\n\",\n \" tensor([ 4.4649, 9.8838, 5.2923, 4.5300, 5.1298, 1.6735, 5.8431, 8.8731,\\n\",\n \" 4.8244, 8.9389, 6.5502, 4.1085, 7.3151, 6.8593, 4.8760, 9.5597,\\n\",\n \" 1.4886, 6.5716, 3.2722, 0.5959, 4.1359, 7.5561, 4.8947, 5.4064,\\n\",\n \" 2.4573, 3.7298, 4.4916, 4.9595, 2.8120, 7.7168, 10.7011, 4.4031,\\n\",\n \" 4.6533, 5.6067, 8.5437, 3.1499, 7.7018, 4.7582, 4.0134, 2.9158,\\n\",\n \" 5.3908, 7.7204, 3.1456, 2.2102, 10.8318, 2.5861])),\\n\",\n \" ('layer_3.normalizer.num_batches_tracked',\\n\",\n \" tensor(670)),\\n\",\n \" ('layer_4.linear.weight',\\n\",\n \" tensor([[ 0.0698, -0.0195, -0.0756, ..., -0.0462, 0.0105, -0.0389],\\n\",\n \" [ 0.1114, 0.1341, 0.3744, ..., -0.1303, -0.5447, 0.0424],\\n\",\n \" [ 0.4859, 0.2065, 0.2659, ..., -0.2024, -0.3696, -0.2466],\\n\",\n \" ...,\\n\",\n \" [ 0.5584, -0.0504, 0.2195, ..., -0.2966, 0.0918, -0.2569],\\n\",\n \" [-0.0763, 0.0830, 0.2275, ..., 0.0154, -0.0329, -0.0522],\\n\",\n \" [-0.1180, 0.2637, 0.1172, ..., 0.1356, -0.6853, 0.2353]])),\\n\",\n \" ('layer_4.linear.bias',\\n\",\n \" tensor([-0.0691, -0.1060, -0.0187, 0.3468, 0.1665, 0.0228, -0.0955, -0.0045,\\n\",\n \" -0.1590, 0.2351, 0.1438, 0.3307, -0.0113, -0.0621, -0.1006, -0.0504,\\n\",\n \" 0.1788, 0.2348, -0.0194, -0.1862, 0.3357, 0.0535, 0.0694, 0.0870,\\n\",\n \" -0.1025, 0.2098, 0.0873, 0.1724, 0.0155, -0.0475, 0.0931, 0.3493])),\\n\",\n \" ('layer_4.normalizer.weight',\\n\",\n \" tensor([ 0.2297, 0.2990, 0.2737, 0.8078, 0.5716, 0.4337, 0.6599, 0.4357,\\n\",\n \" 0.3786, 0.7697, 0.3827, 1.0374, 0.9383, 0.4150, 0.5763, 0.0082,\\n\",\n \" 0.5816, 0.2989, 0.7998, 1.0263, 0.8123, 0.3771, 0.6268, 0.8554,\\n\",\n \" 0.5588, -0.6344, 0.5484, 0.7461, 0.9628, 0.8320, 0.6235, 0.9862])),\\n\",\n \" ('layer_4.normalizer.bias',\\n\",\n \" tensor([-0.1278, 0.4389, 0.4260, 0.3198, -0.1430, 0.3581, -0.2091, -0.0930,\\n\",\n \" 0.3896, 0.3542, -0.1799, 0.2433, 0.2380, -0.1205, 0.3009, -0.0899,\\n\",\n \" 0.2727, 0.4550, -0.1858, 0.1964, 0.4114, -0.0596, 0.0278, -0.2187,\\n\",\n \" 0.0882, -0.0525, -0.0552, 0.2251, 0.2028, 0.1410, -0.1418, 0.2488])),\\n\",\n \" ('layer_4.normalizer.running_mean',\\n\",\n \" tensor([-0.2209, -0.0189, -0.1345, -0.8261, -0.1632, -0.5029, 0.1678, 0.3956,\\n\",\n \" -0.2796, -0.5636, -0.1228, -0.5516, -0.8519, 0.3655, -0.5660, -0.3087,\\n\",\n \" -0.7080, -0.0173, -0.1170, -0.6635, -0.8329, 0.0423, -0.4747, -0.1199,\\n\",\n \" -0.5585, 0.8095, 0.2742, -0.8614, -0.7616, -0.3526, 0.0804, -0.6352])),\\n\",\n \" ('layer_4.normalizer.running_var',\\n\",\n \" tensor([ 0.1660, 9.9155, 9.3877, 10.0118, 1.8771, 8.6701, 0.9420, 1.2411,\\n\",\n \" 7.6982, 7.8437, 0.5575, 8.9085, 10.7996, 1.3539, 6.6764, 0.0753,\\n\",\n \" 6.6262, 9.5189, 0.6057, 5.4100, 8.8232, 1.7903, 4.0475, 1.0141,\\n\",\n \" 3.2860, 5.7909, 1.9748, 6.6539, 7.9738, 4.0276, 0.9936, 6.8559])),\\n\",\n \" ('layer_4.normalizer.num_batches_tracked',\\n\",\n \" tensor(670)),\\n\",\n \" ('output.weight',\\n\",\n \" tensor([[ 0.0907, 0.3841, 0.4231, 0.2701, 0.1529, 0.3734, 0.1848, 0.1908,\\n\",\n \" 0.3445, 0.2799, 0.2094, 0.2791, 0.2999, 0.2314, 0.3558, 0.0392,\\n\",\n \" 0.3215, 0.3956, 0.1413, 0.2460, 0.2496, 0.1880, 0.2432, 0.1266,\\n\",\n \" 0.2394, -0.7282, 0.2006, 0.2259, 0.2687, 0.2108, 0.1736, 0.2261]])),\\n\",\n \" ('output.bias', tensor([0.1369]))]))])\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"checker.checkpoints['mse_3']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### reuse model using trainer\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"`xenonpy.model.training.Trainer` can load model and checkpoints from checker or from model directory directly.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from xenonpy.model.training import Trainer\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Trainer(clip_grad=None, cuda=None, epochs=200, loss_func=None,\\n\",\n \" lr_scheduler=None,\\n\",\n \" model=SequentialLinear(\\n\",\n \" (layer_0): LinearLayer(\\n\",\n \" (linear): Linear(in_features=290, out_features=180, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(180, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_1): LinearLayer(\\n\",\n \" (linear): Linear(...\\n\",\n \" (normalizer): BatchNorm1d(46, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_4): LinearLayer(\\n\",\n \" (linear): Linear(in_features=46, out_features=32, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(32, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (output): Linear(in_features=32, out_features=1, bias=True)\\n\",\n \"),\\n\",\n \" non_blocking=False, optimizer=None)\"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"trainer = Trainer.load(from_=checker)\\n\",\n \"trainer\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The following codes show how to reuse model for prediction.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
band_gapcompositiondensitye_above_hullefermielementsfinal_energy_per_atomformation_energy_per_atompretty_formulastructurevolume
mp-10088070.0000{'Rb': 1.0, 'Cu': 1.0, 'O': 1.0}4.7846340.9963721.100617[Rb, Cu, O]-3.302762-0.186408RbCuO[[-3.05935361 -3.05935361 -3.05935361] Rb, [0....57.268924
mp-10096400.0000{'Pr': 1.0, 'N': 1.0}8.1457770.7593935.213442[Pr, N]-7.082624-0.714336PrN[[0. 0. 0.] Pr, [1.57925232 1.57925232 1.58276...31.579717
mp-10168250.7745{'Hf': 1.0, 'Mg': 1.0, 'O': 3.0}6.1658880.5895502.424570[Hf, Mg, O]-7.911723-3.060060HfMgO3[[2.03622802 2.03622802 2.03622802] Hf, [0. 0....67.541269
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" band_gap composition density \\\\\\n\",\n \"mp-1008807 0.0000 {'Rb': 1.0, 'Cu': 1.0, 'O': 1.0} 4.784634 \\n\",\n \"mp-1009640 0.0000 {'Pr': 1.0, 'N': 1.0} 8.145777 \\n\",\n \"mp-1016825 0.7745 {'Hf': 1.0, 'Mg': 1.0, 'O': 3.0} 6.165888 \\n\",\n \"\\n\",\n \" e_above_hull efermi elements final_energy_per_atom \\\\\\n\",\n \"mp-1008807 0.996372 1.100617 [Rb, Cu, O] -3.302762 \\n\",\n \"mp-1009640 0.759393 5.213442 [Pr, N] -7.082624 \\n\",\n \"mp-1016825 0.589550 2.424570 [Hf, Mg, O] -7.911723 \\n\",\n \"\\n\",\n \" formation_energy_per_atom pretty_formula \\\\\\n\",\n \"mp-1008807 -0.186408 RbCuO \\n\",\n \"mp-1009640 -0.714336 PrN \\n\",\n \"mp-1016825 -3.060060 HfMgO3 \\n\",\n \"\\n\",\n \" structure volume \\n\",\n \"mp-1008807 [[-3.05935361 -3.05935361 -3.05935361] Rb, [0.... 57.268924 \\n\",\n \"mp-1009640 [[0. 0. 0.] Pr, [1.57925232 1.57925232 1.58276... 31.579717 \\n\",\n \"mp-1016825 [[2.03622802 2.03622802 2.03622802] Hf, [0. 0.... 67.541269 \"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# if you have not had the samples data\\n\",\n \"# preset.build('mp_samples', api_key=)\\n\",\n \"from xenonpy.datatools import preset\\n\",\n \"\\n\",\n \"data = preset.mp_samples\\n\",\n \"data.head(3)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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ave:atomic_numberave:atomic_radiusave:atomic_radius_rahmave:atomic_volumeave:atomic_weightave:boiling_pointave:bulk_modulusave:c6_gbave:covalent_radius_corderoave:covalent_radius_pyykko...min:num_s_valencemin:periodmin:specific_heatmin:thermal_conductivitymin:vdw_radiusmin:vdw_radius_alvarezmin:vdw_radius_mm3min:vdw_radius_uffmin:sound_velocitymin:Polarizability
mp-100880724.666667174.067140209.33333325.66666755.0042671297.06333372.8686801646.90139.333333128.333333...1.02.00.3600.02658152.0150.0182.0349.5317.50.802
mp-100964033.000000137.000000232.50000019.05000077.4573301931.20000043.1824411892.85137.000000123.500000...2.02.00.1920.02583155.0166.0193.0360.6333.61.100
mp-101682521.600000153.120852203.40000013.92000050.1584001420.71400076.663625343.82102.80000096.000000...2.02.00.1460.02658152.0150.0182.0302.1317.50.802
\\n\",\n \"

3 rows × 290 columns

\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" ave:atomic_number ave:atomic_radius ave:atomic_radius_rahm \\\\\\n\",\n \"mp-1008807 24.666667 174.067140 209.333333 \\n\",\n \"mp-1009640 33.000000 137.000000 232.500000 \\n\",\n \"mp-1016825 21.600000 153.120852 203.400000 \\n\",\n \"\\n\",\n \" ave:atomic_volume ave:atomic_weight ave:boiling_point \\\\\\n\",\n \"mp-1008807 25.666667 55.004267 1297.063333 \\n\",\n \"mp-1009640 19.050000 77.457330 1931.200000 \\n\",\n \"mp-1016825 13.920000 50.158400 1420.714000 \\n\",\n \"\\n\",\n \" ave:bulk_modulus ave:c6_gb ave:covalent_radius_cordero \\\\\\n\",\n \"mp-1008807 72.868680 1646.90 139.333333 \\n\",\n \"mp-1009640 43.182441 1892.85 137.000000 \\n\",\n \"mp-1016825 76.663625 343.82 102.800000 \\n\",\n \"\\n\",\n \" ave:covalent_radius_pyykko ... min:num_s_valence min:period \\\\\\n\",\n \"mp-1008807 128.333333 ... 1.0 2.0 \\n\",\n \"mp-1009640 123.500000 ... 2.0 2.0 \\n\",\n \"mp-1016825 96.000000 ... 2.0 2.0 \\n\",\n \"\\n\",\n \" min:specific_heat min:thermal_conductivity min:vdw_radius \\\\\\n\",\n \"mp-1008807 0.360 0.02658 152.0 \\n\",\n \"mp-1009640 0.192 0.02583 155.0 \\n\",\n \"mp-1016825 0.146 0.02658 152.0 \\n\",\n \"\\n\",\n \" min:vdw_radius_alvarez min:vdw_radius_mm3 min:vdw_radius_uff \\\\\\n\",\n \"mp-1008807 150.0 182.0 349.5 \\n\",\n \"mp-1009640 166.0 193.0 360.6 \\n\",\n \"mp-1016825 150.0 182.0 302.1 \\n\",\n \"\\n\",\n \" min:sound_velocity min:Polarizability \\n\",\n \"mp-1008807 317.5 0.802 \\n\",\n \"mp-1009640 333.6 1.100 \\n\",\n \"mp-1016825 317.5 0.802 \\n\",\n \"\\n\",\n \"[3 rows x 290 columns]\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
efermi
mp-10088071.100617
mp-10096405.213442
mp-10168252.424570
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" efermi\\n\",\n \"mp-1008807 1.100617\\n\",\n \"mp-1009640 5.213442\\n\",\n \"mp-1016825 2.424570\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from xenonpy.descriptor import Compositions\\n\",\n \"\\n\",\n \"prop = data['efermi'].dropna().to_frame() # reshape to 2-D\\n\",\n \"desc = Compositions(featurizers='classic').transform(data.loc[prop.index]['composition'])\\n\",\n \"\\n\",\n \"desc.head(3)\\n\",\n \"prop.head(3)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Missing directory and/or file name information!\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"y_pred = trainer.predict(x_in=torch.tensor(desc.values, dtype=torch.float)).detach().numpy().flatten()\\n\",\n \"y_true = prop.values.flatten()\\n\",\n \"\\n\",\n \"draw(y_true, y_pred, prop_name='Efermi ($eV$)')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"source\": [],\n \"metadata\": {\n \"collapsed\": false\n }\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}" }, { "path": "docs/source/tutorials/6-transfer_learning.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Transfer Learning\\n\",\n \"\\n\",\n \"There are various methods for transfer learning such as **fine tuning** and **frozen feature extraction**. In this tutorial, we will demonstrate how to perform a **frozen feature extraction** type of transfer learning in XenonPy.\\n\",\n \"\\n\",\n \"This tutorial will use **Refractive Index** data, which are collected from [Polymer Genome](https://www.polymergenome.org). We do not provide these data directly in this tutorial. If you want to rerun this notebook locally, you must collect these data yourself.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### frozen feature extraction\\n\",\n \"\\n\",\n \"A frozen feature extraction type of transfer learning can be split into 2 steps:\\n\",\n \"\\n\",\n \"1. you need to have pre-trained model(s) as source model(s). This can be done by accessing **XenonPy.MDL**.\\n\",\n \"2. you need a feature extractor to generate \\\"neural descriptors\\\" from the source model(s).\\n\",\n \"Here, we would like to introduce you to our feature extractor, ``xenonpy.descriptor.FrozenFeaturizer``.\\n\",\n \"\\n\",\n \"The following codes show a case study of transfer learning between **Refractive Index** of inorganic and organic materials. In this example, the source models will be trained on inorganic compounds and the target will be polymers.\\n\",\n \"\\n\",\n \"Let's do this transfer learning step-by-step.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### useful functions\\n\",\n \"\\n\",\n \"Running the following cell will load some commonly used packages, such as [NumPy](https://numpy.org/), [pandas](https://pandas.pydata.org/), and so on. It will also import some in-house functions used in this tutorial. See *'tools.ipynb'* file to check what will be imported.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%run tools.ipynb\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### access pre-trained models with MDL class\\n\",\n \"\\n\",\n \"We prepared a wide range of APIs to let you query and download our models.\\n\",\n \"These APIs can be accessed via any HTTP requests.\\n\",\n \"For convenience, we implemented some of the most popular APIs and wrapped them into XenonPy.\\n\",\n \"All these functions can be accessed using `xenonpy.mdl.MDL`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"MDL(api_key='anonymous.user.key', endpoint='http://xenon.ism.ac.jp/api')\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"'0.1.1'\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from xenonpy.mdl import MDL\\n\",\n \"\\n\",\n \"mdl = MDL()\\n\",\n \"mdl\\n\",\n \"\\n\",\n \"mdl.version\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 1. query **Refractive Index** models \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"QueryModelDetailsWith(api_key='anonymous.user.key', endpoint='http://xenon.ism.ac.jp/api', variables={'modelset_has': 'Stable', 'property_has': 'refractive'})\\n\",\n \"Queryable: \\n\",\n \" id\\n\",\n \" transferred\\n\",\n \" succeed\\n\",\n \" isRegression\\n\",\n \" deprecated\\n\",\n \" modelset\\n\",\n \" method\\n\",\n \" property\\n\",\n \" descriptor\\n\",\n \" lang\\n\",\n \" accuracy\\n\",\n \" precision\\n\",\n \" recall\\n\",\n \" f1\\n\",\n \" sensitivity\\n\",\n \" prevalence\\n\",\n \" specificity\\n\",\n \" ppv\\n\",\n \" npv\\n\",\n \" meanAbsError\\n\",\n \" maxAbsError\\n\",\n \" meanSquareError\\n\",\n \" rootMeanSquareError\\n\",\n \" r2\\n\",\n \" pValue\\n\",\n \" spearmanCorr\\n\",\n \" pearsonCorr\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"query = mdl(modelset_has=\\\"Stable\\\", property_has=\\\"refractive\\\")\\n\",\n \"query\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
idmodelsetmeanAbsErrorpearsonCorr
3112949Stable inorganic compounds in materials project0.2824340.873065
8474017Stable inorganic compounds in materials project0.2903820.827995
11894702Stable inorganic compounds in materials project0.2931350.876289
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" id modelset meanAbsError \\\\\\n\",\n \"311 2949 Stable inorganic compounds in materials project 0.282434 \\n\",\n \"847 4017 Stable inorganic compounds in materials project 0.290382 \\n\",\n \"1189 4702 Stable inorganic compounds in materials project 0.293135 \\n\",\n \"\\n\",\n \" pearsonCorr \\n\",\n \"311 0.873065 \\n\",\n \"847 0.827995 \\n\",\n \"1189 0.876289 \"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"summary = query('id', 'modelset', 'meanAbsError', 'pearsonCorr').sort_values('meanAbsError')\\n\",\n \"summary.head(3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 2. download the best performing model based on MAE\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"100%|██████████| 1/1 [00:00<00:00, 1.26it/s]\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
idmodel
02335/Users/liuchang/Google 云端硬盘/postdoctoral/tutor...
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" id model\\n\",\n \"0 2335 /Users/liuchang/Google 云端硬盘/postdoctoral/tutor...\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"results = mdl.pull(summary.id[0].item())\\n\",\n \"results\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 3. load **Refractive Index** data from Polymer Genome and calculate the ``Composition`` descriptors\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
SmilesNatomsNtypesVolume of Cell($\\\\AA^3$)Band Gap, PBE(eV)Band Gap, HSE06(eV)Dielectric ConstantDielectric Constant, ElectronicDielectric Constant, IonicAtomization Energy(eV/atom)Density(g/cm$^3$)Refractive IndexIonization Energy(eV)Electron Affinity(eV)Cohesive Energy(eV/atom)compositionFormula
ID_name
MOL1[C@H]([CH]O)(O[C@H]1[C@H](CO)O[C@@H]([CH][C@@H...843572.425.627.483.782.850.93-5.481.881.696.870.83-0.63{'O': 20.0, 'H': 40.0, 'C': 24.0}H40C24O20
MOL2[CH][C@H](C[C@@H](C[C@H](C[CH][C]=[CH])C(=[CH]...12821258.303.944.832.722.640.08-5.901.101.623.561.56-0.63{'C': 64.0, 'H': 64.0}H64C64
MOL3C[C@H](C[CH][CH][CH]C)[CH2].C[C@@H](C[CH][CH][...1082762.106.327.702.612.590.02-5.141.101.616.190.43-0.51{'C': 36.0, 'H': 72.0}H72C36
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\"\n ],\n \"text/plain\": [\n \" Smiles Natoms Ntypes \\\\\\n\",\n \"ID_name \\n\",\n \"MOL1 [C@H]([CH]O)(O[C@H]1[C@H](CO)O[C@@H]([CH][C@@H... 84 3 \\n\",\n \"MOL2 [CH][C@H](C[C@@H](C[C@H](C[CH][C]=[CH])C(=[CH]... 128 2 \\n\",\n \"MOL3 C[C@H](C[CH][CH][CH]C)[CH2].C[C@@H](C[CH][CH][... 108 2 \\n\",\n \"\\n\",\n \" Volume of Cell($\\\\AA^3$) Band Gap, PBE(eV) Band Gap, HSE06(eV) \\\\\\n\",\n \"ID_name \\n\",\n \"MOL1 572.42 5.62 7.48 \\n\",\n \"MOL2 1258.30 3.94 4.83 \\n\",\n \"MOL3 762.10 6.32 7.70 \\n\",\n \"\\n\",\n \" Dielectric Constant Dielectric Constant, Electronic \\\\\\n\",\n \"ID_name \\n\",\n \"MOL1 3.78 2.85 \\n\",\n \"MOL2 2.72 2.64 \\n\",\n \"MOL3 2.61 2.59 \\n\",\n \"\\n\",\n \" Dielectric Constant, Ionic Atomization Energy(eV/atom) \\\\\\n\",\n \"ID_name \\n\",\n \"MOL1 0.93 -5.48 \\n\",\n \"MOL2 0.08 -5.90 \\n\",\n \"MOL3 0.02 -5.14 \\n\",\n \"\\n\",\n \" Density(g/cm$^3$) Refractive Index Ionization Energy(eV) \\\\\\n\",\n \"ID_name \\n\",\n \"MOL1 1.88 1.69 6.87 \\n\",\n \"MOL2 1.10 1.62 3.56 \\n\",\n \"MOL3 1.10 1.61 6.19 \\n\",\n \"\\n\",\n \" Electron Affinity(eV) Cohesive Energy(eV/atom) \\\\\\n\",\n \"ID_name \\n\",\n \"MOL1 0.83 -0.63 \\n\",\n \"MOL2 1.56 -0.63 \\n\",\n \"MOL3 0.43 -0.51 \\n\",\n \"\\n\",\n \" composition Formula \\n\",\n \"ID_name \\n\",\n \"MOL1 {'O': 20.0, 'H': 40.0, 'C': 24.0} H40C24O20 \\n\",\n \"MOL2 {'C': 64.0, 'H': 64.0} H64C64 \\n\",\n \"MOL3 {'C': 36.0, 'H': 72.0} H72C36 \"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pg = \\n\",\n \"pg.head(3)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
ave:atomic_numberave:atomic_radiusave:atomic_radius_rahmave:atomic_volumeave:atomic_weightave:boiling_pointave:bulk_modulusave:c6_gbave:covalent_radius_corderoave:covalent_radius_pyykko...min:num_s_valencemin:periodmin:specific_heatmin:thermal_conductivitymin:vdw_radiusmin:vdw_radius_alvarezmin:vdw_radius_mm3min:vdw_radius_uffmin:sound_velocitymin:Polarizability
ID_name
MOL14.09523898.42891168.33333311.5619057.7210001488.2738154.59650520.76190551.33333351.666667...1.01.00.7110.02658110.0120.0162.0288.6317.50.666793
MOL23.50000085.00000172.0000009.7000006.5095002560.1400044.89982027.20500052.00000053.500000...1.01.00.7110.18050110.0120.0162.0288.61270.00.666793
MOL32.66666783.00000166.00000011.1666674.6756671713.5200048.86642620.30666745.00000046.333333...1.01.00.7110.18050110.0120.0162.0288.61270.00.666793
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3 rows × 290 columns

\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" ave:atomic_number ave:atomic_radius ave:atomic_radius_rahm \\\\\\n\",\n \"ID_name \\n\",\n \"MOL1 4.095238 98.42891 168.333333 \\n\",\n \"MOL2 3.500000 85.00000 172.000000 \\n\",\n \"MOL3 2.666667 83.00000 166.000000 \\n\",\n \"\\n\",\n \" ave:atomic_volume ave:atomic_weight ave:boiling_point \\\\\\n\",\n \"ID_name \\n\",\n \"MOL1 11.561905 7.721000 1488.27381 \\n\",\n \"MOL2 9.700000 6.509500 2560.14000 \\n\",\n \"MOL3 11.166667 4.675667 1713.52000 \\n\",\n \"\\n\",\n \" ave:bulk_modulus ave:c6_gb ave:covalent_radius_cordero \\\\\\n\",\n \"ID_name \\n\",\n \"MOL1 54.596505 20.761905 51.333333 \\n\",\n \"MOL2 44.899820 27.205000 52.000000 \\n\",\n \"MOL3 48.866426 20.306667 45.000000 \\n\",\n \"\\n\",\n \" ave:covalent_radius_pyykko ... min:num_s_valence min:period \\\\\\n\",\n \"ID_name ... \\n\",\n \"MOL1 51.666667 ... 1.0 1.0 \\n\",\n \"MOL2 53.500000 ... 1.0 1.0 \\n\",\n \"MOL3 46.333333 ... 1.0 1.0 \\n\",\n \"\\n\",\n \" min:specific_heat min:thermal_conductivity min:vdw_radius \\\\\\n\",\n \"ID_name \\n\",\n \"MOL1 0.711 0.02658 110.0 \\n\",\n \"MOL2 0.711 0.18050 110.0 \\n\",\n \"MOL3 0.711 0.18050 110.0 \\n\",\n \"\\n\",\n \" min:vdw_radius_alvarez min:vdw_radius_mm3 min:vdw_radius_uff \\\\\\n\",\n \"ID_name \\n\",\n \"MOL1 120.0 162.0 288.6 \\n\",\n \"MOL2 120.0 162.0 288.6 \\n\",\n \"MOL3 120.0 162.0 288.6 \\n\",\n \"\\n\",\n \" min:sound_velocity min:Polarizability \\n\",\n \"ID_name \\n\",\n \"MOL1 317.5 0.666793 \\n\",\n \"MOL2 1270.0 0.666793 \\n\",\n \"MOL3 1270.0 0.666793 \\n\",\n \"\\n\",\n \"[3 rows x 290 columns]\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from xenonpy.descriptor import Compositions\\n\",\n \"\\n\",\n \"pg_desc = Compositions().transform(pg['composition'])\\n\",\n \"pg_desc.head(3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 4. predict Polymer Genome **Refractive Index** directly from a model trained on inorganic compounds\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \" includes:\\n\",\n \"\\\"mse_3\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/mse_3.pth.s\\n\",\n \"\\\"mse_1\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/mse_1.pth.s\\n\",\n \"\\\"mae_2\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/mae_2.pth.s\\n\",\n \"\\\"r2_5\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/r2_5.pth.s\\n\",\n \"\\\"mse_5\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/mse_5.pth.s\\n\",\n \"\\\"r2_1\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/r2_1.pth.s\\n\",\n \"\\\"mae_4\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/mae_4.pth.s\\n\",\n \"\\\"r2_3\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/r2_3.pth.s\\n\",\n \"\\\"mae_3\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/mae_3.pth.s\\n\",\n \"\\\"r2_4\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/r2_4.pth.s\\n\",\n \"\\\"mse_2\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/mse_2.pth.s\\n\",\n \"\\\"mae_1\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/mae_1.pth.s\\n\",\n \"\\\"mae_5\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/mae_5.pth.s\\n\",\n \"\\\"r2_2\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/r2_2.pth.s\\n\",\n \"\\\"mse_4\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/mse_4.pth.s\\n\",\n \"\\\"pearsonr_5\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/pearsonr_5.pth.s\\n\",\n \"\\\"pearsonr_1\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/pearsonr_1.pth.s\\n\",\n \"\\\"pearsonr_3\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/pearsonr_3.pth.s\\n\",\n \"\\\"pearsonr_4\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/pearsonr_4.pth.s\\n\",\n \"\\\"pearsonr_2\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/pearsonr_2.pth.s\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from xenonpy.model.training import Checker\\n\",\n \"\\n\",\n \"checker = Checker(results.model[0])\\n\",\n \"checker.checkpoints\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Trainer(clip_grad=None, cuda=None, epochs=200, loss_func=None,\\n\",\n \" lr_scheduler=None,\\n\",\n \" model=SequentialLinear(\\n\",\n \" (layer_0): LinearLayer(\\n\",\n \" (linear): Linear(in_features=290, out_features=187, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(187, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_1): LinearLayer(\\n\",\n \" (linear): Linear(...\\n\",\n \" (normalizer): BatchNorm1d(110, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_3): LinearLayer(\\n\",\n \" (linear): Linear(in_features=110, out_features=102, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(102, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (output): Linear(in_features=102, out_features=1, bias=True)\\n\",\n \"),\\n\",\n \" non_blocking=False, optimizer=None)\"\n ]\n },\n \"execution_count\": 35,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# --- pre-trained model for prediction\\n\",\n \"from xenonpy.model.training import Trainer\\n\",\n \"\\n\",\n \"trainer = Trainer.load(from_=checker)\\n\",\n \"trainer\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Missing directory and/or file name information!\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"trainer.reset(to='mae_1')\\n\",\n \"\\n\",\n \"y_pred = trainer.predict(x_in=torch.tensor(pg_desc.values, dtype=torch.float)).detach().numpy().flatten()\\n\",\n \"y_true = pg['Refractive Index'].values\\n\",\n \"\\n\",\n \"draw(y_true, y_pred, prop_name='Refractive Index')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 5. frozen feature extraction\\n\",\n \"\\n\",\n \"``FrozenFeaturizer`` accepts a [Pytorch](https://pytorch.org) model as its input.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"FrozenFeaturizer(cuda=False, depth=None,\\n\",\n \" model=SequentialLinear(\\n\",\n \" (layer_0): LinearLayer(\\n\",\n \" (linear): Linear(in_features=290, out_features=187, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(187, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_1): LinearLayer(\\n\",\n \" (linear): Linear(in_features=187, out_features=151, bias=...\\n\",\n \" (normalizer): BatchNorm1d(110, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_3): LinearLayer(\\n\",\n \" (linear): Linear(in_features=110, out_features=102, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(102, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (output): Linear(in_features=102, out_features=1, bias=True)\\n\",\n \"),\\n\",\n \" on_errors='raise', return_type='any')\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from xenonpy.descriptor import FrozenFeaturizer\\n\",\n \"\\n\",\n \"# --- init FrozenFeaturizer with NN model\\n\",\n \"ff = FrozenFeaturizer(model=trainer.model)\\n\",\n \"ff\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The following code will generate new \\\"neural descriptors\\\" from the corresponding neural network model.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"neural_descriptors = ff.transform(pg_desc, depth=2 ,return_type='df')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here, ``depth=x`` means that the last x hidden layer from the output neuron(s) will be concatenated and used as the neural descriptor.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
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MOL1-1.4994971.254353-1.071701-2.697761-1.807236-2.01011-1.944547-1.533609-1.380140-0.416400...-0.146019-0.599903-0.1688260.1351780.6077790.700811-1.405137-0.209901-0.2971320.071797
MOL2-1.2951681.335794-1.657156-3.456876-1.372355-2.39925-1.761726-1.736894-1.464569-1.349594...0.026329-0.161609-0.5422760.2460881.0470520.067110-1.963629-0.109732-0.3286460.103320
MOL3-1.5146081.205772-1.270560-2.831598-1.677628-2.02982-1.933016-1.568646-1.363735-0.656209...-0.118927-0.526747-0.1727020.1540830.6568890.607651-1.418730-0.193771-0.2783430.091769
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3 rows × 212 columns

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\"\n ],\n \"text/plain\": [\n \" L(-2)_1 L(-2)_2 L(-2)_3 L(-2)_4 L(-2)_5 L(-2)_6 L(-2)_7 \\\\\\n\",\n \"ID_name \\n\",\n \"MOL1 -1.499497 1.254353 -1.071701 -2.697761 -1.807236 -2.01011 -1.944547 \\n\",\n \"MOL2 -1.295168 1.335794 -1.657156 -3.456876 -1.372355 -2.39925 -1.761726 \\n\",\n \"MOL3 -1.514608 1.205772 -1.270560 -2.831598 -1.677628 -2.02982 -1.933016 \\n\",\n \"\\n\",\n \" L(-2)_8 L(-2)_9 L(-2)_10 ... L(-1)_93 L(-1)_94 L(-1)_95 \\\\\\n\",\n \"ID_name ... \\n\",\n \"MOL1 -1.533609 -1.380140 -0.416400 ... -0.146019 -0.599903 -0.168826 \\n\",\n \"MOL2 -1.736894 -1.464569 -1.349594 ... 0.026329 -0.161609 -0.542276 \\n\",\n \"MOL3 -1.568646 -1.363735 -0.656209 ... -0.118927 -0.526747 -0.172702 \\n\",\n \"\\n\",\n \" L(-1)_96 L(-1)_97 L(-1)_98 L(-1)_99 L(-1)_100 L(-1)_101 \\\\\\n\",\n \"ID_name \\n\",\n \"MOL1 0.135178 0.607779 0.700811 -1.405137 -0.209901 -0.297132 \\n\",\n \"MOL2 0.246088 1.047052 0.067110 -1.963629 -0.109732 -0.328646 \\n\",\n \"MOL3 0.154083 0.656889 0.607651 -1.418730 -0.193771 -0.278343 \\n\",\n \"\\n\",\n \" L(-1)_102 \\n\",\n \"ID_name \\n\",\n \"MOL1 0.071797 \\n\",\n \"MOL2 0.103320 \\n\",\n \"MOL3 0.091769 \\n\",\n \"\\n\",\n \"[3 rows x 212 columns]\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"neural_descriptors.head(3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As an example, **-1** in the column names denotes the last layer.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"DescriptorHeatmap(bc=True, col_cluster=True, col_colors=None, col_linkage=None,\\n\",\n \" figsize=(70, 10), mask=None, method='average',\\n\",\n \" metric='euclidean', pivot_kws=None, row_cluster=False,\\n\",\n \" row_colors=None, row_linkage=None, save=None)\"\n ]\n },\n \"execution_count\": 41,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from xenonpy.visualization import DescriptorHeatmap\\n\",\n \"\\n\",\n \"sorted_prop = pg['Refractive Index'].sort_values()\\n\",\n \"sorted_desc = neural_descriptors.loc[sorted_prop.index]\\n\",\n \"\\n\",\n \"heatmap = DescriptorHeatmap( \\n\",\n \" bc=True, # use box-cox transform \\n\",\n \"# save=dict(fname='heatmap_density', dpi=150, bbox_inches='tight'), # save figure to file\\n\",\n \" figsize=(70, 10))\\n\",\n \"heatmap.fit(sorted_desc)\\n\",\n \"heatmap.draw(sorted_prop)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 6. use neural descriptors to train new models.\\n\",\n \"\\n\",\n \"In this case, **Random Forest** model and **Bayesian Ridge Linear** model will be trained.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# split data\\n\",\n \"from xenonpy.datatools import Splitter\\n\",\n \"\\n\",\n \"y = pg['Refractive Index']\\n\",\n \"splitter = Splitter(len(y), test_size=0.2)\\n\",\n \"\\n\",\n \"X_train, X_test, y_train, y_test = splitter.split(neural_descriptors, y.values.reshape(-1, 1))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\\n\",\n \" max_features='auto', max_leaf_nodes=None,\\n\",\n \" min_impurity_decrease=0.0, min_impurity_split=None,\\n\",\n \" min_samples_leaf=1, min_samples_split=2,\\n\",\n \" min_weight_fraction_leaf=0.0, n_estimators=100,\\n\",\n \" n_jobs=None, oob_score=False, random_state=None,\\n\",\n \" verbose=0, warm_start=False)\"\n ]\n },\n \"execution_count\": 43,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# random forest\\n\",\n \"from sklearn.ensemble import RandomForestRegressor\\n\",\n \"\\n\",\n \"rf = RandomForestRegressor(n_estimators=100)\\n\",\n \"rf.fit(X_train, y_train.ravel())\\n\",\n \"y_pred = rf.predict(X_test)\\n\",\n \"y_fit_pred = rf.predict(X_train)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Missing directory and/or file name information!\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"draw(y_test.ravel(), y_pred, y_train.ravel(), y_fit_pred, prop_name='refractive index')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, compute_score=False, copy_X=True,\\n\",\n \" fit_intercept=True, lambda_1=1e-06, lambda_2=1e-06, n_iter=300,\\n\",\n \" normalize=False, tol=0.001, verbose=False)\"\n ]\n },\n \"execution_count\": 45,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# bayesian linear\\n\",\n \"from sklearn.linear_model import BayesianRidge\\n\",\n \"\\n\",\n \"br = BayesianRidge()\\n\",\n \"br.fit(X_train, y_train.ravel())\\n\",\n \"y_pred = br.predict(X_test)\\n\",\n \"y_fit_pred = br.predict(X_train)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Missing directory and/or file name information!\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"draw(y_test.ravel(), y_pred, y_train.ravel(), y_fit_pred, prop_name='refractive index')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n },\n \"pycharm\": {\n \"stem_cell\": {\n \"cell_type\": \"raw\",\n \"source\": [],\n \"metadata\": {\n \"collapsed\": false\n }\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}" }, { "path": "docs/source/tutorials/7-inverse-design.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# XenonPy-iQSPR tutorial\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This tutorial provides step by step guidance to all the essential components for running iQSPR, an inverse molecular design algorithm based on machine learning. We provide a set of in-house data and pre-trained models for demonstration purpose. We reserve all rights for using these resources outside of this tutorial. We recommend readers to have prior knowledge about python programming, use of numpy and pandas packages, building models with scikit-learn, and understanding on the fundamental functions of XenonPy (refer to the tutorial on the descriptor calculation and model building with XenonPy). For any question, please contact the developer of XenonPy.\\n\",\n \"\\n\",\n \"Overview - We are interested in finding molecular structures 𝑆 such that their properties 𝑌 have a high probability of falling into a target region 𝑈, i.e., we want to sample from the posterior probability 𝑝(𝑆|𝑌∈𝑈) that is proportional to 𝑝(𝑌∈𝑈|𝑆)𝑝(𝑆) by the Bayes’ theorem. 𝑝(𝑌∈𝑈|𝑆) is the likelihood function that can be derived from any machine learning models predicting 𝑌 for a given 𝑆. 𝑝(𝑆) is the prior that represents all possible candidates of S to be considered. iQSPR is a numerical implementation for this Bayesian formulation based on sequential Monte Carlo sampling, which requires a likelihood model and a prior model, to begin with.\\n\",\n \"\\n\",\n \"This tutorial will proceed as follow:\\n\",\n \"1. initial setup and data preparation\\n\",\n \"2. descriptor preparation for forward model (likelihood)\\n\",\n \"3. forward model (likelihood) preparation\\n\",\n \"4. `NGram` (prior) preparation\\n\",\n \"5. a complete iQSPR run\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'0.6.2'\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import xenonpy\\n\",\n \"xenonpy.__version__\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Useful functions\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, we import some basic libraries and functions that are not directly related to the use of iQSPR. You may look into the \\\"tools.ipynb\\\" for the content. We also pre-set a numpy random seed for reproducibility. The same seed number is used throughout this tutorial.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%run tools.ipynb\\n\",\n \"\\n\",\n \"np.random.seed(201906)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Data preparation (in-house data)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here is a data set that contains 16674 SMILES randomly selected from pubchem with 2 material properties, the internal energy E (kJ/mol) and the HOMO-LUMO gap (eV). The property values are obtained from single-point calculations in DFT (density functional theory) simulations, with compounds geometry optimized at the B3LYP/6-31G(d) level of theory using GAMESS. This data set is prepared by our previous developers of iQSPR. XenonPy supports pandas dataframe as the main input source. When there are mismatch in the number of data points available in each material property, i.e., there exists missing values, please simply fill in the missing values with NaN, and XenonPy will automatically handle them during model training.\\n\",\n \"\\n\",\n \"You can download the in-house data at https://github.com/yoshida-lab/XenonPy/releases/download/v0.4.1/iQSPR_sample_data.csv\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Index(['SMILES', 'E', 'HOMO-LUMO gap'], dtype='object')\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
SMILESEHOMO-LUMO gap
1CC(=O)OC(CC(=O)[O-])C[N+](C)(C)C177.5774.352512
2CC(=O)OC(CC(=O)O)C[N+](C)(C)C185.7357.513497
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\"\n ],\n \"text/plain\": [\n \" SMILES E HOMO-LUMO gap\\n\",\n \"1 CC(=O)OC(CC(=O)[O-])C[N+](C)(C)C 177.577 4.352512\\n\",\n \"2 CC(=O)OC(CC(=O)O)C[N+](C)(C)C 185.735 7.513497\\n\",\n \"3 C1=CC(C(C(=C1)C(=O)O)O)O 98.605 4.581348\\n\",\n \"4 CC(CN)O 83.445 8.034841\\n\",\n \"5 C(C(=O)COP(=O)(O)O)N 90.877 5.741310\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# load in-house data\\n\",\n \"data = pd.read_csv(\\\"./iQSPR_sample_data.csv\\\", index_col=0)\\n\",\n \"\\n\",\n \"# take a look at the data\\n\",\n \"data.columns\\n\",\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let us visualize some statistics of the data set. We can see that the mean character length of the SMILES is around 30. This roughly corresponds to around 20 tokens in iQSPR due to the way we combine some characters into a single token to be handled during the molecule modification step. Furthermore, the data shows a clear Pareto front at the high HOMO-LUMO gap region and the lack of molecules for HOMO-LUMO gap below 3. In this tutorial, we will try to populate the region of HOMO-LUMO gap less than 3 and internal energy less than 200.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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Rt2hQrVqwwVclERPQMtf7Lfffu6cu1majVWiEtLdOEFVWd2tpbbe0LYG81VU3uzcxMgcaNS//5AF7lloiIZGNoEBGRbAwNIiKSjaFBRESy8dLotYzVS/VgaVH606rVWpV6X25eITIzckxRFhHVEgyNWsbSQgXPGboKzbtnuTdq5vkeRPSicPcUERHJxtAgIiLZGBpERCQbQ4OIiGRjaBARkWwMDSIiko2hQUREsjE0iIhINoYGERHJxtAgIiLZGBpERCQbQ4OIiGRjaBARkWwMDSIiko2hQUREsjE0iIhINoYGERHJxtAgIiLZGBpERCQbQ4OIiGRjaBARkWwqUy583bp12LdvHwDA2dkZs2bNwpw5cxATE4N69eoBAKZMmYJ+/frh8uXL8PPzQ1ZWFrp27YoFCxZApVLh1q1bmDlzJu7du4eWLVsiKCgIDRo0MGXZRERUCpNtaZw8eRInTpzArl27EB4ejkuXLuHAgQOIjY3Fli1boNPpoNPp0K9fPwDAzJkzMX/+fOzfvx9CCOzYsQMAsGDBAowYMQKRkZFwdHREcHCwqUomIqJnMFloaLVa+Pr6wtzcHGq1GnZ2drh16xZu3bqFuXPnwtPTE2vWrIHRaERycjJyc3PRqVMnAICPjw8iIyNRUFCA6OhouLq6FhknIqKqYbLdU23atJH+jo+Px759+7B161ZERUUhICAAVlZWmDhxIkJCQtCmTRtotVppeq1Wi9TUVDx48AAajQYqlarIeHk0bqwpd+1arVW556ktamrvNbVuOdhbzVRbezPpMQ0AuH79OiZOnIhZs2ahVatW+O6776T7Ro8ejfDwcNjZ2UGhUEjjQggoFArpv096+vaz3Lunh9EoZE+v1VohLS2zXOuoTp73hVoTe6/pz1lZ2FvNVJN7MzNTlPlh26RnT8XExODDDz/EjBkzMHDgQFy9ehX79++X7hdCQKVSoUmTJkhLS5PG7969CxsbG1hbWyMzMxMGgwEAkJaWBhsbG1OWTEREZTBZaKSkpGDy5MkICgqCu7s7gEchsXjxYqSnp6OgoAA///wz+vXrB1tbW1hYWCAmJgYAoNPp4OTkBLVaja5duyIiIgIAEB4eDicnJ1OVTEREz2Cy3VObNm1CXl4eli5dKo0NGzYMEyZMwPDhw1FYWAgXFxd4eHgAAIKCguDv7w+9Xg8HBweMGTMGABAQEABfX1+sX78eTZs2xYoVK0xVMhERPYNCCCF/h38NVBePaXjO0FVo3j3LvWtk7zX9OSsLe6uZanJvVXpMg4iIaheGBhERyWbyU26p7rB6qR4sLSr2ksrNK0RmRk4lV0RElY2hQZXG0kL1XMdTauYeYKK6hbuniIhINoYGERHJxt1TJMkvMNTa6+UQUeVgaJDEXK2s8DEJ4NFxCSKq3bh7ioiIZGNoEBGRbAwNIiKSjaFBRESyMTSIiEg2hgYREcnG0CAiItkYGkREJBtDg4iIZGNoEBGRbAwNIiKSjaFBRESyMTSIiEg2hgYREcnG0CAiItkYGkREJBtDg4iIZGNoEBGRbAwNIiKSzaShsW7dOri7u8Pd3R2BgYEAgJMnT8LT0xMuLi5YuXKlNO3ly5fh4+MDV1dX+Pn5obCwEABw69YtjBw5Em5ubvj000+RlZVlypKJiKgMJguNkydP4sSJE9i1axfCw8Nx6dIl7N27F3PnzkVwcDAiIiIQGxuLo0ePAgBmzpyJ+fPnY//+/RBCYMeOHQCABQsWYMSIEYiMjISjoyOCg4NNVTIRET2DyUJDq9XC19cX5ubmUKvVsLOzQ3x8PFq0aIHmzZtDpVLB09MTkZGRSE5ORm5uLjp16gQA8PHxQWRkJAoKChAdHQ1XV9ci40REVDVMFhpt2rSRQiA+Ph779u2DQqGAVquVprGxsUFqairu3LlTZFyr1SI1NRUPHjyARqOBSqUqMk5ERFVDZeoVXL9+HRMnTsSsWbOgVCoRHx8v3SeEgEKhgNFohEKhKDb++L9Pevr2szRurCl3zVqtVbnnoef3PI97bX7O2FvNVFt7M2loxMTE4LPPPsPcuXPh7u6OqKgopKWlSfenpaXBxsYGTZo0KTJ+9+5d2NjYwNraGpmZmTAYDFAqldL05XHvnh5Go5A9vVZrhbS0zHKtozqpyS/Uij7uNf05Kwt7q5lqcm9mZooyP2ybbPdUSkoKJk+ejKCgILi7uwMAOnbsiLi4OCQkJMBgMGDv3r1wcnKCra0tLCwsEBMTAwDQ6XRwcnKCWq1G165dERERAQAIDw+Hk5OTqUomIqJnMNmWxqZNm5CXl4elS5dKY8OGDcPSpUsxdepU5OXlwdnZGW5ubgCAoKAg+Pv7Q6/Xw8HBAWPGjAEABAQEwNfXF+vXr0fTpk2xYsUKU5VcbVi9VA+WFibfc0hEVG4me2fy9/eHv79/ifft3r272Ji9vT1CQkKKjdva2mLz5s2VXl91ZmmhgucMXYXm3bPcu5KrISL6D34jnIiIZGNoEBGRbAwNIiKSjaFBRESyMTSIiEg2hgYREcnG0CAiItkYGkREJBtDg4iIZGNoEBGRbAwNIiKSjaFBRESyMTSIiEg2hgYREckmOzQSExMBAEeOHMF3332HzMya+atURERUcbJCY/78+di4cSP++usv+Pv7IykpCXPnzjV1bUREVM3ICo3Y2Fh89dVXOHDgAAYOHIglS5YgOTnZ1LUREVE1Iys0hBAwMzPD77//jh49egAAcnNzTVoYERFVP7JC49VXX8X48eORlJSEbt26YcaMGWjXrp2payMiompG1m+EL1myBAcOHMAbb7wBtVqNrl27YuDAgaaujYiIqhlZWxqLFi2Ct7c3mjVrBgAYPnw4Zs2aZdLCiIio+ilzSyMgIACpqamIiYnB/fv3pfHCwkLpFFwiIqo7ygyNQYMG4fr167h69SpcXV2lcaVSiU6dOpm6NiIiqmbKDI3XX38dr7/+Onr16oUmTZq8qJqoDsovMECrtarQvHnPMW9uXiEyM3IqNC9RXSTrQHhKSgpmzpyJ9PR0CCGk8T179pisMKpbzNVKeM7QVWjePcu9n2teXtuASD5ZoTF//nz4+Pjgtddeg0KhMHVNRERUTckKDZVKhY8++sjUtRARUTUn65TbNm3a4OrVq+VeuF6vh4eHB5KSkgAAc+bMgYuLC7y9veHt7Y0DBw4AAC5fvgwfHx+4urrCz88PhYWFAIBbt25h5MiRcHNzw6effoqsrKxy10BERJVHVmgkJibigw8+gIuLCzw9PaV/ZTl//jyGDx+O+Ph4aSw2NhZbtmyBTqeDTqdDv379AAAzZ87E/PnzsX//fgghsGPHDgDAggULMGLECERGRsLR0RHBwcEVbJOIiCqDrN1T06dPL/eCd+zYgYCAAOlLgDk5Obh16xbmzp2L1NRU9OvXD1OmTEFKSgpyc3OlU3h9fHywZs0aDB48GNHR0fjuu++k8VGjRmHmzJnlroWIiCqHrNBo27ZtuRf8zTffFLl99+5d9OjRAwEBAbCyssLEiRMREhKCNm3aQKvVStNptVqkpqbiwYMH0Gg0UKlURcZrCquX6sHSQtbDS0RUY8h6V+vRowcUCgWEENLZU1qtFseOHZO9oubNm0tbDQAwevRohIeHw87OrsgZWY/X8eS6HqvImVuNG2vKPU9Fz/l/2vOcBkovTmU936ZS3et7Huyt5pEVGleuXJH+zs/Px969exEXF1euFV29ehXx8fHSN8uFEFCpVGjSpAnS0tKk6e7evQsbGxtYW1sjMzMTBoMBSqUSaWlpsLGxKdc6AeDePT2MRvHsCf9Nq7VCWtrzn7lfW18wtVFlPN+mUlmvx+qIvVVPZmaKMj9sl/s3ws3NzeHj44Pff/+9XPMJIbB48WKkp6ejoKAAP//8M/r16wdbW1tYWFggJiYGAKDT6eDk5CRdTTciIgIAEB4eDicnp/KWS0RElUjWlsbDhw+lv4UQiI2NRUZGRrlWZG9vjwkTJmD48OEoLCyEi4sLPDw8AABBQUHw9/eHXq+Hg4MDxowZA+DRBRN9fX2xfv16NG3aFCtWrCjXOomIqHKV+5gGADRu3Bh+fn6yVnD48GHp75EjR2LkyJHFprG3t0dISEixcVtbW2zevFnWeoiIyPTKfUyDiIjqLlmhYTQasWnTJhw7dgyFhYXo3bs3Jk2aJJ0OS0REdYOsA+HLly/H6dOnMXbsWHz00Uf4888/ERgYaOraiIiompG1qXD8+HGEhoZCrVYDAN5++214eXlh7ty5Ji2OiIiqF1lbGkIIKTCAR6fdPnmbiIjqBlmhYW9vj8WLF+PmzZtITEzE4sWLK3RpESIiqtlkhUZAQAAyMjIwbNgwDB48GA8ePMC8efNMXRsREVUzZYZGfn4+Zs+ejVOnTmHp0qU4efIkOnToAKVSCY2m/Nd0IiKimq3M0FizZg30ej26dOkijS1cuBAZGRlYu3atyYsjIqLqpczQOHLkCJYvX47GjRtLY6+88goCAwNx8OBBkxdHRETVS5mhoVarYWlpWWxco9HA3NzcZEUREVH1VGZomJmZQa/XFxvX6/XS73gTEVHdUWZoeHh4wN/fH9nZ2dJYdnY2/P394eLiYvLiiIioeikzNMaOHQsrKyv07t0bQ4YMwaBBg9C7d2+89NJLmDx58ouqkYiIqokyLyNiZmaGhQsXYtKkSbh06RLMzMzQoUOHCv2CHhER1Xyyrj1la2sLW1tbU9dCRETVXLl/7pWIiOouhgYREcnG0CAiItkYGkREJBtDg4iIZGNoEBGRbAwNIiKSjaFBRESyMTSIiEg2hgYREcnG0CAiItlMGhp6vR4eHh5ISkoCAJw8eRKenp5wcXHBypUrpekuX74MHx8fuLq6ws/PT/qtjlu3bmHkyJFwc3PDp59+iqysLFOWS0REz2Cy0Dh//jyGDx+O+Ph4AEBubi7mzp2L4OBgREREIDY2FkePHgUAzJw5E/Pnz8f+/fshhMCOHTsAAAsWLMCIESMQGRkJR0dHBAcHm6pcIiKSwWShsWPHDgQEBEiXUb9w4QJatGiB5s2bQ6VSwdPTE5GRkUhOTkZubi46deoEAPDx8UFkZCQKCgoQHR0NV1fXIuNERFR1ZF0avSK++eabIrfv3LkDrVYr3baxsUFqamqxca1Wi9TUVDx48AAajQYqlarIOBERVR2ThcbTjEYjFAqFdFsIAYVCUer44/8+6enbcjRurCn3PFqtVbnnoZqruj/f1b2+58Heap4XFhpNmjRBWlqadDstLQ02NjbFxu/evQsbGxtYW1sjMzMTBoMBSqVSmr687t3Tw2gUsqfXaq2QlpZZ7vWUtByqGSrj+TaVyno9VkfsrXoyM1OU+WH7hZ1y27FjR8TFxSEhIQEGgwF79+6Fk5MTbG1tYWFhgZiYGACATqeDk5MT1Go1unbtioiICABAeHg4nJycXlS5RERUghe2pWFhYYGlS5di6tSpyMvLg7OzM9zc3AAAQUFB8Pf3h16vh4ODA8aMGQMACAgIgK+vL9avX4+mTZtixYoVL6pcqiPyCwwV3irMzStEZkZOJVdEVL2ZPDQOHz4s/d2zZ0/s3r272DT29vYICQkpNm5ra4vNmzebtD6q28zVSnjO0FVo3j3LvVEzd0AQVRy/EU5ERLIxNIiISDaGBhERycbQICIi2RgaREQkG0ODiIhkY2gQEZFsDA0iIpKNoUFERLIxNIiISDaGBhERycbQICIi2RgaREQkG0ODiIhkY2gQEZFsDA0iIpKNoUFERLIxNIiISDaGBhERycbQICIi2VRVXQBRTZVfYIBWa1Xh+XPzCpGZkVOJFRGZHkODqILM1Up4ztBVeP49y72RWYn1EL0I3D1FRESyMTSIiEg2hgYREcnG0CAiItkYGkREJBtDg4iIZKuSU25Hjx6N+/fvQ6V6tPqvv/4aWVlZWLJkCfLy8tC/f39Mnz4dAHD58mX4+fkhKysLXbt2xYIFC6T5iIjoxXrh775CCMTHx+O3336T3vxzc3Ph5uaGzZs3o2nTppg4cSKOHj0KZ2dnzJw5E4sWLUKnTp0wd+5c7NixAyNGjHjRZRMREapg99Tff/8NABg3bhy8vLywZcsWXLhwAS1atEDz5s2hUqng6emJyMhIJCcnIzc3F506dQIA+Pj4IDIy8kWXTERE//bCtzQyMjLQs2dPzJs3DwUFBRgzZgw++eQTaLVaaRobGxukpqbizp07Rca1Wi1SU1PLtb7GjTXlrvF5Lg1BVB5yXmu1+fXI3mqeFx4anTt3RufOnaXbgwYNwpo1a/DGG29IY0IIKBQKGI1GKBSKYuPlce+eHkajkD29VmuFtLTnv7hDbX3BUOV61mutsl6P1RF7q57MzBRlfth+4bunzp49i1OnTkm3hRCwtbVFWlqaNJaWlgYbGxs0adKkyPjdu3dhY2PzQuslIqL/eOGhkZmZicDAQOTl5UGv12PXrl344osvEBcXh4SEBBgMBuzduxdOTk6wtbWFhYUFYmJiAAA6nQ5OTk4vumQiIvq3F757qm/fvjh//jwGDBgAo9GIESNGoHPnzli6dCmmTp2KvLw8ODs7w83NDQAQFBQEf39/6PV6ODg4YMyYMS+6ZCIi+rcq+cLDtGnTMG3atCJjPXv2xO7du4tNa29vj5CQkBdUGRERlYXfCCciItkYGkREJBtDg4iIZGNoEBGRbAwNIiKSjaFBRESyMTSIiEg2hgYREcnGXzMiqiL5BYYKX+U2N68QmRk5piiLqEwMDaIqYq5WwnOGrkLz7lnujZp5DVWq6bh7ioiIZGNoEBGRbAwNIiKSjaFBRESyMTSIiEg2nj1VCquX6sHSgg8PEdGT+K5YCksLVYVPhwQenRJJRFTbcPcUERHJxtAgIiLZGBpERCQbQ4OIiGRjaBARkWw8e4qoBpJ7hdyS8Aq59DwYGkQ1EK+QS1WFu6eIiEg2bmkQ1THctUXPg6FBVMdw1xY9jxqxe2rPnj14//334eLigq1bt1Z1OUREdVa139JITU3FypUrERYWBnNzcwwbNgzdu3dH69atq7o0ojrneXZt5eUbYGGuLDJWnmVx11j1UO1D4+TJk+jRowcaNmwIAHB1dUVkZCSmTJkia34zM0W51/l4HptG9co975OeZ/6aOG9VrrsmzluV667ovOZqJT5e9GuF5t3k71LheQFg/ex3Kx5YeYXQ63MrvO6KqMh7T3XwrLoVQgjxgmqpkA0bNiA7OxvTp08HAOzcuRMXLlzAwoULq7gyIqK6p9of0zAajVAo/pN8Qogit4mI6MWp9qHRpEkTpKWlSbfT0tJgY2NThRUREdVd1T40evXqhVOnTuH+/fvIycnBr7/+Cicnp6oui4ioTqr2B8JfeeUVTJ8+HWPGjEFBQQEGDRqEDh06VHVZRER1UrU/EE5ERNVHtd89RURE1QdDg4iIZGNoEBGRbAwNIiKSjaHxb7Xtoojr1q2Du7s73N3dERgYCODRJVk8PT3h4uKClStXVnGFz2fZsmXw9fUFUHv6Onz4MHx8fNC/f38sWrQIQO3pTafTSa/HZcuWAaj5ven1enh4eCApKQlA6f1cvnwZPj4+cHV1hZ+fHwoLC6uq5MohSNy+fVv07dtXPHjwQGRlZQlPT09x/fr1qi6rwn7//XcxdOhQkZeXJ/Lz88WYMWPEnj17hLOzs7h586YoKCgQ48aNE0eOHKnqUivk5MmTonv37mL27NkiJyenVvR18+ZN0adPH5GSkiLy8/PF8OHDxZEjR2pFb9nZ2eLNN98U9+7dEwUFBWLQoEHi0KFDNbq3c+fOCQ8PD+Hg4CASExPLfB26u7uLP//8UwghxJw5c8TWrVursPLnxy0NFL0oYv369aWLItZUWq0Wvr6+MDc3h1qthp2dHeLj49GiRQs0b94cKpUKnp6eNbLHhw8fYuXKlZg0aRIA4MKFC7WirwMHDuD9999HkyZNoFarsXLlStSrV69W9GYwGGA0GpGTk4PCwkIUFhZCo9HU6N527NiBgIAA6eoUpb0Ok5OTkZubi06dOgEAfHx8alSfJan2X+57Ee7cuQOtVivdtrGxwYULF6qwoufTpk0b6e/4+Hjs27cPo0aNKtZjampqVZT3XObPn4/p06cjJSUFQMnPXU3sKyEhAWq1GpMmTUJKSgrefvtttGnTplb0ptFo8Pnnn6N///6oV68e3nzzzRr/vH3zzTdFbpfWz9PjWq22RvVZEm5poPZeFPH69esYN24cZs2ahebNm9f4Hnfu3ImmTZuiZ8+e0lhtee4MBgNOnTqFxYsX4+eff8aFCxeQmJhYK3q7cuUKQkND8dtvv+H48eMwMzNDfHx8rejtsdJeh7Xl9fkkbmng0UURz549K92uDRdFjImJwWeffYa5c+fC3d0dUVFRNf7CjxEREUhLS4O3tzfS09ORnZ2N5ORkKJX/+WGfmtgXAPzXf/0XevbsCWtrawDAe++9h8jIyFrR24kTJ9CzZ080btwYwKNdNJs2baoVvT1W2oVVnx6/e/duje4T4JYGgNp3UcSUlBRMnjwZQUFBcHd3BwB07NgRcXFxSEhIgMFgwN69e2tcj//85z+xd+9e6HQ6fPbZZ3jnnXfwww8/1Pi+AKBv3744ceIEMjIyYDAYcPz4cbi5udWK3uzt7XHy5ElkZ2dDCIHDhw/Xitfjk0rrx9bWFhYWFoiJiQHw6CyymtwnwC0NALXvooibNm1CXl4eli5dKo0NGzYMS5cuxdSpU5GXlwdnZ2e4ublVYZWVw8LColb01bFjR3zyyScYMWIECgoK0Lt3bwwfPhytWrWq8b316dMH//rXv+Dj4wO1Wo3XX38dU6dORe/evWt8b4+V9ToMCgqCv78/9Ho9HBwcMGbMmCqu9vnwgoVERCQbd08REZFsDA0iIpKNoUFERLIxNIiISDaGBhERycbQqKPOnTuH0aNHw9PTEx4eHvjkk09w/fp1AEBSUhLatWuHUaNGFZvP19cX7dq1w/379wEA77zzDi5evFjs7yclJSWhffv28Pb2LvYvPz8fAHDkyBEMHToUXl5ecHd3x+eff47bt2+XWPuT669MmZmZRU6HLM96jh49+sKv1Lpu3TocPHgQALB69WqEh4ebdH1hYWGYOHGiSddRksTEREydOrXSlpeSkoIpU6bAaDRW2jLrEn5Pow7Kz8/HxIkT8eOPP8LBwQHAoy8djR8/HocOHQLw6LzzuLg4JCcnw9bWFgCQnZ2NP/74o0LrtLS0hE6nK/G+1NRUzJ49G2FhYdK61q9fj2nTpuH//u//KrS+ikhPTy8x9J5Fr9cjKCgIO3bsMEFVpTtz5gxat24NAPj8889f6LpfpFu3biEuLq7Slte0aVPY29tj27ZtJX4worIxNOqgnJwcZGZmIjs7Wxrz8vKCRqOBwWAAACiVSvTv3x979uyRrij766+/4t1338WPP/5YqfU8ePAABQUFReoZO3Ys7O3tnznvzp07sX37dhiNRjRs2BDz5s2DnZ0dfH19odFocPXqVdy+fRvt2rXDsmXL0KBBAxw9ehRBQUEwMzND+/btcfLkSWzbtg1z5sxBbm4uvL29ERYWBgBYu3Ytzp8/j4cPH+Ljjz/GyJEji9Wwbds29OnTB/Xq1QMAnD9/HosWLUJOTg7UajVmzZqFnj174uzZswgMDJTGp02bBicnJ4SFhWH//v3YsGEDABS5XVof4eHhiI2NRWBgIJRKJQ4dOoQ2bdrg448/xuuvv44JEybg999/x507d6QvDRoMBgQGBuLw4cOwsrJChw4d8Ndff2Hz5s3FetqwYQN27doFlUqFFi1aSF8UTUtLw4QJE5CSkgKlUonly5fDzs4O586dw7fffov8/HykpaWhV69eWLx4MZKSkjBy5EjY2dkhOTkZmzdvRlhYGA4dOoTc3Fzk5ORg9uzZ6NevHwoLC/Htt9/iyJEjUCqV6Ny5MwICAuDv74/U1FR8/PHH2LRpE/744w8EBQUhJycHZmZmmDJlCvr27YuwsDCEhIQgJycHGo0GK1aswOzZs/HgwQMAgLOzM6ZNmwYAGDx4MAYNGoQhQ4bA3Ny8HK9W4u9p1FE//vij6NChg3jnnXfEl19+KXbu3Cmys7OFEEIkJiaKTp06iYsXLwo3NzdpnrFjx4qrV6+Ktm3binv37gkhhOjbt6+4cOFCsb+flJiYKOzt7YWXl1eRf1999ZU0zZIlS4SDg4Po37+/8PPzE3v37hUFBQUl1v54/WfOnBEjRoyQ6j5+/LhU7+zZs4v8psiAAQNESEiIuH//vujWrZu4fPmyEEKIsLAw0bZtW5GYmCj1/eR6Nm3aJIQQ4tKlS8LR0VHk5+cXq2fgwIHi9OnTQggh8vPzRe/evcVvv/0mhBDi4sWLwsPDQ9y/f1/07NlTnDt3TgghxLVr10S3bt3EzZs3RWhoqJgwYYK0vCdvl9aHEEKMGjVK7Nu3T5ruhx9+kOrevHmztH5HR0eRm5srtm/fLkaOHClyc3NFXl6eGDdunBg1alSxfg4ePChcXFzEw4cPhRBCLF68WAQHB4vQ0FDRtWtXER8fL4QQYuHChWLOnDlCCCGmT58uPQZ6vV50795dXLx4USQmJoq2bduK6OhoIYQQSUlJYvTo0SInJ0cIIcTevXuFh4eHEEKIn376SYwcOVLk5OQIg8EgPv/8c7Fr1y5x+vRp4e7uLoQQ4uHDh8LFxUUkJiYKIR79Fo6Tk5NITk4WoaGh4s033xSZmZlCCCHWrVsn5s2bJ4QQIisrS0ybNk1kZGRIfXp4eIhTp04V65/Kxi2NOuqjjz7C4MGDER0djejoaGzcuBEbN25ESEiINI2joyOUSiViY2PRuHFjZGVloW3bthVaX1m7p4BHx0omTpyIqKgoREdHIzAwEJs3b8bWrVuLXNjuSUeOHEFCQgKGDRsmjWVkZODhw4cAgLfeekv6FNm2bVukp6fj7NmzsLOzk7ZiBg4cKP1KXkk8PDwAAO3bt0d+fj70ej0aNWpUZJq4uDi0aNECAHDt2jWYmZnh7bffBvDoMdyzZw+OHj2KV199FR07dgTw6PL1Xbp0QVRU1DOvelpSH8/y7rvvAgAcHByQn5+P7OxsHD16FN7e3rCwsAAADB06tMStjFOnTsHNzQ0vv/wyAGDOnDkAHm0BdejQQeq1ffv2OHDgAABg6dKlOHbsGL7//nv8/fffyMvLQ3Z2Nho2bAiVSiX9noStrS0CAwOxZ88eJCQk4Pz588jKygLw6HdtvL29YWlpCQBYtWoVgEe74R47d+4c0tLSMHnyZGlMoVDg6tWrAB4dh9JoNNLj9nirqFevXpgxYwasrKyk+Zo1a4a4uDj06NHjmY8n/QcPhNdBMTEx+OGHH6DRaNC3b1/MmjULv/zyCxQKBX7//fci03p5eWH37t3Q6XTw9vY2ST2HDh1CaGgoGjVqBFdXV/j7+yMiIgI3btzAv/71r1LnMxqN8Pb2hk6ng06nw65duxAaGiq92T1+8wEevbEIIaBUKiGeunKOmVnp/xuoVCppfgDF5n183+ODqkqlslgIXLt2DQaDodi4EAKFhYVSbY8VFBQUma6kPp7lcTA8WffjXh4rre+ne8jIyJB+0vTJZTxZy6hRo3D06FG0atUKkydPho2NjXSfubm5NN+lS5cwdOhQ6PV69O7dG5988om0vKfru3v3Lu7cuVNkzGAwwM7OTnrOdTodfv75Z/Tp0wcAUL9+fWnaDh064NChQxg6dCiSk5MxePBgxMbGSver1epSP5BQ6RgadZC1tTXWr19f7HLwer2+2JaEt7c3IiMjERERIX3qrmwNGjTAihUrcOPGDWksMTERSqUSr776aqnz9enTB7/88ov0xrJ9+3aMHTu2zHV16dIF8fHxuHLlCgBg//79yMjIgEKhgEqlgsFgkPWm/KR//OMfuHnzJgCgVatWRcL30qVLGDt2LDp27Ii///5b+nGv69evIzo6Gt26dYO1tTWuX7+OvLw8FBQUYP/+/bLWq1Qqy/V7087Ozti9ezfy8/NRWFiIXbt2lThdr169cODAAej1egCPjuv8z//8T6nLzcjIwMWLF/Hll1/CxcUFt2/fxs2bN0s8Oyk6OhqOjo746KOP0K1bNxw6dEg6jtazZ0/s3bsX+fn5MBqN+Oqrr/DLL79AqVRKQdqpUyckJCQgOjoawKPf33Z1dS3xh42CgoIQHByM9957D35+fmjdurV0hiDw6Ky+Vq1ayXvwSMLdU3VQy5Yt8d1332HlypW4ffs2LCwsYGVlhcWLF6NVq1bSp0rg0RWA7ezsYGVlhYYNGz5z2aNGjSryCfbLL7+Es7OzdID5aUuXLkWPHj0wb948zJ49G5mZmVAqldBqtdi4caO01VCSPn36YPz48Rg3bhwUCgU0Gg3WrVtX5u6ehg0bSgdIzczM4OjoCJVKhXr16uHll19Ghw4d4O7ujq1btz6z18fc3Nxw/Phx9OjRA+bm5li7di0WL16MwMBAqNVqrF27Fo0bN8bq1auxcOFC5ObmQqFQYMmSJWjZsiWaN2+ON998E/3794dWq0X37t2l3S1leeedd7BixYpiWyal8fHxQVxcHAYMGID69eujWbNm0sH7Jzk7O+PGjRsYPnw4AKB169ZYuHAhfv311xKX+9JLL2HChAkYOHAg6tevj1deeQVdunRBQkICmjdvXmRaDw8P/Prrr+jfvz+MRiP69u2L9PR06PV6DBs2DMnJyfDx8YEQAt26dcPo0aOh1+thYWGBQYMGYefOnVizZg0CAwORl5cHIQQCAwPRrFkzREVFFVnX2LFj4evrCw8PD5ibm6Ndu3bSTwXcvXsX9+7dQ5cuXWQ9dvQfvMot1Sl6vR7BwcGYOnUq6tWrh0uXLmHixIk4fvx4hX9RTa/XY8iQIQgNDS3xTbi6OHHiBO7duyeF96JFi2BhYYGZM2dWcWUv3tq1a2FtbV3i2XBUNm5pUJ2i0WigVqsxaNAgqFQqqFQqrFq16rl+glOj0eCLL77A+vXr8cUXX1RitZWrTZs22LRpE3744QcYjUbY29vjq6++quqyXriUlBRcunQJ3333XVWXUiNxS4OIiGTjgXAiIpKNoUFERLIxNIiISDaGBhERycbQICIi2RgaREQk2/8HQqWtNJLKpDsAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# check the SMILES length\\n\",\n \"count = [len(x) for x in data['SMILES']]\\n\",\n \"_ = plt.hist(count, bins=20) # arguments are passed to np.histogram\\n\",\n \"_ = plt.title('Histogram for length of SMILES strings')\\n\",\n \"_ = plt.xlabel('SMILES length (counting characters)')\\n\",\n \"_ = plt.ylabel('Counts')\\n\",\n \"_ = plt.show()\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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3aa33qQ4lC6TxKPBqtSSKflUHbE97S0nIwX/S93gfRS0PAeZZJ5NNLQ6QQbU94S0vLgTwmDaUUgkEngtZpbGlpuQwekzHKlpaWliuhNZQtLS0tR9AaypaWlpYjaA1lS0tLyxG0hrKlpaXlCB6TWe9LcVB7oxTi4V5WS0vLw0jrUV7AbnujJwVFpZjm9cO9pJaWloeZ1lBewEHtjS0tLY9tWkN5AVEoabTGGEujNVHYvkUtLY912hjlBRzU3tjS0vLYpjWUF9C2N7a0tFzIY9ZQttntlpaWy+UxG4Brs9stLS2Xy2PWo9yX3Wae3b5gu916nS0tLfAY9igvJ7vdep0tLS3wGPYoLye7fTleZ0tLyxc/j1lDeTnZ7YNGRrS0tDz2aL/5l6CtqWxpaYHWUF6StqaypaUFHsPJnJaWlpbL5THrUV5p6U9bKtTS8tjlmnqUWZbx/Oc/n1OnTgHwjne8g+c///ncdtttvOY1r6GuH75ymyst/WlLhVpaHrtcM0P5iU98gpe85CXcc889ANx99938p//0n3j729/Oe9/7Xowx/NZv/da1Ov0+jLWMZxUbOwXjWYWx9orl1Fr5tZaWxy7XzFDeeeedvPa1r+XYsWMAhGHIa1/7WrrdLkIIvuRLvoTTp09fq9Pv4yBvcLfgXGnD1qTgzHbG5x8Ys5OVGGsveo5Wfq2l5bGLsPYAq7CH2WzGL//yL/Mnf/IneJ7Hc57zHF71qlcRhpdXKvPc5z6X//yf/zM33XTT4rbt7W2++7u/m5/5mZ/ha7/2ax/cKzgCYyyfvW8bpS1J7NOJQ4w1HF/pMM4qzu3MGE9L4jBECvACycmVDoNuxDiryKuGptH4nofSmiDwSKOAQTdCyjZG2dLyWODIZM5P/dRPIaXkNa95DdZa7rzzTl7/+tfzute97qpOeO7cOV75yldy++23X5WRHA4zjLmkbV+wvt7jC/cNGe7kaAUIi/Qkx5cThvPfB11pVGUodQ1YlDLYWjOcF5vnpWJjJyf0Jb1OyNpSQggMq+aK137UWjc3pw/pc14r2rVeGx5Na4VH13ovtVYpBaur3Us+/khDedddd/EHf/AHi7+/7uu+jr/39/7eFS7T8fnPf55XvvKV3HHHHXz/93//VT3HlVLVhqVuTF4pqkrhCbGvcDwKJUioGwVW4Pnutt2Y5GSWI5BYBFrB9rhiuRtfl7W3tLQ8MjjSUB47dozt7W1WVlYAyPOc5eXlKz5RlmX8g3/wD/jn//yf86IXveiKH3+17LYhduOAKJAkkb+vrKeXhhhr2Z6UYAUrg2huSF1cUxuLsprUD0BY919LS8tjiiMN5YkTJ7j99tv5tm/7NjzP44/+6I9YW1vj9a9/PeC25pfDO9/5Tra2tvi1X/s1fu3Xfg1w8csf/uEffhDLP5qj2hClECx3Y5a78aJWcmtUEgSCKPTod0JkCYHvIz3BSr/1JltaHmscaShvvfVWbr311sXfV7rt/uAHPwjA3//7f5+///f//pWt7iHgStoQd7PjrvzHiWA88cbBRYXmLS0tjy2ONJT/9J/+04tuy/OcNE2vyYIeTg6SVet98b3MlpaWK+RIQ/mHf/iH/Pt//+/J8xxrLcYYRqMRH//4x6/H+q4bxloqpZhMGzqJRxh6dOJgn5dZVArAeagHPL5tcWxp+eLkyKrpn/3Zn+VVr3oVJ0+e5LWvfS3Petaz+N7v/d7rsbbryjSvEQIMlge2MkZZTScJLrsjp21xbGn54uVIQ5kkCd/xHd/BM57xDKIo4t/8m3/DH//xH1+HpV1fqtrQNJbI9zi+3MVqmBXNZXfkHGRQD2qdbGlpefRxpKGMooi6rrnlllv49Kc/jZQS8SjdUl7KcEWhJC8bpBQYa+gkuzHKkCTy0cZeUrz3IIPaepktLV8cHBmjfO5zn8sP/MAP8O/+3b/jxS9+MR/72Meuqo7ykcCl4o29NKTfDffFKKNQXnbW/KAypK1RufAyfSvZGhdtDLOl5VHIkYbyVa96FS94wQs4fvw4/+E//Ac++tGP8vznP/96rO0h51LDwqQQ3LjWpZ9eWSnQhUmctaV4YQB3i909Kzm1mdEohRQSZZwjf1BSqKWl5ZHHkYby3e9+976/B4MBf/EXf8GTnvQknvjEJ16rdV0TjhoWtus9mvR84flR3t9RXirA1qikbgwrvYSmcckgX7YTHVtaHi0caSjf85738Jd/+Zd83dd9HZ7n8ad/+qfcfPPNTCYTfvAHf5AXv/jF12OdDwmXOyzsckuCAIpKU1SaqW7wPQkIBnu81EEnoqoNQsBOVmG0xWD5kpuX9j2PMS5+2m7NW1oeeRxpKIUQvPOd71x4j/fffz+vf/3r+Y3f+A1e+tKXPqIN5UHGZzfeeKm6xyuZ591oTVbUJGFAVtT4/sXGLQolo8xgrUVrQxx5+9dpLfedHXNqIyONg3Zr3tLyCONIQ7m5ublvi33zzTdz7tw5ut0unudd4pEPP+OsOtQzvJTXuDe2OMpKpBSHenmB59FLApSGXhIQHPCe7CZ2+klItOSTRj5Ncz7jPs1rprUmCvwr2pq3Re4tLdeHI8uDBoMB73jHO9Bao5TiHe94B0tLS9x9990Y88geh1DW6sBicWMtW6OSSVaTlQ2elPsKyXdLgiazGotgkEaHlvckkUcc+Sz3IuLIJ4kuNpRSCNaWYvrdkG4coI3ZV49Z1YZe4gSFpRTkZXNgveaF5U3jWdWWH7W0XAeONJRvfOMbede73sWXf/mX8/SnP533ve99vOENb+AP/uAP+Ef/6B9djzVeNXHoH1gsPs1rtLUYaykKxSgr9xmm3dhiJwkIPcEoqykrRVHpi85xVJ3lrnErKk2jDZVy/y8qvajljEJJ4HtUjebc9gwhBZ0kuOg57j4zZmMnRwgoKsX2pGzn+LS0XAeO3HrfcsstvP3tb2cymeB5Hp2O2w++6lWvuuaLe7AMuhHDyL8oeVPVhpVuRF41lJVBSg5M7DRaMy0akjBgWjR4vjxwu3upOsu9W3ywNNoQeJLAk4stfy8NKZRBCsEN612iUDIrmn1hglnZMJ5V1JWlbgzHl1OwgkbrQ7P4LS0tDw2X/c3q9/vXch3XBCkPLhbfjUF24pAw0BeJ+e4SeB7dJERpQzcJCTzvijLicHFiaGdScWw53ZcoGnSEm8UTByhtoAYp9CJ7XtWGstLkhaKoFLOqIfAlq0sxeanYmVR0OwErsd9mzltargGPSRfkcsuEXLzREnjh3GPzjsyIX+hxBoGgqs97fd1OcKAX2DQuex4HPhvbM+LYxTt7aUgUSu4+47bZ2rcobSkqjRSCwJOsLyVsZxWfvmebNPJZ6sYUlcJYixRisZZOEjArmtaQtrRcIY9JQ3lQW+JBW+qDDWp9yaL1Cz3OKPRI9mz/1/YYq71GOgx8eknAKKuRviT2vX1b8zhyvehLvZimUdRKsT2u6HdC8qpBKc32uKIIFXVjObacsD2u6CT+Yi2TvJ5v+y/PG25paXFc0lAaY/jABz7Axz72MYQQ/J2/83d43vOe94gvC7oaDttSX2hQj/JGL/Q4m8ZybDne9xwHhQOSyCeOfNLGYowljv1FgmbQEdxyvMfmqCTLaiZ5Q78XMikqlDEIK8hL5Wo4BZwdzpiVFVHou1EW87VsjUrAeaNJ5LEySPYZyrbcqKXlYA7NeldVxctf/nJ+5Vd+Bc/zUErxH/7Df+BlL3sZZVlezzVeFy5Hd/JyDMnlyrJdyKAbkUQ+ng+ej6u13PP4QSfi+HKCMppaGcbjmnFWMytrPB+MhrV+zNmdGWe2M84Mc2ZlwwObU5Q2VEoxnBTcc3ZKWWuG44JRVu1bwxej2tFu00ErddfyYDjUo/yVX/kVvuzLvox/9a/+1eI2ay0//dM/zS//8i/zoz/6o9dlgQ8VRxm5o/rA4WKv88IY4OHb9aPZTTx1koAHtjLuOTMhDCU3Heti5qVMk7zmnrMTslyxthSjZpZQSp7xpAG+P+WuLwzJMleDaYydq7YbJrOaMJQIAf1uSF0rDIKdScl4Vi3ei3Fe87n7RoyzikE34km3LF3W1nz3vXUlUJrA8xbx1YfbI71U00FLy+VyqKH80Ic+xJ133rnvNiEEP/ZjP8btt9/+qDOUR2WrL8fAXdjXnRX1Iht+qe36lWxpd+OXvTQAK9gel/hSMslrRtMKYQW1UgwnFZEvGOfOK+wmAU1j8DzBuZ2cwJfsZBVf+SXH6CSBi8t2I06dy8jKCq3hbz1hZd+6P3//iL8+tU3k+ZwdzRACblnvLdY/KxWjrGJpp0ArxcnVDr48r7tZVC4h1UsCwF70Hj8c7Gs6OKIdtaXlMA41lNZaoujiizyOY6S8vO3kI4mjstWXozvp6iorrLFMMoXnCQY3x/uec1d5aK9R3GukZ2XDJK+JfP9Ao1nVBoxL7oBFKUNVG7KZq+fs9yLyUqG0IeklRKHPNK/ZHleEgc/mOGeSVZTKsLaUcu+5KYNeiBCC+06P+dzpEbPKsNwNsdaeDzN04PTmlKbWaAwGy+nNKXD+R2Y4LtmeFKRJyHS+bb95vbd4byeqxmjYHJWsLSWAoLfn/QgC9zqbxh742q9FjDQOfbbaWtOWB8mhV01VVVRVdZGxrKrqEd+6eBCXs7Xe5bAvbOB5CARFrel1A8radfUsdeNFT3ilXIwv8v2Ft7bXSFe1IS8UJ1aDAz3bKJQgoW4UWIHnu9u6nYDRtGKtF/GFB0YACODG9Q5VbZgUFXVTM5yUjLOKKAoIPMjyitG0Ig597j03JS8NQQBaWb5w/4STK93Fe1FrS1EZ4kiQzTSe13D/5pRpVpPEAVlRIaRga1wQCMska2D9/Htb1poz2xlx7KG3Db1OyKxo0Mbge5KNMzlaGVYHCZ4nMNay3D0/J/1yalSv1Jhe2HTQSYIHVWv6aE94PdrX/3BxqGv43Oc+lze/+c0X3f7zP//z/N2/+3cv68mzLOP5z38+p06dAuB//s//yW233cb/8X/8H7zpTW+6uhVfIRe2EDb66Njh3qTGrGx4YCtjY6eg0RphBav9hDQMWB8k5JXinjMTskLRS0ImWU1ZaWZlzSRrnFEJxCLBk5dOQf2wpFEvDVlfigkCSRBI1pYSemnIydUOS72IrGhYX+nw+BsHdKOQfD7XpywVjRGEnkB4HmnkIaSkqDXDnYqN7ZxpXlPWiqJQ5HXDpKgW74WxljTxqeqGc9s5ZV3jC8sDGzOUsWRFzTSr+N+f3+QTn93g43+ziTbny5dcCZQiiQO6YUilDFleobVhktfsZBVVpZnmDbUyaAXb4/3JpMtJqF1pwmk39nts2WX4Z0VzyccfNefo0ZDwutRreDSs/5HIoW7VP/tn/4xXvOIVvPjFL+arvuqrUErx0Y9+lDRN+dVf/dUjn/gTn/gEP/VTP8U999wDQFmW/ORP/iRve9vbOHnyJD/4gz/If//v/51nP/vZD9mLOYgLWwiTyDsybnaQB3hsxScvFeOyYlbWpLFPPtGE8VwJ3UJRa9I4YGOnoJf6zEqFNRbfl/TTkKax9LshnhSLrPhB4sHL3Xifp7V7+83rPbJZww3rPSqlKEqNMpZeGhJ6HivdmHE3RiuLRuABkS85thLzqbuHlLWmqhusEIDl5KqLM2yNSoqmoWkMnSQg8AQg8AMPIUBpw0ov4mPbM5rGkoSWWak4vVXwt594Pmyx0k/oNYYwlKjtgtCTRJGPmpR4nmv/jEOPpjGEvgSx3wgd5fXviplobYgip8K0N4RykLd0IUWlKStFphW+B3v1Qy+8Xg7yaq+04eB6hBcu5FKv4UokBFvOc6hHmSQJv/mbv8kdd9xBVVVorXnlK1/Jr//6rxOGR2dy77zzTl772tdy7NgxAD75yU9y6623cvPNN+P7Prfddhu///u//9C9kkO43HGze9lb4rPrAeaVwmpY7SaEkUdRafxAEvke2hoQlqpSRKEkjqS7X0iWezFZppBCcGw54ca1Lp04OFJE4zCPptsJKJoGrV1bYydxjz+2lpLEHieXO6wsJfT7AXmpiUOf1eWEQIIvLXEUkAaSThKRxuHCuzg7zEEbPF+S1wZtoRP5aGURVhBHPgjB+nLMTScHLPUjRtn+MrGVQYTng1KGKPLopM6YxZGHwbI6SIgiHyFBepKV/v4fg6MERo4SM3HvW85oWrKxkzOe7fdYASqlOLudszMtObudUyl1RdfLUeVfR3lsl+PRPdjpnZd6DZdTvtZOD72YS0a2Pc/j+c9//lXNyHnDG96w7++NjQ3W19cXfx87doxz585d8fOurnav6PgbTvTJK0XoS2plSCOf5Qu+oDCvt8sqylqxvNxhxUKlNHEa4Hkek6wiikM6iU9Rui9XEnvMcgUCPClBWtLQJwgC7j03JUkCpC+5cbVDpxOyvubWfnx+TqUMpzYmjLKKM6OClX6C0oYoiej1PIbjgp2i4fhyh0E3QkrB8nKHu+7eYmtU8Libu6wuJ4RxyDO+9CRryxO2pyXys1ucHeV0YzB4DCc13V7K8qAiCCUYCEJJXmt2ZjXLg4Q4CsD3kFIy6MfMihqFpDuIObGScGK1y01rfT53ZkSzOUVpy43rHfwoWKxtdbXL+qp7DyPfwwqoG83xYz2EhaJRNI0mDHySyF88bu9nEM4/gzDwELufQeiOVULwpF7KrKyZFQ1lrUg7Mf78uYZ5Ta+bEEUeRakYFYozWxnxnjVuZCVR0tAoRRSFJGnI+npvsQY/Ci55vSwvdzi1MWEyq+n3Ym461sf3zxsbJQSDgXTTPI1FabP43C/n/vX1HjuTkkRIBgO3hvCQa/YwLvUaVle7i+t8933d+xkA7ExKIgR4iqxo8EPDLScGFx23u94L2ftdOuwcDwcHrfVyOdRQ3nbbbZd84H/7b//tik5kjNk35tZae1Vjb4fDDGMu7xdufb1HXdYUec1ovtXxbchm1Vx07K62Y+Cd3/YNOhHSl0zzium0RFuLbyNGWYXAIkzMZFpQ1JpuEuD7kqZsKCvNdFIwGef0uiGTSUG/GzLLyn1brXs3JpzZnFHVGi/02dzO8ISkE/tzXUrniTZlw3C+HgDPwg1zYY1iWpGNS44tJ3QCj85Kh483Zwk9QSA9pkXJx+46w83Hu5xcSdjJamqjSKSHFIbP3rvD6qDAEzDNS2JfIKwk7oZgLLEnCATs7MxY6nh0A5+y1hhrObEUcerMiM3Qu6ieVM+FiX3A1O6HJQTCwMNYw3A74/TZyb7t597PYHtaYBGs9mK2tGY434bv3j+d3z8ZFwzn94/GhdvWBz7b0wJtLI8/OeDUmRHDuYf62XuGbG4XRIGP7wNGc2xPmEMZw7nhjGzW0O0EnFzt7LtedtcYeh7Tack9tdq3NZ9mJVujAmtASFhbSvD3xggvcf/6eo/NzSkbOwWeFAtjOjIWdcA1exjG2iOveR9QVcPwgOfd2CmYlS4UI6VgtFOgLnide9d7IXs/x93P7uEuEztsrcDiR/5SHGoo8zynqipe8IIX8KxnPetBty2eOHGCzc3Nxd+bm5uLbfm15HLHzV4Yu3Hak+ezo7ee7C1qHNeXYoy1jCY1Ra1JQp9BGnFuuyBNfBplCAPBcNIwLRqi0KPfCTm7nbM1LlgbuATN5rBAIBFCu7rHUcUtJ/vMCld6NC0a15sdeOyNpR0Vy1PaMs5r0tCnUppOGPD4k0tsdyvW8oZ7zo4YdCPy3G3JsrzmiTct0U9csigJJEK6ovHAc6LGUgi6aczffrJL1MyKmjQKKCrNqXMZ/V7ESjc6tBBfCrGIz22N3I/O7vHAYrbQ7mdgtCUvFQIWs4iOLSeLz0pKJ6iMcHHHnUlFmvoo7UqqtLGsD5ILtp81VaUpak1eNfieNy9jOs9s/p4fW05ptN4nd7f3Otl7XmDfD6DFvdaibGAkkEJcdL/7/8FEoWRWumstLxv63XDxnl4Ol3vNH0YUSjZHDVHg75txf7nP98UYBz3UUP7RH/0Rf/EXf8G73vUu/p//5//huc99Lt/1Xd/Fk570pKs60dOf/nTuvvtu7r33Xm666Sbe9773cfvtt1/1wh9qLjQ+jTZQ2YsD4vMPfDxzghNKacZ5TV464zYrarKiYTipiQKP4aQkTZxaujaWfnreOHiBYDQqaGpLrgxJKJ0H4AdsT1zSYqkTXTSL56ji+LWlkI3RjOmswfNhfTkijjzWREQS+MyKiuG0QltDUSmefMMy1lpuOtbl3E7B1k6BNZb15Xhx7n4a4nmC0HokaYhWhlpZalUjJRhtyKuGThyyPSnpxMFF791ufE5rg7F2cfzuF2nvZ1DUikoZeoQHziIKA49aa6rCLIrcA08iY1ealSTeRUmzqjYEviSNA6QE1RjEBWH6o34wd9Wg9hbX732NTWNZ7cXMyhqwWGMPvH/XW9w7EmTv5zvJa/JC0Ul8PCmY5vV188o6SYCQgrPDDD/wSGN/ocV6Ocb6SkrxHi1c8hV81Vd9FV/1VV9FWZZ84AMf4Gd+5mfIsowXvvCFvPSlL72iE0VRxL/9t/+WH/qhH6KqKp797Gfzbd/2bQ9q8VeKMoYzF2yr/Hnx/IXGh0q7L94hX5iicq16yliaWmM8SzapaGpLrTVR6FNWDUnso7Rxno62xJHEl5KtcYE1hnHWIIQh8WM6sU8nDugkAWWlUY1mc6cgCgVl5S8u1KM8hl4Sc8NaF6wAYTm21KETB/jSY7kvObczxYzdl9iXkllZI6VgfanDci/hbCdjOC7ZGpWEvs9KP16UEG2PKwa9iNCDadZgASEkWdEAgjDQYMWBHsWuEYoin6JQlJVZ6IFe+Bl00oB+R2LM+VlEe7O5nhRoYykrRS8J6HciFxurKiLfpztXiFd6v/JTGHj0OwFGW4KOpJPu/woc9YO5qwa1M6kW57WWxWsMAsHmqGRnUuJJwdpSsq+o/3KMiBTO2J9YDRYG9Xp6ZbOiYakbopT7vqhGEQeS8ay6qBrjIK62jfeRzGWZ+jiO+fZv/3bSNOXXfu3XeNOb3nTZhvKDH/zg4t9f//Vfz3vf+96rW+lDwJnhjNG0Igld8Ta4zhI4aLtSXfSFMaXrktkcNQgpWOqG+FISeh6ntzOUcdtJXRjywhnJ01sZnue8kCT2GRQhw2lJGgVgJINOSBB6nDzWRWjLoBMxnlVYWJTxWAtZ0XD3mfFi236pX/ZeJ+BG08UiELiSpL2vrW4gjQK6nYBs1hAFASv9mHvOTlwNaKHwPMn6ckLVaMazmhvXuouypd14z/1iymha0U0ChpOCSimSKCUKvX0anFHoMZ5VTIsKYyz9TkTdKKRk3xdp72ewMCiLmPF+LdDId9nx/rrL3FsL2/PYsSfn73fkc3Kty+Y8BthLQ06sdTg7zAk8STcJWBvs33of9YO5UIOCxXkvNHgCi+9JGqWxxuy7/3KNyEPhlV1tKVJVGyLfxxjLUifG9wVGC7bHl2coH+zW/5HIke/+X/7lX/Lud7+bD3zgAzz1qU/lJS95yWUXnD/cXDiudpK5NkDfFyQ4I8H6wY896AtTVIqmMUSBT143jLKaWd5QK03ge6TzrHEaBYxnGUXtAuWJ7xP6EtUYzg5zBAZvANOixvcEaeg6fkaZK8eYlQpfCmZFAxYMsBqHaMU+jcrDWgOX+zHGCvdACauD/Rf38dWYWiua2hBFkuOr7v40CrDaoozBWMPdD0yRHmjt5pD7F7SuCiGYFYqzw5xeGrI6l20z1jKmYntSghVUyhmbQRqxnVVkecOx5fSSX9zL1QLde5wnBP1OtN+T3cNuLerSPB56UK3l7pd8txV1VtbOYHRjtDFHGrymsaz0EpZ6MWeHOWe2c24+3mNt7uFerhF5KLyyK1Xj32XXSGtjUVaT+oGreRUPTZnQo7E76FBD+Uu/9Eu8973vJU1TXvSiF/Ge97yHtbW167m2B82FyjFCQlE3JAQUdcNS7/CL5sIL2tiS+87NEEg8T8x/ZQ2rg5jPPzCiUA1KawQhm+OcNAnopxE7WUU/CaiVIQgk/W7AdFZx1z07pJHPrFIsdUOSNCb0Pf763h1X/yegUZasrOnHAVHokgxhILAWJnnNJKtJ44A600gBK71ksT1cX4pd58v84t4bX7rlRB+Q1LUmDD1uOdGlaSxr/YRjSynjrOZvTu3QTQIqbdieFpwZzhbe9973d5SVhL7HKCvpdQJuWusuwgO7ccqz2zM6sU/SDVjtxWhjj/zCHmRQOkngunzmoy/WdsU+LvBCpRALT3ZnUrKxU+z7Ql6Oodo1MrvGfTKrWVuKD/R+97K7Bheqsdyw2iHw5EVJoaM47PmvxMhcbVJl9zX2OyGyhMD3kZ5Y1L3urkEJwWyP+tTlcrUG/OHkkobyhhtu4MSJE/zZn/0Zf/Znf7bv/v/4H//jNV/cg+VC5ZilboQ2TmBiqRctOlMul8D3mRUN2dR5ksu9mBOrkvXlFD20MN+G9Tohy92IsjakgYdS1jl3AgJPsjUumZUN/W5AKgIaBYNuyMZGNffQMurGkMQ+cegzmrnE0PHVDnfdvcOsbOgmAbec7LE9LZlMK5a6EUu9mKLSbI9LkAKtLb3EZ1Y2ZEWzEOIYdCKecIM8WLgDz3m5sY/ve3TSkCQ82PueFTVR6OEhEaHrK9/14KdFxSB13l0aB8wKRS89uBvpcjkqI32hF+YSRueLu+Hyv5B7jcxe475bjH2Yodpdw2ExzAfLlRiZq92+7xrpvbuWvd734kdkIK/K0D0as+KHvnNvfOMbr6rO8ZHEhcoxnXhez3XIdvtSNI3lpvUud58ZYwvoxAFZUbOT+az2IzppgC8lvU7AcFywuZOz1IsJQ4HWAowhrzWfO7XD506NCAOJLwWdNCDwPTZ2ck5tTqmUoWwMRanodyI6SYiZVQgknzs1plGawBcUdcOnPrfFyiCmqDUWS3ZvgyegrDSb44IkCVgdREgEQghWuglIWLe7Hl29KJvZHY9b1YbV5ZRauQSUFZbAE3Q7wUXvSRQEqAaSxKMoNFqfr3M0xrKdVaz24kUm/7COm8vlqPbDC72wjZ2C0D88IXcpT+gwI3OUoVqsgcNjmHB57ZYHcSVG5sFu3w/zag/s/LkCQ/dozIofusLv+q7vup7ruCb00pBGm0WNXRDIi7Zhe9n1FnZjaysDpy0phVh8uNbCSjdxJT9ZwdmtDGMNqtHgW/JSUjYagWA6bWiM2wKuLqXsTEvuOzsjCgVaweZOznTm86VPWEUbzU5W43lQ14q8ajiznc+VhDR4MMkqGqNRCmqlmOUVte5RK02ZRoS+JA581z7pSc5sZDSNpqoVN6118QaCnaxknJUcW0mpKo01kNeKbupzbCllbSlmZRARR5K/uWdM3SgGyyF5WfP5B8asDCKWlzuMZxVpLMlyQ1k7QY049s8bMilQBrSxdOLgIYlDHTQ++FJEoetM2S0ROrTk65BrBy42Mpfb670rwgLi0HbMCw3ucY7mSozMtUqq7K7hML2Co3g0ZsUPfYV33HHHPo/S8zyWlpZ49rOfzYte9KLrsbYHzXQ+TOvYcsr2tGBYG5Y6IV84PUEpw4m1lOMrKUWpqGozF5pwdX55oRlOck6udTm52nEdG9sF25MCoy1rpJS1oZsGeAi0dCK7nl/jS4mUku1ZiTWWoHYe3Tir8YWksQJrjfvSxwGzWc3KIKXX8ZnOGmptiEKPvCo5M4Qnnhy42r2mYXtckgQBk6JCSqjmccZGafqdkNGkRAjJrHBG9YGNjCCQxEGB77uWy8CXfD4f40lX2lPXGmNDesn5wmat4PhagtYxk1nFaCoQQnHf2Ql/c2oM2rC2lLC2nOJLydpSzGhWMclqt1UvFUu9aFEkDvuNSNk0jCY1jdYcW0m5ca2LLw+em75rYA8aH3wpemlIGPmM5p7sbgZbCCgrdWCx+N51HrSGowzV7gz2utbMCk2/B8fS5KIfiQMN7mVwPYzMwmGYx7hX+vHCYdi7BnUZSlwH8WjMih9qKF/2spft+9sYw3A45G1vexs7Ozu84hWvuOaLe7CUtcKTkqx0BiYIPOpaU5ROofz0Rsa9Z8aLBE1RK7pJQBQEaCzWCk5vzLj/3NTV9iWujGY4KihKhe9J+t2QKPCoag+sYJxVIDSjvKEqGyeAoWFnWuD5HmWjyMqGpraUjWI7qzi+kpBlNQK3Vk9Y8tIwKzWqKVHrHRoj8YXbRjZaA25U7XhW0bPhwpMc5zWqcTNxAFaXUqSErFKc28lplOH4Ssr2ToEnJYNuyLRsaJRhpZeS5Q3dNGCcVexkFXVt3JyexhIHHhujnHqYEfketVLcuN6nm7jyo6LSdBMONWQLJfSy4a57dqgazXI/Qm/OFhnpvYZmY1shfcFKLyaJvLmAg4cnA0ZZyaw0jGeHj5yQQrDcj/e0/7nkXlkppkVDNwkP9CwvJbS8G6JYeIzz7fzuGqraUNdurlEUekyymn56cbH4g40fXksj47qnCrQChGVzVO4LKez+e31P6dUXO4d+Ot/6rd964O233XYbd9xxx6PCUMahzygr0cq1wdWNcVvGKHTtY0JwanNE6AXEkc8sr9lgxvqgQydx87c11olgWMEoK6kbQz8J6HWc7No9p6f4vmDQCel2IiQuI22scdlCKbAYZ4gGCZNZwKmNDKUNcSipasVo1nDDcbcFK4uKB4YzPCGotUVK+Kt7IY1CmkbR74ZsT2rqqmGcG7qxR1kpBr0Qfy7y64yGQFsN1uIJD08IgkBijGE0LTm1OUUKqJuU0ayml4Z8+p4hSeRz64k+w6mLsxal+9K7rHpMXjcM+gl53nB6KyevDDcfd+MiGq3npUputK7vi33Z9qo2+FJyeitnOMnxpMSbCcpa00td7HivodHGsjMqSUMnkbdb7L01KrEIeknIxs7+ttC9rZJVbfCjYLGGvYmWbhLS7zh5vAu3z3vbFIfjiklWccN6F2XOD3qDar6N35/QiELJ5o4LtxhjSePgwBjeI3n7WdUGayAMXevorsr+o8kDfKi54ijqYDB41CR5dlVLrITVpRhj4P5zCqdIIGiUJq80uVHozBV2h3OvTwjo90KqyhCmPqe3pgwnJZ509Y9Frel2fMLQbV03RyWBJ5CeYLkXEvk+nz+9wyTXpGFAHHpEvitYNsbQjX1q7cRBRpOCL5waIazrqW5qKLTLomvgnM3pxg3Hl13pznhaMc0btAHTKHq9iEnWkIYhymiqWiOEKxKulGGp55JYaRgwHhec2S6YZBXausy1QFJUhqxoWO1FDMcF958ZM5s/T+w7ybTtaUGjLRYYTyt8z/WCn9nMODfMWeqGeL6Ydy1JilLt6+aIQsm5HSciUtSKLNd05173Lcd7i2N2DU1ZV/Q6IUqzSBokkUfdaOLIJ68UWoGV7DNW56XMJGe2Z0zG+cKQ7mauN0clw1HpkltL++tM95b4jKYl/Y6bRwTgy/1dRhfGKntpSL93vnQrmrelXsgjefsZhRIhoa6dfKD0Dn4NjyWu2FBaa1EXaPg9UpFSsDZI9nV4PO1Jq+Sl4uxWjhCQhpLhTs2sbvB8j8edjFlfShZbaxkJokBy9wOKsnHxNwtsZzlh1GW1F9MYg9YQhgHWWIbjgrzSNErTKENv2cXVNoYFFks3CcgKRRgJqlITrHQYjnMCX5CVmsCHWsPuu6xqaHzt+n/LhslMUWknqmALMLJhkFjqpuHMsCArG4Qx5I1FK81SNyKOPZQx3HN2SjP3VGd5zTi3rA9STFkTRTGnt2ZoAaO5KnwnCZGJE/UIA59z2zmTac32xNWHboxyEBCHAVWjyGvFU25eJg4D6kaxPSlZ7saLSZLjaYVSmkBKuolLVPXTkNmsZjyrSGIfpOXu02Mslo4NnOjxnmRMEnlMixrVON1LbWCSwaxs6KXhwohlZUOEWBTq73qWW+PCdU6FPnIuYLG35Gd3e70zqVjqRQTzzHk+L+m6f3PK2eGMwJOcXOtirV1snaUQ3LjWpZ9enNF+tBRa721X3Y1RPpI83oeDQw3laDQ68La3ve1tPOMZz7iGS3poOWiLs9pzF/PdpyfsTEpqBV7heoeFFdTa0ElD1pcSGm1QyiCkZKmTsNpLuOfMGG0NEsEoryjyGmvh/k2FqjVZpbhhOaUxrqxGWwgCj9D36KqQMo1JQs3WtHTajJ6kMtZltbXBSoGHZe/PkWpcm80kU5T6/O0NMJ4ZqrxmqRfTKI3SGmvBKJhViuG44Nx2yaDjUzeKvNZobRDCKTfPqoqgkVjAKEUQ+NTaoud1iEu9mO2s5NbjA25Y67I5KSgbg+e7zP3OpOLG9S5p6DOalWx2Y5Z6rsQpiYOFIdoaFXgSsIKlboIQAulLAiHwfBd6mOQ1vidZ6keuBdBatN3fTuinkmnRsD2piGKPpW6IMRZhXHxt1yOsKkUUh8SRdNJtYydkYrUgDgLi2KcbB4wm7vaDsuH7EzMhWdEwyWqWOjHDScnZYc7jTvb2GZLDvMWHstD6Whrdw1T2H8scaii/7uu+DiEEdh6sFUKwsrLCN33TN/GTP/mT122BD5ZLbXHC0M2l6cZOGUcbixBu+70yiIl8H19a1tZj7j83ZWNUMJqVeJ4lCX2sgcYaZrVha5xRllA3NVYI6loRhT5bY4UUHt0kYGk5pGoarLAoo5EStDHcfXZMHPhoIJACz4L0wWvcADFlIbAQBGAO+S40Fipt0UqhNKjG3WYr2J6UlHVD00RUjaKqDNqA9KAbeyjrJM08KSnKCj9w4YGqMWhryIqKKPA5szVFeB5og9GKceEqBTyvYXPktmZrywnDiUsEDHpu6/nAVsa5Led5DroRk7xmnCvSOKLWijD08YSgrBSzUhN4gtV+zG58bJBGi7hgUSnyUjErFMu9iHFRUVaa9eVoMRpibb6VnhWu2DyNXLwZ4UqDwlCzNS4Yz0pWBjFCcKBiUFFptHGq8Mtzr+qv791ZtMGuDRIapa+qiN23ThjlSusod3mwRvfR4t0+UjjUUH7mM5+5nuu47kzzGk8KumnI1mhK4DmDOujOg/zG9WV3uz73bdTkuUIpPe+tDljqhIShh64M3cTn3FAQBYKyccXljbKAJvTAm4s/xIHHII2YDRT3nm1c/LQ2TEtFFVl6cYBSGj8UCG3RuA/IB6Qv3FgFD8oLtFZ9wPNcoXnTWIo90wW0dV6j5/uUjUtqKdyoh1S6VkNfSnqJT+R7ZEWFrmqE9BA4kWA5b53spRFbo5JpUTEuFBJXjB4GHlmhWO2HCARCCjqpz/pyQqMMZzZysrJBW03ou9bG9eWUfifi3jNTRllFHHoUjaLfCRFSHBgf2zUm92/MP69ehBEWbSzdOFhkj/d2loRxyOmzE5LI1dEOxyWjrGJzpyAOPcLIdWk1exoTKqWZ5tVCXLe7FCyM0O40zMtpg72QvZnuvQIeu5l1I+VltwQ+2O6WR2Mb4cPJVZXE/8Iv/AI/+qM/+lCv5bqxd0iVNXByLcVYS+h7BL5gMqspygqlBfdtKs4Nc9LIZQC1UhSNwlhDVimWuxFaWZLIxeR8D8pKEEVuqx37kpPLXXxfsLlT0ksDlLIEUlBpqIylasD3NMZKisbVOXZjD6M1ysDywCMMA2ptCXyPTqCZXWAsIw+OLceMp/lCYXG3Mi+IfITSGGOY5AoJDDo+pdbktWapE7qCfM9HjuVc+EPQ9Z3nJBH0kgghpEuKSUHka7KyxmKdUlAAealIYw3WsDKISKKAzz+wSVbUKGWZ1Q3jrKSXJpxcS+inEaNpyayQaCNoGre1XltKDoyPLYaY9RKaxsxjzAGNNgd2/VxYHrSTlQgs2awhDCTrg4gw8FHKkHTPj7SdzGonISdcKEYgFtvQ3bbXq2mDPUzAo641s1Jx88nLbwl8sN0tReU0Nae6WQgjDx6BiaVHCldlKH/zN3/zUW0o9w6pqmuDMYJOJ3TKOcrQGE1dC5TR7IxLNncKBr2AbhKjrMVaSS8OyUpF3Rji0CcJBWUFTeOK2k8sxTwwLCgaEJtjilrj+x7HVxKsMShtMBjKufdnESjFfAsuCANBEGgCoFbQiT3iMKDXDQkjjZjUZHNjGXjQ7/qcXOnwN/duuzIhD4rGebNRILHastxz8mfGWoQUJMJDG0vVGMqdCt9rCD2XzFAarDVEQUAn8Zzaeui8j9V+TFM3SGFdDaY2pHHAUj+iVAZPOuXw4ahkZ+rqIpU2VJWiiSw3rrlkyfakBJxcXS8N2Zm6XvHjKymPv6F/aBG4H7iun6bR1ErRSYPL2j7uKvvMCtdg0GgIfYFELsIzxlr+6u4hk1lNNw3BWvzq/HP6UjpxkPUr374eJuAxKzRpElxRS+CDLS9qtBMeTsLgQGHklv1claHcjVs+Wqlqw0o3Iq8aBv2QbNYgMIShh/RcHeLnH5ix0k+YlIpO6mE0FFXNOGt43ImuS/LMhYBvOtFlbTklTTQ30MH3LFXthHqLvEHNW/x6vkQ3UNaVi/8ikdYgPBj0QjCCpW5AVlWMJxW1gsh3Bdy11qz2Q6zVnN7IXTwT6KaSfi/AwyNOAgadkHxYkytXWpQErid7tRO7GGGtyUtFow1CSDxpMRaUrqmVx3LHJ47C+ahdQeA7T7GTuox+PwlYX0v53L0jgjDgeCdAqRhrBYHvE3mCY0spy12nXtRPI2ZljTUWEKz0IuJQ0ksitsY5K32XgDo9zNDasL6cMJvHFg4rAt9VMq9rQ+pJljsR06Lmga0ZwkKaOuFerVjUUYKbwDjJnEixsRqspGwavCBwhftzEQgpBVHgUVaaIHQCIQcZxUsVph+pGbrH0PV7wSVHGB/Egy0vCjyP3vwHcVcYueVwrspQPlrqKC9k17Cd3crxfcmNax1OrnbQy3begWIoK8WZ7RmNMpzbnhF6gtKAlBJfeqwMAs6NC7ZHOdZKkshjOJQEoc9N611CXzKeNczyjJV+zM5YUDUNYNxsndAyyRsmsxqERATgadBaEHkCzxPcuNqnLCdIqfE9AdJQ14bRpALrvCDhSkFR2jAtNHGg2dwuCUNJEkFtIDCwthzhS8G4qIkDj7VBwsSr2RwVVHWD1uD5mm7iORXxWYMfBmht0MbgK4EAtAkRAnpJSCeOWOpGSAHdNGJjlFM1iqVuDFh63bl3IyxL3YCdSYC1rk9eG8gLzfFVyZNuWgLgvjPThaBvpTRlpfGld2ARuJSC0PMYV5VTWIrcbTuTilFWcXKly5nNGVHkccu6m8BZzEfCelLQiX2mhWLQi/GkIK8atIJzO4Urmm8sx5ZSdrIKo92PyEo/5oGtjMnUjS5WxhmVvSIdZd1gEdywGlxxzO9gNfZrSxK5Iv69wsgth3OooXz/+99/4O3Wuozoo5FdhfOlbsT2pODM9ozHnejTS0OUMXzis1vce2ZKoxt86erwkshnuRfRaEPge4R+xKc+v0XZWCLPUNVwblSwvuxEL9I4YFrUzIqastHMipJGG6QMKaqGL5yqmBVuG26sy05LAZ4waDyyvCGNA/7W45e558yU0axA15Yo1JzamqG1IY19ogSKDFQFfq3orPhkRUXTCFYHKY22NI2iqgW5V6Os0xdU2tDvxAgheGCYYYGqBoSmE4EXBYvOHikkVWNpjALpNDgV8Libl1lfSTm3lVMrTdO4lsWlTkgceVjjZgr1u06tPUkkRWNYWUqIA4+sqhllNcfmfeLbnZKT610wYDRsjmYXCZjsjljAwKx2g83iyGdSVEyLhlMbmdNP9ASelNTzAWShLxnN+6gj3ycJA3qpSwBNi4ok8N38nXnN59ogQRmPFREvBntJIZhkNVHoU6vdwnNvn0jHTuauq8tNruz1Rg9SY3+wHBUWeCR3Bj0SOdRQvu1tbzv0QV/+5V9+TRZzLdgrxHD3AxPC0CMvXcdJXZvFBXRuO6eslRs5MFN4wnkEvU7ADatd6tqwOckZTSt8z2el7zyjuna1gpEnGI5LVwitLJNCUZWKKPaRtSGOfFYHCac3p4S+xPMks1LhSYgTQZwEeAAGNrdLrLRM8ooitwgJAkNd1Qhh8T1BVbFI2kgPxoUizWqENHiey1hrLQl9izKwPkj40luW+NQ92yRBQFk3LCUBk7LBGKgqEGhOpBG9OCQvGma1JpjrWpaFJvNqQinxpIfSFt/ziCOfpU7AaFbxgHFlR2vdmKpRhIFENZbjyx2kkICllwasL6UobZgVDZ0kcEXss5rA94gDDyGd97dXRxLciAULNJXFTwRCwHRaM6sNg27IOKtQyqKNYdB3tZWu59q9U7vJj0o59e7tcYVEEPQlWIEx81GvpSslumG9w6DjMv1pHMzHt0pmhWa5LzH2vEjH0jzZcznbZ2MtW2NXQhVHmjQKLlsU43LZrVvdzdwba9u6yAfBVRnKRxMLIYZKo7RhvFMR+h6eB6uDdDHdLps19BPXCigRlMqwMvcmAk+yvBahrGFzVBJGAl16CKFI5p6U9CRFVjHKwfcEoZQQ+wSeJPLdF7yoFcKXhKHElz7b0xIhwLOCqqqpSoPnOy+oqQXWGKIQZhV4wjpvTVjq2hnY3bpzrZwnVqsajEAJ94Xud90YXSEFxmg+f3pMKAVxJEmUz9YYpCfmxhdCDwLfQwgQUtIJPIwVVI0m6jiFIGMEZd0QzFs149hje+I6iUbDnCj06EQBp7cyDJan3rrOOK/oxSGzpiHwfbKyYaUXU9WGST5D4Ly9UinCQNCJAs4Oc6SEfhrhvFvBSs+Nnw3n3mY/jZC+YCkKMHMjPcpKbj7WYakXMZqVEEiktfs8KG1cWc7qIGZzJ2eS1XQ7AWEoqWrXyeSER9z1My0qlLYEvkvA9HvhYjSF276GC+N7mObmXg+vUsppfRoX/y0bw/Hl/bN7Hizb4wqtmLfYmovm3bTlQVfGFcUov/M7v5N3vetd12ot14Td2NZUN9y41uGue7dRxikBdVOf05szwCUAmmnlYmrTkjgULA0iGmWYzGqSxOeGtQ6BL/jEZzVQEMeJi3HWBiMEaRJTTXKq2rjCcGtBgFIWhEFpTewLciWYlRXGQhq7cMbWuMGzsL6SuG4UpfD8edzIKMoGklRSVQ3SE3BBmNgKGGWK2PdY6USsL3ewCOrS7e0bbTg7LDmxHJGEPloH9JIAkxls4CZKxpFPmrhJkDeuCZRx3lXa+Ny01qU/72FfW05Z6gYMRyUboxxjLINuRCgl0hMYY2gURGGANppsVpNXNVjB9jSnH4cEKwlRKNkaucyrNjWedLFRYaEoNZ4v2KgKbjwm6afhwiMMQ49wryZipZg1DetLKb4vWO7FGG3pJD6r/YSzGxOkaBbJj42dAk8K4sBHzudzH1tOFz3qu9vn3U6e3XEQ1sJNx7qLXUgS+zywNWNnUhAG7vo4LJGz1zBNspo08pChR1UpvD2CHQdxVcXhixk34sB5N0eJILfs54oM5aMx271bhuF7knPjwm2JLBjtailPrHQoKrUIqJe1E5/wfdc1szKIiX2PM1sZjTIYY1jpxfSS0IkHADPjahNDT6OtKy+KgoBZVRGHkuPHUoSQbGznNFaw1EsJfDfnu5ME1I2l0uUiMWGsoDGWUAj0vJEx9EEYV/dpsTiNHtfvHez+3xN4ngRjSUKPc9s5G6OSQRoQBpLYgwc2ZhhcUuPxNywzHOdMiga0wQ/cuIwb1/tgDH7o8cBGRjYfZ5uGISuDmJuP91BVs5j1bbWlbiwYzc60wQpDpAKWOhE708p50syFFrR17ZHGeXkGy91nJwvVn2NLMWkUIKVmlFXM8gZPQnqTTxQ6UZFO7DplJlnNsbTDF86OkFIS+E5RKC8b4sg7VIV7cU1ISa0tYeAtbt87PXK3k+fCcRC7nNvO0drQSdy2f3dELVzsne3+YAvhvkent3JuXO/Q7QR04uCShu9qvL+VfszmqEQpg/TkYt7NLgeJILfdOofzyNdgf5Cc/6UWixnRUkoXz2oMYDizWaGsE5AddBP6CRhc1vuBMxnTqkJYGGYlReG6R+LAYzhtEEgwhllZgXDjHUTg008Dji0nKG0pS0tjK5S1CO1+bNIoII9qkD5lXSBhLtAAO+OcOPJRxlA3sDv8UBnLoOvTaDcKQgJB6ArLUx/iwCOJJBbJ5+4dUc37vjdHGikMSRhQa8X95yasdkNWlpzEWhJ5xB03kqLXifjbT17l7DDngY2M48spSe5RNoYo8kljn3FWUswaolDy1MevctOxLvefzTi9JUiigCCQJIHP6lJCVtTcsN6ZF/FrBrFkvZ+iGidQUTYKrTSVcRJsBov0BCq3NI3BDyVIwXBccXw52TMqVtNLLErDDatdLJZ+Ei56srtJ4GpGD4gZ7oo+3HduSlUa1pcjZvPEXRKdLzy/cOzuhXHHbOYMzSSv5tqW+tA6yF3jXM7FOfrdyMWo/aPbF6+mC2dXaPewFsmDRJDb7fjhXJGhfP3rX/+QnPQ973kPb3nLWwD4pm/6Jn78x3/8IXneg9itNxt0YFbWNI3LXtfzERFGWHwkUexxdsuJNnie4L6zU85sZRhj6XUCxnlDVRviwAXzy1pTVg1h6FE2miJXeJ5HkoZY0yAEZHlNXiuwLEbaxkmANRqMQOIxmeTkpXbZ50ahtSUMXOZ2OFaoeQuiNWCswfd9urEEAUY3RL7ECLso2ep2QzwhOT0rKEpDr+OxNa5pGlCpppMEeL7HzrRiWhqsUWwWik4sWF/ucsvJHqu9xMW1pqXr0ol9TqxErA6cCPADWzOkMggJKybGl5ITqx2EdLHGoq4RVlI1mn5nPgo3dko93TRCSEC4gv+tYUmtXdG+EAKlLGtLCTuTilop0iQk9CRG230GYm95S6pc4Xzk+yz39wzBEvWBJTe7UyJ9KekM3LgQUbuSpLWl87OEgkAsvNi9z7HredVaM5lVhKEzMqv9+NBEzr6hY2m00MLUxh7ptV1NF85RdZbn3z8XX220ZmfT7WrCjndVs3C+mLnkO37u3Dne8pa38LGPfQwhBH/n7/wdVldXOXny5FWfsCgK3vCGN/D7v//79Pt9XvKSl/A//+f/5Bu+4Ruu+jkP48K53ku9iOGo5PR2RlHWGKO598wYg2ClExFGHqEnmJaKnUmJwTJrDHam5hlxn7zURIFwmdBOgKmd0ZvmitWVgOVeyM7EMsoaohCa2pIkEotHN/ZJEtcrHPkQR5ZJZrBzFZ/QFxgpERaqxqCcJCXefER3oyCqaqQI0ErTTZ3HoxREoUcYuL9vXE05OyywsmY6q+ezZQzS8/E9pxI0awxUJUJarIbaC8mLmv/16Q1UbbFYJ/YbBcxGDaerGWkSkBeKleUUz3dJgvvPZnQSn+nMlfx005CybshLxYnVDtYafN8jjkKEbWh0Q1EJTqwljGcV2rr61ao2GDTHlp1qzcn1jusgmqu+GyxRmC4+273JmcNm8uyqcJ8z5qItZVWbAzPZB5XtHFuOL0rGeFJwcqXDA1szlNKsL6csdSMn2GvtRbOZLhw6tjux83KM3rUo5TkouRVHPtO5UEA8965bHIe+E2fOnOHFL34x3/Zt38YP//APU9c1H/nIR/ie7/ke3vGOd3DjjTde1Qm1dj3HRVGQpilKKaLo2rj3F871jkKPJPaR1vUuT2eKcVY7NWdPEmvDxBjGswZtNFIIPAx5aQgDwTSv5yUngiCUGGUpmoZGWTodDyEtgZTEgcCXHpujCmMNzUyQBpKyNnhSs9qPiQPBX99fOSFc65IxSgus0ShrSaMAXzgFIGVdrWUowZNz1Wlt0QYaA0qDr13vblUrmtrQKIVRmlJBLF35jLGW4bRhqRsihasbyXKN9EBWih2r2ZnWnFztIozFDyQbOzmb4wIpDONJiZTw1PAYHoKsrLj/XMZyNwFpGSQhTeMGmznhV00S+ww6IWnskxcNk6mLB1eVYlgb1gYx41nlRt/GktWBuxYCz+P4csIoa6hLRVPbhablPsNzGR7PQVvKKJQLxfJZUSOk4Ox2zrntGWkcsDZI6O5RJ98nBryV4fuStX7CzetdjGUxG2g8qy65fb0ao3ctRH73PuduciuaD9osKrVQS2pxHGoo3/zmN/MjP/Ij+waJfeu3fitPfepTefOb38zP/dzPXdUJu90uP/zDP8y3f/u3kyQJX/3VX81XfMVXXPbjV1e7l33sma2MY+s9ZkVNU8CoUIS+xy03LnF2mNHvxUzyChAoa6mNRQrJzSf6nNl0nkKv4+Zch4FkMmuIY4+qMviem2nT70euRzuUGGOptMEPA0zduIzrKAdl8HsxN6x2kT5kM8XWtCKr6kX2uqgh8AX9TkBZVpSNWdRJAgS+i1V20hBlrJvT02iMdoZSa0upDL4HVdbgB07j0bcaIyUIQS8J6MRgrCAMQqwxUM0l2aQir2FlIF1vtgU9szTG6VJmhcHQcMNahwc2cnpdn9CThGFImHoYZdnOGo6tpkSBT3+QcnIlZVLUbIxLvEwyqwyDQcJKPyL0fcpGESURSRITJwZfeHQ7KevrPfwoIK8UK/PWwH4aksQBYeSzvCcxYYxlnFXkVUPTaKfvGfkLdXuATjdmMHDZbGMsShuOr3QYZxVlrSiqho1RwdZOwTQ35E2J5/t0kogbjnedsIYQDAaSaV6xPOhQN4YwjrBScMNaZ7Gm3eP2nmt9bf81e6lpi+vrvcu+vh8qdt/r0Jf0+gnpBe/xpXg41nu1PJi1Hmoo77rrLv7dv/t3F91+++23L+KLV8NnPvMZfvu3f5sPfehD9Ho9/uW//Jf8p//0n3jlK195WY8fDl3c8HKIo4BPf34DrSCrXCF4N/ExWHZGObNCYbTTXSxq1752YjXBKp9ZUVI1lhvXUjxPIqwrn6iVQSnN1lwJaBBHaGPZHGVUNayvOBWcze0ZVgg8q7EStndy+pFkUhg8aagqg9UabaBuwBfQS33XXy4hrzRVALp22++mgSh0QhK91CkVVQrqua3NS0hpEKHnyosKEJ57XjBkeY0UcHIlRSmYVZaqEXQiD+VpcjeQkLJSfPruDbLSOGUkrTDCKehMpiVWG2443seM3Y9OJGFn6FonJ7OG4ysxeV6Rxj7bOznDcclwVHBircO5YeZiflXqiqCNYTgpEVaBkRip+dx9Q25Ycd5Zkdec2phhraGc1QS+II6CPcPCzntwReVEHnqJ69gZzr21MA6599QIbS0r3QhlnCc3nFdw+MDZjYx7z04ZTQoabQgDj3NmisSw2g/ZrBpm8/NMshptDFiYjHM8T7LaDdicr2m2x6Pc3VpfbrfN+nqPzc3pZR37UGKspchrRvPQhG/Dxeu5FA/Xeq+GS61VSnGkA3aoobxUKVAYXr1L/id/8id8/dd/Paurq4CbH/5bv/Vbl20or4S9M3N8TzBIIyyW0JeUPUUaBVir2Z7WVJUTgp0VLpO90nczdpS2pKmrd9PaMhwVrixNwua4JC8VvU7IKGs4uZbgex6TzGXAq0pRNa70wpeSB7ZyIl/S7cYoUzgFnNqp/0gf4tAjDgQbtaWsXceMxcUv4xBkCFaDUgopJKoxaMBjXnxu3UXf1C7RIowrRpc+czVzyyR3WfuVJCGbNcxmNbUChFtHXWs3qsFzoYQ81/MBae75O9YSRj661kgBs2YupLvTcMN6l+V+RJbXnB1mrnB8Z0boe4ympStFKWt6qatXXF9K2Ngp2BxWBIGLkekkWDQBDDoRo07Fmc3ZXETXcHLd7T93Y4YPbGQYa5mVzbzF0NJLd8e/1kS4Dp7RpKCoGm45vl+N3FjLxmjG5mjGOGvw5+GN9V5KNz4f99x9zKxw7Zzre4yusZYHtjKyWbNHkOPgwvOD2NUgODMqUI3i5GoHX57fT1zrsp1H8vyeRwqHGkrP8zh37hzHj+/fKJw7d+5BGcqnPOUp/NzP/Rx5npMkCR/84AevWUuklGJRT2asZXtSsDof77Dcj5nlCmwHT/pYaxmOczdCoDbzMbSCWs2LruMAIeRCScY3kqYu2diZsbnjitbPSUE3aRhNa5QybE9d1trDkMYGA3heyLnhhKrWzHJn4ISGbuA6d87ulExmFTOnfwG4Y1QNxxOPJPFpjKCqKhp7/n6AsoJeYJ1Yr3XeZm1ANuALS1XVrC0JTq51SAKPWV6hjKZo3LkEbouvtKvbnM4qRlM3eiKUEAVwfDlirR8xHrs54VLUbI1mZIXCYrFGUSvL5rhgOmtomgYpPTpJQOx7PPnWZVZ6MWnsI4TFGs3GKHOyb6HHcj/k1MaMSbcm8Dy2dgpG04qy0cSBR+BLIt91NRllyYqK0cypEwkpuGG9Q6ODRZmPkQ07WYVAUpZu7pCTWJuX8ijFaFyhtUV6LokmpEV6Fm0195wbszMpmeYNgzTixLGUstJsjgq6nYC1JOCBrYwzmzM86cYHn1zvcOux/uI6PMrQ7WoQ3HA8YmvbXUs379kmHhRj3VUv2s3Og5ORO+j52/rIB8+hhvJ7v/d7+cmf/El+8Rd/kW7XuaXD4ZAf+7Ef46UvfelVn/CZz3wmd911F9/1Xd9FEAR8+Zd/OT/wAz9w1c93OQgsSRBQlAUPnJvS70ecXOmwMSyYlA3jouLMZobSmk4QIANnOM4NC2oDsY/TklxKedyJAZ99YETTWPKyomygUs6rK+uCSexKXIRwSRhwhiyvwFpF6HlMMrdt3jVwFqeqM81r4tC6Yy94DRY4O9as9Sxg0VZw4VE1zgOW0nmH2bx92OCSQmjopQFJ6DPOKuoalJkrpM+9zqqGMIBZbVC1XcznkZ47dnNcUzfGdf00mlHWoK2g3w3mCj4lgfTIqwYBbE1cR8ux5ZhwkDhRjm7AJKupa8O5nQLhub77rGi4654hx5ZSPndfhhVwdlgQepZuEnNqY8LGOCf0JTvTikEnpqhcrWQUevi+pKzUHk+u5sxOjlIWKaDUinPDGWuDlM2dHC+QbG4XTPKKJPLpJE4cZb0f00md6tLdpyZMK1eG5dZYs77S4dhySqM1s6Jhc1ggkISBpKxgc1jsM5TjWbUQ9OCAvutJ1szHCFdOXDlrYP3853pgHSXnjefmqIT5eOTNkRPyuHGtuzCGD0V95EHG9rHEoYbyJS95Cffddx/PetazeNKTnoRSinvuuYeXv/zl3H777Q/qpD/wAz9wzY3jLrtirdOiJqpc7FHPu3KkgLLRlKUTcAikh/QETaPY2Clo6ppZrvADD98XNFpzw0oXpSzWGKyQNMpZEgOUGrzGUtXn1cV3EXOloKxuaBrwfWd4dj05YaEqXWfQpeQRamUwBtcJcwBCCgIrEFj8PQPKds+TF4qiVkyLirxy0mfSc73iQeT0L5d7CcNRwW6UKpy3ADUahpOKU+emZHmB1pbtcUGpNNoIAulimZ3U0iiNkBKl3fPvdkN94fSEJ9ywRBL5ZGXBZFahGuUEkxvDZNpw79kpSRQQBR61atjcqQmCiqbRJHHA1qgiDCVlrZDzcRbrSwmB9KiN6zj5m/tH1LVycnMC+t2QRnkE0mM8l7jLy4aiaqiVpVTu1+n4UocbjnVY7cVsT0vXxpnXTK2bGJmEHqt913PuWcnmTsHWpKCsFGuDFKQljvcbETefp0ZaMa95tYuxulVtmJU125OSVQPDnZybj+9POgSB2CdwsbaU7DOeGBcS6CSSKPCZTBv6ab2nHOnBq5kfZGx395qPBY/1koVSP/7jP873fd/38clPfhKApz/96RdtxR/p7Bbrbk9K6sqpBdWVYTor6HUCPCQrvRilNPiWrNDMCteLO6s0GtCNwfc8tsc1oVdSNxXgCqp3jdqu2crnyZXd23cvFzP/r5gZagvhnsdY3BwcDRdb2AuYFi5mqQ87QLh2Qild/aXaY08tsD0rsadGrkVTq31b/LoEETqvNElcaxu47fvuukIL29OcnVFJrxOQlzXDzJ1LSljt+ggb0qjKDVIzrli+KGvKxrDUC7n33Jh+x7WBhqHHeNOdx5OSJPYZTkpWBq47Ki8aNkc5nTTCGENa+RRNQ6lwnSVKUzaa08McT7he7PvOTPjC6TG+7+J8TaNI4wCj4cRqgtGGM9slldJ4wgkyWysxSlP1NNO8YVZMMcpwbmfG1miGxCk+FWXDX9+3Q1Y2TPOKprEIC1nZMCvGrK+mfOmtK/s+klnRMJo6T7sxGl/i9C3ns7+10mAsedmgG2jmKvS7xsZYS1Y21LUhDCUrNt5XhI6Exuh5+6uhk+wvFq+UYmN7hpQSYwwn1o+2khcav92uo/1e7fyafAx09Bw5rjaOY77ma77motuXlpau5boeMna3CFprtNFEvk+lFEIY+mlItdTwmXtzpmXjlF9Cgac8au0MUq0s1ip2GndRb+wUTHMnaGEOsFaW/RviXU8OIIpc/M/Wzrvcu3k+1PAd8PyXOnaUuf8HwEF5y6qCzaYE6zQ79iJw69qZlFhcTDK/cJCZB5vbBWXZIIXzMgPpPB0Jbm6QD1Ho40mL5zVg5kLC0iCFYGOnYGunYn05QQpJHLgvoEA62bnApyw1VakYZw1JIN18Iyyq1owzNxXy2CDAYuiJgH4nYntS0ijLA5sZQoK2FmstG6OCm9YDeqnPOG8YTSsard3OYlYhrKXfjZFhgFKGe8+MGU1r8lo7BXopwLoi7F7HZ1rWnBvmTMsaoyxJ4rni/EAySEJ8ed7ATfOa0aSiLDV+KhFGkheasXS9/hvbOeO8odGW5dDHpIZSacazatGCeGY7I/I9BmlM3ShG04rHnxwAzluMQhe3HU4L1gcJYegtpOXAecLSl0grwJfUzdFX24XGr9EGsAd2Bz3YQWePBo4cV7vL3iy4EIJPf/rT13ZlDxG7Gb1jqx3ObOYIIQgCnxtXYxd8z1znSjd2dYVh4HHL8YT7zk1Q2kPpiryx1AVIaambiqI2+DjDkgauY+aQnTCx72J+Srm1eN5c0EK6La1WbgStOuTxV8te+7aSClcOpPd7hxdigdKC1G7WTjeV+JWZz6dxRe+Vgr4v0dbNvJ6V7jUGocRog0JQNoZBJyCMnEBFUTT4vkcwVxcaTirW+glhIOnEPkv9gKww81G6khNrMVVjOTecOcWlOCAOXNvlpGxYGSR4nmCSl8wqxbFByvpyMm8KqJxwb9kQ+tKN+vV8oshjktdONMTC+lKHslaMZxW1sgzSgFJZRpPKeZhIZsX5+s1uFJCmAda4+G8Uewgv5N4zE06PFL0ocOr06z2XRJpnwidZjee7uUVVYwg9iTYGg6WoGwLfjZyolPMItXHNBtuTkk7sRjRUlaaRrs0TK8C6cEMvDZnkGVmm6HcCYu1jcaIhnSRYdKXlpaIbBkSR5wSN91Xo7mfXuJ/enBFHPsG8nRGE02o9oFD+wQ46ezRw6Ct60YtexMc//nGe+9zncvvtt/OkJz3peq7rISfyfU6sJK6LxYNGWRSufe3GtQ7DwCPLa6LAIw4CVpdStkclaWrwaw8bGZQyTsShASOdd3iU3mqp3H/AwpoKII5wnpZyI2WvJbW2XIkofdFA4kNVuUz9rudsrXvvqspluMvaEgfzZFFtkKFgKQ3whaDRgn7goxpLIdW8xMgwmymk1BxfjrDaDVUrSjfXwvckURJwZisnDHzSOCRNQsqicWVUgSQOArbHBWJWk5cRVaOJfYnSmuGkpt8JuPV4h7vPGqZFg/Vca+jWTk6/E1JWGmU1yhg8T7DUjdDKOHET6wynK8kyxLHr4+/3IhCCrZ2SIJBY6wSIhRRYa5DGuvil59SWBr2IKJRMpq5kKYlCprMZ41lNHAV0I59ZUZHGAWVlWV6KELhmAl84TYKdaUEvjVjpx6z0I06dc+r2UeixvupimNO8XiivN9oQ+ZI0Dhl0IobTgvvOTannXVrJ3JB7PqwMDt8W73qSSeQt2hmTyHNF/IeUED0W1NIPNZT/9t/+W4qi4P3vfz9veMMbyPOcF7zgBdx22230+/3DHvaIZVcEwJeSrUnBPaenVEohpRswFfoS6UkaZcjKmiTweOJNS9xzeozoWLKioao0RaPwfCiq/Vtg1019eVggLy5/u/1gMfribfalsDgvt1HzonXPlQ01bhItZaMQAvxQkgQCYwS11qwPnB7krlr4tKgwxqkaVY0i8nx8zwdhmeWGoSxIfI8oDCibhl43YbkTMi0aOkAa+XRjn6ZR+L50vfaFYlZqBt2EvFB0E8n2pGZrXNGJA6zW1EYy6DrB4puO97n3gR0aY2jmgiOxCBBW4AvBjWs9osBtL7u9EN8XnN6cORGT+YxybSAOBavztr6N0YxRVpGEAZ04JAo0nu9RlYrGGIajnEZpenN1nkEa8fkHxsSBz9rAqQadHZbcuOZzbDmi0SEWOLHW5TN3b2E0dKKAstRs2cLtigYhnTAEyUKSbmtUUtaaujZ002DRrw5waiNz44NDN++8ajQ3H4svylgfFosMO7tzgRQr/eiSxu+xUId5SR85SRJe+MIX8sIXvpCzZ8/ynve8h5e//OU87nGP481vfvN1WuLVYaxlZ1IuxAk6iZvGd8/ZCV84NWaUVVRa4wuLLz2Weym3HutR15rNSU6vE3F83iHymXuGTrJLafppzKwoyfecS+Iyq9UVGKPrZSQFEASu/KesLg4R7I1lLgrX54+THqSRRPoeRaVJpNuGe54rdhfCEgUhoR/Q6/gkccDOuGRWNSwPIura0AkDlHXycVJ6pImrLugmAUXTsN5P2ZyULiwhBbW2+J6kGweUtaHfiSiVIg58+t0YdM7GqEInBi91LaPCCtZXYprasj1pyIc5/U5EP42Io4CbTw44N8ywQqAa47LlQhJGAWv9CM+XdFKf9aUO06JECNf3XVWKQMJKN2KSF1TSoK2Zi6MoVgcJj4t7nNsp2J5VhIGH1oaq1hSlJgkNoS/JcldVkcT+IgnS74QsDSKMFazPpeM6ScBaP0Epg++7VtKibLBS8vhjfTzPtck2jV2MXE4ij1mp2JmWnFzvLAxaU7uWUCkhkD6ePN+PvpcLY5GV0kxzvciwn1hLv+gSM1fDZQcTtre32d7eZmdnZ9FV80hmmtckQu6buyKFoKlcVlhg0cpQKfB9xc3HfM6NZkyy2mViZyXbk4JBJ5p39bhf/VpphuP959qtU7wSr/J60swLxuPAdQLtMtdAYF69sxACBudBagWe55R5lM4pZiAC119d12B9pzrj+ZAmPr6UWCHoJAFl3pBXBtkV+J4g8iXCmxsN33JsTeAZN8Gx3wnwJHNRXp9bj3dRDUy9BolEeqC1y6ROspqVfkoQSGaFwsPy+BtX2By5+spaGZS1TLIKk8LWqKATgjUCawW7CiSTWUXTGLTV3Hisx/ogJfIlw6mlm4TccsxHayent7IUsZ2VbA1n9JKAXGmW0pBOHJJEEmMkyrgwxUo3RhlLHAm2xgUAvueUq3YmFWWl6CQhN93QW4gB79ZUrq91mWUlZ7dzRtOSWrkpk8uDGGUMQojFyInhuHGKUXGALz08n321k+urCWc2Z9QNWAzrqwe7e3sFhctKsTHKCXyfNNyNwu9n33z1+Sjgh7sU6HqUJ13SUJ45c4b3vve9vOc978HzPF7wghdw5513PipKhKraLMQJfCvZGhfUtaHWCk+6+JVSBn8+te+v7t7k3E5JHLi41awx9GOfx58YsLmTU2lDPqvZGuWUan9mWwCR52KWjzRDaXElSw2uoHwXD7dWjVt/J3CjJnbGhjRy3l+DBqE5vpIQ+5L79QTdOKGM2gINdLsxoS9c91LiZhEZKyhrg9KKqvFdt4uVrPYDRtaNwc0LxXIaIeKATscFOqNQUjeCG451uOsLO4yzipVews3He4ynDUu9ENN0mJUVjRWUlZujHfoSawXjac3JtZTJrHYzyT03hsP4Hrfe0GM4Ljl1rsZoDdISBpKqspSVYgws92OMNqRhwGrf5/RwRhQIzm7lZHmBMoqssnSSkH7qtrReI7jpRIc08VHWMp3PB98Z18SxTxp5fO7UmDT2OLGcsjGaEXqS9eVkYfQ2dgqCQOAF/mK2kzIuHpnG8971UelKjHyP46sJceQvetu7HR9t3FZ811DsGs1s1tDtBJw8xFDuFRSeFg2B9EkCnzj26cYBzQVbkL0e6O4o4Ifb47we5UmHGso77riDu+++m+/4ju/g53/+5/lbf+tvPaQnvtZEoZwXZ1u2swqBJQ5d7WPVGFeuEUiUARpD1Vh8zzKrLEoXNEqw3A0Zz2oaZdgZlUzLirLeX9YTS/ADlwkd55dY0MNMA4tF+5yv69y9uWgg0IZ+iosjegKlNWWhObeTkUYhaeSzUzd4HsQCfAtl1TDodhhParZGBQJLPu/LtkJQlDV+4CNRGK3pxiGeD+OsphOHHFtKiUKPvG4oa003DPjC/WOyoiEMPLYnJX4AT3n8CkZb0jDg9Jabf25sTFUpikJxcjWhamLWBjFVY6iVwpMet9zQpcgrikIxnFTU2pU1pUHArHaKOUWlyXLX43/riQFbo4Kz2zMiT2KkZXtaY6zHseUIz/eRwuIHHmWpUEpzy8kutTLsTEqWujGd2GPHGJLQFdxHocfWpKKTRNy41nMD3HAZbiEgL2s2TpfszGr6kWsLO76cMujGGGM5tTVFCsGxpQ6nt6ec3rSs9mI3hqTUCE9QVprC6kXnz6AT0U/djKMolId6WLtb9eG4cv6jNHNVK0sUyIsy2Pvmq++OAn6YY5PXozzpUEP50Y9+lCiK+K//9b/yzne+c3G7tU5N+3/9r//10K7kIaaXhoSRz8g4Adp+x8n9787G6XUCosinVorpzM1lURqqugErEVKwsZVjtcWi0bjYkO+BsS6pAZAkLg6ktSH0zrf8PRgubk58aJ/T88HMM/E+oDgvDNyJfJR2A7ukcP3yk6wG4+bp7ExcY7hn3WMms5rlXoyQFlVq15Ezn+booZHCW3SDjDKXJMsKTRgGjLKS9UGE0oai1Kz3U5b6IX/6V26GzuogJss1TWO59Vh/kcRAQlbUCCs4W9XoBo6vpwTzFsbH3zBAa+OqEipNUSimuaKXBKShh9au2rVSbvzEJKtZXYrYmZRUTcPaIKWTBCx1I8pGk8QRx1dcHedoUjIrFSbx8X3J8RXX+z3ouvG4rhwo5OYTPe57YErZaM4NZ27ExySnCCJuOtFhbSlma1SSlzX1XC2+qAw+bq77rFD0UjfGoqkNS70Y33cJqO1xwVo/wQKDbkDdaKyxBHvmkwNHjqvdu2UV0kn1deOI4aSkag7OYO8tBaqV6zPfK479cHTlXI/ypEOf8Y/+6I8e8pNdT6QQTkewcrNdZmXDcJRz/8Z0Pk2vR5Y3+J7AaFwRctOgNZTWEAUC7RvGWT0fZGeQ0smZ7drCQM6TJcJNupN7syEPAg8WrYdXEvc8rMj8Qnx5/vkV7iIQwmXHJ7lCeq5LJ45ABrixFcJgrUcUuB8U4RpCUMbSKCeAbKwmCgOnhD5rMPNSrDTyqRpFoy1lbZC+G4AW+ZLtac3jTvbxpEuouBkXlq1ZQRS42NnKIF18+aQQ3HK8xz1nJxSlG1rWaENRK5rGDQe75XiCkJK61swajZCux31WNaSJRyeIWF4K2ZlU3HN2grCG+89NiUIfbUO0wQkgS48gkKSJR+C5ERbJfHu8thQTeB6BJzk7dIIYFji2nFIrTT8NscJyZjjD8zywmkZZ1pZ8diYFD2xldJOA2Y4mCl3Beq0UpyfuekUKRlnFyiBifTVhktUkBCgFK/1kUS9ZVZrRtMIKWPa9RZ3l9riirg2TsmacVWyOCr7mb53Yp0q0d8tqtEHipnseX0nPlwNdwN5SoDTymWU87F0516M86VBDuatg/qlPfWoxCuIrvuIreNrTnvaQL+Ja4wpza6Z5Q1lp+p2A8aymKBW9NODESkCjNLMywKDmepceda2IA+nm4zRmX5eNL1ynjREaZV0ZiXoQVeO7nh3z/4ec9/Qu+zl8EMqJY1xoYL35Dbs95mZ+jnp+nnhuo6SANPIwWlMb8GqLF1lqBb2OZKkf02gwysXRuonHZKZdIgePOPTQyrDUixAIDNZ5NEiEMEShz6xQRJ7bnYyzkv/9hZon3ThACJhkNf1OhEWQ1w1JGHJybf8+atCJ6CcRRkM/CWhCy72nM1YHEev9mKx03mO/G+IpzbnNjLyqMdZNaXzcyRgpBONpRV7WqMYyK10xeidZdrWUxnLDasI4L4lDn34nZDgtUMawPIjcHHDPZ2tSEPmSolQUjebscMZS6uook8jn1hM9KqXZHpVYC56QJHHIZOp2N/2eEwjxA4kX+MxMCY3g2LJTWJJCzOONM7JZw9pKQjf10Yq5sIcgCAVntwrKWnFsOWVl4LqUtrKCaj4LaJo3nBnO9qkS7d2ydpLQjfno7c798RbHHZQsGXScI3L67ORh78q5HuVJhxpKYwyvfvWr+bM/+zO+8iu/krqu+f/+v/+Pr/mar+Hnf/7n3a/kowQpBJHvc2ylQ5Y33Lc5QSsXgxEChtOSIPBZX0qZ5TWToiGQgrIRZGVD2ViX1TSuy4ZmnrxpnPxZGFgnovsgMjn7jBow6MH2VWiiJgn0Ap9BGlCUiqpuyAq3thDoxK6GMw7ca/GVU1dP/HlyR0NZa8LAKQk11hIAUSBczCvyXUtbrTFaY3DeZpKEGKOx1lJWDTed6NELA0ZZjfRhOC7YGhfYrJ6PbJVIz6nHDzpOtV1biCOPG9c73HpyQKOM65vXF8+gWVuK53OMQpq8Ig5gOCqRCE6s+cxKzXI/XnjYcRCQVQ2BL/jsvTtsjkrysmEny0mTmLrReNapkmdFQz8OGfRCOon7Ee0kPo0KSX2fe85NOL01ZaWXstwNOb7a4a/v2eH0xhQtnA4ows0d0toSeh7KGmZZzQ3rXcLQSco1jeXGtS7dpOJzp0YMkoBZpaiqmlFW8qSbl3ACFpEzcOssOn7ywu2GlHYXz4nVhCxXpPF5T/ALD4zxpcT3JP00JpvtVyXau2WNQonvB66N9wKv7FLqR4+Frhy4hKH8tV/7NbTWfOhDH1roT85mM17zmtfwq7/6q/zDf/gPr9siHyzKGLYmBZ+9b4fxXLuw1pZOIl1g3UoS36KUdKUdoUfVWAJfoC2oWpM1TlnHmvOiFGruAjb6oa2L1MDW9MrjlIViLmkWIL0ArGE0c7uxcK4QVNYQx27tWrv6Tz8FIT2s1k4/U7nvhDVO9NdagxGCG4918YXgr0+NsEpjCOh3PerGoLHktcVqxfHVlOm0ZiZqgtAj8twWUWvris+1pqwsx5ZcVndlEBN6HsK6ur17zyjyeQui8Nxr2lvmNehEdBLXGTPKSrQxTAondDyauoFla0spRaXJC+WEMIxgOHVJmrzUxJFHUSrq2iJtg/Q9fDnvXfckW5OSe89MCAOPk+spZzdn5KXi7M6MrKxJg4B4zcMPJGc2p5wdztiaOMm4buQT1ud72LOywZeS3iAhm7ldTK8jqZRia1RSKUUvDdncybnvbIYQsDqI+Ot7R9y03iWJvH1DyiLf58RqQFY2nNnMEJ4kSSL6nZjI9xce1g3rXR7YyPCkoKwblpf2b4kvZ0AbOFV9ow1h4C9ioLuG8rHQlQOXMJS/8zu/w6//+q/vE+ntdDr89E//NHfcccejylCemfcMB4H79UuTkLVln6Yxri+5635Je2lA6EumRY2eFNRaMyvdKMRong0xc0Ppe3Njos5vmR8MB+lPXg3KgDaCTiLJS8FcAAixK+mmYNAJyKzCakuvF2G0pdEGzwsJvIa6tk5kWIInBN04xCjD6WGGLz0Xc6zdXJ6i1kS+wFqJ7xmk9LDGYAQY6xEJQRx5WASB5+F7HnEU4ElLmkbE0byLZ1a6DhKlUNbi+ZJxXlFXGrvikkN7h33NCjck7ZYTPf763hFJHOBJ6T7TSpOXzthWtebssMTzYXOncqIkGLKZRkoXVxYeJIGHUoYsKxHSjdedlQ3jrF6UzlSNYmsnxwg3l91ow5mtnMmsJK9qamWR2rA9dl7zBMEtx/tUlebkcodOJ0AIp725a/w9KZhMG7Q1zErlWhirhu0dQ5oaopO9i2J/u15cGvnEkUelDEEgCQJXZ7nrfR9bSagbQ1U7df5q3tt+0WTIo7as87inU1LZ/bfjsdCVA0dsvXu9i4fxDAaDS46JeCSxO672/nNTOlFILw24cb1LrTVLnZAHtmZ4wiKEZDwrCTyPW471UMpwl9Jsjius0jTWyYi53mS3ZQ18l/02AdjmoTGWD5ZQgOfBeFoQxR7SuliqtnsMr3UKP0JYknkGeDRzAppBAAhnbDupoGlwI3AbQxj65Lmilwo8CXmtscb1OIZdN7VQWDvfYtcs9SNOLrvyFGstndhjlLkYKMLS7UR82eMG7EwqilKjhcAay9bIKacHvsTzPHzfsjOu2Z7WpJHk+HJn0WoX+T4n17rsTCu2xgVKa5LIqeN4nuC+cxOOrfeo6obhVkWvE+B7gqoR7kfOui3wiZUOWlumVc0krxl0QuLQxw8kVVMzmimKUjPOK6dIhCAra+4+O2F1ELM5LjDW0o0DRllBoRRxkFDNrxctLKO8Io59Bt2AKPSIfB9Pinl80OOBrYJ+J+RenChG3Wg86XY0uzO2TWrnCRg9V/MR3HrStRM3jZsIWu0pExLALcd6ZGVDUTj1lb1G93ILtVcGEVujAqXMolf8ws63L0YNyr0caijzPMcYJxqxF2MMTXM5udWHn3FWMSsbpJTcvzEl8AW1atDWspNV9JOAWilGWe3m41iN7wtODWcuHukLTOhk0dwFC3EskFisFUhpscrdrh7k3vvB/vQI/v/23j3Y0qys7/+std77vp5rd0/PDDOMoIiiRhMYoUTUgDIYFKkEMBo1VkJChQStIBIiSUpSYBknUUylKlLGGGKMicTSikQTi3gBMVoK+APk2sylT3ef276+13X5/bH23n369OX0zJzu6W72t2pquk/v875r7/2+z/us5/k+36+3bygaKLXD2jHWXa5KFEhQgQLjOYDDsWE+rDMX7xBAVDm0gGKsCUOfQQdSICVMCks3DphWPiDllaGjfdbVaOfPIQRxoNifeF8hJIRKMK1qekHMl5zuc2KlzX0nezPZtRwD7A29edf6SoJuHMYYEJJpUfJ4bXn0wpReGnL3yTZrvZQ4CNhYSckrzd5w5sfuLHmlWevGWMcs4xI8en6Mw6KkYKPX4u6NFaZ5TV4Z8kojjKWxjtHU11TLxrCx0mKlHWOdwVnBSiehbCzC2YVHUd0YRrkhDSCNQvrtiGlhaKWK/VFJGAqskUzKisGk4q61FkHgp8aaxjEtGqIwQEWKtW5CWTmy1LHSjgmkXNT+DnapwY8vHuwwf/LRgjNnvc1IHCk2VhI6mbcGRjiSWC2C7kEL3sMd64MBNAw9JUtKAUKw2osXosOHJ9+ebuL5jZzQuWqgfP7zn88v/MIv8P3f//2X/Pw973kPX//1X38sJ7/RKGtNXRs6WcAkV2ztTOh2YlZbIWVlkVLw+ONT2mno65K1V8aeVo23Up2ljdoar5LdClHC15zCQNFPA8Z5yXjqg6Uxi3r3ZbqUNxoOmBx4fu0XV35dYaAYXDn/PSgkPKlmpQV8VtoYw964YG9U4YQhlIpuOyaNPEdyd1AwLRpq6wiEQGvDmfNjsjQkTUL2hlOEhLVuhna+SbPeT7yo7aRkf1KyPSy8wHJtmBSlHwYQjtOrHc7vTxmMa5RUJGnIuKh5/leeIpDOe4dnEeNpRV4a8rpEIomUoKgt53fHaCMo65rhpCEOA/rthI3VlDQN+cLWiEBZPn12SO282jwC4pkQRyuSpIliVFp2ht6u48RKa/a+c6QQZCEYJwiVQyKQSmIR1NrQSUNE5Ok+SsBwHFDUNUoFJFFAJw3pdz2ZvSkbitqgG0PdOHZGOUoJ4kgdSax+5OyIzz46REhfXxbO8SWnV7wh2swr/mDD5WrHOxhAdwYFDsHarBs+37IfnHy7VTQob+SEzlUD5Zve9CZe+9rX8rGPfYyv+7qvQ2vNhz/8YT73uc/xy7/8y8dy8huNJAqYFp6/JmfdQwFc2Cm8P7a2hEqwdWFMFPqgp8JgYWI1mJYYA+2WAuO5dVYIFL42o41hWvm6peGiSG8aQhrDpOBYCOhPB+bLVnijssnUgIBWEqBkQKUNeVFjrTdhKzUEoWJvUGCM9XPKSeDtXNdStnanNI2lCQwIwZlzI+7fHs8UwgWffWzA7rCiMv6mTqOAIFQ4B3vjiuG0Ia+N3wWUDed2p+jGsbmZcm7Pd5gfuzCh0gajIU0CppWmFwYgFYHztUuBRAjBF86OCZXkvrv7gGNvVFEbaKwjECCUpN2KSNOQvXHFWj+hnyUMbMVoqhEiZ72fsTuoiSNJr5NQVhoVSO5ab3kZN+G1JsvaEocKp6ERjkd3x8RK0WnHPPvu3mICp9tLqaqaT35un6oxi4kapTw/9FriuQDn9qaUpiYiojY15/dzeq34EiOygw2Xgx3rg+OU47ym24oQAsbTmsY4klCSxRdrxAcn326VbveNnNC56rtbXV3lv/23/8Yv/dIv8bu/+7sA/KW/9Jd417vetTAbu9XRa8d0OyGfPDMmUJIw9KZU2lhs0WCt8TqCWchkUpOXhn4UgnWeAlNClEgCoQhiwaSovLiqcTRNw6jgMn+cAD8JMZpAfbWF3UYwwGT2RmIJI62RUnsupTFMK0saKvKyodGGSvsJn7KpSPKKKJSw1qKpNdZCGgVYB9Oq5k/+v/P0ewlxKLgwyCkbSxp58vP2MOf+U32sdQhhKcrGk9vNzDaiFjy+M2a1F1NpzTCvUcJ7qkspKLVGVXD/PSlu5h/udwISUMhAMMobtvcKHt+eUlYNlXaECoRSCOUIZEArVlQIytJRNYZuGiOcl15zDtqtkNG0RglJEil67YQwDGgFCusMrSyglYTsDguCULA3LEnTiMZ4KtX2fkW3lSyCjdHQ78TEsWJ3WFI3Fm0soYoAQRxJ9oaVHzGM1CWiFEIJYhkQRwpRBQh1kaR/pYbLwY61sW7RXDLOeZM45YOhUpK81JSN5cRMgejg5Nut0u2+kVSlax6p0+ncNBOwGwEpPVl3b1SSF76e1mjj64sCvnA+pyw1WeanSTgg9WWRhJEkDmaGY9pSa7CNXQTHK22tNX7beieimj8RZunmOHd0MsOgbhAIJuVFkQ2jvbixwW85O62ILAtwArSxRFIyqTRtbdnamVJbb12bV97jppV6VyGHY3/S0E5C8sYwGFUI4QhWWpw5N6JsDKc3OmRRQJIotscGrR0r3YS1XkIaKDpp6O0trMMYiDO/NRbCN/zCQDAuJKH0bphhaJFWEUYCrOIZp9pMipqt3Zpp7Xw5Qgmc9WpBaRTM3AAc92x2CAKvazotNIHyFhGnN9uUjWFaNjSNoduKkFISxWLBXey1YxDOaxBoUEpQNoaq1pzbzel2QmIkrTSY1RoNQ/zFtjesvAVxoJDS0mrF3Hfi2rqxBwPohf1i0VxabccM84qiMmyuZAgpaGqDmqmqz393Pvl2q+BGUpWuGihf//rXX/MX/92/+3fHtogbBWt9cTebyUGFNqRpLHVjGUxKsiQkCiXj0kv5W+vIy5qyMmA1ceBFNKZ5g5N+Prq8BRWCni7U1l+UgfIh7aD9rnNevX13L6cXh3TaIRLBzjBnPNXEkfdIN9qyN6hQRpNrh248RaaVKIx1M1MxR3etxc6wIC8bhBM4a9GN4fHzE6rK0M0C74hpfY2xFSmee98qw8oyyhtWegl3V21290tK7ehHkhP9mEnVUDQGbEMgfQBsZwmbKwnrnYxWFtBvx/TaEfvjisGkIZDOWzjUDfec6GBsxN7MGdLgSAJJEgdkSUAaheyOCiai4d4TXf+AKA1xLAiUVyya60TOfeiNcYzyGue8ze60aNhYyVBSsDcq6beSxfZyb1RircNouHvDB2OBYrOf8uz7Vq77uzyYjWlrWe/5Nc1rfk3kM7SDhme3Wtf7RlKVrhooX/ayly3+/NM//dO88Y1vPP6z32AMJxVF5Qm+F/an5KUlDLxBjZhNdzS1RshZcboV0zSWHEOtHbFyGGtBgTBQL4PkZTAOqgI//sjFz8fgCe7aWoZFhRYBWMHOfk1tNWEQsb1fMppU5GUDUiGcV49PQsHdJzqkccRmN+MTkwonJXEUsdEVFHWDCgJ2xxX3nOhSacPOUDMpDSqQRBE0WnPm/NS7aDqLcpLagAqVzzRjRaW9YEqsJPuNI0ljTq23WO154nYvC7gwKHns/BZpGtJvRWz0UvLSEAYCKb0zYxxJdBsQjk4asdZLOHthuhCyWO9lNNrwpfeuECg4t5t7zmQkfd37wPa514pndUk/sTRXp1fKG4gVpaExZlFXnOSasmpIk5AkDjm12qGdBgsS//XiWtnYlX52K3a9bySuGii/8zu/c/HnX/iFX7jk708Vv/M7v8O73/1uiqLghS98IW9729uO7dgHUdb+aXhufwpC0G9HlLWfZ+2mMePS26mudRT745KdwYRAKYRz3vLAWPrdmLrx9gO3AlfyVoECei0vpjGeWUQcfojECiIlqSrjdREzzy6wlWVSWEJVo22ABCalXigygWNnL+crntmm24m4f7PD7rRkMCmJIgiDgGmjiVRIpCQnVlI+8pltdvYLhPT2sVVtWFupaGUpe6MKCQjnRYU3eylZGvqZ9G4yUw23aOtoZyHCCrS2DKYNe6MSpSAvNb0sIgqlF8+wjnAmqLK+kpLGIe3EE8rjICCMJEXdkBJS1A3ddrTgQGrtqT/WeJ/18QFNx3lWVNWexjQta/JSU1WaOJQLv5u9YcWkrLHWIZVXeBpMS9JI0c5i+u3kMi3Ja+Fq2djVMrRbset9I3Fd1U5xjCn1o48+ytvf/nZ+5Vd+hbW1Nf7W3/pb/N//+3958YtffGznmCMKFXtjX6wPpaTTD6m1YHdYctd6xtndEecGJRI/hWJQYLxUmnGeR1nWhkBJktAxLpf55BxhCHEcoXWDUo7DH43Ck/KVlIzLBmt8syCvarQBKS1xFLPajmgsuL2CZqY2VDcwmFaEgRdRzpKA9iAilJLtQYHAokJLJ0vptaKZm2KNcQbbOLRxJE7RaIgjGE58U0RIWG1FNNYwnFqkA20a9kcl46pGIKjqhsoZsiSkmJUIHIqVbjhrcngLhn6WICNf6+y2I8JQLYR0G2O4e7PNpGjY3i28Q6VzTMtmQcBvGkun7ZkXZ7enAKytXWySXpy+CSkbixJikdWN85pWGmCMw0iLcY6y9hbK3VaMBAaTks2V7IZ9//Outza+jCWluGW24DcCN72n/9u//du8/OUv5+TJkwA8/PDDxPGNSdmF8y5/aaKY5g3jqURbw2o/ZX9aI4XiZC9jWFRM8holnO984mCmjt1oL2e+zCYvRdnAYFgThDN9zkP/bvA1ykobL0cXKiZ5A9LNHA99Qy2vDNZ68nZjPdVKBd458sIoJ01CpBSc3Z6wPy4Br2TUbyesdGMaa7GNQwpLHIfgHGVVE0qBs4ZPfn6XSV57WhCG87VBW8fGSot77urw0c/ssLVXEAaOorac2dKcXM0QwjEpGyaFJlSSogqpqoZQBayvJIxlxWDsszndGLpZTL8XEwfBYmpGCq/WHgcB5/amiASyRNHtxGhtaLTFGG/3UFSa4YEu4MGt8ImV9JIANKfBJLEhLy2RkKy2E06vtwiUmHGEObZmxpWI3POu96PTGoegl3m911FeL8SC76SgedVAORgMFn82xjAcDi8ZXez3+0/qhF/4whcIw5DXv/71bG1t8Y3f+I38o3/0j57UsY5CpQ1rnYTVTszW7pTHtyec3mhzaq3FZx8bMlSVn5AovY6fRBKIBhUo8sYQRjAd+9HW4/bdvhNQGK6pBlIZCGuDjWAtjRA4qsaRJYrVTuKD4f4U52YOj8weSNpvdS/slcRhiLOWR7dHFIVBCIcMBG7qGJc1K62UVuKDU1M11MbXALJOwP6wZHucM5oYGmsIJGyuBN7ne6beMxjV6EbTSmOmeUmJtzK2zgf4SEkCKciLhryqCaWh0A3rnQyl8MK8exNaScxf+rIN+psxVe2FNXaHBa1ZQyeNA84PCnpZjJSQxt7K98RK6mXlnC8VzW/IazUmrphtpr5eGAcBUXix8XIc0ypXI3KvzFwp593yuqiZlpqTq+EdV7e8aqB8wQtegBBiERyf//znL/5NCMEnPvGJJ3VCYwx//Md/zC/+4i+SZRl/7+/9Pd73vvfxqle96rp+/+D25Cjsj0psL/VaiVLR7ngqR1FrprXGSUUYQJwGtIlpZRHbezlCVVjhGAwaz4VcBsknjbr2cnTbTYG2s1FPobkwrAiFoWiMb1gcCrjWwO4w977Z0k/YOAm7+wVCCqSsiULBYFyx2ktBCMJAYZxDKslqP2V7v2R/aDDOz5hrC9NSs74qKSqLExIrQIaSad5QWUuqBAZHVVu6WcBXPXsVJ+GTn9ulmIlPlLXm7M6Efi9CBYo0jqmM4f87s0+nk3LviZ5XI48DxkXNykrmPYZCRTsOWVMtTqx4MeK80gtTtCQKWOkmV/oYL8HaWpvhpKKsNSdPdD2tCBY/SyJPNZJSsD8qSYWk1/PniGJ/DmvdFV9/JWghFvVIax3aWDbW/X1418nu4j1MteHuldbi+AdfdxhP5PzHhY0DWpxPFFcNlJ/85Cef9EGvhfX1dR588EFWV1cB+JZv+RY++tGPXneg3N2dzIR1j8baWpvdvQk7w8JTTFoxn/nCLpPSsNlLMI1hZ1gBlns2WqRRwPbuiLI05NPGj9At8ZRQz2Y5y/LAD6cW66rLrHPn0Pjgem6nYjStEBZkJJlMLbWBA7kniWg4v1sgBPSzkCSUGAdnHhswzd3iHPNLJi8t57ZHDOKA7b0JsXTU+ABalGCVw+yMUUphdcgXzgo6rYjhyAvjjscWa+2M46gBwWon8tmdkly4MKWuNGudhEprnHWcuzDm/F5OEkoK68jigHPnx6z3E4q8ZjDL9k6trbG9fakI6bUywgDQVcN2WV/yGplF7M74jQf5kdY6Btahq4bhtLo4pmgMuwfqn4fPNT3w2jmR+7y1REnEY1tD34VXXqZvOi3RlV68bvsqAjpXOv+NzD43NjqXfbZzSCmOTMBueo3yJS95CT/yIz/CaDSi1Wrxe7/3e3zzN3/zDTmXlJd2EKUU3jq07buS/U5Ctx2SJAF/+sltxkXF+d0S6Xwz5zadPrzlUV3nA6hxMM19WIz1PEheinLmBhkruDBokBKk9NNBh8kxc3kXbRxVbVBIpkWDnhHbI+Vf5KxERoogCNgeTNkdV2jjZ7CFg6aBOHK4wKK1YzCFLInptyKUchSlpskuajwOpxXWOXbHBWavIIoFd82ym4Pb63lGdTA4VlovttTz7ezhgKatZXdYLYQGtPVeQEVl2BuXWO1oZwFRpGglnlO8MygxxhLHAVns7S1G+YTRpCZLQmojFvXGMBSLUsXBhlIqJKHyygZprFhPE7Z2p1zYz2m3QtbTkKvhZhiCHSdueqD8qq/6Kn7wB3+Q173udTRNwwtf+EK+67u+64acay6zNi6qRUapjWV7OCWa+YuEgWB/UhOFinpowVnfVFhut28JaLzEnRfU9Q26KynJV/MgesBe0nDRR0gBWewbRdPcEMVemGNSawQQhN6qwlpAOmzTMC0ETW2RgXc9l3g5OBk4rINWGmKt8COGgSerP7Lt57iVgvVeBtTsDkqSQFFWnu4zbQA3ZW9Ucu+JzoI7CT5IPro95txuTqgkjTZs9DPSKLwYULi0Znh+PycO1EJY97ELE06sZJSVRhuDEJJp6efQ50HOOId1jqLQ1I0miQNG44Y48jqtk8J4tfi1kKr22eHmysWywBXpQTSESrK5ktEYw7Rorpol3m7K6E/L6l796lfz6le/+oafZ04472Uxe5OKrZ0pSaLQe45xXnJyrUUaKR49N+b0RptSW/ZGfvojCaBqluXJWwHzGmMc+PHI+gmMiDb431nrChCKvaFGA0XuGOUNiYIs9YK/jZfXpK4dUw2xqYiVJAsDppW3M+zGimHRUGs/MXN6tY0KArR1PLI9YWMlpSgM5z9+ni+5t8+JfkZeNSRhQBgExJFmb1zNVKgkaXRRkRx84+Tcrk+jjYGqMWwPCz8MYbx82vm9nPN7Odb6Uc2yNMStgLmwbjPL1iZGk0URDsdqJ8FYt1D+WW3H5FWz6JCHStFKvZiJlJLRtPRePVfJ+K4kilFUhqIyjE2zcN7sXSVLvN2U0W/tMP4UMSecI2by/sOClXbMZj8hb4x/CgeSQms+8YVdpkXFtGoQauZzLaGVCqyDYb4MmU8nGgdNdXH7/ETg8LuLRutLaF7+5yCFxDiLMZAoH1ylhLoAGXvqUiAcWlhKI30dVFqSIKTBsdoOySuNAcaThsG0IY2gLA2jvEYpz7dUQjDMG2qt2R37bnUYeivleUCpakskFY21BAEEWpKEwWIe3DrH9iBnXDREgeLszoTVboIKWAjrbqylNMYQKBgXDe2ZCMf894um4dxOQRhCO4lY7fpxxdHUMilrmgb6nYQoUldVB7qSKMYonzApatIoZFLUBMHVmzO3mzL6HR0okyhgx/in3KSoiaNgNpscoGYctno2JbG1mzOa+EmdJAnJIsf+SHt5/zuDCnbbQ/DkR0idk8hAcLjy7AREkd/Ty0QAggiHro3Xl7MCpSAUiiQJCJRnTSShpJdFOKlopTFxKHlsZ8rWpEQ4iFcyDIbRuKbdCkl7inYasjMpmJQ1ujZI5/j84yO6aUwaK9bW2n4GPguYTL3mQJYE3HuyvfCoubBfIKWk1woxCzvgkM2VbFGzbKUh08Ln0iqQhEotfHfGeU1eNOyPCiZVQy+LWOl5gWAhoB1HiBRWewmBlLNjepWic3v5onGTxopnrnfQszl18FlpOwkYTRuMNZSVuWQ883bGHR0oe+2Y3Thgf1TRSUOyNODxCxOK0vuNIByPnZ/SWMNKO8LZjElZoWuNNg7nAMtVu7NL3Fwc9TWIq7wmVjAtLXF8eT6qLTOt0ohKW4qqQTeWOAoxThOEin4nIYkkeWmojaWbpVgcURQQBIqdUYEzBmsNoVBo48iLhi88NubEWov11QQlBWms2Owl3ndnpl4aBgJr7IJw3skiauPN8IpS0217J8jFe4kkSRRQlA1xpMBZep1okZ1pa9na9da27VbIqbXWJV7eRWV45NyEvDS00xCtHWe3p5xabbPaSRfdcaMdayv+mPMOdVlpRoUn70sBQRSQBnIRCNNYMc4dcahAeJ/zg+OZtzOezE7mtkMUKi+dZh1aO2IVUNaGqrJ+Brds2B7kXNjP0QaK2iEEZAlkaUASQRp9kXxYdwAOfk/RrBFUGhjml+ejAjzNRwraaUgaBTgpKKoGa0BJhcQxzGsa543ZpmXDtPAq7yd6Kf1O7DviMmBzNeUZp7vIIKBxjjRTbO/nfOyzu3zkMzto42glPvglsaSfJYyKxjtGVg1SCMpK000jnnGiy3ha84kze4vOeSeLuPdkmywNqJqGJPIqRXZGw9nanTIYV4SBYjCu2NqdXvJ+G2OYlBUqEFTa4PAMgEp7KbfhxLtCxtGlwbWoDNuDkvG0YZrXxGHA/qRinF9UXe1kEXLGLsmSgNV2PGvy3P64ozPKeTOn24oYTErObk/otCJWOgmfeWyfRnstw7oxfOxzO2jnfHcxcjhjZzJZAbUOGE0rMM5PoyxxS8JL8oIK/by4wU9UXetWtXhBDq0195/qEa9mqFHN7iDHOEfd1Hzi0YpQQRyG7AlBGCm+7J4VJkXD2Z0xrTTi3pNdHtsaMS01qRO0Y0UUKbb3CtJAzQzbAtLQT9CMppZGazppRL8VMylqmsYQhYrJtCGNQspaI5CUlblk0mWtkyKFWHht7w4rrw/ZTha/GwSClPAyL+9QKe5ab3N+tyRQgrqxDCfVzJNdsj+xrPbSS+bEG+NLV1EoOL9XsdpJsM7SSSOqorlIbxKC9V56GefyenAj/W6OA3d0oCxrjRCCC/sFk7yiKC1rPYUUgjQJKQc153ZyJnlFK1GsdkIGE0Ne1YjQGzjlpcE6g1CSIDLIYim1dquim8K0BhwLEd6jvisHFLX/zzw+pNcKcAR0spiy1osmD0AQWGotaLdCxqWmqhv2xw2bqwKBt49oipoo8Grnw3FNGEiGQtBvhQQy4sJ+RagEd29k7IxKkigkSwJqbRhOavSsRjma+BqldoZ+K77EFAyu7LXda8U4AVs7U7rtACG9GdpBpLHinhMdOmnE2d2c2DlacUhRWYxtWO+nOMOC2qOtZWdYsD/0epur/RirLVHgVZQWH84MT7abPZxWi8CP9IFzXpe9FXBHB8okCji7PWZ3UGOF15bcH1dEKuDESooxlr1RSRIHnF7rMipruq0GIQxSBUTSzya3soyiahhNG+qqum7C9BI3F+PiqYmXjEoYlRqJ9oF2VvCMAz9iWVY+22knIVobUIKNXoJuLBfqktVOzKm1lMbA3rhkrZ8wmnohEItvJhVFTaedsd5L6HVi8rIhDBWVNrTSkCKvaKchUgjOVzlZErDRSy/JzqxzTHLvW95KLaH0IiPe6yb0wr/TkpVuyqm1S9vK88AVqYA09u+j0oZ8z/uZ748Kep2YnWFBJ4vY2p1SVYZWEqGdpydtbmaESqEbzfYgZ2dQLtwZr9TNvp5s8UqBfxkobxI6WcT5vZy8tHQ7AaudmEZb70kSSaxx3LvZpagbLuxPmW41dFsJ3VbMvZsdPrc1wlmHFY5eK0A4KIoKWxwcolviVsFxfR+Wi9NDAs+nDQNwxlFry9buhCQKWetl9Doh+xPfxd4eFkQCrJA0xiKFIFQQBIHfsbQTWpFXe0/CEOtqVropZaXppCHdVsz+/pT9keGujRYnnp1xfi9nd1heMukyzmvS2G9t86IhiRUnNzLv9904kkjRTjukcXBJIwcupeXEkeTCfk44s70YjBr63Zg0CBfuAJNpw1o3pWoMRQVOw+n1thfKqP1kEs6yMygu4YMexHBasb1fLKhHJ9dT7tnoXBosnfD/zbig/s+3Du7oQDmaVFgEFsNo5BjJmhPrLdb7CVIIRu2awcxsrNGOuzc7tNOQojKc3Z1inaOVKXb3Ch4b1iShZG0lxbqCaXn0+Ze4/TF/IGrtM0tbOppG004M1ji+sFXTbnkzsKp0MFPHF/j/4iCgk4U86+4+GysZYSgZ5TWPnhuDhbtPtpiWhse3SwrtqOqaXpZQVJrBtKKuDUr6Bs84r1lpJ1S1pd+OaYxlYhqcgFJr/vgTF9gZ5rTikDQJOLnWZrUXU5T6itlcJ4uwzrE3rDi93qKXxSSRIk1CsthT6dqtkMG4Io1CHI5+J16Q1lGSKPREd63tVccQ90Ylw/yiC9/53Sn9VnxJUO13Ix45N2E4NUSRYuMJiN/cDNzRgXJ7kLPWjalqzWTakKQhq51kQVmYb0sePT/mrvU27TTg7PYEbQxJJAlUxIVBzu6owgmLCBTDQbVoFCiW8+BfLIiEL8f5UUWotUApQ2UsqtIYbbHOUTaW9V6CNoIoUAglWOn64DKv2eWlZqWTkEYhXzg7wjrHei9jez8nVoLuRgQOHjs/oZ1GRJGkri17w4qVdkIcSc7vFyjhdSCLRvPnn9ojLxu0djyyP2a1lxAGik8/OmCtm1zR63reAJpvcYfTimnZUNeGC3sF3U7IiVXf1JlMG/qdi/dMHElqK6gbDU6gAi7plF8CJzCNI44VjbaE8vJJHyl8p9yrzRsmRcNKO7llGjp3dKB0AjpJRBpXOOfoZKFXdZl9SYGUnJ7JQG3tTPxcrxKEsUKPLY/v5YxGFY1xBFKyuzel0M5vw6Tfki3xxYHaQeppiwgFUSARQrLSTmi0ozI+o2olilob0tC7eq52EvrtmG7nYgZ1sDNtLDjhUIGgnUXs7Bc469DWEkbSD7cjsFgmpebCfkEY+uaRFII4CbwdR1HT6yQMxhVZqnAIokixtTPl5ErrusQn/HSN15TM0tD74ZSaezY6l3TO568No5DROAcnWO3FV23c9LsR5/YmnN+ryWJJdzW7LKg2jSMKFJ3U04smE80wqxbZ69PdCb+jA+VGP+ORxwZoYxEIolAymFScWM0WBeadQUljvC5iUVYkM3pGbSzKCYSAyhjPb2u8lHdt/M2yxNOLYFbSuhm14iSEOPSddK0Bp4kT6S0jKoOSijiwxIEijiSVNtRjvzOJI3FJrXC+nU1cQN00jHNNKBT9lYxWrBjmFeu9lLs32+wOK7+t1YYs9sGrqs0sy/Q1yke3J4yKmryqZzxJzelWTICgxnFuN6eVqoV60NUgZ34/J1fDBfH8aoFVCsFqP+WBpn/kZyeFYL2fkUSNV3RPLu+Gx5Fke9AQhwHWWVqpYm9YLax5n24h4Ds6UK50EqSUrHaShQhxUZnFKFdRaYyxTIoG6QRxoCgqy2BaIgX0+zHWaYxtmFYaFQrSUDDNLfkym3zacSNU52Phs8f5oRVetShSEIeKQHpJNQCpJEEQ0A8FzgWsdWPSNODRC77+uNKJaKcxZ7cL1vsXo818+7q1M6Xdmnnx5AUnNtvcdU8fLyYRY51byKUVexqst6fI4oC6bnAIdsY51lqeebLD586PUFJw34kOnSymaiynNzOMdTy+M2Glmx7po3MjVH2axrHeTdnsZ37qx14+1tjJItqtkPPbBUEArSxCCkdRiesS2bjRuKMDpZSCdhYQh3JBOwhDP3JVVIay0hSVYTguEUp4C4FYsd7JmFQ1Ra2xSJQKiAPIpCfoSulvIKH81McSdwYCoN0W1JVDW589GgtRDN00pG4cEkUQ+OtJ4EV9HZ56NpiU7OcSawTP2GhRG9DGd4arWl/igX3PbNChaSL67ZRx4aX+rPPajuAzsbnYRNlotBZYC3WjkVLQbyUMxxVZP0QIeO696xhruWu9RVlZhtOKJPJjiidW2lT15dJnh6k783HJ41D1mR97LnPYbycYa68YfKUQdLOIqjcjvwqotaWsr09k40bjjg2U2lrOnB2wOyxptKHbihlOayKliMIxxlrGRUOWBIiRZDJpuPdEB+0s7SSgPN8QKT9pEIVwYd+PeknhC/qNAbPkB91RMHi+pHN+fLXR3iAN4X10EJYLI82FUUUWw0onQ0lvV6uNoyoNgYA4U4ymmt5stHEzVRgL07Lhwn7trSo6CZNcEym/fa8aMwsi0WX+2aNxw2o7YVxUlLWhG4esdhOq2pDEir1RSb+VUNuGOFa0ksj75qSK0aS+ZDt7eCt9NT+cK223DwfVo1TB58eeyxyOpjXr/eSqwbdpHGudZLHtP7eX00kDtIFOGnolsKcJd2yg3NqdglKstBP2RgXn9nM6SchKJ2YwrjDaogJvZ+uNmLzIbxYFM5VoRxpHpDGc2RpitCUNFGXj60XhfChhKZhx20Hh7YkPjyGHCrRzyAAQijRSZKkXR6kbL+os8CLBgcGLPk9LZOC70mBRKJIwoBLai+72Eu490fEivI2f8tkflWSRV+DJywalvOf8l963hq0vffpWtQ9wtbZ0s4Sq0az3vCvjWNSs91KUkmAd/V6LLAkZTEpwgn7X038mE72oUR5uolxLafxwYLTOJwvzoHrQNfJKry8qszj22kwP81o1xsPb/m47JFTywDjkMlAeOybThs2NGFNr1noZ+5N91nsZQSAQIuTseMxKJyGOAoaTitVeRlFr6tqSporNXsbnzw9wDrQ21MaB8LJRzGpYYhkkb0tYILzCLk4CVQlRCuv9hLK2WKOpjbe+zavZVlx629lxoUlihTXW02QQrHQCtIV+O+UFX3GKU2stxnnNpx8ZIIRgUtT02yH74xpjLdOypp1GrPUTVjqJ94Q6ZAURhD64TQtDtxMt+I+jvGZ/XLE/KlECJmVDKAW1sSSxYmdQEMeSlW5CHASksaKVhuxPSvaGFQhHEHg7B2+ze1Gzct7oNM6x2o4pKs20bOi3kkVQPegaCZdnp42xWGepa8O0MLTbAfsTQdO4K3axD48/rs/k4m4Fcd87NlC2W15MVWtHUTes9VKKuiElpKgbuq2IThqyPSjptyOSSNHNQh7bHqNEhKVCNw6lYK2fMpgU5LVFCm8OFQaCkWig9EFz2du5+ZD4me6rjZReTXZN4F0eQy61IjbWC/YKB5FSBIliMHb024p9Z6m1JpzfMU6QxYpuO6LbjgiVZFzUTErDejflS+7qEQcB06LBOkcYScrSezftjiov44fXkjQzzca5Z844rxd8xnGhZxlnvNCWHOc1e5OSCzs5Z3dz9scl/W5EnmuMdWysZFhjydIQiyKrLf2WoteKGU4rdgaFLxsJhzZeh9PYS/1w5o1O6xx51dBKInDeNCxEUWlNVWv2JvUi6B3OTkF4S4gZ3aiqNNu1Za2TXLGLfaXxx1tF3PeODZSn1loU2vLJ82PCULDSixlPawbjko21lHYa0jSWjX7CuGgIA0UU+SL91v6UOBKEkaTRls2VDG0caaAxSLJE0BiJtlOs1TAznFrixiAJoLxCPTiOvLbu1QLllYKkv31BzEoncQjUnmIURRAqQRhItHEkSUQ70zjrg/JKJyIKFVpbkjRko5+iEDjrKWRCBCSRQkpACM9DrDTTQnN6rc2FYc6FXW/1kKUBG/2MKAiY5A1V++KbqGqfhdXakkYBVaMx1s62oZJp2fAXnx+gFAwnJWmk2N0raWcBjXFUlaFu9KxemYFlsaWuaouzEEW+i6y1JQ4CNg8I8M4DXhwHTPOGnWHJeKppty/6oRvrUIFavEf/fVzeMZe1WNCNtve9efvtYih2EHdsoAykZKUTc8+J9kLhfKUVk8TBJU/OwyrQ/Y7X1BNO8IyTgv3hbPvRS5AI4lBxYX9KqBxRGNDBj48tU8obh/oqTbOyfuIlYoO/6KUEoX3DZvFvFlY7MUHgu81xHJCXEqyf1zbWcqLfYlrXRFHAiX6LNFE8tj0llAH9lYBuOyYUkiz2RPCy0uyOCrb2phRlTV4bpBLome91pPBuiAdqh3Ek2d73AhTWOrLEy6VtrmRIKahrL2QRSx84L+wXSOWghJV2TBRJ8toSBYpw5iw5P34cSYTE11SF90A/XLecB7wsDtgfFTTGstIJCJVnjGyuJFzYL0hCRXEg6K33/YTPpVvlehE8hTxgy3EbGIodxO2z0ieBuSrzzqAkCgW1dnQy/6X2Wj7NP8zLurA/U7wQ0Eoieq2QsjKc2TI4oGo0URCireFEP0Fb2J+W5Hm5VEK/QbiaWNOT/bgd3jdcA+GMihI4CCReVq10tE6GhIEiUBInHGtRwv6kZHfsnRPX+wmhCogiwTM2W5zbL+gkMa0sAOeQSjCaVoyKGmMc53Zyirr2mZqUVI1hWmhaUcTmWop1jq2dCdNpRSsN6XbChXVsHEmCIFxse6eF4b6THcZ5RZmF5I2hnUhCFbLZS4iTgGec7BCHAVLCavdip/ngfDfCXfJvcxysFXZaEXdnMUrJSwjoVzIXu9LW+eCx1vs+az1oe3u74I4OlHWjFyZMg0mJlPKyJ9nhTl0YevtRJSXGGECilCCNQ4Z5SVlblHJUxhFGilgIah3RSkqvaWiWqkK3Ig7O5SfRzPu79BuBwPkteBoJ1roZddXw+M6UKIC80ASBQkpLtx1zYrVFvxNjLSSZwhpHXvluuJI+INy92WK9n3J2OycvDMO8pNMOGJ7LUYlERZIOMd1WyJfe1190k1eUXGxjT6+36WaX8hvnjY1uJ0QIrzYkhOBLTvc5udZiktcUleHkuieVX6lpcni++0o4rDBUVF7X9eC9cyVzsaOOdTvjjg6UYai8A502SJVeIkwwx+FOXd2YhYKQFH7WFfBaME4QhQJjJWkISkrKSuOcYb2fcn5QMC6erne7xLVwcC7A2EuzUYPf3tvS4YwmTSKUcoxyw2ovJY0Vg7zhZD+ll0UIBxbfdd4dlaSh5Fn39NEastRzHKXwD9zhtCYJArZ2p+S1t7ld6XZY62asdPx89Oe3hhgNrXa9MPSa73guz85qrLtYA7z3ZBclBQJBEgesdH0AvCI38kngakK8UghWugn6i0Tw4Gm1gXnXu97FW97ylht2/ChUDCcle6OC0aQkvMKY6yWdOqUoSm/7tNJJqI3FOD91ESgYl36MLAklvU5CFkd0OzEn19oIKTHNF7evTvYUH7tHfXYBnppzFJtOzv7LlJdGO4xSwwGrF89acKANbO0WTIqCqnacWM2450SXr7x/k2ee6CKE9DJjEsZlQ1lp+lkEAsrKsNZLkcJbJ2ztTnns/JhzexM+uzUALKudlH478iWdtlcF+vzWkPG0xhjLtNDsTaqrqvDMu+Fl1TAaNzTGcGqtRSsJL+laH76mn4pvzTwj3FxJF8K8X4x42u7rD33oQ7zvfe+7oeeYTCrK2lLVjnHhzd6Lma7fHF7AQLM/LvnM2QH7owKcwzpLv+0tO1e6CUoqVtohaRrRb8ds9FOedU+PbhoxzjVJqIhjRRwefSPfiVDMOIZP4HdC/OvjwAfBOICDRonzWzIAUgntzNcRk9g7K6pDrzv4e5bZpM2BGKGAVuiN4g4/MyWw0vbWtPtTQ6AESSQJpaRoGpI4mImpeBuRVhLS7yTMZw52hhW744J2K6SuLef3crRztNOQvNRIGfDA6VWec98KgfKSYqGSGA1REHgHBAFqNrYIviw0nFZc2C9mTohm0Q2PIz91Mx9JnDdSdgbeHKzSelE/vKr82RLXjadl6z0YDHj44Yd5/etfzyc/+ckbdp5x0bDeSxnlFe0sXEwVHKQlzKWltocFSgqyNGJaaj+kn4UoJXDWcddai/tPdnDCb7kr7WkjSRzQb4U8njfEoSRQsPdFNgAe4Ot+gfKmXqGAsrm43Z3XB4PZ/+fbXgHUQGT9WOh82knPtsbzzLCVQRxHBFJQyAYVCIwVNI1mcgUPo/m5WqnPEhMLcRLQ1Jp45lpYNpbBqKaYFZTj0He2V7sJTgjSmeJ4rxOijWOzl7LaT2kl4aLjuz+piSJJWcPJfsxqJ6abRezpkqI29FoReaXpd30nfa2bUDaae05kxEGAkgIdW/LSq6H3uwmxYpG1XYnAPS0u7YbPr+WDrxUCBpMa4epLlNGXePJ4WgLlj/3Yj/GmN72Jra2tG3qebitiZ2+KlILhpGKtm1zWzPFy/YpIKoxzxKGk0d6ic6WbsLmSMc5rHjk/Zpw3XuvP+W6hkpJAzQRaBWhbc36v+aIV81VK4vSlBUAx+y8EhJyRwGe6BxafUbZSia8CC7rtgNG0xjroZgHTQmMdBMLzFaWwNFrSihVxGCCEptG+i10fOG8cQbeVEoYSox3dTsx4XLHaSwik4FNnRyChFfkAHUUhSkiMdaz3Mx443UU47+kdKIk1lrI0XjegHdLrhGRpzWPnx9y72eWBu3tI/NTJajehk04ZTxvSSHH/yR5xGKCNXYjfTotmRsEJKRuLEoIsDgjcxZz8SgTubodLuuHzbHFuKTvSNXujCq0Nd2+2UVJcJoTxZPFEZ73vJNz0QPkrv/IrnDp1igcffJBf/dVffcK/f71fjrUORgXtTkJdazZXW6ytZLSSkF47XkxBAEwbw96k8Z3QxrLaS3j23SuLwng0LGgQbF2YMi4b7t5ssdZvsT8uUFHA6krGIK/ZXOvikNidnPxJcPxuN2QRRDONRvCBLw5nRlyzTM17WXomgLAXs8RY+WxPCE/JCYQgCgP6nYRaG4SQflLGK9R661ljsFLSTyLKxoIQ3LXWRjvHzrgiazTGCax2BAqiKGCtn3Ci36JsNKfXvaLP+d0JSvh6Z2N98F7rJmyspNSNY30lw0nJ3Se6NI2m206w1qKkIgwED9y9wjivObmhuf90j2nlqWPWQdoKiKOAr3j2JnuDksZaTq21uPdEjyC4uAW21jGcVJS15uSJ7sVrsnuxGx3EfrosCjwVJ4sDeu148XtJdGCSxliKxpHEIaUuWO9lZO2UOPQd8431y++bg2tIouCy++Iw9kclqZD0en49w0nFxkbnqVxCNxVPZa3COXdT7+fv//7vZ3t7G6UUw+GQPM/5ju/4Dt761rde1+/v7k58EDwCw2lF2koYD4tFFnm1p+q5vZy8bBjlFdZCpxXywF19pBBY5/jYZ3eZFjWjvEYKQSvzEvlnzo2pCk1tNKO8oiy9wdSF/Zy8vLNsbSX+/QQH/iwkRIGvGwolCQOFtoYsCtkfV56reAVf7fmt6A4cu9sR9NOYMAoYT0qmpaYyfipHSi+WW1f+z8rbtGAspAmkcUxd1zipUMKgDeAcnSzl7lMdrLE0xlJVhkY7hHCMpjXOWox1WAebaxnPPNUnUJI4UrP1OSIlCYQC5SgqSz8LabdjVtoxWRKgrWVvWOIsTCtNK1GsdbNLrrnr9aze2OiwvT1e/P16fs/XLjWDccWkbJjkXq7Ne2xnVLXh7hOtq5p+HfbgvlbmeWHfl6fm6j69fkrwBMPH0+XfffizPQgpxZEJ2E3PKH/+539+8edf/dVf5Y/+6I+uO0g+EVS1pdeTSCkInGRnWFz1y/GqJF7Z5CB5Fnztp2w0ZWMYTWu0MTSNf6qPphXtJEZrb4M7HDU467eei2DCpZnl7eqzMw92UoAMwDRgrf+/jCAJBJ12jNWG9V5KnlcI52uWc4Ty4mehD0RPCQROkMaelyiFxDk/c21n6aixfsSwrP1/i8+1Audq4jgkkI66kUgcQRRgrGMwyrFG0soUw7whiSSd2Ht256Wlk4UEYUi/FTOalpxca5PGAdOpZlo13H9Xl71RxWhcYxpoxQHb+znGWDZEyiRvCJRC4+sJzl4+ondVKbMjcD0cxPn2PE1CcAIphBd+QVI1eiGica3fvd6RwsMjikkUPGF60JP9LJ5u3LE8yoOTA3uTCoG7ZC714JdzkCsWR1489dxeTmMMo0lDGnsnxkBB1TjG04baGd/xLiuGw5pQSrJUMi191hMG3mPFOPwsuJ51NWdbzifD2JgH35sFgd9K140nU2vraTSRvVh7DMLZ9hjQjcZay5mtIVIJAhyJg0r7ba7DZ59h6PU8bQPVzMqhrC3TokEgyauGUAksDiUhL3ynWinBZFaInJ/fGAgCRRwKWnHE3qgiygKSUKGNY1JYIunYHzuUsExySz+NWGnHBFIQBSHtLKTXir0S+HZOrx0ynmrW+zF1Y1npJAwnJd1OxM6wZLXrnQ1DpZhWuefnRiGl1uTDGiEEQrKYRHmiAemJ4OC4Yd1oOmFIEnn7hHn9cmdQXpYgaGvZGRXsjwq6rYSVbkQnvTZn4TCnsteO2X2CgfJGfhY3Ek9roHzVq17Fq171qhty7IOTA0oIuq34ql/OwSf3fDsynw+f550yFFS5Iw5CkkiSyYA4UOSlwQpHGoes9hL2RyWdVrAQNggVtLMIrS3ndqcgBaGUDKaNV6t+ApFPCV/bK/WNr38KoJ1CK1ZMKoNCUNaOyng9RoBEQSsNqZoGIRU433DR2hBFAVI6QuXIcDjncMjZvDC0lWQwrmHGPQ2VACGIIkmXiLJuvGCuccQhxGmA0dpPwOBro9UsgAsc1gpUqFjrJUxKT40JcGTtkLK0SCxplpAmIXEoOLHWpt+J2NkvmRQNG6sZO4OcutY8eqHCSzxqX2pJQ5LIsyaSKGB/XLG5knoKzoxnawpHmiiqyn96B7+f47BXuNqW9WDw2lzJLgmGB7fWhxOErd0p2vjhisGoQilf773RuBFWEzcDt8cqnwQOTg7Mvxx5aAzrSpg/8cbGO+U55zX70qiiSgztLKQoDVkroJtERGHASjtESMFg0tBNE9Z7avF0vjDMWe9nWNOAFOyPK6JQsCotlfazs3P905Cra2sIvLBscxOCJEAvFaz2E0ARyprGOqZFg8JntQ4oDcS1RghJO/YBZWegsU5itSVMFP0swgHOSeJQkcSSfEZ1qY0hs440CmlnAWVtaacxYdDQGEEU+JnpNBLURqJ1iLUFaRziHFS1oZX5bfO48nSt9X5GPC0Z5Q0qDFjrtwikF0mJQ0UYBPTbIau9FCUlK+2UstEMhjXjvKFoNAKHMWIh4JAlftpmWmqEczgh6MxI3lkc4gwgHMO8ZnOlRb8dz2xXHdb5/6ZlA05f063wWrjalvVa2/NrZW+TaUMWRQSBoJU4P712RK3w8BoOC/deD6426XOr444NlAfxRL6cMBRsD0qm04ZKGzZX/AjbXRst1roJ08Kw1oWqdvTaMRurKVVlyKuGTquhbhytRIEQWONFgIeTmrpxPPN0n939gmHZ0E4CBhNNFgvaWUWeW6rGzx0fNM2a1+Jmyl03JUgmIQShopuE3HOyx+6w5Nz+lHDaLLyty3o2KRMGBIH3cC0qQ1n7La6KIlph4IUZlKLfjtgbV4ynNUJKTq+1SQJFHCmMtZzbKzHaoIKKWEmSWACSfifEOUFHSKQSbJiE/WFJEAacXFP0OhFJELE/LlESWmlAowParYReFtHvJUjnQAjGRTPLgA3aOr7k7u7s5jcoKfnM4xaJp9okcYCSktObbbSxZHHozcWUpCgN3Y63JljtBOSVpqo0SRh4JfADQhE+CzT0W56aJoV4Us2LJ7NlvVb2NneCnOuz9jtH1wkPr+GwcO/14Had/f6iCJTX++XMVaMH44JASOJQYJwPrmEo2R0UNI2l1oaTGy1vhlQb2nFE10SLbuneyEvxB6HXJDy/N+WRrSnghVCTUICLaKU+Y2mpDu1UszeqkNJijaPRjsb5rfa08HW86TGP1V5N2LYVSzppQBwrHNBtheyNJWpWJI3VjNStIAhgvZcxKTS1trQzRVFbYiXI0pC1WZ3u0Qs5k7zECcFGL6FsLM955hq6cXzm8X3aacBKlrEzqrAKTm92Z9xFjQpgMK7QjcEKSyuJSWJFK/HBrNaabjeil8bEkSQKQk6vtYhiiVAhTVmxMyo9Yb1qCJRgey9ntZOQxorNlZRpWbO+kiKUQtuC0bRmpZewN6tJtrOQzzy6z7QynFzNvEBFU5LFAf12QhxKNiK18KGeP5B3BuWx1OSezJb1WgnC3AlyMm0W3M4nuoYn08y5XfFFESivF+O8ZjLRrHa8rWYYSs+7bMXsT0oQklYagYRuFtE07pKboGkcmyvpQpllTqeIg5DGGkajimnlPVI6acD+yHgbXNFwar1FHCrK2rtbGWsw2jKpDUpZT3k5BhxsCPVSGB+YbHFAGsJGL6G2kmlpMPtTBqOSomwIAr/1zxvfxAmkN1grSz2zQzBIJcnCkDBUSCm9S6ETxOEUEygsjlo7tLZYbVnvZ2wPpqRRiLWWJFEIYL2XsL1fkLVC8pnKd10BSiGkoN9K6aQhSDDaMa0bwNHvJGyuKMq6oSgdqysxwnrB2d1BgwwMeaH91EzZMH9UOAuhUOD8zHQoHWttb3fcbcWUtZlt1QOMdoybhk4a4RCXmGZJIS4JhMdVk3syW9ZrJQhz18M48Fnw9WS5x9HMuV1xRwdKa/2s7PVytg4aOUkpycuG1a7nwe2NPDEyiSVZHC4krIpKEzjJ3rikqP0ceb/rzzOcVkzKmu39Ked3vEm9AlqtmK3tHIfgOc9Y5dzelGlp6GYh1llipZg2kkBatJO0U9jer9D60hHAJwqF3zYjPPWm0ZdSlUL/T5w5nyOl72ZrbxWECqEu/eulgH5PzYQYJJNSI5wkUIqi0dRO8MyNNqfXWoymDVVjqbUXiZU4jNbgQgbjhm5myJKQrd0xzhiMs97jJVSsdlJGeY0S3l5hHDbU2vM0N9YyJIKTaylVbXn03Igz58YUteb+k102VzMabb2y9l5Bg6ZxhsBIokCy0o3Z2pmw0c+YFpogFAgpONFvsTcu6KYdNlcysiSgqDSRcQShRMxUgbS1ZIkii0OGub/GrPM1u4PyZsdVk7tS0HsqnMQnQ9M5vIZrkdPvNNzRgXI4uXrX70qII4m2fvt80MhpnNdY67AW8lJTNpYTKymtNGSU1zx6YUxdW1Y6EU1jefTcmCyNCKVXuP7s2SHDvPJ0JQ3GVhR1QxQEnN8tsM7RygJOrXVoZ5Efd9OGrd0cJyrK2hHPZqHL0m/DjwqWEohnLWKH94ipnVf1vppephCe2lRVvpts7cXALAwEwlu5CglIwXo3pTbQNDVCWlQQEjmLFJJuGrIzKr1eY60JlUQFkguDnHYWce+pHuOpJ0mvdFM2pw3DaUUYSFqxF5KotWZSVIwmNUpAvxsjBXTbEUkgvTfMYk48YL2XIIVkNG1YX81Y76XsFxo3G4/MZnLfYaLY3a/JEjUzi3PYBk5vtslLTRgpylkT0OIIlEIpSTeLCJRkZ78kiX2QPEg92xkUOMRlnjA3qib3VDiJtytN5+nCHRsorXNc2JsymjQksSGLwyMvhvnTPpCKle7FJ3RVeyWhedFezo7/ha0xxjk6acxAVzQGhDBc2C9Yd9BOI7I4QCHACcrakEaKvNIo4UnBReMzgrs2u9x3ssvW/hQlBZOiwjpDWRmMsQSRpG4sQvkRwSvVFwW+fhiFEElBEIdMyxoz96pWfrs8Ka9Mhp/1PHx33VyabTo8JzSYHT8vNUo1nFzJUMIxLTUSRxKGJLH0I4wOnnlXl7O7OaO8AiSnVjJWegnteCbUIARp4HVDT220qWvvMeOcAye8V1Hib+Sittx/qseX3NunlfhmymBSMZzUILyCdpoEhFKR55o0DGnHIc841WFaGh49P0Jb/0A7t5ujnWBSNTSNYTrVrKzErPX9CGUny0jjiED5OnUrnSmMK8WznuGDUdM4P5Ip/Y5iMm3IknARfIrKANe/o3mieCrB7nal6TxduGM/nXFeY/CjVgezwGthvrWw2UW7zrnqeVUb2knoRTOMpaoNWhtGZUNdW7QzjAcVrSgiiRV1Y6kCzWii6XVST2TOIqRUhC1BpARZErE7rGgljgfu6aMU3LPZZjAp2Rsaeq0Aay2jqaEXwlQ2WGNozOVBUuFVe8Qs+hkFuq4JpZcOUwKyJMJZgbYllfZ1uflxAiBJoJtGjKgpD0TJOXdRCL/tjiJFFgd00tgTvqV3JBRS0koC4lDRVA3tVkSjHVVjKGvLSjuESBJFge/kS+i3Y/qtiLzWaGtZbaecWPFv5KOf26GThtTG0m9L1rsZL3/h/QdcCjXaWBpt2BtXXNgrOLnaYqUb0W5lfuhgJlQRBYL1fkKWhDPaF9SNoaoNu4PSC6bUlqlsuO9Ud1ZbDtgdlzNqlhfuSGN1yVjidFdTVJrVTsxgUjLIvZ5kFPnSBJW7YVMoTyXY3a40nacLd2ygrGrL+nqLfFJRzTK4q10MRxm9x5EijYPFRUVlCJVEW0dT+z9LpyiMJkkUK50M5zzNZG0lwaAZTVvsDkq6rRAHRIFkvdfirs0WeeHpJ61WiAAGY+i1E+rGEoSOla6krr0pbhIqpDSMS7/2dgpl5YOeCHxAjCLpxwADRzuJyGLJ9qD0qu34aRvrnXaBeRCFzRW/7uH0ol6nwEuobfY8Tcc4QacdUpcNzliGtbdIXe0kPOe+NS4McqLA22fsDyse2x6zP63xczaw2c8IlaTSlnYS0GtFrK2kbKxmbA9ydvYrKu0bWa04YDKt6XZipPO8znmGX1WawaRmnNec2/MPoUZbhnnFxmrKqbWWH+eLQ0aDAoTj5Hpr4WFd1g02DTizNSaQAotls+upQKfX24vr4WrDCvNtbxQIhlPNZ7dK0jigFQdMS70wrAuVPJbt7ZXqkdcKdkfVL29Xms7ThTs2UMaRtxydZ4EH57ePCozTQtNvx4e62cmBi8rXPgPpbzoZCDbaMeu9lE4WLkQG1nq+E9pKA+Ig4PwgZ5JXtJKYXidiZ7/AOsn9p7p85QObfOzT56gqQysJuLBXUMzqZKYRuNhCIem1Q4wxGOObO3GoUBicg247IQklURKS5xWNdkyrhsHUoKSgncw4iSqgShzjokJbaEcBVvoabJYGtLOQMDQzDU9LKAVRGHLfqQ6DcYN2jkBI6qahajQCwXBc8KlHdmksPPNUz3MPs5CdYUErDLzGYxyinUVaCAOJdZLBtCavNafXOmgD7VZAGqY8emFMrxWSJSHaGKLQTz4Np5Xnup6tZmZvlqLUaOPY6GUkkaKVRATSj++t9VLs6d5l33uv48cQu62I3WFBM3I0esQDd/cv84yZlg2ToiEvG7rtaHa9+G2vdYJAKMLAEklFlkScXM0w1l2W8cWRekLNxYO4Wj3yasHudp2pvlVxxwbKa5kfHb6IpmVDv5Vc1P0TzcLx7kpbmvmxpmVDtxPSbycYa4mj6DIe3ZyGUfdT1ropZ3cnpElIEiqcdUwLQxhK9kYFg6F34FPKT4/sjwPakURIibWGQDiqWgOKJAStfGc/jhSBFIShII4DjDYIB9o66sYr5mz2EtI0wFk/G336VMyF3dzXGoVYiHngBFEgvTSasbTSyM9RJyGN8YT8tW6bwWDKvp49XGrDtPQB9NR6m3N7E7Q2VNphnPW+2wKqHU2tDa0s4sRqirCwP/HMgtNrbfaHJa0sJAwld2+0ObfnPY6G05p2GhAoyfm93BtdWU1e6VnTx9BYS17XFLVkcy1bfFdz5kNRmUWNMY0Vq70WX9ga04olIynR2lKVhiy5/Lse5TV5oWmlXmx3nNeLIKgkRLHEyYAo9FzT+TVzOOOzzt205suyWXO8uGMD5bVwmSCq05cExrk51NXqN/OMY94RPxwY5xfkXMr/3E5OEge0OyHnBzlbOxPiOJip62jObk/ptlOccEyKBiEEYRRw/11djHNIJwlC2FhpsTsqObc7pWoMUlqyJEIbh5SSzX7s7XPHFY2xrHZiqjqgKGuKpqHBoYRkPQ2IpCQJFa0kpJtEXBjlDIclgRJIB/1WRBAGtFPf3W1nEYNx5cdAa00aBlRJgMV5FXHl9SO1saRRSK41xjmyMGBvUiKcwzhBrCRV1fCFLU0WSRBeSOTM1ohuK2QwqWknEbvjEqUESkm0sySht281ztFNQqxz5IX2ZQwFlbaM85oTKxm9VrTIHnfzmt39gkAKpmXjuZezyux6P2F7UNBpRRjrCAPF3rAimqngz7O+OAg4uRYu5MUOelhPC023Jem3IgbTGnXAwO7w9vbCfrG47q6kaHUQV3IHrWpz3dnpsllzvLhjP71xXpMKeUXFoMMX0WovvmImeNQT+PCNMA+Mh7f0aawYFzWTovGZn5QUpSaLAjb6KSCoGs0zTnQZFzW68cbi6/3E8/MMxLHiOc9o8alH9jyFJvFiBg5HvxXQ7yac3ujMiOrwmbP77I1K2llEOwvYH9UEQtBvxax3MyyCB+7qE4SCR86PGOcNSRJQG0cQB3Q7Cc975hqNhsYatrZzzu5MUQjuvzdmlHvVXeugmwbU1qGsZTRpOH1flyyNWOlEbO8WKCU8+V74YwVWkjcNURiTRoqVLGVvXHJfu8taP6XRBmstQaAYjEucduyOS7pZhDEOHYESil479nayUUi3FdJuRYRKMJzWlLUXxmi1E8aTiknR0J/VfR2G/VHFyfUM4wxlY+imMU54RZ1eK7ok67tS0LnSw/LESnrN7fTB41xJ0erEoev3YPZ5uE4+z06VlFzYz9kZFrPSz+ViGctmzVPHHRsoD+pRHt56XOkiup7AeBSutqUPMsm4aHh8e8zp9RatLOLCXoEAspm4wsm1NkVesd73F/vnz44wxtJtBWRxwCivMNbSNI5ACda6vnlRN4Z+N+au9RZxFCCcwsXwZfes8tjOhKpuqBrY6CcoIVCBwljDXRsdnnFXl8G4WpDC94Y1rVQhhCJSksGkRs+2rqGSZLGk0t62t52FrEUxa7XhC+eHCAtZHJCm3tum3448abyXIKeQl4aq0RgLSRbgSi+Ie7KfkaaKKAoIA8n9p3pIIfjIZ3aY5g1RFFCWFqsdDkscBygJYSRJ4lnw0A2tJCKJAz/VZBxGeB7oaPbgAsFgWlLWiiwN6aReDagV+4xRCIFz0vMyD21Z59njUw06B6+7KzaJDuDwrudwnXyenU7KBqPBSa5bLGOJJ447NlAe1KO8kk/OjbiIDl/c1jbsjksmRUVTW06vt0D4pkp8wosKZHFIFEnSxDtrzW+mKJIMR5paV4ynFZ22nyVf6UU0ztA0ijTWnFzNeN6z1rEWylrjZnSoVhZy/119WqniC2fHaO1wwjIcV+SVod/2dBhrrd96zjxhdAPtliBLQgSSe09kfOxzJd12TJIFVJUmVILKOrIkZnM1oNYGa6DXSrjnrjbOwko7pjGWHXKmpeJLTnfYHnpbVhA88IwWw7zGCMtGv8NqL6GTXszGWqlCa4N10G4HJJHiWfesYJ3jsQsTrHbEqaRpfLd+pZMgkYyKkk4aIKUgLzXG+BpiKr2gxWha05kZhzkH7SzAuRlrQHrq0+H69LWulyfSNDncJLqWotVRW+f5v1eVBuFIYnWZcd4Sx4c7NlBeq5lznDhYS6q0XvDvGmOIIkldG5raEUhFO4sQeNrQXRutRSZrnSOKQwb7Bd7g3m/JnPBiEP1OPNNxFDz3meuEjw7YH5ac3mhjjHcTPLmecf9dXaZFwyPnJjOPmIwk9upFeakpSk2ShHSzkNMbbR7bGTMaN/SzmLLRbPRjLII09BnWxlqCsdDLUoqm4b5TPYbjEuskdRPQbYcMRzW9zCv1WAHbuyX3n+7yzNM9pkXDSidmtZexPy5opQkyEBjtCKTk1Ik2SeiFLTrppUrca70U6wRY6HViNvoJK23f9d7o+7HFvGw4uRF7TcVxBU5wIvMUoEgpysZnoKEUrLZjtLWLz9I5ZsyEdFF2iSPvLzMtmuvOHq/UNJnzcK/V3T5qazz/u29C2ZkIaHXZ1npaaHBe7m1Zi7xxWH6qTxEHMwolBca6hRk9CNqx34JOiob9UUkchnQ74SU3z+F66pyelESaUzMx1Tjw9aleS7LeTTnRz5iWPgB6J0lf91xpJ/QeiC+5Udf7KY9fmLDaTdgblWyuZt4bJggIgpq6say3E+5d9yOU1sLuKGcyrem0Yu4/3WU8qXDW8ex7V1lfa7N1bsRoWjEOGhKhiOOA/YkvHfTb0SLYxEHAXesB7TSkKBs67YjRpKGVqEVWZ6y7LAubbx8PNzvmx0wjzyYw1rHWSVnr+GGCgw+uEysp9929ypnH9hYBaf0KgfBw2eWJ7DaulPldT5Z51K5m8e9UM9K6vOLW+koNxSWOH3dsoLxWM+c4cTCjiAPv07K5mACqFpaku+MSbRwr3YsUk/l6DtdT5/SkQEkmhd8qHswW5jdnWVlwgjgJrrntWukk4AR5WQMJurEMJyVF0xAGivVeRlE3WOdY66bebKmX8PmzIwQCazztJ0tCTq5mBHHIMPFGVEr52fiiarh7s+11G4NgQcc5aK262m3Ta8UMM/+5zLO6K2VBVwskR21JD/9eEMjLjnOc4hJXygyPS1ptftxrHeuLoRaprWVrd8pk2tBuhZxaay14sjcLd2ygvFYzB47PDe5aN+7Bm6iTRMjMK89QgxSG3mw9YSjY3i/Y3y9Awlovnl0I3ihqzv2bH+8gj3PeRDl47sMZjXOOjX5CUYWMY0/xmZYahWRlJcFYRzuNqLVhd1ziZkIWjfVq5Z1OwoX9nDQJSGPFvb2MShsevTCm0Za1bkwrSZmWDVHga26NsSgpaCXemzsIwsW652Ii+6OKditkPQ2v+/O+Ed3cp0LOvlKgOk5qzpLm420rBuOKNJqVWIB7brJN7h37qV+rmQPHN7lwrRv34E1Uab34sidFTRAcCsoSwCEO/F7viG3Z1bZdl/FE8Q6H+6OKdhKQV5ZJ7jmJ6ysJaegzVqmgnpHHa22IggCBN6eKIjnLFDVnd8Y++KcRzkJeGTrSzep/PqjPxzzTKKSd+tHC+ey8dX4rubnibV2nRXOZrWsY+s/noGTZXB38uDOo4yZnH2cwX9J8vLhwGoUEgSAlZDJtYOPmruGODZRHNXOO6+a43hs3VIpOGqINdFI/5jhH0zg2N1IC6xZeK0/l3FfKQuYPgc+fHZIXBhUIJDCaNkTd4JKaKggEgklZkYQBQ1PRTkOyxHdWR9MarLeBAIdSnuqyeYnoSHVVzuDhEdHD89OhUmzv50xLQysOENLvAOaCyMeNxZSNkwwmpS+jPIVdxnEG8y+GrfVRSFLFmceGGOeNAu+7u3fT13DHBsqD5mJXws3e0sy9wy+azV8MlEdlv08UV8tCOllEYyxhKEkTRRwEaGMvq6nOa6PdLEZbRxKHXrl81lntdjyJu240OIEKWFijXmkNhzmDVxsRPfjwmuaaqrH0WhF1bdkbVjcsUM7XujMocQh6Wbycj76F4AWxvFShE/DEH11PHXdsoLTOsT8qubBfXDE7uNlbmmud77ipTFfLQqQQnFxrMRhXJOHlplIXz3tpbfQwZebuzS511Sy8ga7kLHhQsq7Smgt7Ba1UEUXqqiOiBzO7UV7TWEur8KK5HKOO49U+r6r2dVUpBWK2vi/mTO5G43Cf4OB1dvCeLQrDvSe6BIFAa0dRHJMvyhPAHRsoj+p634wtzZUaRr3W5Tf8UdnvcZx3/pC4lqnUtWqjhzvJK+3kyAzPOsfjOxOGoxqEY1JYerMu9JUmoQ5mdp2Wn2GflJokVpxav/IXdVxNOVg2Tm42DvcJRnlNqORlfYMn4xh53HharoR3v/vd/OZv/iYAL37xi3nzm9987Oc4qut9M/B0SV1d67yBlL5jeBOK4eO8ZjSpSeIA6yxRMJOlu0ogO5jZdVvRQlFeKXnVz+04P+Nl4+Tm4nCfYH9UsbmSXXbPPhnHyOPGTQ+UH/zgB/n93/993ve+9yGE4Ad/8Af57d/+bf7qX/2rx3qeMBRsDwv293OEhPX+tdXNbwTmFwLCT1jsjzy14clmPdebPd0qEltVbcmSkKbxZm3TwrDSPZr/FoYz/5nZWOFctORq5ziu97psnNxcHM7g263wirXrm/lwvxpueqDc2NjgLW95C1Hkn9YPPPAAZ8+ePfbzWOcYT0v2JxVRJFl1N6YRcC3ML4SiMgvi+FPJeq43ezocaJ6OhwTMzdp8YMzLZmHWdj1ws5L9Uf3/5Xb59sXhDP5KU1O3CoRz7sm6nz5lnDlzhte+9rX80i/9Evfdd9+xHvsvzuxSa8/Bq2pLFAi+9L61Yz3HUbDWMZxUPHp+RJaE9NoJ4NDGcmq9/YSPt7UzIZhZC1h79ePsDQrO7eUYa8lLTS8LObHepjej5NwszN9/WWuiUCEcVNqQRME113K97/PwOY467hJLPFk8bY/fT3/60/zdv/t3efOb3/yEguTu7gRrj47t+4OCVitmfz+nrj0lZnt7/BRW/OSRhZIir9CVXmQ92weeT9Y5oiTi7LnRNbfU0+lF+90rHWeOC/sFQsDufsFoWjEMA6q6oZWEx1Ij3djoPKHPMgD296eLte8Yw+4BbudhXO/7PHwOXTXsHmqIPdG1Pp24ndYKt9d6r7VWKQVra9dOXG7uwOQMf/Inf8L3fd/38cM//MN853d+5w05x2ovRoVe4l8F/u9PFzpZRBoHC7GMw1uKcV57C9tZh36c10/qOHPEkSdOj6cVUgqcsF7d6JDm4c3EJbVEdbn+4kFc7/tcYombhZueUW5tbfGGN7yBhx9+mAcffPCGnafXiomSCFebK0rt30wc1SS43g799TYbWmlIfs57yoShYqWdkpfem+ZG4lrNpmUt8ebgOOlSS1zETb9a3/Oe91BVFe985zsXP3vNa17Da1/72mM9z3FzE28kjnsyZ1o0JFFAe2aMdXZ3wv2nezf8YXGtZtNR+orXe5wlro3lZ3djcNMD5dve9jbe9ra33ezT3tI47smcqrZEShAqQS+LcM7RPSKzOI5M5FpUnaP0Fa/3OEtcG8vP7sZguf+5BXDc2W8cSbb3LZ0sxlpHGMojhTaOIxO5nu319dzIy236k8fys7sxeFqaOUvcWHSyiG4npGo0YSiJI3mZaMVhPJFmy7XOe1QTJo4kjTGLMsOV1rVs5jx5LD+7G4M79nFzlCjGnQwpBKfX23SzS7fS18JxZCLX02y6njHB5YTMk8fys7sxuGMD5c2ygrhV8URvmJs157y8kZe4HXHHBspbQRTjdsIygC2xxNVxx9YoD1NujqrRLbHEEktcDXds9OhkEdmyqL3EEkscA+7YrfftRDhfYoklbm3csRnlEkssscRxYRkol1hiiSWOwDJQLrHEEkscgWWgXGKJJZY4AstAucQSSyxxBJaBcokllljiCCwD5RJLLLHEEbjteJRP1DjqdjKaWq71xmC51huH22m9V1vr9byHp9WFcYkllljidsBy673EEksscQSWgXKJJZZY4ggsA+USSyyxxBFYBsolllhiiSOwDJRLLLHEEkdgGSiXWGKJJY7AMlAuscQSSxyBZaBcYoklljgCy0C5xBJLLHEEloFyiSWWWOII3JGB8td//dd5+ctfzktf+lLe+973Pt3LWWAymfCKV7yCxx57DIAPfvCDfPu3fzsvfelLefjhhxev+8QnPsGrXvUqXvayl/FP/sk/QWt9U9f57ne/m4ceeoiHHnqIn/iJn7il1wrwb/7Nv+HlL385Dz30ED//8z9/y6/3Xe96F295y1tu+XV+z/d8Dw899BCvfOUreeUrX8lHPvKRW3a9v/M7v8OrXvUqvu3bvo0f//EfB475s3V3GM6dO+de8pKXuP39fTedTt23f/u3u09/+tNP97Lcn/3Zn7lXvOIV7rnPfa579NFHXVEU7sUvfrF75JFHXNM07gd+4AfcBz7wAeeccw899JD70z/9U+eccz/6oz/q3vve9960df7BH/yB+xt/42+4qqpcXdfue7/3e92v//qv35Jrdc65D3/4w+41r3mNa5rGFUXhXvKSl7hPfOITt+x6P/jBD7rnP//57kd+5Edu2WvAOeeste5FL3qRa5pm8bNbdb2PPPKIe9GLXuS2trZcXdfuta99rfvABz5wrGu94zLKD37wg7zgBS+g3++TZRkve9nLeP/73/90L4v/+l//K29/+9vZ3NwE4KMf/SjPeMYzuOeeewiCgG//9m/n/e9/P48//jhlWfLVX/3VALzqVa+6qevf2NjgLW95C1EUEYYhDzzwAGfOnLkl1wrwV/7KX+E//sf/SBAE7O7uYoxhNBrdkusdDAY8/PDDvP71rwdu3WsA4HOf+xwAP/ADP8Bf+2t/jf/0n/7TLbve3/7t3+blL385J0+eJAxDHn74YdI0Pda13nGB8sKFC2xsbCz+vrm5yfnz55/GFXm84x3v4Ou+7usWf7/aOg//fGNj46au/1nPetbiIjpz5gy/+Zu/iRDillzrHGEY8tM//dM89NBDPPjgg7fsZ/tjP/ZjvOlNb6Lb7QK37jUAMBqNePDBB/nZn/1Z/sN/+A/8l//yXzh79uwtud4vfOELGGN4/etfzytf+Ur+83/+z8f+2d5xgdJaixAX9eWcc5f8/VbB1dZ5q6z/05/+ND/wAz/Am9/8Zu65555beq0Ab3zjG/nQhz7E1tYWZ86cueXW+yu/8iucOnWKBx98cPGzW/ka+Jqv+Rp+4id+gk6nw+rqKq9+9av56Z/+6VtyvcYYPvShD/Ev/+W/5Jd/+Zf56Ec/yqOPPnqsa73thHuPwsmTJ/njP/7jxd+3t7cX291bCSdPnmR7e3vx9/k6D/98Z2fnpq//T/7kT3jjG9/IW9/6Vh566CH+6I/+6JZd62c/+1nquuY5z3kOaZry0pe+lPe///0opW6p9f7P//k/2d7e5pWvfCXD4ZA8z3n88cdvuXXO8cd//Mc0TbMI7M45Tp8+fUteB+vr6zz44IOsrq4C8C3f8i3Hfg3ccRnl13/91/OhD32Ivb09iqLgt37rt/iGb/iGp3tZl+Grvuqr+PznP7/YNvzGb/wG3/AN38Dp06eJ45g/+ZM/AeDXfu3Xbur6t7a2eMMb3sBP/uRP8tBDD93SawV47LHHeNvb3kZd19R1zf/5P/+H17zmNbfcen/+53+e3/iN3+DXfu3XeOMb38g3fdM38XM/93O33DrnGI/H/MRP/ARVVTGZTHjf+97HD/3QD92S633JS17C7//+7zMajTDG8Hu/93t867d+67Gu9Y7LKE+cOMGb3vQmvvd7v5emaXj1q1/N8573vKd7WZchjmPe+c538g/+wT+gqipe/OIX863f+q0A/ORP/iRve9vbmEwmPPe5z+V7v/d7b9q63vOe91BVFe985zsXP3vNa15zS64V4MUvfjEf/ehH+Y7v+A6UUrz0pS/loYceYnV19ZZc70HcqtcA+ODzkY98hO/4ju/AWsvrXvc6vuZrvuaWXO9XfdVX8YM/+IO87nWvo2kaXvjCF/La176WZz7zmce21qUVxBJLLLHEEbjjtt5LLLHEEseNZaBcYoklljgCy0C5xBJLLHEEloFyiSWWWOIILAPlEkssscQRWAbKJZ40vumbvomPfexj13zNeDy+qVSRL/3SL2Vvb++mnW+JLw4sA+USNxTD4fDIYLrEErc67jjC+RJPD77yK7+Sv/N3/g5/8Ad/wIULFxYE4B/90R+lLEte+cpX8qu/+qucOXOGd7zjHQwGA4wxfM/3fA+vfvWr+fCHP8w73vEOsixjOp3y5je/mZ/92Z/lnnvu4dOf/jRaa/75P//nfO3Xfi2f//zn+Rf/4l8wnU7Z3t7my77sy/jX//pfE8fxVdf32c9+9qrnffjhh694nrqu+cmf/En+3//7fxhj+PIv/3Le9ra30W63+aZv+iae97zn8Rd/8Rf80A/9ECdOnOCf/bN/RtM03HvvvZw9e5a3vOUt/Pqv/zpra2u86U1vAvwkyG/91m/xsz/7szfrq1niOHCcunBLfHHhJS95ifvoRz/qnHPu2c9+tvvFX/xF55xzH/vYx9xXfMVXuLIs3aOPPuq++qu/2jnnXNM07uUvf7n78z//c+ecc6PRyH3bt32b+9M//VP3h3/4h+7LvuzL3GOPPeacc+4P//AP3XOe8xz38Y9/3Dnn3Hve8x733d/93c455975zne6//E//odzzrm6rt0rXvEK9/73v3+xjt3d3UvWedR5r3aen/mZn3HvfOc7nbXWOefcv/pX/8q9/e1vX7z3d7/73Yvjf8M3fMNC7/BDH/qQ+9Iv/VL3h3/4h+7jH/+4e+ELX7jQdXzd617nfvd3f/cYPv0lbiaWGeUSx4Zv/uZvBuC5z30udV2T5/kl/37mzBkeeeQR3vrWty5+VpYlH//4x3nggQc4deoUp0+fXvzbXXfdxXOe8xwAvvzLv5z3ve99APzjf/yP+YM/+AP+/b//95w5c4YLFy5cdq4nct6rnecDH/gA4/GYD37wgwA0TcPa2triGHPZvE996lOAH6cEeMELXsCznvUsAJ7znOdw991384EPfID777+fCxcu8KIXvej6PtAlbhksA+USx4b51ncuW+UOTccaY+h0Ovzar/3a4mc7Ozt0Oh3+7M/+jCzLLnl9kiSLPwshFsf7oR/6IYwxfNu3fRvf+I3fyNbW1mXneiLnvdp5rLW89a1vXQTA6XRKVVWL187Xq5S67PwHlWu++7u/m//+3/879913H3/9r//1W1L2b4lrY9nMWeKGIggCjDE457j//vtJkmQRsLa2tnjFK17Bn//5nz+hY/7+7/8+b3jDG3j5y18OwEc+8hGMMVd9/ZM974te9CLe+973Utc11lr+6T/9p/zUT/3UZa974IEHiKKI3/3d3wW8cvmnPvWpRUB82ctexic+8Qn+1//6X3zXd33XE3qvS9waWGaUS9xQbGxs8LznPY+HHnqI9773vfzbf/tvecc73sHP/dzPobXmH/7Df8jXfu3X8uEPf/i6j/mmN72JN7zhDWRZRrvd5i//5b/MI488ctXXR1H0pM779//+3+dd73oX3/md34kxhuc85zkLU7CDCIKAn/mZn+Htb387P/VTP8V9993H+vr6IlONooiXvexl7OzsLDQTl7i9sFQPWmKJY8C73vUu/vbf/tusr6+ztbXFK1/5Sv73//7fdLtd8jznb/7Nv8mP/diPLWw2lri9sMwol1jiGHD69Gm+7/u+jyAIcM7x4z/+43S7XX7v936PH/7hH+a1r33tMkjexlhmlEssscQSR2DZzFliiSWWOALLQLnEEksscQSWgXKJJZZY4ggsA+USSyyxxBFYBsolllhiiSPw/wNFWhf4IxlK4gAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# check target properties: E & HOMO-LUMO gap\\n\",\n \"_ = plt.figure(figsize=(5,5))\\n\",\n \"_ = plt.scatter(data['E'],data['HOMO-LUMO gap'],s=15,c='b',alpha = 0.1)\\n\",\n \"_ = plt.title('iQSPR sample data')\\n\",\n \"_ = plt.xlabel('Internal energy')\\n\",\n \"_ = plt.ylabel('HOMO-LUMO gap')\\n\",\n \"_ = plt.show()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A complete iQSPR run may take a very long time. For practical purposes, the full design process can be separately done in multiple steps, and we recommend taking advantage of parallel computing if possible. To save time in this tutorial, we will extract only a subset of the full in-house data set for demonstration. You will see that the subset represents the full data set in our target property space well. Readers can try to repeat this tutorial with the full data set if sufficient computing resource is available.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" index SMILES E HOMO-LUMO gap\\n\",\n \"0 23630 CC(C)CN1CC(C1)C2=CC=CC=C2 193.542 5.914093\\n\",\n \"1 19222 C1=CC(=CN=C1)C(=O)OCCO 110.998 5.186226\\n\",\n \"2 20189 C1=CC(=CC=C1CC(C(=O)O)N)N(CCCl)CCCl 202.960 5.400097\\n\",\n \"3 7775 CCCCCC(=O)OCC(C)C 191.230 7.727368\\n\",\n \"4 11881 C1=CC(=C(C=C1O)C(=O)O)C(=O)O 91.482 4.856441\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"np.random.seed(201903) # fix the random seed\\n\",\n \"\\n\",\n \"# extract a subset from the full data set\\n\",\n \"data_ss = data.sample(3000).reset_index()\\n\",\n \"print(data_ss.head())\\n\",\n \"\\n\",\n \"# check target properties: E & HOMO-LUMO gap\\n\",\n \"_ = plt.figure(figsize=(5,5))\\n\",\n \"_ = plt.scatter(data['E'],data['HOMO-LUMO gap'],s=15,c='b',alpha = 0.1,label=\\\"full data\\\")\\n\",\n \"_ = plt.scatter(data_ss['E'],data_ss['HOMO-LUMO gap'],s=15,c='r',alpha = 0.1,label=\\\"subset\\\")\\n\",\n \"_ = plt.legend(loc='upper right')\\n\",\n \"_ = plt.title('iQSPR sample data')\\n\",\n \"_ = plt.xlabel('Internal energy')\\n\",\n \"_ = plt.ylabel('HOMO-LUMO gap')\\n\",\n \"_ = plt.show()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Descriptor and forward model preparation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"XenonPy provides out-of-the-box fingerprint calculators. We currently support all fingerprints and descriptors in the RDKit (Mordred will be added soon). In this tutorial, we only use the ECFP + MACCS in RDKit. You may combine as many descriptors as you need. We currently support `input_type` to be 'smiles' or 'mols' (the RDKit internal mol format) and some of the basic options in RDKit fingerprints. The output will be a pandas dataframe that is supported scikit-learn when building forward model with various machine learning methods.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from xenonpy.descriptor import Fingerprints\\n\",\n \"\\n\",\n \"RDKit_FPs = Fingerprints(featurizers=['ECFP', 'MACCS'], input_type='smiles')\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To get the calculated fingerprints, simply use the transform function. The column names are pre-defined in XenonPy.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" maccs:0 maccs:1 maccs:2 maccs:3 maccs:4 maccs:5 maccs:6 maccs:7 \\\\\\n\",\n \"0 0 0 0 0 0 0 0 0 \\n\",\n \"1 0 0 0 0 0 0 0 0 \\n\",\n \"2 0 0 0 0 0 0 0 0 \\n\",\n \"3 0 0 0 0 0 0 0 0 \\n\",\n \"4 0 0 0 0 0 0 0 0 \\n\",\n \"\\n\",\n \" maccs:8 maccs:9 ... ecfp6:2038 ecfp6:2039 ecfp6:2040 ecfp6:2041 \\\\\\n\",\n \"0 1 0 ... 0 0 0 0 \\n\",\n \"1 0 0 ... 0 0 0 0 \\n\",\n \"2 0 0 ... 0 0 0 0 \\n\",\n \"3 0 0 ... 0 0 0 0 \\n\",\n \"4 0 0 ... 0 0 0 0 \\n\",\n \"\\n\",\n \" ecfp6:2042 ecfp6:2043 ecfp6:2044 ecfp6:2045 ecfp6:2046 ecfp6:2047 \\n\",\n \"0 0 0 0 0 0 0 \\n\",\n \"1 0 0 0 0 0 0 \\n\",\n \"2 0 0 0 0 0 0 \\n\",\n \"3 0 0 0 0 0 0 \\n\",\n \"4 0 0 0 0 0 0 \\n\",\n \"\\n\",\n \"[5 rows x 2215 columns]\\n\"\n ]\n }\n ],\n \"source\": [\n \"tmp_FPs = RDKit_FPs.transform(data_ss['SMILES'])\\n\",\n \"print(tmp_FPs.head())\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"XenonPy also allows users to flexibly organize the desired descriptors. The first step is to import `BaseDescriptor` and all necessary descriptors (fingerprints) supported by XenonPy, which will be used to construct a customized set of descriptors for forward prediction. Then, users can simply pack all descriptors needed in the `BaseDescriptor`. For other descriptors, users can use the `BaseFeaturizer` class to construct for their own needs. If so, we would appreciate very much if you could share the code as a contribution to this project: https://github.com/yoshida-lab/XenonPy/tree/master/xenonpy/contrib. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# XenonPy descriptor calculation library\\n\",\n \"from xenonpy.descriptor.base import BaseDescriptor\\n\",\n \"from xenonpy.descriptor import ECFP, MACCS\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Defining your own `BaseDescriptor` allows you to customize the options for each fingerprint you wanted. You may combine multiple descriptors by adding more \\\"self.XXX\\\" in the `BaseDescriptor`. All descriptors with the same name \\\"XXX\\\" will be concatenated as one long descriptor. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# prepare descriptor function from XenonPy (possible to combine multiple descriptors)\\n\",\n \"class RDKitDesc(BaseDescriptor):\\n\",\n \" def __init__(self, n_jobs=-1, on_errors='nan'):\\n\",\n \" super().__init__()\\n\",\n \" self.n_jobs = n_jobs\\n\",\n \"\\n\",\n \" self.rdkit_fp = ECFP(n_jobs, on_errors=on_errors, input_type='smiles')\\n\",\n \" self.rdkit_fp = MACCS(n_jobs, on_errors=on_errors, input_type='smiles')\\n\",\n \"\\n\",\n \"ECFP_plus_MACCS = RDKitDesc()\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" ecfp6:0 ecfp6:1 ecfp6:2 ecfp6:3 ecfp6:4 ecfp6:5 ecfp6:6 ecfp6:7 \\\\\\n\",\n \"0 0 1 0 0 0 0 0 0 \\n\",\n \"1 0 0 0 0 0 0 0 0 \\n\",\n \"2 0 1 0 0 0 0 0 0 \\n\",\n \"3 0 1 0 0 0 0 0 0 \\n\",\n \"4 0 0 0 0 0 0 0 0 \\n\",\n \"\\n\",\n \" ecfp6:8 ecfp6:9 ... maccs:157 maccs:158 maccs:159 maccs:160 \\\\\\n\",\n \"0 0 0 ... 0 1 0 1 \\n\",\n \"1 0 0 ... 1 0 1 0 \\n\",\n \"2 0 0 ... 1 1 1 0 \\n\",\n \"3 0 0 ... 1 0 1 1 \\n\",\n \"4 0 0 ... 1 0 1 0 \\n\",\n \"\\n\",\n \" maccs:161 maccs:162 maccs:163 maccs:164 maccs:165 maccs:166 \\n\",\n \"0 1 1 1 0 1 0 \\n\",\n \"1 1 1 1 1 1 0 \\n\",\n \"2 1 1 1 1 1 0 \\n\",\n \"3 0 0 0 1 0 0 \\n\",\n \"4 0 1 1 1 1 0 \\n\",\n \"\\n\",\n \"[5 rows x 2215 columns]\\n\"\n ]\n }\n ],\n \"source\": [\n \"tmp_FPs = ECFP_plus_MACCS.transform(data_ss['SMILES'])\\n\",\n \"print(tmp_FPs.head())\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"##### train forward models inside XenonPy\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Warning: the next module may take 1-2min to complete!**\\n\",\n \"\\n\",\n \"The prepared descriptor class will be added to the forward model class used in iQSPR. The forward model calculates the likelihood value for a given molecule. iQSPR provides a Gaussian likelihood template, but users can also write their own BaseLogLikelihood class. To prepare the Gaussian likelihood, iQSPR provides two options:\\n\",\n \"1. use the default setting - Bayesian linear model\\n\",\n \"2. use your own pre-trained model that output mean and standard deviation.\\n\",\n \"\\n\",\n \"Here, we start with the first option. Note that there are a few ways to set the target region of the properties.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 29.6 s, sys: 363 ms, total: 30 s\\n\",\n \"Wall time: 30.9 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"# Forward model template in XenonPy-iQSPR \\n\",\n \"from xenonpy.inverse.iqspr import GaussianLogLikelihood\\n\",\n \"\\n\",\n \"# write down list of property name(s) for forward models and decide the target region\\n\",\n \"# (they will be used as a key in whole iQSPR run)\\n\",\n \"prop = ['E','HOMO-LUMO gap']\\n\",\n \"target_range = {'E': (0,200), 'HOMO-LUMO gap': (-np.inf, 3)}\\n\",\n \"\\n\",\n \"# import descriptor class to iQSPR and set the target of region of the properties\\n\",\n \"prd_mdls = GaussianLogLikelihood(descriptor=RDKit_FPs, targets = target_range)\\n\",\n \"\\n\",\n \"# train forward models inside iQSPR\\n\",\n \"prd_mdls.fit(data_ss['SMILES'], data_ss[prop])\\n\",\n \"\\n\",\n \"# target region can also be updated afterward\\n\",\n \"prd_mdls.update_targets(reset=True,**target_range)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The variable directly contains models for each property trained this way. Note that the input of these models is the preset descriptor (RDKit_FPs in this case).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"BayesianRidge(compute_score=True)\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"prd_mdls['E']\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The variable can be also used for prediction directly from mol/smiles, where the output is a dataframe.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" E: mean E: std HOMO-LUMO gap: mean HOMO-LUMO gap: std\\n\",\n \"0 216.536505 30.798743 6.448873 0.665549\\n\",\n \"1 149.048033 30.669020 5.069425 0.662847\\n\",\n \"2 196.936252 29.520508 5.396384 0.642558\\n\",\n \"3 223.675992 29.193763 7.784497 0.634831\\n\",\n \"4 87.747905 30.040543 4.707319 0.648994\\n\",\n \"CPU times: user 3.77 s, sys: 137 ms, total: 3.9 s\\n\",\n \"Wall time: 4.84 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"pred = prd_mdls.predict(data_ss['SMILES'])\\n\",\n \"print(pred.head())\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let us take a look at the distribution of the likelihood values for all the molecules in our data as a validation. Note that directly calling the object is equivalent to calling the `log_likelihood` function. The output will be a pandas dataframe with properties on the columns. To get the final log-likelihood, you can simply sum over the columns. You will see that all molecules have a small likelihood because they are far from our target region. Note that when setting the target region for the likelihood calculation, \\\"np.inf\\\" is supported. Also, we use only log-likelihood in iQSPR to avoid numerial issue.\\n\",\n \"\\n\",\n \"Note that we can also update the target region when calling the `log_likelihood` function, but this will automatically overwrite the existing target region!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" E HOMO-LUMO gap\\n\",\n \"0 -1.218542 -16.024975\\n\",\n \"1 -0.049529 -7.015274\\n\",\n \"2 -0.613727 -9.251676\\n\",\n \"3 -1.566933 -31.356100\\n\",\n \"4 -0.001840 -5.458423\\n\",\n \"CPU times: user 3.71 s, sys: 120 ms, total: 3.83 s\\n\",\n \"Wall time: 4.76 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"# calculate log-likelihood for a given target property region\\n\",\n \"tmp_ll = prd_mdls(data_ss['SMILES'], **target_range)\\n\",\n \"print(tmp_ll.head())\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# plot histogram of log-likelihood values\\n\",\n \"tmp = tmp_ll.sum(axis = 1, skipna = True)\\n\",\n \"\\n\",\n \"_ = plt.figure(figsize=(8,5))\\n\",\n \"_ = plt.hist(tmp, bins=50)\\n\",\n \"_ = plt.title('ln(likelihood) for SMILES in the data subset')\\n\",\n \"_ = plt.xlabel('ln(likelihood)')\\n\",\n \"_ = plt.ylabel('Counts')\\n\",\n \"_ = plt.show()\\n\",\n \"\\n\",\n \"# plot histogram of likelihood values\\n\",\n \"_ = plt.figure(figsize=(8,5))\\n\",\n \"_ = plt.hist(np.exp(tmp), bins=50)\\n\",\n \"_ = plt.title('Likelihood for SMILES in the data subset')\\n\",\n \"_ = plt.xlabel('Likelihood')\\n\",\n \"_ = plt.ylabel('Counts')\\n\",\n \"_ = plt.show()\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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Hbok7IPvHAnPzm7hTh+cRRrVb7Hv0hpNgWf1jg6iwwTe18rs0i3QIvJg6fF1jdSbFCgOmqDAs0qpKKBftk1OMWBKVEZF4e2xTyUo413hxwGCvzLAgwK0auyKx52ifwonN0e+GIc5AH/z6MSTKZJmQpf43JfvF6BQ6IMFd1wsC7zcPf1lhVvnVwmMtGJUTnUxe8TfXCkJmr4KwxVaXAqTz2pptu4otf/CJdXV3U1NTw4IMP8pnPfGbk91deeSVdXV1s2bKFFStW8MMf/pBVq1ZNeNxK8D4JAQuCowP16YLGlsRxxAm9QVkXJl/QTLBGVzmbPy4nXjdizdBMLCMuf00EOOCELifUR7gxDVSPc15rQmRujAyJ0BSV42IN1zKOOOGoc0zPs+rtTtjtHUtzquFBJwQn1KoF7NEBuVHhmhA5rML0UeWBbYmjwwkFoBXbGbSqnhIwjws8M07d/5AIB72wbBxGxTZv13g4KusmUDicHqA5/xymT5GN97KDIRy3Te1pFVQwIdra2vj4xz/OBz7wAdI05Z577mHt2rV8+MMf5mMf+xhr1qzhT//0T/md3/kdBgcHaW9v5/Of//yEx60E71EYwAZlCihXZ3FCZkVLVPY4Ybt3NKpOmqo3XS34lo//rPdUAYuc0B6Va9JAKqdmi/OjkokyK+hpfR0DUKUwQ5WjIjxVcDREM1PwQGde/+3Nt4iDeQ3+eP5zQ1QaIkzT8fVIqvLzGAS2OqFFjYnSjdAO7HTCnsRxKMJtJxk4DCOUABUhYAtkCmzxjiEHtVFYrsoQRpecrsrsqAwgtE+ilNEC/JfBFJiYVTIaR4F/riqQAXOApik8t4JLA+cz8wa46667uOuuu8b831e+8pWRf69bt46vf/3rUzpmJXiPwpDAgIDDmosTBe8FUekNkYPesc9NTQekXGZRoBFrLvZjtL2m03yTGpQzZp8KvJA4ukSYHyJHnLDBO2Y45foMXnbCC4lnZrTgv83Z4/Y4x27nWBqsIXtrFgjAi970q1dnkWos0G72joiSKOxLHIfzID0nBlqAHQibnHDVOAYOzWqLYhGlCtjgLWNvjkp9hNl5Nv98YuyN5fluYPZJ1zWS67uMcw2mErTLqAFmxMiACGcei6igggsHleA9CtMV1oaIV6if5HMWB6WgcWTMvYzJ2mUJcEUWCcAPvGM3cHM6PkVtPPRhZZaywuAw1pR8wXuiQA8wP1jN/VA+KVoCCji8gGSRXd7RL8KiGEeadZu88MOiY15QFohQrVaC2eIdu7ywOgvURZh3UpDudcqCk8oto9/raC/M43l5aJoqUW0HsibYe/cy/vXLgK8VPUed8IuljPZz4BVRC/w/w4EItDZA56s4VsTYPjU6cR+kggsIFT3vixvC5DWo+4B9TpilyvKodIhwWKx5dUiETYljToiTYmSUXeJrgNp8FP6ICH1iVLEEy6qPjeKbgwXqZwqeksDVaaRZlauyiCjExLEfoQmlDiVgWfuAQHO05x711my9LCg9oiMLQAQ6nWN6tDJKnSoRq3m3xEifOGpUuDUL9Aj8JPHMjpFa4Jg4BkSYe5pdSAp0i9CoVsPuzzPvRxJPyUFnFNZmkSGBulGXrg847oTaqBx2jgGBbhztnBunn7PJ2MfDXmeSuHV5M/pi1W95w6ESvC9eKBaMI1bTneizeSU35+2NyooQ2VCw2/SGNHBMLBsezcgY/TplD8TRXOkGLON2WN36ucQm65LMhmMOOuFF76jNg0J5QCRRe3yvKC96T7NGrgmRJVHZ6IQfV3l2iNAVIq1RuTONpMCPCx5RGHDCHWlgeFSwdFippN3BtKg8UfA0qXJlFrk1swWpNueOHxZhyMEeHOsw6mN1VKadZs3a7oXdztGeH68+X9zWhEBnFObni9XJjcsNiaPXCUuzyD2llB4cSy9Ab80EcAKFC9Qso4Lxcb5r3ucDleCdo1esngvQpGFM8AnAei8MC1yT2Yh4e7T69JyoVCkjQahKYZEqNZllwp0i7PHC0hCZpqNEqYDr0zCGCVL+MIawhaRf4CpVMsCPwzc3CmHgqMDDxYTDAquDY3kItKlyGKEpWq15n8CPqhK8KneVAocFXs6HddpRCid9G9tUaQuw31n9vxdb2BKgSpXnEk9LjCwOSgiRmbla3nWpSdaermRQpVbCqTqpzNSsdp6nwzRVelV42TuaVVl7FlOqrwVmR6UxZwJVgvfFg0rwvohRq8aRNt7z2N/1CjxcSCiJMjMGlkXNx9NPZNXX543E8ja5rL63KTFN64IKa4MFeo0WwEYbKqRYRj5NlUxgriqZwqOJ55gT3plm3JjXwkcHhSpsanO/QK845oaUV5xQElgWIs0aOYrwihO2eKFRhSMSR/jjOxLH5eH0QlHtUUmJY3Q8esSO3+EcK0Ng1SgmyERc78VRCUQGxdg9k60LXx7s2r+QeI6KMCDn3pDiXEDgtEygCio4l3jDBe8SVtMt0+C6BfY4YWFUrj0Nk6NerfY8JONzvjU/To2eGryWBGVTzuMeAvpFRgZsslGPewlYn5woJ1yVRoaBb1R5+kR4WRy3EukRm6KcqZFVweiAu53QrrA8BhpU2ZR4FJgdI3Oi0usgcdZ0VFU2JiYhqwhNeuYMdgh4Jd+R1KeBOvJhoyzSOCqgR+w9ToTICceeRh1fxjZgQ0INeoLxI0CLwtKcx91YCZAVnEtUat4XNlLgqcSTio1LNyjs8I5OJwSxoDkeCsDPlTJKMn5WVa5HF1RpAKZHZWkelFpUceo47G3cvDkPeLu9UBLP0hARGGFYhKg8nDjao7IiKneWAru9sCav7/aKMOygQy3r3eWFPicsCpHVeWlmKERKOYsDjILXGZUlCPu8o89FCsHc0Ld4x2bvWHuaIaPR28ny30UYE3RLwLOJowFYwpmzbw8syGmMp5PU3e6EXd4xO+qY83LAkkk2lAcnOI8KKrjY8YYI3uUhlKJaIzHIiax3frCa8rwJhkCqYdyJRrCmoQeGndCPUcXmj9J5nh0j+zEtkiaFK9PA8YInBTZ5R1FgFXBjKXB/0fGDYoGWqPz6QInl0dgsZbRHJYzKeltiZL841oQ4MtSz9KQAN03h1izyolNmR5gbbSexN2/QDpwhi6gHrivrgpzmMcMCfU5w+b9PLjudjKVRaVRj1Iwux4xcT0DE9M7PBn9X9DxW8NwzFLgjTHHUsoI3JPzcCFMwY/BTNGM4H7jkg/cw8O2iZ58XbitFrs2dcMq0uBqUNZmOKxA1WbSqclMaOC7wQCFhOjqGIrYkKo7Ik0lCm0Zc3sxqiMpcVXZ7RxuWwR5yjmMoIsqGgmdGahKmEVuAymPrZRx2jioROpxQzCcjy4tGmh+zDugXOJK7us/OpU4XRqVRIw0TKOHVq73+ERGKOcukjhM7xwaFtWmkGWusToRBjD2SAlVEXsmbojdnkSJWF29LA7VnWRp52QuDAi97uKMSuyuYBMJ+R+ifwheuTuDa83c+k8F5Dd7f/va3+Yu/+AsAbrvtNj71qU+dz5cbF6lY3TYVIWOsuFGPmAhUAWN+jDcOPlnUAn0IbWpslNGyr8fEBP5f8YJkwtwYqcXElpZEZXEMzABeEMvgl4RIHcJhJyMBck+uJNiSc7nLqFelU4U+hGcSYU5eaghYKeN47vpTO04Wa/VsY6SMzu4HYcQ4uDwsdCif1jwsYmYN+fSj5texSZVZTG7ApYg9vg9hQJWfFhKT342RJdHKI6+m6fdvBzM2FjzXpZXIXcHkUGGbjMLg4CD/7b/9Nx544AEaGxv51//6X/PYY49x0003na+XHBf1Cm9PI31ZHNGvLmNEgOoc0bpmqrI6RLM5G/X/1QptEeo1clWuhT0YT5gCBOAxYGvOp65Sc69ZEeLIgiJYKUFGlab7sUy9ChuxH0QIWBOzQZVeEbYlxnS5NVOuy3nk5dc9lltsHRdhSTyhbvhSLjQ1P8QRJklBy18W5ThQ9vnY40witl/gV5ncF8pjzkTlJudlWWBYhLZzRNteACyoBO4KpoBK8B6FEAIxRgYHB6mtrSXLMqqqXo0Hy9mjRZUWPTEgU8Rqz41qGbdjcu4wEdiVZ8MLojIgMIAwMy8llLAJOw80ZSfKF7WYXsjohnbtSccdwoL8vGDGwy0nyczOj0pVGugW4XjeOPVYvT2KjekvCIHnC57DDq7MIktipCs6tnvHEMINIYzUxRVT8zsiwnUnydI2ROWYyJhdSkteGjoswtMFxzHnSKNxursEPMIhmLRxgGDn74FfSM9N1O7HFp5pZ7Awq6CCcVFhm5xAfX09//7f/3ve/va3U1NTw7XXXstVV111vl5uUjgqwnOJo8gJ/ZCp1LoPOXg0cTQB09PAC94zKGZ2MDsP5r25C83gSTzkM41JFzEv0wNZZEY+mbfPCT/ythCsziJHnGNr/vPTwJ2p6alcn1kArSYXa1JryBZQVCXPxgV1SncUmvL6dsDGzdtGaXAELJguizquBVht/qdOlS2JI8FxdRb5hVJGL8JlGHf7dNjlhD3OsSoEmkddm0Fs8Xy1o+RHc8nc7jNYmFVQwaWC8xa8t2zZwje+8Q1+9KMf0dDQwH/6T/+Jv/zLv+RDH/rQpJ7f3DxZaShDS0vDhI+pBvZjAWgWnMJymAgv5X83AoswKdFuLNtszv8UsEV5IRMvzkexQDwbWAs0zrT3PAzsBrZiVmTb83MPWMbcjhkmjCfX/g6s3l6HlWJW5Oc7BysnjNbweAu5cBWwDdgLXJEfn/y1t+XPLf/fTIx18mx+rMb8+WXUneFzeBG7Phkm3wrwCnZd5+Sv/WpQh12faUx+BzAeJvNdutBReQ+XPs5b8H7kkUe48cYbaW5uBuA973kPf//3fz/p4H30aB9xkpzelpYGOjqPs8ULA2Iei6fLulZhGV7XJI7bjzEX2qKNi9c6YY5zrAiRY6oswgJlxBp1ijUAD4mwSY0OtyzX6iB/XHnEfAj4biFhmxcuD5FVTbVsONrHbudYEQJNTljiHE8njk1e0Ki0R7guRFpUmRGV/VHpEWG7E54oOGbEyM+lkRkKHUCTgIjQJcKzIhzQSHtujbZfhLlRacQWoM2JZ8jB9izi8+u+2Qm7E8euUdrcAdMDny9CEShFHWlStrQ00Nl5/LTXc45Y2aph1HP2eOGYd6RRmTOB2cJksCD/+2yVASd6DxcD3kjvwTmZcqI3HlTsz1Qe/3rjvAXvFStW8Id/+IcMDAxQU1PDD3/4wxGPtvMB0wNxZFjpojwAUsp/bsrV+qbyhvc4G2rZ7WBFsIbnghhGMuqTa+VHBR4seHZ7sypbFSItpcDMXJXvuZz9cVUuLAVKnxMG1RaBvU7ocbBLhQaFq7JIlxN6ELq9o1Mii0qRVXlwXe8du5zwROJ4uuBpUKWajHelgacSjwOuTgN7Co79TjiIp1Zhu7OSyZos8oslq/lfEQJHozB71II5W5WeqMwd9X/lAZo5MbJmkl6Pw/l7a1JOUVlcEoyu2BRPfF47c0u1JlXq9NLmswbs2iTY5OoFEBMquEhw3u6LW265hZdeeon3vOc9FAoF1qxZw6/+6q+er5ejBlgZIkPYTd+Vy6du944D+QTiVAxzjwOPFjz7PSxLTQNEsnjKAEwZw8CGxDMkwrRodl4tqjTmi8gQFry7gNYorArKghjpDI6aCD/AlOgWZcpOLxwUoS6ak0ytRnYjTI86huNdQKkRYXWwmngBc7UpI8My/eVZYG8hoduBqCLi6BUhURnpmk9T87IsYB6bP048DWoqgpPVHxnCgu/J8qr7nbAzcSQKb84bxGn+J+Syt2UcdcIriVESZ0Zl/klTlpcajolRQJ3AtDSMoUgOAj05ZfNcSdZWcBpMMfO+EFbZ85rU/Oqv/up5DdgnoxwEDovwQmLyqTNixCNTbl71ivk9ejWVwVqVMUYCJ8NhinlXZ5E5MTAnmiNOua7e5YReoF8cvRIZRul2jtn5kEwE+pyjPkSOiWPYwTvSSIJSgzKQ5kM3+aCOACuCMj8GahR+vhQ46qCoQlFhXRp4PvG8UPAsyiLtuUrguizQGczFfk48Md1Y5pHPz0frX06EKvUszxeQDme7l2VRaddwisZLn8ALQG/iuS4by5mfoUq/mj55pwhVKE8lNjg1LyiXZ4ED3pGosihYSSgTxedKhhNhCOOlN0WzblPgJS/0iu0uXv2m+vyhNt9dFFRPmeAdj7JZQQVlXJI70oRctQ8bLV8Yw5RdTWYpvLUUWJ8I5FnwIRFqdfwsyBznAy964YDzVOU6JmVMU2VFUIYl47Jgk4StUekWYXGZz507xE9H6Yuwy8GCaHKpReDxgqeEcmWmI/TEchCtA7Y5x0EnLAyR+VFRsR1BLcqaECkozFSYHk71p+zJZQO683LKNWlABGZGZWde+06iOQ1Nz0tQ25xw0DnWBCslDWOLS8CYPYIyQ20Rm6eRIyIc8EJrbgbRj+0MjuYLx04vXJVG3p7LyvbkfPWJsNU79nthTm4EXQIOOkcmlrnWn2HHNYCVkGZEndD27nygFtNnH4+pVh+VLpExphQVVFDGJRm8m3NOchHG1Ln3O2G7c6wMcYwRQhkZloHWqTUorwkREcdhJzxfsHHuQimcVhypQJ4Vy6lMlgaFd500OLI2RLY44aXEcxM2LTlD4XCMPFtMeKLguSENNORNT6fWgPSnyUdro1LIb/Z6Na53wIK/aLlOLhzKr8H8/H0czQPuvJyqWARmqA3iDIgFGKdGlSyJG5mu3OcdARubvywqNwDHssBeLzxS8MwKym25ANjyEKmPNlVao3ANkesyoykmCntFKXnHDi8cyYzCOHrxGwL+peAoAD+TxjELaL0qBZWRElUV1qPow0yiz4SNibnTX3aGktj5xukokpflPZZXM/lbweRQGdK5gDBept3hhH4HHQpt4wzgHcxrvc2q/Gwe/Ndlpj39rULC1kR42jsWxECfwGPek6DcNGprfllU5k5Bl6PfCUHM5qseu5HbIrSHSJWY3+SDVWaE9jPDgfm5Jvh4qAJUGXntmaPq7VXY4vVYwefKfieet9F7+pxlww1YED/ghM3e0eWE29PALWlga24YXOZkL8oi2/wJ7vhMIFXY6RzdmJlv2eShQWHFqGA8K5og2L68Ofm2LFBEURGq81ujbHgMUKORzYnHCVwVIrNHrV+LozL3JF733EkG4gZVelRelbbN+YJAJXC/lrgA6thTwSUbvEcjYIHpsiyCt3HwbtGRacMyugV6nZDoiS10ggWeq0Og03k6vLAjCgWFlxJhv3g8GW8ZZcYwuh4csCZeDbZaB8Ze9FVZZI4T6rFa/X5nxgk3pYGFEb5VdJTy0ffHC569UUlDZFmIKCc0QA6L8FTiqUPpUGjJg/MrTniy4KlWZU4+TFRgrLTqEMoO55kVA1EtOK8Okb58ynK/E1IRlmSRy4ItjApsz7PWnUBr/v4TLMuerkprULqc0B71lOxyjxM2eTdiv3ZrGrg93ymUg/BxMbldgCsyY84UgNaTNh7C5CZkx8OKoCwO52agR7no7v8KyqhMWF6Y2OAtyKzJIsMiPJd4djvh7lIYs/2epVbKmJ1rbI/GiqAcl8gO79jrHDemgQVBGU6iGSzk6BbY4D1zYmRxVJ5PHN1iprqHnHDE2b/LWXE1lp1uBI4kjk3esSURNkfhllJku08YEqU1RA46wSGETHk4MXf4O1JrWG5KHA6lLioLRzW3jgvsdsK0CIsUFgar3Xc4E7FSoAZhRYzMz5RWjUzXfCufBo6ImSeIQKMTFudBv1dsLP6gE1ZnJ9H/8ibnowWfT33GMc1exVhAXWLaLPUoa/NSyegvZKPCnGifRbPCnafhgZfyY04UgMtmGE2jZGi3enjZeW5JA9MmeP7pEICnE8chEW5JAzPP8jgVvH6olE0uUAzlNd1hMW1tj4k5nWylNVOVN6dh3EW1bMrrgaao1ANvTwN16qgSG3RpxNgUAw724lgYA/0IGTYuf0yEfU5IvHBzdiKAVKtJ0nYBVRoJJNQH29I3ElmVWfAVjHs9KyiPFBwqcG0aqAdaQ+RQwdF8kibK8qB05wbDbar8sOAYFGF6JiO84qty8999TuhGuCqLOIwemQo0RjXWzahsvVpN32VuDCwfhwlxyAk9AtOjURDBdj+BfMxelX5njeAFKL0CbScdJsFeY7+YxOt4SoNDmMFGFDPYGM/pqIwXE8cREZaHmNuxwZerCxwTYZcI/ybNRiY/p4IhYIu3ZnEV9r04E/pyTZxmPVXLvIIKJos3RPC+IrPsuDEvh9xVCgTGt9IaHbh3OeGQswnIRjVGxzV59tePlROiCJuc4+lqz21Z4IosEjKbgvRYuaU/F6/yBF7xCT0i9MoJTfE64E3ArjRQlXiKBOZGZUFQ/u1QhmCBf6Y6IsqACG1BqcWcewBaFZoiHPSOpTEwKPCjgscpFMVojL0i1KoF0cWjuNP1CgFlu1hxY1Ds/5wqEXv/W7xjfcFzdWamyVWc8O0cxjLxcuAbwByKaoHlMVKHZcdPJZ6SwNVZYJoqC1RI1EyMZ+e7gC3eJkLXhkiDGpPkmAi9qlyXl1UG8lKLAzKxRTkCwwj14+RER0TIxHY4Tk5Yq3mslPZIwXoX271jyQTfpf78NaeNepk6YE0a6S466iaotQfg2VwTZ1U4VemygtcHhdkRVzFjuPBQg2lq/zTxLAjjswqGMDpeuTarWOOt5OBwPMFkKGOHdxzwgqoyJMqgCDvEcQ1jh4HqFepRDovwcOI54GSE2zsaHsvcr88CUTy7Hfy06LkiM2ZHnSovJrDVe/Z7G8aZHk/ok09Tm4SsVWVfLtO6y1kwWxZNKbA1Kr0hMj0fbS+jHyghrMjLRdUKB5wwJEI1FoxfcY56VUoI5U1jWUmxHJSb8uNVA21R6ZcT2XqKBeY+gWWZsCwozTFSlQtjFfPH7M8pfkej0QRbo3LUC8dE2JIbKx/0jqVZZEn+/q/Kg/oMVQaABwuOGoW3ZEYbfCGxydurMusVjN6ZfHQ48M40ssc55kzApR4GnimYjd5VaWDGqIe3YdZ3w04ohVMHlcqwJqRdxwvRQPmNitIBRzYw+Q8kqRV4fXX23hjBG0yAqSSmYU3UEQuyflE6xEoJi0Lk+mAlA8Eyow41ulkvsC1x1KmyPFjwPCbCsqisDYGf5FNyGxI3kp2PRoezjK0mLzesTxyiOaVQ4HYsEFZhFD9JhBLCUP58BzREqHdKrcJu52jJJVnLDbvyJOIjiWemKsNBuUyVa1PLfouAYsMrz3vHoqgUFbZ5x4CQe2AqTxQ8JezL0ajKNu8ZzjnfzWoljm6BiE1BClZb3w8EJ7SdZiqyTZUmFYp5FvyIV/60poo3lTJ+c9iahiuDmSy350F/flQKGlmfOPYkjoaooGNrjmVKYY8Y93xTYoyaKzJz92lQtaxcdVwW0sJ8FmAiCCA6Sgd+FJpUaYowXeNIZj+M0U9HM1kcNshVYnxGVAUVTBZvmOC9OCoNWWR6HhT6xOqy+7wjqJVIOpw51ZQz8zZVGgJ8p5BwzJndWZ0KC0KgXZX2XKzpuEAiYoMieUDd4oVOcVwRrMywOCh9BKahNEd4PheCAnPgaQD2FjwFhWuzwC2pmSlMV+WIGG1vSYzcMGTb++cS4aHcB/Nn8sXiee/Y6B1XZoH2CHeeVP/uAXZ6x7OJ0BKUxwrQGI0dAkKHCIPYLiQBrksDDcBT3tEhjgYnHBaTAdiYn8/aLLIsBI45z78ALYnj8jwrHo1ajHY5DDTnv/vrqiJ7vfCd6gIfKEUWqjInKnNO+uxmqNKiSjEqlwVlUKzklQJdeTlMgGcTTz+wJAu0qZVxHFbq2pcbT9S8Cv2QIjaIlXGqn2e9wi3ZiQUgA55JrDxydRZGSmTAlDV2KngNUGGbXLioYiz3t1FhaYgsCHajzfFCRAhY/XRbXjZojpE9iZACl5Uii1RPcSUXTMekPlOuyB1iDjpHSaA7L7kMitDlHMeBxSGwPC9R7BThySrPANAsEBwcCMIiVarzjPKggz4HHQgLo7IsRLY7T8HBlsTxlvw1f1h09CC0qONnT2qaDQBPJJ79IkyPMA1ll3i6nbIwWinn0YJnboxckUb2JrYYLQ/K5TGySx09IiOBfUbu+FOlytFcWKkZawCf3DRUjPHTI8KVIYxkpv86LfFlV8WqLGNb4mg6KciN/uyuHrWbKfPct+eaKTOjXfcqNX2KW7I45jj9AlsTBwp1ag3peuWsJiony7tWcm0ZsVr8AWfDROP1WQax3c901ZHBqQpeY1SC98UBxcoV5Qz7uIAP0KiRWWo30+bESim3l5RbSxklJ1wf4ri1zHqFm3KWypBASWFNFjkkxmcewIL7Lm9aHVdmsCh/7WGHiU8BK7PAlsSzPXG0jGJOLA5KppHafNy9DrgzNbXCsj7JbgcvO/OyvCc7tQTQ5SA6WKKRxVmkVmEojWxOHAnWcBwSHdnO9yMMOmF+sPNoyuVj+0WYFSJzgu1mdjhhv3dURfjXwNE0nBIUU0wnJhXoiSdG3t9fgl8oDedmxHKKtsdoHMUW1UyEQ15YFqwUlGg+YYntWDJOLUdUK0yLph9zWMQMn1W58qTy1lYnrAcWCKfMAIxGn1iwbYt2/cdDAWO/pDnLaFviqMr57CczTA474aAXOlWYHcMb86Z8nVGhCl4E6BZ4wXvaNLIyb1BtzXngs6IyN4skwIDIiPbG9UEhf6xiU4HCWAnPWqz0cn/RDI3fN5TRklMDj4lQCpHhCF0e9omjRS1wLI7K3aWMxUBXhKPR1AJHT1HWAcNOWO8dT3r4mdTKNrePCj6vOIeIcaVrRGzUMkeHCJvzzPPGLHJcTChrZRZpSwMpQgGlVc07U1Sp0Uid2vRjuf5/yAmNKC95Cz8tGkbG29vVNNRPDtwBGxSqzevlbVHpx3oPTm3nMC0q14RTg1oZx4CvVhfIgIUh0gBUiXB5VGaOmqwsMn6j0Moddq12OcHJWC/Q8ud6wDlqsFJMk57+9nxFhJe90IWVyU6HuvzAw4BXcyAaL2FricqRvAH9hrshLxCc7+B9//338+Uvf5ksy/jgBz/IvffeO+b3X/rSl/jGN75BY2MjAP/qX/2rUx5zMt5w35UuEUoODqpjeTB50lnRnMzb8gA9KCYa5dXKC2XscsJuL/QiVAk0pGEMZayE0dJ2OqGuyvNzw4ElWWSXdzxV8DyTa3M8WgANsCKXW50djfI3hIkUQZ5FO2Fxvpioms714qh0OGg/KWasjMrtpcCMGE8ZDT8kwrPOcVm0huSLiTEm9jmhM5eSvTqzRuiWxNEpwvFczW5+tAVhbrQ/JeCQM470S96RiXBFNpZ5MRq9ArvyEffLM2vmPZ/Y2L2PSsynWpmkX/CCECk5YfaoIafJoBw0F0SleRz5AgHW5gJbZ6L7RaDDm/fnglEaM+M1J8toU6Uxl1sYT8dkNAW1gksPhw8f5gtf+ALf/OY3KRaLvO997+P6669n6dKlI4/ZuHEjf/zHf8yVV1456eO+4YL3nKhk2djyx5yoI43GFGsmpsDluS43WGzZ6R19UjYyhme85/osjDSvlkVlTRroLXo6xPwU64DLQ6QTz/SoDGE+lIed0JJnpKPhsSC+y1vNfH8hMUlYzCuzPipLgumCeE4EpdlRuadkmisZlm1PV1Ol+/+Knk4vLIhW/50ZI3tFGMJ8Nx2moVKjSqJCtcBQPu1YOCnHKALXZqab/mjB0wM8kiQsjJE7IFcDtFJFNTZYMyt/j2V6ZGOuprgoKlVRaZhgWGU6cO9QSiTnkk+xLhyBzbkswposjlt3BpvibOHMLjyCaccsCTryvkrAk/mCeE02dkEv4+Q+yWj0Y+qHM+Nro9s9DGz3Jn2wcIrX8pLFeax5P/bYY9xwww00NTUBcOedd/LAAw9w3333jTxm48aN/Pmf/zn79+/n2muv5VOf+tSEhu2XZPAOWDNLsIA6OtupIm9IJo5SFrks//L2AN8tJtSp0qomkFQ3auvsMVbGMTGH92cKRp/rF6OgQc7hFWgJyooQedk7SliwvCUEWtThxUSnstxsYDxUAcuCycN2ifHJX3ZmxvDeEBgQ4cm8Ubc8H6CpVaVBjap4QEwVsCVGtiZCQKmNQnNUnk48nbmGyg4c7yyZo3xdfq3mjVrEiowfdI5hY/GLQ6DbOQ45q2lnGINngzdO+I35ruFk2mBZpncqokvNU3jsyRjC+ONRLEhONEhzJgjGFx+SE0NeijUmo5ywxZuKmfKGxNGT73Qufw10u4/mLCuv0BbDGReWCs6MgwcPEsLYbWNjY+NI+QOgo6ODlpYTs7utra1s2LBh5Of+/n5WrlzJJz7xCRYsWMBv/uZv8md/9md8/OMfP+NrX5LB+7jAbu9AYFYaTsm0MqN60ydWiugRGETY5YUE4fqhFAeEUQMpwBga27osMiA2Uj+I6YdUY1ZszgltqRKdWVz9pOBpULhnOBtpTi0Oxlo5LlYPBbvxDzlhmupIQ3OAwPfF0Z8k1KiZIrzkTS8lU4jesTkRmqOwJNiwyj7n6AAO5wMtMSpvTTNa1YaFbLERhrHG25IQacn5y6e7kXvzTCMDvl20KdG1WeTWLDAtl9EtYtKxTuzv08EGVV4ddjvhsBNW5pOYZ0INcFmI9MkJmuKrQRWM6UlUAdekgQExzRbFAvxk32OdQp8yaSXKV4tpUWnKdzwV1cIcZ5l533vvvezfv3/Mr+677z5+/dd/feTnGCMySv9IVcf8XFdXx1e+8pWRn3/5l3+Z3/qt33pjBu96hbnRhm3GE7JfEZTZMbDee15IjObWGpRZ+Rh8jdokXRDTDhmPvjZTdSSu73fCrsSh0bLyHU74adFzdylQF5UnqwsMx8g279jiHZ1eSIk0qrndFIFWLCBtSGxEaGkIDOZmykuDsjXYDmKjc+zyjgZV9jj4fmLFk/dkKYui0qAhHxU3HZeszBV3Qo8IN+WZdpeYlnUDSq06lsexmi4ZVuOvxgZQnk5MZKofZa8TqlWZGa2GvXhUQGxT5YY0MICVE85HcFDg5VxLfL/IGKnZ8SCc//JAfX5efU5QNdbRmdgzo7E6RJaGM5dWziXqgBsqNfYxONuG5Ve/+tVxM+/RaG9v55lnnhn5ubOzk9bW1pGfDxw4wGOPPcY999xjx1YlSSYOzZdk8E7gjLZRxlO2ybx+NWGoIKZTUqemsVGlFsAKE3yiA0AqUIiwMEbqVHkpETIR+rCt9PIQOZhT3OpQikFZGSI7nbAxz8J/gtW5X3aOalXSxNOoygveccwJjRo5jtDtzXmnLio7vONl51kRA/PzenZVzl9WgbVBWRcyeoGvVycj05gDApsKjkyhMcCSGEamO8s4KsIO78ycOA30inAsLxXMi5Fb08DS09z/3SI8npgmydvTcEbucgk770Y91cDidBAskz7szNzhbNGPyRyUKZevFg0Ka9NIFMate58Ojsq05euNsw3es2bNmvCxN910E1/84hfp6uqipqaGBx98kM985jMjv6+uruYP//APuf7665k7dy5f/epXedvb3jbhcS/J4D1ZrAmRlcECyMMFz3EH1UFHNEaUsdSzNP97NB1uW2IGx3ODTRUqcM9w4JGC48Gi5/o0sDpE5oppWThVRIyS93LBszAqs2LkReBQ4lgaAtPUMttyCWJI4OXE0Y/j9jTjrpKt9AIUySzzFRvCGcrpaFVYeagcFNdmSioWJLpEuCaN9IhwYxbZlnj2OnvP5S9EvZpuSA3KNGyBa8YCXhWnlpRGQ/LXKImwyznmj6LTKbbg1WBBa33i6BIr+UzFyabMfnk12O9MGvcoQns8PVVxKmgbtSOr4CLCeWxYtrW18fGPf5wPfOADpGnKPffcw9q1a/nwhz/Mxz72MdasWcOnP/1pPvKRj5CmKVdddRW/9Eu/NOFx39DBW7BA7LGsuTUVpkUrC8w/aYy6LD0KNiLdLUK3mPWYiDBdrVHaL3YDZ2Lshk0Fz5tKgcujpalP5hrXQznz4ZiYPOohwAcz3QUbiVdn3OC2oLQ56JfIzLz5Vw28LYt0R6Ux2pRjF7DHO1qjMntUACliLIgdzlzpE4wBMz3aziBggV6BDiwrnxOUmzNjr5TErs8+EVRgCBN6Oh3mROWdacZB51h00g7oZSfs9I458YSprsCkjIbHQ4/YVOfZlBzaVTmSi1+dTeBOsd1ZpeFXwUS46667uOuuu8b83+g695133smdd945pWO+oYN3GQ4rs/SI8mTiUYGmkzjcWR7EwDLhTbnWdXWe/W3yjscSu6CXhciNpcD9xYQtAjuqCizRyM1pYHXOVJiR0+M8Vl9vBOaGwEHn2JgbCXuUywM0OXj/cMpPEs8TVQkdaeS9qQ2ntOWvPzsqM4Ky1Tu2JDAzU3Y7YU0eILtE+EHRm6pfsInEPQUbwrmlFKhTC0YPFxN2emGlRFrSwFOJDR2tSwOHCo7d3iYkdzlhyMkId3v0QifYhOJuVZ5LhGXBgvMMNWpizFk6oKzNIv1TLDOUcViE9QVHF7agLh5nBciAF70pFa7N4pjSUKPCTWdZ+00xA4ZBEa46zVh/BRWcT7zhgne57nsyArBXTHhpnuopnf96tewVLDDNDZGd3tGbP/eYs5pwjzj6URodHHfQ44zGtw1HtVdu1cgOZ27pi4LpfouanOplwRT0NnrP/lwG9YAod4RIKqYJvlNtISm/l9488+x0whFvTJLLM5tmHF1WUGyAyGzJIi1BGfKmVbLN22Thnjy7rlPTTzkkZlXWrso12Os0KdRF6HYOj3LACfudQ4B3jLpeO725Ag05a47OQNkX4YYsMFPjiFTsycyN0ejNWSsni0DBiUy9A+WRYsIW7/jwYHqKqNWgQKeXnF0kVL2KGvloBGz3lALDJ020vlaIGFsKrGk8FXriRBjGdmxTqSRc1BDrE03l8a833lDBe38eNFfkXo6jccDBP1V5upxD0kiHKHNU6QNeShwzoqkNplgQnBWVvR6CmA52dYAdTuhwylHn2C5Cp8Cb08DSoGz3wgyFDc7xdNHkYDPgkEJBlSuwSUiwrK4bK7uoV4Yzo7ndlkUWxzji4diRZ541EdpipEnNUeemNJ6ylW9X5frM6tzzQqROYE6a0RDhiDNJ2B7vWKqR+Znxjdd7x9IYac51Ta7LAldl5esl9IkJWg3mX+SDnFDMawjKTFHqIywKkX3OMSsafe7kaz8eugWeTjwJMD1GekcpNB4RY+XMjMr1aWR9onQ74ZXEMeekTLpeYVkuwVo8TYANWJO1bgoBuBq4KgsMidD6Og269IixbgCadWz23ycwhDBDpx7Udzlhu3fMjSckJC51VLVGfGny7zUpvv7R+w0VvDudMCxmVTYLpSOv/zarIio0IHSrcsTBxoKjNg08XjD50+VZZFoMPFzwIFYamZFzvOdF42zPjsphJzzrYXshYUFUbk4j81WZq2Za3CnCnqgUVJkXIoe8bb1/DLxY5ZmuShXC5TFyPOau796xBZgXxykN5P2xWVHpEpNOHa8G25k7Ag0Bm71nQJSFmbI8KoujOQsdEeO6lz0wLwuR6Soj3OhC/mcYq+0XUGaq6Z5s88KjwK4qT50Kh5zRNG9MA7OjsiyOby93OghWzlI1n0sn0JUrNHbldfojTrgtwHuHMw44N64FmmAiYM8ljie84/IQR5glipVeXnFCjzNK5bwpnGOTctqMuxvY4+DyeOImi9huokbHMnv25I5NK8LY6c+UXBcGxm3O1ukJLfPRlNgSJkc7jNEQp8qk6cmTiGNy+qb0pYbhDkc6BTOGQm0leL+mWJZFmpxlSsfEHFY8cEMamKXKe4czgsI+byPsXk10qUGV+THy06LnkdyhfU0WWZE7uEcsi06wIZCi9zQrzMnZIgN5M9M8C5W1GcwPygJVWoi8LMJL+TGaAyzTjCY1RsoTiWdzwTjN5cGXAaxu3KTK9BiZoaYP0u2F42piSaP51X3ARu847GCPc7iorI22s7Dtv938R/Ns+pXEcWVm2fv8aKP45XKTMXNMN9xGxAPTo3I48ewHdiWe2qgkovgoIwydqX7VpylcnyvwlWmKZYOGeVEJWKBT4MqgLImn969UTDM9il2Lo/mOoSSwoeA4LEbhbJ2kvspk8P+rLbAr8fzCUMYvlWy7stMJjxYcjQrvLpmujmJ0xTJnfbRjU0e+m3AKM2I4hU5ojehTa/ZCPizlTgyATQXLQqRRZYxp9KUOZWplkwvhyryhgncdJ0SHotrWN1G7CTyM+AnOz93QHbAkRPrFoQhD0Wqoq9MTY/Up8GziOI7xr+eosjhEBjFa3nGBDTkNsVeFw15oUVPDE7WM+WAitAOdURkS2OscbbkLzM1ZYFBMZ6TbCUm00fwUqNfITwoJDar8/FBGQ1SmqeljbHUmLrUqn/RrUqVDbVT+UOK4omQiU48WnJV+skgRxYm9RhmHRdiYONpzdog5tQv1WAOyIW90tudUwtY0ICK0BmvM1p5lAOjJs+vpaporo82Ja7Bs+pnEswPrRZypYSiYyXJPNKXIZxIbcroyi9REmI+yIgSaz+EdWQKi6ogTEljTeL9zHFM1c2Y9M2e9UZXqaDZ6Z1a5GIsCVuIqyfimzROhlhOSxRVcuLjkg3ev2A1y8pe4HtvSO069CO6kf9dgY899opb9iXkxNqrdpH0iPFxw/FgdM2NkkcJNacShPJ94djg4Lo4msSx2ABmjF700RHqwmvfT3tGIBcU5MbApcTxc8Aw5oW4449acS17esWcou8WxNfHckBkDZQijDAaxRursqFyTRVYCLybCXmf2Z48ljh3eUQ8sFps8nRPDmC14j9gQ0lExwYB6bPR7bbBMuHz9bkhNI+OZLBIEOp0ZEO/3jpYpMjr6sG1/p4PaaIJcC04KJiWBQWfXYFjGn6QdjbIUQDpahwa4Mefzn40xw5nwXwZStiVw3ShO5YoQ6cisqTz6fE/HWW9QuDULU6Ygg7238SY8U0ykywErQ8W9/mLGJR28+7Cml2Bb8JPlOiej4DYvKtVZpFNgl3N0i9mhHRGhqEo9pnOyyQsvJo4dPqE/RFZlkfnRmmRRHIuDGQnXo7RGy1TLqFczgNjphGKMHPBwm8ZcXwUKmHzqgNi4+jVpIOTj17WlwMteCXJCVbUa2/qO1vIQ8gUrU9YS2OmEVwp2ba5OI0NqHOwlJ7EWFuTvf/qo820dZxClBtgObEgSjjrTZmmKyoKcl97H6fW2T4bDdkIDCEUxPZeTg3ejwro0jkzD9kxAN+wSE86SnNrXkGe9490AJczGrlZhSTw727R2oP0kMnwj8I7c4WiyTcRzySABK0EddHbUuTGc0XTijYRyQjSVx7/euKSDdzkrHFRTblsc1CbgJokB7HkNqqwMprh3Tc78OCrCc0XHmizSqsq7hjMa1LPLGTOlPeqIKfBW7+j1wuVpGHFYV3LTXjGDg91YcN2Sa35vdsoNBJYF5cqc1XBzfuOXRf7BRuDn59OBo2ui7dE43wec6XqMDkB1QBALuJdnkWqF71QlTI+R9lIY2aUEbFFpyKctAya0VcCas6OPGTAD4oNeaYrCnBi5JueAd4qwPjHK5HVZJMUy59MF21rg6jTQnThKzlxzxkOrGvvnscRTcnBVGkcaeCdjbl4nn64wU213cvw0ZYVuEfY5cxiaHU0aYH/u3nM6OdnJ4lwH49MhYte9iI4pKTWqMjvX/Tmbksoli/M4YXm+cEkH71psO7/FCQcTx1aEtnEswk6H40445qwJuDhYXXV6Tu/4XsFoh8ecMDuYk3xTtCZkO0qvExryALdYFR8tI1bg0cSxyxmLwzthRlTeBLySBYrB8UzR0ZhnmiVAEBYE4573c6rg/zQ1pkgqOmKKcCRXL+xQYdZJDcytTvheIWG6KplCp9gkp6qMkCcy4MnE8VzimRciN2Z2w2/xjkSgMbUgfzRfyOZFZS5wXaaszgKtOYWwM+dCZ0BJZIQJMeTgytR47uPhiIPNBY8DOrLAMMYGOTlzF3JdcwU5Qz5UA6wYdU1/VPBkAtenkZl5DbrM7Z+Wa53U5Mydp71j2EG1yhk1c8rIMM/OYTEH+9djAvOICM8nlgjcmJ5odhZgZHCrglGoBO8LD1XAIlXSqOPyi/c54YgTlmVxJCiWpVnrVFmcmdBQ+UINAFu9aUIvC0ofFqQGxAZtWjXSoDaF2COQIVyd+zpWY4Hj+cQmHb1G2iJMj8pyYHka6S46hoLV1cEofsdFaJTI/y069jvHnaUwEogAjgk8N4o5U4sxVWZGpSGObXYNYzou+73QH6HJWxOtNUbqFJ4tmB6LYFZwA6Ls9EIdjrVZGLHzqtKysbAndUAWuQGT4O3J9UK2eMegg4VZ5OosUp033srB9kxZqFehhNmmHXSOV5wwJ0RWnxR4Esy7MmX8YZ7x0CHCS4lDFdZJZLcYr7lWrSfRJ8K6Udn+0hjZj0xaS2VQ4KgXglqZouZ1YG0UsYWuoG+Am/wcoFI2uUDRqONTqhRrYg0LNI0S6d/nhC2Joy7C4hh5KW9WXptFXnbCt4t22VangSOJZy9wV5pRBRwWY2ZUKTxU9BwV4fZSYF5+Axcxs+Jt3vHmLNKgyiMFTyewUIQlMVIQ4YosMgBs8sI+D4PiOOAdw8BSr9QS2emFecG0OaqwrPyBojffzczKFj1ijUlRuCzXcK5VaAvKW9KUnd50wmuBRCyoDgBpbsG2RzzrvaNZIlXeDA2K8UTtelaMHMTlNEgL+Bu8lRzqo1JSoQFjnRxxQqI6Ysx7Omof2FTkjcF0W2rUNFecwoZxatFVMCU2RlnfW1Roj8r+fEqxW4THgJg4bhnVIzGnpcnfrvUKi/Py0IzXiW7XpLaQJ0yuz1DBxYc3RPA+HQSTaz3ihJZRN2ejKtUKLRrHrLCbvfC49/TkvODpKCVR+pxjT7Rx9z4H+8XRFgNPeEdJhGU+MG9U82pOVGoINKryZOJ4qOhYCMwDFodIP45uL/SrnWRthIao4JRqsSbgeu/YlDh2i7I2RIJCgnI0H8DZpcJu5zgmSpdz9DoYypSrs8AMlMagDIpjupqd18pgNMVhbKK0rJfdL5bR7vbgMVu3jfmCty6LLI7KTI0j9esarNRQVOXKEDkajT++o+DoF6EnRNYGPe1IfBmLgvnRLAjKdFUGomXEL+QOMLPiqQ3oyaJZldvTMEITXRCVaRp4xQlPA9Xu1QU8wRYXMKplkBPMnNcS48nM9mOa4zOinnOGzcWMSuZ9EWJ0VjWEjQZPV3hTemKIYloaqFLzbGwW5fossDZEVgRlcQj8sGhZ4SBWa5wXIn1OWBiVjrwplmL1xoCJWmUCRY0m25rT/36SOBSl5BwbxGRme51t16cpHFGlJcDjiadOYWaItKtphwRnGd+qLDCISbh2eyHFdMVLCk15aWRWVLrFat0qsDgPkI/k3p1DKFucMEeVFVlklphKYhOWTafOxuMXOeGgEzpFWBAis7BzuCkNHBPY6ITHC97kX1UpOWHpOHKEwxjdb0BsEfWY3vqMUbulBoUkVwCsPc0U6VQwOut32Gtv855pwMIwtcBm3PdTs//jAi8WHEHts34thl4Uu56nwwuJs+9mFll+ht3E8XwXNtlSVAWvPSYM3v39/fzpn/4pjzzyCN57br/9dn7t136NYvHS24wdzCfaDijcngYOiTAkJ4Z3loRIKdjUYZ4Um/t7XjIYFmGWKnWqtETlxixwDGG/d6RYJuoxUatO52hVZU6qVKuyA3iw6PEos7PILLVA1pzBsFrzdGaEGo1sTBLaY+TeYWuidgtsVRv13+Ude52wPFPmZZFtBceOxLEsRGbm9c+rcmpj6hwhmnelYo26PjEe8tV5WaEJq+HWxEghX2gOOTdyc4uajsajiRk3LMSy1i3es98Jxx2kCrPVss+Tv3C7c72ZDhGaVVlDPG1tuQa4+jTlr11OGBRYGqzWW/58JotegSZs0GjdFHjpgxgdVcUonKN3A0WF6mjMnurXKFfb7oQeYKaTcQdt6tUmfs+0+PWINZUFuC4Nb4wAfik2LH/nd34H5xz/+T//Z1SVr33ta3z2s5/l05/+9GtxfucUKabb0DgOawHM2qwpp/kNAS8mpltdq9GE+73QHpTtHg46x8ycu90cLWDPC8rL3tEvRtW6PCg7HWxSa4oOB8vOlkdl+SiDguszG1P/EUZR6ykI/UFZg3J1anXrF52nOUIPMjK2Pz0Pxk8knucSoS3aSHURqBJlvip1GB+8OT/Po8AjBccg5gG5IgSeSDyLQ2RxiHTlAk27nbFa+lE2JJ5BtUVrCKWgdq0CypqgvOI9mxPHTuAtieP2LFKvkS7nWZUqV8XIcawcc7K3ZY9YcNO8mXk2zb1+YHti3pHNGqlV5TnvmaZWUiqXKyIW1McbTFkclHqNXAZjpiJHY7szVcZVWRwJaEFs14Dkao+jTr8KU1GMvHZ1515n7J7e0+iSrAmRoXBm5x7F5Bec5iPjF0KN4HzjUgzeL730Et/73vdGfr7hhht45zvfeV5P6nxhhxN2ecesqFwxik3QKcLu3F7sxjzrikBLVKv5qpUNurEAlADNKDu8py+zDPtlJzxV8KZOhzJDbSqwqKZ9PUOVLd70tQex+m2zWiZahcmpdqcZP0ksc34+cfRIgcsHSrSqqQV6bBv7IkJzbu7wkjMneQEiys+WAqAsiEqTwpygoIHLQ6TbCZud8EjBszU3Hj4mCVfFSAkzSeh0juvSQJtCEKUtQnMwwa1UbAFs1ECfOLYnnjQL+AjTogXFFxPHnGgGFcvzLHhuVLY5G6l/2TsWjKJrXhaUJo1Mz5uuo4NcxF5vomZkDdaAHRDrDXQ5YchBqsLxAGm+IL2QOFKEq7NT6+XV2OLUwPjBu4TtalSM674s30WUpYIjMi5v/bWuS67MosWh03DjJ2O51qTm3TreZPKlikuy5t3a2kpXVxczZswAYGBggOnTp5/3EztXKGHskaY823ZA1UmX/hVvGZXHMSNnaCiMCfBrs4j3wiuFBFAuTyP7nRv50A+XZVVRZqpR9J7xwsMFz0HnOKiKQ+iRSJ/YePKy3DoNLCivySJe4UkvPJskHHVWVkCVw/lUXEuMJAi7E88DKFu9py0oM1S5NQ0UBDYWPBscrMkCLyXmNN/rhFVRWZAFqtQagQe8IxDxmQ27dCBEbBG4PFOGBVqjMscrSbRa9mHvOO4cfSLUROVvqwrs846fKaUsBXZH47gvDEqfFxbki0yrKoeimSKPRjlongwF1nvHUWeOQ81qDb/RTb+jYrS/+VHHUPv6VdGoLIzKS4mjV0zDvM8ZfW9wivKvYIvKkhB5ouDYXPBsjcrbc0MMG4K5EG5n2y21AJ2v8jhvtMnLSzJ4t7e3c/fdd/OzP/uzeO/5wQ9+wMyZM/nsZz8LWFnlQsZ+J2xPHEWF29JA2zjqbEtCpEDOFgF+WPB0ibAmM2PfdrWs8LKgdIuNiq/LlewStUbk2hB4DEeLE6oUni84nko8imltr84iQYRnEgs/rVG5/KTzqNd88lBglgbmBeh3wjaEXfmk4dygLAzmR7nZebZ4R5NEulXY7QusC9EU5RQyhfYA+wqCUwVVFkf4t0MZ/6foqFHHrLw4vMk5isD8LFASxzGJDDszhGiOSqOYSUUSI4fFcW3MqI/wdCKIWB18NlbXXpmXFVpGZdhNCrflfOxnEpMZuDI18a3xoNjWvyRGDxwUR4vqyNQmwPq8rn4gRu7IM04wlT4vQrezz0byac6WLJJx9vS9VjVf0ZcKjp6o5vp+0sIziJUupsfxS3Oj0YftVBpVuTyc3Rg+WINyi3fUqLIsKhFb9D3WkL8AdvgVnAdMGLwXLFjAggULRn6+2EomTaoUI7TpibLDyWhWaM7LJUPYF3+Td2x3wq1Z5E2p8ZJr1W78NKfUFdSGc6rUgtN1QdmMMCeaBZkoNGnknlJghtpxA56d/oSMbBkZ0OVgdoj04cnUtLnnBGV7wdEW7d99TnhnKaNHhO9Ue2YATSpkkjvFBFicBTYlns0FTzORNoUGhDnBstf/XZXwdDGhJkSuLgWqImwrGm3wimiliqF8OlKcbaFr1bLkpqCs5ERQ/rWhjO0i9Ocyuu4017iMQTGN6pe9Y0iEt5XCuGURB1wZAjvUsc2b/ZrPIsNY3yICVars9kK1CH3hxBZ/VlQez3ccV2eBy0Ic0TJ5NahR0y7vE/vOjDcq/2JSnrqNE04y9jqh2wn9KiwL4azr4t35NK3L5YCPYsHcyh7hlHJOwHoN9VrhgI/gUqx533fffaf838DAALW1E1XOXn9EbIu8LoSRsfEzoR/jJN9QCgwXLYgNo2z2wlFnioFduaVWrwgve+Fo3gC9PFi55NY822yJSpcIRTFRpCLKvKgMEXgl8RwTsdo3ttXtwoSvvJiL+17vcGr12+Go7EkcB5xnjioLQzTNkzRyuUSWp5EDXqhX46j/NPH8uOhZEgK3psrSoEyLkYVROS5w3MNxIDrz3lwsNlTUFG1cPBVT6dudL3jTRwW+DKMJ1qnSrKYTMlOVHlUGgLrT1FojtuDVq3loDopSG82IIGALQ5/AknCCBtioNt3Y7YTqYO87FZPgPebARRs4qotjFfQWRuVwjPSKlcMWZ+dmk+sx5cX6vCnaOs5hq/NrNZ6i38mYGZUl+S7l1QTRaXmTvSbasFUtttB4xreY2+6EPd4xI9/JVAC1LZHsUnPSeeihh/if//N/MjAwgKoSY6S7u5vnn3/+tTi/V4VOEdZ703e4LZ04s9maODqcMCso95QyNnlHQYQ9eWCsBWbmllozVNmJsN8JxxCC6Ag3HKyJdk2IdOTB6YmCpyUq06LSkjcAn04c+9Sc2Y9jlL8qVQZFuC4NPFXw7HBCj8BhZ271hyKk6mmLgRtHlQqagvJ44vkn71ifCJkTrsqsQbounMhuDyBckUJ9SHmymLC5IKweitycRa7IBbASjOc8J5qi3wC2wPQB367ydCKsjjqimdEvlg3Pw4ZS6vSE1GjAgvJuJxxyjuUhckWwydJjmNRuzHcy9RizZ/GYgSm4NQ1s9I693lGKZuW2W4SCV5YHK1+czMteGSL75VSN7DJ6ga9XeQoKd5dOLaWdDnvzMlxBYUYuezAaq0JkcZxYohYsYF8WX/3CUo1pqJRRB9yUS8meTvL15BqvAttyDfjLszjp63GpYKDTkQ5O/rMo1FwEwfvzn/88v/Ebv8E//MM/8OEPf5iHHnqIurqznW177ZBhjckSwl4Hs51w+QQ3yvQ8W3YoO70f0exeHSLtubKex4JRT65ZUa9WykCVl5xwzDmWBpPanJPrqaz3wiFs/HwaJtA0Q83ANqpyPBdsqgMShMMO+sXc6efGyFwgqqCJ47gTMhf5TtFzbRZZm2e6qcBBb/XYfhEuCxk35xrbo3HYSV4DtmxNVLg+M3ZJb16TL2Cj1du946AXZkZlXRb5UcHxXD41uriU8cOCDQsl2DV4Fqj2jtXEEW58Ocvryt/7IBaoX/H2/rya4UB/Xqa5Ts07s18sINVidesERRBqFG5OAw252uNlwcyWjR5n72FOVBrzXcjp0OmsySwCx7JAbR77eoAfJ54lWWDuOM+ry2majTq+FrbnzGP/rxXOdGMvjUqLRupHXZ8SsDf/THqcUHsOFpWLCpdi2aSmpoZ3vOMdbN68maqqKv7Lf/kvvPOd7+RTn/rUhAf/4Q9/yJe+9CUGBwe5+eabX7Pm5hA2OOGAxSFQFKPAEc+sKLgoKhlxhKu9INrzZ+gJuloJeCr3B1RVDuW6GIPOsvxqVV5KPAuCDfr0iGVq+51jZhZ5xVk9uSUqd5XMdaZRzaGmL40cdVa+OZKPga/Ig9MygW844bAqRxRaUA46YXXI68wKSzLlaglEEe4cDiyPxh8/mmfDddiU4hBKlThWZ2ZjNiDC47lbfQGr5XfkfOFEoSnaTuGwc4gKt2S2OH2jytMU4bo0G7Hb6hcZUzKIOed9Qb7rKDuSN0elR4QrQ6RThJ0FT5cTNmaRVxJHQ7RFrezEMyOCYrz6KqA5z3o9pv9ywDmr4wKSWYnoTFgQ4ZbcZm3WqMrBPwDfrfa0B8fvD6anPK9Nlab8tV/rcfdzhbJv6xC24DXm3+/LTtKAfyPhkmSbVFVVUSqVmD9/Pps3b+b6669HZOJlZ+/evfze7/0e//RP/0RzczMf/OAH+fGPf8yb3vSmc3LiZ0JJYCifbV+uSsNJZgJnQrcT9uWljmUxsjHx1Ks5rzvyEXqx2vicaMGqXBufFSIbvHBYHANeuSW14JJEmCY2gl4TTTdlnxO2Jo6rc/ZEI3BEYFGw4PSSRo7n045g4+LtUenyjuqgeeNO+am3wZ59zjFXrYRg2bg19V7OR9RnRdPz6HSOveJIVFmsJle6MbGmIMDszOr6x8TRCFydmTfk4wVHlwjrssAvpIEnE0dbUKpEWR3NUPkZYLuDtWLn2+GEtqi0xUiiytMFn++IrHSikNP/lCLKzAh7E0eVgpcT5rpDwObEnIFmamR21DESt0fFSi81qlSr0UInQoKJdx1zwjAnJg5nAdUI7WdY6E9usHYLvOA97TGOUXu8kJFhU5SDDtamkTbVcSmbbyRcbO9+wuB9xx138Ku/+qv8wR/8Ae9973t59tlnJ8Xz/v73v8873vEO2tvbAfjCF75AVdVUtN/OHo1qNDSwhlqdTt4DcGWI7HE2al6elMxypeg9Tngx5x4PSlmy1MbOg8LDRcesYOWQ4ahs8EKCZbNXp5FrQ2RQoCdG9onQL8IBBz3ORKCOJI6OEM0J3gkDIhxQc5HpEvOFXO+FTCM4xyERXvSerYknqnIwwvuG4Sc+ocvBW9NAhwgdTsjyRtpelzMRBIaC8aevygLLMWu3PgddXqjLA/AM5yiibPGOkkAhH9RZFCMvq6MlWPklFVMjHBaTgq0mssk7qrEM15gPSn++Cxi9S21QeO+w7VKOilmnLQlx5MtZxHoMvbnU7sm4MgSORVsoiozd0QZsAVOsXDC61LHD5427aCYRAO8GlvQNM5VJhiMiDDs4KI7LYrgoMnLFrk1U2x1ddJHrXOMiLJuI6sRpyoEDB5g9ezYvvfQSTz/9NO9617tobj4dQ9fwe7/3exQKBfbt28fBgwd585vfzG/8xm9MKms/l9gFbALmA2sm+ZzjwA5gJlaHToGj+f9vALZhGeOtwJuxRt4fY/XcFfkxdubPnYPVUYeAX8l/VmAvdvM8lj+2Dcv6Hs9/txC4FrgSy96fAfYBe/LHtwLTMQ3xjZhkakt+/I78eHdjdlxP5Me7Evgu8I/5OS/Gssi7gZX5eT+N2ZnNx4JxAiwAvg1sBW7Kz2tBftwkvw71wMvA/81fuwl4DrgaeEt+vUL+Z6LGcSl/TAkrIU3DFtFj2A5FgC359V3Mme+jLuwaK3Aj9pmWsQ14MT+3N8OUAjZY07PcaN2VH7tlisd4PdGPfS9ncEHEotcVL/14iNIUGpbFGuHyN1VP/MDziAkz729961tjfp42bRrPPPMMS5cuZcmSJad9XgiBZ555hr/927+ltraWj3zkI/zzP/8z73nPeyZ1YkeP9hEnuY1raWmgs/P4uL/b74QjOe2ufZK0qD5gS8ETBZZlgScKnmo1/vAiBe+EKpQFWWRArUu/vzphSOCXB1KGRCBxHEU4rsqjRY8ILB/KuGPUOTyfeF5IhCERrq6vZlHncb5bnXA08axMA5cNZ1RjZgsDefmmNnHMQDjqhEKM/OJw4HJnJgpbRHjBC1V5fflwGpgdlFXALi88qrZALCt4XnZ2boui8kqEtszquL2Jw4nQlkV6xYLozKhUFRNIHE8ozEkz5meRRSK5zZyN/M9uaWDusQGGUB5MHCrC8HDG0VFCXlPB04njmAhL8xLLNu+oxUwwNuQ6KUl6ZqZICfC57slQFsdMHjYBtTkN9CGFN2WB1jN8l0ZjGHis4ClhjdWyvd6rnWw8FzjT/TAejpzHczlbTPY9OCc0N78hpLNOwYTB+9vf/jYvvPACN9xwA957Hn/8cebNm0dvby//7t/9O9773veO+7yZM2dy4403jozVv/Wtb2XDhg2TDt7nCoujaWeciXlwMoqYeUCmwsvOsUscTpSrs0hrVJZE2CfC+lzQaXPiGHCOeSEiYuWOJlVUlfUFT4Z5Oi4OJwaBjosN+DSokqgtGD8teBIRrsgyfiYNI9OH09UMlAHemov8/0shoVqUo844xbeWAlLwPOQcqTPfzs3es9eZh+Ym72kEPjRUYoYqz3jHtGAysYNidfErs8Be59jhhSHg8mDvN8EmSEtiJaE+sTrxyX6gNcB1mdXDh0QYRjnq4YjCJu9pUKttF9SUDMcrL3TlvPi5+aRgeXvfI8L2vAw1MwSaojF9Tpf7ZNhiUWR8Iw7y38+N0ONMu12wXdZkFhqHNXaDQOENX3Owz2hrLkG8MsSLbvhHyUW4pvD41xsTBm8R4etf//pIlr13714++9nP8nd/93e8//3vP23wvv322/nUpz5Fb28vdXV1/PSnP+Utb3nLuT37SSAhdzufBBSbPEv0hMrcTxPHHm8Ute8njnkKs0LkoaLn+cSzIgvcmkWuTDNaolIblfUFxx5nAbBG4No05cageIT9ojxc8DRGG5uvE0efFzqxoFVQcJhMag/KYjUnmecSx7CY8/l0hRtDRilXBuzxQjEqRYXLYuSYWLa+2UNRPKWo7HRCQeChgufKLFLItUIOOKu9L8qZKe0xMoAZ7h72nnlRuSE1A4XWGNjvZUREqkNO+FeWc58MC/CLQmRr4ngxSagjMCRwFKGopnrYPs7kn2Ij76mDmEUWBuN01ypsz23F5kel0wkFTDK3vAAMAjtzLXZFub+YMEON0TOaLlnC9NS92uLUrsrMnHVyWIRngIJ3Y7RSxkMBW6hSTvUUfTXoygPIZIbKLiT0CexzJtzVHmVKRt8VnB0mDN6dnZ1jyiPz5s3j8OHD1NfX4/3pRgBg3bp1fOhDH+L9738/aZpy8803c/fdd5+bsz5HiFizqRrjBR/KG5JdOcsj5pS+BSHS74Qj3jqYO525xPdgOtvtMfJ0kvC9oueYy9jrTFhqSRZYmClVCE95R2dB2OXhgPfUx8g9w9CmMDMNZMBm76jFSjGPFROuCJFbs0BR4QeFhJkxslKEQw72ecfCLDIn6gjjpQ7l8izixaRjX06EalUWh8ghF9njbTJxrxP6nLDHCce8Y1WWsTqYs/oVIdIUhV4Hr3gbYimVG1piYlqKsNMJ/7doPO9SFlkTInvJTRpCpAVj7gwBhzD+9TVpoN9J7oN5wjSgCuN4d0puUKGOOoVNOSVzWYhUY65Hi/LgPSCwzwvTMwvgjyaOf6r2SDST6APOszBGbs4Cc0bF4V4RDovgBOZHkzUo3wQ9YhnkcSfEcHoqYMz/FJm4ft8ncECE2fmcwOkwDOz25qVZxHS0x1MpvFBRl7OhSvkMwcUGlSlm3hdAk2DC4D1t2jT+z//5P9xzzz2oKt/4xjdoamrilVdeIcYzZyf33HMP99xzzzk72XONThGeS04wIpyaiFG1minBAWcX6EPDGQfzx2ZiWdfcXO51XlRqVdjlHDsToSom1KmSoAzmLI8eYKczwaQ+TIekTS0rvy4N1Knxi/cmQlswPZEBJzwtxoQ44B17PbRE2A98p5BQI8qMICwPkSqNPF1wIy45R5xjh4chhHZVNiWeAsbKaI5CQJmXKd7ZDmNZsN7AfhGuC5E1qhBhXZYxkE9cPuIdTxZMoH91iAygTIvQ60w/ZocTNgMvViVckUaWxUh7UBShI/EcFDHDAzXOd0eEbTkX/6o0sDFx7BO7NqtCpFWV3WofSLMqh6PyYsFRCmbZNuCU5hj5YcFTlQ/2HBBHfwKzI9RE5ao00HrSV9Sc4W3s/2S50wVRaQM0Oz1jJMXG88u7oAY9c5llm3d0OqE3nnkUfasXdnjTY1kcdYQ3f7HASmuVUfvXEhMG79///d/nk5/8JP/1v/5XRISrrrqKz33uc9x///185CMfeS3O8byhKjcAiFhZYX5Ubsgtz15wwv6qhC6sbDHsbFu4PvH8zHDKLTFwNFfde6DoAWVZagFyVYwMl4yFcMQ7BlBecQkqlgm3ZMrNWeCOUgAxsaaVQE8uYDU7BJ4sJJREaQIOYxoeUeGnRU/BQa/CjxNhEGHYmdXWkmATnc8kwnbnKYkykGfpDnjTcGBz4lmKcFspY7f3dDkYQHm24EmAmcM6MrJdD2QojyaenyaOTIQZapn9vKD0SWRxUJ7Km34ZJtG60Ztn5sF82rQ2CI1OSBReSoT+TIjeZAaqxKhqs0LkYOKpVivfFLCFxWG6K8edELGMu0UDMzLY6oT1ianprcgiWQpdojSq4/Y046ZxhKEKwJpgr9ErljGWs+cqYDawG8usHZY5i1o9f0cuFXBUBMn/PpBTGy/PrARzMlqj0i1C6wTN90a1xWRlFlgYx9q8HXDCbicsDSZWVsHFh/vvv58vf/nLZFnGBz/4Qe69995xH/fwww/z6U9/mh/+8IcTHnPC4D1//nz+8R//kd7eXrz3I6Pxv/ZrvzbF079w0JU33tqjcksaeCTx7E4EzU4MWbQCq7PInBjZmFgN+5go7TGyJRE6cLQEYxm86G0ke1aM3Jqa8t1RLywJkSvSYAM5PnIs52u3qHJlFtnhhccTR684LgPmqg3SHHLCZcFq18PYlKVTYVvBEVEaA2wpOKpUiC6wJFNecY5hIju8y3VEhGmq9GCli6IKe71jvxcGnGdeiDyeOHYljqW51khzNOuu8niKxzLK/XldfGmI/GIpGzEhfq7gaYh2Pb3A7RhrRbFA2B6tCThNBRdtl9KqwiteyNTRHkxzuzzKvrSU0Z2XrLY44cGiY06Eny0FVoRIW4zMD8qM/PwKmCjTTFXelEWaFY55oT4G1gVlozcZgtnjBM4tTnii4GlX5Z2lEwM5W4AXCp7ZITIjKk8UPPUKdTHySDGhMa/3G+3R0SnCfFUOe6F9HAGsuVGZEwMlbJinUccvxyyMyuxo+jvlRm05m9/hbMe3y0PLORLZqmAslPM3YXn48GG+8IUv8M1vfpNiscj73vc+rr/+epYuXTrmcUeOHOEP/uAPJn3cSRt9NDY2Tv5sL2AMAy8kRvHyWp4si+zBMXPUJ7IgKi3Rml2PJ0KnM1/I6WREgceThGHvGIo21bckRFaFyIbE8XjiKaK85B3vHc7oEjMn2OUE74QV0TS3/6a6wEvejBk6gEbv8KrUIgw42Jl4kmgiUVUa2SUJDSo0E5iuNiS0uhR5seDYkAh71bEsU/Y4YTqRIKYq16rm1l4Qm+50KDudGdEWMMbJ7UGpDsrWxPF8npEuD5H5USlEmC5WO5+VXyMBZgalOqfuJcCbgJ1ppAqrS7/oHf3OsdXB3GjuP7NUWRgEBJZHk2odArYkjppoWb9gu4VDznFE4K1YNrrwpM+y05nH5sJgynxNqvRirkX7cyGsw0BLPFVA6oiz4aWhvPZeHuIaxgauXvaO/1sQXvGm836t2nBREJgRlV4xmYO5GpkdzHLudFCs4XxcbFE+3eh+EeNeP5d4alGuyKy8szRGdjv7DlVwnnCWQzoHDx4khLHTuI2NjWPi5WOPPcYNN9xAU1MTAHfeeScPPPDAKYqtv/M7v8N9993Hf//v/31Sp/CGco+PkFtXKQMi1OTr59KoLD1pHFo4wSJoi0oP0Fnw3FEKvCkNzAoZXd5kXfc5a2DUqAWBOdFU8HYUPN2iXJ8pjcCdaSSTwK1pZJt3NGtE1dsUKHDEOfZ4y75nBWgPymMFT0Etsx4SYaZG3lkKvBKVI054oejY4hzDGDe7ISqLo5UaHLA58dw8nDFX7L3vcIGd3lGFbdGdwJVZ5JrMJiL/pWha4q0o3ysmvGnYdgJHveDCie/36iyyr8rYMVcEY6h8FYgFx1vSQGtUZjg4iNIWzch5jiot0XwxazmRgXblOwXnhDklM7xdmkVe9EKrKgNOmDZOwKtRm7xszuvOS6IyJ9ce3+tAFWZpHPdLfkWI9IswK46lta0CCllkqxfEO6La2P3KEFkRlAa1667YAtcwDl0xw6igDaOaoeVseqLwOyBCv8Aw1oT22M5hvN1DBecQZxm87733Xvbv3z/mV/fddx+//uu/PvJzR0cHLS0nxrdaW1vZsGHDmOf87//9v7n88stZt27dpE/hDRO8d+TqdqvyQFWuq5aNB+r19J9de1QaUI6rjYl3emFFiCxLI53eHOPnB2VILHO6LjOxpf0JdDvH0izlB1WeEsLPlwLNCvUh0haV2yTQ4TzLauHrMTCYZ3azFVoUel1ktzNq21B+giomxtSdKw/Wq3JHGmhWpcM5FgXL2jYkjkNRmSXGA0+ANi8szCKbE5Oo7fDCswVveireIl5zVLrFsuG9VSY01YBSP6of1eWEV8QhDhaFyCbveQkYzo87Lyq1MRITz3EnXJMFWtSMGDYkjulRmRtNcjao8oJzZCg+cdyaRRao8vOlwPH8eoAFxXIpByzTPhShhI5ktldmVqba5s/sUDNN4R3pqfolRYy/XhvMyq6xYJ6lBYUZeeity88nYlOo5fp4Gdu8sMc55kQzZHDAVVlkQGRCjZ1mVdaEaM7zZ3xkBecSZ1s2+epXvzpu5j0aMcYxk+WqOubnbdu28eCDD/LXf/3XHDp0aNLncMbgHWPk+9//Ps8++ywiwpVXXsnb3va2M1IEL1R0OLPU6sqZHrluFY8UHAMirM1OyJiCBeH+nOlQD7ynFNnilO3eascRR1cCHuGaEJgTlb+pTtjvhOdxHBVhfqZcmwUSgRedOaz0a8KtwRgm03Pe8oM+8gOgH2F2VA44x3FVy14jiEYOFqABk0H9fsHzsvO0auDG1IZZatTkZBtUaczLD4fyOvygmJ3ao96x2zu6RZkelegsiB0VMx+eHSCKcm0WecI7tkcz71WsYTdrlFDUPmflifagdDpnJQSgmAVe8MK/FBNWBitXrMgi63Lt8X5gm3PUeNgdrCTU4WxQZr9z9DgHZPh82GNOVA44Ic39OttVuSoXCdvhHD8pOI67hBXBdhxll53yzZjBiNnDVNCgcLkqnfmC1e1kZBEpY2O+8M3NIm/J4pgAfnIiV4Nl8BPBYVLCFby2qJ8RydLJX/ekYJ/urFmzJnxse3s7zzzzzMjPnZ2dtLa2jvz8wAMP0NnZyd13302apnR0dPD+97+fv//7vz/zOZzuF8PDw/zKr/wKfX193HjjjZRKJf7sz/6Mv/mbv+F//a//RXX1xZEXKJYdLYiR3TjmjroxDjljRmQwxk9SsdH1PoEVeY2yOiq93lGI0IRSjJGdBU9JhCYVBhLhCKB5RjmYOHObCfC9gueQE3rF7Kq+5hO6nfC2NNAYIt8uJvQANXlAfNkL05ARNcT1iSPgmB+U6ZnSmZgm99IM3lkKfLU6YZuDFamy0Tuit4bsuhDpD5Ci/GMxoVOEg0445qEmws+WMtaGQMz1mwdFuHHY5GnnOuWOLJApbCoYZbH8ZdnmHdu9o0nNgGJr4vAR3go8HSP/b1WBbicszKwRWdQTGtfLorIvRCI25NPpBEWpjcJMVWpE2e4dS6Jy2EGfCtsSx/p8gagGqqOyKirLYuR7Yhn29BCZm4t6eaBRbRDpsVwD/dacN50CwzK+5vZhMTZJEVvsI1ZP73Hjs0U6nHBQhOHEcW2INOXlm4XBmo+vRtc7ABu8mVpfkcVzOgh0vhCBgw6mxTNb4V2I6OtylIYm/4EVqydfY7npppv44he/SFdXFzU1NTz44IN85jOfGfn9xz72MT72sY8BsG/fPj7wgQ9MGLjhDMH7y1/+MitXruS3f/u3R/5PVfnMZz7Dn/7pn/If/+N/nPTJv57Y7qzpdESEmShHndCQ34hOrU7qc772cbGbr1GhBmWIE9rUf1fleapgQytXBuWgdzQqHMuDzVOJ0QVXBqUqp5J5HC8k8FzBWAlvLkWaY+RrVQX6PFRrZC12gw5gbJNlwXjIToRMlX8pmIv8gCp1CPsLjiG15y6NkUxsuGePcwxpoM8LPZjH4nQ1h5+jUlZChPkhUINpX+/wntQJN6WBNUF5JbdF2+SEemBRFnmh4JgblCvSgFd42ttwzyYnzM0pfFdl5gTUCMwJ1tgkwm1p5MEqT7dArQrXBRviaFRlRlTmiPCywkFvjbzLs8D6gmcIyLBFcxCjGM7MB0C6RPhBMaF1OGVuVO4ezjggjlY1Pvw0InOjaaD3AesTz4DAokxp0MizeXlldR7oy+gDnkocw8BSbw3qiDA3RgYRepxQf1IAvzo1aYGmaFROgBe9uTGtCnFcadpBYJcTmvTETmY8HBN4ORHq1Lwu6y6CbHyDdzxY5ZkelF8azi6qmuz5ZJu0tbXx8Y9/nA984AOkaco999zD2rVr+fCHP8zHPvYx1qyZrGTeWJz2+v7oRz/ia1/72pj/ExE++clPcvfdd180wbtkQ5HUaqTAWEnRNlVuzXndw5iBQ48wUnZYmQfHbU4YRuh1wopUWRoi/UBvIqwMSq9CR8EREa6JJiY1IwrzYmBeUF5OHDOi0Q53OejypmvSLUK/UxZmyjRgRRqpVeh1jrkh8t1iwmEvVKmyNkZeKnjqorI78abVojYq3xyVXjHmxvI0UhMiwTsOOgt2O73QlgWeL3r6E8dlaWBvYlzsbTgOirAjDRTFgvHu3N29PhG6xMSgFmbChoItSseBQQf9wWrYx4CD3nEAuEzhI0MZDsjyCdV9iWdHAvM1cBzTKRn0wqosstvBXnEcSWwCsz0aS2eaOrrEAvitaeBGjJf9nWJCEUYcj54oeAZEKIVIo56Y2gQrVVwWIj3OGpsK9GKuRRlWwtnphYYIGxLhn4qemUDBCTPVNN2P5INDe5zJAvTnujUveUevCG9Ow5hhn24Rgtg1Gm9DvcsJPy4mDAD3DmfjjpErJtvbh9CgEVFjEM3KmUcXIiJwSIR+oOECmD680HDXXXdx1113jfm/r3zlK6c8bu7cuZPieMMZgreqjqu/XV1djXMXg2KxYXkwze1GtW1/uVrfLRByWplg21SPBYVELCgdEeW5RGhVK7u0Dxlta0mw6cq5qjzvHfOjMhADB/EcddAe4M1pxvU5tWv2YMojiWdjwZQGp0dowKY5B8TxgaGMGfWwJyo/KXg6HJQKjhJQI8pVaSQ4YUEWGUKYGSKJ2K7ggNiQUXtQ1oVIowprQqQnKt3A31cl7PYORQnYdOW+xNOg5oDzfOJ4IfHsd8LVIXJrGuhUZXPBkWIBQ0RJne0EWqKyywsDCD1i9ffd3rLvZuwmHtE5UQueB7xl+iWUXc6z2TuuzAJPFBwvi2Nr4pgZlYeKnpVRWR4soy2HtWrgh4kJZtWq0ifwWOKZHyKFfJe0NoumP65G4+sSMx9el5nqYKNaIC4JeJS2aP2LDuf4QSK8lPuXZkCXc/zMsC1A+xzsdcY6ebjg8cC83Js0CvRE24WQlzema6Q+mgZ7pwgDovRjXpr1ahOmA0A99j7aTpPCJRjLZHEwZ6ZUcr2XCzQDP5Rfj8tC5C3D4aLKuoGLUs/7jDXv4eHhUwL48PDwhGPxFxIKcMpUWj/wbOIJwLW50FMNNqq+PJ+YmxGU7QVHosIhwYSZ1IYt9jjocjbIMcsZ7S3LlD1FYQ+OGpTnk4SuLPLm1KhvDjjohFVpZFWWsSvxzFRlVabMQlkE7EZZFK0e3OssmCxOI7NR+vJ9XSNKMQJOWKA2/XjImyFuQdVMAaKwJiovONvuR4XZQVibpex3xiU/6M3B3mPmygi0BWVYTZN6E0ajK7MlqiJcFa0G/6RPyMS29AMOGiI0RGUW8Lw30+NaYIsXDjpnmhcKXy8mdIswHXihIBxxniQoN5cCvbleyUEn3JgGHCeYGUMY97lbzKvzkIgZIjv4t0MZqZiQk2AN6e8UPHu8Y00Wid6NjKX3SZkVYpOZkj9nliqb1ah/tgApe/PpzaMibEmsWdkYYVXewF4eIgOYtvpXqwvUoXhNOeocHpNe2JQvOPODslGMPbQmi7y9lNHpzAP1sBg9cKbqSAYvGDtlMK/NH3PKcWRSpsanQ8BKiJlYQnOuM/iCWq9gYWBEDfOiwqUUvO+44w7+5E/+5BSvyj/6oz/irW9963k/sfOJhHySUBjzJTZGALRnuUVXFpku0OkcPV4I0eqPz3pTtatWZVmI1CiUvDAvRpqDMkeVAS8cEZtMbFSlDmVOZsMcNQrDahTERSHytWLCIWCm96yOkZvSyAanPFpw9DqhKoPLNOJVOSKC845dCI8mngUhY04wOdp6Ij9ynnkFT0sp8OOip0+EuUFZFczMt0WVzYnpke/IKXvXZoE7hwOHvfBQVcJRES4PkWl5vbVBIXVKl5qC4K1ZoAFlVVDmhsgeZ2Wh/wVkxYRQCtyU61wUgTVZYLN3JOIo5PXnAeCgKCUnXJVGFmaBFxLPIMbCSUZVFauBW7PAdrHyTLcXkpx73QgjBcgh4FnveCzx1KCkamP8YGWPl7zjgJBTKoVehBvSwC2qNKmy2QldNUXIp0AHRdjsHR0O9ovjHVnGDbkt3B5nvpkDKI05l3x2UIYFarHPuwC0RtvRqQolUToEDnkrS+1xCSUxrvjsqNw8SvukmH9HwQK52cedPY4LOUsKWmI8J6p/JWynVY19r27MvT0v1NLOmXA+a97nC6cN3h/72Mf4pV/6Jd773vdyzTXXkGUZTz/9NLW1tfzVX/3Va3mO5xxVwPVZGPnijQfBpixbgefEhIJac0bGoqBkYiJHq/PgMCMqfWS0qWU2zUHpFJMyLURhgxeOCGwU4RdLkdo0MDsqr3jHc4ljHzCv4Lh5KHDMCQNeeDlxNEShREZdNLGs9YmnA+U4SrcXipLwq0MpGxPH3xcL7Eocg1nGbWI86GqU2Ro55mCTMwODZZkyRyNd3tGmyso00uEd27zQi9KNsCoqN+bZ3wExxkc/sDpYCWJWNGNiESF18L+rCuwDUi/chn25VwRlbrSacHsMPJk3ZBdFozc6hX7vaFFlXoRjUZmmlumX3XTIj7Uui1wDbAiKFqE92CJXxlGBhwsJT3hBRSFCk0A1xp55ypnwU5cTpmUBxQa1thccM9PAdWmkr+iZBrShLM2UnoJyZynlX6oSHMqiPDuOwNbc83OGwr8bss9nFjA7CyNJ2Q2pCVwVsTJdl5iMwrAog17wKAcQDntHwxk8Mz3wasm5Rk1VUpiStv3pMIj1iILANblA18XAiDkdLqngXVNTw1e/+lX+5V/+hRdeeAGAD33oQ9x5550XJc/7ZEwk5dkjRh2bE818eBioBRbEwCA2pOIVHkk8TWrMgukIqUCBSBDHroJjKEa2FIWjCDsTR4eaFGmrKntFaNRIXRSagZvSwIpgQkYlD20RkMiTiaPHQ0OIPJUIO71naRaYo8qgKvcXPT1YGaElKmszy+SqFBYEZUUwzvhmLww7q4m/PYt8zTn2ijBUND9M0fx9e9iunvlR2ecERZiWlxS6BL5XNBrlDu+5LFjt+KA4+rCseVviWBWVWtURLY92hXenY8tt/yoNdITIzLwefHNq2h4v5wyhmTGyLCoHxKRSB0Ro1sjqoDjMgaguD0T9eQOzAWFuZlOXg0AJYZ+HzUXPVgerskhNVG7LIusToYCZQ9Rh+t4CzCnZoM90hZI47ihF9uVToBucUI1lsgFYm0W+W0joc/CLpYzZkXzq1jRsmvK7vFyPB5vuXBusab3RO3qjUHOe9+EFTm9KcTZIBUrOpASGERouiHD2xsIZ+wree971rnfxrne967U6nwsGG72n38FwiLlpgWNZzvmuB+qj8rwXXkiEY5JwcxaZn5vmNiokAnNiBLWtc58o82LkOI5egQXROMc7nbnwzAVmRXPlmR8id5UCqPJk4ticJByJkeuiImo6Jw4hi5EfFq0u3wRck9fX26NyKOd01wJrgtHntgRhWyIcccIOZ+YFmSjTg/kv7nGwITHu+mwJDIvQ6RztqibUpMrziSdiXN7WqPSIMC1E5onQh3Gfd3thk4OS88yJkVWjNDki5NRBWwxHN+BqsFrxQ0WjC1Z5T3dQqtVYHt0C0zAjZrAabr8q7dEYN1dmkStSk5Pd6IV93tMvNnBTH2FxtADaiLLbO9Zm1sN4OvGm0ZJFdmHaN6g1Zq9MIw0oGY59XjhUTLgql/FtVjM8PuZs0T4mwmxMw2Zv4jgY4bbs1IzaaKR2XuuyyIE80F9MaFRznQ95Geqix6VU8z6Z1nIy7r///nN+MhcS2mNktzhaI+z11ugZPOkxjXrCNsursjyecCe/Jg2sETgowsbEMQ1lN556DbxrODAd5a+rEvZ7R6pGXdvtrDSxw3luzALVIsZKidGYJd6y5rlq1MUDiQOERoXLQ2CmGkvmgaLjtjSwPFMiJr3aL3B3KfB9bNLvoYK3MXUNXFNSlmmkVPAsCCYje30WbduvSq8Trsgi3c5KP5cF5cos0Ivwf6s8UR2L86x1qyoZynbvaceC7uhN5m5nWXSdKjdlkR8kji2J4+2lwP+/vT8Pj+uu8vzx1/ncW6VdtiRLlrxvsZN4yUrihJAADQkkOEkH5iHAJD3DTKb5DSHzhH5I0zQPvTB0A0NPaJbvdDPDMAwTulkmC+lpQtJploZsZHUWJ/G+W5Ily9ZedT+f8/vjXMmyLdmSE1kq+76epx67pFLVvVV1zz33fM55v5cGZb9Yr/heZ9OmkcK5SWC+07RLw7ow/iV2PJaLiDRiRfraa4ueDbmInUBQHe7oWOmVWwaL/H0+Zk/a4rfcK9uiiIAgqlSKLe4OAAdFSdLXr1Ll9VSpsdMJFySBearUJJrausHvFBM89pxgPfZ71b5DJ6ISy4pfiiLO9iadUCo0qU6P+sEZypjBu6+vj8HBQa6//nre9ra3nRalkhPRLabdXI0N7ywJVr+sTZRDo+hSzA7KRYmSqGVnrWItYzOCUoXVGXc44aDAAcwsQdQWjgpqjjS16ULPQsqoSwLPxo4I4ek4olNMw/m8xLMtcmyNTKCp3gdeihxVXpjjA8vSRUcP/LQiz8bYUVB4uw8cFAeitKXtjtVqphAvOEevCJE6dsUwt+iZqfD2JHBZIeFgZDZvQcyot9+ZVGsuWN36l/mI18TRLxA5uK7fczbQUvQcdI6FGnBqEri7BA46YZbN7rDFCYiNvz8fR+yKhB/n4b0Fb5rYPnB5YnrrcdoJNPOoFrlGtXJHXgPPxkIZQrMG2uwj4LIksAXHQRH60HRoSelQG7bJq1JAyKOgJkMwL13j2O+VvaIMiLDTCa+kuunLk8AFSaBalVciu6ooiGm2z0mvul5zNg/wlqKnBottrznhQFquGm3qcqszjZo9TmjIlAOnhtMp83700Ud5+umnue+++/izP/sz3vnOd3LTTTcdo0FbygxNu81SCxS/TZ1d1hZNCnbo8xmpS9GLjVfXpUH+bYnnpUjYHDmej4QGlF4sc20Kyn6Un+dyeDXPw84IXhShpQDrismwGa8Az6TTinucYyDtrW4KcE5QDjqlVa32vNQre5wZROwRx2wNDKbtZn3Oxs5fjYVycSxJlM3OarsHRWgXIVLlAu+pUBu22eMc2yOlVs2kYGMckaRjzrNSg+KyoLyUi0gEDgVlT9o54jQQqWNn5LgG6PNQ9J7VPvBqZAtaL0WOTueY4ZQrixagI7X3vtYH8s4RBDbHJrE7KyidAkvD2At153il2Rd5KhfxlLMF4+oAByPoFuE8sf7sylSedlEw3e5H89YB1OWsDS+o0h454qAUPSwAXhehywlLfGBn5DgnUSrULNgWBeXJyNEjsCNyzEjfswERBlB2RObjODTJ2y7wm1zErLTvu3qUEsM5PrAvkmnbw32mUGrv/nFr3hdffDEXX3wxAwMDPPLII/zlX/4lPT093HDDDXz4wx8+Vds4aexOpwn3BniLtyx75PjRQbGugnnBhjr6sX7jfoHzfKA5DbqiNjQy2wfKMUnP9bGjXGGTCHucTeUt94F2HDksKJ2dZlkBeBA7yGeq8v8bTMwSKwCYwl+fmDzqCu+Zo2bb9kjeWuueiJ1ZsvkAmpoFqGO2T/DA1tjRDTyv4ARWFQNzCCwoJuxLhbZmB+XsEHg6dgQRckFZ7JVXY5Oc7RFhVlB6nTBb4fpCQqsIz+QcWyJoD1Y3XxACW2PHRiLO955d6RDL65ENrvQ54aIk0AP8Kuc45Bzneht8GhChQ+CpfMR8r9SrLWYezR4nbHNife2iXJhY33Vz2pGjQnplkb5vaVCsxOr0u51NkroQiLHXXIx9lp3YFVgQK2VdlHgbjEq7YLY5WzM4IMI1RU+dKl3O7O7anDDXB/qcmSMDbEk9KbuFMd10ZqmS87bAnDE1nFbdJiMpLy/nve99L5WVlXznO9/h7rvvPi2Cd6MqrcEWCmsULi1aAN/nhF3O4dSMh/sQdjvlgNiEYoxYmxvwTM6lNmCCU+GW/iJ7UrGrjkgITliV2MTZ5T7QG5RWJ8xNg5JiHpcO07KYF0xf5CyvrHfwamzj3wu8TVm+HEdESWBlUHKDnq9VxuwVeCDvgIi8wrLE3FuWBNOsLktbEtsiYZZXKnOOPUE4RwKqyiKvJkWrJme7ywnzgvlSbhbTRNnpzFj4gmKgDJsMPAvTth5M1RDBrka8WmnEi43e/zyVnJ3vhWcjx9k+sDQoL6vQrXBeolzgA/3Ar3IR5pRp2iT706x5qKUzAI/Hjl9Gjl5nWuzvLniWpa//zoKnzVkb3mYnzA1mXqyY3dx5iafZ2TZ141iigbmJck4S+HVsp+6ZqixNTBpXgS5nn3WN2km3DGW+miVZjJV0fpNelZyTBM4d0dXREpQ+MUmFsVrpNjlhW+Ro0OP7XGZMMtOgFDIRThi8n3/+ee6//34eeeQRVq5cyYc+9KGSH9IZYobCW0d0AwwdXFudI3FQ503BrizAplgoU7tcnxXMuWWDE56MI3Y6aPSBixJPnxO6RVjpPcsCnKeeDTlHi7d2uH6xRc7XUnEij11+LwTWDQYGBZb4wGyFuRrYJxEvxJa97XdCRTDvygaE5YlNWx7MCQPBXHHyKDMIHIgdO70NwcwLStBAMRcxC2VJ4ml3jv0IS9TaAQsidGEnkVU+HTF3jjlqDjttzvFqzrGgcFgLZlMkzFbl/MGEFlXagM3OkQ/KxrxjC465QckJLCwGloZAIsI+ER4qj2j0gVsGD/sydjq7ajnbey4sBp7ORaZNkwTOTYNzhwgvivBM3pFT4ayQ4NJF0YMCbZFDsGnRFYkNGil2lbUhLXO0hMAhJyQoVWra7L/JR/w8drQAi9PFy9Ua2O3glSiiIShXJ9abv9MJXSJscuYKvzE9WeeU4dbFIRYGZX7wdIoMGzQczUTLraMRsOy+cjqkhBmnhDGD9ze+8Q1+8pOfUFlZyY033sgDDzzArFmzTuW2TRmrfWCfmgEumJsNWFBdEHS4tNIclHqvtHhY4m3MfrNzeAf7ggwHnOZioE2EX+UiNoqQONjhLAOd7wP7nNAC/F4hMQPf2PEikAtwbtGmE1+OhLyHFyKh18UsDUpzSDjHBw5FUB4sc16ZelP2KmyPHLOwK4wWH0giYWXRc0kS+HHebMqag02JHgS+W5ZjHoG3FWwMPlFlUKzPu9sJUdEmUDsFnogjtjphsSpL0p7yHVj/7xZnZsWCcGWxyMWJCW55zDjiqVh41QkvRzErfHFY32Ojc8xUe412Z1OUmyNHd+zYii0Qz/NKk8BSD83ec/2gaam/FJmQVBHYFgkHccRqDki/jaxn22NXBJE6Li56XowcW2PHvsgGrxqDshwTYDNRKtuOnbEwkCjPp50qETosvLVNrIxSGWxNY7QpyHYRnosd5cBlxWMfszQojfrGJGS3pL3xM1WZc/JPk1FCHDd4z5kzh+bmZp544gmeeOKJI37/N3/zN5O+cVNFoyqNaUJewLKpPDawU8QWxGambWLvSjyRKk/lIn4TOZZ4z1leWXBUfbNP7PJ4e2QdGtXBxuTL0989B8zHVO5eiSIOYM3i5yaB8qBszEU4lB2xQ1VYrQkFYJbC2wthWBCqJr30DthC2GZn7urbooh5PrBYlUrM8KHVOV6Klc/0F/lNLmKnwC4c3887FoZAayR0IlRi4/VrfGBALPt1YoNOkQ/DFmXLgP2qvC0tEdSrqTNG2L7visyQIglKUWz7Rn4Bl4TANufoB16JHRVqo/QHI9iPkBdlOcrNgwm/M+ipRqlTuyLZEzlQWJEECuIoOOjE8ULkmKXKNQXPSu95IY7Yn9bdm4LnX/IRs9N+7IsTWFEDj3nln/KO/TjODYFzioHGoLwcOQYcXFkInK3Wf/5MznEAWOXDmOPrcXrL6ejmwxG2AP5GSLDs2waTMiZKbV2gOAEzhlxu6mssYwbvv/iLvzjCqudMJY/VgRXrx30mdrSJGQ53OOtKWJAEKrBe74M4KvVYw9t5QVkWFJVArOag867UWf7/pcM0j+Ycb0mUfRJ4Pe/wCGep4pzJwrYEKPNWArmg6NnthB6ERcHKA9b1YBlhF/BC5Hg9jugBykSpCEKbCIUoYpX3VKIsSCzzvyAJLPKB9bGwMYrYKUKELfItSwLNgKjyk5wpEi5NAq/mI54vi5k74FkVAjMwHQ6AFYWEfg6XAzwmFdDsbYFvuVcuKfoj3IsWBKWA2bftccIVxcCSEGgPQusI8SbB9EN+lovZ5+BtSUJFMLmCuaokSaCI9bbvFyHGlAfnBujxgQQ7+TYoNA16KtRaO+vT/U2cBcMtkQXvdxWtDXNnJHgVyrBFxlZn+icCPB85khBYOMqiZMMp0P1YGpRKDWyMHL/CTqSlPK5+qjnY5RgcHH/wLiub+tg4ZvC+6aabTuV2TGtGmshuEevOaA62sNflhHOBawqeusjx07zjb+KYtxUDK1Lp0IMiLAh2AHsch3Cs8R4FFgfldwqeVyvtEv3FWKhFafQWpWqB30YRXcB1ScKyQmB9HLEvPYnsdsJAUZiPLa6pCLkQeDkXsddFtAuUq43IL/NKUYTtzg72KxJb3NsSOd6SeDrFscs5JCirE8/5Xk3f25lm97/kY16KI5aGwGVFT4eDNudoc2kD9wi2ps87pEzY7YSlwQZ4+sWe72e5iFcim7K8yHubTMWkclcngfkhsC3t7BgyPRg6ZAaA7ZEpEe52jnPSQZ0hJcOWoLzFB84KRV6PHF1i+uDLvdIrVvZwMNzNopgBQiWw0AcG4whBSbCfeUyzpkeslREgVvO57HZCTmCXcywcQ6PkjQTSAUxtsW7EuP3R5DCHp15nmfxI6YCM05Mxg/ctt9xyROYdRREzZ87kqquu4sYbbzwV2zbtKAIzgFywS+RqhYqglKmJ5T+Vc2yLIpyaGfHWSNjpIl6NhSuKnhlpJ0lRhSoiKiILUtckgbXAPwcb+a4CygXm+kC7mJTpDGxYZoHayH5XEMRZzHw9F/GaWhvfJUlgbQGaIiGvgbwzV/qiwMuxlRDq1A72IZfzpd4C/65I6BMzrOhxjr0amInyZC5mDxCJXV2UqfLb2FEbYE5IaAlm6LxFTWr3HG+CVkGgP11MXOYDguN1ZwuWHc5Ro4E9kVBMAouCDGuui5rW+obI0S9CJ1Anh1X2djir11eHQHdqWvBSJDigWq1kNHQumammvVEQ27+9aiejAbFSx9mpQfE+J6yPHDuBcjGtmX4O26VFWHvoEG1ikrHlCu9Mr4JOxnuyD9OCqQ0m2DVaPrclEnY7k/19a6rQ2Cemq1I54nEeW0geBPKnIHAPYu/bTFVmlPh5QsVuE3n8VDNm8P7X//pfH3E/hEBHRwff+973OHDgAP/23/7bSd+4qaQPk4wd2R1QhmlR9ImNaOfVSg7r44hDKDODiRutLnoWBxufbs0JSxKhUm0hTFU45GALwuKg7HQww8NOTAhrANgnJtWaVyjDuiHmeOWQKq+mGezGCBYF6/f+ec7xfC5iVdEzTxURmO09eWeLZOViAk17I0dfUC4Z9MSpfVuNmsFvpQpnJYHdCIHDPcoeG2SpFft9hSr7nONn+ZiV3rPYm+XYr+OI3UCZWBfKxUmgSc1x6OdxxLIQzC4tduxwMMsndONInMmo1qWBz2EdEzFmxLvNCW/zgWp0OJB2OlMI7BKsGwYL7D1iuiS9Dj4wcDjQLveBXZH1YD8RO3ZHZrrQIxF1IaFG4dnIscdBM9aFtMwHqlSHWxDhsHs92JXFDmcSudVqeu8nQ7sTWp0NT80JflSVy1qF3VhZaEBgX2RGFQedeY8OoZAKpEHabTmpbHWm4ZJL1wxKx6Ll9GDM4H3NNdeM+vN169Zxyy23nNbBewB4KmdmDRcmNjbenmaQs9UcahJssXGfs1NwY4CF3lNdsA6Nl9MFt2uKll0u9oHdIjSHQLOCV2GLc2m2FNgJbIgdqjaiPSdAVVB2xY6rioFOER4sj3EqvDNJCFhrYB+OBoQPDCbUqWWlf5ePeSE2U4iVSeD9AwUC8GrsaFZYpJ4NLkqDtnln7oocC5NAwZl34/xCQrtzFBkaKArpIp/y92VWlmgJsDYxg95egVZgV95xQTFwrlh3yDOxs64MZx0X+wXeXkg45IRf5LDXCodHyStVqQ92stoW20BTuSr7nFCLLcguTey9HEgHX4a0x/8559jrHAWx9/KcguegWOY6P5ha40EnmM+sEqnwWhRxVghEYmYJFwJelYai9eYPJVid6YmhLihLQ6ArMiW9xWH0hcoidjKJFQZTaYXRpkVnBaUuKDOCHvE8IX0/Imy9ZFbww0qYixKbej3azb5esc8IczGabKqxq6RaRr9imCivObvCWO0DjW/C853uTNitaMaMGaf9QuZ2J7zgHHNSYaFOEe7PRxwS4d2FhNbIAoQE0y+pCsrFPtCe1sDLrMMQSNv0EjM33hs5GoFyr/wy5ywbxw7UBrVhocXextTnqC22LU5V9ZI0e84J7JSIGVjnx44oYl4IHHKODpS94tgcCQcxDQ9JSyAXJoHLBz292Gu6yILi3DR4zFPT/s5roE5hkZqS4AuR0OKhNTJN75aC53eKnh4nrEgXasuCshgrVyTe1PVedcJF3toQdzjhkMCDuRwFgZiE+mAeovODlXF+mYuoD6YhMyhQJNAtsDkSXnUxszAz4jq1adf3FE2a94AIj8Yxi0JgSaKsdEpHuo8bIiGIlYJecqZ2+GQcMSNY94yIMksDs4KywAeq1E7KT8SOxV5NZwVz7nkutpOFOCH2pmJYidAyRsb9UuTYl9brG9Ss3UYbf6/CFsRHUsAWxosIFyXeymgjfr88jPiCHUW9Qh3Qfvyv+JvCvKA0BGt9fKMRwWMOVoo50J/7xjdvQoz9jo79+KlmwsFbVUmSZDK2ZdpwwAmLQ6AxWCDrFmUQIcYczHvEAvwCVRaqsjy1LnvdObxY9nOJt2GWGMvCnFoN+4AIL8ZuOHN6S+J5OY6oBd5T8LQ64Wyv1KtSjIQK0pY8Ed6ReOoSZXssNAcre+SwK4WOtFUuCCQB5jlzhF/rle3OHGQqFJ7LRYjCWUVPT84WQstEeFXg53GOfgfXFhJmpvuNOCrTMlFOzdB5WVAYEYhymP7KKmBhIWFD7HgxjlAJrE1Mf/sXcUR9CGyLrf1wSyTsiuyKoTkoO2JT7asJgdYoosnDDK90iWV1g6lzEQI9kta3gUdj4Rd5R6SOtd4WJCs10JG2HA4Kw9n3C3FEU7D2xFdjx4WJZ1aw9/fcNAhvwbTBN0dCczrA9Upk1mZVwVpAn8xFNIbAReki6WgMvTs5FCcTO9AGxMb3VW28fzovPFa8Sc8TAZVBeSF2NE/FkOlEJ6WmQf465neqq6tr1J9973vf4/zzz5/ETZp6zvWBejm8ABUpvLuYEGMTcy0hUBHbtN4FSWCAw0Ml2yJHi8CaYJ9vAjydBuuliWe7c3g1M946VSrV+n/LsAy8V4R7yhxVwRxPlqhyUQhsAstKI+vtdpi2xmuR6X8/FdvIfhmB9fkc/SqUh4Ruhmy/HE14EqDNwW/KYnZEjjJVzgqK4mh1ZjSQpN/MMrWhGlRY7q0SPtqEYHdaFlkNnBuU9gB7YqFDrBukWmF5CPQmsNJbJ8SWyIJmu3PMD54n0/pxY2r2+0LOsSDAxcVAIgwPR+13wgw9fJm+NNgYe5TmToMCq72yNHj2iF2BRAqKtXeuTE2Lt0XmIp87KodaCnSosmBERr04BHY6xzle6RDLEtuc4xzvjymFDAK/jB27I8d5ieft3nraR3vfxqJGTQY34bCBw5lAEGF5evV1yjmdgvfatWvTBTb78ogI9fX1XHnllXzmM585ZRs4FdTqkVZRL8aOAyIs84Ec5vwdq0m/RsBLsaMLwaUH/c7IERGYr4pTy8gHgK4oYktkk39r0hH41525xJ8D9KqyWaBXHBtioQ+rKV+aBOZ65ZeR41AqlLVWrUd6UdqadnbBroa2ibDFBTY7eCUX0RWUVanlW07NsupZJ/ysLMdu55irgZxXmjQwN9WTXjnCf3J2MD3vp2PHlsgxPwnclFh/epnCXLVFxX1il7xnY68xA5NdrUjfxjlBqQ2e7+cjCsB7CgkdznFZMfCzsohdsaM7MfedvIMWLxTSk2EemBP88BBSfkQ8W+mVOwaKafnJetNr1TI5h9KlsCgEzvWBQ2KyrJ1iGjILvVKP1bO3OCEPXAG8JS1jBKz75/XIMSeYz2WVgmJO9aPVsLtEeD0VL+sURwV++D0YL4KVJM40zvaeXZF5rp5qTquyyauvvnoqt2NaYk43wkG1Nrmhg3C/CIPAv8QRIfHMCMqhyDK8gKJB2OYcvxKIxVzCZ2KKfr0CfVjP8lLv2Z7qpA9g9cp1BU9NDP8UOzY7x1439MUy6dECSreD9eJYGnR4KGQoEVioyu8WE55yjp/HEdsQYueoR9ioJo51TrApzNkusNjDMsxcoU7h6cjxQhyxyptJrU+ffKcIm52jPQeXehsG6cV6nQsiw+bN9+ZiZqlyeeKpxEbMt6VDOfud0JX6aC7ycG06/LI8CXSQ7qez92uxD7wYmVTAcq90uojZaU91TVAuScJwd0N3OiizMCgdIvwyF1FU64K5MPEjLu3tvWpWpTltx+4ReCwXsSF2LElsoazLmQJiIW2vfC1yDPrA4lSC4Jxg2u2Px44lXoffp41O2CtCZTA1xAv8m3v9v1/MRHluMGGv0416hfpTsdJ6mjDhmjfAX/3VX/EHf/AHb/a2TDu6xNTenMIFRc+QssucoOwTpQeh1TkuSzx1aqPXA2plhA4ntDuoQKj2Vv5YlWqm/HPeLGXfi+leHxRoUfinSOjDcbH3NATlJzmYH3S4XLHUm5tMTmyBM6eWHY5s0XKYPvgmp3hRnForWnXa3w3mJ3lzqqA4Q20QpQBsFhsoSsTqrrsQOtMWwUWYtdusoFSnGfV2Z8YDMxXOTjx7sXJEZ1DWpCe7B3MR7c6xTwJXeM+SoKha7dgDz8aOWIRPDCZsiRzPRo4eUbZF8NN8TFskHBJlaSGhQ6yEVcS0R4a8In+Sj5ip8LsFz4F0TeLRfMycoLhCwgVe2e6svW5BONz1kTadUKlQ75X6oGzFTJGFwJJgXqBFAk0+0JXqceexHv4eEXZEMDsxIa9tkePFyHFWCCxJJzjfTPY4m57dHrlho5CjKWKluoyJcVpl3sfjnnvuOSOCd6UqPpgS4PooYkkILFKlEss+G1WpILWyUutA6MYWABu9EtQ6HPqcQ1RZ6pV+JxTVgmUh1YnuR7gnwPNxzJwAizSwUAOLg2W3T0YRy4PnHFUOYDrbC4OyyzleT11rZgcdHtjYJcLLziY5y0U5PzH/y7PS7hnFesoPijAvBJuGFMdOJ/QCF4VAQ1CejiMGgZlidfGKgqcCZRZQG5SimGlEUwjMTU8yM4IN2myKHJEP5NOOmhpsOvDGQjI83dgPHExFoPrERvQX+8ALccQm5+hOa5/zg2desMXIAREqVXkxdnSI4FXJIRTELMuqFbqTQDF27BTrBuoQ+EUc0RGZCe/aopW/XojMaWdV4jlPYWds6oAFTZ16sL7+i4Bf5yI2iikcLgsmo/vbnLBflScix0pv3plnhUBlgBpvQXSiB9ggtgA8Ws/0oqAURWkZY5inW2xIahY2Hj9az3jG6cNJBe+hOvjpTq8IkTNRpDJV9jrHIm/X2zlgRTDHl10iNKtyUbqot1uErQLexcxQpU9gpzgSrK2qMUA+mCNLjAVTJ1ATlHnp7aAILapsdI5BlCImM/tIHPNMpFSrp00su99IxEwH53urCz8VO6rV+pGrFean2erQhz2IZawC/CJ2vOYcHSoEZ4tx23H0SmC5D7SmC7c2ju6IVMn5wBOR4/VYOCdRlgYb9OkFWrzSFcEOcXRH5lJ04WBCUawev2jEd6cCmzDtT3vCHXbpvLboGYwdb0s8WyMrufQhvByBF6E+mDhWv1jwbwnKlcVkuDxik53m9FOrSp8IbVE6VJM6CtWq0uGEINAXZLgXvRlYm1rKPRI7upywygdqVOlChgeFBsSy3EfzMU1Af9FODBcm9t6+Ejv2q5Wnxru21Zp2Is1U5cIk0CP23RiaXqxV62Qai35syKsP6woqPzMO0zeH02nB8nic7n3eQ1Sm5YEVIVCHMueok1YReMFFdAgIwurgKVd4NI7Ylg6DVKIsClAfAlsix1mJZ49TvAgbnSMSaPbKW4FVxYQBEfqxzG+ptx7kejXBpY1O2I5QFGGHg6Uh0JZOTm52UK2O8nQSsVrh7GDu6NUIC0aY4baKcAihVu3vn4wiVGGV98QCPSjrnemvLFETbTogVpcfkkudhS34Nav1iO9O6845UWYGoSiWiVdgo8S/jSO6nHDjYMJSVfaL0Jl2kcw+KsiUYR6Ulaq0O0enMw2X3ZFQpTAjUpp8YEWwFsQ2sWA1dC1bozAbpcJbN0+E8pZioMFZAKxJpzLP9YFDmGHCC7HY6D6Qixw5VXMgSk9ea1Oxq6FstibN8uvT13zNOQ7kHLNDoEwVFehTK9WM92jpFnuvDmG+p8/G1kPzlsSPa/y8QZWzvYmIHc+VR7FhrsCRZaQzmtMpeD/88MOj/lxVCeNwxT4dqAauSPyYn2uMHcSPRxF5lCq1Pu/XxbpLYizzMycYpajCXhexFOhUC74iJgd6fgS/FmGHc3QCF3nPbmeX8YvSOmu5wnICB4MwQwO/iSKeFeteWaymwb1Ew7AC3wZnZY8ojWqKZd1dYlcS5QrBCS4tdYDVqYsiFFH+JY6pUzNCaFYbbImBBuCdSUI/QmO6WLdClbxAqw+8HEXM8MrM1GS5AqXdQUGVVgeLPbwYmd5KkcBMrPZerXZCfDZ2DKYDKpE33ZBNkaM/DTTVaouOi73yVAw1wUpQYFcIdQr/vr9AXTBNGNIumyQXsd/ZQNBbi2asUAc8kI/Ynk6bdmHqjpcmgYVeiTVwng/YKsVhGhTeUfRcknh2OMf2yKzZqtQ6XtYklq1PZGR8flBcEqhN1zOGvnfjfY4IK600cvwhnUNi9n4ANWNYzZ1pnFY17+9973tj/tHq1asnZWOmI8c7cAQ4KwTagmWRZwfPAbEFx11YYDzXBy5NB0NectaL7RDOwTL6ivQ1ugNsFeEJccwEOp1lus2ASw+uxao0pWL+zznHb0XodHBugHcn1prYIaZ9UgbsVXPJ2YGj0ymt6SRml1ov7dwQaFS40odUTEsoYieVFg0cwGRpQzr+PEfVgj8WIOtQXnCO/U5Y4QPnA4cULks8j0aO/y9fRkvwnJVewcwKdoXiUJpDYF/qM7nL2bTpFYldKXSn29ElZhe3NCjlBIht8XhF0bM3djwRC/m0dXCote6V2NHmhDk+sGzEIVaWvo/3l+UoivlY/rtBT6szDZc+YE3RNNK7g135nHOCkkcOE77qDsqvY+vNr04XlhtPIiCWAUtGdJFcWjTlyeoT/F2rCHsjM0yuHcfLVqRXH57pPQB0qim1d+KkgveZxGsiPCuONRpYmXZmdGMH1JDo/VBLWLPCQg3ME+F55yAol6WWaZAGrlTfZJXq8Nn+KXG8mFhfsAjsUkGc4+IQmK2BF13EUrXgXJM+lwMuDMoWVZaFQIsqW0XY7kxsam0ILAyBjTh6neNpEX4TOc5SpTty1vctjvN8YJYIPWKtaDWqnO8Da1QZxDMgphoH1tnyfLqQuDAEWp1jo5PUpxFeUXgxjmgJgQ1RRLdAf+SY7z2zgy1aDrW4nROUs4Nl1QexYCLYuPiqxNoEX4kdURJoVmWGKhXBdLkLzjRKXk2z5cXhsMdlhVqHjaoZFdenv8uTLigL7I8cu8Q+s5YA8xPPr/IRr+Qi7gB6iok5y2OljGq1A2Wns1LXinSBduj3r0fWddOvcH4hedN0tMf7PC9H9r3ZHDkuOE5NPGBZd6Xa/IAy/qz+dGdm7WlkxjAav/u7v8t99903Wdsy7VDgJRexywlBhWVJwlYRdomjTgM9YsJSFwVvl+cp9aosCdbRMPIAbFJll5gxb60G9ovQi9AudmDNA9Z4z07naFbl/BDYltaYO7DFtiHOC4EGlNmRI0oXqsoxIaSq9GEbXESHOHqwNsBEHDO954IQ2CDCDLXFrRogH0zqdrcIj7uIgRBYq1Z3BjthPSmOnc6Mcl9yjoKYh+VZwU4s2xV6gOeiiKXeE2M+kivSGvC8o+qrQ8MoXs27s0A6GKTKq9jwUpfYSbFaD5ew+oCOoFSKmVGYlJaxwivzvefF2PEbZ7rrb0sDWoOaDOxudazw5oqkQF4Eh6PbmQnyjDRwb4jMiLo5KKt9YENkVwR7U/nXIeNgl65PzPVHSrSO/B7tcqZkOBk15oUhsN055pxguGW7EzamVmmXTGAh9Uyg65BjYAJmDOUTNGN48MEH+W//7b+RJAm/93u/x0c+8pEjfv/II4/wta99jRACq1ev5s///M/J5/NjPJsxoeB9pnSZDCHAxcEjWLaV53DtO8ayltGylwhYOuK9KgIHEHqwRawDiGlji7WTrQmBqhiqfEItMJgGfgeUqdKKBclC+twRFuTmofSHwAGE5yRiPoG14bBH4nwN7EGI1aYIO51Si5ILsFSsjr5cld3YyWGHc6wXM/A96OyKogzrEvmhi9gRRSzxnkpVdriIXJpJt6jyvHN0pbXnuqBUIPyHQjKcERcUnoxs4EXSzpw1wQLoprRFrwzLaF9xjghlodcjnGmG3ucqzLGnHeGVnNCljgHMJX5I8wSULZHQL441qergfFX+40CRV13EXLVSVgFbdHxLkrAqURZVwT9Hjg4n5NLPMBHL4nvShd61qeZJknaDVAHvKnh6MGW8Fj1S3/qQmEmElSmspfDNZGkwOYATMaRUeOJHnoFM4oJla2srd999N/feey/5fJ6bb76ZSy+9lGXLlgHQ19fHn//5n3Pfffcxa9Ys7rzzTu677z4++MEPHvd5T6rb5ExioSoL/eGv+zJVmtVa8opqLWWjXd4OAjsRZmA14u3iTGcEpR/lbA3sV2tRW4TSEh1eZDpaYnQHwjaxb1cdynlqC2gxsFKVlwWekIidwL9S01UexGrU56vnAYmZodAQAuegzESpRlilgVlYN8tuhG61CdBuNcnaZ8VxngY2i6MrNfQtitCNIFj9e78ITzvHDmd14wWqzNPATD2yz3gAy6I3OqEbxw5xhCTh/GDONAfS8kyPHNarrk97rXc6wau1Uzak9fynY0cQ86ycny7ojmSlVw6IvU8owye+RQEKBHZFjn0aaAnKxYnnfIT6tCzW7qyMVKHWUbM4mOVdjdrV01BgPssrDcEWJgE2p+5G+9OrhCHKFSrTz7pyCiuri4ItImd17lPLY489xtq1a5k5cyZgctsPPfQQt99+OwCVlZX88z//M7lcjv7+fjo6OqitrT3h804oeP/n//yfJ77lpxkR1sHgsUxmrLrkVoSN4igDlqXBdqkqDRooIORRq3Fjwypj8Rt1PEVEPTBfAjE2mr8dYSuO5XjaVOgGlgC9CAnK40R0IbRgVwwdOM7Ds1ID81CSEcF1J8I2HMUgnKfmsVkQoYikCYmyWj3NxcCLUcQhhIt9IC/mlDMLGFTrWFnmPfNHbP/Q0EkNcJ5Xtgq87KyF8KA4BM8af7j+6oFZPhAwAao2JzyUi3gsipijgXcWPVeketZ5zDTBFj3Naq5VTLFwmVfOT2zq9emcQ9PFuWXe9qFCTGSqhaE+agtoOcyz8758xGtlESt84N8OWpCvVGsFHTpocliJZ4gyVXakglQjKcN6x5UjD7gOscXSBemI/WQTYe2EbzaDHDblLuls8CTqSHv37sX7Iz/v2traI4JvW1sbjY2HFcqbmppYv379EX+Ty+X45S9/yV133UVTUxNXXHHFCV/7uO91a2sr3/rWt3jmmWcQES644AIaGhpoaWkZ144BfOlLX+LAgQN88YtfHPfflAIv4ziAcBaBOUdlUzsRniZiD3ZJXo4wWwOLUP6FiE6Ec/HUYPXPkRlqAF7Darlna6ANh1NhNp5VGlgvjh8Ts0ntEn47wlJRFqhyFtaPfhBhp5qiXr0I71TPLEx5rw7LLl/BUQGUo+zABmzaBRZhZZ4BVaqw3mYQZgLNKDtVaRfTqT5bA+cHq/POBbYIbHIRdamIVKsIrzirsZ6f1sUv957FKkSqnJO2nI68Yl3vhMdyEQuDck6wXvteBBXlgEJRLRhelHj6xba7NXK0KTQHz7Z08XAH0JwqOx5wjvJgvfVPxxFOFJ+2So42BdmopkWjYoNaxbRtb+YJ4l6vCPOCMihDp6HDjFbn3hw5ekR4PRJmJ6VbzHg+dsPTuueeoO4+XTnZVsGPfOQj7N69+4jf3X777XziE58Yvh9COGI2RlVHnZW56qqrePLJJ/mv//W/8qd/+qf81V/91XG3YczgvXfvXj74wQ/ynve8h//0n/4ThUKBJ598kn/1r/4VP/jBD5g7d+4Jd/Dxxx/nvvvu4+1vf/sJH1tKKNCaBse5wB4VduJYqoG9Ao8R0S9CM4F64HUcRZReDTyhQhuORlHehScotCFsKkAdwkyUNmwhtBdhFYF6rHzwGo7N6oaFsRSlBitT1EpaAwe61Lou+tLM+2xRelXZhWO3WM29A3PaCenkZaJWIuoT084+gGXUBTyqyiuYyNXqEJgnSiLCJhydosxSZVbaTjjyK9nHkE+mya8eFGFZCMxUYWvk2CaORj0sMLVfYENk5aU2hKJYwPzXhSLzQkSrEyrSxd0ZClWqbEnH4C9KbJ1gmQ9swYZlZgdwBHKJ0iumgrgnEvYj9Ihjc6zEybEmCZXAukJCkxcUQUYMAB2PuV7pjoT545yDWOCV1yPTji9lhtZ+SpmTDd733HPPqJn3SJqbm3n66aeH77e3t9PU1DR8v6uri5deemk42163bh133nnnCbdhzOD91a9+lU9+8pNHmA1fc801rFy5kq9+9av8l//yX477xF1dXdx999187GMfO+0UCoc6imLsQN+FG65xH0QoA2Zr4HLxxAi7FDZpxADCbnW04diugQEH6zViO7AgwABCM8pSrOuhXpQmVRYpPCYxitKi1pvdINAblIIXfiUxb5OEeTFsxrEbIUFYRqAmrffuIKIdoUuFRaL0KszH+swPqrBFHe0I75GEQ85RjTKfwKtE9KvVag+q8DKOd6unKMqLEvGMOJpQzsLTgumQDLVGzlMlTmvCe9OJ0kpgTggENXuwIWGtgwIvRBE7nCBpV8hQpjtL4SJvbY8jF437BQ45YdA5dkSwLJgyYjfwVD7mvGLgralhwtAgSlNitfqX0mxxLLnWBoV5Cq1OeCWKeNs4MuNm1WEDh/FwosfvE+HVyLE4hCMWbqcDCmxOjZxXJIGiWJmrZDnJBcvxVCEuv/xyvv71r9PZ2UlFRQUPP/wwn//854d/r6p86lOf4v/+3//LnDlzeOihh7jwwgtP+LxjBu9XXnmFL33pS8f8/P3vfz/f+ta3TvjEn/vc57jzzjvZu3fvCR87Gg0NJxpNOJLGxpoTP+hNwissL9rC3uIczFXY7WFxDF0BViosiaA8TSmbA/gB6PQwuwAzBRaW56guh/yglRyaHKxoqKJGzEy3Ks0wXynA7sQWvdqKgINaB5eXw68H4FcD9ny7y+DKapifwJZBiD0cwJToNgRoVQuS5+egJoYqb881N4LlDl7pNQ/HuRXwrpx9N3sUnkps3PxSB48M2hDO+vRv8gr1QKOD5zy0JXBhfQ2NI2oEc9J/dwXYH8wF/gKxtshqgdr0IChTy7JbgAXASmCWwNDVZU7tuRoxk2MA1Mb0W7F+60Pp3xaxosVBoBarTW/C3s/lWAljLnZSG80JprGxBo9ZcSXpYzsxe7GmUR4/iBkEN8ARLaNvlC1Y50wPTNjTcbKPh27sPVGsjLV0El7jVB7Tk8ns2bO58847ufXWWykWi3zgAx9gzZo13Hbbbdxxxx2sXr2az3/+8/z+7/8+IsKyZcv4sz/7sxM+75jB+3htgSfqP/zRj35ES0sLl112Gffee+8JN2I0Ojp6COPMNhoba2hv7z6p1zlZ5qe9wINpIJmDHcQV6a07vYFl6k4dbcFxqSrlolyWBIo9MFutk6KpoZrO/d28FoRd6pglSr0or3tHl5qvY68K20VoJPDKocBOdeQkIh88OqD8rAdmEVAcXWoWabs1sEcd3SosdJ5+B+dFnn51HAIOonQqFH1MryqPdCtbnHJRbAuSixlSBYTV6vgtjjKU5zBhqLfjGQRel5jHy8uZ191LO8eWAcoww4ky7KQyVFIZHPGYs7HXU7H2xGdTfZUAPBtFxGJTh4nA+sh6zmeqdZ8MivByUFoSz0VAWc76ng8F5YDA07GdUXq9XdXMS8fxe47azsbGGv6lo4c9kWNlErhAlc1O2Bo7IoUriv6YzpZXnbAjdpSnLupvFg0CPZEw20P7BLLaU3E8eCCOHX0IxcS/6Z6Z490H52TCid5UsG7dOtatW3fEz/77f//vw/9/17vexbve9a4JPeeYwTuKIlpbW5k9e/YRP29tbT1h8P7Hf/xH2tvbueGGGzh48CB9fX38xV/8xWnlwONk/NNpOYHzJFCDsk8dZ4lSJTCosMsLPQHaC1AMEfUa8Aq9QKc6DgLniqfOKa8HRxyEtsSUAEXhwshTI8pWdawvwLJIqXLQTOBi8dQ4+G2A/UE4FIQDKOLgXAL71bL29UQsdoHXvOMVHG1AfSiyIlJGVu9WE1hMoBfhpXRAaS6mbfKrdJHvNcwObTSGOnPaRHhBhP3Ocb73rEg1QKrSW1FNrlUx9cChoZmhuuSudDhnv1h5Y4VXHstbH/2QaNbbi4dPIFVq2ikBG1QZFKFHTfHvaDqA5+KIGarsdzDb23O+7qyDZbQDZqbCboUGPdZI+PhHyvGZobBmmpoTRFiv/emCit0m8vipZszgffPNN/OZz3yGv/7rv6a62s5sHR0d3HXXXXz4wx8+7pN+5zvfGf7/vffey1NPPXVaBe6TZYlTFqonSj/4HYnj2SRCg3JxugC3J3HsCcJb8p59RDSJZb0Hgo2u16mjVYT2IMx1JkalCj1B2aURfSpcGCdUIxxwwsJYeZfzbC4Kv9CYTi90Rx6PsNWbbOoiF9iPUOsSngvWllh31JezR2GrOhpRmpyyClsgHJoovJTA687KFMqJy4c9IrRj/e9z9MgJ1Rxwtg8cEDO+KAfe4v3wGsMcVQ6ivKdoQz3dYs5EvZhLff2IeDfUnndpGmi2OWslbBrlqs4DzwFlokhQ5nplixNaI2GpV8p09BN2syoNRX/EwTQ0fbnUBxZPs3r10ewX4aXIMS+YVnlGaTBm8P7Qhz7Ejh07eNvb3sayZctIkoRt27Zx66238v73v/9UbmPJ4JXhwDwWI39fiZJTpdrBVRXQ2hN4yufwQE/iWF0WcEH558GYA8EyeAecLZ6qCOolcCA4CiosRoHAgMKhIHQFgcjRE3mqHdRH0BiUgQCPDMQISn2sxAgNTmkWO2gvDAl9Cp1B2BWs02IHjmLaatctwmz1w48fYg2BNWUw2Gv9zK9g7u3ncuzIeGPaMvi6c8NlpoDJqvaqslKtg6VVHK874dwQhl2AwDLStSOyvkHVVK7WxKzqUp0UxTL41yJHSwi8Je0smR+ONQ4Ge2/rgY4Aq1Lbti2xo19tOGfRcdrgckfd70idezqcTPvg3SZQFBMIWzaOSc3TkdMq8wb4wz/8Q37v935vuKH8vPPOO6aMciJuuukmbrrpppPfwhJhvxc2J47mKLAwHt/BWuVgoVNyaoGmOoLzY09bEJbkAjMEvBsaxRdqUWbHSkgfH6ldvtaKcn4+8DY8m7yjkI7T14xIE2dFyu+4hGcGHb8uxuQUqsSTi6E9CHMiG9x5yTv2B4eiVAs4p3QBe4OjQuCyyA8vIo4kBzSmU6K9QDvWSdKNHDNVKMBsYPaIFrldIvw0ihAgThK8WOBtUWW+HplNH/M+AiuDZckvRo6AZZAJ1i2yMRJ2OyuFnOfH1hYR4EKgOVVubE+lBZaGwKoJ9i+fmxpZNJ8gcCs2eVqmjKqLcipYGBQVa63MKB3GDN5dXV0AlJeXc8kllxzz86FRzwzjULBa7YEgLBxnx2iZU+qc6W3nxboxzooDF4yILpHAW8s8hxRmOaVPYWPBkajwzECMCKyu8DRHigjMjAKDCpqDQrCuFVU7uQCcFQU2DEA+CtQ7JUnbB+dgNeECJtkao9QIzHeBrcHKK9UOWtzhfUsU9gWhXGzbhqgEFhFMW3uc70UFUK+BTqwm3elMkKta9QiZ0yIWZEd+cXOkju+x2a9tQ1gWTAvmnMR67w9Ewh5xrGF078chHIflCRpVuarox1zbKHJsxj1EjTI8Nn88djszFS5XuDzxUzKhWIXJCWSUFmN+V9auXXvMVNAQIsKGDRsmd8tKjHmxUi6Bme7Ig6DXQ48X6mMld1QUqBBoIdAX7HEbBiICcE6Zp3pEAK9xSn9BGAjCTm9eZU25QGcsdAehdkRL3Z6C0Fp0HEyUcicsLQvMyClbitbI2uAC5+UtSKzMBw6qUJ9uc17g3MiTRMIMFJdu7zyvxALzXGCkmNr+IGwJjligWuxyu0etdrxIJhYMGlR5X+J5JoroSAdqVmng/NQAAmBX2gs+EzNXGKkBE2FWapsiGzJqFXg57TC5rBjYoY4WHZ+S3khT5+ion/eJaZ5scsKuyLHCh2Et8ZNhaBE2Y2qZ6OcwHT6zMYP3jTfeyHPPPcc73/lO3v/+9w8rYGWMTl5gzlHlElXYNBBRCFDUwLyjvKmKCrsSh1doSdKAodbJMpKuorC74IiAmlxAnaNe4F1VCX0BvIfneyJa8oED3vHagGNrv1Amgq9OqCsqrYljbj7QECs9CrUuUCVQfdTJpmZoIzicsb9UdJQ7GBCBEY+vcmaEUCHWhdEf4AW1E9AaPDPS/TgAbCCihZDW5kenOr0VgQu8p5bDC5+HBJ6KIzZHjuWpJdnRAl51qswNSlla8/aYf+XG2FGnyooTZJe7nfAI0JmPOS/xnH1UUN7ohO2xo8UHesQmQA+kPeujYcbK1u0yVvY+LyhVR2mmZEwBJzmkM5WM+X354he/SH9/Pw8//DBf+MIX6Ovr4/rrr2fdunXjUrzKsGy4JlK6VKgY5eiNgeY40OOFpjxE5bbYV350hh5ZWaXCKUvKlK5CYOeAoypS6mLl6R5HtxcUx9Jyz54BoTtKx5YVdg46eryQi6EugrpofItS3QE2FRydXpgtekSg1zRVWeM85WInHJHDmerIXejEfDl3q6MRTwWjL+yWYz6am8Sc7UdO7EXpEM/ixMwiRnb2Bmzarwi8pehN1xy4MFE6RNkcOfrF9LTHKnOABe9OYL+DPZFjRTiyxDIotgg5KMJqH9gfhNnHybpficxfdEESjjgRFNN/c1gMOF49vxToFDOnnneSDkLTgYaqQOF4xp9HkY+nPnof92RfUVHBDTfcwA033MC+fft44IEHuPXWW1m0aBFf/epXT9Emlh79Hrb2RVRHgUXlAV9mnSIjCWrdKfPzFgUrIihLI17roNCXCHMrAnkHVRGsqvLDGhLd3urZLg1wfV7oTYRZkac2hnfPSNhXKewfEGoi6FQlL47yCX7fYrHtXhQr5+Q9/UF4tSDMiQN9CFu8o1qU1Tlb6SoXuEBsqKZixGvNRVECBxWeIaIJ5ZzUyaYAdKWLseXAAREOOschhSb1w6JdVTCso330wl63wNbUk7FRA9VpAGlQpU6hGjMFHm2aciQrvPlpdhQ9y8KxydUKH2hUoQ/oQY7RRDmaRISgqTlySh9mxixY6WeqFinfTDZFjl4RuiOhsUQFtjr6HAOF8Qfv8vw0D94j6ezspLOzkwMHDtDQ0DCZ21Ty9CbCgIdCcMwp98cEboCdvY6DBWFeVaB+xBm/GGBHn8MHqI2FhvR3Q5lqa79wYMBR5pQFZYHyCOaXKaFMqU4/zXxkWtidOHoSOLfao6JURGN/OQvBXvtQurA5O69UOlhdZt0leYHX+63Es7vomJFLSytHPc9oBiPlwDKUlzHRqYERv9sswm4cdSgXaTBtcYWZo2h0jxXoKtUCdRGGA/cQDusVH8QC5/GC5QyFZUC7V7rEMsqhrDjBdMVfihzdqeFCVdFTc5zj/dwkMHeEjRxYIC+IomnZZVoUT98gc4PyWiQsKGGBrdOq5g2mLPiTn/yEBx54gCiKuP766/nhD3844XbBM42ZOWUwBCoiiMcodvZ5a6UbOCpRUYWBgjAQIDfKot/QwJ1TYWdPRGWsrKq2oZuaEcE576DcKU5It2Psr1uisLEn4pC3IFUZQU3kqY4PXw2AlXj2JY6mnFIXKVVpyWS01sHRWCaBxjTLHqJcwTn7F2zI560T7DXOYaqCYzEIPBlHJGLZ7olMenvScXoVK8PMTBcof5mP6FMA693OneB5yuAY15yhqUnhSLedUmZuUOaE43fxZLz5jBm8b7nlFrZu3cq1117LV77yFc4999xTuV0lTexg7lhydSmLqz29RaHCKa2HhIpUgycA9Tnr5T66OwWguVypzQUGirC331HwQkUE7qhAn3ewoibgQ1qfPgGKlUjyKOqF0ZKo5pzSnDscWGdM8GgtE2g6KmdZhNIU/BGa5ifL0DMfvVkesyxLsNuJcGonhDBiEVGwIac+4K2JZ17Q49bPx0LglBgvnGpKPXCfVpn3b3/7W8rKyvjRj37Ej3/84+GfDwmJP/vss6dkA09XyiMoj5Q9XUJXv2NgN8yrtp8vqbUAWTHKpxM7qHVWIimPA5FYG2FZTo/I8gsJ7D3kaOsVDhWFs+o8C+pG/8rFAmdVe5IA/Ymwq0/Y0RexWDzlEaTlZILC3lSJq6VMj+mKORmE8bukH48B4NnYnInOSwK7nZBTs2WrxDLuBBjtLQiYR2WE6aJUYgJYymHVwWVBaSp6qnTieiXdAn0iNIQSd5rJmFaM+V169NFHT+V2nLFUlcGWdsEL7Fdh9gwdNWgPkXjY3elwDubUBdoOCQf7HDUVyty6w+lyV7/QPSDsPBgRidKai5hTm1AMsO+QQ4P5VDbVBGZUWFDpHhQKmhoje9h8MKIiVpbOCDiBPg/7ChbJZ8R+uMbuFfYWheKAudePt4wyvE+YrVsMzEfHLfg1kj4xvRSAlyLhF7kYD3x4MGHhUYM+I/u4wRZJX44cDmVB+rOjrwQiRg/8JyIBno0j+oHlBJZM81H5M5bTqVVwyCnnpZdeGrZBu/DCC1m1atUp27gzgRkVysKGQFwObhyl3kIC/UXzliwkFihH+97Vliu9BWXZjISOXkcYVDa3RiCmUdLWLbTUKF39wowK5dCgsK/XesmX1nn6EmFXr6O1zxED82oD5Y7huvpQO6NX2Nzv2FUU+vpMKrdiHF9sVesycZip8Q4sa67DzBwmeizNVDjL28lLgV5RDohjUywsLB5eXH0xcnSIcIH3w2YPZaklWoRQzuFWvjcDAXKqFOWNKQxmTDKnU/AOIfCpT32KJ554gosuuohCocDf/u3fcskll/CVr3yFKBpLISJjosytC9TOhENdJ87KyvPQUGPWYWU5aMopMyo8+aM+yfIczKkJbO6OmBFDcELilbI8VJUpM8sDXoWGyrTNL1ZyAmU5pTy2+0EDu7yjpyj0FYUZZcryqiOL4QeKwv6i0FV0LI8gP56iMmac8KJawD4XT6VYd4kHHieiCmU1YUwdkiEGgUNpy+SQAJQC7x307I2UCj2scjjkDO/FZAyGukCqsdF0wTLuN1MJOwIuTgIDwnE7U04l+0ToEtM0OVH75JnENPl4xs2Ywfs73/kO3nt+/vOfD+t39/b28kd/9Ef8z//5P7nttttO2Uae7jiBirx1OYznsY1HRYGKMVK67l4h50AimN2QsKctJhZlTmNIR98PP09FDubVeDq7Hb0DQk2FMqtCKfpAIQiVY4htVURKZQTnRJ41tdC5f3z7HI24VQGXpAYOuzG/y2L674mCy6uRY68T5vrA6jR4C3BRUA6kPd9Db2sOWJUEDoq1Dw7RD+x3Qv0klTTymOvQdKAAvBy7tHQUWJ6VcYDSXLAcs7z4//7f/+Pzn//8EcYLVVVVfP7zn+fBBx88JRuX8caorVaqKpQ5jZ6cEypySpIIPT2CH6VEc7BP6CsI7QcdfQNCT5+JUS2sCeTGSIHLBMpV0QAT0eavFrhIPBdI2m6I3ZpS78zlqb/mEDtFeMo5ujjywClTJdJjR+VzWJve0X3ds1VZHvSIx78aOzbEjhfH6us8jYiB+qAnXcPPmD4ct2xSU3Osh9yMGTOOa5GWMX3I52BOo0XUEGBWCHR1Cp3tjsJAYFbTkZ/jzEolSZTIwY42x/5uR0NNoLk+UFc9+md+qAi7eoSaPPSfoGRSUOhWoVrUgv4oVxp5bKDnaLY4c+553jlUzHF9URqI5wf/hi7/K4OtsladAd9rB5zvA95nWirHMA3q2BNhzFSjr6+PMEqzbwiBYvHNXNLJOBU4BzNqlepqs0EbbcmishwWNAVmVNpj8rHiIsbMugE6BxzlTkChJr1I2zcobO5zDB719dmSODYUHVuSiWe4y0NghlrG6DGdbmDYPu2N5MzLg3J50Z8xsqhHS+pmlCZjfucvvfRSvvvd7x7z829/+9tcfvnlk7pRGZNHfYMyZ65n5nHUkKorlYVNnvOXJCyZ7ak+zsBRVaz4BGbllEhszH7foONgUThQPDKVyYv1hpdNUC4WoEWVi0LgghBo1MBS/+aNYgu2aHn6F00yxkRO4jbFjHkCvvPOO/nQhz7Eiy++yMUXX0ySJDz55JNs2bKFH/zgB6dyGzPeREQgN46etfJRHhMC9A0K+VjJp+OFDpsIHSgKPrVqq8+ZUuKMoxY5F0VKczpSPxZerQ97ND0YsI6RTnH0RlDrzcm9gOmO1Colq2p3PIa6ZTImj9NqwbK+vp4f//jHnH322fzqV7/iscce47zzzuNHP/pRJgl7hnKgW9i737F3fzQ8cl+dU3oLwmBB8cFODgsqlHOrTdtlJE6g0h2rVz6EV3gGx/0a83oY/UGDqbTrAFY+AdgrwmbneDFyFI6z/YqZ7XaWUCR8zQk/z0W0TnTyKWNC6Encpprjlr5qamr4D//hP5yqbcmYpnTuF/p7HbmqgAjEIwWwIqjNKYrQMzi+5+vwwm4vzI9M4GqIBNP83oeQI2KBJsdk6Y2qrA5HSrzWYj3iM08wft4pwrOxwwGXJsdXBJwu7HXW1rffwezSVFstDU6nIZ2Pfexjx/3Dv/mbv3nTNyZj+hEC9HbbOH2Vg/lNnjg+PAIfO6ivCAx4oaoMDvWd+Dl3eaE/CHvhiOBdJnAhnpxGzCeMOpHogOajSiN1Clck5gh/vGMqhwX3CDN3KAVW+UC7wIKsHzvjKMYM3tdcc83w/7/2ta9xxx13nJINOhlCSEBLuwPGJwMnvQ++F4p7hahOyb2JUuuqoAk0NBYpDkJVDcTpN6aQZtn5PDSnHaU5d3S39ejMi5S9wJxRIuhCUeaTWCI0gexmPN0TtQprizZJ+WZMFgZswnMypxRnqTIri9uTTmNloJCM/42e1k46v/u7vzv8/+9+97tH3J9uaEhIkoETP3Aa45PopPehsE/wB4SkG2TGm9eF0dsu9B8QKuuVqjSCJEUoFqGtLXWuaQzk05itYXy+MA2R0nCc1PfNUCsci7G20AP7nEyohe7FyNHmhOU+sDDLjEua9j5Hf3H8n2HFWCvqp5BxfVclWyyZ1sSzFC1AVG/3w0EI2x2uSXHN4/tCahH6dwsSQcUchQiSAqY+mK4C+gJ07nMoijrGnR0PxbXJDMpvlL2pS04rsJrje12CLVgdTFXBxiNrkDG9UbHbRB4/1WS9+qcBrgrKzjocpLVT0AEIbTLu4O37haRbUIF8gxJVQk2TUqyBXNrnnSRCUgBxwqzmQJyD+ARRzits6XYUgrCk2h9X7nYqqUht12rghGJYYCeuC7znQBCasqw7YwoY81Dq6uoa/r/3noMHDx4xFj9z5szJ3K6MN0CUBmypG+WXCVaoreSI1b2oUsnNBBxE5ZB0w2CrkK8HlwbofLlSOyu1LatgXCvuxQD9XvBq/1aMIXA11TQoXFH0NAOd4/ybGoWa07Cv/EzktMq8165di4gMB+xLL710+HciwoYNGyZ/685UJjKV0QeyW9AGDrvlVkC0ePSgEl4XtAdkLri5h/VSJYaK+Yf/ptgphAFhsB1y6fOKg6qZEwtW5RHMqwwUAsyYLtJ6Y5BnfFl3RsZ0YMzg/eqrr57K7cjYCfE2IThwnYI/W9GGEwc72SVIm0An6KXHefwgyEZBtgtaB5oo4RWBIrgVeox1TL5RUQ+5+je4X0B92fQO2hkZpUgm5zAdKAIbwbUL8esOPEjH+P5UZ4HmgBPVtnsFDgiuCqKFiqtTtEfQfoH+Y9P8qBIqlyi5CWbaGRmlyGRPWD744INce+21XH311dxzzz3H/P6f/umfuOGGG7j++uv5j//xP3Lw4METPmcWvKcDOWAhhFqleLEnzFHC/HF+PeoVvTSgC0/w+BpFWxRdrNCiSA3IPEXm6fSxeMnImComUZiqtbWVu+++m+9///vcf//9/OAHP2DTpk3Dv+/p6eFP//RP+da3vsVPfvITVqxYwde//vUTPu80Xfs/A1kKSd2QjdckBNMcsOzI53Vz3tjrFHqh2C9UzNDhRc2MjJLkJMfj9+7diz/K2aS2tvYI/afHHnuMtWvXDjd5XHPNNTz00EPcfvvtABSLRf7kT/6E2bNnA7BixYpxGd5kwTvj5AjQvdeZQFWAqqYsey9VArAnbcKfG3Q6yHacck5WVfAjH/kIu3fvPuJ3t99+O5/4xCeG77e1tdHY2Dh8v6mpifXr1w/fr6ur493vfjcAAwMDfOtb3+KWW2454TZkwTvj5HBQVqMMdgvx+AYrM6YpXQKvRGYGXa2ebJlj/Nxzzz2jZt4jCSEcMeioqqMOPnZ3d/Pxj3+cs88+e1wT7Vnwzjhpqmcr1Y1a8isn252wxwkrfKDxxA8/7ShXu0Xp/89UTmbXW1paTviY5uZmnn766eH77e3tNDU1HfGYtrY2/t2/+3esXbuWz3zmM+N67RI/7DKmnNPgG7TFOXpE2Dud5/cnkUrgrYlnbeKP7hjNeBO4/PLLefzxx+ns7KS/v5+HH36YK6+8cvj33ns+9rGP8d73vpc//uM/HrccSZZ5Z5zxnOsDeyI5o2VXz/jhpEnU8549ezZ33nknt956K8VikQ984AOsWbOG2267jTvuuIN9+/bxyiuv4L3nZz/7GQCrVq3iC1/4wvE3QaepFXxHRw9hnAdTfV2OtrbxDjVPTxrqq+jo7J3qzXhDNDXV03mgtKV5GxtraG/vnurNeEOcSfvgnNDQUP2GX+/hzQP0TUAStjIWrl46tdcpWeadkZFxxlOKHpZZ8M7IyDjjmV0WKExANC0fTf36SBa8MzIyznj2DboJl02mmtOgVyAjIyPjzCPLvDMyMjJK0D0+y7wzMjIySpAs887IyMgowcx7UoP3N77xDX76058CcNVVV3HXXXdN5stlZGRknBSl2Co4aWWTxx57jF//+tfcd9993H///bz88ss88sgjk/VyGRkZGSfNZJsxTAaTlnk3Njby6U9/mnw+D8DSpUvZs2fPZL1cRkZGxslTgmWTUzIev23bNj70oQ/xd3/3dyxatOhNf36fDOCTwTf9eTMmRhSXEcWZtFFG6fGP2yY+Hn/totN8PH7jxo38/u//PnfdddeEAvdEtU1KXRfk9NA2KTtjNDWmM2fSPrxZ2ialyKS2Cj7zzDP8m3/zb/iDP/iDcYmLZ2RkZEwFKhO/TTWTlnnv3buXj3/849x9991cdtllk/UyGRkZGWckkxa8v/3tbzM4OMgXv/jF4Z/dfPPNfOhDH5qsl8zIyMg4KSaaTZ/WmfdnP/tZPvvZz07W02dkZGSc0WTj8RkZGRklSDYen5GRccZTihOWWfDOyMg445mTCwy68YfkssyMISMjI2Pq2ZNM0IxBhYsmcXvGQxa8MzIyMkpwPD4L3hkZGRlMjzr2RMi6TTIyMjJKkCzzzsjIOOMpxW6TLPPOyMjIKEGyzDsjIyMDpsUi5ETIgndGRkZG1m2SkZGRUXqUYs07C94ZGRlnPKUYvLMFy4yMjIwSJMu8MzIyMkqw5p1l3hkZGRmTzIMPPsi1117L1VdfzT333DPm4+666y7uvffecT1nFrwzMjIy5CRu46S1tZW7776b73//+9x///384Ac/YNOmTcc85mMf+xg/+9nPxv28WfDOyMg449GTuI2Xxx57jLVr1zJz5kwqKyu55ppreOihh454zIMPPsjv/M7v8N73vnfcz5vVvDMyMs54TrbbZO/evXjvj/hdbW0ttbW1w/fb2tpobGwcvt/U1MT69euP+Jt//+//PQDPPPPMuLchC94ZGRlnPPOiwOAEwveQGcNHPvIRdu/efcTvbr/9dj7xiU8M3w8hIHK4zqKqR9w/WbLgnZGRccazKzh6w/iDd5UIlwD33HPPqJn3SJqbm3n66aeH77e3t9PU1PSGthey4J2RkZFx0rS0tJzwMZdffjlf//rX6ezspKKigocffpjPf/7zb/i1swXLjIyMjElk9uzZ3Hnnndx6663ceOONvO9972PNmjXcdtttvPjiiyf9vKKq02HS8xg6OnoI47yMqa/L0dbWOclbNLk01FfR0dk71ZvxhmhqqqfzQHGqN+MN0dhYQ3t791RvxhviTNoH54SGhuo3/Hr/t3WAXj+BskkkvH92+Rt+3TdClnlnZGRklCBZzTsjI+OMR8VuE3n8VJNl3hkZGRklSBa8MzIyMkqQrGySkZGRwfTQ6J4IWfDOyMjIKEFJ2Cx4Z2RkZMC0CMgTIQveGRkZZzyZDVpGRkZGxikhy7wzMjLOeLLMOyMjIyPjlJBl3hkZGRlZt0lGRkZG6bFAAgMy/mJI+ZtgpvBGyYJ3RkbGGc8OHD0TqGRXI6ydxO0ZD1nwzsjIOOMpxQXLLHhnZGSc8ZRi8M66TTIyMjJKkCx4Z2RkZJQgWdkkIyMjowRbBbPMOyMjI6MEmdTg/eCDD3Lttddy9dVXc88990zmS2VkZGScNEM2aBO5TTWTVjZpbW3l7rvv5t577yWfz3PzzTdz6aWXsmzZssl6yYyMjIyTIus2GcFjjz3G2rVrmTlzJpWVlVxzzTU89NBDk/VyGRkZGWcUk5Z5t7W10djYOHy/qamJ9evXj/vvGxqqx/3YEBKamuontH3TkaamsqnehDeEuJjGxvKp3ow3TGNjzVRvwhsm24cJUoILlpMWvEMIyIj5f1U94v6J6OjoIYTxXZw0NtbQ3tE/4W2cTjQ21tDe3j3Vm/GGaGwsPw324XT4HM6cfXBOJpTonU5MWtmkubmZ9vb24fvt7e00NTVN1stlZGRknDR6ErepZtKC9+WXX87jjz9OZ2cn/f39PPzww1x55ZWT9XIZGRkZJ4+cxG2KmbSyyezZs7nzzju59dZbKRaLfOADH2DNmjWT9XIZGRkZb4jpkE1PhEmdsFy3bh3r1q2bzJfIyMjIeOOU4IJlNmGZkZGRUYJk2iYZGRlnPItCYGCc3W0A5dMg9c6Cd0ZGxhnPVufonkDVu8YJb53E7RkPWdkkIyMjY5I5kc7Thg0buOmmm7jmmmv44z/+Y5IkOeFzZsE7IyMjAyatTXBI5+n73/8+999/Pz/4wQ/YtGnTEY/51Kc+xec+9zl+9rOfoar88Ic/POHzTtuyiXMTe4cm+vjpSLYP04NsH6YH49mHN2s/KyNBwwQen77u3r178d4f8bva2lpqa2uH74/UeQKGdZ5uv/12AHbv3s3AwADnn38+ADfddBNf+9rX+PCHP3zcbZi2wbuurmpCjz8dRmSzfZgeZPswPTiV+3BT1cR1hQYGBrjhhhs4ePDgET+//fbb+cQnPjF8/0Q6T0f/vrGxkdbW1hO+/rQN3hkZGRnTmUKhwL333nvMz0dm3XBinaeT1YHKgndGRkbGSXB0eWQsmpubefrpp4fvH63zdLQO1P79+8elA5UtWGZkZGRMIifSeZo7dy5lZWU888wzADzwwAPj0oESVS21kf6MjIyMkuLBBx/kb//2b4d1nm677TZuu+027rjjDlavXs2rr77KZz/7WXp6eli5ciV/+Zd/ST6fP+5zZsE7IyMjowTJyiYZGRkZJUgWvDMyMjJKkCx4Z2RkZJQgWfDOyMjIKEFKOnifSOxlutHT08P73vc+du3aBdjY7Lp167j66qu5++67hx93MiI1p4JvfOMbXHfddVx33XV8+ctfBkpvHwD++q//mmuvvZbrrruO73znO0Bp7seXvvQlPv3pTwOluf233HIL1113HTfccAM33HADL7zwQknux5ShJcq+ffv0He94hx44cEB7e3t13bp1unHjxqnerDF5/vnn9X3ve5+uXLlSd+7cqf39/XrVVVfpjh07tFgs6kc/+lH9xS9+oaqq1113nT733HOqqvpHf/RHes8990zhlhu/+c1v9IMf/KAODg5qoVDQW2+9VR988MGS2gdV1SeffFJvvvlmLRaL2t/fr+94xzt0w4YNJbcfjz32mF566aX6h3/4hyX3XVJVDSHoFVdcocVicfhnpbgfU0nJZt4jxV4qKyuHxV6mKz/84Q/5kz/5k+HJqfXr17Nw4ULmz59PHMesW7eOhx56aFSRmumwX42NjXz6058mn8+Ty+VYunQp27ZtK6l9ALjkkkv43//7fxPHMR0dHXjvOXToUEntR1dXF3fffTcf+9jHgNL7LgFs2bIFgI9+9KNcf/31/J//839Kcj+mkpIN3qOJvYxHzGWq+MIXvsDFF188fH+s7T9ZkZrJ5qyzzho+eLZt28ZPf/pTRKSk9mGIXC7H1772Na677jouu+yykvssPve5z3HnnXcOj2aX2vYDHDp0iMsuu4xvfvOb/K//9b/4+7//e/bs2VNy+zGVlGzwPlkxl+nCWNs/3fdr48aNfPSjH+Wuu+5i/vz5JbkPAHfccQePP/44e/fuZdu2bSWzHz/60Y9oaWnhsssuG/5ZKX6XLrjgAr785S9TU1NDfX09H/jAB/ja175WcvsxlZSsMNWJxF6mO0eL0Qxt/8mK1JwKnnnmGe644w4+85nPcN111/HUU0+V3D5s3ryZQqHAOeecQ0VFBVdffTUPPfQQURQNP2Y678c//uM/0t7ePixF2tfXx+7du0tm+4d4+umnKRaLwychVWXu3Lkl932aSko28z6R2Mt057zzzmPr1q1s374d7z3/8A//wJVXXnnSIjWTzd69e/n4xz/OV77yFa677jqg9PYBYNeuXXz2s5+lUChQKBR49NFHufnmm0tmP77zne/wD//wDzzwwAPccccdvPOd7+R//I//UTLbP0R3dzdf/vKXGRwcpKenh/vuu49PfvKTJbcfU0nJZt6zZ8/mzjvv5NZbbx0We1mzZs1Ub9a4KSsr44tf/CKf+MQnGBwc5KqrruI973kPAF/5yleOEKm59dZbp3hr4dvf/jaDg4N88YtfHP7ZzTffXFL7AHDVVVexfv16brzxRqIo4uqrr+a6666jvr6+pPZjJKX2XQJ4xzvewQsvvMCNN95ICIEPf/jDXHDBBSW3H1NJJkyVkZGRUYKUbNkkIyMj40wmC94ZGRkZJUgWvDMyMjJKkCx4Z2RkZJQgWfDOyMjIKEFKtlUw48xgxYoVLF++HOeOzDO++c1vMm/evCnaqoyMqScL3hnTnu9+97vU19dP9WZkZEwrsrJJRkZGRgmSDelkTGtGK5vMmzePb37zm1O4VRkZU09WNsmY9mRlk4yMY8nKJhkZGRklSBa8MzIyMkqQrOadMa0Zq1Xwk5/8JFddddUUbVVGxtSTBe+MjIyMEiQrm2RkZGSUIFnwzsjIyChBsuCdkZGRUYJkwTsjIyOjBMmCd0ZGRkYJkgXvjIyMjBIkC94ZGRkZJUgWvDMyMjJKkP8/2/wrnCUjA7cAAAAASUVORK5CYII=\\n\",\n 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# check the predicted likelihood\\n\",\n \"dot_scale = 0.1\\n\",\n \"l_std = np.sqrt(pred['E: std']**2+pred['HOMO-LUMO gap: std']**2)\\n\",\n \"\\n\",\n \"_ = plt.figure(figsize=(6,5))\\n\",\n \"rectangle = plt.Rectangle((0,0),200,3,fc='y',alpha=0.1)\\n\",\n \"_ = plt.gca().add_patch(rectangle)\\n\",\n \"im = plt.scatter(pred['E: mean'], pred['HOMO-LUMO gap: mean'], s=l_std*dot_scale, c=np.exp(tmp),alpha = 0.2,cmap=plt.get_cmap('cool'))\\n\",\n \"_ = plt.title('Pubchem data')\\n\",\n \"_ = plt.xlabel('E')\\n\",\n \"_ = plt.ylabel('HOMO-LUMO gap')\\n\",\n \"_ = plt.colorbar(im)\\n\",\n \"_ = plt.show()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Warning: the next module may take 5-10min to complete!**\\n\",\n \"\\n\",\n \"The second option is to prepare your own forward model. In fact, this may often be the case because you may want to first validate your model before using it. When the training takes a long time, you want to avoid repeating the calculation that is simply wasting computing resources. In this tutorial, we will use gradient boosting. Let us first verify the performance of the model on our data set using a 10-fold cross-validation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 2min 36s, sys: 1.42 s, total: 2min 37s\\n\",\n \"Wall time: 2min 46s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"# forward model library from scikit-learn\\n\",\n \"#from sklearn.ensemble import RandomForestRegressor\\n\",\n \"#from sklearn.linear_model import ElasticNetCV\\n\",\n \"from sklearn.ensemble import GradientBoostingRegressor\\n\",\n \"#from sklearn.linear_model import ElasticNet\\n\",\n \"# xenonpy library for data splitting (cross-validation)\\n\",\n \"from xenonpy.datatools import Splitter\\n\",\n \"\\n\",\n \"np.random.seed(201906)\\n\",\n \"\\n\",\n \"# property name will be used as a reference for calling models\\n\",\n \"prop = ['E','HOMO-LUMO gap']\\n\",\n \"\\n\",\n \"# prepare indices for cross-validation data sets\\n\",\n \"sp = Splitter(data_ss.shape[0], test_size=0, k_fold=10)\\n\",\n \"\\n\",\n \"# initialize output variables\\n\",\n \"y_trues, y_preds = [[] for i in range(len(prop))], [[] for i in range(len(prop))]\\n\",\n \"y_trues_fit, y_preds_fit = [[] for i in range(len(prop))], [[] for i in range(len(prop))]\\n\",\n \"mdls = {key: [] for key in prop}\\n\",\n \"\\n\",\n \"# cross-validation test\\n\",\n \"for iTr, iTe in sp.cv():\\n\",\n \" x_train = data_ss['SMILES'].iloc[iTr]\\n\",\n \" x_test = data_ss['SMILES'].iloc[iTe]\\n\",\n \" \\n\",\n \" fps_train = RDKit_FPs.transform(x_train)\\n\",\n \" fps_test = RDKit_FPs.transform(x_test)\\n\",\n \" \\n\",\n \" y_train = data_ss[prop].iloc[iTr]\\n\",\n \" y_test = data_ss[prop].iloc[iTe]\\n\",\n \" for i in range(len(prop)):\\n\",\n \" #mdl = RandomForestRegressor(500, n_jobs=-1, max_features='sqrt')\\n\",\n \" #mdl = ElasticNetCV(cv=5)\\n\",\n \" mdl = GradientBoostingRegressor(loss='ls')\\n\",\n \" #mdl = ElasticNet(alpha=0.5)\\n\",\n \" \\n\",\n \" mdl.fit(fps_train, y_train.iloc[:,i])\\n\",\n \" prd_train = mdl.predict(fps_train)\\n\",\n \" prd_test = mdl.predict(fps_test)\\n\",\n \"\\n\",\n \" y_trues[i].append(y_test.iloc[:,i].values)\\n\",\n \" y_trues_fit[i].append(y_train.iloc[:,i].values)\\n\",\n \" y_preds[i].append(prd_test)\\n\",\n \" y_preds_fit[i].append(prd_train)\\n\",\n \"\\n\",\n \" mdls[prop[i]].append(mdl)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You will see that the performance is not so bad, especially inside the target region that we are aiming at.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# plot results\\n\",\n \"for i, x in enumerate(prop):\\n\",\n \" y_true = np.concatenate(y_trues[i])\\n\",\n \" y_pred = np.concatenate(y_preds[i])\\n\",\n \" y_true_fit = np.concatenate(y_trues_fit[i])\\n\",\n \" y_pred_fit = np.concatenate(y_preds_fit[i])\\n\",\n \" xy_min = min(np.concatenate([y_true,y_true_fit,y_pred,y_pred_fit]))*0.95\\n\",\n \" xy_max = max(np.concatenate([y_true,y_true_fit,y_pred,y_pred_fit]))*1.05\\n\",\n \" xy_diff = xy_max - xy_min\\n\",\n \" \\n\",\n \" _ = plt.figure(figsize=(5,5))\\n\",\n \" _ = plt.scatter(y_pred_fit, y_true_fit, c='b', alpha=0.1, label='train')\\n\",\n \" _ = plt.scatter(y_pred, y_true, c='r', alpha=0.2, label='test')\\n\",\n \" _ = plt.text(xy_min+xy_diff*0.7,xy_min+xy_diff*0.05,'MAE: %5.2f' % np.mean(np.abs(y_true - y_pred)),fontsize=12)\\n\",\n \" _ = plt.title('Property: ' + x)\\n\",\n \" _ = plt.xlim(xy_min,xy_max)\\n\",\n \" _ = plt.ylim(xy_min,xy_max)\\n\",\n \" _ = plt.legend(loc='upper left')\\n\",\n \" _ = plt.xlabel('Prediction')\\n\",\n \" _ = plt.ylabel('Observation')\\n\",\n \" _ = plt.plot([xy_min,xy_max],[xy_min,xy_max],ls=\\\"--\\\",c='k')\\n\",\n \" _ = plt.show()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here, we define a class that return mean and std based on a list of models.\\n\",\n \"Note that the class must have a function called \\\"predict\\\" that takes a descriptor input and return first mean, then standard deviation. Here, we add a constant noise variance to the model variable calculated from the bootstrapped models.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class bootstrap_fcn():\\n\",\n \" def __init__(self, ms, var):\\n\",\n \" self.Ms = ms\\n\",\n \" self.Var = var\\n\",\n \" def predict(self, x):\\n\",\n \" val = np.array([m.predict(x) for m in self.Ms])\\n\",\n \" return np.mean(val, axis = 0), np.sqrt(np.var(val, axis = 0) + self.Var)\\n\",\n \" \\n\",\n \"# include basic measurement noise\\n\",\n \"c_var = {'E': 650, 'HOMO-LUMO gap': 0.5}\\n\",\n \"\\n\",\n \"# generate a dictionary for the final models used to create the likelihood\\n\",\n \"custom_mdls = {key: bootstrap_fcn(value, c_var[key]) for key, value in mdls.items()}\\n\",\n \" \\n\",\n \"# import descriptor calculator and forward model to iQSPR\\n\",\n \"prd_mdls = GaussianLogLikelihood(descriptor=RDKit_FPs, targets=target_range, **custom_mdls)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Once again, we can make predictions and calculate the likelihood values for the full data set.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" E: mean E: std HOMO-LUMO gap: mean HOMO-LUMO gap: std\\n\",\n \"0 196.772149 26.245443 5.871948 0.707692\\n\",\n \"1 124.620714 25.908334 5.461932 0.708542\\n\",\n \"2 198.678938 26.283615 5.275575 0.708301\\n\",\n \"3 222.938083 25.850088 7.515128 0.707791\\n\",\n \"4 85.459304 25.555155 5.184740 0.708865\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 7.8 s, sys: 324 ms, total: 8.12 s\\n\",\n \"Wall time: 9.92 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"pred = prd_mdls.predict(data_ss['SMILES'])\\n\",\n \"print(pred.head())\\n\",\n \"\\n\",\n \"# calculate log-likelihood for a given target property region\\n\",\n \"tmp_ll = prd_mdls.log_likelihood(data_ss['SMILES'])\\n\",\n \"tmp = tmp_ll.sum(axis = 1, skipna = True)\\n\",\n \"\\n\",\n \"# plot histogram of likelihood values\\n\",\n \"_ = plt.figure(figsize=(8,5))\\n\",\n \"_ = plt.hist(np.exp(tmp), bins=50)\\n\",\n \"_ = plt.title('Likelihood for SMILES in the data subset')\\n\",\n \"_ = plt.xlabel('Likelihood')\\n\",\n \"_ = plt.ylabel('Counts')\\n\",\n \"_ = plt.show()\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# check the predicted likelihood\\n\",\n \"dot_scale = 0.5\\n\",\n \"l_std = np.sqrt(pred['E: std']**2+pred['HOMO-LUMO gap: std']**2)\\n\",\n \"\\n\",\n \"_ = plt.figure(figsize=(6,5))\\n\",\n \"rectangle = plt.Rectangle((0,0),200,3,fc='y',alpha=0.1)\\n\",\n \"_ = plt.gca().add_patch(rectangle)\\n\",\n \"im = plt.scatter(pred['E: mean'], pred['HOMO-LUMO gap: mean'], s=l_std*dot_scale, c=np.exp(tmp),alpha = 0.2,cmap=plt.get_cmap('cool'))\\n\",\n \"_ = plt.title('Pubchem data')\\n\",\n \"_ = plt.xlabel('E')\\n\",\n \"_ = plt.ylabel('HOMO-LUMO gap')\\n\",\n \"_ = plt.colorbar(im)\\n\",\n \"_ = plt.show()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Warning: the next module may take 1-2min to complete!**\\n\",\n \"\\n\",\n \"Use of multiple likelihood models is also supported. It is unnecessary in this example, but we will demonstrate how to do it by naively creating separate models for the two properties. Note that you can assign different descriptors to different likelihood models.\\n\",\n \"\\n\",\n \"By creating your own `BaseLogLikelihoodSet` object, you can combine likelihood models as you need. However, make sure you setup the models before hand, including training of the models and setting up the target regions.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 31.1 s, sys: 342 ms, total: 31.4 s\\n\",\n \"Wall time: 33.2 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"from xenonpy.inverse.base import BaseLogLikelihoodSet\\n\",\n \"\\n\",\n \"prd_mdl1 = GaussianLogLikelihood(descriptor = RDKit_FPs, targets = {'E': (0, 200)})\\n\",\n \"prd_mdl2 = GaussianLogLikelihood(descriptor = ECFP_plus_MACCS, targets = {'HOMO-LUMO gap': (-np.inf, 3)})\\n\",\n \"\\n\",\n \"prd_mdl1.fit(data_ss['SMILES'], data_ss['E'])\\n\",\n \"prd_mdl2.fit(data_ss['SMILES'], data_ss['HOMO-LUMO gap'])\\n\",\n \"\\n\",\n \"class MyLogLikelihood(BaseLogLikelihoodSet):\\n\",\n \" def __init__(self):\\n\",\n \" super().__init__()\\n\",\n \" \\n\",\n \" self.loglike = prd_mdl1\\n\",\n \" self.loglike = prd_mdl2\\n\",\n \" \\n\",\n \"like_mdl = MyLogLikelihood()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Calculating likelihood values is the same as before, but `BaseLogLikelihoodSet` does not support direct prediction of the properties unless users define one themselves.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 6.56 s, sys: 217 ms, total: 6.77 s\\n\",\n \"Wall time: 8.53 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"# calculate log-likelihood for a given target property region\\n\",\n \"tmp_ll = like_mdl(data_ss['SMILES'])\\n\",\n \"tmp = tmp_ll.sum(axis = 1, skipna = True)\\n\",\n \"\\n\",\n \"# plot histogram of likelihood values\\n\",\n \"_ = plt.figure(figsize=(8,5))\\n\",\n \"_ = plt.hist(np.exp(tmp), bins=50)\\n\",\n \"_ = plt.title('Likelihood for SMILES in the data subset')\\n\",\n \"_ = plt.xlabel('Likelihood')\\n\",\n \"_ = plt.ylabel('Counts')\\n\",\n \"_ = plt.show()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### N-gram preparation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Warning: the next module may take 3-5min to complete!**\\n\",\n \"\\n\",\n \"For prior, which is simply a molecule generator, we currently provide a N-gram model based on the extended SMILES language developed by our previous iQSPR developers. \\n\",\n \"\\n\",\n \"Note that because the default sample_order in `NGram` is 10, setting train_order=5 (max order to be used in iQSPR) when fitting will cause a warning, and XenonPy will automatically set sample_order=5 (actual order to be used when running the iQSPR).\\n\",\n \"\\n\",\n \"There is a default lower bound to the train_order and sample_order = 1, representing to use at least one character as a reference for generating the next character. We do not recommend altering this number.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/opt/miniconda3/envs/MB16_xepy37_v06_test_env/lib/python3.7/site-packages/xenonpy/inverse/iqspr/modifier.py:169: RuntimeWarning: max : 10 is greater than max : 5,max will be reduced to max \\n\",\n \" (self.sample_order[1], self._train_order[1]), RuntimeWarning)\\n\",\n \"100%|██████████| 3000/3000 [01:22<00:00, 36.26it/s]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 1min 22s, sys: 1.48 s, total: 1min 23s\\n\",\n \"Wall time: 1min 22s\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"NGram(ngram_table=[[[ C ( N & ! O Cl Br F S ... [Si] [Se] \\\\\\n\",\n \"['C'] 4670 2029 490 2371 621 534 38 19 34 57 ... 10 1 \\n\",\n \"[')'] 2023 417 716 0 0 1014 252 48 72 80 ... 3 0 \\n\",\n \"['N'] 381 261 29 187 318 9 1 0 0 1 ... 1 0 \\n\",\n \"[0] 113 8 31 2 552 33 12 4 6 5 ... 0 0 \\n\",\n \"['O'] 787 0 11 0 715 7 0 0 0 4 ... 10 0 \\n\",\n \"['Cl'] 0 0 0 0 304 0 0 0 0 0 ... 0 0 \\n\",\n \"['Br'] 0 0 0 0 73 0 0 1 0 0 ... 0 0 \\n\",\n \"['F'] 0 0 0 0 112 0 0 0 0 0 ... 1 0 \\n\",\n \"['S'] 84 38 3 0 20 0 0 0 0 7 ... 0 0 \\n\",\n \"['B'] 0 4 0 0 0 0 0 0 0 0 ... 0 0 \\n\",\n \"['[N+]'] 0 84 0 1 0 0 0 0 0 0 ... 0 0 \\n\",\n \"['[O-]'] 0 0 0 0 85 0 0...\\n\",\n \"['(', '=O', ')', 'C', '=C'] 1 0 0 0 0 0 0 0 0\\n\",\n \"['=O', ')', 'C', '=C', 'C'] 0 0 0 0 0 0 0 0 1\\n\",\n \"[1, 'C', '&', '=C', '('] 1 0 0 0 0 0 0 0 0\\n\",\n \"['(', 'C', '=C', 'C', '=C'] 0 0 0 0 0 0 0 0 1,\\n\",\n \" =C 1 ( C\\n\",\n \"['(', 'C', '=C', 'C', '&'] 1 0 0 0\\n\",\n \"['C', '=C', 'C', '&', '=C'] 0 2 0 0\\n\",\n \"['&', '=C', 1, 'C', '&'] 1 0 0 0\\n\",\n \"['=C', 1, 'C', '&', '=C'] 0 0 1 0\\n\",\n \"[1, 'C', '&', '=C', '('] 0 0 0 1\\n\",\n \"['C', '&', '=C', '(', 'C'] 1 0 0 0\\n\",\n \"['&', '=C', '(', 'C', '=C'] 0 1 0 0\\n\",\n \"[')', 'C', '=C', 'C', '&'] 1 0 0 0]]],\\n\",\n \" sample_order=(1, 5))\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"# N-gram library in XenonPy-iQSPR\\n\",\n \"from xenonpy.inverse.iqspr import NGram\\n\",\n \"\\n\",\n \"# initialize a new n-gram\\n\",\n \"n_gram = NGram()\\n\",\n \"\\n\",\n \"# train the n-gram with SMILES of available molecules\\n\",\n \"n_gram.fit(data_ss['SMILES'],train_order=5)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let us take a look at the molecules generated by our trained N-gram.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Round 0 ['CC(C)CN1CC(C1)C1=CC=CC=C1', 'C1=CC(=CN=C1)S', 'C1=CC(=CC=C1CC(C(=O)O)N)Cl', 'CCCCCC(=O)OCC1=CC=C(C=C1)O', 'C1=CC(=C(C=C1O)C(=O)O)[N+](=O)[O-]']\\n\",\n \"Round 1 ['CC(C)CN1CC(C1)C1=CC=CC=C1', 'C1=CC(=CN=C1)Cl', 'C1=CC(=CC=C1CCCC(=O)O)OC', 'CCCCCC(=O)OCC1=CC=C(C=C1)C(C)N(CCCCC)C=C(C(=C)O)C(=O)N(C)S(=O)(=O)O', 'C1=CC(=C(C=C1O)C(=O)O)CCO']\\n\",\n \"Round 2 ['CC(C)CN1CC(C1)C1=CC=CC=C1', 'C1=CC(=CN=C1)C1=CC(=CC(=C1)OC)O', 'C1=CC(=CC=C1CCCC(=O)O)C(=O)C', 'CCCCCC(=O)OCC1=CC=C(C=C1)C(C)N(CCCCC)C=C(C(=C)O)C(=O)N(C)S(=O)C1=CC(=CC=C1NC(=O)C(CCCC1C(C(C1)O)O)N=C(N)N)O', 'C1=CC(=C(C=C1O)C(=O)O)CCN']\\n\",\n \"Round 3 ['CC(C)CN1CC(C1)C1=CC=CC=C1', 'C1=CC(=CN=C1)C1=CC(=CC=C1)Cl', 'C1=CC(=CC=C1CCCC(=O)O)Cl', 'CCCCCC(=O)OCC1=CC=C(C=C1)C(C)N(CCCCC)C=C(C(=C)O)C(=O)N(C)S(=O)C1=CC(=CC=C1NC(=O)C(CCCC1C(C(C1)O)O)N)OCC(CO)N', 'C1=CC(=C(C=C1O)O)N']\\n\",\n \"Round 4 ['CC(C)CN1CC(C1)C1=CC=CC=C1', 'C1=CC(=CN=C1)C#N', 'C1=CC(=CC=C1CCCCCC(=O)C(C1=CC=CC2=CC=CC=C2CCC2=CC=C(C=C2)Cl)NC(=N1)NCCCOC)C', 'CCCCCC(=O)OCC1=CC=C(C=C1)C(C)N(CCCCC)C=C(C(=C)O)C(=O)N(C)S(=O)C1=CC(=CC=C1NC(=O)C(CCCC1C(C(C1)O)O)N)OCC(=O)C1=CC=C(C=C1)CCO', 'C1=CC(=C(C=C1)C(=N)N=[N+]=[N-])Cl']\\n\"\n ]\n }\n ],\n \"source\": [\n \"np.random.seed(201903) # fix the random seed\\n\",\n \"\\n\",\n \"# perform pure iQSPR molecule generation starting with 5 initial molecules\\n\",\n \"n_loop = 5\\n\",\n \"tmp = data_ss['SMILES'][:5]\\n\",\n \"for i in range(n_loop):\\n\",\n \" tmp = n_gram.proposal(tmp)\\n\",\n \" print('Round %i' % i,tmp)\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Our N-gram-based molecular generator runs as follow:\\n\",\n \"1. given a tokenized SMILES in the extended SMILES format, randomly delete N tokens from the tail,\\n\",\n \"2. generate the next token using the N-gram table until we hit a termination token or a pre-set maximum length,\\n\",\n \"3. if generation ended without a termination token, a simple gramma check will be performed trying to fix any invalid parts, and the generated molecule will be abandoned if this step fails. \\n\",\n \"\\n\",\n \"Because we always start the modification from the tail and SMILES is not a 1-to-1 representation of a molecule, we recommend users to use the re-order function to randomly re-order the extended SMILES, so that you will not keep modifying the same part of the molecule. To do so, you can use the \\\"set_params\\\" function or do so when you initialize the N-gram using \\\"NGram(...)\\\". In fact, you can adjust other parameters in our N-gram model this way.\\n\",\n \"\\n\",\n \"Note that we did not re-order the SMILES when training our N-gram model, it is highly possible that a re-ordered SMILES during the generation process will lead to substrings that cannot be found in the pre-trained N-gram table, causing a warning message and the molecule will be kept the same.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Round 0 ['CC(C)CN1CC(C1)C1(CCN(CC1)C)C1=CNC2=C1C=C(C=C2)Br', 'C1=CC(=CN=C1)C(=O)OCCO', 'C1=CC(=CC=C1CC(C(=O)O)N)N(CCCl)C(=O)CN1CCN(C1)C1=CC(=C(C(=C1)Cl)N=NC1=C(C=CC(=C1Cl)C)OC)N', 'O=C(CCCCC)Cl', 'C1=CC(=C(C=C1O)C(=O)O)O']\\n\",\n \"Round 1 ['C1C(C2(c3c[nH]c4ccc(Br)cc34)CCN(C)CC2)CCC2=CC(=O)N2C1', 'C1=CC(=CN=C1)C(=O)OCCO', 'C1=CC(=CC=C1CC(C(=O)O)N)N(CCCl)C(=O)CN1CCN(C1)C1=CC(=C(C(=C1)Cl)N=NC1=C(C=CC(=C1Cl)C)OCC1=CC=CS1)O', 'O=C(C(=O)O)S', 'C1=CC(=C(C=C1O)C(C(=O)O)O)Cl']\\n\",\n \"Round 2 ['C1C(C2(c3c[nH]c4ccc(Br)cc34)CCN(C)CC2)CCC2=CC(=O)N2C1', 'C1=CC(=CN=C1)C(=O)OC1=CC(=CC(=C1C)C(=O)O)C(=O)OCCCCCCCBr', 'C1=CC(=CC=C1CC(C(=O)O)N)N(CCCl)C(=O)CN1CCN(C1)C1=CC(=C(C(=C1)Cl)N=NC1=C(C=CC(=C1Cl)C)OCC1=CC=CC=C1)N', 'O=C(CCl)N1CCCCCOCCCCOCCCCOC(C(O1)OCC1=CC=CC=C1)COC(=O)C(=C)C(=O)OCCN(C)CC1=CNC2=CC=CC=C12', 'C1=CC(=C(C=C1O)C(C(=O)O)NC(C)C)C(=O)OC1=CC(=C(C=C1)Cl)O']\\n\",\n \"Round 3 ['C1C(C2(c3c[nH]c4ccc(Br)cc34)CCN(C)CC2)CCC2=CC(=O)N2C1', 'C1=CC(=CN=C1)C(=O)OC1=CC(=CC(=C1C)C(=O)O)C(=O)OCCCCF', 'C1=CC(=CC=C1CC(C(=O)O)N)N(CCCl)C(=O)CN1CCN(C1)C1=CC(=C(C(=C1)Cl)N=NC1=C(C=CC(=C1Cl)C)OCCN(CC)C)Cl', 'O=C(CCl)N1CCCCCOCCCCOCCCCOC(C(O1)OCC1=CC=CC=C1)COC(=O)C(=C)C(=O)OCCN(C)CC1=CNC2=C1C(=O)N2', 'C1=CC(=C(C=C1O)C(C(=O)O)NC(C)C)C(=O)OC1=CC(=C(C=C1)Cl)O']\\n\",\n \"Round 4 ['C1C(C2(c3c[nH]c4ccc(Br)cc34)CCN(C)CC2)CCC2=CC(=O)N2C1', 'C1=CC(=CN=C1)C(=O)OC1=CC(=CC(=C1C)C(=O)O)C(=O)OCCCC', 'c1(Cl)c(C)ccc(OCCN(C)CC)c1N=Nc1c(Cl)cc(N2CCN(CC(=O)N(CCCl)c3ccc(CC(N)C(=O)O)cc3)C)OC1C(C=C2)CCCCCC', 'O=C(CCl)N1CCCCCOCCCCOCCCCOC(C(O1)OCC1=CC=CC=C1)COC(=O)C(=C)C(=O)OCCN(C)CC1=CNC2=C1C=C(C=C2)COC(=O)C=C(C#N)C', 'C1=CC(=C(C=C1O)C(C(=O)O)NC(C)C)C(=O)OC1=CC(=C(C=C1)Cl)F']\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"RDKit ERROR: [10:15:49] Can't kekulize mol. Unkekulized atoms: 18 19 20\\n\",\n \"RDKit ERROR: \\n\"\n ]\n }\n ],\n \"source\": [\n \"# change internal parameters of n_gram generator\\n\",\n \"_ = n_gram.set_params(del_range=[1,10], max_len=500, reorder_prob=0.5)\\n\",\n \"\\n\",\n \"np.random.seed(201906) # fix the random seed\\n\",\n \"\\n\",\n \"# perform pure iQSPR molecule generation starting with 10 PG molecules\\n\",\n \"n_loop = 5\\n\",\n \"tmp = data_ss['SMILES'][:5]\\n\",\n \"for i in range(n_loop):\\n\",\n \" tmp = n_gram.proposal(tmp)\\n\",\n \" print('Round %i' % i,tmp)\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To avoid getting stuck at certain molecule structures due to the re-ordering issue, we recommend two solutions: \\n\",\n \"\\n\",\n \"1. Avoid re-ordering: standardize the SMILES to a single format and avoid re-ordering during generation process. To avoid not modifying only certain part of the molecule, pick a larger maximum number for \\\"del_range\\\". This way, you will save time on training the N-gram model, but probably need more iteration steps during the actual iQSPR run for convergence to your target property region. This is recommend only if you are really out of computing power for training a big N-gram table.\\n\",\n \"\\n\",\n \"2. Data augmentation: expand your training set of SMILES by adding re-ordered SMILES of each originally available SMILES. Be careful of the bias to larger molecules due to more re-order patterns for longer SMILES. This way, you will need longer time to train the N-gram model, but probably converge faster in the actual iQSPR run.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Warning: the next module may take 5-10min to complete!**\\n\",\n \"\\n\",\n \"You can skip this part and proceed forward\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"RDKit WARNING: [10:17:27] WARNING: not removing hydrogen atom without neighbors\\n\",\n \"100%|██████████| 3000/3000 [04:09<00:00, 12.02it/s]\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 4min 9s, sys: 2.07 s, total: 4min 11s\\n\",\n \"Wall time: 4min 10s\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"# Method 1: use canonical SMILES in RDKit with no reordering\\n\",\n \"cans = [Chem.MolToSmiles(Chem.MolFromSmiles(smi)) for smi in data_ss['SMILES']]\\n\",\n \"n_gram_cans = NGram(reorder_prob=0)\\n\",\n \"n_gram_cans.fit(cans)\\n\",\n \"\\n\",\n \"# save results\\n\",\n \"with open('ngram_cans.obj', 'wb') as f:\\n\",\n \" pk.dump(n_gram_cans, f)\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Warning: the next TWO modules may take 5-10hr to complete!**\\n\",\n \"\\n\",\n \"You can skip the next TWO cells for time-saving and use our pre-trained `NGram` to proceed forward. Downloading is available at:\\n\",\n \"* https://github.com/yoshida-lab/XenonPy/releases/download/v0.6.2/ngram_pubchem_ikebata_reO15_O10.xz\\n\",\n \"* https://github.com/yoshida-lab/XenonPy/releases/download/v0.6.2/ngram_pubchem_ikebata_reO15_O11to20.xz\\n\",\n \"\\n\",\n \"Using the merge table function in `NGram`, we can train and store the big `NGram` tables separately, and combine them later, as will be demonstrated below. Note that the most efficient way to manually parallelize the training of `NGram` is NOT to split the order, but to split the training set of SMILES strings, which is not what we are doing here.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"RDKit WARNING: [10:21:49] WARNING: not removing hydrogen atom without neighbors\\n\",\n \"RDKit ERROR: [10:21:52] Explicit valence for atom # 1 Br, 5, is greater than permitted\\n\",\n \"RDKit WARNING: [10:21:52] WARNING: not removing hydrogen atom without neighbors\\n\",\n \"RDKit WARNING: [10:21:52] WARNING: not removing hydrogen atom without neighbors\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"FBr(F)(F)(F)F\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"RDKit WARNING: [10:21:53] WARNING: not removing hydrogen atom without neighbors\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 24.5 s, sys: 74.4 ms, total: 24.6 s\\n\",\n \"Wall time: 24.6 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"# N-gram library in XenonPy-iQSPR\\n\",\n \"from xenonpy.inverse.iqspr import NGram\\n\",\n \"\\n\",\n \"np.random.seed(201906) # fix the random seed\\n\",\n \"\\n\",\n \"# Method 2: expand n-gram training set with randomly reordered SMILES\\n\",\n \"# (we show one of the many possible ways of doing it)\\n\",\n \"n_reorder = 15 # pick a fixed number of re-ordering\\n\",\n \"\\n\",\n \"# convert the SMILES to canonical SMILES in RDKit (not necessary in general)\\n\",\n \"cans = []\\n\",\n \"for smi in data['SMILES']:\\n\",\n \" # remove some molecules in the full SMILES list that may lead to error\\n\",\n \" try:\\n\",\n \" cans.append(Chem.MolToSmiles(Chem.MolFromSmiles(smi)))\\n\",\n \" except:\\n\",\n \" print(smi)\\n\",\n \" pass\\n\",\n \"\\n\",\n \"mols = [Chem.MolFromSmiles(smi) for smi in cans]\\n\",\n \"smi_reorder_all = []\\n\",\n \"smi_reorder = []\\n\",\n \"for mol in mols:\\n\",\n \" idx = list(range(mol.GetNumAtoms()))\\n\",\n \" tmp = [Chem.MolToSmiles(mol,rootedAtAtom=x) for x in range(len(idx))]\\n\",\n \" smi_reorder_all.append(np.array(list(set(tmp))))\\n\",\n \" smi_reorder.append(np.random.choice(smi_reorder_all[-1], n_reorder,\\n\",\n \" replace=(len(smi_reorder_all[-1]) < n_reorder)))\\n\",\n \"\\n\",\n \"n_uni = [len(x) for x in smi_reorder_all]\\n\",\n \"_ = plt.hist(n_uni, bins='auto') # arguments are passed to np.histogram\\n\",\n \"_ = plt.title(\\\"Histogram for number of SMILES variants\\\")\\n\",\n \"_ = plt.show()\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"# flatten out the list and train the N-gram\\n\",\n \"flat_list = [item for sublist in smi_reorder for item in sublist]\\n\",\n \"\\n\",\n \"# first train up to order 10\\n\",\n \"n_gram_reorder1 = NGram(reorder_prob=0.5)\\n\",\n \"_ = n_gram_reorder1.fit(flat_list, train_order=10)\\n\",\n \"# save results\\n\",\n \"_ = joblib.dump(n_gram_reorder1, 'ngram_pubchem_ikebata_reO15_O10.xz')\\n\",\n \"# with open('ngram_pubchem_ikebata_reO15_O10.obj', 'wb') as f:\\n\",\n \"# pk.dump(n_gram_reorder1, f)\\n\",\n \" \\n\",\n \"# Then, train up from order 11 to 20\\n\",\n \"n_gram_reorder2 = NGram(reorder_prob=0.5, sample_order=(11, 20))\\n\",\n \"_ = n_gram_reorder2.fit(flat_list, train_order=(11, 20))\\n\",\n \"# save results\\n\",\n \"_ = joblib.dump(n_gram_reorder2, 'ngram_pubchem_ikebata_reO15_O11to20.xz')\\n\",\n \"# with open('ngram_pubchem_ikebata_reO15_O11to20.obj', 'wb') as f:\\n\",\n \"# pk.dump(n_gram_reorder2, f)\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here, we will load the two parts of the pre-trained `NGram` files and combine them for our use in the example. Note that you have the option to overwrite an existing `NGram` while combining or creating a new one. We recommend overwriting the existing one (default setting of merge_table) if the `NGram` table is expected to be very large in order to avoid memory issue.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 75,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 9.79 s, sys: 1 s, total: 10.8 s\\n\",\n \"Wall time: 10.8 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"# load a pre-trained n-gram from the pickle file\\n\",\n \"n_gram = joblib.load('ngram_pubchem_ikebata_reO15_O10.xz')\\n\",\n \"# with open('ngram_pubchem_ikebata_reO15_O10.obj', 'rb') as f:\\n\",\n \"# n_gram = pk.load(f)\\n\",\n \" \\n\",\n \"# load a pre-trained n-gram from the pickle file\\n\",\n \"n_gram2 = joblib.load('ngram_pubchem_ikebata_reO15_O11to20.xz')\\n\",\n \"# with open('ngram_pubchem_ikebata_reO15_O11to20.obj', 'rb') as f:\\n\",\n \"# n_gram2 = pk.load(f)\\n\",\n \"\\n\",\n \"_ = n_gram.merge_table(n_gram2)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### iQSPR: sequential Monte Carlo\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After the preparation of forward model (likelihood) and `NGram` model (prior), we are now ready to perform the actual iteration of iQSPR to generate molecules in our target property region.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"##### run iQSPR\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We need to first set up some initial molecules as a starting point of our iQSPR iteration. Note that the number of molecules in this initial set governs the number of molecules generated in each iteration step. In practice, you may want at least 100 or even 1000 molecules per step depending your computing resources to avoid getting trapped in a local region when searching the whole molecular space defined by your N-gram model.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 76,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['CC(Cc1ccccc1)NCCn1cnc2c1c(=O)n(C)c(=O)n2C' 'CN1CCN(C)C1=O' 'N#CO'\\n\",\n \" 'COC1C(O)C(N)C(OC2OC(C(C)N)CCC2N)C(O)C1N(C)C(=O)CN' 'CCCCOP(N)(=O)OCCCC'\\n\",\n \" 'NS(=O)(=O)Cl' 'CCSC(=O)N(CC)CC' 'CCCCCC=CCC=CCC=CCCCCC(=O)O'\\n\",\n \" 'CCCCCC=CCC=CCC=CC=CC(O)CCCC(=O)O'\\n\",\n \" 'CC1NCCc2c(C(=O)N3CCCCC3)[nH]c3cccc1c23' 'ClC1C=CC(Cl)C(Cl)C1Cl'\\n\",\n \" 'CC1=CC(=O)CC1' 'CC1C(=O)NC(=O)N(C2CCCCC2)C1=O'\\n\",\n \" 'O=[N+]([O-])c1cc2nc(C(F)(F)F)[nH]c2cc1Cl' 'O=C1C=C2NC(C(=O)O)C=C2CC1=O'\\n\",\n \" 'CCSC(=O)N(CC)CC' 'CCCCCCCCOc1ccc(C(=O)c2ccccc2O)c(O)c1'\\n\",\n \" 'COc1ccc2c(c1)CCC(c1ccccc1)C2(O)c1ccccn1'\\n\",\n \" 'CCN(CCOC(=O)C(OC)(c1ccccc1)c1ccccc1)CC(C)C' 'COc1ccccc1NC(=O)CC(C)=O'\\n\",\n \" 'CC1CC2C(=CC1=O)CCC1C2CCC2(C)C1CCC2(C)O' 'C1COCCN1' 'NCCNCCO'\\n\",\n \" 'CCC(C)Cc1cccc(O)c1C' 'COC(F)(F)CCl']\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"RDKit WARNING: [23:16:50] WARNING: not removing hydrogen atom without neighbors\\n\"\n ]\n }\n ],\n \"source\": [\n \"# set up initial molecules for iQSPR\\n\",\n \"np.random.seed(201906) # fix the random seed\\n\",\n \"cans = [Chem.MolToSmiles(Chem.MolFromSmiles(smi)) for i, smi in enumerate(data_ss['SMILES'])\\n\",\n \" if (data_ss['HOMO-LUMO gap'].iloc[i] > 4)]\\n\",\n \"init_samples = np.random.choice(cans, 25)\\n\",\n \"print(init_samples)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For any sequential Monte Carlo algorithm, annealing is usually recommended to avoid getting trapped in a local mode. In iQSPR, we use the beta vector to control our annealing schedule. We recommend starting with a small number close to 0 to minimize the influence from the likelihood at the beginning steps and using some kind of exponential-like schedule to increase the beta value to 1, which represents the state of the original likelihood. The length of the beta vector directly controls the number of iteration in iQSPR. We recommend adding more steps with beta=1 at the end to allow exploration of the posterior distribution (your target property region). In practice, iteration of the order of 100 or 1000 steps is recommended depending your computing resources.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 77,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Number of steps: 50\\n\",\n \"[0.01 0.02 0.03 0.04 0.05 0.06\\n\",\n \" 0.07 0.08 0.09 0.1 0.11 0.12\\n\",\n \" 0.13 0.14 0.15 0.16 0.17 0.18\\n\",\n \" 0.19 0.2 0.21 0.23111111 0.25222222 0.27333333\\n\",\n \" 0.29444444 0.31555556 0.33666667 0.35777778 0.37888889 0.4\\n\",\n \" 0.4 0.46666667 0.53333333 0.6 0.66666667 0.73333333\\n\",\n \" 0.8 0.86666667 0.93333333 1. 1. 1.\\n\",\n \" 1. 1. 1. 1. 1. 1.\\n\",\n \" 1. 1. ]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# set up annealing schedule in iQSPR\\n\",\n \"beta = np.hstack([np.linspace(0.01,0.2,20),np.linspace(0.21,0.4,10),np.linspace(0.4,1,10),np.linspace(1,1,10)])\\n\",\n \"print('Number of steps: %i' % len(beta))\\n\",\n \"print(beta)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Warning: the next module may take 2-3min to complete!**\\n\",\n \"\\n\",\n \"Putting together the initial molecules, beta vector, forward model (likelihood), N-gram model (prior), you can now use a for-loop over the IQSPR class to get the generated molecules at each iteration step. More information can be extracted from the loop by setting \\\"yield_lpf\\\" to True (l: log-likelihood, p: probability of resampling, f: frequency of appearence). Note that the length of generated molecules in each step may not equal to the length of intial molecules because we only track the unique molecules and record their appearance frequency separately.\\n\",\n \"\\n\",\n \"We will use the previously trained `NGram` here. Please make sure you have loaded the `NGram` models and merged them property in the previous section. We will reset the parameters again to make sure the values are what we expected them to be. \\n\",\n \"\\n\",\n \"Note that warnings will be thrown out if there are molecules generated from the `NGram` that cannot be converted to RDKit's MOL format. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 79,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"# library for running iQSPR in XenonPy-iQSPR\\n\",\n \"from xenonpy.inverse.iqspr import IQSPR\\n\",\n \"\\n\",\n \"# update NGram parameters for this exampleHOMO-LUMO gap\\n\",\n \"n_gram.set_params(del_range=[1,20],max_len=500, reorder_prob=0.5, sample_order=(1,20))\\n\",\n \"\\n\",\n \"# set up likelihood and n-gram models in iQSPR\\n\",\n \"iqspr_reorder = IQSPR(estimator=prd_mdls, modifier=n_gram)\\n\",\n \" \\n\",\n \"np.random.seed(201906) # fix the random seed\\n\",\n \"# main loop of iQSPR\\n\",\n \"iqspr_samples1, iqspr_loglike1, iqspr_prob1, iqspr_freq1 = [], [], [], []\\n\",\n \"for s, ll, p, freq in iqspr_reorder(init_samples, beta, yield_lpf=True):\\n\",\n \" iqspr_samples1.append(s)\\n\",\n \" iqspr_loglike1.append(ll)\\n\",\n \" iqspr_prob1.append(p)\\n\",\n \" iqspr_freq1.append(freq)\\n\",\n \"# record all outputs\\n\",\n \"iqspr_results_reorder = {\\n\",\n \" \\\"samples\\\": iqspr_samples1,\\n\",\n \" \\\"loglike\\\": iqspr_loglike1,\\n\",\n \" \\\"prob\\\": iqspr_prob1,\\n\",\n \" \\\"freq\\\": iqspr_freq1,\\n\",\n \" \\\"beta\\\": beta\\n\",\n \"}\\n\",\n \"# save results\\n\",\n \"with open('iQSPR_results_reorder.obj', 'wb') as f:\\n\",\n \" pk.dump(iqspr_results_reorder, f)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"##### plot results\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let us take a look at the results.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"with open('iQSPR_results_reorder.obj', 'rb') as f:\\n\",\n \" iqspr_results_reorder = pk.load(f)\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, we look at how the likelihood values of the generated molecules converged to a distribution peaked around 1 (hitting the target property region). The gradient correlates well to the annealing schedule.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# plot the likelihood evolution\\n\",\n \"\\n\",\n \"# set up the min and max boundary for the plots\\n\",\n \"tmp_list = [x.sum(axis = 1, skipna = True).values for x in iqspr_results_reorder[\\\"loglike\\\"]]\\n\",\n \"flat_list = np.asarray([item for sublist in tmp_list for item in sublist])\\n\",\n \"y_max, y_min = max(flat_list), min(flat_list)\\n\",\n \"\\n\",\n \"_ = plt.figure(figsize=(10,5))\\n\",\n \"_ = plt.xlim(0,len(iqspr_results_reorder[\\\"loglike\\\"]))\\n\",\n \"_ = plt.ylim(y_min,y_max)\\n\",\n \"_ = plt.xlabel('Step')\\n\",\n \"_ = plt.ylabel('Log-likelihood')\\n\",\n \"for i, ll in enumerate(tmp_list):\\n\",\n \" _ = plt.scatter([i]*len(ll), ll ,s=10, c='b', alpha=0.2)\\n\",\n \"_ = plt.show()\\n\",\n \"#plt.savefig('iqspr_loglike_reorder.png',dpi = 500)\\n\",\n \"#plt.close()\\n\",\n \"\\n\",\n \"y_max, y_min = np.exp(y_max), np.exp(y_min)\\n\",\n \"_ = plt.figure(figsize=(10,5))\\n\",\n \"_ = plt.xlim(0,len(iqspr_results_reorder[\\\"loglike\\\"]))\\n\",\n \"_ = plt.ylim(y_min,y_max)\\n\",\n \"_ = plt.xlabel('Step')\\n\",\n \"_ = plt.ylabel('Likelihood')\\n\",\n \"for i, ll in enumerate(tmp_list):\\n\",\n \" _ = plt.scatter([i]*len(ll), np.exp(ll) ,s=10, c='b', alpha=0.2)\\n\",\n \"_ = plt.show()\\n\",\n \"#plt.savefig('iqspr_like_reorder.png',dpi = 500)\\n\",\n \"#plt.close()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Warning: the next module may take 1-2min to complete!**\\n\",\n \"\\n\",\n \"Next, we plot the evolution of the generated molecules in the target property space. Note that if your forward models are BayesRidge, you need to add return_std=True to the predict function.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Range of std. dev.: (25.5192,33.1820)\\n\",\n \"CPU times: user 18.7 s, sys: 182 ms, total: 18.8 s\\n\",\n \"Wall time: 19.9 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"# re-calculate the property values for the proposed molecules\\n\",\n \"x_mean, x_std, y_mean, y_std = [], [], [], []\\n\",\n \"r_std = []\\n\",\n \"FPs_samples = []\\n\",\n \"for i, smis in enumerate(iqspr_results_reorder[\\\"samples\\\"]):\\n\",\n \" tmp_fps = RDKit_FPs.transform(smis)\\n\",\n \" FPs_samples.append(tmp_fps)\\n\",\n \" \\n\",\n \" tmp1, tmp2 = prd_mdls[\\\"E\\\"].predict(tmp_fps)\\n\",\n \" x_mean.append(tmp1)\\n\",\n \" x_std.append(tmp2)\\n\",\n \" \\n\",\n \" tmp1, tmp2 = prd_mdls[\\\"HOMO-LUMO gap\\\"].predict(tmp_fps)\\n\",\n \" y_mean.append(tmp1)\\n\",\n \" y_std.append(tmp2)\\n\",\n \" \\n\",\n \" r_std.append([np.sqrt(x_std[-1][i]**2 + y_std[-1][i]**2) for i in range(len(x_std[-1]))])\\n\",\n \"\\n\",\n \"# flatten the list for max/min calculation\\n\",\n \"flat_list = [item for sublist in r_std for item in sublist]\\n\",\n \"print('Range of std. dev.: (%.4f,%.4f)' % (min(flat_list),max(flat_list)))\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Warning: the next module may take 1-2min to complete!**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 19.3 s, sys: 395 ms, total: 19.7 s\\n\",\n \"Wall time: 19.7 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"import os\\n\",\n \"\\n\",\n \"# prepare a folder to save all the figures\\n\",\n \"ini_dir = './iQSPR_tutorial_prd/'\\n\",\n \"if not os.path.exists(ini_dir):\\n\",\n \" os.makedirs(ini_dir)\\n\",\n \"\\n\",\n \"flat_list = np.asarray([item for sublist in r_std for item in sublist])\\n\",\n \"s_max, s_min = max(flat_list), min(flat_list)\\n\",\n \"flat_list = np.concatenate((data_ss[\\\"E\\\"],\\n\",\n \" np.asarray([item for sublist in x_mean for item in sublist])))\\n\",\n \"x_max, x_min = max(flat_list), min(flat_list)\\n\",\n \"flat_list = np.concatenate((data_ss[\\\"HOMO-LUMO gap\\\"],\\n\",\n \" np.asarray([item for sublist in y_mean for item in sublist])))\\n\",\n \"y_max, y_min = max(flat_list), min(flat_list)\\n\",\n \"tmp_beta = iqspr_results_reorder[\\\"beta\\\"]\\n\",\n \"\\n\",\n \"for i in range(len(r_std)):\\n\",\n \" dot_size = 45*((np.asarray(r_std[i])-s_min)/(s_max-s_min)) + 5\\n\",\n \" \\n\",\n \" _ = plt.figure(figsize=(5,5))\\n\",\n \" rectangle = plt.Rectangle((0,0),200,3,fc='y',alpha=0.1)\\n\",\n \" _ = plt.gca().add_patch(rectangle)\\n\",\n \" _ = plt.scatter(data_ss[\\\"E\\\"], data_ss[\\\"HOMO-LUMO gap\\\"],s=3, c='b', alpha=0.2)\\n\",\n \" _ = plt.scatter(x_mean[i], y_mean[i],s=dot_size, c='r', alpha=0.5)\\n\",\n \" _ = plt.title('Step: %i (beta = %.3f)' % (i,tmp_beta[i]))\\n\",\n \" _ = plt.xlim(x_min,x_max)\\n\",\n \" _ = plt.ylim(y_min,y_max)\\n\",\n \" _ = plt.xlabel('Internal energy')\\n\",\n \" _ = plt.ylabel('HOMO-LUMO gap')\\n\",\n \" #plt.show()\\n\",\n \" _ = plt.savefig(ini_dir+'Step_%02i.png' % i,dpi = 500)\\n\",\n \" _ = plt.close()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Warning: the next module may take 10-15min to complete!**\\n\",\n \"\\n\",\n \"Finally, let us take a look at the generated molecular structures.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 4min 16s, sys: 7.13 s, total: 4min 23s\\n\",\n \"Wall time: 4min 25s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"import os\\n\",\n \"\\n\",\n \"# prepare a folder to save all the figures\\n\",\n \"ini_dir = './iQSPR_tutorial_smiles/'\\n\",\n \"if not os.path.exists(ini_dir):\\n\",\n \" os.makedirs(ini_dir)\\n\",\n \"\\n\",\n \"tmp_list = [x.sum(axis = 1, skipna = True).values for x in iqspr_results_reorder[\\\"loglike\\\"]] \\n\",\n \"\\n\",\n \"n_S = 25\\n\",\n \"for i, smis in enumerate(iqspr_results_reorder['samples']):\\n\",\n \" tmp_smis = iqspr_results_reorder['samples'][i][\\n\",\n \" np.argsort(tmp_list[i])[::-1]]\\n\",\n \" fig, ax = plt.subplots(5, 5)\\n\",\n \" _ = fig.set_size_inches(20, 20)\\n\",\n \" _ = fig.set_tight_layout(True)\\n\",\n \" for j in range(n_S):\\n\",\n \" xaxis = j // 5\\n\",\n \" yaxis = j % 5\\n\",\n \" try:\\n\",\n \" img = Draw.MolToImage(Chem.MolFromSmiles(tmp_smis[j]))\\n\",\n \" _ = ax[xaxis, yaxis].clear()\\n\",\n \" _ = ax[xaxis, yaxis].set_frame_on(False)\\n\",\n \" _ = ax[xaxis, yaxis].imshow(img)\\n\",\n \" except:\\n\",\n \" pass\\n\",\n \" _ = ax[xaxis, yaxis].set_axis_off()\\n\",\n \" _ = fig.savefig(ini_dir+'Step_%02i.png' % i,dpi = 500)\\n\",\n \" _ = plt.close()\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can now compare our generated molecules with the 16 existing molecules in our data set that are in the target region. You can see that our generated molecules contain substructures that are similar to a few of the existing molecules.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"target_smis = [Chem.MolToSmiles(Chem.MolFromSmiles(smi)) for i, smi in enumerate(data_ss['SMILES'])\\n\",\n \" if ((data_ss['HOMO-LUMO gap'].iloc[i] <= 3) and (data_ss['E'].iloc[i] <= 200))]\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['O=C1N=c2cc([N+](=O)[O-])c([N+](=O)[O-])cc2=NC1=O', 'O=C1C(O)=C(c2ccccc2)C(=O)C(O)=C1c1ccccc1', 'O=c1ccc2[n+]([O-])c3ccc(O)cc3oc-2c1', 'Nc1ccc2nc3ccccc3nc2c1N', 'Cc1cc(Cl)cc2c1C(=O)C(=C1Sc3cc(Cl)cc(C)c3C1=O)S2', 'Nc1ccc(N)c2c1C(=O)c1c(N)ccc(N)c1C2=O', 'COc1cc(N)c2c(c1N)C(=O)c1ccccc1C2=O', 'Nc1ccc(O)c2c1C(=O)c1ccccc1C2=O', 'CNc1ccc(N(CCO)CCO)cc1[N+](=O)[O-]', 'C1=Cc2c3ccccc3cc3c4c(cc1c23)=CCCC=4', 'O=C(CN1CCOCC1)NN=Cc1ccc([N+](=O)[O-])o1', 'CNc1ccc(O)c2c1C(=O)c1c(O)ccc(NC)c1C2=O', 'O=C1c2c(O)ccc(O)c2C(=O)c2c(O)ccc(O)c21', 'O=C1C=CC(=C2C=CC(=O)C=C2)C=C1', 'c1ccc2c(c1)ccc1c2ccc2ccc3ccccc3c21', 'NC1=NC(=O)C2=C(CNC3C=CC(O)C3O)C=NC2=N1']\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(target_smis)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 6.38 s, sys: 1.38 s, total: 7.75 s\\n\",\n \"Wall time: 7.75 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"import os\\n\",\n \"\\n\",\n \"# prepare a folder to save all the figures\\n\",\n \"ini_dir = './iQSPR_tutorial_target_smiles/'\\n\",\n \"if not os.path.exists(ini_dir):\\n\",\n \" os.makedirs(ini_dir)\\n\",\n \"\\n\",\n \"n_S = 25\\n\",\n \"\\n\",\n \"fig, ax = plt.subplots(5, 5)\\n\",\n \"_ = fig.set_size_inches(20, 20)\\n\",\n \"_ = fig.set_tight_layout(True)\\n\",\n \"for j in range(n_S):\\n\",\n \" xaxis = j // 5\\n\",\n \" yaxis = j % 5\\n\",\n \" try:\\n\",\n \" img = Draw.MolToImage(Chem.MolFromSmiles(target_smis[j]))\\n\",\n \" _ = ax[xaxis, yaxis].clear()\\n\",\n \" _ = ax[xaxis, yaxis].set_frame_on(False)\\n\",\n \" _ = ax[xaxis, yaxis].imshow(img)\\n\",\n \" except:\\n\",\n \" pass\\n\",\n \" _ = ax[xaxis, yaxis].set_axis_off()\\n\",\n \"_ = fig.savefig(ini_dir+'target_region.png',dpi = 500)\\n\",\n \"_ = plt.close()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"##### run iQSPR with different annealing schedules\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In some cases, you may want to give priority to certain property reaching the target region. This can be done by adjusting the annealing schedule for each property. To do so, you can input a dictionary for beta, instead. Here, we try to reach a low value for internal energy first. Hence, the annealing schedule for HOMO-LUMO gap has a longer flat region.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" E HOMO-LUMO gap\\n\",\n \"0 0.010000 0.005714\\n\",\n \"1 0.020000 0.011429\\n\",\n \"2 0.030000 0.017143\\n\",\n \"3 0.040000 0.022857\\n\",\n \"4 0.050000 0.028571\\n\",\n \"5 0.060000 0.034286\\n\",\n \"6 0.070000 0.040000\\n\",\n \"7 0.080000 0.045714\\n\",\n \"8 0.090000 0.051429\\n\",\n \"9 0.100000 0.057143\\n\",\n \"10 0.110000 0.062857\\n\",\n \"11 0.120000 0.068571\\n\",\n \"12 0.130000 0.074286\\n\",\n \"13 0.140000 0.080000\\n\",\n \"14 0.150000 0.085714\\n\",\n \"15 0.160000 0.091429\\n\",\n \"16 0.170000 0.097143\\n\",\n \"17 0.180000 0.102857\\n\",\n \"18 0.190000 0.108571\\n\",\n \"19 0.200000 0.114286\\n\",\n \"20 0.210000 0.120000\\n\",\n \"21 0.231111 0.125714\\n\",\n \"22 0.252222 0.131429\\n\",\n \"23 0.273333 0.137143\\n\",\n \"24 0.294444 0.142857\\n\",\n \"25 0.315556 0.148571\\n\",\n \"26 0.336667 0.154286\\n\",\n \"27 0.357778 0.160000\\n\",\n \"28 0.378889 0.165714\\n\",\n \"29 0.400000 0.171429\\n\",\n \"30 0.400000 0.177143\\n\",\n \"31 0.466667 0.182857\\n\",\n \"32 0.533333 0.188571\\n\",\n \"33 0.600000 0.194286\\n\",\n \"34 0.666667 0.200000\\n\",\n \"35 0.733333 0.210000\\n\",\n \"36 0.800000 0.257500\\n\",\n \"37 0.866667 0.305000\\n\",\n \"38 0.933333 0.352500\\n\",\n \"39 1.000000 0.400000\\n\",\n \"40 1.000000 0.400000\\n\",\n \"41 1.000000 0.550000\\n\",\n \"42 1.000000 0.700000\\n\",\n \"43 1.000000 0.850000\\n\",\n \"44 1.000000 1.000000\\n\",\n \"45 1.000000 1.000000\\n\",\n \"46 1.000000 1.000000\\n\",\n \"47 1.000000 1.000000\\n\",\n \"48 1.000000 1.000000\\n\",\n \"49 1.000000 1.000000\\n\"\n ]\n }\n ],\n \"source\": [\n \"# set up annealing schedule in iQSPR\\n\",\n \"beta1 = np.hstack([np.linspace(0.01,0.2,20),np.linspace(0.21,0.4,10),np.linspace(0.4,1,10),np.linspace(1,1,10)])\\n\",\n \"beta2 = np.hstack([np.linspace(0.2/35,0.2,35),np.linspace(0.21,0.4,5),np.linspace(0.4,1,5),np.linspace(1,1,5)])\\n\",\n \"beta = pd.DataFrame({'E': beta1, 'HOMO-LUMO gap': beta2})\\n\",\n \"print(beta)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let us try to re-run the IQSPR to see how the results may differ\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 80,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"# library for running iQSPR in XenonPy-iQSPR\\n\",\n \"from xenonpy.inverse.iqspr import IQSPR\\n\",\n \"\\n\",\n \"# update NGram parameters for this exampleHOMO-LUMO gap\\n\",\n \"n_gram.set_params(del_range=[1,20],max_len=500, reorder_prob=0.5)\\n\",\n \"\\n\",\n \"# set up likelihood and n-gram models in iQSPR\\n\",\n \"iqspr_reorder = IQSPR(estimator=prd_mdls, modifier=n_gram)\\n\",\n \" \\n\",\n \"np.random.seed(201909) # fix the random seed\\n\",\n \"# main loop of iQSPR\\n\",\n \"iqspr_samples1, iqspr_loglike1, iqspr_prob1, iqspr_freq1 = [], [], [], []\\n\",\n \"for s, ll, p, freq in iqspr_reorder(init_samples, beta, yield_lpf=True):\\n\",\n \" iqspr_samples1.append(s)\\n\",\n \" iqspr_loglike1.append(ll)\\n\",\n \" iqspr_prob1.append(p)\\n\",\n \" iqspr_freq1.append(freq)\\n\",\n \"# record all outputs\\n\",\n \"iqspr_results_reorder = {\\n\",\n \" \\\"samples\\\": iqspr_samples1,\\n\",\n \" \\\"loglike\\\": iqspr_loglike1,\\n\",\n \" \\\"prob\\\": iqspr_prob1,\\n\",\n \" \\\"freq\\\": iqspr_freq1,\\n\",\n \" \\\"beta\\\": beta\\n\",\n \"}\\n\",\n \"# save results\\n\",\n \"with open('iQSPR_results_reorder2.obj', 'wb') as f:\\n\",\n \" pk.dump(iqspr_results_reorder, f)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"##### plot results\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let us take a look at the results.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"with open('iQSPR_results_reorder2.obj', 'rb') as f:\\n\",\n \" iqspr_results_reorder = pk.load(f)\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, we look at how the likelihood values of the generated molecules converged to a distribution peaked around 1 (hitting the target property region). The gradient correlates well to the annealing schedule.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# plot the likelihood evolution\\n\",\n \"\\n\",\n \"# set up the min and max boundary for the plots\\n\",\n \"tmp_list = [x.sum(axis = 1, skipna = True).values for x in iqspr_results_reorder[\\\"loglike\\\"]]\\n\",\n \"flat_list = np.asarray([item for sublist in tmp_list for item in sublist])\\n\",\n \"y_max, y_min = max(flat_list), min(flat_list)\\n\",\n \"\\n\",\n \"_ = plt.figure(figsize=(10,5))\\n\",\n \"_ = plt.xlim(0,len(iqspr_results_reorder[\\\"loglike\\\"]))\\n\",\n \"_ = plt.ylim(y_min,y_max)\\n\",\n \"_ = plt.xlabel('Step')\\n\",\n \"_ = plt.ylabel('Log-likelihood')\\n\",\n \"for i, ll in enumerate(tmp_list):\\n\",\n \" _ = plt.scatter([i]*len(ll), ll ,s=10, c='b', alpha=0.2)\\n\",\n \"_ = plt.show()\\n\",\n \"#plt.savefig('iqspr_loglike_reorder.png',dpi = 500)\\n\",\n \"#plt.close()\\n\",\n \"\\n\",\n \"y_max, y_min = np.exp(y_max), np.exp(y_min)\\n\",\n \"_ = plt.figure(figsize=(10,5))\\n\",\n \"_ = plt.xlim(0,len(iqspr_results_reorder[\\\"loglike\\\"]))\\n\",\n \"_ = plt.ylim(y_min,y_max)\\n\",\n \"_ = plt.xlabel('Step')\\n\",\n \"_ = plt.ylabel('Likelihood')\\n\",\n \"for i, ll in enumerate(tmp_list):\\n\",\n \" _ = plt.scatter([i]*len(ll), np.exp(ll) ,s=10, c='b', alpha=0.2)\\n\",\n \"_ = plt.show()\\n\",\n \"#plt.savefig('iqspr_like_reorder.png',dpi = 500)\\n\",\n \"#plt.close()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Warning: the next module may take 1-2min to complete!**\\n\",\n \"\\n\",\n \"Next, we plot the evolution of the generated molecules in the target property space. Note that if your forward models are BayesRidge, you need to add return_std=True to the predict function.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Range of std. dev.: (25.5198,33.2724)\\n\",\n \"CPU times: user 19.1 s, sys: 190 ms, total: 19.3 s\\n\",\n \"Wall time: 20.2 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"# re-calculate the property values for the proposed molecules\\n\",\n \"x_mean, x_std, y_mean, y_std = [], [], [], []\\n\",\n \"r_std = []\\n\",\n \"FPs_samples = []\\n\",\n \"for i, smis in enumerate(iqspr_results_reorder[\\\"samples\\\"]):\\n\",\n \" tmp_fps = RDKit_FPs.transform(smis)\\n\",\n \" FPs_samples.append(tmp_fps)\\n\",\n \" \\n\",\n \" tmp1, tmp2 = prd_mdls[\\\"E\\\"].predict(tmp_fps)\\n\",\n \" x_mean.append(tmp1)\\n\",\n \" x_std.append(tmp2)\\n\",\n \" \\n\",\n \" tmp1, tmp2 = prd_mdls[\\\"HOMO-LUMO gap\\\"].predict(tmp_fps)\\n\",\n \" y_mean.append(tmp1)\\n\",\n \" y_std.append(tmp2)\\n\",\n \" \\n\",\n \" r_std.append([np.sqrt(x_std[-1][i]**2 + y_std[-1][i]**2) for i in range(len(x_std[-1]))])\\n\",\n \"\\n\",\n \"# flatten the list for max/min calculation\\n\",\n \"flat_list = [item for sublist in r_std for item in sublist]\\n\",\n \"print('Range of std. dev.: (%.4f,%.4f)' % (min(flat_list),max(flat_list)))\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Warning: the next module may take 1-2min to complete!**\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can see that the algorithm spent a longer time exploring the lower internal energy region, which ends up not able to find candidates within our target region in this particular test case. In practice, a certain direction may be preferred to guide the search based on prior knowledge.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 19.2 s, sys: 398 ms, total: 19.6 s\\n\",\n \"Wall time: 19.6 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"import os\\n\",\n \"\\n\",\n \"# prepare a folder to save all the figures\\n\",\n \"ini_dir = './iQSPR_tutorial_prd2/'\\n\",\n \"if not os.path.exists(ini_dir):\\n\",\n \" os.makedirs(ini_dir)\\n\",\n \"\\n\",\n \"flat_list = np.asarray([item for sublist in r_std for item in sublist])\\n\",\n \"s_max, s_min = max(flat_list), min(flat_list)\\n\",\n \"flat_list = np.concatenate((data_ss[\\\"E\\\"],\\n\",\n \" np.asarray([item for sublist in x_mean for item in sublist])))\\n\",\n \"x_max, x_min = max(flat_list), min(flat_list)\\n\",\n \"flat_list = np.concatenate((data_ss[\\\"HOMO-LUMO gap\\\"],\\n\",\n \" np.asarray([item for sublist in y_mean for item in sublist])))\\n\",\n \"y_max, y_min = max(flat_list), min(flat_list)\\n\",\n \"tmp_beta = iqspr_results_reorder[\\\"beta\\\"]\\n\",\n \"\\n\",\n \"for i in range(len(r_std)):\\n\",\n \" dot_size = 45*((np.asarray(r_std[i])-s_min)/(s_max-s_min)) + 5\\n\",\n \" \\n\",\n \" _ = plt.figure(figsize=(5,5))\\n\",\n \" rectangle = plt.Rectangle((0,0),200,3,fc='y',alpha=0.1)\\n\",\n \" _ = plt.gca().add_patch(rectangle)\\n\",\n \" _ = plt.scatter(data_ss[\\\"E\\\"], data_ss[\\\"HOMO-LUMO gap\\\"],s=3, c='b', alpha=0.2)\\n\",\n \" _ = plt.scatter(x_mean[i], y_mean[i],s=dot_size, c='r', alpha=0.5)\\n\",\n \" _ = plt.title('Step: %i (beta = %.3f, %.3f)' % (i,tmp_beta.iloc[i,0],tmp_beta.iloc[i,1]))\\n\",\n \" _ = plt.xlim(x_min,x_max)\\n\",\n \" _ = plt.ylim(y_min,y_max)\\n\",\n \" _ = plt.xlabel('Internal energy')\\n\",\n \" _ = plt.ylabel('HOMO-LUMO gap')\\n\",\n \" #plt.show()\\n\",\n \" _ = plt.savefig(ini_dir+'Step_%02i.png' % i,dpi = 500)\\n\",\n \" _ = plt.close()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Thank you for using XenonPy-iQSPR. We would appreciate any feedback and code contribution to this open-source project.\\n\",\n \"\\n\",\n \"The sample code can be downloaded at:\\n\",\n \"https://github.com/yoshida-lab/XenonPy/blob/master/samples/iQSPR.ipynb\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"MB16_xepy37_v06_test_env\",\n \"language\": \"python\",\n \"name\": \"mb16_xepy37_v06_test_env\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.9\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "docs/source/xenonpy.contrib.extend_descriptors.descriptor.rst", "content": "xenonpy.contrib.extend\\_descriptors.descriptor package\n======================================================\n\nSubmodules\n----------\n\nxenonpy.contrib.extend\\_descriptors.descriptor.frozen\\_featurizer\\_descriptor module\n------------------------------------------------------------------------------------\n\n.. automodule:: xenonpy.contrib.extend_descriptors.descriptor.frozen_featurizer_descriptor\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.contrib.extend\\_descriptors.descriptor.mordred\\_descriptor module\n-------------------------------------------------------------------------\n\n.. automodule:: xenonpy.contrib.extend_descriptors.descriptor.mordred_descriptor\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.contrib.extend\\_descriptors.descriptor.organic\\_comp\\_descriptor module\n-------------------------------------------------------------------------------\n\n.. automodule:: xenonpy.contrib.extend_descriptors.descriptor.organic_comp_descriptor\n :members:\n :undoc-members:\n :show-inheritance:\n\nModule contents\n---------------\n\n.. automodule:: xenonpy.contrib.extend_descriptors.descriptor\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/source/xenonpy.contrib.extend_descriptors.rst", "content": "xenonpy.contrib.extend\\_descriptors package\n===========================================\n\nSubpackages\n-----------\n\n.. toctree::\n :maxdepth: 4\n\n xenonpy.contrib.extend_descriptors.descriptor\n\nModule contents\n---------------\n\n.. automodule:: xenonpy.contrib.extend_descriptors\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/source/xenonpy.contrib.ismd.rst", "content": "xenonpy.contrib.ismd package\n============================\n\nSubmodules\n----------\n\nxenonpy.contrib.ismd.reactant\\_pool module\n------------------------------------------\n\n.. automodule:: xenonpy.contrib.ismd.reactant_pool\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.contrib.ismd.reactor module\n-----------------------------------\n\n.. automodule:: xenonpy.contrib.ismd.reactor\n :members:\n :undoc-members:\n :show-inheritance:\n\nModule contents\n---------------\n\n.. automodule:: xenonpy.contrib.ismd\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/source/xenonpy.contrib.rst", "content": "xenonpy.contrib package\n=======================\n\nSubpackages\n-----------\n\n.. toctree::\n :maxdepth: 4\n\n xenonpy.contrib.extend_descriptors\n xenonpy.contrib.ismd\n\nModule contents\n---------------\n\n.. automodule:: xenonpy.contrib\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/source/xenonpy.datatools.rst", "content": "xenonpy.datatools package\n=========================\n\nSubmodules\n----------\n\nxenonpy.datatools.dataset module\n--------------------------------\n\n.. automodule:: xenonpy.datatools.dataset\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.datatools.preset module\n-------------------------------\n\n.. automodule:: xenonpy.datatools.preset\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.datatools.splitter module\n---------------------------------\n\n.. automodule:: xenonpy.datatools.splitter\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.datatools.transform module\n----------------------------------\n\n.. automodule:: xenonpy.datatools.transform\n :members:\n :undoc-members:\n :show-inheritance:\n\nModule contents\n---------------\n\n.. automodule:: xenonpy.datatools\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/source/xenonpy.descriptor.rst", "content": "xenonpy.descriptor package\n==========================\n\nSubmodules\n----------\n\nxenonpy.descriptor.base module\n------------------------------\n\n.. automodule:: xenonpy.descriptor.base\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.descriptor.cgcnn module\n-------------------------------\n\n.. automodule:: xenonpy.descriptor.cgcnn\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.descriptor.compositions module\n--------------------------------------\n\n.. automodule:: xenonpy.descriptor.compositions\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.descriptor.fingerprint module\n-------------------------------------\n\n.. automodule:: xenonpy.descriptor.fingerprint\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.descriptor.frozen\\_featurizer module\n--------------------------------------------\n\n.. automodule:: xenonpy.descriptor.frozen_featurizer\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.descriptor.structure module\n-----------------------------------\n\n.. automodule:: xenonpy.descriptor.structure\n :members:\n :undoc-members:\n :show-inheritance:\n\nModule contents\n---------------\n\n.. automodule:: xenonpy.descriptor\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/source/xenonpy.inverse.iqspr.rst", "content": "xenonpy.inverse.iqspr package\n=============================\n\nSubmodules\n----------\n\nxenonpy.inverse.iqspr.estimator module\n--------------------------------------\n\n.. automodule:: xenonpy.inverse.iqspr.estimator\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.inverse.iqspr.iqspr module\n----------------------------------\n\n.. automodule:: xenonpy.inverse.iqspr.iqspr\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.inverse.iqspr.iqspr4df module\n-------------------------------------\n\n.. automodule:: xenonpy.inverse.iqspr.iqspr4df\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.inverse.iqspr.modifier module\n-------------------------------------\n\n.. automodule:: xenonpy.inverse.iqspr.modifier\n :members:\n :undoc-members:\n :show-inheritance:\n\nModule contents\n---------------\n\n.. automodule:: xenonpy.inverse.iqspr\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/source/xenonpy.inverse.rst", "content": "xenonpy.inverse package\n=======================\n\nSubpackages\n-----------\n\n.. toctree::\n :maxdepth: 4\n\n xenonpy.inverse.iqspr\n\nSubmodules\n----------\n\nxenonpy.inverse.base module\n---------------------------\n\n.. automodule:: xenonpy.inverse.base\n :members:\n :undoc-members:\n :show-inheritance:\n\nModule contents\n---------------\n\n.. automodule:: xenonpy.inverse\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/source/xenonpy.mdl.rst", "content": "xenonpy.mdl package\n===================\n\nSubmodules\n----------\n\nxenonpy.mdl.base module\n-----------------------\n\n.. automodule:: xenonpy.mdl.base\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.mdl.descriptor module\n-----------------------------\n\n.. automodule:: xenonpy.mdl.descriptor\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.mdl.mdl module\n----------------------\n\n.. automodule:: xenonpy.mdl.mdl\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.mdl.method module\n-------------------------\n\n.. automodule:: xenonpy.mdl.method\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.mdl.model module\n------------------------\n\n.. automodule:: xenonpy.mdl.model\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.mdl.modelset module\n---------------------------\n\n.. automodule:: xenonpy.mdl.modelset\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.mdl.property module\n---------------------------\n\n.. automodule:: xenonpy.mdl.property\n :members:\n :undoc-members:\n :show-inheritance:\n\nModule contents\n---------------\n\n.. automodule:: xenonpy.mdl\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/source/xenonpy.model.nn.rst", "content": "xenonpy.model.nn package\n========================\n\nSubmodules\n----------\n\nxenonpy.model.nn.layer module\n-----------------------------\n\n.. automodule:: xenonpy.model.nn.layer\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.model.nn.wrap module\n----------------------------\n\n.. automodule:: xenonpy.model.nn.wrap\n :members:\n :undoc-members:\n :show-inheritance:\n\nModule contents\n---------------\n\n.. automodule:: xenonpy.model.nn\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/source/xenonpy.model.rst", "content": "xenonpy.model package\n=====================\n\nSubpackages\n-----------\n\n.. toctree::\n :maxdepth: 4\n\n xenonpy.model.nn\n xenonpy.model.training\n xenonpy.model.utils\n\nSubmodules\n----------\n\nxenonpy.model.cgcnn module\n--------------------------\n\n.. automodule:: xenonpy.model.cgcnn\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.model.extern module\n---------------------------\n\n.. automodule:: xenonpy.model.extern\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.model.sequential module\n-------------------------------\n\n.. automodule:: xenonpy.model.sequential\n :members:\n :undoc-members:\n :show-inheritance:\n\nModule contents\n---------------\n\n.. automodule:: xenonpy.model\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/source/xenonpy.model.training.dataset.rst", "content": "xenonpy.model.training.dataset package\n======================================\n\nSubmodules\n----------\n\nxenonpy.model.training.dataset.array module\n-------------------------------------------\n\n.. automodule:: xenonpy.model.training.dataset.array\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.model.training.dataset.cgcnn module\n-------------------------------------------\n\n.. automodule:: xenonpy.model.training.dataset.cgcnn\n :members:\n :undoc-members:\n :show-inheritance:\n\nModule contents\n---------------\n\n.. automodule:: xenonpy.model.training.dataset\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/source/xenonpy.model.training.extension.rst", "content": "xenonpy.model.training.extension package\n========================================\n\nSubmodules\n----------\n\nxenonpy.model.training.extension.persist module\n-----------------------------------------------\n\n.. automodule:: xenonpy.model.training.extension.persist\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.model.training.extension.tensor\\_convert module\n-------------------------------------------------------\n\n.. automodule:: xenonpy.model.training.extension.tensor_convert\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.model.training.extension.validator module\n-------------------------------------------------\n\n.. automodule:: xenonpy.model.training.extension.validator\n :members:\n :undoc-members:\n :show-inheritance:\n\nModule contents\n---------------\n\n.. automodule:: xenonpy.model.training.extension\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/source/xenonpy.model.training.rst", "content": "xenonpy.model.training package\n==============================\n\nSubpackages\n-----------\n\n.. toctree::\n :maxdepth: 4\n\n xenonpy.model.training.dataset\n xenonpy.model.training.extension\n\nSubmodules\n----------\n\nxenonpy.model.training.base module\n----------------------------------\n\n.. automodule:: xenonpy.model.training.base\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.model.training.checker module\n-------------------------------------\n\n.. automodule:: xenonpy.model.training.checker\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.model.training.clip\\_grad module\n----------------------------------------\n\n.. automodule:: xenonpy.model.training.clip_grad\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.model.training.loss module\n----------------------------------\n\n.. automodule:: xenonpy.model.training.loss\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.model.training.lr\\_scheduler module\n-------------------------------------------\n\n.. automodule:: xenonpy.model.training.lr_scheduler\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.model.training.optimizer module\n---------------------------------------\n\n.. automodule:: xenonpy.model.training.optimizer\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.model.training.trainer module\n-------------------------------------\n\n.. automodule:: xenonpy.model.training.trainer\n :members:\n :undoc-members:\n :show-inheritance:\n\nModule contents\n---------------\n\n.. automodule:: xenonpy.model.training\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/source/xenonpy.model.utils.rst", "content": "xenonpy.model.utils package\n===========================\n\nSubmodules\n----------\n\nxenonpy.model.utils.metrics module\n----------------------------------\n\n.. automodule:: xenonpy.model.utils.metrics\n :members:\n :undoc-members:\n :show-inheritance:\n\nModule contents\n---------------\n\n.. automodule:: xenonpy.model.utils\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/source/xenonpy.rst", "content": "xenonpy package\n===============\n\nSubpackages\n-----------\n\n.. toctree::\n :maxdepth: 4\n\n xenonpy.contrib\n xenonpy.datatools\n xenonpy.descriptor\n xenonpy.inverse\n xenonpy.mdl\n xenonpy.model\n xenonpy.utils\n xenonpy.visualization\n\nModule contents\n---------------\n\n.. automodule:: xenonpy\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/source/xenonpy.utils.math.rst", "content": "xenonpy.utils.math package\n==========================\n\nSubmodules\n----------\n\nxenonpy.utils.math.product module\n---------------------------------\n\n.. automodule:: xenonpy.utils.math.product\n :members:\n :undoc-members:\n :show-inheritance:\n\nModule contents\n---------------\n\n.. automodule:: xenonpy.utils.math\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/source/xenonpy.utils.rst", "content": "xenonpy.utils package\n=====================\n\nSubpackages\n-----------\n\n.. toctree::\n :maxdepth: 4\n\n xenonpy.utils.math\n\nSubmodules\n----------\n\nxenonpy.utils.parameter\\_gen module\n-----------------------------------\n\n.. automodule:: xenonpy.utils.parameter_gen\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.utils.useful\\_cls module\n--------------------------------\n\n.. automodule:: xenonpy.utils.useful_cls\n :members:\n :undoc-members:\n :show-inheritance:\n\nxenonpy.utils.useful\\_func module\n---------------------------------\n\n.. automodule:: xenonpy.utils.useful_func\n :members:\n :undoc-members:\n :show-inheritance:\n\nModule contents\n---------------\n\n.. automodule:: xenonpy.utils\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/source/xenonpy.visualization.rst", "content": "xenonpy.visualization package\n=============================\n\nSubmodules\n----------\n\nxenonpy.visualization.heatmap module\n------------------------------------\n\n.. automodule:: xenonpy.visualization.heatmap\n :members:\n :undoc-members:\n :show-inheritance:\n\nModule contents\n---------------\n\n.. automodule:: xenonpy.visualization\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "hooks/build", "content": "#!/bin/bash\n\ndocker build --build-arg key=$api_key -f $DOCKERFILE_PATH -t $IMAGE_NAME ." }, { "path": "licences/.gitkeep", "content": "" }, { "path": "mi_book/README.md", "content": "# 演習問題\n\nこのリポジトリは，共立出版社が出版する「マテリアルズインフォマティクス」の演習問題1~22をまとめている．\nその内，演習16と17は，現在訓練済みモデルのダウンロードサービスのメンテナンスにより実行できない状態になってる．サービス開始後演習16と17を追加する．\n\n## 実行環境\n\n実行環境の構築に関しては，我々は[conda](https://docs.conda.io/en/latest/miniconda.html)の利用をおすすめします．\ncondaを利用した場合は，[XenonPy installation](https://xenonpy.readthedocs.io/en/latest/installation.html#using-conda-and-pip)を参考してください．\n\n独自で実行環境を構築する場合，以下の条件を満たすこと．\n\n* Python >= 3.7\n* Pymatgen >= 2020.10.9\n* rdkit == 2020.09\n* xenonpy >= 0.6\n* Pytorch >= 1.7.0\n\n## 演習用データの獲得\n\nこれらの演習を実行する前に，下記のデータセットを用意してください．\n\n* `retrieve_materials_project.ipynb`を実行して，Materials Projectから無機結晶データをダウンロード．\n* [In house data](https://github.com/yoshida-lab/XenonPy/releases/download/v0.6.5/data.zip)をダウンロードして，README.mdと同じフォルダに解凍してください．\n\nフォルダのストラクチャーのイメージ：\n```\ndata\n |- QC_AC_data.pd.xz\n |- mp_ids.txt\n |- ...\noutput\n |- <演習中の出力>\n |- ...\nexercise_2-10.ipynb\nexercise...\n```\n\n## 目次\n\n### 1.3\n\n* 演習1）[XenonPy installation](https://xenonpy.readthedocs.io/en/latest/installation.html#using-conda-and-pip)を参考にXenonPyをインストールせよ．\n\n### 1.3.1\n\nNotebook: [Exercise 2~10](exercise_2-10.ipynb)\n\n* 演習2）配布したSMILES形式の化学構造をMOLオブジェクトという形式に変換し，化学構造を描画せよ．\n\n* 演習3）例題の化学構造の内，GetSubstructMatchを用いてベンゼン’c1ccccc1’にマッチする原子のインデックスを取得し，RDKitのモジュールを使用して，ベンゼンをカラーハイライトして化学構造を図示せよ．\n\n* 演習4）アスピリンCC(=O)Oc1ccccc1C(=O)OのECFPフィンガープリント（Morganフィンガープリント）を計算せよ．ビット数をB=2048，半径をR=2とする．\n\n* 演習5）演習4の各ビットの部分構造を取り出し，化学構造を描画せよ．\n\n* 演習6）カウント型のECFPフィンガープリントとアトムペアフィンガープリントを計算し，それぞれのベクトルの数値を可視化せよ．\n\n* 演習7）分子動力学シミュレーションのサンプルデータを用いて，モノマー構造のECFPフィンガープリント記述子から熱伝導率を予測するモデルを構築せよ．ここでは，ランダムフォレスト回帰とニューラルネットワークを用いる．また，訓練データの数やフィンガープリントの種類等を変更し，予測精度の変化を調べて解析結果をまとめよ．\n\n* 演習8）ポリエチレンCCC(=O)Oの単量体，二量体，三量体のSMILES文字列を生成し．ECFP記述子を計算せよ（B=2048,R=2）．\n\n* 演習9）演習8の各ビットの部分構造を描画し，比較せよ．\n\n* 演習10）サンプルデータの10化合物のRDKitの2次元記述子とmordredの2次元記述子を計算せよ．\n\n### 1.3.2\n\nNotebook: [Exercise 11](exercise_11.ipynb)\n\n* 演習11）XenonPy の 58 種類の元素特徴量を抽出せよ．\n\nNotebook: [Exercise 12](exercise_12.ipynb)\n\n* 演習12）サンプルデータの化学組成の記述子を計算せよ．\n\n### 1.4.1\n\nNotebook: [Exercise 13](exercise_13.ipynb)\n\n* 演習13）分子動力学シミュレーションのサンプルデータにある300種類のポリマーの構造物性相関データを用いて，密度，定圧熱容量，熱伝導率，線膨張係数をの予測モデルを構築せよ．以下の説明やサンプルコードを参考にモデルの作成方法を自ら工夫し，その結果を考察せよ．\n\n### 1.4.2\n\nNotebook: [Exercise 14](exercise_14.ipynb)\n\n* 演習14）以下の説明とサンプルコードを参考にして，Materials Project に収録されているデータを用いて，化学組成や結晶構造から形成エネルギー，バンドギャップ，密度を予測するモデルを構築せよ．\n\n### 1.4.3\n\nNotebook: [Exercise 15](exercise_15.ipynb)\n\n* 演習15）以下の説明とサンプルコードを参考に，任意の化学組成が形成する三つの構造クラス（準結晶・近似結晶・通常の周期結晶）を判別するモデルを構築せよ（3 クラス分類問題）．\n\n### 1.5.2\n\nNotebook: [修正中](/)\n\n* 演習16）API を用いて，XenonPy.MDL から組成から形成エネルギーを予測するモデル集合を抽出せよ．\n\n* 演習17）同じく，XenonPy.MDL からポリマーの繰り返し単位の化学構造から誘電率を予測するモデル集合を抽出せよ．\n\n### 1.5.3\n\nNotebook: [Exercise 18](exercise_18.ipynb)\n\n* 演習18）以下の説明やサンプルコードを参考にし，転移学習を適用して無機化合物の格子熱伝導率の予測モデルを構築せよ．\n\n### 1.5.4\n\nNotebook: [Exercise 19](exercise_19.ipynb)\n\n* 演習19）以下の説明とサンプルコードを参考にし，ポリマーと無機化合物の屈折率の間で転移学習を実行せよ．\n\n### 1.6.2\n\nNotebook: [Exercise 20](exercise_20.ipynb)\n\n* 演習20）サンプルコードを実行し，エピガロカテキン(Epigallocatechingallate)に変異・挿入・欠失・伸長の操作を施し，生成される化学構造を可視化せよ.\n\nNotebook: [Exercise 21](exercise_21.ipynb)\n\n* 演習21）サンプルコードを実行し，フラグメントの確率的な組み換えを行い，所望のHOMO（highest occupied molecular orbital），LUMO（lowest unoccupied molecular orbital）を有する分子（有機薄膜太陽電池のドナー分子）を生成せよ．計算のフローについては，Algorithm3を参照せよ．\n\n### 1.7.5\n\nNotebook: [Exercise 22](exercise_22.ipynb)\n\n* 演習22）以下の解説とサンプルコードを参考に，所望の熱伝導率と線膨張係数を持つ高分子のモノマーを設計せよ．物性の目標範囲を変化させて，生成されるモノマーの構造的な違いを考察せよ．\n" }, { "path": "mi_book/common_setting.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import warnings\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\", message=\\\"numpy.dtype size changed\\\")\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\", message=\\\"numpy.ufunc size changed\\\")\\n\",\n \"\\n\",\n \"# user-friendly print\\n\",\n \"from IPython.core.interactiveshell import InteractiveShell\\n\",\n \"InteractiveShell.ast_node_interactivity = \\\"all\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from itertools import product\\n\",\n \"\\n\",\n \"class ConfusionMatrixDisplay:\\n\",\n \" \\\"\\\"\\\"Confusion Matrix visualization.\\n\",\n \" It is recommend to use :func:`~sklearn.metrics.plot_confusion_matrix` to\\n\",\n \" create a :class:`ConfusionMatrixDisplay`. All parameters are stored as\\n\",\n \" attributes.\\n\",\n \" Read more in the :ref:`User Guide `.\\n\",\n \" Parameters\\n\",\n \" ----------\\n\",\n \" confusion_matrix : ndarray of shape (n_classes, n_classes)\\n\",\n \" Confusion matrix.\\n\",\n \" display_labels : ndarray of shape (n_classes,)\\n\",\n \" Display labels for plot.\\n\",\n \" Attributes\\n\",\n \" ----------\\n\",\n \" im_ : matplotlib AxesImage\\n\",\n \" Image representing the confusion matrix.\\n\",\n \" text_ : ndarray of shape (n_classes, n_classes), dtype=matplotlib Text, \\\\\\n\",\n \" or None\\n\",\n \" Array of matplotlib axes. `None` if `include_values` is false.\\n\",\n \" ax_ : matplotlib Axes\\n\",\n \" Axes with confusion matrix.\\n\",\n \" figure_ : matplotlib Figure\\n\",\n \" Figure containing the confusion matrix.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" def __init__(self, confusion_matrix, display_labels):\\n\",\n \" self.confusion_matrix = confusion_matrix\\n\",\n \" self.display_labels = display_labels\\n\",\n \"\\n\",\n \" def plot(self, *, include_values=True, cmap='viridis',\\n\",\n \" xticks_rotation='horizontal', values_format=None, ax=None, anno_fontsize=14, tick_fontsize=14, label_fontsize=20):\\n\",\n \" \\\"\\\"\\\"Plot visualization.\\n\",\n \" Parameters\\n\",\n \" ----------\\n\",\n \" include_values : bool, default=True\\n\",\n \" Includes values in confusion matrix.\\n\",\n \" cmap : str or matplotlib Colormap, default='viridis'\\n\",\n \" Colormap recognized by matplotlib.\\n\",\n \" xticks_rotation : {'vertical', 'horizontal'} or float, \\\\\\n\",\n \" default='vertical'\\n\",\n \" Rotation of xtick labels.\\n\",\n \" values_format : str, default=None\\n\",\n \" Format specification for values in confusion matrix. If `None`,\\n\",\n \" the format specification is '.2f' for a normalized matrix, and\\n\",\n \" 'd' for a unnormalized matrix.\\n\",\n \" ax : matplotlib axes, default=None\\n\",\n \" Axes object to plot on. If `None`, a new figure and axes is\\n\",\n \" created.\\n\",\n \" Returns\\n\",\n \" -------\\n\",\n \" display : :class:`~sklearn.metrics.ConfusionMatrixDisplay`\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \" if ax is None:\\n\",\n \" fig, ax = plt.subplots()\\n\",\n \" else:\\n\",\n \" fig = ax.figure\\n\",\n \"\\n\",\n \" cm = self.confusion_matrix\\n\",\n \" n_classes = cm.shape[0]\\n\",\n \" self.im_ = ax.imshow(cm, interpolation='nearest', cmap=cmap)\\n\",\n \" self.text_ = None\\n\",\n \"\\n\",\n \" cmap_min, cmap_max = self.im_.cmap(0), self.im_.cmap(256)\\n\",\n \"\\n\",\n \" if include_values:\\n\",\n \" self.text_ = np.empty_like(cm, dtype=object)\\n\",\n \" if values_format is None:\\n\",\n \" values_format = '.2g'\\n\",\n \"\\n\",\n \" # print text with appropriate color depending on background\\n\",\n \" thresh = (cm.max() - cm.min()) / 2.\\n\",\n \" for i, j in product(range(n_classes), range(n_classes)):\\n\",\n \" color = cmap_max if cm[i, j] < thresh else cmap_min\\n\",\n \" self.text_[i, j] = ax.text(j, i,\\n\",\n \" format(cm[i, j], values_format),\\n\",\n \" ha=\\\"center\\\", va=\\\"center\\\",\\n\",\n \" color=color, fontsize=anno_fontsize)\\n\",\n \"\\n\",\n \" cbar = fig.colorbar(self.im_, ax=ax)\\n\",\n \" cbar.ax.tick_params(labelsize=anno_fontsize) \\n\",\n \" ax.set(xticks=np.arange(n_classes),\\n\",\n \" yticks=np.arange(n_classes),\\n\",\n \" xticklabels=self.display_labels,\\n\",\n \" yticklabels=self.display_labels,\\n\",\n \" ylabel=\\\"True label\\\",\\n\",\n \" xlabel=\\\"Predicted label\\\")\\n\",\n \"\\n\",\n \" ax.set_ylim((n_classes - 0.5, -0.5))\\n\",\n \" plt.setp(ax.get_xticklabels(), rotation=xticks_rotation)\\n\",\n \"\\n\",\n \" ax.xaxis.label.set_size(label_fontsize)\\n\",\n \" ax.yaxis.label.set_size(label_fontsize)\\n\",\n \" ax.tick_params(labelsize=tick_fontsize)\\n\",\n \" \\n\",\n \" self.figure_ = fig\\n\",\n \" self.ax_ = ax\\n\",\n \" return self\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 196,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"\\n\",\n \"import matplotlib.ticker as plticker\\n\",\n \"\\n\",\n \"from matplotlib import pyplot as plt\\n\",\n \"from xenonpy.model.utils import regression_metrics\\n\",\n \"\\n\",\n \"def cv_plot(pred, true, pred_fit=None, true_fit=None, *, unit='', lim=None, n_ticks=5, title='', ax=None, color_style=True, show_label=True, show_legend=True, **kwargs):\\n\",\n \" pred, true = pred.flatten(), true.flatten()\\n\",\n \" scores = regression_metrics(true, pred)\\n\",\n \"\\n\",\n \" if ax is None:\\n\",\n \" _, ax = plt.subplots(figsize=(8, 8), dpi=150)\\n\",\n \"\\n\",\n \" if color_style:\\n\",\n \" if pred_fit is not None and true_fit is not None:\\n\",\n \" ax.scatter(pred_fit, true_fit, alpha=0.4, s=13, marker='D', label='Train', **kwargs)\\n\",\n \" ax.scatter(pred, true, alpha=1, s=35, ec='w', marker='o', label='Test', **kwargs)\\n\",\n \" else:\\n\",\n \" if pred_fit is not None and true_fit is not None:\\n\",\n \" ax.scatter(pred_fit, true_fit, alpha=0.6, s=15, ec='grey', c='none', marker='D', label='Train', **kwargs)\\n\",\n \" ax.scatter(pred, true, alpha=1, s=35, ec='w', c='k', marker='o', label='Test', **kwargs)\\n\",\n \" \\n\",\n \" # adjust lims\\n\",\n \" if lim is None:\\n\",\n \" temp_data = np.concatenate([pred, true]) if pred_fit is None or true_fit is None else np.concatenate([pred, true, pred_fit, true_fit])\\n\",\n \" lim = (temp_data.min(), temp_data.max())\\n\",\n \" shift = (lim[1] - lim[0]) * 0.05\\n\",\n \" lim = (lim[0] - shift, lim[1] + shift)\\n\",\n \" ax.set_xlim(*lim)\\n\",\n \" ax.set_ylim(*lim)\\n\",\n \"\\n\",\n \" # plot diagonal\\n\",\n \" ax.plot(lim, lim, ls=\\\"--\\\", c=\\\"0.3\\\", alpha=0.7, lw=1.5)\\n\",\n \" \\n\",\n \" # align ticks\\n\",\n \" base = round((lim[1] - lim[0]) / n_ticks)\\n\",\n \" \\n\",\n \" if base > 0:\\n\",\n \" loc = plticker.MultipleLocator(base=base) # this locator puts ticks at regular intervals\\n\",\n \" ax.xaxis.set_major_locator(loc)\\n\",\n \" ax.yaxis.set_major_locator(loc)\\n\",\n \" \\n\",\n \" if unit != '':\\n\",\n \" unit = f' ({unit})'\\n\",\n \" if show_label:\\n\",\n \" ax.set_xlabel(f'Prediction{unit}', fontsize='x-large')\\n\",\n \" ax.set_ylabel(f'Observation{unit}', fontsize='x-large')\\n\",\n \" if show_legend:\\n\",\n \" legend = ax.legend(markerscale=2.5, fontsize='larger', loc=0)\\n\",\n \" for lh in legend.legendHandles:\\n\",\n \" lh.set_alpha(1.0)\\n\",\n \" ax.grid(color='gray', linestyle='--', linewidth=0.5, alpha=0.5)\\n\",\n \" ax.set_title(title, fontsize='xx-large')\\n\",\n \" shift = (lim[1] - lim[0]) / 30\\n\",\n \" ax.text(lim[1] - shift, lim[0] + shift,\\n\",\n \" 'R2: %.3f\\\\nMAE: %.3f\\\\nCorrelation: %.3f' % (scores['r2'],scores['mae'],scores['pearsonr']),\\n\",\n \" horizontalalignment='right', verticalalignment='bottom', fontsize='x-large',\\n\",\n \" bbox=dict(boxstyle='square', facecolor='grey', alpha=0.2, ec='black'),\\n\",\n \" )\\n\",\n \" ax.tick_params(axis='both', which='major', labelsize='larger')\\n\",\n \" return ax\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "mi_book/exercise_11.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \" new idx \\n\",\n \"[47625, 376787] ---> [623953, 376787] \\n\",\n \"[197650, 50311] ---> [502976, 50311] \\n\",\n \"[251279] ---> [202607] \\n\",\n \"[447145] ---> [535337] \\n\",\n \"[612438, 46951] ---> [612438, 611055] \\n\"\n ]\n }\n ],\n \"source\": [\n \"old_list = [str(r) for r in init_samples[\\\"reactant_idx\\\"][:5]]\\n\",\n \"new_list = [mol_pool.single_proposal(reactant) for reactant in init_samples[\\\"reactant_idx\\\"]]\\n\",\n \"init_samples[\\\"reactant_idx\\\"] = new_list\\n\",\n \"print(\\\"{:25} ---> {:25}\\\".format(\\\"old idx\\\",\\\"new idx\\\"))\\n\",\n \"for o,n in zip(old_list[:5], new_list[:5]):\\n\",\n \" print(\\\"{:25} ---> {:25}\\\".format(str(o),str(n)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### lookup pool\\n\",\n \"By looking up the reactant pool, convert reactant id to SMILES.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"reactant idx ---> reactant SMILES \\n\",\n \"[623953, 376787] ---> CCN(C)C(OC)OC.Cc1ccc([N+](=O)[O-])c(F)c1\\n\",\n \"[502976, 50311] ---> CC(C)C(N)C(C)(C)O.CCC#N \\n\",\n \"[202607] ---> C=CCc1cc2ccc(=O)oc2c(OC)c1O\\n\",\n \"[535337] ---> Brc1cc(OCc2ccccc2)c2ccnn2c1\\n\",\n \"[612438, 611055] ---> Cc1ccc2[nH]ccc2c1.CC(=O)C(=O)Cl\\n\"\n ]\n }\n ],\n \"source\": [\n \"init_samples[\\\"reactant_smiles\\\"] = [mol_pool.single_index2reactant(id_str) for id_str in init_samples[\\\"reactant_idx\\\"]]\\n\",\n \"print(\\\"{:25} ---> {:25}\\\".format(\\\"reactant idx\\\",\\\"reactant SMILES\\\"))\\n\",\n \"for o,n in zip(init_samples[\\\"reactant_idx\\\"][:5], init_samples[\\\"reactant_smiles\\\"][:5]):\\n\",\n \" print(\\\"{:25} ---> {:25}\\\".format(str(o),str(n)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### reactor\\n\",\n \"The reactor can predict the product given the reactant set.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
reactant_idxreactant_smilesproduct_smiles
0[623953, 376787]CCN(C)C(OC)OC.Cc1ccc([N+](=O)[O-])c(F)c1CCN(C)C=Cc1ccc([N+](=O)[O-])c(F)c1
1[502976, 50311]CC(C)C(N)C(C)(C)O.CCC#NCCC(=NC(C(C)C)C(C)(C)O)NCCC#N
2[202607]C=CCc1cc2ccc(=O)oc2c(OC)c1OC=CCc1cc2ccc(=O)oc2c(OC)c1OC
3[535337]Brc1cc(OCc2ccccc2)c2ccnn2c1Brc1cc(OCc2ccccc2)c2ccnn2c1-c1cc(OCc2ccccc2)c2...
4[612438, 611055]Cc1ccc2[nH]ccc2c1.CC(=O)C(=O)ClCC(=O)C(=O)c1c[nH]c2ccc(C)cc12
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" reactant_idx reactant_smiles \\\\\\n\",\n \"0 [623953, 376787] CCN(C)C(OC)OC.Cc1ccc([N+](=O)[O-])c(F)c1 \\n\",\n \"1 [502976, 50311] CC(C)C(N)C(C)(C)O.CCC#N \\n\",\n \"2 [202607] C=CCc1cc2ccc(=O)oc2c(OC)c1O \\n\",\n \"3 [535337] Brc1cc(OCc2ccccc2)c2ccnn2c1 \\n\",\n \"4 [612438, 611055] Cc1ccc2[nH]ccc2c1.CC(=O)C(=O)Cl \\n\",\n \"\\n\",\n \" product_smiles \\n\",\n \"0 CCN(C)C=Cc1ccc([N+](=O)[O-])c(F)c1 \\n\",\n \"1 CCC(=NC(C(C)C)C(C)(C)O)NCCC#N \\n\",\n \"2 C=CCc1cc2ccc(=O)oc2c(OC)c1OC \\n\",\n \"3 Brc1cc(OCc2ccccc2)c2ccnn2c1-c1cc(OCc2ccccc2)c2... \\n\",\n \"4 CC(=O)C(=O)c1c[nH]c2ccc(C)cc12 \"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"init_samples[\\\"product_smiles\\\"] = mol_pool._reactor.react(init_samples[\\\"reactant_smiles\\\"])\\n\",\n \"init_samples.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4. a complete ismd run\\n\",\n \"In this tutorial, we use IQSPR4DF as the SMC framework, which is usable for pandas dataframe. Merge the estimator and the modifier into the IQSPR4DF module, then, a complete implementation is ready to run.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from xenonpy.inverse.iqspr import IQSPR4DF\\n\",\n \"\\n\",\n \"ISMD = IQSPR4DF(estimator=likelihood_calculator, modifier=mol_pool, sample_col='product_smiles')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([0.01 , 0.02 , 0.03 , 0.04 , 0.05 ,\\n\",\n \" 0.06 , 0.07 , 0.08 , 0.09 , 0.1 ,\\n\",\n \" 0.11 , 0.12 , 0.13 , 0.14 , 0.15 ,\\n\",\n \" 0.16 , 0.17 , 0.18 , 0.19 , 0.2 ,\\n\",\n \" 0.21 , 0.23111111, 0.25222222, 0.27333333, 0.29444444,\\n\",\n \" 0.31555556, 0.33666667, 0.35777778, 0.37888889, 0.4 ,\\n\",\n \" 0.4 , 0.46666667, 0.53333333, 0.6 , 0.66666667,\\n\",\n \" 0.73333333, 0.8 , 0.86666667, 0.93333333, 1. ,\\n\",\n \" 1. , 1. , 1. , 1. , 1. ,\\n\",\n \" 1. , 1. , 1. , 1. , 1. ])\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"\\n\",\n \"beta = np.hstack([np.linspace(0.01,0.2,20),np.linspace(0.21,0.4,10),np.linspace(0.4,1,10),np.linspace(1,1,10)])\\n\",\n \"beta\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 52min 9s, sys: 1min 6s, total: 53min 15s\\n\",\n \"Wall time: 7min 53s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"np.random.seed(201906) # fix the random seed\\n\",\n \"# main loop of iQSPR\\n\",\n \"iqspr_samples1, iqspr_loglike1, iqspr_prob1, iqspr_freq1 = [], [], [], []\\n\",\n \"for s, ll, p, freq in ISMD(init_samples, beta, yield_lpf=True):\\n\",\n \" iqspr_samples1.append(s)\\n\",\n \" iqspr_loglike1.append(ll)\\n\",\n \" iqspr_prob1.append(p)\\n\",\n \" iqspr_freq1.append(freq)\\n\",\n \"# record all outputs\\n\",\n \"iqspr_results_reorder = {\\n\",\n \" \\\"samples\\\": iqspr_samples1,\\n\",\n \" \\\"loglike\\\": iqspr_loglike1,\\n\",\n \" \\\"prob\\\": iqspr_prob1,\\n\",\n \" \\\"freq\\\": iqspr_freq1,\\n\",\n \" \\\"beta\\\": beta\\n\",\n \"}\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"By looking at the likelihood of each steps, we can say that after few steps, most of the particles sampled have high likelihood.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"iqspr_results_reorder = {\\n\",\n \" \\\"samples\\\": iqspr_samples1,\\n\",\n \" \\\"loglike\\\": iqspr_loglike1,\\n\",\n \" \\\"prob\\\": iqspr_prob1,\\n\",\n \" \\\"freq\\\": iqspr_freq1,\\n\",\n \" \\\"beta\\\": beta\\n\",\n \"}\\n\",\n \"# set up the min and max boundary for the plots\\n\",\n \"tmp_list = [x.sum(axis = 1, skipna = True).values for x in iqspr_results_reorder[\\\"loglike\\\"]]\\n\",\n \"flat_list = np.asarray([item for sublist in tmp_list for item in sublist])\\n\",\n \"y_max, y_min = max(flat_list), min(flat_list)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(10,5))\\n\",\n \"plt.xlim(0,len(iqspr_results_reorder[\\\"loglike\\\"]))\\n\",\n \"plt.ylim(-30,y_max)\\n\",\n \"plt.xlabel('Step')\\n\",\n \"plt.ylabel('Log-likelihood')\\n\",\n \"for i, ll in enumerate(tmp_list):\\n\",\n \" plt.scatter([i]*len(ll), ll ,s=10, c='b', alpha=0.2)\\n\",\n \"plt.show()\\n\",\n \"#plt.savefig('iqspr_loglike_reorder.png',dpi = 500)\\n\",\n \"#plt.close()\\n\",\n \"\\n\",\n \"y_max, y_min = np.exp(y_max), np.exp(y_min)\\n\",\n \"plt.figure(figsize=(10,5))\\n\",\n \"plt.xlim(0,len(iqspr_results_reorder[\\\"loglike\\\"]))\\n\",\n \"plt.ylim(y_min,y_max)\\n\",\n \"plt.xlabel('Step')\\n\",\n \"plt.ylabel('Likelihood')\\n\",\n \"for i, ll in enumerate(tmp_list):\\n\",\n \" plt.scatter([i]*len(ll), np.exp(ll) ,s=10, c='b', alpha=0.2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.9\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "samples/kernel_neural_network.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ongoing-parameter\",\n \"metadata\": {},\n \"source\": [\n \"This Jupyter notebook contains a set of sample codes that are necessary for obtaining the calculation results of three different datasets as described in the paper titled \\\"Functional Output Regression for Machine Learning in Materials Science\\\". Readers can use the code to perform model training and test with full hyperparameter search, which will take a long time. Instead, readers can also test the performance of the best performing models that are downloadable from our Github repository. \\n\",\n \"\\n\",\n \"We provide three pre-trained models for each dataset.\\n\",\n \"- Dataset 1: https://github.com/yoshida-lab/XenonPy/releases/download/v0.6.3/pretrained_models.zip\\n\",\n \"- Dataset 2: https://github.com/yoshida-lab/XenonPy/releases/download/v0.6.3/dataset2.zip\\n\",\n \"- USGS: https://github.com/yoshida-lab/XenonPy/releases/download/v0.6.3/USGS.zip\\n\",\n \"\\n\",\n \"Please update the variable \\\"model_dir\\\" in the \\\"User parameter setting\\\" section accordingly.\\n\",\n \"\\n\",\n \"To obtain the datasets used in this study, please access the following references.\\n\",\n \"- For Dataset I and Dataset II:\\n\",\n \"Urbina et al. (2021) UV-adVISor: Attention-Based Recurrent Neural Networks to Predict UV–Vis Spectra. Analytical Chemistry, 93(48), 16076-16085.\\n\",\n \"(https://pubs.acs.org/doi/10.1021/acs.analchem.1c03741)\\n\",\n \"- USGS:\\n\",\n \"Kokaly et al. (2017) USGS Spectral Library Version 7 Data: U.S. Geological Survey data release (https://dx.doi.org/10.5066/F7RR1WDJ)\\n\",\n \"\\n\",\n \"For Dataset I and II, please directly pick the files \\\"Datasets I, II, III spectra and SMILES/Dataset_I.csv\\\" and \\\"Datasets I, II, III spectra and SMILES/Dataset_II.csv\\\" in the downloaded zip file, respectively, as the input csv files. For the USGS dataset, please process the downloaded data using \\\"process_dataset_usgs.ipynb\\\" (https://github.com/yoshida-lab/XenonPy/blob/master/samples/process_dataset_usgs.ipynb) to obtain the compact csv files used in our study. A file named \\\"Dataset_USGS.csv\\\" shall be produced after running the codes in the notebook. If there is any trouble in processing the data, please contact us for help.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"rational-throw\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"RDKit WARNING: [23:24:15] Enabling RDKit 2019.09.1 jupyter extensions\\n\",\n \"[23:24:15] Enabling RDKit 2019.09.1 jupyter extensions\\n\"\n ]\n }\n ],\n \"source\": [\n \"# python packages used in the sample codes\\n\",\n \"\\n\",\n \"from pathlib import Path\\n\",\n \"\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"import torch\\n\",\n \"import torch.nn as nn\\n\",\n \"import torch.utils.data\\n\",\n \"from torch import optim\\n\",\n \"\\n\",\n \"from rdkit.Chem.Fingerprints import FingerprintMols\\n\",\n \"from rdkit.Avalon import pyAvalonTools\\n\",\n \"from rdkit.Chem import Draw\\n\",\n \"from rdkit import rdBase, Chem, DataStructs\\n\",\n \"from rdkit.Chem import AllChem\\n\",\n \"from rdkit.Chem.AtomPairs import Pairs, Torsions\\n\",\n \"\\n\",\n \"from sklearn.metrics import mean_squared_error\\n\",\n \"from sklearn.metrics import r2_score\\n\",\n \"from sklearn.metrics import mean_absolute_error\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"\\n\",\n \"# user-friendly print out\\n\",\n \"from IPython.core.interactiveshell import InteractiveShell\\n\",\n \"InteractiveShell.ast_node_interactivity = \\\"all\\\"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"representative-hepatitis\",\n \"metadata\": {},\n \"source\": [\n \"# Preparation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"hearing-resource\",\n \"metadata\": {},\n \"source\": [\n \"#### Utility function: define RBFKernel class to define the radial basis function kernel that will be used in our models\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"horizontal-province\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"\\n\",\n \"from typing import Union, Sequence, Callable, Tuple\\n\",\n \"\\n\",\n \"\\n\",\n \"class RBFKernel():\\n\",\n \" def __init__(self, sigmas_squared: Union[float, int, np.ndarray, Sequence], hight=10, *, dtype='float32'):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Radial Basis Function (RBF) kernel function.\\n\",\n \" Ref: https://en.wikipedia.org/wiki/Radial_basis_function_kernel\\n\",\n \"\\n\",\n \" Parameters\\n\",\n \" ----------\\n\",\n \" sigmas:\\n\",\n \" The squared standard deviations (SD).\\n\",\n \" Can be a single number or a 1d array-like object.\\n\",\n \" x_i: np.ndarray\\n\",\n \" Should be a 1d array.\\n\",\n \" x_j: np.ndarray\\n\",\n \" Should be a 1d array.\\n\",\n \"\\n\",\n \" Returns\\n\",\n \" -------\\n\",\n \" np.ndarray\\n\",\n \" Distribution under RBF kernel.\\n\",\n \"\\n\",\n \" Raises\\n\",\n \" ------\\n\",\n \" ValueError\\n\",\n \" Raise error if sigmas has wrong dimension.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" sigmas_squared = np.asarray(sigmas_squared)\\n\",\n \" if sigmas_squared.ndim == 0:\\n\",\n \" sigmas_squared = sigmas_squared[np.newaxis]\\n\",\n \" if sigmas_squared.ndim != 1:\\n\",\n \" raise ValueError('parameter `sigmas_squared` must be a array-like object which has dimension 1')\\n\",\n \" self._sigmas_squared = sigmas_squared\\n\",\n \" self.hight = hight\\n\",\n \" self.dtype = dtype\\n\",\n \" \\n\",\n \" @property\\n\",\n \" def sigmas_squared(self):\\n\",\n \" return np.copy(self._sigmas_squared)\\n\",\n \" \\n\",\n \" def __call__(self, x_i: np.ndarray, x_j: Union[np.ndarray, int, float]):\\n\",\n \" # K(x_i, x_j) = exp(-||x_i - x_j||^2 / (2 * sigma^2))\\n\",\n \" p1 = np.power(np.expand_dims(x_i, axis=x_i.ndim) - x_j, 2)\\n\",\n \" p2 = self._sigmas_squared * 2\\n\",\n \" dists = self.hight * np.exp(-np.expand_dims(p1, axis=p1.ndim) / p2)\\n\",\n \"\\n\",\n \" if dists.shape[2] == 1:\\n\",\n \" return dists[:, :, 0].astype(self.dtype)\\n\",\n \" return dists.astype(self.dtype)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"canadian-procurement\",\n \"metadata\": {},\n \"source\": [\n \"#### Model definition: define KernelNet class to be the neural network based kernel function model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"caroline-final\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from xenonpy.model import SequentialLinear\\n\",\n \"\\n\",\n \"# model parameter initialization\\n\",\n \"@torch.no_grad()\\n\",\n \"def weights_init(m):\\n\",\n \" if isinstance(m, nn.Linear):\\n\",\n \" nn.init.xavier_normal_(m.weight)\\n\",\n \" nn.init.constant_(m.bias, 0)\\n\",\n \" elif isinstance(m, nn.BatchNorm1d):\\n\",\n \" nn.init.normal_(m.weight, 1.0, 0.02)\\n\",\n \" nn.init.constant_(m.bias, 0)\\n\",\n \"\\n\",\n \" \\n\",\n \"class KernelNet(nn.Module):\\n\",\n \" def __init__(self, *,\\n\",\n \" n_neurons=(1024, 896, 512, 256, 128),\\n\",\n \" activation_func=nn.LeakyReLU(0.2, inplace=True),\\n\",\n \" kernel_grids: Union[int, np.ndarray] = 128,\\n\",\n \" wavelength_points: Union[int, np.ndarray] =181,\\n\",\n \" kernel_func=RBFKernel(sigmas_squared=0.05, hight=1),\\n\",\n \" ):\\n\",\n \" super().__init__()\\n\",\n \"\\n\",\n \" # initializing fixed params (not updated through training)\\n\",\n \" if isinstance(kernel_grids, int):\\n\",\n \" kernel_grids = np.linspace(0, 1, kernel_grids)\\n\",\n \" if isinstance(kernel_grids, np.ndarray):\\n\",\n \" self.kernel_grids = kernel_grids\\n\",\n \" else:\\n\",\n \" raise ValueError('kernel_grids error!')\\n\",\n \" \\n\",\n \" if isinstance(wavelength_points, int):\\n\",\n \" wavelength_points = np.linspace(0, 1, wavelength_points)\\n\",\n \" if isinstance(wavelength_points, np.ndarray):\\n\",\n \" self.wavelength_points = wavelength_points\\n\",\n \" else:\\n\",\n \" raise ValueError('wavelength_points error!')\\n\",\n \" \\n\",\n \" # fully connected layers\\n\",\n \" self.fc_layers = SequentialLinear(\\n\",\n \" in_features=n_neurons[0], out_features=n_neurons[-1], h_neurons=n_neurons[1:-1], h_activation_funcs=activation_func\\n\",\n \" )\\n\",\n \" \\n\",\n \" # baseline parameters\\n\",\n \" self.mu = torch.nn.Parameter(torch.zeros(wavelength_points.size))\\n\",\n \" \\n\",\n \" # kernel\\n\",\n \" self.kernel = torch.from_numpy(\\n\",\n \" kernel_func(self.kernel_grids, self.wavelength_points)\\n\",\n \" )\\n\",\n \" \\n\",\n \" def to(self, *arg, **kwarg):\\n\",\n \" self.kernel = self.kernel.to(*arg, **kwarg)\\n\",\n \" return super().to(*arg, **kwarg)\\n\",\n \"\\n\",\n \" def forward(self, fingerprint_input):\\n\",\n \" x = self.fc_layers(fingerprint_input)\\n\",\n \" x = x.view(x.size(0), self.kernel_grids.size, -1) \\n\",\n \" x = x * self.kernel\\n\",\n \" x = torch.sum(x, dim=1)\\n\",\n \" x = self.mu + x\\n\",\n \" return x\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"85ac5ce3-c56c-4aff-84c7-8053095f70ee\",\n \"metadata\": {},\n \"source\": [\n \"# User parameter setting\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"0d814c7d-a2ea-4f54-a2d7-e4d26b2edac2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Perform best case only or not: True - best model only; False - all hyperparameter search\\n\",\n \"best_case = True\\n\",\n \"\\n\",\n \"# Target dataset: 1 - Dataset I; 2 - Dataset II; 3 - USGS\\n\",\n \"data_case = 1\\n\",\n \"\\n\",\n \"# Full directory of the processed data file\\n\",\n \"data_file = \\\"Dataset_I.csv\\\"\\n\",\n \"\\n\",\n \"# Directory and folder name for saving all the outputs\\n\",\n \"out_directory = \\\"spectrum_results\\\"\\n\",\n \"\\n\",\n \"# Directory and folder name for saving or loading of models\\n\",\n \"if data_case == 1:\\n\",\n \" model_dir = 'model/dataset1'\\n\",\n \"elif data_case == 2:\\n\",\n \" model_dir = 'model/dataset2'\\n\",\n \"elif data_case == 3:\\n\",\n \" model_dir = 'model/usgs'\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"34310578-9373-4f64-a34e-2564fa3b5443\",\n \"metadata\": {},\n \"source\": [\n \"# Main codes\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"detected-swaziland\",\n \"metadata\": {},\n \"source\": [\n \"### Loading data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"distant-driving\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"number of data: 949\\n\"\n ]\n }\n ],\n \"source\": [\n \"# load csv file\\n\",\n \"input_file = pd.read_csv(data_file,sep=\\\",\\\")\\n\",\n \"\\n\",\n \"# Figure\\n\",\n \"out_result = Path(out_directory)\\n\",\n \"out_result.mkdir(exist_ok=True, parents=True)\\n\",\n \"\\n\",\n \"# drop \\\"OC(=O)C(=C\\\\C1=CC=[Cl]C=[Cl]1)\\\\C1=CN=CC=C1\\\" because of Explicit valence error (only relevant for Dataset I)\\n\",\n \"input_file = input_file[input_file.SMILES != \\\"OC(=O)C(=C\\\\C1=CC=[Cl]C=[Cl]1)\\\\C1=CN=CC=C1\\\"]\\n\",\n \"# drop data containing NAN　because of Explicit valence error (only relevant for Dataset II)\\n\",\n \"input_file = input_file.dropna()\\n\",\n \"\\n\",\n \"file_smiles_spectrum = input_file.values\\n\",\n \"smiles_list = (file_smiles_spectrum[:,1])\\n\",\n \"spectrum_list = file_smiles_spectrum[:,2:]\\n\",\n \"spectrum_list = np.array(spectrum_list, dtype='float32')\\n\",\n \"\\n\",\n \"print(\\\"number of data:\\\", len(smiles_list))\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"usual-think\",\n \"metadata\": {},\n \"source\": [\n \"#### Convert smiles to fingerprint\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"considerable-scale\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"torch.Size([949, 1024])\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"torch.Size([949, 182])\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# convert smiles to mol file\\n\",\n \"mol_list = [Chem.MolFromSmiles(s) for s in smiles_list if s is not None]\\n\",\n \" \\n\",\n \"# Morgan fingerprint (ECFP) with radius=3 and 1024 bits\\n\",\n \"fps_bit = [AllChem.GetMorganFingerprintAsBitVect(m, 3, 1024) for m in mol_list]\\n\",\n \"fps_list = []\\n\",\n \"for fps in fps_bit:\\n\",\n \" fps_arr = np.zeros((1,))\\n\",\n \" DataStructs.ConvertToNumpyArray(fps, fps_arr)\\n\",\n \" fps_list.append(fps_arr)\\n\",\n \"fps_list = np.array(fps_list, dtype='float32')\\n\",\n \"\\n\",\n \"fps_list_max = np.amax(fps_list)\\n\",\n \"fps_list_min = np.amin(fps_list)\\n\",\n \"fps_norm = ((fps_list - fps_list_min) / (fps_list_max - fps_list_min))\\n\",\n \"X_data = torch.from_numpy(fps_norm)\\n\",\n \"\\n\",\n \"# process spectrum data\\n\",\n \"spectrum_list_max = np.amax(spectrum_list)\\n\",\n \"spectrum_list_min = np.amin(spectrum_list)\\n\",\n \"spectrum_norm = ((spectrum_list - spectrum_list_min) / (spectrum_list_max - spectrum_list_min))\\n\",\n \"Y_data = torch.from_numpy(spectrum_norm)\\n\",\n \"data_number = torch.zeros((X_data.shape[0],1))\\n\",\n \"Y_data = torch.cat((data_number, Y_data), dim=1)\\n\",\n \"for i in range(Y_data.shape[0]):\\n\",\n \" number = torch.tensor([i]) \\n\",\n \" Y_data[i][0] = number\\n\",\n \" \\n\",\n \"# show data size\\n\",\n \"X_data.shape\\n\",\n \"Y_data.shape\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"listed-vessel\",\n \"metadata\": {},\n \"source\": [\n \"### Set up hyperparams\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"charming-reader\",\n \"metadata\": {},\n \"source\": [\n \"#### 1. Model training parameters\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"tutorial-hearing\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"n_epoch = 3000 # epoch\\n\",\n \"lr = 0.0002 # learning rate\\n\",\n \"ngpu = 2 # number of GPU\\n\",\n \"display_interval = 100\\n\",\n \"\\n\",\n \"model_path = Path(model_dir)\\n\",\n \"model_path.mkdir(exist_ok=True, parents=True)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"varying-marking\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# use GPU if available\\n\",\n \"device = torch.device(\\\"cuda:0\\\" if (torch.cuda.is_available() and ngpu > 0) else \\\"cpu\\\")\\n\",\n \"\\n\",\n \"# split train:val:test \\n\",\n \"if data_case == 1:\\n\",\n \" split_size = 0.316 # DatasetI:0.316, DatasetII:0.301, USGS:0.2\\n\",\n \" split_val_size = 0.5 # DatasetI:0.5, DatasetII:0.488, USGS:0.5\\n\",\n \"elif data_case == 2:\\n\",\n \" split_size = 0.301 # DatasetI:0.316, DatasetII:0.301, USGS:0.2\\n\",\n \" split_val_size = 0.488 # DatasetI:0.5, DatasetII:0.488, USGS:0.5\\n\",\n \"elif data_case == 3:\\n\",\n \" split_size = 0.2 # DatasetI:0.316, DatasetII:0.301, USGS:0.2\\n\",\n \" split_val_size = 0.5 # DatasetI:0.5, DatasetII:0.488, USGS:0.5\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"fbc29884-4885-4f06-bfbf-ed7bd6c7df4b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# indexing corresponding to model parameters below\\n\",\n \"if best_case:\\n\",\n \" if data_case == 1:\\n\",\n \" start_layer = 3\\n\",\n \" end_layer = 4\\n\",\n \" start_hyper = 7\\n\",\n \" end_hyper = 8\\n\",\n \" wavelength_points = 181\\n\",\n \" kernel_grids = 128\\n\",\n \" elif data_case == 2:\\n\",\n \" start_layer = 2\\n\",\n \" end_layer = 3\\n\",\n \" start_hyper = 7\\n\",\n \" end_hyper = 8\\n\",\n \" wavelength_points = 171\\n\",\n \" kernel_grids = 128\\n\",\n \" elif data_case == 3:\\n\",\n \" start_layer = 2\\n\",\n \" end_layer = 3\\n\",\n \" start_hyper = 6\\n\",\n \" end_hyper = 7\\n\",\n \" wavelength_points = 2151\\n\",\n \" kernel_grids = 256\\n\",\n \"else:\\n\",\n \" start_layer = 0\\n\",\n \" end_layer = 4\\n\",\n \" start_hyper = 0\\n\",\n \" end_hyper = 12\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"unauthorized-smell\",\n \"metadata\": {},\n \"source\": [\n \"#### 2. Model parameters\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"sporting-intranet\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# the number of hidden layers\\n\",\n \"if data_case == 3:\\n\",\n \" n_neurons = [\\n\",\n \" (1024, 640, 256), # DatasetUSGS: 1 hidden layer\\n\",\n \" (1024, 768, 512, 256), # DatasetUSGS: 2 hidden layers\\n\",\n \" (1024, 832, 640, 448, 256), # DatasetUSGS: 3 hidden layers\\n\",\n \" (1024, 873, 718, 564, 410, 256), # DatasetUSGS: 4 hidden layers\\n\",\n \" ]\\n\",\n \"else:\\n\",\n \" n_neurons = [\\n\",\n \" (1024, 512, 128), # DatasetI,II: 1 hidden layer\\n\",\n \" (1024, 512, 256, 128), # DatasetI,II: 2 hidden layers\\n\",\n \" (1024, 896, 512, 256, 128), # DatasetI,II: 3 hidden layers\\n\",\n \" (1024, 896, 640, 512, 256, 128), # DatasetI,II: 4 hidden layers\\n\",\n \" ]\\n\",\n \" \\n\",\n \"# the length and variance of RBF kernel\\n\",\n \"kernel_hypers = (\\n\",\n \" [10, 0.00005], [5, 0.00005], [1, 0.00005], [0.5, 0.00005],\\n\",\n \" [10, 0.0005], [5, 0.0005], [1, 0.0005], [0.5, 0.0005],\\n\",\n \" [10, 0.00025], [5, 0.00025], [1, 0.00025], [0.5, 0.00025],\\n\",\n \")\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"requested-egypt\",\n \"metadata\": {},\n \"source\": [\n \"### Begin model training\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"strategic-capitol\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# fixing random seeds for reproducing the results\\n\",\n \"np.random.seed(0)\\n\",\n \"torch.manual_seed(0)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"activated-recruitment\",\n \"metadata\": {},\n \"source\": [\n \"#### Train with different hyperparameters\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"changed-marshall\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"if (data_case == 1 or data_case == 2):\\n\",\n \" batch_number = 50\\n\",\n \"elif (data_case == 3):\\n\",\n \" batch_number = 9\\n\",\n \"\\n\",\n \"# repeat for different number of layers in the neural network\\n\",\n \"for layer_number in range(start_layer, end_layer):\\n\",\n \" # repeat for different sigma values for the kernel\\n\",\n \" for hyper_number in range(start_hyper, end_hyper):\\n\",\n \" # repeat for three times to check sensitivity to data splitting\\n\",\n \" for random_number in range(3):\\n\",\n \" # split data with different random seed\\n\",\n \" X_train, X_test_val, Y_train, Y_test_val = train_test_split(X_data, Y_data, test_size=split_size, random_state=random_number) \\n\",\n \" X_test, X_val, Y_test, Y_val = train_test_split(X_test_val, Y_test_val, test_size=split_val_size, random_state=random_number)\\n\",\n \" Dataset_train = torch.utils.data.TensorDataset(X_train, Y_train)\\n\",\n \" \\n\",\n \" loader_train = torch.utils.data.DataLoader(Dataset_train, batch_size=batch_number, shuffle=True)\\n\",\n \" Dataset_val = torch.utils.data.TensorDataset(X_val, Y_val)\\n\",\n \" loader_val = torch.utils.data.DataLoader(Dataset_val, shuffle=False)\\n\",\n \"\\n\",\n \" # bulid model\\n\",\n \" netG = KernelNet(\\n\",\n \" n_neurons=n_neurons[layer_number],\\n\",\n \" wavelength_points=wavelength_points,\\n\",\n \" kernel_grids=kernel_grids,\\n\",\n \" kernel_func=RBFKernel(\\n\",\n \" sigmas_squared=kernel_hypers[hyper_number][1],\\n\",\n \" hight=kernel_hypers[hyper_number][0]\\n\",\n \" ),\\n\",\n \" ).to(device)\\n\",\n \"\\n\",\n \" # Handle multi-gpu if desired\\n\",\n \"\\n\",\n \" # if (device.type == 'cuda') and (torch.cuda.device_count() > ngpu):\\n\",\n \" # netG = nn.DataParallel(netG, device_ids=range(ngpu)) \\n\",\n \" netG = netG.apply(weights_init)\\n\",\n \"\\n\",\n \" # setup optimizer for training model\\n\",\n \" regu = torch.Tensor([1]).to(device) # parameter regularization\\n\",\n \" criterion = nn.MSELoss()\\n\",\n \" optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(0.5, 0.999), weight_decay=1e-5)\\n\",\n \" \\n\",\n \" # train with fixed number of epochs\\n\",\n \" losses = []\\n\",\n \" print(f\\\"training {layer_number + 1} hidden layer model with hyper_number {hyper_number + 1} and random_number {random_number}...\\\")\\n\",\n \" \\n\",\n \" for epoch in range(n_epoch):\\n\",\n \" for itr, data in enumerate(loader_train):\\n\",\n \" real_fps = data[0][:,:].to(device)\\n\",\n \" real_spectrum = data[1][:,1:].to(device)\\n\",\n \" optimizerG.zero_grad()\\n\",\n \"\\n\",\n \" pred_spectrum = netG(real_fps)\\n\",\n \"\\n\",\n \" mu_parameter = netG.mu\\n\",\n \" mu_para_diff = torch.diff(mu_parameter)\\n\",\n \" delta_mu = torch.sum(torch.pow((mu_para_diff),2))\\n\",\n \" \\n\",\n \" loss = criterion(pred_spectrum, real_spectrum[:,:]) + regu * delta_mu\\n\",\n \"\\n\",\n \" loss.backward()\\n\",\n \" optimizerG.step()\\n\",\n \" losses.append(loss.item())\\n\",\n \"\\n\",\n \" if epoch % display_interval == 0 and itr % display_interval == 0:\\n\",\n \" print('[{}/{}][{}/{}] Loss: {:.7f} '.format(epoch + 1, n_epoch, itr + 1, len(loader_train), loss.item()))\\n\",\n \" \\n\",\n \" # save model\\n\",\n \" PATH_G = '{}/generator_time{:03d}_layer{:03d}_hyper{:03d}.pth'.format(model_path, random_number, layer_number+1, hyper_number+1) \\n\",\n \" torch.save(netG.state_dict(), PATH_G)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"minor-disposal\",\n \"metadata\": {},\n \"source\": [\n \"### Test model performance\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"civic-blocking\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"#####################\\n\",\n \"## output results ##\\n\",\n \"#####################\\n\",\n \"KernelNet(\\n\",\n \" (fc_layers): SequentialLinear(\\n\",\n \" (layer_0): LinearLayer(\\n\",\n \" (linear): Linear(in_features=1024, out_features=896, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (normalizer): BatchNorm1d(896, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): LeakyReLU(negative_slope=0.2, inplace=True)\\n\",\n \" )\\n\",\n \" (layer_1): LinearLayer(\\n\",\n \" (linear): Linear(in_features=896, out_features=640, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (normalizer): BatchNorm1d(640, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): LeakyReLU(negative_slope=0.2, inplace=True)\\n\",\n \" )\\n\",\n \" (layer_2): LinearLayer(\\n\",\n \" (linear): Linear(in_features=640, out_features=512, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (normalizer): BatchNorm1d(512, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): LeakyReLU(negative_slope=0.2, inplace=True)\\n\",\n \" )\\n\",\n \" (layer_3): LinearLayer(\\n\",\n \" (linear): Linear(in_features=512, out_features=256, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" (normalizer): BatchNorm1d(256, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): LeakyReLU(negative_slope=0.2, inplace=True)\\n\",\n \" )\\n\",\n \" (output): Linear(in_features=256, out_features=128, bias=True)\\n\",\n \" )\\n\",\n \")\\n\",\n \"Median of RMSE: 0.124644004 std: 0.011839285\\n\",\n \"Median of R square: 0.7629515670007748 std: 0.06191901874519955\\n\",\n \"Median of MAE: 0.083352484 std: 0.0091531025\\n\",\n \"Median of RMSE_derivative 0.013060271 std: 0.0008302324\\n\"\n ]\n }\n ],\n \"source\": [\n \"# repeat for different number of layers in the neural network\\n\",\n \"for layer_number in range(start_layer, end_layer):\\n\",\n \" # repeat for different sigma values for the kernel\\n\",\n \" for hyper_number in range(start_hyper, end_hyper):\\n\",\n \" # initializing variables for performance evaluation\\n\",\n \" anal_index = [] \\n\",\n \" anal_real_list = []\\n\",\n \" anal_pred_list = []\\n\",\n \"\\n\",\n \" peak_position_real_list = []\\n\",\n \" peak_position_pred_list = []\\n\",\n \" peak_intensity_real_list = []\\n\",\n \" peak_intensity_pred_list = []\\n\",\n \"\\n\",\n \" rmse_median = []\\n\",\n \" rmse_data = []\\n\",\n \" rsquare_median = []\\n\",\n \" mae_median = []\\n\",\n \" drmse_median = []\\n\",\n \" \\n\",\n \" # repeat for three times to check sensitivity to data splitting\\n\",\n \" for random_number in range(3):\\n\",\n \" # split data with different random seed\\n\",\n \" X_train, X_test_val, Y_train, Y_test_val = train_test_split(X_data, Y_data, test_size=split_size, random_state=random_number) \\n\",\n \" X_test, X_val, Y_test, Y_val = train_test_split(X_test_val, Y_test_val, test_size=split_val_size, random_state=random_number)\\n\",\n \" Dataset_train = torch.utils.data.TensorDataset(X_train, Y_train)\\n\",\n \" loader_train = torch.utils.data.DataLoader(Dataset_train, batch_size=50, shuffle=True)\\n\",\n \" Dataset_val = torch.utils.data.TensorDataset(X_val, Y_val)\\n\",\n \" loader_val = torch.utils.data.DataLoader(Dataset_val, shuffle=False)\\n\",\n \"\\n\",\n \" # bulid model\\n\",\n \" netG = KernelNet(\\n\",\n \" n_neurons=n_neurons[layer_number],\\n\",\n \" wavelength_points=wavelength_points,\\n\",\n \" kernel_grids=kernel_grids,\\n\",\n \" kernel_func=RBFKernel(\\n\",\n \" sigmas_squared=kernel_hypers[hyper_number][1],\\n\",\n \" hight=kernel_hypers[hyper_number][0]\\n\",\n \" ),\\n\",\n \" ).to(device)\\n\",\n \" \\n\",\n \" #########\\n\",\n \" ## Load trained model\\n\",\n \" #########\\n\",\n \" trained_netG_path = '{}/generator_time{:03d}_layer{:03d}_hyper{:03d}.pth'.format(model_path, random_number, layer_number+1, hyper_number+1)\\n\",\n \" netG.load_state_dict(torch.load(trained_netG_path)) \\n\",\n \"\\n\",\n \" ############\\n\",\n \" ### Analysis\\n\",\n \" ############\\n\",\n \" pred_spectrum = netG(X_test.to(device))\\n\",\n \" anal_real = Y_test[:,1:].to('cpu').detach().numpy().copy()\\n\",\n \" anal_pred = pred_spectrum[:].to('cpu').detach().numpy().copy()\\n\",\n \" anal_index.extend(Y_test[:,0].to('cpu').detach().numpy().copy())\\n\",\n \" anal_real_list.extend(anal_real)\\n\",\n \" anal_pred_list.extend(anal_pred)\\n\",\n \"\\n\",\n \" rmse_list = []\\n\",\n \" rsquare_list = []\\n\",\n \" mae_list = []\\n\",\n \" drmse_list = []\\n\",\n \" \\n\",\n \" for i in range(anal_real.shape[0]):\\n\",\n \" # RMSE\\n\",\n \" rmse = np.sqrt(mean_squared_error(anal_real[i], anal_pred[i]))\\n\",\n \" rmse_list.append(rmse)\\n\",\n \" rmse_data.append(rmse)\\n\",\n \" # R square\\n\",\n \" rsquare = r2_score(anal_real[i], anal_pred[i])\\n\",\n \" rsquare_list.append(rsquare)\\n\",\n \" # MAE\\n\",\n \" mae = mean_absolute_error(anal_real[i], anal_pred[i])\\n\",\n \" mae_list.append(mae)\\n\",\n \" # RMSE derivative\\n\",\n \" real_d = anal_real[:,:-1] - anal_real[:,1:]\\n\",\n \" pred_d = anal_pred[:,:-1] - anal_pred[:,1:]\\n\",\n \" drmse = np.sqrt(np.sum(((real_d - pred_d) ** 2), axis=1) / anal_real.shape[0])\\n\",\n \" drmse_list.append(drmse)\\n\",\n \" # calculate median\\n\",\n \" rmse_median.append(np.median(rmse_list))\\n\",\n \" rsquare_median.append(np.median(rsquare_list))\\n\",\n \" mae_median.append(np.median(mae_list))\\n\",\n \" drmse_median.append(np.median(drmse_list))\\n\",\n \"\\n\",\n \" # all test results\\n\",\n \" anal_index = np.array(anal_index, dtype='int32')\\n\",\n \" anal_real_list = np.array(anal_real_list, dtype='float32')\\n\",\n \" anal_pred_list = np.array(anal_pred_list, dtype='float32')\\n\",\n \"\\n\",\n \"\\n\",\n \" print(\\\"#####################\\\")\\n\",\n \" print(\\\"## output results ##\\\")\\n\",\n \" print(\\\"#####################\\\")\\n\",\n \" print(netG)\\n\",\n \"\\n\",\n \" # output profiles\\n\",\n \" device_cpu = torch.device('cpu')\\n\",\n \" exp = pd.DataFrame(anal_real_list)\\n\",\n \" exp.insert(loc = 0, column='smiles', value= smiles_list[anal_index])\\n\",\n \" if data_case == 1:\\n\",\n \" exp.to_csv('{}/exp_profiles_dataset1.csv'.format(out_result))\\n\",\n \" elif data_case == 2:\\n\",\n \" exp.to_csv('{}/exp_profiles_dataset2.csv'.format(out_result))\\n\",\n \" elif data_case == 3:\\n\",\n \" exp.to_csv('{}/exp_profiles_USGS.csv'.format(out_result))\\n\",\n \" pred = pd.DataFrame(anal_pred_list)\\n\",\n \" pred.insert(loc = 0, column='smiles', value= smiles_list[anal_index])\\n\",\n \" if data_case == 1:\\n\",\n \" pred.to_csv('{}/pred_profiles_dataset1.csv'.format(out_result))\\n\",\n \" elif data_case == 2:\\n\",\n \" pred.to_csv('{}/pred_profiles_dataset2.csv'.format(out_result))\\n\",\n \" elif data_case == 3:\\n\",\n \" pred.to_csv('{}/pred_profiles_USGS.csv'.format(out_result))\\n\",\n \" \\n\",\n \" # RMSE \\n\",\n \" rmse_df = pd.DataFrame([rmse_data])\\n\",\n \" rmse_df = rmse_df.transpose()\\n\",\n \" rmse_df.columns = [\\\"rmse\\\"]\\n\",\n \" rmse_sort = rmse_df.sort_values('rmse')\\n\",\n \" rmse_sort_index = rmse_sort.index.values\\n\",\n \" print(\\\"Median of RMSE:\\\", np.mean(rmse_median), \\\"std:\\\", np.std(rmse_median))\\n\",\n \" # R2\\n\",\n \" print(\\\"Median of R square:\\\", np.mean(rsquare_median), \\\"std:\\\", np.std(rsquare_median))\\n\",\n \" # MAE\\n\",\n \" print(\\\"Median of MAE:\\\", np.mean(mae_median), \\\"std:\\\",np.std(mae_median)) \\n\",\n \" # dRMSE\\n\",\n \" print(\\\"Median of RMSE_derivative\\\", np.mean(drmse_median), \\\"std:\\\",np.std(drmse_median))\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7b07da15\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "samples/mp_ids.txt", "content": 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}, { "path": "samples/nn_model_build.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%run tools.ipynb\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Model building\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This tutorial shows how to build neuron network models.\\n\",\n \"At end, will calculate compositional descriptors using `xenonpy.descriptor.Compositions` calculator and train our model using `xenonpy.model.training` modules.\\n\",\n \"\\n\",\n \"ALso, we will use some inorganic sample data from materials project to execute this tutorial. \\n\",\n \"If you don't have it, please see https://github.com/yoshida-lab/XenonPy/blob/master/samples/build_sample_data.ipynb.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import torch\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import plotly.express as px\\n\",\n \"import seaborn as sns\\n\",\n \"from torch.utils.data import DataLoader\\n\",\n \"from pymatgen import Structure\\n\",\n \"\\n\",\n \"from xenonpy.model.training import Trainer, SGD, MSELoss, Adam, ReduceLROnPlateau, ExponentialLR, ClipNorm\\n\",\n \"from xenonpy.model.training.extension import Validator, TensorConverter, Persist\\n\",\n \"from xenonpy.model.training.dataset import ArrayDataset, CrystalGraphDataset\\n\",\n \"from xenonpy.model.utils import regression_metrics\\n\",\n \"from xenonpy.model import CrystalGraphConvNet\\n\",\n \"\\n\",\n \"from xenonpy.datatools import preset, Splitter\\n\",\n \"from xenonpy.descriptor import Compositions, CrystalGraphFeaturizer\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## sample data\\n\",\n \"\\n\",\n \"If you followed the tutorial in https://github.com/yoshida-lab/XenonPy/blob/master/samples/build_sample_data.ipynb,\\n\",\n \"the sample data should be save at `~/.xenonpy/userdata` with name `mp_samples.pd.xz`.\\n\",\n \"\\n\",\n \"You can use `pd.read_pickle` to load this file but we suggest that you use our `xenonpy.datatools.preset` module.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
band_gapcompositiondensitye_above_hullefermielementsfinal_energy_per_atomformation_energy_per_atompretty_formulastructurevolume
mp-10088070.0000{'Rb': 1.0, 'Cu': 1.0, 'O': 1.0}4.7846340.9963721.100617[Rb, Cu, O]-3.302762-0.186408RbCuO[[-3.05935361 -3.05935361 -3.05935361] Rb, [0....57.268924
mp-10096400.0000{'Pr': 1.0, 'N': 1.0}8.1457770.7593935.213442[Pr, N]-7.082624-0.714336PrN[[0. 0. 0.] Pr, [1.57925232 1.57925232 1.58276...31.579717
mp-10168250.7745{'Hf': 1.0, 'Mg': 1.0, 'O': 3.0}6.1658880.5895502.424570[Hf, Mg, O]-7.911723-3.060060HfMgO3[[2.03622802 2.03622802 2.03622802] Hf, [0. 0....67.541269
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" band_gap composition density e_above_hull ... formation_energy_per_atom pretty_formula structure volume\\n\",\n \"mp-1008807 0.0000 {'Rb': 1.0, 'Cu': 1.0, 'O': 1.0} 4.784634 0.996372 ... -0.186408 RbCuO [[-3.05935361 -3.05935361 -3.05935361] Rb, [0.... 57.268924\\n\",\n \"mp-1009640 0.0000 {'Pr': 1.0, 'N': 1.0} 8.145777 0.759393 ... -0.714336 PrN [[0. 0. 0.] Pr, [1.57925232 1.57925232 1.58276... 31.579717\\n\",\n \"mp-1016825 0.7745 {'Hf': 1.0, 'Mg': 1.0, 'O': 3.0} 6.165888 0.589550 ... -3.060060 HfMgO3 [[2.03622802 2.03622802 2.03622802] Hf, [0. 0.... 67.541269\\n\",\n \"\\n\",\n \"[3 rows x 11 columns]\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from xenonpy.datatools import preset\\n\",\n \"\\n\",\n \"data = preset.mp_samples\\n\",\n \"data.head(3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## compositional descriptor\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
ave:atomic_numberave:atomic_radiusave:atomic_radius_rahmave:atomic_volumeave:atomic_weightave:boiling_pointave:bulk_modulusave:c6_gbave:covalent_radius_corderoave:covalent_radius_pyykko...min:num_s_valencemin:periodmin:specific_heatmin:thermal_conductivitymin:vdw_radiusmin:vdw_radius_alvarezmin:vdw_radius_mm3min:vdw_radius_uffmin:sound_velocitymin:Polarizability
mp-100880724.666667174.067140209.33333325.66666755.0042671297.06333372.8686801646.90139.333333128.333333...1.02.00.3600.02658152.0150.0182.0349.5317.50.802
mp-100964033.000000137.000000232.50000019.05000077.4573301931.20000043.1824411892.85137.000000123.500000...2.02.00.1920.02583155.0166.0193.0360.6333.61.100
mp-101682521.600000153.120852203.40000013.92000050.1584001420.71400076.663625343.82102.80000096.000000...2.02.00.1460.02658152.0150.0182.0302.1317.50.802
mp-101758259.400000139.000000232.80000011.020000147.2336944226.000000150.2000001037.58137.600000124.800000...1.02.00.13313.00000170.0177.0204.0275.42475.01.670
mp-102151132.000000140.500000226.00000014.30000072.237000877.91200024.850000272.50124.500000119.500000...2.03.00.2320.20500180.0189.0215.0284.82310.02.900
\\n\",\n \"

5 rows × 290 columns

\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" ave:atomic_number ave:atomic_radius ave:atomic_radius_rahm \\\\\\n\",\n \"mp-1008807 24.666667 174.067140 209.333333 \\n\",\n \"mp-1009640 33.000000 137.000000 232.500000 \\n\",\n \"mp-1016825 21.600000 153.120852 203.400000 \\n\",\n \"mp-1017582 59.400000 139.000000 232.800000 \\n\",\n \"mp-1021511 32.000000 140.500000 226.000000 \\n\",\n \"\\n\",\n \" ave:atomic_volume ave:atomic_weight ave:boiling_point \\\\\\n\",\n \"mp-1008807 25.666667 55.004267 1297.063333 \\n\",\n \"mp-1009640 19.050000 77.457330 1931.200000 \\n\",\n \"mp-1016825 13.920000 50.158400 1420.714000 \\n\",\n \"mp-1017582 11.020000 147.233694 4226.000000 \\n\",\n \"mp-1021511 14.300000 72.237000 877.912000 \\n\",\n \"\\n\",\n \" ave:bulk_modulus ave:c6_gb ave:covalent_radius_cordero \\\\\\n\",\n \"mp-1008807 72.868680 1646.90 139.333333 \\n\",\n \"mp-1009640 43.182441 1892.85 137.000000 \\n\",\n \"mp-1016825 76.663625 343.82 102.800000 \\n\",\n \"mp-1017582 150.200000 1037.58 137.600000 \\n\",\n \"mp-1021511 24.850000 272.50 124.500000 \\n\",\n \"\\n\",\n \" ave:covalent_radius_pyykko ... min:num_s_valence min:period \\\\\\n\",\n \"mp-1008807 128.333333 ... 1.0 2.0 \\n\",\n \"mp-1009640 123.500000 ... 2.0 2.0 \\n\",\n \"mp-1016825 96.000000 ... 2.0 2.0 \\n\",\n \"mp-1017582 124.800000 ... 1.0 2.0 \\n\",\n \"mp-1021511 119.500000 ... 2.0 3.0 \\n\",\n \"\\n\",\n \" min:specific_heat min:thermal_conductivity min:vdw_radius \\\\\\n\",\n \"mp-1008807 0.360 0.02658 152.0 \\n\",\n \"mp-1009640 0.192 0.02583 155.0 \\n\",\n \"mp-1016825 0.146 0.02658 152.0 \\n\",\n \"mp-1017582 0.133 13.00000 170.0 \\n\",\n \"mp-1021511 0.232 0.20500 180.0 \\n\",\n \"\\n\",\n \" min:vdw_radius_alvarez min:vdw_radius_mm3 min:vdw_radius_uff \\\\\\n\",\n \"mp-1008807 150.0 182.0 349.5 \\n\",\n \"mp-1009640 166.0 193.0 360.6 \\n\",\n \"mp-1016825 150.0 182.0 302.1 \\n\",\n \"mp-1017582 177.0 204.0 275.4 \\n\",\n \"mp-1021511 189.0 215.0 284.8 \\n\",\n \"\\n\",\n \" min:sound_velocity min:Polarizability \\n\",\n \"mp-1008807 317.5 0.802 \\n\",\n \"mp-1009640 333.6 1.100 \\n\",\n \"mp-1016825 317.5 0.802 \\n\",\n \"mp-1017582 2475.0 1.670 \\n\",\n \"mp-1021511 2310.0 2.900 \\n\",\n \"\\n\",\n \"[5 rows x 290 columns]\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
volume
mp-100880757.268924
mp-100964031.579717
mp-101682567.541269
mp-101758285.579224
mp-102151192.890725
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" volume\\n\",\n \"mp-1008807 57.268924\\n\",\n \"mp-1009640 31.579717\\n\",\n \"mp-1016825 67.541269\\n\",\n \"mp-1017582 85.579224\\n\",\n \"mp-1021511 92.890725\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"prop = data[data.volume <= 2500]['volume'].to_frame()\\n\",\n \"desc = Compositions(featurizers='classic').transform(data.loc[prop.index]['composition'])\\n\",\n \"\\n\",\n \"desc.head(5)\\n\",\n \"prop.head(5)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sp = Splitter(prop.shape[0])\\n\",\n \"x_train, x_val, y_train, y_val = sp.split(desc, prop)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(738, 290)\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"(738, 1)\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"(185, 290)\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"(185, 1)\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x_train.shape\\n\",\n \"y_train.shape\\n\",\n \"x_val.shape\\n\",\n \"y_val.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Sequential(\\n\",\n \" (0): Linear(in_features=290, out_features=200, bias=True)\\n\",\n \" (1): BatchNorm1d(200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (2): ReLU()\\n\",\n \" (3): Dropout(p=0.1)\\n\",\n \" (4): Linear(in_features=200, out_features=100, bias=True)\\n\",\n \" (5): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (6): ReLU()\\n\",\n \" (7): Dropout(p=0.1)\\n\",\n \" (8): Linear(in_features=100, out_features=1, bias=True)\\n\",\n \")\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model = torch.nn.Sequential(\\n\",\n \" torch.nn.Linear(290, 200),\\n\",\n \" torch.nn.BatchNorm1d(200),\\n\",\n \" torch.nn.ReLU(),\\n\",\n \" torch.nn.Dropout(0.1),\\n\",\n \" torch.nn.Linear(200, 100),\\n\",\n \" torch.nn.BatchNorm1d(100),\\n\",\n \" torch.nn.ReLU(),\\n\",\n \" torch.nn.Dropout(0.1),\\n\",\n \" torch.nn.Linear(100, 1),\\n\",\n \")\\n\",\n \"model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"trainer = Trainer(\\n\",\n \" optimizer=Adam(lr=0.01),\\n\",\n \" loss_func=MSELoss(),\\n\",\n \" clip_grad=ClipNorm(max_norm=0.5),\\n\",\n \" lr_scheduler=ExponentialLR(gamma=0.99),\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"trainer.extend(\\n\",\n \" TensorConverter(),\\n\",\n \" Validator(metrics_func=regression_metrics, early_stopping=5, trace_order=5, mae=0.0, pearsonr=1.0),\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"train_dataset = DataLoader(ArrayDataset(x_train, y_train), shuffle=True, batch_size=1000)\\n\",\n \"val_dataset = DataLoader(ArrayDataset(x_val, y_val), batch_size=1000)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"persist = Persist('test_model', increment=True, sync_training_step=True)\\n\",\n \"trainer.extend(persist)\\n\",\n \"trainer.reset(to=model)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 100%|██████████| 10/10 [00:00<00:00, 25.04it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"trainer.fit(training_dataset=train_dataset, validation_dataset=val_dataset, epochs=10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Sequential(\\n\",\n \" (0): Linear(in_features=290, out_features=200, bias=True)\\n\",\n \" (1): BatchNorm1d(200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (2): ReLU()\\n\",\n \" (3): Dropout(p=0.1)\\n\",\n \" (4): Linear(in_features=200, out_features=100, bias=True)\\n\",\n \" (5): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (6): ReLU()\\n\",\n \" (7): Dropout(p=0.1)\\n\",\n \" (8): Linear(in_features=100, out_features=1, bias=True)\\n\",\n \")\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"'Sequential(\\\\n (0): Linear(in_features=290, out_features=200, bias=True)\\\\n (1): BatchNorm1d(200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\\\\n (2): ReLU()\\\\n (3): Dropout(p=0.1)\\\\n (4): Linear(in_features=200, out_features=100, bias=True)\\\\n (5): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\\\\n (6): ReLU()\\\\n (7): Dropout(p=0.1)\\\\n (8): Linear(in_features=100, out_features=1, bias=True)\\\\n)'\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"trainer['persist'].model_structure\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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total_itersi_epochi_batchtrain_mse_lossval_maeval_mseval_rmseval_r2val_pearsonrval_spearmanrval_p_valueval_max_ae
0111412426.46875440.195770386787.81250621.922668-1.0038710.043379-0.0364050.5576822450.045898
1221412127.28125439.935394386543.06250621.725891-1.0026030.0553320.0359120.4544172449.945557
2331411895.40625439.896790386497.71875621.689392-1.0023690.0916080.0321200.2149152449.918945
3441411632.93750439.770111386365.50000621.583069-1.0016830.1318990.0596360.0735052449.862305
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\\n\",\n \" \\n\",\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"y_pred, y_true = trainer.predict(dataset=val_dataset, checkpoint='pearsonr:1')\\n\",\n \"fig = px.scatter(pd.DataFrame({'pred': y_pred.flatten(), 'true': y_true.flatten()}), x='pred', y='true', width=800, height=800, marginal_x='histogram', marginal_y='histogram')\\n\",\n \"fig.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## crystal graph cnn\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
atom_featureneighbor_featureneighbor_idx
mp-1008807[[tensor(0.), tensor(1.), tensor(0.), tensor(0...[[[tensor(0.), tensor(0.), tensor(0.), tensor(...[[tensor(2), tensor(2), tensor(2), tensor(2), ...
mp-1009640[[tensor(1.), tensor(0.), tensor(0.), tensor(0...[[[tensor(0.), tensor(0.), tensor(0.), tensor(...[[tensor(1), tensor(1), tensor(1), tensor(1), ...
mp-1016825[[tensor(0.), tensor(0.), tensor(0.), tensor(0...[[[tensor(1.4013e-45), tensor(2.4653e-37), ten...[[tensor(2), tensor(3), tensor(4), tensor(2), ...
mp-1017582[[tensor(1.), tensor(0.), tensor(0.), tensor(0...[[[tensor(0.), tensor(0.), tensor(0.), tensor(...[[tensor(1), tensor(2), tensor(3), tensor(1), ...
mp-1021511[[tensor(0.), tensor(0.), tensor(0.), tensor(0...[[[tensor(0.), tensor(0.), tensor(0.), tensor(...[[tensor(1), tensor(1), tensor(1), tensor(0), ...
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" atom_feature \\\\\\n\",\n \"mp-1008807 [[tensor(0.), tensor(1.), tensor(0.), tensor(0... \\n\",\n \"mp-1009640 [[tensor(1.), tensor(0.), tensor(0.), tensor(0... \\n\",\n \"mp-1016825 [[tensor(0.), tensor(0.), tensor(0.), tensor(0... \\n\",\n \"mp-1017582 [[tensor(1.), tensor(0.), tensor(0.), tensor(0... \\n\",\n \"mp-1021511 [[tensor(0.), tensor(0.), tensor(0.), tensor(0... \\n\",\n \"\\n\",\n \" neighbor_feature \\\\\\n\",\n \"mp-1008807 [[[tensor(0.), tensor(0.), tensor(0.), tensor(... \\n\",\n \"mp-1009640 [[[tensor(0.), tensor(0.), tensor(0.), tensor(... \\n\",\n \"mp-1016825 [[[tensor(1.4013e-45), tensor(2.4653e-37), ten... \\n\",\n \"mp-1017582 [[[tensor(0.), tensor(0.), tensor(0.), tensor(... \\n\",\n \"mp-1021511 [[[tensor(0.), tensor(0.), tensor(0.), tensor(... \\n\",\n \"\\n\",\n \" neighbor_idx \\n\",\n \"mp-1008807 [[tensor(2), tensor(2), tensor(2), tensor(2), ... \\n\",\n \"mp-1009640 [[tensor(1), tensor(1), tensor(1), tensor(1), ... \\n\",\n \"mp-1016825 [[tensor(2), tensor(3), tensor(4), tensor(2), ... \\n\",\n \"mp-1017582 [[tensor(1), tensor(2), tensor(3), tensor(1), ... \\n\",\n \"mp-1021511 [[tensor(1), tensor(1), tensor(1), tensor(0), ... \"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
volume
mp-100880757.268924
mp-100964031.579717
mp-101682567.541269
mp-101758285.579224
mp-102151192.890725
\\n\",\n \"
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total_itersi_epochi_batchtrain_lossval_maeval_mseval_rmseval_r2val_pearsonrval_spearmanrval_p_valueval_max_error
0111343781.875000500.848969552324.3750743.185303-1.273074e+090.171313-0.0585530.0193903324.069580
1212800263.812500500.569000552043.5000742.996277-1.418644e+090.2054300.0006090.0049103323.794434
2313349451.875000500.286285551760.0625742.805542-1.600670e+090.2418360.1061980.0008833323.517578
3414150381.171875500.001923551475.2500742.613770-1.751977e+090.2694440.2146990.0002003323.239990
4515253343.234375499.715851551189.0625742.421082-1.777963e+090.2831570.3056130.0000903322.961670
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c8dewgde8aBa3w1SHDNC/v1/C125FK7LqeYgmHtj0SVAi3Z5o3bSKRoG1D2HZHKrL95hQ335/i5oc6lugJ8G9t9NrUxA/Mj+l8j4pey0qukzA/jjfs9U8UndKlTpEJDl027oxKbXbH9x/Uv19+xw8OtWrZwk9KndphX1mZf7dVfp7fc8g+sEmrU4u9b7elFtu76IY771fXzkWaML7yeXaXHkjtxt+QC9hfRfLzMtTQbXrccdLwoa6mTHX1+F+kG798wAxvDDlWyIqfnemankNug7dtyBgiWtwMv1dYQ79vI4oVqmrZHkNl+xPas4+kbaFquFoUNi87pr1lce0/kKjF3uwSJgHbs4TP4zC1WO3KanvpZpsfUGnb2nmFaS/PfB5nyWm2tqU3WrBeLZHtq13crlClpckgUJG5/9HqDeXyqz5eL9d0jWzftkBF7Qr8oURVt9vnpBY71OiW6yZo/catuueXj6VW8xeBWgucY6a4797N046dEvmHas3GjggggAACCCCAAAIIIIAAAk0gEJng0I9/+2cznOxDvwfQrHlL/dnG7Oxldjln1CC99N9pslPW795Tqif+9YqGDuxjEljnqrB1Sw3sd7we/cfL/ralyz/Wa5Nm6ewzBpXzZ5rpp668dKwe+sntevH1qfr1I0+Vb+MOArURcM07bYKZ4j7HJKpevMTR7DmReevVpvrsgwACCCCAAAIIIIAAAgggEGCByAwrmz1/mf7yzGs+te3pc/5Zg3X9leP9x5eaHEHT5izWpz73HdNLSDqmU5EevO/28ma56/ZrddO3fqWh42/0x9OeP3qILjpnRPl2117Zm8Umob7/7pv01e8+INsz6fJLxpTvwx0EjiTQurU0/oK4nnomppdecdW9u6c2heRbOJIb2xFAAAEEEEAAAQQQQAABBBpXwDEzdUXm6nTHzt3asm2HitsX+tPSV6Wzial3menrOxa3rbrJf7x2wxYz9XieWpoeRbVd1m7ZW9td2S/gAjbnUFFhttZvLW3Ukv7r6ZjmL3TU9RhPX/hc3CSCa9TTcXAjYHMO5edmasuOfXhETKBFTjLn0Pbdh/LGRayKaVudgvwsk3MoTs6hCL4C2rfOVol5z5JzKHqN26ltrvhuHL12tTmHClpmalMJ36Oi1ro2B1xWZkwlu5LpWJq6fvYzgyU4ApEa22ITT/fo2rHawJAlt4GfmgJDdnsnEzSqS2DIPocFgboKjB8XV+tW0kcfO5r0TqTegnWlYH8EEEAAAQQQQAABBBBAAIEACHBlGoBGoAjpJZBr8g5dclHcH+L45luu1qyl61B6vQKoLQIIIIAAAggggAACCCAQLAGCQ8FqD0qTJgI9e3gaNiShuJnF9+lnXe1nREyatDzVRAABBBBAAAEEEEAAAQSCJ0BwKHhtQonSRGDs6ISKizxt3uLolYm8FdOk2akmAggggAACCCCAAAIIIBA4Aa5IA9ckFChdBDLMXIGXXZJQRkyaMdPV0mUML0uXtqeeCCCAAAIIIIAAAggggECQBAgOBak1KEvaCRQXexpjehDZKQP//UJMu/ekHQEVRgABBBBAAAEEEEAAAQQQaGYBgkPN3ACcHoHhQxPq0d3Trl3S8/8x3YhYEEAAAQQQQAABBBBAAAEEEGhCAYJDTYjNqRCoTsAxo8kuNbOX5ZhZzBYvcTR7Dm/L6pxYhwACCCCAAAIIIIAAAggg0DgCXIU2jitHRaBOAq1bS+MviPvPeekVV1u3kX+oToDsjAACCCCAAAIIIIAAAgggcNQCBIeOmo4nItCwAv36ejq5j6d9ZSb/0POun4eoYc/A0RBAAAEEEEAAAQQQQAABBBA4XIDg0OEmrEGg2QTGj4srP19a9aEZXjabt2ezNQQnRgABBBBAAAEEEEAAAQTSSICrzzRqbKoafIFck3do3HnJ4WWvvOZqp0lSzYIAAggggAACCCCAAAIIIIBAYwoQHGpMXY6NwFEI9DnJ0wnHeyotlf7vZWYvOwpCnoIAAggggAACCCCAAAIIIFAHAYJDdcBiVwSaSmD8+XFlZ0kLFzlavoLk1E3lznkQQAABBBBAAAEEEEAAgXQUIDiUjq1OnQMvYGcvGzUy4Zfz5VddJbzAF5kCIoAAAggggAACCCCAAAIIhFSA4FBIG45iR19g2NCECgs9bdjoaBbJqaPf4NQQAQQQQAABBBBAAAEEEGgmAYJDzQTPaRE4kkCGSTd07thk76H/vuH6OYiO9By2I4AAAggggAACCCCAAAIIIFBXAYJDdRVjfwSaUOCkEz316O5p9x7pzbd5uzYhPadCAAEEEEAAAQQQQAABBNJGgKvNtGlqKhpWgfPPScg1OamnzXC1ZSvJqcPajpQbAQQQQAABBBBAAAEEEAiqAMGhoLYM5ULgoECHDp5O7Z9QPC69MpG3LC8MBBBAAAEEEEAAAQQQQACBhhXgSrNhPTkaAo0iMGZ0QtnZ0pL3mdq+UYA5KAIIIIAAAggggAACCCCQxgIEh9K48al6eATyW5ip7U9navvwtBglRQABBBBAAAEEEEAAAQTCI0BwKDxtRUnTXICp7dP8BUD1EUAAAQQQQAABBBBAAIFGEiA41EiwHBaBhhZgavuGFuV4CCCAAAIIIIAAAggggAACVoDgEK8DBEIkwNT2IWosiooAAggggAACCCCAAAIIhESA4FBIGopiIpASYGr7lAR/EUAAAQQQQAABBBBAAAEEGkKA4FBDKHIMBJpQgKntmxCbUyGAAAIIIIAAAggggAACaSBAcCgNGpkqRk+Aqe2j16bUCAEEEEAAAQQQQAABBBBoLgGCQ80lz3kRqIcAU9vXA4+nIoAAAggggAACCCCAAAIIVBIgOFSJgwcIhEeAqe3D01aUFAEEEEAAAQQQQAABBBAIsgDBoSC3DmVD4BMEmNr+E3DYhAACCCCAAAIIIIAAAgggUGsBgkO1pmJHBIInwNT2wWsTSoQAAggggAACCCCAAAIIhE2A4FDYWozyIlBFwE5t7zjStBmutmw1d1gQQAABBBBAAAEEEEAAAQQQqIMAwaE6YLErAkEUsFPbD+ifUDwuvTKRt3QQ24gyIYAAAggggAACCCCAAAJBFuBKMsitQ9kQqKWAP7V9lrTkfUcffUzvoVqysRsCCCCAAAIIIIAAAggggIARIDjEywCBCAjYqe3t7GV2ef0N3tYRaFKqgAACCCCAAAIIIIAAAgg0mQBXkU1GzYkQaFyB4cMSysuVVq5ytHwFvYcaV5ujI4AAAggggAACCCCAAALRESA4FJ22pCZpLpCTLZ0+Itl76LX/uvLS3IPqI4AAAggggAACCCCAAAII1E6A4FDtnNgLgVAIDDktoZb50pq1jt43+YdYEEAAAQQQQAABBBBAAAEEEDiSAMGhCkIbNm/Tnr2lFdZIm7du1wsTp1RaxwMEgiqQmSmNPD3Ze+itSby9g9pOlAsBBBBAAAEEEEAAAQQQCJJARpAK0xBl2Ve2X5+/7Sfau69Mz/7xh/4hp85epOvu+Nlhh5/4j1+qU3Fbrfhwrb7y7d9o9bqN/j4XjBmqe79xnTIzM7R0xWrd9bM/6cKzhx/2fFYgEESBgQMSevsd1+89tOwDR8cdywCzILYTZUIAAQQQQAABBBBAAAEEgiIQqeCQ53n6zk/+oIVLV6ln147lxna9XZ577EdyzH+ppbhdgX/3B/c/oZ7dOurpP9yjNes365pb7tNzr07WZePOSO3KXwRCI2BimhphklO/MtGV7T103LHx0JSdgiKAAAIIIIAAAggggAACCDS9QKTGnfzu0We1zPT0+fJVF1Yr2atbJ/XqfugWi8W0bftOzZz3vq799LlmpqccHdeji8aOHKCJb82s9hhvTJ6jy66/W3MXLa92OysRCILAaQPNzGV50kcfO/7sZUEoE2VAAAEEEEAAAQQQQAABBBAIpkBkeg7ZvED/fOFN/f3hu/T6O7Or1f76Dx5WZkaGTjmply65YKRysrO0cXOJbM+ibl2Ky5/TrUsHzV+8svxx6s6UmQv1tXse0nduvdo/hl3fKs8keWGJhIDtU+Y4TjTa1ASGzjpdevFVT+9MjpnX66Eec5ForDpWIuY6yohFpG3rWPeo756Z4co0bzTet1FvrDrWL9W2GbFI/Y5VR4Vo7m4/k1vkZCiRYNhzFFuY78bRa1XXfD+271vaNnpta78fx8y/s7Rt9Nr2aGoUieDQ7PlLde+vn9Tvf/o1de7Q7jCHdm1a6/KLx6iwoKUJBm3Trx75lyZNn6+H7rtNO3bu9vfPNoGi1JKdlamdu/ekHvp/5yxYplu/94C+efMVmjBuVPm2xMEha+UruBNaARsYMpFCRaVNRwyT/vu2tMx0cluxylP3bqFtmnoX3F5a2rdqVNq23iAROoAN7nvmvUvbRqhRU1U5+O8rbZsCic5fGxKy713aNjptWrEmtGtFjWjcP/gVmfdsNJqzSi2idf1TpXI8rKNAJIJDz/zfJLUtbOXnCbK5gpYu/1jrN23TPfc/rtuvv8wfKvbd264upxk+qK/u+P6DKtmxS61atvDX20TWqcXeb5Vvul4cXMr2H9ANd96vrp2LNGH8GanV/t9dew9UesyD8ArYX0Va5MQUpTYdMtjVm2+7euk1T1df8cm5hzZscDRnrqMsEycdfWZyxrPwtmblkmdnuuYXLzdSbVu5hun7yPY+sL96Rel9m76tWbnmtsdQ2f649uz75M+uys/iURgEcrNifrvuPxCtf2vCYN/YZbS9D/g8bmzlpj9+pvk8zjLfpWjbprdv7DPmZcdM2zbf9Q89lhq7het2/Ej01R59+qmyM4zZAJG95eXlmIsF17/vmgvCqktxu0J/VWlpmYpMUmrbY+Sj1RvKd1v18XqzPrmPXWm333LdBK3fuFX3/PKx8v24g0DQBYYOSch2irOzlq1d98lDy96d7mjK1GQwafv2oNeM8iGAAAIIIIAAAggggAACCDSUQCR6Do0eMUD2llqeeOpVbd5Sops/f4m/6q/PvqaORW016JQTtKd0n2zi6h7HdFCHojb+9oH9jtej/3hZJx3fXWvWbdJrk2bp6zd+NnU4k6copisvHau+vXvoC3f8VG0KWuk20yOJBYGgC+TlSqcNSuidKcmZyy7/TM2/wO/ffyh4NGmyq/EX8Itu0NuX8iGAAAIIIIAAAggggAACDSEQieDQkSBKtu/STx/8mw4cSF4Y9zSzlv3i7pvKn3bX7dfqpm/9SkPH3+jnJTl/9BBddM6I8u2p3kc2kfX95nlf/e4Dsr2PLr9kTPk+3EEgqALDzbT202a4WrLE0YaNjoqLbLaHw5dt2w6tmz3H1aiRCTPs8tA67iGAAAIIIIAAAggggAACCERTwDEJAau/UoxYfW0eoQ2btio3J1vt2xZUW7u1G7aopck11LKF6W5Ry2Xtlr213JPdgi5gcw4VFWZr/dbSoBe1zuV76RVX705z1bePp89MqL730E9+kaE9Jg97926eVn3oaOjghC44Lxq9h2zOofzcTG3Zsa/Odjwh2AKpnEPbdx/KGxfsElO62goU5GeRc6i2WCHbr33rbJWY9yw5h0LWcLUobqe2ueK7cS2gQraLzTlU0DJTm0r4HhWypjticVM5h0p2lR1x38bYwX5msARH4PCEPMEpW4OWxM5A1rVzcY2BIXuyTsVt6xQYatACcjAEGlFgxPCEycMlLVrkmCGXh4aPpU5pRlv6gSHzNtE4ExCye8yabRIP7krtwV8EEEAAAQQQQAABBBBAAIGoCqRNcCiqDUi9EKiNgB0edmr/hJmCVHp70uFv+23bkgGjwkJPxcWeTjzRM7/oSpPfPXzf2pyPfRBAAAEEEEAAAQQQQAABBMIjEKgrPzu1/FP/eUu/fuQpLViy0ld8YeIUTZm5MDyilBSBgAqMHOGZ6dyleQscpYJBqaJuPZhvqPDgJH1nmHxDNlw0Y6ar3WaoGQsCCCCAAAIIIIAAAggggEB0BQITHLL5fs6/8k7d/YtH9chf/qMlH3zkq7+34APd+aPfK56IRu6T6L6UqFnQBQoKPPXrZ3oPmbfS25MrDy3bujX5uI3pOWSXjh08nXCCZ/J9SFPoPRT0pqV8CCCAAAIIIIAAAggggEC9BAITHPrHc/9VkUkU/fxjP9Zpp/Qur9SFZw/X1m07tHrtpvJ13EEAgaMTGGV6D7kmDvTeXFfbdxw6RmqmsjZtDq2zvYfsMt3MdLaHvOuHYLiHAAIIIIAAAggggAACCERMIDDBoRdfn6qLzz9dvbp3qkTctUux/3hrSYUr2Up78AABBGor0Latpz5mxrK4mbBs8pRDb/+tqZxDpndRauncydNxx3raZyYveHfqoX1T2/mLAAIIIIAAAggggAACCCAQDYHAXPEVtMrXOjO0rOqydMXH/io7kxgLAgjUX8DPJ2R6D1WcjSwVHKrYc8ie6cxRyd5D06a72lta/3NzBAQQQAABBBBAAAEEEEAAgeAJBCY4dMbQU/TCxHf18hvT/fxCCZMYZf7iFfrhr55Q72O7qrh9hfEuwXOkRAiERqCovZmNrPeh2chsL6Id2+UPN7N5iSoux3Tx1KunJzvV/dRpgfm4qFhE7iOAAAIIIIAAAggggAACCNRTIKOez2+wp3/5mk9p0bJV+to9D/nHnD1/qf+3uF2hfv69GxvsPBwIAQQk23to8eKYZsxydZKZtt5OcV9oAkN2NrOqi+09tHxFzA8ODRuaUE521T14jAACCCCAAAIIIIAAAgggEGaBwASHsjIz9PBP7tCM95Zo4furtNPMn939mA46a/ipym+RG2Zjyo5A4ATsbGTHHedp6TJHL76UjAilprGvWthuXT316O5p5SpHdnhZKlF11f14jAACCCCAAAIIIIAAAgggEE6BwASHLF/CdF84rX9v/+Z5nh8k2rh5G8GhcL62KHXABWyQZ+mymNauqzyNfXXFtr2HVq6K+Ymphw1JKCurur1YhwACCCCAAAIIIIAAAgggEEaBagaRNE81fvfosxp03pf8aettCT5/20/12Rvu0YXXfluP//Pl5ikUZ0UgwgKpfEKpKlZNRp1ab//ankO2B5Gd0n6amdqeBQEEEEAAAQQQQAABBBBAIDoCgbnKe3fWQo0/e5jaFLaSvT9j7hJ99QuX6qoJZ+uv/349OuLUBIEACVQcIta6deVk1FWLmZq5bIqZ1r5sf9WtPEYAAQQQQAABBBBAAAEEEAirQGCCQxs3bdOJx3bzHafPWeIPJfviFeP05as/pdVrN+nD1RvCaky5EQisQPdunoabJNNjRyf83kGfVFA7a5ntbbR7tzTTJLJmQQABBBBAAAEEEEAAAQQQiIZAYK7w2rZprVWr1/uqb737nvqc0F0ZGTGVHeyisGOnuSJlQQCBBhc475yERp2eMAHZIx861dPonSmu9h848v7sgQACCCCAAAIIIIAAAgggEHyBwASHzh45UH9+eqLOv/JOvb/8Y332U6N9vbemzpXjOOrauTj4mpQQgYgLHG9mOOvcydOuXdKs2YH5+Ii4OtVDAAEEEEAAAQQQQAABBBpXIDBXd5/77Hn6yucuVqfitrr9S5/WuWee5tf8zSlzdKHJRdS6VS26NTSuFUdHAAEjUN57aLKrA3FIEEAAAQQQQAABBBBAAAEEwi4QmKnsY7GYbjLBoarLwz+5o+oqHiOAQDMKnHCCpw4dPK1f72j2HFeDByWasTScGgEEEEAAAQQQQAABBBBAoL4CgQkO2YocMN0Q5ixYptXrNh1Wr/FjhykzM1DFPayMrEAgHQQcU8kzRyb093/FNMn0Hhp4akImtsuCAAIIIIAAAggggAACCCAQUoHARFvenPKevnHv/2j3ntJqKc8c3l+FrVtWu42VCCDQtAIn9vZUXORpw0ZH781LBoiatgScDQEEEEAAAQQQQAABBBBAoKEEAhMc+u0fn1bnju1137euV4eiNoq5ldMh5bfIbag6cxwEEKingMkRr1Gm99C/no5p8hRHA/rLJI6v50F5OgIIIIAAAggggAACCCCAQLMIBCY4tHtvqc47c7B6H9u1WSA4KQII1E2gz4meJhZIm7c4en+po94mFxELAggggAACCCCAAAIIIIBA+AQqd89pxvIP7HeCZs1b2owl4NQIIFAXAdu5b/jQZDLqd6YE5qOkLlVgXwQQQAABBBBAAAEEEEAAASMQmJ5Dt35xgi685lv66e/+qqL2hYc1zpWXnq0sElIf5sIKBJpTYIBJRv3GW64++tjxb12PofdQc7YH50YAAQQQQAABBBBAAAEEjkYgMMGhqbMWaW/pPj3x1Kt+PZwqCUwuPu90ZZGQ+mjamOcg0GgCWZnyp7J/a5Krye+66npMvNHOxYERQAABBBBAAAEEEEAAAQQaRyAwwaEnTVCo7wk99at7vqKidoVyXbLbNk6Tc1QEGlZgyOCEHxh6/31H27Y5Kiyk91DDCnM0BBBAAAEEEEAAAQQQQKBxBQKTKGSPSUh92qm9/ZnKCAw1bqNzdAQaUiC/hXRyH08JExOaOp2gbkPaciwEEEAAAQQQQAABBBBAoCkEAhMcGjmkn+zQMs+j10FTNDznQKAhBYYNSSamnvOeq31lDXlkjoUAAggggAACCCCAAAIIINDYAoEZVnZMpyL9+emJ+vZ9j6i4fZvD6v3lqy9Ubk72YetZgQACzS/QoYOnHt09rVzlyAaIhpqhZiwIIIAAAggggAACCCCAAALhEAhMcOi9hR+ooFW+3p46r1q5z332PIJD1cqwEoFgCNiA0MpVMX9o2ZDTpCo55YNRSEqBAAIIIIAAAggggAACCCBwmEBggkO/uOvGwwrHCgQQCI/ACSd4KizwtHWro6XLHJ1wPENEw9N6lBQBBBBAAAEEEEAAAQTSWSAwOYdq0wjjrv6m1qzfXJtd2QcBBJpYwE4wOGRwMiD07rRQfbQ0sRSnQwABBBBAAAEEEEAAAQSCJRC6KzgSVgfrBURpEKgoMODUhLKzpJUrHW3YyMxlFW24jwACCCCAAAIIIIAAAggEVSB0waGgQlIuBBCQbM74U/snZPsPTZ1GcIjXBAIIIIAAAggggAACCCAQBgGCQ2FoJcqIQIgE7NAym4x63nxXe/aEqOAUFQEEEEAAAQQQQAABBBBIUwGCQ2na8FQbgcYSaNvG0/HHedp/QJo5m4+YxnLmuAgggAACCCCAAAIIIIBAQwlw5dZQkhwHAQTKBey09naZPsNVPHm3fBt3EEAAAQQQQAABBBBAAAEEgiVAcChY7UFpEIiEQM+enoqKPO3YKS1cRO6hSDQqlUAAAQQQQAABBBBAAIHIChAcimzTUjEEmk/AhoOGHZzWfirT2jdfQ3BmBBBAAAEEEEAAAQQQQKAWAmkVHNq1e682bi6pkWXD5m3as7e00vbNW7frhYlTKq3jAQIIHFmgX7+E8vKk1Wscfbya3kNHFmMPBBBAAAEEEEAAAQQQQKB5BEIVHHr8N99Sx+K2nyi1r2y/rrjph7rkuu+V72fXfe2ehzR0/E0a85nbNf7qb+rD1RvKt6/4cK3Ov/JOjf3MHRoy7kbd+aPfa7/NpmuWpStW666f/al8X+4ggEDtBDIzpEEDkgmH3qX3UO3Q2AsBBBBAAAEEEEAAAQQQaAYBc/nWfMt9D/xFcxZ8UKsC/PH+b6hdm9afuK/nefrOT/6ghUtXqWfXjuX7PvPi25o2e7FeePzHKmpXqNvvflD3/uZJPfLzr/v7/OD+J9SzW0c9/Yd7tGb9Zl1zy3167tXJumzcGeXH4A4CCNRdYPBpCU2e4mrxYkc7dkitWtX9GDwDAQQQQAABBBBAAAEEEECgcQWateeQDdR061Jcq5vrHHlYyu8efVbLTE+fL191YSW1V9+eqXPPPE09TMCoRV6Orv3MOZo6a6F2mmFm27bv1Mx57+vaT5+rvNwcHdeji8aOHKCJb82sdIzUgzcmz9Fl19+tuYuWp1bxFwEEahBo1VI66STPn7Fsmpm5jAUBBBBAAAEEEEAAAQQQQCB4As3ac+i6yy9oMBGbF+ifL7ypvz98l15/Z3al427YtFVnDjulfF3XzsVKJDxt3lKiMjN8zPY4skGq1NKtSwfNX7wy9bD875SZC/3had+59WqdclIvf32LnGYlLC8bd+ovYAOQNgRJm9bfsuIRRo+U5i+QZs9xdcE5rrIyK25tmvsZMUcx16Ftm4a7Sc+SnenKpW2b1LypTmbft+adK6cWPw41VZk4T8MI2PdsbnZMWRn8aNAwosE6Ct+jgtUeDVEa+x3Kfk+mbRtCM1jHsJ/DMfPvLW0brHZprtIELrJhgzbxePwwj0ybwKSGZfb8pbr310/q9z/9mjp3aHfYXjt37VFOTnb5+uysLP/+DrO+zOQjskt2dnKdf99cve7cvcdfn/rfnAXLdOv3HtA3b75CE8aNSq2W/fLKEiEB8w8fbdqw7dmzu9S9q6dVH0nvzXU0fEjDHr82R7Nfauz1JW1bG61w7WMDB/ZTmLYNV7vVprT2QsRzPdq2Nlgh28e+Z2P2vRsLWcEpbq0E+DyuFVOodvJ/QOV7VKjarLaFdU2M3rYv79vaikV7v5ojLk1c763bduibP35EM+cukU0gXXWZ/PzvVNAqv+pq//Ez/zdJbQtb+XmCbK6gpcs/1vpN23TP/Y/r9usvU6uWLSodc19Zmf+8Vvl5fs8h+6DiOe19uy212N5FN9x5v7p2LtKE8ZXzEG3ffXhZU8/jb7gE7Aej/SWTNm34djvtNMcEh2J6Y1JCJ/WN+xfzDX+Wmo9oe5fk52bStjUThXaL/aXLfqHhfRvaJqyx4AX5jvk3Oq49+w7/wajGJ7EhFAL2l+pdpQe0/0By0oJQFJpC1krAfibzeVwrqlDtlBlzlZHB96hQNVotC5tne3GaXv3N9b6lx1ItG6qJdgtMf94HTL6g9xYu02cvGu1X/avXXarv3na1OpnZyfr3PVa5FXr+VLUZffqpumDMUD9AZINEeSavUIb5ELP3XRMOtbmNPqowO9mqj9f7wxDaty0w2wr8LutVt9vnpBb7y/Qt103Q+o1bdc8vH0ut5i8CCNRS4KQTPT8Z9cZNjlassL8ZsyCAAAIIIIAAAggggAACCARFIDDBoTnzl+mqCWfrVhOEscuY0wfo8ovH6JYvTvCTTCcSNf+6NHrEAN38+UvKbyOH9PNnNrPr8lvk6pxRg/TSf6fJTlm/e0+pnvjXKxo6sI+/rbB1Sw3sd7we/cfL/jbb6+i1SbN09hmDytsoMyOmKy8dq4d+crtefH2qfv3IU+XbuIMAAkcWMLFaDR7EtPZHlmIPBBBAAAEEEEAAAQQQQKDpBQITHLI5ftoUtDK5gbL8oM1Hazb6Gv1O7OUHbRYt/fCodS41OYJO699bn/rcdzR0/I3+dPXfvuWq8uPddfu1Wr5qjb/t0i/eJRtcuuicEeXbbe8ju9gk1PfffZP+9PeX9LdnXy/fzh0EEDiywKCBCdnUYcs+cLRlC72HjizGHggggAACCCCAAAIIIIBA0wgEJueQzSf08dpkQKh/n2P1z+ff0OmDT9a02Yt8CTtErLbLNZedI3tLLdlmIOWvf3CzbGLqXWb6+o5mqFrFpVf3Tnrlbz/X2g1b1NLkGmppehulluGD+mjWK/+beqgzhvXXvNf/WP6YOwggUDuBPPO26tcvoVmzXU2d7mjc+V7tnsheCCCAAAIIIIAAAggggAACjSoQmJ5DJx7XzeQc+sCv7BVmCNfkGQs0ZNyNflJpO+yr+zEd6g1hAz9VA0MVD2rzG1UMDFXcxn0EEKi/wLAhnp+Mes5cV6Wl9T8eR0AAAQQQQAABBBBAAAEEEKi/QGB6Dt1753XltTlj6Cl65Bdf19tT58kGjc47a3D5Nu4ggEB4BYrae+rZ09Nyk5R61hxXI4bVnEssvLWk5PIoIOUAAEAASURBVAgggAACCCCAAAIIIIBAuAQCExz667OvqVe3zhp8am9/9rBhJmG0vbEggEC0BIYOTpjgUEzTZrgaNjQhl/RD0WpgaoMAAggggAACCCCAAAKhEwjMsLLpcxbrC3f8VOOv+ZYeMzOHlezYFTpMCowAAkcWOP44T23beCopkZYsITJ0ZDH2QAABBBBAAAEEEEAAAQQaVyAwwaFffv8revgnd+i4Hp31q0f+pbMm3KY7f/R7zZ6/tHEFODoCCDSpgGPiQUNN7iG7TJ0emI+gJjXgZAgggAACCCCAAAIIIIBAkAQCM6wsZqaLHzW0n3/bWrJTz78yWc+89Lau/uqPdWz3zvrbw99TXm5OkOwoCwIIHKVA/1MSev2/rlZ96GjdekcdOzBz2VFS8jQEEEAAAQQQQAABBBBAoN4CgfzZ3k5rf3yvY0wvoi5+BVd+tE7798frXVkOgAACwRDIzpIGnJpMRv3utEB+DAUDilIggAACCCCAAAIIIIAAAk0gEJieQ7au6zdu1bMvTfJva9ZvVoeiNrr585fo0nGj1LpViybg4BQIINBUAkMGe5o6TVqwwNE5Y6V83uJNRc95EEAAAQQQQAABBBBAAIFKAoEJDv3sob/ryadekWP+O31IP33n1qv8v3a4GQsCCERPoLDA0wkneFpsklLPmOnqrDOY1j56rUyNEEAAAQQQQAABBBBAIAwCgQkO7S3dpxuuuUgTLhjl9xgKAx5lRACB+gkMG5IwwaGYZsxyNfL0hDJi9Tsez0YAAQQQQAABBBBAAAEEEKi7QGCCQ3ffcW3dS88zEEAg1ALdu3nqYJJRrzdJqRcsdNS/H4mpQ92gFB4BBBBAAAEEEEAAAQRCKcCYrVA2G4VGIDoCww5Oa09i6ui0KTVBAAEEEEAAAQQQQACBcAkQHApXe1FaBCIncHLfhJ+Met06x5/aPnIVpEIIIIAAAggggAACCCCAQMAFCA4FvIEoHgJRF7B5hgYNTCajnjqdj6Sotzf1QwABBBBAAAEEEEAAgeAJcCUWvDahRAikncDgQQnFTJBoiZm5rKTESbv6U2EEEEAAAQQQQAABBBBAoDkFCA41pz7nRgABXyA/X+rbx1PC5KOeNoPgEC8LBBBAAAEEEEAAAQQQQKApBQgONaU250IAgRoF7LT2dpk1x1VZWY27sQEBBBBAAAEEEEAAAQQQQKCBBQgONTAoh0MAgaMT6NTRU7eunkpLpTlz+Wg6OkWehQACCCCAAAIIIIAAAgjUXYArsLqb8QwEEGgkgVTvoWnTHZkRZiwIIIAAAggggAACCCCAAAJNIEBwqAmQOQUCCNROoHdvTwWtpc1bHH3wAbmHaqfGXggggAACCCCAAAIIIIBA/QQIDtXPj2cjgEADCrgmHjT4tGTuoXen8fHUgLQcCgEEEEAAAQQQQAABBBCoUYCrrxpp2IAAAs0hMHBAQlmZ0vLljjZvpvdQc7QB50QAAQQQQAABBBBAAIH0EiA4lF7tTW0RCLxAbo50Sr+En3Noqsk9xIIAAggggAACCCCAAAIIINC4AgSHGteXoyOAwFEIDB3syYaF3pvn+rOXHcUheAoCCCCAAAIIIIAAAggggEAtBQgO1RKK3RBAoOkE2rf31KuXp7IyacYsPqaaTp4zIYAAAggggAACCCCAQDoKcNWVjq1OnREIgcDwocnE1FOnu4rHQ1BgiogAAggggAACCCCAAAIIhFSA4FBIG45iIxB1AdtzqLjY086d0rwFfFRFvb2pHwIIIIAAAggggAACCDSfAFdczWfPmRFA4BMEbM6hEcOSvYcmv+v4Cao/YXc2IYAAAggggAACCCCAAAIIHKUAwaGjhONpCCDQ+AIn9/XUqpW0caOjDz5g5rLGF+cMCCCAAAIIIIAAAgggkI4CBIfSsdWpMwIhEYiZT6ihg1O9h/i4CkmzUUwEEEAAAQQQQAABBBAImQBXWyFrMIqLQLoJDBqYUHa2tGKlo3Xr6D2Ubu1PfRFAAAEEEEAAAQQQQKDxBQgONb4xZ0AAgXoI5JjA0KAB9B6qByFPRQABBBBAAAEEEEAAAQQ+UYDg0CfysBEBBIIgMHRIQnaI2YJFjkq2B6FElAEBBBBAAAEEEEAAAQQQiI4AwaHotCU1QSCyAq1NUuq+fTwlTAeiqdP42IpsQ1MxBBBAAAEEEEAAAQQQaBYBrrKahZ2TIoBAXQVGDE/IZhyaNdtVaWldn83+CCCAAAIIIIAAAggggAACNQkQHKpJhvUIIBAogQ7Fnnr29LSvTJoxi4+uQDUOhUEAAQQQQAABBBBAAIFQC3CFFermo/AIpJfAiGHJxNRTp7uKx9Or7tQWAQQQQAABBBBAAAEEEGgsAYJDjSXLcRFAoMEFevXyZHsQ7dwpzVvAx1eDA3NABBBAAAEEEEAAAQQQSEsBrq7SstmpNALhFLA5h1K9hya/68gLZzUoNQIIIIAAAggggAACCCAQKIFIBYdKTTKSD1dv0OatRzfX9YbN27Rnb+VMt/ZYL0ycEqhGozAIpLNA376eWpnZyzZudPTBBzZcxIIAAggggAACCCCAAAIIIFAfgYz6PDlIz/1/P/wfvfzGNDPVdbIvwSkn9dLvfnSr2hS20tTZi3TdHT87rLgT//FLdSpuqxUfrtVXvv0brV630d/ngjFDde83rlNmZoaWrlitu372J1149vDDns8KBBBoeoGYCWkPG5LQKxNdTX7X1XHHknyo6VuBMyKAAAIIIIAAAggggECUBCITHDqmU3s9+cB31Of47ibIs0nX3PJj/eWZ1/TV6y6V5yUDRs899iMzFfahngbF7Qr8tvzB/U+oZ7eOevoP92jN+s3muffpuVcn67JxZ0SprakLApERGDggoTffdrVipaN16xx17MgAs8g0LhVBAAEEEEAAAQQQQACBJheIzLCyW66boP59jvV7+3Tu2F6u66p16/xKoL26dVKv7odusVhM27bv1Mx57+vaT5+rvNwcHdeji8aOHKCJb82s9NzUgzcmz9Fl19+tuYuWp1bxFwEEmlggJ1saZAJEdnnH9B5iQQABBBBAAAEEEEAAAQQQOHqByPQcsgT7yvbrf554XpOnz/d7EF1y3umVZL7+g4eVmZEhO+TskgtGKic7Sxs3l/g9i7p1KS7ft1uXDpq/eGX549SdKTMX6mv3PKTv3Hq1fwy7Pjc7ltrM35ALuAf7ldGm4WjIs0ZKU6dLCxc5uvD8mAqTHQGrLXymGYsWcx3er9XqhHtlZoZtWz6Lw92K1Zc+I2Z6+nouieer5wn1WtdxlJMZk9/Goa4Jha9OgO9R1amEe12G+dHdvm9p23C3Y3WlzzLfo+xnMW1bnU76rYtUcCgej2v5qjV+UumSHTFt37FbLfPz1K5Na11+8Rhz8djSBIO26VeP/EuTTADpoftu046du/1WzzaBotSSnZWpnbv3pB76f+csWKZbv/eAvnnzFZowblT5NvvlhiUiAnbE4cEvrBGpUaSrkdNOGniKp+mzPb071dWlFx4aMlq14uY7jW1a/2Kk6jYeh1vAfqFx/PdtuOtB6Q8XsBciGeZLq21flmgJOOYzOTPTtG+C71DRatlkbfhuHL1WtR/D9kbbRq9tY+Z7VCpgH73aUaO6CkQqOGSHhf323lv8nkDX3nqffvPHp/Xz793gDxX77m1Xl9sMH9RXd3z/QZXs2KVWLVv4622vo9Ri77cyQaXUUrb/gG6483517VykCeMr5yHatqsstRt/Qy5gPxizC13RpuFpyEGnOZoxO6Yp0zwNG7ZfOTnVlz0701V+biZtWz1PqNe2yMnwf/HavvvQZ3ioK0ThywUK8rNUtj+uPftIOl+OEpE77Vtna+eeA9p/IDk8OCLVohpGIDc7l39rI/hKsD2wC1ryPSqCTas8Mwomy3R2KGmma1r7mcESHIFIJuuwvzL2OKajNpkhY9Utxe0K/dWlpWUqMkmp7f4frd5Qvuuqj9eb9cl97Eq73eY0Wr9xq+755WPl+3EHAQSaV6BDsadevTwzpFSaMSuSH2fNC8zZEUAAAQQQQAABBBBAIC0EInE1ZYeG3fubJ7V0+cd+3qHpcxbrlTena2C/4/1G/Ouzr8kmkt65a482mGFlv3v0WRM86qAORW1U2Lqlv9+j/3hZu/eU+sd4bdIsnX3GoPIXQGZGTFdeOlYP/eR2vfj6VP36kafKt3EHAQSaV2D40OQvz1OnuzIjS1kQQAABBBBAAAEEEEAAAQTqKBCJYWWOSSgy87339bdnXy+v/gWjh+j6q8b7j0u279JPH/ybDhxIXjn2NLOW/eLum8r3vev2a3XTt36loeNvNEPSpPPNcy86Z0T5djvzmV1sIuv7zfO++t0HZHsfXX7JmPJ9uIMAAs0jYHsO2R5E6zc4mjff1an9GabQPC3BWRFAAAEEEEAAAQQQQCCsAo5nlrAWvmq5d+7eq81bSvwhYS3yKicfsXmENmzaqlwzB3b7ttVPa7R2wxY/gXXLFrUf+7h2y96qxeBxSAVszqGiwmyt31oa0hqkb7HnznP09L9jKiry9JUb4mbeucpLKufQlh37Km/gUegFyDkU+iassQLkHKqRJvQbbM6hEpMnjJxDoW/KwyrQqW2u+G58GEvoV6RyDm0q4XtU6BuzSgWaO+eQ/cxgCY5AJIaVpThtUKdH146qGhiy2+0MZF07F9cYGLL7dCpuq7oEhuxzWBBAoPkF+vb11LqVtHGjow8+qBoaav7yUQIEEEAAAQQQQAABBBBAIMgCkQoOBRmasiGAQOMJmEk0NHRIcjjZ5Hf5WGs8aY6MAAIIIIAAAggggAACURTgKiqKrUqdEEhDgUEDEjKjRrVipaO16+g9lIYvAaqMAAIIIIAAAggggAACRylAcOgo4XgaAggESyDbBIYGmgCRXeg9FKy2oTQIIIAAAggggAACCCAQbAGCQ8FuH0qHAAJ1EBhmhpbZIWYLFzkq2V6HJ7IrAggggAACCCCAAAIIIJDGAgSH0rjxqToCURNoZZJS2+TUCdOBaPIUPt6i1r7UBwEEEEAAAQQQQAABBBpHgKunxnHlqAgg0EwCo0Yk5JiUQ7PmuNqxs5kKwWkRQAABBBBAAAEEEEAAgRAJEBwKUWNRVAQQOLJA+/ae+pzk6cABadI7fMQdWYw9EEAAAQQQQAABBBBAIN0FuHJK91cA9UcgggJnjqL3UASblSohgAACCCCAAAIIIIBAIwkQHGokWA6LAALNJ1BUoffQO5P5mGu+luDMCCCAAAIIIIAAAgggEAYBrprC0EqUEQEE6iyQ6j00Y5arzVvq/HSegAACCCCAAAIIIIAAAgikjQDBobRpaiqKQHoJ2N5Dp/ZPKB6XXnzZZKhmQQABBBBAAAEEEEAAAQQQqFaA4FC1LKxEAIEoCIwdnVB2trRoiaMly7woVIk6IIAAAggggAACCCCAAAINLkBwqMFJOSACCARFIL+FNOr0hF+cp5/3lCA+FJSmoRwIIIAAAggggAACCCAQIAGCQwFqDIqCAAINLzBsaEJtCqW16zzNNPmHWBBAAAEEEEAAAQQQQAABBCoLcKVU2YNHCCAQMYGMmDTuvGSXoTfedFVaGrEKUh0EEEAAAQQQQAABBBBAoJ4CBIfqCcjTEUAg+AJ9T/J0XC9Hu/dIb77Nx17wW4wSIoAAAggggAACCCCAQFMKcJXUlNqcCwEEmk3gsoscuWbSsmkzXG3ZwuxlzdYQnBgBBBBAAAEEEEAAAQQCJ0BwKHBNQoEQQKAxBLp0cnTqqcmp7V+eyEdfYxhzTAQQQAABBBBAAAEEEAinAFdI4Ww3So0AAkchMPas5NT27y919MFyeg8dBSFPQQABBBBAAAEEEEAAgQgKEByKYKNSJQQQqF6ghZna/oyRyantX37VVSJ5t/qdWYsAAggggAACCCCAAAIIpIkAwaE0aWiqiQACSYGhQ8zU9m08bdzkaAZT2/OyQAABBBBAAAEEEEAAAQREcIgXAQIIpJWAndr+3LHJLkNvvOVqL1Pbp1X7U1kEEEAAAQQQQAABBBA4XIDg0OEmrEEAgYgLnNjbU88envbYqe1NgIgFAQQQQAABBBBAAAEEEEhnAa6K0rn1qTsCaSxw/jkJf2r76WZq+82bSU6dxi8Fqo4AAggggAACCCCAQNoLEBxK+5cAAAikp0BxsaeBA8zU9maEGVPbR/M1sH1HNOtFrRBAAAEEEEAAAQQQaGiBjIY+IMdDAAEEwiIw2kxtP3+Bq6XLHC1531HvE7ywFJ1y1kLgmX/HlGH+lRs7OqGOHWjbWpAd1S6r1zhaucqRYzrgueYnJ/vX3nJzPPXo7qlVq6M6LE9CAAEEEEAAAQQQaEIBgkNNiM2pEEAgWAIt8iQbIPq/l10990JMnTsfUMv8YJWR0hy9wK5d0iYzZHDZBzGd3NfTmDOTM9Ud/RF5ZnUCNrj65ts1d0Quau/ptEGeBpmeejGTEJ7l6AT27JVmmhkWbQAuFYSzf22S/Y4dPXUwAVDXBOVYEEAAAQQQQACBoxEgOHQ0ajwHAQQiIzBkcMLvOfTBcke2p8k1V8b9Xg+RqWAaV2TbtkNXyvMXOFq0KOYPJTxjVIIgYAO+LnbvTh4sK0t+AMgznbTsrWR7skfRxk2OXnzJ0ZSpjsaYYOzJfTzeY0fhv3Wro9f+W3MQLidHfu/HM0cSBD0KXp6CAAIIIIBA2gsQHEr7lwAACKS3gA0fXHpxXA/9T4aWr3A0abKrUacnp7pPb5lw1/5AXIqbW8xcS9/21QN6w/Rsee89V9Nnupoz19VQExQ8fUTCDH0Kdz2DUPrde5JBuEsuiqvPiZWH79mcXkuXmqDGG642mSDRU8/ENHmKp7PHJHRsr8r7BqEuQS6DnV0xtQwfmvADcAnju2+fo48+lraaYOh7cx3Nmx9T/34JnTnKU0EBxikz/iKAAAIIIIDAJwsQHPpkH7YigEAaCOS3kOyF7Z//FtPr5iK2XVtPJ1W5yE0DhkhVcbcZUmYvi1uYtm3dWrr4woRGDPP89l28OBkEtEN0xp0fV7+TuYCuT+OnghZ2mGbVxQbnTuzt6YQT4pprgnL/fdPVuvWOnvhLTDYfkQ0SdemMf1W36h6nnE/p5+k8M9ti1cX2LJo02QaIXM02gdC586UB/RMaZXoStSbvU1UuHiOAAAIIIIBAFYGa+ydX2ZGHCCCAQJQFjjs2eaFqh8M8/WxMH68+NCQpyvWOat127U62X37+ocBD+3ae/r9Px/WlL8bVq6envaXSU6at7a10X1QlGr9eqWFl1QWHUme3uXBONYGKW28+oHPPTigvV34S60f+GPOHc+Kfkqr5b6qHVou8Q6/pinu3aePpIhME/epX4qbnkCfPxI9mmADobx7I8POq7TQBUxYEEEAAAQQQQKAmAYJDNcmwHgEE0k7g9OEJnTYwof0HpL/+I6aKOWvSDiPkFbbJqO2SX02C8c6dPF17VVwXfyohmydn3nxHD/0+wwzNISCYVKvb/1NBi7wW1QctKh7Nzh43YljCH+pnh29mZErvzTP+Zljnqg/xr2hV9X6q51BeNT20Ku7bptDzh8refFPcT8Ruh/ZNne7qtw9m+EMqK+7LfQQQQAABBBBAICVAcCglwV8EEEDACIw7PyHbi8j2hnjyr672mhmCWMInsGvXwZ5DnxCwsENubvxSXDZYVFIi/emxmD/syeZxYamdQMLEg+x7xPYMyjW9gWq72OTJY0cndJPxt8PKSrZLjz1h8hG9y9eSmgx3H8w59Ek9tCo+1w6P/fSlcX3lhrifqHqf6R337HOu/v6vmOzMZywIIIAAAggggEBFAb6FVdTgPgIIpL2AnRr6M5fF/WmhN29x9Ld/xmSTG7OES2DXwRm0qus5VLEmbc1QnC9+IV6ehNxOyf5HEySi11hFpZrv294sdiimDQwdzTTqbU0A44ufj5vkySbBsjnNKxNdf5jZAdN7j6WywJ6Dib/zahhWVnnvQ4+K2nu64rNxTTCJ93OypUUm55btKbdyFT21Dilxr7kFbFDeviZffMnVY0/G9KfHk7eXXnH912zqM725y8n5EUAAgSgLkJA6yq1L3RBA4KgEss1Qo6suj+t//5gc6vLv52OacImZ4v6ojsaTmkPgk4aVVS2PTZpse7HY2bOe/ncy39RD/xvTJZ+Kk5i8KlaVx3sO5naqzZCyKk8tf2gDsqPPTKhjh6S/HWa2ZWtMX/6clFOH3kjlB4zondoOK6up+jaRdbeucfMad/XhR45/AW6H0o4+K+HP6lfT81iPQGMJ2CCwnSV08RJHS5a6Sr3GK57PDjd9d5r8f3/bmGByT5PIfvBpnoqLjjyMteJxuI8AAgggcGSB2PfNcuTd2KMmgZ17+XmzJpuwrXccRy1yM7SLNg1b0x2xvBkxR1mZMe3dV/suQNnmF/aePTyTj8bV2nWOn9zVPmYJlkBWhivXdFnZt7/yWDA7W9OmzY6fBLmofe3KXFCQTJpcUuJonWnzhYtcmY8FdevmERisgXCjmZ7ezo5ljU/tX7/3R/t20gnHefpguaMNGx3NmmsvBKW69pSpoaihXz1pih3m6mjkCM+YHF117HC+/qeY17N5XdsAkb0tN949utdtWODRnf3Qs1rkZKjUvGcTdlwiS6QEWuZlqrbfjW0voZcnxvwZDPfvl9qZSQMGDfD8npz278l9PbVta3olmgCynWTADhe2/x7PnOlqzVpHRUXV55WLFGhAKhMz/87mZJshqaW1/x4VkKJTjCMIZJrvUTHzK1lpWfO0rf3MYAmOAD2HgtMWlAQBBAIm0KHY84eY/eXvMb01yZUNHgw8tXIQ4miKbGdD62zyrJzYmymmj8avNs9JzaCVb6ayr8uSay6ePzPB5sFx9aoZ4mSnXreBCtuLyCavZqksUJ4Hp47OlY9y6FGxec992cwmZ/Pi2B4DDz3imBm4HPUzF4npvpT30qrjsLKqbuYaT2edkfBn7LMz9a1e4+hh01Nu3PlmljMTOGJBoKkEdh7MDdfdBOAvvCCh9mYIZNXF9ui0ix12tm69o7mmZ+HsOa6WLjOBzRUxf0jqyBEJP4BU9bk8RgABBBComwA9h+rmddjetf115LAnsiJwAvQcClyTNFiBjqbnUOrkbdskf5l8f6mjpeYWi5meJP9/e+cBH0WZ/vFndxMCgRB66B2kdwSRKmKv2LCiop69lzu9O8923l9PPbuevXdFRQ8BQUEUlSYdpIP03hOyu//n905ms0k2m7abnd39vXyGnZ3Mzrzv9532/uYpzYs+wNrrl/S5SR9ux+nb0t+Xu2TGDOsBF7FEEGS2vNYAJe0zkf9enOXQ1B8sK4vBA8tnZdGsqd8IeMvU1QEDkiXa921bV651RTz026rV1rHcSgd37dXqJxIlVV8idlMXKG+OCkRrRRYvdgvcT2C5B4uXZCwYGH87Wa3ktP1wgYwEh8zMfEu5DXqML17qFsRZa9PaL8gqF81Cy6Fo0o3ttstiOYQsenv2uOTskT5p3Cj89QPHfM0MMQkj+vTRrKJqabR+vUtWapwi3E9bNBOpHiGROrYEnbl3Wg45s18iUStaDkWCYuJsQw01E6ccys6RNes3y7YdmvYkRNm3/6Bs2aYpaYopm7ft1Awehwr8Fdv6cuKPBZbxCwmQQHIR6NPLJycer6MzfTidpAO0sV+4Tbr78lBYqMFg7YJBMEzjsc2nnvPI0zphHsvCPybbW+BncQTsbGXVa5SfJLLWXaVWLHibvUWth1542WPeVhe3z2RcbscIibSwiThQ5450ycjT/MYiYNp0t7z7gUeQcSsZiwn8rQ2vpiJyJIQhmyECVJ+jlnIjT/cKYq3NX2AFq167Lv86Za/LTxKINAHbciijRtm2nK6xyJBZ9NJLvMaiF/fM51+ysh0iQD4LCZAACZBA+QhE+d1Q+SpVnl/d8cALMn7KzwH/9e6d2sgzD90kdWrXlOycw3L3wy/JN9/9ah6qWjTJkmcfvkVaNM0yu1q5ZoNcd/eTsn7jFvP9pOH95cE7x0hqaoosW7le/v7Iq3LqiAHlqRZ/QwIkkCAEjurnk1qZfoEbBuLZIFbH6af6BObwZSkLFlmDrksv9kqzZn4T72PxErfAMgkxcrb+4JKpP4hk1hTjdtaxA4LIWgPksuwnmdfVS77k5IjoJdxkZ6oIC6QDv2qMV1OAe0zGHLgYwiVnyKDIWG9UpG5O+K3tvhetN/YD+um5UMsrH3zsMefIf1/1yIXn+aSOZplLplLeTGWlZQR3subNvPLRp5Y4jUxROMaHaBY5WCuxkEA0COzfZ221vCJ+Kw1Ofd3VuTJ+gltmz3abbIdLlsINOPmuEdHoH26TBEgg+QgkjOVQs8b15a2n75G5E1+WcW8+LOs2bJF3Pp1kevTTr6bKz7MXy5dv/FNmjHteGjesLw8++Vagt+9//E1p3aKR/PzV8/LpKw/I1Bnz5PMJ0wN/5wwJkAAJgACEGqTdRpaU7Ttc8poOoL78yl1qa4bNmzULk7ptYCDdUh9qIV50OMIvZ+pb+ztvyxUIRv2PtOIQ7d4jApP71970yCOPpRhxAg+9hxkDv8SD0bYaqlEBq6HgncCi4rxzvDJiuApC+gfEIXrvQ48cSlIrlmA2+/PSq1evYByc4G0WnscA8E8q0DXQ826rBsB+8RWPrFyVXIpFILZTOQNRF2Ya6jsEtysu92rAa5/A+mLK93r90WvcrtDG2KE2kTDLFqqIj6D0tEKJXpfi+on7Ga6vVSoQjxa/P/0Un1x0gde4neHFzXMvemSWxiViIQESIAESKBuBhLly3jjmLOnRua2x9mnSqL6aobslM9OyU50wdaYcP7SvtGreSON6VJXR5x4nM2YtlL3qZrZz916ZOW+pjD7neEnXSKTtWjWVYwf1konfzwxJcsr0OXL2lffKb4tWhPw7F5IACSQ2AaTbvvpKryD1tlvjD/06yy3PPJ9irEpKSrxjWw110kDUhd/GIxsLYqqcdIJPbr051wTlHTzQZ7K3HDgoMuc3l3Gr+b9/p8iHn3hkwUKXHCroBZvY4MvQurKksS/tZiFFYNCMAQhcGiDUvahuZhArkrlEy62sMNPatf1ypQoXEFMP6vnw5jseI54WXi9Rv1cWZ7jzQQSFUI34LtZAO8VcbxKVbah2jZ/gMW5K/9LrLazWZqpVys6dyX2uh+JUkWX784JRl9dqqPC+4QZ83TW50r2rX2A9+vmXbkE2NMTrYiEBEiABEigdgYRxK0Nz4T72wptfyPRf5kvn9i3lzBMGGgqbt+6QoUd1DxBprm5lSJ+6bfsuvYHk6pshddvIczHDSi2aNpT5i1cF1rdnfpy5UG677zm556aLBW5rKGmaHpslMQhgsI5HP/ZpYvRncCtSU1wqxrgi17f6lvP44ZoSWh9CPx7rkrXrxGRXqq3ZzJB+t2kTTcmrgaxr1xYTwNquy6Il1uCiR7eS69K6hYpFOp18vMiWrX5ZuFgE4tIfG/RThaEFCz0mFksz3Ve7tiLt9cG4WVMr5a+9v2T4RLBxBMoMPm+z80SzmhkFl0eCR+cOIo2u8ctb71kplRGHaMQwvwpHyccePG13p9qZbu2DSBDO3wb6NUXVCnu7+Lz0QpEJ36pVy1SRr8drAOWtbjnjVH+B8yx/C4kzl5NnpVazRuSP6VCUOrQTueV66/qGaw8E6ZX6WHTqSf4Ku2pif0gAYYLJRzKAUqiGlGMZAh3DchMFQiSsiDCh1NFrelt9/DtCg693aC9RD9xtdhqH/wVfj4urfuA6HcFjGteIC85VK18VkXFv/vlXBFl3y4Xn+hmsuriOKOXyFL0e47wtTd+WcpNczSEEcJ8t/BzlkKqxGjEgkFDikNfrlRWr/zBBpXft8ejNfb9k1EiXvfsOSFVEXcwraXn5iPfo8hy8XtCSBrvUvJKm9q17bRvuvGVzFvwuN/3tafnz9RfIWScPtleVGtUoDgVgxP0M1CEX+zTu+7FoAyAMIctYpM/XGire3HGDyA8z/DJ5qrq8bBOZONkaRKAWsAbCYKJBfXUHOwyXGCvzWbeOlrBTtKahl9RorkKRTqeqULRDY+r/Nt8v8xaKDtb8skaFKUyTprikmlq0HNHWJR110NLxCJcg01qiF49CxviyhrbdLjnZfp31S51aOJ+1EyJcsK87bhT5aKxffvrFL19PULFOB9Dnj3Rr3JYI78zhmztwwHot36Cup0AfRKLalvDnVvfL/HMK2z3rVHXL1Hhdb3/gl19miXHxvHK0W+/3kdirM7eRk2Md07Uyo3NMh2o1jvNrx+j17Se/fPKFX61nNHOcCtxDB+k00GUs6EL9rjTLYKFULc1jXs6VZv3KXOcPk7fEp+7DaL9bMxX6ZcnvIsuW+2XHTpFf1LD8l5naD+oe3LeXS4Ypj2S41palD0pzrz1sH9NRuE4P6i/SrLHIf1/3abp7kWdecMmYS9zSUu+jLOUjAGFI38VE/DmqfLXhryJJAMIQ+rc0520k98ttOZNAQolDcAt76sEbzcPG6Jselidf+UQe/dvVahpd3VgV2V2QjUilWmqqcATLIRRYHdkF8/ibXbDO1Xc9Ls2bNJCzThliLzaf2/dY2yqwkF/ikgAEhAa104R9GpfdF7bSaaluvemlRq1vO3cR6aTTihWa8lzdjRCPaMcOffu826VvLUUnDOys0qG9T3bus6479rIyfeqgqpsaQmLCpWzVapc+/Lpkue4baajnqnA0dz626NcBi1/fcvtNaupW6rIWpIGXaZdOXhkpsSEi7N6ffw3fvA2CkFtSqni1zy3xIhptOEHFunbtXPL5OI+xHnvkKZ9ajvlMqnEIdYleEI9l//4UY3GZ7c1R1pFtca0aVfQe7ZUD2d4iG27ZWmTMpZar5YpVIv96wicXnOeVhur2mYhl+07rmHanRPeYDsWuY2eRP2W5THy11Wtc8tU3frXeUssZjcHWs7tPWrfWgPkF9btQmymwrH5mmjlnD+dG7/wssMMyfFm5Fo3xqOWnX1ypOYL2Y8LxvnGTdb2F9Sbmp0zzy7Qf/dJXU6sP1gDe1fMfHcuwx8RatXHdaqW6125Uqz9cp6tE6TqdoZa8V6m4CbdAZN974lmfnKQZznCNZik7gVRVdGtlRO85quw14i8iRSBdhfoqqRpfbl9sxrS4ZrA4h0BCiUM2VqifrZo1MmntsaxBvdqyVlPc22X1uk36Rt8l9evW0mB4uUYtxd+zdD0U/B2/sQu2h5hGz70+Vu577HW5/47L7T/xkwRIgAQMAQwnIMRgskuujml3aZyK7SoU2YIRsgJFqsAI8oj2fjNhmwgcu2KFW5arWLRSJ+wT08+/GqM4Y1afWdMvNWuqOJ6R94nvGVZ2tJo6n5IAd4VoxBwqrs/a6MD4es2W891Ut/z0s9vEoFq42G3itvTqkdgZzeBygzhbEMJgJVfZpVEjDVR9Za68r8HBMfh7+TWPCe7euVPkzrHKblNx+9u/31Jf0tNj07b69fxy+WivQByaNl2vMSpGI+39/AUec/3o3s0nPbr5pX792NSvOG7lWY5rJgrE9eCij4LSWI85THAjRcDqH2e4ZZ5ywLmPLJZHH+WTAf19KngE/5LzoQjYmQ5rRNHiL0PvbZfpcfuNZjNDgocvxrll3Xq4R/oS4l4XiiuXkQAJkEBFCCTAMEBkz9798tSrn8q5pwyVFs0aym8Ll2va+l/kwpEjDJvjBveRZ177TC44c7hk1a8jb370jfTv3VlNgi2lsne39vLaB+OlU/uW8sfGrTJp2iy5/ZrzAlxTUzR17shjpUuHVnL5rf+nrgo15eYrzw78nTMkQAIkEIqAXjpMQOl69fDXggONUOtXdFmtTJHe+la0dy9r0P7HH/lWRZiHaIJMXohbpHJRkd1hSTV98w2RKDNIQAoISnnLnD7w2Zc3kK5RPfrMARE8jjvWJz17+E0AVGTSQjDUWbNdcooOQjCYTMRSGZnKSuIG155LL/Eaq5Y5Ojj/UK0Ejh6g1lsaVLms1iwl7SuWf0dQepRYW6a0bOGXli28JibPb/PcJlA+MjBCMJo2XaRJY79AAO/axVchtzOrtbH5H9aeKHVKcMuFOHnWmV4ZOMClbr1uWbrMZTIZ/qJxboYMtixU4M7MEprA3rw09tEUh7BnuDAi0UPTJn5j5YnrxCbNHDrqHJ/UrpWY1+bQxLmUBEiABEomkBDikEtfWc6cu1Te+0yjVOaVk47pJ1dedIr5NlJjBP08Z7Gcduk9JjZFs8YN5NmHb7FXlb/fMlqu/csT0v+Ua4zZ8In629OP09dCeQWZz1AQhPrxe6+VG/76tLEyOl/FJhYSIAEScCIBDIybNUWQar8MHWyJRRCH9uxx6QRR3frcbX/PW4asSAgyvGkTWlVUQMJS9eA1AlKwBVJ1HaSnajDQlBTLxQRv2cNNuKwW93fUHX/DOpmZ6g6XHzIOuy+xVKblUHBlYF2BLE8IHI5sR+tVkPuvBqzu09unfeCTaA+CgutSGfP2m3/0fSwLRNgzT/NJo4ZiLAR++NFt2EOww4AwEYrNOt0hbksQj5FNcbDm/Vi3Xt1ZNZvi/IVuFZ4hPrtMP7RXq0a4nbXVYPkYoMdL2ZFnOVSvbumOnawsv1w4ymsyu0381m2s2JAl68cZLhmuWS27dlH3tNCX0nhBEpV62tZwlSXid9MEElkNvPLeR25j9fXCSx45W8U9ZDlL1oLIGrBu3qlxtiBkIm5bpgpmieiCnqx9zHaTQFkJuDRTV8JcFZGaHhnI4BKGlPWFCwJT79N1GmXVLfwn833D5u0mgHVGnkVRyJUKLdywPe91XqHl/Bp/BOyYQ5t2MD94/PVe+BrnxxzKS/kTfvWk/StuBhiE2gJSQDjKE5KMqKRiUl6otkrjhAfWBg0soQuD/aYqeCGdPEqomEOPP5liXOxuudEbszfDiAdlXM3U7cSrIS5S9VUMrLoGqNsJLLwSoSxa7DJZ+jpq7Jnzzy0aF6iibQwXc6i4bSP1OmKM2AIh3P4gzLVoHt+POv9+IkUFXZHbb841bqHFtT+Wy9VL38Rcm/Ob28Rfg8shCsTDHup2hkyOdfMEF8Qc2qVxwpwYc+iRx1L0WbH8rBF3btJkt2aZtBShhioeQagMdjm2yCTm/4gfUppn4xdVOIeQeNUYb6WKuIf0Ee/Tzz3mWIVoN2yIT4ZovKhEFvDQZli27YAQlCcGYX6fXlMKXxkh5LZR9/gunXwmrpidz8eOObR1F5+jEu3MZcyhROvRirUnISyHbAQQdcIJO8hchqm40rgY0ai49bmcBEiABBKJAIYycNPBm9zGjdCywo+NVmvh4lJYQEL8mcOHXYIBIgaFeO0QbvKpYFLc3+3fe/FWc5dLM06KTpaLHGqAetbRQWazJvoQ20ozubXUAajGloClEWpsCwM1aoSuv64S9RLsavatupwgyxNiXsDlpJ2mwe6r1kR4Yx3PAxInuJUV7kiIQNdfkys//uQ2aawRrH3FSo+6QvnNABBiUTwWWPShOMVyyKpNwf8Rr6xLZx1UdvaacxZuZ7AogkgyXfvjx59EWrb0C2JxDepX8LdO+XZIx70QhmAFmaHWUeUpHTSNevv2XkH7p3xnuTC9+Y7HsDnpBK+5xpZnu4n2G7g4o1T2dbqqvjs+X4PXT51m9c9k7SOIVCPP8Bqr2ETjjPYgccUnY9U0qFCBtVAttc6trWFWcU/eq2IRYm4t+x2TR1LGaSxFvU916ezT0Br6Y73PspAACSQ2gYQShxK7q9g6EiABEnAGAVjtpFfzS8Ms1Ce6g20ISLBY2qDBX+G+AlctPMgjzsl2jQ0yd55VhypqmtNY453AFQSBwOGKBmudWBe4mo06x2sGyD9oXBa4nOEN7tJlHqml2XSQOadXT3U5i7FrVnk42a5OsXYrK1x3HJ/HHuMzsYd+VkEOwYIRSHn1Go9xs4SVAAQ6a2ha+NfO+56tggWOaQiO8RIwHtZ+AzX208ABYs7XmRp/a/4CtxmkrlrtkXH/80rnji7p1lUTiKhg5BSRFNcVFASjrsjxAddYuNQh9hKOwSnf67mvGc5WrEwxlirIbhZPrnaRPitw14AIhxKLax/6FtcBxMj6+FOPuSa/qG5mo8716X0tuvc00+hK/g9WQig4z04/RWMtaSY+THANx7EaXCBEL1piHa+rVVSCJdySpR75XAXTrmpNdOSRroSNoxfMgfMkkKwEPP/QkqyNj0S79x7UV9ssCUEAWemqV1NzcvZpQvRncCOQ6hxpOg+GSIkdvB7nnUcAD7N401u/nloJqdUHgj4POtonHfXtfEONMZNRw7JWgrXQrt0uIyKhFbU0bkL/I53zkA8BBe5XsBjCPGI8wLwfwathUQTrCliFQDAq9KzuvE7JqxHcyiDWdepoufxFuqJVq3jUJc8vh73l60eIg7AYOrKvT+AasXmLS13PXZpdygoejEFpPfUyd4owURw/xAfDMYIMg0f1Kx+L4rZdGcuRDbGDxh/qp/1QRy0U4OKCwSpSwc81Qa3dAstDDFRjbRm1Zo1LFmm2wRYt1AoqAlnvYM3YvJlfRTCfOfZwDP6+3CULNMMZMmnVrx8/53tpj5WM9FQp6dn4kPb31B/c5toOkSZWBUHHu6iAh2yHuAbjeIQljfXiI1a1ivx+YcGHFywnn+gz96Daep/BfTXUtQ9Wc0iigMDyfVTExD3p8GHrfrVxk2iiBcsaroEeu7EQ9iJPh1tMTXFrzCm3HMqJvHt4aejimsHiHAIUhyrYFyXdACu4ef68EglQHKpE2JW8K4pDlQw8yrvDAy0GVnjr27u7W4YOdEn3HrlGCIB5PKwrEPy5mwaCdVrBgzcGi/36+s1njrriQbDAoHGuxmpZqNYFPp/LvNV1elY41BcDqh5qIZHVIPKkKyoO2TVCwGq4m0GcgPgA1rAQWaABlOfNRwwtl4mF41TecPOYNcet1iywNHPeMW1zLukT5yUGnb1U4B12dKq4U3wmNhhcRxEr6md1uVy+wiVer8sM0GPRHxCGYGUG8bl1q8ixxkC8uwZEbtTQbzJl4ZxfuMiKzQSBMlHikOEYKI04BCEfLrYQYvrFWMRH3/To5jfuyBC7F6vVDKwi8TIC4l4ilB819h3Os6P6qUCr15HSFpyDiPOHlzL9+8BiWAOur7euoTNnuc11NEstiO0YgKXdLtdzFgGKQ87qj1jXhuJQBXuA4lAFATro5xSHHNQZEa4KxaEIA3XQ5qroGy+32sX79F9dHWRhQIcHfScKQ8HYIHDhIb2rxmiBWxnc4JAlaac+wGOADFcovM1GUHBYVbi0jcW96Q3ebmXO/6qDA9S3b2+/sQiJ9L4jJQ7Z9UJ8DWTvg0gEcXHrVlhwWdZbP+ngaYVacR065DLCIjLyOaUg7TasnZCNDRmXEqHUr61uoE280lf7ojXcynQQDks6WBQh3gkGs3C/RP949WV2ulp5VYabKEQ4iIe9elpCTqRZ11MLSJwvOP5gybFtm0vs1Oro31hbTkWivaURh7bkieENVFSG8BDrAhEIsaJgvYY4ZXBhhlVnOw3MXNZsmbFuS6j9T57iEbinHjPUL9XykjmEWi/csvRqLunZNUU6dDxszslNavm3Ua9Nv85U4Wm3qCWv39yjwm2Df3MmAYpDzuyXWNVK3+OwkAAJkAAJkAAJxIoA3G7sjDkYECPbE1xPIBJhsgssL+ppDKMGOsCsX18/zSTGyiiSb7gR5wkDCUyHsiGY2PNwCXJZy3UZBk8oTos5ZPMq7hMcj1R3ib69xfCdqW4S4A3rFUzjJ1gDHViPdFJXQKQqj2WxA38n4tt5HEFw/cMEl5clGrj9t/lWbCKIJ5imTbfcX3DMt1CrO1jeNW9mHfeR7pcdO6wtIuZQtArOVbiXdldXMwTqxoSA9YhDhiD1cNNs39ZyP41WHWK93UC8IbXwdFLprUI9Yg598JHHCETPv5Qi557lFcTFitcClzDE7YM4jjT1FS2wyj3pBCumGzJyzp3rltkqqs5Tlzxk5BysboKIOcZCAiQQnwQoDsVnv7HWJEACJEACCUYAg0bEJerYwSsHVXxZpkIR4rJsVesCuG/t0bezeFu7SeM+BEcqwe8Q+wFZf+C2hu+YMBgIzOctc3usQLtwXYNFRnaOFQMGWZqyVfjBZ44ug0BU2lI9vQwrl3ajlbAerLcwGG/X1mvaDIEIgVh/V8sVi7NLAwnrgEqtCRDsHC5BEPKqaTB2WHiYSefxJh6co1XsTGXVNYtgIhdYBnVVV9CuXbwm6yHcu5BlCRZ0CEIPaxNMv86yKMD6xhKKLMEI/VPRfoC7F0o0xSGr9laAcYjCEIoQsBpWS1awetQBLlfqOqsuPXDFgwstjkE7rbi9jXj9DGQqc+AxDdZXX5VrAlVDnH/jbU8gwL11dMQXdVjj4cqBANRqgBqxgusiglsPOtpvsvLN1zhacA2drWIRrDMRkD4RLOHKCgz3TtxbWEggXglQHIrXnmO9SYAESIAEEpYA3Jq6q3scJrtAyNmqIhGmLeoSZYtGu1U02rMXU2meSEteBw+2GISmaR2qplluFXBpg3sFvgfmzTJ/3FkO2TyDPxFbo7MGIO7cyWtEM1hFwZpjyTK3uvaJce/D91AFS8EkXUUyDIYg0AWXYKGtLPP2uoiDgpJMAy1Yd7VVlx5MKBAyYUUEoQjWXev0E2m3F2r2P0wo6ENY08GyAdkU8R0T+iPwmWoJqPnf9e9YT5cj/hes5NCXlWkNh/qeerJPhg31mXg3OM7QPrjqIDaP3T6clxCtmjS2xEqIGBDECh9vBobD/0MCARS03YkFVnoXXeA1ogdS3k+YZKW7P+M0r6Tp8RJPBVk9URCzLBqljopOZ53plcEDXfLtd2oFp4kKfvjRbdzNjurvkwFHWQkBorFvJ27z1Tc8coQG4EcihCqF7gVOrC/rRAKFCVAcKkyE30mABEiABEjAgQQwKEFwUEzBxavJfjDYwtt4DKIx+XQZpgLzWOa13iJ73PoWWS2LsM2A2AMhCIKPLkvmN5+wuLIsivxyqt/KMrVxoxWEFYFqDxx0Cax5DmgsqAMHVFDQT1h6HVTLq+15bknB/ROp+eoqPCVrseNFIWbU0UepZZuCQFBxiEVr14qs0c8d+t0W0oIt68rKrDKshkLVCdZ/sCKCuyOEQcQj+mODGKspWE4h9hQsm7bpYP83DaSOAkspCGKWZZFeH1QwaqBuUZp4yNElYDnkUHHIsFXEw4dZ6e4//dxjRLpNmzwyYrhmy1TXP6sHHI3ZVM5OYx/t4xpun6PO8cpGFXEhEiF2GNzOYE0EKyJYE0GUTeSC+IBr89yT4S6KdsOFOR4F3ETuJ7YtPAGKQ+H58K8kQAIkQAIk4GgCGAjCxD+zZkHRKHSlS7NO6F8m41KIZPU1zlN9jfNkSRJFKWAgD4sTWzRCjI/gEiy0lXcev8MbehaLAAbm9er6zdSrh7Vsvwp2EIwg4B1UAQ+WdugLTHCVzDHzmp0u77v5xDLzN12et360LCzK0nfmuNPBNlLdI6U4CkTgLSoQrVehaEOeaAR3U4hGmGbNsfYAqyvEzYFgZE1WrLLgY89aM3b/B2IOOdCtrDAVBKq++gqvvPeRBitXzu9rPCK4+sEqBjHJnD7wty2HypKlrDCDsnxvpGwuOt9rhNtvp1jxwyZ+6zbWRP2PVLFERaJEFboh4tpXaVyHvpmoMcXUimrg0Zb7qNOPlbL0M9dNXAIUhxK3b9kyEiABEiABEiCBKBPAoBtxhxCLyAnCQpSb69jNY8CZH//KHqKVvbq2O1/ZfxndX0AExsAbk6h1EcrhXDGWGnC5+0PdD2FptF2zHsKCKt+KyrIGxO+CBaNaKjZCZItFsd3KKtN9ryLtrKPufBCIEBfqe3UzA+9PPvPIOHW17dLZJ7004xqs2pxYcDygRNtyqHDbEQ/ssku8JnEBYmrBHRSfU39wG8tMBGSH+xXEzEQp9jk3QIXDNq01FpO2F8vGT7DEMVgS9YUlUQK1OVH6ju3IJ8DDM58F50iABEiABEiABEiABJKYgJMsbErqBgwy7aDc9roIKr8B1kUQjPQTohHiFyHANya7IK4OglwblzQVjmrVEsnIsAKsR9otDbLJQbXs2rPHZWJ4IdA+SoYG0Y+XAtdGuAj17OEzmblmz7VS3s/SbIezZlvWWT3VyqtHN83WpcHSnVJ2BMSh2NSodSu/tG5lWRIhFhEC/i9ZisljYtt16qiZ+zS2HjIWxtO5F4qmLQ41VaHQTnaARAfBIpFtSdRHXUgpEoWiyGWxJkBxKNY9wP2TAAmQAAmQAAmQAAmQQAQIIJi8NSDPF17gcgeRKCAYqWgE6x1k48JUuKSoEIL4MNaUH9zbXpZWpegyiCcaokuWVPXJ+k0I5K4ZFk0wd5G9Og8rp+CCvcaL5VBwvTGgR8r23r2suFBzfnPJXE3jjhhRE7/VeDuT3dJChQ5YxbRRYaSeuqWCTSwKXCkRuB39WTMz/3iIRV0gYl5wnte4fc5f6JZ5GjcLYgqym82ea7lGd1Nrou5dNW5Wg9jWtTx8UOOAOBQUF9AWiRCDCSIRBNv/faOWRNPdMmigT/rosZRI1lPlYcffOIsAxSFn9QdrQwIkQAIkQAIkQAIkQAIRIwCXu/bt/GayN4osfLZghJhFEHL2asZDxM/K1aD2uQi4rlPo4N5FBSV7uxodSWeLRsRGBsaaGhetpsZHw2ed2hIz0SS/rhWbg/AzYrjfBK6GyDbnN7csVauYVautCVuHFVZdXS+rgeikwofGksrKErXUir5bH6yGIFpEOo19RahBEETsof5HWjHCEFwd086dLpmmgsm06RozS7Pw9emFbJ2+uMkOt1NZI1EBMvDVyixKCOdfu3ZeWbbMEokg1H49Hu11m2DdXTRbJtwXWUgg1gQoDsW6B7h/EiABEiABEiABEiABEqhEAnYQ+06aeatwgThkgngjWHeOK2jeWp4dYhkCZsMtKLN6irhSck2AfEsIssSgRE7rjaxxlvjmFbj1QShauhTxZkSQLWzLFkwi84OiPMFaxIhl6oIGwSRdY5alq4hnTTqvbn+Yh7BXLV0zSZYj09eOvOyJdesW7mFnfK+rQeWPGeqXYUNF1mnGQVgTLVCrok3qdjjua7XEmuQWWBP11JhOhbN0OqMF+bVYr/G+UMLVE5IqLMrat7dEoslqSYTsbpPU2mzSZCvzYEMVDpH5DSIiAtIjEQGOLxYSqCwCFIcqizT3QwIkQAIkQAIkQAIkQAIOJwA3pBSIEzqFztJXVFCym9S4rkc2bFdVKUkL3PpgBdKlEyyorGx5yCpnJhWINqtQhAnuXrDssQWc0BZa+RDRJwHhyBaRihGU0lVMwrqxCkadX+vSzUE0sWNnnXi8z8Qk+mWmW1arBdavs9w6WcIJRCJYE9VQMc1pZf16y5ounDhk1zlYJFquMYnmLbAszqzjBGtZ28Ic+h0iGoQikzlTRSMcYxCM4K4Ii7yqVXWZnquJLMCCBUvlEKA4VDmcuRcSIAESIAESIAESIAESIIEkIoD05XaWuOBmIx4QAnRDJEJMqAMHLLckuCaZebj12fP6adbXdfeo619+CZ7PX1p4rrLS2Bfeb3m+Q/DorOJaZxXXtmocp9lz4HbmNuLaNxPVyuZbzXamLlq9NDA4PiMdPL08dcZvQsUbKmlb6D07JhGs9WAxtdUIiWLaDrFotwaTtwRFbC18f4NFVYhFKhpaopH1HUzdbr8JgI24YTgm7fhhVTR+WIYRG/1y2OsyApMdayw1b93wey2plfx7vBGgOBRvPcb6kgAJkAAJkAAJkAAJkAAJxC0BWHnUU4uQegGXr+KtsdBIBPRGxjcIR0ZsFtZJAAAes0lEQVRMMuJReEHJDgJe2WnsI9UpsJQ5fgTiOvkEAZ0R0wkxe+xsZ7AggiVRD812lpUVnl+k6hRqO7awA7dKCIHlKbAQgtVRYcsjiIIIdr51qxiBbNt2De6uy/y6m1wcExoj7NBBK1YY1sWxsV+PkaIl1LLgtVBvrUShgjbli0kaiD5PWML+s9WFEnWxrZggQoWaCv7db6yhsJ4Vn8kvjQcX2im/xpQAxaGY4ufOSYAESIAESIAESIAESIAESKB4AsiSlpoXzDt/rfBCBAbusD6qrkGS47lAXOhwhF8nK9sZLIlmz7ViOU3/yS3Tf7KEhhbNIbapJYzGcaqZYX1m6CdEJGwjUsWrVj779lsB3GH5tXiJ2wRxR5ygNHX5imSBGNO4kQoojbDV8P2NeiGg/MFDlliEeUxY7vNZGQMRSwzHRXBMMa9aDOUeVtHxoM9YqJl18uKKBa9bkuVS6dpdVKQ6juJQ6dBV0loUhyoJNHdDAiRAAiRAAiRAAiRAAiRAApVBABYfmSEyZ1XGvqO1DwTvHtDfp5Nm29O08HNUJFqk4sy+fSILF0F4KCo+uHURfgehCMJRdY3JBDawXoFwAgur3FwVSMynZZGDeWu5/R0CiuXeh9+EKoWtfkKtE81laA/aWb16KBEp1DKrNulpHrUIcsuufUUbBguhgECkApItKsGiCEIYxCufBqMHE0ywogr+bi9HwHpr3mWtp3yNS+X+ov0VTUbcdskEKA6VzIhrkAAJkAAJkAAJkAAJkAAJkAAJOISAHcvplJN8sl3drdatF9m124rjhNhMEB/26ud+WPmoeLR3nwoRG1H5igkSEGEQ8Nu2TrI/kbEu0QpEIDs+UUmWS6Vre+IxKl2742ctikPx01esKQmQAAmQAAmQAAmQAAmQAAmQQBABZPSqa+I3FRUfYMkS7AZ2QGP02FZCEHrgspeSorFwzCfmrclabn/Xv6uVDCxl8BsWEkhUAhSHErVn2S4SIAESIAESIAESIAESIAESSGICiDdUMy8OkYWhqICUxHjYdBIoQCCC4bkKbJdfSIAESIAESIAESIAESIAESIAESIAESIAE4oAAxaE46CRWkQRIgARIgARIgARIgARIgARIgARIgASiRYDiULTIcrskQAIkQAIkQAIkQAIkQAIkQAIkQAIkEAcEKA7FQSexiiRAAiRAAiRAAiRAAiRAAiRAAiRAAiQQLQIUh6JFltslARIgARIgARIgARIgARIgARIgARIggTggQHEoDjqJVSQBEiABEiABEiABEiABEiABEiABEiCBaBGgOBQtstwuCZAACZAACZAACZAACZAACZAACZAACcQBAYpDcdBJrCIJkAAJkAAJkAAJkAAJkAAJkAAJkAAJRIsAxaFokeV2SYAESIAESIAESIAESIAESIAESIAESCAOCFAcioNOYhVJgARIgARIgARIgARIgARIgARIgARIIFoEKA5Fiyy3SwIkQAIkQAIkQAIkQAIkQAIkQAIkQAJxQIDiUBx0EqtIAiRAAiRAAiRAAiRAAiRAAiRAAiRAAtEiQHEoWmS5XRIgARIgARIgARIgARIgARIgARIgARKIAwIuv5Y4qCerSAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkEAUCtByKAlRukgRIgARIgARIgARIgARIgARIgARIgATihQDFoXjpKdaTBEiABEiABEiABEiABEiABEiABEiABKJAgOJQFKByk4lBYN/+g7Jl267EaEwStMLr9UpxXrIl9eXmbTvlwMFDSUApPpt4+HBuuSqOY+KPTdvE6/OV6/f8UfQIoG82bN4u+w8Uf96FOy/Zt9Hrm4pu+eChbFm3YYv4fOWLWlDS9bqi9ePvy0/gUHaOrFm/Wbbt2B1yIyWdl+HO6ZAb5MJKI7Brzz5ZtXaj4PwrT2HfloeaM34Tru9KOqed0QLWIpIEUiK5MW6LBOKFwIDTrpPde/YXqO4d14ySS887QbJzDsvdD78k33z3q7hcIi2aZMmzD98iLZpmFVifX5xDAH15+mX3yD9uu1SGDugRqFhJfblyzQa57u4nZf3GLeY3Jw3vLw/eOUZSU3lpDECM8czk6bPl1n88J3MnvlygJvc9/oZ8+MWUAsu6dmwt7z//d7Psk6+myj+feltyDh+WKlVS5d5bL5XTjhtQYH1+iQ2Btz6eIP956WPBQBPn2pE9Osrfb71EmjaqbypU0nnJvo1Nv5Vmr3+68zGZ/usCI9TXqZUhJwzrJ/fcdFHgp7z3BlDE3cwdD7wg46f8HBD9undqI888dJPUqV3TtCXceVnSOR13MBKowpu27JBLbvyneZGCZqWkeOTCkSPkzmtHmVbOmL1Ixtz6SJEWT/zgMWmcVVfYt0XQOHLB4t/XyMU3/FOuuPBkufri00wdS+q7cOe0IxvJSkWGAAJSs5BAshHof8q1/tfe/59/+ao/AtPO3XsNhnc/neQ/+rTr/XrR9OsbFP+Vt//bf8XtjyYborhp7+33P+/vOeIKf6cho/2Tf5hdoN4l9eXoGx/2X/uXJ/z7Dxz0L1u5zo/j4qNx3xXYBr/EhsD2Hbv9x4263fRr92PHFKnEvf9+zX/FbY8Ezl+cy+s3bjXrbdm209912GV+FY/8anXkf/uTif5uwy/379i1p8h2uKDyCegDp3/i9zPN9fX3Vev9J190l1+FvEBFwp2X7NsAJkfOPPrc+/6FS1f7Dx7M9n/z3S/m/J01b2mgrrz3BlDE3cyTL3/sn7Pgd39OzmHzfDTw9Ov9T738iWlHSedluHM67kAkWIVx33zqlU/M/TPX6/V/9OV35rxdunytaemPMxeY77hWBz8z5+bmmr+zb51/QGzeutM/7Kyb/XiWev6NzwMVDtd3JZ3TgY1wJuEI0K0sMhobtxKHBBo2qCNtWjYOTLVq1jCtmDB1phw/tK+0at5IqqdXldHnHiczZi2UveU0tY1DNHFV5buuO1++fPNhqZpWpUi9w/WlioEyc95SGX3O8ZJeraq0a9VUjh3US3TQWmQ7XFD5BGplZsirT9wlD941ptid16ieHjh/cS43aVjPrDt5+hzJrFldzjl1qHkLev4Zw6Va1TSZMn1usdviHyqPwMiTBsmxg3ub62vblk1kYL9uMv2X+aYCJZ2X7NvK66fy7On2a86TTu1bSNWqVeS4IX2lQb1a2rcLCmyK994COOLmy41jzpIendsaa78mauXndrslM9N6bgp3XpZ0TscNgAStKO6bN1w+0tw/PdqnTRrV0751SUaN9AItbtMi/3kZ91uPxyPs2wKIHPnl0KEctZB/Qk44pp90bt8yUMeS+i7cOR3YCGcSkgB9JxKyW9mo0hB465MJMlGFoGaNG8jZpwwJuDRs3rpDhh7VPbCJ5upWhtgJ27bvkozq1QLLOeMMAvXqZFoVURfAwiVcX+ZoHBuV+wu4C7Zo2lDmL15VeDP8HgMCeDjFQ2vd2nn9G6IOvy1aIbfd95xA2B0+qLcM6NPZrIV+x3ltF2yrWeP6guUsziKAa+uMWYvkiLbNTcUQ5y3cecm+dVb/havNitUbTNy+Du2svrXX5b3XJhF/n3DVfuHNL4yYi4HmmScMNI0Id16WdE7HH4XErPGS5WvlA3XVnvbzPLnywlOkkbqMBRe10pbUlBSBO+GZKvDjhRz7NpiQ8+Zxf73zwRdUpK8tt199nrqVPRSoZEl9F+6cDmyEMwlJgJZDCdmtbFRJBE4+tr/079nJDD4hEI26+n6B3zXK3n0H9K1nWmATaVUsi5Q9upwlvgiE68s9e62YU2lBFkdpGptm7372czz0cpcOreQkfRPWsllDDWy8Ta68/VH5/Jvppup79h6Q4H7FQsQdovWf83r2kefek3Ua8+va0Wfk9V3485J967w+DFUjXHtv+cczxtLkmIG9Aqvw3htAEZczCE67YvUfJoEDAhjbsRvDnZe818ZHV+/YtVc263Owug0K5u2A8ngBB+vb1mo5lJaWKk+89JGJA4hWsW+d3bdP/PdDvb9ulUf/drWxBguubUl9F+6cDt4O5xOPAC2HEq9P2aJSEPjrTRcH1rrqolNl+Lm3yvczfpPzThsmNTOqm6DU9grZOTlmtmYhE1v77/x0LoFwfQnLIRS8CbUL5tnPNg1nf5598pACFbz1H8/K2PE/yOnHH63ncLoJRB28QnY2+zaYhxPmX3nva3n/88ny1AM3GvdA1AnnLEpx5yX71uBx9H/IVnbDPU+ZweXTD94ocFWxC++9Non4/IQL9lPap7DuG33Tw/LkK5+YgWe487Kkczo+SSRerWF5iwkWIyPOu10GHtlVXe17G5f7v96c/8w8oE8XFYeeFYiD7FtnHwfvjv1W+nbvII8+/4Gp6FrNIqmxOdUCzGPcubGQ91pn92Esapd/x47F3rlPEnAAgRrqKlZdH3gO6QMtCswv12qqVrusXrfJKO7169ayF/EzTgiE60vEwnBpOrrCfY3fsMQfgSztN/jWo2TVr1OgX/EGdL0+FLFvndOvGgBVNDCmPKeZIAf37xaoWEnnJfs2gMqRMxgwIrMRLDDffOruQCarUJXlvTcUlfhYhntnq2aNZKu6gaKEOy9LOqfjo8XJU0v0ZXq1tEDfFm457rUouN+ybwvTcdb3P+nLb1hZ19WMgpjgFpiusVQRK6ykvgt3TjurlaxNpAlQHIo0UW7P8QTmLV4hr38wXjZu3m5ubi+9M0627tgl/Xp1MnU/bnAf+d/kn016zv0HDsmbH30j/Xt3FjzIsjiPACyAjCjgt96A2AIBahquL2trwOPe3drLa3osoJ+XrVgnk6bNkhFD+jivkUlYI7yZRl/CxB3FzOdZe+E70tQvWrbGvPWaNW+ZfD5heiDm0DFH9zTuDoifoNnK5J1PJ8pBTZs+7Oge+ClLjAmg715+92u597bRJq7FKhXgMSG1fUnnJfs2xp0XZvea3VMuuPYBY1Fw3+2XyW513UW/rlNhFoX33jDwHP4nuKA8+ORb5j4JS4Nf5iwWzUZn7qGoerjzsqRz2uFNT/jqTdEEDu98OslYDB04eEiee32s7NNnop5d25m2v/vZJE3mMMeEXNi8bac889pnKgw2FASWZ986+/CAZ8T1l50ZmBppn/XXsQ4sr0vqu3DntLNbzdpVlIBLH8B1SMVCAslDAEFsr/vLf0yWBbQaZtLIsAKXMhQ8+Nz14ItGKNCXYyaw7bP6dhs3QxbnEbjo+odE0+sWqNi0sU9LnVoZJfYlAqZqKnsTswZXwhM1hs0//3yFycZSYIP8UukEECxx2Nk3F9gvzKNff/LPZtnZV94ri39fY+bxFvvEYUfKA3eOMVmSsFDT8RoBSVPzmrdlf791tJyRFzzV/Ij/xYzAqGvu18DvK4vs//X//Fn69uigMU3Cn5fs2yLoHLFgg75wGXHebUXqgmsxrsm89xZBEzcLEK/tYr3XajrzQJ0R8+0BzSZpZwoNd16WdE4HNsqZSifw/U9z5S8PvxSIH4Usvbdp8GL7mRhi0Ytvfym5uV5TN8QeQgybDnlJBNi3ld5l5d7hhdc9KIM0O+jVl5xmtlFS34U7p8tdCf7Q8QQoDjm+i1jBaBCAmwmshbL1TTUyIiElZ+GCgJp4E1o4Y0Ph9fjd+QRK6ksMapC2ldnonN+XwTXE2+ztO/eoS0NtI/IG/w3zeJhFsOrGWfVMSvvCf+d3ZxMId16yb53dd8XVjvfe4sjEx3KIRMjcChddiAiFS0nnZbhzuvC2+L3yCMBOYOv23Wq9mR3ymRgvTRGLqJomaykuxAL7tvL6K9J7Ctd3JZ3Tka4Ltxd7AhSHYt8HrAEJkAAJkAAJkAAJkAAJkAAJkAAJkAAJxIwAYw7FDD13TAIkQAIkQAIkQAIkQAIkQAIkQAIkQAKxJ0BxKPZ9wBqQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQMwIUByKGXrumARIgARIgARIgARIgARIgARIgARIgARiT4DiUOz7gDUgARIgARIgARIgARIgARIgARIgARIggZgRoDgUM/TcMQmQAAmQAAmQAAmQAAmQAAmQAAmQAAnEngDFodj3AWtAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAjEjQHEoZui5YxIgARIgARIgARIgARIgARIgARIgARKIPQGKQ7HvA9aABEiABEiABEiABEiABEiABEiABEiABGJGgOJQzNBzxyRAAiRAAi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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = px.line(trainer.training_info, x='total_iters', y='val_mse')\\n\",\n \"fig.show()\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "samples/pre-trained_model_library.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Pre-trained Model Library\\n\",\n \"\\n\",\n \"**XenonPy.MDL** is a library of pre-trained models that were obtained by feeding diverse materials data on structure-property relationships into neural networks and some other supervised learning models.\\n\",\n \"\\n\",\n \"XenonPy offers a simple-to-use toolchain to perform **transfer learning** with the given **pre-trained models** seamlessly.\\n\",\n \"In this tutorial, we will focus on model querying and retrieving.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### useful functions\\n\",\n \"\\n\",\n \"Running the following cell will load some commonly used packages, such as [NumPy](https://numpy.org/), [pandas](https://pandas.pydata.org/), and so on. It will also import some in-house functions used in this tutorial. See *'tools.ipynb'* file to check what will be imported.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%run tools.ipynb\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### access pre-trained models with MDL class\\n\",\n \"\\n\",\n \"We prepared a wide range of APIs to let you query and download our models.\\n\",\n \"These APIs can be accessed via any HTTP requests.\\n\",\n \"For convenience, we implemented some of the most popular APIs and wrapped them into XenonPy.\\n\",\n \"All these functions can be accessed using `xenonpy.mdl.MDL`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# --- import necessary libraries\\n\",\n \"\\n\",\n \"from xenonpy.mdl import MDL\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"MDL(api_key='anonymous.user.key', endpoint='http://xenon.ism.ac.jp/api')\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"'0.1.1'\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# --- init and check\\n\",\n \"\\n\",\n \"mdl = MDL()\\n\",\n \"mdl\\n\",\n \"\\n\",\n \"mdl.version\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Noticed that ``mdl`` contains optional parameters ``api_key`` and ``endpoint``.\\n\",\n \"``endpoint`` point to where data is fetched. ``api_key`` is an access token used to validate the authorization and action permissions.\\n\",\n \"At this moment, the defaulat key, ``anonymous.user.key``, is the only valid option.\\n\",\n \"We will open the public registration system when the system is ready.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### querying\\n\",\n \"\\n\",\n \"There are many ways to query models. The most straightforward method is to use `mdl`.\\n\",\n \"It accepts variable keywords as input and any hit keyword will be returned. For example, to query models that predict property **refractive index**:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"QueryModelDetails(api_key='anonymous.user.key', endpoint='http://xenon.ism.ac.jp/api', variables={'query': ('refractive',)})\\n\",\n \"Queryable: \\n\",\n \" id\\n\",\n \" transferred\\n\",\n \" succeed\\n\",\n \" isRegression\\n\",\n \" deprecated\\n\",\n \" modelset\\n\",\n \" method\\n\",\n \" property\\n\",\n \" descriptor\\n\",\n \" lang\\n\",\n \" accuracy\\n\",\n \" precision\\n\",\n \" recall\\n\",\n \" f1\\n\",\n \" sensitivity\\n\",\n \" prevalence\\n\",\n \" specificity\\n\",\n \" ppv\\n\",\n \" npv\\n\",\n \" meanAbsError\\n\",\n \" maxAbsError\\n\",\n \" meanSquareError\\n\",\n \" rootMeanSquareError\\n\",\n \" r2\\n\",\n \" pValue\\n\",\n \" spearmanCorr\\n\",\n \" pearsonCorr\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# --- query data\\n\",\n \"\\n\",\n \"query = mdl('refractive')\\n\",\n \"query\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can see that run a querying method does not execute the querying immediately, but simply return a queryable object.\\n\",\n \"If you print out the object, the `queryable` list will be shown. Only the variables in the list can be fetched from the server.\\n\",\n \"\\n\",\n \"Another way to get the `queryable` list is call `query.queryable` as below:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['id',\\n\",\n \" 'transferred',\\n\",\n \" 'succeed',\\n\",\n \" 'isRegression',\\n\",\n \" 'deprecated',\\n\",\n \" 'modelset',\\n\",\n \" 'method',\\n\",\n \" 'property',\\n\",\n \" 'descriptor',\\n\",\n \" 'lang',\\n\",\n \" 'accuracy',\\n\",\n \" 'precision',\\n\",\n \" 'recall',\\n\",\n \" 'f1',\\n\",\n \" 'sensitivity',\\n\",\n \" 'prevalence',\\n\",\n \" 'specificity',\\n\",\n \" 'ppv',\\n\",\n \" 'npv',\\n\",\n \" 'meanAbsError',\\n\",\n \" 'maxAbsError',\\n\",\n \" 'meanSquareError',\\n\",\n \" 'rootMeanSquareError',\\n\",\n \" 'r2',\\n\",\n \" 'pValue',\\n\",\n \" 'spearmanCorr',\\n\",\n \" 'pearsonCorr']\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"query.queryable\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's say we only want to know the variables of `modelset`, `method`, `property`, `descriptor`, `meanAbsError`, `meanSquareError`, `pValue`, and `pearsonCorr`. Execute the query as follow:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
modelsetmethodpropertydescriptormeanAbsErrormeanSquareErrorpValuepearsonCorrid
0Stable inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositions0.4229600.418109None0.6310212335
1Stable inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositions0.5453811.641444None0.5786462338
2Stable inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositions0.7080273.087893None0.4390892339
3Stable inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositions0.5857782.238217None0.5311372341
4Stable inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositions0.5428112.174997None0.5585262342
..............................
3595Polymer Genome Datasetpytorch.nn.neural_networkorganic.polymer.refractive_indexxenonpy.compositions0.0823310.019165None0.82468431191
3596Polymer Genome Datasetpytorch.nn.neural_networkorganic.polymer.refractive_indexxenonpy.compositions0.0945220.023909None0.66740431192
3597Polymer Genome Datasetpytorch.nn.neural_networkorganic.polymer.refractive_indexxenonpy.compositions0.1285090.039972None0.68013631193
3598Polymer Genome Datasetpytorch.nn.neural_networkorganic.polymer.refractive_indexxenonpy.compositions0.0966440.025381None0.70492031194
3599Polymer Genome Datasetpytorch.nn.neural_networkorganic.polymer.refractive_indexxenonpy.compositions0.1220350.040172None0.67964431195
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3600 rows × 9 columns

\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" modelset \\\\\\n\",\n \"0 Stable inorganic compounds in materials project \\n\",\n \"1 Stable inorganic compounds in materials project \\n\",\n \"2 Stable inorganic compounds in materials project \\n\",\n \"3 Stable inorganic compounds in materials project \\n\",\n \"4 Stable inorganic compounds in materials project \\n\",\n \"... ... \\n\",\n \"3595 Polymer Genome Dataset \\n\",\n \"3596 Polymer Genome Dataset \\n\",\n \"3597 Polymer Genome Dataset \\n\",\n \"3598 Polymer Genome Dataset \\n\",\n \"3599 Polymer Genome Dataset \\n\",\n \"\\n\",\n \" method property \\\\\\n\",\n \"0 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"1 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"2 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"3 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"4 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"... ... ... \\n\",\n \"3595 pytorch.nn.neural_network organic.polymer.refractive_index \\n\",\n \"3596 pytorch.nn.neural_network organic.polymer.refractive_index \\n\",\n \"3597 pytorch.nn.neural_network organic.polymer.refractive_index \\n\",\n \"3598 pytorch.nn.neural_network organic.polymer.refractive_index \\n\",\n \"3599 pytorch.nn.neural_network organic.polymer.refractive_index \\n\",\n \"\\n\",\n \" descriptor meanAbsError meanSquareError pValue pearsonCorr \\\\\\n\",\n \"0 xenonpy.compositions 0.422960 0.418109 None 0.631021 \\n\",\n \"1 xenonpy.compositions 0.545381 1.641444 None 0.578646 \\n\",\n \"2 xenonpy.compositions 0.708027 3.087893 None 0.439089 \\n\",\n \"3 xenonpy.compositions 0.585778 2.238217 None 0.531137 \\n\",\n \"4 xenonpy.compositions 0.542811 2.174997 None 0.558526 \\n\",\n \"... ... ... ... ... ... \\n\",\n \"3595 xenonpy.compositions 0.082331 0.019165 None 0.824684 \\n\",\n \"3596 xenonpy.compositions 0.094522 0.023909 None 0.667404 \\n\",\n \"3597 xenonpy.compositions 0.128509 0.039972 None 0.680136 \\n\",\n \"3598 xenonpy.compositions 0.096644 0.025381 None 0.704920 \\n\",\n \"3599 xenonpy.compositions 0.122035 0.040172 None 0.679644 \\n\",\n \"\\n\",\n \" id \\n\",\n \"0 2335 \\n\",\n \"1 2338 \\n\",\n \"2 2339 \\n\",\n \"3 2341 \\n\",\n \"4 2342 \\n\",\n \"... ... \\n\",\n \"3595 31191 \\n\",\n \"3596 31192 \\n\",\n \"3597 31193 \\n\",\n \"3598 31194 \\n\",\n \"3599 31195 \\n\",\n \"\\n\",\n \"[3600 rows x 9 columns]\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"query(\\n\",\n \" 'modelset',\\n\",\n \" 'method',\\n\",\n \" 'property',\\n\",\n \" 'descriptor',\\n\",\n \" 'meanAbsError', \\n\",\n \" 'meanSquareError',\\n\",\n \" 'pValue',\\n\",\n \" 'pearsonCorr' \\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If everything goes right, you will get a pandas DataFrame in return.\\n\",\n \"You can see that 3600 models matched the keyword and these models are contained in two sets of models.\\n\",\n \"Note that all variables in column `pValue` are `None`. This is not a querying error, all variables are `None` indeed, because they were not recorded during training.\\n\",\n \"\\n\",\n \"You can also retrieve the last querying result from the query object via the `results` property.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
modelsetmethodpropertydescriptormeanAbsErrormeanSquareErrorpValuepearsonCorrid
0Stable inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositions0.4229600.418109None0.6310212335
1Stable inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositions0.5453811.641444None0.5786462338
2Stable inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositions0.7080273.087893None0.4390892339
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" modelset method \\\\\\n\",\n \"0 Stable inorganic compounds in materials project pytorch.nn.neural_network \\n\",\n \"1 Stable inorganic compounds in materials project pytorch.nn.neural_network \\n\",\n \"2 Stable inorganic compounds in materials project pytorch.nn.neural_network \\n\",\n \"\\n\",\n \" property descriptor meanAbsError \\\\\\n\",\n \"0 inorganic.crystal.refractive_index xenonpy.compositions 0.422960 \\n\",\n \"1 inorganic.crystal.refractive_index xenonpy.compositions 0.545381 \\n\",\n \"2 inorganic.crystal.refractive_index xenonpy.compositions 0.708027 \\n\",\n \"\\n\",\n \" meanSquareError pValue pearsonCorr id \\n\",\n \"0 0.418109 None 0.631021 2335 \\n\",\n \"1 1.641444 None 0.578646 2338 \\n\",\n \"2 3.087893 None 0.439089 2339 \"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"query.results.head(3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Querying with `mdl` is simple but not efficient enough. In most cases, we may know exactly what we want.\\n\",\n \"\\n\",\n \"Let's say we want to retrieve some models that were trained in the inorganic modelset and can predict the property of refractive index.\\n\",\n \"In this case, we need to feed the parameter `modelset_has` with **inorganic** and the `property_has` with **refractive**, respectively.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
idtransferredsucceedisRegressiondeprecatedmodelsetmethodpropertydescriptorlangmeanAbsErrormaxAbsErrormeanSquareErrorrootMeanSquareErrorr2pValuespearmanCorrpearsonCorr
02335FalseTrueTrueFalseStable inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositionspython0.4229601.7823200.4181090.6466130.226642None0.8051470.631021
12338FalseTrueTrueFalseStable inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositionspython0.5453818.9489271.6414441.2811880.330978None0.8081880.578646
22339FalseTrueTrueFalseStable inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositionspython0.70802710.1422183.0878931.7572400.173911None0.8398000.439089
32341FalseTrueTrueFalseStable inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositionspython0.58577811.8358472.2382171.4960670.241867None0.6909570.531137
42342FalseTrueTrueFalseStable inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositionspython0.54281111.0458352.1749971.4747870.275737None0.8864050.558526
.........................................................
23954863FalseTrueTrueFalseAll inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositionspython0.4296422.2048490.4340430.6588190.441885None0.8082800.712245
23964865FalseTrueTrueFalseAll inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositionspython0.5695797.6221731.6561911.2869310.215002None0.8295440.475225
23974867FalseTrueTrueFalseAll inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositionspython0.4361522.6351260.5590990.7477290.426260None0.8342260.655400
23984868FalseTrueTrueFalseAll inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositionspython0.3835062.4732030.3396530.5827970.599966None0.8400160.798442
23994871FalseTrueTrueFalseAll inorganic compounds in materials projectpytorch.nn.neural_networkinorganic.crystal.refractive_indexxenonpy.compositionspython0.3948622.4901650.3974130.6304070.674495None0.8006930.839631
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2400 rows × 18 columns

\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" id transferred succeed isRegression deprecated \\\\\\n\",\n \"0 2335 False True True False \\n\",\n \"1 2338 False True True False \\n\",\n \"2 2339 False True True False \\n\",\n \"3 2341 False True True False \\n\",\n \"4 2342 False True True False \\n\",\n \"... ... ... ... ... ... \\n\",\n \"2395 4863 False True True False \\n\",\n \"2396 4865 False True True False \\n\",\n \"2397 4867 False True True False \\n\",\n \"2398 4868 False True True False \\n\",\n \"2399 4871 False True True False \\n\",\n \"\\n\",\n \" modelset \\\\\\n\",\n \"0 Stable inorganic compounds in materials project \\n\",\n \"1 Stable inorganic compounds in materials project \\n\",\n \"2 Stable inorganic compounds in materials project \\n\",\n \"3 Stable inorganic compounds in materials project \\n\",\n \"4 Stable inorganic compounds in materials project \\n\",\n \"... ... \\n\",\n \"2395 All inorganic compounds in materials project \\n\",\n \"2396 All inorganic compounds in materials project \\n\",\n \"2397 All inorganic compounds in materials project \\n\",\n \"2398 All inorganic compounds in materials project \\n\",\n \"2399 All inorganic compounds in materials project \\n\",\n \"\\n\",\n \" method property \\\\\\n\",\n \"0 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"1 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"2 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"3 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"4 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"... ... ... \\n\",\n \"2395 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"2396 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"2397 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"2398 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"2399 pytorch.nn.neural_network inorganic.crystal.refractive_index \\n\",\n \"\\n\",\n \" descriptor lang meanAbsError maxAbsError \\\\\\n\",\n \"0 xenonpy.compositions python 0.422960 1.782320 \\n\",\n \"1 xenonpy.compositions python 0.545381 8.948927 \\n\",\n \"2 xenonpy.compositions python 0.708027 10.142218 \\n\",\n \"3 xenonpy.compositions python 0.585778 11.835847 \\n\",\n \"4 xenonpy.compositions python 0.542811 11.045835 \\n\",\n \"... ... ... ... ... \\n\",\n \"2395 xenonpy.compositions python 0.429642 2.204849 \\n\",\n \"2396 xenonpy.compositions python 0.569579 7.622173 \\n\",\n \"2397 xenonpy.compositions python 0.436152 2.635126 \\n\",\n \"2398 xenonpy.compositions python 0.383506 2.473203 \\n\",\n \"2399 xenonpy.compositions python 0.394862 2.490165 \\n\",\n \"\\n\",\n \" meanSquareError rootMeanSquareError r2 pValue spearmanCorr \\\\\\n\",\n \"0 0.418109 0.646613 0.226642 None 0.805147 \\n\",\n \"1 1.641444 1.281188 0.330978 None 0.808188 \\n\",\n \"2 3.087893 1.757240 0.173911 None 0.839800 \\n\",\n \"3 2.238217 1.496067 0.241867 None 0.690957 \\n\",\n \"4 2.174997 1.474787 0.275737 None 0.886405 \\n\",\n \"... ... ... ... ... ... \\n\",\n \"2395 0.434043 0.658819 0.441885 None 0.808280 \\n\",\n \"2396 1.656191 1.286931 0.215002 None 0.829544 \\n\",\n \"2397 0.559099 0.747729 0.426260 None 0.834226 \\n\",\n \"2398 0.339653 0.582797 0.599966 None 0.840016 \\n\",\n \"2399 0.397413 0.630407 0.674495 None 0.800693 \\n\",\n \"\\n\",\n \" pearsonCorr \\n\",\n \"0 0.631021 \\n\",\n \"1 0.578646 \\n\",\n \"2 0.439089 \\n\",\n \"3 0.531137 \\n\",\n \"4 0.558526 \\n\",\n \"... ... \\n\",\n \"2395 0.712245 \\n\",\n \"2396 0.475225 \\n\",\n \"2397 0.655400 \\n\",\n \"2398 0.798442 \\n\",\n \"2399 0.839631 \\n\",\n \"\\n\",\n \"[2400 rows x 18 columns]\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# --- query data\\n\",\n \"\\n\",\n \"mdl(modelset_has='inorganic', property_has='refractive')()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can see that only the models that belong to **All inorganic compounds in materials project** modelset were returned. If you call a query object without parameters, all queryable variables will be returned.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### list/get_detail variables\\n\",\n \"\\n\",\n \"You can check some meta info of the database. To do so, we use `mdl.list_*` and `mdl.get_*_detail` methods. For example, `mdl.list_properties` will return:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
namefullNamesymbolunitdescribe
0inorganic.crystal.efermi
1inorganic.crystal.refractive_index
2inorganic.crystal.band_gap
3inorganic.crystal.density
4inorganic.crystal.total_magnetization
5inorganic.crystal.dielectric_const_elec
6inorganic.crystal.dielectric_const_total
7inorganic.crystal.final_energy_per_atom
8inorganic.crystal.formation_energy_per_atom
9inorganic.crystal.volume
10organic.polymer.band_gap_pbe
11organic.polymer.ionization_energy
12organic.polymer.electron_affinity
13organic.polymer.atomization_energy
14organic.polymer.cohesive_energy
15organic.polymer.refractive_index
16organic.polymer.density
17organic.polymer.dielectric_constant
18organic.polymer.volume_of_cell
19organic.polymer.dielectric_constant_electronic
20organic.polymer.dielectric_constant_ionic
21organic.polymer.band_gap_hse06
22organic.small_molecule
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" name fullName symbol unit \\\\\\n\",\n \"0 inorganic.crystal.efermi \\n\",\n \"1 inorganic.crystal.refractive_index \\n\",\n \"2 inorganic.crystal.band_gap \\n\",\n \"3 inorganic.crystal.density \\n\",\n \"4 inorganic.crystal.total_magnetization \\n\",\n \"5 inorganic.crystal.dielectric_const_elec \\n\",\n \"6 inorganic.crystal.dielectric_const_total \\n\",\n \"7 inorganic.crystal.final_energy_per_atom \\n\",\n \"8 inorganic.crystal.formation_energy_per_atom \\n\",\n \"9 inorganic.crystal.volume \\n\",\n \"10 organic.polymer.band_gap_pbe \\n\",\n \"11 organic.polymer.ionization_energy \\n\",\n \"12 organic.polymer.electron_affinity \\n\",\n \"13 organic.polymer.atomization_energy \\n\",\n \"14 organic.polymer.cohesive_energy \\n\",\n \"15 organic.polymer.refractive_index \\n\",\n \"16 organic.polymer.density \\n\",\n \"17 organic.polymer.dielectric_constant \\n\",\n \"18 organic.polymer.volume_of_cell \\n\",\n \"19 organic.polymer.dielectric_constant_electronic \\n\",\n \"20 organic.polymer.dielectric_constant_ionic \\n\",\n \"21 organic.polymer.band_gap_hse06 \\n\",\n \"22 organic.small_molecule \\n\",\n \"\\n\",\n \" describe \\n\",\n \"0 \\n\",\n \"1 \\n\",\n \"2 \\n\",\n \"3 \\n\",\n \"4 \\n\",\n \"5 \\n\",\n \"6 \\n\",\n \"7 \\n\",\n \"8 \\n\",\n \"9 \\n\",\n \"10 \\n\",\n \"11 \\n\",\n \"12 \\n\",\n \"13 \\n\",\n \"14 \\n\",\n \"15 \\n\",\n \"16 \\n\",\n \"17 \\n\",\n \"18 \\n\",\n \"19 \\n\",\n \"20 \\n\",\n \"21 \\n\",\n \"22 \"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"mdl.list_properties()()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"and `mdl.get_property_detail` will return the property information including how many models are relevant to this property:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'name': 'inorganic.crystal.efermi',\\n\",\n \" 'fullName': '',\\n\",\n \" 'symbol': '',\\n\",\n \" 'unit': '',\\n\",\n \" 'describe': '',\\n\",\n \" 'count': 2481}\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"mdl.get_property_detail('inorganic.crystal.efermi')()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"These querying statements are very useful when you want to know what is in the database.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### get training info/env and download url by model ID\\n\",\n \"\\n\",\n \"Note that all models have their unique IDs. We can use model ID to get more information about a particular model. The following shows how to get training info/env by model ID.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
total_itersi_epochi_batchtrain_mse_lossval_maeval_mseval_rmseval_r2val_pearsonrval_spearmanrval_p_valueval_max_ae
69469422232.0609051.0988122.1067171.4514530.7013760.8504550.8470060.012.112278
69569522242.0498121.1620042.3317021.5269910.6694850.8444440.8399160.012.192457
69669622252.1561361.0924422.1665511.4719210.6928950.8375540.8283050.012.244514
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" total_iters i_epoch i_batch train_mse_loss val_mae val_mse \\\\\\n\",\n \"694 694 22 23 2.060905 1.098812 2.106717 \\n\",\n \"695 695 22 24 2.049812 1.162004 2.331702 \\n\",\n \"696 696 22 25 2.156136 1.092442 2.166551 \\n\",\n \"\\n\",\n \" val_rmse val_r2 val_pearsonr val_spearmanr val_p_value val_max_ae \\n\",\n \"694 1.451453 0.701376 0.850455 0.847006 0.0 12.112278 \\n\",\n \"695 1.526991 0.669485 0.844444 0.839916 0.0 12.192457 \\n\",\n \"696 1.471921 0.692895 0.837554 0.828305 0.0 12.244514 \"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"info = mdl.get_training_info(model_id=1234)()\\n\",\n \"_, ax = plt.subplots(figsize=(10, 5), dpi=100)\\n\",\n \"info.tail(3)\\n\",\n \"info.plot(y=['train_mse_loss', 'val_mse'], ax=ax)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'python': '3.7.4 (default, Aug 13 2019, 20:35:49) \\\\n[GCC 7.3.0]',\\n\",\n \" 'system': '#60-Ubuntu SMP Tue Jul 2 18:22:20 UTC 2019',\\n\",\n \" 'numpy': '1.16.4',\\n\",\n \" 'torch': '1.1.0',\\n\",\n \" 'xenonpy': '0.4.0.beta4',\\n\",\n \" 'device': 'cuda:2',\\n\",\n \" 'start': '2019/09/17 20:55:56',\\n\",\n \" 'finish': '2019/09/17 21:03:04',\\n\",\n \" 'time_elapsed': '5 days, 15:33:23.552799',\\n\",\n \" 'author': 'Chang Liu',\\n\",\n \" 'email': 'liu.chang@ism.ac.jp',\\n\",\n \" 'dataset': 'materials project'}\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"mdl.get_training_env(model_id=1234)()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"nbsphinx\": \"hidden\"\n },\n \"source\": [\n \"Getting download url can be done in a similar way.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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idetagurl
012349274418b5ee2026ea8714b7edc7d012e-1http://xenon.ism.ac.jp/mdl/inorganic.crystal.e...
15678c5a962fce76a773b208cc59631999a25-1http://xenon.ism.ac.jp/mdl/inorganic.crystal.b...
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" id etag \\\\\\n\",\n \"0 1234 9274418b5ee2026ea8714b7edc7d012e-1 \\n\",\n \"1 5678 c5a962fce76a773b208cc59631999a25-1 \\n\",\n \"\\n\",\n \" url \\n\",\n \"0 http://xenon.ism.ac.jp/mdl/inorganic.crystal.e... \\n\",\n \"1 http://xenon.ism.ac.jp/mdl/inorganic.crystal.b... \"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"mdl.get_model_urls(1234, 5678)()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The output dataframe contains the column named `url`. If you only want to get the string of `url`, just use `queryable` specification.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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url
0http://xenon.ism.ac.jp/mdl/inorganic.crystal.e...
1http://xenon.ism.ac.jp/mdl/inorganic.crystal.b...
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" url\\n\",\n \"0 http://xenon.ism.ac.jp/mdl/inorganic.crystal.e...\\n\",\n \"1 http://xenon.ism.ac.jp/mdl/inorganic.crystal.b...\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"mdl.get_model_urls(1234, 5678)('url')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Also, if you don't want to get a dataframe or you want to control the output type yourself, you can set `return_json` to `True`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[{'url': 'http://xenon.ism.ac.jp/mdl/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3.tar.gz'},\\n\",\n \" {'url': 'http://xenon.ism.ac.jp/mdl/inorganic.crystal.band_gap/xenonpy.compositions/pytorch.nn.neural_network/290-168-132-111-1-$Nbe3TKMYM.tar.gz'}]\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"mdl.get_model_urls(1234, 5678)('url', return_json=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can use these urls to download models yourself, but we suggest you to use `mdl.pull`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\\u001b[0;31mSignature:\\u001b[0m\\n\",\n \"\\u001b[0mmdl\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mpull\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0mmodel_ids\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mint\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mpandas\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mcore\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mseries\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mSeries\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mpandas\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mcore\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mframe\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mDataFrame\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0msave_to\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mstr\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;34m'.'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;34m->\\u001b[0m \\u001b[0mpandas\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mcore\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mframe\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mDataFrame\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mDocstring:\\u001b[0m\\n\",\n \"Download model(s) from XenonPy.MDL server.\\n\",\n \"\\n\",\n \"Parameters\\n\",\n \"----------\\n\",\n \"model_ids\\n\",\n \" Model ids.\\n\",\n \" It can be given by a dataframe.\\n\",\n \" In this case, the column with name ``id`` will be used.\\n\",\n \"save_to\\n\",\n \" Path to save models.\\n\",\n \"\\n\",\n \"Returns\\n\",\n \"-------\\n\",\n \"\\u001b[0;31mFile:\\u001b[0m ~/projects/XenonPy/xenonpy/mdl/mdl.py\\n\",\n \"\\u001b[0;31mType:\\u001b[0m method\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"mdl.pull?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"nbsphinx\": \"hidden\"\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"100%|██████████| 2/2 [00:01<00:00, 1.49it/s]\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
idmodel
01234/Users/liuchang/Google 云端硬盘/postdoctoral/tutor...
15678/Users/liuchang/Google 云端硬盘/postdoctoral/tutor...
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" id model\\n\",\n \"0 1234 /Users/liuchang/Google 云端硬盘/postdoctoral/tutor...\\n\",\n \"1 5678 /Users/liuchang/Google 云端硬盘/postdoctoral/tutor...\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ret = mdl.pull(1234, 5678)\\n\",\n \"ret\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The column named `model` contains the local path of the downloaded models.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### upload model\\n\",\n \"\\n\",\n \"Uploading models is not yet availiable until we open the public registration. If you try to upload models, you will get ***'operation needs a logged in user and the corresponded api_key'*** error.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"ValueError\",\n \"evalue\": \"operation needs a logged in user and the corresponded api_key\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mValueError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m\\u001b[0m\\n\\u001b[1;32m 13\\u001b[0m \\u001b[0mtraining_info\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mdict\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0ma\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mb\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 14\\u001b[0m \\u001b[0msupplementary\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mdict\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtrue\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;36m3\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;36m4\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mpred\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;36m4\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 15\\u001b[0;31m )(file='data')\\n\\u001b[0m\",\n \"\\u001b[0;32m~/projects/XenonPy/xenonpy/mdl/base.py\\u001b[0m in \\u001b[0;36m__call__\\u001b[0;34m(self, file, return_json, *querying_vars)\\u001b[0m\\n\\u001b[1;32m 104\\u001b[0m \\u001b[0mret\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mret\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mjson\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 105\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0;34m'errors'\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0mret\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 106\\u001b[0;31m \\u001b[0;32mraise\\u001b[0m \\u001b[0mValueError\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mret\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m'errors'\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m0\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m'message'\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 107\\u001b[0m \\u001b[0mquery_name\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m__class__\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m__name__\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 108\\u001b[0m \\u001b[0mret\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mret\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m'data'\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mquery_name\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m0\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mlower\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;34m+\\u001b[0m \\u001b[0mquery_name\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mValueError\\u001b[0m: operation needs a logged in user and the corresponded api_key\"\n ]\n }\n ],\n \"source\": [\n \"mdl.upload_model(\\n\",\n \" modelset_id=2, \\n\",\n \" describe=dict(\\n\",\n \" property='test2',\\n\",\n \" descriptor='test2',\\n\",\n \" method='test',\\n\",\n \" lang='test',\\n\",\n \" deprecated=True,\\n\",\n \" isRegression=True,\\n\",\n \" meanAbsError=1.11,\\n\",\n \" pearsonCorr=0.85,\\n\",\n \" ),\\n\",\n \" training_info=dict(a=1, b=2),\\n\",\n \" supplementary=dict(true=[1,2,3,4], pred=[1,2,2,4])\\n\",\n \")(file='data')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### retrieve model\\n\",\n \"\\n\",\n \"You can use `xenonpy.model.training.Checker` to load the downloaded models. For example, to load the model with id **1234**:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from xenonpy.model.training import Checker\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/usr/local/miniconda3/envs/xepy37/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: `item` has been deprecated and will be removed in a future version\\n\",\n \" \\\"\\\"\\\"Entry point for launching an IPython kernel.\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \" includes:\\n\",\n \"\\\"final_state\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/final_state.pth.s\\n\",\n \"\\\"training_info\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/training_info.pd.xz\\n\",\n \"\\\"model_class\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/model_class.pkl.z\\n\",\n \"\\\"init_state\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/init_state.pth.s\\n\",\n \"\\\"model\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/model.pth.m\\n\",\n \"\\\"splitter\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/splitter.pkl.z\\n\",\n \"\\\"model_structure\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/model_structure.pkl.z\\n\",\n \"\\\"describe\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/describe.pkl.z\\n\",\n \"\\\"data_indices\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/data_indices.pkl.z\\n\",\n \"\\\"model_params\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/model_params.pkl.z\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"checker = Checker(ret[ret.id == 1234].model.item())\\n\",\n \"checker\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note that the random string *$WEnkZ6e3* in the file name is a magic number to guarantee that each model has a unique name.\\n\",\n \"\\n\",\n \"To load a model into python, call `checker.model` property.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"SequentialLinear(\\n\",\n \" (layer_0): LinearLayer(\\n\",\n \" (linear): Linear(in_features=290, out_features=180, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(180, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_1): LinearLayer(\\n\",\n \" (linear): Linear(in_features=180, out_features=177, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(177, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_2): LinearLayer(\\n\",\n \" (linear): Linear(in_features=177, out_features=162, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(162, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_3): LinearLayer(\\n\",\n \" (linear): Linear(in_features=162, out_features=46, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(46, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_4): LinearLayer(\\n\",\n \" (linear): Linear(in_features=46, out_features=32, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(32, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (output): Linear(in_features=32, out_features=1, bias=True)\\n\",\n \")\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"checker.model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Use `checker.checkpoints` to list checkpoints.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \" includes:\\n\",\n \"\\\"mse_3\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/mse_3.pth.s\\n\",\n \"\\\"mse_1\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/mse_1.pth.s\\n\",\n \"\\\"mae_2\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/mae_2.pth.s\\n\",\n \"\\\"r2_5\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/r2_5.pth.s\\n\",\n \"\\\"mse_5\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/mse_5.pth.s\\n\",\n \"\\\"r2_1\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/r2_1.pth.s\\n\",\n \"\\\"mae_4\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/mae_4.pth.s\\n\",\n \"\\\"r2_3\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/r2_3.pth.s\\n\",\n \"\\\"mae_3\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/mae_3.pth.s\\n\",\n \"\\\"r2_4\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/r2_4.pth.s\\n\",\n \"\\\"mse_2\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/mse_2.pth.s\\n\",\n \"\\\"mae_1\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/mae_1.pth.s\\n\",\n \"\\\"mae_5\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/mae_5.pth.s\\n\",\n \"\\\"r2_2\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/r2_2.pth.s\\n\",\n \"\\\"mse_4\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/mse_4.pth.s\\n\",\n \"\\\"pearsonr_5\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/pearsonr_5.pth.s\\n\",\n \"\\\"pearsonr_1\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/pearsonr_1.pth.s\\n\",\n \"\\\"pearsonr_3\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/pearsonr_3.pth.s\\n\",\n \"\\\"pearsonr_4\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/pearsonr_4.pth.s\\n\",\n \"\\\"pearsonr_2\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.efermi/xenonpy.compositions/pytorch.nn.neural_network/290-180-177-162-46-32-1-$WEnkZ6e3/checkpoints/pearsonr_2.pth.s\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"checker.checkpoints\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"and `checker.checkpoints[]` to load information from a specific checkpoint.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"OrderedDict([('id', 'mse_3'),\\n\",\n \" ('iterations', 670),\\n\",\n \" ('model_state',\\n\",\n \" OrderedDict([('layer_0.linear.weight',\\n\",\n \" tensor([[-2.7413, 2.2444, -0.6347, ..., -0.4093, 1.7569, 0.3331],\\n\",\n \" [-3.4710, 3.1230, -1.5549, ..., 0.7105, 0.1097, -0.5686],\\n\",\n \" [-1.0452, -0.0773, -1.8657, ..., -2.4842, -2.4716, 0.0133],\\n\",\n \" ...,\\n\",\n \" [-2.8635, 2.0810, -1.8306, ..., -0.7263, 0.1966, 0.6690],\\n\",\n \" [-1.4911, 0.3969, -2.4763, ..., -2.1228, -2.3481, -0.2280],\\n\",\n \" [-4.5753, 3.2695, -3.0195, ..., -1.5596, -0.2794, 0.9001]])),\\n\",\n \" ('layer_0.linear.bias',\\n\",\n \" tensor([ 1.8674e-01, 1.2594e+00, 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0.1193,\\n\",\n \" 0.2693, -0.2330, -0.2963, 0.0207, 0.0744, 0.3772, -0.0594, 0.2716,\\n\",\n \" -0.5286, -0.0988, 0.0071, 0.0785, -0.4082, -0.0996, -0.2286, -0.1072,\\n\",\n \" -0.0199, -0.0759])),\\n\",\n \" ('layer_2.normalizer.running_mean',\\n\",\n \" tensor([-0.4771, 0.3641, 0.8236, -0.5117, -0.0686, -0.1060, -0.2271, 1.1366,\\n\",\n \" -0.2673, -0.6526, -0.0598, -0.3061, -0.8754, -0.5231, -0.1757, 0.4500,\\n\",\n \" -0.8126, -0.2425, -0.5052, -0.7121, -0.4026, -0.9407, -0.2459, -0.9544,\\n\",\n \" 0.4757, -0.5780, -0.4919, 0.7358, -0.7519, -1.2301, -0.5611, -0.1652,\\n\",\n \" -0.2985, -1.5551, -0.4821, 0.0284, 0.7578, -0.6171, 1.1162, -0.2456,\\n\",\n \" 0.0706, 0.7641, -0.7250, -0.0534, 0.3452, -1.3787, -0.1682, -0.4565,\\n\",\n \" 0.0713, -0.7465, -0.1761, -0.3235, -0.1148, -1.7422, -0.4193, -0.4965,\\n\",\n \" 1.6195, -0.7968, -0.8410, -0.0312, -0.3054, -1.1875, -0.0566, -0.3492,\\n\",\n \" 0.0022, -0.2289, 0.0654, -0.3188, 1.0440, -0.6859, 0.0535, -0.2057,\\n\",\n \" -0.9382, -1.3899, 0.4321, 0.1345, -0.8927, 0.0757, -0.8157, -0.7032,\\n\",\n \" -0.0305, -0.7336, 0.8059, -0.1560, 0.6057, 0.1573, 1.3014, -0.1614,\\n\",\n \" 0.6065, -0.0084, -1.0559, -0.9398, -1.2521, 0.4449, -0.5810, -1.4539,\\n\",\n \" -0.2939, 0.2501, -0.4125, 0.1147, -0.6748, 0.5822, -1.2044, -0.1167,\\n\",\n \" 0.0862, 0.5174, -0.8685, -1.8570, 0.0200, -0.6124, -0.3150, 0.5020,\\n\",\n \" -0.7448, -0.5465, -1.0633, -0.2995, 0.4695, -0.5782, -0.0652, -0.5671,\\n\",\n \" -1.0913, -0.6757, 0.6314, -0.3749, -0.8141, -0.3002, 0.3342, -0.5968,\\n\",\n \" -0.5119, 0.0035, 0.1476, 0.0465, 0.4401, 0.0067, 0.2266, -0.7001,\\n\",\n \" -0.3087, -0.8820, -0.2069, 0.8888, -0.2508, 1.0863, -0.4786, 0.1945,\\n\",\n \" 0.5957, -0.1170, -0.3074, 0.1786, -0.8405, 0.0903, 0.1077, 0.4824,\\n\",\n \" -0.3188, 0.6018, 0.2859, -1.1954, -0.3282, -0.7685, -0.7977, -0.6385,\\n\",\n \" 0.9811, 0.7143])),\\n\",\n \" ('layer_2.normalizer.running_var',\\n\",\n \" tensor([1.2081, 0.7104, 0.2791, 2.3922, 1.3909, 2.2632, 2.7923, 3.4170, 0.2371,\\n\",\n \" 0.8223, 1.3578, 1.3718, 3.6416, 1.1920, 1.7282, 2.5161, 1.9402, 0.8729,\\n\",\n \" 1.1787, 1.5293, 0.9767, 3.6726, 1.8250, 1.4192, 3.7494, 1.0485, 2.5048,\\n\",\n \" 2.8385, 2.6879, 1.5259, 0.3320, 5.0883, 2.3942, 2.9072, 2.1876, 4.2835,\\n\",\n \" 2.2496, 1.4994, 1.1825, 3.0025, 1.3385, 4.1513, 1.3618, 1.3812, 3.3829,\\n\",\n \" 2.7717, 2.4212, 2.5637, 0.7795, 2.8931, 1.4690, 1.9977, 3.8444, 2.4258,\\n\",\n \" 0.9577, 3.2737, 2.3147, 2.9155, 1.6753, 1.4580, 1.7667, 2.2222, 1.0000,\\n\",\n \" 3.4903, 4.8764, 1.9582, 2.8803, 1.6848, 3.7632, 2.2867, 2.3507, 2.0570,\\n\",\n \" 1.8109, 2.8613, 3.5178, 2.9047, 1.7047, 0.8481, 2.2359, 1.6086, 1.2468,\\n\",\n \" 1.9544, 4.4804, 1.5151, 2.4366, 5.1584, 4.9039, 7.4413, 3.6987, 1.0244,\\n\",\n \" 2.5796, 1.8977, 1.5950, 2.9663, 2.5948, 1.5380, 4.0055, 1.2963, 1.2724,\\n\",\n \" 1.0664, 1.9278, 1.7581, 1.5861, 3.4465, 1.5621, 5.3650, 1.7151, 5.2662,\\n\",\n \" 1.2776, 1.0102, 0.9393, 3.4590, 2.4199, 1.5162, 2.2081, 1.1188, 3.0619,\\n\",\n \" 2.5459, 1.2963, 0.4084, 2.9916, 1.2197, 1.8776, 2.3511, 2.1200, 1.6047,\\n\",\n \" 2.1336, 2.0964, 1.9538, 3.3240, 2.2276, 1.0014, 0.3187, 2.3750, 2.5523,\\n\",\n \" 1.7299, 1.6644, 0.9426, 1.8765, 4.0248, 2.9807, 2.4890, 1.6497, 4.3925,\\n\",\n \" 3.5190, 3.1066, 2.3513, 1.8139, 2.6495, 4.8435, 3.8340, 3.7585, 1.8870,\\n\",\n \" 2.6590, 2.8758, 2.4845, 1.1878, 1.7869, 1.4710, 2.2284, 2.6282, 3.5244])),\\n\",\n \" ('layer_2.normalizer.num_batches_tracked',\\n\",\n \" tensor(670)),\\n\",\n \" ('layer_3.linear.weight',\\n\",\n \" tensor([[ 0.1380, -0.1854, 0.0619, ..., -0.2357, 0.0291, -0.0855],\\n\",\n \" [ 0.1408, 0.0109, 0.0273, ..., -0.1300, -0.2806, 0.0283],\\n\",\n \" [-0.0817, 0.0103, -0.1799, ..., 0.0643, -0.0872, -0.2488],\\n\",\n \" ...,\\n\",\n \" [-0.1365, 0.2386, 0.0441, ..., 0.1613, 0.0506, 0.0980],\\n\",\n \" [ 0.0804, 0.0271, -0.0292, ..., 0.1756, -0.0669, 0.1731],\\n\",\n \" [ 0.0381, 0.2557, -0.1221, ..., 0.2586, 0.0182, -0.0256]])),\\n\",\n \" ('layer_3.linear.bias',\\n\",\n \" tensor([ 1.7908e-02, -1.6474e-02, -1.1066e-01, 2.7437e-01, 7.9325e-01,\\n\",\n \" 7.8987e-02, 3.3952e-01, -8.7965e-02, 1.5753e-01, -1.2900e-01,\\n\",\n \" 7.6526e-02, 7.7629e-03, 5.0898e-01, -3.8382e-01, -6.2505e-02,\\n\",\n \" 7.2312e-01, 4.5212e-02, 9.1312e-01, 4.5915e-02, 1.3952e-02,\\n\",\n \" 2.2207e-01, -5.3636e-04, 1.4154e-01, 1.2470e-01, 1.3919e-01,\\n\",\n \" 1.1014e-01, 1.1680e-01, -3.1510e-02, 4.0542e-02, -1.7219e-04,\\n\",\n \" -1.0441e-01, 5.2226e-01, -5.1104e-02, 3.9572e-01, -2.4648e-01,\\n\",\n \" -3.9327e-01, -1.4421e-01, -6.7385e-02, 2.2774e-02, -1.5080e-01,\\n\",\n \" -5.9285e-03, 1.3464e-01, 1.1147e-01, -2.1358e-01, -3.9889e-01,\\n\",\n \" 1.2666e-01])),\\n\",\n \" ('layer_3.normalizer.weight',\\n\",\n \" tensor([ 0.4035, 0.9273, 0.3436, 0.8313, -0.5483, 0.5186, 0.3613, 0.3362,\\n\",\n \" 0.7600, 0.4670, 1.1825, 0.7006, 0.4275, 1.0126, 0.9773, 0.3357,\\n\",\n \" 0.2955, 0.3329, 0.3705, -0.0120, 0.8153, 0.9983, 0.7633, 0.8837,\\n\",\n \" 0.3240, 0.8691, 0.9471, 0.8034, 0.6000, 0.8582, 0.3787, 0.6470,\\n\",\n \" 1.0252, 0.7739, 0.3396, 0.8057, 0.8804, 0.7036, 0.8939, 0.5345,\\n\",\n \" 0.9509, 0.7852, 0.8444, 0.6482, 0.7647, 0.9864])),\\n\",\n \" ('layer_3.normalizer.bias',\\n\",\n \" tensor([ 0.3733, 0.2051, -0.0269, 0.2724, -0.4801, -0.1726, 0.4889, 0.4670,\\n\",\n \" 0.0054, 0.3607, -0.0175, 0.0667, 0.3025, -0.1931, 0.0393, 0.4796,\\n\",\n \" -0.4264, 0.4658, 0.4557, -0.0588, 0.1771, 0.1360, -0.1883, 0.3348,\\n\",\n \" -0.0160, 0.1065, 0.2562, -0.0518, 0.2925, 0.1273, 0.6136, 0.2823,\\n\",\n \" -0.1507, 0.1460, 0.0915, -0.2639, 0.0656, 0.1196, -0.1569, 0.1028,\\n\",\n \" 0.0275, 0.4489, 0.1869, -0.4018, -0.2103, -0.0286])),\\n\",\n \" ('layer_3.normalizer.running_mean',\\n\",\n \" tensor([-0.9715, -2.2032, -1.8011, 0.9685, 0.6200, -0.9597, 0.7768, -2.1247,\\n\",\n \" -2.0955, -2.6104, 1.6368, -1.4368, 1.0332, 1.4593, 0.8677, 1.6211,\\n\",\n \" -1.0680, 1.0735, -0.7232, 1.2941, -0.8071, -1.6285, -2.1765, -1.8546,\\n\",\n \" -1.0937, 0.5600, 0.9602, 0.9767, -0.7245, -1.1330, -2.3668, 0.4982,\\n\",\n \" 0.3226, -1.4879, -3.2258, -0.0994, -2.7357, -1.0780, -2.0709, -1.0271,\\n\",\n \" -1.8341, 1.0092, -0.7226, -0.3674, 3.3634, -0.3263])),\\n\",\n \" ('layer_3.normalizer.running_var',\\n\",\n \" tensor([ 4.4649, 9.8838, 5.2923, 4.5300, 5.1298, 1.6735, 5.8431, 8.8731,\\n\",\n \" 4.8244, 8.9389, 6.5502, 4.1085, 7.3151, 6.8593, 4.8760, 9.5597,\\n\",\n \" 1.4886, 6.5716, 3.2722, 0.5959, 4.1359, 7.5561, 4.8947, 5.4064,\\n\",\n \" 2.4573, 3.7298, 4.4916, 4.9595, 2.8120, 7.7168, 10.7011, 4.4031,\\n\",\n \" 4.6533, 5.6067, 8.5437, 3.1499, 7.7018, 4.7582, 4.0134, 2.9158,\\n\",\n \" 5.3908, 7.7204, 3.1456, 2.2102, 10.8318, 2.5861])),\\n\",\n \" ('layer_3.normalizer.num_batches_tracked',\\n\",\n \" tensor(670)),\\n\",\n \" ('layer_4.linear.weight',\\n\",\n \" tensor([[ 0.0698, -0.0195, -0.0756, ..., -0.0462, 0.0105, -0.0389],\\n\",\n \" [ 0.1114, 0.1341, 0.3744, ..., -0.1303, -0.5447, 0.0424],\\n\",\n \" [ 0.4859, 0.2065, 0.2659, ..., -0.2024, -0.3696, -0.2466],\\n\",\n \" ...,\\n\",\n \" [ 0.5584, -0.0504, 0.2195, ..., -0.2966, 0.0918, -0.2569],\\n\",\n \" [-0.0763, 0.0830, 0.2275, ..., 0.0154, -0.0329, -0.0522],\\n\",\n \" [-0.1180, 0.2637, 0.1172, ..., 0.1356, -0.6853, 0.2353]])),\\n\",\n \" ('layer_4.linear.bias',\\n\",\n \" tensor([-0.0691, -0.1060, -0.0187, 0.3468, 0.1665, 0.0228, -0.0955, -0.0045,\\n\",\n \" -0.1590, 0.2351, 0.1438, 0.3307, -0.0113, -0.0621, -0.1006, -0.0504,\\n\",\n \" 0.1788, 0.2348, -0.0194, -0.1862, 0.3357, 0.0535, 0.0694, 0.0870,\\n\",\n \" -0.1025, 0.2098, 0.0873, 0.1724, 0.0155, -0.0475, 0.0931, 0.3493])),\\n\",\n \" ('layer_4.normalizer.weight',\\n\",\n \" tensor([ 0.2297, 0.2990, 0.2737, 0.8078, 0.5716, 0.4337, 0.6599, 0.4357,\\n\",\n \" 0.3786, 0.7697, 0.3827, 1.0374, 0.9383, 0.4150, 0.5763, 0.0082,\\n\",\n \" 0.5816, 0.2989, 0.7998, 1.0263, 0.8123, 0.3771, 0.6268, 0.8554,\\n\",\n \" 0.5588, -0.6344, 0.5484, 0.7461, 0.9628, 0.8320, 0.6235, 0.9862])),\\n\",\n \" ('layer_4.normalizer.bias',\\n\",\n \" tensor([-0.1278, 0.4389, 0.4260, 0.3198, -0.1430, 0.3581, -0.2091, -0.0930,\\n\",\n \" 0.3896, 0.3542, -0.1799, 0.2433, 0.2380, -0.1205, 0.3009, -0.0899,\\n\",\n \" 0.2727, 0.4550, -0.1858, 0.1964, 0.4114, -0.0596, 0.0278, -0.2187,\\n\",\n \" 0.0882, -0.0525, -0.0552, 0.2251, 0.2028, 0.1410, -0.1418, 0.2488])),\\n\",\n \" ('layer_4.normalizer.running_mean',\\n\",\n \" tensor([-0.2209, -0.0189, -0.1345, -0.8261, -0.1632, -0.5029, 0.1678, 0.3956,\\n\",\n \" -0.2796, -0.5636, -0.1228, -0.5516, -0.8519, 0.3655, -0.5660, -0.3087,\\n\",\n \" -0.7080, -0.0173, -0.1170, -0.6635, -0.8329, 0.0423, -0.4747, -0.1199,\\n\",\n \" -0.5585, 0.8095, 0.2742, -0.8614, -0.7616, -0.3526, 0.0804, -0.6352])),\\n\",\n \" ('layer_4.normalizer.running_var',\\n\",\n \" tensor([ 0.1660, 9.9155, 9.3877, 10.0118, 1.8771, 8.6701, 0.9420, 1.2411,\\n\",\n \" 7.6982, 7.8437, 0.5575, 8.9085, 10.7996, 1.3539, 6.6764, 0.0753,\\n\",\n \" 6.6262, 9.5189, 0.6057, 5.4100, 8.8232, 1.7903, 4.0475, 1.0141,\\n\",\n \" 3.2860, 5.7909, 1.9748, 6.6539, 7.9738, 4.0276, 0.9936, 6.8559])),\\n\",\n \" ('layer_4.normalizer.num_batches_tracked',\\n\",\n \" tensor(670)),\\n\",\n \" ('output.weight',\\n\",\n \" tensor([[ 0.0907, 0.3841, 0.4231, 0.2701, 0.1529, 0.3734, 0.1848, 0.1908,\\n\",\n \" 0.3445, 0.2799, 0.2094, 0.2791, 0.2999, 0.2314, 0.3558, 0.0392,\\n\",\n \" 0.3215, 0.3956, 0.1413, 0.2460, 0.2496, 0.1880, 0.2432, 0.1266,\\n\",\n \" 0.2394, -0.7282, 0.2006, 0.2259, 0.2687, 0.2108, 0.1736, 0.2261]])),\\n\",\n \" ('output.bias', tensor([0.1369]))]))])\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"checker.checkpoints['mse_3']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### resue model using trainer\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"`xenonpy.model.training.Trainer` can load model and checkpoints from checker or from model directory directly.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from xenonpy.model.training import Trainer\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Trainer(clip_grad=None, cuda=None, epochs=200, loss_func=None,\\n\",\n \" lr_scheduler=None,\\n\",\n \" model=SequentialLinear(\\n\",\n \" (layer_0): LinearLayer(\\n\",\n \" (linear): Linear(in_features=290, out_features=180, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(180, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_1): LinearLayer(\\n\",\n \" (linear): Linear(...\\n\",\n \" (normalizer): BatchNorm1d(46, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_4): LinearLayer(\\n\",\n \" (linear): Linear(in_features=46, out_features=32, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(32, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (output): Linear(in_features=32, out_features=1, bias=True)\\n\",\n \"),\\n\",\n \" non_blocking=False, optimizer=None)\"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"trainer = Trainer.load(from_=checker)\\n\",\n \"trainer\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The following codes show how to reuse model for prediction.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
band_gapcompositiondensitye_above_hullefermielementsfinal_energy_per_atomformation_energy_per_atompretty_formulastructurevolume
mp-10088070.0000{'Rb': 1.0, 'Cu': 1.0, 'O': 1.0}4.7846340.9963721.100617[Rb, Cu, O]-3.302762-0.186408RbCuO[[-3.05935361 -3.05935361 -3.05935361] Rb, [0....57.268924
mp-10096400.0000{'Pr': 1.0, 'N': 1.0}8.1457770.7593935.213442[Pr, N]-7.082624-0.714336PrN[[0. 0. 0.] Pr, [1.57925232 1.57925232 1.58276...31.579717
mp-10168250.7745{'Hf': 1.0, 'Mg': 1.0, 'O': 3.0}6.1658880.5895502.424570[Hf, Mg, O]-7.911723-3.060060HfMgO3[[2.03622802 2.03622802 2.03622802] Hf, [0. 0....67.541269
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" band_gap composition density \\\\\\n\",\n \"mp-1008807 0.0000 {'Rb': 1.0, 'Cu': 1.0, 'O': 1.0} 4.784634 \\n\",\n \"mp-1009640 0.0000 {'Pr': 1.0, 'N': 1.0} 8.145777 \\n\",\n \"mp-1016825 0.7745 {'Hf': 1.0, 'Mg': 1.0, 'O': 3.0} 6.165888 \\n\",\n \"\\n\",\n \" e_above_hull efermi elements final_energy_per_atom \\\\\\n\",\n \"mp-1008807 0.996372 1.100617 [Rb, Cu, O] -3.302762 \\n\",\n \"mp-1009640 0.759393 5.213442 [Pr, N] -7.082624 \\n\",\n \"mp-1016825 0.589550 2.424570 [Hf, Mg, O] -7.911723 \\n\",\n \"\\n\",\n \" formation_energy_per_atom pretty_formula \\\\\\n\",\n \"mp-1008807 -0.186408 RbCuO \\n\",\n \"mp-1009640 -0.714336 PrN \\n\",\n \"mp-1016825 -3.060060 HfMgO3 \\n\",\n \"\\n\",\n \" structure volume \\n\",\n \"mp-1008807 [[-3.05935361 -3.05935361 -3.05935361] Rb, [0.... 57.268924 \\n\",\n \"mp-1009640 [[0. 0. 0.] Pr, [1.57925232 1.57925232 1.58276... 31.579717 \\n\",\n \"mp-1016825 [[2.03622802 2.03622802 2.03622802] Hf, [0. 0.... 67.541269 \"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# if you have not had the samples data\\n\",\n \"# preset.build('mp_samples', api_key=)\\n\",\n \"from xenonpy.datatools import preset\\n\",\n \"\\n\",\n \"data = preset.mp_samples\\n\",\n \"data.head(3)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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ave:atomic_numberave:atomic_radiusave:atomic_radius_rahmave:atomic_volumeave:atomic_weightave:boiling_pointave:bulk_modulusave:c6_gbave:covalent_radius_corderoave:covalent_radius_pyykko...min:num_s_valencemin:periodmin:specific_heatmin:thermal_conductivitymin:vdw_radiusmin:vdw_radius_alvarezmin:vdw_radius_mm3min:vdw_radius_uffmin:sound_velocitymin:Polarizability
mp-100880724.666667174.067140209.33333325.66666755.0042671297.06333372.8686801646.90139.333333128.333333...1.02.00.3600.02658152.0150.0182.0349.5317.50.802
mp-100964033.000000137.000000232.50000019.05000077.4573301931.20000043.1824411892.85137.000000123.500000...2.02.00.1920.02583155.0166.0193.0360.6333.61.100
mp-101682521.600000153.120852203.40000013.92000050.1584001420.71400076.663625343.82102.80000096.000000...2.02.00.1460.02658152.0150.0182.0302.1317.50.802
\\n\",\n \"

3 rows × 290 columns

\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" ave:atomic_number ave:atomic_radius ave:atomic_radius_rahm \\\\\\n\",\n \"mp-1008807 24.666667 174.067140 209.333333 \\n\",\n \"mp-1009640 33.000000 137.000000 232.500000 \\n\",\n \"mp-1016825 21.600000 153.120852 203.400000 \\n\",\n \"\\n\",\n \" ave:atomic_volume ave:atomic_weight ave:boiling_point \\\\\\n\",\n \"mp-1008807 25.666667 55.004267 1297.063333 \\n\",\n \"mp-1009640 19.050000 77.457330 1931.200000 \\n\",\n \"mp-1016825 13.920000 50.158400 1420.714000 \\n\",\n \"\\n\",\n \" ave:bulk_modulus ave:c6_gb ave:covalent_radius_cordero \\\\\\n\",\n \"mp-1008807 72.868680 1646.90 139.333333 \\n\",\n \"mp-1009640 43.182441 1892.85 137.000000 \\n\",\n \"mp-1016825 76.663625 343.82 102.800000 \\n\",\n \"\\n\",\n \" ave:covalent_radius_pyykko ... min:num_s_valence min:period \\\\\\n\",\n \"mp-1008807 128.333333 ... 1.0 2.0 \\n\",\n \"mp-1009640 123.500000 ... 2.0 2.0 \\n\",\n \"mp-1016825 96.000000 ... 2.0 2.0 \\n\",\n \"\\n\",\n \" min:specific_heat min:thermal_conductivity min:vdw_radius \\\\\\n\",\n \"mp-1008807 0.360 0.02658 152.0 \\n\",\n \"mp-1009640 0.192 0.02583 155.0 \\n\",\n \"mp-1016825 0.146 0.02658 152.0 \\n\",\n \"\\n\",\n \" min:vdw_radius_alvarez min:vdw_radius_mm3 min:vdw_radius_uff \\\\\\n\",\n \"mp-1008807 150.0 182.0 349.5 \\n\",\n \"mp-1009640 166.0 193.0 360.6 \\n\",\n \"mp-1016825 150.0 182.0 302.1 \\n\",\n \"\\n\",\n \" min:sound_velocity min:Polarizability \\n\",\n \"mp-1008807 317.5 0.802 \\n\",\n \"mp-1009640 333.6 1.100 \\n\",\n \"mp-1016825 317.5 0.802 \\n\",\n \"\\n\",\n \"[3 rows x 290 columns]\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
efermi
mp-10088071.100617
mp-10096405.213442
mp-10168252.424570
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" efermi\\n\",\n \"mp-1008807 1.100617\\n\",\n \"mp-1009640 5.213442\\n\",\n \"mp-1016825 2.424570\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from xenonpy.descriptor import Compositions\\n\",\n \"\\n\",\n \"prop = data['efermi'].dropna().to_frame() # reshape to 2-D\\n\",\n \"desc = Compositions(featurizers='classic').transform(data.loc[prop.index]['composition'])\\n\",\n \"\\n\",\n \"desc.head(3)\\n\",\n \"prop.head(3)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Missing directory and/or file name information!\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"y_pred = trainer.predict(x_in=torch.tensor(desc.values, dtype=torch.float)).detach().numpy().flatten()\\n\",\n \"y_true = prop.values.flatten()\\n\",\n \"\\n\",\n \"draw(y_true, y_pred, prop_name='Efermi ($eV$)')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "samples/predict_hypermaterials/OTHERS_data.csv", "content": ",composition,elements,label,for_test\nmp-1077980,\"{'Sr': 1.0, 'Ag': 4.0, 'Sb': 2.0}\",\"('Ag', 'Sb', 'Sr')\",OTHERS,False\nmp-30079,\"{'Li': 1.0, 'Mg': 2.0, 'Ge': 1.0}\",\"('Ge', 'Li', 'Mg')\",OTHERS,False\nmvc-2967,\"{'Ba': 2.0, 'Tl': 1.0, 'Fe': 2.0, 'O': 7.0}\",\"('Ba', 'Fe', 'O', 'Tl')\",OTHERS,False\nmp-1202279,\"{'K': 18.0, 'Ho': 18.0, 'F': 72.0}\",\"('F', 'Ho', 'K')\",OTHERS,False\nmp-34421,\"{'Bi': 3.0, 'Cd': 1.0, 'O': 7.0}\",\"('Bi', 'Cd', 'O')\",OTHERS,False\nmp-976583,\"{'K': 6.0, 'Co': 2.0}\",\"('Co', 'K')\",OTHERS,False\nmp-626550,\"{'Ti': 6.0, 'H': 4.0, 'O': 14.0}\",\"('H', 'O', 'Ti')\",OTHERS,False\nmp-555012,\"{'La': 24.0, 'S': 16.0, 'N': 12.0, 'Cl': 4.0}\",\"('Cl', 'La', 'N', 'S')\",OTHERS,False\nmp-755743,\"{'Li': 4.0, 'Ti': 2.0, 'Cr': 2.0, 'O': 8.0}\",\"('Cr', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-766253,\"{'La': 2.0, 'Th': 11.0, 'O': 26.0}\",\"('La', 'O', 'Th')\",OTHERS,False\nmp-753335,\"{'Li': 4.0, 'V': 4.0, 'F': 14.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-1176451,\"{'Mn': 5.0, 'V': 1.0, 'O': 12.0}\",\"('Mn', 'O', 'V')\",OTHERS,False\nmp-21893,\"{'Mn': 4.0, 'Te': 8.0}\",\"('Mn', 'Te')\",OTHERS,False\nmp-568695,\"{'Ca': 3.0, 'Cd': 3.0, 'Sn': 3.0}\",\"('Ca', 'Cd', 'Sn')\",OTHERS,False\nmp-753169,\"{'Mn': 4.0, 'F': 10.0}\",\"('F', 'Mn')\",OTHERS,False\nmp-1040366,\"{'K': 1.0, 'Li': 1.0, 'Mg': 30.0, 'O': 31.0}\",\"('K', 'Li', 'Mg', 'O')\",OTHERS,False\nmp-1208310,\"{'Tb': 4.0, 'Si': 4.0, 'Pd': 8.0}\",\"('Pd', 'Si', 'Tb')\",OTHERS,False\nmp-1215696,\"{'Zn': 2.0, 'In': 1.0, 'Cu': 1.0, 'S': 4.0}\",\"('Cu', 'In', 'S', 'Zn')\",OTHERS,False\nmp-1187611,\"{'Tm': 1.0, 'Lu': 1.0, 'Ag': 2.0}\",\"('Ag', 'Lu', 'Tm')\",OTHERS,False\nmp-1194239,\"{'Pu': 4.0, 'Si': 2.0, 'O': 20.0}\",\"('O', 'Pu', 'Si')\",OTHERS,False\nmp-1097220,\"{'Sc': 2.0, 'Zn': 1.0, 'Hg': 1.0}\",\"('Hg', 'Sc', 'Zn')\",OTHERS,False\nmp-1096426,\"{'Cs': 1.0, 'Na': 2.0, 'Bi': 1.0}\",\"('Bi', 'Cs', 'Na')\",OTHERS,False\nmp-19725,\"{'Fe': 6.0, 'Ge': 6.0, 'Hf': 1.0}\",\"('Fe', 'Ge', 'Hf')\",OTHERS,False\nmvc-9863,\"{'La': 2.0, 'Ti': 2.0, 'Zn': 2.0, 'Cr': 2.0, 'O': 12.0}\",\"('Cr', 'La', 'O', 'Ti', 'Zn')\",OTHERS,False\nmp-1112161,\"{'Cs': 2.0, 'Rb': 1.0, 'Bi': 1.0, 'I': 6.0}\",\"('Bi', 'Cs', 'I', 'Rb')\",OTHERS,False\nmp-1245490,\"{'Ba': 8.0, 'V': 2.0, 'N': 8.0}\",\"('Ba', 'N', 'V')\",OTHERS,False\nmp-559407,\"{'Na': 3.0, 'Gd': 3.0, 'H': 6.0, 'S': 6.0, 'O': 27.0}\",\"('Gd', 'H', 'Na', 'O', 'S')\",OTHERS,False\nmp-1207970,\"{'Tm': 10.0, 'Bi': 2.0, 'Pt': 4.0}\",\"('Bi', 'Pt', 'Tm')\",OTHERS,False\nmp-1246472,\"{'Mg': 2.0, 'In': 1.0, 'Mo': 3.0, 'S': 8.0}\",\"('In', 'Mg', 'Mo', 'S')\",OTHERS,False\nmp-571474,\"{'Sm': 8.0, 'Sn': 6.0}\",\"('Sm', 'Sn')\",OTHERS,False\nmp-1016058,\"{'Cr': 4.0, 'N': 8.0}\",\"('Cr', 'N')\",OTHERS,False\nmp-772720,\"{'Cr': 12.0, 'Co': 8.0, 'O': 48.0}\",\"('Co', 'Cr', 'O')\",OTHERS,False\nmp-1039782,\"{'Na': 1.0, 'Mg': 30.0, 'W': 1.0, 'O': 32.0}\",\"('Mg', 'Na', 'O', 'W')\",OTHERS,False\nmp-1198768,\"{'Ca': 4.0, 'Si': 4.0, 'B': 4.0, 'O': 20.0}\",\"('B', 'Ca', 'O', 'Si')\",OTHERS,False\nmp-675857,\"{'Cd': 4.0, 'Sn': 2.0, 'O': 8.0}\",\"('Cd', 'O', 'Sn')\",OTHERS,False\nmvc-16729,\"{'Y': 2.0, 'Mn': 4.0, 'S': 8.0}\",\"('Mn', 'S', 'Y')\",OTHERS,False\nmp-5808,\"{'Rb': 1.0, 'Te': 2.0, 'Nd': 1.0}\",\"('Nd', 'Rb', 'Te')\",OTHERS,False\nmp-677726,\"{'Al': 12.0, 'Si': 12.0, 'Ag': 16.0, 'S': 5.0, 'O': 48.0}\",\"('Ag', 'Al', 'O', 'S', 'Si')\",OTHERS,False\nmp-1093949,\"{'Ba': 2.0, 'Tl': 1.0, 'Ag': 1.0}\",\"('Ag', 'Ba', 'Tl')\",OTHERS,False\nmp-21604,\"{'S': 24.0, 'In': 4.0, 'Sm': 12.0}\",\"('In', 'S', 'Sm')\",OTHERS,False\nmp-1225810,\"{'Cu': 2.0, 'Si': 1.0, 'Te': 3.0}\",\"('Cu', 'Si', 'Te')\",OTHERS,False\nmp-540651,\"{'Ba': 8.0, 'Mn': 8.0, 'Sn': 8.0, 'S': 32.0}\",\"('Ba', 'Mn', 'S', 'Sn')\",OTHERS,False\nmp-1194912,\"{'Y': 4.0, 'V': 4.0, 'Te': 8.0, 'O': 32.0}\",\"('O', 'Te', 'V', 'Y')\",OTHERS,False\nmp-1176954,\"{'Li': 6.0, 'Nb': 1.0, 'Ni': 3.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'Nb', 'Ni', 'O', 'P')\",OTHERS,False\nmp-1183146,\"{'B': 1.0, 'Au': 3.0}\",\"('Au', 'B')\",OTHERS,False\nmp-16521,\"{'Al': 3.0, 'Os': 2.0}\",\"('Al', 'Os')\",OTHERS,False\nmp-690550,\"{'Co': 14.0, 'Ru': 4.0, 'O': 24.0}\",\"('Co', 'O', 'Ru')\",OTHERS,False\nmp-9201,\"{'K': 4.0, 'Ge': 2.0, 'Au': 7.0}\",\"('Au', 'Ge', 'K')\",OTHERS,False\nmp-1201395,\"{'Tb': 16.0, 'Te': 12.0, 'N': 8.0}\",\"('N', 'Tb', 'Te')\",OTHERS,False\nmp-645338,\"{'Sm': 16.0, 'B': 48.0, 'O': 96.0}\",\"('B', 'O', 'Sm')\",OTHERS,False\nmp-1205720,\"{'Ho': 2.0, 'Mn': 4.0, 'Si': 2.0, 'C': 2.0}\",\"('C', 'Ho', 'Mn', 'Si')\",OTHERS,False\nmp-29507,\"{'O': 26.0, 'V': 6.0, 'Cu': 11.0}\",\"('Cu', 'O', 'V')\",OTHERS,False\nmp-1199783,\"{'Re': 12.0, 'H': 12.0, 'C': 48.0, 'O': 48.0}\",\"('C', 'H', 'O', 'Re')\",OTHERS,False\nmp-1176347,\"{'Na': 10.0, 'Fe': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Fe', 'Na', 'O', 'P')\",OTHERS,False\nmp-1183114,\"{'Ac': 2.0, 'Cu': 6.0}\",\"('Ac', 'Cu')\",OTHERS,False\nmp-531201,\"{'Cd': 10.0, 'Ho': 20.0, 'Se': 40.0}\",\"('Cd', 'Ho', 'Se')\",OTHERS,False\nmp-766427,\"{'K': 4.0, 'Na': 2.0, 'Zn': 4.0, 'H': 10.0, 'C': 8.0, 'O': 28.0}\",\"('C', 'H', 'K', 'Na', 'O', 'Zn')\",OTHERS,False\nmp-1093703,\"{'Zr': 2.0, 'Zn': 1.0, 'Cd': 1.0}\",\"('Cd', 'Zn', 'Zr')\",OTHERS,False\nmp-1216881,\"{'U': 1.0, 'Al': 9.0, 'Fe': 3.0}\",\"('Al', 'Fe', 'U')\",OTHERS,False\nmp-865403,\"{'Tm': 4.0, 'Mg': 2.0, 'Ge': 4.0}\",\"('Ge', 'Mg', 'Tm')\",OTHERS,False\nmp-28601,\"{'Br': 40.0, 'Nb': 8.0}\",\"('Br', 'Nb')\",OTHERS,False\nmp-1209795,\"{'Pr': 4.0, 'Al': 18.0, 'Ir': 6.0}\",\"('Al', 'Ir', 'Pr')\",OTHERS,False\nmp-571165,\"{'Dy': 8.0, 'Fe': 56.0, 'C': 4.0}\",\"('C', 'Dy', 'Fe')\",OTHERS,False\nmp-8689,\"{'V': 4.0, 'Ge': 1.0, 'Se': 8.0}\",\"('Ge', 'Se', 'V')\",OTHERS,False\nmp-772938,\"{'Li': 4.0, 'Ta': 8.0, 'O': 22.0}\",\"('Li', 'O', 'Ta')\",OTHERS,False\nmp-862621,\"{'Ga': 2.0, 'Pt': 6.0}\",\"('Ga', 'Pt')\",OTHERS,False\nmp-754167,\"{'Mn': 5.0, 'O': 1.0, 'F': 11.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-1217987,\"{'Ta': 3.0, 'Co': 2.0, 'Mo': 1.0}\",\"('Co', 'Mo', 'Ta')\",OTHERS,False\nmp-505750,\"{'Rb': 8.0, 'Sn': 16.0, 'Se': 32.0}\",\"('Rb', 'Se', 'Sn')\",OTHERS,False\nmp-1020647,\"{'Sr': 8.0, 'Li': 4.0, 'Be': 4.0, 'B': 12.0, 'O': 32.0}\",\"('B', 'Be', 'Li', 'O', 'Sr')\",OTHERS,False\nmp-1238141,\"{'V': 4.0, 'Ni': 2.0, 'H': 16.0, 'O': 20.0}\",\"('H', 'Ni', 'O', 'V')\",OTHERS,False\nmp-1203000,\"{'Nd': 4.0, 'H': 44.0, 'S': 12.0, 'O': 64.0}\",\"('H', 'Nd', 'O', 'S')\",OTHERS,False\nmp-867847,\"{'La': 2.0, 'Zn': 1.0, 'Ru': 1.0}\",\"('La', 'Ru', 'Zn')\",OTHERS,False\nmp-1196071,\"{'Ba': 28.0, 'Fe': 28.0, 'O': 70.0}\",\"('Ba', 'Fe', 'O')\",OTHERS,False\nmp-30747,\"{'Zn': 22.0, 'Ir': 4.0}\",\"('Ir', 'Zn')\",OTHERS,False\nmp-1190441,\"{'Li': 4.0, 'Sb': 4.0, 'F': 16.0}\",\"('F', 'Li', 'Sb')\",OTHERS,False\nmp-1275,\"{'Si': 2.0, 'Mo': 6.0}\",\"('Mo', 'Si')\",OTHERS,False\nmp-676556,\"{'Sm': 2.0, 'Zr': 8.0, 'O': 19.0}\",\"('O', 'Sm', 'Zr')\",OTHERS,False\nmp-20441,\"{'Mn': 2.0, 'Co': 2.0, 'C': 1.0}\",\"('C', 'Co', 'Mn')\",OTHERS,False\nmp-1187045,\"{'Sn': 3.0, 'Mo': 1.0}\",\"('Mo', 'Sn')\",OTHERS,False\nmp-22401,\"{'Si': 20.0, 'Nd': 8.0, 'Re': 12.0}\",\"('Nd', 'Re', 'Si')\",OTHERS,False\nmp-559537,\"{'Ca': 8.0, 'P': 12.0, 'O': 38.0}\",\"('Ca', 'O', 'P')\",OTHERS,False\nmp-1228000,\"{'Ca': 2.0, 'Mn': 7.0, 'Si': 10.0, 'H': 12.0, 'O': 35.0}\",\"('Ca', 'H', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1207109,\"{'Sm': 1.0, 'Hg': 2.0}\",\"('Hg', 'Sm')\",OTHERS,False\nmp-13498,\"{'Mg': 1.0, 'Tb': 2.0}\",\"('Mg', 'Tb')\",OTHERS,False\nmp-768358,\"{'Sc': 2.0, 'In': 2.0, 'O': 6.0}\",\"('In', 'O', 'Sc')\",OTHERS,False\nmp-753061,\"{'Ti': 1.0, 'Co': 3.0, 'O': 8.0}\",\"('Co', 'O', 'Ti')\",OTHERS,False\nmp-1184981,\"{'Li': 2.0, 'Nd': 1.0, 'Pb': 1.0}\",\"('Li', 'Nd', 'Pb')\",OTHERS,False\nmvc-11754,\"{'V': 4.0, 'S': 10.0}\",\"('S', 'V')\",OTHERS,False\nmp-1096757,\"{'Nb': 1.0, 'Cr': 2.0, 'Re': 1.0}\",\"('Cr', 'Nb', 'Re')\",OTHERS,False\nmp-772337,\"{'Cr': 12.0, 'Cu': 8.0, 'O': 48.0}\",\"('Cr', 'Cu', 'O')\",OTHERS,False\nmp-1218557,\"{'Sr': 6.0, 'Co': 2.0, 'Sb': 4.0, 'O': 18.0}\",\"('Co', 'O', 'Sb', 'Sr')\",OTHERS,False\nmp-1247120,\"{'Cd': 22.0, 'C': 4.0, 'N': 20.0}\",\"('C', 'Cd', 'N')\",OTHERS,False\nmp-868632,\"{'Mn': 2.0, 'O': 2.0, 'F': 2.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-1187278,\"{'Tb': 3.0, 'Dy': 1.0}\",\"('Dy', 'Tb')\",OTHERS,False\nmp-772269,\"{'Mn': 6.0, 'P': 6.0, 'O': 24.0}\",\"('Mn', 'O', 'P')\",OTHERS,False\nmp-1073410,\"{'Mg': 4.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-19858,{'Sr': 6.0},\"('Sr',)\",OTHERS,False\nmp-1211266,\"{'Li': 16.0, 'Mo': 12.0, 'O': 48.0}\",\"('Li', 'Mo', 'O')\",OTHERS,False\nmp-554598,\"{'B': 4.0, 'H': 20.0, 'C': 4.0, 'N': 2.0, 'Cl': 2.0, 'O': 6.0}\",\"('B', 'C', 'Cl', 'H', 'N', 'O')\",OTHERS,False\nmp-1095988,\"{'Sr': 2.0, 'Ag': 1.0, 'Hg': 1.0}\",\"('Ag', 'Hg', 'Sr')\",OTHERS,False\nmp-1064647,\"{'K': 2.0, 'N': 2.0}\",\"('K', 'N')\",OTHERS,False\nmp-1184199,\"{'Eu': 2.0, 'Ag': 1.0, 'Pb': 1.0}\",\"('Ag', 'Eu', 'Pb')\",OTHERS,False\nmp-1213927,\"{'Ce': 12.0, 'P': 4.0, 'Br': 12.0}\",\"('Br', 'Ce', 'P')\",OTHERS,False\nmp-1174745,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1210027,\"{'Rb': 36.0, 'Co': 8.0, 'S': 28.0}\",\"('Co', 'Rb', 'S')\",OTHERS,False\nmp-757597,\"{'Li': 4.0, 'Sn': 4.0, 'P': 16.0, 'O': 48.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,False\nmp-1197135,\"{'La': 2.0, 'N': 6.0, 'O': 26.0}\",\"('La', 'N', 'O')\",OTHERS,False\nmp-759865,\"{'Li': 3.0, 'V': 3.0, 'Cr': 3.0, 'P': 6.0, 'H': 6.0, 'O': 30.0}\",\"('Cr', 'H', 'Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1228512,\"{'Ba': 3.0, 'Fe': 4.0, 'P': 8.0, 'Pb': 1.0, 'O': 28.0}\",\"('Ba', 'Fe', 'O', 'P', 'Pb')\",OTHERS,False\nmp-1210921,\"{'Li': 2.0, 'Zn': 8.0, 'Sn': 2.0, 'O': 16.0}\",\"('Li', 'O', 'Sn', 'Zn')\",OTHERS,False\nmp-1232186,\"{'Ce': 8.0, 'Mg': 4.0, 'S': 16.0}\",\"('Ce', 'Mg', 'S')\",OTHERS,False\nmp-1202615,\"{'Ho': 40.0, 'Ir': 8.0, 'Br': 72.0}\",\"('Br', 'Ho', 'Ir')\",OTHERS,False\nmp-12797,\"{'Be': 4.0, 'O': 14.0, 'Si': 4.0, 'Ba': 2.0}\",\"('Ba', 'Be', 'O', 'Si')\",OTHERS,False\nmp-1220054,\"{'Ni': 1.0, 'Rh': 1.0}\",\"('Ni', 'Rh')\",OTHERS,False\nmp-1239257,\"{'Zr': 4.0, 'Cr': 4.0, 'Cu': 4.0, 'S': 16.0}\",\"('Cr', 'Cu', 'S', 'Zr')\",OTHERS,False\nmp-13903,\"{'F': 6.0, 'Zn': 1.0, 'Sn': 1.0}\",\"('F', 'Sn', 'Zn')\",OTHERS,False\nmp-765733,\"{'Mn': 8.0, 'O': 14.0, 'F': 2.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-1246065,\"{'Ti': 4.0, 'Mo': 4.0, 'N': 12.0}\",\"('Mo', 'N', 'Ti')\",OTHERS,False\nmp-26035,\"{'Li': 18.0, 'O': 58.0, 'P': 16.0, 'Mn': 6.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-754685,\"{'Li': 4.0, 'Cr': 1.0, 'Fe': 1.0, 'W': 2.0, 'O': 12.0}\",\"('Cr', 'Fe', 'Li', 'O', 'W')\",OTHERS,False\nmp-1200101,\"{'Er': 12.0, 'P': 36.0, 'O': 108.0}\",\"('Er', 'O', 'P')\",OTHERS,False\nmp-23227,\"{'Cu': 2.0, 'Br': 2.0}\",\"('Br', 'Cu')\",OTHERS,False\nmp-1193242,\"{'Cs': 8.0, 'Co': 4.0, 'Cl': 16.0}\",\"('Cl', 'Co', 'Cs')\",OTHERS,False\nmp-1201134,\"{'Cu': 4.0, 'C': 8.0, 'O': 24.0}\",\"('C', 'Cu', 'O')\",OTHERS,False\nmp-864624,\"{'Ho': 1.0, 'Zn': 1.0, 'Rh': 2.0}\",\"('Ho', 'Rh', 'Zn')\",OTHERS,False\nmp-20596,\"{'Si': 2.0, 'Ni': 2.0, 'U': 1.0}\",\"('Ni', 'Si', 'U')\",OTHERS,False\nmp-31352,\"{'O': 18.0, 'Se': 6.0, 'Er': 4.0}\",\"('Er', 'O', 'Se')\",OTHERS,False\nmp-1019524,\"{'Ba': 3.0, 'P': 6.0, 'N': 8.0, 'O': 6.0}\",\"('Ba', 'N', 'O', 'P')\",OTHERS,False\nmp-1221447,\"{'Na': 2.0, 'Al': 1.0, 'Ge': 7.0, 'O': 16.0}\",\"('Al', 'Ge', 'Na', 'O')\",OTHERS,False\nmp-6391,\"{'Na': 4.0, 'Zn': 2.0, 'Si': 2.0, 'O': 8.0}\",\"('Na', 'O', 'Si', 'Zn')\",OTHERS,False\nmp-643586,\"{'Cs': 2.0, 'H': 2.0, 'S': 4.0, 'O': 12.0, 'F': 4.0}\",\"('Cs', 'F', 'H', 'O', 'S')\",OTHERS,False\nmp-715438,\"{'Fe': 6.0, 'O': 8.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-754430,\"{'Mn': 7.0, 'O': 12.0}\",\"('Mn', 'O')\",OTHERS,False\nmp-306,\"{'B': 6.0, 'O': 9.0}\",\"('B', 'O')\",OTHERS,False\nmvc-10016,\"{'La': 1.0, 'V': 1.0, 'Cr': 1.0, 'O': 6.0}\",\"('Cr', 'La', 'O', 'V')\",OTHERS,False\nmp-1183975,\"{'Ga': 1.0, 'Ge': 3.0}\",\"('Ga', 'Ge')\",OTHERS,False\nmp-1180190,\"{'Mo': 2.0, 'O': 10.0}\",\"('Mo', 'O')\",OTHERS,False\nmp-1174068,\"{'Li': 6.0, 'Mn': 4.0, 'O': 10.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-756691,\"{'Li': 6.0, 'Co': 1.0, 'Ni': 1.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Co', 'Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-764826,\"{'Li': 24.0, 'Mn': 12.0, 'F': 60.0}\",\"('F', 'Li', 'Mn')\",OTHERS,False\nmp-771487,\"{'Na': 10.0, 'Mn': 4.0, 'P': 2.0, 'C': 8.0, 'O': 32.0}\",\"('C', 'Mn', 'Na', 'O', 'P')\",OTHERS,False\nmp-1177323,\"{'Li': 4.0, 'Nb': 1.0, 'V': 3.0, 'Cr': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Cr', 'Li', 'Nb', 'O', 'P', 'V')\",OTHERS,False\nmp-1216790,\"{'V': 2.0, 'Mo': 1.0, 'O': 8.0}\",\"('Mo', 'O', 'V')\",OTHERS,False\nmp-1190399,\"{'Na': 4.0, 'Zn': 2.0, 'Ge': 4.0, 'S': 12.0}\",\"('Ge', 'Na', 'S', 'Zn')\",OTHERS,False\nmp-571347,\"{'Pr': 6.0, 'Cu': 2.0, 'Ge': 2.0, 'Se': 14.0}\",\"('Cu', 'Ge', 'Pr', 'Se')\",OTHERS,False\nmp-753420,\"{'Li': 2.0, 'Mn': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-650442,\"{'Tl': 12.0, 'Ag': 4.0, 'Te': 8.0}\",\"('Ag', 'Te', 'Tl')\",OTHERS,False\nmp-756392,\"{'Li': 2.0, 'Cu': 2.0, 'Si': 4.0, 'O': 10.0}\",\"('Cu', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1104788,\"{'Co': 2.0, 'B': 4.0, 'O': 8.0}\",\"('B', 'Co', 'O')\",OTHERS,False\nmp-722571,\"{'Re': 20.0, 'H': 20.0, 'C': 80.0, 'O': 80.0}\",\"('C', 'H', 'O', 'Re')\",OTHERS,False\nmp-567459,\"{'Ag': 4.0, 'C': 8.0, 'N': 12.0}\",\"('Ag', 'C', 'N')\",OTHERS,False\nmp-753624,\"{'Li': 2.0, 'Mn': 2.0, 'P': 2.0, 'O': 8.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-19997,\"{'Ni': 2.0, 'In': 4.0, 'Eu': 2.0}\",\"('Eu', 'In', 'Ni')\",OTHERS,False\nmp-980944,\"{'Yb': 6.0, 'Y': 2.0}\",\"('Y', 'Yb')\",OTHERS,False\nmp-864655,\"{'Pa': 1.0, 'Co': 3.0}\",\"('Co', 'Pa')\",OTHERS,False\nmp-753040,\"{'Li': 1.0, 'Mn': 1.0, 'P': 3.0, 'H': 1.0, 'O': 10.0}\",\"('H', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1239310,\"{'Sr': 4.0, 'Y': 2.0, 'Cr': 2.0, 'Cu': 4.0, 'O': 14.0}\",\"('Cr', 'Cu', 'O', 'Sr', 'Y')\",OTHERS,False\nmp-19237,\"{'O': 4.0, 'Sr': 2.0, 'Mo': 1.0}\",\"('Mo', 'O', 'Sr')\",OTHERS,False\nmp-1174721,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1208970,\"{'Sm': 3.0, 'P': 6.0, 'Pd': 20.0}\",\"('P', 'Pd', 'Sm')\",OTHERS,False\nmp-1196827,\"{'K': 16.0, 'Li': 4.0, 'H': 12.0, 'S': 16.0, 'O': 64.0}\",\"('H', 'K', 'Li', 'O', 'S')\",OTHERS,False\nmp-570286,\"{'Bi': 2.0, 'Se': 2.0}\",\"('Bi', 'Se')\",OTHERS,False\nmp-754127,\"{'Li': 2.0, 'Co': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Co', 'Li', 'O', 'P')\",OTHERS,False\nmp-662575,\"{'Eu': 2.0, 'Sr': 4.0, 'Ga': 2.0, 'Cu': 4.0, 'O': 14.0}\",\"('Cu', 'Eu', 'Ga', 'O', 'Sr')\",OTHERS,False\nmp-14901,\"{'O': 48.0, 'Na': 12.0, 'Zn': 12.0, 'As': 12.0}\",\"('As', 'Na', 'O', 'Zn')\",OTHERS,False\nmp-568989,\"{'Li': 56.0, 'Ni': 8.0, 'N': 32.0}\",\"('Li', 'N', 'Ni')\",OTHERS,False\nmp-760071,\"{'Li': 10.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1106340,\"{'La': 10.0, 'Mn': 2.0, 'Pb': 6.0}\",\"('La', 'Mn', 'Pb')\",OTHERS,False\nmp-776447,\"{'Li': 4.0, 'Mn': 6.0, 'B': 6.0, 'O': 18.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1244597,\"{'Mg': 4.0, 'Ta': 4.0, 'Bi': 4.0, 'O': 20.0}\",\"('Bi', 'Mg', 'O', 'Ta')\",OTHERS,False\nmp-1199645,\"{'Mn': 8.0, 'As': 8.0, 'O': 36.0}\",\"('As', 'Mn', 'O')\",OTHERS,False\nmp-1184926,\"{'Ho': 1.0, 'Np': 3.0}\",\"('Ho', 'Np')\",OTHERS,False\nmp-1200738,\"{'Pr': 10.0, 'Sn': 20.0, 'Rh': 8.0}\",\"('Pr', 'Rh', 'Sn')\",OTHERS,False\nmp-780653,\"{'Li': 20.0, 'Mn': 2.0, 'Si': 4.0, 'O': 20.0}\",\"('Li', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1073360,\"{'Mg': 4.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-863354,\"{'Co': 2.0, 'Sn': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Co', 'O', 'P', 'Sn')\",OTHERS,False\nmp-755639,\"{'Li': 4.0, 'Mn': 1.0, 'Fe': 3.0, 'B': 4.0, 'O': 12.0}\",\"('B', 'Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1190642,\"{'Er': 2.0, 'Fe': 12.0, 'P': 7.0}\",\"('Er', 'Fe', 'P')\",OTHERS,False\nmp-1200403,\"{'Nb': 8.0, 'Ag': 4.0, 'O': 22.0}\",\"('Ag', 'Nb', 'O')\",OTHERS,False\nmp-30136,\"{'O': 14.0, 'Mo': 4.0, 'Hg': 4.0}\",\"('Hg', 'Mo', 'O')\",OTHERS,False\nmp-1184848,\"{'K': 3.0, 'Mo': 1.0}\",\"('K', 'Mo')\",OTHERS,False\nmp-1210839,\"{'Lu': 4.0, 'Be': 4.0, 'Ge': 2.0, 'O': 14.0}\",\"('Be', 'Ge', 'Lu', 'O')\",OTHERS,False\nmp-1182249,\"{'Fe': 24.0, 'O': 32.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-1103642,\"{'Ag': 1.0, 'Mo': 6.0, 'Se': 8.0}\",\"('Ag', 'Mo', 'Se')\",OTHERS,False\nmp-753891,\"{'Zr': 4.0, 'Ti': 4.0, 'O': 16.0}\",\"('O', 'Ti', 'Zr')\",OTHERS,False\nmp-1195911,\"{'Sr': 2.0, 'Yb': 8.0, 'Si': 10.0, 'O': 34.0}\",\"('O', 'Si', 'Sr', 'Yb')\",OTHERS,False\nmp-560815,\"{'Al': 18.0, 'P': 18.0, 'O': 72.0}\",\"('Al', 'O', 'P')\",OTHERS,False\nmp-26393,\"{'Li': 4.0, 'O': 48.0, 'P': 12.0, 'Mn': 16.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-759338,\"{'Mn': 6.0, 'O': 8.0, 'F': 4.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-1203491,\"{'Ho': 12.0, 'In': 4.0, 'S': 24.0}\",\"('Ho', 'In', 'S')\",OTHERS,False\nmp-1193222,\"{'Li': 3.0, 'Mg': 3.0, 'Al': 3.0, 'F': 18.0}\",\"('Al', 'F', 'Li', 'Mg')\",OTHERS,False\nmp-571057,\"{'K': 4.0, 'Y': 4.0, 'P': 8.0, 'Se': 24.0}\",\"('K', 'P', 'Se', 'Y')\",OTHERS,False\nmvc-7568,\"{'Mg': 4.0, 'Ti': 6.0, 'O': 16.0}\",\"('Mg', 'O', 'Ti')\",OTHERS,False\nmp-1219110,\"{'Sm': 2.0, 'Al': 2.0, 'Zn': 2.0}\",\"('Al', 'Sm', 'Zn')\",OTHERS,False\nmp-1025324,\"{'Si': 1.0, 'Sn': 1.0, 'Pt': 5.0}\",\"('Pt', 'Si', 'Sn')\",OTHERS,False\nmp-1229121,\"{'Ag': 1.0, 'Bi': 1.0, 'Pb': 1.0, 'S': 3.0}\",\"('Ag', 'Bi', 'Pb', 'S')\",OTHERS,False\nmp-27513,\"{'Cl': 10.0, 'Sc': 7.0}\",\"('Cl', 'Sc')\",OTHERS,False\nmp-16866,\"{'N': 6.0, 'O': 22.0, 'K': 2.0, 'Np': 2.0}\",\"('K', 'N', 'Np', 'O')\",OTHERS,False\nmp-1208837,\"{'Sr': 1.0, 'In': 2.0, 'Cu': 2.0}\",\"('Cu', 'In', 'Sr')\",OTHERS,False\nmp-1212460,\"{'H': 2.0, 'I': 2.0, 'N': 4.0}\",\"('H', 'I', 'N')\",OTHERS,False\nmp-1101509,\"{'Li': 1.0, 'Mo': 1.0, 'P': 2.0, 'O': 8.0}\",\"('Li', 'Mo', 'O', 'P')\",OTHERS,False\nmp-1182212,\"{'Ca': 16.0, 'Si': 8.0, 'O': 40.0}\",\"('Ca', 'O', 'Si')\",OTHERS,False\nmp-505731,\"{'O': 32.0, 'P': 8.0, 'Co': 4.0, 'Ba': 8.0}\",\"('Ba', 'Co', 'O', 'P')\",OTHERS,False\nmvc-13307,\"{'Y': 2.0, 'Mg': 1.0, 'Mn': 2.0, 'S': 2.0, 'O': 5.0}\",\"('Mg', 'Mn', 'O', 'S', 'Y')\",OTHERS,False\nmp-1219174,\"{'Sm': 1.0, 'Zn': 2.0, 'Ga': 2.0}\",\"('Ga', 'Sm', 'Zn')\",OTHERS,False\nmp-19058,\"{'Ba': 6.0, 'Co': 2.0, 'Ru': 4.0, 'O': 18.0}\",\"('Ba', 'Co', 'O', 'Ru')\",OTHERS,False\nmp-1228920,\"{'Cs': 2.0, 'Fe': 2.0, 'Cu': 2.0, 'F': 12.0}\",\"('Cs', 'Cu', 'F', 'Fe')\",OTHERS,False\nmp-32530,\"{'Li': 12.0, 'V': 8.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1008920,\"{'Mn': 2.0, 'Ge': 1.0}\",\"('Ge', 'Mn')\",OTHERS,False\nmp-27410,\"{'P': 3.0, 'Sn': 4.0}\",\"('P', 'Sn')\",OTHERS,False\nmp-1095969,\"{'Mn': 1.0, 'Nb': 2.0, 'Cr': 1.0}\",\"('Cr', 'Mn', 'Nb')\",OTHERS,False\nmp-1225885,\"{'Cu': 2.0, 'Ni': 1.0, 'Sn': 1.0, 'Se': 4.0}\",\"('Cu', 'Ni', 'Se', 'Sn')\",OTHERS,False\nmp-1247345,\"{'Ba': 6.0, 'Fe': 4.0, 'N': 8.0}\",\"('Ba', 'Fe', 'N')\",OTHERS,False\nmp-21065,\"{'Si': 4.0, 'P': 8.0}\",\"('P', 'Si')\",OTHERS,False\nmp-1103571,\"{'Al': 2.0, 'Si': 2.0, 'O': 11.0}\",\"('Al', 'O', 'Si')\",OTHERS,False\nmp-1095419,\"{'Ta': 4.0, 'Cr': 4.0, 'P': 4.0}\",\"('Cr', 'P', 'Ta')\",OTHERS,False\nmp-7574,\"{'B': 2.0, 'Al': 2.0, 'Mo': 2.0}\",\"('Al', 'B', 'Mo')\",OTHERS,False\nmp-26875,\"{'Li': 8.0, 'O': 16.0, 'P': 4.0, 'Cu': 4.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-1103857,\"{'U': 2.0, 'Mn': 8.0, 'P': 4.0}\",\"('Mn', 'P', 'U')\",OTHERS,False\nmp-1217081,\"{'Ti': 3.0, 'Al': 5.0}\",\"('Al', 'Ti')\",OTHERS,False\nmp-758997,\"{'Li': 3.0, 'Cu': 3.0, 'C': 3.0, 'O': 9.0}\",\"('C', 'Cu', 'Li', 'O')\",OTHERS,False\nmp-1182130,\"{'C': 6.0, 'I': 2.0, 'N': 2.0}\",\"('C', 'I', 'N')\",OTHERS,False\nmp-1226793,\"{'Ce': 4.0, 'Ni': 8.0, 'B': 12.0}\",\"('B', 'Ce', 'Ni')\",OTHERS,False\nmp-542181,\"{'Eu': 2.0, 'K': 6.0, 'V': 4.0, 'O': 16.0}\",\"('Eu', 'K', 'O', 'V')\",OTHERS,False\nmp-864991,\"{'Ca': 2.0, 'Ag': 1.0, 'Pb': 1.0}\",\"('Ag', 'Ca', 'Pb')\",OTHERS,False\nmp-504495,\"{'Na': 4.0, 'Zn': 4.0, 'Mo': 4.0, 'O': 20.0, 'H': 4.0}\",\"('H', 'Mo', 'Na', 'O', 'Zn')\",OTHERS,False\nmp-653559,\"{'P': 16.0, 'Se': 12.0, 'I': 8.0}\",\"('I', 'P', 'Se')\",OTHERS,False\nmp-23996,\"{'H': 16.0, 'Be': 2.0, 'O': 16.0, 'S': 2.0}\",\"('Be', 'H', 'O', 'S')\",OTHERS,False\nmp-570527,\"{'Yb': 20.0, 'Au': 16.0}\",\"('Au', 'Yb')\",OTHERS,False\nmp-1249385,\"{'Ba': 2.0, 'Ti': 2.0, 'Al': 1.0, 'Tl': 1.0, 'O': 7.0}\",\"('Al', 'Ba', 'O', 'Ti', 'Tl')\",OTHERS,False\nmp-1206002,\"{'Er': 6.0, 'Fe': 1.0, 'Bi': 2.0}\",\"('Bi', 'Er', 'Fe')\",OTHERS,False\nmp-581681,\"{'Ca': 42.0, 'Mn': 8.0, 'Sb': 36.0}\",\"('Ca', 'Mn', 'Sb')\",OTHERS,False\nmp-1206973,\"{'Fe': 2.0, 'Co': 2.0, 'Ge': 2.0}\",\"('Co', 'Fe', 'Ge')\",OTHERS,False\nmp-1218764,\"{'Sr': 2.0, 'Tb': 1.0, 'Cu': 2.0, 'Ir': 1.0, 'O': 8.0}\",\"('Cu', 'Ir', 'O', 'Sr', 'Tb')\",OTHERS,False\nmp-554747,\"{'Li': 20.0, 'La': 12.0, 'Nb': 8.0, 'O': 48.0}\",\"('La', 'Li', 'Nb', 'O')\",OTHERS,False\nmp-1187264,\"{'Tb': 2.0, 'Ni': 1.0, 'Ir': 1.0}\",\"('Ir', 'Ni', 'Tb')\",OTHERS,False\nmvc-16518,\"{'Ca': 4.0, 'Ta': 4.0, 'Cr': 2.0, 'O': 16.0}\",\"('Ca', 'Cr', 'O', 'Ta')\",OTHERS,False\nmp-1227730,\"{'Ca': 4.0, 'Fe': 3.0, 'As': 8.0, 'Pt': 1.0}\",\"('As', 'Ca', 'Fe', 'Pt')\",OTHERS,False\nmp-1184192,\"{'Er': 6.0, 'Tm': 2.0}\",\"('Er', 'Tm')\",OTHERS,False\nmp-505262,\"{'Gd': 2.0, 'Se': 2.0, 'Cu': 2.0, 'O': 2.0}\",\"('Cu', 'Gd', 'O', 'Se')\",OTHERS,False\nmp-1174519,\"{'Li': 5.0, 'Mn': 3.0, 'O': 8.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-569696,\"{'La': 1.0, 'Al': 2.0, 'Ga': 2.0}\",\"('Al', 'Ga', 'La')\",OTHERS,False\nmp-1184692,\"{'Ho': 2.0, 'Zn': 1.0, 'Pt': 1.0}\",\"('Ho', 'Pt', 'Zn')\",OTHERS,False\nmp-772617,\"{'Zn': 4.0, 'N': 8.0, 'O': 24.0}\",\"('N', 'O', 'Zn')\",OTHERS,False\nmp-1210330,\"{'Na': 8.0, 'Zr': 4.0, 'Ge': 10.0, 'H': 2.0, 'O': 32.0}\",\"('Ge', 'H', 'Na', 'O', 'Zr')\",OTHERS,False\nmp-29743,\"{'O': 56.0, 'Sr': 20.0, 'U': 12.0}\",\"('O', 'Sr', 'U')\",OTHERS,False\nmp-1210278,\"{'Pu': 8.0, 'Mo': 16.0, 'O': 64.0}\",\"('Mo', 'O', 'Pu')\",OTHERS,False\nmp-1203156,\"{'Y': 8.0, 'Cd': 8.0, 'Bi': 8.0, 'O': 32.0}\",\"('Bi', 'Cd', 'O', 'Y')\",OTHERS,False\nmp-1205834,\"{'Er': 2.0, 'Al': 4.0, 'Ni': 1.0, 'Ge': 2.0}\",\"('Al', 'Er', 'Ge', 'Ni')\",OTHERS,False\nmp-4007,\"{'Si': 2.0, 'Ni': 2.0, 'Nd': 1.0}\",\"('Nd', 'Ni', 'Si')\",OTHERS,False\nmp-1018900,\"{'Pr': 2.0, 'Cu': 2.0, 'Pb': 2.0}\",\"('Cu', 'Pb', 'Pr')\",OTHERS,False\nmp-1228832,\"{'Al': 1.0, 'Ni': 6.0, 'Ge': 1.0}\",\"('Al', 'Ge', 'Ni')\",OTHERS,False\nmp-1205555,\"{'U': 2.0, 'Si': 2.0, 'Os': 4.0, 'C': 2.0}\",\"('C', 'Os', 'Si', 'U')\",OTHERS,False\nmp-1178538,\"{'Ba': 4.0, 'Ca': 4.0, 'I': 16.0}\",\"('Ba', 'Ca', 'I')\",OTHERS,False\nmp-754942,\"{'Sr': 2.0, 'Sm': 4.0, 'O': 8.0}\",\"('O', 'Sm', 'Sr')\",OTHERS,False\nmp-652101,\"{'Zn': 20.0, 'Fe': 2.0, 'B': 16.0, 'Rh': 36.0}\",\"('B', 'Fe', 'Rh', 'Zn')\",OTHERS,False\nmp-972472,\"{'Sn': 1.0, 'As': 3.0}\",\"('As', 'Sn')\",OTHERS,False\nmp-769626,\"{'Li': 4.0, 'Mn': 2.0, 'Nb': 3.0, 'Fe': 3.0, 'O': 16.0}\",\"('Fe', 'Li', 'Mn', 'Nb', 'O')\",OTHERS,False\nmp-573051,\"{'Re': 6.0, 'O': 18.0, 'F': 6.0}\",\"('F', 'O', 'Re')\",OTHERS,False\nmp-1186693,\"{'Pr': 2.0, 'Mg': 1.0, 'Cd': 1.0}\",\"('Cd', 'Mg', 'Pr')\",OTHERS,False\nmp-2503,\"{'Se': 8.0, 'Pd': 14.0}\",\"('Pd', 'Se')\",OTHERS,False\nmp-1186346,\"{'Np': 1.0, 'Sn': 1.0, 'O': 3.0}\",\"('Np', 'O', 'Sn')\",OTHERS,False\nmp-1196261,\"{'Sb': 8.0, 'Xe': 4.0, 'F': 38.0}\",\"('F', 'Sb', 'Xe')\",OTHERS,False\nmp-1103729,\"{'Na': 1.0, 'Ge': 6.0, 'As': 6.0}\",\"('As', 'Ge', 'Na')\",OTHERS,False\nmp-510728,\"{'Co': 1.0, 'Mn': 1.0, 'N': 12.0, 'H': 18.0, 'C': 6.0}\",\"('C', 'Co', 'H', 'Mn', 'N')\",OTHERS,False\nmp-765725,\"{'Na': 3.0, 'Li': 5.0, 'Mn': 5.0, 'O': 9.0}\",\"('Li', 'Mn', 'Na', 'O')\",OTHERS,False\nmp-756075,\"{'Cr': 1.0, 'Te': 1.0, 'W': 2.0, 'O': 12.0}\",\"('Cr', 'O', 'Te', 'W')\",OTHERS,False\nmp-722344,\"{'K': 1.0, 'Ca': 4.0, 'B': 22.0, 'H': 18.0, 'Cl': 1.0, 'O': 46.0}\",\"('B', 'Ca', 'Cl', 'H', 'K', 'O')\",OTHERS,False\nmp-362,\"{'Ni': 6.0, 'S': 4.0}\",\"('Ni', 'S')\",OTHERS,False\nmp-1181143,\"{'K': 4.0, 'Tb': 4.0, 'N': 16.0, 'O': 56.0}\",\"('K', 'N', 'O', 'Tb')\",OTHERS,False\nmp-1094552,\"{'Mg': 6.0, 'Sb': 6.0}\",\"('Mg', 'Sb')\",OTHERS,False\nmp-1219321,\"{'Sm': 4.0, 'Cr': 1.0, 'Fe': 33.0}\",\"('Cr', 'Fe', 'Sm')\",OTHERS,False\nmp-1178360,\"{'Dy': 4.0, 'In': 4.0, 'O': 12.0}\",\"('Dy', 'In', 'O')\",OTHERS,False\nmp-1218644,\"{'Sr': 5.0, 'Ca': 1.0, 'Cu': 4.0, 'Bi': 4.0, 'O': 8.0}\",\"('Bi', 'Ca', 'Cu', 'O', 'Sr')\",OTHERS,False\nmp-1029125,\"{'Te': 2.0, 'Mo': 1.0, 'W': 3.0, 'Se': 2.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-753120,\"{'W': 4.0, 'O': 4.0, 'F': 16.0}\",\"('F', 'O', 'W')\",OTHERS,False\nmp-29151,\"{'Li': 5.0, 'N': 1.0, 'Cl': 2.0}\",\"('Cl', 'Li', 'N')\",OTHERS,False\nmp-759214,\"{'Co': 4.0, 'P': 12.0, 'O': 36.0}\",\"('Co', 'O', 'P')\",OTHERS,False\nmp-569913,\"{'Ce': 6.0, 'Si': 2.0, 'Pt': 10.0}\",\"('Ce', 'Pt', 'Si')\",OTHERS,False\nmp-1220045,\"{'Os': 3.0, 'W': 3.0, 'C': 2.0}\",\"('C', 'Os', 'W')\",OTHERS,False\nmp-1245610,\"{'Si': 12.0, 'Sb': 20.0, 'N': 36.0}\",\"('N', 'Sb', 'Si')\",OTHERS,False\nmp-1225095,\"{'H': 4.0, 'Ru': 4.0, 'Rh': 4.0, 'C': 24.0, 'O': 24.0}\",\"('C', 'H', 'O', 'Rh', 'Ru')\",OTHERS,False\nmp-541870,\"{'K': 4.0, 'Al': 8.0, 'P': 8.0, 'O': 44.0, 'H': 20.0}\",\"('Al', 'H', 'K', 'O', 'P')\",OTHERS,False\nmp-714885,\"{'V': 2.0, 'O': 2.0}\",\"('O', 'V')\",OTHERS,False\nmp-1038709,\"{'Cs': 1.0, 'Mg': 30.0, 'Ga': 1.0, 'O': 32.0}\",\"('Cs', 'Ga', 'Mg', 'O')\",OTHERS,False\nmp-743603,\"{'Li': 3.0, 'Ti': 3.0, 'Fe': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Fe', 'Li', 'O', 'P', 'Ti')\",OTHERS,False\nmp-1100586,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1179515,\"{'Sc': 6.0, 'Ru': 2.0, 'C': 8.0}\",\"('C', 'Ru', 'Sc')\",OTHERS,False\nmp-1223486,\"{'La': 18.0, 'Al': 10.0, 'Se': 42.0}\",\"('Al', 'La', 'Se')\",OTHERS,False\nmp-770831,\"{'Na': 6.0, 'Co': 2.0, 'B': 2.0, 'S': 2.0, 'O': 14.0}\",\"('B', 'Co', 'Na', 'O', 'S')\",OTHERS,False\nmp-11734,\"{'O': 6.0, 'Mg': 1.0, 'Sr': 2.0, 'Re': 1.0}\",\"('Mg', 'O', 'Re', 'Sr')\",OTHERS,False\nmp-1193559,\"{'Rb': 4.0, 'S': 4.0, 'N': 4.0, 'O': 16.0}\",\"('N', 'O', 'Rb', 'S')\",OTHERS,False\nmp-764980,\"{'Li': 4.0, 'Mn': 4.0, 'F': 20.0}\",\"('F', 'Li', 'Mn')\",OTHERS,False\nmp-778364,\"{'Na': 12.0, 'Ti': 4.0, 'O': 14.0}\",\"('Na', 'O', 'Ti')\",OTHERS,False\nmp-8176,\"{'O': 2.0, 'Rb': 1.0, 'Tl': 1.0}\",\"('O', 'Rb', 'Tl')\",OTHERS,False\nmp-1196125,\"{'Si': 14.0, 'H': 84.0, 'C': 28.0}\",\"('C', 'H', 'Si')\",OTHERS,False\nmp-690435,\"{'Li': 7.0, 'Mn': 11.0, 'O': 24.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-5900,\"{'F': 32.0, 'Zr': 4.0, 'Ba': 8.0}\",\"('Ba', 'F', 'Zr')\",OTHERS,False\nmp-775804,\"{'Li': 12.0, 'Sb': 4.0, 'S': 12.0}\",\"('Li', 'S', 'Sb')\",OTHERS,False\nmp-29598,\"{'N': 14.0, 'Na': 2.0, 'P': 8.0}\",\"('N', 'Na', 'P')\",OTHERS,False\nmp-1080277,\"{'Ce': 6.0, 'Se': 12.0}\",\"('Ce', 'Se')\",OTHERS,False\nmp-1185851,\"{'Mg': 2.0, 'C': 2.0}\",\"('C', 'Mg')\",OTHERS,False\nmp-761745,\"{'Sr': 2.0, 'Cd': 4.0, 'H': 32.0, 'Br': 12.0, 'O': 16.0}\",\"('Br', 'Cd', 'H', 'O', 'Sr')\",OTHERS,False\nmp-2201,\"{'Se': 1.0, 'Pb': 1.0}\",\"('Pb', 'Se')\",OTHERS,False\nmp-1227444,\"{'Bi': 1.0, 'Pb': 1.0, 'Br': 1.0, 'O': 2.0}\",\"('Bi', 'Br', 'O', 'Pb')\",OTHERS,False\nmp-755409,\"{'Li': 5.0, 'Mn': 2.0, 'Cu': 3.0, 'O': 10.0}\",\"('Cu', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-510262,\"{'O': 2.0, 'Cs': 6.0}\",\"('Cs', 'O')\",OTHERS,False\nmp-1195553,\"{'Zr': 2.0, 'Zn': 40.0, 'Fe': 4.0}\",\"('Fe', 'Zn', 'Zr')\",OTHERS,False\nmp-1226753,\"{'Cd': 1.0, 'Bi': 1.0, 'I': 1.0, 'O': 2.0}\",\"('Bi', 'Cd', 'I', 'O')\",OTHERS,False\nmp-693655,\"{'Ba': 4.0, 'Sm': 7.0, 'Si': 12.0, 'B': 1.0, 'N': 27.0}\",\"('B', 'Ba', 'N', 'Si', 'Sm')\",OTHERS,False\nmp-760261,\"{'Li': 12.0, 'Cr': 4.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Cr', 'Li', 'O')\",OTHERS,False\nmp-1215547,\"{'Zn': 3.0, 'Cr': 8.0, 'Fe': 1.0, 'S': 16.0}\",\"('Cr', 'Fe', 'S', 'Zn')\",OTHERS,False\nmp-540610,\"{'K': 4.0, 'U': 14.0, 'O': 44.0}\",\"('K', 'O', 'U')\",OTHERS,False\nmp-24055,\"{'H': 24.0, 'N': 24.0, 'O': 32.0, 'K': 4.0, 'Co': 4.0}\",\"('Co', 'H', 'K', 'N', 'O')\",OTHERS,False\nmp-22711,\"{'Al': 14.0, 'Gd': 2.0, 'Au': 6.0}\",\"('Al', 'Au', 'Gd')\",OTHERS,False\nmp-1205588,\"{'Mn': 6.0, 'Ge': 2.0, 'N': 2.0}\",\"('Ge', 'Mn', 'N')\",OTHERS,False\nmp-627291,\"{'V': 6.0, 'H': 12.0, 'O': 20.0}\",\"('H', 'O', 'V')\",OTHERS,False\nmp-1237686,\"{'Rb': 8.0, 'Al': 4.0, 'H': 16.0, 'N': 8.0}\",\"('Al', 'H', 'N', 'Rb')\",OTHERS,False\nmp-1079888,\"{'Ti': 4.0, 'Se': 4.0}\",\"('Se', 'Ti')\",OTHERS,False\nmp-29412,\"{'Br': 28.0, 'Ta': 12.0}\",\"('Br', 'Ta')\",OTHERS,False\nmp-4736,\"{'O': 56.0, 'P': 20.0, 'Nd': 4.0}\",\"('Nd', 'O', 'P')\",OTHERS,False\nmp-18998,\"{'Sr': 16.0, 'Mn': 12.0, 'O': 40.0}\",\"('Mn', 'O', 'Sr')\",OTHERS,False\nmp-4495,\"{'Li': 2.0, 'K': 2.0, 'Te': 2.0}\",\"('K', 'Li', 'Te')\",OTHERS,False\nmp-774722,\"{'Li': 1.0, 'La': 20.0, 'Cu': 9.0, 'O': 40.0}\",\"('Cu', 'La', 'Li', 'O')\",OTHERS,False\nmp-1187727,\"{'Yb': 1.0, 'Eu': 1.0, 'Zn': 2.0}\",\"('Eu', 'Yb', 'Zn')\",OTHERS,False\nmp-1210713,\"{'Mn': 13.0, 'Fe': 1.0, 'Si': 2.0, 'O': 22.0}\",\"('Fe', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-980049,\"{'Yb': 3.0, 'Re': 1.0}\",\"('Re', 'Yb')\",OTHERS,False\nmp-18282,\"{'O': 16.0, 'Sc': 2.0, 'Zn': 2.0, 'Mo': 6.0}\",\"('Mo', 'O', 'Sc', 'Zn')\",OTHERS,False\nmp-531213,\"{'Ca': 13.0, 'F': 43.0, 'Y': 6.0}\",\"('Ca', 'F', 'Y')\",OTHERS,False\nmp-757595,\"{'H': 24.0, 'S': 8.0, 'N': 8.0, 'O': 24.0}\",\"('H', 'N', 'O', 'S')\",OTHERS,False\nmp-1008865,\"{'Nd': 1.0, 'Cd': 2.0}\",\"('Cd', 'Nd')\",OTHERS,False\nmp-1097314,\"{'Mn': 1.0, 'Ga': 1.0, 'Fe': 2.0}\",\"('Fe', 'Ga', 'Mn')\",OTHERS,False\nmp-1221406,\"{'Na': 3.0, 'Eu': 3.0, 'F': 12.0}\",\"('Eu', 'F', 'Na')\",OTHERS,False\nmp-1201218,\"{'Si': 18.0, 'C': 16.0, 'N': 2.0, 'O': 38.0}\",\"('C', 'N', 'O', 'Si')\",OTHERS,False\nmp-1187561,\"{'Yb': 1.0, 'Ga': 1.0, 'O': 3.0}\",\"('Ga', 'O', 'Yb')\",OTHERS,False\nmp-1072492,\"{'Ca': 2.0, 'C': 4.0}\",\"('C', 'Ca')\",OTHERS,False\nmp-1205778,\"{'Ho': 3.0, 'Cd': 3.0, 'Au': 3.0}\",\"('Au', 'Cd', 'Ho')\",OTHERS,False\nmp-776576,\"{'Li': 2.0, 'Ti': 2.0, 'Mn': 1.0, 'Cr': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Cr', 'Li', 'Mn', 'O', 'P', 'Ti')\",OTHERS,False\nmp-1223695,\"{'In': 1.0, 'Sn': 1.0, 'Te': 2.0}\",\"('In', 'Sn', 'Te')\",OTHERS,False\nmp-1001835,\"{'Li': 2.0, 'B': 2.0}\",\"('B', 'Li')\",OTHERS,False\nmp-973895,\"{'Ni': 3.0, 'H': 1.0}\",\"('H', 'Ni')\",OTHERS,False\nmp-999263,\"{'Rh': 2.0, 'N': 2.0}\",\"('N', 'Rh')\",OTHERS,False\nmp-1114653,\"{'K': 1.0, 'Rb': 2.0, 'Al': 1.0, 'Cl': 6.0}\",\"('Al', 'Cl', 'K', 'Rb')\",OTHERS,False\nmp-1094554,\"{'Mg': 8.0, 'Sb': 4.0}\",\"('Mg', 'Sb')\",OTHERS,False\nmp-34501,\"{'Fe': 4.0, 'Co': 2.0, 'O': 8.0}\",\"('Co', 'Fe', 'O')\",OTHERS,False\nmp-1025347,\"{'Ti': 2.0, 'Co': 1.0, 'Se': 4.0}\",\"('Co', 'Se', 'Ti')\",OTHERS,False\nmp-556458,\"{'Ba': 3.0, 'Ca': 3.0, 'C': 6.0, 'O': 18.0}\",\"('Ba', 'C', 'Ca', 'O')\",OTHERS,False\nmp-757826,\"{'Cs': 1.0, 'Ca': 1.0, 'N': 3.0, 'O': 6.0}\",\"('Ca', 'Cs', 'N', 'O')\",OTHERS,False\nmp-1223419,\"{'K': 2.0, 'Si': 10.0, 'B': 2.0, 'O': 26.0}\",\"('B', 'K', 'O', 'Si')\",OTHERS,False\nmp-1096027,\"{'Ti': 2.0, 'Zn': 1.0, 'Rh': 1.0}\",\"('Rh', 'Ti', 'Zn')\",OTHERS,False\nmp-1200006,\"{'Ag': 16.0, 'H': 128.0, 'C': 48.0, 'S': 32.0, 'N': 16.0, 'O': 48.0}\",\"('Ag', 'C', 'H', 'N', 'O', 'S')\",OTHERS,False\nmp-1210341,\"{'Na': 8.0, 'Al': 6.0, 'Si': 6.0, 'N': 2.0, 'O': 24.0}\",\"('Al', 'N', 'Na', 'O', 'Si')\",OTHERS,False\nmp-30843,\"{'Sr': 28.0, 'Pt': 12.0}\",\"('Pt', 'Sr')\",OTHERS,False\nmp-556201,\"{'Ag': 2.0, 'Sb': 2.0, 'C': 4.0, 'N': 4.0, 'Cl': 4.0, 'F': 12.0}\",\"('Ag', 'C', 'Cl', 'F', 'N', 'Sb')\",OTHERS,False\nmvc-15475,\"{'Al': 1.0, 'Ag': 1.0, 'O': 3.0}\",\"('Ag', 'Al', 'O')\",OTHERS,False\nmp-1208552,\"{'Tb': 4.0, 'Al': 18.0, 'Ir': 6.0}\",\"('Al', 'Ir', 'Tb')\",OTHERS,False\nmp-665747,\"{'Eu': 4.0, 'Ag': 4.0}\",\"('Ag', 'Eu')\",OTHERS,False\nmp-6425,\"{'Li': 6.0, 'O': 24.0, 'P': 6.0, 'In': 4.0}\",\"('In', 'Li', 'O', 'P')\",OTHERS,False\nmp-759823,\"{'Li': 12.0, 'Cu': 12.0, 'P': 12.0, 'O': 48.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-27556,\"{'O': 24.0, 'Nb': 8.0, 'Cs': 8.0}\",\"('Cs', 'Nb', 'O')\",OTHERS,False\nmp-27760,\"{'Pr': 2.0, 'Si': 4.0}\",\"('Pr', 'Si')\",OTHERS,False\nmp-28645,\"{'N': 2.0, 'Ca': 2.0, 'Ni': 2.0}\",\"('Ca', 'N', 'Ni')\",OTHERS,False\nmp-753239,\"{'Li': 2.0, 'Sn': 2.0, 'P': 2.0, 'H': 2.0, 'O': 10.0}\",\"('H', 'Li', 'O', 'P', 'Sn')\",OTHERS,False\nmp-755944,\"{'Li': 3.0, 'Fe': 2.0, 'Co': 2.0, 'O': 8.0}\",\"('Co', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-646609,\"{'U': 4.0, 'In': 2.0, 'Rh': 4.0}\",\"('In', 'Rh', 'U')\",OTHERS,False\nmp-973738,\"{'Ge': 1.0, 'B': 1.0, 'O': 3.0}\",\"('B', 'Ge', 'O')\",OTHERS,False\nmp-1209298,\"{'Rb': 2.0, 'P': 30.0}\",\"('P', 'Rb')\",OTHERS,False\nmp-684642,\"{'S': 18.0, 'N': 18.0}\",\"('N', 'S')\",OTHERS,False\nmp-10209,\"{'Al': 3.0, 'Ni': 1.0, 'Ge': 2.0, 'Y': 3.0}\",\"('Al', 'Ge', 'Ni', 'Y')\",OTHERS,False\nmp-12569,\"{'B': 16.0, 'Pr': 4.0}\",\"('B', 'Pr')\",OTHERS,False\nmp-1223708,\"{'K': 2.0, 'Mo': 8.0, 'O': 13.0}\",\"('K', 'Mo', 'O')\",OTHERS,False\nmp-560060,\"{'Nd': 2.0, 'Sn': 4.0, 'P': 20.0, 'Cl': 74.0, 'O': 24.0}\",\"('Cl', 'Nd', 'O', 'P', 'Sn')\",OTHERS,False\nmp-571079,\"{'Tl': 2.0, 'Cl': 2.0}\",\"('Cl', 'Tl')\",OTHERS,False\nmp-1199182,\"{'K': 8.0, 'Cu': 16.0, 'Sb': 8.0, 'Se': 24.0}\",\"('Cu', 'K', 'Sb', 'Se')\",OTHERS,False\nmp-1076681,\"{'Ba': 1.0, 'Sr': 7.0, 'Co': 6.0, 'Cu': 2.0, 'O': 24.0}\",\"('Ba', 'Co', 'Cu', 'O', 'Sr')\",OTHERS,False\nmp-1019091,\"{'La': 2.0, 'Se': 4.0}\",\"('La', 'Se')\",OTHERS,False\nmp-645158,\"{'La': 4.0, 'Mn': 4.0, 'O': 12.0}\",\"('La', 'Mn', 'O')\",OTHERS,False\nmvc-5781,\"{'Zn': 4.0, 'Ir': 2.0, 'W': 2.0, 'O': 12.0}\",\"('Ir', 'O', 'W', 'Zn')\",OTHERS,False\nmp-974417,\"{'Re': 3.0, 'Sb': 1.0}\",\"('Re', 'Sb')\",OTHERS,False\nmp-12651,\"{'Mg': 4.0, 'Pt': 4.0}\",\"('Mg', 'Pt')\",OTHERS,False\nmp-1038767,\"{'Mg': 4.0, 'Cd': 4.0}\",\"('Cd', 'Mg')\",OTHERS,False\nmp-1179617,\"{'W': 17.0, 'O': 47.0}\",\"('O', 'W')\",OTHERS,False\nmvc-10584,\"{'Ca': 2.0, 'V': 6.0, 'P': 6.0, 'O': 26.0}\",\"('Ca', 'O', 'P', 'V')\",OTHERS,False\nmvc-9655,\"{'Pr': 2.0, 'W': 4.0, 'O': 12.0}\",\"('O', 'Pr', 'W')\",OTHERS,False\nmp-635469,\"{'Ce': 2.0, 'Cu': 2.0, 'S': 2.0, 'O': 2.0}\",\"('Ce', 'Cu', 'O', 'S')\",OTHERS,False\nmp-770815,\"{'Cs': 12.0, 'Y': 4.0, 'O': 12.0}\",\"('Cs', 'O', 'Y')\",OTHERS,False\nmp-752655,\"{'Li': 4.0, 'Nb': 2.0, 'Fe': 3.0, 'Ni': 3.0, 'O': 16.0}\",\"('Fe', 'Li', 'Nb', 'Ni', 'O')\",OTHERS,False\nmp-1225750,\"{'Er': 2.0, 'Si': 3.0}\",\"('Er', 'Si')\",OTHERS,False\nmp-669435,\"{'K': 4.0, 'La': 4.0, 'C': 4.0, 'O': 16.0}\",\"('C', 'K', 'La', 'O')\",OTHERS,False\nmp-555835,\"{'Gd': 12.0, 'Sc': 8.0, 'Ga': 12.0, 'O': 48.0}\",\"('Ga', 'Gd', 'O', 'Sc')\",OTHERS,False\nmp-1077593,\"{'Sm': 2.0, 'Sn': 4.0}\",\"('Sm', 'Sn')\",OTHERS,False\nmp-849295,\"{'Fe': 2.0, 'B': 2.0, 'O': 6.0}\",\"('B', 'Fe', 'O')\",OTHERS,False\nmp-6331,\"{'B': 8.0, 'O': 28.0, 'Na': 8.0, 'Y': 8.0}\",\"('B', 'Na', 'O', 'Y')\",OTHERS,False\nmp-1185369,\"{'Li': 1.0, 'Lu': 1.0, 'In': 2.0}\",\"('In', 'Li', 'Lu')\",OTHERS,False\nmp-1213040,\"{'Er': 1.0, 'Mn': 4.0, 'Cu': 3.0, 'O': 12.0}\",\"('Cu', 'Er', 'Mn', 'O')\",OTHERS,False\nmp-555273,\"{'Ca': 12.0, 'Ge': 4.0, 'Cl': 8.0, 'O': 16.0}\",\"('Ca', 'Cl', 'Ge', 'O')\",OTHERS,False\nmp-1201508,\"{'K': 16.0, 'Si': 16.0}\",\"('K', 'Si')\",OTHERS,False\nmp-684969,\"{'Sc': 4.0, 'S': 6.0}\",\"('S', 'Sc')\",OTHERS,False\nmp-758790,\"{'Li': 4.0, 'Fe': 4.0, 'P': 16.0, 'O': 48.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-1208093,\"{'V': 2.0, 'Cu': 3.0, 'O': 11.0}\",\"('Cu', 'O', 'V')\",OTHERS,False\nmp-540482,\"{'Li': 2.0, 'Cr': 2.0, 'P': 4.0, 'O': 14.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-684606,\"{'Cu': 27.0, 'Se': 20.0}\",\"('Cu', 'Se')\",OTHERS,False\nmp-773139,\"{'Li': 4.0, 'V': 3.0, 'Sb': 5.0, 'O': 16.0}\",\"('Li', 'O', 'Sb', 'V')\",OTHERS,False\nmp-1006330,\"{'Ce': 3.0, 'Cu': 1.0}\",\"('Ce', 'Cu')\",OTHERS,False\nmp-626644,\"{'Ca': 1.0, 'H': 2.0, 'O': 2.0}\",\"('Ca', 'H', 'O')\",OTHERS,False\nmp-1094383,\"{'Mg': 4.0, 'Ti': 4.0}\",\"('Mg', 'Ti')\",OTHERS,False\nmp-1074645,\"{'Mg': 8.0, 'Si': 4.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1193092,\"{'Er': 2.0, 'Be': 26.0}\",\"('Be', 'Er')\",OTHERS,False\nmp-977534,\"{'Zr': 1.0, 'Nb': 1.0, 'Tc': 2.0}\",\"('Nb', 'Tc', 'Zr')\",OTHERS,False\nmp-1192121,\"{'Tb': 4.0, 'Re': 4.0, 'B': 16.0}\",\"('B', 'Re', 'Tb')\",OTHERS,False\nmp-977012,\"{'H': 6.0, 'Pb': 1.0, 'C': 1.0, 'Br': 3.0, 'N': 1.0}\",\"('Br', 'C', 'H', 'N', 'Pb')\",OTHERS,False\nmp-542314,\"{'Nd': 2.0, 'Cu': 2.0, 'S': 2.0, 'O': 2.0}\",\"('Cu', 'Nd', 'O', 'S')\",OTHERS,False\nmp-1174327,\"{'Li': 8.0, 'Co': 6.0, 'O': 14.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-1100768,\"{'Tb': 1.0, 'Cd': 1.0, 'Hg': 2.0}\",\"('Cd', 'Hg', 'Tb')\",OTHERS,False\nmp-1018737,\"{'K': 2.0, 'Mg': 2.0, 'P': 2.0}\",\"('K', 'Mg', 'P')\",OTHERS,False\nmp-1222593,\"{'Lu': 3.0, 'Mn': 1.0, 'Fe': 3.0, 'O': 10.0}\",\"('Fe', 'Lu', 'Mn', 'O')\",OTHERS,False\nmp-1247078,\"{'Mg': 2.0, 'Ga': 1.0, 'W': 3.0, 'S': 8.0}\",\"('Ga', 'Mg', 'S', 'W')\",OTHERS,False\nmp-1183230,\"{'Ac': 1.0, 'Sm': 3.0}\",\"('Ac', 'Sm')\",OTHERS,False\nmp-1193901,\"{'Ho': 4.0, 'Mo': 4.0, 'Br': 4.0, 'O': 16.0}\",\"('Br', 'Ho', 'Mo', 'O')\",OTHERS,False\nmp-753231,\"{'Li': 2.0, 'V': 4.0, 'O': 3.0, 'F': 8.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-20106,\"{'Li': 4.0, 'Sb': 4.0, 'Nd': 2.0}\",\"('Li', 'Nd', 'Sb')\",OTHERS,False\nmp-977550,\"{'Zr': 1.0, 'Sc': 1.0, 'Os': 2.0}\",\"('Os', 'Sc', 'Zr')\",OTHERS,False\nmp-29665,\"{'Sb': 3.0, 'Au': 1.0}\",\"('Au', 'Sb')\",OTHERS,False\nmp-1219165,\"{'Sm': 4.0, 'Fe': 3.0, 'Co': 1.0, 'As': 4.0, 'O': 4.0}\",\"('As', 'Co', 'Fe', 'O', 'Sm')\",OTHERS,False\nmp-639160,\"{'Gd': 4.0, 'In': 2.0, 'Ge': 4.0}\",\"('Gd', 'Ge', 'In')\",OTHERS,False\nmp-1186935,\"{'Sc': 2.0, 'Cd': 1.0, 'Os': 1.0}\",\"('Cd', 'Os', 'Sc')\",OTHERS,False\nmp-1246245,\"{'Ba': 8.0, 'Fe': 3.0, 'N': 8.0}\",\"('Ba', 'Fe', 'N')\",OTHERS,False\nmp-1193953,\"{'U': 4.0, 'N': 4.0, 'F': 20.0}\",\"('F', 'N', 'U')\",OTHERS,False\nmp-22339,\"{'Co': 3.0, 'Sn': 3.0, 'Th': 3.0}\",\"('Co', 'Sn', 'Th')\",OTHERS,False\nmp-1096227,\"{'Ca': 1.0, 'Cd': 2.0, 'In': 1.0}\",\"('Ca', 'Cd', 'In')\",OTHERS,False\nmp-1215860,\"{'Yb': 1.0, 'Lu': 2.0, 'Se': 4.0}\",\"('Lu', 'Se', 'Yb')\",OTHERS,False\nmp-1213479,\"{'Dy': 4.0, 'Cu': 2.0, 'W': 8.0, 'O': 32.0}\",\"('Cu', 'Dy', 'O', 'W')\",OTHERS,False\nmp-17623,\"{'Si': 4.0, 'Co': 18.0, 'Sm': 2.0}\",\"('Co', 'Si', 'Sm')\",OTHERS,False\nmp-1218040,\"{'Sr': 2.0, 'Nd': 2.0, 'Sc': 2.0, 'O': 8.0}\",\"('Nd', 'O', 'Sc', 'Sr')\",OTHERS,False\nmp-1225871,\"{'Cu': 8.0, 'Ge': 4.0, 'S': 12.0}\",\"('Cu', 'Ge', 'S')\",OTHERS,False\nmp-758355,\"{'Li': 8.0, 'V': 8.0, 'P': 8.0, 'O': 40.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmvc-3621,\"{'Mn': 4.0, 'Zn': 6.0, 'O': 14.0}\",\"('Mn', 'O', 'Zn')\",OTHERS,False\nmp-541582,\"{'Re': 4.0, 'Se': 8.0}\",\"('Re', 'Se')\",OTHERS,False\nmp-1188815,\"{'U': 4.0, 'Co': 2.0, 'S': 10.0}\",\"('Co', 'S', 'U')\",OTHERS,False\nmp-1176443,\"{'Mn': 4.0, 'Fe': 4.0, 'B': 4.0, 'O': 16.0}\",\"('B', 'Fe', 'Mn', 'O')\",OTHERS,False\nmp-570596,\"{'Nd': 4.0, 'Ni': 34.0}\",\"('Nd', 'Ni')\",OTHERS,False\nmp-1220390,\"{'Nb': 1.0, 'Au': 4.0}\",\"('Au', 'Nb')\",OTHERS,False\nmp-560570,\"{'Rb': 2.0, 'Na': 1.0, 'Al': 6.0, 'F': 21.0}\",\"('Al', 'F', 'Na', 'Rb')\",OTHERS,False\nmp-17891,\"{'O': 16.0, 'Na': 4.0, 'Si': 4.0, 'Y': 4.0}\",\"('Na', 'O', 'Si', 'Y')\",OTHERS,False\nmp-1226322,\"{'Cr': 4.0, 'In': 1.0, 'Ag': 1.0, 'Se': 8.0}\",\"('Ag', 'Cr', 'In', 'Se')\",OTHERS,False\nmp-1255465,\"{'Zn': 2.0, 'Cu': 2.0, 'Si': 4.0, 'O': 12.0}\",\"('Cu', 'O', 'Si', 'Zn')\",OTHERS,False\nmp-978101,\"{'Pr': 2.0, 'Fe': 2.0, 'Ge': 4.0}\",\"('Fe', 'Ge', 'Pr')\",OTHERS,False\nmp-14206,\"{'K': 3.0, 'As': 2.0, 'Ag': 3.0}\",\"('Ag', 'As', 'K')\",OTHERS,False\nmp-22747,\"{'C': 4.0, 'O': 8.0, 'Pb': 2.0}\",\"('C', 'O', 'Pb')\",OTHERS,False\nmp-532178,\"{'O': 48.0, 'Yb': 16.0, 'Zr': 12.0}\",\"('O', 'Yb', 'Zr')\",OTHERS,False\nmp-560189,\"{'Na': 8.0, 'Li': 16.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Li', 'Na', 'O')\",OTHERS,False\nmp-622930,\"{'Ba': 8.0, 'In': 12.0, 'O': 26.0}\",\"('Ba', 'In', 'O')\",OTHERS,False\nmp-1221141,\"{'Na': 5.0, 'Br': 4.0, 'Cl': 1.0}\",\"('Br', 'Cl', 'Na')\",OTHERS,False\nmp-1210580,\"{'Na': 2.0, 'Lu': 2.0, 'Re': 8.0, 'O': 40.0}\",\"('Lu', 'Na', 'O', 'Re')\",OTHERS,False\nmp-672315,\"{'P': 6.0, 'C': 12.0, 'S': 12.0, 'N': 18.0}\",\"('C', 'N', 'P', 'S')\",OTHERS,False\nmp-22136,\"{'O': 12.0, 'Co': 4.0, 'Ge': 4.0}\",\"('Co', 'Ge', 'O')\",OTHERS,False\nmp-30696,\"{'Cu': 8.0, 'Cd': 20.0}\",\"('Cd', 'Cu')\",OTHERS,False\nmp-1253602,\"{'Ba': 6.0, 'Al': 3.0, 'W': 6.0, 'F': 33.0}\",\"('Al', 'Ba', 'F', 'W')\",OTHERS,False\nmp-11558,\"{'Sc': 5.0, 'Re': 24.0}\",\"('Re', 'Sc')\",OTHERS,False\nmp-22652,\"{'Fe': 12.0, 'S': 12.0}\",\"('Fe', 'S')\",OTHERS,False\nmp-558911,\"{'Cs': 4.0, 'Co': 6.0, 'S': 8.0}\",\"('Co', 'Cs', 'S')\",OTHERS,False\nmp-759893,\"{'Mn': 12.0, 'O': 7.0, 'F': 17.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-558861,\"{'K': 4.0, 'I': 4.0, 'O': 8.0, 'F': 8.0}\",\"('F', 'I', 'K', 'O')\",OTHERS,False\nmp-1216890,\"{'Ti': 3.0, 'Ni': 3.0}\",\"('Ni', 'Ti')\",OTHERS,False\nmp-1199416,\"{'Eu': 8.0, 'Co': 4.0, 'Si': 12.0, 'O': 40.0}\",\"('Co', 'Eu', 'O', 'Si')\",OTHERS,False\nmvc-5860,\"{'Bi': 16.0, 'O': 32.0}\",\"('Bi', 'O')\",OTHERS,False\nmp-555559,\"{'Sm': 12.0, 'Nb': 4.0, 'Se': 12.0, 'O': 16.0}\",\"('Nb', 'O', 'Se', 'Sm')\",OTHERS,False\nmp-1174544,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-16488,\"{'Al': 18.0, 'Co': 4.0}\",\"('Al', 'Co')\",OTHERS,False\nmp-753298,\"{'Pr': 1.0, 'Y': 1.0, 'O': 2.0}\",\"('O', 'Pr', 'Y')\",OTHERS,False\nmp-1246650,\"{'Mn': 24.0, 'Se': 16.0, 'N': 16.0}\",\"('Mn', 'N', 'Se')\",OTHERS,False\nmp-1105794,\"{'Pu': 4.0, 'Si': 10.0, 'Pt': 6.0}\",\"('Pt', 'Pu', 'Si')\",OTHERS,False\nmp-849270,\"{'Ti': 34.0, 'N': 12.0, 'O': 48.0}\",\"('N', 'O', 'Ti')\",OTHERS,False\nmp-1187876,\"{'Y': 1.0, 'Rh': 2.0, 'Pb': 1.0}\",\"('Pb', 'Rh', 'Y')\",OTHERS,False\nmp-555282,\"{'Ga': 8.0, 'S': 20.0, 'N': 20.0, 'Cl': 28.0}\",\"('Cl', 'Ga', 'N', 'S')\",OTHERS,False\nmp-777339,\"{'Li': 2.0, 'V': 6.0, 'O': 2.0, 'F': 22.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-1114521,\"{'Rb': 2.0, 'Al': 1.0, 'In': 1.0, 'I': 6.0}\",\"('Al', 'I', 'In', 'Rb')\",OTHERS,False\nmp-1190610,\"{'Ho': 14.0, 'Te': 4.0, 'Au': 4.0}\",\"('Au', 'Ho', 'Te')\",OTHERS,False\nmp-569348,\"{'Tl': 8.0, 'Ga': 8.0, 'Br': 32.0}\",\"('Br', 'Ga', 'Tl')\",OTHERS,False\nmp-1028409,\"{'Mo': 3.0, 'W': 1.0, 'Se': 8.0}\",\"('Mo', 'Se', 'W')\",OTHERS,False\nmp-1264019,\"{'Mn': 8.0, 'Zn': 12.0, 'Si': 12.0, 'O': 48.0}\",\"('Mn', 'O', 'Si', 'Zn')\",OTHERS,False\nmp-1185613,\"{'Mg': 1.0, 'Tl': 1.0, 'O': 3.0}\",\"('Mg', 'O', 'Tl')\",OTHERS,False\nmp-1245870,\"{'Sc': 6.0, 'Si': 12.0, 'N': 22.0}\",\"('N', 'Sc', 'Si')\",OTHERS,False\nmp-1194659,\"{'Cs': 4.0, 'Nb': 4.0, 'V': 8.0, 'O': 32.0}\",\"('Cs', 'Nb', 'O', 'V')\",OTHERS,False\nmp-1206701,\"{'Sr': 2.0, 'Yb': 1.0, 'U': 1.0, 'O': 6.0}\",\"('O', 'Sr', 'U', 'Yb')\",OTHERS,False\nmp-567379,{'Bi': 4.0},\"('Bi',)\",OTHERS,False\nmp-1039255,\"{'Mg': 8.0, 'Cd': 4.0}\",\"('Cd', 'Mg')\",OTHERS,False\nmp-21083,\"{'O': 20.0, 'Co': 4.0, 'Ba': 4.0, 'Yb': 8.0}\",\"('Ba', 'Co', 'O', 'Yb')\",OTHERS,False\nmp-975395,\"{'Nd': 1.0, 'Mg': 16.0, 'Al': 12.0}\",\"('Al', 'Mg', 'Nd')\",OTHERS,False\nmp-6113,\"{'Li': 4.0, 'O': 20.0, 'Ti': 4.0, 'As': 4.0}\",\"('As', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-1216536,\"{'Tm': 1.0, 'Pa': 1.0, 'O': 4.0}\",\"('O', 'Pa', 'Tm')\",OTHERS,False\nmp-549389,\"{'Li': 4.0, 'O': 4.0, 'C': 1.0}\",\"('C', 'Li', 'O')\",OTHERS,False\nmp-1093564,\"{'Li': 1.0, 'Mg': 1.0, 'Ga': 2.0}\",\"('Ga', 'Li', 'Mg')\",OTHERS,False\nmp-557653,\"{'Si': 4.0, 'O': 8.0}\",\"('O', 'Si')\",OTHERS,False\nmp-1078874,\"{'Ba': 2.0, 'Ce': 1.0, 'Ta': 1.0, 'O': 6.0}\",\"('Ba', 'Ce', 'O', 'Ta')\",OTHERS,False\nmp-1194887,\"{'Ca': 32.0, 'As': 24.0}\",\"('As', 'Ca')\",OTHERS,False\nmp-1849,\"{'P': 3.0, 'Mn': 6.0}\",\"('Mn', 'P')\",OTHERS,False\nmp-1216722,\"{'Ti': 1.0, 'Mo': 1.0, 'O': 4.0}\",\"('Mo', 'O', 'Ti')\",OTHERS,False\nmp-998308,\"{'Cs': 1.0, 'Sc': 1.0, 'Br': 3.0}\",\"('Br', 'Cs', 'Sc')\",OTHERS,False\nmp-756463,\"{'Mn': 3.0, 'Sb': 1.0, 'P': 4.0, 'O': 16.0}\",\"('Mn', 'O', 'P', 'Sb')\",OTHERS,False\nmp-849268,\"{'Ni': 6.0, 'O': 2.0, 'F': 10.0}\",\"('F', 'Ni', 'O')\",OTHERS,False\nmp-975190,\"{'Rb': 1.0, 'Te': 1.0, 'O': 3.0}\",\"('O', 'Rb', 'Te')\",OTHERS,False\nmp-7219,\"{'Na': 4.0, 'Se': 6.0, 'Zr': 2.0}\",\"('Na', 'Se', 'Zr')\",OTHERS,False\nmp-1246886,\"{'Ta': 2.0, 'C': 12.0, 'N': 18.0}\",\"('C', 'N', 'Ta')\",OTHERS,False\nmp-1213497,\"{'Gd': 4.0, 'Ga': 18.0, 'Ir': 6.0}\",\"('Ga', 'Gd', 'Ir')\",OTHERS,False\nmp-1216457,\"{'V': 6.0, 'Co': 1.0, 'Rh': 1.0}\",\"('Co', 'Rh', 'V')\",OTHERS,False\nmp-757767,\"{'Li': 32.0, 'Mn': 8.0, 'P': 16.0, 'O': 72.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1227151,\"{'Ca': 2.0, 'Sm': 2.0, 'Mn': 4.0, 'O': 12.0}\",\"('Ca', 'Mn', 'O', 'Sm')\",OTHERS,False\nmp-1080041,\"{'Cs': 2.0, 'Na': 1.0, 'Bi': 1.0, 'Cl': 6.0}\",\"('Bi', 'Cl', 'Cs', 'Na')\",OTHERS,False\nmp-1197020,{'Ge': 34.0},\"('Ge',)\",OTHERS,False\nmp-738654,\"{'U': 4.0, 'H': 64.0, 'C': 16.0, 'N': 16.0, 'O': 48.0}\",\"('C', 'H', 'N', 'O', 'U')\",OTHERS,False\nmp-752507,\"{'Li': 4.0, 'Cu': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-441,\"{'Rb': 2.0, 'Te': 1.0}\",\"('Rb', 'Te')\",OTHERS,False\nmp-1181880,\"{'Fe': 6.0, 'S': 6.0, 'O': 60.0}\",\"('Fe', 'O', 'S')\",OTHERS,False\nmp-676060,\"{'Gd': 4.0, 'Cu': 2.0, 'O': 8.0}\",\"('Cu', 'Gd', 'O')\",OTHERS,False\nmp-1217020,\"{'Ti': 1.0, 'Al': 1.0, 'Fe': 2.0}\",\"('Al', 'Fe', 'Ti')\",OTHERS,False\nmp-757486,\"{'Li': 9.0, 'Cr': 3.0, 'P': 6.0, 'O': 24.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-1245936,\"{'Ba': 28.0, 'Zr': 4.0, 'N': 24.0}\",\"('Ba', 'N', 'Zr')\",OTHERS,False\nmp-22579,\"{'O': 16.0, 'Mn': 4.0, 'Se': 4.0}\",\"('Mn', 'O', 'Se')\",OTHERS,False\nmp-758978,\"{'Li': 3.0, 'Ni': 4.0, 'O': 8.0}\",\"('Li', 'Ni', 'O')\",OTHERS,False\nmp-631335,\"{'Mg': 1.0, 'Ta': 1.0, 'Zn': 1.0}\",\"('Mg', 'Ta', 'Zn')\",OTHERS,False\nmp-776457,\"{'Li': 1.0, 'V': 4.0, 'O': 1.0, 'F': 11.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-976147,\"{'Pr': 1.0, 'Er': 3.0}\",\"('Er', 'Pr')\",OTHERS,False\nmp-392,\"{'Sb': 8.0, 'U': 6.0}\",\"('Sb', 'U')\",OTHERS,False\nmp-975652,\"{'Pr': 2.0, 'Te': 4.0}\",\"('Pr', 'Te')\",OTHERS,False\nmp-1058581,{'Ba': 2.0},\"('Ba',)\",OTHERS,False\nmp-640073,\"{'Fe': 2.0, 'Cu': 2.0, 'S': 4.0}\",\"('Cu', 'Fe', 'S')\",OTHERS,False\nmp-1226362,\"{'Cu': 6.0, 'Te': 2.0, 'Mo': 2.0, 'H': 2.0, 'Cl': 4.0, 'O': 15.0}\",\"('Cl', 'Cu', 'H', 'Mo', 'O', 'Te')\",OTHERS,False\nmp-757153,\"{'Li': 1.0, 'Fe': 5.0, 'O': 8.0}\",\"('Fe', 'Li', 'O')\",OTHERS,False\nmp-28810,\"{'C': 2.0, 'Na': 4.0, 'S': 6.0}\",\"('C', 'Na', 'S')\",OTHERS,False\nmp-1174704,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1178410,\"{'Cu': 4.0, 'Ni': 2.0, 'O': 8.0}\",\"('Cu', 'Ni', 'O')\",OTHERS,False\nmp-1012103,\"{'V': 16.0, 'Ni': 8.0, 'O': 48.0}\",\"('Ni', 'O', 'V')\",OTHERS,False\nmp-1226313,\"{'Cr': 1.0, 'Mo': 1.0, 'O': 3.0}\",\"('Cr', 'Mo', 'O')\",OTHERS,False\nmp-1195013,\"{'Na': 4.0, 'Pd': 2.0, 'S': 16.0, 'O': 52.0}\",\"('Na', 'O', 'Pd', 'S')\",OTHERS,False\nmp-1181862,\"{'Cd': 2.0, 'S': 2.0}\",\"('Cd', 'S')\",OTHERS,False\nmp-640345,\"{'Cs': 2.0, 'Mn': 1.0, 'Fe': 1.0, 'C': 6.0, 'N': 6.0}\",\"('C', 'Cs', 'Fe', 'Mn', 'N')\",OTHERS,False\nmp-1187675,\"{'Yb': 1.0, 'Ho': 3.0}\",\"('Ho', 'Yb')\",OTHERS,False\nmp-19292,\"{'O': 6.0, 'Co': 1.0, 'As': 2.0}\",\"('As', 'Co', 'O')\",OTHERS,False\nmp-1105218,\"{'Na': 4.0, 'Os': 4.0, 'O': 12.0}\",\"('Na', 'O', 'Os')\",OTHERS,False\nmp-1245863,\"{'Mg': 1.0, 'Fe': 10.0, 'N': 8.0}\",\"('Fe', 'Mg', 'N')\",OTHERS,False\nmp-776506,\"{'Li': 6.0, 'Mn': 3.0, 'V': 3.0, 'P': 6.0, 'H': 6.0, 'O': 30.0}\",\"('H', 'Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-1180521,\"{'Na': 16.0, 'W': 8.0, 'O': 48.0}\",\"('Na', 'O', 'W')\",OTHERS,False\nmp-768613,\"{'Zr': 1.0, 'Mn': 6.0, 'Sn': 6.0}\",\"('Mn', 'Sn', 'Zr')\",OTHERS,False\nmp-1197056,\"{'Sm': 8.0, 'Ga': 16.0, 'Se': 32.0}\",\"('Ga', 'Se', 'Sm')\",OTHERS,False\nmp-1217973,\"{'Sr': 1.0, 'Pr': 1.0, 'V': 1.0, 'O': 4.0}\",\"('O', 'Pr', 'Sr', 'V')\",OTHERS,False\nmp-625367,\"{'Lu': 2.0, 'H': 2.0, 'O': 4.0}\",\"('H', 'Lu', 'O')\",OTHERS,False\nmp-1191988,\"{'Y': 4.0, 'Mn': 4.0, 'B': 16.0}\",\"('B', 'Mn', 'Y')\",OTHERS,False\nmp-648788,\"{'Te': 14.0, 'Os': 2.0, 'O': 10.0, 'F': 64.0}\",\"('F', 'O', 'Os', 'Te')\",OTHERS,False\nmp-1018779,\"{'Li': 3.0, 'Pr': 1.0, 'Sb': 2.0}\",\"('Li', 'Pr', 'Sb')\",OTHERS,False\nmp-10846,\"{'Ni': 4.0, 'As': 4.0, 'Se': 4.0}\",\"('As', 'Ni', 'Se')\",OTHERS,False\nmp-753323,\"{'Li': 4.0, 'Mn': 2.0, 'O': 1.0, 'F': 7.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1206244,\"{'Lu': 2.0, 'O': 4.0}\",\"('Lu', 'O')\",OTHERS,False\nmp-1104583,\"{'Pt': 1.0, 'S': 2.0, 'N': 4.0, 'O': 8.0}\",\"('N', 'O', 'Pt', 'S')\",OTHERS,False\nmp-1827,\"{'Ga': 4.0, 'Sr': 1.0}\",\"('Ga', 'Sr')\",OTHERS,False\nmp-1178188,\"{'Ho': 10.0, 'Ti': 10.0, 'O': 34.0}\",\"('Ho', 'O', 'Ti')\",OTHERS,False\nmp-1101948,\"{'Nb': 4.0, 'P': 4.0, 'Rh': 4.0}\",\"('Nb', 'P', 'Rh')\",OTHERS,False\nmp-1188137,\"{'Cd': 4.0, 'Hg': 4.0, 'S': 4.0, 'Br': 4.0}\",\"('Br', 'Cd', 'Hg', 'S')\",OTHERS,False\nmp-998928,\"{'Tl': 1.0, 'Cd': 1.0, 'S': 2.0}\",\"('Cd', 'S', 'Tl')\",OTHERS,False\nmp-761797,\"{'Li': 6.0, 'Mn': 15.0, 'O': 32.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-1058171,\"{'Na': 1.0, 'S': 1.0}\",\"('Na', 'S')\",OTHERS,False\nmp-1220172,\"{'Nd': 1.0, 'Al': 2.0, 'Pt': 3.0}\",\"('Al', 'Nd', 'Pt')\",OTHERS,False\nmp-1072645,\"{'Si': 4.0, 'Os': 2.0}\",\"('Os', 'Si')\",OTHERS,False\nmp-20313,\"{'O': 16.0, 'Si': 4.0, 'Fe': 8.0}\",\"('Fe', 'O', 'Si')\",OTHERS,False\nmp-1226506,\"{'Ce': 1.0, 'Th': 2.0, 'O': 6.0}\",\"('Ce', 'O', 'Th')\",OTHERS,False\nmp-997002,\"{'K': 2.0, 'Au': 2.0, 'O': 4.0}\",\"('Au', 'K', 'O')\",OTHERS,False\nmp-756149,\"{'Li': 3.0, 'Nb': 4.0, 'Fe': 1.0, 'O': 12.0}\",\"('Fe', 'Li', 'Nb', 'O')\",OTHERS,False\nmp-1186176,\"{'Na': 1.0, 'Sn': 3.0}\",\"('Na', 'Sn')\",OTHERS,False\nmp-675293,\"{'Yb': 2.0, 'Y': 4.0, 'S': 8.0}\",\"('S', 'Y', 'Yb')\",OTHERS,False\nmp-8308,\"{'B': 2.0, 'Ca': 3.0, 'Ni': 7.0}\",\"('B', 'Ca', 'Ni')\",OTHERS,False\nmp-530985,\"{'La': 20.0, 'Mn': 18.0, 'O': 60.0}\",\"('La', 'Mn', 'O')\",OTHERS,False\nmp-11436,\"{'Er': 1.0, 'V': 2.0, 'Ga': 4.0}\",\"('Er', 'Ga', 'V')\",OTHERS,False\nmp-1185050,\"{'La': 2.0, 'Tl': 1.0, 'Ag': 1.0}\",\"('Ag', 'La', 'Tl')\",OTHERS,False\nmp-1225980,\"{'Cu': 19.0, 'Sn': 16.0}\",\"('Cu', 'Sn')\",OTHERS,False\nmp-557407,\"{'K': 6.0, 'Na': 2.0, 'U': 2.0, 'C': 6.0, 'O': 22.0}\",\"('C', 'K', 'Na', 'O', 'U')\",OTHERS,False\nmp-768091,\"{'Li': 8.0, 'Ni': 14.0, 'O': 22.0}\",\"('Li', 'Ni', 'O')\",OTHERS,False\nmvc-10602,\"{'Cr': 6.0, 'P': 6.0, 'O': 26.0}\",\"('Cr', 'O', 'P')\",OTHERS,False\nmp-570451,\"{'Nb': 4.0, 'Te': 6.0}\",\"('Nb', 'Te')\",OTHERS,False\nmp-756258,\"{'Li': 3.0, 'Co': 4.0, 'Cu': 1.0, 'O': 8.0}\",\"('Co', 'Cu', 'Li', 'O')\",OTHERS,False\nmp-541695,\"{'W': 4.0, 'C': 24.0, 'O': 24.0}\",\"('C', 'O', 'W')\",OTHERS,False\nmp-1197831,\"{'Li': 8.0, 'Br': 8.0, 'O': 24.0}\",\"('Br', 'Li', 'O')\",OTHERS,False\nmp-1037722,\"{'Hf': 1.0, 'Mg': 30.0, 'Al': 1.0, 'O': 32.0}\",\"('Al', 'Hf', 'Mg', 'O')\",OTHERS,False\nmp-1199697,\"{'Ce': 12.0, 'Al': 24.0, 'Pt': 16.0}\",\"('Al', 'Ce', 'Pt')\",OTHERS,False\nmp-776417,\"{'Li': 1.0, 'V': 6.0, 'O': 7.0, 'F': 5.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-540632,\"{'Rb': 2.0, 'Ta': 2.0, 'Ge': 6.0, 'O': 18.0}\",\"('Ge', 'O', 'Rb', 'Ta')\",OTHERS,False\nmp-1114397,\"{'Rb': 2.0, 'Y': 1.0, 'In': 1.0, 'F': 6.0}\",\"('F', 'In', 'Rb', 'Y')\",OTHERS,False\nmp-973283,\"{'Ho': 1.0, 'Lu': 1.0, 'Ag': 2.0}\",\"('Ag', 'Ho', 'Lu')\",OTHERS,False\nmp-26407,\"{'Li': 8.0, 'O': 28.0, 'P': 8.0, 'Sn': 4.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,False\nmp-676762,\"{'Yb': 2.0, 'Ce': 4.0, 'S': 8.0}\",\"('Ce', 'S', 'Yb')\",OTHERS,False\nmp-1212695,\"{'Ga': 2.0, 'Hg': 5.0, 'Se': 8.0}\",\"('Ga', 'Hg', 'Se')\",OTHERS,False\nmp-582540,\"{'Eu': 4.0, 'Mo': 4.0, 'O': 16.0, 'F': 4.0}\",\"('Eu', 'F', 'Mo', 'O')\",OTHERS,False\nmp-1227974,\"{'Ba': 1.0, 'Bi': 2.0, 'Br': 2.0, 'O': 2.0}\",\"('Ba', 'Bi', 'Br', 'O')\",OTHERS,False\nmp-1215344,\"{'Zr': 8.0, 'Ag': 1.0, 'Pd': 3.0}\",\"('Ag', 'Pd', 'Zr')\",OTHERS,False\nmp-542292,\"{'Ag': 2.0, 'Mn': 6.0, 'As': 6.0, 'O': 24.0, 'H': 4.0}\",\"('Ag', 'As', 'H', 'Mn', 'O')\",OTHERS,False\nmp-770653,\"{'Sr': 12.0, 'W': 8.0, 'O': 36.0}\",\"('O', 'Sr', 'W')\",OTHERS,False\nmp-1239256,\"{'Zr': 4.0, 'Cr': 4.0, 'Cu': 4.0, 'S': 16.0}\",\"('Cr', 'Cu', 'S', 'Zr')\",OTHERS,False\nmp-775905,\"{'Li': 8.0, 'Mn': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-572621,\"{'Ni': 4.0, 'P': 4.0, 'C': 12.0, 'O': 18.0}\",\"('C', 'Ni', 'O', 'P')\",OTHERS,False\nmp-1209523,\"{'Rb': 6.0, 'Sc': 4.0, 'Br': 18.0}\",\"('Br', 'Rb', 'Sc')\",OTHERS,False\nmp-27941,\"{'O': 12.0, 'Cl': 4.0, 'Ag': 4.0}\",\"('Ag', 'Cl', 'O')\",OTHERS,False\nmp-556282,\"{'Ba': 8.0, 'Al': 4.0, 'In': 4.0, 'O': 20.0}\",\"('Al', 'Ba', 'In', 'O')\",OTHERS,False\nmp-559809,\"{'Rb': 8.0, 'Fe': 4.0, 'O': 10.0}\",\"('Fe', 'O', 'Rb')\",OTHERS,False\nmp-779351,\"{'Li': 4.0, 'Mn': 1.0, 'P': 6.0, 'H': 8.0, 'O': 22.0}\",\"('H', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-755421,\"{'Ca': 6.0, 'Hf': 1.0, 'O': 8.0}\",\"('Ca', 'Hf', 'O')\",OTHERS,False\nmp-1213505,\"{'Er': 8.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Er', 'O')\",OTHERS,False\nmp-1197097,\"{'K': 8.0, 'P': 8.0, 'H': 24.0, 'O': 32.0}\",\"('H', 'K', 'O', 'P')\",OTHERS,False\nmp-1223001,\"{'La': 4.0, 'Th': 4.0, 'U': 4.0, 'S': 20.0}\",\"('La', 'S', 'Th', 'U')\",OTHERS,False\nmp-1226829,\"{'Cd': 4.0, 'In': 1.0, 'Te': 4.0, 'As': 1.0}\",\"('As', 'Cd', 'In', 'Te')\",OTHERS,False\nmvc-11797,\"{'Ca': 2.0, 'Cu': 4.0, 'O': 8.0}\",\"('Ca', 'Cu', 'O')\",OTHERS,False\nmvc-1659,\"{'Zn': 2.0, 'Sb': 4.0, 'O': 12.0}\",\"('O', 'Sb', 'Zn')\",OTHERS,False\nmp-1036104,\"{'Mg': 14.0, 'Mn': 1.0, 'Cr': 1.0, 'O': 16.0}\",\"('Cr', 'Mg', 'Mn', 'O')\",OTHERS,False\nmp-1187900,\"{'Yb': 4.0, 'Mg': 2.0}\",\"('Mg', 'Yb')\",OTHERS,False\nmp-698096,\"{'La': 5.0, 'Fe': 1.0, 'Re': 3.0, 'O': 16.0}\",\"('Fe', 'La', 'O', 'Re')\",OTHERS,False\nmp-1246482,\"{'Mg': 2.0, 'Ni': 2.0, 'W': 2.0, 'S': 8.0}\",\"('Mg', 'Ni', 'S', 'W')\",OTHERS,False\nmp-1191803,\"{'Ag': 10.0, 'Te': 2.0, 'P': 2.0, 'O': 8.0}\",\"('Ag', 'O', 'P', 'Te')\",OTHERS,False\nmp-683867,\"{'Tb': 16.0, 'B': 48.0, 'O': 96.0}\",\"('B', 'O', 'Tb')\",OTHERS,False\nmp-1206680,\"{'Li': 2.0, 'Sn': 1.0, 'Pd': 1.0}\",\"('Li', 'Pd', 'Sn')\",OTHERS,False\nmp-2375,\"{'Pd': 3.0, 'Np': 1.0}\",\"('Np', 'Pd')\",OTHERS,False\nmp-1199954,\"{'Mn': 8.0, 'B': 16.0, 'O': 32.0}\",\"('B', 'Mn', 'O')\",OTHERS,False\nmp-1204230,\"{'Sr': 64.0, 'Ga': 32.0, 'O': 112.0}\",\"('Ga', 'O', 'Sr')\",OTHERS,False\nmp-778241,\"{'Li': 2.0, 'V': 3.0, 'F': 8.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-1174542,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1205833,\"{'Ba': 2.0, 'Nb': 1.0, 'Rh': 1.0, 'O': 6.0}\",\"('Ba', 'Nb', 'O', 'Rh')\",OTHERS,False\nmp-1184670,\"{'Hf': 6.0, 'Al': 2.0}\",\"('Al', 'Hf')\",OTHERS,False\nmp-19719,\"{'Mn': 2.0, 'Se': 8.0, 'Yb': 4.0}\",\"('Mn', 'Se', 'Yb')\",OTHERS,False\nmvc-2232,\"{'Ba': 4.0, 'Mg': 2.0, 'Fe': 4.0, 'Cu': 2.0, 'F': 28.0}\",\"('Ba', 'Cu', 'F', 'Fe', 'Mg')\",OTHERS,False\nmp-6027,\"{'O': 12.0, 'Cu': 2.0, 'Ba': 4.0, 'Tl': 4.0}\",\"('Ba', 'Cu', 'O', 'Tl')\",OTHERS,False\nmp-1204777,\"{'Zn': 4.0, 'H': 48.0, 'C': 24.0, 'S': 16.0, 'N': 8.0}\",\"('C', 'H', 'N', 'S', 'Zn')\",OTHERS,False\nmp-1221554,\"{'Mn': 2.0, 'Si': 4.0, 'Ni': 2.0}\",\"('Mn', 'Ni', 'Si')\",OTHERS,False\nmp-775748,\"{'Li': 4.0, 'Cr': 3.0, 'Fe': 2.0, 'Sn': 3.0, 'O': 16.0}\",\"('Cr', 'Fe', 'Li', 'O', 'Sn')\",OTHERS,False\nmp-1211634,\"{'K': 2.0, 'Pr': 2.0, 'Cl': 8.0, 'O': 8.0}\",\"('Cl', 'K', 'O', 'Pr')\",OTHERS,False\nmp-768490,\"{'Bi': 8.0, 'S': 12.0, 'O': 48.0}\",\"('Bi', 'O', 'S')\",OTHERS,False\nmp-1198194,\"{'Cs': 2.0, 'P': 4.0, 'H': 10.0, 'O': 16.0}\",\"('Cs', 'H', 'O', 'P')\",OTHERS,False\nmp-557324,\"{'Cs': 2.0, 'Cr': 1.0, 'F': 6.0}\",\"('Cr', 'Cs', 'F')\",OTHERS,False\nmp-1216744,\"{'U': 1.0, 'Al': 8.0, 'Cu': 4.0}\",\"('Al', 'Cu', 'U')\",OTHERS,False\nmp-764273,\"{'Na': 4.0, 'Li': 2.0, 'Mn': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Li', 'Mn', 'Na', 'O', 'P')\",OTHERS,False\nmp-8713,\"{'S': 4.0, 'K': 4.0, 'Mn': 2.0}\",\"('K', 'Mn', 'S')\",OTHERS,False\nmp-766800,\"{'Li': 6.0, 'Fe': 12.0, 'P': 12.0, 'O': 48.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-1095778,\"{'Sr': 1.0, 'Pb': 1.0, 'Au': 2.0}\",\"('Au', 'Pb', 'Sr')\",OTHERS,False\nmp-1106187,\"{'Er': 7.0, 'Fe': 1.0, 'I': 12.0}\",\"('Er', 'Fe', 'I')\",OTHERS,False\nmp-1188147,\"{'B': 16.0, 'Os': 4.0}\",\"('B', 'Os')\",OTHERS,False\nmp-1185744,\"{'Mg': 17.0, 'Al': 11.0, 'Rh': 1.0}\",\"('Al', 'Mg', 'Rh')\",OTHERS,False\nmp-757889,\"{'Li': 4.0, 'Sn': 4.0, 'P': 12.0, 'O': 36.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,False\nmp-1216438,\"{'V': 2.0, 'Co': 6.0, 'As': 2.0, 'O': 16.0}\",\"('As', 'Co', 'O', 'V')\",OTHERS,False\nmp-1064529,\"{'Rb': 2.0, 'N': 2.0}\",\"('N', 'Rb')\",OTHERS,False\nmp-1222213,\"{'Mg': 4.0, 'Al': 4.0, 'Si': 2.0, 'O': 18.0}\",\"('Al', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-1247324,\"{'Re': 8.0, 'Pb': 12.0, 'N': 16.0}\",\"('N', 'Pb', 'Re')\",OTHERS,False\nmp-1087238,\"{'Y': 2.0, 'Cu': 4.0, 'Sn': 4.0}\",\"('Cu', 'Sn', 'Y')\",OTHERS,False\nmp-1180425,\"{'Mg': 2.0, 'N': 2.0, 'Cl': 6.0, 'O': 12.0}\",\"('Cl', 'Mg', 'N', 'O')\",OTHERS,False\nmp-756689,\"{'La': 4.0, 'Sb': 2.0, 'O': 12.0}\",\"('La', 'O', 'Sb')\",OTHERS,False\nmp-31467,\"{'Ga': 3.0, 'Pd': 7.0}\",\"('Ga', 'Pd')\",OTHERS,False\nmp-22756,\"{'O': 6.0, 'K': 4.0, 'Pb': 2.0}\",\"('K', 'O', 'Pb')\",OTHERS,False\nmp-1094138,\"{'Na': 4.0, 'Co': 8.0, 'P': 8.0, 'O': 36.0}\",\"('Co', 'Na', 'O', 'P')\",OTHERS,False\nmp-675912,\"{'Yb': 4.0, 'Sm': 2.0, 'S': 8.0}\",\"('S', 'Sm', 'Yb')\",OTHERS,False\nmp-1206552,\"{'Ba': 2.0, 'Bi': 2.0, 'Br': 2.0, 'O': 4.0}\",\"('Ba', 'Bi', 'Br', 'O')\",OTHERS,False\nmp-1216581,\"{'V': 4.0, 'Fe': 2.0, 'Si': 2.0}\",\"('Fe', 'Si', 'V')\",OTHERS,False\nmp-772579,\"{'La': 8.0, 'W': 4.0, 'O': 24.0}\",\"('La', 'O', 'W')\",OTHERS,False\nmp-5284,\"{'Si': 2.0, 'Cr': 2.0, 'U': 1.0}\",\"('Cr', 'Si', 'U')\",OTHERS,False\nmp-1174579,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmvc-13134,\"{'Cr': 4.0, 'O': 12.0}\",\"('Cr', 'O')\",OTHERS,False\nmp-504957,\"{'Rb': 6.0, 'Re': 6.0, 'Cl': 24.0}\",\"('Cl', 'Rb', 'Re')\",OTHERS,False\nmp-995200,\"{'H': 2.0, 'C': 6.0}\",\"('C', 'H')\",OTHERS,False\nmp-755052,\"{'Li': 2.0, 'V': 4.0, 'O': 8.0}\",\"('Li', 'O', 'V')\",OTHERS,False\nmp-758539,\"{'Li': 2.0, 'Co': 3.0, 'O': 6.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-767672,\"{'Li': 4.0, 'V': 4.0, 'F': 20.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-9167,\"{'C': 4.0, 'N': 8.0, 'Sr': 4.0}\",\"('C', 'N', 'Sr')\",OTHERS,False\nmp-1221664,\"{'Mn': 1.0, 'Cd': 2.0, 'Te': 3.0}\",\"('Cd', 'Mn', 'Te')\",OTHERS,False\nmp-770170,\"{'Li': 6.0, 'V': 6.0, 'B': 4.0, 'O': 20.0}\",\"('B', 'Li', 'O', 'V')\",OTHERS,False\nmp-679630,\"{'Ba': 4.0, 'Be': 2.0, 'S': 2.0, 'O': 8.0, 'F': 8.0}\",\"('Ba', 'Be', 'F', 'O', 'S')\",OTHERS,False\nmp-758652,\"{'Zn': 2.0, 'Co': 2.0, 'O': 6.0}\",\"('Co', 'O', 'Zn')\",OTHERS,False\nmp-1096845,\"{'Ba': 2.0, 'Ag': 2.0, 'O': 4.0}\",\"('Ag', 'Ba', 'O')\",OTHERS,False\nmp-1223371,\"{'La': 2.0, 'Al': 3.0, 'Ga': 1.0, 'Pd': 4.0}\",\"('Al', 'Ga', 'La', 'Pd')\",OTHERS,False\nmp-1093586,\"{'Hf': 2.0, 'V': 1.0, 'Tc': 1.0}\",\"('Hf', 'Tc', 'V')\",OTHERS,False\nmp-1199260,\"{'B': 4.0, 'P': 8.0, 'Pb': 8.0, 'O': 36.0}\",\"('B', 'O', 'P', 'Pb')\",OTHERS,False\nmp-24234,\"{'H': 16.0, 'N': 4.0, 'O': 4.0, 'S': 4.0, 'Mo': 2.0}\",\"('H', 'Mo', 'N', 'O', 'S')\",OTHERS,False\nmp-1207223,\"{'Zr': 3.0, 'Zn': 3.0, 'Ge': 3.0}\",\"('Ge', 'Zn', 'Zr')\",OTHERS,False\nmp-29209,\"{'Mg': 2.0, 'Ba': 1.0, 'Bi': 2.0}\",\"('Ba', 'Bi', 'Mg')\",OTHERS,False\nmp-559781,\"{'K': 2.0, 'Y': 2.0, 'C': 16.0, 'O': 16.0}\",\"('C', 'K', 'O', 'Y')\",OTHERS,False\nmp-774906,\"{'Li': 8.0, 'Mn': 4.0, 'Ni': 12.0, 'O': 32.0}\",\"('Li', 'Mn', 'Ni', 'O')\",OTHERS,False\nmp-761277,\"{'Li': 4.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1209334,\"{'Rb': 2.0, 'Ho': 2.0, 'W': 4.0, 'O': 16.0}\",\"('Ho', 'O', 'Rb', 'W')\",OTHERS,False\nmp-1229327,\"{'Ba': 8.0, 'Gd': 4.0, 'Cu': 12.0, 'O': 27.0}\",\"('Ba', 'Cu', 'Gd', 'O')\",OTHERS,False\nmp-1244565,\"{'Ca': 2.0, 'Sn': 4.0, 'O': 10.0}\",\"('Ca', 'O', 'Sn')\",OTHERS,False\nmp-780263,\"{'Ba': 24.0, 'Ge': 12.0, 'O': 48.0}\",\"('Ba', 'Ge', 'O')\",OTHERS,False\nmp-1095164,\"{'Gd': 2.0, 'B': 4.0, 'Ru': 4.0}\",\"('B', 'Gd', 'Ru')\",OTHERS,False\nmp-1096534,\"{'Y': 1.0, 'Zr': 1.0, 'Pd': 2.0}\",\"('Pd', 'Y', 'Zr')\",OTHERS,False\nmp-11301,\"{'Cd': 2.0, 'Ho': 1.0}\",\"('Cd', 'Ho')\",OTHERS,False\nmp-676083,\"{'Ce': 4.0, 'U': 2.0, 'Se': 8.0}\",\"('Ce', 'Se', 'U')\",OTHERS,False\nmp-26155,\"{'Li': 2.0, 'O': 24.0, 'P': 8.0, 'Mn': 2.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1223424,\"{'La': 4.0, 'Pr': 1.0, 'Co': 10.0, 'P': 10.0}\",\"('Co', 'La', 'P', 'Pr')\",OTHERS,False\nmp-1180326,\"{'Na': 6.0, 'Np': 2.0, 'O': 12.0}\",\"('Na', 'Np', 'O')\",OTHERS,False\nmp-31433,\"{'Ni': 2.0, 'Sn': 8.0, 'Lu': 2.0}\",\"('Lu', 'Ni', 'Sn')\",OTHERS,False\nmp-541991,\"{'Cu': 6.0, 'Mo': 6.0, 'O': 24.0}\",\"('Cu', 'Mo', 'O')\",OTHERS,False\nmp-5878,\"{'F': 3.0, 'K': 1.0, 'Zn': 1.0}\",\"('F', 'K', 'Zn')\",OTHERS,False\nmp-777593,\"{'Li': 32.0, 'Ti': 3.0, 'Cr': 13.0, 'O': 48.0}\",\"('Cr', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-752793,\"{'Li': 8.0, 'Ag': 4.0, 'F': 12.0}\",\"('Ag', 'F', 'Li')\",OTHERS,False\nmp-989564,\"{'La': 1.0, 'Os': 1.0, 'N': 3.0}\",\"('La', 'N', 'Os')\",OTHERS,False\nmp-760468,\"{'Li': 2.0, 'Si': 4.0, 'Bi': 2.0, 'O': 12.0}\",\"('Bi', 'Li', 'O', 'Si')\",OTHERS,False\nmp-3035,\"{'Si': 2.0, 'Fe': 2.0, 'Ce': 1.0}\",\"('Ce', 'Fe', 'Si')\",OTHERS,False\nmp-36946,\"{'Tl': 2.0, 'Bi': 2.0, 'S': 4.0}\",\"('Bi', 'S', 'Tl')\",OTHERS,False\nmp-28592,\"{'Li': 7.0, 'O': 2.0, 'Br': 3.0}\",\"('Br', 'Li', 'O')\",OTHERS,False\nmp-1188906,\"{'Sm': 2.0, 'Ga': 2.0, 'Co': 15.0}\",\"('Co', 'Ga', 'Sm')\",OTHERS,False\nmp-10247,\"{'N': 3.0, 'Cr': 1.0, 'U': 2.0}\",\"('Cr', 'N', 'U')\",OTHERS,False\nmp-865902,\"{'Yb': 4.0, 'B': 16.0, 'Os': 4.0}\",\"('B', 'Os', 'Yb')\",OTHERS,False\nmp-1206794,\"{'Li': 1.0, 'Au': 1.0}\",\"('Au', 'Li')\",OTHERS,False\nmp-1037802,\"{'Ca': 1.0, 'Mg': 30.0, 'Mn': 1.0, 'O': 32.0}\",\"('Ca', 'Mg', 'Mn', 'O')\",OTHERS,False\nmp-570414,\"{'Tm': 8.0, 'Fe': 56.0, 'C': 4.0}\",\"('C', 'Fe', 'Tm')\",OTHERS,False\nmp-21792,\"{'O': 32.0, 'Na': 40.0, 'Co': 8.0}\",\"('Co', 'Na', 'O')\",OTHERS,False\nmvc-3232,\"{'Zn': 2.0, 'Si': 2.0, 'Ni': 2.0, 'O': 10.0}\",\"('Ni', 'O', 'Si', 'Zn')\",OTHERS,False\nmp-1078033,\"{'U': 1.0, 'Ga': 5.0, 'Rh': 1.0}\",\"('Ga', 'Rh', 'U')\",OTHERS,False\nmp-31337,\"{'Pd': 3.0, 'In': 1.0}\",\"('In', 'Pd')\",OTHERS,False\nmp-20132,\"{'In': 1.0, 'Hg': 1.0}\",\"('Hg', 'In')\",OTHERS,False\nmp-1097671,\"{'Ta': 1.0, 'Be': 1.0, 'Rh': 2.0}\",\"('Be', 'Rh', 'Ta')\",OTHERS,False\nmp-989405,\"{'Cs': 2.0, 'Li': 1.0, 'In': 1.0, 'Br': 6.0}\",\"('Br', 'Cs', 'In', 'Li')\",OTHERS,False\nmp-863669,\"{'Pm': 2.0, 'Cu': 1.0, 'Pt': 1.0}\",\"('Cu', 'Pm', 'Pt')\",OTHERS,False\nmp-26317,\"{'Li': 2.0, 'Cr': 2.0, 'P': 2.0, 'O': 8.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-1096445,\"{'Cs': 1.0, 'Rb': 2.0, 'Bi': 1.0}\",\"('Bi', 'Cs', 'Rb')\",OTHERS,False\nmp-834,\"{'N': 1.0, 'Th': 1.0}\",\"('N', 'Th')\",OTHERS,False\nmp-560714,\"{'Ba': 4.0, 'V': 16.0, 'As': 16.0, 'O': 76.0}\",\"('As', 'Ba', 'O', 'V')\",OTHERS,False\nmp-601286,\"{'Tb': 4.0, 'Cu': 4.0, 'Pb': 4.0, 'Se': 12.0}\",\"('Cu', 'Pb', 'Se', 'Tb')\",OTHERS,False\nmp-1174046,\"{'Li': 6.0, 'Mn': 2.0, 'Co': 2.0, 'O': 10.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-11247,\"{'Li': 3.0, 'Au': 1.0}\",\"('Au', 'Li')\",OTHERS,False\nmp-6504,\"{'C': 2.0, 'O': 6.0, 'Mg': 1.0, 'Ba': 1.0}\",\"('Ba', 'C', 'Mg', 'O')\",OTHERS,False\nmp-680690,\"{'La': 1.0, 'Cu': 3.0, 'Ru': 4.0, 'O': 12.0}\",\"('Cu', 'La', 'O', 'Ru')\",OTHERS,False\nmp-1189013,\"{'K': 2.0, 'Pt': 1.0, 'N': 4.0, 'O': 10.0}\",\"('K', 'N', 'O', 'Pt')\",OTHERS,False\nmp-779341,\"{'Li': 9.0, 'Mn': 12.0, 'B': 12.0, 'O': 36.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1209044,\"{'Sc': 20.0, 'Sb': 12.0}\",\"('Sb', 'Sc')\",OTHERS,False\nmp-1196608,\"{'Nd': 12.0, 'Tl': 2.0, 'Fe': 26.0}\",\"('Fe', 'Nd', 'Tl')\",OTHERS,False\nmp-559994,\"{'La': 24.0, 'S': 16.0, 'Br': 4.0, 'N': 12.0}\",\"('Br', 'La', 'N', 'S')\",OTHERS,False\nmp-510357,\"{'La': 8.0, 'Br': 10.0, 'B': 8.0}\",\"('B', 'Br', 'La')\",OTHERS,False\nmp-1019108,\"{'Sm': 2.0, 'Co': 2.0, 'Si': 2.0}\",\"('Co', 'Si', 'Sm')\",OTHERS,False\nmp-1191536,\"{'Eu': 2.0, 'Zn': 22.0}\",\"('Eu', 'Zn')\",OTHERS,False\nmp-19039,\"{'O': 8.0, 'Mo': 2.0, 'Cd': 2.0}\",\"('Cd', 'Mo', 'O')\",OTHERS,False\nmp-1066,\"{'N': 1.0, 'Yb': 1.0}\",\"('N', 'Yb')\",OTHERS,False\nmp-1211215,\"{'Li': 8.0, 'Nd': 4.0, 'N': 20.0, 'O': 60.0}\",\"('Li', 'N', 'Nd', 'O')\",OTHERS,False\nmp-19356,\"{'O': 18.0, 'Mn': 6.0, 'Yb': 6.0}\",\"('Mn', 'O', 'Yb')\",OTHERS,False\nmp-1173397,\"{'Sc': 6.0, 'Nb': 2.0, 'O': 14.0}\",\"('Nb', 'O', 'Sc')\",OTHERS,False\nmp-972439,\"{'Zn': 6.0, 'P': 4.0, 'O': 16.0}\",\"('O', 'P', 'Zn')\",OTHERS,False\nmp-1227705,\"{'Ba': 1.0, 'Sr': 1.0, 'Ca': 1.0, 'Mg': 1.0, 'Si': 2.0, 'O': 8.0}\",\"('Ba', 'Ca', 'Mg', 'O', 'Si', 'Sr')\",OTHERS,False\nmp-6574,\"{'C': 1.0, 'N': 2.0, 'O': 2.0, 'Er': 2.0}\",\"('C', 'Er', 'N', 'O')\",OTHERS,False\nmvc-657,\"{'Fe': 2.0, 'W': 4.0, 'O': 16.0}\",\"('Fe', 'O', 'W')\",OTHERS,False\nmp-675374,\"{'Al': 4.0, 'Pb': 2.0, 'Se': 8.0}\",\"('Al', 'Pb', 'Se')\",OTHERS,False\nmp-1214906,\"{'Ag': 2.0, 'Mo': 2.0, 'O': 8.0}\",\"('Ag', 'Mo', 'O')\",OTHERS,False\nmp-13283,\"{'S': 12.0, 'Sm': 2.0, 'Er': 6.0}\",\"('Er', 'S', 'Sm')\",OTHERS,False\nmp-1179608,\"{'S': 2.0, 'O': 8.0}\",\"('O', 'S')\",OTHERS,False\nmp-616615,\"{'Hf': 16.0, 'Mn': 2.0, 'Te': 12.0}\",\"('Hf', 'Mn', 'Te')\",OTHERS,False\nmp-1189383,\"{'Li': 4.0, 'Cd': 2.0, 'Ge': 2.0, 'S': 8.0}\",\"('Cd', 'Ge', 'Li', 'S')\",OTHERS,False\nmp-1222873,\"{'Li': 2.0, 'Al': 1.0, 'Ni': 3.0, 'O': 6.0}\",\"('Al', 'Li', 'Ni', 'O')\",OTHERS,False\nmp-625211,\"{'Li': 2.0, 'H': 6.0, 'O': 4.0}\",\"('H', 'Li', 'O')\",OTHERS,False\nmp-1245835,\"{'Pd': 2.0, 'C': 4.0, 'N': 4.0}\",\"('C', 'N', 'Pd')\",OTHERS,False\nmp-1200792,\"{'Fe': 4.0, 'P': 16.0, 'C': 16.0, 'O': 40.0}\",\"('C', 'Fe', 'O', 'P')\",OTHERS,False\nmp-1175669,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-22417,\"{'Li': 4.0, 'O': 16.0, 'P': 4.0, 'Ni': 4.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-631267,\"{'Ta': 1.0, 'Fe': 1.0, 'Sb': 1.0}\",\"('Fe', 'Sb', 'Ta')\",OTHERS,False\nmp-1222349,\"{'Li': 2.0, 'Ge': 2.0, 'Rh': 2.0, 'O': 8.0}\",\"('Ge', 'Li', 'O', 'Rh')\",OTHERS,False\nmp-1076897,\"{'Ca': 28.0, 'Mg': 4.0, 'Mn': 32.0, 'O': 80.0}\",\"('Ca', 'Mg', 'Mn', 'O')\",OTHERS,False\nmp-619577,\"{'K': 16.0, 'V': 16.0, 'P': 32.0, 'O': 120.0}\",\"('K', 'O', 'P', 'V')\",OTHERS,False\nmp-1227547,\"{'Cu': 6.0, 'Pb': 2.0, 'Se': 6.0, 'N': 2.0, 'O': 27.0}\",\"('Cu', 'N', 'O', 'Pb', 'Se')\",OTHERS,False\nmp-1147764,\"{'Na': 4.0, 'Cu': 2.0, 'I': 4.0, 'Cl': 4.0}\",\"('Cl', 'Cu', 'I', 'Na')\",OTHERS,False\nmp-983388,\"{'K': 3.0, 'Hg': 1.0}\",\"('Hg', 'K')\",OTHERS,False\nmp-722979,\"{'K': 4.0, 'Zn': 2.0, 'H': 16.0, 'N': 8.0}\",\"('H', 'K', 'N', 'Zn')\",OTHERS,False\nmp-1176187,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-560223,\"{'K': 8.0, 'Se': 4.0, 'S': 8.0, 'O': 24.0}\",\"('K', 'O', 'S', 'Se')\",OTHERS,False\nmp-1217234,\"{'Ti': 8.0, 'Co': 3.0, 'Ni': 1.0, 'Sn': 4.0}\",\"('Co', 'Ni', 'Sn', 'Ti')\",OTHERS,False\nmp-1093568,\"{'Y': 1.0, 'Zn': 2.0, 'Pd': 1.0}\",\"('Pd', 'Y', 'Zn')\",OTHERS,False\nmp-1040315,\"{'K': 1.0, 'Mg': 30.0, 'B': 1.0, 'O': 31.0}\",\"('B', 'K', 'Mg', 'O')\",OTHERS,False\nmp-771282,\"{'Li': 24.0, 'Ti': 7.0, 'Cr': 5.0, 'O': 36.0}\",\"('Cr', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-758332,\"{'Ca': 4.0, 'Gd': 16.0, 'O': 28.0}\",\"('Ca', 'Gd', 'O')\",OTHERS,False\nmvc-8343,\"{'Ca': 4.0, 'Cr': 4.0, 'Ni': 4.0, 'P': 8.0, 'O': 36.0}\",\"('Ca', 'Cr', 'Ni', 'O', 'P')\",OTHERS,False\nmp-1097023,\"{'K': 4.0, 'Fe': 4.0, 'O': 12.0}\",\"('Fe', 'K', 'O')\",OTHERS,False\nmp-752931,\"{'Li': 2.0, 'Mn': 1.0, 'F': 4.0}\",\"('F', 'Li', 'Mn')\",OTHERS,False\nmp-774311,\"{'Li': 12.0, 'Mn': 4.0, 'V': 4.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-1189104,\"{'Eu': 6.0, 'Ga': 4.0, 'P': 8.0}\",\"('Eu', 'Ga', 'P')\",OTHERS,False\nmp-570759,\"{'Tb': 8.0, 'Bi': 6.0}\",\"('Bi', 'Tb')\",OTHERS,False\nmp-582084,\"{'Cd': 18.0, 'I': 36.0}\",\"('Cd', 'I')\",OTHERS,False\nmp-1198634,\"{'Yb': 2.0, 'P': 6.0, 'H': 12.0, 'O': 12.0}\",\"('H', 'O', 'P', 'Yb')\",OTHERS,False\nmp-1214425,\"{'C': 96.0, 'Br': 8.0, 'F': 72.0}\",\"('Br', 'C', 'F')\",OTHERS,False\nmp-758373,\"{'Li': 8.0, 'Co': 8.0, 'P': 8.0, 'O': 32.0}\",\"('Co', 'Li', 'O', 'P')\",OTHERS,False\nmp-1218226,\"{'Sr': 2.0, 'La': 2.0, 'Ga': 2.0, 'Cu': 2.0, 'O': 10.0}\",\"('Cu', 'Ga', 'La', 'O', 'Sr')\",OTHERS,False\nmp-1095715,\"{'Ca': 1.0, 'La': 1.0, 'Rh': 2.0}\",\"('Ca', 'La', 'Rh')\",OTHERS,False\nmp-14883,\"{'S': 10.0, 'Zr': 3.0, 'Ba': 4.0}\",\"('Ba', 'S', 'Zr')\",OTHERS,False\nmp-24664,\"{'H': 16.0, 'C': 4.0, 'N': 8.0, 'O': 4.0, 'Cl': 4.0, 'Zn': 2.0}\",\"('C', 'Cl', 'H', 'N', 'O', 'Zn')\",OTHERS,False\nmp-850538,\"{'Mn': 2.0, 'Fe': 3.0, 'Sn': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Fe', 'Mn', 'O', 'P', 'Sn')\",OTHERS,False\nmp-1225646,\"{'Dy': 12.0, 'Cu': 4.0, 'Se': 24.0}\",\"('Cu', 'Dy', 'Se')\",OTHERS,False\nmp-1196142,\"{'Te': 8.0, 'N': 4.0, 'O': 24.0}\",\"('N', 'O', 'Te')\",OTHERS,False\nmp-1239184,\"{'Cr': 8.0, 'Hg': 4.0, 'S': 16.0}\",\"('Cr', 'Hg', 'S')\",OTHERS,False\nmp-8720,\"{'Mn': 2.0, 'Rb': 4.0, 'Te': 4.0}\",\"('Mn', 'Rb', 'Te')\",OTHERS,False\nmp-677349,\"{'Li': 22.0, 'Mn': 2.0, 'As': 12.0}\",\"('As', 'Li', 'Mn')\",OTHERS,False\nmp-1217575,\"{'Th': 14.0, 'Ag': 51.0}\",\"('Ag', 'Th')\",OTHERS,False\nmp-1205783,\"{'Na': 1.0, 'Sc': 1.0, 'N': 2.0, 'F': 6.0}\",\"('F', 'N', 'Na', 'Sc')\",OTHERS,False\nmp-675711,\"{'Li': 2.0, 'Mo': 12.0, 'S': 16.0}\",\"('Li', 'Mo', 'S')\",OTHERS,False\nmp-770619,\"{'Li': 24.0, 'Mn': 11.0, 'Cr': 1.0, 'O': 36.0}\",\"('Cr', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1025084,\"{'Ho': 2.0, 'B': 2.0, 'C': 2.0}\",\"('B', 'C', 'Ho')\",OTHERS,False\nmvc-8947,\"{'Ca': 2.0, 'La': 2.0, 'Fe': 2.0, 'Ni': 2.0, 'O': 12.0}\",\"('Ca', 'Fe', 'La', 'Ni', 'O')\",OTHERS,False\nmp-1186661,\"{'Pu': 1.0, 'Cl': 1.0}\",\"('Cl', 'Pu')\",OTHERS,False\nmp-685369,\"{'Ti': 6.0, 'Fe': 14.0, 'O': 30.0}\",\"('Fe', 'O', 'Ti')\",OTHERS,False\nmp-7921,\"{'F': 6.0, 'Mg': 1.0, 'Pd': 1.0}\",\"('F', 'Mg', 'Pd')\",OTHERS,False\nmp-541047,\"{'Al': 1.0, 'Pd': 2.0, 'Zr': 1.0}\",\"('Al', 'Pd', 'Zr')\",OTHERS,False\nmp-1246515,\"{'Ca': 12.0, 'Te': 6.0, 'N': 4.0}\",\"('Ca', 'N', 'Te')\",OTHERS,False\nmp-28550,\"{'F': 14.0, 'Ag': 3.0, 'Hf': 2.0}\",\"('Ag', 'F', 'Hf')\",OTHERS,False\nmp-23194,\"{'La': 1.0, 'I': 2.0}\",\"('I', 'La')\",OTHERS,False\nmp-1201679,\"{'Ba': 4.0, 'La': 8.0, 'W': 4.0, 'O': 28.0}\",\"('Ba', 'La', 'O', 'W')\",OTHERS,False\nmp-753188,\"{'Li': 5.0, 'Mn': 2.0, 'Co': 5.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-19112,\"{'O': 8.0, 'S': 2.0, 'Fe': 2.0}\",\"('Fe', 'O', 'S')\",OTHERS,False\nmp-21895,\"{'Na': 30.0, 'Pb': 8.0}\",\"('Na', 'Pb')\",OTHERS,False\nmp-982243,\"{'Y': 6.0, 'Sn': 2.0}\",\"('Sn', 'Y')\",OTHERS,False\nmp-12466,\"{'Fe': 2.0, 'Se': 4.0, 'Pd': 4.0}\",\"('Fe', 'Pd', 'Se')\",OTHERS,False\nmp-669913,\"{'Sr': 4.0, 'Pb': 6.0}\",\"('Pb', 'Sr')\",OTHERS,False\nmvc-13624,\"{'Ca': 1.0, 'La': 2.0, 'Ti': 1.0, 'O': 6.0}\",\"('Ca', 'La', 'O', 'Ti')\",OTHERS,False\nmp-759047,\"{'Li': 12.0, 'Cr': 3.0, 'P': 6.0, 'O': 24.0, 'F': 6.0}\",\"('Cr', 'F', 'Li', 'O', 'P')\",OTHERS,False\nmp-1199246,\"{'Ba': 10.0, 'B': 6.0, 'Br': 2.0, 'O': 18.0}\",\"('B', 'Ba', 'Br', 'O')\",OTHERS,False\nmp-775072,\"{'Li': 4.0, 'Mn': 3.0, 'Cu': 2.0, 'Sb': 3.0, 'O': 16.0}\",\"('Cu', 'Li', 'Mn', 'O', 'Sb')\",OTHERS,False\nmp-1188841,\"{'Ba': 1.0, 'Nb': 2.0, 'V': 2.0, 'O': 11.0}\",\"('Ba', 'Nb', 'O', 'V')\",OTHERS,False\nmp-3782,\"{'O': 24.0, 'Sb': 10.0, 'Nd': 6.0}\",\"('Nd', 'O', 'Sb')\",OTHERS,False\nmp-1112894,\"{'Cs': 2.0, 'In': 1.0, 'As': 1.0, 'Cl': 6.0}\",\"('As', 'Cl', 'Cs', 'In')\",OTHERS,False\nmp-675755,\"{'Ti': 1.0, 'Cu': 3.0, 'O': 4.0}\",\"('Cu', 'O', 'Ti')\",OTHERS,False\nmp-752935,\"{'Li': 2.0, 'Fe': 4.0, 'O': 8.0}\",\"('Fe', 'Li', 'O')\",OTHERS,False\nmp-1079395,\"{'Ce': 4.0, 'Ge': 4.0}\",\"('Ce', 'Ge')\",OTHERS,False\nmp-777583,\"{'Li': 8.0, 'Mn': 3.0, 'Fe': 5.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-812,\"{'O': 24.0, 'Ho': 16.0}\",\"('Ho', 'O')\",OTHERS,False\nmp-24798,\"{'H': 12.0, 'B': 12.0, 'Cl': 1.0, 'Rb': 3.0}\",\"('B', 'Cl', 'H', 'Rb')\",OTHERS,False\nmvc-2321,\"{'Ba': 4.0, 'Mg': 2.0, 'Co': 4.0, 'Cu': 2.0, 'F': 28.0}\",\"('Ba', 'Co', 'Cu', 'F', 'Mg')\",OTHERS,False\nmp-1176570,\"{'Mn': 3.0, 'Ni': 1.0, 'Sn': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Mn', 'Ni', 'O', 'P', 'Sn')\",OTHERS,False\nmp-1183235,\"{'Ac': 1.0, 'Nd': 3.0}\",\"('Ac', 'Nd')\",OTHERS,False\nmp-6073,\"{'O': 48.0, 'Mg': 12.0, 'Al': 8.0, 'Si': 12.0}\",\"('Al', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-1209845,\"{'Nd': 4.0, 'Si': 4.0, 'Pd': 8.0}\",\"('Nd', 'Pd', 'Si')\",OTHERS,False\nmp-758399,\"{'Li': 6.0, 'V': 3.0, 'Cr': 3.0, 'P': 6.0, 'H': 6.0, 'O': 30.0}\",\"('Cr', 'H', 'Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1024052,\"{'Na': 8.0, 'Zn': 4.0, 'Se': 8.0}\",\"('Na', 'Se', 'Zn')\",OTHERS,False\nmp-570807,\"{'Sr': 4.0, 'In': 13.0, 'Au': 9.0}\",\"('Au', 'In', 'Sr')\",OTHERS,False\nmp-1782,\"{'Sc': 1.0, 'Se': 1.0}\",\"('Sc', 'Se')\",OTHERS,False\nmp-1222754,\"{'La': 1.0, 'Y': 1.0, 'Mg': 6.0}\",\"('La', 'Mg', 'Y')\",OTHERS,False\nmp-1178224,\"{'Fe': 4.0, 'O': 4.0, 'F': 4.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-11327,\"{'Se': 1.0, 'Rb': 2.0}\",\"('Rb', 'Se')\",OTHERS,False\nmp-1184476,\"{'Gd': 2.0, 'In': 1.0, 'Hg': 1.0}\",\"('Gd', 'Hg', 'In')\",OTHERS,False\nmp-558982,\"{'Hg': 4.0, 'C': 8.0, 'O': 8.0, 'F': 12.0}\",\"('C', 'F', 'Hg', 'O')\",OTHERS,False\nmp-1245091,\"{'Cr': 32.0, 'O': 48.0}\",\"('Cr', 'O')\",OTHERS,False\nmp-7795,\"{'Rb': 1.0, 'Sn': 1.0, 'S': 2.0}\",\"('Rb', 'S', 'Sn')\",OTHERS,False\nmp-723002,\"{'Ag': 4.0, 'H': 24.0, 'S': 2.0, 'N': 8.0, 'O': 8.0}\",\"('Ag', 'H', 'N', 'O', 'S')\",OTHERS,False\nmp-1080016,\"{'Tl': 6.0, 'Ir': 2.0}\",\"('Ir', 'Tl')\",OTHERS,False\nmp-20353,\"{'Ga': 2.0, 'Gd': 2.0}\",\"('Ga', 'Gd')\",OTHERS,False\nmp-574486,\"{'Ag': 8.0, 'C': 4.0, 'N': 8.0}\",\"('Ag', 'C', 'N')\",OTHERS,False\nmp-1203864,\"{'B': 8.0, 'P': 24.0, 'Pb': 24.0, 'O': 96.0}\",\"('B', 'O', 'P', 'Pb')\",OTHERS,False\nmp-768846,\"{'Rb': 2.0, 'Fe': 4.0, 'O': 7.0}\",\"('Fe', 'O', 'Rb')\",OTHERS,False\nmvc-6932,\"{'Ca': 2.0, 'Cr': 4.0, 'O': 8.0}\",\"('Ca', 'Cr', 'O')\",OTHERS,False\nmp-9771,\"{'O': 6.0, 'Ti': 2.0, 'U': 1.0}\",\"('O', 'Ti', 'U')\",OTHERS,False\nmp-774237,\"{'Li': 5.0, 'Cr': 2.0, 'Ni': 5.0, 'O': 12.0}\",\"('Cr', 'Li', 'Ni', 'O')\",OTHERS,False\nmp-1203988,\"{'H': 30.0, 'Pd': 2.0, 'Ru': 2.0, 'N': 18.0, 'O': 22.0}\",\"('H', 'N', 'O', 'Pd', 'Ru')\",OTHERS,False\nmp-757668,\"{'Li': 4.0, 'Mn': 3.0, 'Ni': 2.0, 'Sb': 3.0, 'O': 16.0}\",\"('Li', 'Mn', 'Ni', 'O', 'Sb')\",OTHERS,False\nmp-1177871,\"{'Li': 2.0, 'Nb': 1.0, 'Co': 3.0, 'O': 8.0}\",\"('Co', 'Li', 'Nb', 'O')\",OTHERS,False\nmp-1209024,\"{'Sc': 12.0, 'Sn': 10.0}\",\"('Sc', 'Sn')\",OTHERS,False\nmp-19803,\"{'O': 8.0, 'Cd': 2.0, 'In': 4.0}\",\"('Cd', 'In', 'O')\",OTHERS,False\nmp-752820,\"{'Li': 4.0, 'V': 2.0, 'P': 4.0, 'O': 14.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1228657,\"{'B': 4.0, 'Ru': 2.0, 'W': 2.0}\",\"('B', 'Ru', 'W')\",OTHERS,False\nmp-1101695,\"{'Na': 2.0, 'Fe': 24.0, 'O': 38.0}\",\"('Fe', 'Na', 'O')\",OTHERS,False\nmp-1175904,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-779498,\"{'Ba': 24.0, 'Cl': 16.0, 'O': 16.0}\",\"('Ba', 'Cl', 'O')\",OTHERS,False\nmp-1192618,\"{'Nb': 8.0, 'Fe': 8.0, 'Si': 14.0}\",\"('Fe', 'Nb', 'Si')\",OTHERS,False\nmp-19875,\"{'Si': 2.0, 'Ru': 2.0, 'Gd': 2.0}\",\"('Gd', 'Ru', 'Si')\",OTHERS,False\nmp-1104829,\"{'Nb': 5.0, 'Sn': 1.0, 'Se': 8.0}\",\"('Nb', 'Se', 'Sn')\",OTHERS,False\nmp-1185170,\"{'La': 3.0, 'Lu': 1.0}\",\"('La', 'Lu')\",OTHERS,False\nmp-865429,\"{'Yb': 1.0, 'Y': 1.0, 'Rh': 2.0}\",\"('Rh', 'Y', 'Yb')\",OTHERS,False\nmp-1184327,\"{'Eu': 3.0, 'V': 1.0}\",\"('Eu', 'V')\",OTHERS,False\nmp-1173284,\"{'Sr': 24.0, 'Fe': 12.0, 'Mo': 12.0, 'O': 72.0}\",\"('Fe', 'Mo', 'O', 'Sr')\",OTHERS,False\nmp-995122,\"{'Nb': 1.0, 'S': 2.0}\",\"('Nb', 'S')\",OTHERS,False\nmp-1193749,\"{'Rb': 4.0, 'Mg': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Mg', 'O', 'P', 'Rb')\",OTHERS,False\nmp-1100345,\"{'Ca': 2.0, 'Sn': 2.0, 'S': 6.0}\",\"('Ca', 'S', 'Sn')\",OTHERS,False\nmp-3779,\"{'Si': 2.0, 'Cr': 2.0, 'Te': 6.0}\",\"('Cr', 'Si', 'Te')\",OTHERS,False\nmp-1223321,\"{'K': 4.0, 'Ta': 4.0, 'Si': 8.0, 'O': 28.0}\",\"('K', 'O', 'Si', 'Ta')\",OTHERS,False\nmp-760022,\"{'Mn': 12.0, 'O': 10.0, 'F': 14.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-1206382,\"{'Tm': 2.0, 'Ni': 2.0, 'Pb': 1.0}\",\"('Ni', 'Pb', 'Tm')\",OTHERS,False\nmp-561712,\"{'Cs': 4.0, 'Er': 4.0, 'P': 16.0, 'O': 48.0}\",\"('Cs', 'Er', 'O', 'P')\",OTHERS,False\nmp-1066131,\"{'Y': 1.0, 'Tl': 1.0, 'S': 2.0}\",\"('S', 'Tl', 'Y')\",OTHERS,False\nmp-865751,\"{'Yb': 1.0, 'Dy': 1.0, 'Rh': 2.0}\",\"('Dy', 'Rh', 'Yb')\",OTHERS,False\nmp-1184505,\"{'Gd': 1.0, 'Pa': 1.0, 'Tc': 2.0}\",\"('Gd', 'Pa', 'Tc')\",OTHERS,False\nmp-1215634,\"{'Zr': 4.0, 'Te': 6.0}\",\"('Te', 'Zr')\",OTHERS,False\nmp-554461,\"{'K': 2.0, 'Os': 4.0, 'O': 12.0}\",\"('K', 'O', 'Os')\",OTHERS,False\nmp-755927,\"{'Li': 2.0, 'Cr': 3.0, 'Fe': 1.0, 'O': 8.0}\",\"('Cr', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-861894,\"{'Li': 2.0, 'La': 1.0, 'Sn': 1.0}\",\"('La', 'Li', 'Sn')\",OTHERS,False\nmp-1222347,\"{'Li': 2.0, 'Ti': 2.0, 'Cr': 2.0, 'O': 8.0}\",\"('Cr', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-1216437,\"{'V': 6.0, 'Ge': 1.0, 'Os': 1.0}\",\"('Ge', 'Os', 'V')\",OTHERS,False\nmp-1221259,\"{'Na': 3.0, 'Ca': 1.0, 'Mg': 1.0, 'Cr': 3.0, 'Si': 8.0, 'O': 24.0}\",\"('Ca', 'Cr', 'Mg', 'Na', 'O', 'Si')\",OTHERS,False\nmp-559871,\"{'Al': 6.0, 'F': 18.0}\",\"('Al', 'F')\",OTHERS,False\nmp-35031,\"{'Cu': 1.0, 'Pr': 5.0, 'Se': 8.0}\",\"('Cu', 'Pr', 'Se')\",OTHERS,False\nmp-600029,\"{'Si': 24.0, 'O': 48.0}\",\"('O', 'Si')\",OTHERS,False\nmp-757062,\"{'Li': 4.0, 'Nb': 3.0, 'V': 3.0, 'Sn': 2.0, 'O': 16.0}\",\"('Li', 'Nb', 'O', 'Sn', 'V')\",OTHERS,False\nmp-1212579,\"{'H': 24.0, 'C': 24.0, 'N': 40.0}\",\"('C', 'H', 'N')\",OTHERS,False\nmp-1175048,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-556289,\"{'Na': 8.0, 'Er': 4.0, 'Mo': 4.0, 'P': 4.0, 'O': 32.0}\",\"('Er', 'Mo', 'Na', 'O', 'P')\",OTHERS,False\nmp-1246047,\"{'Te': 2.0, 'C': 2.0, 'N': 4.0}\",\"('C', 'N', 'Te')\",OTHERS,False\nmp-2176,\"{'Zn': 1.0, 'Te': 1.0}\",\"('Te', 'Zn')\",OTHERS,False\nmp-1202079,\"{'Ti': 42.0, 'Mn': 50.0}\",\"('Mn', 'Ti')\",OTHERS,False\nmvc-5090,\"{'Ca': 4.0, 'Ti': 2.0, 'W': 2.0, 'O': 12.0}\",\"('Ca', 'O', 'Ti', 'W')\",OTHERS,False\nmp-984716,\"{'Ca': 1.0, 'Sn': 4.0, 'P': 6.0, 'O': 24.0}\",\"('Ca', 'O', 'P', 'Sn')\",OTHERS,False\nmp-1175920,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-566602,\"{'La': 4.0, 'Mn': 4.0, 'O': 12.0}\",\"('La', 'Mn', 'O')\",OTHERS,False\nmp-1079810,\"{'Hf': 3.0, 'Sn': 6.0}\",\"('Hf', 'Sn')\",OTHERS,False\nmp-505042,\"{'Bi': 8.0, 'Cu': 4.0, 'O': 16.0}\",\"('Bi', 'Cu', 'O')\",OTHERS,False\nmp-36526,\"{'Ac': 1.0, 'F': 1.0, 'O': 1.0}\",\"('Ac', 'F', 'O')\",OTHERS,False\nmp-683950,\"{'Ca': 20.0, 'Pr': 8.0, 'Cu': 48.0, 'O': 82.0}\",\"('Ca', 'Cu', 'O', 'Pr')\",OTHERS,False\nmp-777883,\"{'Ba': 24.0, 'Y': 4.0, 'Cl': 60.0}\",\"('Ba', 'Cl', 'Y')\",OTHERS,False\nmp-558048,\"{'Na': 2.0, 'V': 2.0, 'Ge': 4.0, 'O': 12.0}\",\"('Ge', 'Na', 'O', 'V')\",OTHERS,False\nmp-1204281,\"{'Hf': 12.0, 'Ge': 24.0, 'Ru': 12.0}\",\"('Ge', 'Hf', 'Ru')\",OTHERS,False\nmp-1216310,\"{'V': 2.0, 'Mo': 2.0, 'As': 4.0}\",\"('As', 'Mo', 'V')\",OTHERS,False\nmp-1102373,\"{'Y': 4.0, 'Ge': 4.0, 'Ir': 4.0}\",\"('Ge', 'Ir', 'Y')\",OTHERS,False\nmp-975051,\"{'Rb': 3.0, 'Ag': 1.0}\",\"('Ag', 'Rb')\",OTHERS,False\nmp-1183999,\"{'Cs': 1.0, 'Yb': 3.0}\",\"('Cs', 'Yb')\",OTHERS,False\nmp-1222787,\"{'La': 2.0, 'Ti': 2.0, 'Nb': 2.0, 'O': 12.0}\",\"('La', 'Nb', 'O', 'Ti')\",OTHERS,False\nmp-1195884,\"{'K': 4.0, 'Nd': 2.0, 'N': 10.0, 'O': 34.0}\",\"('K', 'N', 'Nd', 'O')\",OTHERS,False\nmp-1185611,\"{'Lu': 3.0, 'Pu': 1.0}\",\"('Lu', 'Pu')\",OTHERS,False\nmp-771523,\"{'Li': 4.0, 'Cr': 8.0, 'O': 16.0}\",\"('Cr', 'Li', 'O')\",OTHERS,False\nmp-541997,\"{'K': 12.0, 'Fe': 4.0, 'Se': 12.0}\",\"('Fe', 'K', 'Se')\",OTHERS,False\nmp-16037,\"{'O': 2.0, 'Te': 1.0, 'Dy': 2.0}\",\"('Dy', 'O', 'Te')\",OTHERS,False\nmp-1190394,\"{'Nd': 8.0, 'Fe': 2.0, 'S': 12.0, 'O': 2.0}\",\"('Fe', 'Nd', 'O', 'S')\",OTHERS,False\nmp-1099080,\"{'Mg': 14.0, 'Fe': 1.0, 'Co': 1.0}\",\"('Co', 'Fe', 'Mg')\",OTHERS,False\nmp-1245707,\"{'Sb': 2.0, 'Te': 2.0, 'N': 2.0}\",\"('N', 'Sb', 'Te')\",OTHERS,False\nmp-705526,\"{'H': 64.0, 'Au': 4.0, 'C': 24.0, 'S': 8.0, 'N': 16.0, 'Cl': 4.0, 'O': 16.0}\",\"('Au', 'C', 'Cl', 'H', 'N', 'O', 'S')\",OTHERS,False\nmp-31378,\"{'O': 52.0, 'Cr': 16.0, 'Cs': 8.0}\",\"('Cr', 'Cs', 'O')\",OTHERS,False\nmp-267,\"{'Ru': 2.0, 'Te': 4.0}\",\"('Ru', 'Te')\",OTHERS,False\nmp-504357,\"{'Cr': 16.0, 'P': 12.0, 'O': 48.0}\",\"('Cr', 'O', 'P')\",OTHERS,False\nmp-1224618,\"{'Ho': 5.0, 'Co': 19.0, 'P': 12.0}\",\"('Co', 'Ho', 'P')\",OTHERS,False\nmp-1190699,\"{'C': 8.0, 'O': 16.0}\",\"('C', 'O')\",OTHERS,False\nmp-1217824,\"{'Ta': 1.0, 'V': 1.0, 'C': 1.0, 'N': 1.0}\",\"('C', 'N', 'Ta', 'V')\",OTHERS,False\nmp-21683,\"{'In': 2.0, 'Ni': 21.0, 'P': 6.0}\",\"('In', 'Ni', 'P')\",OTHERS,False\nmp-754078,\"{'Li': 1.0, 'Fe': 2.0, 'P': 2.0, 'O': 8.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-1226274,\"{'Cs': 2.0, 'K': 1.0, 'Ti': 1.0, 'O': 2.0, 'F': 3.0}\",\"('Cs', 'F', 'K', 'O', 'Ti')\",OTHERS,False\nmp-560022,\"{'Ba': 18.0, 'Nb': 4.0, 'C': 2.0, 'N': 20.0, 'O': 2.0}\",\"('Ba', 'C', 'N', 'Nb', 'O')\",OTHERS,False\nmp-1187717,{'Y': 4.0},\"('Y',)\",OTHERS,False\nmp-1103473,\"{'Cs': 1.0, 'Cr': 1.0, 'P': 2.0, 'S': 7.0}\",\"('Cr', 'Cs', 'P', 'S')\",OTHERS,False\nmp-24066,\"{'H': 2.0, 'O': 2.0, 'Cl': 2.0, 'Sr': 2.0}\",\"('Cl', 'H', 'O', 'Sr')\",OTHERS,False\nmp-557825,\"{'H': 8.0, 'C': 2.0, 'N': 4.0, 'O': 2.0}\",\"('C', 'H', 'N', 'O')\",OTHERS,False\nmp-974469,\"{'Re': 2.0, 'Te': 2.0}\",\"('Re', 'Te')\",OTHERS,False\nmp-1189057,\"{'Lu': 4.0, 'Ge': 4.0, 'Pd': 8.0}\",\"('Ge', 'Lu', 'Pd')\",OTHERS,False\nmp-1235846,\"{'Sr': 2.0, 'Li': 1.0, 'O': 12.0}\",\"('Li', 'O', 'Sr')\",OTHERS,False\nmp-556459,\"{'V': 2.0, 'P': 2.0, 'O': 10.0}\",\"('O', 'P', 'V')\",OTHERS,False\nmp-1245656,\"{'K': 2.0, 'Pd': 2.0, 'N': 2.0}\",\"('K', 'N', 'Pd')\",OTHERS,False\nmp-1220381,\"{'Nb': 6.0, 'Ga': 1.0, 'Ge': 1.0}\",\"('Ga', 'Ge', 'Nb')\",OTHERS,False\nmp-675743,\"{'Mg': 2.0, 'Mo': 12.0, 'Se': 16.0}\",\"('Mg', 'Mo', 'Se')\",OTHERS,False\nmp-1184233,\"{'Er': 1.0, 'Mg': 1.0, 'Ag': 2.0}\",\"('Ag', 'Er', 'Mg')\",OTHERS,False\nmp-1094804,\"{'Mg': 4.0, 'Cd': 8.0}\",\"('Cd', 'Mg')\",OTHERS,False\nmp-560090,\"{'Mn': 4.0, 'Ni': 12.0, 'Te': 6.0, 'Pb': 2.0, 'O': 36.0}\",\"('Mn', 'Ni', 'O', 'Pb', 'Te')\",OTHERS,False\nmp-4788,\"{'O': 12.0, 'K': 2.0, 'Os': 4.0}\",\"('K', 'O', 'Os')\",OTHERS,False\nmp-734561,\"{'Rb': 6.0, 'P': 6.0, 'H': 8.0, 'O': 24.0}\",\"('H', 'O', 'P', 'Rb')\",OTHERS,False\nmp-755567,\"{'Fe': 6.0, 'O': 8.0, 'F': 4.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-1104131,\"{'Nb': 2.0, 'P': 2.0, 'O': 10.0}\",\"('Nb', 'O', 'P')\",OTHERS,False\nmp-707290,\"{'Nb': 20.0, 'Pb': 28.0, 'O': 78.0}\",\"('Nb', 'O', 'Pb')\",OTHERS,False\nmp-1219465,\"{'Sm': 4.0, 'Re': 12.0, 'O': 60.0}\",\"('O', 'Re', 'Sm')\",OTHERS,False\nmp-1212327,\"{'Ho': 16.0, 'Al': 8.0, 'O': 36.0}\",\"('Al', 'Ho', 'O')\",OTHERS,False\nmp-752651,\"{'Li': 4.0, 'Ti': 2.0, 'Nb': 3.0, 'Cu': 3.0, 'O': 16.0}\",\"('Cu', 'Li', 'Nb', 'O', 'Ti')\",OTHERS,False\nmp-1194276,\"{'Ca': 4.0, 'Ho': 8.0, 'S': 16.0}\",\"('Ca', 'Ho', 'S')\",OTHERS,False\nmp-1225693,\"{'Dy': 1.0, 'Mn': 2.0, 'Si': 1.0, 'Ge': 1.0}\",\"('Dy', 'Ge', 'Mn', 'Si')\",OTHERS,False\nmp-11873,\"{'As': 1.0, 'Pd': 5.0, 'Tl': 1.0}\",\"('As', 'Pd', 'Tl')\",OTHERS,False\nmp-778809,\"{'Fe': 10.0, 'O': 14.0, 'F': 6.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-8219,\"{'P': 2.0, 'Cu': 2.0, 'Zr': 1.0}\",\"('Cu', 'P', 'Zr')\",OTHERS,False\nmp-1191259,\"{'Cu': 3.0, 'Bi': 2.0, 'P': 2.0, 'O': 14.0}\",\"('Bi', 'Cu', 'O', 'P')\",OTHERS,False\nmp-11523,\"{'Ni': 3.0, 'Sn': 1.0}\",\"('Ni', 'Sn')\",OTHERS,False\nmp-631495,\"{'Ba': 1.0, 'Y': 1.0, 'Sc': 1.0}\",\"('Ba', 'Sc', 'Y')\",OTHERS,False\nmp-21620,\"{'Si': 20.0, 'Tc': 12.0, 'U': 8.0}\",\"('Si', 'Tc', 'U')\",OTHERS,False\nmp-1182246,\"{'Ba': 2.0, 'Ge': 6.0, 'O': 16.0}\",\"('Ba', 'Ge', 'O')\",OTHERS,False\nmp-752890,\"{'Li': 2.0, 'Cu': 4.0, 'C': 4.0, 'O': 14.0}\",\"('C', 'Cu', 'Li', 'O')\",OTHERS,False\nmp-23405,\"{'Br': 6.0, 'Te': 1.0, 'Cs': 2.0}\",\"('Br', 'Cs', 'Te')\",OTHERS,False\nmp-1198640,\"{'Mg': 1.0, 'Hg': 6.0, 'N': 6.0, 'O': 26.0}\",\"('Hg', 'Mg', 'N', 'O')\",OTHERS,False\nmp-1185830,\"{'Mg': 5.0, 'In': 1.0}\",\"('In', 'Mg')\",OTHERS,False\nmp-765020,\"{'Sr': 24.0, 'Fe': 16.0, 'O': 53.0}\",\"('Fe', 'O', 'Sr')\",OTHERS,False\nmp-752865,\"{'Li': 4.0, 'Mn': 5.0, 'Cu': 3.0, 'O': 16.0}\",\"('Cu', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-767518,\"{'Na': 4.0, 'Sc': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Na', 'O', 'P', 'Sc')\",OTHERS,False\nmp-774463,\"{'Li': 6.0, 'Cr': 4.0, 'P': 6.0, 'O': 24.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-676663,\"{'Zn': 6.0, 'Sn': 6.0, 'Sb': 12.0}\",\"('Sb', 'Sn', 'Zn')\",OTHERS,False\nmp-555509,\"{'Ho': 6.0, 'Cu': 2.0, 'Ge': 2.0, 'S': 14.0}\",\"('Cu', 'Ge', 'Ho', 'S')\",OTHERS,False\nmp-1219565,\"{'Rb': 1.0, 'O': 2.0}\",\"('O', 'Rb')\",OTHERS,False\nmp-1218104,\"{'Sr': 1.0, 'Pr': 3.0, 'Fe': 4.0, 'O': 12.0}\",\"('Fe', 'O', 'Pr', 'Sr')\",OTHERS,False\nmp-1114631,\"{'Rb': 3.0, 'Sb': 1.0, 'Cl': 6.0}\",\"('Cl', 'Rb', 'Sb')\",OTHERS,False\nmp-765146,\"{'Li': 20.0, 'V': 4.0, 'C': 16.0, 'O': 48.0}\",\"('C', 'Li', 'O', 'V')\",OTHERS,False\nmp-768438,\"{'Li': 16.0, 'Ti': 4.0, 'Cr': 4.0, 'O': 24.0}\",\"('Cr', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-557969,\"{'Li': 2.0, 'La': 4.0, 'S': 4.0, 'O': 16.0, 'F': 6.0}\",\"('F', 'La', 'Li', 'O', 'S')\",OTHERS,False\nmp-754659,\"{'Zr': 6.0, 'N': 4.0, 'O': 6.0}\",\"('N', 'O', 'Zr')\",OTHERS,False\nmp-677732,\"{'Nd': 2.0, 'Ti': 4.0, 'Cd': 2.0, 'O': 12.0, 'F': 2.0}\",\"('Cd', 'F', 'Nd', 'O', 'Ti')\",OTHERS,False\nmvc-5764,\"{'Ca': 8.0, 'Mn': 16.0, 'O': 32.0}\",\"('Ca', 'Mn', 'O')\",OTHERS,False\nmp-1222544,\"{'Li': 4.0, 'Co': 1.0, 'Ru': 1.0, 'O': 6.0}\",\"('Co', 'Li', 'O', 'Ru')\",OTHERS,False\nmp-1029975,\"{'Al': 4.0, 'C': 6.0, 'N': 12.0}\",\"('Al', 'C', 'N')\",OTHERS,False\nmp-1199077,\"{'Ba': 4.0, 'B': 12.0, 'H': 8.0, 'O': 26.0}\",\"('B', 'Ba', 'H', 'O')\",OTHERS,False\nmp-770175,\"{'Li': 4.0, 'Mn': 6.0, 'Fe': 2.0, 'O': 16.0}\",\"('Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-865402,\"{'Tm': 3.0, 'Al': 3.0, 'Cu': 3.0}\",\"('Al', 'Cu', 'Tm')\",OTHERS,False\nmp-505802,\"{'Sn': 2.0, 'Mo': 8.0, 'O': 12.0}\",\"('Mo', 'O', 'Sn')\",OTHERS,False\nmp-1209222,\"{'Rb': 2.0, 'Lu': 2.0, 'Be': 2.0, 'F': 12.0}\",\"('Be', 'F', 'Lu', 'Rb')\",OTHERS,False\nmp-1186131,\"{'Na': 1.0, 'Dy': 3.0}\",\"('Dy', 'Na')\",OTHERS,False\nmp-24356,\"{'H': 16.0, 'N': 4.0, 'Cl': 12.0, 'Cd': 4.0}\",\"('Cd', 'Cl', 'H', 'N')\",OTHERS,False\nmp-1216388,\"{'Zr': 18.0, 'Co': 5.0, 'Sn': 4.0}\",\"('Co', 'Sn', 'Zr')\",OTHERS,False\nmp-561525,\"{'K': 6.0, 'Ga': 6.0, 'B': 6.0, 'O': 21.0}\",\"('B', 'Ga', 'K', 'O')\",OTHERS,False\nmp-555073,\"{'La': 12.0, 'As': 8.0, 'Cl': 4.0, 'O': 28.0}\",\"('As', 'Cl', 'La', 'O')\",OTHERS,False\nmp-554131,\"{'Sr': 4.0, 'Li': 4.0, 'Co': 4.0, 'F': 24.0}\",\"('Co', 'F', 'Li', 'Sr')\",OTHERS,False\nmp-762505,\"{'Ti': 3.0, 'Co': 3.0, 'Sb': 2.0, 'O': 16.0}\",\"('Co', 'O', 'Sb', 'Ti')\",OTHERS,False\nmp-1101,\"{'As': 1.0, 'Tm': 1.0}\",\"('As', 'Tm')\",OTHERS,False\nmp-27570,\"{'Cl': 16.0, 'Fe': 4.0, 'Cs': 4.0}\",\"('Cl', 'Cs', 'Fe')\",OTHERS,False\nmp-1187310,\"{'Tb': 1.0, 'Be': 1.0, 'O': 3.0}\",\"('Be', 'O', 'Tb')\",OTHERS,False\nmp-1212001,\"{'K': 12.0, 'Si': 24.0, 'H': 24.0, 'N': 44.0}\",\"('H', 'K', 'N', 'Si')\",OTHERS,False\nmp-1218980,\"{'Sm': 2.0, 'Ga': 2.0, 'Si': 2.0}\",\"('Ga', 'Si', 'Sm')\",OTHERS,False\nmp-13136,\"{'C': 1.0, 'W': 1.0}\",\"('C', 'W')\",OTHERS,False\nmvc-16045,\"{'Sr': 4.0, 'Ca': 1.0, 'Cr': 2.0, 'S': 2.0, 'O': 6.0}\",\"('Ca', 'Cr', 'O', 'S', 'Sr')\",OTHERS,False\nmp-582227,\"{'Eu': 1.0, 'Mn': 2.0, 'Sb': 2.0}\",\"('Eu', 'Mn', 'Sb')\",OTHERS,False\nmp-1237680,\"{'Li': 4.0, 'Cr': 4.0, 'O': 18.0}\",\"('Cr', 'Li', 'O')\",OTHERS,False\nmp-757440,\"{'Cr': 3.0, 'S': 6.0, 'O': 24.0}\",\"('Cr', 'O', 'S')\",OTHERS,False\nmp-530787,\"{'O': 53.0, 'Y': 6.0, 'Zr': 22.0}\",\"('O', 'Y', 'Zr')\",OTHERS,False\nmp-21153,\"{'Er': 4.0, 'Ge': 4.0, 'Ir': 4.0}\",\"('Er', 'Ge', 'Ir')\",OTHERS,False\nmp-1101284,\"{'Ti': 6.0, 'Nb': 2.0, 'O': 16.0}\",\"('Nb', 'O', 'Ti')\",OTHERS,False\nmp-1203703,\"{'Tm': 4.0, 'N': 12.0, 'O': 36.0}\",\"('N', 'O', 'Tm')\",OTHERS,False\nmp-1193375,\"{'Yb': 2.0, 'Ni': 21.0, 'B': 6.0}\",\"('B', 'Ni', 'Yb')\",OTHERS,False\nmp-25992,\"{'Ni': 6.0, 'P': 8.0, 'O': 28.0}\",\"('Ni', 'O', 'P')\",OTHERS,False\nmp-1174245,\"{'Li': 7.0, 'Co': 5.0, 'O': 12.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-840,\"{'Rh': 4.0, 'Sm': 2.0}\",\"('Rh', 'Sm')\",OTHERS,False\nmp-766870,\"{'Mn': 5.0, 'O': 9.0, 'F': 1.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-1178581,\"{'Ba': 16.0, 'Ti': 12.0, 'O': 40.0}\",\"('Ba', 'O', 'Ti')\",OTHERS,False\nmp-707839,\"{'Si': 2.0, 'H': 28.0, 'O': 8.0, 'F': 12.0}\",\"('F', 'H', 'O', 'Si')\",OTHERS,False\nmp-4689,\"{'Al': 28.0, 'Fe': 4.0, 'Cu': 8.0}\",\"('Al', 'Cu', 'Fe')\",OTHERS,False\nmvc-10589,\"{'Ba': 4.0, 'Mg': 4.0, 'Mo': 4.0, 'F': 28.0}\",\"('Ba', 'F', 'Mg', 'Mo')\",OTHERS,False\nmp-675372,\"{'Ca': 2.0, 'Zr': 8.0, 'O': 18.0}\",\"('Ca', 'O', 'Zr')\",OTHERS,False\nmp-755243,\"{'Li': 5.0, 'Cu': 1.0, 'S': 1.0, 'O': 2.0}\",\"('Cu', 'Li', 'O', 'S')\",OTHERS,False\nmp-1178259,\"{'Fe': 4.0, 'Co': 12.0, 'O': 32.0}\",\"('Co', 'Fe', 'O')\",OTHERS,False\nmp-1008823,\"{'Os': 1.0, 'N': 2.0}\",\"('N', 'Os')\",OTHERS,False\nmp-1189981,\"{'Zr': 10.0, 'Zn': 2.0, 'Sn': 6.0}\",\"('Sn', 'Zn', 'Zr')\",OTHERS,False\nmp-1227768,\"{'Ba': 1.0, 'Sr': 2.0, 'P': 2.0, 'O': 8.0}\",\"('Ba', 'O', 'P', 'Sr')\",OTHERS,False\nmp-653838,\"{'Zr': 4.0, 'Fe': 48.0, 'Si': 8.0, 'B': 4.0}\",\"('B', 'Fe', 'Si', 'Zr')\",OTHERS,False\nmp-1182142,\"{'Ba': 2.0, 'Al': 4.0, 'O': 12.0}\",\"('Al', 'Ba', 'O')\",OTHERS,False\nmp-32308,\"{'O': 12.0, 'Ni': 2.0, 'Ta': 4.0}\",\"('Ni', 'O', 'Ta')\",OTHERS,False\nmp-684911,\"{'Sm': 16.0, 'Te': 24.0}\",\"('Sm', 'Te')\",OTHERS,False\nmp-1219038,\"{'Sm': 1.0, 'U': 1.0, 'Te': 6.0}\",\"('Sm', 'Te', 'U')\",OTHERS,False\nmp-1183962,\"{'Eu': 2.0, 'Cd': 1.0, 'Pb': 1.0}\",\"('Cd', 'Eu', 'Pb')\",OTHERS,False\nmp-1097234,\"{'V': 1.0, 'Cr': 1.0, 'W': 2.0}\",\"('Cr', 'V', 'W')\",OTHERS,False\nmp-1183799,\"{'Dy': 1.0, 'Ho': 1.0, 'Zn': 2.0}\",\"('Dy', 'Ho', 'Zn')\",OTHERS,False\nmp-760939,\"{'Cu': 6.0, 'O': 1.0, 'F': 11.0}\",\"('Cu', 'F', 'O')\",OTHERS,False\nmp-1232092,\"{'Sm': 8.0, 'Mg': 4.0, 'S': 16.0}\",\"('Mg', 'S', 'Sm')\",OTHERS,False\nmvc-5962,\"{'Ca': 2.0, 'Ta': 1.0, 'W': 1.0, 'O': 6.0}\",\"('Ca', 'O', 'Ta', 'W')\",OTHERS,False\nmp-13323,\"{'O': 6.0, 'Nb': 1.0, 'Ba': 2.0, 'Eu': 1.0}\",\"('Ba', 'Eu', 'Nb', 'O')\",OTHERS,False\nmp-540879,\"{'Eu': 4.0, 'B': 8.0, 'O': 16.0}\",\"('B', 'Eu', 'O')\",OTHERS,False\nmp-10208,\"{'P': 4.0, 'Ni': 2.0, 'Mo': 2.0}\",\"('Mo', 'Ni', 'P')\",OTHERS,False\nmp-1193690,\"{'Ge': 7.0, 'H': 1.0, 'O': 20.0}\",\"('Ge', 'H', 'O')\",OTHERS,False\nmp-1201385,\"{'Ba': 6.0, 'Fe': 52.0, 'O': 82.0}\",\"('Ba', 'Fe', 'O')\",OTHERS,False\nmp-1105702,\"{'Zr': 8.0, 'Nb': 2.0, 'Ga': 6.0}\",\"('Ga', 'Nb', 'Zr')\",OTHERS,False\nmp-1097724,\"{'Na': 4.0, 'W': 4.0, 'O': 10.0}\",\"('Na', 'O', 'W')\",OTHERS,False\nmp-766541,\"{'P': 10.0, 'W': 8.0, 'O': 48.0}\",\"('O', 'P', 'W')\",OTHERS,False\nmp-1245342,\"{'Li': 2.0, 'Lu': 2.0, 'S': 4.0, 'O': 16.0}\",\"('Li', 'Lu', 'O', 'S')\",OTHERS,False\nmp-560656,\"{'Ca': 20.0, 'Te': 20.0, 'O': 60.0}\",\"('Ca', 'O', 'Te')\",OTHERS,False\nmp-721094,\"{'Na': 5.0, 'Ca': 2.0, 'Ce': 3.0, 'Ti': 8.0, 'Nb': 2.0, 'O': 30.0}\",\"('Ca', 'Ce', 'Na', 'Nb', 'O', 'Ti')\",OTHERS,False\nmp-534778,\"{'Ca': 4.0, 'Na': 2.0, 'O': 36.0, 'Sc': 2.0, 'Si': 12.0, 'Zn': 4.0}\",\"('Ca', 'Na', 'O', 'Sc', 'Si', 'Zn')\",OTHERS,False\nmp-542654,\"{'In': 9.0, 'S': 15.0, 'Rb': 3.0}\",\"('In', 'Rb', 'S')\",OTHERS,False\nmp-2977,\"{'Cu': 1.0, 'Ag': 1.0, 'Te': 2.0}\",\"('Ag', 'Cu', 'Te')\",OTHERS,False\nmp-1218356,\"{'Sr': 1.0, 'Al': 1.0, 'Ga': 1.0}\",\"('Al', 'Ga', 'Sr')\",OTHERS,False\nmp-4586,\"{'Li': 2.0, 'Al': 2.0, 'Te': 4.0}\",\"('Al', 'Li', 'Te')\",OTHERS,False\nmp-27376,\"{'O': 23.0, 'Si': 10.0, 'Rb': 6.0}\",\"('O', 'Rb', 'Si')\",OTHERS,False\nmp-706622,\"{'P': 4.0, 'H': 24.0, 'N': 8.0, 'O': 8.0}\",\"('H', 'N', 'O', 'P')\",OTHERS,False\nmp-1171010,\"{'Ca': 1.0, 'Fe': 2.0, 'Si': 4.0, 'O': 12.0}\",\"('Ca', 'Fe', 'O', 'Si')\",OTHERS,False\nmp-1176721,\"{'Li': 1.0, 'Fe': 5.0, 'O': 5.0, 'F': 1.0}\",\"('F', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1186598,\"{'Pm': 1.0, 'Ho': 1.0, 'Ir': 2.0}\",\"('Ho', 'Ir', 'Pm')\",OTHERS,False\nmp-1207138,\"{'Sm': 1.0, 'Eu': 2.0, 'Ta': 1.0, 'O': 6.0}\",\"('Eu', 'O', 'Sm', 'Ta')\",OTHERS,False\nmp-605406,\"{'Rb': 2.0, 'U': 4.0, 'H': 10.0, 'C': 8.0, 'O': 30.0}\",\"('C', 'H', 'O', 'Rb', 'U')\",OTHERS,False\nmp-1229046,\"{'Al': 1.0, 'In': 1.0, 'Cu': 2.0, 'Se': 4.0}\",\"('Al', 'Cu', 'In', 'Se')\",OTHERS,False\nmp-1077939,\"{'Cr': 1.0, 'Te': 4.0, 'Rh': 2.0}\",\"('Cr', 'Rh', 'Te')\",OTHERS,False\nmp-1220441,\"{'Nb': 4.0, 'Rh': 1.0}\",\"('Nb', 'Rh')\",OTHERS,False\nmp-1080124,\"{'Cd': 2.0, 'Cl': 8.0}\",\"('Cd', 'Cl')\",OTHERS,False\nmp-27887,\"{'Si': 8.0, 'P': 12.0, 'Ba': 6.0}\",\"('Ba', 'P', 'Si')\",OTHERS,False\nmp-1199218,\"{'Li': 12.0, 'As': 28.0, 'H': 156.0, 'N': 52.0}\",\"('As', 'H', 'Li', 'N')\",OTHERS,False\nmp-1187266,\"{'Tb': 3.0, 'Au': 1.0}\",\"('Au', 'Tb')\",OTHERS,False\nmp-625282,\"{'Fe': 4.0, 'H': 4.0, 'O': 8.0}\",\"('Fe', 'H', 'O')\",OTHERS,False\nmp-1114539,\"{'K': 1.0, 'Rb': 2.0, 'In': 1.0, 'Br': 6.0}\",\"('Br', 'In', 'K', 'Rb')\",OTHERS,False\nmp-746820,\"{'Ni': 1.0, 'H': 6.0, 'S': 2.0, 'O': 12.0}\",\"('H', 'Ni', 'O', 'S')\",OTHERS,False\nmp-958,\"{'Cr': 6.0, 'Rh': 2.0}\",\"('Cr', 'Rh')\",OTHERS,False\nmp-753885,\"{'Li': 4.0, 'Cr': 2.0, 'Co': 3.0, 'Sb': 3.0, 'O': 16.0}\",\"('Co', 'Cr', 'Li', 'O', 'Sb')\",OTHERS,False\nmp-1183424,\"{'Be': 3.0, 'Ge': 1.0}\",\"('Be', 'Ge')\",OTHERS,False\nmp-1222801,\"{'La': 1.0, 'Nd': 1.0, 'Cu': 1.0, 'O': 4.0}\",\"('Cu', 'La', 'Nd', 'O')\",OTHERS,False\nmp-1194348,\"{'Eu': 12.0, 'Ga': 4.0, 'P': 12.0}\",\"('Eu', 'Ga', 'P')\",OTHERS,False\nmp-772241,\"{'Li': 6.0, 'Mn': 2.0, 'Al': 4.0, 'O': 12.0}\",\"('Al', 'Li', 'Mn', 'O')\",OTHERS,False\nmvc-14316,\"{'Ca': 2.0, 'Fe': 2.0, 'F': 10.0}\",\"('Ca', 'F', 'Fe')\",OTHERS,False\nmp-3808,\"{'O': 4.0, 'K': 2.0, 'U': 1.0}\",\"('K', 'O', 'U')\",OTHERS,False\nmp-1190793,\"{'P': 4.0, 'N': 4.0, 'O': 16.0}\",\"('N', 'O', 'P')\",OTHERS,False\nmp-1214797,\"{'Ba': 4.0, 'Er': 2.0, 'Re': 2.0, 'O': 12.0}\",\"('Ba', 'Er', 'O', 'Re')\",OTHERS,False\nmp-1197539,\"{'K': 12.0, 'As': 4.0, 'Se': 16.0}\",\"('As', 'K', 'Se')\",OTHERS,False\nmp-1227902,\"{'Ca': 12.0, 'Al': 4.0, 'Fe': 4.0, 'Si': 12.0, 'O': 48.0}\",\"('Al', 'Ca', 'Fe', 'O', 'Si')\",OTHERS,False\nmp-540439,\"{'Mn': 2.0, 'P': 6.0, 'O': 18.0}\",\"('Mn', 'O', 'P')\",OTHERS,False\nmp-510240,\"{'Rb': 4.0, 'Ag': 8.0, 'S': 6.0}\",\"('Ag', 'Rb', 'S')\",OTHERS,False\nmvc-10945,\"{'Ca': 4.0, 'V': 2.0, 'N': 6.0}\",\"('Ca', 'N', 'V')\",OTHERS,False\nmvc-4438,\"{'Mg': 12.0, 'Co': 8.0, 'Ge': 12.0, 'O': 48.0}\",\"('Co', 'Ge', 'Mg', 'O')\",OTHERS,False\nmp-767412,\"{'Li': 3.0, 'Co': 4.0, 'S': 8.0}\",\"('Co', 'Li', 'S')\",OTHERS,False\nmp-1210026,\"{'Nd': 10.0, 'Ge': 6.0, 'O': 26.0}\",\"('Ge', 'Nd', 'O')\",OTHERS,False\nmp-865184,\"{'Li': 1.0, 'Sc': 1.0, 'Pd': 2.0}\",\"('Li', 'Pd', 'Sc')\",OTHERS,False\nmp-769030,\"{'Bi': 20.0, 'B': 4.0, 'O': 40.0}\",\"('B', 'Bi', 'O')\",OTHERS,False\nmp-608469,\"{'Co': 8.0, 'C': 32.0, 'O': 32.0}\",\"('C', 'Co', 'O')\",OTHERS,False\nmp-1212594,\"{'In': 12.0, 'Te': 12.0, 'Br': 4.0}\",\"('Br', 'In', 'Te')\",OTHERS,False\nmp-1184962,\"{'K': 1.0, 'Cd': 3.0}\",\"('Cd', 'K')\",OTHERS,False\nmp-1200758,\"{'Ba': 12.0, 'V': 8.0, 'Se': 16.0, 'O': 64.0}\",\"('Ba', 'O', 'Se', 'V')\",OTHERS,False\nmp-560043,\"{'K': 1.0, 'Sb': 4.0, 'F': 13.0}\",\"('F', 'K', 'Sb')\",OTHERS,False\nmp-645189,\"{'Cs': 2.0, 'K': 2.0, 'Pt': 12.0, 'S': 12.0, 'O': 56.0}\",\"('Cs', 'K', 'O', 'Pt', 'S')\",OTHERS,False\nmp-1184478,{'In': 1.0},\"('In',)\",OTHERS,False\nmp-1039462,\"{'Ca': 4.0, 'Mg': 2.0}\",\"('Ca', 'Mg')\",OTHERS,False\nmp-1222929,\"{'La': 1.0, 'Al': 1.0, 'Ni': 4.0}\",\"('Al', 'La', 'Ni')\",OTHERS,False\nmvc-5816,\"{'Co': 8.0, 'O': 16.0}\",\"('Co', 'O')\",OTHERS,False\nmp-1226531,\"{'Ce': 1.0, 'Si': 1.0, 'B': 1.0, 'Ir': 3.0}\",\"('B', 'Ce', 'Ir', 'Si')\",OTHERS,False\nmp-1096308,\"{'Al': 1.0, 'Fe': 1.0, 'Ru': 2.0}\",\"('Al', 'Fe', 'Ru')\",OTHERS,False\nmp-1185441,\"{'Li': 1.0, 'Sm': 2.0, 'Ga': 1.0}\",\"('Ga', 'Li', 'Sm')\",OTHERS,False\nmp-1208525,\"{'Sr': 2.0, 'Y': 1.0, 'Cu': 2.0, 'Pb': 1.0, 'O': 7.0}\",\"('Cu', 'O', 'Pb', 'Sr', 'Y')\",OTHERS,False\nmvc-16754,\"{'Y': 2.0, 'Ti': 4.0, 'O': 8.0}\",\"('O', 'Ti', 'Y')\",OTHERS,False\nmp-1218581,\"{'Sr': 1.0, 'Ca': 3.0, 'V': 4.0, 'O': 12.0}\",\"('Ca', 'O', 'Sr', 'V')\",OTHERS,False\nmp-34355,\"{'As': 13.0, 'Li': 31.0, 'Zr': 2.0}\",\"('As', 'Li', 'Zr')\",OTHERS,False\nmp-867673,\"{'Na': 3.0, 'Pt': 12.0, 'O': 16.0}\",\"('Na', 'O', 'Pt')\",OTHERS,False\nmp-1187640,\"{'Tm': 5.0, 'Mg': 1.0}\",\"('Mg', 'Tm')\",OTHERS,False\nmp-1246700,\"{'Ca': 4.0, 'Ni': 4.0, 'S': 2.0, 'F': 12.0}\",\"('Ca', 'F', 'Ni', 'S')\",OTHERS,False\nmp-985477,\"{'Al': 8.0, 'Tl': 8.0, 'S': 16.0}\",\"('Al', 'S', 'Tl')\",OTHERS,False\nmp-1111618,\"{'K': 2.0, 'Na': 1.0, 'Sc': 1.0, 'I': 6.0}\",\"('I', 'K', 'Na', 'Sc')\",OTHERS,False\nmp-1147536,\"{'Ca': 2.0, 'Cu': 1.0, 'S': 2.0, 'O': 2.0}\",\"('Ca', 'Cu', 'O', 'S')\",OTHERS,False\nmp-1535,\"{'Si': 2.0, 'Tb': 1.0}\",\"('Si', 'Tb')\",OTHERS,False\nmp-13863,\"{'O': 12.0, 'Ge': 4.0, 'Ba': 4.0}\",\"('Ba', 'Ge', 'O')\",OTHERS,False\nmp-1187683,\"{'U': 3.0, 'Hg': 1.0}\",\"('Hg', 'U')\",OTHERS,False\nmp-1221701,\"{'Mn': 1.0, 'V': 1.0, 'S': 2.0}\",\"('Mn', 'S', 'V')\",OTHERS,False\nmp-1100530,\"{'Li': 9.0, 'Mn': 7.0, 'O': 16.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-30828,\"{'Sr': 8.0, 'Pb': 4.0}\",\"('Pb', 'Sr')\",OTHERS,False\nmp-861974,\"{'Pa': 1.0, 'Cd': 1.0, 'Pt': 2.0}\",\"('Cd', 'Pa', 'Pt')\",OTHERS,False\nmp-1239191,\"{'Cs': 4.0, 'Cr': 8.0, 'S': 16.0}\",\"('Cr', 'Cs', 'S')\",OTHERS,False\nmp-1199915,\"{'Lu': 8.0, 'B': 24.0, 'Os': 4.0}\",\"('B', 'Lu', 'Os')\",OTHERS,False\nmp-898,\"{'Al': 15.0, 'Ho': 5.0}\",\"('Al', 'Ho')\",OTHERS,False\nmp-685503,\"{'K': 4.0, 'Zr': 48.0, 'I': 112.0}\",\"('I', 'K', 'Zr')\",OTHERS,False\nmp-11489,\"{'Li': 3.0, 'Pd': 1.0}\",\"('Li', 'Pd')\",OTHERS,False\nmp-1175220,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-545436,\"{'O': 6.0, 'Bi': 1.0, 'Ba': 2.0, 'Yb': 1.0}\",\"('Ba', 'Bi', 'O', 'Yb')\",OTHERS,False\nmp-22394,\"{'C': 1.0, 'Ru': 3.0, 'Th': 1.0}\",\"('C', 'Ru', 'Th')\",OTHERS,False\nmp-675748,\"{'Zn': 2.0, 'Ge': 1.0, 'S': 4.0}\",\"('Ge', 'S', 'Zn')\",OTHERS,False\nmp-1211431,\"{'La': 4.0, 'Cu': 20.0, 'Sn': 4.0}\",\"('Cu', 'La', 'Sn')\",OTHERS,False\nmp-581345,\"{'Nd': 24.0, 'Ge': 24.0, 'O': 84.0}\",\"('Ge', 'Nd', 'O')\",OTHERS,False\nmp-1187948,\"{'Zn': 6.0, 'Ni': 2.0}\",\"('Ni', 'Zn')\",OTHERS,False\nmvc-12644,\"{'Ca': 2.0, 'Ni': 4.0, 'O': 8.0}\",\"('Ca', 'Ni', 'O')\",OTHERS,False\nmp-4449,\"{'O': 16.0, 'Se': 4.0, 'Tl': 8.0}\",\"('O', 'Se', 'Tl')\",OTHERS,False\nmp-1183967,\"{'Ga': 1.0, 'Ag': 3.0}\",\"('Ag', 'Ga')\",OTHERS,False\nmp-1228179,\"{'Ba': 4.0, 'Pr': 1.0, 'Dy': 1.0, 'Cu': 6.0, 'O': 14.0}\",\"('Ba', 'Cu', 'Dy', 'O', 'Pr')\",OTHERS,False\nmp-1221933,\"{'Mn': 3.0, 'Zn': 9.0, 'P': 8.0, 'O': 32.0}\",\"('Mn', 'O', 'P', 'Zn')\",OTHERS,False\nmp-709031,\"{'Al': 8.0, 'P': 8.0, 'H': 22.0, 'C': 6.0, 'N': 2.0, 'O': 36.0}\",\"('Al', 'C', 'H', 'N', 'O', 'P')\",OTHERS,False\nmp-26618,\"{'Li': 4.0, 'O': 16.0, 'P': 4.0, 'Cr': 2.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-1178754,\"{'W': 4.0, 'O': 14.0}\",\"('O', 'W')\",OTHERS,False\nmp-23179,\"{'Ni': 4.0, 'Bi': 12.0}\",\"('Bi', 'Ni')\",OTHERS,False\nmp-1187262,\"{'Sr': 3.0, 'U': 1.0}\",\"('Sr', 'U')\",OTHERS,False\nmp-11407,\"{'Ga': 1.0, 'Pt': 3.0}\",\"('Ga', 'Pt')\",OTHERS,False\nmp-1210237,\"{'Na': 1.0, 'Eu': 1.0, 'Cu': 2.0, 'F': 8.0}\",\"('Cu', 'Eu', 'F', 'Na')\",OTHERS,False\nmp-554002,\"{'Al': 4.0, 'H': 12.0, 'O': 12.0}\",\"('Al', 'H', 'O')\",OTHERS,False\nmp-1207916,\"{'Tm': 12.0, 'Sc': 8.0, 'Al': 12.0, 'O': 48.0}\",\"('Al', 'O', 'Sc', 'Tm')\",OTHERS,False\nmp-1216500,\"{'Zr': 2.0, 'Mn': 12.0, 'Ga': 1.0, 'Sn': 11.0}\",\"('Ga', 'Mn', 'Sn', 'Zr')\",OTHERS,False\nmp-1217476,\"{'Tb': 1.0, 'Si': 2.0, 'Ir': 1.0, 'Rh': 1.0}\",\"('Ir', 'Rh', 'Si', 'Tb')\",OTHERS,False\nmp-755045,\"{'Li': 1.0, 'Ni': 2.0, 'O': 4.0}\",\"('Li', 'Ni', 'O')\",OTHERS,False\nmp-1195190,\"{'Ba': 16.0, 'Fe': 12.0, 'Se': 24.0, 'F': 16.0}\",\"('Ba', 'F', 'Fe', 'Se')\",OTHERS,False\nmp-10714,\"{'C': 1.0, 'Rh': 3.0, 'Tm': 1.0}\",\"('C', 'Rh', 'Tm')\",OTHERS,False\nmp-1104672,\"{'Rb': 1.0, 'Hg': 3.0, 'N': 1.0, 'Cl': 8.0, 'O': 2.0}\",\"('Cl', 'Hg', 'N', 'O', 'Rb')\",OTHERS,False\nmp-1077241,\"{'Y': 2.0, 'Cu': 4.0}\",\"('Cu', 'Y')\",OTHERS,False\nmp-1200258,\"{'Hg': 4.0, 'S': 8.0, 'O': 28.0}\",\"('Hg', 'O', 'S')\",OTHERS,False\nmp-1080723,\"{'Hf': 2.0, 'Cu': 2.0, 'Ge': 2.0, 'As': 2.0}\",\"('As', 'Cu', 'Ge', 'Hf')\",OTHERS,False\nmp-1112618,\"{'Cs': 2.0, 'Sm': 1.0, 'Cu': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cs', 'Cu', 'Sm')\",OTHERS,False\nmp-1208097,\"{'V': 8.0, 'S': 12.0, 'N': 8.0, 'O': 48.0}\",\"('N', 'O', 'S', 'V')\",OTHERS,False\nmp-4041,\"{'Al': 2.0, 'Ge': 2.0, 'Yb': 1.0}\",\"('Al', 'Ge', 'Yb')\",OTHERS,False\nmp-754467,\"{'Li': 1.0, 'V': 2.0, 'O': 3.0, 'F': 3.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-6603,\"{'O': 12.0, 'Sr': 4.0, 'Ce': 2.0, 'Ir': 2.0}\",\"('Ce', 'Ir', 'O', 'Sr')\",OTHERS,False\nmp-559961,\"{'Tl': 8.0, 'Se': 4.0, 'O': 16.0}\",\"('O', 'Se', 'Tl')\",OTHERS,False\nmp-1196648,\"{'Nd': 12.0, 'Zn': 2.0, 'Fe': 26.0}\",\"('Fe', 'Nd', 'Zn')\",OTHERS,False\nmp-1209139,\"{'Sb': 4.0, 'Te': 7.0, 'Pb': 1.0}\",\"('Pb', 'Sb', 'Te')\",OTHERS,False\nmp-1178445,\"{'Cr': 8.0, 'P': 16.0, 'O': 48.0}\",\"('Cr', 'O', 'P')\",OTHERS,False\nmp-1208558,\"{'Te': 2.0, 'Br': 12.0, 'O': 16.0}\",\"('Br', 'O', 'Te')\",OTHERS,False\nmp-998592,\"{'Rb': 4.0, 'Ca': 4.0, 'I': 12.0}\",\"('Ca', 'I', 'Rb')\",OTHERS,False\nmp-606691,\"{'K': 4.0, 'U': 8.0, 'P': 12.0, 'O': 56.0}\",\"('K', 'O', 'P', 'U')\",OTHERS,False\nmp-768598,\"{'Na': 4.0, 'Ni': 2.0, 'C': 2.0, 'S': 2.0, 'O': 14.0}\",\"('C', 'Na', 'Ni', 'O', 'S')\",OTHERS,False\nmp-10108,\"{'P': 1.0, 'Np': 1.0}\",\"('Np', 'P')\",OTHERS,False\nmp-1191394,\"{'Li': 8.0, 'Nd': 7.0, 'Ge': 10.0}\",\"('Ge', 'Li', 'Nd')\",OTHERS,False\nmp-27805,\"{'Si': 4.0, 'S': 20.0, 'Ba': 12.0}\",\"('Ba', 'S', 'Si')\",OTHERS,False\nmp-4187,\"{'N': 4.0, 'O': 2.0, 'Ge': 4.0}\",\"('Ge', 'N', 'O')\",OTHERS,False\nmp-1069477,\"{'Gd': 1.0, 'Si': 3.0, 'Ir': 1.0}\",\"('Gd', 'Ir', 'Si')\",OTHERS,False\nmp-754751,\"{'Li': 3.0, 'Nb': 3.0, 'Te': 1.0, 'O': 12.0}\",\"('Li', 'Nb', 'O', 'Te')\",OTHERS,False\nmp-510006,\"{'Pu': 4.0, 'P': 8.0, 'K': 12.0, 'S': 32.0}\",\"('K', 'P', 'Pu', 'S')\",OTHERS,False\nmp-756583,\"{'Li': 4.0, 'V': 5.0, 'Cu': 1.0, 'Cl': 1.0, 'O': 15.0}\",\"('Cl', 'Cu', 'Li', 'O', 'V')\",OTHERS,False\nmp-22569,\"{'O': 16.0, 'P': 4.0, 'In': 4.0}\",\"('In', 'O', 'P')\",OTHERS,False\nmp-1180367,\"{'Nd': 4.0, 'C': 12.0, 'O': 40.0}\",\"('C', 'Nd', 'O')\",OTHERS,False\nmp-973114,\"{'Ho': 1.0, 'Lu': 1.0, 'Ru': 2.0}\",\"('Ho', 'Lu', 'Ru')\",OTHERS,False\nmp-504532,\"{'O': 44.0, 'As': 8.0, 'W': 8.0}\",\"('As', 'O', 'W')\",OTHERS,False\nmp-1214361,\"{'Ca': 4.0, 'Mn': 6.0, 'Si': 6.0, 'H': 6.0, 'O': 28.0}\",\"('Ca', 'H', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1185110,\"{'K': 3.0, 'Rh': 1.0}\",\"('K', 'Rh')\",OTHERS,False\nmp-768883,\"{'Sn': 4.0, 'S': 6.0, 'O': 24.0}\",\"('O', 'S', 'Sn')\",OTHERS,False\nmp-1223761,\"{'La': 2.0, 'Ga': 7.0, 'Au': 1.0}\",\"('Au', 'Ga', 'La')\",OTHERS,False\nmp-1189317,\"{'Mg': 4.0, 'Cr': 2.0, 'B': 4.0, 'Ir': 10.0}\",\"('B', 'Cr', 'Ir', 'Mg')\",OTHERS,False\nmp-865202,\"{'Tm': 1.0, 'Cd': 1.0, 'Pt': 2.0}\",\"('Cd', 'Pt', 'Tm')\",OTHERS,False\nmp-2497,\"{'Zn': 1.0, 'Gd': 1.0}\",\"('Gd', 'Zn')\",OTHERS,False\nmp-29970,\"{'O': 56.0, 'Sb': 20.0, 'Cs': 12.0}\",\"('Cs', 'O', 'Sb')\",OTHERS,False\nmp-1214469,\"{'Ca': 3.0, 'Cd': 3.0, 'N': 12.0, 'O': 36.0}\",\"('Ca', 'Cd', 'N', 'O')\",OTHERS,False\nmp-1206320,\"{'Rb': 2.0, 'Sc': 2.0, 'Cl': 6.0}\",\"('Cl', 'Rb', 'Sc')\",OTHERS,False\nmp-27714,\"{'H': 12.0, 'O': 4.0, 'F': 4.0}\",\"('F', 'H', 'O')\",OTHERS,False\nmp-644443,\"{'Ca': 2.0, 'P': 2.0, 'H': 4.0, 'O': 12.0}\",\"('Ca', 'H', 'O', 'P')\",OTHERS,False\nmp-1034881,\"{'Cs': 1.0, 'Na': 1.0, 'Mg': 14.0, 'O': 15.0}\",\"('Cs', 'Mg', 'Na', 'O')\",OTHERS,False\nmp-757241,\"{'V': 10.0, 'W': 6.0, 'O': 40.0}\",\"('O', 'V', 'W')\",OTHERS,False\nmp-1245853,\"{'Ba': 6.0, 'Y': 2.0, 'N': 6.0}\",\"('Ba', 'N', 'Y')\",OTHERS,False\nmp-1103677,\"{'Tb': 3.0, 'Ga': 9.0, 'Pt': 2.0}\",\"('Ga', 'Pt', 'Tb')\",OTHERS,False\nmp-1246559,\"{'Dy': 2.0, 'Mg': 2.0, 'Cr': 2.0, 'S': 8.0}\",\"('Cr', 'Dy', 'Mg', 'S')\",OTHERS,False\nmp-865483,\"{'Li': 2.0, 'Gd': 1.0, 'In': 1.0}\",\"('Gd', 'In', 'Li')\",OTHERS,False\nmp-1183768,\"{'Ce': 1.0, 'Nd': 1.0, 'Hg': 2.0}\",\"('Ce', 'Hg', 'Nd')\",OTHERS,False\nmp-779893,\"{'Dy': 4.0, 'P': 20.0, 'O': 56.0}\",\"('Dy', 'O', 'P')\",OTHERS,False\nmp-1099956,\"{'K': 4.0, 'Na': 28.0, 'V': 24.0, 'Mo': 8.0, 'O': 80.0}\",\"('K', 'Mo', 'Na', 'O', 'V')\",OTHERS,False\nmp-756525,\"{'Mn': 3.0, 'Cu': 1.0, 'O': 8.0}\",\"('Cu', 'Mn', 'O')\",OTHERS,False\nmp-1096407,\"{'Al': 1.0, 'Ni': 1.0, 'Pt': 2.0}\",\"('Al', 'Ni', 'Pt')\",OTHERS,False\nmp-680331,\"{'Cd': 4.0, 'Hg': 12.0, 'C': 24.0, 'S': 24.0, 'N': 24.0, 'Cl': 8.0}\",\"('C', 'Cd', 'Cl', 'Hg', 'N', 'S')\",OTHERS,False\nmp-557634,\"{'Na': 2.0, 'V': 6.0, 'P': 6.0, 'O': 24.0}\",\"('Na', 'O', 'P', 'V')\",OTHERS,False\nmvc-545,\"{'Ba': 1.0, 'Ca': 1.0, 'Ag': 4.0, 'O': 8.0}\",\"('Ag', 'Ba', 'Ca', 'O')\",OTHERS,False\nmp-1173947,\"{'Li': 6.0, 'Mn': 4.0, 'O': 10.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-1245883,\"{'Sc': 16.0, 'Te': 12.0, 'N': 8.0}\",\"('N', 'Sc', 'Te')\",OTHERS,False\nmp-1185192,\"{'Li': 3.0, 'La': 1.0}\",\"('La', 'Li')\",OTHERS,False\nmp-1215156,\"{'Al': 18.0, 'P': 18.0, 'H': 42.0, 'O': 72.0}\",\"('Al', 'H', 'O', 'P')\",OTHERS,False\nmp-774293,\"{'Li': 8.0, 'Ti': 8.0, 'Mn': 8.0, 'Co': 2.0, 'O': 36.0}\",\"('Co', 'Li', 'Mn', 'O', 'Ti')\",OTHERS,False\nmp-561261,\"{'Ce': 8.0, 'P': 8.0, 'S': 32.0}\",\"('Ce', 'P', 'S')\",OTHERS,False\nmp-623261,\"{'Pr': 4.0, 'Ga': 4.0, 'Co': 4.0}\",\"('Co', 'Ga', 'Pr')\",OTHERS,False\nmp-777233,\"{'Ba': 4.0, 'Y': 4.0, 'F': 20.0}\",\"('Ba', 'F', 'Y')\",OTHERS,False\nmp-755337,\"{'Li': 4.0, 'Nb': 1.0, 'Fe': 3.0, 'O': 8.0}\",\"('Fe', 'Li', 'Nb', 'O')\",OTHERS,False\nmp-974606,\"{'Re': 2.0, 'H': 12.0, 'C': 2.0, 'N': 6.0, 'O': 8.0}\",\"('C', 'H', 'N', 'O', 'Re')\",OTHERS,False\nmp-19764,\"{'In': 1.0, 'Pr': 3.0}\",\"('In', 'Pr')\",OTHERS,False\nmp-1112421,\"{'K': 2.0, 'Pr': 1.0, 'Au': 1.0, 'Cl': 6.0}\",\"('Au', 'Cl', 'K', 'Pr')\",OTHERS,False\nmp-758453,\"{'Na': 8.0, 'P': 8.0, 'H': 16.0, 'N': 8.0, 'O': 20.0}\",\"('H', 'N', 'Na', 'O', 'P')\",OTHERS,False\nmp-1215853,\"{'Yb': 1.0, 'Tm': 2.0, 'Se': 4.0}\",\"('Se', 'Tm', 'Yb')\",OTHERS,False\nmp-1176482,\"{'Mn': 3.0, 'Cr': 2.0, 'Fe': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Cr', 'Fe', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1245073,\"{'Cr': 8.0, 'Fe': 24.0, 'O': 48.0}\",\"('Cr', 'Fe', 'O')\",OTHERS,False\nmp-1245737,\"{'Na': 2.0, 'Cr': 2.0, 'N': 2.0}\",\"('Cr', 'N', 'Na')\",OTHERS,False\nmp-849745,\"{'Li': 12.0, 'Mn': 8.0, 'P': 16.0, 'O': 56.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-672187,\"{'Pr': 4.0, 'Pd': 4.0, 'Pb': 2.0}\",\"('Pb', 'Pd', 'Pr')\",OTHERS,False\nmp-1185197,\"{'Li': 6.0, 'Ho': 2.0}\",\"('Ho', 'Li')\",OTHERS,False\nmp-1014005,\"{'Mn': 2.0, 'Si': 1.0, 'Rh': 1.0}\",\"('Mn', 'Rh', 'Si')\",OTHERS,False\nmp-4989,\"{'Cr': 3.0, 'Ni': 3.0, 'As': 3.0}\",\"('As', 'Cr', 'Ni')\",OTHERS,False\nmp-1078429,\"{'Eu': 2.0, 'Ga': 6.0, 'Pd': 2.0}\",\"('Eu', 'Ga', 'Pd')\",OTHERS,False\nmp-766132,\"{'Li': 8.0, 'Zr': 4.0, 'Fe': 4.0, 'P': 12.0, 'O': 48.0}\",\"('Fe', 'Li', 'O', 'P', 'Zr')\",OTHERS,False\nmp-21660,\"{'Co': 10.0, 'Ga': 20.0, 'Hf': 10.0}\",\"('Co', 'Ga', 'Hf')\",OTHERS,False\nmp-1078398,\"{'Ce': 2.0, 'As': 2.0, 'O': 6.0}\",\"('As', 'Ce', 'O')\",OTHERS,False\nmp-20961,\"{'S': 8.0, 'Pd': 6.0, 'Eu': 2.0}\",\"('Eu', 'Pd', 'S')\",OTHERS,False\nmp-1033751,\"{'Mg': 14.0, 'Bi': 1.0, 'C': 1.0, 'O': 16.0}\",\"('Bi', 'C', 'Mg', 'O')\",OTHERS,False\nmp-1658,\"{'O': 24.0, 'Tl': 16.0}\",\"('O', 'Tl')\",OTHERS,False\nmp-767562,\"{'Na': 4.0, 'Lu': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Lu', 'Na', 'O', 'P')\",OTHERS,False\nmp-30626,\"{'Zn': 12.0, 'Dy': 4.0}\",\"('Dy', 'Zn')\",OTHERS,False\nmp-1209407,\"{'Pr': 4.0, 'Re': 6.0, 'O': 19.0}\",\"('O', 'Pr', 'Re')\",OTHERS,False\nmp-1188269,\"{'Pb': 4.0, 'S': 8.0, 'N': 8.0}\",\"('N', 'Pb', 'S')\",OTHERS,False\nmp-7979,\"{'F': 6.0, 'K': 2.0, 'Pd': 1.0}\",\"('F', 'K', 'Pd')\",OTHERS,False\nmp-849092,\"{'Ni': 2.0, 'S': 4.0}\",\"('Ni', 'S')\",OTHERS,False\nmp-554852,\"{'Y': 4.0, 'Te': 10.0, 'O': 26.0}\",\"('O', 'Te', 'Y')\",OTHERS,False\nmp-1223138,\"{'La': 4.0, 'Ti': 2.0, 'Mn': 2.0, 'O': 12.0}\",\"('La', 'Mn', 'O', 'Ti')\",OTHERS,False\nmp-1245321,\"{'Ti': 30.0, 'O': 60.0}\",\"('O', 'Ti')\",OTHERS,False\nmp-697789,\"{'Li': 2.0, 'Cr': 2.0, 'P': 4.0, 'O': 14.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-1190173,\"{'Cu': 2.0, 'Pb': 6.0, 'Cl': 4.0, 'O': 8.0}\",\"('Cl', 'Cu', 'O', 'Pb')\",OTHERS,False\nmp-1173113,\"{'Tb': 10.0, 'Yb': 1.0, 'Se': 16.0}\",\"('Se', 'Tb', 'Yb')\",OTHERS,False\nmp-643004,\"{'Sr': 1.0, 'Mg': 2.0, 'Fe': 1.0, 'H': 8.0}\",\"('Fe', 'H', 'Mg', 'Sr')\",OTHERS,False\nmp-1223901,\"{'In': 1.0, 'Bi': 3.0}\",\"('Bi', 'In')\",OTHERS,False\nmp-1214016,\"{'Ca': 10.0, 'Sn': 6.0}\",\"('Ca', 'Sn')\",OTHERS,False\nmp-1198305,\"{'Ce': 40.0, 'Se': 56.0, 'O': 4.0}\",\"('Ce', 'O', 'Se')\",OTHERS,False\nmp-1196696,\"{'Na': 28.0, 'U': 4.0, 'H': 16.0, 'S': 16.0, 'Cl': 4.0, 'O': 80.0}\",\"('Cl', 'H', 'Na', 'O', 'S', 'U')\",OTHERS,False\nmp-756452,\"{'Sm': 4.0, 'Dy': 4.0, 'O': 12.0}\",\"('Dy', 'O', 'Sm')\",OTHERS,False\nmp-1114497,\"{'Rb': 3.0, 'Ga': 1.0, 'Br': 6.0}\",\"('Br', 'Ga', 'Rb')\",OTHERS,False\nmp-1208173,\"{'V': 4.0, 'Pb': 4.0, 'O': 8.0}\",\"('O', 'Pb', 'V')\",OTHERS,False\nmp-1177282,\"{'Li': 4.0, 'Ti': 2.0, 'Mn': 3.0, 'V': 3.0, 'O': 16.0}\",\"('Li', 'Mn', 'O', 'Ti', 'V')\",OTHERS,False\nmp-1070498,\"{'Rb': 2.0, 'Te': 2.0, 'Pt': 1.0}\",\"('Pt', 'Rb', 'Te')\",OTHERS,False\nmp-555688,\"{'W': 4.0, 'S': 8.0, 'N': 12.0, 'Cl': 12.0}\",\"('Cl', 'N', 'S', 'W')\",OTHERS,False\nmp-1194130,\"{'Zr': 16.0, 'Pd': 8.0, 'N': 4.0}\",\"('N', 'Pd', 'Zr')\",OTHERS,False\nmp-1239263,\"{'Hf': 4.0, 'Cr': 4.0, 'Cu': 4.0, 'S': 16.0}\",\"('Cr', 'Cu', 'Hf', 'S')\",OTHERS,False\nmp-779467,\"{'Li': 9.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1095823,\"{'Zn': 2.0, 'Ag': 1.0, 'Ir': 1.0}\",\"('Ag', 'Ir', 'Zn')\",OTHERS,False\nmp-33554,\"{'Cl': 6.0, 'Gd': 1.0, 'Na': 3.0}\",\"('Cl', 'Gd', 'Na')\",OTHERS,False\nmp-985301,\"{'Ac': 1.0, 'Dy': 3.0}\",\"('Ac', 'Dy')\",OTHERS,False\nmp-22017,\"{'In': 4.0, 'La': 2.0, 'Ir': 2.0}\",\"('In', 'Ir', 'La')\",OTHERS,False\nmp-1223441,\"{'K': 1.0, 'Ba': 1.0, 'Li': 1.0, 'Zn': 1.0, 'F': 6.0}\",\"('Ba', 'F', 'K', 'Li', 'Zn')\",OTHERS,False\nmp-1178389,\"{'Fe': 2.0, 'Co': 3.0, 'Sb': 3.0, 'O': 16.0}\",\"('Co', 'Fe', 'O', 'Sb')\",OTHERS,False\nmp-31169,\"{'Al': 1.0, 'Sc': 1.0, 'Ag': 2.0}\",\"('Ag', 'Al', 'Sc')\",OTHERS,False\nmp-570463,\"{'Ta': 8.0, 'Co': 16.0}\",\"('Co', 'Ta')\",OTHERS,False\nmp-14718,\"{'Na': 12.0, 'Mn': 6.0, 'Cr': 6.0, 'F': 42.0}\",\"('Cr', 'F', 'Mn', 'Na')\",OTHERS,False\nmp-1225248,\"{'Fe': 36.0, 'B': 4.0, 'P': 8.0}\",\"('B', 'Fe', 'P')\",OTHERS,False\nmp-1260870,\"{'Ba': 2.0, 'Ti': 3.0, 'Al': 1.0, 'O': 7.0}\",\"('Al', 'Ba', 'O', 'Ti')\",OTHERS,False\nmp-1189173,\"{'La': 6.0, 'Bi': 8.0, 'Pt': 6.0}\",\"('Bi', 'La', 'Pt')\",OTHERS,False\nmp-778476,\"{'Li': 6.0, 'Mn': 5.0, 'Fe': 1.0, 'B': 6.0, 'O': 18.0}\",\"('B', 'Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-698362,\"{'H': 88.0, 'Ir': 4.0, 'C': 12.0, 'N': 24.0, 'O': 32.0}\",\"('C', 'H', 'Ir', 'N', 'O')\",OTHERS,False\nmp-1095898,\"{'Sc': 2.0, 'Ge': 1.0, 'Pd': 1.0}\",\"('Ge', 'Pd', 'Sc')\",OTHERS,False\nmp-985557,\"{'Ce': 3.0, 'Dy': 1.0}\",\"('Ce', 'Dy')\",OTHERS,False\nmp-567241,\"{'Pr': 3.0, 'Ag': 4.0, 'Sn': 4.0}\",\"('Ag', 'Pr', 'Sn')\",OTHERS,False\nmp-7187,\"{'Al': 1.0, 'Ti': 1.0, 'Ni': 2.0}\",\"('Al', 'Ni', 'Ti')\",OTHERS,False\nmp-11441,\"{'Ga': 6.0, 'Hf': 4.0}\",\"('Ga', 'Hf')\",OTHERS,False\nmp-1202551,\"{'Cu': 8.0, 'As': 8.0, 'H': 24.0, 'O': 40.0}\",\"('As', 'Cu', 'H', 'O')\",OTHERS,False\nmp-1213357,\"{'K': 12.0, 'Sm': 8.0, 'N': 36.0, 'O': 108.0}\",\"('K', 'N', 'O', 'Sm')\",OTHERS,False\nmp-752642,\"{'V': 4.0, 'As': 4.0, 'O': 16.0}\",\"('As', 'O', 'V')\",OTHERS,False\nmp-570360,\"{'Rb': 4.0, 'Nd': 8.0, 'I': 20.0}\",\"('I', 'Nd', 'Rb')\",OTHERS,False\nmp-774496,\"{'Li': 30.0, 'Mn': 6.0, 'Si': 24.0, 'O': 72.0}\",\"('Li', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1038896,\"{'Mg': 4.0, 'Bi': 8.0}\",\"('Bi', 'Mg')\",OTHERS,False\nmp-10669,\"{'Al': 2.0, 'Ge': 2.0, 'Ba': 3.0}\",\"('Al', 'Ba', 'Ge')\",OTHERS,False\nmp-10963,\"{'Se': 8.0, 'Ag': 4.0, 'Sn': 2.0, 'Hg': 2.0}\",\"('Ag', 'Hg', 'Se', 'Sn')\",OTHERS,False\nmp-1201365,\"{'Pu': 8.0, 'Re': 4.0, 'B': 24.0}\",\"('B', 'Pu', 'Re')\",OTHERS,False\nmp-1223006,\"{'La': 1.0, 'Al': 2.0, 'Ag': 3.0}\",\"('Ag', 'Al', 'La')\",OTHERS,False\nmp-1247428,\"{'Ca': 6.0, 'Re': 2.0, 'N': 6.0}\",\"('Ca', 'N', 'Re')\",OTHERS,False\nmp-1207311,\"{'Nd': 2.0, 'Ag': 1.0, 'Sb': 3.0}\",\"('Ag', 'Nd', 'Sb')\",OTHERS,False\nmp-27725,\"{'I': 4.0, 'Au': 4.0}\",\"('Au', 'I')\",OTHERS,False\nmp-1208738,\"{'Tb': 12.0, 'Ni': 6.0, 'Pb': 1.0}\",\"('Ni', 'Pb', 'Tb')\",OTHERS,False\nmp-861886,\"{'Li': 1.0, 'Dy': 1.0, 'Hg': 2.0}\",\"('Dy', 'Hg', 'Li')\",OTHERS,False\nmp-560920,\"{'Si': 32.0, 'O': 64.0}\",\"('O', 'Si')\",OTHERS,False\nmp-1227645,\"{'Ba': 1.0, 'Sr': 1.0, 'Hf': 2.0, 'O': 6.0}\",\"('Ba', 'Hf', 'O', 'Sr')\",OTHERS,False\nmp-568476,\"{'Bi': 4.0, 'Pd': 8.0, 'Pb': 4.0}\",\"('Bi', 'Pb', 'Pd')\",OTHERS,False\nmp-867627,\"{'Ba': 12.0, 'Pt': 9.0, 'O': 27.0}\",\"('Ba', 'O', 'Pt')\",OTHERS,False\nmp-758743,\"{'Li': 8.0, 'Cu': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Cu', 'Li', 'O', 'Si')\",OTHERS,False\nmp-555318,\"{'K': 4.0, 'Zn': 2.0, 'Si': 4.0, 'O': 12.0}\",\"('K', 'O', 'Si', 'Zn')\",OTHERS,False\nmp-25372,\"{'Li': 1.0, 'Cu': 1.0, 'O': 2.0}\",\"('Cu', 'Li', 'O')\",OTHERS,False\nmp-1080177,\"{'Pu': 3.0, 'Al': 3.0, 'Ni': 3.0}\",\"('Al', 'Ni', 'Pu')\",OTHERS,False\nmp-864904,\"{'Ho': 1.0, 'Cd': 1.0, 'Ag': 2.0}\",\"('Ag', 'Cd', 'Ho')\",OTHERS,False\nmp-505271,\"{'O': 12.0, 'Ni': 2.0, 'Sb': 4.0}\",\"('Ni', 'O', 'Sb')\",OTHERS,False\nmp-1019783,\"{'K': 16.0, 'S': 16.0, 'O': 24.0}\",\"('K', 'O', 'S')\",OTHERS,False\nmp-1095608,\"{'K': 2.0, 'P': 2.0, 'O': 8.0}\",\"('K', 'O', 'P')\",OTHERS,False\nmp-1095957,\"{'Mg': 1.0, 'Os': 2.0, 'W': 1.0}\",\"('Mg', 'Os', 'W')\",OTHERS,False\nmp-758324,\"{'Ti': 10.0, 'Nb': 4.0, 'O': 28.0}\",\"('Nb', 'O', 'Ti')\",OTHERS,False\nmp-1106407,\"{'Hf': 3.0, 'Co': 9.0, 'B': 6.0}\",\"('B', 'Co', 'Hf')\",OTHERS,False\nmp-18547,\"{'O': 16.0, 'Na': 4.0, 'Ge': 4.0, 'Gd': 4.0}\",\"('Gd', 'Ge', 'Na', 'O')\",OTHERS,False\nmp-1264900,\"{'Mg': 8.0, 'Mn': 8.0, 'Si': 12.0, 'O': 48.0}\",\"('Mg', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1204063,\"{'Li': 88.0, 'Ge': 20.0}\",\"('Ge', 'Li')\",OTHERS,False\nmp-1174644,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-8348,\"{'Tl': 1.0, 'Sb': 1.0, 'F': 6.0}\",\"('F', 'Sb', 'Tl')\",OTHERS,False\nmp-1194936,\"{'Mg': 64.0, 'Si': 32.0, 'Bi': 1.0}\",\"('Bi', 'Mg', 'Si')\",OTHERS,False\nmp-1197529,\"{'Sr': 2.0, 'Au': 4.0, 'S': 8.0, 'O': 32.0}\",\"('Au', 'O', 'S', 'Sr')\",OTHERS,False\nmp-5156,\"{'O': 28.0, 'P': 8.0, 'Th': 4.0}\",\"('O', 'P', 'Th')\",OTHERS,False\nmp-640915,\"{'Na': 8.0, 'La': 4.0, 'P': 4.0, 'S': 8.0, 'O': 28.0}\",\"('La', 'Na', 'O', 'P', 'S')\",OTHERS,False\nmp-1113880,\"{'Rb': 2.0, 'Al': 1.0, 'Cu': 1.0, 'F': 6.0}\",\"('Al', 'Cu', 'F', 'Rb')\",OTHERS,False\nmp-1205230,\"{'Si': 8.0, 'H': 64.0, 'C': 32.0, 'I': 32.0}\",\"('C', 'H', 'I', 'Si')\",OTHERS,False\nmp-863732,\"{'Pm': 2.0, 'Pt': 1.0, 'Rh': 1.0}\",\"('Pm', 'Pt', 'Rh')\",OTHERS,False\nmp-1175245,\"{'Li': 7.0, 'Mn': 4.0, 'Co': 1.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-558862,\"{'Na': 2.0, 'Ge': 8.0, 'P': 6.0, 'O': 24.0}\",\"('Ge', 'Na', 'O', 'P')\",OTHERS,False\nmp-1102925,\"{'Nb': 4.0, 'Fe': 4.0, 'Si': 4.0}\",\"('Fe', 'Nb', 'Si')\",OTHERS,False\nmp-29443,\"{'P': 2.0, 'I': 4.0}\",\"('I', 'P')\",OTHERS,False\nmp-1078343,\"{'U': 1.0, 'Ga': 5.0, 'Ru': 1.0}\",\"('Ga', 'Ru', 'U')\",OTHERS,False\nmp-1262565,\"{'Mg': 8.0, 'Ti': 8.0, 'Si': 16.0, 'O': 48.0}\",\"('Mg', 'O', 'Si', 'Ti')\",OTHERS,False\nmp-756010,\"{'Fe': 6.0, 'O': 10.0, 'F': 2.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-1186353,\"{'Nd': 6.0, 'Np': 2.0}\",\"('Nd', 'Np')\",OTHERS,False\nmvc-6707,\"{'Mg': 2.0, 'Sn': 2.0, 'P': 4.0, 'O': 14.0}\",\"('Mg', 'O', 'P', 'Sn')\",OTHERS,False\nmp-769713,\"{'Na': 8.0, 'B': 8.0, 'Sb': 4.0, 'S': 2.0, 'O': 32.0}\",\"('B', 'Na', 'O', 'S', 'Sb')\",OTHERS,False\nmp-1173939,\"{'Li': 4.0, 'Mn': 2.0, 'O': 6.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-1189044,\"{'Ti': 12.0, 'Ge': 4.0}\",\"('Ge', 'Ti')\",OTHERS,False\nmp-771043,\"{'Y': 8.0, 'Zr': 8.0, 'O': 28.0}\",\"('O', 'Y', 'Zr')\",OTHERS,False\nmp-775289,\"{'Mn': 3.0, 'Cr': 1.0, 'Cu': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Cr', 'Cu', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1245323,\"{'Al': 40.0, 'O': 60.0}\",\"('Al', 'O')\",OTHERS,False\nmp-733936,\"{'Cs': 8.0, 'Mg': 4.0, 'H': 32.0, 'C': 8.0, 'O': 40.0}\",\"('C', 'Cs', 'H', 'Mg', 'O')\",OTHERS,False\nmp-557035,\"{'K': 6.0, 'S': 2.0, 'O': 8.0, 'F': 2.0}\",\"('F', 'K', 'O', 'S')\",OTHERS,False\nmp-771232,\"{'Li': 5.0, 'Mn': 2.0, 'Ni': 5.0, 'O': 12.0}\",\"('Li', 'Mn', 'Ni', 'O')\",OTHERS,False\nmp-606102,\"{'Ce': 3.0, 'In': 3.0, 'Ru': 2.0}\",\"('Ce', 'In', 'Ru')\",OTHERS,False\nmp-1218851,\"{'Sr': 2.0, 'Ca': 1.0, 'Ti': 3.0, 'O': 9.0}\",\"('Ca', 'O', 'Sr', 'Ti')\",OTHERS,False\nmp-755552,\"{'Li': 1.0, 'Y': 1.0, 'S': 2.0}\",\"('Li', 'S', 'Y')\",OTHERS,False\nmp-19453,\"{'O': 28.0, 'Mo': 4.0, 'Te': 8.0}\",\"('Mo', 'O', 'Te')\",OTHERS,False\nmp-605735,\"{'Sr': 6.0, 'In': 8.0, 'Pb': 2.0}\",\"('In', 'Pb', 'Sr')\",OTHERS,False\nmp-25382,\"{'O': 8.0, 'F': 2.0, 'P': 2.0, 'Fe': 2.0}\",\"('F', 'Fe', 'O', 'P')\",OTHERS,False\nmp-560247,\"{'Li': 4.0, 'Ca': 4.0, 'Si': 10.0, 'O': 26.0}\",\"('Ca', 'Li', 'O', 'Si')\",OTHERS,False\nmp-558997,\"{'Sm': 2.0, 'Mo': 2.0, 'Cl': 2.0, 'O': 8.0}\",\"('Cl', 'Mo', 'O', 'Sm')\",OTHERS,False\nmp-1215148,\"{'Ce': 4.0, 'W': 8.0, 'S': 6.0, 'O': 28.0}\",\"('Ce', 'O', 'S', 'W')\",OTHERS,False\nmp-1225405,\"{'Dy': 2.0, 'In': 3.0, 'Cu': 1.0}\",\"('Cu', 'Dy', 'In')\",OTHERS,False\nmp-18340,\"{'O': 24.0, 'Mo': 4.0, 'Tb': 8.0}\",\"('Mo', 'O', 'Tb')\",OTHERS,False\nmp-504927,\"{'O': 8.0, 'Cr': 2.0, 'Cu': 2.0}\",\"('Cr', 'Cu', 'O')\",OTHERS,False\nmp-1106252,\"{'Nd': 10.0, 'Ga': 6.0}\",\"('Ga', 'Nd')\",OTHERS,False\nmp-3107,\"{'O': 7.0, 'P': 1.0, 'Ga': 3.0}\",\"('Ga', 'O', 'P')\",OTHERS,False\nmp-1212904,\"{'Dy': 10.0, 'Co': 4.0, 'Bi': 2.0}\",\"('Bi', 'Co', 'Dy')\",OTHERS,False\nmp-1181689,\"{'Cs': 6.0, 'Ba': 8.0, 'C': 6.0, 'O': 18.0, 'F': 10.0}\",\"('Ba', 'C', 'Cs', 'F', 'O')\",OTHERS,False\nmp-567114,\"{'Sr': 2.0, 'Bi': 4.0, 'Pd': 4.0}\",\"('Bi', 'Pd', 'Sr')\",OTHERS,False\nmp-1181604,\"{'Fe': 4.0, 'H': 4.0, 'O': 8.0}\",\"('Fe', 'H', 'O')\",OTHERS,False\nmp-654008,\"{'K': 3.0, 'Cr': 11.0, 'S': 18.0}\",\"('Cr', 'K', 'S')\",OTHERS,False\nmp-675532,\"{'La': 2.0, 'Ti': 12.0, 'O': 24.0}\",\"('La', 'O', 'Ti')\",OTHERS,False\nmp-1179601,\"{'Sb': 8.0, 'N': 12.0, 'Cl': 28.0, 'O': 4.0}\",\"('Cl', 'N', 'O', 'Sb')\",OTHERS,False\nmp-1093852,\"{'Y': 1.0, 'Sc': 1.0, 'Pt': 2.0}\",\"('Pt', 'Sc', 'Y')\",OTHERS,False\nmp-21294,\"{'Y': 4.0, 'In': 2.0}\",\"('In', 'Y')\",OTHERS,False\nmp-1216675,\"{'Ti': 1.0, 'Mo': 2.0}\",\"('Mo', 'Ti')\",OTHERS,False\nmp-1291,\"{'In': 3.0, 'Er': 1.0}\",\"('Er', 'In')\",OTHERS,False\nmp-557432,\"{'Ge': 4.0, 'Cl': 4.0, 'O': 8.0, 'F': 20.0}\",\"('Cl', 'F', 'Ge', 'O')\",OTHERS,False\nmp-1096958,\"{'Dy': 2.0, 'Mn': 4.0, 'O': 8.0}\",\"('Dy', 'Mn', 'O')\",OTHERS,False\nmp-1226961,\"{'Ce': 2.0, 'Pr': 4.0, 'S': 8.0}\",\"('Ce', 'Pr', 'S')\",OTHERS,False\nmp-649944,\"{'Na': 8.0, 'Cu': 12.0, 'Si': 16.0, 'O': 48.0}\",\"('Cu', 'Na', 'O', 'Si')\",OTHERS,False\nmp-569879,\"{'Cs': 2.0, 'Ta': 6.0, 'Pb': 1.0, 'Cl': 18.0}\",\"('Cl', 'Cs', 'Pb', 'Ta')\",OTHERS,False\nmp-1246896,\"{'Mn': 6.0, 'Al': 2.0, 'N': 6.0}\",\"('Al', 'Mn', 'N')\",OTHERS,False\nmp-631582,\"{'Al': 1.0, 'Tc': 2.0, 'Pb': 1.0}\",\"('Al', 'Pb', 'Tc')\",OTHERS,False\nmp-1074259,\"{'Mg': 8.0, 'Si': 6.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1227844,\"{'Ba': 1.0, 'La': 1.0, 'Mn': 2.0, 'O': 6.0}\",\"('Ba', 'La', 'Mn', 'O')\",OTHERS,False\nmp-775903,\"{'Li': 4.0, 'Mn': 2.0, 'P': 4.0, 'O': 14.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1095340,\"{'Ba': 2.0, 'Tb': 2.0, 'Ag': 2.0, 'S': 6.0}\",\"('Ag', 'Ba', 'S', 'Tb')\",OTHERS,False\nmp-556062,\"{'Na': 2.0, 'Nb': 8.0, 'Tl': 6.0, 'P': 4.0, 'O': 34.0}\",\"('Na', 'Nb', 'O', 'P', 'Tl')\",OTHERS,False\nmp-677029,\"{'Ca': 4.0, 'Mg': 2.0, 'Al': 4.0, 'Si': 6.0, 'O': 24.0}\",\"('Al', 'Ca', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-1095985,\"{'Mg': 1.0, 'Ti': 1.0, 'Au': 2.0}\",\"('Au', 'Mg', 'Ti')\",OTHERS,False\nmp-1223342,\"{'La': 4.0, 'Mn': 2.0, 'Cr': 2.0, 'O': 12.0}\",\"('Cr', 'La', 'Mn', 'O')\",OTHERS,False\nmp-1030704,\"{'Na': 30.0, 'W': 14.0, 'N': 38.0}\",\"('N', 'Na', 'W')\",OTHERS,False\nmp-552113,\"{'V': 4.0, 'O': 6.0}\",\"('O', 'V')\",OTHERS,False\nmp-1196520,\"{'V': 8.0, 'P': 8.0, 'N': 8.0, 'O': 48.0}\",\"('N', 'O', 'P', 'V')\",OTHERS,False\nmp-863767,\"{'Fe': 26.0, 'Sn': 4.0, 'O': 40.0}\",\"('Fe', 'O', 'Sn')\",OTHERS,False\nmp-779587,\"{'Ti': 6.0, 'P': 6.0, 'O': 24.0}\",\"('O', 'P', 'Ti')\",OTHERS,False\nmp-38284,\"{'Ce': 8.0, 'Nd': 8.0, 'O': 28.0}\",\"('Ce', 'Nd', 'O')\",OTHERS,False\nmp-756490,\"{'Li': 12.0, 'Mn': 2.0, 'S': 8.0}\",\"('Li', 'Mn', 'S')\",OTHERS,False\nmp-1112437,\"{'Rb': 2.0, 'Pr': 1.0, 'Cu': 1.0, 'I': 6.0}\",\"('Cu', 'I', 'Pr', 'Rb')\",OTHERS,False\nmp-1203006,\"{'Lu': 6.0, 'Ge': 26.0, 'Ru': 8.0}\",\"('Ge', 'Lu', 'Ru')\",OTHERS,False\nmp-695119,\"{'Na': 6.0, 'Zr': 4.0, 'Si': 4.0, 'P': 2.0, 'O': 24.0}\",\"('Na', 'O', 'P', 'Si', 'Zr')\",OTHERS,False\nmp-1101512,\"{'Li': 4.0, 'Fe': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-22025,\"{'O': 7.0, 'Co': 2.0, 'As': 2.0}\",\"('As', 'Co', 'O')\",OTHERS,False\nmp-27359,\"{'Cl': 6.0, 'Sc': 2.0, 'Cs': 2.0}\",\"('Cl', 'Cs', 'Sc')\",OTHERS,False\nmp-972448,\"{'Sm': 4.0, 'B': 4.0, 'S': 12.0}\",\"('B', 'S', 'Sm')\",OTHERS,False\nmp-674423,\"{'Sr': 20.0, 'Ta': 8.0, 'O': 39.0}\",\"('O', 'Sr', 'Ta')\",OTHERS,False\nmp-775128,\"{'Fe': 3.0, 'Sb': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Fe', 'O', 'P', 'Sb')\",OTHERS,False\nmp-978490,\"{'Si': 1.0, 'Pd': 1.0, 'O': 3.0}\",\"('O', 'Pd', 'Si')\",OTHERS,False\nmp-569943,\"{'K': 8.0, 'Ag': 4.0, 'I': 12.0}\",\"('Ag', 'I', 'K')\",OTHERS,False\nmp-1096843,\"{'Ba': 1.0, 'Cu': 1.0, 'S': 2.0}\",\"('Ba', 'Cu', 'S')\",OTHERS,False\nmp-640950,\"{'Na': 6.0, 'B': 54.0, 'Se': 48.0}\",\"('B', 'Na', 'Se')\",OTHERS,False\nmp-19137,\"{'O': 6.0, 'K': 2.0, 'Mn': 1.0, 'Se': 2.0}\",\"('K', 'Mn', 'O', 'Se')\",OTHERS,False\nmp-1215786,\"{'Zn': 2.0, 'Cd': 6.0, 'Ge': 2.0, 'S': 12.0}\",\"('Cd', 'Ge', 'S', 'Zn')\",OTHERS,False\nmp-571580,\"{'Er': 56.0, 'In': 12.0, 'Pd': 12.0}\",\"('Er', 'In', 'Pd')\",OTHERS,False\nmp-556481,\"{'Sr': 2.0, 'U': 2.0, 'Se': 4.0, 'O': 16.0}\",\"('O', 'Se', 'Sr', 'U')\",OTHERS,False\nmp-759603,\"{'H': 36.0, 'C': 4.0, 'N': 20.0, 'Cl': 8.0}\",\"('C', 'Cl', 'H', 'N')\",OTHERS,False\nmp-1184082,\"{'Er': 2.0, 'Zn': 1.0, 'Hg': 1.0}\",\"('Er', 'Hg', 'Zn')\",OTHERS,False\nmp-16342,\"{'Ge': 2.0, 'Ho': 2.0}\",\"('Ge', 'Ho')\",OTHERS,False\nmp-1188076,\"{'Th': 1.0, 'Mg': 149.0}\",\"('Mg', 'Th')\",OTHERS,False\nmp-1147741,\"{'Li': 32.0, 'Zn': 8.0, 'P': 16.0, 'S': 64.0}\",\"('Li', 'P', 'S', 'Zn')\",OTHERS,False\nmp-11604,\"{'S': 8.0, 'K': 2.0, 'Cu': 2.0, 'Sm': 4.0}\",\"('Cu', 'K', 'S', 'Sm')\",OTHERS,False\nmp-1079860,\"{'Hf': 3.0, 'Ga': 3.0, 'Ni': 3.0}\",\"('Ga', 'Hf', 'Ni')\",OTHERS,False\nmp-1194612,\"{'Y': 4.0, 'Nb': 4.0, 'Te': 8.0, 'O': 32.0}\",\"('Nb', 'O', 'Te', 'Y')\",OTHERS,False\nmp-11275,\"{'Be': 8.0, 'Re': 4.0}\",\"('Be', 'Re')\",OTHERS,False\nmp-554805,\"{'Sr': 12.0, 'Se': 12.0, 'Cl': 8.0, 'O': 32.0}\",\"('Cl', 'O', 'Se', 'Sr')\",OTHERS,False\nmp-560833,\"{'B': 12.0, 'Pb': 24.0, 'Cl': 4.0, 'O': 40.0}\",\"('B', 'Cl', 'O', 'Pb')\",OTHERS,False\nmp-1074408,\"{'Mg': 8.0, 'Si': 6.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-17395,\"{'B': 10.0, 'O': 20.0, 'Cu': 2.0, 'Tm': 2.0}\",\"('B', 'Cu', 'O', 'Tm')\",OTHERS,False\nmp-10003,\"{'Si': 2.0, 'Co': 2.0, 'Nb': 8.0}\",\"('Co', 'Nb', 'Si')\",OTHERS,False\nmp-219,\"{'P': 1.0, 'Mo': 1.0}\",\"('Mo', 'P')\",OTHERS,False\nmp-1019253,\"{'Sm': 2.0, 'Se': 4.0}\",\"('Se', 'Sm')\",OTHERS,False\nmp-1250551,\"{'Si': 112.0, 'O': 224.0}\",\"('O', 'Si')\",OTHERS,False\nmp-27474,\"{'Pa': 2.0, 'Br': 8.0}\",\"('Br', 'Pa')\",OTHERS,False\nmp-616507,\"{'Cs': 4.0, 'Na': 4.0, 'Ir': 2.0, 'O': 8.0}\",\"('Cs', 'Ir', 'Na', 'O')\",OTHERS,False\nmp-1208821,\"{'Sr': 2.0, 'Zn': 2.0, 'O': 10.0}\",\"('O', 'Sr', 'Zn')\",OTHERS,False\nmvc-2541,\"{'Sr': 2.0, 'Y': 1.0, 'Tl': 1.0, 'V': 2.0, 'O': 7.0}\",\"('O', 'Sr', 'Tl', 'V', 'Y')\",OTHERS,False\nmp-1112505,\"{'Cs': 2.0, 'Al': 1.0, 'Tl': 1.0, 'Cl': 6.0}\",\"('Al', 'Cl', 'Cs', 'Tl')\",OTHERS,False\nmp-567511,\"{'H': 32.0, 'Pt': 4.0, 'C': 4.0, 'N': 32.0}\",\"('C', 'H', 'N', 'Pt')\",OTHERS,False\nmp-17314,\"{'Li': 2.0, 'O': 32.0, 'P': 6.0, 'Mo': 6.0}\",\"('Li', 'Mo', 'O', 'P')\",OTHERS,False\nmp-776165,\"{'Ag': 4.0, 'Au': 4.0, 'O': 12.0}\",\"('Ag', 'Au', 'O')\",OTHERS,False\nmp-1211437,\"{'La': 4.0, 'W': 4.0, 'O': 8.0}\",\"('La', 'O', 'W')\",OTHERS,False\nmp-569543,\"{'Cs': 2.0, 'La': 2.0, 'Zr': 12.0, 'Fe': 2.0, 'Cl': 36.0}\",\"('Cl', 'Cs', 'Fe', 'La', 'Zr')\",OTHERS,False\nmp-707055,\"{'Na': 16.0, 'Ge': 8.0, 'H': 96.0, 'S': 16.0, 'O': 56.0}\",\"('Ge', 'H', 'Na', 'O', 'S')\",OTHERS,False\nmp-1219928,\"{'Pd': 1.0, 'Pt': 1.0, 'Au': 1.0}\",\"('Au', 'Pd', 'Pt')\",OTHERS,False\nmp-1200211,\"{'Ca': 6.0, 'Sn': 26.0, 'Ir': 8.0}\",\"('Ca', 'Ir', 'Sn')\",OTHERS,False\nmp-1102043,\"{'La': 4.0, 'Os': 8.0}\",\"('La', 'Os')\",OTHERS,False\nmp-760329,\"{'Li': 6.0, 'V': 4.0, 'F': 24.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-1208095,\"{'Yb': 3.0, 'N': 21.0, 'O': 45.0}\",\"('N', 'O', 'Yb')\",OTHERS,False\nmp-1102036,\"{'Ni': 6.0, 'P': 6.0}\",\"('Ni', 'P')\",OTHERS,False\nmp-1201500,\"{'Ca': 2.0, 'Fe': 10.0, 'P': 10.0, 'O': 44.0}\",\"('Ca', 'Fe', 'O', 'P')\",OTHERS,False\nmp-11595,\"{'Ge': 6.0, 'Yb': 10.0}\",\"('Ge', 'Yb')\",OTHERS,False\nmp-1080202,\"{'Fe': 4.0, 'N': 8.0}\",\"('Fe', 'N')\",OTHERS,False\nmp-1191502,\"{'V': 2.0, 'In': 2.0, 'Se': 4.0, 'O': 16.0}\",\"('In', 'O', 'Se', 'V')\",OTHERS,False\nmp-1025223,\"{'Hf': 4.0, 'Al': 3.0}\",\"('Al', 'Hf')\",OTHERS,False\nmp-631436,\"{'V': 1.0, 'Os': 1.0, 'Cl': 1.0}\",\"('Cl', 'Os', 'V')\",OTHERS,False\nmp-1101293,\"{'Ti': 3.0, 'V': 1.0, 'Ni': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Ni', 'O', 'P', 'Ti', 'V')\",OTHERS,False\nmp-973391,\"{'Li': 4.0, 'Si': 4.0, 'B': 24.0}\",\"('B', 'Li', 'Si')\",OTHERS,False\nmp-778744,\"{'Li': 4.0, 'Mn': 2.0, 'Ni': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Li', 'Mn', 'Ni', 'O', 'P')\",OTHERS,False\nmp-570491,\"{'Ta': 1.0, 'Ni': 3.0}\",\"('Ni', 'Ta')\",OTHERS,False\nmp-1198791,\"{'Sb': 8.0, 'Te': 12.0, 'W': 4.0, 'C': 16.0, 'O': 16.0, 'F': 48.0}\",\"('C', 'F', 'O', 'Sb', 'Te', 'W')\",OTHERS,False\nmp-1249484,\"{'K': 2.0, 'Mg': 6.0, 'Al': 2.0, 'Si': 6.0, 'O': 20.0, 'F': 4.0}\",\"('Al', 'F', 'K', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-542658,\"{'K': 4.0, 'Mo': 8.0, 'O': 26.0}\",\"('K', 'Mo', 'O')\",OTHERS,False\nmp-764498,\"{'Mn': 12.0, 'O': 7.0, 'F': 17.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-1225451,\"{'Dy': 12.0, 'Co': 9.0}\",\"('Co', 'Dy')\",OTHERS,False\nmp-1205629,\"{'Cs': 3.0, 'Fe': 1.0, 'F': 6.0}\",\"('Cs', 'F', 'Fe')\",OTHERS,False\nmp-1096801,\"{'Ba': 2.0, 'Zr': 2.0, 'P': 4.0}\",\"('Ba', 'P', 'Zr')\",OTHERS,False\nmp-1079720,\"{'Ag': 4.0, 'O': 4.0}\",\"('Ag', 'O')\",OTHERS,False\nmp-1216030,\"{'Yb': 4.0, 'Fe': 1.0, 'S': 7.0}\",\"('Fe', 'S', 'Yb')\",OTHERS,False\nmp-997048,\"{'Ag': 2.0, 'Br': 2.0, 'O': 4.0}\",\"('Ag', 'Br', 'O')\",OTHERS,False\nmp-1185542,\"{'Cs': 1.0, 'K': 2.0, 'Rb': 1.0}\",\"('Cs', 'K', 'Rb')\",OTHERS,False\nmp-30029,\"{'Ni': 39.0, 'Ga': 9.0, 'Ge': 18.0}\",\"('Ga', 'Ge', 'Ni')\",OTHERS,False\nmp-1095337,\"{'Sm': 2.0, 'V': 2.0, 'O': 8.0}\",\"('O', 'Sm', 'V')\",OTHERS,False\nmp-8179,\"{'Li': 2.0, 'O': 4.0, 'Tl': 2.0}\",\"('Li', 'O', 'Tl')\",OTHERS,False\nmp-20373,\"{'B': 4.0, 'Co': 12.0}\",\"('B', 'Co')\",OTHERS,False\nmp-756672,\"{'Li': 16.0, 'Si': 16.0, 'Bi': 8.0, 'O': 52.0}\",\"('Bi', 'Li', 'O', 'Si')\",OTHERS,False\nmp-755081,\"{'Li': 8.0, 'Ni': 5.0, 'O': 10.0}\",\"('Li', 'Ni', 'O')\",OTHERS,False\nmp-19271,\"{'O': 12.0, 'Cr': 4.0, 'Ba': 4.0}\",\"('Ba', 'Cr', 'O')\",OTHERS,False\nmvc-4661,\"{'Zn': 2.0, 'Sb': 4.0, 'O': 8.0}\",\"('O', 'Sb', 'Zn')\",OTHERS,False\nmp-772819,\"{'K': 6.0, 'Cu': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Cu', 'K', 'O', 'P')\",OTHERS,False\nmp-1096787,\"{'Al': 2.0, 'Zn': 2.0, 'S': 5.0}\",\"('Al', 'S', 'Zn')\",OTHERS,False\nmp-4165,\"{'N': 4.0, 'O': 4.0, 'Ta': 4.0}\",\"('N', 'O', 'Ta')\",OTHERS,False\nmp-510569,\"{'Cs': 2.0, 'Ce': 2.0, 'Cu': 2.0, 'S': 6.0}\",\"('Ce', 'Cs', 'Cu', 'S')\",OTHERS,False\nmp-11563,\"{'Ti': 1.0, 'Rh': 1.0}\",\"('Rh', 'Ti')\",OTHERS,False\nmp-554115,\"{'Zn': 24.0, 'S': 24.0}\",\"('S', 'Zn')\",OTHERS,False\nmp-10200,\"{'Be': 2.0, 'Si': 2.0, 'Zr': 2.0}\",\"('Be', 'Si', 'Zr')\",OTHERS,False\nmp-554142,\"{'Ge': 2.0, 'Te': 4.0, 'O': 12.0}\",\"('Ge', 'O', 'Te')\",OTHERS,False\nmp-1216888,\"{'U': 3.0, 'Al': 3.0, 'Co': 1.0, 'Rh': 2.0}\",\"('Al', 'Co', 'Rh', 'U')\",OTHERS,False\nmp-755936,\"{'Lu': 2.0, 'W': 2.0, 'O': 8.0}\",\"('Lu', 'O', 'W')\",OTHERS,False\nmp-1224789,\"{'Ga': 2.0, 'As': 1.0, 'P': 1.0}\",\"('As', 'Ga', 'P')\",OTHERS,False\nmp-29516,\"{'Br': 32.0, 'Rh': 4.0, 'Bi': 28.0}\",\"('Bi', 'Br', 'Rh')\",OTHERS,False\nmp-1216452,\"{'V': 6.0, 'Si': 1.0, 'C': 1.0}\",\"('C', 'Si', 'V')\",OTHERS,False\nmp-1220245,\"{'Nd': 2.0, 'Zn': 1.0, 'Sb': 4.0}\",\"('Nd', 'Sb', 'Zn')\",OTHERS,False\nmp-38077,\"{'Ce': 5.0, 'K': 1.0, 'S': 8.0}\",\"('Ce', 'K', 'S')\",OTHERS,False\nmp-27167,\"{'F': 6.0, 'Sn': 4.0, 'I': 2.0}\",\"('F', 'I', 'Sn')\",OTHERS,False\nmp-780803,\"{'Li': 16.0, 'Fe': 16.0, 'F': 48.0}\",\"('F', 'Fe', 'Li')\",OTHERS,False\nmp-759065,\"{'Li': 12.0, 'V': 8.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1191432,\"{'Ce': 6.0, 'Al': 2.0, 'Cd': 2.0, 'S': 14.0}\",\"('Al', 'Cd', 'Ce', 'S')\",OTHERS,False\nmp-978255,\"{'Mg': 2.0, 'Ti': 6.0}\",\"('Mg', 'Ti')\",OTHERS,False\nmp-546707,\"{'Na': 4.0, 'O': 4.0, 'C': 1.0}\",\"('C', 'Na', 'O')\",OTHERS,False\nmp-20699,\"{'Y': 4.0, 'Mn': 4.0, 'O': 12.0}\",\"('Mn', 'O', 'Y')\",OTHERS,False\nmp-1216095,\"{'Y': 2.0, 'Mn': 3.0, 'Fe': 1.0}\",\"('Fe', 'Mn', 'Y')\",OTHERS,False\nmp-973198,{'Na': 3.0},\"('Na',)\",OTHERS,False\nmp-639904,\"{'Ce': 12.0, 'Mo': 4.0, 'O': 28.0}\",\"('Ce', 'Mo', 'O')\",OTHERS,False\nmp-1220251,\"{'Nd': 4.0, 'Mn': 1.0, 'Ni': 3.0, 'O': 12.0}\",\"('Mn', 'Nd', 'Ni', 'O')\",OTHERS,False\nmp-1219193,\"{'Si': 1.0, 'Ni': 6.0, 'Ge': 1.0}\",\"('Ge', 'Ni', 'Si')\",OTHERS,False\nmp-31070,\"{'S': 18.0, 'As': 16.0}\",\"('As', 'S')\",OTHERS,False\nmp-9066,\"{'Li': 4.0, 'O': 8.0, 'Na': 2.0, 'As': 2.0}\",\"('As', 'Li', 'Na', 'O')\",OTHERS,False\nmp-9899,\"{'P': 2.0, 'Ag': 2.0, 'Ba': 2.0}\",\"('Ag', 'Ba', 'P')\",OTHERS,False\nmp-1188605,\"{'V': 10.0, 'P': 6.0}\",\"('P', 'V')\",OTHERS,False\nmp-6651,\"{'O': 12.0, 'F': 24.0, 'K': 8.0, 'Ta': 8.0}\",\"('F', 'K', 'O', 'Ta')\",OTHERS,False\nmp-1208632,\"{'Zn': 12.0, 'N': 72.0}\",\"('N', 'Zn')\",OTHERS,False\nmp-1222245,\"{'Mn': 2.0, 'Fe': 8.0, 'Bi': 10.0, 'O': 30.0}\",\"('Bi', 'Fe', 'Mn', 'O')\",OTHERS,False\nmp-604496,\"{'Ce': 2.0, 'Si': 2.0, 'H': 2.0, 'Ru': 2.0}\",\"('Ce', 'H', 'Ru', 'Si')\",OTHERS,False\nmp-2965,\"{'Si': 2.0, 'Mn': 2.0, 'Ce': 1.0}\",\"('Ce', 'Mn', 'Si')\",OTHERS,False\nmp-1205100,\"{'Sn': 2.0, 'S': 12.0, 'N': 4.0, 'O': 42.0}\",\"('N', 'O', 'S', 'Sn')\",OTHERS,False\nmvc-9486,\"{'Ti': 8.0, 'Zn': 4.0, 'P': 8.0, 'O': 32.0}\",\"('O', 'P', 'Ti', 'Zn')\",OTHERS,False\nmp-1224432,\"{'Hf': 1.0, 'U': 3.0, 'S': 3.0}\",\"('Hf', 'S', 'U')\",OTHERS,False\nmp-1179922,\"{'Pt': 4.0, 'N': 8.0, 'O': 16.0}\",\"('N', 'O', 'Pt')\",OTHERS,False\nmp-1197024,\"{'Fe': 4.0, 'Bi': 4.0, 'Se': 12.0, 'O': 36.0}\",\"('Bi', 'Fe', 'O', 'Se')\",OTHERS,False\nmp-1029965,\"{'K': 30.0, 'Os': 14.0, 'N': 38.0}\",\"('K', 'N', 'Os')\",OTHERS,False\nmp-1226910,\"{'Cs': 1.0, 'Nb': 2.0, 'Si': 8.0, 'O': 24.0}\",\"('Cs', 'Nb', 'O', 'Si')\",OTHERS,False\nmp-1097178,\"{'Ta': 1.0, 'Si': 1.0, 'Tc': 2.0}\",\"('Si', 'Ta', 'Tc')\",OTHERS,False\nmp-18520,\"{'O': 24.0, 'P': 4.0, 'V': 4.0, 'Ba': 4.0}\",\"('Ba', 'O', 'P', 'V')\",OTHERS,False\nmp-1093946,\"{'Be': 1.0, 'Ge': 1.0, 'Ir': 2.0}\",\"('Be', 'Ge', 'Ir')\",OTHERS,False\nmp-1220311,\"{'Nd': 1.0, 'Er': 3.0, 'Fe': 34.0}\",\"('Er', 'Fe', 'Nd')\",OTHERS,False\nmp-761240,\"{'Li': 4.0, 'Mn': 2.0, 'V': 2.0, 'P': 8.0, 'O': 28.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-2591,\"{'O': 4.0, 'Ti': 12.0}\",\"('O', 'Ti')\",OTHERS,False\nmp-758932,\"{'Li': 40.0, 'Fe': 8.0, 'H': 8.0, 'O': 32.0}\",\"('Fe', 'H', 'Li', 'O')\",OTHERS,False\nmp-643392,\"{'Ca': 4.0, 'H': 8.0, 'O': 8.0}\",\"('Ca', 'H', 'O')\",OTHERS,False\nmp-1245618,\"{'Ge': 4.0, 'Te': 2.0, 'N': 4.0}\",\"('Ge', 'N', 'Te')\",OTHERS,False\nmp-779534,\"{'Li': 9.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-19388,\"{'O': 8.0, 'Ni': 2.0, 'As': 2.0, 'Ba': 1.0}\",\"('As', 'Ba', 'Ni', 'O')\",OTHERS,False\nmp-715811,\"{'Fe': 12.0, 'O': 16.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-1177898,\"{'Li': 4.0, 'Mn': 2.0, 'Fe': 2.0, 'O': 8.0}\",\"('Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-765572,\"{'Co': 4.0, 'B': 4.0, 'O': 12.0}\",\"('B', 'Co', 'O')\",OTHERS,False\nmp-1216756,\"{'Tm': 2.0, 'B': 6.0, 'Rh': 9.0}\",\"('B', 'Rh', 'Tm')\",OTHERS,False\nmp-1192271,\"{'Cs': 4.0, 'U': 2.0, 'Pd': 6.0, 'S': 12.0}\",\"('Cs', 'Pd', 'S', 'U')\",OTHERS,False\nmp-770464,\"{'La': 4.0, 'Bi': 4.0, 'O': 16.0}\",\"('Bi', 'La', 'O')\",OTHERS,False\nmp-1094041,\"{'Sr': 1.0, 'Ca': 3.0, 'Mn': 4.0, 'O': 12.0}\",\"('Ca', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-1096616,\"{'Mg': 1.0, 'Zr': 1.0, 'Cd': 2.0}\",\"('Cd', 'Mg', 'Zr')\",OTHERS,False\nmp-31082,\"{'Li': 4.0, 'Ge': 2.0, 'Zr': 1.0}\",\"('Ge', 'Li', 'Zr')\",OTHERS,False\nmp-752784,\"{'Mn': 8.0, 'O': 8.0, 'F': 8.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-510366,\"{'K': 12.0, 'Co': 4.0, 'O': 10.0}\",\"('Co', 'K', 'O')\",OTHERS,False\nmp-1078409,\"{'Ba': 2.0, 'La': 1.0, 'Bi': 1.0, 'O': 6.0}\",\"('Ba', 'Bi', 'La', 'O')\",OTHERS,False\nmp-849471,\"{'Li': 4.0, 'Mn': 3.0, 'Cr': 2.0, 'Fe': 3.0, 'O': 16.0}\",\"('Cr', 'Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1222442,\"{'Li': 5.0, 'Au': 1.0, 'O': 4.0}\",\"('Au', 'Li', 'O')\",OTHERS,False\nmp-1076208,\"{'K': 20.0, 'Na': 12.0, 'Nb': 16.0, 'W': 16.0, 'O': 80.0}\",\"('K', 'Na', 'Nb', 'O', 'W')\",OTHERS,False\nmp-961681,\"{'Zr': 1.0, 'Ge': 1.0, 'Pt': 1.0}\",\"('Ge', 'Pt', 'Zr')\",OTHERS,False\nmp-1188003,\"{'Zr': 2.0, 'Tc': 1.0, 'Ru': 1.0}\",\"('Ru', 'Tc', 'Zr')\",OTHERS,False\nmp-1220432,\"{'Nd': 4.0, 'Ti': 2.0, 'Cu': 2.0, 'O': 12.0}\",\"('Cu', 'Nd', 'O', 'Ti')\",OTHERS,False\nmp-1025290,\"{'La': 2.0, 'Fe': 2.0, 'Si': 2.0, 'C': 1.0}\",\"('C', 'Fe', 'La', 'Si')\",OTHERS,False\nmp-776648,\"{'Li': 4.0, 'Fe': 6.0, 'F': 16.0}\",\"('F', 'Fe', 'Li')\",OTHERS,False\nmp-1093709,\"{'Sc': 1.0, 'Cu': 2.0, 'Ag': 1.0}\",\"('Ag', 'Cu', 'Sc')\",OTHERS,False\nmp-1074189,\"{'Mg': 8.0, 'Si': 14.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-612405,\"{'Fe': 6.0, 'O': 8.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-1188851,\"{'Ho': 10.0, 'Bi': 2.0, 'Au': 4.0}\",\"('Au', 'Bi', 'Ho')\",OTHERS,False\nmp-1096783,\"{'Ba': 8.0, 'V': 4.0, 'Fe': 4.0, 'O': 24.0}\",\"('Ba', 'Fe', 'O', 'V')\",OTHERS,False\nmp-1229004,\"{'Al': 1.0, 'Cr': 4.0, 'Cu': 1.0, 'Se': 8.0}\",\"('Al', 'Cr', 'Cu', 'Se')\",OTHERS,False\nmp-757110,\"{'Na': 6.0, 'Ni': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Na', 'Ni', 'O', 'P')\",OTHERS,False\nmvc-8188,\"{'Ca': 2.0, 'Fe': 4.0, 'O': 8.0}\",\"('Ca', 'Fe', 'O')\",OTHERS,False\nmp-1097499,\"{'Nb': 1.0, 'Cr': 2.0, 'W': 1.0}\",\"('Cr', 'Nb', 'W')\",OTHERS,False\nmp-29538,\"{'Cl': 10.0, 'Ba': 2.0, 'Gd': 2.0}\",\"('Ba', 'Cl', 'Gd')\",OTHERS,False\nmp-29759,\"{'V': 4.0, 'Xe': 8.0, 'F': 68.0}\",\"('F', 'V', 'Xe')\",OTHERS,False\nmp-560071,\"{'F': 6.0, 'K': 1.0, 'Co': 1.0, 'Rb': 2.0}\",\"('Co', 'F', 'K', 'Rb')\",OTHERS,False\nmp-1189962,\"{'Pr': 4.0, 'Sc': 4.0, 'S': 12.0}\",\"('Pr', 'S', 'Sc')\",OTHERS,False\nmvc-16379,\"{'Y': 4.0, 'Co': 12.0, 'O': 24.0}\",\"('Co', 'O', 'Y')\",OTHERS,False\nmp-1211841,\"{'K': 2.0, 'H': 16.0, 'W': 2.0, 'N': 4.0, 'F': 12.0}\",\"('F', 'H', 'K', 'N', 'W')\",OTHERS,False\nmp-568922,\"{'Ce': 14.0, 'C': 6.0, 'I': 20.0}\",\"('C', 'Ce', 'I')\",OTHERS,False\nmp-768144,\"{'Na': 4.0, 'Sb': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Na', 'O', 'P', 'Sb')\",OTHERS,False\nmp-1039728,\"{'Na': 1.0, 'Hf': 1.0, 'Mg': 30.0, 'O': 31.0}\",\"('Hf', 'Mg', 'Na', 'O')\",OTHERS,False\nmp-11718,\"{'F': 1.0, 'Rb': 1.0}\",\"('F', 'Rb')\",OTHERS,False\nmp-1228334,\"{'Ba': 4.0, 'Eu': 2.0, 'Cu': 6.0, 'O': 13.0}\",\"('Ba', 'Cu', 'Eu', 'O')\",OTHERS,False\nmp-675423,\"{'Sr': 3.0, 'Cr': 2.0, 'O': 7.0}\",\"('Cr', 'O', 'Sr')\",OTHERS,False\nmp-1178173,\"{'Ho': 2.0, 'Te': 1.0, 'O': 2.0}\",\"('Ho', 'O', 'Te')\",OTHERS,False\nmp-9964,\"{'N': 1.0, 'Ti': 3.0, 'Tl': 1.0}\",\"('N', 'Ti', 'Tl')\",OTHERS,False\nmp-2824,\"{'Al': 4.0, 'Pd': 8.0}\",\"('Al', 'Pd')\",OTHERS,False\nmp-1227522,\"{'Ca': 12.0, 'Al': 2.0, 'Cr': 6.0, 'Si': 12.0, 'O': 48.0}\",\"('Al', 'Ca', 'Cr', 'O', 'Si')\",OTHERS,False\nmp-1215420,\"{'Zr': 3.0, 'Ta': 1.0, 'Fe': 8.0}\",\"('Fe', 'Ta', 'Zr')\",OTHERS,False\nmp-1184147,\"{'Cu': 2.0, 'Ge': 6.0}\",\"('Cu', 'Ge')\",OTHERS,False\nmp-773043,\"{'Li': 8.0, 'Fe': 16.0, 'O': 32.0}\",\"('Fe', 'Li', 'O')\",OTHERS,False\nmp-1176270,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-976008,\"{'Nd': 1.0, 'Tm': 1.0, 'Mg': 2.0}\",\"('Mg', 'Nd', 'Tm')\",OTHERS,False\nmp-1218804,\"{'Sr': 4.0, 'In': 17.0, 'Au': 15.0}\",\"('Au', 'In', 'Sr')\",OTHERS,False\nmp-1020620,\"{'Rb': 5.0, 'Li': 6.0, 'B': 11.0, 'O': 22.0}\",\"('B', 'Li', 'O', 'Rb')\",OTHERS,False\nmp-23915,\"{'H': 4.0, 'O': 52.0, 'Al': 12.0, 'Si': 12.0, 'Ca': 8.0}\",\"('Al', 'Ca', 'H', 'O', 'Si')\",OTHERS,False\nmp-23414,\"{'N': 2.0, 'Cl': 12.0, 'Sc': 8.0}\",\"('Cl', 'N', 'Sc')\",OTHERS,False\nmp-1192042,\"{'Tm': 6.0, 'Al': 2.0, 'Ni': 16.0}\",\"('Al', 'Ni', 'Tm')\",OTHERS,False\nmp-532537,\"{'Ag': 4.0, 'Ca': 8.0, 'Mn': 8.0, 'O': 48.0, 'V': 12.0}\",\"('Ag', 'Ca', 'Mn', 'O', 'V')\",OTHERS,False\nmp-1175273,\"{'Li': 7.0, 'Mn': 4.0, 'Co': 1.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1179346,\"{'Te': 2.0, 'C': 8.0, 'S': 8.0, 'N': 16.0, 'F': 8.0}\",\"('C', 'F', 'N', 'S', 'Te')\",OTHERS,False\nmp-1187253,\"{'Ta': 1.0, 'Ti': 3.0}\",\"('Ta', 'Ti')\",OTHERS,False\nmp-1221943,\"{'Mg': 1.0, 'Ge': 1.0, 'P': 2.0}\",\"('Ge', 'Mg', 'P')\",OTHERS,False\nmp-720985,\"{'Ca': 1.0, 'Er': 15.0, 'Si': 8.0, 'Se': 11.0, 'Cl': 1.0, 'O': 28.0}\",\"('Ca', 'Cl', 'Er', 'O', 'Se', 'Si')\",OTHERS,False\nmp-1192759,\"{'Ir': 3.0, 'N': 10.0, 'Cl': 10.0}\",\"('Cl', 'Ir', 'N')\",OTHERS,False\nmp-1213589,\"{'Eu': 9.0, 'Ni': 26.0, 'P': 12.0}\",\"('Eu', 'Ni', 'P')\",OTHERS,False\nmp-556748,\"{'Ca': 2.0, 'Cr': 2.0, 'F': 10.0}\",\"('Ca', 'Cr', 'F')\",OTHERS,False\nmp-29974,\"{'Li': 28.0, 'Ge': 32.0, 'Rb': 4.0}\",\"('Ge', 'Li', 'Rb')\",OTHERS,False\nmp-505131,\"{'Li': 2.0, 'O': 8.0, 'V': 2.0, 'Cu': 2.0}\",\"('Cu', 'Li', 'O', 'V')\",OTHERS,False\nmp-31214,\"{'P': 8.0, 'Ti': 24.0}\",\"('P', 'Ti')\",OTHERS,False\nmp-1093948,\"{'Sr': 1.0, 'La': 1.0, 'Ag': 2.0}\",\"('Ag', 'La', 'Sr')\",OTHERS,False\nmp-1228713,\"{'Al': 4.0, 'Ni': 15.0}\",\"('Al', 'Ni')\",OTHERS,False\nmp-1208398,\"{'Tl': 8.0, 'Se': 12.0, 'O': 48.0}\",\"('O', 'Se', 'Tl')\",OTHERS,False\nmp-765857,\"{'Li': 3.0, 'Cr': 3.0, 'Si': 6.0, 'O': 18.0}\",\"('Cr', 'Li', 'O', 'Si')\",OTHERS,False\nmp-773065,\"{'Li': 5.0, 'V': 5.0, 'Cr': 2.0, 'O': 12.0}\",\"('Cr', 'Li', 'O', 'V')\",OTHERS,False\nmp-773108,\"{'Li': 10.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-753626,\"{'Li': 4.0, 'Nd': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Li', 'Nd', 'O', 'P')\",OTHERS,False\nmp-570806,\"{'Mg': 60.0, 'Al': 48.0, 'Ag': 38.0}\",\"('Ag', 'Al', 'Mg')\",OTHERS,False\nmvc-4994,\"{'Ca': 4.0, 'Mo': 2.0, 'W': 2.0, 'O': 12.0}\",\"('Ca', 'Mo', 'O', 'W')\",OTHERS,False\nmp-1219106,\"{'Sm': 1.0, 'Y': 1.0, 'Mn': 4.0, 'Ge': 4.0}\",\"('Ge', 'Mn', 'Sm', 'Y')\",OTHERS,False\nmp-1226593,\"{'Ce': 2.0, 'Y': 2.0, 'Ga': 4.0, 'Pd': 8.0}\",\"('Ce', 'Ga', 'Pd', 'Y')\",OTHERS,False\nmp-19034,\"{'O': 16.0, 'Mg': 6.0, 'V': 4.0}\",\"('Mg', 'O', 'V')\",OTHERS,False\nmp-1186495,\"{'Pm': 3.0, 'Er': 1.0}\",\"('Er', 'Pm')\",OTHERS,False\nmp-7928,\"{'S': 2.0, 'K': 2.0, 'Pt': 1.0}\",\"('K', 'Pt', 'S')\",OTHERS,False\nmp-1201038,\"{'K': 4.0, 'Na': 4.0, 'Al': 24.0, 'Si': 24.0, 'H': 16.0, 'O': 96.0}\",\"('Al', 'H', 'K', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1180271,\"{'Mg': 1.0, 'O': 2.0}\",\"('Mg', 'O')\",OTHERS,False\nmp-18378,\"{'Al': 4.0, 'K': 12.0, 'Te': 12.0}\",\"('Al', 'K', 'Te')\",OTHERS,False\nmp-21634,\"{'Na': 8.0, 'W': 8.0, 'O': 28.0}\",\"('Na', 'O', 'W')\",OTHERS,False\nmp-1221985,\"{'Mg': 1.0, 'Cd': 1.0, 'Ag': 2.0}\",\"('Ag', 'Cd', 'Mg')\",OTHERS,False\nmp-762116,\"{'Li': 4.0, 'Cu': 16.0, 'P': 12.0, 'O': 48.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-772390,\"{'Li': 4.0, 'Al': 4.0, 'Fe': 4.0, 'O': 16.0}\",\"('Al', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-864953,\"{'Mn': 1.0, 'V': 2.0, 'Cr': 1.0}\",\"('Cr', 'Mn', 'V')\",OTHERS,False\nmp-1177443,\"{'Li': 4.0, 'Fe': 2.0, 'Cu': 3.0, 'Sn': 3.0, 'O': 16.0}\",\"('Cu', 'Fe', 'Li', 'O', 'Sn')\",OTHERS,False\nmp-1211512,\"{'K': 4.0, 'Y': 2.0, 'Sb': 2.0, 'O': 14.0}\",\"('K', 'O', 'Sb', 'Y')\",OTHERS,False\nmp-1936,\"{'As': 2.0, 'Ta': 2.0}\",\"('As', 'Ta')\",OTHERS,False\nmp-1187199,\"{'Ta': 3.0, 'Bi': 1.0}\",\"('Bi', 'Ta')\",OTHERS,False\nmp-1245498,\"{'Sr': 8.0, 'Ir': 2.0, 'N': 8.0}\",\"('Ir', 'N', 'Sr')\",OTHERS,False\nmp-570798,\"{'Er': 3.0, 'P': 6.0, 'Pd': 20.0}\",\"('Er', 'P', 'Pd')\",OTHERS,False\nmp-1247357,\"{'Y': 8.0, 'In': 4.0, 'N': 12.0}\",\"('In', 'N', 'Y')\",OTHERS,False\nmp-867291,\"{'Tb': 2.0, 'Ir': 1.0, 'Ru': 1.0}\",\"('Ir', 'Ru', 'Tb')\",OTHERS,False\nmp-1245767,\"{'Ca': 8.0, 'Ir': 4.0, 'N': 12.0}\",\"('Ca', 'Ir', 'N')\",OTHERS,False\nmp-22643,\"{'Co': 2.0, 'Ge': 4.0, 'Tb': 3.0}\",\"('Co', 'Ge', 'Tb')\",OTHERS,False\nmp-1205556,\"{'Ba': 2.0, 'Nd': 1.0, 'Re': 1.0, 'O': 6.0}\",\"('Ba', 'Nd', 'O', 'Re')\",OTHERS,False\nmp-1080127,\"{'Yb': 3.0, 'Cd': 3.0, 'Pb': 3.0}\",\"('Cd', 'Pb', 'Yb')\",OTHERS,False\nmp-780575,\"{'Na': 8.0, 'Fe': 4.0, 'Si': 24.0, 'O': 60.0}\",\"('Fe', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1832,\"{'La': 1.0, 'Pt': 5.0}\",\"('La', 'Pt')\",OTHERS,False\nmp-542501,\"{'U': 3.0, 'Te': 2.0, 'Rb': 2.0, 'O': 14.0}\",\"('O', 'Rb', 'Te', 'U')\",OTHERS,False\nmp-1186401,\"{'Pa': 1.0, 'Si': 1.0, 'O': 3.0}\",\"('O', 'Pa', 'Si')\",OTHERS,False\nmp-505478,\"{'Na': 2.0, 'Fe': 2.0, 'B': 2.0, 'P': 4.0, 'O': 20.0, 'H': 6.0}\",\"('B', 'Fe', 'H', 'Na', 'O', 'P')\",OTHERS,False\nmp-1025510,\"{'Fe': 1.0, 'Cu': 2.0, 'Si': 1.0, 'Se': 4.0}\",\"('Cu', 'Fe', 'Se', 'Si')\",OTHERS,False\nmp-767157,\"{'Ti': 16.0, 'Cu': 1.0, 'S': 32.0}\",\"('Cu', 'S', 'Ti')\",OTHERS,False\nmp-1246884,\"{'Y': 3.0, 'Mg': 2.0, 'V': 1.0, 'S': 8.0}\",\"('Mg', 'S', 'V', 'Y')\",OTHERS,False\nmp-1469,\"{'P': 16.0, 'S': 12.0}\",\"('P', 'S')\",OTHERS,False\nmp-11714,\"{'C': 4.0, 'Si': 4.0}\",\"('C', 'Si')\",OTHERS,False\nmp-1198442,\"{'Cu': 8.0, 'P': 4.0, 'C': 8.0, 'S': 4.0, 'O': 52.0}\",\"('C', 'Cu', 'O', 'P', 'S')\",OTHERS,False\nmp-766048,\"{'Li': 8.0, 'V': 4.0, 'Si': 2.0, 'Ge': 2.0, 'O': 20.0}\",\"('Ge', 'Li', 'O', 'Si', 'V')\",OTHERS,False\nmp-1094475,\"{'Mg': 6.0, 'Zn': 6.0}\",\"('Mg', 'Zn')\",OTHERS,False\nmp-541868,\"{'Te': 6.0, 'Mo': 2.0, 'Cl': 32.0}\",\"('Cl', 'Mo', 'Te')\",OTHERS,False\nmp-1094493,\"{'Mg': 6.0, 'Zn': 6.0}\",\"('Mg', 'Zn')\",OTHERS,False\nmp-975738,\"{'Pr': 2.0, 'B': 4.0, 'Os': 4.0}\",\"('B', 'Os', 'Pr')\",OTHERS,False\nmp-1186483,\"{'Pm': 4.0, 'Mg': 2.0}\",\"('Mg', 'Pm')\",OTHERS,False\nmp-1176410,\"{'Na': 8.0, 'Ni': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Na', 'Ni', 'O', 'P')\",OTHERS,False\nmp-690644,\"{'Tl': 1.0, 'In': 1.0, 'S': 2.0}\",\"('In', 'S', 'Tl')\",OTHERS,False\nmp-752502,\"{'Li': 7.0, 'Cr': 3.0, 'Si': 2.0, 'O': 12.0}\",\"('Cr', 'Li', 'O', 'Si')\",OTHERS,False\nmp-30170,\"{'Ge': 40.0, 'Pd': 6.0, 'Ba': 8.0}\",\"('Ba', 'Ge', 'Pd')\",OTHERS,False\nmp-1203462,\"{'Na': 4.0, 'Ho': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Ho', 'Na', 'O', 'P')\",OTHERS,False\nmp-1175073,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-14222,\"{'O': 4.0, 'Ga': 2.0, 'Tl': 2.0}\",\"('Ga', 'O', 'Tl')\",OTHERS,False\nmp-583476,\"{'Nb': 28.0, 'S': 8.0, 'I': 76.0}\",\"('I', 'Nb', 'S')\",OTHERS,False\nmp-35555,\"{'F': 1.0, 'La': 1.0, 'O': 1.0}\",\"('F', 'La', 'O')\",OTHERS,False\nmp-1078621,\"{'Tl': 2.0, 'In': 2.0, 'S': 4.0}\",\"('In', 'S', 'Tl')\",OTHERS,False\nmp-1215536,\"{'Zr': 4.0, 'Si': 2.0, 'Mo': 6.0}\",\"('Mo', 'Si', 'Zr')\",OTHERS,False\nmp-19981,{'Gd': 4.0},\"('Gd',)\",OTHERS,False\nmp-9835,\"{'Co': 2.0, 'Sb': 4.0}\",\"('Co', 'Sb')\",OTHERS,False\nmp-1209369,\"{'Rb': 5.0, 'Dy': 3.0, 'I': 12.0}\",\"('Dy', 'I', 'Rb')\",OTHERS,False\nmp-16690,\"{'P': 8.0, 'S': 28.0, 'K': 8.0, 'Nd': 4.0}\",\"('K', 'Nd', 'P', 'S')\",OTHERS,False\nmp-1096411,\"{'Zr': 2.0, 'In': 1.0, 'Tc': 1.0}\",\"('In', 'Tc', 'Zr')\",OTHERS,False\nmp-570883,\"{'Ca': 42.0, 'Zn': 72.0, 'Ni': 4.0}\",\"('Ca', 'Ni', 'Zn')\",OTHERS,False\nmp-16496,\"{'Al': 2.0, 'Cu': 4.0, 'Re': 4.0}\",\"('Al', 'Cu', 'Re')\",OTHERS,False\nmp-1210367,\"{'Na': 6.0, 'P': 6.0, 'N': 6.0, 'O': 14.0}\",\"('N', 'Na', 'O', 'P')\",OTHERS,False\nmp-1113333,\"{'Tb': 1.0, 'Cs': 2.0, 'Cu': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cs', 'Cu', 'Tb')\",OTHERS,False\nmp-675073,\"{'Na': 8.0, 'Pt': 2.0, 'O': 8.0}\",\"('Na', 'O', 'Pt')\",OTHERS,False\nmp-1245354,\"{'Li': 56.0, 'Cr': 8.0, 'N': 32.0}\",\"('Cr', 'Li', 'N')\",OTHERS,False\nmp-23202,\"{'In': 2.0, 'I': 2.0}\",\"('I', 'In')\",OTHERS,False\nmp-753471,\"{'Li': 4.0, 'Cu': 4.0, 'B': 4.0, 'O': 12.0}\",\"('B', 'Cu', 'Li', 'O')\",OTHERS,False\nmp-29216,\"{'Cl': 8.0, 'Cs': 4.0, 'Pt': 2.0}\",\"('Cl', 'Cs', 'Pt')\",OTHERS,False\nmp-1207427,\"{'Zr': 18.0, 'Mo': 8.0, 'O': 6.0}\",\"('Mo', 'O', 'Zr')\",OTHERS,False\nmp-1077262,\"{'Tb': 1.0, 'Cu': 5.0}\",\"('Cu', 'Tb')\",OTHERS,False\nmp-1024977,\"{'Yb': 1.0, 'Ni': 4.0, 'Au': 1.0}\",\"('Au', 'Ni', 'Yb')\",OTHERS,False\nmp-778047,\"{'Na': 32.0, 'Ni': 8.0, 'O': 32.0}\",\"('Na', 'Ni', 'O')\",OTHERS,False\nmp-1222759,\"{'La': 1.0, 'U': 3.0, 'C': 8.0}\",\"('C', 'La', 'U')\",OTHERS,False\nmp-1195560,\"{'C': 24.0, 'F': 40.0}\",\"('C', 'F')\",OTHERS,False\nmp-865778,\"{'Lu': 10.0, 'Rh': 6.0}\",\"('Lu', 'Rh')\",OTHERS,False\nmp-28313,\"{'Nd': 4.0, 'Cl': 8.0}\",\"('Cl', 'Nd')\",OTHERS,False\nmp-1032034,\"{'Sr': 1.0, 'Mg': 6.0, 'Zn': 1.0, 'O': 8.0}\",\"('Mg', 'O', 'Sr', 'Zn')\",OTHERS,False\nmp-542606,{'Pu': 17.0},\"('Pu',)\",OTHERS,False\nmp-1186636,\"{'Pm': 2.0, 'Rh': 6.0}\",\"('Pm', 'Rh')\",OTHERS,False\nmp-995196,\"{'C': 2.0, 'O': 6.0}\",\"('C', 'O')\",OTHERS,False\nmp-4651,\"{'O': 6.0, 'Ti': 2.0, 'Sr': 2.0}\",\"('O', 'Sr', 'Ti')\",OTHERS,False\nmp-771566,\"{'Lu': 8.0, 'Te': 4.0, 'O': 24.0}\",\"('Lu', 'O', 'Te')\",OTHERS,False\nmp-1228701,\"{'Al': 4.0, 'Ge': 2.0, 'W': 3.0}\",\"('Al', 'Ge', 'W')\",OTHERS,False\nmp-753560,\"{'Li': 2.0, 'V': 2.0, 'P': 2.0, 'H': 2.0, 'O': 10.0}\",\"('H', 'Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1226828,\"{'Ce': 2.0, 'Pd': 3.0, 'Rh': 3.0}\",\"('Ce', 'Pd', 'Rh')\",OTHERS,False\nmp-27080,\"{'Li': 4.0, 'Ni': 12.0, 'P': 16.0, 'O': 60.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-8948,\"{'As': 2.0, 'Pd': 2.0, 'Ce': 2.0}\",\"('As', 'Ce', 'Pd')\",OTHERS,False\nmp-1188397,\"{'Pr': 4.0, 'Mg': 2.0, 'Ir': 2.0, 'O': 12.0}\",\"('Ir', 'Mg', 'O', 'Pr')\",OTHERS,False\nmp-861906,\"{'Ba': 1.0, 'Na': 2.0, 'Mg': 1.0, 'P': 2.0, 'O': 8.0}\",\"('Ba', 'Mg', 'Na', 'O', 'P')\",OTHERS,False\nmp-1232293,\"{'Mg': 1.0, 'Al': 2.0, 'Sn': 2.0}\",\"('Al', 'Mg', 'Sn')\",OTHERS,False\nmp-1208510,\"{'Sr': 2.0, 'Y': 1.0, 'Fe': 3.0, 'O': 8.0}\",\"('Fe', 'O', 'Sr', 'Y')\",OTHERS,False\nmp-541075,\"{'Rb': 4.0, 'Mo': 6.0, 'O': 20.0}\",\"('Mo', 'O', 'Rb')\",OTHERS,False\nmp-1210000,\"{'Nd': 6.0, 'Cd': 4.0, 'Si': 6.0, 'O': 26.0}\",\"('Cd', 'Nd', 'O', 'Si')\",OTHERS,False\nmp-1019740,\"{'Ga': 1.0, 'B': 3.0, 'N': 4.0}\",\"('B', 'Ga', 'N')\",OTHERS,False\nmp-1897,\"{'Rh': 4.0, 'Pu': 2.0}\",\"('Pu', 'Rh')\",OTHERS,False\nmp-683598,\"{'P': 4.0, 'W': 4.0, 'C': 20.0, 'Br': 12.0, 'O': 20.0}\",\"('Br', 'C', 'O', 'P', 'W')\",OTHERS,False\nmp-1216917,\"{'Ti': 1.0, 'In': 1.0, 'Ni': 2.0}\",\"('In', 'Ni', 'Ti')\",OTHERS,False\nmp-703350,\"{'La': 4.0, 'Ge': 6.0, 'O': 18.0}\",\"('Ge', 'La', 'O')\",OTHERS,False\nmp-504891,\"{'La': 6.0, 'Ga': 2.0, 'Mn': 2.0, 'S': 14.0}\",\"('Ga', 'La', 'Mn', 'S')\",OTHERS,False\nmp-1185469,\"{'Li': 1.0, 'Yb': 3.0}\",\"('Li', 'Yb')\",OTHERS,False\nmp-1076679,\"{'La': 7.0, 'Sm': 1.0, 'V': 7.0, 'Cr': 1.0, 'O': 24.0}\",\"('Cr', 'La', 'O', 'Sm', 'V')\",OTHERS,False\nmp-1194017,\"{'Th': 4.0, 'P': 4.0, 'O': 20.0}\",\"('O', 'P', 'Th')\",OTHERS,False\nmp-1652,\"{'Zn': 2.0, 'Pd': 2.0}\",\"('Pd', 'Zn')\",OTHERS,False\nmp-1190316,\"{'Yb': 1.0, 'Fe': 4.0, 'Cu': 3.0, 'O': 12.0}\",\"('Cu', 'Fe', 'O', 'Yb')\",OTHERS,False\nmp-542333,\"{'S': 5.0, 'Cu': 9.0}\",\"('Cu', 'S')\",OTHERS,False\nmp-604537,\"{'Mn': 10.0, 'Ga': 10.0, 'Ni': 20.0}\",\"('Ga', 'Mn', 'Ni')\",OTHERS,False\nmp-1080537,\"{'K': 2.0, 'Na': 1.0, 'V': 1.0, 'F': 6.0}\",\"('F', 'K', 'Na', 'V')\",OTHERS,False\nmp-1226956,\"{'Cd': 10.0, 'P': 6.0, 'O': 26.0}\",\"('Cd', 'O', 'P')\",OTHERS,False\nmp-1105011,\"{'W': 2.0, 'S': 4.0, 'N': 4.0, 'O': 4.0}\",\"('N', 'O', 'S', 'W')\",OTHERS,False\nmp-1074542,\"{'Mg': 16.0, 'Si': 12.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-556523,\"{'La': 2.0, 'Pb': 12.0, 'Cl': 2.0, 'O': 14.0}\",\"('Cl', 'La', 'O', 'Pb')\",OTHERS,False\nmp-1097575,\"{'Sc': 2.0, 'Al': 1.0, 'Au': 1.0}\",\"('Al', 'Au', 'Sc')\",OTHERS,False\nmp-505108,\"{'Fe': 1.0, 'Sn': 1.0, 'F': 6.0, 'O': 6.0, 'H': 12.0}\",\"('F', 'Fe', 'H', 'O', 'Sn')\",OTHERS,False\nmp-558356,\"{'Na': 4.0, 'Cr': 2.0, 'Ni': 2.0, 'F': 14.0}\",\"('Cr', 'F', 'Na', 'Ni')\",OTHERS,False\nmp-1154732,\"{'Ca': 4.0, 'Si': 16.0, 'W': 4.0, 'O': 40.0}\",\"('Ca', 'O', 'Si', 'W')\",OTHERS,False\nmp-640715,\"{'Yb': 16.0, 'Rb': 24.0, 'P': 24.0, 'O': 96.0}\",\"('O', 'P', 'Rb', 'Yb')\",OTHERS,False\nmp-1227967,\"{'Ba': 1.0, 'Ga': 3.0, 'Sn': 1.0}\",\"('Ba', 'Ga', 'Sn')\",OTHERS,False\nmp-772830,\"{'Li': 4.0, 'V': 2.0, 'Si': 1.0, 'Ge': 1.0, 'O': 10.0}\",\"('Ge', 'Li', 'O', 'Si', 'V')\",OTHERS,False\nmp-8675,\"{'Pd': 4.0, 'Hg': 4.0}\",\"('Hg', 'Pd')\",OTHERS,False\nmp-1096979,\"{'Na': 24.0, 'V': 24.0, 'Zn': 24.0, 'O': 96.0}\",\"('Na', 'O', 'V', 'Zn')\",OTHERS,False\nmp-1211010,\"{'Ni': 2.0, 'P': 2.0, 'N': 2.0, 'O': 20.0}\",\"('N', 'Ni', 'O', 'P')\",OTHERS,False\nmp-18820,\"{'O': 7.0, 'Fe': 2.0, 'Sr': 3.0}\",\"('Fe', 'O', 'Sr')\",OTHERS,False\nmp-759523,\"{'Li': 4.0, 'V': 2.0, 'Fe': 2.0, 'P': 8.0, 'O': 28.0}\",\"('Fe', 'Li', 'O', 'P', 'V')\",OTHERS,False\nmp-20979,\"{'Mg': 3.0, 'In': 3.0, 'Tm': 3.0}\",\"('In', 'Mg', 'Tm')\",OTHERS,False\nmp-1245514,\"{'Sb': 2.0, 'C': 3.0, 'N': 6.0}\",\"('C', 'N', 'Sb')\",OTHERS,False\nmp-775023,\"{'Li': 2.0, 'Fe': 3.0, 'Sn': 1.0, 'O': 8.0}\",\"('Fe', 'Li', 'O', 'Sn')\",OTHERS,False\nmp-1244965,\"{'Si': 34.0, 'O': 68.0}\",\"('O', 'Si')\",OTHERS,False\nmp-849952,\"{'Li': 2.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1183938,\"{'Cs': 1.0, 'K': 2.0, 'As': 1.0}\",\"('As', 'Cs', 'K')\",OTHERS,False\nmp-1239391,\"{'Zn': 4.0, 'Co': 8.0, 'Cu': 8.0, 'O': 32.0}\",\"('Co', 'Cu', 'O', 'Zn')\",OTHERS,False\nmp-1178487,\"{'Bi': 10.0, 'O': 14.0, 'F': 2.0}\",\"('Bi', 'F', 'O')\",OTHERS,False\nmp-1246778,\"{'Li': 4.0, 'Cd': 4.0, 'N': 4.0}\",\"('Cd', 'Li', 'N')\",OTHERS,False\nmp-976380,\"{'Nd': 2.0, 'Ni': 4.0, 'Sb': 4.0}\",\"('Nd', 'Ni', 'Sb')\",OTHERS,False\nmp-776246,\"{'Zr': 16.0, 'N': 16.0, 'O': 8.0}\",\"('N', 'O', 'Zr')\",OTHERS,False\nmp-33569,\"{'La': 4.0, 'Se': 8.0, 'Sr': 2.0}\",\"('La', 'Se', 'Sr')\",OTHERS,False\nmp-1213705,\"{'Cs': 4.0, 'Na': 2.0, 'Ga': 2.0, 'P': 4.0}\",\"('Cs', 'Ga', 'Na', 'P')\",OTHERS,False\nmp-23060,\"{'I': 6.0, 'Cs': 2.0, 'Pt': 1.0}\",\"('Cs', 'I', 'Pt')\",OTHERS,False\nmp-1180066,\"{'Ni': 1.0, 'I': 2.0, 'O': 6.0}\",\"('I', 'Ni', 'O')\",OTHERS,False\nmp-744841,\"{'Si': 2.0, 'Mo': 24.0, 'H': 48.0, 'C': 12.0, 'N': 6.0, 'O': 80.0}\",\"('C', 'H', 'Mo', 'N', 'O', 'Si')\",OTHERS,False\nmp-1078818,\"{'Mg': 4.0, 'Ni': 2.0, 'H': 4.0}\",\"('H', 'Mg', 'Ni')\",OTHERS,False\nmp-1184504,\"{'Eu': 6.0, 'W': 2.0}\",\"('Eu', 'W')\",OTHERS,False\nmp-849361,\"{'Li': 4.0, 'V': 4.0, 'F': 20.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-1212011,\"{'In': 4.0, 'Sb': 4.0, 'Se': 12.0}\",\"('In', 'Sb', 'Se')\",OTHERS,False\nmp-1384,\"{'La': 1.0, 'Ir': 5.0}\",\"('Ir', 'La')\",OTHERS,False\nmp-773918,\"{'Zn': 1.0, 'Bi': 38.0, 'O': 60.0}\",\"('Bi', 'O', 'Zn')\",OTHERS,False\nmp-759435,\"{'Li': 4.0, 'Cu': 4.0, 'P': 12.0, 'O': 36.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-1220982,\"{'Na': 1.0, 'Li': 1.0, 'Ga': 2.0, 'Si': 4.0, 'O': 12.0}\",\"('Ga', 'Li', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1225162,\"{'Eu': 1.0, 'Si': 3.0, 'Ni': 1.0}\",\"('Eu', 'Ni', 'Si')\",OTHERS,False\nmp-1205433,\"{'Dy': 2.0, 'In': 8.0, 'Pt': 8.0}\",\"('Dy', 'In', 'Pt')\",OTHERS,False\nmp-752904,\"{'Li': 3.0, 'Fe': 3.0, 'Si': 1.0, 'O': 8.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-758846,\"{'Li': 3.0, 'Mn': 2.0, 'Ni': 5.0, 'O': 12.0}\",\"('Li', 'Mn', 'Ni', 'O')\",OTHERS,False\nmp-6805,\"{'O': 48.0, 'P': 12.0, 'K': 12.0, 'Ga': 8.0}\",\"('Ga', 'K', 'O', 'P')\",OTHERS,False\nmp-1185485,\"{'Lu': 1.0, 'Al': 1.0, 'O': 3.0}\",\"('Al', 'Lu', 'O')\",OTHERS,False\nmp-1184799,\"{'In': 1.0, 'Os': 1.0, 'O': 3.0}\",\"('In', 'O', 'Os')\",OTHERS,False\nmp-1186244,\"{'Pm': 1.0, 'Nd': 3.0}\",\"('Nd', 'Pm')\",OTHERS,False\nmp-768042,\"{'Na': 8.0, 'Fe': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Fe', 'Na', 'O', 'P')\",OTHERS,False\nmp-1225429,\"{'Eu': 2.0, 'Al': 3.0, 'Ag': 1.0}\",\"('Ag', 'Al', 'Eu')\",OTHERS,False\nmp-1187603,\"{'Tm': 1.0, 'Sb': 1.0, 'Rh': 2.0}\",\"('Rh', 'Sb', 'Tm')\",OTHERS,False\nmp-1190160,\"{'K': 4.0, 'Se': 4.0, 'O': 14.0}\",\"('K', 'O', 'Se')\",OTHERS,False\nmp-1219871,\"{'Pd': 4.0, 'Se': 4.0, 'S': 4.0}\",\"('Pd', 'S', 'Se')\",OTHERS,False\nmp-1078780,\"{'Zn': 1.0, 'Cd': 3.0, 'S': 4.0}\",\"('Cd', 'S', 'Zn')\",OTHERS,False\nmp-984454,\"{'K': 1.0, 'Tl': 1.0, 'O': 3.0}\",\"('K', 'O', 'Tl')\",OTHERS,False\nmp-1201660,\"{'Ho': 10.0, 'Ge': 20.0, 'Os': 8.0}\",\"('Ge', 'Ho', 'Os')\",OTHERS,False\nmp-15963,\"{'P': 3.0, 'Ru': 3.0, 'Hf': 3.0}\",\"('Hf', 'P', 'Ru')\",OTHERS,False\nmp-1096584,\"{'Li': 1.0, 'Ga': 2.0, 'Tc': 1.0}\",\"('Ga', 'Li', 'Tc')\",OTHERS,False\nmp-696490,\"{'Ca': 8.0, 'H': 8.0, 'S': 8.0, 'O': 28.0}\",\"('Ca', 'H', 'O', 'S')\",OTHERS,False\nmp-1238814,\"{'H': 4.0, 'N': 1.0}\",\"('H', 'N')\",OTHERS,False\nmp-1246350,\"{'Sr': 8.0, 'V': 4.0, 'N': 8.0}\",\"('N', 'Sr', 'V')\",OTHERS,False\nmp-11143,\"{'Si': 8.0, 'Cu': 18.0, 'Ba': 2.0}\",\"('Ba', 'Cu', 'Si')\",OTHERS,False\nmp-631358,\"{'Sr': 1.0, 'Ta': 2.0, 'Mn': 1.0}\",\"('Mn', 'Sr', 'Ta')\",OTHERS,False\nmp-1221508,\"{'Na': 11.0, 'Ti': 1.0, 'Nb': 2.0, 'Si': 4.0, 'P': 2.0, 'O': 25.0, 'F': 1.0}\",\"('F', 'Na', 'Nb', 'O', 'P', 'Si', 'Ti')\",OTHERS,False\nmp-1215195,\"{'Zr': 1.0, 'U': 1.0, 'C': 2.0}\",\"('C', 'U', 'Zr')\",OTHERS,False\nmp-1224102,\"{'In': 2.0, 'Cu': 2.0, 'Sn': 2.0, 'S': 8.0}\",\"('Cu', 'In', 'S', 'Sn')\",OTHERS,False\nmp-1217816,\"{'Ta': 1.0, 'V': 1.0, 'H': 4.0}\",\"('H', 'Ta', 'V')\",OTHERS,False\nmp-676020,\"{'Ba': 2.0, 'C': 2.0, 'O': 6.0}\",\"('Ba', 'C', 'O')\",OTHERS,False\nmp-764781,\"{'Li': 3.0, 'Co': 4.0, 'O': 8.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-561352,\"{'Ba': 2.0, 'Na': 4.0, 'Ti': 4.0, 'Si': 8.0, 'O': 28.0}\",\"('Ba', 'Na', 'O', 'Si', 'Ti')\",OTHERS,False\nmp-753245,\"{'Li': 2.0, 'Mn': 4.0, 'O': 6.0, 'F': 2.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-20701,\"{'K': 2.0, 'Ba': 1.0, 'Co': 1.0, 'N': 6.0, 'O': 12.0}\",\"('Ba', 'Co', 'K', 'N', 'O')\",OTHERS,False\nmp-16509,\"{'Al': 8.0, 'Ni': 2.0, 'Lu': 2.0}\",\"('Al', 'Lu', 'Ni')\",OTHERS,False\nmp-1800,\"{'C': 12.0, 'Nd': 8.0}\",\"('C', 'Nd')\",OTHERS,False\nmp-1221192,\"{'Na': 8.0, 'Li': 2.0, 'Nb': 10.0, 'O': 30.0}\",\"('Li', 'Na', 'Nb', 'O')\",OTHERS,False\nmp-694915,\"{'Ca': 2.0, 'La': 2.0, 'Mn': 2.0, 'Sn': 2.0, 'O': 12.0}\",\"('Ca', 'La', 'Mn', 'O', 'Sn')\",OTHERS,False\nmp-753828,\"{'Li': 4.0, 'Co': 2.0, 'Si': 2.0, 'O': 8.0}\",\"('Co', 'Li', 'O', 'Si')\",OTHERS,False\nmp-759326,\"{'Li': 6.0, 'Mn': 11.0, 'O': 2.0, 'F': 24.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1025261,\"{'Mg': 1.0, 'P': 1.0, 'Pd': 5.0}\",\"('Mg', 'P', 'Pd')\",OTHERS,False\nmp-1036235,\"{'Mg': 14.0, 'Mn': 1.0, 'Co': 1.0, 'O': 16.0}\",\"('Co', 'Mg', 'Mn', 'O')\",OTHERS,False\nmp-865554,\"{'Mn': 2.0, 'V': 1.0, 'Ge': 1.0}\",\"('Ge', 'Mn', 'V')\",OTHERS,False\nmp-1175727,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-770910,\"{'Li': 12.0, 'Mn': 4.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1198784,\"{'Cs': 4.0, 'U': 2.0, 'Nb': 12.0, 'Cl': 30.0, 'O': 6.0}\",\"('Cl', 'Cs', 'Nb', 'O', 'U')\",OTHERS,False\nmp-1104791,\"{'Li': 1.0, 'Mg': 8.0, 'Si': 4.0}\",\"('Li', 'Mg', 'Si')\",OTHERS,False\nmp-27493,\"{'O': 32.0, 'P': 8.0, 'Sn': 12.0}\",\"('O', 'P', 'Sn')\",OTHERS,False\nmp-761260,\"{'Li': 4.0, 'V': 2.0, 'O': 4.0, 'F': 4.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-31474,\"{'Rb': 2.0, 'Hg': 7.0}\",\"('Hg', 'Rb')\",OTHERS,False\nmp-1221556,\"{'Na': 4.0, 'Mg': 1.0, 'V': 10.0, 'H': 44.0, 'O': 52.0}\",\"('H', 'Mg', 'Na', 'O', 'V')\",OTHERS,False\nmp-553949,\"{'Ag': 4.0, 'Hg': 4.0, 'Te': 6.0, 'O': 24.0}\",\"('Ag', 'Hg', 'O', 'Te')\",OTHERS,False\nmp-867164,\"{'Pm': 3.0, 'Sn': 1.0}\",\"('Pm', 'Sn')\",OTHERS,False\nmp-31476,\"{'H': 6.0, 'C': 12.0, 'Cs': 6.0}\",\"('C', 'Cs', 'H')\",OTHERS,False\nmp-603940,\"{'Fe': 2.0, 'H': 24.0, 'C': 8.0, 'N': 2.0, 'Cl': 8.0}\",\"('C', 'Cl', 'Fe', 'H', 'N')\",OTHERS,False\nmp-756542,\"{'Mn': 2.0, 'V': 2.0, 'O': 8.0}\",\"('Mn', 'O', 'V')\",OTHERS,False\nmp-31447,\"{'Tb': 2.0, 'Ag': 2.0, 'Pb': 2.0}\",\"('Ag', 'Pb', 'Tb')\",OTHERS,False\nmp-1027480,\"{'Te': 2.0, 'Mo': 2.0, 'W': 2.0, 'Se': 2.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-1198290,\"{'Na': 4.0, 'U': 4.0, 'H': 36.0, 'Se': 8.0, 'O': 48.0}\",\"('H', 'Na', 'O', 'Se', 'U')\",OTHERS,False\nmp-1177564,\"{'Li': 12.0, 'Mn': 4.0, 'V': 4.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-1206924,\"{'Ba': 1.0, 'Zn': 2.0, 'Ge': 2.0}\",\"('Ba', 'Ge', 'Zn')\",OTHERS,False\nmp-780704,\"{'V': 6.0, 'O': 8.0, 'F': 4.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-8312,\"{'As': 4.0, 'Ta': 5.0}\",\"('As', 'Ta')\",OTHERS,False\nmp-851099,\"{'Li': 9.0, 'Mn': 7.0, 'Cr': 12.0, 'O': 48.0}\",\"('Cr', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-21013,\"{'Li': 1.0, 'C': 6.0, 'N': 6.0, 'Fe': 1.0, 'Cs': 2.0}\",\"('C', 'Cs', 'Fe', 'Li', 'N')\",OTHERS,False\nmp-1070789,\"{'Cr': 1.0, 'Au': 4.0}\",\"('Au', 'Cr')\",OTHERS,False\nmp-1224477,\"{'H': 11.0, 'I': 1.0, 'N': 2.0, 'O': 6.0}\",\"('H', 'I', 'N', 'O')\",OTHERS,False\nmp-684784,\"{'Sr': 1.0, 'Nd': 7.0, 'Fe': 8.0, 'As': 8.0, 'O': 8.0}\",\"('As', 'Fe', 'Nd', 'O', 'Sr')\",OTHERS,False\nmp-1218452,\"{'Sr': 5.0, 'Fe': 4.0, 'Co': 1.0, 'O': 10.0}\",\"('Co', 'Fe', 'O', 'Sr')\",OTHERS,False\nmp-9548,\"{'Ge': 6.0, 'As': 6.0}\",\"('As', 'Ge')\",OTHERS,False\nmp-683616,\"{'Li': 5.0, 'Ti': 7.0, 'In': 1.0, 'P': 12.0, 'O': 48.0}\",\"('In', 'Li', 'O', 'P', 'Ti')\",OTHERS,False\nmp-30495,\"{'Sm': 1.0, 'Cd': 2.0}\",\"('Cd', 'Sm')\",OTHERS,False\nmp-769486,\"{'Na': 6.0, 'Ni': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Na', 'Ni', 'O', 'P')\",OTHERS,False\nmp-28318,\"{'Hg': 4.0, 'Te': 4.0, 'O': 12.0}\",\"('Hg', 'O', 'Te')\",OTHERS,False\nmp-1175750,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-27491,\"{'Ca': 2.0, 'Fe': 8.0, 'O': 12.0}\",\"('Ca', 'Fe', 'O')\",OTHERS,False\nmp-1227175,\"{'Ca': 2.0, 'Nd': 2.0, 'Cr': 2.0, 'O': 8.0}\",\"('Ca', 'Cr', 'Nd', 'O')\",OTHERS,False\nmp-1188236,\"{'Gd': 4.0, 'Mg': 2.0, 'Ir': 2.0, 'O': 12.0}\",\"('Gd', 'Ir', 'Mg', 'O')\",OTHERS,False\nmvc-601,\"{'Y': 1.0, 'Sn': 1.0, 'W': 2.0, 'O': 8.0}\",\"('O', 'Sn', 'W', 'Y')\",OTHERS,False\nmp-562541,\"{'Cs': 8.0, 'Na': 12.0, 'Tl': 4.0, 'O': 16.0}\",\"('Cs', 'Na', 'O', 'Tl')\",OTHERS,False\nmp-1238482,\"{'Mn': 2.0, 'Al': 4.0, 'P': 4.0, 'H': 40.0, 'O': 32.0}\",\"('Al', 'H', 'Mn', 'O', 'P')\",OTHERS,False\nmp-643432,\"{'H': 12.0, 'N': 4.0}\",\"('H', 'N')\",OTHERS,False\nmp-1099868,\"{'K': 8.0, 'Na': 24.0, 'V': 16.0, 'Mo': 16.0, 'O': 80.0}\",\"('K', 'Mo', 'Na', 'O', 'V')\",OTHERS,False\nmp-1247030,\"{'Lu': 1.0, 'Mg': 2.0, 'Mo': 3.0, 'S': 8.0}\",\"('Lu', 'Mg', 'Mo', 'S')\",OTHERS,False\nmp-1018809,\"{'Mo': 2.0, 'S': 4.0}\",\"('Mo', 'S')\",OTHERS,False\nmp-1206203,\"{'Yb': 2.0, 'Se': 2.0, 'I': 2.0}\",\"('I', 'Se', 'Yb')\",OTHERS,False\nmvc-16572,\"{'Ca': 4.0, 'Mo': 4.0, 'O': 12.0}\",\"('Ca', 'Mo', 'O')\",OTHERS,False\nmp-30599,\"{'Ti': 2.0, 'Cu': 3.0}\",\"('Cu', 'Ti')\",OTHERS,False\nmp-1204662,\"{'Sr': 18.0, 'W': 6.0, 'O': 36.0}\",\"('O', 'Sr', 'W')\",OTHERS,False\nmp-755970,\"{'Li': 6.0, 'Mn': 2.0, 'Si': 2.0, 'B': 2.0, 'O': 14.0}\",\"('B', 'Li', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1221713,\"{'Mn': 2.0, 'Fe': 8.0, 'Si': 6.0}\",\"('Fe', 'Mn', 'Si')\",OTHERS,False\nmp-1206795,\"{'Sm': 2.0, 'Fe': 4.0, 'Si': 2.0, 'C': 2.0}\",\"('C', 'Fe', 'Si', 'Sm')\",OTHERS,False\nmp-1095591,\"{'Tm': 3.0, 'Cu': 4.0, 'Sn': 4.0}\",\"('Cu', 'Sn', 'Tm')\",OTHERS,False\nmp-1228562,\"{'Al': 9.0, 'Ge': 3.0, 'W': 6.0}\",\"('Al', 'Ge', 'W')\",OTHERS,False\nmp-1214122,\"{'Ca': 2.0, 'Ce': 2.0, 'Al': 2.0, 'Fe': 4.0, 'Si': 6.0, 'O': 26.0}\",\"('Al', 'Ca', 'Ce', 'Fe', 'O', 'Si')\",OTHERS,False\nmp-772977,\"{'Ba': 10.0, 'Li': 1.0, 'W': 7.0, 'O': 30.0}\",\"('Ba', 'Li', 'O', 'W')\",OTHERS,False\nmp-697008,\"{'Cu': 2.0, 'H': 24.0, 'N': 4.0, 'O': 4.0, 'F': 8.0}\",\"('Cu', 'F', 'H', 'N', 'O')\",OTHERS,False\nmp-1209449,\"{'Rb': 12.0, 'Lu': 4.0, 'Cl': 24.0}\",\"('Cl', 'Lu', 'Rb')\",OTHERS,False\nmp-1214127,\"{'Ca': 2.0, 'Al': 4.0, 'Si': 12.0, 'H': 20.0, 'O': 42.0}\",\"('Al', 'Ca', 'H', 'O', 'Si')\",OTHERS,False\nmp-1218750,\"{'Sr': 2.0, 'Nb': 1.0, 'In': 1.0, 'O': 6.0}\",\"('In', 'Nb', 'O', 'Sr')\",OTHERS,False\nmp-760101,\"{'Li': 6.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1072320,\"{'Hg': 2.0, 'S': 2.0, 'Br': 2.0}\",\"('Br', 'Hg', 'S')\",OTHERS,False\nmp-1070456,\"{'Be': 3.0, 'N': 2.0}\",\"('Be', 'N')\",OTHERS,False\nmp-769464,\"{'Li': 4.0, 'La': 12.0, 'Mn': 4.0, 'O': 28.0}\",\"('La', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1199385,\"{'Er': 20.0, 'In': 40.0, 'Ni': 18.0}\",\"('Er', 'In', 'Ni')\",OTHERS,False\nmp-1235,\"{'Ti': 2.0, 'Ir': 2.0}\",\"('Ir', 'Ti')\",OTHERS,False\nmp-1179195,\"{'Si': 20.0, 'O': 30.0}\",\"('O', 'Si')\",OTHERS,False\nmp-1223968,\"{'Ho': 2.0, 'Si': 1.0, 'Ge': 1.0}\",\"('Ge', 'Ho', 'Si')\",OTHERS,False\nmp-70,{'Rb': 1.0},\"('Rb',)\",OTHERS,False\nmp-1226388,\"{'Cr': 4.0, 'Cd': 2.0, 'Se': 6.0, 'S': 2.0}\",\"('Cd', 'Cr', 'S', 'Se')\",OTHERS,False\nmp-19253,\"{'O': 3.0, 'V': 1.0, 'Nd': 1.0}\",\"('Nd', 'O', 'V')\",OTHERS,False\nmp-11307,\"{'Mg': 2.0, 'Cd': 2.0}\",\"('Cd', 'Mg')\",OTHERS,False\nmp-1177924,\"{'Li': 2.0, 'Mn': 3.0, 'W': 1.0, 'O': 8.0}\",\"('Li', 'Mn', 'O', 'W')\",OTHERS,False\nmp-1205739,\"{'Li': 2.0, 'Sn': 1.0, 'F': 6.0}\",\"('F', 'Li', 'Sn')\",OTHERS,False\nmp-1184083,\"{'Er': 2.0, 'Zn': 1.0, 'Ag': 1.0}\",\"('Ag', 'Er', 'Zn')\",OTHERS,False\nmp-1176364,\"{'Na': 6.0, 'Li': 6.0, 'Fe': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Fe', 'Li', 'Na', 'O', 'P')\",OTHERS,False\nmp-865050,\"{'Hf': 1.0, 'Mg': 1.0, 'Rh': 2.0}\",\"('Hf', 'Mg', 'Rh')\",OTHERS,False\nmp-1216330,\"{'V': 2.0, 'Fe': 2.0, 'Sb': 2.0, 'O': 12.0}\",\"('Fe', 'O', 'Sb', 'V')\",OTHERS,False\nmp-694910,\"{'Fe': 35.0, 'O': 36.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-849757,\"{'Li': 4.0, 'Mn': 4.0, 'Si': 16.0, 'O': 40.0}\",\"('Li', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1184359,\"{'Eu': 1.0, 'Hg': 1.0, 'O': 3.0}\",\"('Eu', 'Hg', 'O')\",OTHERS,False\nmp-12867,\"{'O': 6.0, 'Sr': 2.0, 'Sn': 2.0}\",\"('O', 'Sn', 'Sr')\",OTHERS,False\nmp-1222296,\"{'Li': 1.0, 'Sm': 1.0, 'S': 2.0}\",\"('Li', 'S', 'Sm')\",OTHERS,False\nmp-1232422,\"{'Yb': 3.0, 'Hf': 1.0}\",\"('Hf', 'Yb')\",OTHERS,False\nmp-1183580,\"{'Cd': 3.0, 'B': 1.0}\",\"('B', 'Cd')\",OTHERS,False\nmp-17189,\"{'K': 6.0, 'Ce': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Ce', 'K', 'O', 'P')\",OTHERS,False\nmp-561051,\"{'Rb': 4.0, 'Sb': 8.0, 'S': 14.0}\",\"('Rb', 'S', 'Sb')\",OTHERS,False\nmp-759536,\"{'V': 8.0, 'O': 6.0, 'F': 18.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-1226037,\"{'Co': 3.0, 'Ni': 3.0, 'P': 3.0}\",\"('Co', 'Ni', 'P')\",OTHERS,False\nmp-1215636,\"{'Zn': 1.0, 'Ni': 4.0, 'O': 5.0}\",\"('Ni', 'O', 'Zn')\",OTHERS,False\nmp-1198572,\"{'Yb': 2.0, 'Al': 2.0, 'B': 28.0}\",\"('Al', 'B', 'Yb')\",OTHERS,False\nmp-753486,\"{'Ba': 2.0, 'Ca': 2.0, 'I': 8.0}\",\"('Ba', 'Ca', 'I')\",OTHERS,False\nmp-1100520,\"{'Li': 9.0, 'Mn': 7.0, 'O': 16.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-22350,\"{'N': 1.0, 'Sn': 1.0, 'Nd': 3.0}\",\"('N', 'Nd', 'Sn')\",OTHERS,False\nmvc-9204,\"{'Ti': 2.0, 'Cr': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Cr', 'O', 'P', 'Ti')\",OTHERS,False\nmp-1227996,\"{'Ca': 6.0, 'Fe': 14.0, 'Si': 12.0, 'O': 48.0}\",\"('Ca', 'Fe', 'O', 'Si')\",OTHERS,False\nmp-504372,\"{'Ni': 12.0, 'P': 14.0, 'O': 48.0}\",\"('Ni', 'O', 'P')\",OTHERS,False\nmp-1104792,\"{'Mn': 1.0, 'Be': 12.0}\",\"('Be', 'Mn')\",OTHERS,False\nmp-1224812,\"{'Ga': 1.0, 'Co': 4.0}\",\"('Co', 'Ga')\",OTHERS,False\nmp-1037110,\"{'Mg': 30.0, 'Zn': 1.0, 'Bi': 1.0, 'O': 32.0}\",\"('Bi', 'Mg', 'O', 'Zn')\",OTHERS,False\nmp-862555,\"{'Li': 1.0, 'Y': 2.0, 'Al': 1.0}\",\"('Al', 'Li', 'Y')\",OTHERS,False\nmp-697813,\"{'Li': 4.0, 'Cr': 8.0, 'P': 12.0, 'O': 48.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-694534,\"{'Li': 2.0, 'Fe': 2.0, 'P': 4.0, 'O': 14.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-772498,\"{'Na': 6.0, 'Li': 6.0, 'Mn': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Li', 'Mn', 'Na', 'O', 'P')\",OTHERS,False\nmp-16373,\"{'Ge': 4.0, 'U': 2.0}\",\"('Ge', 'U')\",OTHERS,False\nmp-763099,\"{'Li': 2.0, 'V': 1.0, 'O': 2.0, 'F': 1.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-705201,\"{'Fe': 16.0, 'P': 16.0, 'O': 64.0}\",\"('Fe', 'O', 'P')\",OTHERS,False\nmp-1075482,\"{'Mg': 12.0, 'Si': 16.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1219995,\"{'Pr': 12.0, 'Al': 6.0, 'Fe': 22.0}\",\"('Al', 'Fe', 'Pr')\",OTHERS,False\nmvc-1666,\"{'Ta': 4.0, 'Zn': 2.0, 'O': 12.0}\",\"('O', 'Ta', 'Zn')\",OTHERS,False\nmp-778347,\"{'Li': 4.0, 'Fe': 4.0, 'F': 16.0}\",\"('F', 'Fe', 'Li')\",OTHERS,False\nmp-1198871,\"{'Zn': 6.0, 'P': 12.0, 'H': 12.0, 'O': 38.0}\",\"('H', 'O', 'P', 'Zn')\",OTHERS,False\nmp-705845,\"{'Ba': 4.0, 'Fe': 4.0, 'O': 12.0}\",\"('Ba', 'Fe', 'O')\",OTHERS,False\nmp-1113297,\"{'Eu': 1.0, 'Rb': 2.0, 'Cu': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cu', 'Eu', 'Rb')\",OTHERS,False\nmp-12170,\"{'S': 10.0, 'Sn': 2.0, 'La': 4.0}\",\"('La', 'S', 'Sn')\",OTHERS,False\nmp-655613,\"{'Tb': 6.0, 'Cs': 2.0, 'Te': 7.0, 'N': 2.0}\",\"('Cs', 'N', 'Tb', 'Te')\",OTHERS,False\nmp-1183141,\"{'Ag': 2.0, 'C': 2.0}\",\"('Ag', 'C')\",OTHERS,False\nmvc-1471,\"{'Y': 2.0, 'Ti': 6.0, 'Se': 4.0, 'Cl': 2.0, 'O': 16.0}\",\"('Cl', 'O', 'Se', 'Ti', 'Y')\",OTHERS,False\nmp-3465,\"{'B': 2.0, 'Rh': 3.0, 'La': 1.0}\",\"('B', 'La', 'Rh')\",OTHERS,False\nmp-1203539,\"{'Gd': 4.0, 'Sb': 4.0, 'Mo': 8.0, 'O': 36.0}\",\"('Gd', 'Mo', 'O', 'Sb')\",OTHERS,False\nmp-1194954,\"{'Na': 4.0, 'Nb': 8.0, 'H': 28.0, 'O': 36.0}\",\"('H', 'Na', 'Nb', 'O')\",OTHERS,False\nmp-569580,\"{'Na': 4.0, 'La': 16.0, 'I': 28.0, 'N': 8.0}\",\"('I', 'La', 'N', 'Na')\",OTHERS,False\nmp-1075132,\"{'Mg': 6.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-979427,\"{'Y': 1.0, 'Ho': 1.0, 'Zn': 2.0}\",\"('Ho', 'Y', 'Zn')\",OTHERS,False\nmp-1079845,\"{'Mn': 3.0, 'Ni': 3.0, 'As': 3.0}\",\"('As', 'Mn', 'Ni')\",OTHERS,False\nmp-1209115,\"{'Sb': 2.0, 'H': 1.0, 'F': 13.0}\",\"('F', 'H', 'Sb')\",OTHERS,False\nmp-780393,\"{'Ho': 12.0, 'B': 12.0, 'O': 36.0}\",\"('B', 'Ho', 'O')\",OTHERS,False\nmp-8769,\"{'Co': 2.0, 'Se': 4.0, 'Rb': 4.0}\",\"('Co', 'Rb', 'Se')\",OTHERS,False\nmp-754502,\"{'Sm': 1.0, 'Y': 1.0, 'O': 2.0}\",\"('O', 'Sm', 'Y')\",OTHERS,False\nmp-779727,\"{'Ti': 34.0, 'N': 12.0, 'O': 48.0}\",\"('N', 'O', 'Ti')\",OTHERS,False\nmp-570113,\"{'Bi': 2.0, 'Au': 4.0}\",\"('Au', 'Bi')\",OTHERS,False\nmp-1019802,\"{'K': 2.0, 'Al': 2.0, 'P': 8.0, 'O': 24.0}\",\"('Al', 'K', 'O', 'P')\",OTHERS,False\nmp-1093563,\"{'Cs': 1.0, 'Rb': 1.0, 'Au': 2.0}\",\"('Au', 'Cs', 'Rb')\",OTHERS,False\nmp-1217793,\"{'Sr': 1.0, 'Ti': 1.0, 'Fe': 3.0, 'Bi': 3.0, 'O': 12.0}\",\"('Bi', 'Fe', 'O', 'Sr', 'Ti')\",OTHERS,False\nmp-1209862,\"{'P': 4.0, 'N': 4.0, 'F': 24.0}\",\"('F', 'N', 'P')\",OTHERS,False\nmp-559832,\"{'Hg': 12.0, 'S': 8.0, 'Br': 8.0}\",\"('Br', 'Hg', 'S')\",OTHERS,False\nmp-556908,\"{'Nd': 10.0, 'As': 8.0, 'Cl': 6.0, 'O': 24.0}\",\"('As', 'Cl', 'Nd', 'O')\",OTHERS,False\nmvc-12627,\"{'Mg': 2.0, 'V': 4.0, 'O': 8.0}\",\"('Mg', 'O', 'V')\",OTHERS,False\nmp-776547,\"{'Li': 4.0, 'Fe': 2.0, 'B': 4.0, 'O': 12.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1223477,\"{'K': 1.0, 'As': 2.0, 'Rh': 1.0}\",\"('As', 'K', 'Rh')\",OTHERS,False\nmp-758261,\"{'Li': 8.0, 'Cr': 4.0, 'Si': 16.0, 'O': 40.0}\",\"('Cr', 'Li', 'O', 'Si')\",OTHERS,False\nmp-676586,\"{'Na': 1.0, 'V': 2.0, 'S': 4.0}\",\"('Na', 'S', 'V')\",OTHERS,False\nmp-531340,\"{'Al': 28.0, 'Mg': 14.0, 'O': 56.0}\",\"('Al', 'Mg', 'O')\",OTHERS,False\nmp-642625,\"{'Hg': 36.0, 'Sb': 3.0, 'Br': 3.0, 'Cl': 6.0, 'O': 18.0}\",\"('Br', 'Cl', 'Hg', 'O', 'Sb')\",OTHERS,False\nmp-773084,\"{'Ba': 6.0, 'Li': 2.0, 'Ti': 7.0, 'Nb': 9.0, 'O': 42.0}\",\"('Ba', 'Li', 'Nb', 'O', 'Ti')\",OTHERS,False\nmp-557180,\"{'Ca': 2.0, 'As': 4.0, 'Xe': 8.0, 'F': 40.0}\",\"('As', 'Ca', 'F', 'Xe')\",OTHERS,False\nmp-1202141,\"{'Tm': 24.0, 'Ga': 16.0}\",\"('Ga', 'Tm')\",OTHERS,False\nmp-1195366,\"{'Cu': 8.0, 'Pb': 16.0, 'Cl': 24.0, 'O': 16.0}\",\"('Cl', 'Cu', 'O', 'Pb')\",OTHERS,False\nmp-754279,\"{'Li': 4.0, 'Cr': 3.0, 'Ni': 1.0, 'O': 8.0}\",\"('Cr', 'Li', 'Ni', 'O')\",OTHERS,False\nmp-998988,\"{'Ti': 3.0, 'Ga': 1.0}\",\"('Ga', 'Ti')\",OTHERS,False\nmp-1238924,\"{'Tl': 3.0, 'As': 1.0, 'F': 6.0}\",\"('As', 'F', 'Tl')\",OTHERS,False\nmp-504555,\"{'U': 8.0, 'K': 4.0, 'F': 36.0}\",\"('F', 'K', 'U')\",OTHERS,False\nmp-775263,\"{'Li': 6.0, 'Co': 1.0, 'Ni': 9.0, 'O': 20.0}\",\"('Co', 'Li', 'Ni', 'O')\",OTHERS,False\nmp-1183555,\"{'Ca': 1.0, 'Nd': 1.0, 'Ag': 2.0}\",\"('Ag', 'Ca', 'Nd')\",OTHERS,False\nmp-772665,\"{'Be': 4.0, 'Cr': 4.0, 'O': 16.0}\",\"('Be', 'Cr', 'O')\",OTHERS,False\nmp-581443,\"{'Cs': 24.0, 'U': 12.0, 'Mo': 12.0, 'O': 84.0}\",\"('Cs', 'Mo', 'O', 'U')\",OTHERS,False\nmp-26796,\"{'Cu': 10.0, 'P': 6.0, 'O': 26.0}\",\"('Cu', 'O', 'P')\",OTHERS,False\nmp-1176952,\"{'Li': 12.0, 'Mn': 2.0, 'V': 6.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-1196989,\"{'Zn': 2.0, 'P': 4.0, 'H': 16.0, 'O': 20.0}\",\"('H', 'O', 'P', 'Zn')\",OTHERS,False\nmp-42981,\"{'F': 4.0, 'Gd': 4.0, 'Na': 4.0, 'Nb': 4.0, 'O': 24.0, 'Ti': 4.0}\",\"('F', 'Gd', 'Na', 'Nb', 'O', 'Ti')\",OTHERS,False\nmp-1101151,\"{'Na': 1.0, 'Ho': 1.0, 'O': 2.0}\",\"('Ho', 'Na', 'O')\",OTHERS,False\nmp-1074455,\"{'Mg': 8.0, 'Si': 6.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-5401,\"{'B': 8.0, 'O': 20.0, 'Sr': 8.0}\",\"('B', 'O', 'Sr')\",OTHERS,False\nmp-1228760,\"{'Ba': 6.0, 'Ca': 6.0, 'Cu': 9.0, 'Hg': 3.0, 'O': 25.0}\",\"('Ba', 'Ca', 'Cu', 'Hg', 'O')\",OTHERS,False\nmp-1195488,\"{'Sr': 2.0, 'Fe': 2.0, 'Ni': 4.0, 'P': 6.0, 'O': 24.0}\",\"('Fe', 'Ni', 'O', 'P', 'Sr')\",OTHERS,False\nmp-560410,\"{'Sr': 4.0, 'U': 2.0, 'Ni': 2.0, 'O': 12.0}\",\"('Ni', 'O', 'Sr', 'U')\",OTHERS,False\nmp-999126,\"{'Tb': 1.0, 'Na': 1.0, 'S': 2.0}\",\"('Na', 'S', 'Tb')\",OTHERS,False\nmp-1232041,\"{'La': 8.0, 'Mg': 4.0, 'Se': 16.0}\",\"('La', 'Mg', 'Se')\",OTHERS,False\nmp-1268088,\"{'Ba': 1.0, 'Al': 1.0, 'Cu': 1.0, 'Ag': 1.0, 'O': 5.0}\",\"('Ag', 'Al', 'Ba', 'Cu', 'O')\",OTHERS,False\nmp-1095972,\"{'Sr': 2.0, 'Hg': 1.0, 'Ge': 1.0}\",\"('Ge', 'Hg', 'Sr')\",OTHERS,False\nmp-4124,\"{'C': 1.0, 'N': 2.0, 'Ca': 1.0}\",\"('C', 'Ca', 'N')\",OTHERS,False\nmp-570175,\"{'Ce': 10.0, 'Si': 6.0}\",\"('Ce', 'Si')\",OTHERS,False\nmp-14970,\"{'B': 2.0, 'O': 10.0, 'Cu': 2.0, 'Ba': 2.0, 'La': 2.0}\",\"('B', 'Ba', 'Cu', 'La', 'O')\",OTHERS,False\nmp-1096469,\"{'Hf': 2.0, 'Re': 1.0, 'Pt': 1.0}\",\"('Hf', 'Pt', 'Re')\",OTHERS,False\nmp-773055,\"{'Li': 6.0, 'Mg': 6.0, 'Ti': 12.0, 'O': 32.0}\",\"('Li', 'Mg', 'O', 'Ti')\",OTHERS,False\nmp-679985,\"{'Ba': 6.0, 'In': 8.0, 'Cu': 6.0, 'O': 24.0}\",\"('Ba', 'Cu', 'In', 'O')\",OTHERS,False\nmp-569136,\"{'K': 6.0, 'Cu': 22.0, 'Te': 32.0}\",\"('Cu', 'K', 'Te')\",OTHERS,False\nmp-17885,\"{'Ge': 8.0, 'Te': 20.0, 'Ba': 8.0}\",\"('Ba', 'Ge', 'Te')\",OTHERS,False\nmp-1214682,\"{'Ba': 12.0, 'Gd': 4.0, 'Al': 8.0, 'O': 30.0}\",\"('Al', 'Ba', 'Gd', 'O')\",OTHERS,False\nmp-1248937,\"{'Y': 4.0, 'Si': 2.0, 'Sb': 2.0, 'W': 13.0, 'O': 28.0}\",\"('O', 'Sb', 'Si', 'W', 'Y')\",OTHERS,False\nmp-977549,\"{'Zr': 2.0, 'Os': 1.0, 'Ru': 1.0}\",\"('Os', 'Ru', 'Zr')\",OTHERS,False\nmp-23776,\"{'H': 32.0, 'Be': 4.0, 'O': 16.0, 'Cl': 8.0}\",\"('Be', 'Cl', 'H', 'O')\",OTHERS,False\nmp-5690,\"{'O': 12.0, 'Sc': 4.0, 'Gd': 4.0}\",\"('Gd', 'O', 'Sc')\",OTHERS,False\nmp-1197552,\"{'U': 8.0, 'Pb': 4.0, 'Se': 20.0}\",\"('Pb', 'Se', 'U')\",OTHERS,False\nmp-17646,\"{'O': 24.0, 'Yb': 8.0, 'W': 4.0}\",\"('O', 'W', 'Yb')\",OTHERS,False\nmp-34508,\"{'S': 8.0, 'Sm': 4.0, 'Sr': 2.0}\",\"('S', 'Sm', 'Sr')\",OTHERS,False\nmp-756639,\"{'Li': 6.0, 'Nb': 3.0, 'O': 3.0, 'F': 15.0}\",\"('F', 'Li', 'Nb', 'O')\",OTHERS,False\nmp-754549,\"{'Na': 4.0, 'Cu': 2.0, 'O': 4.0}\",\"('Cu', 'Na', 'O')\",OTHERS,False\nmp-567908,\"{'Lu': 4.0, 'B': 3.0, 'C': 4.0}\",\"('B', 'C', 'Lu')\",OTHERS,False\nmp-1184869,\"{'Ho': 1.0, 'Lu': 1.0, 'Hg': 2.0}\",\"('Hg', 'Ho', 'Lu')\",OTHERS,False\nmp-1114593,\"{'Rb': 2.0, 'Na': 1.0, 'Nd': 1.0, 'Cl': 6.0}\",\"('Cl', 'Na', 'Nd', 'Rb')\",OTHERS,False\nmp-1073501,\"{'Mg': 4.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-560029,\"{'Y': 6.0, 'Br': 2.0, 'O': 8.0}\",\"('Br', 'O', 'Y')\",OTHERS,False\nmp-1096815,\"{'Ba': 6.0, 'Ta': 8.0, 'Nb': 2.0, 'O': 30.0}\",\"('Ba', 'Nb', 'O', 'Ta')\",OTHERS,False\nmp-24150,\"{'H': 1.0, 'Li': 1.0, 'O': 3.0, 'Nd': 2.0}\",\"('H', 'Li', 'Nd', 'O')\",OTHERS,False\nmvc-10122,\"{'Mg': 6.0, 'Ni': 12.0, 'O': 24.0}\",\"('Mg', 'Ni', 'O')\",OTHERS,False\nmvc-9680,\"{'Pr': 2.0, 'Zn': 2.0, 'W': 4.0, 'O': 12.0}\",\"('O', 'Pr', 'W', 'Zn')\",OTHERS,False\nmp-4227,\"{'S': 6.0, 'V': 2.0, 'Ba': 2.0}\",\"('Ba', 'S', 'V')\",OTHERS,False\nmp-1227384,\"{'Ca': 2.0, 'Fe': 6.0, 'Si': 8.0, 'O': 24.0}\",\"('Ca', 'Fe', 'O', 'Si')\",OTHERS,False\nmp-3882,\"{'Si': 2.0, 'Rh': 2.0, 'Sm': 1.0}\",\"('Rh', 'Si', 'Sm')\",OTHERS,False\nmp-1184828,\"{'Ho': 1.0, 'Tm': 1.0, 'In': 2.0}\",\"('Ho', 'In', 'Tm')\",OTHERS,False\nmp-1246713,\"{'Sr': 2.0, 'Zn': 4.0, 'N': 4.0}\",\"('N', 'Sr', 'Zn')\",OTHERS,False\nmp-1246665,\"{'Li': 2.0, 'Cr': 4.0, 'N': 6.0}\",\"('Cr', 'Li', 'N')\",OTHERS,False\nmp-1113314,\"{'Na': 2.0, 'Cu': 1.0, 'Sb': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cu', 'Na', 'Sb')\",OTHERS,False\nmp-20497,\"{'Sr': 2.0, 'In': 4.0, 'Ir': 2.0}\",\"('In', 'Ir', 'Sr')\",OTHERS,False\nmp-28749,\"{'Cr': 4.0, 'Xe': 4.0, 'F': 24.0}\",\"('Cr', 'F', 'Xe')\",OTHERS,False\nmp-856,\"{'O': 4.0, 'Sn': 2.0}\",\"('O', 'Sn')\",OTHERS,False\nmp-1186611,\"{'Pm': 1.0, 'Nd': 1.0, 'Ga': 2.0}\",\"('Ga', 'Nd', 'Pm')\",OTHERS,False\nmp-1382,\"{'S': 8.0, 'Ce': 6.0}\",\"('Ce', 'S')\",OTHERS,False\nmp-4756,\"{'Zn': 2.0, 'Sn': 2.0, 'Sb': 4.0}\",\"('Sb', 'Sn', 'Zn')\",OTHERS,False\nmp-1207452,\"{'Zr': 6.0, 'U': 4.0, 'Ge': 8.0}\",\"('Ge', 'U', 'Zr')\",OTHERS,False\nmp-755658,\"{'Ce': 2.0, 'Pr': 2.0, 'O': 4.0}\",\"('Ce', 'O', 'Pr')\",OTHERS,False\nmp-867235,\"{'Li': 1.0, 'Y': 2.0, 'Ir': 1.0}\",\"('Ir', 'Li', 'Y')\",OTHERS,False\nmp-1176835,\"{'Li': 8.0, 'Fe': 7.0, 'P': 12.0, 'W': 1.0, 'O': 48.0}\",\"('Fe', 'Li', 'O', 'P', 'W')\",OTHERS,False\nmvc-12108,\"{'Ca': 2.0, 'Mn': 4.0, 'O': 8.0}\",\"('Ca', 'Mn', 'O')\",OTHERS,False\nmp-3042,\"{'F': 6.0, 'Si': 1.0, 'K': 2.0}\",\"('F', 'K', 'Si')\",OTHERS,False\nmp-1003403,\"{'Mn': 16.0, 'H': 2.0, 'O': 32.0}\",\"('H', 'Mn', 'O')\",OTHERS,False\nmp-774794,\"{'Li': 20.0, 'Sm': 12.0, 'Sb': 8.0, 'O': 48.0}\",\"('Li', 'O', 'Sb', 'Sm')\",OTHERS,False\nmp-1194649,\"{'Rb': 2.0, 'Na': 4.0, 'Mn': 6.0, 'P': 8.0, 'Cl': 2.0, 'O': 28.0}\",\"('Cl', 'Mn', 'Na', 'O', 'P', 'Rb')\",OTHERS,False\nmp-1178641,\"{'Zn': 1.0, 'Cu': 3.0, 'Ni': 1.0, 'Se': 4.0}\",\"('Cu', 'Ni', 'Se', 'Zn')\",OTHERS,False\nmp-1039529,\"{'Ca': 6.0, 'Mg': 12.0}\",\"('Ca', 'Mg')\",OTHERS,False\nmp-12107,\"{'Ti': 1.0, 'Pt': 3.0}\",\"('Pt', 'Ti')\",OTHERS,False\nmp-1186622,\"{'Pm': 1.0, 'P': 3.0}\",\"('P', 'Pm')\",OTHERS,False\nmp-1211637,\"{'K': 2.0, 'Ca': 2.0, 'P': 2.0, 'H': 2.0, 'O': 8.0}\",\"('Ca', 'H', 'K', 'O', 'P')\",OTHERS,False\nmp-542709,\"{'Eu': 2.0, 'Si': 12.0}\",\"('Eu', 'Si')\",OTHERS,False\nmp-638,\"{'Zn': 5.0, 'Sr': 1.0}\",\"('Sr', 'Zn')\",OTHERS,False\nmp-1183254,\"{'Ag': 6.0, 'S': 2.0}\",\"('Ag', 'S')\",OTHERS,False\nmp-571333,\"{'Ca': 10.0, 'Si': 4.0, 'N': 12.0}\",\"('Ca', 'N', 'Si')\",OTHERS,False\nmp-1064861,\"{'Ga': 1.0, 'C': 3.0}\",\"('C', 'Ga')\",OTHERS,False\nmp-866187,\"{'Ti': 2.0, 'Zn': 1.0, 'Tc': 1.0}\",\"('Tc', 'Ti', 'Zn')\",OTHERS,False\nmp-1224473,\"{'Hf': 2.0, 'Ni': 1.0, 'S': 4.0}\",\"('Hf', 'Ni', 'S')\",OTHERS,False\nmp-556074,\"{'K': 2.0, 'Cu': 3.0, 'Se': 4.0, 'O': 12.0}\",\"('Cu', 'K', 'O', 'Se')\",OTHERS,False\nmp-568827,\"{'Be': 12.0, 'W': 1.0}\",\"('Be', 'W')\",OTHERS,False\nmp-9664,\"{'B': 2.0, 'P': 4.0, 'K': 6.0}\",\"('B', 'K', 'P')\",OTHERS,False\nmvc-12125,\"{'Fe': 4.0, 'O': 8.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-22424,\"{'O': 8.0, 'Na': 4.0, 'S': 2.0}\",\"('Na', 'O', 'S')\",OTHERS,False\nmp-1111572,\"{'K': 2.0, 'Tl': 1.0, 'Pd': 1.0, 'F': 6.0}\",\"('F', 'K', 'Pd', 'Tl')\",OTHERS,False\nmp-1194940,\"{'Na': 16.0, 'Fe': 8.0, 'P': 8.0, 'O': 32.0, 'F': 8.0}\",\"('F', 'Fe', 'Na', 'O', 'P')\",OTHERS,False\nmp-13082,\"{'Si': 1.0, 'Mn': 2.0, 'Co': 1.0}\",\"('Co', 'Mn', 'Si')\",OTHERS,False\nmp-997004,\"{'Ca': 2.0, 'Ag': 2.0, 'O': 4.0}\",\"('Ag', 'Ca', 'O')\",OTHERS,False\nmp-757284,\"{'Mn': 3.0, 'Cr': 1.0, 'Co': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Co', 'Cr', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1213119,\"{'Dy': 2.0, 'Cu': 1.0, 'Si': 4.0, 'O': 12.0}\",\"('Cu', 'Dy', 'O', 'Si')\",OTHERS,False\nmp-530539,\"{'O': 36.0, 'Sr': 16.0, 'U': 8.0}\",\"('O', 'Sr', 'U')\",OTHERS,False\nmp-510662,\"{'Cs': 2.0, 'U': 2.0, 'Ag': 2.0, 'Se': 6.0}\",\"('Ag', 'Cs', 'Se', 'U')\",OTHERS,False\nmp-630391,\"{'Cs': 4.0, 'Zn': 4.0, 'Ag': 4.0, 'C': 16.0, 'S': 16.0, 'N': 16.0}\",\"('Ag', 'C', 'Cs', 'N', 'S', 'Zn')\",OTHERS,False\nmp-1104531,\"{'Sm': 4.0, 'Te': 10.0}\",\"('Sm', 'Te')\",OTHERS,False\nmp-974744,\"{'Nd': 1.0, 'Zn': 2.0, 'Ag': 1.0}\",\"('Ag', 'Nd', 'Zn')\",OTHERS,False\nmp-1198063,\"{'Na': 4.0, 'Ti': 4.0, 'Si': 4.0, 'O': 22.0}\",\"('Na', 'O', 'Si', 'Ti')\",OTHERS,False\nmp-542934,\"{'Cu': 18.0, 'Ce': 2.0, 'Mg': 4.0}\",\"('Ce', 'Cu', 'Mg')\",OTHERS,False\nmp-583193,\"{'Cs': 12.0, 'P': 4.0, 'Se': 16.0}\",\"('Cs', 'P', 'Se')\",OTHERS,False\nmp-686717,\"{'Fe': 2.0, 'Ni': 1.0, 'Se': 4.0}\",\"('Fe', 'Ni', 'Se')\",OTHERS,False\nmp-29398,\"{'I': 6.0, 'Cs': 2.0, 'Hf': 1.0}\",\"('Cs', 'Hf', 'I')\",OTHERS,False\nmp-12666,\"{'Be': 12.0, 'Pd': 1.0}\",\"('Be', 'Pd')\",OTHERS,False\nmp-1184880,\"{'K': 3.0, 'Hf': 1.0}\",\"('Hf', 'K')\",OTHERS,False\nmp-1229278,\"{'Al': 4.0, 'H': 50.0, 'S': 7.0, 'O': 52.0}\",\"('Al', 'H', 'O', 'S')\",OTHERS,False\nmp-562965,\"{'Cs': 4.0, 'Cr': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Cr', 'Cs', 'O', 'P')\",OTHERS,False\nmp-1203391,\"{'K': 64.0, 'Sr': 16.0, 'Si': 48.0, 'O': 144.0}\",\"('K', 'O', 'Si', 'Sr')\",OTHERS,False\nmp-1215290,\"{'Zr': 2.0, 'Nb': 2.0, 'Fe': 8.0}\",\"('Fe', 'Nb', 'Zr')\",OTHERS,False\nmp-1216039,\"{'Zr': 2.0, 'Ti': 3.0, 'Pb': 5.0, 'O': 15.0}\",\"('O', 'Pb', 'Ti', 'Zr')\",OTHERS,False\nmp-1185930,\"{'Mn': 6.0, 'Sb': 2.0}\",\"('Mn', 'Sb')\",OTHERS,False\nmp-13525,\"{'In': 3.0, 'Ho': 3.0, 'Ir': 3.0}\",\"('Ho', 'In', 'Ir')\",OTHERS,False\nmp-1227614,\"{'Ca': 4.0, 'Mg': 3.0, 'Si': 8.0, 'Ni': 1.0, 'O': 24.0}\",\"('Ca', 'Mg', 'Ni', 'O', 'Si')\",OTHERS,False\nmp-1183913,\"{'Cs': 1.0, 'Pd': 1.0, 'O': 3.0}\",\"('Cs', 'O', 'Pd')\",OTHERS,False\nmp-1232353,\"{'Sr': 6.0, 'Nd': 2.0, 'Ta': 2.0, 'Al': 6.0, 'O': 24.0}\",\"('Al', 'Nd', 'O', 'Sr', 'Ta')\",OTHERS,False\nmp-1196488,\"{'Zn': 8.0, 'H': 40.0, 'Cl': 16.0, 'O': 20.0}\",\"('Cl', 'H', 'O', 'Zn')\",OTHERS,False\nmp-746730,\"{'Fe': 4.0, 'P': 16.0, 'O': 44.0}\",\"('Fe', 'O', 'P')\",OTHERS,False\nmp-755959,\"{'Na': 4.0, 'Li': 4.0, 'O': 4.0}\",\"('Li', 'Na', 'O')\",OTHERS,False\nmp-800,\"{'B': 2.0, 'Tm': 1.0}\",\"('B', 'Tm')\",OTHERS,False\nmp-1202414,\"{'Nd': 40.0, 'Se': 56.0, 'O': 4.0}\",\"('Nd', 'O', 'Se')\",OTHERS,False\nmp-1213254,\"{'Cs': 2.0, 'Gd': 2.0, 'W': 4.0, 'O': 16.0}\",\"('Cs', 'Gd', 'O', 'W')\",OTHERS,False\nmp-24179,\"{'H': 24.0, 'C': 8.0, 'N': 4.0, 'O': 16.0, 'S': 8.0, 'K': 4.0}\",\"('C', 'H', 'K', 'N', 'O', 'S')\",OTHERS,False\nmp-1078860,\"{'Sr': 2.0, 'Ge': 2.0, 'Au': 6.0}\",\"('Au', 'Ge', 'Sr')\",OTHERS,False\nmp-1180133,\"{'Na': 1.0, 'Ca': 1.0, 'H': 6.0, 'Ir': 1.0}\",\"('Ca', 'H', 'Ir', 'Na')\",OTHERS,False\nmp-759404,\"{'Li': 6.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1207695,\"{'Tm': 4.0, 'S': 4.0, 'I': 4.0}\",\"('I', 'S', 'Tm')\",OTHERS,False\nmp-1103788,\"{'Sr': 2.0, 'Ni': 4.0, 'P': 8.0}\",\"('Ni', 'P', 'Sr')\",OTHERS,False\nmp-1255862,\"{'Mg': 8.0, 'Si': 14.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1219137,\"{'Sm': 2.0, 'Ga': 3.0, 'Cu': 1.0}\",\"('Cu', 'Ga', 'Sm')\",OTHERS,False\nmp-1222005,\"{'Mn': 3.0, 'Ge': 1.0}\",\"('Ge', 'Mn')\",OTHERS,False\nmp-849328,\"{'Li': 4.0, 'Mn': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1080815,\"{'Zr': 3.0, 'Sn': 3.0, 'Rh': 3.0}\",\"('Rh', 'Sn', 'Zr')\",OTHERS,False\nmp-1211549,\"{'La': 12.0, 'Sb': 4.0, 'O': 28.0}\",\"('La', 'O', 'Sb')\",OTHERS,False\nmp-1079542,\"{'Co': 3.0, 'Ni': 3.0, 'As': 3.0}\",\"('As', 'Co', 'Ni')\",OTHERS,False\nmp-676893,\"{'Ca': 4.0, 'Th': 1.0, 'F': 12.0}\",\"('Ca', 'F', 'Th')\",OTHERS,False\nmp-1182283,\"{'Ca': 4.0, 'Mn': 2.0, 'As': 4.0, 'O': 20.0}\",\"('As', 'Ca', 'Mn', 'O')\",OTHERS,False\nmp-1207424,\"{'Zr': 8.0, 'In': 4.0, 'Au': 8.0}\",\"('Au', 'In', 'Zr')\",OTHERS,False\nmp-774920,\"{'Li': 2.0, 'Fe': 2.0, 'Sb': 2.0, 'O': 8.0}\",\"('Fe', 'Li', 'O', 'Sb')\",OTHERS,False\nmp-1214265,\"{'Bi': 12.0, 'Br': 5.0, 'O': 16.0}\",\"('Bi', 'Br', 'O')\",OTHERS,False\nmp-758147,\"{'Li': 4.0, 'Cu': 4.0, 'P': 12.0, 'O': 36.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-557981,\"{'K': 24.0, 'Co': 24.0, 'P': 24.0, 'O': 96.0}\",\"('Co', 'K', 'O', 'P')\",OTHERS,False\nmp-1176362,\"{'Na': 6.0, 'Li': 6.0, 'Fe': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Fe', 'Li', 'Na', 'O', 'P')\",OTHERS,False\nmp-961655,\"{'Y': 1.0, 'Cd': 1.0, 'Ga': 1.0}\",\"('Cd', 'Ga', 'Y')\",OTHERS,False\nmp-1215709,\"{'Y': 1.0, 'Zr': 1.0, 'Fe': 4.0}\",\"('Fe', 'Y', 'Zr')\",OTHERS,False\nmp-1214488,\"{'Ba': 8.0, 'As': 6.0}\",\"('As', 'Ba')\",OTHERS,False\nmp-505669,\"{'La': 8.0, 'Co': 8.0, 'O': 20.0}\",\"('Co', 'La', 'O')\",OTHERS,False\nmp-28575,\"{'S': 16.0, 'Cl': 12.0, 'W': 2.0}\",\"('Cl', 'S', 'W')\",OTHERS,False\nmp-17702,\"{'Yb': 8.0, 'Si': 4.0, 'O': 20.0}\",\"('O', 'Si', 'Yb')\",OTHERS,False\nmp-565518,\"{'Na': 2.0, 'V': 6.0, 'Fe': 6.0, 'O': 24.0}\",\"('Fe', 'Na', 'O', 'V')\",OTHERS,False\nmp-1004373,\"{'Li': 4.0, 'Mn': 4.0, 'O': 8.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-1174854,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1104073,\"{'C': 11.0, 'N': 4.0}\",\"('C', 'N')\",OTHERS,False\nmp-1029176,\"{'Te': 2.0, 'Mo': 2.0, 'W': 2.0, 'Se': 4.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-766923,\"{'Li': 4.0, 'V': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Li', 'O', 'Si', 'V')\",OTHERS,False\nmp-1194852,\"{'Na': 8.0, 'Cd': 8.0, 'P': 24.0, 'H': 64.0, 'O': 80.0}\",\"('Cd', 'H', 'Na', 'O', 'P')\",OTHERS,False\nmp-27581,\"{'Pb': 7.0, 'Cl': 2.0, 'F': 12.0}\",\"('Cl', 'F', 'Pb')\",OTHERS,False\nmp-652036,\"{'Gd': 4.0, 'Fe': 20.0, 'P': 12.0}\",\"('Fe', 'Gd', 'P')\",OTHERS,False\nmp-29034,\"{'Ge': 8.0, 'Te': 24.0, 'Tl': 16.0}\",\"('Ge', 'Te', 'Tl')\",OTHERS,False\nmp-1009830,\"{'Be': 2.0, 'Zn': 1.0}\",\"('Be', 'Zn')\",OTHERS,False\nmp-1219533,\"{'Re': 1.0, 'Ir': 1.0}\",\"('Ir', 'Re')\",OTHERS,False\nmp-1227624,\"{'Fe': 4.0, 'Cu': 26.0, 'Ge': 4.0, 'S': 32.0}\",\"('Cu', 'Fe', 'Ge', 'S')\",OTHERS,False\nmp-1076445,\"{'Sr': 8.0, 'Mn': 1.0, 'Fe': 7.0, 'O': 24.0}\",\"('Fe', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-694881,\"{'Na': 2.0, 'Nd': 6.0, 'Ti': 6.0, 'Mn': 2.0, 'O': 24.0}\",\"('Mn', 'Na', 'Nd', 'O', 'Ti')\",OTHERS,False\nmp-636950,\"{'Mo': 8.0, 'P': 8.0, 'O': 44.0}\",\"('Mo', 'O', 'P')\",OTHERS,False\nmp-1210917,\"{'Lu': 4.0, 'Sn': 4.0, 'F': 28.0}\",\"('F', 'Lu', 'Sn')\",OTHERS,False\nmp-1175736,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1186819,\"{'Rb': 4.0, 'Mg': 2.0}\",\"('Mg', 'Rb')\",OTHERS,False\nmp-5673,\"{'P': 16.0, 'Ni': 2.0, 'Mo': 2.0}\",\"('Mo', 'Ni', 'P')\",OTHERS,False\nmp-1019781,\"{'K': 4.0, 'Na': 6.0, 'P': 6.0, 'O': 20.0}\",\"('K', 'Na', 'O', 'P')\",OTHERS,False\nmp-1226346,\"{'Cr': 3.0, 'Te': 2.0, 'Se': 2.0}\",\"('Cr', 'Se', 'Te')\",OTHERS,False\nmp-554930,\"{'Nb': 8.0, 'Ag': 4.0, 'P': 4.0, 'S': 40.0}\",\"('Ag', 'Nb', 'P', 'S')\",OTHERS,False\nmp-704492,\"{'Mn': 16.0, 'P': 16.0, 'O': 64.0}\",\"('Mn', 'O', 'P')\",OTHERS,False\nmp-1239130,\"{'Nb': 2.0, 'Cr': 6.0, 'Cu': 4.0, 'S': 16.0}\",\"('Cr', 'Cu', 'Nb', 'S')\",OTHERS,False\nmp-1096686,\"{'Tl': 1.0, 'Bi': 1.0, 'Pb': 2.0}\",\"('Bi', 'Pb', 'Tl')\",OTHERS,False\nmp-1222926,\"{'La': 1.0, 'Cu': 1.0, 'Si': 2.0, 'Ni': 1.0}\",\"('Cu', 'La', 'Ni', 'Si')\",OTHERS,False\nmp-28137,\"{'O': 8.0, 'Cl': 2.0, 'Co': 1.0}\",\"('Cl', 'Co', 'O')\",OTHERS,False\nmp-867175,\"{'Sm': 4.0, 'Ag': 4.0, 'As': 8.0}\",\"('Ag', 'As', 'Sm')\",OTHERS,False\nmp-22829,\"{'B': 32.0, 'O': 56.0, 'Co': 8.0}\",\"('B', 'Co', 'O')\",OTHERS,False\nmp-571055,\"{'Fe': 11.0, 'Mo': 6.0, 'C': 5.0}\",\"('C', 'Fe', 'Mo')\",OTHERS,False\nmp-1219723,\"{'Pr': 1.0, 'Th': 1.0, 'C': 1.0, 'N': 1.0}\",\"('C', 'N', 'Pr', 'Th')\",OTHERS,False\nmp-1183940,\"{'Cs': 6.0, 'K': 2.0}\",\"('Cs', 'K')\",OTHERS,False\nmp-756426,\"{'Zr': 2.0, 'Nb': 2.0, 'O': 8.0}\",\"('Nb', 'O', 'Zr')\",OTHERS,False\nmp-1097154,\"{'Y': 2.0, 'Hg': 1.0, 'Au': 1.0}\",\"('Au', 'Hg', 'Y')\",OTHERS,False\nmp-760798,\"{'Li': 8.0, 'Cu': 4.0, 'F': 20.0}\",\"('Cu', 'F', 'Li')\",OTHERS,False\nmp-1076037,\"{'Eu': 4.0, 'Ti': 4.0, 'O': 10.0}\",\"('Eu', 'O', 'Ti')\",OTHERS,False\nmp-1020020,\"{'Li': 8.0, 'N': 1.0, 'O': 3.0}\",\"('Li', 'N', 'O')\",OTHERS,False\nmp-27957,\"{'O': 8.0, 'Ni': 2.0, 'Ba': 6.0}\",\"('Ba', 'Ni', 'O')\",OTHERS,False\nmp-1224124,\"{'In': 4.0, 'P': 6.0, 'Pb': 2.0, 'O': 23.0}\",\"('In', 'O', 'P', 'Pb')\",OTHERS,False\nmp-1212113,\"{'I': 8.0, 'N': 12.0}\",\"('I', 'N')\",OTHERS,False\nmp-1193402,\"{'Eu': 6.0, 'Ga': 8.0, 'Ge': 12.0}\",\"('Eu', 'Ga', 'Ge')\",OTHERS,False\nmp-1222094,\"{'Mg': 4.0, 'Te': 3.0, 'Se': 1.0}\",\"('Mg', 'Se', 'Te')\",OTHERS,False\nmp-1939,\"{'Ni': 4.0, 'Er': 2.0}\",\"('Er', 'Ni')\",OTHERS,False\nmp-542817,\"{'U': 8.0, 'Pt': 8.0}\",\"('Pt', 'U')\",OTHERS,False\nmp-1037497,\"{'Mg': 30.0, 'Zn': 1.0, 'Co': 1.0, 'O': 32.0}\",\"('Co', 'Mg', 'O', 'Zn')\",OTHERS,False\nmp-1174772,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-541111,\"{'Ga': 4.0, 'Al': 4.0, 'Cl': 16.0}\",\"('Al', 'Cl', 'Ga')\",OTHERS,False\nmp-973631,\"{'Lu': 2.0, 'Al': 1.0, 'Ru': 1.0}\",\"('Al', 'Lu', 'Ru')\",OTHERS,False\nmp-1220274,\"{'Nd': 2.0, 'Y': 1.0}\",\"('Nd', 'Y')\",OTHERS,False\nmp-1176630,\"{'Li': 4.0, 'Mn': 4.0, 'F': 12.0}\",\"('F', 'Li', 'Mn')\",OTHERS,False\nmp-1174881,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1246137,\"{'Sr': 32.0, 'Mn': 16.0, 'N': 32.0}\",\"('Mn', 'N', 'Sr')\",OTHERS,False\nmp-1035244,\"{'Mg': 14.0, 'Cu': 1.0, 'C': 1.0, 'O': 16.0}\",\"('C', 'Cu', 'Mg', 'O')\",OTHERS,False\nmp-1176247,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-695948,\"{'P': 12.0, 'H': 48.0, 'S': 12.0, 'N': 12.0, 'O': 24.0}\",\"('H', 'N', 'O', 'P', 'S')\",OTHERS,False\nmp-556303,\"{'Cu': 2.0, 'Bi': 4.0, 'Se': 8.0, 'O': 24.0}\",\"('Bi', 'Cu', 'O', 'Se')\",OTHERS,False\nmp-504263,\"{'Li': 14.0, 'Co': 9.0, 'P': 16.0, 'O': 56.0}\",\"('Co', 'Li', 'O', 'P')\",OTHERS,False\nmp-779434,\"{'Li': 20.0, 'La': 12.0, 'Nb': 8.0, 'O': 48.0}\",\"('La', 'Li', 'Nb', 'O')\",OTHERS,False\nmp-757136,\"{'Li': 2.0, 'Co': 3.0, 'Sn': 1.0, 'O': 8.0}\",\"('Co', 'Li', 'O', 'Sn')\",OTHERS,False\nmp-1032467,\"{'Li': 1.0, 'Mg': 6.0, 'Fe': 1.0, 'O': 8.0}\",\"('Fe', 'Li', 'Mg', 'O')\",OTHERS,False\nmp-1184081,\"{'Er': 2.0, 'Ru': 1.0, 'Au': 1.0}\",\"('Au', 'Er', 'Ru')\",OTHERS,False\nmp-780624,\"{'V': 6.0, 'O': 5.0, 'F': 19.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-570584,\"{'Ta': 2.0, 'Si': 4.0, 'H': 70.0, 'C': 24.0, 'N': 8.0}\",\"('C', 'H', 'N', 'Si', 'Ta')\",OTHERS,False\nmp-18560,\"{'O': 16.0, 'F': 4.0, 'P': 4.0, 'Np': 4.0}\",\"('F', 'Np', 'O', 'P')\",OTHERS,False\nmp-1245128,\"{'Mg': 40.0, 'O': 40.0}\",\"('Mg', 'O')\",OTHERS,False\nmp-1154546,\"{'Ca': 4.0, 'Si': 16.0, 'Sn': 4.0, 'O': 40.0}\",\"('Ca', 'O', 'Si', 'Sn')\",OTHERS,False\nmp-1223844,\"{'In': 2.0, 'Sb': 1.0, 'As': 1.0}\",\"('As', 'In', 'Sb')\",OTHERS,False\nmvc-8814,\"{'Ti': 4.0, 'Zn': 2.0, 'P': 6.0, 'O': 24.0}\",\"('O', 'P', 'Ti', 'Zn')\",OTHERS,False\nmp-9972,\"{'Si': 2.0, 'Y': 2.0}\",\"('Si', 'Y')\",OTHERS,False\nmp-1073692,\"{'Mg': 6.0, 'Si': 6.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1030535,\"{'Mo': 2.0, 'W': 2.0, 'Se': 6.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-30977,\"{'H': 48.0, 'B': 40.0, 'Br': 8.0}\",\"('B', 'Br', 'H')\",OTHERS,False\nmp-554909,\"{'Ba': 6.0, 'Mg': 2.0, 'Ir': 4.0, 'O': 18.0}\",\"('Ba', 'Ir', 'Mg', 'O')\",OTHERS,False\nmp-1216804,\"{'Tl': 2.0, 'V': 6.0, 'Cd': 8.0, 'O': 24.0}\",\"('Cd', 'O', 'Tl', 'V')\",OTHERS,False\nmp-1184464,\"{'Eu': 2.0, 'Zn': 1.0, 'In': 1.0}\",\"('Eu', 'In', 'Zn')\",OTHERS,False\nmp-20726,\"{'Ca': 4.0, 'Sr': 4.0, 'Sn': 4.0}\",\"('Ca', 'Sn', 'Sr')\",OTHERS,False\nmp-560710,\"{'Cs': 2.0, 'Tl': 1.0, 'Mo': 1.0, 'F': 6.0}\",\"('Cs', 'F', 'Mo', 'Tl')\",OTHERS,False\nmp-5893,\"{'O': 3.0, 'Si': 1.0, 'Ca': 1.0}\",\"('Ca', 'O', 'Si')\",OTHERS,False\nmp-759285,\"{'Li': 4.0, 'Fe': 7.0, 'O': 2.0, 'F': 14.0}\",\"('F', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-755738,\"{'Rb': 8.0, 'O': 4.0}\",\"('O', 'Rb')\",OTHERS,False\nmp-772636,\"{'Na': 2.0, 'Li': 10.0, 'Fe': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Fe', 'Li', 'Na', 'O', 'P')\",OTHERS,False\nmp-774649,\"{'V': 3.0, 'Ni': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Ni', 'O', 'P', 'V')\",OTHERS,False\nmp-553968,\"{'Mo': 8.0, 'P': 8.0, 'Cl': 40.0, 'O': 24.0}\",\"('Cl', 'Mo', 'O', 'P')\",OTHERS,False\nmp-42255,\"{'Ca': 2.0, 'F': 2.0, 'Na': 2.0, 'Nb': 4.0, 'O': 12.0}\",\"('Ca', 'F', 'Na', 'Nb', 'O')\",OTHERS,False\nmp-1094096,\"{'Ni': 4.0, 'O': 3.0}\",\"('Ni', 'O')\",OTHERS,False\nmp-1217321,\"{'Th': 2.0, 'Fe': 1.0, 'Si': 3.0}\",\"('Fe', 'Si', 'Th')\",OTHERS,False\nmp-1202377,\"{'Zr': 4.0, 'Te': 8.0, 'Br': 4.0, 'O': 22.0}\",\"('Br', 'O', 'Te', 'Zr')\",OTHERS,False\nmp-1094407,\"{'Y': 6.0, 'Mg': 6.0}\",\"('Mg', 'Y')\",OTHERS,False\nmp-762515,\"{'Li': 12.0, 'Cu': 18.0, 'C': 18.0, 'O': 54.0}\",\"('C', 'Cu', 'Li', 'O')\",OTHERS,False\nmp-1196505,\"{'Na': 18.0, 'Fe': 2.0, 'Mo': 12.0, 'O': 48.0}\",\"('Fe', 'Mo', 'Na', 'O')\",OTHERS,False\nmp-866517,\"{'Ce': 6.0, 'Mg': 2.0, 'Al': 2.0, 'S': 14.0}\",\"('Al', 'Ce', 'Mg', 'S')\",OTHERS,False\nmp-13573,\"{'Si': 7.0, 'Sc': 5.0, 'Pt': 9.0}\",\"('Pt', 'Sc', 'Si')\",OTHERS,False\nmp-1222550,\"{'Mg': 4.0, 'Fe': 14.0, 'O': 24.0}\",\"('Fe', 'Mg', 'O')\",OTHERS,False\nmp-1227581,\"{'Cd': 2.0, 'Cu': 10.0, 'Sb': 4.0, 'S': 13.0}\",\"('Cd', 'Cu', 'S', 'Sb')\",OTHERS,False\nmp-1009652,\"{'Rh': 1.0, 'C': 1.0}\",\"('C', 'Rh')\",OTHERS,False\nmp-1180318,\"{'Na': 8.0, 'P': 8.0, 'O': 28.0}\",\"('Na', 'O', 'P')\",OTHERS,False\nmp-1226733,\"{'Cd': 1.0, 'Hg': 1.0, 'Se': 2.0}\",\"('Cd', 'Hg', 'Se')\",OTHERS,False\nmp-616173,\"{'Ce': 20.0, 'Ni': 4.0, 'Ge': 8.0}\",\"('Ce', 'Ge', 'Ni')\",OTHERS,False\nmp-628,\"{'Co': 2.0, 'Zr': 4.0}\",\"('Co', 'Zr')\",OTHERS,False\nmp-1078567,\"{'Sr': 2.0, 'Bi': 4.0, 'Pd': 4.0}\",\"('Bi', 'Pd', 'Sr')\",OTHERS,False\nmp-765523,\"{'Ca': 7.0, 'Cu': 17.0, 'O': 24.0}\",\"('Ca', 'Cu', 'O')\",OTHERS,False\nmp-17561,\"{'Mn': 2.0, 'Nb': 4.0, 'Zn': 4.0, 'O': 16.0}\",\"('Mn', 'Nb', 'O', 'Zn')\",OTHERS,False\nmp-1175424,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-31249,\"{'O': 22.0, 'Te': 8.0, 'La': 4.0}\",\"('La', 'O', 'Te')\",OTHERS,False\nmp-561259,\"{'Na': 12.0, 'Nb': 4.0, 'O': 4.0, 'F': 24.0}\",\"('F', 'Na', 'Nb', 'O')\",OTHERS,False\nmp-1238863,\"{'Ti': 4.0, 'Cr': 4.0, 'Ag': 4.0, 'S': 16.0}\",\"('Ag', 'Cr', 'S', 'Ti')\",OTHERS,False\nmp-1104388,\"{'Y': 9.0, 'Pd': 6.0}\",\"('Pd', 'Y')\",OTHERS,False\nmp-6227,\"{'O': 14.0, 'Si': 4.0, 'Ca': 4.0, 'Zn': 2.0}\",\"('Ca', 'O', 'Si', 'Zn')\",OTHERS,False\nmp-979986,\"{'Yb': 2.0, 'Ba': 1.0, 'Ca': 1.0}\",\"('Ba', 'Ca', 'Yb')\",OTHERS,False\nmp-1215708,\"{'Y': 1.0, 'U': 1.0, 'Sb': 2.0}\",\"('Sb', 'U', 'Y')\",OTHERS,False\nmp-1182880,\"{'Al': 1.0, 'Cu': 3.0, 'Hg': 1.0, 'Se': 4.0}\",\"('Al', 'Cu', 'Hg', 'Se')\",OTHERS,False\nmp-1224397,\"{'K': 3.0, 'Ge': 7.0, 'H': 1.0, 'O': 20.0}\",\"('Ge', 'H', 'K', 'O')\",OTHERS,False\nmp-1215485,\"{'Zn': 1.0, 'In': 2.0, 'Ge': 1.0, 'As': 4.0}\",\"('As', 'Ge', 'In', 'Zn')\",OTHERS,False\nmp-1183620,\"{'Ca': 1.0, 'Eu': 3.0}\",\"('Ca', 'Eu')\",OTHERS,False\nmp-1177363,\"{'Li': 4.0, 'Mn': 3.0, 'Sn': 3.0, 'Sb': 2.0, 'O': 16.0}\",\"('Li', 'Mn', 'O', 'Sb', 'Sn')\",OTHERS,False\nmp-1214367,\"{'Ba': 2.0, 'Tb': 2.0, 'Fe': 8.0, 'O': 14.0}\",\"('Ba', 'Fe', 'O', 'Tb')\",OTHERS,False\nmp-1187718,\"{'Y': 2.0, 'Cu': 1.0, 'Os': 1.0}\",\"('Cu', 'Os', 'Y')\",OTHERS,False\nmp-675430,\"{'Sm': 8.0, 'Ag': 4.0, 'Se': 14.0}\",\"('Ag', 'Se', 'Sm')\",OTHERS,False\nmp-1112048,\"{'K': 2.0, 'Ce': 1.0, 'Cu': 1.0, 'I': 6.0}\",\"('Ce', 'Cu', 'I', 'K')\",OTHERS,False\nmp-849291,\"{'Li': 20.0, 'Fe': 12.0, 'F': 56.0}\",\"('F', 'Fe', 'Li')\",OTHERS,False\nmp-20478,\"{'Si': 2.0, 'Ti': 2.0, 'Lu': 2.0}\",\"('Lu', 'Si', 'Ti')\",OTHERS,False\nmp-1197173,\"{'Pr': 12.0, 'Fe': 26.0, 'Sn': 2.0}\",\"('Fe', 'Pr', 'Sn')\",OTHERS,False\nmp-21099,\"{'O': 8.0, 'S': 2.0, 'K': 2.0, 'Pb': 1.0}\",\"('K', 'O', 'Pb', 'S')\",OTHERS,False\nmp-22433,\"{'Si': 4.0, 'Ir': 4.0, 'Np': 2.0}\",\"('Ir', 'Np', 'Si')\",OTHERS,False\nmp-5517,\"{'P': 2.0, 'Ni': 2.0, 'Tb': 1.0}\",\"('Ni', 'P', 'Tb')\",OTHERS,False\nmp-864931,\"{'Mg': 4.0, 'Co': 8.0}\",\"('Co', 'Mg')\",OTHERS,False\nmp-776654,\"{'Li': 7.0, 'V': 5.0, 'O': 12.0}\",\"('Li', 'O', 'V')\",OTHERS,False\nmp-1190013,\"{'Ti': 8.0, 'Ni': 8.0}\",\"('Ni', 'Ti')\",OTHERS,False\nmp-1176150,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1225792,\"{'Er': 5.0, 'P': 12.0, 'Ir': 19.0}\",\"('Er', 'Ir', 'P')\",OTHERS,False\nmp-1192413,\"{'Cs': 2.0, 'In': 2.0, 'Te': 6.0, 'O': 16.0}\",\"('Cs', 'In', 'O', 'Te')\",OTHERS,False\nmp-780338,\"{'Li': 8.0, 'Cr': 8.0, 'S': 16.0, 'O': 64.0}\",\"('Cr', 'Li', 'O', 'S')\",OTHERS,False\nmp-1104185,\"{'Ti': 1.0, 'Tl': 1.0, 'V': 4.0, 'S': 8.0}\",\"('S', 'Ti', 'Tl', 'V')\",OTHERS,False\nmp-1026910,\"{'Mo': 2.0, 'W': 2.0, 'Se': 4.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-744711,\"{'Zn': 2.0, 'Cr': 2.0, 'H': 56.0, 'C': 12.0, 'N': 24.0, 'Cl': 10.0, 'O': 16.0}\",\"('C', 'Cl', 'Cr', 'H', 'N', 'O', 'Zn')\",OTHERS,False\nmp-1020645,\"{'Na': 8.0, 'N': 1.0, 'O': 3.0}\",\"('N', 'Na', 'O')\",OTHERS,False\nmp-1079819,\"{'Lu': 3.0, 'Al': 3.0, 'Ni': 3.0}\",\"('Al', 'Lu', 'Ni')\",OTHERS,False\nmp-1223512,\"{'K': 1.0, 'Mg': 3.0, 'Al': 1.0, 'Si': 3.0, 'O': 10.0, 'F': 2.0}\",\"('Al', 'F', 'K', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-26785,\"{'Li': 4.0, 'O': 28.0, 'P': 8.0, 'Ni': 4.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-767961,\"{'Ti': 3.0, 'Mn': 2.0, 'P': 6.0, 'W': 1.0, 'O': 24.0}\",\"('Mn', 'O', 'P', 'Ti', 'W')\",OTHERS,False\nmp-752400,\"{'Ce': 4.0, 'Sm': 4.0, 'O': 14.0}\",\"('Ce', 'O', 'Sm')\",OTHERS,False\nmp-611448,{'C': 12.0},\"('C',)\",OTHERS,False\nmp-1186527,\"{'Pm': 6.0, 'Nd': 2.0}\",\"('Nd', 'Pm')\",OTHERS,False\nmp-12532,\"{'P': 1.0, 'S': 4.0, 'K': 1.0, 'Ag': 2.0}\",\"('Ag', 'K', 'P', 'S')\",OTHERS,False\nmp-1225978,\"{'La': 2.0, 'In': 3.0, 'Sn': 3.0}\",\"('In', 'La', 'Sn')\",OTHERS,False\nmp-10837,\"{'F': 28.0, 'Na': 8.0, 'Mg': 4.0, 'In': 4.0}\",\"('F', 'In', 'Mg', 'Na')\",OTHERS,False\nmp-1211485,\"{'La': 12.0, 'Au': 4.0, 'I': 12.0}\",\"('Au', 'I', 'La')\",OTHERS,False\nmp-30051,\"{'C': 4.0, 'Si': 12.0, 'Cl': 28.0}\",\"('C', 'Cl', 'Si')\",OTHERS,False\nmp-767722,\"{'V': 1.0, 'Fe': 1.0, 'P': 4.0, 'O': 14.0}\",\"('Fe', 'O', 'P', 'V')\",OTHERS,False\nmp-1076424,\"{'Sr': 6.0, 'Ca': 2.0, 'Fe': 8.0, 'O': 24.0}\",\"('Ca', 'Fe', 'O', 'Sr')\",OTHERS,False\nmp-1222533,\"{'Li': 4.0, 'Si': 1.0, 'Ge': 3.0, 'O': 10.0}\",\"('Ge', 'Li', 'O', 'Si')\",OTHERS,False\nmp-861664,\"{'Li': 1.0, 'Tm': 1.0, 'Au': 2.0}\",\"('Au', 'Li', 'Tm')\",OTHERS,False\nmp-1201999,\"{'Y': 40.0, 'Ir': 8.0, 'Br': 72.0}\",\"('Br', 'Ir', 'Y')\",OTHERS,False\nmp-699473,\"{'Li': 4.0, 'B': 24.0, 'H': 52.0, 'O': 14.0}\",\"('B', 'H', 'Li', 'O')\",OTHERS,False\nmp-1147612,\"{'Mg': 1.0, 'Cu': 3.0, 'N': 3.0}\",\"('Cu', 'Mg', 'N')\",OTHERS,False\nmp-778259,\"{'Pr': 8.0, 'Hf': 8.0, 'O': 32.0}\",\"('Hf', 'O', 'Pr')\",OTHERS,False\nmvc-8266,\"{'Ca': 4.0, 'Fe': 4.0, 'Ni': 4.0, 'P': 8.0, 'O': 36.0}\",\"('Ca', 'Fe', 'Ni', 'O', 'P')\",OTHERS,False\nmp-1220987,\"{'Na': 1.0, 'Li': 1.0, 'Mn': 1.0, 'S': 2.0}\",\"('Li', 'Mn', 'Na', 'S')\",OTHERS,False\nmp-1218309,\"{'Sr': 1.0, 'La': 2.0, 'Ce': 1.0, 'Mn': 2.0, 'Cr': 2.0, 'O': 12.0}\",\"('Ce', 'Cr', 'La', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-686311,\"{'Na': 7.0, 'Bi': 3.0, 'P': 12.0, 'Pb': 10.0, 'O': 48.0}\",\"('Bi', 'Na', 'O', 'P', 'Pb')\",OTHERS,False\nmvc-13997,\"{'Al': 2.0, 'V': 2.0, 'O': 6.0}\",\"('Al', 'O', 'V')\",OTHERS,False\nmp-1203517,\"{'Cs': 4.0, 'H': 24.0, 'C': 8.0, 'S': 8.0, 'N': 4.0, 'O': 16.0}\",\"('C', 'Cs', 'H', 'N', 'O', 'S')\",OTHERS,False\nmp-1180715,\"{'Na': 12.0, 'As': 4.0, 'H': 64.0, 'S': 16.0, 'O': 32.0}\",\"('As', 'H', 'Na', 'O', 'S')\",OTHERS,False\nmp-1205702,\"{'Co': 1.0, 'Re': 1.0, 'Pb': 2.0, 'O': 6.0}\",\"('Co', 'O', 'Pb', 'Re')\",OTHERS,False\nmp-1197745,\"{'Cs': 2.0, 'Ti': 4.0, 'P': 6.0, 'O': 20.0, 'F': 8.0}\",\"('Cs', 'F', 'O', 'P', 'Ti')\",OTHERS,False\nmp-600190,\"{'La': 4.0, 'H': 96.0, 'C': 12.0, 'N': 12.0, 'Cl': 24.0, 'O': 12.0}\",\"('C', 'Cl', 'H', 'La', 'N', 'O')\",OTHERS,False\nmp-978513,\"{'Sm': 1.0, 'Er': 1.0, 'Mg': 2.0}\",\"('Er', 'Mg', 'Sm')\",OTHERS,False\nmp-771347,\"{'Li': 11.0, 'Ti': 4.0, 'Fe': 9.0, 'O': 32.0}\",\"('Fe', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-669445,\"{'Cu': 2.0, 'Bi': 8.0, 'Pb': 6.0, 'S': 19.0}\",\"('Bi', 'Cu', 'Pb', 'S')\",OTHERS,False\nmp-1174194,\"{'Li': 6.0, 'Mn': 2.0, 'Co': 2.0, 'O': 10.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-541913,\"{'Hg': 1.0, 'Fe': 1.0, 'S': 4.0, 'C': 4.0, 'N': 4.0}\",\"('C', 'Fe', 'Hg', 'N', 'S')\",OTHERS,False\nmp-553919,\"{'Ag': 6.0, 'Mo': 2.0, 'Cl': 1.0, 'O': 7.0, 'F': 3.0}\",\"('Ag', 'Cl', 'F', 'Mo', 'O')\",OTHERS,False\nmp-20922,\"{'C': 1.0, 'Ga': 1.0, 'Dy': 3.0}\",\"('C', 'Dy', 'Ga')\",OTHERS,False\nmp-546079,\"{'C': 2.0, 'O': 6.0, 'Mg': 2.0}\",\"('C', 'Mg', 'O')\",OTHERS,False\nmp-1226134,\"{'Cs': 4.0, 'Tl': 1.0, 'Sb': 1.0, 'Cl': 12.0}\",\"('Cl', 'Cs', 'Sb', 'Tl')\",OTHERS,False\nmp-758100,\"{'Li': 4.0, 'Cu': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-1080525,\"{'Fe': 4.0, 'B': 4.0}\",\"('B', 'Fe')\",OTHERS,False\nmp-1216180,\"{'Yb': 29.0, 'Se': 32.0}\",\"('Se', 'Yb')\",OTHERS,False\nmp-1183876,\"{'Cd': 2.0, 'Hg': 4.0, 'Se': 2.0, 'O': 12.0}\",\"('Cd', 'Hg', 'O', 'Se')\",OTHERS,False\nmp-1186897,\"{'Rb': 1.0, 'Rh': 1.0, 'O': 3.0}\",\"('O', 'Rb', 'Rh')\",OTHERS,False\nmp-1189166,\"{'Tm': 4.0, 'Ge': 4.0, 'Pd': 8.0}\",\"('Ge', 'Pd', 'Tm')\",OTHERS,False\nmp-1211495,\"{'La': 12.0, 'Pt': 4.0, 'I': 12.0}\",\"('I', 'La', 'Pt')\",OTHERS,False\nmp-866282,\"{'Ca': 1.0, 'Th': 1.0, 'Rh': 2.0}\",\"('Ca', 'Rh', 'Th')\",OTHERS,False\nmp-1105483,\"{'Ce': 6.0, 'Sn': 12.0, 'Ru': 2.0}\",\"('Ce', 'Ru', 'Sn')\",OTHERS,False\nmp-1210221,\"{'Na': 4.0, 'W': 4.0, 'O': 15.0}\",\"('Na', 'O', 'W')\",OTHERS,False\nmp-1095484,\"{'Pr': 2.0, 'Al': 8.0, 'Ni': 2.0}\",\"('Al', 'Ni', 'Pr')\",OTHERS,False\nmp-1215302,\"{'Zr': 4.0, 'Al': 4.0, 'Pd': 4.0}\",\"('Al', 'Pd', 'Zr')\",OTHERS,False\nmp-8620,\"{'Si': 10.0, 'Rh': 6.0, 'La': 4.0}\",\"('La', 'Rh', 'Si')\",OTHERS,False\nmp-13494,\"{'Fe': 29.0, 'Nd': 3.0}\",\"('Fe', 'Nd')\",OTHERS,False\nmp-647075,\"{'Zn': 64.0, 'S': 64.0}\",\"('S', 'Zn')\",OTHERS,False\nmp-1112784,\"{'Cs': 2.0, 'K': 1.0, 'Ce': 1.0, 'I': 6.0}\",\"('Ce', 'Cs', 'I', 'K')\",OTHERS,False\nmp-1219622,\"{'Rb': 2.0, 'Li': 2.0, 'Mg': 2.0, 'B': 8.0, 'H': 32.0}\",\"('B', 'H', 'Li', 'Mg', 'Rb')\",OTHERS,False\nmp-1095573,\"{'Nb': 4.0, 'Co': 4.0, 'Si': 4.0}\",\"('Co', 'Nb', 'Si')\",OTHERS,False\nmp-763324,\"{'Li': 2.0, 'Mn': 2.0, 'P': 4.0, 'H': 3.0, 'O': 16.0}\",\"('H', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-697408,\"{'H': 20.0, 'C': 4.0, 'N': 4.0, 'O': 10.0}\",\"('C', 'H', 'N', 'O')\",OTHERS,False\nmp-1222651,\"{'Li': 2.0, 'Mg': 1.0, 'Ge': 1.0}\",\"('Ge', 'Li', 'Mg')\",OTHERS,False\nmp-11713,\"{'C': 5.0, 'Si': 5.0}\",\"('C', 'Si')\",OTHERS,False\nmp-1184847,\"{'Ho': 1.0, 'Lu': 1.0, 'Ir': 2.0}\",\"('Ho', 'Ir', 'Lu')\",OTHERS,False\nmp-1020627,\"{'Sr': 4.0, 'Li': 4.0, 'Al': 12.0, 'N': 16.0}\",\"('Al', 'Li', 'N', 'Sr')\",OTHERS,False\nmp-1210310,\"{'Rb': 4.0, 'Pr': 4.0, 'S': 8.0, 'O': 48.0}\",\"('O', 'Pr', 'Rb', 'S')\",OTHERS,False\nmp-1220011,\"{'P': 6.0, 'N': 6.0, 'O': 6.0}\",\"('N', 'O', 'P')\",OTHERS,False\nmp-755266,\"{'Li': 4.0, 'Ti': 3.0, 'O': 8.0}\",\"('Li', 'O', 'Ti')\",OTHERS,False\nmp-1247036,\"{'Mg': 2.0, 'Ti': 3.0, 'Cr': 1.0, 'S': 8.0}\",\"('Cr', 'Mg', 'S', 'Ti')\",OTHERS,False\nmp-1246298,\"{'Sr': 8.0, 'Ru': 2.0, 'N': 8.0}\",\"('N', 'Ru', 'Sr')\",OTHERS,False\nmp-7007,\"{'Se': 2.0, 'Nb': 1.0}\",\"('Nb', 'Se')\",OTHERS,False\nmp-1215917,\"{'Zr': 16.0, 'Ni': 4.0, 'Mo': 4.0}\",\"('Mo', 'Ni', 'Zr')\",OTHERS,False\nmp-1175812,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-30971,\"{'Cl': 4.0, 'Cu': 4.0, 'Te': 8.0}\",\"('Cl', 'Cu', 'Te')\",OTHERS,False\nmp-1221774,\"{'Mn': 2.0, 'Cr': 2.0, 'Te': 4.0}\",\"('Cr', 'Mn', 'Te')\",OTHERS,False\nmp-1204384,\"{'Ag': 4.0, 'H': 40.0, 'S': 32.0, 'N': 32.0, 'Cl': 4.0, 'O': 24.0}\",\"('Ag', 'Cl', 'H', 'N', 'O', 'S')\",OTHERS,False\nmp-866141,\"{'Ti': 1.0, 'Fe': 2.0, 'Si': 1.0}\",\"('Fe', 'Si', 'Ti')\",OTHERS,False\nmp-1238823,\"{'Cs': 4.0, 'Cr': 8.0, 'S': 16.0}\",\"('Cr', 'Cs', 'S')\",OTHERS,False\nmp-1096050,\"{'Mg': 1.0, 'Al': 1.0, 'Pt': 2.0}\",\"('Al', 'Mg', 'Pt')\",OTHERS,False\nmp-27211,\"{'F': 13.0, 'K': 1.0, 'Sb': 4.0}\",\"('F', 'K', 'Sb')\",OTHERS,False\nmp-30542,\"{'P': 32.0, 'Ni': 8.0, 'W': 2.0}\",\"('Ni', 'P', 'W')\",OTHERS,False\nmp-27734,\"{'Cr': 6.0, 'Br': 18.0}\",\"('Br', 'Cr')\",OTHERS,False\nmp-1196152,\"{'Y': 4.0, 'B': 12.0, 'H': 96.0, 'N': 16.0}\",\"('B', 'H', 'N', 'Y')\",OTHERS,False\nmp-768492,\"{'Li': 8.0, 'Ti': 1.0, 'Mn': 3.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'Mn', 'O', 'P', 'Ti')\",OTHERS,False\nmp-1214538,\"{'Ba': 8.0, 'Na': 8.0, 'Ga': 8.0, 'F': 48.0}\",\"('Ba', 'F', 'Ga', 'Na')\",OTHERS,False\nmvc-932,\"{'Ba': 1.0, 'Zn': 1.0, 'Cr': 4.0, 'O': 8.0}\",\"('Ba', 'Cr', 'O', 'Zn')\",OTHERS,False\nmp-1013713,\"{'Ba': 3.0, 'Bi': 1.0, 'P': 1.0}\",\"('Ba', 'Bi', 'P')\",OTHERS,False\nmp-1211986,\"{'K': 8.0, 'Hg': 12.0, 'S': 16.0}\",\"('Hg', 'K', 'S')\",OTHERS,False\nmp-1270,\"{'S': 12.0, 'Dy': 8.0}\",\"('Dy', 'S')\",OTHERS,False\nmp-570598,\"{'Sr': 2.0, 'Si': 14.0, 'N': 20.0}\",\"('N', 'Si', 'Sr')\",OTHERS,False\nmp-8258,\"{'Al': 8.0, 'S': 14.0, 'Ba': 2.0}\",\"('Al', 'Ba', 'S')\",OTHERS,False\nmp-1017631,\"{'Sc': 1.0, 'B': 1.0, 'Pd': 3.0}\",\"('B', 'Pd', 'Sc')\",OTHERS,False\nmp-1202872,\"{'K': 8.0, 'B': 8.0, 'C': 8.0, 'N': 8.0, 'F': 24.0}\",\"('B', 'C', 'F', 'K', 'N')\",OTHERS,False\nmp-1174179,\"{'Li': 8.0, 'Co': 6.0, 'O': 14.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-24473,\"{'H': 16.0, 'Be': 4.0, 'N': 4.0, 'O': 16.0, 'P': 4.0}\",\"('Be', 'H', 'N', 'O', 'P')\",OTHERS,False\nmvc-4358,\"{'Mg': 4.0, 'Ta': 2.0, 'Cr': 2.0, 'O': 12.0}\",\"('Cr', 'Mg', 'O', 'Ta')\",OTHERS,False\nmp-22916,\"{'Na': 1.0, 'Br': 1.0}\",\"('Br', 'Na')\",OTHERS,False\nmp-760269,\"{'Li': 10.0, 'Fe': 4.0, 'B': 4.0, 'O': 16.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-582819,{'Pu': 8.0},\"('Pu',)\",OTHERS,False\nmp-1193528,\"{'Ca': 2.0, 'I': 4.0, 'O': 24.0}\",\"('Ca', 'I', 'O')\",OTHERS,False\nmp-557821,\"{'Ca': 6.0, 'Cr': 6.0, 'P': 8.0, 'O': 32.0}\",\"('Ca', 'Cr', 'O', 'P')\",OTHERS,False\nmp-1188244,\"{'Lu': 8.0, 'Re': 4.0, 'C': 8.0}\",\"('C', 'Lu', 'Re')\",OTHERS,False\nmp-720765,\"{'H': 12.0, 'S': 4.0, 'N': 4.0, 'O': 16.0}\",\"('H', 'N', 'O', 'S')\",OTHERS,False\nmp-1211808,\"{'K': 6.0, 'Na': 14.0, 'Tl': 18.0, 'Hg': 1.0}\",\"('Hg', 'K', 'Na', 'Tl')\",OTHERS,False\nmp-1094575,\"{'Li': 6.0, 'Mg': 6.0}\",\"('Li', 'Mg')\",OTHERS,False\nmp-1238861,\"{'Ti': 2.0, 'Cr': 2.0, 'Cu': 2.0, 'S': 8.0}\",\"('Cr', 'Cu', 'S', 'Ti')\",OTHERS,False\nmp-1195212,\"{'Rb': 16.0, 'Si': 40.0, 'Ni': 8.0, 'O': 96.0}\",\"('Ni', 'O', 'Rb', 'Si')\",OTHERS,False\nmp-1211727,\"{'K': 2.0, 'Rb': 1.0, 'Yb': 1.0, 'V': 2.0, 'O': 8.0}\",\"('K', 'O', 'Rb', 'V', 'Yb')\",OTHERS,False\nmp-18150,\"{'O': 24.0, 'P': 6.0, 'K': 9.0, 'Lu': 3.0}\",\"('K', 'Lu', 'O', 'P')\",OTHERS,False\nmp-758196,\"{'Mg': 9.0, 'As': 6.0, 'O': 24.0}\",\"('As', 'Mg', 'O')\",OTHERS,False\nmp-1208360,\"{'Tb': 2.0, 'Tl': 2.0, 'W': 4.0, 'O': 16.0}\",\"('O', 'Tb', 'Tl', 'W')\",OTHERS,False\nmp-829,\"{'Al': 1.0, 'Pd': 1.0}\",\"('Al', 'Pd')\",OTHERS,False\nmp-1185776,\"{'Mg': 2.0, 'Ir': 2.0, 'O': 5.0}\",\"('Ir', 'Mg', 'O')\",OTHERS,False\nmp-1106254,\"{'La': 2.0, 'Lu': 6.0, 'S': 12.0}\",\"('La', 'Lu', 'S')\",OTHERS,False\nmp-510713,\"{'O': 16.0, 'K': 6.0, 'Mn': 4.0}\",\"('K', 'Mn', 'O')\",OTHERS,False\nmvc-3064,\"{'Ba': 2.0, 'Tl': 2.0, 'Zn': 2.0, 'Fe': 3.0, 'O': 10.0}\",\"('Ba', 'Fe', 'O', 'Tl', 'Zn')\",OTHERS,False\nmp-1208140,\"{'Tl': 6.0, 'Ge': 2.0, 'F': 14.0}\",\"('F', 'Ge', 'Tl')\",OTHERS,False\nmp-23149,\"{'K': 8.0, 'Al': 6.0, 'Si': 6.0, 'Cl': 2.0, 'O': 24.0}\",\"('Al', 'Cl', 'K', 'O', 'Si')\",OTHERS,False\nmp-1079801,\"{'Nb': 1.0, 'Ni': 3.0, 'H': 2.0, 'C': 2.0}\",\"('C', 'H', 'Nb', 'Ni')\",OTHERS,False\nmp-1210861,\"{'Na': 8.0, 'Si': 24.0, 'O': 48.0}\",\"('Na', 'O', 'Si')\",OTHERS,False\nmp-756093,\"{'Na': 12.0, 'Nb': 1.0, 'O': 7.0}\",\"('Na', 'Nb', 'O')\",OTHERS,False\nmp-1105556,\"{'La': 1.0, 'As': 12.0, 'Os': 4.0}\",\"('As', 'La', 'Os')\",OTHERS,False\nmp-9266,\"{'Na': 12.0, 'S': 8.0, 'Fe': 2.0}\",\"('Fe', 'Na', 'S')\",OTHERS,False\nmp-754157,\"{'Al': 2.0, 'In': 2.0, 'O': 6.0}\",\"('Al', 'In', 'O')\",OTHERS,False\nmp-1205879,\"{'Pr': 3.0, 'Cd': 3.0, 'Au': 3.0}\",\"('Au', 'Cd', 'Pr')\",OTHERS,False\nmp-1175375,\"{'Li': 7.0, 'Mn': 5.0, 'O': 12.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-769567,\"{'Li': 10.0, 'Ti': 11.0, 'Cr': 9.0, 'O': 40.0}\",\"('Cr', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-3312,\"{'Se': 8.0, 'Sb': 4.0, 'Cs': 2.0}\",\"('Cs', 'Sb', 'Se')\",OTHERS,False\nmp-554736,\"{'Ca': 2.0, 'B': 4.0, 'H': 24.0, 'O': 20.0}\",\"('B', 'Ca', 'H', 'O')\",OTHERS,False\nmp-1099277,\"{'Li': 1.0, 'Mg': 6.0, 'Fe': 1.0}\",\"('Fe', 'Li', 'Mg')\",OTHERS,False\nmp-850900,\"{'Li': 6.0, 'V': 4.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1796,\"{'Te': 6.0, 'Tl': 10.0}\",\"('Te', 'Tl')\",OTHERS,False\nmp-568709,\"{'Dy': 8.0, 'Ni': 28.0, 'Sn': 12.0}\",\"('Dy', 'Ni', 'Sn')\",OTHERS,False\nmp-1097565,\"{'Hf': 2.0, 'Re': 1.0, 'Ru': 1.0}\",\"('Hf', 'Re', 'Ru')\",OTHERS,False\nmp-1216037,\"{'Zn': 3.0, 'Rh': 2.0, 'C': 12.0, 'N': 12.0}\",\"('C', 'N', 'Rh', 'Zn')\",OTHERS,False\nmp-1182211,\"{'Ba': 2.0, 'Yb': 2.0, 'Co': 8.0, 'O': 14.0}\",\"('Ba', 'Co', 'O', 'Yb')\",OTHERS,False\nmp-10992,\"{'Cu': 2.0, 'As': 4.0, 'Dy': 2.0}\",\"('As', 'Cu', 'Dy')\",OTHERS,False\nmp-532650,\"{'Ca': 8.0, 'Mn': 6.0, 'B': 6.0, 'C': 2.0, 'O': 30.0}\",\"('B', 'C', 'Ca', 'Mn', 'O')\",OTHERS,False\nmp-31508,\"{'K': 4.0, 'Br': 20.0, 'Pb': 8.0}\",\"('Br', 'K', 'Pb')\",OTHERS,False\nmp-554370,\"{'Ti': 2.0, 'S': 2.0, 'Cl': 12.0, 'O': 2.0}\",\"('Cl', 'O', 'S', 'Ti')\",OTHERS,False\nmp-1228875,\"{'Cs': 2.0, 'Ni': 2.0, 'P': 2.0}\",\"('Cs', 'Ni', 'P')\",OTHERS,False\nmp-1147714,\"{'P': 8.0, 'S': 12.0}\",\"('P', 'S')\",OTHERS,False\nmp-568135,\"{'Cs': 8.0, 'Sc': 12.0, 'C': 2.0, 'Cl': 26.0}\",\"('C', 'Cl', 'Cs', 'Sc')\",OTHERS,False\nmp-762236,\"{'Al': 6.0, 'Cr': 2.0, 'O': 12.0}\",\"('Al', 'Cr', 'O')\",OTHERS,False\nmp-1078913,\"{'Rb': 3.0, 'Ce': 1.0, 'F': 6.0}\",\"('Ce', 'F', 'Rb')\",OTHERS,False\nmp-558346,\"{'Nd': 4.0, 'I': 12.0, 'O': 36.0}\",\"('I', 'Nd', 'O')\",OTHERS,False\nmp-761234,\"{'Li': 3.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1226091,\"{'Cr': 8.0, 'Cu': 3.0, 'Ni': 1.0, 'S': 16.0}\",\"('Cr', 'Cu', 'Ni', 'S')\",OTHERS,False\nmp-976818,\"{'Ni': 3.0, 'Au': 1.0}\",\"('Au', 'Ni')\",OTHERS,False\nmvc-3244,\"{'Mg': 2.0, 'Cr': 2.0, 'P': 2.0, 'O': 10.0}\",\"('Cr', 'Mg', 'O', 'P')\",OTHERS,False\nmp-1213043,\"{'Cs': 1.0, 'Mg': 1.0, 'As': 1.0, 'H': 6.0, 'O': 4.0}\",\"('As', 'Cs', 'H', 'Mg', 'O')\",OTHERS,False\nmp-1076751,\"{'K': 4.0, 'Na': 4.0, 'Nb': 1.0, 'W': 7.0, 'O': 24.0}\",\"('K', 'Na', 'Nb', 'O', 'W')\",OTHERS,False\nmp-571225,\"{'Cd': 7.0, 'I': 14.0}\",\"('Cd', 'I')\",OTHERS,False\nmp-707816,\"{'H': 10.0, 'C': 2.0, 'S': 2.0, 'N': 4.0, 'Cl': 2.0, 'O': 8.0}\",\"('C', 'Cl', 'H', 'N', 'O', 'S')\",OTHERS,False\nmp-768682,\"{'Rb': 10.0, 'Fe': 2.0, 'O': 8.0}\",\"('Fe', 'O', 'Rb')\",OTHERS,False\nmp-1205657,\"{'Gd': 4.0, 'Mg': 2.0, 'Ni': 4.0}\",\"('Gd', 'Mg', 'Ni')\",OTHERS,False\nmp-570037,\"{'Co': 6.0, 'Au': 12.0, 'C': 24.0, 'N': 24.0}\",\"('Au', 'C', 'Co', 'N')\",OTHERS,False\nmp-1178244,\"{'Fe': 18.0, 'O': 18.0, 'F': 18.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-776035,\"{'Li': 18.0, 'Cu': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-973921,\"{'Lu': 2.0, 'Ni': 12.0, 'As': 7.0}\",\"('As', 'Lu', 'Ni')\",OTHERS,False\nmp-866121,\"{'V': 3.0, 'Os': 1.0}\",\"('Os', 'V')\",OTHERS,False\nmp-1213994,\"{'Ca': 2.0, 'H': 12.0, 'Pt': 2.0, 'O': 12.0}\",\"('Ca', 'H', 'O', 'Pt')\",OTHERS,False\nmp-611378,\"{'Ba': 1.0, 'Lu': 1.0, 'Fe': 1.0, 'Cu': 1.0, 'O': 5.0}\",\"('Ba', 'Cu', 'Fe', 'Lu', 'O')\",OTHERS,False\nmp-709921,\"{'Cs': 4.0, 'Na': 8.0, 'Cu': 4.0, 'Si': 24.0, 'H': 8.0, 'O': 62.0}\",\"('Cs', 'Cu', 'H', 'Na', 'O', 'Si')\",OTHERS,False\nmp-561257,\"{'Li': 2.0, 'Pr': 4.0, 'C': 4.0, 'N': 8.0, 'F': 6.0}\",\"('C', 'F', 'Li', 'N', 'Pr')\",OTHERS,False\nmp-1186256,\"{'Nd': 2.0, 'Tl': 1.0, 'Ag': 1.0}\",\"('Ag', 'Nd', 'Tl')\",OTHERS,False\nmp-1199071,\"{'Pr': 2.0, 'Cd': 40.0, 'Pd': 4.0}\",\"('Cd', 'Pd', 'Pr')\",OTHERS,False\nmp-1209430,\"{'Pr': 3.0, 'P': 6.0, 'Pd': 20.0}\",\"('P', 'Pd', 'Pr')\",OTHERS,False\nmp-1219697,\"{'Rb': 3.0, 'Ag': 9.0, 'Sb': 4.0, 'S': 12.0}\",\"('Ag', 'Rb', 'S', 'Sb')\",OTHERS,False\nmp-755205,\"{'Zr': 8.0, 'N': 8.0, 'O': 4.0}\",\"('N', 'O', 'Zr')\",OTHERS,False\nmp-1228011,\"{'Ba': 1.0, 'La': 2.0, 'Fe': 2.0, 'O': 5.0, 'F': 4.0}\",\"('Ba', 'F', 'Fe', 'La', 'O')\",OTHERS,False\nmp-1703,\"{'Zn': 1.0, 'Yb': 1.0}\",\"('Yb', 'Zn')\",OTHERS,False\nmp-754903,\"{'Y': 2.0, 'B': 6.0, 'O': 12.0}\",\"('B', 'O', 'Y')\",OTHERS,False\nmp-761223,\"{'Mn': 6.0, 'O': 2.0, 'F': 16.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-698125,\"{'Np': 2.0, 'H': 32.0, 'C': 8.0, 'N': 16.0, 'Cl': 2.0, 'O': 12.0}\",\"('C', 'Cl', 'H', 'N', 'Np', 'O')\",OTHERS,False\nmp-1213423,\"{'Eu': 16.0, 'As': 24.0}\",\"('As', 'Eu')\",OTHERS,False\nmp-776063,\"{'Li': 16.0, 'Mn': 8.0, 'P': 8.0, 'O': 32.0, 'F': 8.0}\",\"('F', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1182253,\"{'Ca': 2.0, 'N': 4.0, 'O': 16.0}\",\"('Ca', 'N', 'O')\",OTHERS,False\nmp-1225717,\"{'Cu': 3.0, 'N': 1.0}\",\"('Cu', 'N')\",OTHERS,False\nmp-1186395,\"{'Pa': 1.0, 'Hg': 1.0, 'O': 3.0}\",\"('Hg', 'O', 'Pa')\",OTHERS,False\nmp-1095090,\"{'Ba': 1.0, 'Mn': 2.0, 'O': 5.0}\",\"('Ba', 'Mn', 'O')\",OTHERS,False\nmp-1207267,\"{'Pr': 4.0, 'Co': 1.0, 'I': 5.0}\",\"('Co', 'I', 'Pr')\",OTHERS,False\nmp-1225403,\"{'Dy': 2.0, 'Cu': 6.0, 'Se': 6.0}\",\"('Cu', 'Dy', 'Se')\",OTHERS,False\nmp-555983,\"{'Er': 8.0, 'Si': 4.0, 'O': 20.0}\",\"('Er', 'O', 'Si')\",OTHERS,False\nmp-1215741,\"{'Zn': 2.0, 'Cr': 3.0, 'Fe': 1.0, 'O': 8.0}\",\"('Cr', 'Fe', 'O', 'Zn')\",OTHERS,False\nmvc-9983,\"{'Mg': 2.0, 'Mo': 4.0, 'O': 8.0}\",\"('Mg', 'Mo', 'O')\",OTHERS,False\nmp-21625,\"{'Co': 6.0, 'Ga': 18.0, 'Y': 4.0}\",\"('Co', 'Ga', 'Y')\",OTHERS,False\nmp-759513,\"{'Li': 14.0, 'Ni': 2.0, 'O': 8.0, 'F': 4.0}\",\"('F', 'Li', 'Ni', 'O')\",OTHERS,False\nmp-1211804,\"{'K': 6.0, 'Na': 14.0, 'Tl': 18.0, 'Cd': 1.0}\",\"('Cd', 'K', 'Na', 'Tl')\",OTHERS,False\nmp-27684,\"{'O': 6.0, 'Tl': 8.0}\",\"('O', 'Tl')\",OTHERS,False\nmp-1226714,\"{'Ce': 1.0, 'Er': 4.0, 'S': 7.0}\",\"('Ce', 'Er', 'S')\",OTHERS,False\nmp-21011,\"{'As': 6.0, 'Ce': 8.0}\",\"('As', 'Ce')\",OTHERS,False\nmp-680415,\"{'Os': 6.0, 'C': 20.0, 'Br': 4.0, 'O': 20.0}\",\"('Br', 'C', 'O', 'Os')\",OTHERS,False\nmp-759653,\"{'Li': 4.0, 'Bi': 8.0, 'P': 4.0, 'O': 24.0}\",\"('Bi', 'Li', 'O', 'P')\",OTHERS,False\nmp-768795,\"{'Li': 8.0, 'Bi': 8.0, 'B': 16.0, 'O': 40.0}\",\"('B', 'Bi', 'Li', 'O')\",OTHERS,False\nmp-780308,\"{'Li': 5.0, 'Mn': 6.0, 'B': 6.0, 'O': 18.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-12557,\"{'Sr': 2.0, 'Cu': 2.0, 'As': 2.0}\",\"('As', 'Cu', 'Sr')\",OTHERS,False\nmp-707519,\"{'Mg': 16.0, 'Si': 8.0, 'H': 1.0, 'O': 32.0}\",\"('H', 'Mg', 'O', 'Si')\",OTHERS,False\nmvc-11654,\"{'Mn': 4.0, 'Zn': 1.0, 'O': 8.0}\",\"('Mn', 'O', 'Zn')\",OTHERS,False\nmp-1096340,\"{'Na': 1.0, 'Hg': 2.0, 'Bi': 1.0}\",\"('Bi', 'Hg', 'Na')\",OTHERS,False\nmp-1207971,\"{'Tm': 2.0, 'Co': 8.0, 'Ge': 4.0}\",\"('Co', 'Ge', 'Tm')\",OTHERS,False\nmp-1179218,\"{'Sr': 2.0, 'Br': 2.0, 'O': 10.0}\",\"('Br', 'O', 'Sr')\",OTHERS,False\nmp-1212562,\"{'Na': 4.0, 'Cr': 4.0, 'H': 48.0, 'S': 8.0, 'O': 32.0}\",\"('Cr', 'H', 'Na', 'O', 'S')\",OTHERS,False\nmp-1229210,\"{'Ba': 10.0, 'Er': 5.0, 'Cu': 15.0, 'O': 34.0}\",\"('Ba', 'Cu', 'Er', 'O')\",OTHERS,False\nmp-1246127,\"{'Ca': 6.0, 'Ir': 4.0, 'N': 8.0}\",\"('Ca', 'Ir', 'N')\",OTHERS,False\nmp-850215,\"{'Li': 2.0, 'Ti': 2.0, 'Mn': 2.0, 'O': 8.0}\",\"('Li', 'Mn', 'O', 'Ti')\",OTHERS,False\nmp-1245746,\"{'Na': 1.0, 'Sb': 1.0, 'N': 2.0}\",\"('N', 'Na', 'Sb')\",OTHERS,False\nmp-20138,\"{'Pd': 3.0, 'In': 3.0, 'La': 3.0}\",\"('In', 'La', 'Pd')\",OTHERS,False\nmp-1176673,\"{'Na': 10.0, 'Ni': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Na', 'Ni', 'O', 'P')\",OTHERS,False\nmp-1201474,\"{'K': 16.0, 'S': 16.0, 'O': 64.0}\",\"('K', 'O', 'S')\",OTHERS,False\nmp-19917,\"{'Si': 2.0, 'Zr': 2.0, 'Te': 2.0}\",\"('Si', 'Te', 'Zr')\",OTHERS,False\nmp-1180621,\"{'K': 1.0, 'S': 1.0}\",\"('K', 'S')\",OTHERS,False\nmp-607371,\"{'Ru': 2.0, 'N': 4.0}\",\"('N', 'Ru')\",OTHERS,False\nmp-662599,\"{'Ag': 36.0, 'As': 12.0, 'Se': 36.0}\",\"('Ag', 'As', 'Se')\",OTHERS,False\nmp-1097065,\"{'Li': 2.0, 'Hf': 1.0, 'N': 2.0}\",\"('Hf', 'Li', 'N')\",OTHERS,False\nmp-685374,\"{'Ti': 6.0, 'Fe': 10.0, 'O': 24.0}\",\"('Fe', 'O', 'Ti')\",OTHERS,False\nmp-33255,\"{'Ni': 15.0, 'O': 16.0}\",\"('Ni', 'O')\",OTHERS,False\nmp-753451,\"{'Li': 2.0, 'Fe': 4.0, 'P': 4.0, 'H': 2.0, 'O': 16.0}\",\"('Fe', 'H', 'Li', 'O', 'P')\",OTHERS,False\nmp-974843,\"{'Rb': 3.0, 'Os': 1.0}\",\"('Os', 'Rb')\",OTHERS,False\nmp-998536,\"{'Rb': 4.0, 'Ca': 4.0, 'Br': 12.0}\",\"('Br', 'Ca', 'Rb')\",OTHERS,False\nmp-1211668,\"{'La': 2.0, 'Nb': 16.0, 'O': 28.0}\",\"('La', 'Nb', 'O')\",OTHERS,False\nmp-6239,\"{'O': 48.0, 'Si': 12.0, 'Mn': 12.0, 'Fe': 8.0}\",\"('Fe', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1207622,\"{'Yb': 10.0, 'Pt': 18.0}\",\"('Pt', 'Yb')\",OTHERS,False\nmp-705468,\"{'Li': 6.0, 'Mn': 6.0, 'P': 8.0, 'O': 32.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1186813,\"{'Pu': 3.0, 'Br': 1.0}\",\"('Br', 'Pu')\",OTHERS,False\nmp-1096395,\"{'Li': 1.0, 'Mg': 1.0, 'Pb': 2.0}\",\"('Li', 'Mg', 'Pb')\",OTHERS,False\nmp-1199323,\"{'Th': 4.0, 'P': 8.0, 'H': 8.0, 'C': 4.0, 'O': 32.0}\",\"('C', 'H', 'O', 'P', 'Th')\",OTHERS,False\nmp-28444,\"{'O': 16.0, 'Si': 4.0, 'Fe': 6.0}\",\"('Fe', 'O', 'Si')\",OTHERS,False\nmvc-6475,\"{'Mg': 2.0, 'Ti': 4.0, 'O': 8.0}\",\"('Mg', 'O', 'Ti')\",OTHERS,False\nmp-780146,\"{'Fe': 6.0, 'O': 4.0, 'F': 8.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-1219095,\"{'Sm': 2.0, 'Bi': 2.0, 'Ru': 4.0, 'O': 14.0}\",\"('Bi', 'O', 'Ru', 'Sm')\",OTHERS,False\nmp-1175110,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-3569,\"{'Sb': 12.0, 'Nd': 1.0, 'Os': 4.0}\",\"('Nd', 'Os', 'Sb')\",OTHERS,False\nmp-755642,\"{'Li': 6.0, 'V': 2.0, 'S': 8.0}\",\"('Li', 'S', 'V')\",OTHERS,False\nmp-1209956,\"{'Na': 1.0, 'Tb': 1.0, 'Pd': 6.0, 'O': 8.0}\",\"('Na', 'O', 'Pd', 'Tb')\",OTHERS,False\nmp-1220313,\"{'Nd': 2.0, 'Sb': 1.0, 'O': 2.0}\",\"('Nd', 'O', 'Sb')\",OTHERS,False\nmp-2501,\"{'B': 2.0, 'Mo': 4.0}\",\"('B', 'Mo')\",OTHERS,False\nmp-771834,\"{'Tb': 12.0, 'Nb': 4.0, 'O': 28.0}\",\"('Nb', 'O', 'Tb')\",OTHERS,False\nmp-669325,\"{'Te': 2.0, 'W': 2.0, 'Cl': 18.0}\",\"('Cl', 'Te', 'W')\",OTHERS,False\nmp-1079552,\"{'Tb': 2.0, 'Ga': 4.0, 'Ni': 2.0}\",\"('Ga', 'Ni', 'Tb')\",OTHERS,False\nmp-7022,\"{'V': 10.0, 'As': 6.0}\",\"('As', 'V')\",OTHERS,False\nmp-9930,\"{'P': 2.0, 'Se': 2.0, 'U': 2.0}\",\"('P', 'Se', 'U')\",OTHERS,False\nmp-12540,\"{'O': 48.0, 'P': 8.0, 'Zr': 8.0, 'Mo': 4.0}\",\"('Mo', 'O', 'P', 'Zr')\",OTHERS,False\nmp-558786,\"{'Ag': 12.0, 'Ge': 2.0, 'S': 2.0, 'O': 16.0}\",\"('Ag', 'Ge', 'O', 'S')\",OTHERS,False\nmp-25923,\"{'Li': 6.0, 'O': 32.0, 'P': 8.0, 'Mn': 9.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1074028,\"{'Mg': 18.0, 'Si': 10.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1196619,\"{'Ir': 8.0, 'Se': 36.0, 'Cl': 24.0}\",\"('Cl', 'Ir', 'Se')\",OTHERS,False\nmp-1113353,\"{'Cs': 2.0, 'Cu': 1.0, 'Sb': 1.0, 'I': 6.0}\",\"('Cs', 'Cu', 'I', 'Sb')\",OTHERS,False\nmp-8111,\"{'La': 2.0, 'O': 2.0, 'F': 2.0}\",\"('F', 'La', 'O')\",OTHERS,False\nmp-998435,\"{'Cs': 4.0, 'Sc': 4.0, 'Br': 12.0}\",\"('Br', 'Cs', 'Sc')\",OTHERS,False\nmp-504962,\"{'In': 4.0, 'Na': 12.0, 'K': 8.0, 'O': 16.0}\",\"('In', 'K', 'Na', 'O')\",OTHERS,False\nmp-769138,\"{'Dy': 12.0, 'Sb': 12.0, 'O': 42.0}\",\"('Dy', 'O', 'Sb')\",OTHERS,False\nmp-1245714,\"{'Sr': 16.0, 'C': 8.0, 'N': 20.0}\",\"('C', 'N', 'Sr')\",OTHERS,False\nmp-1019796,\"{'K': 8.0, 'P': 4.0, 'O': 14.0}\",\"('K', 'O', 'P')\",OTHERS,False\nmp-1069771,\"{'Rb': 2.0, 'Pd': 1.0, 'Se': 2.0}\",\"('Pd', 'Rb', 'Se')\",OTHERS,False\nmp-2568,\"{'Si': 2.0, 'Y': 1.0}\",\"('Si', 'Y')\",OTHERS,False\nmp-568801,\"{'Pr': 6.0, 'Cu': 2.0, 'Si': 2.0, 'Se': 14.0}\",\"('Cu', 'Pr', 'Se', 'Si')\",OTHERS,False\nmp-755361,\"{'Na': 6.0, 'V': 2.0, 'B': 2.0, 'As': 2.0, 'O': 14.0}\",\"('As', 'B', 'Na', 'O', 'V')\",OTHERS,False\nmp-36329,\"{'O': 4.0, 'U': 1.0, 'Sr': 1.0}\",\"('O', 'Sr', 'U')\",OTHERS,False\nmp-974358,\"{'Ir': 3.0, 'Ru': 1.0}\",\"('Ir', 'Ru')\",OTHERS,False\nmp-766544,\"{'Li': 4.0, 'Mn': 4.0, 'Si': 6.0, 'O': 20.0}\",\"('Li', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-26194,\"{'O': 52.0, 'P': 12.0, 'Mo': 8.0}\",\"('Mo', 'O', 'P')\",OTHERS,False\nmp-1030390,\"{'Te': 2.0, 'Mo': 2.0, 'W': 2.0, 'Se': 2.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-1193254,\"{'Sr': 4.0, 'C': 8.0, 'O': 16.0}\",\"('C', 'O', 'Sr')\",OTHERS,False\nmp-556213,\"{'K': 16.0, 'Mn': 16.0, 'V': 16.0, 'O': 64.0}\",\"('K', 'Mn', 'O', 'V')\",OTHERS,False\nmp-559,\"{'Y': 1.0, 'Pd': 3.0}\",\"('Pd', 'Y')\",OTHERS,False\nmp-867195,\"{'Sr': 1.0, 'Os': 1.0, 'O': 3.0}\",\"('O', 'Os', 'Sr')\",OTHERS,False\nmp-780722,\"{'Y': 6.0, 'N': 2.0, 'O': 5.0}\",\"('N', 'O', 'Y')\",OTHERS,False\nmvc-1326,\"{'Ba': 2.0, 'Y': 2.0, 'W': 8.0, 'O': 14.0}\",\"('Ba', 'O', 'W', 'Y')\",OTHERS,False\nmp-1093905,\"{'Li': 1.0, 'La': 2.0, 'Tl': 1.0}\",\"('La', 'Li', 'Tl')\",OTHERS,False\nmp-1692,\"{'Cu': 2.0, 'O': 2.0}\",\"('Cu', 'O')\",OTHERS,False\nmp-1211502,\"{'La': 10.0, 'B': 6.0, 'O': 26.0}\",\"('B', 'La', 'O')\",OTHERS,False\nmp-1209181,\"{'Rb': 1.0, 'N': 1.0}\",\"('N', 'Rb')\",OTHERS,False\nmp-1223843,\"{'Ho': 1.0, 'Bi': 1.0, 'Te': 3.0}\",\"('Bi', 'Ho', 'Te')\",OTHERS,False\nmp-1204607,\"{'K': 8.0, 'I': 12.0, 'N': 4.0, 'O': 48.0}\",\"('I', 'K', 'N', 'O')\",OTHERS,False\nmp-1074288,\"{'Mg': 8.0, 'Si': 6.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-9571,\"{'Ca': 1.0, 'Zn': 2.0, 'As': 2.0}\",\"('As', 'Ca', 'Zn')\",OTHERS,False\nmp-1112124,\"{'Cs': 2.0, 'Rb': 1.0, 'Pr': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cs', 'Pr', 'Rb')\",OTHERS,False\nmp-1113404,\"{'Rb': 2.0, 'In': 1.0, 'Cu': 1.0, 'Br': 6.0}\",\"('Br', 'Cu', 'In', 'Rb')\",OTHERS,False\nmp-1217818,\"{'Ta': 1.0, 'V': 1.0, 'B': 4.0}\",\"('B', 'Ta', 'V')\",OTHERS,False\nmp-1209668,\"{'Sr': 6.0, 'H': 10.0, 'Os': 4.0, 'O': 16.0}\",\"('H', 'O', 'Os', 'Sr')\",OTHERS,False\nmp-849432,\"{'Ni': 1.0, 'H': 12.0, 'S': 2.0, 'N': 2.0, 'O': 10.0}\",\"('H', 'N', 'Ni', 'O', 'S')\",OTHERS,False\nmvc-14736,\"{'Y': 4.0, 'W': 4.0, 'O': 12.0}\",\"('O', 'W', 'Y')\",OTHERS,False\nmp-1187413,\"{'Th': 2.0, 'Ag': 6.0}\",\"('Ag', 'Th')\",OTHERS,False\nmp-764769,\"{'Li': 8.0, 'V': 2.0, 'F': 14.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-973498,\"{'In': 3.0, 'Au': 1.0}\",\"('Au', 'In')\",OTHERS,False\nmp-1219700,\"{'Sc': 2.0, 'Mn': 12.0, 'Ga': 1.0, 'Ge': 11.0}\",\"('Ga', 'Ge', 'Mn', 'Sc')\",OTHERS,False\nmp-1183082,\"{'Ac': 3.0, 'Zr': 1.0}\",\"('Ac', 'Zr')\",OTHERS,False\nmp-1193798,\"{'La': 6.0, 'Fe': 2.0, 'W': 2.0, 'S': 6.0, 'O': 12.0}\",\"('Fe', 'La', 'O', 'S', 'W')\",OTHERS,False\nmp-557531,\"{'Pb': 4.0, 'S': 2.0, 'O': 8.0, 'F': 4.0}\",\"('F', 'O', 'Pb', 'S')\",OTHERS,False\nmp-1209816,\"{'Np': 4.0, 'F': 24.0}\",\"('F', 'Np')\",OTHERS,False\nmp-7400,\"{'Na': 2.0, 'Al': 2.0, 'Ge': 2.0}\",\"('Al', 'Ge', 'Na')\",OTHERS,False\nmp-1220782,\"{'Nb': 12.0, 'Ga': 3.0, 'Ge': 1.0}\",\"('Ga', 'Ge', 'Nb')\",OTHERS,False\nmp-1021328,\"{'H': 4.0, 'C': 1.0}\",\"('C', 'H')\",OTHERS,False\nmp-20103,\"{'Tm': 3.0, 'In': 3.0, 'Pt': 3.0}\",\"('In', 'Pt', 'Tm')\",OTHERS,False\nmp-23623,\"{'B': 20.0, 'O': 36.0, 'Br': 4.0, 'Pb': 8.0}\",\"('B', 'Br', 'O', 'Pb')\",OTHERS,False\nmp-1226559,\"{'Co': 3.0, 'Ni': 1.0}\",\"('Co', 'Ni')\",OTHERS,False\nmp-1196645,\"{'Cd': 8.0, 'S': 4.0, 'O': 24.0}\",\"('Cd', 'O', 'S')\",OTHERS,False\nmp-1211992,\"{'K': 18.0, 'Nd': 2.0, 'P': 8.0, 'S': 32.0}\",\"('K', 'Nd', 'P', 'S')\",OTHERS,False\nmp-626112,\"{'H': 28.0, 'N': 4.0, 'O': 24.0}\",\"('H', 'N', 'O')\",OTHERS,False\nmp-1213882,\"{'Ce': 4.0, 'Ta': 4.0, 'O': 18.0}\",\"('Ce', 'O', 'Ta')\",OTHERS,False\nmp-1096146,\"{'Y': 2.0, 'Rh': 1.0, 'Pb': 1.0}\",\"('Pb', 'Rh', 'Y')\",OTHERS,False\nmp-1222785,\"{'La': 1.0, 'Nd': 1.0, 'Al': 4.0}\",\"('Al', 'La', 'Nd')\",OTHERS,False\nmp-557769,\"{'Ca': 8.0, 'H': 16.0, 'C': 16.0, 'O': 40.0}\",\"('C', 'Ca', 'H', 'O')\",OTHERS,False\nmp-1080594,\"{'Dy': 3.0, 'In': 3.0, 'Cu': 3.0}\",\"('Cu', 'Dy', 'In')\",OTHERS,False\nmp-567471,\"{'Hg': 8.0, 'I': 16.0}\",\"('Hg', 'I')\",OTHERS,False\nmp-716814,\"{'Fe': 12.0, 'O': 18.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-759251,\"{'Li': 4.0, 'Sb': 4.0, 'P': 16.0, 'O': 48.0}\",\"('Li', 'O', 'P', 'Sb')\",OTHERS,False\nmp-561315,\"{'Cs': 6.0, 'Al': 2.0, 'Ge': 4.0, 'O': 14.0}\",\"('Al', 'Cs', 'Ge', 'O')\",OTHERS,False\nmp-865928,\"{'Ti': 2.0, 'Tc': 1.0, 'Pt': 1.0}\",\"('Pt', 'Tc', 'Ti')\",OTHERS,False\nmp-35589,\"{'F': 7.0, 'Na': 3.0, 'U': 1.0}\",\"('F', 'Na', 'U')\",OTHERS,False\nmp-1225373,\"{'Dy': 2.0, 'P': 7.0, 'Rh': 12.0}\",\"('Dy', 'P', 'Rh')\",OTHERS,False\nmp-12022,\"{'B': 8.0, 'O': 14.0, 'Hg': 2.0}\",\"('B', 'Hg', 'O')\",OTHERS,False\nmp-27658,\"{'Li': 5.0, 'B': 4.0}\",\"('B', 'Li')\",OTHERS,False\nmp-752549,\"{'Li': 1.0, 'Ag': 1.0, 'F': 2.0}\",\"('Ag', 'F', 'Li')\",OTHERS,False\nmp-9665,\"{'B': 2.0, 'K': 6.0, 'As': 4.0}\",\"('As', 'B', 'K')\",OTHERS,False\nmp-505396,\"{'Pb': 4.0, 'Cs': 4.0, 'Na': 12.0, 'O': 16.0}\",\"('Cs', 'Na', 'O', 'Pb')\",OTHERS,False\nmp-1217591,\"{'Yb': 12.0, 'Cd': 72.0}\",\"('Cd', 'Yb')\",OTHERS,False\nmp-742801,\"{'V': 4.0, 'Cu': 2.0, 'H': 12.0, 'N': 4.0, 'O': 12.0}\",\"('Cu', 'H', 'N', 'O', 'V')\",OTHERS,False\nmp-1185360,\"{'Li': 1.0, 'Mn': 1.0, 'Ir': 2.0}\",\"('Ir', 'Li', 'Mn')\",OTHERS,False\nmp-863436,\"{'Co': 8.0, 'Te': 16.0, 'O': 40.0}\",\"('Co', 'O', 'Te')\",OTHERS,False\nmp-1094352,\"{'Sr': 8.0, 'Mg': 4.0}\",\"('Mg', 'Sr')\",OTHERS,False\nmp-757997,\"{'Li': 4.0, 'Sn': 4.0, 'P': 16.0, 'O': 48.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,False\nmp-1224936,\"{'Fe': 15.0, 'O': 16.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-7591,\"{'Ge': 1.0, 'As': 1.0}\",\"('As', 'Ge')\",OTHERS,False\nmp-557865,\"{'N': 6.0, 'O': 12.0}\",\"('N', 'O')\",OTHERS,False\nmp-651122,\"{'Mn': 12.0, 'Bi': 4.0, 'C': 60.0, 'O': 60.0}\",\"('Bi', 'C', 'Mn', 'O')\",OTHERS,False\nmp-1095889,\"{'Ta': 1.0, 'Nb': 1.0, 'Os': 2.0}\",\"('Nb', 'Os', 'Ta')\",OTHERS,False\nmp-1184534,\"{'Gd': 1.0, 'Lu': 1.0, 'Cd': 2.0}\",\"('Cd', 'Gd', 'Lu')\",OTHERS,False\nmp-1172894,\"{'Ca': 2.0, 'Ga': 4.0}\",\"('Ca', 'Ga')\",OTHERS,False\nmp-1183430,\"{'Ca': 2.0, 'Mg': 4.0}\",\"('Ca', 'Mg')\",OTHERS,False\nmp-1217436,\"{'Th': 4.0, 'U': 8.0, 'S': 20.0}\",\"('S', 'Th', 'U')\",OTHERS,False\nmp-7851,\"{'Co': 9.0, 'Ta': 3.0}\",\"('Co', 'Ta')\",OTHERS,False\nmp-768986,\"{'Li': 40.0, 'Bi': 8.0, 'O': 32.0}\",\"('Bi', 'Li', 'O')\",OTHERS,False\nmp-867551,\"{'Li': 12.0, 'Ni': 12.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-1214566,\"{'Ba': 6.0, 'Co': 2.0, 'O': 10.0}\",\"('Ba', 'Co', 'O')\",OTHERS,False\nmp-770367,\"{'Li': 2.0, 'V': 4.0, 'S': 6.0, 'O': 24.0}\",\"('Li', 'O', 'S', 'V')\",OTHERS,False\nmp-768430,\"{'Li': 24.0, 'Ti': 7.0, 'Cr': 5.0, 'O': 36.0}\",\"('Cr', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-976280,\"{'Li': 2.0, 'Br': 2.0}\",\"('Br', 'Li')\",OTHERS,False\nmp-13230,\"{'F': 12.0, 'K': 6.0, 'Y': 2.0}\",\"('F', 'K', 'Y')\",OTHERS,False\nmp-27539,\"{'O': 10.0, 'La': 4.0, 'Re': 2.0}\",\"('La', 'O', 'Re')\",OTHERS,False\nmp-30011,\"{'F': 28.0, 'Kr': 4.0, 'Sb': 4.0}\",\"('F', 'Kr', 'Sb')\",OTHERS,False\nmp-1100775,\"{'Sr': 1.0, 'Eu': 1.0, 'O': 3.0}\",\"('Eu', 'O', 'Sr')\",OTHERS,False\nmp-25420,\"{'Li': 2.0, 'Ti': 2.0, 'P': 2.0, 'O': 8.0, 'F': 2.0}\",\"('F', 'Li', 'O', 'P', 'Ti')\",OTHERS,False\nmp-1095730,\"{'Tl': 2.0, 'Cd': 1.0, 'In': 1.0}\",\"('Cd', 'In', 'Tl')\",OTHERS,False\nmp-774912,\"{'Li': 4.0, 'Nb': 2.0, 'Co': 3.0, 'Sb': 3.0, 'O': 16.0}\",\"('Co', 'Li', 'Nb', 'O', 'Sb')\",OTHERS,False\nmp-1095377,\"{'Er': 4.0, 'Sn': 4.0, 'Pd': 4.0}\",\"('Er', 'Pd', 'Sn')\",OTHERS,False\nmp-510122,\"{'Ce': 2.0, 'Co': 6.0, 'Pu': 8.0}\",\"('Ce', 'Co', 'Pu')\",OTHERS,False\nmvc-4004,\"{'Al': 4.0, 'W': 4.0, 'O': 12.0}\",\"('Al', 'O', 'W')\",OTHERS,False\nmp-1218918,\"{'Sr': 10.0, 'P': 6.0, 'Br': 1.0, 'O': 24.0, 'F': 1.0}\",\"('Br', 'F', 'O', 'P', 'Sr')\",OTHERS,False\nmp-1181006,\"{'Li': 4.0, 'V': 4.0, 'Te': 4.0, 'O': 20.0}\",\"('Li', 'O', 'Te', 'V')\",OTHERS,False\nmp-28866,\"{'Cl': 12.0, 'Te': 2.0, 'Os': 1.0}\",\"('Cl', 'Os', 'Te')\",OTHERS,False\nmp-556235,\"{'Ca': 4.0, 'C': 4.0, 'O': 12.0}\",\"('C', 'Ca', 'O')\",OTHERS,False\nmp-1185435,\"{'Li': 1.0, 'Yb': 1.0, 'Au': 2.0}\",\"('Au', 'Li', 'Yb')\",OTHERS,False\nmp-1220063,\"{'Pr': 7.0, 'In': 6.0, 'Ni': 5.0, 'Ge': 3.0}\",\"('Ge', 'In', 'Ni', 'Pr')\",OTHERS,False\nmp-1228917,\"{'Al': 1.0, 'In': 1.0, 'Ga': 1.0, 'Sb': 3.0}\",\"('Al', 'Ga', 'In', 'Sb')\",OTHERS,False\nmp-1214309,\"{'C': 16.0, 'N': 16.0, 'O': 32.0}\",\"('C', 'N', 'O')\",OTHERS,False\nmp-1212306,\"{'Ho': 16.0, 'Cd': 4.0, 'Pt': 4.0}\",\"('Cd', 'Ho', 'Pt')\",OTHERS,False\nmp-1215589,\"{'Zr': 3.0, 'Al': 1.0, 'N': 4.0}\",\"('Al', 'N', 'Zr')\",OTHERS,False\nmp-30792,\"{'Na': 16.0, 'Pb': 16.0}\",\"('Na', 'Pb')\",OTHERS,False\nmp-743872,\"{'V': 4.0, 'P': 4.0, 'H': 20.0, 'N': 4.0, 'O': 24.0}\",\"('H', 'N', 'O', 'P', 'V')\",OTHERS,False\nmp-1221881,\"{'Mn': 2.0, 'Al': 1.0, 'Cu': 3.0}\",\"('Al', 'Cu', 'Mn')\",OTHERS,False\nmp-1218661,\"{'Sr': 6.0, 'Ti': 2.0, 'Nb': 8.0, 'O': 30.0}\",\"('Nb', 'O', 'Sr', 'Ti')\",OTHERS,False\nmp-1246203,\"{'Ca': 6.0, 'Ni': 2.0, 'N': 6.0}\",\"('Ca', 'N', 'Ni')\",OTHERS,False\nmp-1209473,\"{'Rb': 2.0, 'Na': 6.0, 'Fe': 14.0, 'P': 12.0, 'O': 48.0}\",\"('Fe', 'Na', 'O', 'P', 'Rb')\",OTHERS,False\nmp-777271,\"{'Li': 9.0, 'Mn': 12.0, 'B': 12.0, 'O': 36.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1035964,\"{'Cs': 1.0, 'K': 1.0, 'Mg': 14.0, 'O': 15.0}\",\"('Cs', 'K', 'Mg', 'O')\",OTHERS,False\nmp-1101681,\"{'Nd': 2.0, 'Bi': 7.0, 'O': 14.0}\",\"('Bi', 'Nd', 'O')\",OTHERS,False\nmp-1207153,\"{'Gd': 3.0, 'Ga': 1.0, 'C': 1.0}\",\"('C', 'Ga', 'Gd')\",OTHERS,False\nmp-1210743,\"{'Li': 2.0, 'C': 1.0}\",\"('C', 'Li')\",OTHERS,False\nmp-1079313,\"{'V': 6.0, 'Ge': 2.0, 'C': 2.0}\",\"('C', 'Ge', 'V')\",OTHERS,False\nmp-1198817,\"{'Gd': 8.0, 'Si': 6.0, 'S': 24.0}\",\"('Gd', 'S', 'Si')\",OTHERS,False\nmp-1189433,\"{'La': 10.0, 'Sn': 6.0, 'C': 2.0}\",\"('C', 'La', 'Sn')\",OTHERS,False\nmp-1223414,\"{'La': 4.0, 'Te': 12.0, 'W': 2.0, 'O': 36.0}\",\"('La', 'O', 'Te', 'W')\",OTHERS,False\nmp-1080709,\"{'Nd': 4.0, 'Au': 4.0}\",\"('Au', 'Nd')\",OTHERS,False\nmp-762322,\"{'Li': 2.0, 'Ti': 4.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'O', 'P', 'Ti')\",OTHERS,False\nmp-775202,\"{'Cr': 1.0, 'Sb': 3.0, 'P': 6.0, 'O': 24.0}\",\"('Cr', 'O', 'P', 'Sb')\",OTHERS,False\nmp-1038009,\"{'Sr': 1.0, 'Ca': 1.0, 'Mg': 30.0, 'O': 32.0}\",\"('Ca', 'Mg', 'O', 'Sr')\",OTHERS,False\nmp-13065,\"{'O': 3.0, 'Ho': 2.0}\",\"('Ho', 'O')\",OTHERS,False\nmp-541487,\"{'Tl': 2.0, 'Pt': 4.0, 'Se': 6.0}\",\"('Pt', 'Se', 'Tl')\",OTHERS,False\nmp-9517,\"{'N': 4.0, 'Ti': 2.0, 'Sr': 2.0}\",\"('N', 'Sr', 'Ti')\",OTHERS,False\nmp-1188299,\"{'Tm': 4.0, 'Si': 4.0, 'Pd': 8.0}\",\"('Pd', 'Si', 'Tm')\",OTHERS,False\nmp-721365,\"{'V': 4.0, 'H': 40.0, 'S': 4.0, 'O': 40.0}\",\"('H', 'O', 'S', 'V')\",OTHERS,False\nmp-17746,\"{'Hf': 6.0, 'Ge': 6.0, 'Pd': 6.0}\",\"('Ge', 'Hf', 'Pd')\",OTHERS,False\nmvc-12592,\"{'Mg': 2.0, 'Cr': 4.0, 'O': 8.0}\",\"('Cr', 'Mg', 'O')\",OTHERS,False\nmp-1187112,\"{'Sr': 6.0, 'Dy': 2.0}\",\"('Dy', 'Sr')\",OTHERS,False\nmp-1078810,\"{'Sc': 4.0, 'Sn': 2.0, 'Au': 4.0}\",\"('Au', 'Sc', 'Sn')\",OTHERS,False\nmp-1180312,\"{'Mg': 8.0, 'O': 8.0}\",\"('Mg', 'O')\",OTHERS,False\nmp-1225422,\"{'Fe': 20.0, 'Bi': 16.0, 'O': 52.0, 'F': 4.0}\",\"('Bi', 'F', 'Fe', 'O')\",OTHERS,False\nmp-1187318,\"{'Tb': 6.0, 'Pr': 2.0}\",\"('Pr', 'Tb')\",OTHERS,False\nmp-1224843,\"{'Ga': 1.0, 'Mo': 4.0, 'Se': 4.0, 'S': 4.0}\",\"('Ga', 'Mo', 'S', 'Se')\",OTHERS,False\nmp-1216295,\"{'V': 1.0, 'Re': 11.0, 'B': 4.0}\",\"('B', 'Re', 'V')\",OTHERS,False\nmp-1217611,\"{'Ti': 2.0, 'Fe': 4.0, 'Bi': 4.0, 'Pb': 2.0, 'O': 18.0}\",\"('Bi', 'Fe', 'O', 'Pb', 'Ti')\",OTHERS,False\nmp-1186280,\"{'Nd': 3.0, 'Ti': 1.0}\",\"('Nd', 'Ti')\",OTHERS,False\nmp-1192484,\"{'Y': 20.0, 'Cd': 6.0, 'Ru': 2.0}\",\"('Cd', 'Ru', 'Y')\",OTHERS,False\nmp-1201180,\"{'Mn': 2.0, 'Fe': 4.0, 'P': 4.0, 'O': 32.0}\",\"('Fe', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1191469,\"{'Cd': 5.0, 'N': 2.0, 'O': 16.0}\",\"('Cd', 'N', 'O')\",OTHERS,False\nmp-1199635,\"{'Cu': 4.0, 'C': 32.0, 'N': 8.0, 'Cl': 16.0}\",\"('C', 'Cl', 'Cu', 'N')\",OTHERS,False\nmp-27252,\"{'Cd': 4.0, 'I': 12.0, 'Tl': 4.0}\",\"('Cd', 'I', 'Tl')\",OTHERS,False\nmp-1076508,\"{'La': 4.0, 'Cr': 4.0, 'O': 10.0}\",\"('Cr', 'La', 'O')\",OTHERS,False\nmp-17283,\"{'K': 12.0, 'Se': 16.0, 'Nb': 4.0}\",\"('K', 'Nb', 'Se')\",OTHERS,False\nmp-485,\"{'Ni': 6.0, 'Zr': 2.0}\",\"('Ni', 'Zr')\",OTHERS,False\nmvc-2260,\"{'Cr': 9.0, 'O': 13.0}\",\"('Cr', 'O')\",OTHERS,False\nmp-1029726,\"{'Li': 12.0, 'Ir': 2.0, 'N': 8.0}\",\"('Ir', 'Li', 'N')\",OTHERS,False\nmp-30403,\"{'Li': 2.0, 'Au': 1.0, 'Pb': 1.0}\",\"('Au', 'Li', 'Pb')\",OTHERS,False\nmp-1212145,\"{'K': 12.0, 'Be': 12.0, 'B': 12.0, 'P': 24.0, 'O': 100.0}\",\"('B', 'Be', 'K', 'O', 'P')\",OTHERS,False\nmp-1207603,\"{'Y': 1.0, 'Ga': 3.0, 'B': 4.0, 'O': 12.0}\",\"('B', 'Ga', 'O', 'Y')\",OTHERS,False\nmp-1113032,\"{'Cs': 2.0, 'K': 1.0, 'Ta': 1.0, 'F': 6.0}\",\"('Cs', 'F', 'K', 'Ta')\",OTHERS,False\nmvc-10179,\"{'Ca': 4.0, 'P': 8.0, 'W': 8.0, 'O': 32.0}\",\"('Ca', 'O', 'P', 'W')\",OTHERS,False\nmp-862657,\"{'Li': 1.0, 'Cu': 1.0, 'Pd': 2.0}\",\"('Cu', 'Li', 'Pd')\",OTHERS,False\nmp-1078573,\"{'Ge': 2.0, 'H': 8.0}\",\"('Ge', 'H')\",OTHERS,False\nmvc-10236,\"{'Mg': 6.0, 'Ni': 12.0, 'O': 24.0}\",\"('Mg', 'Ni', 'O')\",OTHERS,False\nmp-1201338,\"{'V': 12.0, 'O': 26.0}\",\"('O', 'V')\",OTHERS,False\nmp-1196235,\"{'Sm': 8.0, 'U': 4.0, 'Se': 20.0}\",\"('Se', 'Sm', 'U')\",OTHERS,False\nmp-1247301,\"{'Co': 3.0, 'C': 6.0, 'N': 9.0}\",\"('C', 'Co', 'N')\",OTHERS,False\nmp-770332,\"{'Na': 2.0, 'Bi': 2.0, 'C': 2.0, 'S': 2.0, 'O': 14.0}\",\"('Bi', 'C', 'Na', 'O', 'S')\",OTHERS,False\nmp-31080,\"{'Al': 2.0, 'Si': 2.0, 'Y': 2.0}\",\"('Al', 'Si', 'Y')\",OTHERS,False\nmp-1202849,\"{'Sr': 2.0, 'H': 36.0, 'Cl': 4.0, 'O': 34.0}\",\"('Cl', 'H', 'O', 'Sr')\",OTHERS,False\nmp-556573,\"{'K': 8.0, 'W': 4.0, 'C': 8.0, 'O': 36.0}\",\"('C', 'K', 'O', 'W')\",OTHERS,False\nmp-1248182,\"{'Na': 4.0, 'Al': 11.0, 'Si': 13.0, 'Ag': 7.0, 'O': 48.0}\",\"('Ag', 'Al', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1209156,\"{'Sb': 4.0, 'C': 8.0, 'O': 20.0}\",\"('C', 'O', 'Sb')\",OTHERS,False\nmp-1228559,\"{'Al': 5.0, 'Ni': 41.0, 'B': 12.0}\",\"('Al', 'B', 'Ni')\",OTHERS,False\nmp-766112,\"{'Li': 8.0, 'Mn': 2.0, 'V': 6.0, 'P': 16.0, 'O': 56.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-1247552,\"{'Na': 2.0, 'Ti': 1.0, 'N': 2.0}\",\"('N', 'Na', 'Ti')\",OTHERS,False\nmp-1040029,\"{'Rb': 1.0, 'La': 1.0, 'Mg': 30.0, 'O': 32.0}\",\"('La', 'Mg', 'O', 'Rb')\",OTHERS,False\nmp-1204691,\"{'Pr': 4.0, 'B': 20.0, 'O': 40.0}\",\"('B', 'O', 'Pr')\",OTHERS,False\nmp-13126,\"{'N': 2.0, 'Zr': 2.0}\",\"('N', 'Zr')\",OTHERS,False\nmp-559631,\"{'Bi': 12.0, 'Rh': 24.0, 'O': 58.0}\",\"('Bi', 'O', 'Rh')\",OTHERS,False\nmp-1039945,\"{'Rb': 1.0, 'Mg': 30.0, 'Si': 1.0, 'O': 32.0}\",\"('Mg', 'O', 'Rb', 'Si')\",OTHERS,False\nmp-1025175,\"{'Lu': 2.0, 'Cr': 2.0, 'C': 3.0}\",\"('C', 'Cr', 'Lu')\",OTHERS,False\nmp-772255,\"{'Cu': 6.0, 'P': 6.0, 'O': 18.0}\",\"('Cu', 'O', 'P')\",OTHERS,False\nmp-1227177,\"{'Ca': 1.0, 'Eu': 1.0, 'Si': 4.0}\",\"('Ca', 'Eu', 'Si')\",OTHERS,False\nmp-777003,\"{'Fe': 7.0, 'Te': 1.0, 'P': 12.0, 'O': 48.0}\",\"('Fe', 'O', 'P', 'Te')\",OTHERS,False\nmp-1217422,\"{'Tc': 1.0, 'Rh': 1.0}\",\"('Rh', 'Tc')\",OTHERS,False\nmp-1215887,\"{'Y': 1.0, 'U': 1.0, 'Cu': 4.0, 'Si': 4.0}\",\"('Cu', 'Si', 'U', 'Y')\",OTHERS,False\nmp-765294,\"{'Sr': 2.0, 'P': 4.0, 'H': 8.0, 'O': 12.0}\",\"('H', 'O', 'P', 'Sr')\",OTHERS,False\nmp-1223068,\"{'Li': 14.0, 'Eu': 16.0, 'Si': 8.0, 'Cl': 14.0, 'O': 32.0}\",\"('Cl', 'Eu', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1176274,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1038357,\"{'Hf': 1.0, 'Mg': 30.0, 'Bi': 1.0, 'O': 32.0}\",\"('Bi', 'Hf', 'Mg', 'O')\",OTHERS,False\nmp-1211511,\"{'K': 4.0, 'Tl': 8.0}\",\"('K', 'Tl')\",OTHERS,False\nmp-1213389,\"{'Cs': 2.0, 'Lu': 2.0, 'Ta': 12.0, 'Br': 36.0}\",\"('Br', 'Cs', 'Lu', 'Ta')\",OTHERS,False\nmp-755385,\"{'Fe': 6.0, 'O': 2.0, 'F': 10.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-704268,\"{'Mn': 36.0, 'Ag': 48.0, 'O': 96.0}\",\"('Ag', 'Mn', 'O')\",OTHERS,False\nmp-35822,\"{'Li': 8.0, 'O': 8.0, 'Si': 2.0}\",\"('Li', 'O', 'Si')\",OTHERS,False\nmp-1192080,\"{'Nd': 2.0, 'Mn': 3.0, 'Sb': 4.0, 'S': 12.0}\",\"('Mn', 'Nd', 'S', 'Sb')\",OTHERS,False\nmp-684493,\"{'Li': 4.0, 'Sn': 8.0, 'P': 12.0, 'O': 40.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,False\nmp-1193479,\"{'U': 6.0, 'O': 22.0}\",\"('O', 'U')\",OTHERS,False\nmp-1200133,\"{'Mg': 4.0, 'Cu': 4.0, 'C': 4.0, 'O': 20.0}\",\"('C', 'Cu', 'Mg', 'O')\",OTHERS,False\nmp-30247,\"{'H': 2.0, 'O': 2.0, 'Mg': 1.0}\",\"('H', 'Mg', 'O')\",OTHERS,False\nmp-1079587,\"{'Yb': 4.0, 'Pd': 4.0}\",\"('Pd', 'Yb')\",OTHERS,False\nmp-14578,\"{'S': 44.0, 'Cs': 16.0, 'Ta': 8.0}\",\"('Cs', 'S', 'Ta')\",OTHERS,False\nmp-12565,\"{'Mn': 1.0, 'Au': 4.0}\",\"('Au', 'Mn')\",OTHERS,False\nmp-766028,\"{'K': 4.0, 'Li': 5.0, 'Ti': 8.0, 'P': 8.0, 'O': 40.0}\",\"('K', 'Li', 'O', 'P', 'Ti')\",OTHERS,False\nmp-1096760,\"{'Ti': 1.0, 'Al': 1.0, 'Ir': 2.0}\",\"('Al', 'Ir', 'Ti')\",OTHERS,False\nmp-561108,\"{'Y': 4.0, 'I': 12.0, 'O': 36.0}\",\"('I', 'O', 'Y')\",OTHERS,False\nmvc-1192,\"{'Ba': 2.0, 'Sn': 3.0, 'O': 7.0}\",\"('Ba', 'O', 'Sn')\",OTHERS,False\nmp-849518,\"{'Mn': 12.0, 'O': 7.0, 'F': 17.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-1175125,\"{'Li': 7.0, 'Mn': 4.0, 'Co': 1.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-561008,\"{'Ca': 8.0, 'P': 16.0, 'O': 48.0}\",\"('Ca', 'O', 'P')\",OTHERS,False\nmp-1190821,\"{'Y': 2.0, 'Fe': 17.0, 'C': 3.0}\",\"('C', 'Fe', 'Y')\",OTHERS,False\nmp-11473,\"{'Hg': 1.0, 'Tl': 1.0}\",\"('Hg', 'Tl')\",OTHERS,False\nmp-2242,\"{'S': 4.0, 'Ge': 4.0}\",\"('Ge', 'S')\",OTHERS,False\nmp-772590,\"{'Li': 12.0, 'V': 4.0, 'B': 8.0, 'S': 2.0, 'O': 32.0}\",\"('B', 'Li', 'O', 'S', 'V')\",OTHERS,False\nmp-759535,\"{'Na': 12.0, 'Cu': 20.0, 'O': 16.0}\",\"('Cu', 'Na', 'O')\",OTHERS,False\nmp-504735,\"{'Ni': 12.0, 'Ti': 12.0, 'O': 4.0}\",\"('Ni', 'O', 'Ti')\",OTHERS,False\nmp-755794,\"{'Rb': 6.0, 'Lu': 2.0, 'O': 6.0}\",\"('Lu', 'O', 'Rb')\",OTHERS,False\nmp-972866,\"{'Sc': 2.0, 'Pd': 1.0, 'Au': 1.0}\",\"('Au', 'Pd', 'Sc')\",OTHERS,False\nmp-13240,\"{'Sc': 1.0, 'Cu': 4.0, 'In': 1.0}\",\"('Cu', 'In', 'Sc')\",OTHERS,False\nmp-1198692,\"{'Ti': 20.0, 'Ge': 16.0}\",\"('Ge', 'Ti')\",OTHERS,False\nmp-770173,\"{'Mg': 4.0, 'Mn': 6.0, 'O': 16.0}\",\"('Mg', 'Mn', 'O')\",OTHERS,False\nmp-1211262,\"{'La': 2.0, 'B': 2.0, 'Pt': 8.0}\",\"('B', 'La', 'Pt')\",OTHERS,False\nmp-8806,\"{'Sr': 4.0, 'Cu': 4.0, 'O': 6.0}\",\"('Cu', 'O', 'Sr')\",OTHERS,False\nmp-1198034,\"{'Cd': 4.0, 'H': 20.0, 'Br': 12.0, 'N': 8.0}\",\"('Br', 'Cd', 'H', 'N')\",OTHERS,False\nmp-1185732,\"{'Mg': 16.0, 'Ta': 1.0, 'Al': 12.0}\",\"('Al', 'Mg', 'Ta')\",OTHERS,False\nmp-865025,\"{'Hf': 1.0, 'Ta': 1.0, 'Re': 2.0}\",\"('Hf', 'Re', 'Ta')\",OTHERS,False\nmp-734875,\"{'H': 4.0, 'Pb': 8.0, 'C': 2.0, 'Cl': 12.0, 'O': 8.0}\",\"('C', 'Cl', 'H', 'O', 'Pb')\",OTHERS,False\nmp-1195066,\"{'Cs': 14.0, 'Fe': 8.0, 'S': 16.0}\",\"('Cs', 'Fe', 'S')\",OTHERS,False\nmp-1210767,\"{'Pr': 12.0, 'In': 1.0, 'Co': 6.0}\",\"('Co', 'In', 'Pr')\",OTHERS,False\nmp-758993,\"{'Li': 8.0, 'Cr': 8.0, 'P': 8.0, 'O': 28.0, 'F': 8.0}\",\"('Cr', 'F', 'Li', 'O', 'P')\",OTHERS,False\nmp-504815,\"{'Re': 10.0, 'Ni': 4.0, 'As': 24.0}\",\"('As', 'Ni', 'Re')\",OTHERS,False\nmp-30532,\"{'Zn': 2.0, 'I': 8.0, 'Tl': 4.0}\",\"('I', 'Tl', 'Zn')\",OTHERS,False\nmp-6578,\"{'B': 4.0, 'O': 48.0, 'P': 12.0, 'Ba': 12.0}\",\"('B', 'Ba', 'O', 'P')\",OTHERS,False\nmp-1227336,\"{'Ba': 1.0, 'Sr': 1.0, 'Ti': 2.0, 'O': 6.0}\",\"('Ba', 'O', 'Sr', 'Ti')\",OTHERS,False\nmp-1102559,\"{'Nb': 4.0, 'Se': 8.0}\",\"('Nb', 'Se')\",OTHERS,False\nmp-1174192,\"{'Li': 4.0, 'Mn': 3.0, 'Co': 1.0, 'O': 8.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-20064,\"{'Ga': 2.0, 'Dy': 1.0}\",\"('Dy', 'Ga')\",OTHERS,False\nmp-1222723,\"{'La': 1.0, 'U': 1.0, 'O': 4.0}\",\"('La', 'O', 'U')\",OTHERS,False\nmp-744386,\"{'Al': 9.0, 'Fe': 2.0, 'Si': 4.0, 'H': 1.0, 'O': 24.0}\",\"('Al', 'Fe', 'H', 'O', 'Si')\",OTHERS,False\nmp-865032,\"{'Mn': 2.0, 'Pt': 1.0, 'Rh': 1.0}\",\"('Mn', 'Pt', 'Rh')\",OTHERS,False\nmp-643801,\"{'K': 2.0, 'H': 6.0, 'Pb': 1.0, 'O': 6.0}\",\"('H', 'K', 'O', 'Pb')\",OTHERS,False\nmp-1071252,\"{'La': 1.0, 'Ga': 2.0, 'Rh': 3.0}\",\"('Ga', 'La', 'Rh')\",OTHERS,False\nmp-1202411,\"{'Fe': 8.0, 'P': 4.0, 'H': 4.0, 'O': 12.0, 'F': 8.0}\",\"('F', 'Fe', 'H', 'O', 'P')\",OTHERS,False\nmp-20787,\"{'Fe': 4.0, 'B': 4.0}\",\"('B', 'Fe')\",OTHERS,False\nmp-556050,\"{'P': 32.0, 'S': 8.0, 'N': 48.0, 'F': 48.0}\",\"('F', 'N', 'P', 'S')\",OTHERS,False\nmp-1104424,\"{'Ba': 8.0, 'Sb': 4.0, 'H': 2.0, 'O': 1.0}\",\"('Ba', 'H', 'O', 'Sb')\",OTHERS,False\nmp-19918,\"{'Ge': 2.0, 'Gd': 2.0}\",\"('Gd', 'Ge')\",OTHERS,False\nmp-1245079,\"{'Be': 50.0, 'O': 50.0}\",\"('Be', 'O')\",OTHERS,False\nmp-1228533,\"{'Ba': 2.0, 'Sr': 1.0, 'Ca': 3.0, 'Mg': 2.0, 'Si': 4.0, 'O': 16.0}\",\"('Ba', 'Ca', 'Mg', 'O', 'Si', 'Sr')\",OTHERS,False\nmp-1186660,\"{'Pu': 1.0, 'Cd': 1.0, 'Hg': 2.0}\",\"('Cd', 'Hg', 'Pu')\",OTHERS,False\nmp-979137,\"{'Sr': 1.0, 'Ga': 1.0, 'Si': 1.0, 'H': 1.0}\",\"('Ga', 'H', 'Si', 'Sr')\",OTHERS,False\nmp-754756,\"{'Li': 2.0, 'Ni': 1.0, 'O': 2.0}\",\"('Li', 'Ni', 'O')\",OTHERS,False\nmp-1217486,\"{'Tb': 2.0, 'Mn': 2.0, 'Fe': 2.0}\",\"('Fe', 'Mn', 'Tb')\",OTHERS,False\nmp-29912,\"{'Sb': 16.0, 'Mo': 8.0, 'Se': 8.0}\",\"('Mo', 'Sb', 'Se')\",OTHERS,False\nmp-1226515,\"{'Ce': 1.0, 'Mn': 1.0, 'Si': 2.0, 'Pd': 1.0}\",\"('Ce', 'Mn', 'Pd', 'Si')\",OTHERS,False\nmp-24420,\"{'H': 2.0, 'Se': 1.0}\",\"('H', 'Se')\",OTHERS,False\nmp-1219026,\"{'Sm': 1.0, 'Y': 1.0, 'Fe': 17.0}\",\"('Fe', 'Sm', 'Y')\",OTHERS,False\nmp-753172,\"{'Li': 4.0, 'Cr': 5.0, 'O': 10.0}\",\"('Cr', 'Li', 'O')\",OTHERS,False\nmp-1184970,\"{'Li': 2.0, 'Hg': 1.0, 'Bi': 1.0}\",\"('Bi', 'Hg', 'Li')\",OTHERS,False\nmp-974061,\"{'K': 1.0, 'Si': 1.0, 'O': 3.0}\",\"('K', 'O', 'Si')\",OTHERS,False\nmp-1097667,\"{'Y': 1.0, 'Sc': 1.0, 'Tl': 2.0}\",\"('Sc', 'Tl', 'Y')\",OTHERS,False\nmp-26304,\"{'O': 32.0, 'P': 8.0, 'Cu': 9.0}\",\"('Cu', 'O', 'P')\",OTHERS,False\nmp-775479,\"{'Li': 8.0, 'Fe': 12.0, 'Cu': 4.0, 'O': 32.0}\",\"('Cu', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1019797,\"{'K': 20.0, 'B': 4.0, 'S': 16.0, 'O': 64.0}\",\"('B', 'K', 'O', 'S')\",OTHERS,False\nmp-1218425,\"{'Sr': 3.0, 'Ta': 1.0, 'Co': 1.0, 'O': 7.0}\",\"('Co', 'O', 'Sr', 'Ta')\",OTHERS,False\nmp-1208499,\"{'Tb': 12.0, 'Nb': 8.0, 'Al': 86.0}\",\"('Al', 'Nb', 'Tb')\",OTHERS,False\nmp-1025834,\"{'Te': 2.0, 'Mo': 1.0, 'W': 2.0, 'Se': 4.0}\",\"('Mo', 'Se', 'Te', 'W')\",OTHERS,False\nmp-1207401,\"{'Zn': 2.0, 'Se': 2.0, 'O': 10.0}\",\"('O', 'Se', 'Zn')\",OTHERS,False\nmp-11560,\"{'Si': 6.0, 'Ti': 6.0, 'Ru': 6.0}\",\"('Ru', 'Si', 'Ti')\",OTHERS,False\nmp-770905,\"{'Li': 2.0, 'Mn': 2.0, 'As': 2.0, 'C': 2.0, 'O': 14.0}\",\"('As', 'C', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-754642,\"{'Mn': 4.0, 'Fe': 2.0, 'O': 8.0}\",\"('Fe', 'Mn', 'O')\",OTHERS,False\nmp-753461,\"{'Li': 4.0, 'Mn': 3.0, 'O': 1.0, 'F': 11.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1192180,\"{'Tm': 8.0, 'P': 8.0, 'S': 8.0}\",\"('P', 'S', 'Tm')\",OTHERS,False\nmp-1185721,\"{'Mg': 16.0, 'Al': 12.0, 'Zn': 1.0}\",\"('Al', 'Mg', 'Zn')\",OTHERS,False\nmp-1182691,\"{'Mg': 16.0, 'Si': 24.0, 'O': 92.0}\",\"('Mg', 'O', 'Si')\",OTHERS,False\nmp-555117,\"{'Cs': 16.0, 'Bi': 32.0, 'O': 56.0}\",\"('Bi', 'Cs', 'O')\",OTHERS,False\nmp-772482,\"{'Mn': 3.0, 'Sn': 3.0, 'Te': 2.0, 'O': 16.0}\",\"('Mn', 'O', 'Sn', 'Te')\",OTHERS,False\nmp-1266421,\"{'Si': 12.0, 'Bi': 18.0, 'O': 52.0}\",\"('Bi', 'O', 'Si')\",OTHERS,False\nmp-1245456,\"{'Cu': 16.0, 'Ge': 16.0, 'N': 32.0}\",\"('Cu', 'Ge', 'N')\",OTHERS,False\nmp-757620,\"{'Li': 4.0, 'V': 4.0, 'O': 4.0, 'F': 12.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-771660,\"{'Mn': 2.0, 'P': 4.0, 'O': 14.0}\",\"('Mn', 'O', 'P')\",OTHERS,False\nmp-24199,\"{'H': 1.0, 'Li': 1.0, 'F': 2.0}\",\"('F', 'H', 'Li')\",OTHERS,False\nmvc-10039,\"{'La': 1.0, 'Cr': 1.0, 'Ni': 1.0, 'O': 6.0}\",\"('Cr', 'La', 'Ni', 'O')\",OTHERS,False\nmp-1201811,\"{'Sr': 4.0, 'O': 40.0}\",\"('O', 'Sr')\",OTHERS,False\nmp-4248,\"{'C': 2.0, 'Co': 1.0, 'Y': 1.0}\",\"('C', 'Co', 'Y')\",OTHERS,False\nmp-1025194,\"{'Pr': 2.0, 'Fe': 2.0, 'Si': 2.0, 'C': 1.0}\",\"('C', 'Fe', 'Pr', 'Si')\",OTHERS,False\nmp-998883,{'Ge': 1.0},\"('Ge',)\",OTHERS,False\nmp-9312,\"{'C': 2.0, 'Ni': 1.0, 'Pr': 1.0}\",\"('C', 'Ni', 'Pr')\",OTHERS,False\nmp-1227930,\"{'Ba': 2.0, 'La': 2.0, 'Sm': 2.0, 'Mn': 6.0, 'O': 18.0}\",\"('Ba', 'La', 'Mn', 'O', 'Sm')\",OTHERS,False\nmp-1027002,\"{'Te': 2.0, 'Mo': 1.0, 'W': 3.0, 'Se': 2.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-24672,\"{'H': 20.0, 'Ni': 2.0, 'Zr': 12.0, 'Sn': 4.0}\",\"('H', 'Ni', 'Sn', 'Zr')\",OTHERS,False\nmp-977368,\"{'K': 2.0, 'Dy': 4.0, 'Cu': 4.0, 'Se': 9.0}\",\"('Cu', 'Dy', 'K', 'Se')\",OTHERS,False\nmp-1407,\"{'Se': 8.0, 'Rh': 3.0}\",\"('Rh', 'Se')\",OTHERS,False\nmp-29593,\"{'P': 12.0, 'Cl': 96.0, 'Re': 8.0}\",\"('Cl', 'P', 'Re')\",OTHERS,False\nmp-1216835,\"{'Tl': 1.0, 'V': 12.0, 'S': 16.0}\",\"('S', 'Tl', 'V')\",OTHERS,False\nmp-1218367,\"{'Sr': 3.0, 'Ca': 1.0, 'S': 4.0}\",\"('Ca', 'S', 'Sr')\",OTHERS,False\nmp-1245115,\"{'Sn': 40.0, 'O': 40.0}\",\"('O', 'Sn')\",OTHERS,False\nmp-758707,\"{'Li': 8.0, 'Mn': 12.0, 'W': 4.0, 'O': 32.0}\",\"('Li', 'Mn', 'O', 'W')\",OTHERS,False\nmp-14322,\"{'O': 9.0, 'Ba': 3.0, 'Tm': 4.0}\",\"('Ba', 'O', 'Tm')\",OTHERS,False\nmp-672344,\"{'Ce': 4.0, 'Al': 20.0, 'Pt': 12.0}\",\"('Al', 'Ce', 'Pt')\",OTHERS,False\nmp-1192281,\"{'Cs': 1.0, 'B': 12.0, 'O': 12.0}\",\"('B', 'Cs', 'O')\",OTHERS,False\nmp-1178459,\"{'Cr': 12.0, 'Fe': 7.0, 'O': 48.0}\",\"('Cr', 'Fe', 'O')\",OTHERS,False\nmp-568619,\"{'Si': 5.0, 'C': 5.0}\",\"('C', 'Si')\",OTHERS,False\nmp-1071438,\"{'Gd': 1.0, 'Ni': 4.0, 'Au': 1.0}\",\"('Au', 'Gd', 'Ni')\",OTHERS,False\nmp-1209830,\"{'P': 1.0, 'Br': 2.0}\",\"('Br', 'P')\",OTHERS,False\nmp-764879,\"{'V': 8.0, 'O': 6.0, 'F': 18.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-22919,\"{'Ag': 1.0, 'I': 1.0}\",\"('Ag', 'I')\",OTHERS,False\nmp-16830,\"{'B': 6.0, 'Ni': 12.0, 'Sr': 1.0}\",\"('B', 'Ni', 'Sr')\",OTHERS,False\nmp-1102848,\"{'La': 3.0, 'Ir': 9.0}\",\"('Ir', 'La')\",OTHERS,False\nmp-1096160,\"{'Sr': 2.0, 'In': 1.0, 'Hg': 1.0}\",\"('Hg', 'In', 'Sr')\",OTHERS,False\nmp-704317,\"{'K': 8.0, 'Cu': 12.0, 'Mo': 16.0, 'O': 64.0}\",\"('Cu', 'K', 'Mo', 'O')\",OTHERS,False\nmp-1093901,\"{'Zr': 1.0, 'Sb': 1.0, 'Ru': 2.0}\",\"('Ru', 'Sb', 'Zr')\",OTHERS,False\nmp-1205061,\"{'Zr': 5.0, 'Tl': 20.0, 'Se': 20.0}\",\"('Se', 'Tl', 'Zr')\",OTHERS,False\nmp-1183178,\"{'Ag': 2.0, 'As': 2.0}\",\"('Ag', 'As')\",OTHERS,False\nmp-19123,\"{'Li': 2.0, 'O': 8.0, 'V': 2.0, 'Rb': 4.0}\",\"('Li', 'O', 'Rb', 'V')\",OTHERS,False\nmp-752757,\"{'V': 4.0, 'O': 2.0, 'F': 10.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-1215929,\"{'Y': 2.0, 'Mg': 2.0, 'Al': 8.0}\",\"('Al', 'Mg', 'Y')\",OTHERS,False\nmp-669564,\"{'Cs': 4.0, 'Re': 24.0, 'S': 32.0, 'Br': 12.0}\",\"('Br', 'Cs', 'Re', 'S')\",OTHERS,False\nmp-1080131,\"{'Cu': 2.0, 'Si': 1.0, 'Hg': 1.0, 'Te': 4.0}\",\"('Cu', 'Hg', 'Si', 'Te')\",OTHERS,False\nmp-1091371,\"{'Bi': 4.0, 'O': 6.0}\",\"('Bi', 'O')\",OTHERS,False\nmp-7342,\"{'Sm': 12.0, 'Ir': 4.0}\",\"('Ir', 'Sm')\",OTHERS,False\nmp-1019596,\"{'Ce': 2.0, 'Zr': 2.0, 'O': 8.0}\",\"('Ce', 'O', 'Zr')\",OTHERS,False\nmp-1216527,\"{'Tl': 1.0, 'In': 1.0}\",\"('In', 'Tl')\",OTHERS,False\nmp-766254,\"{'Cu': 4.0, 'P': 12.0, 'O': 36.0}\",\"('Cu', 'O', 'P')\",OTHERS,False\nmp-849611,\"{'Li': 8.0, 'Mn': 12.0, 'B': 12.0, 'O': 36.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1216714,\"{'Ti': 2.0, 'Nb': 2.0, 'Al': 2.0, 'C': 2.0}\",\"('Al', 'C', 'Nb', 'Ti')\",OTHERS,False\nmp-1210996,\"{'Mg': 4.0, 'Al': 6.0, 'Si': 12.0, 'O': 36.0}\",\"('Al', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-30340,\"{'Ag': 2.0, 'In': 1.0, 'Er': 1.0}\",\"('Ag', 'Er', 'In')\",OTHERS,False\nmp-1215622,\"{'Zn': 1.0, 'Cu': 2.0, 'Sn': 1.0, 'Se': 3.0, 'S': 1.0}\",\"('Cu', 'S', 'Se', 'Sn', 'Zn')\",OTHERS,False\nmp-1185959,\"{'Mg': 2.0, 'S': 4.0}\",\"('Mg', 'S')\",OTHERS,False\nmp-8860,\"{'Na': 3.0, 'As': 1.0}\",\"('As', 'Na')\",OTHERS,False\nmp-770833,\"{'Li': 8.0, 'Sn': 4.0, 'B': 4.0, 'O': 16.0}\",\"('B', 'Li', 'O', 'Sn')\",OTHERS,False\nmp-631388,\"{'Cd': 1.0, 'Ir': 1.0, 'Ru': 1.0}\",\"('Cd', 'Ir', 'Ru')\",OTHERS,False\nmp-867352,\"{'Tc': 2.0, 'Rh': 6.0}\",\"('Rh', 'Tc')\",OTHERS,False\nmp-777902,\"{'Ti': 3.0, 'Mn': 2.0, 'Cu': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Cu', 'Mn', 'O', 'P', 'Ti')\",OTHERS,False\nmp-772144,\"{'Mn': 5.0, 'V': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Mn', 'O', 'P', 'V')\",OTHERS,False\nmvc-11476,\"{'Mg': 1.0, 'V': 4.0, 'S': 8.0}\",\"('Mg', 'S', 'V')\",OTHERS,False\nmp-505603,\"{'Pb': 20.0, 'S': 4.0, 'O': 32.0}\",\"('O', 'Pb', 'S')\",OTHERS,False\nmp-780319,\"{'Li': 6.0, 'Mn': 2.0, 'P': 4.0, 'H': 4.0, 'O': 18.0}\",\"('H', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-752550,\"{'Li': 3.0, 'Ni': 1.0, 'O': 1.0, 'F': 3.0}\",\"('F', 'Li', 'Ni', 'O')\",OTHERS,False\nmp-1207436,\"{'Zr': 9.0, 'Pd': 11.0}\",\"('Pd', 'Zr')\",OTHERS,False\nmp-4431,\"{'S': 6.0, 'As': 2.0, 'Ag': 6.0}\",\"('Ag', 'As', 'S')\",OTHERS,False\nmp-555707,\"{'S': 16.0, 'N': 8.0, 'O': 12.0, 'F': 4.0}\",\"('F', 'N', 'O', 'S')\",OTHERS,False\nmp-13998,\"{'O': 32.0, 'Na': 40.0, 'Al': 8.0}\",\"('Al', 'Na', 'O')\",OTHERS,False\nmp-863295,\"{'Cs': 4.0, 'V': 4.0, 'Ga': 4.0, 'P': 8.0, 'O': 40.0}\",\"('Cs', 'Ga', 'O', 'P', 'V')\",OTHERS,False\nmp-504714,\"{'O': 26.0, 'I': 2.0, 'Ni': 6.0, 'B': 14.0}\",\"('B', 'I', 'Ni', 'O')\",OTHERS,False\nmp-773135,\"{'Li': 6.0, 'Cu': 6.0, 'P': 4.0, 'O': 16.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-1101665,\"{'Na': 2.0, 'Li': 10.0, 'Fe': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Fe', 'Li', 'Na', 'O', 'P')\",OTHERS,False\nmp-1226052,\"{'Co': 1.0, 'Cu': 2.0, 'Ge': 1.0, 'Se': 4.0}\",\"('Co', 'Cu', 'Ge', 'Se')\",OTHERS,False\nmp-19090,\"{'O': 12.0, 'Mn': 2.0, 'Mo': 2.0, 'Te': 2.0}\",\"('Mn', 'Mo', 'O', 'Te')\",OTHERS,False\nmp-1207317,\"{'U': 2.0, 'P': 3.0}\",\"('P', 'U')\",OTHERS,False\nmp-1184294,\"{'Fe': 2.0, 'Ag': 6.0}\",\"('Ag', 'Fe')\",OTHERS,False\nmp-13236,\"{'Si': 6.0, 'Ho': 10.0}\",\"('Ho', 'Si')\",OTHERS,False\nmp-862543,\"{'Sc': 2.0, 'Al': 1.0, 'Tc': 1.0}\",\"('Al', 'Sc', 'Tc')\",OTHERS,False\nmp-762282,\"{'Li': 3.0, 'Cr': 5.0, 'O': 10.0}\",\"('Cr', 'Li', 'O')\",OTHERS,False\nmp-1187636,\"{'Tm': 3.0, 'Hg': 1.0}\",\"('Hg', 'Tm')\",OTHERS,False\nmp-1080467,\"{'K': 1.0, 'Cd': 4.0, 'P': 3.0}\",\"('Cd', 'K', 'P')\",OTHERS,False\nmp-866031,\"{'Al': 2.0, 'Fe': 1.0, 'Ir': 1.0}\",\"('Al', 'Fe', 'Ir')\",OTHERS,False\nmp-1020636,\"{'Zn': 16.0, 'Si': 8.0, 'O': 32.0}\",\"('O', 'Si', 'Zn')\",OTHERS,False\nmp-24105,\"{'H': 2.0, 'O': 2.0, 'Co': 1.0}\",\"('Co', 'H', 'O')\",OTHERS,False\nmp-1103596,\"{'Ce': 1.0, 'Ge': 4.0, 'Rh': 6.0}\",\"('Ce', 'Ge', 'Rh')\",OTHERS,False\nmp-1186751,\"{'Pr': 1.0, 'Ho': 1.0, 'Tl': 2.0}\",\"('Ho', 'Pr', 'Tl')\",OTHERS,False\nmvc-12394,\"{'Mg': 4.0, 'Ti': 8.0, 'O': 16.0}\",\"('Mg', 'O', 'Ti')\",OTHERS,False\nmp-1198811,\"{'Ca': 4.0, 'Hg': 4.0, 'C': 16.0, 'S': 16.0, 'N': 16.0, 'O': 12.0}\",\"('C', 'Ca', 'Hg', 'N', 'O', 'S')\",OTHERS,False\nmp-1185886,\"{'Mg': 2.0, 'Hg': 4.0}\",\"('Hg', 'Mg')\",OTHERS,False\nmp-1247072,\"{'Ba': 12.0, 'Ru': 8.0, 'N': 16.0}\",\"('Ba', 'N', 'Ru')\",OTHERS,False\nmp-1147572,\"{'La': 2.0, 'Pd': 1.0, 'O': 2.0, 'F': 2.0}\",\"('F', 'La', 'O', 'Pd')\",OTHERS,False\nmp-1113362,\"{'Cs': 2.0, 'Ce': 1.0, 'Cu': 1.0, 'I': 6.0}\",\"('Ce', 'Cs', 'Cu', 'I')\",OTHERS,False\nmp-1018707,\"{'Gd': 2.0, 'Se': 4.0}\",\"('Gd', 'Se')\",OTHERS,False\nmp-765988,\"{'Li': 1.0, 'V': 1.0, 'Fe': 1.0, 'P': 2.0, 'H': 2.0, 'O': 10.0}\",\"('Fe', 'H', 'Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1039915,\"{'Na': 1.0, 'Mg': 30.0, 'Si': 1.0, 'O': 32.0}\",\"('Mg', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1219577,\"{'Rb': 2.0, 'Ta': 4.0, 'O': 12.0}\",\"('O', 'Rb', 'Ta')\",OTHERS,False\nmp-11507,\"{'Ni': 4.0, 'Mo': 1.0}\",\"('Mo', 'Ni')\",OTHERS,False\nmp-1220452,\"{'Nb': 1.0, 'Cu': 3.0, 'Se': 2.0, 'S': 2.0}\",\"('Cu', 'Nb', 'S', 'Se')\",OTHERS,False\nmp-1183920,\"{'Cs': 1.0, 'Pa': 1.0, 'O': 3.0}\",\"('Cs', 'O', 'Pa')\",OTHERS,False\nmp-542884,\"{'S': 8.0, 'Rb': 8.0, 'Be': 4.0, 'O': 40.0, 'H': 16.0}\",\"('Be', 'H', 'O', 'Rb', 'S')\",OTHERS,False\nmp-754224,\"{'Ba': 2.0, 'Sr': 4.0, 'I': 12.0}\",\"('Ba', 'I', 'Sr')\",OTHERS,False\nmp-975422,\"{'Rb': 2.0, 'F': 6.0}\",\"('F', 'Rb')\",OTHERS,False\nmp-767435,\"{'P': 12.0, 'W': 8.0, 'O': 48.0}\",\"('O', 'P', 'W')\",OTHERS,False\nmp-1184260,\"{'Fe': 3.0, 'Cu': 1.0}\",\"('Cu', 'Fe')\",OTHERS,False\nmp-1202260,\"{'Lu': 4.0, 'Ni': 34.0}\",\"('Lu', 'Ni')\",OTHERS,False\nmp-759330,\"{'Rb': 16.0, 'Ge': 36.0, 'H': 60.0, 'N': 20.0}\",\"('Ge', 'H', 'N', 'Rb')\",OTHERS,False\nmp-674982,\"{'Li': 19.0, 'Mn': 5.0, 'N': 9.0}\",\"('Li', 'Mn', 'N')\",OTHERS,False\nmp-9295,\"{'Cu': 3.0, 'Te': 4.0, 'Ta': 1.0}\",\"('Cu', 'Ta', 'Te')\",OTHERS,False\nmp-31950,\"{'Li': 2.0, 'O': 24.0, 'P': 8.0, 'Mn': 2.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1197228,\"{'Ni': 2.0, 'P': 8.0, 'H': 36.0, 'C': 4.0, 'N': 4.0, 'O': 30.0}\",\"('C', 'H', 'N', 'Ni', 'O', 'P')\",OTHERS,False\nmp-18199,\"{'O': 4.0, 'As': 12.0, 'Sr': 12.0, 'Ta': 4.0}\",\"('As', 'O', 'Sr', 'Ta')\",OTHERS,False\nmp-1245716,\"{'Mg': 4.0, 'Re': 4.0, 'N': 8.0}\",\"('Mg', 'N', 'Re')\",OTHERS,False\nmp-685928,\"{'Rb': 10.0, 'Tc': 6.0, 'S': 14.0}\",\"('Rb', 'S', 'Tc')\",OTHERS,False\nmp-1218958,\"{'Sn': 1.0, 'Pb': 4.0, 'Se': 5.0}\",\"('Pb', 'Se', 'Sn')\",OTHERS,False\nmp-1103838,\"{'Pd': 4.0, 'Pb': 2.0, 'O': 8.0}\",\"('O', 'Pb', 'Pd')\",OTHERS,False\nmp-1013560,\"{'Ba': 3.0, 'As': 2.0}\",\"('As', 'Ba')\",OTHERS,False\nmp-1188045,\"{'Zr': 2.0, 'Mn': 6.0}\",\"('Mn', 'Zr')\",OTHERS,False\nmp-1227286,\"{'Ba': 1.0, 'Y': 1.0, 'Mn': 1.0, 'Co': 1.0, 'O': 5.0}\",\"('Ba', 'Co', 'Mn', 'O', 'Y')\",OTHERS,False\nmp-1227505,\"{'Ca': 1.0, 'V': 4.0, 'Co': 1.0, 'Cu': 2.0, 'O': 12.0}\",\"('Ca', 'Co', 'Cu', 'O', 'V')\",OTHERS,False\nmp-771121,\"{'La': 8.0, 'Hf': 4.0, 'O': 20.0}\",\"('Hf', 'La', 'O')\",OTHERS,False\nmp-767737,\"{'Co': 9.0, 'Cu': 3.0, 'O': 16.0}\",\"('Co', 'Cu', 'O')\",OTHERS,False\nmp-1245668,\"{'Cu': 2.0, 'As': 3.0}\",\"('As', 'Cu')\",OTHERS,False\nmp-1113444,\"{'Cs': 2.0, 'Tl': 1.0, 'Ag': 1.0, 'Br': 6.0}\",\"('Ag', 'Br', 'Cs', 'Tl')\",OTHERS,False\nmp-1196028,\"{'Lu': 8.0, 'B': 8.0, 'N': 8.0, 'O': 52.0}\",\"('B', 'Lu', 'N', 'O')\",OTHERS,False\nmp-1072114,\"{'Ho': 2.0, 'O': 2.0, 'F': 2.0}\",\"('F', 'Ho', 'O')\",OTHERS,False\nmp-1094261,\"{'Zr': 4.0, 'Sn': 4.0}\",\"('Sn', 'Zr')\",OTHERS,False\nmp-1217340,\"{'Th': 2.0, 'U': 2.0, 'B': 16.0}\",\"('B', 'Th', 'U')\",OTHERS,False\nmp-861594,\"{'Pr': 2.0, 'Tl': 1.0, 'Cd': 1.0}\",\"('Cd', 'Pr', 'Tl')\",OTHERS,False\nmp-32780,\"{'Au': 4.0, 'Cl': 4.0}\",\"('Au', 'Cl')\",OTHERS,False\nmp-1220382,\"{'Nb': 5.0, 'Se': 2.0, 'S': 2.0}\",\"('Nb', 'S', 'Se')\",OTHERS,False\nmp-1030467,\"{'Te': 4.0, 'Mo': 3.0, 'W': 1.0, 'S': 4.0}\",\"('Mo', 'S', 'Te', 'W')\",OTHERS,False\nmp-1216859,\"{'Ti': 2.0, 'Cr': 2.0, 'Sb': 2.0, 'O': 12.0}\",\"('Cr', 'O', 'Sb', 'Ti')\",OTHERS,False\nmp-1219677,\"{'Si': 18.0, 'C': 20.0, 'N': 2.0, 'O': 36.0}\",\"('C', 'N', 'O', 'Si')\",OTHERS,False\nmp-1196428,\"{'Rb': 4.0, 'B': 8.0, 'Se': 12.0, 'O': 40.0}\",\"('B', 'O', 'Rb', 'Se')\",OTHERS,False\nmp-763313,\"{'Li': 2.0, 'Co': 2.0, 'Si': 2.0, 'O': 8.0}\",\"('Co', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1223233,\"{'La': 4.0, 'V': 2.0, 'Fe': 2.0, 'O': 12.0}\",\"('Fe', 'La', 'O', 'V')\",OTHERS,False\nmp-622785,\"{'Fe': 16.0, 'S': 16.0, 'N': 16.0, 'O': 16.0}\",\"('Fe', 'N', 'O', 'S')\",OTHERS,False\nmp-768944,\"{'Li': 8.0, 'V': 8.0, 'B': 16.0, 'O': 40.0}\",\"('B', 'Li', 'O', 'V')\",OTHERS,False\nmp-1203099,\"{'Si': 12.0, 'O': 26.0}\",\"('O', 'Si')\",OTHERS,False\nmvc-10022,\"{'W': 4.0, 'O': 8.0}\",\"('O', 'W')\",OTHERS,False\nmp-768648,\"{'Li': 8.0, 'Fe': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-756332,\"{'Li': 2.0, 'Cr': 4.0, 'O': 6.0}\",\"('Cr', 'Li', 'O')\",OTHERS,False\nmp-546292,\"{'O': 2.0, 'Cl': 2.0, 'Yb': 2.0}\",\"('Cl', 'O', 'Yb')\",OTHERS,False\nmp-569673,\"{'Pr': 4.0, 'I': 8.0}\",\"('I', 'Pr')\",OTHERS,False\nmp-772624,\"{'Al': 8.0, 'C': 4.0, 'O': 4.0}\",\"('Al', 'C', 'O')\",OTHERS,False\nmp-21319,\"{'C': 1.0, 'Ce': 3.0, 'Tl': 1.0}\",\"('C', 'Ce', 'Tl')\",OTHERS,False\nmp-755771,\"{'Sr': 6.0, 'As': 4.0, 'O': 16.0}\",\"('As', 'O', 'Sr')\",OTHERS,False\nmp-1191945,\"{'Tb': 2.0, 'Fe': 17.0, 'N': 3.0}\",\"('Fe', 'N', 'Tb')\",OTHERS,False\nmp-10871,\"{'Al': 1.0, 'Au': 1.0}\",\"('Al', 'Au')\",OTHERS,False\nmp-1191664,\"{'Ta': 20.0, 'Ni': 4.0}\",\"('Ni', 'Ta')\",OTHERS,False\nmp-1481,\"{'Sn': 1.0, 'Te': 1.0}\",\"('Sn', 'Te')\",OTHERS,False\nmp-22175,\"{'C': 4.0, 'N': 4.0, 'S': 4.0, 'Eu': 2.0}\",\"('C', 'Eu', 'N', 'S')\",OTHERS,False\nmp-560038,\"{'Mn': 6.0, 'B': 14.0, 'I': 2.0, 'O': 26.0}\",\"('B', 'I', 'Mn', 'O')\",OTHERS,False\nmp-1208346,\"{'Tb': 4.0, 'Fe': 4.0, 'B': 16.0}\",\"('B', 'Fe', 'Tb')\",OTHERS,False\nmp-767279,\"{'Li': 4.0, 'Dy': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Dy', 'Li', 'O', 'P')\",OTHERS,False\nmp-1174794,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-640747,\"{'Sr': 12.0, 'Nb': 8.0, 'Co': 4.0, 'O': 36.0}\",\"('Co', 'Nb', 'O', 'Sr')\",OTHERS,False\nmp-560642,\"{'Na': 12.0, 'Sb': 4.0, 'P': 8.0, 'O': 36.0}\",\"('Na', 'O', 'P', 'Sb')\",OTHERS,False\nmp-644389,\"{'Li': 2.0, 'H': 2.0, 'Pd': 1.0}\",\"('H', 'Li', 'Pd')\",OTHERS,False\nmp-1079726,\"{'Zr': 4.0, 'Se': 2.0, 'N': 4.0}\",\"('N', 'Se', 'Zr')\",OTHERS,False\nmp-551283,\"{'O': 6.0, 'Cu': 2.0, 'Na': 2.0, 'Te': 1.0}\",\"('Cu', 'Na', 'O', 'Te')\",OTHERS,False\nmp-6501,\"{'O': 20.0, 'Zn': 4.0, 'Ba': 4.0, 'Ho': 8.0}\",\"('Ba', 'Ho', 'O', 'Zn')\",OTHERS,False\nmp-1195624,\"{'Ba': 12.0, 'Sb': 8.0, 'S': 28.0}\",\"('Ba', 'S', 'Sb')\",OTHERS,False\nmp-30943,\"{'Mg': 2.0, 'P': 2.0, 'Se': 6.0}\",\"('Mg', 'P', 'Se')\",OTHERS,False\nmp-1208029,\"{'Tm': 16.0, 'Mg': 4.0, 'Ni': 4.0}\",\"('Mg', 'Ni', 'Tm')\",OTHERS,False\nmp-699888,\"{'Na': 8.0, 'Ti': 4.0, 'Fe': 4.0, 'P': 12.0, 'O': 48.0}\",\"('Fe', 'Na', 'O', 'P', 'Ti')\",OTHERS,False\nmp-764848,\"{'Li': 28.0, 'Mn': 10.0, 'F': 48.0}\",\"('F', 'Li', 'Mn')\",OTHERS,False\nmp-1182901,\"{'Al': 12.0, 'B': 10.0, 'O': 36.0}\",\"('Al', 'B', 'O')\",OTHERS,False\nmp-1201991,\"{'Yb': 2.0, 'N': 6.0, 'O': 28.0}\",\"('N', 'O', 'Yb')\",OTHERS,False\nmp-980949,\"{'W': 2.0, 'Cl': 8.0}\",\"('Cl', 'W')\",OTHERS,False\nmp-14391,\"{'O': 92.0, 'Na': 12.0, 'P': 32.0, 'V': 4.0}\",\"('Na', 'O', 'P', 'V')\",OTHERS,False\nmp-1245788,\"{'Ba': 2.0, 'Zn': 4.0, 'N': 4.0}\",\"('Ba', 'N', 'Zn')\",OTHERS,False\nmp-773007,\"{'Li': 2.0, 'Cu': 10.0, 'P': 4.0, 'O': 16.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-866811,\"{'Ca': 4.0, 'Sn': 4.0, 'S': 12.0}\",\"('Ca', 'S', 'Sn')\",OTHERS,False\nmp-1184593,\"{'Ho': 2.0, 'Ir': 1.0, 'Pd': 1.0}\",\"('Ho', 'Ir', 'Pd')\",OTHERS,False\nmp-1185011,\"{'La': 3.0, 'Zr': 1.0}\",\"('La', 'Zr')\",OTHERS,False\nmp-1183543,\"{'Ca': 1.0, 'In': 1.0, 'O': 3.0}\",\"('Ca', 'In', 'O')\",OTHERS,False\nmp-1210770,\"{'Lu': 2.0, 'Ta': 10.0, 'Ag': 4.0, 'O': 30.0}\",\"('Ag', 'Lu', 'O', 'Ta')\",OTHERS,False\nmp-9029,\"{'N': 6.0, 'Ca': 6.0, 'V': 2.0}\",\"('Ca', 'N', 'V')\",OTHERS,False\nmp-1183442,\"{'Bi': 1.0, 'Pd': 2.0, 'Au': 1.0}\",\"('Au', 'Bi', 'Pd')\",OTHERS,False\nmp-703684,\"{'Mn': 1.0, 'H': 12.0, 'N': 2.0, 'Cl': 4.0, 'O': 2.0}\",\"('Cl', 'H', 'Mn', 'N', 'O')\",OTHERS,False\nmp-1205507,\"{'Eu': 2.0, 'Mg': 1.0, 'W': 1.0, 'O': 6.0}\",\"('Eu', 'Mg', 'O', 'W')\",OTHERS,False\nmp-1183201,\"{'Au': 3.0, 'Cl': 1.0}\",\"('Au', 'Cl')\",OTHERS,False\nmp-769008,\"{'Li': 8.0, 'Bi': 6.0, 'O': 16.0}\",\"('Bi', 'Li', 'O')\",OTHERS,False\nmp-1204674,\"{'Ni': 18.0, 'Te': 10.0, 'Pb': 12.0, 'O': 60.0}\",\"('Ni', 'O', 'Pb', 'Te')\",OTHERS,False\nmp-582159,\"{'Si': 8.0, 'Ni': 28.0, 'P': 20.0}\",\"('Ni', 'P', 'Si')\",OTHERS,False\nmp-17668,\"{'Ga': 14.0, 'Yb': 2.0, 'Au': 6.0}\",\"('Au', 'Ga', 'Yb')\",OTHERS,False\nmp-1213326,\"{'Cs': 2.0, 'Tm': 2.0, 'Nb': 12.0, 'Br': 36.0}\",\"('Br', 'Cs', 'Nb', 'Tm')\",OTHERS,False\nmp-30930,\"{'Al': 4.0, 'I': 12.0}\",\"('Al', 'I')\",OTHERS,False\nmp-757220,\"{'Lu': 2.0, 'H': 12.0, 'Cl': 6.0, 'O': 30.0}\",\"('Cl', 'H', 'Lu', 'O')\",OTHERS,False\nmp-1219669,\"{'Pu': 2.0, 'O': 3.0}\",\"('O', 'Pu')\",OTHERS,False\nmp-1213575,\"{'Er': 2.0, 'Zr': 12.0, 'P': 18.0, 'O': 72.0}\",\"('Er', 'O', 'P', 'Zr')\",OTHERS,False\nmp-972912,\"{'La': 3.0, 'Er': 1.0}\",\"('Er', 'La')\",OTHERS,False\nmp-720379,\"{'K': 6.0, 'Na': 2.0, 'P': 8.0, 'H': 8.0, 'O': 28.0}\",\"('H', 'K', 'Na', 'O', 'P')\",OTHERS,False\nmp-1247479,\"{'Zn': 10.0, 'Fe': 2.0, 'N': 8.0}\",\"('Fe', 'N', 'Zn')\",OTHERS,False\nmp-1097438,\"{'Hf': 2.0, 'Fe': 1.0, 'Co': 1.0}\",\"('Co', 'Fe', 'Hf')\",OTHERS,False\nmp-754496,\"{'Li': 20.0, 'Sb': 4.0, 'S': 4.0}\",\"('Li', 'S', 'Sb')\",OTHERS,False\nmp-730460,\"{'Na': 10.0, 'H': 6.0, 'C': 8.0, 'O': 24.0}\",\"('C', 'H', 'Na', 'O')\",OTHERS,False\nmp-695259,\"{'Ba': 6.0, 'Li': 2.0, 'Ti': 10.0, 'Sb': 6.0, 'O': 42.0}\",\"('Ba', 'Li', 'O', 'Sb', 'Ti')\",OTHERS,False\nmp-1213861,\"{'Co': 2.0, 'P': 4.0, 'O': 20.0}\",\"('Co', 'O', 'P')\",OTHERS,False\nmp-1182052,\"{'Ca': 10.0, 'P': 6.0, 'O': 24.0}\",\"('Ca', 'O', 'P')\",OTHERS,False\nmp-1025029,\"{'Pr': 2.0, 'H': 2.0, 'Se': 2.0}\",\"('H', 'Pr', 'Se')\",OTHERS,False\nmp-1094085,\"{'Nd': 3.0, 'Sn': 1.0, 'N': 1.0}\",\"('N', 'Nd', 'Sn')\",OTHERS,False\nmp-1186282,\"{'Nd': 3.0, 'Ti': 1.0}\",\"('Nd', 'Ti')\",OTHERS,False\nmp-676932,\"{'Eu': 2.0, 'Sr': 2.0, 'Li': 2.0, 'Te': 2.0, 'O': 12.0}\",\"('Eu', 'Li', 'O', 'Sr', 'Te')\",OTHERS,False\nmp-1194454,\"{'Si': 2.0, 'N': 8.0, 'O': 6.0, 'F': 12.0}\",\"('F', 'N', 'O', 'Si')\",OTHERS,False\nmp-1186932,\"{'Sc': 2.0, 'Ag': 1.0, 'Pt': 1.0}\",\"('Ag', 'Pt', 'Sc')\",OTHERS,False\nmp-779057,\"{'Li': 8.0, 'Fe': 8.0, 'B': 8.0, 'O': 28.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmvc-10387,\"{'Ba': 4.0, 'Zn': 4.0, 'Sn': 4.0, 'F': 28.0}\",\"('Ba', 'F', 'Sn', 'Zn')\",OTHERS,False\nmp-558446,\"{'Ag': 4.0, 'Hg': 4.0, 'S': 4.0, 'I': 4.0}\",\"('Ag', 'Hg', 'I', 'S')\",OTHERS,False\nmp-1228501,\"{'Ba': 3.0, 'La': 3.0, 'Cu': 6.0, 'O': 14.0}\",\"('Ba', 'Cu', 'La', 'O')\",OTHERS,False\nmp-23108,\"{'Na': 1.0, 'Cl': 6.0, 'Cs': 2.0, 'U': 1.0}\",\"('Cl', 'Cs', 'Na', 'U')\",OTHERS,False\nmp-715572,\"{'Fe': 16.0, 'O': 24.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-1078767,\"{'Hf': 3.0, 'Al': 3.0, 'Ni': 3.0}\",\"('Al', 'Hf', 'Ni')\",OTHERS,False\nmp-5438,\"{'B': 1.0, 'Rh': 3.0, 'Tm': 1.0}\",\"('B', 'Rh', 'Tm')\",OTHERS,False\nmp-647818,\"{'F': 12.0, 'Ni': 4.0, 'Cs': 2.0}\",\"('Cs', 'F', 'Ni')\",OTHERS,False\nmp-1212653,\"{'K': 12.0, 'Fe': 4.0, 'H': 12.0, 'C': 24.0, 'O': 48.0}\",\"('C', 'Fe', 'H', 'K', 'O')\",OTHERS,False\nmp-1066856,\"{'Ag': 2.0, 'O': 2.0}\",\"('Ag', 'O')\",OTHERS,False\nmp-1194844,\"{'Mg': 2.0, 'Bi': 4.0, 'I': 16.0, 'O': 16.0}\",\"('Bi', 'I', 'Mg', 'O')\",OTHERS,False\nmp-1106245,\"{'Zr': 10.0, 'Al': 2.0, 'Sb': 6.0}\",\"('Al', 'Sb', 'Zr')\",OTHERS,False\nmp-559713,\"{'Fe': 12.0, 'P': 16.0, 'O': 56.0}\",\"('Fe', 'O', 'P')\",OTHERS,False\nmp-1215198,\"{'Zr': 1.0, 'U': 1.0, 'B': 24.0}\",\"('B', 'U', 'Zr')\",OTHERS,False\nmp-1185235,\"{'Li': 3.0, 'Rh': 1.0}\",\"('Li', 'Rh')\",OTHERS,False\nmp-1252085,\"{'Al': 1.0, 'W': 3.0, 'O': 8.0}\",\"('Al', 'O', 'W')\",OTHERS,False\nmp-1226663,\"{'Er': 18.0, 'B': 10.0, 'S': 42.0}\",\"('B', 'Er', 'S')\",OTHERS,False\nmp-765686,\"{'Li': 4.0, 'Co': 8.0, 'O': 2.0, 'F': 16.0}\",\"('Co', 'F', 'Li', 'O')\",OTHERS,False\nmp-753532,\"{'Nb': 4.0, 'O': 6.0, 'F': 4.0}\",\"('F', 'Nb', 'O')\",OTHERS,False\nmp-572,\"{'Cd': 1.0, 'Sm': 1.0}\",\"('Cd', 'Sm')\",OTHERS,False\nmp-1211442,\"{'Rb': 4.0, 'Pr': 4.0, 'Se': 8.0, 'O': 52.0}\",\"('O', 'Pr', 'Rb', 'Se')\",OTHERS,False\nmp-1228150,\"{'Ba': 3.0, 'La': 1.0, 'Nb': 3.0, 'O': 12.0}\",\"('Ba', 'La', 'Nb', 'O')\",OTHERS,False\nmp-754647,\"{'Li': 3.0, 'Al': 3.0, 'V': 3.0, 'O': 12.0}\",\"('Al', 'Li', 'O', 'V')\",OTHERS,False\nmp-999068,\"{'Ti': 2.0, 'Al': 1.0, 'V': 1.0}\",\"('Al', 'Ti', 'V')\",OTHERS,False\nmp-1202245,\"{'Yb': 24.0, 'Sn': 16.0, 'Pd': 16.0}\",\"('Pd', 'Sn', 'Yb')\",OTHERS,False\nmp-8479,\"{'Si': 2.0, 'S': 8.0, 'Tl': 8.0}\",\"('S', 'Si', 'Tl')\",OTHERS,False\nmp-758188,\"{'Li': 8.0, 'Cr': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Cr', 'Li', 'O', 'Si')\",OTHERS,False\nmp-980757,\"{'Sm': 10.0, 'Sb': 6.0}\",\"('Sb', 'Sm')\",OTHERS,False\nmp-12494,\"{'Cd': 2.0, 'Te': 6.0, 'Cs': 2.0, 'Pr': 2.0}\",\"('Cd', 'Cs', 'Pr', 'Te')\",OTHERS,False\nmp-1100865,\"{'Yb': 10.0, 'Ti': 6.0, 'O': 27.0}\",\"('O', 'Ti', 'Yb')\",OTHERS,False\nmp-1181630,\"{'Er': 34.0, 'Te': 6.0, 'Ru': 12.0}\",\"('Er', 'Ru', 'Te')\",OTHERS,False\nmp-695295,\"{'H': 22.0, 'Ru': 1.0, 'N': 7.0, 'Cl': 4.0, 'O': 8.0}\",\"('Cl', 'H', 'N', 'O', 'Ru')\",OTHERS,False\nmp-755975,\"{'Li': 2.0, 'Ti': 1.0, 'V': 3.0, 'O': 8.0}\",\"('Li', 'O', 'Ti', 'V')\",OTHERS,False\nmp-1187453,\"{'Ti': 2.0, 'Al': 1.0, 'Ni': 1.0}\",\"('Al', 'Ni', 'Ti')\",OTHERS,False\nmp-772995,\"{'Li': 7.0, 'Ti': 12.0, 'Fe': 5.0, 'O': 32.0}\",\"('Fe', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-763535,\"{'Li': 1.0, 'P': 5.0, 'W': 3.0, 'O': 19.0}\",\"('Li', 'O', 'P', 'W')\",OTHERS,False\nmp-1198459,\"{'Nd': 8.0, 'S': 12.0, 'O': 64.0}\",\"('Nd', 'O', 'S')\",OTHERS,False\nmp-764838,\"{'K': 8.0, 'Co': 4.0, 'P': 16.0, 'H': 40.0, 'O': 68.0}\",\"('Co', 'H', 'K', 'O', 'P')\",OTHERS,False\nmp-561166,\"{'Na': 4.0, 'Zr': 2.0, 'Ge': 4.0, 'O': 14.0}\",\"('Ge', 'Na', 'O', 'Zr')\",OTHERS,False\nmp-753835,\"{'Li': 4.0, 'Mn': 8.0, 'O': 12.0, 'F': 4.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1224716,\"{'Fe': 2.0, 'Co': 1.0, 'Se': 4.0}\",\"('Co', 'Fe', 'Se')\",OTHERS,False\nmp-1027135,\"{'Te': 2.0, 'Mo': 2.0, 'W': 2.0, 'Se': 4.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-1247400,\"{'Sr': 2.0, 'Os': 14.0, 'N': 20.0}\",\"('N', 'Os', 'Sr')\",OTHERS,False\nmvc-11660,\"{'Ca': 1.0, 'Co': 4.0, 'O': 8.0}\",\"('Ca', 'Co', 'O')\",OTHERS,False\nmp-30021,\"{'K': 5.0, 'Sb': 4.0}\",\"('K', 'Sb')\",OTHERS,False\nmp-757214,\"{'Li': 3.0, 'Ti': 1.0, 'Co': 3.0, 'O': 8.0}\",\"('Co', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-1076683,\"{'Ba': 12.0, 'Sr': 20.0, 'Co': 16.0, 'Cu': 16.0, 'O': 80.0}\",\"('Ba', 'Co', 'Cu', 'O', 'Sr')\",OTHERS,False\nmvc-10594,\"{'Ca': 2.0, 'Cr': 6.0, 'P': 6.0, 'O': 26.0}\",\"('Ca', 'Cr', 'O', 'P')\",OTHERS,False\nmp-554160,\"{'K': 8.0, 'Cu': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Cu', 'K', 'O', 'P')\",OTHERS,False\nmp-1221439,\"{'Na': 4.0, 'Li': 5.0, 'W': 10.0, 'O': 30.0}\",\"('Li', 'Na', 'O', 'W')\",OTHERS,False\nmp-1177825,\"{'Li': 8.0, 'V': 12.0, 'W': 4.0, 'O': 32.0}\",\"('Li', 'O', 'V', 'W')\",OTHERS,False\nmp-1225817,\"{'Dy': 2.0, 'Fe': 4.0, 'Co': 4.0, 'B': 2.0}\",\"('B', 'Co', 'Dy', 'Fe')\",OTHERS,False\nmp-753274,\"{'Fe': 8.0, 'O': 8.0, 'F': 8.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-571584,\"{'Li': 1.0, 'Mg': 1.0, 'Sb': 1.0, 'Pt': 1.0}\",\"('Li', 'Mg', 'Pt', 'Sb')\",OTHERS,False\nmp-1106202,\"{'Ce': 4.0, 'Zn': 10.0, 'Sn': 2.0}\",\"('Ce', 'Sn', 'Zn')\",OTHERS,False\nmp-1218525,\"{'Sr': 3.0, 'La': 1.0, 'Ti': 2.0, 'Mn': 2.0, 'O': 12.0}\",\"('La', 'Mn', 'O', 'Sr', 'Ti')\",OTHERS,False\nmp-1225692,\"{'Cu': 4.0, 'Br': 1.0, 'Cl': 3.0}\",\"('Br', 'Cl', 'Cu')\",OTHERS,False\nmp-581601,\"{'Zn': 12.0, 'S': 12.0}\",\"('S', 'Zn')\",OTHERS,False\nmp-603128,\"{'O': 28.0, 'Fe': 8.0, 'Sr': 4.0, 'Eu': 8.0}\",\"('Eu', 'Fe', 'O', 'Sr')\",OTHERS,False\nmp-556103,\"{'Cs': 18.0, 'Ga': 12.0, 'F': 54.0}\",\"('Cs', 'F', 'Ga')\",OTHERS,False\nmp-1095382,\"{'Fe': 5.0, 'O': 7.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-23602,\"{'Ta': 4.0, 'Bi': 16.0, 'Cl': 4.0, 'O': 32.0}\",\"('Bi', 'Cl', 'O', 'Ta')\",OTHERS,False\nmp-865797,\"{'Lu': 1.0, 'Co': 2.0, 'Sn': 1.0}\",\"('Co', 'Lu', 'Sn')\",OTHERS,False\nmp-756834,\"{'Ho': 4.0, 'Cr': 4.0, 'O': 12.0}\",\"('Cr', 'Ho', 'O')\",OTHERS,False\nmp-1216633,\"{'U': 1.0, 'Ru': 2.0, 'Rh': 1.0}\",\"('Rh', 'Ru', 'U')\",OTHERS,False\nmp-1227870,\"{'Ce': 4.0, 'Zr': 2.0, 'Co': 32.0}\",\"('Ce', 'Co', 'Zr')\",OTHERS,False\nmp-1101492,\"{'Li': 2.0, 'V': 4.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-977552,\"{'Be': 3.0, 'Tc': 1.0}\",\"('Be', 'Tc')\",OTHERS,False\nmp-1200581,\"{'Bi': 16.0, 'B': 24.0, 'H': 8.0, 'O': 64.0}\",\"('B', 'Bi', 'H', 'O')\",OTHERS,False\nmp-998610,\"{'Tl': 1.0, 'Ag': 1.0, 'Cl': 3.0}\",\"('Ag', 'Cl', 'Tl')\",OTHERS,False\nmp-754833,\"{'Hf': 8.0, 'N': 8.0, 'O': 4.0}\",\"('Hf', 'N', 'O')\",OTHERS,False\nmp-1027286,\"{'Te': 2.0, 'Mo': 2.0, 'W': 2.0, 'Se': 2.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-1228939,\"{'Al': 6.0, 'N': 2.0, 'O': 6.0}\",\"('Al', 'N', 'O')\",OTHERS,False\nmp-761589,\"{'Nb': 2.0, 'Co': 6.0, 'O': 16.0}\",\"('Co', 'Nb', 'O')\",OTHERS,False\nmp-567891,\"{'Nd': 1.0, 'Ag': 2.0}\",\"('Ag', 'Nd')\",OTHERS,False\nmp-17383,\"{'Ni': 8.0, 'Ge': 4.0}\",\"('Ge', 'Ni')\",OTHERS,False\nmp-541636,\"{'Er': 4.0, 'P': 16.0, 'K': 4.0, 'O': 48.0}\",\"('Er', 'K', 'O', 'P')\",OTHERS,False\nmp-1238486,\"{'Mn': 2.0, 'H': 6.0, 'C': 10.0, 'N': 2.0, 'O': 12.0}\",\"('C', 'H', 'Mn', 'N', 'O')\",OTHERS,False\nmp-1192886,\"{'Ni': 6.0, 'N': 6.0, 'F': 18.0}\",\"('F', 'N', 'Ni')\",OTHERS,False\nmp-674420,\"{'Li': 8.0, 'Ni': 4.0, 'O': 12.0}\",\"('Li', 'Ni', 'O')\",OTHERS,False\nmp-9630,\"{'S': 2.0, 'Cr': 1.0, 'Tl': 1.0}\",\"('Cr', 'S', 'Tl')\",OTHERS,False\nmp-865519,\"{'Y': 1.0, 'Ta': 1.0, 'Ru': 2.0}\",\"('Ru', 'Ta', 'Y')\",OTHERS,False\nmp-1245297,\"{'B': 50.0, 'N': 50.0}\",\"('B', 'N')\",OTHERS,False\nmp-21677,\"{'Ca': 6.0, 'Ni': 16.0, 'In': 8.0}\",\"('Ca', 'In', 'Ni')\",OTHERS,False\nmp-753593,\"{'Ti': 10.0, 'O': 13.0}\",\"('O', 'Ti')\",OTHERS,False\nmp-1244998,\"{'Ga': 50.0, 'N': 50.0}\",\"('Ga', 'N')\",OTHERS,False\nmp-771945,\"{'Sn': 1.0, 'P': 6.0, 'W': 3.0, 'O': 24.0}\",\"('O', 'P', 'Sn', 'W')\",OTHERS,False\nmp-761141,\"{'Li': 11.0, 'V': 12.0, 'Co': 4.0, 'O': 32.0}\",\"('Co', 'Li', 'O', 'V')\",OTHERS,False\nmp-559369,\"{'Zr': 4.0, 'H': 24.0, 'N': 20.0, 'O': 70.0}\",\"('H', 'N', 'O', 'Zr')\",OTHERS,False\nmp-3629,\"{'O': 48.0, 'Y': 8.0, 'W': 12.0}\",\"('O', 'W', 'Y')\",OTHERS,False\nmp-1095424,\"{'Pr': 4.0, 'Mn': 2.0, 'As': 5.0}\",\"('As', 'Mn', 'Pr')\",OTHERS,False\nmp-559676,\"{'Sn': 2.0, 'S': 2.0}\",\"('S', 'Sn')\",OTHERS,False\nmp-862618,\"{'Li': 1.0, 'Er': 1.0, 'Hg': 2.0}\",\"('Er', 'Hg', 'Li')\",OTHERS,False\nmp-1206490,\"{'Nb': 4.0, 'B': 4.0, 'Mo': 2.0}\",\"('B', 'Mo', 'Nb')\",OTHERS,False\nmp-865877,\"{'Ti': 2.0, 'Re': 1.0, 'Pt': 1.0}\",\"('Pt', 'Re', 'Ti')\",OTHERS,False\nmp-1182555,\"{'Cd': 4.0, 'C': 28.0, 'S': 12.0, 'N': 16.0}\",\"('C', 'Cd', 'N', 'S')\",OTHERS,False\nmvc-1239,\"{'Zn': 4.0, 'Ni': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Ni', 'O', 'P', 'Zn')\",OTHERS,False\nmp-13654,\"{'Y': 6.0, 'Sb': 8.0, 'Au': 6.0}\",\"('Au', 'Sb', 'Y')\",OTHERS,False\nmp-1209357,\"{'Rb': 3.0, 'Y': 1.0, 'P': 2.0, 'O': 8.0}\",\"('O', 'P', 'Rb', 'Y')\",OTHERS,False\nmp-1186269,\"{'Nd': 3.0, 'Hf': 1.0}\",\"('Hf', 'Nd')\",OTHERS,False\nmp-1183324,\"{'Ba': 1.0, 'Sr': 3.0}\",\"('Ba', 'Sr')\",OTHERS,False\nmp-1222962,\"{'La': 1.0, 'Nd': 3.0, 'Mn': 4.0, 'O': 12.0}\",\"('La', 'Mn', 'Nd', 'O')\",OTHERS,False\nmp-1179999,\"{'Pb': 20.0, 'S': 2.0, 'O': 26.0, 'F': 4.0}\",\"('F', 'O', 'Pb', 'S')\",OTHERS,False\nmp-1177365,\"{'Li': 4.0, 'Nb': 3.0, 'Ni': 3.0, 'Te': 2.0, 'O': 16.0}\",\"('Li', 'Nb', 'Ni', 'O', 'Te')\",OTHERS,False\nmp-1224422,\"{'Hf': 6.0, 'Ga': 6.0, 'Pd': 4.0, 'Pt': 2.0}\",\"('Ga', 'Hf', 'Pd', 'Pt')\",OTHERS,False\nmp-755487,\"{'Li': 4.0, 'Bi': 2.0, 'O': 6.0}\",\"('Bi', 'Li', 'O')\",OTHERS,False\nmp-1200162,\"{'C': 164.0, 'F': 56.0}\",\"('C', 'F')\",OTHERS,False\nmp-1207159,\"{'Cs': 3.0, 'Eu': 1.0, 'F': 6.0}\",\"('Cs', 'Eu', 'F')\",OTHERS,False\nmp-1206697,\"{'Eu': 1.0, 'Ni': 1.0, 'P': 1.0}\",\"('Eu', 'Ni', 'P')\",OTHERS,False\nmp-1197353,\"{'Ba': 8.0, 'Li': 2.0, 'Ga': 10.0, 'Se': 24.0}\",\"('Ba', 'Ga', 'Li', 'Se')\",OTHERS,False\nmp-1191101,\"{'K': 4.0, 'Mg': 4.0, 'B': 4.0, 'O': 12.0}\",\"('B', 'K', 'Mg', 'O')\",OTHERS,False\nmp-759946,\"{'Sc': 16.0, 'O': 15.0}\",\"('O', 'Sc')\",OTHERS,False\nmp-11500,\"{'Mn': 1.0, 'Ni': 1.0}\",\"('Mn', 'Ni')\",OTHERS,False\nmp-752802,\"{'Fe': 5.0, 'O': 4.0, 'F': 8.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-1101055,\"{'Ta': 2.0, 'In': 2.0, 'S': 4.0}\",\"('In', 'S', 'Ta')\",OTHERS,False\nmp-721246,\"{'Sr': 12.0, 'Co': 2.0, 'C': 4.0, 'N': 14.0}\",\"('C', 'Co', 'N', 'Sr')\",OTHERS,False\nmp-989525,\"{'Cs': 2.0, 'Rb': 1.0, 'Pb': 1.0, 'F': 6.0}\",\"('Cs', 'F', 'Pb', 'Rb')\",OTHERS,False\nmp-696633,\"{'Ca': 10.0, 'Ta': 1.0, 'Ti': 8.0, 'Al': 1.0, 'Si': 10.0, 'O': 50.0}\",\"('Al', 'Ca', 'O', 'Si', 'Ta', 'Ti')\",OTHERS,False\nmp-775969,\"{'V': 3.0, 'Cr': 3.0, 'W': 2.0, 'O': 16.0}\",\"('Cr', 'O', 'V', 'W')\",OTHERS,False\nmp-5752,\"{'Si': 2.0, 'Tb': 1.0, 'Ir': 2.0}\",\"('Ir', 'Si', 'Tb')\",OTHERS,False\nmp-1208504,\"{'Tb': 3.0, 'Fe': 2.0, 'Si': 7.0}\",\"('Fe', 'Si', 'Tb')\",OTHERS,False\nmp-1223852,\"{'In': 1.0, 'Ga': 1.0, 'Ag': 2.0, 'S': 4.0}\",\"('Ag', 'Ga', 'In', 'S')\",OTHERS,False\nmp-1201586,\"{'Zr': 4.0, 'P': 8.0, 'O': 32.0}\",\"('O', 'P', 'Zr')\",OTHERS,False\nmp-12846,\"{'Ga': 4.0, 'Ge': 4.0, 'Yb': 4.0}\",\"('Ga', 'Ge', 'Yb')\",OTHERS,False\nmp-864800,\"{'Yb': 2.0, 'Pd': 1.0, 'Au': 1.0}\",\"('Au', 'Pd', 'Yb')\",OTHERS,False\nmp-1247225,\"{'Mg': 16.0, 'Ge': 4.0, 'N': 16.0}\",\"('Ge', 'Mg', 'N')\",OTHERS,False\nmp-761137,\"{'Li': 10.0, 'Ti': 3.0, 'Mn': 5.0, 'O': 16.0}\",\"('Li', 'Mn', 'O', 'Ti')\",OTHERS,False\nmp-1027091,\"{'Mo': 2.0, 'W': 2.0, 'Se': 4.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-780936,\"{'Li': 5.0, 'V': 6.0, 'O': 5.0, 'F': 19.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-1075363,\"{'Mg': 10.0, 'Si': 12.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-754998,\"{'Ce': 2.0, 'Se': 1.0, 'O': 2.0}\",\"('Ce', 'O', 'Se')\",OTHERS,False\nmp-743613,\"{'K': 1.0, 'Mn': 1.0, 'H': 5.0, 'S': 2.0, 'O': 10.0}\",\"('H', 'K', 'Mn', 'O', 'S')\",OTHERS,False\nmp-1202713,\"{'Ir': 8.0, 'Cl': 8.0, 'O': 8.0, 'F': 48.0}\",\"('Cl', 'F', 'Ir', 'O')\",OTHERS,False\nmp-22512,\"{'Sr': 4.0, 'In': 4.0, 'O': 10.0}\",\"('In', 'O', 'Sr')\",OTHERS,False\nmp-1210571,\"{'Na': 4.0, 'Cd': 6.0, 'P': 8.0, 'N': 2.0, 'Cl': 2.0, 'O': 28.0}\",\"('Cd', 'Cl', 'N', 'Na', 'O', 'P')\",OTHERS,False\nmp-676104,\"{'Tm': 4.0, 'Cu': 2.0, 'O': 8.0}\",\"('Cu', 'O', 'Tm')\",OTHERS,False\nmp-1207090,\"{'Cu': 1.0, 'Sn': 1.0, 'Pd': 2.0}\",\"('Cu', 'Pd', 'Sn')\",OTHERS,False\nmp-1211794,\"{'K': 2.0, 'Na': 1.0, 'O': 2.0}\",\"('K', 'Na', 'O')\",OTHERS,False\nmp-542121,\"{'Rb': 4.0, 'Nb': 2.0, 'Si': 8.0, 'O': 23.0}\",\"('Nb', 'O', 'Rb', 'Si')\",OTHERS,False\nmp-1214990,\"{'Ba': 4.0, 'C': 8.0, 'O': 24.0}\",\"('Ba', 'C', 'O')\",OTHERS,False\nmp-24333,\"{'H': 24.0, 'O': 12.0, 'Cl': 6.0, 'U': 2.0}\",\"('Cl', 'H', 'O', 'U')\",OTHERS,False\nmp-1102232,\"{'H': 4.0, 'C': 4.0, 'N': 4.0}\",\"('C', 'H', 'N')\",OTHERS,False\nmp-1215852,\"{'Yb': 1.0, 'Eu': 1.0, 'Si': 4.0, 'Au': 4.0}\",\"('Au', 'Eu', 'Si', 'Yb')\",OTHERS,False\nmp-1201518,\"{'K': 6.0, 'Fe': 10.0, 'F': 30.0}\",\"('F', 'Fe', 'K')\",OTHERS,False\nmp-1193079,\"{'Nd': 4.0, 'Cu': 24.0}\",\"('Cu', 'Nd')\",OTHERS,False\nmp-1177831,\"{'Li': 4.0, 'V': 4.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-16855,\"{'O': 26.0, 'Si': 4.0, 'K': 6.0, 'Ta': 6.0}\",\"('K', 'O', 'Si', 'Ta')\",OTHERS,False\nmp-774356,\"{'Li': 6.0, 'Mn': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Li', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1095397,\"{'Sn': 4.0, 'S': 8.0}\",\"('S', 'Sn')\",OTHERS,False\nmp-560925,\"{'Na': 10.0, 'Fe': 6.0, 'F': 28.0}\",\"('F', 'Fe', 'Na')\",OTHERS,False\nmp-1215427,\"{'Zn': 1.0, 'Cd': 1.0, 'Se': 2.0}\",\"('Cd', 'Se', 'Zn')\",OTHERS,False\nmp-19864,\"{'O': 12.0, 'Ca': 4.0, 'Fe': 2.0, 'W': 2.0}\",\"('Ca', 'Fe', 'O', 'W')\",OTHERS,False\nmp-766206,\"{'Sr': 1.0, 'Li': 4.0, 'La': 15.0, 'Co': 4.0, 'O': 32.0}\",\"('Co', 'La', 'Li', 'O', 'Sr')\",OTHERS,False\nmp-1101173,\"{'W': 2.0, 'N': 4.0}\",\"('N', 'W')\",OTHERS,False\nmp-644369,\"{'Ba': 2.0, 'Bi': 2.0, 'O': 6.0}\",\"('Ba', 'Bi', 'O')\",OTHERS,False\nmp-1201230,\"{'Bi': 4.0, 'Pb': 12.0, 'S': 18.0}\",\"('Bi', 'Pb', 'S')\",OTHERS,False\nmp-1190421,\"{'Cs': 2.0, 'Dy': 4.0, 'Ag': 6.0, 'Te': 10.0}\",\"('Ag', 'Cs', 'Dy', 'Te')\",OTHERS,False\nmp-1174284,\"{'Li': 6.0, 'Mn': 2.0, 'Co': 2.0, 'O': 10.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-9575,\"{'Li': 2.0, 'Be': 2.0, 'Sb': 2.0}\",\"('Be', 'Li', 'Sb')\",OTHERS,False\nmp-22317,\"{'Ga': 4.0, 'Eu': 2.0}\",\"('Eu', 'Ga')\",OTHERS,False\nmp-1223853,\"{'K': 2.0, 'Zr': 1.0, 'P': 2.0, 'O': 8.0}\",\"('K', 'O', 'P', 'Zr')\",OTHERS,False\nmp-1232235,\"{'Pm': 1.0, 'S': 1.0}\",\"('Pm', 'S')\",OTHERS,False\nmp-530036,\"{'Tb': 22.0, 'S': 32.0}\",\"('S', 'Tb')\",OTHERS,False\nmp-1184742,\"{'Gd': 1.0, 'Zr': 1.0, 'Ru': 2.0}\",\"('Gd', 'Ru', 'Zr')\",OTHERS,False\nmp-1056027,{'K': 1.0},\"('K',)\",OTHERS,False\nmp-541971,\"{'Tm': 2.0, 'Ni': 12.0, 'P': 7.0}\",\"('Ni', 'P', 'Tm')\",OTHERS,False\nmp-30622,\"{'Ru': 4.0, 'Dy': 10.0}\",\"('Dy', 'Ru')\",OTHERS,False\nmp-558122,\"{'Gd': 4.0, 'S': 4.0, 'Cl': 4.0, 'O': 16.0}\",\"('Cl', 'Gd', 'O', 'S')\",OTHERS,False\nmp-640922,\"{'Ce': 3.0, 'In': 3.0, 'Pt': 3.0}\",\"('Ce', 'In', 'Pt')\",OTHERS,False\nmp-867315,\"{'Ca': 1.0, 'Gd': 1.0, 'Hg': 2.0}\",\"('Ca', 'Gd', 'Hg')\",OTHERS,False\nmp-12574,\"{'C': 1.0, 'Dy': 2.0}\",\"('C', 'Dy')\",OTHERS,False\nmp-758011,\"{'Li': 4.0, 'Fe': 8.0, 'Si': 8.0, 'O': 26.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-685153,\"{'Fe': 64.0, 'O': 96.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-1201969,\"{'Ce': 2.0, 'N': 16.0, 'O': 36.0}\",\"('Ce', 'N', 'O')\",OTHERS,False\nmp-30334,\"{'Al': 14.0, 'Th': 4.0}\",\"('Al', 'Th')\",OTHERS,False\nmp-505148,\"{'Th': 2.0, 'Pb': 2.0, 'I': 12.0}\",\"('I', 'Pb', 'Th')\",OTHERS,False\nmp-686004,\"{'Li': 12.0, 'Sc': 4.0, 'Cl': 24.0}\",\"('Cl', 'Li', 'Sc')\",OTHERS,False\nmp-30675,\"{'Ga': 8.0, 'Ti': 10.0}\",\"('Ga', 'Ti')\",OTHERS,False\nmp-1208788,\"{'Sr': 6.0, 'Ti': 2.0, 'Si': 8.0, 'H': 4.0, 'O': 26.0}\",\"('H', 'O', 'Si', 'Sr', 'Ti')\",OTHERS,False\nmp-677127,\"{'Y': 1.0, 'Al': 6.0, 'Si': 18.0, 'N': 30.0, 'O': 2.0}\",\"('Al', 'N', 'O', 'Si', 'Y')\",OTHERS,False\nmp-557824,\"{'Rb': 8.0, 'Re': 12.0, 'S': 24.0}\",\"('Rb', 'Re', 'S')\",OTHERS,False\nmp-581276,\"{'Cu': 4.0, 'Te': 4.0, 'Cl': 4.0, 'O': 10.0}\",\"('Cl', 'Cu', 'O', 'Te')\",OTHERS,False\nmp-1212761,\"{'Gd': 1.0, 'Fe': 4.0, 'P': 12.0}\",\"('Fe', 'Gd', 'P')\",OTHERS,False\nmp-1105942,\"{'Sm': 10.0, 'Ga': 6.0}\",\"('Ga', 'Sm')\",OTHERS,False\nmp-1219003,\"{'Sn': 8.0, 'Se': 4.0, 'S': 8.0}\",\"('S', 'Se', 'Sn')\",OTHERS,False\nmp-1226283,\"{'Cr': 1.0, 'W': 1.0, 'O': 3.0}\",\"('Cr', 'O', 'W')\",OTHERS,False\nmvc-9917,\"{'Mg': 6.0, 'Fe': 12.0, 'O': 24.0}\",\"('Fe', 'Mg', 'O')\",OTHERS,False\nmp-976254,\"{'Li': 3.0, 'Mg': 1.0}\",\"('Li', 'Mg')\",OTHERS,False\nmp-1185144,\"{'K': 2.0, 'Th': 6.0}\",\"('K', 'Th')\",OTHERS,False\nmp-1208894,\"{'Sm': 4.0, 'Si': 10.0, 'Pd': 6.0}\",\"('Pd', 'Si', 'Sm')\",OTHERS,False\nmp-559162,\"{'Si': 4.0, 'B': 44.0, 'H': 40.0, 'C': 16.0, 'F': 44.0}\",\"('B', 'C', 'F', 'H', 'Si')\",OTHERS,False\nmp-777342,\"{'V': 8.0, 'O': 6.0, 'F': 18.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-972205,\"{'V': 2.0, 'Mo': 1.0, 'Os': 1.0}\",\"('Mo', 'Os', 'V')\",OTHERS,False\nmp-1205601,\"{'Yb': 4.0, 'Sn': 2.0, 'Pd': 4.0}\",\"('Pd', 'Sn', 'Yb')\",OTHERS,False\nmp-510400,\"{'Gd': 1.0, 'Ir': 3.0}\",\"('Gd', 'Ir')\",OTHERS,False\nmp-765840,\"{'Li': 4.0, 'V': 3.0, 'Co': 1.0, 'P': 4.0, 'O': 16.0}\",\"('Co', 'Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1228163,\"{'Ca': 1.0, 'Mn': 1.0, 'Zn': 1.0, 'As': 2.0, 'H': 3.0, 'O': 10.0}\",\"('As', 'Ca', 'H', 'Mn', 'O', 'Zn')\",OTHERS,False\nmp-759878,\"{'Rb': 2.0, 'Mg': 2.0, 'H': 24.0, 'Cl': 6.0, 'O': 12.0}\",\"('Cl', 'H', 'Mg', 'O', 'Rb')\",OTHERS,False\nmp-753083,\"{'Li': 3.0, 'Fe': 4.0, 'O': 8.0}\",\"('Fe', 'Li', 'O')\",OTHERS,False\nmp-1195598,\"{'Ba': 2.0, 'Er': 8.0, 'Si': 10.0, 'O': 34.0}\",\"('Ba', 'Er', 'O', 'Si')\",OTHERS,False\nmp-1177942,\"{'Li': 4.0, 'Hf': 4.0, 'O': 10.0}\",\"('Hf', 'Li', 'O')\",OTHERS,False\nmp-1095822,\"{'Zr': 2.0, 'Pt': 1.0, 'Rh': 1.0}\",\"('Pt', 'Rh', 'Zr')\",OTHERS,False\nmp-1199886,\"{'Sn': 4.0, 'Mo': 30.0, 'Se': 38.0}\",\"('Mo', 'Se', 'Sn')\",OTHERS,False\nmp-1072085,\"{'Ce': 1.0, 'Si': 5.0}\",\"('Ce', 'Si')\",OTHERS,False\nmp-6080,\"{'S': 10.0, 'Co': 2.0, 'Ba': 2.0, 'Nd': 4.0}\",\"('Ba', 'Co', 'Nd', 'S')\",OTHERS,False\nmp-567197,\"{'Y': 2.0, 'Sn': 2.0, 'Au': 2.0}\",\"('Au', 'Sn', 'Y')\",OTHERS,False\nmp-1104194,\"{'Ca': 1.0, 'Mo': 6.0, 'S': 8.0}\",\"('Ca', 'Mo', 'S')\",OTHERS,False\nmp-540391,\"{'O': 16.0, 'P': 4.0, 'Co': 4.0}\",\"('Co', 'O', 'P')\",OTHERS,False\nmp-1229080,\"{'Ce': 5.0, 'Cu': 19.0, 'P': 12.0}\",\"('Ce', 'Cu', 'P')\",OTHERS,False\nmp-1020638,\"{'Na': 8.0, 'P': 1.0, 'O': 3.0}\",\"('Na', 'O', 'P')\",OTHERS,False\nmp-542327,\"{'Ce': 5.0, 'Co': 19.0}\",\"('Ce', 'Co')\",OTHERS,False\nmp-1178789,\"{'V': 2.0, 'Zn': 1.0, 'O': 6.0}\",\"('O', 'V', 'Zn')\",OTHERS,False\nmp-614951,\"{'Rb': 9.0, 'N': 9.0, 'O': 27.0}\",\"('N', 'O', 'Rb')\",OTHERS,False\nmp-1247652,\"{'Ca': 32.0, 'Mn': 24.0, 'Cr': 8.0, 'O': 84.0}\",\"('Ca', 'Cr', 'Mn', 'O')\",OTHERS,False\nmp-614502,{'Gd': 1.0},\"('Gd',)\",OTHERS,False\nmp-557785,\"{'Mn': 2.0, 'H': 42.0, 'C': 14.0, 'S': 6.0, 'N': 2.0}\",\"('C', 'H', 'Mn', 'N', 'S')\",OTHERS,False\nmp-973633,\"{'Lu': 2.0, 'Si': 4.0, 'Ni': 2.0}\",\"('Lu', 'Ni', 'Si')\",OTHERS,False\nmp-1388,\"{'Rh': 4.0, 'Dy': 2.0}\",\"('Dy', 'Rh')\",OTHERS,False\nmp-9580,\"{'Ga': 2.0, 'Se': 4.0, 'Tl': 2.0}\",\"('Ga', 'Se', 'Tl')\",OTHERS,False\nmp-562517,\"{'Ca': 2.0, 'Mg': 2.0, 'Si': 4.0, 'O': 12.0}\",\"('Ca', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-982558,\"{'Ho': 1.0, 'Ge': 1.0, 'O': 3.0}\",\"('Ge', 'Ho', 'O')\",OTHERS,False\nmp-1218474,\"{'Sr': 3.0, 'Fe': 2.0, 'Ru': 1.0, 'O': 9.0}\",\"('Fe', 'O', 'Ru', 'Sr')\",OTHERS,False\nmp-1247135,\"{'Si': 14.0, 'Ge': 2.0, 'N': 20.0}\",\"('Ge', 'N', 'Si')\",OTHERS,False\nmp-30922,\"{'Co': 1.0, 'Ho': 6.0, 'Bi': 2.0}\",\"('Bi', 'Co', 'Ho')\",OTHERS,False\nmp-18562,\"{'Na': 8.0, 'Si': 8.0, 'Se': 20.0}\",\"('Na', 'Se', 'Si')\",OTHERS,False\nmvc-15567,\"{'Mg': 4.0, 'Fe': 4.0, 'O': 8.0}\",\"('Fe', 'Mg', 'O')\",OTHERS,False\nmp-1016146,\"{'Mg': 4.0, 'Mn': 16.0, 'O': 32.0}\",\"('Mg', 'Mn', 'O')\",OTHERS,False\nmp-571222,\"{'Cs': 1.0, 'Br': 1.0}\",\"('Br', 'Cs')\",OTHERS,False\nmp-510494,\"{'La': 10.0, 'Sn': 6.0}\",\"('La', 'Sn')\",OTHERS,False\nmp-998428,\"{'Cs': 4.0, 'Ca': 4.0, 'I': 12.0}\",\"('Ca', 'Cs', 'I')\",OTHERS,False\nmp-698214,\"{'K': 1.0, 'Mn': 1.0, 'H': 24.0, 'C': 14.0, 'N': 8.0}\",\"('C', 'H', 'K', 'Mn', 'N')\",OTHERS,False\nmvc-11373,\"{'Ca': 2.0, 'Mn': 2.0, 'W': 2.0, 'O': 12.0}\",\"('Ca', 'Mn', 'O', 'W')\",OTHERS,False\nmp-1223474,\"{'K': 2.0, 'H': 2.0, 'C': 2.0, 'O': 6.0}\",\"('C', 'H', 'K', 'O')\",OTHERS,False\nmp-1199576,\"{'U': 12.0, 'Ag': 8.0, 'Ge': 8.0, 'O': 64.0}\",\"('Ag', 'Ge', 'O', 'U')\",OTHERS,False\nmp-1186222,\"{'Nb': 1.0, 'Ga': 1.0, 'Os': 2.0}\",\"('Ga', 'Nb', 'Os')\",OTHERS,False\nmvc-3251,\"{'Zn': 2.0, 'Cr': 2.0, 'P': 2.0, 'O': 10.0}\",\"('Cr', 'O', 'P', 'Zn')\",OTHERS,False\nmp-1039906,\"{'Sr': 1.0, 'Mg': 30.0, 'Si': 1.0, 'O': 32.0}\",\"('Mg', 'O', 'Si', 'Sr')\",OTHERS,False\nmp-705842,\"{'Fe': 9.0, 'Cu': 3.0, 'O': 16.0}\",\"('Cu', 'Fe', 'O')\",OTHERS,False\nmp-1068732,\"{'Th': 1.0, 'Si': 3.0, 'Ru': 1.0}\",\"('Ru', 'Si', 'Th')\",OTHERS,False\nmp-649616,\"{'Pd': 4.0, 'Xe': 8.0, 'F': 64.0}\",\"('F', 'Pd', 'Xe')\",OTHERS,False\nmp-532701,\"{'In': 2.0, 'Mg': 6.0, 'Na': 6.0, 'O': 48.0, 'S': 12.0}\",\"('In', 'Mg', 'Na', 'O', 'S')\",OTHERS,False\nmp-559328,\"{'Fe': 4.0, 'H': 16.0, 'C': 12.0, 'O': 20.0}\",\"('C', 'Fe', 'H', 'O')\",OTHERS,False\nmp-1097636,\"{'Ti': 1.0, 'Zn': 2.0, 'Pt': 1.0}\",\"('Pt', 'Ti', 'Zn')\",OTHERS,False\nmvc-3773,\"{'Y': 6.0, 'Ni': 6.0, 'O': 18.0}\",\"('Ni', 'O', 'Y')\",OTHERS,False\nmp-758950,\"{'V': 4.0, 'O': 5.0, 'F': 7.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-677288,\"{'Mg': 5.0, 'Al': 1.0, 'H': 21.0, 'O': 17.0}\",\"('Al', 'H', 'Mg', 'O')\",OTHERS,False\nmp-1183610,\"{'Ca': 2.0, 'Sm': 6.0}\",\"('Ca', 'Sm')\",OTHERS,False\nmp-976596,\"{'Li': 1.0, 'Lu': 2.0, 'Ga': 1.0}\",\"('Ga', 'Li', 'Lu')\",OTHERS,False\nmp-775441,\"{'Li': 4.0, 'V': 3.0, 'Fe': 3.0, 'Sb': 2.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'Sb', 'V')\",OTHERS,False\nmvc-12661,\"{'Zn': 4.0, 'Fe': 8.0, 'O': 16.0}\",\"('Fe', 'O', 'Zn')\",OTHERS,False\nmp-1228165,\"{'Al': 5.0, 'Se': 8.0}\",\"('Al', 'Se')\",OTHERS,False\nmp-1032984,\"{'Ca': 1.0, 'Mg': 6.0, 'Ni': 1.0, 'O': 8.0}\",\"('Ca', 'Mg', 'Ni', 'O')\",OTHERS,False\nmp-532676,\"{'Al': 6.0, 'Li': 2.0, 'Mg': 2.0, 'O': 48.0, 'Se': 12.0}\",\"('Al', 'Li', 'Mg', 'O', 'Se')\",OTHERS,False\nmp-1210779,\"{'Mo': 2.0, 'C': 8.0, 'O': 8.0, 'F': 12.0}\",\"('C', 'F', 'Mo', 'O')\",OTHERS,False\nmp-1193682,\"{'Mo': 4.0, 'S': 16.0, 'N': 8.0}\",\"('Mo', 'N', 'S')\",OTHERS,False\nmp-559427,\"{'Ba': 2.0, 'Sr': 8.0, 'U': 6.0, 'O': 28.0}\",\"('Ba', 'O', 'Sr', 'U')\",OTHERS,False\nmp-774931,\"{'Li': 2.0, 'La': 28.0, 'Cu': 12.0, 'O': 56.0}\",\"('Cu', 'La', 'Li', 'O')\",OTHERS,False\nmp-677056,\"{'Na': 6.0, 'U': 3.0, 'I': 18.0}\",\"('I', 'Na', 'U')\",OTHERS,False\nmp-559091,\"{'Si': 4.0, 'O': 8.0}\",\"('O', 'Si')\",OTHERS,False\nmp-1226939,\"{'Cd': 2.0, 'Te': 1.0, 'Se': 1.0}\",\"('Cd', 'Se', 'Te')\",OTHERS,False\nmp-849316,\"{'Co': 6.0, 'O': 2.0, 'F': 10.0}\",\"('Co', 'F', 'O')\",OTHERS,False\nmp-765543,\"{'Sr': 3.0, 'Rh': 16.0, 'O': 32.0}\",\"('O', 'Rh', 'Sr')\",OTHERS,False\nmp-772890,\"{'Np': 4.0, 'Se': 8.0, 'O': 24.0}\",\"('Np', 'O', 'Se')\",OTHERS,False\nmp-775275,\"{'Mn': 3.0, 'Sb': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Mn', 'O', 'P', 'Sb')\",OTHERS,False\nmp-1185726,\"{'Mg': 16.0, 'Sc': 1.0, 'Al': 12.0}\",\"('Al', 'Mg', 'Sc')\",OTHERS,False\nmp-863365,\"{'Li': 4.0, 'Sn': 2.0, 'P': 8.0, 'O': 24.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,False\nmp-28611,\"{'O': 14.0, 'Ta': 4.0, 'Th': 2.0}\",\"('O', 'Ta', 'Th')\",OTHERS,False\nmp-1212398,\"{'Li': 8.0, 'Eu': 20.0, 'Sn': 28.0}\",\"('Eu', 'Li', 'Sn')\",OTHERS,False\nmp-1199190,\"{'Ba': 12.0, 'Bi': 3.0, 'Ir': 9.0, 'O': 36.0}\",\"('Ba', 'Bi', 'Ir', 'O')\",OTHERS,False\nmp-1071163,\"{'Ti': 3.0, 'O': 3.0}\",\"('O', 'Ti')\",OTHERS,False\nmp-1079725,\"{'Er': 6.0, 'Co': 1.0, 'Te': 2.0}\",\"('Co', 'Er', 'Te')\",OTHERS,False\nmp-21109,\"{'La': 3.0, 'Al': 12.0}\",\"('Al', 'La')\",OTHERS,False\nmp-1202524,\"{'K': 4.0, 'Cu': 2.0, 'H': 28.0, 'C': 8.0, 'N': 12.0, 'O': 16.0}\",\"('C', 'Cu', 'H', 'K', 'N', 'O')\",OTHERS,False\nmp-867863,\"{'Sm': 1.0, 'Cu': 4.0, 'Ag': 1.0}\",\"('Ag', 'Cu', 'Sm')\",OTHERS,False\nmp-1001612,\"{'Lu': 2.0, 'Si': 2.0}\",\"('Lu', 'Si')\",OTHERS,False\nmp-559235,\"{'B': 2.0, 'H': 22.0, 'C': 8.0, 'N': 2.0, 'Cl': 2.0, 'F': 8.0}\",\"('B', 'C', 'Cl', 'F', 'H', 'N')\",OTHERS,False\nmp-768605,\"{'Ho': 8.0, 'Ti': 4.0, 'O': 20.0}\",\"('Ho', 'O', 'Ti')\",OTHERS,False\nmp-6977,\"{'Be': 6.0, 'N': 4.0}\",\"('Be', 'N')\",OTHERS,False\nmp-755442,\"{'Mn': 4.0, 'Co': 1.0, 'O': 8.0}\",\"('Co', 'Mn', 'O')\",OTHERS,False\nmp-1215570,\"{'Zr': 3.0, 'Sn': 4.0, 'Sb': 2.0}\",\"('Sb', 'Sn', 'Zr')\",OTHERS,False\nmp-1177309,\"{'Li': 8.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-772840,\"{'Sm': 8.0, 'Ge': 8.0, 'O': 28.0}\",\"('Ge', 'O', 'Sm')\",OTHERS,False\nmp-5709,\"{'O': 8.0, 'P': 2.0, 'Tl': 6.0}\",\"('O', 'P', 'Tl')\",OTHERS,False\nmp-1232230,\"{'Eu': 2.0, 'Se': 4.0}\",\"('Eu', 'Se')\",OTHERS,False\nmp-705895,\"{'Ba': 2.0, 'Nb': 4.0, 'Fe': 4.0, 'P': 12.0, 'O': 48.0}\",\"('Ba', 'Fe', 'Nb', 'O', 'P')\",OTHERS,False\nmp-619575,\"{'Cl': 8.0, 'F': 24.0}\",\"('Cl', 'F')\",OTHERS,False\nmp-697144,\"{'Rb': 2.0, 'H': 4.0, 'N': 2.0}\",\"('H', 'N', 'Rb')\",OTHERS,False\nmp-1223497,\"{'La': 2.0, 'Ge': 4.0, 'Pd': 3.0, 'Pt': 1.0}\",\"('Ge', 'La', 'Pd', 'Pt')\",OTHERS,False\nmp-1222541,\"{'Li': 4.0, 'Fe': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1076543,\"{'Ca': 7.0, 'Mg': 1.0, 'Ti': 1.0, 'Mn': 7.0, 'O': 24.0}\",\"('Ca', 'Mg', 'Mn', 'O', 'Ti')\",OTHERS,False\nmp-20989,\"{'Ba': 1.0, 'Gd': 1.0, 'Fe': 2.0, 'O': 5.0}\",\"('Ba', 'Fe', 'Gd', 'O')\",OTHERS,False\nmp-862840,\"{'Pr': 11.0, 'In': 9.0, 'Ni': 4.0}\",\"('In', 'Ni', 'Pr')\",OTHERS,False\nmp-18343,\"{'O': 28.0, 'Mg': 4.0, 'P': 8.0, 'Ba': 4.0}\",\"('Ba', 'Mg', 'O', 'P')\",OTHERS,False\nmp-754365,\"{'Li': 4.0, 'Cr': 4.0, 'O': 14.0}\",\"('Cr', 'Li', 'O')\",OTHERS,False\nmp-1104364,\"{'Cd': 2.0, 'Br': 4.0, 'O': 8.0}\",\"('Br', 'Cd', 'O')\",OTHERS,False\nmp-1178324,\"{'Fe': 6.0, 'O': 2.0, 'F': 14.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-11102,\"{'Rh': 4.0, 'In': 4.0, 'Yb': 4.0}\",\"('In', 'Rh', 'Yb')\",OTHERS,False\nmp-1232260,\"{'Mg': 4.0, 'In': 8.0, 'S': 16.0}\",\"('In', 'Mg', 'S')\",OTHERS,False\nmp-1209414,\"{'Sm': 8.0, 'Mn': 2.0, 'S': 14.0}\",\"('Mn', 'S', 'Sm')\",OTHERS,False\nmp-510570,\"{'Ca': 1.0, 'Mn': 4.0, 'Cu': 3.0, 'O': 12.0}\",\"('Ca', 'Cu', 'Mn', 'O')\",OTHERS,False\nmp-1192361,\"{'Zn': 22.0, 'Co': 4.0}\",\"('Co', 'Zn')\",OTHERS,False\nmp-1200625,\"{'Ba': 2.0, 'Ti': 2.0, 'Mn': 4.0, 'Si': 4.0, 'O': 20.0}\",\"('Ba', 'Mn', 'O', 'Si', 'Ti')\",OTHERS,False\nmp-1033061,\"{'Ba': 1.0, 'Mg': 6.0, 'Al': 1.0, 'O': 8.0}\",\"('Al', 'Ba', 'Mg', 'O')\",OTHERS,False\nmp-756259,\"{'Li': 4.0, 'Mn': 5.0, 'Nb': 1.0, 'O': 12.0}\",\"('Li', 'Mn', 'Nb', 'O')\",OTHERS,False\nmp-1187499,\"{'Ti': 1.0, 'Mn': 1.0, 'Ir': 2.0}\",\"('Ir', 'Mn', 'Ti')\",OTHERS,False\nmp-1176872,\"{'Li': 14.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1193772,\"{'Tl': 8.0, 'Cr': 4.0, 'O': 16.0}\",\"('Cr', 'O', 'Tl')\",OTHERS,False\nmp-1212777,\"{'K': 24.0, 'Ge': 48.0, 'O': 96.0}\",\"('Ge', 'K', 'O')\",OTHERS,False\nmp-17562,\"{'B': 6.0, 'O': 18.0, 'Sc': 2.0, 'Sr': 6.0}\",\"('B', 'O', 'Sc', 'Sr')\",OTHERS,False\nmp-1213601,\"{'Dy': 22.0, 'Ge': 36.0}\",\"('Dy', 'Ge')\",OTHERS,False\nmp-557611,\"{'Mn': 4.0, 'Si': 4.0, 'Pb': 4.0, 'O': 18.0}\",\"('Mn', 'O', 'Pb', 'Si')\",OTHERS,False\nmp-1038735,\"{'Cs': 1.0, 'Mg': 30.0, 'Bi': 1.0, 'O': 32.0}\",\"('Bi', 'Cs', 'Mg', 'O')\",OTHERS,False\nmp-1216009,\"{'Y': 2.0, 'Lu': 2.0, 'Ga': 12.0}\",\"('Ga', 'Lu', 'Y')\",OTHERS,False\nmp-10334,\"{'P': 6.0, 'Cd': 6.0, 'Pr': 2.0}\",\"('Cd', 'P', 'Pr')\",OTHERS,False\nmp-1179679,\"{'Rb': 2.0, 'P': 2.0}\",\"('P', 'Rb')\",OTHERS,False\nmp-1203086,\"{'Sr': 18.0, 'W': 6.0, 'O': 36.0}\",\"('O', 'Sr', 'W')\",OTHERS,False\nmp-559644,\"{'K': 8.0, 'Cu': 4.0, 'P': 12.0, 'S': 36.0}\",\"('Cu', 'K', 'P', 'S')\",OTHERS,False\nmp-648893,\"{'Na': 32.0, 'V': 16.0, 'O': 56.0}\",\"('Na', 'O', 'V')\",OTHERS,False\nmp-1214545,\"{'Ba': 10.0, 'As': 6.0, 'S': 2.0, 'O': 24.0}\",\"('As', 'Ba', 'O', 'S')\",OTHERS,False\nmp-1186437,\"{'Pd': 3.0, 'W': 1.0}\",\"('Pd', 'W')\",OTHERS,False\nmp-93,{'U': 30.0},\"('U',)\",OTHERS,False\nmp-759155,\"{'Li': 8.0, 'Fe': 8.0, 'C': 8.0, 'O': 28.0}\",\"('C', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1195788,\"{'K': 8.0, 'Mo': 8.0, 'Se': 8.0, 'O': 44.0}\",\"('K', 'Mo', 'O', 'Se')\",OTHERS,False\nmp-740930,\"{'La': 2.0, 'P': 6.0, 'H': 12.0, 'O': 12.0}\",\"('H', 'La', 'O', 'P')\",OTHERS,False\nmp-1195859,\"{'Na': 2.0, 'B': 2.0, 'H': 68.0, 'C': 24.0, 'N': 8.0}\",\"('B', 'C', 'H', 'N', 'Na')\",OTHERS,False\nmp-550745,\"{'O': 6.0, 'Fe': 1.0, 'Bi': 2.0, 'Ti': 1.0}\",\"('Bi', 'Fe', 'O', 'Ti')\",OTHERS,False\nmp-1200868,\"{'La': 8.0, 'P': 18.0, 'Rh': 16.0}\",\"('La', 'P', 'Rh')\",OTHERS,False\nmp-4856,\"{'Co': 1.0, 'Ga': 5.0, 'Ho': 1.0}\",\"('Co', 'Ga', 'Ho')\",OTHERS,False\nmp-1091398,\"{'Y': 3.0, 'Si': 3.0, 'Ag': 3.0}\",\"('Ag', 'Si', 'Y')\",OTHERS,False\nmp-775999,\"{'Li': 4.0, 'Ti': 3.0, 'Nb': 2.0, 'Fe': 3.0, 'O': 16.0}\",\"('Fe', 'Li', 'Nb', 'O', 'Ti')\",OTHERS,False\nmp-1246548,\"{'In': 1.0, 'Fe': 2.0, 'N': 2.0}\",\"('Fe', 'In', 'N')\",OTHERS,False\nmp-768082,\"{'Na': 10.0, 'Ni': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Na', 'Ni', 'O', 'P')\",OTHERS,False\nmp-1195803,\"{'Na': 12.0, 'Nb': 12.0, 'O': 36.0}\",\"('Na', 'Nb', 'O')\",OTHERS,False\nmp-1106224,\"{'Mn': 4.0, 'V': 4.0, 'O': 12.0}\",\"('Mn', 'O', 'V')\",OTHERS,False\nmp-1212897,\"{'Er': 8.0, 'Si': 12.0, 'Pd': 4.0}\",\"('Er', 'Pd', 'Si')\",OTHERS,False\nmp-1026016,\"{'Te': 2.0, 'Mo': 2.0, 'W': 1.0, 'Se': 2.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-1036160,\"{'Sr': 1.0, 'Mg': 14.0, 'Mn': 1.0, 'O': 16.0}\",\"('Mg', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-676338,\"{'Mg': 2.0, 'In': 4.0, 'O': 8.0}\",\"('In', 'Mg', 'O')\",OTHERS,False\nmp-1180166,\"{'Na': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Na', 'O', 'P')\",OTHERS,False\nmp-754322,\"{'Sr': 2.0, 'Cu': 1.0, 'O': 4.0}\",\"('Cu', 'O', 'Sr')\",OTHERS,False\nmp-1211441,\"{'Nd': 46.0, 'Mg': 8.0, 'Ni': 14.0}\",\"('Mg', 'Nd', 'Ni')\",OTHERS,False\nmvc-4829,\"{'Zn': 2.0, 'Mo': 4.0, 'O': 8.0}\",\"('Mo', 'O', 'Zn')\",OTHERS,False\nmp-639789,\"{'Fe': 8.0, 'Re': 12.0}\",\"('Fe', 'Re')\",OTHERS,False\nmp-974950,\"{'Rb': 3.0, 'Li': 1.0}\",\"('Li', 'Rb')\",OTHERS,False\nmvc-2902,\"{'Ca': 8.0, 'Ti': 10.0, 'Te': 6.0, 'O': 36.0}\",\"('Ca', 'O', 'Te', 'Ti')\",OTHERS,False\nmp-1177125,\"{'Li': 5.0, 'Fe': 3.0, 'O': 2.0, 'F': 10.0}\",\"('F', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-755735,\"{'Li': 2.0, 'Co': 4.0, 'O': 3.0, 'F': 8.0}\",\"('Co', 'F', 'Li', 'O')\",OTHERS,False\nmp-758939,\"{'Li': 4.0, 'Fe': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1224991,\"{'K': 2.0, 'Na': 2.0, 'Ca': 4.0, 'Mg': 15.0, 'Si': 24.0, 'O': 72.0}\",\"('Ca', 'K', 'Mg', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1185553,\"{'Cs': 3.0, 'Hg': 1.0}\",\"('Cs', 'Hg')\",OTHERS,False\nmp-1191623,\"{'Ce': 2.0, 'Ni': 12.0, 'As': 7.0}\",\"('As', 'Ce', 'Ni')\",OTHERS,False\nmp-1094363,\"{'Y': 3.0, 'Mg': 3.0}\",\"('Mg', 'Y')\",OTHERS,False\nmp-3141,\"{'Rh': 3.0, 'In': 3.0, 'Er': 3.0}\",\"('Er', 'In', 'Rh')\",OTHERS,False\nmp-12789,\"{'Al': 20.0, 'Fe': 4.0, 'Yb': 2.0}\",\"('Al', 'Fe', 'Yb')\",OTHERS,False\nmp-541318,\"{'Cs': 2.0, 'Ti': 6.0, 'P': 12.0, 'Si': 4.0, 'O': 50.0}\",\"('Cs', 'O', 'P', 'Si', 'Ti')\",OTHERS,False\nmp-1182147,\"{'C': 4.0, 'S': 4.0, 'N': 8.0}\",\"('C', 'N', 'S')\",OTHERS,False\nmp-1184781,\"{'In': 1.0, 'Ge': 3.0}\",\"('Ge', 'In')\",OTHERS,False\nmp-864926,\"{'Hf': 1.0, 'Pa': 1.0, 'Tc': 2.0}\",\"('Hf', 'Pa', 'Tc')\",OTHERS,False\nmp-38950,\"{'Ce': 4.0, 'O': 14.0, 'Y': 4.0}\",\"('Ce', 'O', 'Y')\",OTHERS,False\nmvc-34,\"{'Mn': 4.0, 'S': 8.0}\",\"('Mn', 'S')\",OTHERS,False\nmp-1177986,\"{'Li': 2.0, 'Fe': 1.0, 'S': 2.0}\",\"('Fe', 'Li', 'S')\",OTHERS,False\nmp-980189,\"{'Yb': 3.0, 'Na': 1.0}\",\"('Na', 'Yb')\",OTHERS,False\nmp-1184097,\"{'Er': 2.0, 'Cu': 1.0, 'Tc': 1.0}\",\"('Cu', 'Er', 'Tc')\",OTHERS,False\nmp-1220765,\"{'Na': 2.0, 'Hf': 6.0, 'N': 6.0, 'Cl': 6.0}\",\"('Cl', 'Hf', 'N', 'Na')\",OTHERS,False\nmp-2398,\"{'Sc': 4.0, 'Sn': 8.0}\",\"('Sc', 'Sn')\",OTHERS,False\nmp-8249,\"{'P': 2.0, 'S': 6.0, 'Tl': 2.0}\",\"('P', 'S', 'Tl')\",OTHERS,False\nmp-1252404,\"{'Ba': 2.0, 'Al': 2.0, 'Ni': 8.0, 'O': 14.0}\",\"('Al', 'Ba', 'Ni', 'O')\",OTHERS,False\nmp-698580,\"{'Co': 7.0, 'Re': 17.0, 'O': 48.0}\",\"('Co', 'O', 'Re')\",OTHERS,False\nmp-1095669,\"{'Hf': 4.0, 'Tc': 8.0}\",\"('Hf', 'Tc')\",OTHERS,False\nmp-1076439,\"{'Ba': 4.0, 'Co': 4.0, 'O': 10.0}\",\"('Ba', 'Co', 'O')\",OTHERS,False\nmp-1205088,\"{'La': 26.0, 'Hg': 116.0}\",\"('Hg', 'La')\",OTHERS,False\nmp-11140,\"{'Sb': 20.0, 'Ho': 8.0}\",\"('Ho', 'Sb')\",OTHERS,False\nmp-1182477,\"{'As': 1.0, 'N': 1.0, 'O': 2.0, 'F': 6.0}\",\"('As', 'F', 'N', 'O')\",OTHERS,False\nmp-768437,\"{'Li': 8.0, 'Bi': 4.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Bi', 'Li', 'O')\",OTHERS,False\nmp-554911,\"{'K': 2.0, 'Mn': 2.0, 'Zn': 4.0, 'Si': 4.0, 'O': 15.0}\",\"('K', 'Mn', 'O', 'Si', 'Zn')\",OTHERS,False\nmp-1079272,\"{'Sr': 2.0, 'Zr': 1.0, 'V': 1.0, 'O': 6.0}\",\"('O', 'Sr', 'V', 'Zr')\",OTHERS,False\nmp-1095833,\"{'Tl': 2.0, 'Hg': 1.0, 'Te': 1.0}\",\"('Hg', 'Te', 'Tl')\",OTHERS,False\nmp-1027058,\"{'Te': 4.0, 'Mo': 2.0, 'W': 2.0, 'S': 4.0}\",\"('Mo', 'S', 'Te', 'W')\",OTHERS,False\nmp-1029665,\"{'Sr': 6.0, 'B': 2.0, 'N': 6.0}\",\"('B', 'N', 'Sr')\",OTHERS,False\nmp-1188711,\"{'U': 4.0, 'V': 4.0, 'S': 12.0}\",\"('S', 'U', 'V')\",OTHERS,False\nmp-753374,\"{'Li': 4.0, 'V': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Li', 'O', 'Si', 'V')\",OTHERS,False\nmp-607450,\"{'In': 12.0, 'Ru': 4.0}\",\"('In', 'Ru')\",OTHERS,False\nmp-1199496,\"{'Cd': 48.0, 'As': 32.0}\",\"('As', 'Cd')\",OTHERS,False\nmp-1097684,\"{'Mn': 1.0, 'V': 2.0, 'Mo': 1.0}\",\"('Mn', 'Mo', 'V')\",OTHERS,False\nmp-864894,\"{'Be': 6.0, 'Rh': 2.0}\",\"('Be', 'Rh')\",OTHERS,False\nmp-1113012,\"{'Cs': 2.0, 'Li': 1.0, 'Mo': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cs', 'Li', 'Mo')\",OTHERS,False\nmp-1246262,\"{'Zn': 4.0, 'Ir': 2.0, 'N': 6.0}\",\"('Ir', 'N', 'Zn')\",OTHERS,False\nmp-1226937,\"{'Ce': 3.0, 'Th': 3.0, 'Si': 4.0}\",\"('Ce', 'Si', 'Th')\",OTHERS,False\nmp-1220898,\"{'Pr': 32.0, 'In': 3.0, 'Rh': 19.0}\",\"('In', 'Pr', 'Rh')\",OTHERS,False\nmp-1185664,\"{'Mg': 16.0, 'Al': 12.0, 'Ga': 1.0}\",\"('Al', 'Ga', 'Mg')\",OTHERS,False\nmp-1187970,\"{'Yb': 3.0, 'Cr': 1.0}\",\"('Cr', 'Yb')\",OTHERS,False\nmp-23082,\"{'O': 12.0, 'Cl': 4.0, 'K': 4.0, 'Cr': 4.0}\",\"('Cl', 'Cr', 'K', 'O')\",OTHERS,False\nmp-759825,\"{'Bi': 4.0, 'O': 4.0, 'F': 4.0}\",\"('Bi', 'F', 'O')\",OTHERS,False\nmp-1185607,\"{'La': 1.0, 'Eu': 1.0, 'Hg': 2.0}\",\"('Eu', 'Hg', 'La')\",OTHERS,False\nmp-720868,\"{'Ca': 4.0, 'Cd': 2.0, 'H': 48.0, 'Cl': 12.0, 'O': 24.0}\",\"('Ca', 'Cd', 'Cl', 'H', 'O')\",OTHERS,False\nmp-1188308,\"{'Er': 10.0, 'Sb': 2.0, 'Au': 4.0}\",\"('Au', 'Er', 'Sb')\",OTHERS,False\nmp-1120802,\"{'Li': 12.0, 'Mn': 1.0, 'Co': 1.0, 'Ni': 10.0, 'O': 24.0}\",\"('Co', 'Li', 'Mn', 'Ni', 'O')\",OTHERS,False\nmp-1096508,\"{'K': 2.0, 'Hg': 1.0, 'Au': 1.0}\",\"('Au', 'Hg', 'K')\",OTHERS,False\nmp-675142,\"{'Yb': 12.0, 'Dy': 4.0, 'Sb': 12.0}\",\"('Dy', 'Sb', 'Yb')\",OTHERS,False\nmp-571189,\"{'Na': 2.0, 'Hf': 4.0, 'Cu': 2.0, 'Se': 10.0}\",\"('Cu', 'Hf', 'Na', 'Se')\",OTHERS,False\nmp-567352,\"{'Ga': 8.0, 'Sb': 32.0, 'Br': 32.0}\",\"('Br', 'Ga', 'Sb')\",OTHERS,False\nmp-754595,\"{'Li': 2.0, 'Ti': 3.0, 'Fe': 1.0, 'O': 8.0}\",\"('Fe', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-675535,\"{'La': 4.0, 'Sn': 2.0, 'Sb': 8.0}\",\"('La', 'Sb', 'Sn')\",OTHERS,False\nmp-1211930,\"{'K': 4.0, 'Ho': 4.0, 'S': 8.0, 'O': 40.0}\",\"('Ho', 'K', 'O', 'S')\",OTHERS,False\nmp-1226457,\"{'Cs': 16.0, 'Mn': 12.0, 'O': 24.0}\",\"('Cs', 'Mn', 'O')\",OTHERS,False\nmp-1179495,\"{'Si': 1.0, 'Hg': 2.0, 'O': 4.0, 'F': 6.0}\",\"('F', 'Hg', 'O', 'Si')\",OTHERS,False\nmp-605006,\"{'Li': 8.0, 'H': 64.0, 'C': 16.0, 'S': 16.0, 'N': 8.0, 'O': 40.0}\",\"('C', 'H', 'Li', 'N', 'O', 'S')\",OTHERS,False\nmp-1228565,\"{'Ba': 3.0, 'Sr': 3.0, 'Np': 2.0}\",\"('Ba', 'Np', 'Sr')\",OTHERS,False\nmp-31186,\"{'Al': 1.0, 'Fe': 2.0, 'Ni': 1.0}\",\"('Al', 'Fe', 'Ni')\",OTHERS,False\nmp-1202543,\"{'Na': 4.0, 'H': 48.0, 'Ru': 4.0, 'C': 16.0, 'S': 8.0, 'Cl': 16.0, 'O': 8.0}\",\"('C', 'Cl', 'H', 'Na', 'O', 'Ru', 'S')\",OTHERS,False\nmp-1222201,\"{'Mg': 6.0, 'Fe': 2.0, 'Si': 4.0, 'O': 16.0}\",\"('Fe', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-1173915,\"{'Li': 4.0, 'Mn': 2.0, 'Co': 2.0, 'O': 8.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1215285,\"{'Zr': 2.0, 'Cr': 2.0, 'Cu': 2.0, 'Se': 8.0}\",\"('Cr', 'Cu', 'Se', 'Zr')\",OTHERS,False\nmp-1100673,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-2921,\"{'O': 48.0, 'Ga': 20.0, 'Gd': 12.0}\",\"('Ga', 'Gd', 'O')\",OTHERS,False\nmvc-8002,\"{'Zn': 4.0, 'Co': 6.0, 'O': 16.0}\",\"('Co', 'O', 'Zn')\",OTHERS,False\nmp-1211408,\"{'Lu': 6.0, 'Al': 60.0, 'Re': 12.0}\",\"('Al', 'Lu', 'Re')\",OTHERS,False\nmp-1246181,\"{'Mn': 16.0, 'Si': 40.0, 'N': 64.0}\",\"('Mn', 'N', 'Si')\",OTHERS,False\nmp-11572,\"{'Tc': 1.0, 'Ta': 1.0}\",\"('Ta', 'Tc')\",OTHERS,False\nmp-761916,\"{'Na': 4.0, 'H': 16.0, 'Au': 4.0, 'Br': 16.0, 'O': 8.0}\",\"('Au', 'Br', 'H', 'Na', 'O')\",OTHERS,False\nmp-27842,\"{'Cr': 2.0, 'Br': 2.0, 'O': 2.0}\",\"('Br', 'Cr', 'O')\",OTHERS,False\nmp-1186466,\"{'Pr': 1.0, 'Nd': 1.0, 'Ir': 2.0}\",\"('Ir', 'Nd', 'Pr')\",OTHERS,False\nmp-1188989,\"{'Tb': 6.0, 'Co': 4.0, 'Ge': 6.0}\",\"('Co', 'Ge', 'Tb')\",OTHERS,False\nmp-449,\"{'Si': 6.0, 'Fe': 10.0}\",\"('Fe', 'Si')\",OTHERS,False\nmp-1095201,\"{'Zr': 2.0, 'Cu': 2.0, 'Ge': 4.0}\",\"('Cu', 'Ge', 'Zr')\",OTHERS,False\nmp-765566,\"{'Li': 3.0, 'Ag': 6.0, 'F': 18.0}\",\"('Ag', 'F', 'Li')\",OTHERS,False\nmp-1218787,\"{'Sr': 4.0, 'Ce': 1.0, 'Y': 3.0, 'Cu': 4.0, 'Ru': 2.0, 'O': 20.0}\",\"('Ce', 'Cu', 'O', 'Ru', 'Sr', 'Y')\",OTHERS,False\nmp-1244586,\"{'Zn': 4.0, 'Cr': 4.0, 'Bi': 4.0, 'O': 20.0}\",\"('Bi', 'Cr', 'O', 'Zn')\",OTHERS,False\nmp-22904,\"{'Cl': 4.0, 'Ca': 2.0}\",\"('Ca', 'Cl')\",OTHERS,False\nmp-1175376,\"{'Li': 9.0, 'Co': 7.0, 'O': 16.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-1178362,\"{'Dy': 2.0, 'Cl': 2.0, 'O': 2.0}\",\"('Cl', 'Dy', 'O')\",OTHERS,False\nmp-1184860,\"{'Ho': 2.0, 'Mg': 4.0}\",\"('Ho', 'Mg')\",OTHERS,False\nmp-1178916,\"{'V': 4.0, 'P': 4.0, 'H': 44.0, 'O': 36.0}\",\"('H', 'O', 'P', 'V')\",OTHERS,False\nmp-1227166,\"{'Ca': 2.0, 'Nd': 2.0, 'Mn': 4.0, 'O': 12.0}\",\"('Ca', 'Mn', 'Nd', 'O')\",OTHERS,False\nmp-971855,\"{'Tm': 4.0, 'Co': 6.0, 'Si': 10.0}\",\"('Co', 'Si', 'Tm')\",OTHERS,False\nmp-780133,\"{'Mn': 12.0, 'O': 14.0, 'F': 10.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-654,\"{'Fe': 17.0, 'Ce': 2.0}\",\"('Ce', 'Fe')\",OTHERS,False\nmp-755514,\"{'Li': 5.0, 'Mn': 2.0, 'Fe': 3.0, 'O': 10.0}\",\"('Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1013531,\"{'Sr': 3.0, 'Bi': 1.0, 'N': 1.0}\",\"('Bi', 'N', 'Sr')\",OTHERS,False\nmvc-4609,\"{'Mg': 2.0, 'Cu': 4.0, 'O': 8.0}\",\"('Cu', 'Mg', 'O')\",OTHERS,False\nmp-1185395,\"{'Li': 1.0, 'Pm': 3.0}\",\"('Li', 'Pm')\",OTHERS,False\nmvc-4854,\"{'Mg': 4.0, 'Cr': 4.0, 'O': 8.0}\",\"('Cr', 'Mg', 'O')\",OTHERS,False\nmp-27907,\"{'S': 26.0, 'Sb': 12.0, 'Pb': 8.0}\",\"('Pb', 'S', 'Sb')\",OTHERS,False\nmp-1185168,\"{'K': 2.0, 'Rb': 6.0}\",\"('K', 'Rb')\",OTHERS,False\nmp-780535,\"{'Ba': 2.0, 'Lu': 4.0, 'O': 8.0}\",\"('Ba', 'Lu', 'O')\",OTHERS,False\nmp-1224792,\"{'Ga': 1.0, 'Ag': 1.0, 'Se': 2.0}\",\"('Ag', 'Ga', 'Se')\",OTHERS,False\nmp-1093681,\"{'Ca': 1.0, 'In': 1.0, 'Ag': 2.0}\",\"('Ag', 'Ca', 'In')\",OTHERS,False\nmp-555785,\"{'Nd': 4.0, 'Ti': 4.0, 'O': 14.0}\",\"('Nd', 'O', 'Ti')\",OTHERS,False\nmp-2136,\"{'O': 12.0, 'Sb': 8.0}\",\"('O', 'Sb')\",OTHERS,False\nmp-568348,{'P': 84.0},\"('P',)\",OTHERS,False\nmp-555941,\"{'Al': 6.0, 'P': 6.0, 'O': 24.0}\",\"('Al', 'O', 'P')\",OTHERS,False\nmp-1184424,\"{'Dy': 1.0, 'Y': 1.0, 'In': 2.0}\",\"('Dy', 'In', 'Y')\",OTHERS,False\nmp-1208770,\"{'Sr': 6.0, 'Li': 12.0, 'Nb': 4.0, 'O': 22.0}\",\"('Li', 'Nb', 'O', 'Sr')\",OTHERS,False\nmp-764402,\"{'Mn': 2.0, 'Fe': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Fe', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1199399,\"{'Ti': 2.0, 'Si': 14.0, 'H': 96.0, 'C': 32.0, 'O': 4.0}\",\"('C', 'H', 'O', 'Si', 'Ti')\",OTHERS,False\nmp-755678,\"{'Ca': 2.0, 'La': 4.0, 'I': 16.0}\",\"('Ca', 'I', 'La')\",OTHERS,False\nmp-5915,\"{'N': 8.0, 'O': 24.0, 'Tl': 8.0}\",\"('N', 'O', 'Tl')\",OTHERS,False\nmp-1184636,\"{'Hf': 1.0, 'Th': 1.0, 'Tc': 2.0}\",\"('Hf', 'Tc', 'Th')\",OTHERS,False\nmp-1112957,\"{'Cs': 2.0, 'Hg': 1.0, 'Sb': 1.0, 'F': 6.0}\",\"('Cs', 'F', 'Hg', 'Sb')\",OTHERS,False\nmp-1190722,\"{'Mg': 1.0, 'U': 2.0, 'Si': 2.0, 'O': 16.0}\",\"('Mg', 'O', 'Si', 'U')\",OTHERS,False\nmp-561093,\"{'Rb': 8.0, 'Cr': 8.0, 'O': 28.0}\",\"('Cr', 'O', 'Rb')\",OTHERS,False\nmp-867167,\"{'Li': 1.0, 'Ca': 2.0, 'Tl': 1.0}\",\"('Ca', 'Li', 'Tl')\",OTHERS,False\nmp-1194999,\"{'Na': 2.0, 'Co': 4.0, 'As': 6.0, 'O': 20.0}\",\"('As', 'Co', 'Na', 'O')\",OTHERS,False\nmp-1013719,\"{'Ba': 3.0, 'Bi': 1.0, 'Sb': 1.0}\",\"('Ba', 'Bi', 'Sb')\",OTHERS,False\nmp-1184539,\"{'Eu': 2.0, 'Th': 6.0}\",\"('Eu', 'Th')\",OTHERS,False\nmp-18787,\"{'O': 2.0, 'Mn': 3.0, 'Sb': 2.0, 'Ba': 2.0}\",\"('Ba', 'Mn', 'O', 'Sb')\",OTHERS,False\nmp-764587,\"{'Li': 4.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-755126,\"{'Mn': 6.0, 'O': 11.0, 'F': 1.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-973066,\"{'Sc': 1.0, 'Ge': 1.0, 'Rh': 2.0}\",\"('Ge', 'Rh', 'Sc')\",OTHERS,False\nmp-550566,\"{'O': 4.0, 'Bi': 2.0, 'Eu': 1.0, 'Cl': 1.0}\",\"('Bi', 'Cl', 'Eu', 'O')\",OTHERS,False\nmp-755519,\"{'Li': 3.0, 'Mn': 1.0, 'Cr': 3.0, 'O': 8.0}\",\"('Cr', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-607479,\"{'Eu': 4.0, 'Rb': 4.0, 'Si': 4.0, 'S': 16.0}\",\"('Eu', 'Rb', 'S', 'Si')\",OTHERS,False\nmp-1224835,\"{'Fe': 2.0, 'Pb': 3.0, 'W': 1.0, 'O': 9.0}\",\"('Fe', 'O', 'Pb', 'W')\",OTHERS,False\nmp-17642,\"{'O': 24.0, 'Cr': 4.0, 'Rb': 4.0, 'U': 4.0}\",\"('Cr', 'O', 'Rb', 'U')\",OTHERS,False\nmvc-11925,\"{'Mg': 2.0, 'Bi': 2.0, 'P': 2.0, 'O': 12.0}\",\"('Bi', 'Mg', 'O', 'P')\",OTHERS,False\nmp-780570,\"{'Ti': 8.0, 'S': 16.0, 'O': 64.0}\",\"('O', 'S', 'Ti')\",OTHERS,False\nmp-1094067,\"{'Sc': 4.0, 'F': 12.0}\",\"('F', 'Sc')\",OTHERS,False\nmp-18444,\"{'O': 12.0, 'Cu': 2.0, 'Sr': 6.0, 'Rh': 2.0}\",\"('Cu', 'O', 'Rh', 'Sr')\",OTHERS,False\nmp-849415,\"{'Li': 8.0, 'Fe': 8.0, 'P': 16.0, 'O': 56.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-4840,\"{'Cu': 2.0, 'Ga': 2.0, 'Se': 4.0}\",\"('Cu', 'Ga', 'Se')\",OTHERS,False\nmp-1220322,\"{'Nb': 1.0, 'Re': 1.0}\",\"('Nb', 'Re')\",OTHERS,False\nmvc-15235,\"{'Y': 4.0, 'Cr': 12.0, 'O': 36.0}\",\"('Cr', 'O', 'Y')\",OTHERS,False\nmp-26453,\"{'Li': 6.0, 'Mn': 4.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-9848,\"{'P': 10.0, 'Pr': 2.0}\",\"('P', 'Pr')\",OTHERS,False\nmp-1197740,\"{'Ce': 8.0, 'Si': 4.0, 'S': 20.0}\",\"('Ce', 'S', 'Si')\",OTHERS,False\nmp-1075184,\"{'Mg': 6.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1214076,\"{'Ca': 2.0, 'Al': 4.0, 'Si': 8.0, 'H': 4.0, 'O': 24.0}\",\"('Al', 'Ca', 'H', 'O', 'Si')\",OTHERS,False\nmp-1036621,\"{'Y': 1.0, 'Mg': 14.0, 'Al': 1.0, 'O': 16.0}\",\"('Al', 'Mg', 'O', 'Y')\",OTHERS,False\nmp-1223672,\"{'K': 2.0, 'Ba': 1.0, 'Bi': 3.0, 'O': 9.0}\",\"('Ba', 'Bi', 'K', 'O')\",OTHERS,False\nmp-2633,\"{'O': 8.0, 'Ge': 4.0}\",\"('Ge', 'O')\",OTHERS,False\nmvc-5236,\"{'Sn': 12.0, 'O': 24.0}\",\"('O', 'Sn')\",OTHERS,False\nmp-1216036,\"{'Zn': 6.0, 'Co': 8.0, 'Sb': 4.0, 'O': 24.0}\",\"('Co', 'O', 'Sb', 'Zn')\",OTHERS,False\nmp-569341,\"{'Nd': 3.0, 'B': 3.0, 'Pt': 6.0}\",\"('B', 'Nd', 'Pt')\",OTHERS,False\nmp-1194728,\"{'Ba': 6.0, 'Na': 2.0, 'Gd': 6.0, 'Si': 12.0, 'O': 40.0}\",\"('Ba', 'Gd', 'Na', 'O', 'Si')\",OTHERS,False\nmp-558498,\"{'Zn': 8.0, 'Ga': 8.0, 'N': 8.0, 'O': 8.0}\",\"('Ga', 'N', 'O', 'Zn')\",OTHERS,False\nmp-18737,\"{'Nd': 4.0, 'Ni': 2.0, 'O': 8.0}\",\"('Nd', 'Ni', 'O')\",OTHERS,False\nmp-17167,\"{'Be': 6.0, 'O': 24.0, 'Si': 6.0, 'Cd': 8.0, 'Te': 2.0}\",\"('Be', 'Cd', 'O', 'Si', 'Te')\",OTHERS,False\nmp-530524,\"{'F': 60.0, 'Tl': 4.0, 'Zr': 12.0}\",\"('F', 'Tl', 'Zr')\",OTHERS,False\nmp-1196849,\"{'Fe': 6.0, 'W': 20.0, 'C': 6.0}\",\"('C', 'Fe', 'W')\",OTHERS,False\nmp-17540,\"{'O': 18.0, 'Co': 2.0, 'Se': 6.0, 'Sr': 4.0}\",\"('Co', 'O', 'Se', 'Sr')\",OTHERS,False\nmp-541929,\"{'Fe': 4.0, 'P': 12.0, 'Si': 4.0, 'O': 44.0}\",\"('Fe', 'O', 'P', 'Si')\",OTHERS,False\nmp-600006,\"{'Si': 20.0, 'O': 40.0}\",\"('O', 'Si')\",OTHERS,False\nmp-1222757,\"{'Li': 2.0, 'Mg': 1.0, 'Ti': 9.0, 'O': 20.0}\",\"('Li', 'Mg', 'O', 'Ti')\",OTHERS,False\nmp-1189806,\"{'Yb': 4.0, 'Cu': 2.0, 'Ge': 12.0}\",\"('Cu', 'Ge', 'Yb')\",OTHERS,False\nmp-768139,\"{'Li': 16.0, 'Sb': 4.0, 'S': 2.0}\",\"('Li', 'S', 'Sb')\",OTHERS,False\nmp-772646,\"{'Li': 12.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-15753,\"{'Li': 4.0, 'As': 4.0, 'Ba': 3.0}\",\"('As', 'Ba', 'Li')\",OTHERS,False\nmp-755859,\"{'Li': 4.0, 'Mn': 1.0, 'Fe': 3.0, 'B': 4.0, 'O': 12.0}\",\"('B', 'Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1180419,\"{'Na': 8.0, 'Ag': 8.0, 'I': 16.0, 'O': 24.0}\",\"('Ag', 'I', 'Na', 'O')\",OTHERS,False\nmp-867913,\"{'Li': 1.0, 'Ho': 2.0, 'Ir': 1.0}\",\"('Ho', 'Ir', 'Li')\",OTHERS,False\nmp-26552,\"{'O': 44.0, 'P': 12.0, 'Co': 10.0}\",\"('Co', 'O', 'P')\",OTHERS,False\nmp-998762,\"{'Mn': 1.0, 'Tl': 1.0, 'F': 3.0}\",\"('F', 'Mn', 'Tl')\",OTHERS,False\nmp-863389,\"{'Nb': 16.0, 'Tl': 16.0, 'O': 53.0}\",\"('Nb', 'O', 'Tl')\",OTHERS,False\nmp-772663,\"{'Mn': 2.0, 'H': 8.0, 'S': 4.0, 'O': 20.0}\",\"('H', 'Mn', 'O', 'S')\",OTHERS,False\nmvc-8085,\"{'Zn': 2.0, 'Cu': 2.0, 'Ge': 4.0, 'O': 12.0}\",\"('Cu', 'Ge', 'O', 'Zn')\",OTHERS,False\nmp-1080559,\"{'In': 2.0, 'Ga': 4.0, 'Au': 4.0}\",\"('Au', 'Ga', 'In')\",OTHERS,False\nmp-1246818,\"{'Sr': 40.0, 'Cd': 8.0, 'N': 32.0}\",\"('Cd', 'N', 'Sr')\",OTHERS,False\nmp-6872,\"{'N': 2.0, 'O': 6.0, 'F': 12.0, 'Si': 2.0, 'K': 6.0}\",\"('F', 'K', 'N', 'O', 'Si')\",OTHERS,False\nmp-774354,\"{'Li': 4.0, 'Mn': 8.0, 'F': 20.0}\",\"('F', 'Li', 'Mn')\",OTHERS,False\nmp-1226833,\"{'Ce': 2.0, 'Zn': 3.0, 'Cu': 7.0}\",\"('Ce', 'Cu', 'Zn')\",OTHERS,False\nmp-1203228,\"{'La': 6.0, 'Rh': 8.0, 'Pb': 26.0}\",\"('La', 'Pb', 'Rh')\",OTHERS,False\nmp-1038195,\"{'Mg': 30.0, 'Al': 1.0, 'C': 1.0, 'O': 32.0}\",\"('Al', 'C', 'Mg', 'O')\",OTHERS,False\nmp-778583,\"{'Li': 3.0, 'Mn': 2.0, 'Co': 2.0, 'O': 8.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-667447,\"{'Te': 8.0, 'As': 8.0, 'Se': 16.0, 'F': 48.0}\",\"('As', 'F', 'Se', 'Te')\",OTHERS,False\nmp-1189078,\"{'Na': 2.0, 'Al': 2.0, 'C': 2.0, 'O': 10.0}\",\"('Al', 'C', 'Na', 'O')\",OTHERS,False\nmp-1181438,\"{'Er': 10.0, 'Bi': 2.0, 'Au': 4.0}\",\"('Au', 'Bi', 'Er')\",OTHERS,False\nmp-22253,\"{'S': 4.0, 'Zn': 1.0, 'In': 2.0}\",\"('In', 'S', 'Zn')\",OTHERS,False\nmp-758848,\"{'Ti': 5.0, 'Nb': 1.0, 'O': 12.0}\",\"('Nb', 'O', 'Ti')\",OTHERS,False\nmp-25466,\"{'Li': 2.0, 'C': 2.0, 'O': 14.0, 'P': 2.0, 'Cu': 2.0}\",\"('C', 'Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-1096900,\"{'Cr': 4.0, 'N': 8.0}\",\"('Cr', 'N')\",OTHERS,False\nmp-865409,\"{'U': 1.0, 'Tc': 2.0, 'Sn': 1.0}\",\"('Sn', 'Tc', 'U')\",OTHERS,False\nmp-11243,\"{'Er': 2.0, 'Au': 2.0}\",\"('Au', 'Er')\",OTHERS,False\nmp-864637,\"{'Nd': 8.0, 'Al': 8.0}\",\"('Al', 'Nd')\",OTHERS,False\nmp-1080454,\"{'Dy': 3.0, 'Pd': 3.0, 'Pb': 3.0}\",\"('Dy', 'Pb', 'Pd')\",OTHERS,False\nmvc-9392,\"{'Pr': 2.0, 'Mg': 2.0, 'Mo': 4.0, 'O': 12.0}\",\"('Mg', 'Mo', 'O', 'Pr')\",OTHERS,False\nmp-1203188,\"{'Ca': 8.0, 'As': 4.0, 'H': 16.0, 'F': 52.0}\",\"('As', 'Ca', 'F', 'H')\",OTHERS,False\nmp-1246325,\"{'Ta': 2.0, 'Mn': 4.0, 'N': 6.0}\",\"('Mn', 'N', 'Ta')\",OTHERS,False\nmp-754598,\"{'Hg': 9.0, 'Se': 3.0, 'O': 18.0}\",\"('Hg', 'O', 'Se')\",OTHERS,False\nmp-1210098,\"{'Rb': 8.0, 'Ce': 4.0, 'Cl': 20.0}\",\"('Ce', 'Cl', 'Rb')\",OTHERS,False\nmp-865876,\"{'Ti': 2.0, 'Tc': 1.0, 'Os': 1.0}\",\"('Os', 'Tc', 'Ti')\",OTHERS,False\nmp-505765,\"{'Ba': 20.0, 'Co': 20.0, 'N': 20.0}\",\"('Ba', 'Co', 'N')\",OTHERS,False\nmp-697563,\"{'Ca': 4.0, 'Mg': 1.0, 'B': 4.0, 'H': 6.0, 'C': 2.0, 'O': 18.0}\",\"('B', 'C', 'Ca', 'H', 'Mg', 'O')\",OTHERS,False\nmp-756308,\"{'Li': 4.0, 'Ti': 3.0, 'Fe': 3.0, 'Sn': 2.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'Sn', 'Ti')\",OTHERS,False\nmp-1173971,\"{'Li': 6.0, 'Mn': 2.0, 'Co': 2.0, 'O': 10.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1105758,\"{'Al': 2.0, 'Tl': 4.0, 'O': 2.0, 'F': 10.0}\",\"('Al', 'F', 'O', 'Tl')\",OTHERS,False\nmp-28538,\"{'H': 12.0, 'Si': 4.0, 'I': 4.0}\",\"('H', 'I', 'Si')\",OTHERS,False\nmp-569557,\"{'Dy': 6.0, 'Si': 2.0, 'Cu': 2.0, 'Se': 14.0}\",\"('Cu', 'Dy', 'Se', 'Si')\",OTHERS,False\nmp-3255,\"{'O': 6.0, 'Cu': 4.0, 'Sr': 2.0}\",\"('Cu', 'O', 'Sr')\",OTHERS,False\nmp-17620,\"{'B': 6.0, 'O': 18.0, 'Tm': 6.0}\",\"('B', 'O', 'Tm')\",OTHERS,False\nmp-1185759,\"{'Mg': 17.0, 'Al': 11.0, 'Tl': 1.0}\",\"('Al', 'Mg', 'Tl')\",OTHERS,False\nmp-735563,\"{'Cs': 1.0, 'Co': 1.0, 'H': 18.0, 'N': 6.0, 'Cl': 4.0, 'O': 8.0}\",\"('Cl', 'Co', 'Cs', 'H', 'N', 'O')\",OTHERS,False\nmp-8371,\"{'C': 2.0, 'Lu': 1.0}\",\"('C', 'Lu')\",OTHERS,False\nmp-1205435,\"{'K': 8.0, 'Fe': 8.0, 'P': 8.0, 'O': 28.0, 'F': 8.0}\",\"('F', 'Fe', 'K', 'O', 'P')\",OTHERS,False\nmp-504722,\"{'Ni': 4.0, 'N': 8.0, 'O': 24.0}\",\"('N', 'Ni', 'O')\",OTHERS,False\nmp-7237,\"{'O': 4.0, 'Cu': 2.0, 'Ag': 2.0}\",\"('Ag', 'Cu', 'O')\",OTHERS,False\nmp-1078200,\"{'V': 2.0, 'Co': 1.0, 'Se': 4.0}\",\"('Co', 'Se', 'V')\",OTHERS,False\nmp-1177476,\"{'Li': 3.0, 'V': 5.0, 'O': 12.0}\",\"('Li', 'O', 'V')\",OTHERS,False\nmp-1187133,\"{'Sr': 6.0, 'Fe': 2.0}\",\"('Fe', 'Sr')\",OTHERS,False\nmp-720332,\"{'Na': 5.0, 'Ca': 1.0, 'Al': 5.0, 'Fe': 1.0, 'Si': 12.0, 'O': 36.0}\",\"('Al', 'Ca', 'Fe', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1219396,\"{'Sc': 2.0, 'Te': 3.0}\",\"('Sc', 'Te')\",OTHERS,False\nmvc-1364,\"{'Ba': 2.0, 'V': 3.0, 'O': 7.0}\",\"('Ba', 'O', 'V')\",OTHERS,False\nmp-760095,\"{'Li': 6.0, 'Mn': 6.0, 'F': 18.0}\",\"('F', 'Li', 'Mn')\",OTHERS,False\nmp-761214,\"{'Li': 3.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1024991,\"{'Er': 2.0, 'Cu': 4.0}\",\"('Cu', 'Er')\",OTHERS,False\nmp-1175447,\"{'Li': 9.0, 'Co': 7.0, 'O': 16.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-1225379,\"{'Fe': 30.0, 'Si': 6.0, 'P': 6.0}\",\"('Fe', 'P', 'Si')\",OTHERS,False\nmp-20294,\"{'Rh': 1.0, 'In': 5.0, 'Ce': 1.0}\",\"('Ce', 'In', 'Rh')\",OTHERS,False\nmp-977207,\"{'Li': 4.0, 'Mg': 2.0}\",\"('Li', 'Mg')\",OTHERS,False\nmp-1199265,\"{'Nb': 4.0, 'Pb': 4.0, 'Se': 8.0, 'O': 30.0}\",\"('Nb', 'O', 'Pb', 'Se')\",OTHERS,False\nmp-1181157,\"{'K': 2.0, 'La': 2.0, 'C': 8.0, 'O': 22.0}\",\"('C', 'K', 'La', 'O')\",OTHERS,False\nmp-774219,\"{'Li': 3.0, 'Mn': 1.0, 'Co': 3.0, 'O': 8.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-17217,\"{'Ge': 4.0, 'Te': 12.0, 'Tl': 12.0}\",\"('Ge', 'Te', 'Tl')\",OTHERS,False\nmp-1096250,\"{'Ti': 1.0, 'V': 1.0, 'Mo': 2.0}\",\"('Mo', 'Ti', 'V')\",OTHERS,False\nmp-1222270,\"{'Li': 1.0, 'Mg': 1.0}\",\"('Li', 'Mg')\",OTHERS,False\nmp-1221952,\"{'Mg': 1.0, 'Ti': 2.0, 'Ni': 1.0, 'O': 6.0}\",\"('Mg', 'Ni', 'O', 'Ti')\",OTHERS,False\nmp-1215969,\"{'Yb': 20.0, 'Cd': 3.0, 'Au': 9.0}\",\"('Au', 'Cd', 'Yb')\",OTHERS,False\nmp-1174093,\"{'Li': 4.0, 'Mn': 1.0, 'Co': 3.0, 'O': 8.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-672653,\"{'Nb': 6.0, 'Ni': 16.0, 'Ge': 7.0}\",\"('Ge', 'Nb', 'Ni')\",OTHERS,False\nmp-1215993,\"{'Y': 1.0, 'Bi': 3.0, 'Ru': 4.0, 'O': 14.0}\",\"('Bi', 'O', 'Ru', 'Y')\",OTHERS,False\nmp-26716,\"{'O': 28.0, 'P': 8.0, 'Co': 4.0}\",\"('Co', 'O', 'P')\",OTHERS,False\nmp-15317,\"{'F': 6.0, 'Na': 1.0, 'Rb': 2.0, 'Ho': 1.0}\",\"('F', 'Ho', 'Na', 'Rb')\",OTHERS,False\nmp-862287,\"{'Be': 1.0, 'Al': 1.0, 'Rh': 2.0}\",\"('Al', 'Be', 'Rh')\",OTHERS,False\nmp-30187,\"{'O': 11.0, 'Ga': 2.0, 'Te': 4.0}\",\"('Ga', 'O', 'Te')\",OTHERS,False\nmp-560092,\"{'Rb': 2.0, 'Na': 10.0, 'Be': 16.0, 'O': 22.0}\",\"('Be', 'Na', 'O', 'Rb')\",OTHERS,False\nmp-1097242,\"{'Y': 1.0, 'Sc': 1.0, 'Au': 2.0}\",\"('Au', 'Sc', 'Y')\",OTHERS,False\nmp-698218,\"{'La': 2.0, 'H': 26.0, 'C': 6.0, 'S': 6.0, 'O': 22.0}\",\"('C', 'H', 'La', 'O', 'S')\",OTHERS,False\nmp-1080742,\"{'Sr': 2.0, 'Zr': 1.0, 'Cr': 1.0, 'O': 6.0}\",\"('Cr', 'O', 'Sr', 'Zr')\",OTHERS,False\nmp-560791,\"{'Cs': 2.0, 'Na': 2.0, 'Ti': 2.0, 'O': 6.0}\",\"('Cs', 'Na', 'O', 'Ti')\",OTHERS,False\nmp-698183,\"{'Mg': 3.0, 'P': 2.0, 'H': 44.0, 'O': 30.0}\",\"('H', 'Mg', 'O', 'P')\",OTHERS,False\nmp-11765,\"{'Ti': 5.0, 'Se': 4.0}\",\"('Se', 'Ti')\",OTHERS,False\nmp-29062,\"{'F': 24.0, 'Zr': 4.0, 'Th': 2.0}\",\"('F', 'Th', 'Zr')\",OTHERS,False\nmp-554144,\"{'F': 7.0, 'K': 3.0, 'Mn': 2.0}\",\"('F', 'K', 'Mn')\",OTHERS,False\nmp-1111199,\"{'K': 2.0, 'Al': 1.0, 'Tl': 1.0, 'I': 6.0}\",\"('Al', 'I', 'K', 'Tl')\",OTHERS,False\nmp-733610,\"{'H': 14.0, 'C': 6.0, 'N': 6.0, 'O': 10.0}\",\"('C', 'H', 'N', 'O')\",OTHERS,False\nmp-19241,\"{'O': 6.0, 'Ni': 2.0, 'Ba': 2.0}\",\"('Ba', 'Ni', 'O')\",OTHERS,False\nmp-1207591,\"{'Yb': 2.0, 'Eu': 2.0, 'Cu': 2.0, 'S': 6.0}\",\"('Cu', 'Eu', 'S', 'Yb')\",OTHERS,False\nmp-757117,\"{'Li': 4.0, 'Fe': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1246919,\"{'Mn': 10.0, 'Ge': 4.0, 'N': 12.0}\",\"('Ge', 'Mn', 'N')\",OTHERS,False\nmp-768303,\"{'Dy': 2.0, 'Nb': 2.0, 'O': 8.0}\",\"('Dy', 'Nb', 'O')\",OTHERS,False\nmp-1080375,\"{'Ce': 24.0, 'Se': 48.0}\",\"('Ce', 'Se')\",OTHERS,False\nmvc-2270,\"{'Ca': 2.0, 'Mn': 9.0, 'O': 13.0}\",\"('Ca', 'Mn', 'O')\",OTHERS,False\nmp-1080314,\"{'Ce': 6.0, 'Se': 12.0}\",\"('Ce', 'Se')\",OTHERS,False\nmp-979963,\"{'Yb': 3.0, 'Ti': 1.0}\",\"('Ti', 'Yb')\",OTHERS,False\nmp-21172,\"{'Pb': 3.0, 'Pu': 1.0}\",\"('Pb', 'Pu')\",OTHERS,False\nmp-1185438,\"{'Lu': 3.0, 'Hg': 1.0}\",\"('Hg', 'Lu')\",OTHERS,False\nmvc-16196,\"{'Zn': 4.0, 'Bi': 4.0, 'O': 10.0}\",\"('Bi', 'O', 'Zn')\",OTHERS,False\nmp-1229223,\"{'As': 14.0, 'H': 6.0, 'O': 31.0}\",\"('As', 'H', 'O')\",OTHERS,False\nmp-12716,\"{'Li': 1.0, 'Pd': 2.0, 'Tl': 1.0}\",\"('Li', 'Pd', 'Tl')\",OTHERS,False\nmp-1095753,\"{'La': 2.0, 'Tl': 1.0, 'Ag': 1.0}\",\"('Ag', 'La', 'Tl')\",OTHERS,False\nmp-849302,\"{'Li': 6.0, 'Fe': 8.0, 'F': 38.0}\",\"('F', 'Fe', 'Li')\",OTHERS,False\nmp-1192181,\"{'Au': 2.0, 'C': 8.0, 'N': 8.0, 'O': 4.0}\",\"('Au', 'C', 'N', 'O')\",OTHERS,False\nmp-567601,\"{'Sr': 16.0, 'C': 4.0, 'N': 16.0}\",\"('C', 'N', 'Sr')\",OTHERS,False\nmp-1220026,\"{'Pr': 1.0, 'Er': 1.0, 'Co': 17.0}\",\"('Co', 'Er', 'Pr')\",OTHERS,False\nmp-8351,\"{'O': 16.0, 'Na': 4.0, 'Al': 4.0, 'Si': 4.0}\",\"('Al', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1079201,\"{'B': 2.0, 'C': 4.0, 'N': 2.0}\",\"('B', 'C', 'N')\",OTHERS,False\nmp-641661,\"{'Xe': 16.0, 'F': 96.0}\",\"('F', 'Xe')\",OTHERS,False\nmp-1222046,\"{'Mg': 1.0, 'Fe': 4.0, 'Cu': 1.0, 'O': 8.0}\",\"('Cu', 'Fe', 'Mg', 'O')\",OTHERS,False\nmp-761163,\"{'Na': 2.0, 'Ni': 7.0, 'O': 14.0}\",\"('Na', 'Ni', 'O')\",OTHERS,False\nmp-755855,\"{'Li': 2.0, 'Fe': 3.0, 'Bi': 1.0, 'O': 8.0}\",\"('Bi', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1193346,\"{'Zr': 16.0, 'Co': 8.0, 'N': 4.0}\",\"('Co', 'N', 'Zr')\",OTHERS,False\nmp-9069,\"{'Na': 2.0, 'Al': 2.0, 'K': 4.0, 'As': 4.0}\",\"('Al', 'As', 'K', 'Na')\",OTHERS,False\nmp-1176348,\"{'Na': 10.0, 'Fe': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Fe', 'Na', 'O', 'P')\",OTHERS,False\nmp-1226096,\"{'Co': 7.0, 'Ge': 4.0}\",\"('Co', 'Ge')\",OTHERS,False\nmp-1246494,\"{'Sr': 12.0, 'Rh': 8.0, 'N': 16.0}\",\"('N', 'Rh', 'Sr')\",OTHERS,False\nmp-1204289,\"{'Cs': 32.0, 'Bi': 8.0, 'As': 24.0, 'Se': 56.0}\",\"('As', 'Bi', 'Cs', 'Se')\",OTHERS,False\nmp-685863,\"{'Li': 24.0, 'Si': 6.0, 'O': 24.0}\",\"('Li', 'O', 'Si')\",OTHERS,False\nmp-743584,\"{'H': 12.0, 'W': 1.0, 'N': 3.0, 'O': 3.0, 'F': 3.0}\",\"('F', 'H', 'N', 'O', 'W')\",OTHERS,False\nmp-20318,\"{'B': 2.0, 'Mn': 4.0}\",\"('B', 'Mn')\",OTHERS,False\nmvc-10529,\"{'P': 8.0, 'W': 8.0, 'O': 32.0}\",\"('O', 'P', 'W')\",OTHERS,False\nmp-756442,\"{'Mg': 2.0, 'Co': 4.0, 'O': 8.0}\",\"('Co', 'Mg', 'O')\",OTHERS,False\nmp-753010,\"{'Li': 8.0, 'Co': 2.0, 'O': 8.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-1221833,\"{'Mn': 2.0, 'Ni': 1.0, 'Sb': 2.0, 'Pt': 1.0}\",\"('Mn', 'Ni', 'Pt', 'Sb')\",OTHERS,False\nmp-23870,\"{'H': 1.0, 'Na': 1.0}\",\"('H', 'Na')\",OTHERS,False\nmp-1182867,\"{'Al': 2.0, 'W': 2.0, 'O': 8.0}\",\"('Al', 'O', 'W')\",OTHERS,False\nmp-1218922,\"{'Sn': 1.0, 'Te': 1.0, 'Pb': 4.0, 'S': 4.0}\",\"('Pb', 'S', 'Sn', 'Te')\",OTHERS,False\nmp-777761,\"{'Li': 6.0, 'Mn': 1.0, 'V': 3.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-1208574,\"{'Ta': 16.0, 'Se': 32.0, 'S': 4.0, 'I': 32.0}\",\"('I', 'S', 'Se', 'Ta')\",OTHERS,False\nmp-1071985,\"{'Ce': 2.0, 'Sn': 2.0, 'Au': 2.0}\",\"('Au', 'Ce', 'Sn')\",OTHERS,False\nmp-1185215,\"{'Li': 6.0, 'Fe': 2.0}\",\"('Fe', 'Li')\",OTHERS,False\nmp-1080356,\"{'Ce': 18.0, 'Se': 36.0}\",\"('Ce', 'Se')\",OTHERS,False\nmp-1112935,\"{'Cs': 2.0, 'Ta': 1.0, 'Ag': 1.0, 'I': 6.0}\",\"('Ag', 'Cs', 'I', 'Ta')\",OTHERS,False\nmp-22291,\"{'Se': 6.0, 'Cs': 2.0, 'Gd': 2.0, 'Hg': 2.0}\",\"('Cs', 'Gd', 'Hg', 'Se')\",OTHERS,False\nmp-505089,\"{'Li': 16.0, 'Tb': 4.0, 'F': 32.0}\",\"('F', 'Li', 'Tb')\",OTHERS,False\nmp-1237354,\"{'H': 14.0, 'C': 4.0, 'N': 14.0, 'O': 12.0}\",\"('C', 'H', 'N', 'O')\",OTHERS,False\nmp-1185407,\"{'Li': 1.0, 'Pr': 1.0, 'Au': 2.0}\",\"('Au', 'Li', 'Pr')\",OTHERS,False\nmp-28204,\"{'H': 28.0, 'O': 16.0, 'Cs': 4.0}\",\"('Cs', 'H', 'O')\",OTHERS,False\nmp-1245424,\"{'Ge': 4.0, 'Pb': 28.0, 'N': 24.0}\",\"('Ge', 'N', 'Pb')\",OTHERS,False\nmp-1212627,\"{'Mn': 2.0, 'C': 12.0, 'O': 12.0}\",\"('C', 'Mn', 'O')\",OTHERS,False\nmp-510564,\"{'Nd': 1.0, 'Rh': 1.0, 'In': 5.0}\",\"('In', 'Nd', 'Rh')\",OTHERS,False\nmvc-9167,\"{'Mg': 4.0, 'Co': 12.0, 'O': 28.0}\",\"('Co', 'Mg', 'O')\",OTHERS,False\nmp-1210992,\"{'Li': 2.0, 'Sm': 2.0, 'S': 4.0, 'O': 16.0}\",\"('Li', 'O', 'S', 'Sm')\",OTHERS,False\nmp-891,\"{'Ni': 6.0, 'Ta': 2.0}\",\"('Ni', 'Ta')\",OTHERS,False\nmp-1216097,\"{'Y': 3.0, 'Bi': 1.0, 'Ru': 4.0, 'O': 14.0}\",\"('Bi', 'O', 'Ru', 'Y')\",OTHERS,False\nmp-1104382,\"{'Cr': 4.0, 'Ga': 2.0, 'S': 8.0}\",\"('Cr', 'Ga', 'S')\",OTHERS,False\nmp-1224071,\"{'K': 10.0, 'Cu': 2.0, 'C': 2.0, 'O': 10.0}\",\"('C', 'Cu', 'K', 'O')\",OTHERS,False\nmp-1196392,\"{'Li': 8.0, 'B': 8.0, 'H': 40.0, 'N': 8.0}\",\"('B', 'H', 'Li', 'N')\",OTHERS,False\nmp-1076766,\"{'La': 20.0, 'Sm': 12.0, 'V': 4.0, 'Cr': 28.0, 'O': 80.0}\",\"('Cr', 'La', 'O', 'Sm', 'V')\",OTHERS,False\nmp-779989,\"{'Gd': 8.0, 'P': 16.0, 'O': 52.0}\",\"('Gd', 'O', 'P')\",OTHERS,False\nmp-1184280,\"{'Fe': 6.0, 'W': 2.0}\",\"('Fe', 'W')\",OTHERS,False\nmp-1246053,\"{'Ba': 6.0, 'Nb': 4.0, 'N': 8.0}\",\"('Ba', 'N', 'Nb')\",OTHERS,False\nmp-1193310,\"{'Cr': 22.0, 'W': 1.0, 'C': 6.0}\",\"('C', 'Cr', 'W')\",OTHERS,False\nmp-772781,\"{'Lu': 4.0, 'B': 8.0, 'O': 18.0}\",\"('B', 'Lu', 'O')\",OTHERS,False\nmp-17639,\"{'Zn': 17.0, 'Y': 2.0}\",\"('Y', 'Zn')\",OTHERS,False\nmp-1025934,\"{'Mo': 2.0, 'W': 1.0, 'Se': 2.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-776574,\"{'V': 6.0, 'O': 9.0, 'F': 9.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-17712,\"{'O': 22.0, 'Si': 4.0, 'V': 5.0, 'Pr': 4.0}\",\"('O', 'Pr', 'Si', 'V')\",OTHERS,False\nmp-1183407,\"{'Be': 3.0, 'Si': 1.0}\",\"('Be', 'Si')\",OTHERS,False\nmp-1105280,\"{'Li': 4.0, 'Ta': 4.0, 'O': 12.0}\",\"('Li', 'O', 'Ta')\",OTHERS,False\nmp-1202587,\"{'K': 4.0, 'Ho': 8.0, 'F': 28.0}\",\"('F', 'Ho', 'K')\",OTHERS,False\nmp-1185267,\"{'Li': 1.0, 'Np': 1.0, 'Ru': 2.0}\",\"('Li', 'Np', 'Ru')\",OTHERS,False\nmp-1217134,\"{'Ti': 3.0, 'V': 1.0, 'S': 4.0}\",\"('S', 'Ti', 'V')\",OTHERS,False\nmp-1222159,\"{'Mg': 4.0, 'Fe': 1.0, 'O': 5.0}\",\"('Fe', 'Mg', 'O')\",OTHERS,False\nmp-1020683,\"{'Na': 2.0, 'Ca': 2.0, 'P': 6.0, 'O': 18.0}\",\"('Ca', 'Na', 'O', 'P')\",OTHERS,False\nmp-1097672,\"{'Cs': 2.0, 'Hg': 1.0, 'Te': 1.0}\",\"('Cs', 'Hg', 'Te')\",OTHERS,False\nmp-758647,\"{'Li': 4.0, 'V': 11.0, 'O': 22.0}\",\"('Li', 'O', 'V')\",OTHERS,False\nmp-1189298,\"{'Yb': 4.0, 'B': 16.0}\",\"('B', 'Yb')\",OTHERS,False\nmp-1213811,\"{'Cs': 4.0, 'Co': 2.0, 'S': 4.0, 'O': 28.0}\",\"('Co', 'Cs', 'O', 'S')\",OTHERS,False\nmp-620058,\"{'Pb': 12.0, 'N': 72.0}\",\"('N', 'Pb')\",OTHERS,False\nmp-672394,\"{'Yb': 2.0, 'In': 8.0, 'Ni': 2.0}\",\"('In', 'Ni', 'Yb')\",OTHERS,False\nmp-1197699,\"{'Pt': 8.0, 'Cl': 32.0, 'O': 28.0}\",\"('Cl', 'O', 'Pt')\",OTHERS,False\nmp-1229219,\"{'Ag': 3.0, 'Te': 32.0, 'Au': 13.0}\",\"('Ag', 'Au', 'Te')\",OTHERS,False\nmp-1175016,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmvc-15019,\"{'V': 4.0, 'P': 6.0, 'O': 24.0}\",\"('O', 'P', 'V')\",OTHERS,False\nmp-695887,\"{'K': 3.0, 'Cu': 2.0, 'P': 4.0, 'H': 1.0, 'O': 14.0}\",\"('Cu', 'H', 'K', 'O', 'P')\",OTHERS,False\nmp-1174763,\"{'Li': 5.0, 'Mn': 3.0, 'O': 8.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-774530,\"{'Ba': 8.0, 'Ti': 22.0, 'O': 52.0}\",\"('Ba', 'O', 'Ti')\",OTHERS,False\nmp-753484,\"{'Cr': 4.0, 'O': 4.0, 'F': 4.0}\",\"('Cr', 'F', 'O')\",OTHERS,False\nmp-1204490,\"{'Nb': 8.0, 'Cu': 4.0, 'O': 24.0}\",\"('Cu', 'Nb', 'O')\",OTHERS,False\nmp-556136,\"{'K': 4.0, 'Mo': 4.0, 'I': 4.0, 'O': 24.0}\",\"('I', 'K', 'Mo', 'O')\",OTHERS,False\nmp-1217894,\"{'Ta': 1.0, 'Re': 1.0}\",\"('Re', 'Ta')\",OTHERS,False\nmp-1208931,\"{'Sr': 2.0, 'Er': 1.0, 'Cu': 2.0, 'Bi': 2.0, 'O': 8.0}\",\"('Bi', 'Cu', 'Er', 'O', 'Sr')\",OTHERS,False\nmp-1185751,\"{'Mg': 17.0, 'Al': 11.0, 'Si': 1.0}\",\"('Al', 'Mg', 'Si')\",OTHERS,False\nmp-775168,\"{'Mn': 1.0, 'Cu': 3.0, 'P': 4.0, 'O': 16.0}\",\"('Cu', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1038690,\"{'Ba': 1.0, 'Mg': 30.0, 'Cr': 1.0, 'O': 32.0}\",\"('Ba', 'Cr', 'Mg', 'O')\",OTHERS,False\nmp-1196002,\"{'Mg': 16.0, 'Si': 16.0, 'O': 48.0}\",\"('Mg', 'O', 'Si')\",OTHERS,False\nmp-684265,\"{'Mg': 4.0, 'Al': 8.0, 'Si': 10.0, 'O': 36.0}\",\"('Al', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-769592,\"{'Na': 6.0, 'V': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Na', 'O', 'P', 'V')\",OTHERS,False\nmvc-10706,\"{'Mg': 1.0, 'Sb': 4.0, 'O': 8.0}\",\"('Mg', 'O', 'Sb')\",OTHERS,False\nmp-865174,\"{'Hf': 1.0, 'Cd': 1.0, 'Rh': 2.0}\",\"('Cd', 'Hf', 'Rh')\",OTHERS,False\nmp-675307,\"{'Fe': 10.0, 'Ni': 14.0, 'S': 32.0}\",\"('Fe', 'Ni', 'S')\",OTHERS,False\nmp-867993,\"{'V': 12.0, 'Co': 4.0, 'O': 32.0}\",\"('Co', 'O', 'V')\",OTHERS,False\nmvc-8797,\"{'Ca': 1.0, 'Sn': 4.0, 'O': 8.0}\",\"('Ca', 'O', 'Sn')\",OTHERS,False\nmp-557309,\"{'Na': 6.0, 'Li': 2.0, 'W': 2.0, 'O': 10.0}\",\"('Li', 'Na', 'O', 'W')\",OTHERS,False\nmp-1220427,\"{'Nb': 6.0, 'Ag': 1.0, 'Se': 8.0}\",\"('Ag', 'Nb', 'Se')\",OTHERS,False\nmp-1198112,\"{'Nb': 16.0, 'P': 16.0, 'Cl': 96.0, 'O': 32.0}\",\"('Cl', 'Nb', 'O', 'P')\",OTHERS,False\nmp-1195102,\"{'Rb': 8.0, 'Ag': 40.0, 'P': 16.0, 'S': 64.0}\",\"('Ag', 'P', 'Rb', 'S')\",OTHERS,False\nmvc-7422,\"{'Mg': 8.0, 'Ti': 8.0, 'Bi': 8.0, 'O': 40.0}\",\"('Bi', 'Mg', 'O', 'Ti')\",OTHERS,False\nmp-867328,\"{'K': 2.0, 'Y': 2.0, 'Si': 2.0, 'S': 8.0}\",\"('K', 'S', 'Si', 'Y')\",OTHERS,False\nmp-777110,\"{'Li': 32.0, 'Ti': 13.0, 'Cr': 3.0, 'O': 48.0}\",\"('Cr', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-581963,\"{'K': 16.0, 'Hf': 12.0, 'Te': 68.0}\",\"('Hf', 'K', 'Te')\",OTHERS,False\nmp-979274,\"{'U': 1.0, 'Ag': 3.0}\",\"('Ag', 'U')\",OTHERS,False\nmp-772992,\"{'Li': 9.0, 'Fe': 3.0, 'W': 7.0, 'O': 28.0}\",\"('Fe', 'Li', 'O', 'W')\",OTHERS,False\nmp-866716,\"{'Rb': 4.0, 'Li': 4.0, 'H': 48.0, 'Se': 12.0, 'N': 16.0}\",\"('H', 'Li', 'N', 'Rb', 'Se')\",OTHERS,False\nmvc-9232,\"{'Zn': 4.0, 'Fe': 4.0, 'Bi': 4.0, 'O': 20.0}\",\"('Bi', 'Fe', 'O', 'Zn')\",OTHERS,False\nmp-770492,\"{'Li': 24.0, 'Mn': 11.0, 'Cr': 1.0, 'O': 36.0}\",\"('Cr', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-757637,\"{'Li': 14.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1063280,\"{'La': 1.0, 'Hg': 2.0}\",\"('Hg', 'La')\",OTHERS,False\nmp-761246,\"{'Li': 4.0, 'Mn': 2.0, 'Fe': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Fe', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-6246,\"{'C': 4.0, 'O': 12.0, 'F': 8.0, 'Ca': 8.0}\",\"('C', 'Ca', 'F', 'O')\",OTHERS,False\nmp-1192187,\"{'Na': 12.0, 'Tl': 12.0}\",\"('Na', 'Tl')\",OTHERS,False\nmp-31480,\"{'Ca': 12.0, 'Ga': 8.0, 'Pd': 8.0}\",\"('Ca', 'Ga', 'Pd')\",OTHERS,False\nmp-757837,\"{'Li': 4.0, 'Co': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Co', 'Li', 'O', 'P')\",OTHERS,False\nmp-768418,\"{'Li': 12.0, 'Mn': 4.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-760024,\"{'Na': 4.0, 'Ti': 20.0, 'O': 40.0}\",\"('Na', 'O', 'Ti')\",OTHERS,False\nmp-694955,\"{'Na': 1.0, 'Li': 2.0, 'La': 3.0, 'Ti': 6.0, 'O': 18.0}\",\"('La', 'Li', 'Na', 'O', 'Ti')\",OTHERS,False\nmp-1016841,\"{'Hf': 1.0, 'Hg': 1.0, 'O': 3.0}\",\"('Hf', 'Hg', 'O')\",OTHERS,False\nmp-1102535,\"{'Pt': 1.0, 'C': 4.0, 'S': 2.0, 'I': 2.0, 'O': 2.0}\",\"('C', 'I', 'O', 'Pt', 'S')\",OTHERS,False\nmp-1184306,\"{'Eu': 1.0, 'Bi': 1.0, 'Pd': 2.0}\",\"('Bi', 'Eu', 'Pd')\",OTHERS,False\nmp-1195356,\"{'Ge': 8.0, 'Bi': 8.0, 'N': 8.0, 'O': 56.0}\",\"('Bi', 'Ge', 'N', 'O')\",OTHERS,False\nmp-1226325,\"{'Cr': 4.0, 'Cd': 1.0, 'Fe': 1.0, 'S': 8.0}\",\"('Cd', 'Cr', 'Fe', 'S')\",OTHERS,False\nmp-1112892,\"{'Cs': 2.0, 'In': 1.0, 'Bi': 1.0, 'Br': 6.0}\",\"('Bi', 'Br', 'Cs', 'In')\",OTHERS,False\nmp-17599,\"{'O': 12.0, 'Ca': 1.0, 'Cu': 3.0, 'Ge': 4.0}\",\"('Ca', 'Cu', 'Ge', 'O')\",OTHERS,False\nmp-708035,\"{'Na': 2.0, 'Zn': 2.0, 'H': 18.0, 'O': 12.0}\",\"('H', 'Na', 'O', 'Zn')\",OTHERS,False\nmp-23581,\"{'B': 14.0, 'O': 26.0, 'Cr': 6.0, 'Br': 2.0}\",\"('B', 'Br', 'Cr', 'O')\",OTHERS,False\nmp-772563,\"{'Li': 8.0, 'V': 4.0, 'O': 8.0, 'F': 12.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-1214142,\"{'Ca': 11.0, 'Si': 4.0, 'Cl': 1.0, 'O': 18.0}\",\"('Ca', 'Cl', 'O', 'Si')\",OTHERS,False\nmp-496,\"{'Si': 8.0, 'Sr': 4.0}\",\"('Si', 'Sr')\",OTHERS,False\nmp-753869,\"{'Li': 2.0, 'V': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1266944,\"{'Al': 2.0, 'Mo': 6.0, 'Se': 4.0, 'Cl': 2.0, 'O': 16.0}\",\"('Al', 'Cl', 'Mo', 'O', 'Se')\",OTHERS,False\nmp-1033843,\"{'Mg': 14.0, 'Cr': 1.0, 'Bi': 1.0, 'O': 16.0}\",\"('Bi', 'Cr', 'Mg', 'O')\",OTHERS,False\nmp-669554,\"{'Mn': 12.0, 'Si': 4.0, 'Ni': 8.0}\",\"('Mn', 'Ni', 'Si')\",OTHERS,False\nmp-1191291,\"{'Hg': 4.0, 'S': 2.0, 'Br': 8.0, 'N': 4.0, 'O': 6.0}\",\"('Br', 'Hg', 'N', 'O', 'S')\",OTHERS,False\nmp-1228072,\"{'Ba': 4.0, 'Ca': 1.0, 'Zr': 5.0, 'O': 15.0}\",\"('Ba', 'Ca', 'O', 'Zr')\",OTHERS,False\nmp-1228271,\"{'Ba': 8.0, 'Cu': 4.0, 'Hg': 4.0, 'O': 17.0}\",\"('Ba', 'Cu', 'Hg', 'O')\",OTHERS,False\nmp-1219237,\"{'Si': 4.0, 'Mo': 1.0, 'W': 1.0}\",\"('Mo', 'Si', 'W')\",OTHERS,False\nmp-1246099,\"{'Ca': 12.0, 'In': 8.0, 'N': 16.0}\",\"('Ca', 'In', 'N')\",OTHERS,False\nmp-1095433,\"{'Tm': 2.0, 'Ga': 8.0, 'Ni': 2.0}\",\"('Ga', 'Ni', 'Tm')\",OTHERS,False\nmp-1201686,\"{'Tl': 4.0, 'Mo': 6.0, 'Se': 2.0, 'O': 24.0}\",\"('Mo', 'O', 'Se', 'Tl')\",OTHERS,False\nmp-697818,\"{'Sr': 4.0, 'Pr': 4.0, 'Mn': 2.0, 'Cu': 2.0, 'O': 16.0}\",\"('Cu', 'Mn', 'O', 'Pr', 'Sr')\",OTHERS,False\nmp-1198628,\"{'Na': 12.0, 'Ru': 4.0, 'O': 16.0}\",\"('Na', 'O', 'Ru')\",OTHERS,False\nmp-762239,\"{'Mn': 2.0, 'C': 4.0, 'O': 12.0}\",\"('C', 'Mn', 'O')\",OTHERS,False\nmp-1105173,\"{'Li': 4.0, 'Fe': 2.0, 'Sn': 2.0, 'S': 8.0}\",\"('Fe', 'Li', 'S', 'Sn')\",OTHERS,False\nmp-1143551,\"{'Si': 12.0, 'W': 8.0, 'O': 48.0}\",\"('O', 'Si', 'W')\",OTHERS,False\nmp-27181,\"{'Cl': 16.0, 'K': 4.0, 'Au': 4.0}\",\"('Au', 'Cl', 'K')\",OTHERS,False\nmp-568646,\"{'Ta': 24.0, 'Si': 8.0}\",\"('Si', 'Ta')\",OTHERS,False\nmp-1227655,\"{'Ce': 4.0, 'V': 2.0, 'Co': 32.0}\",\"('Ce', 'Co', 'V')\",OTHERS,False\nmp-1084749,\"{'Ti': 1.0, 'Zn': 1.0, 'Bi': 2.0, 'O': 6.0}\",\"('Bi', 'O', 'Ti', 'Zn')\",OTHERS,False\nmp-676095,\"{'Na': 3.0, 'Pr': 5.0, 'Cl': 18.0}\",\"('Cl', 'Na', 'Pr')\",OTHERS,False\nmp-1036771,\"{'Mg': 30.0, 'V': 1.0, 'Sn': 1.0, 'O': 32.0}\",\"('Mg', 'O', 'Sn', 'V')\",OTHERS,False\nmp-1188091,\"{'Ce': 4.0, 'Ge': 10.0, 'Ir': 6.0}\",\"('Ce', 'Ge', 'Ir')\",OTHERS,False\nmp-8180,\"{'Li': 2.0, 'N': 2.0, 'O': 6.0}\",\"('Li', 'N', 'O')\",OTHERS,False\nmp-553924,\"{'Ag': 16.0, 'P': 4.0, 'I': 4.0, 'O': 16.0}\",\"('Ag', 'I', 'O', 'P')\",OTHERS,False\nmp-1185571,\"{'Cs': 2.0, 'As': 2.0, 'H': 4.0, 'O': 8.0}\",\"('As', 'Cs', 'H', 'O')\",OTHERS,False\nmp-1079086,\"{'U': 3.0, 'Al': 3.0, 'Ni': 3.0}\",\"('Al', 'Ni', 'U')\",OTHERS,False\nmp-770773,\"{'Rb': 8.0, 'S': 8.0, 'O': 28.0}\",\"('O', 'Rb', 'S')\",OTHERS,False\nmp-1174181,\"{'Li': 6.0, 'Co': 4.0, 'O': 10.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-1006888,\"{'K': 1.0, 'Y': 1.0, 'S': 2.0}\",\"('K', 'S', 'Y')\",OTHERS,False\nmvc-6200,\"{'Ca': 6.0, 'Ti': 6.0, 'As': 8.0, 'O': 32.0}\",\"('As', 'Ca', 'O', 'Ti')\",OTHERS,False\nmp-560084,\"{'Ni': 4.0, 'As': 8.0, 'S': 24.0, 'N': 16.0, 'O': 32.0, 'F': 64.0}\",\"('As', 'F', 'N', 'Ni', 'O', 'S')\",OTHERS,False\nmp-1218778,\"{'Sr': 8.0, 'Li': 4.0, 'Si': 4.0, 'N': 12.0}\",\"('Li', 'N', 'Si', 'Sr')\",OTHERS,False\nmp-10739,\"{'P': 3.0, 'Ti': 3.0, 'Ru': 3.0}\",\"('P', 'Ru', 'Ti')\",OTHERS,False\nmp-772810,\"{'V': 4.0, 'Sb': 2.0, 'P': 6.0, 'O': 24.0}\",\"('O', 'P', 'Sb', 'V')\",OTHERS,False\nmp-720245,\"{'Cs': 2.0, 'K': 2.0, 'Na': 2.0, 'Li': 26.0, 'Si': 8.0, 'O': 32.0}\",\"('Cs', 'K', 'Li', 'Na', 'O', 'Si')\",OTHERS,False\nmp-754578,\"{'Tm': 4.0, 'Ga': 4.0, 'O': 12.0}\",\"('Ga', 'O', 'Tm')\",OTHERS,False\nmp-17894,\"{'P': 4.0, 'S': 16.0, 'Rb': 6.0, 'Sm': 2.0}\",\"('P', 'Rb', 'S', 'Sm')\",OTHERS,False\nmp-1232089,\"{'Tm': 16.0, 'Mg': 8.0, 'Se': 32.0}\",\"('Mg', 'Se', 'Tm')\",OTHERS,False\nmp-1216070,\"{'Y': 2.0, 'Mn': 3.0, 'Co': 1.0}\",\"('Co', 'Mn', 'Y')\",OTHERS,False\nmp-557704,\"{'Tl': 9.0, 'N': 9.0, 'O': 27.0}\",\"('N', 'O', 'Tl')\",OTHERS,False\nmp-6873,\"{'O': 8.0, 'F': 2.0, 'Al': 2.0, 'Si': 2.0, 'Ca': 2.0}\",\"('Al', 'Ca', 'F', 'O', 'Si')\",OTHERS,False\nmp-554023,\"{'Be': 4.0, 'B': 2.0, 'O': 6.0, 'F': 2.0}\",\"('B', 'Be', 'F', 'O')\",OTHERS,False\nmp-772513,\"{'Li': 4.0, 'Cr': 12.0, 'O': 32.0}\",\"('Cr', 'Li', 'O')\",OTHERS,False\nmp-865629,\"{'Li': 2.0, 'Ce': 1.0, 'Al': 1.0}\",\"('Al', 'Ce', 'Li')\",OTHERS,False\nmp-1009007,\"{'Lu': 1.0, 'Sb': 1.0}\",\"('Lu', 'Sb')\",OTHERS,False\nmp-552131,\"{'Cl': 2.0, 'O': 4.0, 'Cs': 2.0}\",\"('Cl', 'Cs', 'O')\",OTHERS,False\nmp-1227047,\"{'Ca': 1.0, 'Yb': 3.0}\",\"('Ca', 'Yb')\",OTHERS,False\nmp-1201292,\"{'K': 8.0, 'Zr': 4.0, 'Si': 12.0, 'O': 40.0}\",\"('K', 'O', 'Si', 'Zr')\",OTHERS,False\nmp-1216314,\"{'V': 1.0, 'Ni': 3.0}\",\"('Ni', 'V')\",OTHERS,False\nmp-1247339,\"{'Ba': 12.0, 'Mo': 8.0, 'N': 16.0}\",\"('Ba', 'Mo', 'N')\",OTHERS,False\nmp-1201907,\"{'Ba': 20.0, 'Hg': 103.0}\",\"('Ba', 'Hg')\",OTHERS,False\nmp-20550,\"{'C': 1.0, 'Ce': 3.0, 'Pb': 1.0}\",\"('C', 'Ce', 'Pb')\",OTHERS,False\nmp-1223187,\"{'La': 6.0, 'Co': 1.0, 'Si': 2.0, 'S': 14.0}\",\"('Co', 'La', 'S', 'Si')\",OTHERS,False\nmp-1018732,\"{'Ho': 2.0, 'Te': 4.0}\",\"('Ho', 'Te')\",OTHERS,False\nmp-1225908,\"{'Cs': 1.0, 'Ti': 3.0, 'Al': 1.0, 'O': 8.0}\",\"('Al', 'Cs', 'O', 'Ti')\",OTHERS,False\nmp-2556,\"{'Sn': 1.0, 'Tl': 1.0}\",\"('Sn', 'Tl')\",OTHERS,False\nmp-1224342,\"{'Hf': 1.0, 'Sc': 3.0, 'Si': 8.0}\",\"('Hf', 'Sc', 'Si')\",OTHERS,False\nmp-1078473,\"{'Mn': 2.0, 'C': 2.0, 'O': 6.0}\",\"('C', 'Mn', 'O')\",OTHERS,False\nmp-570245,\"{'Ba': 4.0, 'Ga': 2.0, 'Ge': 2.0, 'N': 2.0}\",\"('Ba', 'Ga', 'Ge', 'N')\",OTHERS,False\nmp-28431,\"{'I': 4.0, 'Sb': 2.0, 'F': 12.0}\",\"('F', 'I', 'Sb')\",OTHERS,False\nmp-1096004,\"{'Hf': 2.0, 'Nb': 1.0, 'In': 1.0}\",\"('Hf', 'In', 'Nb')\",OTHERS,False\nmp-1111122,\"{'K': 2.0, 'Y': 1.0, 'Tl': 1.0, 'F': 6.0}\",\"('F', 'K', 'Tl', 'Y')\",OTHERS,False\nmp-754333,\"{'Ti': 2.0, 'O': 2.0}\",\"('O', 'Ti')\",OTHERS,False\nmp-1192761,\"{'Cs': 8.0, 'Hg': 4.0, 'Cl': 16.0}\",\"('Cl', 'Cs', 'Hg')\",OTHERS,False\nmp-1225182,\"{'Gd': 4.0, 'Nb': 2.0, 'Ga': 2.0, 'O': 14.0}\",\"('Ga', 'Gd', 'Nb', 'O')\",OTHERS,False\nmp-21819,\"{'P': 19.0, 'Cr': 30.0, 'U': 2.0}\",\"('Cr', 'P', 'U')\",OTHERS,False\nmp-1059160,\"{'Br': 1.0, 'N': 1.0}\",\"('Br', 'N')\",OTHERS,False\nmp-1103670,\"{'Tb': 4.0, 'Ga': 4.0, 'Ni': 4.0}\",\"('Ga', 'Ni', 'Tb')\",OTHERS,False\nmp-556562,\"{'Li': 2.0, 'As': 2.0, 'H': 12.0, 'O': 6.0, 'F': 12.0}\",\"('As', 'F', 'H', 'Li', 'O')\",OTHERS,False\nmp-1039188,\"{'Mg': 2.0, 'Bi': 2.0}\",\"('Bi', 'Mg')\",OTHERS,False\nmp-1096899,\"{'Cr': 4.0, 'N': 8.0}\",\"('Cr', 'N')\",OTHERS,False\nmp-1245841,\"{'K': 12.0, 'Mo': 2.0, 'N': 8.0}\",\"('K', 'Mo', 'N')\",OTHERS,False\nmvc-6139,\"{'Zn': 2.0, 'Ni': 4.0, 'O': 8.0}\",\"('Ni', 'O', 'Zn')\",OTHERS,False\nmp-1221104,\"{'Na': 2.0, 'Ca': 2.0, 'Al': 2.0, 'Si': 4.0, 'O': 14.0}\",\"('Al', 'Ca', 'Na', 'O', 'Si')\",OTHERS,False\nmp-770555,\"{'Mn': 8.0, 'P': 8.0, 'O': 32.0}\",\"('Mn', 'O', 'P')\",OTHERS,False\nmvc-8355,\"{'Ca': 2.0, 'Co': 2.0, 'Ge': 4.0, 'O': 12.0}\",\"('Ca', 'Co', 'Ge', 'O')\",OTHERS,False\nmp-1245831,\"{'Ba': 6.0, 'Sc': 2.0, 'N': 6.0}\",\"('Ba', 'N', 'Sc')\",OTHERS,False\nmp-1030853,\"{'Rb': 1.0, 'Mg': 6.0, 'Si': 1.0, 'O': 8.0}\",\"('Mg', 'O', 'Rb', 'Si')\",OTHERS,False\nmp-558712,\"{'Ag': 8.0, 'Bi': 4.0, 'O': 12.0}\",\"('Ag', 'Bi', 'O')\",OTHERS,False\nmp-1196766,\"{'Ce': 2.0, 'Ni': 4.0, 'N': 34.0, 'O': 48.0}\",\"('Ce', 'N', 'Ni', 'O')\",OTHERS,False\nmp-1213621,\"{'Dy': 4.0, 'H': 52.0, 'C': 32.0, 'N': 24.0, 'O': 52.0}\",\"('C', 'Dy', 'H', 'N', 'O')\",OTHERS,False\nmp-1200692,\"{'Si': 17.0, 'C': 17.0}\",\"('C', 'Si')\",OTHERS,False\nmp-1185311,\"{'Li': 1.0, 'Cr': 1.0, 'O': 3.0}\",\"('Cr', 'Li', 'O')\",OTHERS,False\nmp-556202,\"{'Cu': 2.0, 'C': 8.0, 'O': 8.0}\",\"('C', 'Cu', 'O')\",OTHERS,False\nmp-1093705,\"{'K': 2.0, 'Hg': 1.0, 'Se': 1.0}\",\"('Hg', 'K', 'Se')\",OTHERS,False\nmp-757023,\"{'V': 4.0, 'Cr': 4.0, 'O': 12.0}\",\"('Cr', 'O', 'V')\",OTHERS,False\nmp-760026,\"{'Li': 6.0, 'Mn': 5.0, 'Sb': 1.0, 'O': 12.0}\",\"('Li', 'Mn', 'O', 'Sb')\",OTHERS,False\nmp-1238525,\"{'Al': 4.0, 'S': 8.0, 'N': 4.0, 'O': 80.0}\",\"('Al', 'N', 'O', 'S')\",OTHERS,False\nmp-25359,\"{'O': 32.0, 'P': 6.0, 'Mo': 6.0}\",\"('Mo', 'O', 'P')\",OTHERS,False\nmp-16852,\"{'Ni': 6.0, 'Ga': 14.0}\",\"('Ga', 'Ni')\",OTHERS,False\nmp-744260,\"{'Li': 8.0, 'La': 8.0, 'Mo': 16.0, 'O': 64.0}\",\"('La', 'Li', 'Mo', 'O')\",OTHERS,False\nmp-1219975,\"{'Rb': 8.0, 'Mo': 12.0, 'O': 44.0}\",\"('Mo', 'O', 'Rb')\",OTHERS,False\nmp-1018941,\"{'Pr': 2.0, 'Te': 2.0, 'Cl': 2.0}\",\"('Cl', 'Pr', 'Te')\",OTHERS,False\nmp-1253012,\"{'Al': 8.0, 'Fe': 6.0, 'O': 24.0}\",\"('Al', 'Fe', 'O')\",OTHERS,False\nmp-1507,\"{'Te': 2.0, 'Hg': 2.0}\",\"('Hg', 'Te')\",OTHERS,False\nmvc-6155,\"{'Ti': 6.0, 'As': 8.0, 'O': 32.0}\",\"('As', 'O', 'Ti')\",OTHERS,False\nmp-1076878,\"{'La': 28.0, 'Sm': 4.0, 'V': 32.0, 'O': 80.0}\",\"('La', 'O', 'Sm', 'V')\",OTHERS,False\nmp-1200699,\"{'P': 8.0, 'O': 12.0, 'F': 16.0}\",\"('F', 'O', 'P')\",OTHERS,False\nmp-766885,\"{'Sb': 18.0, 'P': 6.0, 'O': 42.0}\",\"('O', 'P', 'Sb')\",OTHERS,False\nmp-973433,\"{'Lu': 1.0, 'Sc': 1.0, 'Ru': 2.0}\",\"('Lu', 'Ru', 'Sc')\",OTHERS,False\nmp-103,{'Hf': 2.0},\"('Hf',)\",OTHERS,False\nmp-1238169,\"{'Hg': 12.0, 'H': 16.0, 'I': 8.0, 'O': 48.0}\",\"('H', 'Hg', 'I', 'O')\",OTHERS,False\nmp-556697,\"{'Ca': 2.0, 'Ta': 4.0, 'Bi': 4.0, 'O': 18.0}\",\"('Bi', 'Ca', 'O', 'Ta')\",OTHERS,False\nmp-1019052,\"{'Re': 3.0, 'N': 3.0}\",\"('N', 'Re')\",OTHERS,False\nmp-1213132,\"{'Er': 4.0, 'Mg': 4.0, 'B': 20.0, 'O': 40.0}\",\"('B', 'Er', 'Mg', 'O')\",OTHERS,False\nmp-567969,\"{'Ho': 1.0, 'B': 2.0, 'Rh': 2.0, 'C': 1.0}\",\"('B', 'C', 'Ho', 'Rh')\",OTHERS,False\nmp-705520,\"{'Sn': 12.0, 'H': 16.0, 'N': 8.0, 'O': 40.0}\",\"('H', 'N', 'O', 'Sn')\",OTHERS,False\nmp-1075152,\"{'Mg': 6.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-13941,\"{'O': 6.0, 'Sr': 2.0, 'Yb': 1.0, 'Re': 1.0}\",\"('O', 'Re', 'Sr', 'Yb')\",OTHERS,False\nmp-510659,\"{'Ag': 4.0, 'Cu': 4.0, 'V': 4.0, 'O': 16.0}\",\"('Ag', 'Cu', 'O', 'V')\",OTHERS,False\nmp-7343,\"{'Y': 12.0, 'Pt': 4.0}\",\"('Pt', 'Y')\",OTHERS,False\nmp-706241,\"{'Rb': 8.0, 'W': 13.0, 'O': 43.0}\",\"('O', 'Rb', 'W')\",OTHERS,False\nmp-3345,\"{'S': 8.0, 'Cu': 6.0, 'As': 2.0}\",\"('As', 'Cu', 'S')\",OTHERS,False\nmp-10636,\"{'Sr': 2.0, 'Sb': 4.0}\",\"('Sb', 'Sr')\",OTHERS,False\nmp-770547,\"{'Mn': 8.0, 'P': 4.0, 'O': 20.0}\",\"('Mn', 'O', 'P')\",OTHERS,False\nmp-1019610,\"{'Cs': 16.0, 'Mg': 8.0, 'Si': 40.0, 'O': 96.0}\",\"('Cs', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-1196867,\"{'La': 8.0, 'N': 24.0}\",\"('La', 'N')\",OTHERS,False\nmp-559938,\"{'Li': 2.0, 'V': 6.0, 'Te': 4.0, 'O': 24.0}\",\"('Li', 'O', 'Te', 'V')\",OTHERS,False\nmp-505682,\"{'Dy': 24.0, 'Te': 12.0}\",\"('Dy', 'Te')\",OTHERS,False\nmp-31763,\"{'O': 12.0, 'Ca': 4.0, 'Fe': 2.0, 'Re': 2.0}\",\"('Ca', 'Fe', 'O', 'Re')\",OTHERS,False\nmp-1191401,\"{'Hf': 8.0, 'Fe': 16.0}\",\"('Fe', 'Hf')\",OTHERS,False\nmp-560879,\"{'C': 4.0, 'S': 8.0, 'N': 12.0, 'Cl': 12.0}\",\"('C', 'Cl', 'N', 'S')\",OTHERS,False\nmp-972869,\"{'Sc': 2.0, 'Ga': 1.0, 'Pt': 1.0}\",\"('Ga', 'Pt', 'Sc')\",OTHERS,False\nmp-1203456,\"{'Hg': 2.0, 'H': 32.0, 'Br': 12.0, 'N': 8.0}\",\"('Br', 'H', 'Hg', 'N')\",OTHERS,False\nmvc-5646,\"{'Ti': 2.0, 'Zn': 4.0, 'Ir': 2.0, 'O': 12.0}\",\"('Ir', 'O', 'Ti', 'Zn')\",OTHERS,False\nmp-775268,\"{'Li': 8.0, 'Mn': 7.0, 'Fe': 1.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1180695,\"{'Rb': 24.0, 'V': 8.0, 'O': 64.0}\",\"('O', 'Rb', 'V')\",OTHERS,False\nmp-1205898,\"{'Sc': 6.0, 'Te': 2.0, 'Ru': 1.0}\",\"('Ru', 'Sc', 'Te')\",OTHERS,False\nmp-763417,\"{'Co': 6.0, 'O': 2.0, 'F': 10.0}\",\"('Co', 'F', 'O')\",OTHERS,False\nmp-1221769,\"{'Mn': 8.0, 'B': 2.0}\",\"('B', 'Mn')\",OTHERS,False\nmp-568069,\"{'Ti': 4.0, 'Bi': 2.0}\",\"('Bi', 'Ti')\",OTHERS,False\nmp-1223482,\"{'Li': 6.0, 'Mo': 30.0, 'Se': 38.0}\",\"('Li', 'Mo', 'Se')\",OTHERS,False\nmp-1099867,\"{'K': 2.0, 'Na': 6.0, 'V': 4.0, 'Mo': 4.0, 'O': 24.0}\",\"('K', 'Mo', 'Na', 'O', 'V')\",OTHERS,False\nmp-16517,\"{'Al': 4.0, 'Ni': 2.0, 'Tm': 2.0}\",\"('Al', 'Ni', 'Tm')\",OTHERS,False\nmp-531051,\"{'Ca': 16.0, 'Nb': 8.0, 'O': 36.0}\",\"('Ca', 'Nb', 'O')\",OTHERS,False\nmp-773026,\"{'Ti': 12.0, 'Fe': 5.0, 'O': 32.0}\",\"('Fe', 'O', 'Ti')\",OTHERS,False\nmp-1086664,\"{'Rb': 1.0, 'Cd': 4.0, 'As': 3.0}\",\"('As', 'Cd', 'Rb')\",OTHERS,False\nmp-27479,\"{'O': 36.0, 'Pr': 8.0, 'W': 8.0}\",\"('O', 'Pr', 'W')\",OTHERS,False\nmp-1175629,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1210353,\"{'Na': 8.0, 'Al': 6.0, 'Si': 6.0, 'S': 2.0, 'O': 32.0}\",\"('Al', 'Na', 'O', 'S', 'Si')\",OTHERS,False\nmp-1210174,\"{'Nb': 8.0, 'Co': 8.0, 'Si': 12.0}\",\"('Co', 'Nb', 'Si')\",OTHERS,False\nmp-1224049,\"{'K': 6.0, 'H': 2.0, 'Se': 4.0, 'O': 16.0}\",\"('H', 'K', 'O', 'Se')\",OTHERS,False\nmp-1189619,\"{'Eu': 10.0, 'Al': 10.0}\",\"('Al', 'Eu')\",OTHERS,False\nmp-505248,\"{'Cr': 2.0, 'Rb': 6.0, 'O': 12.0, 'H': 12.0}\",\"('Cr', 'H', 'O', 'Rb')\",OTHERS,False\nmp-13274,\"{'C': 1.0, 'O': 4.0, 'Na': 4.0}\",\"('C', 'Na', 'O')\",OTHERS,False\nmp-864949,\"{'Mn': 1.0, 'Ga': 1.0, 'Rh': 2.0}\",\"('Ga', 'Mn', 'Rh')\",OTHERS,False\nmp-1214720,\"{'Ba': 2.0, 'Gd': 1.0, 'Cu': 4.0, 'O': 8.0}\",\"('Ba', 'Cu', 'Gd', 'O')\",OTHERS,False\nmp-1198331,\"{'Rb': 16.0, 'Li': 4.0, 'H': 12.0, 'S': 16.0, 'O': 64.0}\",\"('H', 'Li', 'O', 'Rb', 'S')\",OTHERS,False\nmp-1177172,\"{'Li': 8.0, 'V': 10.0, 'Cr': 8.0, 'O': 36.0}\",\"('Cr', 'Li', 'O', 'V')\",OTHERS,False\nmp-695254,\"{'Ca': 10.0, 'La': 6.0, 'Mg': 3.0, 'Ti': 13.0, 'O': 48.0}\",\"('Ca', 'La', 'Mg', 'O', 'Ti')\",OTHERS,False\nmp-1228048,\"{'Ba': 4.0, 'Ta': 2.0, 'Ti': 1.0, 'Zn': 1.0, 'O': 12.0}\",\"('Ba', 'O', 'Ta', 'Ti', 'Zn')\",OTHERS,False\nmp-7386,\"{'F': 8.0, 'Na': 2.0, 'Ag': 2.0}\",\"('Ag', 'F', 'Na')\",OTHERS,False\nmp-978785,\"{'Si': 16.0, 'Mo': 4.0, 'Pt': 12.0}\",\"('Mo', 'Pt', 'Si')\",OTHERS,False\nmp-763763,\"{'Li': 4.0, 'Cr': 3.0, 'Fe': 2.0, 'Co': 3.0, 'O': 16.0}\",\"('Co', 'Cr', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-758488,\"{'Li': 2.0, 'P': 6.0, 'W': 4.0, 'O': 26.0}\",\"('Li', 'O', 'P', 'W')\",OTHERS,False\nmp-3193,\"{'O': 20.0, 'Na': 8.0, 'Si': 8.0}\",\"('Na', 'O', 'Si')\",OTHERS,False\nmp-757655,\"{'Li': 17.0, 'Ni': 11.0, 'O': 28.0}\",\"('Li', 'Ni', 'O')\",OTHERS,False\nmp-1225167,\"{'Gd': 3.0, 'Y': 3.0, 'Fe': 23.0}\",\"('Fe', 'Gd', 'Y')\",OTHERS,False\nmp-867574,\"{'Li': 4.0, 'Mn': 8.0, 'Si': 8.0, 'O': 26.0}\",\"('Li', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-574176,\"{'Ba': 4.0, 'In': 8.0, 'Au': 8.0}\",\"('Au', 'Ba', 'In')\",OTHERS,False\nmp-1204738,\"{'Fe': 4.0, 'As': 8.0, 'S': 16.0, 'O': 32.0, 'F': 48.0}\",\"('As', 'F', 'Fe', 'O', 'S')\",OTHERS,False\nmp-631631,\"{'Mn': 5.0, 'V': 2.0, 'Pb': 3.0, 'O': 16.0}\",\"('Mn', 'O', 'Pb', 'V')\",OTHERS,False\nmp-1222471,\"{'Mg': 7.0, 'Ti': 1.0, 'Al': 6.0, 'Si': 8.0, 'H': 8.0, 'O': 38.0}\",\"('Al', 'H', 'Mg', 'O', 'Si', 'Ti')\",OTHERS,False\nmp-561037,\"{'K': 8.0, 'U': 1.0, 'Cr': 4.0, 'N': 2.0, 'O': 24.0}\",\"('Cr', 'K', 'N', 'O', 'U')\",OTHERS,False\nmp-759405,\"{'Bi': 12.0, 'O': 8.0, 'F': 20.0}\",\"('Bi', 'F', 'O')\",OTHERS,False\nmp-569576,\"{'La': 20.0, 'Si': 12.0, 'N': 36.0}\",\"('La', 'N', 'Si')\",OTHERS,False\nmp-1106150,\"{'Ce': 1.0, 'Mn': 4.0, 'Cu': 3.0, 'O': 12.0}\",\"('Ce', 'Cu', 'Mn', 'O')\",OTHERS,False\nmp-770899,\"{'Li': 4.0, 'V': 6.0, 'O': 16.0}\",\"('Li', 'O', 'V')\",OTHERS,False\nmp-569456,\"{'Ti': 4.0, 'Hg': 24.0, 'As': 16.0, 'Br': 28.0}\",\"('As', 'Br', 'Hg', 'Ti')\",OTHERS,False\nmp-850091,\"{'Li': 9.0, 'Nb': 10.0, 'Cr': 6.0, 'P': 24.0, 'O': 96.0}\",\"('Cr', 'Li', 'Nb', 'O', 'P')\",OTHERS,False\nmp-18807,\"{'O': 7.0, 'V': 2.0, 'Zn': 2.0}\",\"('O', 'V', 'Zn')\",OTHERS,False\nmp-683975,\"{'Yb': 12.0, 'K': 24.0, 'P': 20.0, 'S': 80.0}\",\"('K', 'P', 'S', 'Yb')\",OTHERS,False\nmvc-16255,\"{'Ba': 1.0, 'Al': 1.0, 'Cu': 1.0, 'W': 1.0, 'O': 5.0}\",\"('Al', 'Ba', 'Cu', 'O', 'W')\",OTHERS,False\nmp-504876,\"{'Hg': 19.0, 'Br': 18.0, 'As': 10.0}\",\"('As', 'Br', 'Hg')\",OTHERS,False\nmp-1251156,\"{'Sr': 4.0, 'Al': 2.0, 'Cu': 6.0, 'O': 14.0}\",\"('Al', 'Cu', 'O', 'Sr')\",OTHERS,False\nmp-1187229,\"{'Sr': 1.0, 'La': 3.0}\",\"('La', 'Sr')\",OTHERS,False\nmp-8914,\"{'Ga': 4.0, 'Sr': 4.0, 'Sn': 4.0}\",\"('Ga', 'Sn', 'Sr')\",OTHERS,False\nmp-1208283,\"{'Ti': 10.0, 'Co': 2.0, 'Sn': 6.0}\",\"('Co', 'Sn', 'Ti')\",OTHERS,False\nmp-1104706,\"{'Na': 2.0, 'Ca': 2.0, 'Si': 2.0, 'O': 8.0}\",\"('Ca', 'Na', 'O', 'Si')\",OTHERS,False\nmp-777669,\"{'Li': 16.0, 'Fe': 8.0, 'F': 32.0}\",\"('F', 'Fe', 'Li')\",OTHERS,False\nmp-757690,\"{'Li': 2.0, 'Nb': 2.0, 'P': 8.0, 'O': 24.0}\",\"('Li', 'Nb', 'O', 'P')\",OTHERS,False\nmp-26005,\"{'Li': 2.0, 'O': 48.0, 'P': 14.0, 'Mn': 12.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-627,\"{'Se': 1.0, 'Np': 1.0}\",\"('Np', 'Se')\",OTHERS,False\nmp-3262,\"{'Si': 2.0, 'Co': 2.0, 'Tm': 1.0}\",\"('Co', 'Si', 'Tm')\",OTHERS,False\nmp-1195876,\"{'U': 8.0, 'B': 24.0, 'Mo': 4.0}\",\"('B', 'Mo', 'U')\",OTHERS,False\nmp-1186882,\"{'Rb': 3.0, 'Zr': 1.0}\",\"('Rb', 'Zr')\",OTHERS,False\nmp-1245820,\"{'Sr': 2.0, 'C': 14.0, 'N': 20.0}\",\"('C', 'N', 'Sr')\",OTHERS,False\nmp-1066655,\"{'K': 3.0, 'Rh': 1.0}\",\"('K', 'Rh')\",OTHERS,False\nmp-20433,\"{'Ti': 6.0, 'In': 8.0}\",\"('In', 'Ti')\",OTHERS,False\nmp-6121,\"{'O': 20.0, 'Zn': 4.0, 'Y': 8.0, 'Ba': 4.0}\",\"('Ba', 'O', 'Y', 'Zn')\",OTHERS,False\nmp-1027109,\"{'Mo': 3.0, 'W': 1.0, 'Se': 6.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-624762,\"{'Fe': 16.0, 'Te': 8.0, 'C': 44.0, 'O': 44.0}\",\"('C', 'Fe', 'O', 'Te')\",OTHERS,False\nmp-745021,\"{'Sr': 8.0, 'Mg': 8.0, 'V': 12.0, 'O': 56.0}\",\"('Mg', 'O', 'Sr', 'V')\",OTHERS,False\nmp-759173,\"{'Li': 1.0, 'V': 3.0, 'O': 8.0}\",\"('Li', 'O', 'V')\",OTHERS,False\nmp-19910,\"{'Mg': 2.0, 'Ni': 4.0, 'Ce': 4.0}\",\"('Ce', 'Mg', 'Ni')\",OTHERS,False\nmp-1112949,\"{'Cs': 3.0, 'Tl': 1.0, 'I': 6.0}\",\"('Cs', 'I', 'Tl')\",OTHERS,False\nmp-1111166,\"{'K': 3.0, 'Sb': 1.0, 'Br': 6.0}\",\"('Br', 'K', 'Sb')\",OTHERS,False\nmp-552623,\"{'O': 4.0, 'Na': 4.0, 'C': 1.0}\",\"('C', 'Na', 'O')\",OTHERS,False\nmp-1205678,\"{'Ho': 4.0, 'Mg': 2.0, 'Si': 4.0}\",\"('Ho', 'Mg', 'Si')\",OTHERS,False\nmp-776169,\"{'Li': 8.0, 'Mn': 4.0, 'F': 20.0}\",\"('F', 'Li', 'Mn')\",OTHERS,False\nmp-849764,\"{'H': 112.0, 'Pt': 8.0, 'Br': 20.0, 'N': 36.0, 'O': 8.0}\",\"('Br', 'H', 'N', 'O', 'Pt')\",OTHERS,False\nmvc-7907,\"{'Mg': 3.0, 'Ta': 6.0, 'Fe': 4.0, 'O': 24.0}\",\"('Fe', 'Mg', 'O', 'Ta')\",OTHERS,False\nmp-5014,\"{'S': 4.0, 'Cu': 4.0, 'Ag': 4.0}\",\"('Ag', 'Cu', 'S')\",OTHERS,False\nmp-974332,\"{'S': 3.0, 'Br': 1.0}\",\"('Br', 'S')\",OTHERS,False\nmp-1097379,\"{'Ti': 1.0, 'Nb': 2.0, 'Mo': 1.0}\",\"('Mo', 'Nb', 'Ti')\",OTHERS,False\nmp-676749,\"{'Li': 6.0, 'U': 3.0, 'Cl': 18.0}\",\"('Cl', 'Li', 'U')\",OTHERS,False\nmp-673703,\"{'Rb': 3.0, 'Bi': 7.0, 'Pb': 3.0, 'I': 10.0, 'O': 10.0}\",\"('Bi', 'I', 'O', 'Pb', 'Rb')\",OTHERS,False\nmp-757001,\"{'Y': 15.0, 'Tm': 1.0, 'O': 24.0}\",\"('O', 'Tm', 'Y')\",OTHERS,False\nmp-1013549,\"{'Ba': 3.0, 'Bi': 1.0, 'N': 1.0}\",\"('Ba', 'Bi', 'N')\",OTHERS,False\nmp-1232160,\"{'Tb': 4.0, 'Mg': 4.0, 'S': 12.0}\",\"('Mg', 'S', 'Tb')\",OTHERS,False\nmp-559642,\"{'Ca': 2.0, 'Zr': 2.0, 'Al': 18.0, 'B': 2.0, 'O': 36.0}\",\"('Al', 'B', 'Ca', 'O', 'Zr')\",OTHERS,False\nmp-1183505,\"{'Bi': 6.0, 'As': 2.0}\",\"('As', 'Bi')\",OTHERS,False\nmp-1186550,\"{'Pm': 1.0, 'Cd': 1.0, 'Cu': 2.0}\",\"('Cd', 'Cu', 'Pm')\",OTHERS,False\nmp-1204045,\"{'U': 4.0, 'Fe': 30.0, 'Ge': 4.0}\",\"('Fe', 'Ge', 'U')\",OTHERS,False\nmp-640286,\"{'Ce': 4.0, 'Cu': 16.0, 'Sn': 4.0}\",\"('Ce', 'Cu', 'Sn')\",OTHERS,False\nmp-1097589,\"{'Ta': 1.0, 'V': 1.0, 'Mo': 2.0}\",\"('Mo', 'Ta', 'V')\",OTHERS,False\nmp-1100379,\"{'Ca': 12.0, 'Sn': 12.0, 'S': 36.0}\",\"('Ca', 'S', 'Sn')\",OTHERS,False\nmp-30820,\"{'Li': 2.0, 'Al': 1.0, 'Rh': 1.0}\",\"('Al', 'Li', 'Rh')\",OTHERS,False\nmp-22903,\"{'Rb': 1.0, 'I': 1.0}\",\"('I', 'Rb')\",OTHERS,False\nmp-510607,\"{'Sr': 4.0, 'Cr': 4.0, 'O': 16.0}\",\"('Cr', 'O', 'Sr')\",OTHERS,False\nmp-10193,\"{'P': 1.0, 'Lu': 1.0, 'Pt': 1.0}\",\"('Lu', 'P', 'Pt')\",OTHERS,False\nmp-31049,\"{'O': 4.0, 'Nd': 2.0}\",\"('Nd', 'O')\",OTHERS,False\nmp-768225,\"{'Cu': 4.0, 'Ni': 2.0, 'O': 8.0}\",\"('Cu', 'Ni', 'O')\",OTHERS,False\nmp-772825,\"{'Ta': 8.0, 'Be': 4.0, 'O': 24.0}\",\"('Be', 'O', 'Ta')\",OTHERS,False\nmp-1027112,\"{'Mo': 1.0, 'W': 3.0, 'Se': 4.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-416,\"{'Cr': 6.0, 'Os': 2.0}\",\"('Cr', 'Os')\",OTHERS,False\nmp-754992,\"{'Lu': 1.0, 'Tl': 1.0, 'O': 2.0}\",\"('Lu', 'O', 'Tl')\",OTHERS,False\nmp-1194289,\"{'Ga': 2.0, 'Co': 21.0, 'B': 6.0}\",\"('B', 'Co', 'Ga')\",OTHERS,False\nmp-865343,\"{'Tm': 2.0, 'Ir': 1.0, 'Os': 1.0}\",\"('Ir', 'Os', 'Tm')\",OTHERS,False\nmp-1995,\"{'C': 2.0, 'Pr': 1.0}\",\"('C', 'Pr')\",OTHERS,False\nmp-22958,\"{'O': 3.0, 'K': 1.0, 'Br': 1.0}\",\"('Br', 'K', 'O')\",OTHERS,False\nmp-1203528,\"{'K': 8.0, 'Ba': 2.0, 'V': 12.0, 'O': 36.0}\",\"('Ba', 'K', 'O', 'V')\",OTHERS,False\nmp-1245213,\"{'Zn': 50.0, 'S': 50.0}\",\"('S', 'Zn')\",OTHERS,False\nmp-1222070,\"{'Mg': 1.0, 'Ti': 4.0, 'P': 6.0, 'O': 24.0}\",\"('Mg', 'O', 'P', 'Ti')\",OTHERS,False\nmp-1037934,\"{'Ca': 1.0, 'Mg': 30.0, 'Cd': 1.0, 'O': 32.0}\",\"('Ca', 'Cd', 'Mg', 'O')\",OTHERS,False\nmp-4991,\"{'O': 14.0, 'Sn': 4.0, 'Tb': 4.0}\",\"('O', 'Sn', 'Tb')\",OTHERS,False\nmp-505824,\"{'Cs': 2.0, 'Pd': 1.0, 'C': 2.0}\",\"('C', 'Cs', 'Pd')\",OTHERS,False\nmp-662527,\"{'Ce': 8.0, 'Si': 8.0, 'O': 28.0}\",\"('Ce', 'O', 'Si')\",OTHERS,False\nmp-542566,\"{'Pr': 6.0, 'Pd': 12.0, 'Sb': 10.0}\",\"('Pd', 'Pr', 'Sb')\",OTHERS,False\nmp-1228668,\"{'Ba': 2.0, 'Cu': 3.0, 'O': 6.0}\",\"('Ba', 'Cu', 'O')\",OTHERS,False\nmp-1187370,\"{'Tb': 1.0, 'Pm': 1.0, 'Ir': 2.0}\",\"('Ir', 'Pm', 'Tb')\",OTHERS,False\nmp-754919,\"{'V': 2.0, 'O': 2.0, 'F': 6.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-1221791,\"{'Mn': 2.0, 'Sb': 2.0, 'Pt': 1.0, 'Au': 1.0}\",\"('Au', 'Mn', 'Pt', 'Sb')\",OTHERS,False\nmp-1147685,\"{'Li': 20.0, 'Zn': 2.0, 'P': 8.0, 'S': 32.0}\",\"('Li', 'P', 'S', 'Zn')\",OTHERS,False\nmp-19108,\"{'Ca': 4.0, 'Mn': 2.0, 'W': 2.0, 'O': 12.0}\",\"('Ca', 'Mn', 'O', 'W')\",OTHERS,False\nmp-29982,\"{'B': 6.0, 'C': 3.0, 'Nb': 7.0}\",\"('B', 'C', 'Nb')\",OTHERS,False\nmvc-16311,\"{'Y': 1.0, 'Sb': 1.0, 'W': 2.0, 'O': 8.0}\",\"('O', 'Sb', 'W', 'Y')\",OTHERS,False\nmp-1187312,\"{'Tb': 1.0, 'As': 3.0}\",\"('As', 'Tb')\",OTHERS,False\nmp-1106223,\"{'Nd': 4.0, 'Ge': 8.0, 'Pt': 4.0}\",\"('Ge', 'Nd', 'Pt')\",OTHERS,False\nmp-1232246,\"{'Ce': 32.0, 'Mg': 16.0, 'S': 64.0}\",\"('Ce', 'Mg', 'S')\",OTHERS,False\nmp-1208599,\"{'Sr': 2.0, 'Li': 2.0, 'Ti': 2.0, 'F': 12.0}\",\"('F', 'Li', 'Sr', 'Ti')\",OTHERS,False\nmp-753127,\"{'Na': 3.0, 'Li': 4.0, 'Mn': 5.0, 'O': 9.0}\",\"('Li', 'Mn', 'Na', 'O')\",OTHERS,False\nmp-27276,\"{'Si': 12.0, 'Ni': 31.0}\",\"('Ni', 'Si')\",OTHERS,False\nmp-766414,\"{'Mn': 2.0, 'Fe': 6.0, 'P': 8.0, 'O': 32.0}\",\"('Fe', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1178773,\"{'Y': 10.0, 'Fe': 20.0}\",\"('Fe', 'Y')\",OTHERS,False\nmp-1247451,\"{'Mg': 6.0, 'Ga': 6.0, 'N': 10.0}\",\"('Ga', 'Mg', 'N')\",OTHERS,False\nmp-1278,\"{'Si': 2.0, 'Zr': 4.0}\",\"('Si', 'Zr')\",OTHERS,False\nmp-1227648,\"{'Ca': 1.0, 'In': 6.0, 'Cu': 6.0}\",\"('Ca', 'Cu', 'In')\",OTHERS,False\nmp-1056985,\"{'Ti': 1.0, 'Pd': 1.0}\",\"('Pd', 'Ti')\",OTHERS,False\nmp-1190582,\"{'Mg': 6.0, 'Nb': 1.0, 'H': 16.0}\",\"('H', 'Mg', 'Nb')\",OTHERS,False\nmp-1214524,\"{'Ba': 8.0, 'Dy': 2.0, 'Cu': 6.0, 'O': 18.0}\",\"('Ba', 'Cu', 'Dy', 'O')\",OTHERS,False\nmp-4068,\"{'Na': 8.0, 'S': 12.0, 'Ge': 4.0}\",\"('Ge', 'Na', 'S')\",OTHERS,False\nmp-771164,\"{'Na': 4.0, 'Mn': 2.0, 'B': 2.0, 'As': 2.0, 'O': 14.0}\",\"('As', 'B', 'Mn', 'Na', 'O')\",OTHERS,False\nmp-767576,\"{'Li': 12.0, 'Mn': 2.0, 'O': 8.0, 'F': 2.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-703595,\"{'Cd': 13.0, 'I': 28.0}\",\"('Cd', 'I')\",OTHERS,False\nmp-676552,\"{'Nd': 2.0, 'Th': 8.0, 'O': 19.0}\",\"('Nd', 'O', 'Th')\",OTHERS,False\nmp-510342,\"{'Dy': 4.0, 'Te': 8.0, 'O': 22.0}\",\"('Dy', 'O', 'Te')\",OTHERS,False\nmp-1215613,\"{'Yb': 7.0, 'Mg': 1.0, 'Pt': 4.0}\",\"('Mg', 'Pt', 'Yb')\",OTHERS,False\nmp-1190998,\"{'Pr': 8.0, 'P': 8.0, 'S': 8.0}\",\"('P', 'Pr', 'S')\",OTHERS,False\nmp-1207084,\"{'Al': 1.0, 'Ni': 3.0, 'C': 1.0}\",\"('Al', 'C', 'Ni')\",OTHERS,False\nmp-755470,\"{'Li': 2.0, 'Bi': 1.0, 'S': 2.0}\",\"('Bi', 'Li', 'S')\",OTHERS,False\nmp-756054,\"{'Zr': 8.0, 'N': 8.0, 'O': 4.0}\",\"('N', 'O', 'Zr')\",OTHERS,False\nmp-626467,\"{'Al': 8.0, 'H': 24.0, 'O': 24.0}\",\"('Al', 'H', 'O')\",OTHERS,False\nmp-1080178,\"{'Gd': 3.0, 'Zn': 3.0, 'Pd': 3.0}\",\"('Gd', 'Pd', 'Zn')\",OTHERS,False\nmp-1209792,\"{'Rb': 1.0, 'Cu': 2.0, 'O': 10.0}\",\"('Cu', 'O', 'Rb')\",OTHERS,False\nmp-601846,\"{'Yb': 2.0, 'In': 8.0, 'Ir': 1.0}\",\"('In', 'Ir', 'Yb')\",OTHERS,False\nmp-558775,\"{'Al': 6.0, 'Pb': 10.0, 'F': 38.0}\",\"('Al', 'F', 'Pb')\",OTHERS,False\nmp-972924,\"{'La': 1.0, 'Mg': 16.0, 'Al': 12.0}\",\"('Al', 'La', 'Mg')\",OTHERS,False\nmp-1176372,\"{'Na': 6.0, 'Mn': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Mn', 'Na', 'O', 'P')\",OTHERS,False\nmp-557463,\"{'Na': 12.0, 'Sc': 4.0, 'Si': 8.0, 'O': 28.0}\",\"('Na', 'O', 'Sc', 'Si')\",OTHERS,False\nmp-1094255,\"{'Zr': 6.0, 'Sn': 2.0}\",\"('Sn', 'Zr')\",OTHERS,False\nmp-865439,\"{'Th': 6.0, 'O': 4.0}\",\"('O', 'Th')\",OTHERS,False\nmp-1178904,\"{'V': 4.0, 'Cu': 2.0, 'P': 4.0, 'O': 30.0}\",\"('Cu', 'O', 'P', 'V')\",OTHERS,False\nmp-9351,\"{'Ge': 2.0, 'Lu': 2.0, 'Au': 2.0}\",\"('Au', 'Ge', 'Lu')\",OTHERS,False\nmp-565679,\"{'Hg': 16.0, 'Se': 16.0, 'O': 36.0}\",\"('Hg', 'O', 'Se')\",OTHERS,False\nmp-1187402,\"{'Th': 1.0, 'Al': 1.0, 'Cu': 2.0}\",\"('Al', 'Cu', 'Th')\",OTHERS,False\nmp-14017,\"{'K': 6.0, 'Sb': 2.0}\",\"('K', 'Sb')\",OTHERS,False\nmp-758528,\"{'Li': 6.0, 'Mn': 10.0, 'O': 4.0, 'F': 18.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-778474,\"{'Li': 12.0, 'Co': 4.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Co', 'Li', 'O')\",OTHERS,False\nmp-1179605,{'Sb': 2.0},\"('Sb',)\",OTHERS,False\nmp-29973,\"{'N': 4.0, 'Sr': 4.0}\",\"('N', 'Sr')\",OTHERS,False\nmp-1024968,\"{'Pu': 1.0, 'B': 2.0, 'Rh': 3.0}\",\"('B', 'Pu', 'Rh')\",OTHERS,False\nmp-8613,\"{'P': 2.0, 'S': 6.0, 'Mn': 2.0}\",\"('Mn', 'P', 'S')\",OTHERS,False\nmp-974364,\"{'Ru': 1.0, 'Au': 3.0}\",\"('Au', 'Ru')\",OTHERS,False\nmp-775876,\"{'Na': 2.0, 'Li': 2.0, 'Mn': 2.0, 'P': 4.0, 'O': 14.0}\",\"('Li', 'Mn', 'Na', 'O', 'P')\",OTHERS,False\nmvc-15619,\"{'Mg': 4.0, 'Co': 2.0, 'Sb': 2.0, 'O': 12.0}\",\"('Co', 'Mg', 'O', 'Sb')\",OTHERS,False\nmp-1104601,\"{'Co': 1.0, 'N': 2.0, 'O': 10.0}\",\"('Co', 'N', 'O')\",OTHERS,False\nmp-673798,\"{'K': 6.0, 'Cl': 2.0, 'O': 2.0}\",\"('Cl', 'K', 'O')\",OTHERS,False\nmp-1095641,\"{'Tb': 5.0, 'S': 7.0}\",\"('S', 'Tb')\",OTHERS,False\nmp-1209090,\"{'Rb': 2.0, 'Tm': 2.0, 'Be': 2.0, 'F': 12.0}\",\"('Be', 'F', 'Rb', 'Tm')\",OTHERS,False\nmp-9721,\"{'O': 1.0, 'Ca': 3.0, 'Ge': 1.0}\",\"('Ca', 'Ge', 'O')\",OTHERS,False\nmp-32509,\"{'V': 2.0, 'P': 8.0, 'O': 24.0}\",\"('O', 'P', 'V')\",OTHERS,False\nmp-1070152,\"{'Ca': 1.0, 'Co': 2.0, 'Si': 2.0}\",\"('Ca', 'Co', 'Si')\",OTHERS,False\nmp-22588,\"{'O': 12.0, 'Fe': 4.0, 'Lu': 4.0}\",\"('Fe', 'Lu', 'O')\",OTHERS,False\nmp-1227199,\"{'Ca': 1.0, 'Pr': 3.0, 'Co': 4.0, 'O': 12.0}\",\"('Ca', 'Co', 'O', 'Pr')\",OTHERS,False\nmp-1187114,\"{'Sr': 3.0, 'Cr': 1.0}\",\"('Cr', 'Sr')\",OTHERS,False\nmp-1073927,\"{'Mg': 12.0, 'Si': 10.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1246426,\"{'Ge': 4.0, 'C': 4.0, 'N': 8.0}\",\"('C', 'Ge', 'N')\",OTHERS,False\nmp-6652,\"{'B': 6.0, 'O': 18.0, 'K': 2.0, 'Zn': 8.0}\",\"('B', 'K', 'O', 'Zn')\",OTHERS,False\nmp-867782,\"{'Mn': 2.0, 'O': 2.0, 'F': 2.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-14214,\"{'Si': 8.0, 'P': 32.0, 'Ba': 32.0}\",\"('Ba', 'P', 'Si')\",OTHERS,False\nmp-850284,\"{'Li': 4.0, 'Co': 2.0, 'O': 2.0, 'F': 6.0}\",\"('Co', 'F', 'Li', 'O')\",OTHERS,False\nmp-697264,\"{'Cd': 4.0, 'H': 8.0, 'Se': 4.0, 'O': 16.0}\",\"('Cd', 'H', 'O', 'Se')\",OTHERS,False\nmp-1188370,\"{'La': 6.0, 'Sn': 10.0}\",\"('La', 'Sn')\",OTHERS,False\nmp-1031330,\"{'K': 1.0, 'La': 1.0, 'Mg': 6.0, 'O': 8.0}\",\"('K', 'La', 'Mg', 'O')\",OTHERS,False\nmp-21974,\"{'O': 48.0, 'Fe': 20.0, 'Dy': 12.0}\",\"('Dy', 'Fe', 'O')\",OTHERS,False\nmp-1176791,\"{'Li': 18.0, 'Mn': 2.0, 'Si': 4.0, 'O': 20.0}\",\"('Li', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-705429,\"{'Li': 12.0, 'Co': 10.0, 'P': 16.0, 'O': 56.0}\",\"('Co', 'Li', 'O', 'P')\",OTHERS,False\nmp-1214619,\"{'C': 40.0, 'Cl': 36.0, 'O': 2.0}\",\"('C', 'Cl', 'O')\",OTHERS,False\nmp-1105966,\"{'Gd': 8.0, 'Te': 12.0}\",\"('Gd', 'Te')\",OTHERS,False\nmp-1173997,\"{'Li': 5.0, 'Mn': 1.0, 'Co': 2.0, 'O': 8.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-28473,\"{'C': 2.0, 'F': 6.0, 'Cl': 2.0}\",\"('C', 'Cl', 'F')\",OTHERS,False\nmp-1203464,\"{'Bi': 8.0, 'As': 4.0, 'O': 24.0}\",\"('As', 'Bi', 'O')\",OTHERS,False\nmp-1192255,\"{'Sm': 2.0, 'Mn': 3.0, 'Sb': 4.0, 'S': 12.0}\",\"('Mn', 'S', 'Sb', 'Sm')\",OTHERS,False\nmp-5391,\"{'O': 16.0, 'Mg': 8.0, 'Si': 4.0}\",\"('Mg', 'O', 'Si')\",OTHERS,False\nmp-1213756,\"{'Cr': 7.0, 'Te': 8.0}\",\"('Cr', 'Te')\",OTHERS,False\nmp-756866,\"{'Li': 2.0, 'Ti': 1.0, 'Mn': 3.0, 'O': 8.0}\",\"('Li', 'Mn', 'O', 'Ti')\",OTHERS,False\nmp-1224328,\"{'Hf': 1.0, 'Nb': 1.0, 'B': 4.0}\",\"('B', 'Hf', 'Nb')\",OTHERS,False\nmp-1205810,\"{'Eu': 2.0, 'Br': 6.0}\",\"('Br', 'Eu')\",OTHERS,False\nmp-1111564,\"{'Li': 2.0, 'Al': 1.0, 'Ag': 1.0, 'F': 6.0}\",\"('Ag', 'Al', 'F', 'Li')\",OTHERS,False\nmp-1210883,\"{'Nd': 28.0, 'V': 8.0, 'B': 4.0, 'O': 68.0}\",\"('B', 'Nd', 'O', 'V')\",OTHERS,False\nmp-1211154,\"{'Na': 6.0, 'Ca': 8.0, 'Ti': 2.0, 'Si': 8.0, 'O': 32.0, 'F': 4.0}\",\"('Ca', 'F', 'Na', 'O', 'Si', 'Ti')\",OTHERS,False\nmp-1206671,\"{'Lu': 2.0, 'Se': 2.0, 'I': 2.0}\",\"('I', 'Lu', 'Se')\",OTHERS,False\nmp-780706,\"{'Li': 2.0, 'Mn': 2.0, 'P': 4.0, 'O': 14.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1183219,\"{'Ba': 2.0, 'Mg': 1.0, 'Tl': 1.0}\",\"('Ba', 'Mg', 'Tl')\",OTHERS,False\nmp-5479,\"{'F': 4.0, 'Al': 1.0, 'Rb': 1.0}\",\"('Al', 'F', 'Rb')\",OTHERS,False\nmp-568808,\"{'Tb': 6.0, 'Se': 8.0}\",\"('Se', 'Tb')\",OTHERS,False\nmp-1183933,\"{'Cs': 1.0, 'Np': 1.0, 'O': 3.0}\",\"('Cs', 'Np', 'O')\",OTHERS,False\nmp-1033732,\"{'Mg': 14.0, 'B': 1.0, 'Sb': 1.0, 'O': 16.0}\",\"('B', 'Mg', 'O', 'Sb')\",OTHERS,False\nmp-705432,\"{'Li': 12.0, 'Ni': 10.0, 'P': 16.0, 'O': 56.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-975933,\"{'Nd': 1.0, 'Ho': 1.0, 'In': 2.0}\",\"('Ho', 'In', 'Nd')\",OTHERS,False\nmp-1198649,\"{'K': 9.0, 'Y': 3.0, 'Si': 12.0, 'O': 32.0, 'F': 2.0}\",\"('F', 'K', 'O', 'Si', 'Y')\",OTHERS,False\nmp-1176194,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-642649,\"{'Ba': 2.0, 'H': 4.0, 'O': 6.0}\",\"('Ba', 'H', 'O')\",OTHERS,False\nmp-1215921,\"{'Y': 1.0, 'Ho': 1.0, 'C': 4.0}\",\"('C', 'Ho', 'Y')\",OTHERS,False\nmp-754057,\"{'Li': 4.0, 'Fe': 2.0, 'Cu': 3.0, 'Sb': 3.0, 'O': 16.0}\",\"('Cu', 'Fe', 'Li', 'O', 'Sb')\",OTHERS,False\nmp-779706,\"{'Ag': 4.0, 'Ge': 4.0, 'O': 12.0}\",\"('Ag', 'Ge', 'O')\",OTHERS,False\nmp-1178561,\"{'Ag': 1.0, 'Cl': 1.0, 'O': 3.0}\",\"('Ag', 'Cl', 'O')\",OTHERS,False\nmp-571467,\"{'Cs': 2.0, 'Na': 1.0, 'Y': 1.0, 'Br': 6.0}\",\"('Br', 'Cs', 'Na', 'Y')\",OTHERS,False\nmp-1220708,\"{'Na': 1.0, 'Ho': 1.0, 'F': 4.0}\",\"('F', 'Ho', 'Na')\",OTHERS,False\nmp-23280,\"{'Cl': 12.0, 'As': 4.0}\",\"('As', 'Cl')\",OTHERS,False\nmp-1028947,\"{'Mo': 1.0, 'W': 3.0, 'Se': 4.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-753719,\"{'Sr': 1.0, 'Li': 1.0, 'Nb': 2.0, 'O': 6.0, 'F': 1.0}\",\"('F', 'Li', 'Nb', 'O', 'Sr')\",OTHERS,False\nmp-985297,\"{'Ag': 3.0, 'Rh': 1.0}\",\"('Ag', 'Rh')\",OTHERS,False\nmp-757169,\"{'Li': 4.0, 'Mn': 2.0, 'Si': 4.0, 'O': 12.0}\",\"('Li', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1211819,\"{'K': 6.0, 'Hf': 8.0, 'Ag': 4.0, 'F': 46.0}\",\"('Ag', 'F', 'Hf', 'K')\",OTHERS,False\nmp-25823,\"{'Li': 2.0, 'Ni': 4.0, 'P': 10.0, 'O': 30.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-542132,\"{'Yb': 2.0, 'As': 4.0, 'Cu': 2.0}\",\"('As', 'Cu', 'Yb')\",OTHERS,False\nmp-849955,\"{'Li': 9.0, 'Mn': 15.0, 'Fe': 6.0, 'O': 36.0}\",\"('Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-7151,\"{'Cu': 2.0, 'Se': 6.0, 'Cs': 2.0, 'U': 2.0}\",\"('Cs', 'Cu', 'Se', 'U')\",OTHERS,False\nmp-20269,\"{'Mn': 1.0, 'Ga': 1.0, 'Pt': 1.0}\",\"('Ga', 'Mn', 'Pt')\",OTHERS,False\nmp-1095694,\"{'Ag': 8.0, 'S': 4.0}\",\"('Ag', 'S')\",OTHERS,False\nmp-1177438,\"{'Li': 4.0, 'Mn': 2.0, 'Cr': 3.0, 'Co': 3.0, 'O': 16.0}\",\"('Co', 'Cr', 'Li', 'Mn', 'O')\",OTHERS,False\nmvc-671,\"{'W': 3.0, 'O': 8.0}\",\"('O', 'W')\",OTHERS,False\nmp-1478,\"{'Cu': 8.0, 'O': 6.0}\",\"('Cu', 'O')\",OTHERS,False\nmp-1183679,\"{'Cr': 1.0, 'H': 3.0}\",\"('Cr', 'H')\",OTHERS,False\nmp-601396,\"{'Cu': 4.0, 'H': 40.0, 'C': 8.0, 'N': 16.0, 'O': 40.0}\",\"('C', 'Cu', 'H', 'N', 'O')\",OTHERS,False\nmp-1187595,\"{'Yb': 1.0, 'Ni': 1.0, 'O': 3.0}\",\"('Ni', 'O', 'Yb')\",OTHERS,False\nmp-1179005,\"{'V': 4.0, 'Re': 4.0, 'O': 22.0}\",\"('O', 'Re', 'V')\",OTHERS,False\nmp-1196075,\"{'Si': 20.0, 'H': 114.0, 'C': 38.0}\",\"('C', 'H', 'Si')\",OTHERS,False\nmp-556377,\"{'Ba': 17.0, 'Dy': 16.0, 'Zn': 8.0, 'Pt': 4.0, 'O': 57.0}\",\"('Ba', 'Dy', 'O', 'Pt', 'Zn')\",OTHERS,False\nmp-3787,\"{'S': 6.0, 'Fe': 4.0, 'Rb': 2.0}\",\"('Fe', 'Rb', 'S')\",OTHERS,False\nmp-1247046,\"{'Fe': 1.0, 'Co': 10.0, 'N': 8.0}\",\"('Co', 'Fe', 'N')\",OTHERS,False\nmp-1211487,\"{'K': 8.0, 'Yb': 4.0, 'F': 20.0}\",\"('F', 'K', 'Yb')\",OTHERS,False\nmp-1196069,\"{'Nd': 12.0, 'Al': 86.0, 'W': 8.0}\",\"('Al', 'Nd', 'W')\",OTHERS,False\nmp-1224629,\"{'Gd': 2.0, 'Fe': 4.0, 'Bi': 2.0, 'O': 12.0}\",\"('Bi', 'Fe', 'Gd', 'O')\",OTHERS,False\nmp-975812,\"{'Pr': 1.0, 'Sm': 3.0}\",\"('Pr', 'Sm')\",OTHERS,False\nmp-1183972,\"{'Cs': 1.0, 'Sr': 1.0, 'O': 3.0}\",\"('Cs', 'O', 'Sr')\",OTHERS,False\nmp-10106,\"{'Ca': 4.0, 'As': 2.0}\",\"('As', 'Ca')\",OTHERS,False\nmp-1218394,\"{'Sr': 3.0, 'Be': 2.0, 'B': 5.0, 'H': 1.0, 'O': 13.0}\",\"('B', 'Be', 'H', 'O', 'Sr')\",OTHERS,False\nmp-1214083,\"{'Ca': 12.0, 'Si': 4.0, 'O': 16.0, 'F': 4.0}\",\"('Ca', 'F', 'O', 'Si')\",OTHERS,False\nmp-567939,\"{'Cs': 1.0, 'Sm': 1.0, 'Cl': 3.0}\",\"('Cl', 'Cs', 'Sm')\",OTHERS,False\nmp-1245720,\"{'Zn': 1.0, 'Pb': 2.0, 'N': 2.0}\",\"('N', 'Pb', 'Zn')\",OTHERS,False\nmp-1225560,\"{'Er': 2.0, 'O': 3.0}\",\"('Er', 'O')\",OTHERS,False\nmp-773502,\"{'K': 2.0, 'Co': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Co', 'K', 'O', 'P')\",OTHERS,False\nmp-1094985,\"{'Mg': 8.0, 'Cd': 4.0}\",\"('Cd', 'Mg')\",OTHERS,False\nmp-1095452,\"{'U': 3.0, 'Fe': 2.0, 'Ge': 7.0}\",\"('Fe', 'Ge', 'U')\",OTHERS,False\nmp-1183556,\"{'Ca': 1.0, 'Y': 1.0, 'Cd': 2.0}\",\"('Ca', 'Cd', 'Y')\",OTHERS,False\nmp-30216,\"{'K': 4.0, 'I': 12.0, 'Re': 2.0}\",\"('I', 'K', 'Re')\",OTHERS,False\nmp-1210455,\"{'Na': 3.0, 'La': 1.0, 'As': 2.0, 'O': 8.0}\",\"('As', 'La', 'Na', 'O')\",OTHERS,False\nmp-1186570,\"{'Pr': 1.0, 'Dy': 1.0, 'In': 2.0}\",\"('Dy', 'In', 'Pr')\",OTHERS,False\nmp-755576,\"{'Zr': 8.0, 'N': 8.0, 'O': 4.0}\",\"('N', 'O', 'Zr')\",OTHERS,False\nmp-1226984,\"{'La': 20.0, 'Ga': 6.0, 'Co': 14.0}\",\"('Co', 'Ga', 'La')\",OTHERS,False\nmp-1018740,\"{'La': 2.0, 'Co': 2.0, 'Si': 2.0}\",\"('Co', 'La', 'Si')\",OTHERS,False\nmp-1005761,\"{'Sm': 2.0, 'Gd': 6.0}\",\"('Gd', 'Sm')\",OTHERS,False\nmp-1186674,\"{'Pm': 1.0, 'Zn': 2.0, 'Hg': 1.0}\",\"('Hg', 'Pm', 'Zn')\",OTHERS,False\nmp-162,{'Yb': 1.0},\"('Yb',)\",OTHERS,False\nmp-1191851,\"{'Yb': 6.0, 'Si': 4.0, 'Ni': 12.0}\",\"('Ni', 'Si', 'Yb')\",OTHERS,False\nmp-977446,\"{'Nd': 2.0, 'V': 2.0, 'Ge': 6.0}\",\"('Ge', 'Nd', 'V')\",OTHERS,False\nmp-1207829,\"{'Yb': 12.0, 'Mn': 8.0, 'Al': 86.0}\",\"('Al', 'Mn', 'Yb')\",OTHERS,False\nmp-1207780,\"{'Y': 12.0, 'Rh': 4.0}\",\"('Rh', 'Y')\",OTHERS,False\nmp-770370,\"{'V': 12.0, 'B': 4.0, 'O': 24.0}\",\"('B', 'O', 'V')\",OTHERS,False\nmp-1217198,\"{'V': 4.0, 'Cd': 4.0, 'Ni': 4.0, 'P': 8.0, 'O': 52.0}\",\"('Cd', 'Ni', 'O', 'P', 'V')\",OTHERS,False\nmp-565711,\"{'P': 24.0, 'O': 24.0, 'F': 36.0}\",\"('F', 'O', 'P')\",OTHERS,False\nmp-1099622,\"{'K': 5.0, 'Na': 3.0, 'Nb': 7.0, 'W': 1.0, 'O': 24.0}\",\"('K', 'Na', 'Nb', 'O', 'W')\",OTHERS,False\nmp-1177697,\"{'Li': 3.0, 'Cu': 4.0, 'Sb': 1.0, 'O': 8.0}\",\"('Cu', 'Li', 'O', 'Sb')\",OTHERS,False\nmp-1246934,\"{'Mg': 2.0, 'Ti': 3.0, 'Ni': 1.0, 'S': 8.0}\",\"('Mg', 'Ni', 'S', 'Ti')\",OTHERS,False\nmp-1178865,\"{'W': 4.0, 'O': 20.0}\",\"('O', 'W')\",OTHERS,False\nmp-1195268,\"{'Co': 8.0, 'Bi': 16.0, 'S': 8.0, 'O': 56.0}\",\"('Bi', 'Co', 'O', 'S')\",OTHERS,False\nmp-685478,\"{'Na': 32.0, 'Pd': 16.0, 'O': 48.0}\",\"('Na', 'O', 'Pd')\",OTHERS,False\nmp-28238,\"{'Se': 44.0, 'Sb': 16.0, 'Ba': 16.0}\",\"('Ba', 'Sb', 'Se')\",OTHERS,False\nmp-780897,\"{'Li': 4.0, 'Mn': 4.0, 'P': 8.0, 'H': 12.0, 'O': 34.0}\",\"('H', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-557295,\"{'V': 8.0, 'Se': 16.0, 'O': 48.0}\",\"('O', 'Se', 'V')\",OTHERS,False\nmp-29357,\"{'C': 2.0, 'Cl': 28.0, 'Zr': 12.0}\",\"('C', 'Cl', 'Zr')\",OTHERS,False\nmp-1205990,\"{'Nb': 1.0, 'N': 2.0, 'Cl': 6.0}\",\"('Cl', 'N', 'Nb')\",OTHERS,False\nmp-1185618,\"{'Mg': 1.0, 'V': 1.0, 'Rh': 2.0}\",\"('Mg', 'Rh', 'V')\",OTHERS,False\nmp-973926,\"{'K': 2.0, 'Co': 2.0, 'H': 6.0, 'C': 6.0, 'O': 12.0}\",\"('C', 'Co', 'H', 'K', 'O')\",OTHERS,False\nmp-1091374,\"{'Nb': 2.0, 'Cr': 2.0, 'N': 4.0}\",\"('Cr', 'N', 'Nb')\",OTHERS,False\nmp-18190,\"{'O': 12.0, 'Ca': 1.0, 'Cu': 3.0, 'Sn': 4.0}\",\"('Ca', 'Cu', 'O', 'Sn')\",OTHERS,False\nmp-697873,\"{'Sn': 4.0, 'H': 4.0, 'C': 8.0, 'O': 12.0}\",\"('C', 'H', 'O', 'Sn')\",OTHERS,False\nmp-13456,\"{'S': 5.0, 'Zn': 5.0}\",\"('S', 'Zn')\",OTHERS,False\nmp-3654,\"{'F': 3.0, 'Ca': 1.0, 'Rb': 1.0}\",\"('Ca', 'F', 'Rb')\",OTHERS,False\nmp-1101241,\"{'V': 14.0, 'Fe': 5.0, 'O': 32.0}\",\"('Fe', 'O', 'V')\",OTHERS,False\nmp-1176633,\"{'Li': 8.0, 'Ni': 8.0, 'P': 8.0, 'O': 32.0, 'F': 8.0}\",\"('F', 'Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-7935,\"{'Sb': 2.0, 'Te': 2.0, 'U': 2.0}\",\"('Sb', 'Te', 'U')\",OTHERS,False\nmp-23408,\"{'S': 20.0, 'Tl': 16.0, 'Bi': 8.0}\",\"('Bi', 'S', 'Tl')\",OTHERS,False\nmp-977358,\"{'Ho': 2.0, 'Ag': 1.0, 'Ir': 1.0}\",\"('Ag', 'Ho', 'Ir')\",OTHERS,False\nmp-31084,\"{'Zr': 12.0, 'Ge': 24.0, 'Rh': 12.0}\",\"('Ge', 'Rh', 'Zr')\",OTHERS,False\nmp-766402,\"{'Li': 8.0, 'V': 4.0, 'Si': 12.0, 'O': 32.0}\",\"('Li', 'O', 'Si', 'V')\",OTHERS,False\nmp-556291,\"{'Ba': 12.0, 'Sn': 8.0, 'S': 28.0}\",\"('Ba', 'S', 'Sn')\",OTHERS,False\nmp-1099087,\"{'Mg': 14.0, 'Co': 1.0, 'Sn': 1.0}\",\"('Co', 'Mg', 'Sn')\",OTHERS,False\nmp-555891,\"{'Si': 4.0, 'O': 8.0}\",\"('O', 'Si')\",OTHERS,False\nmp-1207733,\"{'Y': 4.0, 'Cr': 4.0, 'Ge': 4.0, 'O': 20.0}\",\"('Cr', 'Ge', 'O', 'Y')\",OTHERS,False\nmp-1039117,\"{'Ca': 1.0, 'Mg': 3.0}\",\"('Ca', 'Mg')\",OTHERS,False\nmp-505212,\"{'Cs': 6.0, 'Au': 2.0, 'O': 2.0}\",\"('Au', 'Cs', 'O')\",OTHERS,False\nmp-2976,\"{'B': 1.0, 'Pd': 3.0, 'Pr': 1.0}\",\"('B', 'Pd', 'Pr')\",OTHERS,False\nmp-765596,\"{'Li': 4.0, 'V': 8.0, 'O': 20.0, 'F': 4.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-1112603,\"{'Cs': 2.0, 'Hg': 1.0, 'As': 1.0, 'F': 6.0}\",\"('As', 'Cs', 'F', 'Hg')\",OTHERS,False\nmp-643743,\"{'Cu': 4.0, 'H': 4.0, 'Cl': 4.0, 'O': 4.0}\",\"('Cl', 'Cu', 'H', 'O')\",OTHERS,False\nmp-1197511,\"{'Ti': 18.0, 'Mn': 4.0, 'B': 16.0, 'Ru': 36.0}\",\"('B', 'Mn', 'Ru', 'Ti')\",OTHERS,False\nmp-1197271,\"{'Eu': 4.0, 'V': 8.0, 'I': 4.0, 'O': 36.0}\",\"('Eu', 'I', 'O', 'V')\",OTHERS,False\nmp-557784,\"{'La': 4.0, 'Nb': 4.0, 'Te': 4.0, 'O': 24.0}\",\"('La', 'Nb', 'O', 'Te')\",OTHERS,False\nmp-1203752,\"{'Tb': 4.0, 'Fe': 20.0, 'P': 12.0}\",\"('Fe', 'P', 'Tb')\",OTHERS,False\nmp-1244920,\"{'V': 30.0, 'O': 75.0}\",\"('O', 'V')\",OTHERS,False\nmp-1197808,\"{'Ca': 4.0, 'Sn': 4.0, 'O': 24.0}\",\"('Ca', 'O', 'Sn')\",OTHERS,False\nmp-769159,\"{'Li': 4.0, 'Cu': 4.0, 'P': 4.0, 'H': 8.0, 'O': 20.0}\",\"('Cu', 'H', 'Li', 'O', 'P')\",OTHERS,False\nmp-556638,\"{'K': 4.0, 'Th': 2.0, 'Mo': 6.0, 'O': 24.0}\",\"('K', 'Mo', 'O', 'Th')\",OTHERS,False\nmp-1195226,\"{'Pb': 8.0, 'Br': 6.0, 'N': 2.0, 'O': 32.0}\",\"('Br', 'N', 'O', 'Pb')\",OTHERS,False\nmp-1919,\"{'Cr': 8.0, 'Zr': 4.0}\",\"('Cr', 'Zr')\",OTHERS,False\nmp-774106,\"{'Li': 6.0, 'Fe': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-759331,\"{'Fe': 18.0, 'O': 18.0, 'F': 18.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-1176549,\"{'Li': 1.0, 'V': 3.0, 'O': 6.0}\",\"('Li', 'O', 'V')\",OTHERS,False\nmp-1016825,\"{'Hf': 1.0, 'Mg': 1.0, 'O': 3.0}\",\"('Hf', 'Mg', 'O')\",OTHERS,False\nmp-30304,\"{'O': 14.0, 'Sb': 2.0, 'Bi': 6.0}\",\"('Bi', 'O', 'Sb')\",OTHERS,False\nmp-753099,\"{'Li': 3.0, 'Co': 2.0, 'Si': 2.0, 'O': 8.0}\",\"('Co', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1197179,\"{'Er': 22.0, 'In': 12.0, 'Ge': 8.0}\",\"('Er', 'Ge', 'In')\",OTHERS,False\nmp-1223689,\"{'K': 2.0, 'Cd': 3.0, 'Sn': 1.0, 'As': 4.0}\",\"('As', 'Cd', 'K', 'Sn')\",OTHERS,False\nmp-766530,\"{'Li': 4.0, 'Fe': 4.0, 'Si': 6.0, 'O': 20.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1251718,\"{'Ba': 8.0, 'Al': 16.0, 'O': 48.0}\",\"('Al', 'Ba', 'O')\",OTHERS,False\nmp-1197754,\"{'Np': 2.0, 'U': 2.0, 'P': 8.0, 'H': 8.0, 'C': 4.0, 'O': 32.0}\",\"('C', 'H', 'Np', 'O', 'P', 'U')\",OTHERS,False\nmp-1080076,\"{'Sr': 2.0, 'N': 1.0, 'O': 6.0}\",\"('N', 'O', 'Sr')\",OTHERS,False\nmp-1176923,\"{'Li': 12.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-505401,\"{'K': 4.0, 'Mn': 4.0, 'F': 16.0, 'O': 4.0, 'H': 8.0}\",\"('F', 'H', 'K', 'Mn', 'O')\",OTHERS,False\nmp-1221649,\"{'Mn': 1.0, 'Co': 1.0, 'Ni': 1.0, 'Sn': 1.0}\",\"('Co', 'Mn', 'Ni', 'Sn')\",OTHERS,False\nmp-1252068,\"{'K': 2.0, 'Al': 22.0, 'O': 34.0}\",\"('Al', 'K', 'O')\",OTHERS,False\nmp-1213734,\"{'Cs': 6.0, 'Nb': 12.0, 'S': 2.0, 'Br': 34.0}\",\"('Br', 'Cs', 'Nb', 'S')\",OTHERS,False\nmvc-7738,\"{'Ca': 4.0, 'Bi': 6.0, 'O': 16.0}\",\"('Bi', 'Ca', 'O')\",OTHERS,False\nmp-11131,\"{'S': 8.0, 'K': 2.0, 'Ge': 2.0, 'Hg': 3.0}\",\"('Ge', 'Hg', 'K', 'S')\",OTHERS,False\nmp-25259,\"{'O': 20.0, 'Fe': 2.0, 'Se': 8.0}\",\"('Fe', 'O', 'Se')\",OTHERS,False\nmp-1095161,\"{'Sm': 2.0, 'Mn': 2.0, 'Ge': 4.0}\",\"('Ge', 'Mn', 'Sm')\",OTHERS,False\nmp-1025575,\"{'Mo': 1.0, 'W': 2.0, 'Se': 6.0}\",\"('Mo', 'Se', 'W')\",OTHERS,False\nmp-685267,\"{'Cr': 36.0, 'Ga': 12.0, 'S': 72.0}\",\"('Cr', 'Ga', 'S')\",OTHERS,False\nmp-610787,\"{'Gd': 1.0, 'Cr': 2.0, 'Si': 2.0}\",\"('Cr', 'Gd', 'Si')\",OTHERS,False\nmp-752911,\"{'Li': 4.0, 'V': 4.0, 'F': 12.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-1220519,\"{'Nb': 4.0, 'N': 3.0}\",\"('N', 'Nb')\",OTHERS,False\nmp-1180517,\"{'Mg': 1.0, 'S': 1.0, 'O': 9.0}\",\"('Mg', 'O', 'S')\",OTHERS,False\nmp-778,\"{'P': 3.0, 'Fe': 6.0}\",\"('Fe', 'P')\",OTHERS,False\nmp-1074528,\"{'Mg': 16.0, 'Si': 12.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1039344,\"{'Sn': 6.0, 'Bi': 2.0}\",\"('Bi', 'Sn')\",OTHERS,False\nmp-766465,\"{'Sr': 8.0, 'Mn': 4.0, 'C': 4.0, 'O': 12.0, 'F': 20.0}\",\"('C', 'F', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-1215189,\"{'Zr': 1.0, 'Ti': 1.0, 'S': 4.0}\",\"('S', 'Ti', 'Zr')\",OTHERS,False\nmp-1223667,\"{'K': 2.0, 'Ba': 1.0, 'Sn': 1.0, 'Te': 4.0}\",\"('Ba', 'K', 'Sn', 'Te')\",OTHERS,False\nmp-1197995,\"{'Na': 4.0, 'Mg': 2.0, 'P': 8.0, 'H': 24.0, 'O': 36.0}\",\"('H', 'Mg', 'Na', 'O', 'P')\",OTHERS,False\nmp-1206770,\"{'Np': 1.0, 'Ga': 5.0, 'Rh': 1.0}\",\"('Ga', 'Np', 'Rh')\",OTHERS,False\nmp-1080154,\"{'Te': 2.0, 'Mo': 3.0, 'W': 1.0, 'Se': 4.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-28702,\"{'Gd': 2.0, 'B': 3.0, 'C': 2.0}\",\"('B', 'C', 'Gd')\",OTHERS,False\nmp-9457,\"{'O': 12.0, 'Ca': 6.0, 'Re': 2.0}\",\"('Ca', 'O', 'Re')\",OTHERS,False\nmp-1218722,\"{'Sr': 2.0, 'Pr': 2.0, 'Cr': 1.0, 'Ni': 1.0, 'O': 8.0}\",\"('Cr', 'Ni', 'O', 'Pr', 'Sr')\",OTHERS,False\nmp-568621,\"{'Mg': 16.0, 'B': 2.0, 'Rh': 8.0}\",\"('B', 'Mg', 'Rh')\",OTHERS,False\nmp-1099088,\"{'K': 1.0, 'Mg': 14.0, 'Co': 1.0}\",\"('Co', 'K', 'Mg')\",OTHERS,False\nmp-1019808,\"{'K': 6.0, 'Be': 12.0, 'B': 18.0, 'O': 42.0}\",\"('B', 'Be', 'K', 'O')\",OTHERS,False\nmp-675710,\"{'K': 4.0, 'U': 4.0, 'O': 14.0}\",\"('K', 'O', 'U')\",OTHERS,False\nmp-1190482,\"{'Mn': 4.0, 'N': 4.0, 'Cl': 12.0}\",\"('Cl', 'Mn', 'N')\",OTHERS,False\nmp-560812,\"{'K': 2.0, 'U': 4.0, 'P': 6.0, 'O': 24.0}\",\"('K', 'O', 'P', 'U')\",OTHERS,False\nmp-1246012,\"{'Ni': 2.0, 'C': 12.0, 'N': 18.0}\",\"('C', 'N', 'Ni')\",OTHERS,False\nmp-1227951,\"{'Ba': 1.0, 'La': 1.0, 'Co': 2.0, 'O': 6.0}\",\"('Ba', 'Co', 'La', 'O')\",OTHERS,False\nmp-22188,\"{'Cu': 2.0, 'Sn': 2.0, 'Dy': 2.0}\",\"('Cu', 'Dy', 'Sn')\",OTHERS,False\nmp-1221057,\"{'Na': 2.0, 'Eu': 2.0, 'Ti': 2.0, 'Nb': 2.0, 'O': 12.0, 'F': 2.0}\",\"('Eu', 'F', 'Na', 'Nb', 'O', 'Ti')\",OTHERS,False\nmp-865524,\"{'Y': 1.0, 'Cd': 1.0, 'Pt': 2.0}\",\"('Cd', 'Pt', 'Y')\",OTHERS,False\nmp-5746,\"{'O': 20.0, 'Mg': 4.0, 'Te': 8.0}\",\"('Mg', 'O', 'Te')\",OTHERS,False\nmp-17388,\"{'Cu': 6.0, 'Sn': 5.0, 'Yb': 3.0}\",\"('Cu', 'Sn', 'Yb')\",OTHERS,False\nmp-781626,\"{'Li': 4.0, 'Nb': 2.0, 'O': 6.0}\",\"('Li', 'Nb', 'O')\",OTHERS,False\nmp-26742,\"{'O': 24.0, 'P': 6.0, 'Cu': 8.0}\",\"('Cu', 'O', 'P')\",OTHERS,False\nmp-1078838,\"{'Ti': 2.0, 'Fe': 2.0, 'H': 4.0}\",\"('Fe', 'H', 'Ti')\",OTHERS,False\nmp-568791,\"{'Ca': 3.0, 'Si': 1.0, 'Br': 2.0}\",\"('Br', 'Ca', 'Si')\",OTHERS,False\nmp-865406,\"{'Lu': 2.0, 'Zn': 1.0, 'Au': 1.0}\",\"('Au', 'Lu', 'Zn')\",OTHERS,False\nmp-776483,\"{'Li': 8.0, 'Mn': 4.0, 'P': 4.0, 'O': 16.0, 'F': 4.0}\",\"('F', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-622508,\"{'Gd': 1.0, 'Si': 2.0, 'Rh': 3.0}\",\"('Gd', 'Rh', 'Si')\",OTHERS,False\nmp-1028539,\"{'Te': 4.0, 'Mo': 2.0, 'W': 2.0, 'Se': 4.0}\",\"('Mo', 'Se', 'Te', 'W')\",OTHERS,False\nmp-1223170,\"{'La': 2.0, 'Ti': 3.0, 'Ni': 1.0, 'O': 10.0}\",\"('La', 'Ni', 'O', 'Ti')\",OTHERS,False\nmp-769557,\"{'Li': 2.0, 'Mn': 2.0, 'Nb': 2.0, 'O': 8.0}\",\"('Li', 'Mn', 'Nb', 'O')\",OTHERS,False\nmp-1226264,\"{'Cr': 4.0, 'Ga': 1.0, 'S': 8.0}\",\"('Cr', 'Ga', 'S')\",OTHERS,False\nmp-1204207,\"{'Nb': 30.0, 'Ge': 21.0}\",\"('Ge', 'Nb')\",OTHERS,False\nmp-1247458,\"{'Sr': 24.0, 'Pb': 3.0, 'N': 18.0}\",\"('N', 'Pb', 'Sr')\",OTHERS,False\nmp-757855,\"{'V': 8.0, 'Co': 4.0, 'H': 16.0, 'O': 32.0}\",\"('Co', 'H', 'O', 'V')\",OTHERS,False\nmp-1080793,\"{'Pu': 2.0, 'Te': 6.0}\",\"('Pu', 'Te')\",OTHERS,False\nmp-1179633,\"{'S': 4.0, 'N': 2.0, 'O': 18.0}\",\"('N', 'O', 'S')\",OTHERS,False\nmp-768954,\"{'Ba': 8.0, 'Y': 4.0, 'Cl': 28.0}\",\"('Ba', 'Cl', 'Y')\",OTHERS,False\nmp-1023124,\"{'Co': 2.0, 'Mo': 1.0, 'S': 4.0}\",\"('Co', 'Mo', 'S')\",OTHERS,False\nmp-29646,\"{'As': 8.0, 'Hf': 4.0}\",\"('As', 'Hf')\",OTHERS,False\nmp-654301,\"{'Nb': 10.0, 'P': 14.0, 'O': 60.0}\",\"('Nb', 'O', 'P')\",OTHERS,False\nmp-754116,\"{'Ba': 2.0, 'Y': 4.0, 'Br': 16.0}\",\"('Ba', 'Br', 'Y')\",OTHERS,False\nmp-1093757,\"{'Zr': 1.0, 'Ti': 1.0, 'Au': 2.0}\",\"('Au', 'Ti', 'Zr')\",OTHERS,False\nmp-768865,\"{'Lu': 8.0, 'Ge': 4.0, 'O': 20.0}\",\"('Ge', 'Lu', 'O')\",OTHERS,False\nmp-34322,\"{'Dy': 4.0, 'O': 14.0, 'Ti': 4.0}\",\"('Dy', 'O', 'Ti')\",OTHERS,False\nmp-756266,\"{'Li': 4.0, 'Cr': 3.0, 'Ga': 1.0, 'O': 8.0}\",\"('Cr', 'Ga', 'Li', 'O')\",OTHERS,False\nmvc-3477,\"{'Mn': 2.0, 'Zn': 2.0, 'F': 8.0}\",\"('F', 'Mn', 'Zn')\",OTHERS,False\nmp-582278,\"{'Tm': 16.0, 'In': 4.0, 'Rh': 4.0}\",\"('In', 'Rh', 'Tm')\",OTHERS,False\nmp-753874,\"{'Li': 4.0, 'Si': 4.0, 'Ni': 2.0, 'O': 12.0}\",\"('Li', 'Ni', 'O', 'Si')\",OTHERS,False\nmp-756160,\"{'Er': 6.0, 'Ta': 2.0, 'O': 14.0}\",\"('Er', 'O', 'Ta')\",OTHERS,False\nmp-1038781,\"{'Mg': 8.0, 'Bi': 4.0}\",\"('Bi', 'Mg')\",OTHERS,False\nmp-779091,\"{'Li': 2.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1225460,\"{'Fe': 4.0, 'H': 36.0, 'S': 8.0, 'O': 48.0}\",\"('Fe', 'H', 'O', 'S')\",OTHERS,False\nmp-1095497,\"{'Y': 3.0, 'Ir': 9.0}\",\"('Ir', 'Y')\",OTHERS,False\nmvc-2762,\"{'Ba': 2.0, 'Tl': 2.0, 'Zn': 2.0, 'Sn': 3.0, 'O': 10.0}\",\"('Ba', 'O', 'Sn', 'Tl', 'Zn')\",OTHERS,False\nmp-1224741,\"{'Gd': 4.0, 'Ti': 2.0, 'Cu': 2.0, 'O': 12.0}\",\"('Cu', 'Gd', 'O', 'Ti')\",OTHERS,False\nmvc-2773,\"{'Sr': 2.0, 'Sn': 2.0, 'P': 4.0, 'O': 16.0}\",\"('O', 'P', 'Sn', 'Sr')\",OTHERS,False\nmp-1200788,\"{'Cu': 12.0, 'P': 32.0, 'Se': 24.0, 'Br': 12.0}\",\"('Br', 'Cu', 'P', 'Se')\",OTHERS,False\nmvc-2768,\"{'Ba': 2.0, 'Ca': 2.0, 'Tl': 2.0, 'Sn': 3.0, 'O': 10.0}\",\"('Ba', 'Ca', 'O', 'Sn', 'Tl')\",OTHERS,False\nmp-675462,\"{'Mo': 8.0, 'Ru': 4.0, 'Se': 16.0}\",\"('Mo', 'Ru', 'Se')\",OTHERS,False\nmp-1216820,\"{'U': 2.0, 'Cu': 1.0, 'Si': 3.0}\",\"('Cu', 'Si', 'U')\",OTHERS,False\nmp-1209821,\"{'Np': 4.0, 'Ge': 4.0, 'O': 8.0}\",\"('Ge', 'Np', 'O')\",OTHERS,False\nmp-865762,\"{'Tl': 1.0, 'W': 2.0, 'Cl': 6.0, 'O': 2.0}\",\"('Cl', 'O', 'Tl', 'W')\",OTHERS,False\nmp-22502,\"{'N': 1.0, 'Mn': 3.0, 'Ir': 1.0}\",\"('Ir', 'Mn', 'N')\",OTHERS,False\nmp-776754,\"{'Li': 2.0, 'Fe': 3.0, 'F': 8.0}\",\"('F', 'Fe', 'Li')\",OTHERS,False\nmp-1189473,\"{'Fe': 1.0, 'Sb': 1.0, 'Cl': 8.0, 'O': 8.0}\",\"('Cl', 'Fe', 'O', 'Sb')\",OTHERS,False\nmp-628185,\"{'K': 6.0, 'Na': 2.0, 'Sn': 6.0, 'Se': 16.0}\",\"('K', 'Na', 'Se', 'Sn')\",OTHERS,False\nmp-30186,\"{'Si': 46.0, 'Ba': 6.0}\",\"('Ba', 'Si')\",OTHERS,False\nmp-636380,\"{'Li': 2.0, 'O': 8.0, 'Mo': 4.0}\",\"('Li', 'Mo', 'O')\",OTHERS,False\nmp-1221707,\"{'Mn': 9.0, 'Al': 4.0, 'V': 3.0}\",\"('Al', 'Mn', 'V')\",OTHERS,False\nmp-1204916,\"{'K': 8.0, 'Fe': 4.0, 'F': 20.0}\",\"('F', 'Fe', 'K')\",OTHERS,False\nmp-579552,\"{'La': 24.0, 'C': 12.0, 'O': 60.0}\",\"('C', 'La', 'O')\",OTHERS,False\nmp-1213172,\"{'Cs': 2.0, 'Tm': 2.0, 'Mo': 4.0, 'O': 16.0}\",\"('Cs', 'Mo', 'O', 'Tm')\",OTHERS,False\nmp-1206129,\"{'Rb': 1.0, 'As': 2.0, 'Ru': 2.0}\",\"('As', 'Rb', 'Ru')\",OTHERS,False\nmp-1199646,\"{'Li': 4.0, 'Zn': 8.0, 'B': 20.0, 'H': 80.0}\",\"('B', 'H', 'Li', 'Zn')\",OTHERS,False\nmp-1185264,\"{'La': 3.0, 'Eu': 1.0}\",\"('Eu', 'La')\",OTHERS,False\nmp-776286,\"{'Ti': 3.0, 'Cr': 1.0, 'Sn': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Cr', 'O', 'P', 'Sn', 'Ti')\",OTHERS,False\nmp-1195771,\"{'Zn': 8.0, 'As': 8.0, 'O': 36.0}\",\"('As', 'O', 'Zn')\",OTHERS,False\nmp-867169,\"{'Sr': 2.0, 'Sn': 1.0, 'Hg': 1.0}\",\"('Hg', 'Sn', 'Sr')\",OTHERS,False\nmp-1226470,\"{'Ce': 1.0, 'Y': 1.0, 'Co': 4.0}\",\"('Ce', 'Co', 'Y')\",OTHERS,False\nmp-1181758,\"{'Mn': 2.0, 'B': 8.0, 'O': 32.0}\",\"('B', 'Mn', 'O')\",OTHERS,False\nmp-1213420,\"{'Eu': 2.0, 'Sb': 3.0, 'Pd': 3.0}\",\"('Eu', 'Pd', 'Sb')\",OTHERS,False\nmp-1016823,\"{'Ba': 1.0, 'Ge': 1.0, 'O': 3.0}\",\"('Ba', 'Ge', 'O')\",OTHERS,False\nmp-557748,\"{'Sb': 8.0, 'Ir': 8.0, 'C': 16.0, 'O': 16.0, 'F': 48.0}\",\"('C', 'F', 'Ir', 'O', 'Sb')\",OTHERS,False\nmvc-11241,\"{'V': 1.0, 'S': 2.0}\",\"('S', 'V')\",OTHERS,False\nmp-30741,\"{'Ir': 3.0, 'Pa': 1.0}\",\"('Ir', 'Pa')\",OTHERS,False\nmp-769344,\"{'Ba': 16.0, 'Ta': 8.0, 'O': 36.0}\",\"('Ba', 'O', 'Ta')\",OTHERS,False\nmp-771042,\"{'Li': 6.0, 'Nb': 3.0, 'Fe': 3.0, 'O': 16.0}\",\"('Fe', 'Li', 'Nb', 'O')\",OTHERS,False\nmp-1175222,\"{'Li': 7.0, 'Mn': 4.0, 'Co': 1.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-770212,\"{'Li': 24.0, 'Ti': 8.0, 'S': 24.0, 'O': 4.0}\",\"('Li', 'O', 'S', 'Ti')\",OTHERS,False\nmp-1073254,\"{'Mg': 4.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1222420,\"{'Li': 1.0, 'Fe': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-722485,\"{'Sr': 2.0, 'P': 4.0, 'H': 12.0, 'O': 18.0}\",\"('H', 'O', 'P', 'Sr')\",OTHERS,False\nmp-1183498,\"{'Ca': 3.0, 'Ho': 1.0}\",\"('Ca', 'Ho')\",OTHERS,False\nmp-1025183,\"{'Ho': 2.0, 'B': 4.0, 'C': 1.0}\",\"('B', 'C', 'Ho')\",OTHERS,False\nmp-1176918,\"{'Li': 12.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmvc-13706,\"{'Cr': 3.0, 'O': 8.0}\",\"('Cr', 'O')\",OTHERS,False\nmp-1210164,\"{'Ni': 2.0, 'N': 8.0, 'O': 16.0}\",\"('N', 'Ni', 'O')\",OTHERS,False\nmp-561551,\"{'Y': 4.0, 'Si': 4.0, 'O': 14.0}\",\"('O', 'Si', 'Y')\",OTHERS,False\nmp-29238,\"{'Se': 4.0, 'Ag': 4.0, 'Tl': 4.0}\",\"('Ag', 'Se', 'Tl')\",OTHERS,False\nmp-1191807,\"{'Cu': 8.0, 'Cl': 4.0, 'O': 12.0}\",\"('Cl', 'Cu', 'O')\",OTHERS,False\nmp-683983,\"{'Ta': 16.0, 'O': 32.0}\",\"('O', 'Ta')\",OTHERS,False\nmp-1204030,\"{'Si': 12.0, 'C': 16.0, 'N': 4.0, 'O': 38.0}\",\"('C', 'N', 'O', 'Si')\",OTHERS,False\nmp-1198773,\"{'Yb': 8.0, 'Cr': 8.0, 'O': 40.0}\",\"('Cr', 'O', 'Yb')\",OTHERS,False\nmp-7224,\"{'C': 4.0, 'Th': 2.0}\",\"('C', 'Th')\",OTHERS,False\nmp-1213192,\"{'Dy': 4.0, 'Fe': 34.0, 'C': 6.0}\",\"('C', 'Dy', 'Fe')\",OTHERS,False\nmp-867296,\"{'Re': 4.0, 'P': 4.0, 'O': 20.0}\",\"('O', 'P', 'Re')\",OTHERS,False\nmp-777558,\"{'Li': 32.0, 'Ti': 3.0, 'Cr': 13.0, 'O': 48.0}\",\"('Cr', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-1188832,\"{'Nd': 4.0, 'Ga': 4.0, 'Pd': 8.0}\",\"('Ga', 'Nd', 'Pd')\",OTHERS,False\nmp-1215656,\"{'Zn': 1.0, 'Fe': 4.0, 'Cu': 1.0, 'O': 8.0}\",\"('Cu', 'Fe', 'O', 'Zn')\",OTHERS,False\nmp-2226,\"{'Pd': 1.0, 'Dy': 1.0}\",\"('Dy', 'Pd')\",OTHERS,False\nmp-1184190,\"{'Er': 1.0, 'Mg': 16.0, 'Al': 12.0}\",\"('Al', 'Er', 'Mg')\",OTHERS,False\nmp-1183917,\"{'Cs': 1.0, 'Mo': 1.0, 'O': 3.0}\",\"('Cs', 'Mo', 'O')\",OTHERS,False\nmp-1205020,\"{'Ca': 1.0, 'Cu': 4.0, 'As': 4.0, 'H': 2.0, 'O': 26.0}\",\"('As', 'Ca', 'Cu', 'H', 'O')\",OTHERS,False\nmp-1178405,\"{'Cs': 4.0, 'Hf': 2.0, 'O': 6.0}\",\"('Cs', 'Hf', 'O')\",OTHERS,False\nmp-1218070,\"{'Ta': 6.0, 'Cr': 1.0, 'C': 3.0, 'S': 6.0}\",\"('C', 'Cr', 'S', 'Ta')\",OTHERS,False\nmp-1213412,\"{'Gd': 16.0, 'Si': 8.0, 'Ni': 8.0, 'Bi': 4.0}\",\"('Bi', 'Gd', 'Ni', 'Si')\",OTHERS,False\nmp-541897,\"{'Cd': 2.0, 'Rb': 4.0, 'Se': 12.0, 'P': 4.0}\",\"('Cd', 'P', 'Rb', 'Se')\",OTHERS,False\nmp-1206747,\"{'Lu': 1.0, 'Tl': 1.0}\",\"('Lu', 'Tl')\",OTHERS,False\nmp-973892,\"{'Eu': 1.0, 'Al': 1.0, 'O': 3.0}\",\"('Al', 'Eu', 'O')\",OTHERS,False\nmp-1198386,\"{'Al': 16.0, 'Mo': 24.0}\",\"('Al', 'Mo')\",OTHERS,False\nmp-1100777,\"{'V': 1.0, 'Te': 1.0, 'O': 3.0}\",\"('O', 'Te', 'V')\",OTHERS,False\nmp-1201340,\"{'U': 4.0, 'P': 8.0, 'H': 8.0, 'C': 4.0, 'O': 52.0}\",\"('C', 'H', 'O', 'P', 'U')\",OTHERS,False\nmp-570480,\"{'Tc': 8.0, 'Br': 32.0}\",\"('Br', 'Tc')\",OTHERS,False\nmp-1093552,\"{'Mg': 2.0, 'Cd': 1.0, 'Pt': 1.0}\",\"('Cd', 'Mg', 'Pt')\",OTHERS,False\nmp-1056955,\"{'Ag': 1.0, 'N': 1.0}\",\"('Ag', 'N')\",OTHERS,False\nmp-710,\"{'P': 1.0, 'Sm': 1.0}\",\"('P', 'Sm')\",OTHERS,False\nmp-1086666,\"{'Sr': 2.0, 'Ta': 1.0, 'In': 1.0, 'O': 6.0}\",\"('In', 'O', 'Sr', 'Ta')\",OTHERS,False\nmp-16281,\"{'O': 14.0, 'Cd': 4.0, 'Sb': 4.0}\",\"('Cd', 'O', 'Sb')\",OTHERS,False\nmp-624417,\"{'Cd': 11.0, 'I': 22.0}\",\"('Cd', 'I')\",OTHERS,False\nmp-1222567,\"{'Li': 2.0, 'Nb': 2.0, 'W': 2.0, 'O': 14.0}\",\"('Li', 'Nb', 'O', 'W')\",OTHERS,False\nmp-1029595,\"{'Na': 12.0, 'Os': 4.0, 'N': 8.0}\",\"('N', 'Na', 'Os')\",OTHERS,False\nmp-1094963,\"{'Ce': 2.0, 'Mg': 2.0}\",\"('Ce', 'Mg')\",OTHERS,False\nmp-1021490,\"{'Sn': 4.0, 'S': 1.0, 'F': 6.0}\",\"('F', 'S', 'Sn')\",OTHERS,False\nmp-1111200,\"{'K': 2.0, 'Tl': 1.0, 'As': 1.0, 'I': 6.0}\",\"('As', 'I', 'K', 'Tl')\",OTHERS,False\nmp-695314,\"{'Ca': 10.0, 'Si': 4.0, 'H': 2.0, 'O': 18.0, 'F': 2.0}\",\"('Ca', 'F', 'H', 'O', 'Si')\",OTHERS,False\nmp-1176983,\"{'Li': 12.0, 'Mn': 2.0, 'V': 6.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-1099574,\"{'Ce': 16.0, 'Se': 32.0}\",\"('Ce', 'Se')\",OTHERS,False\nmp-1214495,\"{'Ba': 6.0, 'Ti': 2.0, 'O': 10.0}\",\"('Ba', 'O', 'Ti')\",OTHERS,False\nmp-1182967,\"{'Al': 2.0, 'Cu': 4.0, 'As': 2.0, 'O': 24.0}\",\"('Al', 'As', 'Cu', 'O')\",OTHERS,False\nmp-867369,\"{'Tc': 2.0, 'F': 6.0}\",\"('F', 'Tc')\",OTHERS,False\nmp-755692,\"{'Nb': 2.0, 'V': 2.0, 'O': 10.0}\",\"('Nb', 'O', 'V')\",OTHERS,False\nmp-1219459,\"{'Sb': 1.0, 'Te': 1.0}\",\"('Sb', 'Te')\",OTHERS,False\nmp-768407,\"{'Li': 24.0, 'Ti': 5.0, 'Cr': 7.0, 'O': 36.0}\",\"('Cr', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-1035190,\"{'Mg': 14.0, 'Zn': 1.0, 'Co': 1.0, 'O': 16.0}\",\"('Co', 'Mg', 'O', 'Zn')\",OTHERS,False\nmp-1203220,\"{'In': 8.0, 'Rh': 8.0, 'O': 24.0}\",\"('In', 'O', 'Rh')\",OTHERS,False\nmp-866013,\"{'Dy': 1.0, 'Tm': 1.0, 'Mg': 2.0}\",\"('Dy', 'Mg', 'Tm')\",OTHERS,False\nmp-12553,\"{'Al': 6.0, 'Pr': 2.0}\",\"('Al', 'Pr')\",OTHERS,False\nmvc-1258,\"{'Mg': 4.0, 'Cr': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Cr', 'Mg', 'O', 'P')\",OTHERS,False\nmp-1210149,\"{'Sm': 46.0, 'Cd': 8.0, 'Rh': 14.0}\",\"('Cd', 'Rh', 'Sm')\",OTHERS,False\nmp-722501,\"{'Sn': 4.0, 'H': 12.0, 'S': 16.0, 'N': 16.0, 'Cl': 20.0, 'O': 4.0}\",\"('Cl', 'H', 'N', 'O', 'S', 'Sn')\",OTHERS,False\nmp-560687,\"{'Nb': 12.0, 'Te': 32.0, 'I': 28.0, 'O': 4.0}\",\"('I', 'Nb', 'O', 'Te')\",OTHERS,False\nmp-1008498,{'Ca': 4.0},\"('Ca',)\",OTHERS,False\nmp-1186803,\"{'Pu': 1.0, 'S': 3.0}\",\"('Pu', 'S')\",OTHERS,False\nmp-22965,\"{'Te': 1.0, 'I': 1.0, 'Bi': 1.0}\",\"('Bi', 'I', 'Te')\",OTHERS,False\nmp-1208543,\"{'Tb': 4.0, 'Co': 2.0, 'Pt': 2.0, 'O': 12.0}\",\"('Co', 'O', 'Pt', 'Tb')\",OTHERS,False\nmp-1079779,\"{'Tb': 4.0, 'In': 2.0, 'Cu': 4.0}\",\"('Cu', 'In', 'Tb')\",OTHERS,False\nmp-1078217,\"{'Na': 2.0, 'Fe': 2.0, 'F': 6.0}\",\"('F', 'Fe', 'Na')\",OTHERS,False\nmp-1193698,\"{'Zr': 12.0, 'Fe': 12.0, 'C': 4.0}\",\"('C', 'Fe', 'Zr')\",OTHERS,False\nmp-1192827,\"{'Eu': 16.0, 'In': 6.0}\",\"('Eu', 'In')\",OTHERS,False\nmp-761164,\"{'Li': 4.0, 'Nb': 4.0, 'F': 20.0}\",\"('F', 'Li', 'Nb')\",OTHERS,False\nmp-754117,\"{'Y': 2.0, 'H': 2.0, 'O': 4.0}\",\"('H', 'O', 'Y')\",OTHERS,False\nmp-555560,\"{'Cd': 2.0, 'Te': 2.0, 'Mo': 2.0, 'O': 12.0}\",\"('Cd', 'Mo', 'O', 'Te')\",OTHERS,False\nmp-13501,\"{'C': 2.0, 'Co': 1.0, 'Er': 1.0}\",\"('C', 'Co', 'Er')\",OTHERS,False\nmp-13992,\"{'S': 4.0, 'Cs': 2.0, 'Pt': 3.0}\",\"('Cs', 'Pt', 'S')\",OTHERS,False\nmp-19814,\"{'Ni': 2.0, 'As': 4.0}\",\"('As', 'Ni')\",OTHERS,False\nmp-20828,\"{'Pt': 3.0, 'Pb': 1.0}\",\"('Pb', 'Pt')\",OTHERS,False\nmp-35876,\"{'Ca': 2.0, 'Nd': 4.0, 'S': 8.0}\",\"('Ca', 'Nd', 'S')\",OTHERS,False\nmp-1076151,\"{'Ca': 4.0, 'Ti': 4.0, 'O': 10.0}\",\"('Ca', 'O', 'Ti')\",OTHERS,False\nmp-1099116,\"{'Ce': 1.0, 'Mg': 14.0, 'C': 1.0}\",\"('C', 'Ce', 'Mg')\",OTHERS,False\nmp-23415,\"{'Tl': 4.0, 'Fe': 4.0, 'I': 12.0}\",\"('Fe', 'I', 'Tl')\",OTHERS,False\nmp-1246175,\"{'Ca': 6.0, 'Zr': 4.0, 'N': 8.0}\",\"('Ca', 'N', 'Zr')\",OTHERS,False\nmp-1181826,\"{'Co': 1.0, 'Cu': 1.0, 'P': 2.0, 'O': 7.0}\",\"('Co', 'Cu', 'O', 'P')\",OTHERS,False\nmp-1075724,\"{'Mg': 10.0, 'Si': 18.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-541787,\"{'Na': 32.0, 'Hg': 12.0}\",\"('Hg', 'Na')\",OTHERS,False\nmp-765350,\"{'La': 8.0, 'H': 8.0, 'Se': 24.0, 'O': 80.0}\",\"('H', 'La', 'O', 'Se')\",OTHERS,False\nmp-1184176,\"{'Dy': 4.0, 'Sc': 4.0, 'S': 12.0}\",\"('Dy', 'S', 'Sc')\",OTHERS,False\nmp-1212202,\"{'Mn': 8.0, 'Cr': 12.0, 'N': 8.0, 'O': 48.0}\",\"('Cr', 'Mn', 'N', 'O')\",OTHERS,False\nmp-1200365,\"{'Cs': 4.0, 'Mo': 10.0, 'O': 32.0}\",\"('Cs', 'Mo', 'O')\",OTHERS,False\nmp-1104964,\"{'Lu': 2.0, 'Ni': 8.0, 'As': 4.0}\",\"('As', 'Lu', 'Ni')\",OTHERS,False\nmp-1183595,\"{'Ca': 1.0, 'Yb': 1.0, 'Tl': 2.0}\",\"('Ca', 'Tl', 'Yb')\",OTHERS,False\nmp-1210069,\"{'Na': 1.0, 'Dy': 1.0, 'Pd': 6.0, 'O': 8.0}\",\"('Dy', 'Na', 'O', 'Pd')\",OTHERS,False\nmp-20082,\"{'As': 4.0, 'Rh': 6.0, 'Yb': 1.0}\",\"('As', 'Rh', 'Yb')\",OTHERS,False\nmp-1183340,\"{'Ba': 3.0, 'Ho': 1.0}\",\"('Ba', 'Ho')\",OTHERS,False\nmp-1214186,\"{'C': 12.0, 'I': 4.0, 'F': 8.0}\",\"('C', 'F', 'I')\",OTHERS,False\nmp-543094,\"{'Li': 4.0, 'Fe': 8.0, 'P': 12.0, 'O': 48.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-1190199,\"{'Cu': 1.0, 'Sb': 6.0, 'S': 2.0, 'O': 16.0}\",\"('Cu', 'O', 'S', 'Sb')\",OTHERS,False\nmp-19467,\"{'O': 32.0, 'P': 6.0, 'Mo': 6.0, 'Ag': 2.0}\",\"('Ag', 'Mo', 'O', 'P')\",OTHERS,False\nmp-504353,\"{'Li': 2.0, 'O': 24.0, 'P': 8.0, 'Sb': 2.0}\",\"('Li', 'O', 'P', 'Sb')\",OTHERS,False\nmp-1096596,\"{'Ti': 1.0, 'Cu': 1.0, 'Au': 2.0}\",\"('Au', 'Cu', 'Ti')\",OTHERS,False\nmp-23662,\"{'Li': 8.0, 'B': 8.0, 'H': 32.0, 'O': 32.0}\",\"('B', 'H', 'Li', 'O')\",OTHERS,False\nmp-764989,\"{'Li': 8.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1207893,\"{'V': 5.0, 'Se': 8.0}\",\"('Se', 'V')\",OTHERS,False\nmp-554756,\"{'P': 12.0, 'H': 72.0, 'C': 12.0, 'N': 12.0, 'O': 36.0}\",\"('C', 'H', 'N', 'O', 'P')\",OTHERS,False\nmp-1246486,\"{'Ca': 14.0, 'Re': 2.0, 'N': 12.0}\",\"('Ca', 'N', 'Re')\",OTHERS,False\nmp-849498,\"{'Li': 10.0, 'Mn': 12.0, 'B': 12.0, 'O': 36.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-29922,\"{'O': 40.0, 'P': 4.0, 'Sb': 20.0}\",\"('O', 'P', 'Sb')\",OTHERS,False\nmp-1216294,\"{'U': 2.0, 'Al': 2.0, 'Co': 2.0}\",\"('Al', 'Co', 'U')\",OTHERS,False\nmp-1106389,\"{'K': 2.0, 'Bi': 2.0, 'F': 12.0}\",\"('Bi', 'F', 'K')\",OTHERS,False\nmp-1175781,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-753920,\"{'Tm': 2.0, 'Se': 1.0, 'O': 2.0}\",\"('O', 'Se', 'Tm')\",OTHERS,False\nmp-8341,\"{'O': 40.0, 'Na': 8.0, 'Ge': 8.0, 'Sb': 8.0}\",\"('Ge', 'Na', 'O', 'Sb')\",OTHERS,False\nmp-757511,\"{'Li': 8.0, 'Ni': 4.0, 'P': 16.0, 'O': 48.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-29487,\"{'Cl': 12.0, 'Pd': 6.0}\",\"('Cl', 'Pd')\",OTHERS,False\nmp-22005,\"{'O': 28.0, 'V': 8.0, 'Pb': 8.0}\",\"('O', 'Pb', 'V')\",OTHERS,False\nmp-677233,\"{'Na': 4.0, 'Al': 12.0, 'Tl': 8.0, 'Si': 12.0, 'O': 48.0}\",\"('Al', 'Na', 'O', 'Si', 'Tl')\",OTHERS,False\nmp-1102542,\"{'Hf': 8.0, 'Ni': 2.0, 'P': 2.0}\",\"('Hf', 'Ni', 'P')\",OTHERS,False\nmp-1225482,\"{'La': 1.0, 'Ce': 1.0, 'Al': 2.0, 'O': 6.0}\",\"('Al', 'Ce', 'La', 'O')\",OTHERS,False\nmvc-16792,\"{'Y': 2.0, 'Mn': 4.0, 'S': 8.0}\",\"('Mn', 'S', 'Y')\",OTHERS,False\nmp-770680,\"{'Li': 36.0, 'Mn': 8.0, 'Al': 4.0, 'O': 32.0}\",\"('Al', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-12497,\"{'Cd': 2.0, 'Te': 6.0, 'Cs': 2.0, 'Sm': 2.0}\",\"('Cd', 'Cs', 'Sm', 'Te')\",OTHERS,False\nmp-1205553,\"{'Sr': 2.0, 'Lu': 1.0, 'Ta': 1.0, 'O': 6.0}\",\"('Lu', 'O', 'Sr', 'Ta')\",OTHERS,False\nmp-985288,\"{'As': 6.0, 'W': 2.0}\",\"('As', 'W')\",OTHERS,False\nmp-556631,\"{'K': 4.0, 'Zr': 4.0, 'Sn': 4.0, 'F': 28.0}\",\"('F', 'K', 'Sn', 'Zr')\",OTHERS,False\nmp-690794,\"{'Sr': 2.0, 'H': 1.0, 'N': 1.0}\",\"('H', 'N', 'Sr')\",OTHERS,False\nmp-1007636,\"{'K': 1.0, 'Lu': 1.0, 'S': 2.0}\",\"('K', 'Lu', 'S')\",OTHERS,False\nmp-1176171,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-755601,\"{'Fe': 6.0, 'O': 8.0, 'F': 4.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-559638,\"{'K': 12.0, 'Np': 4.0, 'O': 8.0, 'F': 20.0}\",\"('F', 'K', 'Np', 'O')\",OTHERS,False\nmp-1186475,\"{'Pm': 2.0, 'Mg': 1.0, 'Ge': 1.0}\",\"('Ge', 'Mg', 'Pm')\",OTHERS,False\nmp-1185635,\"{'Mg': 149.0, 'Tl': 1.0}\",\"('Mg', 'Tl')\",OTHERS,False\nmp-1225147,\"{'Fe': 4.0, 'Ni': 4.0, 'Pd': 1.0, 'S': 8.0}\",\"('Fe', 'Ni', 'Pd', 'S')\",OTHERS,False\nmp-4322,\"{'B': 2.0, 'Co': 2.0, 'Pr': 1.0}\",\"('B', 'Co', 'Pr')\",OTHERS,False\nmp-696989,\"{'Na': 2.0, 'P': 2.0, 'H': 10.0, 'C': 4.0, 'S': 2.0, 'N': 6.0, 'O': 8.0}\",\"('C', 'H', 'N', 'Na', 'O', 'P', 'S')\",OTHERS,False\nmp-1113667,\"{'Rb': 2.0, 'Bi': 1.0, 'Au': 1.0, 'Cl': 6.0}\",\"('Au', 'Bi', 'Cl', 'Rb')\",OTHERS,False\nmp-1093629,\"{'Mn': 1.0, 'Al': 2.0, 'Fe': 1.0}\",\"('Al', 'Fe', 'Mn')\",OTHERS,False\nmp-707845,\"{'K': 12.0, 'Zr': 4.0, 'H': 4.0, 'S': 4.0, 'O': 20.0, 'F': 20.0}\",\"('F', 'H', 'K', 'O', 'S', 'Zr')\",OTHERS,False\nmp-32502,\"{'Li': 2.0, 'V': 2.0, 'P': 8.0, 'O': 24.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-978544,\"{'Sm': 1.0, 'Lu': 1.0, 'Tl': 2.0}\",\"('Lu', 'Sm', 'Tl')\",OTHERS,False\nmp-1184930,\"{'Ho': 2.0, 'Lu': 6.0}\",\"('Ho', 'Lu')\",OTHERS,False\nmp-1173714,\"{'Na': 8.0, 'Al': 6.0, 'Si': 6.0, 'N': 2.0, 'O': 28.0}\",\"('Al', 'N', 'Na', 'O', 'Si')\",OTHERS,False\nmp-11467,\"{'Nd': 1.0, 'Hg': 1.0}\",\"('Hg', 'Nd')\",OTHERS,False\nmp-1202582,\"{'Mo': 4.0, 'Rh': 4.0, 'N': 20.0, 'Cl': 4.0, 'O': 16.0}\",\"('Cl', 'Mo', 'N', 'O', 'Rh')\",OTHERS,False\nmp-1246734,\"{'Fe': 16.0, 'Si': 16.0, 'N': 32.0}\",\"('Fe', 'N', 'Si')\",OTHERS,False\nmp-559764,\"{'Nd': 12.0, 'Si': 8.0, 'Cl': 4.0, 'O': 32.0}\",\"('Cl', 'Nd', 'O', 'Si')\",OTHERS,False\nmp-1102114,\"{'Ta': 4.0, 'N': 8.0}\",\"('N', 'Ta')\",OTHERS,False\nmp-1112617,\"{'Cs': 2.0, 'Pr': 1.0, 'Cu': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cs', 'Cu', 'Pr')\",OTHERS,False\nmp-1037960,\"{'Ca': 1.0, 'Mg': 30.0, 'Cr': 1.0, 'O': 32.0}\",\"('Ca', 'Cr', 'Mg', 'O')\",OTHERS,False\nmp-850786,\"{'Li': 6.0, 'Mn': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-20547,\"{'Ca': 12.0, 'Fe': 24.0, 'O': 48.0}\",\"('Ca', 'Fe', 'O')\",OTHERS,False\nmp-1247673,\"{'Ca': 8.0, 'Ti': 3.0, 'Mn': 5.0, 'O': 21.0}\",\"('Ca', 'Mn', 'O', 'Ti')\",OTHERS,False\nmp-1173025,\"{'Ag': 6.0, 'S': 2.0, 'I': 2.0}\",\"('Ag', 'I', 'S')\",OTHERS,False\nmp-1074466,\"{'Mg': 8.0, 'Si': 6.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1188666,\"{'Sm': 6.0, 'Cu': 6.0, 'Sb': 8.0}\",\"('Cu', 'Sb', 'Sm')\",OTHERS,False\nmp-2363,\"{'Mo': 4.0, 'Hf': 2.0}\",\"('Hf', 'Mo')\",OTHERS,False\nmp-973662,\"{'Lu': 2.0, 'Zn': 1.0, 'Cu': 1.0}\",\"('Cu', 'Lu', 'Zn')\",OTHERS,False\nmp-1232329,\"{'K': 2.0, 'Zn': 2.0, 'Cu': 1.0, 'S': 2.0, 'O': 2.0}\",\"('Cu', 'K', 'O', 'S', 'Zn')\",OTHERS,False\nmp-1102396,\"{'Ba': 8.0, 'In': 4.0}\",\"('Ba', 'In')\",OTHERS,False\nmp-1221960,\"{'Mn': 1.0, 'Fe': 2.0, 'Pb': 2.0, 'O': 3.0, 'F': 12.0}\",\"('F', 'Fe', 'Mn', 'O', 'Pb')\",OTHERS,False\nmp-1210466,\"{'Na': 4.0, 'Mg': 2.0, 'Sc': 2.0, 'F': 14.0}\",\"('F', 'Mg', 'Na', 'Sc')\",OTHERS,False\nmp-1227504,\"{'Bi': 8.0, 'Se': 8.0, 'S': 4.0}\",\"('Bi', 'S', 'Se')\",OTHERS,False\nmvc-20,\"{'Nb': 4.0, 'Te': 2.0, 'O': 16.0}\",\"('Nb', 'O', 'Te')\",OTHERS,False\nmp-758588,\"{'K': 8.0, 'Li': 6.0, 'Fe': 4.0, 'O': 16.0}\",\"('Fe', 'K', 'Li', 'O')\",OTHERS,False\nmp-769110,\"{'Li': 4.0, 'Co': 8.0, 'S': 12.0, 'O': 48.0}\",\"('Co', 'Li', 'O', 'S')\",OTHERS,False\nmp-1246819,\"{'In': 1.0, 'C': 1.0, 'N': 2.0}\",\"('C', 'In', 'N')\",OTHERS,False\nmp-1040297,\"{'K': 1.0, 'Mg': 30.0, 'B': 1.0, 'O': 31.0}\",\"('B', 'K', 'Mg', 'O')\",OTHERS,False\nmp-12602,\"{'Zn': 5.0, 'Pr': 1.0}\",\"('Pr', 'Zn')\",OTHERS,False\nmp-1208877,\"{'Sr': 2.0, 'Eu': 1.0, 'Ta': 1.0, 'Cu': 2.0, 'O': 8.0}\",\"('Cu', 'Eu', 'O', 'Sr', 'Ta')\",OTHERS,False\nmp-1188001,\"{'Zr': 2.0, 'Tc': 1.0, 'Ir': 1.0}\",\"('Ir', 'Tc', 'Zr')\",OTHERS,False\nmp-27628,\"{'Cl': 8.0, 'Te': 12.0}\",\"('Cl', 'Te')\",OTHERS,False\nmp-1111912,\"{'K': 2.0, 'Rb': 1.0, 'Ir': 1.0, 'F': 6.0}\",\"('F', 'Ir', 'K', 'Rb')\",OTHERS,False\nmp-11291,\"{'Cd': 1.0, 'Ce': 1.0}\",\"('Cd', 'Ce')\",OTHERS,False\nmp-1220232,\"{'Ni': 10.0, 'Ge': 2.0, 'B': 4.0, 'O': 20.0}\",\"('B', 'Ge', 'Ni', 'O')\",OTHERS,False\nmp-4393,\"{'O': 68.0, 'Cs': 16.0, 'U': 20.0}\",\"('Cs', 'O', 'U')\",OTHERS,False\nmp-862635,\"{'Sc': 1.0, 'Sb': 1.0, 'Rh': 2.0}\",\"('Rh', 'Sb', 'Sc')\",OTHERS,False\nmp-1259193,\"{'Sr': 4.0, 'Y': 2.0, 'Mn': 4.0, 'Al': 2.0, 'O': 14.0}\",\"('Al', 'Mn', 'O', 'Sr', 'Y')\",OTHERS,False\nmp-8387,\"{'O': 6.0, 'Te': 1.0, 'Tl': 6.0}\",\"('O', 'Te', 'Tl')\",OTHERS,False\nmp-31245,\"{'O': 22.0, 'Te': 8.0, 'Tb': 4.0}\",\"('O', 'Tb', 'Te')\",OTHERS,False\nmp-558153,\"{'Ba': 24.0, 'Nb': 12.0, 'O': 18.0, 'F': 72.0}\",\"('Ba', 'F', 'Nb', 'O')\",OTHERS,False\nmp-31147,\"{'Si': 2.0, 'Zn': 2.0, 'Ba': 2.0}\",\"('Ba', 'Si', 'Zn')\",OTHERS,False\nmp-1027143,\"{'Mo': 2.0, 'W': 2.0, 'Se': 2.0, 'S': 6.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-1079307,\"{'Dy': 3.0, 'Co': 2.0, 'Ge': 4.0}\",\"('Co', 'Dy', 'Ge')\",OTHERS,False\nmp-849522,\"{'Li': 16.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1215607,\"{'Zr': 2.0, 'Ti': 1.0, 'Sn': 1.0, 'O': 8.0}\",\"('O', 'Sn', 'Ti', 'Zr')\",OTHERS,False\nmp-1211000,\"{'Li': 2.0, 'Sm': 2.0, 'W': 4.0, 'O': 16.0}\",\"('Li', 'O', 'Sm', 'W')\",OTHERS,False\nmp-1078813,\"{'Y': 3.0, 'Zn': 3.0, 'Pd': 3.0}\",\"('Pd', 'Y', 'Zn')\",OTHERS,False\nmp-740746,\"{'Na': 4.0, 'H': 8.0, 'Br': 4.0, 'O': 20.0}\",\"('Br', 'H', 'Na', 'O')\",OTHERS,False\nmp-1072004,\"{'U': 2.0, 'Co': 2.0, 'Ge': 2.0}\",\"('Co', 'Ge', 'U')\",OTHERS,False\nmp-673681,\"{'Ta': 22.0, 'O': 4.0}\",\"('O', 'Ta')\",OTHERS,False\nmp-735521,\"{'Mn': 4.0, 'H': 24.0, 'O': 12.0, 'F': 12.0}\",\"('F', 'H', 'Mn', 'O')\",OTHERS,False\nmp-581635,\"{'Cs': 6.0, 'Te': 34.0, 'Mo': 30.0}\",\"('Cs', 'Mo', 'Te')\",OTHERS,False\nmp-1077288,\"{'Na': 4.0, 'Cl': 2.0}\",\"('Cl', 'Na')\",OTHERS,False\nmp-758298,\"{'Li': 8.0, 'Ni': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-1209465,\"{'Rb': 4.0, 'Mo': 2.0, 'O': 4.0, 'F': 8.0}\",\"('F', 'Mo', 'O', 'Rb')\",OTHERS,False\nmp-1187161,\"{'Sr': 6.0, 'Pm': 2.0}\",\"('Pm', 'Sr')\",OTHERS,False\nmp-1174413,\"{'Li': 6.0, 'Mn': 2.0, 'Co': 2.0, 'O': 10.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-3775,\"{'F': 18.0, 'Na': 6.0, 'Si': 3.0}\",\"('F', 'Na', 'Si')\",OTHERS,False\nmp-510083,\"{'Tb': 8.0, 'Ti': 12.0, 'Si': 16.0}\",\"('Si', 'Tb', 'Ti')\",OTHERS,False\nmp-866159,\"{'Y': 1.0, 'Zr': 1.0, 'Ru': 2.0}\",\"('Ru', 'Y', 'Zr')\",OTHERS,False\nmp-686466,\"{'Pr': 32.0, 'Si': 32.0, 'N': 54.0, 'Cl': 6.0, 'O': 28.0}\",\"('Cl', 'N', 'O', 'Pr', 'Si')\",OTHERS,False\nmp-746692,\"{'K': 4.0, 'Co': 6.0, 'P': 8.0, 'H': 8.0, 'O': 32.0}\",\"('Co', 'H', 'K', 'O', 'P')\",OTHERS,False\nmp-1114468,\"{'Rb': 2.0, 'Al': 1.0, 'Tl': 1.0, 'F': 6.0}\",\"('Al', 'F', 'Rb', 'Tl')\",OTHERS,False\nmp-1182659,\"{'Cu': 4.0, 'H': 28.0, 'C': 12.0, 'S': 4.0, 'N': 12.0, 'O': 16.0}\",\"('C', 'Cu', 'H', 'N', 'O', 'S')\",OTHERS,False\nmp-1213233,\"{'Dy': 8.0, 'Ni': 2.0}\",\"('Dy', 'Ni')\",OTHERS,False\nmp-972868,\"{'Si': 2.0, 'Au': 6.0}\",\"('Au', 'Si')\",OTHERS,False\nmp-1181940,\"{'Cd': 1.0, 'Cl': 2.0}\",\"('Cd', 'Cl')\",OTHERS,False\nmp-1106156,\"{'Cs': 4.0, 'Hg': 6.0, 'Se': 8.0}\",\"('Cs', 'Hg', 'Se')\",OTHERS,False\nmp-778232,\"{'Li': 8.0, 'V': 12.0, 'Cr': 8.0, 'O': 48.0}\",\"('Cr', 'Li', 'O', 'V')\",OTHERS,False\nmp-849641,\"{'V': 8.0, 'O': 12.0, 'F': 4.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-1112651,\"{'Cs': 3.0, 'In': 1.0, 'Br': 6.0}\",\"('Br', 'Cs', 'In')\",OTHERS,False\nmp-685168,\"{'Be': 16.0, 'F': 32.0}\",\"('Be', 'F')\",OTHERS,False\nmp-1220473,\"{'Nb': 2.0, 'Tl': 2.0, 'Te': 2.0, 'O': 12.0}\",\"('Nb', 'O', 'Te', 'Tl')\",OTHERS,False\nmp-530184,\"{'Al': 16.0, 'Ni': 8.0, 'O': 32.0}\",\"('Al', 'Ni', 'O')\",OTHERS,False\nmp-2970,\"{'O': 8.0, 'Na': 8.0, 'Ge': 2.0}\",\"('Ge', 'Na', 'O')\",OTHERS,False\nmp-556705,\"{'As': 4.0, 'S': 4.0, 'Cl': 12.0, 'F': 24.0}\",\"('As', 'Cl', 'F', 'S')\",OTHERS,False\nmp-1218571,\"{'Sr': 3.0, 'Li': 4.0, 'Nb': 6.0, 'O': 20.0}\",\"('Li', 'Nb', 'O', 'Sr')\",OTHERS,False\nmp-754976,\"{'Cs': 2.0, 'Be': 1.0, 'O': 2.0}\",\"('Be', 'Cs', 'O')\",OTHERS,False\nmp-777532,\"{'Li': 32.0, 'Ti': 5.0, 'Cr': 11.0, 'O': 48.0}\",\"('Cr', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-865753,\"{'Yb': 1.0, 'Er': 1.0, 'Pd': 2.0}\",\"('Er', 'Pd', 'Yb')\",OTHERS,False\nmp-1180739,\"{'La': 4.0, 'Ga': 4.0, 'O': 12.0}\",\"('Ga', 'La', 'O')\",OTHERS,False\nmp-1228871,\"{'Cd': 1.0, 'Fe': 4.0, 'Cu': 10.0, 'Sn': 5.0, 'Se': 20.0}\",\"('Cd', 'Cu', 'Fe', 'Se', 'Sn')\",OTHERS,False\nmp-557597,\"{'K': 2.0, 'Ca': 1.0, 'N': 4.0, 'O': 8.0}\",\"('Ca', 'K', 'N', 'O')\",OTHERS,False\nmp-752398,\"{'Ba': 1.0, 'Cu': 1.0, 'O': 2.0}\",\"('Ba', 'Cu', 'O')\",OTHERS,False\nmp-755103,\"{'Li': 4.0, 'Fe': 4.0, 'F': 12.0}\",\"('F', 'Fe', 'Li')\",OTHERS,False\nmp-21608,\"{'B': 6.0, 'Ni': 21.0, 'Tm': 2.0}\",\"('B', 'Ni', 'Tm')\",OTHERS,False\nmvc-10576,\"{'Ba': 2.0, 'Mg': 3.0, 'Tl': 2.0, 'Sn': 4.0, 'O': 12.0}\",\"('Ba', 'Mg', 'O', 'Sn', 'Tl')\",OTHERS,False\nmp-1006328,\"{'Li': 8.0, 'Ce': 8.0, 'Sn': 8.0}\",\"('Ce', 'Li', 'Sn')\",OTHERS,False\nmp-27713,\"{'O': 1.0, 'V': 1.0, 'Br': 2.0}\",\"('Br', 'O', 'V')\",OTHERS,False\nmp-1244982,\"{'Al': 40.0, 'O': 60.0}\",\"('Al', 'O')\",OTHERS,False\nmp-29618,\"{'P': 6.0, 'Ni': 2.0, 'W': 4.0}\",\"('Ni', 'P', 'W')\",OTHERS,False\nmp-675419,\"{'Li': 2.0, 'Pr': 2.0, 'S': 4.0}\",\"('Li', 'Pr', 'S')\",OTHERS,False\nmvc-3060,\"{'Ba': 2.0, 'Mn': 3.0, 'Tl': 2.0, 'Zn': 2.0, 'O': 10.0}\",\"('Ba', 'Mn', 'O', 'Tl', 'Zn')\",OTHERS,False\nmp-1100672,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-768293,\"{'Lu': 8.0, 'Se': 8.0, 'O': 24.0}\",\"('Lu', 'O', 'Se')\",OTHERS,False\nmp-1189443,\"{'H': 4.0, 'N': 4.0, 'F': 8.0}\",\"('F', 'H', 'N')\",OTHERS,False\nmp-754041,\"{'Gd': 4.0, 'Hf': 2.0, 'O': 10.0}\",\"('Gd', 'Hf', 'O')\",OTHERS,False\nmp-1196472,\"{'Pr': 8.0, 'U': 4.0, 'S': 20.0}\",\"('Pr', 'S', 'U')\",OTHERS,False\nmp-907,\"{'O': 49.0, 'W': 18.0}\",\"('O', 'W')\",OTHERS,False\nmp-1247466,\"{'Mg': 2.0, 'Sc': 3.0, 'Cr': 1.0, 'S': 8.0}\",\"('Cr', 'Mg', 'S', 'Sc')\",OTHERS,False\nmp-561179,\"{'Ba': 8.0, 'Cu': 4.0, 'I': 4.0, 'O': 8.0}\",\"('Ba', 'Cu', 'I', 'O')\",OTHERS,False\nmp-1702,\"{'Rh': 4.0, 'La': 2.0}\",\"('La', 'Rh')\",OTHERS,False\nmp-850985,\"{'Li': 4.0, 'Fe': 4.0, 'P': 8.0, 'H': 4.0, 'O': 28.0}\",\"('Fe', 'H', 'Li', 'O', 'P')\",OTHERS,False\nmp-1226856,\"{'Ce': 3.0, 'Nd': 1.0, 'C': 8.0}\",\"('C', 'Ce', 'Nd')\",OTHERS,False\nmp-541785,\"{'Ge': 2.0, 'Pd': 2.0, 'S': 6.0}\",\"('Ge', 'Pd', 'S')\",OTHERS,False\nmp-1001784,\"{'Li': 1.0, 'Ti': 1.0, 'S': 2.0}\",\"('Li', 'S', 'Ti')\",OTHERS,False\nmp-1203368,\"{'Zn': 48.0, 'As': 32.0}\",\"('As', 'Zn')\",OTHERS,False\nmp-27077,\"{'Li': 14.0, 'O': 64.0, 'P': 18.0, 'Cr': 8.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-1198461,\"{'Na': 80.0, 'Fe': 16.0, 'P': 32.0, 'O': 128.0, 'F': 32.0}\",\"('F', 'Fe', 'Na', 'O', 'P')\",OTHERS,False\nmp-1228042,\"{'Ca': 4.0, 'Eu': 2.0, 'Cu': 4.0, 'O': 12.0}\",\"('Ca', 'Cu', 'Eu', 'O')\",OTHERS,False\nmp-580329,\"{'Pu': 3.0, 'Sn': 1.0}\",\"('Pu', 'Sn')\",OTHERS,False\nmp-743619,\"{'Co': 2.0, 'H': 12.0, 'C': 4.0, 'S': 4.0, 'N': 4.0, 'O': 6.0}\",\"('C', 'Co', 'H', 'N', 'O', 'S')\",OTHERS,False\nmp-768232,\"{'Rb': 12.0, 'Sb': 4.0, 'O': 12.0}\",\"('O', 'Rb', 'Sb')\",OTHERS,False\nmp-705194,\"{'Mn': 16.0, 'Sn': 8.0, 'C': 80.0, 'Br': 8.0, 'O': 80.0}\",\"('Br', 'C', 'Mn', 'O', 'Sn')\",OTHERS,False\nmp-1216288,\"{'W': 12.0, 'N': 3.0, 'O': 36.0}\",\"('N', 'O', 'W')\",OTHERS,False\nmp-705871,\"{'Ca': 14.0, 'Al': 6.0, 'Si': 20.0, 'N': 42.0}\",\"('Al', 'Ca', 'N', 'Si')\",OTHERS,False\nmp-774348,\"{'Li': 6.0, 'Mn': 2.0, 'Fe': 4.0, 'B': 6.0, 'O': 18.0}\",\"('B', 'Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1245231,\"{'B': 50.0, 'N': 50.0}\",\"('B', 'N')\",OTHERS,False\nmp-1174645,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-29584,\"{'Be': 1.0, 'K': 4.0, 'As': 2.0}\",\"('As', 'Be', 'K')\",OTHERS,False\nmp-1198503,\"{'U': 2.0, 'Zn': 40.0, 'Rh': 4.0}\",\"('Rh', 'U', 'Zn')\",OTHERS,False\nmvc-10098,\"{'Zn': 6.0, 'Cr': 12.0, 'O': 24.0}\",\"('Cr', 'O', 'Zn')\",OTHERS,False\nmp-1222879,\"{'La': 1.0, 'Mn': 1.0, 'Ni': 4.0}\",\"('La', 'Mn', 'Ni')\",OTHERS,False\nmp-634378,\"{'Na': 4.0, 'H': 4.0, 'S': 4.0, 'O': 16.0}\",\"('H', 'Na', 'O', 'S')\",OTHERS,False\nmp-558281,\"{'F': 8.0, 'Cr': 2.0, 'Sr': 2.0}\",\"('Cr', 'F', 'Sr')\",OTHERS,False\nmp-1178661,\"{'Yb': 2.0, 'Mo': 2.0, 'Cl': 2.0, 'O': 8.0}\",\"('Cl', 'Mo', 'O', 'Yb')\",OTHERS,False\nmp-1174818,\"{'Li': 6.0, 'Mn': 3.0, 'Co': 1.0, 'O': 10.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-19844,\"{'Na': 1.0, 'Fe': 4.0, 'Sb': 12.0}\",\"('Fe', 'Na', 'Sb')\",OTHERS,False\nmp-1191530,\"{'Er': 6.0, 'Ga': 2.0, 'Co': 2.0, 'S': 14.0}\",\"('Co', 'Er', 'Ga', 'S')\",OTHERS,False\nmp-1186326,\"{'Nd': 1.0, 'Re': 1.0, 'O': 3.0}\",\"('Nd', 'O', 'Re')\",OTHERS,False\nmp-554243,\"{'Si': 6.0, 'O': 12.0}\",\"('O', 'Si')\",OTHERS,False\nmp-1178513,\"{'Ba': 2.0, 'Sn': 2.0, 'O': 6.0}\",\"('Ba', 'O', 'Sn')\",OTHERS,False\nmp-775155,\"{'Li': 12.0, 'Fe': 3.0, 'O': 3.0, 'F': 15.0}\",\"('F', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-773119,\"{'Na': 4.0, 'Mn': 2.0, 'As': 2.0, 'C': 2.0, 'O': 14.0}\",\"('As', 'C', 'Mn', 'Na', 'O')\",OTHERS,False\nmp-30094,\"{'H': 16.0, 'C': 8.0, 'N': 16.0}\",\"('C', 'H', 'N')\",OTHERS,False\nmp-1219720,\"{'Pt': 1.0, 'Se': 1.0, 'S': 1.0}\",\"('Pt', 'S', 'Se')\",OTHERS,False\nmvc-10215,\"{'Ho': 2.0, 'Mn': 4.0, 'Zn': 2.0, 'O': 12.0}\",\"('Ho', 'Mn', 'O', 'Zn')\",OTHERS,False\nmp-763331,\"{'Li': 2.0, 'Mn': 4.0, 'F': 10.0}\",\"('F', 'Li', 'Mn')\",OTHERS,False\nmp-11417,\"{'Ga': 2.0, 'Tb': 2.0}\",\"('Ga', 'Tb')\",OTHERS,False\nmp-776391,\"{'Li': 4.0, 'Fe': 3.0, 'Co': 3.0, 'Cu': 2.0, 'O': 16.0}\",\"('Co', 'Cu', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-866163,\"{'Y': 1.0, 'In': 1.0, 'Rh': 2.0}\",\"('In', 'Rh', 'Y')\",OTHERS,False\nmp-1208590,\"{'Ta': 1.0, 'In': 1.0, 'Se': 2.0}\",\"('In', 'Se', 'Ta')\",OTHERS,False\nmp-556600,\"{'Cs': 4.0, 'S': 8.0, 'N': 12.0, 'O': 16.0}\",\"('Cs', 'N', 'O', 'S')\",OTHERS,False\nmp-20529,\"{'O': 18.0, 'Na': 2.0, 'Ba': 6.0, 'Ir': 4.0}\",\"('Ba', 'Ir', 'Na', 'O')\",OTHERS,False\nmp-1210859,\"{'Li': 1.0, 'Ti': 1.0, 'S': 2.0, 'O': 1.0}\",\"('Li', 'O', 'S', 'Ti')\",OTHERS,False\nmp-777833,\"{'Fe': 10.0, 'O': 4.0, 'F': 16.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-761192,\"{'Li': 4.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-8066,\"{'Cu': 18.0, 'As': 6.0}\",\"('As', 'Cu')\",OTHERS,False\nmp-1174255,\"{'Li': 6.0, 'Mn': 2.0, 'Co': 2.0, 'O': 10.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-757590,\"{'Li': 8.0, 'Sn': 8.0, 'P': 8.0, 'O': 32.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,False\nmp-29749,\"{'Ni': 10.0, 'Ga': 6.0, 'Ge': 4.0}\",\"('Ga', 'Ge', 'Ni')\",OTHERS,False\nmp-771330,\"{'Mn': 4.0, 'I': 8.0, 'O': 24.0}\",\"('I', 'Mn', 'O')\",OTHERS,False\nmp-1224395,\"{'Hf': 1.0, 'Al': 6.0, 'Fe': 6.0}\",\"('Al', 'Fe', 'Hf')\",OTHERS,False\nmp-1246997,\"{'Nb': 2.0, 'Cr': 6.0, 'Ag': 4.0, 'S': 16.0}\",\"('Ag', 'Cr', 'Nb', 'S')\",OTHERS,False\nmp-1068510,\"{'In': 2.0, 'Te': 3.0}\",\"('In', 'Te')\",OTHERS,False\nmp-1025448,\"{'Y': 4.0, 'Pt': 4.0}\",\"('Pt', 'Y')\",OTHERS,False\nmp-1177560,\"{'Li': 12.0, 'Mn': 4.0, 'V': 4.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-1079891,\"{'Ba': 4.0, 'Br': 4.0, 'O': 2.0}\",\"('Ba', 'Br', 'O')\",OTHERS,False\nmp-1039895,\"{'Rb': 1.0, 'Mg': 30.0, 'B': 1.0, 'O': 31.0}\",\"('B', 'Mg', 'O', 'Rb')\",OTHERS,False\nmp-775126,\"{'Mn': 12.0, 'O': 7.0, 'F': 17.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-560939,\"{'Na': 4.0, 'La': 4.0, 'P': 8.0, 'O': 28.0}\",\"('La', 'Na', 'O', 'P')\",OTHERS,False\nmp-743952,\"{'Mo': 8.0, 'H': 12.0, 'C': 16.0, 'O': 32.0}\",\"('C', 'H', 'Mo', 'O')\",OTHERS,False\nmvc-13320,\"{'Sr': 4.0, 'Y': 2.0, 'Cr': 4.0, 'O': 14.0}\",\"('Cr', 'O', 'Sr', 'Y')\",OTHERS,False\nmp-755907,\"{'Co': 8.0, 'O': 4.0, 'F': 12.0}\",\"('Co', 'F', 'O')\",OTHERS,False\nmp-16695,\"{'O': 28.0, 'P': 8.0, 'K': 8.0, 'Zn': 4.0}\",\"('K', 'O', 'P', 'Zn')\",OTHERS,False\nmp-28673,\"{'O': 24.0, 'P': 16.0, 'S': 4.0}\",\"('O', 'P', 'S')\",OTHERS,False\nmp-759278,\"{'Li': 8.0, 'Cu': 4.0, 'P': 16.0, 'O': 48.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-1220225,\"{'Nd': 2.0, 'Cu': 2.0, 'Ag': 2.0}\",\"('Ag', 'Cu', 'Nd')\",OTHERS,False\nmp-504900,\"{'Eu': 2.0, 'Sb': 4.0, 'Pd': 4.0}\",\"('Eu', 'Pd', 'Sb')\",OTHERS,False\nmp-1177698,\"{'Li': 12.0, 'Mn': 8.0, 'P': 16.0, 'O': 56.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-675063,\"{'K': 6.0, 'Ba': 12.0, 'As': 30.0}\",\"('As', 'Ba', 'K')\",OTHERS,False\nmp-608777,\"{'Eu': 2.0, 'In': 4.0, 'As': 4.0}\",\"('As', 'Eu', 'In')\",OTHERS,False\nmp-1200525,\"{'Cs': 4.0, 'Fe': 4.0, 'P': 6.0, 'O': 16.0, 'F': 14.0}\",\"('Cs', 'F', 'Fe', 'O', 'P')\",OTHERS,False\nmp-1076062,\"{'Sr': 7.0, 'Ca': 1.0, 'Fe': 5.0, 'Co': 3.0, 'O': 24.0}\",\"('Ca', 'Co', 'Fe', 'O', 'Sr')\",OTHERS,False\nmp-1096118,\"{'Sc': 1.0, 'Cd': 2.0, 'Ag': 1.0}\",\"('Ag', 'Cd', 'Sc')\",OTHERS,False\nmp-22514,\"{'Mn': 4.0, 'Sn': 2.0}\",\"('Mn', 'Sn')\",OTHERS,False\nmp-1209976,\"{'Nd': 16.0, 'Cd': 4.0, 'Pd': 4.0}\",\"('Cd', 'Nd', 'Pd')\",OTHERS,False\nmp-1226201,\"{'Cu': 7.0, 'Te': 2.0, 'S': 2.0, 'O': 20.0}\",\"('Cu', 'O', 'S', 'Te')\",OTHERS,False\nmp-560322,\"{'K': 4.0, 'Ru': 2.0, 'N': 6.0, 'Cl': 6.0, 'O': 10.0}\",\"('Cl', 'K', 'N', 'O', 'Ru')\",OTHERS,False\nmp-9675,\"{'B': 2.0, 'P': 4.0, 'Cs': 6.0}\",\"('B', 'Cs', 'P')\",OTHERS,False\nmp-1215605,\"{'Yb': 2.0, 'Ag': 2.0, 'Sn': 2.0}\",\"('Ag', 'Sn', 'Yb')\",OTHERS,False\nmp-1078811,\"{'Nb': 4.0, 'Ge': 2.0, 'C': 2.0}\",\"('C', 'Ge', 'Nb')\",OTHERS,False\nmp-14780,\"{'Na': 12.0, 'Mn': 2.0, 'Se': 8.0}\",\"('Mn', 'Na', 'Se')\",OTHERS,False\nmp-1218153,\"{'Sr': 1.0, 'Li': 1.0, 'Al': 1.0, 'Sb': 2.0}\",\"('Al', 'Li', 'Sb', 'Sr')\",OTHERS,False\nmp-540914,\"{'K': 12.0, 'Mn': 4.0, 'O': 12.0}\",\"('K', 'Mn', 'O')\",OTHERS,False\nmp-18099,\"{'F': 20.0, 'Sb': 4.0, 'Ba': 4.0}\",\"('Ba', 'F', 'Sb')\",OTHERS,False\nmp-1201031,\"{'Zn': 4.0, 'As': 8.0, 'S': 16.0, 'O': 32.0, 'F': 48.0}\",\"('As', 'F', 'O', 'S', 'Zn')\",OTHERS,False\nmp-694629,\"{'V': 9.0, 'P': 12.0, 'O': 48.0}\",\"('O', 'P', 'V')\",OTHERS,False\nmp-1224233,\"{'Ho': 3.0, 'Mn': 3.0, 'Ga': 2.0, 'Ge': 1.0}\",\"('Ga', 'Ge', 'Ho', 'Mn')\",OTHERS,False\nmp-1189135,\"{'Dy': 6.0, 'Cu': 6.0, 'Sb': 8.0}\",\"('Cu', 'Dy', 'Sb')\",OTHERS,False\nmp-557482,\"{'Ca': 10.0, 'As': 6.0, 'O': 24.0, 'F': 2.0}\",\"('As', 'Ca', 'F', 'O')\",OTHERS,False\nmp-1027147,\"{'Mo': 2.0, 'W': 2.0, 'Se': 4.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-1218062,\"{'Ta': 3.0, 'W': 1.0, 'S': 8.0}\",\"('S', 'Ta', 'W')\",OTHERS,False\nmp-1224078,\"{'In': 2.0, 'Fe': 2.0, 'Co': 2.0, 'O': 8.0}\",\"('Co', 'Fe', 'In', 'O')\",OTHERS,False\nmvc-157,\"{'Mg': 1.0, 'W': 4.0, 'O': 8.0}\",\"('Mg', 'O', 'W')\",OTHERS,False\nmp-21416,\"{'Al': 4.0, 'Pu': 2.0}\",\"('Al', 'Pu')\",OTHERS,False\nmp-9842,\"{'C': 4.0, 'O': 12.0, 'Rb': 2.0, 'Sm': 2.0}\",\"('C', 'O', 'Rb', 'Sm')\",OTHERS,False\nmp-16338,\"{'Si': 1.0, 'S': 8.0, 'Ga': 1.0, 'Mo': 4.0}\",\"('Ga', 'Mo', 'S', 'Si')\",OTHERS,False\nmp-568598,\"{'Cu': 2.0, 'Hg': 1.0, 'I': 4.0}\",\"('Cu', 'Hg', 'I')\",OTHERS,False\nmp-1080556,\"{'Eu': 2.0, 'Ni': 2.0, 'Ge': 4.0}\",\"('Eu', 'Ge', 'Ni')\",OTHERS,False\nmp-1034626,\"{'Na': 1.0, 'Mg': 14.0, 'B': 1.0, 'O': 16.0}\",\"('B', 'Mg', 'Na', 'O')\",OTHERS,False\nmp-21833,\"{'P': 24.0, 'Mo': 32.0}\",\"('Mo', 'P')\",OTHERS,False\nmp-540923,\"{'Ba': 12.0, 'Sn': 4.0, 'P': 12.0}\",\"('Ba', 'P', 'Sn')\",OTHERS,False\nmp-1218331,\"{'Sr': 3.0, 'Ca': 1.0, 'Si': 8.0}\",\"('Ca', 'Si', 'Sr')\",OTHERS,False\nmp-1216976,\"{'Tl': 5.0, 'Sb': 10.0, 'As': 3.0, 'S': 22.0}\",\"('As', 'S', 'Sb', 'Tl')\",OTHERS,False\nmp-1214523,\"{'Be': 52.0, 'Mo': 4.0}\",\"('Be', 'Mo')\",OTHERS,False\nmp-779427,\"{'Sm': 4.0, 'V': 4.0, 'O': 14.0}\",\"('O', 'Sm', 'V')\",OTHERS,False\nmp-1193529,\"{'Pr': 2.0, 'Be': 26.0}\",\"('Be', 'Pr')\",OTHERS,False\nmp-1218633,\"{'Sr': 3.0, 'Nb': 2.0, 'Cu': 1.0, 'O': 9.0}\",\"('Cu', 'Nb', 'O', 'Sr')\",OTHERS,False\nmp-30737,\"{'Mg': 3.0, 'Y': 3.0, 'Ag': 3.0}\",\"('Ag', 'Mg', 'Y')\",OTHERS,False\nmp-1178817,\"{'V': 1.0, 'H': 6.0, 'O': 3.0, 'F': 3.0}\",\"('F', 'H', 'O', 'V')\",OTHERS,False\nmp-1111218,\"{'K': 2.0, 'Na': 1.0, 'Y': 1.0, 'Cl': 6.0}\",\"('Cl', 'K', 'Na', 'Y')\",OTHERS,False\nmp-1186111,\"{'Na': 1.0, 'Ac': 1.0, 'In': 2.0}\",\"('Ac', 'In', 'Na')\",OTHERS,False\nmp-753262,\"{'Li': 4.0, 'Co': 2.0, 'Si': 6.0, 'O': 16.0}\",\"('Co', 'Li', 'O', 'Si')\",OTHERS,False\nmp-555419,\"{'Te': 12.0, 'As': 8.0, 'C': 6.0, 'N': 6.0, 'F': 48.0}\",\"('As', 'C', 'F', 'N', 'Te')\",OTHERS,False\nmp-23416,\"{'Li': 8.0, 'Cl': 16.0, 'Zn': 4.0}\",\"('Cl', 'Li', 'Zn')\",OTHERS,False\nmp-1212666,\"{'Ga': 5.0, 'Cu': 1.0, 'Se': 8.0}\",\"('Cu', 'Ga', 'Se')\",OTHERS,False\nmp-1246876,\"{'Mg': 2.0, 'Cr': 2.0, 'Ga': 2.0, 'S': 8.0}\",\"('Cr', 'Ga', 'Mg', 'S')\",OTHERS,False\nmp-754558,\"{'Fe': 2.0, 'Ni': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Fe', 'Ni', 'O', 'P')\",OTHERS,False\nmp-1192810,\"{'Mo': 6.0, 'N': 4.0, 'O': 20.0}\",\"('Mo', 'N', 'O')\",OTHERS,False\nmp-1215551,\"{'Yb': 2.0, 'Zn': 2.0, 'Ga': 2.0}\",\"('Ga', 'Yb', 'Zn')\",OTHERS,False\nmp-867186,\"{'Ce': 1.0, 'Zn': 2.0, 'Ag': 1.0}\",\"('Ag', 'Ce', 'Zn')\",OTHERS,False\nmp-1222821,\"{'Li': 1.0, 'Ni': 3.0, 'O': 6.0}\",\"('Li', 'Ni', 'O')\",OTHERS,False\nmp-867688,\"{'Li': 10.0, 'Fe': 2.0, 'P': 8.0, 'O': 28.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-1097084,\"{'Fe': 2.0, 'Ir': 1.0, 'Rh': 1.0}\",\"('Fe', 'Ir', 'Rh')\",OTHERS,False\nmp-1112975,\"{'Cs': 3.0, 'Er': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cs', 'Er')\",OTHERS,False\nmp-754764,\"{'Fe': 4.0, 'O': 4.0, 'F': 4.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-555580,\"{'Sr': 1.0, 'Zr': 4.0, 'P': 6.0, 'O': 24.0}\",\"('O', 'P', 'Sr', 'Zr')\",OTHERS,False\nmp-757526,\"{'Li': 4.0, 'V': 3.0, 'Co': 3.0, 'W': 2.0, 'O': 16.0}\",\"('Co', 'Li', 'O', 'V', 'W')\",OTHERS,False\nmp-1220034,\"{'P': 2.0, 'H': 2.0, 'Pb': 2.0, 'O': 8.0}\",\"('H', 'O', 'P', 'Pb')\",OTHERS,False\nmp-29514,\"{'H': 4.0, 'F': 16.0, 'P': 4.0}\",\"('F', 'H', 'P')\",OTHERS,False\nmp-1187049,\"{'Th': 3.0, 'Pb': 1.0}\",\"('Pb', 'Th')\",OTHERS,False\nmp-676121,\"{'Ag': 6.0, 'S': 2.0, 'I': 2.0}\",\"('Ag', 'I', 'S')\",OTHERS,False\nmvc-2200,\"{'Ca': 2.0, 'Mn': 2.0, 'P': 8.0, 'O': 24.0}\",\"('Ca', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1213873,\"{'Ce': 10.0, 'Sn': 6.0, 'Au': 2.0}\",\"('Au', 'Ce', 'Sn')\",OTHERS,False\nmp-1076602,\"{'Sr': 3.0, 'Ca': 5.0, 'Fe': 4.0, 'Co': 4.0, 'O': 24.0}\",\"('Ca', 'Co', 'Fe', 'O', 'Sr')\",OTHERS,False\nmp-764645,\"{'Li': 6.0, 'Mn': 2.0, 'F': 14.0}\",\"('F', 'Li', 'Mn')\",OTHERS,False\nmp-1193618,\"{'Rb': 8.0, 'Sn': 2.0, 'S': 8.0, 'O': 8.0}\",\"('O', 'Rb', 'S', 'Sn')\",OTHERS,False\nmp-1203867,\"{'Ce': 4.0, 'Co': 20.0, 'P': 12.0}\",\"('Ce', 'Co', 'P')\",OTHERS,False\nmp-1213449,\"{'K': 8.0, 'Te': 4.0, 'Mo': 12.0, 'O': 60.0}\",\"('K', 'Mo', 'O', 'Te')\",OTHERS,False\nmp-17954,\"{'S': 12.0, 'Cu': 6.0, 'Sn': 3.0, 'Ba': 3.0}\",\"('Ba', 'Cu', 'S', 'Sn')\",OTHERS,False\nmp-1211351,\"{'La': 6.0, 'Be': 2.0, 'Al': 2.0, 'S': 14.0}\",\"('Al', 'Be', 'La', 'S')\",OTHERS,False\nmp-1181590,\"{'Mg': 2.0, 'I': 4.0, 'O': 32.0}\",\"('I', 'Mg', 'O')\",OTHERS,False\nmp-1101836,\"{'Gd': 2.0, 'H': 4.0, 'Cl': 2.0, 'O': 4.0}\",\"('Cl', 'Gd', 'H', 'O')\",OTHERS,False\nmp-778822,\"{'Mn': 4.0, 'S': 6.0, 'O': 24.0}\",\"('Mn', 'O', 'S')\",OTHERS,False\nmp-557311,\"{'Sr': 16.0, 'Ge': 8.0, 'O': 32.0}\",\"('Ge', 'O', 'Sr')\",OTHERS,False\nmp-851030,\"{'Li': 8.0, 'Fe': 4.0, 'P': 4.0, 'H': 4.0, 'O': 20.0}\",\"('Fe', 'H', 'Li', 'O', 'P')\",OTHERS,False\nmp-18685,\"{'S': 12.0, 'Cu': 6.0, 'Ge': 3.0, 'Sr': 3.0}\",\"('Cu', 'Ge', 'S', 'Sr')\",OTHERS,False\nmvc-13442,\"{'Cu': 4.0, 'S': 2.0}\",\"('Cu', 'S')\",OTHERS,False\nmp-1196732,\"{'Rb': 12.0, 'H': 24.0, 'I': 36.0, 'O': 108.0}\",\"('H', 'I', 'O', 'Rb')\",OTHERS,False\nmp-776293,\"{'Na': 8.0, 'Sb': 4.0, 'C': 8.0, 'S': 2.0, 'O': 32.0}\",\"('C', 'Na', 'O', 'S', 'Sb')\",OTHERS,False\nmp-558546,\"{'K': 6.0, 'S': 6.0, 'O': 18.0}\",\"('K', 'O', 'S')\",OTHERS,False\nmp-1199891,\"{'K': 8.0, 'P': 4.0, 'H': 16.0, 'S': 12.0, 'O': 8.0}\",\"('H', 'K', 'O', 'P', 'S')\",OTHERS,False\nmp-556776,\"{'Sr': 4.0, 'P': 8.0, 'O': 24.0}\",\"('O', 'P', 'Sr')\",OTHERS,False\nmp-1114561,\"{'Rb': 2.0, 'Li': 1.0, 'Bi': 1.0, 'Br': 6.0}\",\"('Bi', 'Br', 'Li', 'Rb')\",OTHERS,False\nmp-1193946,\"{'Mn': 4.0, 'Nb': 8.0, 'S': 16.0}\",\"('Mn', 'Nb', 'S')\",OTHERS,False\nmp-1232043,\"{'La': 8.0, 'Mg': 4.0, 'Se': 16.0}\",\"('La', 'Mg', 'Se')\",OTHERS,False\nmp-1097457,\"{'Sr': 2.0, 'Li': 1.0, 'Al': 1.0}\",\"('Al', 'Li', 'Sr')\",OTHERS,False\nmp-570756,\"{'Cs': 3.0, 'Li': 2.0, 'Cl': 5.0}\",\"('Cl', 'Cs', 'Li')\",OTHERS,False\nmp-1080857,\"{'Ce': 16.0, 'Se': 32.0}\",\"('Ce', 'Se')\",OTHERS,False\nmp-707788,\"{'Na': 4.0, 'Ca': 4.0, 'B': 20.0, 'H': 40.0, 'O': 56.0}\",\"('B', 'Ca', 'H', 'Na', 'O')\",OTHERS,False\nmp-1215942,\"{'Y': 1.0, 'Sc': 1.0, 'Fe': 4.0}\",\"('Fe', 'Sc', 'Y')\",OTHERS,False\nmp-1225255,\"{'Gd': 2.0, 'Co': 1.0, 'Ni': 3.0}\",\"('Co', 'Gd', 'Ni')\",OTHERS,False\nmp-1192708,\"{'U': 8.0, 'Co': 1.0, 'S': 17.0}\",\"('Co', 'S', 'U')\",OTHERS,False\nmp-1186284,\"{'Nd': 3.0, 'Pa': 1.0}\",\"('Nd', 'Pa')\",OTHERS,False\nmp-1197420,\"{'Mg': 2.0, 'Cr': 2.0, 'H': 44.0, 'O': 30.0}\",\"('Cr', 'H', 'Mg', 'O')\",OTHERS,False\nmp-984806,\"{'Ba': 4.0, 'Pr': 2.0, 'Ru': 2.0, 'O': 12.0}\",\"('Ba', 'O', 'Pr', 'Ru')\",OTHERS,False\nmp-758226,\"{'Cr': 1.0, 'Fe': 3.0, 'Co': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Co', 'Cr', 'Fe', 'O', 'P')\",OTHERS,False\nmp-10577,\"{'N': 20.0, 'Sr': 20.0, 'Nb': 4.0}\",\"('N', 'Nb', 'Sr')\",OTHERS,False\nmp-1033692,\"{'Hf': 1.0, 'Mg': 14.0, 'Cd': 1.0, 'O': 16.0}\",\"('Cd', 'Hf', 'Mg', 'O')\",OTHERS,False\nmp-17756,\"{'S': 12.0, 'Rb': 3.0, 'Ag': 6.0, 'Sb': 3.0}\",\"('Ag', 'Rb', 'S', 'Sb')\",OTHERS,False\nmp-540635,\"{'Ba': 2.0, 'Sn': 2.0, 'Ge': 6.0, 'O': 18.0}\",\"('Ba', 'Ge', 'O', 'Sn')\",OTHERS,False\nmp-567768,\"{'Yb': 2.0, 'Cu': 2.0, 'Ge': 2.0}\",\"('Cu', 'Ge', 'Yb')\",OTHERS,False\nmp-1245612,\"{'Mn': 28.0, 'Si': 4.0, 'N': 24.0}\",\"('Mn', 'N', 'Si')\",OTHERS,False\nmvc-684,\"{'Al': 2.0, 'Cr': 2.0, 'W': 4.0, 'O': 16.0}\",\"('Al', 'Cr', 'O', 'W')\",OTHERS,False\nmp-1211961,\"{'K': 2.0, 'Si': 4.0, 'O': 8.0}\",\"('K', 'O', 'Si')\",OTHERS,False\nmp-28691,\"{'Te': 8.0, 'I': 2.0, 'Ta': 2.0}\",\"('I', 'Ta', 'Te')\",OTHERS,False\nmp-3433,\"{'Mg': 1.0, 'Ni': 4.0, 'Y': 1.0}\",\"('Mg', 'Ni', 'Y')\",OTHERS,False\nmp-30223,\"{'Fe': 4.0, 'I': 2.0, 'La': 4.0}\",\"('Fe', 'I', 'La')\",OTHERS,False\nmp-1218090,\"{'Ta': 3.0, 'Al': 1.0, 'Fe': 8.0}\",\"('Al', 'Fe', 'Ta')\",OTHERS,False\nmp-766351,\"{'Ba': 8.0, 'Ca': 4.0, 'I': 24.0}\",\"('Ba', 'Ca', 'I')\",OTHERS,False\nmp-1246792,\"{'Mg': 4.0, 'Cu': 4.0, 'N': 4.0}\",\"('Cu', 'Mg', 'N')\",OTHERS,False\nmp-1223097,\"{'Li': 16.0, 'H': 8.0, 'N': 8.0}\",\"('H', 'Li', 'N')\",OTHERS,False\nmp-618964,\"{'Ba': 10.0, 'Fe': 8.0, 'S': 22.0}\",\"('Ba', 'Fe', 'S')\",OTHERS,False\nmp-861934,\"{'Ho': 1.0, 'Al': 1.0, 'Ag': 2.0}\",\"('Ag', 'Al', 'Ho')\",OTHERS,False\nmp-1195152,\"{'Ba': 12.0, 'V': 16.0, 'Zn': 4.0, 'O': 56.0}\",\"('Ba', 'O', 'V', 'Zn')\",OTHERS,False\nmvc-3104,\"{'Ba': 2.0, 'Ca': 2.0, 'Tl': 2.0, 'W': 3.0, 'O': 10.0}\",\"('Ba', 'Ca', 'O', 'Tl', 'W')\",OTHERS,False\nmp-31308,\"{'S': 40.0, 'K': 8.0, 'Ta': 8.0}\",\"('K', 'S', 'Ta')\",OTHERS,False\nmp-771914,\"{'Ba': 6.0, 'La': 4.0, 'Cl': 24.0}\",\"('Ba', 'Cl', 'La')\",OTHERS,False\nmp-1016155,\"{'Na': 1.0, 'Mn': 4.0, 'O': 8.0}\",\"('Mn', 'Na', 'O')\",OTHERS,False\nmp-1227871,\"{'Ca': 6.0, 'Cd': 4.0, 'Ga': 8.0}\",\"('Ca', 'Cd', 'Ga')\",OTHERS,False\nmp-675479,\"{'Pu': 2.0, 'Pa': 2.0, 'O': 8.0}\",\"('O', 'Pa', 'Pu')\",OTHERS,False\nmvc-5756,\"{'Ca': 1.0, 'W': 1.0, 'O': 3.0}\",\"('Ca', 'O', 'W')\",OTHERS,False\nmp-7917,\"{'Ge': 2.0, 'Se': 2.0, 'Hf': 2.0}\",\"('Ge', 'Hf', 'Se')\",OTHERS,False\nmvc-16087,\"{'Y': 4.0, 'Ti': 12.0, 'O': 24.0}\",\"('O', 'Ti', 'Y')\",OTHERS,False\nmp-1221205,\"{'Na': 3.0, 'P': 1.0, 'O': 4.0}\",\"('Na', 'O', 'P')\",OTHERS,False\nmp-1212260,\"{'Ho': 6.0, 'Al': 24.0, 'Ru': 8.0}\",\"('Al', 'Ho', 'Ru')\",OTHERS,False\nmp-1181699,\"{'Cl': 2.0, 'O': 6.0}\",\"('Cl', 'O')\",OTHERS,False\nmp-980060,\"{'Tb': 1.0, 'Ag': 3.0}\",\"('Ag', 'Tb')\",OTHERS,False\nmp-1215532,\"{'Zn': 4.0, 'Fe': 1.0, 'Se': 5.0}\",\"('Fe', 'Se', 'Zn')\",OTHERS,False\nmp-1213005,\"{'Er': 4.0, 'Si': 4.0, 'Ni': 4.0}\",\"('Er', 'Ni', 'Si')\",OTHERS,False\nmp-23059,\"{'Cl': 6.0, 'Rb': 2.0, 'Sn': 1.0}\",\"('Cl', 'Rb', 'Sn')\",OTHERS,False\nmp-754334,\"{'Na': 2.0, 'Tm': 2.0, 'O': 4.0}\",\"('Na', 'O', 'Tm')\",OTHERS,False\nmp-1181627,\"{'Cu': 2.0, 'H': 28.0, 'C': 8.0, 'N': 8.0, 'O': 16.0}\",\"('C', 'Cu', 'H', 'N', 'O')\",OTHERS,False\nmp-757010,\"{'Li': 3.0, 'Fe': 1.0, 'Ni': 4.0, 'O': 8.0}\",\"('Fe', 'Li', 'Ni', 'O')\",OTHERS,False\nmvc-6842,\"{'Zn': 4.0, 'Ag': 8.0, 'O': 16.0}\",\"('Ag', 'O', 'Zn')\",OTHERS,False\nmp-1203168,\"{'Sn': 8.0, 'H': 48.0, 'C': 16.0, 'Cl': 8.0, 'O': 4.0}\",\"('C', 'Cl', 'H', 'O', 'Sn')\",OTHERS,False\nmp-1036763,\"{'Mg': 30.0, 'V': 1.0, 'Sn': 1.0, 'O': 32.0}\",\"('Mg', 'O', 'Sn', 'V')\",OTHERS,False\nmp-5622,\"{'P': 2.0, 'Co': 2.0, 'Nd': 1.0}\",\"('Co', 'Nd', 'P')\",OTHERS,False\nmp-568758,\"{'Bi': 8.0, 'Br': 8.0}\",\"('Bi', 'Br')\",OTHERS,False\nmp-1206153,\"{'Eu': 2.0, 'Zr': 1.0, 'O': 4.0}\",\"('Eu', 'O', 'Zr')\",OTHERS,False\nmp-753293,\"{'Li': 4.0, 'Ni': 2.0, 'P': 4.0, 'O': 14.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-1006112,\"{'Na': 3.0, 'Co': 1.0}\",\"('Co', 'Na')\",OTHERS,False\nmp-753722,\"{'Li': 1.0, 'Cu': 1.0, 'S': 2.0}\",\"('Cu', 'Li', 'S')\",OTHERS,False\nmp-758573,\"{'H': 16.0, 'Ru': 3.0, 'C': 6.0, 'Cl': 6.0, 'O': 14.0}\",\"('C', 'Cl', 'H', 'O', 'Ru')\",OTHERS,False\nmp-1087537,\"{'Cr': 3.0, 'P': 3.0, 'Pd': 3.0}\",\"('Cr', 'P', 'Pd')\",OTHERS,False\nmp-755178,\"{'Mn': 6.0, 'O': 4.0, 'F': 8.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-1094248,\"{'Y': 2.0, 'Sn': 6.0}\",\"('Sn', 'Y')\",OTHERS,False\nmp-675744,\"{'Yb': 8.0, 'Zr': 6.0, 'O': 24.0}\",\"('O', 'Yb', 'Zr')\",OTHERS,False\nmp-1173749,\"{'Na': 8.0, 'Al': 6.0, 'Si': 6.0, 'O': 25.0}\",\"('Al', 'Na', 'O', 'Si')\",OTHERS,False\nmp-568342,\"{'Tb': 2.0, 'Cl': 2.0}\",\"('Cl', 'Tb')\",OTHERS,False\nmp-696457,\"{'Sc': 2.0, 'P': 6.0, 'H': 12.0, 'O': 24.0}\",\"('H', 'O', 'P', 'Sc')\",OTHERS,False\nmp-1105105,\"{'I': 10.0, 'N': 4.0}\",\"('I', 'N')\",OTHERS,False\nmp-776954,\"{'Li': 4.0, 'Mn': 2.0, 'P': 2.0, 'H': 2.0, 'O': 10.0}\",\"('H', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1208318,\"{'Tb': 4.0, 'Si': 4.0, 'Pt': 8.0}\",\"('Pt', 'Si', 'Tb')\",OTHERS,False\nmp-1094231,\"{'Mg': 2.0, 'Sn': 10.0}\",\"('Mg', 'Sn')\",OTHERS,False\nmp-568031,\"{'Ga': 8.0, 'Hg': 16.0, 'Sb': 8.0, 'Cl': 32.0}\",\"('Cl', 'Ga', 'Hg', 'Sb')\",OTHERS,False\nmp-758669,\"{'Li': 2.0, 'V': 4.0, 'C': 8.0, 'O': 24.0}\",\"('C', 'Li', 'O', 'V')\",OTHERS,False\nmp-554955,\"{'Mg': 1.0, 'Cr': 1.0, 'F': 6.0}\",\"('Cr', 'F', 'Mg')\",OTHERS,False\nmp-772496,\"{'Mn': 3.0, 'Cu': 1.0, 'Ni': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Cu', 'Mn', 'Ni', 'O', 'P')\",OTHERS,False\nmp-676110,\"{'Tb': 14.0, 'Pb': 3.0, 'Se': 24.0}\",\"('Pb', 'Se', 'Tb')\",OTHERS,False\nmp-1247846,\"{'Al': 16.0, 'O': 24.0}\",\"('Al', 'O')\",OTHERS,False\nmp-754649,\"{'Mn': 2.0, 'C': 2.0, 'S': 2.0, 'O': 14.0}\",\"('C', 'Mn', 'O', 'S')\",OTHERS,False\nmp-1099669,\"{'K': 4.0, 'Na': 28.0, 'V': 28.0, 'Mo': 4.0, 'O': 80.0}\",\"('K', 'Mo', 'Na', 'O', 'V')\",OTHERS,False\nmp-1219595,\"{'Rb': 2.0, 'Hg': 2.0, 'Sb': 2.0, 'Te': 6.0}\",\"('Hg', 'Rb', 'Sb', 'Te')\",OTHERS,False\nmp-10179,\"{'Li': 1.0, 'Mg': 1.0, 'Pd': 1.0, 'Sb': 1.0}\",\"('Li', 'Mg', 'Pd', 'Sb')\",OTHERS,False\nmp-1247446,\"{'Sr': 6.0, 'Zr': 6.0, 'N': 10.0}\",\"('N', 'Sr', 'Zr')\",OTHERS,False\nmp-1196501,\"{'Al': 4.0, 'Si': 6.0, 'N': 4.0, 'O': 20.0}\",\"('Al', 'N', 'O', 'Si')\",OTHERS,False\nmp-1073302,\"{'Mg': 4.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-21653,\"{'Ba': 12.0, 'Ni': 12.0, 'N': 12.0}\",\"('Ba', 'N', 'Ni')\",OTHERS,False\nmp-1218180,\"{'Sr': 1.0, 'Nd': 2.0, 'Fe': 2.0, 'O': 7.0}\",\"('Fe', 'Nd', 'O', 'Sr')\",OTHERS,False\nmp-5170,\"{'Si': 4.0, 'Co': 2.0, 'Nd': 2.0}\",\"('Co', 'Nd', 'Si')\",OTHERS,False\nmp-1025876,\"{'Te': 2.0, 'Mo': 3.0, 'Se': 4.0}\",\"('Mo', 'Se', 'Te')\",OTHERS,False\nmp-1246783,\"{'Hf': 2.0, 'Cr': 2.0, 'Ag': 2.0, 'S': 8.0}\",\"('Ag', 'Cr', 'Hf', 'S')\",OTHERS,False\nmp-767204,\"{'Li': 4.0, 'V': 2.0, 'Si': 12.0, 'O': 30.0}\",\"('Li', 'O', 'Si', 'V')\",OTHERS,False\nmp-1203166,\"{'Er': 4.0, 'Mo': 6.0, 'H': 4.0, 'Se': 4.0, 'O': 34.0}\",\"('Er', 'H', 'Mo', 'O', 'Se')\",OTHERS,False\nmp-669546,\"{'Al': 13.0, 'Pd': 13.0}\",\"('Al', 'Pd')\",OTHERS,False\nmp-554396,\"{'Mg': 4.0, 'Si': 2.0, 'O': 8.0}\",\"('Mg', 'O', 'Si')\",OTHERS,False\nmp-1246732,\"{'Mg': 2.0, 'Co': 2.0, 'W': 2.0, 'S': 8.0}\",\"('Co', 'Mg', 'S', 'W')\",OTHERS,False\nmp-1206309,\"{'Er': 1.0, 'Cr': 2.0, 'Si': 2.0, 'C': 1.0}\",\"('C', 'Cr', 'Er', 'Si')\",OTHERS,False\nmp-976264,\"{'Li': 3.0, 'Nd': 1.0}\",\"('Li', 'Nd')\",OTHERS,False\nmp-974325,\"{'Nd': 2.0, 'Dy': 6.0}\",\"('Dy', 'Nd')\",OTHERS,False\nmp-1022965,\"{'K': 6.0, 'V': 4.0, 'P': 6.0, 'O': 24.0}\",\"('K', 'O', 'P', 'V')\",OTHERS,False\nmp-1194676,\"{'Zn': 2.0, 'H': 48.0, 'Au': 4.0, 'C': 24.0, 'S': 16.0, 'N': 8.0, 'Cl': 8.0}\",\"('Au', 'C', 'Cl', 'H', 'N', 'S', 'Zn')\",OTHERS,False\nmp-20646,\"{'Mn': 2.0, 'Ge': 2.0, 'Sm': 1.0}\",\"('Ge', 'Mn', 'Sm')\",OTHERS,False\nmp-754682,\"{'Li': 2.0, 'Ti': 1.0, 'Co': 3.0, 'O': 8.0}\",\"('Co', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-21903,\"{'S': 8.0, 'K': 2.0, 'Ge': 2.0, 'Eu': 2.0}\",\"('Eu', 'Ge', 'K', 'S')\",OTHERS,False\nmp-756024,\"{'Cr': 2.0, 'Sb': 4.0, 'O': 12.0}\",\"('Cr', 'O', 'Sb')\",OTHERS,False\nmp-865513,\"{'Y': 1.0, 'Sb': 1.0, 'Pd': 2.0}\",\"('Pd', 'Sb', 'Y')\",OTHERS,False\nmp-863426,\"{'Li': 6.0, 'Mn': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1200045,\"{'Bi': 24.0, 'N': 20.0, 'O': 104.0}\",\"('Bi', 'N', 'O')\",OTHERS,False\nmp-29381,\"{'Na': 16.0, 'As': 8.0, 'Te': 16.0}\",\"('As', 'Na', 'Te')\",OTHERS,False\nmp-546152,\"{'O': 4.0, 'Bi': 2.0, 'Sm': 1.0, 'Cl': 1.0}\",\"('Bi', 'Cl', 'O', 'Sm')\",OTHERS,False\nmp-695154,\"{'Na': 7.0, 'Bi': 3.0, 'P': 12.0, 'Pb': 10.0, 'O': 48.0}\",\"('Bi', 'Na', 'O', 'P', 'Pb')\",OTHERS,False\nmp-3200,\"{'O': 14.0, 'Ti': 4.0, 'Lu': 4.0}\",\"('Lu', 'O', 'Ti')\",OTHERS,False\nmp-755104,\"{'Ba': 4.0, 'Y': 2.0, 'Cl': 14.0}\",\"('Ba', 'Cl', 'Y')\",OTHERS,False\nmp-1094629,\"{'Mg': 2.0, 'Ga': 4.0}\",\"('Ga', 'Mg')\",OTHERS,False\nmp-5169,\"{'Cu': 8.0, 'P': 4.0, 'O': 18.0}\",\"('Cu', 'O', 'P')\",OTHERS,False\nmp-40802,\"{'Ba': 1.0, 'Bi': 1.0, 'La': 1.0, 'O': 6.0, 'Sr': 1.0}\",\"('Ba', 'Bi', 'La', 'O', 'Sr')\",OTHERS,False\nmp-1175840,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-30859,\"{'Zr': 9.0, 'Pt': 11.0}\",\"('Pt', 'Zr')\",OTHERS,False\nmvc-8434,\"{'Cr': 4.0, 'Ge': 8.0, 'O': 24.0}\",\"('Cr', 'Ge', 'O')\",OTHERS,False\nmp-1183368,\"{'Ba': 6.0, 'Pr': 2.0}\",\"('Ba', 'Pr')\",OTHERS,False\nmp-1114554,\"{'Tb': 1.0, 'K': 1.0, 'Rb': 2.0, 'Cl': 6.0}\",\"('Cl', 'K', 'Rb', 'Tb')\",OTHERS,False\nmp-1217705,\"{'Tb': 2.0, 'Ge': 3.0}\",\"('Ge', 'Tb')\",OTHERS,False\nmp-1183070,\"{'Ac': 2.0, 'Tl': 1.0, 'Sn': 1.0}\",\"('Ac', 'Sn', 'Tl')\",OTHERS,False\nmp-1207257,\"{'K': 2.0, 'Cd': 1.0, 'Sb': 2.0}\",\"('Cd', 'K', 'Sb')\",OTHERS,False\nmp-1201169,\"{'P': 16.0, 'Se': 12.0, 'Br': 8.0}\",\"('Br', 'P', 'Se')\",OTHERS,False\nmp-777779,\"{'Li': 4.0, 'Mn': 1.0, 'Te': 3.0, 'O': 12.0}\",\"('Li', 'Mn', 'O', 'Te')\",OTHERS,False\nmp-1203277,\"{'K': 12.0, 'Ho': 4.0, 'Si': 12.0, 'O': 40.0}\",\"('Ho', 'K', 'O', 'Si')\",OTHERS,False\nmp-28760,\"{'S': 4.0, 'K': 4.0, 'Rb': 4.0}\",\"('K', 'Rb', 'S')\",OTHERS,False\nmp-1104486,\"{'Tm': 6.0, 'Ge': 8.0}\",\"('Ge', 'Tm')\",OTHERS,False\nmp-866804,\"{'Ti': 1.0, 'Mn': 1.0, 'H': 12.0, 'O': 6.0, 'F': 6.0}\",\"('F', 'H', 'Mn', 'O', 'Ti')\",OTHERS,False\nmp-1222497,\"{'Lu': 1.0, 'Al': 8.0, 'Ni': 3.0}\",\"('Al', 'Lu', 'Ni')\",OTHERS,False\nmp-556725,\"{'Cu': 2.0, 'H': 16.0, 'C': 4.0, 'Br': 6.0, 'N': 2.0, 'O': 2.0}\",\"('Br', 'C', 'Cu', 'H', 'N', 'O')\",OTHERS,False\nmp-755170,\"{'Fe': 8.0, 'O': 2.0, 'F': 14.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-510062,\"{'Sm': 2.0, 'Cs': 2.0, 'Cd': 2.0, 'Se': 6.0}\",\"('Cd', 'Cs', 'Se', 'Sm')\",OTHERS,False\nmp-571212,\"{'Yb': 10.0, 'Ag': 6.0}\",\"('Ag', 'Yb')\",OTHERS,False\nmvc-1887,\"{'Ba': 4.0, 'Mg': 2.0, 'Cu': 2.0, 'Sb': 4.0, 'F': 28.0}\",\"('Ba', 'Cu', 'F', 'Mg', 'Sb')\",OTHERS,False\nmp-1219789,\"{'Rb': 4.0, 'Ba': 2.0, 'Cl': 8.0}\",\"('Ba', 'Cl', 'Rb')\",OTHERS,False\nmp-1106192,\"{'Eu': 4.0, 'Zr': 4.0, 'Se': 12.0}\",\"('Eu', 'Se', 'Zr')\",OTHERS,False\nmp-1227727,\"{'Ba': 1.0, 'Sr': 4.0, 'Fe': 5.0, 'O': 15.0}\",\"('Ba', 'Fe', 'O', 'Sr')\",OTHERS,False\nmp-980066,\"{'Sm': 8.0, 'Cu': 12.0, 'As': 12.0}\",\"('As', 'Cu', 'Sm')\",OTHERS,False\nmp-1190681,\"{'Zr': 8.0, 'Fe': 16.0}\",\"('Fe', 'Zr')\",OTHERS,False\nmp-1215652,\"{'Zn': 4.0, 'Cd': 1.0, 'Se': 5.0}\",\"('Cd', 'Se', 'Zn')\",OTHERS,False\nmp-5351,\"{'Ge': 2.0, 'Pd': 2.0, 'Tb': 1.0}\",\"('Ge', 'Pd', 'Tb')\",OTHERS,False\nmp-971911,\"{'Zn': 2.0, 'N': 2.0}\",\"('N', 'Zn')\",OTHERS,False\nmp-1244700,\"{'Cr': 12.0, 'O': 28.0}\",\"('Cr', 'O')\",OTHERS,False\nmp-9194,\"{'O': 2.0, 'Cu': 2.0, 'Se': 2.0, 'Sm': 2.0}\",\"('Cu', 'O', 'Se', 'Sm')\",OTHERS,False\nmp-754161,\"{'Rb': 6.0, 'Ho': 2.0, 'O': 6.0}\",\"('Ho', 'O', 'Rb')\",OTHERS,False\nmp-754801,\"{'Li': 3.0, 'Ti': 6.0, 'O': 13.0}\",\"('Li', 'O', 'Ti')\",OTHERS,False\nmp-1199509,\"{'Mg': 8.0, 'As': 4.0, 'O': 20.0}\",\"('As', 'Mg', 'O')\",OTHERS,False\nmp-698941,\"{'Nd': 10.0, 'Fe': 10.0, 'As': 10.0, 'O': 8.0, 'F': 2.0}\",\"('As', 'F', 'Fe', 'Nd', 'O')\",OTHERS,False\nmp-13925,\"{'F': 6.0, 'Na': 1.0, 'Y': 1.0, 'Cs': 2.0}\",\"('Cs', 'F', 'Na', 'Y')\",OTHERS,False\nmp-1221346,\"{'Na': 4.0, 'Nd': 8.0, 'N': 2.0, 'Cl': 18.0, 'O': 2.0}\",\"('Cl', 'N', 'Na', 'Nd', 'O')\",OTHERS,False\nmp-13196,\"{'O': 16.0, 'Sb': 4.0, 'Sm': 4.0}\",\"('O', 'Sb', 'Sm')\",OTHERS,False\nmp-21170,\"{'K': 4.0, 'Pb': 8.0}\",\"('K', 'Pb')\",OTHERS,False\nmp-570981,\"{'Ce': 2.0, 'Al': 16.0, 'Pt': 9.0}\",\"('Al', 'Ce', 'Pt')\",OTHERS,False\nmp-1132757,\"{'Mg': 2.0, 'V': 2.0, 'Si': 4.0, 'O': 12.0}\",\"('Mg', 'O', 'Si', 'V')\",OTHERS,False\nmp-1177267,\"{'Li': 4.0, 'V': 3.0, 'Cu': 1.0, 'P': 4.0, 'O': 16.0}\",\"('Cu', 'Li', 'O', 'P', 'V')\",OTHERS,False\nmp-867842,\"{'Sc': 1.0, 'In': 1.0, 'Rh': 2.0}\",\"('In', 'Rh', 'Sc')\",OTHERS,False\nmp-1076669,\"{'La': 7.0, 'Sm': 1.0, 'Mn': 6.0, 'Fe': 2.0, 'O': 24.0}\",\"('Fe', 'La', 'Mn', 'O', 'Sm')\",OTHERS,False\nmp-1174421,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-637194,\"{'Cs': 6.0, 'Sc': 4.0, 'Cl': 18.0}\",\"('Cl', 'Cs', 'Sc')\",OTHERS,False\nmp-31075,\"{'Se': 8.0, 'Br': 8.0, 'Hg': 12.0}\",\"('Br', 'Hg', 'Se')\",OTHERS,False\nmp-1194742,\"{'Ba': 6.0, 'B': 6.0, 'Pb': 4.0, 'Br': 2.0, 'O': 18.0}\",\"('B', 'Ba', 'Br', 'O', 'Pb')\",OTHERS,False\nmp-1097647,\"{'Y': 2.0, 'Pd': 1.0, 'Pt': 1.0}\",\"('Pd', 'Pt', 'Y')\",OTHERS,False\nmp-769605,\"{'Li': 8.0, 'V': 2.0, 'Cr': 6.0, 'O': 16.0}\",\"('Cr', 'Li', 'O', 'V')\",OTHERS,False\nmp-20773,\"{'As': 1.0, 'Pu': 1.0}\",\"('As', 'Pu')\",OTHERS,False\nmp-1225195,\"{'Fe': 2.0, 'C': 6.0, 'N': 6.0, 'O': 4.0}\",\"('C', 'Fe', 'N', 'O')\",OTHERS,False\nmp-30756,\"{'Ru': 8.0, 'Sn': 24.0, 'La': 8.0}\",\"('La', 'Ru', 'Sn')\",OTHERS,False\nmp-1191680,\"{'Ta': 6.0, 'Se': 18.0}\",\"('Se', 'Ta')\",OTHERS,False\nmp-1187021,\"{'Sm': 1.0, 'Mg': 5.0}\",\"('Mg', 'Sm')\",OTHERS,False\nmp-1212458,\"{'Ho': 12.0, 'Mn': 8.0, 'Al': 86.0}\",\"('Al', 'Ho', 'Mn')\",OTHERS,False\nmp-639876,\"{'U': 4.0, 'Sn': 2.0, 'Rh': 4.0}\",\"('Rh', 'Sn', 'U')\",OTHERS,False\nmp-1079393,\"{'Co': 6.0, 'Si': 2.0}\",\"('Co', 'Si')\",OTHERS,False\nmp-569505,\"{'Tb': 2.0, 'Re': 4.0, 'Si': 2.0, 'C': 2.0}\",\"('C', 'Re', 'Si', 'Tb')\",OTHERS,False\nmp-11621,\"{'Ni': 1.0, 'Zn': 1.0, 'Sb': 1.0}\",\"('Ni', 'Sb', 'Zn')\",OTHERS,False\nmp-1204729,\"{'Yb': 12.0, 'Al': 86.0, 'W': 8.0}\",\"('Al', 'W', 'Yb')\",OTHERS,False\nmp-18041,\"{'O': 16.0, 'Sm': 2.0, 'W': 4.0, 'Tl': 2.0}\",\"('O', 'Sm', 'Tl', 'W')\",OTHERS,False\nmp-1245963,\"{'Ba': 2.0, 'Fe': 4.0, 'N': 4.0}\",\"('Ba', 'Fe', 'N')\",OTHERS,False\nmp-1228860,\"{'Ba': 4.0, 'Cr': 13.0, 'O': 28.0}\",\"('Ba', 'Cr', 'O')\",OTHERS,False\nmp-758664,\"{'Fe': 18.0, 'O': 18.0, 'F': 18.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-555837,\"{'Rb': 6.0, 'Si': 10.0, 'O': 23.0}\",\"('O', 'Rb', 'Si')\",OTHERS,False\nmp-1104225,\"{'Lu': 3.0, 'Ga': 9.0, 'Pd': 2.0}\",\"('Ga', 'Lu', 'Pd')\",OTHERS,False\nmp-1203797,\"{'Nd': 26.0, 'B': 8.0, 'O': 52.0}\",\"('B', 'Nd', 'O')\",OTHERS,False\nmp-866873,\"{'Ca': 6.0, 'Sn': 4.0, 'S': 14.0}\",\"('Ca', 'S', 'Sn')\",OTHERS,False\nmp-1018136,\"{'La': 1.0, 'Bi': 1.0, 'Pt': 1.0}\",\"('Bi', 'La', 'Pt')\",OTHERS,False\nmp-1224692,\"{'Fe': 2.0, 'Pt': 1.0}\",\"('Fe', 'Pt')\",OTHERS,False\nmp-20509,\"{'Ba': 2.0, 'Y': 1.0, 'Cu': 4.0, 'O': 8.0}\",\"('Ba', 'Cu', 'O', 'Y')\",OTHERS,False\nmvc-13704,\"{'Zn': 4.0, 'Sb': 2.0, 'Mo': 2.0, 'O': 12.0}\",\"('Mo', 'O', 'Sb', 'Zn')\",OTHERS,False\nmp-778408,\"{'Hg': 2.0, 'Bi': 2.0, 'O': 6.0}\",\"('Bi', 'Hg', 'O')\",OTHERS,False\nmp-771151,\"{'Li': 4.0, 'Mn': 2.0, 'As': 2.0, 'C': 2.0, 'O': 14.0}\",\"('As', 'C', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-973451,\"{'Nd': 2.0, 'Bi': 1.0, 'O': 2.0}\",\"('Bi', 'Nd', 'O')\",OTHERS,False\nmp-768200,\"{'Li': 4.0, 'Ho': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Ho', 'Li', 'O', 'P')\",OTHERS,False\nmp-753777,\"{'Li': 6.0, 'Fe': 1.0, 'Co': 1.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Co', 'Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-3411,\"{'Se': 6.0, 'Mo': 6.0, 'Tl': 2.0}\",\"('Mo', 'Se', 'Tl')\",OTHERS,False\nmp-12174,\"{'Ni': 30.0, 'Hf': 10.0}\",\"('Hf', 'Ni')\",OTHERS,False\nmp-977455,\"{'Pa': 1.0, 'Ag': 1.0, 'O': 3.0}\",\"('Ag', 'O', 'Pa')\",OTHERS,False\nmp-1181167,\"{'Na': 8.0, 'B': 16.0, 'O': 44.0}\",\"('B', 'Na', 'O')\",OTHERS,False\nmp-10872,\"{'Al': 1.0, 'Hf': 1.0, 'Au': 2.0}\",\"('Al', 'Au', 'Hf')\",OTHERS,False\nmp-755125,\"{'Li': 2.0, 'Mn': 2.0, 'Si': 2.0, 'O': 8.0}\",\"('Li', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1099332,\"{'Sr': 1.0, 'Ce': 1.0, 'Mg': 6.0}\",\"('Ce', 'Mg', 'Sr')\",OTHERS,False\nmp-1103088,\"{'Dy': 3.0, 'Al': 9.0}\",\"('Al', 'Dy')\",OTHERS,False\nmp-760954,\"{'Li': 5.0, 'Ti': 2.0, 'V': 3.0, 'O': 10.0}\",\"('Li', 'O', 'Ti', 'V')\",OTHERS,False\nmp-1180456,\"{'Li': 3.0, 'V': 3.0, 'O': 6.0}\",\"('Li', 'O', 'V')\",OTHERS,False\nmp-1094709,\"{'Mg': 3.0, 'Sb': 1.0}\",\"('Mg', 'Sb')\",OTHERS,False\nmp-22461,\"{'Fe': 3.0, 'Sn': 1.0}\",\"('Fe', 'Sn')\",OTHERS,False\nmp-1095709,\"{'Tc': 2.0, 'Ge': 1.0, 'W': 1.0}\",\"('Ge', 'Tc', 'W')\",OTHERS,False\nmp-773100,\"{'La': 4.0, 'Bi': 2.0, 'O': 10.0}\",\"('Bi', 'La', 'O')\",OTHERS,False\nmp-1214654,\"{'Ba': 4.0, 'La': 2.0, 'Nb': 2.0, 'Cu': 4.0, 'O': 16.0}\",\"('Ba', 'Cu', 'La', 'Nb', 'O')\",OTHERS,False\nmp-1184074,\"{'Dy': 1.0, 'Tm': 1.0, 'Ru': 2.0}\",\"('Dy', 'Ru', 'Tm')\",OTHERS,False\nmp-1221048,\"{'Na': 2.0, 'Li': 2.0, 'V': 4.0, 'O': 12.0}\",\"('Li', 'Na', 'O', 'V')\",OTHERS,False\nmp-1215695,\"{'Zn': 6.0, 'Ga': 2.0, 'Ag': 2.0, 'S': 10.0}\",\"('Ag', 'Ga', 'S', 'Zn')\",OTHERS,False\nmp-1207493,\"{'Yb': 4.0, 'Si': 4.0, 'Pd': 4.0}\",\"('Pd', 'Si', 'Yb')\",OTHERS,False\nmp-1210775,\"{'Lu': 4.0, 'O': 4.0, 'F': 4.0}\",\"('F', 'Lu', 'O')\",OTHERS,False\nmp-23626,\"{'Rb': 4.0, 'Li': 8.0, 'I': 12.0, 'O': 36.0}\",\"('I', 'Li', 'O', 'Rb')\",OTHERS,False\nmp-722891,\"{'Na': 4.0, 'Ni': 2.0, 'H': 28.0, 'N': 12.0}\",\"('H', 'N', 'Na', 'Ni')\",OTHERS,False\nmp-1226724,\"{'Cd': 1.0, 'Sb': 1.0}\",\"('Cd', 'Sb')\",OTHERS,False\nmp-1245570,\"{'Mn': 16.0, 'Se': 12.0, 'N': 8.0}\",\"('Mn', 'N', 'Se')\",OTHERS,False\nmp-25827,\"{'Li': 2.0, 'O': 22.0, 'P': 6.0, 'Mo': 4.0}\",\"('Li', 'Mo', 'O', 'P')\",OTHERS,False\nmp-1184919,\"{'K': 6.0, 'Na': 2.0}\",\"('K', 'Na')\",OTHERS,False\nmp-540619,\"{'K': 12.0, 'Sn': 4.0, 'Te': 12.0}\",\"('K', 'Sn', 'Te')\",OTHERS,False\nmp-768960,\"{'Li': 40.0, 'B': 8.0, 'O': 32.0}\",\"('B', 'Li', 'O')\",OTHERS,False\nmp-1221608,\"{'Mo': 6.0, 'Pb': 1.0, 'Se': 4.0, 'S': 4.0}\",\"('Mo', 'Pb', 'S', 'Se')\",OTHERS,False\nmp-1173753,\"{'Na': 4.0, 'Ga': 4.0, 'Se': 6.0}\",\"('Ga', 'Na', 'Se')\",OTHERS,False\nmp-1186717,\"{'Pr': 3.0, 'Hf': 1.0}\",\"('Hf', 'Pr')\",OTHERS,False\nmp-1120736,\"{'Na': 10.0, 'Ti': 6.0, 'F': 28.0}\",\"('F', 'Na', 'Ti')\",OTHERS,False\nmp-1217173,\"{'Ti': 16.0, 'Mn': 8.0, 'O': 4.0}\",\"('Mn', 'O', 'Ti')\",OTHERS,False\nmp-28513,\"{'O': 12.0, 'Cl': 4.0, 'Pb': 2.0}\",\"('Cl', 'O', 'Pb')\",OTHERS,False\nmp-1179787,\"{'Rb': 1.0, 'Cd': 6.0, 'C': 12.0, 'Br': 1.0, 'O': 26.0}\",\"('Br', 'C', 'Cd', 'O', 'Rb')\",OTHERS,False\nmp-1206284,\"{'La': 2.0, 'C': 2.0, 'Br': 2.0}\",\"('Br', 'C', 'La')\",OTHERS,False\nmvc-13426,\"{'Ca': 1.0, 'Cu': 2.0, 'N': 2.0}\",\"('Ca', 'Cu', 'N')\",OTHERS,False\nmp-1095427,\"{'Hf': 4.0, 'Co': 4.0, 'P': 4.0}\",\"('Co', 'Hf', 'P')\",OTHERS,False\nmp-556346,\"{'Pr': 4.0, 'I': 12.0, 'O': 36.0}\",\"('I', 'O', 'Pr')\",OTHERS,False\nmp-1225036,\"{'Gd': 1.0, 'Al': 6.0, 'Cu': 6.0}\",\"('Al', 'Cu', 'Gd')\",OTHERS,False\nmp-15796,\"{'Li': 1.0, 'Se': 2.0, 'Ho': 1.0}\",\"('Ho', 'Li', 'Se')\",OTHERS,False\nmp-1105352,\"{'Ce': 1.0, 'Fe': 4.0, 'Cu': 3.0, 'O': 12.0}\",\"('Ce', 'Cu', 'Fe', 'O')\",OTHERS,False\nmp-1247144,\"{'La': 2.0, 'Mg': 2.0, 'Mo': 2.0, 'S': 8.0}\",\"('La', 'Mg', 'Mo', 'S')\",OTHERS,False\nmp-762249,\"{'V': 2.0, 'C': 4.0, 'O': 12.0}\",\"('C', 'O', 'V')\",OTHERS,False\nmp-775938,\"{'Ti': 12.0, 'O': 24.0}\",\"('O', 'Ti')\",OTHERS,False\nmp-765521,\"{'Li': 4.0, 'V': 4.0, 'O': 4.0, 'F': 12.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-1232150,\"{'Mg': 4.0, 'Sc': 4.0, 'Se': 12.0}\",\"('Mg', 'Sc', 'Se')\",OTHERS,False\nmp-560262,\"{'Zn': 2.0, 'In': 4.0, 'S': 8.0}\",\"('In', 'S', 'Zn')\",OTHERS,False\nmp-867247,\"{'Tb': 2.0, 'Br': 6.0}\",\"('Br', 'Tb')\",OTHERS,False\nmp-765086,\"{'Li': 10.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1186042,\"{'Na': 6.0, 'Ca': 2.0}\",\"('Ca', 'Na')\",OTHERS,False\nmp-1217247,\"{'Ti': 16.0, 'Co': 4.0, 'Ni': 4.0}\",\"('Co', 'Ni', 'Ti')\",OTHERS,False\nmp-1017629,\"{'Mg': 1.0, 'Ni': 1.0, 'H': 3.0}\",\"('H', 'Mg', 'Ni')\",OTHERS,False\nmp-1190932,\"{'Co': 3.0, 'Te': 2.0, 'P': 2.0, 'O': 14.0}\",\"('Co', 'O', 'P', 'Te')\",OTHERS,False\nmp-1239190,\"{'Ta': 2.0, 'Cr': 6.0, 'Ag': 4.0, 'S': 16.0}\",\"('Ag', 'Cr', 'S', 'Ta')\",OTHERS,False\nmp-1205523,\"{'Sc': 4.0, 'Co': 2.0, 'Ge': 4.0}\",\"('Co', 'Ge', 'Sc')\",OTHERS,False\nmp-1206590,\"{'Co': 1.0, 'Au': 3.0}\",\"('Au', 'Co')\",OTHERS,False\nmp-36577,\"{'As': 2.0, 'S': 4.0, 'Sr': 1.0}\",\"('As', 'S', 'Sr')\",OTHERS,False\nmp-1037480,\"{'Y': 1.0, 'Mg': 30.0, 'Cr': 1.0, 'O': 32.0}\",\"('Cr', 'Mg', 'O', 'Y')\",OTHERS,False\nmp-1100897,\"{'Y': 2.0, 'Zr': 8.0, 'O': 19.0}\",\"('O', 'Y', 'Zr')\",OTHERS,False\nmp-8885,\"{'S': 8.0, 'Cd': 4.0, 'Ba': 4.0}\",\"('Ba', 'Cd', 'S')\",OTHERS,False\nmp-1097227,\"{'Sr': 2.0, 'Hg': 1.0, 'Sb': 1.0}\",\"('Hg', 'Sb', 'Sr')\",OTHERS,False\nmp-1220301,\"{'Nb': 2.0, 'Tl': 2.0, 'W': 2.0, 'O': 12.0}\",\"('Nb', 'O', 'Tl', 'W')\",OTHERS,False\nmp-757780,\"{'Li': 24.0, 'Co': 24.0, 'P': 24.0, 'O': 96.0}\",\"('Co', 'Li', 'O', 'P')\",OTHERS,False\nmp-1188749,\"{'Tb': 4.0, 'Ga': 4.0, 'Pd': 8.0}\",\"('Ga', 'Pd', 'Tb')\",OTHERS,False\nmp-1218281,\"{'Sr': 1.0, 'Eu': 1.0, 'Ni': 1.0, 'O': 4.0}\",\"('Eu', 'Ni', 'O', 'Sr')\",OTHERS,False\nmp-1076317,\"{'Mg': 1.0, 'Cu': 1.0, 'O': 3.0}\",\"('Cu', 'Mg', 'O')\",OTHERS,False\nmp-1214856,\"{'Al': 4.0, 'S': 4.0, 'N': 4.0, 'Cl': 12.0}\",\"('Al', 'Cl', 'N', 'S')\",OTHERS,False\nmp-722280,\"{'Li': 4.0, 'Cu': 4.0, 'H': 16.0, 'Cl': 12.0, 'O': 8.0}\",\"('Cl', 'Cu', 'H', 'Li', 'O')\",OTHERS,False\nmp-1176501,\"{'Lu': 2.0, 'Bi': 2.0, 'O': 6.0}\",\"('Bi', 'Lu', 'O')\",OTHERS,False\nmp-1181934,\"{'Mg': 2.0, 'H': 32.0, 'C': 8.0, 'S': 8.0, 'N': 4.0, 'O': 32.0, 'F': 24.0}\",\"('C', 'F', 'H', 'Mg', 'N', 'O', 'S')\",OTHERS,False\nmp-1018064,\"{'Er': 1.0, 'Fe': 1.0, 'C': 2.0}\",\"('C', 'Er', 'Fe')\",OTHERS,False\nmp-1245216,\"{'Zn': 50.0, 'S': 50.0}\",\"('S', 'Zn')\",OTHERS,False\nmp-1202863,\"{'Na': 8.0, 'Mg': 8.0, 'P': 8.0, 'C': 16.0, 'O': 48.0}\",\"('C', 'Mg', 'Na', 'O', 'P')\",OTHERS,False\nmp-767927,\"{'Li': 8.0, 'Ti': 2.0, 'V': 8.0, 'Cr': 8.0, 'O': 36.0}\",\"('Cr', 'Li', 'O', 'Ti', 'V')\",OTHERS,False\nmp-18825,\"{'Y': 2.0, 'Cr': 2.0, 'O': 8.0}\",\"('Cr', 'O', 'Y')\",OTHERS,False\nmp-8073,\"{'As': 4.0, 'Pd': 8.0}\",\"('As', 'Pd')\",OTHERS,False\nmp-27568,\"{'Al': 6.0, 'Ge': 14.0, 'Ba': 20.0}\",\"('Al', 'Ba', 'Ge')\",OTHERS,False\nmp-1201050,\"{'Sm': 4.0, 'Nb': 4.0, 'S': 14.0, 'O': 60.0}\",\"('Nb', 'O', 'S', 'Sm')\",OTHERS,False\nmp-1219618,\"{'Rb': 4.0, 'H': 16.0, 'O': 12.0}\",\"('H', 'O', 'Rb')\",OTHERS,False\nmp-1181620,\"{'Co': 1.0, 'I': 2.0, 'O': 6.0}\",\"('Co', 'I', 'O')\",OTHERS,False\nmp-1190024,\"{'Ba': 6.0, 'Na': 2.0, 'Sb': 2.0, 'O': 12.0}\",\"('Ba', 'Na', 'O', 'Sb')\",OTHERS,False\nmp-1102309,\"{'Yb': 8.0, 'Ga': 4.0}\",\"('Ga', 'Yb')\",OTHERS,False\nmp-780850,\"{'Li': 6.0, 'Mn': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1174348,\"{'Li': 8.0, 'Mn': 6.0, 'O': 14.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-755848,\"{'Li': 6.0, 'Cr': 3.0, 'Fe': 3.0, 'O': 12.0}\",\"('Cr', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1246916,\"{'Mg': 2.0, 'Ni': 10.0, 'N': 8.0}\",\"('Mg', 'N', 'Ni')\",OTHERS,False\nmp-1246348,\"{'Fe': 3.0, 'Co': 8.0, 'N': 8.0}\",\"('Co', 'Fe', 'N')\",OTHERS,False\nmp-1227523,\"{'Ce': 4.0, 'U': 8.0, 'S': 20.0}\",\"('Ce', 'S', 'U')\",OTHERS,False\nmp-1221600,\"{'Mn': 2.0, 'Re': 2.0, 'B': 4.0}\",\"('B', 'Mn', 'Re')\",OTHERS,False\nmp-1206101,\"{'Ag': 1.0, 'Pb': 1.0, 'F': 6.0}\",\"('Ag', 'F', 'Pb')\",OTHERS,False\nmp-3627,\"{'S': 8.0, 'Mo': 6.0, 'Eu': 1.0}\",\"('Eu', 'Mo', 'S')\",OTHERS,False\nmp-1101817,\"{'Sm': 4.0, 'Ni': 4.0, 'Ge': 4.0}\",\"('Ge', 'Ni', 'Sm')\",OTHERS,False\nmp-757453,\"{'Fe': 6.0, 'O': 6.0, 'F': 6.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-1009494,\"{'Np': 1.0, 'N': 1.0}\",\"('N', 'Np')\",OTHERS,False\nmp-1208255,\"{'Th': 2.0, 'N': 8.0, 'O': 34.0}\",\"('N', 'O', 'Th')\",OTHERS,False\nmp-1246406,\"{'Mg': 2.0, 'V': 1.0, 'Mo': 3.0, 'S': 8.0}\",\"('Mg', 'Mo', 'S', 'V')\",OTHERS,False\nmp-1187388,\"{'Tb': 1.0, 'Tm': 1.0, 'Ir': 2.0}\",\"('Ir', 'Tb', 'Tm')\",OTHERS,False\nmp-1080058,\"{'K': 2.0, 'I': 2.0, 'O': 6.0}\",\"('I', 'K', 'O')\",OTHERS,False\nmp-756968,\"{'K': 6.0, 'Ca': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Ca', 'K', 'O', 'P')\",OTHERS,False\nmp-864661,\"{'Zn': 1.0, 'Cu': 1.0, 'Pd': 2.0}\",\"('Cu', 'Pd', 'Zn')\",OTHERS,False\nmp-761143,\"{'K': 3.0, 'Li': 3.0, 'Sb': 3.0, 'P': 6.0, 'O': 24.0}\",\"('K', 'Li', 'O', 'P', 'Sb')\",OTHERS,False\nmp-1225300,\"{'Dy': 2.0, 'Al': 3.0, 'Co': 1.0}\",\"('Al', 'Co', 'Dy')\",OTHERS,False\nmp-1213415,\"{'Cu': 6.0, 'Hg': 18.0, 'As': 12.0, 'Cl': 18.0}\",\"('As', 'Cl', 'Cu', 'Hg')\",OTHERS,False\nmp-1187213,\"{'Ta': 2.0, 'Re': 1.0, 'Os': 1.0}\",\"('Os', 'Re', 'Ta')\",OTHERS,False\nmp-6407,\"{'F': 160.0, 'Se': 32.0, 'Sb': 32.0, 'Te': 16.0}\",\"('F', 'Sb', 'Se', 'Te')\",OTHERS,False\nmp-29419,\"{'Cl': 6.0, 'Te': 4.0, 'Hf': 1.0}\",\"('Cl', 'Hf', 'Te')\",OTHERS,False\nmp-1198750,\"{'Mg': 2.0, 'H': 44.0, 'S': 2.0, 'O': 30.0}\",\"('H', 'Mg', 'O', 'S')\",OTHERS,False\nmp-555732,\"{'Yb': 8.0, 'P': 8.0, 'S': 32.0}\",\"('P', 'S', 'Yb')\",OTHERS,False\nmp-505647,\"{'Gd': 2.0, 'W': 4.0, 'K': 2.0, 'O': 16.0}\",\"('Gd', 'K', 'O', 'W')\",OTHERS,False\nmp-1208134,\"{'Tl': 1.0, 'Cu': 2.0, 'H': 1.0, 'Se': 2.0, 'O': 10.0}\",\"('Cu', 'H', 'O', 'Se', 'Tl')\",OTHERS,False\nmp-581156,\"{'Cs': 14.0, 'U': 16.0, 'V': 4.0, 'Cl': 2.0, 'O': 64.0}\",\"('Cl', 'Cs', 'O', 'U', 'V')\",OTHERS,False\nmp-1096373,\"{'Mg': 1.0, 'Sc': 1.0, 'Tl': 2.0}\",\"('Mg', 'Sc', 'Tl')\",OTHERS,False\nmp-1245419,\"{'Li': 12.0, 'Mn': 4.0, 'N': 8.0}\",\"('Li', 'Mn', 'N')\",OTHERS,False\nmp-758457,\"{'Li': 4.0, 'Ti': 4.0, 'P': 16.0, 'O': 48.0}\",\"('Li', 'O', 'P', 'Ti')\",OTHERS,False\nmp-505186,\"{'Cs': 8.0, 'Pr': 12.0, 'I': 26.0, 'C': 4.0}\",\"('C', 'Cs', 'I', 'Pr')\",OTHERS,False\nmp-1119132,\"{'Cs': 20.0, 'Os': 20.0, 'N': 40.0}\",\"('Cs', 'N', 'Os')\",OTHERS,False\nmp-567076,\"{'Li': 2.0, 'Bi': 1.0, 'Au': 1.0}\",\"('Au', 'Bi', 'Li')\",OTHERS,False\nmp-983229,\"{'Pm': 2.0, 'Zn': 1.0, 'Rh': 1.0}\",\"('Pm', 'Rh', 'Zn')\",OTHERS,False\nmp-604910,\"{'Mn': 2.0, 'Se': 2.0}\",\"('Mn', 'Se')\",OTHERS,False\nmp-766086,\"{'Li': 4.0, 'La': 8.0, 'Ti': 8.0, 'Cr': 4.0, 'O': 36.0}\",\"('Cr', 'La', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-1192303,\"{'Pr': 2.0, 'Mn': 3.0, 'Sb': 4.0, 'S': 12.0}\",\"('Mn', 'Pr', 'S', 'Sb')\",OTHERS,False\nmp-561125,\"{'Ba': 10.0, 'P': 6.0, 'Br': 2.0, 'O': 24.0}\",\"('Ba', 'Br', 'O', 'P')\",OTHERS,False\nmp-1114171,\"{'K': 2.0, 'Na': 1.0, 'Ru': 1.0, 'F': 6.0}\",\"('F', 'K', 'Na', 'Ru')\",OTHERS,False\nmp-558557,\"{'Ba': 1.0, 'La': 4.0, 'Cu': 5.0, 'O': 12.0}\",\"('Ba', 'Cu', 'La', 'O')\",OTHERS,False\nmp-759528,\"{'Li': 3.0, 'V': 3.0, 'Fe': 3.0, 'P': 6.0, 'H': 6.0, 'O': 30.0}\",\"('Fe', 'H', 'Li', 'O', 'P', 'V')\",OTHERS,False\nmp-8632,{'V': 1.0},\"('V',)\",OTHERS,False\nmp-1224665,\"{'Lu': 12.0, 'Co': 31.0, 'Sn': 16.0}\",\"('Co', 'Lu', 'Sn')\",OTHERS,False\nmp-753508,\"{'Li': 4.0, 'Cu': 4.0, 'S': 4.0}\",\"('Cu', 'Li', 'S')\",OTHERS,False\nmp-7739,\"{'F': 6.0, 'Zn': 1.0, 'Ba': 2.0}\",\"('Ba', 'F', 'Zn')\",OTHERS,False\nmp-1194394,\"{'Na': 6.0, 'C': 4.0, 'O': 16.0}\",\"('C', 'Na', 'O')\",OTHERS,False\nmp-705355,\"{'Li': 4.0, 'Mn': 2.0, 'P': 10.0, 'O': 30.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1206453,\"{'Sm': 4.0, 'Cd': 2.0, 'Cu': 4.0}\",\"('Cd', 'Cu', 'Sm')\",OTHERS,False\nmp-1213807,\"{'Ce': 8.0, 'Si': 8.0, 'Pd': 8.0}\",\"('Ce', 'Pd', 'Si')\",OTHERS,False\nmp-978929,\"{'Sr': 1.0, 'Ac': 1.0, 'Zn': 2.0}\",\"('Ac', 'Sr', 'Zn')\",OTHERS,False\nmp-555331,\"{'K': 2.0, 'Gd': 2.0, 'Pd': 2.0, 'O': 6.0}\",\"('Gd', 'K', 'O', 'Pd')\",OTHERS,False\nmp-1210918,\"{'Mg': 12.0, 'Si': 8.0, 'O': 36.0}\",\"('Mg', 'O', 'Si')\",OTHERS,False\nmp-14568,\"{'F': 8.0, 'Mg': 2.0, 'Ba': 2.0}\",\"('Ba', 'F', 'Mg')\",OTHERS,False\nmp-567808,\"{'Tm': 1.0, 'Cu': 2.0, 'Ge': 2.0}\",\"('Cu', 'Ge', 'Tm')\",OTHERS,False\nmp-758847,\"{'Li': 12.0, 'Fe': 4.0, 'P': 8.0, 'O': 32.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-1232213,\"{'Y': 2.0, 'Se': 4.0}\",\"('Se', 'Y')\",OTHERS,False\nmp-1197266,\"{'Nd': 14.0, 'Se': 8.0, 'Cl': 6.0, 'O': 34.0}\",\"('Cl', 'Nd', 'O', 'Se')\",OTHERS,False\nmp-1218397,\"{'Sr': 5.0, 'Zn': 1.0, 'Cd': 4.0}\",\"('Cd', 'Sr', 'Zn')\",OTHERS,False\nmp-30313,\"{'O': 28.0, 'Se': 8.0, 'Tb': 8.0}\",\"('O', 'Se', 'Tb')\",OTHERS,False\nmp-1018651,\"{'Ba': 2.0, 'H': 3.0, 'I': 1.0}\",\"('Ba', 'H', 'I')\",OTHERS,False\nmp-31362,\"{'Na': 6.0, 'Cl': 12.0, 'Y': 2.0}\",\"('Cl', 'Na', 'Y')\",OTHERS,False\nmp-27421,\"{'F': 6.0, 'Rb': 1.0, 'Bi': 1.0}\",\"('Bi', 'F', 'Rb')\",OTHERS,False\nmp-17048,\"{'S': 16.0, 'Rb': 8.0, 'W': 4.0}\",\"('Rb', 'S', 'W')\",OTHERS,False\nmp-849672,\"{'Mn': 12.0, 'O': 2.0, 'F': 22.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-866308,\"{'Ca': 4.0, 'B': 8.0, 'H': 56.0, 'N': 8.0}\",\"('B', 'Ca', 'H', 'N')\",OTHERS,False\nmp-600219,\"{'Mn': 4.0, 'H': 48.0, 'C': 8.0, 'N': 8.0, 'Cl': 16.0}\",\"('C', 'Cl', 'H', 'Mn', 'N')\",OTHERS,False\nmp-21298,\"{'Si': 4.0, 'Np': 2.0}\",\"('Np', 'Si')\",OTHERS,False\nmvc-5849,\"{'Ca': 6.0, 'Mn': 12.0, 'O': 24.0}\",\"('Ca', 'Mn', 'O')\",OTHERS,False\nmp-1094476,\"{'Mg': 2.0, 'Zn': 6.0}\",\"('Mg', 'Zn')\",OTHERS,False\nmp-867721,\"{'Mn': 6.0, 'Si': 6.0, 'O': 20.0}\",\"('Mn', 'O', 'Si')\",OTHERS,False\nmp-753711,\"{'Ca': 3.0, 'P': 2.0, 'O': 8.0}\",\"('Ca', 'O', 'P')\",OTHERS,False\nmp-554311,\"{'F': 6.0, 'Cr': 1.0, 'Rb': 1.0}\",\"('Cr', 'F', 'Rb')\",OTHERS,False\nmp-1228510,\"{'Ba': 2.0, 'Sr': 3.0, 'Ca': 1.0, 'Mg': 2.0, 'Si': 4.0, 'O': 16.0}\",\"('Ba', 'Ca', 'Mg', 'O', 'Si', 'Sr')\",OTHERS,False\nmp-773098,\"{'Li': 4.0, 'Mn': 3.0, 'V': 1.0, 'O': 8.0}\",\"('Li', 'Mn', 'O', 'V')\",OTHERS,False\nmp-867634,\"{'Li': 8.0, 'Mn': 8.0, 'P': 8.0, 'O': 32.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-975031,\"{'Rb': 4.0, 'Be': 4.0, 'H': 12.0}\",\"('Be', 'H', 'Rb')\",OTHERS,False\nmp-1245220,\"{'Zn': 50.0, 'S': 50.0}\",\"('S', 'Zn')\",OTHERS,False\nmp-30328,\"{'Fe': 1.0, 'Ho': 6.0, 'Bi': 2.0}\",\"('Bi', 'Fe', 'Ho')\",OTHERS,False\nmp-1077378,\"{'Cs': 2.0, 'Mn': 2.0, 'P': 2.0}\",\"('Cs', 'Mn', 'P')\",OTHERS,False\nmp-758284,\"{'Zn': 4.0, 'P': 8.0, 'H': 28.0, 'N': 4.0, 'O': 32.0}\",\"('H', 'N', 'O', 'P', 'Zn')\",OTHERS,False\nmp-1099865,\"{'La': 28.0, 'Sm': 4.0, 'Mn': 32.0, 'O': 80.0}\",\"('La', 'Mn', 'O', 'Sm')\",OTHERS,False\nmp-1187703,\"{'Y': 2.0, 'Ir': 1.0, 'Ru': 1.0}\",\"('Ir', 'Ru', 'Y')\",OTHERS,False\nmp-20720,\"{'N': 6.0, 'O': 12.0, 'Ni': 1.0, 'Pb': 2.0}\",\"('N', 'Ni', 'O', 'Pb')\",OTHERS,False\nmp-1186262,\"{'Nd': 3.0, 'Dy': 1.0}\",\"('Dy', 'Nd')\",OTHERS,False\nmp-1200604,\"{'Si': 1.0, 'P': 4.0, 'H': 28.0, 'C': 10.0, 'Cl': 4.0}\",\"('C', 'Cl', 'H', 'P', 'Si')\",OTHERS,False\nmp-1176406,\"{'Na': 8.0, 'Bi': 4.0, 'O': 12.0}\",\"('Bi', 'Na', 'O')\",OTHERS,False\nmp-767798,\"{'Ni': 17.0, 'As': 12.0, 'O': 48.0}\",\"('As', 'Ni', 'O')\",OTHERS,False\nmp-756058,\"{'Li': 2.0, 'Cr': 3.0, 'Fe': 1.0, 'O': 8.0}\",\"('Cr', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1175878,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1175965,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1072586,\"{'Sm': 1.0, 'Cd': 1.0, 'Ni': 4.0}\",\"('Cd', 'Ni', 'Sm')\",OTHERS,False\nmp-1039919,\"{'La': 1.0, 'Hf': 1.0, 'Mg': 30.0, 'O': 32.0}\",\"('Hf', 'La', 'Mg', 'O')\",OTHERS,False\nmp-756748,\"{'Sc': 4.0, 'H': 12.0, 'Cl': 8.0, 'O': 40.0}\",\"('Cl', 'H', 'O', 'Sc')\",OTHERS,False\nmp-758780,\"{'Li': 4.0, 'Bi': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Bi', 'Li', 'O', 'P')\",OTHERS,False\nmp-1101069,\"{'Cr': 2.0, 'P': 2.0, 'Se': 6.0}\",\"('Cr', 'P', 'Se')\",OTHERS,False\nmp-1227088,\"{'Ca': 6.0, 'Ag': 3.0, 'Ge': 5.0}\",\"('Ag', 'Ca', 'Ge')\",OTHERS,False\nmp-759159,\"{'Li': 5.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1095204,\"{'Tm': 4.0, 'Ni': 4.0, 'Sn': 2.0}\",\"('Ni', 'Sn', 'Tm')\",OTHERS,False\nmp-1220795,\"{'Nb': 12.0, 'Sn': 3.0, 'Sb': 1.0}\",\"('Nb', 'Sb', 'Sn')\",OTHERS,False\nmp-862965,\"{'Pm': 1.0, 'Tl': 1.0, 'Au': 2.0}\",\"('Au', 'Pm', 'Tl')\",OTHERS,False\nmp-27617,\"{'F': 4.0, 'Se': 1.0, 'Ce': 2.0}\",\"('Ce', 'F', 'Se')\",OTHERS,False\nmp-641587,\"{'Fe': 21.0, 'Mo': 2.0, 'C': 6.0}\",\"('C', 'Fe', 'Mo')\",OTHERS,False\nmp-641924,\"{'Bi': 8.0, 'Pb': 4.0, 'S': 16.0}\",\"('Bi', 'Pb', 'S')\",OTHERS,False\nmp-1225439,\"{'Fe': 2.0, 'Ni': 6.0, 'Mo': 12.0, 'N': 4.0}\",\"('Fe', 'Mo', 'N', 'Ni')\",OTHERS,False\nmp-1183803,\"{'Ce': 1.0, 'Dy': 1.0, 'Mg': 2.0}\",\"('Ce', 'Dy', 'Mg')\",OTHERS,False\nmp-1182869,\"{'Al': 4.0, 'O': 8.0}\",\"('Al', 'O')\",OTHERS,False\nmp-1106325,\"{'Ca': 1.0, 'Cu': 3.0, 'Pt': 4.0, 'O': 12.0}\",\"('Ca', 'Cu', 'O', 'Pt')\",OTHERS,False\nmp-862978,\"{'Er': 2.0, 'Ag': 1.0, 'Ru': 1.0}\",\"('Ag', 'Er', 'Ru')\",OTHERS,False\nmp-1224851,\"{'Ga': 1.0, 'Cu': 1.0, 'Ge': 1.0, 'Se': 4.0}\",\"('Cu', 'Ga', 'Ge', 'Se')\",OTHERS,False\nmp-1071036,\"{'Tb': 2.0, 'Sc': 2.0, 'Sb': 2.0}\",\"('Sb', 'Sc', 'Tb')\",OTHERS,False\nmp-1182891,\"{'Al': 2.0, 'C': 2.0, 'N': 2.0, 'O': 10.0}\",\"('Al', 'C', 'N', 'O')\",OTHERS,False\nmp-1213382,\"{'Cs': 2.0, 'Pr': 2.0, 'Nb': 12.0, 'Br': 36.0}\",\"('Br', 'Cs', 'Nb', 'Pr')\",OTHERS,False\nmp-22915,\"{'Ag': 1.0, 'I': 1.0}\",\"('Ag', 'I')\",OTHERS,False\nmp-1210351,\"{'Na': 6.0, 'Yb': 2.0, 'Br': 12.0}\",\"('Br', 'Na', 'Yb')\",OTHERS,False\nmp-1205905,\"{'Dy': 4.0, 'Ge': 4.0, 'Ru': 2.0}\",\"('Dy', 'Ge', 'Ru')\",OTHERS,False\nmvc-6278,\"{'Zn': 8.0, 'Mo': 16.0, 'O': 32.0}\",\"('Mo', 'O', 'Zn')\",OTHERS,False\nmp-767219,\"{'Li': 8.0, 'Ti': 8.0, 'Mn': 2.0, 'Co': 8.0, 'O': 36.0}\",\"('Co', 'Li', 'Mn', 'O', 'Ti')\",OTHERS,False\nmp-1113723,\"{'Rb': 2.0, 'Ag': 1.0, 'Sb': 1.0, 'I': 6.0}\",\"('Ag', 'I', 'Rb', 'Sb')\",OTHERS,False\nmp-770191,\"{'Li': 8.0, 'V': 8.0, 'S': 12.0, 'O': 48.0}\",\"('Li', 'O', 'S', 'V')\",OTHERS,False\nmp-996997,\"{'Li': 2.0, 'Ag': 2.0, 'O': 4.0}\",\"('Ag', 'Li', 'O')\",OTHERS,False\nmp-541090,\"{'Ce': 4.0, 'Se': 8.0, 'O': 24.0}\",\"('Ce', 'O', 'Se')\",OTHERS,False\nmp-23058,\"{'O': 2.0, 'Cl': 2.0, 'Nd': 2.0}\",\"('Cl', 'Nd', 'O')\",OTHERS,False\nmp-1227957,\"{'Ba': 1.0, 'Fe': 1.0, 'As': 2.0, 'Ru': 1.0}\",\"('As', 'Ba', 'Fe', 'Ru')\",OTHERS,False\nmp-1222669,\"{'Li': 2.0, 'I': 1.0, 'Br': 1.0}\",\"('Br', 'I', 'Li')\",OTHERS,False\nmp-1219556,\"{'Sb': 8.0, 'Te': 16.0, 'Pd': 12.0}\",\"('Pd', 'Sb', 'Te')\",OTHERS,False\nmp-1187158,\"{'Sr': 2.0, 'Br': 4.0}\",\"('Br', 'Sr')\",OTHERS,False\nmp-1209311,\"{'Rb': 2.0, 'Gd': 2.0, 'Mo': 4.0, 'O': 16.0}\",\"('Gd', 'Mo', 'O', 'Rb')\",OTHERS,False\nmp-753738,\"{'V': 3.0, 'Co': 1.0, 'O': 8.0}\",\"('Co', 'O', 'V')\",OTHERS,False\nmp-3407,\"{'Cr': 5.0, 'Se': 8.0, 'Tl': 1.0}\",\"('Cr', 'Se', 'Tl')\",OTHERS,False\nmp-21208,\"{'F': 9.0, 'Rh': 3.0}\",\"('F', 'Rh')\",OTHERS,False\nmp-755700,\"{'Fe': 6.0, 'O': 8.0, 'F': 4.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-1112490,\"{'Cs': 2.0, 'Tl': 1.0, 'In': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cs', 'In', 'Tl')\",OTHERS,False\nmp-1224400,\"{'In': 2.0, 'Cu': 2.0, 'Se': 2.0, 'S': 2.0}\",\"('Cu', 'In', 'S', 'Se')\",OTHERS,False\nmp-1229240,\"{'Ca': 12.0, 'Ti': 12.0, 'Si': 8.0, 'Ge': 4.0, 'O': 60.0}\",\"('Ca', 'Ge', 'O', 'Si', 'Ti')\",OTHERS,False\nmp-27301,\"{'Cl': 16.0, 'Rb': 6.0, 'Au': 6.0}\",\"('Au', 'Cl', 'Rb')\",OTHERS,False\nmp-1229313,\"{'Ca': 2.0, 'Eu': 4.0, 'Ge': 8.0, 'O': 24.0}\",\"('Ca', 'Eu', 'Ge', 'O')\",OTHERS,False\nmp-1105201,\"{'Ba': 5.0, 'In': 4.0, 'Te': 4.0, 'S': 7.0}\",\"('Ba', 'In', 'S', 'Te')\",OTHERS,False\nmp-555948,\"{'C': 74.0, 'F': 42.0}\",\"('C', 'F')\",OTHERS,False\nmp-510232,\"{'Nd': 12.0, 'Co': 6.0, 'Sn': 1.0}\",\"('Co', 'Nd', 'Sn')\",OTHERS,False\nmp-1207440,\"{'Zr': 14.0, 'Pt': 20.0}\",\"('Pt', 'Zr')\",OTHERS,False\nmp-7238,\"{'B': 4.0, 'O': 12.0, 'Nd': 4.0}\",\"('B', 'Nd', 'O')\",OTHERS,False\nmp-1175576,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-623837,\"{'As': 2.0, 'C': 6.0, 'N': 6.0}\",\"('As', 'C', 'N')\",OTHERS,False\nmp-867529,\"{'Li': 2.0, 'Ni': 4.0, 'P': 6.0, 'O': 20.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-8215,\"{'S': 6.0, 'La': 2.0, 'Yb': 2.0}\",\"('La', 'S', 'Yb')\",OTHERS,False\nmp-1114337,\"{'Na': 2.0, 'Li': 1.0, 'Ir': 1.0, 'F': 6.0}\",\"('F', 'Ir', 'Li', 'Na')\",OTHERS,False\nmp-6691,\"{'O': 8.0, 'Cu': 4.0, 'Ba': 2.0, 'Dy': 1.0}\",\"('Ba', 'Cu', 'Dy', 'O')\",OTHERS,False\nmp-1246459,\"{'Ca': 10.0, 'Fe': 2.0, 'N': 8.0}\",\"('Ca', 'Fe', 'N')\",OTHERS,False\nmp-768749,\"{'Li': 8.0, 'Al': 2.0, 'Cr': 6.0, 'O': 16.0}\",\"('Al', 'Cr', 'Li', 'O')\",OTHERS,False\nmp-1226293,\"{'Cr': 4.0, 'In': 1.0, 'Ag': 1.0, 'S': 8.0}\",\"('Ag', 'Cr', 'In', 'S')\",OTHERS,False\nmp-1113491,\"{'Cs': 3.0, 'Sc': 1.0, 'I': 6.0}\",\"('Cs', 'I', 'Sc')\",OTHERS,False\nmp-723036,\"{'Fe': 4.0, 'H': 64.0, 'C': 16.0, 'S': 16.0, 'N': 32.0, 'Cl': 8.0}\",\"('C', 'Cl', 'Fe', 'H', 'N', 'S')\",OTHERS,False\nmp-1211285,\"{'K': 1.0, 'Ta': 1.0, 'P': 2.0, 'O': 8.0}\",\"('K', 'O', 'P', 'Ta')\",OTHERS,False\nmp-16955,\"{'Li': 12.0, 'O': 16.0, 'K': 8.0, 'Fe': 4.0}\",\"('Fe', 'K', 'Li', 'O')\",OTHERS,False\nmp-1104460,\"{'Zn': 2.0, 'P': 4.0, 'O': 8.0}\",\"('O', 'P', 'Zn')\",OTHERS,False\nmvc-15145,\"{'Zn': 3.0, 'Cu': 10.0, 'Te': 6.0, 'O': 36.0}\",\"('Cu', 'O', 'Te', 'Zn')\",OTHERS,False\nmp-17056,\"{'Li': 6.0, 'O': 24.0, 'P': 6.0, 'Zn': 6.0}\",\"('Li', 'O', 'P', 'Zn')\",OTHERS,False\nmp-554259,\"{'Sr': 4.0, 'Ag': 4.0, 'B': 28.0, 'O': 48.0}\",\"('Ag', 'B', 'O', 'Sr')\",OTHERS,False\nmp-1204743,\"{'Rb': 8.0, 'Cd': 4.0, 'C': 8.0, 'Cl': 8.0, 'O': 20.0}\",\"('C', 'Cd', 'Cl', 'O', 'Rb')\",OTHERS,False\nmp-1176538,\"{'Li': 4.0, 'V': 4.0, 'F': 16.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-567769,\"{'Ca': 1.0, 'Si': 2.0, 'Pd': 2.0}\",\"('Ca', 'Pd', 'Si')\",OTHERS,False\nmp-1187241,\"{'Ta': 2.0, 'Ir': 6.0}\",\"('Ir', 'Ta')\",OTHERS,False\nmp-27422,\"{'F': 6.0, 'Cs': 1.0, 'Bi': 1.0}\",\"('Bi', 'Cs', 'F')\",OTHERS,False\nmp-1019270,\"{'Ta': 2.0, 'Ti': 1.0, 'N': 3.0}\",\"('N', 'Ta', 'Ti')\",OTHERS,False\nmp-1222465,\"{'Lu': 12.0, 'Co': 9.0}\",\"('Co', 'Lu')\",OTHERS,False\nmp-1096564,\"{'Sr': 2.0, 'Zn': 1.0, 'Cd': 1.0}\",\"('Cd', 'Sr', 'Zn')\",OTHERS,False\nmp-560547,\"{'K': 2.0, 'Cr': 2.0, 'F': 6.0}\",\"('Cr', 'F', 'K')\",OTHERS,False\nmp-1218965,\"{'Sm': 1.0, 'U': 1.0, 'N': 2.0}\",\"('N', 'Sm', 'U')\",OTHERS,False\nmp-1184811,\"{'In': 2.0, 'Ga': 6.0}\",\"('Ga', 'In')\",OTHERS,False\nmp-25929,\"{'Li': 6.0, 'O': 24.0, 'P': 6.0, 'Bi': 4.0}\",\"('Bi', 'Li', 'O', 'P')\",OTHERS,False\nmp-1111388,\"{'K': 3.0, 'Pr': 1.0, 'Br': 6.0}\",\"('Br', 'K', 'Pr')\",OTHERS,False\nmp-1194097,\"{'In': 1.0, 'Co': 22.0, 'B': 6.0}\",\"('B', 'Co', 'In')\",OTHERS,False\nmp-1080849,\"{'K': 16.0, 'Re': 16.0, 'N': 32.0}\",\"('K', 'N', 'Re')\",OTHERS,False\nmvc-10961,\"{'V': 8.0, 'O': 18.0}\",\"('O', 'V')\",OTHERS,False\nmp-582399,\"{'Pu': 6.0, 'Pd': 10.0}\",\"('Pd', 'Pu')\",OTHERS,False\nmp-554529,\"{'Ba': 24.0, 'Ti': 12.0, 'O': 48.0}\",\"('Ba', 'O', 'Ti')\",OTHERS,False\nmp-1147675,\"{'Rh': 6.0, 'Cl': 8.0}\",\"('Cl', 'Rh')\",OTHERS,False\nmp-557176,\"{'Ba': 10.0, 'B': 8.0, 'O': 20.0, 'F': 4.0}\",\"('B', 'Ba', 'F', 'O')\",OTHERS,False\nmp-1028528,\"{'Te': 2.0, 'Mo': 2.0, 'W': 2.0, 'Se': 4.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-1194044,\"{'V': 16.0, 'Co': 8.0, 'N': 4.0}\",\"('Co', 'N', 'V')\",OTHERS,False\nmp-761151,\"{'Zr': 6.0, 'O': 11.0}\",\"('O', 'Zr')\",OTHERS,False\nmp-1099932,\"{'K': 1.0, 'Na': 7.0, 'V': 6.0, 'Cr': 2.0, 'O': 24.0}\",\"('Cr', 'K', 'Na', 'O', 'V')\",OTHERS,False\nmp-1220910,\"{'Na': 2.0, 'Ta': 2.0, 'W': 2.0, 'O': 14.0}\",\"('Na', 'O', 'Ta', 'W')\",OTHERS,False\nmp-780186,\"{'Li': 9.0, 'Mn': 10.0, 'O': 20.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-1218235,\"{'Sr': 1.0, 'Gd': 1.0, 'Ni': 1.0, 'O': 4.0}\",\"('Gd', 'Ni', 'O', 'Sr')\",OTHERS,False\nmp-752542,\"{'Li': 2.0, 'Ti': 2.0, 'Mn': 3.0, 'O': 10.0}\",\"('Li', 'Mn', 'O', 'Ti')\",OTHERS,False\nmp-758792,\"{'Li': 18.0, 'Cr': 6.0, 'Si': 12.0, 'O': 42.0}\",\"('Cr', 'Li', 'O', 'Si')\",OTHERS,False\nmp-7280,\"{'P': 8.0, 'S': 8.0, 'Pd': 8.0}\",\"('P', 'Pd', 'S')\",OTHERS,False\nmp-614572,\"{'Ca': 12.0, 'In': 4.0, 'P': 12.0}\",\"('Ca', 'In', 'P')\",OTHERS,False\nmp-1218689,\"{'Sr': 2.0, 'V': 11.0, 'Ni': 1.0, 'O': 22.0}\",\"('Ni', 'O', 'Sr', 'V')\",OTHERS,False\nmp-978285,\"{'Mg': 3.0, 'Mn': 1.0}\",\"('Mg', 'Mn')\",OTHERS,False\nmp-1346,\"{'B': 12.0, 'O': 2.0}\",\"('B', 'O')\",OTHERS,False\nmp-1203746,\"{'As': 4.0, 'S': 4.0, 'Xe': 4.0, 'N': 4.0, 'F': 40.0}\",\"('As', 'F', 'N', 'S', 'Xe')\",OTHERS,False\nmp-1084775,\"{'Sc': 3.0, 'Sn': 3.0, 'Pt': 3.0}\",\"('Pt', 'Sc', 'Sn')\",OTHERS,False\nmp-1186318,\"{'Nd': 2.0, 'Lu': 6.0}\",\"('Lu', 'Nd')\",OTHERS,False\nmp-1080851,\"{'Ce': 12.0, 'Se': 24.0}\",\"('Ce', 'Se')\",OTHERS,False\nmp-1219139,\"{'Sm': 1.0, 'Er': 3.0, 'Fe': 34.0}\",\"('Er', 'Fe', 'Sm')\",OTHERS,False\nmp-1080018,\"{'Th': 2.0, 'Co': 4.0, 'Sn': 4.0}\",\"('Co', 'Sn', 'Th')\",OTHERS,False\nmp-1246063,\"{'V': 2.0, 'Co': 8.0, 'N': 8.0}\",\"('Co', 'N', 'V')\",OTHERS,False\nmp-1184754,\"{'K': 2.0, 'Rb': 1.0, 'Bi': 1.0}\",\"('Bi', 'K', 'Rb')\",OTHERS,False\nmp-759818,\"{'W': 8.0, 'O': 8.0, 'F': 32.0}\",\"('F', 'O', 'W')\",OTHERS,False\nmp-752429,\"{'Sc': 4.0, 'Tl': 4.0, 'O': 12.0}\",\"('O', 'Sc', 'Tl')\",OTHERS,False\nmp-684604,\"{'Cu': 2.0, 'S': 4.0}\",\"('Cu', 'S')\",OTHERS,False\nmvc-9199,\"{'Mg': 4.0, 'Fe': 12.0, 'O': 28.0}\",\"('Fe', 'Mg', 'O')\",OTHERS,False\nmp-19061,\"{'Li': 4.0, 'O': 24.0, 'Si': 8.0, 'Fe': 4.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1232039,\"{'La': 16.0, 'Mg': 8.0, 'Se': 32.0}\",\"('La', 'Mg', 'Se')\",OTHERS,False\nmp-1186392,\"{'Pa': 1.0, 'Be': 1.0, 'O': 3.0}\",\"('Be', 'O', 'Pa')\",OTHERS,False\nmp-7541,\"{'P': 6.0, 'Sn': 2.0}\",\"('P', 'Sn')\",OTHERS,False\nmp-31090,\"{'Pd': 4.0, 'Er': 4.0, 'Pb': 2.0}\",\"('Er', 'Pb', 'Pd')\",OTHERS,False\nmp-1492,\"{'Cd': 3.0, 'Te': 3.0}\",\"('Cd', 'Te')\",OTHERS,False\nmp-1191414,\"{'Y': 6.0, 'Cl': 4.0, 'O': 12.0}\",\"('Cl', 'O', 'Y')\",OTHERS,False\nmp-1205603,\"{'Tb': 2.0, 'Si': 2.0, 'Ru': 4.0, 'C': 2.0}\",\"('C', 'Ru', 'Si', 'Tb')\",OTHERS,False\nmp-559405,\"{'Rb': 1.0, 'U': 2.0, 'Sb': 1.0, 'S': 8.0}\",\"('Rb', 'S', 'Sb', 'U')\",OTHERS,False\nmp-1039962,\"{'Ce': 1.0, 'Mg': 30.0, 'B': 1.0, 'O': 32.0}\",\"('B', 'Ce', 'Mg', 'O')\",OTHERS,False\nmp-758334,\"{'Li': 6.0, 'Cr': 3.0, 'Sb': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Cr', 'Li', 'O', 'P', 'Sb')\",OTHERS,False\nmp-1245578,\"{'Li': 1.0, 'Pt': 1.0, 'N': 1.0}\",\"('Li', 'N', 'Pt')\",OTHERS,False\nmp-568666,\"{'Rb': 2.0, 'Au': 2.0, 'I': 6.0}\",\"('Au', 'I', 'Rb')\",OTHERS,False\nmp-758498,\"{'Li': 4.0, 'Cu': 2.0, 'Si': 2.0, 'O': 8.0}\",\"('Cu', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1225306,\"{'Dy': 2.0, 'Si': 3.0, 'Ni': 1.0}\",\"('Dy', 'Ni', 'Si')\",OTHERS,False\nmp-865764,\"{'Yb': 1.0, 'Gd': 1.0, 'Rh': 2.0}\",\"('Gd', 'Rh', 'Yb')\",OTHERS,False\nmp-1216863,\"{'Ti': 2.0, 'Nb': 2.0, 'Bi': 6.0, 'O': 18.0}\",\"('Bi', 'Nb', 'O', 'Ti')\",OTHERS,False\nmp-1094619,\"{'Mg': 8.0, 'Ga': 4.0}\",\"('Ga', 'Mg')\",OTHERS,False\nmp-1183990,\"{'Ga': 2.0, 'O': 2.0}\",\"('Ga', 'O')\",OTHERS,False\nmp-26585,\"{'Li': 10.0, 'O': 36.0, 'P': 10.0, 'Sb': 4.0}\",\"('Li', 'O', 'P', 'Sb')\",OTHERS,False\nmp-2563,\"{'Se': 1.0, 'Ce': 1.0}\",\"('Ce', 'Se')\",OTHERS,False\nmp-1218906,\"{'Sr': 10.0, 'Mg': 5.0, 'Mo': 3.0, 'W': 2.0, 'O': 30.0}\",\"('Mg', 'Mo', 'O', 'Sr', 'W')\",OTHERS,False\nmp-974627,\"{'K': 3.0, 'Tl': 1.0}\",\"('K', 'Tl')\",OTHERS,False\nmp-1174401,\"{'Li': 7.0, 'Co': 5.0, 'O': 12.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-23565,\"{'O': 14.0, 'Cr': 4.0, 'Ag': 1.0, 'Bi': 1.0}\",\"('Ag', 'Bi', 'Cr', 'O')\",OTHERS,False\nmp-631535,\"{'Be': 1.0, 'W': 2.0, 'Cl': 1.0}\",\"('Be', 'Cl', 'W')\",OTHERS,False\nmp-768536,\"{'Cs': 12.0, 'La': 4.0, 'O': 12.0}\",\"('Cs', 'La', 'O')\",OTHERS,False\nmp-1212318,\"{'Na': 1.0, 'Yb': 3.0, 'Se': 6.0}\",\"('Na', 'Se', 'Yb')\",OTHERS,False\nmp-1232409,\"{'Nb': 16.0, 'Si': 4.0, 'Te': 16.0}\",\"('Nb', 'Si', 'Te')\",OTHERS,False\nmp-1210642,\"{'Mn': 4.0, 'Te': 4.0, 'O': 12.0}\",\"('Mn', 'O', 'Te')\",OTHERS,False\nmp-9348,\"{'F': 6.0, 'K': 1.0, 'Sc': 1.0, 'Rb': 2.0}\",\"('F', 'K', 'Rb', 'Sc')\",OTHERS,False\nmp-694992,\"{'Co': 1.0, 'P': 2.0, 'H': 18.0, 'N': 6.0, 'F': 12.0}\",\"('Co', 'F', 'H', 'N', 'P')\",OTHERS,False\nmp-23548,\"{'Ag': 2.0, 'Bi': 2.0, 'O': 6.0}\",\"('Ag', 'Bi', 'O')\",OTHERS,False\nmp-1076893,\"{'Ba': 8.0, 'Sr': 24.0, 'Co': 20.0, 'Cu': 12.0, 'O': 80.0}\",\"('Ba', 'Co', 'Cu', 'O', 'Sr')\",OTHERS,False\nmp-1247384,\"{'Mg': 2.0, 'Mn': 1.0, 'W': 3.0, 'S': 8.0}\",\"('Mg', 'Mn', 'S', 'W')\",OTHERS,False\nmp-2607,\"{'Ge': 3.0, 'U': 1.0}\",\"('Ge', 'U')\",OTHERS,False\nmp-1095886,\"{'Hf': 2.0, 'Ni': 1.0, 'Rh': 1.0}\",\"('Hf', 'Ni', 'Rh')\",OTHERS,False\nmp-1219653,\"{'Pu': 1.0, 'Zr': 1.0}\",\"('Pu', 'Zr')\",OTHERS,False\nmp-1185355,\"{'Li': 2.0, 'Eu': 6.0}\",\"('Eu', 'Li')\",OTHERS,False\nmp-865615,\"{'Ga': 1.0, 'Si': 1.0, 'Ru': 2.0}\",\"('Ga', 'Ru', 'Si')\",OTHERS,False\nmp-971975,\"{'Sr': 1.0, 'Hg': 1.0, 'O': 3.0}\",\"('Hg', 'O', 'Sr')\",OTHERS,False\nmp-762514,\"{'Li': 4.0, 'Mn': 12.0, 'O': 24.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-1246222,\"{'Mn': 6.0, 'Si': 12.0, 'N': 22.0}\",\"('Mn', 'N', 'Si')\",OTHERS,False\nmp-504450,\"{'Tl': 4.0, 'F': 20.0, 'Be': 8.0}\",\"('Be', 'F', 'Tl')\",OTHERS,False\nmp-1183144,{'Al': 4.0},\"('Al',)\",OTHERS,False\nmp-1073115,\"{'Mg': 8.0, 'Si': 12.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1211663,\"{'K': 4.0, 'Gd': 8.0, 'Cl': 28.0}\",\"('Cl', 'Gd', 'K')\",OTHERS,False\nmp-1218860,\"{'Sr': 6.0, 'Tl': 3.0, 'Cu': 3.0, 'O': 16.0}\",\"('Cu', 'O', 'Sr', 'Tl')\",OTHERS,False\nmp-672695,\"{'K': 4.0, 'Ag': 4.0, 'C': 4.0, 'O': 12.0}\",\"('Ag', 'C', 'K', 'O')\",OTHERS,False\nmp-1195181,\"{'Co': 16.0, 'Se': 12.0, 'Cl': 8.0, 'O': 36.0}\",\"('Cl', 'Co', 'O', 'Se')\",OTHERS,False\nmp-1225645,\"{'Er': 4.0, 'Fe': 1.0, 'S': 7.0}\",\"('Er', 'Fe', 'S')\",OTHERS,False\nmp-1199833,\"{'Cs': 40.0, 'Tl': 24.0, 'Si': 4.0, 'O': 16.0}\",\"('Cs', 'O', 'Si', 'Tl')\",OTHERS,False\nmp-571634,\"{'Yb': 10.0, 'Pb': 6.0}\",\"('Pb', 'Yb')\",OTHERS,False\nmp-1079538,\"{'V': 2.0, 'H': 2.0, 'O': 5.0}\",\"('H', 'O', 'V')\",OTHERS,False\nmp-1200466,\"{'Rb': 8.0, 'Sb': 8.0, 'S': 4.0, 'O': 20.0, 'F': 24.0}\",\"('F', 'O', 'Rb', 'S', 'Sb')\",OTHERS,False\nmp-1080734,\"{'Ce': 2.0, 'B': 4.0, 'Os': 4.0}\",\"('B', 'Ce', 'Os')\",OTHERS,False\nmp-867704,\"{'Li': 2.0, 'Ni': 2.0, 'P': 6.0, 'O': 18.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-13570,\"{'Mg': 2.0, 'Sc': 4.0, 'Ga': 4.0}\",\"('Ga', 'Mg', 'Sc')\",OTHERS,False\nmp-1228844,\"{'Er': 20.0, 'Cu': 6.0, 'Pb': 9.0, 'S': 42.0}\",\"('Cu', 'Er', 'Pb', 'S')\",OTHERS,False\nmp-1025238,\"{'Tm': 1.0, 'Ti': 2.0, 'Ga': 4.0}\",\"('Ga', 'Ti', 'Tm')\",OTHERS,False\nmp-981387,\"{'Hg': 3.0, 'Bi': 1.0}\",\"('Bi', 'Hg')\",OTHERS,False\nmp-1096807,\"{'Al': 1.0, 'O': 3.0, 'F': 3.0}\",\"('Al', 'F', 'O')\",OTHERS,False\nmp-752849,\"{'Y': 4.0, 'Pb': 4.0, 'O': 14.0}\",\"('O', 'Pb', 'Y')\",OTHERS,False\nmp-1101094,\"{'Cs': 2.0, 'Ce': 1.0, 'Cl': 6.0}\",\"('Ce', 'Cl', 'Cs')\",OTHERS,False\nmp-1226220,\"{'Cr': 1.0, 'Ga': 1.0, 'Fe': 2.0}\",\"('Cr', 'Fe', 'Ga')\",OTHERS,False\nmp-542120,\"{'Ba': 8.0, 'Fe': 4.0, 'O': 16.0}\",\"('Ba', 'Fe', 'O')\",OTHERS,False\nmp-1227295,\"{'Be': 4.0, 'Ni': 1.0, 'Ge': 1.0}\",\"('Be', 'Ge', 'Ni')\",OTHERS,False\nmp-1197847,\"{'Pr': 5.0, 'Mg': 41.0}\",\"('Mg', 'Pr')\",OTHERS,False\nmp-1211742,\"{'K': 4.0, 'Tb': 4.0, 'H': 4.0, 'S': 8.0, 'O': 32.0}\",\"('H', 'K', 'O', 'S', 'Tb')\",OTHERS,False\nmp-1215799,\"{'Yb': 5.0, 'Se': 7.0}\",\"('Se', 'Yb')\",OTHERS,False\nmp-1229104,\"{'Al': 2.0, 'H': 30.0, 'O': 12.0, 'F': 12.0}\",\"('Al', 'F', 'H', 'O')\",OTHERS,False\nmp-1200518,\"{'In': 2.0, 'Re': 8.0, 'C': 28.0, 'I': 6.0, 'O': 28.0}\",\"('C', 'I', 'In', 'O', 'Re')\",OTHERS,False\nmp-1186950,\"{'Sc': 2.0, 'In': 1.0, 'Hg': 1.0}\",\"('Hg', 'In', 'Sc')\",OTHERS,False\nmp-26887,\"{'Li': 4.0, 'V': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1093847,\"{'Ba': 2.0, 'Pd': 1.0, 'Au': 1.0}\",\"('Au', 'Ba', 'Pd')\",OTHERS,False\nmp-981208,\"{'Y': 1.0, 'Tm': 1.0, 'Hg': 2.0}\",\"('Hg', 'Tm', 'Y')\",OTHERS,False\nmp-26855,\"{'Li': 4.0, 'O': 24.0, 'P': 8.0, 'Sn': 2.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,False\nmp-1178425,\"{'Co': 3.0, 'F': 9.0}\",\"('Co', 'F')\",OTHERS,False\nmp-1216851,\"{'V': 32.0, 'N': 3.0}\",\"('N', 'V')\",OTHERS,False\nmp-8842,\"{'Si': 4.0, 'Ca': 4.0, 'Pd': 4.0}\",\"('Ca', 'Pd', 'Si')\",OTHERS,False\nmp-1030405,\"{'Te': 4.0, 'Mo': 2.0, 'W': 2.0, 'Se': 2.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-979420,\"{'Y': 1.0, 'Er': 1.0, 'Ir': 2.0}\",\"('Er', 'Ir', 'Y')\",OTHERS,False\nmp-759667,\"{'Li': 8.0, 'Sn': 4.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Li', 'O', 'Sn')\",OTHERS,False\nmvc-15702,\"{'Zn': 1.0, 'Sn': 2.0, 'N': 2.0}\",\"('N', 'Sn', 'Zn')\",OTHERS,False\nmp-1105934,\"{'Li': 2.0, 'Mn': 2.0, 'As': 2.0, 'O': 10.0}\",\"('As', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1019532,\"{'Ba': 4.0, 'Al': 16.0, 'O': 28.0}\",\"('Al', 'Ba', 'O')\",OTHERS,False\nmp-769266,\"{'Ca': 12.0, 'Ta': 8.0, 'O': 32.0}\",\"('Ca', 'O', 'Ta')\",OTHERS,False\nmp-645962,\"{'La': 8.0, 'B': 28.0, 'O': 54.0}\",\"('B', 'La', 'O')\",OTHERS,False\nmp-753668,\"{'Ba': 2.0, 'Gd': 1.0, 'Sb': 1.0, 'O': 6.0}\",\"('Ba', 'Gd', 'O', 'Sb')\",OTHERS,False\nmp-753816,\"{'Na': 12.0, 'Mg': 2.0, 'O': 8.0}\",\"('Mg', 'Na', 'O')\",OTHERS,False\nmp-14116,\"{'O': 2.0, 'Cu': 1.0, 'Rh': 1.0}\",\"('Cu', 'O', 'Rh')\",OTHERS,False\nmp-1070603,\"{'Sm': 1.0, 'Si': 2.0, 'Ir': 2.0}\",\"('Ir', 'Si', 'Sm')\",OTHERS,False\nmp-646420,\"{'Nd': 4.0, 'In': 2.0, 'Rh': 4.0}\",\"('In', 'Nd', 'Rh')\",OTHERS,False\nmp-1038779,\"{'Mg': 3.0, 'Al': 3.0}\",\"('Al', 'Mg')\",OTHERS,False\nmp-771143,\"{'Na': 6.0, 'Ni': 2.0, 'B': 2.0, 'S': 2.0, 'O': 14.0}\",\"('B', 'Na', 'Ni', 'O', 'S')\",OTHERS,False\nmp-780697,\"{'Mn': 10.0, 'O': 5.0, 'F': 15.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-695452,\"{'K': 2.0, 'Al': 11.0, 'Si': 13.0, 'Ag': 9.0, 'O': 48.0}\",\"('Ag', 'Al', 'K', 'O', 'Si')\",OTHERS,False\nmp-1227292,\"{'Be': 4.0, 'Cu': 1.0, 'Ge': 1.0}\",\"('Be', 'Cu', 'Ge')\",OTHERS,False\nmp-554763,\"{'Np': 4.0, 'Ag': 4.0, 'Se': 4.0, 'O': 20.0}\",\"('Ag', 'Np', 'O', 'Se')\",OTHERS,False\nmp-642821,\"{'K': 6.0, 'H': 10.0, 'Pt': 2.0}\",\"('H', 'K', 'Pt')\",OTHERS,False\nmp-1186245,\"{'Nd': 2.0, 'Ho': 2.0, 'O': 5.0}\",\"('Ho', 'Nd', 'O')\",OTHERS,False\nmp-1094517,\"{'Mg': 4.0, 'Sn': 2.0}\",\"('Mg', 'Sn')\",OTHERS,False\nmp-615760,\"{'Ba': 10.0, 'In': 4.0, 'Sb': 12.0}\",\"('Ba', 'In', 'Sb')\",OTHERS,False\nmp-1020711,\"{'Rb': 4.0, 'Si': 2.0, 'S': 12.0, 'O': 42.0}\",\"('O', 'Rb', 'S', 'Si')\",OTHERS,False\nmp-23237,\"{'F': 12.0, 'Bi': 4.0}\",\"('Bi', 'F')\",OTHERS,False\nmp-1079460,\"{'Ti': 6.0, 'Sn': 2.0}\",\"('Sn', 'Ti')\",OTHERS,False\nmp-23811,\"{'H': 6.0, 'B': 2.0, 'O': 20.0, 'P': 4.0, 'Ca': 2.0, 'Ni': 2.0}\",\"('B', 'Ca', 'H', 'Ni', 'O', 'P')\",OTHERS,False\nmp-757227,\"{'Li': 8.0, 'Fe': 8.0, 'P': 8.0, 'O': 32.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-1097440,\"{'Ca': 1.0, 'Sc': 1.0, 'Rh': 2.0}\",\"('Ca', 'Rh', 'Sc')\",OTHERS,False\nmp-1114417,\"{'Rb': 2.0, 'Li': 1.0, 'Ce': 1.0, 'Cl': 6.0}\",\"('Ce', 'Cl', 'Li', 'Rb')\",OTHERS,False\nmp-1114491,\"{'Rb': 3.0, 'Au': 1.0, 'Br': 6.0}\",\"('Au', 'Br', 'Rb')\",OTHERS,False\nmp-547069,\"{'Fe': 1.0, 'Bi': 1.0, 'O': 3.0}\",\"('Bi', 'Fe', 'O')\",OTHERS,False\nmp-1207406,\"{'Zr': 10.0, 'Al': 6.0, 'N': 2.0}\",\"('Al', 'N', 'Zr')\",OTHERS,False\nmp-1028492,\"{'Te': 2.0, 'W': 4.0, 'S': 6.0}\",\"('S', 'Te', 'W')\",OTHERS,False\nmp-1183621,\"{'Cd': 3.0, 'Pd': 1.0}\",\"('Cd', 'Pd')\",OTHERS,False\nmp-675287,\"{'Cs': 2.0, 'Cu': 2.0, 'Cl': 6.0}\",\"('Cl', 'Cs', 'Cu')\",OTHERS,False\nmp-1256151,\"{'Zr': 10.0, 'Al': 2.0, 'Ni': 8.0}\",\"('Al', 'Ni', 'Zr')\",OTHERS,False\nmp-976804,\"{'Ni': 1.0, 'Ag': 1.0, 'Te': 2.0}\",\"('Ag', 'Ni', 'Te')\",OTHERS,False\nmp-1183266,\"{'Ac': 2.0, 'Pr': 6.0}\",\"('Ac', 'Pr')\",OTHERS,False\nmp-556938,\"{'Pr': 30.0, 'Ti': 24.0, 'Se': 58.0, 'I': 8.0, 'O': 25.0}\",\"('I', 'O', 'Pr', 'Se', 'Ti')\",OTHERS,False\nmp-1184616,\"{'Ho': 2.0, 'Mg': 1.0, 'Os': 1.0}\",\"('Ho', 'Mg', 'Os')\",OTHERS,False\nmp-1210013,\"{'Nb': 4.0, 'Fe': 8.0, 'O': 18.0}\",\"('Fe', 'Nb', 'O')\",OTHERS,False\nmp-647218,\"{'Re': 8.0, 'P': 8.0, 'O': 44.0}\",\"('O', 'P', 'Re')\",OTHERS,False\nmp-1036617,\"{'Mg': 14.0, 'Al': 1.0, 'V': 1.0, 'O': 16.0}\",\"('Al', 'Mg', 'O', 'V')\",OTHERS,False\nmp-1214317,\"{'Ca': 12.0, 'Ga': 8.0, 'Sn': 12.0, 'O': 48.0}\",\"('Ca', 'Ga', 'O', 'Sn')\",OTHERS,False\nmvc-4610,\"{'Y': 2.0, 'Co': 4.0, 'O': 8.0}\",\"('Co', 'O', 'Y')\",OTHERS,False\nmp-22036,\"{'Zr': 4.0, 'Pd': 4.0, 'Sb': 4.0}\",\"('Pd', 'Sb', 'Zr')\",OTHERS,False\nmp-680450,\"{'K': 32.0, 'Li': 16.0, 'Sn': 64.0}\",\"('K', 'Li', 'Sn')\",OTHERS,False\nmp-769627,\"{'V': 2.0, 'Cr': 1.0, 'Fe': 3.0, 'P': 6.0, 'O': 24.0}\",\"('Cr', 'Fe', 'O', 'P', 'V')\",OTHERS,False\nmp-667315,\"{'Sc': 4.0, 'Nb': 24.0, 'Cl': 52.0, 'O': 12.0}\",\"('Cl', 'Nb', 'O', 'Sc')\",OTHERS,False\nmp-768602,\"{'Li': 4.0, 'Fe': 5.0, 'Sb': 3.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'Sb')\",OTHERS,False\nmp-1226309,\"{'Cr': 2.0, 'Cu': 2.0, 'S': 8.0}\",\"('Cr', 'Cu', 'S')\",OTHERS,False\nmp-1218614,\"{'Sr': 4.0, 'Li': 1.0, 'Ru': 3.0, 'O': 12.0}\",\"('Li', 'O', 'Ru', 'Sr')\",OTHERS,False\nmp-28558,\"{'Ge': 4.0, 'Br': 12.0, 'Rb': 4.0}\",\"('Br', 'Ge', 'Rb')\",OTHERS,False\nmp-1237663,\"{'Tl': 8.0, 'P': 4.0, 'O': 16.0}\",\"('O', 'P', 'Tl')\",OTHERS,False\nmp-555869,\"{'Li': 2.0, 'V': 4.0, 'O': 10.0}\",\"('Li', 'O', 'V')\",OTHERS,False\nmp-1221599,\"{'Mn': 1.0, 'V': 1.0, 'Co': 4.0, 'Si': 2.0}\",\"('Co', 'Mn', 'Si', 'V')\",OTHERS,False\nmp-12628,{'Rb': 8.0},\"('Rb',)\",OTHERS,False\nmp-559019,\"{'Nd': 24.0, 'S': 16.0, 'Br': 4.0, 'N': 12.0}\",\"('Br', 'N', 'Nd', 'S')\",OTHERS,False\nmp-22707,\"{'Ag': 2.0, 'Sb': 2.0, 'Eu': 2.0}\",\"('Ag', 'Eu', 'Sb')\",OTHERS,False\nmp-626307,\"{'Fe': 4.0, 'H': 4.0, 'O': 8.0}\",\"('Fe', 'H', 'O')\",OTHERS,False\nmp-1221059,\"{'Na': 2.0, 'Sb': 2.0, 'W': 2.0, 'O': 12.0}\",\"('Na', 'O', 'Sb', 'W')\",OTHERS,False\nmp-1211359,\"{'La': 1.0, 'Mn': 3.0, 'Cr': 4.0, 'O': 12.0}\",\"('Cr', 'La', 'Mn', 'O')\",OTHERS,False\nmp-1218554,\"{'Sr': 7.0, 'Y': 6.0, 'O': 1.0, 'F': 30.0}\",\"('F', 'O', 'Sr', 'Y')\",OTHERS,False\nmp-1009770,\"{'Ru': 1.0, 'N': 1.0}\",\"('N', 'Ru')\",OTHERS,False\nmp-972486,\"{'Sm': 3.0, 'Ti': 1.0}\",\"('Sm', 'Ti')\",OTHERS,False\nmp-1187986,\"{'Yb': 6.0, 'Sc': 2.0}\",\"('Sc', 'Yb')\",OTHERS,False\nmp-1097707,\"{'Cu': 96.0, 'Se': 48.0}\",\"('Cu', 'Se')\",OTHERS,False\nmp-1225740,\"{'Er': 2.0, 'Mn': 3.0, 'Sb': 3.0, 'O': 14.0}\",\"('Er', 'Mn', 'O', 'Sb')\",OTHERS,False\nmp-1221596,\"{'Mn': 2.0, 'Mo': 2.0, 'As': 4.0}\",\"('As', 'Mn', 'Mo')\",OTHERS,False\nmp-621852,\"{'La': 4.0, 'Ge': 10.0, 'Ru': 6.0}\",\"('Ge', 'La', 'Ru')\",OTHERS,False\nmp-21635,\"{'O': 24.0, 'Mn': 4.0, 'Ge': 8.0, 'Ce': 2.0}\",\"('Ce', 'Ge', 'Mn', 'O')\",OTHERS,False\nmp-626553,\"{'Ca': 2.0, 'H': 32.0, 'O': 20.0}\",\"('Ca', 'H', 'O')\",OTHERS,False\nmp-765076,\"{'Li': 4.0, 'Mn': 4.0, 'O': 4.0, 'F': 8.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1224583,\"{'Hf': 4.0, 'Ga': 4.0, 'Co': 4.0}\",\"('Co', 'Ga', 'Hf')\",OTHERS,False\nmp-690136,\"{'Mg': 4.0, 'Al': 3.0, 'H': 17.0, 'O': 17.0}\",\"('Al', 'H', 'Mg', 'O')\",OTHERS,False\nmp-1213827,\"{'Ce': 8.0, 'Ge': 1.0, 'Pd': 24.0}\",\"('Ce', 'Ge', 'Pd')\",OTHERS,False\nmp-251,\"{'N': 1.0, 'Ta': 1.0}\",\"('N', 'Ta')\",OTHERS,False\nmp-981396,\"{'Y': 2.0, 'B': 8.0, 'Ir': 8.0}\",\"('B', 'Ir', 'Y')\",OTHERS,False\nmp-1184569,{'Hg': 2.0},\"('Hg',)\",OTHERS,False\nmp-1203078,\"{'Sm': 4.0, 'Er': 11.0, 'S': 22.0}\",\"('Er', 'S', 'Sm')\",OTHERS,False\nmp-977548,\"{'Nd': 4.0, 'Mg': 2.0, 'Ge': 4.0}\",\"('Ge', 'Mg', 'Nd')\",OTHERS,False\nmp-1182250,\"{'Cd': 8.0, 'Se': 8.0, 'O': 30.0}\",\"('Cd', 'O', 'Se')\",OTHERS,False\nmp-568570,\"{'Cs': 4.0, 'Sn': 4.0, 'I': 12.0}\",\"('Cs', 'I', 'Sn')\",OTHERS,False\nmp-1213056,\"{'Dy': 2.0, 'Al': 20.0, 'Fe': 4.0}\",\"('Al', 'Dy', 'Fe')\",OTHERS,False\nmp-20792,\"{'Ca': 4.0, 'Pd': 4.0, 'In': 2.0}\",\"('Ca', 'In', 'Pd')\",OTHERS,False\nmp-1106250,\"{'Gd': 10.0, 'Pb': 6.0}\",\"('Gd', 'Pb')\",OTHERS,False\nmp-1102945,\"{'Hf': 4.0, 'Ge': 4.0, 'Rh': 4.0}\",\"('Ge', 'Hf', 'Rh')\",OTHERS,False\nmp-1199032,\"{'Ni': 6.0, 'B': 6.0, 'P': 6.0, 'O': 36.0}\",\"('B', 'Ni', 'O', 'P')\",OTHERS,False\nmp-504616,\"{'Ce': 4.0, 'Co': 14.0, 'B': 6.0}\",\"('B', 'Ce', 'Co')\",OTHERS,False\nmp-1187954,\"{'Zn': 1.0, 'In': 3.0}\",\"('In', 'Zn')\",OTHERS,False\nmp-557250,\"{'Hg': 4.0, 'I': 4.0, 'N': 4.0, 'O': 12.0}\",\"('Hg', 'I', 'N', 'O')\",OTHERS,False\nmp-1206322,\"{'Ce': 2.0, 'Ag': 4.0}\",\"('Ag', 'Ce')\",OTHERS,False\nmp-117,{'Sn': 2.0},\"('Sn',)\",OTHERS,False\nmp-1215507,\"{'Yb': 1.0, 'Zn': 1.0, 'Cu': 1.0, 'As': 2.0}\",\"('As', 'Cu', 'Yb', 'Zn')\",OTHERS,False\nmp-34234,\"{'Al': 12.0, 'Ni': 6.0, 'O': 24.0}\",\"('Al', 'Ni', 'O')\",OTHERS,False\nmp-1228896,\"{'Ba': 10.0, 'Bi': 2.0, 'Pb': 8.0, 'O': 30.0}\",\"('Ba', 'Bi', 'O', 'Pb')\",OTHERS,False\nmp-555988,\"{'Na': 8.0, 'U': 2.0, 'Cr': 6.0, 'O': 28.0}\",\"('Cr', 'Na', 'O', 'U')\",OTHERS,False\nmp-1184036,\"{'Ga': 2.0, 'Ag': 6.0}\",\"('Ag', 'Ga')\",OTHERS,False\nmp-18297,\"{'N': 16.0, 'Ga': 8.0, 'Ba': 12.0}\",\"('Ba', 'Ga', 'N')\",OTHERS,False\nmp-1239251,\"{'Ta': 2.0, 'Cr': 6.0, 'Cu': 4.0, 'S': 16.0}\",\"('Cr', 'Cu', 'S', 'Ta')\",OTHERS,False\nmp-867819,\"{'Li': 3.0, 'Cr': 3.0, 'S': 6.0, 'O': 24.0}\",\"('Cr', 'Li', 'O', 'S')\",OTHERS,False\nmp-26675,\"{'Li': 4.0, 'O': 48.0, 'P': 16.0, 'Sb': 4.0}\",\"('Li', 'O', 'P', 'Sb')\",OTHERS,False\nmp-22097,\"{'O': 12.0, 'Mo': 2.0, 'Rh': 4.0}\",\"('Mo', 'O', 'Rh')\",OTHERS,False\nmp-24681,\"{'H': 18.0, 'N': 6.0, 'O': 8.0, 'Cl': 4.0, 'Cr': 1.0, 'Cs': 1.0}\",\"('Cl', 'Cr', 'Cs', 'H', 'N', 'O')\",OTHERS,False\nmp-605437,\"{'Fe': 4.0, 'H': 4.0, 'O': 8.0}\",\"('Fe', 'H', 'O')\",OTHERS,False\nmp-580,\"{'B': 2.0, 'Pu': 1.0}\",\"('B', 'Pu')\",OTHERS,False\nmp-1187938,\"{'Yb': 1.0, 'Ag': 1.0, 'Au': 2.0}\",\"('Ag', 'Au', 'Yb')\",OTHERS,False\nmp-569302,\"{'Gd': 1.0, 'Si': 2.0, 'Ru': 2.0}\",\"('Gd', 'Ru', 'Si')\",OTHERS,False\nmp-768149,\"{'Dy': 6.0, 'In': 6.0, 'O': 18.0}\",\"('Dy', 'In', 'O')\",OTHERS,False\nmp-14139,\"{'O': 48.0, 'Fe': 20.0, 'Sm': 12.0}\",\"('Fe', 'O', 'Sm')\",OTHERS,False\nmp-23084,\"{'O': 4.0, 'Cl': 2.0, 'Pb': 2.0, 'Bi': 2.0}\",\"('Bi', 'Cl', 'O', 'Pb')\",OTHERS,False\nmp-1225289,\"{'Dy': 2.0, 'Te': 1.0, 'S': 2.0}\",\"('Dy', 'S', 'Te')\",OTHERS,False\nmp-559355,\"{'Hg': 6.0, 'As': 2.0, 'S': 8.0, 'Cl': 2.0}\",\"('As', 'Cl', 'Hg', 'S')\",OTHERS,False\nmp-573514,\"{'Cs': 8.0, 'Sb': 8.0}\",\"('Cs', 'Sb')\",OTHERS,False\nmp-640447,\"{'Ce': 10.0, 'Ni': 2.0, 'Pb': 6.0}\",\"('Ce', 'Ni', 'Pb')\",OTHERS,False\nmp-771054,\"{'V': 12.0, 'P': 8.0, 'O': 32.0}\",\"('O', 'P', 'V')\",OTHERS,False\nmp-540287,\"{'Li': 4.0, 'Cr': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-757008,\"{'Mg': 1.0, 'Fe': 10.0, 'O': 16.0}\",\"('Fe', 'Mg', 'O')\",OTHERS,False\nmvc-4454,\"{'Ti': 4.0, 'Al': 2.0, 'O': 8.0}\",\"('Al', 'O', 'Ti')\",OTHERS,False\nmp-1209091,\"{'Rb': 2.0, 'Tb': 6.0, 'F': 20.0}\",\"('F', 'Rb', 'Tb')\",OTHERS,False\nmp-1213177,\"{'Cs': 4.0, 'Tm': 4.0, 'I': 12.0}\",\"('Cs', 'I', 'Tm')\",OTHERS,False\nmp-780199,\"{'Li': 5.0, 'Mn': 6.0, 'B': 6.0, 'O': 18.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1218665,\"{'Sr': 6.0, 'Ta': 8.0, 'Ti': 8.0, 'O': 42.0}\",\"('O', 'Sr', 'Ta', 'Ti')\",OTHERS,False\nmp-4972,\"{'Cu': 2.0, 'In': 1.0, 'Lu': 1.0}\",\"('Cu', 'In', 'Lu')\",OTHERS,False\nmp-1223169,\"{'La': 3.0, 'Y': 1.0, 'Hf': 4.0, 'O': 14.0}\",\"('Hf', 'La', 'O', 'Y')\",OTHERS,False\nmp-1203935,\"{'Fe': 4.0, 'S': 4.0, 'O': 32.0}\",\"('Fe', 'O', 'S')\",OTHERS,False\nmp-1183874,\"{'Ce': 6.0, 'Sc': 2.0}\",\"('Ce', 'Sc')\",OTHERS,False\nmp-223,\"{'O': 6.0, 'Ge': 3.0}\",\"('Ge', 'O')\",OTHERS,False\nmp-755172,\"{'Li': 5.0, 'V': 3.0, 'Fe': 2.0, 'O': 10.0}\",\"('Fe', 'Li', 'O', 'V')\",OTHERS,False\nmp-1194158,\"{'Er': 4.0, 'Mo': 4.0, 'Br': 4.0, 'O': 16.0}\",\"('Br', 'Er', 'Mo', 'O')\",OTHERS,False\nmp-1074738,\"{'Mg': 8.0, 'Si': 4.0}\",\"('Mg', 'Si')\",OTHERS,False\nmvc-1288,\"{'Ca': 4.0, 'V': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Ca', 'O', 'P', 'V')\",OTHERS,False\nmp-1076570,\"{'Sr': 7.0, 'Ca': 1.0, 'Ti': 7.0, 'Mn': 1.0, 'O': 24.0}\",\"('Ca', 'Mn', 'O', 'Sr', 'Ti')\",OTHERS,False\nmp-1186604,\"{'Pm': 1.0, 'Lu': 1.0, 'Al': 2.0}\",\"('Al', 'Lu', 'Pm')\",OTHERS,False\nmp-1013705,\"{'Sr': 3.0, 'Bi': 1.0, 'As': 1.0}\",\"('As', 'Bi', 'Sr')\",OTHERS,False\nmp-866079,\"{'Mg': 1.0, 'Sc': 2.0, 'Ru': 1.0}\",\"('Mg', 'Ru', 'Sc')\",OTHERS,False\nmp-1190646,\"{'K': 4.0, 'Cu': 2.0, 'Te': 2.0, 'O': 14.0}\",\"('Cu', 'K', 'O', 'Te')\",OTHERS,False\nmp-1224855,\"{'Fe': 1.0, 'O': 2.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-1198448,\"{'Na': 4.0, 'Al': 12.0, 'Si': 12.0, 'H': 8.0, 'O': 48.0}\",\"('Al', 'H', 'Na', 'O', 'Si')\",OTHERS,False\nmp-30815,\"{'Al': 3.0, 'Pd': 2.0, 'La': 1.0}\",\"('Al', 'La', 'Pd')\",OTHERS,False\nmp-1106110,\"{'Eu': 8.0, 'S': 12.0}\",\"('Eu', 'S')\",OTHERS,False\nmp-862805,\"{'Pr': 1.0, 'Sn': 1.0, 'Au': 2.0}\",\"('Au', 'Pr', 'Sn')\",OTHERS,False\nmp-1079890,\"{'Al': 4.0, 'Cu': 2.0, 'Ir': 2.0}\",\"('Al', 'Cu', 'Ir')\",OTHERS,False\nmp-1214638,\"{'Ba': 6.0, 'Fe': 2.0, 'Ru': 4.0, 'O': 18.0}\",\"('Ba', 'Fe', 'O', 'Ru')\",OTHERS,False\nmp-849734,\"{'Li': 2.0, 'V': 2.0, 'F': 8.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-1183973,\"{'Ga': 3.0, 'Si': 1.0}\",\"('Ga', 'Si')\",OTHERS,False\nmp-1208086,\"{'Tm': 3.0, 'P': 6.0, 'Pd': 20.0}\",\"('P', 'Pd', 'Tm')\",OTHERS,False\nmp-561224,\"{'Te': 4.0, 'O': 8.0}\",\"('O', 'Te')\",OTHERS,False\nmp-3346,\"{'Zr': 2.0, 'Sb': 2.0, 'Ho': 2.0}\",\"('Ho', 'Sb', 'Zr')\",OTHERS,False\nmp-23918,\"{'H': 8.0, 'Na': 2.0, 'Ga': 2.0}\",\"('Ga', 'H', 'Na')\",OTHERS,False\nmp-1093912,\"{'Ca': 1.0, 'Hg': 2.0, 'Bi': 1.0}\",\"('Bi', 'Ca', 'Hg')\",OTHERS,False\nmp-763659,\"{'Li': 3.0, 'V': 2.0, 'Fe': 2.0, 'O': 8.0}\",\"('Fe', 'Li', 'O', 'V')\",OTHERS,False\nmp-1220867,\"{'Na': 2.0, 'Tl': 2.0, 'O': 2.0}\",\"('Na', 'O', 'Tl')\",OTHERS,False\nmp-24034,\"{'H': 12.0, 'Al': 2.0, 'K': 6.0}\",\"('Al', 'H', 'K')\",OTHERS,False\nmp-977324,\"{'Ga': 3.0, 'Ni': 20.0, 'B': 6.0}\",\"('B', 'Ga', 'Ni')\",OTHERS,False\nmp-1176897,\"{'Li': 14.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1181173,\"{'K': 16.0, 'Ag': 8.0, 'Sn': 12.0, 'S': 36.0, 'O': 8.0}\",\"('Ag', 'K', 'O', 'S', 'Sn')\",OTHERS,False\nmp-555885,\"{'Sr': 2.0, 'V': 8.0, 'Ag': 4.0, 'O': 24.0}\",\"('Ag', 'O', 'Sr', 'V')\",OTHERS,False\nmp-1197960,\"{'Bi': 24.0, 'S': 24.0, 'O': 108.0}\",\"('Bi', 'O', 'S')\",OTHERS,False\nmp-1025917,\"{'Mo': 1.0, 'W': 2.0, 'Se': 2.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-2562,\"{'Mn': 2.0, 'S': 2.0}\",\"('Mn', 'S')\",OTHERS,False\nmp-1216361,\"{'V': 4.0, 'P': 8.0, 'Pb': 4.0, 'O': 32.0}\",\"('O', 'P', 'Pb', 'V')\",OTHERS,False\nmp-1220506,\"{'Nd': 2.0, 'Fe': 15.0, 'Si': 2.0, 'H': 2.0}\",\"('Fe', 'H', 'Nd', 'Si')\",OTHERS,False\nmvc-14571,\"{'Ti': 2.0, 'Zn': 4.0, 'Sb': 2.0, 'O': 12.0}\",\"('O', 'Sb', 'Ti', 'Zn')\",OTHERS,False\nmp-1111985,\"{'K': 2.0, 'In': 1.0, 'Ag': 1.0, 'Cl': 6.0}\",\"('Ag', 'Cl', 'In', 'K')\",OTHERS,False\nmp-6809,\"{'O': 10.0, 'Si': 2.0, 'Ca': 2.0, 'Sn': 2.0}\",\"('Ca', 'O', 'Si', 'Sn')\",OTHERS,False\nmp-1018806,\"{'Mo': 2.0, 'Se': 2.0, 'S': 2.0}\",\"('Mo', 'S', 'Se')\",OTHERS,False\nmp-21370,\"{'O': 6.0, 'Sb': 1.0, 'Ba': 2.0, 'Eu': 1.0}\",\"('Ba', 'Eu', 'O', 'Sb')\",OTHERS,False\nmp-9770,\"{'S': 24.0, 'Ge': 4.0, 'Ag': 32.0}\",\"('Ag', 'Ge', 'S')\",OTHERS,False\nmvc-8878,\"{'Mg': 2.0, 'Ti': 2.0, 'Cu': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Cu', 'Mg', 'O', 'P', 'Ti')\",OTHERS,False\nmp-7310,\"{'F': 12.0, 'Zr': 2.0, 'Pb': 2.0}\",\"('F', 'Pb', 'Zr')\",OTHERS,False\nmp-1016875,\"{'Cd': 1.0, 'Rh': 1.0, 'O': 3.0}\",\"('Cd', 'O', 'Rh')\",OTHERS,False\nmp-1186973,\"{'Sc': 1.0, 'Ag': 3.0}\",\"('Ag', 'Sc')\",OTHERS,False\nmp-862677,\"{'Li': 1.0, 'Ho': 1.0, 'Tl': 2.0}\",\"('Ho', 'Li', 'Tl')\",OTHERS,False\nmp-1224110,\"{'Ho': 6.0, 'Mn': 1.0, 'Ge': 2.0, 'S': 14.0}\",\"('Ge', 'Ho', 'Mn', 'S')\",OTHERS,False\nmp-850999,\"{'Na': 4.0, 'Cr': 16.0, 'O': 48.0}\",\"('Cr', 'Na', 'O')\",OTHERS,False\nmp-1245407,\"{'Y': 6.0, 'Ge': 12.0, 'N': 22.0}\",\"('Ge', 'N', 'Y')\",OTHERS,False\nmp-11869,\"{'Pd': 1.0, 'Sn': 1.0, 'Hf': 1.0}\",\"('Hf', 'Pd', 'Sn')\",OTHERS,False\nmp-704988,\"{'Ni': 4.0, 'P': 10.0, 'O': 30.0}\",\"('Ni', 'O', 'P')\",OTHERS,False\nmp-1105787,\"{'Pb': 8.0, 'O': 4.0, 'F': 8.0}\",\"('F', 'O', 'Pb')\",OTHERS,False\nmp-570017,\"{'Mg': 20.0, 'Au': 60.0}\",\"('Au', 'Mg')\",OTHERS,False\nmp-30474,\"{'Ca': 22.0, 'Ga': 14.0}\",\"('Ca', 'Ga')\",OTHERS,False\nmp-4198,\"{'O': 8.0, 'P': 2.0, 'Ag': 6.0}\",\"('Ag', 'O', 'P')\",OTHERS,False\nmvc-321,\"{'Mg': 2.0, 'Ti': 8.0, 'O': 12.0}\",\"('Mg', 'O', 'Ti')\",OTHERS,False\nmvc-12619,\"{'Mg': 4.0, 'Co': 4.0, 'O': 8.0}\",\"('Co', 'Mg', 'O')\",OTHERS,False\nmp-1104560,\"{'Dy': 4.0, 'Cd': 2.0, 'Te': 8.0}\",\"('Cd', 'Dy', 'Te')\",OTHERS,False\nmp-1223315,\"{'K': 1.0, 'Y': 1.0, 'Nb': 6.0, 'Cl': 18.0}\",\"('Cl', 'K', 'Nb', 'Y')\",OTHERS,False\nmp-685779,\"{'Sn': 5.0, 'Mo': 36.0, 'S': 48.0}\",\"('Mo', 'S', 'Sn')\",OTHERS,False\nmp-12894,\"{'O': 2.0, 'S': 1.0, 'Y': 2.0}\",\"('O', 'S', 'Y')\",OTHERS,False\nmp-764484,\"{'Li': 3.0, 'Mn': 3.0, 'V': 3.0, 'P': 12.0, 'O': 42.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-1187398,\"{'Tc': 6.0, 'W': 2.0}\",\"('Tc', 'W')\",OTHERS,False\nmp-1174246,\"{'Li': 7.0, 'Co': 5.0, 'O': 12.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-1102672,\"{'Sr': 8.0, 'Al': 4.0}\",\"('Al', 'Sr')\",OTHERS,False\nmp-1218475,\"{'Sr': 4.0, 'Mg': 1.0, 'Ti': 1.0, 'Fe': 2.0, 'As': 2.0, 'O': 6.0}\",\"('As', 'Fe', 'Mg', 'O', 'Sr', 'Ti')\",OTHERS,False\nmp-1101621,\"{'As': 8.0, 'Pd': 10.0, 'Pb': 2.0}\",\"('As', 'Pb', 'Pd')\",OTHERS,False\nmp-775225,\"{'Nb': 3.0, 'Ni': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Nb', 'Ni', 'O', 'P')\",OTHERS,False\nmp-361,\"{'Cu': 4.0, 'O': 2.0}\",\"('Cu', 'O')\",OTHERS,False\nmp-755752,\"{'Rb': 8.0, 'Be': 8.0, 'O': 12.0}\",\"('Be', 'O', 'Rb')\",OTHERS,False\nmp-20627,\"{'Co': 1.0, 'Ga': 5.0, 'Yb': 1.0}\",\"('Co', 'Ga', 'Yb')\",OTHERS,False\nmp-27346,\"{'Al': 8.0, 'Cl': 32.0, 'Hg': 12.0}\",\"('Al', 'Cl', 'Hg')\",OTHERS,False\nmp-24816,\"{'H': 4.0, 'O': 10.0, 'K': 2.0, 'Cr': 2.0, 'Cd': 1.0}\",\"('Cd', 'Cr', 'H', 'K', 'O')\",OTHERS,False\nmp-1202708,\"{'Yb': 8.0, 'Ni': 2.0, 'B': 26.0}\",\"('B', 'Ni', 'Yb')\",OTHERS,False\nmp-1197925,\"{'Sr': 8.0, 'Ga': 4.0, 'P': 8.0, 'O': 32.0, 'F': 8.0}\",\"('F', 'Ga', 'O', 'P', 'Sr')\",OTHERS,False\nmp-1194266,\"{'K': 12.0, 'Sb': 4.0, 'S': 12.0}\",\"('K', 'S', 'Sb')\",OTHERS,False\nmp-8911,\"{'S': 2.0, 'Cu': 2.0, 'Ag': 2.0}\",\"('Ag', 'Cu', 'S')\",OTHERS,False\nmp-568790,\"{'Ca': 56.0, 'Ga': 4.0, 'As': 44.0}\",\"('As', 'Ca', 'Ga')\",OTHERS,False\nmp-4434,\"{'O': 12.0, 'Al': 4.0, 'Tb': 4.0}\",\"('Al', 'O', 'Tb')\",OTHERS,False\nmp-1039108,\"{'Ca': 4.0, 'Mg': 8.0}\",\"('Ca', 'Mg')\",OTHERS,False\nmp-1181373,\"{'H': 12.0, 'N': 8.0, 'O': 18.0}\",\"('H', 'N', 'O')\",OTHERS,False\nmp-1189560,\"{'Y': 4.0, 'Si': 12.0}\",\"('Si', 'Y')\",OTHERS,False\nmp-1175317,\"{'Li': 7.0, 'Mn': 5.0, 'O': 12.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmvc-10699,\"{'Ca': 1.0, 'Sb': 4.0, 'O': 8.0}\",\"('Ca', 'O', 'Sb')\",OTHERS,False\nmp-756318,\"{'Al': 4.0, 'Co': 2.0, 'Cl': 16.0}\",\"('Al', 'Cl', 'Co')\",OTHERS,False\nmp-767521,\"{'Na': 4.0, 'In': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'In', 'Na', 'O', 'P')\",OTHERS,False\nmp-861884,\"{'Ac': 1.0, 'Ga': 1.0, 'Te': 2.0}\",\"('Ac', 'Ga', 'Te')\",OTHERS,False\nmp-1077942,\"{'Rb': 2.0, 'Te': 1.0, 'Se': 4.0}\",\"('Rb', 'Se', 'Te')\",OTHERS,False\nmp-759877,\"{'Li': 4.0, 'Cu': 1.0, 'F': 7.0}\",\"('Cu', 'F', 'Li')\",OTHERS,False\nmp-1214815,\"{'Co': 2.0, 'N': 8.0, 'F': 24.0}\",\"('Co', 'F', 'N')\",OTHERS,False\nmp-1211272,\"{'La': 6.0, 'Nb': 2.0, 'Cl': 6.0, 'O': 12.0}\",\"('Cl', 'La', 'Nb', 'O')\",OTHERS,False\nmp-1185724,\"{'Mg': 16.0, 'Mn': 1.0, 'Al': 12.0}\",\"('Al', 'Mg', 'Mn')\",OTHERS,False\nmp-6265,\"{'O': 6.0, 'Sb': 1.0, 'Ba': 2.0, 'Tb': 1.0}\",\"('Ba', 'O', 'Sb', 'Tb')\",OTHERS,False\nmp-22749,\"{'C': 4.0, 'Mn': 10.0}\",\"('C', 'Mn')\",OTHERS,False\nmp-1098218,\"{'Mg': 30.0, 'Cu': 1.0, 'Si': 1.0, 'O': 32.0}\",\"('Cu', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-1216181,\"{'Y': 2.0, 'B': 6.0, 'Rh': 9.0}\",\"('B', 'Rh', 'Y')\",OTHERS,False\nmp-1207848,\"{'Y': 12.0, 'Ge': 8.0, 'Rh': 8.0}\",\"('Ge', 'Rh', 'Y')\",OTHERS,False\nmp-549490,\"{'O': 5.0, 'Nb': 4.0, 'K': 1.0, 'F': 1.0}\",\"('F', 'K', 'Nb', 'O')\",OTHERS,False\nmp-1184572,\"{'Hf': 1.0, 'Mg': 1.0, 'Pt': 2.0}\",\"('Hf', 'Mg', 'Pt')\",OTHERS,False\nmp-761175,\"{'Na': 2.0, 'Ni': 8.0, 'H': 18.0, 'C': 6.0, 'O': 30.0}\",\"('C', 'H', 'Na', 'Ni', 'O')\",OTHERS,False\nmp-567849,\"{'Er': 4.0, 'Al': 6.0, 'Co': 2.0}\",\"('Al', 'Co', 'Er')\",OTHERS,False\nmp-1038570,\"{'Mg': 30.0, 'Cr': 1.0, 'Cd': 1.0, 'O': 32.0}\",\"('Cd', 'Cr', 'Mg', 'O')\",OTHERS,False\nmp-567787,\"{'Li': 1.0, 'In': 1.0, 'Ag': 2.0}\",\"('Ag', 'In', 'Li')\",OTHERS,False\nmp-15919,\"{'Be': 12.0, 'O': 48.0, 'P': 12.0, 'Ag': 12.0}\",\"('Ag', 'Be', 'O', 'P')\",OTHERS,False\nmp-1208371,\"{'Tl': 4.0, 'N': 8.0, 'Cl': 20.0, 'O': 4.0}\",\"('Cl', 'N', 'O', 'Tl')\",OTHERS,False\nmp-631449,\"{'Li': 1.0, 'Ta': 1.0, 'Be': 1.0}\",\"('Be', 'Li', 'Ta')\",OTHERS,False\nmp-561848,\"{'C': 24.0, 'O': 16.0}\",\"('C', 'O')\",OTHERS,False\nmp-21278,\"{'In': 4.0, 'Ce': 2.0, 'Ir': 2.0}\",\"('Ce', 'In', 'Ir')\",OTHERS,False\nmp-1208045,\"{'Tl': 1.0, 'Fe': 1.0, 'S': 2.0, 'O': 8.0}\",\"('Fe', 'O', 'S', 'Tl')\",OTHERS,False\nmp-1079141,\"{'Pr': 2.0, 'Ge': 4.0, 'Ir': 4.0}\",\"('Ge', 'Ir', 'Pr')\",OTHERS,False\nmp-754151,\"{'V': 2.0, 'O': 2.0, 'F': 2.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-979073,\"{'Tm': 1.0, 'Mg': 2.0, 'Sc': 1.0}\",\"('Mg', 'Sc', 'Tm')\",OTHERS,False\nmp-29505,\"{'O': 12.0, 'Cs': 12.0, 'Bi': 4.0}\",\"('Bi', 'Cs', 'O')\",OTHERS,False\nmp-1175732,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmvc-1912,\"{'Y': 1.0, 'Cu': 3.0, 'Bi': 4.0, 'O': 12.0}\",\"('Bi', 'Cu', 'O', 'Y')\",OTHERS,False\nmp-27552,\"{'I': 6.0, 'Tl': 2.0, 'Pb': 2.0}\",\"('I', 'Pb', 'Tl')\",OTHERS,False\nmp-672254,\"{'Ca': 3.0, 'Pd': 3.0, 'Pb': 3.0}\",\"('Ca', 'Pb', 'Pd')\",OTHERS,False\nmp-1218038,\"{'Ta': 3.0, 'V': 7.0, 'Si': 6.0}\",\"('Si', 'Ta', 'V')\",OTHERS,False\nmp-778109,\"{'Li': 12.0, 'Nb': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'Nb', 'O', 'P')\",OTHERS,False\nmp-1019087,\"{'Ca': 2.0, 'Sn': 2.0, 'Hg': 2.0}\",\"('Ca', 'Hg', 'Sn')\",OTHERS,False\nmp-1246920,\"{'Pb': 2.0, 'C': 16.0, 'N': 12.0}\",\"('C', 'N', 'Pb')\",OTHERS,False\nmp-1212980,\"{'Eu': 6.0, 'In': 6.0, 'O': 18.0}\",\"('Eu', 'In', 'O')\",OTHERS,False\nmp-772294,\"{'Na': 10.0, 'Li': 2.0, 'Fe': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Fe', 'Li', 'Na', 'O', 'P')\",OTHERS,False\nmp-1074753,\"{'Mg': 8.0, 'Si': 4.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1247069,\"{'Cd': 6.0, 'Ga': 4.0, 'N': 8.0}\",\"('Cd', 'Ga', 'N')\",OTHERS,False\nmp-1112710,\"{'Cs': 2.0, 'K': 1.0, 'Sc': 1.0, 'I': 6.0}\",\"('Cs', 'I', 'K', 'Sc')\",OTHERS,False\nmp-862720,\"{'Sr': 2.0, 'Cd': 1.0, 'In': 1.0}\",\"('Cd', 'In', 'Sr')\",OTHERS,False\nmp-1095596,\"{'Ca': 2.0, 'B': 1.0, 'As': 1.0, 'O': 8.0}\",\"('As', 'B', 'Ca', 'O')\",OTHERS,False\nmp-2333,\"{'Pd': 3.0, 'Nd': 1.0}\",\"('Nd', 'Pd')\",OTHERS,False\nmvc-2075,\"{'Mg': 2.0, 'Sn': 9.0, 'O': 13.0}\",\"('Mg', 'O', 'Sn')\",OTHERS,False\nmp-1199234,\"{'Tl': 4.0, 'Mo': 30.0, 'Se': 38.0}\",\"('Mo', 'Se', 'Tl')\",OTHERS,False\nmp-1214395,\"{'Ba': 8.0, 'In': 8.0, 'F': 40.0}\",\"('Ba', 'F', 'In')\",OTHERS,False\nmp-1203378,\"{'Cs': 14.0, 'In': 6.0, 'As': 12.0}\",\"('As', 'Cs', 'In')\",OTHERS,False\nmp-1186671,\"{'Pm': 1.0, 'Y': 3.0}\",\"('Pm', 'Y')\",OTHERS,False\nmp-1197938,\"{'Co': 6.0, 'B': 14.0, 'O': 26.0, 'F': 2.0}\",\"('B', 'Co', 'F', 'O')\",OTHERS,False\nmp-1173940,\"{'Li': 3.0, 'Mn': 1.0, 'Co': 2.0, 'O': 6.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1028535,\"{'Te': 2.0, 'Mo': 1.0, 'W': 3.0, 'Se': 2.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-973626,\"{'Hg': 6.0, 'Au': 2.0}\",\"('Au', 'Hg')\",OTHERS,False\nmp-758112,\"{'Co': 2.0, 'Ni': 3.0, 'Sb': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Co', 'Ni', 'O', 'P', 'Sb')\",OTHERS,False\nmp-722865,\"{'H': 32.0, 'Se': 8.0, 'N': 8.0, 'O': 20.0}\",\"('H', 'N', 'O', 'Se')\",OTHERS,False\nmp-1222794,\"{'La': 1.0, 'Ga': 3.0, 'Pt': 1.0}\",\"('Ga', 'La', 'Pt')\",OTHERS,False\nmp-567348,\"{'Tm': 1.0, 'In': 1.0, 'Pd': 2.0}\",\"('In', 'Pd', 'Tm')\",OTHERS,False\nmp-1202721,\"{'Ag': 4.0, 'H': 24.0, 'C': 8.0, 'N': 20.0, 'O': 16.0}\",\"('Ag', 'C', 'H', 'N', 'O')\",OTHERS,False\nmp-6562,\"{'O': 4.0, 'Cu': 1.0, 'Ba': 2.0, 'Hg': 1.0}\",\"('Ba', 'Cu', 'Hg', 'O')\",OTHERS,False\nmp-669389,\"{'Sr': 6.0, 'In': 2.0, 'Rh': 2.0, 'O': 12.0}\",\"('In', 'O', 'Rh', 'Sr')\",OTHERS,False\nmp-1095566,\"{'La': 2.0, 'Co': 8.0, 'B': 2.0}\",\"('B', 'Co', 'La')\",OTHERS,False\nmp-571514,\"{'Cd': 8.0, 'I': 16.0}\",\"('Cd', 'I')\",OTHERS,False\nmp-1213682,\"{'Cs': 1.0, 'Er': 1.0, 'Mo': 2.0, 'O': 8.0}\",\"('Cs', 'Er', 'Mo', 'O')\",OTHERS,False\nmp-973958,\"{'K': 1.0, 'Ru': 1.0, 'O': 3.0}\",\"('K', 'O', 'Ru')\",OTHERS,False\nmp-773087,\"{'Li': 2.0, 'Mg': 1.0, 'Cu': 2.0, 'Si': 4.0, 'O': 12.0}\",\"('Cu', 'Li', 'Mg', 'O', 'Si')\",OTHERS,False\nmvc-16726,\"{'Al': 2.0, 'Co': 4.0, 'S': 8.0}\",\"('Al', 'Co', 'S')\",OTHERS,False\nmp-1228149,\"{'Ba': 3.0, 'Sr': 9.0, 'Al': 4.0, 'O': 16.0, 'F': 4.0}\",\"('Al', 'Ba', 'F', 'O', 'Sr')\",OTHERS,False\nmp-1228037,\"{'Ba': 1.0, 'Ca': 1.0, 'Dy': 2.0, 'F': 10.0}\",\"('Ba', 'Ca', 'Dy', 'F')\",OTHERS,False\nmp-1195436,\"{'Cd': 12.0, 'B': 4.0, 'P': 4.0, 'O': 28.0}\",\"('B', 'Cd', 'O', 'P')\",OTHERS,False\nmp-389,\"{'Si': 4.0, 'Pd': 4.0}\",\"('Pd', 'Si')\",OTHERS,False\nmp-1200220,\"{'Al': 24.0, 'Ir': 8.0}\",\"('Al', 'Ir')\",OTHERS,False\nmp-1073599,\"{'Mg': 8.0, 'Si': 16.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-862360,\"{'Sc': 2.0, 'Ni': 1.0, 'Ir': 1.0}\",\"('Ir', 'Ni', 'Sc')\",OTHERS,False\nmp-569435,\"{'K': 4.0, 'Bi': 4.0, 'P': 8.0, 'Se': 24.0}\",\"('Bi', 'K', 'P', 'Se')\",OTHERS,False\nmp-1113276,\"{'Rb': 2.0, 'Bi': 1.0, 'Au': 1.0, 'F': 6.0}\",\"('Au', 'Bi', 'F', 'Rb')\",OTHERS,False\nmp-1029750,\"{'Sr': 6.0, 'Ru': 2.0, 'N': 6.0}\",\"('N', 'Ru', 'Sr')\",OTHERS,False\nmp-1173734,\"{'Na': 1.0, 'Ca': 3.0, 'Fe': 4.0, 'Si': 8.0, 'O': 24.0}\",\"('Ca', 'Fe', 'Na', 'O', 'Si')\",OTHERS,False\nmp-558475,\"{'K': 16.0, 'Nb': 8.0, 'S': 40.0, 'O': 4.0}\",\"('K', 'Nb', 'O', 'S')\",OTHERS,False\nmp-11366,\"{'Cu': 2.0, 'Zn': 1.0, 'Zr': 1.0}\",\"('Cu', 'Zn', 'Zr')\",OTHERS,False\nmp-756488,\"{'Co': 4.0, 'O': 8.0}\",\"('Co', 'O')\",OTHERS,False\nmp-22392,\"{'F': 2.0, 'Cu': 2.0, 'Se': 2.0, 'Sm': 2.0}\",\"('Cu', 'F', 'Se', 'Sm')\",OTHERS,False\nmp-1201992,\"{'La': 2.0, 'V': 2.0, 'H': 4.0, 'I': 8.0, 'O': 30.0}\",\"('H', 'I', 'La', 'O', 'V')\",OTHERS,False\nmp-1215891,\"{'Y': 1.0, 'Th': 1.0}\",\"('Th', 'Y')\",OTHERS,False\nmp-774859,\"{'Li': 4.0, 'Nb': 4.0, 'P': 4.0, 'O': 20.0}\",\"('Li', 'Nb', 'O', 'P')\",OTHERS,False\nmp-690667,\"{'Ag': 7.0, 'N': 1.0, 'O': 8.0}\",\"('Ag', 'N', 'O')\",OTHERS,False\nmp-867293,\"{'Li': 1.0, 'Co': 2.0, 'Si': 1.0}\",\"('Co', 'Li', 'Si')\",OTHERS,False\nmp-690540,\"{'V': 2.0, 'Bi': 4.0, 'O': 11.0}\",\"('Bi', 'O', 'V')\",OTHERS,False\nmp-654900,\"{'Cu': 4.0, 'H': 36.0, 'C': 16.0, 'N': 4.0, 'O': 24.0}\",\"('C', 'Cu', 'H', 'N', 'O')\",OTHERS,False\nmp-540631,\"{'K': 2.0, 'Ta': 2.0, 'Ge': 6.0, 'O': 18.0}\",\"('Ge', 'K', 'O', 'Ta')\",OTHERS,False\nmp-1077413,\"{'Na': 2.0, 'P': 2.0, 'O': 2.0}\",\"('Na', 'O', 'P')\",OTHERS,False\nmp-21437,\"{'O': 12.0, 'Fe': 4.0, 'Te': 2.0}\",\"('Fe', 'O', 'Te')\",OTHERS,False\nmp-1219732,\"{'Pt': 4.0, 'Se': 1.0, 'S': 3.0}\",\"('Pt', 'S', 'Se')\",OTHERS,False\nmp-582119,\"{'Tb': 16.0, 'In': 4.0, 'Rh': 4.0}\",\"('In', 'Rh', 'Tb')\",OTHERS,False\nmp-1219236,\"{'Re': 6.0, 'Br': 16.0, 'Cl': 2.0}\",\"('Br', 'Cl', 'Re')\",OTHERS,False\nmp-22428,\"{'Sr': 4.0, 'Ce': 4.0, 'O': 12.0}\",\"('Ce', 'O', 'Sr')\",OTHERS,False\nmp-867873,\"{'Li': 1.0, 'Tm': 2.0, 'Al': 1.0}\",\"('Al', 'Li', 'Tm')\",OTHERS,False\nmp-21827,\"{'Cu': 8.0, 'Se': 32.0, 'In': 18.0}\",\"('Cu', 'In', 'Se')\",OTHERS,False\nmp-1027137,\"{'Te': 2.0, 'Mo': 3.0, 'W': 1.0, 'Se': 4.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-675325,\"{'Al': 1.0, 'Ag': 9.0, 'S': 6.0}\",\"('Ag', 'Al', 'S')\",OTHERS,False\nmp-972119,\"{'Sr': 1.0, 'Ca': 1.0, 'Tl': 2.0}\",\"('Ca', 'Sr', 'Tl')\",OTHERS,False\nmp-1174250,\"{'Li': 8.0, 'Co': 6.0, 'O': 14.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-557509,\"{'Mn': 4.0, 'H': 44.0, 'C': 20.0, 'N': 4.0, 'O': 24.0}\",\"('C', 'H', 'Mn', 'N', 'O')\",OTHERS,False\nmp-626791,\"{'V': 4.0, 'H': 4.0, 'O': 8.0}\",\"('H', 'O', 'V')\",OTHERS,False\nmp-1232167,\"{'Mg': 8.0, 'Sc': 16.0, 'Se': 32.0}\",\"('Mg', 'Sc', 'Se')\",OTHERS,False\nmp-1098705,\"{'Ce': 4.0, 'Se': 8.0}\",\"('Ce', 'Se')\",OTHERS,False\nmp-1184689,\"{'Ho': 2.0, 'Ni': 1.0, 'Ru': 1.0}\",\"('Ho', 'Ni', 'Ru')\",OTHERS,False\nmp-1208379,\"{'Tb': 4.0, 'W': 4.0, 'O': 20.0}\",\"('O', 'Tb', 'W')\",OTHERS,False\nmp-778265,\"{'Li': 4.0, 'Mn': 3.0, 'Fe': 1.0, 'B': 4.0, 'O': 12.0}\",\"('B', 'Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-27017,\"{'Li': 8.0, 'O': 32.0, 'P': 8.0, 'Sn': 8.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,False\nmp-1184920,\"{'Ho': 2.0, 'Os': 6.0}\",\"('Ho', 'Os')\",OTHERS,False\nmp-18854,\"{'O': 4.0, 'Cr': 1.0, 'Sr': 2.0}\",\"('Cr', 'O', 'Sr')\",OTHERS,False\nmp-768869,\"{'Li': 8.0, 'V': 4.0, 'B': 4.0, 'O': 16.0}\",\"('B', 'Li', 'O', 'V')\",OTHERS,False\nmvc-5152,\"{'Mg': 6.0, 'Cu': 12.0, 'O': 24.0}\",\"('Cu', 'Mg', 'O')\",OTHERS,False\nmp-570419,\"{'Zr': 6.0, 'Al': 6.0, 'C': 10.0}\",\"('Al', 'C', 'Zr')\",OTHERS,False\nmp-639511,\"{'Si': 32.0, 'O': 64.0}\",\"('O', 'Si')\",OTHERS,False\nmp-1213744,\"{'Cr': 2.0, 'Cu': 2.0, 'W': 4.0, 'O': 16.0}\",\"('Cr', 'Cu', 'O', 'W')\",OTHERS,False\nmp-770065,\"{'Gd': 8.0, 'Ti': 4.0, 'O': 20.0}\",\"('Gd', 'O', 'Ti')\",OTHERS,False\nmp-1227586,\"{'Ca': 4.0, 'Fe': 4.0, 'O': 10.0}\",\"('Ca', 'Fe', 'O')\",OTHERS,False\nmp-1073454,\"{'Mg': 8.0, 'Si': 16.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1018683,\"{'Dy': 2.0, 'Se': 4.0}\",\"('Dy', 'Se')\",OTHERS,False\nmp-772993,\"{'Li': 2.0, 'Ti': 7.0, 'Nb': 6.0, 'O': 30.0}\",\"('Li', 'Nb', 'O', 'Ti')\",OTHERS,False\nmp-1246702,\"{'Ca': 10.0, 'Co': 4.0, 'N': 12.0}\",\"('Ca', 'Co', 'N')\",OTHERS,False\nmp-1215756,\"{'Zr': 6.0, 'Bi': 2.0, 'F': 30.0}\",\"('Bi', 'F', 'Zr')\",OTHERS,False\nmp-1198330,\"{'Cu': 1.0, 'C': 32.0, 'N': 8.0, 'F': 16.0}\",\"('C', 'Cu', 'F', 'N')\",OTHERS,False\nmp-1186050,\"{'Na': 3.0, 'Hg': 1.0}\",\"('Hg', 'Na')\",OTHERS,False\nmp-571638,\"{'Rb': 4.0, 'Cu': 2.0, 'Br': 4.0, 'Cl': 4.0}\",\"('Br', 'Cl', 'Cu', 'Rb')\",OTHERS,False\nmp-13361,\"{'O': 8.0, 'P': 2.0, 'Cu': 1.0, 'Cd': 2.0}\",\"('Cd', 'Cu', 'O', 'P')\",OTHERS,False\nmp-1224834,\"{'Ga': 1.0, 'Si': 1.0, 'Ni': 6.0}\",\"('Ga', 'Ni', 'Si')\",OTHERS,False\nmp-861982,\"{'Pa': 1.0, 'Ga': 3.0}\",\"('Ga', 'Pa')\",OTHERS,False\nmp-29987,\"{'Al': 4.0, 'I': 4.0, 'La': 6.0}\",\"('Al', 'I', 'La')\",OTHERS,False\nmp-695492,\"{'Ca': 2.0, 'Fe': 3.0, 'Sb': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Ca', 'Fe', 'O', 'P', 'Sb')\",OTHERS,False\nmp-1174526,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1193030,\"{'Ce': 8.0, 'Co': 2.0, 'S': 14.0}\",\"('Ce', 'Co', 'S')\",OTHERS,False\nmp-677743,\"{'Na': 12.0, 'Ca': 8.0, 'Ta': 12.0, 'Ti': 8.0, 'O': 60.0}\",\"('Ca', 'Na', 'O', 'Ta', 'Ti')\",OTHERS,False\nmvc-10720,\"{'Mg': 1.0, 'Bi': 4.0, 'O': 8.0}\",\"('Bi', 'Mg', 'O')\",OTHERS,False\nmvc-10393,\"{'Ho': 2.0, 'W': 4.0, 'O': 12.0}\",\"('Ho', 'O', 'W')\",OTHERS,False\nmp-1213571,\"{'Cs': 4.0, 'Np': 4.0, 'Mo': 4.0, 'O': 24.0}\",\"('Cs', 'Mo', 'Np', 'O')\",OTHERS,False\nmp-1111173,\"{'K': 3.0, 'In': 1.0, 'Cl': 6.0}\",\"('Cl', 'In', 'K')\",OTHERS,False\nmp-1193370,\"{'Nb': 8.0, 'Cu': 4.0, 'S': 16.0}\",\"('Cu', 'Nb', 'S')\",OTHERS,False\nmp-1174287,\"{'Li': 8.0, 'Mn': 6.0, 'O': 14.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-1222993,\"{'La': 1.0, 'Eu': 1.0, 'Pd': 6.0}\",\"('Eu', 'La', 'Pd')\",OTHERS,False\nmp-768940,\"{'Cr': 8.0, 'S': 12.0, 'O': 48.0}\",\"('Cr', 'O', 'S')\",OTHERS,False\nmp-1245461,\"{'Cd': 8.0, 'Ru': 2.0, 'N': 8.0}\",\"('Cd', 'N', 'Ru')\",OTHERS,False\nmp-758200,\"{'Li': 8.0, 'Ni': 8.0, 'P': 8.0, 'O': 32.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-772973,\"{'Li': 4.0, 'P': 20.0, 'W': 4.0, 'O': 60.0}\",\"('Li', 'O', 'P', 'W')\",OTHERS,False\nmp-780099,\"{'Fe': 8.0, 'O': 4.0, 'F': 12.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-723120,\"{'Ba': 2.0, 'H': 12.0, 'C': 8.0, 'O': 14.0}\",\"('Ba', 'C', 'H', 'O')\",OTHERS,False\nmp-20512,\"{'Fe': 2.0, 'Sn': 2.0}\",\"('Fe', 'Sn')\",OTHERS,False\nmp-1014339,\"{'Cr': 12.0, 'N': 24.0}\",\"('Cr', 'N')\",OTHERS,False\nmp-1223899,\"{'Ho': 1.0, 'Al': 1.0, 'Ni': 4.0}\",\"('Al', 'Ho', 'Ni')\",OTHERS,False\nmp-1105724,\"{'Ba': 4.0, 'Er': 2.0, 'Ga': 2.0, 'Te': 10.0}\",\"('Ba', 'Er', 'Ga', 'Te')\",OTHERS,False\nmp-1207967,\"{'Y': 12.0, 'Cr': 8.0, 'Ga': 12.0, 'O': 48.0}\",\"('Cr', 'Ga', 'O', 'Y')\",OTHERS,False\nmp-14500,\"{'As': 12.0, 'Sr': 2.0, 'Pt': 8.0}\",\"('As', 'Pt', 'Sr')\",OTHERS,False\nmp-1215907,\"{'Y': 1.0, 'Ag': 1.0, 'Te': 2.0}\",\"('Ag', 'Te', 'Y')\",OTHERS,False\nmp-1209388,\"{'Sc': 8.0, 'In': 4.0, 'Au': 8.0}\",\"('Au', 'In', 'Sc')\",OTHERS,False\nmp-761102,\"{'Na': 3.0, 'Mn': 20.0, 'O': 40.0}\",\"('Mn', 'Na', 'O')\",OTHERS,False\nmp-760078,\"{'Ba': 4.0, 'Ti': 11.0, 'O': 26.0}\",\"('Ba', 'O', 'Ti')\",OTHERS,False\nmp-1211068,\"{'Mn': 2.0, 'P': 4.0, 'O': 20.0}\",\"('Mn', 'O', 'P')\",OTHERS,False\nmp-21272,\"{'Ni': 1.0, 'Sb': 1.0, 'Er': 1.0}\",\"('Er', 'Ni', 'Sb')\",OTHERS,False\nmp-1247291,\"{'Mg': 2.0, 'V': 3.0, 'In': 1.0, 'S': 8.0}\",\"('In', 'Mg', 'S', 'V')\",OTHERS,False\nmp-708135,\"{'Y': 1.0, 'H': 16.0, 'C': 4.0, 'N': 9.0, 'O': 16.0}\",\"('C', 'H', 'N', 'O', 'Y')\",OTHERS,False\nmp-623006,\"{'U': 1.0, 'Ga': 5.0, 'Ir': 1.0}\",\"('Ga', 'Ir', 'U')\",OTHERS,False\nmp-973850,\"{'Ho': 5.0, 'Ga': 15.0}\",\"('Ga', 'Ho')\",OTHERS,False\nmp-1200889,\"{'Ti': 8.0, 'Bi': 8.0, 'O': 28.0}\",\"('Bi', 'O', 'Ti')\",OTHERS,False\nmp-23057,\"{'Li': 2.0, 'Br': 4.0, 'Cs': 2.0}\",\"('Br', 'Cs', 'Li')\",OTHERS,False\nmp-7531,\"{'O': 13.0, 'Al': 6.0, 'Ca': 4.0}\",\"('Al', 'Ca', 'O')\",OTHERS,False\nmp-1196701,\"{'Na': 4.0, 'Al': 4.0, 'Si': 6.0, 'O': 28.0}\",\"('Al', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1093886,\"{'Ta': 2.0, 'Mn': 1.0, 'Ru': 1.0}\",\"('Mn', 'Ru', 'Ta')\",OTHERS,False\nmp-1218927,\"{'Sr': 10.0, 'Cu': 5.0, 'Mo': 1.0, 'W': 4.0, 'O': 30.0}\",\"('Cu', 'Mo', 'O', 'Sr', 'W')\",OTHERS,False\nmp-1102537,\"{'Nb': 4.0, 'V': 4.0, 'P': 4.0}\",\"('Nb', 'P', 'V')\",OTHERS,False\nmp-755111,\"{'Li': 1.0, 'V': 4.0, 'O': 1.0, 'F': 11.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-863971,\"{'K': 2.0, 'Mn': 3.0, 'P': 4.0, 'H': 4.0, 'O': 12.0}\",\"('H', 'K', 'Mn', 'O', 'P')\",OTHERS,False\nmp-541338,\"{'Al': 12.0, 'P': 12.0, 'O': 48.0}\",\"('Al', 'O', 'P')\",OTHERS,False\nmp-759426,\"{'Li': 4.0, 'V': 1.0, 'P': 6.0, 'O': 18.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-864807,\"{'Hf': 1.0, 'Zn': 1.0, 'Ni': 2.0}\",\"('Hf', 'Ni', 'Zn')\",OTHERS,False\nmp-1221286,\"{'Na': 3.0, 'Mn': 2.0, 'B': 1.0, 'O': 6.0}\",\"('B', 'Mn', 'Na', 'O')\",OTHERS,False\nmp-1261478,\"{'Al': 4.0, 'Ni': 12.0, 'O': 24.0}\",\"('Al', 'Ni', 'O')\",OTHERS,False\nmp-1181996,{'C': 2.0},\"('C',)\",OTHERS,False\nmp-1218849,\"{'Sr': 2.0, 'Gd': 1.0, 'Cu': 2.0, 'Ir': 1.0, 'O': 8.0}\",\"('Cu', 'Gd', 'Ir', 'O', 'Sr')\",OTHERS,False\nmp-1174059,\"{'Li': 5.0, 'Mn': 1.0, 'Co': 2.0, 'O': 8.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-755159,\"{'Tl': 4.0, 'Ge': 4.0, 'O': 14.0}\",\"('Ge', 'O', 'Tl')\",OTHERS,False\nmp-18819,\"{'O': 7.0, 'P': 2.0, 'Ni': 2.0}\",\"('Ni', 'O', 'P')\",OTHERS,False\nmp-766000,\"{'Li': 1.0, 'V': 1.0, 'Cr': 1.0, 'P': 4.0, 'O': 14.0}\",\"('Cr', 'Li', 'O', 'P', 'V')\",OTHERS,False\nmp-18987,\"{'Li': 2.0, 'O': 14.0, 'P': 4.0, 'Mo': 2.0}\",\"('Li', 'Mo', 'O', 'P')\",OTHERS,False\nmp-755273,\"{'Li': 4.0, 'Fe': 8.0, 'O': 4.0, 'F': 12.0}\",\"('F', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-643923,\"{'Zr': 8.0, 'Co': 4.0, 'H': 20.0}\",\"('Co', 'H', 'Zr')\",OTHERS,False\nmp-1175059,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmvc-4380,\"{'Ca': 4.0, 'Ta': 2.0, 'Ni': 2.0, 'O': 12.0}\",\"('Ca', 'Ni', 'O', 'Ta')\",OTHERS,False\nmp-1181255,\"{'Li': 16.0, 'P': 16.0, 'O': 68.0}\",\"('Li', 'O', 'P')\",OTHERS,False\nmp-753794,\"{'Hf': 4.0, 'P': 4.0, 'O': 18.0}\",\"('Hf', 'O', 'P')\",OTHERS,False\nmp-559287,\"{'Ho': 4.0, 'Sn': 6.0, 'Pb': 6.0, 'S': 24.0}\",\"('Ho', 'Pb', 'S', 'Sn')\",OTHERS,False\nmp-761140,\"{'Na': 4.0, 'Ti': 7.0, 'O': 16.0}\",\"('Na', 'O', 'Ti')\",OTHERS,False\nmp-622086,\"{'La': 8.0, 'Ge': 4.0, 'S': 20.0}\",\"('Ge', 'La', 'S')\",OTHERS,False\nmvc-5715,\"{'Mg': 4.0, 'Cu': 2.0, 'Ir': 2.0, 'O': 12.0}\",\"('Cu', 'Ir', 'Mg', 'O')\",OTHERS,False\nmp-18084,\"{'O': 20.0, 'Zn': 4.0, 'Ba': 4.0, 'Tb': 8.0}\",\"('Ba', 'O', 'Tb', 'Zn')\",OTHERS,False\nmp-1208803,\"{'Sr': 2.0, 'La': 1.0, 'Cu': 2.0, 'Hg': 1.0, 'O': 6.0}\",\"('Cu', 'Hg', 'La', 'O', 'Sr')\",OTHERS,False\nmp-758428,\"{'Ti': 6.0, 'Nb': 2.0, 'O': 16.0}\",\"('Nb', 'O', 'Ti')\",OTHERS,False\nmp-1101460,\"{'Nb': 4.0, 'Cr': 4.0, 'O': 16.0}\",\"('Cr', 'Nb', 'O')\",OTHERS,False\nmp-1177320,\"{'Li': 8.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-625762,\"{'Al': 8.0, 'H': 24.0, 'O': 24.0}\",\"('Al', 'H', 'O')\",OTHERS,False\nmp-1207411,\"{'Zr': 12.0, 'Ga': 4.0, 'Ni': 2.0, 'H': 20.0}\",\"('Ga', 'H', 'Ni', 'Zr')\",OTHERS,False\nmp-1183475,\"{'Ca': 2.0, 'Br': 2.0}\",\"('Br', 'Ca')\",OTHERS,False\nmp-979751,\"{'Ta': 1.0, 'Ti': 1.0, 'Fe': 2.0}\",\"('Fe', 'Ta', 'Ti')\",OTHERS,False\nmp-1222066,\"{'Mg': 1.0, 'Ti': 4.0, 'Mn': 1.0, 'Zn': 1.0, 'Ni': 1.0, 'O': 12.0}\",\"('Mg', 'Mn', 'Ni', 'O', 'Ti', 'Zn')\",OTHERS,False\nmp-1214618,\"{'Ba': 6.0, 'Cd': 2.0, 'Ir': 4.0, 'O': 18.0}\",\"('Ba', 'Cd', 'Ir', 'O')\",OTHERS,False\nmp-1227456,\"{'Ca': 10.0, 'Ge': 6.0, 'F': 1.0}\",\"('Ca', 'F', 'Ge')\",OTHERS,False\nmp-1246475,\"{'Ga': 4.0, 'Fe': 4.0, 'N': 8.0}\",\"('Fe', 'Ga', 'N')\",OTHERS,False\nmp-20745,{'Pb': 2.0},\"('Pb',)\",OTHERS,False\nmp-1185981,\"{'Mn': 2.0, 'Ir': 1.0, 'Rh': 1.0}\",\"('Ir', 'Mn', 'Rh')\",OTHERS,False\nmp-28274,\"{'F': 24.0, 'In': 4.0, 'Ba': 6.0}\",\"('Ba', 'F', 'In')\",OTHERS,False\nmp-1216875,\"{'Tm': 2.0, 'Cu': 6.0, 'Te': 6.0}\",\"('Cu', 'Te', 'Tm')\",OTHERS,False\nmp-755620,\"{'Mn': 1.0, 'Co': 5.0, 'O': 12.0}\",\"('Co', 'Mn', 'O')\",OTHERS,False\nmp-540469,\"{'Li': 2.0, 'Co': 3.0, 'P': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'O', 'P')\",OTHERS,False\nmp-20725,\"{'Pb': 2.0, 'O': 4.0}\",\"('O', 'Pb')\",OTHERS,False\nmp-40374,\"{'Na': 6.0, 'Ti': 1.0, 'Mn': 1.0, 'Si': 6.0, 'O': 18.0}\",\"('Mn', 'Na', 'O', 'Si', 'Ti')\",OTHERS,False\nmp-1080096,\"{'Pt': 1.0, 'S': 6.0, 'N': 2.0}\",\"('N', 'Pt', 'S')\",OTHERS,False\nmp-1208658,\"{'Ta': 2.0, 'P': 8.0, 'S': 20.0, 'Cl': 10.0}\",\"('Cl', 'P', 'S', 'Ta')\",OTHERS,False\nmp-8013,\"{'F': 6.0, 'K': 1.0, 'Ru': 1.0}\",\"('F', 'K', 'Ru')\",OTHERS,False\nmp-21879,\"{'O': 6.0, 'Ba': 2.0, 'Ce': 1.0, 'Pt': 1.0}\",\"('Ba', 'Ce', 'O', 'Pt')\",OTHERS,False\nmp-1225453,\"{'Er': 14.0, 'Ag': 51.0}\",\"('Ag', 'Er')\",OTHERS,False\nmp-1217994,\"{'Ta': 2.0, 'Zn': 1.0, 'S': 4.0}\",\"('S', 'Ta', 'Zn')\",OTHERS,False\nmp-1246036,\"{'Ba': 4.0, 'Sn': 4.0, 'N': 8.0}\",\"('Ba', 'N', 'Sn')\",OTHERS,False\nmp-1199458,\"{'Co': 12.0, 'B': 6.0, 'P': 24.0, 'H': 12.0, 'Pb': 24.0, 'Cl': 6.0, 'O': 108.0}\",\"('B', 'Cl', 'Co', 'H', 'O', 'P', 'Pb')\",OTHERS,False\nmp-978960,\"{'Sm': 3.0, 'Tm': 1.0}\",\"('Sm', 'Tm')\",OTHERS,False\nmp-1029059,\"{'Te': 4.0, 'Mo': 1.0, 'W': 3.0, 'Se': 2.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-626785,\"{'K': 2.0, 'H': 2.0, 'O': 2.0}\",\"('H', 'K', 'O')\",OTHERS,False\nmp-1183547,\"{'Ca': 1.0, 'Sb': 1.0, 'Pd': 2.0}\",\"('Ca', 'Pd', 'Sb')\",OTHERS,False\nmp-7074,\"{'O': 12.0, 'Na': 2.0, 'Si': 4.0, 'Sc': 2.0}\",\"('Na', 'O', 'Sc', 'Si')\",OTHERS,False\nmp-1213477,\"{'Cs': 4.0, 'Hf': 24.0, 'C': 4.0, 'Cl': 60.0}\",\"('C', 'Cl', 'Cs', 'Hf')\",OTHERS,False\nmp-651051,\"{'Te': 8.0, 'W': 12.0, 'C': 60.0, 'O': 60.0}\",\"('C', 'O', 'Te', 'W')\",OTHERS,False\nmp-24142,\"{'H': 8.0, 'O': 4.0, 'F': 10.0, 'Mg': 2.0, 'Al': 2.0}\",\"('Al', 'F', 'H', 'Mg', 'O')\",OTHERS,False\nmp-772808,\"{'K': 4.0, 'Bi': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('Bi', 'C', 'K', 'O', 'P')\",OTHERS,False\nmp-1191331,\"{'Ti': 16.0, 'Co': 8.0}\",\"('Co', 'Ti')\",OTHERS,False\nmp-698397,\"{'Sn': 1.0, 'H': 24.0, 'C': 6.0, 'N': 2.0, 'O': 2.0, 'F': 6.0}\",\"('C', 'F', 'H', 'N', 'O', 'Sn')\",OTHERS,False\nmp-780421,\"{'La': 6.0, 'Al': 6.0, 'O': 18.0}\",\"('Al', 'La', 'O')\",OTHERS,False\nmp-1210288,\"{'Na': 6.0, 'Mo': 2.0, 'O': 8.0}\",\"('Mo', 'Na', 'O')\",OTHERS,False\nmp-531773,\"{'Li': 28.0, 'Mg': 22.0, 'N': 25.0}\",\"('Li', 'Mg', 'N')\",OTHERS,False\nmp-1040443,\"{'K': 2.0, 'Ba': 2.0, 'Bi': 2.0, 'Te': 2.0, 'O': 12.0}\",\"('Ba', 'Bi', 'K', 'O', 'Te')\",OTHERS,False\nmp-625175,\"{'H': 12.0, 'Cl': 4.0, 'O': 20.0}\",\"('Cl', 'H', 'O')\",OTHERS,False\nmp-1215110,\"{'Co': 4.0, 'Mo': 12.0, 'O': 72.0}\",\"('Co', 'Mo', 'O')\",OTHERS,False\nmp-769122,\"{'Li': 8.0, 'Sb': 4.0, 'H': 12.0, 'O': 6.0, 'F': 20.0}\",\"('F', 'H', 'Li', 'O', 'Sb')\",OTHERS,False\nmp-867753,\"{'Cu': 1.0, 'Sb': 1.0, 'Rh': 2.0}\",\"('Cu', 'Rh', 'Sb')\",OTHERS,False\nmp-1200385,\"{'Eu': 12.0, 'Sb': 16.0, 'Se': 36.0}\",\"('Eu', 'Sb', 'Se')\",OTHERS,False\nmp-733877,\"{'Ca': 4.0, 'B': 12.0, 'H': 52.0, 'O': 48.0}\",\"('B', 'Ca', 'H', 'O')\",OTHERS,False\nmp-1211998,\"{'La': 2.0, 'In': 4.0, 'Cu': 18.0}\",\"('Cu', 'In', 'La')\",OTHERS,False\nmp-1188828,\"{'Ba': 4.0, 'Br': 8.0, 'O': 4.0}\",\"('Ba', 'Br', 'O')\",OTHERS,False\nmp-1200777,\"{'Er': 10.0, 'Ge': 20.0, 'Rh': 8.0}\",\"('Er', 'Ge', 'Rh')\",OTHERS,False\nmp-1195619,\"{'Pb': 16.0, 'N': 8.0, 'O': 40.0}\",\"('N', 'O', 'Pb')\",OTHERS,False\nmp-784631,\"{'Cr': 1.0, 'Ni': 2.0}\",\"('Cr', 'Ni')\",OTHERS,False\nmp-849556,\"{'Li': 6.0, 'Mn': 9.0, 'Si': 6.0, 'O': 24.0}\",\"('Li', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1210612,\"{'P': 4.0, 'N': 8.0, 'O': 16.0}\",\"('N', 'O', 'P')\",OTHERS,False\nmp-13610,\"{'F': 6.0, 'Zn': 1.0, 'Pb': 1.0}\",\"('F', 'Pb', 'Zn')\",OTHERS,False\nmp-776594,\"{'Mn': 8.0, 'O': 13.0, 'F': 3.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-1120780,\"{'Au': 4.0, 'Cl': 4.0}\",\"('Au', 'Cl')\",OTHERS,False\nmp-549,\"{'Sc': 1.0, 'Sb': 1.0}\",\"('Sb', 'Sc')\",OTHERS,False\nmp-1184898,\"{'K': 3.0, 'Ga': 1.0}\",\"('Ga', 'K')\",OTHERS,False\nmp-1205959,\"{'Lu': 3.0, 'Mg': 3.0, 'Tl': 3.0}\",\"('Lu', 'Mg', 'Tl')\",OTHERS,False\nmp-555515,\"{'Cu': 2.0, 'Ag': 8.0, 'Te': 2.0, 'O': 12.0}\",\"('Ag', 'Cu', 'O', 'Te')\",OTHERS,False\nmp-1232078,\"{'Er': 8.0, 'Mg': 4.0, 'Se': 16.0}\",\"('Er', 'Mg', 'Se')\",OTHERS,False\nmp-766058,\"{'Na': 2.0, 'Fe': 16.0, 'O': 24.0}\",\"('Fe', 'Na', 'O')\",OTHERS,False\nmp-1096055,\"{'Y': 2.0, 'Zn': 1.0, 'Pd': 1.0}\",\"('Pd', 'Y', 'Zn')\",OTHERS,False\nmp-1206018,\"{'Mn': 2.0, 'Ge': 2.0, 'As': 4.0}\",\"('As', 'Ge', 'Mn')\",OTHERS,False\nmvc-649,\"{'Ba': 2.0, 'Y': 1.0, 'Sn': 3.0, 'O': 8.0}\",\"('Ba', 'O', 'Sn', 'Y')\",OTHERS,False\nmp-1215982,\"{'Yb': 3.0, 'Fe': 3.0, 'S': 8.0}\",\"('Fe', 'S', 'Yb')\",OTHERS,False\nmp-561529,\"{'Eu': 8.0, 'K': 4.0, 'Si': 16.0, 'O': 40.0, 'F': 4.0}\",\"('Eu', 'F', 'K', 'O', 'Si')\",OTHERS,False\nmp-1183203,\"{'Al': 1.0, 'Ge': 3.0}\",\"('Al', 'Ge')\",OTHERS,False\nmp-1147737,\"{'Li': 4.0, 'Zn': 1.0, 'P': 2.0, 'S': 8.0}\",\"('Li', 'P', 'S', 'Zn')\",OTHERS,False\nmp-29391,\"{'Sb': 4.0, 'Cl': 12.0, 'F': 8.0}\",\"('Cl', 'F', 'Sb')\",OTHERS,False\nmp-762632,\"{'Li': 2.0, 'Fe': 2.0, 'Si': 2.0, 'O': 8.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1245632,\"{'Re': 2.0, 'Ni': 2.0, 'N': 4.0}\",\"('N', 'Ni', 'Re')\",OTHERS,False\nmp-1175401,\"{'Li': 9.0, 'Co': 7.0, 'O': 16.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmvc-4436,\"{'Zn': 12.0, 'Co': 8.0, 'Ge': 12.0, 'O': 48.0}\",\"('Co', 'Ge', 'O', 'Zn')\",OTHERS,False\nmp-18473,\"{'O': 16.0, 'Rb': 2.0, 'Mo': 4.0, 'La': 2.0}\",\"('La', 'Mo', 'O', 'Rb')\",OTHERS,False\nmp-1215594,\"{'Yb': 1.0, 'Gd': 3.0, 'F': 12.0}\",\"('F', 'Gd', 'Yb')\",OTHERS,False\nmp-758890,\"{'Li': 2.0, 'Ag': 4.0, 'F': 8.0}\",\"('Ag', 'F', 'Li')\",OTHERS,False\nmp-764009,\"{'Mn': 6.0, 'O': 7.0, 'F': 5.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-20771,\"{'Zn': 2.0, 'Ge': 2.0, 'Eu': 1.0}\",\"('Eu', 'Ge', 'Zn')\",OTHERS,False\nmp-17138,\"{'O': 24.0, 'P': 4.0, 'K': 4.0, 'Mo': 4.0}\",\"('K', 'Mo', 'O', 'P')\",OTHERS,False\nmp-1097593,\"{'Cu': 1.0, 'Sb': 1.0, 'Pd': 2.0}\",\"('Cu', 'Pd', 'Sb')\",OTHERS,False\nmp-1184788,\"{'Ge': 2.0, 'Rh': 6.0}\",\"('Ge', 'Rh')\",OTHERS,False\nmp-776410,\"{'Mg': 8.0, 'N': 16.0, 'O': 48.0}\",\"('Mg', 'N', 'O')\",OTHERS,False\nmp-5077,\"{'Li': 2.0, 'Na': 1.0, 'Sb': 1.0}\",\"('Li', 'Na', 'Sb')\",OTHERS,False\nmp-1112164,\"{'K': 2.0, 'Er': 1.0, 'Cu': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cu', 'Er', 'K')\",OTHERS,False\nmp-978534,\"{'Si': 2.0, 'Ge': 2.0}\",\"('Ge', 'Si')\",OTHERS,False\nmp-758776,\"{'Li': 6.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1223668,\"{'K': 1.0, 'Mn': 1.0, 'Sn': 3.0, 'O': 8.0}\",\"('K', 'Mn', 'O', 'Sn')\",OTHERS,False\nmp-1204107,\"{'Al': 8.0, 'Fe': 4.0, 'Si': 10.0, 'O': 36.0}\",\"('Al', 'Fe', 'O', 'Si')\",OTHERS,False\nmp-1228111,\"{'Ba': 1.0, 'Nb': 2.0, 'Fe': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Ba', 'Fe', 'Nb', 'O', 'P')\",OTHERS,False\nmp-1222388,\"{'Li': 1.0, 'Mo': 6.0, 'Se': 6.0, 'S': 2.0}\",\"('Li', 'Mo', 'S', 'Se')\",OTHERS,False\nmp-778867,\"{'Li': 9.0, 'Mn': 12.0, 'B': 12.0, 'O': 36.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-557961,\"{'Sb': 6.0, 'C': 6.0, 'N': 6.0, 'Cl': 24.0, 'O': 6.0}\",\"('C', 'Cl', 'N', 'O', 'Sb')\",OTHERS,False\nmp-1079874,\"{'Er': 3.0, 'Sn': 3.0, 'Pd': 3.0}\",\"('Er', 'Pd', 'Sn')\",OTHERS,False\nmp-1199988,\"{'Al': 2.0, 'Pb': 6.0, 'S': 2.0, 'O': 22.0}\",\"('Al', 'O', 'Pb', 'S')\",OTHERS,False\nmp-1097485,\"{'Ca': 1.0, 'La': 1.0, 'Au': 2.0}\",\"('Au', 'Ca', 'La')\",OTHERS,False\nmp-27642,\"{'Br': 20.0, 'Pa': 4.0}\",\"('Br', 'Pa')\",OTHERS,False\nmvc-3734,\"{'Zn': 4.0, 'Cr': 4.0, 'F': 20.0}\",\"('Cr', 'F', 'Zn')\",OTHERS,False\nmp-730585,\"{'Li': 8.0, 'B': 8.0, 'H': 16.0, 'O': 8.0, 'F': 32.0}\",\"('B', 'F', 'H', 'Li', 'O')\",OTHERS,False\nmp-1094069,\"{'Sr': 2.0, 'Cd': 2.0, 'Cu': 1.0, 'S': 2.0, 'O': 2.0}\",\"('Cd', 'Cu', 'O', 'S', 'Sr')\",OTHERS,False\nmp-759591,\"{'Sr': 20.0, 'V': 15.0, 'O': 49.0}\",\"('O', 'Sr', 'V')\",OTHERS,False\nmp-865430,\"{'Y': 1.0, 'Te': 1.0}\",\"('Te', 'Y')\",OTHERS,False\nmp-1178550,\"{'Al': 4.0, 'In': 4.0, 'O': 12.0}\",\"('Al', 'In', 'O')\",OTHERS,False\nmp-1214951,\"{'Al': 2.0, 'Zn': 20.0, 'B': 16.0, 'Rh': 36.0}\",\"('Al', 'B', 'Rh', 'Zn')\",OTHERS,False\nmp-774064,\"{'Li': 2.0, 'Sb': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Li', 'O', 'P', 'Sb')\",OTHERS,False\nmp-1228974,\"{'Al': 4.0, 'V': 4.0, 'Co': 4.0}\",\"('Al', 'Co', 'V')\",OTHERS,False\nmp-770431,\"{'Ho': 8.0, 'Se': 12.0, 'O': 48.0}\",\"('Ho', 'O', 'Se')\",OTHERS,False\nmp-1194267,\"{'Cu': 6.0, 'Te': 2.0, 'Pb': 2.0, 'O': 16.0}\",\"('Cu', 'O', 'Pb', 'Te')\",OTHERS,False\nmp-849741,\"{'Ni': 8.0, 'P': 24.0, 'O': 72.0}\",\"('Ni', 'O', 'P')\",OTHERS,False\nmp-1193985,\"{'Ta': 2.0, 'Co': 21.0, 'B': 6.0}\",\"('B', 'Co', 'Ta')\",OTHERS,False\nmp-1019738,\"{'Eu': 8.0, 'Ba': 4.0, 'O': 16.0}\",\"('Ba', 'Eu', 'O')\",OTHERS,False\nmp-1208033,\"{'Tl': 2.0, 'Pt': 6.0, 'O': 8.0}\",\"('O', 'Pt', 'Tl')\",OTHERS,False\nmp-677716,\"{'Rb': 4.0, 'Al': 12.0, 'Cd': 4.0, 'Si': 12.0, 'O': 48.0}\",\"('Al', 'Cd', 'O', 'Rb', 'Si')\",OTHERS,False\nmp-1224061,\"{'In': 2.0, 'Te': 1.0, 'As': 1.0}\",\"('As', 'In', 'Te')\",OTHERS,False\nmp-1215182,\"{'Zr': 1.0, 'Ti': 1.0, 'C': 2.0}\",\"('C', 'Ti', 'Zr')\",OTHERS,False\nmp-1223990,\"{'In': 4.0, 'Bi': 1.0}\",\"('Bi', 'In')\",OTHERS,False\nmp-690672,\"{'K': 6.0, 'Na': 2.0, 'Fe': 2.0, 'Cl': 12.0}\",\"('Cl', 'Fe', 'K', 'Na')\",OTHERS,False\nmp-1222492,\"{'Li': 3.0, 'Er': 1.0, 'Br': 6.0}\",\"('Br', 'Er', 'Li')\",OTHERS,False\nmp-1227726,\"{'Ca': 8.0, 'Mn': 4.0, 'Al': 4.0, 'O': 22.0}\",\"('Al', 'Ca', 'Mn', 'O')\",OTHERS,False\nmp-7818,\"{'Se': 2.0, 'Pd': 8.0}\",\"('Pd', 'Se')\",OTHERS,False\nmp-6563,\"{'Al': 36.0, 'Si': 24.0, 'Fe': 16.0, 'Tb': 4.0}\",\"('Al', 'Fe', 'Si', 'Tb')\",OTHERS,False\nmp-1181047,\"{'Ho': 10.0, 'Sb': 2.0, 'Au': 4.0}\",\"('Au', 'Ho', 'Sb')\",OTHERS,False\nmp-733612,\"{'Rb': 4.0, 'H': 12.0, 'S': 8.0, 'O': 32.0}\",\"('H', 'O', 'Rb', 'S')\",OTHERS,False\nmp-1203791,\"{'B': 8.0, 'P': 4.0, 'H': 80.0, 'C': 20.0, 'N': 4.0}\",\"('B', 'C', 'H', 'N', 'P')\",OTHERS,False\nmp-6782,\"{'Li': 2.0, 'O': 6.0, 'Zr': 1.0, 'Te': 1.0}\",\"('Li', 'O', 'Te', 'Zr')\",OTHERS,False\nmp-865274,\"{'Tl': 8.0, 'In': 8.0, 'S': 16.0}\",\"('In', 'S', 'Tl')\",OTHERS,False\nmp-1185024,\"{'La': 1.0, 'Ag': 3.0}\",\"('Ag', 'La')\",OTHERS,False\nmp-1211967,\"{'K': 4.0, 'Sr': 24.0, 'Sc': 4.0, 'Si': 16.0, 'O': 64.0}\",\"('K', 'O', 'Sc', 'Si', 'Sr')\",OTHERS,False\nmp-24013,\"{'H': 144.0, 'N': 48.0, 'Cl': 40.0, 'Cr': 8.0, 'Cu': 8.0}\",\"('Cl', 'Cr', 'Cu', 'H', 'N')\",OTHERS,False\nmp-30053,\"{'C': 2.0, 'N': 2.0, 'Na': 2.0}\",\"('C', 'N', 'Na')\",OTHERS,False\nmp-1401,\"{'Zn': 4.0, 'Zr': 2.0}\",\"('Zn', 'Zr')\",OTHERS,False\nmp-1185522,\"{'Lu': 6.0, 'Tl': 2.0}\",\"('Lu', 'Tl')\",OTHERS,False\nmp-697924,\"{'Mg': 1.0, 'Co': 1.0, 'H': 16.0, 'Cl': 4.0, 'O': 8.0}\",\"('Cl', 'Co', 'H', 'Mg', 'O')\",OTHERS,False\nmp-1213915,\"{'Ce': 6.0, 'Zn': 6.0, 'Co': 12.0}\",\"('Ce', 'Co', 'Zn')\",OTHERS,False\nmp-1074264,\"{'Mg': 8.0, 'Si': 6.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1211564,\"{'Li': 4.0, 'Er': 16.0, 'Ge': 16.0}\",\"('Er', 'Ge', 'Li')\",OTHERS,False\nmp-30557,\"{'Co': 9.0, 'Ga': 6.0, 'Y': 3.0}\",\"('Co', 'Ga', 'Y')\",OTHERS,False\nmp-978522,\"{'Sm': 1.0, 'Y': 1.0, 'Ir': 2.0}\",\"('Ir', 'Sm', 'Y')\",OTHERS,False\nmp-755210,\"{'Y': 4.0, 'Cu': 2.0, 'O': 7.0}\",\"('Cu', 'O', 'Y')\",OTHERS,False\nmp-1228930,\"{'Ba': 6.0, 'La': 2.0, 'V': 6.0, 'O': 24.0}\",\"('Ba', 'La', 'O', 'V')\",OTHERS,False\nmp-1238874,\"{'Ti': 4.0, 'Cr': 4.0, 'Ag': 4.0, 'S': 16.0}\",\"('Ag', 'Cr', 'S', 'Ti')\",OTHERS,False\nmp-1223292,\"{'La': 2.0, 'Cu': 5.0, 'Ni': 5.0}\",\"('Cu', 'La', 'Ni')\",OTHERS,False\nmp-672689,\"{'Hf': 6.0, 'Co': 16.0, 'Si': 7.0}\",\"('Co', 'Hf', 'Si')\",OTHERS,False\nmp-781060,\"{'Mn': 8.0, 'O': 4.0, 'F': 12.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-12024,\"{'V': 4.0, 'Se': 8.0, 'Rb': 4.0}\",\"('Rb', 'Se', 'V')\",OTHERS,False\nmp-1206169,\"{'Sr': 2.0, 'Cu': 2.0, 'Si': 2.0}\",\"('Cu', 'Si', 'Sr')\",OTHERS,False\nmp-1219552,\"{'Rb': 2.0, 'Ti': 6.0, 'Nb': 2.0, 'O': 18.0}\",\"('Nb', 'O', 'Rb', 'Ti')\",OTHERS,False\nmp-685632,\"{'Ba': 7.0, 'U': 7.0, 'O': 24.0}\",\"('Ba', 'O', 'U')\",OTHERS,False\nmp-1190037,\"{'Nb': 6.0, 'Fe': 2.0, 'Se': 12.0}\",\"('Fe', 'Nb', 'Se')\",OTHERS,False\nmp-754634,\"{'Li': 6.0, 'W': 1.0, 'O': 6.0}\",\"('Li', 'O', 'W')\",OTHERS,False\nmp-1029236,\"{'Te': 6.0, 'Mo': 2.0, 'W': 2.0, 'Se': 2.0}\",\"('Mo', 'Se', 'Te', 'W')\",OTHERS,False\nmp-631405,\"{'Zr': 1.0, 'Tc': 1.0, 'Cl': 1.0}\",\"('Cl', 'Tc', 'Zr')\",OTHERS,False\nmp-1181340,\"{'Fe': 6.0, 'O': 8.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-1187300,\"{'Tb': 3.0, 'Pm': 1.0}\",\"('Pm', 'Tb')\",OTHERS,False\nmp-1245945,\"{'K': 2.0, 'Sn': 1.0, 'N': 2.0}\",\"('K', 'N', 'Sn')\",OTHERS,False\nmp-1201836,\"{'Ti': 20.0, 'As': 12.0}\",\"('As', 'Ti')\",OTHERS,False\nmp-642651,\"{'Yb': 8.0, 'Ge': 16.0, 'Ru': 12.0}\",\"('Ge', 'Ru', 'Yb')\",OTHERS,False\nmp-625465,\"{'Tm': 2.0, 'H': 6.0, 'O': 6.0}\",\"('H', 'O', 'Tm')\",OTHERS,False\nmp-30364,\"{'Ba': 1.0, 'Au': 5.0}\",\"('Au', 'Ba')\",OTHERS,False\nmp-1074924,\"{'Mg': 6.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-865329,\"{'Lu': 2.0, 'Mg': 1.0, 'Tc': 1.0}\",\"('Lu', 'Mg', 'Tc')\",OTHERS,False\nmp-759096,\"{'Na': 7.0, 'W': 12.0, 'O': 36.0}\",\"('Na', 'O', 'W')\",OTHERS,False\nmp-1207492,\"{'Zn': 3.0, 'P': 2.0, 'H': 4.0, 'O': 8.0}\",\"('H', 'O', 'P', 'Zn')\",OTHERS,False\nmp-867669,\"{'Li': 8.0, 'V': 2.0, 'F': 12.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-772826,\"{'K': 8.0, 'Li': 4.0, 'Ti': 8.0, 'As': 8.0, 'O': 40.0}\",\"('As', 'K', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-1207673,\"{'Yb': 8.0, 'Sn': 20.0, 'Pd': 12.0}\",\"('Pd', 'Sn', 'Yb')\",OTHERS,False\nmp-862331,\"{'La': 2.0, 'Cu': 1.0, 'Ru': 1.0}\",\"('Cu', 'La', 'Ru')\",OTHERS,False\nmp-1096982,\"{'H': 2.0, 'C': 2.0, 'S': 1.0}\",\"('C', 'H', 'S')\",OTHERS,False\nmp-867815,\"{'Ac': 2.0, 'Ga': 6.0}\",\"('Ac', 'Ga')\",OTHERS,False\nmp-1184248,\"{'Er': 1.0, 'Tm': 1.0, 'Pd': 2.0}\",\"('Er', 'Pd', 'Tm')\",OTHERS,False\nmp-23130,\"{'O': 2.0, 'Cl': 2.0, 'Co': 1.0, 'Sr': 2.0}\",\"('Cl', 'Co', 'O', 'Sr')\",OTHERS,False\nmvc-277,\"{'Sr': 4.0, 'Cu': 4.0, 'Sn': 2.0, 'O': 14.0}\",\"('Cu', 'O', 'Sn', 'Sr')\",OTHERS,False\nmp-683792,\"{'Fe': 8.0, 'Os': 4.0, 'C': 48.0, 'O': 48.0}\",\"('C', 'Fe', 'O', 'Os')\",OTHERS,False\nmp-557145,\"{'Cr': 4.0, 'Sb': 4.0, 'F': 40.0}\",\"('Cr', 'F', 'Sb')\",OTHERS,False\nmp-1212754,\"{'Gd': 2.0, 'Cl': 6.0, 'O': 24.0}\",\"('Cl', 'Gd', 'O')\",OTHERS,False\nmp-1211805,\"{'Pr': 16.0, 'Sb': 28.0, 'Rh': 34.0}\",\"('Pr', 'Rh', 'Sb')\",OTHERS,False\nmp-510709,\"{'Sr': 8.0, 'As': 8.0, 'O': 40.0, 'H': 24.0}\",\"('As', 'H', 'O', 'Sr')\",OTHERS,False\nmp-18493,\"{'O': 24.0, 'Na': 8.0, 'P': 6.0, 'Fe': 4.0}\",\"('Fe', 'Na', 'O', 'P')\",OTHERS,False\nmp-684000,\"{'Eu': 8.0, 'Si': 12.0, 'Ni': 32.0}\",\"('Eu', 'Ni', 'Si')\",OTHERS,False\nmp-1352,\"{'N': 1.0, 'Zr': 1.0}\",\"('N', 'Zr')\",OTHERS,False\nmp-1187999,\"{'Yb': 6.0, 'Lu': 2.0}\",\"('Lu', 'Yb')\",OTHERS,False\nmp-1190145,\"{'Na': 4.0, 'In': 4.0, 'Ge': 2.0, 'S': 12.0}\",\"('Ge', 'In', 'Na', 'S')\",OTHERS,False\nmp-24366,\"{'K': 4.0, 'Zn': 2.0, 'H': 24.0, 'S': 4.0, 'O': 28.0}\",\"('H', 'K', 'O', 'S', 'Zn')\",OTHERS,False\nmp-1025266,\"{'Tb': 2.0, 'Cr': 2.0, 'C': 3.0}\",\"('C', 'Cr', 'Tb')\",OTHERS,False\nmp-9529,\"{'Ge': 4.0, 'Rh': 4.0, 'Dy': 4.0}\",\"('Dy', 'Ge', 'Rh')\",OTHERS,False\nmp-754050,\"{'Mn': 2.0, 'O': 3.0, 'F': 1.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-1036237,\"{'Mg': 14.0, 'Mn': 1.0, 'Fe': 1.0, 'O': 16.0}\",\"('Fe', 'Mg', 'Mn', 'O')\",OTHERS,False\nmp-1211532,\"{'K': 4.0, 'Tm': 4.0, 'Mo': 8.0, 'O': 32.0}\",\"('K', 'Mo', 'O', 'Tm')\",OTHERS,False\nmp-556939,\"{'Np': 2.0, 'I': 2.0, 'O': 2.0}\",\"('I', 'Np', 'O')\",OTHERS,False\nmp-978543,\"{'Sm': 1.0, 'Er': 1.0, 'Tl': 2.0}\",\"('Er', 'Sm', 'Tl')\",OTHERS,False\nmp-1182571,\"{'Ca': 8.0, 'Be': 16.0, 'P': 12.0, 'O': 80.0}\",\"('Be', 'Ca', 'O', 'P')\",OTHERS,False\nmp-1173794,\"{'Na': 18.0, 'O': 3.0}\",\"('Na', 'O')\",OTHERS,False\nmp-961711,\"{'Zr': 1.0, 'Si': 1.0, 'Pt': 1.0}\",\"('Pt', 'Si', 'Zr')\",OTHERS,False\nmp-1211960,\"{'K': 4.0, 'Ge': 4.0, 'O': 6.0}\",\"('Ge', 'K', 'O')\",OTHERS,False\nmp-36539,\"{'Bi': 2.0, 'K': 2.0, 'Se': 4.0}\",\"('Bi', 'K', 'Se')\",OTHERS,False\nmp-20231,\"{'Pd': 4.0, 'In': 2.0, 'Ce': 2.0}\",\"('Ce', 'In', 'Pd')\",OTHERS,False\nmp-1183867,\"{'Ce': 2.0, 'Cd': 1.0, 'In': 1.0}\",\"('Cd', 'Ce', 'In')\",OTHERS,False\nmp-1096521,\"{'Al': 2.0, 'Fe': 1.0, 'Cu': 1.0}\",\"('Al', 'Cu', 'Fe')\",OTHERS,False\nmp-999576,\"{'Mn': 2.0, 'Si': 1.0, 'Ru': 1.0}\",\"('Mn', 'Ru', 'Si')\",OTHERS,False\nmp-1209037,\"{'Sr': 8.0, 'Cr': 8.0, 'F': 40.0}\",\"('Cr', 'F', 'Sr')\",OTHERS,False\nmp-8766,\"{'S': 4.0, 'Co': 2.0, 'Rb': 4.0}\",\"('Co', 'Rb', 'S')\",OTHERS,False\nmp-1191014,\"{'Ce': 12.0, 'Ni': 4.0, 'Ge': 8.0}\",\"('Ce', 'Ge', 'Ni')\",OTHERS,False\nmp-755271,\"{'Li': 3.0, 'Ni': 3.0, 'B': 3.0, 'O': 9.0}\",\"('B', 'Li', 'Ni', 'O')\",OTHERS,False\nmp-978,\"{'Sr': 8.0, 'Sn': 4.0}\",\"('Sn', 'Sr')\",OTHERS,False\nmp-867908,\"{'La': 1.0, 'Bi': 1.0, 'Au': 2.0}\",\"('Au', 'Bi', 'La')\",OTHERS,False\nmp-8895,\"{'B': 4.0, 'Ca': 2.0, 'Rh': 5.0}\",\"('B', 'Ca', 'Rh')\",OTHERS,False\nmp-753482,\"{'Li': 2.0, 'Mn': 1.0, 'Co': 1.0, 'O': 4.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-759117,\"{'Li': 8.0, 'Sb': 4.0, 'P': 12.0, 'O': 40.0}\",\"('Li', 'O', 'P', 'Sb')\",OTHERS,False\nmp-1248957,\"{'Si': 32.0, 'O': 64.0}\",\"('O', 'Si')\",OTHERS,False\nmp-1205991,\"{'La': 1.0, 'Bi': 2.0, 'Cl': 1.0, 'O': 4.0}\",\"('Bi', 'Cl', 'La', 'O')\",OTHERS,False\nmp-863250,\"{'Ta': 1.0, 'Mn': 2.0, 'Ge': 1.0}\",\"('Ge', 'Mn', 'Ta')\",OTHERS,False\nmp-510085,\"{'Er': 8.0, 'Ti': 12.0, 'Si': 16.0}\",\"('Er', 'Si', 'Ti')\",OTHERS,False\nmp-1209592,\"{'P': 8.0, 'O': 24.0}\",\"('O', 'P')\",OTHERS,False\nmp-1077164,\"{'Li': 1.0, 'Ti': 1.0, 'S': 2.0, 'O': 2.0}\",\"('Li', 'O', 'S', 'Ti')\",OTHERS,False\nmp-1246108,\"{'Mn': 10.0, 'Zn': 1.0, 'N': 8.0}\",\"('Mn', 'N', 'Zn')\",OTHERS,False\nmp-1203960,\"{'U': 4.0, 'H': 24.0, 'Br': 8.0, 'O': 20.0}\",\"('Br', 'H', 'O', 'U')\",OTHERS,False\nmp-763558,\"{'Li': 1.0, 'V': 2.0, 'C': 4.0, 'O': 12.0}\",\"('C', 'Li', 'O', 'V')\",OTHERS,False\nmp-1206944,\"{'La': 3.0, 'Mg': 3.0, 'Cu': 3.0}\",\"('Cu', 'La', 'Mg')\",OTHERS,False\nmp-29903,\"{'F': 20.0, 'Te': 4.0, 'Tl': 4.0}\",\"('F', 'Te', 'Tl')\",OTHERS,False\nmp-1062310,\"{'Lu': 1.0, 'Cd': 2.0}\",\"('Cd', 'Lu')\",OTHERS,False\nmp-695900,\"{'Sb': 4.0, 'H': 4.0, 'C': 4.0, 'S': 4.0, 'O': 12.0, 'F': 32.0}\",\"('C', 'F', 'H', 'O', 'S', 'Sb')\",OTHERS,False\nmp-30145,\"{'Se': 26.0, 'Rb': 4.0, 'Bi': 16.0}\",\"('Bi', 'Rb', 'Se')\",OTHERS,False\nmp-1221327,\"{'Na': 2.0, 'Mn': 1.0, 'Ni': 1.0, 'O': 4.0}\",\"('Mn', 'Na', 'Ni', 'O')\",OTHERS,False\nmp-1237078,\"{'Rb': 2.0, 'U': 1.0, 'Br': 4.0, 'O': 4.0}\",\"('Br', 'O', 'Rb', 'U')\",OTHERS,False\nmp-1212239,\"{'Hg': 6.0, 'Pt': 2.0, 'N': 4.0, 'Cl': 16.0}\",\"('Cl', 'Hg', 'N', 'Pt')\",OTHERS,False\nmp-1182773,\"{'Ba': 4.0, 'Gd': 8.0, 'Te': 16.0}\",\"('Ba', 'Gd', 'Te')\",OTHERS,False\nmp-1102758,\"{'Lu': 4.0, 'Ga': 4.0, 'Ni': 4.0}\",\"('Ga', 'Lu', 'Ni')\",OTHERS,False\nmp-30823,\"{'Os': 6.0, 'Pu': 10.0}\",\"('Os', 'Pu')\",OTHERS,False\nmp-1436,\"{'Ag': 4.0, 'Eu': 2.0}\",\"('Ag', 'Eu')\",OTHERS,False\nmp-973964,\"{'Lu': 2.0, 'Ga': 1.0, 'Ag': 1.0}\",\"('Ag', 'Ga', 'Lu')\",OTHERS,False\nmp-541751,\"{'Bi': 24.0, 'Ni': 8.0, 'I': 6.0}\",\"('Bi', 'I', 'Ni')\",OTHERS,False\nmp-1199681,\"{'Al': 4.0, 'Sn': 4.0, 'P': 4.0, 'H': 72.0, 'C': 24.0, 'Cl': 16.0}\",\"('Al', 'C', 'Cl', 'H', 'P', 'Sn')\",OTHERS,False\nmp-23253,\"{'Rb': 2.0, 'Bi': 4.0}\",\"('Bi', 'Rb')\",OTHERS,False\nmp-616565,\"{'Rb': 8.0, 'Cr': 16.0, 'O': 52.0}\",\"('Cr', 'O', 'Rb')\",OTHERS,False\nmp-851093,\"{'Ni': 6.0, 'P': 8.0, 'O': 28.0}\",\"('Ni', 'O', 'P')\",OTHERS,False\nmp-1114365,\"{'Rb': 2.0, 'Na': 1.0, 'Ir': 1.0, 'F': 6.0}\",\"('F', 'Ir', 'Na', 'Rb')\",OTHERS,False\nmp-755892,\"{'Sr': 2.0, 'La': 4.0, 'Cl': 16.0}\",\"('Cl', 'La', 'Sr')\",OTHERS,False\nmp-1225227,\"{'Fe': 3.0, 'Co': 3.0, 'Si': 2.0}\",\"('Co', 'Fe', 'Si')\",OTHERS,False\nmvc-14364,\"{'Mg': 4.0, 'Sb': 4.0, 'F': 20.0}\",\"('F', 'Mg', 'Sb')\",OTHERS,False\nmp-1245791,\"{'K': 4.0, 'Ni': 4.0, 'N': 4.0}\",\"('K', 'N', 'Ni')\",OTHERS,False\nmp-2819,\"{'In': 1.0, 'Te': 1.0}\",\"('In', 'Te')\",OTHERS,False\nmvc-14220,\"{'Mg': 2.0, 'Mo': 2.0, 'F': 10.0}\",\"('F', 'Mg', 'Mo')\",OTHERS,False\nmp-850908,\"{'Li': 2.0, 'Mg': 11.0, 'W': 12.0, 'O': 48.0}\",\"('Li', 'Mg', 'O', 'W')\",OTHERS,False\nmp-778284,\"{'Li': 4.0, 'V': 3.0, 'Co': 2.0, 'Sn': 3.0, 'O': 16.0}\",\"('Co', 'Li', 'O', 'Sn', 'V')\",OTHERS,False\nmp-1218618,\"{'Sr': 12.0, 'Ca': 2.0, 'Mn': 8.0, 'O': 26.0}\",\"('Ca', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-1246706,\"{'Na': 16.0, 'Ir': 16.0, 'N': 32.0}\",\"('Ir', 'N', 'Na')\",OTHERS,False\nmp-1245695,\"{'Sr': 28.0, 'Re': 4.0, 'N': 24.0}\",\"('N', 'Re', 'Sr')\",OTHERS,False\nmp-1221056,\"{'Na': 2.0, 'Dy': 2.0, 'Ti': 4.0, 'O': 12.0}\",\"('Dy', 'Na', 'O', 'Ti')\",OTHERS,False\nmp-1867,\"{'Ni': 2.0, 'Ta': 4.0}\",\"('Ni', 'Ta')\",OTHERS,False\nmp-555993,\"{'Cr': 2.0, 'Ag': 6.0, 'Cl': 2.0, 'O': 8.0}\",\"('Ag', 'Cl', 'Cr', 'O')\",OTHERS,False\nmp-1114513,\"{'Rb': 2.0, 'Na': 1.0, 'Ce': 1.0, 'I': 6.0}\",\"('Ce', 'I', 'Na', 'Rb')\",OTHERS,False\nmp-762019,\"{'Li': 1.0, 'V': 1.0, 'C': 2.0, 'O': 6.0}\",\"('C', 'Li', 'O', 'V')\",OTHERS,False\nmp-3155,\"{'C': 4.0, 'Sc': 3.0, 'Fe': 1.0}\",\"('C', 'Fe', 'Sc')\",OTHERS,False\nmp-778904,\"{'Mn': 12.0, 'O': 14.0, 'F': 10.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-1106022,\"{'Y': 4.0, 'Ge': 10.0, 'Rh': 6.0}\",\"('Ge', 'Rh', 'Y')\",OTHERS,False\nmp-1018639,\"{'Ti': 1.0, 'Sn': 1.0, 'O': 3.0}\",\"('O', 'Sn', 'Ti')\",OTHERS,False\nmp-553898,\"{'Ge': 12.0, 'Pb': 12.0, 'O': 36.0}\",\"('Ge', 'O', 'Pb')\",OTHERS,False\nmp-1215539,\"{'Yb': 1.0, 'Mn': 2.0, 'Bi': 1.0, 'Sb': 1.0}\",\"('Bi', 'Mn', 'Sb', 'Yb')\",OTHERS,False\nmp-698074,\"{'Ca': 6.0, 'H': 6.0, 'S': 6.0, 'O': 27.0}\",\"('Ca', 'H', 'O', 'S')\",OTHERS,False\nmp-690772,\"{'V': 10.0, 'S': 16.0}\",\"('S', 'V')\",OTHERS,False\nmp-1222856,\"{'La': 2.0, 'Ge': 4.0, 'Pd': 2.0, 'Rh': 2.0}\",\"('Ge', 'La', 'Pd', 'Rh')\",OTHERS,False\nmp-1184657,{'Hg': 8.0},\"('Hg',)\",OTHERS,False\nmp-1223244,\"{'La': 2.0, 'Al': 3.0, 'Ge': 1.0}\",\"('Al', 'Ge', 'La')\",OTHERS,False\nmp-1016883,\"{'Zr': 1.0, 'Zn': 1.0, 'O': 3.0}\",\"('O', 'Zn', 'Zr')\",OTHERS,False\nmp-1025068,\"{'Nd': 1.0, 'B': 2.0, 'Ir': 3.0}\",\"('B', 'Ir', 'Nd')\",OTHERS,False\nmp-865746,\"{'Ga': 1.0, 'Tc': 2.0, 'W': 1.0}\",\"('Ga', 'Tc', 'W')\",OTHERS,False\nmp-15397,\"{'O': 15.0, 'Al': 2.0, 'Ti': 7.0}\",\"('Al', 'O', 'Ti')\",OTHERS,False\nmp-1142857,\"{'Si': 12.0, 'Mo': 12.0, 'O': 48.0}\",\"('Mo', 'O', 'Si')\",OTHERS,False\nmp-769734,\"{'Sc': 4.0, 'Fe': 4.0, 'O': 12.0}\",\"('Fe', 'O', 'Sc')\",OTHERS,False\nmp-1188757,\"{'Y': 12.0, 'Os': 4.0}\",\"('Os', 'Y')\",OTHERS,False\nmp-505225,\"{'U': 4.0, 'P': 4.0, 'O': 20.0}\",\"('O', 'P', 'U')\",OTHERS,False\nmp-1205693,\"{'Ba': 2.0, 'Dy': 1.0, 'U': 1.0, 'O': 6.0}\",\"('Ba', 'Dy', 'O', 'U')\",OTHERS,False\nmp-29564,\"{'Mg': 6.0, 'Si': 8.0, 'Ba': 4.0}\",\"('Ba', 'Mg', 'Si')\",OTHERS,False\nmp-760432,\"{'Cu': 4.0, 'O': 6.0}\",\"('Cu', 'O')\",OTHERS,False\nmp-558954,\"{'Na': 8.0, 'Sr': 8.0, 'Al': 8.0, 'P': 4.0, 'O': 16.0, 'F': 36.0}\",\"('Al', 'F', 'Na', 'O', 'P', 'Sr')\",OTHERS,False\nmvc-10142,\"{'Mg': 6.0, 'Fe': 12.0, 'O': 24.0}\",\"('Fe', 'Mg', 'O')\",OTHERS,False\nmp-1097204,\"{'Al': 1.0, 'Ga': 1.0, 'Ir': 2.0}\",\"('Al', 'Ga', 'Ir')\",OTHERS,False\nmp-849457,\"{'Li': 3.0, 'Ni': 1.0, 'Sb': 4.0, 'O': 12.0}\",\"('Li', 'Ni', 'O', 'Sb')\",OTHERS,False\nmp-779007,\"{'Li': 10.0, 'Fe': 2.0, 'Co': 3.0, 'Ni': 3.0, 'O': 16.0}\",\"('Co', 'Fe', 'Li', 'Ni', 'O')\",OTHERS,False\nmp-1219869,\"{'Pr': 2.0, 'Fe': 1.0, 'Si': 3.0}\",\"('Fe', 'Pr', 'Si')\",OTHERS,False\nmvc-1093,\"{'Ca': 4.0, 'W': 4.0, 'O': 8.0}\",\"('Ca', 'O', 'W')\",OTHERS,False\nmp-29477,\"{'C': 4.0, 'S': 14.0, 'Cl': 12.0}\",\"('C', 'Cl', 'S')\",OTHERS,False\nmp-29519,\"{'K': 4.0, 'I': 16.0, 'Au': 4.0}\",\"('Au', 'I', 'K')\",OTHERS,False\nmp-1209818,\"{'Ni': 7.0, 'Mo': 9.0, 'P': 6.0}\",\"('Mo', 'Ni', 'P')\",OTHERS,False\nmp-2847,\"{'B': 16.0, 'Er': 4.0}\",\"('B', 'Er')\",OTHERS,False\nmp-1176980,\"{'Li': 12.0, 'Mn': 2.0, 'V': 6.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-849732,\"{'Li': 1.0, 'Sb': 1.0, 'Te': 3.0, 'O': 12.0}\",\"('Li', 'O', 'Sb', 'Te')\",OTHERS,False\nmp-1211672,\"{'Nd': 6.0, 'N': 8.0, 'O': 33.0}\",\"('N', 'Nd', 'O')\",OTHERS,False\nmp-774389,\"{'Li': 4.0, 'Mn': 1.0, 'V': 3.0, 'P': 8.0, 'O': 28.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-27133,\"{'P': 8.0, 'S': 32.0, 'Bi': 8.0}\",\"('Bi', 'P', 'S')\",OTHERS,False\nmp-1191148,\"{'Sr': 2.0, 'Cu': 4.0, 'B': 4.0, 'O': 12.0}\",\"('B', 'Cu', 'O', 'Sr')\",OTHERS,False\nmp-1223195,\"{'La': 4.0, 'U': 2.0, 'Te': 10.0}\",\"('La', 'Te', 'U')\",OTHERS,False\nmp-1221325,\"{'Na': 2.0, 'Pt': 1.0, 'O': 3.0}\",\"('Na', 'O', 'Pt')\",OTHERS,False\nmp-28166,\"{'K': 3.0, 'O': 1.0, 'Br': 1.0}\",\"('Br', 'K', 'O')\",OTHERS,False\nmp-1211737,\"{'K': 8.0, 'Tb': 4.0, 'Cl': 20.0}\",\"('Cl', 'K', 'Tb')\",OTHERS,False\nmvc-6095,\"{'Zn': 2.0, 'Fe': 1.0, 'W': 1.0, 'O': 6.0}\",\"('Fe', 'O', 'W', 'Zn')\",OTHERS,False\nmp-569330,\"{'Ce': 8.0, 'C': 8.0, 'Br': 6.0}\",\"('Br', 'C', 'Ce')\",OTHERS,False\nmp-1222923,\"{'La': 1.0, 'Pr': 3.0, 'Mn': 4.0, 'O': 12.0}\",\"('La', 'Mn', 'O', 'Pr')\",OTHERS,False\nmp-759298,\"{'Li': 10.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1113420,\"{'Eu': 1.0, 'Cs': 2.0, 'Au': 1.0, 'Cl': 6.0}\",\"('Au', 'Cl', 'Cs', 'Eu')\",OTHERS,False\nmp-2514,\"{'Mg': 24.0, 'P': 16.0}\",\"('Mg', 'P')\",OTHERS,False\nmp-26691,\"{'Li': 6.0, 'Ni': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-753178,\"{'Li': 1.0, 'Fe': 2.0, 'C': 3.0, 'O': 9.0}\",\"('C', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-680322,\"{'Bi': 17.0, 'P': 5.0, 'Pb': 5.0, 'O': 43.0}\",\"('Bi', 'O', 'P', 'Pb')\",OTHERS,False\nmp-865015,\"{'Mn': 1.0, 'Si': 1.0, 'Rh': 2.0}\",\"('Mn', 'Rh', 'Si')\",OTHERS,False\nmp-1232361,\"{'Cs': 1.0, 'In': 1.0, 'Cu': 6.0, 'O': 8.0}\",\"('Cs', 'Cu', 'In', 'O')\",OTHERS,False\nmp-1226713,\"{'Ce': 1.0, 'Dy': 4.0, 'S': 7.0}\",\"('Ce', 'Dy', 'S')\",OTHERS,False\nmp-631661,\"{'Y': 2.0, 'Fe': 1.0, 'B': 1.0}\",\"('B', 'Fe', 'Y')\",OTHERS,False\nmp-300,\"{'Yb': 1.0, 'Pt': 3.0}\",\"('Pt', 'Yb')\",OTHERS,False\nmp-1111512,\"{'Na': 2.0, 'Sb': 1.0, 'Au': 1.0, 'F': 6.0}\",\"('Au', 'F', 'Na', 'Sb')\",OTHERS,False\nmp-568580,\"{'Na': 40.0, 'Ga': 24.0, 'Sn': 12.0}\",\"('Ga', 'Na', 'Sn')\",OTHERS,False\nmp-643074,\"{'Cs': 4.0, 'P': 4.0, 'H': 4.0, 'O': 14.0}\",\"('Cs', 'H', 'O', 'P')\",OTHERS,False\nmp-753270,\"{'Li': 2.0, 'V': 3.0, 'P': 4.0, 'O': 14.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-975390,\"{'Rb': 1.0, 'Ca': 3.0}\",\"('Ca', 'Rb')\",OTHERS,False\nmp-560801,\"{'Gd': 3.0, 'Al': 9.0, 'B': 12.0, 'O': 36.0}\",\"('Al', 'B', 'Gd', 'O')\",OTHERS,False\nmp-1186463,\"{'Pd': 1.0, 'S': 3.0}\",\"('Pd', 'S')\",OTHERS,False\nmp-1176417,\"{'Mn': 4.0, 'Te': 4.0, 'O': 12.0}\",\"('Mn', 'O', 'Te')\",OTHERS,False\nmp-555085,\"{'K': 2.0, 'Au': 2.0, 'N': 8.0, 'O': 24.0}\",\"('Au', 'K', 'N', 'O')\",OTHERS,False\nmp-779318,\"{'Li': 2.0, 'Cr': 4.0, 'P': 6.0, 'O': 26.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-1223222,\"{'La': 4.0, 'C': 2.0, 'I': 5.0}\",\"('C', 'I', 'La')\",OTHERS,False\nmp-568716,\"{'Sr': 20.0, 'Ag': 20.0}\",\"('Ag', 'Sr')\",OTHERS,False\nmp-557693,\"{'Cs': 8.0, 'Zn': 8.0, 'P': 8.0, 'O': 32.0}\",\"('Cs', 'O', 'P', 'Zn')\",OTHERS,False\nmp-22687,\"{'In': 4.0, 'Ba': 1.0}\",\"('Ba', 'In')\",OTHERS,False\nmp-28341,\"{'Li': 4.0, 'Cl': 16.0, 'Ga': 4.0}\",\"('Cl', 'Ga', 'Li')\",OTHERS,False\nmvc-8344,\"{'Mg': 2.0, 'Mn': 4.0, 'O': 10.0}\",\"('Mg', 'Mn', 'O')\",OTHERS,False\nmp-1186320,\"{'Np': 1.0, 'Hg': 1.0, 'O': 3.0}\",\"('Hg', 'Np', 'O')\",OTHERS,False\nmp-1214561,\"{'Ba': 2.0, 'Tb': 1.0, 'W': 1.0, 'O': 6.0}\",\"('Ba', 'O', 'Tb', 'W')\",OTHERS,False\nmp-752663,\"{'Li': 8.0, 'Fe': 4.0, 'B': 4.0, 'O': 16.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1073794,\"{'Mg': 6.0, 'Si': 6.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-984353,\"{'As': 2.0, 'Au': 6.0}\",\"('As', 'Au')\",OTHERS,False\nmp-1194892,\"{'Si': 2.0, 'P': 8.0, 'Pd': 2.0, 'Pb': 2.0, 'O': 28.0}\",\"('O', 'P', 'Pb', 'Pd', 'Si')\",OTHERS,False\nmp-707342,\"{'Zn': 2.0, 'H': 36.0, 'Ru': 4.0, 'Br': 14.0, 'N': 12.0}\",\"('Br', 'H', 'N', 'Ru', 'Zn')\",OTHERS,False\nmp-1093916,\"{'Hf': 1.0, 'Zr': 1.0, 'Co': 2.0}\",\"('Co', 'Hf', 'Zr')\",OTHERS,False\nmp-680695,\"{'Ce': 6.0, 'B': 4.0, 'Cl': 6.0, 'O': 12.0}\",\"('B', 'Ce', 'Cl', 'O')\",OTHERS,False\nmp-686526,\"{'Na': 4.0, 'Ca': 4.0, 'Al': 4.0, 'Si': 8.0, 'O': 28.0}\",\"('Al', 'Ca', 'Na', 'O', 'Si')\",OTHERS,False\nmp-16932,\"{'Th': 2.0, 'Co': 17.0}\",\"('Co', 'Th')\",OTHERS,False\nmp-696993,\"{'Sn': 8.0, 'H': 8.0, 'S': 4.0, 'O': 20.0, 'F': 8.0}\",\"('F', 'H', 'O', 'S', 'Sn')\",OTHERS,False\nmp-1245621,\"{'Fe': 10.0, 'Ge': 4.0, 'N': 12.0}\",\"('Fe', 'Ge', 'N')\",OTHERS,False\nmvc-1072,\"{'Ca': 4.0, 'Cu': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Ca', 'Cu', 'O', 'P')\",OTHERS,False\nmp-764057,\"{'V': 6.0, 'O': 12.0, 'F': 6.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-13385,\"{'P': 4.0, 'Se': 12.0, 'Ag': 2.0, 'Tm': 2.0}\",\"('Ag', 'P', 'Se', 'Tm')\",OTHERS,False\nmp-773515,\"{'Te': 3.0, 'W': 1.0, 'O': 12.0}\",\"('O', 'Te', 'W')\",OTHERS,False\nmp-13918,\"{'Ge': 2.0, 'Sr': 1.0, 'Au': 2.0}\",\"('Au', 'Ge', 'Sr')\",OTHERS,False\nmvc-1098,\"{'Ba': 2.0, 'V': 3.0, 'O': 8.0}\",\"('Ba', 'O', 'V')\",OTHERS,False\nmp-28989,\"{'Li': 10.0, 'N': 3.0, 'Br': 1.0}\",\"('Br', 'Li', 'N')\",OTHERS,False\nmp-1103877,\"{'Nd': 6.0, 'Ga': 2.0, 'Co': 6.0}\",\"('Co', 'Ga', 'Nd')\",OTHERS,False\nmp-6345,\"{'O': 18.0, 'Ru': 4.0, 'Ba': 6.0, 'Tb': 2.0}\",\"('Ba', 'O', 'Ru', 'Tb')\",OTHERS,False\nmp-510113,\"{'Bi': 8.0, 'Mn': 10.0, 'Ni': 4.0}\",\"('Bi', 'Mn', 'Ni')\",OTHERS,False\nmvc-2664,\"{'Mg': 4.0, 'Ta': 4.0, 'Fe': 2.0, 'O': 16.0}\",\"('Fe', 'Mg', 'O', 'Ta')\",OTHERS,False\nmp-1207719,\"{'Yb': 12.0, 'Ta': 8.0, 'Al': 86.0}\",\"('Al', 'Ta', 'Yb')\",OTHERS,False\nmp-682548,\"{'Cs': 5.0, 'Te': 3.0, 'H': 4.0, 'N': 1.0, 'Cl': 18.0}\",\"('Cl', 'Cs', 'H', 'N', 'Te')\",OTHERS,False\nmp-1190813,\"{'Cs': 4.0, 'U': 2.0, 'Pt': 6.0, 'Se': 12.0}\",\"('Cs', 'Pt', 'Se', 'U')\",OTHERS,False\nmp-1096504,\"{'Sr': 2.0, 'Li': 1.0, 'Pd': 1.0}\",\"('Li', 'Pd', 'Sr')\",OTHERS,False\nmvc-4008,\"{'Zn': 4.0, 'Ni': 4.0, 'O': 12.0}\",\"('Ni', 'O', 'Zn')\",OTHERS,False\nmp-766243,\"{'Y': 8.0, 'Zr': 8.0, 'O': 24.0}\",\"('O', 'Y', 'Zr')\",OTHERS,False\nmp-26692,\"{'Li': 4.0, 'Ni': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-759092,\"{'Li': 12.0, 'Fe': 12.0, 'P': 12.0, 'O': 48.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-1104197,\"{'Ba': 1.0, 'Yb': 1.0, 'Fe': 4.0, 'O': 7.0}\",\"('Ba', 'Fe', 'O', 'Yb')\",OTHERS,False\nmp-21515,\"{'S': 8.0, 'Fe': 6.0}\",\"('Fe', 'S')\",OTHERS,False\nmp-1006256,\"{'Ce': 1.0, 'Y': 1.0, 'Tl': 2.0}\",\"('Ce', 'Tl', 'Y')\",OTHERS,False\nmp-1188061,\"{'Zr': 6.0, 'Sn': 2.0}\",\"('Sn', 'Zr')\",OTHERS,False\nmp-1205648,\"{'Rb': 4.0, 'Ni': 2.0, 'As': 4.0}\",\"('As', 'Ni', 'Rb')\",OTHERS,False\nmp-30097,\"{'Cl': 16.0, 'Te': 14.0, 'Bi': 4.0}\",\"('Bi', 'Cl', 'Te')\",OTHERS,False\nmp-768522,\"{'La': 8.0, 'Ce': 8.0, 'O': 28.0}\",\"('Ce', 'La', 'O')\",OTHERS,False\nmp-1177753,\"{'Li': 6.0, 'Fe': 4.0, 'O': 12.0}\",\"('Fe', 'Li', 'O')\",OTHERS,False\nmp-1244909,\"{'Al': 40.0, 'O': 60.0}\",\"('Al', 'O')\",OTHERS,False\nmp-1189350,\"{'La': 10.0, 'As': 2.0, 'Pb': 6.0}\",\"('As', 'La', 'Pb')\",OTHERS,False\nmp-1080340,\"{'Ce': 2.0, 'Se': 4.0}\",\"('Ce', 'Se')\",OTHERS,False\nmp-780005,\"{'Li': 8.0, 'Mn': 2.0, 'V': 6.0, 'P': 16.0, 'O': 56.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-771026,\"{'Na': 4.0, 'B': 2.0, 'Sb': 2.0, 'S': 2.0, 'O': 14.0}\",\"('B', 'Na', 'O', 'S', 'Sb')\",OTHERS,False\nmp-1075098,\"{'Mg': 6.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-626219,\"{'H': 20.0, 'Cl': 4.0, 'O': 24.0}\",\"('Cl', 'H', 'O')\",OTHERS,False\nmp-1097373,\"{'Sc': 1.0, 'In': 1.0, 'Hg': 2.0}\",\"('Hg', 'In', 'Sc')\",OTHERS,False\nmp-9942,\"{'S': 4.0, 'Ti': 2.0, 'Fe': 1.0}\",\"('Fe', 'S', 'Ti')\",OTHERS,False\nmp-1185069,\"{'La': 1.0, 'Lu': 1.0, 'Tl': 2.0}\",\"('La', 'Lu', 'Tl')\",OTHERS,False\nmp-1184253,\"{'Er': 1.0, 'Tm': 1.0, 'Ag': 2.0}\",\"('Ag', 'Er', 'Tm')\",OTHERS,False\nmp-1212971,\"{'Dy': 2.0, 'Be': 2.0, 'N': 2.0, 'F': 12.0}\",\"('Be', 'Dy', 'F', 'N')\",OTHERS,False\nmp-1074201,\"{'Mg': 8.0, 'Si': 14.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1095140,\"{'Er': 4.0, 'Co': 2.0, 'Ge': 4.0}\",\"('Co', 'Er', 'Ge')\",OTHERS,False\nmp-1200829,\"{'Na': 4.0, 'Sb': 4.0, 'F': 24.0}\",\"('F', 'Na', 'Sb')\",OTHERS,False\nmp-1197782,\"{'K': 6.0, 'V': 10.0, 'Te': 8.0, 'H': 16.0, 'O': 48.0}\",\"('H', 'K', 'O', 'Te', 'V')\",OTHERS,False\nmp-764723,\"{'Li': 2.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1095131,\"{'Sr': 1.0, 'Ta': 2.0, 'O': 7.0}\",\"('O', 'Sr', 'Ta')\",OTHERS,False\nmp-1097388,\"{'Y': 1.0, 'Zn': 1.0, 'Pd': 2.0}\",\"('Pd', 'Y', 'Zn')\",OTHERS,False\nmp-1672,\"{'S': 1.0, 'Ca': 1.0}\",\"('Ca', 'S')\",OTHERS,False\nmp-1175066,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-23297,\"{'F': 6.0, 'Br': 2.0}\",\"('Br', 'F')\",OTHERS,False\nmp-1197684,\"{'Na': 20.0, 'Al': 4.0, 'H': 32.0, 'O': 32.0}\",\"('Al', 'H', 'Na', 'O')\",OTHERS,False\nmp-16455,\"{'S': 10.0, 'Zn': 2.0, 'Ba': 2.0, 'Nd': 4.0}\",\"('Ba', 'Nd', 'S', 'Zn')\",OTHERS,False\nmp-1198676,\"{'Cs': 16.0, 'Fe': 4.0, 'O': 14.0}\",\"('Cs', 'Fe', 'O')\",OTHERS,False\nmp-770745,\"{'Li': 8.0, 'Cr': 8.0, 'O': 28.0}\",\"('Cr', 'Li', 'O')\",OTHERS,False\nmp-760396,\"{'Ta': 2.0, 'Al': 2.0, 'O': 8.0}\",\"('Al', 'O', 'Ta')\",OTHERS,False\nmp-757516,\"{'Li': 4.0, 'Ni': 16.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-1193512,\"{'Cu': 3.0, 'I': 6.0, 'O': 20.0}\",\"('Cu', 'I', 'O')\",OTHERS,False\nmp-1246055,\"{'Fe': 2.0, 'C': 12.0, 'N': 18.0}\",\"('C', 'Fe', 'N')\",OTHERS,False\nmp-20205,\"{'Yb': 8.0, 'Cu': 8.0, 'O': 20.0}\",\"('Cu', 'O', 'Yb')\",OTHERS,False\nmp-1095701,\"{'Mg': 30.0, 'Sn': 1.0, 'C': 1.0, 'O': 32.0}\",\"('C', 'Mg', 'O', 'Sn')\",OTHERS,False\nmp-1209321,\"{'Rb': 8.0, 'O': 16.0, 'F': 8.0}\",\"('F', 'O', 'Rb')\",OTHERS,False\nmp-1194932,\"{'Sm': 12.0, 'Se': 6.0, 'O': 6.0, 'F': 12.0}\",\"('F', 'O', 'Se', 'Sm')\",OTHERS,False\nmp-1079462,\"{'Sm': 2.0, 'Ge': 4.0, 'Pt': 4.0}\",\"('Ge', 'Pt', 'Sm')\",OTHERS,False\nmp-976339,\"{'Li': 1.0, 'Tm': 2.0, 'In': 1.0}\",\"('In', 'Li', 'Tm')\",OTHERS,False\nmp-862762,\"{'Pr': 12.0, 'Co': 4.0}\",\"('Co', 'Pr')\",OTHERS,False\nmp-760823,\"{'Li': 22.0, 'V': 8.0, 'F': 48.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-1187498,\"{'Tl': 3.0, 'Zn': 1.0}\",\"('Tl', 'Zn')\",OTHERS,False\nmp-569898,\"{'Ce': 3.0, 'Ga': 1.0, 'Br': 3.0}\",\"('Br', 'Ce', 'Ga')\",OTHERS,False\nmp-1229238,\"{'Ba': 10.0, 'Gd': 4.0, 'Y': 1.0, 'Cu': 15.0, 'O': 35.0}\",\"('Ba', 'Cu', 'Gd', 'O', 'Y')\",OTHERS,False\nmp-1245543,\"{'Ti': 24.0, 'C': 24.0, 'N': 56.0}\",\"('C', 'N', 'Ti')\",OTHERS,False\nmp-984772,\"{'Ce': 3.0, 'Zn': 1.0}\",\"('Ce', 'Zn')\",OTHERS,False\nmp-1185862,\"{'Mg': 1.0, 'F': 3.0}\",\"('F', 'Mg')\",OTHERS,False\nmp-570900,\"{'K': 16.0, 'Sn': 36.0}\",\"('K', 'Sn')\",OTHERS,False\nmp-1094375,\"{'Y': 10.0, 'Mg': 2.0}\",\"('Mg', 'Y')\",OTHERS,False\nmp-1213845,\"{'Co': 2.0, 'Cu': 4.0, 'Ge': 2.0, 'Se': 8.0}\",\"('Co', 'Cu', 'Ge', 'Se')\",OTHERS,False\nmp-1246723,\"{'Sm': 2.0, 'Mg': 2.0, 'Cr': 2.0, 'S': 8.0}\",\"('Cr', 'Mg', 'S', 'Sm')\",OTHERS,False\nmvc-5049,\"{'Mg': 4.0, 'V': 2.0, 'W': 2.0, 'O': 12.0}\",\"('Mg', 'O', 'V', 'W')\",OTHERS,False\nmp-1186143,\"{'Na': 1.0, 'Lu': 3.0}\",\"('Lu', 'Na')\",OTHERS,False\nmp-867761,\"{'Sc': 2.0, 'Co': 1.0, 'Ru': 1.0}\",\"('Co', 'Ru', 'Sc')\",OTHERS,False\nmp-1215347,\"{'Zr': 6.0, 'Co': 2.0, 'O': 1.0}\",\"('Co', 'O', 'Zr')\",OTHERS,False\nmp-1006367,\"{'Ce': 8.0, 'Hf': 4.0, 'Se': 20.0}\",\"('Ce', 'Hf', 'Se')\",OTHERS,False\nmp-1005829,\"{'Sm': 2.0, 'Mn': 12.0, 'P': 7.0}\",\"('Mn', 'P', 'Sm')\",OTHERS,False\nmp-1207518,\"{'Yb': 4.0, 'Rh': 4.0, 'O': 12.0}\",\"('O', 'Rh', 'Yb')\",OTHERS,False\nmp-601820,\"{'Fe': 3.0, 'Co': 1.0}\",\"('Co', 'Fe')\",OTHERS,False\nmp-1079700,\"{'Dy': 6.0, 'Co': 1.0, 'Te': 2.0}\",\"('Co', 'Dy', 'Te')\",OTHERS,False\nmp-1175544,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-758091,\"{'Li': 2.0, 'V': 2.0, 'F': 8.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-1076488,\"{'Sr': 24.0, 'Ca': 8.0, 'Fe': 12.0, 'Co': 20.0, 'O': 80.0}\",\"('Ca', 'Co', 'Fe', 'O', 'Sr')\",OTHERS,False\nmvc-6059,\"{'Zn': 2.0, 'Cu': 4.0, 'O': 8.0}\",\"('Cu', 'O', 'Zn')\",OTHERS,False\nmp-1177982,\"{'Li': 2.0, 'Fe': 1.0, 'B': 2.0, 'O': 6.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-680614,\"{'Yb': 32.0, 'Li': 4.0, 'Ge': 52.0}\",\"('Ge', 'Li', 'Yb')\",OTHERS,False\nmp-460,\"{'Zn': 1.0, 'Pr': 1.0}\",\"('Pr', 'Zn')\",OTHERS,False\nmp-722342,\"{'Na': 4.0, 'Ca': 8.0, 'B': 36.0, 'H': 32.0, 'O': 80.0}\",\"('B', 'Ca', 'H', 'Na', 'O')\",OTHERS,False\nmp-735491,\"{'Ca': 1.0, 'Mg': 2.0, 'H': 24.0, 'Cl': 6.0, 'O': 12.0}\",\"('Ca', 'Cl', 'H', 'Mg', 'O')\",OTHERS,False\nmp-1223246,\"{'La': 2.0, 'Ga': 1.0, 'Ni': 1.0, 'O': 6.0}\",\"('Ga', 'La', 'Ni', 'O')\",OTHERS,False\nmp-1219249,\"{'Si': 3.0, 'P': 3.0, 'Ir': 1.0}\",\"('Ir', 'P', 'Si')\",OTHERS,False\nmp-1185514,\"{'Lu': 1.0, 'Ge': 1.0, 'O': 3.0}\",\"('Ge', 'Lu', 'O')\",OTHERS,False\nmp-1187545,\"{'Tl': 3.0, 'Ag': 1.0}\",\"('Ag', 'Tl')\",OTHERS,False\nmp-695708,\"{'Ba': 14.0, 'Si': 2.0, 'B': 6.0, 'N': 2.0, 'O': 26.0}\",\"('B', 'Ba', 'N', 'O', 'Si')\",OTHERS,False\nmp-780778,\"{'Li': 4.0, 'Mn': 14.0, 'P': 16.0, 'O': 56.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-667288,\"{'K': 16.0, 'P': 32.0, 'Pb': 8.0, 'O': 96.0}\",\"('K', 'O', 'P', 'Pb')\",OTHERS,False\nmp-753723,\"{'Li': 4.0, 'Mn': 4.0, 'O': 2.0, 'F': 12.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1013561,\"{'Sr': 3.0, 'Sb': 1.0, 'As': 1.0}\",\"('As', 'Sb', 'Sr')\",OTHERS,False\nmp-1220780,\"{'Na': 1.0, 'La': 1.0, 'Nb': 4.0, 'O': 12.0}\",\"('La', 'Na', 'Nb', 'O')\",OTHERS,False\nmp-1220298,\"{'Ni': 17.0, 'Ge': 4.0, 'Se': 4.0}\",\"('Ge', 'Ni', 'Se')\",OTHERS,False\nmp-1189374,\"{'Ho': 12.0, 'Ga': 2.0, 'Ni': 4.0}\",\"('Ga', 'Ho', 'Ni')\",OTHERS,False\nmp-17849,\"{'O': 1.0, 'Fe': 16.0, 'Ho': 6.0}\",\"('Fe', 'Ho', 'O')\",OTHERS,False\nmp-1205212,\"{'B': 128.0, 'F': 192.0}\",\"('B', 'F')\",OTHERS,False\nmp-755821,\"{'Li': 5.0, 'Cu': 1.0, 'P': 2.0, 'O': 8.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-757073,\"{'Ba': 4.0, 'Li': 1.0, 'Y': 2.0, 'Cu': 5.0, 'O': 14.0}\",\"('Ba', 'Cu', 'Li', 'O', 'Y')\",OTHERS,False\nmp-1207212,\"{'Li': 1.0, 'Hf': 1.0, 'S': 2.0}\",\"('Hf', 'Li', 'S')\",OTHERS,False\nmp-1219172,\"{'Sm': 2.0, 'Sb': 1.0, 'S': 1.0}\",\"('S', 'Sb', 'Sm')\",OTHERS,False\nmp-569207,\"{'K': 4.0, 'Os': 2.0, 'N': 2.0, 'Cl': 10.0}\",\"('Cl', 'K', 'N', 'Os')\",OTHERS,False\nmp-1220985,\"{'Nd': 10.0, 'Ta': 30.0, 'O': 90.0}\",\"('Nd', 'O', 'Ta')\",OTHERS,False\nmp-780275,\"{'Ag': 2.0, 'B': 2.0, 'O': 6.0}\",\"('Ag', 'B', 'O')\",OTHERS,False\nmp-1213452,\"{'H': 20.0, 'Pb': 4.0, 'C': 8.0, 'S': 4.0, 'I': 4.0, 'N': 24.0}\",\"('C', 'H', 'I', 'N', 'Pb', 'S')\",OTHERS,False\nmp-8001,\"{'Li': 1.0, 'O': 2.0, 'Al': 1.0}\",\"('Al', 'Li', 'O')\",OTHERS,False\nmp-1173595,\"{'Sr': 14.0, 'Nb': 6.0, 'O': 29.0}\",\"('Nb', 'O', 'Sr')\",OTHERS,False\nmp-505343,\"{'U': 2.0, 'Co': 8.0, 'B': 8.0}\",\"('B', 'Co', 'U')\",OTHERS,False\nmp-1071904,\"{'Pr': 2.0, 'Cu': 4.0}\",\"('Cu', 'Pr')\",OTHERS,False\nmp-761193,\"{'Li': 2.0, 'Cu': 8.0, 'P': 6.0, 'O': 24.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-865234,\"{'Ta': 1.0, 'Zn': 1.0, 'Ru': 2.0}\",\"('Ru', 'Ta', 'Zn')\",OTHERS,False\nmp-1035359,\"{'Mg': 14.0, 'Nb': 1.0, 'Cu': 1.0, 'O': 16.0}\",\"('Cu', 'Mg', 'Nb', 'O')\",OTHERS,False\nmp-1178636,\"{'Zr': 8.0, 'N': 16.0, 'F': 48.0}\",\"('F', 'N', 'Zr')\",OTHERS,False\nmp-1229224,\"{'Al': 2.0, 'B': 4.0, 'Pb': 12.0, 'O': 14.0, 'F': 14.0}\",\"('Al', 'B', 'F', 'O', 'Pb')\",OTHERS,False\nmp-36641,\"{'Cd': 1.0, 'Ga': 6.0, 'Te': 10.0}\",\"('Cd', 'Ga', 'Te')\",OTHERS,False\nmp-676296,\"{'U': 1.0, 'Bi': 4.0, 'O': 8.0}\",\"('Bi', 'O', 'U')\",OTHERS,False\nmp-1212634,\"{'Er': 4.0, 'Te': 4.0, 'As': 4.0}\",\"('As', 'Er', 'Te')\",OTHERS,False\nmp-24416,\"{'H': 2.0, 'Mn': 2.0}\",\"('H', 'Mn')\",OTHERS,False\nmp-760378,\"{'Li': 2.0, 'Ti': 4.0, 'O': 8.0}\",\"('Li', 'O', 'Ti')\",OTHERS,False\nmp-18474,\"{'Ge': 2.0, 'Se': 12.0, 'Ag': 16.0}\",\"('Ag', 'Ge', 'Se')\",OTHERS,False\nmp-755773,\"{'Mn': 6.0, 'O': 7.0, 'F': 5.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-865361,\"{'Tm': 2.0, 'Mg': 1.0, 'Os': 1.0}\",\"('Mg', 'Os', 'Tm')\",OTHERS,False\nmp-1111384,\"{'K': 3.0, 'Pr': 1.0, 'Cl': 6.0}\",\"('Cl', 'K', 'Pr')\",OTHERS,False\nmp-861905,\"{'Li': 1.0, 'Bi': 1.0, 'Pd': 2.0}\",\"('Bi', 'Li', 'Pd')\",OTHERS,False\nmp-1217074,\"{'Ti': 2.0, 'H': 1.0, 'C': 1.0}\",\"('C', 'H', 'Ti')\",OTHERS,False\nmp-1250217,\"{'Zn': 6.0, 'Cu': 4.0, 'Si': 8.0, 'O': 28.0}\",\"('Cu', 'O', 'Si', 'Zn')\",OTHERS,False\nmp-1219194,\"{'Sm': 12.0, 'Ta': 4.0, 'Ti': 8.0, 'O': 42.0}\",\"('O', 'Sm', 'Ta', 'Ti')\",OTHERS,False\nmp-1075975,\"{'Eu': 1.0, 'Co': 1.0, 'O': 3.0}\",\"('Co', 'Eu', 'O')\",OTHERS,False\nmp-1228231,\"{'Ba': 6.0, 'Ti': 2.0, 'Fe': 4.0, 'O': 18.0}\",\"('Ba', 'Fe', 'O', 'Ti')\",OTHERS,False\nmp-1014910,\"{'Cr': 4.0, 'N': 8.0}\",\"('Cr', 'N')\",OTHERS,False\nmp-736122,\"{'Ba': 18.0, 'Ca': 6.0, 'Zr': 6.0, 'W': 6.0, 'O': 54.0}\",\"('Ba', 'Ca', 'O', 'W', 'Zr')\",OTHERS,False\nmp-1080442,\"{'U': 3.0, 'Ga': 3.0, 'Pd': 3.0}\",\"('Ga', 'Pd', 'U')\",OTHERS,False\nmp-1228558,\"{'Al': 1.0, 'Si': 5.0, 'C': 2.0, 'O': 12.0}\",\"('Al', 'C', 'O', 'Si')\",OTHERS,False\nmp-558203,\"{'Si': 32.0, 'O': 64.0}\",\"('O', 'Si')\",OTHERS,False\nmp-744517,\"{'Li': 8.0, 'Mo': 8.0, 'H': 48.0, 'N': 8.0, 'O': 40.0}\",\"('H', 'Li', 'Mo', 'N', 'O')\",OTHERS,False\nmp-1220796,\"{'Na': 10.0, 'Sr': 2.0, 'Nb': 2.0, 'As': 8.0}\",\"('As', 'Na', 'Nb', 'Sr')\",OTHERS,False\nmp-1110992,\"{'Na': 2.0, 'Tl': 1.0, 'Cu': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cu', 'Na', 'Tl')\",OTHERS,False\nmp-1200509,\"{'Mn': 1.0, 'H': 36.0, 'C': 60.0, 'N': 12.0}\",\"('C', 'H', 'Mn', 'N')\",OTHERS,False\nmp-1187007,\"{'Si': 1.0, 'Pb': 3.0}\",\"('Pb', 'Si')\",OTHERS,False\nmp-1224945,\"{'Fe': 1.0, 'Cu': 1.0}\",\"('Cu', 'Fe')\",OTHERS,False\nmp-1075570,\"{'Mg': 10.0, 'Si': 12.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-545512,\"{'O': 2.0, 'Ca': 2.0}\",\"('Ca', 'O')\",OTHERS,False\nmp-1176011,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1103472,\"{'Na': 2.0, 'U': 1.0, 'F': 8.0}\",\"('F', 'Na', 'U')\",OTHERS,False\nmp-1014230,\"{'Ti': 1.0, 'Zn': 1.0}\",\"('Ti', 'Zn')\",OTHERS,False\nmp-555962,\"{'Li': 4.0, 'Mo': 4.0, 'As': 4.0, 'O': 24.0}\",\"('As', 'Li', 'Mo', 'O')\",OTHERS,False\nmp-774712,\"{'Li': 2.0, 'Cu': 2.0, 'S': 2.0}\",\"('Cu', 'Li', 'S')\",OTHERS,False\nmp-29276,\"{'O': 34.0, 'P': 12.0, 'Cd': 4.0}\",\"('Cd', 'O', 'P')\",OTHERS,False\nmp-27439,\"{'O': 2.0, 'I': 2.0, 'Tm': 2.0}\",\"('I', 'O', 'Tm')\",OTHERS,False\nmvc-7669,\"{'Ba': 1.0, 'Y': 1.0, 'Mn': 1.0, 'Cu': 1.0, 'O': 5.0}\",\"('Ba', 'Cu', 'Mn', 'O', 'Y')\",OTHERS,False\nmp-540948,\"{'Zn': 2.0, 'Ni': 2.0, 'P': 8.0, 'O': 24.0}\",\"('Ni', 'O', 'P', 'Zn')\",OTHERS,False\nmp-6075,\"{'Li': 2.0, 'B': 10.0, 'O': 20.0, 'Ba': 4.0}\",\"('B', 'Ba', 'Li', 'O')\",OTHERS,False\nmvc-13558,\"{'Fe': 4.0, 'S': 8.0}\",\"('Fe', 'S')\",OTHERS,False\nmp-18348,\"{'O': 28.0, 'P': 8.0, 'K': 4.0, 'Ga': 4.0}\",\"('Ga', 'K', 'O', 'P')\",OTHERS,False\nmp-555439,\"{'Cs': 2.0, 'Li': 6.0, 'F': 8.0}\",\"('Cs', 'F', 'Li')\",OTHERS,False\nmp-1245934,\"{'Cd': 2.0, 'C': 16.0, 'N': 12.0}\",\"('C', 'Cd', 'N')\",OTHERS,False\nmp-1092278,\"{'Mn': 2.0, 'B': 4.0, 'Mo': 4.0}\",\"('B', 'Mn', 'Mo')\",OTHERS,False\nmp-7719,\"{'O': 16.0, 'Na': 2.0, 'S': 4.0, 'Tm': 2.0}\",\"('Na', 'O', 'S', 'Tm')\",OTHERS,False\nmp-18962,\"{'O': 6.0, 'Ca': 1.0, 'Sr': 2.0, 'Mo': 1.0}\",\"('Ca', 'Mo', 'O', 'Sr')\",OTHERS,False\nmp-1238791,\"{'Cr': 4.0, 'Cu': 2.0, 'S': 8.0}\",\"('Cr', 'Cu', 'S')\",OTHERS,False\nmp-1176421,\"{'Mn': 2.0, 'Zn': 8.0, 'O': 10.0}\",\"('Mn', 'O', 'Zn')\",OTHERS,False\nmp-1253743,\"{'Ca': 4.0, 'Ti': 2.0, 'Al': 2.0, 'O': 10.0}\",\"('Al', 'Ca', 'O', 'Ti')\",OTHERS,False\nmp-1245401,\"{'Mn': 1.0, 'Cr': 1.0, 'N': 2.0}\",\"('Cr', 'Mn', 'N')\",OTHERS,False\nmp-772613,\"{'Li': 4.0, 'Cr': 6.0, 'Ni': 2.0, 'O': 16.0}\",\"('Cr', 'Li', 'Ni', 'O')\",OTHERS,False\nmp-1114453,\"{'Rb': 2.0, 'Na': 1.0, 'Pr': 1.0, 'I': 6.0}\",\"('I', 'Na', 'Pr', 'Rb')\",OTHERS,False\nmp-1245337,\"{'Si': 40.0, 'Ni': 16.0, 'N': 64.0}\",\"('N', 'Ni', 'Si')\",OTHERS,False\nmp-753509,\"{'Li': 3.0, 'Mn': 1.0, 'O': 1.0, 'F': 4.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1112902,\"{'Cs': 2.0, 'Hg': 1.0, 'Sb': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cs', 'Hg', 'Sb')\",OTHERS,False\nmp-1032862,\"{'Mg': 6.0, 'Mn': 1.0, 'Fe': 1.0, 'O': 8.0}\",\"('Fe', 'Mg', 'Mn', 'O')\",OTHERS,False\nmp-10740,{'Pa': 1.0},\"('Pa',)\",OTHERS,False\nmp-685705,\"{'Mn': 17.0, 'Nb': 31.0, 'N': 48.0}\",\"('Mn', 'N', 'Nb')\",OTHERS,False\nmp-768177,\"{'Li': 8.0, 'V': 4.0, 'B': 4.0, 'O': 16.0}\",\"('B', 'Li', 'O', 'V')\",OTHERS,False\nmp-1096162,\"{'Mg': 2.0, 'Cd': 1.0, 'Ga': 1.0}\",\"('Cd', 'Ga', 'Mg')\",OTHERS,False\nmp-560409,\"{'P': 24.0, 'S': 24.0, 'N': 48.0, 'F': 40.0}\",\"('F', 'N', 'P', 'S')\",OTHERS,False\nmp-1025034,\"{'Th': 1.0, 'Ni': 2.0, 'B': 2.0, 'C': 1.0}\",\"('B', 'C', 'Ni', 'Th')\",OTHERS,False\nmp-1226011,\"{'Co': 1.0, 'Si': 4.0, 'Ni': 3.0}\",\"('Co', 'Ni', 'Si')\",OTHERS,False\nmvc-9211,\"{'Ti': 2.0, 'Zn': 2.0, 'Ni': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Ni', 'O', 'P', 'Ti', 'Zn')\",OTHERS,False\nmp-1229165,\"{'Ag': 3.0, 'Bi': 7.0, 'S': 12.0}\",\"('Ag', 'Bi', 'S')\",OTHERS,False\nmp-13156,\"{'Ag': 2.0, 'Hf': 2.0}\",\"('Ag', 'Hf')\",OTHERS,False\nmp-1173858,\"{'Na': 7.0, 'Sr': 6.0, 'Nd': 7.0, 'Ti': 20.0, 'O': 60.0}\",\"('Na', 'Nd', 'O', 'Sr', 'Ti')\",OTHERS,False\nmp-1147677,\"{'Ca': 1.0, 'Cu': 6.0, 'O': 8.0}\",\"('Ca', 'Cu', 'O')\",OTHERS,False\nmp-1103519,\"{'K': 4.0, 'Cu': 4.0, 'O': 4.0}\",\"('Cu', 'K', 'O')\",OTHERS,False\nmp-1104636,\"{'Er': 2.0, 'Al': 6.0, 'C': 6.0}\",\"('Al', 'C', 'Er')\",OTHERS,False\nmp-772436,\"{'Li': 16.0, 'Al': 8.0, 'Fe': 8.0, 'O': 32.0}\",\"('Al', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1078698,\"{'Zr': 2.0, 'Cu': 2.0, 'Ge': 2.0, 'As': 2.0}\",\"('As', 'Cu', 'Ge', 'Zr')\",OTHERS,False\nmp-1211200,\"{'Mg': 2.0, 'U': 4.0, 'P': 4.0, 'H': 40.0, 'O': 44.0}\",\"('H', 'Mg', 'O', 'P', 'U')\",OTHERS,False\nmvc-6262,\"{'Mg': 4.0, 'Bi': 8.0, 'O': 16.0}\",\"('Bi', 'Mg', 'O')\",OTHERS,False\nmvc-4222,\"{'Ge': 12.0, 'Te': 8.0, 'O': 48.0}\",\"('Ge', 'O', 'Te')\",OTHERS,False\nmp-733,\"{'O': 6.0, 'Ge': 3.0}\",\"('Ge', 'O')\",OTHERS,False\nmp-1194955,\"{'Na': 6.0, 'Gd': 2.0, 'C': 6.0, 'O': 18.0}\",\"('C', 'Gd', 'Na', 'O')\",OTHERS,False\nmp-780360,\"{'Na': 32.0, 'Gd': 8.0, 'O': 28.0}\",\"('Gd', 'Na', 'O')\",OTHERS,False\nmp-3419,\"{'F': 8.0, 'Rb': 2.0, 'Au': 2.0}\",\"('Au', 'F', 'Rb')\",OTHERS,False\nmp-775805,\"{'Zr': 8.0, 'N': 8.0, 'O': 4.0}\",\"('N', 'O', 'Zr')\",OTHERS,False\nmp-781594,\"{'Li': 2.0, 'Mn': 6.0, 'B': 6.0, 'O': 18.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1065668,\"{'Gd': 1.0, 'Ni': 1.0, 'C': 2.0}\",\"('C', 'Gd', 'Ni')\",OTHERS,False\nmp-33581,\"{'Ba': 9.0, 'Nb': 10.0, 'O': 30.0}\",\"('Ba', 'Nb', 'O')\",OTHERS,False\nmp-1221677,\"{'Mn': 1.0, 'H': 6.0, 'N': 2.0, 'Cl': 2.0}\",\"('Cl', 'H', 'Mn', 'N')\",OTHERS,False\nmp-12838,\"{'Si': 6.0, 'Fe': 2.0, 'Tm': 6.0}\",\"('Fe', 'Si', 'Tm')\",OTHERS,False\nmp-580539,\"{'Gd': 3.0, 'Al': 1.0, 'C': 1.0}\",\"('Al', 'C', 'Gd')\",OTHERS,False\nmp-1225045,\"{'Er': 2.0, 'Al': 3.0, 'Co': 1.0}\",\"('Al', 'Co', 'Er')\",OTHERS,False\nmp-12243,\"{'F': 14.0, 'Si': 2.0, 'Rb': 6.0}\",\"('F', 'Rb', 'Si')\",OTHERS,False\nmp-1194349,\"{'Nb': 12.0, 'Ni': 12.0, 'C': 4.0}\",\"('C', 'Nb', 'Ni')\",OTHERS,False\nmp-975079,\"{'Mn': 2.0, 'Zn': 6.0}\",\"('Mn', 'Zn')\",OTHERS,False\nmp-1192447,\"{'Nd': 6.0, 'Mg': 23.0, 'C': 1.0}\",\"('C', 'Mg', 'Nd')\",OTHERS,False\nmp-14091,\"{'Al': 2.0, 'Se': 4.0, 'Ag': 2.0}\",\"('Ag', 'Al', 'Se')\",OTHERS,False\nmp-1186120,\"{'Np': 1.0, 'Co': 3.0}\",\"('Co', 'Np')\",OTHERS,False\nmp-768399,\"{'Ba': 6.0, 'Y': 4.0, 'Br': 24.0}\",\"('Ba', 'Br', 'Y')\",OTHERS,False\nmp-759281,\"{'Li': 12.0, 'Fe': 12.0, 'Si': 12.0, 'O': 48.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-722369,\"{'Na': 12.0, 'B': 20.0, 'H': 8.0, 'O': 40.0}\",\"('B', 'H', 'Na', 'O')\",OTHERS,False\nmp-540550,\"{'Na': 4.0, 'Cl': 4.0, 'O': 20.0, 'H': 8.0}\",\"('Cl', 'H', 'Na', 'O')\",OTHERS,False\nmp-1228892,\"{'Cs': 2.0, 'Cr': 2.0, 'Cu': 2.0, 'F': 12.0}\",\"('Cr', 'Cs', 'Cu', 'F')\",OTHERS,False\nmp-759320,\"{'Li': 9.0, 'Mn': 9.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1205384,\"{'La': 4.0, 'Te': 8.0}\",\"('La', 'Te')\",OTHERS,False\nmp-510547,\"{'Cu': 8.0, 'Cl': 4.0, 'O': 12.0, 'H': 12.0}\",\"('Cl', 'Cu', 'H', 'O')\",OTHERS,False\nmp-8248,\"{'O': 6.0, 'Rb': 4.0, 'Te': 2.0}\",\"('O', 'Rb', 'Te')\",OTHERS,False\nmp-1120786,\"{'V': 2.0, 'Bi': 4.0}\",\"('Bi', 'V')\",OTHERS,False\nmp-1209066,\"{'Sb': 4.0, 'As': 2.0, 'S': 4.0}\",\"('As', 'S', 'Sb')\",OTHERS,False\nmp-866114,\"{'Hf': 1.0, 'Tc': 2.0, 'W': 1.0}\",\"('Hf', 'Tc', 'W')\",OTHERS,False\nmp-1183173,\"{'Al': 1.0, 'Ga': 1.0, 'Ni': 2.0}\",\"('Al', 'Ga', 'Ni')\",OTHERS,False\nmp-12071,\"{'Ti': 6.0, 'As': 2.0}\",\"('As', 'Ti')\",OTHERS,False\nmp-1100700,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1208320,\"{'Tb': 4.0, 'Ni': 4.0, 'P': 4.0}\",\"('Ni', 'P', 'Tb')\",OTHERS,False\nmp-1238871,\"{'Ti': 4.0, 'Cr': 4.0, 'Ag': 4.0, 'S': 16.0}\",\"('Ag', 'Cr', 'S', 'Ti')\",OTHERS,False\nmp-556006,\"{'Os': 4.0, 'Cl': 16.0, 'O': 4.0}\",\"('Cl', 'O', 'Os')\",OTHERS,False\nmp-1195604,\"{'Mg': 1.0, 'H': 30.0, 'C': 2.0, 'S': 2.0, 'O': 18.0}\",\"('C', 'H', 'Mg', 'O', 'S')\",OTHERS,False\nmp-19771,\"{'Co': 2.0, 'Ge': 2.0, 'Dy': 1.0}\",\"('Co', 'Dy', 'Ge')\",OTHERS,False\nmp-6452,\"{'O': 20.0, 'Zn': 4.0, 'Ba': 4.0, 'Sm': 8.0}\",\"('Ba', 'O', 'Sm', 'Zn')\",OTHERS,False\nmp-3922,\"{'S': 8.0, 'Ag': 4.0, 'Sb': 4.0}\",\"('Ag', 'S', 'Sb')\",OTHERS,False\nmvc-8489,\"{'Ta': 4.0, 'Ni': 4.0, 'P': 8.0, 'O': 36.0}\",\"('Ni', 'O', 'P', 'Ta')\",OTHERS,False\nmp-1221218,\"{'Na': 8.0, 'Eu': 4.0, 'Sn': 2.0, 'As': 8.0}\",\"('As', 'Eu', 'Na', 'Sn')\",OTHERS,False\nmp-1020018,\"{'Li': 20.0, 'B': 4.0, 'S': 16.0, 'O': 64.0}\",\"('B', 'Li', 'O', 'S')\",OTHERS,False\nmp-1201589,\"{'Fe': 6.0, 'Se': 6.0, 'O': 20.0}\",\"('Fe', 'O', 'Se')\",OTHERS,False\nmp-1214100,\"{'Ca': 2.0, 'H': 1.0, 'I': 2.0}\",\"('Ca', 'H', 'I')\",OTHERS,False\nmp-24653,\"{'H': 32.0, 'N': 8.0, 'F': 20.0, 'Ti': 4.0}\",\"('F', 'H', 'N', 'Ti')\",OTHERS,False\nmp-1226695,\"{'Co': 2.0, 'H': 30.0, 'Br': 4.0, 'N': 12.0, 'O': 4.0}\",\"('Br', 'Co', 'H', 'N', 'O')\",OTHERS,False\nmp-1225584,\"{'Er': 1.0, 'Al': 1.0, 'Cu': 4.0}\",\"('Al', 'Cu', 'Er')\",OTHERS,False\nmp-1222566,\"{'Li': 9.0, 'Mn': 3.0, 'N': 4.0}\",\"('Li', 'Mn', 'N')\",OTHERS,False\nmp-1079120,\"{'Nb': 2.0, 'B': 4.0, 'Mo': 4.0}\",\"('B', 'Mo', 'Nb')\",OTHERS,False\nmp-569132,\"{'K': 12.0, 'In': 4.0, 'Cl': 24.0}\",\"('Cl', 'In', 'K')\",OTHERS,False\nmp-978527,\"{'Sm': 1.0, 'Dy': 1.0, 'Tl': 2.0}\",\"('Dy', 'Sm', 'Tl')\",OTHERS,False\nmp-22750,\"{'O': 8.0, 'P': 2.0, 'Pb': 3.0}\",\"('O', 'P', 'Pb')\",OTHERS,False\nmp-12697,\"{'Sc': 1.0, 'Mn': 6.0, 'Sn': 6.0}\",\"('Mn', 'Sc', 'Sn')\",OTHERS,False\nmp-1178571,\"{'Ag': 16.0, 'Ge': 16.0, 'O': 48.0}\",\"('Ag', 'Ge', 'O')\",OTHERS,False\nmp-1197685,\"{'Os': 16.0, 'C': 24.0}\",\"('C', 'Os')\",OTHERS,False\nmp-1069658,\"{'Eu': 1.0, 'Ge': 3.0, 'Rh': 1.0}\",\"('Eu', 'Ge', 'Rh')\",OTHERS,False\nmvc-11047,\"{'Mn': 2.0, 'S': 4.0}\",\"('Mn', 'S')\",OTHERS,False\nmp-735063,\"{'H': 28.0, 'C': 4.0, 'S': 4.0, 'N': 12.0, 'O': 16.0}\",\"('C', 'H', 'N', 'O', 'S')\",OTHERS,False\nmp-775750,\"{'Li': 4.0, 'Fe': 4.0, 'Si': 24.0, 'O': 60.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1216427,\"{'V': 6.0, 'Si': 1.0, 'Os': 1.0}\",\"('Os', 'Si', 'V')\",OTHERS,False\nmp-1227066,\"{'Ce': 1.0, 'Pr': 3.0, 'Fe': 34.0}\",\"('Ce', 'Fe', 'Pr')\",OTHERS,False\nmp-22627,\"{'Ga': 18.0, 'Ru': 6.0, 'Dy': 4.0}\",\"('Dy', 'Ga', 'Ru')\",OTHERS,False\nmp-765639,\"{'Sm': 11.0, 'Y': 5.0, 'O': 24.0}\",\"('O', 'Sm', 'Y')\",OTHERS,False\nmp-21447,\"{'P': 2.0, 'Co': 2.0, 'Nb': 8.0}\",\"('Co', 'Nb', 'P')\",OTHERS,False\nmvc-81,\"{'Ca': 4.0, 'Nb': 4.0, 'Sn': 2.0, 'O': 16.0}\",\"('Ca', 'Nb', 'O', 'Sn')\",OTHERS,False\nmp-1186542,\"{'Pm': 6.0, 'Tm': 2.0}\",\"('Pm', 'Tm')\",OTHERS,False\nmp-757811,\"{'Li': 6.0, 'V': 6.0, 'P': 8.0, 'O': 32.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1266684,\"{'Li': 8.0, 'Si': 8.0, 'Bi': 8.0, 'O': 32.0}\",\"('Bi', 'Li', 'O', 'Si')\",OTHERS,False\nmp-705305,\"{'V': 4.0, 'P': 12.0, 'O': 36.0}\",\"('O', 'P', 'V')\",OTHERS,False\nmp-1228382,\"{'Ba': 2.0, 'Nb': 1.0, 'Co': 1.0, 'O': 6.0}\",\"('Ba', 'Co', 'Nb', 'O')\",OTHERS,False\nmp-1181041,\"{'Ho': 4.0, 'W': 4.0, 'C': 8.0}\",\"('C', 'Ho', 'W')\",OTHERS,False\nmp-545488,\"{'Si': 4.0, 'O': 8.0}\",\"('O', 'Si')\",OTHERS,False\nmp-1078877,\"{'Mg': 1.0, 'Si': 1.0, 'H': 2.0, 'O': 4.0}\",\"('H', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-1194939,\"{'Na': 12.0, 'Mn': 3.0, 'S': 6.0, 'O': 24.0, 'F': 8.0}\",\"('F', 'Mn', 'Na', 'O', 'S')\",OTHERS,False\nmp-1216840,\"{'U': 5.0, 'P': 1.0, 'S': 4.0}\",\"('P', 'S', 'U')\",OTHERS,False\nmp-611062,\"{'Eu': 2.0, 'Sb': 4.0}\",\"('Eu', 'Sb')\",OTHERS,False\nmvc-3987,\"{'Mg': 4.0, 'Cr': 4.0, 'O': 12.0}\",\"('Cr', 'Mg', 'O')\",OTHERS,False\nmp-1213227,\"{'Dy': 4.0, 'Si': 12.0, 'Ni': 20.0}\",\"('Dy', 'Ni', 'Si')\",OTHERS,False\nmp-1233677,\"{'Ca': 1.0, 'Al': 2.0, 'Si': 6.0, 'O': 16.0}\",\"('Al', 'Ca', 'O', 'Si')\",OTHERS,False\nmp-864930,\"{'Li': 1.0, 'Ti': 1.0, 'Ir': 2.0}\",\"('Ir', 'Li', 'Ti')\",OTHERS,False\nmp-12722,\"{'Al': 4.0, 'Pd': 4.0, 'Tb': 4.0}\",\"('Al', 'Pd', 'Tb')\",OTHERS,False\nmp-1093670,\"{'Li': 1.0, 'Hg': 2.0, 'Rh': 1.0}\",\"('Hg', 'Li', 'Rh')\",OTHERS,False\nmp-1219105,\"{'Sm': 1.0, 'Al': 3.0, 'Cu': 1.0}\",\"('Al', 'Cu', 'Sm')\",OTHERS,False\nmvc-4456,\"{'Y': 2.0, 'Sn': 4.0, 'O': 8.0}\",\"('O', 'Sn', 'Y')\",OTHERS,False\nmp-1185219,\"{'La': 1.0, 'Ce': 1.0, 'Zn': 2.0}\",\"('Ce', 'La', 'Zn')\",OTHERS,False\nmp-758121,\"{'Fe': 4.0, 'C': 6.0, 'O': 18.0}\",\"('C', 'Fe', 'O')\",OTHERS,False\nmvc-3034,\"{'Mg': 8.0, 'Co': 10.0, 'Te': 6.0, 'O': 36.0}\",\"('Co', 'Mg', 'O', 'Te')\",OTHERS,False\nmp-1228574,\"{'Ba': 6.0, 'Ca': 6.0, 'Mg': 1.0, 'C': 13.0, 'O': 39.0}\",\"('Ba', 'C', 'Ca', 'Mg', 'O')\",OTHERS,False\nmp-1111171,\"{'K': 3.0, 'Pr': 1.0, 'I': 6.0}\",\"('I', 'K', 'Pr')\",OTHERS,False\nmp-676799,\"{'Ag': 8.0, 'Ge': 1.0, 'Te': 6.0}\",\"('Ag', 'Ge', 'Te')\",OTHERS,False\nmp-3285,\"{'O': 22.0, 'Na': 4.0, 'Ta': 8.0}\",\"('Na', 'O', 'Ta')\",OTHERS,False\nmp-1214095,\"{'Ca': 2.0, 'La': 1.0, 'Mn': 2.0, 'O': 7.0}\",\"('Ca', 'La', 'Mn', 'O')\",OTHERS,False\nmp-753089,\"{'Li': 2.0, 'Cu': 4.0, 'F': 12.0}\",\"('Cu', 'F', 'Li')\",OTHERS,False\nmp-561531,\"{'Y': 4.0, 'Si': 4.0, 'O': 14.0}\",\"('O', 'Si', 'Y')\",OTHERS,False\nmp-753724,\"{'Li': 3.0, 'Ni': 5.0, 'O': 1.0, 'F': 11.0}\",\"('F', 'Li', 'Ni', 'O')\",OTHERS,False\nmp-771823,\"{'Li': 4.0, 'V': 4.0, 'Ni': 4.0, 'O': 16.0}\",\"('Li', 'Ni', 'O', 'V')\",OTHERS,False\nmp-1227032,\"{'Ca': 1.0, 'Mn': 1.0, 'O': 2.0}\",\"('Ca', 'Mn', 'O')\",OTHERS,False\nmp-1201953,\"{'Ba': 6.0, 'Al': 4.0, 'B': 20.0, 'H': 8.0, 'O': 46.0}\",\"('Al', 'B', 'Ba', 'H', 'O')\",OTHERS,False\nmp-1223115,\"{'La': 8.0, 'Sb': 4.0, 'Pb': 2.0}\",\"('La', 'Pb', 'Sb')\",OTHERS,False\nmvc-8890,\"{'Zn': 4.0, 'Bi': 8.0, 'O': 20.0}\",\"('Bi', 'O', 'Zn')\",OTHERS,False\nmp-756912,\"{'Li': 4.0, 'Zn': 4.0, 'B': 4.0, 'O': 12.0}\",\"('B', 'Li', 'O', 'Zn')\",OTHERS,False\nmp-1202407,\"{'La': 4.0, 'B': 20.0, 'H': 20.0, 'N': 4.0, 'O': 56.0}\",\"('B', 'H', 'La', 'N', 'O')\",OTHERS,False\nmp-1079661,\"{'B': 1.0, 'C': 7.0}\",\"('B', 'C')\",OTHERS,False\nmp-1175277,\"{'Li': 7.0, 'Mn': 4.0, 'Co': 1.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1106184,\"{'Mn': 4.0, 'B': 16.0}\",\"('B', 'Mn')\",OTHERS,False\nmp-1212495,\"{'Ge': 4.0, 'P': 8.0}\",\"('Ge', 'P')\",OTHERS,False\nmp-978802,\"{'Sm': 3.0, 'Pd': 1.0}\",\"('Pd', 'Sm')\",OTHERS,False\nmp-1196282,\"{'Cs': 8.0, 'H': 16.0, 'F': 24.0}\",\"('Cs', 'F', 'H')\",OTHERS,False\nmp-12237,\"{'O': 56.0, 'P': 20.0, 'Ho': 4.0}\",\"('Ho', 'O', 'P')\",OTHERS,False\nmp-865091,\"{'Hf': 1.0, 'Mn': 1.0, 'Rh': 2.0}\",\"('Hf', 'Mn', 'Rh')\",OTHERS,False\nmp-775910,\"{'Zr': 10.0, 'O': 20.0}\",\"('O', 'Zr')\",OTHERS,False\nmp-1187292,\"{'Tb': 6.0, 'Ge': 10.0}\",\"('Ge', 'Tb')\",OTHERS,False\nmp-573196,\"{'Cs': 6.0, 'Li': 4.0, 'Ga': 2.0, 'Mo': 8.0, 'O': 32.0}\",\"('Cs', 'Ga', 'Li', 'Mo', 'O')\",OTHERS,False\nmp-1204227,\"{'Al': 4.0, 'N': 12.0, 'O': 72.0}\",\"('Al', 'N', 'O')\",OTHERS,False\nmp-1207421,\"{'Zr': 12.0, 'Zn': 8.0}\",\"('Zn', 'Zr')\",OTHERS,False\nmp-570183,\"{'Pr': 1.0, 'As': 2.0, 'Pd': 2.0}\",\"('As', 'Pd', 'Pr')\",OTHERS,False\nmp-1222685,\"{'La': 1.0, 'Yb': 1.0, 'Al': 4.0}\",\"('Al', 'La', 'Yb')\",OTHERS,False\nmp-1223719,\"{'K': 4.0, 'Co': 4.0, 'Se': 6.0, 'O': 18.0}\",\"('Co', 'K', 'O', 'Se')\",OTHERS,False\nmp-1134210,\"{'Al': 4.0, 'Co': 13.0, 'Si': 2.0, 'Sb': 2.0, 'O': 28.0}\",\"('Al', 'Co', 'O', 'Sb', 'Si')\",OTHERS,False\nmp-1186070,\"{'Na': 3.0, 'Pr': 1.0}\",\"('Na', 'Pr')\",OTHERS,False\nmp-684766,\"{'Na': 1.0, 'Co': 2.0, 'H': 3.0, 'S': 2.0, 'O': 10.0}\",\"('Co', 'H', 'Na', 'O', 'S')\",OTHERS,False\nmp-1216708,\"{'V': 18.0, 'Ni': 12.0}\",\"('Ni', 'V')\",OTHERS,False\nmp-1188041,\"{'Zr': 3.0, 'Mn': 1.0}\",\"('Mn', 'Zr')\",OTHERS,False\nmp-1220691,\"{'Nb': 2.0, 'Cu': 1.0, 'S': 4.0}\",\"('Cu', 'Nb', 'S')\",OTHERS,False\nmp-1219682,\"{'Rb': 4.0, 'Nb': 12.0, 'P': 12.0, 'O': 60.0}\",\"('Nb', 'O', 'P', 'Rb')\",OTHERS,False\nmp-1185020,\"{'La': 1.0, 'Ho': 1.0, 'Tl': 2.0}\",\"('Ho', 'La', 'Tl')\",OTHERS,False\nmp-1066791,\"{'Tm': 1.0, 'Tl': 1.0, 'S': 2.0}\",\"('S', 'Tl', 'Tm')\",OTHERS,False\nmp-1213189,\"{'Cs': 2.0, 'Lu': 2.0, 'Mo': 4.0, 'O': 16.0}\",\"('Cs', 'Lu', 'Mo', 'O')\",OTHERS,False\nmp-772024,\"{'Ba': 12.0, 'La': 4.0, 'Br': 36.0}\",\"('Ba', 'Br', 'La')\",OTHERS,False\nmp-981214,\"{'Sr': 1.0, 'Mg': 5.0}\",\"('Mg', 'Sr')\",OTHERS,False\nmp-1203905,\"{'K': 8.0, 'In': 4.0, 'Cl': 20.0, 'O': 4.0}\",\"('Cl', 'In', 'K', 'O')\",OTHERS,False\nmp-1206823,\"{'Pr': 1.0, 'I': 6.0}\",\"('I', 'Pr')\",OTHERS,False\nmp-1212903,\"{'Er': 4.0, 'Zn': 4.0, 'Rh': 4.0}\",\"('Er', 'Rh', 'Zn')\",OTHERS,False\nmp-720860,\"{'H': 36.0, 'C': 4.0, 'S': 4.0, 'O': 28.0, 'F': 12.0}\",\"('C', 'F', 'H', 'O', 'S')\",OTHERS,False\nmp-1174936,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1365,\"{'Ti': 12.0, 'Ge': 10.0}\",\"('Ge', 'Ti')\",OTHERS,False\nmp-1190931,\"{'Na': 2.0, 'Fe': 2.0, 'Mo': 4.0, 'O': 16.0}\",\"('Fe', 'Mo', 'Na', 'O')\",OTHERS,False\nmp-779863,\"{'Hf': 16.0, 'N': 16.0, 'O': 8.0}\",\"('Hf', 'N', 'O')\",OTHERS,False\nmp-569158,\"{'Dy': 4.0, 'Co': 14.0, 'B': 6.0}\",\"('B', 'Co', 'Dy')\",OTHERS,False\nmp-1217432,\"{'V': 20.0, 'Ga': 1.0, 'Ge': 4.0, 'S': 40.0}\",\"('Ga', 'Ge', 'S', 'V')\",OTHERS,False\nmp-1198712,\"{'Y': 20.0, 'Ir': 12.0}\",\"('Ir', 'Y')\",OTHERS,False\nmp-559420,\"{'Sb': 16.0, 'Pt': 4.0, 'C': 16.0, 'O': 16.0, 'F': 88.0}\",\"('C', 'F', 'O', 'Pt', 'Sb')\",OTHERS,False\nmp-2235,\"{'Rh': 4.0, 'Yb': 2.0}\",\"('Rh', 'Yb')\",OTHERS,False\nmp-1008734,\"{'Th': 1.0, 'H': 2.0}\",\"('H', 'Th')\",OTHERS,False\nmp-11694,\"{'Cs': 2.0, 'Pd': 3.0, 'Se': 4.0}\",\"('Cs', 'Pd', 'Se')\",OTHERS,False\nmp-774897,\"{'Li': 3.0, 'Mn': 2.0, 'Cr': 1.0, 'O': 6.0}\",\"('Cr', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1201880,\"{'Tb': 6.0, 'Co': 8.0, 'Sn': 26.0}\",\"('Co', 'Sn', 'Tb')\",OTHERS,False\nmp-971769,{'Tm': 4.0},\"('Tm',)\",OTHERS,False\nmp-1245979,\"{'Zn': 4.0, 'Co': 4.0, 'N': 8.0}\",\"('Co', 'N', 'Zn')\",OTHERS,False\nmp-654051,\"{'Nb': 12.0, 'S': 2.0, 'I': 18.0}\",\"('I', 'Nb', 'S')\",OTHERS,False\nmp-755038,\"{'Li': 5.0, 'Fe': 1.0, 'O': 3.0, 'F': 2.0}\",\"('F', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1195541,\"{'P': 8.0, 'Pb': 8.0, 'O': 24.0}\",\"('O', 'P', 'Pb')\",OTHERS,False\nmp-1339,\"{'Nb': 1.0, 'Ir': 3.0}\",\"('Ir', 'Nb')\",OTHERS,False\nmp-1209461,\"{'Rb': 4.0, 'W': 6.0, 'Se': 2.0, 'O': 24.0}\",\"('O', 'Rb', 'Se', 'W')\",OTHERS,False\nmp-677094,\"{'Mg': 1.0, 'Ti': 3.0, 'Nb': 2.0, 'Pb': 6.0, 'O': 18.0}\",\"('Mg', 'Nb', 'O', 'Pb', 'Ti')\",OTHERS,False\nmp-30260,\"{'Ni': 21.0, 'Zr': 8.0}\",\"('Ni', 'Zr')\",OTHERS,False\nmp-1227122,\"{'Ca': 1.0, 'Ta': 4.0, 'O': 14.0}\",\"('Ca', 'O', 'Ta')\",OTHERS,False\nmp-1096097,\"{'Sr': 2.0, 'Li': 1.0, 'Ir': 1.0}\",\"('Ir', 'Li', 'Sr')\",OTHERS,False\nmp-774592,\"{'Li': 6.0, 'Co': 3.0, 'Sb': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Co', 'Li', 'O', 'P', 'Sb')\",OTHERS,False\nmp-1105624,\"{'Na': 6.0, 'La': 2.0, 'N': 12.0}\",\"('La', 'N', 'Na')\",OTHERS,False\nmp-772076,\"{'Sr': 4.0, 'Li': 4.0, 'Nb': 5.0, 'Fe': 1.0, 'O': 20.0}\",\"('Fe', 'Li', 'Nb', 'O', 'Sr')\",OTHERS,False\nmp-779424,\"{'Gd': 6.0, 'Ta': 2.0, 'O': 14.0}\",\"('Gd', 'O', 'Ta')\",OTHERS,False\nmp-770822,\"{'Li': 6.0, 'Fe': 2.0, 'Si': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1211449,\"{'K': 2.0, 'Ho': 2.0, 'Mo': 4.0, 'O': 16.0}\",\"('Ho', 'K', 'Mo', 'O')\",OTHERS,False\nmp-1229226,\"{'Al': 8.0, 'Cu': 4.0, 'Si': 8.0, 'O': 32.0, 'F': 4.0}\",\"('Al', 'Cu', 'F', 'O', 'Si')\",OTHERS,False\nmp-1175560,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-757167,\"{'Li': 6.0, 'Si': 3.0, 'Ni': 3.0, 'O': 12.0}\",\"('Li', 'Ni', 'O', 'Si')\",OTHERS,False\nmp-672314,\"{'Mg': 12.0, 'In': 4.0}\",\"('In', 'Mg')\",OTHERS,False\nmp-1207310,\"{'Sm': 2.0, 'Cd': 1.0, 'Sb': 3.0}\",\"('Cd', 'Sb', 'Sm')\",OTHERS,False\nmp-643471,\"{'Ba': 2.0, 'Y': 1.0, 'Cu': 4.0, 'O': 6.0}\",\"('Ba', 'Cu', 'O', 'Y')\",OTHERS,False\nmvc-5821,\"{'Mn': 2.0, 'Zn': 4.0, 'Ir': 2.0, 'O': 12.0}\",\"('Ir', 'Mn', 'O', 'Zn')\",OTHERS,False\nmp-10379,\"{'Cu': 4.0, 'Ge': 2.0, 'Hf': 3.0}\",\"('Cu', 'Ge', 'Hf')\",OTHERS,False\nmp-16253,\"{'Si': 4.0, 'Ca': 4.0, 'Ba': 4.0}\",\"('Ba', 'Ca', 'Si')\",OTHERS,False\nmp-706664,\"{'B': 80.0, 'H': 104.0}\",\"('B', 'H')\",OTHERS,False\nmp-1211478,\"{'K': 2.0, 'Ho': 6.0, 'F': 20.0}\",\"('F', 'Ho', 'K')\",OTHERS,False\nmp-779241,\"{'Li': 2.0, 'Cu': 10.0, 'P': 4.0, 'O': 16.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-1112629,\"{'Cs': 2.0, 'Al': 1.0, 'Hg': 1.0, 'Br': 6.0}\",\"('Al', 'Br', 'Cs', 'Hg')\",OTHERS,False\nmp-1183976,\"{'Cs': 1.0, 'Te': 1.0, 'O': 3.0}\",\"('Cs', 'O', 'Te')\",OTHERS,False\nmp-696275,\"{'Sn': 1.0, 'H': 8.0, 'N': 2.0, 'Cl': 6.0}\",\"('Cl', 'H', 'N', 'Sn')\",OTHERS,False\nmp-771195,\"{'Na': 10.0, 'Ni': 4.0, 'As': 2.0, 'C': 8.0, 'O': 32.0}\",\"('As', 'C', 'Na', 'Ni', 'O')\",OTHERS,False\nmp-697038,\"{'Ba': 2.0, 'H': 6.0, 'Ru': 1.0}\",\"('Ba', 'H', 'Ru')\",OTHERS,False\nmp-1221115,\"{'Na': 1.0, 'Cr': 12.0, 'Si': 8.0, 'Br': 1.0, 'O': 28.0}\",\"('Br', 'Cr', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1100459,\"{'Mg': 8.0, 'Si': 16.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1182139,\"{'Ba': 4.0, 'I': 8.0, 'O': 4.0}\",\"('Ba', 'I', 'O')\",OTHERS,False\nmp-1217550,\"{'Tb': 4.0, 'Nd': 4.0, 'Co': 56.0, 'B': 4.0}\",\"('B', 'Co', 'Nd', 'Tb')\",OTHERS,False\nmp-1100158,\"{'Ba': 1.0, 'Hf': 1.0, 'Mg': 6.0}\",\"('Ba', 'Hf', 'Mg')\",OTHERS,False\nmp-863292,\"{'Na': 6.0, 'V': 2.0, 'P': 4.0, 'O': 18.0}\",\"('Na', 'O', 'P', 'V')\",OTHERS,False\nmp-735553,\"{'V': 12.0, 'P': 8.0, 'H': 48.0, 'C': 12.0, 'N': 8.0, 'O': 60.0}\",\"('C', 'H', 'N', 'O', 'P', 'V')\",OTHERS,False\nmp-1175354,\"{'Li': 7.0, 'Mn': 5.0, 'O': 12.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-1224321,\"{'Hf': 2.0, 'Al': 2.0, 'Zn': 2.0}\",\"('Al', 'Hf', 'Zn')\",OTHERS,False\nmp-892,\"{'Sc': 1.0, 'Pt': 1.0}\",\"('Pt', 'Sc')\",OTHERS,False\nmp-8958,\"{'F': 10.0, 'Rb': 2.0, 'Pd': 4.0}\",\"('F', 'Pd', 'Rb')\",OTHERS,False\nmp-754336,\"{'Li': 2.0, 'Mn': 1.0, 'Fe': 1.0, 'O': 4.0}\",\"('Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1101034,\"{'Ti': 1.0, 'H': 12.0, 'N': 3.0, 'F': 7.0}\",\"('F', 'H', 'N', 'Ti')\",OTHERS,False\nmp-1076939,\"{'Gd': 2.0, 'Mn': 4.0}\",\"('Gd', 'Mn')\",OTHERS,False\nmp-989626,\"{'La': 2.0, 'W': 2.0, 'N': 6.0}\",\"('La', 'N', 'W')\",OTHERS,False\nmp-1039741,\"{'Na': 1.0, 'Hf': 1.0, 'Mg': 30.0, 'O': 31.0}\",\"('Hf', 'Mg', 'Na', 'O')\",OTHERS,False\nmp-867872,\"{'Li': 1.0, 'Cd': 1.0, 'Ag': 2.0}\",\"('Ag', 'Cd', 'Li')\",OTHERS,False\nmp-20223,\"{'O': 16.0, 'Mn': 8.0, 'Ge': 4.0}\",\"('Ge', 'Mn', 'O')\",OTHERS,False\nmvc-7973,\"{'Ca': 4.0, 'Mn': 4.0, 'As': 8.0, 'O': 28.0}\",\"('As', 'Ca', 'Mn', 'O')\",OTHERS,False\nmp-31387,\"{'Ga': 4.0, 'Pd': 4.0, 'Er': 4.0}\",\"('Er', 'Ga', 'Pd')\",OTHERS,False\nmp-1076601,\"{'Sr': 20.0, 'Ca': 12.0, 'Mn': 24.0, 'Fe': 8.0, 'O': 80.0}\",\"('Ca', 'Fe', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-1199267,\"{'Tb': 8.0, 'Fe': 8.0, 'Sb': 24.0}\",\"('Fe', 'Sb', 'Tb')\",OTHERS,False\nmp-555030,\"{'Na': 4.0, 'Y': 4.0, 'P': 16.0, 'O': 48.0}\",\"('Na', 'O', 'P', 'Y')\",OTHERS,False\nmp-1174529,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1178335,\"{'Ga': 2.0, 'Pt': 2.0, 'O': 4.0}\",\"('Ga', 'O', 'Pt')\",OTHERS,False\nmp-1070573,\"{'La': 1.0, 'Al': 3.0, 'Cu': 1.0}\",\"('Al', 'Cu', 'La')\",OTHERS,False\nmp-1111664,\"{'K': 2.0, 'Li': 1.0, 'Sb': 1.0, 'Cl': 6.0}\",\"('Cl', 'K', 'Li', 'Sb')\",OTHERS,False\nmp-779330,\"{'Li': 8.0, 'Mn': 4.0, 'H': 32.0, 'S': 8.0, 'O': 48.0}\",\"('H', 'Li', 'Mn', 'O', 'S')\",OTHERS,False\nmp-1073003,\"{'Mg': 8.0, 'Si': 12.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-31285,\"{'Se': 32.0, 'Pd': 4.0, 'Cs': 8.0}\",\"('Cs', 'Pd', 'Se')\",OTHERS,False\nmp-862791,\"{'Ga': 1.0, 'Cu': 1.0, 'Pt': 2.0}\",\"('Cu', 'Ga', 'Pt')\",OTHERS,False\nmp-1017519,\"{'Ir': 1.0, 'C': 1.0}\",\"('C', 'Ir')\",OTHERS,False\nmp-1220361,\"{'Nb': 1.0, 'O': 3.0}\",\"('Nb', 'O')\",OTHERS,False\nmp-707483,\"{'Ti': 4.0, 'P': 8.0, 'H': 16.0, 'O': 36.0}\",\"('H', 'O', 'P', 'Ti')\",OTHERS,False\nmp-1179847,\"{'Pt': 4.0, 'N': 8.0, 'Cl': 8.0}\",\"('Cl', 'N', 'Pt')\",OTHERS,False\nmp-771773,\"{'Na': 4.0, 'Bi': 2.0, 'B': 2.0, 'P': 2.0, 'O': 14.0}\",\"('B', 'Bi', 'Na', 'O', 'P')\",OTHERS,False\nmp-757324,\"{'Ce': 2.0, 'Zr': 12.0, 'O': 28.0}\",\"('Ce', 'O', 'Zr')\",OTHERS,False\nmp-23893,\"{'H': 20.0, 'N': 4.0, 'O': 4.0, 'Br': 4.0}\",\"('Br', 'H', 'N', 'O')\",OTHERS,False\nmp-942702,\"{'Li': 2.0, 'Mn': 2.0, 'S': 2.0, 'O': 8.0, 'F': 2.0}\",\"('F', 'Li', 'Mn', 'O', 'S')\",OTHERS,False\nmp-605196,\"{'Cs': 2.0, 'Cu': 6.0, 'As': 16.0, 'H': 48.0, 'C': 16.0, 'I': 8.0, 'O': 16.0}\",\"('As', 'C', 'Cs', 'Cu', 'H', 'I', 'O')\",OTHERS,False\nmp-1062228,\"{'Ca': 1.0, 'O': 2.0}\",\"('Ca', 'O')\",OTHERS,False\nmp-1213281,\"{'Eu': 8.0, 'Mo': 4.0, 'O': 24.0}\",\"('Eu', 'Mo', 'O')\",OTHERS,False\nmp-770630,\"{'Rb': 2.0, 'Li': 4.0, 'Cr': 4.0, 'B': 6.0, 'O': 18.0}\",\"('B', 'Cr', 'Li', 'O', 'Rb')\",OTHERS,False\nmp-1208725,\"{'Sr': 3.0, 'Fe': 2.0, 'Ag': 2.0, 'S': 2.0, 'O': 5.0}\",\"('Ag', 'Fe', 'O', 'S', 'Sr')\",OTHERS,False\nmp-1147772,\"{'Ba': 2.0, 'Na': 2.0, 'Cu': 2.0, 'Br': 2.0, 'O': 4.0}\",\"('Ba', 'Br', 'Cu', 'Na', 'O')\",OTHERS,False\nmp-1206431,\"{'In': 2.0, 'Au': 3.0}\",\"('Au', 'In')\",OTHERS,False\nmp-1196267,\"{'Be': 4.0, 'Al': 4.0, 'Si': 4.0, 'O': 20.0}\",\"('Al', 'Be', 'O', 'Si')\",OTHERS,False\nmp-1095714,\"{'La': 1.0, 'Sb': 1.0, 'Pd': 2.0}\",\"('La', 'Pd', 'Sb')\",OTHERS,False\nmp-542664,\"{'Ce': 8.0, 'Se': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Ce', 'O', 'Se', 'Si')\",OTHERS,False\nmp-1248251,\"{'Al': 4.0, 'V': 4.0, 'O': 12.0}\",\"('Al', 'O', 'V')\",OTHERS,False\nmp-1209733,\"{'Sm': 2.0, 'Zr': 12.0, 'P': 18.0, 'O': 72.0}\",\"('O', 'P', 'Sm', 'Zr')\",OTHERS,False\nmp-687239,\"{'K': 10.0, 'Y': 2.0, 'Ni': 4.0, 'N': 24.0, 'O': 48.0}\",\"('K', 'N', 'Ni', 'O', 'Y')\",OTHERS,False\nmp-1199878,\"{'Ba': 4.0, 'U': 8.0, 'S': 20.0}\",\"('Ba', 'S', 'U')\",OTHERS,False\nmp-865014,\"{'Mg': 4.0, 'Cu': 2.0}\",\"('Cu', 'Mg')\",OTHERS,False\nmp-1245839,\"{'Sr': 6.0, 'Ni': 6.0, 'N': 10.0}\",\"('N', 'Ni', 'Sr')\",OTHERS,False\nmp-1174374,\"{'Li': 7.0, 'Co': 5.0, 'O': 12.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-1181299,\"{'Mg': 2.0, 'U': 2.0, 'S': 4.0, 'O': 42.0}\",\"('Mg', 'O', 'S', 'U')\",OTHERS,False\nmp-21048,\"{'P': 4.0, 'Cr': 4.0}\",\"('Cr', 'P')\",OTHERS,False\nmp-1076487,\"{'Sr': 28.0, 'Ca': 4.0, 'Mn': 4.0, 'Fe': 28.0, 'O': 80.0}\",\"('Ca', 'Fe', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-1095465,\"{'Y': 4.0, 'Ge': 4.0, 'Rh': 4.0}\",\"('Ge', 'Rh', 'Y')\",OTHERS,False\nmp-30432,\"{'Rh': 6.0, 'Ba': 2.0, 'Pb': 12.0}\",\"('Ba', 'Pb', 'Rh')\",OTHERS,False\nmp-561248,\"{'Sm': 8.0, 'Cu': 8.0, 'Te': 8.0, 'S': 8.0}\",\"('Cu', 'S', 'Sm', 'Te')\",OTHERS,False\nmp-35612,\"{'F': 1.0, 'O': 1.0, 'Pu': 1.0}\",\"('F', 'O', 'Pu')\",OTHERS,False\nmp-1214392,\"{'Ca': 3.0, 'S': 6.0, 'O': 30.0}\",\"('Ca', 'O', 'S')\",OTHERS,False\nmp-978270,\"{'Mg': 2.0, 'Zr': 6.0}\",\"('Mg', 'Zr')\",OTHERS,False\nmp-1192374,\"{'K': 4.0, 'Ge': 12.0, 'As': 12.0}\",\"('As', 'Ge', 'K')\",OTHERS,False\nmp-764985,\"{'Li': 8.0, 'Fe': 3.0, 'Co': 7.0, 'O': 20.0}\",\"('Co', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1223490,\"{'K': 4.0, 'C': 4.0, 'N': 4.0}\",\"('C', 'K', 'N')\",OTHERS,False\nmp-18673,\"{'O': 16.0, 'P': 4.0, 'Zn': 4.0, 'Cs': 4.0}\",\"('Cs', 'O', 'P', 'Zn')\",OTHERS,False\nmp-1185331,\"{'Li': 1.0, 'Dy': 2.0, 'Rh': 1.0}\",\"('Dy', 'Li', 'Rh')\",OTHERS,False\nmp-1208793,\"{'Sm': 1.0, 'Mg': 5.0, 'Sb': 4.0}\",\"('Mg', 'Sb', 'Sm')\",OTHERS,False\nmp-866536,\"{'Eu': 2.0, 'Na': 1.0, 'Sn': 1.0}\",\"('Eu', 'Na', 'Sn')\",OTHERS,False\nmp-753609,\"{'Li': 2.0, 'Mn': 1.0, 'P': 2.0, 'O': 8.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1247106,\"{'Mn': 4.0, 'Sn': 4.0, 'N': 8.0}\",\"('Mn', 'N', 'Sn')\",OTHERS,False\nmp-1078472,\"{'Ti': 2.0, 'Cu': 2.0, 'Ge': 2.0, 'As': 2.0}\",\"('As', 'Cu', 'Ge', 'Ti')\",OTHERS,False\nmp-1225239,\"{'Fe': 3.0, 'Ge': 2.0}\",\"('Fe', 'Ge')\",OTHERS,False\nmp-1232202,\"{'Ce': 16.0, 'Mg': 8.0, 'S': 32.0}\",\"('Ce', 'Mg', 'S')\",OTHERS,False\nmp-560929,\"{'F': 12.0, 'Na': 6.0, 'Cr': 2.0}\",\"('Cr', 'F', 'Na')\",OTHERS,False\nmp-16623,\"{'Al': 14.0, 'Dy': 2.0, 'Au': 6.0}\",\"('Al', 'Au', 'Dy')\",OTHERS,False\nmp-685124,\"{'Ni': 19.0, 'Se': 20.0}\",\"('Ni', 'Se')\",OTHERS,False\nmvc-5154,\"{'Ca': 4.0, 'Al': 2.0, 'Ag': 2.0, 'O': 10.0}\",\"('Ag', 'Al', 'Ca', 'O')\",OTHERS,False\nmp-1174756,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmvc-2876,\"{'Zn': 1.0, 'Sb': 4.0, 'O': 8.0}\",\"('O', 'Sb', 'Zn')\",OTHERS,False\nmp-7359,\"{'As': 2.0, 'Ag': 2.0, 'Ba': 2.0}\",\"('Ag', 'As', 'Ba')\",OTHERS,False\nmp-1075486,\"{'Mg': 10.0, 'Si': 12.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1370,\"{'Si': 32.0, 'Cs': 32.0}\",\"('Cs', 'Si')\",OTHERS,False\nmp-36733,\"{'Cd': 2.0, 'La': 4.0, 'Se': 8.0}\",\"('Cd', 'La', 'Se')\",OTHERS,False\nmp-559434,\"{'Tb': 4.0, 'B': 12.0, 'O': 24.0}\",\"('B', 'O', 'Tb')\",OTHERS,False\nmvc-8440,\"{'Mg': 4.0, 'Ti': 4.0, 'Si': 16.0, 'O': 40.0}\",\"('Mg', 'O', 'Si', 'Ti')\",OTHERS,False\nmp-989584,\"{'Ca': 6.0, 'Sn': 2.0, 'N': 1.0, 'O': 1.0}\",\"('Ca', 'N', 'O', 'Sn')\",OTHERS,False\nmp-6531,\"{'O': 48.0, 'S': 12.0, 'K': 8.0, 'Mn': 8.0}\",\"('K', 'Mn', 'O', 'S')\",OTHERS,False\nmp-1216268,\"{'Y': 9.0, 'Lu': 3.0, 'Al': 20.0, 'O': 48.0}\",\"('Al', 'Lu', 'O', 'Y')\",OTHERS,False\nmp-759604,\"{'Mn': 12.0, 'O': 4.0, 'F': 32.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-862332,\"{'La': 2.0, 'Hg': 6.0}\",\"('Hg', 'La')\",OTHERS,False\nmp-1212221,\"{'Ho': 10.0, 'Bi': 2.0, 'Pd': 4.0}\",\"('Bi', 'Ho', 'Pd')\",OTHERS,False\nmp-975322,\"{'Rb': 1.0, 'Fe': 1.0, 'O': 3.0}\",\"('Fe', 'O', 'Rb')\",OTHERS,False\nmp-1225359,\"{'Fe': 4.0, 'Cu': 8.0, 'B': 4.0, 'O': 20.0}\",\"('B', 'Cu', 'Fe', 'O')\",OTHERS,False\nmp-568746,\"{'Bi': 4.0, 'Pd': 2.0}\",\"('Bi', 'Pd')\",OTHERS,False\nmp-758740,\"{'Li': 2.0, 'Fe': 8.0, 'P': 6.0, 'O': 24.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-1025296,\"{'Sn': 1.0, 'P': 1.0, 'Pd': 5.0}\",\"('P', 'Pd', 'Sn')\",OTHERS,False\nmp-1177021,\"{'Li': 12.0, 'Mn': 2.0, 'V': 6.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-1202959,\"{'Zn': 18.0, 'S': 18.0}\",\"('S', 'Zn')\",OTHERS,False\nmp-3742,\"{'O': 38.0, 'Fe': 24.0, 'Sr': 2.0}\",\"('Fe', 'O', 'Sr')\",OTHERS,False\nmp-600167,\"{'Cu': 4.0, 'H': 24.0, 'C': 16.0, 'O': 32.0}\",\"('C', 'Cu', 'H', 'O')\",OTHERS,False\nmp-1211979,\"{'K': 4.0, 'Nb': 12.0, 'Cu': 4.0, 'O': 36.0}\",\"('Cu', 'K', 'Nb', 'O')\",OTHERS,False\nmp-1025470,\"{'Lu': 2.0, 'Te': 6.0}\",\"('Lu', 'Te')\",OTHERS,False\nmp-622171,\"{'V': 4.0, 'Sb': 8.0, 'O': 20.0}\",\"('O', 'Sb', 'V')\",OTHERS,False\nmp-1180870,\"{'K': 6.0, 'Na': 2.0, 'Cr': 4.0, 'O': 16.0}\",\"('Cr', 'K', 'Na', 'O')\",OTHERS,False\nmp-25393,\"{'Li': 4.0, 'O': 8.0, 'F': 2.0, 'P': 2.0, 'Fe': 2.0}\",\"('F', 'Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-1220523,\"{'Nb': 12.0, 'Zn': 4.0, 'Au': 4.0}\",\"('Au', 'Nb', 'Zn')\",OTHERS,False\nmp-1102657,\"{'Y': 8.0, 'Pt': 4.0}\",\"('Pt', 'Y')\",OTHERS,False\nmp-1223480,\"{'K': 1.0, 'Nd': 9.0, 'Si': 6.0, 'O': 26.0}\",\"('K', 'Nd', 'O', 'Si')\",OTHERS,False\nmp-915,\"{'Y': 1.0, 'Cd': 1.0}\",\"('Cd', 'Y')\",OTHERS,False\nmp-643367,\"{'Y': 2.0, 'C': 2.0, 'Br': 2.0}\",\"('Br', 'C', 'Y')\",OTHERS,False\nmp-865353,\"{'Tm': 2.0, 'I': 6.0}\",\"('I', 'Tm')\",OTHERS,False\nmp-23453,\"{'Br': 28.0, 'Sb': 20.0, 'Hg': 24.0}\",\"('Br', 'Hg', 'Sb')\",OTHERS,False\nmp-1113725,\"{'Rb': 2.0, 'Er': 1.0, 'Ag': 1.0, 'Cl': 6.0}\",\"('Ag', 'Cl', 'Er', 'Rb')\",OTHERS,False\nmp-1112106,\"{'Cs': 2.0, 'Y': 1.0, 'Tl': 1.0, 'Br': 6.0}\",\"('Br', 'Cs', 'Tl', 'Y')\",OTHERS,False\nmp-997589,\"{'La': 8.0, 'Al': 7.0, 'In': 1.0, 'O': 24.0}\",\"('Al', 'In', 'La', 'O')\",OTHERS,False\nmp-1097494,\"{'Mg': 1.0, 'Pd': 2.0, 'Pb': 1.0}\",\"('Mg', 'Pb', 'Pd')\",OTHERS,False\nmp-27722,\"{'N': 8.0, 'S': 24.0, 'Pd': 4.0}\",\"('N', 'Pd', 'S')\",OTHERS,False\nmp-1212103,\"{'K': 8.0, 'Fe': 8.0, 'P': 12.0, 'O': 48.0}\",\"('Fe', 'K', 'O', 'P')\",OTHERS,False\nmp-773014,\"{'Mn': 3.0, 'S': 6.0, 'O': 24.0}\",\"('Mn', 'O', 'S')\",OTHERS,False\nmp-626547,\"{'Ti': 6.0, 'H': 4.0, 'O': 14.0}\",\"('H', 'O', 'Ti')\",OTHERS,False\nmp-1205255,\"{'U': 4.0, 'N': 4.0, 'O': 32.0}\",\"('N', 'O', 'U')\",OTHERS,False\nmp-1073904,\"{'Mg': 12.0, 'Si': 10.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-11654,\"{'B': 3.0, 'O': 12.0, 'As': 3.0}\",\"('As', 'B', 'O')\",OTHERS,False\nmp-1228556,\"{'Al': 6.0, 'Cu': 2.0, 'B': 4.0, 'O': 17.0}\",\"('Al', 'B', 'Cu', 'O')\",OTHERS,False\nmp-1029282,\"{'Te': 2.0, 'Mo': 2.0, 'W': 2.0, 'Se': 2.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-4136,\"{'O': 16.0, 'P': 4.0, 'Ce': 4.0}\",\"('Ce', 'O', 'P')\",OTHERS,False\nmp-1172967,\"{'Li': 2.0, 'Ni': 4.0, 'O': 8.0}\",\"('Li', 'Ni', 'O')\",OTHERS,False\nmp-1222642,\"{'Li': 2.0, 'Ti': 3.0, 'Zn': 1.0, 'O': 8.0}\",\"('Li', 'O', 'Ti', 'Zn')\",OTHERS,False\nmp-1203386,\"{'K': 8.0, 'H': 12.0, 'Ir': 4.0, 'N': 4.0, 'Cl': 20.0}\",\"('Cl', 'H', 'Ir', 'K', 'N')\",OTHERS,False\nmp-556059,\"{'K': 4.0, 'Ni': 2.0, 'N': 8.0, 'O': 24.0}\",\"('K', 'N', 'Ni', 'O')\",OTHERS,False\nmp-774245,\"{'Li': 5.0, 'Mn': 5.0, 'Ni': 2.0, 'O': 12.0}\",\"('Li', 'Mn', 'Ni', 'O')\",OTHERS,False\nmp-10906,\"{'Al': 2.0, 'Zr': 2.0, 'Pt': 4.0}\",\"('Al', 'Pt', 'Zr')\",OTHERS,False\nmp-685418,\"{'Fe': 16.0, 'Cu': 8.0, 'O': 32.0}\",\"('Cu', 'Fe', 'O')\",OTHERS,False\nmvc-5883,\"{'Ca': 2.0, 'Cu': 4.0, 'O': 8.0}\",\"('Ca', 'Cu', 'O')\",OTHERS,False\nmp-757238,\"{'Gd': 2.0, 'As': 2.0, 'O': 8.0}\",\"('As', 'Gd', 'O')\",OTHERS,False\nmp-1175769,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1195843,\"{'Fe': 4.0, 'H': 32.0, 'S': 4.0, 'O': 32.0}\",\"('Fe', 'H', 'O', 'S')\",OTHERS,False\nmp-1038234,\"{'K': 1.0, 'Mg': 30.0, 'Al': 1.0, 'O': 32.0}\",\"('Al', 'K', 'Mg', 'O')\",OTHERS,False\nmp-862960,\"{'Pm': 1.0, 'Sn': 3.0}\",\"('Pm', 'Sn')\",OTHERS,False\nmp-763524,\"{'Li': 1.0, 'Fe': 2.0, 'C': 4.0, 'O': 12.0}\",\"('C', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1217676,\"{'Tb': 24.0, 'Se': 45.0}\",\"('Se', 'Tb')\",OTHERS,False\nmp-1228524,\"{'Al': 4.0, 'Te': 6.0}\",\"('Al', 'Te')\",OTHERS,False\nmp-3390,\"{'Si': 2.0, 'Cu': 2.0, 'Y': 1.0}\",\"('Cu', 'Si', 'Y')\",OTHERS,False\nmp-1225279,\"{'Dy': 1.0, 'Cu': 1.0, 'Ge': 1.0}\",\"('Cu', 'Dy', 'Ge')\",OTHERS,False\nmp-998421,\"{'K': 2.0, 'Ca': 2.0, 'Cl': 6.0}\",\"('Ca', 'Cl', 'K')\",OTHERS,False\nmp-12075,\"{'B': 2.0, 'Fe': 2.0, 'Tb': 1.0}\",\"('B', 'Fe', 'Tb')\",OTHERS,False\nmp-1185183,\"{'La': 1.0, 'Ce': 1.0, 'Hg': 2.0}\",\"('Ce', 'Hg', 'La')\",OTHERS,False\nmp-1247638,\"{'Sr': 4.0, 'Ca': 28.0, 'Mn': 32.0, 'O': 96.0}\",\"('Ca', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-1197458,\"{'K': 8.0, 'Th': 2.0, 'C': 16.0, 'O': 40.0}\",\"('C', 'K', 'O', 'Th')\",OTHERS,False\nmp-1178447,\"{'Fe': 8.0, 'O': 4.0, 'F': 12.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-1175152,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1194972,\"{'S': 8.0, 'Xe': 4.0, 'O': 24.0, 'F': 8.0}\",\"('F', 'O', 'S', 'Xe')\",OTHERS,False\nmp-562263,\"{'Cs': 8.0, 'Pb': 4.0, 'O': 8.0}\",\"('Cs', 'O', 'Pb')\",OTHERS,False\nmp-531661,\"{'Nd': 10.0, 'O': 39.0, 'Ti': 12.0}\",\"('Nd', 'O', 'Ti')\",OTHERS,False\nmp-1223123,\"{'Li': 2.0, 'Ta': 2.0, 'W': 2.0, 'O': 13.0}\",\"('Li', 'O', 'Ta', 'W')\",OTHERS,False\nmp-1222841,\"{'La': 3.0, 'Si': 3.0, 'B': 3.0, 'O': 15.0}\",\"('B', 'La', 'O', 'Si')\",OTHERS,False\nmp-1192792,\"{'Sr': 4.0, 'Ru': 4.0, 'O': 12.0}\",\"('O', 'Ru', 'Sr')\",OTHERS,False\nmp-1221033,\"{'Na': 1.0, 'Sr': 12.0, 'Ni': 7.0, 'O': 23.0}\",\"('Na', 'Ni', 'O', 'Sr')\",OTHERS,False\nmp-3682,\"{'F': 6.0, 'K': 2.0, 'Cu': 2.0}\",\"('Cu', 'F', 'K')\",OTHERS,False\nmp-15994,\"{'O': 48.0, 'P': 16.0, 'Hg': 8.0}\",\"('Hg', 'O', 'P')\",OTHERS,False\nmp-1106129,\"{'Bi': 4.0, 'Te': 2.0, 'Br': 2.0, 'O': 9.0}\",\"('Bi', 'Br', 'O', 'Te')\",OTHERS,False\nmp-1187893,\"{'Y': 1.0, 'Np': 3.0}\",\"('Np', 'Y')\",OTHERS,False\nmp-867159,\"{'Sm': 1.0, 'Dy': 1.0, 'Mg': 2.0}\",\"('Dy', 'Mg', 'Sm')\",OTHERS,False\nmp-768959,\"{'Na': 8.0, 'Sb': 4.0, 'C': 8.0, 'S': 2.0, 'O': 32.0}\",\"('C', 'Na', 'O', 'S', 'Sb')\",OTHERS,False\nmp-759658,\"{'Li': 4.0, 'Ag': 4.0, 'F': 8.0}\",\"('Ag', 'F', 'Li')\",OTHERS,False\nmp-1196928,\"{'Mg': 2.0, 'Zr': 2.0, 'P': 4.0, 'H': 16.0, 'O': 24.0}\",\"('H', 'Mg', 'O', 'P', 'Zr')\",OTHERS,False\nmp-1201244,\"{'Rb': 12.0, 'Ir': 4.0, 'Br': 24.0, 'O': 4.0}\",\"('Br', 'Ir', 'O', 'Rb')\",OTHERS,False\nmp-1183984,\"{'Cu': 3.0, 'Te': 1.0}\",\"('Cu', 'Te')\",OTHERS,False\nmp-1186207,\"{'Nb': 2.0, 'Re': 1.0, 'Ru': 1.0}\",\"('Nb', 'Re', 'Ru')\",OTHERS,False\nmp-1195614,\"{'La': 4.0, 'Tl': 4.0, 'P': 8.0, 'S': 24.0}\",\"('La', 'P', 'S', 'Tl')\",OTHERS,False\nmp-1174064,\"{'Li': 6.0, 'Mn': 2.0, 'Co': 2.0, 'O': 10.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-510377,\"{'B': 1.0, 'Eu': 1.0, 'Rh': 3.0}\",\"('B', 'Eu', 'Rh')\",OTHERS,False\nmp-570506,\"{'Zr': 4.0, 'I': 8.0}\",\"('I', 'Zr')\",OTHERS,False\nmp-1191016,\"{'Tm': 6.0, 'B': 14.0, 'W': 2.0}\",\"('B', 'Tm', 'W')\",OTHERS,False\nmp-756977,\"{'Ni': 1.0, 'H': 12.0, 'S': 1.0, 'O': 9.0}\",\"('H', 'Ni', 'O', 'S')\",OTHERS,False\nmp-1202032,\"{'Fe': 4.0, 'Sn': 4.0, 'O': 24.0}\",\"('Fe', 'O', 'Sn')\",OTHERS,False\nmp-1039058,\"{'Mg': 4.0, 'Bi': 8.0}\",\"('Bi', 'Mg')\",OTHERS,False\nmp-1072824,\"{'Dy': 2.0, 'Cu': 2.0, 'Sn': 2.0}\",\"('Cu', 'Dy', 'Sn')\",OTHERS,False\nmp-1246430,\"{'Zn': 6.0, 'Mo': 2.0, 'N': 8.0}\",\"('Mo', 'N', 'Zn')\",OTHERS,False\nmp-1203025,\"{'Np': 16.0, 'N': 24.0}\",\"('N', 'Np')\",OTHERS,False\nmp-12990,\"{'C': 2.0, 'Al': 2.0, 'Ti': 4.0}\",\"('Al', 'C', 'Ti')\",OTHERS,False\nmp-1220641,\"{'Nb': 3.0, 'B': 4.0, 'W': 3.0}\",\"('B', 'Nb', 'W')\",OTHERS,False\nmp-554497,\"{'Cu': 1.0, 'Sb': 2.0, 'Xe': 4.0, 'F': 20.0}\",\"('Cu', 'F', 'Sb', 'Xe')\",OTHERS,False\nmp-1024954,\"{'Ho': 2.0, 'Cu': 2.0, 'Sn': 2.0}\",\"('Cu', 'Ho', 'Sn')\",OTHERS,False\nmp-1232305,\"{'Y': 4.0, 'Mg': 4.0, 'Se': 12.0}\",\"('Mg', 'Se', 'Y')\",OTHERS,False\nmp-29815,\"{'As': 16.0, 'La': 8.0}\",\"('As', 'La')\",OTHERS,False\nmp-850399,\"{'Li': 2.0, 'V': 2.0, 'P': 2.0, 'H': 2.0, 'O': 10.0}\",\"('H', 'Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1103416,\"{'Na': 2.0, 'B': 2.0, 'H': 8.0}\",\"('B', 'H', 'Na')\",OTHERS,False\nmp-1175120,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1219621,\"{'Rb': 1.0, 'Cu': 2.0, 'H': 3.0, 'S': 2.0, 'O': 10.0}\",\"('Cu', 'H', 'O', 'Rb', 'S')\",OTHERS,False\nmp-1080109,\"{'Cu': 4.0, 'S': 3.0, 'N': 1.0}\",\"('Cu', 'N', 'S')\",OTHERS,False\nmp-1197395,\"{'K': 4.0, 'Mn': 6.0, 'H': 12.0, 'S': 6.0, 'O': 32.0}\",\"('H', 'K', 'Mn', 'O', 'S')\",OTHERS,False\nmp-1073181,\"{'Mg': 8.0, 'Si': 12.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1187060,\"{'Th': 6.0, 'V': 2.0}\",\"('Th', 'V')\",OTHERS,False\nmp-1178327,\"{'Fe': 8.0, 'O': 14.0, 'F': 2.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-756365,\"{'Ca': 4.0, 'Ce': 4.0, 'O': 12.0}\",\"('Ca', 'Ce', 'O')\",OTHERS,False\nmp-1187802,\"{'Y': 6.0, 'Ge': 2.0}\",\"('Ge', 'Y')\",OTHERS,False\nmvc-9587,\"{'Ca': 2.0, 'Bi': 4.0, 'O': 8.0}\",\"('Bi', 'Ca', 'O')\",OTHERS,False\nmp-1037923,\"{'Mg': 30.0, 'Mn': 1.0, 'Fe': 1.0, 'O': 32.0}\",\"('Fe', 'Mg', 'Mn', 'O')\",OTHERS,False\nmp-1099625,\"{'Sm': 4.0, 'Ni': 4.0, 'O': 10.0}\",\"('Ni', 'O', 'Sm')\",OTHERS,False\nmp-572159,\"{'K': 2.0, 'Fe': 2.0, 'C': 12.0, 'N': 6.0, 'O': 6.0}\",\"('C', 'Fe', 'K', 'N', 'O')\",OTHERS,False\nmp-1178781,\"{'Zn': 4.0, 'P': 6.0, 'O': 32.0}\",\"('O', 'P', 'Zn')\",OTHERS,False\nmp-1101036,\"{'Tl': 90.0, 'Sb': 10.0, 'Te': 60.0}\",\"('Sb', 'Te', 'Tl')\",OTHERS,False\nmp-1113520,\"{'Cs': 2.0, 'Sc': 1.0, 'In': 1.0, 'Br': 6.0}\",\"('Br', 'Cs', 'In', 'Sc')\",OTHERS,False\nmp-672244,\"{'Gd': 1.0, 'Al': 4.0, 'Ge': 2.0, 'Au': 1.0}\",\"('Al', 'Au', 'Gd', 'Ge')\",OTHERS,False\nmp-865562,\"{'Be': 3.0, 'Ru': 1.0}\",\"('Be', 'Ru')\",OTHERS,False\nmp-22541,\"{'Mn': 1.0, 'Ni': 1.0, 'Sb': 1.0}\",\"('Mn', 'Ni', 'Sb')\",OTHERS,False\nmp-1179643,{'S': 4.0},\"('S',)\",OTHERS,False\nmp-685931,\"{'Ba': 20.0, 'Nb': 19.0, 'Se': 60.0}\",\"('Ba', 'Nb', 'Se')\",OTHERS,False\nmp-1194498,\"{'Ba': 2.0, 'Fe': 6.0, 'P': 6.0, 'O': 24.0}\",\"('Ba', 'Fe', 'O', 'P')\",OTHERS,False\nmp-1101339,\"{'Tb': 2.0, 'Ga': 2.0, 'O': 6.0}\",\"('Ga', 'O', 'Tb')\",OTHERS,False\nmp-1073623,\"{'Mg': 4.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-530399,\"{'Al': 20.0, 'Li': 4.0, 'O': 32.0}\",\"('Al', 'Li', 'O')\",OTHERS,False\nmp-1216133,\"{'Yb': 4.0, 'Zr': 1.0, 'Co': 33.0}\",\"('Co', 'Yb', 'Zr')\",OTHERS,False\nmp-1187680,\"{'Yb': 2.0, 'Nb': 6.0}\",\"('Nb', 'Yb')\",OTHERS,False\nmp-1246820,\"{'Re': 2.0, 'Te': 2.0, 'N': 2.0}\",\"('N', 'Re', 'Te')\",OTHERS,False\nmp-12337,\"{'O': 12.0, 'Na': 2.0, 'La': 4.0, 'Os': 2.0}\",\"('La', 'Na', 'O', 'Os')\",OTHERS,False\nmp-1224966,\"{'Fe': 1.0, 'Co': 1.0, 'Se': 2.0}\",\"('Co', 'Fe', 'Se')\",OTHERS,False\nmp-1198056,\"{'Na': 8.0, 'Al': 8.0, 'Si': 8.0, 'O': 32.0}\",\"('Al', 'Na', 'O', 'Si')\",OTHERS,False\nmvc-10213,\"{'Zn': 6.0, 'Co': 12.0, 'O': 24.0}\",\"('Co', 'O', 'Zn')\",OTHERS,False\nmp-20257,\"{'As': 2.0, 'Pd': 6.0, 'Pb': 4.0}\",\"('As', 'Pb', 'Pd')\",OTHERS,False\nmp-1219023,\"{'Sm': 1.0, 'Ga': 3.0, 'Ni': 1.0}\",\"('Ga', 'Ni', 'Sm')\",OTHERS,False\nmvc-9088,\"{'Zn': 2.0, 'Ni': 4.0, 'P': 4.0, 'O': 20.0}\",\"('Ni', 'O', 'P', 'Zn')\",OTHERS,False\nmp-778187,\"{'Li': 8.0, 'Co': 6.0, 'P': 8.0, 'H': 36.0, 'O': 48.0}\",\"('Co', 'H', 'Li', 'O', 'P')\",OTHERS,False\nmp-567199,\"{'Pb': 5.0, 'I': 10.0}\",\"('I', 'Pb')\",OTHERS,False\nmp-555101,\"{'K': 3.0, 'Co': 2.0, 'F': 7.0}\",\"('Co', 'F', 'K')\",OTHERS,False\nmp-1028769,\"{'W': 4.0, 'Se': 6.0, 'S': 2.0}\",\"('S', 'Se', 'W')\",OTHERS,False\nmp-1028795,\"{'Te': 2.0, 'Mo': 1.0, 'W': 3.0, 'Se': 2.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-1214583,\"{'Ba': 2.0, 'Y': 1.0, 'Cu': 2.0, 'Hg': 1.0, 'O': 7.0}\",\"('Ba', 'Cu', 'Hg', 'O', 'Y')\",OTHERS,False\nmp-1190719,\"{'Bi': 16.0, 'Mo': 6.0}\",\"('Bi', 'Mo')\",OTHERS,False\nmp-677627,\"{'Li': 12.0, 'Nd': 12.0, 'Te': 8.0, 'O': 48.0}\",\"('Li', 'Nd', 'O', 'Te')\",OTHERS,False\nmp-755418,\"{'Li': 4.0, 'Mn': 3.0, 'Nb': 3.0, 'Sb': 2.0, 'O': 16.0}\",\"('Li', 'Mn', 'Nb', 'O', 'Sb')\",OTHERS,False\nmp-1099747,\"{'Eu': 4.0, 'Mn': 4.0, 'O': 10.0}\",\"('Eu', 'Mn', 'O')\",OTHERS,False\nmp-754393,\"{'Li': 5.0, 'Mn': 7.0, 'O': 12.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-3526,\"{'In': 3.0, 'Er': 3.0, 'Pt': 3.0}\",\"('Er', 'In', 'Pt')\",OTHERS,False\nmp-9939,\"{'Ge': 2.0, 'Hf': 4.0}\",\"('Ge', 'Hf')\",OTHERS,False\nmp-705147,\"{'Fe': 8.0, 'Ge': 4.0, 'Se': 16.0}\",\"('Fe', 'Ge', 'Se')\",OTHERS,False\nmp-24390,\"{'H': 4.0, 'O': 16.0, 'P': 4.0, 'Ca': 4.0}\",\"('Ca', 'H', 'O', 'P')\",OTHERS,False\nmp-555271,\"{'Ba': 4.0, 'Zn': 4.0, 'Cl': 2.0, 'F': 14.0}\",\"('Ba', 'Cl', 'F', 'Zn')\",OTHERS,False\nmp-1215723,\"{'Zr': 1.0, 'Al': 6.0, 'Fe': 6.0}\",\"('Al', 'Fe', 'Zr')\",OTHERS,False\nmp-867677,\"{'Li': 4.0, 'Ag': 4.0, 'F': 24.0}\",\"('Ag', 'F', 'Li')\",OTHERS,False\nmp-765480,\"{'Li': 4.0, 'V': 4.0, 'F': 20.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-558298,\"{'Zn': 3.0, 'Te': 1.0, 'As': 2.0, 'Pb': 3.0, 'O': 14.0}\",\"('As', 'O', 'Pb', 'Te', 'Zn')\",OTHERS,False\nmp-1190355,\"{'Li': 2.0, 'Al': 2.0, 'Si': 4.0, 'O': 14.0}\",\"('Al', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1213036,\"{'Er': 3.0, 'Ga': 9.0, 'Os': 3.0}\",\"('Er', 'Ga', 'Os')\",OTHERS,False\nmp-1192146,\"{'Nb': 4.0, 'Te': 16.0, 'Os': 4.0}\",\"('Nb', 'Os', 'Te')\",OTHERS,False\nmp-213,\"{'Ca': 1.0, 'Pd': 1.0}\",\"('Ca', 'Pd')\",OTHERS,False\nmp-570293,\"{'Nb': 10.0, 'Ga': 6.0}\",\"('Ga', 'Nb')\",OTHERS,False\nmp-972285,\"{'Sr': 3.0, 'Li': 1.0}\",\"('Li', 'Sr')\",OTHERS,False\nmp-21309,\"{'O': 6.0, 'Mg': 1.0, 'Te': 1.0, 'Pb': 2.0}\",\"('Mg', 'O', 'Pb', 'Te')\",OTHERS,False\nmp-1195937,\"{'Na': 8.0, 'Be': 8.0, 'Si': 24.0, 'O': 64.0}\",\"('Be', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1194721,\"{'Sc': 6.0, 'Mn': 6.0, 'Al': 14.0, 'Si': 10.0}\",\"('Al', 'Mn', 'Sc', 'Si')\",OTHERS,False\nmp-1216668,\"{'V': 6.0, 'Zn': 4.0, 'Fe': 2.0, 'O': 22.0}\",\"('Fe', 'O', 'V', 'Zn')\",OTHERS,False\nmp-1183346,\"{'Ba': 2.0, 'Ca': 6.0}\",\"('Ba', 'Ca')\",OTHERS,False\nmp-3886,\"{'C': 2.0, 'Al': 2.0, 'Zr': 4.0}\",\"('Al', 'C', 'Zr')\",OTHERS,False\nmp-10960,\"{'O': 5.0, 'S': 2.0, 'Ti': 2.0, 'Tb': 2.0}\",\"('O', 'S', 'Tb', 'Ti')\",OTHERS,False\nmp-31268,\"{'Al': 2.0, 'Br': 12.0, 'Bi': 2.0}\",\"('Al', 'Bi', 'Br')\",OTHERS,False\nmp-1187431,\"{'Ti': 2.0, 'Cu': 1.0, 'Ir': 1.0}\",\"('Cu', 'Ir', 'Ti')\",OTHERS,False\nmvc-8819,\"{'Si': 16.0, 'Ni': 4.0, 'O': 40.0}\",\"('Ni', 'O', 'Si')\",OTHERS,False\nmp-5836,\"{'O': 8.0, 'Si': 2.0, 'Th': 2.0}\",\"('O', 'Si', 'Th')\",OTHERS,False\nmvc-11655,\"{'Mg': 1.0, 'Mn': 4.0, 'O': 8.0}\",\"('Mg', 'Mn', 'O')\",OTHERS,False\nmp-1096338,\"{'Na': 1.0, 'Li': 1.0, 'Hg': 2.0}\",\"('Hg', 'Li', 'Na')\",OTHERS,False\nmp-8261,\"{'O': 9.0, 'Ba': 3.0, 'Yb': 4.0}\",\"('Ba', 'O', 'Yb')\",OTHERS,False\nmp-1105554,\"{'Tm': 8.0, 'Re': 4.0, 'C': 8.0}\",\"('C', 'Re', 'Tm')\",OTHERS,False\nmp-1175203,\"{'Li': 7.0, 'Mn': 4.0, 'Co': 1.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-754990,\"{'Li': 4.0, 'V': 2.0, 'P': 4.0, 'H': 2.0, 'O': 16.0}\",\"('H', 'Li', 'O', 'P', 'V')\",OTHERS,False\nmp-625477,\"{'Eu': 2.0, 'H': 6.0, 'O': 6.0}\",\"('Eu', 'H', 'O')\",OTHERS,False\nmp-1100664,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1202854,\"{'Pr': 20.0, 'Mo': 12.0, 'O': 64.0}\",\"('Mo', 'O', 'Pr')\",OTHERS,False\nmp-22896,\"{'Cl': 6.0, 'La': 2.0}\",\"('Cl', 'La')\",OTHERS,False\nmp-706995,\"{'Zn': 4.0, 'Co': 4.0, 'H': 72.0, 'N': 24.0, 'Cl': 20.0}\",\"('Cl', 'Co', 'H', 'N', 'Zn')\",OTHERS,False\nmp-1247556,\"{'K': 1.0, 'Ba': 1.0, 'N': 1.0}\",\"('Ba', 'K', 'N')\",OTHERS,False\nmp-753781,\"{'Eu': 1.0, 'Hf': 1.0, 'O': 3.0}\",\"('Eu', 'Hf', 'O')\",OTHERS,False\nmp-21554,\"{'P': 6.0, 'Pb': 10.0, 'O': 24.0, 'F': 2.0}\",\"('F', 'O', 'P', 'Pb')\",OTHERS,False\nmp-1184239,\"{'Er': 1.0, 'Sc': 1.0, 'Zn': 2.0}\",\"('Er', 'Sc', 'Zn')\",OTHERS,False\nmp-861915,\"{'Li': 1.0, 'Dy': 2.0, 'Ir': 1.0}\",\"('Dy', 'Ir', 'Li')\",OTHERS,False\nmp-21708,\"{'Cu': 24.0, 'Ce': 4.0}\",\"('Ce', 'Cu')\",OTHERS,False\nmp-1220197,\"{'Nd': 1.0, 'Al': 1.0, 'Cu': 4.0}\",\"('Al', 'Cu', 'Nd')\",OTHERS,False\nmp-6183,\"{'O': 12.0, 'Ru': 2.0, 'Ba': 4.0, 'Pr': 2.0}\",\"('Ba', 'O', 'Pr', 'Ru')\",OTHERS,False\nmp-1224097,\"{'K': 4.0, 'N': 2.0, 'O': 5.0}\",\"('K', 'N', 'O')\",OTHERS,False\nmp-1198084,\"{'Ca': 6.0, 'S': 6.0, 'O': 27.0}\",\"('Ca', 'O', 'S')\",OTHERS,False\nmp-1206315,\"{'Sm': 3.0, 'Cd': 3.0, 'Au': 3.0}\",\"('Au', 'Cd', 'Sm')\",OTHERS,False\nmp-560014,\"{'Pr': 6.0, 'Cu': 2.0, 'Sn': 2.0, 'S': 14.0}\",\"('Cu', 'Pr', 'S', 'Sn')\",OTHERS,False\nmp-1218470,\"{'Sr': 1.0, 'Ca': 1.0, 'Ni': 8.0, 'Sn': 4.0}\",\"('Ca', 'Ni', 'Sn', 'Sr')\",OTHERS,False\nmp-7109,\"{'Si': 2.0, 'Lu': 1.0, 'Pt': 2.0}\",\"('Lu', 'Pt', 'Si')\",OTHERS,False\nmp-555276,\"{'Sm': 8.0, 'U': 4.0, 'S': 20.0}\",\"('S', 'Sm', 'U')\",OTHERS,False\nmp-1244551,\"{'Cr': 8.0, 'Bi': 8.0, 'O': 40.0}\",\"('Bi', 'Cr', 'O')\",OTHERS,False\nmp-581967,\"{'Nb': 28.0, 'O': 70.0}\",\"('Nb', 'O')\",OTHERS,False\nmp-1217922,\"{'Ta': 1.0, 'Re': 1.0, 'Se': 4.0}\",\"('Re', 'Se', 'Ta')\",OTHERS,False\nmp-1211736,\"{'K': 2.0, 'Rb': 1.0, 'Pr': 1.0, 'V': 2.0, 'O': 8.0}\",\"('K', 'O', 'Pr', 'Rb', 'V')\",OTHERS,False\nmvc-7646,\"{'Mg': 4.0, 'Sn': 6.0, 'O': 16.0}\",\"('Mg', 'O', 'Sn')\",OTHERS,False\nmp-1093680,\"{'Mg': 1.0, 'Cr': 1.0, 'Pt': 2.0}\",\"('Cr', 'Mg', 'Pt')\",OTHERS,False\nmp-21742,\"{'Ba': 20.0, 'Al': 4.0, 'Ir': 8.0, 'O': 44.0}\",\"('Al', 'Ba', 'Ir', 'O')\",OTHERS,False\nmp-722351,\"{'H': 12.0, 'Pb': 4.0, 'S': 8.0, 'N': 12.0}\",\"('H', 'N', 'Pb', 'S')\",OTHERS,False\nmp-1006891,\"{'K': 1.0, 'Sm': 1.0, 'Se': 2.0}\",\"('K', 'Se', 'Sm')\",OTHERS,False\nmp-766589,\"{'Li': 10.0, 'Co': 16.0, 'O': 32.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-1246592,\"{'Mg': 2.0, 'Fe': 10.0, 'N': 8.0}\",\"('Fe', 'Mg', 'N')\",OTHERS,False\nmp-626879,\"{'V': 12.0, 'H': 8.0, 'O': 32.0}\",\"('H', 'O', 'V')\",OTHERS,False\nmp-1078876,\"{'Er': 3.0, 'Sn': 3.0, 'Ir': 3.0}\",\"('Er', 'Ir', 'Sn')\",OTHERS,False\nmp-1187242,\"{'Ta': 1.0, 'Nb': 1.0, 'Re': 2.0}\",\"('Nb', 'Re', 'Ta')\",OTHERS,False\nmp-1027894,\"{'Mo': 3.0, 'W': 1.0, 'Se': 4.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-1252808,\"{'Al': 8.0, 'B': 24.0, 'H': 112.0, 'N': 8.0}\",\"('Al', 'B', 'H', 'N')\",OTHERS,False\nmp-766215,\"{'Na': 5.0, 'Li': 1.0, 'Fe': 6.0, 'Si': 12.0, 'O': 36.0}\",\"('Fe', 'Li', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1246474,\"{'Zr': 4.0, 'Cr': 4.0, 'Ag': 4.0, 'S': 16.0}\",\"('Ag', 'Cr', 'S', 'Zr')\",OTHERS,False\nmp-1018692,\"{'Er': 2.0, 'Te': 4.0}\",\"('Er', 'Te')\",OTHERS,False\nmp-11447,\"{'Te': 1.0, 'U': 1.0}\",\"('Te', 'U')\",OTHERS,False\nmp-20750,\"{'C': 1.0, 'Sn': 1.0, 'Tb': 3.0}\",\"('C', 'Sn', 'Tb')\",OTHERS,False\nmp-558544,\"{'Mo': 2.0, 'S': 4.0}\",\"('Mo', 'S')\",OTHERS,False\nmp-1216639,\"{'U': 2.0, 'Al': 3.0, 'Si': 3.0}\",\"('Al', 'Si', 'U')\",OTHERS,False\nmp-759027,\"{'Fe': 12.0, 'O': 12.0, 'F': 12.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-1221042,\"{'Na': 2.0, 'Ga': 12.0, 'Ag': 2.0, 'Te': 20.0}\",\"('Ag', 'Ga', 'Na', 'Te')\",OTHERS,False\nmp-677152,\"{'Ho': 10.0, 'Te': 7.0, 'S': 10.0}\",\"('Ho', 'S', 'Te')\",OTHERS,False\nmp-645044,\"{'Mn': 8.0, 'Hg': 16.0, 'C': 48.0, 'S': 48.0, 'N': 48.0}\",\"('C', 'Hg', 'Mn', 'N', 'S')\",OTHERS,False\nmp-640118,\"{'Ce': 4.0, 'Zn': 12.0}\",\"('Ce', 'Zn')\",OTHERS,False\nmp-1094364,\"{'Mg': 12.0, 'Ti': 12.0}\",\"('Mg', 'Ti')\",OTHERS,False\nmvc-2412,\"{'Zn': 2.0, 'Cr': 9.0, 'O': 13.0}\",\"('Cr', 'O', 'Zn')\",OTHERS,False\nmp-359,\"{'P': 6.0, 'Cu': 18.0}\",\"('Cu', 'P')\",OTHERS,False\nmp-1104235,\"{'Hg': 1.0, 'H': 4.0, 'C': 2.0, 'N': 4.0, 'Cl': 2.0}\",\"('C', 'Cl', 'H', 'Hg', 'N')\",OTHERS,False\nmp-1214830,\"{'B': 6.0, 'H': 12.0, 'C': 12.0}\",\"('B', 'C', 'H')\",OTHERS,False\nmp-1220068,\"{'Rb': 4.0, 'U': 12.0, 'Pt': 8.0, 'Se': 34.0}\",\"('Pt', 'Rb', 'Se', 'U')\",OTHERS,False\nmp-1213030,\"{'Er': 8.0, 'Fe': 2.0}\",\"('Er', 'Fe')\",OTHERS,False\nmp-1175581,\"{'Li': 7.0, 'Mn': 5.0, 'O': 12.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-13008,\"{'Cr': 2.0, 'Ge': 6.0, 'Nd': 2.0}\",\"('Cr', 'Ge', 'Nd')\",OTHERS,False\nmp-30934,\"{'S': 24.0, 'As': 1.0, 'I': 3.0}\",\"('As', 'I', 'S')\",OTHERS,False\nmvc-6779,\"{'Zn': 4.0, 'Mo': 8.0, 'O': 16.0}\",\"('Mo', 'O', 'Zn')\",OTHERS,False\nmp-1112650,\"{'Cs': 3.0, 'Sm': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cs', 'Sm')\",OTHERS,False\nmvc-3788,\"{'Ti': 4.0, 'Zn': 4.0, 'O': 12.0}\",\"('O', 'Ti', 'Zn')\",OTHERS,False\nmp-1183941,\"{'Ce': 4.0, 'Mg': 2.0}\",\"('Ce', 'Mg')\",OTHERS,False\nmp-1215958,\"{'Y': 2.0, 'Fe': 2.0, 'Co': 2.0}\",\"('Co', 'Fe', 'Y')\",OTHERS,False\nmp-1196079,\"{'Mg': 24.0, 'Bi': 16.0}\",\"('Bi', 'Mg')\",OTHERS,False\nmvc-5228,\"{'Mg': 6.0, 'Mn': 12.0, 'O': 24.0}\",\"('Mg', 'Mn', 'O')\",OTHERS,False\nmp-1078566,\"{'Ce': 2.0, 'Mn': 2.0, 'Se': 2.0, 'O': 3.0}\",\"('Ce', 'Mn', 'O', 'Se')\",OTHERS,False\nmp-1204595,\"{'Gd': 16.0, 'Si': 8.0, 'S': 12.0, 'O': 28.0}\",\"('Gd', 'O', 'S', 'Si')\",OTHERS,False\nmp-26612,\"{'Fe': 6.0, 'O': 24.0, 'P': 6.0}\",\"('Fe', 'O', 'P')\",OTHERS,False\nmvc-7248,\"{'Ba': 1.0, 'Cu': 1.0, 'Re': 1.0, 'O': 5.0}\",\"('Ba', 'Cu', 'O', 'Re')\",OTHERS,False\nmp-779704,\"{'Si': 2.0, 'Hg': 4.0, 'O': 8.0}\",\"('Hg', 'O', 'Si')\",OTHERS,False\nmp-556063,\"{'Pr': 1.0, 'P': 3.0, 'H': 6.0, 'O': 12.0}\",\"('H', 'O', 'P', 'Pr')\",OTHERS,False\nmp-1062510,\"{'Nd': 1.0, 'Pd': 2.0}\",\"('Nd', 'Pd')\",OTHERS,False\nmp-1099583,\"{'Sm': 1.0, 'Ti': 1.0, 'O': 3.0}\",\"('O', 'Sm', 'Ti')\",OTHERS,False\nmvc-10170,\"{'Mg': 4.0, 'Cu': 8.0, 'Te': 8.0, 'O': 32.0}\",\"('Cu', 'Mg', 'O', 'Te')\",OTHERS,False\nmp-1225530,\"{'Dy': 1.0, 'Ho': 1.0, 'Mn': 4.0}\",\"('Dy', 'Ho', 'Mn')\",OTHERS,False\nmp-1079825,\"{'Ca': 1.0, 'Ti': 2.0, 'O': 6.0}\",\"('Ca', 'O', 'Ti')\",OTHERS,False\nmp-21071,\"{'Eu': 2.0, 'S': 1.0, 'O': 2.0}\",\"('Eu', 'O', 'S')\",OTHERS,False\nmp-31040,\"{'Cl': 8.0, 'Nb': 2.0}\",\"('Cl', 'Nb')\",OTHERS,False\nmp-1030745,\"{'Mo': 2.0, 'W': 2.0, 'Se': 2.0, 'S': 6.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-6328,\"{'O': 1.0, 'Na': 2.0, 'Ti': 2.0, 'Sb': 2.0}\",\"('Na', 'O', 'Sb', 'Ti')\",OTHERS,False\nmp-1185443,\"{'Li': 1.0, 'Yb': 1.0, 'Pb': 2.0}\",\"('Li', 'Pb', 'Yb')\",OTHERS,False\nmp-1029679,\"{'Ba': 2.0, 'Ge': 14.0, 'N': 20.0}\",\"('Ba', 'Ge', 'N')\",OTHERS,False\nmp-1184015,\"{'Ga': 2.0, 'C': 2.0}\",\"('C', 'Ga')\",OTHERS,False\nmp-1205204,\"{'Zn': 8.0, 'Ni': 4.0, 'P': 8.0, 'H': 32.0, 'O': 48.0}\",\"('H', 'Ni', 'O', 'P', 'Zn')\",OTHERS,False\nmp-1096661,\"{'Hf': 2.0, 'Nb': 1.0, 'Ir': 1.0}\",\"('Hf', 'Ir', 'Nb')\",OTHERS,False\nmp-1208206,\"{'Tm': 10.0, 'In': 8.0}\",\"('In', 'Tm')\",OTHERS,False\nmp-865138,\"{'Hf': 2.0, 'Mn': 1.0, 'Ir': 1.0}\",\"('Hf', 'Ir', 'Mn')\",OTHERS,False\nmp-864789,\"{'Yb': 2.0, 'Sn': 1.0, 'Hg': 1.0}\",\"('Hg', 'Sn', 'Yb')\",OTHERS,False\nmp-704745,\"{'Cu': 8.0, 'O': 1.0}\",\"('Cu', 'O')\",OTHERS,False\nmp-1218378,\"{'Sr': 4.0, 'Br': 1.0, 'N': 2.0, 'Cl': 1.0}\",\"('Br', 'Cl', 'N', 'Sr')\",OTHERS,False\nmp-1226014,\"{'Co': 4.0, 'P': 4.0, 'Se': 4.0}\",\"('Co', 'P', 'Se')\",OTHERS,False\nmp-16479,\"{'S': 4.0, 'Yb': 2.0}\",\"('S', 'Yb')\",OTHERS,False\nmp-766711,\"{'Li': 16.0, 'Si': 8.0, 'Ni': 8.0, 'O': 32.0}\",\"('Li', 'Ni', 'O', 'Si')\",OTHERS,False\nmp-560894,\"{'Li': 8.0, 'Be': 6.0, 'P': 6.0, 'Cl': 2.0, 'O': 24.0}\",\"('Be', 'Cl', 'Li', 'O', 'P')\",OTHERS,False\nmp-1027358,\"{'Te': 4.0, 'Mo': 4.0, 'S': 4.0}\",\"('Mo', 'S', 'Te')\",OTHERS,False\nmp-1222061,\"{'Mg': 1.0, 'Al': 1.0, 'Cr': 3.0, 'Fe': 1.0, 'O': 8.0}\",\"('Al', 'Cr', 'Fe', 'Mg', 'O')\",OTHERS,False\nmp-1195450,\"{'Li': 8.0, 'Ca': 4.0, 'B': 32.0, 'H': 28.0, 'O': 70.0}\",\"('B', 'Ca', 'H', 'Li', 'O')\",OTHERS,False\nmp-1245994,\"{'Al': 2.0, 'Cr': 4.0, 'N': 6.0}\",\"('Al', 'Cr', 'N')\",OTHERS,False\nmp-1226126,\"{'Co': 1.0, 'Re': 2.0, 'Mo': 2.0, 'S': 8.0}\",\"('Co', 'Mo', 'Re', 'S')\",OTHERS,False\nmp-707110,\"{'Co': 12.0, 'C': 28.0, 'S': 8.0, 'O': 28.0}\",\"('C', 'Co', 'O', 'S')\",OTHERS,False\nmp-20543,\"{'C': 1.0, 'Sn': 1.0, 'Pr': 3.0}\",\"('C', 'Pr', 'Sn')\",OTHERS,False\nmp-765036,\"{'Li': 4.0, 'Mn': 2.0, 'O': 2.0, 'F': 6.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-14665,\"{'O': 32.0, 'S': 8.0, 'K': 4.0, 'Nd': 4.0}\",\"('K', 'Nd', 'O', 'S')\",OTHERS,False\nmp-1175158,\"{'Li': 7.0, 'Mn': 4.0, 'Co': 1.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1186532,\"{'Pm': 3.0, 'Ti': 1.0}\",\"('Pm', 'Ti')\",OTHERS,False\nmp-1175088,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-571448,\"{'Cs': 2.0, 'Pu': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cs', 'Pu')\",OTHERS,False\nmp-5518,\"{'Ga': 2.0, 'Se': 4.0, 'Ag': 2.0}\",\"('Ag', 'Ga', 'Se')\",OTHERS,False\nmp-849423,\"{'Li': 6.0, 'Mn': 3.0, 'V': 3.0, 'P': 6.0, 'H': 6.0, 'O': 30.0}\",\"('H', 'Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-761204,\"{'Li': 12.0, 'Mn': 2.0, 'V': 6.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-1113566,\"{'Eu': 1.0, 'Cs': 2.0, 'Ag': 1.0, 'Cl': 6.0}\",\"('Ag', 'Cl', 'Cs', 'Eu')\",OTHERS,False\nmp-1190904,\"{'Tb': 12.0, 'Si': 8.0, 'Ni': 4.0}\",\"('Ni', 'Si', 'Tb')\",OTHERS,False\nmp-760091,\"{'Nb': 8.0, 'S': 16.0, 'O': 64.0}\",\"('Nb', 'O', 'S')\",OTHERS,False\nmp-1225825,\"{'La': 3.0, 'Gd': 1.0, 'Br': 4.0, 'O': 4.0}\",\"('Br', 'Gd', 'La', 'O')\",OTHERS,False\nmp-551135,\"{'O': 5.0, 'B': 2.0, 'Cu': 1.0, 'Ba': 1.0}\",\"('B', 'Ba', 'Cu', 'O')\",OTHERS,False\nmp-1079059,\"{'Er': 4.0, 'In': 2.0, 'Au': 4.0}\",\"('Au', 'Er', 'In')\",OTHERS,False\nmp-69,{'Sm': 4.0},\"('Sm',)\",OTHERS,False\nmp-779428,\"{'Sr': 8.0, 'Hf': 2.0, 'O': 12.0}\",\"('Hf', 'O', 'Sr')\",OTHERS,False\nmp-1245132,\"{'Ga': 50.0, 'N': 50.0}\",\"('Ga', 'N')\",OTHERS,False\nmp-1095025,\"{'Pr': 1.0, 'Cr': 2.0, 'B': 6.0}\",\"('B', 'Cr', 'Pr')\",OTHERS,False\nmp-24,{'C': 8.0},\"('C',)\",OTHERS,False\nmp-1093899,\"{'Zr': 1.0, 'Cd': 1.0, 'Pt': 2.0}\",\"('Cd', 'Pt', 'Zr')\",OTHERS,False\nmp-778102,\"{'Ba': 4.0, 'Y': 4.0, 'F': 20.0}\",\"('Ba', 'F', 'Y')\",OTHERS,False\nmp-758280,\"{'Li': 2.0, 'V': 3.0, 'C': 6.0, 'O': 18.0}\",\"('C', 'Li', 'O', 'V')\",OTHERS,False\nmvc-6533,\"{'Ca': 2.0, 'W': 4.0, 'O': 8.0}\",\"('Ca', 'O', 'W')\",OTHERS,False\nmp-1078100,\"{'Cd': 1.0, 'P': 1.0, 'Pd': 5.0}\",\"('Cd', 'P', 'Pd')\",OTHERS,False\nmp-690014,\"{'Ni': 2.0, 'P': 4.0, 'O': 14.0}\",\"('Ni', 'O', 'P')\",OTHERS,False\nmp-1096129,\"{'La': 2.0, 'Hg': 1.0, 'Ru': 1.0}\",\"('Hg', 'La', 'Ru')\",OTHERS,False\nmp-1080452,\"{'K': 1.0, 'Cd': 4.0, 'As': 3.0}\",\"('As', 'Cd', 'K')\",OTHERS,False\nmp-629397,\"{'Fe': 1.0, 'Ni': 1.0, 'N': 1.0}\",\"('Fe', 'N', 'Ni')\",OTHERS,False\nmp-1193017,\"{'Gd': 6.0, 'Zn': 23.0}\",\"('Gd', 'Zn')\",OTHERS,False\nmp-771304,\"{'Li': 6.0, 'Si': 2.0, 'Sb': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Li', 'O', 'Sb', 'Si')\",OTHERS,False\nmp-1206263,\"{'Tm': 3.0, 'Mg': 3.0, 'Ag': 3.0}\",\"('Ag', 'Mg', 'Tm')\",OTHERS,False\nmp-1027051,\"{'Mo': 3.0, 'W': 1.0, 'Se': 4.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-1194116,\"{'Ce': 4.0, 'Mo': 4.0, 'Br': 4.0, 'O': 16.0}\",\"('Br', 'Ce', 'Mo', 'O')\",OTHERS,False\nmp-1200004,\"{'Ho': 18.0, 'C': 18.0, 'O': 72.0}\",\"('C', 'Ho', 'O')\",OTHERS,False\nmp-31426,\"{'Ni': 3.0, 'In': 3.0, 'Ho': 3.0}\",\"('Ho', 'In', 'Ni')\",OTHERS,False\nmp-11334,{'W': 8.0},\"('W',)\",OTHERS,False\nmp-674343,\"{'Ti': 10.0, 'Cu': 7.0, 'S': 20.0}\",\"('Cu', 'S', 'Ti')\",OTHERS,False\nmp-984744,\"{'Ca': 6.0, 'Tl': 2.0}\",\"('Ca', 'Tl')\",OTHERS,False\nmp-1072798,\"{'Er': 2.0, 'Al': 2.0, 'Zn': 2.0}\",\"('Al', 'Er', 'Zn')\",OTHERS,False\nmp-553972,\"{'Cu': 8.0, 'C': 8.0, 'S': 8.0, 'N': 8.0}\",\"('C', 'Cu', 'N', 'S')\",OTHERS,False\nmp-1179159,\"{'Tl': 2.0, 'C': 8.0, 'S': 8.0, 'N': 2.0}\",\"('C', 'N', 'S', 'Tl')\",OTHERS,False\nmp-7723,\"{'F': 4.0, 'Na': 1.0, 'Al': 1.0}\",\"('Al', 'F', 'Na')\",OTHERS,False\nmp-1102615,\"{'Ce': 4.0, 'Te': 4.0, 'Cl': 4.0}\",\"('Ce', 'Cl', 'Te')\",OTHERS,False\nmp-754254,\"{'P': 4.0, 'W': 2.0, 'O': 16.0}\",\"('O', 'P', 'W')\",OTHERS,False\nmp-505611,\"{'O': 2.0, 'S': 2.0, 'Ni': 1.0, 'Cu': 2.0, 'Sr': 2.0}\",\"('Cu', 'Ni', 'O', 'S', 'Sr')\",OTHERS,False\nmp-1114383,\"{'Rb': 2.0, 'Tl': 2.0, 'I': 6.0}\",\"('I', 'Rb', 'Tl')\",OTHERS,False\nmp-1215213,\"{'Zr': 1.0, 'Ta': 1.0, 'Cr': 4.0}\",\"('Cr', 'Ta', 'Zr')\",OTHERS,False\nmvc-16046,\"{'Sr': 3.0, 'Mg': 1.0, 'Mn': 2.0, 'S': 2.0, 'O': 5.0}\",\"('Mg', 'Mn', 'O', 'S', 'Sr')\",OTHERS,False\nmp-1207675,\"{'Tm': 4.0, 'Si': 4.0, 'Ir': 4.0}\",\"('Ir', 'Si', 'Tm')\",OTHERS,False\nmp-777475,\"{'Li': 6.0, 'Fe': 2.0, 'F': 12.0}\",\"('F', 'Fe', 'Li')\",OTHERS,False\nmp-1007685,\"{'In': 1.0, 'Ag': 1.0, 'Se': 2.0}\",\"('Ag', 'In', 'Se')\",OTHERS,False\nmp-26745,\"{'Li': 2.0, 'Sb': 2.0, 'P': 4.0, 'O': 14.0}\",\"('Li', 'O', 'P', 'Sb')\",OTHERS,False\nmp-755301,\"{'Ni': 6.0, 'O': 1.0, 'F': 11.0}\",\"('F', 'Ni', 'O')\",OTHERS,False\nmp-28241,\"{'Zr': 6.0, 'I': 12.0}\",\"('I', 'Zr')\",OTHERS,False\nmp-1191093,\"{'Gd': 2.0, 'Fe': 17.0, 'H': 3.0}\",\"('Fe', 'Gd', 'H')\",OTHERS,False\nmp-1026440,\"{'Hf': 1.0, 'Mg': 14.0, 'Cd': 1.0}\",\"('Cd', 'Hf', 'Mg')\",OTHERS,False\nmp-753398,\"{'W': 4.0, 'O': 4.0, 'F': 12.0}\",\"('F', 'O', 'W')\",OTHERS,False\nmp-1001918,\"{'Hf': 1.0, 'C': 1.0}\",\"('C', 'Hf')\",OTHERS,False\nmp-28828,\"{'Li': 6.0, 'Cl': 8.0, 'Fe': 1.0}\",\"('Cl', 'Fe', 'Li')\",OTHERS,False\nmp-546007,\"{'La': 2.0, 'Mo': 1.0, 'O': 6.0}\",\"('La', 'Mo', 'O')\",OTHERS,False\nmp-1038045,\"{'Ca': 1.0, 'Mg': 30.0, 'Fe': 1.0, 'O': 32.0}\",\"('Ca', 'Fe', 'Mg', 'O')\",OTHERS,False\nmp-972423,\"{'Tb': 2.0, 'Ga': 4.0, 'Pd': 2.0}\",\"('Ga', 'Pd', 'Tb')\",OTHERS,False\nmp-753334,\"{'V': 4.0, 'O': 7.0, 'F': 5.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-1229107,\"{'Ag': 3.0, 'Sn': 1.0}\",\"('Ag', 'Sn')\",OTHERS,False\nmp-1210906,\"{'Na': 2.0, 'Mn': 1.0, 'Sb': 2.0}\",\"('Mn', 'Na', 'Sb')\",OTHERS,False\nmp-20836,\"{'Ni': 4.0, 'As': 4.0, 'Nd': 2.0}\",\"('As', 'Nd', 'Ni')\",OTHERS,False\nmp-1098064,\"{'Cs': 1.0, 'Hf': 1.0, 'Mg': 14.0, 'O': 15.0}\",\"('Cs', 'Hf', 'Mg', 'O')\",OTHERS,False\nmp-20857,\"{'B': 4.0, 'Co': 4.0}\",\"('B', 'Co')\",OTHERS,False\nmp-1186267,\"{'Nd': 6.0, 'Er': 2.0}\",\"('Er', 'Nd')\",OTHERS,False\nmp-29997,\"{'As': 4.0, 'I': 12.0, 'La': 12.0}\",\"('As', 'I', 'La')\",OTHERS,False\nmp-41701,\"{'Li': 2.0, 'Co': 4.0, 'P': 8.0, 'H': 6.0, 'O': 28.0}\",\"('Co', 'H', 'Li', 'O', 'P')\",OTHERS,False\nmp-23965,\"{'H': 16.0, 'O': 8.0, 'Co': 2.0, 'Br': 4.0}\",\"('Br', 'Co', 'H', 'O')\",OTHERS,False\nmp-569819,\"{'Th': 14.0, 'Os': 6.0}\",\"('Os', 'Th')\",OTHERS,False\nmp-21547,\"{'Na': 24.0, 'In': 8.0, 'Sb': 16.0}\",\"('In', 'Na', 'Sb')\",OTHERS,False\nmp-1192391,\"{'Rb': 2.0, 'Li': 4.0, 'Cd': 2.0, 'C': 4.0, 'O': 12.0, 'F': 2.0}\",\"('C', 'Cd', 'F', 'Li', 'O', 'Rb')\",OTHERS,False\nmp-1183106,\"{'Ac': 2.0, 'Zn': 1.0, 'In': 1.0}\",\"('Ac', 'In', 'Zn')\",OTHERS,False\nmp-1207531,\"{'Zn': 12.0, 'B': 28.0, 'I': 4.0, 'O': 52.0}\",\"('B', 'I', 'O', 'Zn')\",OTHERS,False\nmp-771902,\"{'Li': 4.0, 'Ti': 3.0, 'Cr': 2.0, 'Fe': 3.0, 'O': 16.0}\",\"('Cr', 'Fe', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-751967,\"{'V': 8.0, 'Ni': 4.0, 'P': 8.0, 'H': 24.0, 'O': 52.0}\",\"('H', 'Ni', 'O', 'P', 'V')\",OTHERS,False\nmp-13002,\"{'F': 12.0, 'Si': 2.0, 'K': 4.0}\",\"('F', 'K', 'Si')\",OTHERS,False\nmp-1222646,\"{'Li': 2.0, 'Mg': 1.0, 'Hg': 1.0}\",\"('Hg', 'Li', 'Mg')\",OTHERS,False\nmp-1105961,\"{'Th': 4.0, 'Ta': 4.0, 'N': 12.0}\",\"('N', 'Ta', 'Th')\",OTHERS,False\nmp-775811,\"{'Li': 4.0, 'V': 4.0, 'Fe': 4.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'V')\",OTHERS,False\nmvc-4608,\"{'Y': 2.0, 'Cr': 4.0, 'O': 8.0}\",\"('Cr', 'O', 'Y')\",OTHERS,False\nmp-555458,\"{'Ba': 4.0, 'Fe': 2.0, 'S': 4.0, 'I': 5.0}\",\"('Ba', 'Fe', 'I', 'S')\",OTHERS,False\nmp-7167,\"{'C': 1.0, 'Rh': 3.0, 'Sm': 1.0}\",\"('C', 'Rh', 'Sm')\",OTHERS,False\nmp-755965,\"{'Li': 2.0, 'Ti': 2.0, 'Fe': 2.0, 'O': 8.0}\",\"('Fe', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-982730,\"{'Ho': 2.0, 'Zn': 1.0, 'Ir': 1.0}\",\"('Ho', 'Ir', 'Zn')\",OTHERS,False\nmp-561398,\"{'Na': 2.0, 'Al': 2.0, 'Mo': 4.0, 'O': 16.0}\",\"('Al', 'Mo', 'Na', 'O')\",OTHERS,False\nmp-766887,\"{'Li': 13.0, 'Co': 15.0, 'O': 28.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-755278,\"{'Li': 2.0, 'V': 1.0, 'Cr': 1.0, 'O': 4.0}\",\"('Cr', 'Li', 'O', 'V')\",OTHERS,False\nmp-777897,\"{'Li': 4.0, 'Ti': 2.0, 'V': 3.0, 'Cu': 3.0, 'O': 16.0}\",\"('Cu', 'Li', 'O', 'Ti', 'V')\",OTHERS,False\nmp-1205148,\"{'Pr': 2.0, 'H': 40.0, 'S': 8.0, 'N': 10.0, 'O': 32.0}\",\"('H', 'N', 'O', 'Pr', 'S')\",OTHERS,False\nmp-560577,\"{'F': 28.0, 'Ca': 4.0, 'Cr': 4.0, 'Ba': 4.0}\",\"('Ba', 'Ca', 'Cr', 'F')\",OTHERS,False\nmp-754198,\"{'Ba': 1.0, 'La': 2.0, 'I': 8.0}\",\"('Ba', 'I', 'La')\",OTHERS,False\nmp-777464,\"{'Li': 4.0, 'Ti': 5.0, 'Cr': 3.0, 'O': 16.0}\",\"('Cr', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-755995,\"{'P': 4.0, 'W': 2.0, 'O': 14.0}\",\"('O', 'P', 'W')\",OTHERS,False\nmp-1221200,\"{'Na': 8.0, 'Li': 8.0, 'Mn': 2.0, 'Fe': 6.0, 'P': 8.0, 'O': 32.0, 'F': 8.0}\",\"('F', 'Fe', 'Li', 'Mn', 'Na', 'O', 'P')\",OTHERS,False\nmp-1029396,\"{'Sr': 2.0, 'Hf': 2.0, 'N': 4.0}\",\"('Hf', 'N', 'Sr')\",OTHERS,False\nmvc-15700,\"{'Te': 1.0, 'F': 6.0}\",\"('F', 'Te')\",OTHERS,False\nmp-10141,\"{'B': 2.0, 'Sm': 1.0}\",\"('B', 'Sm')\",OTHERS,False\nmp-1225916,\"{'Cs': 2.0, 'C': 2.0, 'N': 2.0, 'O': 2.0}\",\"('C', 'Cs', 'N', 'O')\",OTHERS,False\nmp-570369,\"{'Ce': 32.0, 'Ru': 18.0}\",\"('Ce', 'Ru')\",OTHERS,False\nmp-673174,\"{'Fe': 24.0, 'N': 9.0}\",\"('Fe', 'N')\",OTHERS,False\nmp-771034,\"{'Li': 6.0, 'Mn': 2.0, 'As': 2.0, 'C': 2.0, 'O': 14.0}\",\"('As', 'C', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-983556,\"{'Er': 2.0, 'Mg': 1.0, 'Tl': 1.0}\",\"('Er', 'Mg', 'Tl')\",OTHERS,False\nmp-1238813,\"{'Li': 2.0, 'Cr': 4.0, 'S': 8.0}\",\"('Cr', 'Li', 'S')\",OTHERS,False\nmp-669471,\"{'Hg': 4.0, 'S': 16.0, 'N': 12.0, 'Cl': 12.0}\",\"('Cl', 'Hg', 'N', 'S')\",OTHERS,False\nmp-752571,\"{'Li': 5.0, 'Co': 7.0, 'O': 3.0, 'F': 13.0}\",\"('Co', 'F', 'Li', 'O')\",OTHERS,False\nmp-532220,\"{'Cr': 48.0, 'Dy': 16.0, 'S': 96.0}\",\"('Cr', 'Dy', 'S')\",OTHERS,False\nmp-1029256,\"{'Te': 8.0, 'Mo': 2.0, 'W': 2.0}\",\"('Mo', 'Te', 'W')\",OTHERS,False\nmp-554665,\"{'Si': 8.0, 'O': 16.0}\",\"('O', 'Si')\",OTHERS,False\nmp-1184254,\"{'Ga': 1.0, 'Pd': 3.0}\",\"('Ga', 'Pd')\",OTHERS,False\nmp-1192847,\"{'Ho': 6.0, 'Zn': 23.0}\",\"('Ho', 'Zn')\",OTHERS,False\nmp-5788,\"{'Na': 4.0, 'U': 4.0, 'O': 12.0}\",\"('Na', 'O', 'U')\",OTHERS,False\nmp-19805,\"{'Ge': 2.0, 'Pt': 2.0, 'U': 1.0}\",\"('Ge', 'Pt', 'U')\",OTHERS,False\nmp-558592,\"{'Sb': 32.0, 'Br': 8.0, 'O': 44.0}\",\"('Br', 'O', 'Sb')\",OTHERS,False\nmp-1206332,\"{'Sr': 2.0, 'La': 1.0, 'Sb': 1.0, 'O': 6.0}\",\"('La', 'O', 'Sb', 'Sr')\",OTHERS,False\nmp-1070394,\"{'Ce': 1.0, 'Si': 3.0, 'Rh': 1.0}\",\"('Ce', 'Rh', 'Si')\",OTHERS,False\nmp-1214183,\"{'Ca': 12.0, 'Al': 86.0, 'W': 8.0}\",\"('Al', 'Ca', 'W')\",OTHERS,False\nmp-1204837,\"{'Na': 4.0, 'Fe': 8.0, 'Si': 24.0, 'O': 60.0}\",\"('Fe', 'Na', 'O', 'Si')\",OTHERS,False\nmp-684496,\"{'Sb': 4.0, 'P': 8.0, 'O': 28.0}\",\"('O', 'P', 'Sb')\",OTHERS,False\nmp-1246291,\"{'Dy': 2.0, 'Mg': 2.0, 'W': 2.0, 'S': 8.0}\",\"('Dy', 'Mg', 'S', 'W')\",OTHERS,False\nmp-1038514,\"{'Hf': 1.0, 'Mg': 30.0, 'Cd': 1.0, 'O': 32.0}\",\"('Cd', 'Hf', 'Mg', 'O')\",OTHERS,False\nmp-850751,\"{'Li': 4.0, 'V': 4.0, 'P': 8.0, 'H': 8.0, 'O': 32.0}\",\"('H', 'Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1102068,\"{'Dy': 4.0, 'As': 4.0, 'Pd': 4.0}\",\"('As', 'Dy', 'Pd')\",OTHERS,False\nmp-981262,\"{'Yb': 2.0, 'F': 2.0}\",\"('F', 'Yb')\",OTHERS,False\nmp-1184403,\"{'Gd': 2.0, 'Al': 1.0, 'Cd': 1.0}\",\"('Al', 'Cd', 'Gd')\",OTHERS,False\nmvc-3750,\"{'Mg': 4.0, 'Mo': 4.0, 'F': 20.0}\",\"('F', 'Mg', 'Mo')\",OTHERS,False\nmp-756919,\"{'Mg': 6.0, 'As': 4.0, 'O': 16.0}\",\"('As', 'Mg', 'O')\",OTHERS,False\nmp-1099096,\"{'Ce': 1.0, 'Mg': 14.0, 'Cd': 1.0}\",\"('Cd', 'Ce', 'Mg')\",OTHERS,False\nmp-866108,\"{'Hf': 2.0, 'Zn': 6.0}\",\"('Hf', 'Zn')\",OTHERS,False\nmp-1232334,\"{'Sr': 3.0, 'Cu': 2.0, 'O': 4.0, 'F': 4.0}\",\"('Cu', 'F', 'O', 'Sr')\",OTHERS,False\nmp-21372,\"{'O': 2.0, 'P': 2.0, 'Ru': 2.0, 'Ce': 2.0}\",\"('Ce', 'O', 'P', 'Ru')\",OTHERS,False\nmp-9518,\"{'O': 2.0, 'P': 2.0, 'Zn': 2.0, 'Nd': 2.0}\",\"('Nd', 'O', 'P', 'Zn')\",OTHERS,False\nmp-1219419,\"{'Sc': 5.0, 'P': 12.0, 'Ir': 19.0}\",\"('Ir', 'P', 'Sc')\",OTHERS,False\nmp-863697,\"{'Pm': 2.0, 'Li': 1.0, 'Si': 1.0}\",\"('Li', 'Pm', 'Si')\",OTHERS,False\nmp-755334,\"{'Co': 6.0, 'O': 4.0, 'F': 8.0}\",\"('Co', 'F', 'O')\",OTHERS,False\nmp-769649,\"{'Gd': 2.0, 'Ta': 6.0, 'O': 18.0}\",\"('Gd', 'O', 'Ta')\",OTHERS,False\nmp-1211869,\"{'La': 12.0, 'Sc': 8.0, 'Ga': 12.0, 'O': 48.0}\",\"('Ga', 'La', 'O', 'Sc')\",OTHERS,False\nmp-1216689,\"{'Tl': 1.0, 'Cr': 5.0, 'Te': 8.0}\",\"('Cr', 'Te', 'Tl')\",OTHERS,False\nmp-1236984,\"{'Na': 2.0, 'Mg': 4.0, 'S': 6.0, 'O': 24.0}\",\"('Mg', 'Na', 'O', 'S')\",OTHERS,False\nmp-1251411,\"{'Na': 4.0, 'Al': 4.0, 'H': 32.0, 'N': 16.0}\",\"('Al', 'H', 'N', 'Na')\",OTHERS,False\nmp-762052,\"{'Na': 4.0, 'Ga': 22.0, 'O': 34.0}\",\"('Ga', 'Na', 'O')\",OTHERS,False\nmp-1214838,\"{'Al': 18.0, 'Cr': 6.0, 'Si': 2.0}\",\"('Al', 'Cr', 'Si')\",OTHERS,False\nmp-1247387,\"{'Li': 12.0, 'C': 4.0, 'N': 8.0}\",\"('C', 'Li', 'N')\",OTHERS,False\nmp-1014296,\"{'C': 6.0, 'N': 2.0}\",\"('C', 'N')\",OTHERS,False\nmp-1247156,\"{'Co': 1.0, 'Ni': 2.0, 'N': 2.0}\",\"('Co', 'N', 'Ni')\",OTHERS,False\nmp-1079296,\"{'Hf': 2.0, 'Au': 6.0}\",\"('Au', 'Hf')\",OTHERS,False\nmp-569698,\"{'U': 1.0, 'Al': 2.0, 'Pd': 5.0}\",\"('Al', 'Pd', 'U')\",OTHERS,False\nmp-31456,\"{'Ru': 1.0, 'Sb': 1.0, 'Hf': 1.0}\",\"('Hf', 'Ru', 'Sb')\",OTHERS,False\nmp-1014993,\"{'Cr': 4.0, 'N': 8.0}\",\"('Cr', 'N')\",OTHERS,False\nmp-1173467,\"{'Rb': 2.0, 'Na': 2.0, 'Mg': 10.0, 'Si': 24.0, 'O': 60.0}\",\"('Mg', 'Na', 'O', 'Rb', 'Si')\",OTHERS,False\nmp-780172,\"{'Tm': 8.0, 'Te': 4.0, 'O': 24.0}\",\"('O', 'Te', 'Tm')\",OTHERS,False\nmp-753051,\"{'Li': 2.0, 'Cr': 2.0, 'P': 2.0, 'H': 2.0, 'O': 10.0}\",\"('Cr', 'H', 'Li', 'O', 'P')\",OTHERS,False\nmp-677074,\"{'Pb': 2.0, 'O': 2.0}\",\"('O', 'Pb')\",OTHERS,False\nmp-26739,\"{'Cr': 8.0, 'Li': 12.0, 'O': 48.0, 'P': 12.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-1232447,\"{'Li': 7.0, 'Mn': 4.0, 'Co': 1.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1245775,\"{'Sr': 12.0, 'Ta': 8.0, 'N': 16.0}\",\"('N', 'Sr', 'Ta')\",OTHERS,False\nmp-26532,\"{'O': 20.0, 'P': 6.0, 'Cu': 4.0}\",\"('Cu', 'O', 'P')\",OTHERS,False\nmp-1523,\"{'P': 8.0, 'Zr': 4.0}\",\"('P', 'Zr')\",OTHERS,False\nmp-21268,\"{'Si': 4.0, 'As': 8.0}\",\"('As', 'Si')\",OTHERS,False\nmp-1228516,\"{'Ba': 2.0, 'Nd': 2.0, 'Fe': 1.0, 'Co': 3.0, 'O': 12.0}\",\"('Ba', 'Co', 'Fe', 'Nd', 'O')\",OTHERS,False\nmp-17546,\"{'O': 16.0, 'Ru': 4.0, 'Cs': 8.0}\",\"('Cs', 'O', 'Ru')\",OTHERS,False\nmp-30978,\"{'Al': 8.0, 'K': 4.0, 'Br': 28.0}\",\"('Al', 'Br', 'K')\",OTHERS,False\nmp-1246166,\"{'Zr': 1.0, 'Fe': 2.0, 'N': 2.0}\",\"('Fe', 'N', 'Zr')\",OTHERS,False\nmvc-2922,\"{'Ca': 4.0, 'Mn': 4.0, 'O': 8.0}\",\"('Ca', 'Mn', 'O')\",OTHERS,False\nmp-1194311,\"{'Sc': 3.0, 'Ni': 20.0, 'B': 6.0}\",\"('B', 'Ni', 'Sc')\",OTHERS,False\nmp-1183956,\"{'Cs': 2.0, 'La': 2.0}\",\"('Cs', 'La')\",OTHERS,False\nmp-510471,\"{'Cu': 4.0, 'Gd': 4.0, 'S': 8.0}\",\"('Cu', 'Gd', 'S')\",OTHERS,False\nmp-560637,\"{'Ga': 8.0, 'S': 40.0, 'N': 40.0, 'Cl': 32.0}\",\"('Cl', 'Ga', 'N', 'S')\",OTHERS,False\nmp-1201689,\"{'La': 8.0, 'Ni': 24.0, 'B': 8.0}\",\"('B', 'La', 'Ni')\",OTHERS,False\nmp-1078916,\"{'Tm': 4.0, 'In': 2.0, 'Cu': 4.0}\",\"('Cu', 'In', 'Tm')\",OTHERS,False\nmp-1098545,\"{'Sr': 1.0, 'Hf': 1.0, 'Mg': 30.0, 'O': 32.0}\",\"('Hf', 'Mg', 'O', 'Sr')\",OTHERS,False\nmp-1201234,\"{'Cu': 4.0, 'H': 32.0, 'S': 12.0, 'O': 40.0}\",\"('Cu', 'H', 'O', 'S')\",OTHERS,False\nmp-644245,\"{'Na': 4.0, 'P': 2.0, 'H': 6.0, 'O': 10.0}\",\"('H', 'Na', 'O', 'P')\",OTHERS,False\nmp-1196828,\"{'Na': 4.0, 'Li': 4.0, 'Mg': 4.0, 'P': 4.0, 'O': 16.0, 'F': 4.0}\",\"('F', 'Li', 'Mg', 'Na', 'O', 'P')\",OTHERS,False\nmp-772972,\"{'Li': 8.0, 'Ti': 8.0, 'O': 20.0}\",\"('Li', 'O', 'Ti')\",OTHERS,False\nmp-752698,\"{'Li': 4.0, 'Cr': 2.0, 'Cu': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Cr', 'Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-24118,\"{'H': 20.0, 'O': 16.0, 'S': 2.0}\",\"('H', 'O', 'S')\",OTHERS,False\nmp-1079367,\"{'U': 3.0, 'Al': 3.0, 'Co': 3.0}\",\"('Al', 'Co', 'U')\",OTHERS,False\nmp-1182361,\"{'Ba': 2.0, 'B': 10.0, 'O': 20.0}\",\"('B', 'Ba', 'O')\",OTHERS,False\nmp-12464,\"{'Co': 2.0, 'Se': 4.0, 'Pd': 4.0}\",\"('Co', 'Pd', 'Se')\",OTHERS,False\nmp-1223550,\"{'K': 1.0, 'P': 4.0, 'N': 3.0, 'O': 16.0}\",\"('K', 'N', 'O', 'P')\",OTHERS,False\nmp-765515,\"{'Li': 1.0, 'Co': 15.0, 'O': 16.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-1183449,\"{'Be': 1.0, 'V': 1.0, 'O': 3.0}\",\"('Be', 'O', 'V')\",OTHERS,False\nmp-237,\"{'Te': 1.0, 'Tm': 1.0}\",\"('Te', 'Tm')\",OTHERS,False\nmp-1103859,\"{'Tl': 1.0, 'Mo': 6.0, 'S': 8.0}\",\"('Mo', 'S', 'Tl')\",OTHERS,False\nmp-15257,\"{'Se': 12.0, 'Tb': 8.0}\",\"('Se', 'Tb')\",OTHERS,False\nmp-1185847,\"{'Mg': 1.0, 'Bi': 1.0, 'O': 3.0}\",\"('Bi', 'Mg', 'O')\",OTHERS,False\nmvc-353,\"{'Ba': 2.0, 'Tl': 2.0, 'Co': 4.0, 'O': 12.0}\",\"('Ba', 'Co', 'O', 'Tl')\",OTHERS,False\nmp-637220,\"{'Gd': 2.0, 'Si': 4.0, 'Pt': 4.0}\",\"('Gd', 'Pt', 'Si')\",OTHERS,False\nmp-29536,\"{'C': 4.0, 'F': 16.0, 'Ag': 20.0}\",\"('Ag', 'C', 'F')\",OTHERS,False\nmp-27100,\"{'Cr': 8.0, 'O': 52.0, 'P': 12.0}\",\"('Cr', 'O', 'P')\",OTHERS,False\nmp-1211688,\"{'K': 2.0, 'Li': 2.0, 'Ho': 4.0, 'Mo': 8.0, 'O': 32.0}\",\"('Ho', 'K', 'Li', 'Mo', 'O')\",OTHERS,False\nmp-29979,\"{'B': 6.0, 'C': 2.0, 'Nb': 6.0}\",\"('B', 'C', 'Nb')\",OTHERS,False\nmp-1247586,\"{'Sr': 1.0, 'Ca': 7.0, 'Mn': 6.0, 'Cr': 2.0, 'O': 21.0}\",\"('Ca', 'Cr', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-21526,\"{'K': 16.0, 'Pb': 16.0}\",\"('K', 'Pb')\",OTHERS,False\nmp-1080388,\"{'Pu': 1.0, 'Pt': 5.0}\",\"('Pt', 'Pu')\",OTHERS,False\nmp-770537,\"{'Li': 4.0, 'Mn': 8.0, 'O': 12.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-707040,\"{'Ca': 2.0, 'Al': 4.0, 'H': 32.0, 'N': 18.0}\",\"('Al', 'Ca', 'H', 'N')\",OTHERS,False\nmp-568685,\"{'Tm': 4.0, 'Al': 4.0, 'Rh': 4.0}\",\"('Al', 'Rh', 'Tm')\",OTHERS,False\nmp-779987,\"{'Fe': 6.0, 'O': 2.0, 'F': 10.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-8456,\"{'As': 2.0, 'Sr': 2.0, 'Pt': 2.0}\",\"('As', 'Pt', 'Sr')\",OTHERS,False\nmp-775274,\"{'K': 8.0, 'Cr': 4.0, 'O': 16.0}\",\"('Cr', 'K', 'O')\",OTHERS,False\nmp-753971,\"{'Li': 5.0, 'Fe': 1.0, 'Ni': 4.0, 'O': 10.0}\",\"('Fe', 'Li', 'Ni', 'O')\",OTHERS,False\nmp-504748,\"{'Pb': 10.0, 'P': 6.0, 'O': 24.0, 'Cl': 2.0}\",\"('Cl', 'O', 'P', 'Pb')\",OTHERS,False\nmp-1228616,\"{'Ba': 4.0, 'Pb': 8.0, 'S': 12.0, 'O': 48.0}\",\"('Ba', 'O', 'Pb', 'S')\",OTHERS,False\nmp-1210400,\"{'Na': 6.0, 'Li': 2.0, 'Ti': 4.0, 'F': 24.0}\",\"('F', 'Li', 'Na', 'Ti')\",OTHERS,False\nmp-22784,\"{'Ni': 3.0, 'In': 1.0}\",\"('In', 'Ni')\",OTHERS,False\nmp-780690,\"{'Co': 6.0, 'O': 8.0, 'F': 4.0}\",\"('Co', 'F', 'O')\",OTHERS,False\nmp-744713,\"{'Na': 4.0, 'Fe': 12.0, 'P': 8.0, 'H': 32.0, 'O': 56.0}\",\"('Fe', 'H', 'Na', 'O', 'P')\",OTHERS,False\nmp-770328,\"{'Li': 8.0, 'Cr': 6.0, 'Fe': 2.0, 'O': 16.0}\",\"('Cr', 'Fe', 'Li', 'O')\",OTHERS,False\nmvc-6159,\"{'Ca': 4.0, 'Y': 2.0, 'Sb': 2.0, 'O': 10.0}\",\"('Ca', 'O', 'Sb', 'Y')\",OTHERS,False\nmp-1099308,\"{'Ba': 1.0, 'Li': 1.0, 'Mg': 6.0}\",\"('Ba', 'Li', 'Mg')\",OTHERS,False\nmp-552185,\"{'O': 6.0, 'N': 2.0, 'Ag': 2.0}\",\"('Ag', 'N', 'O')\",OTHERS,False\nmp-1175655,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-866021,\"{'Eu': 1.0, 'In': 1.0, 'Au': 2.0}\",\"('Au', 'Eu', 'In')\",OTHERS,False\nmp-1100441,\"{'Cr': 1.0, 'Cu': 1.0, 'B': 1.0}\",\"('B', 'Cr', 'Cu')\",OTHERS,False\nmp-1190799,\"{'Ca': 2.0, 'P': 4.0, 'O': 18.0}\",\"('Ca', 'O', 'P')\",OTHERS,False\nmp-1177411,\"{'Li': 4.0, 'Fe': 3.0, 'Ni': 2.0, 'Sb': 3.0, 'O': 16.0}\",\"('Fe', 'Li', 'Ni', 'O', 'Sb')\",OTHERS,False\nmp-1225961,\"{'Cs': 2.0, 'Sc': 2.0, 'Cu': 2.0, 'F': 12.0}\",\"('Cs', 'Cu', 'F', 'Sc')\",OTHERS,False\nmp-1183321,\"{'Ba': 1.0, 'Sr': 2.0, 'Ca': 1.0}\",\"('Ba', 'Ca', 'Sr')\",OTHERS,False\nmp-755996,\"{'Li': 5.0, 'Fe': 3.0, 'Cu': 2.0, 'O': 10.0}\",\"('Cu', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1207480,\"{'Y': 4.0, 'Mn': 2.0, 'C': 8.0}\",\"('C', 'Mn', 'Y')\",OTHERS,False\nmp-672215,\"{'Gd': 1.0, 'P': 2.0, 'Ru': 2.0}\",\"('Gd', 'P', 'Ru')\",OTHERS,False\nmp-755893,\"{'Ca': 4.0, 'Bi': 4.0, 'O': 12.0}\",\"('Bi', 'Ca', 'O')\",OTHERS,False\nmp-1238269,\"{'Tl': 8.0, 'C': 2.0, 'O': 10.0}\",\"('C', 'O', 'Tl')\",OTHERS,False\nmp-983565,\"{'Cu': 2.0, 'B': 2.0, 'Se': 4.0}\",\"('B', 'Cu', 'Se')\",OTHERS,False\nmp-353,\"{'O': 2.0, 'Ag': 4.0}\",\"('Ag', 'O')\",OTHERS,False\nmp-1196047,\"{'Cs': 4.0, 'V': 8.0, 'Sb': 4.0, 'O': 32.0}\",\"('Cs', 'O', 'Sb', 'V')\",OTHERS,False\nmp-556666,\"{'Ca': 4.0, 'Bi': 4.0, 'C': 4.0, 'O': 16.0, 'F': 4.0}\",\"('Bi', 'C', 'Ca', 'F', 'O')\",OTHERS,False\nmvc-6574,\"{'Y': 2.0, 'Fe': 6.0, 'F': 30.0}\",\"('F', 'Fe', 'Y')\",OTHERS,False\nmp-17033,\"{'F': 24.0, 'Al': 4.0, 'Pd': 4.0, 'Cs': 4.0}\",\"('Al', 'Cs', 'F', 'Pd')\",OTHERS,False\nmp-774402,\"{'V': 1.0, 'Cr': 3.0, 'P': 6.0, 'O': 24.0}\",\"('Cr', 'O', 'P', 'V')\",OTHERS,False\nmp-1078086,\"{'In': 2.0, 'Ni': 3.0, 'Se': 2.0}\",\"('In', 'Ni', 'Se')\",OTHERS,False\nmp-4057,\"{'B': 4.0, 'F': 16.0, 'Cs': 4.0}\",\"('B', 'Cs', 'F')\",OTHERS,False\nmp-1228779,\"{'Ba': 8.0, 'Ca': 5.0, 'Mn': 3.0, 'Fe': 8.0, 'F': 56.0}\",\"('Ba', 'Ca', 'F', 'Fe', 'Mn')\",OTHERS,False\nmp-984356,\"{'Cs': 1.0, 'Ir': 1.0, 'O': 3.0}\",\"('Cs', 'Ir', 'O')\",OTHERS,False\nmp-1005693,\"{'Sm': 2.0, 'In': 1.0, 'Hg': 1.0}\",\"('Hg', 'In', 'Sm')\",OTHERS,False\nmp-1186252,\"{'Nd': 4.0, 'Mg': 2.0}\",\"('Mg', 'Nd')\",OTHERS,False\nmp-754349,\"{'Gd': 2.0, 'O': 2.0, 'F': 2.0}\",\"('F', 'Gd', 'O')\",OTHERS,False\nmp-1223232,\"{'La': 4.0, 'Ce': 1.0, 'Zr': 3.0, 'O': 14.0}\",\"('Ce', 'La', 'O', 'Zr')\",OTHERS,False\nmp-1224476,\"{'Ho': 6.0, 'Ni': 4.0, 'Ru': 8.0}\",\"('Ho', 'Ni', 'Ru')\",OTHERS,False\nmp-1079997,\"{'V': 1.0, 'Pt': 8.0}\",\"('Pt', 'V')\",OTHERS,False\nmp-1207693,\"{'Yb': 12.0, 'Al': 12.0, 'Cr': 8.0, 'O': 48.0}\",\"('Al', 'Cr', 'O', 'Yb')\",OTHERS,False\nmp-25969,\"{'Li': 2.0, 'O': 24.0, 'P': 6.0, 'Mn': 8.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1223665,\"{'Ir': 1.0, 'Pt': 1.0}\",\"('Ir', 'Pt')\",OTHERS,False\nmp-1075406,\"{'Mg': 10.0, 'Si': 12.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-553880,\"{'Zn': 26.0, 'S': 26.0}\",\"('S', 'Zn')\",OTHERS,False\nmp-8578,\"{'C': 1.0, 'Sc': 3.0, 'In': 1.0}\",\"('C', 'In', 'Sc')\",OTHERS,False\nmp-1079654,\"{'Sm': 2.0, 'Mn': 1.0, 'Ga': 6.0}\",\"('Ga', 'Mn', 'Sm')\",OTHERS,False\nmp-849527,\"{'Rb': 12.0, 'In': 4.0, 'O': 12.0}\",\"('In', 'O', 'Rb')\",OTHERS,False\nmp-1246754,\"{'Li': 24.0, 'Ir': 8.0, 'N': 16.0}\",\"('Ir', 'Li', 'N')\",OTHERS,False\nmp-1986,\"{'Zn': 1.0, 'O': 1.0}\",\"('O', 'Zn')\",OTHERS,False\nmp-15886,\"{'Na': 4.0, 'S': 6.0, 'U': 2.0}\",\"('Na', 'S', 'U')\",OTHERS,False\nmp-571479,\"{'Yb': 8.0, 'Ba': 8.0, 'Sn': 24.0}\",\"('Ba', 'Sn', 'Yb')\",OTHERS,False\nmp-1218879,\"{'Sr': 4.0, 'Al': 4.0, 'Si': 2.0, 'O': 14.0}\",\"('Al', 'O', 'Si', 'Sr')\",OTHERS,False\nmp-677780,\"{'Ca': 4.0, 'Mg': 2.0, 'Si': 4.0, 'O': 14.0}\",\"('Ca', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-1228432,\"{'Ba': 8.0, 'Ta': 2.0, 'Ru': 3.0, 'Br': 2.0, 'O': 18.0}\",\"('Ba', 'Br', 'O', 'Ru', 'Ta')\",OTHERS,False\nmp-1102399,\"{'Fe': 2.0, 'P': 2.0, 'O': 7.0}\",\"('Fe', 'O', 'P')\",OTHERS,False\nmp-775340,\"{'Li': 4.0, 'Cr': 2.0, 'Si': 12.0, 'O': 30.0}\",\"('Cr', 'Li', 'O', 'Si')\",OTHERS,False\nmp-556884,\"{'Ca': 12.0, 'Si': 24.0, 'N': 24.0, 'O': 24.0}\",\"('Ca', 'N', 'O', 'Si')\",OTHERS,False\nmp-6250,\"{'O': 20.0, 'Zn': 4.0, 'Ba': 4.0, 'Tm': 8.0}\",\"('Ba', 'O', 'Tm', 'Zn')\",OTHERS,False\nmp-1194202,\"{'Ba': 2.0, 'Ho': 2.0, 'Fe': 8.0, 'O': 14.0}\",\"('Ba', 'Fe', 'Ho', 'O')\",OTHERS,False\nmp-1101361,\"{'V': 4.0, 'Co': 1.0, 'Cu': 1.0, 'O': 12.0}\",\"('Co', 'Cu', 'O', 'V')\",OTHERS,False\nmp-631308,\"{'Sn': 1.0, 'Ir': 1.0, 'Se': 2.0}\",\"('Ir', 'Se', 'Sn')\",OTHERS,False\nmp-866207,\"{'Lu': 1.0, 'Cd': 1.0, 'Pd': 2.0}\",\"('Cd', 'Lu', 'Pd')\",OTHERS,False\nmvc-4607,\"{'Mo': 4.0, 'O': 10.0}\",\"('Mo', 'O')\",OTHERS,False\nmp-773785,\"{'K': 8.0, 'Te': 4.0, 'O': 16.0}\",\"('K', 'O', 'Te')\",OTHERS,False\nmp-1105840,\"{'Nb': 6.0, 'V': 2.0, 'Se': 12.0}\",\"('Nb', 'Se', 'V')\",OTHERS,False\nmp-694564,\"{'Li': 2.0, 'Fe': 1.0, 'P': 8.0, 'O': 24.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-772609,\"{'La': 8.0, 'Zn': 8.0, 'O': 20.0}\",\"('La', 'O', 'Zn')\",OTHERS,False\nmp-29644,\"{'Ge': 1.0, 'Bi': 4.0, 'Te': 7.0}\",\"('Bi', 'Ge', 'Te')\",OTHERS,False\nmp-1100537,\"{'Li': 9.0, 'Mn': 7.0, 'O': 16.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-649632,\"{'K': 4.0, 'Co': 4.0, 'C': 16.0, 'O': 16.0}\",\"('C', 'Co', 'K', 'O')\",OTHERS,False\nmp-983509,\"{'Na': 6.0, 'Cd': 2.0}\",\"('Cd', 'Na')\",OTHERS,False\nmp-850185,\"{'Li': 6.0, 'Bi': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Bi', 'Li', 'O', 'P')\",OTHERS,False\nmp-755593,\"{'Li': 8.0, 'Co': 2.0, 'O': 6.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-755992,\"{'Fe': 6.0, 'O': 8.0, 'F': 4.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-767409,\"{'Li': 5.0, 'Sb': 1.0, 'S': 1.0}\",\"('Li', 'S', 'Sb')\",OTHERS,False\nmp-1176896,\"{'Li': 12.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1020631,\"{'Zn': 2.0, 'Ge': 2.0, 'O': 6.0}\",\"('Ge', 'O', 'Zn')\",OTHERS,False\nmp-1184567,\"{'Hf': 2.0, 'Tc': 1.0, 'Ir': 1.0}\",\"('Hf', 'Ir', 'Tc')\",OTHERS,False\nmp-1184119,\"{'Cu': 1.0, 'Pd': 3.0}\",\"('Cu', 'Pd')\",OTHERS,False\nmp-675212,\"{'Li': 3.0, 'W': 8.0, 'O': 24.0}\",\"('Li', 'O', 'W')\",OTHERS,False\nmp-1178257,\"{'Fe': 7.0, 'P': 8.0, 'O': 32.0}\",\"('Fe', 'O', 'P')\",OTHERS,False\nmp-862870,\"{'Li': 1.0, 'Tc': 1.0, 'O': 3.0}\",\"('Li', 'O', 'Tc')\",OTHERS,False\nmp-554411,\"{'Cs': 4.0, 'Mn': 4.0, 'F': 16.0}\",\"('Cs', 'F', 'Mn')\",OTHERS,False\nmp-4962,\"{'S': 2.0, 'Co': 2.0, 'Sb': 2.0}\",\"('Co', 'S', 'Sb')\",OTHERS,False\nmp-18761,\"{'O': 8.0, 'K': 3.0, 'V': 3.0}\",\"('K', 'O', 'V')\",OTHERS,False\nmp-1177877,\"{'Li': 2.0, 'Mn': 1.0, 'V': 1.0, 'P': 2.0, 'O': 8.0, 'F': 2.0}\",\"('F', 'Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-1173697,\"{'Na': 2.0, 'La': 4.0, 'Ti': 4.0, 'Mn': 2.0, 'O': 18.0}\",\"('La', 'Mn', 'Na', 'O', 'Ti')\",OTHERS,False\nmp-989511,\"{'Cs': 2.0, 'As': 1.0, 'Br': 1.0, 'Cl': 6.0}\",\"('As', 'Br', 'Cl', 'Cs')\",OTHERS,False\nmp-1222477,\"{'Li': 6.0, 'V': 6.0, 'Zn': 6.0, 'O': 24.0}\",\"('Li', 'O', 'V', 'Zn')\",OTHERS,False\nmp-720295,\"{'U': 4.0, 'H': 40.0, 'N': 8.0, 'O': 28.0}\",\"('H', 'N', 'O', 'U')\",OTHERS,False\nmp-1193284,\"{'Nd': 4.0, 'Mo': 4.0, 'Br': 4.0, 'O': 16.0}\",\"('Br', 'Mo', 'Nd', 'O')\",OTHERS,False\nmp-1246767,\"{'Ga': 20.0, 'Ge': 12.0, 'N': 36.0}\",\"('Ga', 'Ge', 'N')\",OTHERS,False\nmp-972095,\"{'Zn': 6.0, 'Hg': 2.0}\",\"('Hg', 'Zn')\",OTHERS,False\nmp-753268,\"{'Li': 6.0, 'Cu': 1.0, 'F': 8.0}\",\"('Cu', 'F', 'Li')\",OTHERS,False\nmp-1211810,\"{'K': 2.0, 'Li': 2.0, 'Tm': 4.0, 'Mo': 8.0, 'O': 32.0}\",\"('K', 'Li', 'Mo', 'O', 'Tm')\",OTHERS,False\nmp-1217538,\"{'Te': 2.0, 'W': 2.0, 'N': 2.0, 'O': 15.0}\",\"('N', 'O', 'Te', 'W')\",OTHERS,False\nmp-1018140,\"{'Mg': 1.0, 'Ni': 1.0}\",\"('Mg', 'Ni')\",OTHERS,False\nmp-1218068,\"{'Sr': 2.0, 'Pr': 2.0, 'Zn': 2.0, 'Ru': 2.0, 'O': 12.0}\",\"('O', 'Pr', 'Ru', 'Sr', 'Zn')\",OTHERS,False\nmp-1192619,{'C': 29.0},\"('C',)\",OTHERS,False\nmp-1229303,\"{'Ba': 10.0, 'Gd': 1.0, 'Y': 4.0, 'Cu': 15.0, 'O': 35.0}\",\"('Ba', 'Cu', 'Gd', 'O', 'Y')\",OTHERS,False\nmp-1194450,\"{'Ti': 6.0, 'Al': 16.0, 'Rh': 7.0}\",\"('Al', 'Rh', 'Ti')\",OTHERS,False\nmp-1211025,\"{'Na': 6.0, 'Fe': 6.0, 'B': 6.0, 'P': 12.0, 'O': 66.0}\",\"('B', 'Fe', 'Na', 'O', 'P')\",OTHERS,False\nmp-755878,\"{'Cu': 6.0, 'O': 1.0, 'F': 11.0}\",\"('Cu', 'F', 'O')\",OTHERS,False\nmp-19954,\"{'Al': 16.0, 'Cr': 10.0}\",\"('Al', 'Cr')\",OTHERS,False\nmp-20991,\"{'O': 6.0, 'Ba': 2.0, 'Pb': 2.0}\",\"('Ba', 'O', 'Pb')\",OTHERS,False\nmp-1223258,\"{'La': 2.0, 'Si': 3.0, 'Ni': 1.0}\",\"('La', 'Ni', 'Si')\",OTHERS,False\nmp-756391,\"{'Mn': 3.0, 'Ni': 1.0, 'P': 4.0, 'O': 16.0}\",\"('Mn', 'Ni', 'O', 'P')\",OTHERS,False\nmp-982013,\"{'Sr': 6.0, 'Tm': 2.0}\",\"('Sr', 'Tm')\",OTHERS,False\nmp-780658,\"{'Li': 2.0, 'Mn': 6.0, 'P': 12.0, 'O': 40.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-755619,\"{'Sn': 4.0, 'P': 4.0, 'O': 14.0}\",\"('O', 'P', 'Sn')\",OTHERS,False\nmp-1195322,\"{'Rb': 2.0, 'Pd': 1.0, 'S': 8.0, 'O': 26.0}\",\"('O', 'Pd', 'Rb', 'S')\",OTHERS,False\nmp-20424,\"{'C': 1.0, 'Mg': 1.0, 'Co': 3.0}\",\"('C', 'Co', 'Mg')\",OTHERS,False\nmp-861937,\"{'Ba': 2.0, 'As': 1.0, 'Au': 1.0}\",\"('As', 'Au', 'Ba')\",OTHERS,False\nmp-3211,\"{'O': 2.0, 'S': 1.0, 'Nd': 2.0}\",\"('Nd', 'O', 'S')\",OTHERS,False\nmp-1194001,\"{'Pr': 8.0, 'P': 2.0, 'Cl': 2.0, 'O': 16.0}\",\"('Cl', 'O', 'P', 'Pr')\",OTHERS,False\nmp-698063,\"{'Na': 8.0, 'P': 8.0, 'H': 8.0, 'O': 28.0}\",\"('H', 'Na', 'O', 'P')\",OTHERS,False\nmp-1147614,\"{'Li': 24.0, 'Zn': 12.0, 'P': 16.0, 'S': 64.0}\",\"('Li', 'P', 'S', 'Zn')\",OTHERS,False\nmp-1187062,\"{'Th': 6.0, 'Se': 2.0}\",\"('Se', 'Th')\",OTHERS,False\nmp-1208652,\"{'Sr': 2.0, 'Ge': 2.0, 'O': 8.0}\",\"('Ge', 'O', 'Sr')\",OTHERS,False\nmp-1112033,\"{'K': 2.0, 'Hg': 1.0, 'Bi': 1.0, 'Cl': 6.0}\",\"('Bi', 'Cl', 'Hg', 'K')\",OTHERS,False\nmp-1114287,\"{'K': 2.0, 'Ta': 1.0, 'Ag': 1.0, 'F': 6.0}\",\"('Ag', 'F', 'K', 'Ta')\",OTHERS,False\nmp-1246307,\"{'Mg': 22.0, 'C': 4.0, 'N': 20.0}\",\"('C', 'Mg', 'N')\",OTHERS,False\nmvc-13239,\"{'Ti': 2.0, 'F': 8.0}\",\"('F', 'Ti')\",OTHERS,False\nmp-29403,\"{'Cu': 1.0, 'Ga': 1.0, 'I': 4.0}\",\"('Cu', 'Ga', 'I')\",OTHERS,False\nmp-758047,\"{'Mn': 7.0, 'Co': 1.0, 'P': 12.0, 'O': 48.0}\",\"('Co', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1213387,\"{'Dy': 30.0, 'C': 40.0}\",\"('C', 'Dy')\",OTHERS,False\nmp-1184761,\"{'Ir': 3.0, 'Os': 1.0}\",\"('Ir', 'Os')\",OTHERS,False\nmp-757647,\"{'Li': 8.0, 'Mn': 8.0, 'Si': 24.0, 'O': 60.0}\",\"('Li', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1025495,\"{'Nd': 2.0, 'Si': 4.0, 'Rh': 2.0}\",\"('Nd', 'Rh', 'Si')\",OTHERS,False\nmp-1096039,\"{'Mn': 1.0, 'Ag': 1.0, 'Pd': 2.0}\",\"('Ag', 'Mn', 'Pd')\",OTHERS,False\nmp-1176979,\"{'Li': 6.0, 'Fe': 1.0, 'S': 4.0}\",\"('Fe', 'Li', 'S')\",OTHERS,False\nmp-567329,\"{'Cr': 4.0, 'Hg': 24.0, 'As': 16.0, 'Br': 28.0}\",\"('As', 'Br', 'Cr', 'Hg')\",OTHERS,False\nmp-1218483,\"{'Sr': 4.0, 'Zn': 1.0, 'Cu': 1.0, 'W': 2.0, 'O': 12.0}\",\"('Cu', 'O', 'Sr', 'W', 'Zn')\",OTHERS,False\nmp-1179593,\"{'Sr': 12.0, 'S': 24.0, 'O': 120.0}\",\"('O', 'S', 'Sr')\",OTHERS,False\nmp-1176833,\"{'Li': 8.0, 'Mn': 1.0, 'Fe': 7.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1219260,\"{'Sm': 2.0, 'Fe': 17.0, 'C': 1.0, 'N': 1.0}\",\"('C', 'Fe', 'N', 'Sm')\",OTHERS,False\nmvc-2597,\"{'Ba': 2.0, 'Tl': 1.0, 'Bi': 2.0, 'O': 7.0}\",\"('Ba', 'Bi', 'O', 'Tl')\",OTHERS,False\nmp-1100539,\"{'Li': 9.0, 'Mn': 7.0, 'O': 16.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-754605,\"{'Li': 2.0, 'Lu': 2.0, 'O': 4.0}\",\"('Li', 'Lu', 'O')\",OTHERS,False\nmp-779011,\"{'Li': 6.0, 'Mn': 1.0, 'Fe': 5.0, 'B': 6.0, 'O': 18.0}\",\"('B', 'Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1178835,\"{'V': 4.0, 'Re': 4.0, 'O': 22.0}\",\"('O', 'Re', 'V')\",OTHERS,False\nmp-779877,\"{'Li': 6.0, 'Mn': 1.0, 'Fe': 5.0, 'B': 6.0, 'O': 18.0}\",\"('B', 'Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1084832,\"{'Fe': 5.0, 'Co': 3.0}\",\"('Co', 'Fe')\",OTHERS,False\nmp-554174,\"{'Ca': 8.0, 'P': 8.0, 'H': 32.0, 'O': 44.0}\",\"('Ca', 'H', 'O', 'P')\",OTHERS,False\nmp-775320,\"{'Li': 4.0, 'V': 3.0, 'O': 8.0}\",\"('Li', 'O', 'V')\",OTHERS,False\nmp-655591,\"{'Li': 2.0, 'B': 26.0, 'C': 4.0}\",\"('B', 'C', 'Li')\",OTHERS,False\nmp-976055,\"{'Li': 3.0, 'In': 1.0}\",\"('In', 'Li')\",OTHERS,False\nmp-768905,\"{'Li': 24.0, 'Mn': 5.0, 'Cr': 7.0, 'O': 36.0}\",\"('Cr', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1175170,\"{'Li': 7.0, 'Mn': 4.0, 'Co': 1.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-23737,\"{'H': 3.0, 'Mg': 1.0, 'K': 1.0}\",\"('H', 'K', 'Mg')\",OTHERS,False\nmp-26750,\"{'Fe': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Fe', 'O', 'P')\",OTHERS,False\nmp-1188053,\"{'Zr': 6.0, 'Ta': 2.0}\",\"('Ta', 'Zr')\",OTHERS,False\nmp-753638,\"{'Li': 3.0, 'Nb': 2.0, 'Fe': 3.0, 'O': 10.0}\",\"('Fe', 'Li', 'Nb', 'O')\",OTHERS,False\nmp-770852,\"{'Li': 4.0, 'Ti': 2.0, 'Ni': 4.0, 'O': 10.0}\",\"('Li', 'Ni', 'O', 'Ti')\",OTHERS,False\nmp-567705,\"{'Ti': 4.0, 'Al': 8.0}\",\"('Al', 'Ti')\",OTHERS,False\nmp-15793,\"{'Li': 1.0, 'Se': 2.0, 'Tb': 1.0}\",\"('Li', 'Se', 'Tb')\",OTHERS,False\nmvc-7052,\"{'Mo': 2.0, 'S': 4.0}\",\"('Mo', 'S')\",OTHERS,False\nmp-1206907,\"{'Tl': 1.0, 'N': 1.0}\",\"('N', 'Tl')\",OTHERS,False\nmp-1093731,\"{'Ti': 2.0, 'Sn': 1.0, 'Mo': 1.0}\",\"('Mo', 'Sn', 'Ti')\",OTHERS,False\nmp-759907,\"{'Li': 2.0, 'Fe': 3.0, 'Cu': 1.0, 'O': 8.0}\",\"('Cu', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1039319,\"{'Mg': 8.0, 'Cd': 4.0}\",\"('Cd', 'Mg')\",OTHERS,False\nmp-673633,\"{'In': 4.0, 'S': 6.0}\",\"('In', 'S')\",OTHERS,False\nmp-763247,\"{'Li': 2.0, 'V': 2.0, 'Si': 2.0, 'O': 10.0}\",\"('Li', 'O', 'Si', 'V')\",OTHERS,False\nmp-1247354,\"{'Re': 2.0, 'C': 12.0, 'N': 18.0}\",\"('C', 'N', 'Re')\",OTHERS,False\nmp-1224733,\"{'Gd': 2.0, 'Zn': 1.0, 'Ag': 1.0}\",\"('Ag', 'Gd', 'Zn')\",OTHERS,False\nmp-1177532,\"{'Li': 6.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1226389,\"{'Cr': 1.0, 'O': 3.0, 'F': 3.0}\",\"('Cr', 'F', 'O')\",OTHERS,False\nmp-1184550,\"{'Ge': 2.0, 'C': 2.0}\",\"('C', 'Ge')\",OTHERS,False\nmp-756763,\"{'Li': 3.0, 'Cr': 1.0, 'Co': 4.0, 'O': 8.0}\",\"('Co', 'Cr', 'Li', 'O')\",OTHERS,False\nmp-1220146,\"{'Ni': 16.0, 'Bi': 16.0, 'S': 2.0, 'I': 4.0}\",\"('Bi', 'I', 'Ni', 'S')\",OTHERS,False\nmp-767597,\"{'Li': 4.0, 'Mn': 4.0, 'F': 20.0}\",\"('F', 'Li', 'Mn')\",OTHERS,False\nmp-1199056,\"{'Rb': 16.0, 'Ge': 16.0}\",\"('Ge', 'Rb')\",OTHERS,False\nmp-865681,\"{'Ti': 1.0, 'Si': 1.0, 'Ru': 2.0}\",\"('Ru', 'Si', 'Ti')\",OTHERS,False\nmp-1093540,\"{'Li': 2.0, 'Ga': 1.0, 'Bi': 1.0}\",\"('Bi', 'Ga', 'Li')\",OTHERS,False\nmp-1183738,\"{'Ce': 1.0, 'Sb': 3.0}\",\"('Ce', 'Sb')\",OTHERS,False\nmp-1226019,\"{'Co': 5.0, 'Ni': 1.0, 'S': 8.0}\",\"('Co', 'Ni', 'S')\",OTHERS,False\nmp-653887,\"{'Co': 16.0, 'Pb': 4.0, 'C': 64.0, 'O': 64.0}\",\"('C', 'Co', 'O', 'Pb')\",OTHERS,False\nmp-759662,\"{'V': 6.0, 'O': 9.0, 'F': 3.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-1183523,\"{'Ca': 6.0, 'Dy': 2.0}\",\"('Ca', 'Dy')\",OTHERS,False\nmp-1208294,\"{'U': 4.0, 'Cl': 8.0}\",\"('Cl', 'U')\",OTHERS,False\nmp-1025231,\"{'Pr': 2.0, 'Cr': 1.0, 'S': 4.0}\",\"('Cr', 'Pr', 'S')\",OTHERS,False\nmp-1205187,\"{'Sr': 2.0, 'H': 16.0, 'Cl': 4.0, 'O': 24.0}\",\"('Cl', 'H', 'O', 'Sr')\",OTHERS,False\nmp-778861,\"{'Li': 4.0, 'V': 8.0, 'O': 4.0, 'F': 20.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-626216,\"{'Sr': 1.0, 'H': 16.0, 'O': 10.0}\",\"('H', 'O', 'Sr')\",OTHERS,False\nmp-1196708,\"{'Ge': 14.0, 'N': 4.0, 'O': 30.0}\",\"('Ge', 'N', 'O')\",OTHERS,False\nmp-1036480,\"{'Rb': 1.0, 'Mg': 14.0, 'Al': 1.0, 'O': 16.0}\",\"('Al', 'Mg', 'O', 'Rb')\",OTHERS,False\nmp-775774,\"{'Ag': 2.0, 'Bi': 2.0, 'O': 6.0}\",\"('Ag', 'Bi', 'O')\",OTHERS,False\nmp-568998,\"{'Cu': 6.0, 'Se': 6.0}\",\"('Cu', 'Se')\",OTHERS,False\nmp-1102248,\"{'Tm': 4.0, 'Al': 4.0, 'Au': 4.0}\",\"('Al', 'Au', 'Tm')\",OTHERS,False\nmp-15559,\"{'S': 32.0, 'K': 4.0, 'Sb': 20.0}\",\"('K', 'S', 'Sb')\",OTHERS,False\nmp-780798,\"{'Li': 1.0, 'Mn': 2.0, 'Cr': 2.0, 'O': 8.0}\",\"('Cr', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-696416,\"{'Na': 2.0, 'Si': 6.0, 'B': 2.0, 'O': 16.0}\",\"('B', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1033646,\"{'Rb': 1.0, 'Hf': 1.0, 'Mg': 6.0, 'O': 7.0}\",\"('Hf', 'Mg', 'O', 'Rb')\",OTHERS,False\nmp-1207390,\"{'Zn': 2.0, 'Pt': 6.0, 'O': 8.0}\",\"('O', 'Pt', 'Zn')\",OTHERS,False\nmp-756050,\"{'Ta': 4.0, 'Cu': 4.0, 'O': 12.0}\",\"('Cu', 'O', 'Ta')\",OTHERS,False\nmp-558579,\"{'Tl': 4.0, 'H': 4.0, 'C': 4.0, 'O': 8.0}\",\"('C', 'H', 'O', 'Tl')\",OTHERS,False\nmp-1029952,\"{'Te': 6.0, 'Mo': 2.0, 'W': 2.0, 'S': 2.0}\",\"('Mo', 'S', 'Te', 'W')\",OTHERS,False\nmp-1183784,\"{'Ce': 3.0, 'Cl': 1.0}\",\"('Ce', 'Cl')\",OTHERS,False\nmp-768257,\"{'Na': 8.0, 'Bi': 4.0, 'C': 8.0, 'S': 2.0, 'O': 32.0}\",\"('Bi', 'C', 'Na', 'O', 'S')\",OTHERS,False\nmp-763176,\"{'Mn': 6.0, 'O': 8.0, 'F': 4.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-1036441,\"{'Cs': 1.0, 'Mg': 14.0, 'Mn': 1.0, 'O': 16.0}\",\"('Cs', 'Mg', 'Mn', 'O')\",OTHERS,False\nmp-604147,\"{'Na': 8.0, 'Li': 4.0, 'H': 24.0, 'N': 12.0}\",\"('H', 'Li', 'N', 'Na')\",OTHERS,False\nmp-559918,\"{'Ti': 8.0, 'Cu': 4.0, 'S': 16.0}\",\"('Cu', 'S', 'Ti')\",OTHERS,False\nmp-1096643,\"{'Li': 1.0, 'Tl': 2.0, 'Pd': 1.0}\",\"('Li', 'Pd', 'Tl')\",OTHERS,False\nmp-1244813,\"{'Y': 4.0, 'V': 13.0, 'Si': 2.0, 'Sb': 2.0, 'O': 28.0}\",\"('O', 'Sb', 'Si', 'V', 'Y')\",OTHERS,False\nmp-560159,\"{'B': 12.0, 'H': 42.0, 'C': 10.0, 'N': 4.0, 'O': 2.0}\",\"('B', 'C', 'H', 'N', 'O')\",OTHERS,False\nmp-1196513,\"{'Sc': 12.0, 'Ge': 24.0, 'Ru': 12.0}\",\"('Ge', 'Ru', 'Sc')\",OTHERS,False\nmp-541041,\"{'Nd': 4.0, 'Ag': 4.0, 'P': 16.0, 'O': 48.0}\",\"('Ag', 'Nd', 'O', 'P')\",OTHERS,False\nmp-1019586,\"{'Ca': 2.0, 'Al': 4.0, 'Si': 2.0, 'O': 12.0}\",\"('Al', 'Ca', 'O', 'Si')\",OTHERS,False\nmp-557299,\"{'Nd': 12.0, 'Mo': 4.0, 'O': 28.0}\",\"('Mo', 'Nd', 'O')\",OTHERS,False\nmp-1095875,\"{'Sc': 1.0, 'Zn': 1.0, 'Ir': 2.0}\",\"('Ir', 'Sc', 'Zn')\",OTHERS,False\nmp-504627,\"{'Er': 8.0, 'Ni': 2.0, 'B': 26.0}\",\"('B', 'Er', 'Ni')\",OTHERS,False\nmp-779250,\"{'Li': 16.0, 'Fe': 8.0, 'B': 8.0, 'O': 32.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-757415,\"{'Na': 8.0, 'Fe': 4.0, 'O': 8.0}\",\"('Fe', 'Na', 'O')\",OTHERS,False\nmp-849411,\"{'Li': 8.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-27943,\"{'W': 6.0, 'N': 3.0}\",\"('N', 'W')\",OTHERS,False\nmp-758806,\"{'B': 80.0, 'P': 16.0, 'H': 64.0, 'Cl': 16.0}\",\"('B', 'Cl', 'H', 'P')\",OTHERS,False\nmp-23037,\"{'Cs': 1.0, 'Pb': 1.0, 'Cl': 3.0}\",\"('Cl', 'Cs', 'Pb')\",OTHERS,False\nmp-775921,\"{'Li': 6.0, 'V': 4.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1213696,\"{'Cs': 4.0, 'Co': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Co', 'Cs', 'O', 'P')\",OTHERS,False\nmp-861913,\"{'Li': 1.0, 'Dy': 2.0, 'Al': 1.0}\",\"('Al', 'Dy', 'Li')\",OTHERS,False\nmp-27897,\"{'N': 8.0, 'O': 16.0, 'Ba': 4.0}\",\"('Ba', 'N', 'O')\",OTHERS,False\nmp-1217682,\"{'Tb': 2.0, 'Hf': 1.0, 'Al': 9.0}\",\"('Al', 'Hf', 'Tb')\",OTHERS,False\nmp-1208703,\"{'Sr': 4.0, 'Cr': 3.0, 'O': 10.0}\",\"('Cr', 'O', 'Sr')\",OTHERS,False\nmp-7806,\"{'P': 4.0, 'Cr': 12.0}\",\"('Cr', 'P')\",OTHERS,False\nmp-1029155,\"{'Te': 2.0, 'Mo': 2.0, 'W': 2.0, 'Se': 2.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-759735,\"{'Li': 4.0, 'Bi': 2.0, 'P': 10.0, 'O': 30.0}\",\"('Bi', 'Li', 'O', 'P')\",OTHERS,False\nmp-568357,\"{'K': 8.0, 'La': 12.0, 'Os': 2.0, 'I': 28.0}\",\"('I', 'K', 'La', 'Os')\",OTHERS,False\nmp-764248,\"{'Li': 4.0, 'V': 2.0, 'Cr': 2.0, 'P': 8.0, 'O': 28.0}\",\"('Cr', 'Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1105271,\"{'Mn': 4.0, 'Bi': 4.0, 'O': 12.0}\",\"('Bi', 'Mn', 'O')\",OTHERS,False\nmvc-2146,\"{'Zn': 4.0, 'Co': 2.0, 'N': 4.0}\",\"('Co', 'N', 'Zn')\",OTHERS,False\nmp-1101240,\"{'V': 3.0, 'Co': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Co', 'O', 'P', 'V')\",OTHERS,False\nmp-5473,\"{'Mg': 4.0, 'Si': 4.0, 'Ca': 4.0}\",\"('Ca', 'Mg', 'Si')\",OTHERS,False\nmp-977415,\"{'Ho': 1.0, 'Cd': 1.0, 'Au': 2.0}\",\"('Au', 'Cd', 'Ho')\",OTHERS,False\nmp-1209209,\"{'Sb': 4.0, 'N': 1.0, 'F': 13.0}\",\"('F', 'N', 'Sb')\",OTHERS,False\nmp-1218178,\"{'Sr': 2.0, 'La': 2.0, 'Ni': 2.0, 'O': 8.0}\",\"('La', 'Ni', 'O', 'Sr')\",OTHERS,False\nmp-559227,\"{'Rb': 4.0, 'Ge': 4.0, 'Bi': 4.0, 'S': 16.0}\",\"('Bi', 'Ge', 'Rb', 'S')\",OTHERS,False\nmp-1222934,\"{'La': 2.0, 'Ge': 2.0, 'Pd': 2.0}\",\"('Ge', 'La', 'Pd')\",OTHERS,False\nmp-1093902,\"{'Ba': 1.0, 'Mg': 1.0, 'Tl': 2.0}\",\"('Ba', 'Mg', 'Tl')\",OTHERS,False\nmp-15988,\"{'Li': 2.0, 'Cu': 1.0, 'Sb': 1.0}\",\"('Cu', 'Li', 'Sb')\",OTHERS,False\nmp-1247705,\"{'Sr': 4.0, 'Ca': 28.0, 'Mn': 24.0, 'Al': 8.0, 'O': 92.0}\",\"('Al', 'Ca', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-1111896,\"{'Na': 3.0, 'Er': 1.0, 'Cl': 6.0}\",\"('Cl', 'Er', 'Na')\",OTHERS,False\nmp-1246758,\"{'Fe': 2.0, 'Cu': 1.0, 'N': 2.0}\",\"('Cu', 'Fe', 'N')\",OTHERS,False\nmp-27351,\"{'N': 12.0, 'Cl': 16.0, 'Sb': 4.0}\",\"('Cl', 'N', 'Sb')\",OTHERS,False\nmp-684013,\"{'Ba': 4.0, 'In': 26.0, 'Ir': 8.0}\",\"('Ba', 'In', 'Ir')\",OTHERS,False\nmp-780525,\"{'Mn': 12.0, 'O': 2.0, 'F': 22.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmvc-5035,\"{'Zn': 4.0, 'Te': 2.0, 'W': 2.0, 'O': 12.0}\",\"('O', 'Te', 'W', 'Zn')\",OTHERS,False\nmp-752975,\"{'Fe': 4.0, 'O': 4.0, 'F': 4.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-1093607,\"{'Hf': 2.0, 'Mn': 1.0, 'Co': 1.0}\",\"('Co', 'Hf', 'Mn')\",OTHERS,False\nmp-1186211,\"{'Na': 2.0, 'Zn': 6.0}\",\"('Na', 'Zn')\",OTHERS,False\nmp-1113281,\"{'Cs': 2.0, 'Al': 1.0, 'In': 1.0, 'Br': 6.0}\",\"('Al', 'Br', 'Cs', 'In')\",OTHERS,False\nmp-9952,\"{'Zn': 2.0, 'Sb': 6.0, 'Hf': 10.0}\",\"('Hf', 'Sb', 'Zn')\",OTHERS,False\nmp-669497,\"{'Dy': 20.0, 'Cl': 44.0}\",\"('Cl', 'Dy')\",OTHERS,False\nmp-10436,\"{'Al': 2.0, 'Si': 2.0, 'Tb': 1.0}\",\"('Al', 'Si', 'Tb')\",OTHERS,False\nmp-1113018,\"{'Cs': 2.0, 'Li': 1.0, 'Dy': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cs', 'Dy', 'Li')\",OTHERS,False\nmp-1195438,\"{'Al': 8.0, 'B': 56.0, 'Pd': 120.0}\",\"('Al', 'B', 'Pd')\",OTHERS,False\nmp-1227605,\"{'Bi': 12.0, 'I': 6.0, 'Br': 6.0}\",\"('Bi', 'Br', 'I')\",OTHERS,False\nmp-1225536,\"{'Dy': 1.0, 'Tm': 1.0, 'Mn': 4.0}\",\"('Dy', 'Mn', 'Tm')\",OTHERS,False\nmp-19752,\"{'O': 12.0, 'P': 3.0, 'Fe': 3.0}\",\"('Fe', 'O', 'P')\",OTHERS,False\nmvc-11245,\"{'Zn': 2.0, 'Fe': 4.0, 'S': 8.0}\",\"('Fe', 'S', 'Zn')\",OTHERS,False\nmp-3415,\"{'Si': 2.0, 'Ru': 2.0, 'Yb': 1.0}\",\"('Ru', 'Si', 'Yb')\",OTHERS,False\nmp-1101531,\"{'Li': 18.0, 'Co': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Co', 'Li', 'O', 'P')\",OTHERS,False\nmp-570019,\"{'Cd': 8.0, 'I': 16.0}\",\"('Cd', 'I')\",OTHERS,False\nmp-1096127,\"{'Y': 2.0, 'Cu': 1.0, 'Ag': 1.0}\",\"('Ag', 'Cu', 'Y')\",OTHERS,False\nmp-721699,\"{'Ca': 2.0, 'H': 32.0, 'C': 8.0, 'N': 20.0, 'O': 20.0}\",\"('C', 'Ca', 'H', 'N', 'O')\",OTHERS,False\nmp-1226086,\"{'Cr': 10.0, 'Te': 12.0}\",\"('Cr', 'Te')\",OTHERS,False\nmp-624243,\"{'Fe': 12.0, 'W': 12.0, 'C': 2.0}\",\"('C', 'Fe', 'W')\",OTHERS,False\nmp-635413,\"{'Cs': 3.0, 'Bi': 1.0}\",\"('Bi', 'Cs')\",OTHERS,False\nmp-1228266,\"{'Al': 6.0, 'C': 1.0, 'O': 7.0}\",\"('Al', 'C', 'O')\",OTHERS,False\nmp-18837,\"{'O': 8.0, 'K': 1.0, 'Sc': 1.0, 'Mo': 2.0}\",\"('K', 'Mo', 'O', 'Sc')\",OTHERS,False\nmp-1181356,\"{'Ga': 3.0, 'S': 2.0, 'O': 15.0}\",\"('Ga', 'O', 'S')\",OTHERS,False\nmp-20092,\"{'O': 6.0, 'Eu': 1.0, 'Ta': 2.0}\",\"('Eu', 'O', 'Ta')\",OTHERS,False\nmp-29351,\"{'Ba': 4.0, 'Mo': 24.0, 'O': 40.0}\",\"('Ba', 'Mo', 'O')\",OTHERS,False\nmp-24577,\"{'H': 12.0, 'O': 6.0, 'F': 6.0, 'Ni': 1.0, 'Sn': 1.0}\",\"('F', 'H', 'Ni', 'O', 'Sn')\",OTHERS,False\nmp-1195802,\"{'Sm': 8.0, 'Ge': 6.0, 'S': 24.0}\",\"('Ge', 'S', 'Sm')\",OTHERS,False\nmp-1104856,\"{'La': 6.0, 'Pt': 8.0}\",\"('La', 'Pt')\",OTHERS,False\nmp-756641,\"{'Li': 4.0, 'B': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('B', 'C', 'Li', 'O', 'P')\",OTHERS,False\nmp-1195554,\"{'Th': 4.0, 'Te': 8.0, 'Mo': 4.0, 'O': 36.0}\",\"('Mo', 'O', 'Te', 'Th')\",OTHERS,False\nmp-706986,\"{'P': 8.0, 'H': 32.0, 'N': 8.0, 'O': 24.0}\",\"('H', 'N', 'O', 'P')\",OTHERS,False\nmp-983410,\"{'Ho': 2.0, 'Cu': 1.0, 'Pd': 1.0}\",\"('Cu', 'Ho', 'Pd')\",OTHERS,False\nmp-2840,\"{'Ir': 4.0, 'Pu': 2.0}\",\"('Ir', 'Pu')\",OTHERS,False\nmvc-3757,\"{'Zn': 4.0, 'Mo': 4.0, 'F': 20.0}\",\"('F', 'Mo', 'Zn')\",OTHERS,False\nmp-558428,\"{'Ba': 9.0, 'Sc': 2.0, 'Si': 6.0, 'O': 24.0}\",\"('Ba', 'O', 'Sc', 'Si')\",OTHERS,False\nmp-771568,\"{'Sr': 4.0, 'Ca': 8.0, 'I': 24.0}\",\"('Ca', 'I', 'Sr')\",OTHERS,False\nmp-775995,\"{'Mn': 3.0, 'Cr': 1.0, 'Sn': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Cr', 'Mn', 'O', 'P', 'Sn')\",OTHERS,False\nmp-1247545,\"{'Nb': 4.0, 'Mo': 4.0, 'N': 12.0}\",\"('Mo', 'N', 'Nb')\",OTHERS,False\nmp-774235,\"{'Li': 1.0, 'Cr': 3.0, 'O': 4.0}\",\"('Cr', 'Li', 'O')\",OTHERS,False\nmp-1102313,\"{'Yb': 4.0, 'Sb': 4.0, 'Ir': 4.0}\",\"('Ir', 'Sb', 'Yb')\",OTHERS,False\nmp-1076680,\"{'Sr': 16.0, 'Ca': 16.0, 'Ti': 8.0, 'Mn': 24.0, 'O': 80.0}\",\"('Ca', 'Mn', 'O', 'Sr', 'Ti')\",OTHERS,False\nmp-669550,\"{'Ce': 3.0, 'Mn': 1.0, 'Al': 24.0, 'Cu': 8.0}\",\"('Al', 'Ce', 'Cu', 'Mn')\",OTHERS,False\nmp-16721,\"{'Al': 8.0, 'Tm': 8.0}\",\"('Al', 'Tm')\",OTHERS,False\nmp-555726,\"{'Na': 4.0, 'Cl': 4.0, 'O': 12.0}\",\"('Cl', 'Na', 'O')\",OTHERS,False\nmp-1187918,\"{'Zn': 3.0, 'P': 1.0}\",\"('P', 'Zn')\",OTHERS,False\nmp-1221653,\"{'Mn': 1.0, 'Nb': 4.0, 'C': 2.0, 'S': 4.0}\",\"('C', 'Mn', 'Nb', 'S')\",OTHERS,False\nmp-1224283,\"{'Hf': 1.0, 'Ta': 1.0, 'B': 4.0}\",\"('B', 'Hf', 'Ta')\",OTHERS,False\nmp-752670,\"{'Li': 6.0, 'Fe': 2.0, 'S': 6.0}\",\"('Fe', 'Li', 'S')\",OTHERS,False\nmp-989546,\"{'Sr': 2.0, 'Tc': 2.0, 'N': 6.0}\",\"('N', 'Sr', 'Tc')\",OTHERS,False\nmp-1224413,\"{'H': 3.0, 'Pd': 4.0}\",\"('H', 'Pd')\",OTHERS,False\nmp-756396,\"{'Fe': 8.0, 'O': 12.0, 'F': 4.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-1221052,\"{'Na': 14.0, 'Fe': 14.0, 'P': 12.0, 'O': 48.0, 'F': 6.0}\",\"('F', 'Fe', 'Na', 'O', 'P')\",OTHERS,False\nmp-759775,\"{'Li': 16.0, 'Mn': 18.0, 'O': 36.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-1099889,\"{'Sr': 28.0, 'Ca': 4.0, 'Ti': 28.0, 'Mn': 4.0, 'O': 80.0}\",\"('Ca', 'Mn', 'O', 'Sr', 'Ti')\",OTHERS,False\nmp-22402,\"{'O': 14.0, 'Co': 8.0, 'In': 2.0, 'Ba': 2.0}\",\"('Ba', 'Co', 'In', 'O')\",OTHERS,False\nmp-21431,\"{'Ho': 1.0, 'In': 3.0}\",\"('Ho', 'In')\",OTHERS,False\nmp-31015,\"{'S': 1.0, 'Br': 7.0, 'Ta': 3.0}\",\"('Br', 'S', 'Ta')\",OTHERS,False\nmp-1202437,\"{'Cs': 4.0, 'H': 8.0, 'S': 4.0, 'N': 4.0, 'O': 12.0}\",\"('Cs', 'H', 'N', 'O', 'S')\",OTHERS,False\nmp-1222178,\"{'Mn': 2.0, 'Fe': 8.0, 'Bi': 10.0, 'O': 30.0}\",\"('Bi', 'Fe', 'Mn', 'O')\",OTHERS,False\nmp-1206808,\"{'Cs': 2.0, 'Li': 1.0, 'Al': 1.0, 'F': 6.0}\",\"('Al', 'Cs', 'F', 'Li')\",OTHERS,False\nmp-510657,\"{'O': 4.0, 'V': 1.0, 'Cu': 3.0}\",\"('Cu', 'O', 'V')\",OTHERS,False\nmp-1208612,\"{'Sr': 1.0, 'Li': 1.0, 'Si': 1.0}\",\"('Li', 'Si', 'Sr')\",OTHERS,False\nmp-1212738,\"{'Gd': 12.0, 'Si': 4.0, 'Br': 12.0}\",\"('Br', 'Gd', 'Si')\",OTHERS,False\nmp-646548,\"{'Eu': 4.0, 'K': 4.0, 'As': 4.0, 'S': 12.0}\",\"('As', 'Eu', 'K', 'S')\",OTHERS,False\nmp-999439,\"{'Nb': 3.0, 'Cr': 1.0}\",\"('Cr', 'Nb')\",OTHERS,False\nmp-753930,\"{'Li': 4.0, 'V': 2.0, 'Fe': 2.0, 'O': 10.0}\",\"('Fe', 'Li', 'O', 'V')\",OTHERS,False\nmp-1105411,\"{'Li': 2.0, 'Cr': 2.0, 'Ge': 4.0, 'O': 12.0}\",\"('Cr', 'Ge', 'Li', 'O')\",OTHERS,False\nmp-30818,\"{'Li': 2.0, 'Al': 1.0, 'Pt': 1.0}\",\"('Al', 'Li', 'Pt')\",OTHERS,False\nmp-777308,\"{'Mn': 8.0, 'O': 14.0, 'F': 2.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-771140,\"{'Li': 5.0, 'Mn': 5.0, 'Sn': 2.0, 'O': 12.0}\",\"('Li', 'Mn', 'O', 'Sn')\",OTHERS,False\nmp-571438,\"{'Be': 12.0, 'V': 1.0}\",\"('Be', 'V')\",OTHERS,False\nmp-978967,\"{'Sr': 1.0, 'Dy': 3.0}\",\"('Dy', 'Sr')\",OTHERS,False\nmp-1246060,\"{'Zr': 6.0, 'Ag': 3.0, 'N': 9.0}\",\"('Ag', 'N', 'Zr')\",OTHERS,False\nmp-29655,\"{'H': 32.0, 'B': 16.0, 'C': 8.0}\",\"('B', 'C', 'H')\",OTHERS,False\nmp-5456,\"{'O': 3.0, 'Cu': 1.0, 'Sr': 2.0}\",\"('Cu', 'O', 'Sr')\",OTHERS,False\nmp-1074413,\"{'Mg': 8.0, 'Si': 6.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-29107,\"{'Rb': 8.0, 'Te': 16.0, 'Hg': 12.0}\",\"('Hg', 'Rb', 'Te')\",OTHERS,False\nmp-542232,\"{'Ca': 4.0, 'Fe': 4.0, 'F': 20.0}\",\"('Ca', 'F', 'Fe')\",OTHERS,False\nmp-1174631,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1185380,\"{'Li': 2.0, 'Mg': 2.0}\",\"('Li', 'Mg')\",OTHERS,False\nmp-8121,\"{'Si': 2.0, 'Cu': 2.0, 'Sm': 2.0}\",\"('Cu', 'Si', 'Sm')\",OTHERS,False\nmp-1204381,\"{'Ce': 8.0, 'Ni': 28.0}\",\"('Ce', 'Ni')\",OTHERS,False\nmp-1204517,\"{'Er': 4.0, 'Fe': 28.0, 'Si': 6.0}\",\"('Er', 'Fe', 'Si')\",OTHERS,False\nmp-765008,\"{'Li': 6.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-4093,\"{'O': 8.0, 'P': 2.0, 'Cu': 3.0}\",\"('Cu', 'O', 'P')\",OTHERS,False\nmp-772423,\"{'Ni': 3.0, 'S': 6.0, 'O': 24.0}\",\"('Ni', 'O', 'S')\",OTHERS,False\nmp-974882,\"{'Rb': 3.0, 'Mo': 1.0}\",\"('Mo', 'Rb')\",OTHERS,False\nmp-23612,\"{'B': 6.0, 'O': 10.0, 'K': 3.0, 'Br': 1.0}\",\"('B', 'Br', 'K', 'O')\",OTHERS,False\nmp-1193894,\"{'Cs': 12.0, 'Sb': 4.0, 'S': 12.0}\",\"('Cs', 'S', 'Sb')\",OTHERS,False\nmp-625619,\"{'H': 4.0, 'N': 2.0, 'O': 3.0}\",\"('H', 'N', 'O')\",OTHERS,False\nmp-571248,\"{'Cs': 4.0, 'Na': 2.0, 'Au': 6.0, 'C': 12.0, 'N': 12.0}\",\"('Au', 'C', 'Cs', 'N', 'Na')\",OTHERS,False\nmp-1245647,\"{'Zn': 4.0, 'Ag': 4.0, 'N': 4.0}\",\"('Ag', 'N', 'Zn')\",OTHERS,False\nmp-556171,\"{'Cd': 4.0, 'P': 8.0, 'Xe': 20.0, 'F': 88.0}\",\"('Cd', 'F', 'P', 'Xe')\",OTHERS,False\nmp-1227506,\"{'Ba': 4.0, 'Sr': 4.0, 'Si': 16.0}\",\"('Ba', 'Si', 'Sr')\",OTHERS,False\nmp-1019717,\"{'Cs': 4.0, 'B': 4.0, 'S': 12.0, 'O': 44.0}\",\"('B', 'Cs', 'O', 'S')\",OTHERS,False\nmvc-2247,\"{'Ba': 4.0, 'Ca': 2.0, 'Mn': 4.0, 'Cu': 2.0, 'F': 28.0}\",\"('Ba', 'Ca', 'Cu', 'F', 'Mn')\",OTHERS,False\nmp-694908,\"{'Sr': 3.0, 'La': 7.0, 'Ti': 3.0, 'Mn': 7.0, 'O': 30.0}\",\"('La', 'Mn', 'O', 'Sr', 'Ti')\",OTHERS,False\nmp-1200512,\"{'Er': 12.0, 'Al': 86.0, 'W': 8.0}\",\"('Al', 'Er', 'W')\",OTHERS,False\nmp-754517,\"{'Sr': 1.0, 'Ca': 2.0, 'I': 6.0}\",\"('Ca', 'I', 'Sr')\",OTHERS,False\nmp-675833,\"{'Mg': 2.0, 'Mo': 12.0, 'Se': 16.0}\",\"('Mg', 'Mo', 'Se')\",OTHERS,False\nmp-990083,\"{'Mo': 18.0, 'S': 36.0}\",\"('Mo', 'S')\",OTHERS,False\nmp-1245166,\"{'Cr': 4.0, 'Fe': 28.0, 'O': 48.0}\",\"('Cr', 'Fe', 'O')\",OTHERS,False\nmp-865538,\"{'Ti': 2.0, 'Re': 1.0, 'Pd': 1.0}\",\"('Pd', 'Re', 'Ti')\",OTHERS,False\nmp-16000,\"{'Ti': 12.0, 'Se': 54.0, 'Ag': 2.0, 'Cs': 10.0}\",\"('Ag', 'Cs', 'Se', 'Ti')\",OTHERS,False\nmp-1185817,\"{'Mg': 5.0, 'In': 1.0}\",\"('In', 'Mg')\",OTHERS,False\nmp-1208259,\"{'Ti': 6.0, 'Co': 2.0, 'S': 12.0}\",\"('Co', 'S', 'Ti')\",OTHERS,False\nmp-1212693,\"{'Nd': 8.0, 'In': 24.0, 'Pt': 8.0}\",\"('In', 'Nd', 'Pt')\",OTHERS,False\nmp-567747,\"{'Co': 7.0, 'Mo': 6.0}\",\"('Co', 'Mo')\",OTHERS,False\nmp-778508,\"{'Li': 12.0, 'Mn': 4.0, 'V': 4.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-867809,\"{'V': 6.0, 'P': 6.0, 'O': 24.0}\",\"('O', 'P', 'V')\",OTHERS,False\nmp-672388,\"{'Th': 2.0, 'Pb': 2.0}\",\"('Pb', 'Th')\",OTHERS,False\nmp-758778,\"{'Li': 2.0, 'Fe': 8.0, 'P': 6.0, 'O': 24.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-24229,\"{'H': 20.0, 'C': 4.0, 'O': 22.0, 'Na': 4.0, 'Ca': 2.0}\",\"('C', 'Ca', 'H', 'Na', 'O')\",OTHERS,False\nmp-1206175,\"{'Eu': 2.0, 'Al': 2.0, 'Ge': 2.0}\",\"('Al', 'Eu', 'Ge')\",OTHERS,False\nmp-676089,\"{'Fe': 2.0, 'Co': 1.0, 'Se': 4.0}\",\"('Co', 'Fe', 'Se')\",OTHERS,False\nmp-706400,\"{'Ba': 4.0, 'P': 8.0, 'H': 16.0, 'O': 32.0}\",\"('Ba', 'H', 'O', 'P')\",OTHERS,False\nmp-632672,\"{'Li': 1.0, 'H': 1.0, 'F': 2.0}\",\"('F', 'H', 'Li')\",OTHERS,False\nmp-1218676,\"{'Sr': 4.0, 'La': 1.0, 'Fe': 2.0, 'Co': 3.0, 'O': 15.0}\",\"('Co', 'Fe', 'La', 'O', 'Sr')\",OTHERS,False\nmp-1210772,\"{'Li': 2.0, 'H': 6.0, 'Pt': 1.0, 'O': 6.0}\",\"('H', 'Li', 'O', 'Pt')\",OTHERS,False\nmp-542533,\"{'Cs': 4.0, 'Na': 4.0, 'O': 52.0, 'B': 16.0, 'H': 48.0}\",\"('B', 'Cs', 'H', 'Na', 'O')\",OTHERS,False\nmp-774411,\"{'Li': 2.0, 'V': 1.0, 'O': 2.0}\",\"('Li', 'O', 'V')\",OTHERS,False\nmp-1208008,\"{'Tm': 8.0, 'Si': 12.0, 'Pd': 4.0}\",\"('Pd', 'Si', 'Tm')\",OTHERS,False\nmp-1216020,\"{'Zn': 35.0, 'Cu': 17.0}\",\"('Cu', 'Zn')\",OTHERS,False\nmvc-4363,\"{'Al': 8.0, 'Si': 12.0, 'W': 12.0, 'O': 48.0}\",\"('Al', 'O', 'Si', 'W')\",OTHERS,False\nmp-973261,\"{'Mg': 4.0, 'Sn': 2.0, 'O': 8.0}\",\"('Mg', 'O', 'Sn')\",OTHERS,False\nmp-1197032,\"{'Ag': 28.0, 'P': 4.0, 'S': 24.0}\",\"('Ag', 'P', 'S')\",OTHERS,False\nmvc-14714,\"{'Zn': 1.0, 'Mo': 1.0, 'F': 6.0}\",\"('F', 'Mo', 'Zn')\",OTHERS,False\nmp-1029354,\"{'Mn': 2.0, 'Zn': 4.0, 'N': 6.0}\",\"('Mn', 'N', 'Zn')\",OTHERS,False\nmp-13182,\"{'Li': 4.0, 'O': 10.0, 'Ti': 2.0, 'Ge': 2.0}\",\"('Ge', 'Li', 'O', 'Ti')\",OTHERS,False\nmvc-8331,\"{'V': 4.0, 'Zn': 1.0, 'Cu': 3.0, 'O': 12.0}\",\"('Cu', 'O', 'V', 'Zn')\",OTHERS,False\nmp-761118,\"{'Ti': 7.0, 'Sn': 3.0, 'O': 20.0}\",\"('O', 'Sn', 'Ti')\",OTHERS,False\nmp-9612,\"{'O': 44.0, 'La': 12.0, 'Ir': 12.0}\",\"('Ir', 'La', 'O')\",OTHERS,False\nmp-768854,\"{'Li': 10.0, 'Mn': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-780461,\"{'Mn': 12.0, 'O': 10.0, 'F': 14.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-768350,\"{'Ba': 8.0, 'Y': 4.0, 'F': 28.0}\",\"('Ba', 'F', 'Y')\",OTHERS,False\nmp-1228377,\"{'Ba': 3.0, 'Sr': 1.0, 'Co': 4.0, 'O': 12.0}\",\"('Ba', 'Co', 'O', 'Sr')\",OTHERS,False\nmp-1216661,\"{'Ti': 1.0, 'Zn': 1.0, 'Cu': 2.0}\",\"('Cu', 'Ti', 'Zn')\",OTHERS,False\nmp-1095821,\"{'Ti': 2.0, 'Cr': 1.0, 'Co': 1.0}\",\"('Co', 'Cr', 'Ti')\",OTHERS,False\nmp-557121,\"{'K': 4.0, 'Sn': 2.0, 'Au': 4.0, 'S': 8.0}\",\"('Au', 'K', 'S', 'Sn')\",OTHERS,False\nmp-616577,\"{'Sr': 12.0, 'B': 4.0, 'C': 4.0, 'N': 4.0, 'Cl': 8.0}\",\"('B', 'C', 'Cl', 'N', 'Sr')\",OTHERS,False\nmp-867860,\"{'Sm': 1.0, 'Zn': 1.0, 'Au': 2.0}\",\"('Au', 'Sm', 'Zn')\",OTHERS,False\nmp-1245973,\"{'Mn': 16.0, 'Ge': 4.0, 'N': 16.0}\",\"('Ge', 'Mn', 'N')\",OTHERS,False\nmp-570613,\"{'Cd': 6.0, 'I': 12.0}\",\"('Cd', 'I')\",OTHERS,False\nmp-1220922,\"{'Na': 4.0, 'Ti': 4.0, 'Fe': 4.0, 'O': 16.0}\",\"('Fe', 'Na', 'O', 'Ti')\",OTHERS,False\nmp-1208192,\"{'Tm': 12.0, 'In': 2.0, 'Fe': 3.0}\",\"('Fe', 'In', 'Tm')\",OTHERS,False\nmp-1094126,\"{'Sm': 2.0, 'Fe': 2.0, 'O': 5.0}\",\"('Fe', 'O', 'Sm')\",OTHERS,False\nmp-977590,\"{'Nd': 1.0, 'In': 1.0, 'Pd': 2.0}\",\"('In', 'Nd', 'Pd')\",OTHERS,False\nmp-1096261,\"{'Mg': 1.0, 'Ga': 1.0, 'Pt': 2.0}\",\"('Ga', 'Mg', 'Pt')\",OTHERS,False\nmp-560209,\"{'Li': 18.0, 'Al': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Al', 'Li', 'O', 'P')\",OTHERS,False\nmp-1179413,\"{'Ta': 6.0, 'S': 12.0}\",\"('S', 'Ta')\",OTHERS,False\nmp-675037,\"{'Eu': 2.0, 'Sm': 4.0, 'S': 8.0}\",\"('Eu', 'S', 'Sm')\",OTHERS,False\nmp-677292,\"{'Cs': 6.0, 'Ca': 3.0, 'Al': 12.0, 'Si': 12.0, 'O': 48.0}\",\"('Al', 'Ca', 'Cs', 'O', 'Si')\",OTHERS,False\nmp-1035858,\"{'Y': 1.0, 'Mg': 14.0, 'B': 1.0, 'O': 16.0}\",\"('B', 'Mg', 'O', 'Y')\",OTHERS,False\nmp-1206849,\"{'Sr': 2.0, 'P': 2.0, 'Au': 2.0}\",\"('Au', 'P', 'Sr')\",OTHERS,False\nmvc-7989,\"{'Zn': 4.0, 'Cr': 4.0, 'As': 8.0, 'O': 28.0}\",\"('As', 'Cr', 'O', 'Zn')\",OTHERS,False\nmp-973675,\"{'Hf': 18.0, 'As': 2.0, 'W': 8.0}\",\"('As', 'Hf', 'W')\",OTHERS,False\nmp-699393,\"{'Yb': 2.0, 'Si': 12.0, 'H': 108.0, 'C': 36.0, 'N': 6.0}\",\"('C', 'H', 'N', 'Si', 'Yb')\",OTHERS,False\nmp-780578,\"{'Li': 8.0, 'Mn': 2.0, 'Si': 6.0, 'O': 18.0}\",\"('Li', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1204346,\"{'Fe': 4.0, 'S': 4.0, 'O': 34.0}\",\"('Fe', 'O', 'S')\",OTHERS,False\nmp-1218440,\"{'Sr': 3.0, 'Fe': 2.0, 'Te': 1.0, 'O': 9.0}\",\"('Fe', 'O', 'Sr', 'Te')\",OTHERS,False\nmp-683971,\"{'Sm': 15.0, 'Ga': 44.0, 'Ni': 52.0}\",\"('Ga', 'Ni', 'Sm')\",OTHERS,False\nmp-554094,\"{'Ca': 2.0, 'Mg': 4.0, 'S': 6.0, 'O': 24.0}\",\"('Ca', 'Mg', 'O', 'S')\",OTHERS,False\nmvc-10399,\"{'Ho': 2.0, 'Zn': 2.0, 'W': 4.0, 'O': 12.0}\",\"('Ho', 'O', 'W', 'Zn')\",OTHERS,False\nmp-603327,\"{'H': 24.0, 'S': 8.0, 'N': 8.0, 'O': 24.0}\",\"('H', 'N', 'O', 'S')\",OTHERS,False\nmp-1208698,\"{'Sm': 2.0, 'P': 6.0, 'O': 18.0}\",\"('O', 'P', 'Sm')\",OTHERS,False\nmp-1187496,\"{'Tl': 3.0, 'Si': 1.0}\",\"('Si', 'Tl')\",OTHERS,False\nmp-34144,\"{'Al': 12.0, 'Mg': 6.0, 'O': 24.0}\",\"('Al', 'Mg', 'O')\",OTHERS,False\nmp-1245031,\"{'Ti': 39.0, 'O': 65.0}\",\"('O', 'Ti')\",OTHERS,False\nmp-695597,\"{'Na': 4.0, 'Mg': 12.0, 'Si': 16.0, 'O': 44.0, 'F': 4.0}\",\"('F', 'Mg', 'Na', 'O', 'Si')\",OTHERS,False\nmp-733998,\"{'Tb': 4.0, 'H': 48.0, 'S': 8.0, 'N': 4.0, 'O': 48.0}\",\"('H', 'N', 'O', 'S', 'Tb')\",OTHERS,False\nmp-1225133,\"{'Fe': 3.0, 'Cu': 1.0, 'As': 2.0}\",\"('As', 'Cu', 'Fe')\",OTHERS,False\nmp-780871,\"{'Li': 6.0, 'Fe': 12.0, 'Si': 12.0, 'O': 48.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-541353,\"{'K': 8.0, 'Tc': 12.0, 'S': 24.0}\",\"('K', 'S', 'Tc')\",OTHERS,False\nmp-24809,\"{'H': 4.0, 'Ca': 2.0}\",\"('Ca', 'H')\",OTHERS,False\nmp-757703,\"{'Mn': 3.0, 'Co': 1.0, 'Sn': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Co', 'Mn', 'O', 'P', 'Sn')\",OTHERS,False\nmp-560584,\"{'Si': 6.0, 'Ag': 18.0, 'O': 21.0}\",\"('Ag', 'O', 'Si')\",OTHERS,False\nmp-27821,\"{'P': 11.0, 'Ag': 3.0}\",\"('Ag', 'P')\",OTHERS,False\nmp-976355,\"{'Lu': 3.0, 'Si': 1.0}\",\"('Lu', 'Si')\",OTHERS,False\nmp-1208963,\"{'Sm': 16.0, 'Mg': 4.0, 'Pd': 4.0}\",\"('Mg', 'Pd', 'Sm')\",OTHERS,False\nmp-1220924,\"{'Na': 2.0, 'Pr': 2.0, 'Ti': 4.0, 'O': 12.0}\",\"('Na', 'O', 'Pr', 'Ti')\",OTHERS,False\nmvc-13319,\"{'Mg': 4.0, 'Nb': 4.0, 'Fe': 2.0, 'O': 16.0}\",\"('Fe', 'Mg', 'Nb', 'O')\",OTHERS,False\nmp-1093588,\"{'Mg': 1.0, 'Sn': 1.0, 'Pt': 2.0}\",\"('Mg', 'Pt', 'Sn')\",OTHERS,False\nmp-1001021,\"{'Y': 4.0, 'Zn': 2.0, 'Se': 8.0}\",\"('Se', 'Y', 'Zn')\",OTHERS,False\nmp-1228040,\"{'Ca': 1.0, 'U': 2.0, 'P': 2.0, 'O': 18.0}\",\"('Ca', 'O', 'P', 'U')\",OTHERS,False\nmp-754297,\"{'Ti': 2.0, 'P': 2.0, 'H': 2.0, 'O': 10.0}\",\"('H', 'O', 'P', 'Ti')\",OTHERS,False\nmp-1186351,\"{'Nd': 1.0, 'Sm': 1.0, 'In': 2.0}\",\"('In', 'Nd', 'Sm')\",OTHERS,False\nmp-1227127,\"{'Ca': 4.0, 'Mg': 4.0, 'Cd': 4.0}\",\"('Ca', 'Cd', 'Mg')\",OTHERS,False\nmp-1038146,\"{'K': 1.0, 'Mg': 30.0, 'Mn': 1.0, 'O': 32.0}\",\"('K', 'Mg', 'Mn', 'O')\",OTHERS,False\nmp-543028,\"{'Tl': 16.0, 'Te': 8.0, 'O': 24.0}\",\"('O', 'Te', 'Tl')\",OTHERS,False\nmp-865270,\"{'Tm': 1.0, 'Ta': 1.0, 'Os': 2.0}\",\"('Os', 'Ta', 'Tm')\",OTHERS,False\nmp-1394,\"{'O': 1.0, 'Rb': 2.0}\",\"('O', 'Rb')\",OTHERS,False\nmp-1110633,\"{'K': 1.0, 'Rb': 2.0, 'Sc': 1.0, 'I': 6.0}\",\"('I', 'K', 'Rb', 'Sc')\",OTHERS,False\nmp-1103367,\"{'C': 4.0, 'N': 4.0, 'O': 4.0}\",\"('C', 'N', 'O')\",OTHERS,False\nmp-759637,\"{'Na': 8.0, 'Sr': 4.0, 'Li': 4.0, 'V': 4.0, 'P': 8.0, 'O': 36.0}\",\"('Li', 'Na', 'O', 'P', 'Sr', 'V')\",OTHERS,False\nmp-1217722,\"{'Tb': 4.0, 'Gd': 8.0, 'Ga': 20.0, 'O': 48.0}\",\"('Ga', 'Gd', 'O', 'Tb')\",OTHERS,False\nmp-1220518,\"{'Nb': 4.0, 'Ni': 1.0, 'C': 2.0, 'S': 4.0}\",\"('C', 'Nb', 'Ni', 'S')\",OTHERS,False\nmp-675980,\"{'Cr': 4.0, 'P': 12.0, 'S': 36.0}\",\"('Cr', 'P', 'S')\",OTHERS,False\nmp-862962,\"{'Ca': 1.0, 'La': 1.0, 'Zn': 2.0}\",\"('Ca', 'La', 'Zn')\",OTHERS,False\nmp-1211671,\"{'La': 6.0, 'Ge': 10.0}\",\"('Ge', 'La')\",OTHERS,False\nmp-1104114,\"{'Eu': 1.0, 'Mo': 6.0, 'Se': 8.0}\",\"('Eu', 'Mo', 'Se')\",OTHERS,False\nmp-626529,\"{'H': 28.0, 'N': 4.0, 'O': 24.0}\",\"('H', 'N', 'O')\",OTHERS,False\nmp-1213891,\"{'Cs': 12.0, 'Pr': 4.0, 'Cl': 24.0}\",\"('Cl', 'Cs', 'Pr')\",OTHERS,False\nmp-1176710,\"{'Li': 8.0, 'Mn': 8.0, 'P': 16.0, 'O': 56.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1206860,\"{'Nd': 3.0, 'Mg': 3.0, 'In': 3.0}\",\"('In', 'Mg', 'Nd')\",OTHERS,False\nmp-1214940,\"{'Al': 4.0, 'P': 4.0, 'H': 8.0, 'O': 16.0}\",\"('Al', 'H', 'O', 'P')\",OTHERS,False\nmp-1187907,\"{'Yb': 1.0, 'Br': 1.0}\",\"('Br', 'Yb')\",OTHERS,False\nmp-1076638,\"{'Rb': 1.0, 'V': 1.0, 'O': 3.0}\",\"('O', 'Rb', 'V')\",OTHERS,False\nmp-1185055,\"{'Hf': 8.0, 'In': 4.0, 'Ni': 8.0}\",\"('Hf', 'In', 'Ni')\",OTHERS,False\nmp-1221134,\"{'Na': 1.0, 'Ca': 2.0, 'Mg': 5.0, 'Al': 1.0, 'Si': 7.0, 'O': 22.0, 'F': 2.0}\",\"('Al', 'Ca', 'F', 'Mg', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1021466,\"{'Cs': 1.0, 'P': 1.0, 'Pb': 1.0, 'I': 2.0, 'F': 6.0}\",\"('Cs', 'F', 'I', 'P', 'Pb')\",OTHERS,False\nmp-1203434,\"{'Na': 16.0, 'Fe': 16.0, 'F': 56.0}\",\"('F', 'Fe', 'Na')\",OTHERS,False\nmp-1183797,\"{'Ce': 4.0, 'Si': 10.0, 'Ni': 6.0}\",\"('Ce', 'Ni', 'Si')\",OTHERS,False\nmp-720118,\"{'Sr': 3.0, 'Nd': 10.0, 'Al': 12.0, 'Si': 18.0, 'N': 36.0, 'O': 18.0}\",\"('Al', 'N', 'Nd', 'O', 'Si', 'Sr')\",OTHERS,False\nmp-775190,\"{'Li': 2.0, 'V': 3.0, 'Cu': 1.0, 'O': 8.0}\",\"('Cu', 'Li', 'O', 'V')\",OTHERS,False\nmp-1079706,\"{'Ti': 5.0, 'Al': 2.0, 'C': 3.0}\",\"('Al', 'C', 'Ti')\",OTHERS,False\nmvc-15532,\"{'Zn': 1.0, 'Cr': 2.0, 'N': 2.0}\",\"('Cr', 'N', 'Zn')\",OTHERS,False\nmp-1186461,\"{'Pm': 2.0, 'Ga': 1.0, 'Hg': 1.0}\",\"('Ga', 'Hg', 'Pm')\",OTHERS,False\nmp-22304,\"{'Se': 4.0, 'Cd': 1.0, 'In': 2.0}\",\"('Cd', 'In', 'Se')\",OTHERS,False\nmp-556569,\"{'Sr': 2.0, 'Ta': 4.0, 'Bi': 4.0, 'O': 18.0}\",\"('Bi', 'O', 'Sr', 'Ta')\",OTHERS,False\nmp-556665,\"{'Tl': 4.0, 'Bi': 4.0, 'P': 8.0, 'S': 28.0}\",\"('Bi', 'P', 'S', 'Tl')\",OTHERS,False\nmp-11567,\"{'Sc': 1.0, 'Zn': 12.0}\",\"('Sc', 'Zn')\",OTHERS,False\nmp-1194580,\"{'Rb': 4.0, 'Na': 8.0, 'B': 24.0, 'Cl': 4.0, 'O': 40.0}\",\"('B', 'Cl', 'Na', 'O', 'Rb')\",OTHERS,False\nmp-1174913,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-850403,\"{'Li': 4.0, 'Fe': 4.0, 'P': 4.0, 'H': 8.0, 'O': 20.0}\",\"('Fe', 'H', 'Li', 'O', 'P')\",OTHERS,False\nmp-1214494,\"{'Ba': 10.0, 'Y': 4.0, 'Zr': 2.0, 'Al': 4.0, 'O': 26.0}\",\"('Al', 'Ba', 'O', 'Y', 'Zr')\",OTHERS,False\nmp-1084796,\"{'Ca': 1.0, 'Zn': 3.0, 'Se': 4.0}\",\"('Ca', 'Se', 'Zn')\",OTHERS,False\nmp-977592,\"{'Bi': 4.0, 'Rh': 6.0, 'S': 4.0}\",\"('Bi', 'Rh', 'S')\",OTHERS,False\nmp-754860,\"{'Co': 10.0, 'O': 5.0, 'F': 15.0}\",\"('Co', 'F', 'O')\",OTHERS,False\nmvc-15097,\"{'Y': 2.0, 'Cu': 3.0, 'W': 6.0, 'O': 24.0}\",\"('Cu', 'O', 'W', 'Y')\",OTHERS,False\nmp-10224,\"{'Na': 1.0, 'S': 2.0, 'V': 1.0}\",\"('Na', 'S', 'V')\",OTHERS,False\nmp-1102017,\"{'Cd': 4.0, 'Br': 4.0, 'O': 4.0}\",\"('Br', 'Cd', 'O')\",OTHERS,False\nmp-1017983,\"{'Ti': 1.0, 'Mo': 3.0}\",\"('Mo', 'Ti')\",OTHERS,False\nmp-1037677,\"{'Li': 1.0, 'Mg': 30.0, 'Fe': 1.0, 'O': 32.0}\",\"('Fe', 'Li', 'Mg', 'O')\",OTHERS,False\nmp-1076858,\"{'Sr': 7.0, 'Ca': 1.0, 'Fe': 7.0, 'Co': 1.0, 'O': 24.0}\",\"('Ca', 'Co', 'Fe', 'O', 'Sr')\",OTHERS,False\nmp-973779,\"{'Nd': 2.0, 'Cd': 1.0, 'Hg': 1.0}\",\"('Cd', 'Hg', 'Nd')\",OTHERS,False\nmp-560164,\"{'Li': 3.0, 'Al': 1.0, 'Mo': 2.0, 'As': 2.0, 'O': 14.0}\",\"('Al', 'As', 'Li', 'Mo', 'O')\",OTHERS,False\nmp-758877,\"{'Li': 14.0, 'Mn': 14.0, 'P': 12.0, 'O': 48.0, 'F': 6.0}\",\"('F', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1250185,\"{'Ca': 8.0, 'Mn': 8.0, 'Si': 12.0, 'O': 48.0}\",\"('Ca', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1114466,\"{'Rb': 2.0, 'Al': 1.0, 'Tl': 1.0, 'Br': 6.0}\",\"('Al', 'Br', 'Rb', 'Tl')\",OTHERS,False\nmp-1223817,\"{'K': 4.0, 'Mo': 1.0, 'W': 1.0, 'O': 8.0}\",\"('K', 'Mo', 'O', 'W')\",OTHERS,False\nmp-1247276,\"{'Mg': 2.0, 'Ti': 1.0, 'Mn': 3.0, 'S': 8.0}\",\"('Mg', 'Mn', 'S', 'Ti')\",OTHERS,False\nmp-770456,\"{'Li': 6.0, 'Ti': 2.0, 'O': 7.0}\",\"('Li', 'O', 'Ti')\",OTHERS,False\nmp-570629,\"{'Ce': 8.0, 'Ru': 6.0}\",\"('Ce', 'Ru')\",OTHERS,False\nmp-13003,\"{'O': 12.0, 'Ge': 4.0, 'Cd': 4.0}\",\"('Cd', 'Ge', 'O')\",OTHERS,False\nmp-772842,\"{'Na': 8.0, 'Ge': 8.0, 'O': 20.0}\",\"('Ge', 'Na', 'O')\",OTHERS,False\nmp-865386,\"{'Gd': 2.0, 'Ga': 6.0}\",\"('Ga', 'Gd')\",OTHERS,False\nmp-1202680,\"{'Co': 12.0, 'Te': 8.0, 'O': 32.0}\",\"('Co', 'O', 'Te')\",OTHERS,False\nmp-978512,\"{'Sm': 1.0, 'Tm': 1.0, 'Mg': 2.0}\",\"('Mg', 'Sm', 'Tm')\",OTHERS,False\nmp-505014,\"{'Cs': 8.0, 'Sn': 4.0, 'Te': 14.0}\",\"('Cs', 'Sn', 'Te')\",OTHERS,False\nmp-569940,\"{'K': 6.0, 'Bi': 2.0}\",\"('Bi', 'K')\",OTHERS,False\nmp-677659,\"{'Sr': 7.0, 'La': 7.0, 'Cu': 7.0, 'Ru': 7.0, 'O': 42.0}\",\"('Cu', 'La', 'O', 'Ru', 'Sr')\",OTHERS,False\nmp-1196332,\"{'Fe': 8.0, 'H': 40.0, 'S': 8.0, 'O': 44.0}\",\"('Fe', 'H', 'O', 'S')\",OTHERS,False\nmp-6008,\"{'O': 48.0, 'Al': 8.0, 'Si': 12.0, 'Ca': 12.0}\",\"('Al', 'Ca', 'O', 'Si')\",OTHERS,False\nmp-504944,\"{'P': 8.0, 'Na': 8.0, 'O': 32.0, 'H': 16.0}\",\"('H', 'Na', 'O', 'P')\",OTHERS,False\nmp-1095460,\"{'Hg': 4.0, 'Sb': 1.0, 'H': 1.0, 'O': 6.0}\",\"('H', 'Hg', 'O', 'Sb')\",OTHERS,False\nmp-1192354,\"{'Eu': 4.0, 'Ga': 4.0, 'Ge': 2.0, 'S': 14.0}\",\"('Eu', 'Ga', 'Ge', 'S')\",OTHERS,False\nmp-1180149,\"{'Na': 2.0, 'V': 4.0, 'O': 10.0}\",\"('Na', 'O', 'V')\",OTHERS,False\nmp-1213703,\"{'Cs': 3.0, 'Ca': 2.0, 'Cl': 7.0}\",\"('Ca', 'Cl', 'Cs')\",OTHERS,False\nmp-1186408,\"{'Pa': 2.0, 'Si': 6.0}\",\"('Pa', 'Si')\",OTHERS,False\nmp-567614,\"{'Tb': 4.0, 'Co': 4.0, 'I': 2.0}\",\"('Co', 'I', 'Tb')\",OTHERS,False\nmp-849366,\"{'Li': 2.0, 'Co': 6.0, 'O': 2.0, 'F': 10.0}\",\"('Co', 'F', 'Li', 'O')\",OTHERS,False\nmp-769001,\"{'Li': 12.0, 'Sn': 4.0, 'B': 8.0, 'S': 2.0, 'O': 32.0}\",\"('B', 'Li', 'O', 'S', 'Sn')\",OTHERS,False\nmp-774516,\"{'Li': 8.0, 'Mg': 3.0, 'Cu': 9.0, 'Si': 16.0, 'O': 48.0}\",\"('Cu', 'Li', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-1180667,\"{'La': 6.0, 'Fe': 2.0, 'O': 12.0}\",\"('Fe', 'La', 'O')\",OTHERS,False\nmp-1197627,\"{'Pu': 4.0, 'Te': 8.0, 'Cl': 4.0, 'O': 22.0}\",\"('Cl', 'O', 'Pu', 'Te')\",OTHERS,False\nmp-1187466,\"{'Ti': 3.0, 'Cd': 1.0}\",\"('Cd', 'Ti')\",OTHERS,False\nmp-673029,\"{'Li': 2.0, 'Sn': 4.0, 'P': 10.0, 'O': 32.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,False\nmp-1205209,\"{'Fe': 8.0, 'S': 8.0, 'O': 56.0}\",\"('Fe', 'O', 'S')\",OTHERS,False\nmp-1080029,\"{'Ba': 2.0, 'Mn': 2.0, 'Se': 2.0, 'O': 1.0, 'F': 2.0}\",\"('Ba', 'F', 'Mn', 'O', 'Se')\",OTHERS,False\nmp-1196548,\"{'H': 36.0, 'C': 32.0, 'N': 4.0, 'O': 36.0}\",\"('C', 'H', 'N', 'O')\",OTHERS,False\nmp-707443,\"{'Na': 7.0, 'Al': 1.0, 'H': 2.0, 'C': 4.0, 'O': 12.0, 'F': 4.0}\",\"('Al', 'C', 'F', 'H', 'Na', 'O')\",OTHERS,False\nmp-1227803,\"{'Ba': 4.0, 'Sn': 8.0, 'Bi': 4.0}\",\"('Ba', 'Bi', 'Sn')\",OTHERS,False\nmp-22239,\"{'Ge': 4.0, 'Rh': 4.0}\",\"('Ge', 'Rh')\",OTHERS,False\nmp-1222518,\"{'Li': 4.0, 'Te': 4.0, 'H': 20.0, 'O': 24.0}\",\"('H', 'Li', 'O', 'Te')\",OTHERS,False\nmp-973538,\"{'Lu': 2.0, 'Al': 1.0, 'Zn': 1.0}\",\"('Al', 'Lu', 'Zn')\",OTHERS,False\nmp-756535,\"{'Li': 2.0, 'Fe': 2.0, 'F': 8.0}\",\"('F', 'Fe', 'Li')\",OTHERS,False\nmp-768369,\"{'Ba': 8.0, 'Y': 4.0, 'F': 28.0}\",\"('Ba', 'F', 'Y')\",OTHERS,False\nmvc-6817,\"{'Zn': 4.0, 'Cr': 8.0, 'O': 16.0}\",\"('Cr', 'O', 'Zn')\",OTHERS,False\nmp-1202237,\"{'Mg': 4.0, 'B': 8.0, 'H': 52.0, 'N': 12.0}\",\"('B', 'H', 'Mg', 'N')\",OTHERS,False\nmp-849419,\"{'Fe': 10.0, 'O': 9.0, 'F': 11.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-1208841,\"{'Sr': 2.0, 'Fe': 1.0, 'As': 2.0, 'F': 1.0}\",\"('As', 'F', 'Fe', 'Sr')\",OTHERS,False\nmp-862676,\"{'Li': 1.0, 'Ho': 1.0, 'In': 2.0}\",\"('Ho', 'In', 'Li')\",OTHERS,False\nmp-1180387,\"{'Mg': 1.0, 'Mn': 2.0, 'Br': 6.0, 'O': 12.0}\",\"('Br', 'Mg', 'Mn', 'O')\",OTHERS,False\nmp-1196485,\"{'Sm': 12.0, 'In': 4.0, 'S': 24.0}\",\"('In', 'S', 'Sm')\",OTHERS,False\nmp-1028607,\"{'Te': 4.0, 'Mo': 1.0, 'W': 3.0, 'Se': 4.0}\",\"('Mo', 'Se', 'Te', 'W')\",OTHERS,False\nmp-773118,\"{'Ba': 6.0, 'Li': 2.0, 'Ta': 9.0, 'Ti': 7.0, 'O': 42.0}\",\"('Ba', 'Li', 'O', 'Ta', 'Ti')\",OTHERS,False\nmp-1096115,\"{'Li': 1.0, 'Pd': 2.0, 'Au': 1.0}\",\"('Au', 'Li', 'Pd')\",OTHERS,False\nmp-1218186,\"{'Sr': 1.0, 'Mn': 1.0, 'Te': 1.0, 'O': 6.0}\",\"('Mn', 'O', 'Sr', 'Te')\",OTHERS,False\nmvc-6562,\"{'Y': 2.0, 'W': 6.0, 'F': 30.0}\",\"('F', 'W', 'Y')\",OTHERS,False\nmp-1213751,\"{'Cs': 8.0, 'B': 48.0, 'H': 4.0, 'F': 48.0}\",\"('B', 'Cs', 'F', 'H')\",OTHERS,False\nmp-684950,\"{'K': 1.0, 'Na': 1.0, 'Zr': 2.0, 'Be': 1.0, 'P': 4.0, 'O': 16.0}\",\"('Be', 'K', 'Na', 'O', 'P', 'Zr')\",OTHERS,False\nmp-1213907,\"{'Ce': 4.0, 'Ga': 18.0, 'Rh': 6.0}\",\"('Ce', 'Ga', 'Rh')\",OTHERS,False\nmp-1112422,\"{'K': 2.0, 'Nd': 1.0, 'Au': 1.0, 'Cl': 6.0}\",\"('Au', 'Cl', 'K', 'Nd')\",OTHERS,False\nmp-28642,\"{'F': 72.0, 'Al': 8.0, 'Ba': 24.0}\",\"('Al', 'Ba', 'F')\",OTHERS,False\nmp-16591,\"{'O': 28.0, 'Si': 8.0, 'S': 12.0, 'Tm': 16.0}\",\"('O', 'S', 'Si', 'Tm')\",OTHERS,False\nmp-28143,\"{'H': 32.0, 'N': 8.0, 'S': 20.0}\",\"('H', 'N', 'S')\",OTHERS,False\nmp-1080155,\"{'Te': 4.0, 'Mo': 2.0, 'W': 2.0, 'Se': 2.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-690522,\"{'Mo': 2.0, 'Cl': 4.0, 'O': 1.0}\",\"('Cl', 'Mo', 'O')\",OTHERS,False\nmvc-6418,\"{'Ca': 2.0, 'Cr': 4.0, 'O': 8.0}\",\"('Ca', 'Cr', 'O')\",OTHERS,False\nmp-758463,\"{'Li': 1.0, 'Ni': 1.0, 'Sn': 1.0, 'O': 4.0}\",\"('Li', 'Ni', 'O', 'Sn')\",OTHERS,False\nmp-1203016,\"{'Ba': 18.0, 'Al': 18.0, 'As': 30.0}\",\"('Al', 'As', 'Ba')\",OTHERS,False\nmp-556591,\"{'Si': 18.0, 'O': 36.0}\",\"('O', 'Si')\",OTHERS,False\nmp-1011376,\"{'Ce': 1.0, 'Se': 2.0}\",\"('Ce', 'Se')\",OTHERS,False\nmp-23486,\"{'Cl': 6.0, 'Ni': 2.0, 'Rb': 2.0}\",\"('Cl', 'Ni', 'Rb')\",OTHERS,False\nmp-867583,\"{'Ag': 6.0, 'Sb': 6.0, 'O': 18.0}\",\"('Ag', 'O', 'Sb')\",OTHERS,False\nmp-9205,\"{'O': 4.0, 'F': 2.0, 'K': 2.0, 'Se': 2.0}\",\"('F', 'K', 'O', 'Se')\",OTHERS,False\nmp-758923,\"{'Sb': 4.0, 'As': 4.0, 'Au': 4.0, 'F': 36.0}\",\"('As', 'Au', 'F', 'Sb')\",OTHERS,False\nmp-18852,\"{'O': 8.0, 'Na': 4.0, 'Mo': 2.0}\",\"('Mo', 'Na', 'O')\",OTHERS,False\nmp-1101165,\"{'Yb': 2.0, 'Bi': 2.0, 'O': 6.0}\",\"('Bi', 'O', 'Yb')\",OTHERS,False\nmp-1203225,\"{'Al': 4.0, 'Se': 6.0, 'O': 24.0}\",\"('Al', 'O', 'Se')\",OTHERS,False\nmp-1212687,\"{'Fe': 2.0, 'Ni': 2.0, 'Ag': 4.0, 'F': 14.0}\",\"('Ag', 'F', 'Fe', 'Ni')\",OTHERS,False\nmp-757269,\"{'Li': 6.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-867161,\"{'Li': 1.0, 'In': 3.0}\",\"('In', 'Li')\",OTHERS,False\nmp-865533,\"{'Ti': 2.0, 'Re': 1.0, 'Os': 1.0}\",\"('Os', 'Re', 'Ti')\",OTHERS,False\nmp-1226930,\"{'Cr': 19.0, 'S': 30.0}\",\"('Cr', 'S')\",OTHERS,False\nmp-966801,\"{'Li': 4.0, 'Ca': 4.0, 'B': 4.0, 'O': 12.0}\",\"('B', 'Ca', 'Li', 'O')\",OTHERS,False\nmp-1184131,\"{'Er': 2.0, 'Ag': 1.0, 'Os': 1.0}\",\"('Ag', 'Er', 'Os')\",OTHERS,False\nmp-1222558,\"{'Li': 3.0, 'Ag': 3.0, 'Ge': 2.0}\",\"('Ag', 'Ge', 'Li')\",OTHERS,False\nmp-695325,\"{'Ti': 5.0, 'Zn': 1.0, 'Bi': 2.0, 'Pb': 4.0, 'O': 18.0}\",\"('Bi', 'O', 'Pb', 'Ti', 'Zn')\",OTHERS,False\nmp-675045,\"{'Li': 14.0, 'V': 2.0, 'P': 8.0}\",\"('Li', 'P', 'V')\",OTHERS,False\nmp-758947,\"{'Li': 14.0, 'Mn': 14.0, 'P': 12.0, 'O': 48.0, 'F': 6.0}\",\"('F', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1200662,\"{'Hg': 8.0, 'S': 8.0, 'O': 32.0}\",\"('Hg', 'O', 'S')\",OTHERS,False\nmp-1217221,\"{'Ti': 4.0, 'Fe': 1.0, 'Co': 1.0, 'S': 8.0}\",\"('Co', 'Fe', 'S', 'Ti')\",OTHERS,False\nmp-10647,\"{'Se': 1.0, 'Tl': 1.0}\",\"('Se', 'Tl')\",OTHERS,False\nmp-15960,\"{'Li': 40.0, 'O': 32.0, 'Al': 8.0}\",\"('Al', 'Li', 'O')\",OTHERS,False\nmp-9847,\"{'P': 10.0, 'Yb': 2.0}\",\"('P', 'Yb')\",OTHERS,False\nmvc-1915,\"{'Ca': 2.0, 'Ti': 9.0, 'O': 13.0}\",\"('Ca', 'O', 'Ti')\",OTHERS,False\nmvc-9330,\"{'Pr': 2.0, 'Zn': 2.0, 'Cu': 4.0, 'O': 12.0}\",\"('Cu', 'O', 'Pr', 'Zn')\",OTHERS,False\nmp-1210714,\"{'V': 4.0, 'H': 48.0, 'S': 4.0, 'O': 40.0}\",\"('H', 'O', 'S', 'V')\",OTHERS,False\nmp-1079433,\"{'Ba': 1.0, 'Mg': 4.0, 'Si': 3.0}\",\"('Ba', 'Mg', 'Si')\",OTHERS,False\nmp-1224891,\"{'Ga': 3.0, 'Cu': 3.0, 'Si': 1.0, 'Se': 8.0}\",\"('Cu', 'Ga', 'Se', 'Si')\",OTHERS,False\nmvc-7066,\"{'Mg': 4.0, 'Mo': 8.0, 'O': 16.0}\",\"('Mg', 'Mo', 'O')\",OTHERS,False\nmp-1183046,\"{'Zr': 2.0, 'Ti': 6.0}\",\"('Ti', 'Zr')\",OTHERS,False\nmp-1114452,\"{'Rb': 2.0, 'Na': 1.0, 'Pr': 1.0, 'F': 6.0}\",\"('F', 'Na', 'Pr', 'Rb')\",OTHERS,False\nmp-861726,\"{'Pu': 1.0, 'Bi': 1.0, 'Pd': 2.0}\",\"('Bi', 'Pd', 'Pu')\",OTHERS,False\nmp-1225302,\"{'Fe': 10.0, 'P': 6.0, 'O': 34.0}\",\"('Fe', 'O', 'P')\",OTHERS,False\nmp-12304,\"{'Sc': 22.0, 'Ir': 8.0}\",\"('Ir', 'Sc')\",OTHERS,False\nmp-676275,\"{'Nd': 2.0, 'Pb': 5.0, 'F': 16.0}\",\"('F', 'Nd', 'Pb')\",OTHERS,False\nmp-1080472,\"{'Pt': 1.0, 'N': 2.0, 'Cl': 6.0}\",\"('Cl', 'N', 'Pt')\",OTHERS,False\nmp-1211232,\"{'La': 2.0, 'Al': 6.0, 'Ni': 4.0}\",\"('Al', 'La', 'Ni')\",OTHERS,False\nmp-510581,\"{'Pr': 4.0, 'Ni': 4.0, 'Sn': 4.0, 'H': 8.0}\",\"('H', 'Ni', 'Pr', 'Sn')\",OTHERS,False\nmp-1079846,\"{'Th': 3.0, 'Al': 3.0, 'Ir': 3.0}\",\"('Al', 'Ir', 'Th')\",OTHERS,False\nmp-752636,\"{'Li': 4.0, 'Sb': 4.0, 'O': 12.0}\",\"('Li', 'O', 'Sb')\",OTHERS,False\nmp-1201161,\"{'Pr': 16.0, 'As': 4.0, 'Br': 4.0, 'O': 32.0}\",\"('As', 'Br', 'O', 'Pr')\",OTHERS,False\nmp-546790,\"{'O': 2.0, 'Te': 2.0, 'La': 2.0, 'Cu': 2.0}\",\"('Cu', 'La', 'O', 'Te')\",OTHERS,False\nmp-540771,\"{'Ba': 4.0, 'Zr': 4.0, 'S': 12.0}\",\"('Ba', 'S', 'Zr')\",OTHERS,False\nmp-1103880,\"{'Tm': 3.0, 'Ga': 9.0, 'Pt': 2.0}\",\"('Ga', 'Pt', 'Tm')\",OTHERS,False\nmp-1030340,\"{'Te': 6.0, 'Mo': 3.0, 'W': 1.0, 'S': 2.0}\",\"('Mo', 'S', 'Te', 'W')\",OTHERS,False\nmp-1097176,\"{'Sr': 1.0, 'Li': 1.0, 'Tl': 2.0}\",\"('Li', 'Sr', 'Tl')\",OTHERS,False\nmp-30718,\"{'K': 20.0, 'Hg': 28.0}\",\"('Hg', 'K')\",OTHERS,False\nmp-1221880,\"{'Mn': 2.0, 'Cu': 1.0, 'Sb': 2.0, 'Pd': 1.0}\",\"('Cu', 'Mn', 'Pd', 'Sb')\",OTHERS,False\nmp-705479,\"{'Sn': 4.0, 'H': 24.0, 'Cl': 16.0, 'O': 12.0}\",\"('Cl', 'H', 'O', 'Sn')\",OTHERS,False\nmp-1018735,\"{'Ba': 1.0, 'Ti': 2.0, 'As': 2.0, 'O': 1.0}\",\"('As', 'Ba', 'O', 'Ti')\",OTHERS,False\nmp-1195997,\"{'Y': 4.0, 'B': 12.0, 'H': 120.0, 'N': 24.0}\",\"('B', 'H', 'N', 'Y')\",OTHERS,False\nmp-1097659,\"{'Y': 2.0, 'Ga': 1.0, 'Pd': 1.0}\",\"('Ga', 'Pd', 'Y')\",OTHERS,False\nmp-768514,\"{'Li': 9.0, 'Ti': 1.0, 'Mn': 3.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'Mn', 'O', 'P', 'Ti')\",OTHERS,False\nmp-1205964,\"{'Ca': 2.0, 'Fe': 1.0, 'H': 6.0}\",\"('Ca', 'Fe', 'H')\",OTHERS,False\nmp-977584,\"{'Zr': 2.0, 'Zn': 1.0, 'Ag': 2.0, 'F': 14.0}\",\"('Ag', 'F', 'Zn', 'Zr')\",OTHERS,False\nmp-675539,\"{'Na': 7.0, 'Cu': 3.0, 'O': 8.0}\",\"('Cu', 'Na', 'O')\",OTHERS,False\nmp-1114444,\"{'Rb': 2.0, 'Na': 1.0, 'Lu': 1.0, 'Cl': 6.0}\",\"('Cl', 'Lu', 'Na', 'Rb')\",OTHERS,False\nmp-16194,\"{'O': 36.0, 'Si': 12.0, 'Rb': 24.0}\",\"('O', 'Rb', 'Si')\",OTHERS,False\nmp-560091,\"{'Cs': 2.0, 'Zr': 4.0, 'P': 6.0, 'O': 24.0}\",\"('Cs', 'O', 'P', 'Zr')\",OTHERS,False\nmp-559422,\"{'Bi': 32.0, 'Au': 16.0, 'O': 72.0}\",\"('Au', 'Bi', 'O')\",OTHERS,False\nmp-1184411,\"{'Eu': 1.0, 'Mo': 1.0, 'O': 3.0}\",\"('Eu', 'Mo', 'O')\",OTHERS,False\nmp-1105153,\"{'Sr': 4.0, 'Os': 4.0, 'O': 12.0}\",\"('O', 'Os', 'Sr')\",OTHERS,False\nmp-559025,\"{'Ba': 2.0, 'Er': 2.0, 'Ag': 2.0, 'S': 6.0}\",\"('Ag', 'Ba', 'Er', 'S')\",OTHERS,False\nmp-985,\"{'Cu': 1.0, 'Tm': 1.0}\",\"('Cu', 'Tm')\",OTHERS,False\nmp-774791,\"{'Li': 4.0, 'Mn': 3.0, 'Cr': 3.0, 'Te': 2.0, 'O': 16.0}\",\"('Cr', 'Li', 'Mn', 'O', 'Te')\",OTHERS,False\nmp-1076134,\"{'Sr': 12.0, 'Ca': 20.0, 'Ti': 12.0, 'Mn': 20.0, 'O': 80.0}\",\"('Ca', 'Mn', 'O', 'Sr', 'Ti')\",OTHERS,False\nmp-1190342,\"{'Lu': 10.0, 'Pt': 6.0}\",\"('Lu', 'Pt')\",OTHERS,False\nmp-866101,\"{'Ac': 1.0, 'Cr': 1.0, 'O': 3.0}\",\"('Ac', 'Cr', 'O')\",OTHERS,False\nmp-1193367,\"{'Na': 2.0, 'Ca': 4.0, 'Si': 6.0, 'O': 18.0}\",\"('Ca', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1076247,\"{'Ba': 6.0, 'Sr': 2.0, 'Co': 1.0, 'Cu': 7.0, 'O': 24.0}\",\"('Ba', 'Co', 'Cu', 'O', 'Sr')\",OTHERS,False\nmp-5036,\"{'P': 8.0, 'S': 32.0, 'Er': 8.0}\",\"('Er', 'P', 'S')\",OTHERS,False\nmp-1225500,\"{'Dy': 1.0, 'Ho': 1.0, 'Al': 4.0}\",\"('Al', 'Dy', 'Ho')\",OTHERS,False\nmp-1101457,\"{'Ni': 2.0, 'Bi': 4.0, 'O': 12.0}\",\"('Bi', 'Ni', 'O')\",OTHERS,False\nmp-1229089,\"{'Ag': 1.0, 'Bi': 1.0, 'Se': 1.0, 'S': 1.0}\",\"('Ag', 'Bi', 'S', 'Se')\",OTHERS,False\nmvc-1114,\"{'Ba': 2.0, 'Y': 2.0, 'Ti': 8.0, 'O': 14.0}\",\"('Ba', 'O', 'Ti', 'Y')\",OTHERS,False\nmp-1199390,\"{'Ag': 4.0, 'H': 80.0, 'S': 24.0, 'N': 20.0, 'O': 36.0}\",\"('Ag', 'H', 'N', 'O', 'S')\",OTHERS,False\nmp-1598,\"{'Na': 6.0, 'P': 2.0}\",\"('Na', 'P')\",OTHERS,False\nmp-1036503,\"{'Cs': 1.0, 'Mg': 14.0, 'Al': 1.0, 'O': 16.0}\",\"('Al', 'Cs', 'Mg', 'O')\",OTHERS,False\nmp-555629,\"{'Sb': 4.0, 'Te': 4.0, 'Cl': 12.0, 'F': 24.0}\",\"('Cl', 'F', 'Sb', 'Te')\",OTHERS,False\nmp-1076184,\"{'Sr': 8.0, 'Ca': 24.0, 'Mn': 32.0, 'O': 80.0}\",\"('Ca', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-1112900,\"{'Cs': 2.0, 'Tl': 1.0, 'Hg': 1.0, 'Br': 6.0}\",\"('Br', 'Cs', 'Hg', 'Tl')\",OTHERS,False\nmp-540079,\"{'Ni': 4.0, 'P': 16.0, 'O': 48.0}\",\"('Ni', 'O', 'P')\",OTHERS,False\nmp-1572,\"{'Ga': 4.0, 'Se': 4.0}\",\"('Ga', 'Se')\",OTHERS,False\nmp-510751,\"{'Cu': 16.0, 'O': 16.0}\",\"('Cu', 'O')\",OTHERS,False\nmp-8925,\"{'Ag': 3.0, 'Te': 2.0, 'Tl': 1.0}\",\"('Ag', 'Te', 'Tl')\",OTHERS,False\nmp-568537,\"{'Yb': 1.0, 'In': 1.0, 'Au': 2.0}\",\"('Au', 'In', 'Yb')\",OTHERS,False\nmp-1183402,\"{'Ba': 2.0, 'Li': 2.0, 'B': 18.0, 'O': 30.0}\",\"('B', 'Ba', 'Li', 'O')\",OTHERS,False\nmp-1894,\"{'C': 1.0, 'W': 1.0}\",\"('C', 'W')\",OTHERS,False\nmp-30006,\"{'N': 8.0, 'O': 24.0, 'Co': 4.0}\",\"('Co', 'N', 'O')\",OTHERS,False\nmp-22978,\"{'Rb': 2.0, 'Mn': 1.0, 'Cl': 4.0}\",\"('Cl', 'Mn', 'Rb')\",OTHERS,False\nmp-1174104,\"{'Li': 5.0, 'Co': 3.0, 'O': 8.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-1113426,\"{'Cs': 1.0, 'Rb': 2.0, 'As': 1.0, 'Br': 6.0}\",\"('As', 'Br', 'Cs', 'Rb')\",OTHERS,False\nmp-543019,\"{'Gd': 2.0, 'Ni': 4.0}\",\"('Gd', 'Ni')\",OTHERS,False\nmp-569340,\"{'Rb': 8.0, 'Zn': 4.0, 'N': 48.0}\",\"('N', 'Rb', 'Zn')\",OTHERS,False\nmp-1221426,\"{'Mo': 1.0, 'Ir': 4.0}\",\"('Ir', 'Mo')\",OTHERS,False\nmp-674513,\"{'Dy': 6.0, 'Ta': 2.0, 'O': 14.0}\",\"('Dy', 'O', 'Ta')\",OTHERS,False\nmp-1245567,\"{'Fe': 1.0, 'Co': 2.0, 'N': 2.0}\",\"('Co', 'Fe', 'N')\",OTHERS,False\nmp-571265,\"{'Rb': 4.0, 'U': 2.0, 'Br': 12.0}\",\"('Br', 'Rb', 'U')\",OTHERS,False\nmp-20331,\"{'Cu': 4.0, 'Se': 8.0, 'Sb': 4.0}\",\"('Cu', 'Sb', 'Se')\",OTHERS,False\nmp-1037241,\"{'Y': 1.0, 'Mg': 30.0, 'C': 1.0, 'O': 32.0}\",\"('C', 'Mg', 'O', 'Y')\",OTHERS,False\nmp-1029153,\"{'Te': 2.0, 'Mo': 2.0, 'W': 2.0, 'Se': 4.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-1219452,\"{'Sc': 2.0, 'Tl': 4.0, 'Mo': 30.0, 'S': 38.0}\",\"('Mo', 'S', 'Sc', 'Tl')\",OTHERS,False\nmp-694855,\"{'Li': 4.0, 'Mo': 2.0, 'O': 6.0}\",\"('Li', 'Mo', 'O')\",OTHERS,False\nmp-21862,\"{'C': 1.0, 'V': 1.0, 'Ru': 3.0}\",\"('C', 'Ru', 'V')\",OTHERS,False\nmp-570009,\"{'Np': 1.0, 'Mn': 2.0, 'Si': 2.0}\",\"('Mn', 'Np', 'Si')\",OTHERS,False\nmp-569894,\"{'La': 4.0, 'B': 4.0, 'Cl': 5.0}\",\"('B', 'Cl', 'La')\",OTHERS,False\nmp-973601,\"{'Hg': 1.0, 'I': 3.0}\",\"('Hg', 'I')\",OTHERS,False\nmp-1112225,\"{'Rb': 2.0, 'In': 1.0, 'Hg': 1.0, 'F': 6.0}\",\"('F', 'Hg', 'In', 'Rb')\",OTHERS,False\nmp-510567,\"{'Ce': 6.0, 'Cu': 2.0, 'Sn': 2.0, 'S': 14.0}\",\"('Ce', 'Cu', 'S', 'Sn')\",OTHERS,False\nmp-758596,\"{'Li': 4.0, 'Fe': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-36484,\"{'V': 16.0, 'Ni': 4.0, 'O': 30.0}\",\"('Ni', 'O', 'V')\",OTHERS,False\nmvc-4401,\"{'Ge': 12.0, 'W': 8.0, 'O': 48.0}\",\"('Ge', 'O', 'W')\",OTHERS,False\nmp-557635,\"{'Sb': 16.0, 'Te': 8.0, 'Se': 8.0, 'F': 80.0}\",\"('F', 'Sb', 'Se', 'Te')\",OTHERS,False\nmp-1222222,\"{'Mn': 4.0, 'Ga': 3.0, 'Co': 8.0, 'Ge': 1.0}\",\"('Co', 'Ga', 'Ge', 'Mn')\",OTHERS,False\nmp-1099847,\"{'Sr': 4.0, 'Ca': 28.0, 'Fe': 4.0, 'Co': 28.0, 'O': 80.0}\",\"('Ca', 'Co', 'Fe', 'O', 'Sr')\",OTHERS,False\nmp-849364,\"{'Li': 4.0, 'Mn': 4.0, 'P': 4.0, 'H': 24.0, 'O': 30.0}\",\"('H', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1229221,\"{'As': 8.0, 'Se': 12.0, 'S': 12.0, 'N': 16.0, 'F': 48.0}\",\"('As', 'F', 'N', 'S', 'Se')\",OTHERS,False\nmp-1218095,\"{'Sr': 1.0, 'Nd': 1.0, 'Ga': 1.0, 'O': 4.0}\",\"('Ga', 'Nd', 'O', 'Sr')\",OTHERS,False\nmp-1025937,\"{'Te': 4.0, 'Mo': 3.0, 'S': 2.0}\",\"('Mo', 'S', 'Te')\",OTHERS,False\nmp-1023939,\"{'Mo': 2.0, 'S': 4.0}\",\"('Mo', 'S')\",OTHERS,False\nmp-1063133,\"{'Ca': 1.0, 'Pd': 2.0}\",\"('Ca', 'Pd')\",OTHERS,False\nmp-568627,\"{'Rb': 8.0, 'Tm': 2.0, 'I': 12.0}\",\"('I', 'Rb', 'Tm')\",OTHERS,False\nmp-769301,\"{'Ca': 16.0, 'Ta': 8.0, 'O': 36.0}\",\"('Ca', 'O', 'Ta')\",OTHERS,False\nmp-1210989,\"{'Li': 12.0, 'Er': 12.0, 'Te': 8.0, 'O': 48.0}\",\"('Er', 'Li', 'O', 'Te')\",OTHERS,False\nmp-642729,\"{'La': 1.0, 'H': 3.0, 'C': 3.0, 'O': 6.0}\",\"('C', 'H', 'La', 'O')\",OTHERS,False\nmp-763252,\"{'Li': 2.0, 'Sb': 2.0, 'W': 2.0, 'O': 12.0}\",\"('Li', 'O', 'Sb', 'W')\",OTHERS,False\nmp-1202794,\"{'Si': 20.0, 'H': 114.0, 'C': 38.0}\",\"('C', 'H', 'Si')\",OTHERS,False\nmp-759168,\"{'Li': 6.0, 'V': 2.0, 'O': 4.0, 'F': 4.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-1198240,\"{'La': 10.0, 'Sn': 20.0, 'Ir': 8.0}\",\"('Ir', 'La', 'Sn')\",OTHERS,False\nmp-1079156,\"{'Ag': 2.0, 'Cl': 2.0, 'O': 4.0}\",\"('Ag', 'Cl', 'O')\",OTHERS,False\nmp-1475,\"{'Be': 12.0, 'Mo': 1.0}\",\"('Be', 'Mo')\",OTHERS,False\nmp-542610,\"{'Mo': 16.0, 'O': 44.0}\",\"('Mo', 'O')\",OTHERS,False\nmp-1195057,\"{'K': 6.0, 'Ba': 6.0, 'Li': 4.0, 'Al': 8.0, 'B': 12.0, 'O': 40.0, 'F': 2.0}\",\"('Al', 'B', 'Ba', 'F', 'K', 'Li', 'O')\",OTHERS,False\nmp-19391,\"{'Na': 1.0, 'V': 1.0, 'O': 2.0}\",\"('Na', 'O', 'V')\",OTHERS,False\nmp-1096653,\"{'Y': 2.0, 'Ag': 1.0, 'Pb': 1.0}\",\"('Ag', 'Pb', 'Y')\",OTHERS,False\nmp-1217891,\"{'Ta': 1.0, 'Ti': 1.0, 'Al': 6.0}\",\"('Al', 'Ta', 'Ti')\",OTHERS,False\nmp-850747,\"{'Li': 4.0, 'Fe': 8.0, 'P': 8.0, 'H': 4.0, 'O': 32.0}\",\"('Fe', 'H', 'Li', 'O', 'P')\",OTHERS,False\nmp-761592,\"{'Li': 3.0, 'Fe': 2.0, 'Co': 1.0, 'O': 6.0}\",\"('Co', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1174578,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-13452,\"{'Be': 1.0, 'Pd': 2.0}\",\"('Be', 'Pd')\",OTHERS,False\nmp-979273,\"{'Te': 1.0, 'Pd': 3.0}\",\"('Pd', 'Te')\",OTHERS,False\nmp-1180197,\"{'Mo': 2.0, 'O': 10.0}\",\"('Mo', 'O')\",OTHERS,False\nmp-1182486,\"{'Ba': 2.0, 'H': 32.0, 'O': 20.0}\",\"('Ba', 'H', 'O')\",OTHERS,False\nmp-850763,\"{'Li': 4.0, 'Cr': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-961724,\"{'Zr': 1.0, 'Bi': 1.0, 'Rh': 1.0}\",\"('Bi', 'Rh', 'Zr')\",OTHERS,False\nmp-1183367,\"{'Ba': 1.0, 'Eu': 3.0}\",\"('Ba', 'Eu')\",OTHERS,False\nmp-1173098,\"{'Ba': 6.0, 'Ti': 2.0, 'Nb': 10.0, 'Si': 8.0, 'O': 51.0}\",\"('Ba', 'Nb', 'O', 'Si', 'Ti')\",OTHERS,False\nmp-1224687,\"{'Fe': 2.0, 'Cu': 1.0, 'S': 3.0}\",\"('Cu', 'Fe', 'S')\",OTHERS,False\nmp-636253,\"{'Gd': 1.0, 'Cu': 5.0}\",\"('Cu', 'Gd')\",OTHERS,False\nmp-1073122,\"{'Mg': 8.0, 'Si': 12.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1211854,\"{'La': 4.0, 'Mo': 4.0, 'O': 20.0}\",\"('La', 'Mo', 'O')\",OTHERS,False\nmp-569603,\"{'Ba': 4.0, 'Ca': 16.0, 'Cu': 8.0, 'N': 16.0}\",\"('Ba', 'Ca', 'Cu', 'N')\",OTHERS,False\nmp-1229286,\"{'Ba': 51.0, 'Pd': 24.0, 'O': 10.0}\",\"('Ba', 'O', 'Pd')\",OTHERS,False\nmp-758291,\"{'Li': 6.0, 'Sn': 6.0, 'P': 8.0, 'O': 32.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,False\nmp-1190320,\"{'Al': 12.0, 'Ge': 10.0}\",\"('Al', 'Ge')\",OTHERS,False\nmp-1097596,\"{'Sc': 2.0, 'Zn': 1.0, 'Rh': 1.0}\",\"('Rh', 'Sc', 'Zn')\",OTHERS,False\nmp-630227,{'C': 60.0},\"('C',)\",OTHERS,False\nmp-1111483,\"{'Rb': 2.0, 'Cu': 1.0, 'Pd': 1.0, 'F': 6.0}\",\"('Cu', 'F', 'Pd', 'Rb')\",OTHERS,False\nmp-2484,\"{'Sn': 3.0, 'Sm': 1.0}\",\"('Sm', 'Sn')\",OTHERS,False\nmp-552488,\"{'O': 2.0, 'La': 2.0, 'Cu': 2.0, 'Se': 2.0}\",\"('Cu', 'La', 'O', 'Se')\",OTHERS,False\nmp-7394,\"{'S': 4.0, 'Ge': 1.0, 'Ag': 2.0, 'Ba': 1.0}\",\"('Ag', 'Ba', 'Ge', 'S')\",OTHERS,False\nmp-1017533,\"{'Y': 2.0, 'N': 2.0}\",\"('N', 'Y')\",OTHERS,False\nmp-1227818,\"{'Ba': 1.0, 'Sr': 11.0, 'Al': 4.0, 'O': 16.0, 'F': 4.0}\",\"('Al', 'Ba', 'F', 'O', 'Sr')\",OTHERS,False\nmp-866216,\"{'Ca': 1.0, 'Dy': 1.0, 'Rh': 2.0}\",\"('Ca', 'Dy', 'Rh')\",OTHERS,False\nmp-15237,\"{'C': 10.0, 'Tb': 8.0}\",\"('C', 'Tb')\",OTHERS,False\nmp-985575,\"{'Li': 2.0, 'Ag': 2.0, 'C': 4.0, 'O': 8.0}\",\"('Ag', 'C', 'Li', 'O')\",OTHERS,False\nmp-619775,\"{'Y': 14.0, 'C': 4.0, 'I': 24.0, 'N': 2.0}\",\"('C', 'I', 'N', 'Y')\",OTHERS,False\nmp-1181787,\"{'Fe': 4.0, 'C': 8.0, 'Cl': 4.0, 'O': 18.0}\",\"('C', 'Cl', 'Fe', 'O')\",OTHERS,False\nmp-1247688,\"{'Ca': 8.0, 'Ti': 2.0, 'Mn': 6.0, 'O': 22.0}\",\"('Ca', 'Mn', 'O', 'Ti')\",OTHERS,False\nmvc-10123,\"{'Ho': 2.0, 'Zn': 2.0, 'Mo': 4.0, 'O': 12.0}\",\"('Ho', 'Mo', 'O', 'Zn')\",OTHERS,False\nmp-1202829,\"{'Ba': 8.0, 'Ga': 4.0, 'Bi': 4.0, 'Te': 20.0}\",\"('Ba', 'Bi', 'Ga', 'Te')\",OTHERS,False\nmp-1185600,\"{'Mg': 149.0, 'Os': 1.0}\",\"('Mg', 'Os')\",OTHERS,False\nmp-985594,\"{'Na': 2.0, 'Ag': 2.0, 'C': 4.0, 'O': 8.0}\",\"('Ag', 'C', 'Na', 'O')\",OTHERS,False\nmp-776355,\"{'Li': 1.0, 'V': 6.0, 'O': 7.0, 'F': 5.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-985540,\"{'Ac': 6.0, 'La': 2.0}\",\"('Ac', 'La')\",OTHERS,False\nmp-768377,\"{'Fe': 3.0, 'Cu': 3.0, 'P': 6.0, 'O': 24.0}\",\"('Cu', 'Fe', 'O', 'P')\",OTHERS,False\nmp-756230,\"{'Mn': 2.0, 'P': 2.0, 'H': 2.0, 'O': 10.0}\",\"('H', 'Mn', 'O', 'P')\",OTHERS,False\nmp-776335,\"{'Zr': 16.0, 'N': 16.0, 'O': 8.0}\",\"('N', 'O', 'Zr')\",OTHERS,False\nmp-1111497,\"{'K': 3.0, 'Bi': 1.0, 'Cl': 6.0}\",\"('Bi', 'Cl', 'K')\",OTHERS,False\nmp-1211590,\"{'La': 5.0, 'Mg': 41.0}\",\"('La', 'Mg')\",OTHERS,False\nmp-755480,\"{'K': 2.0, 'Rb': 2.0, 'O': 2.0}\",\"('K', 'O', 'Rb')\",OTHERS,False\nmp-639496,\"{'Eu': 2.0, 'In': 4.0, 'Au': 2.0}\",\"('Au', 'Eu', 'In')\",OTHERS,False\nmp-28606,\"{'K': 6.0, 'Se': 26.0, 'Au': 2.0}\",\"('Au', 'K', 'Se')\",OTHERS,False\nmp-17673,\"{'Lu': 12.0, 'O': 8.0, 'F': 20.0}\",\"('F', 'Lu', 'O')\",OTHERS,False\nmp-1223171,\"{'Li': 19.0, 'V': 12.0, 'O': 32.0}\",\"('Li', 'O', 'V')\",OTHERS,False\nmp-1100171,\"{'Cs': 1.0, 'Mg': 6.0, 'Mo': 1.0}\",\"('Cs', 'Mg', 'Mo')\",OTHERS,False\nmp-22211,\"{'V': 6.0, 'Sn': 2.0}\",\"('Sn', 'V')\",OTHERS,False\nmp-1096622,\"{'Li': 2.0, 'Ca': 1.0, 'Al': 1.0}\",\"('Al', 'Ca', 'Li')\",OTHERS,False\nmp-773466,\"{'Na': 4.0, 'Cu': 12.0, 'O': 16.0}\",\"('Cu', 'Na', 'O')\",OTHERS,False\nmvc-10947,\"{'Al': 2.0, 'Fe': 4.0, 'O': 8.0}\",\"('Al', 'Fe', 'O')\",OTHERS,False\nmp-530048,\"{'Fe': 21.0, 'O': 32.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-1236246,\"{'Li': 1.0, 'Ti': 1.0, 'O': 2.0}\",\"('Li', 'O', 'Ti')\",OTHERS,False\nmp-772237,\"{'Na': 2.0, 'Li': 10.0, 'Fe': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Fe', 'Li', 'Na', 'O', 'P')\",OTHERS,False\nmp-23745,\"{'H': 16.0, 'N': 4.0, 'O': 8.0, 'Se': 2.0}\",\"('H', 'N', 'O', 'Se')\",OTHERS,False\nmp-769502,\"{'Li': 4.0, 'Nb': 2.0, 'Fe': 3.0, 'O': 10.0}\",\"('Fe', 'Li', 'Nb', 'O')\",OTHERS,False\nmp-1187598,\"{'Tm': 2.0, 'Mg': 1.0, 'Cd': 1.0}\",\"('Cd', 'Mg', 'Tm')\",OTHERS,False\nmp-19,{'Te': 3.0},\"('Te',)\",OTHERS,False\nmp-40685,\"{'Na': 2.0, 'O': 28.0, 'Sb': 4.0, 'Sm': 6.0, 'Ti': 4.0}\",\"('Na', 'O', 'Sb', 'Sm', 'Ti')\",OTHERS,False\nmp-1196623,\"{'Cu': 6.0, 'Ag': 4.0, 'Bi': 14.0, 'Pb': 6.0, 'S': 32.0}\",\"('Ag', 'Bi', 'Cu', 'Pb', 'S')\",OTHERS,False\nmvc-9221,\"{'Mg': 4.0, 'Fe': 4.0, 'Bi': 4.0, 'O': 20.0}\",\"('Bi', 'Fe', 'Mg', 'O')\",OTHERS,False\nmp-6067,\"{'O': 48.0, 'S': 12.0, 'Cd': 8.0, 'Tl': 8.0}\",\"('Cd', 'O', 'S', 'Tl')\",OTHERS,False\nmp-1180390,\"{'Mg': 4.0, 'Te': 4.0, 'I': 24.0, 'O': 24.0}\",\"('I', 'Mg', 'O', 'Te')\",OTHERS,False\nmp-1217196,\"{'Ti': 5.0, 'Mn': 1.0, 'S': 10.0}\",\"('Mn', 'S', 'Ti')\",OTHERS,False\nmp-581868,\"{'Na': 4.0, 'Y': 4.0, 'I': 16.0, 'O': 48.0}\",\"('I', 'Na', 'O', 'Y')\",OTHERS,False\nmp-1205022,\"{'Cs': 16.0, 'Te': 112.0}\",\"('Cs', 'Te')\",OTHERS,False\nmp-1226853,\"{'Ce': 5.0, 'Bi': 1.0, 'Sb': 4.0}\",\"('Bi', 'Ce', 'Sb')\",OTHERS,False\nmp-542459,\"{'Ba': 4.0, 'Ge': 8.0, 'Ga': 8.0, 'O': 32.0}\",\"('Ba', 'Ga', 'Ge', 'O')\",OTHERS,False\nmp-849731,\"{'Li': 1.0, 'Ti': 1.0, 'V': 2.0, 'O': 6.0}\",\"('Li', 'O', 'Ti', 'V')\",OTHERS,False\nmp-504149,\"{'Fe': 12.0, 'P': 12.0, 'O': 48.0}\",\"('Fe', 'O', 'P')\",OTHERS,False\nmp-1187702,\"{'V': 1.0, 'W': 3.0}\",\"('V', 'W')\",OTHERS,False\nmp-556046,\"{'Sr': 2.0, 'Ga': 4.0, 'B': 4.0, 'O': 14.0}\",\"('B', 'Ga', 'O', 'Sr')\",OTHERS,False\nmp-1222250,\"{'Mg': 4.0, 'Al': 2.0, 'H': 12.0, 'C': 1.0, 'O': 18.0}\",\"('Al', 'C', 'H', 'Mg', 'O')\",OTHERS,False\nmvc-10619,\"{'V': 6.0, 'P': 6.0, 'O': 26.0}\",\"('O', 'P', 'V')\",OTHERS,False\nmp-22765,\"{'Si': 6.0, 'Pd': 2.0, 'Eu': 4.0}\",\"('Eu', 'Pd', 'Si')\",OTHERS,False\nmp-1009746,\"{'Sc': 1.0, 'P': 1.0}\",\"('P', 'Sc')\",OTHERS,False\nmp-546810,\"{'O': 4.0, 'C': 4.0, 'Rb': 2.0}\",\"('C', 'O', 'Rb')\",OTHERS,False\nmp-685544,\"{'Ga': 24.0, 'Cu': 12.0, 'O': 48.0}\",\"('Cu', 'Ga', 'O')\",OTHERS,False\nmp-28168,\"{'C': 2.0, 'Br': 28.0, 'Th': 12.0}\",\"('Br', 'C', 'Th')\",OTHERS,False\nmp-1226584,\"{'Ce': 1.0, 'Si': 1.0, 'B': 1.0, 'Rh': 3.0}\",\"('B', 'Ce', 'Rh', 'Si')\",OTHERS,False\nmp-1246633,\"{'Mg': 2.0, 'V': 2.0, 'Ga': 2.0, 'S': 8.0}\",\"('Ga', 'Mg', 'S', 'V')\",OTHERS,False\nmp-1178260,\"{'Fe': 10.0, 'O': 14.0, 'F': 6.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmvc-3347,\"{'Mg': 4.0, 'Cr': 4.0, 'O': 8.0}\",\"('Cr', 'Mg', 'O')\",OTHERS,False\nmp-1029730,\"{'Ca': 8.0, 'Hf': 2.0, 'N': 8.0}\",\"('Ca', 'Hf', 'N')\",OTHERS,False\nmp-1032466,\"{'Sr': 1.0, 'Mg': 6.0, 'Mn': 1.0, 'O': 8.0}\",\"('Mg', 'Mn', 'O', 'Sr')\",OTHERS,False\nmvc-7046,\"{'Si': 16.0, 'Mo': 8.0, 'O': 48.0}\",\"('Mo', 'O', 'Si')\",OTHERS,False\nmp-1245175,\"{'Ta': 50.0, 'N': 50.0}\",\"('N', 'Ta')\",OTHERS,False\nmp-755110,\"{'Cs': 4.0, 'Mn': 2.0, 'O': 8.0}\",\"('Cs', 'Mn', 'O')\",OTHERS,False\nmp-1036013,\"{'Y': 1.0, 'Mg': 14.0, 'Cu': 1.0, 'O': 16.0}\",\"('Cu', 'Mg', 'O', 'Y')\",OTHERS,False\nmp-1200491,\"{'K': 8.0, 'Al': 8.0, 'P': 8.0, 'O': 40.0}\",\"('Al', 'K', 'O', 'P')\",OTHERS,False\nmp-9596,\"{'B': 8.0, 'La': 2.0, 'Ir': 8.0}\",\"('B', 'Ir', 'La')\",OTHERS,False\nmp-865779,\"{'Ga': 2.0, 'Co': 1.0, 'Ru': 1.0}\",\"('Co', 'Ga', 'Ru')\",OTHERS,False\nmp-1032523,\"{'Mg': 6.0, 'Fe': 1.0, 'Si': 1.0, 'O': 8.0}\",\"('Fe', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-1216131,\"{'Y': 2.0, 'O': 3.0, 'F': 3.0}\",\"('F', 'O', 'Y')\",OTHERS,False\nmp-1747,\"{'K': 2.0, 'Te': 1.0}\",\"('K', 'Te')\",OTHERS,False\nmp-1214225,\"{'C': 16.0, 'S': 24.0, 'I': 8.0}\",\"('C', 'I', 'S')\",OTHERS,False\nmp-1246689,\"{'Mg': 2.0, 'Ga': 2.0, 'Mo': 2.0, 'S': 8.0}\",\"('Ga', 'Mg', 'Mo', 'S')\",OTHERS,False\nmp-637263,\"{'Zr': 10.0, 'Al': 2.0, 'Ni': 8.0}\",\"('Al', 'Ni', 'Zr')\",OTHERS,False\nmp-1191489,\"{'Ca': 2.0, 'Al': 4.0, 'O': 8.0, 'F': 8.0}\",\"('Al', 'Ca', 'F', 'O')\",OTHERS,False\nmp-757982,\"{'In': 4.0, 'H': 12.0, 'O': 12.0}\",\"('H', 'In', 'O')\",OTHERS,False\nmp-765894,\"{'Li': 5.0, 'V': 1.0, 'F': 8.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-541221,\"{'Ba': 6.0, 'N': 12.0, 'O': 30.0, 'H': 12.0}\",\"('Ba', 'H', 'N', 'O')\",OTHERS,False\nmp-2723,\"{'O': 4.0, 'Ir': 2.0}\",\"('Ir', 'O')\",OTHERS,False\nmp-562631,\"{'K': 4.0, 'S': 2.0, 'O': 8.0}\",\"('K', 'O', 'S')\",OTHERS,False\nmp-1111490,\"{'K': 3.0, 'In': 1.0, 'Br': 6.0}\",\"('Br', 'In', 'K')\",OTHERS,False\nmp-571350,\"{'Ba': 2.0, 'As': 4.0, 'Pd': 4.0}\",\"('As', 'Ba', 'Pd')\",OTHERS,False\nmp-1184672,\"{'Hf': 2.0, 'Ti': 6.0}\",\"('Hf', 'Ti')\",OTHERS,False\nmp-632692,\"{'Li': 1.0, 'Fe': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-1205470,\"{'Ca': 4.0, 'Hg': 4.0, 'H': 64.0, 'Br': 16.0, 'O': 32.0}\",\"('Br', 'Ca', 'H', 'Hg', 'O')\",OTHERS,False\nmp-41742,\"{'Ca': 2.0, 'Nd': 2.0, 'Ti': 2.0, 'Mn': 2.0, 'O': 12.0}\",\"('Ca', 'Mn', 'Nd', 'O', 'Ti')\",OTHERS,False\nmp-1818,\"{'F': 4.0, 'Si': 1.0}\",\"('F', 'Si')\",OTHERS,False\nmp-2075,\"{'N': 8.0, 'Si': 6.0}\",\"('N', 'Si')\",OTHERS,False\nmp-18649,\"{'Mn': 6.0, 'Ge': 6.0, 'Rh': 6.0}\",\"('Ge', 'Mn', 'Rh')\",OTHERS,False\nmp-1079867,\"{'Li': 4.0, 'Pr': 2.0, 'Ge': 2.0}\",\"('Ge', 'Li', 'Pr')\",OTHERS,False\nmp-30564,\"{'Co': 2.0, 'Th': 2.0}\",\"('Co', 'Th')\",OTHERS,False\nmp-1219972,\"{'Pr': 2.0, 'Ga': 2.0, 'Si': 2.0}\",\"('Ga', 'Pr', 'Si')\",OTHERS,False\nmp-753276,\"{'Li': 2.0, 'Co': 1.0, 'Ni': 1.0, 'O': 4.0}\",\"('Co', 'Li', 'Ni', 'O')\",OTHERS,False\nmp-23201,\"{'Ba': 1.0, 'Bi': 3.0}\",\"('Ba', 'Bi')\",OTHERS,False\nmp-569935,\"{'La': 3.0, 'B': 2.0, 'N': 4.0}\",\"('B', 'La', 'N')\",OTHERS,False\nmp-504732,\"{'Mn': 12.0, 'Ti': 12.0, 'O': 4.0}\",\"('Mn', 'O', 'Ti')\",OTHERS,False\nmp-1076782,\"{'Ba': 1.0, 'Co': 1.0, 'O': 3.0}\",\"('Ba', 'Co', 'O')\",OTHERS,False\nmp-757068,\"{'Li': 4.0, 'Cr': 1.0, 'Ni': 3.0, 'P': 4.0, 'O': 16.0}\",\"('Cr', 'Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-770610,\"{'Li': 8.0, 'Al': 4.0, 'Co': 4.0, 'O': 16.0}\",\"('Al', 'Co', 'Li', 'O')\",OTHERS,False\nmp-1263325,\"{'Al': 1.0, 'Ni': 1.0, 'F': 5.0}\",\"('Al', 'F', 'Ni')\",OTHERS,False\nmp-698018,\"{'Li': 2.0, 'Al': 2.0, 'Si': 4.0, 'H': 4.0, 'O': 14.0}\",\"('Al', 'H', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1216900,\"{'Tl': 1.0, 'V': 2.0, 'Cr': 3.0, 'S': 8.0}\",\"('Cr', 'S', 'Tl', 'V')\",OTHERS,False\nmp-760794,\"{'Li': 8.0, 'V': 8.0, 'F': 32.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-1229173,\"{'Ba': 12.0, 'Sm': 4.0, 'Al': 3.0, 'Co': 5.0, 'O': 30.0}\",\"('Al', 'Ba', 'Co', 'O', 'Sm')\",OTHERS,False\nmp-1221360,\"{'Na': 2.0, 'Nb': 2.0, 'W': 2.0, 'O': 13.0}\",\"('Na', 'Nb', 'O', 'W')\",OTHERS,False\nmp-756651,\"{'Li': 6.0, 'Mn': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1217172,\"{'Ti': 5.0, 'S': 8.0}\",\"('S', 'Ti')\",OTHERS,False\nmp-1228335,\"{'Ba': 4.0, 'Ti': 1.0, 'Al': 3.0, 'P': 4.0, 'O': 20.0, 'F': 4.0}\",\"('Al', 'Ba', 'F', 'O', 'P', 'Ti')\",OTHERS,False\nmp-12570,\"{'Th': 1.0, 'B': 12.0}\",\"('B', 'Th')\",OTHERS,False\nmp-1086668,\"{'La': 2.0, 'Sn': 4.0, 'Rh': 2.0}\",\"('La', 'Rh', 'Sn')\",OTHERS,False\nmp-1244593,\"{'Ca': 2.0, 'Ti': 2.0, 'Bi': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Bi', 'Ca', 'O', 'P', 'Ti')\",OTHERS,False\nmp-1211455,\"{'K': 2.0, 'Gd': 2.0, 'Mo': 4.0, 'O': 16.0}\",\"('Gd', 'K', 'Mo', 'O')\",OTHERS,False\nmp-1209439,\"{'Rb': 8.0, 'Ta': 6.0, 'O': 19.0}\",\"('O', 'Rb', 'Ta')\",OTHERS,False\nmp-1034435,\"{'Ba': 1.0, 'Mg': 14.0, 'Cr': 1.0, 'O': 16.0}\",\"('Ba', 'Cr', 'Mg', 'O')\",OTHERS,False\nmp-644841,\"{'K': 8.0, 'Pd': 4.0, 'N': 16.0, 'O': 32.0}\",\"('K', 'N', 'O', 'Pd')\",OTHERS,False\nmp-1228840,\"{'Al': 1.0, 'V': 1.0, 'Ni': 2.0}\",\"('Al', 'Ni', 'V')\",OTHERS,False\nmp-20024,\"{'Cu': 2.0, 'Sn': 2.0, 'La': 2.0}\",\"('Cu', 'La', 'Sn')\",OTHERS,False\nmp-707897,\"{'Ca': 4.0, 'Al': 2.0, 'H': 22.0, 'C': 1.0, 'O': 20.0}\",\"('Al', 'C', 'Ca', 'H', 'O')\",OTHERS,False\nmp-1221811,\"{'Mn': 3.0, 'Ni': 1.0, 'O': 4.0}\",\"('Mn', 'Ni', 'O')\",OTHERS,False\nmp-776476,\"{'Cd': 4.0, 'Fe': 4.0, 'O': 12.0}\",\"('Cd', 'Fe', 'O')\",OTHERS,False\nmp-1192549,\"{'Ba': 4.0, 'U': 2.0, 'Cu': 8.0, 'Se': 12.0}\",\"('Ba', 'Cu', 'Se', 'U')\",OTHERS,False\nmp-14324,\"{'Be': 4.0, 'O': 10.0, 'Na': 8.0, 'K': 4.0}\",\"('Be', 'K', 'Na', 'O')\",OTHERS,False\nmp-1218002,\"{'Ta': 2.0, 'Mo': 1.0, 'S': 6.0}\",\"('Mo', 'S', 'Ta')\",OTHERS,False\nmp-561500,\"{'Cs': 4.0, 'Na': 2.0, 'V': 2.0, 'F': 12.0}\",\"('Cs', 'F', 'Na', 'V')\",OTHERS,False\nmp-530214,\"{'Fe': 16.0, 'Ni': 8.0, 'O': 32.0}\",\"('Fe', 'Ni', 'O')\",OTHERS,False\nmp-1224887,\"{'Fe': 6.0, 'Ni': 6.0, 'B': 4.0}\",\"('B', 'Fe', 'Ni')\",OTHERS,False\nmp-643378,\"{'Cu': 1.0, 'H': 4.0, 'Pb': 2.0, 'Cl': 2.0, 'O': 4.0}\",\"('Cl', 'Cu', 'H', 'O', 'Pb')\",OTHERS,False\nmp-1185057,\"{'K': 3.0, 'Yb': 1.0}\",\"('K', 'Yb')\",OTHERS,False\nmp-698602,\"{'Sr': 5.0, 'La': 5.0, 'Mn': 9.0, 'Cu': 1.0, 'O': 30.0}\",\"('Cu', 'La', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-1095643,\"{'Nb': 4.0, 'Cr': 8.0}\",\"('Cr', 'Nb')\",OTHERS,False\nmp-586092,\"{'Li': 12.0, 'Fe': 20.0, 'O': 32.0}\",\"('Fe', 'Li', 'O')\",OTHERS,False\nmp-760397,\"{'Li': 1.0, 'Co': 1.0, 'Cu': 1.0, 'O': 4.0}\",\"('Co', 'Cu', 'Li', 'O')\",OTHERS,False\nmp-1104271,\"{'Ag': 6.0, 'Sb': 2.0, 'S': 6.0}\",\"('Ag', 'S', 'Sb')\",OTHERS,False\nmp-866192,\"{'Yb': 1.0, 'Li': 2.0, 'Sn': 1.0}\",\"('Li', 'Sn', 'Yb')\",OTHERS,False\nmp-1105780,\"{'Bi': 8.0, 'O': 12.0}\",\"('Bi', 'O')\",OTHERS,False\nmp-26944,\"{'O': 20.0, 'P': 6.0, 'Sn': 4.0}\",\"('O', 'P', 'Sn')\",OTHERS,False\nmp-753438,\"{'Li': 8.0, 'V': 4.0, 'O': 8.0, 'F': 4.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-554272,\"{'Cu': 24.0, 'Sb': 8.0, 'S': 24.0}\",\"('Cu', 'S', 'Sb')\",OTHERS,False\nmp-1103730,\"{'Na': 4.0, 'Mg': 1.0, 'Ge': 2.0, 'Se': 6.0}\",\"('Ge', 'Mg', 'Na', 'Se')\",OTHERS,False\nmp-15889,\"{'O': 12.0, 'Ba': 4.0, 'La': 2.0, 'Ir': 2.0}\",\"('Ba', 'Ir', 'La', 'O')\",OTHERS,False\nmp-1209233,\"{'Sc': 22.0, 'Ga': 20.0}\",\"('Ga', 'Sc')\",OTHERS,False\nmp-1214747,\"{'Ba': 2.0, 'Ca': 2.0, 'Nd': 1.0, 'Ti': 2.0, 'Cu': 2.0, 'O': 12.0}\",\"('Ba', 'Ca', 'Cu', 'Nd', 'O', 'Ti')\",OTHERS,False\nmp-17968,\"{'O': 24.0, 'Na': 4.0, 'Ca': 2.0, 'V': 8.0}\",\"('Ca', 'Na', 'O', 'V')\",OTHERS,False\nmp-3928,\"{'O': 20.0, 'Na': 10.0, 'P': 6.0}\",\"('Na', 'O', 'P')\",OTHERS,False\nmp-559826,\"{'Er': 4.0, 'Cu': 4.0, 'S': 8.0}\",\"('Cu', 'Er', 'S')\",OTHERS,False\nmp-772060,\"{'Li': 3.0, 'Nb': 1.0, 'Fe': 3.0, 'O': 8.0}\",\"('Fe', 'Li', 'Nb', 'O')\",OTHERS,False\nmp-644278,\"{'Y': 2.0, 'H': 2.0, 'C': 1.0}\",\"('C', 'H', 'Y')\",OTHERS,False\nmp-1186783,\"{'Pt': 3.0, 'Cl': 1.0}\",\"('Cl', 'Pt')\",OTHERS,False\nmp-762271,\"{'Li': 2.0, 'V': 3.0, 'O': 6.0}\",\"('Li', 'O', 'V')\",OTHERS,False\nmp-1097261,\"{'Y': 1.0, 'Hf': 1.0, 'Rh': 2.0}\",\"('Hf', 'Rh', 'Y')\",OTHERS,False\nmp-1213717,\"{'Cs': 8.0, 'Sb': 4.0, 'F': 20.0}\",\"('Cs', 'F', 'Sb')\",OTHERS,False\nmp-622258,\"{'Rb': 4.0, 'V': 8.0, 'Sb': 4.0, 'O': 32.0}\",\"('O', 'Rb', 'Sb', 'V')\",OTHERS,False\nmp-555387,\"{'Ba': 4.0, 'Si': 8.0, 'B': 8.0, 'O': 32.0}\",\"('B', 'Ba', 'O', 'Si')\",OTHERS,False\nmp-554084,\"{'Ba': 1.0, 'Bi': 6.0, 'P': 4.0, 'O': 20.0}\",\"('Ba', 'Bi', 'O', 'P')\",OTHERS,False\nmp-771949,\"{'Ti': 1.0, 'Mn': 3.0, 'P': 4.0, 'O': 16.0}\",\"('Mn', 'O', 'P', 'Ti')\",OTHERS,False\nmp-1112417,\"{'K': 2.0, 'Ta': 1.0, 'Au': 1.0, 'F': 6.0}\",\"('Au', 'F', 'K', 'Ta')\",OTHERS,False\nmp-5400,\"{'Si': 2.0, 'Cr': 2.0, 'Th': 1.0}\",\"('Cr', 'Si', 'Th')\",OTHERS,False\nmp-756644,\"{'Li': 8.0, 'Fe': 7.0, 'O': 15.0}\",\"('Fe', 'Li', 'O')\",OTHERS,False\nmp-1223910,\"{'Ho': 2.0, 'Co': 2.0, 'Ni': 2.0}\",\"('Co', 'Ho', 'Ni')\",OTHERS,False\nmp-1188063,\"{'Yb': 3.0, 'Ce': 1.0}\",\"('Ce', 'Yb')\",OTHERS,False\nmp-4533,\"{'O': 6.0, 'Na': 4.0, 'Si': 2.0}\",\"('Na', 'O', 'Si')\",OTHERS,False\nmp-621981,\"{'Tl': 8.0, 'Ag': 8.0, 'C': 16.0, 'N': 16.0}\",\"('Ag', 'C', 'N', 'Tl')\",OTHERS,False\nmp-559985,\"{'Ca': 4.0, 'P': 16.0, 'O': 44.0}\",\"('Ca', 'O', 'P')\",OTHERS,False\nmp-1206259,\"{'Tm': 3.0, 'Mg': 3.0, 'Au': 3.0}\",\"('Au', 'Mg', 'Tm')\",OTHERS,False\nmp-1033791,\"{'Mg': 14.0, 'Cr': 1.0, 'C': 1.0, 'O': 16.0}\",\"('C', 'Cr', 'Mg', 'O')\",OTHERS,False\nmp-12962,\"{'Fe': 1.0, 'Zr': 6.0, 'Sb': 2.0}\",\"('Fe', 'Sb', 'Zr')\",OTHERS,False\nmp-27659,\"{'Pu': 1.0, 'Pt': 4.0}\",\"('Pt', 'Pu')\",OTHERS,False\nmp-976972,\"{'Ho': 1.0, 'Tm': 1.0, 'Cu': 2.0}\",\"('Cu', 'Ho', 'Tm')\",OTHERS,False\nmp-1175361,\"{'Li': 7.0, 'Mn': 4.0, 'Co': 1.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-865258,\"{'Tb': 1.0, 'Yb': 1.0, 'Rh': 2.0}\",\"('Rh', 'Tb', 'Yb')\",OTHERS,False\nmp-12664,\"{'Be': 12.0, 'Au': 1.0}\",\"('Au', 'Be')\",OTHERS,False\nmp-1077045,\"{'Ti': 4.0, 'H': 2.0}\",\"('H', 'Ti')\",OTHERS,False\nmp-1217848,\"{'Tb': 2.0, 'Cu': 6.0, 'S': 6.0}\",\"('Cu', 'S', 'Tb')\",OTHERS,False\nmp-1078823,\"{'Cs': 2.0, 'Zr': 1.0, 'Ag': 2.0, 'Te': 4.0}\",\"('Ag', 'Cs', 'Te', 'Zr')\",OTHERS,False\nmp-977372,\"{'Pm': 2.0, 'Sn': 1.0, 'Hg': 1.0}\",\"('Hg', 'Pm', 'Sn')\",OTHERS,False\nmp-1189442,\"{'Tm': 12.0, 'Pt': 4.0}\",\"('Pt', 'Tm')\",OTHERS,False\nmp-13771,\"{'O': 48.0, 'Sc': 8.0, 'Ge': 12.0, 'Cd': 12.0}\",\"('Cd', 'Ge', 'O', 'Sc')\",OTHERS,False\nmp-1104709,\"{'Sm': 1.0, 'Mn': 6.0, 'Sn': 6.0}\",\"('Mn', 'Sm', 'Sn')\",OTHERS,False\nmp-1111060,\"{'K': 1.0, 'Na': 2.0, 'Al': 1.0, 'F': 6.0}\",\"('Al', 'F', 'K', 'Na')\",OTHERS,False\nmp-560464,\"{'U': 4.0, 'Tl': 8.0, 'Te': 8.0, 'O': 32.0}\",\"('O', 'Te', 'Tl', 'U')\",OTHERS,False\nmp-1077866,\"{'Ba': 1.0, 'Zn': 1.0, 'B': 1.0, 'O': 3.0, 'F': 1.0}\",\"('B', 'Ba', 'F', 'O', 'Zn')\",OTHERS,False\nmp-990091,\"{'Ag': 8.0, 'W': 4.0, 'S': 16.0}\",\"('Ag', 'S', 'W')\",OTHERS,False\nmp-978096,\"{'Pu': 3.0, 'Au': 1.0}\",\"('Au', 'Pu')\",OTHERS,False\nmp-647867,\"{'La': 16.0, 'Mo': 28.0, 'O': 108.0}\",\"('La', 'Mo', 'O')\",OTHERS,False\nmp-1218971,\"{'Sm': 1.0, 'Th': 1.0, 'N': 2.0}\",\"('N', 'Sm', 'Th')\",OTHERS,False\nmp-30774,\"{'Mg': 1.0, 'Ni': 2.0, 'Sn': 1.0}\",\"('Mg', 'Ni', 'Sn')\",OTHERS,False\nmp-15359,\"{'B': 4.0, 'O': 12.0, 'Al': 3.0, 'Gd': 1.0}\",\"('Al', 'B', 'Gd', 'O')\",OTHERS,False\nmp-773158,\"{'Li': 12.0, 'Fe': 8.0, 'P': 12.0, 'O': 48.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-600032,\"{'Si': 48.0, 'O': 96.0}\",\"('O', 'Si')\",OTHERS,False\nmp-541628,\"{'Cs': 4.0, 'Te': 4.0, 'F': 20.0}\",\"('Cs', 'F', 'Te')\",OTHERS,False\nmp-753795,\"{'Li': 12.0, 'Bi': 4.0, 'O': 12.0}\",\"('Bi', 'Li', 'O')\",OTHERS,False\nmp-1073575,\"{'Mg': 4.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-685196,\"{'Tl': 4.0, 'Ag': 32.0, 'Te': 22.0}\",\"('Ag', 'Te', 'Tl')\",OTHERS,False\nmp-1078889,\"{'La': 2.0, 'As': 4.0, 'Ir': 4.0}\",\"('As', 'Ir', 'La')\",OTHERS,False\nmp-1177019,\"{'Li': 7.0, 'Fe': 1.0, 'Ni': 3.0, 'P': 6.0, 'O': 24.0}\",\"('Fe', 'Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-774797,\"{'Li': 8.0, 'Ti': 12.0, 'Zn': 4.0, 'O': 32.0}\",\"('Li', 'O', 'Ti', 'Zn')\",OTHERS,False\nmp-1227409,\"{'Bi': 1.0, 'As': 1.0, 'Pd': 6.0}\",\"('As', 'Bi', 'Pd')\",OTHERS,False\nmp-1008631,\"{'V': 1.0, 'Co': 1.0, 'Te': 1.0}\",\"('Co', 'Te', 'V')\",OTHERS,False\nmp-1114589,\"{'Eu': 1.0, 'Rb': 2.0, 'Na': 1.0, 'Cl': 6.0}\",\"('Cl', 'Eu', 'Na', 'Rb')\",OTHERS,False\nmp-1095431,\"{'Yb': 4.0, 'Ga': 4.0, 'Ni': 4.0}\",\"('Ga', 'Ni', 'Yb')\",OTHERS,False\nmp-1246312,\"{'Hf': 4.0, 'Cr': 4.0, 'Cu': 4.0, 'S': 16.0}\",\"('Cr', 'Cu', 'Hf', 'S')\",OTHERS,False\nmp-17092,\"{'Sc': 6.0, 'Pd': 6.0, 'Sn': 6.0}\",\"('Pd', 'Sc', 'Sn')\",OTHERS,False\nmp-1211829,\"{'K': 2.0, 'Ce': 2.0, 'S': 4.0, 'O': 16.0}\",\"('Ce', 'K', 'O', 'S')\",OTHERS,False\nmp-1226015,\"{'Co': 2.0, 'Ni': 2.0, 'Ge': 2.0}\",\"('Co', 'Ge', 'Ni')\",OTHERS,False\nmp-1112922,\"{'Cs': 2.0, 'Sb': 1.0, 'Au': 1.0, 'I': 6.0}\",\"('Au', 'Cs', 'I', 'Sb')\",OTHERS,False\nmp-1184372,\"{'Eu': 2.0, 'Sb': 1.0, 'Au': 1.0}\",\"('Au', 'Eu', 'Sb')\",OTHERS,False\nmp-866009,\"{'Dy': 1.0, 'Zr': 1.0, 'Ru': 2.0}\",\"('Dy', 'Ru', 'Zr')\",OTHERS,False\nmp-1202819,\"{'Sr': 8.0, 'B': 20.0, 'H': 4.0, 'O': 40.0}\",\"('B', 'H', 'O', 'Sr')\",OTHERS,False\nmp-776264,\"{'Li': 2.0, 'Fe': 2.0, 'F': 8.0}\",\"('F', 'Fe', 'Li')\",OTHERS,False\nmp-755520,\"{'Na': 1.0, 'Mn': 1.0, 'O': 2.0}\",\"('Mn', 'Na', 'O')\",OTHERS,False\nmp-2041,\"{'Zn': 4.0, 'Ho': 2.0}\",\"('Ho', 'Zn')\",OTHERS,False\nmp-866106,\"{'Ba': 2.0, 'Hg': 1.0, 'Pb': 1.0}\",\"('Ba', 'Hg', 'Pb')\",OTHERS,False\nmp-1094098,\"{'Na': 2.0, 'Ga': 2.0, 'Te': 4.0}\",\"('Ga', 'Na', 'Te')\",OTHERS,False\nmp-865141,\"{'Lu': 4.0, 'Ni': 13.0, 'C': 4.0}\",\"('C', 'Lu', 'Ni')\",OTHERS,False\nmp-1196551,\"{'Rb': 7.0, 'Sm': 1.0, 'Fe': 6.0, 'P': 8.0, 'O': 34.0}\",\"('Fe', 'O', 'P', 'Rb', 'Sm')\",OTHERS,False\nmp-8678,\"{'O': 8.0, 'F': 2.0, 'Na': 2.0, 'Al': 2.0, 'P': 2.0}\",\"('Al', 'F', 'Na', 'O', 'P')\",OTHERS,False\nmp-12160,\"{'Li': 24.0, 'B': 12.0, 'O': 36.0, 'Ho': 4.0}\",\"('B', 'Ho', 'Li', 'O')\",OTHERS,False\nmp-33333,\"{'Na': 2.0, 'Sb': 2.0, 'Se': 4.0}\",\"('Na', 'Sb', 'Se')\",OTHERS,False\nmp-555319,\"{'Rb': 12.0, 'Ti': 12.0, 'P': 20.0, 'O': 80.0}\",\"('O', 'P', 'Rb', 'Ti')\",OTHERS,False\nmp-4473,\"{'Cu': 8.0, 'Se': 8.0, 'Ba': 4.0}\",\"('Ba', 'Cu', 'Se')\",OTHERS,False\nmp-1232136,\"{'Pr': 4.0, 'Mg': 4.0, 'Se': 12.0}\",\"('Mg', 'Pr', 'Se')\",OTHERS,False\nmp-9947,\"{'C': 7.0, 'Si': 7.0}\",\"('C', 'Si')\",OTHERS,False\nmp-760113,\"{'Li': 8.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1208544,\"{'Tb': 8.0, 'Si': 16.0, 'C': 4.0, 'N': 24.0}\",\"('C', 'N', 'Si', 'Tb')\",OTHERS,False\nmp-18622,\"{'P': 8.0, 'Hf': 14.0}\",\"('Hf', 'P')\",OTHERS,False\nmp-19187,\"{'O': 14.0, 'Na': 2.0, 'P': 4.0, 'Co': 1.0, 'Zr': 1.0}\",\"('Co', 'Na', 'O', 'P', 'Zr')\",OTHERS,False\nmp-1206826,\"{'La': 4.0, 'Si': 1.0, 'I': 5.0}\",\"('I', 'La', 'Si')\",OTHERS,False\nmp-1023934,\"{'Mo': 1.0, 'Se': 2.0}\",\"('Mo', 'Se')\",OTHERS,False\nmp-644758,\"{'Fe': 8.0, 'Mo': 16.0, 'N': 4.0}\",\"('Fe', 'Mo', 'N')\",OTHERS,False\nmp-7152,\"{'Cu': 2.0, 'Se': 6.0, 'Zr': 2.0, 'Cs': 2.0}\",\"('Cs', 'Cu', 'Se', 'Zr')\",OTHERS,False\nmp-559307,\"{'Cu': 12.0, 'P': 16.0, 'S': 12.0, 'I': 12.0}\",\"('Cu', 'I', 'P', 'S')\",OTHERS,False\nmp-776250,\"{'Zr': 4.0, 'N': 4.0, 'O': 2.0}\",\"('N', 'O', 'Zr')\",OTHERS,False\nmp-1224088,\"{'Ho': 1.0, 'Al': 6.0, 'Fe': 6.0}\",\"('Al', 'Fe', 'Ho')\",OTHERS,False\nmp-757698,\"{'Mn': 23.0, 'Fe': 1.0, 'O': 32.0}\",\"('Fe', 'Mn', 'O')\",OTHERS,False\nmp-1211074,\"{'Li': 3.0, 'Pt': 3.0, 'O': 6.0}\",\"('Li', 'O', 'Pt')\",OTHERS,False\nmp-1069042,\"{'Eu': 1.0, 'Zn': 2.0, 'Sb': 2.0}\",\"('Eu', 'Sb', 'Zn')\",OTHERS,False\nmp-1225649,\"{'Er': 4.0, 'Al': 4.0, 'Fe': 4.0}\",\"('Al', 'Er', 'Fe')\",OTHERS,False\nmp-1198347,\"{'Tb': 12.0, 'Al': 86.0, 'Cr': 8.0}\",\"('Al', 'Cr', 'Tb')\",OTHERS,False\nmp-773011,\"{'Dy': 8.0, 'Ge': 8.0, 'O': 28.0}\",\"('Dy', 'Ge', 'O')\",OTHERS,False\nmvc-5939,\"{'Mg': 2.0, 'Ti': 1.0, 'W': 1.0, 'O': 6.0}\",\"('Mg', 'O', 'Ti', 'W')\",OTHERS,False\nmp-581877,\"{'Cs': 6.0, 'Zr': 3.0, 'Si': 18.0, 'O': 45.0}\",\"('Cs', 'O', 'Si', 'Zr')\",OTHERS,False\nmp-1185250,\"{'Er': 1.0, 'Mg': 149.0}\",\"('Er', 'Mg')\",OTHERS,False\nmp-26173,\"{'Li': 1.0, 'Bi': 3.0, 'P': 2.0, 'O': 10.0}\",\"('Bi', 'Li', 'O', 'P')\",OTHERS,False\nmp-1217056,\"{'U': 7.0, 'V': 5.0, 'Ag': 3.0, 'O': 35.0}\",\"('Ag', 'O', 'U', 'V')\",OTHERS,False\nmp-1199352,\"{'Sb': 4.0, 'Xe': 4.0, 'F': 28.0}\",\"('F', 'Sb', 'Xe')\",OTHERS,False\nmp-1094395,\"{'Mg': 6.0, 'Ti': 2.0}\",\"('Mg', 'Ti')\",OTHERS,False\nmp-1215095,\"{'B': 8.0, 'N': 4.0, 'O': 22.0}\",\"('B', 'N', 'O')\",OTHERS,False\nmp-1174569,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1208866,\"{'Si': 2.0, 'P': 4.0, 'Ir': 1.0}\",\"('Ir', 'P', 'Si')\",OTHERS,False\nmp-1201801,\"{'Np': 4.0, 'Co': 2.0, 'O': 16.0, 'F': 20.0}\",\"('Co', 'F', 'Np', 'O')\",OTHERS,False\nmp-1173004,\"{'Mo': 6.0, 'O': 16.0}\",\"('Mo', 'O')\",OTHERS,False\nmp-1186313,\"{'Nd': 1.0, 'Ho': 1.0, 'Zn': 2.0}\",\"('Ho', 'Nd', 'Zn')\",OTHERS,False\nmp-1227010,\"{'Ce': 6.0, 'Al': 3.0, 'Ga': 3.0, 'Ni': 6.0}\",\"('Al', 'Ce', 'Ga', 'Ni')\",OTHERS,False\nmp-1218506,\"{'Sr': 4.0, 'Cu': 3.0, 'Ni': 1.0, 'O': 8.0}\",\"('Cu', 'Ni', 'O', 'Sr')\",OTHERS,False\nmp-573471,\"{'Li': 85.0, 'Sn': 20.0}\",\"('Li', 'Sn')\",OTHERS,False\nmp-1213686,\"{'Cs': 1.0, 'Cr': 1.0, 'Mo': 2.0, 'O': 8.0}\",\"('Cr', 'Cs', 'Mo', 'O')\",OTHERS,False\nmp-6198,\"{'O': 32.0, 'Si': 8.0, 'Ga': 8.0, 'Sr': 4.0}\",\"('Ga', 'O', 'Si', 'Sr')\",OTHERS,False\nmp-722382,\"{'P': 4.0, 'H': 44.0, 'C': 4.0, 'N': 8.0, 'O': 16.0}\",\"('C', 'H', 'N', 'O', 'P')\",OTHERS,False\nmp-21007,\"{'Sn': 3.0, 'Sm': 3.0, 'Pt': 3.0}\",\"('Pt', 'Sm', 'Sn')\",OTHERS,False\nmp-1244820,\"{'Sr': 4.0, 'Y': 2.0, 'Bi': 4.0, 'O': 14.0}\",\"('Bi', 'O', 'Sr', 'Y')\",OTHERS,False\nmp-558801,\"{'K': 4.0, 'Pr': 8.0, 'Cl': 18.0, 'O': 4.0}\",\"('Cl', 'K', 'O', 'Pr')\",OTHERS,False\nmp-1215660,\"{'Zn': 1.0, 'Hg': 4.0, 'Te': 5.0}\",\"('Hg', 'Te', 'Zn')\",OTHERS,False\nmp-530409,\"{'Bi': 20.0, 'Cl': 36.0, 'Hf': 6.0}\",\"('Bi', 'Cl', 'Hf')\",OTHERS,False\nmp-1977,\"{'Sn': 3.0, 'Nd': 1.0}\",\"('Nd', 'Sn')\",OTHERS,False\nmp-559588,\"{'Cd': 2.0, 'Sb': 2.0, 'S': 4.0, 'Br': 2.0}\",\"('Br', 'Cd', 'S', 'Sb')\",OTHERS,False\nmp-1025283,\"{'Pr': 1.0, 'In': 5.0, 'Rh': 1.0}\",\"('In', 'Pr', 'Rh')\",OTHERS,False\nmp-764927,\"{'Li': 12.0, 'Cr': 3.0, 'P': 9.0, 'O': 36.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-1225944,\"{'Cs': 2.0, 'Sb': 2.0, 'W': 2.0, 'O': 12.0}\",\"('Cs', 'O', 'Sb', 'W')\",OTHERS,False\nmp-1096132,\"{'Y': 2.0, 'Hg': 1.0, 'Pd': 1.0}\",\"('Hg', 'Pd', 'Y')\",OTHERS,False\nmp-36890,\"{'Bi': 3.0, 'O': 7.0, 'Ta': 1.0}\",\"('Bi', 'O', 'Ta')\",OTHERS,False\nmp-27398,\"{'Tl': 8.0, 'Br': 16.0}\",\"('Br', 'Tl')\",OTHERS,False\nmp-555092,\"{'K': 12.0, 'W': 4.0, 'S': 12.0, 'Cl': 4.0, 'O': 4.0}\",\"('Cl', 'K', 'O', 'S', 'W')\",OTHERS,False\nmp-1174835,\"{'Li': 6.0, 'Mn': 3.0, 'Co': 1.0, 'O': 10.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-767627,\"{'Mn': 6.0, 'O': 4.0, 'F': 12.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmvc-16348,\"{'Zn': 6.0, 'Sb': 12.0, 'O': 24.0}\",\"('O', 'Sb', 'Zn')\",OTHERS,False\nmp-683660,\"{'Mn': 8.0, 'C': 32.0, 'Br': 8.0, 'O': 32.0}\",\"('Br', 'C', 'Mn', 'O')\",OTHERS,False\nmp-1175864,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-27008,\"{'Fe': 4.0, 'P': 6.0, 'O': 20.0}\",\"('Fe', 'O', 'P')\",OTHERS,False\nmp-567655,\"{'Sr': 2.0, 'C': 1.0, 'N': 2.0, 'Cl': 2.0}\",\"('C', 'Cl', 'N', 'Sr')\",OTHERS,False\nmp-531021,\"{'Li': 11.0, 'Mn': 13.0, 'O': 32.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-1247615,\"{'Ca': 16.0, 'Mn': 12.0, 'Cr': 4.0, 'O': 44.0}\",\"('Ca', 'Cr', 'Mn', 'O')\",OTHERS,False\nmp-570163,\"{'Tm': 8.0, 'Mn': 8.0, 'Sn': 14.0}\",\"('Mn', 'Sn', 'Tm')\",OTHERS,False\nmp-1073495,\"{'Mg': 4.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1218624,\"{'Sr': 5.0, 'Ca': 5.0, 'P': 6.0, 'Cl': 2.0, 'O': 24.0}\",\"('Ca', 'Cl', 'O', 'P', 'Sr')\",OTHERS,False\nmp-1183774,\"{'Co': 6.0, 'Sn': 2.0}\",\"('Co', 'Sn')\",OTHERS,False\nmp-1237806,\"{'H': 16.0, 'C': 4.0, 'N': 12.0, 'O': 14.0}\",\"('C', 'H', 'N', 'O')\",OTHERS,False\nmp-758769,\"{'Li': 4.0, 'Ni': 4.0, 'C': 8.0, 'O': 24.0}\",\"('C', 'Li', 'Ni', 'O')\",OTHERS,False\nmp-861891,\"{'Se': 2.0, 'Br': 4.0}\",\"('Br', 'Se')\",OTHERS,False\nmvc-525,\"{'Ca': 2.0, 'Cu': 6.0, 'P': 8.0, 'O': 28.0}\",\"('Ca', 'Cu', 'O', 'P')\",OTHERS,False\nmp-849509,\"{'Li': 16.0, 'V': 8.0, 'P': 8.0, 'O': 32.0, 'F': 8.0}\",\"('F', 'Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1208619,\"{'Sr': 2.0, 'Li': 2.0, 'Ga': 2.0, 'F': 12.0}\",\"('F', 'Ga', 'Li', 'Sr')\",OTHERS,False\nmp-1194315,\"{'Ni': 14.0, 'P': 14.0}\",\"('Ni', 'P')\",OTHERS,False\nmp-1074011,\"{'Mg': 12.0, 'Si': 10.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1245956,\"{'Al': 2.0, 'Pb': 6.0, 'N': 6.0}\",\"('Al', 'N', 'Pb')\",OTHERS,False\nmp-1219254,\"{'Sc': 1.0, 'In': 1.0, 'P': 2.0}\",\"('In', 'P', 'Sc')\",OTHERS,False\nmp-1227158,\"{'Ce': 2.0, 'Ta': 14.0, 'O': 38.0}\",\"('Ce', 'O', 'Ta')\",OTHERS,False\nmp-1037735,\"{'Y': 1.0, 'Mg': 30.0, 'V': 1.0, 'O': 32.0}\",\"('Mg', 'O', 'V', 'Y')\",OTHERS,False\nmp-1076123,\"{'Eu': 4.0, 'Co': 4.0, 'O': 10.0}\",\"('Co', 'Eu', 'O')\",OTHERS,False\nmp-1198344,\"{'Ir': 4.0, 'Rh': 4.0, 'Br': 24.0, 'N': 20.0, 'Cl': 4.0}\",\"('Br', 'Cl', 'Ir', 'N', 'Rh')\",OTHERS,False\nmp-1184519,\"{'In': 6.0, 'Cl': 2.0}\",\"('Cl', 'In')\",OTHERS,False\nmp-720100,\"{'Na': 2.0, 'H': 36.0, 'C': 18.0, 'I': 2.0, 'N': 6.0, 'O': 6.0}\",\"('C', 'H', 'I', 'N', 'Na', 'O')\",OTHERS,False\nmp-1212858,\"{'Dy': 10.0, 'Sb': 2.0, 'Pt': 4.0}\",\"('Dy', 'Pt', 'Sb')\",OTHERS,False\nmp-11493,\"{'Lu': 1.0, 'Pb': 2.0}\",\"('Lu', 'Pb')\",OTHERS,False\nmp-675192,\"{'Ce': 3.0, 'Dy': 2.0, 'O': 9.0}\",\"('Ce', 'Dy', 'O')\",OTHERS,False\nmp-759552,\"{'Li': 4.0, 'Cr': 2.0, 'P': 8.0, 'H': 32.0, 'O': 40.0}\",\"('Cr', 'H', 'Li', 'O', 'P')\",OTHERS,False\nmvc-10489,\"{'Ca': 4.0, 'Co': 8.0, 'Cu': 8.0, 'O': 32.0}\",\"('Ca', 'Co', 'Cu', 'O')\",OTHERS,False\nmp-1245357,\"{'Ba': 12.0, 'Mn': 24.0, 'N': 48.0}\",\"('Ba', 'Mn', 'N')\",OTHERS,False\nmp-1027293,\"{'Mo': 2.0, 'W': 2.0, 'Se': 2.0, 'S': 6.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-25380,\"{'O': 8.0, 'F': 2.0, 'P': 2.0, 'Cu': 2.0}\",\"('Cu', 'F', 'O', 'P')\",OTHERS,False\nmp-1229007,\"{'Ag': 1.0, 'Sb': 1.0, 'Te': 1.0, 'Se': 1.0}\",\"('Ag', 'Sb', 'Se', 'Te')\",OTHERS,False\nmp-1182617,\"{'Co': 6.0, 'P': 6.0, 'H': 18.0, 'O': 30.0}\",\"('Co', 'H', 'O', 'P')\",OTHERS,False\nmp-1200195,\"{'Sc': 12.0, 'Mn': 12.0, 'Ge': 24.0}\",\"('Ge', 'Mn', 'Sc')\",OTHERS,False\nmp-760595,\"{'Cu': 4.0, 'C': 4.0, 'O': 12.0}\",\"('C', 'Cu', 'O')\",OTHERS,False\nmp-1223475,\"{'K': 2.0, 'Na': 2.0, 'Ca': 2.0, 'Th': 2.0, 'Si': 16.0, 'O': 40.0}\",\"('Ca', 'K', 'Na', 'O', 'Si', 'Th')\",OTHERS,False\nmp-10478,\"{'As': 4.0, 'Rh': 4.0, 'Ce': 4.0}\",\"('As', 'Ce', 'Rh')\",OTHERS,False\nmp-1025282,\"{'Ti': 1.0, 'V': 2.0, 'Te': 4.0}\",\"('Te', 'Ti', 'V')\",OTHERS,False\nmp-556146,\"{'Sn': 4.0, 'C': 4.0, 'S': 4.0, 'N': 4.0, 'F': 4.0}\",\"('C', 'F', 'N', 'S', 'Sn')\",OTHERS,False\nmp-641962,\"{'K': 4.0, 'Au': 4.0, 'C': 8.0, 'S': 8.0, 'N': 8.0}\",\"('Au', 'C', 'K', 'N', 'S')\",OTHERS,False\nmp-1215061,\"{'Ba': 4.0, 'Na': 2.0, 'Ti': 4.0, 'Mn': 2.0, 'Re': 4.0, 'Si': 16.0, 'H': 2.0, 'O': 52.0, 'F': 2.0}\",\"('Ba', 'F', 'H', 'Mn', 'Na', 'O', 'Re', 'Si', 'Ti')\",OTHERS,False\nmp-1101555,\"{'Li': 4.0, 'Mo': 2.0, 'P': 8.0, 'O': 26.0}\",\"('Li', 'Mo', 'O', 'P')\",OTHERS,False\nmp-1094254,\"{'Zr': 5.0, 'Sn': 1.0}\",\"('Sn', 'Zr')\",OTHERS,False\nmp-865913,\"{'Li': 2.0, 'Ac': 1.0, 'Sn': 1.0}\",\"('Ac', 'Li', 'Sn')\",OTHERS,False\nmp-1222706,\"{'La': 1.0, 'Zn': 1.0, 'Ag': 1.0, 'As': 2.0}\",\"('Ag', 'As', 'La', 'Zn')\",OTHERS,False\nmp-510484,\"{'Sn': 4.0, 'Ni': 8.0, 'Ca': 2.0}\",\"('Ca', 'Ni', 'Sn')\",OTHERS,False\nmp-1219530,\"{'Sb': 28.0, 'Mo': 9.0, 'Ru': 3.0}\",\"('Mo', 'Ru', 'Sb')\",OTHERS,False\nmp-1207948,\"{'V': 8.0, 'As': 8.0, 'N': 8.0, 'O': 32.0, 'F': 8.0}\",\"('As', 'F', 'N', 'O', 'V')\",OTHERS,False\nmp-753741,\"{'Li': 12.0, 'Cu': 4.0, 'S': 8.0}\",\"('Cu', 'Li', 'S')\",OTHERS,False\nmp-1094171,\"{'La': 6.0, 'Mg': 2.0}\",\"('La', 'Mg')\",OTHERS,False\nmvc-12664,\"{'Mg': 4.0, 'Ni': 4.0, 'O': 8.0}\",\"('Mg', 'Ni', 'O')\",OTHERS,False\nmvc-9385,\"{'Pr': 2.0, 'Mg': 2.0, 'Cr': 4.0, 'O': 12.0}\",\"('Cr', 'Mg', 'O', 'Pr')\",OTHERS,False\nmp-4240,\"{'Rb': 1.0, 'Te': 2.0, 'Ce': 1.0}\",\"('Ce', 'Rb', 'Te')\",OTHERS,False\nmp-865080,\"{'Na': 1.0, 'Ce': 1.0, 'Au': 2.0}\",\"('Au', 'Ce', 'Na')\",OTHERS,False\nmp-1216971,\"{'Ti': 3.0, 'Nb': 6.0, 'O': 21.0}\",\"('Nb', 'O', 'Ti')\",OTHERS,False\nmp-1211760,\"{'K': 4.0, 'Eu': 8.0, 'Cl': 28.0}\",\"('Cl', 'Eu', 'K')\",OTHERS,False\nmp-1176051,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1177350,\"{'Li': 4.0, 'Mn': 5.0, 'Sb': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'Mn', 'O', 'P', 'Sb')\",OTHERS,False\nmp-1183370,\"{'Ba': 4.0, 'Sn': 4.0, 'S': 12.0}\",\"('Ba', 'S', 'Sn')\",OTHERS,False\nmp-1095603,\"{'Y': 4.0, 'As': 4.0, 'Se': 4.0}\",\"('As', 'Se', 'Y')\",OTHERS,False\nmp-1182493,\"{'Ca': 4.0, 'V': 4.0, 'Si': 16.0, 'O': 60.0}\",\"('Ca', 'O', 'Si', 'V')\",OTHERS,False\nmp-1190638,\"{'Dy': 8.0, 'Ge': 8.0, 'Pd': 8.0}\",\"('Dy', 'Ge', 'Pd')\",OTHERS,False\nmp-1223896,\"{'Hg': 1.0, 'Br': 1.0, 'N': 1.0}\",\"('Br', 'Hg', 'N')\",OTHERS,False\nmp-680232,\"{'K': 16.0, 'Ag': 8.0, 'C': 24.0, 'S': 24.0, 'N': 24.0}\",\"('Ag', 'C', 'K', 'N', 'S')\",OTHERS,False\nmp-1226025,\"{'K': 3.0, 'Zr': 4.0, 'Si': 12.0, 'H': 1.0, 'O': 44.0}\",\"('H', 'K', 'O', 'Si', 'Zr')\",OTHERS,False\nmp-759399,\"{'Na': 4.0, 'Co': 12.0, 'O': 16.0}\",\"('Co', 'Na', 'O')\",OTHERS,False\nmp-1001790,\"{'Li': 1.0, 'O': 3.0}\",\"('Li', 'O')\",OTHERS,False\nmvc-5807,\"{'Zn': 4.0, 'Cr': 2.0, 'Ir': 2.0, 'O': 12.0}\",\"('Cr', 'Ir', 'O', 'Zn')\",OTHERS,False\nmp-778251,\"{'Li': 4.0, 'Ti': 1.0, 'Co': 2.0, 'Ni': 3.0, 'P': 6.0, 'O': 24.0}\",\"('Co', 'Li', 'Ni', 'O', 'P', 'Ti')\",OTHERS,False\nmp-1188179,\"{'Er': 4.0, 'Al': 6.0, 'Ge': 8.0}\",\"('Al', 'Er', 'Ge')\",OTHERS,False\nmp-540702,\"{'Fe': 21.0, 'Se': 24.0}\",\"('Fe', 'Se')\",OTHERS,False\nmp-572674,\"{'Re': 3.0, 'C': 6.0, 'I': 6.0, 'O': 6.0}\",\"('C', 'I', 'O', 'Re')\",OTHERS,False\nmp-1227758,\"{'Ca': 4.0, 'Ti': 2.0, 'Cr': 2.0, 'O': 12.0}\",\"('Ca', 'Cr', 'O', 'Ti')\",OTHERS,False\nmp-1201100,\"{'Ce': 3.0, 'Ag': 3.0, 'S': 6.0, 'O': 27.0}\",\"('Ag', 'Ce', 'O', 'S')\",OTHERS,False\nmp-1226632,\"{'Cr': 24.0, 'P': 11.0, 'C': 1.0}\",\"('C', 'Cr', 'P')\",OTHERS,False\nmvc-5754,\"{'Mg': 4.0, 'Bi': 8.0, 'O': 16.0}\",\"('Bi', 'Mg', 'O')\",OTHERS,False\nmp-1224896,\"{'Fe': 2.0, 'Sb': 12.0, 'Pd': 2.0}\",\"('Fe', 'Pd', 'Sb')\",OTHERS,False\nmp-1096989,\"{'H': 24.0, 'C': 4.0, 'N': 4.0, 'Cl': 4.0}\",\"('C', 'Cl', 'H', 'N')\",OTHERS,False\nmp-867537,\"{'Li': 4.0, 'Co': 3.0, 'Ni': 1.0, 'O': 8.0}\",\"('Co', 'Li', 'Ni', 'O')\",OTHERS,False\nmp-1199851,\"{'Er': 4.0, 'C': 12.0, 'O': 32.0}\",\"('C', 'Er', 'O')\",OTHERS,False\nmp-14046,\"{'O': 48.0, 'Si': 12.0, 'V': 8.0, 'Mn': 12.0}\",\"('Mn', 'O', 'Si', 'V')\",OTHERS,False\nmp-2199,\"{'Si': 1.0, 'Fe': 3.0}\",\"('Fe', 'Si')\",OTHERS,False\nmp-29422,\"{'Cl': 8.0, 'Hf': 2.0}\",\"('Cl', 'Hf')\",OTHERS,False\nmp-556044,\"{'Si': 12.0, 'O': 24.0}\",\"('O', 'Si')\",OTHERS,False\nmp-705154,\"{'Li': 2.0, 'Cr': 8.0, 'P': 14.0, 'O': 48.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-1247606,\"{'Sr': 4.0, 'Ca': 28.0, 'Mn': 28.0, 'Cr': 4.0, 'O': 88.0}\",\"('Ca', 'Cr', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-1094704,\"{'Mg': 10.0, 'Cd': 2.0}\",\"('Cd', 'Mg')\",OTHERS,False\nmp-1027049,\"{'Te': 2.0, 'Mo': 2.0, 'W': 2.0, 'Se': 4.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-772659,\"{'Li': 12.0, 'Cr': 4.0, 'O': 16.0}\",\"('Cr', 'Li', 'O')\",OTHERS,False\nmp-755444,\"{'Li': 1.0, 'Zn': 1.0, 'Fe': 4.0, 'O': 8.0}\",\"('Fe', 'Li', 'O', 'Zn')\",OTHERS,False\nmp-1187775,\"{'Yb': 2.0, 'Pu': 6.0}\",\"('Pu', 'Yb')\",OTHERS,False\nmp-1214188,\"{'Ce': 8.0, 'Ta': 12.0, 'Se': 8.0, 'O': 32.0}\",\"('Ce', 'O', 'Se', 'Ta')\",OTHERS,False\nmp-7432,\"{'Sr': 1.0, 'Cd': 2.0, 'Sb': 2.0}\",\"('Cd', 'Sb', 'Sr')\",OTHERS,False\nmp-752797,\"{'Co': 6.0, 'O': 1.0, 'F': 11.0}\",\"('Co', 'F', 'O')\",OTHERS,False\nmp-2544,\"{'Be': 17.0, 'Zr': 2.0}\",\"('Be', 'Zr')\",OTHERS,False\nmp-7463,\"{'P': 6.0, 'Cu': 18.0}\",\"('Cu', 'P')\",OTHERS,True\nmp-1195659,\"{'Th': 4.0, 'Se': 8.0, 'O': 24.0}\",\"('O', 'Se', 'Th')\",OTHERS,True\nmp-1018786,\"{'Li': 2.0, 'Mn': 2.0, 'Sb': 2.0}\",\"('Li', 'Mn', 'Sb')\",OTHERS,True\nmp-651173,\"{'K': 6.0, 'W': 2.0, 'C': 8.0, 'N': 8.0, 'O': 2.0, 'F': 2.0}\",\"('C', 'F', 'K', 'N', 'O', 'W')\",OTHERS,True\nmp-1095230,\"{'Ca': 2.0, 'Se': 2.0, 'O': 8.0}\",\"('Ca', 'O', 'Se')\",OTHERS,True\nmp-1218639,\"{'Sr': 5.0, 'Mn': 4.0, 'C': 1.0, 'O': 13.0}\",\"('C', 'Mn', 'O', 'Sr')\",OTHERS,True\nmp-21313,\"{'C': 1.0, 'Mn': 3.0, 'Ga': 1.0}\",\"('C', 'Ga', 'Mn')\",OTHERS,True\nmp-768659,\"{'Li': 4.0, 'Co': 4.0, 'S': 6.0, 'O': 24.0}\",\"('Co', 'Li', 'O', 'S')\",OTHERS,True\nmp-1210938,\"{'Sm': 10.0, 'In': 20.0, 'Pd': 10.0}\",\"('In', 'Pd', 'Sm')\",OTHERS,True\nmvc-2827,\"{'Ca': 4.0, 'Ta': 4.0, 'Fe': 2.0, 'O': 16.0}\",\"('Ca', 'Fe', 'O', 'Ta')\",OTHERS,True\nmp-762265,\"{'Li': 3.0, 'V': 5.0, 'O': 10.0}\",\"('Li', 'O', 'V')\",OTHERS,True\nmp-779031,\"{'Na': 4.0, 'V': 4.0, 'P': 8.0, 'O': 32.0}\",\"('Na', 'O', 'P', 'V')\",OTHERS,True\nmp-1246289,\"{'Mg': 6.0, 'Bi': 6.0, 'N': 10.0}\",\"('Bi', 'Mg', 'N')\",OTHERS,True\nmp-1200207,\"{'Cs': 16.0, 'Ga': 22.0}\",\"('Cs', 'Ga')\",OTHERS,True\nmp-1111691,\"{'K': 3.0, 'Tl': 1.0, 'F': 6.0}\",\"('F', 'K', 'Tl')\",OTHERS,True\nmp-9820,\"{'F': 6.0, 'As': 1.0, 'Cs': 1.0}\",\"('As', 'Cs', 'F')\",OTHERS,True\nmp-22724,\"{'F': 14.0, 'Zr': 2.0, 'Ce': 2.0}\",\"('Ce', 'F', 'Zr')\",OTHERS,True\nmp-1207797,\"{'Y': 3.0, 'Ga': 9.0, 'Cu': 2.0}\",\"('Cu', 'Ga', 'Y')\",OTHERS,True\nmp-753198,\"{'Mg': 2.0, 'Fe': 4.0, 'O': 8.0}\",\"('Fe', 'Mg', 'O')\",OTHERS,True\nmp-1029165,\"{'Te': 2.0, 'Mo': 2.0, 'W': 2.0, 'Se': 6.0}\",\"('Mo', 'Se', 'Te', 'W')\",OTHERS,True\nmp-684769,\"{'Sr': 2.0, 'La': 6.0, 'Ti': 1.0, 'Cu': 3.0, 'O': 16.0}\",\"('Cu', 'La', 'O', 'Sr', 'Ti')\",OTHERS,True\nmp-541923,\"{'Tl': 4.0, 'Cu': 20.0, 'Se': 12.0}\",\"('Cu', 'Se', 'Tl')\",OTHERS,True\nmp-1188122,\"{'Ba': 4.0, 'Er': 2.0, 'In': 2.0, 'Se': 10.0}\",\"('Ba', 'Er', 'In', 'Se')\",OTHERS,True\nmp-1112401,\"{'Cs': 1.0, 'Rb': 2.0, 'Ir': 1.0, 'F': 6.0}\",\"('Cs', 'F', 'Ir', 'Rb')\",OTHERS,True\nmp-1020010,\"{'Li': 8.0, 'Ca': 12.0, 'N': 24.0}\",\"('Ca', 'Li', 'N')\",OTHERS,True\nmp-863410,\"{'Li': 4.0, 'Nb': 2.0, 'P': 10.0, 'O': 30.0}\",\"('Li', 'Nb', 'O', 'P')\",OTHERS,True\nmp-558487,\"{'Gd': 4.0, 'P': 20.0, 'O': 56.0}\",\"('Gd', 'O', 'P')\",OTHERS,True\nmp-8184,\"{'Li': 4.0, 'O': 8.0, 'Zn': 2.0, 'Ge': 2.0}\",\"('Ge', 'Li', 'O', 'Zn')\",OTHERS,True\nmp-29776,\"{'Ge': 18.0, 'Rh': 26.0, 'Ce': 8.0}\",\"('Ce', 'Ge', 'Rh')\",OTHERS,True\nmp-582282,\"{'Gd': 2.0, 'Ag': 2.0, 'Se': 4.0}\",\"('Ag', 'Gd', 'Se')\",OTHERS,True\nmp-985626,\"{'Na': 3.0, 'Mn': 2.0, 'Sb': 1.0, 'O': 6.0}\",\"('Mn', 'Na', 'O', 'Sb')\",OTHERS,True\nmp-1025513,\"{'Zr': 4.0, 'Ge': 4.0}\",\"('Ge', 'Zr')\",OTHERS,True\nmp-1209864,\"{'Nd': 6.0, 'Zr': 2.0, 'Sb': 10.0}\",\"('Nd', 'Sb', 'Zr')\",OTHERS,True\nmp-1217847,\"{'Sr': 2.0, 'Ti': 8.0, 'Bi': 8.0, 'O': 30.0}\",\"('Bi', 'O', 'Sr', 'Ti')\",OTHERS,True\nmp-9705,\"{'B': 3.0, 'N': 6.0, 'Na': 1.0, 'Ba': 4.0}\",\"('B', 'Ba', 'N', 'Na')\",OTHERS,True\nmp-1036805,\"{'Ba': 1.0, 'Mg': 30.0, 'V': 1.0, 'O': 32.0}\",\"('Ba', 'Mg', 'O', 'V')\",OTHERS,True\nmp-557328,\"{'Li': 3.0, 'Zn': 2.0, 'Sb': 1.0, 'O': 6.0}\",\"('Li', 'O', 'Sb', 'Zn')\",OTHERS,True\nmp-26684,\"{'Li': 6.0, 'O': 36.0, 'P': 10.0, 'Cr': 4.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,True\nmp-554873,\"{'Ag': 8.0, 'B': 32.0, 'O': 52.0}\",\"('Ag', 'B', 'O')\",OTHERS,True\nmp-10609,\"{'C': 1.0, 'Sn': 1.0, 'Lu': 3.0}\",\"('C', 'Lu', 'Sn')\",OTHERS,True\nmp-1009213,\"{'Mn': 1.0, 'Sb': 1.0}\",\"('Mn', 'Sb')\",OTHERS,True\nmp-1187601,\"{'Tm': 2.0, 'Tl': 1.0, 'Zn': 1.0}\",\"('Tl', 'Tm', 'Zn')\",OTHERS,True\nmp-1188132,\"{'Ho': 4.0, 'Mo': 4.0, 'C': 8.0}\",\"('C', 'Ho', 'Mo')\",OTHERS,True\nmvc-14545,\"{'Ca': 1.0, 'La': 2.0, 'Sb': 1.0, 'O': 6.0}\",\"('Ca', 'La', 'O', 'Sb')\",OTHERS,True\nmp-1222495,\"{'Li': 3.0, 'Cd': 1.0}\",\"('Cd', 'Li')\",OTHERS,True\nmp-27331,\"{'K': 12.0, 'Ge': 4.0, 'Te': 12.0}\",\"('Ge', 'K', 'Te')\",OTHERS,True\nmp-1226404,\"{'Cu': 4.0, 'Si': 4.0, 'H': 72.0, 'C': 28.0, 'S': 12.0, 'I': 4.0}\",\"('C', 'Cu', 'H', 'I', 'S', 'Si')\",OTHERS,True\nmp-569020,\"{'In': 2.0, 'Sb': 2.0}\",\"('In', 'Sb')\",OTHERS,True\nmvc-4646,\"{'Ca': 8.0, 'Bi': 16.0, 'O': 32.0}\",\"('Bi', 'Ca', 'O')\",OTHERS,True\nmp-28319,\"{'I': 8.0, 'Pt': 4.0}\",\"('I', 'Pt')\",OTHERS,True\nmp-1093914,\"{'Mn': 1.0, 'Be': 2.0, 'Ru': 1.0}\",\"('Be', 'Mn', 'Ru')\",OTHERS,True\nmp-1222654,\"{'Li': 2.0, 'Ga': 1.0, 'Si': 1.0}\",\"('Ga', 'Li', 'Si')\",OTHERS,True\nmp-780712,\"{'Ce': 4.0, 'Cr': 4.0, 'O': 12.0}\",\"('Ce', 'Cr', 'O')\",OTHERS,True\nmp-26399,\"{'Li': 6.0, 'O': 16.0, 'P': 4.0, 'V': 2.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-559813,\"{'Ba': 9.0, 'Cu': 7.0, 'Cl': 2.0, 'O': 15.0}\",\"('Ba', 'Cl', 'Cu', 'O')\",OTHERS,True\nmvc-5780,\"{'Mg': 4.0, 'Fe': 2.0, 'Ir': 2.0, 'O': 12.0}\",\"('Fe', 'Ir', 'Mg', 'O')\",OTHERS,True\nmp-862846,\"{'Ca': 1.0, 'La': 1.0, 'Hg': 2.0}\",\"('Ca', 'Hg', 'La')\",OTHERS,True\nmp-29844,\"{'Al': 9.0, 'Cl': 36.0, 'Tb': 3.0}\",\"('Al', 'Cl', 'Tb')\",OTHERS,True\nmp-31145,\"{'Cu': 2.0, 'Ba': 2.0, 'Bi': 2.0}\",\"('Ba', 'Bi', 'Cu')\",OTHERS,True\nmp-20519,\"{'S': 2.0, 'Cd': 1.0, 'In': 1.0}\",\"('Cd', 'In', 'S')\",OTHERS,True\nmp-1033291,\"{'Mg': 6.0, 'B': 1.0, 'C': 1.0, 'O': 8.0}\",\"('B', 'C', 'Mg', 'O')\",OTHERS,True\nmp-541441,\"{'Zn': 2.0, 'Te': 2.0}\",\"('Te', 'Zn')\",OTHERS,True\nmp-1095711,\"{'Mg': 1.0, 'Sc': 1.0, 'Au': 2.0}\",\"('Au', 'Mg', 'Sc')\",OTHERS,True\nmp-771838,\"{'La': 8.0, 'Al': 12.0, 'O': 30.0}\",\"('Al', 'La', 'O')\",OTHERS,True\nmp-758006,\"{'Ca': 8.0, 'Si': 4.0, 'O': 16.0}\",\"('Ca', 'O', 'Si')\",OTHERS,True\nmp-706611,\"{'Li': 4.0, 'Be': 4.0, 'H': 16.0, 'N': 4.0, 'F': 16.0}\",\"('Be', 'F', 'H', 'Li', 'N')\",OTHERS,True\nmp-680205,\"{'Pb': 15.0, 'I': 30.0}\",\"('I', 'Pb')\",OTHERS,True\nmp-5577,\"{'Ge': 2.0, 'Rh': 2.0, 'Ce': 1.0}\",\"('Ce', 'Ge', 'Rh')\",OTHERS,True\nmp-1197388,\"{'Gd': 8.0, 'Fe': 56.0, 'C': 4.0}\",\"('C', 'Fe', 'Gd')\",OTHERS,True\nmp-546266,\"{'O': 4.0, 'Bi': 2.0, 'Dy': 1.0, 'I': 1.0}\",\"('Bi', 'Dy', 'I', 'O')\",OTHERS,True\nmp-574430,\"{'Ba': 2.0, 'U': 2.0, 'I': 12.0}\",\"('Ba', 'I', 'U')\",OTHERS,True\nmp-30782,\"{'Mg': 23.0, 'Sr': 6.0}\",\"('Mg', 'Sr')\",OTHERS,True\nmp-21657,\"{'Se': 12.0, 'Tl': 20.0}\",\"('Se', 'Tl')\",OTHERS,True\nmp-1208739,\"{'Sr': 4.0, 'Al': 4.0, 'H': 4.0, 'O': 4.0, 'F': 16.0}\",\"('Al', 'F', 'H', 'O', 'Sr')\",OTHERS,True\nmp-755999,\"{'Rb': 4.0, 'Be': 4.0, 'O': 6.0}\",\"('Be', 'O', 'Rb')\",OTHERS,True\nmp-561414,\"{'K': 12.0, 'Hg': 12.0, 'S': 8.0, 'Br': 20.0, 'O': 24.0}\",\"('Br', 'Hg', 'K', 'O', 'S')\",OTHERS,True\nmp-1227126,\"{'Ca': 2.0, 'Y': 2.0, 'Mn': 4.0, 'O': 12.0}\",\"('Ca', 'Mn', 'O', 'Y')\",OTHERS,True\nmp-6065,\"{'O': 6.0, 'Ga': 1.0, 'Sr': 2.0, 'Sb': 1.0}\",\"('Ga', 'O', 'Sb', 'Sr')\",OTHERS,True\nmp-555139,\"{'Zr': 4.0, 'Cu': 6.0, 'H': 64.0, 'O': 32.0, 'F': 28.0}\",\"('Cu', 'F', 'H', 'O', 'Zr')\",OTHERS,True\nmp-1095804,\"{'Nb': 1.0, 'Zn': 1.0, 'Ni': 2.0}\",\"('Nb', 'Ni', 'Zn')\",OTHERS,True\nmp-780528,\"{'Na': 12.0, 'Cr': 4.0, 'C': 8.0, 'S': 2.0, 'O': 32.0}\",\"('C', 'Cr', 'Na', 'O', 'S')\",OTHERS,True\nmp-31024,\"{'C': 1.0, 'Cl': 8.0, 'Sc': 5.0}\",\"('C', 'Cl', 'Sc')\",OTHERS,True\nmp-756507,\"{'Li': 2.0, 'Fe': 2.0, 'P': 2.0, 'H': 2.0, 'O': 10.0}\",\"('Fe', 'H', 'Li', 'O', 'P')\",OTHERS,True\nmp-1246688,\"{'Zr': 2.0, 'Sn': 2.0, 'N': 4.0}\",\"('N', 'Sn', 'Zr')\",OTHERS,True\nmp-30301,\"{'Li': 4.0, 'O': 16.0, 'Cl': 4.0}\",\"('Cl', 'Li', 'O')\",OTHERS,True\nmp-757560,\"{'Li': 6.0, 'Sn': 4.0, 'P': 14.0, 'O': 42.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,True\nmp-1094591,\"{'Li': 6.0, 'Mg': 2.0}\",\"('Li', 'Mg')\",OTHERS,True\nmvc-10249,\"{'Mg': 2.0, 'Sb': 6.0, 'P': 6.0, 'O': 26.0}\",\"('Mg', 'O', 'P', 'Sb')\",OTHERS,True\nmp-22243,\"{'Li': 4.0, 'O': 16.0, 'Ni': 4.0, 'As': 4.0}\",\"('As', 'Li', 'Ni', 'O')\",OTHERS,True\nmp-697108,\"{'K': 6.0, 'H': 2.0, 'Rh': 2.0, 'C': 10.0, 'N': 10.0, 'O': 2.0}\",\"('C', 'H', 'K', 'N', 'O', 'Rh')\",OTHERS,True\nmp-1265272,\"{'Li': 2.0, 'Mg': 2.0, 'Al': 6.0, 'S': 12.0, 'O': 48.0}\",\"('Al', 'Li', 'Mg', 'O', 'S')\",OTHERS,True\nmp-601275,\"{'K': 12.0, 'Al': 4.0, 'H': 24.0}\",\"('Al', 'H', 'K')\",OTHERS,True\nmp-29281,\"{'P': 132.0, 'Th': 24.0}\",\"('P', 'Th')\",OTHERS,True\nmp-1079861,\"{'Tm': 2.0, 'Co': 2.0, 'Ge': 4.0}\",\"('Co', 'Ge', 'Tm')\",OTHERS,True\nmp-1200003,\"{'Fe': 8.0, 'As': 8.0, 'N': 8.0, 'O': 32.0, 'F': 8.0}\",\"('As', 'F', 'Fe', 'N', 'O')\",OTHERS,True\nmp-1218092,\"{'Ta': 4.0, 'Nb': 2.0, 'Te': 2.0, 'I': 14.0}\",\"('I', 'Nb', 'Ta', 'Te')\",OTHERS,True\nmp-1245479,\"{'Ca': 10.0, 'Hf': 4.0, 'N': 12.0}\",\"('Ca', 'Hf', 'N')\",OTHERS,True\nmp-865758,\"{'Yb': 1.0, 'Ge': 1.0, 'O': 3.0}\",\"('Ge', 'O', 'Yb')\",OTHERS,True\nmp-26576,\"{'Li': 4.0, 'Fe': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,True\nmp-757666,\"{'Ca': 4.0, 'B': 20.0, 'H': 12.0, 'O': 40.0}\",\"('B', 'Ca', 'H', 'O')\",OTHERS,True\nmp-1213960,\"{'Ce': 4.0, 'Ni': 6.0, 'Ge': 10.0}\",\"('Ce', 'Ge', 'Ni')\",OTHERS,True\nmp-566150,\"{'In': 4.0, 'Cu': 6.0, 'P': 8.0, 'O': 32.0}\",\"('Cu', 'In', 'O', 'P')\",OTHERS,True\nmp-19811,\"{'Ru': 3.0, 'Sn': 3.0, 'U': 3.0}\",\"('Ru', 'Sn', 'U')\",OTHERS,True\nmp-20079,\"{'O': 12.0, 'Ca': 4.0, 'Pb': 4.0}\",\"('Ca', 'O', 'Pb')\",OTHERS,True\nmp-862362,\"{'Sc': 2.0, 'Tc': 1.0, 'Pt': 1.0}\",\"('Pt', 'Sc', 'Tc')\",OTHERS,True\nmp-1228819,\"{'As': 4.0, 'S': 2.0}\",\"('As', 'S')\",OTHERS,True\nmp-849339,\"{'Mn': 6.0, 'O': 10.0, 'F': 2.0}\",\"('F', 'Mn', 'O')\",OTHERS,True\nmp-18909,\"{'O': 8.0, 'P': 2.0, 'Ni': 2.0, 'Ba': 1.0}\",\"('Ba', 'Ni', 'O', 'P')\",OTHERS,True\nmp-1192640,\"{'Na': 2.0, 'Nb': 4.0, 'Se': 4.0, 'O': 19.0}\",\"('Na', 'Nb', 'O', 'Se')\",OTHERS,True\nmp-1096678,\"{'Nb': 2.0, 'Tc': 1.0, 'Ru': 1.0}\",\"('Nb', 'Ru', 'Tc')\",OTHERS,True\nmp-998152,\"{'Tl': 1.0, 'Ag': 1.0, 'F': 3.0}\",\"('Ag', 'F', 'Tl')\",OTHERS,True\nmp-758113,\"{'Li': 3.0, 'Fe': 6.0, 'Si': 6.0, 'O': 24.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,True\nmp-753571,\"{'Li': 1.0, 'V': 1.0, 'F': 6.0}\",\"('F', 'Li', 'V')\",OTHERS,True\nmp-867971,\"{'Mn': 15.0, 'O': 32.0}\",\"('Mn', 'O')\",OTHERS,True\nmp-757177,\"{'Li': 12.0, 'Cu': 2.0, 'S': 8.0}\",\"('Cu', 'Li', 'S')\",OTHERS,True\nmp-19739,\"{'Sc': 1.0, 'Fe': 6.0, 'Ge': 6.0}\",\"('Fe', 'Ge', 'Sc')\",OTHERS,True\nmp-27721,\"{'H': 12.0, 'As': 4.0}\",\"('As', 'H')\",OTHERS,True\nmp-1221702,\"{'Mn': 1.0, 'Al': 1.0, 'Rh': 2.0}\",\"('Al', 'Mn', 'Rh')\",OTHERS,True\nmp-504703,\"{'Eu': 4.0, 'Fe': 4.0, 'O': 12.0}\",\"('Eu', 'Fe', 'O')\",OTHERS,True\nmp-15821,\"{'Li': 3.0, 'Ge': 3.0, 'Nd': 3.0}\",\"('Ge', 'Li', 'Nd')\",OTHERS,True\nmp-756603,\"{'Li': 4.0, 'Al': 2.0, 'Ni': 2.0, 'O': 8.0}\",\"('Al', 'Li', 'Ni', 'O')\",OTHERS,True\nmp-38125,\"{'Cd': 2.0, 'Se': 8.0, 'Sm': 4.0}\",\"('Cd', 'Se', 'Sm')\",OTHERS,True\nmp-42022,\"{'Cs': 2.0, 'F': 5.0, 'O': 1.0, 'Rb': 1.0, 'Zr': 1.0}\",\"('Cs', 'F', 'O', 'Rb', 'Zr')\",OTHERS,True\nmp-776555,\"{'Na': 4.0, 'V': 12.0, 'P': 8.0, 'O': 52.0}\",\"('Na', 'O', 'P', 'V')\",OTHERS,True\nmp-760888,\"{'V': 8.0, 'O': 2.0, 'F': 22.0}\",\"('F', 'O', 'V')\",OTHERS,True\nmp-1030319,\"{'Te': 8.0, 'Mo': 4.0}\",\"('Mo', 'Te')\",OTHERS,True\nmp-862811,\"{'Cs': 2.0, 'K': 1.0, 'U': 2.0, 'Si': 4.0, 'O': 14.0}\",\"('Cs', 'K', 'O', 'Si', 'U')\",OTHERS,True\nmp-1183639,\"{'Cd': 1.0, 'Hg': 3.0}\",\"('Cd', 'Hg')\",OTHERS,True\nmp-1185021,\"{'La': 1.0, 'Pm': 1.0, 'Ru': 2.0}\",\"('La', 'Pm', 'Ru')\",OTHERS,True\nmp-556288,\"{'Li': 4.0, 'Re': 2.0, 'O': 6.0}\",\"('Li', 'O', 'Re')\",OTHERS,True\nmp-770413,\"{'La': 4.0, 'Ho': 4.0, 'O': 12.0}\",\"('Ho', 'La', 'O')\",OTHERS,True\nmp-1200278,\"{'Sn': 4.0, 'H': 28.0, 'Cl': 12.0, 'O': 16.0}\",\"('Cl', 'H', 'O', 'Sn')\",OTHERS,True\nmp-698483,\"{'U': 4.0, 'Cu': 2.0, 'As': 4.0, 'H': 48.0, 'O': 48.0}\",\"('As', 'Cu', 'H', 'O', 'U')\",OTHERS,True\nmp-1232173,\"{'Ho': 8.0, 'Mg': 4.0, 'Se': 16.0}\",\"('Ho', 'Mg', 'Se')\",OTHERS,True\nmp-778584,\"{'Li': 1.0, 'Mn': 2.0, 'Co': 2.0, 'O': 8.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmvc-8898,\"{'Mg': 4.0, 'Ti': 12.0, 'O': 28.0}\",\"('Mg', 'O', 'Ti')\",OTHERS,True\nmp-1094218,\"{'La': 15.0, 'Mg': 3.0}\",\"('La', 'Mg')\",OTHERS,True\nmp-2061,\"{'Tl': 3.0, 'Th': 1.0}\",\"('Th', 'Tl')\",OTHERS,True\nmp-1114645,\"{'Rb': 2.0, 'Tl': 1.0, 'Hg': 1.0, 'Br': 6.0}\",\"('Br', 'Hg', 'Rb', 'Tl')\",OTHERS,True\nmp-569338,\"{'Yb': 4.0, 'Al': 4.0, 'Pd': 4.0}\",\"('Al', 'Pd', 'Yb')\",OTHERS,True\nmp-1186681,\"{'Pm': 1.0, 'Y': 1.0, 'Ir': 2.0}\",\"('Ir', 'Pm', 'Y')\",OTHERS,True\nmp-1221085,\"{'Na': 1.0, 'Ce': 1.0, 'Mo': 2.0, 'O': 8.0}\",\"('Ce', 'Mo', 'Na', 'O')\",OTHERS,True\nmp-1199784,\"{'Zr': 4.0, 'C': 12.0, 'N': 12.0, 'O': 48.0}\",\"('C', 'N', 'O', 'Zr')\",OTHERS,True\nmp-1355,\"{'Mn': 23.0, 'Y': 6.0}\",\"('Mn', 'Y')\",OTHERS,True\nmp-1221209,\"{'Na': 3.0, 'W': 2.0, 'Cl': 4.0, 'O': 4.0}\",\"('Cl', 'Na', 'O', 'W')\",OTHERS,True\nmp-862694,\"{'Al': 2.0, 'Ir': 1.0, 'Rh': 1.0}\",\"('Al', 'Ir', 'Rh')\",OTHERS,True\nmp-1039936,\"{'Na': 1.0, 'Ce': 1.0, 'Mg': 30.0, 'O': 32.0}\",\"('Ce', 'Mg', 'Na', 'O')\",OTHERS,True\nmp-1245109,\"{'Zn': 40.0, 'O': 40.0}\",\"('O', 'Zn')\",OTHERS,True\nmp-1227830,\"{'Ba': 2.0, 'Sr': 4.0, 'Sn': 6.0, 'O': 18.0}\",\"('Ba', 'O', 'Sn', 'Sr')\",OTHERS,True\nmp-1179597,\"{'Sb': 8.0, 'F': 48.0}\",\"('F', 'Sb')\",OTHERS,True\nmp-1099098,\"{'Ce': 1.0, 'Mg': 14.0, 'Bi': 1.0}\",\"('Bi', 'Ce', 'Mg')\",OTHERS,True\nmp-1100995,\"{'Tl': 1.0, 'Cu': 7.0, 'S': 4.0}\",\"('Cu', 'S', 'Tl')\",OTHERS,True\nmp-1226373,\"{'Cr': 12.0, 'Sb': 4.0, 'As': 8.0}\",\"('As', 'Cr', 'Sb')\",OTHERS,True\nmvc-940,\"{'Ba': 1.0, 'Ca': 1.0, 'Mn': 4.0, 'O': 8.0}\",\"('Ba', 'Ca', 'Mn', 'O')\",OTHERS,True\nmp-723406,\"{'La': 2.0, 'P': 6.0, 'H': 6.0, 'O': 20.0}\",\"('H', 'La', 'O', 'P')\",OTHERS,True\nmp-1190729,\"{'Ba': 4.0, 'Ge': 4.0, 'O': 16.0}\",\"('Ba', 'Ge', 'O')\",OTHERS,True\nmp-1093633,\"{'K': 1.0, 'Na': 2.0, 'As': 1.0}\",\"('As', 'K', 'Na')\",OTHERS,True\nmp-1147571,\"{'K': 1.0, 'Cu': 2.0, 'Ge': 1.0, 'S': 4.0}\",\"('Cu', 'Ge', 'K', 'S')\",OTHERS,True\nmp-1232324,\"{'Cd': 1.0, 'Pd': 2.0, 'O': 3.0}\",\"('Cd', 'O', 'Pd')\",OTHERS,True\nmp-414,\"{'Se': 1.0, 'Lu': 1.0}\",\"('Lu', 'Se')\",OTHERS,True\nmp-1029850,\"{'K': 30.0, 'Cr': 14.0, 'N': 38.0}\",\"('Cr', 'K', 'N')\",OTHERS,True\nmp-1186105,\"{'Na': 1.0, 'Ac': 3.0}\",\"('Ac', 'Na')\",OTHERS,True\nmp-628726,\"{'Sr': 2.0, 'Al': 4.0, 'Cl': 16.0}\",\"('Al', 'Cl', 'Sr')\",OTHERS,True\nmp-557082,\"{'H': 24.0, 'O': 12.0}\",\"('H', 'O')\",OTHERS,True\nmp-18256,\"{'B': 8.0, 'O': 20.0, 'Mg': 8.0}\",\"('B', 'Mg', 'O')\",OTHERS,True\nmp-1186581,\"{'Pm': 2.0, 'Ge': 6.0}\",\"('Ge', 'Pm')\",OTHERS,True\nmp-1101443,\"{'Nd': 2.0, 'Ta': 6.0, 'O': 18.0}\",\"('Nd', 'O', 'Ta')\",OTHERS,True\nmp-989517,\"{'Cs': 2.0, 'S': 1.0, 'Br': 1.0, 'Cl': 6.0}\",\"('Br', 'Cl', 'Cs', 'S')\",OTHERS,True\nmp-760336,\"{'Li': 15.0, 'Mn': 15.0, 'Co': 3.0, 'O': 36.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-583553,\"{'Mo': 24.0, 'Pb': 4.0, 'Cl': 56.0}\",\"('Cl', 'Mo', 'Pb')\",OTHERS,True\nmp-637249,\"{'Eu': 8.0, 'Li': 4.0, 'Ge': 12.0}\",\"('Eu', 'Ge', 'Li')\",OTHERS,True\nmp-776405,\"{'Li': 12.0, 'Sb': 4.0, 'S': 14.0}\",\"('Li', 'S', 'Sb')\",OTHERS,True\nmp-704203,\"{'Li': 4.0, 'V': 2.0, 'P': 8.0, 'O': 26.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-972877,\"{'Sc': 2.0, 'Al': 1.0, 'Ir': 1.0}\",\"('Al', 'Ir', 'Sc')\",OTHERS,True\nmp-1202899,\"{'Pt': 4.0, 'N': 24.0, 'O': 24.0}\",\"('N', 'O', 'Pt')\",OTHERS,True\nmp-982596,\"{'Tb': 1.0, 'Sc': 1.0}\",\"('Sc', 'Tb')\",OTHERS,True\nmp-510582,\"{'Nd': 4.0, 'Ni': 4.0, 'Sn': 4.0, 'H': 8.0}\",\"('H', 'Nd', 'Ni', 'Sn')\",OTHERS,True\nmp-685218,\"{'Fe': 24.0, 'Co': 12.0, 'O': 48.0}\",\"('Co', 'Fe', 'O')\",OTHERS,True\nmp-554824,\"{'La': 12.0, 'Ru': 4.0, 'O': 28.0}\",\"('La', 'O', 'Ru')\",OTHERS,True\nmp-1025346,\"{'Ga': 1.0, 'As': 1.0, 'Pd': 5.0}\",\"('As', 'Ga', 'Pd')\",OTHERS,True\nmp-867806,\"{'Li': 1.0, 'La': 2.0, 'Ir': 1.0}\",\"('Ir', 'La', 'Li')\",OTHERS,True\nmp-1112759,\"{'Cs': 2.0, 'K': 1.0, 'In': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cs', 'In', 'K')\",OTHERS,True\nmp-25838,\"{'Li': 2.0, 'O': 24.0, 'P': 6.0, 'Cr': 4.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,True\nmp-1198575,\"{'Rb': 18.0, 'La': 6.0, 'Cl': 36.0, 'O': 12.0}\",\"('Cl', 'La', 'O', 'Rb')\",OTHERS,True\nmp-15362,\"{'Li': 12.0, 'B': 12.0, 'O': 36.0, 'Nd': 8.0}\",\"('B', 'Li', 'Nd', 'O')\",OTHERS,True\nmp-1216465,\"{'V': 2.0, 'As': 4.0, 'H': 8.0, 'O': 18.0}\",\"('As', 'H', 'O', 'V')\",OTHERS,True\nmp-647265,\"{'V': 16.0, 'Bi': 56.0, 'O': 120.0}\",\"('Bi', 'O', 'V')\",OTHERS,True\nmp-673024,\"{'Li': 2.0, 'Sn': 8.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,True\nmp-1218050,\"{'Sr': 2.0, 'Pr': 2.0, 'Ga': 6.0, 'O': 14.0}\",\"('Ga', 'O', 'Pr', 'Sr')\",OTHERS,True\nmp-1202122,\"{'Pd': 3.0, 'N': 16.0, 'O': 16.0}\",\"('N', 'O', 'Pd')\",OTHERS,True\nmvc-1338,\"{'Ca': 4.0, 'Cr': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Ca', 'Cr', 'O', 'P')\",OTHERS,True\nmp-865144,\"{'Cd': 2.0, 'Au': 6.0}\",\"('Au', 'Cd')\",OTHERS,True\nmp-756600,\"{'Li': 7.0, 'Mn': 5.0, 'O': 12.0}\",\"('Li', 'Mn', 'O')\",OTHERS,True\nmp-1040512,\"{'Nb': 3.0, 'Si': 1.0, 'O': 10.0}\",\"('Nb', 'O', 'Si')\",OTHERS,True\nmp-1186198,\"{'Na': 1.0, 'Zn': 1.0, 'O': 3.0}\",\"('Na', 'O', 'Zn')\",OTHERS,True\nmp-1076534,\"{'Rb': 1.0, 'Ta': 1.0, 'O': 3.0}\",\"('O', 'Rb', 'Ta')\",OTHERS,True\nmp-1199244,\"{'Cs': 4.0, 'Cu': 4.0, 'S': 24.0}\",\"('Cs', 'Cu', 'S')\",OTHERS,True\nmp-1184390,\"{'Gd': 2.0, 'Tl': 1.0, 'Zn': 1.0}\",\"('Gd', 'Tl', 'Zn')\",OTHERS,True\nmp-770785,\"{'Na': 12.0, 'Mn': 4.0, 'B': 8.0, 'As': 2.0, 'O': 32.0}\",\"('As', 'B', 'Mn', 'Na', 'O')\",OTHERS,True\nmp-1246233,\"{'Na': 24.0, 'Ga': 8.0, 'N': 16.0}\",\"('Ga', 'N', 'Na')\",OTHERS,True\nmp-1219019,\"{'Sm': 1.0, 'Ga': 3.0, 'Pt': 1.0}\",\"('Ga', 'Pt', 'Sm')\",OTHERS,True\nmp-1208977,\"{'Sm': 12.0, 'Si': 8.0, 'Rh': 8.0}\",\"('Rh', 'Si', 'Sm')\",OTHERS,True\nmp-571660,\"{'Cd': 12.0, 'I': 24.0}\",\"('Cd', 'I')\",OTHERS,True\nmp-5510,\"{'Si': 2.0, 'Au': 2.0, 'U': 1.0}\",\"('Au', 'Si', 'U')\",OTHERS,True\nmp-1177102,\"{'Li': 5.0, 'Fe': 1.0, 'S': 4.0}\",\"('Fe', 'Li', 'S')\",OTHERS,True\nmp-758309,\"{'Li': 4.0, 'Co': 8.0, 'P': 12.0, 'O': 48.0}\",\"('Co', 'Li', 'O', 'P')\",OTHERS,True\nmp-1094334,\"{'Sr': 2.0, 'Mg': 4.0}\",\"('Mg', 'Sr')\",OTHERS,True\nmp-540639,\"{'Fe': 1.0, 'S': 4.0, 'N': 6.0, 'C': 4.0, 'H': 8.0}\",\"('C', 'Fe', 'H', 'N', 'S')\",OTHERS,True\nmp-569299,\"{'Be': 4.0, 'B': 8.0, 'C': 8.0}\",\"('B', 'Be', 'C')\",OTHERS,True\nmp-977128,\"{'Na': 1.0, 'Ru': 3.0}\",\"('Na', 'Ru')\",OTHERS,True\nmp-1225216,\"{'Fe': 2.0, 'Co': 2.0, 'Bi': 4.0, 'As': 4.0, 'O': 24.0}\",\"('As', 'Bi', 'Co', 'Fe', 'O')\",OTHERS,True\nmp-1199772,\"{'Sb': 16.0, 'Xe': 8.0, 'Cl': 8.0, 'F': 88.0}\",\"('Cl', 'F', 'Sb', 'Xe')\",OTHERS,True\nmp-740750,\"{'Na': 8.0, 'P': 8.0, 'H': 12.0, 'O': 30.0}\",\"('H', 'Na', 'O', 'P')\",OTHERS,True\nmp-761105,\"{'Li': 2.0, 'Co': 4.0, 'O': 2.0, 'F': 6.0}\",\"('Co', 'F', 'Li', 'O')\",OTHERS,True\nmp-562987,\"{'V': 1.0, 'Cu': 1.0, 'O': 3.0}\",\"('Cu', 'O', 'V')\",OTHERS,True\nmp-567368,\"{'Zr': 24.0, 'V': 16.0, 'Ga': 32.0}\",\"('Ga', 'V', 'Zr')\",OTHERS,True\nmvc-4147,\"{'Mg': 4.0, 'Ta': 2.0, 'Cu': 2.0, 'O': 12.0}\",\"('Cu', 'Mg', 'O', 'Ta')\",OTHERS,True\nmp-1227560,\"{'Ce': 4.0, 'Ga': 2.0, 'S': 5.0, 'O': 4.0}\",\"('Ce', 'Ga', 'O', 'S')\",OTHERS,True\nmp-1185990,\"{'Mg': 1.0, 'Tl': 3.0}\",\"('Mg', 'Tl')\",OTHERS,True\nmp-1112047,\"{'K': 2.0, 'Cu': 1.0, 'Mo': 1.0, 'Br': 6.0}\",\"('Br', 'Cu', 'K', 'Mo')\",OTHERS,True\nmp-1210173,\"{'Pr': 12.0, 'Co': 4.0, 'Br': 12.0}\",\"('Br', 'Co', 'Pr')\",OTHERS,True\nmp-981247,\"{'Zn': 7.0, 'Sb': 8.0, 'Ru': 9.0}\",\"('Ru', 'Sb', 'Zn')\",OTHERS,True\nmp-4300,\"{'O': 7.0, 'Si': 2.0, 'Yb': 2.0}\",\"('O', 'Si', 'Yb')\",OTHERS,True\nmp-1100617,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1228596,\"{'Al': 1.0, 'H': 12.0, 'C': 4.0, 'N': 1.0, 'F': 4.0}\",\"('Al', 'C', 'F', 'H', 'N')\",OTHERS,True\nmp-754693,\"{'Lu': 2.0, 'Bi': 6.0, 'O': 12.0}\",\"('Bi', 'Lu', 'O')\",OTHERS,True\nmp-2496,\"{'Sn': 46.0, 'Cs': 8.0}\",\"('Cs', 'Sn')\",OTHERS,True\nmp-1074941,\"{'Mg': 6.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,True\nmp-23541,\"{'K': 2.0, 'Br': 10.0, 'Sn': 4.0}\",\"('Br', 'K', 'Sn')\",OTHERS,True\nmp-975199,\"{'Rb': 1.0, 'Sr': 3.0}\",\"('Rb', 'Sr')\",OTHERS,True\nmp-1212246,\"{'Ho': 10.0, 'In': 8.0, 'Pt': 4.0}\",\"('Ho', 'In', 'Pt')\",OTHERS,True\nmp-1027335,\"{'Mo': 2.0, 'W': 2.0, 'S': 8.0}\",\"('Mo', 'S', 'W')\",OTHERS,True\nmp-706616,\"{'Zn': 4.0, 'H': 32.0, 'O': 16.0, 'F': 8.0}\",\"('F', 'H', 'O', 'Zn')\",OTHERS,True\nmp-769544,\"{'Mg': 1.0, 'Cr': 3.0, 'Se': 2.0, 'S': 4.0, 'O': 24.0}\",\"('Cr', 'Mg', 'O', 'S', 'Se')\",OTHERS,True\nmp-1209887,\"{'Nb': 4.0, 'Fe': 4.0, 'Si': 4.0}\",\"('Fe', 'Nb', 'Si')\",OTHERS,True\nmp-1186906,\"{'Rb': 3.0, 'Gd': 1.0}\",\"('Gd', 'Rb')\",OTHERS,True\nmp-1212910,\"{'Fe': 2.0, 'Bi': 12.0, 'P': 4.0, 'O': 28.0, 'F': 4.0}\",\"('Bi', 'F', 'Fe', 'O', 'P')\",OTHERS,True\nmp-761212,\"{'Li': 2.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,True\nmvc-2643,\"{'Zn': 2.0, 'W': 10.0, 'O': 14.0}\",\"('O', 'W', 'Zn')\",OTHERS,True\nmp-1198558,\"{'Cd': 8.0, 'C': 8.0, 'O': 24.0}\",\"('C', 'Cd', 'O')\",OTHERS,True\nmp-773138,\"{'Na': 2.0, 'Cr': 2.0, 'As': 2.0, 'C': 2.0, 'O': 14.0}\",\"('As', 'C', 'Cr', 'Na', 'O')\",OTHERS,True\nmp-23310,\"{'Yb': 8.0, 'Bi': 6.0}\",\"('Bi', 'Yb')\",OTHERS,True\nmp-756869,\"{'Tb': 2.0, 'K': 4.0, 'O': 6.0}\",\"('K', 'O', 'Tb')\",OTHERS,True\nmp-559347,\"{'Si': 16.0, 'O': 32.0}\",\"('O', 'Si')\",OTHERS,True\nmp-581476,\"{'Zn': 12.0, 'S': 12.0}\",\"('S', 'Zn')\",OTHERS,True\nmp-1224906,\"{'Ge': 4.0, 'Pb': 10.0, 'S': 2.0, 'O': 24.0}\",\"('Ge', 'O', 'Pb', 'S')\",OTHERS,True\nmp-1215422,\"{'Zr': 2.0, 'S': 1.0, 'O': 3.0}\",\"('O', 'S', 'Zr')\",OTHERS,True\nmp-753630,\"{'Li': 9.0, 'V': 6.0, 'P': 12.0, 'H': 6.0, 'O': 48.0}\",\"('H', 'Li', 'O', 'P', 'V')\",OTHERS,True\nmp-17439,\"{'O': 24.0, 'P': 8.0, 'K': 2.0, 'Sm': 2.0}\",\"('K', 'O', 'P', 'Sm')\",OTHERS,True\nmp-780843,\"{'Li': 4.0, 'Fe': 2.0, 'Ni': 3.0, 'Sn': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Fe', 'Li', 'Ni', 'O', 'P', 'Sn')\",OTHERS,True\nmp-1212582,\"{'Eu': 4.0, 'Al': 6.0, 'O': 16.0}\",\"('Al', 'Eu', 'O')\",OTHERS,True\nmp-28837,\"{'Cl': 24.0, 'K': 12.0, 'Rh': 4.0}\",\"('Cl', 'K', 'Rh')\",OTHERS,True\nmp-532510,\"{'Al': 28.0, 'Ni': 14.0, 'O': 56.0}\",\"('Al', 'Ni', 'O')\",OTHERS,True\nmp-568793,\"{'Ca': 28.0, 'Ga': 11.0}\",\"('Ca', 'Ga')\",OTHERS,True\nmp-39888,\"{'Nd': 1.0, 'Ti': 3.0, 'Fe': 1.0, 'Cu': 3.0, 'O': 12.0}\",\"('Cu', 'Fe', 'Nd', 'O', 'Ti')\",OTHERS,True\nmp-772334,\"{'Li': 3.0, 'Fe': 3.0, 'Te': 1.0, 'O': 8.0}\",\"('Fe', 'Li', 'O', 'Te')\",OTHERS,True\nmp-1199106,\"{'Tm': 20.0, 'Pt': 16.0}\",\"('Pt', 'Tm')\",OTHERS,True\nmp-567313,{'Te': 3.0},\"('Te',)\",OTHERS,True\nmp-12866,\"{'O': 6.0, 'Sr': 2.0, 'Sn': 2.0}\",\"('O', 'Sn', 'Sr')\",OTHERS,True\nmp-755087,\"{'Li': 3.0, 'Fe': 1.0, 'F': 6.0}\",\"('F', 'Fe', 'Li')\",OTHERS,True\nmp-1184072,\"{'Dy': 2.0, 'Pd': 1.0, 'Rh': 1.0}\",\"('Dy', 'Pd', 'Rh')\",OTHERS,True\nmp-26134,\"{'Cr': 4.0, 'P': 10.0, 'O': 32.0}\",\"('Cr', 'O', 'P')\",OTHERS,True\nmp-2331,\"{'B': 4.0, 'Mo': 2.0}\",\"('B', 'Mo')\",OTHERS,True\nmp-1217053,\"{'U': 1.0, 'Fe': 10.0, 'Si': 2.0}\",\"('Fe', 'Si', 'U')\",OTHERS,True\nmp-769253,\"{'Dy': 2.0, 'Se': 1.0, 'O': 2.0}\",\"('Dy', 'O', 'Se')\",OTHERS,True\nmp-1078684,\"{'Er': 3.0, 'Cd': 3.0, 'Ga': 3.0}\",\"('Cd', 'Er', 'Ga')\",OTHERS,True\nmp-567858,\"{'Dy': 24.0, 'C': 12.0, 'I': 34.0}\",\"('C', 'Dy', 'I')\",OTHERS,True\nmp-1099903,\"{'K': 20.0, 'Na': 12.0, 'Ta': 24.0, 'Nb': 8.0, 'O': 80.0}\",\"('K', 'Na', 'Nb', 'O', 'Ta')\",OTHERS,True\nmp-1246529,\"{'Fe': 16.0, 'Ge': 16.0, 'N': 32.0}\",\"('Fe', 'Ge', 'N')\",OTHERS,True\nmp-766079,\"{'Sr': 8.0, 'Cu': 8.0, 'O': 17.0}\",\"('Cu', 'O', 'Sr')\",OTHERS,True\nmp-1220481,\"{'Nb': 4.0, 'Al': 1.0}\",\"('Al', 'Nb')\",OTHERS,True\nmp-555202,\"{'K': 2.0, 'Te': 2.0, 'H': 2.0, 'O': 2.0, 'F': 8.0}\",\"('F', 'H', 'K', 'O', 'Te')\",OTHERS,True\nmp-1211077,\"{'Li': 12.0, 'I': 6.0, 'O': 36.0}\",\"('I', 'Li', 'O')\",OTHERS,True\nmp-1212611,\"{'Hf': 4.0, 'As': 8.0, 'O': 36.0}\",\"('As', 'Hf', 'O')\",OTHERS,True\nmp-1219538,\"{'Sb': 10.0, 'O': 28.0}\",\"('O', 'Sb')\",OTHERS,True\nmp-979952,\"{'Yb': 3.0, 'Ti': 1.0}\",\"('Ti', 'Yb')\",OTHERS,True\nmp-1207397,\"{'Zn': 1.0, 'Os': 2.0, 'N': 4.0, 'O': 8.0}\",\"('N', 'O', 'Os', 'Zn')\",OTHERS,True\nmp-1185774,\"{'Mg': 4.0, 'Sc': 2.0}\",\"('Mg', 'Sc')\",OTHERS,True\nmp-1245359,\"{'In': 20.0, 'Si': 12.0, 'N': 36.0}\",\"('In', 'N', 'Si')\",OTHERS,True\nmp-5151,\"{'S': 12.0, 'Ge': 2.0, 'Cd': 8.0}\",\"('Cd', 'Ge', 'S')\",OTHERS,True\nmp-975337,\"{'Rb': 1.0, 'F': 3.0}\",\"('F', 'Rb')\",OTHERS,True\nmp-1220161,\"{'Nd': 2.0, 'Al': 2.0, 'Si': 2.0}\",\"('Al', 'Nd', 'Si')\",OTHERS,True\nmp-504549,\"{'U': 4.0, 'Fe': 4.0, 'O': 16.0}\",\"('Fe', 'O', 'U')\",OTHERS,True\nmp-1099703,\"{'Sr': 6.0, 'Ca': 2.0, 'Ti': 6.0, 'Mn': 2.0, 'O': 24.0}\",\"('Ca', 'Mn', 'O', 'Sr', 'Ti')\",OTHERS,True\nmp-761011,\"{'Li': 6.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-1025028,\"{'Pr': 2.0, 'Si': 2.0, 'Ag': 2.0}\",\"('Ag', 'Pr', 'Si')\",OTHERS,True\nmp-1208914,\"{'Sm': 6.0, 'Al': 24.0, 'Ru': 8.0}\",\"('Al', 'Ru', 'Sm')\",OTHERS,True\nmp-10746,\"{'Mg': 4.0, 'Si': 2.0, 'Cu': 6.0}\",\"('Cu', 'Mg', 'Si')\",OTHERS,True\nmp-556799,\"{'Li': 12.0, 'In': 4.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'In', 'Li', 'O')\",OTHERS,True\nmp-776050,\"{'Li': 2.0, 'Fe': 4.0, 'F': 18.0}\",\"('F', 'Fe', 'Li')\",OTHERS,True\nmp-680168,\"{'K': 8.0, 'Re': 12.0, 'C': 8.0, 'Se': 20.0, 'N': 8.0}\",\"('C', 'K', 'N', 'Re', 'Se')\",OTHERS,True\nmp-3062,\"{'N': 2.0, 'S': 3.0, 'Sm': 4.0}\",\"('N', 'S', 'Sm')\",OTHERS,True\nmp-698396,\"{'Cu': 2.0, 'H': 8.0, 'C': 4.0, 'N': 2.0, 'Cl': 6.0, 'O': 2.0}\",\"('C', 'Cl', 'Cu', 'H', 'N', 'O')\",OTHERS,True\nmp-754557,\"{'Eu': 2.0, 'Y': 4.0, 'O': 8.0}\",\"('Eu', 'O', 'Y')\",OTHERS,True\nmp-1185693,\"{'Mg': 1.0, 'B': 1.0, 'O': 3.0}\",\"('B', 'Mg', 'O')\",OTHERS,True\nmp-778080,\"{'Ba': 2.0, 'Y': 6.0, 'F': 22.0}\",\"('Ba', 'F', 'Y')\",OTHERS,True\nmp-738686,\"{'Sb': 2.0, 'H': 48.0, 'C': 12.0, 'N': 6.0, 'Cl': 12.0}\",\"('C', 'Cl', 'H', 'N', 'Sb')\",OTHERS,True\nmp-766205,\"{'Sr': 1.0, 'Li': 1.0, 'La': 15.0, 'Co': 4.0, 'O': 32.0}\",\"('Co', 'La', 'Li', 'O', 'Sr')\",OTHERS,True\nmp-1223863,\"{'In': 1.0, 'Cu': 1.0, 'Te': 2.0}\",\"('Cu', 'In', 'Te')\",OTHERS,True\nmp-697313,\"{'Al': 24.0, 'Tl': 10.0, 'Cd': 7.0, 'Si': 24.0, 'O': 96.0}\",\"('Al', 'Cd', 'O', 'Si', 'Tl')\",OTHERS,True\nmp-1187237,\"{'Sr': 6.0, 'Ce': 2.0}\",\"('Ce', 'Sr')\",OTHERS,True\nmp-763345,\"{'Li': 8.0, 'V': 4.0, 'O': 8.0, 'F': 4.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,True\nmvc-6576,\"{'Ca': 2.0, 'Sn': 4.0, 'O': 8.0}\",\"('Ca', 'O', 'Sn')\",OTHERS,True\nmp-561942,\"{'Cs': 3.0, 'Ta': 1.0, 'O': 8.0}\",\"('Cs', 'O', 'Ta')\",OTHERS,True\nmp-1222633,\"{'Mg': 4.0, 'Fe': 1.0, 'Cl': 1.0, 'O': 13.0}\",\"('Cl', 'Fe', 'Mg', 'O')\",OTHERS,True\nmp-867271,\"{'Be': 2.0, 'Co': 1.0, 'Ni': 1.0}\",\"('Be', 'Co', 'Ni')\",OTHERS,True\nmvc-513,\"{'Ba': 1.0, 'Zn': 1.0, 'Cu': 4.0, 'O': 8.0}\",\"('Ba', 'Cu', 'O', 'Zn')\",OTHERS,True\nmp-554810,\"{'Ba': 4.0, 'V': 2.0, 'P': 4.0, 'O': 18.0}\",\"('Ba', 'O', 'P', 'V')\",OTHERS,True\nmp-1100068,\"{'Ba': 4.0, 'Sr': 28.0, 'Co': 20.0, 'Cu': 12.0, 'O': 80.0}\",\"('Ba', 'Co', 'Cu', 'O', 'Sr')\",OTHERS,True\nmp-1238596,\"{'Mg': 2.0, 'V': 10.0, 'H': 40.0, 'N': 2.0, 'O': 44.0}\",\"('H', 'Mg', 'N', 'O', 'V')\",OTHERS,True\nmp-21281,\"{'Mn': 2.0, 'Ge': 2.0, 'Th': 1.0}\",\"('Ge', 'Mn', 'Th')\",OTHERS,True\nmp-770400,\"{'Li': 8.0, 'Al': 4.0, 'Ni': 4.0, 'O': 16.0}\",\"('Al', 'Li', 'Ni', 'O')\",OTHERS,True\nmp-685216,\"{'Nb': 24.0, 'Tl': 2.0, 'Te': 32.0}\",\"('Nb', 'Te', 'Tl')\",OTHERS,True\nmp-31157,\"{'N': 1.0, 'Ca': 3.0, 'Sb': 1.0}\",\"('Ca', 'N', 'Sb')\",OTHERS,True\nmp-1221080,\"{'Na': 1.0, 'Ce': 1.0, 'Ti': 2.0, 'O': 6.0}\",\"('Ce', 'Na', 'O', 'Ti')\",OTHERS,True\nmp-1208022,\"{'Tl': 1.0, 'S': 2.0, 'N': 1.0, 'O': 8.0}\",\"('N', 'O', 'S', 'Tl')\",OTHERS,True\nmp-1018687,\"{'Er': 2.0, 'S': 4.0}\",\"('Er', 'S')\",OTHERS,True\nmp-1219596,\"{'Rb': 5.0, 'Bi': 4.0}\",\"('Bi', 'Rb')\",OTHERS,True\nmp-1237926,\"{'Lu': 4.0, 'Mg': 4.0, 'Se': 12.0}\",\"('Lu', 'Mg', 'Se')\",OTHERS,True\nmp-777676,\"{'Li': 4.0, 'V': 4.0, 'F': 12.0}\",\"('F', 'Li', 'V')\",OTHERS,True\nmp-12221,\"{'O': 16.0, 'Ca': 4.0, 'Te': 4.0}\",\"('Ca', 'O', 'Te')\",OTHERS,True\nmp-1212003,\"{'La': 12.0, 'Fe': 20.0, 'O': 48.0}\",\"('Fe', 'La', 'O')\",OTHERS,True\nmp-1176989,\"{'Li': 12.0, 'Mn': 2.0, 'V': 6.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,True\nmp-22753,\"{'C': 4.0, 'Fe': 2.0, 'U': 2.0}\",\"('C', 'Fe', 'U')\",OTHERS,True\nmp-756780,\"{'Ti': 3.0, 'Mn': 3.0, 'Cr': 2.0, 'O': 16.0}\",\"('Cr', 'Mn', 'O', 'Ti')\",OTHERS,True\nmp-3804,\"{'P': 2.0, 'Sr': 1.0, 'Ru': 2.0}\",\"('P', 'Ru', 'Sr')\",OTHERS,True\nmp-1190833,\"{'Nd': 2.0, 'Si': 4.0, 'Ni': 18.0}\",\"('Nd', 'Ni', 'Si')\",OTHERS,True\nmp-653248,\"{'Cr': 24.0, 'B': 56.0, 'Cl': 8.0, 'O': 104.0}\",\"('B', 'Cl', 'Cr', 'O')\",OTHERS,True\nmp-705411,\"{'W': 6.0, 'O': 18.0}\",\"('O', 'W')\",OTHERS,True\nmp-1220084,\"{'Nd': 2.0, 'Ti': 2.0, 'Cd': 2.0, 'Sb': 2.0, 'O': 14.0}\",\"('Cd', 'Nd', 'O', 'Sb', 'Ti')\",OTHERS,True\nmp-849636,\"{'Sr': 4.0, 'Ta': 4.0, 'N': 4.0, 'O': 8.0}\",\"('N', 'O', 'Sr', 'Ta')\",OTHERS,True\nmp-19166,\"{'O': 12.0, 'Ni': 2.0, 'Sr': 6.0, 'Pt': 2.0}\",\"('Ni', 'O', 'Pt', 'Sr')\",OTHERS,True\nmp-1093897,\"{'Li': 2.0, 'La': 1.0, 'Al': 1.0}\",\"('Al', 'La', 'Li')\",OTHERS,True\nmp-707274,\"{'H': 26.0, 'Se': 4.0, 'N': 6.0, 'O': 16.0}\",\"('H', 'N', 'O', 'Se')\",OTHERS,True\nmp-18973,\"{'O': 20.0, 'Co': 4.0, 'Se': 8.0}\",\"('Co', 'O', 'Se')\",OTHERS,True\nmp-557364,\"{'Cd': 8.0, 'B': 16.0, 'F': 64.0}\",\"('B', 'Cd', 'F')\",OTHERS,True\nmp-540592,\"{'Fe': 8.0, 'Te': 12.0, 'O': 36.0}\",\"('Fe', 'O', 'Te')\",OTHERS,True\nmp-5568,\"{'B': 4.0, 'Co': 11.0, 'Nd': 3.0}\",\"('B', 'Co', 'Nd')\",OTHERS,True\nmp-19435,\"{'O': 8.0, 'Co': 2.0, 'Mo': 2.0}\",\"('Co', 'Mo', 'O')\",OTHERS,True\nmp-1227989,\"{'Ba': 1.0, 'Sr': 1.0, 'Al': 20.0, 'Cu': 2.0, 'O': 34.0}\",\"('Al', 'Ba', 'Cu', 'O', 'Sr')\",OTHERS,True\nmp-504578,\"{'Mg': 4.0, 'Cr': 4.0, 'O': 16.0}\",\"('Cr', 'Mg', 'O')\",OTHERS,True\nmp-1178644,\"{'Zn': 1.0, 'Ga': 1.0, 'Cu': 3.0, 'Se': 4.0}\",\"('Cu', 'Ga', 'Se', 'Zn')\",OTHERS,True\nmp-28980,\"{'K': 12.0, 'Bi': 4.0, 'Se': 12.0}\",\"('Bi', 'K', 'Se')\",OTHERS,True\nmp-1197535,\"{'Y': 8.0, 'Nb': 12.0, 'Ge': 16.0}\",\"('Ge', 'Nb', 'Y')\",OTHERS,True\nmp-540258,\"{'O': 24.0, 'P': 8.0, 'Ti': 2.0}\",\"('O', 'P', 'Ti')\",OTHERS,True\nmp-989643,\"{'La': 2.0, 'Co': 2.0, 'N': 6.0}\",\"('Co', 'La', 'N')\",OTHERS,True\nmp-752900,\"{'Li': 4.0, 'Fe': 2.0, 'Si': 2.0, 'O': 8.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,True\nmp-33320,\"{'Ca': 4.0, 'U': 4.0, 'S': 8.0}\",\"('Ca', 'S', 'U')\",OTHERS,True\nmp-1174359,\"{'Li': 8.0, 'Mn': 6.0, 'O': 14.0}\",\"('Li', 'Mn', 'O')\",OTHERS,True\nmp-1207174,\"{'Li': 1.0, 'Al': 2.0, 'Ge': 1.0}\",\"('Al', 'Ge', 'Li')\",OTHERS,True\nmp-1178290,\"{'Fe': 8.0, 'O': 2.0, 'F': 14.0}\",\"('F', 'Fe', 'O')\",OTHERS,True\nmp-672654,\"{'Zr': 6.0, 'Co': 16.0, 'Ge': 7.0}\",\"('Co', 'Ge', 'Zr')\",OTHERS,True\nmp-567353,\"{'Mn': 2.0, 'Si': 2.0, 'Ni': 2.0}\",\"('Mn', 'Ni', 'Si')\",OTHERS,True\nmp-1198694,\"{'Cs': 4.0, 'V': 4.0, 'P': 4.0, 'O': 20.0}\",\"('Cs', 'O', 'P', 'V')\",OTHERS,True\nmp-1009113,\"{'Ho': 1.0, 'Hg': 2.0}\",\"('Hg', 'Ho')\",OTHERS,True\nmp-1175674,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-19911,\"{'Co': 1.0, 'In': 5.0, 'Tm': 1.0}\",\"('Co', 'In', 'Tm')\",OTHERS,True\nmp-1207835,\"{'Tm': 6.0, 'Sb': 2.0, 'O': 14.0}\",\"('O', 'Sb', 'Tm')\",OTHERS,True\nmp-1208811,\"{'Sr': 2.0, 'Lu': 1.0, 'Cu': 2.0, 'Bi': 2.0, 'O': 8.0}\",\"('Bi', 'Cu', 'Lu', 'O', 'Sr')\",OTHERS,True\nmp-562762,\"{'Ba': 4.0, 'Co': 2.0, 'Se': 4.0, 'Cl': 4.0, 'O': 12.0}\",\"('Ba', 'Cl', 'Co', 'O', 'Se')\",OTHERS,True\nmp-1208733,\"{'Sr': 2.0, 'Ta': 1.0, 'Co': 1.0, 'O': 6.0}\",\"('Co', 'O', 'Sr', 'Ta')\",OTHERS,True\nmp-1189703,\"{'Yb': 4.0, 'Ge': 8.0, 'Pd': 4.0}\",\"('Ge', 'Pd', 'Yb')\",OTHERS,True\nmp-1175349,\"{'Li': 7.0, 'Mn': 5.0, 'O': 12.0}\",\"('Li', 'Mn', 'O')\",OTHERS,True\nmp-1025271,\"{'Al': 1.0, 'P': 1.0, 'Pt': 5.0}\",\"('Al', 'P', 'Pt')\",OTHERS,True\nmp-29154,\"{'P': 2.0, 'Co': 2.0, 'Hf': 4.0}\",\"('Co', 'Hf', 'P')\",OTHERS,True\nmp-1193044,\"{'Bi': 2.0, 'P': 6.0, 'N': 2.0, 'O': 20.0}\",\"('Bi', 'N', 'O', 'P')\",OTHERS,True\nmp-744576,\"{'Co': 4.0, 'H': 32.0, 'S': 4.0, 'O': 32.0}\",\"('Co', 'H', 'O', 'S')\",OTHERS,True\nmp-13938,\"{'O': 6.0, 'Sr': 2.0, 'Dy': 1.0, 'Re': 1.0}\",\"('Dy', 'O', 'Re', 'Sr')\",OTHERS,True\nmp-1204722,\"{'Ba': 4.0, 'Zr': 2.0, 'C': 16.0, 'O': 38.0}\",\"('Ba', 'C', 'O', 'Zr')\",OTHERS,True\nmp-763560,\"{'Li': 1.0, 'Mn': 2.0, 'C': 4.0, 'O': 12.0}\",\"('C', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-21479,\"{'Rh': 1.0, 'In': 5.0, 'La': 1.0}\",\"('In', 'La', 'Rh')\",OTHERS,True\nmp-569098,\"{'Rb': 4.0, 'Na': 44.0, 'W': 16.0, 'N': 48.0}\",\"('N', 'Na', 'Rb', 'W')\",OTHERS,True\nmp-976135,\"{'Pr': 1.0, 'Er': 3.0}\",\"('Er', 'Pr')\",OTHERS,True\nmp-752428,\"{'Rb': 8.0, 'Pr': 4.0, 'O': 12.0}\",\"('O', 'Pr', 'Rb')\",OTHERS,True\nmp-773438,\"{'Li': 10.0, 'Cu': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,True\nmp-22998,\"{'S': 2.0, 'Cr': 2.0, 'Br': 2.0}\",\"('Br', 'Cr', 'S')\",OTHERS,True\nmp-568225,\"{'Tb': 4.0, 'B': 16.0}\",\"('B', 'Tb')\",OTHERS,True\nmp-1101228,\"{'V': 4.0, 'F': 18.0}\",\"('F', 'V')\",OTHERS,True\nmp-849589,\"{'Li': 5.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,True\nmp-204,\"{'Fe': 4.0, 'Ce': 2.0}\",\"('Ce', 'Fe')\",OTHERS,True\nmp-851103,\"{'Li': 4.0, 'V': 4.0, 'P': 8.0, 'H': 8.0, 'O': 32.0}\",\"('H', 'Li', 'O', 'P', 'V')\",OTHERS,True\nmp-1198901,\"{'Cs': 4.0, 'Cu': 2.0, 'H': 16.0, 'Se': 4.0, 'O': 24.0}\",\"('Cs', 'Cu', 'H', 'O', 'Se')\",OTHERS,True\nmp-1102476,\"{'Tb': 4.0, 'As': 4.0, 'Se': 4.0}\",\"('As', 'Se', 'Tb')\",OTHERS,True\nmp-1209123,\"{'Rh': 4.0, 'W': 2.0, 'O': 12.0}\",\"('O', 'Rh', 'W')\",OTHERS,True\nmp-1211067,\"{'Li': 2.0, 'Tm': 4.0, 'Br': 10.0}\",\"('Br', 'Li', 'Tm')\",OTHERS,True\nmp-569273,\"{'Mg': 88.0, 'Ir': 14.0}\",\"('Ir', 'Mg')\",OTHERS,True\nmp-5259,\"{'F': 8.0, 'Al': 2.0, 'Rb': 2.0}\",\"('Al', 'F', 'Rb')\",OTHERS,True\nmp-11644,\"{'Li': 4.0, 'Ca': 2.0}\",\"('Ca', 'Li')\",OTHERS,True\nmp-1246153,\"{'Ti': 6.0, 'N': 4.0}\",\"('N', 'Ti')\",OTHERS,True\nmp-757163,\"{'Li': 1.0, 'V': 1.0, 'F': 6.0}\",\"('F', 'Li', 'V')\",OTHERS,True\nmp-28177,\"{'Na': 2.0, 'Cl': 12.0, 'Sb': 2.0}\",\"('Cl', 'Na', 'Sb')\",OTHERS,True\nmp-756074,\"{'Bi': 4.0, 'P': 2.0, 'O': 12.0}\",\"('Bi', 'O', 'P')\",OTHERS,True\nmp-1189145,\"{'Dy': 4.0, 'Si': 8.0, 'Mo': 6.0}\",\"('Dy', 'Mo', 'Si')\",OTHERS,True\nmp-1104602,\"{'Cu': 2.0, 'Ag': 1.0, 'S': 2.0, 'O': 10.0}\",\"('Ag', 'Cu', 'O', 'S')\",OTHERS,True\nmp-1210034,\"{'Nd': 12.0, 'Ti': 4.0, 'Cl': 20.0, 'O': 16.0}\",\"('Cl', 'Nd', 'O', 'Ti')\",OTHERS,True\nmp-1224293,\"{'Ho': 4.0, 'Fe': 2.0, 'Mo': 2.0, 'O': 14.0}\",\"('Fe', 'Ho', 'Mo', 'O')\",OTHERS,True\nmp-1211602,\"{'Li': 4.0, 'Lu': 16.0, 'Ge': 16.0}\",\"('Ge', 'Li', 'Lu')\",OTHERS,True\nmp-3222,\"{'O': 14.0, 'Ru': 2.0, 'La': 6.0}\",\"('La', 'O', 'Ru')\",OTHERS,True\nmp-1177550,\"{'Li': 8.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-14763,\"{'N': 6.0, 'Ca': 6.0, 'Mn': 2.0}\",\"('Ca', 'Mn', 'N')\",OTHERS,True\nmp-1179693,\"{'Rb': 2.0, 'Mn': 2.0, 'As': 2.0}\",\"('As', 'Mn', 'Rb')\",OTHERS,True\nmp-754054,\"{'Li': 2.0, 'V': 1.0, 'Cr': 1.0, 'O': 4.0}\",\"('Cr', 'Li', 'O', 'V')\",OTHERS,True\nmp-1211839,\"{'K': 2.0, 'Al': 4.0, 'Si': 8.0, 'H': 4.0, 'O': 24.0}\",\"('Al', 'H', 'K', 'O', 'Si')\",OTHERS,True\nmp-758542,\"{'Li': 24.0, 'Cu': 24.0, 'P': 24.0, 'O': 96.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,True\nmp-754447,\"{'Cd': 6.0, 'Co': 5.0, 'O': 15.0}\",\"('Cd', 'Co', 'O')\",OTHERS,True\nmp-2776,\"{'Ga': 2.0, 'Au': 1.0}\",\"('Au', 'Ga')\",OTHERS,True\nmp-1214847,\"{'B': 8.0, 'H': 16.0, 'O': 16.0}\",\"('B', 'H', 'O')\",OTHERS,True\nmp-1199853,\"{'In': 2.0, 'S': 6.0, 'N': 6.0, 'O': 24.0}\",\"('In', 'N', 'O', 'S')\",OTHERS,True\nmp-759232,\"{'Li': 1.0, 'Mn': 1.0, 'V': 1.0, 'P': 4.0, 'O': 14.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,True\nmvc-7769,\"{'Mg': 2.0, 'Ni': 4.0, 'O': 8.0}\",\"('Mg', 'Ni', 'O')\",OTHERS,True\nmp-17165,\"{'C': 12.0, 'N': 12.0, 'Na': 2.0, 'Cr': 2.0, 'Cs': 4.0}\",\"('C', 'Cr', 'Cs', 'N', 'Na')\",OTHERS,True\nmp-772650,\"{'Li': 8.0, 'Cr': 20.0, 'O': 48.0}\",\"('Cr', 'Li', 'O')\",OTHERS,True\nmp-862934,\"{'Pm': 1.0, 'Li': 2.0, 'Tl': 1.0}\",\"('Li', 'Pm', 'Tl')\",OTHERS,True\nmp-20061,\"{'Ge': 3.0, 'Pt': 6.0}\",\"('Ge', 'Pt')\",OTHERS,True\nmvc-12291,\"{'Ca': 2.0, 'V': 4.0, 'Ni': 4.0, 'P': 8.0, 'O': 36.0}\",\"('Ca', 'Ni', 'O', 'P', 'V')\",OTHERS,True\nmp-766441,\"{'Li': 4.0, 'V': 2.0, 'C': 4.0, 'O': 12.0}\",\"('C', 'Li', 'O', 'V')\",OTHERS,True\nmp-1217015,\"{'Ti': 1.0, 'Al': 2.0, 'V': 1.0}\",\"('Al', 'Ti', 'V')\",OTHERS,True\nmp-6853,\"{'O': 14.0, 'Si': 2.0, 'Ca': 3.0, 'Ga': 3.0, 'Ta': 1.0}\",\"('Ca', 'Ga', 'O', 'Si', 'Ta')\",OTHERS,True\nmp-752601,\"{'Na': 1.0, 'Mn': 3.0, 'O': 4.0}\",\"('Mn', 'Na', 'O')\",OTHERS,True\nmp-780053,\"{'Na': 16.0, 'Ge': 16.0, 'O': 40.0}\",\"('Ge', 'Na', 'O')\",OTHERS,True\nmp-680260,\"{'Ti': 45.0, 'Se': 16.0}\",\"('Se', 'Ti')\",OTHERS,True\nmp-17230,\"{'Ga': 2.0, 'Nb': 10.0, 'Sn': 4.0}\",\"('Ga', 'Nb', 'Sn')\",OTHERS,True\nmp-1228976,\"{'Ba': 9.0, 'Bi': 18.0, 'S': 36.0}\",\"('Ba', 'Bi', 'S')\",OTHERS,True\nmp-1180189,\"{'Na': 1.0, 'Al': 3.0, 'Cr': 2.0, 'O': 14.0}\",\"('Al', 'Cr', 'Na', 'O')\",OTHERS,True\nmp-1017467,\"{'Ca': 1.0, 'Mn': 1.0, 'O': 3.0}\",\"('Ca', 'Mn', 'O')\",OTHERS,True\nmp-675771,\"{'Al': 12.0, 'P': 12.0, 'O': 48.0}\",\"('Al', 'O', 'P')\",OTHERS,True\nmp-1196864,\"{'K': 4.0, 'In': 4.0, 'P': 8.0, 'O': 32.0}\",\"('In', 'K', 'O', 'P')\",OTHERS,True\nmp-30960,\"{'O': 24.0, 'Dy': 4.0, 'W': 6.0}\",\"('Dy', 'O', 'W')\",OTHERS,True\nmp-1096377,\"{'Ti': 2.0, 'Ga': 1.0, 'Tc': 1.0}\",\"('Ga', 'Tc', 'Ti')\",OTHERS,True\nmp-1020025,\"{'Li': 16.0, 'B': 48.0, 'O': 80.0}\",\"('B', 'Li', 'O')\",OTHERS,True\nmp-760690,\"{'Li': 8.0, 'Te': 1.0, 'O': 6.0}\",\"('Li', 'O', 'Te')\",OTHERS,True\nmp-1206098,\"{'Zr': 1.0, 'Mn': 1.0, 'F': 6.0}\",\"('F', 'Mn', 'Zr')\",OTHERS,True\nmp-1175530,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1204808,\"{'Na': 4.0, 'Li': 4.0, 'Ni': 2.0, 'P': 12.0, 'H': 48.0, 'O': 60.0}\",\"('H', 'Li', 'Na', 'Ni', 'O', 'P')\",OTHERS,True\nmp-999145,\"{'Sm': 2.0, 'Ni': 2.0}\",\"('Ni', 'Sm')\",OTHERS,True\nmp-1177080,\"{'Li': 10.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-861904,\"{'La': 6.0, 'Al': 2.0, 'Cr': 2.0, 'S': 14.0}\",\"('Al', 'Cr', 'La', 'S')\",OTHERS,True\nmp-557322,\"{'Bi': 4.0, 'Te': 2.0, 'Br': 2.0, 'O': 9.0}\",\"('Bi', 'Br', 'O', 'Te')\",OTHERS,True\nmp-707958,\"{'P': 2.0, 'H': 18.0, 'C': 4.0, 'N': 8.0, 'O': 10.0}\",\"('C', 'H', 'N', 'O', 'P')\",OTHERS,True\nmp-28838,\"{'C': 2.0, 'Si': 2.0, 'U': 3.0}\",\"('C', 'Si', 'U')\",OTHERS,True\nmp-1261917,\"{'Ba': 2.0, 'Al': 2.0, 'W': 8.0, 'O': 14.0}\",\"('Al', 'Ba', 'O', 'W')\",OTHERS,True\nmp-3849,\"{'S': 4.0, 'Fe': 2.0, 'Tl': 2.0}\",\"('Fe', 'S', 'Tl')\",OTHERS,True\nmp-26903,\"{'Li': 6.0, 'V': 4.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-684690,\"{'Sc': 4.0, 'Se': 6.0}\",\"('Sc', 'Se')\",OTHERS,True\nmvc-8750,\"{'Zn': 1.0, 'Sn': 4.0, 'O': 8.0}\",\"('O', 'Sn', 'Zn')\",OTHERS,True\nmp-645796,\"{'Tl': 4.0, 'Fe': 4.0, 'Mo': 8.0, 'O': 32.0}\",\"('Fe', 'Mo', 'O', 'Tl')\",OTHERS,True\nmp-759038,\"{'Li': 4.0, 'Ti': 2.0, 'Fe': 3.0, 'Co': 3.0, 'O': 16.0}\",\"('Co', 'Fe', 'Li', 'O', 'Ti')\",OTHERS,True\nmp-614444,\"{'Zr': 1.0, 'Zn': 1.0}\",\"('Zn', 'Zr')\",OTHERS,True\nmp-1192359,\"{'Cs': 4.0, 'Mn': 2.0, 'Te': 4.0, 'S': 12.0}\",\"('Cs', 'Mn', 'S', 'Te')\",OTHERS,True\nmp-22623,\"{'Cu': 4.0, 'Ge': 4.0, 'Ce': 3.0}\",\"('Ce', 'Cu', 'Ge')\",OTHERS,True\nmp-22399,\"{'O': 4.0, 'S': 8.0, 'Yb': 8.0}\",\"('O', 'S', 'Yb')\",OTHERS,True\nmp-1211411,\"{'Li': 24.0, 'Sn': 24.0, 'Pt': 16.0}\",\"('Li', 'Pt', 'Sn')\",OTHERS,True\nmp-1181733,\"{'K': 4.0, 'Ba': 4.0, 'V': 20.0, 'O': 72.0}\",\"('Ba', 'K', 'O', 'V')\",OTHERS,True\nmp-30656,\"{'Ti': 3.0, 'Ni': 3.0, 'Ga': 3.0}\",\"('Ga', 'Ni', 'Ti')\",OTHERS,True\nmp-1245020,\"{'W': 34.0, 'O': 68.0}\",\"('O', 'W')\",OTHERS,True\nmp-1207202,\"{'La': 1.0, 'P': 2.0, 'Pd': 2.0}\",\"('La', 'P', 'Pd')\",OTHERS,True\nmp-1209593,\"{'Rb': 4.0, 'Cu': 2.0, 'Cl': 8.0, 'O': 4.0}\",\"('Cl', 'Cu', 'O', 'Rb')\",OTHERS,True\nmp-764374,\"{'Li': 4.0, 'Mn': 8.0, 'O': 2.0, 'F': 16.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1247221,\"{'Lu': 3.0, 'Mg': 2.0, 'Mn': 1.0, 'S': 8.0}\",\"('Lu', 'Mg', 'Mn', 'S')\",OTHERS,True\nmp-4865,\"{'Ni': 2.0, 'Ge': 2.0, 'Er': 1.0}\",\"('Er', 'Ge', 'Ni')\",OTHERS,True\nmp-3071,\"{'B': 2.0, 'Ni': 13.0, 'Nd': 3.0}\",\"('B', 'Nd', 'Ni')\",OTHERS,True\nmp-1100553,\"{'Li': 9.0, 'Mn': 7.0, 'O': 16.0}\",\"('Li', 'Mn', 'O')\",OTHERS,True\nmp-1199487,\"{'Mo': 4.0, 'H': 32.0, 'S': 16.0, 'N': 8.0}\",\"('H', 'Mo', 'N', 'S')\",OTHERS,True\nmp-570525,\"{'La': 8.0, 'Zn': 8.0, 'Rh': 8.0}\",\"('La', 'Rh', 'Zn')\",OTHERS,True\nmp-1246782,\"{'Al': 4.0, 'Ni': 2.0, 'N': 6.0}\",\"('Al', 'N', 'Ni')\",OTHERS,True\nmp-1180,\"{'Mn': 1.0, 'Pt': 3.0}\",\"('Mn', 'Pt')\",OTHERS,True\nmp-1217101,\"{'Ti': 3.0, 'S': 4.0}\",\"('S', 'Ti')\",OTHERS,True\nmp-772728,\"{'Na': 10.0, 'Li': 2.0, 'Mn': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Li', 'Mn', 'Na', 'O', 'P')\",OTHERS,True\nmp-1037039,\"{'Mg': 30.0, 'Fe': 1.0, 'Co': 1.0, 'O': 32.0}\",\"('Co', 'Fe', 'Mg', 'O')\",OTHERS,True\nmp-1200531,\"{'Mn': 1.0, 'H': 44.0, 'C': 12.0, 'N': 8.0, 'Cl': 2.0, 'O': 10.0}\",\"('C', 'Cl', 'H', 'Mn', 'N', 'O')\",OTHERS,True\nmp-1205455,\"{'Sn': 4.0, 'Se': 4.0}\",\"('Se', 'Sn')\",OTHERS,True\nmp-1178831,\"{'V': 8.0, 'O': 14.0}\",\"('O', 'V')\",OTHERS,True\nmp-1040135,\"{'Li': 1.0, 'Mg': 30.0, 'B': 1.0, 'O': 32.0}\",\"('B', 'Li', 'Mg', 'O')\",OTHERS,True\nmp-1222656,\"{'Li': 2.0, 'Ti': 1.0, 'O': 3.0}\",\"('Li', 'O', 'Ti')\",OTHERS,True\nmp-1084826,\"{'Nd': 4.0, 'Co': 6.0}\",\"('Co', 'Nd')\",OTHERS,True\nmp-1077232,\"{'W': 2.0, 'N': 4.0}\",\"('N', 'W')\",OTHERS,True\nmp-867795,\"{'Sc': 1.0, 'Nb': 1.0, 'Tc': 2.0}\",\"('Nb', 'Sc', 'Tc')\",OTHERS,True\nmp-1183996,\"{'Cu': 6.0, 'F': 2.0}\",\"('Cu', 'F')\",OTHERS,True\nmp-1185104,\"{'K': 1.0, 'In': 3.0}\",\"('In', 'K')\",OTHERS,True\nmp-758310,\"{'Li': 12.0, 'Ti': 8.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'O', 'P', 'Ti')\",OTHERS,True\nmp-1184494,\"{'Gd': 1.0, 'Cd': 1.0, 'Pt': 2.0}\",\"('Cd', 'Gd', 'Pt')\",OTHERS,True\nmp-757851,\"{'Li': 8.0, 'Co': 12.0, 'Ni': 4.0, 'O': 32.0}\",\"('Co', 'Li', 'Ni', 'O')\",OTHERS,True\nmp-775258,\"{'Li': 8.0, 'Mn': 2.0, 'Fe': 6.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1073477,\"{'Mg': 4.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,True\nmp-979057,\"{'Sm': 2.0, 'Zn': 1.0, 'Ag': 1.0}\",\"('Ag', 'Sm', 'Zn')\",OTHERS,True\nmp-21005,\"{'In': 1.0, 'Gd': 1.0}\",\"('Gd', 'In')\",OTHERS,True\nmp-772075,\"{'Li': 12.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-1114575,\"{'Rb': 2.0, 'Li': 1.0, 'Sb': 1.0, 'Br': 6.0}\",\"('Br', 'Li', 'Rb', 'Sb')\",OTHERS,True\nmp-684620,\"{'Nb': 25.0, 'S': 48.0}\",\"('Nb', 'S')\",OTHERS,True\nmp-1183381,\"{'Ba': 2.0, 'Zn': 1.0, 'In': 1.0}\",\"('Ba', 'In', 'Zn')\",OTHERS,True\nmp-1093817,\"{'Cs': 2.0, 'Hg': 1.0, 'Au': 1.0}\",\"('Au', 'Cs', 'Hg')\",OTHERS,True\nmp-1147544,\"{'Ba': 2.0, 'Tl': 1.0, 'Cu': 3.0, 'O': 7.0}\",\"('Ba', 'Cu', 'O', 'Tl')\",OTHERS,True\nmp-1177993,\"{'Li': 8.0, 'Mn': 12.0, 'Sn': 4.0, 'O': 32.0}\",\"('Li', 'Mn', 'O', 'Sn')\",OTHERS,True\nmp-1178068,\"{'Li': 24.0, 'Ti': 1.0, 'Cr': 11.0, 'O': 36.0}\",\"('Cr', 'Li', 'O', 'Ti')\",OTHERS,True\nmp-645504,\"{'Yb': 46.0, 'Mg': 8.0, 'Cu': 14.0}\",\"('Cu', 'Mg', 'Yb')\",OTHERS,True\nmp-1245152,{'Al': 100.0},\"('Al',)\",OTHERS,True\nmp-571607,\"{'Cd': 12.0, 'I': 24.0}\",\"('Cd', 'I')\",OTHERS,True\nmp-1197275,\"{'Tb': 8.0, 'Fe': 56.0, 'C': 4.0}\",\"('C', 'Fe', 'Tb')\",OTHERS,True\nmp-1207321,\"{'Pr': 2.0, 'Cu': 1.0, 'Sb': 3.0}\",\"('Cu', 'Pr', 'Sb')\",OTHERS,True\nmp-1266446,\"{'Li': 20.0, 'Cu': 2.0, 'Si': 4.0, 'O': 20.0}\",\"('Cu', 'Li', 'O', 'Si')\",OTHERS,True\nmp-1193638,\"{'Nb': 8.0, 'Fe': 2.0, 'Se': 16.0}\",\"('Fe', 'Nb', 'Se')\",OTHERS,True\nmp-757601,\"{'Ba': 8.0, 'Fe': 8.0, 'O': 21.0}\",\"('Ba', 'Fe', 'O')\",OTHERS,True\nmp-1028604,\"{'Te': 4.0, 'W': 4.0, 'S': 4.0}\",\"('S', 'Te', 'W')\",OTHERS,True\nmp-1078906,\"{'Co': 1.0, 'Te': 1.0, 'Pb': 2.0, 'O': 6.0}\",\"('Co', 'O', 'Pb', 'Te')\",OTHERS,True\nmp-1080715,\"{'Sc': 3.0, 'Si': 3.0, 'Ru': 3.0}\",\"('Ru', 'Sc', 'Si')\",OTHERS,True\nmp-1246994,\"{'Zr': 4.0, 'Cr': 4.0, 'Ag': 4.0, 'S': 16.0}\",\"('Ag', 'Cr', 'S', 'Zr')\",OTHERS,True\nmp-1026034,\"{'Mo': 1.0, 'W': 2.0, 'S': 6.0}\",\"('Mo', 'S', 'W')\",OTHERS,True\nmp-999508,\"{'Mn': 1.0, 'Si': 1.0, 'Ni': 2.0}\",\"('Mn', 'Ni', 'Si')\",OTHERS,True\nmp-1912,\"{'Zn': 1.0, 'Ag': 1.0}\",\"('Ag', 'Zn')\",OTHERS,True\nmp-1212351,\"{'Na': 3.0, 'Cr': 1.0, 'O': 6.0}\",\"('Cr', 'Na', 'O')\",OTHERS,True\nmp-559931,\"{'F': 6.0, 'V': 2.0}\",\"('F', 'V')\",OTHERS,True\nmp-744914,\"{'Ca': 8.0, 'Mg': 1.0, 'Al': 6.0, 'Si': 5.0, 'O': 28.0}\",\"('Al', 'Ca', 'Mg', 'O', 'Si')\",OTHERS,True\nmp-25886,\"{'Cu': 2.0, 'P': 2.0, 'O': 8.0}\",\"('Cu', 'O', 'P')\",OTHERS,True\nmp-690515,\"{'K': 2.0, 'Co': 1.0, 'H': 2.0, 'Se': 2.0, 'O': 10.0}\",\"('Co', 'H', 'K', 'O', 'Se')\",OTHERS,True\nmp-17745,\"{'F': 12.0, 'S': 2.0, 'Mn': 4.0, 'Hg': 4.0}\",\"('F', 'Hg', 'Mn', 'S')\",OTHERS,True\nmvc-5910,\"{'Mg': 2.0, 'Mo': 1.0, 'W': 1.0, 'O': 6.0}\",\"('Mg', 'Mo', 'O', 'W')\",OTHERS,True\nmp-567772,{'Na': 8.0},\"('Na',)\",OTHERS,True\nmp-21497,\"{'O': 10.0, 'S': 2.0, 'Pb': 4.0}\",\"('O', 'Pb', 'S')\",OTHERS,True\nmp-1221041,\"{'Na': 8.0, 'Zr': 3.0, 'Si': 16.0, 'Sn': 1.0, 'H': 16.0, 'O': 52.0}\",\"('H', 'Na', 'O', 'Si', 'Sn', 'Zr')\",OTHERS,True\nmp-29000,\"{'B': 40.0, 'Na': 24.0, 'S': 72.0}\",\"('B', 'Na', 'S')\",OTHERS,True\nmp-756921,\"{'Li': 1.0, 'Zn': 1.0, 'Fe': 10.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'Zn')\",OTHERS,True\nmp-1194642,\"{'Fe': 2.0, 'H': 8.0, 'C': 6.0, 'O': 16.0}\",\"('C', 'Fe', 'H', 'O')\",OTHERS,True\nmp-1228195,\"{'Ba': 7.0, 'Nb': 4.0, 'Mo': 1.0, 'O': 20.0}\",\"('Ba', 'Mo', 'Nb', 'O')\",OTHERS,True\nmp-1214519,\"{'Ba': 12.0, 'Y': 4.0, 'Al': 8.0, 'O': 30.0}\",\"('Al', 'Ba', 'O', 'Y')\",OTHERS,True\nmp-558759,\"{'Cd': 8.0, 'Te': 12.0, 'Cl': 12.0, 'O': 26.0}\",\"('Cd', 'Cl', 'O', 'Te')\",OTHERS,True\nmp-510459,\"{'O': 8.0, 'V': 2.0, 'Mn': 2.0, 'Cu': 2.0}\",\"('Cu', 'Mn', 'O', 'V')\",OTHERS,True\nmp-610706,\"{'F': 6.0, 'Co': 1.0, 'Cs': 2.0}\",\"('Co', 'Cs', 'F')\",OTHERS,True\nmp-1033754,\"{'Hf': 1.0, 'Mg': 14.0, 'Sb': 1.0, 'O': 16.0}\",\"('Hf', 'Mg', 'O', 'Sb')\",OTHERS,True\nmp-32688,\"{'Nd': 16.0, 'Se': 24.0}\",\"('Nd', 'Se')\",OTHERS,True\nmp-1180728,\"{'Mg': 2.0, 'As': 2.0, 'N': 2.0, 'O': 20.0}\",\"('As', 'Mg', 'N', 'O')\",OTHERS,True\nmp-1208711,\"{'Sm': 4.0, 'Br': 8.0}\",\"('Br', 'Sm')\",OTHERS,True\nmp-974948,\"{'Rb': 3.0, 'In': 1.0}\",\"('In', 'Rb')\",OTHERS,True\nmp-1227254,\"{'Ca': 1.0, 'Mn': 1.0, 'Cu': 3.0, 'Ru': 3.0, 'O': 12.0}\",\"('Ca', 'Cu', 'Mn', 'O', 'Ru')\",OTHERS,True\nmp-1211085,\"{'Li': 2.0, 'Ca': 4.0, 'Fe': 2.0, 'N': 4.0}\",\"('Ca', 'Fe', 'Li', 'N')\",OTHERS,True\nmp-622213,\"{'Ge': 16.0, 'S': 32.0}\",\"('Ge', 'S')\",OTHERS,True\nmp-12275,\"{'Zn': 4.0, 'Sr': 4.0, 'Sb': 8.0}\",\"('Sb', 'Sr', 'Zn')\",OTHERS,True\nmp-706227,\"{'B': 6.0, 'H': 30.0, 'C': 24.0, 'N': 24.0, 'O': 12.0}\",\"('B', 'C', 'H', 'N', 'O')\",OTHERS,True\nmp-1215704,\"{'Zr': 3.0, 'Mn': 8.0, 'Al': 1.0}\",\"('Al', 'Mn', 'Zr')\",OTHERS,True\nmp-753013,\"{'Co': 3.0, 'Te': 1.0, 'O': 8.0}\",\"('Co', 'O', 'Te')\",OTHERS,True\nmp-982981,\"{'Na': 6.0, 'Rh': 2.0}\",\"('Na', 'Rh')\",OTHERS,True\nmp-4476,\"{'Si': 2.0, 'Cu': 2.0, 'Ho': 2.0}\",\"('Cu', 'Ho', 'Si')\",OTHERS,True\nmp-1195797,\"{'Hg': 4.0, 'Sb': 14.0, 'H': 4.0, 'Xe': 6.0, 'F': 118.0}\",\"('F', 'H', 'Hg', 'Sb', 'Xe')\",OTHERS,True\nmp-1079559,\"{'Cd': 2.0, 'P': 2.0, 'Se': 6.0}\",\"('Cd', 'P', 'Se')\",OTHERS,True\nmp-1225419,\"{'Fe': 16.0, 'Ni': 20.0, 'P': 12.0}\",\"('Fe', 'Ni', 'P')\",OTHERS,True\nmp-28630,\"{'B': 4.0, 'N': 8.0, 'Na': 12.0}\",\"('B', 'N', 'Na')\",OTHERS,True\nmp-9566,\"{'Mg': 2.0, 'Sr': 1.0, 'Sb': 2.0}\",\"('Mg', 'Sb', 'Sr')\",OTHERS,True\nmp-755597,\"{'Ca': 4.0, 'Ce': 2.0, 'O': 8.0}\",\"('Ca', 'Ce', 'O')\",OTHERS,True\nmp-541218,\"{'Gd': 4.0, 'P': 8.0, 'O': 40.0, 'H': 36.0}\",\"('Gd', 'H', 'O', 'P')\",OTHERS,True\nmp-1178106,\"{'Li': 4.0, 'Fe': 8.0, 'O': 16.0}\",\"('Fe', 'Li', 'O')\",OTHERS,True\nmp-1112148,\"{'Cs': 2.0, 'Na': 1.0, 'Mo': 1.0, 'I': 6.0}\",\"('Cs', 'I', 'Mo', 'Na')\",OTHERS,True\nmp-19879,\"{'P': 4.0, 'Pd': 12.0}\",\"('P', 'Pd')\",OTHERS,True\nmp-1222057,\"{'Mg': 1.0, 'Fe': 4.0, 'Co': 1.0, 'O': 8.0}\",\"('Co', 'Fe', 'Mg', 'O')\",OTHERS,True\nmp-768809,\"{'Li': 12.0, 'Bi': 4.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Bi', 'Li', 'O')\",OTHERS,True\nmp-27814,\"{'C': 1.0, 'S': 14.0}\",\"('C', 'S')\",OTHERS,True\nmp-685044,\"{'Yb': 16.0, 'S': 29.0}\",\"('S', 'Yb')\",OTHERS,True\nmp-8832,\"{'Si': 2.0, 'Cr': 2.0, 'Tb': 1.0}\",\"('Cr', 'Si', 'Tb')\",OTHERS,True\nmp-1096036,\"{'Sc': 1.0, 'Ga': 1.0, 'Ag': 2.0}\",\"('Ag', 'Ga', 'Sc')\",OTHERS,True\nmp-1114505,\"{'Rb': 2.0, 'Tl': 2.0, 'Br': 6.0}\",\"('Br', 'Rb', 'Tl')\",OTHERS,True\nmp-1218981,\"{'Sm': 1.0, 'Pa': 1.0, 'O': 4.0}\",\"('O', 'Pa', 'Sm')\",OTHERS,True\nmp-1188079,\"{'Zr': 8.0, 'In': 4.0, 'Ni': 8.0}\",\"('In', 'Ni', 'Zr')\",OTHERS,True\nmp-1182698,\"{'Fe': 16.0, 'H': 20.0, 'O': 34.0}\",\"('Fe', 'H', 'O')\",OTHERS,True\nmp-721058,\"{'Co': 8.0, 'P': 8.0, 'H': 24.0, 'O': 32.0}\",\"('Co', 'H', 'O', 'P')\",OTHERS,True\nmp-1206729,\"{'Nb': 1.0, 'Ga': 1.0, 'Pb': 2.0, 'O': 6.0}\",\"('Ga', 'Nb', 'O', 'Pb')\",OTHERS,True\nmp-3788,\"{'O': 14.0, 'Al': 8.0, 'Sr': 2.0}\",\"('Al', 'O', 'Sr')\",OTHERS,True\nmp-758930,\"{'Na': 40.0, 'Fe': 8.0, 'H': 8.0, 'O': 32.0}\",\"('Fe', 'H', 'Na', 'O')\",OTHERS,True\nmp-981384,\"{'Sc': 1.0, 'Os': 3.0}\",\"('Os', 'Sc')\",OTHERS,True\nmp-989537,\"{'Cs': 2.0, 'Tl': 1.0, 'In': 1.0, 'F': 6.0}\",\"('Cs', 'F', 'In', 'Tl')\",OTHERS,True\nmp-757613,\"{'Li': 8.0, 'Co': 12.0, 'Sb': 4.0, 'O': 32.0}\",\"('Co', 'Li', 'O', 'Sb')\",OTHERS,True\nmp-715517,\"{'V': 16.0, 'O': 32.0}\",\"('O', 'V')\",OTHERS,True\nmp-1019543,\"{'Ba': 8.0, 'Ca': 8.0, 'P': 16.0, 'O': 56.0}\",\"('Ba', 'Ca', 'O', 'P')\",OTHERS,True\nmp-557659,\"{'K': 2.0, 'V': 8.0, 'P': 14.0, 'O': 48.0}\",\"('K', 'O', 'P', 'V')\",OTHERS,True\nmp-1009752,\"{'Sc': 1.0, 'Si': 1.0}\",\"('Sc', 'Si')\",OTHERS,True\nmp-5149,\"{'Al': 1.0, 'Co': 2.0, 'Nb': 1.0}\",\"('Al', 'Co', 'Nb')\",OTHERS,True\nmp-684829,\"{'Co': 27.0, 'Se': 32.0}\",\"('Co', 'Se')\",OTHERS,True\nmp-1198456,\"{'Na': 4.0, 'Zn': 2.0, 'P': 8.0, 'H': 32.0, 'O': 40.0}\",\"('H', 'Na', 'O', 'P', 'Zn')\",OTHERS,True\nmp-1191981,\"{'Ta': 7.0, 'B': 8.0, 'Ru': 6.0}\",\"('B', 'Ru', 'Ta')\",OTHERS,True\nmp-1093890,\"{'Li': 2.0, 'Tl': 1.0, 'Cd': 1.0}\",\"('Cd', 'Li', 'Tl')\",OTHERS,True\nmp-756466,\"{'Nb': 1.0, 'Co': 3.0, 'P': 4.0, 'O': 16.0}\",\"('Co', 'Nb', 'O', 'P')\",OTHERS,True\nmp-20276,\"{'F': 18.0, 'U': 4.0}\",\"('F', 'U')\",OTHERS,True\nmp-1102055,\"{'La': 4.0, 'As': 8.0}\",\"('As', 'La')\",OTHERS,True\nmp-1076181,\"{'Ba': 4.0, 'Sr': 28.0, 'Mn': 8.0, 'Fe': 24.0, 'O': 80.0}\",\"('Ba', 'Fe', 'Mn', 'O', 'Sr')\",OTHERS,True\nmp-1184025,\"{'Eu': 2.0, 'Mg': 1.0, 'In': 1.0}\",\"('Eu', 'In', 'Mg')\",OTHERS,True\nmp-1197569,\"{'Zn': 2.0, 'Ni': 21.0, 'B': 20.0}\",\"('B', 'Ni', 'Zn')\",OTHERS,True\nmp-1177426,\"{'Li': 4.0, 'Fe': 3.0, 'Cu': 2.0, 'Sn': 3.0, 'O': 16.0}\",\"('Cu', 'Fe', 'Li', 'O', 'Sn')\",OTHERS,True\nmp-769176,\"{'K': 40.0, 'Y': 8.0, 'O': 32.0}\",\"('K', 'O', 'Y')\",OTHERS,True\nmp-1113946,\"{'K': 1.0, 'Na': 2.0, 'Sb': 1.0, 'F': 6.0}\",\"('F', 'K', 'Na', 'Sb')\",OTHERS,True\nmp-1212862,\"{'Gd': 4.0, 'In': 8.0, 'Cl': 20.0}\",\"('Cl', 'Gd', 'In')\",OTHERS,True\nmp-1212745,\"{'Eu': 4.0, 'Er': 8.0, 'O': 16.0}\",\"('Er', 'Eu', 'O')\",OTHERS,True\nmp-1114541,\"{'K': 1.0, 'Rb': 2.0, 'Lu': 1.0, 'Cl': 6.0}\",\"('Cl', 'K', 'Lu', 'Rb')\",OTHERS,True\nmp-1218730,\"{'Sr': 2.0, 'Si': 3.0, 'Ni': 1.0}\",\"('Ni', 'Si', 'Sr')\",OTHERS,True\nmp-1173503,\"{'Na': 4.0, 'Pr': 8.0, 'Ti': 8.0, 'Mn': 4.0, 'O': 36.0}\",\"('Mn', 'Na', 'O', 'Pr', 'Ti')\",OTHERS,True\nmp-540783,\"{'Os': 2.0, 'O': 8.0}\",\"('O', 'Os')\",OTHERS,True\nmp-1218535,\"{'Sr': 1.0, 'Al': 3.0, 'P': 1.0, 'S': 1.0, 'O': 14.0}\",\"('Al', 'O', 'P', 'S', 'Sr')\",OTHERS,True\nmp-1206061,\"{'K': 3.0, 'U': 1.0, 'F': 6.0}\",\"('F', 'K', 'U')\",OTHERS,True\nmp-2573,\"{'Al': 3.0, 'Np': 1.0}\",\"('Al', 'Np')\",OTHERS,True\nmp-753708,\"{'Ti': 4.0, 'O': 4.0, 'F': 4.0}\",\"('F', 'O', 'Ti')\",OTHERS,True\nmp-1211125,\"{'Nd': 8.0, 'Co': 2.0, 'S': 14.0}\",\"('Co', 'Nd', 'S')\",OTHERS,True\nmp-8825,\"{'O': 44.0, 'Pr': 24.0}\",\"('O', 'Pr')\",OTHERS,True\nmp-772554,\"{'Li': 4.0, 'Nb': 3.0, 'V': 5.0, 'O': 16.0}\",\"('Li', 'Nb', 'O', 'V')\",OTHERS,True\nmp-849735,\"{'Li': 4.0, 'Cr': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Cr', 'Li', 'O', 'Si')\",OTHERS,True\nmp-1147542,\"{'Ba': 2.0, 'Cu': 1.0, 'S': 2.0, 'Br': 2.0}\",\"('Ba', 'Br', 'Cu', 'S')\",OTHERS,True\nmp-763923,\"{'Co': 6.0, 'O': 4.0, 'F': 8.0}\",\"('Co', 'F', 'O')\",OTHERS,True\nmp-976806,\"{'Ni': 2.0, 'Au': 6.0}\",\"('Au', 'Ni')\",OTHERS,True\nmp-1095172,\"{'Cs': 2.0, 'Nd': 2.0, 'S': 4.0}\",\"('Cs', 'Nd', 'S')\",OTHERS,True\nmp-644223,\"{'Li': 2.0, 'B': 2.0, 'H': 8.0}\",\"('B', 'H', 'Li')\",OTHERS,True\nmp-998429,\"{'Ni': 2.0, 'Ag': 2.0, 'F': 6.0}\",\"('Ag', 'F', 'Ni')\",OTHERS,True\nmp-1101595,\"{'Mo': 4.0, 'P': 6.0, 'O': 24.0}\",\"('Mo', 'O', 'P')\",OTHERS,True\nmp-7524,\"{'P': 2.0, 'Se': 2.0, 'Nb': 2.0}\",\"('Nb', 'P', 'Se')\",OTHERS,True\nmp-643651,\"{'Rb': 6.0, 'Zn': 2.0, 'H': 10.0}\",\"('H', 'Rb', 'Zn')\",OTHERS,True\nmvc-16787,\"{'Zn': 2.0, 'Cr': 4.0, 'S': 8.0}\",\"('Cr', 'S', 'Zn')\",OTHERS,True\nmp-36480,\"{'Cu': 2.0, 'La': 4.0, 'O': 8.0}\",\"('Cu', 'La', 'O')\",OTHERS,True\nmp-1029262,\"{'V': 2.0, 'Zn': 4.0, 'N': 6.0}\",\"('N', 'V', 'Zn')\",OTHERS,True\nmp-774239,\"{'Li': 4.0, 'Co': 8.0, 'O': 16.0}\",\"('Co', 'Li', 'O')\",OTHERS,True\nmp-1018712,\"{'Hf': 1.0, 'Ga': 4.0, 'Co': 1.0}\",\"('Co', 'Ga', 'Hf')\",OTHERS,True\nmp-7999,\"{'O': 14.0, 'Si': 4.0, 'Y': 4.0}\",\"('O', 'Si', 'Y')\",OTHERS,True\nmp-673637,\"{'Ti': 27.0, 'S': 30.0}\",\"('S', 'Ti')\",OTHERS,True\nmp-1227962,\"{'Ba': 1.0, 'Cd': 3.0, 'Ga': 1.0}\",\"('Ba', 'Cd', 'Ga')\",OTHERS,True\nmp-31703,\"{'O': 4.0, 'F': 12.0, 'Cr': 4.0}\",\"('Cr', 'F', 'O')\",OTHERS,True\nmp-19314,\"{'Be': 6.0, 'O': 24.0, 'Si': 6.0, 'S': 2.0, 'Mn': 8.0}\",\"('Be', 'Mn', 'O', 'S', 'Si')\",OTHERS,True\nmp-1196301,\"{'La': 16.0, 'Mn': 8.0, 'Se': 16.0, 'O': 16.0}\",\"('La', 'Mn', 'O', 'Se')\",OTHERS,True\nmp-16750,\"{'Cu': 2.0, 'Ho': 2.0, 'Pb': 2.0}\",\"('Cu', 'Ho', 'Pb')\",OTHERS,True\nmp-753393,\"{'Na': 1.0, 'V': 1.0, 'O': 2.0}\",\"('Na', 'O', 'V')\",OTHERS,True\nmp-864742,\"{'Na': 3.0, 'Sn': 1.0}\",\"('Na', 'Sn')\",OTHERS,True\nmp-985829,\"{'Hf': 1.0, 'S': 2.0}\",\"('Hf', 'S')\",OTHERS,True\nmp-532329,\"{'Cu': 8.0, 'Fe': 12.0, 'S': 40.0}\",\"('Cu', 'Fe', 'S')\",OTHERS,True\nmp-1200744,\"{'Rb': 4.0, 'Cd': 6.0, 'B': 32.0, 'O': 56.0}\",\"('B', 'Cd', 'O', 'Rb')\",OTHERS,True\nmp-26765,\"{'Li': 2.0, 'O': 48.0, 'P': 14.0, 'V': 12.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-558193,\"{'K': 2.0, 'Cu': 3.0, 'As': 4.0, 'O': 12.0}\",\"('As', 'Cu', 'K', 'O')\",OTHERS,True\nmp-983539,\"{'Be': 1.0, 'Au': 3.0}\",\"('Au', 'Be')\",OTHERS,True\nmp-761153,\"{'U': 6.0, 'O': 11.0}\",\"('O', 'U')\",OTHERS,True\nmp-30454,\"{'Er': 1.0, 'Pt': 1.0, 'Bi': 1.0}\",\"('Bi', 'Er', 'Pt')\",OTHERS,True\nmp-1094692,\"{'Mg': 48.0, 'Al': 39.0}\",\"('Al', 'Mg')\",OTHERS,True\nmp-569015,\"{'Mg': 8.0, 'Cu': 8.0, 'As': 8.0}\",\"('As', 'Cu', 'Mg')\",OTHERS,True\nmp-1247341,\"{'La': 1.0, 'Mg': 2.0, 'V': 3.0, 'S': 8.0}\",\"('La', 'Mg', 'S', 'V')\",OTHERS,True\nmp-1187044,\"{'Sn': 6.0, 'P': 2.0}\",\"('P', 'Sn')\",OTHERS,True\nmvc-13836,\"{'Ta': 1.0, 'F': 5.0}\",\"('F', 'Ta')\",OTHERS,True\nmp-12855,\"{'Cu': 2.0, 'Ge': 1.0, 'Se': 4.0, 'Hg': 1.0}\",\"('Cu', 'Ge', 'Hg', 'Se')\",OTHERS,True\nmp-1186285,\"{'Nd': 6.0, 'Pa': 2.0}\",\"('Nd', 'Pa')\",OTHERS,True\nmp-1077377,\"{'Yb': 2.0, 'Cu': 2.0, 'Pb': 2.0}\",\"('Cu', 'Pb', 'Yb')\",OTHERS,True\nmp-1623,\"{'S': 1.0, 'Er': 1.0}\",\"('Er', 'S')\",OTHERS,True\nmp-1120785,\"{'Na': 16.0, 'V': 16.0, 'F': 56.0}\",\"('F', 'Na', 'V')\",OTHERS,True\nmp-2192,\"{'B': 2.0, 'Pt': 2.0}\",\"('B', 'Pt')\",OTHERS,True\nmp-1213829,\"{'Ce': 8.0, 'Al': 1.0, 'Pd': 24.0}\",\"('Al', 'Ce', 'Pd')\",OTHERS,True\nmp-2371,\"{'Ga': 2.0, 'Te': 5.0}\",\"('Ga', 'Te')\",OTHERS,True\nmp-1199808,\"{'Tl': 4.0, 'Ni': 2.0, 'H': 24.0, 'S': 4.0, 'O': 28.0}\",\"('H', 'Ni', 'O', 'S', 'Tl')\",OTHERS,True\nmp-1208539,\"{'Tb': 4.0, 'Cu': 2.0, 'Ge': 4.0, 'O': 16.0}\",\"('Cu', 'Ge', 'O', 'Tb')\",OTHERS,True\nmp-1207144,\"{'Sm': 4.0, 'Mg': 2.0, 'Pd': 4.0}\",\"('Mg', 'Pd', 'Sm')\",OTHERS,True\nmp-1018055,\"{'Hf': 2.0, 'Ir': 2.0}\",\"('Hf', 'Ir')\",OTHERS,True\nmp-5124,\"{'Mn': 1.0, 'Ni': 2.0, 'Sb': 1.0}\",\"('Mn', 'Ni', 'Sb')\",OTHERS,True\nmp-12834,\"{'Th': 4.0, 'Cu': 24.0}\",\"('Cu', 'Th')\",OTHERS,True\nmp-1076253,\"{'La': 6.0, 'Sm': 2.0, 'V': 5.0, 'Cr': 3.0, 'O': 24.0}\",\"('Cr', 'La', 'O', 'Sm', 'V')\",OTHERS,True\nmp-1222826,\"{'Li': 2.0, 'Zr': 6.0, 'Mn': 1.0, 'Cl': 15.0}\",\"('Cl', 'Li', 'Mn', 'Zr')\",OTHERS,True\nmp-861643,\"{'Ca': 1.0, 'La': 1.0, 'Mg': 2.0}\",\"('Ca', 'La', 'Mg')\",OTHERS,True\nmp-1216480,\"{'V': 3.0, 'Ga': 1.0}\",\"('Ga', 'V')\",OTHERS,True\nmp-753113,\"{'Li': 4.0, 'V': 1.0, 'F': 7.0}\",\"('F', 'Li', 'V')\",OTHERS,True\nmp-693284,\"{'Ca': 12.0, 'Hf': 8.0, 'Fe': 8.0, 'Si': 4.0, 'O': 48.0}\",\"('Ca', 'Fe', 'Hf', 'O', 'Si')\",OTHERS,True\nmp-1184775,\"{'Fe': 2.0, 'Sn': 6.0}\",\"('Fe', 'Sn')\",OTHERS,True\nmp-22993,\"{'O': 4.0, 'Cl': 1.0, 'Ag': 1.0}\",\"('Ag', 'Cl', 'O')\",OTHERS,True\nmp-935267,\"{'Li': 16.0, 'U': 4.0, 'C': 12.0, 'O': 44.0}\",\"('C', 'Li', 'O', 'U')\",OTHERS,True\nmp-558945,\"{'Hg': 12.0, 'As': 4.0, 'Br': 4.0, 'O': 16.0}\",\"('As', 'Br', 'Hg', 'O')\",OTHERS,True\nmp-1095315,\"{'Ba': 2.0, 'W': 2.0, 'O': 8.0}\",\"('Ba', 'O', 'W')\",OTHERS,True\nmp-754407,\"{'Li': 2.0, 'Fe': 1.0, 'S': 2.0}\",\"('Fe', 'Li', 'S')\",OTHERS,True\nmp-1111091,\"{'Na': 2.0, 'Nb': 1.0, 'Ag': 1.0, 'F': 6.0}\",\"('Ag', 'F', 'Na', 'Nb')\",OTHERS,True\nmp-1209247,\"{'Rb': 2.0, 'Nd': 2.0, 'Mo': 4.0, 'O': 16.0}\",\"('Mo', 'Nd', 'O', 'Rb')\",OTHERS,True\nmvc-16806,\"{'Li': 2.0, 'Cr': 4.0, 'O': 8.0}\",\"('Cr', 'Li', 'O')\",OTHERS,True\nmp-754690,\"{'Mn': 6.0, 'O': 5.0, 'F': 7.0}\",\"('F', 'Mn', 'O')\",OTHERS,True\nmp-677269,\"{'Ca': 16.0, 'Zr': 9.0, 'Ta': 7.0, 'N': 7.0, 'O': 41.0}\",\"('Ca', 'N', 'O', 'Ta', 'Zr')\",OTHERS,True\nmvc-3735,\"{'Ca': 4.0, 'Mo': 4.0, 'F': 20.0}\",\"('Ca', 'F', 'Mo')\",OTHERS,True\nmp-1245817,\"{'Li': 1.0, 'Fe': 1.0, 'N': 1.0}\",\"('Fe', 'Li', 'N')\",OTHERS,True\nmp-973732,\"{'Li': 1.0, 'Ho': 3.0}\",\"('Ho', 'Li')\",OTHERS,True\nmp-1213158,\"{'Er': 10.0, 'Si': 4.0, 'Sb': 4.0}\",\"('Er', 'Sb', 'Si')\",OTHERS,True\nmp-1019105,\"{'K': 2.0, 'Mg': 2.0, 'Bi': 2.0}\",\"('Bi', 'K', 'Mg')\",OTHERS,True\nmp-1111507,\"{'Eu': 1.0, 'Na': 2.0, 'Cu': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cu', 'Eu', 'Na')\",OTHERS,True\nmp-754411,\"{'Ta': 4.0, 'Fe': 4.0, 'O': 16.0}\",\"('Fe', 'O', 'Ta')\",OTHERS,True\nmp-1216534,\"{'U': 2.0, 'Se': 1.0, 'S': 1.0}\",\"('S', 'Se', 'U')\",OTHERS,True\nmp-1246058,\"{'Ca': 8.0, 'Re': 2.0, 'N': 8.0}\",\"('Ca', 'N', 'Re')\",OTHERS,True\nmp-1191181,\"{'Ho': 2.0, 'Fe': 12.0, 'P': 7.0}\",\"('Fe', 'Ho', 'P')\",OTHERS,True\nmp-778008,\"{'Mn': 14.0, 'P': 16.0, 'O': 56.0}\",\"('Mn', 'O', 'P')\",OTHERS,True\nmp-567626,\"{'Mg': 2.0, 'Zn': 4.0}\",\"('Mg', 'Zn')\",OTHERS,True\nmp-11506,\"{'Ni': 6.0, 'Mo': 2.0}\",\"('Mo', 'Ni')\",OTHERS,True\nmp-567618,\"{'Si': 20.0, 'Pt': 48.0}\",\"('Pt', 'Si')\",OTHERS,True\nmp-685281,\"{'Ti': 1.0, 'Zn': 1.0, 'H': 12.0, 'O': 6.0, 'F': 6.0}\",\"('F', 'H', 'O', 'Ti', 'Zn')\",OTHERS,True\nmp-25082,\"{'Mo': 4.0, 'C': 24.0, 'O': 24.0}\",\"('C', 'Mo', 'O')\",OTHERS,True\nmp-1219434,\"{'Sc': 4.0, 'Fe': 6.0, 'Ru': 2.0}\",\"('Fe', 'Ru', 'Sc')\",OTHERS,True\nmp-772176,\"{'Li': 6.0, 'Nb': 14.0, 'O': 38.0}\",\"('Li', 'Nb', 'O')\",OTHERS,True\nmp-3952,\"{'O': 16.0, 'Y': 8.0, 'Ba': 4.0}\",\"('Ba', 'O', 'Y')\",OTHERS,True\nmp-24853,\"{'O': 5.0, 'Co': 2.0, 'Ba': 1.0, 'Nd': 1.0}\",\"('Ba', 'Co', 'Nd', 'O')\",OTHERS,True\nmp-1219863,\"{'Pr': 6.0, 'Mn': 1.0, 'Si': 2.0, 'S': 14.0}\",\"('Mn', 'Pr', 'S', 'Si')\",OTHERS,True\nmp-989189,\"{'Sb': 2.0, 'Br': 2.0, 'O': 2.0}\",\"('Br', 'O', 'Sb')\",OTHERS,True\nmp-780881,\"{'Li': 4.0, 'Mn': 3.0, 'Cr': 3.0, 'O': 12.0}\",\"('Cr', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1176929,\"{'Li': 12.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-541721,\"{'Ba': 6.0, 'Er': 2.0, 'Ru': 4.0, 'O': 18.0}\",\"('Ba', 'Er', 'O', 'Ru')\",OTHERS,True\nmp-1208035,\"{'U': 10.0, 'V': 2.0, 'Sb': 6.0}\",\"('Sb', 'U', 'V')\",OTHERS,True\nmp-1147716,\"{'Cs': 2.0, 'Mo': 2.0, 'C': 12.0}\",\"('C', 'Cs', 'Mo')\",OTHERS,True\nmp-1226784,\"{'Ce': 2.0, 'Mn': 1.0, 'Se': 2.0, 'O': 2.0}\",\"('Ce', 'Mn', 'O', 'Se')\",OTHERS,True\nmp-865600,\"{'Y': 2.0, 'Ir': 1.0, 'Pd': 1.0}\",\"('Ir', 'Pd', 'Y')\",OTHERS,True\nmp-761910,\"{'Li': 6.0, 'Cu': 4.0, 'F': 16.0}\",\"('Cu', 'F', 'Li')\",OTHERS,True\nmp-758217,\"{'Li': 6.0, 'V': 4.0, 'H': 8.0, 'O': 4.0, 'F': 20.0}\",\"('F', 'H', 'Li', 'O', 'V')\",OTHERS,True\nmp-773576,\"{'Li': 1.0, 'Zn': 2.0, 'Fe': 9.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'Zn')\",OTHERS,True\nmp-1228885,\"{'Ba': 2.0, 'V': 2.0, 'Cu': 1.0, 'F': 12.0}\",\"('Ba', 'Cu', 'F', 'V')\",OTHERS,True\nmp-624486,\"{'K': 12.0, 'Sn': 6.0, 'Ge': 18.0, 'O': 54.0}\",\"('Ge', 'K', 'O', 'Sn')\",OTHERS,True\nmp-29849,\"{'P': 14.0, 'Ag': 6.0, 'Sn': 2.0}\",\"('Ag', 'P', 'Sn')\",OTHERS,True\nmvc-10776,\"{'Ca': 4.0, 'Ti': 8.0, 'Be': 12.0, 'Si': 12.0, 'O': 48.0}\",\"('Be', 'Ca', 'O', 'Si', 'Ti')\",OTHERS,True\nmp-1179365,\"{'Sr': 2.0, 'H': 32.0, 'O': 20.0}\",\"('H', 'O', 'Sr')\",OTHERS,True\nmp-14623,\"{'K': 10.0, 'Cu': 2.0, 'As': 4.0}\",\"('As', 'Cu', 'K')\",OTHERS,True\nmp-1174776,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-778479,\"{'Mn': 12.0, 'O': 5.0, 'F': 19.0}\",\"('F', 'Mn', 'O')\",OTHERS,True\nmp-1073705,\"{'Mg': 2.0, 'Si': 2.0}\",\"('Mg', 'Si')\",OTHERS,True\nmp-558335,\"{'Na': 8.0, 'Pd': 4.0, 'N': 16.0, 'O': 32.0}\",\"('N', 'Na', 'O', 'Pd')\",OTHERS,True\nmp-1219132,\"{'Re': 4.0, 'Mo': 2.0, 'Se': 8.0}\",\"('Mo', 'Re', 'Se')\",OTHERS,True\nmp-1227371,\"{'Ba': 1.0, 'Sr': 1.0, 'La': 1.0, 'Bi': 1.0, 'O': 6.0}\",\"('Ba', 'Bi', 'La', 'O', 'Sr')\",OTHERS,True\nmvc-5445,\"{'Ca': 4.0, 'Y': 2.0, 'Bi': 2.0, 'O': 10.0}\",\"('Bi', 'Ca', 'O', 'Y')\",OTHERS,True\nmp-763192,\"{'V': 6.0, 'O': 3.0, 'F': 15.0}\",\"('F', 'O', 'V')\",OTHERS,True\nmp-695,\"{'Ti': 2.0, 'Co': 4.0}\",\"('Co', 'Ti')\",OTHERS,True\nmp-1208695,\"{'Ta': 9.0, 'V': 2.0, 'O': 25.0}\",\"('O', 'Ta', 'V')\",OTHERS,True\nmp-1175910,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-31116,\"{'La': 4.0, 'Sc': 4.0, 'O': 12.0}\",\"('La', 'O', 'Sc')\",OTHERS,True\nmp-1178537,\"{'Co': 6.0, 'C': 6.0, 'O': 21.0}\",\"('C', 'Co', 'O')\",OTHERS,True\nmp-1175280,\"{'Li': 7.0, 'Mn': 4.0, 'Co': 1.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1834,\"{'Nd': 1.0, 'Al': 4.0}\",\"('Al', 'Nd')\",OTHERS,True\nmp-1095291,\"{'Li': 4.0, 'Sn': 2.0, 'Se': 6.0}\",\"('Li', 'Se', 'Sn')\",OTHERS,True\nmp-1208409,\"{'Ta': 6.0, 'Co': 2.0, 'S': 12.0}\",\"('Co', 'S', 'Ta')\",OTHERS,True\nmp-768597,\"{'Sr': 4.0, 'La': 4.0, 'Cl': 20.0}\",\"('Cl', 'La', 'Sr')\",OTHERS,True\nmp-867338,\"{'Ac': 1.0, 'Cd': 1.0, 'Ag': 2.0}\",\"('Ac', 'Ag', 'Cd')\",OTHERS,True\nmp-1190707,\"{'Tm': 2.0, 'Fe': 12.0, 'P': 7.0}\",\"('Fe', 'P', 'Tm')\",OTHERS,True\nmp-1113366,\"{'Cs': 1.0, 'Rb': 2.0, 'Pd': 1.0, 'F': 6.0}\",\"('Cs', 'F', 'Pd', 'Rb')\",OTHERS,True\nmp-849468,\"{'Li': 4.0, 'Fe': 3.0, 'Co': 3.0, 'Te': 2.0, 'O': 16.0}\",\"('Co', 'Fe', 'Li', 'O', 'Te')\",OTHERS,True\nmp-754020,\"{'Li': 2.0, 'Co': 2.0, 'Cu': 2.0, 'O': 8.0}\",\"('Co', 'Cu', 'Li', 'O')\",OTHERS,True\nmp-1218284,\"{'Sr': 2.0, 'Dy': 2.0, 'Co': 2.0, 'O': 8.0}\",\"('Co', 'Dy', 'O', 'Sr')\",OTHERS,True\nmp-1221971,\"{'Mg': 1.0, 'Zn': 3.0, 'S': 4.0}\",\"('Mg', 'S', 'Zn')\",OTHERS,True\nmp-1114421,\"{'Rb': 2.0, 'Li': 1.0, 'Lu': 1.0, 'Cl': 6.0}\",\"('Cl', 'Li', 'Lu', 'Rb')\",OTHERS,True\nmp-9765,\"{'O': 14.0, 'Nd': 4.0, 'Pt': 4.0}\",\"('Nd', 'O', 'Pt')\",OTHERS,True\nmp-1074405,\"{'Mg': 8.0, 'Si': 6.0}\",\"('Mg', 'Si')\",OTHERS,True\nmp-720104,\"{'Na': 8.0, 'Al': 6.0, 'Ge': 6.0, 'N': 2.0, 'O': 28.0}\",\"('Al', 'Ge', 'N', 'Na', 'O')\",OTHERS,True\nmp-1185546,\"{'Cs': 1.0, 'Rb': 2.0, 'Bi': 1.0}\",\"('Bi', 'Cs', 'Rb')\",OTHERS,True\nmp-1181383,\"{'K': 2.0, 'Co': 1.0, 'B': 12.0, 'O': 30.0}\",\"('B', 'Co', 'K', 'O')\",OTHERS,True\nmp-766168,\"{'Ti': 8.0, 'Sn': 2.0, 'O': 20.0}\",\"('O', 'Sn', 'Ti')\",OTHERS,True\nmp-1078736,\"{'Pr': 2.0, 'Ni': 4.0, 'Sb': 4.0}\",\"('Ni', 'Pr', 'Sb')\",OTHERS,True\nmp-1195793,\"{'Ba': 4.0, 'Fe': 4.0, 'P': 4.0, 'H': 4.0, 'O': 20.0}\",\"('Ba', 'Fe', 'H', 'O', 'P')\",OTHERS,True\nmp-542824,\"{'Ce': 3.0, 'Ag': 4.0, 'Sn': 4.0}\",\"('Ag', 'Ce', 'Sn')\",OTHERS,True\nmp-683793,\"{'Fe': 16.0, 'Te': 8.0, 'Mo': 4.0, 'C': 56.0, 'S': 8.0, 'O': 56.0}\",\"('C', 'Fe', 'Mo', 'O', 'S', 'Te')\",OTHERS,True\nmp-1187005,\"{'Si': 2.0, 'Ag': 6.0}\",\"('Ag', 'Si')\",OTHERS,True\nmp-1096299,\"{'Li': 2.0, 'Hg': 1.0, 'Sb': 1.0}\",\"('Hg', 'Li', 'Sb')\",OTHERS,True\nmp-1207770,\"{'Y': 10.0, 'Bi': 2.0, 'Pt': 4.0}\",\"('Bi', 'Pt', 'Y')\",OTHERS,True\nmp-778385,\"{'B': 24.0, 'H': 24.0, 'Se': 8.0, 'O': 72.0}\",\"('B', 'H', 'O', 'Se')\",OTHERS,True\nmp-979286,{'Xe': 1.0},\"('Xe',)\",OTHERS,True\nmp-1228771,\"{'Ba': 2.0, 'Fe': 10.0, 'Bi': 8.0, 'O': 30.0}\",\"('Ba', 'Bi', 'Fe', 'O')\",OTHERS,True\nmp-541879,\"{'Rb': 8.0, 'Ge': 8.0, 'S': 20.0}\",\"('Ge', 'Rb', 'S')\",OTHERS,True\nmp-1187731,\"{'Y': 2.0, 'Zn': 1.0, 'Hg': 1.0}\",\"('Hg', 'Y', 'Zn')\",OTHERS,True\nmp-1206803,\"{'Y': 2.0, 'C': 1.0, 'I': 2.0}\",\"('C', 'I', 'Y')\",OTHERS,True\nmp-1190708,\"{'Al': 12.0, 'Fe': 8.0, 'Si': 4.0}\",\"('Al', 'Fe', 'Si')\",OTHERS,True\nmp-1219347,\"{'Si': 1.0, 'Pb': 7.0, 'Cl': 2.0, 'O': 8.0}\",\"('Cl', 'O', 'Pb', 'Si')\",OTHERS,True\nmp-1246452,\"{'B': 24.0, 'C': 24.0, 'N': 56.0}\",\"('B', 'C', 'N')\",OTHERS,True\nmvc-11253,\"{'Ca': 2.0, 'Cr': 4.0, 'S': 8.0}\",\"('Ca', 'Cr', 'S')\",OTHERS,True\nmp-1093630,\"{'Li': 1.0, 'Ge': 2.0, 'Rh': 1.0}\",\"('Ge', 'Li', 'Rh')\",OTHERS,True\nmp-735556,\"{'Na': 4.0, 'Co': 2.0, 'H': 16.0, 'C': 4.0, 'O': 20.0}\",\"('C', 'Co', 'H', 'Na', 'O')\",OTHERS,True\nmp-1224984,\"{'Fe': 2.0, 'Co': 2.0, 'B': 2.0}\",\"('B', 'Co', 'Fe')\",OTHERS,True\nmp-1219453,\"{'Sr': 12.0, 'Ti': 4.0, 'Si': 16.0, 'O': 61.0}\",\"('O', 'Si', 'Sr', 'Ti')\",OTHERS,True\nmp-27989,\"{'C': 2.0, 'N': 2.0, 'Br': 2.0}\",\"('Br', 'C', 'N')\",OTHERS,True\nmp-1209723,\"{'Np': 2.0, 'Tl': 4.0, 'F': 12.0}\",\"('F', 'Np', 'Tl')\",OTHERS,True\nmp-30704,\"{'Ga': 6.0, 'Nb': 10.0}\",\"('Ga', 'Nb')\",OTHERS,True\nmp-1095964,\"{'Zn': 2.0, 'Ag': 1.0, 'Pd': 1.0}\",\"('Ag', 'Pd', 'Zn')\",OTHERS,True\nmp-554291,\"{'Cu': 8.0, 'Sb': 16.0, 'Cl': 8.0, 'O': 24.0}\",\"('Cl', 'Cu', 'O', 'Sb')\",OTHERS,True\nmp-1185389,\"{'Li': 1.0, 'Nd': 2.0, 'Rh': 1.0}\",\"('Li', 'Nd', 'Rh')\",OTHERS,True\nmp-1221814,\"{'Mn': 1.0, 'Fe': 5.0, 'O': 8.0}\",\"('Fe', 'Mn', 'O')\",OTHERS,True\nmp-1228492,\"{'Ca': 8.0, 'Fe': 16.0, 'Cu': 8.0, 'O': 40.0}\",\"('Ca', 'Cu', 'Fe', 'O')\",OTHERS,True\nmp-849273,\"{'Li': 4.0, 'Co': 4.0, 'O': 8.0}\",\"('Co', 'Li', 'O')\",OTHERS,True\nmp-19838,\"{'Si': 2.0, 'Cr': 2.0, 'Pu': 1.0}\",\"('Cr', 'Pu', 'Si')\",OTHERS,True\nmvc-12951,\"{'Mn': 4.0, 'Zn': 1.0, 'S': 8.0}\",\"('Mn', 'S', 'Zn')\",OTHERS,True\nmp-1177254,\"{'Li': 4.0, 'V': 2.0, 'Co': 3.0, 'Cu': 3.0, 'O': 16.0}\",\"('Co', 'Cu', 'Li', 'O', 'V')\",OTHERS,True\nmp-26241,\"{'Li': 4.0, 'O': 28.0, 'P': 8.0, 'V': 6.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmvc-8408,\"{'V': 4.0, 'Zn': 4.0, 'Ge': 8.0, 'O': 24.0}\",\"('Ge', 'O', 'V', 'Zn')\",OTHERS,True\nmp-582024,\"{'Cs': 12.0, 'Cu': 8.0, 'Cl': 20.0}\",\"('Cl', 'Cs', 'Cu')\",OTHERS,True\nmp-709066,\"{'Rb': 16.0, 'Li': 4.0, 'H': 12.0, 'S': 16.0, 'O': 64.0}\",\"('H', 'Li', 'O', 'Rb', 'S')\",OTHERS,True\nmp-972032,\"{'Tl': 2.0, 'I': 6.0, 'O': 18.0}\",\"('I', 'O', 'Tl')\",OTHERS,True\nmp-997032,\"{'Mg': 2.0, 'Au': 2.0, 'O': 4.0}\",\"('Au', 'Mg', 'O')\",OTHERS,True\nmp-1225357,\"{'Dy': 1.0, 'Al': 7.0, 'Fe': 5.0}\",\"('Al', 'Dy', 'Fe')\",OTHERS,True\nmp-1014212,{'Si': 1.0},\"('Si',)\",OTHERS,True\nmp-962057,\"{'Sr': 1.0, 'Ca': 1.0, 'Si': 1.0}\",\"('Ca', 'Si', 'Sr')\",OTHERS,True\nmp-1219143,\"{'Sm': 6.0, 'Mn': 1.0, 'Si': 2.0, 'S': 14.0}\",\"('Mn', 'S', 'Si', 'Sm')\",OTHERS,True\nmp-1186417,\"{'Pa': 1.0, 'Te': 2.0, 'Pd': 1.0}\",\"('Pa', 'Pd', 'Te')\",OTHERS,True\nmp-769720,\"{'La': 4.0, 'Ti': 6.0, 'O': 18.0}\",\"('La', 'O', 'Ti')\",OTHERS,True\nmp-757305,\"{'Li': 8.0, 'Ti': 4.0, 'P': 12.0, 'O': 40.0}\",\"('Li', 'O', 'P', 'Ti')\",OTHERS,True\nmp-684096,\"{'Bi': 4.0, 'P': 16.0, 'O': 48.0}\",\"('Bi', 'O', 'P')\",OTHERS,True\nmp-19245,\"{'O': 6.0, 'Mn': 2.0, 'Ba': 1.0, 'La': 1.0}\",\"('Ba', 'La', 'Mn', 'O')\",OTHERS,True\nmp-767791,\"{'Mg': 5.0, 'Co': 19.0, 'O': 32.0}\",\"('Co', 'Mg', 'O')\",OTHERS,True\nmp-650178,\"{'Se': 40.0, 'S': 8.0, 'O': 24.0, 'F': 8.0}\",\"('F', 'O', 'S', 'Se')\",OTHERS,True\nmp-1212099,\"{'K': 4.0, 'La': 2.0, 'Ta': 12.0, 'Br': 30.0, 'O': 6.0}\",\"('Br', 'K', 'La', 'O', 'Ta')\",OTHERS,True\nmp-771326,\"{'Nb': 1.0, 'Fe': 5.0, 'P': 6.0, 'O': 24.0}\",\"('Fe', 'Nb', 'O', 'P')\",OTHERS,True\nmp-1186187,\"{'Na': 1.0, 'Tl': 2.0, 'Sn': 1.0}\",\"('Na', 'Sn', 'Tl')\",OTHERS,True\nmp-13396,\"{'Li': 1.0, 'Rh': 1.0, 'In': 2.0}\",\"('In', 'Li', 'Rh')\",OTHERS,True\nmp-756829,\"{'Li': 4.0, 'Fe': 3.0, 'Co': 1.0, 'O': 8.0}\",\"('Co', 'Fe', 'Li', 'O')\",OTHERS,True\nmp-11469,\"{'Pr': 1.0, 'Hg': 1.0}\",\"('Hg', 'Pr')\",OTHERS,True\nmp-861878,\"{'Cd': 2.0, 'Ag': 1.0, 'Rh': 1.0}\",\"('Ag', 'Cd', 'Rh')\",OTHERS,True\nmp-12879,\"{'Si': 10.0, 'Rh': 6.0, 'Tb': 4.0}\",\"('Rh', 'Si', 'Tb')\",OTHERS,True\nmp-530546,\"{'O': 108.0, 'Si': 54.0}\",\"('O', 'Si')\",OTHERS,True\nmp-1184988,\"{'Li': 2.0, 'Sm': 1.0, 'Al': 1.0}\",\"('Al', 'Li', 'Sm')\",OTHERS,True\nmp-569496,\"{'Ce': 1.0, 'Si': 2.0, 'Au': 4.0}\",\"('Au', 'Ce', 'Si')\",OTHERS,True\nmp-18279,\"{'Cu': 2.0, 'Ge': 8.0, 'Zr': 4.0}\",\"('Cu', 'Ge', 'Zr')\",OTHERS,True\nmp-1200277,\"{'Cs': 4.0, 'Mg': 2.0, 'Se': 4.0, 'O': 28.0}\",\"('Cs', 'Mg', 'O', 'Se')\",OTHERS,True\nmp-20826,\"{'Cu': 2.0, 'Te': 2.0}\",\"('Cu', 'Te')\",OTHERS,True\nmp-20063,\"{'Li': 3.0, 'Ge': 4.0, 'Ce': 5.0}\",\"('Ce', 'Ge', 'Li')\",OTHERS,True\nmp-755697,\"{'V': 8.0, 'O': 12.0, 'F': 4.0}\",\"('F', 'O', 'V')\",OTHERS,True\nmp-540050,\"{'Fe': 12.0, 'P': 12.0, 'O': 44.0}\",\"('Fe', 'O', 'P')\",OTHERS,True\nmvc-13015,\"{'Cu': 3.0, 'Sn': 4.0, 'O': 12.0}\",\"('Cu', 'O', 'Sn')\",OTHERS,True\nmp-1220425,\"{'Nb': 6.0, 'Bi': 1.0, 'Se': 8.0}\",\"('Bi', 'Nb', 'Se')\",OTHERS,True\nmp-1219917,\"{'Pd': 1.0, 'Rh': 1.0, 'Pb': 4.0}\",\"('Pb', 'Pd', 'Rh')\",OTHERS,True\nmp-1071078,\"{'Ni': 2.0, 'Se': 4.0}\",\"('Ni', 'Se')\",OTHERS,True\nmp-1114690,\"{'Rb': 3.0, 'Ir': 1.0, 'F': 6.0}\",\"('F', 'Ir', 'Rb')\",OTHERS,True\nmp-677308,\"{'Na': 2.0, 'Pr': 4.0, 'Cl': 12.0}\",\"('Cl', 'Na', 'Pr')\",OTHERS,True\nmp-1213319,\"{'Cs': 2.0, 'Pr': 2.0, 'W': 4.0, 'O': 16.0}\",\"('Cs', 'O', 'Pr', 'W')\",OTHERS,True\nmvc-6921,\"{'Mg': 4.0, 'Mn': 8.0, 'O': 16.0}\",\"('Mg', 'Mn', 'O')\",OTHERS,True\nmp-1213830,\"{'Ce': 6.0, 'W': 2.0, 'Cl': 6.0, 'O': 12.0}\",\"('Ce', 'Cl', 'O', 'W')\",OTHERS,True\nmp-758538,\"{'Li': 8.0, 'V': 14.0, 'O': 24.0}\",\"('Li', 'O', 'V')\",OTHERS,True\nmp-1095082,\"{'In': 2.0, 'Ru': 6.0}\",\"('In', 'Ru')\",OTHERS,True\nmp-1220828,\"{'Nb': 16.0, 'Pb': 12.0, 'O': 48.0, 'F': 8.0}\",\"('F', 'Nb', 'O', 'Pb')\",OTHERS,True\nmp-1206296,\"{'Y': 4.0, 'Co': 2.0, 'Si': 4.0}\",\"('Co', 'Si', 'Y')\",OTHERS,True\nmp-1210619,\"{'Na': 24.0, 'Nd': 8.0, 'P': 16.0, 'O': 64.0}\",\"('Na', 'Nd', 'O', 'P')\",OTHERS,True\nmp-768497,\"{'Li': 6.0, 'Mn': 12.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,True\nmp-697962,\"{'Eu': 6.0, 'Mg': 7.0, 'H': 26.0}\",\"('Eu', 'H', 'Mg')\",OTHERS,True\nmp-744703,\"{'Mn': 4.0, 'Cu': 4.0, 'H': 40.0, 'Se': 8.0, 'Cl': 8.0, 'O': 40.0}\",\"('Cl', 'Cu', 'H', 'Mn', 'O', 'Se')\",OTHERS,True\nmp-1016846,\"{'Ti': 1.0, 'Cd': 1.0, 'O': 3.0}\",\"('Cd', 'O', 'Ti')\",OTHERS,True\nmp-773192,\"{'Gd': 4.0, 'W': 6.0, 'O': 24.0}\",\"('Gd', 'O', 'W')\",OTHERS,True\nmp-30042,\"{'Li': 4.0, 'Ge': 2.0, 'Ce': 2.0}\",\"('Ce', 'Ge', 'Li')\",OTHERS,True\nmp-1246423,\"{'B': 4.0, 'Mo': 4.0, 'N': 12.0}\",\"('B', 'Mo', 'N')\",OTHERS,True\nmp-753148,\"{'Li': 2.0, 'Cu': 7.0, 'F': 16.0}\",\"('Cu', 'F', 'Li')\",OTHERS,True\nmp-1183854,\"{'Ce': 1.0, 'Pm': 3.0}\",\"('Ce', 'Pm')\",OTHERS,True\nmp-1196825,\"{'Zr': 4.0, 'H': 48.0, 'N': 20.0, 'F': 16.0}\",\"('F', 'H', 'N', 'Zr')\",OTHERS,True\nmp-1105795,\"{'Cd': 4.0, 'N': 4.0, 'F': 12.0}\",\"('Cd', 'F', 'N')\",OTHERS,True\nmp-17252,\"{'Cu': 6.0, 'Ge': 3.0, 'Se': 12.0, 'Ba': 3.0}\",\"('Ba', 'Cu', 'Ge', 'Se')\",OTHERS,True\nmp-1176130,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-655580,\"{'Ce': 4.0, 'Cu': 16.0, 'Sn': 4.0}\",\"('Ce', 'Cu', 'Sn')\",OTHERS,True\nmp-1100342,\"{'Ca': 2.0, 'Sn': 3.0, 'S': 8.0}\",\"('Ca', 'S', 'Sn')\",OTHERS,True\nmp-22037,\"{'O': 8.0, 'Cu': 6.0, 'Pb': 1.0}\",\"('Cu', 'O', 'Pb')\",OTHERS,True\nmp-635393,\"{'Ca': 2.0, 'P': 1.0, 'I': 1.0}\",\"('Ca', 'I', 'P')\",OTHERS,True\nmp-567365,{'Kr': 2.0},\"('Kr',)\",OTHERS,True\nmp-1229330,\"{'Bi': 1.0, 'W': 18.0, 'O': 60.0}\",\"('Bi', 'O', 'W')\",OTHERS,True\nmp-17146,\"{'S': 20.0, 'K': 8.0}\",\"('K', 'S')\",OTHERS,True\nmp-1071448,\"{'Ce': 2.0, 'Ga': 2.0, 'Co': 2.0}\",\"('Ce', 'Co', 'Ga')\",OTHERS,True\nmp-1209227,\"{'Rb': 1.0, 'Ca': 1.0, 'Br': 3.0}\",\"('Br', 'Ca', 'Rb')\",OTHERS,True\nmvc-8943,\"{'Zn': 4.0, 'Bi': 12.0, 'O': 28.0}\",\"('Bi', 'O', 'Zn')\",OTHERS,True\nmp-1185748,\"{'Mg': 4.0, 'Ag': 2.0}\",\"('Ag', 'Mg')\",OTHERS,True\nmp-3960,\"{'O': 4.0, 'Ca': 1.0, 'U': 1.0}\",\"('Ca', 'O', 'U')\",OTHERS,True\nmp-1112199,\"{'K': 2.0, 'In': 1.0, 'Hg': 1.0, 'F': 6.0}\",\"('F', 'Hg', 'In', 'K')\",OTHERS,True\nmp-1246024,\"{'Sr': 28.0, 'Hf': 4.0, 'N': 24.0}\",\"('Hf', 'N', 'Sr')\",OTHERS,True\nmvc-13995,\"{'Y': 2.0, 'Ti': 2.0, 'O': 6.0}\",\"('O', 'Ti', 'Y')\",OTHERS,True\nmp-1222406,\"{'Li': 1.0, 'Zr': 2.0, 'Pb': 1.0}\",\"('Li', 'Pb', 'Zr')\",OTHERS,True\nmp-1106139,\"{'In': 8.0, 'Bi': 6.0, 'Pb': 2.0}\",\"('Bi', 'In', 'Pb')\",OTHERS,True\nmp-1214547,\"{'Ba': 4.0, 'Mn': 4.0, 'V': 4.0, 'F': 28.0}\",\"('Ba', 'F', 'Mn', 'V')\",OTHERS,True\nmp-1208575,\"{'Tb': 2.0, 'Cl': 6.0, 'O': 24.0}\",\"('Cl', 'O', 'Tb')\",OTHERS,True\nmp-561973,\"{'K': 4.0, 'Te': 8.0, 'O': 24.0}\",\"('K', 'O', 'Te')\",OTHERS,True\nmp-1218693,\"{'Sr': 4.0, 'V': 6.0, 'Bi': 6.0, 'O': 28.0}\",\"('Bi', 'O', 'Sr', 'V')\",OTHERS,True\nmp-1207120,\"{'Ba': 2.0, 'Li': 1.0, 'I': 1.0, 'O': 6.0}\",\"('Ba', 'I', 'Li', 'O')\",OTHERS,True\nmp-1186295,\"{'Nd': 1.0, 'Cd': 1.0, 'Ag': 2.0}\",\"('Ag', 'Cd', 'Nd')\",OTHERS,True\nmp-2360,\"{'Ca': 2.0, 'Ge': 2.0}\",\"('Ca', 'Ge')\",OTHERS,True\nmvc-13461,\"{'Cr': 2.0, 'N': 4.0}\",\"('Cr', 'N')\",OTHERS,True\nmp-30893,\"{'Sr': 8.0, 'Bi': 6.0}\",\"('Bi', 'Sr')\",OTHERS,True\nmp-1102441,\"{'Re': 4.0, 'N': 8.0}\",\"('N', 'Re')\",OTHERS,True\nmp-1252764,\"{'Al': 2.0, 'F': 6.0}\",\"('Al', 'F')\",OTHERS,True\nmp-1020616,\"{'Rb': 12.0, 'Mg': 12.0, 'P': 12.0, 'O': 48.0}\",\"('Mg', 'O', 'P', 'Rb')\",OTHERS,True\nmp-1214060,\"{'Ca': 2.0, 'Mg': 10.0, 'P': 6.0, 'H': 2.0, 'C': 2.0, 'O': 32.0}\",\"('C', 'Ca', 'H', 'Mg', 'O', 'P')\",OTHERS,True\nmp-765485,\"{'Zr': 15.0, 'V': 1.0, 'O': 32.0}\",\"('O', 'V', 'Zr')\",OTHERS,True\nmp-1214042,\"{'Ca': 6.0, 'Ge': 8.0, 'Au': 14.0}\",\"('Au', 'Ca', 'Ge')\",OTHERS,True\nmp-9018,\"{'Li': 4.0, 'O': 16.0, 'P': 4.0, 'Cd': 4.0}\",\"('Cd', 'Li', 'O', 'P')\",OTHERS,True\nmp-1096047,\"{'Sc': 1.0, 'Cu': 1.0, 'Au': 2.0}\",\"('Au', 'Cu', 'Sc')\",OTHERS,True\nmp-29704,\"{'O': 32.0, 'S': 16.0, 'Cs': 8.0}\",\"('Cs', 'O', 'S')\",OTHERS,True\nmp-1029118,\"{'Te': 2.0, 'Mo': 1.0, 'W': 3.0, 'S': 6.0}\",\"('Mo', 'S', 'Te', 'W')\",OTHERS,True\nmp-1228478,\"{'Ba': 12.0, 'Ti': 9.0, 'Pt': 3.0, 'O': 36.0}\",\"('Ba', 'O', 'Pt', 'Ti')\",OTHERS,True\nmvc-928,\"{'Ba': 1.0, 'Ca': 1.0, 'V': 4.0, 'O': 8.0}\",\"('Ba', 'Ca', 'O', 'V')\",OTHERS,True\nmp-766488,\"{'Li': 6.0, 'Mn': 6.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-626315,\"{'Sr': 2.0, 'H': 32.0, 'O': 20.0}\",\"('H', 'O', 'Sr')\",OTHERS,True\nmp-758372,\"{'Li': 4.0, 'Co': 2.0, 'Si': 2.0, 'O': 8.0}\",\"('Co', 'Li', 'O', 'Si')\",OTHERS,True\nmp-554335,\"{'K': 4.0, 'Cu': 2.0, 'H': 24.0, 'Se': 4.0, 'O': 28.0}\",\"('Cu', 'H', 'K', 'O', 'Se')\",OTHERS,True\nmp-1094512,\"{'Mg': 8.0, 'Sb': 4.0}\",\"('Mg', 'Sb')\",OTHERS,True\nmp-1014226,\"{'Zr': 6.0, 'Zn': 2.0, 'N': 2.0}\",\"('N', 'Zn', 'Zr')\",OTHERS,True\nmp-850798,\"{'Li': 8.0, 'Mn': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Li', 'Mn', 'O', 'Si')\",OTHERS,True\nmp-867571,\"{'Li': 8.0, 'Mn': 8.0, 'P': 8.0, 'O': 36.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,True\nmp-540011,\"{'Li': 4.0, 'Ni': 2.0, 'P': 10.0, 'O': 30.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,True\nmp-534870,\"{'B': 2.0, 'Ga': 6.0, 'H': 8.0, 'Na': 8.0, 'O': 24.0, 'Si': 6.0}\",\"('B', 'Ga', 'H', 'Na', 'O', 'Si')\",OTHERS,True\nmp-1104102,\"{'Yb': 2.0, 'Ni': 8.0, 'As': 4.0}\",\"('As', 'Ni', 'Yb')\",OTHERS,True\nmp-745163,\"{'Mn': 2.0, 'Cu': 5.0, 'P': 4.0, 'H': 2.0, 'O': 18.0}\",\"('Cu', 'H', 'Mn', 'O', 'P')\",OTHERS,True\nmp-1226098,\"{'Co': 1.0, 'Ni': 1.0, 'P': 2.0, 'S': 6.0}\",\"('Co', 'Ni', 'P', 'S')\",OTHERS,True\nmp-1186958,\"{'Sc': 2.0, 'Cu': 1.0, 'Rh': 1.0}\",\"('Cu', 'Rh', 'Sc')\",OTHERS,True\nmp-758910,\"{'Li': 12.0, 'Fe': 6.0, 'Si': 12.0, 'O': 36.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,True\nmp-1194253,\"{'Hf': 18.0, 'W': 8.0, 'Se': 2.0}\",\"('Hf', 'Se', 'W')\",OTHERS,True\nmvc-2883,\"{'Sb': 10.0, 'Te': 6.0, 'O': 36.0}\",\"('O', 'Sb', 'Te')\",OTHERS,True\nmp-1226198,\"{'Co': 8.0, 'Cu': 6.0, 'Ni': 4.0, 'Se': 24.0}\",\"('Co', 'Cu', 'Ni', 'Se')\",OTHERS,True\nmp-22649,\"{'O': 12.0, 'V': 4.0, 'Cd': 4.0}\",\"('Cd', 'O', 'V')\",OTHERS,True\nmp-1077741,\"{'Yb': 1.0, 'Cu': 4.0, 'Ag': 1.0}\",\"('Ag', 'Cu', 'Yb')\",OTHERS,True\nmp-765189,\"{'Li': 3.0, 'V': 3.0, 'O': 5.0, 'F': 3.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,True\nmp-769096,\"{'Mn': 6.0, 'P': 24.0}\",\"('Mn', 'P')\",OTHERS,True\nmp-637614,\"{'Zn': 3.0, 'In': 2.0, 'S': 6.0}\",\"('In', 'S', 'Zn')\",OTHERS,True\nmp-554275,\"{'Na': 16.0, 'V': 4.0, 'H': 4.0, 'O': 20.0}\",\"('H', 'Na', 'O', 'V')\",OTHERS,True\nmp-766558,\"{'Na': 4.0, 'Mn': 12.0, 'O': 24.0}\",\"('Mn', 'Na', 'O')\",OTHERS,True\nmvc-13560,\"{'Ca': 1.0, 'V': 4.0, 'S': 8.0}\",\"('Ca', 'S', 'V')\",OTHERS,True\nmp-2961,\"{'Mg': 2.0, 'Si': 2.0, 'P': 4.0}\",\"('Mg', 'P', 'Si')\",OTHERS,True\nmp-1195435,\"{'Na': 4.0, 'Er': 4.0, 'B': 4.0, 'Te': 2.0, 'O': 20.0}\",\"('B', 'Er', 'Na', 'O', 'Te')\",OTHERS,True\nmp-974368,\"{'Rh': 3.0, 'Pb': 1.0}\",\"('Pb', 'Rh')\",OTHERS,True\nmp-1189706,\"{'Cd': 4.0, 'Tc': 4.0, 'O': 12.0}\",\"('Cd', 'O', 'Tc')\",OTHERS,True\nmp-1207495,\"{'Zr': 12.0, 'Ge': 2.0, 'I': 28.0}\",\"('Ge', 'I', 'Zr')\",OTHERS,True\nmp-1093645,\"{'Ti': 2.0, 'Al': 1.0, 'W': 1.0}\",\"('Al', 'Ti', 'W')\",OTHERS,True\nmp-560282,\"{'K': 8.0, 'Ba': 4.0, 'N': 16.0, 'O': 32.0}\",\"('Ba', 'K', 'N', 'O')\",OTHERS,True\nmvc-16790,\"{'Y': 2.0, 'Ti': 4.0, 'S': 8.0}\",\"('S', 'Ti', 'Y')\",OTHERS,True\nmp-1205595,\"{'Ba': 2.0, 'Tm': 1.0, 'Mo': 1.0, 'O': 6.0}\",\"('Ba', 'Mo', 'O', 'Tm')\",OTHERS,True\nmp-1079224,\"{'V': 6.0, 'As': 2.0, 'N': 2.0}\",\"('As', 'N', 'V')\",OTHERS,True\nmp-996960,\"{'Ag': 2.0, 'N': 2.0, 'O': 4.0}\",\"('Ag', 'N', 'O')\",OTHERS,True\nmp-1221014,\"{'Nb': 24.0, 'I': 42.0, 'Br': 2.0}\",\"('Br', 'I', 'Nb')\",OTHERS,True\nmp-758248,\"{'Li': 4.0, 'Co': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Co', 'Li', 'O', 'P')\",OTHERS,True\nmp-16066,\"{'P': 4.0, 'Ni': 8.0, 'Yb': 2.0}\",\"('Ni', 'P', 'Yb')\",OTHERS,True\nmp-1174032,\"{'Li': 5.0, 'Mn': 1.0, 'Co': 2.0, 'O': 8.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1210319,\"{'Nd': 4.0, 'Fe': 4.0, 'Ge': 8.0, 'O': 28.0}\",\"('Fe', 'Ge', 'Nd', 'O')\",OTHERS,True\nmvc-10716,\"{'Sr': 4.0, 'Y': 2.0, 'Ti': 4.0, 'Ga': 2.0, 'O': 14.0}\",\"('Ga', 'O', 'Sr', 'Ti', 'Y')\",OTHERS,True\nmp-23358,\"{'Hg': 10.0, 'Cl': 4.0, 'O': 8.0}\",\"('Cl', 'Hg', 'O')\",OTHERS,True\nmp-772072,\"{'Li': 4.0, 'Fe': 2.0, 'Si': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Fe', 'Li', 'O', 'Si')\",OTHERS,True\nmp-1239271,\"{'Nb': 1.0, 'Cr': 3.0, 'Ag': 2.0, 'S': 8.0}\",\"('Ag', 'Cr', 'Nb', 'S')\",OTHERS,True\nmp-1212105,\"{'Ho': 1.0, 'Al': 3.0, 'B': 4.0, 'O': 12.0}\",\"('Al', 'B', 'Ho', 'O')\",OTHERS,True\nmp-558956,\"{'K': 8.0, 'V': 4.0, 'P': 4.0, 'O': 24.0}\",\"('K', 'O', 'P', 'V')\",OTHERS,True\nmp-650598,\"{'U': 8.0, 'Pb': 24.0, 'O': 48.0}\",\"('O', 'Pb', 'U')\",OTHERS,True\nmp-1218724,\"{'Sr': 6.0, 'La': 2.0, 'V': 6.0, 'O': 24.0}\",\"('La', 'O', 'Sr', 'V')\",OTHERS,True\nmp-989616,\"{'Ca': 2.0, 'W': 2.0, 'N': 6.0}\",\"('Ca', 'N', 'W')\",OTHERS,True\nmp-1198803,\"{'Sc': 20.0, 'Ge': 16.0}\",\"('Ge', 'Sc')\",OTHERS,True\nmp-1111567,\"{'K': 2.0, 'Sc': 1.0, 'Tl': 1.0, 'F': 6.0}\",\"('F', 'K', 'Sc', 'Tl')\",OTHERS,True\nmp-979028,\"{'Sm': 2.0, 'Mg': 1.0}\",\"('Mg', 'Sm')\",OTHERS,True\nmp-768638,\"{'Dy': 6.0, 'Sb': 10.0, 'O': 24.0}\",\"('Dy', 'O', 'Sb')\",OTHERS,True\nmp-778808,\"{'Li': 24.0, 'La': 12.0, 'Nb': 8.0, 'O': 48.0}\",\"('La', 'Li', 'Nb', 'O')\",OTHERS,True\nmp-867110,\"{'Sc': 2.0, 'Co': 1.0, 'Os': 1.0}\",\"('Co', 'Os', 'Sc')\",OTHERS,True\nmp-973203,\"{'Sb': 3.0, 'Mo': 1.0}\",\"('Mo', 'Sb')\",OTHERS,True\nmp-761257,\"{'Li': 4.0, 'Fe': 8.0, 'O': 2.0, 'F': 16.0}\",\"('F', 'Fe', 'Li', 'O')\",OTHERS,True\nmp-24103,\"{'H': 24.0, 'N': 4.0, 'O': 4.0, 'Cu': 2.0, 'Br': 8.0}\",\"('Br', 'Cu', 'H', 'N', 'O')\",OTHERS,True\nmp-756131,\"{'Sr': 4.0, 'Ca': 2.0, 'I': 12.0}\",\"('Ca', 'I', 'Sr')\",OTHERS,True\nmp-1223416,\"{'La': 2.0, 'Mn': 1.0, 'Pb': 1.0, 'O': 9.0}\",\"('La', 'Mn', 'O', 'Pb')\",OTHERS,True\nmp-772850,\"{'La': 8.0, 'Hf': 8.0, 'O': 28.0}\",\"('Hf', 'La', 'O')\",OTHERS,True\nmp-760076,\"{'Li': 10.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-1198260,\"{'Ce': 6.0, 'Ge': 26.0, 'Ir': 8.0}\",\"('Ce', 'Ge', 'Ir')\",OTHERS,True\nmp-1194197,\"{'La': 12.0, 'Si': 4.0, 'N': 16.0, 'F': 4.0}\",\"('F', 'La', 'N', 'Si')\",OTHERS,True\nmp-760293,\"{'Li': 2.0, 'Mn': 4.0, 'F': 10.0}\",\"('F', 'Li', 'Mn')\",OTHERS,True\nmp-753455,\"{'Bi': 4.0, 'O': 4.0, 'F': 12.0}\",\"('Bi', 'F', 'O')\",OTHERS,True\nmp-1205237,\"{'Ti': 12.0, 'C': 240.0, 'Cl': 48.0}\",\"('C', 'Cl', 'Ti')\",OTHERS,True\nmp-863421,\"{'Li': 12.0, 'V': 6.0, 'Si': 12.0, 'O': 36.0}\",\"('Li', 'O', 'Si', 'V')\",OTHERS,True\nmvc-5503,\"{'Ca': 6.0, 'Co': 12.0, 'O': 24.0}\",\"('Ca', 'Co', 'O')\",OTHERS,True\nmp-1070925,\"{'La': 2.0, 'P': 2.0, 'Rh': 2.0}\",\"('La', 'P', 'Rh')\",OTHERS,True\nmp-752795,\"{'Co': 6.0, 'O': 5.0, 'F': 7.0}\",\"('Co', 'F', 'O')\",OTHERS,True\nmp-766500,\"{'Li': 10.0, 'Mn': 6.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-567967,\"{'Li': 4.0, 'Ga': 4.0, 'I': 16.0}\",\"('Ga', 'I', 'Li')\",OTHERS,True\nmp-32959,\"{'H': 4.0, 'O': 2.0}\",\"('H', 'O')\",OTHERS,True\nmvc-8032,\"{'Zn': 3.0, 'Fe': 4.0, 'Mo': 6.0, 'O': 24.0}\",\"('Fe', 'Mo', 'O', 'Zn')\",OTHERS,True\nmp-36150,\"{'Sm': 4.0, 'Tm': 2.0, 'S': 8.0}\",\"('S', 'Sm', 'Tm')\",OTHERS,True\nmp-1111893,\"{'Na': 3.0, 'Ga': 1.0, 'Cl': 6.0}\",\"('Cl', 'Ga', 'Na')\",OTHERS,True\nmp-675048,\"{'Sr': 6.0, 'Nd': 8.0, 'O': 18.0}\",\"('Nd', 'O', 'Sr')\",OTHERS,True\nmp-603930,\"{'Mg': 4.0, 'Si': 4.0, 'O': 12.0}\",\"('Mg', 'O', 'Si')\",OTHERS,True\nmp-9468,\"{'O': 56.0, 'Mg': 2.0, 'P': 14.0, 'K': 2.0, 'Ca': 18.0}\",\"('Ca', 'K', 'Mg', 'O', 'P')\",OTHERS,True\nmp-1187592,\"{'Tc': 6.0, 'Pb': 2.0}\",\"('Pb', 'Tc')\",OTHERS,True\nmp-4343,\"{'Mn': 2.0, 'As': 2.0, 'Sr': 1.0}\",\"('As', 'Mn', 'Sr')\",OTHERS,True\nmp-1097284,\"{'Ag': 1.0, 'Sn': 1.0, 'Rh': 2.0}\",\"('Ag', 'Rh', 'Sn')\",OTHERS,True\nmp-1114648,\"{'Rb': 2.0, 'In': 1.0, 'As': 1.0, 'Cl': 6.0}\",\"('As', 'Cl', 'In', 'Rb')\",OTHERS,True\nmp-1218046,\"{'Ta': 2.0, 'Co': 8.0, 'Si': 2.0}\",\"('Co', 'Si', 'Ta')\",OTHERS,True\nmp-975109,\"{'Rb': 2.0, 'Na': 6.0}\",\"('Na', 'Rb')\",OTHERS,True\nmp-755904,\"{'Li': 4.0, 'V': 4.0, 'F': 16.0}\",\"('F', 'Li', 'V')\",OTHERS,True\nmp-774589,\"{'Ti': 3.0, 'Mn': 2.0, 'V': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Mn', 'O', 'P', 'Ti', 'V')\",OTHERS,True\nmp-1102662,\"{'Ca': 4.0, 'Mg': 4.0, 'Pd': 4.0}\",\"('Ca', 'Mg', 'Pd')\",OTHERS,True\nmp-615789,\"{'Ba': 8.0, 'Cu': 12.0, 'O': 24.0}\",\"('Ba', 'Cu', 'O')\",OTHERS,True\nmp-974690,\"{'Rb': 2.0, 'Cu': 1.0, 'Cl': 4.0}\",\"('Cl', 'Cu', 'Rb')\",OTHERS,True\nmp-1180949,\"{'K': 1.0, 'Fe': 3.0, 'S': 2.0, 'O': 14.0}\",\"('Fe', 'K', 'O', 'S')\",OTHERS,True\nmp-31502,\"{'Sc': 2.0, 'Cd': 14.0}\",\"('Cd', 'Sc')\",OTHERS,True\nmp-1183604,\"{'Ca': 2.0, 'Eu': 6.0}\",\"('Ca', 'Eu')\",OTHERS,True\nmp-571598,\"{'Ga': 6.0, 'Au': 21.0}\",\"('Au', 'Ga')\",OTHERS,True\nmp-17558,\"{'Ca': 10.0, 'Ga': 4.0, 'As': 12.0}\",\"('As', 'Ca', 'Ga')\",OTHERS,True\nmp-1012879,\"{'Li': 12.0, 'Cr': 8.0, 'Ge': 12.0, 'O': 48.0}\",\"('Cr', 'Ge', 'Li', 'O')\",OTHERS,True\nmp-695285,\"{'Sr': 2.0, 'Al': 2.0, 'Si': 10.0, 'N': 14.0, 'O': 4.0}\",\"('Al', 'N', 'O', 'Si', 'Sr')\",OTHERS,True\nmp-1191492,\"{'La': 8.0, 'P': 8.0, 'S': 8.0}\",\"('La', 'P', 'S')\",OTHERS,True\nmp-979265,\"{'Ta': 2.0, 'C': 1.0, 'N': 3.0}\",\"('C', 'N', 'Ta')\",OTHERS,True\nmp-7881,\"{'Ge': 2.0, 'Sr': 1.0, 'Cd': 2.0}\",\"('Cd', 'Ge', 'Sr')\",OTHERS,True\nmp-1094293,\"{'Sr': 6.0, 'Mg': 2.0}\",\"('Mg', 'Sr')\",OTHERS,True\nmp-1211984,\"{'La': 12.0, 'Ni': 6.0, 'Pb': 1.0}\",\"('La', 'Ni', 'Pb')\",OTHERS,True\nmp-774353,\"{'Li': 3.0, 'Ti': 1.0, 'Ni': 2.0, 'O': 6.0}\",\"('Li', 'Ni', 'O', 'Ti')\",OTHERS,True\nmp-696291,\"{'Zn': 2.0, 'H': 8.0, 'N': 4.0, 'O': 16.0}\",\"('H', 'N', 'O', 'Zn')\",OTHERS,True\nmp-772177,\"{'Li': 2.0, 'Fe': 3.0, 'Cu': 1.0, 'O': 8.0}\",\"('Cu', 'Fe', 'Li', 'O')\",OTHERS,True\nmp-1075761,\"{'Mg': 10.0, 'Si': 18.0}\",\"('Mg', 'Si')\",OTHERS,True\nmp-978847,\"{'Sr': 1.0, 'Ga': 1.0, 'Ge': 1.0, 'H': 1.0}\",\"('Ga', 'Ge', 'H', 'Sr')\",OTHERS,True\nmp-1192592,\"{'Ce': 6.0, 'Mg': 23.0, 'Sb': 1.0}\",\"('Ce', 'Mg', 'Sb')\",OTHERS,True\nmp-765724,\"{'V': 6.0, 'F': 24.0}\",\"('F', 'V')\",OTHERS,True\nmp-865334,\"{'Lu': 2.0, 'Ni': 1.0, 'Os': 1.0}\",\"('Lu', 'Ni', 'Os')\",OTHERS,True\nmp-4103,\"{'O': 14.0, 'Sr': 4.0, 'Sb': 4.0}\",\"('O', 'Sb', 'Sr')\",OTHERS,True\nmp-773170,\"{'Cr': 6.0, 'Sb': 2.0, 'O': 16.0}\",\"('Cr', 'O', 'Sb')\",OTHERS,True\nmp-756710,\"{'Cr': 3.0, 'Fe': 2.0, 'O': 8.0}\",\"('Cr', 'Fe', 'O')\",OTHERS,True\nmp-17394,\"{'O': 16.0, 'Na': 4.0, 'Ge': 4.0, 'Y': 4.0}\",\"('Ge', 'Na', 'O', 'Y')\",OTHERS,True\nmp-26198,\"{'Co': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Co', 'O', 'P')\",OTHERS,True\nmvc-13133,\"{'Cu': 2.0, 'Sn': 1.0, 'Se': 4.0}\",\"('Cu', 'Se', 'Sn')\",OTHERS,True\nmp-759215,\"{'Li': 6.0, 'Mg': 1.0, 'Ni': 12.0, 'O': 26.0}\",\"('Li', 'Mg', 'Ni', 'O')\",OTHERS,True\nmp-1183186,\"{'As': 1.0, 'Pt': 3.0}\",\"('As', 'Pt')\",OTHERS,True\nmp-1078898,\"{'Gd': 2.0, 'B': 4.0, 'C': 4.0}\",\"('B', 'C', 'Gd')\",OTHERS,True\nmp-25208,\"{'O': 8.0, 'Bi': 4.0}\",\"('Bi', 'O')\",OTHERS,True\nmp-561490,\"{'Pd': 2.0, 'Se': 2.0, 'O': 8.0}\",\"('O', 'Pd', 'Se')\",OTHERS,True\nmp-777668,\"{'Li': 4.0, 'Ti': 3.0, 'Co': 3.0, 'Sn': 2.0, 'O': 16.0}\",\"('Co', 'Li', 'O', 'Sn', 'Ti')\",OTHERS,True\nmp-779064,\"{'Li': 8.0, 'Fe': 2.0, 'O': 2.0, 'F': 10.0}\",\"('F', 'Fe', 'Li', 'O')\",OTHERS,True\nmp-1077889,\"{'Ir': 2.0, 'C': 4.0}\",\"('C', 'Ir')\",OTHERS,True\nmp-568313,\"{'Si': 2.0, 'Se': 4.0}\",\"('Se', 'Si')\",OTHERS,True\nmp-1112080,\"{'K': 2.0, 'Ta': 1.0, 'Ag': 1.0, 'I': 6.0}\",\"('Ag', 'I', 'K', 'Ta')\",OTHERS,True\nmp-1218943,\"{'Sr': 10.0, 'Cu': 5.0, 'Mo': 3.0, 'W': 2.0, 'O': 30.0}\",\"('Cu', 'Mo', 'O', 'Sr', 'W')\",OTHERS,True\nmp-1266342,\"{'Na': 11.0, 'Zr': 8.0, 'Si': 7.0, 'P': 5.0, 'O': 48.0}\",\"('Na', 'O', 'P', 'Si', 'Zr')\",OTHERS,True\nmp-1193542,\"{'Li': 3.0, 'Mn': 3.0, 'V': 3.0, 'F': 18.0}\",\"('F', 'Li', 'Mn', 'V')\",OTHERS,True\nmp-1197580,\"{'Li': 12.0, 'Ca': 34.0, 'Hg': 18.0}\",\"('Ca', 'Hg', 'Li')\",OTHERS,True\nmp-1017555,\"{'Cs': 1.0, 'Be': 1.0, 'F': 3.0}\",\"('Be', 'Cs', 'F')\",OTHERS,True\nmp-765010,\"{'Li': 6.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-772568,\"{'Li': 6.0, 'Cr': 12.0, 'O': 39.0}\",\"('Cr', 'Li', 'O')\",OTHERS,True\nmp-1193543,\"{'Nb': 8.0, 'Ni': 2.0, 'Se': 16.0}\",\"('Nb', 'Ni', 'Se')\",OTHERS,True\nmp-1104326,\"{'Ho': 2.0, 'V': 2.0, 'O': 8.0}\",\"('Ho', 'O', 'V')\",OTHERS,True\nmp-559663,\"{'Li': 2.0, 'F': 12.0, 'Ni': 2.0, 'Sr': 2.0}\",\"('F', 'Li', 'Ni', 'Sr')\",OTHERS,True\nmp-1187073,{'Sr': 3.0},\"('Sr',)\",OTHERS,True\nmp-1222755,\"{'La': 1.0, 'Y': 1.0, 'Mn': 4.0, 'Si': 4.0}\",\"('La', 'Mn', 'Si', 'Y')\",OTHERS,True\nmp-1244901,\"{'V': 30.0, 'O': 75.0}\",\"('O', 'V')\",OTHERS,True\nmp-1210151,\"{'Nd': 6.0, 'Re': 4.0, 'O': 18.0}\",\"('Nd', 'O', 'Re')\",OTHERS,True\nmp-774451,\"{'Li': 4.0, 'Mn': 3.0, 'Fe': 2.0, 'Co': 3.0, 'O': 16.0}\",\"('Co', 'Fe', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1069825,\"{'Pr': 1.0, 'Al': 3.0, 'Cu': 1.0}\",\"('Al', 'Cu', 'Pr')\",OTHERS,True\nmp-1247468,\"{'Mg': 10.0, 'Co': 2.0, 'N': 8.0}\",\"('Co', 'Mg', 'N')\",OTHERS,True\nmp-4895,\"{'As': 2.0, 'Te': 2.0, 'U': 2.0}\",\"('As', 'Te', 'U')\",OTHERS,True\nmp-1224472,\"{'Ho': 30.0, 'Si': 18.0, 'C': 2.0}\",\"('C', 'Ho', 'Si')\",OTHERS,True\nmp-757838,\"{'Li': 4.0, 'Sn': 4.0, 'P': 12.0, 'O': 36.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,True\nmp-543029,\"{'La': 2.0, 'I': 2.0, 'Te': 1.0}\",\"('I', 'La', 'Te')\",OTHERS,True\nmp-1068559,\"{'La': 1.0, 'Co': 1.0, 'Si': 3.0}\",\"('Co', 'La', 'Si')\",OTHERS,True\nmp-1180580,\"{'Li': 1.0, 'H': 1.0, 'F': 2.0}\",\"('F', 'H', 'Li')\",OTHERS,True\nmp-1187698,\"{'V': 1.0, 'Zn': 1.0, 'Co': 2.0}\",\"('Co', 'V', 'Zn')\",OTHERS,True\nmp-7130,\"{'C': 1.0, 'Sc': 1.0, 'Ru': 3.0}\",\"('C', 'Ru', 'Sc')\",OTHERS,True\nmp-696152,\"{'Cu': 1.0, 'Sn': 1.0, 'H': 12.0, 'N': 2.0, 'O': 6.0}\",\"('Cu', 'H', 'N', 'O', 'Sn')\",OTHERS,True\nmp-1183208,\"{'Al': 1.0, 'Ga': 3.0}\",\"('Al', 'Ga')\",OTHERS,True\nmp-1206182,\"{'La': 2.0, 'C': 2.0, 'I': 2.0}\",\"('C', 'I', 'La')\",OTHERS,True\nmp-1216113,\"{'Y': 4.0, 'Mn': 1.0, 'S': 7.0}\",\"('Mn', 'S', 'Y')\",OTHERS,True\nmp-1094665,\"{'Li': 1.0, 'Mg': 3.0}\",\"('Li', 'Mg')\",OTHERS,True\nmp-1186627,\"{'Pm': 1.0, 'Pr': 1.0, 'Hg': 2.0}\",\"('Hg', 'Pm', 'Pr')\",OTHERS,True\nmp-972370,\"{'Yb': 2.0, 'In': 1.0, 'Hg': 1.0}\",\"('Hg', 'In', 'Yb')\",OTHERS,True\nmp-1206494,\"{'Lu': 3.0, 'In': 3.0, 'Ni': 3.0}\",\"('In', 'Lu', 'Ni')\",OTHERS,True\nmp-30142,\"{'Be': 12.0, 'N': 18.0, 'O': 57.0}\",\"('Be', 'N', 'O')\",OTHERS,True\nmp-19932,\"{'Se': 12.0, 'In': 16.0}\",\"('In', 'Se')\",OTHERS,True\nmp-1212719,\"{'Fe': 20.0, 'Si': 4.0, 'C': 4.0}\",\"('C', 'Fe', 'Si')\",OTHERS,True\nmp-978539,\"{'Sm': 1.0, 'Tm': 1.0, 'Ru': 2.0}\",\"('Ru', 'Sm', 'Tm')\",OTHERS,True\nmp-1186053,\"{'Na': 3.0, 'Nb': 1.0}\",\"('Na', 'Nb')\",OTHERS,True\nmp-1210663,\"{'Mg': 14.0, 'Ir': 6.0}\",\"('Ir', 'Mg')\",OTHERS,True\nmp-1209975,\"{'Nd': 12.0, 'Se': 12.0, 'N': 4.0}\",\"('N', 'Nd', 'Se')\",OTHERS,True\nmp-1207889,\"{'W': 12.0, 'I': 24.0}\",\"('I', 'W')\",OTHERS,True\nmp-865176,\"{'Hf': 1.0, 'Cu': 3.0}\",\"('Cu', 'Hf')\",OTHERS,True\nmp-21684,\"{'P': 12.0, 'Cu': 19.0, 'Ce': 5.0}\",\"('Ce', 'Cu', 'P')\",OTHERS,True\nmp-680092,\"{'Cd': 10.0, 'I': 20.0}\",\"('Cd', 'I')\",OTHERS,True\nmp-973924,\"{'Li': 1.0, 'Mg': 16.0, 'Al': 12.0}\",\"('Al', 'Li', 'Mg')\",OTHERS,True\nmp-769010,\"{'Li': 12.0, 'Bi': 10.0, 'O': 28.0}\",\"('Bi', 'Li', 'O')\",OTHERS,True\nmp-23621,\"{'O': 48.0, 'Na': 4.0, 'P': 16.0, 'Bi': 4.0}\",\"('Bi', 'Na', 'O', 'P')\",OTHERS,True\nmp-1064227,{'Ca': 4.0},\"('Ca',)\",OTHERS,True\nmp-1076754,\"{'K': 16.0, 'Na': 16.0, 'Mo': 28.0, 'W': 4.0, 'O': 80.0}\",\"('K', 'Mo', 'Na', 'O', 'W')\",OTHERS,True\nmp-28337,\"{'O': 13.0, 'V': 4.0, 'As': 2.0}\",\"('As', 'O', 'V')\",OTHERS,True\nmp-558376,\"{'Na': 8.0, 'Mn': 8.0, 'O': 12.0}\",\"('Mn', 'Na', 'O')\",OTHERS,True\nmp-1199525,\"{'U': 4.0, 'P': 16.0, 'O': 48.0}\",\"('O', 'P', 'U')\",OTHERS,True\nmp-1120721,\"{'Rb': 2.0, 'Ge': 2.0, 'Br': 6.0}\",\"('Br', 'Ge', 'Rb')\",OTHERS,True\nmvc-1323,\"{'Ba': 2.0, 'Y': 2.0, 'Fe': 8.0, 'O': 14.0}\",\"('Ba', 'Fe', 'O', 'Y')\",OTHERS,True\nmp-1228975,\"{'Al': 2.0, 'Cr': 3.0, 'Cu': 1.0, 'S': 8.0}\",\"('Al', 'Cr', 'Cu', 'S')\",OTHERS,True\nmp-1105355,\"{'Ca': 1.0, 'Mn': 7.0, 'O': 12.0}\",\"('Ca', 'Mn', 'O')\",OTHERS,True\nmp-760126,\"{'Na': 8.0, 'V': 8.0, 'P': 12.0, 'O': 48.0}\",\"('Na', 'O', 'P', 'V')\",OTHERS,True\nmp-554085,\"{'Te': 6.0, 'C': 12.0, 'F': 48.0}\",\"('C', 'F', 'Te')\",OTHERS,True\nmp-1096168,\"{'Ca': 2.0, 'Zn': 1.0, 'Ag': 1.0}\",\"('Ag', 'Ca', 'Zn')\",OTHERS,True\nmp-683902,\"{'Rb': 6.0, 'Nb': 4.0, 'As': 2.0, 'Se': 22.0}\",\"('As', 'Nb', 'Rb', 'Se')\",OTHERS,True\nmp-669347,\"{'K': 8.0, 'Cu': 8.0, 'F': 24.0}\",\"('Cu', 'F', 'K')\",OTHERS,True\nmp-1027946,\"{'Y': 1.0, 'Mg': 14.0, 'Sb': 1.0}\",\"('Mg', 'Sb', 'Y')\",OTHERS,True\nmp-1192712,\"{'K': 4.0, 'I': 4.0, 'Cl': 16.0, 'O': 4.0}\",\"('Cl', 'I', 'K', 'O')\",OTHERS,True\nmp-1182074,\"{'Ca': 2.0, 'Co': 1.0, 'O': 3.0}\",\"('Ca', 'Co', 'O')\",OTHERS,True\nmp-27342,\"{'F': 24.0, 'Ge': 10.0}\",\"('F', 'Ge')\",OTHERS,True\nmp-1113456,\"{'Rb': 2.0, 'Sb': 1.0, 'Au': 1.0, 'Cl': 6.0}\",\"('Au', 'Cl', 'Rb', 'Sb')\",OTHERS,True\nmp-690725,\"{'Tl': 2.0, 'Cu': 2.0, 'H': 2.0, 'S': 2.0, 'O': 10.0}\",\"('Cu', 'H', 'O', 'S', 'Tl')\",OTHERS,True\nmp-1193418,\"{'Y': 2.0, 'I': 6.0, 'O': 22.0}\",\"('I', 'O', 'Y')\",OTHERS,True\nmp-766575,\"{'Li': 32.0, 'Ti': 4.0, 'S': 24.0}\",\"('Li', 'S', 'Ti')\",OTHERS,True\nmp-555076,\"{'K': 4.0, 'Na': 4.0, 'Sn': 4.0, 'F': 24.0}\",\"('F', 'K', 'Na', 'Sn')\",OTHERS,True\nmp-5563,\"{'O': 16.0, 'Ge': 4.0, 'Ag': 20.0}\",\"('Ag', 'Ge', 'O')\",OTHERS,True\nmp-1183288,\"{'Ba': 1.0, 'In': 1.0, 'O': 3.0}\",\"('Ba', 'In', 'O')\",OTHERS,True\nmp-1210101,\"{'Na': 1.0, 'Be': 2.0, 'Tl': 3.0, 'F': 8.0}\",\"('Be', 'F', 'Na', 'Tl')\",OTHERS,True\nmp-6980,\"{'S': 2.0, 'Sc': 1.0, 'Cu': 1.0}\",\"('Cu', 'S', 'Sc')\",OTHERS,True\nmp-1226365,\"{'Cs': 2.0, 'Cu': 3.0, 'Ni': 1.0, 'F': 10.0}\",\"('Cs', 'Cu', 'F', 'Ni')\",OTHERS,True\nmp-1025691,\"{'Te': 4.0, 'Mo': 1.0, 'W': 2.0, 'S': 2.0}\",\"('Mo', 'S', 'Te', 'W')\",OTHERS,True\nmp-571136,\"{'Cd': 7.0, 'I': 14.0}\",\"('Cd', 'I')\",OTHERS,True\nmp-1221207,\"{'Na': 8.0, 'Ca': 4.0, 'Si': 2.0, 'P': 8.0}\",\"('Ca', 'Na', 'P', 'Si')\",OTHERS,True\nmp-1217328,\"{'Th': 1.0, 'Mn': 3.0, 'Al': 2.0}\",\"('Al', 'Mn', 'Th')\",OTHERS,True\nmp-1080104,\"{'Pr': 2.0, 'B': 4.0, 'Ir': 4.0}\",\"('B', 'Ir', 'Pr')\",OTHERS,True\nmp-1205147,\"{'Sm': 2.0, 'Ni': 24.0, 'B': 12.0}\",\"('B', 'Ni', 'Sm')\",OTHERS,True\nmp-640818,\"{'Mo': 36.0, 'O': 104.0}\",\"('Mo', 'O')\",OTHERS,True\nmp-1021492,\"{'Cd': 4.0, 'S': 1.0, 'F': 6.0}\",\"('Cd', 'F', 'S')\",OTHERS,True\nmp-7405,\"{'O': 12.0, 'Al': 4.0, 'Sm': 4.0}\",\"('Al', 'O', 'Sm')\",OTHERS,True\nmp-755923,\"{'Fe': 6.0, 'O': 7.0, 'F': 5.0}\",\"('F', 'Fe', 'O')\",OTHERS,True\nmp-1211934,\"{'La': 12.0, 'Al': 2.0, 'Fe': 26.0}\",\"('Al', 'Fe', 'La')\",OTHERS,True\nmp-1226892,\"{'Ce': 2.0, 'Co': 5.0, 'Cu': 5.0}\",\"('Ce', 'Co', 'Cu')\",OTHERS,True\nmp-19276,\"{'O': 8.0, 'Ba': 2.0, 'Mo': 2.0}\",\"('Ba', 'Mo', 'O')\",OTHERS,True\nmp-1198608,\"{'V': 8.0, 'Zn': 4.0, 'P': 12.0, 'N': 4.0, 'O': 60.0}\",\"('N', 'O', 'P', 'V', 'Zn')\",OTHERS,True\nmp-766389,\"{'Li': 12.0, 'Mn': 3.0, 'O': 3.0, 'F': 15.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-759728,\"{'Sb': 14.0, 'O': 12.0, 'F': 18.0}\",\"('F', 'O', 'Sb')\",OTHERS,True\nmp-557650,\"{'Sr': 3.0, 'P': 6.0, 'N': 8.0, 'O': 6.0}\",\"('N', 'O', 'P', 'Sr')\",OTHERS,True\nmp-5308,\"{'O': 36.0, 'La': 4.0, 'Ta': 12.0}\",\"('La', 'O', 'Ta')\",OTHERS,True\nmp-4954,\"{'Si': 2.0, 'Pd': 2.0, 'La': 1.0}\",\"('La', 'Pd', 'Si')\",OTHERS,True\nmp-1111928,\"{'K': 2.0, 'Li': 1.0, 'As': 1.0, 'F': 6.0}\",\"('As', 'F', 'K', 'Li')\",OTHERS,True\nmp-697721,\"{'H': 32.0, 'C': 8.0, 'N': 4.0, 'Cl': 4.0}\",\"('C', 'Cl', 'H', 'N')\",OTHERS,True\nmp-752897,\"{'Li': 3.0, 'Fe': 7.0, 'O': 12.0}\",\"('Fe', 'Li', 'O')\",OTHERS,True\nmp-17876,\"{'O': 16.0, 'Na': 4.0, 'Mn': 4.0, 'As': 4.0}\",\"('As', 'Mn', 'Na', 'O')\",OTHERS,True\nmp-23041,\"{'S': 4.0, 'Sb': 4.0, 'I': 4.0}\",\"('I', 'S', 'Sb')\",OTHERS,True\nmp-1227498,\"{'Ba': 1.0, 'Sr': 1.0, 'Nd': 1.0, 'Cu': 3.0, 'O': 7.0}\",\"('Ba', 'Cu', 'Nd', 'O', 'Sr')\",OTHERS,True\nmp-1028791,\"{'Te': 4.0, 'Mo': 1.0, 'W': 3.0, 'Se': 2.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,True\nmp-1205359,\"{'Ba': 2.0, 'U': 1.0, 'Cu': 1.0, 'O': 6.0}\",\"('Ba', 'Cu', 'O', 'U')\",OTHERS,True\nmp-1197910,\"{'K': 8.0, 'Si': 32.0, 'H': 216.0, 'C': 72.0, 'O': 24.0}\",\"('C', 'H', 'K', 'O', 'Si')\",OTHERS,True\nmp-15626,\"{'F': 6.0, 'Na': 1.0, 'Ti': 1.0, 'Rb': 2.0}\",\"('F', 'Na', 'Rb', 'Ti')\",OTHERS,True\nmp-561499,\"{'Dy': 6.0, 'Cu': 2.0, 'Sn': 2.0, 'S': 14.0}\",\"('Cu', 'Dy', 'S', 'Sn')\",OTHERS,True\nmp-1210171,\"{'Pu': 8.0, 'Ni': 2.0}\",\"('Ni', 'Pu')\",OTHERS,True\nmp-570847,\"{'Co': 1.0, 'H': 3.0, 'C': 6.0, 'N': 6.0}\",\"('C', 'Co', 'H', 'N')\",OTHERS,True\nmp-767404,\"{'Gd': 4.0, 'As': 8.0, 'O': 22.0}\",\"('As', 'Gd', 'O')\",OTHERS,True\nmp-721047,\"{'Ca': 2.0, 'H': 16.0, 'Cl': 4.0, 'O': 8.0}\",\"('Ca', 'Cl', 'H', 'O')\",OTHERS,True\nmp-560756,\"{'Th': 9.0, 'Mo': 18.0, 'O': 72.0}\",\"('Mo', 'O', 'Th')\",OTHERS,True\nmp-867750,\"{'Ti': 3.0, 'P': 1.0, 'O': 7.0}\",\"('O', 'P', 'Ti')\",OTHERS,True\nmp-694318,\"{'In': 16.0, 'W': 24.0, 'O': 96.0}\",\"('In', 'O', 'W')\",OTHERS,True\nmp-10895,\"{'Al': 1.0, 'V': 1.0, 'Mn': 2.0}\",\"('Al', 'Mn', 'V')\",OTHERS,True\nmp-605166,\"{'K': 4.0, 'Dy': 4.0, 'H': 8.0, 'C': 8.0, 'S': 4.0, 'O': 36.0}\",\"('C', 'Dy', 'H', 'K', 'O', 'S')\",OTHERS,True\nmp-1213975,\"{'Ca': 4.0, 'B': 16.0}\",\"('B', 'Ca')\",OTHERS,True\nmp-1215913,\"{'Y': 1.0, 'Co': 1.0, 'C': 1.0}\",\"('C', 'Co', 'Y')\",OTHERS,True\nmp-765473,\"{'Co': 4.0, 'Te': 8.0, 'O': 20.0}\",\"('Co', 'O', 'Te')\",OTHERS,True\nmp-754099,\"{'Li': 4.0, 'Ti': 2.0, 'Fe': 4.0, 'O': 10.0}\",\"('Fe', 'Li', 'O', 'Ti')\",OTHERS,True\nmp-531110,\"{'Ca': 18.0, 'F': 40.0, 'Pr': 2.0}\",\"('Ca', 'F', 'Pr')\",OTHERS,True\nmp-1064933,{'S': 4.0},\"('S',)\",OTHERS,True\nmp-11565,\"{'Y': 1.0, 'Rh': 5.0}\",\"('Rh', 'Y')\",OTHERS,True\nmp-759999,\"{'Mn': 12.0, 'O': 14.0, 'F': 10.0}\",\"('F', 'Mn', 'O')\",OTHERS,True\nmp-23259,\"{'Li': 1.0, 'Br': 1.0}\",\"('Br', 'Li')\",OTHERS,True\nmp-9873,\"{'Zn': 2.0, 'Ge': 2.0, 'Eu': 2.0}\",\"('Eu', 'Ge', 'Zn')\",OTHERS,True\nmp-768432,\"{'Mn': 2.0, 'Tl': 2.0, 'O': 6.0}\",\"('Mn', 'O', 'Tl')\",OTHERS,True\nmp-12984,\"{'Cu': 4.0, 'Se': 8.0, 'Tb': 4.0}\",\"('Cu', 'Se', 'Tb')\",OTHERS,True\nmp-759261,\"{'Li': 4.0, 'Co': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Co', 'Li', 'O', 'Si')\",OTHERS,True\nmp-1198465,\"{'Er': 8.0, 'B': 24.0, 'Ru': 4.0}\",\"('B', 'Er', 'Ru')\",OTHERS,True\nmp-559672,\"{'K': 2.0, 'Ni': 2.0, 'As': 2.0, 'O': 8.0}\",\"('As', 'K', 'Ni', 'O')\",OTHERS,True\nmp-770540,\"{'Mn': 8.0, 'P': 4.0, 'O': 20.0}\",\"('Mn', 'O', 'P')\",OTHERS,True\nmp-1210604,\"{'Na': 3.0, 'Eu': 1.0, 'V': 2.0, 'O': 8.0}\",\"('Eu', 'Na', 'O', 'V')\",OTHERS,True\nmp-571033,\"{'Ni': 2.0, 'Se': 2.0}\",\"('Ni', 'Se')\",OTHERS,True\nmp-1223557,\"{'K': 2.0, 'U': 4.0, 'Sb': 2.0, 'Se': 16.0}\",\"('K', 'Sb', 'Se', 'U')\",OTHERS,True\nmp-676117,\"{'Li': 4.0, 'Cu': 2.0, 'Ge': 2.0}\",\"('Cu', 'Ge', 'Li')\",OTHERS,True\nmp-771335,\"{'Na': 6.0, 'Sn': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Na', 'O', 'P', 'Sn')\",OTHERS,True\nmp-759841,\"{'Li': 8.0, 'Mn': 16.0, 'O': 4.0, 'F': 32.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-761267,\"{'Li': 4.0, 'V': 2.0, 'O': 2.0, 'F': 8.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,True\nmp-22988,\"{'Cl': 3.0, 'Ge': 1.0, 'Cs': 1.0}\",\"('Cl', 'Cs', 'Ge')\",OTHERS,True\nmp-21105,\"{'Si': 4.0, 'Pu': 2.0}\",\"('Pu', 'Si')\",OTHERS,True\nmp-1215070,\"{'Ca': 8.0, 'Al': 16.0, 'Si': 16.0, 'W': 16.0, 'O': 64.0}\",\"('Al', 'Ca', 'O', 'Si', 'W')\",OTHERS,True\nmp-865531,\"{'Ti': 1.0, 'Mn': 2.0, 'Al': 1.0}\",\"('Al', 'Mn', 'Ti')\",OTHERS,True\nmp-1202107,\"{'Hg': 4.0, 'C': 32.0, 'N': 8.0, 'Cl': 16.0}\",\"('C', 'Cl', 'Hg', 'N')\",OTHERS,True\nmp-561743,\"{'Li': 8.0, 'Co': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Co', 'Li', 'O', 'Si')\",OTHERS,True\nmp-1112506,\"{'Cs': 2.0, 'Al': 1.0, 'Tl': 1.0, 'Br': 6.0}\",\"('Al', 'Br', 'Cs', 'Tl')\",OTHERS,True\nmp-10096,\"{'Na': 6.0, 'P': 8.0, 'Ga': 2.0, 'Sr': 6.0}\",\"('Ga', 'Na', 'P', 'Sr')\",OTHERS,True\nmp-631314,\"{'K': 1.0, 'Ta': 1.0, 'Pt': 1.0}\",\"('K', 'Pt', 'Ta')\",OTHERS,True\nmp-1176603,\"{'Na': 8.0, 'Fe': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Fe', 'Na', 'O', 'P')\",OTHERS,True\nmp-644281,\"{'Mg': 7.0, 'Ti': 1.0, 'H': 16.0}\",\"('H', 'Mg', 'Ti')\",OTHERS,True\nmp-1027772,\"{'Te': 2.0, 'Mo': 3.0, 'W': 1.0, 'Se': 2.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,True\nmvc-1665,\"{'Ba': 4.0, 'Mg': 2.0, 'Cu': 6.0, 'F': 28.0}\",\"('Ba', 'Cu', 'F', 'Mg')\",OTHERS,True\nmp-1078544,\"{'Sc': 4.0, 'In': 2.0, 'Cu': 4.0}\",\"('Cu', 'In', 'Sc')\",OTHERS,True\nmp-1061865,\"{'Te': 1.0, 'N': 2.0}\",\"('N', 'Te')\",OTHERS,True\nmp-1199711,\"{'Si': 72.0, 'O': 116.0}\",\"('O', 'Si')\",OTHERS,True\nmp-4730,\"{'Ga': 2.0, 'Se': 4.0, 'Hg': 1.0}\",\"('Ga', 'Hg', 'Se')\",OTHERS,True\nmp-1177524,\"{'Li': 12.0, 'Ti': 8.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'O', 'P', 'Ti')\",OTHERS,True\nmp-766596,\"{'Ti': 8.0, 'Si': 16.0, 'O': 48.0}\",\"('O', 'Si', 'Ti')\",OTHERS,True\nmp-30527,\"{'Ta': 6.0, 'S': 18.0}\",\"('S', 'Ta')\",OTHERS,True\nmp-1101268,\"{'V': 1.0, 'Fe': 7.0, 'P': 12.0, 'O': 48.0}\",\"('Fe', 'O', 'P', 'V')\",OTHERS,True\nmp-676881,\"{'Ho': 8.0, 'Zr': 6.0, 'O': 24.0}\",\"('Ho', 'O', 'Zr')\",OTHERS,True\nmp-675633,\"{'Ag': 4.0, 'Au': 1.0, 'S': 2.0}\",\"('Ag', 'Au', 'S')\",OTHERS,True\nmp-684705,\"{'Ca': 2.0, 'La': 2.0, 'Mn': 2.0, 'Mo': 2.0, 'O': 12.0}\",\"('Ca', 'La', 'Mn', 'Mo', 'O')\",OTHERS,True\nmp-215,\"{'Y': 1.0, 'Sb': 1.0}\",\"('Sb', 'Y')\",OTHERS,True\nmp-611836,{'O': 2.0},\"('O',)\",OTHERS,True\nmp-779159,\"{'Na': 32.0, 'Cu': 8.0, 'O': 28.0}\",\"('Cu', 'Na', 'O')\",OTHERS,True\nmp-27557,\"{'Na': 32.0, 'Fe': 8.0, 'O': 28.0}\",\"('Fe', 'Na', 'O')\",OTHERS,True\nmp-1244816,\"{'Sr': 2.0, 'Y': 1.0, 'Tl': 1.0, 'Ni': 2.0, 'O': 7.0}\",\"('Ni', 'O', 'Sr', 'Tl', 'Y')\",OTHERS,True\nmp-585476,\"{'Cs': 14.0, 'Np': 6.0, 'Cl': 24.0, 'O': 12.0}\",\"('Cl', 'Cs', 'Np', 'O')\",OTHERS,True\nmp-1214575,\"{'Ba': 10.0, 'Mn': 6.0, 'O': 24.0, 'F': 2.0}\",\"('Ba', 'F', 'Mn', 'O')\",OTHERS,True\nmp-865482,\"{'V': 1.0, 'Tc': 2.0, 'Ge': 1.0}\",\"('Ge', 'Tc', 'V')\",OTHERS,True\nmp-27841,\"{'O': 2.0, 'V': 2.0, 'Br': 2.0}\",\"('Br', 'O', 'V')\",OTHERS,True\nmp-774749,\"{'Rb': 1.0, 'Na': 3.0, 'Li': 12.0, 'Ti': 4.0, 'O': 16.0}\",\"('Li', 'Na', 'O', 'Rb', 'Ti')\",OTHERS,True\nmp-1208535,\"{'Tb': 4.0, 'Ga': 18.0, 'Ir': 6.0}\",\"('Ga', 'Ir', 'Tb')\",OTHERS,True\nmp-617632,\"{'La': 6.0, 'Ag': 2.0, 'Ge': 2.0, 'S': 14.0}\",\"('Ag', 'Ge', 'La', 'S')\",OTHERS,True\nmp-1176994,\"{'Li': 7.0, 'V': 3.0, 'Si': 2.0, 'O': 12.0}\",\"('Li', 'O', 'Si', 'V')\",OTHERS,True\nmp-1194802,\"{'V': 8.0, 'Co': 4.0, 'O': 32.0}\",\"('Co', 'O', 'V')\",OTHERS,True\nmp-1114386,\"{'Rb': 2.0, 'Al': 1.0, 'In': 1.0, 'F': 6.0}\",\"('Al', 'F', 'In', 'Rb')\",OTHERS,True\nmp-754908,\"{'Nb': 1.0, 'Cr': 1.0, 'O': 4.0}\",\"('Cr', 'Nb', 'O')\",OTHERS,True\nmvc-8086,\"{'Sn': 2.0, 'Ge': 4.0, 'O': 12.0}\",\"('Ge', 'O', 'Sn')\",OTHERS,True\nmp-754409,\"{'Ho': 2.0, 'B': 2.0, 'O': 6.0}\",\"('B', 'Ho', 'O')\",OTHERS,True\nmp-10274,\"{'C': 1.0, 'Ga': 1.0, 'Er': 3.0}\",\"('C', 'Er', 'Ga')\",OTHERS,True\nmp-1017579,\"{'In': 2.0, 'Au': 3.0}\",\"('Au', 'In')\",OTHERS,True\nmvc-3996,\"{'Ca': 4.0, 'W': 4.0, 'O': 12.0}\",\"('Ca', 'O', 'W')\",OTHERS,True\nmp-756246,\"{'Nd': 4.0, 'Zr': 4.0, 'O': 12.0}\",\"('Nd', 'O', 'Zr')\",OTHERS,True\nmp-4499,\"{'Mg': 4.0, 'Al': 4.0, 'Si': 4.0}\",\"('Al', 'Mg', 'Si')\",OTHERS,True\nmp-1191015,\"{'Li': 8.0, 'Nd': 8.0, 'Sn': 8.0}\",\"('Li', 'Nd', 'Sn')\",OTHERS,True\nmp-775302,\"{'Li': 8.0, 'Cr': 4.0, 'Si': 24.0, 'O': 60.0}\",\"('Cr', 'Li', 'O', 'Si')\",OTHERS,True\nmp-29266,\"{'S': 2.0, 'Cs': 2.0}\",\"('Cs', 'S')\",OTHERS,True\nmp-650280,\"{'Cs': 16.0, 'As': 64.0, 'S': 104.0}\",\"('As', 'Cs', 'S')\",OTHERS,True\nmp-976784,\"{'Ni': 1.0, 'Au': 3.0}\",\"('Au', 'Ni')\",OTHERS,True\nmp-775189,\"{'Li': 12.0, 'Mn': 6.0, 'Fe': 6.0, 'P': 12.0, 'O': 48.0}\",\"('Fe', 'Li', 'Mn', 'O', 'P')\",OTHERS,True\nmp-23828,\"{'H': 12.0, 'Br': 2.0, 'Sr': 7.0}\",\"('Br', 'H', 'Sr')\",OTHERS,True\nmp-1239164,\"{'Nb': 2.0, 'Cr': 6.0, 'Cu': 4.0, 'S': 16.0}\",\"('Cr', 'Cu', 'Nb', 'S')\",OTHERS,True\nmp-1207192,\"{'Nd': 2.0, 'H': 2.0, 'Se': 2.0}\",\"('H', 'Nd', 'Se')\",OTHERS,True\nmp-757653,\"{'Li': 16.0, 'Fe': 16.0, 'P': 16.0, 'O': 64.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,True\nmp-1094377,\"{'Mg': 12.0, 'Ti': 6.0}\",\"('Mg', 'Ti')\",OTHERS,True\nmp-5431,\"{'O': 6.0, 'Te': 2.0, 'Ba': 2.0}\",\"('Ba', 'O', 'Te')\",OTHERS,True\nmvc-4372,\"{'Ca': 4.0, 'Ta': 2.0, 'Co': 2.0, 'O': 12.0}\",\"('Ca', 'Co', 'O', 'Ta')\",OTHERS,True\nmp-778376,\"{'Li': 32.0, 'Mn': 5.0, 'Cr': 11.0, 'O': 48.0}\",\"('Cr', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1096710,\"{'Ti': 1.0, 'V': 2.0, 'W': 1.0}\",\"('Ti', 'V', 'W')\",OTHERS,True\nmp-6105,\"{'O': 32.0, 'Se': 8.0, 'Pr': 8.0, 'Ta': 12.0}\",\"('O', 'Pr', 'Se', 'Ta')\",OTHERS,True\nmp-752880,\"{'Fe': 8.0, 'O': 8.0, 'F': 8.0}\",\"('F', 'Fe', 'O')\",OTHERS,True\nmp-1187765,\"{'Y': 2.0, 'Al': 1.0, 'Ru': 1.0}\",\"('Al', 'Ru', 'Y')\",OTHERS,True\nmp-1076884,\"{'Sr': 24.0, 'Ca': 8.0, 'Fe': 28.0, 'Co': 4.0, 'O': 80.0}\",\"('Ca', 'Co', 'Fe', 'O', 'Sr')\",OTHERS,True\nmp-642648,\"{'K': 2.0, 'P': 2.0, 'H': 4.0, 'O': 4.0}\",\"('H', 'K', 'O', 'P')\",OTHERS,True\nmp-730972,\"{'U': 4.0, 'P': 12.0, 'H': 40.0, 'Cl': 4.0, 'O': 32.0}\",\"('Cl', 'H', 'O', 'P', 'U')\",OTHERS,True\nmp-505629,\"{'Cr': 12.0, 'Ni': 8.0, 'Si': 4.0}\",\"('Cr', 'Ni', 'Si')\",OTHERS,True\nmp-230,\"{'Sb': 8.0, 'O': 16.0}\",\"('O', 'Sb')\",OTHERS,True\nmp-1211816,\"{'K': 4.0, 'In': 4.0, 'P': 8.0, 'Se': 24.0}\",\"('In', 'K', 'P', 'Se')\",OTHERS,True\nmp-21473,\"{'P': 2.0, 'Ni': 2.0, 'Gd': 1.0}\",\"('Gd', 'Ni', 'P')\",OTHERS,True\nmp-1198684,\"{'K': 12.0, 'Mo': 14.0, 'O': 68.0}\",\"('K', 'Mo', 'O')\",OTHERS,True\nmp-1214277,\"{'Ce': 6.0, 'N': 8.0, 'O': 33.0}\",\"('Ce', 'N', 'O')\",OTHERS,True\nmp-867313,\"{'Li': 1.0, 'Tm': 1.0, 'Pt': 2.0}\",\"('Li', 'Pt', 'Tm')\",OTHERS,True\nmp-9694,\"{'Ge': 4.0, 'Pd': 4.0, 'Tm': 3.0}\",\"('Ge', 'Pd', 'Tm')\",OTHERS,True\nmp-568405,\"{'K': 4.0, 'Nb': 2.0, 'Cl': 12.0}\",\"('Cl', 'K', 'Nb')\",OTHERS,True\nmp-21650,\"{'Li': 8.0, 'O': 32.0, 'Ni': 4.0, 'Ge': 12.0}\",\"('Ge', 'Li', 'Ni', 'O')\",OTHERS,True\nmp-994,\"{'P': 1.0, 'Y': 1.0}\",\"('P', 'Y')\",OTHERS,True\nmp-680167,\"{'Li': 2.0, 'Mo': 12.0, 'Cl': 26.0}\",\"('Cl', 'Li', 'Mo')\",OTHERS,True\nmp-1247016,\"{'Si': 12.0, 'Sb': 6.0, 'N': 22.0}\",\"('N', 'Sb', 'Si')\",OTHERS,True\nmp-1180215,\"{'N': 6.0, 'O': 3.0}\",\"('N', 'O')\",OTHERS,True\nmp-1228626,\"{'Ba': 2.0, 'Si': 3.0, 'Ge': 5.0, 'O': 18.0}\",\"('Ba', 'Ge', 'O', 'Si')\",OTHERS,True\nmp-1098076,\"{'Ce': 1.0, 'Mg': 6.0, 'B': 1.0}\",\"('B', 'Ce', 'Mg')\",OTHERS,True\nmp-757960,\"{'Li': 4.0, 'Ni': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,True\nmp-15779,\"{'Mg': 2.0, 'Si': 1.0, 'Ni': 3.0}\",\"('Mg', 'Ni', 'Si')\",OTHERS,True\nmp-1114642,\"{'Rb': 2.0, 'Li': 1.0, 'Ga': 1.0, 'F': 6.0}\",\"('F', 'Ga', 'Li', 'Rb')\",OTHERS,True\nmp-1019361,\"{'Th': 2.0, 'Sb': 2.0, 'Te': 2.0}\",\"('Sb', 'Te', 'Th')\",OTHERS,True\nmp-1179094,\"{'Sr': 4.0, 'H': 8.0}\",\"('H', 'Sr')\",OTHERS,True\nmp-1096013,\"{'Ta': 2.0, 'Mn': 1.0, 'Fe': 1.0}\",\"('Fe', 'Mn', 'Ta')\",OTHERS,True\nmp-18732,\"{'O': 6.0, 'Ti': 2.0, 'Ni': 2.0}\",\"('Ni', 'O', 'Ti')\",OTHERS,True\nmp-4135,\"{'O': 12.0, 'P': 4.0, 'Rb': 4.0}\",\"('O', 'P', 'Rb')\",OTHERS,True\nmp-20821,\"{'Ga': 3.0, 'Np': 1.0}\",\"('Ga', 'Np')\",OTHERS,True\nmp-18838,\"{'O': 16.0, 'F': 12.0, 'S': 4.0, 'K': 8.0, 'Mn': 4.0}\",\"('F', 'K', 'Mn', 'O', 'S')\",OTHERS,True\nmp-1103779,\"{'Mg': 2.0, 'Cr': 4.0, 'O': 8.0}\",\"('Cr', 'Mg', 'O')\",OTHERS,True\nmp-22966,\"{'C': 2.0, 'F': 4.0, 'Cl': 4.0}\",\"('C', 'Cl', 'F')\",OTHERS,True\nmp-30560,\"{'Co': 1.0, 'Nb': 1.0, 'Sn': 1.0}\",\"('Co', 'Nb', 'Sn')\",OTHERS,True\nmp-19377,\"{'Li': 2.0, 'O': 12.0, 'Mo': 2.0, 'La': 4.0}\",\"('La', 'Li', 'Mo', 'O')\",OTHERS,True\nmp-865508,\"{'U': 2.0, 'Ni': 12.0, 'As': 7.0}\",\"('As', 'Ni', 'U')\",OTHERS,True\nmp-1224129,\"{'In': 1.0, 'Cu': 1.0, 'Sn': 1.0, 'Se': 4.0}\",\"('Cu', 'In', 'Se', 'Sn')\",OTHERS,True\nmp-17153,\"{'Te': 16.0, 'Ba': 4.0, 'Tb': 8.0}\",\"('Ba', 'Tb', 'Te')\",OTHERS,True\nmp-1223740,\"{'K': 4.0, 'H': 16.0, 'Pb': 4.0, 'I': 12.0, 'O': 8.0}\",\"('H', 'I', 'K', 'O', 'Pb')\",OTHERS,True\nmp-1075430,\"{'Mg': 10.0, 'Si': 12.0}\",\"('Mg', 'Si')\",OTHERS,True\nmp-1228981,\"{'Ba': 10.0, 'Fe': 8.0, 'Pt': 2.0, 'Cl': 2.0, 'O': 25.0}\",\"('Ba', 'Cl', 'Fe', 'O', 'Pt')\",OTHERS,True\nmp-1190043,\"{'Tl': 2.0, 'Cr': 6.0, 'Se': 10.0}\",\"('Cr', 'Se', 'Tl')\",OTHERS,True\nmp-2233,\"{'Ag': 2.0, 'Th': 4.0}\",\"('Ag', 'Th')\",OTHERS,True\nmp-990086,\"{'Mo': 18.0, 'H': 2.0, 'S': 36.0}\",\"('H', 'Mo', 'S')\",OTHERS,True\nmp-1012835,\"{'Sm': 2.0, 'Cu': 2.0, 'W': 4.0, 'O': 16.0}\",\"('Cu', 'O', 'Sm', 'W')\",OTHERS,True\nmvc-9555,\"{'Mg': 4.0, 'Ti': 8.0, 'P': 8.0, 'O': 32.0}\",\"('Mg', 'O', 'P', 'Ti')\",OTHERS,True\nmp-1198511,\"{'Tb': 6.0, 'Co': 8.0, 'Ge': 26.0}\",\"('Co', 'Ge', 'Tb')\",OTHERS,True\nmp-1202667,\"{'Ni': 4.0, 'P': 8.0, 'H': 104.0, 'C': 32.0, 'S': 16.0, 'N': 8.0, 'O': 8.0}\",\"('C', 'H', 'N', 'Ni', 'O', 'P', 'S')\",OTHERS,True\nmp-1194543,\"{'K': 8.0, 'Ge': 2.0, 'Se': 8.0, 'O': 8.0}\",\"('Ge', 'K', 'O', 'Se')\",OTHERS,True\nmp-1111493,\"{'K': 3.0, 'Er': 1.0, 'Cl': 6.0}\",\"('Cl', 'Er', 'K')\",OTHERS,True\nmp-1186864,\"{'Rb': 3.0, 'Pm': 1.0}\",\"('Pm', 'Rb')\",OTHERS,True\nmp-695941,\"{'Ag': 12.0, 'H': 52.0, 'Se': 4.0, 'N': 16.0, 'O': 20.0}\",\"('Ag', 'H', 'N', 'O', 'Se')\",OTHERS,True\nmp-1186732,\"{'Pr': 6.0, 'Pa': 2.0}\",\"('Pa', 'Pr')\",OTHERS,True\nmp-772170,\"{'Ca': 4.0, 'La': 4.0, 'I': 20.0}\",\"('Ca', 'I', 'La')\",OTHERS,True\nmp-1214216,\"{'C': 36.0, 'Br': 2.0, 'F': 26.0}\",\"('Br', 'C', 'F')\",OTHERS,True\nmp-760034,\"{'Li': 6.0, 'V': 3.0, 'Fe': 3.0, 'P': 6.0, 'H': 6.0, 'O': 30.0}\",\"('Fe', 'H', 'Li', 'O', 'P', 'V')\",OTHERS,True\nmp-1186185,\"{'Na': 1.0, 'Tl': 2.0, 'In': 1.0}\",\"('In', 'Na', 'Tl')\",OTHERS,True\nmp-764566,\"{'Li': 1.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,True\nmp-1181558,\"{'Cs': 1.0, 'Zr': 1.0, 'Cu': 3.0, 'Se': 4.0}\",\"('Cs', 'Cu', 'Se', 'Zr')\",OTHERS,True\nmp-1220728,\"{'Na': 1.0, 'Li': 1.0, 'As': 2.0, 'S': 4.0}\",\"('As', 'Li', 'Na', 'S')\",OTHERS,True\nmp-1221452,\"{'Na': 1.0, 'Fe': 6.0, 'H': 12.0, 'S': 4.0, 'O': 29.0}\",\"('Fe', 'H', 'Na', 'O', 'S')\",OTHERS,True\nmp-1118832,\"{'Ca': 4.0, 'W': 2.0, 'N': 4.0}\",\"('Ca', 'N', 'W')\",OTHERS,True\nmp-1200080,\"{'Pr': 6.0, 'Rh': 8.0, 'Pb': 26.0}\",\"('Pb', 'Pr', 'Rh')\",OTHERS,True\nmp-570644,\"{'Ba': 14.0, 'Na': 21.0, 'Ca': 1.0, 'N': 6.0}\",\"('Ba', 'Ca', 'N', 'Na')\",OTHERS,True\nmp-1074263,\"{'Mg': 16.0, 'Si': 12.0}\",\"('Mg', 'Si')\",OTHERS,True\nmp-1096345,\"{'Al': 1.0, 'Ga': 1.0, 'Ru': 2.0}\",\"('Al', 'Ga', 'Ru')\",OTHERS,True\nmp-556053,\"{'Te': 4.0, 'C': 32.0, 'O': 16.0, 'F': 48.0}\",\"('C', 'F', 'O', 'Te')\",OTHERS,True\nmp-1246774,\"{'Si': 14.0, 'Ni': 2.0, 'N': 20.0}\",\"('N', 'Ni', 'Si')\",OTHERS,True\nmp-975275,\"{'Rb': 1.0, 'Na': 2.0, 'Sb': 1.0}\",\"('Na', 'Rb', 'Sb')\",OTHERS,True\nmp-632490,\"{'Eu': 8.0, 'Sn': 4.0, 'S': 16.0}\",\"('Eu', 'S', 'Sn')\",OTHERS,True\nmp-3523,\"{'O': 16.0, 'Sr': 4.0, 'Gd': 8.0}\",\"('Gd', 'O', 'Sr')\",OTHERS,True\nmp-31104,\"{'K': 16.0, 'Bi': 16.0}\",\"('Bi', 'K')\",OTHERS,True\nmp-1224521,\"{'Ho': 12.0, 'Si': 5.0, 'S': 28.0}\",\"('Ho', 'S', 'Si')\",OTHERS,True\nmp-1195463,\"{'U': 4.0, 'Si': 24.0, 'H': 216.0, 'C': 72.0, 'N': 12.0, 'F': 4.0}\",\"('C', 'F', 'H', 'N', 'Si', 'U')\",OTHERS,True\nmp-1197109,\"{'Fe': 4.0, 'S': 4.0, 'O': 44.0}\",\"('Fe', 'O', 'S')\",OTHERS,True\nmp-1215271,\"{'Zr': 2.0, 'Fe': 2.0, 'Co': 2.0}\",\"('Co', 'Fe', 'Zr')\",OTHERS,True\nmp-1215747,\"{'Yb': 4.0, 'S': 7.0}\",\"('S', 'Yb')\",OTHERS,True\nmp-866140,\"{'Ti': 1.0, 'Ga': 1.0, 'Pd': 2.0}\",\"('Ga', 'Pd', 'Ti')\",OTHERS,True\nmp-28432,\"{'Cd': 4.0, 'I': 14.0, 'Tl': 6.0}\",\"('Cd', 'I', 'Tl')\",OTHERS,True\nmp-1095001,\"{'Ca': 3.0, 'Zn': 3.0}\",\"('Ca', 'Zn')\",OTHERS,True\nmp-556418,\"{'Al': 2.0, 'S': 2.0, 'Cl': 6.0, 'O': 4.0}\",\"('Al', 'Cl', 'O', 'S')\",OTHERS,True\nmp-1039958,\"{'Ce': 1.0, 'Mg': 30.0, 'B': 1.0, 'O': 32.0}\",\"('B', 'Ce', 'Mg', 'O')\",OTHERS,True\nmp-1218486,\"{'Sr': 2.0, 'Y': 1.0, 'Tl': 1.0, 'Cu': 2.0, 'O': 7.0}\",\"('Cu', 'O', 'Sr', 'Tl', 'Y')\",OTHERS,True\nmp-7163,{'Tb': 1.0},\"('Tb',)\",OTHERS,True\nmp-1112936,\"{'Cs': 2.0, 'Ta': 1.0, 'Ag': 1.0, 'Br': 6.0}\",\"('Ag', 'Br', 'Cs', 'Ta')\",OTHERS,True\nmp-769391,\"{'Ba': 8.0, 'Zr': 2.0, 'O': 12.0}\",\"('Ba', 'O', 'Zr')\",OTHERS,True\nmp-752857,\"{'V': 3.0, 'O': 1.0, 'F': 11.0}\",\"('F', 'O', 'V')\",OTHERS,True\nmp-774838,\"{'Li': 2.0, 'Y': 14.0, 'Ti': 14.0, 'S': 14.0, 'O': 35.0}\",\"('Li', 'O', 'S', 'Ti', 'Y')\",OTHERS,True\nmp-3998,\"{'O': 8.0, 'La': 2.0, 'Ta': 2.0}\",\"('La', 'O', 'Ta')\",OTHERS,True\nmp-1223620,\"{'La': 12.0, 'Ge': 5.0, 'S': 28.0}\",\"('Ge', 'La', 'S')\",OTHERS,True\nmp-551131,\"{'Co': 4.0, 'As': 2.0, 'Cl': 2.0, 'O': 8.0}\",\"('As', 'Cl', 'Co', 'O')\",OTHERS,True\nmp-640381,\"{'Yb': 4.0, 'Cu': 4.0, 'S': 8.0}\",\"('Cu', 'S', 'Yb')\",OTHERS,True\nmp-1101091,\"{'Dy': 3.0, 'In': 4.0, 'Co': 2.0}\",\"('Co', 'Dy', 'In')\",OTHERS,True\nmp-1459,\"{'N': 1.0, 'Ta': 1.0}\",\"('N', 'Ta')\",OTHERS,True\nmp-342,\"{'Te': 1.0, 'Sm': 1.0}\",\"('Sm', 'Te')\",OTHERS,True\nmp-1245418,\"{'Cd': 4.0, 'Sn': 4.0, 'N': 8.0}\",\"('Cd', 'N', 'Sn')\",OTHERS,True\nmp-1209320,\"{'Rb': 4.0, 'Ag': 4.0, 'Te': 4.0, 'S': 12.0}\",\"('Ag', 'Rb', 'S', 'Te')\",OTHERS,True\nmp-865908,\"{'Yb': 2.0, 'Hg': 1.0, 'Pb': 1.0}\",\"('Hg', 'Pb', 'Yb')\",OTHERS,True\nmp-766123,\"{'Ce': 8.0, 'Th': 2.0, 'O': 20.0}\",\"('Ce', 'O', 'Th')\",OTHERS,True\nmp-973834,\"{'Pd': 1.0, 'Au': 3.0}\",\"('Au', 'Pd')\",OTHERS,True\nmp-772199,\"{'Rb': 18.0, 'Cl': 6.0, 'O': 6.0}\",\"('Cl', 'O', 'Rb')\",OTHERS,True\nmp-1200191,\"{'Ga': 42.0, 'Rh': 8.0}\",\"('Ga', 'Rh')\",OTHERS,True\nmp-7711,\"{'O': 1.0, 'Ag': 2.0}\",\"('Ag', 'O')\",OTHERS,True\nmvc-12018,\"{'Ta': 4.0, 'Mn': 2.0, 'Zn': 3.0, 'O': 16.0}\",\"('Mn', 'O', 'Ta', 'Zn')\",OTHERS,True\nmp-604594,\"{'Ce': 4.0, 'In': 10.0, 'Pd': 8.0}\",\"('Ce', 'In', 'Pd')\",OTHERS,True\nmp-779538,\"{'Fe': 10.0, 'O': 14.0, 'F': 6.0}\",\"('F', 'Fe', 'O')\",OTHERS,True\nmp-1210680,\"{'Mg': 4.0, 'Pb': 8.0}\",\"('Mg', 'Pb')\",OTHERS,True\nmp-605809,\"{'Ba': 22.0, 'In': 12.0, 'O': 6.0}\",\"('Ba', 'In', 'O')\",OTHERS,True\nmp-631322,\"{'K': 1.0, 'Sn': 1.0, 'Os': 1.0}\",\"('K', 'Os', 'Sn')\",OTHERS,True\nmp-1211509,\"{'K': 4.0, 'Sm': 8.0, 'I': 20.0}\",\"('I', 'K', 'Sm')\",OTHERS,True\nmp-1196241,\"{'Sr': 8.0, 'Be': 8.0, 'H': 32.0, 'O': 32.0}\",\"('Be', 'H', 'O', 'Sr')\",OTHERS,True\nmp-755545,\"{'Li': 6.0, 'Fe': 1.0, 'O': 4.0, 'F': 2.0}\",\"('F', 'Fe', 'Li', 'O')\",OTHERS,True\nmp-1222405,\"{'Li': 1.0, 'Cd': 3.0}\",\"('Cd', 'Li')\",OTHERS,True\nmp-1194603,\"{'Mn': 8.0, 'Bi': 8.0, 'O': 24.0}\",\"('Bi', 'Mn', 'O')\",OTHERS,True\nmp-697126,\"{'Ca': 4.0, 'H': 12.0, 'Rh': 3.0}\",\"('Ca', 'H', 'Rh')\",OTHERS,True\nmp-1204339,\"{'U': 2.0, 'Cr': 8.0, 'H': 36.0, 'O': 48.0}\",\"('Cr', 'H', 'O', 'U')\",OTHERS,True\nmp-19872,\"{'Al': 2.0, 'Cu': 1.0, 'U': 1.0}\",\"('Al', 'Cu', 'U')\",OTHERS,True\nmp-1095163,\"{'Pt': 1.0, 'Cl': 6.0, 'O': 2.0}\",\"('Cl', 'O', 'Pt')\",OTHERS,True\nmp-19766,\"{'Pd': 3.0, 'Sn': 3.0, 'Ce': 3.0}\",\"('Ce', 'Pd', 'Sn')\",OTHERS,True\nmp-1219205,\"{'Sm': 6.0, 'S': 2.0, 'O': 2.0, 'F': 10.0}\",\"('F', 'O', 'S', 'Sm')\",OTHERS,True\nmp-1104836,\"{'Re': 2.0, 'Cl': 6.0, 'O': 6.0}\",\"('Cl', 'O', 'Re')\",OTHERS,True\nmp-1079374,\"{'Th': 2.0, 'Ta': 2.0, 'N': 6.0}\",\"('N', 'Ta', 'Th')\",OTHERS,True\nmp-1215234,\"{'Zr': 2.0, 'Mn': 2.0, 'Ni': 2.0}\",\"('Mn', 'Ni', 'Zr')\",OTHERS,True\nmvc-4357,\"{'Ca': 4.0, 'Ta': 2.0, 'V': 2.0, 'O': 12.0}\",\"('Ca', 'O', 'Ta', 'V')\",OTHERS,True\nmp-761156,\"{'Li': 3.0, 'La': 5.0, 'Ti': 6.0, 'Nb': 2.0, 'O': 26.0}\",\"('La', 'Li', 'Nb', 'O', 'Ti')\",OTHERS,True\nmp-1185991,\"{'Mn': 1.0, 'Cd': 3.0}\",\"('Cd', 'Mn')\",OTHERS,True\nmp-504922,\"{'U': 4.0, 'Pb': 4.0, 'O': 16.0}\",\"('O', 'Pb', 'U')\",OTHERS,True\nmp-559335,\"{'Zn': 2.0, 'Ag': 4.0, 'C': 8.0, 'S': 8.0, 'N': 8.0}\",\"('Ag', 'C', 'N', 'S', 'Zn')\",OTHERS,True\nmp-1221638,\"{'Mn': 1.0, 'Cr': 3.0, 'Te': 4.0}\",\"('Cr', 'Mn', 'Te')\",OTHERS,True\nmp-19217,\"{'O': 18.0, 'Mn': 6.0, 'Er': 6.0}\",\"('Er', 'Mn', 'O')\",OTHERS,True\nmp-707494,\"{'B': 44.0, 'H': 36.0, 'C': 4.0, 'Br': 24.0, 'O': 16.0}\",\"('B', 'Br', 'C', 'H', 'O')\",OTHERS,True\nmp-1186219,\"{'Nb': 6.0, 'Se': 2.0}\",\"('Nb', 'Se')\",OTHERS,True\nmp-568591,\"{'La': 6.0, 'C': 2.0, 'I': 10.0}\",\"('C', 'I', 'La')\",OTHERS,True\nmp-1097181,\"{'Zr': 1.0, 'Sc': 1.0, 'Zn': 2.0}\",\"('Sc', 'Zn', 'Zr')\",OTHERS,True\nmp-604368,\"{'K': 4.0, 'Na': 2.0, 'V': 10.0, 'H': 20.0, 'O': 38.0}\",\"('H', 'K', 'Na', 'O', 'V')\",OTHERS,True\nmp-1202855,\"{'C': 20.0, 'I': 6.0, 'N': 8.0}\",\"('C', 'I', 'N')\",OTHERS,True\nmp-1094064,\"{'Sb': 1.0, 'H': 3.0}\",\"('H', 'Sb')\",OTHERS,True\nmp-1097218,\"{'Li': 1.0, 'Cd': 2.0, 'In': 1.0}\",\"('Cd', 'In', 'Li')\",OTHERS,True\nmp-759611,\"{'Ba': 4.0, 'Li': 4.0, 'Ca': 4.0, 'V': 4.0, 'P': 8.0, 'O': 36.0}\",\"('Ba', 'Ca', 'Li', 'O', 'P', 'V')\",OTHERS,True\nmp-1227942,\"{'Ba': 1.0, 'Ga': 1.0, 'Ge': 1.0}\",\"('Ba', 'Ga', 'Ge')\",OTHERS,True\nmp-1175397,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1102504,\"{'Co': 4.0, 'P': 4.0, 'W': 4.0}\",\"('Co', 'P', 'W')\",OTHERS,True\nmp-559366,\"{'P': 6.0, 'Br': 6.0, 'N': 6.0, 'F': 6.0}\",\"('Br', 'F', 'N', 'P')\",OTHERS,True\nmp-1204560,\"{'Rb': 4.0, 'Ce': 2.0, 'N': 10.0, 'O': 38.0}\",\"('Ce', 'N', 'O', 'Rb')\",OTHERS,True\nmp-1214450,\"{'Ba': 1.0, 'Nb': 6.0, 'H': 1.0, 'O': 16.0}\",\"('Ba', 'H', 'Nb', 'O')\",OTHERS,True\nmp-554732,\"{'Rb': 4.0, 'Sb': 4.0, 'S': 4.0, 'O': 16.0, 'F': 8.0}\",\"('F', 'O', 'Rb', 'S', 'Sb')\",OTHERS,True\nmp-1202662,\"{'Sc': 8.0, 'Si': 16.0, 'W': 12.0}\",\"('Sc', 'Si', 'W')\",OTHERS,True\nmp-1029702,\"{'Mg': 4.0, 'Ti': 4.0, 'N': 8.0}\",\"('Mg', 'N', 'Ti')\",OTHERS,True\nmp-1207402,\"{'Zr': 12.0, 'Al': 4.0, 'Fe': 2.0, 'H': 20.0}\",\"('Al', 'Fe', 'H', 'Zr')\",OTHERS,True\nmp-1211034,\"{'Mg': 4.0, 'Si': 8.0, 'O': 22.0}\",\"('Mg', 'O', 'Si')\",OTHERS,True\nmp-1247565,\"{'Sb': 16.0, 'Te': 12.0, 'N': 8.0}\",\"('N', 'Sb', 'Te')\",OTHERS,True\nmp-773137,\"{'K': 2.0, 'Ca': 2.0, 'Bi': 2.0}\",\"('Bi', 'Ca', 'K')\",OTHERS,True\nmp-1079270,\"{'Tm': 2.0, 'Ni': 1.0, 'Sn': 6.0}\",\"('Ni', 'Sn', 'Tm')\",OTHERS,True\nmp-31143,\"{'Er': 8.0, 'Sn': 4.0, 'Au': 8.0}\",\"('Au', 'Er', 'Sn')\",OTHERS,True\nmp-554673,\"{'K': 4.0, 'Zn': 4.0, 'B': 4.0, 'P': 8.0, 'O': 32.0}\",\"('B', 'K', 'O', 'P', 'Zn')\",OTHERS,True\nmp-1208274,\"{'Th': 6.0, 'Si': 24.0, 'Ru': 8.0}\",\"('Ru', 'Si', 'Th')\",OTHERS,True\nmp-1193963,\"{'Mg': 8.0, 'Si': 4.0, 'S': 16.0}\",\"('Mg', 'S', 'Si')\",OTHERS,True\nmp-761209,\"{'Li': 6.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,True\nmp-24211,\"{'Co': 2.0, 'H': 16.0, 'I': 4.0, 'O': 20.0}\",\"('Co', 'H', 'I', 'O')\",OTHERS,True\nmp-1179383,\"{'Ti': 4.0, 'Si': 8.0, 'C': 12.0, 'N': 8.0, 'Cl': 20.0}\",\"('C', 'Cl', 'N', 'Si', 'Ti')\",OTHERS,True\nmp-1027053,\"{'Te': 4.0, 'Mo': 3.0, 'W': 1.0, 'Se': 2.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,True\nmp-972591,\"{'Sm': 1.0, 'Mg': 16.0, 'Al': 12.0}\",\"('Al', 'Mg', 'Sm')\",OTHERS,True\nmp-1177772,\"{'Li': 3.0, 'Cr': 2.0, 'Fe': 3.0, 'O': 10.0}\",\"('Cr', 'Fe', 'Li', 'O')\",OTHERS,True\nmp-1224003,\"{'Ho': 3.0, 'Mn': 3.0, 'Ga': 2.0, 'Si': 1.0}\",\"('Ga', 'Ho', 'Mn', 'Si')\",OTHERS,True\nmp-1219252,\"{'Sc': 1.0, 'Tl': 1.0, 'F': 6.0}\",\"('F', 'Sc', 'Tl')\",OTHERS,True\nmp-559879,\"{'P': 4.0, 'S': 8.0, 'N': 4.0, 'Cl': 24.0, 'O': 16.0}\",\"('Cl', 'N', 'O', 'P', 'S')\",OTHERS,True\nmp-1210257,\"{'Na': 6.0, 'Ti': 2.0, 'F': 12.0}\",\"('F', 'Na', 'Ti')\",OTHERS,True\nmp-1075111,\"{'Mg': 6.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,True\nmp-675211,\"{'W': 7.0, 'O': 21.0}\",\"('O', 'W')\",OTHERS,True\nmp-1225635,\"{'Fe': 8.0, 'Co': 2.0, 'Bi': 10.0, 'O': 30.0}\",\"('Bi', 'Co', 'Fe', 'O')\",OTHERS,True\nmp-1112599,\"{'Cs': 2.0, 'K': 1.0, 'Au': 1.0, 'F': 6.0}\",\"('Au', 'Cs', 'F', 'K')\",OTHERS,True\nmp-777673,\"{'Li': 8.0, 'Fe': 4.0, 'F': 16.0}\",\"('F', 'Fe', 'Li')\",OTHERS,True\nmp-758671,\"{'Li': 4.0, 'Mn': 4.0, 'P': 12.0, 'O': 36.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,True\nmp-1095043,\"{'Sc': 3.0, 'Os': 1.0, 'C': 4.0}\",\"('C', 'Os', 'Sc')\",OTHERS,True\nmp-1245772,\"{'Na': 4.0, 'Mg': 4.0, 'N': 4.0}\",\"('Mg', 'N', 'Na')\",OTHERS,True\nmp-36216,\"{'Ag': 4.0, 'S': 2.0}\",\"('Ag', 'S')\",OTHERS,True\nmp-1246241,\"{'Fe': 8.0, 'Ru': 2.0, 'N': 8.0}\",\"('Fe', 'N', 'Ru')\",OTHERS,True\nmp-771359,\"{'Cu': 16.0, 'O': 24.0}\",\"('Cu', 'O')\",OTHERS,True\nmp-642281,\"{'Sr': 21.0, 'In': 8.0, 'Pb': 7.0}\",\"('In', 'Pb', 'Sr')\",OTHERS,True\nmp-32833,\"{'Ho': 24.0, 'Se': 44.0}\",\"('Ho', 'Se')\",OTHERS,True\nmp-861600,\"{'Ho': 2.0, 'Zn': 1.0, 'In': 1.0}\",\"('Ho', 'In', 'Zn')\",OTHERS,True\nmp-1008733,{'Ge': 2.0},\"('Ge',)\",OTHERS,True\nmp-1076337,\"{'Ba': 2.0, 'Sr': 6.0, 'Ti': 7.0, 'Mn': 1.0, 'O': 24.0}\",\"('Ba', 'Mn', 'O', 'Sr', 'Ti')\",OTHERS,True\nmp-1198309,\"{'Sc': 40.0, 'Os': 8.0, 'Cl': 72.0}\",\"('Cl', 'Os', 'Sc')\",OTHERS,True\nmp-1025314,\"{'Mn': 1.0, 'Al': 2.0, 'S': 4.0}\",\"('Al', 'Mn', 'S')\",OTHERS,True\nmp-28918,\"{'W': 2.0, 'Sb': 4.0, 'O': 12.0}\",\"('O', 'Sb', 'W')\",OTHERS,True\nmp-1213579,\"{'Gd': 8.0, 'Sc': 12.0, 'Si': 16.0}\",\"('Gd', 'Sc', 'Si')\",OTHERS,True\nmp-766514,\"{'Li': 8.0, 'Fe': 6.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,True\nmp-1183872,\"{'Ce': 3.0, 'Cu': 1.0}\",\"('Ce', 'Cu')\",OTHERS,True\nmp-18890,\"{'Ca': 2.0, 'Fe': 2.0, 'Si': 4.0, 'O': 12.0}\",\"('Ca', 'Fe', 'O', 'Si')\",OTHERS,True\nmp-1188603,\"{'La': 1.0, 'Sb': 12.0, 'Ru': 4.0}\",\"('La', 'Ru', 'Sb')\",OTHERS,True\nmp-1211019,\"{'Na': 6.0, 'Mg': 10.0, 'Si': 16.0, 'H': 4.0, 'O': 48.0}\",\"('H', 'Mg', 'Na', 'O', 'Si')\",OTHERS,True\nmp-697593,\"{'Te': 28.0, 'W': 4.0, 'Cl': 20.0, 'O': 4.0}\",\"('Cl', 'O', 'Te', 'W')\",OTHERS,True\nmp-24065,\"{'H': 20.0, 'B': 4.0, 'O': 18.0, 'Ca': 2.0}\",\"('B', 'Ca', 'H', 'O')\",OTHERS,True\nmp-760183,\"{'Li': 10.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-865280,\"{'Nb': 1.0, 'Al': 1.0, 'Fe': 2.0}\",\"('Al', 'Fe', 'Nb')\",OTHERS,True\nmp-866712,\"{'Rb': 2.0, 'Mn': 2.0, 'P': 6.0, 'H': 2.0, 'O': 20.0}\",\"('H', 'Mn', 'O', 'P', 'Rb')\",OTHERS,True\nmp-775429,\"{'Ba': 4.0, 'Li': 1.0, 'Bi': 3.0, 'O': 11.0}\",\"('Ba', 'Bi', 'Li', 'O')\",OTHERS,True\nmp-2102,\"{'Ge': 4.0, 'Pr': 4.0}\",\"('Ge', 'Pr')\",OTHERS,True\nmp-1225821,\"{'Cu': 2.0, 'P': 2.0, 'O': 7.0}\",\"('Cu', 'O', 'P')\",OTHERS,True\nmp-622570,\"{'Ti': 4.0, 'Cu': 6.0}\",\"('Cu', 'Ti')\",OTHERS,True\nmp-757962,\"{'Li': 6.0, 'Co': 6.0, 'P': 6.0, 'O': 24.0}\",\"('Co', 'Li', 'O', 'P')\",OTHERS,True\nmp-10180,\"{'Li': 2.0, 'Pd': 1.0, 'Sb': 1.0}\",\"('Li', 'Pd', 'Sb')\",OTHERS,True\nmp-1078870,\"{'U': 3.0, 'Ga': 3.0, 'Rh': 3.0}\",\"('Ga', 'Rh', 'U')\",OTHERS,True\nmp-1104125,\"{'Y': 4.0, 'Cd': 2.0, 'Se': 8.0}\",\"('Cd', 'Se', 'Y')\",OTHERS,True\nmp-1247481,\"{'Li': 4.0, 'Mn': 4.0, 'N': 4.0}\",\"('Li', 'Mn', 'N')\",OTHERS,True\nmp-768385,\"{'Ba': 8.0, 'Y': 4.0, 'Br': 28.0}\",\"('Ba', 'Br', 'Y')\",OTHERS,True\nmp-1179389,\"{'Te': 2.0, 'C': 8.0, 'S': 8.0, 'N': 16.0, 'Cl': 4.0, 'O': 4.0}\",\"('C', 'Cl', 'N', 'O', 'S', 'Te')\",OTHERS,True\nmp-2850,\"{'B': 4.0, 'Os': 2.0}\",\"('B', 'Os')\",OTHERS,True\nmp-9275,\"{'O': 38.0, 'Ta': 12.0, 'Th': 4.0}\",\"('O', 'Ta', 'Th')\",OTHERS,True\nmp-1190622,\"{'Ba': 4.0, 'Cr': 4.0, 'O': 16.0}\",\"('Ba', 'Cr', 'O')\",OTHERS,True\nmp-972300,\"{'Sr': 3.0, 'Lu': 1.0}\",\"('Lu', 'Sr')\",OTHERS,True\nmp-1190981,\"{'Sr': 8.0, 'Zn': 4.0, 'S': 12.0}\",\"('S', 'Sr', 'Zn')\",OTHERS,True\nmp-30677,\"{'Ga': 20.0, 'Tm': 12.0}\",\"('Ga', 'Tm')\",OTHERS,True\nmp-557755,\"{'Ba': 6.0, 'Cr': 4.0, 'Mo': 2.0, 'O': 18.0}\",\"('Ba', 'Cr', 'Mo', 'O')\",OTHERS,True\nmp-642834,\"{'Ba': 2.0, 'H': 10.0, 'Cl': 2.0, 'O': 6.0}\",\"('Ba', 'Cl', 'H', 'O')\",OTHERS,True\nmp-754462,\"{'Sb': 2.0, 'Te': 4.0, 'F': 12.0}\",\"('F', 'Sb', 'Te')\",OTHERS,True\nmp-1101328,\"{'V': 18.0, 'O': 44.0}\",\"('O', 'V')\",OTHERS,True\nmp-1192903,\"{'Bi': 6.0, 'As': 4.0, 'O': 20.0}\",\"('As', 'Bi', 'O')\",OTHERS,True\nmp-976405,\"{'Na': 1.0, 'Er': 3.0}\",\"('Er', 'Na')\",OTHERS,True\nmp-510638,\"{'Ga': 2.0, 'Mn': 2.0, 'F': 10.0, 'O': 4.0, 'H': 8.0}\",\"('F', 'Ga', 'H', 'Mn', 'O')\",OTHERS,True\nmp-1112215,\"{'K': 2.0, 'Tm': 1.0, 'Cu': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cu', 'K', 'Tm')\",OTHERS,True\nmp-1029458,\"{'Zn': 4.0, 'Cr': 2.0, 'N': 6.0}\",\"('Cr', 'N', 'Zn')\",OTHERS,True\nmp-3772,\"{'Ga': 2.0, 'Se': 4.0, 'Cd': 1.0}\",\"('Cd', 'Ga', 'Se')\",OTHERS,True\nmp-10444,\"{'O': 10.0, 'Al': 4.0, 'Ca': 4.0}\",\"('Al', 'Ca', 'O')\",OTHERS,True\nmp-773707,\"{'Li': 4.0, 'Ti': 3.0, 'Mn': 2.0, 'V': 3.0, 'O': 16.0}\",\"('Li', 'Mn', 'O', 'Ti', 'V')\",OTHERS,True\nmp-1177635,\"{'Li': 3.0, 'Mn': 2.0, 'Sb': 1.0, 'O': 6.0}\",\"('Li', 'Mn', 'O', 'Sb')\",OTHERS,True\nmp-756151,\"{'Li': 4.0, 'Cr': 5.0, 'W': 1.0, 'O': 12.0}\",\"('Cr', 'Li', 'O', 'W')\",OTHERS,True\nmp-1220278,\"{'Nd': 4.0, 'Fe': 4.0, 'Co': 12.0, 'B': 3.0, 'C': 1.0}\",\"('B', 'C', 'Co', 'Fe', 'Nd')\",OTHERS,True\nmp-1213404,\"{'Dy': 2.0, 'N': 6.0, 'O': 28.0}\",\"('Dy', 'N', 'O')\",OTHERS,True\nmp-1206434,\"{'Nd': 1.0, 'Ga': 2.0, 'Cu': 2.0}\",\"('Cu', 'Ga', 'Nd')\",OTHERS,True\nmp-1029069,\"{'Mo': 2.0, 'W': 2.0, 'Se': 4.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,True\nmp-1225998,\"{'Co': 1.0, 'Pt': 1.0}\",\"('Co', 'Pt')\",OTHERS,True\nmp-977129,\"{'Hg': 2.0, 'Pd': 6.0}\",\"('Hg', 'Pd')\",OTHERS,True\nmp-1212660,\"{'Ga': 2.0, 'Cu': 2.0, 'Br': 8.0}\",\"('Br', 'Cu', 'Ga')\",OTHERS,True\nmp-722270,\"{'Zr': 4.0, 'H': 28.0, 'C': 4.0, 'N': 16.0, 'F': 20.0}\",\"('C', 'F', 'H', 'N', 'Zr')\",OTHERS,True\nmp-976588,\"{'Ho': 1.0, 'Th': 3.0}\",\"('Ho', 'Th')\",OTHERS,True\nmp-769616,\"{'Na': 4.0, 'Cr': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Cr', 'Na', 'O', 'P')\",OTHERS,True\nmp-1095493,\"{'Cd': 4.0, 'Se': 8.0}\",\"('Cd', 'Se')\",OTHERS,True\nmp-1223773,\"{'K': 5.0, 'Bi': 4.0}\",\"('Bi', 'K')\",OTHERS,True\nmp-982557,\"{'Ho': 2.0, 'Ga': 6.0}\",\"('Ga', 'Ho')\",OTHERS,True\nmp-1190171,{'C': 16.0},\"('C',)\",OTHERS,True\nmp-673096,\"{'Li': 3.0, 'Sn': 1.0, 'P': 6.0, 'O': 18.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,True\nmp-1189833,\"{'Yb': 4.0, 'Si': 4.0, 'Pd': 8.0}\",\"('Pd', 'Si', 'Yb')\",OTHERS,True\nmp-1103785,\"{'Pr': 3.0, 'Al': 11.0}\",\"('Al', 'Pr')\",OTHERS,True\nmp-1245450,\"{'Ca': 32.0, 'Mn': 12.0, 'N': 36.0}\",\"('Ca', 'Mn', 'N')\",OTHERS,True\nmp-1002223,\"{'Dy': 1.0, 'P': 1.0}\",\"('Dy', 'P')\",OTHERS,True\nmp-772087,\"{'Er': 6.0, 'Sb': 10.0, 'O': 24.0}\",\"('Er', 'O', 'Sb')\",OTHERS,True\nmp-862599,\"{'Li': 1.0, 'Er': 1.0, 'Au': 2.0}\",\"('Au', 'Er', 'Li')\",OTHERS,True\nmp-1225237,\"{'Ga': 2.0, 'P': 4.0, 'C': 6.0, 'N': 4.0, 'O': 16.0, 'F': 2.0}\",\"('C', 'F', 'Ga', 'N', 'O', 'P')\",OTHERS,True\nmp-1219858,\"{'Pr': 2.0, 'Si': 3.0, 'Ni': 1.0}\",\"('Ni', 'Pr', 'Si')\",OTHERS,True\nmp-1217980,\"{'Ta': 2.0, 'Be': 8.0, 'Mo': 2.0}\",\"('Be', 'Mo', 'Ta')\",OTHERS,True\nmp-1097071,\"{'Li': 1.0, 'Y': 2.0, 'Co': 1.0}\",\"('Co', 'Li', 'Y')\",OTHERS,True\nmp-1019575,\"{'Ca': 16.0, 'Al': 24.0, 'S': 4.0, 'O': 64.0}\",\"('Al', 'Ca', 'O', 'S')\",OTHERS,True\nmp-757131,\"{'Sn': 2.0, 'P': 8.0, 'O': 24.0}\",\"('O', 'P', 'Sn')\",OTHERS,True\nmp-774845,\"{'Li': 4.0, 'Ti': 2.0, 'Co': 3.0, 'Sn': 3.0, 'O': 16.0}\",\"('Co', 'Li', 'O', 'Sn', 'Ti')\",OTHERS,True\nmp-569471,\"{'U': 2.0, 'B': 4.0, 'C': 2.0}\",\"('B', 'C', 'U')\",OTHERS,True\nmp-1180514,\"{'Li': 1.0, 'Ti': 1.0, 'S': 2.0, 'O': 2.0}\",\"('Li', 'O', 'S', 'Ti')\",OTHERS,True\nmp-761229,\"{'Na': 8.0, 'Mn': 4.0, 'O': 12.0}\",\"('Mn', 'Na', 'O')\",OTHERS,True\nmp-18468,\"{'C': 4.0, 'Si': 4.0, 'Mn': 20.0}\",\"('C', 'Mn', 'Si')\",OTHERS,True\nmp-1219558,\"{'Sb': 2.0, 'Pb': 4.0, 'S': 4.0, 'I': 6.0}\",\"('I', 'Pb', 'S', 'Sb')\",OTHERS,True\nmp-778813,\"{'Li': 7.0, 'Mn': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1094397,\"{'Y': 6.0, 'Mg': 2.0}\",\"('Mg', 'Y')\",OTHERS,True\nmp-722900,\"{'Ca': 16.0, 'H': 48.0, 'N': 16.0, 'O': 80.0}\",\"('Ca', 'H', 'N', 'O')\",OTHERS,True\nmp-1246336,\"{'Li': 12.0, 'Rh': 4.0, 'N': 8.0}\",\"('Li', 'N', 'Rh')\",OTHERS,True\nmp-683875,\"{'Te': 4.0, 'S': 28.0, 'Br': 8.0}\",\"('Br', 'S', 'Te')\",OTHERS,True\nmp-510475,\"{'Y': 8.0, 'Cu': 8.0, 'O': 20.0}\",\"('Cu', 'O', 'Y')\",OTHERS,True\nmp-1176777,\"{'Li': 3.0, 'Co': 3.0, 'B': 3.0, 'O': 9.0}\",\"('B', 'Co', 'Li', 'O')\",OTHERS,True\nmp-781617,\"{'Li': 5.0, 'Mn': 6.0, 'B': 6.0, 'O': 18.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1215056,\"{'Ba': 3.0, 'Mn': 15.0, 'S': 18.0, 'O': 72.0}\",\"('Ba', 'Mn', 'O', 'S')\",OTHERS,True\nmp-11790,\"{'Cu': 4.0, 'Se': 8.0, 'La': 4.0}\",\"('Cu', 'La', 'Se')\",OTHERS,True\nmp-703476,\"{'P': 4.0, 'H': 32.0, 'N': 8.0, 'O': 14.0}\",\"('H', 'N', 'O', 'P')\",OTHERS,True\nmp-676923,\"{'Cd': 4.0, 'Bi': 12.0, 'O': 22.0}\",\"('Bi', 'Cd', 'O')\",OTHERS,True\nmp-677490,\"{'Li': 16.0, 'Nd': 12.0, 'Sb': 4.0, 'Te': 4.0, 'O': 48.0}\",\"('Li', 'Nd', 'O', 'Sb', 'Te')\",OTHERS,True\nmp-685969,\"{'Ag': 8.0, 'Ge': 1.0, 'Te': 6.0}\",\"('Ag', 'Ge', 'Te')\",OTHERS,True\nmp-1204057,\"{'Si': 72.0, 'O': 144.0}\",\"('O', 'Si')\",OTHERS,True\nmp-18628,\"{'B': 8.0, 'C': 20.0, 'Dy': 20.0}\",\"('B', 'C', 'Dy')\",OTHERS,True\nmp-1216793,\"{'U': 3.0, 'Ni': 3.0, 'P': 6.0}\",\"('Ni', 'P', 'U')\",OTHERS,True\nmp-557104,\"{'Li': 4.0, 'Al': 4.0, 'B': 8.0, 'O': 20.0}\",\"('Al', 'B', 'Li', 'O')\",OTHERS,True\nmp-636284,\"{'W': 4.0, 'O': 12.0}\",\"('O', 'W')\",OTHERS,True\nmp-757790,\"{'Li': 1.0, 'P': 2.0, 'W': 1.0, 'O': 8.0}\",\"('Li', 'O', 'P', 'W')\",OTHERS,True\nmp-1202256,\"{'K': 8.0, 'Cd': 6.0, 'P': 12.0, 'O': 48.0}\",\"('Cd', 'K', 'O', 'P')\",OTHERS,True\nmp-766510,\"{'Li': 12.0, 'Mn': 1.0, 'Fe': 3.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Fe', 'Li', 'Mn', 'O', 'P')\",OTHERS,True\nmp-1212595,\"{'Ho': 4.0, 'Hf': 4.0, 'Mo': 14.0, 'O': 56.0}\",\"('Hf', 'Ho', 'Mo', 'O')\",OTHERS,True\nmp-1203775,\"{'Er': 6.0, 'Ge': 26.0, 'Ir': 8.0}\",\"('Er', 'Ge', 'Ir')\",OTHERS,True\nmp-1200490,\"{'Tb': 16.0, 'Al': 8.0, 'O': 36.0}\",\"('Al', 'O', 'Tb')\",OTHERS,True\nmp-20290,\"{'Tb': 2.0, 'Cu': 2.0, 'Pb': 2.0}\",\"('Cu', 'Pb', 'Tb')\",OTHERS,True\nmp-560402,\"{'U': 6.0, 'O': 16.0}\",\"('O', 'U')\",OTHERS,True\nmp-698171,\"{'Cu': 8.0, 'H': 14.0, 'S': 2.0, 'O': 22.0}\",\"('Cu', 'H', 'O', 'S')\",OTHERS,True\nmvc-13555,\"{'Cr': 1.0, 'S': 2.0}\",\"('Cr', 'S')\",OTHERS,True\nmp-1103228,\"{'Bi': 4.0, 'Ir': 4.0, 'Se': 4.0}\",\"('Bi', 'Ir', 'Se')\",OTHERS,True\nmvc-9427,\"{'Ca': 4.0, 'Ti': 8.0, 'P': 8.0, 'O': 32.0}\",\"('Ca', 'O', 'P', 'Ti')\",OTHERS,True\nmp-1176119,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-2656,\"{'S': 8.0, 'Nd': 6.0}\",\"('Nd', 'S')\",OTHERS,True\nmvc-13985,\"{'Sn': 2.0, 'F': 8.0}\",\"('F', 'Sn')\",OTHERS,True\nmp-1199220,\"{'Eu': 6.0, 'Rh': 8.0, 'Pb': 26.0}\",\"('Eu', 'Pb', 'Rh')\",OTHERS,True\nmp-697834,\"{'Ca': 4.0, 'La': 4.0, 'Mn': 7.0, 'Ni': 1.0, 'O': 24.0}\",\"('Ca', 'La', 'Mn', 'Ni', 'O')\",OTHERS,True\nmp-24595,\"{'H': 2.0, 'B': 5.0, 'O': 10.0, 'Cl': 1.0, 'Ca': 2.0}\",\"('B', 'Ca', 'Cl', 'H', 'O')\",OTHERS,True\nmp-1195483,\"{'Ba': 4.0, 'Th': 8.0, 'S': 20.0}\",\"('Ba', 'S', 'Th')\",OTHERS,True\nmp-24510,\"{'H': 4.0, 'O': 14.0, 'I': 4.0, 'Ba': 2.0}\",\"('Ba', 'H', 'I', 'O')\",OTHERS,True\nmp-1105605,\"{'Tb': 7.0, 'Fe': 1.0, 'I': 12.0}\",\"('Fe', 'I', 'Tb')\",OTHERS,True\nmp-18903,\"{'O': 6.0, 'Sr': 2.0, 'Cd': 1.0, 'W': 1.0}\",\"('Cd', 'O', 'Sr', 'W')\",OTHERS,True\nmp-1198571,\"{'Ba': 8.0, 'Eu': 7.0, 'Cl': 34.0}\",\"('Ba', 'Cl', 'Eu')\",OTHERS,True\nmp-16223,\"{'Te': 8.0, 'Tl': 8.0}\",\"('Te', 'Tl')\",OTHERS,True\nmvc-3948,\"{'Mg': 4.0, 'Cr': 4.0, 'O': 10.0}\",\"('Cr', 'Mg', 'O')\",OTHERS,True\nmp-1184410,\"{'Eu': 2.0, 'Mg': 2.0}\",\"('Eu', 'Mg')\",OTHERS,True\nmp-1102773,\"{'K': 8.0, 'Bi': 1.0, 'O': 3.0}\",\"('Bi', 'K', 'O')\",OTHERS,True\nmp-1191873,\"{'Sm': 6.0, 'Mn': 2.0, 'Ga': 2.0, 'S': 14.0}\",\"('Ga', 'Mn', 'S', 'Sm')\",OTHERS,True\nmp-27358,\"{'O': 20.0, 'Se': 8.0}\",\"('O', 'Se')\",OTHERS,True\nmp-31088,\"{'Pd': 4.0, 'Dy': 4.0, 'Pb': 2.0}\",\"('Dy', 'Pb', 'Pd')\",OTHERS,True\nmp-672671,\"{'Pb': 3.0, 'I': 6.0}\",\"('I', 'Pb')\",OTHERS,True\nmp-755419,\"{'Bi': 2.0, 'Te': 1.0, 'O': 2.0}\",\"('Bi', 'O', 'Te')\",OTHERS,True\nmp-1185263,\"{'Li': 1.0, 'Pa': 1.0, 'O': 3.0}\",\"('Li', 'O', 'Pa')\",OTHERS,True\nmp-1183100,\"{'Ac': 6.0, 'Cd': 2.0}\",\"('Ac', 'Cd')\",OTHERS,True\nmp-1094181,\"{'Hf': 3.0, 'Mg': 3.0}\",\"('Hf', 'Mg')\",OTHERS,True\nmvc-11366,\"{'Co': 2.0, 'Si': 4.0, 'O': 12.0}\",\"('Co', 'O', 'Si')\",OTHERS,True\nmp-649676,\"{'Ca': 12.0, 'Si': 12.0, 'O': 36.0}\",\"('Ca', 'O', 'Si')\",OTHERS,True\nmp-1227184,\"{'Ca': 1.0, 'Nd': 3.0, 'Cr': 4.0, 'O': 16.0}\",\"('Ca', 'Cr', 'Nd', 'O')\",OTHERS,True\nmp-1226017,\"{'Co': 1.0, 'Ni': 1.0, 'Te': 2.0}\",\"('Co', 'Ni', 'Te')\",OTHERS,True\nmp-705416,\"{'Fe': 24.0, 'O': 32.0}\",\"('Fe', 'O')\",OTHERS,True\nmp-765089,\"{'Ti': 3.0, 'V': 2.0, 'Cu': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Cu', 'O', 'P', 'Ti', 'V')\",OTHERS,True\nmp-1226182,\"{'Cs': 2.0, 'Al': 1.0, 'Te': 3.0, 'O': 12.0}\",\"('Al', 'Cs', 'O', 'Te')\",OTHERS,True\nmp-1072256,\"{'Hf': 4.0, 'Ge': 2.0}\",\"('Ge', 'Hf')\",OTHERS,True\nmp-14653,\"{'F': 12.0, 'Ag': 1.0, 'Sb': 2.0}\",\"('Ag', 'F', 'Sb')\",OTHERS,True\nmp-863703,\"{'Pm': 2.0, 'Mg': 1.0, 'Sn': 1.0}\",\"('Mg', 'Pm', 'Sn')\",OTHERS,True\nmp-1199443,\"{'Sn': 8.0, 'H': 48.0, 'C': 8.0, 'N': 24.0, 'Cl': 24.0}\",\"('C', 'Cl', 'H', 'N', 'Sn')\",OTHERS,True\nmp-1175604,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1094416,\"{'Mg': 8.0, 'Ti': 8.0}\",\"('Mg', 'Ti')\",OTHERS,True\nmp-942701,\"{'Li': 2.0, 'Co': 2.0, 'S': 2.0, 'O': 8.0, 'F': 2.0}\",\"('Co', 'F', 'Li', 'O', 'S')\",OTHERS,True\nmp-16867,\"{'O': 16.0, 'Cu': 2.0, 'Yb': 2.0, 'W': 4.0}\",\"('Cu', 'O', 'W', 'Yb')\",OTHERS,True\nmvc-5180,\"{'Ca': 4.0, 'Cr': 4.0, 'O': 8.0}\",\"('Ca', 'Cr', 'O')\",OTHERS,True\nmp-755433,\"{'Li': 2.0, 'Co': 4.0, 'O': 4.0, 'F': 6.0}\",\"('Co', 'F', 'Li', 'O')\",OTHERS,True\nmp-1079075,\"{'Y': 2.0, 'Ga': 4.0, 'Pd': 2.0}\",\"('Ga', 'Pd', 'Y')\",OTHERS,True\nmp-671913,\"{'Mo': 8.0, 'Se': 16.0, 'Cl': 32.0, 'O': 8.0}\",\"('Cl', 'Mo', 'O', 'Se')\",OTHERS,True\nmp-759864,\"{'Li': 2.0, 'V': 2.0, 'Cr': 2.0, 'P': 4.0, 'H': 4.0, 'O': 20.0}\",\"('Cr', 'H', 'Li', 'O', 'P', 'V')\",OTHERS,True\nmp-972176,\"{'Zr': 10.0, 'Al': 6.0, 'C': 2.0}\",\"('Al', 'C', 'Zr')\",OTHERS,True\nmp-1177874,\"{'Li': 8.0, 'Ni': 12.0, 'W': 4.0, 'O': 32.0}\",\"('Li', 'Ni', 'O', 'W')\",OTHERS,True\nmp-759672,\"{'Li': 2.0, 'V': 2.0, 'O': 2.0, 'F': 6.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,True\nmp-1101598,\"{'Li': 2.0, 'Ni': 2.0, 'P': 4.0, 'O': 14.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,True\nmp-1226589,\"{'Ce': 1.0, 'Nd': 1.0, 'Al': 2.0, 'O': 6.0}\",\"('Al', 'Ce', 'Nd', 'O')\",OTHERS,True\nmp-1184159,\"{'Er': 1.0, 'Hf': 1.0, 'Ru': 2.0}\",\"('Er', 'Hf', 'Ru')\",OTHERS,True\nmp-1023942,\"{'Te': 4.0, 'W': 2.0}\",\"('Te', 'W')\",OTHERS,True\nmp-761063,\"{'Li': 6.0, 'Fe': 2.0, 'Ni': 6.0, 'O': 16.0}\",\"('Fe', 'Li', 'Ni', 'O')\",OTHERS,True\nmp-1036224,\"{'Mg': 14.0, 'Mn': 1.0, 'Sn': 1.0, 'O': 16.0}\",\"('Mg', 'Mn', 'O', 'Sn')\",OTHERS,True\nmp-1188434,\"{'Y': 10.0, 'Tl': 6.0}\",\"('Tl', 'Y')\",OTHERS,True\nmp-961650,\"{'Al': 1.0, 'V': 1.0, 'Ni': 1.0}\",\"('Al', 'Ni', 'V')\",OTHERS,True\nmp-1185128,\"{'La': 6.0, 'Hf': 2.0}\",\"('Hf', 'La')\",OTHERS,True\nmp-780086,\"{'Li': 8.0, 'Mn': 1.0, 'O': 4.0, 'F': 2.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1100580,\"{'Li': 9.0, 'Mn': 7.0, 'O': 16.0}\",\"('Li', 'Mn', 'O')\",OTHERS,True\nmp-1201749,\"{'Sr': 16.0, 'As': 16.0, 'O': 56.0}\",\"('As', 'O', 'Sr')\",OTHERS,True\nmp-703370,\"{'Ca': 13.0, 'Si': 10.0, 'H': 12.0, 'O': 34.0, 'F': 10.0}\",\"('Ca', 'F', 'H', 'O', 'Si')\",OTHERS,True\nmp-865757,\"{'Yb': 1.0, 'Ga': 1.0, 'Rh': 2.0}\",\"('Ga', 'Rh', 'Yb')\",OTHERS,True\nmp-862319,\"{'Ac': 2.0, 'Cd': 1.0, 'Sn': 1.0}\",\"('Ac', 'Cd', 'Sn')\",OTHERS,True\nmp-1178498,\"{'Cd': 2.0, 'Co': 4.0, 'O': 8.0}\",\"('Cd', 'Co', 'O')\",OTHERS,True\nmp-24123,\"{'H': 12.0, 'B': 12.0, 'Rb': 2.0}\",\"('B', 'H', 'Rb')\",OTHERS,True\nmp-758074,\"{'Li': 4.0, 'V': 4.0, 'P': 4.0, 'O': 20.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-761579,\"{'Fe': 2.0, 'Co': 6.0, 'O': 16.0}\",\"('Co', 'Fe', 'O')\",OTHERS,True\nmp-778885,\"{'Li': 10.0, 'Mn': 3.0, 'Cr': 2.0, 'Ni': 3.0, 'O': 16.0}\",\"('Cr', 'Li', 'Mn', 'Ni', 'O')\",OTHERS,True\nmp-24110,\"{'H': 18.0, 'N': 6.0, 'O': 8.0, 'Cl': 4.0, 'Ru': 1.0, 'Cs': 1.0}\",\"('Cl', 'Cs', 'H', 'N', 'O', 'Ru')\",OTHERS,True\nmp-775136,\"{'Li': 4.0, 'Cr': 3.0, 'Co': 3.0, 'Te': 2.0, 'O': 16.0}\",\"('Co', 'Cr', 'Li', 'O', 'Te')\",OTHERS,True\nmp-1069346,\"{'Pr': 1.0, 'V': 1.0, 'O': 3.0}\",\"('O', 'Pr', 'V')\",OTHERS,True\nmp-1209336,\"{'Pr': 8.0, 'Sn': 12.0}\",\"('Pr', 'Sn')\",OTHERS,True\nmp-1106064,\"{'Ho': 4.0, 'Ga': 12.0, 'Ni': 1.0}\",\"('Ga', 'Ho', 'Ni')\",OTHERS,True\nmp-559557,\"{'Cs': 4.0, 'Na': 4.0, 'B': 20.0, 'O': 34.0}\",\"('B', 'Cs', 'Na', 'O')\",OTHERS,True\nmp-1227976,\"{'Ba': 6.0, 'Br': 2.0, 'N': 3.0, 'Cl': 1.0}\",\"('Ba', 'Br', 'Cl', 'N')\",OTHERS,True\nmp-569472,\"{'Gd': 4.0, 'Al': 18.0, 'Ir': 6.0}\",\"('Al', 'Gd', 'Ir')\",OTHERS,True\nmp-22383,\"{'Si': 2.0, 'Co': 2.0, 'Pu': 1.0}\",\"('Co', 'Pu', 'Si')\",OTHERS,True\nmp-541312,\"{'Ge': 3.0, 'Te': 6.0, 'As': 2.0}\",\"('As', 'Ge', 'Te')\",OTHERS,True\nmp-56,{'Ba': 2.0},\"('Ba',)\",OTHERS,True\nmp-759124,\"{'Li': 6.0, 'Co': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Co', 'Li', 'O', 'Si')\",OTHERS,True\nmp-1204519,\"{'Al': 8.0, 'P': 4.0, 'O': 32.0}\",\"('Al', 'O', 'P')\",OTHERS,True\nmp-1176039,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1111297,\"{'K': 3.0, 'Y': 1.0, 'F': 6.0}\",\"('F', 'K', 'Y')\",OTHERS,True\nmp-14704,\"{'Li': 24.0, 'B': 12.0, 'O': 36.0, 'Y': 4.0}\",\"('B', 'Li', 'O', 'Y')\",OTHERS,True\nmp-738615,\"{'Hg': 4.0, 'H': 12.0, 'C': 4.0, 'S': 4.0, 'O': 12.0}\",\"('C', 'H', 'Hg', 'O', 'S')\",OTHERS,True\nmp-771303,\"{'Li': 4.0, 'Mn': 4.0, 'B': 8.0, 'O': 20.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-775861,\"{'Zr': 16.0, 'N': 16.0, 'O': 8.0}\",\"('N', 'O', 'Zr')\",OTHERS,True\nmp-758129,\"{'Li': 4.0, 'Mn': 2.0, 'Cr': 3.0, 'Co': 3.0, 'O': 16.0}\",\"('Co', 'Cr', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1003322,\"{'Ca': 2.0, 'Mn': 2.0, 'O': 4.0}\",\"('Ca', 'Mn', 'O')\",OTHERS,True\nmp-1203536,\"{'Ho': 20.0, 'Co': 24.0, 'Sn': 72.0}\",\"('Co', 'Ho', 'Sn')\",OTHERS,True\nmp-558301,\"{'Si': 16.0, 'O': 32.0}\",\"('O', 'Si')\",OTHERS,True\nmvc-14231,\"{'Ca': 2.0, 'Fe': 2.0, 'F': 8.0}\",\"('Ca', 'F', 'Fe')\",OTHERS,True\nmp-2294,\"{'Al': 6.0, 'Ir': 2.0}\",\"('Al', 'Ir')\",OTHERS,True\nmp-776437,\"{'La': 4.0, 'Ta': 8.0, 'N': 4.0, 'O': 20.0}\",\"('La', 'N', 'O', 'Ta')\",OTHERS,True\nmp-1191365,\"{'Gd': 8.0, 'P': 8.0, 'S': 8.0}\",\"('Gd', 'P', 'S')\",OTHERS,True\nmp-758077,\"{'Li': 2.0, 'Fe': 16.0, 'O': 24.0}\",\"('Fe', 'Li', 'O')\",OTHERS,True\nmp-979940,\"{'Yb': 3.0, 'Zr': 1.0}\",\"('Yb', 'Zr')\",OTHERS,True\nmp-2124,\"{'Sb': 6.0, 'Ho': 8.0}\",\"('Ho', 'Sb')\",OTHERS,True\nmp-556155,\"{'Zn': 20.0, 'S': 20.0}\",\"('S', 'Zn')\",OTHERS,True\nmp-779108,\"{'Be': 16.0, 'P': 16.0, 'O': 56.0}\",\"('Be', 'O', 'P')\",OTHERS,True\nmp-753503,\"{'Li': 2.0, 'Mn': 4.0, 'F': 14.0}\",\"('F', 'Li', 'Mn')\",OTHERS,True\nmp-559417,\"{'La': 2.0, 'B': 4.0, 'Cl': 2.0, 'O': 8.0}\",\"('B', 'Cl', 'La', 'O')\",OTHERS,True\nmp-680400,\"{'K': 12.0, 'Ta': 8.0, 'S': 44.0}\",\"('K', 'S', 'Ta')\",OTHERS,True\nmp-1227917,\"{'Ba': 1.0, 'La': 2.0, 'Fe': 2.0, 'O': 7.0}\",\"('Ba', 'Fe', 'La', 'O')\",OTHERS,True\nmp-1077112,\"{'Eu': 2.0, 'Zn': 2.0, 'Ge': 2.0}\",\"('Eu', 'Ge', 'Zn')\",OTHERS,True\nmp-1073061,\"{'Mg': 8.0, 'Si': 12.0}\",\"('Mg', 'Si')\",OTHERS,True\nmp-752643,\"{'Na': 2.0, 'Ti': 4.0, 'O': 8.0}\",\"('Na', 'O', 'Ti')\",OTHERS,True\nmvc-11563,\"{'Ca': 2.0, 'V': 4.0, 'O': 8.0}\",\"('Ca', 'O', 'V')\",OTHERS,True\nmp-1220094,\"{'Nd': 1.0, 'Sm': 1.0, 'Fe': 17.0, 'N': 3.0}\",\"('Fe', 'N', 'Nd', 'Sm')\",OTHERS,True\nmp-1100344,\"{'Ca': 8.0, 'Sn': 8.0, 'S': 24.0}\",\"('Ca', 'S', 'Sn')\",OTHERS,True\nmp-1184208,\"{'Er': 1.0, 'Tl': 1.0, 'Rh': 2.0}\",\"('Er', 'Rh', 'Tl')\",OTHERS,True\nmp-1246527,\"{'Mn': 4.0, 'S': 3.0, 'N': 2.0}\",\"('Mn', 'N', 'S')\",OTHERS,True\nmp-540915,\"{'Eu': 6.0, 'O': 8.0, 'Br': 2.0}\",\"('Br', 'Eu', 'O')\",OTHERS,True\nmp-560563,\"{'K': 6.0, 'Pd': 4.0, 'O': 8.0}\",\"('K', 'O', 'Pd')\",OTHERS,True\nmp-558250,\"{'Mo': 4.0, 'H': 8.0, 'C': 8.0, 'O': 16.0}\",\"('C', 'H', 'Mo', 'O')\",OTHERS,True\nmp-1100166,\"{'Y': 1.0, 'Mg': 3.0}\",\"('Mg', 'Y')\",OTHERS,True\nmp-1198990,\"{'Na': 8.0, 'Pr': 4.0, 'Ge': 4.0, 'O': 20.0}\",\"('Ge', 'Na', 'O', 'Pr')\",OTHERS,True\nmp-780171,\"{'Hg': 4.0, 'H': 40.0, 'N': 8.0, 'Cl': 16.0, 'O': 4.0}\",\"('Cl', 'H', 'Hg', 'N', 'O')\",OTHERS,True\nmp-23443,\"{'Mn': 4.0, 'I': 12.0, 'Tl': 4.0}\",\"('I', 'Mn', 'Tl')\",OTHERS,True\nmp-1223876,\"{'K': 2.0, 'Th': 1.0, 'F': 6.0}\",\"('F', 'K', 'Th')\",OTHERS,True\nmp-1209305,\"{'Rb': 3.0, 'Yb': 1.0, 'P': 2.0, 'O': 8.0}\",\"('O', 'P', 'Rb', 'Yb')\",OTHERS,True\nmp-768754,\"{'Li': 4.0, 'Bi': 8.0, 'S': 12.0, 'O': 48.0}\",\"('Bi', 'Li', 'O', 'S')\",OTHERS,True\nmp-680674,\"{'Cs': 6.0, 'Bi': 4.0, 'Br': 18.0}\",\"('Bi', 'Br', 'Cs')\",OTHERS,True\nmp-1246838,\"{'V': 4.0, 'Co': 6.0, 'N': 8.0}\",\"('Co', 'N', 'V')\",OTHERS,True\nmp-1100156,\"{'Rb': 1.0, 'Mg': 6.0, 'Sb': 1.0}\",\"('Mg', 'Rb', 'Sb')\",OTHERS,True\nmp-1247033,\"{'Mg': 2.0, 'Ti': 3.0, 'Ga': 1.0, 'S': 8.0}\",\"('Ga', 'Mg', 'S', 'Ti')\",OTHERS,True\nmp-1218551,\"{'Sr': 3.0, 'Y': 2.0, 'Fe': 1.0, 'Cu': 3.0, 'Bi': 1.0, 'O': 12.0}\",\"('Bi', 'Cu', 'Fe', 'O', 'Sr', 'Y')\",OTHERS,True\nmp-1216284,\"{'V': 1.0, 'Re': 1.0, 'O': 4.0}\",\"('O', 'Re', 'V')\",OTHERS,True\nmvc-13322,\"{'Sr': 4.0, 'Y': 4.0, 'Mn': 4.0, 'O': 14.0}\",\"('Mn', 'O', 'Sr', 'Y')\",OTHERS,True\nmp-17770,\"{'O': 24.0, 'P': 8.0, 'Cs': 2.0, 'Pr': 2.0}\",\"('Cs', 'O', 'P', 'Pr')\",OTHERS,True\nmp-17542,\"{'O': 24.0, 'P': 4.0, 'Mo': 4.0, 'Tl': 4.0}\",\"('Mo', 'O', 'P', 'Tl')\",OTHERS,True\nmp-1217298,\"{'Th': 5.0, 'C': 1.0}\",\"('C', 'Th')\",OTHERS,True\nmp-1001917,\"{'Ho': 1.0, 'As': 1.0}\",\"('As', 'Ho')\",OTHERS,True\nmp-849665,\"{'Mn': 12.0, 'O': 4.0, 'F': 32.0}\",\"('F', 'Mn', 'O')\",OTHERS,True\nmp-651688,\"{'Si': 8.0, 'Ag': 20.0, 'O': 26.0}\",\"('Ag', 'O', 'Si')\",OTHERS,True\nmp-1214757,\"{'Ba': 2.0, 'Ca': 1.0, 'Sm': 2.0, 'Ti': 3.0, 'Cu': 2.0, 'O': 14.0}\",\"('Ba', 'Ca', 'Cu', 'O', 'Sm', 'Ti')\",OTHERS,True\nmp-640051,\"{'Pu': 4.0, 'Ni': 4.0, 'Sn': 2.0}\",\"('Ni', 'Pu', 'Sn')\",OTHERS,True\nmp-760107,\"{'V': 28.0, 'O': 24.0, 'F': 36.0}\",\"('F', 'O', 'V')\",OTHERS,True\nmp-1245615,\"{'Nb': 2.0, 'Te': 2.0, 'N': 2.0}\",\"('N', 'Nb', 'Te')\",OTHERS,True\nmp-1101620,\"{'Dy': 6.0, 'Nb': 2.0, 'O': 14.0}\",\"('Dy', 'Nb', 'O')\",OTHERS,True\nmp-1190414,\"{'Yb': 7.0, 'In': 1.0, 'Ni': 4.0, 'Ge': 12.0}\",\"('Ge', 'In', 'Ni', 'Yb')\",OTHERS,True\nmp-1174208,\"{'Li': 6.0, 'Mn': 2.0, 'Co': 2.0, 'O': 10.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1245525,\"{'Ge': 4.0, 'Pb': 4.0, 'N': 8.0}\",\"('Ge', 'N', 'Pb')\",OTHERS,True\nmp-546142,\"{'O': 4.0, 'U': 1.0, 'Na': 2.0}\",\"('Na', 'O', 'U')\",OTHERS,True\nmp-27837,\"{'Na': 1.0, 'H': 1.0, 'F': 2.0}\",\"('F', 'H', 'Na')\",OTHERS,True\nmp-760093,\"{'Li': 2.0, 'Ti': 16.0, 'O': 32.0}\",\"('Li', 'O', 'Ti')\",OTHERS,True\nmp-1223957,\"{'K': 4.0, 'Mg': 1.0, 'Cu': 3.0, 'F': 12.0}\",\"('Cu', 'F', 'K', 'Mg')\",OTHERS,True\nmp-1186167,\"{'Na': 1.0, 'Si': 1.0, 'O': 3.0}\",\"('Na', 'O', 'Si')\",OTHERS,True\nmp-777800,\"{'Fe': 4.0, 'O': 6.0, 'F': 2.0}\",\"('F', 'Fe', 'O')\",OTHERS,True\nmp-1096592,\"{'Hf': 2.0, 'Zn': 1.0, 'Fe': 1.0}\",\"('Fe', 'Hf', 'Zn')\",OTHERS,True\nmp-795607,\"{'V': 8.0, 'N': 16.0, 'O': 44.0}\",\"('N', 'O', 'V')\",OTHERS,True\nmp-1204701,\"{'Cd': 4.0, 'N': 8.0, 'O': 24.0}\",\"('Cd', 'N', 'O')\",OTHERS,True\nmp-675199,\"{'Na': 2.0, 'Pr': 2.0, 'S': 4.0}\",\"('Na', 'Pr', 'S')\",OTHERS,True\nmp-552191,\"{'O': 6.0, 'Ba': 2.0, 'Tb': 1.0, 'Bi': 1.0}\",\"('Ba', 'Bi', 'O', 'Tb')\",OTHERS,True\nmp-615444,\"{'Cr': 2.0, 'P': 2.0, 'C': 10.0, 'Br': 6.0, 'O': 10.0}\",\"('Br', 'C', 'Cr', 'O', 'P')\",OTHERS,True\nmp-753155,\"{'Fe': 8.0, 'O': 14.0, 'F': 2.0}\",\"('F', 'Fe', 'O')\",OTHERS,True\nmp-1183521,\"{'Ca': 12.0, 'Si': 4.0, 'O': 4.0}\",\"('Ca', 'O', 'Si')\",OTHERS,True\nmp-754905,\"{'Ni': 6.0, 'O': 2.0, 'F': 10.0}\",\"('F', 'Ni', 'O')\",OTHERS,True\nmp-759157,\"{'V': 6.0, 'O': 4.0, 'F': 12.0}\",\"('F', 'O', 'V')\",OTHERS,True\nmp-674976,\"{'Zr': 9.0, 'Sc': 1.0, 'O': 20.0}\",\"('O', 'Sc', 'Zr')\",OTHERS,True\nmp-1030461,\"{'Te': 4.0, 'Mo': 4.0, 'S': 4.0}\",\"('Mo', 'S', 'Te')\",OTHERS,True\nmp-10533,\"{'S': 8.0, 'Cu': 4.0, 'Y': 4.0}\",\"('Cu', 'S', 'Y')\",OTHERS,True\nmp-640315,\"{'Y': 2.0, 'Zn': 40.0, 'Ru': 4.0}\",\"('Ru', 'Y', 'Zn')\",OTHERS,True\nmp-976378,\"{'La': 6.0, 'Sn': 8.0}\",\"('La', 'Sn')\",OTHERS,True\nmp-14652,\"{'P': 6.0, 'Cs': 4.0}\",\"('Cs', 'P')\",OTHERS,True\nmp-1019055,\"{'Re': 2.0, 'N': 4.0}\",\"('N', 'Re')\",OTHERS,True\nmp-1079174,\"{'Pu': 4.0, 'Pd': 4.0}\",\"('Pd', 'Pu')\",OTHERS,True\nmp-764759,\"{'Li': 6.0, 'Mn': 5.0, 'O': 12.0}\",\"('Li', 'Mn', 'O')\",OTHERS,True\nmp-1208624,\"{'Sr': 1.0, 'P': 4.0, 'N': 2.0, 'O': 12.0}\",\"('N', 'O', 'P', 'Sr')\",OTHERS,True\nmp-1224902,\"{'Gd': 2.0, 'Er': 2.0, 'Al': 12.0}\",\"('Al', 'Er', 'Gd')\",OTHERS,True\nmp-1194897,\"{'Na': 8.0, 'Lu': 4.0, 'C': 8.0, 'O': 24.0, 'F': 4.0}\",\"('C', 'F', 'Lu', 'Na', 'O')\",OTHERS,True\nmp-1174322,\"{'Li': 8.0, 'Mn': 6.0, 'O': 14.0}\",\"('Li', 'Mn', 'O')\",OTHERS,True\nmp-1104360,\"{'Er': 3.0, 'Ga': 8.0, 'Ir': 3.0}\",\"('Er', 'Ga', 'Ir')\",OTHERS,True\nmp-1222773,\"{'Li': 2.0, 'Fe': 1.0, 'P': 2.0, 'S': 6.0}\",\"('Fe', 'Li', 'P', 'S')\",OTHERS,True\nmp-1209861,\"{'Nd': 6.0, 'Hf': 2.0, 'Sb': 10.0}\",\"('Hf', 'Nd', 'Sb')\",OTHERS,True\nmp-1218687,\"{'Sr': 4.0, 'La': 1.0, 'Co': 5.0, 'O': 15.0}\",\"('Co', 'La', 'O', 'Sr')\",OTHERS,True\nmp-1245701,\"{'Fe': 2.0, 'Ge': 14.0, 'N': 20.0}\",\"('Fe', 'Ge', 'N')\",OTHERS,True\nmp-981390,\"{'Hg': 1.0, 'Bi': 3.0}\",\"('Bi', 'Hg')\",OTHERS,True\nmp-1210236,\"{'Nd': 2.0, 'Fe': 21.0, 'B': 6.0}\",\"('B', 'Fe', 'Nd')\",OTHERS,True\nmp-17926,\"{'Ni': 6.0, 'Zr': 6.0, 'Sb': 8.0}\",\"('Ni', 'Sb', 'Zr')\",OTHERS,True\nmp-559476,\"{'Dy': 4.0, 'I': 12.0, 'O': 36.0}\",\"('Dy', 'I', 'O')\",OTHERS,True\nmp-768941,\"{'Sr': 6.0, 'Te': 2.0, 'O': 12.0}\",\"('O', 'Sr', 'Te')\",OTHERS,True\nmp-504908,\"{'In': 6.0, 'Te': 3.0, 'O': 18.0}\",\"('In', 'O', 'Te')\",OTHERS,True\nmp-1114094,\"{'Rb': 2.0, 'In': 1.0, 'Ag': 1.0, 'F': 6.0}\",\"('Ag', 'F', 'In', 'Rb')\",OTHERS,True\nmp-998911,\"{'Tm': 2.0, 'Ge': 2.0}\",\"('Ge', 'Tm')\",OTHERS,True\nmp-1076202,\"{'Sr': 28.0, 'Ca': 4.0, 'Ti': 20.0, 'Mn': 12.0, 'O': 80.0}\",\"('Ca', 'Mn', 'O', 'Sr', 'Ti')\",OTHERS,True\nmp-1213341,\"{'Gd': 14.0, 'Br': 6.0, 'O': 36.0}\",\"('Br', 'Gd', 'O')\",OTHERS,True\nmp-7131,\"{'Te': 1.0, 'Pr': 1.0}\",\"('Pr', 'Te')\",OTHERS,True\nmp-1185293,\"{'Li': 1.0, 'Ac': 2.0, 'Tl': 1.0}\",\"('Ac', 'Li', 'Tl')\",OTHERS,True\nmp-1201128,\"{'Na': 4.0, 'Ni': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Na', 'Ni', 'O', 'P')\",OTHERS,True\nmp-1027492,\"{'Mo': 4.0, 'Se': 4.0, 'S': 4.0}\",\"('Mo', 'S', 'Se')\",OTHERS,True\nmp-17578,\"{'Ge': 6.0, 'Yb': 4.0, 'Ir': 7.0}\",\"('Ge', 'Ir', 'Yb')\",OTHERS,True\nmp-1217419,\"{'Tc': 3.0, 'Mo': 1.0}\",\"('Mo', 'Tc')\",OTHERS,True\nmvc-16583,\"{'Mg': 2.0, 'Cr': 2.0, 'F': 8.0}\",\"('Cr', 'F', 'Mg')\",OTHERS,True\nmp-557498,\"{'Na': 4.0, 'Bi': 8.0, 'Au': 4.0, 'O': 20.0}\",\"('Au', 'Bi', 'Na', 'O')\",OTHERS,True\nmp-1221047,\"{'Na': 1.0, 'Er': 1.0, 'Mo': 2.0, 'O': 8.0}\",\"('Er', 'Mo', 'Na', 'O')\",OTHERS,True\nmp-1228324,\"{'Ba': 12.0, 'Na': 8.0, 'Sb': 16.0}\",\"('Ba', 'Na', 'Sb')\",OTHERS,True\nmp-753321,\"{'Li': 2.0, 'Fe': 2.0, 'P': 2.0, 'O': 8.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,True\nmp-1093982,\"{'Ti': 1.0, 'Cd': 1.0, 'Pd': 2.0}\",\"('Cd', 'Pd', 'Ti')\",OTHERS,True\nmp-760983,\"{'Li': 10.0, 'V': 5.0, 'Cr': 3.0, 'O': 16.0}\",\"('Cr', 'Li', 'O', 'V')\",OTHERS,True\nmp-775157,\"{'Li': 12.0, 'Mn': 2.0, 'V': 6.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,True\nmp-9442,\"{'Si': 6.0, 'Fe': 4.0, 'Dy': 6.0}\",\"('Dy', 'Fe', 'Si')\",OTHERS,True\nmp-1203729,\"{'Ba': 2.0, 'Cu': 1.0, 'C': 12.0, 'O': 18.0}\",\"('Ba', 'C', 'Cu', 'O')\",OTHERS,True\nmp-1208576,\"{'Ta': 4.0, 'Co': 4.0, 'As': 4.0}\",\"('As', 'Co', 'Ta')\",OTHERS,True\nmp-849278,\"{'Fe': 1.0, 'Ni': 3.0, 'Sn': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Fe', 'Ni', 'O', 'P', 'Sn')\",OTHERS,True\nmp-1199794,\"{'Tm': 20.0, 'In': 40.0, 'Ni': 18.0}\",\"('In', 'Ni', 'Tm')\",OTHERS,True\nmp-772660,\"{'Nb': 4.0, 'Cr': 4.0, 'O': 16.0}\",\"('Cr', 'Nb', 'O')\",OTHERS,True\nmp-752401,\"{'Rb': 4.0, 'Zr': 2.0, 'O': 6.0}\",\"('O', 'Rb', 'Zr')\",OTHERS,True\nmp-776544,\"{'Li': 4.0, 'Fe': 8.0, 'O': 4.0, 'F': 20.0}\",\"('F', 'Fe', 'Li', 'O')\",OTHERS,True\nmp-1221673,\"{'Mn': 2.0, 'Cr': 2.0, 'P': 4.0}\",\"('Cr', 'Mn', 'P')\",OTHERS,True\nmp-11201,\"{'Sc': 6.0, 'Fe': 1.0, 'Sb': 2.0}\",\"('Fe', 'Sb', 'Sc')\",OTHERS,True\nmp-1183066,\"{'Ac': 2.0, 'Zn': 1.0, 'Au': 1.0}\",\"('Ac', 'Au', 'Zn')\",OTHERS,True\nmp-763588,\"{'Li': 2.0, 'Fe': 2.0, 'C': 4.0, 'O': 12.0}\",\"('C', 'Fe', 'Li', 'O')\",OTHERS,True\nmp-27441,\"{'O': 22.0, 'Se': 8.0, 'Au': 4.0}\",\"('Au', 'O', 'Se')\",OTHERS,True\nmp-28085,\"{'F': 28.0, 'K': 4.0, 'As': 8.0}\",\"('As', 'F', 'K')\",OTHERS,True\nmp-1176394,\"{'Na': 8.0, 'Mn': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Mn', 'Na', 'O', 'P')\",OTHERS,True\nmp-4051,\"{'O': 8.0, 'Al': 2.0, 'P': 2.0}\",\"('Al', 'O', 'P')\",OTHERS,True\nmp-1099978,\"{'La': 24.0, 'Sm': 8.0, 'Cr': 20.0, 'Fe': 12.0, 'O': 80.0}\",\"('Cr', 'Fe', 'La', 'O', 'Sm')\",OTHERS,True\nmp-1210094,\"{'Na': 1.0, 'As': 4.0, 'I': 1.0, 'O': 6.0}\",\"('As', 'I', 'Na', 'O')\",OTHERS,True\nmp-1220949,\"{'Na': 14.0, 'Fe': 8.0, 'P': 18.0, 'O': 64.0}\",\"('Fe', 'Na', 'O', 'P')\",OTHERS,True\nmp-18162,\"{'Li': 4.0, 'O': 8.0, 'Cu': 8.0}\",\"('Cu', 'Li', 'O')\",OTHERS,True\nmp-759605,\"{'Li': 4.0, 'Fe': 2.0, 'C': 4.0, 'O': 12.0}\",\"('C', 'Fe', 'Li', 'O')\",OTHERS,True\nmp-1223118,\"{'La': 4.0, 'Nb': 1.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'La', 'Nb', 'O')\",OTHERS,True\nmp-1196614,\"{'Cs': 8.0, 'Tl': 4.0, 'Cl': 20.0, 'O': 4.0}\",\"('Cl', 'Cs', 'O', 'Tl')\",OTHERS,True\nmp-758595,\"{'Li': 8.0, 'Cr': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Cr', 'Li', 'O', 'Si')\",OTHERS,True\nmp-1097274,\"{'K': 2.0, 'Rb': 1.0, 'As': 1.0}\",\"('As', 'K', 'Rb')\",OTHERS,True\nmp-1197565,\"{'Ce': 2.0, 'Cd': 40.0, 'Pd': 4.0}\",\"('Cd', 'Ce', 'Pd')\",OTHERS,True\nmp-1095993,\"{'Zr': 1.0, 'In': 1.0, 'Ag': 2.0}\",\"('Ag', 'In', 'Zr')\",OTHERS,True\nmp-1247445,\"{'Mg': 2.0, 'In': 1.0, 'N': 2.0}\",\"('In', 'Mg', 'N')\",OTHERS,True\nmvc-2250,\"{'Y': 1.0, 'Cu': 3.0, 'Ni': 4.0, 'O': 12.0}\",\"('Cu', 'Ni', 'O', 'Y')\",OTHERS,True\nmp-8850,\"{'S': 12.0, 'Dy': 8.0}\",\"('Dy', 'S')\",OTHERS,True\nmp-29136,\"{'N': 10.0, 'Cu': 6.0, 'Sr': 12.0}\",\"('Cu', 'N', 'Sr')\",OTHERS,True\nmp-1220559,\"{'Nb': 4.0, 'H': 3.0, 'S': 8.0}\",\"('H', 'Nb', 'S')\",OTHERS,True\nmp-681,\"{'Zn': 4.0, 'Eu': 2.0}\",\"('Eu', 'Zn')\",OTHERS,True\nmp-568036,\"{'La': 8.0, 'Ga': 4.0, 'N': 12.0}\",\"('Ga', 'La', 'N')\",OTHERS,True\nmp-1209055,\"{'Sb': 1.0, 'Br': 6.0, 'N': 2.0}\",\"('Br', 'N', 'Sb')\",OTHERS,True\nmp-867896,\"{'Sc': 1.0, 'Zn': 1.0, 'Cu': 2.0}\",\"('Cu', 'Sc', 'Zn')\",OTHERS,True\nmp-1196269,\"{'In': 4.0, 'Re': 8.0, 'C': 36.0, 'O': 36.0}\",\"('C', 'In', 'O', 'Re')\",OTHERS,True\nmp-1245099,\"{'Co': 30.0, 'O': 60.0}\",\"('Co', 'O')\",OTHERS,True\nmp-20491,\"{'Cu': 2.0, 'In': 1.0, 'La': 1.0}\",\"('Cu', 'In', 'La')\",OTHERS,True\nmp-1198754,\"{'Zn': 4.0, 'Sn': 4.0, 'P': 56.0}\",\"('P', 'Sn', 'Zn')\",OTHERS,True\nmp-1197223,\"{'Na': 4.0, 'Zn': 1.0, 'P': 4.0, 'H': 16.0, 'C': 2.0, 'N': 2.0, 'O': 16.0}\",\"('C', 'H', 'N', 'Na', 'O', 'P', 'Zn')\",OTHERS,True\nmp-38994,\"{'Na': 1.0, 'Al': 1.0, 'Si': 4.0}\",\"('Al', 'Na', 'Si')\",OTHERS,True\nmp-1037599,\"{'K': 1.0, 'Mg': 30.0, 'Fe': 1.0, 'O': 32.0}\",\"('Fe', 'K', 'Mg', 'O')\",OTHERS,True\nmp-25829,\"{'Li': 2.0, 'Mn': 4.0, 'P': 6.0, 'O': 22.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,True\nmp-1079449,\"{'Nd': 2.0, 'Si': 4.0, 'Ir': 4.0}\",\"('Ir', 'Nd', 'Si')\",OTHERS,True\nmp-1225580,\"{'La': 1.0, 'Ce': 1.0, 'Cu': 4.0, 'Si': 4.0}\",\"('Ce', 'Cu', 'La', 'Si')\",OTHERS,True\nmp-1182301,\"{'Co': 12.0, 'B': 28.0, 'I': 4.0, 'O': 52.0}\",\"('B', 'Co', 'I', 'O')\",OTHERS,True\nmp-1199982,\"{'Rb': 8.0, 'U': 4.0, 'Se': 8.0, 'O': 48.0}\",\"('O', 'Rb', 'Se', 'U')\",OTHERS,True\nmp-1027663,\"{'Te': 4.0, 'Mo': 3.0, 'W': 1.0, 'Se': 4.0}\",\"('Mo', 'Se', 'Te', 'W')\",OTHERS,True\nmp-19987,\"{'Pa': 6.0, 'Sb': 8.0}\",\"('Pa', 'Sb')\",OTHERS,True\nmp-626014,\"{'H': 20.0, 'I': 4.0, 'O': 24.0}\",\"('H', 'I', 'O')\",OTHERS,True\nmp-580354,\"{'Ce': 6.0, 'Ni': 18.0}\",\"('Ce', 'Ni')\",OTHERS,True\nmp-974395,\"{'Pt': 3.0, 'Rh': 1.0}\",\"('Pt', 'Rh')\",OTHERS,True\nmp-21320,\"{'Ni': 3.0, 'Ga': 3.0, 'U': 3.0}\",\"('Ga', 'Ni', 'U')\",OTHERS,True\nmp-941,\"{'Ni': 30.0, 'As': 12.0}\",\"('As', 'Ni')\",OTHERS,True\nmp-504457,\"{'Ba': 2.0, 'Ti': 12.0, 'O': 26.0}\",\"('Ba', 'O', 'Ti')\",OTHERS,True\nmp-19479,\"{'O': 32.0, 'Na': 10.0, 'Mo': 8.0, 'La': 2.0}\",\"('La', 'Mo', 'Na', 'O')\",OTHERS,True\nmp-1097603,\"{'Ba': 2.0, 'Mg': 1.0, 'Zn': 1.0}\",\"('Ba', 'Mg', 'Zn')\",OTHERS,True\nmp-1176477,\"{'Mn': 4.0, 'O': 6.0, 'F': 2.0}\",\"('F', 'Mn', 'O')\",OTHERS,True\nmp-1187143,\"{'Sr': 1.0, 'Be': 1.0, 'O': 3.0}\",\"('Be', 'O', 'Sr')\",OTHERS,True\nmp-20874,\"{'B': 6.0, 'Eu': 1.0}\",\"('B', 'Eu')\",OTHERS,True\nmp-623836,\"{'Th': 8.0, 'Ni': 8.0}\",\"('Ni', 'Th')\",OTHERS,True\nmp-27192,\"{'Cs': 4.0, 'Be': 8.0, 'F': 20.0}\",\"('Be', 'Cs', 'F')\",OTHERS,True\nmp-1245215,\"{'Al': 36.0, 'O': 18.0}\",\"('Al', 'O')\",OTHERS,True\nmp-1226501,\"{'Ce': 1.0, 'Sc': 1.0, 'Pd': 6.0}\",\"('Ce', 'Pd', 'Sc')\",OTHERS,True\nmp-568190,\"{'La': 16.0, 'C': 16.0, 'Cl': 10.0}\",\"('C', 'Cl', 'La')\",OTHERS,True\nmp-631254,\"{'K': 1.0, 'Mn': 1.0, 'Ru': 1.0}\",\"('K', 'Mn', 'Ru')\",OTHERS,True\nmp-1187796,\"{'U': 4.0, 'B': 16.0, 'Mo': 4.0}\",\"('B', 'Mo', 'U')\",OTHERS,True\nmp-695772,\"{'Mo': 2.0, 'P': 8.0, 'O': 24.0}\",\"('Mo', 'O', 'P')\",OTHERS,True\nmp-29305,\"{'O': 24.0, 'Re': 6.0, 'Pb': 3.0}\",\"('O', 'Pb', 'Re')\",OTHERS,True\nmp-1244872,\"{'B': 50.0, 'N': 50.0}\",\"('B', 'N')\",OTHERS,True\nmp-774573,\"{'Li': 4.0, 'Mn': 4.0, 'Ni': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'Mn', 'Ni', 'O', 'P')\",OTHERS,True\nmp-1074562,\"{'Mg': 8.0, 'Si': 6.0}\",\"('Mg', 'Si')\",OTHERS,True\nmp-1207270,\"{'Tl': 2.0, 'Cu': 1.0, 'Te': 2.0}\",\"('Cu', 'Te', 'Tl')\",OTHERS,True\nmp-629560,\"{'Fe': 6.0, 'C': 18.0, 'Se': 4.0, 'O': 18.0}\",\"('C', 'Fe', 'O', 'Se')\",OTHERS,True\nmp-649470,\"{'K': 12.0, 'Nd': 8.0, 'N': 36.0, 'O': 108.0}\",\"('K', 'N', 'Nd', 'O')\",OTHERS,True\nmvc-680,\"{'Y': 2.0, 'Ni': 2.0, 'W': 4.0, 'O': 16.0}\",\"('Ni', 'O', 'W', 'Y')\",OTHERS,True\nmp-2400,\"{'Na': 4.0, 'S': 4.0}\",\"('Na', 'S')\",OTHERS,True\nmp-1097288,\"{'Sc': 2.0, 'Pt': 1.0, 'Rh': 1.0}\",\"('Pt', 'Rh', 'Sc')\",OTHERS,True\nmp-20692,\"{'N': 8.0, 'Mn': 4.0, 'Ge': 4.0}\",\"('Ge', 'Mn', 'N')\",OTHERS,True\nmp-1177526,\"{'Li': 6.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-772822,\"{'Li': 3.0, 'Cu': 3.0, 'Te': 1.0, 'O': 8.0}\",\"('Cu', 'Li', 'O', 'Te')\",OTHERS,True\nmp-738670,\"{'H': 40.0, 'C': 16.0, 'N': 8.0, 'O': 24.0}\",\"('C', 'H', 'N', 'O')\",OTHERS,True\nmp-1185577,\"{'Cs': 2.0, 'Hg': 6.0}\",\"('Cs', 'Hg')\",OTHERS,True\nmp-866287,\"{'Dy': 1.0, 'Sn': 1.0, 'Au': 2.0}\",\"('Au', 'Dy', 'Sn')\",OTHERS,True\nmp-770495,\"{'Li': 4.0, 'Ti': 2.0, 'Mn': 3.0, 'Fe': 3.0, 'O': 16.0}\",\"('Fe', 'Li', 'Mn', 'O', 'Ti')\",OTHERS,True\nmp-1186561,\"{'Pm': 2.0, 'Cd': 6.0}\",\"('Cd', 'Pm')\",OTHERS,True\nmp-1094981,\"{'Mg': 4.0, 'Bi': 8.0}\",\"('Bi', 'Mg')\",OTHERS,True\nmp-760102,\"{'W': 8.0, 'O': 4.0, 'F': 32.0}\",\"('F', 'O', 'W')\",OTHERS,True\nmp-8869,\"{'O': 4.0, 'S': 8.0, 'Y': 8.0}\",\"('O', 'S', 'Y')\",OTHERS,True\nmp-1226212,\"{'Cr': 1.0, 'Ni': 1.0, 'Sb': 2.0}\",\"('Cr', 'Ni', 'Sb')\",OTHERS,True\nmp-781628,\"{'Li': 7.0, 'Mn': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-974613,\"{'Nd': 1.0, 'P': 3.0}\",\"('Nd', 'P')\",OTHERS,True\nmp-1185501,\"{'Lu': 1.0, 'Mg': 1.0, 'Tl': 2.0}\",\"('Lu', 'Mg', 'Tl')\",OTHERS,True\nmp-971720,\"{'Zn': 1.0, 'Ga': 3.0}\",\"('Ga', 'Zn')\",OTHERS,True\nmp-1204398,\"{'Co': 4.0, 'Mo': 4.0, 'H': 96.0, 'N': 24.0, 'Cl': 4.0, 'O': 28.0}\",\"('Cl', 'Co', 'H', 'Mo', 'N', 'O')\",OTHERS,True\nmp-1226498,\"{'Ce': 1.0, 'Pr': 1.0, 'Al': 4.0}\",\"('Al', 'Ce', 'Pr')\",OTHERS,True\nmp-1106292,\"{'C': 8.0, 'Se': 4.0, 'N': 8.0}\",\"('C', 'N', 'Se')\",OTHERS,True\nmp-753740,\"{'Cr': 1.0, 'O': 3.0}\",\"('Cr', 'O')\",OTHERS,True\nmp-613327,\"{'Fe': 2.0, 'B': 4.0, 'W': 4.0}\",\"('B', 'Fe', 'W')\",OTHERS,True\nmp-11352,\"{'Al': 5.0, 'Ni': 2.0, 'Pr': 1.0}\",\"('Al', 'Ni', 'Pr')\",OTHERS,True\nmp-1195,\"{'P': 28.0, 'Ba': 6.0}\",\"('Ba', 'P')\",OTHERS,True\nmp-759743,\"{'Sb': 20.0, 'O': 28.0, 'F': 4.0}\",\"('F', 'O', 'Sb')\",OTHERS,True\nmp-28024,\"{'Si': 4.0, 'Y': 4.0, 'Pd': 8.0}\",\"('Pd', 'Si', 'Y')\",OTHERS,True\nmp-624929,\"{'Cd': 1.0, 'In': 1.0, 'Ga': 1.0, 'S': 4.0}\",\"('Cd', 'Ga', 'In', 'S')\",OTHERS,True\nmp-1183403,\"{'Be': 1.0, 'Bi': 1.0, 'O': 3.0}\",\"('Be', 'Bi', 'O')\",OTHERS,True\nmp-7046,\"{'S': 2.0, 'Rb': 1.0, 'Dy': 1.0}\",\"('Dy', 'Rb', 'S')\",OTHERS,True\nmp-1030570,\"{'Te': 4.0, 'Mo': 2.0, 'W': 2.0, 'Se': 2.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,True\nmp-697807,\"{'Li': 2.0, 'Mn': 8.0, 'P': 14.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,True\nmp-23983,\"{'H': 8.0, 'O': 14.0, 'P': 2.0, 'V': 2.0, 'Ni': 1.0}\",\"('H', 'Ni', 'O', 'P', 'V')\",OTHERS,True\nmp-567157,\"{'Ba': 14.0, 'Na': 10.0, 'Li': 6.0, 'N': 6.0}\",\"('Ba', 'Li', 'N', 'Na')\",OTHERS,True\nmp-559431,\"{'K': 4.0, 'V': 4.0, 'P': 4.0, 'O': 20.0}\",\"('K', 'O', 'P', 'V')\",OTHERS,True\nmp-1094557,\"{'Mg': 6.0, 'Sb': 6.0}\",\"('Mg', 'Sb')\",OTHERS,True\nmp-755434,\"{'Li': 4.0, 'Mn': 2.0, 'F': 8.0}\",\"('F', 'Li', 'Mn')\",OTHERS,True\nmp-743704,\"{'Ca': 8.0, 'Y': 4.0, 'Al': 14.0, 'Cr': 4.0, 'Si': 2.0, 'O': 48.0}\",\"('Al', 'Ca', 'Cr', 'O', 'Si', 'Y')\",OTHERS,True\nmp-772093,\"{'Li': 2.0, 'Fe': 2.0, 'P': 2.0, 'O': 8.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,True\nmp-17350,\"{'O': 24.0, 'Te': 4.0, 'La': 8.0}\",\"('La', 'O', 'Te')\",OTHERS,True\nmp-1106039,\"{'Sm': 4.0, 'As': 8.0, 'Au': 4.0}\",\"('As', 'Au', 'Sm')\",OTHERS,True\nmp-1226133,\"{'Co': 1.0, 'Mo': 4.0, 'N': 5.0}\",\"('Co', 'Mo', 'N')\",OTHERS,True\nmp-1187097,\"{'Sr': 2.0, 'Pd': 1.0, 'Pt': 1.0}\",\"('Pd', 'Pt', 'Sr')\",OTHERS,True\nmp-1211576,\"{'Nd': 8.0, 'Co': 2.0, 'Si': 8.0, 'C': 8.0, 'O': 44.0}\",\"('C', 'Co', 'Nd', 'O', 'Si')\",OTHERS,True\nmp-762029,\"{'Li': 4.0, 'Ag': 8.0, 'F': 16.0}\",\"('Ag', 'F', 'Li')\",OTHERS,True\nmp-1034331,\"{'Hf': 1.0, 'Mg': 14.0, 'V': 1.0, 'O': 16.0}\",\"('Hf', 'Mg', 'O', 'V')\",OTHERS,True\nmp-722246,\"{'Si': 2.0, 'H': 20.0, 'O': 8.0, 'F': 12.0}\",\"('F', 'H', 'O', 'Si')\",OTHERS,True\nmp-755290,\"{'V': 6.0, 'O': 8.0, 'F': 4.0}\",\"('F', 'O', 'V')\",OTHERS,True\nmp-1205997,\"{'Na': 1.0, 'U': 1.0, 'F': 6.0}\",\"('F', 'Na', 'U')\",OTHERS,True\nmp-1120819,\"{'Na': 8.0, 'Co': 16.0, 'Bi': 8.0, 'O': 48.0}\",\"('Bi', 'Co', 'Na', 'O')\",OTHERS,True\nmp-755473,\"{'Li': 3.0, 'V': 1.0, 'Cr': 3.0, 'O': 8.0}\",\"('Cr', 'Li', 'O', 'V')\",OTHERS,True\nmvc-15452,\"{'Y': 1.0, 'Mo': 1.0, 'O': 3.0}\",\"('Mo', 'O', 'Y')\",OTHERS,True\nmp-571636,\"{'In': 2.0, 'Cl': 2.0}\",\"('Cl', 'In')\",OTHERS,True\nmp-989628,\"{'Y': 4.0, 'Re': 4.0, 'N': 12.0}\",\"('N', 'Re', 'Y')\",OTHERS,True\nmp-1181813,\"{'Fe': 24.0, 'O': 32.0}\",\"('Fe', 'O')\",OTHERS,True\nmp-1177398,\"{'Li': 4.0, 'Mn': 3.0, 'V': 1.0, 'O': 8.0}\",\"('Li', 'Mn', 'O', 'V')\",OTHERS,True\nmp-1247002,\"{'Zr': 2.0, 'Cr': 2.0, 'Ag': 2.0, 'S': 8.0}\",\"('Ag', 'Cr', 'S', 'Zr')\",OTHERS,True\nmp-1093601,\"{'Li': 1.0, 'Ca': 2.0, 'Zn': 1.0}\",\"('Ca', 'Li', 'Zn')\",OTHERS,True\nmp-1212628,\"{'Ho': 12.0, 'Ga': 60.0, 'Ru': 16.0}\",\"('Ga', 'Ho', 'Ru')\",OTHERS,True\nmp-1189414,\"{'Yb': 4.0, 'Si': 12.0}\",\"('Si', 'Yb')\",OTHERS,True\nmp-1096646,\"{'Li': 2.0, 'Y': 1.0, 'Ga': 1.0}\",\"('Ga', 'Li', 'Y')\",OTHERS,True\nmp-1080612,\"{'Gd': 3.0, 'Al': 3.0, 'Cu': 3.0}\",\"('Al', 'Cu', 'Gd')\",OTHERS,True\nmp-5733,\"{'O': 36.0, 'Si': 12.0, 'Ca': 12.0}\",\"('Ca', 'O', 'Si')\",OTHERS,True\nmp-504650,\"{'La': 6.0, 'Cu': 2.0, 'Si': 2.0, 'S': 14.0}\",\"('Cu', 'La', 'S', 'Si')\",OTHERS,True\nmp-542280,\"{'Ca': 2.0, 'P': 12.0, 'Na': 8.0, 'O': 36.0}\",\"('Ca', 'Na', 'O', 'P')\",OTHERS,True\nmp-1181830,\"{'Ca': 4.0, 'C': 8.0, 'O': 16.0}\",\"('C', 'Ca', 'O')\",OTHERS,True\nmp-758336,\"{'Li': 6.0, 'V': 2.0, 'P': 6.0, 'O': 22.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-1183482,\"{'Ca': 2.0, 'Cu': 1.0, 'Rh': 1.0}\",\"('Ca', 'Cu', 'Rh')\",OTHERS,True\nmp-755241,\"{'Mn': 8.0, 'O': 8.0, 'F': 8.0}\",\"('F', 'Mn', 'O')\",OTHERS,True\nmp-1209892,\"{'Nd': 1.0, 'Fe': 4.0, 'As': 12.0}\",\"('As', 'Fe', 'Nd')\",OTHERS,True\nmp-1232437,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1184722,\"{'Ho': 6.0, 'Th': 2.0}\",\"('Ho', 'Th')\",OTHERS,True\nmp-1025066,\"{'Er': 1.0, 'In': 1.0, 'Co': 4.0}\",\"('Co', 'Er', 'In')\",OTHERS,True\nmp-754962,\"{'Mn': 2.0, 'Fe': 1.0, 'O': 6.0}\",\"('Fe', 'Mn', 'O')\",OTHERS,True\nmp-1217477,\"{'Tb': 1.0, 'Pr': 1.0, 'Fe': 4.0}\",\"('Fe', 'Pr', 'Tb')\",OTHERS,True\nmp-26387,\"{'Li': 4.0, 'Ni': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,True\nmp-766091,\"{'Li': 4.0, 'La': 5.0, 'Ti': 6.0, 'Nb': 2.0, 'O': 26.0}\",\"('La', 'Li', 'Nb', 'O', 'Ti')\",OTHERS,True\nmp-632329,{'C': 2.0},\"('C',)\",OTHERS,True\nmp-774775,\"{'Na': 4.0, 'Mn': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Mn', 'Na', 'O', 'P')\",OTHERS,True\nmp-759689,\"{'V': 6.0, 'O': 11.0, 'F': 1.0}\",\"('F', 'O', 'V')\",OTHERS,True\nmp-754103,\"{'Ca': 2.0, 'Ni': 2.0, 'O': 6.0}\",\"('Ca', 'Ni', 'O')\",OTHERS,True\nmp-1177257,\"{'Li': 4.0, 'Ti': 3.0, 'V': 3.0, 'Ni': 2.0, 'O': 16.0}\",\"('Li', 'Ni', 'O', 'Ti', 'V')\",OTHERS,True\nmp-1110640,\"{'K': 2.0, 'In': 1.0, 'As': 1.0, 'Br': 6.0}\",\"('As', 'Br', 'In', 'K')\",OTHERS,True\nmp-510057,\"{'Gd': 12.0, 'Nb': 4.0, 'S': 12.0, 'O': 16.0}\",\"('Gd', 'Nb', 'O', 'S')\",OTHERS,True\nmp-17173,\"{'S': 12.0, 'Rh': 8.0}\",\"('Rh', 'S')\",OTHERS,True\nmvc-10598,\"{'Ba': 4.0, 'Mg': 4.0, 'Co': 4.0, 'F': 28.0}\",\"('Ba', 'Co', 'F', 'Mg')\",OTHERS,True\nmp-974065,\"{'Lu': 1.0, 'Sc': 1.0, 'Pd': 2.0}\",\"('Lu', 'Pd', 'Sc')\",OTHERS,True\nmp-864876,\"{'Tb': 1.0, 'In': 1.0, 'Rh': 2.0}\",\"('In', 'Rh', 'Tb')\",OTHERS,True\nmp-1225861,\"{'Dy': 14.0, 'Au': 51.0}\",\"('Au', 'Dy')\",OTHERS,True\nmp-1094626,\"{'Mg': 10.0, 'Ga': 2.0}\",\"('Ga', 'Mg')\",OTHERS,True\nmp-1205196,\"{'Li': 10.0, 'Ce': 10.0, 'Si': 8.0, 'N': 24.0}\",\"('Ce', 'Li', 'N', 'Si')\",OTHERS,True\nmp-1196242,\"{'Dy': 6.0, 'Al': 4.0, 'Ni': 12.0, 'H': 18.0}\",\"('Al', 'Dy', 'H', 'Ni')\",OTHERS,True\nmp-637600,\"{'Gd': 6.0, 'Co': 1.0, 'Br': 10.0}\",\"('Br', 'Co', 'Gd')\",OTHERS,True\nmp-771717,\"{'Mn': 16.0, 'O': 24.0}\",\"('Mn', 'O')\",OTHERS,True\nmp-1226382,\"{'Cr': 4.0, 'Cd': 2.0, 'Se': 2.0, 'S': 6.0}\",\"('Cd', 'Cr', 'S', 'Se')\",OTHERS,True\nmp-755393,\"{'Sr': 2.0, 'La': 2.0, 'I': 10.0}\",\"('I', 'La', 'Sr')\",OTHERS,True\nmp-1187488,\"{'Ti': 1.0, 'Te': 1.0, 'O': 3.0}\",\"('O', 'Te', 'Ti')\",OTHERS,True\nmp-1099244,\"{'Mg': 3.0, 'W': 1.0, 'O': 4.0}\",\"('Mg', 'O', 'W')\",OTHERS,True\nmp-1246277,\"{'Na': 12.0, 'Re': 2.0, 'N': 8.0}\",\"('N', 'Na', 'Re')\",OTHERS,True\nmp-1245017,\"{'Y': 40.0, 'O': 60.0}\",\"('O', 'Y')\",OTHERS,True\nmp-540806,\"{'Ir': 3.0, 'Ca': 6.0, 'O': 12.0}\",\"('Ca', 'Ir', 'O')\",OTHERS,True\nmp-1214640,\"{'Ba': 6.0, 'Ce': 2.0, 'Ir': 4.0, 'O': 18.0}\",\"('Ba', 'Ce', 'Ir', 'O')\",OTHERS,True\nmp-771957,\"{'Ta': 8.0, 'Pb': 4.0, 'O': 24.0}\",\"('O', 'Pb', 'Ta')\",OTHERS,True\nmp-560268,\"{'K': 4.0, 'Mo': 6.0, 'As': 8.0, 'O': 40.0}\",\"('As', 'K', 'Mo', 'O')\",OTHERS,True\nmp-1094724,\"{'Mg': 3.0, 'Sb': 1.0}\",\"('Mg', 'Sb')\",OTHERS,True\nmvc-5466,\"{'Co': 16.0, 'O': 32.0}\",\"('Co', 'O')\",OTHERS,True\nmp-768579,\"{'Na': 4.0, 'Mn': 2.0, 'B': 2.0, 'P': 2.0, 'O': 14.0}\",\"('B', 'Mn', 'Na', 'O', 'P')\",OTHERS,True\nmp-1205479,\"{'K': 44.0, 'Sb': 22.0, 'F': 110.0}\",\"('F', 'K', 'Sb')\",OTHERS,True\nmp-1214564,\"{'Ba': 8.0, 'Na': 8.0, 'Cr': 8.0, 'F': 48.0}\",\"('Ba', 'Cr', 'F', 'Na')\",OTHERS,True\nmp-777874,\"{'Li': 12.0, 'Mn': 2.0, 'V': 6.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,True\nmp-19155,\"{'Cr': 4.0, 'Hg': 4.0, 'O': 16.0}\",\"('Cr', 'Hg', 'O')\",OTHERS,True\nmp-1094437,\"{'Mg': 8.0, 'Zn': 4.0}\",\"('Mg', 'Zn')\",OTHERS,True\nmp-676260,\"{'Ag': 9.0, 'Te': 4.0, 'Br': 3.0}\",\"('Ag', 'Br', 'Te')\",OTHERS,True\nmp-1025268,\"{'Pu': 2.0, 'W': 2.0, 'C': 3.0}\",\"('C', 'Pu', 'W')\",OTHERS,True\nmp-1102875,\"{'Er': 8.0, 'Al': 4.0}\",\"('Al', 'Er')\",OTHERS,True\nmp-720995,\"{'Sr': 8.0, 'C': 8.0, 'N': 12.0}\",\"('C', 'N', 'Sr')\",OTHERS,True\nmp-1219640,\"{'Rb': 3.0, 'I': 2.0, 'Br': 1.0}\",\"('Br', 'I', 'Rb')\",OTHERS,True\nmp-1079481,\"{'Al': 2.0, 'S': 2.0, 'Br': 6.0}\",\"('Al', 'Br', 'S')\",OTHERS,True\nmp-559134,\"{'Eu': 6.0, 'As': 4.0, 'S': 16.0}\",\"('As', 'Eu', 'S')\",OTHERS,True\nmp-1213307,\"{'Cs': 2.0, 'Nd': 2.0, 'Mo': 4.0, 'O': 16.0}\",\"('Cs', 'Mo', 'Nd', 'O')\",OTHERS,True\nmp-1205613,\"{'Rb': 3.0, 'Sm': 1.0, 'F': 6.0}\",\"('F', 'Rb', 'Sm')\",OTHERS,True\nmp-11666,\"{'Zn': 6.0, 'Ge': 3.0, 'Pr': 2.0}\",\"('Ge', 'Pr', 'Zn')\",OTHERS,True\nmp-1199580,\"{'Zn': 2.0, 'Cu': 2.0, 'C': 8.0, 'O': 24.0}\",\"('C', 'Cu', 'O', 'Zn')\",OTHERS,True\nmp-770731,\"{'Li': 4.0, 'Zr': 8.0, 'O': 16.0}\",\"('Li', 'O', 'Zr')\",OTHERS,True\nmp-1106337,\"{'Mn': 4.0, 'Pb': 4.0, 'O': 12.0}\",\"('Mn', 'O', 'Pb')\",OTHERS,True\nmp-1245403,\"{'Pd': 3.0, 'C': 24.0, 'N': 18.0}\",\"('C', 'N', 'Pd')\",OTHERS,True\nmp-1224874,\"{'Gd': 2.0, 'Si': 3.0}\",\"('Gd', 'Si')\",OTHERS,True\nmp-636934,\"{'Bi': 12.0, 'O': 18.0}\",\"('Bi', 'O')\",OTHERS,True\nmp-14643,\"{'Te': 12.0, 'Er': 8.0}\",\"('Er', 'Te')\",OTHERS,True\nmp-562050,\"{'Cs': 6.0, 'Na': 3.0, 'Ti': 3.0, 'F': 18.0}\",\"('Cs', 'F', 'Na', 'Ti')\",OTHERS,True\nmp-1654,\"{'Ni': 4.0, 'Ce': 2.0}\",\"('Ce', 'Ni')\",OTHERS,True\nmp-754085,\"{'Li': 4.0, 'Bi': 2.0, 'S': 4.0}\",\"('Bi', 'Li', 'S')\",OTHERS,True\nmp-1213293,\"{'Cs': 2.0, 'Tb': 2.0, 'Mo': 4.0, 'O': 16.0}\",\"('Cs', 'Mo', 'O', 'Tb')\",OTHERS,True\nmp-1202486,\"{'La': 28.0, 'Ru': 12.0}\",\"('La', 'Ru')\",OTHERS,True\nmp-1246113,\"{'Cr': 4.0, 'Fe': 6.0, 'N': 8.0}\",\"('Cr', 'Fe', 'N')\",OTHERS,True\nmp-1015582,\"{'Cr': 4.0, 'N': 8.0}\",\"('Cr', 'N')\",OTHERS,True\nmp-1245702,\"{'Ca': 10.0, 'Ir': 4.0, 'N': 12.0}\",\"('Ca', 'Ir', 'N')\",OTHERS,True\nmp-1217895,\"{'Ta': 1.0, 'Mo': 1.0}\",\"('Mo', 'Ta')\",OTHERS,True\nmp-3205,\"{'C': 2.0, 'O': 6.0, 'Ca': 2.0}\",\"('C', 'Ca', 'O')\",OTHERS,True\nmp-974341,\"{'Ru': 2.0, 'Rh': 6.0}\",\"('Rh', 'Ru')\",OTHERS,True\nmp-779357,\"{'V': 6.0, 'O': 2.0, 'F': 22.0}\",\"('F', 'O', 'V')\",OTHERS,True\nmvc-2670,\"{'Ta': 4.0, 'Zn': 4.0, 'Ni': 2.0, 'O': 16.0}\",\"('Ni', 'O', 'Ta', 'Zn')\",OTHERS,True\nmp-569952,\"{'K': 8.0, 'Cu': 4.0, 'Cl': 12.0}\",\"('Cl', 'Cu', 'K')\",OTHERS,True\nmp-1104205,\"{'Ba': 1.0, 'Er': 1.0, 'Fe': 4.0, 'O': 7.0}\",\"('Ba', 'Er', 'Fe', 'O')\",OTHERS,True\nmp-1184577,\"{'Ho': 2.0, 'Os': 1.0, 'Rh': 1.0}\",\"('Ho', 'Os', 'Rh')\",OTHERS,True\nmp-21410,\"{'Fe': 4.0, 'S': 4.0}\",\"('Fe', 'S')\",OTHERS,True\nmp-25813,\"{'Li': 6.0, 'O': 32.0, 'P': 8.0, 'Fe': 6.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,True\nmp-1135,\"{'Nb': 1.0, 'Pd': 3.0}\",\"('Nb', 'Pd')\",OTHERS,True\nmp-780462,\"{'Na': 12.0, 'Mn': 4.0, 'P': 2.0, 'C': 8.0, 'O': 32.0}\",\"('C', 'Mn', 'Na', 'O', 'P')\",OTHERS,True\nmp-1175970,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1203397,\"{'Zr': 4.0, 'Cd': 4.0, 'H': 40.0, 'C': 24.0, 'O': 68.0}\",\"('C', 'Cd', 'H', 'O', 'Zr')\",OTHERS,True\nmp-557661,\"{'Sb': 16.0, 'Pd': 4.0, 'C': 16.0, 'O': 16.0, 'F': 88.0}\",\"('C', 'F', 'O', 'Pd', 'Sb')\",OTHERS,True\nmp-1101545,\"{'Li': 4.0, 'Co': 4.0, 'P': 16.0, 'O': 48.0}\",\"('Co', 'Li', 'O', 'P')\",OTHERS,True\nmp-555392,\"{'K': 12.0, 'Na': 2.0, 'Au': 4.0, 'I': 2.0, 'O': 16.0}\",\"('Au', 'I', 'K', 'Na', 'O')\",OTHERS,True\nmp-1191428,\"{'Sc': 2.0, 'Co': 12.0, 'P': 7.0}\",\"('Co', 'P', 'Sc')\",OTHERS,True\nmp-1173448,\"{'Re': 12.0, 'Se': 8.0, 'Cl': 20.0}\",\"('Cl', 'Re', 'Se')\",OTHERS,True\nmp-753441,\"{'Li': 4.0, 'Mn': 4.0, 'O': 2.0, 'F': 12.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-15318,\"{'F': 6.0, 'Na': 1.0, 'Rb': 2.0, 'Ho': 1.0}\",\"('F', 'Ho', 'Na', 'Rb')\",OTHERS,True\nmp-567446,\"{'La': 46.0, 'Cd': 8.0, 'Pt': 14.0}\",\"('Cd', 'La', 'Pt')\",OTHERS,True\nmp-1175046,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-985283,\"{'Ag': 3.0, 'Ge': 1.0}\",\"('Ag', 'Ge')\",OTHERS,True\nmp-984726,\"{'Dy': 1.0, 'Al': 8.0, 'Cu': 4.0}\",\"('Al', 'Cu', 'Dy')\",OTHERS,True\nmp-1196275,\"{'Ba': 8.0, 'Fe': 8.0, 'O': 8.0, 'F': 40.0}\",\"('Ba', 'F', 'Fe', 'O')\",OTHERS,True\nmp-1104171,\"{'Yb': 2.0, 'Ga': 4.0, 'Se': 8.0}\",\"('Ga', 'Se', 'Yb')\",OTHERS,True\nmp-15108,\"{'O': 40.0, 'As': 8.0, 'Rb': 8.0, 'Sn': 8.0}\",\"('As', 'O', 'Rb', 'Sn')\",OTHERS,True\nmp-17491,\"{'Li': 4.0, 'Ge': 12.0, 'Ba': 8.0}\",\"('Ba', 'Ge', 'Li')\",OTHERS,True\nmp-1205361,\"{'Te': 12.0, 'Rh': 2.0, 'I': 6.0}\",\"('I', 'Rh', 'Te')\",OTHERS,True\nmp-1178273,\"{'Fe': 10.0, 'O': 4.0, 'F': 16.0}\",\"('F', 'Fe', 'O')\",OTHERS,True\nmvc-12970,\"{'Mg': 2.0, 'Fe': 4.0, 'S': 8.0}\",\"('Fe', 'Mg', 'S')\",OTHERS,True\nmp-865156,\"{'Dy': 2.0, 'Zn': 1.0, 'Ga': 1.0}\",\"('Dy', 'Ga', 'Zn')\",OTHERS,True\nmp-1038168,\"{'Mg': 30.0, 'Mn': 1.0, 'V': 1.0, 'O': 32.0}\",\"('Mg', 'Mn', 'O', 'V')\",OTHERS,True\nmp-20704,\"{'U': 1.0, 'Mn': 4.0, 'Al': 8.0}\",\"('Al', 'Mn', 'U')\",OTHERS,True\nmp-980205,\"{'Y': 4.0, 'B': 16.0, 'Os': 4.0}\",\"('B', 'Os', 'Y')\",OTHERS,True\nmp-4669,\"{'O': 64.0, 'Mo': 16.0, 'Th': 8.0}\",\"('Mo', 'O', 'Th')\",OTHERS,True\nmp-33737,\"{'Mn': 3.0, 'Cu': 3.0, 'O': 8.0}\",\"('Cu', 'Mn', 'O')\",OTHERS,True\nmvc-14333,\"{'V': 2.0, 'Zn': 2.0, 'F': 10.0}\",\"('F', 'V', 'Zn')\",OTHERS,True\nmp-1178743,\"{'Y': 4.0, 'Al': 1.0, 'Si': 2.0, 'O': 10.0, 'F': 5.0}\",\"('Al', 'F', 'O', 'Si', 'Y')\",OTHERS,True\nmp-674804,\"{'Sm': 4.0, 'Pb': 2.0, 'Se': 8.0}\",\"('Pb', 'Se', 'Sm')\",OTHERS,True\nmp-8710,\"{'O': 8.0, 'Ca': 2.0, 'Pt': 3.0}\",\"('Ca', 'O', 'Pt')\",OTHERS,True\nmp-1193652,\"{'Re': 4.0, 'Te': 8.0, 'Cl': 16.0}\",\"('Cl', 'Re', 'Te')\",OTHERS,True\nmp-7138,\"{'Sb': 4.0, 'Yb': 2.0}\",\"('Sb', 'Yb')\",OTHERS,True\nmp-1225677,\"{'Cu': 1.0, 'Au': 1.0}\",\"('Au', 'Cu')\",OTHERS,True\nmvc-867,\"{'Mg': 2.0, 'Co': 6.0, 'P': 8.0, 'O': 28.0}\",\"('Co', 'Mg', 'O', 'P')\",OTHERS,True\nmp-1078956,\"{'Pt': 1.0, 'N': 4.0, 'Cl': 2.0, 'O': 1.0}\",\"('Cl', 'N', 'O', 'Pt')\",OTHERS,True\nmp-1232372,\"{'Sc': 2.0, 'B': 2.0, 'C': 2.0}\",\"('B', 'C', 'Sc')\",OTHERS,True\nmp-18772,\"{'N': 4.0, 'O': 4.0, 'Na': 8.0, 'Mo': 2.0}\",\"('Mo', 'N', 'Na', 'O')\",OTHERS,True\nmp-696878,\"{'K': 3.0, 'Ca': 1.0, 'P': 2.0, 'H': 1.0, 'O': 8.0}\",\"('Ca', 'H', 'K', 'O', 'P')\",OTHERS,True\nmp-3884,\"{'O': 14.0, 'Sn': 4.0, 'Ho': 4.0}\",\"('Ho', 'O', 'Sn')\",OTHERS,True\nmp-764043,\"{'Li': 6.0, 'Mn': 6.0, 'Ni': 2.0, 'O': 16.0}\",\"('Li', 'Mn', 'Ni', 'O')\",OTHERS,True\nmp-1038154,\"{'Mg': 30.0, 'Mn': 1.0, 'Sn': 1.0, 'O': 32.0}\",\"('Mg', 'Mn', 'O', 'Sn')\",OTHERS,True\nmp-1228077,\"{'Ba': 4.0, 'Na': 1.0, 'Ir': 3.0, 'O': 12.0}\",\"('Ba', 'Ir', 'Na', 'O')\",OTHERS,True\nmp-20038,\"{'P': 2.0, 'Co': 2.0, 'Eu': 1.0}\",\"('Co', 'Eu', 'P')\",OTHERS,True\nmp-3890,\"{'Cr': 2.0, 'Fe': 15.0, 'Sm': 2.0}\",\"('Cr', 'Fe', 'Sm')\",OTHERS,True\nmp-1247009,\"{'V': 8.0, 'Fe': 3.0, 'N': 8.0}\",\"('Fe', 'N', 'V')\",OTHERS,True\nmp-542081,\"{'La': 4.0, 'P': 8.0, 'S': 28.0, 'K': 8.0}\",\"('K', 'La', 'P', 'S')\",OTHERS,True\nmp-1039999,\"{'Na': 1.0, 'La': 1.0, 'Mg': 30.0, 'O': 32.0}\",\"('La', 'Mg', 'Na', 'O')\",OTHERS,True\nmp-1185684,\"{'Mg': 16.0, 'Al': 12.0, 'O': 1.0}\",\"('Al', 'Mg', 'O')\",OTHERS,True\nmp-29088,\"{'N': 4.0, 'Ge': 4.0, 'Sr': 6.0}\",\"('Ge', 'N', 'Sr')\",OTHERS,True\nmp-1201569,\"{'Sc': 10.0, 'V': 5.0, 'O': 25.0}\",\"('O', 'Sc', 'V')\",OTHERS,True\nmp-778169,\"{'Li': 2.0, 'Cu': 4.0, 'S': 6.0, 'O': 24.0}\",\"('Cu', 'Li', 'O', 'S')\",OTHERS,True\nmp-1076592,\"{'Ba': 1.0, 'Sr': 7.0, 'Mn': 1.0, 'Fe': 7.0, 'O': 24.0}\",\"('Ba', 'Fe', 'Mn', 'O', 'Sr')\",OTHERS,True\nmp-755993,\"{'Mn': 3.0, 'Nb': 2.0, 'Fe': 3.0, 'O': 16.0}\",\"('Fe', 'Mn', 'Nb', 'O')\",OTHERS,True\nmp-754889,\"{'Li': 2.0, 'Mn': 4.0, 'B': 4.0, 'O': 12.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1210156,\"{'Re': 2.0, 'Pd': 1.0, 'N': 4.0, 'O': 8.0}\",\"('N', 'O', 'Pd', 'Re')\",OTHERS,True\nmp-1182808,\"{'Cr': 4.0, 'P': 8.0, 'H': 64.0, 'N': 12.0, 'O': 40.0}\",\"('Cr', 'H', 'N', 'O', 'P')\",OTHERS,True\nmp-602355,\"{'Cr': 6.0, 'H': 2.0, 'O': 16.0}\",\"('Cr', 'H', 'O')\",OTHERS,True\nmp-979416,\"{'Ta': 2.0, 'Be': 2.0, 'O': 5.0}\",\"('Be', 'O', 'Ta')\",OTHERS,True\nmp-1079250,\"{'Sm': 2.0, 'Ni': 2.0, 'Sb': 4.0}\",\"('Ni', 'Sb', 'Sm')\",OTHERS,True\nmp-22875,\"{'Br': 1.0, 'Tl': 1.0}\",\"('Br', 'Tl')\",OTHERS,True\nmp-1184421,\"{'Gd': 6.0, 'Cd': 2.0}\",\"('Cd', 'Gd')\",OTHERS,True\nmp-1100745,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-24111,\"{'H': 24.0, 'O': 28.0, 'S': 4.0, 'Co': 2.0, 'Rb': 4.0}\",\"('Co', 'H', 'O', 'Rb', 'S')\",OTHERS,True\nmp-1180144,\"{'Mn': 1.0, 'Mo': 3.0}\",\"('Mn', 'Mo')\",OTHERS,True\nmp-556745,\"{'Tl': 4.0, 'C': 4.0, 'O': 8.0}\",\"('C', 'O', 'Tl')\",OTHERS,True\nmp-1215244,\"{'Zr': 1.0, 'Nb': 2.0, 'Pd': 9.0}\",\"('Nb', 'Pd', 'Zr')\",OTHERS,True\nmp-41191,\"{'F': 1.0, 'Na': 3.0, 'O': 10.0, 'P': 2.0, 'Ti': 2.0}\",\"('F', 'Na', 'O', 'P', 'Ti')\",OTHERS,True\nmp-1207921,\"{'U': 2.0, 'Fe': 8.0, 'B': 2.0}\",\"('B', 'Fe', 'U')\",OTHERS,True\nmp-1184948,\"{'K': 1.0, 'Mn': 1.0, 'O': 3.0}\",\"('K', 'Mn', 'O')\",OTHERS,True\nmvc-8328,\"{'Zn': 1.0, 'Fe': 4.0, 'Cu': 3.0, 'O': 12.0}\",\"('Cu', 'Fe', 'O', 'Zn')\",OTHERS,True\nmp-985587,\"{'Al': 6.0, 'O': 9.0}\",\"('Al', 'O')\",OTHERS,True\nmp-1223322,\"{'K': 1.0, 'Rb': 1.0, 'Mn': 1.0, 'F': 6.0}\",\"('F', 'K', 'Mn', 'Rb')\",OTHERS,True\nmp-1216066,\"{'Y': 2.0, 'Co': 1.0, 'Ru': 3.0}\",\"('Co', 'Ru', 'Y')\",OTHERS,True\nmp-1027645,\"{'Mo': 3.0, 'W': 1.0, 'S': 8.0}\",\"('Mo', 'S', 'W')\",OTHERS,True\nmp-1178643,\"{'Zn': 1.0, 'Cr': 1.0, 'Cu': 3.0, 'Se': 4.0}\",\"('Cr', 'Cu', 'Se', 'Zn')\",OTHERS,True\nmp-783904,\"{'Ni': 2.0, 'H': 40.0, 'S': 4.0, 'N': 4.0, 'O': 28.0}\",\"('H', 'N', 'Ni', 'O', 'S')\",OTHERS,True\nmp-683897,\"{'Pr': 6.0, 'Sn': 26.0, 'Rh': 8.0}\",\"('Pr', 'Rh', 'Sn')\",OTHERS,True\nmp-778793,\"{'Li': 4.0, 'Fe': 3.0, 'Si': 3.0, 'O': 12.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,True\nmp-766308,\"{'Ba': 4.0, 'Ca': 4.0, 'I': 16.0}\",\"('Ba', 'Ca', 'I')\",OTHERS,True\nmp-686280,\"{'Nd': 12.0, 'Al': 3.0, 'Si': 15.0, 'N': 21.0, 'O': 21.0}\",\"('Al', 'N', 'Nd', 'O', 'Si')\",OTHERS,True\nmp-1216446,\"{'V': 9.0, 'Te': 8.0}\",\"('Te', 'V')\",OTHERS,True\nmp-626872,\"{'V': 12.0, 'H': 8.0, 'O': 32.0}\",\"('H', 'O', 'V')\",OTHERS,True\nmp-5960,\"{'O': 12.0, 'Ca': 6.0, 'U': 2.0}\",\"('Ca', 'O', 'U')\",OTHERS,True\nmp-1198997,\"{'C': 16.0, 'S': 8.0, 'N': 8.0, 'O': 8.0, 'F': 8.0}\",\"('C', 'F', 'N', 'O', 'S')\",OTHERS,True\nmp-1219422,\"{'Sc': 28.0, 'As': 12.0}\",\"('As', 'Sc')\",OTHERS,True\nmp-757593,\"{'Li': 4.0, 'Co': 5.0, 'Sb': 1.0, 'O': 12.0}\",\"('Co', 'Li', 'O', 'Sb')\",OTHERS,True\nmp-1245874,\"{'Ca': 6.0, 'B': 6.0, 'N': 10.0}\",\"('B', 'Ca', 'N')\",OTHERS,True\n" }, { "path": "samples/predict_hypermaterials/QC_AC_data.csv", "content": ",composition,elements,label,for_test\nZn 58 Mg 40 Dy 2,\"{'Zn': 58.0, 'Mg': 40.0, 'Dy': 2.0}\",\"('Dy', 'Mg', 'Zn')\",QC,False\nAl 88.6 Cu 19.4 Li 50.3,\"{'Al': 88.6, 'Cu': 19.4, 'Li': 50.3}\",\"('Al', 'Cu', 'Li')\",AC,False\nGa 43 Co 47 Cu 10,\"{'Ga': 43.0, 'Co': 47.0, 'Cu': 10.0}\",\"('Co', 'Cu', 'Ga')\",QC,False\nZn 58 Mg 40 Tm 2,\"{'Zn': 58.0, 'Mg': 40.0, 'Tm': 2.0}\",\"('Mg', 'Tm', 'Zn')\",QC,False\nCd 65 Mg 20 Lu 15,\"{'Cd': 65.0, 'Mg': 20.0, 'Lu': 15.0}\",\"('Cd', 'Lu', 'Mg')\",QC,False\nAl 39 Pd 21 Fe 2,\"{'Al': 39.0, 'Pd': 21.0, 'Fe': 2.0}\",\"('Al', 'Fe', 'Pd')\",AC,False\nAu 60.7 Sn 25.2 Eu 14.1,\"{'Au': 60.7, 'Sn': 25.2, 'Eu': 14.1}\",\"('Au', 'Eu', 'Sn')\",AC,False\nAl 70 Pd 21 Tc 9,\"{'Al': 70.0, 'Pd': 21.0, 'Tc': 9.0}\",\"('Al', 'Pd', 'Tc')\",QC,False\nAl 70.5 Pd 21 Mn 8.5,\"{'Al': 70.5, 'Pd': 21.0, 'Mn': 8.5}\",\"('Al', 'Mn', 'Pd')\",QC,False\nZn 76 Yb 14 Mg 10,\"{'Zn': 76.0, 'Yb': 14.0, 'Mg': 10.0}\",\"('Mg', 'Yb', 'Zn')\",QC,False\nAl 70 Ni 20 Rh 10,\"{'Al': 70.0, 'Ni': 20.0, 'Rh': 10.0}\",\"('Al', 'Ni', 'Rh')\",QC,False\nCd 6 Yb 1,\"{'Cd': 6.0, 'Yb': 1.0}\",\"('Cd', 'Yb')\",AC,False\nAg 42.5 Ga 42.5 Yb 15,\"{'Ag': 42.5, 'Ga': 42.5, 'Yb': 15.0}\",\"('Ag', 'Ga', 'Yb')\",AC,False\nAl 68 Pd 20 Ru 12,\"{'Al': 68.0, 'Pd': 20.0, 'Ru': 12.0}\",\"('Al', 'Pd', 'Ru')\",AC,False\nAl 73 Ir 14.5 Os 12.5,\"{'Al': 73.0, 'Ir': 14.5, 'Os': 12.5}\",\"('Al', 'Ir', 'Os')\",QC,False\nAu 49.7 In 35.4 Ce 14.9,\"{'Au': 49.7, 'In': 35.4, 'Ce': 14.9}\",\"('Au', 'Ce', 'In')\",AC,False\nAl 65 Cu 20 Ru 15,\"{'Al': 65.0, 'Cu': 20.0, 'Ru': 15.0}\",\"('Al', 'Cu', 'Ru')\",QC,False\nZn 80 Sc 15 Mg 5,\"{'Zn': 80.0, 'Sc': 15.0, 'Mg': 5.0}\",\"('Mg', 'Sc', 'Zn')\",QC,False\nGa 35 V 45 Ni 6 Si 14,\"{'Ga': 35.0, 'V': 45.0, 'Ni': 6.0, 'Si': 14.0}\",\"('Ga', 'Ni', 'Si', 'V')\",QC,False\nAl 71.5 Co 28.5,\"{'Al': 71.5, 'Co': 28.5}\",\"('Al', 'Co')\",AC,False\nTa 181 Te 112,\"{'Ta': 181.0, 'Te': 112.0}\",\"('Ta', 'Te')\",AC,False\nCd 65 Mg 20 Y 15,\"{'Cd': 65.0, 'Mg': 20.0, 'Y': 15.0}\",\"('Cd', 'Mg', 'Y')\",QC,False\nAu 61.2 Sn 23.9 Dy 15.2,\"{'Au': 61.2, 'Sn': 23.9, 'Dy': 15.2}\",\"('Au', 'Dy', 'Sn')\",AC,False\nAg 42.9 In 43.6 Eu 13.5,\"{'Ag': 42.9, 'In': 43.6, 'Eu': 13.5}\",\"('Ag', 'Eu', 'In')\",AC,False\nGa 33 Fe 46 Cu 3 Si 18,\"{'Ga': 33.0, 'Fe': 46.0, 'Cu': 3.0, 'Si': 18.0}\",\"('Cu', 'Fe', 'Ga', 'Si')\",QC,False\nAl 76 Co 24,\"{'Al': 76.0, 'Co': 24.0}\",\"('Al', 'Co')\",AC,False\nAg 47.7 In 38.7 Ce 14.2,\"{'Ag': 47.7, 'In': 38.7, 'Ce': 14.2}\",\"('Ag', 'Ce', 'In')\",AC,False\nZn 77 Sc 7 Tm 9 Fe 7,\"{'Zn': 77.0, 'Sc': 7.0, 'Tm': 9.0, 'Fe': 7.0}\",\"('Fe', 'Sc', 'Tm', 'Zn')\",QC,False\nAg 42 In 45 Ca 13,\"{'Ag': 42.0, 'In': 45.0, 'Ca': 13.0}\",\"('Ag', 'Ca', 'In')\",AC,False\nZn 40 Mg 39.5 Ga 25,\"{'Zn': 40.0, 'Mg': 39.5, 'Ga': 25.0}\",\"('Ga', 'Mg', 'Zn')\",QC,False\nAl 70.5 Mn 16.5 Pd 13,\"{'Al': 70.5, 'Mn': 16.5, 'Pd': 13.0}\",\"('Al', 'Mn', 'Pd')\",QC,False\nMn 82 Si 15 Al 3,\"{'Mn': 82.0, 'Si': 15.0, 'Al': 3.0}\",\"('Al', 'Mn', 'Si')\",QC,False\nAl 65 Cu 20 Fe 15,\"{'Al': 65.0, 'Cu': 20.0, 'Fe': 15.0}\",\"('Al', 'Cu', 'Fe')\",QC,False\nTa 1.6 Te 1,\"{'Ta': 1.6, 'Te': 1.0}\",\"('Ta', 'Te')\",QC,False\nCd 65 Mg 20 Tm 15,\"{'Cd': 65.0, 'Mg': 20.0, 'Tm': 15.0}\",\"('Cd', 'Mg', 'Tm')\",QC,False\nCd 84 Yb 16,\"{'Cd': 84.0, 'Yb': 16.0}\",\"('Cd', 'Yb')\",QC,False\nAl 73.2 Co 26.8,\"{'Al': 73.2, 'Co': 26.8}\",\"('Al', 'Co')\",AC,False\nCd 65 Mg 20 Dy 15,\"{'Cd': 65.0, 'Mg': 20.0, 'Dy': 15.0}\",\"('Cd', 'Dy', 'Mg')\",QC,False\nAu 44.2 In 41.7 Ca 14.1,\"{'Au': 44.2, 'In': 41.7, 'Ca': 14.1}\",\"('Au', 'Ca', 'In')\",QC,False\nZn 75 Sc 15 Ag 10,\"{'Zn': 75.0, 'Sc': 15.0, 'Ag': 10.0}\",\"('Ag', 'Sc', 'Zn')\",QC,False\nCd 25 Eu 4,\"{'Cd': 25.0, 'Eu': 4.0}\",\"('Cd', 'Eu')\",AC,False\nZn 58 Mg 40 Er 2,\"{'Zn': 58.0, 'Mg': 40.0, 'Er': 2.0}\",\"('Er', 'Mg', 'Zn')\",QC,False\nAu 50.5 Ga 35.9 Ca 13.6,\"{'Au': 50.5, 'Ga': 35.9, 'Ca': 13.6}\",\"('Au', 'Ca', 'Ga')\",AC,False\nAg 2 In 4 Yb 1,\"{'Ag': 2.0, 'In': 4.0, 'Yb': 1.0}\",\"('Ag', 'In', 'Yb')\",AC,False\nZn 58 Mg 40 Y 2,\"{'Zn': 58.0, 'Mg': 40.0, 'Y': 2.0}\",\"('Mg', 'Y', 'Zn')\",QC,False\nZn 56.8 Er 8.7 Mg 34.6,\"{'Zn': 56.8, 'Er': 8.7, 'Mg': 34.6}\",\"('Er', 'Mg', 'Zn')\",QC,False\nCd 76 Ca 13,\"{'Cd': 76.0, 'Ca': 13.0}\",\"('Ca', 'Cd')\",AC,False\nZn 73.6 Mg 2.5 Sc 11.2,\"{'Zn': 73.6, 'Mg': 2.5, 'Sc': 11.2}\",\"('Mg', 'Sc', 'Zn')\",AC,False\nV 15 Ni 10 Si 1,\"{'V': 15.0, 'Ni': 10.0, 'Si': 1.0}\",\"('Ni', 'Si', 'V')\",QC,False\nMn 4 Si 1,\"{'Mn': 4.0, 'Si': 1.0}\",\"('Mn', 'Si')\",QC,False\nGa 3.22 Ni 2.78 Zr 1,\"{'Ga': 3.22, 'Ni': 2.78, 'Zr': 1.0}\",\"('Ga', 'Ni', 'Zr')\",AC,False\nZn 77 Sc 8 Ho 8 Fe 7,\"{'Zn': 77.0, 'Sc': 8.0, 'Ho': 8.0, 'Fe': 7.0}\",\"('Fe', 'Ho', 'Sc', 'Zn')\",QC,False\nZn 84 Ti 8 Mg 8,\"{'Zn': 84.0, 'Ti': 8.0, 'Mg': 8.0}\",\"('Mg', 'Ti', 'Zn')\",QC,False\nAl 70 Pd 23 Mn 6 Si 1,\"{'Al': 70.0, 'Pd': 23.0, 'Mn': 6.0, 'Si': 1.0}\",\"('Al', 'Mn', 'Pd', 'Si')\",AC,False\nAu 47.2 In 37.2 Gd 15.6,\"{'Au': 47.2, 'In': 37.2, 'Gd': 15.6}\",\"('Au', 'Gd', 'In')\",AC,False\nGa 3.64 Ni 2.36 Sc 1,\"{'Ga': 3.64, 'Ni': 2.36, 'Sc': 1.0}\",\"('Ga', 'Ni', 'Sc')\",AC,False\nMg 43 Al 42 Pd 15,\"{'Mg': 43.0, 'Al': 42.0, 'Pd': 15.0}\",\"('Al', 'Mg', 'Pd')\",QC,False\nAu 62.3 Sn 23.1 Gd 14.6,\"{'Au': 62.3, 'Sn': 23.1, 'Gd': 14.6}\",\"('Au', 'Gd', 'Sn')\",AC,False\nCd 65 Mg 20 Ca 15,\"{'Cd': 65.0, 'Mg': 20.0, 'Ca': 15.0}\",\"('Ca', 'Cd', 'Mg')\",QC,False\nAg 41 In 44 Yb 15,\"{'Ag': 41.0, 'In': 44.0, 'Yb': 15.0}\",\"('Ag', 'In', 'Yb')\",AC,False\nAl 74.8 Co 17.0 Ni 8.2,\"{'Al': 74.8, 'Co': 17.0, 'Ni': 8.2}\",\"('Al', 'Co', 'Ni')\",AC,False\nAl 72 Pd 17 Os 11,\"{'Al': 72.0, 'Pd': 17.0, 'Os': 11.0}\",\"('Al', 'Os', 'Pd')\",QC,False\nCd 85 Ca 15,\"{'Cd': 85.0, 'Ca': 15.0}\",\"('Ca', 'Cd')\",QC,False\nAl 71 Pd 21 Re 8,\"{'Al': 71.0, 'Pd': 21.0, 'Re': 8.0}\",\"('Al', 'Pd', 'Re')\",QC,False\nV 3 Ni 2,\"{'V': 3.0, 'Ni': 2.0}\",\"('Ni', 'V')\",QC,False\nCd 65 Mg 20 Tb 15,\"{'Cd': 65.0, 'Mg': 20.0, 'Tb': 15.0}\",\"('Cd', 'Mg', 'Tb')\",QC,False\nAg 2 In 4 Ca 1,\"{'Ag': 2.0, 'In': 4.0, 'Ca': 1.0}\",\"('Ag', 'Ca', 'In')\",AC,False\nAl 40 Mn 10.1 Si 7.4,\"{'Al': 40.0, 'Mn': 10.1, 'Si': 7.4}\",\"('Al', 'Mn', 'Si')\",AC,False\nAg 42.2 In 42.6 Tm 15.2,\"{'Ag': 42.2, 'In': 42.6, 'Tm': 15.2}\",\"('Ag', 'In', 'Tm')\",AC,False\nAu 61.2 Sn 24.3 Ca 14.5,\"{'Au': 61.2, 'Sn': 24.3, 'Ca': 14.5}\",\"('Au', 'Ca', 'Sn')\",AC,False\nZn 65 Mg 26 Ho 9,\"{'Zn': 65.0, 'Mg': 26.0, 'Ho': 9.0}\",\"('Ho', 'Mg', 'Zn')\",QC,False\nAu 64.2 Sn 21.3 Pr 14.5,\"{'Au': 64.2, 'Sn': 21.3, 'Pr': 14.5}\",\"('Au', 'Pr', 'Sn')\",AC,False\nCd 6 Gd 1,\"{'Cd': 6.0, 'Gd': 1.0}\",\"('Cd', 'Gd')\",AC,False\nAg 43.4 In 42.8 Eu 13.8,\"{'Ag': 43.4, 'In': 42.8, 'Eu': 13.8}\",\"('Ag', 'Eu', 'In')\",AC,False\nAl 66.6 Rh 26.1 Si 7.3,\"{'Al': 66.6, 'Rh': 26.1, 'Si': 7.3}\",\"('Al', 'Rh', 'Si')\",AC,False\nAl 67 Pd 11 Mn 14 Si 7,\"{'Al': 67.0, 'Pd': 11.0, 'Mn': 14.0, 'Si': 7.0}\",\"('Al', 'Mn', 'Pd', 'Si')\",AC,False\nZn 61.4 Mg 24.5 Er 14.1,\"{'Zn': 61.4, 'Mg': 24.5, 'Er': 14.1}\",\"('Er', 'Mg', 'Zn')\",AC,False\nGa 3.85 Ni 2.15 Hf 1,\"{'Ga': 3.85, 'Ni': 2.15, 'Hf': 1.0}\",\"('Ga', 'Hf', 'Ni')\",AC,False\nAl 75 Os 10 Pd 15,\"{'Al': 75.0, 'Os': 10.0, 'Pd': 15.0}\",\"('Al', 'Os', 'Pd')\",QC,False\nAg 42 In 42 Yb 16,\"{'Ag': 42.0, 'In': 42.0, 'Yb': 16.0}\",\"('Ag', 'In', 'Yb')\",QC,False\nZn 77 Mg 18 Hf 5,\"{'Zn': 77.0, 'Mg': 18.0, 'Hf': 5.0}\",\"('Hf', 'Mg', 'Zn')\",AC,False\nAu 61.1 Ga 25.0 Ca 13.9,\"{'Au': 61.1, 'Ga': 25.0, 'Ca': 13.9}\",\"('Au', 'Ca', 'Ga')\",AC,False\nZn 55 Mg 40 Nd 5,\"{'Zn': 55.0, 'Mg': 40.0, 'Nd': 5.0}\",\"('Mg', 'Nd', 'Zn')\",QC,False\nAl 57.3 Cu 31.4 Ru 11.3,\"{'Al': 57.3, 'Cu': 31.4, 'Ru': 11.3}\",\"('Al', 'Cu', 'Ru')\",AC,False\nCd 65 Mg 20 Er 15,\"{'Cd': 65.0, 'Mg': 20.0, 'Er': 15.0}\",\"('Cd', 'Er', 'Mg')\",QC,False\nAl 71.8 Co 21.1 Ni 7.1,\"{'Al': 71.8, 'Co': 21.1, 'Ni': 7.1}\",\"('Al', 'Co', 'Ni')\",AC,False\nZn 75 Sc 15 Co 10,\"{'Zn': 75.0, 'Sc': 15.0, 'Co': 10.0}\",\"('Co', 'Sc', 'Zn')\",QC,False\nAl 65 Cu 20 Ir 15,\"{'Al': 65.0, 'Cu': 20.0, 'Ir': 15.0}\",\"('Al', 'Cu', 'Ir')\",QC,False\nAu 37 In 39.6 Ca 12.6,\"{'Au': 37.0, 'In': 39.6, 'Ca': 12.6}\",\"('Au', 'Ca', 'In')\",AC,False\nZn 77 Sc 8 Er 8 Fe 7,\"{'Zn': 77.0, 'Sc': 8.0, 'Er': 8.0, 'Fe': 7.0}\",\"('Er', 'Fe', 'Sc', 'Zn')\",QC,False\nZn 58 Mg 40 Lu 2,\"{'Zn': 58.0, 'Mg': 40.0, 'Lu': 2.0}\",\"('Lu', 'Mg', 'Zn')\",QC,False\nCu 48 Sc 15 Ga 34 Mg 3,\"{'Cu': 48.0, 'Sc': 15.0, 'Ga': 34.0, 'Mg': 3.0}\",\"('Cu', 'Ga', 'Mg', 'Sc')\",QC,False\nZn 74 Mg 15 Ho 11,\"{'Zn': 74.0, 'Mg': 15.0, 'Ho': 11.0}\",\"('Ho', 'Mg', 'Zn')\",QC,False\nAl 6 Cu 1 Li 3,\"{'Al': 6.0, 'Cu': 1.0, 'Li': 3.0}\",\"('Al', 'Cu', 'Li')\",QC,False\nAl 72 Pd 17 Ru 11,\"{'Al': 72.0, 'Pd': 17.0, 'Ru': 11.0}\",\"('Al', 'Pd', 'Ru')\",QC,False\nZn 47.3 Mg 27 Al 10.7,\"{'Zn': 47.3, 'Mg': 27.0, 'Al': 10.7}\",\"('Al', 'Mg', 'Zn')\",AC,False\nZn 17 Yb 3,\"{'Zn': 17.0, 'Yb': 3.0}\",\"('Yb', 'Zn')\",AC,False\nAg 41.7 In 43.2 Yb 15.1,\"{'Ag': 41.7, 'In': 43.2, 'Yb': 15.1}\",\"('Ag', 'In', 'Yb')\",AC,False\nCd 65 Mg 20 Yb 15,\"{'Cd': 65.0, 'Mg': 20.0, 'Yb': 15.0}\",\"('Cd', 'Mg', 'Yb')\",QC,False\nZn 64 Mg 25 Y 11,\"{'Zn': 64.0, 'Mg': 25.0, 'Y': 11.0}\",\"('Mg', 'Y', 'Zn')\",QC,False\nZn 77 Mg 17.5 Ti 5.5,\"{'Zn': 77.0, 'Mg': 17.5, 'Ti': 5.5}\",\"('Mg', 'Ti', 'Zn')\",AC,False\nCd 6 Ca 1,\"{'Cd': 6.0, 'Ca': 1.0}\",\"('Ca', 'Cd')\",AC,False\nAl 65 Cu 15 Rh 20,\"{'Al': 65.0, 'Cu': 15.0, 'Rh': 20.0}\",\"('Al', 'Cu', 'Rh')\",QC,False\nZn 58 Mg 40 Ho 2,\"{'Zn': 58.0, 'Mg': 40.0, 'Ho': 2.0}\",\"('Ho', 'Mg', 'Zn')\",QC,False\nCd 6 Dy 1,\"{'Cd': 6.0, 'Dy': 1.0}\",\"('Cd', 'Dy')\",AC,False\nCd 6 Sr 1,\"{'Cd': 6.0, 'Sr': 1.0}\",\"('Cd', 'Sr')\",AC,False\nCr 5 Ni 3 Si 2,\"{'Cr': 5.0, 'Ni': 3.0, 'Si': 2.0}\",\"('Cr', 'Ni', 'Si')\",QC,False\nCd 6 Sm 1,\"{'Cd': 6.0, 'Sm': 1.0}\",\"('Cd', 'Sm')\",AC,False\nAl 70 Pd 20 Re 10,\"{'Al': 70.0, 'Pd': 20.0, 'Re': 10.0}\",\"('Al', 'Pd', 'Re')\",QC,False\nAl 72.5 Pd 27.5,\"{'Al': 72.5, 'Pd': 27.5}\",\"('Al', 'Pd')\",AC,False\nAu 42 In 42 Yb 16,\"{'Au': 42.0, 'In': 42.0, 'Yb': 16.0}\",\"('Au', 'In', 'Yb')\",AC,False\nZn 75 Sc 15 Fe 10,\"{'Zn': 75.0, 'Sc': 15.0, 'Fe': 10.0}\",\"('Fe', 'Sc', 'Zn')\",QC,False\nAg 46.4 In 39.7 Gd 13.9,\"{'Ag': 46.4, 'In': 39.7, 'Gd': 13.9}\",\"('Ag', 'Gd', 'In')\",AC,False\nBe 17 Ru 3,\"{'Be': 17.0, 'Ru': 3.0}\",\"('Be', 'Ru')\",AC,False\nAl 65 Cu 20 Co 15,\"{'Al': 65.0, 'Cu': 20.0, 'Co': 15.0}\",\"('Al', 'Co', 'Cu')\",QC,False\nZn 56.8 Mg 34.6 Tb 8.7,\"{'Zn': 56.8, 'Mg': 34.6, 'Tb': 8.7}\",\"('Mg', 'Tb', 'Zn')\",QC,False\nAl 71.5 Co 23.6 Cu 4.9,\"{'Al': 71.5, 'Co': 23.6, 'Cu': 4.9}\",\"('Al', 'Co', 'Cu')\",AC,False\nCd 65 Mg 20 Gd 15,\"{'Cd': 65.0, 'Mg': 20.0, 'Gd': 15.0}\",\"('Cd', 'Gd', 'Mg')\",QC,False\nAl 55 Cu 25.5 Fe 12.5 Si 7,\"{'Al': 55.0, 'Cu': 25.5, 'Fe': 12.5, 'Si': 7.0}\",\"('Al', 'Cu', 'Fe', 'Si')\",AC,False\nZn 75 Sc 15 Mn 10,\"{'Zn': 75.0, 'Sc': 15.0, 'Mn': 10.0}\",\"('Mn', 'Sc', 'Zn')\",QC,False\nZn 76 Mg 17 Hf 7,\"{'Zn': 76.0, 'Mg': 17.0, 'Hf': 7.0}\",\"('Hf', 'Mg', 'Zn')\",QC,False\nAl 75 Ru 10 Pd 15,\"{'Al': 75.0, 'Ru': 10.0, 'Pd': 15.0}\",\"('Al', 'Pd', 'Ru')\",QC,False\nAu 12.2 In 6.3 Ca 3,\"{'Au': 12.2, 'In': 6.3, 'Ca': 3.0}\",\"('Au', 'Ca', 'In')\",AC,False\nAl 55.1 Cu 14.6 Ru 20.2 Si 10.1,\"{'Al': 55.1, 'Cu': 14.6, 'Ru': 20.2, 'Si': 10.1}\",\"('Al', 'Cu', 'Ru', 'Si')\",AC,False\nZn 75 Sc 15 Au 10,\"{'Zn': 75.0, 'Sc': 15.0, 'Au': 10.0}\",\"('Au', 'Sc', 'Zn')\",QC,False\nCd 19 Pr 3,\"{'Cd': 19.0, 'Pr': 3.0}\",\"('Cd', 'Pr')\",AC,False\nZn 72 Sc 16 Cu 12,\"{'Zn': 72.0, 'Sc': 16.0, 'Cu': 12.0}\",\"('Cu', 'Sc', 'Zn')\",QC,True\nAu 42.9 In 41.9 Yb 15.2,\"{'Au': 42.9, 'In': 41.9, 'Yb': 15.2}\",\"('Au', 'In', 'Yb')\",AC,True\nZn 75 Sc 15 Ni 10,\"{'Zn': 75.0, 'Sc': 15.0, 'Ni': 10.0}\",\"('Ni', 'Sc', 'Zn')\",QC,True\nGa 2.6 Cu 3.4 Lu 1,\"{'Ga': 2.6, 'Cu': 3.4, 'Lu': 1.0}\",\"('Cu', 'Ga', 'Lu')\",AC,True\nAl 70 Ni 20 Ru 10,\"{'Al': 70.0, 'Ni': 20.0, 'Ru': 10.0}\",\"('Al', 'Ni', 'Ru')\",QC,True\nZn 75 Sc 15 Pd 10,\"{'Zn': 75.0, 'Sc': 15.0, 'Pd': 10.0}\",\"('Pd', 'Sc', 'Zn')\",QC,True\nAl 65 Cu 20 Os 15,\"{'Al': 65.0, 'Cu': 20.0, 'Os': 15.0}\",\"('Al', 'Cu', 'Os')\",QC,True\nGa 2.3 Cu 3.7 Sc 1,\"{'Ga': 2.3, 'Cu': 3.7, 'Sc': 1.0}\",\"('Cu', 'Ga', 'Sc')\",AC,True\nAg 46.9 In 38.7 Pr 14.4,\"{'Ag': 46.9, 'In': 38.7, 'Pr': 14.4}\",\"('Ag', 'In', 'Pr')\",AC,True\nZn 34.6 Mg 40 Al 25.4,\"{'Zn': 34.6, 'Mg': 40.0, 'Al': 25.4}\",\"('Al', 'Mg', 'Zn')\",AC,True\nAu 65 Sn 20 Ce 15,\"{'Au': 65.0, 'Sn': 20.0, 'Ce': 15.0}\",\"('Au', 'Ce', 'Sn')\",AC,True\nCd 6 Nd 1,\"{'Cd': 6.0, 'Nd': 1.0}\",\"('Cd', 'Nd')\",AC,True\nZn 75 Sc 15 Pt 10,\"{'Zn': 75.0, 'Sc': 15.0, 'Pt': 10.0}\",\"('Pt', 'Sc', 'Zn')\",QC,True\nZn 40 Mg 39.5 Ga 16.4 Al 4.1,\"{'Zn': 40.0, 'Mg': 39.5, 'Ga': 16.4, 'Al': 4.1}\",\"('Al', 'Ga', 'Mg', 'Zn')\",AC,True\nZn 37 Mg 46 Al 17,\"{'Zn': 37.0, 'Mg': 46.0, 'Al': 17.0}\",\"('Al', 'Mg', 'Zn')\",AC,True\nZn 17 Sc 3,\"{'Zn': 17.0, 'Sc': 3.0}\",\"('Sc', 'Zn')\",AC,True\nAg 42 In 42 Ca 16,\"{'Ag': 42.0, 'In': 42.0, 'Ca': 16.0}\",\"('Ag', 'Ca', 'In')\",QC,True\nAu 61.2 Sn 24.5 Eu 14.3,\"{'Au': 61.2, 'Sn': 24.5, 'Eu': 14.3}\",\"('Au', 'Eu', 'Sn')\",AC,True\nCd 6 Y 1,\"{'Cd': 6.0, 'Y': 1.0}\",\"('Cd', 'Y')\",AC,True\nZn 84 Mg 7 Zr 9,\"{'Zn': 84.0, 'Mg': 7.0, 'Zr': 9.0}\",\"('Mg', 'Zn', 'Zr')\",QC,True\nCd 65 Mg 20 Ho 15,\"{'Cd': 65.0, 'Mg': 20.0, 'Ho': 15.0}\",\"('Cd', 'Ho', 'Mg')\",QC,True\nZn 56.8 Mg 34.6 Dy 8.7,\"{'Zn': 56.8, 'Mg': 34.6, 'Dy': 8.7}\",\"('Dy', 'Mg', 'Zn')\",QC,True\nAl 71.5 Cu 8.5 Ru 20,\"{'Al': 71.5, 'Cu': 8.5, 'Ru': 20.0}\",\"('Al', 'Cu', 'Ru')\",AC,True\nAl 75 Mn 20 Pd 5,\"{'Al': 75.0, 'Mn': 20.0, 'Pd': 5.0}\",\"('Al', 'Mn', 'Pd')\",AC,True\nTa 97 Te 60,\"{'Ta': 97.0, 'Te': 60.0}\",\"('Ta', 'Te')\",AC,True\nZn 77 Mg 18 Zr 5,\"{'Zn': 77.0, 'Mg': 18.0, 'Zr': 5.0}\",\"('Mg', 'Zn', 'Zr')\",AC,True\nNi 70.6 Cr 29.4,\"{'Ni': 70.6, 'Cr': 29.4}\",\"('Cr', 'Ni')\",QC,True\nAl 40 Mn 25 Fe 15 Ge 20,\"{'Al': 40.0, 'Mn': 25.0, 'Fe': 15.0, 'Ge': 20.0}\",\"('Al', 'Fe', 'Ge', 'Mn')\",QC,True\nAl 75 Pd 13 Ru 12,\"{'Al': 75.0, 'Pd': 13.0, 'Ru': 12.0}\",\"('Al', 'Pd', 'Ru')\",AC,True\nAu 60.3 Sn 24.6 Yb 15.1,\"{'Au': 60.3, 'Sn': 24.6, 'Yb': 15.1}\",\"('Au', 'Sn', 'Yb')\",AC,True\nCu 46 Sc 16 Al 38,\"{'Cu': 46.0, 'Sc': 16.0, 'Al': 38.0}\",\"('Al', 'Cu', 'Sc')\",QC,True\nAl 70 Ni 15 Fe 15,\"{'Al': 70.0, 'Ni': 15.0, 'Fe': 15.0}\",\"('Al', 'Fe', 'Ni')\",QC,True\n" }, { "path": "samples/predict_hypermaterials/model_training_and_vitural_screening.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This notebook shows how to train a random forest classifier that classifies any given chemical composition into one of three kinds of material types: quasicrystals (QCs), approximant crystals (ACs), and others (OTHERS) including ordinary periodic crystals, and to perform a virtual screening to predict a set of chemical compositions to form quasicrystalline phases. The dataset includes chemical compositions of 80 QCs, 78 ACs, and 10,000 ordinary crystals that have been discovered to date.\\n\",\n \"\\n\",\n \"You can assess [our paper](https://onlinelibrary.wiley.com/doi/10.1002/adma.202102507) to know the details of the data analysis workflow. The pre-trained model can be downloaded from [here](https://github.com/yoshida-lab/XenonPy/releases/download/v0.6.5/best_QC_AC_classification_model.pkl.z).\\n\",\n \"\\n\",\n \"XenonPy with version v0.6.5 or higher is needed to execute the following codes. Please follow the [official installation guide](https://xenonpy.readthedocs.io/en/latest/installation.html#using-conda-and-pip) to prepare your local environment.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%run tools.ipynb\\n\",\n \"\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"import seaborn as sns\\n\",\n \"import matplotlib\\n\",\n \"\\n\",\n \"from pathlib import Path\\n\",\n \"from matplotlib import pyplot as plt\\n\",\n \"\\n\",\n \"# user-friendly print\\n\",\n \"from IPython.core.interactiveshell import InteractiveShell\\n\",\n \"InteractiveShell.ast_node_interactivity = \\\"all\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'0.6.5'\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# version should greater than 0.6.5\\n\",\n \"\\n\",\n \"import xenonpy as xe\\n\",\n \"xe.__version__\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Model training\\n\",\n \"\\n\",\n \"The model is trained using the optimized hyperparameters in the previous paper; the list of chemical compositions for `QC` and `AC` was collected from the literature, and the list of 10,000 compositions for `OTHERS` was randomly selected from the [Materials Project database](https://materialsproject.org/). \\n\",\n \"\\n\",\n \"#### load training data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
compositionelementslabelfor_test
mp-1077980{'Sr': 1.0, 'Ag': 4.0, 'Sb': 2.0}(Ag, Sb, Sr)OTHERSFalse
mp-30079{'Li': 1.0, 'Mg': 2.0, 'Ge': 1.0}(Ge, Li, Mg)OTHERSFalse
mvc-2967{'Ba': 2.0, 'Tl': 1.0, 'Fe': 2.0, 'O': 7.0}(Ba, Fe, O, Tl)OTHERSFalse
mp-1202279{'K': 18.0, 'Ho': 18.0, 'F': 72.0}(F, Ho, K)OTHERSFalse
mp-34421{'Bi': 3.0, 'Cd': 1.0, 'O': 7.0}(Bi, Cd, O)OTHERSFalse
...............
mp-5960{'O': 12.0, 'Ca': 6.0, 'U': 2.0}(Ca, O, U)OTHERSTrue
mp-1198997{'C': 16.0, 'S': 8.0, 'N': 8.0, 'O': 8.0, 'F':...(C, F, N, O, S)OTHERSTrue
mp-1219422{'Sc': 28.0, 'As': 12.0}(As, Sc)OTHERSTrue
mp-757593{'Li': 4.0, 'Co': 5.0, 'Sb': 1.0, 'O': 12.0}(Co, Li, O, Sb)OTHERSTrue
mp-1245874{'Ca': 6.0, 'B': 6.0, 'N': 10.0}(B, Ca, N)OTHERSTrue
\\n\",\n \"

10158 rows × 4 columns

\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" composition \\\\\\n\",\n \"mp-1077980 {'Sr': 1.0, 'Ag': 4.0, 'Sb': 2.0} \\n\",\n \"mp-30079 {'Li': 1.0, 'Mg': 2.0, 'Ge': 1.0} \\n\",\n \"mvc-2967 {'Ba': 2.0, 'Tl': 1.0, 'Fe': 2.0, 'O': 7.0} \\n\",\n \"mp-1202279 {'K': 18.0, 'Ho': 18.0, 'F': 72.0} \\n\",\n \"mp-34421 {'Bi': 3.0, 'Cd': 1.0, 'O': 7.0} \\n\",\n \"... ... \\n\",\n \"mp-5960 {'O': 12.0, 'Ca': 6.0, 'U': 2.0} \\n\",\n \"mp-1198997 {'C': 16.0, 'S': 8.0, 'N': 8.0, 'O': 8.0, 'F':... \\n\",\n \"mp-1219422 {'Sc': 28.0, 'As': 12.0} \\n\",\n \"mp-757593 {'Li': 4.0, 'Co': 5.0, 'Sb': 1.0, 'O': 12.0} \\n\",\n \"mp-1245874 {'Ca': 6.0, 'B': 6.0, 'N': 10.0} \\n\",\n \"\\n\",\n \" elements label for_test \\n\",\n \"mp-1077980 (Ag, Sb, Sr) OTHERS False \\n\",\n \"mp-30079 (Ge, Li, Mg) OTHERS False \\n\",\n \"mvc-2967 (Ba, Fe, O, Tl) OTHERS False \\n\",\n \"mp-1202279 (F, Ho, K) OTHERS False \\n\",\n \"mp-34421 (Bi, Cd, O) OTHERS False \\n\",\n \"... ... ... ... \\n\",\n \"mp-5960 (Ca, O, U) OTHERS True \\n\",\n \"mp-1198997 (C, F, N, O, S) OTHERS True \\n\",\n \"mp-1219422 (As, Sc) OTHERS True \\n\",\n \"mp-757593 (Co, Li, O, Sb) OTHERS True \\n\",\n \"mp-1245874 (B, Ca, N) OTHERS True \\n\",\n \"\\n\",\n \"[10158 rows x 4 columns]\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"array(['OTHERS', 'QC', 'AC'], dtype=object)\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"training_data = pd.read_csv(\\n\",\n \" 'training_data.csv',\\n\",\n \" index_col=0,\\n\",\n \" converters={'composition': eval, 'elements': eval},\\n\",\n \")\\n\",\n \"\\n\",\n \"training_data\\n\",\n \"training_data.label.unique()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As described above, the dataset consists of three class labels, `QC`, `AC`, and `OTHERS`.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Calculate compositional descriptors\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We use `xenonpy.descriptor.Compositions` to calculate the 232-dimensional compositional descriptors. As described in [our paper](https://onlinelibrary.wiley.com/doi/full/10.1002/adma.202102507), we use `weighted average`, `weighted variance`, `max-pooling`, and `min-pooling` of the 58 element features (4 $\\\\times$ 58 = 232-dimension).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\\u001b[0;31mSignature:\\u001b[0m \\u001b[0mcompositions\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mfit_transform\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mX\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0my\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mfit_params\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mDocstring:\\u001b[0m\\n\",\n \"Fit to data, then transform it.\\n\",\n \"\\n\",\n \"Fits transformer to `X` and `y` with optional parameters `fit_params`\\n\",\n \"and returns a transformed version of `X`.\\n\",\n \"\\n\",\n \"Parameters\\n\",\n \"----------\\n\",\n \"X : array-like of shape (n_samples, n_features)\\n\",\n \" Input samples.\\n\",\n \"\\n\",\n \"y : array-like of shape (n_samples,) or (n_samples, n_outputs), default=None\\n\",\n \" Target values (None for unsupervised transformations).\\n\",\n \"\\n\",\n \"**fit_params : dict\\n\",\n \" Additional fit parameters.\\n\",\n \"\\n\",\n \"Returns\\n\",\n \"-------\\n\",\n \"X_new : ndarray array of shape (n_samples, n_features_new)\\n\",\n \" Transformed array.\\n\",\n \"\\u001b[0;31mFile:\\u001b[0m ~/mambaforge/envs/xepy38/lib/python3.8/site-packages/sklearn/base.py\\n\",\n \"\\u001b[0;31mType:\\u001b[0m method\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from xenonpy.descriptor import Compositions\\n\",\n \"\\n\",\n \"# use specific featurizers\\n\",\n \"featurizers = ['WeightedAverage', 'WeightedVariance', 'MaxPooling', 'MinPooling']\\n\",\n \"\\n\",\n \"compositions = Compositions(featurizers=featurizers, n_jobs=20)\\n\",\n \"compositions.fit_transform?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To use the following calculator, users need to feed an iterable object containing the compositional information of compounds to `cal.transform` or `cal.fit_transform` method. The data type in the iterable object can be `pymatgen.core.Compositon`, or a python `dict`, for example, {‘H’: 2, ‘O’: 1}.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 3.05 s, sys: 567 ms, total: 3.61 s\\n\",\n \"Wall time: 5.91 s\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
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ave:atomic_numberave:atomic_radiusave:atomic_radius_rahmave:atomic_volumeave:atomic_weightave:boiling_pointave:bulk_modulusave:c6_gbave:covalent_radius_corderoave:covalent_radius_pyykko...min:num_s_valencemin:periodmin:specific_heatmin:thermal_conductivitymin:vdw_radiusmin:vdw_radius_alvarezmin:vdw_radius_mm3min:vdw_radius_uffmin:sound_velocitymin:Polarizability
mp-107798046.857143158.428571238.71428615.957143108.9446862201.85714379.497211800.285714150.428571139.571429...1.05.00.20524.00000206.0247.0243.0314.82600.0000006.600
mp-3007914.750000153.000000233.50000013.67500032.0450001736.78750049.384485758.250000132.500000133.000000...1.02.00.32260.00000173.0212.0243.0245.14602.0000005.840
mvc-296725.083333157.534162206.41666717.28333358.5597501018.94416777.9118571066.825000110.083333100.750000...2.02.00.1280.02658152.0150.0182.0291.2317.5000000.802
mp-120227920.333333158.349076193.16666722.06666746.670374725.84000038.3826261038.466667103.833333103.000000...1.02.00.1640.02770147.0146.0171.0336.42000.0000000.557
mp-3442132.090909153.400904198.63636415.90909177.395291665.30272760.703844187.35454595.45454593.636364...2.02.00.1240.02658152.0150.0182.0284.8317.5000000.802
..................................................................
mp-596020.000000160.620852211.90000018.62000045.425691983.01400060.763625928.149258112.000000106.100000...2.02.00.1150.02658152.0150.0182.0339.5317.5000000.802
mp-11989978.666667113.537506184.50000012.41666717.5144011861.73733342.32831148.06666774.16666775.166667...2.02.00.7110.02583147.0146.0171.0336.4317.5000000.557
mp-121942224.600000155.100000253.40000014.43000053.9456142435.60000046.5000001177.000000154.700000139.900000...2.04.00.32816.00000185.0188.0236.0329.53638.6782664.310
mp-75759313.363636143.564411194.90909112.37727328.9169991053.54000086.330568393.15454596.95454590.136364...1.02.00.2050.02658152.0150.0182.0245.1317.5000000.802
mp-124587410.000000122.272727210.90909117.27272720.2453641586.454545117.984038646.918182103.181818102.090909...2.02.00.6530.02583155.0166.0193.0339.9333.6000001.100
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10158 rows × 232 columns

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\"\n ],\n \"text/plain\": [\n \" ave:atomic_number ave:atomic_radius ave:atomic_radius_rahm \\\\\\n\",\n \"mp-1077980 46.857143 158.428571 238.714286 \\n\",\n \"mp-30079 14.750000 153.000000 233.500000 \\n\",\n \"mvc-2967 25.083333 157.534162 206.416667 \\n\",\n \"mp-1202279 20.333333 158.349076 193.166667 \\n\",\n \"mp-34421 32.090909 153.400904 198.636364 \\n\",\n \"... ... ... ... \\n\",\n \"mp-5960 20.000000 160.620852 211.900000 \\n\",\n \"mp-1198997 8.666667 113.537506 184.500000 \\n\",\n \"mp-1219422 24.600000 155.100000 253.400000 \\n\",\n \"mp-757593 13.363636 143.564411 194.909091 \\n\",\n \"mp-1245874 10.000000 122.272727 210.909091 \\n\",\n \"\\n\",\n \" ave:atomic_volume ave:atomic_weight ave:boiling_point \\\\\\n\",\n \"mp-1077980 15.957143 108.944686 2201.857143 \\n\",\n \"mp-30079 13.675000 32.045000 1736.787500 \\n\",\n \"mvc-2967 17.283333 58.559750 1018.944167 \\n\",\n \"mp-1202279 22.066667 46.670374 725.840000 \\n\",\n \"mp-34421 15.909091 77.395291 665.302727 \\n\",\n \"... ... ... ... \\n\",\n \"mp-5960 18.620000 45.425691 983.014000 \\n\",\n \"mp-1198997 12.416667 17.514401 1861.737333 \\n\",\n \"mp-1219422 14.430000 53.945614 2435.600000 \\n\",\n \"mp-757593 12.377273 28.916999 1053.540000 \\n\",\n \"mp-1245874 17.272727 20.245364 1586.454545 \\n\",\n \"\\n\",\n \" ave:bulk_modulus ave:c6_gb ave:covalent_radius_cordero \\\\\\n\",\n \"mp-1077980 79.497211 800.285714 150.428571 \\n\",\n \"mp-30079 49.384485 758.250000 132.500000 \\n\",\n \"mvc-2967 77.911857 1066.825000 110.083333 \\n\",\n \"mp-1202279 38.382626 1038.466667 103.833333 \\n\",\n \"mp-34421 60.703844 187.354545 95.454545 \\n\",\n \"... ... ... ... \\n\",\n \"mp-5960 60.763625 928.149258 112.000000 \\n\",\n \"mp-1198997 42.328311 48.066667 74.166667 \\n\",\n \"mp-1219422 46.500000 1177.000000 154.700000 \\n\",\n \"mp-757593 86.330568 393.154545 96.954545 \\n\",\n \"mp-1245874 117.984038 646.918182 103.181818 \\n\",\n \"\\n\",\n \" ave:covalent_radius_pyykko ... min:num_s_valence min:period \\\\\\n\",\n \"mp-1077980 139.571429 ... 1.0 5.0 \\n\",\n \"mp-30079 133.000000 ... 1.0 2.0 \\n\",\n \"mvc-2967 100.750000 ... 2.0 2.0 \\n\",\n \"mp-1202279 103.000000 ... 1.0 2.0 \\n\",\n \"mp-34421 93.636364 ... 2.0 2.0 \\n\",\n \"... ... ... ... ... \\n\",\n \"mp-5960 106.100000 ... 2.0 2.0 \\n\",\n \"mp-1198997 75.166667 ... 2.0 2.0 \\n\",\n \"mp-1219422 139.900000 ... 2.0 4.0 \\n\",\n \"mp-757593 90.136364 ... 1.0 2.0 \\n\",\n \"mp-1245874 102.090909 ... 2.0 2.0 \\n\",\n \"\\n\",\n \" min:specific_heat min:thermal_conductivity min:vdw_radius \\\\\\n\",\n \"mp-1077980 0.205 24.00000 206.0 \\n\",\n \"mp-30079 0.322 60.00000 173.0 \\n\",\n \"mvc-2967 0.128 0.02658 152.0 \\n\",\n \"mp-1202279 0.164 0.02770 147.0 \\n\",\n \"mp-34421 0.124 0.02658 152.0 \\n\",\n \"... ... ... ... \\n\",\n \"mp-5960 0.115 0.02658 152.0 \\n\",\n \"mp-1198997 0.711 0.02583 147.0 \\n\",\n \"mp-1219422 0.328 16.00000 185.0 \\n\",\n \"mp-757593 0.205 0.02658 152.0 \\n\",\n \"mp-1245874 0.653 0.02583 155.0 \\n\",\n \"\\n\",\n \" min:vdw_radius_alvarez min:vdw_radius_mm3 min:vdw_radius_uff \\\\\\n\",\n \"mp-1077980 247.0 243.0 314.8 \\n\",\n \"mp-30079 212.0 243.0 245.1 \\n\",\n \"mvc-2967 150.0 182.0 291.2 \\n\",\n \"mp-1202279 146.0 171.0 336.4 \\n\",\n \"mp-34421 150.0 182.0 284.8 \\n\",\n \"... ... ... ... \\n\",\n \"mp-5960 150.0 182.0 339.5 \\n\",\n \"mp-1198997 146.0 171.0 336.4 \\n\",\n \"mp-1219422 188.0 236.0 329.5 \\n\",\n \"mp-757593 150.0 182.0 245.1 \\n\",\n \"mp-1245874 166.0 193.0 339.9 \\n\",\n \"\\n\",\n \" min:sound_velocity min:Polarizability \\n\",\n \"mp-1077980 2600.000000 6.600 \\n\",\n \"mp-30079 4602.000000 5.840 \\n\",\n \"mvc-2967 317.500000 0.802 \\n\",\n \"mp-1202279 2000.000000 0.557 \\n\",\n \"mp-34421 317.500000 0.802 \\n\",\n \"... ... ... \\n\",\n \"mp-5960 317.500000 0.802 \\n\",\n \"mp-1198997 317.500000 0.557 \\n\",\n \"mp-1219422 3638.678266 4.310 \\n\",\n \"mp-757593 317.500000 0.802 \\n\",\n \"mp-1245874 333.600000 1.100 \\n\",\n \"\\n\",\n \"[10158 rows x 232 columns]\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"desc = compositions.fit_transform(training_data.composition)\\n\",\n \"\\n\",\n \"desc\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Train the model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In [our paper](https://onlinelibrary.wiley.com/doi/full/10.1002/adma.202102507), we performed a grid searching to find the best hyperparameters of the random forest classifier. For simplicity as a demonstration, we use the already optimized hyperparameters.\\n\",\n \"\\n\",\n \"\\n\",\n \"```python\\n\",\n \"param_grid = dict(\\n\",\n \" random_state=[0],\\n\",\n \" max_depth=[5, 10, 15, 20, 25],\\n\",\n \" n_estimators=[50, 100, 200, 300],\\n\",\n \" max_features=['sqrt', 'log2', None],\\n\",\n \" bootstrap=[True, False],\\n\",\n \" criterion=[\\\"gini\\\", \\\"entropy\\\"]\\n\",\n \")\\n\",\n \"```\\n\",\n \"\\n\",\n \"Best parameters:\\n\",\n \"```python\\n\",\n \"hyperparams = dict(\\n\",\n \" random_state=0,\\n\",\n \" max_depth=25,\\n\",\n \" n_estimators=200,\\n\",\n \" max_features='log2',\\n\",\n \" bootstrap=False,\\n\",\n \" criterion=\\\"entropy\\\",\\n\",\n \")\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In order to evaluate the model performance, we split the entire dataset into `train` and `test` datasets. We assigned the flag `for_test` to the data to indicate which samples were used for testing in the model construction process in the previous work. While you can use this flag to reproduce our work, here we would like to show you the standard procedure on how to learn the model.\\n\",\n \"\\n\",\n \"There are lots of choices to do this work, here we use the `xenonpy.datatools.Splitter` class.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\\u001b[0;31mInit signature:\\u001b[0m\\n\",\n \"\\u001b[0mSplitter\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0msize\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mint\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mtest_size\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mfloat\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mint\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;36m0.2\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mk_fold\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mint\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mIterable\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mNoneType\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mrandom_state\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mint\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mNoneType\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mshuffle\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mbool\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mDocstring:\\u001b[0m Data splitter for train and test\\n\",\n \"\\u001b[0;31mInit docstring:\\u001b[0m\\n\",\n \"Parameters\\n\",\n \"----------\\n\",\n \"size\\n\",\n \" Total sample size.\\n\",\n \" All data must have same length of their first dim,\\n\",\n \"test_size\\n\",\n \" If float, should be between ``0.0`` and ``1.0`` and represent the proportion\\n\",\n \" of the dataset to include in the test split. If int, represents the\\n\",\n \" absolute number of test samples. Can be ``0`` if cv is ``None``.\\n\",\n \" In this case, :meth:`~Splitter.cv` will yield a tuple only contains ``training`` and ``validation``\\n\",\n \" on each step. By default, the value is set to 0.2.\\n\",\n \"k_fold\\n\",\n \" Number of k-folds.\\n\",\n \" If ``int``, Must be at least 2.\\n\",\n \" If ``Iterable``, it should provide label for each element which will be used for group cv.\\n\",\n \" In this case, the input of :meth:`~Splitter.cv` must be a :class:`pandas.DataFrame` object.\\n\",\n \" Default value is None to specify no cv.\\n\",\n \"random_state\\n\",\n \" If int, random_state is the seed used by the random number generator;\\n\",\n \" Default is None.\\n\",\n \"shuffle\\n\",\n \" Whether or not to shuffle the data before splitting.\\n\",\n \"\\u001b[0;31mFile:\\u001b[0m ~/mambaforge/envs/xepy38/lib/python3.8/site-packages/xenonpy/datatools/splitter.py\\n\",\n \"\\u001b[0;31mType:\\u001b[0m type\\n\",\n \"\\u001b[0;31mSubclasses:\\u001b[0m \\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from xenonpy.datatools import Splitter\\n\",\n \"\\n\",\n \"Splitter?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
Splitter(random_state=0, size=10158)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
\"\n ],\n \"text/plain\": [\n \"Splitter(random_state=0, size=10158)\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"splitter = Splitter(size=desc.shape[0], random_state=0, test_size=0.2)\\n\",\n \"splitter\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(8126, 232)\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"(8126,)\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"(2032, 232)\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"(2032,)\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x_train, x_test, y_train, y_test = splitter.split(desc, training_data.label)\\n\",\n \"\\n\",\n \"x_train.shape\\n\",\n \"y_train.shape\\n\",\n \"x_test.shape\\n\",\n \"y_test.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Use [scikit-learn random forest classifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn-ensemble-randomforestclassifier) to train the model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
RandomForestClassifier(bootstrap=False, criterion='entropy', max_depth=25,\\n\",\n       \"                       max_features='log2', n_estimators=200, random_state=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
\"\n ],\n \"text/plain\": [\n \"RandomForestClassifier(bootstrap=False, criterion='entropy', max_depth=25,\\n\",\n \" max_features='log2', n_estimators=200, random_state=0)\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.ensemble import RandomForestClassifier\\n\",\n \"\\n\",\n \"# prepare the random forest class\\n\",\n \"rfc = RandomForestClassifier(\\n\",\n \" random_state=0,\\n\",\n \" max_depth=25,\\n\",\n \" n_estimators=200,\\n\",\n \" max_features='log2',\\n\",\n \" bootstrap=False,\\n\",\n \" criterion=\\\"entropy\\\",\\n\",\n \")\\n\",\n \"\\n\",\n \"rfc\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
RandomForestClassifier(bootstrap=False, criterion='entropy', max_depth=25,\\n\",\n       \"                       max_features='log2', n_estimators=200, random_state=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
\"\n ],\n \"text/plain\": [\n \"RandomForestClassifier(bootstrap=False, criterion='entropy', max_depth=25,\\n\",\n \" max_features='log2', n_estimators=200, random_state=0)\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# train the model with training data\\n\",\n \"rfc.fit(x_train, y_train)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Predict the class label of training and test data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"y_pred, y_true = rfc.predict(x_test), y_test\\n\",\n \"y_pred_fit, y_true_fit = rfc.predict(x_train), y_train\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### confusion matrix\\n\",\n \"\\n\",\n \"The confusion matrix is a common measure used to summarize the predicted results of a classification task. For more information, see [here](https://scikit-learn.org/stable/modules/model_evaluation.html#confusion-matrix).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Testing CM:\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1993, 1, 1],\\n\",\n \" [ 3, 8, 3],\\n\",\n \" [ 5, 5, 13]])\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"Training CM:\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"array([[8005, 0, 0],\\n\",\n \" [ 0, 66, 0],\\n\",\n \" [ 0, 0, 55]])\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# import a library for evaluating the model performance based on confusion matrix\\n\",\n \"from sklearn.metrics import confusion_matrix\\n\",\n \"\\n\",\n \"labels = ['OTHERS', 'QC', 'AC']\\n\",\n \"cm = confusion_matrix(y_true, y_pred, labels=labels)\\n\",\n \"cm_fit = confusion_matrix(y_true_fit, y_pred_fit, labels=labels)\\n\",\n \"\\n\",\n \"print('Testing CM:')\\n\",\n \"cm\\n\",\n \"\\n\",\n \"print('\\\\nTraining CM:')\\n\",\n \"cm_fit\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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vV/o716Pnt5dB+Rjc3TLlUy9fLp/+Zmef0SfIGik6svy7/LuvIzKfpXfLnuf2lkaRuYUOopK6RmbfRu1bDv5drhAzkwPLxz1ll7czMvJWiZ2dl+r4ur7OaV1d5vXLNh2vqSDOt78GImBoR74uISyJidkQsj4iMiJ6RaT22rrOugxIRm1NMZ7MhRWNkf9PI9DQ07hkRj1Xb6J3edkbFufvT+/+tP9WoSq1jtfSsJ7MKuHAQ5/VcE9fkumu3ApCZ8yhGKVam7+uaatcgxXSpUKy70moXlI9vj4gfRsSLI2J6zTNqGymfV7//VrNYI+bJnjpUObfy3+pGfQ9GxISIeEtE/D4iHomIpT3/Vst/rz1rvGwz1MqXllCs3TNk5Q8Sp1BMr703vdN2nZWZQ+p0IEmSJFXKzMX03n/PqjjUs39JRdoHKEYHbhMRM2HN+puHlkmGGg/W66oax6rFIY2KgTrFcD6DvtO3VvozvWuWdsJvL+vEclD89hERH4uIKyJiTkSsrIjlbimTTa52/nBFxK4U0wyPLx//rZ9kPb+9HD/Aby89v9vM6CcPSeoa49pdAUlqsLMoRh3uCLwFOHuA9D0NDg/XTFWMGN2aKovQs+56FNX0XVsSKBobIqJmGooRWD3WauSNiF2Ai1i74WQxxWi+nsaizcvHwY4mHVBETKJY93B7iqD0RVV6Vm5VPk4qt4FMrtiv/Oxr/b0eqiPf/mxRPj6ZmYsGcd5grqHK9H1V+7tD79++nsb9hsrMH0TEMynWSnlFuRERd1GsDfrtzLxuEFmOlM+rnvzr+ffc99/qZhTBf2UP56UUjas9Qf+mFI3+w/23OqdGY3HdMvPeiPg3er9Pr2D4I00lSZKkSn+mWJfxiIgYV3bIm1Ueu7hP2ksoYs9ZwN0US970xI7rjNZssKHEIY2KgTpFUz6DzFwaEU9S/HbRlN9eWPt3lYHSrPO7eUQcSrFUyLSKlxdS/P6SwFiK2cSgiOeepIHKTsm/pmhkvRY4NTOzn6Q9v730rI86kMkDJ5GkkcsRoZK6SmY+TO+P9R+KiHpu+KC4YR1OulVVXm+V71A0gt4HnEyxFsb6mblZZm7B2qNAo5/zhyyKFp/vAgdTjBp7QWY+USV5z9QwX8vMqGPbvpF1rVO918JQzxtq/m2Tme8CnkEx/fSFFA3sOwFvBa6NiC8OJdsGpxspvkDRCDqHYi3RLTNzUmZumplblP9ee3pRD/ffakO+l8rR9a+teGkXejsOSJIkSY3Q04A5hd7RgEeXj5f0Sdt3etyex8qRpZ1otMZAlUbsby8RMY5iGZ9pwI3APwEbZObUzNy8jOUOqTylweVPpJjKdkeKpWJe2N+SQaWe314+UOdvL7MaWVdJ6jQ2hErqRv8JzKPoQfjeAdL29CbcdoB0PSMtqzXwtU1EbEvvtK6nZOZPM3Nun2TNbLT4JMUaJquAl2XmzTXSPlY+Vlvvo5bKnp+1pvcd6tS/j5aPmw5yDdaRcA31BIvr1UizYa0MMvOuzPzPzPwnYBOKqad+UR4+PSJeWGddRsLn1RRlg+KJ5dO3Z+Z3MvOxPmkqexB3iv+k+DFqIcWaP5sA/xsR3kdKkiSpUf5KsUYmwKxyfdDNgLszs++sPz0NobP6PF6WmSvoPMONgXpGKA45nusAA34GEbEeRawBnRkHHkoxhewqig7gF5brbFZq5m8v36EYNb0QOCEzH62Rdji/vUhS1/EHLEldJzPnA58qn76X2tPK9KzBcUy1H/XL9Rd6GteqrSHRTpWBxA1V0jy7GQVHxOuBD5RP35mZvxvglJ71PA6JiMGuQXE9vdP8HlMj3bGDzLfHX8vHscDxgzhvzTouEdFv8BkR06hYF2ZItRueeeVjv0FnREwFdqs3s8xcnZlXUjSAP1C+/Jw6Tx8Jn1ezbErvjxfV/q0eQfUfOCqnuW1o7+JqIuI44D3l07cBLwNWUPwbfH8r6iBJkqTul5lLgSvLp8fQz/qgFWnvoVy+JiL2oHd90KFMi9tzj93M++vhxkA147nSwTWOteI9DqTnM3hWjTSz6J2OthPjwJ7P/4lyNrL+NOu3l48Dp1D8LV+ZmX8b4JSe316eP4iZ0iSpa9kQKqlbfZkiMJoKfKhGuvPKx62BN1ZJ8/Hy8UmKtf06zYKK/X36HiwbuWp9BkMSEccCXyuffjkzv1LHad8DllA0Nv53OfqtWv5jymAQWNPA/fvy6Rllb9G+5zyb3tGxg5KZdwF/KZ9+MiI2qPPU8yl66K5H9YahDwITKRqQzh9K/YapJ0h6aZXjZ1DUbx3l9Dv9ysxVwPLyab1TFI2Ez6tZnqJ3iqf+/q2OAz4xwPk9pjWuWv0r1zM9l+IHkx9m5ncz81rg38okH4+IWj+4SJIkSYPR05B5OL0dLS+ukrangfQDwKQ+5w9Gzz32tCGcW6/hxkA98dxz+5u9qIzND+37eoVWvMeB9Pz2cmjZ2XItZSz0kfLpTZl5U8tqVr+e3142j4jN+x6MiG2Adza60Ig4Ffhw+fSMzPxVHad9kyL2nAZ8ZoD8x9tYKqnb2RAqqSuVvUnPKp+eUCPd1fQGGWdHxNsjYjJARGwREd+kWHMT4MNlvp3mFnpH5X07Ig7oORARh1IEjhs1ssCI2JnicxsP/IbeEWM1ldOA9owgfT7wh4g4vKdBNAq7RsR7gJuAF/TJ4sMUDW67Ar+OiGeU542LiJcBP6ZYv3KoTgeWAjsDl0fE88rpTImIyRFxcER8rWxw7XlPDwNfKp9+ICI+1tOAGxHTIuLfgfeVxz8/wPQ1zfLD8vG5Zf02KOs3PSI+SdFQPr/KuVdFxJcjYlZl0B0RW0XE2RRrhUJxHQxohHxeTZGZC+ntmfv5iDi2ZyR6ROxJ8RkeCCyqcv58oKfn8evKHwuaolz79xyKqZ3uBd5ScfizwB8oemv/cBCdBiRJkqRaehoy16c3jl9nRGif108pH58GrhtCmT0NbntExJA61Q6kATHQjylGAm5Ccf+9TXnupIh4LfBzoO/yOJV63uM/RcRQl5IZrvOBq8r9H0fEKyti7R3K4z2NuWe2oX71uIwiVguK97ALFMubRMRzKX57aej6rhFxOPCt8unXM/ML9ZyXmTcCXyyfviUifhIR+5ZxXk+d94mIDwN3A/s2st6S1GlsCJXUzc4Bbqsj3RsogqgJwNnAgoiYCzxC7yjRz2bm16qc31aZmRRTVq4E9gCujYhFEbGIYrrXXYGXN7jYw+ntTXoo8HBEPFZl+1LliZn5ZYrAZhXFlEeXAYsj4kmKNWFuBT5HMVVr9jn3WuCt5evHArdFxHyKNTJ+BDxO7wjeQSuDhRdR9PTcE7gQWFReD4sopmr6Z6Bvb8kPUgSnQdGLdU55zhx6R+P+kN5enK12Dr0/KnwEmF/WbzZFw/T76e1l3Nc04B3l+U9HxLyIWEjRIPf2Ms0XMvP3Vc7vT6d/Xs30LopraWvgIopr/yngHxT/Ht5EMfq8mp7voXcACyPigYi4LyLOq3HOULybYorolcCrMnPNaNTyO+c1FOv27AB8tcFlS5IkaXS6kmIGIShmEbo3Mx+okvaSinQAl2bmyippa7kYuL3M5/KImFveX98XEScNIb9qhhwDZeYd9M4ccwLwYBkHP0UR6/0JqDVD07kUHX53Ah4o4/Se97hNjfMappxN6KXAzRTrmX6fIp6ZB9wDvJCisff0zLywFXUarMxcQDGbEsBRwO0R8TTF7xG/pXhfr2twsc+h+K0K4KU1fnd5LCLO6HPu++htDD2JYnmWnt9elgI3Uvx+si0NbsCVpE5jQ6ikrlXeaH+wjnQLKNapeANFEPQ0RUPXYxS9Eo/JzPdVzaADZOYFFDfiv6YY2TeOojHlO8D+mXlRE4vfCNi8xrbOGiiZ+RmKBtovAH+nuAmfRhFAXAP8P4opbn/Qz7nfoGiI/RVFr9eJwP3AfwLPpHf9lCEpG/R2pgg0b6AIxCcB9wG/o2gI/VOfc5Zn5sspArsLKYLZqeXjhcCJmfnKzFwxnLoNVflv4fnARyk6ByynCHR+DzwnMz9b4/RXlOddRDEycALFSOD7KRqfn5WZdY0IrqhPR39ezZSZ11Fcpz+m+Dc6huI758fAYZn5vQGy+CTFyOVrKabN2gaYQTFysyEiYj+Kf08AH8vMK/qmKUd3n0ZxHb2y7IkuSZIkDVlmLqfozNuj2mjQnsbBxypeGsq0uJSNp8+iGHV3H8Vo1Bnl1rDpQocbA2XmR4BTKRqLF1E03N5IMXPLidRYqiQz76TodPlLis6Mm9D7Hps2y0w/9XiYYgac99Db6D0ZeJBiGZ0Dyo7THavsIP98it+OFlJ8fg9TdKrfh6KDa7NMp/ZvL2tdr5m5KjPfDewPfIOiwX8VxW808yhmKzoL2DczL0eSulgUnfolSZIkSZIkSZIkqXs4IlSSJEmSJEmSJElS17EhVJIkSZIkSZIkSVLXsSFUkiRJkiRJkiRJUtexIVSSJEmSJEmSJElS17EhVJIkSZIkSZIkSVLXsSFUkiRJkiRJkiRJUtexIVSSJEmSJEmSJElS17EhVJIkSZIkSZIkSVLXsSFUkiRJkiRJkiRJUtexIVSSJEmSJEmSJElS1xnX7gpInSYiHgMmAw+2uy6SJEnDtC2wODO3aHdF+hMRvwRmtrjYuzPzhS0uU5JazthWkiR1EWPbdRnb1smGUGldk4kxU2Pihru3uyJSq+y645btroIkqQnuvvtuJowfP7Xd9ahhJjFm95i4YUsKy2ULIFe3pCxJ6gDGthp1jG0lqTsZ267N2HZwbAiV1vVgTNxw94m7vrLd9ZBa5vpr/qvdVZAkNcH+++zR7ioMKCZuSKvuu5bd9gNy6byWlCVJHcDYVqOOsa0kdSdj27UZ2w6ODaGSJEmS2iggxrSuLEmSJEmSGs7YtlO16q8iSZIkSZIkSZIkSS3jiFBJkiRJ7RX2ZpUkSZIkjXDGth3JEaGSJEmSJEmSJEmSuo4jQiVJkiS1V8vWUZEkSZIkqUmMbTuSfxVJkiRJkiRJkiRJXceGUEmSJEmSJEmSJEldx6lxJUmSJLVXRLtrIEmSJEnS8BjbdiRHhEqSJEmSJEmSJEnqOo4IlSRJktRGAdGq/pn2zpUkSZIkNYOxbadyRKgkSZIkSZIkSZKkruOIUEmSJEntE7RuHRU7zUqSJEmSmsHYtmM5IlSSJEmSJEmSJElS13FEqCRJkqT2atk6KpIkSZIkNYmxbUfyryJJkiRJkiRJkiSp69gQKkmSJEmSJEmSJKnrODWuJEmSpPaKaHcNJEmSJEkaHmPbjuSIUEmSJEmSJEmSJEldxxGhkiRJktooIFrVP9PeuZIkSZKkZjC27VSOCJUkSZIkSZIkSZLUdRwRKkmSJKm9XEdFkiRJkjTSGdt2JEeESpIkSZIkSZIkSeo6jgiVJEmS1F4tW0dFkiRJkqQmMbbtSP5VJEmSJEmSJEmSJHUdR4RKkiRJap+gdeuouFyLJEmSJKkZjG07liNCJUmSJEmSJEmSJHUdG0IlSZIkSZIkSZIkdR2nxpUkSZLURgHRqv6Zzh8kSZIkSWoGY9tO5YhQSZIkSZIkSZIkSV3HEaGSJEmS2qtlvWYlSZIkSWoSY9uO5F9FkiRJkiRJkiRJUtdxRKgkSZKk9hrj+iaSJEmSpBHO2LYjOSJUkiRJkiRJkiRJUtdxRKgkSZKkNooWrqNi71xJkiRJUjMY23YqR4RKkiRJkiRJkiRJ6jqOCJUkSZLUPgFEi3qz2mlWkiRJktQMxrYdyxGhkiRJkiRJkiRJkrqODaGSJEmSJEmSJEmSuo5T40qSJElqr7B/piRJkiRphDO27Uj+VSRJkiRJkiRJkiR1HUeESpIkSWqviHbXQJIkSZKk4TG27UiOCJUkSZIkSZIkSZLUdRwRKkmSJKmNooXrqNg7V5IkSZLUDMa2ncoRoZIkSZIkSZIkSZK6jiNCJUmSJLWX66hIkiRJkkY6Y9uO5IhQSZIkSZIkSZIkSV3HEaGSJEmS2qtl66hIkiRJktQkxrYdyb+KJEmSJEmSJEmSpK5jQ6gkSZIkSZIkSZKkruPUuJIkSZLaJ4CI1pUlSZIkSVKjGdt2LEeESpIkSZIkSZIkSeo6jgiVJEmS1EYB0ar+mXablSRJkiQ1g7Ftp3JEqCRJkiRJkiRJkqSu44hQSZIkSe3VqnVUJEmSJElqFmPbjuSIUEmSJEmSJEmSJEldxxGhkiRJktqrZeuoSJIkSZLUJMa2Hcm/iiRJkiRJkiRJkqSuY0OoJEmSJEmSJEmSpK7j1LiSJEmS2svpgyRJkiRJI52xbUfyryJJkiRJkiRJkiSp6zgiVJIkSVIbBUS0rixJkiRJkhrO2LZTOSJUkiRJkiRJkiRJUtdxRKgkSZKk9glat46KnWYlSZIkSc1gbNuxHBEqSZIkSZIkSZIkqes4IlSSJElSe7VsHRVJkiRJkprE2LYjOSJUkiRJkiRJkiRJUtdxRKgkSZKk9mrVOiqSJEmSJDWLsW1H8q8iSZIkSZIkSZIkqevYECpJkiRJkiRJkiSp6zg1riRJkqQ2CohoXVmSJEmSJDWcsW2nckSoJEmSJEmSJEmSpK7jiFBJkiRJbRUt6zUrSZIkSVJzGNt2JkeESpIkSZIkSZIkSeo6jgiVJEmS1Fb2mpUkSZIkjXTGtp3JEaGSJEmSJEmSJEmSuo4jQiVJkiS1T5Rbq8qSJEmSJKnRjG07liNCJUmSJEmSJEmSJHUdR4RKkiRJaivXUZEkSZIkjXTGtp3JEaGSJEmSJEmSJEmSuo4NoZIkSZIkSZIkSZK6jlPjSmq5KZMnsveu27Lfbtux327bsc+u27HjttMZM6bom7H3Cz/Cg4/ObXMtNdI8vWgp3z7/Ui7489+458EnWLx0OZttsgGH7juTU190GIfvv1PDyrrvoSf5n5/+hT/+9RYenj0fgK03m8azD9ud1590JDtss2ld+WQmP/nttfz4wmu45a6HmTN/EZtMW5/dd9qak593IC87/qC6p9QYbp3eetb3+OGvr6qrrH86em++/9k315VWzeM17zXfLYLWTR8UQLakJEnSaGBsq2bwPt/7/NHGa95rvlsY23YuG0Iltdyvv/Eu9n7Gtu2uhrrI3257kFPP/OY6PzI88MgcHnhkDj/6zdW8+eVH86n3njTsG5Lzfn0V7/3Uj1i8dPlar99272Pcdu9jfPv8y/jcB17OK55/cM185i1YxKvf903+esNda73+6BMLePSJBVx0xS189xd/5fuffTPTNpjckjpp5PCa95qXJEntZ2yrRvM+3/v80cZr3mteagUbQiW1XOWNy4KnF/P32x9i5+03Z4vpG7axVhqpHnhkDief/hWemPs0AK9+4aG85Dn7s+HUSfz99of48nf/yH0PP8k3fnQJ600cz8fe8eIhl/XbS//B2z7+v6xenUyZPJF3vPrZHHHgzkQEl15zB1/+3h9YtGQ5b//37zNtg8k878i9+s1nxcpVnPLer3PV3+4B4PD9d+JNLzua7bbcmAcencs3f3wJl19/F3+94S5e8Z6vccHXTmfcuLFNrVOly3/4wZrHp05Zb8A81Dxe817z3ahVvWYlSWokY1s1kvf53uePNl7zXvPdyNi2M9kQWkNEPA94IXAEsCWwITAPeBi4GPhZZl7Wz3mzgD83oAqXZOasMs+zgI+Wr78uM88Z6OSI2B64t29efdJU5luvBZk5rUZZfa0CngYeBK4Bvp+Zf6q3sIg4DDgVOASYAUwFFgBPAI8D1wGXApdn5hODeSNqj//95RXMmbeQG259gHseLP5kv/ra6QaLGpIPffFna26aP33GSbz55bPWHDtgj+158bP24zmv/yx3P/AE//W/F3Hy8w5iz523HnQ5S5Yu572f+hGrVycTJ4zjl199J/vtPmPN8UP3nclzDt+d49/0BZYtX8kZn/4xs565K+tNHL9OXt85/9I1N80vfvZ+/M8nXrdm+qz9dp/BC2btwxs/9B1+8ccbuOpv9/Cdn13Gm152dFPrVGn3nbYa9Oej1vGa95qXpMEytq3K2FbDYmyrRvI+3/v80cZr3mteapUx7a5AJ4qIAyPiKuBC4F+AvYDpwHhgM2A/4N3ApRHxu4ho3ETl3WksMI3ic3w9cFFE/CIias4NEBEbRMT5wOXAW4B9gY0oGvA3AXYFjgbeA/ycImDUCPCNH13C+b+/bk2gKA3VHfc9xq/+/DcADtprh7VumntstOH6fPqMkwFYvTr54rm/H1JZP/z1VTxSrtXwL6ccs9YNao/9dp/Bv5xyDAAPPz6P8/pZo2H16tV88dw/ALD+pAl8/gOvWHPT3GPs2DF8/gOvYPJ6EwD4wjm/J3Pdmf8bVSeNHF7zXvPdKYhozVaspCKNHsa2DWdsq7UY26pRvM/3Pn+08Zr3mu9OxradyobQPiLiJcBfgGeWL90EnAk8GzgAOI6il+nd5fHjgCsj4vCKbK6hCIyqbddWpH1ujXSva+y7G9CHatSlcjt0gHyu7ZN+X4rP6TPAkjLNi4DvVMsgIsZSBOsnli89DHwMeB6wP3Aw8GLg48CVwOo636OkLvJ/f7xhzf5pLzm8arpjD9mNbbfcGIDfXXoTS/qsvdDIsiqP/aLinB5X/u0eHn1iAQAvPHY/Ntpw/X7z2WjD9Xnhs/YFinUmrix7HDajTho5vOa95iWpXsa2xraSRg7v873PH2285r3mpVayIbRCRDwTOA+YBCTwfmDfzPxMZl6Umddn5h8y8+PA7sBny1M3AX5VTqFDZi7KzJuqbcCiimLvqJG22nQ8zfJwrXpXbLcOkE/f9/+38nM7k6KXa8//sV4WEftUyeN1wGHl/iXArpl5Vmb+LjNvyMyrM/P/MvOjmXkosAPwxWG+f0kjzGXX3blm/8iDdqmaLiI46sDi+MLFy7jxtgcHVc7KlavW3LjO2GoTZmw9vWraGVtPZ8ZWmwBw5d/uZtWqtX/LuryizkfVqDPA0Qc9o9/zGl0njRxe817zXStatEmjhLGtsa2kkcX7fO/zRxuvea/5rmVs25FcI7QUERMoAsUJ5UtnZObnq6XPzOXA+yJiHPAuimltvgcc2eSqjmiZeU1EnAe8pnzpucDf+kl6UsX+OzNz4QD5PgB8rTG1lDRS3HbPowBMmTyRbbfYuGbaZ+ywxZr92+95lEP3nVl3Ofc89ATLV6wEYNcdtxww/TN23IL7H5nDsuUrufehJ9hpxuZrjt1a1rmevCrrfNu9j651rJF16uukd/w3/7jjIeY9tZgp6xef7WH77cSpLzrM9SbazGvea16SBmJs2xrGtpIayft87/NHG695r3mplRwR2utUip6XAJcBX6jzvH8F7ir3j4iIYxtdsS50TcX+dlXSbF+xf0fzqiJppFq2fAWz5z4NwDabbzRg+m0qbqwffGzuoMp68NHe9NtsUUdZm1cv66HH6s9rrTo/Oq9pderroitvZfbcp1mxchXzFizm77c/xNfOu5jDT/kk7/3Uj1i2fMWA5anxvOa95iWpTsa2rWNsK2nYvM/3Pn+08Zr3mpdazRGhvd5Ysf/Z7G8V435k5tKI+C96p655E/CnBtet26yssl9pWcX+HsB1zauOpJFo4eLer4kp6683YPopkyf2e+6gy5pcR1nrV5S1aO2ynl5Uf15r13lp0+oEEAH77rYtxx2+J/vsui1bbrohUNxk//6ym/nJb69l+YqVfPv8S3l8zgK+++k3MmaM/alayWvea75rRTHlVavKkkYBY9vWMbaVNGze53ufP9p4zXvNdy1j245lQygQEVOAA8unS4HfDDKLn9EbLB7doGp1s70q9tddLbpwLbB3uf+1iHhxZj7c3GpJGkmWLO3txTZ+/NgB00+c0Pu/vCVLl9dI2U9Zy3rLmlBPWeN7y1rcp6ylg8irVp0bWSeAT7z7pUzbYPI6r++3+wxeeOx+/PMrZnHSO/6b2XOf5tcX/53v/+pKTn3RYeukV/N4zXvNS9JAjG1bzthW0rB5n+99/mjjNe81L7WaTf+FveltFP5bZg5qjHhmPgjMLp9uGRFb1ErfAFtHxJ4DbUDtVZuHmG9EbDbUikfEDODV5dPlFIF2f74E9PwdDgTujYjfRMSZEXFsREwbah0q6nJzfxtQ/0Tzktpm0nrj1+yvWLFqwPTLlvd20p+03oQaKfspa2JvWcvrKWtFb1mT+5S13iDyqlXnRtYJ6PemudJeu2zDN/79tDXPv/J9B4i0mte813w3i4iWbNIoYGw7iHyNbSV1Au/zvc8fbbzmvea7mbFtZ3JEaGF6xf6jVVPV9hjQE0RNL583y3+UW7vy/RhwVr2ZRsQYYHPgOOATwAY95WXmQ/2dk5l/j4hXAd8B1gfGA8eXG0BGxG0UUzV9PzOvqLc+krrDWtOMLFpaI2Vh0ZLKqUcm1kg5QFmL6yhrrWle1i5r6vpr51V5I71OPkuqT5fSyDrV6+hnPoNdtt+cO+57nNvufYzHn3yKzadvMPCJagivea95SaqDse3g8jW2ldR23ud7nz/aeM17zUut5ojQwtSK/YVDzOPpiv0Nh1GXbnB0RGTPBqwCHgHOAbYGHgLempn/XiuTzPwJsCvF1Ex9g+8AdgPeBvw1Ii4cbG/lzNyjvw24ezD5SGqPiRPGs+nGxdf3w7PnD5h+7cXoN66Rcl2V6R96bF6NlGVZFYvXVy5q3/f5QHmtXeeNmlanwdht5lZr9h+ZPXC5ahyvea/5bmavWalhjG0by9hWUtN5n+99/mjjNe81382MbTuTDaGFykBvyhDzqAw4FwyjLvV4XWbGQBuwQzPyzcyzhln/Cyh6ww4oMx/KzHcDW1FM8/RG4GzgCmBlRdLnAZdHxPR1c5HUrXbbcUsAnl60dK0bw/7cfm/vb067lufVa+a2mzKhXJPhtnsGHlxx+z1FWRMnjGPHbTddu84ze8seKK/KOu/Wp86NrNNgeK/VXl7zXvOSNABj20Hka2wrqVN4n+99/mjjNe81L7WSDaGFJyv2B/dt2quyx+aTVVONDtcCe1VsRwP/AtxUHn8LcEFE1D01cxb+kZn/k5nvzMzDKD7zj9O73sqOFFMbSRolDt9/5zX7l15zR9V0mcml190JwPqTJrDvrtsOqpxx48Zy8D47AnD/I3N44JE5VdM+8Mgc7i+PH7z3jowdu/b/ag+rqPNfatQZ4JJrbq84b6em1WkwbrnrkTX7W242bcj5aGi85r3mu1HQul6zxv4aBYxtG8vYVlJLeJ/vff5o4zXvNd+NjG07lw2hhX/Q2wNzn4ioPsF3PyJiW3rXUHksM5u5hspIsCgzb6rY/pKZXwMOAC4s0zyLQazF0p/MnJOZHwXOrHj5FeHYcGnUeOGz9l2zf87PL6+a7k9X3rrmxvK4I/ZcZ6H6erzoWfut2f/Ozy6rmq7y2Iuevd86xw/dd0c236RYh+GXf7qBeQsW9ZvPvAWL+OVFNwKwxfQNOKS8SW5Gner1l2tu5477Hgdg5xmbs8X00T5bXut5zXvNS9IAjG0by9hWUkt4n+99/mjjNe81L7WSDaFAZj4NXFc+XQ84fpBZvKRi/+JG1KkbZeZy4DVATzeX90XEzAZk/TWKtVoANgacQkgaJXbdcUueP2tvAK75x71848eXrJNm3oJFvP+zPwFgzJjg3a89bp00P/jVlWx00NvZ6KC389azvtdvWae84GC23LS4UfzqD//MDbfcv06aG265n6/+8M8AbLXZNF7x/IPXSTNmzBje9drnALBoyXLe+6kfsXr16rXSrF69mvd86jwWL10OwLteexxjxqz7v+xG1emaf9zLw4/XXh/ipjsf5s0fPnfN87ecMqtmejWH17zXfNeKFm1SlzO2bQ1jW0mN5n2+9/mjjde813zXMrbtSHVP3zIKfAvo+TY5IyJ+lZk50EkRMRF4e8VL32xG5bpFZj4ZEf8BfAGYAHwSePkw81waEXPo7bm8ulZ6td8O20znkH3X/p1gs016lyJ60bP2Y878hWueL1q8jF/+6cZWVU8jzCfedSJX3ngPc+Yv5P2f+Qn/uP0hXnrcAWwwZRL/uOMhvnjuH7jv4WJWt7eecgx7PWObIZUzeb0JfPb9L+fUM7/JsuUreeG/fJl3nvpsjjxoF6CYyuXL3/sjy5avZMyY4DNnvozJVXoqvuHko/jZH67jmn/cx8//eD2z5z7FP798FttuuTEPPjqXr//oYi6//i4ADtprB15/0pFNrdNFV9zC5779O44+6BnMOnhXdpu5JZtMK5YVe/DRufz+8pv5yW+vYdnyYoDJcw7fg9e++PAhfY4aPq95r3lJGoCxbQsY2wqMbdVY3ud7nz/aeM17zUutEnXEQ6NCGfTdDswoX3pXZn6pjvM+C7y3fPrXzBzwmyQiLqZYWwRgh8y8r45zzgI+Wj59XWaeU8c52wP3lk8vycxZjch3qGVVpJ0I3A1sDSSwT2b+o0+aqCdYryj7Hoq+EPOBjes9t0p+N8d6G+0+cddXDjULDeCUFxzMVz56at3pH3hkDvu86KMDJ9SQzbvmv9pdhWG54Zb7OfXMb9XsBfeGk47kM2e+jP5mGPvBr67kbR//XwBOef7BfOWs6tfnD351JWd8+kcsWbai3+OTJo7ns+9/Oa884ZCadZ47fyGvet83ufLGu6umOWTfmXz/M29i4/Imtll1+tQ3fs2nv3lhv8cqRQSnnXg4n3jXiUOajkaN4zXvNV+v/ffZA4Bbbr65I/uMRsTNYzfcevcNX/SZlpS34P/ex6oFD9+SmXu0pECpDYxt6893qGVVpDW2HeWMbTuPsa33+aPlPr9beM17zdfL2HZtxraD44jQUmYui4hTgEuA8cAXImIs8MXMXKcXZkRMAD5Ob6C4AKj/7ncUKz/rTwBfoQjwzgJe2ifZTyPir8C3M7Pq/wkjYgrwbXoHhJ8/nEBR0si03+4z+Ot5H+Tb51/GBX++kXseeoLFS5az2SYbcMg+M3ntiw/j8AN2HjijOrzyhEM4dL+ZfOsnf+GiK25dc7O+9eYb8axDd+MNJx3FjttuOmA+G0+bwq+/fjo//u21/PTCa7jpzoeZu2ARG2+4PnvsvBUnP+8gXnb8Qf1OodLoOr3yhEPZfPqGXH/z/dx050M8MXch8xYsYuWq1Ww4dRI7brsph+wzk1edcAg7b795nZ+Umslr3mu+27gMntQ4xratY2wrqdG8z/c+f7Txmvea7zbGtp3JEaF9RMTJwHcp1lMB+DvwPeB6YB6wCXAI8FpgpzLNPODFmfmXOsu4mM7sNfsh4P8Gyrd0V2YuHUxZfcqdANxB0Us5gf0z88aK41dSTOe0HPg9cCnF3+JJiumBtgAOBV4PbFWe9jiwX2Y+Wud7qFY3e81q1BnpvWYlSf0bKb1mp734sy0pb/4vzmh4r9mImAr8C3AiRXywPvAYxf3rt+qNEeooZzzwCuAkYD9gU4oGk7nAPyju48/NzEWNKE8jn7Gtsa2xrUYjY1tJ6k7Gtmszth0cR4T2kZk/iYj7gf8GDgT2BmqNZ74IeGtm3tGK+jXZf5RbPfYDbhxqQZm5vFxP5ZsUF/nHgBdVJHmQIlicALyg3Gq5BnjVcANFSZIktVhE63rNNriciNgf+Bm9U5D22L7cTo2Is4HThzm95UyKYLC/IHfLcjsOeH9EnJSZ1wy1LHUPY1tjW0mSJLWQsW095bQltrUhtB+ZeTVwUEQcTxHAHEHRS3MDinU6HqaYZuhnjWoFH6XOAT4AzAReGBEHZOZ1AJl5ckTsADwHOJziH8YMYEOKXrZPU/TSvZ7iH+jvnTZIkiRJrVKOGrsQ2Kx86dvAjyjihf2AM4EdgXcAS4D3D7GcKcAfKYJPgLuAzwO3AiuAZwDvBPYBtgN+HxF7ZubDQylP3cXYtmXOwdhWkiRJI9BoiG2dGlfqw+mDNBo5fZAkdacRMX3QtG123/gln2tJeXN//l5WzX+oIdMHRcT5FFMGAbwzM8/uc3xj4EpgZ4rpL/fLzL8PoZzTgS+WTy8Fnp2Zy/ukCYpA9eTypS9m5rsHW5ak7mJsq9HI2FaSupOx7dqMbQdn4BV7JUmSJElrRMSu9AaKV/QNFAEycy5Fj1ko4q4PDLG4Iyv2/71voFiWlcCHq5wjSZIkSdI6Rktsa0OoJEmSpPaKFm2Nc3LF/jdqpPs9cH+5f0JETBpCWRtU7N9XI929FftTh1COJEmSJGk4jG1raVtsa0OoJEmSJA3OrIr9P1VLVPZm7Tk+BThgCGXdUbG/fY10lcfuqJZIkiRJkqTSrIr9ro1tbQiVJEmS1FYR0ZKtgXrWYVmYmQ8MkPaWiv3dh1DWt4BV5f6/RcT4vgnKdVQ+VvHSV4ZQjiRJkiRpGIxta2pbbDuuEZlIkiRJ0mgQEROBzcunAwWKfdPMGGx5mXljRLyBYpqio4F/RMQXKYLQlcAuFOu17A+sBt6XmRcOthxJkiRJ0ugxmmJbG0IlSZIkjSYzI+Lm/g5k5h79vd5H5RolT9eRvjLNkNY3ycxzI+J64O3AG4Gv9pPsPODzmXnNUMqQJEmSJI0oxrZ1siFUkiRJUls1eGqfZptUsb+8jvTLKvYnD6XAiFgPeAVwAtWXNzkeeCoi7s7MuUMpR5IkSZI0dMa2tbUrtrUhVJIkSdJocnedvWOrWVKxP6GO9BMr9hcPtrCI2AS4EDgISODrwDeBWymmC5oJnAq8B3gz8OyIeG5m3jXYsiRJkiRJI4axbZ1sCJUkSZLUNkHres02qJTBTgc0pcq59foyRaAI8PbM/Eqf4zcDH4iIy4FfAjsC3wUOG0JZkiRJkqQhMLYdUNti22pDTyVJkiRJfWTmMmB2+XTbOk6ZUbH/wGDKiohpwMvLp3fT//opPfX6FfDn8umhEbHXYMqSJEmSJI0eoym2tSFUkiRJUvtE0Wu2FVujus0CN5WPUyNiuwHS7l6xf/Mgy9kVGFvuX5uZOUD6a6qUK0mSJElqJmPbWtoa29oQKkmSJEmDc0nF/rHVEkUxL9Ix5dNFwHWDLGdFxf74OtJXpllRNZUkSZIkSaMktrUhVJIkSVJ7RYu2xvlpxf6ba6Q7Dti+3L8gM5cMspx7gdXl/mERMVDAeEzF/t2DLEuSJEmSNBzGttW0Nba1IVSSJEmSBiEzbwF+UT49NCLe3jdNRGwMnF0+XQ18qp80p0VElts5/ZQzF7i0fLoF8MlqdYqI9wL7lk/vBf5ez3uRJEmSJI1OoyW2HTfcDCRJkiRp6Mo1TlpUVgO9BzgCmA6cHRH7AT8EFlAEbR8AdizTfiEzbxxiOWdSBIwTgDMi4iDg28CtFEHoTsCpwPPL9Am8p441VyRJkiRJDWNsO4C2xbY2hEqSJEnSIGXmvRFxPPAzYFvg9eXW11eA9w2jnKsj4iXAuRSB6dHl1p+FwDsy8xdDLU+SJEmSNHqMhtjWhlBJkiRJGoLMvDYi9gT+BTiRogfr+sBjwGXANzPzkgaU85uI2AV4LXA8sBewMUU34PnALcAfgW9n5qPDLU+SJEmSNHp0e2xrQ6gkSZKktmrd9EGNl5lPAZ8ut8Geew5wTp1p5wFfLDdJkiRJUocxtq0rbctj2zGtKkiSJEmSJEmSJEmSWsURoZIkSZLaaiT3mpUkSZIkCYxtO5UjQiVJkiRJkiRJkiR1HUeESpIkSWovO81KkiRJkkY6Y9uO5IhQSZIkSZIkSZIkSV3HEaGSJEmS2idauI6KvXMlSZIkSc1gbNuxHBEqSZIkSZIkSZIkqevYECpJkiRJkiRJkiSp6zg1riRJkqS2CVo3fZCzB0mSJEmSmsHYtnM5IlSSJEmSJEmSJElS13FEqCRJkqS2alWvWUmSJEmSmsXYtjM5IlSSJEmSJEmSJElS13FEqCRJkqQ2ihb2mrV3riRJkiSpGYxtO5UjQiVJkiRJkiRJkiR1HUeESpIkSWovO7NKkiRJkkY6Y9uO5IhQSZIkSZIkSZIkSV3HEaGSJEmS2qp166hIkiRJktQcxradyRGhkiRJkiRJkiRJkrqODaGSJEmSJEmSJEmSuo5T40qSJElqn2jh9EHOUiRJkiRJagZj247liFBJkiRJkiRJkiRJXccRoZIkSZLaqlWdZiVJkiRJahZj287kiFBJkiRJkiRJkiRJXccRoZIkSZLaJmjdOip2zpUkSZIkNYOxbedyRKgkSZIkSZIkSZKkruOIUEmSJElt5ToqkiRJkqSRzti2MzkiVJIkSZIkSZIkSVLXcUSoJEmSpLZq1ToqkiRJkiQ1i7FtZ3JEqCRJkiRJkiRJkqSuY0OoJEmSJEmSJEmSpK7j1LiSJEmS2srZgyRJkiRJI52xbWdyRKgkSZIkSZIkSZKkruOIUEmSJEntEzBmTIu6zdo7V5IkSZLUDMa2HcsRoZIkSZIkSZIkSZK6jiNCJUmSJLVN0Lp1VOw0K0mSJElqBmPbzuWIUEmSJEmSJEmSJEldxxGhkiRJktooiFZ1m7XfrCRJkiSpKYxtO5UjQiVJkiRJkiRJkiR1HRtCJUmSJEmSJEmSJHUdp8aVJEmS1FYtmz1IkiRJkqQmMbbtTI4IlSRJkiRJkiRJktR1HBEqSZIkqa3CbrOSJEmSpBHO2LYzOSJUkiRJkiRJkiRJUtdxRKgkSZKk9okW9pq1c64kSZIkqRmMbTuWI0IlSZIkSZIkSZIkdR1HhEqSJElqK5dRkSRJkiSNdMa2nckRoZIkSZIkSZIkSZK6jiNCJUmSJLVN0Lp1VOycK0mSJElqBmPbzuWIUEmSJEmSJEmSJEldx4ZQSZIkSZIkSZIkSV3HqXElSZIktVWLZg+SJEmSJKlpjG07kyNCJUmSJEmSJEmSJHUdR4RKkiRJaquw26wkSZIkaYQztu1MjgiVJEmSJEmSJEmS1HUcESpJkiSprew0K0mSJEka6YxtO5MjQiVJkiRJkiRJkiR1HUeESpIkSWqfaOE6KvbOlSRJkiQ1g7Ftx3JEqCRJkiRJkiRJkqSu44hQSZIkSW3lOiqSJEmSpJHO2LYzOSJUkiRJkiRJkiRJUtexIVSSJEmSJEmSJElS13FqXEmSJEltEwTRovmDAucpkiRJkiQ1nrFt5xpyQ2hEfKSRFRmMzPx4u8qWJEmSJHUX41tJkiRJ6k7DGRF6FpANqsdgGSiqqXbYdnN+8vNPtrsaUsu8+rvXtbsKUkt98Nid210FqSWWrVjd7irUpUWdZqVazsL4Vl3I2FajjbGtRhtjW40WxrYajpG2RqiXkSRJkiSpGxjfSpIkSVKTDXeNUAM3SZIkScPSqnVUpAF4IUqSJEkaMmPbzjSchtBjGlYLSZIkSZLax/hWkiRJkrrQkBtCM/OSRlZEkiRJ0uhkp1m1m/GtJEmSpOEytu1MI22NUEmSJEmSJEmSJEka0HDXCJUkSZKkoYsWrqNi71xJkiRJUjMY23YsR4RKkiRJkiRJkiRJ6jo2hEqSJEmSJEmSJEnqOm2ZGjcitgOOBGYCGwNTATLzDe2ojyRJkqT2adXsQVIzGN9KkiRJAmPbTtWyhtAoJkd+FXAGsFffw0AC6wSKEfES4B3l07mZeVIz6ylJkiRJUi3Gt5IkSZI0MrSkITQitgJ+CBzR81L5mAy8rOulwA+AiUBGxGGZ+demVFSSJElSSwUQLeo2a+dcNYLxrSRJkqS+jG07V9PXCC2DxCspgsS+f5+enrJVZeaTwM8rXnpZQysoSZIkSVIdjG8lSZIkaWRp6ojQiBgLXABsQ29A+ATwdeDPwFjgD3Vk9QvgFeX+sxtbS0mSJEnt1Kpes9JwGN9KkiRJqsXYtjM1e2rc1wP70hsk/gx4bWYuAoiIGXXm0xNMBrBbRGyUmfMaWVFJkiRJkmowvpUkSZKkEabZU+OeUbF/KfCyniBxMMqg8KGKl3YbbsUkSZIkdYaI1mzSMBnfSpIkSarK2LYzNa0hNCJ2AHaueOn0zFw9jCzvqNjfaRj5SJIkSZJUN+NbSZIkSRqZmjki9KDyMYE7M/Nvw8xvfsX+RsPMS5IkSZKkehnfSpIkSdII1Mw1Qjer2L+lAfktrthfvwH5SZIkSeoA4dw+6nzGt5IkSZJqMrbtTM0cETqlYn/Q66b0Y4MG5ydJkiRJUj2MbyVJkiRpBGrmiNA5FfsbNyC/7arkLUmSJGmkCmhZp1k752rojG8lSZIkVWds27GaOSJ0dvkYwD7DySgipgB7Vbx0/3DykyRJkiRpEIxvJUmSJGkEauaI0Ksq9reMiAMy87oh5vVqYGy5v6xP3pIkSZJGrGjhOip2m9WQGd9KkiRJqsHYtlM1bURoZj4G/KPipY8NJZ+ImAZ8EMhyuzQzlw+7gpIkSZIk1cH4VpIkSZJGpmZOjQvw1Yr94yPiQ4M5OSImAz8GtqG3iftLDaqbJEmSpDYLinVUWrK1+81qpDO+lSRJktQvY9vO1eyG0G8B95b7AXwsIn4QETMGOjEingdcDTyL3t6y12bmb5pVWUmSJEmSqjC+lSRJkqQRpplrhJKZKyPiZcAlwCSKYPHlwMsi4hrgnsr0EfF+YBfgOcDWZfosH+cDpzSzvpIkSZJab0zL1lGRhs74VpIkSVItxradqakNoQCZeV1EvAL4ITC5fHkM8Mxy6xHAJyv2oTdIfAo4OTPXCiwlSZIkSWoV41tJkiRJGlmaPTUuAJl5AXAwcAvrTl+cFVtlgEj5/DbgsMy8qAVVlSRJkiSpKuNbSZIkSRo5WtIQCpCZtwB7Ay8DLgVWUq4fW7FR8Xg98Hpgz/JcSZIkSV0oojVbc+oeUyPizIi4MiKejIglEXFvRHw3Io5qQnnTIuL0iLgoIh6KiGVluTdHxPci4g0RMbHR5WptxreSJEmS+jK2HVR5LYttmz41bqXMTOCnwE8jYjJwCLAtsAkwAXgSeBy4IjOfbGXdJEmSJGkwImJ/4GfAjD6Hti+3UyPibOD0MhYabnkvB84GNu1zaJNy2x14NXARcN9wy1NtxreSJEmSukG3x7YtbQitlJmLgT+1q3xJkiRJnSGa1aW1iSJie+BCYLPypW8DPwLmA/sBZwI7Au8AlgDvH2Z5bwe+TDHCcAHwLeDPwGPAhhQNcEcALxxOORoa41tJkiRJxrZ1ldfy2LZtDaGSJEmSNIJ9jt5A8Z2ZeXbFsasj4ifAlcDOwBkR8f3M/PtQCoqII4EvUQSK1wAvyMzZ/SQ9NyL+mXXXrZQkSZIkqT9dH9u2bI1QSZIkSeorAsa0aGtU59yI2BU4sXx6RZ9AEYDMnEvRYxaKuOsDQyxrDPA/ZR5zgBOqBIo95a7OzFVDKUuSJEmSNDTGtgOW1bbY1oZQSZIkSRqckyv2v1Ej3e+B+8v9EyJi0hDKej5Fz1uAL2Tm40PIQ5IkSZKkvkZFbNvyqXEjYjzFGz4COIhiyO3GQALzgNkUQ2IvA36dmStaXUdJkiRJrTMC11GZVbFfdV3IzMyI+BPwOmAKcABFnDMYr6zY/2HPTkRMBbYAngZmZ+bqQearBjC+lSRJktTD2LamtsW2LWsIjYiJwAeBtwDTKw9V7G8G7EIRRL4beDIivgb8Z2YubVVdJUmSJKmGPcrHhZn5wABpb6nY353BB4uHlY+PZeY9EfEy4L3AMyvSzIuI3wCfzMxb1slBDWd8K0mSJKkLjIrYtiUNoRGxP/ADimGvPYFh9nlc5zRgU+BDwMsj4pWZeX1TKypJkiSp5VrcaXZmRNzc34HM3KO/1yuVDWCbl08HChT7pplRR/rKsqYC25VP7ykb0f65n6QbAa8CTo6IN2bm9wZTjgbH+FaSJElSf4xtq5bV1ti26WuERsQzgYvoDRKz3GKArTLdLsBFEXFQs+srSZIkSTVMrdh/uo70lWmmVk3Vv40r9venCBSfphhduA0wkSLO+jSwGpgAfCcijhpkOaqT8a0kSZKkLjFqYtumjgiNiI2AXwEbsnbgdz1FD9qrgDuBBeWxaRRv9mCK+YL3p7dH7YbALyNit8yc38x6S5IkSepad9fTO7aGSRX7y+tIv6xif/Igy6oMLtcDVgHHZ+blFa/fBXwgIu4FvgaMBT4PHDjIsjQA41tJkiRJHcTYtk7NHhH6CYrpf3oCxNnAiZl5YGZ+PjMvz8zZmbksM5eX+5eXxw4EXgo8XpHfZmWekiRJkrpEtOi/BllSsT+hjvQTK/YXD6MsgPP6BIprZObXgb+XTw+IiN0HWZYGZnwrSZIkqSpj27rKghbHtk1rCC3nF341vUHiY8CRmfmLevPIzJ8DR1EEmD35vLrMW5IkSZJabbDTAU2pcu5gywK4YID0v67YP3iQZakG41tJkiRJXWbUxLbNHBE6i+KD6VkP5fTMvGuwmZTnnF7mQ5nnrMZUUZIkSVK7jYnWbI2QmcsoGrIAtq3jlBkV+w8MsrgnWLun7UDn31+xv9kgy1JtszC+lSRJklSDsW1VbY1tm9kQWvmhzAZ+Ooy8fsraUwjNqJZQkiRJkprspvJxakRsN0Dayml8bh5MIZmZfc4ZO8AplcdXDaYsDcj4VpIkSVK3GRWxbTMbQjcqHxO4vnyjQ5KZq4Hr+slbkiRJ0ggWQES0ZmtctS+p2D+26nuLCOCY8uki1o5p6vXniv2dB0hbefyhIZSl6oxvJUmSJFVlbDugtsW2zWwInV2xP78B+S2o2H+iAflJkiRJ0lBUjgZ8c410xwHbl/sXZOaSIZR1XsX+y6oliohxwInl02TtgFbDZ3wrSZIkqduMiti2mQ2hla20mzcgv8o8HmxAfpIkSZI6QERrtkbJzFuAX5RPD42It6/7nmJj4Ozy6WrgU/2kOS0istzOqVLWDcAF5dPnRsSpVar1KaBnKqPzM/PRet6L6mZ8K0mSJKkmY9vOjG3HDTeDGi4FngamAodExKQhthITEZOBQ8qnC4HLGlNFSZIkSRqS9wBHANOBsyNiP+CHFCP99gU+AOxYpv1CZt44jLLeCRwMbAqcExFHAz+hGKU4A3gD8IIy7ePAu4dRlvpnfCtJkiSpG3V9bNu0EaGZuRT4Ufl0EvCuYWT3LmAyxTDYnw414JQkSZKkRsjMe4Hj6R3N93rgD8DVwDfoDRS/AryvAWU9F7iXIoZ7A/Bb4Hrg5/QGincCx2am64M2mPGtJEmSpG40GmLbZk6NC/BRYG65/5GIOGGwGZTnfIQiSJxf7kuSJEnqBhGMadHW0DmEgMy8FtiToofs1RSxzzLgfuD7wKzMfFtmZgPKuqEs6z0UIwifAFZQ9Jz9I/AvwF7l1EZqDuNbSZIkSf0ztq23rJbHts2cGpfMfDQiXkgx7+804PyI+DLwicycV+vciNgI+BDwjrKe84ETMvPhZtZZkiRJkuqVmU8Bny63wZ57DnDOINIvBr5Qbmox41tJkiRJ3aqbY9shN4RGxFGDSP4+4P8BG1HM6fu2iPgdcBVwB/AURY/YDYFdKNZLOQ6YAARF6/MHgHERcVRm/mWo9ZYkSZLUWRrcmVUaNONbSZIkScNlbNuZhjMi9GKK4G4wkiLwmwicUG7V9FwySRFgfr3ieVNHskqSJEmSRpWLMb6VJEmSpK7TiICr3jbu7PNY69xk3SDUtnRJkiSpC4XdZtU5jG8lSZIkDYmxbWcabkPoYP6qzUorSZIkSdJwGd9KkiRJUpcZTkPoxxpWC0mSJEmjUtC6dVRsjVINxreSJEmShszYtnMNuSE0Mw0UJUmSJEkjnvGtJEmSJHWnRqwRKkmSJElDNsZ1VCRJkiRJI5yxbWca0+4KSJIkSZIkSZIkSVKj2RAqSZIkSZIkSZIkqes4Na4kSZKktnLyIEmSJEnSSGds25kcESpJkiRJkiRJkiSp6zgiVJIkSVJbRdhvVpIkSZI0shnbdqaWNoRGxPbAKcBhwG7ARsCGDG7EcGamDbiSJEmSpLYxvpUkSZKkzteSgCsipgFfAU6mdzpem8YlSZKkUS4CxrQoMrBzrhrB+FaSJElSX8a2navpDaERsQPwJ2A7iuAwKw5X7vc9Rp9jkiRJkiS1jfGtJEmSJI0sTW0IjYiJwM+AGfQGgQHcDzwBHFi+lsAlwAbAlsAWFa8DPAnc3My6SpIkSWoP11HRSGB8K0mSJKkWY9vONGbgJMPyemAfegO+G4EDM3MH4GWVCTPzmMw8IDO3AmYC/wE8RRFYbgxcWqY5psl1liRJkiSpL+NbSZIkSRphmt0QejpFkBjAPcAxmXl9eazaNEFk5r2Z+RFgX+CGsp7/FhH/2dzqSpIkSWq1iNZs0jAZ30qSJEmqyti2MzWtITQitgJ2KZ8m8IHMXDCYPDLzfuC5wH0UweaZEXF0I+spSZIkSVItxreSJEmSNDI1c0ToweVjAE8DvxhKJpn5JPC+ipfOHF61JEmSJEkaFONbSZIkSRqBxjUx783KxwT+lpmraiWOiAmZubzK4Z8D84CNgGdHxMaZObdxVZUkSZLULuHcPup8xreSJEmSajK27UzNHBG6UcX+Y/0cX9bn+aRqGWXmauCa8uk44JDhVU2SJEmSpLoZ30qSJEnSCNTMEaGVPWT76wn7dJ/nWwC11lh5smJ/y6FWSpIkSVJnGWOnWXU+41tJkiRJNRnbdqZmjgh9qmJ/g74HM3MRa/ea3XGA/CZX7G86jHpJkiRJkjQYxreSJEmSNAI1syH0nor9aj1cb6vYP2yA/Par2F8ypBpJkiRJ6ihBENGiDbvnasiMbyVJkiRVZWzbuZrZEHpr+RjAblXSXF+R5pUR0W99IuK5wIyKlx5oSA0lSZIkSRqY8a0kSZIkjUBNawjNzIeAB8unkyNiv36S/awnObA98F99g8WI2B34nzJNT9rLGl5hSZIkSW0RLdqkoTK+lSRJkjQQY9vO1MwRoQB/qtj/p36O/x64t9wP4J+B2yPi7Ij4j4j4KUWv2i3L4wn8MjOfaGKdJUmSJEnqy/hWkiRJkkaYcU3O/3zgtRRB3muBT1QezMwVEfEe4OcUQWAAM4G3ViTrCRABFgLvb3KdJUmSJLVKwJhoUZ9Wu85qeIxvJUmSJPXP2LZjNbsh9HfA2ZQjTyNim3JKoTUy8//KYPGzrB0UrklSvr4QODkz72xynSVJkiRJ6sv4VpIkSZJGmKY2hGbmCuD0OtJ9MSKuAD4KHAtMqDi8EPgFcFZm3tOMekqSJEmSVIvxrSRJkiSNPM0eEVq3zLwK+KeImAzMAKYB84C7y4BTkiRJUhdq1exBUqsY30qSJEmjj7FtZ+qYhtAembkYuLXd9ZDUa9GSZfzit1dx8ZU38/Cjc1iybDmbTJvKPrtvzwnPPpB999hhWPlnJrfe+RDX/O0u/nH7A9z7wOPMXbAQgGlT12fnHbfk6IP34NlH7s3ECeNr5rVixUrueeBxbrv7YW6762Fuv/th7n7gcVauXAXAB9/xUp5/7AHDqq+623rjxvCsZ2zKQdtNY/Op6zFx3BjmL1nB7bMXcvGdT3L77IXDyn/6+hP44kv3qjv9B391Cw/MW7LO60fO3IR/Pnz7QZf/id/dzq2PD+89qLv4HS9JzWN8K3UW73s0mhjbarTxO15SNR3XECqps9x+98N88NPf57En5q/1+qOz5/Ho7Hn89uIbOOn5h/KuN7yAGGKXl5P++TPr5N9j9pwFzJ6zgMuvuY3vnn8xH3vvK9h15tZV8/rK937Hj391+ZDqIW2/8SROnzWTTadMXOv1zaZOZLOpEzly5ib87tbZfO+aB9tUw+FZtTp59Kll7a6GOojf8eoUQ72+JEmql/c9Gk2MbTXa+B2vTmFs25lsCJVU1aOz5/Hefz+HeQsWAfCCZx3AsUfszdTJ63HHvY/w/Z9fyiOPz+Wnv76CiRPG89bXPG9I5Twx5ykANtloKkcfsgf77DaDLTbdiLHjxnD/Q0/wi99dxT9ue4CHHp3Duz76P3zj029lu62n95tXZq7ZHz9uLDvO2IKVK1dx9/2PDaluGj2mrz+B9z1rZzacVPTau/jOJ7nyvrksXr6K7TeezPP33ILNp07kubttxvJVq/nR9Q8Pu8w/3j6bP97+RM00j1cJ7q57YD4fmHPzgGXst800Xr5/cfP990cWMH+Js/Gp4He8JEkaLbzv0WhibKvRxu94SQOxIVQNExHPA14IHAFsCWwIzAUeBv4M/Cwz/zqEfI8EXgAcBWwLbAKsLvO+Bfgr8KPMvK0Bb0MVzv7Ob9bcRLzrjS/g5OcftubY7rtsy7GH7cWb3/9VHnx0Dj/8v0s57qh92Gn7LQddzjNmbsWpL53F4QfuytixY9Y6tttO2/Dco/fl89/4JT/77VU8vWgpX/r2BXzuw6f1m9cBe+7IDttuxq4zt2bmjC0YN24s/3PeH72R0IBeeeA2awLFc69+gD/c1hvE3TNnMVfdP4+z/mlXttxgPZ6/++b89Z65PDh/3Wl9BuOppSt5aP7SIZ27eMUqFs9fNWC6Uw6Ysmb/kjvnDKksdSe/49Upgtato2LfXEn1Mr7tLt73aDQxttVo43e8OoWxbecaM3ASqbaIODAirgIuBP4F2AuYDowHNgf2B94LXB4Rv4mIuiZkj4gDIuJS4C/AmcAhwNbAesBkYBvgOOAs4NaI+ENE7NvAtzaq3f/QbC65suiRt+cztlvrJqLHBlMn8+43nQDA6tXJ//7skiGV9c3/91aOOnj3dW4iekQE73j989lko6kAXHPjXTz19OJ+0x558O686Lhn8oyZWzNu3Ngh1Uejz5YbTOSZMzYC4I7ZC9cKFHssWr6K715dTBs0Zkxwwl5btLSOQ7Hx5PHsteUGACxYsoIbHprf3gqpY/gdL0lS/4xvu4/3PRpNjG012vgdL6keQx4RGhGvaWRFBiMzv9uusrW2iHgJ8H1gUvnSTcB3geuBeRS9Ww8FXgPMBI4HroyIEzLz6hr5vgr4FkVQCHAn8FPgCmB2+doWwGHAi4BnAM8G3gWc1pA3N8r96a83rdl/4XEHVU33zH13ZotNp/HYE/O57JrbWLZsBRMn1l4QfCgmjB/HXrtux8VX3Myq1at5+PG5bDB1csPL0eh0cBkoAvz5zierpvvHI0/xxMJlbDplIvttsyHjxwYrVmXV9O125MxNGDOm6CN22T1z6OCqqsX8jlenGeM6Kmoz41uB8W238r5Ho4mxrUYbv+PVaYxtO9NwpsY9B2jX/3YMFDtARDwTOA+YQHEtfAD4XGb2nc/iDxHxKeATwBnAZsCvI2LfzFxnIYKIOI7ibzwGWEnR2/a/+8kX4P8i4gPAicCnG/POBHDDTfes2T9gr5lV00UEB+w1k1//6TqWLF3ObXc/zD67b9+UOq1Y2XsJjB3jgHY1zm5bTF2zf8tjT9VMe8tjT3P0ThOZNH4sO2wymTtmL2p29YbsqJmbrNm/5C6nDlIvv+MlaR3nYHw7qhnfdi/vezSaGNtqtPE7XlI9GrFGaKuauLMsyz4/HSAiJtAbJAKckZmfr5Y+M5cD74uIcRS9WqdT/NjwnD75TgN+QO+0za/IzPNr1SWL1aXPj4g/AM8d7HtR/+59sOiYPGm9CWyx6bSaabffdrM1+/c9NLspNxLLV6zkptseAIpFxLfecpMBzpDqt/W0otP/khWrmLNoRc20D1ese7L1hpOGFSw+c8ZGHLTdRmw6ZQIRwdPLVnL/3MVc/+B8/nrPXFasHvr/8nbbfAqbb1AMOrhj9kIeWTC09VrUnfyOl6SqjG9HIePb7uZ9j0YTY1uNNn7HS6rHcBtCWznO1zHFneVUoGctlMuAL9R53r8CLwB2Ap4dEYdm5hUVx0+nmG4I4NyBgsRKmfkU8JN606u65StWMnf+QgA2H+Amokiz4Zr9x2bPb0qdfvrrK1hQzqt/xEG7sf6kiU0pR6PPuDHBtEnFdChzFi0fMH1lmunrT6iRcmDbTJu01vOJ4yYwff0JHLDtNF6y95b896X3cucTQwtGj95p+pr9S+6qPiWSRh+/49WJnD1IHcL4dvQyvu1S3vdoNDG21Wjjd7w6kbFtZxpOQ+jrGlYLjURvrNj/bNlrdUCZuTQi/gv4YvnSP1Osi9LjTRX7nxlWDTVki5csW7M/uY7/YU9erzfN4qXLaqQcmjvvfZT/Oe+PQDHX/pte9ZwBzpDqt9743mlKlq7ob4aytS2pSLPe+KEtaL94+Squf3A+tz3+NI8+tYwlK1YxecJYdth4MkfvPJ1tpk1i+pSJ/OtzduE//3DHoAPGSePHcFC5NszSFau48r55Q6qnupPf8ZLUL+Pb0c34tkt536PRxNhWo43f8ZLqNeSG0Mw8t5EV0cgREVOAA8unS4HfDDKLn9EbKB5Tke8zgK3Lp3dn5s3DqKaGYdny3ulTxo8b+GZ4wvjer5Kly2pPvTJYc+c/zfv/83tr8n3H6/6JGVtv2tAyNLpNGNsbLK6sY7qeyjQTxw1+rYd5S1bwjp/+nWUrV69z7LbHF/K722bz6oO25bhdN2PCuDG85YgdOPP/bmbVIKYSOnSHjdfU7cr75vVblkYvv+PVcaJYs6dVZUn9Mb4dvYxvu5v3PRpNjG012vgdr45jbNuxGrFGqEafvem9dv6WmYP6P0dmPhgRs4HNgO0iYtPMfALYvyLZNY2panURUS0Qrb6y9igxccL4NfuVC3xXs3zFyjX7600cXyPl4Dy1cAnv/tg5PP7EfABe/NxncuLxhzQsfwlg+areQGrcmIHvIirTDCUIW7U6awZ+qxO+e/WDbLfRJHbdfCqbT53IAdtuyNX3z6+7DKcOUi1+x0uStJYRH98a21bnfY9GE2NbjTZ+x0uq1+C7+0gwvWL/0SHm8VjFfk/3mMp8Hx9ivmqAyukkKqeZqGbJ0t51JSqnmRiOhYuW8p6PfYe77isusefN2o8z/vlFDclbqrR0RW/AV890QIOdbmio/nD7E2v299hig7rP22baesycvj4AD89fMuR1WNS9/I5XpwmKoKQVm51mJfXD+LaLed+j0cTYVqON3/HqNMa2ncsRoRqKqRX7C4eYx9MV+xuVj5V3Q0PNt26ZuUd/r5e9aXdvdvmdbML4cWy04frMW7CI2U8uGDD9Y2WPJ1h74fGhWrR4Ke/5+He49a6HAHjOUfvwb+94aeumFtCosnJ1smDJCjacNJ5NJk8YMP309XtvlucsWl4j5fA8NG/Jmv2N16+/p2Jlj9m/3D2noXVSd/A7XpKktYz4+NbYtjrvezSaGNtqtPE7XlK9HBGqoagM8qYMMY/KYHNp+fhUA/JVg+y43eZA0aOq8kahP/c9OHvN/g7bbj6schctWcZ7Pn4ON9/xIADPOXJvPvzOkxkzxq8rNc9D84vAbNKEsWwyQGC29bT1es9bsKRGyuGpf9WUXmMDDt9xY6AIgi81WFQVfser00RESzZJ6ofxbZfzvkejibGtRhu/49VpjG07k/8yNRSVk/JvOcQ8tugnv8p8h/d/Iw3bvnvssGb/un/cXTVdZnL9TfcAMGm9Cew6c+shl7loyTLe+/HvcNPtDwDw7CP25sOnv4yxY/2qUnPd+nhvJ/3dB5iqZ7fNi9+5lq5Yxb1zFjetTttuNGnN/rzF9S1Vtf+209hgvSLYvfGh+Ty1dOUAZ2i08jtekqQ1jG+7nPc9Gk2MbTXa+B0vqR7+69RQ/B3ouQPZJyIGtbp0RGwLbFY+XQg8UO5fX5HsoGHVUMN2zGF7rtn/5e+vqZru6hvv5NHZ8wA47MBnMHGIi4333ET847bem4iPvMubCLXG1ffPW7N/zM7Tq6bba6sN2GxqMX3QDQ8tYMWqofRtrc+zd9l0zf4tjz1dI2WvyqmDLr7THrOqzu94dZox0ZpNkvphfNvlvO/RaGJsq9HG73h1GmPbzuS/UA1aZi4EriufrgccP8gsXlKxf3FmZpnv7cAj5eszI6LfdU7UGjtsuzlHHVwsJ3PT7Q/w099csU6ap55ezBe++SsAxowJXn3i0euk+fWfruPwl3yQw1/yQf7jyz/tt6zFS5Zxxr+f402E2uaRBUu59oHihniXzabwnGdsuk6a9SeM5TXP3BaA1auTX9302Dppjpy5Cf/7mgP439ccwJsPm9FvWQdtN63mguZjAk49aFt226LonfvEwmVc98D8Ad/DtEnj2WurosfvvMXL+dsjA6+PodHL73hJkgrGt93P+x6NJsa2Gm38jpdUj3HtroBGrG8CB5f7Z0TEr3oCvloiYiLw9oqXzusn34+W+2cCrx1uRTV073jdP/H3W+9j/lPFDcOd9zzCs4/chymT1+POex/lez+7hEcenwvAy084nF122GrQZSxfsZL3/ce5/P3W+wHYfedtePWJR3P/Q7Nrnrfp9A2Zuv6kdV5fvGQZf77iprVeu/PeR9fs95RT6ZhD92TypImDrru6y/evfYhdNpvCBuuN57UHb8eMjSdzxX1zWbJ8FdttPJkT9tyCzcsesxfe+jgPzBvaGiqnz5rJkwuXcd2D87n7ycU8sXAZy1auZtL4sey4yWSO3nk620wrru3lq1bzjcvvY8XqgXvnHjVzE8aWXcIuvXsOA38ja7TzO16SpDWMb7uc9z0aTYxtNdr4HS9pIDaEaqi+B3wYmAEcCbwT+FId530C2Lncvxv4UZ/jXy7z2gh4TUT8MjPPr6dCEbEBcFxm9t9tR4O21eYb89kPn8a/ffr7PP7kAi646DouuOi6ddKd+LyDedtrB9txujBn3tPceMt9a57fcudDnPaeswc874PveCnPP/aAdV5f8PRiPnl29Uvmgj9eywV/vHat1/bfc0dvJMQTC5fzmYvu4vRZM5m+/gRm7TydWf1MJfSH22bzw+seHlZZ06dM5Lm71V4q6omFy/jaZfdx++yFNdP1OGqnTdbsX3KXUwdpYH7Hq5M4tY+kNjO+7XLe92g0MbbVaON3vDqJsW1nsiFUQ5KZyyPiFcBfgPHAFyJiLPDFzFzdN31ETAA+Dry3fGk18MbMXGu188ycGxGvAi6gmLr5vIh4D/CVzFxVrT4R8WLgs8BlgIFiA+220zZ870un8/PfXsUlV97MQ4/OYemyFWw8bQp777Y9L3zOgey3547trqbUEPfOWcy//vJmnrXLphy43UZsvsFEJo4bw4IlK7hj9kL+fOeT3PZ4fcFbNZ+96E5mTl+fmdPXZ/qUiUyZOJbJE8axYuVqFixdwX1zF3P9gwu4+v55rKyjtyzArptPYYsN1gPg1see5vGnlw2rjho9/I6XJMn4drTwvkejibGtRhu/4yXVEnXM9iJVFREnA9+lWEsF4O8UvWmvB+YBmwCHUEwBtFOZJoG3ZOY3auT7GuAbQE83lzuBnwBXAD1zDmxBMX3Ri4Ce9VbOzczThvmebt5x5113/8nvrxpONtKI8sk/3dnuKkgt9cFjdx44kdQFTj6umOnx7jtu7ch+qRFx8ybb7rT7a//7gpaUd+7bXsCcB++6JTNdq0/SOrotvjW21WhkbKvRxthWo4Wx7dqMbQfHEaEalsz8SUTcD/w3cCCwN/CZGqfMBt6QmTW/ETLzuxFxG/BF4FCK6YY+OEB1fkvRa1aSJEmSpEExvpUkSZK6jw2hGrbMvBo4KCKOp+i9egRFb9aNKKb/6XENcGxm1jX3RpnvYRFxNPAC4ChgG4peuKuBOcAtwOXAeZl5R2PekSRJklrJdVQkdQrjW0mSJA2VsW1nsiFUDZOZFwIXVr4WERsBl1JM7XMQ8Dpg4JWk1873EuCSBlVTkiRJkqSajG8lSZKk7jBm4CTS0GXmPOB5wEPlS1+KiNPaVyNJkiR1lIBo0Ya9cyUNg/GtJEmSqjK27Vg2hKrpMvMhimBxHsU/0W9FxEntrZUkSZIkSYNjfCtJkiSNLE6Nq5bIzJsj4nnAP5Uv7RwRkzJzSTvrJUmSpPYbE3ZnlTRyGN9KkiSpP8a2naltDaERMQ7YCtgYmApEZv6lXfVR82Xm1cDV7a6HJEmSJDWS8e3oY3wrSZIkjQwtbQiNiPWBNwEvBA4G1qs4nP3VJyL2BfYuny7IzP9rcjUlSZIkSarJ+FaSJEmSOl/LGkIj4k3Ap4ENe16q89QJwDkUgeTqiNguMx9tfA0lSZIktVoAY1pYltQIxreSJEmSKhnbdq6W/F0i4tvA14Bp/RzOWueW081cQ+91dEqj6ydJkiRJUj2MbyVJkiRp5Gh6Q2hEfBo4jSLQy/LxAeC7FMFjPY3XP6nYP77BVZQkSZLURhGt2aThMr6VJEmSVI2xbWdqakNoRBwAnEERICYwF3hZZu6QmadRTCVUj1/1ZAkcFhEtXdtUkiRJkjS6Gd9KkiRJ0sjT7IDr4/T2iJ0LHJaZdw42k8y8PSIWAlOA9YBnADc3rJaSJEmS2maMXVo1MhjfSpIkSarK2LYzNW1EaERMAZ5Nb2/Z9w4lSKxwS8X+M4ZTN0mSJEmS6mV8K0mSJEkjUzNHhB4BjC/35wPfG2Z+syv2txhmXpIkSZI6hJ1mNQIY30qSJEmqydi2MzVzjdBtyscErs7M1cPM76mK/anDzEuSJEmSpHoZ30qSJEnSCNTMhtDpFftPNCC/8RX7ww06JUmSJEmql/GtJEmSJI1AzZwad1HF/voNyG/Tiv25DchPkiRJUpsFMKZF0wc5S5GGwfhWkiRJUlXGtp2rmSNCK3vJbj+cjCIigP0qXppdLa0kSZIkSQ1mfCtJkiRJI1AzR4TeXj4GsFdEbJKZc4aY11HAhhXPrx5WzSRJkiR1hoAxYbdZdTzjW0mSJEnVGdt2rKaNCM3MG4AngSzLeeswsnt/xf7tmfn4cOomSZIkSVK9jG8lSZIkaWRq5tS4AOdTtE0H8K8RsfdgM4iItwHPK58mcG7jqidJkiSp3SJas0nDZHwrSZIkqSpj287U7IbQTwDLKAK89YA/RcRx9ZwYERMi4izgy+X5AE8BX2lCPSVJkiRJqsX4VpIkSZJGmGauEUpmPlQGe/9JEextDFwYEZcAPwJmV6aPiM2AZwDHAacC29I723EC78jMp5tZZ0mSJEmtE8AYl1HRCGB8K0mSJKkaY9vO1dSGUIDM/HRE7Aa8hiLYC+DocqsUwKN9nlNxzn9l5v82ubqSJEmSJPXL+FaSJEmSRpamN4SWXgfcC3yY3gCwMhCkn9ey4vlHM/Pfm11JSZIkSa0X9mfVyGJ8K0mSJGkdxradqdlrhAKQhY8BhwO/Ye2Ru8G6I3l7XrsEONIgUZIkSZLUCYxvJUmSJGnkaNWIUAAy8yrgBRGxI/Bs4AiKdVI2ASYATwKPA38FfpeZN7WyfpIkSZIk1cP4VpIkSZI6X0sbQntk5j3AN8pNkiRJ0ig2xtmDNIIZ30qSJEkCY9tO1ZKpcSVJkiRJkiRJkiSpldoyIlSSJEmSethrVpIkSZI00hnbdiZHhEqSJEmSJEmSJEnqOo4IlSRJktQ2QRDRmm6zgd1zJUmSJEmNZ2zbuZraEBoR2zUj38x8oBn5SpIkSZLUH+NbSZIkSRp5mj0i9D4gG5xn4khWSZIkqTtEC9dRsdOshuc+jG8lSZIk9cfYtmO1KuDyzyJJkiRJ6gbGt5IkSZI0QnRqz9O+vWwNNCVJkqQu1aJlVKR2Mb6VJEmSRgFj287U7IbQcweZfiywEbAHsH35WgJzgV81rlqSJEmSNHwRMRX4F+BEYCdgfeAx4FLgW5n5lyaWfTLw44qXzs3M05pVnoxvJUmSJHWnbo5tm9oQmpmvG+q5EbEr8FHg5RTB4zjgtMxc1aDqSZIkSdKQRcT+wM+AGX0ObV9up0bE2cDpmdnQtSUjYlPgK43MU7UZ30qSJEnqRt0e245pZubDkZm3ZeYpwOkUUwe9EvhWe2slSZIkqdHGRLRka6SI2B64kN5A8dvAc4GDgbcA95SvvwP4VEMLL3wFmA483oS81WDGt5IkSVL3M7YdkqbHth3bENojM8+m+OADeE1EnNTmKkmSJEnS54DNyv13ZuYbMvP3mXl1Zn4dOAi4szx+RkTs3aiCI+LlwEnAIuADjcpXzWd8K0mSJKnDdH1s2/ENoaWzKNZSATizjfWQJEmS1EABjInWbI3qN1tOc3pi+fSKsnFrLZk5l6LHLBRxV0OCuojYDPiv8um/Avc1Il+11FkY30qSJEldxdh20GW3LLYdEQ2hmfkQ8DeKv+8BEbFLm6skSZIkafQ6uWL/GzXS/R64v9w/ISImNaDsnmmDLgf+uwH5qcWMbyVJkiR1iFER246IhtDSPRX7+7StFpIkSZIaKqI1WwPNqtj/U7VEmZkVx6cABwyn0Ih4BfBSYBnwhsxcPZz81FbGt5IkSVKXMbatT6tj25HUELqsYn/rttVCkiRJ0mi3R/m4MDMfGCDtLRX7uw+1wD7TBn0sM28fal7qCMa3kiRJktptVMS245pdQANtV7E/kuotSZIkqYYxDVvhpPkiYiKwefl0oECxb5oZwyj6a8AmwA3AZ4aRjzqD8a0kSZLUZYxt69Ly2HZEBFwRsSVwMJDlS0+0sTqSJEmSRq6ZEXFzfwcyc4/+Xu9jasX+03Wkr0wztWqqGiLiFOAlwErg9Zm5cij5qDMY30qSJElqAGPbOnV8Q2hEjKFYpLWnrglc274aSZIkSWqkBq9x0myTKvaX15G+cgrUyYMtLCI2B84un/6/zLxxsHmocxjfSpIkSd3L2La6dsa2HdsQGhFjgeOAs4ADKQLEAO7OzH5buSVJkiRpAHfX2Tu2miUV+xPqSD+xYn/xEMrrmTboNuDjQzhfHcD4VpIkSVKDGdvWqakNoRHxpyGcNg6YBuxM8eFXtqEn8JHh10ySJEmShmSw0wFNqXLugCLilcCLgdXAGzJzWe0z1EzGt5IkSZK6yKiJbZs9InQWveueDEbf4LDntf/OzPOGWylJkiRJnSECxrRo+qBGTFOUmcsiYjawGbBtHafMqNh/YJDFfbR8vA7YLiK26yfN7hX7O0TEK8r9ezPzqkGWp9pmYXwrSZIkqR/GtjW1Nbbt1KlxK4PDAJ4C/jUzv9q+KkmSJEkSADcBxwJTI2K7zKwVBFYGc4OdArVn6qGDgB/Wkf6ocgM4F7AhtDMY30qSJEnqRKMitm1FQ+hg26ZXUQSGs4HrgYuAH2XmokZXTJIkSVL7jWlEd9bWuoQiWKR8PKe/RBERwDHl00UUvV81shnfSpIkSeqXsW1nampDaGaOaWb+kiRJktQGPwU+Vu6/mSrBInAcsH25f0FmLhlMIZm5/UBpImIW8Ofy6bmZedpgylD9jG8lSZIkdZlREdsayEmSJElqq4jWbI2SmbcAvyifHhoRb1/3PcXGwNnl09XAp/pJc1pEZLmd07gaSpIkSZJazdi2M2PbTl0jVJIkSZI62XuAI4DpwNkRsR/FWicLgH2BDwA7lmm/kJk3tqGOkiRJkiTV0vWxbdMaQiNiZ+D4ipd+nJmPNas8SZIkSSNP0Lp1VBpZSmbeGxHHAz8DtgVeX259fQV4XwOLVhsY30qSJEmqxdi2czVzROjzgC+U+/OArzaxLEmSJElqqcy8NiL2BP4FOBHYCVgfeAy4DPhmZl7SxiqqcYxvJUmSJHWlbo9tm9kQOoWiYTqBGzJzRRPLkiRJkqSWy8yngE+X22DPPQc4Z5jlX0xjOwSrf8a3kiRJkrpWN8e2zWwIfbJi//EmliNJkiRpBGvR7EHScBjfSpIkSarJ2LYzjWli3o9W7G/QxHIkSZIkSWom41tJkiRJGoGaOSL0KmAVRWPrnk0sR5IkSdII1szemVKDGN9KkiRJqsnYtjM17e+SmU8AF1PM6TsjIg5sVlmSJEmSJDWL8a0kSZIkjUzNbqD+OLC63P9CRDRzBKokSZKkESgiWrJJw2R8K0mSJKkqY9vO1NSG0My8FPgkRa/Zw4CfRsS0ZpYpSZIkSVKjGd9KkiRJ0sjT9CmLM/MjwLsp1lM5AbgtIj4WEfvbg1aSJElStGiThsv4VpIkSVI1xradaViBWkR8u+LpGZk5t8/xeyqerijL2wz4ULmtiogFwNODKDYzc+YQqyxJkiRJ0jqMbyVJkiSp+wy3x+ppQJb7ZwFz+xzfvuI4Ffs9jdbjgE3KrV45cBJJkiRJI0FEMKZFa5y4looGcBrGt5IkSZKGwNi2czVi6p5g8MHbUIM9/7qSJEmSpGYxvpUkSZKkLtLsNUwewB6ukiRJkqSRz/hWkiRJkkaYpjaEZub2zcxfkiRJ0sjnsDiNBMa3kiRJkmoxtu1MY9pdAUmSJEmSJEmSJElqtGZPjStJkiRJNYXdZiVJkiRJI5yxbWdyRKgkSZIkSZIkSZKkruOIUEmSJEltFXablSRJkiSNcMa2nckRoZIkSZIkSZIkSZK6jiNCJUmSJLVN0LremfbNlSRJkiQ1g7Ft52pEQ2iWj+dFxNIG5DdgeZn5rBaUI0mSJEkaXYxvJUmSJKmLNGpEaAAHNyivgcrJAVNJkiRJGjFcR0UdxvhWkiRJ0qAZ23Ym1wiVJEmSJEmSJEmS1HUauUaoTd2SJEmSpG5gfCtJkiRJXaBRDaEJnAk82aD8JEmSJI0StjipwxjfSpIkSRo0Y9vO1IiG0J51TX6SmQ80ID9JkiRJktrB+FaSJEmSukgjp8aVJEmSpEGLsN+sJEmSJGlkM7btTDaESlVkuysgtdC/Hrtzu6sgtdSnLr6r3VWQWuLJRcvbXQVJUpsZ22o0MbbVaGNsq9HC2FbDYUOoJEmSpLYa0+4KSJIkSZI0TMa2ncm/iyRJkiRJkiRJkqSu44hQSZIkSW0TtG4dFVdrkSRJkiQ1g7Ft53JEqCRJkiRJkiRJkqSu44hQSZIkSW1lb1ZJkiRJ0khnbNuZGjEiNBuQhyRJkiRJ7WZ8K0mSJEldpBENoTZyS5IkSZK6gfGtJEmSJHWR4U6Nu0PF/sPDzEuSJEnSaBMQrWp6solLtRnfSpIkSRoaY9uONayG0My8v1EVkSRJkiSpXYxvJUmSJKn7DHdEqCRJkiQNyxi7s0qSJEmSRjhj287UiDVCJUmSJEmSJEmSJKmjOCJUkiRJUlu1bB0VSZIkSZKaxNi2MzkiVJIkSZIkSZIkSVLXcUSoJEmSpLaJ8r9WlSVJkiRJUqMZ23YuR4RKkiRJkiRJkiRJ6jo2hEqSJEmSJEmSJEnqOk6NK0mSJKmtwll9JEmSJEkjnLFtZ3JEqCRJkiRJkiRJkqSu44hQSZIkSW01BrvNSpIkSZJGNmPbzuSIUEmSJEmSJEmSJEldxxGhkiRJktrKdVQkSZIkSSOdsW1nckSoJEmSJEmSJEmSpK7jiFBJkiRJbWWvWUmSJEnSSGds25kcESpJkiRJkiRJkiSp6zgiVJIkSVLbBBC0ptusnXMlSZKk/9/efcfLUdf7H3990guBhBACEmoA6b0I0kVUxHrFq2IB9apXBfvV67Vf9WdDvfbLVYkFRQUrKnaQLlWl19AhkISE9Pb5/TF7cjabU3b37DnbXs885pGZ2e9857t7Jjnz3vnOdyQNB7Nt6/KOUEmSJEmSJEmSJEkdxwuhkiRJkiRJkiRJkjqOQ+NKkiRJaqpRjusjSZIkSWpzZtvW5B2hkiRJkiRJkiRJkjqOd4RKkiRJaqrAbrOSJEmSpPZmtm1N3hEqSZIkSZIkSZIkqeN4R6gkSZKk5gmIkeo0a+dcSZIkSdJwMNu2LO8IlSRJkiRJkiRJktRxvCNUkiRJUlP5HBVJkiRJUrsz27Ym7wiVJEmSJEmSJEmS1HG8I1SSJElS0wQwaoQ6zdo3V5IkSZI0HMy2rcs7QiVJkiRJkiRJkiR1HC+ESpIkSZIkSZIkSeo4Do0rSZIkqanCgX0kSZIkSW3ObNuavCNUkiRJkiRJkiRJUsfxjlBJkiRJTRV2mpUkSZIktTmzbWvyjlBJkiRJkiRJkiRJHcc7QiVJkiQ1lZ1mJUmSJEntzmzbmrwjVJIkSZIkSZIkSVLH8Y5QSZIkSU0TBKNG6EEqYf9cSZIkSdIwMNu2Lu8IlSRJkiRJkiRJktRxvBAqSZIkSZIkSZIkqeM4NK4kSZKkpnJQH0mSJElSuzPbtibvCJUkSZIkSZIkSZLUcbwjVJIkSVJz2W1WkiRJktTuzLYtyTtCJUmSJEmSJEmSJHUc7wiVJEmS1FRht1lJkiRJUpsz27Ym7wiVJEmSJEmSJEmS1HG8I1SSJElS8wTESHWatXOuJEmSJGk4mG1blneESpIkSZIkSZIkSeo43hEqSZIkqanszCpJkiRJandm29bkHaGSJEmSJEmSJEmSOo4XQiVJkiRJkiRJkiR1HIfGlSRJktRcjh8kSZIkSWp3ZtuW5B2hkiRJkiRJkiRJkjqOd4RKkiRJaqqw26wkSZIkqc2ZbVuTd4RKkiRJkiRJkiRJ6jjeESpJkiSpaQKIEeo0a99cSZIkSdJwMNu2Lu8IlSRJkiRJkiRJktRxvCNUkiRJUlPZm1WSJEmS1O7Mtq3JO0IlSZIkSZIkSZIkdRzvCJUkSZLUXG3cbTYipgD/DrwY2BmYDDwCXAJ8MzP/OsT6AzgYeCZwOLAnMLP08uPA9cDPgB9m5oqh7EuSJEmSNARm24Hqb1q29UKoJEmSJNUhIg4AfgpsX/HSDqXpVRHxZeBtmZl17uaePurvMas0PQ94f0S8LDOvrXM/kiRJkqQu1OnZ1qFxJUmSJKlGEbED8Ft6g9y3gWcBhwJvAu4urT8d+NQQdjWr9PfDwFeBl1P0nj0YeDVween1nYE/RsSuQ9iXJEmSJKmLdEO29Y5QSZIkSU0V7Tl+0JnAlqX5MzLzy2Wv/S0ifgJcCewCvDsizsnMf9Sxn2uB/wf8KjPXVrx2TUR8H/gK8GZgKvBF4MQ69iNJkiRJGgKz7YCalm29I1SSJEmSahARu1E8NwXgioqgCEBmLqDoMQtF7npfPfvKzEMz8+d9BMWe1xN4J8WzWwBOiIjN69mXJEmSJKl7dEu29UKoJEmSpKaKGJmpgU4umz9rgHK/B+4tzT8vIiY2tBUlmbkSuKy0OBrYaTj2I0mSJEnqn9l2aIYr23ohVJIkSZJqc0zZ/J/7K1Tq0drz+ibAgcPYpnFl8332sJUkSZIkqcwxZfMdm229ECpJkiSpqWKEpgbas/T3ksy8b5CyN5fN79HYZhQiYjxweGlxFXDncOxHkiRJktQ/s+3QDFe2HdOISiRJkiSpTcyOiJv6eiEz9+xrfblSMJtZWhwsKFaW2b6K8vU4HZhemv9lZj45TPuRJEmSJLUGs22VvBAqSZIkqbka3KV1mE0pm68mlJWXmdJvqTpFxL7AR0uLK4EPNHofkiRJkqQqmG3rNpzZ1guhkiRJkrrJXdX0jh3AxLL5VVWUX1k2P2kI+91IRMwEfllW7zsz87ZG7kOSJEmS1JLMtlXyQqgkSZKkpor26ja7vGx+XBXlx5fNL2tUIyJiGvA7YLvSqm9k5tcaVb8kSZIkqTZm29qNRLYd1cjKJEmSJKnD1Toc0Cb9bFu3iNiMIijuW1r1XeDNjahbkiRJktQVuibbeiFUkiRJkqqUmSuBeaXFbavYZPuy+fuGuv+I2BS4EDi4tOoHwGmZmUOtW5IkSZLUHbop23ohVJIkSVLTRIzs1CA3lv6eEhHbDVgS9iibv2koO42IKRRB8WmlVT8EXp2Z64ZSryRJkiRpaMy21RvpbOszQiUNatnylfzswqu4+MqbePDh+SxfuYrpU6ewzx478LzjD2K/PXccUv2ZyS13PMDVf7+TG2+7j3vue5QFi5YAMHXKZHbZaWuOOnRPjj9yH8aPGztgXYufXMaV193O9Tfdw+13P8RDjy5g2fKVTJwwjqfM3Jx9S23eafuthtRmdS6Pd3WbCWNGcdyuMzho26nMnDKe8WNGsWjFam6bt4SL75zPbfOWDKn+LSaP4/Mv2qvq8h/49S3ct3D5RuuP2Glz3nD4DjXv/5N/uJ1bHx3ae5D6cDFwXGn+OGBOX4UiIoBjS4tLgWvr3WFZUDystOpc4FWZubbeOiWp23iur27i8a5uY7aV6tIV2dYLoZIGdNtdD/Jfnz6HRx57YoP1D89byMPzFvK7i67nJc89jLe97iSizq4oJ7/xsxvV32Pe/EXMm7+Iy66+le+dfxEfedfL2G32Nn2W/dUfruZz//sL1q7duOPIkqUruP3uh7j97oc4/zdXcPJJT+ctr3k2o0Z5Y7x6ebyr22y/+UTOOGonZmwyfoP1MzYZz4xNxnPETtP5/a3z+P41DzSphUOzdl3yyOKVzW6GqtC4zqwj5jzgo6X5N9BPWAROAHYozV+QmRt/E1KFsqB4eGnVucArvQgqSdXzXF/dxONd3cZsq1Zhth1Ys7KtF0Il9evheQt593/PYeGipQA89xkH8owj9mGTSRO4/Z6H+MHPLuGhRxdw3q+vYNy4sbz51c+uaz+PzV8MwPRpUzj6aXuyz+7bs9WMaYwZM4q5DzzGL353Ff+89T4eeHg+b//wtzjr029mu2222Kie+U88ydq16xg1Kjhg79kcsu/O7LLTU9hsyiSeXLKcv91wBz/97ZUsX7GKH/3yUlauWs273/iC+j8gdRSPd3WbLSaP493H7sxmE4ve2Rff+ThX3buQZavWsv3mkzhxj5nMnDKeE3bbklVr1/Hj6x8a8j7/dNtj/PH2xwYsM+/JvsPddfcv4j9/dfOg+9h/1ma8dP/iS5Z/PrSYJ5avrr2h0iAy8+aI+DnwQuCwiHhrZn6lvExEbA58ubS4DvhUZT0RcSpwdmnxO5l5ah9lNgF+gxdBJalunuurm3i8q9uYbaX6dUu29UKohk1E3AbsWlq8NjMPqqOOScBLgOOBg4AZwFRgCfAIcD3wB+D8zFzcgGarzFfO/s36E+e3v/4kXvLcw9e/tseu23Lc4Xvzhvd+nQcens+5v7iEE47al5132Lrm/ew6+ym86l+O4ekH7cbo0Rv26ttt51k86+j9+PxZv+RnF17FkqUr+NK3L+BzHzx1o3omjBvHS5/3dF72/CPYcovNNnr9wH1m8+xj9+et//V/LHpyGT+/8Cqec+wB7LlrNc+CVqfzeFe3efmB26wPit+7+n7+cFtviLt7/jL+du9CPvzsp7LVphM4cfeZXHHPQu5/oq4Of+stXrmGBxetqGvbZavXsmzR4OfGLzugt6f5xXc9Xte+1ARt2G0WeCdwBLAF8OWI2J/iuSaLgP2A9wE7lcp+ITNvqHUHETEe+HVpPwB/owiduw9y98YDmflErfuT1D/zbXvzXF/dxONd3cZsq5Zitu1Ts7Ot4whoWETEEfSGRIADI2LfGus4HbgH+A7wKmB3in+MYyjC4m7Ay4FvAw9HxKdKt1arAe59YB4XX1k883ivp263wYlzj02nTOId//Y8ANatS77/04vr2tf/febNHHXoHhudOPeICE5/7XOZPq348V59w50sfnLZRuVe9oIjOOO1z+3zxLnHjtvO5DUnH7t++c+X/qOuNquzeLyr22y96XgO3m4aAHc8tmSDoNhj6aq1fO/q+wEYNSo4ac+ZI9rGekybNJa9t94UgEXLV3PDA4ua3CJ1ssy8B3gOcH9p1WspLmD8DTiL3qD4NeA9de5ma+CosuVDgBuAfw4yvbDO/Unqg/m2vXmur27i8a5uY7aVhq4bsq0XQjVcXlf6ewWwtmLdgCJiYkT8CPgSsCWQwG+B04FnAgdSPJj3VOAHwGJgEvDe0mtqgL9cfuP6+eefcHC/5Q7Zbxe2mjEVgMuuvpWVK4dnmIZxY8ew927bAbB23ToeenRB3XUdtM/s9fP3P2SPKnm8q/scsv209fN/uaP/4+KfDz/JY0uK4Xz2m7UZY0e3dtfGI3eazqhRRRsvu2cBa7PJDVLVYoT+NFpmXgPsRdFD9m/AAmAlcC9wDnBMZr4lMz0apfZmvm1jnuurm3i8q9uYbdVqzLatyQuharhSr9WTS4vnA78rzZ9SugV6MGcBLy3N3wkclJknZuZXMvOPmXldZl6Umd/JzFMoHtL7CcCB0hvo+hvvXj9/wN6z+y0XEetfX75iFbfe9eCwtWn1mt5hI0aNqv+/r0bVo87h8a5us9uWm6yfv+WRJwcs2/P6xLGj2XHzScParqE6cvbm6+f/euf8JrZE3SQzF2fmpzPz0MycnpkTMnOHzHxlZg54i0VmzsnMKE2n9vH63LLXa5nmDNf7lbqN+bb9ea6vbuLxrm5jtpUap5Ozrc8I1XB4GTC5ND+HYpifE4HNKW5l/lF/G0bEq4BXlhbnAodl5oDdvDJzIfCBiLgAGPg3nqp2z/3zAJg4Ydz6XoL92WHbLdfPz31gHvvusUPD27Nq9RpuvPU+AMaOGc2srafXXde1/+wNBjtut+UAJdUtPN7VbbaZOhGA5avXMn/ZwN+zlj/35CmbTeD2x5bWvd+Dt5vKwdtNZYvJ44gIlqxcw70LlnHdA4u44p4FrF5Xf8fC3WZuwswpE4BiSKSHFtf3vBZJkiqYb9uc5/rqJh7v6jZmW0nV8EKohkPPEEH3AX+mOM7mA9NLr/UZFCNiNPChslWnDRYSy2XmlXW1VhtZtXoNC55YAsDMQU6cizK9z3F4ZN4Tw9Km8359BYtKz5J4+sG7M2liNZ2vN7Z02Qp+csFlQNED8vgja3q0jzqQx7u6zZhRwdSJYwGYv3TVoOXLy2wxub5jscesUkjtMX7MOKZPHscB207lhXtvxdcvm8sddYbRo2f3fqlysT1m20609shUkrqb+baNea6vbuLxrm5jtlUrMtu2Ji+EqqEiYk/g0NLidzNzHbAqIn5A8QyUZ0TE9pl5bx+bHwfsXJq/OjMvGvYGq0/Llq9cP1/NSeqkCb1llq9YOUDJ+txxz8N8+9w/AsXzJd5wyjPrruvM//0lj81fDMBzjt2f2dtv1ZA2qn15vKvbTBjbO4zUitXrBi2/Yk1vmfJta7Fs1Vquf+AJbp23hIcXrWDFmnXFcETTJ3HU7OnMmjqRLTYZz3ufsQuf+uMd3Pl4bYFx4thRHLRd8WyYFavXctW9C+tqpyRJ5cy37c9zfXUTj3d1G7OtpGp5IVSN9rqy+Tll82dTBMVRwKnAR/vY9riy+V82umGq3spVvUNJjB0zetDy48b2/leyYmVjH2Wz4Ikned//+976et962olst82Muur63vkX8fu/3gDArK2n87bXndSoZqqNebyr24wb3Rv41qwbPCyuXttbZtyY2sPiwuWrOeP8f7Jq7cb7um3eEn5/6zxOOXAWz9xtS8aNGcUbn74D7/vVzaytYSihp+2wOeNLbbvq3oWsXDP4+1JrsdOspBZlvm1znuurm3i8q9uYbdWKzLatySdLq2EiYiy9zz+5JDPv6nktM68H/l5aPC2iz5vEDyibv3p4WqlqjB83dv18+cPo+7Nq9Zr18xPGjx2gZG0WL1nOOz86h0cfewKAFz7rEF78nKfVVdcFf7yGs875AwCbTZnEJ9/3SiZPmtCopqqNebyr25SHtjGjBj8VHFsWLlfVEcLWrss+g2KPdQnfu+YBbn20eAzazCnjOWDWZv2W78tRDh0kSWow821n8Fxf3cTjXd3GbCupWl4IVSM9H+jp3nV2H6/3rNseeEYfr29RNv9oA9vVp4i4qa8JmD3c+2515UOolA+t0p/lK3rH2J84YWhj7PdYsnQF7/ro2dw592EAnn3M/rzrjS+oq67fXXQ9n/n6z8hMNpk8gTM/fBo7bTezIe1U+/N4V7cpHzKomuGAxo+pbbihev3p9t7Hpu251ZSqt9tmswnM3mIyAA8tWlHz0ENqETFCkyRVr23yrdm2f57rq5t4vKvbmG3Vksy2LckLoWqknmGDlgI/6eP1c4DVFWXLbVo2v6SB7VKNxo0dw7TNil+88x5fNGj5R0q9/ABmzqitp1Nfli5bwbs+dja33PkAAM88al/ef/q/0HdH64H94ZK/88kvn8+6dcnkSeP5/IdOY7fZ2wy5jeocHu/qNmvWJYuWF7+Op08aN2j5LSb3lpm/bNUAJYfmgSeWr5/fvIp29Th6594es3+96/EBSkqSVBPzbQfwXF/dxONd3cZsK6laXghVQ0TELOBZpcXzMnOjoJeZjwO/Ki2+KCKmVRRZXDa/SeNbuVF79uxrAu4adOMu0NPLbtnylRucHPdl7v3z1s/vuO3QeuctW76Sd31sDjfdfj8Axx+5Dx8442RGVTHERaU/XfoPPv7Fn7B23TomTxrPmR86jT123XZI7VNn8nhXt+kJZhPHjWb6pIGHwdpms96hpx4sC3SNVv1TU3qNDjh8x82BIgRfeveCxjZKIyRG7I9dZyVVo93yrdl2YJ7rq5t4vKvbmG3VWsy2rcoLoWqUU+k9nuYMUK5n+KDxwCkVr5V3dXGsiybbb88d189f98/+83Nmcv2NdwMwccK4IfXQK06cz+bG2+4D4BlH7MMH3/ZSRo+u48T5sn/wsS/8eIMT572eul3dbVNn83hXt7l1Xu/3ubsPMlRPz+srVq/lngXLhq1N206duH5+wfLqeufuP2sqm04owu4NDyxi8Yo1g2whSVJVTsV82zE811c38XhXtzHbSqqGF0I1ZFGMcXFa2aq/RET2NdHbYxY2Hj7ourL5g4ervarOMYfvtX7+l7+/ut9yf7vhDh6etxCAww96KuPHD9z7qj89J87/vLX3xPlDbx/6ifOkieM584OeOGtgHu/qNlff98T6+WN32aLfcntvPYUZmxTPC7rhwUWsXltP39bqPGPX3nbc8kh1Iwg6dFBnCCBihKZmv1lJLc9823k811c38XhXtzHbqpWYbVuXF0LVCMcCO9Wx3X4RcUDZ8p/L5ut7kroaZsdtZ3LkoXsAcONt93H+b67YqMziJ5fxhf8rsv+oUcErX3z0RmV+8+drOeJF7+eIF72fT3zpvD73tWz5St7933MacuL8l8v/WZw4ry1OnD//odPYazdPnDUwj3d1m4cWreCaUmDcZcYmHL/rjI3KTB43mlcdXAxBtW5dcsFNj25U5oidNue7rzyA777yAP7tsO373NdB204d8AR9VMArD5rFbjOL3rmPLVnJdfc/Meh7mDpxLHttXTx+beGyVfz9ocWDbCFJUlXMtx3Gc311E493dRuzraRqjGl2A9QRXls2/yZgsCeyTwO+VrZtT0/ZP1M8w2Q2cFBEHJuZf2lkQ1Wb0087kX/eMpcnFhcnybff/RDHH7kvm0yawB33PMz3f3oxDz1ajFn/0uc9nV12fErN+1i1eg3/8fHv8I9b7gVg911m8coXH829D8wbcLsZW2zGlMkTN1h36d9u4SOf/xFr165j9KhRvPXUE5k0cRx33/tIv/WMGTOG7bbpv8eYuofHu7rND659gF23nMymE8by6kO2ZfvNJ3Ll3IUsW72W7adN5Ll7bsXMKUWP2Qtvncd9C+t7hsoZR+/E40tWcu0Di7j78aU8tmQVK9esY9K40ew4fRJHzZ7OrNLQQavWruObV9zL6nWD9849cqfNGT2qiKGX3L2AHL4OvZKk7mK+7UCe66ubeLyr25htJQ3GC6EakoiYCry4tHh9Zv5vldu9GdgLOCUi3p2ZKzJzbUR8nN7nrHw7Ig7KzPlV1vk0YHFm3lzbu1B/njJzcz73wVN5/6fPYd7ji/j1n67l13+6dqNyL3r2obzlNc+pax/zFz7JDTfPXb98yx0PcNo7vzzodu8//V848bgDN1h38ZU3sXbtOgDWrlvHZ77+s0Hr2WrGVM476z9qa7Q6kse7us3jS1dx5p/v4oyjd2L65HEcvfMWHL3zxl8m/PG2xzj3ugeHtK8tNhnPs3bbcsAyjy1ZyVmX38tt86obOujI2WVDB91Z1amCWpjD+khqBebbzuW5vrqJx7u6jdlWrcRs25q8EKqhegXQ05Xr3Bq2Oxf4ODCVImj+ACAz50TECcDLgR2AKyLiXzPz+v4qiojNgHcB7wNOAAyKDbTbzrP43v+8jZ9deBUXX3kTDz48nxUrV7P51E3YZ/cdeN4zD2L/veoZOUpqPR7v6jb3LFjGf15wM8/YZQYHbTeVmVPGM27MKBYtX83tjy3lojserzq89efMv9zJ7OmT2WmLSWwxeTybjB/NpHFjWL1mHYtWrObeBcu57oEnuPq+J1hTRW9ZgKduuQlbbToBgFsffZJ5S1YOqY2SJJWYbzuY5/rqJh7v6jZmW0kDifReaw1BRFwL9DwHZcfMnFvldjsDd5QW/5SZx5e9Ngn4Hr09cRP4LfAb4BZgITAF2B44Hng+ReAEODYzL6rv3azf/0077bLbHj/+/VVDqUaS1MI+fdGdzW6CNCJ+918nA7DowbtasmNqRNy086677/Hrv14zIvt77lEHceftt9ycmXuOyA4ltZVOy7dmW0nqfGZbdQuz7YbMtrXxjlDVLSL2ozckXlltSATIzDtLIfNA4LiI2DEz7ym9tiwiXgK8DXg/MAM4sTT1ZynwReDqGt+GJEmSJKnLmW8lSZKkzjSq2Q1QW3td2XwtwwZVbhPAaeUvZOGLwI6l184BbgUWAGuARaXlHwKnAltn5gcyc2kd7ZAkSVITxQj9kaQBmG8lSZI0JGbb1uQdoapbZp4OnD6E7T8HfG6QMkuBOaVJkiRJkqSGM99KkiRJnckLoZIkSZKaJyBGqkOrHWclSZIkScPBbNuyHBpXkiRJkiRJkiRJUsfxjlBJkiRJTWVnVkmSJElSuzPbtibvCJUkSZIkSZIkSZLUcbwQKkmSJEmSJEmSJKnjODSuJEmSpOZy/CBJkiRJUrsz27Yk7wiVJEmSJEmSJEmS1HG8I1SSJElS0wQQI9Rt1s65kiRJkqThYLZtXd4RKkmSJEmSJEmSJKnjeEeoJEmSpKYKu7NKkiRJktqc2bY1eUeoJEmSJEmSJEmSpI7jHaGSJEmSmspOs5IkSZKkdme2bU3eESpJkiRJkiRJkiSp43ghVJIkSZIkSZIkSVLHcWhcSZIkSc3l+EGSJEmSpHZntm1J3hEqSZIkSZIkSZIkqeN4R6gkSZKkpgq7zUqSJEmS2pzZtjV5R6gkSZIkSZIkSZKkjuMdoZIkSZKaKuw0K0mSJElqc2bb1uQdoZIkSZIkSZIkSZI6jneESpIkSWoqO81KkiRJktqd2bY1eUeoJEmSJEmSJEmSpI7jHaGSJEmSmiYYueeo2DtXkiRJkjQczLatyztCJUmSJEmSJEmSJHUcL4RKkiRJkiRJkiRJ6jgOjStJkiSpyRzYR5IkSZLU7sy2rcg7QiVJkiRJkiRJkiR1HO8IlSRJktQ8ATFSnWbtnCtJkiRJGg5m25blHaGSJEmSJEmSJEmSOo53hEqSJElqKjuzSpIkSZLandm2NXlHqCRJkiRJkiRJkqSO4x2hkiRJkppqxJ6jIkmSJEnSMDHbtibvCJUkSZIkSZIkSZLUcbwjVJIkSVJThU9SkSRJkiS1ObNta/KOUEmSJEmSJEmSJEkdxwuhkiRJkiRJkiRJkjqOQ+NKkiRJai5HD5IkSZIktTuzbUvyjlBJkiRJkiRJkiRJHcc7QiVJkiQ1lZ1mJUmSJEntzmzbmrwjVJIkSZIkSZIkSVLH8Y5QSZIkSU0TQIxQt1l750qSJEmShoPZtnV5R6gkSZIkSZIkSZKkjuMdoZIkSZKaKIgR689qv1lJkiRJ0nAw27Yq7wiVJEmSJEmSJEmS1HG8I1SSJElSc9mZVZIkSZLU7sy2Lck7QiVJkiRJkiRJkiR1HC+ESpIkSZIkSZIkSeo4Do0rSZIkqakcPUiSJEmS1O7Mtq3JO0IlSZIkSZIkSZIkdRzvCJUkSZLUVGG3WUmSJElSmzPbtibvCJUkSZIkSZIkSZLUcbwjVJIkSVJThU9SkSRJkiS1ObNta/KOUEmSJEmSJEmSJEkdxztCJUmSJDVPjOBzVOycK0mSJEkaDmbbluUdoZIkSZIkSZIkSZI6jhdCJUmSJEmSJEmSJHUcL4RKkiRJkiRJkiRJ6jheCJUkSZIkSZIkSZLUccY0uwGSJEmSulcAESO3L0mSJEmSGs1s27q8I1SSJEmSJEmSJElSx/GOUEmSJElNFfZnlSRJkiS1ObNta/KOUEmSJEmSJEmSJEkdxztCJUmSJDXVSD1HRZIkSZKk4WK2bU3eESpJkiRJkiRJkiSp43hHqCRJkqSmstOsJEmSJKndmW1bk3eESpIkSZIkSZIkSeo4XgiVJEmSJEmSJEmS1HEcGleSJElSczl+kCRJkiSp3ZltW5J3hEqSJEmSJEmSJEnqON4RKkmSJKmpwm6zkiRJkqQ2Z7ZtTd4RKkmSJEmSJEmSJKnjeEeoJEmSpKYKO81KkiRJktqc2bY1eUeoJEmSJEmSJEmSpI7jHaGSJEmSmiZK00jtS5IkSZKkRjPbti7vCJUkSZIkSZIkSZLUcbwjVJIkSVJz2Z1VkiRJktTuzLYtyTtCJUmSJEmSJEmSJHUcL4RKkiRJkiRJkiRJ6jgOjStJkiSpqcLxgyRJkiRJbc5s25q8I1SSJEmSJEmSJElSx/GOUEmSJEnNExAj1WnWzrmSJEmSpOFgtm1ZkZnNboPUUiJi8dhx46Zsu/1OzW6KJGmYPL50VbObII2IJfPuZ9TosaxesbQlY1JE3DRu3Lg9Zu+884js764772TVqlU3Z+aeI7JDSWois60kdT6zrbqF2XZDZtvaeCFUqhARjwCTgPub3ZYuM7v0911NbYU0cjzm1W085ptjW2BZZm7V7Ib0JSJ+Se+xMVLuysznj/A+JWnEmW2bxnMedRuPeXUbj/nmMNtuzGxbJS+ESmoJEXETgL1Y1C085tVtPOYlSVI38JxH3cZjXt3GY15qP6Oa3QBJkiRJkiRJkiRJajQvhEqSJEmSJEmSJEnqOF4IlSRJkiRJkiRJktRxvBAqSZIkSZIkSZIkqeN4IVSSJEmSJEmSJElSx4nMbHYbJEmSJEmSJEmSJKmhvCNUkiRJkiRJkiRJUsfxQqgkSZIkSZIkSZKkjuOFUEmSJEmSJEmSJEkdxwuhkiRJkiRJkiRJkjqOF0IlSZIkSZIkSZIkdRwvhEqSJEmSJEmSJEnqOF4IlSRJkiRJkiRJktRxvBAqSZIkSZIkSZIkqeN4IVRqYxHx7Ij4WkT8IyIei4hVEfFoRFwXEZ+PiCP62e6YiMgGTBeV1fmRsvWnVtn+Hfqqq6LMR+po1xOD7KtyWhMRC0uf47ci4rhq2l9W9+ER8fWIuD4iFkTE6oh4PCJuiYiLIuLMiHhhRMyopV5JanURcVvZ/6XX1FnHpIh4dUR8NyJuLv0+W136f/mWiPhBRJwWEZs2uv2SJKk1mG3NtpLUTGZbqbONaXYDJNUuIg4Cvgoc0sfLW5am/YF3RMTvgbdk5p0j2MR2MxqYWpr2Bl4bEb8AXpGZy/rbqHTicjbw4j5enl6adgOOBt4J3FZaVoeJiGcDzweOALYGNgMWAA8CfwF+mpmX11HvkcBJwFHAthTH1LpS3TcDlwM/ysxbG/A2pJqUvpDctWzVgRGxb2b+vYY6Tgc+QPF7q9LU0rQb8HLgKxHxZeATmflkve2WJEmtw2zbcGZbDYnZVt3IbCt1Pi+ESm0mIl4EnANMLK26EfgucB2wkOJk8jDg1cBs4ATgyoh4QWZeVtrmaopQ1J+zgYNK888CHuqn3NI630a9PgD8oopyawd5/RrgtLLl0RQnKs8E3krx2b6A4nP4174qiIjRwG+Bw0urHgS+CVwBzAPGUoSGAyh+Bn0Fe7W5Qb64mVmaDgDeFRG/pfji5p4q6j0Q+CJF+OzLJGAWxbH1kYj4I/CezLyh1vcgDcHrSn+voPg/b3Rp3RmDbRgRE4E5wEtLqxK4EPgNcCvFFyKbAttTHOcnlZbfWyp3UWPegiRJahazrdlWrcNsqy5ntpU6nBdCpTYSEYcA5wLjKH6xvg84MzMrw9EfIuJTwCeAd1MEyF9FxAGZOTczl1KEzP72Ux4Cb8/MuQ18G0PxYGb22+4aLO2nnj9ExE+ASyk+45dGxCf76QF2Gr1B8WLgpMxc0ke5XwAfjojtgBMb0Ha1iDq+uHkOxRc3z8vMvw1Q7ykUXzxMKK26AziP3i8iALaiOP5eADwVOB54O3BqQ96cNIiImAKcXFo8H5hG8X/cKRHxnsxcOUgVZ9EbFO8E/jUzr+un7HciYhrwLuA/htZySZLUCsy2Zlu1DrOtupnZVuoOXgiV2kREjKM3KAK8OzM/31/5zFwFvCcixlCcRE4DvgccOcxNbWuZeXVEnEtxgg9Fr+G+wuJLyubP6Ccoltd7H/CNxrRSzTaEL262BH4dEftl5oN91HsCReAcBayhODn+ah/1AvwiIt5HMXzVpxvzzqSqvQyYXJqfQzHMz4nA5sALgR/1t2FEvAp4ZWlxLnBYZj4+0M4ycyHwgYi4AHDoIEmS2pjZdmSYbVUNs61ktpW6wahmN0BS1V4F7FiavxT4QpXb/SdFjySAIyLiuEY3rANdXTa/XT9ldiibv334mqJW088XN5/pJ9CRmasy8z0UwwEBbEFxcl1Z71TgB/T+bn5ZZn6pv3pLdWdmnk8xRNGva383Ut16hg66D/gz8EtgfsVrGykNvfahslWnDRYUy2XmlZl5U41tlSRJrcVsO3LMtuqX2VYCzLZSV/BCqNQ+Xl82/7nMzGo2yswVwFfKVv1bQ1vVmdb0M1+ufGiMPYexLWo9jfji5viIOKzi9bdRDDkE8J1SCKxKZi7OzJ9UW14aiojYEzi0tPjdzFxXulPjB6V1z4iI7fvZ/Dhg59L81Zl50fC1VJIktSiz7cgx22ogZlt1NbOt1D28ECq1gYjYBDiotLiC4oHbtfhp2fzRDWlUZ9u7bP7ufspcUzb/jYjYZhjbo9bSqC9u3lhRpPyLnM/W2TZpJJT3ip1TNn926e9R9P9Mn/I7N37ZuCZJkqR2YLYdcWZbDcRsq25ntpW6hM8IldrDPvT+e/17Zq6uZePMvD8i5lE8w2HriNgqMx9pdCPLbBMRe1VR7inDVO+8zJxXY90AlHp69Yzvv4oNg3a5/6HoPTmWIsjfExF/BC6iCJLXZeYT9bRBratBX9x8sTR/bFm9TwV6vnC4y+FR1KoiYiy9/0dekpl39byWmddHxN+BfYHTIuJjfXyZckDZ/NVIkqRuY7atrV6zrYaF2VbdzmwrdRcvhErtYYuy+YfrrOMRirDYU99whsWPl6Zm1ftR4CPVVhoRo4CZwAnAJ4BNe/aXmQ/0tU1m/iMiTqHoJTaZIjQ+pzQBZETcSvF8gXMy84pq26OW1sgvbraLiBmZ+RieQKt9PB+YUZo/u4/Xz6b4QmR74BnAHyteL/999mijGydJklqe2ba2es22Gi5mW3U7s63URRwaV2oPU8rml9RZx5Nl85sNoS2d4OiIyJ4JWAs8RDEMxjbAA8CbM/O/B6qk9NyK3ShOjCrDdwC7A28BLo+I30bEVg19F2qGRn1x06PnpNsTaLWLnqGDlgJ9PbvnHGB1Rdlym5bN1/v7TJIktS+zbWOZbVUvs626ndlW6iJeCJXaQ3nQ26TOOsoD56IhtKUap2VmDDYBOw5HvZn5kSG2/wL67g22kcx8IDPfQTEU0j4Uz9j4MnAFsKas6LOByyJii41rURtp9Bc300p/ewKtlhcRs4BnlRbPy8yNjtXMfBz4VWnxRRExraLI4rL5en+fSZKk9mW2raFes62GkdlWXctsK3UfL4RK7eHxsvmt66yjvMfm4/2W6g7XAHuXTUcD/w7cWHr9TcAFEVH18OFZ+Gdmfiszz8jMwyk+84/R24NsJ4qhjdS+Gv3FzYrS355Aqx2cSu+545wByvV82TYeOKXitfLfPzMb0ipJktROzLaNZbZVvcy26manYraVuooXQqX28E96e2DuW3qgd9UiYlt6n6HySGYO5zNU2sHSzLyxbPprZn4DOBD4banMM6jhWSx9ycz5mflh4D/KVr8sImIo9aqphuuLG0+g1dJK/2+dVrbqL+XDsFUMyfarsnKVQwhdVzZ/8HC1V5IktSyzbWOZbVUvs626ktlW6k5eCJXaQGY+CVxbWpwAPKfGKl5UNn9RI9rUiTJzFfBqYH5p1XsiYnYDqv4GxbNaADZnw2dmqL38g8Z9cbMEuK807wm0Wt2xFD3/a7VfRBxQtvznsvkXDK1JkiSp3ZhtR4bZVlUw26pbmW2lLuSFUKl9fLNs/t3V9ryMiPHAW8tW/V9DW9VhSs8A+HhpcRzwyQbUuYLeAAqwbqh1qjlKz41o2Bc3mZmlem8DHiqtnx0Rew6poVLjvbZs/k3AyweZ3tzPtn8G7irNHxQRxw5XgyVJUssy244As60GYrZVFzPbSl3IC6FS+/gecG9p/kjgjCq3+wSwS2n+8sz880CFBcDXgQdL8ydHxN6VBWoZAigidgBmlBafABYMsX1qrvIvXIbyxc25A9T7H0gtIiKmAi8uLV6fmf+bmecOMn2d3mdTnRIREwAycy29X8gBfDsiptfQlqdFxB4NeFuSJKl5zLYjx2yrgZht1VXMtlL38kKo1CYycyVFT6TVpVVfiIh3RkSf/44jYlxEfAp4V2nVIuBVw9/S9lf6rD9RWgz6fp7KeRHxroiYNlBdEbEJ8O1SPQDn9/SUVNtqxBc3dwE/qnj9S8DC0vyrI+Jfqm1QRGwaES+ptrxUo1cAE0vzlV9yDKSn7FR6wyaZOQf4YWlxB+CKiNh/oIoiYrOI+BjwV3qH4JIkSW3IbDtyzLYahNlW3cZsK3WpMc1ugKTqZeYVEXEK8F2KoUvOBF4TEd+jeA7DQmA68DTgNcDOpU0XAi/MzLtHvtUNtU1E7FVl2TtLw/bU61vAe4HtgRdFxH6ZeUN5W4DPAZ+MiN8Dl1A8Y+NxiuGBtgIOoxg24ymlbR4FPjiENqkFZOaqiHgZxUnrWIovbkYDX8zMjYaGiohxwMfo/eJmHfD6zFxTXi4zF5T+fV9A0VHp3Ih4J/C1Uk/DPkXECymOxUuB84b6/qQ+vK5s/sc1bPcjenvIvhb4QdlrrwfGU4TIXYBrI+K3wG+AWyh+b02h+D/4eOD5FKFTkiR1ALOt2VbNZ7ZVFzLbSl3KC6FSm8nMn0TEvcBXgYOAfYDPDrDJn4A3Z+btI9G+YfZxNhx2YiD7AzfUu6NSIPg4xZAuAXyUDR9+fj9wKMWzVk4qTQO5GjglMx+ut01qHZl5ZZ1f3CTw75l5UT/1/jYiTgPOojiR/hJwekT8BLgCmFcquhXF8fcCoOeZK5c28j1KABGxH3BAafHKzJxb7baZeWdEXAscCBwXETtm5j2l15aVenq/DXg/xRBrJ5am/iwFvkjx/6kkSWpzZluzrZrPbKtuYbaVupsXQqU2lJl/Aw6OiOdQnCweQXHyuCnFczoeBC4GfpqZf21WOzvAHOB9wGzg+RFxYGZeC5CZJ0fEjsAzgadTnLBvD2xGEQieBO6hCA4/BX7vsEGdpY4vbuYBr8vMCwap97sRcSvFSfFhFD0K3z9Icy6k6DkrNVp5j9lahg4q3+ZAii/dTgM+1PNC6f/EL0bE/wEnU/SOPZBieKBNKcLhw8D1wO8ofqc9WUcbJElSizLbjpg5mG3VD7OtuoTZVupi4bmLJElD08cXN9PY8DncVwPHZeaSGus9mqJH9lHALIqeuOuA+cDNwGXAuR3SK16SJEmS1ERmW0lSJ/JCqCRJwyAiplE8X6dneJ8zMvPLTWySJEmSJEk1MdtKktqdF0IlSRomETGL4vknsyiGlXptZs5paqMkSZIkSaqB2VaS1M68ECpJ0jCKiD0pes9OA9YCL8vM85rbKkmSJEmSqme2lSS1qzHNboAkSZ0sM2+KiGcDJ5ZW7RIREzNzeTPbJUmSJElStcy2kqR25R2hkiRJkiRJkiRJkjrOqGY3QJIkSZIkSZIkSZIazQuhkiRJkiRJkiRJkjqOF0IlSZIkSZIkSZIkdRwvhEqSJEmSJEmSJEnqOF4IlSRJkiRJkiRJktRxvBAqSZIkSZIkSZIkqeN4IVSSJEmSJEmSJElSx/FCqCRJkiRJkiRJkqSO44VQSZIkSZIkSZIkSR3HC6GSpGEXEXMjIkvT3EHKfqSsbEbEMSPSyC4UETtUfNZzhmEfLffzjIg5FW3aodltGoqIOLXi/Zza7DZJkiRJnchs25rMtmZbSRrImGY3QJJaXSncbD9IsXXAIuAJ4BbgauD8zPznsDZOkiRJkqQqmG0lSVI38o5QSWqMUcA0YEfgRODDwD8i4uKI2L2pLVNdRqJHqSRJkiS1GLNthzHbSpK6nRdCJWl4HQVcFxEvbHZDJEmSJEmqk9lWkiS1JYfGlaTavRy4smLdaGA6cADwGuBpZa9NAH4YEUdm5jUj00RJkiRJkgZktpUkSR3PO0IlqXaPZObciumuzPxbZn4jMw8DTgeybJsJwBea09z2kpkfycwomy5qdpskSZIkqQOZbYeR2VaSpNbghVBJGgaZ+RXg8xWrj4iI/ZvRHkmSJEmSamW2lSRJ7c4LoZI0fD4JrK5Yd0IzGiJJkiRJUp3MtpIkqW35jFBJGiaZuSAirgEOK1u9R631RMSWwKHA1sAWwBLgwsy8fZDtJpT2vR0wAwjgMeBO4MrMXFNrW/rYxz7AvqW2LQceBK7PzHuGWvdQRcRTgEMo3vt0YB3wBHA7cENmPtHEtm0OHA5sRfEzXUHxs7khM29qQP2jgSOAXSje/3yKn81lzXzf9YiI2cBuwPbAphTDci0EHgKuyszHhmGfU4GnA9tQHDuPAXcBlzTi301pHztRPHdpBjANWAQ8AlyRmQ81Yh+SJElSI5htm8tsa7Ydwj6nYraVJC+EStIwu58Nw+IWlQUiYi7FiTDAvZm5Q2n9kcCHgGOB0RWbvYMi9GwkIg4F/gs4HpjYT7sWR8QPgY/Vc2IaES8HPgLs2sfLGRGXAP8vMy+so+6PAB8uW3Vstc9SiYjJwJuB1wFPHaDoulKQ/wEwJzMXldUxl96fR7nXRMRrBqjztMycM0DbAvhX4O3AwfQzKkNEPAh8GfhSZi4fYH99bTseeB/wVvo41oCVEfFz4MOZeVstdY+U0s/wJODFwDHAloOUv45iqK5zM3PtEPf9VOCjwAsonn1UaX5EnAX8d60/m1L9k4EzgNcCOw9Q7gaKXvfnZWb2V06SJEkaQWbb2uv+CGZbs63Z1mwrqekcGleShlfUtVHEx4CLKAJfZVDsb5tJEfED4ErgefQfFKHoffhG4I6IeEkN7RoXET+lCFl9BUUo3vNRwG8j4lPV1j1UEfFcYC7wGQYOilD8/jsE+CLwomFtGOt7SF4L/JCiB/RAv3+3AT4F3BwRe9awj22BGyhCfF9BEWA8RWC9PiKG/X3X6YfAucBLGSQolhwAfB/4fUTMqHenEfFC4HqKz6evoAhFD9r/BG6MiH7DXj/1Pwu4myIEDrbtfsCPKd7TZrXsR5IkSRomZtsRYrY125ptJamxvCNUkobXrIrlxwfbICLeDnywbNW9wI3AYmAmsH8f28wALqQ4cS63nOIE+CFgLbAtRY/NsaXXJwE/jojXZ+a3B2nXKOCnwHMrXloNXEUxPM0mwD6l/QC8NyIGfc9DFRHvBD7LxiFsCXAd8CjFsDPTgb0oPscRERGHAL9m4wA3n+Jn8zhFiNul1LYeOwCXRcQxmXnDIPvYmuLLhZ0qXnoC+FtpX1tQBOTNKL5IOBc4rca3MxIqf4aLgZuBecCTFEFuFsVxVv6FyHHAhRFxeGaurHGfhwNnA+NKy4soPrfH2fBz67ETcHFEHFHNUFkR8W/A19n4i5+5wE2l/W1KMRTXtmWvHw9cWnpPT9b4niRJkqRGMtuabc22tTHb9jLbSmoqL4RK0jCJiGnAgRWrbxlksy0pQg/AFcA7MvOqinrHU4SenuVRFD0Ny4PiQ8AHgB9UnjiXnhHxH8B7KU7MA/hqRFybmX8foG3vYsOgmMD/UAxBtLCs/gBOAL5GcVL9CYpAOSxKvT8/x4Y9lG+meP8XZOZG+46IXYGXUPQcrnQExe/HWcAlZevPB949QFM2CsURsRXwczYMildRfBnwx8qhYUo9MT8LvLC0ajOKMH/gIGHh/9gwKC6m+BmfnZmryuofD7yeolfuJsBXBqizmf5O0RP2N5l5c18FImIS8AqK46und+0BFMP/vK/G/X2FIig+SfHv4lsVn9s4iiGpPg1MKa1+CvCdiDh6oCF+IuI4Ng6K5wCf7Ou9RcQxFMNH9XxxsBdwFvDyGt+TJEmS1BBmW7Ot2bZuZluzraRWkJlOTk5OTgNMFD3bsmw6psrtPl2xXQIHVlF/Ar8CxlW5n/dUbHstML2K7V4CrCvb7s8DlN2Gogdu+X7eNEj9WwK39fHe5g6y3Ueq/bwpQtiiivLnAxOr/OzGAFv089oOFfXOqePY+XVFHd8ERlex3f9UbPehAcq+uKLsk8DBg9T/dGBpHz+bmt9jFe+l6p9nz+deY/3bAfeV1b8I2HSQbeb08d6XAE8bZLunlcqVb/e6AcpvAjxSVnYd8Noq3tMmwOUV+zlqgPKnVpQ9tdE/RycnJycnJycnp/afMNuC2dZsW/+/n6p/nj2fe431m23Ntk5OTsM0+YxQSRoGEfEmihBX7vLMvLaKzR8DXpNlvfYG2M/Eiv0sAk7KzPmDbZuZ51H05utxbERUDj/U441s+GyJ8zPzG4PUPw94JcUJ8nB5B8WwKz3+DrwiM5dXs3FmrsnMYRneKCIOAk4sW3UF8IbMXFvF5u8E/lG2/NZSj9e+vL1i+T8z8+qBKs/My4APVdGOEZeZc2ssfx/FcdBjU4rnCNXqvzLzykH2dSUbDu0F8LYBNnkTGw5V9dkcZJiu0n6WUDzPpbzH+0A9tiVJkqRhYbY125pt62O2NdtKah1eCJWk2m0VETtUTLMj4qCIeENEXEoRwsqHs1lJEQCqcVZmLqiy7MuBGWXLX8zMh6vcFuDMiuXn91Pu1RXLH66m8lJo+WUN7alaadikyuF/3p61P0NjuJxRsfz+zKwqOJcC5f+UrZoBHFZZLiJ2AI4sW/UIG34BMJAvUXwx0Ql+BZR/uXJ4jds/Cny1yrJfpnimS4+9B/iS5fSy+SXAf1fboMy8H/hx2aoTBvjCQJIkSaqH2dZsWw2z7cgx20rSMPBCqCTV7ofAPRXTncDVwP9SDM1SbiVwSlY8D2UAP6+hLc+sWP5RDduSmXdTDL3S48jKMhExC9i+bNU/MvOmGnZzTi1tqsG+lD1PBrgtMy8apn3V4/iy+UeAi2vc/i8Vyxv9bCie+VLux1X2yiWL58v8eNCCLSIiRkXElIh4SuWXNRTPNCn/gmX3Gqv/cWauqaZgqVzl51b5cyAinkoxtFGP35R6w9ai/BgYDxxS4/aSJEnSQMy2ZttqmG0byGxrtpU08sY0uwGS1OEuB96YmTdWWX4tGw4bM5jyk9RVwMrSyXMtFtB7Uju7j9cPqliuNvTWW75alT0jLxqm/dQsImYDW5etuhPYPiL62aJP4yqWh+tn85YatxkRETEZOAl4AbAfsAvVn7dMq3F39Xxuby1bPriPMpUB8r46/m1W9rKeDVxSYx2SJElSI5htzbZgtq2Z2RYw20pqMi+ESlJjrAOeBJ4AbgX+Bvw0M2+osZ5F1Tw/BdYPn/OUslXjgLtq3F+lzftYN7Ni+Y5aKszM+yNiBRs+h6URtq5YrqUn73DbtmL5CIre1UPR8J8NcHudbRlWEfF64JNsODRWLTarsXytn0Pl57xlH2Uqj4F3M/RnofR1DEiSJEmNZLbth9kWMNvWxGzbL7OtpBHlhVBJqt2xwzhMzZM1lJ1G44c4n9LPfsotrqPeRTQ+LE6vWF7Y4PqHorJtjTAcP5tFdbZl2ETEF4C3D7GaWv9dDPVz6yvEjdQxIEmSJNXLbFs7s+3QmW2rZ7aVpAbwQqgkta/K4WVGSjZpv4NppXYNx8+mmrGHWukzqFlEnMzGQfEm4FyKnuhzgUeBFZm5smLbuWz4vJ9aDPVz62v7Zh0DkiRJUrsx226oldpltq2D2XZQZltJI8oLoZLUvuZXLN+emU8dhv1U9katdWiWercZzOMVy600tEpl287KzDcOw36G+rMZjp/LUHy0YvmDwCcys5owt+kQ9jvUz62vHtuVx8ArMvOHNe5HkiRJ6gZm2w2Zbc229TLbSlIfGj3shCRphJSet1J+krpjRIwdhl09WrG8Sy0bR8S2NH7oIICHK5b3GIZ91KvyM9t1hPZT08+G4WtXzSJiF2D3slV/zcyPVxMUI2IiMHUIu6/1c6j8nOf1UWakjgFJkiSprZltzbZ97MdsWx+zrST1wQuhktTeLi+bHwscMwz7uKZi+Wk1bn9ooxpS4bKK5aMbXP9QhpS5iQ2ftXFYRAzHMzBa9WdTj8oAdkEN2x7G0IbWGerndnUfZS6vWD6hxn1IkiRJ3cRs28ts2zo/m3qYbSWpxXghVJLa2+8qlv+t0TvIzAeAe8tW7R0Re9ZQxSkNblKPfwCPlS3vHhFHNbD+lRXL46vdMDPXAn+q2PZVjWhUhUsrlk+OiNHVbFjqYf3SxjepblMrlhf1Vagfpw5x3ydHRFWPCyiVq/zcKn8OANex4RBCh0XEXnW2T5IkSep0ZtteZluzbb3MtpLUBy+ESlJ7+x7wRNnyyRHxjGHYz3crliufd9GniDgYeH7jmwOlYWW+VrH6ixExrkG7eKJieesat/9yxfKHI6LWOgaUmXOBS8pWbQX8e5WbnwHMaGR7hqjyWSS7VbNRRBwEvGyI+54JvKXKsqcDW5Yt35iZ11UWysx1bHh8BvClakOpJEmS1GXMthsy25pt62G2laQ+eCFUktpYZj4BfLZi9XkRcUQt9UTE6Ih4cURs3k+R/wVWlC3/S0S8cZA6twS+z/D+rvkSG4a6/YHvR0RVPVwjYkxEbNHXa5m5AphbturgiJhabcMy8yLgD2WrtgR+ExGzqq2j1MYpEfGKAYr8T8XyJ0sBaqA6Dwc+Vks7RsDfK5ZfExFbDbRBROwE/IRi6Kyh+kREDDicUkQ8DfjvitWVn3+5M9mw5+yxwNkRUdNzhSJiu4h4Ti3bSJIkSe3EbGu2xWxrtpWkYeKFUElqf58Bfl+2PBW4KCK+GhFP7W+jiBgbEYdHxKeBu4DzgU37KpuZDwIfqlj9tYg4MyKm9VH3CRTPkdgVWAUsreH9VC0zFwCvZsNnnpwMXB0Rz++vh2JE7BIR/wncCZw0wC7+UjY/CbgwIv41IvaKiB0jYoeyaZM+tn8N8EDZ8n7APyLiP/oLqaX2TSm1/1vAg8An+yubmecDvylbNQX4Y0S8obIHcUSMi4g3AxeW3k9lT9WmycyH2HAYns2Bv5QC2gYiYnxEvB64EtiB4ouMJUPY/UJgMvD7iHhTP5/bmyj+nU0ue+lS4Nv9VZqZi4F/BdaUrX4lcF1EnDJQaIyIrSPitIj4JXB3qR5JkiSpk5ltzbZmW7OtJDWct7BLUpvLzDUR8VKKwHB4afVo4M3AmyPiQeBGYAFFB5hNgVkUw7PU0tvw88DRwHNLy6OAdwKnR8SVFKFmMrAvsF3Zdh8staX8JLthMvNXEfFOih6KPR189gZ+ATwZEdcC8ygC5fTSazOrrP5LFM8/6fl9eShwbj9lTwPmVLTt4Yg4ieJn85TS6mnAp4FPRcQtFEFgEcWzVqYCsykCUJRVtWCQdr4euAzYsbS8GUVP509HxFWl7aeX2r9ZqcxqiiGEvjdI3SPpvcDF9H7euwFXRMRtFMfwKoqf3SFAeTg/HfhAxbpanEER+jYFvg78v4rP7RA2fs7LQ8CrS8ME9Ssz/xwRpwLfovdZPLtT9Cj/dkTcUKprSan9m1O87y03qkySJEnqYGZbsy1mW7OtJA0DL4RKUgfIzEURcQzwKeBtFGGxxzalaTDLgJUD7GNtRPwLRVh6YdlLY4Ej+9nszMz8TKmn5rDJzC9GxN3A2RQn2z2mAMcMod4bIuINFM/DqGnIl7I6/h4R+1OEshPKXgpgj9I0mAF7t5ZC6TEUvTrLe0pPBZ7VxyYrKXpvXlPFvkdMZl4eEf8GnMWGX2Q8lQ3fV4+1wDsz85sR8YEh7PpS4OXAOfSG9r4+tx73AM/KzHuqqTwzzykF3h8CO5e9NI4iiFajZXo4S5IkScPFbGu2NduabSWp0RwaV5I6RGauzsx3UQzZ83Xg0So2mw/8FDgVmJmZDw+yj5WZ+SLgFOD2AYpeDjwvM99dTdsbITN/SdHb9IMUJ/MDWUPRy/TfKYZNGqjesyl6MX4I+CPFcEBL2XDIosHaNi8znwUcBfyc6oZTugf4JvAcYMDnopT2cR9Fj+WPsuGzO8qtoni/B2bmeVW0YcRl5hyKLx8uGqDYCor3cUhmfqlB+z0fOLBUb39fmiyg+EJmr8y8o8b6r6E4jl4JXMGGQwr1ZR1wPcXwYAdk5jtq2Z8kSZLUrsy2ZlvMtkPZr9lWkipEZtW/6yRJbSYidgf2oRgCZSrFCepi4H7gVuDuHMIvgojYl+LZIFsByymGQbkuM+8eUsMbICJmU5z8z6B476soTvbvAG4oPeOiWW0bQxEAd6b42Uyh6LW8iGI4oVsy85Eh1n8EsAuwBUWPyweBSzOzbXpfRsR2wNMphl4aSzEM1EPAZZn55DDud1ppv9tQ9MJ+nOJZQ5dk5uoG7WMK8DSKobw2p+iVvYTiC5w7KI6Bph2jkiRJUisx25ptMdvWs1+zrSThhVBJkiRJkiRJkiRJHcihcSVJkiRJkiRJkiR1HC+ESpIkSZIkSZIkSeo4XgiVJEmSJEmSJEmS1HG8ECpJkiRJkiRJkiSp43ghVJIkSZIkSZIkSVLH8UKoJEmSJEmSJEmSpI7jhVBJkiRJkiRJkiRJHccLoZIkSZIkSZIkSZI6jhdCJUmSJEmSJEmSJHUcL4RKkiRJkiRJkiRJ6jheCJUkSZIkSZIkSZLUcbwQKkmSJEmSJEmSJKnjeCFUkiRJkiRJkiRJUsfxQqgkSZIkSZIkSZKkjuOFUEmSJEmSJEmSJEkdxwuhkiRJkiRJkiRJkjqOF0IlSZIkSZIkSZIkdRwvhEqSJEmSJEmSJEnqOF4IlSRJkiRJkiRJktRxvBAqSZIkSZIkSZIkqeN4IVSSJEmSJEmSJElSx/n/PVG2q9ygJRIAAAAASUVORK5CYII=\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# plot the results\\n\",\n \"fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5), dpi=150)\\n\",\n \"cm = cm / cm.sum(axis=1, keepdims=True)\\n\",\n \"cmd = ConfusionMatrixDisplay(cm, labels)\\n\",\n \"_ = cmd.plot(\\n\",\n \" cmap=plt.cm.Blues, \\n\",\n \" ax=ax1)\\n\",\n \"_ = ax1.set_title(\\\"Normalized confusion matrix\\\")\\n\",\n \"\\n\",\n \"cmd = ConfusionMatrixDisplay(cm, labels)\\n\",\n \"_ = cmd.plot(\\n\",\n \" cmap=plt.cm.Blues, \\n\",\n \" ax=ax2)\\n\",\n \"_ = ax2.set_title(\\\"Without normalize\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, save the model for further use.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['pre_trained_model/classification_model_with_taining_data.pkl.z']\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import joblib\\n\",\n \"\\n\",\n \"Path('pre_trained_model/').mkdir(exist_ok=True, parents=True)\\n\",\n \"joblib.dump(rfc, 'pre_trained_model/classification_model_with_taining_data.pkl.z')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Virtual screening\\n\",\n \"\\n\",\n \"The trained model can be used to predict the structure type (QC, AC, and OTHERS) of any given chemical composition. Here we focus only on ternary systems. In the following, we describe the procedure of virtual screening and its visualization in a ternary phase diagram.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For the sake of demonstration, we will select a system as a prediction target in which quasicrystals are already known to be present.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
elementslabelprediction
Cd 65 Mg 20 Gd 15(Cd, Gd, Mg)QCQC
Zn 75 Sc 15 Ag 10(Ag, Sc, Zn)QCQC
Zn 58 Mg 40 Lu 2(Lu, Mg, Zn)QCQC
Zn 75 Sc 15 Pd 10(Pd, Sc, Zn)QCQC
Al 65 Cu 20 Os 15(Al, Cu, Os)QCQC
Zn 76 Mg 17 Hf 7(Hf, Mg, Zn)QCQC
Al 70 Pd 20 Re 10(Al, Pd, Re)QCQC
Cd 65 Mg 20 Y 15(Cd, Mg, Y)QCQC
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" elements label prediction\\n\",\n \"Cd 65 Mg 20 Gd 15 (Cd, Gd, Mg) QC QC\\n\",\n \"Zn 75 Sc 15 Ag 10 (Ag, Sc, Zn) QC QC\\n\",\n \"Zn 58 Mg 40 Lu 2 (Lu, Mg, Zn) QC QC\\n\",\n \"Zn 75 Sc 15 Pd 10 (Pd, Sc, Zn) QC QC\\n\",\n \"Al 65 Cu 20 Os 15 (Al, Cu, Os) QC QC\\n\",\n \"Zn 76 Mg 17 Hf 7 (Hf, Mg, Zn) QC QC\\n\",\n \"Al 70 Pd 20 Re 10 (Al, Pd, Re) QC QC\\n\",\n \"Cd 65 Mg 20 Y 15 (Cd, Mg, Y) QC QC\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"test_data = training_data.loc[x_test.index].assign(prediction=y_pred).drop(columns=['composition', 'for_test'])\\n\",\n \"\\n\",\n \"test_data[(test_data.label == 'QC') & (test_data.prediction == 'QC')]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's draw a predicted phase diagram of the Al-Cu-Os system.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Virtual composition\\n\",\n \"\\n\",\n \"First, a set of virtual chemical compositions covering the entire compositional space of $\\\\mathrm{Al}_a \\\\mathrm{Cu}_b \\\\mathrm{Os}_c$ is generated ($a$, $b$, $c$ denote the composition ratio). The function `make_virtual_compounds` is provided to simplify this step.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\\u001b[0;31mSignature:\\u001b[0m\\n\",\n \"\\u001b[0mmake_virtual_compounds\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0melements\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0minterval\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0.5\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mpen_size\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m10\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mreorder_elements\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mmax_proportion\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m100\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0melement_ratio\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mDocstring:\\u001b[0m \\n\",\n \"\\u001b[0;31mFile:\\u001b[0m /tmp/ipykernel_577836/3766468394.py\\n\",\n \"\\u001b[0;31mType:\\u001b[0m function\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"make_virtual_compounds?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
compositionelements
0{'Al': 0.25, 'Cu': 99.75, 'Os': 0.0}(Al, Cu, Os)
1{'Al': 0.5, 'Cu': 99.5, 'Os': 0.0}(Al, Cu, Os)
2{'Al': 0.75, 'Cu': 99.25, 'Os': 0.0}(Al, Cu, Os)
3{'Al': 1.0, 'Cu': 99.0, 'Os': 0.0}(Al, Cu, Os)
4{'Al': 1.25, 'Cu': 98.75, 'Os': 0.0}(Al, Cu, Os)
.........
80593{'Al': 0.0, 'Cu': 0.5, 'Os': 99.5}(Al, Cu, Os)
80594{'Al': 0.25, 'Cu': 0.25, 'Os': 99.5}(Al, Cu, Os)
80595{'Al': 0.5, 'Cu': 0.0, 'Os': 99.5}(Al, Cu, Os)
80596{'Al': 0.0, 'Cu': 0.25, 'Os': 99.75}(Al, Cu, Os)
80597{'Al': 0.25, 'Cu': 0.0, 'Os': 99.75}(Al, Cu, Os)
\\n\",\n \"

80598 rows × 2 columns

\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" composition elements\\n\",\n \"0 {'Al': 0.25, 'Cu': 99.75, 'Os': 0.0} (Al, Cu, Os)\\n\",\n \"1 {'Al': 0.5, 'Cu': 99.5, 'Os': 0.0} (Al, Cu, Os)\\n\",\n \"2 {'Al': 0.75, 'Cu': 99.25, 'Os': 0.0} (Al, Cu, Os)\\n\",\n \"3 {'Al': 1.0, 'Cu': 99.0, 'Os': 0.0} (Al, Cu, Os)\\n\",\n \"4 {'Al': 1.25, 'Cu': 98.75, 'Os': 0.0} (Al, Cu, Os)\\n\",\n \"... ... ...\\n\",\n \"80593 {'Al': 0.0, 'Cu': 0.5, 'Os': 99.5} (Al, Cu, Os)\\n\",\n \"80594 {'Al': 0.25, 'Cu': 0.25, 'Os': 99.5} (Al, Cu, Os)\\n\",\n \"80595 {'Al': 0.5, 'Cu': 0.0, 'Os': 99.5} (Al, Cu, Os)\\n\",\n \"80596 {'Al': 0.0, 'Cu': 0.25, 'Os': 99.75} (Al, Cu, Os)\\n\",\n \"80597 {'Al': 0.25, 'Cu': 0.0, 'Os': 99.75} (Al, Cu, Os)\\n\",\n \"\\n\",\n \"[80598 rows x 2 columns]\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"target = ('Al', 'Cu', 'Os')\\n\",\n \"\\n\",\n \"v_comps = make_virtual_compounds(*target, reorder_elements=False, interval=0.25, max_proportion=100)\\n\",\n \"v_comps\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Calculate the descriptors of candidate compositions and predict their class labels\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 17.3 s, sys: 849 ms, total: 18.2 s\\n\",\n \"Wall time: 18.1 s\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"(80598, 232)\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"v_desc = compositions.fit_transform(v_comps)\\n\",\n \"v_desc.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
compositionelementsACOTHERSQCpred_label
0{'Al': 0.25, 'Cu': 99.75, 'Os': 0.0}(Al, Cu, Os)0.0600.8700.070OTHERS
1{'Al': 0.5, 'Cu': 99.5, 'Os': 0.0}(Al, Cu, Os)0.0600.8650.075OTHERS
2{'Al': 0.75, 'Cu': 99.25, 'Os': 0.0}(Al, Cu, Os)0.0600.8650.075OTHERS
3{'Al': 1.0, 'Cu': 99.0, 'Os': 0.0}(Al, Cu, Os)0.0650.8600.075OTHERS
4{'Al': 1.25, 'Cu': 98.75, 'Os': 0.0}(Al, Cu, Os)0.0700.8550.075OTHERS
.....................
80593{'Al': 0.0, 'Cu': 0.5, 'Os': 99.5}(Al, Cu, Os)0.0150.9350.050OTHERS
80594{'Al': 0.25, 'Cu': 0.25, 'Os': 99.5}(Al, Cu, Os)0.0150.9350.050OTHERS
80595{'Al': 0.5, 'Cu': 0.0, 'Os': 99.5}(Al, Cu, Os)0.0200.9350.045OTHERS
80596{'Al': 0.0, 'Cu': 0.25, 'Os': 99.75}(Al, Cu, Os)0.0150.9350.050OTHERS
80597{'Al': 0.25, 'Cu': 0.0, 'Os': 99.75}(Al, Cu, Os)0.0150.9350.050OTHERS
\\n\",\n \"

80598 rows × 6 columns

\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" composition elements AC OTHERS \\\\\\n\",\n \"0 {'Al': 0.25, 'Cu': 99.75, 'Os': 0.0} (Al, Cu, Os) 0.060 0.870 \\n\",\n \"1 {'Al': 0.5, 'Cu': 99.5, 'Os': 0.0} (Al, Cu, Os) 0.060 0.865 \\n\",\n \"2 {'Al': 0.75, 'Cu': 99.25, 'Os': 0.0} (Al, Cu, Os) 0.060 0.865 \\n\",\n \"3 {'Al': 1.0, 'Cu': 99.0, 'Os': 0.0} (Al, Cu, Os) 0.065 0.860 \\n\",\n \"4 {'Al': 1.25, 'Cu': 98.75, 'Os': 0.0} (Al, Cu, Os) 0.070 0.855 \\n\",\n \"... ... ... ... ... \\n\",\n \"80593 {'Al': 0.0, 'Cu': 0.5, 'Os': 99.5} (Al, Cu, Os) 0.015 0.935 \\n\",\n \"80594 {'Al': 0.25, 'Cu': 0.25, 'Os': 99.5} (Al, Cu, Os) 0.015 0.935 \\n\",\n \"80595 {'Al': 0.5, 'Cu': 0.0, 'Os': 99.5} (Al, Cu, Os) 0.020 0.935 \\n\",\n \"80596 {'Al': 0.0, 'Cu': 0.25, 'Os': 99.75} (Al, Cu, Os) 0.015 0.935 \\n\",\n \"80597 {'Al': 0.25, 'Cu': 0.0, 'Os': 99.75} (Al, Cu, Os) 0.015 0.935 \\n\",\n \"\\n\",\n \" QC pred_label \\n\",\n \"0 0.070 OTHERS \\n\",\n \"1 0.075 OTHERS \\n\",\n \"2 0.075 OTHERS \\n\",\n \"3 0.075 OTHERS \\n\",\n \"4 0.075 OTHERS \\n\",\n \"... ... ... \\n\",\n \"80593 0.050 OTHERS \\n\",\n \"80594 0.050 OTHERS \\n\",\n \"80595 0.045 OTHERS \\n\",\n \"80596 0.050 OTHERS \\n\",\n \"80597 0.050 OTHERS \\n\",\n \"\\n\",\n \"[80598 rows x 6 columns]\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pred_proba = pd.DataFrame(rfc.predict_proba(v_desc), columns=rfc.classes_)\\n\",\n \"pred_labels = pred_proba.assign(pred_label=rfc.predict(v_desc))\\n\",\n \"pred_results = pd.concat([v_comps, pred_labels], axis=1)\\n\",\n \"pred_results\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Draw a phase diagram\\n\",\n \"\\n\",\n \"Here, we generate a phase diagram to visualize the predicted structure types (`QC`, `AC`, and `OTHERS`) in the ternary system.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"labels = ['OTHERS', 'QC', 'AC']\\n\",\n \"k_folds = 10\\n\",\n \"cmap = 'YlGnBu'\\n\",\n \"n = 1\\n\",\n \"criteria = 'mae'\\n\",\n \"epa_color = 'orangered'\\n\",\n \"pe_color = 'aqua'\\n\",\n \"\\n\",\n \"norm = matplotlib.colors.BoundaryNorm(np.linspace(-1.5, 1.5, 4), 3)\\n\",\n \"fmt = matplotlib.ticker.FuncFormatter(lambda x, _: ['OTHERS', 'AC', 'QC'][norm(x)])\\n\",\n \"\\n\",\n \"def trans(s):\\n\",\n \" if s == 'QC': return 1.5\\n\",\n \" if s == 'AC': return 0\\n\",\n \" if s == 'OTHERS': return -1.5\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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EANGvemPOvONEFU7ObLD9btVKVqdpr5205+kTXK6CstIJ/3/oscypH1edbUEo1CFNrAVVd+L950ICqUqoGC7nppodZtGgpAMcffxj77rvbOt3h5JOPZtddewEwduxEnnjiJWBpbQ9UKaWUUkqpGokICyuHceMVg3P7LrzyRJq2aIpYsBZCgVxWqkBoq2qpeoBn4IKB/6BR40IA3nz2Y34Y/j3JoLQB3pFSSqmGogFVpdRqZJgwYRT33/8sAAUF+dx554B1vovneTzwwNW559dccz9Ll04FwtoaqFJKKaWUUmtU5s/gtZc/YOSwiQB0264Tx512GKGQy1AFQ27Vv2T3VbECrbdowVmX/g1wQdo7Bz7BtNIR9fY+lFINwK35r6Wtod+Mqg0aUFVKrcY8Lr30DoIgAGDAgDPo3LnDet1pzz135sQTjwBgyZJl3HDDv9EGVUoppZRSqr5YGzJryTDuvO4/uX2X33oGsXisKngqJnp0marZzFSI4h/VgiAnnH0kW3XZEoAR343lf//9iMWpOfX0bpRSSjU0DagqpVahnA8++Jj33vsCgA4dtuDyy8/aoDvecccAiorc0qiHHnqRX34ZBaQ2cJxKKaWUUkqt2bLMOB6651UWznelpw48bHf23H9nrGRrprpoqVBVM7WqQVV0k2ifFYjnJbjkxjNy97/vuqeYsHAE1q6Y06qU2nx4tbSpzYH+JJVSKxB8fxb/+tftuT133jmARo2KNuiuW23VjiuucEHZMAzp1+82RDRLVSmllFJK1S3fVvDL5KE8+e//ApBIxBlw0+nLxUqp9rXFlQCoFkfFCgTZQKt1z/c+dHd2268PAPNnL+Tph15lRvmEenhHSqmGYIypla0WxtHFGCNruT29iusTxpgBxphRxpgKY8xSY8znxphjanjNfGPMncaYOcaYtDFmnDHmlBrOLzDGTDHGzDbGNN7gN70R0oCqUmoFS3n44ef59depAPz+9ztzwglH1Mqd+/c/g06d2gPw8cff8N57HwFltXJvpZRSSimlVmVJahS3Xv0MmbQPwCnnHkXHru3dsv5sEypcsDSw1TJTI4aq6v/ZUgAWCMVw3vVn4cXcn9Uv/Ps1hk8aRjrQVVhKqTqVAr6tYRte7dzvql9ojCkAPgfuBHYAJuO6Rh8AvGGMuZ1Vew4YADQGJgDdgGeMMees5vyBQFfgEhEpX8f3t0nQgKpSqpqQRYsmcd11/87tuf/+gbXyKRpAYWEBgwZdlnver99tZDIzWT43QCmllFJKqdqRChYx5IshfPC2iym0btucs/51PGGUbVq9MGq2ZuqKmasWlq+tGl1bGRg6de/M/51yuHutZJpHb32a8ctG18+bU0rVK4NXK9uGEpH5IrL36jYg+wd9EnhlhcvvAPYGpgE7iEhvEdkW+D8gDVxujDlyufdtTB/gOGAU0EVEdgJ2je5/kzEmtsL53XHB1w9F5PUNfsMbKQ2oKqWqWcC1195PSYnLGj311GPYddcda/UV/vKXP7LPPrsCMHnyDB544FlgSa2+hlJKKaWUUiLC/PLh3Hj54Ny+S675B42aulJW1Zf05wKl0T4virNml/oboiX/0b50lLJqgJP7nUiT5k0A+OzNIXzx3dcsTen8VqnNi6nFgGrtJCzV4NTo8U0RKc29A2O2AP4ZPT1DRHI1SkTkHVzWKsD1K9xvn+jxPhEpjs4fDbwOtAG2X+H8h3C/Li/YoHexkdOAqlIqkmLUqGE8/virADRuXMStt/ar9VcxxiyX9XrjjQ+xYMFEIKj111JKKaWUUr9dpf4UXnjmHX4dOx2AHfpsy/+dcFC1M6qW+q8om6VqcTVTQ0suq1WAjDXRHaBxiyac3P+k3LWPXPs4Y5aMRFZ1Y6WUqkPGmC7AftHTZ1Y4fBSQB0wWkSGruPyx6HEXY8w21fY3jR5XbIIyL3psVu31/wb8AbhNRKas0+A3MRpQVUoBgsg8Lrnkllxn0quvPpctt2xbJ6+28849OfPM4wAoK6tg4MB7Wfl3s1JKKaWUUuvHSsCM+cO5+6bnc/uuuu1MjLfCn8DZmqjL1Uw1uYiqFVc/NfvRvxVIRdmpnoFsZazD/34YnXt0BmDiqIm888p7zCyfURdvTSnVQDaWplRrcArus56ZuFqp1f0uevx6VReKyBxcKYDq5wIsiB5XzETNPp8PYIxpCtwDTMKVFtisaUBVKQWU8dZb7/HFFz8CsM02nbjkklPr9BVvvvkSmjZ1zf6eeuoNRoz4CVdbWymllFJKqQ2zJD2G++54keKlrpTV4cfuS+89egLVGlFJVX3UFWXLAFTVWnVZqn5oCMQFROKm6lgsFuOf15+du/7pO/7Dj7NGEtgQpdRmwpja2epseMYAJ0dPnxURu8Ip3aPHyTXcJptV2qPavk9wvy4vM8bsbYxpbIw5BTgcmFgtE/UmYEvgfBFJr+/72FRoQFWp3zwhlZrJpZdWNfO7++7Lyc/Pq9NXbdu2Fddd50qqiAgXX3wLInPRBlVKKaWUUmpD+GEFI8d8z3OPvwdAQWEel1x3ClRbsg8uOrAiE9U2tNaQCQ2+NQgGa93XKWswYnLB1EAgsGDFsOOefdj9kD0BKF64lJcfeonRS3+ph3eslNoEdTTGTF3dtp733A/oGn39zCqOt4wel9Zwj+yxFtkdIjIDFyxth8tuLYvuHwDnQa5x1fnAKyLySfZaY0xbY0yuJMDmRAOqSv3mLeKee55i+vQ5ABx00J4cddRBa7imdlxwwUl0794FgG++Gc6rr74DlNZ4jVJKKaWUUjVZkBzGTVcOJghcdugZFx3LFh3aEBI1nhJDaKvqpGYZclVVyVipOt9ms1OjxlTVmlQF1tVTzVjwBU696gzieXEA3hn8Jj+MG0aFX1mv718pVTeM8Wplq0OnRo9fr6Z+aUH0mKnhHtnM0sLqO0XkeuBY4DVgCDAY2F1EPosyYx8FKoB/ARhjjowCwwuAZcaYb40x263zO9qIaUBVqd+0gDlzJnDrra72tOd53HffVfVR1wWAvLw87rnnytzzAQPupLJyJqvOF1BKKaWUUqpmFZn5fPDBZ3z92c8AbNmhNSdfcIxrMFVtiX8IuWiqMVEw1UVUo/OMa0YVXeNbCKJwqzWQCg0Zm81gdYFVwdCuc3uOOO3PAASZgP/cOpifFo2u5++CUqoumFr6v8gsEem6um2dx2ZMI1zAE1adnQpVNfZqWo6aHz0mVzwgIm+KyPEicqCInCUio6JDZwN7ANeIyFxjzJ7AW7hftecAVwF9gM+MMc3X7h1t/DSgqtRv2nyuvPJuKircp+bnnnsCvXp1X8M1tetPf9qPP/5xHwBmzZrHXXc9ASyq1zEopZRSSqlNn7WW2aXDuPXKJ3P7/nXDaRQU5VedI8YFSasFV0VcgykRsNYFTyEKuhLVUgWIMlP9EDKhO8/irgnE5OqpHnv+X2nexq2W/fHj7/hyyBAWVi6u43evlPqNOw5oDFTiskhXpTh6bLma49WPFddwTo4xpg1wKzASeCjafSXu1+VhIvK4iNwG9APaA6etzX03BRpQVeo3q5IffviO5557G4AWLZpxww0X1vsojDHcc8+VxONuadQddzzBrFm/An69j0UppZRSSm26ijOTeObRN5k+ZS4AfX/Xk0OO3htwgdRsMLV6mygBqq/NygZbqzeqCi2kbVUgNhUaktYQ2Ki2qrics+x1BY2KOKn/qbnrn7rxMX5aMBJZVfcrpdQmwWBqbcm/oU5WhJ4aPb4uImWrOWdi9LhtDffZZoVz1+QuXL3Vf4pI9tfr74EJIlK9+dXb1Y5tFjSgqtRvkmDtPC655NbcnhtvvIhWrVrUcE3d2X77bbjggpMASCZTXH75IGB+g4xFKaWUUkptegKbYtLsn3jwrlcA96H95bedtVxHbcuq2p9GTaiieqihuH3ZqwRIWXfcF7fEP8w2oxIXbLVR5qrFPQfY55iD2GbHbgDMnDiDd174L5NLp9XFW1dK1ZNaXvJfe+MyZmtg3+jpMzWc+kP0uPdq7tMB2HqFc2t63X2Ak4HHRWRotUPNWLk5SvZ58zXdd1OhAVWlfpNKeeGFNxg61JU86dlzW/75z7816IiuvfZ8WrVqDsBLL73LN998jVutoJRSSimlVM0Wp8Zw783PUV7q5o/HnPQHttvJJVpVX+ZfxRVMzQVCZYUGVVEt1SA0+NGWicoBZO+VsYZM1NnK4vaF0f2M8Tj92nNy93vxnuf4ZsoIMqGuwlJK1bpTcL/UpgNf1HDe27iloN2MMQes4nj2l9bPK2SXrsQYEwceBhbjlvhXtwToaoyJVduXrS242dQ/0YCqUr85lvLyqVEWqHPffVflltw3lBYtmnHzzZfknl988a1YO4dV5REopZRSSimVlQqWMXTYt7z27CcANGpcyIVX/z2aRbqmUaubUYpUW+Yf7TPRft8a0lF91CA6ZgVCMQiG0LqvrXHZrS5w64K3xkD3XXry+6P2B6C8uJSX7n+eYYvG19F3QSlV97xa2mqPcR2lT46e/kdqqC0iIguAx6KnTxpjelS7z5HAZdHTG9bipfsBvYABIrJivdUhQBvgwujeceC6asc2CxpQVeo3ZxG33fY48+a5xk9HHXUgBx+8VwOPyTnrrOPZaSf3O33EiHE888xrwLIGHZNSSimllNp4iQjzK0dw8xVP5GqU/nPA32gZNYUKparxVBUDuJ2BZKuous2KweKaTgWW5YKx2WAqgNiqEgK5QGr0XKIGVaHA8f1PJ6/QNcX67IX/8clPQylOr668oVJqo2a82tlq1364ZfoC/Gctzr8M+D66ZpwxZqQxZjLwDpAP3C0ib9d0A2NMR1yA9CsRWdVr3oRbbnqvMWYiMAv4MzAeeG6t3tUmQAOqSv2mZJg2bSx33/0UAIlEgrvvvqKBx1QlFotx331X5Z5feeU9lJZOw01XlVJKKaWUWl6ZP5u33/yIYd//AkDnbdpz0tlHuCBoFPxccSbp8kuXX+YvuPqnLiAKlaHJXRczkg3BuqZWUhWoDQVsVEeV6H7GuPqq6dDQtG0bDj3zeDeO0PLS7Y/x8eyx2qBKKVVbTo0evxKRNRZqFpEksD9wOfALbil+a+BL4C8i0n8tXvN+IA84bzWv8Uv0Gl8AHYFC4EXgwOj1NwsaUFXqN0RkPn//+wDS6QwA/fqdwrbbdm7gUS3vgAN+x7HHHgrAwoVLuOWWR4CFDTsopZRSSim10bESMnXBUK695KHcvv43nUEskcBWq2uKsFJ9VHDHY6YqezUbVC33o+xUcVmrXrZsgFQFXAPrjok1+JLNVHXB1dBCKjSkosf9Tj6OFlu2BWDCd8N5/71PmVCiDViV2qQY1+yuNrba7EslIqeKiBGR/dfhmoyI3CkiO4lIkYg0F5H9ReSNtbz+GBHJE5FxNZzzk4gcICKF0f1PEpHN6hdfwxZNVErVowr++tdz+O67nwFo0qQRAweeW++j2HXXY5g/v+Y61GEYAuB5Hnfd9STvv/8lY8aMxX0IppRSSimlFCyqHM8ff/dPSksqANhptx7sffCubim+mFyQE6riF9mMIgvEoCrzNAqEZkLXbMpiSJgVArHRY8a6r7N1V28+9iLKFi+tdpZgZfmISSaVBtyKrOf+dT2fP/oc08f+SszTHCelNhXG1GIkVG3yNKCq1G+CEASzGTHil9yeMLS88srXNG7cqF5HMm3aHJYuXbZW51rrFlpNnjwTmAdsXNm0SimllFKqYQRhivHTfmLJwmW5fRXLMvzw7pRcCmo2m7SmGEi2vmq2HmpozXLNqaAqcJrLQsXkGlcZA8vmLKR0WclajTubOLBo1ly+mjeFAzp0W+v3rJRSauOhAVWlfhOKGTz4ZaZMmZnbU1mZZNy4Edx338B6Hcndd3egMCrMX5N02qe0tIxMxieVSvPxx59xyCHHA43rfpBKKaWUUmqjNrdyJINu+A9BEOb2TZk0jVJZyIGH75ULklqivtrGbdWzVhFIhmCtayjlh5CyrqFUzIuCsgLp0NVEDcWQDsFg8LNNqSw0a98Wk5fIjaN6Vmv14KykM5SXliEiZCqTfDR+FHu07UxRQldhKbXxM5haq5qpma6bAw2oKrXZCykunsLVV9+X25Ofn0c6neGhh17knHP+xvbbb1Nvoxk27M01nlNSUsEHHwwnk1nCKae4pln9+t3GqFF7EY93Q8s/K6WUUkr9dlUGxXz1zde89/qXADRqXEBFeQqA+69/ij0O2o38gryoMVVVhmo2yJr9OrTg26hxlYV09HXMc8HXbGaqMWCiyKgXpaZaMQQWfAsXvPBgLsPVirun8dz9s2Lzl2JLK/jfa2/w81vvI9by4YNP0qvztvy9W5+6+2YppWqNLvlX1WlUQqnN3kJuvPEhlixZBsAJJxzBVVedA0AQBPTrd+tG12X0889Hc+CBO/H3v/8fe+zRG4BffpnMo4++ACyt+WKllFJKKbXZstYys3Q4t131eG7fhdecyu779gFg7syFvPTof7EWrBg8Y8BU1VOVaO1+KK5hVFZQvXFV9jxcBipSdW3MQCCGQAwZcRmtGYk267YwalplMS7w6lvstHmkt+7APv88lfxGRQCMeucjPh/6A3MqSuv626aUUqqWaUBVqc1amvHjR/Lggy8AUFhYwB139Kd//zPo1Kk9AB999A3vv/9lQw5yOTNnLiI/P0Hbts3xPI/7768qSXDttQ+wZMlUIFz9DZRSSiml1GZrWWYWr7/4Lr+MnAzAttt34ci//5FLbjqTWMz9efvs/a8xd+6SXMOpbAFUG21VAU+nenaqif5ftv6qL27maaMlukHoslKDEPzQEGIIBNKBC9BmrMEXF1SV6FozbR5+xy2wXpz85i3Y84x/uBcW4eNBD/HylNU2ylZKbVRMLW1qc6ABVaU2YyLz6NfvVoIgAODyy8+iY8ctKSoq5K67BuTO69fvNjKZTEMNM0dE+PLLsey//465fXvs0Zt//OP/ACguLuG66x4AFjTQCJVSSimlVEOxNmDy/B+576b/5PZddNOZWBNj6x5d+PMpfwIgWZniidvcOZINoEZbaA2hNWSqLcev/rWIy2y1FrekXwx+aAhCt4Q/bQ2p0OADYdScClhuyb+NAq6hBVuRwVtaSmaLNgTWBXJ3Pu5oWnTsAMCcUWP5+t33GLl4Xt1945RStcOrpU1tFvRHqdRmq4z33/+Yjz76BoCOHbdkwIAzckePO+4w9tlnVwAmTZrOv//9fIOMsrqRI6fSo0cHioqWb1p1222X0ihaGvXIIy8xZsxwINUAI1RKKaWUUg1lfvIXHrv3FZYsXAbAfn/akz6/7+2yQkM4qd+JNGnmGph+/PoQxgz/FYsLjKZDt/w+pGp5v0g249RgsxsGiWqjpq3Bigu4+gLJ0JC2y2e4Ci6AKhhMtukVxjWwsobYpJkku3WJ9oGIwYsnOOCSc3Pva8gDj/PGhFEEVldhKaXUpkIDqkptloRMZhb9+t2W23PXXZdRVFSYe26M4b77rsoV1r7xxodYsGBxvY80K532GTNmBrvuuu1Kxzp02CJX99VaS79+tyGin+IrpZRSSv1WpMMKRv06lOcefRuARF6c8649nbR1QdLKEPKaNuUfl56Uu+b+qx8n7Vu3RD/aMqEhHRqC0GWqpqIl+pZoaX+0zN+31bbQkAldZmomMPjWVNVbpVqjK+sCppIN2C4rQzAETRu7YK6460IM2+61O1333A2A0gWL+OLZl/h49tS6/0YqpdZfVJN5gze1WdCAqlKbpaU8+OBzTJo0HYC99+7L8ccfttJZu+yyA2ec8RcASkvLufrq++pxjMv76qtx7L13Tzxv1b+W/vWv0+jSxS2N+uyz73nnnQ+AknocoVJKKaWUaihzK0Zx17VP4WdcKauTzv0zW3Zu5wKhFjLWZYQefOLhdOneCYDxP0/kvde+IGUNIi67NBVC2ros1VQI6cDk6qq6RlYu+zRtXdC1wjekJKqTGkJAVWYq0WuHYqL6rCbXzMqK0GjyTMq37Qy414Mo+CqAMRzY7xy8WAyAH/7zCh+NHkZpRldhKbXR0oCqqkYDqkptdgIWLpzIDTc8CLhM1PvvH5jLRF3RzTdfQtOmbmnU00+/ycyZc+ttpFlLlpSxbFk5Xbu2W+05BQX5DBp0ee75v/51O+n0bFguP0AppZRSSm1uSjML+eTTzxny/g8AtN6iJX+/6C+5aWAQBTwBxItx0sBzcte+9MBL+IHNLfsPouCpb6HU98hYlzkaistUzVgXOM1Ey/szoSsLkAk9lw0bNbgybm1/7nqi5lSBuOBtYt5i/FbNkfw8V0sVcs2ujAHPQOsundnluKPce0in+fTBJ3hz+i/19F1VSim1ITSgqtRmZwFXX30fpaXlAJx++rHssssOqz17iy1ac+2157Pzzjtwzz030KlT+/oaaM7nn4/ioIN6r/G8Y445hP333x2AqVNncd99TwNL6nh0SimllFKqoYgI05aN4M6BT+T2nTfwZBo1LsLzIIbLKq1eD7XrHrvQ9w97stu+e/CvO6/AmCgYmquVCskgCpxGy/oz2ZIAgSsJkAoNydCQsR7J0FAZRk2txNVitVEAVqq9dkYMASBhSP6c+VR2au+OSZTYIFX9vcMoo3Xvs/5BYbOmAPzy0RC+/PorppctrdfvsVJqLZla2tRmQQOqSm1WUvz8808MHvwaAE2aNOKWW/qt8aqLLz6Z4cPfoEOH9ixeXFrXg1zOlCnzaN68MS1bNlnjua7u68BcWYCbb36EefN+BYI6HqVSanNgjNnbGPOkMWaSMabcGFNhjJlsjHnKGLPfWt5jB2PMPcaYEcaYxcYY3xizxBjzvTHmNmNMj7p+H0op9VuyIDmZl595mym/zgSg587dOOz4A7BRRmrMZJfquwBoRRT4POPuqzj74WsIlwQkozqqYVTbNBRDKvRcZqs1JKMgaiCGpDWko0ZU6dAFSH1xwdJsY6kwCsIG1gVWAfwoSiICeVNnke7cATGeC/bi6qva6BzPuFqrAPlNmrDfuafm3u+Hgx7mzWnjsKKrsJTaqBiQWto0qLp50ICqUpsNQWQuF198CxJNwK699ny22KL1Gq+Mx+MYYzjwwN58/vmouh5ojrWWb78dz777rj6DdkW9e2/H2WcfD0B5eSVXXXUvML+ORqiU2hwYYwqNMS8CXwOnA1sCU4BJQDvgNOALY8wrxpii1dwjYYx5CBgN9AN6A0uBEbhU+d2AK4BfjDE31/FbUkqp34TAZpgwdwSP3P5Cbt+lN5+NZzwEF0gNxGWopkJIhR5+6LlapvEEKeIUdN2CuWMXUhkafHHnVwSuRqoVV08127TKD9wSfj80ZKzJZa1mM0wlKheQjjJaLVHN1ShoagWoSOGVV5Js1dIt7wcQ14gKXEZttCsXCO5z9J9o260rAPN/ncQXr7/N0EWz6uV7rJRSav1oQFWpzUYpr7/+P77+ehgA3bp14aKL/rFOd2jRojGtWzdj0qT6qaM6bNhkdtyxC/n5iXW67sYbL6Z5c7c06pln3uSnn34AknUwQqXUps4YEwPeA04ASoF/Aq1FpLeI9AFaA2dHx44HPjDGxFe4hwe8BZwHpICrgDYi0l1E9hCR7kDb6N5zgL3r470ppdTmbnbFWB6983lKissA+ONf9qfXrtu5JlKhyy6t9A2+NVQEHqGQq5EaZJtFbdmK5MIyykp90qE7PxkYMqG7Lh0aUoFb4l8aeCQDQ3ng5a4Po2X9oQVrDWEUYPWrBV6zS/8BGk2ZTuk2XbDGFUytyjMVDBZjBBFxY8QFab1YjEMuPTd35ucPP8V7E0aTDnQVllIbFV3yr6rRgKpSmwVLMjmD/v3vzO25554ryMvLW+c77b339nz33XjCMKzNAa6ksjLNhAlz6NNn63W+tk2bllx33fm55xdffCsi89AGVUqpVbgKOABIA4eIyGMikmuhLCIpEXkCOCQ6Z19g4Ar3GAAcHh3/g4jcJiLLFbgTkaUi8hjQE3ijzt6NUkr9RqSCMoaN/p7XnnofgILCfM656lRS1lAZuGX5Jb6hPDCkbLXapLjM0WyjqsB6hD06UTx6FkvTsDjtUe57bkm/def7ApWhIRlAMjCkQrecP2Ndc6ogW3sVovqp4Ef1Ui0u01QEzOISwkQeflEjApsNxhpAiHtCgefmqpaqWWsUd6XLrn3Y7gD3eVzFkmI+feI53p01oX6+2UqptWNM7Wxqs6ABVaU2C4sZNOhJZs50maWHHLI3hx++/3rdKS8vQZ8+Xfnxx0m1OL6VffHFGPbbrxdmPf+Dcv75J7Hddm5p1Pff/8xLL70FlNTiCJVSmzpjTBPg0ujpHSIydHXnRseyn0r9yxjTNLpHY+DyaP8tIvJ9Ta8pIuUi8u8NG7lSSqlpZSMYdPVgwtAC8PcLj6NZ29akAkiFhnRgKPfdlrYGTLVc0GqfsWcsBE0aExiPkoWVlGSiBlPWBUqD0OCHHqUZl+WaigKh6Sgb1dVcdUv2Q4nKA4g7FlYrCRCE0GT6TEq27uSusVUZqgkP8j3Bw5UFsFGg1SOqp4oLyu5/4dnE8tzKrZ9eepNPRgxncaqynr7jSiml1oUGVJXa5PnMmjWe2257HIBYLMa991653oFKgJ126sKUKfOoqEit+eT1sHDhMtJpn06d2qz3PRKJBPfee2Xu+WWX3UVFxUzcZ/5KKQXAn4BmuASkh9fi/Idxv0SaRtcCHAa0iO7xSB2MUSml1AqWpubz/rufMvSLkQC026otx571Z9IWyjKGisCwzPcoybjGUjbq8mKoClhagSCbaSqG9LadCH+dzZKUodT3qAzdcv/K0FBpXZMoVy7ARF+7gGsmWtYfWghCQyoKxFrcfsFFRAvmzifZpjUST7jXx/2HQxBiXtQ8CwPG/YcmbiAW/TUuUcOsxu23ZNcTjgPABgFDHniU16ePq/8fgFJqFWopO9Xouv/NhQZUldrkLeCKKwaRTLrg5/nnn0jPnttu0B2NMey//44MGTKmNga4HBHh889Hc+CBO23wvf74x31zmbhz5izgjjseAxZt8H2VUhudjsaYqavbarhur+jxFxFZsKYXEZH5wPjo6e+jx2w91HEisnj9hq+UUmptWWuZsHgY9177ZG7fP689HZNfQCp0gc7KACqjzNRsMqqVaLm/zdZPhWToaqOKQDqeT6pFc/LmL6YiMJSmDWUZqPANlYGHL64WaiiuBIAI+Lhl/eBqs7r9plqN1ihbNRNQuGARFVtuiRVDiGCMy0hNGHAr/Y2LpQh40XFTbbypqCHWnqf9jcatWwIw8avv+eazz5lUov/5UWqjoDVUVTUaUFVqk1bJd999w4svvgtAq1bNuf76C2vlzltt1ZowDJk/vzi3b8qMJYShRcQV018fEybMYcstW9KsWaNaGec991xBPO76x9x115PMmPEL4NfKvZVSm7yO0eOUdbhmcvTYIXrcKnqsKXCrlFKqlsytnMwLT7zJnOnzAOi9Zy/2OHRvktFSf99CSTpGScZFJbLZqCIuqCoYfInKAkTnB1GW6dIOHSiau4BkWpiXirMgHacs8PCjTNQwuodrPOWCp9a6oGfGQiYKpmZLAAguWNtk+kyKO3XCN4aQ6I9sYbmEtqyYB/kxwTPutTICaWvIiIdnoKCokIMuPDN3/kf3PMKbU8Zg13PurZRSqm5oQFWpGixZsoSnn36av//97/Ts2ZNGjRqRn5/PVlttxdFHH81bb721xnssWLCASy+9lB49elBYWEjLli3ZZ599GDx48HoHJR3B2nlcfPEtuT033XQxLVo024B7Lu/AA3fi889HIyIsWlLOS2+OYumypHt1YZ3HHwQhQ4dO4Pe/367Wxti9+9ZcdNE/AEil0lx22V3AvA2+7yeffMLxxx9P586dKSgooLCwkK5du3LSSSfx5Zdf1nht3f3MlfrNmiUiXVe31XBdk+ixfB1eK3tu0xUe1+UeSim1Udq457bgh2lGTf+Rp+95GQDP8zj96rMp86PaptZQkvFI2apAajbAGViDHxr8IFtf1SMTBSqT1mWzWmIs6dCRpjNmkRFYljGUZVzGazJ0maK+dVmwmSj7VTCEuCX+Im7ZfvXXNuWVxNJpKpq3IGM9V3xKqopQJbyoDIGtqpuaLU2QDiETuLH51p1rgF5/PIj2O7j58uKpM/jqlTf4YcGMDfregs5vldpgnqmdTW0WzCb4y2+TG7DadCUSCYIgyD0vKCggFotRUVGR23fYYYfx+uuvU1RUtNL1w4cP59BDD2XJkiUANG7cmFQqlbvnIYccwjvvvEN+fv56jK6YZ555jNNOc3VEe/Xqzs8/v5XL1qwtX345llatGnPzA98z+pcFPHHPn2nWJJ8e3VoTj8Wis8xaNSv85ptfaN68Eb16da7VMS5bVkr37oeyaNHSaMzPs+++RwPrngUrIpx77rk89thjuX0FBQUYY0gmk7l9/fr145577lnp+rr9mSu13upt5maMmZpf2HzrvgecWyv3Gz7kEdLJZdPWEDhd3VjeAo4G/isif17La/4L/B/wtogcbYx5AzhmXe6h1HrQ+a2qFxv33BYmLBtG//Ou4t2XPnFjOfGPnHbjhaTFYK3LDl1Q6RFiSESpQQYhZqIl+VFAtDJ0GaeCIZ2to2oNgXVlADqPH8vMzt1YbIrI8yyJOBTFhUYJS57nMlKhWo1T3P3BBVJzQVMxbDluLIu6bkNYWAiA57myBXHPUBCzFMXc+cZAHMHz3JgzoUdFmM2yhUZxIeEJcePOmT/mF54+/WIACps24Yp3n+GK3x9FXmzd5/o6v1WboXqNShpjphY12WLrQ/7+2JpPXgsfP38OlWUL1mt+qzYemqGqVA2CIGD33Xfn4YcfZsqUKSSTScrLy5k2bRpnnHEGAB988AHnnHPOSteWlJRwxBFHsGTJErbbbjt++uknysrKqKio4MEHHySRSPDxxx/Tr1+/9RiZpbR0OldccXduz333XVUrwdQgCPH9kCAI+HXSQlq2bclnQ8bx3icTmDazmFfeGsWDT/3AzNklWGsJQgusuQRAWVmSmTMXscMOnTZ4jCtq3rwpt9xS9X28+OJbCMM5rM/fp88880xusvmXv/yFiRMnkkwmqays5Ndff+X//u//ALj33ntXyuKo25+5Umo9zI4et1mHa7JFqKetcI+ta2VESinVgDbeuS2UZ5bx7Y/f8N7LnwLQqGkjjut3MinxsGJIhobFKY8giqNYoiXzFldTNXD1UisDVxM1EBdUTVtDie+RCg2p0MO3HtM7bsOWM6cShFCehvK0ocKHVHSPtDUEZGuqug3JZqg6oUDBkiWkiorIRMFU42VruRqMEeKeW9IfuFRXJBqzqwVb9d4NUhUgNu7cLr23Z5cj/gBAsrSMjx76D5/OGc/60PmtUkrVPg2oKlWDzz//nKFDh3LuuefStWvVh0ddunRh8ODBucnm888/z6xZs5a7dtCgQcyfP5/CwkLef/99dt11VwDy8vI4//zzueGGGwB4/PHHmThx4jqObBG33voICxa4AvV//vPBHHTQnuv5LiEMLWEYMnd+CdNnFzNjdjGTpy3lxrs/57o7P+f9L2exWy9XHP/Ft0bz2jtjGTVmHsNHzyOTCXP3qSmoOmTIaA44YEfM2qSyrofTTz+WPn22B2DkyPE89dSrQHHNF63Cs88+C8C2227LSy+9RLdu3XLHevTowWuvvZb7t/Dqq68ud23d/syVUuvhm+ixpzFmizWdHJ2TrUny3Qr36GWMaV3L41NKqXq1sc5tRYQppSO5/5oncvPJv110AoVRKat0GAVNrakKaFrIhFAZeJT6HpWhoTjtUeJ7BFGgNWUNqcCQiYKYrq4qlOQ1Junl0bysGPAIQ6jMeCxNeixNuuBtENVOzZUVwD0iIBbCUGg5exZLtuqMR/QxflSDNWEgr1p2axQjJYxqtWZCF/CNGdegqjDuymmBu94zbvn/YReeTl5hAQDfv/YuXwz/nsXJsnX63oLOb5WqFbXVkEobU202NKCqVA0OOOCAGo9nP8kHGDZs2HLHshOXv/3tb2y99cqJTRdeeCGNGzcmDENeeOGFdRhVhilTxnDvvc8AkJeXYNCgy9fh+ip+EJBK+3zzw3Q++3oK/xzwNv+65n0G3vYJd/z7S9756Fc+GjKJr36cR/s2BTQuilNR6VOZ9HnujZ+57b4vGDVuHplMUOPrzJmzBGMMW27Zcr3GuTZisRj33z8w93zgwHspKZlOVR7B2pk3L2qA0Lv3KjN+E4kEffr0AaC8fPmSinX3M1dKracPgFIgBpy3FuefH51bDLxf7R7F0f7aqWOglFINZOOc28Li1CzefuN9Rv/4CwAdum7FH046Al88RKDM9ygPqv50DbNNpyxuCw3lvstMTQYemcCQDDzKMoaK0EOsl8s89QV8Hya26ULXhdMRK1hrSPtQmTGUpTwqfBfwtFEt1UBc/VMbbQGGFnPnsqztlth4PBd0DQQ8I+THLHkxFyG14iInhmwDKheczTa/8qgqqSji6rZ6xu1vukUr/nDW39wxa3n7zkf4aPaYda5ZqvNbpZSqfRpQVWoDFBQU5L4Ow6pMzQkTJjBz5kzA1aFalcaNG7PPPvsA8PHHH6/Dq86jf/87yGRcJ/tLLz2drl07ruGalb37ya98PGQKDz75Pc+8MoJX/juaT76cwidfTuGdD3/l5f+OJQwlV0Pqq+GL2a9vm9z1n3w5hY++mMS3P86gMhWQTgeISG7LEhG++GIMBxyw4zqPcV3tu+9uHHfcHwFYtGgpN930ELBwne6R/XR+1KhRy9UYy/J9n5EjRwLkPqGHuv6ZK6XWh4iUAvdGTy83xuyxunOjY5dFT28TkYroHuXAHdH+gcaYGpcDGGMaG2Mu2LCRK6VUw2iIua2VkAkLRvDgDU/n9p169VnYeB4AFb7LRLVRZqi14IeuFmo68EgFHpWhx9J0nNLA1U9dnI5RnDGU+Z7LCrWujmooEIbGZbeaPOY3aUOnZXMJreD7kM5AKgPJDFT4US3W6DWDaLm/BUhnaFK8hNIttkAE0ta4ICkuO7VRzH0K57JTBYsbs29d+YDqWa/xmPuj3ES9auJR1moQZarud/KxtOzgFllM/GEk33/yMVPL5q/19xd0fqtUrfFqaVObBf1RKrUBvvjii9zXO+5YFTAcO3Zs7utevXqt9vrssV9++WUtX7GCTz8dwn//62pLtWvXhiuvPHvtBwxkMgHTZxbz6DNDuefRr7ntvi945e0xvPTWmBqvW1Scxg8t7du6iXYYCiLw4huj+PK7abz7ya9UVPq587Mx1bFjZ7D11lvQuHHhOo1zfd155wAKClxB/Pvvf5YJE0YCmbW+/txzXQLa5MmTOeGEE5g8eXLu2IQJEzj++OOZOnUq22yzzXK1ouruZ66U2kA3A18B+cDHxphzjDG5iIExpsAYcxbwcXTO+8CKHTnuAt6Ljn9qjLncGLNcyr0xppkx5gxgLPCXOns3SilVh+p/bgszysbz1L9fZdE8V8pql/13ZYd9dsO3xtU49b0oEOkCloEY0uIhuBIAZYGhIjCkoxqpFb5HReBR4cdI+oYggKRv8EMIQzdHzc5TZ7fsQLuSBZi074KmQVRKwIeKtKE0bUgG0WtHW2ANbadPZ0HHrQmpqoUqYvAQ4sYS96pW9MaiL3xc9qngXt+KC55mSwIAGCwxI8Q8CKJr8/Lj/O3yM3Pfr9duf4wf5owilLVfhaXzW6VqiS73V9VoQFWp9bRs2TJuu+02APbZZx969OiROzZ37tzc1x06dFjtPbLHSktLV1peszIhCGZxySW35vbcfvulNGnSeK3HXFqW4vnXR3LaJW/w2ddT+e6nWVSmwuUmljX5esRi9t55+RKCv05ezJMvDOPjLyZTWpaiMuljrZsuptM+I0ZMYY89uq/1GDdUly5bMWCAW64WBAGXXnoHMG+trz/yyCO59957ycvL4/XXX6dbt24UFRVRVFTEdtttxxdffMG5557Ljz/+SNOmTXPX1c3PXCm1oUQkAA4DXgWaAo8Ci40xI40xPwOLgceBxsDDwDEiEq5wDwv8GXgEKABuBxYZYyYYY4YaYyZE9xkMbAV8WS9vTimlalH9z20hHSYZMWEYLzz0BgCxeIy/X3WWW5pvDWW+wbduqbxEzaH8KEPVDw0VvqEs44Kolb6h0veoyEBgXWDUiqu9mvFdMNVaIQgEa90mwIQWXei+ZHpuPiwCfgBllVCRFtKhq6nqW0NgDfGycrCW8ibN3GtALlRaGBMK4uBHGagGcsETL8pUtQI2uiLuSW7Jf7YxVdxzQdZCz42vIAZ9D9mL7XbfCYCFs+bz/jOvMnbppLX5sQI6v1VKqbqgAVWl1oO1ln/84x/MmzeP/Px8/v3vfy93vKysqlh8UVHRau9T/Vj1a1ZtKY899hLjxrnJ02677cg//vF/az3m1/43hlfeHs1r74zhh2Gz1nzBKiRTIVNnV9Br26bL7f/0qym89vYY3nzvF555eQRLl1WyeGkl3303nt/9rgeeV7+/ai6//Cw6REuj3nvvCz788BNg7Sd3l1xyCW+++SZt27YFIJlMkkwmAUin05SVlVFSUrLcNXXzM1dK1QYRqRSRvwL7Ac8A84FuQB+gUXTabSJyvoikV3MPX0TOA3YC7gPGAG2AXaLHYcCtwPYicl2dvRmllKoDDTO3hUklo3jw5qfIpN1qosNOPpLWnTuSCiEVQmWUeRqIaxKVCV1QtTI0LMt4lIcelYGhMmo85Qeu8VMQuABqGEIqDUEgZDJCMunqp9roY7NMRlhY0JyCME2jdIXLHI2W+IchpNOGVAYqMlCegcoMtJs5jbmdtiHALdkn20TKiMs4FRfwDaPgrImSFlzQtEq+5/4Yz2awekbwooxVz0iU5Srkee7a0649OzenfvOhlxkxdTipILXG73GWzm+V2nCCqZVNbR40oKrUerj44ot59913AXj44Yfp3bt3Hb9iyNKlU7j22gdye+6/f+BaBSrT6YB580sZcP37XH3rx3zx3fQNGsmIX4rp1a0ZeYnlXzuVCXnjvbG89s5YvvtxJpMmL2DOnKV07OQmbSvWVq1LjRoVceedA3LP+/W7Dd+fzdo0qKqsrOSvf/0rRxxxBJ06deLjjz9m8eLFLFq0iI8//pgddtiB559/nt13353Ro0fX4btQStU2EflKRE4TkW1FpJGIGKqW919hjPnzWtxjnIj0E5E+ItJSRBLR454iMlBE1j5lSCmlNhL1P7eFksxShnz1JV+88zUATVs25cjzTqIyMCRDj2UZjzLfI20NNqpRKkA6cPVTK0MXbE0GhqRvyPgmykQVfF8Iw2w2KlXL+aN9YSgEvrjzAxjTvCs7LJ1arR+Aq2kKrq5qeQUk09B06SJKGjUlnchHxEU/jXFB0UTMBUR9MfhRNi1ULekXolqpgGcsnie5uqlgSXhVwdXCmDu/KO7KAiQMbLN9Vw4+wfUKSFUkeeGuZ/h5Sc0lu7J0fqtULfFM7Wxqs6ABVaXWUf/+/XnwwQcBuPfeezn99NNXOqdJkya5rysrK1d7r+rHql+zsgVcf/2DLF26DIC///0o9txz57Ua772PfcsVN3/EgkWVlJb7a75gDazA0NFL+d1OLVc6NnT4bIaOmMUl17zH++8PZ/p8nwWLyustkFrdCScckfse/frrVB5++Dlco+6aDRgwgFdffZXu3bvz1VdfcfDBB9OqVStat27NwQcfzFdffUX37t1ZvHgx559/fu662v+ZK6Xqg4hcimtcFQNeMsb8oYGHpJRS9aoh5rYiwvilI3jgmidy+/7S7xRo1JjK0FCWMVQGkA5dzVQrLvPUNaPyWJaJkYzqpQaBC1dm66Nms1ODwG3ZrFPfF/yMy0pNZ4RMpupYuVdIabyILZNLXCBVBLLL/32hshKSFSHt5s9mSvOOpK07loiyTD3jHkNxjadstcZTflR+IBtgjXtCQcwFSeNRXCUbSDVR8NRlrgp5niUv5rJVPQ9O/Nc/KGriFlZ89tonfP/jdxSnl632+5yl81ullKp9GlBVah1cdtll3H333QDcddddXHLJJas8r3379rmv58yZs9r7ZY81bdqUxo1XVws1zbhxP/Pwwy8CUFRUyO2391/jWOctKOXbH6fz8FM/8Pr/xq7x/HUxbU4FLZvl0axJYpXHE17I5JnLGPzSSEaOmUtlsnqzqvoJrhpjuP/+q3LPr7vu3yxaNAlX4n/VysrKePzxxwG44IILKCxcuZFWYWEhF1zgGnh/8803LFy4EKjtn7lSqj6JyL+Af+Jqo/Y2xsQbeEhKKVUvGmZuC7PLZ/Lq8/9j8tgpAHTevit9/++PlKXdUv6yjEc69Agx2dgmGQuVUeZqJnRL8f3AHQtDEy3xF3wfwmiJvw2rMlWRqkBrGEIY1VENQghC+KVxJ7qXzARrsQLgjvtRb9OtF89iStMOJEMPP1y+/0B2EW8mcLVW3dVVy3tt9D4MLogaM1WBWLDEor/KLS7YKgiNYtAoylA1UYprs1bNOOGSE3OvO/iGRxm9dFSN82ud3ypVi7QplapGA6pKraUBAwZw1113AXDnnXfSv//qg5rVu2BW7465ouyxnj17rvYckbn063cbYeiKPV155dm5+qCrY63lvse+5bzL32HR0spoUli7vhy2iP12bbPSfmPg931a8+HXc1mwqJzn3xjF9JnFDZKluttuO3HqqccAUFJSxrXX3g8sWO35EydOJAhcwHWbbbZZ7XndunXLfT1t2jSgdn/mSqn6JyKPicj1InJ31MhKKaU2aw01tw1twIjZw3nq9mdz+4657ByKgzhloUcyMFFTJ7fUX8hmphpKMoYS38O3hoq0wfezWalRcDRwH94HgauTGmQDqNEyf7fUH/yMC6qKzX7YL0henOmNt2SbkjmIdY2sAt9lsOZlUrRMlTAt1oZMGjIZl3maDg1eFCANMWQwhNnIKeSyVKN4LiaqjRo35IKohXF3DCAvCp4mPMjzJHeOIVuCQDjq1CPouO1WAIwf/isfvP4ecytXH+zU+a1StciY2tnUZkEDqkqthf79+zNo0CDATTgHDBhQ4/k9evSgU6dOAHz44YerPKeiooKvv3Y1ow455JDV3KmM//3vQz755FsAOnfuwKWXrrwMqzprLfc//i0vvDGKiVOW1Hjuhigu9Smr8Om85fIF6nfs1oyJ08tIZ1y90s+/mcqX30+jrDyT+yS/PoOrt97aj8aN3Rgff/xVRo8eDqy6gH/1mrQzZsxY7T0XLKgKymaXNdXez1wppZRSqm413NwWfi0Zz9P3vkDJkmUA9D10b9ru3Icy3yPpG1LWEFhD2kZL5kNIWo+l6RjL/Bh+6GqlSrQkX6zkslNtKLmGUkEAgS/4GSGddlmq2SmoDasCrtl9ngczGm1B28qlxDMZ0ilIp1091l4l0xjVdGtsVFLA94XKtOCHgjEuWuqHhtC6WImVbDDVLf/P1luNm2wdVRfEjRlLdDnggq0gNIoLeVEdVcEFbKOkWRKJOOdce1bu+zn41qcYPmcEVlbdK0Dnt0opVTc0oKrUGvTv3z+3FGrQoEFrnHBmnXzyyQC8/PLLTJ8+faXjDz30EOXl5cRiMU466aRV3EFIp2dy6aV35PYMGnQZhYUFq33N4mVJZs5Zxs33fMGS4uRajXNDfPfzEvbs3Sr3IVt+wqNn16aMnLAsd04qFfDSm6MZ8+v8XJZtfdpyy7ZcffW5gAs2X3LJrYjMpSoXoMp2222XWwY1ePDg3Kf51YVhmFs21aJFC3r06JE7tuE/c6WUUkqputVwc1uo8Cv4fswPvPvMOwDE8xIceOHZFKc8KkOP8tDLBVMDa0iGUBFAue+aT6V8gx8FNLMNpNJRtqmfcbM7G7rn2bqpmZQlSFsCPxs8FSSquWoDt+w+kYhKC4hhXLMu9Fw2LZfx2jxZSigeS2NNopqrQioNFRUuS9VayIghiBpUZf/AtlQFRIFcAypjXOapQUh44OPGVRhz5zVNWFdT1VStDDamqrGVALvsvyu7HbgbAEvmL+Glh1/h1+IJq/ye6/xWqVqSTRWvla0Wh2XMn4wxbxpj5hpj0saYBcaYb40xN6+qjJUxJmGMGWCMGWWMqTDGLDXGfG6MOaaG18g3xtxpjJkTvcY4Y8wpNZxfYIyZYoyZbYzZbOuAaEBVqRpcfvnluQnnPffcw6WXXrrW1/bv35927dpRWVnJ4YcfzvDhwwHIZDI88sgjXHPNNQCcffbZdO/efRV3WMwDD/yHyZPdJ8n77bc7xx57aI2vefHV/+OKmz6iMlU/q1XTvuWXqaX06dEcgN/v3IrvRy1hxQTU4aPn8skXkymvyCAiWKmaBNeHSy45la5dOwIwZMhQ3nrrfaB0pfMKCws588wzARgxYgRHHnkkY8aMwVqLtZbRo0fzpz/9ie+++y667yXEYrHc9Rv+M1dKKaWUqjsNO7eFkUtG8+iNTxL6bq663yl/IdG2HanQkA5wW2jIhB6hhfIgxpJ0jKXpGOVpVyfV913dU8gGTt1S/mywMwwlerSEGYtNheALNhBC3yIiGOMyW4WqxlTZIOyieDM8a2meKcMGws6V0xjVtAvgsk5FXPDW9yHtG1LRmK1kA6areucu+zSXbSoQ96Kgq0AcF2SNEVIYJxdwhaqGVYIrBSDROE4beCaxuJuHvvno63w/fiiV/soJFTq/VaoWbUQ1VI0xcWPMc8B7wJ+BEBgFlAO7AgOBghWuKQA+B+4EdgAmA0uBA4A3jDG3r+blngMGAI2BCUA34BljzDmrOX8g0BW4RETK1/c9buxMQ9Q13ECb3IDVpmnmzJl07twZcEtl2rRZuV5odf3791+p9tTw4cM59NBDWbLELb1v0qQJqVQK33dNmg455BDeeecd8vPzV7hbwJQpQ+jZ83AyGR9jDCNGvEWfPtuv8rV9P+DHkbP5w7FPr8c73TDGwPGHduSrYYvYbceWvDNk7irPa9+uCXff8CeKChPsvUdnCguyDa1MvZSR+e9/P+XPf3ZdS7t06cD48R9RUNCTFf+LlkwmOeaYY5Zb1pT9+aTT6dy+E044geeee265CSdsyM9cqTpVb8WajDFT8wubb933gHNr5X7DhzxCOrlsmoh0rZUbKrVx0vmtqnMNO7eF+ZULufXRO/j3pfcA0KxtK8585WmkoIjK0JAKDaEYfFsVNCzLeGRCt5w+nXFB0IwPYEgk3JJ8a6Ml/NHSfxFc8DRjET+KluIKnZoYkPAwnsFEE9DsVC4Wc/PaIIBEJs3vyycwLd6GfAImNOm4UnJaImEoKjIU5kM8Bi0KLfkxiBvBM9UyU4GYJ+THIIaQH3MNrApiQsYaBKFZQoh5Qqt8S0HcXZMwQixKZPOiUgB5Mff+wih4O/imwbwz+C0A9jp8H+4YfBd7tdtjpe+9zm/VZqheC5EaY6YWNdti64PPfLJW7vfJ4DOoLFmwQfNbY8wTwJm4IOpZIvJTtWNFwB+AD0TEr7b/fuAiYBpwmIhMiPYfBbwK5ANHicj/ql3TB/g5ep0DRKTYGLMT8AMueLuliITVzu8OjAaGiMhh6/v+NgXaxVap1bDWLvd19bpCq1JevvIHL3379mXcuHHccccdvPvuu8yaNYtGjRrRq1cvTjnlFE4//fTl6hplJZPT6dPnaDJuxsiJJx5RQzA15JuhM7jwqv+t8nhdE4HvRi7mqP3b8/JHs1Z73tz5Zbzz4a+EYcj227Zhy3ZNiMU8ohL9GzSGXXc9hvnzF6/xvPz8PNLpDDNnzmObbQ5kzpwxQNvlziksLOT999/njTfe4Pnnn2f48OEsXLgQYwwdO3Zk991357TTTuPwww9f5Wus789cKaWUUqouNeTc1orllU9f48H+9+b2HXT+adiCQnwLYVQvNRO6+qkhLohakTGEVkilAFxDKZdV6jJSw8AFUq3NZpkKYWAhAJI+Rlz0UzwBC4ILqpooQGmMoerbEt0zFDLksdhrwo7pWfyv+a7Y0AVcs7lIsRjEYoIfgBVDQT5krMsm9TyDxS3tN0A8Cqa613NlAmLGkA7dzfJiLpiaMO68bALbVUdfxLLFxbnv16pmy9aKCw5j+O79b/jrXsfy89iRtClsvdx5Or9VqpZsJP2kjDEH4IKpc4EDRWRp9eMiUgm8s8I1WwD/jJ6ekQ2mRue/Y4y5E7gGuB6oHlzYJ3q8T0SKo/NHG2NeB/4BbA9U71r3EO5X9QUb8h43BZqhqtRGJ8Xw4e+z667H5vbsueduXHjh2as8e+KURZRVZJi/oKy+BriSosI4rZvnMX9xioy/6oL4QC4TYIft2tK+XVM3yYxXn3yt33+hLrhgAEuXLluna4wxzJ79Je3b74l+tqR+AzRDVamNm85v1Wbt12WTuHXQXTx3yxO5fYf97a/svPdeWFzzpjBq5AQuSJlt7GSjrFNBlisrJVFB0aqmp+7/iQChLP8fvihKKZArdGqiVVK5e0bXmui85kElhfgsiDfF4i3XnNvzslmj2ecGz4uW7ZuqeqkeLrC6YqgxW0LRRKUA4sYFXr1qpRVvHnAdJctK1un7HIvH+HTah+zX4aDcvFupzVTDZKie/VSt3O+Tx0/foAxVY8z/gCOAfiJy31pecxbwODBZRLqt4ngHYHb0dFsRmRLtHwjcjMto/bDa+XcAlwF7i8i30b6/AS8B14nIjevz3jYlGkVQaqMiiMzl4otvWW7v99//xN13X8qee+6c2+f7IUuXVXDetfeSSq8+iFnXPAPH/7Ejz/5vBvv1bcObn81Z4zWLKuJc3a83eXkxdurZjvw896tofSd+d9/dgcLCNS8xstZSXp6krKwcEeHKK+/hP/95AOi4Xq+rlFJKKaVqlgkz/DjlZ97490vL7f/iww/Z8pQTsUVNSAYeKeuCqn4IKd8jEwg2hIpKl4kZ+OQaNVnrAqyhL1hxUVEbWkwgkApcQVTBRT3jxq3Jz6Z+WpC4wSuI4XlR8NYPIYiis56hMWl2ZxpTEm1obpOMyu/sygUYQ5OmhnjM3UskCq4aQzwBBQkoyndL8wtjQmFMSHguAzVuIBUat+TfQMJAUdxSmLA0zRMKYq5JVbb5VMut2uLlx13UKJoirzJLNbBUlleSTqUJg5B3XvofW/+zK52b6OeQSm2Oojqo2eYqbxtjdgNOw9U1TQLDgKdEZPYKl/4uevx6VfcVkTnGmGnA1tG5U6JD2eUM2wMfVrsku4R2fjSupsA9wCTgDn4DNKCq1EallFdeeZtvvx0BQNu2rVi40NUquvjiW/jhh1dzy2mefHEYn345pUGDqQC79GzB2EklLFmWYeHSNNt0bMSUWRU1XvPld9MpKfuQg/beho7tm7FFm8YYY6ImAeseVB027M01niMivPLK1/zud9uyyy5HU1xcwrPP/pfzzjuRPfZoDRSu8+sqpZRSSqma/bxkLM/d+yyVpa6EQPP2W7Bs7gKSy0oY+tRz7PjPC1wjKjGIhaRvCAIXNE2m3D2yDaOMibJWrWtuGmSsC0Bma6X6IaRd/QDjGcREpaWy00vrlv4TujqrxA0ShpAM3X4DeMKu4TSG53ehhAK6hb9QZNNUevnk5QmxqLBpNrPVM659VOBDue+ybYvywTMuQOrh/uj2bZStmq2LiqXAsyRirq5qzAMTDU8M3PDWA9F5rp6qibJY48Z1ngEI0wEzv5xC2CJD/+OuAODpO/7DQUftR4dtOxKPZXsWKKVqxcaR+N0bSAAVwF+A21m+4fyRwBXGmFNE5LVq+7Nd4ybXcO8puIBqj2r7PsH9arrMGPMTMBI4FjgcmJjNZAVuArYEDhGRNL8BWuhEqY2GpbJyOgMG3JnbM3jwzfTq5X7v/fTTGJ577m0Avv5hGs+9+jPvfzZhlXeqL4UFMbpu1Yixk0sBGDpmCbv3asnalFAaOWYeb7w7lp/HzKWiwqesPANka2LV/srHCRPm0K5dC7p0ac8NN1yY23/xxbdg7aobaSmllFJKqfVXkinjm+E/8NlLHwCQV1jA4TddTTxqXvTLm2+xZMYMfAE/gEwIQQhBKPi+RPVRo9qpUlUnVQSCpHXZpkmXXWoyFtIWE1hMVC/AQFWB1sBWZaEKEAg2GbhrJEpgDSxb+sUkwwQlYT5YYXheF/qmpwFQUOi5AG61UgOhBYnyGwTwMy6u64euwZZvXRw3I5DnuXMSBhrFoTDuvs7GaKJqBbn7Cy54GrjhuvePK1eQ78GSMXPp2LsdffbsxX5H7A3AsiUlPHfv80wuG1enP1ulfnuqdaXb0M39r76jMWbq6rYaBrJl9JgP3Al8B/SNnncHXsNlCz1vjOld7bqW0eNy9VZXkD3WIrtDRGbggqXtcNmtZcAzuF9N50GucdX5wCsi8knuO2ZMW2NMsxpeb5OmAVWlNhqLuPPOwcyePR+Aww7blyOPPJD77rsqd8aVV97NjJmLOPvS/zJizDwaugTyPru05pufq5pB+YEwakIJu/ZsWcNVVabPWsYb747j8ed+ZMGi0joJpAIEQcjQoRPYay+3KuGf//wbPXtuC8DQoaN44YU3gHWrUaWUUkoppWo2dMEonrzhcSTq/PT7006kTc/t6X3iXwGQMGT0o4/gh4Z0aEgH4GeEVEpIp1wAMbNcnlPUOCoTRSr9EONbTMbHJH2Mb6sqEkfFUE0IZAIXUI2KtBrE7UsFkI7SX63F+D59wtmMymzh7mWh2GtESIwOXinGuIBvGCxfu1WoCqwGFtI+VGYMKeshAmlriFNVOzXuWeKe5GqtZhdoWdzz7PCr3nX2uEHEEDOCX5bEr8zQrENTAM695jTyCvIAeP3Jdxg7fiiVwcqNxZRSm7zG0WMcWAz8SURGiEhGRCYBf8NlkeYBA6tdVxA9Zmq4d/Y37nLLN0XkelxW6mvAEGAwsLuIfGbcEtNHcRmz/wIwxhwZBYUXAMuMMd8aY7Zbj/e6UdOAqlIbBZ+ZM8dzxx2uUH88Hueee9yynYMO2pOjj/4DAPPmLeK0M29h+qxlDTXQnDYt8knEPOYuTC23/5eppXRuX0RRQWyt7vPeJxO486Gv+HjIZKqvoajN2OrQoRPo23dbEglX5SSRSCwXqL788kGUl0/DTWOVUkoppdSGmlk+n/ff+YjxQ0cB0KJDO3of/xfSoWG7v55AYZs2ACz48Qfm/vADYQiZjMtUDQPcsv9Qcsv7XaaqEPohpEK3rD9tXSQzGULG5gKjALlU0sC6Y34YRT1doVZT6WOSIbk0Ut/Sg0VM81uQCWNROqgLwo7I70SfyukEvsuczWQEsdn5atWkNRsEDQIXr00Hrm5qMnSBUhGIITROuCX+obgmViLGDTtKXFuxApZIrvwr4K6ZOWwO2+y+FTEg4cFWnbbgpPP+DEAYhNx/3WBmlo+s9Z+rUr9pppY2Z5aIdF3dVsMoqv8B/riILNedWkQscG/09FBjjLfCdXk13DvbmCS54gEReVNEjheRA0XkLBEZFR06G9gDuEZE5hpj9gTewiXUnwNcBfQBPjPGNK/htTc5GlBVaqMwn8svH0Qq5T4QuvDCv7Pddtvkjg4adDl5ea4G0pBPP8JuBJ8279u3NV8OX7TKY1+PWMy+fdus1X1KytK0btmIbtu0XmnyWBvKy5NMn76QHXbotNz+gw/ei6OOOhBwgerbb38MWPX7UUoppZRSa8+K8O2sn3nh9idz+/a58J+EefmkQ4PNK6DX2f/MHZvy5INkkhnSaRewDAIhCIV0uio+akPXgIm0C3KaMAqeBhaTbSoVuKX+uehlGGWkWnFlAAQXPM1EpQECi0m7TNX8VIrOpphJmVa5zlAmCsraRvnMzW9F54oFhIEbT/XP/rOrrIznmlRlXzoVGMr8GCKGILqgMG7xcH+Im+guVlzkwVp345iBqO9VVEfVfZUwgmeE4lklFDXNp6hpPp7B1V81wukX/YW2W7YC4JtPhvHZx5+xLDO/tn6sSv22GRBjamXbwFqsxdW+Hr+ac7L7m1K11D97XU3LSVc8t0bGmDbArbiM2Iei3Vfi3uFhIvK4iNwG9APa45pnbTY0oKpUg6vk66+/5uWX3wOgdesWXHvt+cudsc02neiz277uiViCZSPreYzL6965MXMXpSivDFZ5fP7iFMbAFq3yV3m8up16bsExf+rJzju2p7ik+gdhtZOi+vnnoznggJ1W2exq0KDLSSQS0ddPMW3aWGpeAaGUUkoppdZkzJJJvD74NRbPngdAp1370H6vvckEhlRoCMXQdp8/0HT7HQFIzpnJ3PfeIoyW/IehC6CGYRScDAUbhJAJMalqy/ejoGhu2hhERUbDavtsVJDUdbPCVGZcdqofBVPTAV5phj6xOYxKb+GuCWX5exjDhKIOdKmYhxcEeN7y8RBjXCDVZLNQYy7rNJ1xWarpEDLWEIpQFM/e1n0dM9khuqBrNuk1iq0CrsGVMZa8GBixzB87j636bInnQV7cxY/jBgobFXDhtafmxnX31YOZXvxznZXVUko1iF+rfZ1azTnV92eXjk6MHret4d7ZrK6JNZxT3V24eqv/FJFsv7zfAxNEpHrzq7erHdtsaEBVqQYlhOFsLr74ltyem2++hObNm1adIcKPI2YxblYL8FzZE5ucTZhaUO+jBYjFDH17tuCnsTXVsoavhi9aqyzVC87YkwvP2pMb7vqURHztygSsrblzlwCG9u1X/SFct25duOSSkwFIpzMMGHAXrsyLUkoppZRaH6kgzVe/DufdR14CwHgev7vwfFI2RoU1+NZQ4XukA0PXMy7OrW+f/+bTpIqL8f0o8TQQJGpKZX2BlHVbKFVNprKBz2ywNFoaT1gVQM0FX32LSUUNrPxofyrApAKa2woKJWBhqshFMQWMdfeJFcXIllwdW9iRnZIzAZeFC1XL8bPBVKQqSzWIgsepMEYQGhKmajj5MSHuLb8KWKIaqWE0dCsuEhI3kB/95b7k14Vs0b0V8USsWgar5JrC/unY/dhpN1eqcNqk2bz81FssTNbU1FsptdZqrSnV+hOROcCM6Ok2qzktuz8NLIm+/iF63HvVb810ALZe4dzVMsbsA5yMKzswtNqhZkDpCqdnnzdf0303JRpQVapBLeOZZ17j559/AWCnnXpw5pnH5Y6GoWXM+PmcdelbhBIn3nynqmOlDdO5c/deLRkxfhlBWPMn3RXJkJnzK9lu6yY1nte+XVNmzynh82+mUVSYqLVxighDhozhwAN3rPG8q68+j7Zt3dKoN974iCFDhgANX1JBKaWUUmpT9P3CcTx311Nkki5BavujjqCo8zYkQ0j6hmRoqAwMlRko7Nqd1vsdBoBNVrDkk7cACPxsRBEXMPVDyARRo6goiBpat4w/dMv5jeCii6F1S//D6HqbLQlgXYZrVBbApEO3pQJ2ab6IEUtaudqsIrnaq5JnEAyBbwkDmB1vSTNbSRObxPMMItmmUia3XN94bugiVXGT0EIQNdTyxWCM0CjhslOz8RWJgqeWaPjR9zNmBC9qXuWnfErnlNC+e6tcZmrMVAVmATxjuPyWs3I/j4dvf5Epc4cRWF2FpdQGq90aqhvilejxlGo1Uqs7PXr8UkSyy0rfBnygmzHmgFVcc070+PMK2aUrMcbEgYdxTbGuXOHwEqCrMaZ6tlT36HExmxENqCrVYCylpdO46qp7c3vuv38gsZj7vZNK+9zz6Dc8+cIwJk5xHyrFirbG5LWia/c+bNXjoHofceOiOB3aFjJhetmaTwaGjS1m5+2aE4+t+r8anmfo1rUVZ136Xx4fdDSeVzv/dQEYO3YGW2+9BY0bF9Z4XtOmjbnttn/lnl9yya2E4Vxqq+SAUkoppdRvxeLkMj795ht+eudTAPIaN2anU0+jPPRIBx4lGY+ytKGyUvAD8H1oc+yZJFq3Y68jj6XNoScShoJkbFVR0WSASYUYP5qbhVG2abTkn2xtVJGq7NLQNZkyfugaV2VcwNVkwlxDKtKusVWH/HKK03lUpONRVqtE/awEk4gjvsWmLRIIxsCIRl3ZcdnUXANVwWWririAaTY7VURwlaWEooQQN4bKqM9VQawqmzVbjcAsF2hxX3jZYKlx2asLRs6l084d8DDLlxyINi/6Yse+3Tn6BPe3QllpBQ/e9iyLkmPq5oeulGoIg4ASYHvgXmNMHoBxLgaOxP16ui17gYgsAB6Lnj5pjOmRPWaMORK4LHp6w1q8fj+gFzBARFastzoEaANcGN07DlxX7dhmQwOqSjWYhdx888MsXOiCpX/5y6Hsv/8eACxeWsHUGUu4/YEvGfzCsNwVxhjy2h5EsezA/rt3qPcR79u3NV+tphHVqoRWGDaumD12XPWS+112bM+Hn09kqw7N2HO3TiscXf/gaiYTMGLEFPbYo/uaTwZOPfUY+vbdAYDRoycwePDLQM0lDZRSSimlVBUR4Zv5o3n25kdz+3Y+7VS8ps3xrUv+TGYgmXZbEIirl1rQio7XP4898O/0Ts+KgqnRkvzKXAFSjLWYMMQEbsMPXR3UbAkAi4tMusFgKnyo8CEIoyX/2YxUH5MKMOmAWBCyQ/Nixi1uQW7uGUT3KowjfujG49tc8LMsXkSFl0+bymLERs2yQgCDF8vWfIV43AVC82OQ8CQKtBoEFyT1o2GHuJeLhp2T7wn5UWYqCMmlFdggpHm7RsvVcM1mwXqmKqhqgH9d8w8aRYkFrzzzMSNHf0smWHEVrlJqnXi1tG0gEVkE/AVIAhcB840xQ4G5wH24YOplIvLFCpdeBnyPW9o/zhgz0hgzGXgHyAfuFpG3qYExpiMuQPqViPxnFafcBFTiAr0TgVnAn3GNsp5b93e78dKAqlINwmfSpNHcd9+zAOTn53HXXZfljr7+zlguv/FjKpNBrrNpljEeyVTItNkV7LBtU+rLlm0KCEJh4dL0Ol03aWY57VoX0KRRfKVjYWi56Z7PeeCWI1bKTt2Q0jLffjue3/1uu1y275p4nsd9912Vez5w4L0UF0+jarGVUkoppZSqyaSS2fz3tfeYPcY1l27euRPbHvV/pEKPCt+jPGNIpyGVEqyFdBqSScHPWEKJMTfenKIgRbN0uQtgZkK8igxeKsBkgmq1U6MM1XR2ab9US/e0uRIBJu1jKnxMIC4YWxlEdVSzpQSgR/NlTF7ahCB0E09Xf9W6Jk4JD5P0McnAvY4BG7os1NFFXdi+ZDphEGKtO2aMy2DNzt3jcbe0vygh5MUEg1AQg7jnYsSVgSEdxY2l2nzf1WS15HmQiIFBiHvC/J9ns/WuWxEzrlRANqIqUhWQdcFUwfOEdu1bcn5/V0rMWstNVwxmYWqENqhSaoNsPGv+ReRToDfwDFAB7AzEccHRA0Rk0CquSQL7A5cDv+CW4rcGvgT+IiL91+Kl7wfygPNWM65fotf4AugIFAIvAgdGr7/Z0ICqUg1iLpdeeju+7wMwYMAZdOmyFQAjxsxh7oIyPv1qSo13GD6+mB27NSMRr71l8jXZZ5fWfD1i/UqefDl8EfvtunKDqp/HzuPME3ejY/vmKxxZ//e0bFkFCxYU0717+3W6bu+9d+VvfzscgCVLlnHjjQ+iDaqUUkoppdbMDwO+mzGKNwY9mdu32wXnkzYJKgJDMg2ZDC4jNYQwsGTSllTKEqYtNu0yQYdLR/r6M1zmaVnGZZZm66FGWavGDzFBVFc1CDGZABNYt/muNqpXlokaWEXZpenonGwt1TCkQDK0b1TJtKWN3ICjTFAC69JKkwGkrYt+km1yBdYKKYkxNa8tXUrmEQQuIxUxuQzTvDzIiwtFCSiISS4IGvcsMWNIB4ZKH5KBwbfk/ioXXGC2MB4lshkh5kHJjGKatGlEQeOEaz5VLStVoguzYZo8zzWx8oDTzjuKTl3aAfD9V2N47+2PqPDn1uG/BKVUfRKRSSJymoh0FJE8EWkjIv8nIl/WcE1GRO4UkZ1EpEhEmovI/iLyxlq+5jHRa622qYuI/CQiB4hIYXT/k0Rk/vq8x42ZBlSVqncVfPTRp/zvf658SPv2bbn88rMQEUrLUpx60eu89f6aG05ZCz+OWcrvereq6wGzw7ZNmTa7gmRq/TI2FxdnSGcsW22xfD3T5s0KuOScvVbIRjUblJ36+eejOOig3pj1uMkdd/SnsLAAgAcffIHx40fhGiMqpZRSSqnV+XHxRJ5/6CXKF7lSVh33/B1t+u5OMjBUZKAyGWWj+i7LM5mETKVF0hYJgACo9ClLJSgN89nKX4yXjpb3+0HUQMrVUiUEQlzQ1Y+yTQMLGQupACp913wqtK6WaiZwy/9TLkPVLf+39NliKSNnN636GF9ctqtEX5t0gInuQ+CCslgh9CEMYGLelnRML8bzfax1pa7EuhqqiTgkPCiIWfJiQtwIBTFLQSwqAQuEuGBqGMVwAxvVTDVCIsp4jRmIWcvC8Qvp0qcdMa9avdRo2IaqplaGqIEVrjlWfkEeA289PfdzunngU8wvHYG1ugpLqfWy8SSoqo2ABlSVqlcW359Fv3652tDccUd/GjduxPOvj+TJF4czacpSJk9bu/qdU2dX0LpZPs0aJ+pqwCTihh27NWP4+BVrTa+br0csYq+dWy+379p/HUiTxvnL7duQYOqMGQspLMynTZtm63V9p07tufxy1xU1CAL+9a/bgM3ugzSllFJKqVpTkknyxehhfPGf1wDw4nF2Pu88SgOPkqShshKXxRlAGAp+BsJ0iGSLiPqhC3qWZTCpgNHlrellFmCihlQkXQ1VfBsFSUOXaepHtVSzQdPKaIl/Ogq6YtxjMnABVisuuzVjaZlI4YllaVkCcev13bmBQF4sKg/gmlNhjKvTarMdpFzmaygwsqATO5VNJwxcGQNrXe3U/LjLFC2KMk1jBvLczVyCrBhEDL4YMqEh6V6OuBEKY1Ht1bi7bsG4+XTo2YZE3MtlpFat2ndlBmLGBXKz9VOz/WANcOgRe7DX/r0BmD1jIU/8+1VK/F/r5x+HUpsbz9TOpjYLGlBVql4V8+ijLzB+vFvO/7vf9eH/jj6UWXOKGXjrx9xyz2frfMfVLaevLb/r3YofxyxdqZbrukqlLZNmlLFjNxfsbNI4j9136bBSdur6stby1Vfj2H//Xhs0zgEDzqBjxy0B+PDDr3n//Y+B8g26p1JKKaXU5urDWeN48c4nCTOulNV2xx5DbItOVKZM1HzKBVLDUMhkIJ3MBidxGaBWXJ3SKFIYBIYpyeZsl7cIk7GYTAipwJ2TiTJFU6HLas1mqmYbWQlgpSrwGoZ4vstuJem77NVkhj4dljFyejMX/Yy79E4jFmIGE2W8GguCuOBHKG7pfzalNCrZuiDRnALxaR5WuOzQmGs45WImkvtjO2aEvKi0v61WGsCKISOGwJrcLDhbBzVmIKxMU7agnC23aeGW+q8g1+PGuMKJ2dqqJpulalxT2+vuOBMvusGDd7/OlFnDCe1mVcpQKaXqnQZUlao3AYsXT+Laax/I7bn//oHc9/h3XHLt+yxaUklFct2X3ywtyVCeDOi0ZVFtDhaAZo0TtG6Wz9TZFbVyv59/XcYO2zQlL+Fx/FE7EoRSa4XxR46cxnbbbUVhYf6aT65BUVEhd945IPe8X79byWRmUVXuXymllFJKAcwoX8qnQ77kl0+/AqCgRQu6n3AyxWmPZeVCELi5XuALvi+EQRT4DImyTF3zKRNE6Z2BC45OSTZjq0QZ+dbH+BYviGqppqPM0dC6DNYghHSYW5qfy14NrUv5TIauDmoqhKgpVaeWSRYtyyMZxKr+Go6W65MXc+OwAOLSPsVlqhorLqAbRjVdrWtQNaJRV3qXTnNL/ROGWMwQ8yAvJgTZmqqeC56G1rhSrJJdoh8lvUqu4gA2iuGKwJwRc9hmtw54xrByDoJEAVMXSI15rt5q9SzV7DU9enbmpDP+CEBlRYrbr32GpelRdflPQ6nNT7a+Rq1sDf1mVG3QgKpS9WYh1133b5YtKwXgL8cdzrLKAp549ife/2TiBt35258X8/verTZoufyq7LdrG74cvqjW7icCP4xewu/7tKJVyyKWLaskla4eRF6/4GoqlWHs2Bn07btNrYzzr3/9E3vttQsAEydO58EHnwU2rOSBUkoppdTmRER4e+po3r7zkdy+HU4/k2S8CZVJl5nq+64RVTotBBkXPCUduIzRTAgWt7zeCqQFk47qnlaGjFrYgp2bLYoKjEbNp5I+piLjygSkA0xlgEkF1Wqohu6+mRBSvstKTQeQ8pFkhpjv06N9BeNnFkWRR+Ou9aNGVCKYUHL9qXIFTq27v0n5mKTvvvYtBqEyXkBJXiM6ZJa4ygFRoNVKVHkgmt/6Nlu5wJCxJldhIOGBZ4zrvSVVdVXLF5URi3k0a1u03Bw/u5zfyza6MhBjhdqquSX/USotQv+BJ9CsRWMA3nhpCD/++DWZcO3KjCmlHDG1s6nNgwZUlaoXKcaMGc6jj74MQKNGhbTuuBsXXvkui5ZWbvDd0xnL+Gml9O7efIPvldVpyyLKkwFLSzK1dk+A6XMrad4kwctvjuCZV34mEd/wX0NffTWOvffumVvKtKGMMdx//8BcY6sbbniQhQsnkp0WK6WUUkr91g1dNIsPXv4viya5UlbNtu1GmwMOozRjSEVL/TNpobICrC+QspCOludHwdRsViq+dQHTTABBiJcMWFyWT0xCmidSLhqZjrJR04HLNk0GLliaiR6TPiblu3NSAZT7SNJH0mls4CMSsv3Wlfw6rQCLB1RlmrqUTs9FOHPpotbVaI2CtcZGY/ejMgS+RawLLE9o1olti2dhQksYmmi4LnKSH8v10XJBVNzLiHUBTzfbdM2wfAup0GCtMOfnuXTdrX0uixVc4FQAL8pOharyAtnEN49qwdVsfVWgZeum9B94Qu7nd+2AwSxLD6u11WJKKfVbowFVpeqcIDKPSy65FRsVIt1z30N455PpzJxTUmuvMnpiCT22bkJ+3ob/z9oY+H3vVnz78+JaGNnKvvhpETv3aIZnDPENDKguWVJKaWklXbu2q6XROX379uL0048FoLS0nKuvvg9YUKuvoZRSSim1KcqEAW+PH8UXjzyT29ftnIsp8+Ok02CtEAQuqCqhBT9EApfVSRi6FExckyhEMKkocBmIiypGKZ4/z2vBzlsUu2sybjm/SVu8lI+XdMFUqcgg5RkXYA0E41skE4INEQmRqKN9UX5I6+YBs+cnIO5BPJZbXy9FCVxR12pv0gJh1MgqtLm0MhNYTGAREYxniMcNiaIEs1u0p/PS2Xi5pf6GhBe60G02UZSq4Ghe1LhKIKqvarBiMEYonbaEZu2bEs9PYG1V01a3vN/VaY1nl/pHx7KB02x4NPuYDazGgVPP+iM9tu8IwIifJvL6yx+QDGZs8L8HpX4ztCmVqkYDqkrVuTLefvsDPv/8BwA6dmzPj78WsnRZqlZfRQS+G7mYvXduvcH36t29OeOnlZLO1E3d0IqUBS/GBaf13eB7ffbZaA46qHctjGplt9zSjyZNGgEwePBrjBz5E6AF/JVSSin12/b+rIl8/NizpJa55IAt9zuQoh69SWdcrdTAJ6qfigtaBtnsztClaRpc4NRWLak3oUQZq5JrLpXMxFhSnqBT4wrXlKrCR1IBpEMkcMv+bTqD+L4LeoaCBDYKploXUI0iiztvn2Hk+ELXiErEBWkBiccgES2azwY7sjVLA1fv1UUno4CrlSiy6eHFIJ4A48HcplvQqqKYQutWdxnEVRGInlkxWGvwgPyY5LJIs5uriSp4oWXJ5MW03m4L0rYqaTabeerhgqnZZlTR7XO1U6uIC6R62bICkJeIc9NdZ+bOuOnqZ1lUMhwRXYWllFLrSgOqStUpIZWaxaWX3p7b027rPckEdfNqs+YnKSqM0apZ3nrfIz/Po8fWTRg9sfayZ1d08H7bsNdePZk0YWYuaxfILbFfW5Mnz6Vly8a0iOpB1bYttmjNNdecB7jlXBdffAsi81nfWq9KKaWUUpu6xclKPvjpR35+9S0AvLw8Op9yHqm0kElD4LvaqTZKNM3VKA1ttfTJKDs1yig1QbXs0ECq0jhDyy+zGtOjbSkmk4aMj2QCJBTwAxdUparJqYggEmKtRcSSrR/apkVIEBqWlUeNqKIGUxLzIN/LnefSQbNjjMabeyPRdVFlUhIeiYQhL89gLeAZZnTYmq3nTiXPg7y45PpXhdk+VwgxT8j3qnrTxD3BAnnGlXFdNm4uzbdvh49HOkrctdHLG+MaT8WjLWYEg5DwqobtygjIcklw2V46MQMHHNSHQw/fDYD585bywKBXqPDHbeg/C6WU+s3RgKpSdWox9933NFOnzgJgq87bMnpqvE5f8athi9l31zbrff3eO7fmu5GLqctySs2a5HPm33elV6/ODBs2Odq7bsHUMLR8992v7LPPDrU/wGouuuhktt22MwBfffUTr7/+DlBap6+plFJKKbWxenHKL3x4z6NIlOHZ8diTiLXcgkxG8H23zN9acUFGibJTs1mngPGMW/ofRLVIg6ol/oQWI9nM1RATWMJSnwlTEvTsFPUdMBYbpLE2xNogl10pWGwYRFmpUi2gCr23SzN6YiEmkXC7MlGw1DNVr23FRT2FXATUBScNWMFYwVhy0cx4viEeVQqQaCtr1AxPhObJUgpiYHHNpjICVgwFMSE/5jJ3s9mmMSCGC5TaihSZ0iSN2rfEF0M6NPjWkE0/iBlImOy1krtH1Sxa8Lxo+X8UsK2+9D/7eOPtp5NIuL9JHrrvv0yaOpQg1FVYStWslpb7e1V56WrTpgFVpepMwNy5v3Lzza7zqed55LXcZZ2zMNdVSbnP4uI0XbdqtM7XtmqWR1FhjFnz63ZCFQRCJmPp02drJk6cQ2VlmnX9tgwbNomddupCfn6ibgYZyc/P4557rsg979//TpLJWYBd/UVKKaWUUpuhsUsX8dmHnzDzh6EA5Lduy5ZHn0A6bUmnssFUd65YXIMoS1XswDOItVE9VAvpwC31Bxe0zHVvirJaKwMkk2Hm3DhtWvrkJXzCMMBaPxdQBfC87Nr67Pwsu07fsG2ngNkLEqT9aFm/4KKfUSTShK4mqsnuz8ou7ZdqY5IoGzZu8OKGqDBALts0tDCrY1c6zpoKUVA5m3wbMyF5MXG1Tz2iLFIhFhMKonqqy0bNplXvjlEjKpMrKRuEYEVymaYmCsDGjKtW4Pa7fdk/8KsHUKXaBrDNtu0554IjAUinfa678mkqguHr+89Cqd+G7CcYtbFpPHWzoAFVperMfK666l4qKtyn6fseeCBzF9fP/+R+GL2E3Xdsybo2vd931zZ8NaxuGlFVt6S4kvkLyzDGsO++vfjyy7HrdH1lZZpJk+bSu/fWdTTC5R1xxAEccsjeAMycOZdBg54A6v77pJRSSim1sQjF8uqksXzxwCO5fV1OORdf8kmnq86zoRAE4uqY+lGAs1oLepOKmlJZ3JatS+pHNVbBZadm3CZhiFgY8UuMnbdPgmRrZ4WAxRgPRLBio0zRqqX+8bila8eASdPzc+cBLqKZiLvXz0g0nmrL+qPgqZtMu0Am0bJ+EjFM4wRezLhTwyjJ1YJYIcjPo7xZcwoXLsJGOa4JIxTFXAA0HgU9Y1FgNRZ9nV5YQrwgQX6zwlwcVzCEAsmwKlRspKp2arZuqjFV2aq5/aYqdrNilirApVccT5u2zQD433+/54svvyQTLFqnfxNKKfVbpgFVpepEip9++oH//MfVlmrUuBG/zG5Vb6/uB8KYiSX07dlira/pulUjFhenKSn363BkzuffTKVJ43wAOnVqQzrts2DBsrW+fsiQMey33451nu2bZYzh3nuvJBaLAXD77U8we/avQN1/r5RSSimlNgYfzprOpy+8TuksV8qq6fY70mT3A0hWVq+ZKoRhFKDMRHVNc3eQqCmVxVgXgTTZplWhxaSrooYmENfEKgSXjxmytMTDWkPrFi5gKiJIFJG1EiASRCueql5xp+4B4yblI2IQsmUHBIlFmaXZAqd+tFmioqW4aGf2mmiNvk14SL7r8mSMK20QVT5wwVxcwHRR+440nz8XE4bEs0FUY3LxXM8IMSPEjUTL+C2l4+bQqneH5WqfxqPVwbFozivVqhJka6JioqzXbBA1m3lLtQDqKlYYN2laxNU3npx7PrD/YErSw3P1aJVSq7JiO7n13dTmQAOqStUBkblcdNEtuefttt6D0or6/cU5bkopW3doRFFBbI3neh7svmNLfhi9pB5GBlt3bkHL5kW55wccsCNDhoxeqwncggXF+L5Px46t63KIK+nZc1vOP/9EACork1xxxSBgfr2OQSmllFKqIZRlMvx3zBiGPfUft8MY2v/jIlJJQxC65f2ubqq4Jf2+rapLGqVvmtDiVfiYdOi2UFwUUHDnQy4zldAi2XshUV1Ul6W6y/YhIq7plCEGhFFA1SI2iM63NC4KadpImLfYlYfKLs8n4UFeLMpWzdZGrfqaUKJ6BZJrpiVWsHEPGudB4zxisargqPGqaqLGPVcr1eKxpP1WtJo9k7hn3RJ/I2TjxzHjhpDw3FL/iikLady5JbFEPBeYNVEDq5gHeTH3Ypaqb2t21XBuif8q/tQwKzyuONM+4R8H0nuXbQAYN2Y6zz79Dslgyrr+81Dqt6PWaqiqzYEGVJWqdSW8+OKb/PDDSABMoimzi+s3+Jf19fDF7LPLml+7b88WjJlYgh/UzyfSu/fZikSi6tdPs2aNaNeuBRMmzKnxOhHh889Hc+CBvet6iKt03XUX0LJlcwBeeOF/fPfdt0Blg4xFKaWUUqq+PDtpIp89+iR+RQUArfY7jHj77i4zNXTB1EzGEqQs+FHGqbWYUKKl+xaSgSsommtbHwVbwyiI6geYdACVaUzaQhBETaeiTFcJSaVh9gJD146ByzgFQhsiEhCGPn6YJggyiAh9e4YM/yXPXW9MLstUXLqoe2PZbNRso6psTVcxmIqgaqxxA0VxKIhh4iZ3O3ArmcS6eycSBt9CxhrKW7aiqLyc/HQ6V9UAokCoVMVVTBBQMWspTbdpmyvXms10TXiQZ6JGVCYqMVBt/X42U3VVjadWrJsqVL3VLM/zuOXOM3PPb73+eeYvHo61mQ3416KUUr8NGlBVqlZZysunctlld+X2xJvt7Go2NYB5i1PEYoa2LfNXe05RQYytOzRi3JT661zfskUR3gqfzO211/YMHTqBIAhXe92vv86mQ4dWNG1atNpz6lLLls256aaLcs8vvvgWrJ3Hyp/3K6WUUkptHmaUlfHB9z8w7b13AfAKimh19Jmk064JlQB+BiQVZaYG1jWcylgXJPVD8ENXOzUboPSitem+xSRDyFgXdPUtkgmx6TQ2DBHJhhdd1qmVkLGTQrp1FuKx6g2oXFmA7Dlbtgkor4TScnGZm9ll+3HPdXGi2qUuvdYt/89m1PohBOLehwgS86AgAQUxogpQiEAsVhXQjHmSS2wVBDGGpVt3oemU6blAphVIeEJe1EjKM7Bs7Gya79DBNeyiKjs1Wx81m6UKUdkBql7fAtaaqHbs8sHT5bZqT1YMqv5ur578+bh9AFi6pIw7b32BymDd+hso9ZuhK/5VNRpQVapWLeaOO55g7tyFAHgF7YkVbtmgI/py2CL27dtmtcf32aU1Xw+vvwZLxhguOmvPFfeSSMTp23dbfvhhwiqvC4KQH3+cyJ57blf3g6zB2Wf/lV69ugMwbNhYnn32NaCkQceklFJKKVVXnpjwK9898FAuJbPNUSdjGrfK1Uy1gcWmQ0iFkA4xlQGkQ8gEUaZpFFwFCAWT7ZqabTwVhJgwhIxrYhVWVhL6mdyye1czP4bn5QEuKDl6IvTuQVRDFbI1VV24MGTH7iEjxttcFqmIIHEPyfOyO1ztVBtFGLN1X6MaqiZT1dhKitwyf/I8jOeiIdl+VdkyAsaAFzOEkl2iD2CwTRph43ESxSWImKgeKsSM4AFBSZIw6ZPftikCeAhxYymMCwlPKIy5GLAxQsK4BlZeNPxc7yz3Urm+X1nZL221x1xwdQXX3XwKhYXu+/vko+8z9tcfCGzZuvwzUUqp3xwNqCpVa3ymTx/LoEFPRc894s37NOSAAKhIhsxeUEmPLk1WOta2ZT6xmGHe4lS9jee80/agU4fmy+3LTv522KETM2cuorw8udJ133//K7vu2o1EIl4Po1y9eDzOffddlXt+5ZX3UFY2larpqlJKKaXU5uG7BQv5/N33KR4zEoC8th1oftCxhKFgrSXwQ4LKEJK+C0j6LtuTbF3SqD6qCaIs0CB73GJSAaY8A2nrgrGZAJtORvVSLdbm+toDBi8KxBoMs+cbWjQVGhWGINl6poIVy3ZdhckzLOlMVWYrCMRjUc1WqWpElY1KZkLwwFjrslOzTajiHpIfc8HY3OoqF5GMJ1wKqWegoMDkmqXGEGLRowXKunamaOpMwLrAqOeCsZ6B8rEzKdqxI5nQfVtiBgpibpm/a0jlGle5TNVq5ReNqRr+Cuv6q48yF0ytFkQ1sHyKKrBVpzZceOmxgEtiuOaypyj3R6zfPxqlNmeaoaqq0YCqUrVmPuf88yZSqTQAsSbd8RJNG3hMzk/jitll++bEY8v/9t63bxu+HLao3sZRVJjg2v4HrFA0v+qJMYYDDtiRzz8fvdx1ZWWVzJq1mJ49O9bPQNfgoIP25M9/PhiA+fMXceutjwELG3ZQSin1/+ydd5xcVd3/3+ecW6Zs32x67yGVEAiBFAIqYkP0eRTlUeyK5ZEqKhZ8xAJSxAY2RBRsP/WxPSpKQhJCKOm99962ZevM3PP749x7p+xudpNsCuG+87rZmVvPzO7OnvnM5/v5RkRERHQjac/jJ6tXs+yRR8N1Ve/4BFrYpFs1XquHbtHQ4mej+iKlyGgjnPpKn0j56l3KF1jTHjSm4VgK3ZI2S2sarzWFl2pBk/alSFPur7Vnckq1RuCXxWvNS6thyliNp9N4XgpPZ3DsDEP6CdZvT4Ewx6bTLUaF1BqR1mg/kkB4Zsmrl89oRMbzM0gFusiBuAWuEWN9LRgpCcVLqUD5c2whQQuBh8k/1VrQatm09KgkvvcgloC01ngaWvZVI0sSkIiR9h+XEBpXGYerqzRSmHgAS7TVYjSQJmusDUTT9vQaX28+bkjVJ2+5jn79Te+Ff/9zCf/8x1ya03tP8KcmIuI8J2pKFZFDJKhGRHQLx/jpz37P0/981tyVLlbJBWd1RLlkMpola6u5ZHxFuG7U4GJ2H2ikoanjzNLu5sH/eQPFyfw818KOpH36VCCEYM+eI+E604hqQvjJ/7nA/fffieOYrrEPPvgztmxZDUQB/hERERERERHnB7/btoP5jz9F88H9ACTHTiE2dhqpVk2m1UP7maci5Zmy/daM70A1jaeEZwRMPJNJKtOZcD95rBVR34puTqMbW/BaWkinGvB0Bs/zjEDqpcl4ad+takRTjReW9tfWCxqaPKrKW0lnmmhtbeCiC+Cl1S1kMmm8jH+M9tAZDzIeOpVBtKaNI9UXfUUGMyFNZSCdAV+8xZFgSbQl0MrPfPVFVMuC4I5tC6QExwYpRFiGH5hfPQ+a+vfB3ncQlUmT0YJU2qNp415io/qGjlEBuNK4VC2hcaQ2QqoI4gX82+F0ONuNKuOJPGG1XeFU5AuthaX/iYTLl792Y3j/C3f8hLrmFb7DNyIiIiKikEhQjYg4ZTTp9F4+9emvhWus0gkIP+fpXGHjjmP06RGjOGFhKcHkMWW8vKb6jF1/xJBK3v22iQVr2xdIZ88ez7PPrkJrzZ49R1BK0bt3+ekf5AkwdOgAbr31/QC0tqa4/fZ7gf1nd1AREREREREREd1AbWsrTy5exrbfPmlWSEXPd3wCzxOkm03DJh2U+AcNqAKRMpU2wioY9yoa2ZgyuaopD+pboa4Fr6kJ3dKMTrfgZVrIZIyIGpb86wxoD097aFK+EzVoUgWel+GlVU1MvkCQSjdSWpzCsT32HGjxH4VHRqcQUhm3ajpNpqkRWjLolMlrzeviFDSUkgKtJDrpoB2FdizTkMr/YN+yfTFSgG0bcVX65flKaIKI2Iy/OErjWJAe2h827QEgtXU/9qCeSKWQmMZTljRxAOYcxpnqWn5jKpEt+Q/8BZJsQ6xAwA20z1w3q4SwwVWue7U9n8Lb/nMmU6cZU8jmTXv48Q/+QHN6ywn//EREnLcEYcWnukScF0SCakTEKVPNO979eZrqTWMnYZejkkPO8pjaZ/6Sw8ycUsUl4ytYsraaTObMdaf/6bffhmXlv+R09LekqCjOkCG9WLVqO88+u4rZs8efgRGeOJ///Efp3ds0/Prf//03zzwzFzh2dgcVEREREREREXGKPLBiIwu++lW8VpOzX3bFW1A9h5BpzZgS/nSOMzXtmRzUtEZkNLRoo+A1p81+TWlfXTSiq2hOkWlowWttxku1kM60kEo3+2X+WYXTCKsZ0OmcPFUjH3ralPo3NjexYXsDFwyTXH5hkueW1YWqoqfTCGHmnp6XwcukwcvgZVLmdsYPLcWU6vs190bFLHZMmGnMr7/3FUmpTPMpIUyZfywmsG2BUhpLmjL/wFWqNVi+y1QCmR5l0NCMPlqHd7AWa0AP42xVmmJLk7RNAypLGnOs9I8LFkFWuBVoP1/Vb1IFeAgytFP6L3Lud6LnSGHxzQc+GlaF3fu1p9h9YCkZL6rCioho80t0SsvZfjAR3cHZ7e4SEfGKJ8NTT/2cP/7u9+Eaq2xSOHk71zhU3QJaM6RfgueXH+n8gG7irdeMYfKEvgVrj/9XZOrUkfzwh/9g/PjBJJOx0ze44zBlytvYv//wcfdpafEzc5XiTW/6KPX107CsEUR/JSMiIiIiIiJeiaw5UsePP3cndWuWAyBsl7I3vJ/WFm1yUMEEd6Y9s/huTWOR1H4Juy+epj1Es99xKaONuHqsFZP+CZoMGa8VjWkuFfh9AkHP0xpBBk97SBHIihrIhOX8qzc3cMMbq9i2p5n6Bo/s5/cCKS2kEAR5rCD8TFZfuUSDCGrkhWk+ZUt0zMJzFDrp+IIqKEtg2f6ZBSQSAtsSobApAgE0R/C0hdFsAyepGDMQ/eJa3ItHIqXAkqa035XaJAz458pte2DliKuhO1W274zSmLgCrQv2EcF2eM3lt3Jwf03733xf54nHHRobW2hsaGHahR9h+54RFDmTO/iJiYiIiHh1EgmqERGnxEHuf/iPeWsun1hGvwH9ztJ4Oqd3ZQzLlrz9NWdmjFIKZl9axW9/u6Bgy/EFR8/zqK1tZPnyrezefebE31y2bdvD0aM1Xdo3k8mQyWT40Y+e5OMf/2+g8rSOLSIiIiIiIiKiu9Fa8z8vr6d6w7pwncJj6qEVJIpLc2rLjWiIhVlcnWONFIiMB3EIA0U1RlT1RdjAjWrEP0H+29K8pE8C16oQOsz91Ci0FmhtIYRHaZHF6MEJqspdhO/nFEIihEaIVM55jagqEAjVknM5PyfVMiGm2lYmPzUjoNEEl0rhO1kxYqVdJ0KjWZBraiRhHWadihwXqRDaZLS2pGhduY1MzCYlNfWCHEFWh2X9YYl+rsOUrJE299kKNO3geWrv2ODZ3LntMNXVtR39COSRyWQ4criOZSue59LJI7BVcZeOi4g4b4k8MxE5RIJqRMRJ0tLSxAsvLmTZS8/lrZ/37L9xer8eIdRZGlnH9OsZ54JhJRytbaU15bFqU9cmU6fC3XdcyY03zsyf+HUhN2bOnJVcfPEIli3bytVXT6asLHkaR9k+DzzQj3jc7XS/hoZmamrMc/nFL36H669/ExUVpUQvsRERERERERGvJP536z5e+vP/0rJ7W7gunUrxl2fmU/nuz4WZqaIlnW3q5AustGYAgfAyiGMps+1YCrSJB6AhBcda/AZTabRO05pu8kv7/aJ1ocw5/Lmi1qYJlQyCSTG19OlMM55O4XkeU8Ym2bC9gSH9bBavbqGuwZxDyRhKKaRUCMw6yKDsBEJK41INbKWOQrsKXRrDK7bRJS661EW7NiiBsCWOK5DKNKEqKRG4jsBRRgBVgTvVd5zGpIclzbakBa7ysIVGLl6HnDURVm2h8vKRFDuQtD2KLONstSS4yne5CrCzGm8o1IZVx/7TQfaZ8cVdjcSItNI/NldQfejBHsTi7cxRc86lPWhoaKG+zkRZffa2H/J//5pAiZxxTjWJjYg402gZ/fxHZIne7UdEnCQ/feqv3P2FzxFmPakEZBrR6XoyxzZhFY8+q+Nrj+mTe/DnuXtpac3wjtcPYMP2elpTp69zZ1Vlgk984NKCnKbO/wjV1Bzj0KFarrxyAuXlRcyZs4K3ve2y0zbOjli8+A+d7rNs2RZSqQzf+c6PePLJv3D0aA133/1dvvOdbwKFMQcREREREREREecmjek0D7+4ht1P/ShcJ9w4uqWJxsX/oujyt+L2HZVtDy+EcalKAAmWHwPgZ6aKhhR4GhE2q8qdc2oynofnpQm7QeX1prdMgyidxpTpB3JhhkwmbRpWAcm4ZEBvhz/PPcaBw5qZFyX5x8JmpLCQMvtWN+hUL6RCCpW1bmoNnin1x5JGLEk4YCuz+HmHQhKW0buuEVWVyIqpQphmUrbfXCruN5OyRI5jdP8RdEUJVnEMNbAHLVv3U35BL6QATwQNqbJu02wAQo77FMKmV6Jgm/8tCR4SGfw0g4Kp9zMLH8y7LxBtjCD//r9tXDChkrdcfRtbt+zl+QWr+d8//JN3/ecwHOvcrcSLiIiIOJOcm0GPERHnOP+cu4zv/eApDu31u16qOHbFtHB7unYNOtN8lkbXPuNHlLJ55zGaWjJ4Gl5ceZRpE09vWfoP7n0LyYSTt64rH2o/88xKrrpqIgBVVaXE4y47dhw8HUM8JVpaUqxatYMpU4bzzW/eTiIRB+AHP3iKNWuWAS3HP0FERERERERExDnCt5dvY+2vfk661kQtxcddTukbPhRur/nj99CptCndh6ySJ0V28fycVd/BKtJGTNUpD1rSaM/D8zw8nSHjtWBkP88v/dd4nud/zZAJnawZPC+D1mkymRQZrxmt02jtcdmkYl5c2YKUFo3NFkfrPAb2sQDpOyk1XsbD0yk0aSwrZtywuYvrK5l+VykN/rtkX+SVAuWHmjq2IB4XJp80J5dUAo7I5qFa0hdXlSn599IZ1I59yKG9SVpQNqyK5r3V6NaUeSK9rPM00KtzM1NFECMgsw2qAsE1dKwG+/o3MtosnjaS9fHJitkH9h1DSkG/AaXcc9+Hw/Vf/OxPqGlcg+dlOj1bRMR5i+imJeK8IBJUIyJOkEwmw8duf4T1y58J19mlk1CxKlRyqFmhU6RrV56lEbbFsSVjh5ewbF11uG7bngbKS2zKiu3Tcs3J4/vwuitGFqzt/K/H9u0HSCZj9OhREq674orxzJ+/JqfD67nB/PmrmT79AqSU9O/fm8997iOA+Rm55ZZvoPW+szzCiIiIiIiIiIjO2VXfyG+fX8Lhf/zWrFA2pW+5iaJL34zVazAArTvX0bjsGYSfoyo8nQ3w9DzwPERzCtGaQbSkTQOq5gy0eujGFr+030OTIZ1p8cVSj0wmHQqkWnt4nsbzUmS8FjTaX5cik2n1xVQjsPasMA7Ug0fTvhApWLq2hUmjbSxlmSxVDZ5OG11Uur6I6l9HZ9BCG3eqknhKoV3fWqokpI0iKaVGSrAssB0jWOoCoVIID1t6xJRH0tIoYUr3bWFK8J3te1FD+hBzJEpqLCmoGN+fQyv2+C22sh5dIbIZrJDvUs0VU8OS/5yS/uxM22zw8MfpZSNu28ds1FrzwoK9XDpzAALFNW+cxpWvucj8jOw8yHcf+jWt3tYT+dGKiDi/CD69ONUl4rwgElQjIk6QBx75M7u3Lkan6wEQTg9kYiAAVukEEEagzDRsxWut7vA8Z5JpEyt5ceXRNhOpeYsPMWtK1Wm55g8fuA7bzn+J6exvh+d5zJ+/hlmzxuWtj8cdRo/uz/Ll2zo48sxz5Eg9NTUNDB3aO1x3220fYNAgUwb1r38t5K9//QdQf5ZGGBERERERERHRNb760iY2P/4IOm0aOBXP+g/syj4IJOVvuincr/afP8VraDTWRw1YynxtziCaMsadmtHQkjENqdIZdGsa0r546qXIZFr8Uv6sjOjLeRiJMoPWgXM144ufKdKZFtKZJjJeK55OM21igueX1/kjk2idIpWG9Vs9xo0QeDpjxFQ0QgqksPC8tIkMSKfx0ilwLaNcWhKKLMK6e8jaO/E1EClwHLPN88Dzm1tZAhwBjjSLFGCFcQAa2dSCVVtPvH85Mb+yXghI9Cwm05qh8UgjIPA8P1aA3AZT2UXmTKsDV2revu18Xz1tkhbSum2ogjnG5NUG6zeuPcrAwaUUJV0EAikU37j/JpQyF3/wvl+xY9dyPO/cqsSLiIiIOBtEgmpExAlQXXuMrz34FOna1eE6u3xyWFojVAyrZGy4LVWz1A/ZP3uUFduUl9hs29PQZlt1XYq6YykG9Ul06zWvv24CY0YUCrWdfxK3dOlWLrhgIPG402bbRRcNY/XqHTQ3t3bTKE+NOXNWhLEEAfF4jG99647w/q23fpOWlp20nb5GRERERERERJwbvLCvmmf+PYeGZfMBkMUVlFz57rBmPDbsQuKjTbRV5thR6hb+NmuR9DxT2t+SQbSmkS0ZU+avMcpiWqNbWvwyfl881Z4f7JlT+6p12IAKNEIYATSb7anxdGt4/OjBCXbtT9HQZJymporJQwjBll2Sfr3AtlNo7WEpFyXjOY/YM+qkrYwTVUk8x7hTte2v12ZM2KaZlQb8ZKds/CqmUZSFh5IalRMDYEvjD3UtTWLLDhLj+mMrYZpNBQcDlZP6s2/ZbjytwyRZ6Qul2WZX+qSqhP1nEt9QTNA7LJfwPQyQTnmsXXmYC6f0Ne5e/4qjxwzmQx+7FoDGxma+fNePaPY2nOBoIiLOE2Q3LRHnBdG3MiLiBPj05x+j4eAy0ObTe5UcgnQq8vZRxSMQVjEAuuUQXtOuMz7OXGZNqWLe4kMdbn9++RGmTazstsoD21Z860uvR+Z1QBSdnr+5uZV163YyefLQdrdLKZk+/QLmz1/TPQM9BbZs2UdZWREVFcVttv3Hf7yemTMvBmDz5h185zs/Bw6f4RFGRERERERERHSOpzVfXLCGXU98L1xX+sYPId1EVoVLe5Rd9UHwmzzVv/gH0rX7zc4taURDK6I5DY0ZI7BmdNCNCTKZMB8VTC6qaRAlfNEOhDBftbnja5nar87XJm81Y/JUPU9jKRg/MsmStbUIhC/GmixSrQUazcur01w8ViD85lRCSP+cxvWqbBsVjxtnqmtBsWNuWzLUebWj/I5ToBQoK+siBbPJlpqYBTGVNbYad6rGUWDX1GFZkkRF0pT6B6X8ftyslYgRr0xSvaMGrbPnDxpT4WenCpG7jvD5KlyXuw3IRhPorKk4EFaNWG2CBASKxS/s58KL+/hu1KzyKoTg8198H+UVJo7rN7/6N4uef460V9Pln7OIiPOC7ir3j8r+zxsiQTUioousWLON3/7+/8g0+LlBwjIl/gUIobDKJoX3UzXL0V76DI0yn0F9EtQdS1Fdl+pwn5aUx9qtdUwaXdYt1/zy7VdSWZHveO3K34t581YzY8ZYpOz4ZWno0N7U1TVy5Ehdh/ucbjIZj4UL1zFz5th2twshePjhu8JP/L/61R9w4MAm4Oz8DEREREREREREdMTjK/ew4o+/J7XXzG/t/iNJXvRaU67vl7yLphROoiclU94CgM6kqPn3T6HJ5KSarNQ0wm9EZdQ6QjHWuEpNbqr2S/A1aXRBqySBCHNUNRqNKdlPp5tpTTfg6RTgccn4MpauqyPjeXhkyOg0nk77oq3JW91/uBUptZ+zKv0xmAZXUrnGmSoEWArtyOx9f/6mhYCYBUIgJMRcEfawymRA+02kFDmipjCPW/qCakx6sGEXJeP7YwmTpxqU6ROU4AuovKA3B9YeIJ3x0MKsCx2puY5SP7O1YNVxG075vl1A5DWpIji/L2zX1bZy5FAjQ4aX+ef3twqJQFJRUcIX7/5AeN7P3Po9WtIbznolXkRERMTZxDrbA4iIeCWgtebDtz5KqmYZwTTGKhmLUPF295exvshYb7zm/ZBpJNO4A6to2BkcsZmwTZtYyR+f2dPpvqs21fKOqwewbksdza0n3/ipZ48kn/jA1MKRdHrc4cN1HDvWxODBvTrd96qrJvKvfy3nHe+YfpKjPDUWL97M+PGDcd2Om3lNmjSGD3/4HfzoR7+hvr6Bu+56iJ/85NtAvzM2zoiIs4lTlmT859/RLedatewJWppquuVcERERERFZGlIZvjV3JUf+/Fi4rvy6TyG0CK2NIpUxUacelE75D46tnoPXWEPjxkWkJm7DLeqDaM74Sh1GQBUCkTZRAEiJ8BRCeH4uqg6FT+O+DEr6RbgdCF2tntdKxmvFDEJQnLToWeGwaEUNIH3xNii+zxiHqk5hWTEWr5HMmuIx50Xtu0qNM1ZZvhtVCbSrzAJGHUUbkTWmwoBSy8rPLw0SC6Qwzao8v7BeCbCBuDINqdSeQ6je5SjXNqfyn1bpC6ueHytg2YqKkT3Zu/oAQy/sHfb5Ksw8DW4H20WOPbUrsmbgVsUXd0EifXfw8/N2cfmsgb4hoODE/s0PfOhN/OSHf2btmq0sXbKeX/3yb9x44yBs1bvNtSIizlsiZ2lEDpFDNSKiC/zmfxeyfMlCvJaDAAirCFVc2ME+ixACq+xChFXErKvejF3cfhn76WTS6DLWbq2jJdW5QKo1LFx2mOmTe5zSNX90/1txnfzPabryN+eZZ1Zw5ZUTO98RKC8vorKymM2b957MEE+JxsYWNm3aw6RJQzrd9557bqa01EQCPPbY71my5EWg6TSPMCIiIiIiIiKia3xl/ib2/OGneI2m8idx4VW4A8f4HZc8hO9SDaZy0klQfvl7SPQezuvecwtOsq9pQhXg16oL7YWl/8KxUMo2ZfeYEvNARBVCIYQNSF/I892hgUtVZ/B0Ck0GISRSKGZe1IPnllX7x8s2n9t7OoNGo6RNKhXn0FHFoL4mdgChUG4CYgpt+Q5U16/Vt0ArgZYCz/ZjAKREWoJkkUQqgQxMrFJjW0aTDdTNoAGVJT0sqXEyaTK7DpEc0dM0qVJGrzWpAsbBGuSkAlQMKad+fz3p5lSemCoLHl8QBxAunSipys9hFX7UQHBOjbmjtcfunbXEEzYVPfKNIgLpO1QFEoVtxfjWgzeH27941w+ord/gC+EREa8SpOiepRsQQtwthNCdLB/r4FhbCHGHEGKFEKJBCHFUCDFHCPG241zPFULcJ4TYI4RoEUKsEULceJz9Y0KILUKI3UKIou54zOcakaAaEdEJLS0pbvvSY6RrlofrrLILcz5Rbx9pl+L0fgPHMn2YckHFcfftbmKOZOSgYlZtqu3yMbsPNBFzFT3K2jaE6gqXXTKQq2YNL1jb+R+LTZv2UlVVSnl5119jp0+/gOefX08mc/Ju2pPh2WdXMWvWuLCc/3hUVVXwpS99AjAui09/+mtovY+oQVVERERERETE2WZHTSO/mrOI+oX/C4BwYpRe8yHTDj5N6EoN68M9DzQkR19B1XVfp7LnIKrsRpNfGogDUpgmTh7Q4gXt7sPQUClVmGcqhI0UFlJIpDSd5k2WapAc6uHpFJkwNkvQr1ec5laPIzUpjAib+yF+IMhqHDvpi4GwdovF6CGtWLZCKBthWWg/K1W7NtpRaCVNQyrHgrhlhFYlkAriCVAy2wtASiOOOpbGFsaZKgRY0sORGtsYX0lv2k18ZD+ElGHpvsCIrkHzKhXsLzVJG0Zc0pftL++BnNJ8nbv4j7RQRG3TaIrsDFz47tlgnFJkn2GER8bzeGnhXi6d3j88QrSZv5szCiG58spLePO1swDYv/8I937jMVLejg5/ziIiIs4IB4GFHSz7CncWQsSAOcB9wFhgM3AUmA38XgjxzQ6u8wvgDqAI2ACMAB4XQny0g/3vAoYCN2utj53UIzvHiQTViIhO+PK3fsvBXYvRmQYApNsLGevbpWOFkKzZUseQ/kkSseMLsN3J9Mk9WLjscKefWhcyf/EhZk6pOuHrCSF49L5rUQWftnWmO2YyGRYtWs/06WNO6HquazNhwmBefnnTiQ71pDlwoIaWlhQDBnT9+fnkJ29g5MjBACxcuJTf/OZPQNdF7oiIiIiIiIiI08Gn/rGWw7/7vhFKgeJZ78Qq6ZFfZ65zxFTP3JceiFZYXtODSeWH/bpzHyFAaHQgwiqTO4oyYqplxbGtBJaKYUnXd6iG/lf/mvil/hnf+WjK9KUQXD6pgkXLqzGl6kZMFXktszVS2CjpIKRxw6YzsHaLYvyoNCoWM2KprSBhm9p8SxqXquOLrLZZJ5RAWYJ4TCL9KbwUYFmCuGOaUAV6sS110LvKuE8bm9ENzdi9y8KnRQuwlA4dowqNq8z+jl+CX94rCQJqDjaEZf0pr/3cVArWFX5ebyIIzPUs4Ttj/dvBuT3tsWbFQYaOLCMWD57PQI71RVTfUSyFHTYR++a9n8JxTPTVd779KzZvWYLntbTzUxYRcX4R5B53y9K9Q/u71np6B8uf2tn/XmA6sA0Yq7WeqLUeDlwLtAB3CiHenHuAEGIS8J/ACmCw1noCMAVTgvlVUeA2E0KMxIiv/9Ba/7/ufbjnDpGgGhFxHPYfrOF7P/496bq1/hq/lP8Es1MWLD3MjFMsp+8qPcocYq5i94ETLy+va0hz8EgLwwecmCP/YzdezLDBhS7czp+jl17axKRJQ8JJ2YkwceIQNm/eS2Pj6Z/Aaa2ZM2clV17ZtgnZ8XAch4ce+nx4/4477qOxcRfG9hERERERERERceZ5evNhXvj3P2netAQAVdqT4kvfZtQ7gMDFGAijmaAbk5+L6mmaMxZ7m5IMLa7PdlqSImun9DNKzfmMNVJK6btUFUKqnEakgqDsX4eKbgatPYKGUuNHlLF5Z1NO1r+RBYMoACEEAgvXKfadr0G8gGD3oTg9StMk475r1pZ4lkTbEm357lTXNmJrwkI6RkxNFuWU+QvTt8pSGkcasVIKjS094kr7maTgSvDW7cC5YGA4E/Z0VneWvpiqhDmP0XM1th/ZOmpqPza/vBcRZMn6x2e0n93K8Uv9Q3laZMv9Jf5X//safG1pTrFp/VHGTaoMjjLO3sClKkR4P1f+GTqsL/998/UAtLam+Oxnvker3tjJT11ExHmC6KblLCGE6AUEMQAf1FpvCLZprf+Mca0C3F1w6Az/67e11tX+/iuB/wdUAYUOqe9jXjg+2W2DPweJBNWIiOPw0dseoenwMvCzgVTRcKRTdsLn2XeoGaUEPSvcbh5hW2ZOqWL+4kMnffwLK48wZVx5G7dpRyTiDl+4ZXaByCw6dac2NDSzZcs+xo8ffFLjFEIwa9Z45s5ddVLHnwgbNuyhb98KSkuTJ3zsG94wi2uumQnA7t37+da3fgwc7uYRRkRERERERER0Tirj8dmnV1P9v4+E68qu/jDSchFeUB6OX+Lvt7TPeIiMRqQ8s/gt6tfXljO8pA5LeYQWTQ/AVx99DU4LgbAsAhXBCJ42SsYQQhlHqS9+SiHxdNaTKRA4tmDM0CQrNtaHx3s6HZamCwRKxbGtOEJYYR6rEMrkptouy7cUMXHEMTNORxnx1FHopI1OWGhHQVwibYm0JJYFrivwezYhJdguRjz1ja1xpYkpI6JK3wHqHaqBmIssTpjHjjCCqh8PIIXGkZCwjCnWMfqu/zggnrDo0b+YfZtNTqyHIB2U/+fomrlG4vwpd3CdrKhqyayQmju9f/n5/UyZ1hspBV6uQitMdqrMybvN89MJ+Mzn3kvv3kaI/cuf5jP3mWdJZ6IqrIiIVwBvARxgs9Z6bjvbf+h/nSyEyO2qXeJ/3V+wfxApUBqsEEJcD7wG+IbWesupD/ncJRJUIyI64KWlm/jn03PwGrebFdLBKhl30uebv+QwMy868XL6E2H4gCIOHmmhriHd+c4dkM5oVmyo4aKx5V3a/4GvvJ6y0oIQ+y5osXPnruKKK8afsNs3lwEDepBKpTlwoPqkz9EZ6XSGl17ayGWXjT7pczz44GexLFNKde+9P2bnznVAazeNMCIiIiIiIiKiazy8aDtb/+/XpI/sAcAdPI7EmMsRXk63o4wHaY1Ia2RzBpHyN3g5opo2Yt+amnLGVVT7kz8NKY+cKFRDoOZZCpAIqVDSRikHKUwWqpRmW3Du3EzUyydV8dKqWtNYikBw9XwHqxH9bBXDsmJh13ohJMpxkbEYWJKaFpd0RlJVmjINqZTAcxQ6ptCuUTdlzEJZAiWhqNiYA5QCxzGiruPrxMbx6WFLjS01ltRYQhOTGm/THhjZP3SVejpbah8cK31nqpJg+ZmqQhgbqxIwfGJPdqw9TCaVCZ9ATxthNted2jY71TS6svyM1tw3+saVKkK3bfWRFhrqUwwYXBJcwX/iA39q0EQs51ti/LIAFBcn+erXbwq333Hbt2lOr8fzzmx/g4iIM07wS3SqS/cyUQjxlN9U6k9CiK8KIcZ2sO+l/tcF7W3UWu/BRAHk7gtwwP9a6EQN7u8HEEKUAA8CmzDRAuc1kaAaEdEOWms+eMsPaK1ZGq6zSsYj1Mk7TI81ptl9oJFRg4u7Y4htUFJw8bhyXlh55JTPtW5rPYP6JEjGj5/7OmxwBe9864SCvwmd/4HYv7+aTCZD//6nHoNw5ZUTmDNnpT/J7n5eeGEDF100HNu2Ot+5A0aPHsYnP3kDAE1Nzdx55/1k/yZFRERERERERJx+Dje08INnllL371+YFUJQfvXHEBkCLc0ogGkP0ZqB1kxY4h/aJIUf/ueX8+9pLqbMbSFppaAlHXpGQ/VPCHICRhG2QlgSadkmm1O5xpkqjWvVCK7SFws15SUOpUUWO/c3E0QDQCZ0TgohsK2Ef9VguwTLQlgKYSu0FGhbsWJPCRMG1BkR1VFQ7ELMlPpbSYVlmxJ/N2ayUk2Zv0ApjW1pHBUkG/jOVGWyT13fqcrO/Yi+PYwbN3jK0EipUX5WquU3orIkSDSORZhzGujQUkmGTezJpqVmrqiD741/O5h35xbiS1+MVTJfqxH+t8Bo2gJQCCQvLNjHZVcMCHNRc2fRYdm/yJUKdPafNsu7briai6YYLWXN6q389Me/I6Pb9L+JiDh/yLV6n+rSvZrqJOBdmKZSbwG+AKwSQjxUmG0KjPS/bj7O+QJX6aicdf/CfKLyGSHEdCFEkRDiRuCNwMYcJ+pXgT7AJ7TW5324ciSoRkS0wy9++ywbVi9Ctx4FQFilqKJhnRzVOS+vqWbymDIs1b2voAAXjS1n+YYa0pnuERbnLznEjE4ctT964K3E3HyhsbMP3E42j7QjSkoS9OtXyfr1u7vlfLnU1zexc+chLrhgwCmf60tf+gQ9ehjX769//TcWLFgANJzyeSMiIiIiIiIiusLn/7meg399DN3SCEBy8utxeg3LiqloaM0gmtPma0sm6AkFqUy2qkiZXFMAtGbp4R5MrjyESOdYJ3Pr0YURNHEsk1eqTA26ESxtRNBgKhBJkf56yawpVTy3rDbnZMFgBVJZvrvVjxMI3a4WKh4HWyKkANeols1WjF1NRQwrP+aX+ZscVWyBsqWJelUQj5uSf9sWuI5JB3BVTo6q0CiMK9USHo4ElUrBvqMwsCdSGiEVTPMqO9BPhCnzV5gc1SB2NsxoFdmnrffgUmoPN9JU12IetQatCybZItCss2Jq4Ext+ybff86QbN9ST3lFjLLyOMHeIshLbWMv9o8OHMg5SCm5/8Gbw/v/8+Ufc/DIajwv1eb4iIiIdhkghNja0dKF4/djMk8vxeSYxoAJwKOYX+Sbga8XHBM0Pjl6nPMG28KSVa31DoxY2hvjbq0HHgfSwMeBoHHVJ4DfaK3/FRwrhOgphAgjAc4nIkE1IqKA5uZW7rj7Z6RqVoTrrPILCz6lPTkyGc2StdVcMr6wgdOpkYgpBvVJsG5rfbed88AR84FSr8r2Xbmvmz2cSy7sX7C2c6F43bpdDBjQg2I/W6o7mDZtNC+9tJF0unubPc2Zs5LZs08tliCgvLyUe+65Obx/881fx/P20e09HiMiIiIiIiIiCli5r5a/zn2ehiX/AEC4CcquvNGoeLYEW5ka9daMyU/1dHZa52lE0JgK/PUi1DfrWh2aWhU9y1qyjai0zhpKg6/KVxSDEFLbQlo2thXDspJZp6q0EdJmSL9iao9lqKlP55zExA9IoXCsEhyryJT3KwdLuSjbQbouSIlWEu0HiWoF2pGsb65kaFEdqtRG2wqRsIjFZdh8Kh4TWAqkNF8tpf1yfY0QHpbwiFkaS2liyiPuO0z1xl2Ikf2xpAjFUVtoUzTvC59SaFzfnWr5T4N5rkDkaNDad/ZeMK0f617c2yb7VJBtOGUaZBH2BWtTNCaCUn8jmGY8zbKX9nPJtP5GXg3zUq3QFaFpW7bfUSXY1GnjuP5drwPg6NE6vvY/PyWttx/nJzEi4hXOOVTyr7V+VGt9p9b6Ra31Ya11i9Z6ldb6JuBOf7dbhRCDcw6L+V+Plz8XOEvzcv201ncDbwd+B8wFfgJcorV+Rpg3zI9iHEO3Aggh3uwLwweAGiHEQiHEyefonYNEgmrEOcE3v/lN84feX47HgQMHuO222xg1ahTxeJyKigpmzJjBT37yk24p+/78N57iyJ4l4DUDIOP9ULHep3zegI07jtG3Kk5R4uRLyAuZeVEV85ecfCOqjliw5FC7ua9CCL779TejlCxYf/zzpVJpXn55E9Omde/rqG1bXHzxCBYtWt9t59yz5whSCvr06T7x+0Mf+k8mTDCVE0uXruHxx38L1HTLuevq6rj33nu57LLLqKqqwnVd+vfvz+zZs7n77rupqWn/Oqf79ykiIiIiIuLVyrkyv9Vac/vf1nL0Lz8IRdHS2f+FKinPKnNB/mlrBpH2G1OZunREym865ZF1n+aUoeN5rNxfyvg+dUYZFCKrGhpV0twOWs1bAh2skwJh2Uhpyv2lVChp4SiHi8eV8OKqRqR0UdL181EFSjo4dpHvYlUILKRQICTStowjNVAhHZORqotj6LIYXpHLKtGXiWofMqb8JlR+tqgUuDGB9IVO2/JL8X0dOCZNPytbQEJ5ZvgSqG+E1hSyR0noYLWVJmGZr5bUxJU5V+gEJiuSakzDqWwrLkNpZRw3ZnFkbz2C7HGCfJdrYZl/4HzNd6oqQLByyUHGjO+Jk1NhZpypFpL8ymDdxQ/9/+frN5FIGI3mR4/+gbVrluB5jV06tjOi+W3EOYfopsWwS2s9tKPlFEf6ALAXsIA356xv9r86xzk2cFQ1FW7QWv9Ba/0OrfWVWusPa60DF9pHgKnAF7XWe4UQ04A/Ahngo8DnMdEEzwghyk7uIZ17RIJqxFlnw4YNfOUrX+nSvkuWLGHs2LE8+OCDbNy4EcuyqK+v57nnnuPDH/4wr3/962lpOfmojp17DvHoT/5Apj4Q5iRW6aSTPl9HzFt8iFnd1KCqV6ULIuso7U4amjLs3NfImKH5ua+f/vA0+vfJd+13xcW5aNF6LrlkJJZ1/GzWk2HMmAHs2nWY+vpTn8BprXn22VXMnj2+G0aWRSnFww/fFd7/3OcepK5uG7TjBDgR5s6dy8iRI/nsZz/LokWLqKmpIZFIsGfPHp599lm+8pWvsH379jbHne7fp4iIiIiIiFcr59L89g9r9rF0zv/RsmM1AFZlP4oue6vJFlXSNGlqTCOa0ib4s9UzYprW4OV3lDfdlnLmLVojUppW4bCjvojhPRrQSpglzAskq/gFDacsYbrWKz9XFVDK5KhaKsb4kS4bt6fIZBRSWKZSTJhYAEvFcezAmWpj2zEsFUMWxdGOlRVtHYVnK7Qj8RIWOq7QcYvdsR6UyBbKnFYjoEqBsgQlJQKlBDJwpKIRwizSL/G3hcaWGeLK14e1h163A2vMQGyZFTNjUvvuUY3tH+snHZisV//pEP7+hdploLmMndqHdS/tQ3teaGwLxidlTqOrnOMKMe5UQVNTmh1baxgzroe/r0QIGyXsMEs1+CbpNvJu4eiCrFXFgP59uOPO9wKQyWS4/baHaPU2n7JwGc1vIyJOHq11BnjRvzsyZ1PQzfl4rqFgW5c6PwshqjDRAsuB7/urP4d5sbhGa/0jrfU3gFuAvsD7u3LeVwKRoBpxVvE8jw9+8IM0Nzczbdq04+5bW1vLm970Jo4cOcLo0aN5+eWXqa+vp6Ghge9973vYts3TTz/NLbfcctLj+citj9JSvYxA4FLFo5B29zeROlTdQmvao2/PWOc7d8LMi6pYcBrcqQGL11QzaZTJfS0riTGofxl33XLFCTeiqqtrZM+eI4weXRgT0D0IIcIGVafK6tU7GDKkF0VF8c53PkGuuGIqb3/71QAcPHiEe+75AXDwpM+3cOFC3vjGN3LgwAFe85rX8Nxzz9HS0kJ1dTWNjY0sXryYu+66i9LSfAH8TPw+RUREREREvBo5l+a3rRmPu/+xipq//yhcV/bmmxCOnQ3wbPEQrWnwQGgPrTXCI+tIdZQpW1eghZH6dPAu0sNX+GBzdTGDKhqxXW2UPmVEUx1UNAXKbE5DFi2EsWcCUlgo5ZJMJBjcz2HjDnCsJI5dhKVcHCtJzC0j5pahlIu0HJRyQfmuVNvvGqWkcaW6FrrIxitx0T0S6JIYlLrIIsXykiFMbtrml/eDbYNjC9M8ygajy2pTmi81rqVxlCZpmzL/QNTkYDWiNIFKxlBCo6SJBUj6+ycsTcw2Q7MFoUAbPB3hEyPCuNngqUMATkzRb1gZO9cdMYZfYRywOYkLIfnGt+B82SCARfN3cemM/mH5P0L6jbxEOzP5IIOg8AoF+/jLp295NwMHmYq+Z/79Ev/313+T0V3SYtolmt9GnKtoKbplOUMEZf25pbEb/a/Dj3Nc0Dxm43H2yeVbmLzVj/lCLsBlwAatdW7zqz/lbDsviATViLPKd7/7XRYuXMgNN9zA6173uuPue//997N//37i8Tj/93//x5QpUwBwHIdPfOIToQvgRz/6ERs3dvV3P8sz81cyZ+5cvCa/uZGMYZVccMLn6SrPLT3M9AtPrcv9mKHF7NzXSENT92aH5qIxua+XTerBD+57C/d+8WqSifwKga7EwASNqLojj7QjevcuRynF7t2HT/ocra1pli7dwtSpIzvf+ST51rfuwHXNc/jtbz/Bpk0rOX6MTfs0Njby3ve+l6amJt7+9rfzz3/+k8svvxwpzUt7PB7noosu4p577mHIkCF5x57u36eIiIiIiIhXK+fS/PZbz29j579+RabGfHgbG30xsXHTcurEtRFTM4AjjcMzaRRDrQTalWhboB0Zlu1rP3NV+1qdtiTaVmjbYlV1BRP71pluTI4yQqflnyuYAkoI7ZW+U1XYFsqOYTtFTBkrWLE+huuU4zilKBlDihi2XYzrlmDbxSjlYtsJVCxuMlPL40ZATdjopINO2pC00CU2utxFJy1IKGRCYcUUrUVFtDgufVqP4riCkmKJ60CRCwkLEpbJO1X4rlShsaVHXGm/QZXG0h5i6z5io/qZ0n6hSSgotU08QFyBIwJxVCP95lG2L4yaWFkdpi4oERTmA/7+UsCI8T3YubGaTEsmdKYGT2Ugooayqb+i0Bt66EAD6ZRHn34lfqaqQqLMbV8SEHlnDURfnVP6ny+uhrKwgEQizjfu/e9w62fu+DZNLZvR+sSrsKL5bUREtzHO/5rbvfkF/+v09g4QQvQDhhTs2yFCiBnAe4Efaa1fzNlUCtQV7B7cL+vsvK8UIkE14qyxbds27rrrLiorK3nooYc63f+JJ54A4Prrr2/zxxPgU5/6FEVFRWQyGZ588skTGksm43HTZx4lXbM0XGeVTkBI+4TOcyI0tWTYuruBccNLTup4SwkmjSpj8ZqT//S3M2ZcOohpUwZSVlHCmGFlzJw6gDe9rjD/tHOBdNeuw9i2Ra9e5adnoDnMnj2eZ59dddJlRgsXrmXatNEo1f2xBAFDhgzg9ts/AEAqleK2274J7Dvh8/ziF79g69atxONxHn300XCi2RVO5+9TRERERETEq5VzaX57uKGFH81dSv3cX5sVUlL21k8gcucLzRlTxp+Tb4oyAilKguvbNeM22rWNQOr4cySdow4KI5ruby0i7niUJDLGlhmzzPGOBa5Cu8o/t39sMI20JEIqKssEjg1H6+I4dhLHTmBbMWJuMbGiHtgl5ch4DBFzEcmYEVCLHSME22a8OqbQMcuU+ZfG0JUJSDrY5S5uUhGPCxwHNvUYwqjanRS5Ho5lBFPX8nCVh6s0ruUZl6ntEVceZY4RWW1Mpqrctg9ncE8sW5rSfqmJWx4J2zNNqyw/h1WaxlaONDqz0aVNtmpgEg5EUZOSoMPGVo6EmC2YMLUXq1/a17bxFH6cbc55BKD8ZlPB92nR/N1cfsVAI6IKyy/XF/kiqhCmQVVwrMg6UPPzVLPrcwfztrdfxfQZFwKwZfMuvvfdJ0nrvSf0MwvR/DYiojsQQrwRGOvffTpn05+AFDBCCDG7nUM/6n9dVuAube8aFvAD4DCmxD+XI8BQIUTum+rAsXTy7qdzjEhQjThrfPjDH6ahoYEHH3yQqqrj54lu2LCBnTt3AnDNNde0u09RUREzZswA4Omnn253n454+Md/Y9vGl9CpWgCEXY5Ktv0j3N0sXVvNuBGlOPaJ/ypOnVDBy2uqyXinJ1i9pNjlzk/N5CufuYpvffkNvPe/ZjJv3mpkXomC6NSdqrVm3rzuzyPtiGQyxrBhfVi1avsJH1tT08DBg7WMGNG3+wdWwGc/+xH69u0JwF/+Mpenn34GOHZC5wgmjddeey09enTd7Xy6f58iIiIiIiJerZxL89tb/7WBw39+FJ0yeZGls67D6TPINyCaLFTRmjHV3ZY05fm2MtKZxAinQUBnMOETGAHW8xDaL+0PLJO+Orj0SA8urDqcFUwtYerdLd+Vil/qHzS6kgIhBcKRTBrdwPLNSYSlELbxa0rLQSSSCNdCOKa8XzsWOmaji110mWvGGjM5qcQtSFjoiji6NIZwJU5CYtkC2xHYtllwLfaX9WJo/V7iliZpa+IS3Jwlocw6k4kaPESNbGlFH67FHlCJIzUxpSlxNEnLOFNdFTSO8jXqoJRf6jzBVIkcIVUaMdURRni1c0TSfoNKaKhPUVfdQqFnoLB6WIQ+V7Ns3lBN737FFBW7RjBF5uigws9SDQRWmXNsQHuiqu9o1dnrCCF44KHbwmq0b9zzU/YfWIvnpU7gpzaa30acy4hsw7tTXbpgSjruSIQYK4T4oRBiYsF6KYR4F/CUv+pvWuuXg+1a6wPAD/27PxVCjMo59s3AZ/y7X+nCMG7BuGDv0LpNxsdcoAr4lH9uC/hyzrbzgkhQjTgr/PjHP+aZZ57hNa95De9973s73X/16tXh7XHjxnW4X7Bt7dq1XR5LbV0D99z/JOnaVeE6u/yi01qaHuBpeHHlUS6dcGKd5EuSFr0qY2zeeWICXFcpLnL43jffwhWXDWXalIFMmdSPSRMGEI877NgR5H12LqYCrFixjeHD+5JIuJ3v3E1ccskIli/fRmvriU3g5sxZcdpjCQKKipLce+/t4f1bbvkGqdRuOm4AkE9LSwuLFy8GYNasWWzdupUPfvCD9O/fH9d16d27N9deey1///vf2xx7On+fIiIiIiIiXq2cS/PbpXtreXrufBqXzAFAJksof+P7UJZASL9bfKsfGWX5nY0saZpMBaJBUEMe3A70M40RQq2ct5I5+x7zHGpTLn2LGvymVxIs35opc5REyGt137+qhSN1Do0tFkIq/7oa6TjI0iTEHXOehAWlLroiBiUOWBJtSzzHOGC9uIUXt6DYhYSNdBTxpMR1IRYTuK7AcQRKQV2f3lRWHyKWacWWGksFzlEPV3jErQxxyyPpmCZTltA4Crz1O4ldMMA36BrnqokDMNuVL4YqYYRYRxE6UlWgLwfaChoVLAKUCo3CoZYtBVw8oy+LF+wNn+5AbNU53wLhC5vBvUwaVi47yJSpfZHCJv/tvwAhs987X0w17tXOZIJ8r2wgtk6cNJIPfOitANTXN/DlL/yANNs7OVeWaH4bcU4junk5NWzgI8ByIcQRIcRSIcRLGPfnU0AJsAD4r3aO/QywCFPav0YIsVwIsRn4M+ACD2it/3S8iwshBmAE0vla65+3s8tXgUbgISHERmAXcB2wDvjFCT/ac5RIUI044+zZs4c77riDeDzOD3/4w84PAPbuzZaL9OvXr8P9gm11dXUcO9Y1sfHmL/yMugPLwDMZljIxCOmeWrbpibBtTwMVpQ6lxV2PF5g5pYr5i7u/EVVRwuHiSf2YNLYP175+DFKaSZlZYNasccyfvwbP010SU1taUqxcuZ2LLx7R7WM9Hkoppk0bzXPPrevyMTt2HCSRcKmqKu18527i3e9+M1Onmg8V167dzKOPPompjuic7du309pqfmZ3797NhAkTeOyxxzh06BCJRIIDBw7w5z//mTe84Q3cdNNNeceezt+niIiIiIiIVyPn0vxWa83N/1xL9f9+N1xXee0HUUnTaFUIYRyqgRJn+eX9toS0Nk1TlMz5iFf4deX4Yqq/JXcymHcbVtX2YGxZNVJok8cKaOHntoYl3FlhVQrN6IFNrN2VNNew/GuWxk3JfsxC275w6tqmnD/uN54SJuNVJyx0zEKXxaAqgSyycGKCeNyIlLZtRFQVNKJSmpgLBwcOpN+e7cZV6guhrjJO02ILkkpj+0O1pEbU1iPR2BVJLKlRfql/0jYJB4FoKsiW8FtCh2Zfcx78TFVtxiZNeX9g6s0XSQ0lZQ6lFS67t9cZ7Tr4fgf7CYkUVljKLxAse+kAkyb3xbadPGdqUPIvffE02F8SNKpqD+H/7/8Tuc7W7D5f/srHKClJAvD4z/7M0iUv4nmNHZwzn2h+GxHRZbYDXwD+BtRgmkxNwjTm+DvwHmC21rqm8ECtdRNwBXAnsBZTit8DmAf8h9b69sJj2uFhwAE+3t5GrfVa/xrPAgOAOEbovdK//nlBJKhGnHE++tGPUltby913383QoUO7dEx9fX14O5FIdLhf7rbcYzpiw+Y9/Pr//Z3MsU1mhVDYpROPf9BpYN7iQ8yacvyysID+veK0tHgcrjnxJkbHQ0rB9ddN4Kffvo7v3/sWbCv/5UEIQSIRY/To/qxYsa1L53zuubVcdtkYlDrzLzUjRvTl0KFaqqs7nyh5nsf8+auZNevMxBIESCl5+OG7wvtf+tJ3OHx4C5Du9Njq6mxVxTe+8Q1s2+ZXv/oVx44do7q6mp07d3L99dcD8Oijj/Lwww+H+5+u36eIiIiIiIhXK+fS/PbX6/ey7pk/0brTNN1x+w+j4oo3h9u9DFnR0lXZr4HrVPmCay5agwQRiKm2bKv6BWqhlKSlZHN9KaPLavyLapDmHGidtVf6WaqjBzexcW+CtPabYAnM16QDxY4p83cVXsxClzpQ6po8VyUgyFEtdvFKXXSRjYhbSFti2+C6AqWMK9VxhCmtl1CeMKX1uqIMK5XCaWgwQ0JjCw9HeSSUxrWMO9WRmpj0yKzbRcmEAQTpAkGZf2DkNd5NvwmVn59q+WKp7btTTYaqEVHdQGCVtDEsZLNRjbt10qW9WPHSAXRG5xnddPiNyHLsWJp9e48xanQvJLkxhjJ74nbIlvbnu1kDObUze13PnhXc9cUP+z82mttuuZ+U3tLh/rlE89uIc5nshySnvpyqQVVrXaO1/prW+k1a62Fa6xKttaO17q21foPW+pda6w47V2utW7XW92mtJ2itE1rrMq31FVrr33fx+m/zr7fmOPu8rLWerbWO++e/QWu9/2Qe77lKJKhGnFF++ctf8re//Y1JkyZx6623nu3h8P5Pf4/W6qUE0xCreAzC6vgP8Omiui5FfUOKQX06v/blF/ZgwdLud6d+7tOzuOHtExk6uJLhQyoLtmZf8qdMGc7q1TtoaTl+OX119TGOHKln+PA+3T7WrnLVVROZM2dFp/stW7aV0aNNpMGZZurUibz3vW8FoKamji9/+TvAgU6P8zwv7/ajjz7K9ddfj20bp/OAAQN48sknufDCCwG45557SKc7F2ojIiIiIiIiToxzaX7bmMrw0MJVVP/lx+G63u/6JFKp0Bwq/DpybUtTLi+FcXl6nt80Kluar32rpVbmQB2IpmRTNbUU2cUvv9e2xZbWMvomGoiJNAhtehwF9evaP1YIYgnoWZFiR3XChI46vmO2PIEuss05HQkJG8pjRsVURvjVlsJzJF7MRpc4UBFHlTq4SYXrl/cryzShsizz+JUCV3nElIctTeOp2uGDqdi6DUUG1/IosjWljiZua+LKLK6l0XuP4FSV4MRtLKmxhcaVpsxf+E2cpC+WupbJYXVUNkdV+o7WwMUalvaLfKkyiFgUaLP4+mfMVYwaV8GaZYfCpzLr9836T4VQvDB/D9NnDfVdpLlLsHdbKaAwJzUbJFC4R6Gani8PffyT72DEyIEAPL9wBb/7zV9Ie533oYnmtxHnOuGHHKe4RJwfRIJqxBnj4MGD3HzzzSil+PGPf4xlWV0+tri4OLzd2NhxyUjuttxj2uOvTy9m8UsL8Zr9D0lUAlVc2MH+zPH8siNMm1h53BfYCSNK2bSjnuZWr+OdTpBkwqFf72I+/eHLuGTyAGTBAIJy/wApJdOnX8D8+as5Hs88s4KrrprQbeM8GXr0KKGoKM727R0LlM3NraxZs5OLLhp2BkeWzze+cSvJpBHTH33016xatRRoPu4xuT/fAwYM4J3vfGebfaSU3HbbbQAcPnyYJUuWtDm2u36fIiIiIiIiXo2ca/Pbh1dsZeefnyBTdxSAkikzKR432Z/LCVPp79eIC1chYgpsZRykMTsI+zR2Sj/zVAc16gpQMkdwDWyX0jhcXStbt+6HgS6v6cGk8sMI7U8mg35JQZ5qzGLSkHqW7y8343AtsCU6Ycr6sVQQRuq7U13TOMvys1ljymSllrgQt7ETklhcYNv4i3GnBuX+QoDEoyLmEVPGXaoALxYjU5yktPooJZZHzDJ5qFbgJFXgeBlath2kdFQvXD8vNWaZMn9baiwI3aiW0Fh+JqoRTX3Hqsgv7y8UUgWB2GKE1Lw4W5/RYyvYta2O5sZ0znEylDoFkv17G7CUolev0vwSfiHCxlSdeeREKKYW7uuRFVR1zm1/3AIcx+ZbD2Q/XPjcnd/hWOPGPMG0PaL5bURExCuJSFCNOGPceeedHDlyhI985COMHj2aY8eO5S1BXg7QZl3fvtmu63v27OnwGsG2kpISioqKOtzP8zw++dkfkq5ZHq6zyyYhZNcnwd1NS8pj7dY6Jo0qa3e7Y0suGFbCsvU13XrdOz81k4e++kaSSacgCisItW/L0KG9qa1t5MiR9stktm7dT0lJgsrKkm4d68kwa9Y4FixY0+EEbv78NcyYcQFSnr2Xw759e3HXXR8DzM/mzTd/Ha33HfeY3Gyo0aM7/iBgzJgx4e0dO3b41+ve36eIiIiIiIhXK+fS/HZXfRO/f3EJB//+/wAQtsOg936cWEz4GaK+O0qCcCTCEr7z1C/Bd6XfKclXEAOBM25D3EJLGYaEaiXQoXoojbhqiawQ64uqh9NJlIKyohTaUdkmVQJwFJXFrWghOJqKG8epLfFcC69HHJ20jfLoSLSroMz1z+u7YF3LnDOmjLDqSBxH4PpLPC5wY4KYC66l/WEZ52lMBrmm2hdDNS1D+pLcuZsimaHE9kjaOjgtMaVp3riX0lG9iDnC6MdS4wiz5D50S2ocyzx9lgz14FB7lrRT8hs4UoNcVZHVnAO09o8Rgosv78NLz2XnikLkZppavLBgF9NnDs2Kof43XoRJrm1dpe3RUZ6qxpTzt7vNX33NGy7n6tdfBsDu3Qd48P6f49HxzzlE89uIVwBtPgE5ySXivCASVCPOGNu2mdzNRx55hOLi4jbLN77xjXDfYN1nPvMZIL9TY24Hx0KCbRdccMFxx3Lf9//Enq2L0WkjCAq3ChkfcHIPrBtZtamWkYOLiTltfzUvm1TJopVH2kRanQqzpw/lgzdcxOuvHFnwut75q/yVV05ot5ze8zyee24tM2eO7bZxngqxmMOYMQNZunRrm22HD9dRV9fIkCG9z8LI8rnllvcxZEh/AObMeYE//envQMe5ThUVFeGksyPhG/Inu8F+3f37FBERERER8WrlXJrffnXxRrY+8X102sQy9bv2nST79TVim1/qb+JLBZYtkUqiPYzh0PWto/7nzzroehTWp/qF3mG3JQmWQgcd5YNyfs93LHqm6ZUWsLSuigsrDueHjFoKHVdM6lXN8kMVRqS1FTpu+25Ty8QRKIlnKSOexmyTraowTariFrrIhriNsCWJhMB2BMoC2wHLEjiWL5hKjSVMiX+ZkyGmNDHLlPs7UpOwNWVxiRrcC2vHPuMgDYy5QqObWkhVN1A6oIyYAldpErZ52gJTr5L+tURWspTCOE2VyDa8ajNtC58WHTazak93yT2uV78krSmPwwebEEL5pfwWEov1aw4xZGgliaQbXiDbeEqFIqsIvxkng3Gpaq399ya5TtXs3PO+B27Bskx+6/33/ZztO1fieR33gYjmtxHnOtmGzae2RJwfRIJqxCuCUaNGMXCgyeH5xz/+0e4+DQ0NLFiwAIDXve51HZ6ruuYYX3/gSdJ12fxku2zyOfHCpjUsXHaY6ZN75K0vL7EpLbLZsbdrHTI7w7IkA/uV8P1vvIXy0jhWXgMq0Xai1w4VFcWUlxexZUu+k3LJki2MGzeIWOzM55F2xOTJQ1m7didNTfkTuDlzVnDVVWe+CVl7xGIu999/Z3j/ttu+SXPzDmiTZZUl+Dlft25dhy6BdevWhbeHDBkCdO/vU0RERERERMTJ0Z1/jxftP8qCOXOofuk5AJyKSvq//b+QwshmSvnNmJTAdbPiKpAtw9caMgXzCYERR1u1UQ+V717NPSZYPA0ZzOILqliSRlwOpRMMStb7dk2TiTqk5Bh7GxM0ZyyQGNdp3AilxGyznxLoEhtdFkNbwoiqRQ7a8TtCOcYWqiyBZQssyzxW1xXYtsk9TTqmhL/I0vSMeSQsI4jaEizfoepID1dq3AEV6MN1WKlWbF8gjVlQs3IXvS/sj2sJY971ExBsGZTl69CJGjSdCcRVu9BpSqhbh09xmJOKrzuLgu0ie2xw49IZfVg0fy9am8xUiUUqBWtW7mfSRf39Y41zldCZ2j4dvw/qWADSeEGKbs5I/W3+qtGjB3PTJ94BQFNTC3d99juk2d7hOCCa30ZERLxyiATViDPGs88+63+K2f7y5S9/Odw3WPftb387XPfe974XgF//+tds3769zfm///3vc+zYMZRS3HDDDR2O41Of/ymNh5eBNp/eq+RQpFPePQ+yG9h9oIm4q6gsywqSs6ZUMW9x9zWi+vynr+BjN05l0ICyvPWFeamdMWPGWBYuXEcmY6aFTU0trF+/m0mThnTbWLsDKSUzZ45l3rzsp9WbNu2lsrKE8vJzp9Tnuutey+zZUwHYunUX3/7240DHAf7vf//7Adi1axe/+c1v2mz3PI8HH3wQMCVUkydPDrd11+9TRERERETEq5lzYX6b0ZpvLlnHzp9/J1w35MaPoeIJI/b57/iChkyOYyIAwjQkS2V3glAX04FWlvEIW8l7GMXPMm3tTR8mPzIgrc2+kK+xScGa+kpGFVejhAdSYDkwoqSW9XUVaMuU+evyODrpoGOW70QVRmCN2aYpleWHj8ZshK0QrkK6CssRJJIC28K4U40WixsuGldoklaGIssjrjzjUFUaR2lc5VHpapKOptQRlE3oT8OqXUZwFdB6qA7lKIoqEr7TVPvl+zos67d8R2vQbMrOc6nmPyW5EmFY4i+zhmDytpuTtFeoX1Ti0Ltvkm2b6pBYIOCl53cy5dIBKL+JmJ8REP7L/SYLIZBCIoQK72f3E118X2B+SDzthbfD9f7Nu774IXr0KAPgt795mucWzCfj1XV4xmh+G3EuEzWlisglElQjXjHcfvvt9O7dm8bGRt74xjeGAeStra088sgjfPGLXwTgIx/5CCNHjmz3HCtWb+N3f/g7mQa//FvYWKVnt3FSe8xbcohZU6oAGNwvQXVdipr6VLecu7w0zs0fvYyPf+DSghfzE39ld12bCRMGs3jxZgCefXY1M2eOPat5pB0xeHAvGhqaOXy4jkzGY9Gi9Uyffm6V+ggh+Pa3Px8+f/fc8wj79m0A2u9eOmPGDP7jP/4DgJtuuonf/OY3pFLm52TXrl3ccMMNLFu2DICvfe1red+X7vh9ioiIiIiIiDg1uuPv8W+37mbtn39H467tAJSMHEOv2a/Luhp1IG4JEglTEi9Vtuu9VDmKX65iF4ilnjaqoO8W1MF8IrBkBvt7/gk8nS3tl8aemdGCdXXljC2vBiUZV3aUNbUVeFIYsdR3pmpHmvDR0J3qQsLyu0JZoCTCARWXSCVMWb/ri6hKhIsQGltpXMsjbmkStkeFq0n4Waomn1QTV5qkBXFlclVdqSmuSiIFtB5twBIe1av20HdSP6Q08QCWANsXVZUwpfqSrDs1iJCVIl+nLiR4isLy/nZcqZKsGThYHxwgkUy+pC8rluwjlc5QV9PM0SONDB1WmSOOyjY5qG2F0pxvun+zPcGnfRerRvt+20IzqfYjAcrLS7j7f24K199+6wOkvG0duk+j+W3EuUxhJMfJLhHnB2evA09ExAlSWlrKX//6V66++mrWrl3LlClTKC4uprm5Ofwj+7rXvY6HHnqo3eO11rz/5u/TeuT5cJ1VMhahYmdk/CdC3bE0B4+0MGJgkosuqOAPzxw/wL2rXH3FcD54wxRibttf/ZP9pGzixCE8+eSz9OlTTlNTC4MG9TzFUZ4+rrpqAv/85zIGDerJxIlDcF37bA+pDRMmjOajH30njzzyKxoaGvn85x/iZz97GOjf7v6PP/44Bw8eZP78+Vx//fW4rksikaC6ujrc50tf+hI33nhj3nGn+vsUERERERERceqc6t/jhlSKHz+/mK0/eyRcN+am/0ZIgfBNg5lM1kRqqvSNxGZZRgDV2s8lFRI8jU5phNBGzUt75sDcOnXthWX+2pJmDunliGOezqqLgWlRa3Y1FjOiZy2VsSbKnFaW1/Ywja0SNtpVpvGUEOjWjLlWWcyU9ascCUIKpCVCEdiyIBEXOK6RDy2/8ZYrIeGLpLbUuH5Wqkm50r5rVFPimMZTMZXtqyWAHhP7c/CFrXgDyykZUEYsbpkEA2GaTmX7cJlz5R6rCr5HmhwBxb8TxAIUulIDwbQ9HTZXiDHOVwvlCCZM7s3Sl/Zw+FAj068YisjmMeRc2/9f5J7BDCjYbmTh9pu4hmMQol0h1FzN8/NZCyIANHzgQ9fyw0f/H6tWbmLZ0vU88fjvef8HemGL9vsYRPPbiIiIVwKRoBrxiuKiiy5izZo13Hvvvfz1r39l165dJJNJxo0bx4033sgHPvCBDt2RT/1hAcufewL8RlTIGKp4xBkc/Ynx4qojvO+tg3lx5VFaU8ef3HSF114xnLe9aSzXXDWqYMuJlfkXIoRg5sxx/PGPi7jxxqtOaYynm7KyIsrKkqxYsY2PfOTqszKGKVPexv79HZfxgyllCvKqfv7zP/LCC8tZt241EG+zbzKZZO7cuTz22GP84he/YPXq1dTX19OvXz9mzJjBpz71KS677LJ2r3Mqv08RERERERER3cOp/D3+zsr1LLjpPXgpkxPf89LLqRgzlqaM2e55mkyGUMTLeKbUX2sjRgoJmbSvpWbMIjKA5ztSA0Uu7YtkWiMC8VRjsk+FQOC7UkVwjG/PzGQAbc6JYFl9L2ZX7uSZo4PM9phl1M9AoVQm3FVroMhGuYpMWoc6n7KMw9bvc0Q8brJTlTLjUUFzKOURtzwc31VabGfFVEuCI4wbNWHl554GZl07YZOsSnJ0/QFGv2UsrtKhAOoo81CtnFJ9Sba0PxBrhf8UBusDQbtQcM1D0Ka0P3dbsF4KO9xrxKhKfv3Eaqp6FlFRmSD7zRHh13whtX1nqP9dPCkum/o+Dhw4Gl6BglsALS3mZ1QpxU0f/Ro/+P5vWbpkNVK2lSSi+W3EOYkwr5Xdda6IVz6iI6v9OcwrbsARZ5/m5laGXPxR9q/9FXgtAMTiCd7y9g9i2+dO86RclBL07RmnviFFTd2plfsLIZg93TgyZXuzs1OkoaGJQ4fq6NOn4px0feayf/9RWlrSDBxYdVYakX3yk3dw9GjNCR3jODbNzesRYgjRX9+Ik+SM/eAIIbYW9+k35N2/+2u3nO+p/3wT9fv2bNNaD+2WE0ZEnJtE89uIE2ZPQyPvevK3LPjoBwh+hAaNHs1bb/oknl+p72V886ivrxljqRE9g2lQUKGf7S9lBEztaZORis43LgZCa/A+Uhdsgxx3qhmI0KAFuNKj3G7maMql1bNMbXywvxQ5dfAC4U9ac9+uFuYQKkvkC5bCCJ221OHplNCFJldTSi9zhVCdJ2JKAc01jWRa0hT3LEIqkbct+Cpop1Q/907BzcL9c5+8Qu9oHjknNdfIqjoawd5dtdi2omef4rYl/sc/cwFdfynSOfv+9ye/QHV1TZePBSgqSnC0dg22HHxCx0VE+JzRN0VCiK2JXv2GXP3In7vlfP+86S00Hojmt690IodqxKuCL3zzVxzeuTgUUwGamxr5/Z/+jV12bnR5L+S103qxcNkRLptUyd+f209jc+akz3Xnp2byvhtntxEQu0NQzGQyPPnkPD784av517+Wc/31M86KUNkV9u+v5oUXNjB0aC8aGlqYNm30GR/DAw/0Ix53O92vtTVFXV0DLS0ttLameOqpP3DDDR8Cyk77GCMiIiIiIiLOfb61cj3rH/0euSLYjvXreb6ugdLJ02hOC5qbjEM10DYzGU0mbZo3BQ5KMMJrOmOq+dNpTbo5A03mQBFkBniA5yFSnn9JDRl/vSQrvgYn1gJaM5DKmO1S8NqK7fzl4DAuLdvLM6lhoI07VSuFVhIRV1gxiVAyPI3nZbNIlSWMsxYoKhbYlkBII5paCuKWR7HlUWQbp6ojPEocHZbnC6EptrKxAMrPOlVoLKn9JlGgjzWzf+lueo+spPFII0On9EX47lZT9p+ThxgIugRuV53XeMac3xdgc/Y123T2vigQZHPOb74KBBZSKF9QNW7TFcsOMHxEJQf2H2PchD707Vfmn0/65xAIIXNE2Gy5f/59s85konYurOYasx58oBfxRL6pIjdqIKCxsYXa2no8z6OhoZGNm5YxZkRvpDz3ItgiItrl3HybG3GWiATViPOePXuP8OjP/pd0/bqctSYjKFO/AVU0DGmdO53eAarKXVxbsudgEwuWHmbmRVX8Y+H+kzpXWWmMWz96WYHIeWpl/rm8+OJGLrxwKD16lNC3bwUbNuxh9Oj28z7PJlpr5s5dyRvfeDHFxXGeemoeEyYMJpk8sxO4xYv/0Ok+x4418ac/vUhlpeSaaz4MwJ133s9b3/paksliOikWi4iIiIiIiDjPeeHgIZ5/+h8cWmaa7tilpaRqawFY+8PvM+W7F4O0syWqvks16BrfXsdphdFAMxkjfmpXmYwALYxoGpTzh9XkOeXjSqA9I6IKtIkICOvnJUjJcPcwOxpLqMdld7qE4bEaNmUqwVKm0ZUlsRMWTsyIgJ4XiKnGC6mUWSxlmmo5tkBK4yy1FMSVR4njkVBGTLUEJG0/UUCYXlcW5is66JeVFVGF0LgSXAWbl+1m+NR+JEpcVm88TGtDC0XFTiioqnbKfo3EacTUQDwNomjDdXlHmGgDKQslzazAmu0TJnMWs0YgaW5KsXn9Yd52/XiGj0zz979s4O3vnGjm/UFwLoH46YGQnRb2C2SXRNXcPNWFLz4WHh0It4V5rVu3HGLnjqM8++xcvvn1x9Aa7rz92/zhT2NwOPMmh4iIk6FttWfEq5koPCTivOejd/yQpsPLQBuHpyoaiSoOckQ90jXLz9rYOmLmRT2Yt/gQAPsPNyME9Krs3NXYHt/52psoKsoXDbtLTD12rIlt2w4wbtwgAC67bDQvvriBdPrk3bSni/Xrd9OvXw9KShIIIbjiivHMnbvqbA+rXebMWcXs2RN4/etn8qY3zQZgz54D3Hvvj4Dj569GREREREREnN+kPY/vLl/D2h/9IFw3/lO3Uj52PAANu3ey+2/BB7gidHcG0adKBQKfwFIizG0P3JDSz0UNhFdjAfXr49FoJcxiCSOWOjKr1JpdDBkvrK13bI8hsVo2Npajbck60Zth6ghWTIAlkEkLq0hh2Sb2SsjsWJQSSCmwLIFtm9slxRLX1iQdKHI1SUuT9ONYbaGNcCo9bKmxJVjK3I7ZRvS0lQ6jABwBrtKmiZXSNOytIVHsUlwew1IwYmpftry0B1v52nA782jz3Om8ZlNBnEDoMC08RhxnTl4QFyCF8hcLgQpFyxcW7mTq5QORUpBIOgweWsG6NQdoe0Xfeao9XxMP/hWOqdDFenzaVqWZ62h0noM1k/F4cdFWpl0+jDvuvJG+fasA+Ntf5/Ovp+fieXVdul5ERETEuUQkqHaB+vp6FixYwBNPPMEDDzzAPffcwwMPPMBjjz3GX/7yF3bt2nW2hxjRAQtfWs+//j0Hr3GHWSEdrNJxWCUXgF9a4jXtJtN8cu7P08GIQUXsP9xMfWM6XDd/ySFmXFTV5XP07JEEYNzoXrzl9WMKJmvd97Ha3LmrmD17fDiZsm2LKVNG8MILG7rtGt1BOp3hpZc2Mm1atiFXv36VZDIZ9u+vPs6RZ569e02gf9++FQA88MCd2LYpofrWt37K9u1rgNazNbyIE+Dxxx/PvkkVgrvvvvtsDykiIiIiJJrfvnL59dadLPvdb2jYuweAyomT6D19FqNv+u9Qodv+q8dpqa42zZp8tycQCpPSt0sG7tVAD/U841CVfo26RqAF6EAZtHxV0TICqw7UwyBToDCuUwhQggmxA6xs6IkXs8C10Eqxij5MlPvAUai4xHIllm0EYEuZsRlBFVwHbFugJMRj4FgaR2li0iOmPFyVIWZlcJXJT3WUR9KCmC+AJpRxnir8/lf4MQFCh8fELbDR7Fm5n+FTeqOExpFQXhnHdRU1e+vzS/DDh+m7XDG6sy1zy/TbFtgH+3eGBCyhTAMqEbhSs+c4cqSRxsZWBgwqC7dMvqg/q1fso7U1k7Nn7pU9tPazHbRu867AiKAidMF2hfZE1YJgXZYt3cnY8f1wXZuiogT3fP1T4dY7bruf5tQWXoG9XV6VvJrnt6Kbl4hXPlHJfwccOnSIxx57jP/3//4fS5cuxfOO32W9Z8+evO51r+OGG27gta99LUpFJblnG601H7n9UVLVS8N1VukEhDRNqKyyCaSPvgRAumYZstfVOZ/Knh2UEky5oJzfPb07b31DU4Zd+xoZPaSY9dvqj3sOIQQfu/ESfvzLl/nhA9fi2Kpge/eMdd++o2it6du3Mm/9BRcM4Fe/mk99fRPFxW270p8NFi1az8UXj8S281/yrrxyAn/5y8u8610zz4nc1yCW4Nprp4brRo4cwn//93t44IHHaG5u4TOf+Ra//e1wYODZG2hERERExCuSaH77yqe2pZVfLVvOxid/blZIydiP/TeekJSOGEWf11zDvn/9H5mGY+x86icM/dgdpNNZoUoKk58alJpr7RtQMbfTqWCu6NerF2L7ByHAM7d1awYh/RNpjfYgaPIEUCKaScoU+zJF6CLLlPgj2O32YKR3hIpkikbHNs5UESSDEmaoWpbvUlXmfjIBjtIkLI3rl/bbEhIWKOEZ16kyTlUpzDpHmugA0DjC3HZ9XdhVGluY23vWHaLfqApcV+X1yRp3aV9e/MdWqvoV5cmapgmWWQLnajD24PkOMM+abjsX9+Nmc7Vo6YuaUsjce+EBWmsWPruNWa8ZlnMigVKKi6cO4sXntzNj9vC8sfpHEoTeZreFIQP+YxKhsNrVJlW55f/ZK3mgJU1NabZsOsg73nVJeLl33fB6Hn3kt7z04mrWrd3KD3/4az71yb5YoleXrhcRcbY4B94yRpxDRA7VAurr67nzzjsZOHAgn/3sZ1m8eHGnk02AgwcP8stf/pJrrrmGIUOG8Pjjj3fpuIjTx89+PYeNqxehU8aBKOxSVDLbRE8lhiBs4wLUqVoyx7aclXHmcvHYcpatryGdaTt5WbymmgtHl2Gpjl/Fhw+p4I5PTOfdb5/IdW8cy8QL+hTs0T1/AYzwt4orr5zQZpsQgtmzxzNnzspuudapUlfXyO7dhxkzpm2ua3FxggEDerB27bnhwlmzZieDB/ekqChfiP7iFz9OVZX5Wf3d7/7BvHnzgcazMMLzm7Fjx+Z94i6E4Gc/+9nZHlZERETEKRPNb88fHlm/kdU//RGZpiYABl3zJoqHDgeMu3Tgf30EFU8AcOBff6Fx+ybTLEmBbYHl9w3KFQWCDE8dRKRq36moRdBlybhRLdM4KlAZtS3N9iACQOWqgtn7k5P7WdzaF13sQMw26mfMOF2XJQdzYeM2lDTOVOn3WhLCuGgdB2IxgRszDtUSX0yNKV9IFRpHesRUhpj0fGHUlO87ysOSHnFlnKNKQNx3qsb8rNRATFUSMi0pjuysYdCYSiPI+sfYEty4Rd+hZexYeyR83oQAiQ5SDULxVeDnrOaV+he6NnMoEFOVAEvaKGnnZaaasxgX8Lat1VRWJSkti4XCqEmv1Qwd3oMjhxuoqWlqp6g/t+DfyxFBc1ysJ+FS7QghYMG8jVw+Y0TWvCCMU/qBh24P9/ufLz/CocNr8bx0B2eKOFmi+W1ExOkjElRzWLZsGRdeeCH33Xcfzc3Nbba7rkv//v2ZNGkSEydOpFevXu1+Ur9r1y7e//738853vvNMDDuiHZqbW/nM3Y+Rrs2KelbZ5DwHqhACu3xyeD9dtwqdaTmj48wlGVf0753o0IGa8TSL11QzdUJFu9v79ynl+usm8JlPzmRgvzLuvu1KU86VQ3d9orZmzU4GDqxqI/wF9OlTgZSCPXuOtLv9TDJ37kpmz57QoQN12rTRLF68iVTq7E7gUqk0S5ZsZurUUW22lZYW8/Wv3xre//Snv0Yms5uuugYiOmfRokWsXbu2zfqf/vSnZ2E0EREREd1HNL89f9hcW8+/nnuenf/8OwBWsohRN34w9Bd6WuCUVzLwnTeaA7Rm20+/A0Ib0TQQLAm++mXkIsgsFfmNqoIQUOXf9r9qIdCWAkuiXQsdt9FxG5xAcMVXEwV93XpqdYx6K2a6QSlp9onbCFfRUlxMsx2jd6oayzK5rmZMYNtgO8K4U4XAVpqEq3GkKdN3pBc6VUtdSFiamKUpsjB5qTnpBEp4prxfgmtp4jbmfJYRVV2l2bFkL6Om9EHJrJQYlO0DDB1fxa6NR8m0psO4gOD8OTpy+AY7tzyfgnMVNgQLvodKKCQWhYJm6CYVJo90yYt7uGTagIKfEOM+1XjMmD2M557dXLA1t9FU1oGqdSC7tif6dv3NQ3ul/wcP1tPamqH/gPI2+18ydRz/9Z43AlBTU89X7n6EDOeGyeF8IZrfngYKO/qd7BJxXhAJqj5z585l5syZbNmS71KsrKzk85//PPPnz+fYsWPs2rWLZcuWsXz5cvbv309TUxPPPPMMt9xyC/369cs79tChQ2fyIUTk8LmvPUnNvmXgmTcOMt4fFWtbQiLdHsiEaaiE10q6bs2ZHGYeMy+qYsGS4//MbNp5jN6VMYqT+aXrE8f25of3X8tH3nMxibixHhQV5Tex6q6S9kD4u/TStsJfLldeOYFnn111VvOQdu8+jFKK3r3bTuICLEtxySUjWbRo/RkcWVsWLlzHpZeOwrLaL6d8//vfxoUXXgDAihXreeyx3wLnVv7rK5mOJpYLFy5kw4ZzKxM4IiIioqtE89vzB6013127nhWPfDdcN+q978MtKw8LszO+ebjvm/+DWB/zfatbs5y6l+dhWUagBMJc1VwyGchkNJ7ni4JS+M2hBMK1TA299G2YMYVwfOtmWO9OVoH0XatCasYlDrNC9jPO1JiFtiTELayYwImDZQvWVwxmTO0O4/b080ctC2KuIB4TuI5x1pbGNHFfQHWkNk2ihCauPFxhnKa2gJjlmSZTlhFZHWVK/mOWca/aAnO8NKKoJaDhSBPp1gw9+xVldWTfuRo8LFvB6Cm9WPvyvuyb6ILptSDfqSrQ4blE+4fkHaeEhZRWfqwAwSCMyLp88T7GTeyN7XQcwVHZo4hE0mXnjqM5LtX8kvzAqWoiALw2e5yMSzX3/YbWmvlzNzJr9gj/Om3dsl/9+idJJo1B48c//D2rVr1MxouqsLqLaH7b/QjZPUvE+UH0rQRWrFjBtddey7Fjx8J1QgjuuOMOtmzZwte+9jVmzJiBZbWNnLVtmyuvvJIHH3yQzZs388ADD1BV1fXmQRHdz47dh/jhY38gUx/8kZBYZZM63N8unQjCTEgyDVvOiku1d48YntYcONL5tectOcQsv0GVlIL/ePNY3nntOGZeNoQeFcmCvbOlHd3FwoXrmDq1Y+EvIJmMMWRIL1at2tFt1z4RtNY8+6xpmtUZo0f3Z8+eo9TVnZ0JXG1tA/v3VzNyZL8O91FK8fDDd4X377rrIWpqthJMgCNOnmPHjvGb3/ymw+3Rp/gRERGvRKL57fnF3H37WfTXv3BkzWoAigYMZMhbrstKZdpPxpTgxByGfSjb9GfPH59ECPC8HJkvR9vSWpPJaNJpfM0r64ZE+tNkIUz3DUsgHJlj/fS/IvwpiTAOVCEYHT/CJt2DtGUbN6syblflSpyYxHUlygIcmwPlvRh6bG/WmWqbiAKlNEpqYsojJj0c6WFLk53qCI+E7ZGwjFhqK0jaHgmlKbI0tr+v0Xy1yVSVZr3jO1iDsv/NL+9h/GX9QhdprpvUf5aQQN+BJTTWtXKstjlIQggFVCh8c52fl9rRdNwCHGlhCbvNnF34gwmkzcaGNDu21TB6bM+cq+i8r4FL9bIZg3lh4XY8T6PJkI8IRU6NyXrQuqM55cm9j9i88RC9+5RQXOLkrc/92evbt4o7P/d+M2rP47Zb7yett0cNqrqBaH4bEXH6edULqo2Njfznf/4n9fXZMmvbtnniiSe47777KC0t7fK5YrEYt956K2vXruW1r33t6RhuRBf40C0/oKV6GYHQpIpHI62iDvcXVgKreAxF5QO47KrrEcrtcN/TxYzJPViw5HCX9j1c3UpLq8fwgcVcMLKK++9+Ax+9cSqqTXm/6PZqgkD4GzWqY+Evl6lTR7Js2RZaW898Of3KldsZPrwPyWSs032FEFx55QTmzFlxBkbWljlzVnLllRM7Fb5nzJjCO95xDQCHDh3lq1/9AXDgDIzw/OY3v/lNnuAwY8aMvO/FE088QTodZXpFRES8cojmt+cXrRmPn65czeofPxquG3fTJ5G+GO5h3KnBXy6toXzK5ZRfdCljrnwdM+/4Yvh3La9Jkq9ZGXdq/jWDqlSlTJap5QgsV2HFLZQlEVL40VL+UiCAuSpNf6uezW5v4251FSJuYRdZxJMC2zbntm1ze29FX3rXH8IljVICyxIoZUrqldT0cDMkHU3SgrjSOBKStrlvS40URih1Lb9ZlWWOjfuCqUkoMCX/xrlqYlwtAQe211LRM0FRiRNmrSqh/QZSZpEia9CddHlfli/c28a7mRVhjVDZ0bQueKZMMoJECmWyUkVhI1nhZ6hKf2/JwnnbuWzm4PD7KUKptS2xmM3I0VWsXL673e3tjarwXEIEGa5dlw2EEKTTGZa8vJOLLx3snzeb2wq5Py6aT99yA4MG9wXg2Tkv8+c//Z2MjqqwTpVofnt6EN20RJwfvOoF1a9//ets2rQpb91DDz3Ef/3Xf530OXv06ME//vEPPv3pT5/q8CJOkKfnLmfevGfxmvaYFTKGVTKm0+NUyVjSRZczqH9vSovs0zzKfC4YVsKOvY00Nhd+cpzFUuDYAtdRxFxFsqycG64dwW0fn0FVZZJ4LH/Mp6tjvRH+Os4jLUQpxbRpo1m4sG12z+mktTXFihXbuPjikV0+plevMmzbZteurgnb3cWOHQeJxRx69uzam9v77ruDWMyI/t/5zi/YsGEFcPayf88HCj+h/8IXvsCsWbPC+wcOHOCvf/3rmR5WRERExEkTzW/PL365ZSvLnnqSJj9uodcll9Lr4qn+Vk3GAw8RioGmy7zggi/cS89Pfo4RDUfQfh6Ap03zqkDQ8jxNOu27U32EMCWpygIZVPpbZhGBsKhM7mpoZg0iOf37FyYOsNQe6DegkghHoCyTieo4EscRWBY4jhFVhRTs6DOE0Ue34jrGler4Ymip5RG3NK70sJXnm2M9kkH5vzKCXdLWlNmaYts0pYr5Zf2OBIVxqjr+fVuCIwHPY8uKg4yc3Nt/7Dos0w8cqHmzXgHF5TGKSl327agL3axgnvtQmGxnqlwYnyiFwBIKJa2CubXIEVGzHNx/DE9revctJutZzUeHPlUPjWbCpL5sXH+QpubWUNLUYX5qZxwvpOD4LHlpF5MuGoDjxxK0zW/N/gzGYg73fevm8Ng773iIxubNx3HMRnSFaH57eogiVM99hBCXCCHuFEL8VQixTghxQAjRIoSoFUJsE0K8IIT4rhDiP4UQZadyrVe1oHr48GEeeuihvHWvfe1r+fjHP37K55ZSct11153yeSK6Tibjcdvdj5GuXhaus8smImTnAmkwiZm3+BCzppy5kjbbEkwYWcritUc73Oeaq0Zw1czhzLh0MBdP6s/k8X1551snMHP6KIb3jxe8IHdveX8uO3cewnVtevYsO6HjRozoy8GDtdTUHOt8525iwYK1TJs2GqVO7CVu9uzxzJt35nJfPc9j/vzVXHHFuC4fM2hQPz7zmQ8BkE6nufXWbwL7T9MIz3/WrVvHokWLwvt9+/blNa95De973/vy9ovKoiIiIl4pRPPb84vqlhb+sGQpG37zKwCEUky46RPmtr+PV3DblPaDVBJtW+wr78OAw7tN1mrBFCeTAe1ly9yl37jKOFMJm1kJYVyjsnBqFZgXJWg/LLTcbkEpOBQrAylQMQvLUTiuJJGQODa4jqAoYcr6pTSl+MdKy4hlUpS0HsNSGkt6xGSGUtdkohbZxnFqC9+papnMVCGMsJpQpimVo4wp1rGCRAKNa0HCNrmqdk4TqU3LDzJ0XA9sW5gMVwjFVCWMg9WPhM177iZM7c2axQfwMrnypO6SWKIAWyiUsHzhtFBMNcq0RCIw7lWtNQvnbefyWYOP40ktxENIzdTLB7Jowfac9TK8UttvZtt7QgRX7Nq8ur6+mV27qhk9prB/hRFVs9EE2ef02uuu4IrZUwDYtnUP3334F6T13i5dL6It0fw24tWGMLxbCLEUeAH4BvAGYCTQA5OuUgQMBC4BPgH8BtglhHhYCNH/ZK77qhZUH3/8cRob8zMT/+d//ue0CVIAzz77bJhpKYRo86LWGY8//nje8XffffdpGecrkc3bNrFl7QvodC0AwqlAJgaf0DmO1rZyrCnNwN6J0zDCtlw6oZKXVx/F6+AD2MryBJ/79Czu+exreeArb+T7976FR771Vt509RimXjKCtWt30dKS8vfu/hL/AK018+at5oorOs8jLSQop3/mmZWnYWRtqa4+xpEjdYwY0feEj00kXEaM6MuKFdtOw8jasnz5NkaN6k88fmIxE5/5zIfo3984Kf7v/+bx978/DZw5wfp8onAi+Z73vAcpJW9/+9tJJrOZxH//+9/Zuzea2EdERJz7RPPb84vvrl3Pyh89itfaCsCIt72dkgEDjAznu02DOaCnswJV4CQVAvaW96bnsSPY6Va0znenZjJ+aX8opGaPC86bFQlzy/zNeaQ0G7Wj/K5Pgotie1kSGwKWRMUkQoITA9c1jlflN8hybCOmKmHEz7it2Td4KMP2bKVMeSQtTalrhFJHmKhWSwSuU/NVSo0tPEpsjxLHF0yFWVyhcYRZF/P7ZwXDFwKaGlIc3lPPgJFlRjz1Hb7B13yZ07/vP3eOqxgyuoL1Kw6Fz3dHv2Lhc0i2zF8JZUr9w4OC4n3pl//nC62b1h2m34BSiou6EGVVcH/goHIaGlo5crghvEr7YmrHrxHZ0v/OWfDsFmZeMbyD15zgTU9B+yshuP/B25C+Yv/Nrz/G3n1r8LwUESdONL89TXSTO1Uc/9ct4gQRQvQC/gX8EphE/gtM4bOde1sDCeBTwBohxLtO9NqvakG1MKR50qRJXHrppWdpNBGnRppjx3bQdHRFuMYum3xSbx4WLjvMtEmVp92KX1pk06PcZcuuhg73eeRbb+GiCf0YO7oXI4b1YPjQSoYPqURJgZSSyy8fw4IFa05LXmouy5dvZdSofiQSJ5cvW1VVSjLpsn37wW4eWVvmzFnBlVdOPOnjp0wZwcqV23OE6tNDS0uK1at3MGXK8BM+NplMcO+9t4f3b7nlG6RSu4kaVJ0YqVSKX/ziF3nrbrzxRgCKiop4+9vfHq7PZDL8/Oc/P6Pji4iIiDgZovnt+cOGmhrmzZ/PrmfnAuCWlTH2Pe/xRT9TRJ3S+RNAI5Zm8zu1qf9nS6+hDD+4FcgR/oK3nIHIKHPe7IdvR43IbfmO1cL5ppCgHIF0JTImGezWcdAupdFyQQqkJVEKXDe/zN+1jHAZU1DkapKOxlEaEi6poiSVdYdJqgxJ5eFKjaU0Qni4yiNpa4odjauM2NorBiW2iQdQwsQFWFKjpFnnSt+5KvNL+Ncs2sOEy/piSYEKXKky22SqjfjhL4HYOmJsBbu21tLSmAJ0G/dv7nPt9/PClgopbV8wzcqoIld1DR2h5uBUKsOKZfu46JLODFT+ce28J5hxxVCee3Zbdp8CNO0Nvr1rtAlByGPf3lqkFPTqXXKccebPV4PnbfyE4Xzww9cBcOxYI1+66/tk2H6c80S0RzS/Pb1EJf/nFkKIkcBy4EryhVTtL3uBVcBCYDGwCWgi/y8gGPfqk0KIb57I9V+1guqRI0dYsmRJ3rqohOmVzAE++OGvgmc+vXdLhyLdHid1ppZWj/Xb6pg4sqwbx9eWmVN6MH/xoXa3lZXEuGhCX14/e0QoCofzuJwX4GHD+lBT08CRI/Xtnqc7aGlJsWrVyQl/ucyaNZ4FC1bjdWTH7Qa2bTtAUVGcHj2ON4k7PkpJLrvMCNWnk/nzVzN9+gXhJ/Enyrve9SYuu+xCADZs2Mb3v/8E0HF0RERb/vznP3PwYFbkv+SSSxgzJpu5XOiweuyxx6KusxEREec00fz2/MHzPL67eh3Lvv/dcN2kD36QRFFRViyl8N2juWeET9PhXmDK6o+VlGJ5HqWt9QgBmYzOaUQlQkdruCZH37MUYV5qrrMqGIdSAuUoYi5c4O1nQ3F/HFfgOGA7UFRkbjs2uDY4li96Co2tTDl/THm40iMpM6SH9qNiz24qnAxlblYMdSUkbSixPeLSw5EeCcuj2NE4Vm5WqiZuaZKWiQmw/MzU4DFJATUHjiEl9OiVMM5Vmd+wi5zHLwuX4DwSJk3rzfJF+8J1OYebqACMmCqFRAoLgeWX8ucKm4EztfCf2bbkxd1MntIPy2p/zljoOc02ssruUVoWp7JHgq2b2+sVINoVeQrnPFmXavtqkNaaBfM2M+OKYe1uz9mzIFM1y5e/8jHKyooBeOLnf+Hll1/C8zo2n0S0JZrfRrxaEEJUAX8HeuasbgF+C1wL9NRa99daT9Raz9RaT9VajwKKgQuBz2EE1iC5RQOfEUJ0OSz+VSuoLlmypM0Lx9SpUzvYO+Lcppmn/z2PFYsXAhCPx1Al2dJ0uySBVZpfwm8VZ8tlpGv7syVhZlzAyo21jBpSjOucnl+RAb3jNDZlOFLb2mZbSbHDh/9rCj964K1YVjvdPnMWgKuumnhau9PPn7/mlIS/gHjcYcyYASxbtrWbRpaP53ksWLCGWbO6nkfaEcOH9+Ho0WMcPXp6hOojR+qprW1k6NDeJ30OIQQPP3xXeP/uu7/HoUNbgI6bm0XkU1gOFXx6H3DFFVcwaNCg8P7mzZuZN2/eGRlbRERExMkQzW/PH/61dx8v/u8fqPabi5UPG8bIN14TuifBCHaO78YUBM2WzH1bBjmgxqmJgI29hjJi/1Yyac9kp5It7y+s6grdqtKU6QOgTawAOitOKmXObdswtmU3m4r7IWyFUgLHETi2aTplK4g5kHQ1cWWEUFdBQpnmUAkFRRYUO+A6kkz/XiR27sX1XaZx5ZFQHsWWR4XjUeZ6lDpQ6RKKrbbSxCzjejWNq7Ll+0EeqiUB7bHmhX1cdFnf8HkqMKHmGRkCETZwsEJQug/9BhSRas1w9FCTed5yjglEVSO2KpSwkb4yLYJ8VKFyyvvbCpX1dc3s31fP8JE9QjEzjAUIhVMJKBA5ynf2OxneuuSygbz8wk4ymXxzQ65A357IWUhHoura1fsZPKSSZLIrFW35HweYly1BVVU5X/jyR8P1t958L2m9IxL8ToBofnv6yPv9PtXlbD+Y84OfAIPJiqH/BEZqrd+ltf6r1vpIewdprT2t9Uqt9X2+wPoRoCbnPA8IISZ1ZQCvWkF1/fr1bdZddNFFZ2EkEaeK1vv5wAe/TPBH+Zq3vIV+fU0Ieuno/pRcMIBk/x6IuB3OqnpOvyA8vu/Vk0FDjykj/Fmi+aO+aPkRpl94ci7X4yEEXDapB88ty/+EeGC/Uj794Wl8/H1TedfbJjJqRGFzrPZfdisqiikrK2LLln3dPtYjR+qpqTl2SsJfLpMnD2Pt2p00N7cVkk+VpUu3MHbsQGIxp1vOd9VVE5gz5/TkvppYggmnfJ4pU8bz/ve/DYDa2nq++MWHgAOnfN5XA7t37+af//xneN9xHK6//vq8fYQQvPe9781bF4X3R0REnMtE89vzg+Z0mp+vWMGqn/4kXHfppz+JVMoX90xJvxIaW2jjysTkiloq68hEGEdo0FCp2Y5REy+hZ11+hVTWnSrC+0r5DZ0sCiIA8N2bZl/t62JJr5WqVB17k1VYypT2FxULioolrisoS2iKHOMcjSnj3JSYHFRLeNjSw7U0caVxpEd8UAX20VqsVAtJS1PmaipdTbFtzhG3IKE8X1D2sKTGEoGwap4D1xdW7VBUNZEAuzdW02dgMfEiO088Jed27hJIlFJkXapC6HDdJbP68vKCvQRRC7bIirhCSKRwkcIoz4ETNVBV2v7LdYFKnpu3nRlXDENK5Z9LZo0VwnQME0IYBywS6TexMo8l3/XqOjbjJvZh6eJd/vbC8v9C0VK0WdWR+Nvammbl8t1cdPHANj/P7aPDr4FMjDaP/6ab3sGo0UMAePGFVfz6qf/Fo7qL5311E81vTz8dfvhygkvEqSGEmA68iezT+ZDW+g1a610nei6t9U+BqcA+si/793Xl2FetoLpnz568+1JKKisrz9JoIk6eev7yl7+zZ+dmwHRAv/DiGbznnZOxSuIM+a/ZDLruMnpedgED3nAxsapSBlx7KaljzQAkB1ThVpqyEreqNBRUAXbubyQRV1SUdo9AFzBhZCkbttfT0pr9dHj4kEqufcMF3HXLbG7/xAxGj6hC5jkFjp+ROnPmWBYuXNft5fRz5qzgqqtOPo+0ECkl06ePZd681d12ToCmplbWrdvFhRcO7bZzVlaWUFKSYOvW/d12ToAtW/ZRXl5ERUVxt5zv61+/laIi48D+8Y9/x4oVi4Hmbjn3+czjjz+e9/vy5je/mYqKijb7FX6q//vf/57a2trTPr6IiIiIkyGa354fPLF5C8t/8QQtNTUADJ49i96TJhKIUMaPqLEkJC1IWCaD1BJ+Gb2vg0mM4Jn2QHvm9ubyAQyp3o30Mnlzy7CZVa4j1d/u+cfqoAmWv6+UZn7quoKJx7aytmIotiOQCuJxcGxBzDYl+64y4qktjQjsKE3M8nCUR5HtO0v93NO4BXFLEBs7gPTaXcSkJqk8imyPpOWZx2uZJlSOZY51pREypciW2gcl/EaANs2vvFSGbWsPM/rCqjaRCR25z0L909+u/HMpadYVFzv07J1g1+ZaLIJ34wIhjGwsRW6Jv1mfL5yK3KP8sQj27q7FcRRVPYv8EUp/H3N89p+iqzLN2HF92b71CI0NrQXHFB4v8r7kIkTWYRvICS88v51LLh2MUp3JC/mSkggfd9apa9s2DzyY7RXw+c89TEPDJjwd9QrojGh+G/Eq4vac27/VWt/e4Z5dQGu9CbgaCBqpvEYI0akD6lUrqNbV1eXdLykpaVPqEnGuo2lp2cmtt2Zzg++//zO89/qL0RNH0mvGOGI9SnBKE5SO7U/PGWOpvHAYVdNGcfjFjQA4FUXs+utLuD1LOdhOZub8xYeZNaXQKXryuI5k9JASVmyoCdcNG1TBo996C5/+0DSKkg6JeL6A25WGU65rM378YBYv3txtY92yZR9lZd0n/AUMGdKL+vomDh+u63znLjJv3mpmzBh3yrEEhcycOZbnnlvbbUJ1JuOxcOE6ZswY2y3nA+jdu4ovfOEmwMQe3Hzz19DauCQi2kdrzWOPPZa3rnBiGTBs2DCmT58e3m9qauKpp546reOLiIiIOFmi+e0rn/2Njfz15ZfZ8PvfA6Ach0s+9lFsobNOS+m7R32hMMz+DOaMOe7JjCdoTWcF07RWbC3tz/DaXeFUIRBM/VOECN/Zmqdj5eynLLBsQVW6loxQ1LrFods1kZTYtqbI1RQ7GVxLmzJ/P5LAkR4xpXElOAIsgkZSRnSNW5pYRRIlNLK+gSIHimwocaHY1hRZRnh1hCnlV4FhU2SfIyM/6rDcX0lYv+QAF0zuhfJjvgo7mLQ3fcr1ZBZKjoHQOvmSXqxacohMxjNOUmkhhYMSju8Y9UXP4/w+Zkv6FVoLnn9uGzNmDg+Fy8Kk1PwjC4XKwKkaemrNfkIwfeYwnpu3xTxYQXZbm/N2hjmutqaZI4caGDq8Kud6ba/drmAb3A71W/Pv6qsv5w1vmAHAnj0Hue/ex9B0fzXe+UQ0vz0zCCm6ZYk4eYQQNnAV5pWjAehy5unx0FqvBh4m+5fgjZ0d86oVVFtaWvLuFxUVdbBnxLnLER5++HG2bNkJwKxZl/C2t72Oospinmtx6P/6bImbFXOxYg69rpjAkZc3o1vTANRu2E3RgCqsZIxUXWObK9QeS3G4uoWh/ZPdMuLLJ/Vg0Yoj4aT13W+fyH++ZRxTLxpI3zbdMMUJvQmaNGkIGzfuobGxpfOdO8HzjPA3c2b3CX+5dGfu66FDtTQ2NjN4cM/Odz5BYjGHceMGsWTJlm453+LFm5kwYTCua3fL+QJuvvl9DBtmSqyeffYl/vCHvwHdJ1ifb8yZM4dt27aF93v27Mk111zT4f6Fk9GoLCoiIuJcJZrfvvL5yYYNLH3kB3hpM1ed+K53UN6nV7a3ujCTyKCkHGGEVa3zJSvpvx9MBWKpMIKLp2FvsorK5lrcdEteZ3qRI64dz/EopYkEcB1wHc3Ymu2sqxxi1ktBMil8B6rGldp3j2ocYWIJHKlJWBBXEFOmDD+mNDFplmLb7ONKTeXEftSu3IUtMkaElabcP2mbrNSYlc0pVf7zYp4rkysbZJ8KAU11LdQcbqLf0Oycu41+2sHUO5t7qEPR1lxTYAmB4yjGTapi+eLDvpiZ70I9XjMn4/dUBCX/AGtW7WPY8CriCSfcp4ORhUthAX92XW7uqqBvvzIyGY8D++sLvs+FR3eMEME1JfPnbmLm7JF+rEFnY8zNfhXHzW391gO3Y1kWAA/c/3O2b1+Jp7s/Nux8IZrfniEKXyJPdok4FaYBQZOcv2qtuzPzLvcX4TWd7fyqFVRdNz8s+9ixY2dpJBEnR5r9+zfw1a/+ADAlbd/+9ucRQvDtNcdIA8LO/lEXvjjplibQGQ/pT050a4ZB/zGd5oM1HV7phZVHmDq+glM1P1aUOhQlLXbuaySZcBg6sJx7v3g1t318OqrgU6quuFILEUIwc+Y4nn121akNFCP8jR/f/cJfQHl5EZWVJWzatPeUzqO1Zs6cld2SR9oRkyYNYf36XTQ1nZpQ3djYwqZNe5g4cUg3jSyL6zo88MCd4f3bb7+XpqadRC7V9imcMN5www3hhL093vGOdxCPx8P7S5YsYcWK7vlAICIiIqI7iea3r2xWHDnMs/96mj2LFgGQrOrBlPdcj63A89+BW8KIpQLTK12GMQA6v3w9R0BVyoiggdNUSMHaiqGMPbqljSO1vfmnUmYRwmSqKgWua0r+BzUc4ECiklblhNvjjiZuexTbHnHLNKBK2pqEbTJSiyyNKz0s6RkHqvCdqUqTkB4ltmccqpZHUZFNSZ8iWnYfpcwxEQdumMGadaEaodOcS0gdNpCygyxYAcsW7uWi6X0LYrWOTzA+mSPQho2myNdFRoypYN/u6rEsawABAABJREFUYzQ2aCRWjkM0u3PHwmj2m9DSmmHN6n1MnNyvnc2iYCFHpPF8E+zxxFfDjCuGM3/epg6bPYn83XPW5oijAnbtPEIi6dKjR5G/x/H/tUUDXsFPr2HUqMF86r/fDUBzcwufvfPbaE44HvFVQzS/jXgVMSDn9tPdeWKt9QYgyE8acLx94VUsqJaU5LsB6+rqou6BrygO8PnPP8ixY8ZV+qEP/ScTJ46mttVj6WETe5E72cj9+93j0tGhzlQyqh+HX95IpqFjsSyV1qzcWMtFF5Sf0ohnXVTFvMWmCcCD//MGPvfpWVSWJ0gm2pb4nywDB1bR0pLiwIGakz5HY2MLGzbsYdKk7hf+cpkx4wIWLVrfptPoibBp016qqkopKzt9DhwppS9Un1ru69y5q5g1a9xpK718y1uu4jWvuQyA7dv38OCDjwGHj3/Qq5Dq6mr++Mc/5q3rqBwqoKSkhOuuuy5vXfQpfkRExLlINL995ZLRHk+sX8OSH/wgXHf5xz9MPBEzQl6O4CQgLPW3pJnW2jLrSlXCrGvNiDAOIMhADah1i/GEpLy5FinJMw4E95UCyxdjw5Jsf5tlCSzSDKzbz5bSfkhhclPLijTlMU2pZYTTuDJOU9tvoGX5+amW8nNP/QiAIn//ngmPEluTlJpS25T3D5vQk4PrDiMznmm4JfzxSZBSh024bH+bTSA8Z98CHNhdj5uwKOsRD8VQKChIF/mSoQobJuE3A8N/zk0uam6DKCEUSjpcPnMIz8/fCYXCZ4GgWNiIKvudhRcWbuOSSwdjKdVmz/Zo15uac93c/QLVvLgkRv8BZWxcd6BgDHlDOd4KPA8WLtjM9Jkj2x1XeyNtt/RfAJj3A7nCqtbwhS9+hKoq8x7s//3uaebNm0dG13fxeq8eovntmaO9nOWTWSJOidxMxj0d7nXy7Mb8Ge20BPZVK6j265f/iZ/neRw+HIkPrwyaWLz4BR5/3PzRKC0t5p57bkYIU9zT0ok+Z8edbF9Jrdn9t5c7veKaLXUM6ZckEeuolOX4DO2f5EhtC7X1KYYOquBd103g+usKXZUnVuLfEVdeOYG5c1ee9BuoZ589vcJfgOPYTJw4hJdf3nhSx2cyGRYtWs/06Rd088jaMmhQT5qaWjh0qPakjj9woIbW1hQDBnRfHm8hQggeeuhzKGV+Rr/xjR+xZ896IH3arvlK5Je//CXNzdmmXRMnTmTixM4brxVOSp988sk2pbURERERZ5tofvvK5endO3jxd7+jZoeJsuoz7gLGXn0llvRLyxVY0hdM/dxRx38npwBy3qQLDekMZHwXoRDgZYwYGrhVhYANVUO5oHobQRl7rmgYuFJVkCuAOd62wY0ZEWxU9Q42lw9EKonjQtw22acJyzSQynWnxi2NqyBmm3HH/JL+pKUpC5yrtkfSMlEBCVtT7HiUOR5VScHoyVVsW7HfPAfCd6SijQdUZBtRWdKIwLYvuCoBUmtWvrCfC6f1Br8VVZ60V6jxYYrRlczGBVjCnNMSMvBnhgdmS/wVvfuUooTkwN7ccvpCUbRAUAzRVFc3UlPdxNBhPXK2tjPAjsTJwkUIhMht+pRdLp46hKVLdpJKZdo5tnNWLN/JqDG9icftTo5p/5yC3Io8jUa3EYZLS4u552v/Hd6/7Zb7yGR2RB8UFRDNb88c7fyWndQScUrkNnk5HTkgwS9Bp93JX7WC6ujRo9usW7JkyVkYScSJodF6H5/+9NfCP6R33/1JqqpM98LVR1vJ+Ht2/EIl0K3GxVq3YXeYp9oZC5YcZsbkHic8YingkvEVLFpxhJhr8bOH34ZlyYIumCde4t8RpaVJ+vSpYMOGE/+w5uDBGlpaUgwcePqEv1wmTBjM5s37aGg48a70L720iQsvHIbjdFzK0p1cddVEnnlmxQlP4IJYgquu6nxSc6qMGzeSj33segAaGhr53OceAPaf9uu+kij85L2zT+8DXvOa19C/f//w/tGjR9s4ASIiIiLONtH89pVJYyrFn9asYPFjPw/XXXnrx7H8z/GNozLXoaqRUiP8OYkUprRfYwTENNDiGc+hBjJhUyoROk+lhIzrcLiogkGNB7BtvzTeNs2mch2r2r+qbYNtCywLitJNFLU2cjBZibIgEYdSV1PqeCQtk28at4wL1ZFZJ6hEY0kPR2psZQTUUtcj6XhUxjQljiamjDO12IYSBxwFA4aWUnOokeZjraHDNLes31XgWEZQDa4VuFQ3rz7M4BGlxGJWdhxBGX9wLv+5M2X9ppFV2OCKrCzqf0f8e9I4U4WLJV2UsBAILp85hIXz2xP9suJqrhhrummZZcHcLcycPTznude+azMIwi1oMoVsc/528ZXjXFesbdlcdPEQXnphe9td25D/WJqbU6xfu49JFw4yx5zQG5m2FYSGQleMueb73n8tkyaNAmD58g387LHf4XHoBK53/hPNbyMAhBBvEEJof9l+nP1sIcQdQogVQogGIcRRIcQcIcTbjnOMK4S4TwixRwjRIoRYI4To8AdNCBETQmwRQuwWQnR3OWmuWNG7m88N0Of/s3fe8XEU5/9/z+4V9WJZlixbtmXLvfdeIUACpBESAgmQ8CU9AdMDIYQEQm+/kEACCaQQIL0QCARc5Ip777aaZUuyZBWrS7fz+2N27/aKmiVLstk3r+XudmdmZ0/yafZzn+d5UJ9Qpe017Bklog8yffp05Wi0/aHbtGkTl112WS/OyqF9qnnjjX+wfv12AMaMGc63v30dAM0+g+f2BUJAPpbh4b0T4V9YVB0uAp/5c++ENnayrIGpY5MY0M9L6emOf3M3bVwyew5XsWTecPr1i2H65NB8SN0nplrMmzeG11/PITt7IC5Xx1y1lvD38Y9Pb79xNyGEYMmSiaxcuZsrrpjZ4X61tQ0cO1bMtdcuPoezCyYxMZa0tGQOHTrB6NHhOa1a4+DBIjIy+pGQENN+427gwQe/yx//+BYVFVX8/vf/5FvfupY5c64Eotvte6ETKTdUWloa77//fof6T58+nePHj/tf//rXv+aaa67p1jk6ODg4dAVnfXt+8saxA6z/1Ss0nlE5bydcfgmDxo8286Aq4VBKtVh0acqVaaAcqIDS2QjIadaP33KmGgYYUomkhiEAiZVaMT9lMLMKdlKS0B80F0IL7w+qb1SU8KcOGH3qKAcHDMfjgSgvKkzfZeDVrRB8A7emhEmXELRIJQp6TIFVF1ZRKYlbQKwZ8h+lq1QGXh28uiTKpa7JkILJ8zLYuf4E8y4dply24E99YEmMkoBQpwloamwh72AFl30uW72fAKb4bBWZsq7VenSZb6RujqGGNAVMK4xeWIWkTPen/6cpiI3zMjQrmQN7Sxk3IcK9vgh7AkBebjkJiVEk9wtdM8rgtiEvA6MJU3yN5AaN7KMaNTqN3TsLqa6uJyEh2tJdW5uwn3VrDjNvQTaaJvBL7kJDWol6W5Flhe3nE45EYoTNVdd1nn72bpYt+SoA9//gZ3zu6ktJTuqHJj6ycoYfZ33bgwjQWv397fxY3YkQIh54sQPtooD/AQsAH7AXiAWWAkuFEI9JKe+J0PX3wNWoyscHgTHAq0KIKCnlLyO0vw8YDlwtpey2hO5CiO8A3yLwDs4B3ujG8VNQ84YOOJM+sp9AKSkpzJgxg82bA+Hef/vb3/jhD394Ts/b1TDqurrwSvQfHXzU1uZy111P+Pc888z3cbtV4aTfHa3ntKmfTkhyMSLehd0BLqWkpa6RI7/94KxnkLO1jMsWpPOX94633xiIjtIZPjiWf68u4Y8vfYL4eK+58AhwLiLr3W4X06aNYOPGgx0OiT94sIiBA/uRmBjb/RNqg8GD+7N16xGKiytIT0/uUJ8VK3axZMnEc56WIJT588fy+uurGTEivUNCdUuLjw8/PNijwm9KSjI//vH3+O53fwLALbc8zIYN09G04XzUA0wi5YW67rrrznq8Dz74gNzcXLKysroyLQcHB4duw1nfnn8U19WwcvNG9vzzLQA8MdEs+/ZX8WjKaaqJgA8A/EZDv9CJBJ9PFa0StjYeDVqkElNBuVIBU6QV/s5C08jrn8nI8nyOpI8AwEpvL01tzgr/14QScvvVnKbB5aUuKo4ojyTGY5DgUW5TjwZuoVymbjO/K0LiMudkFYhyaRKPGfbv1QySPJJEjxJaEUpM9WoEZRdNTo3G49E5VVRDemasX/AM+vWTlstUvdy2oZgpc9LN1AVKWEaAkOparL56QJv2u1NDVuxmW5UvQaCjCXdkoVLAtJmD+esbO8kenYq31WiqQF/DMPhwfT6f+dzksLD3oIsLEkwjCaeddYrC4qWjyVl1kCs/NSVim1DKy2o4c6aBocP629rIkPaB3zH/vDo0teBrlFKahXenc/XnL+HPf3qPU6cqeOgnL/LkU4OAoR0Z9ILGWd/2MH33duoxVBGlfwCfbqfdAiAX+LhZhAkhxCeBPwF3CyHWSSn/bXUQQkxBiak7gaVSygohxCRgI/ATIcTLUkqfrf0o4E7gv1LKv3TXBQohbgeesO2SwFVCiDullM3ddJrPY2bSAda31/gjG/IP8IUvfCHo9c6dO9m4ceM5PWdUVFTQ6/r6+k71Ly1t13V8AVPG44+/zPHj6ouCT3xiMZddtgiAikYffz4aWIzfMymed44HXKRJHkHJmn2c/GAHLZW1Zz2DmroWjhfXMXpYfPuNgYXT+rN2WxmP//BSRmT1Iy012O1+LgXB8eOHkJ9fypkz7f+OWcLfvHnhoYI9wdKlHc/7evLkaQzDYNCglB6YWTAejxKqP/ywY3lfN248yIwZI3G7e/a7q2984xrGjx8JwKZNu/jDH/4KVPXoHPoa9fX1/PGPf+zWMaWUvPLKK906poODg0NXcda35xevH91LzrO/QJrK5/yvXkvigBQlK0n1t8YSVFWouuk4NaU8A0GjoYEMVH0XAoQm/esqq5CUFcZv5UrVdYGmCcoTU4lrqiO2pUG1Mx2ZQgRSBLhdSoyUPoPssnwO988iOgr6xxj090piTDFVF4FCU25hhdGDR5NEuyBKl7h1SYyZEiDOLUmLkf7QfpemxFS3ICiHqWaKq5PmDGTPppN4hHLC2pfS6v2R/pQAVRUN1NY0MWhoYN1uuVmtQlZWqL9uOlNd5nN7sSp/8Sl0NKGjCze68ISLqVYHwKXrzJg1hM0bClr5yQffA+zcdoJx49PxeiOvGQOh/yH7pURKw3yM3CbSaHYGDEjA43Fx/HhFB/rC6pUHWLI09J7BcvBa6RAC+yOH97c2MyPsWq3f40cfW05UlBeA53/2OgcP7sSQnU8bdiHhrG8dAIQQC4BvAH8H/tlGuzSzHcBNlpgKIKX8F/C4+fJHIV0Xmo/PSikrzPa7gL+gwu/HhrT/OeqD5judvZY25n4f4WIqwCDgrm46RxLwgG3X2+31+UgLqjfeeCMxMcEhFffff/85TXKdmJgY9LqkpKRT/Tdt2tSd0zmPaKagYD+PP/4yAC6Xi6efDjjRn9pzhiYgxQufHRJFcb2PvFr1JUlmrE5KYx35/95I0X+3dXkmm/dWMG1sEi697ZXBgH5eoqNcRMfFcO1VU8LE03PtrhRCsGzZJFas2NVu240bDzJ9enaPC38WCQkxDB7cn/3723b+SilZuXI3y5ad+3ykrTFhwlDy8kqoqWn7ZvHMmToKCk4xblxmD80sgMvl4tln7/W/vueeJ6mpySc8N9VHh7/85S9UVXW/qPzqq69iGB/d99XBwaHv4axvzx92l5eQ8847FGzdAUDyoIHM/uJnERKaDeUkbTE3CLgoDfNH6ZPKTWqF/hvSFD3NrcUn/GKkJbTq5nNND7gwBXAkfQTZJ48CSmS12rvdArcrUDg1s+IkJxLS0KNdxHgM4lyq+JRHA5dQ+VGjXNJ0oVoFqFR4vyVaeoUkymUQrRvEugziXCrEXxXECqQFsFbK1g2rJiTxsTpDRyRyeO9pv3CK/3jwtinnBLMWZoRV1va/HwQEX12ArmlomsplKoTpRBUqg6qVL1UTLjPEP3QdH1p4CoZnp1B2qpaqykhrxkD/+voWjhwqZcKkjHZ/ZwL5VKUZXm8QcHUG2kQWVoPb+Z8LWLh4FGtXH2r3c+LI4RJSUuJISrZ/xggCEnTgHkdwtpF4KvSfEFF16NAM7rjzRgBaWlq4844nkRSezQkuGJz1bc9j/zzpytZ98xFRwMtADfDddpp/ElVo6YiUcmWE41bo/jQhxAjb/gTzMTQE/qT56F8ECCGuAS4GHpFSHm3/CtrHzNX6Y9uuQoJF1B8KIT7dxXPEAH9DCcQCOAKsaq/fR1pQTUlJYfny5UH73n//fX7xi190eWzDMCImdM7MzFR/qE127tyJz+cLaxeJ4uJiVqxY0eW5nZ8Uc9ddT9DQoFyn3/velxk9WqW2OFHnY19FM6kewcIBXm4YGcvR6kChqbsmxrH15Q8wqusDMUxdwOeTbN1XwayJ/dpst2h6KhMnDefFxz9FVFSwUNlToeoDB/ZD0wRFReWttjlzpp6CglOMHz+kR+bUGnPnjmHz5kM0N7deJGzfvkKGDEklPr738oEKIUxH7e42261YsZulS3s+LYHFxRfP45OfXAbAyZOneOSRF+EjnMA/NBzq3nvvDbg5OrE1NzeTmhrIg15YWMh7773X05fj0AWEEL+1Jez/VAf79BdC3CuEWCWEOCmEaBJCVAsh9gghfiWEWHqu5+3g0FGc9e35gc8w+Mvhnax6LpB67pLlX8fj9fi//mw2lGhqyUpW+L9PCgwDWqSgSQq/KChR8poB+HzCnyfUcqf63Z72m3pzq4uOpdHtIbmmwr/fLsYCuHwtpJ85RXH/dPrFGvT3+kjwqNB8t+lMjdJVISqX7ZyakOiaBGGgC4MoXeVLTfZKkjymExUVY+nWgkPu1VwkuhnGrwsYP7U/R/edpqnRFya6WnMtyK0mKSWKxGRv0PsuLPepALcmcOsuPJobTbjRcKHjRlPJBEyXrmaKqRqa0P15U4PHjFwPQQjBwiXDWbPqWMiR4P7rc44yb+HwsLRgrSGRZp5baZNIJZGEVXMmBAup9vOo/TExHkaMHMDuXUW2NsHz8fkkmzbmMnfBSKzCVn6x2f+61e4dxhJTQ0VhKeHOu25k0KABALz9nzW8887/MGT3C4rnC876tmexPpO6Zeu+af0QGA3cK6Vsryr1HPNxTaSDZv/ckLYA1rekoU5U63UxgBAiAXgaOIxKLdBd2HN7HAYWSimfIhCS7wLeNAttdVrjFEKMBVYCS2y7vy+lbLd6+UdaUAX1oTNy5MigfcuXL+cPf/jDWY9ZXl7OZZddxnPPPRd2LDY2lrFjA7+HlZWVvPvuux0a97777qOxsePFkC4calmzZg1vvqkc16mp/bj//m/5jxbV+vhCVgzLJ8bz2WHRxLkFhbUtpEVrzEt1c/rQCfZvyevWGR3Kr2Fg/yjiYyI7OudPTyM5JZ7rr5nJnBlDwnIw9SRLl05k1ardrX7jvHLlrl4V/ixcLp2ZM0exYcPBiMebm1vYsuUwc+aM7uGZhZOR0Q8pJSdOnI54vKioHE0TDBzYtuh+rnnqqXv8OYafeuo3HDu2B3te4Y8KR44cIScnJ2jfl770pbMay+VyhYXTRspd5dA3MauMXmXbdWMH+tyMWlw+DCxGpTXcifp2PAu4GVghhFjd3fN1cDhbnPVt32fFiWOs/v2bVJ5Qhp+sWVMZvWQeYIqitmWbhgqBtzBQ+VFbTOVVoEQRy53qM12tLj0QNu8yQ/c1s6i8vfCUMPW2Y2lZDC/NwzBzDFiFq6zb05GluZwYNJTUOEGcSxLrUkWnNE2iCwOPJonSVCi+y59eQOLWleM0Tod4N8S6JdFmASqX6URVxlD12nKe2l2k/hB9DVy6YMqcNLZvLDbfn2DXl2FIdm4qYfrstKBVt99HKQQaGho6GsIUfYNdqZpwIYQLgYYmXGi4Ebiwr+OVkNr2+jmlfywxMW4K861w+uD25adqaWhoYXBmcpvjhCNtj6HP7TJrqCs1lMB8pk0fyp5dx2lsbInYZ+uWPCZOzsTjcYNNTI08XlfvK2SEuUtiY2N49LHAF0Z33PYETc3HzqkDv6/irG8dzNymdwKbgI58azrKfDzSRhvLVWq/6f4f6k/PXUKIBUKIONM1ejlwyOZE/QkwEPi2lPJc/GHfjRJTrVwqN6CKZAmUqPoYsEMI8U1T3G0TIcQsIcSvgO3ATAIJnP8spfxrRyb0kRdUY2Ji+POf/0xcXCC3ZXNzM9dffz1333031dXVHR6roaGBZ555hrFjx/K///2v1XZXXHFF0Os777yTmpq2C5898cQT/OY3v+nwXC4cJD7fCW655WH/nocfXk5SUuDfx8hEncsGRTGjv4dBsS58hmRbeQufHhLNd8fF8e27/xk26qjhXc+/mbO1jEUzAt/kJSZEMaB/DBkDYlkyM527l3+cwQMT0LXgpVxP65ZxcdFkZaWxZ09+2LGionKE6H3hz2Ls2MEUFZVRXR1enGL9+gPMmjWqQ8WgeoJlyyLnfZVSsmrVbpYtm9RLMwuQnT2U5ctvAKCxsYk773ycDhQrvOD4zW9+E/RzmjZtWtCNf2cJTfT/r3/9i7KysrMez6FHuRpVybTSfH25EKJ/a42FEHcBvwLigN8AI6WUmVLKmVLK8UA/4EpUUv5F53LiDg6dwVnf9m1qmhv5754trH9F5T4UmsZlt38TTQi/sc8SFV1Cmn5JlTu1xSfwGdBkCHxmcLkQKuzfHoflskLoTWentf60/hxqEe4CW3Q3JYmpDK4oQhWvCvSJbaghytdIS0oCCR6DGJdBjAvcmkGUJon3SOLcEo8uEcISwgw0JB7NwCMMPLoSUaNcKudqlMsmmAqVJkAjIJBqSHML5D61rmPwsATOVDVxprLBH+JvsX/7KcZM6IfHqwelNRACdCFwCzcu4fLn+7Q2y2WpQvp1NFxKRBUqd6rlxFR0fEE/b2EWG9blYVgVsbDeV0nOqiMsWjKi9c4dRIYIj5giu7Vb+tuE2DxsL3VdY868EWxYG6611NU2cuxoKRMnZqL5HanC9n74R+zytQQwMCK4VK/54seZO28KAAcP5vGLn7+OwUcvD7Ozvu0dujnkP1MIcay1re15CB2wVO+vSZUDpD2sm/7IrqDgY/5veaSU+SixNB3lbj0DvIoyGXzLnM8U4NvAm1JK/2JBCDFACBGcF+js2AgsllL6/7FLKY+h1vb1BKz441E5XE+bkWSvCSF+JoR4SAjxhBDiJSHEe0KI0+aY/we4befJAb7c0Ul95AVVgMmTJ/PPf/4zaNEppeTxxx9n+PDh/OAHP2DdunW0tIQ7fpubm1m5ciW333472dnZ3HbbbZw61XZY7c0334zLFXA27tu3j8WLF7Nu3bqgdoZhsHHjRq688kruukuliBgxout/cM8vKnnllT+xffs+ACZPHsNXv3pVUIskj06CV/PnlWowJJ8fHs0lGV7+/fftHD52mkEZCQwdnEBMlI6ua1zSDQuXUxWNNDUbDBqgws9/et/H+NGdF3PrTdO4aNkEMtKDvxRpLRyoJ5g9exTbth2lqSnwO2wJf0uXTuydSUUgEE4fnPe1qqqWEydOM2bM4F6aWThxcdEMGzaAvXuDiw3s3p1PVlYasbFRrfTsWe6775ukpSm96G9/e4+VK1ehUux8NPD5fPz2t78N2ne2395bzJkzh+zsbP/rpqYmfv/733dpTIce40bz8WlgH2oBFbEUrhBiHvBT8+VtUsqbpJRBd5lSykYp5VvAPODe0DEcHHoTZ33bd/l3wT7ef/7XNNergjozrrqctOxhWMHO1l2xENLvMPVJSYMhaAF8ZhuBRIS4+Cxnq7D1d2sSXUh/CoC2cvgd7zeIgVWleIxAwWQNyciSY5wenkWCxwzt18AllJgapUu8ZpEofyEpU8j16pI4F8S5IcalcqnGaCotgCWeakKaYqc9LYFUBbFMt6uVBsB+8zp7UQabck76NTwBNNY1U3CsmjET+qkxUc5Wj3DhFh5VTEqI8M3K/4kwC0+50DUvbi0al/Co0H9Te+3smj4q2s2o0QPYvfNk0P6jh8sYkBZPQmL3pLIKklSltcew5VoVQa3CxVAYPiKV06drqThtL+IryFl9iIWLxyCEaCNHqyVL+xMwdOVyrCsJOZ9ECMEzzwbSJ/74wRc5dWo/RvvRuRcMzvq29+hDOVTvAKYBT0spd3awj3WD2lbIouUsDfpgklL+CBXl9WdUePzLwCwp5QdCWfVfBGqB2wCEEFeaonAJUCmEWCeEONsK2GuBj0kpK0MPmOLtpYBVjMX+rddY4Isoofde4HbgJlSO10TCk0q/DFwqpexwSKcjqJosW7aM1atXM3z48KD95eXlPPzwwyxYsIC4uDiGDBnC9OnTmTp1KgMHDiQ6Opply5bx9NNPU1QUnLIiIyNyYvERI0Zwzz33BO3btm0bCxYsIC0tjZkzZzJ58mRSUlKYO3cub731FgAXX3xxWL8LG4OqqmPcd98z/j3PPXcfuh7uULQviGJdGp8YHE2SV+M3f9zK6BEpPPz9j/Hw9y/BABbNHcbh3La+lOkY0yYN5HSdxqULBnLpsmyu/cwUPnXpKPoluJg9KzukdS8pqSa6rjN37hjWrdvn37dnjxL+4uJ6Lx9pJNLTk3G5dI4fD3wjunLlLpYtm9TraQlCmT17NFu3HvHnfW1qamb79qPMnj2qnZ49R0JCHI88cpv/9a23/pSWliLaDv+6cHj77bc5ceKE/7Wu63zxi1/s8rjXXntt0GsnLKrvI4TIIlCl9A+AdZdwQytdfoS6F18hpXymlTYASMUj3TFPB4fuxFnf9j2O11SyYt1adv3nfQCiE+K5+Js3KEcowYHObvNOTaLC+61iU4atSJVLgIFQAiZKbFVjSX/FekRAaJUycDNvz49qmTV1lyA3PYsRpbn+4wNqy2iMjUGLi8KtSVyaKirl0SXxHohzS7xuNReXOWOvBtG6JEZX+VN1AbqZS9WlS9zm2NZzSzT1i6oEQvQh8BrwO1aT+3lJTPZQlFftd6luWVfMrAUD0YQV0K8DOioHqimKBq3LNZsAaBWgEghc5n7reNcipCZNzeDg/lIaGpRQ3dIi2bqpgNlzh3V6rFCJ0X4EMF2LIakAJH7JOGicCKHyi5eNZvXKQ/7XJcVVtDQbDBrkN6wFibHhTlX7T6urGEiC8zFLKZkxYzw33qjSoFdVneFHP/wFkvbSR144OOvbC4ZCKeXw1rbWOgkhRqLWqbnAg504X4P56GmjjZV4OqyanpTyb1LKz0spl0kpb7YJuV8DZgP3SylPCCHmAn9H/Un6OkrMnAJ8IIRI6sR8rfOukFKGh7AGjq8FJgI/M+dtTyDdGtYxCewCrpJSfr2zqQocQdXGtGnT2LFjB3fccQdRUeHussbGRgoLC9m2bRs7duyguLg4YsL97OxsXn/9dV577bVWz/XAAw9www3h93ClpaVs2bKFXbt2UVlZ6d//yU9+kn/84x9B3/xf+JTy0EO/oLRUFVS6+urLWLx4Vru9hBB+t+pN107n6zfM4tOXjWXapEFMm5DBxDEDeHdlW2lD2icpMYpv3jCbW2+ez5TJw/juDVPxenVycvaGCH+W0Nul03ULI0dmUFpaRWVlLU1NLWzb1reEPztLlgTyvhYWnsLjcZOWltTb0wrD5dKZM2c069btB2Dt2v3MnTsmoujfm9xww2eYPn08ALt2HeTll98AKtrudIEQuhC86KKLSE9P7/K4oS6AvXv38uGHH3Z5XIdzyo2oxdNaKWUu8BrKsjNVCBGUo0MIMQD4mPnyZz05SQeH7sZZ3/YdpJT8/dhu/vtEINXdRd/4MjFJCcpLaCs4BebdnhT4JDRJzX+H6EI5PT1asKim+qlFpyUwWvlRW0wRNnRNGvZltYDKhGS8LU3ENdXiEj6GlhZQNXQIHk2Jn/Fug2gdYl2BUH3lIFVOWJeQxLgMYl2SGFNo9egSr66KV0XrZqg/+MP8BZZzS7luLReXbj/ubxPYN2NuOjs+LEEakvLSelqaDTIGxSpBVfPg0jy4hFflRA0kFLA92i9d5U8VQkfX3Cp3qggUpLJSAZyNYKhpOnPmDWPDWlXvZdvmAiZPHYzb3bk1o1WwyXoVdjw0FZX/mV07CMmzGtInJUW5ZvNyy5FSsHrVQRYvHWP2DuRmDZV1RdB/fv8x3SM5BJ9LSnjop98jLi4GgJde+is7d27C17rmckHhrG97EdFNW9d4EeU2/WZbQmMErBvAtvL9Wcc6dLMohEhFRXTtQIXaA3wfdZUfl1L+yjQdLAcygK90Yr4dRkpZJaW8FRgGfA/4AHUNoR96yroPe4AXUI7UqVLKf5zNeR1BNYT4+HieeOIJ8vPzeeSRR5g+fXpQ1dLWSE9P5ytf+QorVqzg0KFDXHPNNW266VwuF6+88gqvvvoqWVlZrbbLzs7mlVde4Z///CexsbFndU3nJ01s3Liap59+FYCoKC+PP35nh3tbbtXrvzCNm66dgcfjwpCS5x/7JDkb87s0M49H4xePfYqrrpzAFZeO4bpr5nK8oITc3GJcLp309GSbY7ZLp+pWhBAsWzaJFSt2sm5d3xT+LGJjo8jOHsjOnbmsXr2HJUv6TlqCUEaNGkRxcQUFBaWcOlXFyJGRnTu9iaZpPPfcff7XP/jBs1RUHAU6kmrn/KWkpIT//Oc/Qfu6Gg5lMXLkSGbOnBm0z/kWv+9ihiJZ+ZB+DyClLARWmftuDOky3/Y8BweH8xxnfds32FFWxL9eeoXjew4AMGD4UOZcfYW6w5NKULW7U32SoBQAFkIDt1COVEsLM6SgKcg1qDYDNU5QfxF8V69+pBK32yxcpUHu4BFkFx0lu7yQ6rSBRHk0JYpqPrOglIFXV2H5bl25Tz0aeHVIMMP7o3V1s+kxi03pQprpB8ziUlogTYDlYLVE4CB3qi1MVkPlQdXMLSrKxahxKezdfopNa04yb/EgNOFFw2OKn8H5PlUeVGt8Hc3vQrVcrG6zn92FGXi/VEErPUSc7diCf8iwftScaaSwsJLC/NOMGZfWoX4WyqnZ2Qij1sPzg9yq0nKrquuZt2AkG9YdZv++EwwenEx8QjSWZBo8QsjrsEJd3XEzJJGmD9tOenp/7r3vZkClErlt+eNIWXDBF6hy1re9S9/QU5mO+jD4rRCi2L4Bz5ltMm3755n7LOt5aDitHSsHz6E22th5ApVv9RtSSuvb2HnAwZBUWf+0HTtnSClPSSl/LqW8REqZAgwCJgMLUCkShgHxUspJUsrvSCnf78r5PhpfB58FAwYM4J577uGee+6hurqa7du3k5ubS1lZGQ0NDURHR5OUlERaWhpTpkxh8ODO53YUQnDDDTdwww03sHv3brZu3cqpU6doaWlhwIABTJ8+nSlTpgT1ufHGG7nxxhu75yL7MBUVB1i06EsY5lf03/rWFxk2rPPvsdul+/+oZg1J5u0PDrFjz8l2ekXG69EZO3IAsTFurrxkDLoe+CicN28M//73Zm666WP0dnh/W6SmJqLrOnl5JSxZMqG3p9MmM2eO4sUX32HKlCxiYrztd+glLKH69ddzuPbaxb2WlmDGjM9SXNx24vjo6CgMQxIfn8SYMR+npGQ3qhDjhclvf/vboNyAMTExfOYzn+m28b/0pS+xefNm/+s33niDZ5555iMlDvQgmW0l528rLMpkCZCFygv1Z9v+3wPLgOuEEHdJ6U/AZv3BqZRSdj1HjINDH8FZ3/YeTb4Wnv/Ta7z//Cv+fVfc/jV0t4uWEMVUEyAN061qhvjrQmJIYeYFVa7QhkDNIX9Iv4UQphhrgDTlPwOrGJVygFqOVpeQGJrNt6lJmqOjaIry0r+8hIJpU4jVDWJ1SZxHhfNH6QER1C2USCoRuDRVmMoK6ZdS+t20bk0S6w6E97t1e3i/DMiXluOV4FW1WmIpQTTQWjJuYj9ef+UAWdmJxMfH+AtIhbkyzTGl/0rVm6bOo/poppiqUiNYrS0xVVfipDDM4YTdM9nGTz/w5cXCJSN487VtfPqqyZ1aM7YuiraPJRBL//Xa3xe1x/8olXIdFeVh1JiBrFl1gJu+vjRMzAydl3VcSpXjVBMahr9GjvXTtb4usKT+tpk763pKisvbbCOlCndPSUlh186jjB75MQ4d2YnepgHv/MZZ3zqYCKCtb2U023ErxH8jyiG6IOKAQgxCrZettm1PQIiFwPXAr6SUditzIhAaEmxVw0xqb9zuREpZzDmsyizOw29wzrsJO3SWGt59900uu+z//HuWLFnA177WWpq7jiGlZPX6XGrrOpZjWAjhF2M1TdA/JYbRI1LRNEFcbHDakerqek6fPkNGRgoeT9/+nuLEiXJaWnxkZqb2uZykdgzDoLDwFDExUaSmdkdhwHNHXV0jpaWVpKUlER3dO+Lvd75zJ6dPV7bb7uKLL+bYsWMcO3aMffveZuzYi2g7jY7DBUKP/WMXQhyLHzgo69o/v9Ut4/3x6is4c7KoBShsrU17gqoQ4reoBd9fpZSfs+2PQyXLjwE+KaX8t7n/PuAh4LiUMrPrV+Hg0C7O+vYC553C/Tz6wI/JeeUN/76rbvwSk+fM9DtT7aKoRIDpULWKtYfKX5bgavW1pC1LsrKcr4Exg59Yr5UYGkCgikLF1p5B8/loSUpQKbUsd6kILjxliaKBvoGxdFtbK0eqP4erJZqKgLAJgePBBMpGhSKlwYnCWrxROqkD4swm9nbt//NSgmrwntaxy6id+6fb2NBCackZ+vWPJTa2J9Zf4Z7S9tpblJfXUFvTyODB/dD1Tga2Wr+AIYS/X62/f9/9zr1UVFR26HTXXXcd//jHP2hoqKfqzGaioyajib4ZiefQbfTojay1vr2um9a3r6n1bW4HTAGdQghxI/AKkC+lHBZyLA21nnYDy6SUK0OO/xi4H9gupZzWznlcwHaUaDtaSllhO1aMEnQHWq5VIcRkVFqAN6SUXU/420fo28qPw0cQg+bmQpYvD67tsXr1Op555g6mTBl71iMfOlrGtx/IoaGxYxUgb7p2On//z14M4O7vLGL8mAFctHAE6hvqQLumphbefHMNN910CTk5e/jc5+a3NmSvk59fyr59hfTvn4CmCaZPb8vt37u8//4OZs8ezbZtR/nYx6aQnBzXfqdewDAMXnttFV//+sf5z382c801C3tFqH7qqUHtirkJCYmkpvbn/fdVZMPy5T/lnXfGIsSwHpihg0OXKDzbBacpml5lvgwqVyulrBFC/AO4FhX2/2/zkPUtet/84HFwcDivqGqsZ+WuLax/7a9B+//3n3eY+LWrISoanwEtPqEMghKazdypPlv+02afQDedpPU+QZOh3KsNPiWu+sP/TRHNAFqM4LBuHWg09wkBXk05N62+QkCUbpBYX43rZBFNyYnE+JrwDc8gyq3aR7uUOBrjkgEBVai7Z7dQYfyWUBrrArfpWo13W4W2VHoAt+WK1VT6AmGJtYQqJcodGooVwv/h+iLGT0zn6OEKZs3NJHVAfFjb4NyjAe+rhgbChUu4Q0LFrfV+qE/WCqEPjBeet9Tw97HLmVJK/vrmdm64aQ7vvrOfT181uUNCZfDc7XMysLtfI7lILXeqdSiy0zQgh1su4DPV9fz37d184orJHDxwkksunRShX+gogbGttXDApWq/npCCWUGPAZ5+eiAxxe2LziNGjKSgIJ/a2loAnnn6Vb5/792oSF8Hh27E9oVQd4zV00gpS4QQvwS+A/xaCPFxKeVBACHElcBdZtMHOzDccmACcKNdTDVZCVwDfBd41hRfH7Adu2BwBFWHPsZpXnjhNfbvPwrA0KEZ5OefQErJLbc8zKpVvz9rscrr0TskpiYnRjFpXDpf/Oxk8o5Xogu4+csziY5yhZ1bCMHatfuYN28MAwYkkpAQw7FjxQwf3vWk4N2NYRim4LsAr9fFH/6wivHjhxAV1ffciadPn6GiooaLL55CUlIsH3yws88K1du3H2Ps2Ez69Ytj9OhB7NhxjKlTR7TfsZvZsuVv7bb585/XMm/eaNauXU1h4UnefXctb7/9Hpdffi2ObuRwAXM1EAuUA29HOP47lKB6hRAiRUpZDhw3jyUJIZIjLBQdHBwcOszbhXt559mXaGlSFd5TMjMoLzxB9alyVv76DZZ++ysYhiqHrNuioZVsF1h76poSHFsMM7+qVGH7PhkUGG8LZrfu2U3h09TehKH2WWH3zUagqJQmDPq5DQYcyKNm3EhiYnXcW/bhHtYfj+Ym1i39oqnbpgUamAWqtMDc4/xiKkSbBaysOYW6XAWqQJVNklP/9+d7DUYIdaKaMy0UF9UyZ94QMgYns+LdI3zqc+PD1+xotmrxAberEDq6cNvOFYrl97X3FEGioNXPElZFIPurbQw4sK+EzKH9SO4Xw4SJA9m+tZAZs4ZGOGekOdhf2QVb+34ZIpiGydIRxo58X7V65QEWLRlNWnoiO7blc+pUNf1T41sN/Q+bccTQf/s8rPfQ/h7ZUwLAhk2/C5urchIH5tDc7ONPb2xgwuRUZk1fj2EYPPrIy3zp+isZMigVTet79zkODr3MXagcrHOBvUKIPagbQesG9ikp5T/bGkAIkYkSSHOklL+N0OQnwCeBZ4QQ3wLigXRgPyHmhnOBUH8gpgOLUNfVD5XntQE4DZQCm4FVUsq2c+a1g1OUyqEP4aOs7AgPPBAoqPzHPz7JyJHDAMjJ2cxf/vLfsxr5REk1T/xibZttdF2QEO/hi5+dxC+f+jRzZ2TyzI8/zk9/cCkx0e6wBOtCCCoqaigvryY7WxUiWrRoPGvX7vPnfu1L7NiRy5gxmURHe9A0jYULx5GTs7e3pxWRFSt2sWzZZAD6908gPj6a3NxzlvrkrGloaGLfvgKmTVN/f2bMyGb37nwaG5t7eWbhHD16kqSkOAYNSuWJJ+7y71++/BGamgq50AtUOXykudF8TAGahBDSvgHWHxYPSlgFWGfrv7hnpung4HAhkn/mNKtXr2LP+2sAiOuXxPVP3YfuUr6Wtb//C6ePn/SLqZaearlT7ehCCU4tPvCZ+U+bbWKq/S+5PXulJqwiUOocLk3lOI0yzYtWflOvJkl0S+LLyvAlJ+CKceF1CRidiXawAI9mEOeSROtmwSkRCO93mecB0/mqG3g0tc8l1NiW+1TXwG2KqYFCU+FiauB5sKBpFYbScLMhp5AFS7IQmkZCQhRp6fEcO3waqwRVMAFBTgiXWYCqc/4iaV6fMAtZBTlEw0TcwByam33s3FHE9JlDABg/cSC5R8upq207FZm0/VSlWZwpUKTa7vQMR7R6qy8IfV+xvSo6Xo7LrZGWngBIFi8bw6oV+1XhqjaKXIUeay+1YPDPSECbhb5ChVd1vnXrDjJz9ggmTRrF179xNQC1tfX84N7n8LWeKcjB4aywfxnU1a0XDKoASCnrgSXA3cA+YBTQH1gNfE5KeUcHhnkOtW7+Vivn2GeeYxWQCUQDf0SlGajv0gW0gRBioBDiCZSJ4kNUwaxvAJ8HPgZcCdwA3An8CSgVQqwTQnzqbM/pCKoOfYgSfvjD/0dlpYq0vPHGzzJv3nSefvoef4s77nic+vqGTo2af7ySnI15/O7NrW22u/Vr83jgjov48tVTycxIRAhBdlZ/xo0aENIyEPL/wQc7/cIfQFSUhwkThrJ169FOzfFc09DQxJ49+UyfHnBOZmWlU11dR1lZdRs9e55jx4pJSIghJSUQrrV48QTWrOl7QvXq1XtYsGC8v1KypmksWDCOnJw9vTyzYAzDYN26/SxaNB6Az3/+4yxYMB2Aw4fz+NnPfov6ss7B4cJCCJEFLDRfngCKWtksB+oNAFLKUuB/5r7v9NR8HRwcLiyklLyVt4t/Pf6Cf9/lt36VQWOyWfClzwDga2rmvWdfwlpfSkOF+6ucqAEXol1isiQlnwEtVpi/rb19tWSJqDpqE0C0S+LVpcqJisQlJB4hidIMEvQW4opO0Dw0gxhdoglJYlocOpKo2lp0LeCUtXyGAoluCrZCSDQMEtzK/erVJTEugnKv6qh2wi4whF2lFeZvfy0QwhIyBcUnzuDSNQakxfnFw5lzMtm6+TgtLYYZnmsXY1U/JYYG/uuMtOGfjbAEwPDoNSEEwiYMCjQ2bchjxswhuEwLrxCC+YuGszanvXsG2cpz63Woe9WaZ/BtfnvOUqFUYqSUrMk5zKLFo/zHEhOjGZCWwJHDxdjFzI7kj5VSoom2JYfwn4W1hYurEsO8aklVZR2nSqvJHqnq7vzwR98gOTkBgNf+8B8+3LgOw6hpd44ODp2huwTVc4WU8lUppQjNnxrSpklK+bhZ6T5GSpkkpVwipfxra31C+n9WSumRUrbqzpJSbpZSLpVSRpvjX2cWiDonCCFuB44BtwPq26D2kcAc4O9CiM1CiE6nF3MEVYc+QgO7dm3hl798E4C4uBh++tPlAFx++RIuvVQVoisoOMGTT/66UyMfPlbGg098QHNL6/+mRg3vz21fn89XrpnO5PEDI7pRA5vam5tbQnx8NP37JwSNNWVKFgcOFFJf39ipeZ5LcnL2snDhOL/wZ3HRRZNZsWJnL80qHMMwWLt2n1/4s4iK8jB+/BC2bes7QnVZWTU1NfVkZQUXVxw+PJ3KylrKy8/00szC2bLlCBMnDsPrDYS0Pffcff7f8x//+OeUlh4GOpZf2MHhPOJG1J3YISnlICnl4EgbgWqn04UQE8znP0LpEhcJIZa3dRKhuPscXYODg8N5ytayAj740985eegYAIPHZjPjkx9DSljw1S8Sm5IMwIEVaynathOJEkgjfX1srT8NQ+VF9Rmh7tSAhBaUKdR+Ay+UEBWlKaHTJSQe3SBKhwSPQaJHkpB/nKahGbjdGl4dojVDOVcnDqZm93GQBi5NmukBJC4BXh28mnLQejSIcStHrG7mSnVpAQFBCNA1iWbucwlhiqn2aw2uVxB4rSHQ0NAR6GxYU8C8RcPsPXG7XUyelsHWzcdte4X/0QrxF+YY9r4dElaD5oUpFkYofiT8ijPV1Q2UltSQPWoAVt5XEGQMSqKlxaC0JPKa0XKnBpypHZ1e4Fqs+xdC9gemKYLue/bsKmL4iFRiYr3YBds584az+cNjZnX54KJcocJq2OtO1O2KLK4Go1I3SFat3M+SpeP8809JSeKBB7/pb3fb8sdpMnLbdco6ODicvwgh3EKIv6DcqFaOj9DvH1vbrLYSmAZsFkJ8rDPndwRVhz6ARMqT3HrrT/0OxPvu+yYDBypnqBCCZ565F11Xi5VHH32J48c7/uXG6Yo68goqWz0+ZHASLz7xKZKTY4iKCg77CV3QWSjhby+LF08IO6ZpGosWTWDVqr7hUiwrq6a6uo6srPC8rsnJcaSkJHDkyIlemFk4W7YcYcKEoRHzuk6dOpz9+49TX992aFRPsWJFsDvZTl8SquvqGjl0qIgpU7KC9k+bNp6bblLFzqura/jBD55FFTt3cLgwEOoO68vmy9BEbEGYoUlbzJc3mvvWA/ea+54WQrwkhAhKkCyE8AghLgPWAo9209QdHBwuAJp8Lfz34A7e+/lv/fs+dfe30DSNFglRcXFc9O2v+o+999QvaGn2Ychwl6kVKm8ATYagBUxJSWFYBaWwh/qbrlDbc4kkSpdomqFym2pK8IzSDWJ0g6jmBlzVNZCWjNflI95tkOhRgmhsvIfo1DgajleE+Qh1Ic1NpQ+IcanXHhdE6xLNnKkSUPEXrYoU9hrImarZHKkaGi6/kIrQ2L+nlGHD+xET6zGvV/hdlqPHDuB4QSU1Zxr9b4Ym3GozQ/yt9u3XZoh0PGzWBN9WB99i56w8zKKlo1TeVyFASP8ICxePYM2qIxEKW0Uq3BROuJgZem2RhUn/+2WjsbGF3buOM31GaF5XicfjYvLUIWzdnEe4HtGeY7V9l2roNYUX4rJfIeTnlxEd4yElNS7ovDd//SrGjVcms82b9vL6a3/HkOUdPreDQ3uE/qs6282h2/g18BkCCZglkI8SWK8ikNLAjcoVOxiVP/YWVDFay1EkgCTgb0KIyDf5EXAEVYc+wBn+8Y+3WbnyQwCGD8/k1ltvCGoxduwIvvOd6wCoq6vnnnue7NDIhmHww8fej3hszMhUvnz1FD5/5XhmTRsccrTtBda2bUcZN671gk5Dhw6gvr6RU6eqOjTPc8mKFbu46KLWPxMWLBjH+vUH8Pl6N5y+vr6RgwfDhT8LJVSPZ/Xq3T08s3AOHz5BSkoCycmRCzn16xdPcnIcR4+e7OGZhbNq1W4WL54Q8ff5oYduJT4+FoCXX/4z27dvRuXqdnC4IFgCZKEWVn/oQPtXzcfrzGqkSCkfA74O1AD/BxwRQhQIITYJIXajcmW8A8zjAqta6uDg0DX+V3SA//7yd9RWqLXglMuWMHzaBFpMh2mLhMlXfIz0sSMBKD18jF3/DK8VYN18G5gh/qY71WjFnQqB8P7gMQx0VK5UHdNVqkuidYN4t0GMWxJ/NA8xNpM4D8S7IcoM73drqthU0rh0Kg+WQIsRyMeqq9B+lw4eTRWfcmvKseoRZsEp26aZwqsK/Q93S9rF1FD5wRJYm5sM9u4qZsr0jOCLtI2zcPEI1q3ODYxHINRfjR9aOMrq29Hb40A/zR/ir+Rr+3/HCyqIinaT6i/oZIqvQs0hISGGjEFJHDp4KmR8K7S+vfV5uFDatlwTEJ5D2bDuCLPnDkfXI70HknHjM8jLK6O2ppHWDF+h//l7t2MStURUdb32/LChHYVKZbXmEAsWjQzqD+ByaTzx1G3+/fd9/2dU1RxEyr6VNszBwaHrCCFuAa4j8Il2ApUHNktKeY+U8h9SyqNSygoppSGlrJdSnpRSbpJSPi+l/DTqXuG3BATZGOBfQoj4CKcMwxFUHXoZSUNDAbffHjD2PPXUPURFecNaPvDAd0hJSQLgtdf+zfr129od/flfbyT/eLComZQQReagBL71ldk8+oNLufu7i9G00MVU62PW1zexf38hU6e2nWLjoosm88EHO3s1zOTIkROkpMS3KvwBeL1uJk/OYvPmwz04s3BWrdrD4sUTwtIS2Bk6dAC1tb0rVPt8Bhs2HGDhwnFttlu4cDzr1u3vVaG6pKSSxsZmMjNTIx5PS+vPD3/4bUDluLrlloeR8gQdSznj4NDnsb6ZWy2lzO9A+9eBJlQV0susnVLKXwHDgR8AawAvMAUYCuQCvwIWSSmXddvMHRwczmvK6mv4YNuHbHjjnwC4o7xcvvz/kBIaDeEvOCWFxqW3f9vfb82Lv6G+WoV/W55HaxkpDWjwaTRJgQ/lSjWkOu53e4pA0SRNC9xhasLAqxvEuQ2idYnbzI3q0STRLkmMLomqqkJz6UQnxRCtq+JTUS7lLvWa4fvRbo2UsWmU71dfGFuFpOxOVZcmcaFEWLceEFJd5ma5ZtVqL7DesL74FcJ0pqKbzlTToSpc/hD9TesLmDFncLDwF7J0SRsYjxAaJSdqcQmvGs+fvsue+1SEPCfkeCQirZMs4TYwJ8OQrFtzhAWLsm2tQoplCZg5ZxjbtxTQ3Oxr5xyRMPxiol/G9c8/kis18igVFXWcLq9lRHZo7QhbfwGLFo9m9aoDIfO0NmHbK4OvM4JLNSCi+uhIkS2LXTsKGT0mnehod1B7S3y++GNzuOLKRQCcPFnG44/+mhZ5PNJQDg6dRkN2y+bQNUzB84cEPnjWAxOklH/vzDhSyiIp5VeBL6GCPwSqkNatHenvCKoOvUwZzzzzCrm56o/cRRfN5VOfuihiy+TkRH7yk1v8r5966pU2R66pbeTHT60I2ufSBT/76RU8+oNL+cKnJpKcFE1sbLDLtL3Qn9Wrd7NwYdvCH0BiYixpackcOtQ74fQ+n8H69QdYsKBt4Q9g0qRhHDlygtra3nEnlpZWUV/fyJAhkYU/OxddNIkVK3b1mlC9efMhJk/OwuNxt9nO63UzceIwtmw50kMzC0ZKyYoVu1i2bFKb7b73vS8zcuQwANas2cKf//wvoG8VKnNwOBuklDeaSfmXdrD9aSml1+zzVsixU1LKh6WUi6SUaWYi/gQp5UQp5dellGvOzVU4ODicb0gp+U/BPv7z5C8xWpRAtvQrnychbQDNBqZDFVp8Ap8UDJ06ngmXqo+p+qpq9r/9nn8sVWhKPTYZ6tFnEHCoSiWaWcImKNFSs/XVhArz92rg9rtElXvUq0sS3D6idAPX4UJixg8m1qXcqdG6Khpl5T+15LmEzGTqymppqm2yuU6VyBDjDuRMddkFXaz+gUJUpnSp/i9UblMVHBAqZtpD16Gqsp7T5XVkjUixt4jghhAsXJzN2tXHkFKG1UcID9MPz9saSuQ91jVY+VQDY+7eeZyRo9OIjg6PaLPnUnW7XUybMZQtmwoQiA7nSw2MZYmXwecPOlcrrlSL1SsOsHjZ6HbPNTAjESScPFkZ4WjrblVQ/zYskVXtay2sv3UaGprZv+8kU6YNaeX8iseeXI7brdI7PPv0axw9th2f0XfqWzicv9gd913ZHLrM91Ah+gBHgE9KKSvPdjAp5evAcgIfYsuFEK270kwcQdWhF2nhxIkDPPzwi4AK6X7mme+3KWjefPPnmTt3KtdddxVPPnlvq+0Abvvh29TWNQMq/GPC2DTmzx7Cpz8xjk9dNo74uFAXbPt5lE6dqqK2tpFhw1r/9tbO/Plj2bjxAC0tvvYbdzObNx9m0qRAIaK2EEKwePFEVq3q+XB6JfztbDMtgZ2kpDgGDEjk8OGeF6praxs4cuQkkyYN61D7KVOyOHy4iLq6nl/AHTxYREZGPxITY9ts5/F4ePrpe/yv77zzCerrC4hcEsPBwcHBwcGhLQ5XnWLFe//j0LrNACSlp7Lo+qtpNqDBUM5UHwKDgBy37Lv/R7+hg/nMjV9mylWfQkrwSeH/S+yT0GRoprNV0CQFkoCVJtRvaQWLgyo8FW2G4+umyOkRkgS3QT+PQYIb3EWluDKScUW50TSVFsBt3iW6NRBC+kUAXROkTRlMyfZCJdii8oG6zRQASiyVARHVElyF9AutdpTrNBB+b3dwCjQltNoSGKxZlcuCJVnhb7xfS7NG0IiPj2VYVn/27S32v0vK+Rpe8z7SHUCYU1W01drqo87d0NDCvr0nmTots9W29tmOHpPOiaJKqqvrg84hOnG7HikvantCKkBebhnxCVGkpLSrHQCwaOloclYdjGBuCA7Ttztnrdet51ntGOvWHGbu/GybscU6X/C6NTs7k+/dei0AjY1NfP+u5/CR26VzOzg49Cmutj2/U0pZ0dUBpZTPAzvMl0nAxe31cQRVh16kmHvvfYba2joAvvGNa5g4se1vRl0uF2vX/pEXXvgBGzYcatWlmJt/mj/8ZQcAsdFuZk7J4Nkff4LnH/0Uuq6FhPhb4T9tz9Zy/F10UduOPzsej4tp00bw4YeHOtynO1DC3wkmT46w4GyFzMz+NDf7KCnp8mdRpzh0qIj09OR2hT878+ePY+PGg/h8PStUr1y5myVLJnaggIFCCdUTelyobmnxsWnTIebNG9Oh9pdfvoRLL1VFzgsKTvDkky8DZedwhg4ODg4ODhcePmnwz6O7ePvpF/37Lrvl/xDeKFoww/QJFJGywvLj0wZw0xu/ZtRVVxKdVxQSKA2NPhXmbzlcg4KszTB6a2kibEKqV5PEuQx0TQmiOqpYVLTLR5zHIM4t8coW5PEy3Flpptgp8ejqJtEtzPypZi5UUA5Zb3IMmkuntvQMLiHRhUGsW5ruWNXfSj9gSZKa38kaQAiB0HR/OH9ASBWqAJWwZ4KFgrwK4uI89EuJCYxhGzEgx7oQuADBjFlZ7NxeSFOTJT+L4NbWe9fK0q5jjjKBylor/J3Wrz3K3Pkj2o1oCwwhWbg4mzWrjwbNUWGJwZHGUpK6QG/lWNsYhsGGdUeYv3Bku20t4uK8DB2awv59bdUKUCKnxMDA12UhFaC8rIYz1Q0My+of4Vzh3HPvV0lLU07mf/x9JatWrsZnVHV5Hg4fXew+8K5ujkn17BFCpAITUW/jQSnlP7tx+Mdszx1B1aGvUs+mTRv57W9Viovk5ER+/OPvdainpmnEx8eQmdmf/fsLI7a5/rt/QUr43JXj+el9l3DLzfOZM2MIw4ckh7RsX0i1OHz4BAMGJJKU1LFvby0mTBhKXl4JNTX17TfuJlQhoo4LfxbLlk1ixYrdPRZO39Li48MPDzF//thO9fN4XEyZMrxHheri4gp8Ph+DB4cu4tomMzOVxsZmSkoqz83EIrBx40GmT8/2hzq1hxCCp5/+PrquFuOPPPIrCgv3A83ncJYODg4ODg4XFmuLj7Hyj3+hLE+lsho6ZTzjPraEFgmG6U616rtH6So8H0w5SNNoSe2HVlOHVt/gX58aBjRJgWFAvU+j2Rbi7xFKxrMCrDXTSeoSEo9u4NEM8zwSXTPw6AZxLoO06EDRqfoDRXhHZ+DSNYQIOE1VuL9ExwzjF2pt6EMJo4OnZlC6swiXkMTogTB+y6VqoVytEl2z+VCFhhAeBG6sQlFBBZvMEH67W9UwJB+uL2DOgmEh77rqZxWFEuhoQve7S3VdY9acLD7ccMwUaNtQTztEW33V2KfLazhTXc+wrNR2z6U8mz5AMiAtHpdL42RRlW2eAcetOoMlxVjO3oD/szMztdixrZCx4zKIimo/os3OjFlZ7NiWH5L31ZqHfVNFpqQZ3i87mCc1EqtXtpaWIFKBLEhIiOPHD33L3+qO256iseVYr9a3cDj/8X851MXNoUvMtD0Pr+bYNf5HwPI+s62G4AiqDr2CxDCK+N73HvLvefDB75KSEip2ts3cuWPYvPkwzc0tQftXrz/GvoMljM7uz/9dN4PrPz+NKy4Zg6aFhsF0XEz1+Xxs2HCA+fPbz0caihCCpUsnsXJlz7gUS0oqaG72kZnZOeEPICEhhkGDUjhwoGcSt3/44SGmTRvRYeHPzsSJQ8nN7RmhWkrJypW7WLq04+5kO0qo7pm8r2fO1FNQcIpx49oOMQtl3Lhsvv1tFRpVX9/APfc8CRSfgxk6ODg4ODhceNQ1N/Huvu2s/NUfALX++/gd38SHoEWiRFUpkFKJjn4x1VCuTynVjsaRQ4k+nK/GAJoMtd969MtsQgmoiEAxH4+QppgqSTRzoWrmvigNYnVJvFsVo3IJ8NXU46tpwJ2ehG66U2NcASFONwVWzDMYhkRHCaTeGDf9MhMpP1pOlEvdVOpaoL01V7fNlRqQB3V0oaMJjeCVucpDqsL8g29Td+84yeixqURFBa8ZLbFRCA1NuNGEi1ApMTs7jVOl1VRV1gXyEPhD4TujbLTWNjgX6+qVB1m8dEz70W/+ivb414gLFo1gbc4xkAGpNHDugHhsFesSpicZpCkid2C6JvX1zRw8WMykKZ1bM4JKpzZjVhYb1x8lVMiMTCRRtePv/dEjpfRLiSM5OTSire219fU3Xsm06cq4sXvXYX7z8p/wSWd96+BwnmPPv7ilOwc2UwfkRThPRBxB1aEXqOaPf/w7H364E1BCzje+cU2nR3G5dGbNGsWGDQeC9q/blM+Xr57KK89dxcI5w/B69bMK8bfz4YeHmDp1BB5P54U/gIyMfkgpOXHi9Fn17ygqLcHudgsRtcXcuaPZtOnQOc/7WlNTT15eCePHR0oq3z5CCJYsmdgjQvX+/ccZPLg/CQkx7TeOQGJiLBkZ/Th4sKibZxbOihW7WLq08+5kgB/96LukpCQB8Mc/vsW6dWuBnnNWOzg4ODg4nK/8u2A/7/3itzTU1AIw5ZOXMmDMKHyYhaSkKkIFKp+pJQM1EZz90YiLwfC4cZ+uxGeoY8KAZrOvcooqQVQIaYqzquiUEjQlXiFx6RKXZqAJQx3XDeI8kli3Ct/XBdTtKcRrW4dF6yolgD3TpVn3ShXEQigxVVOC8JAJqZQeLsfX7POnHrCjBFIZKGolLHdqhCr0VmEqUyS0H2uob+bQgVNMmDwwZHzToWk6UgMCZMi6X9NZvHQsq1buD5ldd9jEgsfIPXqKpORYkvvFYd1zRDpXa07NmFgPw0eksHf3SXN0y4VqCsfmZl6Z//9n4/xcm3OI+QuyQ+6TOs7IUQMoKa6iqqqja0XZyvO28fkMPtxwlLnzszs1P1CRjU89c7v/9Y9++ALlFYcwjJY2ejk4tIGQ3bM5dAW7c+zUORj/FOpDql2HmiOoOvQwBjU1x7j77if9e5555vu43Z0LM7EYM2YwRUWnqa6u8+9bumA4n7tyApPGpUdMzN5Znam2toHc3BImThx6VnO0WLZsEitXnluX4oEDxxk0qN9ZC38AbreLmTNHhgnV3c2KFbtZunTSWQl/FoMGpWAYkpMnz51Q3dzcwubNh5g7t2P5SFtj3rwxfPjhwXMqVBcVlaNpgoED+51V/+TkRH7yk1v8r2+55WEMo4izDc1ycHBwcHD4KHCy7gwrN29g69/fBsATG8Pib33FL5S2SKsOu3KHqmr3Ep8RKNtjIYG6EUOIPlZIs6Fu1poJCLGWgOrWlJiqmyH1bnNcjyaJcatQ/ShduVSjTbeqV1dFoXQkTcWViFgveny0Gl0aeHVpWycr0dZatloirMt2/iiXYNS0NA5vKfY7YS2EAI8u/SkAAsHpwi8B+l2KQqDhUnlTgwL9FRvW5jNn/tAIwp9lkrAXrgq0UQKuet0/NZ6YGA+F+eUhKQa6Jqza5+rzGWxYf5j5C0YFjS1E8BUFKtybr0PuDaZOz2TP7pM0NbX4z9HW+QPzD2nXxvKt7NQZ6uqaGDI0pa3LC0NKiZTW74Vg8dLRrF55sBMjWI7qdiZoY+uWPCZOysTrDTW2tNU/cGze/Cl84ZpLASgvr+KhH/8SHwUdnrGDgx0n5L9PYNcxz4X7xxqzXTedI6g69DClPPbYS5w4UQrAlVcu5ZJLFpz1aEIIli2byIoVu/z7Zk8bwvxZoYuu8KqXHWXFil2dKkTUGnFx0QwbNoC9e8/NH3CrEFFXhT+AsWMzKSws48yZuvYbnwWWUzcj4+yEPztKqD53eV83bDjIzJmjcLkiJfvvOG63ixkzRrJxY2cWnR1HSsmqVbtZunRil8a5+ebPM3HiKAC2bt3L7373F6CqG2bo4ODg4OBwYfKPvN3898kX/OrjwpuuI86WykqinJq6pvKRakLlRrU7U60IdAOQbjcNA/oTe6JYOUPNJY7LFC29uhJOvZrErVs5TiHWJYlzSaJ0Ffbv0SQuDTy6QZQucWsqL6pLSOoOFhE1ZpCZj1US6wI3ZmkloYTXgCAp0ITq6zHHiXIpwTZtaCK1VQ00VjcGRdJbhawsMVWz3Kkht5/Kd2kVpbKXbFGcLqujrraZzKFJYf2EmTM1VHC03LChYumCRWNYu+YQhmGvPn82oqogUAgqIBJv32oJf+7wsf3zgfaERJX3dSgb1+V1YkaabVz7OcORUpppCdouBmxvL6WBlIH0Ehb9U+OJinJRWNAZc0NrbtrwXKh1tQ0cO3qKCZMywo7Z56W2gNir/ikG2j786HeIjvYC8OIv/sS+/VsxDCcKy8HhPKXS9rzdsPyzwBqzsq1G4AiqDj1KE3l5+3jiiV8D4Ha7eeqpe7o8alpaMm63TmGhqkpu5UoN3s5u7JMnT2MYkkGDOvftbWvMnj2arVuPhOV97Q42bDjAzJkjzyofaShKqJ4UJFR3F1Y+0mXLuib8WcTHRzNkSCr79kUuUNYVqqvrKCoqY+zYwd0y3rhxmRQUnOLMme5fwO3Zk09WVhpxcdFdGsflcvHss/f6X99zz1NUV+cSfNvn4ODg4ODgALD3dDEr//Nf8reqNVO/zAxmXvNp7EKbJXhG2wozWdkzDdufV8v15zOgduBAYktP0dKkIlu8ZmEnjymgWu5UDRWm7xEG0aZw6tGU2Blthv0nmO5UtyZxC6g/WoI3sz+aW/eH5FuFqEBZcjyaNI+pzaWBV1MuWbeucqNa7SfOzWDPhhP+61DCq2ob8E4GC6UKpdoqYdRqZ45rujrXrspl4ZIs9U7a1vYIjfAFvrnm94uXweeLifYwesxAdu0ssPeg84JqOHV1jRw5XMLESYF8pPZiUq2ZO1ozBAwf0Z/T5bVUVgSbG0LvcQL7I6RRaIUjh0tJHRBPUlLbEW1KoIwkfgbnTF24eBRr1xw+C3ODfezIfXNWH2LhopFh711A3G1tvIC7GiAzM507774BUCaUO297mmbjWCfn6/BRx/pU6Y7NMal2CXseva6FEUdmMOpHdLK9ho6g6tCDFHPnnY/T2NgEwK23Xs/IkcO6ZeQlSyayenX3uhSV8Ne1fKShuFw6c+aMZt26/e037gRnztRRWFjG2LGdTyrfGunpyei6zvHjZd02JsDevQUMG9Z14c/OnDmj2bIlvEBZV7EKUXXVnWyhCpRN7HahuqmphW3bjjJ79qhuGW/Zsrl85jMfA6CkpIyf/vQFzk16GgcHBwcHh/MXQxr8+/BO3nvmV/59H1v+DdweD2C7cRYqzN5yfRqGrUgVwu+oa8F01wFCE1QOGUJKfp7f4amh3KGBsFEVpu/WJElegyiXEly9puDpsQRQTaUAEABNzTSeqMCblYpXV45Ur1lMSgj16NYD6QXsLlO36Yh1m+IuKOdtckoUsfFuSgurTeE2WDCw5061wt+FcKELFeYPtlJFwpTEpOTYkXJSUmNJTDLXjOYyP5BLFJuo2v5aTSKZMm0I+/Yep76hieBssdYYocKdXZgLFhLtztg1qw+ycPEYhBD+ccPHD8y/I/NduDSbnFVHQuYjgzb7/lCBMdJtUUuLj80f5jJ77vBWxg04PtsuNBUQQqOi3IwcOYBdO8+2qG3kc5QUV9PSbDBocHJw6zbv94J/RvZruvW26xicmQbA/97byDtvv4vPOLf1LRwuPMxP7S5vDl1iq+35x7pzYCHEbCDBfLm9vfaOoHoBUl1dzWOPPca8efNITU3F6/UyePBgli5dyo9+9CMqKysj9ispKeH2229n9OjRREdH069fPxYuXMjLL7/cDUJlLatWreYvf3kXgAEDUvjBD77VxTEDxMZGkZ2dwc6dud025r59hQwZkkp8fPcJfwCjRg2iuLiCqqrabhtzxYpdLFvWfcKfxdKlE1m1qvuE6ubmFrZuPdJtwp+Fy6Uze/Zo1q/vvryvx4+X4XLppKcnt9+4Ewwc2A9NExQVlXfbmOvW7WPu3DHoetfSEth58sm78XhUbuNnnnmVI0d2oUpndB+PPvpoRJdFJM7t55ODg4ODQ1+nL65vV584yv9+9wZVJ0sAGDFnOiMXzAlyIUmUsOXSAs+bAcMQajOnYJgCq/UopaQ2ORl3UxOxDbUqVN/8M6+ZOVijdEw3qoFLU2H2VnEoXRh4XZIYl3LH6pqaw5k9x4kZNwhNqHMJwC2UyxWUPmm5VrGEVKFcr9GugNCKOoyOej1h9kD2bipBk0ZQjkBVkEoz85xihuiHhulbApgl5vlo8fnY8mEhs+bazQIaSCvMPrKzMfhHauUqDYhsmiaYO38k69YcJOATDv89sMLHWx83IJaWllTT1NTCoMHJyKDz2ccPPYdo9/cvJSWWuHgv+XkdEf2ssYIjioKvQbJlcx6TpgzG43HZwuTbE0/bOydMnT6EvbuLaGxs7uQYrYwsJTmrOp6WIPLcgt+LmJgoHnnsu/7Xd97xLHWNh7p9Hemsbx0czi1SymIgF/UPfYkQIrEbh/+07fn69ho7guoFxsqVKxk1ahT33HMPGzZsoLKykpiYGIqKili1ahUPPvggeXl5Yf22bt3K+PHjefrppzl06BAul4szZ86wdu1abr75Zi677DIaGxvPclYSn6+IW2/9qX/PT3+6nISEuLMcLzIzZ45k1668bvlD3tzcwpYth5kz52z/iLdOd4fTHz9ehq53v/AHSqgeMWIgu3bldct469btZ86c0V3ORxqJ0aMHcfLk6W4Rqq18pEuWdE9aglC6U6iurKyltLSKkSMzumFmAYYPz+T2278KQFNTM3fc8RhQ3G3jHzx4kAcffLBDbc/t55ODg4ODQ1+nL65va5sa+e+eraz5zRsACF3j4tu+iWGmmhJCyTktMuBFNCT4DGjxCVqk8Ms9UkKTIWjyqUdNU7KcBlQMzyIl1zIMSASGKZqqXKleTRJjaoxRGnjM1Jke0yVq5UQFaKyow9fkw9U/AR9qfrqmilpppjvVZYqo1tw01D63mUNViMANpBASjws0DWKjdEaMSebAzrKIgefC/L9yp0ZwaPo7qbXR9i1FjJ+cjtujhzQJ6ReylBIRDwbn3hyWlcKZ6nrKy2pCjnccfzZUKVm1ch+Ll44NO09rQmpnmLdgOBvX52IY7aVeMp2Y5vOwo1JSW9NAfm4548ZnEPzedB1NE8ydN4J1a46037gDHDxQTMagJOIToiIcjTxnJXEbpqgdue3nrr6YefMnA3DkcCG/eP4NWuQJugtnfXuBY3ftd3FzYv67zB/NRy9wR3cMKIRIAr5hvmwC/tZeH0dQvYBYt24dl19+OSUlJVx88cWsXbuWxsZGKioqqKurY8uWLdx3330kJgYL+FVVVVxxxRWUl5czZswYNm/ezJkzZ6itreX555/H7Xbz3nvvsXz58rOcWSW//vWb7Nyp3IPTpo3nxhs/28WrDUfXNebNG8vatfu6PNb69QeYPfvcCH8AAwYk4fW6KSjoWhh1dxUiaotZs0ayc2cuTU1dE6qrqmopLq5g1KhB3TSzYLpTqN61K4/s7IHExkZaxHWduLhosrLS2LMnv8tjrVix85y4kwG+//2vMXBgKgD//OcHvP/+CqCm7U4dwDAMbrrpJhoaGpg7d26bbc/955ODg4ODQ1+mr65v3z6+j3f/38s0NzQAMONznyR1uErlJjELTEklTLrNOy4poVGCj2CZp1maKQDM3oah1jWaBr6YaJpiYogrL0cX0sxjqkRUj2amANACwqkQEiEM3C71XLfcpoakenchURMDjk/NnJQubOWTbLlRQYmt0S7wuqU/9N8SFazQfiutwciJ/cg7UkVjQ4t/LImV3zM0j6rtHRDW+6P21dU2kZ9bwdjx9lojwtZP0qoa0cHl0OJlY1i9srXIpo6lD5AYHDp4koyMZBISogkXcCOMHLRea/88UVFuxoxNY+f2onbbBrsyQ+cgyVl9mIWLs8+6tkTr51TnGTa8P1WVdZSXd22t2NLiY9uWfGbNGR52TLlprTPLkOftO22FEDz1zG3+n8NPH3qZktK9GEbX04Y569uPBqKbNocu8yrqzynALUKI7ih68gAq3F8A/5RSVrTXwRFULxDq6uq4/vrrqa+v56qrruLdd99l/vz5aJr6EUdHRzN9+nQeeughsrKygvo++eSTFBcXEx0dzdtvv82MGTMA8Hg8fPvb3/Z/y/arX/2KQ4cOdXJmBpWVR7nvvmf8e5577t5uDU22k509kPLyM1RUnP0f8qqqWk6ePM3o0edG+LNYunQiq1fv6ZJLcdeuPEaMOHfCH4Cu68ydO4a1a7uW91WlJZh8ToQ/i+4Qqpuamtm5M5eZM7s3LUEos2ePYvv2YzQ1nf0CLj+/lJgYL6mp3RnlECA+Po5HHw184XfrrT+lpaWIrroZfvazn7Fu3Tquu+46LrnkkjbbntvPJwcHBweHvkxfXd8er63gg5wcdr79gZpHYjyLv/YlQIXVg/pL2YJZvAklpvrMLchnKcEnBT4p0E2R0i4ZgaRySCbJhYW4pYEulGPUJVSYv1sL5EsVSNxCEuuywvQDomf9iQpcSbHoMV7/6LqQuHRVQMql4c+LipnXNUpTLlhNyKDwfl2o8b164LVLSNy6YPq8dLauD0S0qPB+JaSGhkCbb4V6blsPr8vJY96iYa2sGYPXIJHbiLB2oSQnx9IvJY6jR0rbaGUrWRXhNC0tBls25zJr7ghT0OvMGldaft12W06cPIhDB0tpqG/d3OBPQCAtcTE4jUJxcRXSkAzMSOrEHDuDes8XLx1NzsqDXVoqbtqYy7QZQ3G7g+8XlQM34MG13jvZjogaPEfB1GljufGrnwSgurqWB+5/gRbyzn7CJs761sGh55BSHgNeQf3jjwX+JIRwn+14QogrASsniA/4YUf6OYLqBcLvf/97jh07RnR0NC+++KJ/odkRfve73wFwzTXXhC1GAb773e8SFxeHz+fjtdde6+TMSvjxj39OWVmFeY7LWbBgRifH6BwXXTSJDz7Yedb9V6zo3kJErREd7WX06EHs2HF2FSYt4W/WrJHdPLNwRo7MoKys6qyF6vz8UqKiPAwYcG6EPzuqQNnZC9Vr1uxj3rwx6Pq5/XjUdatA2dk5qg3DICdnD4sXnzt3MsCXvvRJZs1Shdn27j3ML3/5R+Ds87/m5uZy3333kZKSwjPPPNNu+3P7+eTg4ODg0Jfpi+tbKSX/zt3NW0++4N+3+Bs3EJWYEOQ88hlWflKwxB6rEJWdFin8YfQaoAx4ZoC8uZRxezSaBqWRdOKkX0R1aSrXqUsECk5ZjlWPLoPaCcOg5lAx3tED0c3ZeAREuSBaD3VNqYIpupk31asH8q96bAJulB4oWiWs/KvAoMw4GutbqCivRwCa0BFCQxcuBMECWaSldmlJDT6fZGBGgik3BtIDSCTBb58IeYRAg/bX8XPnZ/PhhqP4fOHh9EJo7Ywg2fzhUaZOt4S/zqYN6LhTVeV9zWLdmvbuGcwyWDK4FJaUkjWrDrNoybm8Z1BnTO4XQ2JSDMeOlp7VWvxMdQNFxysYPSY9eHQZkFJVSL/PutqwMUSQxBHZE/ijH3+D+PhYAF759T/Zsf1DfEZdp+dr4axvPzp0W8i/Q3fwA+Ar5vYi0JUceAL4qjnWlVLKgx3p5AiqFwjWh/KnPvUp+vfv3+F+Bw8epKCgAICPf/zjEdvExcWxcOFCAN57771OzKqJAwd28LOf/QGA6OgoHnusW9JbtElKSgIJCTHk5nY+32NBwSk8HjdpaUndP7EIzJiRze7d+WeV93Xt2v3dXoioLZYtm8yKFZ0XqpXwt5clSyacg1mFExPjZdSoQezYkdt+4xAqKmooLz9Ddnb35iNtjZEjMygtraKysvNC9fbtxxgzJpPoaM85mFkATdN49tl7/a/vv/85ysuPonw3nefmm2+mtraWp59+mtTU1DbbntvPJwcHBweHvk5fXN9uLzvOir/9m6K96l5rwIhhTPvM5UDgJllK9VfSa4qpQoAhgssf+WUgIVWuU02F3jfbPIsuM8Q/WgdfxgDcZRXozU3owsBl5j3VhSVMGsS6JXFu5Uz1mvlQAaoOFhM1fACaSxWG0oXEpUm/i1UTEl0zq08LcGuSeLcSXP2OWAKylGpvisBCzVszi1VpwJxFGWzIOekvIKWE1GAFwe8utFWrl1KybnUuC5YMi+zcFKEypGYWXbIE3c6pFF6viwmTBrNtS17kBlb+gzARV3DmTAOFBacZMzaD1gpbRcIKWRcI//hCtO9UHTK0H3V1Tba8r62eAasYljWjA/uLyRySTFz8uYtoszNvwXA2bjiGz2d0WlRdveogi5aM8htbrKJZlogaLFxbRb/s6STs90WtBVgL0tP7c+/9N/nPcfvyp2iRx87akOGsbz8qSDOtSte37spf/FFGSnlKSvk725bfhbH+ZRvnvx3t5wiqFwCNjY1s2bIFgMWLF3Ps2DFuuukmBg8ejNfrJT09nU996lO88847YX337Nnjfz5hQuuCl3Vs377OuOlOctttj9LSooSXu+76P4YM6RmhatGi8axZs68DCdwDSCnJydlzTvORhqJpGgsWjCMnZ0/7jW1UVNRw6lT3FyJqi/79E4iLiyY3t6RT/XbsyGX06EFER3vbb9xNKKG68wXKPvhgJxddNOkczSqcs8372tDQxN69BUyfPuIczSyYuXOn8qUvqdCoiooqfvSj54G2QuQi89JLL/HBBx9w8cUXc/3117fb/tx+Pjk4ODg49GX64vq2ydfCO0d38O7PfuPf97Hbv2kKlQHRxpAB4dFyYRpGwJ1qaTZWGL2BEi0loJvuUIHEpatCUB4NhCaoz84k5kg+Xl0SrQekIyECQqolamqmXuerb6KxtBpXZgou000apyvB1R++r6m2mgCvkHjNYlfKBWsKqpqZn5VAqL8mbCIrAf0xIdFD/9QY8o+dQRNaiGPQvPII+uGhA2VkDE4kPi6KMCEsSHS0nnddlJg4aTBHj5yirjZS8Z9QQS4wn5yVh1i0ZHQr+Ugji6OhYp2wjy1aS18QYNGSbHJWHQ0I0K1euylSS0lTcws7thUyY9awNsfuTrxeN+MnZLB9mxINOypSFh2vwOXSSEtPtPWzXKih93ShvurAb0Xw8cBrVRBN+H9m3/nuNWSPHALA2jXb+euf38InOx+F5axvHRw+ujiC6gVAXl4eTU1NABw/fpxJkybxm9/8hlOnThETE0NJSQn/+te/+MQnPsE3v/nNoL4nTgSqGg4a1HrOUOtYdXU1NTUdcdPV8Pbb7/HOOzkADB6czl13/V8nr+zsiYryMGHCULZuPdrhPjt35jJy5CBiYnpO+AMYPjydyspaysvPdLjPihU7ueiiyedwVpFZvHgCa9bs7bBQ3djYzJ49+cyYkX2OZxaMrmvMnz+WNWv2drhPbm4x8fHRpKQknMOZhZOamkhMjJf8/I4LlDk5e1m4cFynQh+7yqOP3kFMTDQAL7zwOnv3bgc6Xnm0qKiIO++8k+joaH75y192qM+5+3xycHBwcOjr9MX17coTB3n/pT9ypuw0AKOXzCNr1lQg4E41pBJ33GYxKE0oX12LqSmpv9wySA6UgGbWJ7dC9S1x1GO5XoGolHh06cN1ptZ/Tk1IdJSL1WVGx7v1wHwqdh8nbsJgvLpAExCjS3Rdomky4DYVSpx1CZUzNdZlCqjgL1gFmO5VlQLAkrEsITaoHYKZcwezffNJWlrCxTRL0LILbc3NPnZuO8GMWYMJEyTD9NdQgdOajbQ17JhbVQjBgsWjyFndXp7KwJrrRFEFmi5IH5hA5wTdSOHprYu2oSQmRdM/NZajR04h/bVY2jqXZNOGXKbNGILuamvNGMnF2TUmTh7E0cOl1Nepf8NtiapSSgzDx5rVh1i4aCSmFtzOXEPft9AOEcT2kEv0eNw8/uSt/tffv/s5auuPYBjtvbcBnPXtRwv1mdk9W/f+i3PoLRxB9QKgoiJQfOyRRx7B7Xbz+uuvU1NTQ0VFBQUFBVxzzTUAvPjiizz33HP+9mfOBES8mJiYVs9hP2bvExmDpqZ8brvtEf+eJ564yy/G9BRTpmRx4EAh9fXtiz6Njc3s2pXX48KfxUUXdTycPi+vhLi4aPr371nhD5RQPX78ELZt61je15ycPSxY0LPCn8WIEQOpqKjh9On2hWrDMFizZh+LF/dMWoJQFi+eSE7Ong4J1WVl1VRX15GVld5u2+5k0KA07r336wD4fD6WL38EKU92uP/Xv/51qqqq+NGPfsTw4eFVWyNxbj6fHBwcHBzOB/ra+raysY7Vu7ey5vd/BUB3u7nolq8hpXK+WSKQJOD8FKh9hgGGlRdVAAh/JLkBeIRE0zCLQ5mh/FrAZap6GES7JNq4oWgHC5BSCaKxboh3S38xLLdN7GwsPwNS4ukXh1tIXMJQYinqJjDKpXKugjpXjCtQoCrIfSpAoFITeF2BG0ghAo5YsNoLNOHC6/EwcfJAdmw92aHK9ls+PM60GYNwuVxB+8PD4e1ni+TqDEuqEOF48DZ4cDItzT5KS6ojtPVfFZgpBtasPsSiJaNaHS/Sddqr04cSyBVrZRgI6WvmDZX4mDV3CFs+LDDzvrYt5lZX11N8soqRowa02S54zt0j8wghmL8wmzU5AaHant4h8Fpdx97dJxg+oj8xsa0ZW/w+6E7MMfj9iSTSfuLyBXzskjkAFBQU88xTv8XgRHjDVnDWtx89tG7aHC4MnJ/lBYBdhDEMgxdffJFrrrkGt1sVOcvMzOS1115j6lT1DfpDDz3kD8M/N5zm5z9/jYMHVQ7L+fOn8YUvfOIcni8ymqaxaNEEVq1qP5x+zZq9zJ8/9pwXImqNfv3iSU6O4+jRtgUqJfzt7TXhD2Dq1OHs319AfX1Tm+3Ky89QVVXH8OE9K/zZWbZscocKlG3depTx44cQFXVu85G2RnS0hzFjMtm+vX2hurfcyQC33fYVhg5V35b/73/r+Pe/3wHaX+D94Q9/4D//+Q9TpkzhtttuO8ezdHBwcHC4EOhr69t3ju/hX0/9Cl+zSic069rPkjhY/U0M1FNXWOHw9v2CgDiqmfbUFqmcqZbD08pTakeiQu0TXEoN0qM8iOR4OFmOVzNwa9J0pKq8fFa6AJBU7DpOwqRMvKZIGudSuU41gT+kXwiruJVBlA5ePTAHv5PKfPRo9ptHGRAH/CKghhAamnAh0Bg9LpXj+VXU1gTWjJHcqWeqGyk5eYbsUSG5J8N0s0hCWncIgKo6/eqVByM6Kf1iJ4K9e06QNbw/sa0Kf2q8s0PYHuzXFRBqPR6diVMGsm3zcZTM3fq5csxCVEKoX8a22obPo+vi6uDMZBobWygttQvVZk5UGRCEm5pa2L2riGkzhkaYR7DgHGmu1n+RafuahRA88dRyf02Kxx99hfzC3RhG2/c54KxvHRwcHEH1giA+Pt7/PDMzky984QthbTRN4/bbbwegrKyMrVu3hvWtq2u9sqH9mL1POC2Ulh7mwQefB9Qfqeeeu6/dnEDniqFDB1Bf38ipU1Wttjl9+gwVFTWMGDGwB2cWzsKF41m3bn/ESqMW27YdY9y43hP+QP0uLVw4gdWr2xaqV6zYybJlPZePNBIpKfEkJcVy7FjrBcrq65s4cOA4U6d27Fvlc8X06SPYu7eAhobWF3CHD58gJSWB5OS4HpxZgOjoKJ588i7/69tue5TGxgLC81oFKC0t5dZbb0XXdV566aUw50lbdP/nk4ODg4PD+UJfWt/mnSln5Qer2LtiHQCxKf2Y85Vrg6Qaa6Wro0RSUzP1/4W02vqkOmKYxU2izfB6extpE9DcmoGbgAMVQIzIQOYVE6MpEdRtpglwiUAe07r8crxpCbii3GZYv3KfghJQvS4V4q8JSZQO8e5ALlV7LSaBEmg1M9TfmqElzGoiIL4pN6nlf1Wv5y3KYl2OVSjU9M7aREuBYO3qPBYsHh64XxCRnKn2d9mU0UTwrWxXbjfiE6LIGJTIoQN2c0NwsavGpmZ27chn+sysDo7aOVEyWDSUrTyHsePTyM89TW2tGU4fQSo9XliJx+MidYBtzSjbkhdD8rv638yu3cMtXjKKnJWHMGTrjtoN644xa05WmLEl/Kcf6b/2CA79j+RSHTtuON/41ucAqK9v5Aff/xkt5LU5qrO+/egSKenE2WwOFwaOoHoBYM+9MmbMmFbbjR071v88Pz8fgIyMQFGjoqKiVvtaxxISEoiLa0vMKeX++5+jqkq51r761auYPr333JRgVaff1WrunhUrdrFsWe84/ux4vW4mThzGli1HIh6vr29i//6CXhf+AIYNG0BtbUOrQvWRIydJTo6jX7/e/+O/aNEE1q5tvUDZ6tW7WbRofK+kJbCjhOpxrQrVPp/Bhg0HWLBgXA/PLJirrrqUxYtnAXD0aAHPPfcqcLrV9nfffTfl5eV87WtfY8yYMdTU1ARtVn48IGxf938+OTg4ODicL/SV9a2Ukndyd/KvJ17w71v2na/ijY3xh8ODJZxKdE3l8bRC4VV+VPUaQNcMPJoKtffawvNlWMpHJbwmuiUJXuk/jwa43ILo7HQaD59EN0VRSwTVAHw+qg+fIn5Uuj91QJSt+LkQKrWAJcR6TJerXZBUc5bomsrJ6ra5UzVhCseW/umvVK+hCd0Wvq6RPjAeKaHk5JmIgmfR8So8bj0g/LVa8T5CYatuZtac4Wzdkm86naV51sB5N6w7wqw5w7s1oi0Qzm+0kRc1+FqFEMxfPJy1q49GbCGlZP3aYyxYNDxMwuxwsLyUgZ+rsELtO9jXf02S+MQo0gfGc/hQZHNDZUUd5eU1jMgeEHoVpkzcPQ7k9pyq9z/wNVJSVDGsN17/LxvWr8UwWo/Ccta3H126K4eqw4WBI6heAPTr18+/6GzLCRr0bbDZzl5Z0F5xMBTr2LhxbYk5DezYsZmXXvoTAPHxsTz88PJ253+uSUqKZcCAJA4fDs+Hc+xYMYmJMaSk9L7wByrv6+HDRdTVhed9Xb16DwsX9r7wZ3HRRZGr0/t8BuvX72fhwvG9MKtwlFA9NKJQfepUFXV1jQwd2l5uqZ4hKyudM2fqKSsLzeEFmzcfZvLkLLxedy/MLIAQgmefvdf/e/jQQy9QXHwQiBxmmZurXCkvvPAC8fHxYdsjjwRyLVv77rpLuWC79/PJwcHBweF8oq+sb7eWFfDem/+g5EgeABnjRjPhExcDyhFqheuDxGu6RD2m/mTPTKlJiY7EhXKkuoQSKX0Ecq8i7A4mg3iXEl81If37XcLAKySJQ5JpPn0GraEpqICULqBy70nix6Sj6RqaABfSL+iCGs8tVM7WGF3i1UOLSmEKtZbj1vScmmkFAkVVLLFNQ6Ah0P3nsLNgcRbrcvLCnKlIwfo1ecxbNMx/4o7pDCLkd0J2yZ1q4XbrTJs+lE0bc/37pPlfZWUdZaeqTeHPnGyHiCzkqVF9KCm+fcEv9HwDM5KQhqSkOHzNuGfXCUZk9yc6xhM2bhu1odqch7A5kdu6dhk0hno+a+4wtm0uoKXFLhircVavPMTipaODz9XOXDqPNV8Z9GAnOTmBBx78hv/17cufosk42qohx1nfOjg4gCOoXjBccsklAOzfv7/VD/79+/f7n2dlqVCV0aNHM2TIEAD++9//RuxXW1vLmjVrgs4TjkTKE9x660/957///m+Rlta/09dyLpg/fywbNhzA5wv8ITcMg7Vr97FoUe86aO0IIVi8eAKrVu0O2l9WVk1tbQPDhqX10szCSUqKIzU1MUyo3rLlCJMmDet14c/OlCnDOXgwWKiWUpru5N5NSxCKKlAWLFTX1jZw5MgJJk0a1juTCmHKlLHcfPPVAJw5U8t99z0DlHT7ebrv88nBwcHB4Xykt9e3Tb4W3j24jfd+8dvAnO74JrqmqfB6wBJ+XCIQcq8Jtc9yp1rh8nFuiDYTpepC4mtF39GQeDRJgkc5RAVKhPXokOCBeA+4dUH/SZmU7SxU0pQ5n5baRhoq6vBmJKniUlih/koMjTJzqXp1FfJvOWntEpkQ0l+YStesIlVKtLQEVrNl0LwDIfjB+2PjPGQOSeLg/lNBx/fuLmZEdn9iYj2thPgH3pGg84Qd74ya2rYgOHpsOsePV3DmTENQCP6qlftYsnRchwpsBVA/WfW7G9q2/dyegbNEdoguWDyCtauOBomkjY0t7N1TzJRpg4POJYNEzjbPHOgV8m/Ocqy2hgxKchEYz+12M3laJps35QXNIz+vjLh4LykpcUHjdzzXa0cJEXlbuYSbbv40EyaqAsVbt+zjD7/7Oz5Z2s1zcda35zP+z9ru2Hr7Yhy6BUdQvUD4yle+AkBhYSFvvvlm2HHDMHj66acBFUI1bdo0/7Hrr78egDfeeIO8vLywvj//+c+pqalB13Wuu+66VmZQzV//+harV28CIDt7KN/73vVduKLuxeNxMW3aCD78MFBpcsuWI0yYMLRPCX8AmZmpNDY2U1JS6d/3wQc7ueiiviX8ASxYMC5IqK6ra+Tw4SImT87q5ZkFE0moPnz4BAMGJJKU1LdCaJKT40hJiQ8Sqlet2s2SJRN7LRdxJH7yk1tJTFTO7lde+RtbtmwEwvNArVq1yl/VNdL2wAMP+Nta+5599ln/vu75fHJwcHBwOB/p7fXtB0UHePsXv6OuUrkAJ358GZmTxiEQ/vyjVsGmaF2F0Xs0GZR/1K0psdVabRo226q0Qv1tDjq3JolxSfp5lagpsdyg4BEGbg10TeVEjU2JQXNpNJWdQTdHObW9kIRJg3GZMaWB9koMdQnLkSr9xbOs4CdhCr1uEXC8aua8LAJFqlQBKmG6UzVhzyEZLlpNmzWYndtO0NzsQwhBU6OPfbuLmTwtg85JCyLkMXh+nRsngjQrhJn386A/DD8/v5TYWA8pqbER+kSau0CZTQJV7IPyeJ6F+9Iq+mXNWwDx8VEMHpLMof2BL7U3rMtlztxhQWkJZIjI2dqXE1broLm1Oc22xOng/WPGplNYUEHNmQZA/dtdv/Yo8xeOjDCv1vPHdgetXb/L5eLJpwPFpX5w7/NUVh8yf4bBOOvbjy5aN20OFwbOz/ICYeHChXzucyqZ9je/+U3efPNNms0qpIWFhVx33XVs374dgIcffjgobPyOO+4gPT2duro6Lr/8cn9C/6amJl544QXuv/9+AL72ta8xatSoCGeX1NcXcMcdj/v3PP30PXi9vVc4KRITJgwlN7eEmpp66uoaOXiwiClT+pbwZ7Fs2SR/3tfDh0+Qmtr3hD9QQvWUKVls2nQYgJUrd7N48YQ+JfxZDBmihOrS0ip8Ph8bNx5k/vy+GUITEKoNiosraG72MXhw33B7W6Sm9uOBB74DqIXiLbc8jJTFdPfCt+ufTw4ODg4O5yu9ub4tr6/hvS0b2finfwHgjvJy8fduUk5UM0+qJXS6hcRlipYCQKoQfV1T4f9uoYo4SYk/S6YljymkX+D0aJJoXRLjxv8nVQmYBrFucOmmQGu6YFMmDaJsVxFSSupKqxEeF+7EGDUeBl7dzJWqg0dXxajcGn4x1S6JqQJUdtHUvFbzWvxFr2zCXuBqgp2bdrepQOB2uZg+czCbNxQCsHFDPjPnDsHl0tuRUyO5UwXB642urDvD+6alJ6LrGkVFpzEMybo1h4OEv/aRpmjXtTVR6HpaRHi/Z8wawo5tx2lu9lFxuo6qynqGDU8JHwtLyPVPsa0z25qFpgxovaOV9kE9Ctujcp4uWjySNatUCq6d248zdlw6UVHBxZwiiZfd5+Vr//qXLpvJpz69BICSktM89sjLtMiCbjp/AGd96+BwYdDxcnQOfZ5XX32V0tJScnJyuOaaa/B6vcTExFBRUeFv88Mf/pAbbrghqF9iYiJvvfUWl156Kfv27WPGjBnEx8fT0NDgX7RecsklPPPMM62c+RTf+96Pyc8vMtsu4Iorlp6Ta+wKQgiWLp3IypW7cbl0Fi+e0GfykYaSmBhLRkY/9u8vZPPmw3zxi4t6e0qtMnHiMF57bRUDBybT1NRMZmZqb0+pVZYtm8Q772wlM7M/U6cOx+Ppmx+BXq+byZOz2LTpEHl5JVx++czenlJEvv3ta/nlL9/g4MFc1q/fzhtv/IMvfvGrQFK3naPrn08ODg4ODuczvbG+lVLyj7xdvH7Xwxg+JfAs/Oo1JKalohPId6pJJThZOUh1TWJI0KVAM6O8DX9+VIlhBEQxaWpElq7j1pR4Ge9WuVjtwdMaqmiU5RjVzGJRAJ4oN3GDkqg6WkZ1Xhn9549UAq5Qof2aLaTfJSwhVfrFVEs8FZhh/uAvbuWyHLiSIAnVCu23rkUTtopXhAqB5gmA7FGp7N5ZzPGCSipO17NoaXarP3d/36BXotVjXSPgELZYtGQU//rnTkaNTmP02IFER4fnI22btoRHS7puu13kvsH3Li6XzvSZQ9i8MZ9Tp2pYtCTyeyoxgvpKZBspFoLF6mARNVL6gvZ/FgJB+sBEhCbIyy3j4IFiPv/FmR3q2/25VCNfhcWjT9zCO2+vo6mpmf/37Ot89f8+w6gR6ehaVDfNw1nfns+olC4ODoq+qSY5nBWxsbGsXLmSl156iUWLFhEbG0tNTQ2DBg3immuuYd26dTz44IMR+06fPp29e/eyfPlyRo4cSXNzM7GxsSxYsICXXnqJd955B6/XG6FnC88//zwvv/wX/55nnvl+n3QoAmRkpFBX18jp02cYMqTvCn8A8+aN4f33dzJp0jA8nr6VlsCOEIIlSybyj39s5KKLJvf2dNokMTGWfv3i2bu3gAkThvb2dNpk0qRh7NqVS2pqEgkJMb0yhxkzPsvgwYta3YYPv5iyMnVDO3bsWL785TuZNm0+4fmzusbZfz45ODg4OJzv9Mb69lBlKY9/7VZKjuYBEBUfx/wvfQ4dlcfUo0lTtJREudQNtlszcJvh8ppmCp7mfXfgr6Iq22TJjxrgFgGhM0pXRasQYKX9F6ZgG+2S6JoqUuWx3cEJAcmjBnB63wmiM5JwR+nopivWo6v8p0ogVa/deiDU3x/yT+DRUpnsoq2uBdoI4fK7D1XzUAdpsDNVM12Kaq6ChUuG8/Y/97FoyYgOyXCt01ZY+NkIHsF9YmK9DBnSj62b8pgyNbOVuZm5cv2h3maBsbYrP3WKjtxTZY9K5djRMqKiXCT3a33N2D2zam8+oe7lYBYtHsnbb+1h7vzhaFqo07itGXbfveW82dczYujlDB8Sebt4ydfxeN1omkZW1nDmzrweg+53qTrr2/OT7sqh6nBhILrzA7+HOO8mfGFTyDXXfIM333zbv+fee29jwoSxvTintpAUFpYDksGD+/dZ4RfA5/NRWFhGQkI0/fol9PZ02qSmpp7y8mrS0pKJiupbqR5CKS4+TWNjC5mZqeZCrm9iGJLCwlK8Xg/p6cm9MofvfOdOTp+ubLddRkYG06dP59///jfx8bFUVx8F+k4BNQegB3PfCyGOxQ8clHXtn9/qlvH+ePUVnDlZlCulHN4tAzo49E2c9W0focXw8fyuFfzosmupKikDwBvl5ZYf309cQnzYh6laSpjh/gKkFP5iU1a+VH9mSHv+VBnqA8TvEJW2vi7NcpIGwvHD5ENDUn/qDJrXTXRSNIB/Pv55Im1uVPOPgu15cJGUgIPVPz9hiaVtuUbNhgEJlVDq65ooK6ulf2oc0dFnaxhoq4BVoE3HaP2f3qlTZ2iob2bQ4KRzFNXWuX/2gfD7YHerlJKiwircbo20gW3fM0R+33puPdzU1EJxcRXJSbHEJ3Sf47MzfPc791JRUdluu9mzZ9PY2MiOHTt4590X+NjF16Jrffue7CNGj97ICSGO9RuUkXXff//RLeM9fNmnOV10wlnfnuf0zXhXh/OEBjZsWB8kpgL8619v8eCDN+Fy9b1fr4MHjzNwYD9cLp2kpNg+7VJ8663NLFkykZycvVxxxUzi46N7e0oRaWnx8dprq/j61y/jnXe2cc01C/usUH3y5Gk2bTrMiBHpVFXV9tkcqgBr1uxl6tQsDh06wZw5o3tFVH3qqUFER7f/zfgll1zCn/+sXOo1NXUUFOxnyJB+BEpwODg4ODg4nB+sL83lf7/7s19MBWhsaGT1h+v5/AO3oNnC+D2aJM4VCJVvkdDkgyYpaDGgRQbWQ4Yh/PlTpQSfVOMYKGeqW4DXXDo3+8AwRc1Et8qv6tHAqwf62CnZWkDy2HSqDpWSMSOT6EQvUbYofI9QuVOtcSzHqTDdsQLlRtXN6epCpRiwcJtiqhB6kCNVvbY5UoUAq0iV5VS0hccahsGfX9/JjTfN4t13DvCZq1sruBkq3IYG+2uINtMMWGN0hMiiZnlZDWtyDjNx0iCOF1aweOnoyL1DcqWGSp7tn92e3KGdtjI8TF8i2ba5gFGj0ygprmbs+IEMzkxqYxQtWOwW7UvT2NsH7RHtXqi90JSUkr/9ZTs3fHUe7769l09+ZrItCq+nvlMSPP10OjHFbRtAvN4oxo0by6uv/haA2297ki3b5uB1TeqzKeMceoa+9NMXQlwJXAZMBwYD/YEWIA94H3hGSpnfSl83cCvwJSAbaAR2AM9LKf/WSh8v8BPgOvNcR4DHpZS/baV9FLAX8AJjpJQ1Z3GZfZq+9PvgcJ5hGEXccsvD/tdDhmQAsGfPIV566U+9Na1WaWmxChGNZc6c0WzdeoTm5pbenlZEiosr8PlUIaKlSyeyYsWu3p5Sq2zceJAZM0aSnBxPZmZ/9u0r7O0pRURKycqVu1m2bBLjxw8hL6+Umpr63p5WRKqr6ygsLGPs2EyWLZvEypW7ujV8rKNs2fI3jh/PaXP7739/z003fZqbb1ZFQ6SU3HXXE0Bxj8/XwcHBwcGhK9Q2N/LB/m188KvX/Ps80cpFt+lv73Dy4FE0JC4hidYNonUDj1n0SQhoMcCHAAN8MsSjKVQ/DYkwC0pJoULzY3SIdqm2PkOJqW4B8WYBqWjzEcKlt6bqelpqm4gbmMjA6YMp3n4cr11M1dT8VE5UWyEqvztVFcyyCmpppphqzdsSWVWxIZt6JkJdotb1ioCYGsLObScYOz6NxORoskemsmfnyQg/heC+kb2oZyuehhK6tvL7dVm98iBLlo5hRPYAyk6dobKirgOjSVTZsY6v2dTVdeyWPEg0NqfaUN/MkcOnmDg5gwWLRrB+7bF21ozBAnDn3znTgRxsaW5zzpbQfvRIGamp8aSkxDFrThYb1x/r9Nm7w5S4/sPfcTT/LY4V/Cdke5tjBe9wrOC/PPv/fsx9P7yO6TOU+WLf3qO89Ks3MTjV5fM7nL+Ibt66gduBbwHTUB8+u4FTwFjgFmCfEOKSsOtQQucK4HFgPEoYPQ0sBf4qhHi0lfP9HrgTiAMOAiOBV4UQX2+l/X3AcODWC1FMBUdQdThrKvn97//C5s27AZgwYRR/+MMT/qP33/9ch0KFe5IPPzzI9OnZuN0uXC6d2bNHs379gd6eVhhSSlas2MWyZZMAGDiwH5omKCoq7+WZhXPmTB0FBacYN07llpo7dwxbthzuk0L1vn2FDBmSSnx8tFmgbBIrVuzu7WlFZOVK9fMXQpCQEMOgQf3Zv/94b08rjObmFrZtO8rs2aP44Q+/Tf/+ykX75ptvs2bNGqD9mw8HBwcHB4e+wn8L9/HOz16hsVb9/ZrzuU9w6TeuA8xCVY+/gI5BlC5JcEmSPUrolEh84A/199nGtDQwK9xeE2AYmEWJJHFuNYZAYiDxSUmUBtG6xK2pPK262d+w6WQCJXaW7ThO6tTBKgdrQjRRsW6qT1SrFAJm8SlNk/68ri4tIKrac6lar60UA9am5E1LFAtIAFpo7lRhOkf9vYKFu/r6Zg4fOsWESQMBmDI9g317S2hstK8ZOyIxRBLyuuNL58CgRw6XkJISR1Kyyke6ZNkYVq08GLlXRIdtZwtNBd7p9htb7lz181ibc5T5i0agCY3oGA8jsvuzZ1ckodqO1sbc255p4P+dwzAMNn+Yy5x5IwCNEdlpnDpVQ1Xl2ZgbukeKCujOwb/tpSXVNDW1kDkkhaeeudPf/sEHXqD89CEMo7lbzu/g0A38BrgYiJdSDpVSzpRSZgGjgBwgBnhNCBEb0u8xYAGQC4yXUk6WUmYDn0I5Ve823a9+hBBTgKuBncAwKeUkYAZQD/xEhIQNCCFGocTX/0op/8IFiiOoOpwFBmfOHOP733/Kv+fZZ+9l4cIZXHvtFQCUl1fy4IPP99YEw6ipqScvr5Tx44f4940ePYiTJ09TVVXbizMLZ9++QjIz+xMfH0gqv3TpRFat2t0rLsW2WLFiN0uXBsK1XC6dWbNG9Tmhurm5hS1bDjNnTiBcKyOjH0LAiRN9S6g+frwMXdeDQvznzh3N5s2HaGnxtdGz51m3bj+zZ48yU2gk8PDDy/3HbrnlYXy+Ipy0gA4ODg4O5wMldVXkbFrPln+8C0BUXAxXfPd6Lr7+k6RmKhHwyObd7F+xlmiXKk4lTJHTJ5VIaiAwLJcqKnTeJSQeU6gE1UcCOmb4vQDrb6VhqOcxLlWIyq0rcdXSvQzM4lCofrUnKvHEReGJjzIFW0nmtAxO7DyJNAzTnaqKUenC7jZVOVWFmVbAuiEUQvrFVVDFswQCTbgIErFCxFUrt6rwi4JG4EQm63Nymbcgy5+/XtM0Zs8byoa1ebafQrBQFtGbGlFL6yavF+DzGWzaeIy5C7L9+1L6xxEX5yU/r7U1Y6hTV0R43jYBUTV0C5a3A++JxqnSGhobWhic2c98YzSmTMtk356TNDUapru383Np10d3Fm/31s15TJqSidfrUl8saILFS0ez2i9Un0MPXxDSPJv1ztjfa/XFyeqV+1mydBwCwdy5k7n2ussBOH26ip88+AsMis7BvBzOC8zP2e7YuuPXW0r5OynlB1LKxpD9R4HPmy/7A4v8lyBEGvAN8+VNUsqDtn7/QrlWAX4UcrqF5uOzUsoKs/0u4C9AKsoVa+fnqH9w3+n8lZ0/OIKqw1lwikce+RUnT6qQh09/+mIuumguAI89dicxMSrX589//kf27TvSa7O0o4S/SUHfxAohWLZsUp8Kp7eEv7lzxwTtj4uLJisrjT178ntpZuEUFZWjaYKBA/sF7R8zZjAnTpymurrvuBPXrz/A7NmjcbmC820tXTqRlSv7jlAtpWTVKiVS23G7XcycOYoNG/qOUF1VVUtxcQWjRg3y77vpps8xebL63d2+fR+vvvonoKqXZujg4ODg4NAxpJT8r2g3/37iBf+a4OPfvI5+/ZOIjfbwhXu+5m/75uMvI5sa/I7RFgMMsxCVlCqPKijXaaAwlFSCqyHw+QSGVPlQrTynQigJskUK4l1KfHVpStAMRWAWwpIGp/YUkzwhw+809erg9rjon9WP8kOncOu2UH5s4f5mzlSXHtjnMudrvzkMhGsHC4aabWbKvaqhESy62tLHUnaqhvqGZgYPSQq6lmFZ/aiuaqDidB2ht6WhYmrAJRuJSKH7rSEJdc/a2bolj4mTM/F4gmtBLFg0kvVrj2AY4f2s90j4ReWwFq3MJVg8DP0vtI2wC6wSclYdZtHS0aYzWEMTGi6Xi9lzh7Ohw+H04efp+Pw7Rm1tI7nHyhg/ISNof2pqPDGxHgoLThP6XoS6RsPndvZzEnYRVQaPc/hgMRkZySQkBGpXPPzI94iJUak/Xnzhz+zZuxXD6JtpwxzOPZronu1cI6UsQYXxg3KqWnwS8ABHpJQrI3T9pfk4TQgxwrbfqsgWmtfNssQnWjuEENegnLOPmOLuBYsjqDp0kiaOHdvDU0/9BgCPx82TT97tPzp4cDr33HMzoKrUL1/+SK+LVZYDMSOjX9ixAQOS8HrdFBT0jXw4GzYcYNasUWHCH8Ds2aPYvv0YTU29H05vCX9WWgI7AaF6Zy/MLJyqqlpOnDjN6NGDwo7FxUUzbFjfEap37cpjxIiBxMaGVz0dO3Ywx4+X9Rmh+oMPdrJs2eSgmxtd13nuufv8r++99xmqqo4SHPzo4ODg4ODQt9hfWcz//vk2udv2AjBg2CCWXHslHt0g2iWZdfFsJsyfCkBZUSlv/fpvtEhoNAtPtRjQbChR1Wd5DYVUblSpjjX4BE0GNBgQ4wo4LaUIuFw1JFFuM3zevOm2VtFWOgErh2r5wVLis1JwuXV0lBtWiaeS9NEplOdVIJta/AWzrEcIhPr7JSqzn7UPLAFWRxBa/MlWlApLMbarA9LMJaoepZSsWXWMRUtGEImFS4azZlWo+NeeKzJUEBW0fbvRtohqtamrbeDY0VNMmGgX/tT1RUV5GDNuIDu3FxIu+oVOtaNqSdtziiywKo4cKiU9PZHExNCitYLhI1KprKijoqLOdNQJ/++U+nEZIAI/p4hnFpG21hzCqk9rW86qQyxcMtr83Ql+3xYsGsW6NUdUwS3bHIO3yPM5O0IEZBHY72sx2LI5l9lzs4N6DBo0gLvu+Sqg7m/vvO1JWmTeWZ7fwaFnEEKMBfqh/mxstx2aYz6uidRPSlmESgVgbwtQYj6GOlGt18XmeROAp4HDqNQCFzSOoOrQSYq5447HaGpSuWNuu+0rjBgxJKjFHXfc5C9Q9d57a/nPf1b19CT9BAoRTWy1zZIlE1m9ek+vC7/V1XUUFZ1mzJjBEY/rus6cOaNZt25fD88snN2788nKSoso/AGkpSXhdrspLOx9oXrFil1cdNGkVhdes2ePYtu2o70uVDc1NbNjxzFmzRoZ8biV93Xlyt53VOfnlxId7WXAgMSwY4sXz+Jzn7sUgNLSch566BfgJPB3cHBwcOij+AyDd49u519Pvezf94V7vkZSjIto3bxZ0uDa+76Opqtbp3+9+CdKT5TR6FNiqkFARnQLs/gTSqrySWgy1KNErU11m1CKDITyJ7jVaysc38KQgfYS8DU2U11YSezw/rh00HWVPkATKlVAtFswauZADm8+GZCvzGWQJlQRKrtrVRcySHBVgrBdeDKf2QpRCVQuz4iOTBGY+9HDZQxIiyMhMfKaMblfDIlJ0eQeC4TTh67YwgXKUFdlJFmzIyJqMDmrD7Fw8UhzzRjeb/KUIRzYf5L6+qaQ89DKOrNz52+/rbrelhaDLZvyzHykoU5exaKlo8hZdbidsexnlEFb5++LIl9r8ckqDJ8kIyMpQnuIjnYzekw6u3Z0vqitXdzvGMHh/f5xzN+wTR8eZdr0Ybjd4caW5bd9maFDVeqP99/fyFv/fhefcTqsncOFT+tfHXRuOydzUwwQQnwW+Je5+0kppf1bq1HmY1uhxJardLRt3/9Qf67uEkIsEELECSFuAC4HDtmcqD8BBgLfDk1FcCHiCKoOnaCGFStW8ve//w+A9PRU7r03vKBbdHQUTzwRSOC9fPkjNDU1hbXrCfbsyWfYsDTi4kK/vQ0QE+Nl9OhB7NiR22qbnmDFip3+QkStMXJkBqWlVVRW9l6RvKamZrZvV4WI2kLlfe1dobqg4BRer5sBA5JabeNy6cyZM4b16/f33MQisGbNPubNG4uuRwrwU6SnJ6PrOsePl/XgzIIxDIOcnL0sWTKh1TZPPHEXXq8HgOee+z2HD+9C5Td3cHBwcHDoW6wtOcJ/f/MGlcXqy78JC2cwfcksvHog76mGYPDIoSy7VuVSbKxv5I0nXwEw845K0yWqxFBdmN4/CT5D0GILK451B+I2LEHTDXh1iVs3VBEqU5R1mQY6w9berUHJjiJSJg3Cows0VJ5WlybN3KrqeeqgeJrqW6g5XY+mqXlZY0OgSJYgOG+qdSwQYm4irFBpc39rAqJttxL+Cpk1d2ibP4M587PYtCEPn8+ILJ6GnSqCJCHsTzq/9iwprqKl2WDQoORW22iaYN6CbNataUuoDCVc/A0n3HEbSVa22LIpjynTh5jCX+QxU/rFkZAYQ96x1kQ/67dKhrxbgfG6uoaXUpKz6iCLl45us92UaZns33eChoZmul9ysr46sMaL9F7DmTP1HC88zZixwWkJLKKjo3j08eX+13fd8TT1jbm9bshx6Hk0IbtlM8kUQhxrbevonIQQXxJCSNQ/7BLgr0ALcJ2U8u6Q5lbYblvfCFjH/B+IUsp8lFiajnK3ngFeNc/zLXMeU4BvA29KKf9nm98AIUS4E+cCwBFUHTqIpKWliFtv/al/zyOP3EZ8fFzE1ldf/XEWLpwBwJEj+bz11qqemGQQzc0tbN9+rF3hD2DGjGx2786jsbF3qjYWFp7C7XaTlpbUZru+kPd17dr9zJ07pk3hD5RQPWpUBjt39o5QLaVk9eo9LFnSujvZYtSoDIqLK3qtQFlFRQ3l5dWMHBl5EWentwuU7diRy+jRg4iO9rbaZtiwwdx5500ANDc3c/vtjxKIEnFwcHBwcOgb1DY18MGezax85U8A6C6dL9/3NTy6+huroURN6y/u5773ZeKS4gHY/M4aKopL8Wjg0cBt5iPVNNC1gFylQvnBJQx0EQir14QVhi/x6JJolxI2vVogn6ldVxIosbSxshZfs4+YAfF4NIlLl0Tpag5RLolbDwix4+ZkcODDE2hSBopZaVYOP1Ng1UxHrT3EGk2F+9tDrM08naqNhmYWSFJHdCWE+jUwdWz75uNMnpoR0fFnIdCI8roZP2EgO7cVERboHkFXszypweHwZyPCqT5SwupVh0zhr+2xhg7rT21NI+Vl4eaG1h2TgTE77qoMnYN6XlPTQEF+OWPHDTTP2foI8xaMYOP6YxiGEeGovaMMeR4QHc/Oqao4sP8kmUP6ERcf6k4OHlPTNObOjyRUt//z7Nz7aXvuf3vVXFavPMCixWPaNLZ89qqLWbR4OgBHjxby/M/+gE+ebLW9g0MPUgqsAzYAhag/QaOA64QQoeGv1j/IthxvlhMmyJUmpfwRcBXwZ2Al8DIwS0r5gVD/eF4EaoHbAIQQV5rCcAlQKYRYJ4QILhZznuMIqg4dpIKXXnqd3bsPATBjxgSuv/7TrbYWQvDcc/cxceIo7rrre3z60xf30DwDqArk4YWIIqFpGvPnjyUnZ28PzCwYS/gLLUTUGqmpicTEeMnPLz3HMwunsrKGU6eqOiT8AcyYMZJdu3pHqN6xI5dRowYRE9O68GchhOCiiybzwQc7e2Bm4Sh38uQOtY2NjSI7eyC7duWd20lFoKGhiT178pkxI7vdtvfc8zUGDUoD4N//Xsm77/4P9UWmg4ODg4ND3+Ddoj28/ezLNDeoe8dLvnQlQ7IHK6HTFDztfseE5HiuuuXLTJw3jTt+eh8Zg1JVqD0E5fA0DPVamo8uTRLrgiiXEko95qYj8epq86cKMDe7/qMLNYZLk5RsKyJ96mC8uhJfY/RAASx/ASqzb2yil6T+0ZTkV5mpAdRx6zz+PjZ3ZyBnZfBtoiZcCKGHFZ8CLUz7Ewhqa5ooyK9kzLh0WhcpA87BCZMyOHqkjIa6ppD21qYkYZXTtTscjIFzHzxQzODBycT7CxFFOn/gnIuXjWHVisiFQoPzhELr82xrv72v9XNQv2A5qw6xaMnoEOEv8lhRXjfjJmSwwxSqgwl2aob6VLvmVJU0N/vYvjWfmbOzOtRjWFZ/zlQ3RBCqO/KzjuTmtb5CECFHBIhggbmoqAJNF6QPTGr7LELw5NN3omnqZ/LTh17iZPE+DKN3DDkOPY/1+dodm/mbWSilHN7a1tF5SSnfk1IukFLOk1IOAUaiQv4/AWwMcYc2mI+eNoa0bqDDqq9JKf8mpfy8lHKZlPJmKaV1A/01YDZwv5TyhBBiLvB3VFDG14F7gSnAB0KIpI5eW1/HEVQdOkALp08f4f77n/Pv+X//7wf+PyatMXXqOHbu/Bef//ylbNvWs8XdAhXIOyb8AYwYMZDKyhrKy3tW9Nm5M5eRIzM6JPxZLF48kZycPa1843zuWLFiV7tpCezousa8eWNZs6ZnherGxmZ2787rkPBnkZqaSHR0zwvVubklxMVF079/QvuNTWbOHMXOnbn+XMY9RU7OXhYsGNfuv32A2NgYHn30dv/r5csfobm5iLMJw3NwcHBwcOhuTtZVkLN2NVvfXgVAfL8ErvruF/FoEq8GXvP7eB9K+HSZeU0v/dLlfP+3DzEgLZXmqjoww/oNAoa3JgmGFDQYAl0TRJuOV0sa0jXQNRXe7zEdpm7znEHFojBdpULlZq0uqCQmJRZvnAeXkLg1lTPVrYGuqTyoLrOjJZaOnZ7G4e0lGD7DJgtKf2EqKwWAEgo0BC6baGkd07FnWLU7QgMV01U7YQqua1cfY8Hi4RHWjIE8lvbwfiEE8xdmszbnWODcNqE3/NG2njgrbVXNv7lZsm1LPrPmDLftbwuNpKQ4+qfGc+Rwe9E3IWKeWcDLyj1rba3l9lR9As9PnqhEAAMzEkPatC46Tpo8mMMHS6mv62z6tXCnameE1Q83HGX6zGERjC2tj7F42RhWr4wsVLclrAZffyttMAtZhRyWUrJm9QEWLwmtsxOZKVNG89WbPgPAmTO1/PAHz+OjoEN9HS4M2vq6pTPbucTMmfo5YC8wCPiO7XCF+RhesTuAdayijTZ+hBCp/5+9846P46zz//uZ3VW3JEuWLEsukqzm3rutFjjKwY+jw3HAhRZywCWBFCC0UALpdI52wBEChF5CCcQqlnsvsqxi9d57331+f8zO7mxVW5U48/ZrLe3MM888M7vafeYzn+/3CzwMXAK+ZV/8CdRDfZWU8ntSyi8D9wCJwO3T6ffFgCGoGkyDdh566Jt0dfUC8I53vJYDB3ZMa0shBDt2pFJW1uCWwH1+8VaBfDrcdtu2Ba1OPzY2wZUrteze7b0QkS9CQ4PIylrDxYvTTq0yZ2pr2wkLCyYubmbpT9LSVtHdPUh398IJ1ceOlXLo0AZMppl9xOXmbqa4uHTBhGqbzcaxY6Xk5PjOR+oNk0nhwIEsjh1buAJlXV0D9PcPk5qaMO1t/v3fX8v+/dsBKCu7yXe+8zT+0/UYGBgYGBjMP1JKipou8/tH/sex7O33vIuY5RF2x6bq19OmA2Z7GL8ChJoFQQrE71hNx8VGxqVkEudFskQtIjVpU4VW7OLluBQ616uNKIsk3IJa+Ep4hm5rspOiqKH+Jmmj7XobsZsSsJggxP7Q3Kg6x5NLaH9QkIn1W1ZQcanDkcJAc6VqUpfQ/Y/L7/aAfmGxh/i7zauE6k51z3va2tIPQMKqKHzJCN5ypa5evZyx8Uk62gfQXIZCl1rAOc7pFG/yh/M4zpy6yc7deuFP69u/DHLg0HrOnKrGavUci1Pg83O8ujbuLb0djyr8VZCd6z2VmTdXsbb80JE0jh+76aXvqVyqs6O/b4SW5l4yMqc/ZwRYvjyMmNgIblb5Mzd4k6OEU6x2vE9159dDSHW+h0qvNZGSGkd4+PSNLQ994b+IilLT3v3kx3/k/LlTWG3D097ewGAhkFJagb/an+7Wraqw//TnPFrv1nYqHkPNt/pB+34BDgLlUkp98as/6NbdEhiCqsEUjHH9+iW+9a1nAAgLC+UrX7l3Rj0oisKRI5spKro2HwP0wF8F8qmIiVlGdHQEN28uTD6cY8dK7YWIZv6nuGvXekpL6xkdnX+hWhX+rpGTM720BO7cdtvC5X3t7h6gp2eQ9etXzXjb0NBgsrJWL1iBsgsXbrJp01pCQvxFXHgnPT2Rrq5+enoWpkCZVjRtJiiKwte+9qDj+Wc/+w06OytRc5cbGBgYGBgsDtd76/nbs3+grlS9zluXlcLL3vYKLHYdxibBapOMS3QFoiRB9lpMQoA5NIjgFREM1PciUZdJpBrub1dvgkyqg9SKFo6v5i0NMUksCgQp6k93vUf7XQvNNwtoKW0lLiseS5AadG+25z8VYC+Mpf0OZntov+Y+Tc1cTmv9AKNDEw7hVmuriZRCqK5ULU+qulRBYPF6DtW8qk6ZUwuvllJSUnSTQznrdUKo5z+33lQxUEB2bjrFhRXaUi97lo6f0q0yvXufU/nDBvpHaG7qIStr6og217HbCAoys3XbGs6frfXavyMfrSaWC21M2k/psZn7No7XQQjKSltYlxxLRIR7PtKpWbM2htGRSTo7hlyOf/rMzKlaZC9E5WlsmVqwPXAojdMnb2K1TmVucD/nroK/Y43XQ5WAYHx8kiuX6tm1J2XKcemJi4vhU59RCzNLKfnoPY9hlbVGgaqXCKYAPRYI7QNcLzacsv887G0DIUQSkOLW1idCiCPAu4DvSSlP61ZFAf1uzbXn0VP1+2LBEFQN/CJlC/fc82WsVvVGw8c//n5Wr57Z3UaA5OR4hoZG6ejoC/QQXZhOBfKpyM7exPHjZfPuUuzuHqC7e5C0tJkLf6AJ1RsXRKi+eLGaDRvWEBo6c+EPIDY2ksjIMKqrWwM8Mk80d/Js2bVrPdeu1c27UD0yMk5ZWQM7dkw7PY4H+fkL46iuqmph+fIIYmKWzXjbvXu38u53vx6A3t5+PvOZr6PmTTcwMDAwMFh4xq2TFN68wG+f+rFj2Xs/ewfBFpNaSEqCDYFNOnPdmYQaTq8oEptUH1ZgWVYC/RVtSF04vTZ7NAstrF7aHaMSk5BYhI3IIFXsDDE5xUCneKZ2pKAJnpLJkXEG2gaJTl5OkII976pTMNIC9BWhD8zXZCaJSRHsPLSKSyebVVFX0QReV4FR6AQ/zRmqOHKC4hig0w3o6cC8cb2NtetiWDYD4U8vvEVGhZKwKorKinamI8DNXMJyXv4WFaih3t6FP+89S5zXB5u2JFFd3cHQ0JiXlgLPS+3ZCW7j45NcvtSg5iOdQgv1VaQpOy+D4oIKe67fmQqqnvgSDxsbugkKMhO/0j2V1fSOPTjYzJata7hwrnYard0Vabf8tX4PU3LyeBV796+flbHlzv96KxmZyQCcPHGZX/7iD1ilEYVlsHQQQgQBr7E/vahb9QdgAkgXQuR52fQObRs3d6m3fZiBbwOdqCH+erqAVKHerdPQLPadUx/BiwNDUDXwwwDPPfd3nn++BIC1axO59973zro3zaU4n3fvLl2qIStrtd8K5FMRHGxhy5Z1nDvn9/Njzhw9eoXbbpuZ48+dlJQEBgZG6Ox0v/kTOEZHx7l+vZ6dO9dP3dgP2dmbKCm5Pq9C9c2bLURHhxMbO3PhT0NRFA4f3jjvBcqKiq5x5MjmaeUj9cWKFZFERIRSUzNVDq/ZY7XaOHGijCNHNs26jy9/+aNERIQB8N3v/pKrV8/jLB5pYGBgYGCwcJxou8Ef/+fnDNhTWe1/5SE27duKlDBpBat0iorBiuYoVfOUOr2RdhSFZenx9N9ocWxjtbsPQ8wQrEjMQt021AThZoiwaAWvVGFTPysWQpdN0z49MAtoON9E4s7VmBXhUUhKdb3atxX2/Kx2ERikI/Q/PiEcmw0624Y9snUKoTjEVe13VUw1u5w7zZXqy+U4OWHjysUmdu1ZO+3XwzPgXbDvQCrnz9YxOWn1vSEwc4HSuZ+mxm7MFhMrE6YX0SY9X32EEGTnZHCssNxtH/pjEh7bzXTcp05Us2dfygyEP8/XJioqlJUJkdys7HBro287ldDqliLA7ZpOSklJcSVHcjJ07WeKZPPWJG7e7GDYq1CtH6uX96G7vdcHvb0jdHb0sz4tfhZjhKAgC48/4awV8MmPf42h4ZvYbFO9Zw1e7AghA/KY+zjEbiHEF4QQHrkDhRAZwJ9QQ/cHge9r66SUbcB37U9/KITI1G33WuB++9OHpjGMe4DNwH1SSvd8qwVAHPARe99m4LO6dbcEhqBq4AMb4+MN3HPPlx1LHn/8fkJDZx5mohEdHUFcXBSVlc2BGKAHWgXyXbvmJvwBbN+eSnl5E8PD8yP6VFe3EhkZRmzs9AsR+ULN+zp/4fRFRdc4fHjTnIQ/gJCQIDZvXsf58/NToMxms3H8eBnZ2bN3J2ukpibQ3z9MV9f8CNUdHX0MDY2SnDy7SZyenJzNHDs2f3lfz52rZOvWZIKDvYf8TYdVq+L55Cc/CKiv0113fQkp5+dzwMDAwMDAwBe9Y0MUXz7DC//3OwAsQRb+4+PvRQBWCZMIVTKSzhykDs+b1FIBCCZtAqtVYJWC4NUxjHcOMjE8AUItYBVk304LvVcEBAkbIXZnKXYRVP/V7SENSVUsHeoYRCiC0Jgwe/oBNU2ADRyh/lpxKc0TqfWjCa3aGPYcXsW5khZM9pBn0Iup6iicOSidhamcoe6+xVSAMydr2bV3LWbz9OaMjv26aQsWi4ntO9dw7kzttPpxMr3cqlJKjhWXk52TNUVfboKhq/wNCBKTYrBaJW2t/bieF18uV2/LfJ/Tvt5h2tv6SUuf/pzRVw2JfQdSOXu6FuukVkLNtd30fauuoqomrF690sj69HjCwtwj2mYiHqkO6CPZGRQXeUvfqN0O8Byta1oG/+/VoqNl5OR5cydPn1e+6jCvfOUhABob23j8sf/FxsKkjTNYHByRAAF4zN0nTgTwKaBCCNEuhDgvhDglhKgDyoF/QS1e8TopZZPbtvcDJ1FD+0uFEJeEEFXAH4Fg4Akp5R/8ngsh1qAKpMVSyp94afIFYBh4SghRATQArwfKgJ/O7pCXHoagauCDHr7+9Z9QVVUHQHb2Ht70plfOudfDhzdy6lS5I4VAIJlJBfKpEEKQk7OZwsKrARiZKzabjZKS62Rnz97xp2f58ghiY5fNi1Dd2dnP4OAIKSkrA9Lf9u0p3LjRyMhI4IXqc+eq2LJl3ZyEPz35+Vt54YXAC9VSyoC4kzVCQoLYtGktFy4EXqgeHh6jsrKZbdtS5tzXPff8JykpqwEoKDjN73//FzzT6hgYGBgYGMwfBS2l/Oax72OdUHN5v+Z9byB+TQKT9iJSNrtoKnGKkSZFzYE6YRdTJyRMSGF/DhKFyC1r6LnSgM2eFtNikthQQ/01TSfMDMH2KapAFURt2Pdn36eUqrBrw5GxkvoLTcRvS3JISCbhTCtgsj/X1jmFVekYuya4AoRHWEhaG0HljV6cI9E5U4VAqzqvhfo7qtC7h/670d83QlvbAGnpcTN4RXz3l7UhgYb6HgYHRgPSn37dtSuNrF+/chqFiNyFQO9iaHaeWp1eSu2V8yUg6l2u0xMZiwrKyc33lo/UP8LL6+UUqus8nMEzl3dcj2F0dJxrV5rYuWudl3YzJ2n1ciYnrHahWhuftzF6z8srHO9dT2G1vq6L8IhgVsTNPqJN49EnPobZrDq5H3/0x9TVX8VmM6KwDBaEy8B/A78FelGLTO0CwoESVLE1U0p51H1DKeUIkAs8AFxHDcVfARQBb5JSTqdozteAIOC/vK2UUl6376MQWAOEAs8A+fb93xIYgqqBFyZpayvn85//FqCKi1/72oNzuoOnERRkZvv2VM6cqZxzX3pmU4F8KtaujWNsbIL29t6A9Qlw/vxNNm9eN6tCRL44fHgjJ0/emEYC95mhFiKafT5SdxRFITt7E4WFgc37Ojw8Rnl5E9u3zz4fqTsxMcuIiYmgqiqwd5orK5uJi4siOjoiYH3u2JFKWVkDIyOBzftaWHiVnJwtAfnbDwkJ5oknPu54/rGPfYXR0XpmO9E2MDAwMDCYCXUDHRz9xwtcKVBrbCxfGctr7ngLEpi0i5gCHN5NqRM6rTa1jVXChE0wYXVuIwFTdDhCEUx0DRBmVr/XhIBJKZESLMKGya7tSHsxIv2UTSti5ZDj7MJs180uliVGYQq1oGYikPbiV+rvZs1FqzjFVK2No0gVDl8mQoEdu+MovdzFxITN7k51CqmOsH80MdUb3qvCFxfeJDs3zWXOoJaNsnmtHu8Uu7xfjgohOJKTTnHhTK4Z/M0p1HGNjU1y9UoDu3a73yyeSTi91lYVFZctC2b1mhhulM10zuitkJaThvpuQsOCZi38eZu/bdi4ivq6bgYHx1wcnXOb6UlOHr/JvgMpKIogUNndcvIyKS68ocv76k009Y+7W9VmExw/VsmhIxkBOfqsrBQ+9OG3AjA6OsYnHvgqVupn3Z/B0sdb8bjZPOaKlLJHSvkNKeUbpZQZUsooKaVFSrlCSnlESvklKaXPXKVSynEp5aNSyq1SyjApZbSUMldK+Ztp7v8NUsogKaXPPHlSyrNSyjwpZai9/3dIKee/qMoCYgiqBl5o48EHn2JgYAiA973vzWzfviFgvW/Zso7q6laGhmZyx9k/s6lAPh3y8wOb93VkZIwbNxrYvn3ujj89wcEWtm1L4exZb6Exs6OyspnY2EiWLw+c8Aewbl08IyNjtLf3BaxPVfjbHBDhT8+RI5s4caIsYEK11Wrl1KlyDh/eGJD+NNQCZZsDWqCsra2X8fEJ1qxZEbA+/+3fXkZ+/n4AamoaeeqpHwEd/jcyMDAwMDCYI1JK/lF/hV8/8j+OZW+993YiIkJRcMpa2ixCX13dZhdT9VNBq66dWUgEkmWbVjNc2mjPXepsrwgbyyxqzlSBtIueKjbp/XcA26SVzsoulmfGE2RSc6IGmZxyXrCiC+dH+106LtY1V6piF1kVRc3HajIrbNsVx8Uz7WjOVN2RI4RaiEroQ6tdpleec+KG+h5CQsysiIvQiaj+3Jpul6Da+XYTthJWRSEUQUtzr9v2M52XO/d38ngl+w+keclH6isU3xv641J/7t2fwsXzdUxMeIvC89WP73mrzSYpOVbJ4WyP9Igzwr1IlRCC7Nx0jhUG6ppB0tM9RE/PMKnr4x3L5n7pJFgWGU5iUgwVN1rd1sxMBHXKpgqXL9WRuWHVrAvteuPBT9/BihXRAPzq2ecpOXYMm20gYP0bLC20PNZzfRjcGhiCqoEbI1y4cIb//V/1xkRkZARf/OLdAd2DEILc3C0By/s5lwrkUxEVFU5CwnLKy93TjsyOwsJrZGfPrRCRL7ZuTaaqqiUgQrXVauPkyRscORJY4U9Dzft6OSBCdXt7L2NjE6xdO5MQs+kRHGxh69Zkzp0LjKP6zJlKtm9PJSjIPHXjGZKcHM/w8CgdHXMXqtW0BIF1J4P6t//Vr37S8f7/0pf+h+bmcmAyoPsxMDAwMDDQc6Grlr8/81tab6rOsfXbMjnw/3KxKPYweuw/FRzLNEHSpsqU2KRQw/F191jNAsz2fKah4RbCEqMYqO3EooXYmyQRZrAo0hGar80AnfeA9RlMnU7T1qstxG5MIMiiqPlYsYfvKyDseVQ1IVURau5WvcDqxLlce6SkRdHeOszQwAQKJrS8qYowIXS5Ux2DcozS8wazlJITJdUczE5xE1F9h7V7Czf3JZBl56RzrKjSbc6obzudsHyVnp4hursGWZ/mLZWVt/1rx6AlaPB9XGaziV17kjlzqtrn/qeHOo4rlxvIyFwZUOFPI2FVFAhBS3NgzA1FhRXk5GXg/tprLu/p4/ka7N2/nvPnau0FyubmJh0dneDG9WZ27Eix7y0wqlZ09DI+/4UPO55/9J7HGLfWzGshZgMDg6WBIaga6JBI2WovGKN+AXz2sx8mPj424HtKSorFZrPR0tI9p34CUYF8Kg4d2sDp0+XTqDTqn46OPkZGxli3bu6FiLyhCdWByPt69mwF27alEBQUmHyk7mhCdUXF3PK+avlI58OdrLFtWwqVlc1zLlA2NDRKdXUrW7a455YKHIFyVN+40UhiYiyRkWEBGpmTLVsyueMONTRqaGiYT37yKaAt4PsxMDAwMDAAGLdOUlB5gb988/8cy/7jUx/ErCjYbM5Qf30uUlBFSs016p7bVAFCTBKLSTrETZOAqIyVDNZ0Ypu0YhaScItkWZAamm8WYPGiVaquUuki7YwPjjHSM0Lk6ihV4BVqMSuTonOnauN29IGHO1XYi1850wGoSxVh4lDOWo4XNtqX6ByqHgV/VBHLGbavrVNFs6uXG0nLWEFoqBlvYqMzlYCGp2PSn6wVHhHMupQVXC/Vh9O7zXNcnrr35txf0dEycvN9mQU8C045k0BMj4zMBJqbeunv95YecPri3ejoJGWlLezYuXba2/jD3aUKvoTqmVNb3UlkZCgxMeHue8UprMpp7ke7HeB8D1osJnbuUoXqucqfJcXlHDycgaLMPcmBO7e/99/Yui0DgEsXb/B/P/4NVt/R1gYvYkSAHga3BoagaqCjj2ef/QMlJecByMhI5sMffse87S0/fxsFBVfn9EUeiArkU2GxmNm1K41Tp8pn3YeUkhdeuMxttwXW8efO6tUrmJiw0traM+s+hoZGqapqYevW5MANzAuHDm3g1KkbcxKqKyqaSEhYTlSU+yQucKgFyrZQUDA3ofro0Svk5gYmH6kvoqMjiIuLmlOBsslJK2fOVHDwoL/Kt3Pj85//b6KjIwH4yU9+x5kzJ1GLQBoYGBgYGASWY61l/OkbP2G4fxCAw6+/jbTtWQjsjlOpq7ps/4rWosG1GaompmpLzYpTRFWAIC0y3qQQlbWK/rJmlgdLQsyqkKrPuKmhOWKd6QVU4dUkoOlCAyt3rHaE+Acp9raAIqRTMBXqc01E1YRhDS2PqodEKhTi4iMICbHQ1DiAWnBKQcE1gsZV7NS7U1VpeWxsguulbWzfmejj7HsWRnIRsqY5J9q9Zx2XLzYwPj6diBbvTtbamg6WRYYSG+svlZVe6ph+4SjH1kKQk5dJUYGvawbh43dXTpRUcuBQWoAj2lz3Fx4RzLrkGMpKZ18rwGazcfJENQcPr/dYJ7HaRXjnefQvrPrIpYsga0MiTY3dDHgVqqdHV+cAg4OjJCe7RrQFKousyWTiiSfvczz/9Ke+SW9fBVIGvhCzwSIinN8Xc30YquqtgSGoGtixMTxcz333PepY8tRTnyQoKPBhJhrLloWydm0c1683zGr7QFYgn4pNm9ZSX9/B4ODsvsgrKppZuXJ+hT+N/PytFBTM3qVYUHB13oU/UIXqnTvXc/r07HI4TU6q+UgPHQpcfl9frFmzgomJCdraZidUt7R0Y7PZSEoKvNvbHWeBstlN4E6dKmf37nQslsCnJdBYsSKGhx76iOP5f//3F7HZmjEKVBkYGBgYBJKesUGKzh3n2C+fAyA4LIS3fux2VTYTai5Us+J5XStQRVRQQ/w1SUgAQSYIN4FFQLBwld8AwpOisPaPIIbHCFJU8VUIVUiy2fdrFmpqAUW47hNgoLUfc4iFsOgQzEJ1mao5VNU8qtp27mH+7mgX7K6OVVCEBUVYEJg5eCSZUyX1SCk9q8Lb54HOnKgazt9OltSy7+A6n8KfZ+V1k3MgPsbtDZNJYe/+FE6dqLaPx3Ucnri6aW02GyePV3L4SKZbK+3YrEwnVcF0iF8ZSVCQicYGX1F4/pWUrq5B+vtGSU5Z4bfdTFHn9a797dqzjkseQvX0lZ6LFxrYuGkVISGuxhbpkiJB61O33us1ivt+nSKnEIKc3A0UFZZNa1zeKCwoIzfP3zXD3Et05eTu5g1vfBkA7e3dfPlL32NSBiZtnIGBwdLEEFQN7HTw2GM/oKFBvUv5ylce4dWvzpn3ve7fn8m5c5VMTMw8h2IgK5BPhRCCvLzZ5X1Vhb8bCyL8AURGhpGUtIKyssYZb9va2oPVamX16sAVIvLH5s3rqK1tm5VQffp0Obt2pc2r8KcnP38bR4/O3FEtpaSg4GrA85H6IijIzI4d6zlzZuZ5XwcGhqmv72DjxjXzMDJX7rzz7WzYoDoaTp++zDPP/A7onff9GhgYGBi8dChousyvv/JdpD3x6es++FaiV8aqofBI9UJI2sVS3XRS/1Wv+TIF9pypQhVhTYq9nbB3AihIzAJS9ibRfKHRKREJXSErnEVJhM70qQiQNknLpWZWbkt0uFNDTE73rBYMrQh1X4ri5qwVzr40Z6ojZysCIcwIYc+ZKgShYRbWp8dSeqUNRRM7nWfBIYw5BTInPd3DduEvxuu5Fx6XmcJNM5vZ/D01LZaOjgH6eodxytvaSL2PQOPShTo2bEwiJMTiJqLafG49F47kZFJSPLtw+qKjN8jJ1wu/8yeqms0m9uxL5vTJmhn3NTIyTmV5G1u2rfayVnr56SpUu54atzQQXiTNlQlRmMwmmppmni6uqrKN2NgIopf7N7YIIRyP2fKVR+8hOFg1JH3j689QWXkRm21uacMMlg7qTanAPAyD6q2BIagaABM0NNzgkUe+D4DZbObJJz+xIHs2m03s3ZvBiRM3ZrTdfFQgn4pVq2IQQtDU1DWj7U6frmDnzvXzUojIFwcOZHL2bMWMwulV4e8KeXnzl4/UHVWo3jrjcPrBwRFqa9vZtCkwuaWmgypUx3DjxsyE6uvXG1i7No5ly0LnaWSebNmyjurq1hkXKDt69Ap5eQtzk8JisfDUU87PmQceeJzBwXqcl5wGBgYGBgazp2aghaN/+wdlpy4BELd6Jf/ynjcgUEPrFbs46vjWsQs8Aqd0aLU5hR8F1VVqUdTCUEjJuG5/ZlTxM9wsWRYdSnCYhYGWfrVrCVqWVItwCp96sRagq7Kd6OTlBAWbHY5ULQ2ANm41j6p0SReg70MvmTlSAQgQwoRJWFAwu3zPb9uZyI1r7YyNTeKtCJPUiY5SJ4gVF9zkSG6qlzOvSblubkP3avNetvSGKoBaEQKy89IoLqjAUwT15XhUhb/y8la2bl+jE1Hnl7CwINanx3P1yszmjDer2omJCWe5h/A3f6JqWno87W399PVp5ga9q9T3fkuKqzh0JA1FcW0j3eZxUucp9kB6/OKX7JwsjhWVz0iotlptnD5VxcHDGdPeBkARyqzcqsnJiXz03ncBMDExyf33PomVuhn1YbC0UQL0MLg1MF5LA6CVBx54nJERVXz58Iff4XCOLQRZWatpbu6mv396ORSdhYgWxvGnJy9PLfo03S9yVfhrY/Pm+StE5A2LxcyePRmcPDl9obqsrJHVq1fMSyEifyQmxiAlNDdP/46zKvxtXRDhT8+BA1mcOTN9oXpiYpJz5yrZvz9z6sYBRCtQNhNHdVNTF4qisGqVd6fJfPCKVxzhNa/JA6C5uZ1HHvkuYCTwNzAwMDCYG1ablaK6Szz7le85lr3jE+8jODgIi91ZarMXmpISF/+lYnecqiH6aki9SSeCCiQmpKNglYZJ2AgxS8Itaj7U1F2rqL/UgtUmHYWs3D2gGooA6/gkPbU9xKTHEWx3pQabnPs1acKortCUXkR1uFJ1LlWLAEWYEVjs4fb6LeztFMHeg2s5dbwGh6DqZ3olkdRUdxG9PJTlMfo5o1NI9Qj1d5uvTVekkg5xV2XFighCw4NoqOvCvwjnvMQtKS7n0OE0XLMSzH+KoZ271nHtSqNdqJ4aq9XG6ZM3OXA43UeLQIuqzt9z8jLsQvX06OgYYHRkgjVrXeeMnsKpLn+q14F479+XABseHkzq+niuXZ2+UH3+bA1bt62d0tjibSgOx+oMhdX77r+dxEQ1V+tzfy7m+b//E6utf9rbGxgYvHgwBNWXPCOUlBzj5z//MwArViznM5/50IKOQAhhr05+eVrt1QrkMQsu/AFERISSkrKSa9emd6exoODqogh/ABs2rKaxsXNaQvXExCRnz1Zw4MD8FSLyR37+lmnnfW1u7gIEiYkLJ/xpWCxmdu9On7ZQfeLEDfbuzcBs9nUJNX8kJcVis0laWqYWqqWUFBZeJT9/4dzJGk888QAWi5p767HHfkhtbSm4eH4MDAwMDAxmxuXumzz341/TYU9ltXH/Vnb/y0EUITEBkxImUR+giqpaKL1A6sRTXei8XWjVHKpC4CjhZBYQYVFzqmrPg0LMxKfE0Fau3ijUi7LabMdkd7wKAS2XmknYtoogsxosbxHSURxLG5NJqD/Nuis4oQv5d8qlEpOQKEJ1hirChCJM9vbCPgbNOShITomlt3eEnu5hF2VJurg51RVWq40zJ+s5cChZt9yfm9G/09EX3tIMABw+sp7jJdVY3RRt5xxS25eNzo4+hofHWLtu/nPYu2MyKew/uJ6TJb5SMLmekwvnatm8dTXBwQsV0ebc/4q4ZYSEWmio1+aM3gt7gXqeiwoqyM7z5vj07v7VZUP1MRbP/LW+nK27dqdw5XL9tAqUDQ2NUV3dzuYt3tISeI7AF+7C6lQCa0REGF/68l2O5/d97AkmJmqwyfl3RxvMPwErSmVwS2AIqi9pJDZbM3fd9bBjyRe/eDfLl0ct+EhWrowmKMhCQ0OH33YLUYF8Kvbty+Dixeopv8ibm7uRUi6K8Af6cPqpXYonT5azZ8/iCH+gCtXJyfGUltb7befMR7plgUbmycaNa2ho6GRgwL9Q3d8/THNzN1lZU0/i5gu1QNnUjuqrV+tITU0gPDxkgUbmJCMjhbvueicAY2Pj9sJ4bQs+DoPAsiJE4X8OLQ/IY0WIMVUxMDCYPiOTo5wsP8efvvNzAISi8M5P3YFFEQQrqpgqASkFQhfmL4RaPAoBir2NGi4vHcWdwkyqAApaGL5aGMqiSCwmMCuqIGSxF7palbWCrppu5Nikox8HwpkXdbR3hMnhCSJXRWIRaj9BJqeQG6Q487ea3C7E9XKm5mBVhVqBIoLUfKlenKnu+S2zc9dzrLBat9a7qHb5QjMbN68kKNiMNzHV053qmR/TH1qIvy+JKyTUQmbWSq5cqvc4BunQ5CTSJikquEFO3uJdM6Suj6e7e4ie7iEva53HNzw8zs2bHWzZOtWccX5cqgCHs9M4fqwKm7v12rFftX1VZTsrV0YSFeWaykpOmUrB3aXsjveCYFL3D+xC9YE0TpRM7agtLrrBkZzMgBlbZpJf9d///dXs268aFcrKqvmf/3kGif/rXIMXB9p3xVwfBrcGxlXKS5pefvzjX3LhQikAW7dm8r73vXnRRpObu4Wiomt+xZ+FqEA+FSaTif37Mzl+3HelSS0f6WI4/vQkJCzHZDLR2Og7jLq/f5impk42bFg84Q9g375Mzp+v8lug7Nq1OlJSVhIRsXD5SN1xOqr9C9VHj14mP39x3Mkay5aFsnZtHNevN/hsMz4+wcWLN9m711eI2fzzqU/9F/Hxqnvk17/+O0VFRYC3iw8DAwMDAwP/nGkv5ZdP/pixYTUn5G1veyXJWSlqwLuECV0+U8X+HR1kF0oVVMFy0stU1ITEbNJJPgIUxUaERRIdpDpKNfFT2NdLBGt2JNJwqdkhSUmd/qjtr+VCE4m7V2NGdaFaFF3ovj3EXwjtp25MwpmtVGtjVsAsFLs71buIpeVF1Qut0TGhRC4Poabam0tRZWR4gpuVnWzemohnnlQvYqqHbDadOdHUQsO2HUncuN7K6Mg4oHcyOn+vqmonLj6S6OiFj2jTk5OfRVGB/8imY0XlHM7OmOacMZCiqvM1DA0NIiNzJVcvewunV99Lk5NWzp2pY/9Bb7lz/QmxXt4Xfg/DmcPXm2s1NS2erq5Bent8zxXbWvuwTlpJSgq8sUUTVrU8q95QFIUnn7rP8fyhz32Hzs5ybHLmhZgNDAyWLoag+pLFRn9/LZ/85FOOJV/96icxmRbHoQgQFhZMenoily97rzS5kBXIpyI9PZH29l56e71/kZeW1pOcHL+owp/GVHlftUJUiyn8gVqgzJ9QPT4+yYULN9m3b2ZJ5ecDTaj2VaCsoaGDoCALK1dGL+zAvLB/fybnzlX6FKpLSso4cCBrUf/2o6KW8fDD9zie33XXl7BaG1mIHGcGBgYGBrcOnSM9FJ8u4dhv/wFAeGQEb7v7nVgUMJtgTOrC7e0uoWCTFh6PKoJKtVCVPixTQRVOVSlS7UGghvmHm8GkSLs7VA3j1/qxCYhNXMbkyCTDPSMuYxX2MfQ29hAWHULIsiBMiuou1UL6FSHtYq+zkJZ+e8fv9mPRBFp1ewH2kH4NKTTnqcSborX/0DrOnKzFanOfMwgECieKazmUnerVdepVTJ1BISr1zPp2pkopHQ8hBPsPpXC8pMohDusdtZNWK2dPV/sQ/haW2NgIIqNCqa3xbm5obxtgfNzK6tXLZ9Dr/MzZt+9cw/XSFkZHJ9zWqOf2/Nl6tu1YjcViclvrnhpCK7sj7OvdXlOvw/d0p3qmAnC2ycnbQEFBmYeDFexpCQrLyMnd4Pd4A4GWCgCcfwfa8717t/DOd70WgN7eAR767LeRNM37mAzmDzUqITAPI+r/1sAQVF+ydPClL32btjb1y/2Nb3wFeXn7F3lMsHt3Oleu1DI25v5Fjr0Q1eILf+A/7+vExCTnz1exb9/CFiLyRXh4CGlpq7hypdZjXWNjJ2aziYSEmUzi5o+MjCRaW3vo6/MUqo8fL2P//sUV/vTk5XnP+yqlpKjoGrm5i5eWQI/ZbGLfvkxOnPB0R/T2DtLR0Ud6euIijMyV//zPN7Bjx0YALl++wQ9/+CzQs7iDMjAwMDB40SCl5HT7VX76he86lr3lrn9n+YoozAImrOikUDUXqUWRhJrU4k+aDDRucxZ4EhKCFfVhUnD4Ou17JMIMYRbV3WoS6HKeuv5M2Z1E3bkmnewkUZCYpI3mq20kbUsgzKQ6ZYMUZ+i+2R7q7+nx0+fis7dTtHEojn1ID5HK+btwvwwUEBxsZsOmlVw63+yyQgAd7YOMjU+S5CL8+col6c2B6H3+7i/EXy+iurMuOYaBgVE6OwdBam5b9XjPna1l2/Y19kJEi39z9uDhdE4er8Jmcw2LV/ORzjYtQSDD2NX3gqIoHDiUyomSm7gLnAMDo9TVdbFxU5KXfbvnr/U/Uv+ZqHytdBVVY1eEsWxZMHW1HY5lqigvuVHWTNLqGJZFLoyxRXOrui1FCMGXHv5vwsPVcXz3u7/i6tXz2OSIZycGLxq0vNVzfRjcGhiC6kuScSorL/PUUz8BIDg4iMceu2+KbRYGk0nh4MENHDtW6rK8qakLk2npCH8AcXFRhIYGU1fX7rJcFf4yFy0fqTf27Mng8uUaxsedQrVWiGipCH+gCdXbPMLpe3uHaGvrISNj8YU/jfDwENavX8XVq7Uuyy9friE9PZGwsODFGZgXMjOTaG7u9hCql9JNCpPJxNe+9qDj+YMPPkVvbzW+ChwYGBgYGBjoqR1o4i+/eY7Ki2qkS9L6Nbz6na9xCI/jdp3GhCREUcXLEJPrha2md6nuUbX4VKhZEmZxikBmITEhWWZRnamqxCqdF8qo8o5NOh2loZHBhC8PpaehF1AFWosCTaXtxKevICjI5ChapYqyEkVRxVXNPat3p5o0MRXXwHv3lAC+c6f6ugSUbN6WwM3KLkaGJ+xbmpBScKzwJtm5aS59+5LP1HnF1O5UVfD1/J73JaK6k52bxrGCShd34tDgGHU1nWzYpJ8zLq6oGhJiYcOmRC5ecK0VUHGjlcSkaCIjFz6HvS+SU1bQ3z9Cd9cQ+lftWGEF2bmZfvKIegvv179L9ALtXF8PdftDR9I5UVKJzWZ17G9iwsqF87Xs3Z/q4l71VuAq0DiKVwnh+DtMTIznE598PwA2m4177v4KNtkwrfe3gYHB0scQVF+StHLvvY8wMaFOlO699z2kpCx+GL1GWtoqenoG6e4eAJz5SPPylo7wp5Gbu4Xi4lLHHee+viFaW3vIyEha5JG5YjIpHDiQxbFj1x3LrlypJS1t1aIUIvJHfHwUwcEWF6H66NHL3HbbtiUh/OnZuzfdXqBM/VsaG5vgypVadu9evHyk3vCW97W2tp2wsBDi4ha+CJ0vjhzZzVvf+moAOjt7+PznvwW0+9/IwMDAwOAlj1VaOdVwkZ8/+kPHsvd95v1YgkwoQjJhc801arYLlfoCTzZgEmexKYndmWrfzuoQVMEiINgkHe5RveDpPlXRhNbV21bRfLUVrFYUYHJ0gu7GflakxRBkwl5IClT3qrZvT9FFE001d6qi6By1CJ37T/iQkNylUM1NavfvCsHBI8mcOFaLdqlYVdFBwqpIlkWGeA3vdz1eTz+tt/bu7ll/blRfRC8PIyY2jOqqbvt2guKiCo7kpCGEmMIJubBs3baGihutjIyMA2qh3fPnVOHPuwd5OgRuXqxP45CTm0FRYQXa69Pa0gdAQkKUW3st1N3k4Xj2/y4hIIJiSIiFrI2ruHSxAU2wPXOqil2712E2C9zzsC60wKpx9z3vJCVFvTYsLDjLH37/V2z0Ldj+DQKLMzpgbg+DWwNDUH3JMcg//vECf/zjUQBWrYrj4x//wCKPyRO9S/Hq1VrWr196wh+oCdyzslZz6ZKa93UpOf7cSU9PpKurn56eQcbHJ7h8uYY9exY/H6k38vJUoVpKSV1dO6GhwUtK+NMwmUwcPLiBkhLVDXPsWCkHD27AZFp6H60rV0YTFGShoaEDm83GsWPXyMnZvNjD8uDRR+8jJER1937jG09z48ZFYHxxB2VgYGBgsKS52lXOr77zc3ra1Nzmu/P3sCNnl7pSqkWmnEKoKgNZXJycKgpOwVJKNSWAouDioww1Q1iQJEhnDRXajpBoRdLd5RrForAyfQXtN9oRAqrPNZO8K5Egu6qruk2lwx1rtoulQnfxrQhn8SxwDR11Ho50iIvCnvtUj0BxyfXobcqatCaasdFJOtuHmZy0cuFsA3v2r5tCItOEU18ORSeaM3U2Iqo7+w6mcvZ0DVarjdbWXqRNsioxGkchI0fXi6uuKorgcHY6JcVqdfozp2rYuWudPR/pXMYWeFF1eUw4y5eHUX2zAyklxYUVHMnN8LInT+F8KsHdhQC8JNu2r6W8rIWRkXEG+odpbuohIyvBbSfS7XfXIlfauvlxjQpCQoJ57PF7HUvuu/cxRkdrkNKIwnoxYoT8G+hZelf9BvOIZHi4mne+0xne/8gj9xIREb6IY/JObOwyIiPDKC9v5NKlmkWtQD4Vu3at59q1OqqqWggOthAfH73YQ/KJKlRf5tix6xw8mLUkhT+A0NBgMjOTuHjxJsXFpeTmLj3hTyM9PZGOjj5qa9vo6RkkLW3VYg/JJ3l5WygqusaFCzfZsGENoaFBiz0kD9auTeSBB9TQqMnJST72sUeAZv8bGRgYGBi8ZBmZHOX5k0f5w3d+CYDJbOIDn36/Y70NZ4i886e0uzqlo6CTlrPUJFRXaphZdbIKAZM2VToKsm9rQidk6kVNCb5UIpOAVekx9DT0093Yj23SxvKECIIU1WlqUqRDMA0x2ceq9Y/TharuU5dmwL7MrLtCd7oNvQle7rgvUZ8fzk2lpLCKc6fr2bZzNRaL2etxuWw3RREqicQmrUhps4tXAXApBlvYvCWJC+fqKS6sJDtXu2bQ9b1ERNU1a2MZGZ6grraTxsZuMjfo54yzdalq2waWA4fWc/pkDaXXmliXHEtERLCHq1N978/hWiIAw1YUwaEj6Rw/VklRQTnZuVk+jC2uOVj1haScLSQ2acMmAyP263ndv+WTl78PgJqaJr721Z9goy0gfRsYGCweU30zGtxSdLNnzxtps9+9T0tbxzve8f8WeUy+yc7exHe/+zde+cpdS6YQkTcUReHQoQ386U9nuOOOVy72cPyyYkUkFouZ+voObrtt22IPxy+7d6fxne/8lR071hMaunTykXrjttu28fTTBbzznfmLPRS/hIUFk5KyktOnK7jzzlctyhh2734Dra3eK91qSCkxmRQSElZx/Pgl0tNzqay8CixbmEEaGBgYGLxoKKw7w6fe9FFH+qX8N95GUmoSE1J1J0qcDk+zPbQ92J471WR3olrtbaRUl2EXTgVqXlVNlA01q6KrwLMMj36ZNw1GDecXpO5O5Oo/q9n7+iyHwOsI4UeT1VSx1OGAFU73LEhHWw3PWbKnWKQ+N7mIYqrwJPGmbEVGhRAdE0bFjXbe+Z69U2pfnhqWq1DldAAGWtSUbNqSyI9+cIK0jDjCl4W4rNNSHywVQ1hOfhZP//g4b3zLbjfhTxvl4oq+QihIaSM42EzWxgSOF1fx3juO4P8MautmNnYpZUCi+taui+VESSUhIUGsTIic2Rh8jFlbrqbRkC7L9X9X2vj37Xn7lPPbiYlJADIzM/nsZ77JL37xVy5duoYiLDMas8HiIezfDYHqy+DFjyGovmSYpKuriro6p9Orq6uPn/70KEFBS8+lBuqXzsjIGEePXubChZuLPRy/9PUNMTo6wbPPlhAUtLS/FJubu5ictPLMM0VLMjWBhtVqY2BghHPnKjwKfy01hoZGGR2d4Pe/P7WkilF5o729l5GRcZ55pnhRHMo1NU10d/dO2U5RFG677TZ++ctfUlvbyPh4I0FBmRiBFQYGBgYGGp0jXZw+c4oJXdHN1soWKv5ZCTiFTYnT6al3dmrylf7y2MXbKdTiUgKn6OlPOrLpFkrpTB0AzovnsaEJJsetXP1nNZYgk4vLVO9GdYzB8VO6FZzSjdllPqcTezxypboLrP4QtLX2MzFu47e/vITwm/TPl/NVC63WmJ+QammT9PcNU3qlmY62wWlts1iMjk4wOjrJ3567RnhEoOeMgT2/nZ0DDA9N8Jtnzy/wnHFmr4+UkpbmPhRF8OzPz0z7+mbaqQmmQXV1Mz09vVO2i4qKYvv27ZSXl3Ozqh5JI5ASsHEYzD9L9+rZYDEwBNWXDG189rPfYGho2LGkp6eX+vpyPv3pDy3iuHzzm9+c4PbbX87zz1/k3/5t35J1KY6NTfDLXx7jzW8+xAsvXOEtbzm82EPySU1NKxUVzaxYEYmUcskVT9Lz/PMXOXgwi/Pnq8jP30Zs7NJ0J9psNp5+upAPfvCV/OlPZ3jrWw+jKEtT9Ovs7Keo6CrbtqVSW9vGy162fcHH8MQTSdP6W964cTPXr5cyNjYGwLe+9VPuueejwIp5HqGBgYGBwYsBKSVn2y7yf1/8rsvyqxeuMhIxwpb9Wx2FpKQEiwlAEmwv/mQSaqGpcZtThjILZ1Eqk6K6U8clhJskZpPTQQqqqxWc/dgkTNhTImrFoSTqOrN9WmCdtHHpL5Xsf9NGKo7Xs+fV6zHZhUoFtV2QPfRfS1EgdK5UrYiWXts0C+EqodpDsAUKinBe6ul/1+MtZFsgaGke4MqlZtYlL6evb4z9B5K9bu/sw11q1ucwnU/XpeDEsZts27mGyvI29uxLZuVK97z7zmMUmlK+CEgpefbnZ3jfHdk896fLvP5NOzGb9f5iTeafS27NwJzr3t5h/vn36+z7f6lcv9bMv7xKTb81lVY5vbygbsK+R6cze32uXm4kJXUFw8MTJK2OJi195TT27+vGw+x44slVhLVObVLKzX0Zzz//DwCGh0c5c+Yk+/YmoIjQOY/BwMBg4VmaV/0GAWaMa9cu8p3v/ByA0NAQx13Gr3zl+zQ2ti7m4LxSW9tOeHgIK1dGk529icLCa4s9JJ8UF1/j8OGNxMZGEhMTQVXV0sz3qBYiuk5OzmZ27EilrKzBUWl0qdHV1U9//zCpqQncdpua93Wpcv78TTZvXkd0dAQbNqzl4sXqxR6ST44evUx+/jbS0lbR0zNId/fAgo/h3Lnf0thY7PdRUfEP3vWuN/Od73zCMcl+6KFv0tFxE7UGs4GBgYHBS53q/lr+9PM/UXNd/d6NT4pzrPv+576H1Wp1yCQWkz0vqknNgwr2UH+d9qOgFqpSUMVUUKWpcLMqwLpnuBT2bfT6lRlVnNFXcjbr3LANV1pZuzmesMhgohPCaa3udThSFQUURWJRcORy1Ryrjufg4q71GJNOTHUegf65K54pAYRDDD5eVM3h7FQyN6yksa6bwcExr324Cqn6quoLIaZCf/8IrS39pGfEk52bQXFhJTYPUU/nkV2AMfmi9GoTKalxREWHsW3HWs6dqXVroY1rLpfogRGLiwsryM7LYPWaGMbHJ2lv759mz+7vypm0ncm2KmNjk1y90sDO3ckcOLSeM6eqsVp9ibpq/+7/AsGpM89QU/93aur/Tm39P1wedQ3qo7jk57zjP17FJx+83bHdPXc/gk3WBWQMBguDEqCHwa2B8Vq+BJCymbvvftiRW+pTn7qTD33oHQAMD4/w8Y8/vpjD88C9Avm6dfGMjIzR3t63yCPzpKtrgN7eIVJT1WqSR45s4sSJG36+yBePCxdusmnTWkJCglAUhSNHNlNUtDSF6hdeuOLI8RoTs4zlyyO4ebNlkUflycjIGDduNLJ9uxqqs3NnKtev1zM6uvSE6srKZmJjI1m+PALQCpRdWeRReaew8CrZ2ZvZvXsLt9/+BgD6+gb49KefAiOBv4GBgcFLngnbBGfqL/CzJ37iWPbAVz9K+tY0AGrKavjHL54HnBKNSahipRaGP2lz+gCDBAQprrlMFaEJmdKRw1SP1kafS1UourQAOgEUYGxonL62IeJSohFCkrptJfXXOpBWG4pQx2ARznypzvB/6cizin4cAkwuzj63QlQ6J6avwkG+ZMWy0jbWpcQQHhGMEILDOWkcK6zy2lZgP6HSVbScn3ypnhwrrCI7Lw0hYFlkCIlJ0VSUu88VXMfhFFUXTlgdH5/kyuUGdu1JBmDjpkTq6joZmlKong1zEwkb6rsJCbEQHx+JEJCdl0lxQbnPfKMuexZiGiH33oTT2YmwJ49Xse/AekwmhaAgM1u3r+H82Vpde1W+EpgQKPabCwvnUNb2ZLNJSopvcCQ7iw/e+RY2bEgF4PSpK/z8md9jld0LNiaDuaG/YTaXh8GtgSGo3vIM8Mc//pUXXjgJQHJyEh/96O187nMfITY2GoCf/exPnDhxYRHH6MrFi9Vs2LDWpQK55lIMVKXFQHH06GWX4k7BwRa2bk3m7NnKRRyVJyMj45SVNbJjR6pjWXJyPMPDo3R0LC2huqqqmZiYCIfwB6pQffx42ZITqgsLVeFfC/FXhepNS06otlptnDx5gyNHNjqWxcYuIyoqjOrqpeVQb2vrZWxsgrVrVafRl750D8uWhQPwve89y6VL54DRRRyhgYGBgcFiU9pTxi+/8Qz9XeocJve1R9h2YCt3PnSHo83/PfZ/DPYNEKSoLlGtEJUNNQxfC9k324VT7QJXFS9Vsc1sD793QXOO+rgg1ousWluTgKozTaTuTUIRArOAIItC6tY4qi62YUInnNq3U5CYhMSkc8eacO5bFV2dA9FELHfHnSL8FXb1nFePj1u5ermZnXvWOJYlrIpECEFLs7c5o7tYuXBCZWNDL8FBZuLilzn2uWdfMhfO1jMxaXVrvbhzyJPHq9i7P9URJSiEIDs3k+LCch9bzNytGQhsNknJsUqO5DjTgkVFhbIq0ZtQ7RvXNBB+2rn8JvD08fn0Y9PTM0x31yDr01Y6tt28eS011Z2MDE3aRdSZuVDn6l4V7mO2d3HpUi1ZGxIJDQ3CYrHw+JP3Obb5+ANPMTh404uz2sDAYKljCKq3NJKxsXo++tGvOJY88cTHCQkJZvnyKD7/+f92LL/rri85HKyLyejoONev17NzZ6rL8qiocBISllNRsXTC6W/ebGH58ghiYlxze27blkJVVTPDw77uOC88RUVXyc7e5JHbMz9/K0ePXlkyQrXVauPEiRscObLJZXlwsIUtW5I5d867O2IxaG/vY2RkzCH8aSQnr2RwcITOzv5FGpknZ89WsG1bikfBtOzszZSUXF8Sf/ugXoQVFFwhP3+rY1lCQhyf+tSdjvV33/0wUjaz2FVwDQwMDAwWh6GJQU5fPs2ff/QHAIKCg3jfg7cjgU27N5LzuhwABnr6efbrP0cIMNuFSWlT86LqA9ODTE43qElx5i5VRU63zKBe9BWHQ9XRVno4+XpaBzCZFZbFhjqEU5OA1euj6W0fYnxoHEUX5u9wqOr2L3TjUQCTUJyyjUM0FS4/phblPNedOVnH7r3rnMKfvc2RnPUcL77pd864kPNJKSUnjlVzOGe9fikWi4kdu9dy9lSNt61023sumy96e4bp7Bj0yO25alW0WlCppVe3NFDjmZ0Ye/VyAxkZK+3GFuf7Z9+BVM6frWXSQ6iezjh8iaJM473qbXuBQKHoaDm5+ZsczlOB6o7Nyd1AUeENL8H9U//z3PtMt/e8AzM6Osn10iZ27ExxjP8VrzjEv74mG4Dm5nYefeQHSJaWycHAE4G0f4bP/SGMa5lbAkNQvaXp4qtf/RHV1Q0A5OXt4/Wvf7lj7Qc+8FY2b84A4Ny5a/zf//1+MQbpQlHRNQ4f9hT+AA4d2sDp0+Wz+CIPPFarjePHyzyEP1DvOOfkbKGg4OoijMyTjo4+hobGWLcu3mNddHQEcXFRVFYuDaH63LlKtm5NJjjY4rFu+/YUKiqaloRQLaX0cCfryc/fxgsvXF7gUXlnaGiUqqoWtm5N9lgXHGxh8+Z1S0aoLi9vYtWqGKKiwl2W33XXu1m/fi0ARUVn+M1v/gwsfP5XAwMDA4PF51r3FX74pe9jtc8H33rnG1mRFI/Nfm36n5+4neAQNcrpzz/5M41V9QSbVRnDitOZKlDD/DVU8dJ+sYs9pymeMpAmaKJ7ru/DiXrBrGDj5rkW0veswmIviGWxC6pCEWw5kMSVk02OFAICCTrRVUtD4ChIBQjVq+p4aGHWilDU3+3/FOHboedNDOrrHaGjfYj1aSs8tguPCGZtcgxl11td+nAe7UKIA86xXrvSQlp6HKFhQR4tsrISaGrsZaDfW0SLlldW+3/+XaBFBTfIzc/yui47T3WpuovR9ldwjuOb7vGp7cbGJrle2sKOXevcthOqUL1rHWe8CtU+ehXCx0Pv8naOcSbCZW1NB5GRIfaiteq50h6JiTFYrTZaW/rQRNaFeZg8loGgpPgGhw5noSiuf7ePPX4fFotaLO6Jx39MbW0pNrn00oYZuKJ//87lYXBrYAiqtyyTtLTc4Itf/A6ghiJ/9aufdMlpYzab+epXP+l4/olPPMnAwOCCj1Sjs7OfwcERUlK8V2a0WMzs2JHK6dMVCzwyT86dq2LLFu/CH8CaNSuYmJigra1ngUfmiir8XeG227b6bHP48EZOnryB1bq4QvXw8BiVlc1s25bidb0QgtzczRQWLr5QXVHRTELCcg/hT2P58qUjVBcUXCU3d4vPfFbbt6dQXt7EyMjiCtWTk1bOnKng4EHPC47g4CCefPLjjuf33vsoIyN1GC5VAwMDg5cW7cOt/PMf/+Ts0bMAxK1awZv+643YpPM7blVSHG/+rzcDYJ208r3P/8A1DN+ORajuVJNdrDQr0lH8Scuj6vLNqTd/ujlX3f1zjoBlAS3l3cSviyQk1OJwvZq0fK1AbFwIQUEKXS2D9jB/55i0/biLuopwikeqO1XLDelEwezzPGpCqHDr/VhhtT0fqfc5w649a7lysYnxcWsAxL6Zo+1tbMzK9WstbN+1Bm9nSA2nz6C40N81wxTOySlHMr3t62o7CY8IJnZFhG6pc5uIiFDWrVtBWWmLbq1waxvIsWu4h9XD8WOVHDiU5tXYApC1IYHGxh4GBgKVesn5F+NN9Pe1jbTBieOVHD6ywUP413rNy99EUeH1BRL6wZes0tU5QP/ACCkpnsaWjIxkPvLf/wHA2Ng4D9z/ONA0n4M0MDAIMIagesvSxoMPPsXg4DAAH/jAW9i61VOouO22Aw7XamtrBw8//N0FHaUerQK5PzZvXkdtbRuDgyMLNCpPVOGvyVGIyBda0Z/FDKevrGwmPj6K6OgIn22Cgszs2LGeM2cWN+9rQcFVcnJ8C38Aa9bEMTY2QVtb78INzI3JSSunT5dz6NAGv+0OH96w6EJ1a2sPk5NWVq9e4bONoijk5GymsHBx876eOlXOzp3rHXfq3Xnta/N5+csPAVBX18STT/4v0LmAIzQwMDAwWEyklFztuMR3H/q+Y9n7P/mfhIWFqOvty2w2eOMdbyA+SU3Jc6bgPKf+qQqwCmoof6hJzanq9ItJVezE1YmqoYmfThep+rvUtZPYizGhKzoyYaWpvIt1m+OxmFQB16LoZSTVibr9YCKXTrYg7M9d0g6gyk3aVorQ38x3H7FeVDThXWzTuUqFY+Q01HUTFhbEihW+54wmk8Luves4c7JOd9QLg9C9QiePV7PvYIojLYGzjfNcrEyIwmw20dzUh9fzIASehak8JHE8hUd/wp/r9jab4ERJFYezs/xuv3tvCpcu1jExYXWsd7iMHQWV3N+h/vft+tBvq7VxpbtriP6+UZJT4r0cs30PQiEnN5Pigvkzt2hH7f7Qj+fihVo2bkwiJMTV2OJwhQrBsmWhrFm7grKyZvy/Zq57D7TQXnC0lLx8z4hGjQc/dQfx8TEA/ObXz1NUVIxNGlFYSxmjKJWBHkNQvSUZ4ezZk/zoR78FIDo6ks9//i6frR9//AFHbsXvf/9ZhoaGF2SUeiorm1mxIsqlEJE3hBDk5W3l6NHFcykWFl4lJ2fzlBUsIyPDSEqK5caNxgUamStWq5VTp8o5dGjjlG23bFlHdXUrQ0OLU+ynra2HiYkJ1qzxLfxpqAXKFk+oPn26wq/wpxEUZGHbtpRFE6o1d7I+H6kv1q6NY3R0nPb2vgUYmScDAyPU13ewadNan22EEDz11CcwmdQ8cQ8//F2amm4AEws0SgMDAwODxaR24Ca//vHvaKhS51Wbd28g//U5CMAkJAJV0JyQEBYWwh0P3u7Y9hff/6Pjd7MiMSkSRZGqA1SozlStMJXQayjac90iraC9PnequsIppmohnZXnW0nduRKTyVt2RTWvq0mB8DATyesjuXm92yGkesdVrNHC+xVd8R31eZCrb89lx875k7AnhrXZJCeP13LwSOqUjr609Hg62gfo69NdLwimUdk9MPR0D9PbO0JKahzu58M5dvXnkZx0jhVV+Jgzasv8FT5yx5fg5n37yxfryNqQqBP+vLc3m83s2bueUyeq7SPzzMOrbq1PAzBT8c/+nvGodq8+igpukJPnzSwgddvDyoRoTCaF5qbeKfY3HXQ3AFzG5K2l+m90ZILyGy1s257iXOoIr3dl3/40zp+r0QnV03vdfI/T10O7eeHKzapWYmIjWL58mc9to6KW8YUvOuua3HP3l7FaG5dMfQsDT3zdbpnpw+DWwHgtbzkkUrZw110PO5Z87nMfJi4uxucWqalruPfe9/L617+C73//ccLDwxZioA6sVisnTpRx+LB/x59GYqJ6LM3N3fM5LK9oFcjXrImbujFw4EAWZ85ULEre19OnK9i+PZWgIP/CH2APp9/C0aNXFmBkrjiFP//uZI3IyDASE2MoL1/4kJjBwRFqa9v8Cn96tm5N5ubNlkURqsvKGlmzZgWRkdP7e1YLlF1elAlcQcEV8vL8u5MBNm1K58473w7A8PAIH//442Ak8DcwMDC45ZmwjnOh9hw/ffJnjmUf/sIHMAnXsh5WqV2sSl72uiMcyN/F69/+Wu5/+G5AFU+DzXrfnepMNQlXOUSAoyCUXmqRuBa0QoJNevocAUb6xhjqGyVubZQjhF//NWcW2kNiEbB5exwVpd2Mj1ld2jlkUQGK8DanE66/2nOp+sNVdpRcvdxMRmY8IaEW+xIfcp4wIRSF7Lx0igurPHSo+cxNqe2rqLCSnLwMFwFZ/09PWHgwKevjKL3W7JHD0/mq2XC+qq7nfS7/xkYnuVHWwvYdydNqn56RQHtrH/19Ix7H4e35TP8pXtJCaOtqbnawfHk4MTERPs+lniO5mRQ7hOrpisy+RUzXfeluFnjJu1pyrIzD2VmYTIrDjTqlUH2yMgDjnK4Qq547m1Vy8kQFhw870xL4+nf77W9gxw71Ovjy5XJ++MNfIunyef4NDAyWDoagesvRx89//jtOnrwIQFZWKv/1X/8+5VZf/OLd/OY3X2N0VNLfv7AO1TNnKu3Cn/d8pN7Iz99CQcHCuhRn4vjTsFjM7NmTzsmTN+ZxZJ4MDo5QU9PGli3rpr1NUlIsNpuNlpaFFapv3GgkKSl22sIfwMGDWYtSoOzo0avk5W2dtgtDE6oXukDZxMQkZ89WcOCA9wII3oiKCichYTkVFQsrVDc1dSGEYNUq3zd99Dz00EeIiYkG4Omn/8jJkyeAxUsBYmBgYGAw/5T1XuPHj/+Uof4hAF711peRtS3dUYhK2NVPG/a8qAoEmwVf/fnnuPuR99Jc2omQErOmTQpXh6lZeAqiegRq3xoSVUi14i5OqhdXZgE3TjWRtT/JEWytR0F1yZoUpzs2yCLYuW8lF063ee5c4NGLJkI5xSi1oQm3+bQQXubL0iGmjoxMcON6O1t3rPLYrVs32pGzIi6c0FALDfULUSvAOZKa6k6io0OJifGew94V9Qh37V7HlUuNjI9P+m0XaEqKyzl0OANlmrG9Qgiy8zZQVFCmG1lgx+ZNKLVabZw8UcnBwxletrB5WSYJDw8mNTWO0qtznzM6x6QXWD1bgaCjY4ChoXHWrZuesQUgMzOR5uYeV0d1QPH++p47d5Ot29b5rLehx2Qy8dRXP+F4/ulPfZ3e3pvYpK/3rMFiIQhcyP/0PhkMljqGoHpLYWVoqJb773/MseSrX/0kFsvUH+Ta3b/8/IV1KQ4NjXLzpvcK5P6IiAglOTme0tL6+RmYF8rLm0hM9KxAPhUbNqyhoaGTgYGFE6qnKkTki/z8bRQUXF0woVorRDQT4Q9UoXr37nROnSqfp5F5ojmiNYf0dFm9egVWq5XW1oUrUHbyZDl79mRgNvsOGvTGoUMbOHVq4YRqKSWFhVfJy9sy7W1iYqL5/OedoVF33fUlbLZmjAJVBgYGBrcmQxMDnLpwkr/87G8AhIaH8r5PvBupEzS1bzuz/SJVc5zapMAUZCY+dTnNNzowm1wvYk1CLU41Fe6ykuZrRAv/l84LbYCOxn5CI4JYtjzEI1+egiTIJAk2QZACZpxjWpcaSV/3GP09Y5pPT7ed23e6cBPHBB4OROdovaEuP1lSy/5D6xCKvzmDth9nX4ey13PiWDU223x//6r7ttlsnD5Rw8HD62e0tcmksO9ACidLqvy0km4P32H306Gzc4DBwVHWJU+dykpPfHwkwSEWGuo93Yn+HKO+8OU21T+/cL6GLVvXeAh/0quY6mTXnhSuXG7wI1T7H5n+L9HjPAt9G7WdlJLCKfKRet2TEOTlbaKwoHQW45wp6pkdGhqjqrKFrVunb2w5cmQXb3nrqwDo7OzhC5//NkYU1tJEETIgD4NbA0NQvaXo4NFHf0BTk3pn+1//NZdXvOLIjHpYuXI5FouJhoaFKfYyW+EPYN++TM6fr2JiYv7v3mmFiLxVIJ8KVajeumBCdUtLNzabJCkpdsbbLlsWytq1cVy/3jAPI/Pk5Mkb7N6dPmU+Um9s3LiG+voOBgbm350opaSg4Ar5+dMX/vRor/9CCNX9/cM0NXWyYcPqGW9rsZjZtSuN06fnr9iAnmvX6khJWUlEROiMtrvjjreyaVM6AGfPXuXpp38D9AZ+gAYGBgYGi871not853Pfw2ZTBZ533vVWYuOXO0Lt9cKMye6iDDbpRE8geeMKWm/2MDE26Qind4T6C28B3zh61sRQvdxm04X6a2iFpJCSinOtZO5Z5bK92p/EokgsCi5pAPTpBQ7krOJEsb2QjtBkVVex0zlvdhU6PUVX5zpvdHcNMzgwxtrk5S4im4cA52WaHhpqIT0zjquX5zOyxbnjSxca2Lh51bQcf+6sT4uns2uQ3h5/5gZviRtm5xItPFpGbv7UNQy8kZ2TRcmxcq9zRu+CuSe+Q/adIfSgFtqtqmxjy9Y1Xtr6O26JyWRi7/71nDx+c1pjcr7DXYtzSaTHWL1Nl6sqW4mLjyQ6embGFoD4lVEEB5tpaAh0GL27UK1SVFRKTs6mGV/fPvLIRwkJCQbgm998hvLyy9jkWCAGamBgME8Yguotwzh1dWU8+ugPADCbzTz55Mdn1VNu7haKiubfpdja2oPV6r8CuT/MZhP792dy/HjZ1I3nyKlT5bMW/gASEpZjMplobJxfoVoV/q7OKC2BO/v3Z3LuXOW8C9UDA8M0NHSycaO3SdzUqHecF8ZRXVpaT3LyzIU/jWXLwlizZgVlZfMvVB89emVGaQnc2bRpLbW1bQwOzq9QPT4+yYULN9m3z1uImX/MZjNf/eonHc8//vHHGRioRfUqGRgYGBjcKnSMNPOXP/ydSyfU7/qk5FW86f2vw2ZTP/G18HqHl05AsMkpAKoZHiUmsyBt1yoqz7Y40gRooqbWUDrauyLdhFNfYqq2XV1pJ6vTY7AEmxyCqiqaqq6kIBNYTE43rTps52/LY0JYFhlEY92AzqOnuA3MmzvVu8PU93xecKywmuzc9Ux1SSjdT4Kd7TtXU1baytjofBWIVI9xZHicyvJ2tm6b6c1i56Bz8jIpLJgqssnTqaounf41UVVlGyvilhEdPbuaFKGhQaRnJHDlsmcU3nRmdv6FVNd2x4rKOZKT5TFnnMqdqo0kLX0lnR0D9Pb6Eqq9i6juSLfz7T6FtVptnDpVycFDmVOMyzfZORs5VnR9Hq9v1UG3tfUyMTHJ6jUzN7asXZvIffe9B4DJyUk+9rFHgYWvGWHgHxGgh8GtgSGo3jK0cv/9jzE6qt7Fuuuud5GRkTKrnsLDQ0hLS+TKldoAjs+V2eQj9UZGRhKtrT309Q0FaGSeaBXIZyv8aeTlbaGo6Nq8CtXXrzewdm0cy5bNTvgDVajeuzeDEyfmN++r9vrPpSrsqlUxKIqgqWn+ErdPTExy/nzVrIQ/PQcOZHH27PwK1Y2NnVgsJhISls+6D1Wo3srRo/Ob9/X48TIOHMjCZJpZWgKNl73sIK973W0AtLR08OUvfxfoCOAIDQwMDAwWE5vNRmnbOf7nCz90LLvzM+8lKNiCs2a3cBM8JSYFtViUfYnFfrUTk7SM0aEJBntG1bymwjUXKvY+9bM0ieetOtea56r4o8lF4yOTtFT3sHZDrJp6QNHaSoIUCDOp+VK91zIXjg73Hkzk3MlWbDYQwqyKYW7uQudoNJ+u8NqdL2pudhMbG0b08lC/7lT349ajKAr7D6VwoqTa/85mhfMytaS4ikNH0uY0Z4yNjSAiIoi62qnMDb6dqlMJq1arjdOnqjh4KH3W4wTYuSuZ0muNjI1NuOxTIn0WHPPlSnUXUjU62vsZH59k9WrXVFbu4qZ3nLcfcvKyKDqqv2ZwF1B9vWa+i1+5XyqdPVPFjh3J0yq064uwsGDS0ldx5XLdrPtwxdOdqhpbrs0olZU7993/HlavTgDgr38p5i9/eR6bHJjLQA0CTKByqM4FoXJQCPEVIUSJEKJLCDEhhOgQQjwvhHiH8POBKYSwCCHuE0JcFkIMCSG6hRBHhRBv8LNNsBDiUSFEkxBiTAhRKoR4t5/2IUKIm0KIRiFExNyOeOliCKq3BMMUFx/j2Wf/CkBcXAyf/vR/zanHPXvSuXy5hrGx+bnjXFbWwJo1K1i2bHZ3bzXUcPpt8+pSVB1/s0tLoCc8PITU1ASuXq0NzMDcmJiY5Ny5Svbvn/3dW42srNU0N3fPm1Dd1NSFyTQ34U8jL28LhYXz56g+fryM/fszZ5yP1B1NqJ6vAmVaPtLc3NlP4jS0PLHNzfMjVPf2DtHe3kt6euKc+nn88QccxeyeeOJ/qa4uBcYDMEIDAwMDg8WmabicZ777G1ob1FRWu45s59Ar9jkEVFW2kdgcQpcqWiqAVTrXC+EUaDL2J1FxuskhpoJTePUm/XiUctIt0C6KTTjD9ivOtpCxexWKIhyuVQVpTwcgsZhUV6xi36EQoNg3dlQzRyEkJIiMjSu4dqnTi+Dk6U5VMLs38UCfT9NmlZw71cC+g8louVi1WvDuItdUNd+TU2Lp7x+juyuwc0Ztr53tg4yNTrJm7cxy2Hvj0JEMTpRUOdJH+MfNNen3LKicP1vDtm1r5yT8gSpUHziYzvFjFS7uZVDfuMIuWDpfL3cB05v4rjsyKSk4ep28/E1ejmn682mBIC4uivCIEOrregDFZUz+a9tPj8HBUWpq2tm0eW7GFoBdu1O5eqU+ANe37mKq+vzGjSZ7od3ZG1vCw8P4yiMfdTy/92OPMj5eh01O5z1r8BIiHzgOPAAcQs19dhn16+XlwNPAn4QQwe4bCiFCgKPAo8AmoAroBvKA3wghvuJjnz8F7gMigHIgHfixEOIOH+0fBFKBu6WUgzM/xBcHhqD6okditTZx111fcix5+OGPEhW1bE69mkwKBw9mUVJyfa4D9ECtQF4540JEvoiPjyIkJIi6uvaA9KenqakLRZl+BfKp2Ls3nUuXahgfD7xQfeLEDfbtm7vwB5pQvZWCgsAL1bMpROSPiIhQUlJWcvVqoO44O+nrG6K1tYeMjKSA9JeVtZqmpm76+wNfoOzKlVrS0lYRHh4SkP7y87fMW4Gyo0cvz9mdDJCWto577vlPAMbHJ7j33kcwEvgbGBgYvPgZt41ypfosT3/9WUCdl37oofchhHAUodLyj2riqgnV/SkAGxJhd6tq3zQKEB4ZzLLoENrr+lS5zP4V55ChpvhacuxHOIVRbZuB7hHGRydZkbQMYRdQTfYQ/1AThJjteVPt2wh7j6qIqt+Henm2ccsKqit6GRvR/Lj6kepxili+x+0qwF660MSmrQkEBVvQXw4KN1Ohp1DnnZy8NIoKKr2crdkFuWrpC6SUFBVWkJ03tyghjZAQC1kbE7l0cTopmKZ27eoZGhqjurqdTVtmnsPeGymp8fT2DtPdNegyFlWI1wRLD3l9Wu/j8vIWEpOW24U/99dGMvXr5rrHI0c2cPxYBarmN0frnRtFhaXk5Gyc85wR1M+RAwczOH4skOYGdVyTk1bOna1i//65v1ff9rZXc+jQDgDKy2v49rd+CsxfNJ7B9BEBcqdqqWDmMhSgBrgLWCmlXC+l3C2ljAXeBYwB/wo85GXbR4DD9u03SSm3SSnTgNfZt3tACPFa1+MW24E3o4q2yVLKrcBuYAT4ghDC5NY+A1V8/ZuU8tdzOtIljiGovujp5Uc/epZLl9Q8otu3b+D22306tWdEWloiXV399PQE9obCyZPl7N078wrk/sjN3Uxx8bVp3nGeHoEW/gBMJhMHDmRRUhLYvK99fUO0tHSTmRkY4Q9g5cpogoIsNDQENoz66tVaUlMTAib8Aezbl8GlS9WzrDTqGzUtwbaATOJAE6oDn/d1bGyCy5dr2LMnMBccoArVyckrKS31zOE1F+rq2gkNDSYuLiog/T344AdJSIgD4He/+wdHjxYAt+xNUAMDA4OXBPUDV/j2l37CyPAoAK9716tIzUoG7KKm7mJUoAqcwXb3pxWwaBetuj61b/KMXQlUXWzDNmlzXBxrBaK0dv4ukIRu32YFh/v0xqlmNh5IxCTAIiQmAcGKOhazkA7HqnseS3Vv6kOfB1VRTOw7vIaTx/TinyafaVKaggkzLuKX25TFXQwcHhqn5mY3m7assvegCrKK0PpxP2O4LdMeznFHL48gJiac6puduvVez57X7Z0PTbJWqapsJyEhkqgob46/mQq26pnYtn0N5WWtjIz4MzcI/Dk1vfktiwtvkO0lH+l0t/dGbv4GCguuu2zneK8I/TkTzvfWFLufnLRx9kw1+w9koAn6iqMv8Hz3u75WCmYUTI73jUAhNDSIDRuSuHypXrfN7I9dW9LS0oPNBolJgTG2AKxPS6CnZ4ju7tnOFYXXZ6dOVrBrdxoWS2CMLU999ROO99LnP/8dOjrKsUmjVsBSwNsn12wec+QMkCml/LqU0sVVJqX8KfB5+9P3C+HMEyKEWAl80P70vVLKct12f0R1rQJ8zm1/WqXzr0ope+ztrwC/BuKADW7tv4X6IfrhmR/aiwtDUJ0G//jHP3jLW97CunXrCAkJITQ0lNTUVN7xjndQVFTkd9u2tjY+9rGPkZmZSWhoKDExMRw5coQf/OAHAXB+Wenrq+aTn3zSseRrX3tw1jkJvXHbbdt44YXLAetPrUDeRVZWYO7eaoSGBpOZuZpLl2oC1udsK5BPRXp6Ih0dfQEVqgORj9QbaoGywOV9HR+f4OLFavbunVtuKXdMJq1AWeAc1XV17YSEBBEfHxjhT2PlyuVYLCYaGgJXoKyk5DoHD2ZhMgX2I33fvgwuXLgZsLyvNpuN4uJrAUlLoLFsWQQPP3yP4/nddz/M5GQjMwlZ09PV1cWPfvQj/uM//oONGzcSHh5OcHAwq1ev5t/+7d/43e9+N2Uf8/u5b2BgYBAYlur8dnCih+OnjvGXZ48CsCw6gvff/w4XR6g+/5yC3S0qcBScUuxfh5qIqa23KBAUbGLthliqrzpvGAt0of92UUqfK1Xbh0mo4q32XPPytdb2ERUbSlhkMIqQDpE2yARmRTqKUHnPmyd1e3eOSAjB6jWRjI5O0tUxghAmFEy4hFILE3i4U90ERk1psz+OF9dyMDtFp+wKnNfbzu3UZXqh1r94uf/Qes6crMVq9eZwnK746RSMJydtnD1dx74D62e0ve92ah4IRREcOpLG8WPujlpc2/rZj3s+1dbWXqxWG0lJMV4Fw+mIp97axcYsI3p5ONU32/Ba2MnttZ3OeT99qopdu1M8hD/du8EhlGrvNcVLKgjnFuq52rEzhRvXmxgbndD1MZ1j16cs0J1jKSkquE5u3kav52su5OVvovBoaQB6Usc8MDBCY2MXGzYEztiya9cm3v3ufwOgr2+Az3z6G0DzrPsz5re3FlLKfimlv7tCf7X/jEEVPDX+HxAEVEkpC7xs9137z51CiPW65ZH2n+6hgC32n44LZiHE24CXAV+WUt70M8ZbAkNQ9YOUkg9+8IP8y7/8C7/61a+or6935KOpqanhmWeeITc3l49+9KNetz9//jybNm3iySefpKKiArPZzMDAACUlJbz//e/nla98JWNjY3MYYQdf+MK36ejoBuAtb3kV2dl75tCfJ7GxkURGhlFTE5gwWlX4m3s+Um/s3p3GtWt1Acn7Oj4+ycWL1XMuROSL224LXN7X+voOgoMtxMdHB6Q/PWFhwaSnJ3H5cmCE6pKSMg4e3BBQ0V8jPT2R9vY+envnLlSrwl8pubmbAzAyT1ShOjDh9D09g3R1DZCWNrd8pN4wm03s25fB8eOBcVRfulRDVtYaQkODAtKfxrvf/Xp27doEwNWrFXz/+78AembVV0JCAu95z3v42c9+RllZGTabDYvFQlNTE3/4wx94wxvewKtf/WqGh72nbZj/z30DAwODubGU57dSSmr7zvPYg993LHv//e8gKibSqR3hFEw1cdWsOPVBi6IKq77qiktgdWYMbfX9jI1MeEhE3lytwssCYRdNsdmovNhOxu6VmIXEotiFW5OrmOs+Foej0Bv2FRLJwZx1nCiuV+cMevFMqG5BTaRSBVZf4pWaD7S9bZDJSRuJSdEIe//6OblwF7ccWurUImFIsIXNW5K4dL5hWqKi93/Os3/uTC07dqr5SB35Zef40Fi7LpahoTE6OwZwOVCXV0kTuqWHgKqdK9DSEpSRm7chYOPUPw4fyeTkiUo1Ck8ItxG4v1v9PwYGRmmo72LDhtUe69SuPd7xbu8N9/3b3xuaUH04i5LiG7q36XSOUWsrXXThsutNrF23Yk6Fdn0RE7uMyKhQqqvbZril8PqsoOAauXmbA359+6WH7yIiQq038v3v/4pLl85gk7NLG2bMbwOHy8fwHB7zjD4UdET3+377z2PeNpJSNqGmAtC3BdD+WNydqNrzVgAhRCTwJFCJmlrglscQVP3w4x//mO9+VxXp3/SmN1FRUcHIyAjDw8PcuHGD173udQA89dRTHnd1+vr6eM1rXkNXVxdZWVmcPXuWgYEBhoaG+OY3v4nFYuH555/nnnvumeXoxigvv8TXvvZ/AISEBPPoo/fN+lj9kZ29iWPHrs85nL6hQa1AvnLl8gCNzBVFUTh8eCPFxdfm3Nfx49fZvz9zXoQ/gBUrIomICKG2dqZf5K5IKSkqCqzjz53du9O4cqV2zkJ1b+8gHR19cy5E5Ast7+sLL8xdqL50qYbMzCRCQz3yeAeE8PAQ0tISAyJUv/DCZW67bWsARuWdjIwkWlt75lygbHR0nGvX6ti1a/3UjWeIoih8/eufcjz/9Ke/Rnd3FZ6On6mZnJxk7969fPvb3+bmzZuMjIwwODhITU0N733vewH461//yh13eOZfn//PfQMDA4O5s5Tnt91j9fz2l3+h9LwahZiatZbXv/vVgPMi1OJ29WLWPdfLQYruiUveOsCkCDbsS6TslOr40r4ttDJENukqNzkKUNnHoA+2vnm5ndRNKwiymBwuWkWojlSQaloAe1vvt1E9l+qFq8ioYOLiw6mp6nNp4VGIyqNX136lhJLCag7npKKJhALFh1joTVaems1bk7hZ1cHw8NwKRA4OjlFf182GTavm1I8/cvIyKSoon9HNbf150s5bWVkzq1fHsGxZKFLKgDv1goOD2Lx1DRfO1zDb6BuNwoJScvM2+RT+1Pe7vqiUZ25eh3Dv0J6dxbuSU+IYGByjs3M2Vemd+5mYsHLhfDV796XNop/pcfjIBk6UlM/h+lYdb1NjF2aTQkJCdMDGppGQEMenPqVGZ0sp+eg9jyBl06zeY8b89iXH2+0/L0sp+3XLNbdYlZ9tNVepvtL1P1C/Ku8XQhwWQkQIId6Nmqe1QudE/QKwCviQlPIlobAbgqof/u//VLEyLS2Nn//856SnO8OUMzMz+dWvfkVqaioAzz77rMu2jz/+OK2trYSGhvKXv/yF3bt3AxAUFMSHPvQhHnroIQC+973vUVFRMYvRtfKxjz3C5KQainv//e9j3brAhRnoCQkJYtOmtZw/P3vHtir8BaYCuT9SUxPo6xumq2s2X+Qqvb2DtLfPn/CnkZOzmeLi0jkJ1Zcu1ZCRkURY2PwIf6AVKNvAsWNzC4154YUr3HbbtgCNyjtxcVGEhwdTWzv7AmVjYxNcu1bH7t3zN4kD2LMnfc5CdU1NK8uWhRIbGzl141miCtVzT/1RXFzKkSMbUZT5+do5eHAn//7vrwGgq6uXhx76JrMpUHX06FFOnz7NnXfe6fh8B0hOTuYHP/iBY6L59NNP09DgWtRi/j/3DQwMDObOUp3f2qSVqrbzfOMLP3Esu+cL78eiy7nv7vAEp8ipCqXqMn1ovbvnTtOBYhPCsVolPR2ujiy9VqG5ULX+9csFMDE8QVvDAGsyl2Oyu1PN9ryp4Cr26nF4IB37UhuqjlG3UGwBuw+s5sKZRiYnbfbt9U5SoW7vVLi8UlneQWJSJMsig9EEMFcPq6uw6i9I2xdCCA5np1FS5C+c3ufWjt+KC8vJzs2cl4g2jejoMFbELeNmVTv+hUrpsl5/jlThr4Z9+13njIEWVbdtW0dlRTPDw3p9QnsNpzevamrqRlEUElZF+2ghdKkf3JbjdJR6f0M4F6rh9P5ScPly0Do5eaKSPXvXB7TehjshIRY2bdGE6ung6c5Vr29LyZmniDaA/77rnaxfvwaAoqKz/OY3f0bSN8VWnhjz28ARqKJUdtYIIap9PWYzPiHETpx5Ur/itlpLSNztpwttncMFJ6WsQxVLE1DdrQPAj4FJ4L/s+90OfAj4pZTyH7rxxAshAptDbwlhCKp+aGlRU0Js27YNs9nzLrDFYmH79u0ADA66hhlrk9W3ve1tpKSkeGz7kY98hIiICKxWKz/72c9mOLIB/va3f/Dcc4UAJCWt5P773zfDPmbGjh2p3LjRwMjI7G40qBXIEwNaiMgX+flbOXp09uLPfOUjdSckJIiNG9dy4cKsPisZG5vg6tXaeRf+ANLSVtHTM0h39+yE6traNiIiQlixYv6EP42cnC0cOzb7AmXFxdc4fHj+hD8NTaguKZld3lebzcaxY9fJyZm/SZxGfHwUoaHB1NXNTqju7Oynv3+YlJSEAI/MlUceuY+wMDU07Fvfeobr1y+hFqucPnl5eX7Xa3fxAc6dO+eybn4/9w0MDAwCw1Kd3zYPXef7X/8lnW3qtdyRV+5jT/YOwPWCRZ/bNMiuKbrO2JxilknRuUsVp+iqtd+0P5Hrp5pdBDBt9uAoHuI2HRQ4w/ivnGpm84FEzIrqSBXaWAUI1MJU3maTTjlT9Ym69u6+N0GQxcKW7au4eK7JvtT1dfOcs7o+n5y0cvFcE7v2rdHtXdjFQRu+BMPZkLR6OePjVtrb+qdu7IWW5l6EEKxKnP/r7wOH1nPmVA1Wq3YOpnfs2jk6faqK3XtSMJmdTl9t3dzcqq5CoxCCIzkbKS5yTcEkXdr7Ga+UFBdOlY/UuS/XUPwpu3fpY/nyCGJXRFJV2eZ2HP46cq7v6xuhpaWHjMz5NbYAbNuWTHl5MyMjUzmqvY/76tV6UtcnEB4+f5R1vLAAAQAASURBVMaW4OAgHn/ifsfzB+5/gpGROqSc2XWOMb8NHEukKJVX7EWnfgdYgN9JKX/h1kQTY/y96bWLJ5d8G1LKzwFvBH4FFAA/APZKKV8Q6pfQ/wBDwEftY3mtXRRuA3qFEMeFEFmzPbaliiGo+kG7e3P58mWHE1TPxMQEly5dAnDcqQEoLy+nvl6tcviqV73Ka98REREcOaIWS3v++ednMCrJxEQj99zzZceSRx+9j/DwsBn0MXMURSE7ezNFRTMPp3dWIA9sISJfxMQsY/nyCG7ebJm6sRt1de2EhQWuAvlU7NyZSllZPaOjMw+NOnaslEOHNgS8EJEv8vNnl/dVy0e6EMIfYK80uoaLF2cuVHd1DdDXN0xq6vwKfxppaavo6hqYVYGyCxdusmnTWkJCApuP1Be5ubN3VB89Ov/uZIDVqxN44AH15pLVauWee76MlDP/HPBHSIjzppDV6qy2Or+f+wYGBgaBYynOb8etI1yqOM1Pv6WmGDBbzPz3596jlrsROrlFOEUkRdFC+KW9EJQqiOkLUWkCp7tLVfs9NCKI2IRwmm/2uoxHL5rq0QRWAXS3DyOlJHZlGGZNsHVsI+0h/564yErCNbxf8XCnOiRaMjfG0VDXy/DgpEcuS/V/7UjdikkB5043smN3EhaXfAma8Md0dcRpk52XTlFhxQwERWEfi+RYUSU5OZlTtA8MQUFmtm5fw/mztVO09BSe+/qHaWnqJSPTe1oCh7wq5QzEVV+io2DNmljGxiZob/N0J07lI752rYGU1Hi7scWfuDkd8dPf2NWfhw5ncvJkpY8CZd725aSwoJS8fN9pCQKJogiOZG+guGgm5gb1bI+PT3L5Ui179sy/seW1r83jtpcdAKC2tomnnvxfJHNLG+eOMb9dNBqklKm+HjPpyO4C/SuwFjgP/KeXZqP2n/4uILU7BCPuK6SUv5VSvkVKmS+lfL+UUnOxfQDYB3xaStkshDiAKuxagTuATwLbgReEENEzOa6ljiGo+uHOO+8EoKqqire//e1UVTlTTZSXl/OWt7yF6upq1q9f75Iz5No1p+i4ebNvEUlbd/36TD7Eu/j2t5/mxg1VLDp4cAdvf/trZrD97Fm3Lp7h4TE6OmYWZjBfFcj9ceTIJo4fL7PfcZ4eWgXynJz5TUugR1EUjhzZNGOhurtbFeHWr5+/3FLuxMYuIyoqjOrqmYVRX7hQzcaNCyf8AezcuZ7S0pkL1UePXiY/f/7ykXrjttu2zjicfmRknLKyRnbsmNH37JwIDQ0mMzOJS5dmlve1qqqZ2Fj1JsdCcO+972XtWtXV8PzzJTz33N9Ro1ICQ2FhoeP3LVucnxXz+7lvYGBgEDiW4vy2buAiX/3sjxi3p8F5+x2vY02K06GmCat6TcosQAhVuNRLiWi/C13OU7s2o2k0NpxCbfqOldRc7cDmCKe3i6bCuW/9cgVASq6eaGLLgUSCFFdJyIZT/NVv7+mkdQ3Xdg/d1kQyx08hOJSdwoniOtd2wnUb53YqA/1jNDf2k565wqV359myK6q6cztXfTUyMpTExGgqymcm+pSVtrAuOZbwZfPn+HNn0+ZEaqo7GRrSTFlThf+rFBWUkZ3nPS2Be65Vx+9+RVV/AqdKbt4mCgqu6frRe1S9X2eNj09y6WIte/am+9jHTMYzvW2Cgixs257MubPe0sX5Fmsb6jsJDjYTH79w0cFr165gZGScjnZfjmrvYz1ecoMDBzIW5PpWCMGTTz7gqO3x5S9/n6amG9j8FnmfGcb8dgZ4M17P5hHIIQkRAfwN2AGUAq9wy52qoVXtjfGyDrd106rwK4SIAx4GLgHfsi/+BOpRvkpK+T0p5ZeBe4BE4Pbp9PtiwRBU/fDa176Wp556iqCgIH7961+Tnp5OWFgYYWFhZGVlUVhYyJ133smZM2eIjHSGMjc3Nzt+T0rynddUW9ff3+8RUuWdSTo6qvjsZ7/hWPK1rz24IHfwNDSX4nTvOKsVyPvnpQK5P4KDLWzdmsy5c/7yLbty8WL1vFQgn4rk5JUMDo7S2Tn90KgXXrhMfv78O/7cyc7eTEnJ9AuUjY6OU1ZWz86dCyf8gSZUb5yRUF1V1cLy5RHExCybx5F5EhsbSWRkGDU10xeqi4qukp29ad7TErize3ca167VTVuotlptnDhxg8OH/YWYBZawsFAee8xZoO+ee77M+HgjsylQ5U5vby9f/rIaHXDkyBEyM50Omvn73DcwMDAILEttfts/1snRwkJe+PMJAGLiovnPe97i0kYvaoLmFJUO4VMrFGWyi6gWRSei4nn96vDSCTCbFdZviaPyoir+aSkCpFt75zeupKGym/jVEYSHW1zy4QlU0cyspTR1G7+b+ukyKo/8lcK9ijusWhUFSFpbtDmj/2sAgaCkqIbDuSm66wWt4JDzuXtXgbiy2Ls/hfNn65ictE7RUt2bKvw1sHtvcgD2Pn2EEBzJzaC4UJ/70Z9tV9LY2E1QkJn4ldMT/jxTAbiMAN9iqitRUWGsWrWcivJmL+31fTn7PHG8nP37vQl/03XM+nruXxnasmUtN2+2MTQ0vTmjlJJjxWVk5yzcnFEjL38zBUevTeP6Vv2r6ekZorOzn7T0hTO2bN6czgc/+FYAhodH+MQnngKaAtK3Mb+dPoHSUgOpqQohwoDngP1ABfAyKWWXj+baB50/a7VWQXi6CXEfQ823+kEppfaBfxAol1LqxZg/6NbdMhiC6hTcfffd/Pa3vyU+Ph6AkZERRkZU9/PY2BgDAwP09fW5bDMw4HRDhYX5DsXXr9Nv45s2PvOZr9HXp7a9/fY3sHv3wrkpAaKjw4mPj6Ky0tcXuStqBfKFF/4Atm1LobKyyS2Bu3dGR8cpLa2flwrk02EmLsXq6laio8OJjV1Y4Q9UoXrz5nXTFqqLiq5x5MjCC38AKSkJDAyMTEuoVoW/Mo4c2bQAI/MkO3sTx45NT6ju6OhjeHiMdeviF2BkriiKwuHDGykunl6BsrNnK9m6NZngYMs8j8yVN7/5VRw5ooapVlXV8fWv/xjwNa+YHjabjXe+8520tLQQHBzMN77xDZf18/e5b2BgYBB4lsr81mazUTtwgSc+9X3Hsg996t1ELAuzO0vt4dJIhD2cHyQmRTpETEeAu90BahZOZypCgpAI4SqQuchCApLSouluHWJ0cNyheepzRyqO3yXWSRtVVzvZsDMei5eaOb4KUbkIl0Ig0BXb8hrq7ykDC0wcyl5PSfFNpJRM5aloaurBbFaIX+mMEtGqt2s9ekpmgbnMt1hM7Ny1lrOna/20cu7r9Mlq9u5LmddCRL5ITIzGZrXR2tI3ZVspbZQUlXPEnpZgujlnXQVsAdKfkOr7NThwMJ0zZ6qYnLR59KkJ5dq/vt4ROtr7SUtPcLSbWakx7VaC/uf0pCAhFHJzNlFcOL054+XLdaRnrJrXQru+iIoKY2VCNJUV7mmihNdnBUevkpe/sNfgAJ976EPExKgi/s+e/hOnTp3EJoen2Mo/xvz2xY0QIgRVqMwGaoHbpJT+XDqn7D8P++gvCUhxa+tv/0eAdwHfk1Ke1q2KAtwvwrXn0VP1+2LCEFT9MDw8zFvf+lZe85rXsHbtWp5//nk6Ozvp6Ojg+eefZ9OmTTz99NPs3buXK1dmnltyZoxy5cp5vvc9tdpqREQYDz/80Xnep3cOHdrIyZM3XPKreKOmppXIyLB5rUDuDyEEOTmbKSy8OmVbVfib/0JEvoiOjiAubmqh2mazUVJynezshclH6o3t21MoL2+askBZZ2c/g4OjJCevXKCReXLbbdPL+3ruXNWiCH8aISFBbN68jvPnvYVGOZFSOoqmLRapqQn09w/T1eV/sjQ0NEpVVTPbtqX4bTcfCCFc3Ptf+MK3aWurAmYfGnXXXXfx5z//GYBvf/vbbNu2ODeKDAwMDObKUprfdo3V8suf/IHK0loANmxL4zVvy3es1ztSNSdqsLcCUJpLVdGJqUhH8Q9NcHX066YHCSHYaC9QpbletRmhyc25WXa+jcwd8ZjdlFPVcKq6U91nkwI3y6uHUOPp/HN3pyp2ATYiIpg1a5dTXuavUKQNm7RyoriWQ9nJHmuli+dX55INcBxq5oYEGht6GBgY9duur2+E9rYB0jIW/maxRk5eJsVF7nlfPZ2qV680sj49nrCwIF2rqUVV1zaq0O/K9IRKi8XMrl2pnDldqdvGu/et4Og18vK3oAhNaNX2PtUNfH9euqmea0iSVscyOWmjrdW/uWFsbIJrV+rZuWthI9r0HDyUwZnTVTpHtXd3bm1Nu73Q7sIbW2JionnooQ87nt9z95ex2RrmUPzMmN/OBn3Btrk85j4OYQF+A7wMaATypZSNU2z2B9QLonQhRJ6X9XfYf150c5d6278Z+DbQiRrir6cLSBWudwsz7D87pxjjiwpDUPXDfffdx7PPPktGRgbFxcW8/OUvJzY2lhUrVvDyl7+c4uJiMjIy6Ozs5EMf+pBju2XLnB+ww8O+7xrp1+m38YaULdx998MOB9unPnUnCQlxsz20OREUZGbnzvWcPu3bBa5VIM/OXhzHn8aaNXGMjU3Q1tbrs01nZz8DAyPzXoF8Kg4f3jClUH3uXBVbtqxbNOEPVJeiKlT7D6dX3cmLJ/wBaqXR2GV+herh4TEqK5sWRfjTs317CjduNPgVqisrm4mPjyI6emHykfoiP38rR4/6d1QXFl4lJ2fLgqYk0bNjx0be+943AdDfP8inPvUU4O/i0zf33nsv3/zmNwF46qmneM973uPRZj4+9w0MDAzmg6Uyv7XaJqhoPsu3H/6pc2wPvx9FUVyEUFekTjB1Fp7SnKlBilMANeHqqfMlOWjCbGx8GGazQnfLIN49kpLhwXF62odZnRrl6FMruwP2VANaa58ah0DB5HAKehai0kal38JVYN21Zy2XLzYyMeF7znj9Whsp62MIC9enshKO/pzPBbinGwgQQgiyPcLpXccCUFxQTm6+93ykC0XEshDWrFnOjTJv5i71xRwbm+TalUZ27lqH+ztqKlHVv1gt3R7+e9qwcQ31dZ12odq9X/W1rKvtIDw8mLg4V2OL5zi9C4fTW+a+3HPsefmbKSzwH05//NgNDh7KXNB6G+5YLGZ27Ezh7BlvGpL66tlskpKSxUlLoPGBO97Cpk1qtPaZM1d5+unfIumdVV/G/HZ2KAF6zAW7UPkz4NVAK6qYOmWhCyllG/Bd+9MfCiEc+R2EEK8F7rc/fWgaw7gH2AzcJ6V0z7daAMQBH7H3bQY+q1t3y2AIqj4YGBjge9/7HgAf/vCHCQ0N9WgTGhrKhz+s3iUqKSmhvV29WE9MdOYLbWryndtEWxcZGUlEhD+BZIDf/e45CgpUF/X69Wu5++7/nMnhBJzNm9dRU9PG4KBH8TcAzp9f2Ark/tBcir6+yBeqAvlUBAVZ2L49hTNnKr2uHx4eo7y8ie3bF+/urcbatXGMjo7T3u49NKqyspm4uMUX/gCOHNEc1d7vxhcUXCUnZ/OiTuJBFaqzs30L1VarlVOnyjl0aPEmcRoxMWqRqaoq99Aolba2HiYmrKxZs8Lr+oXii1+8m8hI9T34wx/+mgsXzuClYKVf7r//fp544gkAHnvsMe6++26v7QL/uW9gYGAQeJbS/LZ1+Br/8/gz9HarEQ+vemM22/dtdIbvC2fxKE0kDbLnRhU4naiarKNdpNozVTpD9x17FJ7OVPt/WoT95v2rKD3dgs0+ZxT2vhS78/Ty8Wa2HUpEKMK1D0BoYq/bcboLacJFrvXmEPXmTjW7PLdYTOzau5YzJ2tdlktsSKyMjY9TeqWV7bv0OQ+1o1lYViZEYjIpNDf1el3fUN9NaGgQK+IWX4TZsy+Fi+frfAjVkpMllew/uF4n/LmLqt5xfQ/4csD6E1Pd3kNCkJu3hcKCq471QmgPVcw/dqyMI9nTnTN6c6JqQr5D+p9mX04kkmXLQlm9OpYbZd7njN3dg/T0DpG6fvEi2jQ2blpNbW0Hg4Ou5gbtrFy8WM3GjWsW9frWbDbz5FMfdzz/5CeeYmDgJjY5s1oBxvz2Rc9bgDfbfx8FfiSEKPHx2OG27f3ASdTQ/lIhxCUhRBXwRyAYeEJK+Qd/OxdCrEEVSIullD/x0uQLwDDwlBCiAmgAXg+UAT/10v5FiyGo+qCiooLJyUkA1q/3nVczPT3d8XtNjXpTQF8BT18Zzx1t3caN/r7sJKOjdXzsY19xLHniiQcIDl5coVIIQV7eFgoKPMPpR0bGuHGjYUErkPsjMjKMxMQYyss9vwQqKxe2AvlUbNmSTHV1K0NDnqFRquNv8YU/Dc2l6C5UW61WTp68weHDGxZpZK4EBVnYti2Fs2c93RFtbb2Mj0+wZs3iuL3dWbcunpGRMTo6PIXq06cr2LEjlaAgs5ctF54jRzZx4kSZh1CtpiW4uqhpCTRWrlzBZz6juquklNx115eQsoXpXk7ed999PPbYYwA8+uij3HvvvT7bBvZz38DAwGB+WCrz2zHrIBdLT/GLHzwHQEhoEHd/9t2AUzDVZjtaWL9Jt0zi6lI1KZ6h+frpiUOykq4/FeG8GFKAkDALq9ZFUnuj296PdLhcO5oGCQo2Eb0i1Ol4dexDOtypDp+hcIq7rvlbdQKqcOa8tC/A/fJMeHh11d/T0uNoaxugv2/EXvLI5tjP6eP17N63xqvjz+ssUur34yuEe/Zk56RTUlypmzOq/dtskuPHKjmck+574wXEbDaxa08yp09We6zr6R6iu3uI1PX6OaO7CDq9Qk/+z66vta6vy6pVyxGKoLmpy2O/Fy9Ws2HDardCu3p3sq/X2VVCnR2u50QC+w9kcP5clVehWk1LsHipzPQIIcjN3URhgT7vq3oeRkbGKbveyPYdixvRBvCylx3g/71OTY3S0tLBV778PVST4vQw5rdzxd1RPtvHnNAnG04GDvl5uFTPk1KOALnAA8B11FD8FUAR8CYppe83hJOvAUHAf3lbKaW8bt9HIbAGCAWeQXXSzszdssQxBFUf6HNp1tXV+WzX1tbm+F2zt2dmZrJ27VoA/va3v3ndbmhoiGPHjgHwL//yL35G0smTT/4vtbWqGPiylx3k//2/26Z1DPNNYmIsNpukpaXbZXlR0TWyszcvWj5Sbxw8mMXp0+UulUatVptd+Fs6H/zqF7mnUN3e3sfY2ARr1y4N4Q8gKiqchITlVFS4CtVnzlSyfXsKQUGLl5bAna1bk6mqanERqrV8pEvBnawnP9/TUT04OEJNTRubN69bxJG5EhxsYevWZM6dc3VU37jRSFJSDJGRvhPXLyQf+ch/kJ6eDEBJyXmeffaPeOZI9+Tee+/l8ccfB9TJ5n333ee3fWA/9w0MDAzmh6Uyv20bvsSjD/7AMS97791vIiEpDu0i0+FOBUfxKYsJD9ep/qcvWUu9dBVICTb7A3Dxidp026Rtjaf2ehcT45MOsVZBcvVUC1sPrPIibTqLVukvlV1TDWhillO0FPawf0dfwj0cX3vm7k50ts/OTaO4sMrlCHp7RujuGiY1LdbLSLXxaVKvL1ebQAh3gXX2ImtYeDDJKSsoveaagunq5UYyMhPchL/FJSNzJS3NvfT3u17zFxWUk5OvRcdO7Sz1JUwKx3+zwVWQyc3dokY26YYwOjrO9dIGdu5ab3+P+U6g4X+k3lpO99rO1ZFrNpvYvSeN06dcw+mrb7YRHRVOTMzSMLYArEqMAaClWY1g1o7kWPF1e72NpWFseeyxex3XWk8++WOqq69ik+NTbmfMb+eO+6fibB9zQUr5YymlmOaj0Mv241LKR6WUW6WUYVLKaCllrpTyN9Pc/xuklEFSSp9V56SUZ6WUeVLKUHv/75iiYNaLkqWjeC0xsrKyHGFQP/jBDxx38/VYrVZH2NTy5cvJzHSkoOBd73oXAL/4xS+ora312PZb3/oWg4ODmEwm3vGOd/gYxSTNzTd4+GE1zYWiKDz11CeWjEMRNJeiU/xZzArk/rBYzOzenc6pU+WOZWfPVrBtW8qi5iP1RlJSLFarldZW9YtcFf4uLwnHnzuHDm3g9OkKxwXR0NAo1dWtbNmSvLgDc0MTqvUFysrLm0hMXDrCn0Z0dDjx8dEueV8LCq6Sm7t4+Uh9sW1bCpWVzQwPq6FRk5NWzpyp4MCBrEUemZOgoCCeesqZJ/3++x9jeLgO/BRkuPfeex1hUI8//viUk02NwHzuGxgYGMwfS2F+OzDezN//9g+OvXABgFVJK7j9Q68HfF9smnXLhHCKrEJ7rl9v/99FetIJqeAUQMFVChOAyQRZu+K5ca7VMZab17tJWh9FcKhrlIiacsBeiMrPV7S3/Jqe3+mqyOq6xL+PKTYunOAQM40NvY5lxwpvciTPd5SYN9lMCHdXrHOMgZp77NqzjiuXGhkfV+eMo6MTXC9tZseutQHpP1AIIcjJy6SowHnNUFvTSWRUKLGx7sKf66vjX5K0n0uvTfTvem+vuF60dXYQERFCamoC1645b44UF5Vy2KXQriaVOuc9c/GhTk9Udf+rgqysJJqauhjoV+eMNpuNEyfKOZy9dOaMGrl5mygsLHU42Ts6+hkcGmVd8tIxtqSlrePuu9XP4/HxCe6//3HAd0g+GPNbA4P5wBBUfRAaGsr73vc+AC5cuMBrX/tarl69is1mw2azceXKFV796ldz4sQJAO6++25MJuck6N577yUhIYHh4WH+9V//lfPnzwMwPj7Od77zHT796U8D8IEPfICMjAy808onPvEkQ0Nqkuc773w7mzf7ars4LFsWyrp18ZSW1usqkC8tx5/Gxo1rqK/vYGBgxF6BvIWtW5MXe1he0QvVFRVNJCQsJyoqfLGH5YHF4lqgbKkKfwCrV69gclIVqicnrZw+Xc7Bg0tvEgeqUH3qVDlWq5WWlm5sNklSUuzUGy4wQghycpxC9cmTN9izJx2LZWmkJdB49atzeMUrDgNQX9/M44//AF8FJh944AHHZPPJJ5/kYx/72LT3E5jPfQMDA4P5Y7Hnt1LaaO6/wMOf+IFj2f2fv52QsGAXSUkTP7VcqmbFU2byNtNw9e8JJAKrFNik0zXqLc+psz+JAJKSoxjoGWOwb4zxcSs1Zd2kb41z2U67iNJH1TtkLy870M+NhFshKm+2RU3qUvQiq66JKtLaOJSdwoljNUgpqa/tIWJZMDGxYXjbSC+G+S6i5HmCnPk59a7VmWEyKew7kMqp4zcBOFFSxcFDaUsqok0jfmUkQUFmGhu6sdlsnDxexcHDadPa1lOmtC+Z1inzJ6q6t1PfbXv3pnPxYjXj45N0dvQzMDhCSspKZzMxdcGs+UVznQvy8jY7ovAunK9h8+a1S8zYop7/iIgQkpPjuV7aAEBBwTXy87cs5sC88skH72DlSvX64He//SdHjxZgkwNe2xrz28ChfS/N9WFwayD8VdxboizYgEdGRnjDG97gYm8PDlbTVYyNOZNVv/3tb+enP/2py4QT4Pz587ziFa+gq6sLUEOmRkdHmZiYAFRL/B//+EdHn25753e/+1/e8Aa1KMDy5VFUVv6d2NjlgTzEgDA5aeWZZ4rYvTuNtrZe8vKWnpNSo6WlmzNnKjGbFbZtS2H16sUtmuOPY8dKiY4O58KFm/z7v+csOZFKQ0rJM88UceBAFlev1vK61+1f7CH5pL9/mOeeO8vq1SuIiVnGpk1LyxWh5+rVWvr7R6ira+e1r93LsmWehUOWCr///Um2bk3h1Kly3v727CUpqJeV3WTLltditVoJDQ2hvPxvrFlzEHBO5Ovr61m3Tk2roCgKcXH+nQj33nuvR96puX3uzysL9qIIIapTUtakVFcfC0h/qalHqKlpqJFSLo3E3AYG88NLYn7bM3aD//7Ix3j6+38BYNeBjfz0uS9jQxM9hSMs3waYhSqAKgKHKKqhoA9/B8WumNqk07lqQ3WnSvu2+m003VBdLh3rtG0Heke5fKKF8GVBrFy7jFXrIl0+SE12d2qQydWdKvB8ro5RVbcUzCgegqo3d6qWFkDRjVc4hFQ9F841YjIJysvaee0bNhESYnbpyfmb4vFcHYte7J3+14XndeRUb2OF3/7qHLt2J3PxYj2vf+POae9roRkeHuePv7tIWkY8ZrOJ7Tt8zRmd51Rx8SrZvdIep3Oq8zud9XrJVqGyopmmpm46Ovp4+b9s96gNob5OMytaNBVyWnkg9e819fe//OUCGRmrOHWqgn9/x+ElNGd0HcfkpI1fPHOM3XvSaG3tJS9vaeR5dedHP/od73vvpwDYsiWds+d/T5B5g8t5vYXntwv65hFCVK9etyrl6OVfBqS//G1vpbGuxZjfvshZmgrNEiE0NJS//OUv/OY3v+Hpp5/m/PnztLe3I4RgzZo17N27l9tvv51//dd/9br9rl27KC0t5ZFHHuHPf/4zDQ0NhIeHs3nzZt797nfznve8x8ddWUlj40Xe+MaPOJY88MD7l6SYCmoC99270/jHPy5x552vXuzh+GXVqhjGxsYZGrItaTEV4MCBLL797b+Qk7NpyYqp4Aynf/bZEt73vpcv9nD8EhkZRlxcJNevN/CBD7xisYfjl82b1/Hd7/6N9PTEJS2mgpr39Yc/fJ63vW3xxNTdu99Aa6t316lGaGiwPRfxOrZvfx1dXdeA1Y71NpvN5Xd9DkFvDA4Oeiyb/ee+gYGBwcKwWPNbq22M//3Jdx1iKsCDD79PFROlJm5Ku0dUzXGqiamgEyo1q6kdE6pUZJUgpLMglHATYDVnqq6OFAhnLlVFcS1kFbU8BJNZ0NEyyPYjSS4pBbTCWRbF9Yrei9HUMVwppd2Z5G7/VDwCr7Xn7uHV3sRUgO07E/nR986wfUciISEWz5PkGJ37aPVn3O7RlXLa3+X6du4Fp7QRO1GPJScvi188c5p3/MfSvQEPEBYWxNrkWC6cr+d9H8j20Ur/jhL2c+nNwevvfE7nXLu30b++kvT0REpKrpOQsNyHmKovEiVxf3/MpgCVU9yfGTk5m/jB9//Bv71+3xISU90RWOx5X//x/GU+eOfiXDPs3fOWKee2ILFYzERHL6e9vZ/0tJdRU3sZE07R1JjfGhjMH4ZDdUnSy09/+j3e9a4HHEte8Yp83v3uty/imPzT1dXP4OAoiYkxS1r8k1LS1NSFlJLVq1cs4S9y1fnb2NjJsmWhxMZGLvZw/DIwMEJ39wArV0YTErJ0Cgt4o6Wlm/HxSdasiVsyieW9YbPZaGjoIDg4iISEpXkzRWNkZJz29l5iYpYtmvj74Q/fR3d375TtcnNzaWlpoby8nJKSZzh06HXA0sqjO08YDlUDg6XNLT+/bR8+w4c++CC//uk/Hcs++OF3s3PPdqfXTSeC6oVUfyfHGfzsfD5VW/1zTQT1otXS2TqIzQorEsPVkHeXbaXD0arvz98AVNepu+ggvG4z/QJAaqHV5sY+QsMsrIjTUkS5in3+xrSQRq+hoTG6u4aIXRFBWNjSnjO2t/czNjpJUlI0isnb6+FdCPfTxN/CWSKQUtJQ34nFYiLRXlRJQ+r+XwqMjU3Q1tpHdHQYkVFLe/7V3T3A4MAoqxJjsFhMU28QYD784QfomcbcFtSIgueee47BwQHaOkqIWb4XRSzda/IAsSgO1YIrgXGo5m01HKq3Arf8X9mLDyuDgzV8/ONPuCz95z+LePLJj7Fx4/Ty9ywkg4Mj/OEPp3nzmw9x4sQNXv/6A4s9JJ9cv97A6tUrMJsVIiJCl2wOVYA//vEM+flbKS6+xr/+6+4lVzxJY2JikmeeKeKDH3wlzz13jre97ciSFaqbmrq4cOEm69evoqurnyNHNi32kHxSVHSNXbvSqKhoYs+edFatipl6o0VASsnPflbIBz/4Kv7wh1O8+c2HMJsXftL5xBNJhIb6DzMKD49gzZrVFBYWAnDXXV/izJk9KMp6FnhOZmBgYPCSYtzax7lLJ/nN0y+4LP/TH/7KnQ++BktIMFYbTNoTkGruT/19T5ubJqSF8EuhOkut9vWae9RkD/lH6r18doT6TEspIHTLNXmxsa6fyLoQlkUHI22SjG1xjr4FUh2fzqHqHurv2I0Ok1AFRC3830Vg1Q1CoKDoUtIg/IdXH32+gv0H13HxfBNHclNZHuOcM7qKxE5RUO+CdRalml3ovz9U847al9Vq49mfn+G9d2Tzlz9d5o1v2b1kb253dgxwoqSKzVtXU1fbRd5t+rz73t2/TnFaO7fOd4f30H/P4k3+8e4lPX2qkk2b1tLS0k1W1mqSk9UcqlLacHWnzibs37+T1X/ov+u2Ukqe/eUJ3vf+l/Hcn8/z+tfvIyjY7Gf7hcL1tohALbT7h9+f4U1vPMCJE+X82+v3LfionnwiidYp5rYgSU5Oob29jf7+fgC+8NB3ePKrq4Glm9rsxUogbz8tzU8+g5li+LKXHB185Svfo7m5HYDMzBRArbh6zz1f9pKraPEpKLhKXt4WVq5cTnCwhfr6jsUeklcmJ62cPVvBgQOZ7NuXwaVLagL3pUhzcxdCQFJSrKNA1VLl5Mkb7N2bQXR0BMnJKyktrV/sIXlFSklh4VXy87eyYcNqGhs76e8fXuxheaWvb4iWlm4yM5PIz99KQcHVJfm3D3DlSi3r168iOjqc3bvTOXWqfOqN5oFz535LY2Ox38dXvvJpHnnkv9iyRU2Yf/58KT/5ya+A3kUZs4GBgcFLhY6RCzx0//cd32Up6xMBaGpo50ff/D3gzDUpkJgVickhIqph6XqxU2C/iBHOHKkOpJs84m4htT9XcGa/VOzLtdWTNknpmTY2701g/aZY6qt6GR+ddOxXEXi4U734Tt2eO282Shdl1/Oy2kVM1Q7KB12dQwwPT7Bm3XKO5KZyrLDaxzi8X75LbF7nGIGad6jFrNTzdfFCHZu3riYqKpSsDau4fHEJzxmPlpOTn0Xq+jh6e4bo6R7Ce9oE92VaWganO1iAj5fQ2/buLfT/PBkaGuPmzRa2bFlHdvZmjhVfx2q16sYyV/R/hza3x1T9u6YaqKhoIWFVNDExERw8mMnxkhs+jmohcXcZqxQWlpKTu5n4ldGEhAZRX7fw17dnzj5LfcNRn4+6hn9ys+Z53vf+d/E/37uf0NAQAL71rZ9z/fpFbHJ0wcdsYPBSwxBUlxTj1NSU8vjj/wuAxWLh17/+OmvXqpPO558v4bnnChdxfJ5oFcgTE9UKg7m5WyguvrYkxR+1AnkGFosZk8nE/v2ZHD9+fbGH5YGU0iFSA6xcuRyLxUxDw1Q5dBae/v5hmpq6yMpS81Du25fBhQs3mZhYekL11at1pKSsJDw8xF5pdCsFBUtTqD569Ar5+VsRQhAREcratXFLUqgeH5/g8uUa9u5NB2DjxjXU13cwMLD0hOq6unZCQoJYtSqWr371k47ln/jEk/T31xLoYg0GBgYGBiqD44384Xd/48zxUkAVU7/xk/sw2UOov/vUr2lr7sIkIFgRWBTVNarh4rC0C3OKQOcWdYqiCmASEiGki3NOuGlWqmgLJsU1tYDWpOJyB8lZy7EEm1AUwZZ9CVw91WLvS91Wb6zUxF3dMD1wdXxqR+XZ0im82oU54fv7SUrJscJqjuSqEaPLY8KJig6hprrL2wh0v7mJgo6n8zd/Hx4e52ZlB1u2rgYE23as40ZZK6OjE/O2z9lSVdlO/MpIoqLUNEY5eVkUFWg3jPWS/lR+NelFrpuJgDq12FhUeI2cnE0IIQgNDSJrw2ouXaq1u1MXAm/vGX2uX3X95KSV06crOHBQvam9Pi2Bru4BenoGmUl6i/lFPd+trb1YrTaSktTosJycTRw7VobN3SY/Q6Sff7Pl9Kkqdu1OJSUlifsfeA+gGrE+9tFHsMmld+3woke7mRaAx6LfSzAICEvl08sAgFbuu+8xxsbGAbjnnnezeXMGjz9+v6PFPfd8mfHx8cUaoAtSSofwoxEWFkxGRhKXLtUs4sg86e8fprGxkw0bnAVo0tMTaW/vo7fXM/H2YnLtWh3JySuJiHDmoszL20JR0dJzKeqFP1ALlO3bl8Hx42WLPDJXxscnuHjxJvv2ZTiWJSQsx2Qy0di4tITq+voOgoMtxMdHO5bt35/J+fNVS06oLikp48CBLEcFaFWo3rLkHNU2m43i4lJyc9UKrfn5B3jDG/4FgLa2Tr70pW8D7Ys4QgMDA4NbE5vNRlv/eR5+8EeOZZ/68nvZtHU9//E+tZDpyPAYTz70E0C9yDTZH5oc6gxGl7qH+hyXZRJFSBcxVu9Cxf7TZA/1F27Ltd9HRiZpuNlH6qZYx7JVa5YxOjLJQPcIZsXN9Yrnc3cUYXYVjYRbuLeuAwUzrg5H31RXdbFiRThR0dqcUXDgUDJnTtZjtU5PUNP27E3UCeS881hRBYdzMhxzRkURHDycRklxJa5C4+KqDJOTVs6ermH/QS0dkEJM7DIio8Koqe50GaMv8dP1mdQWond76pmJgKqntbWXiUkrSatjHct27kzlemkdo6NjHu0DL1z6en9Ij59nTleyc2cKFovJ4XDNy9/E0ReuuqUi0Dt8td/1zwN5LeR6Y0FgdycXXCMvb7N9NJKQUDMZmau4dLnai0vX20P/z7nc9Rhcj2OmRyWxMTAwREN9Bxs2rAJsfOzed7F27SoA/vH8SZ577u/YZN+szoyBb9w/rWb7MLg1MATVJcMQhYVF/OY3fwdg5coVPPjgnQC86U2vJDt7DwBVVXV8/es/XbRR6iktrWfduniPIjS7d6dx9WotY2NL545zQcEV8vK2urgDhBDk52/lhReWjvgzPj7JhQuuwh+oQnVaWiKXLy8dobqhoROLxczKlctdlmdkJNHa2kNf39AijcwTd+FPIy9vC4WFS0eollJSVHSN3NwtLstVoTpzSQnVPT2DdHT0kZ6e6LJ81aoYFEWhqcmbO2ZxuHSphsxM1xyrjz12P8HBai67p576CVVVV4GlcbPKwMDA4FZhcOIG3/naszQ1qOGy2S/bQf4rdwFw9yfeTtTyZQD86dlCLp654XCaariLngqau0e6iJhCQJACISbUvKjoL1ydblVVdHW6W9H3b3cNXT7Zwpb9qxxzRq2fXYcTOV/SjLvr0D1ruPuFskDxEMqcuVNdxRWFIFyFVnxitdo4d7qevQfX2Zeol3VBwWY2bl7J5QtNXrfz7k7Vly5yqwofgDlSe1s/4+NWVq92nTOuS17B4OAYXZ3u5obFkx7On61j2461BAVZ0L9LDh7O4OTxKqRNehVQvf3uSFfhVSB3nufZuBWdwp82Z7S/hxSFQ4c3cKx4seaMbvk3sDE4+P/Ze+/4NrY6f/85knt3HDsuceJup/fuxHZCWZZlKT9YWJbeF1hu73C5vXA7sCz9wrJcWJbev0ASt/SeOC6xHdtJ3OLeq3R+f4xGGkmjZsvlBj15TWTNnHPmjDSaOec9nzJOS0sXq9cstyuZkBBNTEw4TU16D7UdRUftb8VRZDX7uOi1q4ie1TXXWZ6+hKjoUI0ICpu3ZHK56rqX81tfxF9H8VnPktVZtAUzpYerKS5Zbb1WRUSE8+xX7rC2c89dzzMxeXXRzHMCBLgVCQiqiwKJydTKbbc9aV3z9NN3EhMTBSjC38svP2i9WD722H/S2bmwVnVTU4rwt3NnvtM2g8HAnj2rKC+/vAA9c+bGjW6MRqNupvTExFgiI0Npbl4c1mlHj9awc2eBblKfbdtyuXhxcQjVivB3STOIs6EI1Rs4ePDCAvTMmf5+feEPIDIyjJycFC5ebJ7/julw/nwTeXlpREQ4B6DPz19cQvWhQxc4cGCD7rb9+9cvGqF6YmKKqqoWtm61T+iXlZXOXXcprlFTU1PcffezQPsC9DBAgAABbk1M5nHqr53gGy/+AgCj0cDDT3/cElNTErMkii888H5r+acf+A5ms8muDVVAVa1WhbC3OEWzXVj+VsVSxf3fZvxpFFhd9dWyjvR0jTE5YSIpLcq6D1XgjYsLISExnOuNg7Z9W//TvMf+vUEYHQrZi4RSOMTa9FJQO3vqBus2phIS4jxmXLs+hcaGHsZG1TGjzaLSrn9On4H+vmdzO5dSUna4jqIS5zkDQHFJAaWHaj20Mj/2XSPDEzQ391iEP2U/qiAeHhbCmrXLOXum2UUPHS1MLWus3dW6wWtxFt+8cQmvrW1l+fIEYmLCNe0AmMnMTGJwaIzu7iGdfs719N/5uyk9XEVx8RrdRGeF+1ZxpLIWs3kmIQpma7FqX3dqysSZ01fZsTPXqaQyvy2gstzTuerr/rW2zI6WrM59VDDT2tqLwWAgOSXObsu73/MmCvduBqC+/hpf/9r/YKbDj30OELBQDaAlIKguCvr47nd/ysWLSmyeLVvW8OEPv9OuxKZNq/nEJ94DwNDQCF/84svz3Uk7jh5VEhG5yuadnZ1Cf/8wvb3ON/L5RE1EpCf8qRQVraOiomqGN3L/MTAwQkdHH3l5zsIfKBOR3btXUVGx8EL1hQtN5OSk6gp/AElJsYSHh9LSsvBCtWNYAke2bcvjwoUmJicXVqiemJji0qVmJ+FPRbWoXgzu9M3NnURFhbN0aYzu9sjIMLKykrl0qWWee+ZMeXkVhYWrMRicb3cPPPApUlISAfjNbw7y178eAhZXCJAAAQIEeL3SP3Gep770Q8ZGFdfjj3zqH8ktSAfALMFshn/56D+QU6Bkor50tp7f/1+ptb4qpqpYJ6Ka2ahAEUqDjRax1LLdaMAuzqkRjfu/pb7jpNZslpyrbGPDHts4zKB5FcCmHUlcOH3T6k7vLkG90r69mKrv2i0ssorRXkITrq0VR0YmaWnqZdWaZQ49tbQoBLv3ZnKkXE1QpddRe0tKrbWk4zqQMxZVr9R1kpoWrxH+7ImLj2BpYhQN9Z0+tjxbeUJb14DASHlpPUVFBRiEQed7gvUbVlB/pYPxsSkX36XlOxbCUTfX7NOdCOjoEu6YEEr5NzVl4vSpBnbuytOpr7B//1oOH77k4sjnTwJoa+tDCEFKahx6xx0WFsyaNemcO6t64S3cw/jjx66wbXu2y/ltZlYSAwOj9PZ4O791DF+gZ1HrzppVf52UkvLSWopLVjttFULw0sv3Wuc9Tzz+bTo7azHLxRU27PWM32KoBrglCAiqC46Zvr5GHnroJeuaV155SHfy/8QTt1utVr/3vZ9z9uzCCGvaDOTuOHBg4a0UL15sJicnhcjIMJdlwsNDWLUqnXPnrrosMx8cPKhY/LkS/gByclLo7R2mr2/hRJ+JiSkuXmxm2zbnp7daiovXUl5+eUGF6ubmm0REhJKYGOuyjNFoYNeuAioqFjZBWUXFZfbsWWVN0qFHUlIcoaHBXLs2/5lGVcxmMxUVlykqWuu23PbtuZw717igQnVPzxADA6NkZSXrbo+KiuSZZ+62vr/99qeYnr7BQg7mAwQIEOBWYGK6h8qj5fzyf8sAiE+I5q6H/lWJP4rtKhsUZOShZz5prff8Iz9k2JLYUB0OqZNPVSQVwqZTBRksLv7qBNWyGDSTVoOAIKNSX7VwdbRyRcC1xgHikyKIjA6xOXoLdT8SowGCQ4ysWp9A1ZkuJzFVaF6t9qDCQZgRwl7MEqCIKlrrVGdrRUcqy66ypyjL7ZgxbXksE5Mmum4Oq71x7IoDevu0F1199TyZnjZx5lQz23dmuS23a08OJ49f9Truqz7ubMAc1xuwPxME7e0DSClJSY23rlOsU22L0WBk794CystqNS2rAqqyuNd2PVkeejo+pf7x43Vs2ZaNMUgNaKENHaCUiouPImFJNA31C+F9o5zjUkrKSmsoKl6Nu+PesDGD2po2xsYmmT95wr4fg4OjtLX1kZ+vb9iiUrx/DYcPaefgruK8akMLuI6d6m28ZO3+qqpukJmVSGSkvmHLpk2r+NjH3mk5rmEe/uLXkeiHAAkQIMDsCAiqC85NHnvsP+np6QfgX//1n9izZ4tuyaSkBL785c8Dyg3qG994bb46aYcniz+VJUuiiYuLorFxYdxo1Qzk27Y5Pr11ZvPmbC5fvsb4+MLEUGxpuUl4uHvhT+XAgfULKlRXVFxm9273wh9AeHgo+fkLl6BMEf6qKCpybZ2skpubSk/P4IIJ1b2WLKfZ2SkeyxYXr6OsrGrB3OnPnr3K6tUrCAsLcVvOaDSya1cBlZULF/f10KELdknz9PjAB/6Z7duVMtXVDXzzm68BvfPQuwABAgS4NZFS0j12hofv/Y513b1f+jfi4qPsJh6qs+vu4g284a07AbjZ0cfffn/MZlkqbNalegZ/RovQqkpjKqquJXCe7Ni1Y3kzPWWm5txNVm9dZt2m1jM6aGQ5q+JpvTbM2IjzA0Nh/V9YrFO1fXLsvTZ2qsFeUhH227WiamfHENIsSUlVvURcj8f2FmdRUaoXQ9GTxapjH7UxVV3uzomTx5vYtGUlwcH6Fn8qISFBrN+QzplTzd437hV6wqpNIDVgxEAQQhosFn9rcX22KaSvWMr4+BTdXUNeCKiu0LMC9q7O4OAYrTd6KChIteuj1LF63FNYwLFjV3SF6vmwUr18+QYZGUuJinIU/uyP2WAQFO4roLxsPowbHC2zFQ4fukxJiX5YAq1oumRJBPHxkTQ2dGD7rIWm3EzCELgKB+FcZnJymvPnmtm2Pdtt6cee+LzVEOv73/8VZ88exyRHfexXAD30Ht3MZAlwaxAQVBeUCWprz/H1r/8YgPDwMJ599m63NT7/+X9jz57NfOpTH+KJJ+6aj07aoZeB3B379q3hyJGaBbFSrKiotiQi8nyaGwwG9u5dTVlZ1Tz0zB7HDOSeSEiIISYmgqtX5z8eTl/fML29w+TkeBb+QElQVlXVsiBC9blzV1m1Kp3wcPfCn8r+/Rs4dGhhhOqDBy+wf79+PFJHIiJUoXr+LarHxiapqbnGpk3uLU1UcnNT6eoaoL9//oXqhoZ24uOjWLIk2m05g8HAK688ZH3/8MNfpaenAQi4Rt2KCCEKhRDfE0LUCyGGhRAjQogGIcT3hRB7dcrLGS4Zlvo/sLz/gYd+ZTjW1Wz7iA/73ehQt1mnzJQQokMI8f+EEB8UHp6OCiE2CiG+K4S4IoQYtSwtQoijQojnhRBv8+7TD/D3wvBUM//72h+4eLYBgFVrV/KBj74JIWwygypZqa8PPvFR1mzI5gt3fYp3/EuJss1ieaq+qieqao0abMBJoNWitVDV2iNat2v+rj57k9x1SwkKNli32WKvSrSOY0IIthcmc6KiXbctdY0QjnvTs05FI7y6T0gkLRailaVXKSxS78MOVqcO72NiwkhOiaHxin3uBf1fvTvLWH0Ryh1DQ+PcuNFHwSrvxoxr1qVx9WoXIyPO2elnhr01qsBoFVIdbZRrqltZuXIpUVFhDq78tkWNhyoElOxfzeFDlxfk4Xbp4csUW4U/PVdyrO9DQoNYv2Elp0412IWTmA8mJ82cO6sV/txLSCtXJjI6OklX1yA6NuRzhPIZXr/eRUiIkaRl0egnvLL/zPbszePYUa1Q7a/P1LP7/9EjV9i5M9fj/HbZsgS++KVPKTWl5I7bn0XKa4siv8HrHYH0yxLg1iAgqC4oHdx55zNMTysT9/vv/yTp6e4HHSEhIVRUvMazz95GWdn8uvxLKSkvd85A7o7Q0GDWrVvJ6dMNc9gzZ/r6hunpGdRNROSKzMxkhobG6O4e9FzYj5w/30RBwXK7DOSe2LdvDZWV1fMuVCthCdxb/GkxGAwUFq6e9wRl4+OTVFdfY/Nm909vtSxdGkNUVDhNTb7G8JodV692EBcXSUKCe+FPy9atOVy61DLvCcrKyqrYu3eNbkgSPRYq7qvJZObo0Rr27l3jVfmdOzfywQ++HYC+vgEeeeTrwMLH/w3gP4QQ4UKI14AK4GNACtAI1APJwEeBciHET4QQ2kB/R1wsKlUuto/PwWFMuOmPurh6elGvKVMFRABvAv4b+JVw8ktWEEJ8ATgNfBxYCbQBl1CeOOwE7gJ+NftDC3CrYDZP09Z7hme//CPruse+8gmCLBaKZs0cUggQFjf8rOwUfl/+Iu/81z3UnrtJkFBc+d1pKqroKfDgNOtgFqSKYtLS9ujIFJ2tw6zMi7eGClAtZIOFdHbtF7AsJRJplnR1jGp2Y+uwQQQ51XG2YlPrOdxTnY7ZVr625iZpK2KJig7VHJTLigBs27mSM6euMT1t1vTTV+yFHik9u/+XH66jqDjfo0ebihCCvUV5VJTWzaB/1lasrwJbQimhEeiE3TelJtptYvuOHIcYh9JuQdiONzY2gmXJcdRfmY1xg2rZ6O7zsY+3eeNGN8HBBpYlx+Ao/CnfCdZXZbVk3bp0Ghs7GBkZt5W1Jj9S9wH237HZh8U53iuYOXa0jh07cjTxSF3FEbWtK9m/htJDVUhpQvsbAeW4zNLstKjnorrolbEtYJZSs5gVw5ayWvYVrUJKS3xnu3KOdSTBIUGsXb+cM6ebfNy/u76j+f7svwMpzZilpLdvmJs3B8jKSfTq2D/7+feRk6vEqD5SeY6f/ey3mGWfpxMzQIAAPhAQVBeMIf74x7/wpz+VA5CensLdd3/cq5pCCOLiokhMjKW+vm0uO2mHuwzk7ti4MYu6ulZGR/31xNkziquvdxZ/Wg4c2DCv4s/4+CRVVS1s2eK98AcQFhbC2rUrOXOmcY565szVqx3ExESQkKCfiMgVWVnJDA6O0uN1APfZU1ZWRWGh98KfSlHRWior5y/uq9lsprKymn37vLNOVlkIobq7e5CRkXEyMpZ5LqwhMTGWiIgwmpvnT6A8fbqB9eszCA0N9rrO00/fRWRkBAD/9V8/oarqLLAwIUAC+BeLWPgH4F+BQeAzwFIp5QYp5UZgKfApy7b3AX9QBUYpZaHeomn+P1yUmQsXgg5X/dEsrp5ePqUps8lyzI9atr0d+LBjBSHEduBlFI/rl4AUKWWOlHKHlDIbSAA+hCJSBwgAwNBUFV97/qfc7FQm7f/4zzspLFYexEqpsVAVFld+i3CqiKOCzLw4brYNMzYyaXPbF/YLKHXcJYRS0cpV2r+1Us25yjY27E7FIOxtEg0olqna8AGqvSPAjn2pipWqR3d61cJRs2erdaqj8Or6oKamprlwtpUt25ejZ8HnqmZIcBAbNy/nzMkWpZTbz026eeexuJW21n6MRgPLkj2HstKSlhbPtMlMZ8eAD7Vs36wilhrtBFTbegcLYQvHj9azfUc2QUEGfHHZ3r0nl5MnGpmeNvnQV8d+u4ut6RhqQVJeVsO+olVO5czSbBVJtQuYEUKyr6iA8rLLOvuzt8KUdiKi/SIdFmfh0Sbs9feP0tk5QG6efgx7veOUUhITG05iUixXrnQ4tG92aVkrXf7Tfg6qeO187BcvtpCTm0R4RDDSB9F43fp06uvbGRkdn7H1r3N/td+38mqW0noMZYdrKC5Zbb1OOB6xIyEhwTz/gs379YH7XmJ4tAGzeabnbABPbvy+LgFe/wQE1QXBzOTkde644ynrmueeu5eICP3sl64oLFzNsWO1mExzf1H0lIHcHUIIiorWUlqqn2nS3zQ1uc9A7o74+CgSEqLnTaguL7/sMgO5JzZuzKS29gZjY3MvVNuEP+8s/hxRrBQv+LlX+nR3DzI8PEZmpm/CHyhC9erVKzh7dn6E6tOnG1i3bqVPwp9KVlYy/f3D8yZU+2qdrKWoaC0VFVXzIlSPjk5QX9/Khg2ZPtVLS1vGAw8orlEmk4nbb38KKQMB/G8RHgRKUCw83ySl/JaU0mpBKqUcl1J+B8Vic8JS9oEF6ek8IaWclFI+AhyzrHqPTrGPooz3D0sp75RS2gUXllL2SSl/JKUsmdveBni9MG0eoab+BN/52m8AJS7ml5/+qFX4NDvM9w0GoYiWQmIwWF4FbNmTyplK+/j77uww3clfEvvwAo50d4yAgIRlEYp1qrWcJMjgHD9Vq3dGRQWTmh5FQ22/ndWns3Wqw9RZ2P4Qbo/MntPHr7N5WxpBQUE6W13VVY4mf9Uyrl/rZ2TI1zGjK9dji6UqzpaqUkoqyq6wrzjfx30pFJUUUFZa54VrsipUqzFRjdgLq46frzODg6N0dPSTl+ddWAItwcFBbNqcwamTMx0zeifcqly6eJ3s7GVOiYikdGxHFUhtLE9bwtSUic6OfpzFQVs7rgVFfbHWeVEoPVRNUclqpScO1p1mHUtKVRAGMzt3Z3PyRAMm0zRa23N7kdTkYnElgoLzcUsmJia5dPE6m7f4NmYEi0X1PvsEZbPH/ntTfgJK/1tauomIDGFpovcebQBv+cdC3vSm3QBcu9bBiy+8ipmFyW9yyxBQUwNoCAiqC0IvX//6j7hypRmAwsIt/Mu/vMXnVkJCgti4MZOTJ+v93D9nysuVDOQzEf4AVqxIZGJiips3+/3bMQe8zUDujr17VaF6bsWfnp5BBgddZyD3hMFgYN++NZSWzn3c1zNnGlm7dqXHRESuWLIkmvj4KBoa5v4GPlPrZJVNm7KoqbluyTQ6d4yOTlBX18rGjd7FI9VDsaiee6G6vr6NxMRY4uKiZlQ/PDyEVavSOXdu7uO+Hj58iaKitV67GGq5886PkpGRBsDBg8f47W//BMyfZXUA/yOEiEZxSwd4Vkp5wlVZy7avWN7eJYSY2Qn/+kL9PDJ0tqkXp7Pz05UAr3cGJs/y6IOvMjmphLL67G3vIDMr2WbVZiknrP9LgoUSC1W1RAVISArHYISu9hG79vUsTb1FLyKjlJJzR9rZsNsWHspqhWqxgHWc+zqmK9q4NZHLF3qYmlYs4ByjZ6j3Ir3+GpySVrnu/+DgOB3tQ+TkJeqKhJ4+DyEEe4uzKS/zJgSXb1Z2gJ34eflSK5lZiUS4yEDuiejoMJYvX0Jdjd6YUZG8tfFQtZ+HnogqrJaJlvfCthw+VE3xfleJiDyzek0azc3dDA/PJsqLnuhnz8TEFBcvXGPrNm3sXM991rZWXLKa0sPVcxpDU2Km5VoXYRHBJCZGoSdiejq/lPntSk6ddBwzakVS1z3wfi0craxn127P8UhdsTx9CZMT09zs9F+4OKk5TjUsg9ksOVJxhcK9vj+kEELw3It3YTQq15vnnv0B165fxmwOeGEFCOAPAoLqvDPNzZv1PPro1wHlIvfKKw/N+Ea+bl0GjY3tlrg4c0Nv7xD9/d5lIHeHGktxLm/kZ882smaN5wzk7ggJCWbDhkxOnbrix545c+jQRY8ZyD2xcmUSY2MTdHX54hrlG2NjE9TW3mDjRt+f3mrZu3cNR4/WzKlQXV/fRkJCDPHxM9dBlARlaykrm1uL6tLSmQt/KkuWRBMXF0Vj49wJ1SaTiaNHaygsdHQx843Nm7O5fPnanCYo6+zsZ3JyivT0xBnVDw8P4/nn77O+v+uuZ5mYuI7nzKsBFjH/CMQCJuAbXpT/BsoXHmepe6sTYXkd0dmmzhC3z1NfAryOmTDd5NChUv78O0WjX5Ycz233/H92rsECQKpSkFQSTllmIgK0ISrZvCeFc0cVd3pV4NTDk7yiOnELnXJXa/pIXhFNeGSwrQ8ChLC3TtWKrCrquqAgIxu2JHHuRIclNIDBQefSt051dEn3JI5VljZRWJypk4hIQd/V2F5sXJYcg8EA7W2exoyObv/u7H/tXZQnJ6e5eOEGW7ZleNiHe7bvzOTsmRampsxgFU71BVT11ZU1qhDCstiL1teudRMWFkxSku8ebdq2i4tXUXrY39nptY8fBEcqr1iEP5sVrtt+OUr/AmJiwklbvoTa2rnzwjObJZXldezdNzPrZJXVa9NobuqapVCtonwGjm7xfb0j9PYOk5WdNKvWi/evpuxwzZzOby+cb6FgVYrXiXYdWbUqi89+7r0AjI2N8+D9L2Pimj+7+HeFwU9LgFuDwHc573TypS+9wuCgkjfi4x9/N5s3z8yNGpQbeUnJeg4fnjvxR3H1nbnFn0psbCTJyfHU1c2NG62Sgfy61xnI3bF+fQYNDXMnVHubgdwb9u/fwMGDF+bsRl5aWsW+fb7HI3UkNDSY9eszOH16biyqTSYzx47Vsnfv6lm3lZGRxOjo3AnVN28OMDExxYoVMxP+tOzbt4YjR2rmzJ3+5Ml6Nm3KIiTE97AEWhSheg1lZXNjUS2l5NChi7O+Vr3rXW+iuFjRjxobr/HKK68Cve4rBVjM7LG8VkspPWacs8Q+rXGoe0sihAgD3mB5e06nyB8tr3uFEL8UQrxBCBGhUy7A3zlSSnrGzvDwvd+zrnv4iQ8RHR2hSG4W61T11RoH1YAtCZRF/1FFzYjIYJZnRNNY04sQqhDinBnZ25GP9g4pgYlJE/VVPRRsSrS6+QuhvLpy9beJqxpBTxjJyo3jZscoI0MmRbhDWEU8V70UPlintt4YICTESGKS9mGxvqgq7YQ4573uLcqhoqzBizGjr2NKZX/HjjSyfWfWjC3+VIKCgtiyNZOTx68i7D55+3/2PRAa8dTxO7BHSklFeR1FxbN7WAyQkhoPQHtb3yxbcvzMlWPu6xuht3eE7JwUtGeh1dZb6H0u+o8aduzM4fSpq5YEZc7tzNYP+eKFa+QXzFz4UxFCUFRcQHmp1p1+pn3Tr1d6uJri/bOfM8TEhJOSGseVOv+FTtf+lsfHp6ipbmXjpoxZtfnFhz9NQkIcAP/70z9z5Eg5ZnPAC2smOMb1nukS4NYgIKjOK+OcP3+a73znZwBER0fyxBO3z7rVtLQETCYTHR2zvZE709jYTlycf4Q/gD17VnHiRN0sAri7RslAvnbWwh+oT5zXzUncV18zkHsiLi6SZcviuXLF/0+cu7oGGBubYOXK2T29VdmwIZP6+rY5SVB26tQVNmzInLXwpzJXFtWK8Hdh1tbJKqGhwaxbl8Hp09648fnGyMg4jY3trFuX4Zf2MjOXMTw8Rne3/1yjVOrqWklNXUJMzOz0HiEEL7/8kPU68vjj36Cjow4lqXmABSRdCHHV1eKunuXVlyB36o9p+cy6qsuHhRDS1QI0edHGSndtCCHOe9MRIUSkEGIb8GsUt/4R4EWdov9tKQPwTuCvwKAQ4pIQ4rtCiHcLIWbm0xvglmJk6ir//d3fUHu5BYDNW3N5978WY1ItUy0TR9VSVGARU7F3xTc46DlrNi2l4XIP5illzOg4AVVFTm9w1Ioun+pk1ZYkDEYDUto2KRndnSdINiFVtfgTIAyWPwW7i5ZztOyGUy074c/6oh8WQA8pJccqmtmzL9PagL3FqCs3auc2BRAZFUpGZgI1lz2JP95aqdro7x+lu2uInNwknf54i3JWCAwUFKTR1trH0OC4VwKqL3rbhfMt5OUlz1r4UykuWU1pqb+tFJW2Dh+6TMl+vTmD7TGBnpBsE25swmlwcBBbt2Vz/JijcYPQvLoXrl0xPj5F9eUbbNq80sfj1CclNR4pJR3t/Zae6QXu8IT9L1k9lqamLmJiI0hI8E9knx27VKHaP/Nb9QGSRFJZUcvuwjwM3mThc0N8fAyPPPYZ6/u77niOCVPjnFrWBgjw90BAUJ03JFK2c9ttT1ovXA8//DmWLVvql9b379/gd/HHbDZz5EjNjBMR6REcHMSWLTmcOFHntzZBEf5GR8fJyPCP8AewfPlSpqf9L1SfPl3vcwZyT6hCtT8TlEkpOXhwdvFIHVESlPlfqB4ZGaehoZ316zP81mZcXBRJSbF+T1B25UorycnxxMZG+q3NjRszqatr9btQffjwJUpK1s8qLIEjyrXqgt/aA5ieNnHiRB27dxf4pb0NGwr45CeVHD3Dw6M8+OCLwFwkbQ8wD6hPA4d9qKOWnbkfqDM3gSNultNetDHhoQ09K1OVVzXi7TBwEngzcAp4o5SyxrGClNIEvAv4AHAUZfZuBNYCHwf+D2gQQvw9hEYI4AKzeYrrXSf5yuOvWdc98dwnMBgMVrlHOzRVLVQdRVYs642aJSjIwIZtSZw/YTMu99W6R9u+ytDABH1dY6RmxCClzZVfSZCFnX2fRKcBVMnJYH2XmBRNSIiRthtDdqX0/zR4LVZdvthBVk4C4RH2Y0ZnO13tYnDb9pZtK7h4vtUa69Y1jhaw7ik/XEdRifY+7KmGmlDKiAEjBoLsZTwhKC5ZTZmdO/3MBFQtExNTVF26wZatmTNrQIeoqDAyMpZSfXm2Xnj2AnlTUyfR0eEkJDgatng733MW2gsK0mi90cvg4Dj2VqoGDEI9b+yfQDhLrM6C65HKK+zak+fRsMVbgRagqGQVZXZCtfq7886aVq+fZrOZY0eusKcwz+t+eCI42MimzRmcPOGvpLaKmNrTPczQ0AQrM5bO+BGFrTUTH/34P7N2nZJg+uyZWn7037/AJG/6p8t/Rzjaic90CXBrEBBU541Bfv7z31JefgqA3NwMvvCFD/qt9ejocNLTl1Jdfd1vbc4mA7k71qxZQUtLF8PDY35pT3X19ZfFnxYlnIL/hGolA3mbzxnIPRESEsSmTVmcOOG/uK9XrrSxbFk8cXH+E/4A0tOXMjk5RWdnv9/aPHz4EsXF6/wq/AHs2aMmKPOPUD09beL48Tr27Jm9i5kWRahe61ehuqOjD5PJRFpagt/aBIiPjyIhIcavQvXx43Vs3ZpLcLBe5uOZ8fjjtxMbq0xgXn31l5w6dRzwzzUrwIy4LqXMcrW4qaeqG76Yoahl/WlK/ScpZaGrBXiPF210uGtDSvlRN3XrsQmvV1BiygJ0Ai7jcEiFH0sp9wBLUUTYB4EKS5HlwG+EELd0eIQArhmcusQLT/6Yvl7lp/aefy1m684CzNImpFqlEAFGg7MYqnVSd5yYrMyOpa9nnKH+CZ0a3qE17JLA2co2NhamghB2+9Oz+7TpdvadlnbvFYFn597lHK+8YRkzCk09oSkZbC8ouRm2TExMU13VyYbNqU4F9atJyzY9S1ZbJaPRwNYdKzlxtNn1zq3tCYf3+rQ09xARGcrSpe4vtaoYpsZDtbeMtAlgKsuWxREcEkTrjV6r1eVsqSyvs1j8+XcavG17NmfPNjE15Q+PFqEYtlS6ikc683mJIlSvofTwZd39CmGws251t6j09owwODBGVmaSV+Kr+yO3/YuOCmfFiqXUVrdZt3mWpFQrW6Pu1nNnW1i9ZjlhYf6d365ancr1a70MDfljrKhcgcoO11BSssrLT85ze0FBQTz3wh3WtQ8/9A36BmqRMpArwCcCimoADQFBdV6QjI1d4+67v2Jd8+KL9xMS4h83E5Vduwo4fbreLzdyf2Qgd4US93Udhw5d9Et7s81A7o6YmAjS0pZSU+PoxjUzlAzk/hf+ANauXUlTU6dfhGqTSbH487fwp6JaKfpDqFaFv+XL/WPtrUURqrP9JlSfOFHHli05fhX+VFasSGRiYoqbN/tn3dZcPqQA2LtXFapnP4AbGhrl2rUuVq9O91zYBxITl/DII5+3vr/99qeQsp3ZTGICLAjqxTvbhzo5DnVvBZ7SCK/5QD6KRes/AT/xpgEpZZ+U8i9SyqellPuAEpRwAUHAw3PV8QCLl0nTEBeqjvKDb/8JgIiIUL70+IcAmzOyKqxKi0LpKM+p+azBMscUEGyAYGGLY7p9byqnKx0fwknMUlrrucJxW8eNIcLCg4hdEg5gtUg1CInR4C75lS2Oq2O7BqGkvgqPCCYrJ57qS93YzZithQ0+ySInjrSwbecKp3ik7ttQxVTn+6t2zJWds5SurmEG+j2NGbXfkH40WLNZcuxIPYV7cxw+cJvlo1Y4tYmq+iKbvSs/FBWvorys1i9jxt6eYfoHRsnK8p9Hm0pQkJEdO3I4esQfuQIk5842WYQ/x/mir2Mn589tWXIcQUEGbtzomXEPwSa4lh6upmT/ao8CrLoYhMHhDHAtuG7fkc3Zs81MT5msZQxWK2wj9sFD7AV6R8bHprhS186GDSt9Fnq9+Syc477OnIb6ThISooiLn51hi7ReaRVK9m/lHe8sAaCzs5dnn/o+09J/BlkBAvy9ERBU54VuXnjhe1y7pgwG3/zmQt761mK/7yUoyMj27XkcPTr7C7k/MpC7IyVlCSBobZ3djdxkMnHsWC2FhbMPKu6KXbvyOXXqyqzj4nR29jM1NUV6uv+FP9AK1bO3Ujxx4oolEZH/hT9QhOrU1ARqa2enV0gpOXz4IiUlcyP8AaxbpwjVs01QNjw8RnPzTdasWeGnnjnjr7iv1dXXSU9fSnT03OSfCQkJZsOGTE6dmr1QfejQJUpK5uYhxec+928UFCgPlY4ePcdPf/prYMDv+wkwp1RaXlcLIZZ5Kmwpo/qsHp2zXi0wUspG4B0o7v9vFUK8bwZtlAL/ZXm702+dC/C6QErJwMRZHr73e9aHY7ff+25S0hKsCajUGKrqVF4VLFXpw9WdSpVFVEE1PiGMyMhg2q8NWbdreuK2De1+TCbJxeMdbNiVovTHst4gbMKqXqv2e9Ba5kmLOGgrsW5TErVVXUxOmOzrYMBIEPpCqzP9fWP0942Tmb0E4cGF3xGt8Ou0TUqklAgh2FeSQ/lhb8Q/91aqF89f10lEZLNCVf45u/TjUNqVK39ERCg5ucu4eHH2WckPW4S/uSI3L5nOzgEGBkZn1c7Y2CS1te1s3JhhWTObcZ3+91dUvIbyMldxX72XCBobOlmyJIolS3wzbPFWfA0ODmL7jmyOH2vUTTRmO59siwF9wba8rJbCvfm68Uj9YVmbkhKPwWCgva1/RvVVTCY4ebyJXYW5sxJ7HcVUlaee/QKhocrv9Wtf/Qn1DWcxmecmEfOtSMBANYCWgKA650xz40YNTz/9bQCMRiMvvvjAnAmVBQXLaWvrZXBw5jfymzf7/ZaB3B379yuxNGcj/igZyLPnTPgDJe7rtm15HDs2c6HaZvHnv3ikeqSmJgCS9vaZZyUfHh6jqamTtWv9E1TeFbt3F3Dy5OyE6pqaGyxfvnTWiYjcoSYom61FtSL8+TceqSOxsZGkpCyhrm7mMbympqY5fbqeXbv8E4/UFevXZ9DQ0D4robq1tQeDQVge0Pif4OBgXnrpAev7e+99jpGRFny3DgmwgPwJxXXfCHzWi/Kfs5QdwJbl/pZESnkNWzKqJ4UQM7mRqgHj/OtyE2DRM25q549//Cvlh84DkL4iiX//wtsBjUUqNvlG1S+09mN2lqkokxJFRLWJJmqZLbuTlViqZmndhw2JcHDCt9+qcKWqhxU5cQSHKae6ejs2CGkVVVUMmu3aNmx/Wdz6He7pRqORbbvSOXn0hvXghK1FTUmtRZ3zNLvi8FX2lmTiKqmOPmpZz/coKSUJCZGER4ZwvaXXuk5d7HG0UrV+q4yPT1Nb08HGTSssXXe2FpSaf3bH4kNCqS1bs7h04ToTE1Mej80VjY2dxMdF+Cz8+YJi3LCaw4eqPRd2Q0W5KvyB/Tk3k/GHo8Cu/B0ZGUp29jIuXZq5UG0ymTl2tN6v8Uj1yMtLoaO9zypUq1auWlyLl8o51t01xPj4FCtXJnot5totXgquRcWOcV+d++dJsD1zuol1G9IJCQmy/m58FXxtYqqtH2qCsqysNG674/0ATE5Ocf89r2Ci2fsv5O8c9X4x2yXArUFAUJ1z2rn//ucZHVVcaj73ufezenWOhzozRwhhsVK7MKP6c+3qqyUqKpzMzGVUVbXMqP7IyDhXr3awbt3cCn8Aq1Yt58aN7hkL1bW1N0hLm30Gcm/Yv389hw/PXKieS4s/LcHBQWzdmsvx4zNLUDY1Nc2pU1fmXPgDSEtLwGyeuVDd1taDEJCaOjfCn5bduws4caJuxkL1sWO1bN+eR1CQfuwpf6EK1YcPz8yiWkpJaemlOb9W/cM/7OMf/7EIgBs3OvjKV74DdM3pPgP4DynlIPCS5e19Qogdrspatt1refuSlHLIVdlbiJdR4sxmAXaB3YUQ3vjE7ra8+i+Ad4BFj5Rmbg6f5tH7v29d9+jTHyE8PBQzNqtUs2YYYhCKWKqdeGitdPTyd1tFVwGh4UFk58dxpapHaUfYl9NrU8v4uImmuj5y1yleQkZLoSDVOtWhkmuNTyiiI67FzZWZcQz0j9PfO2YtY3CI5yiEs0Ci0tLUS0xsGPFLIhG6wqt+v/RwiqPqwJ7CLI5UXnUKwaMVV6UmM5etr8o3eaSint17cizxSLVyub4FoOM+vMVoNLBrdy5HKmd2qTGZzBw7Us+evXrxSP1LYlIM4eHBXGvpnlH9rq5BRkYmWLnS0aPNX1aqNrZuy+bi+RYXCco8ywSq8OfvfBuOCCEo3r/GSajW+w3arxMIoXq0zc462VvhNSoqjKysJC5fbvVJsFV/qyMjEzQ1drFuXbr1WFyJxLYHT45p6sxoxVTbb1ldJ7nn/g+TnKKcY7/7bRl//cvfMJn6Z/z5BAjw90pAUJ1Txjh69Ag//vHvAEhIiOORR/5jzve6bFkcwcHBXL/u+6S/rs7/GcjdsWNHHufOXfUi06gzhw5dnJNERHooT5yVBFW+Mj1t4uTJ+RH+QBGqV65M5PJl3584t7X1AtJi6Tr3rF6dzrVrXQwN+S5UHztWx7Ztcy/8qcxUqFYGcYpIPR8EBwexZUvOjITqwcFRWlt7KShYPgc9c2b58qVMT5vo6Ojzue6lSy1kZi4jMjJsDnpmz4sv3k9QkGLR9JWvfJeWlmpg5lYyAeadJ4ByIBT4ixDi00II64kjhAgTQnwS+IulTDnw5IL0dJ6RUvZhc9t/SAihvaB+SwjxJyHEu4QQdoMCIcQSIcSjwAcsq75PgL8bhqfr+N43fklTYzsAu/eu5W3v3K0IqWbsElIZUERL1dndikbEtEqFwlk4MGJzxy9Yn8DVun7Gx6dtVj6O7drvwsr5Y+2s277MySxICOkknuqNKmzbLdZ+FpHRfkggUNNcFRatpLKsxbLW3vjb1bhVIJBmyYmj19lZmIFwO02zF1ntwwLYfyruRNWw8GDy8pO4dMGVZ4vlG5HaBFXKvnq7RxgemmBlRhIIrWWqbb/afUuHf0qzehax+mTnLKO3d5i+vhGvyms5e6aJtevnXvhT2Ve0ioqKOsxm38eMpYf0hL/Zxo/Vr280Gti5K48jla688FzPsUZHJ2io72D9+rkLZaUlKSmGsLBgrl2zCdV67v961F/pYNmyWGJj596wBZQEZefPNXs9v9X+BsrLatlblG93bM4W3prfj9NvTCukYhVRtVGgJZKoqHCeeOpz1rr33PUi49MNfkvEfKviyq9gpkuA1z8BQXXOkJjNrdx2m21O9vjjtxEfHzsvey8pWUdpaZVPF8Xp6blNRKSH0Whkx458jhyp8alee3svZrPZ7xnI3ZGcHI/RaOTGDd+eOB87Vuv3DOSe2LEjnzNnGnxKUKbGI50P62SVmSYoU4S/blatmh/hDyA6OpwVKxKprvYtcHtVVQsZGcuIigqfo545s2bNCotQ7VuCskOHLrB//9yGJXBkJnFfJyenOHeukR075tbFTCU/P4svfEEx3hsfn+Dee58DOuZl3wFmj5RyGngL8DMgBvgm0C2EOC+EOAd0A9+2bPs/4C2WOouNZCFEpYflLTNo9wVgDCVx1785bPsH4BfAgBDiihDihBCiAWhHSUQlgB8BX5/5YQV4PWEyj9PceoqXnvkZAAaDgSef+zhCCGy2T7b88Kr1pypaOl7phdX90cHOVCgZrAwGrBKh0SDYvGsZZ452WK1f1UmpUejZcCk96esZY2RokuQVMUqfLdtVV39HC1ftCsdJr+p8a7BGyNAKgraScQnhxMaF0dLU71Lk0eP82TZWrVlGWGiwpj3HKbi91apj+8JJYHW///WbUqmp7mR83PlBoWofh/XzNVjbLD1cS3GJ/pzBnSuyIxJpZxHrjpL9qzl8SC87vWtGRyeov9LBhg3zI/wBhIeHkJ+fwoULLT7Va6jvIDEphrg4e8MWNYyGzWLYf+TkJtPTPURf37DOVtffX1lpDfuKCuZ1zLivqICK8jq788T1OaZYpyrz2wZ278mdn05iEap35nL0iHcW1aoo2tkxwPSUibS0Jdb1ErMmyZy0rjdLs5OYiuYqLKXJWtdmsapd4P3/9ma2bVsDQE11E9/51s+Ylo4JAAM4I/20BLgVCAiqc0Y/P/rRLzh9ugqAdevy+OQn/2Xe9h4REUpeXioXLjR5XWcuM5C7Iy8vlc7OPvr7vXvirFr8zXU8Uj0Uodp7K8WhoVGuX+/2ewZyTyiZRn0Tqi9fvkZGRtK8Cn+gJCgzGAw+JShTE1HN5yAOYOfOfE6frvdaqJ6cnObs2fkT/lRUodoXi+rr17sIDg5m2bK4ueuYDjExEaSnL6WmxvsEZZWVNezaVYDROD/WyQBf+tJnSUxUBrg/+9mfqKioAGaXdCLA/CGlHJVSvhcoAn6AoojnAnlAJ/BDoFhK+S9SysX6xYYCezwsHhNvOSKlvAl81/JWa6X6YeCdKAL0WSAC2ASkAM3A/wBvklJ+SEo5u6yNAV43DE5d4JlHfsSw5YHdBz/2RtZtyMIswWRWFlXsNFoE0SBnpdOKvU2jarJqP8YyIjAIxV4zdUU0Y6PT9PUo8belsCzYpEYDiliqygtnKtvZsjcVqYmzanNat8e7iZGuNOwk7OzYk86pozcwm23u9O7GLWNjUzRc6WLt+lSnnriPl+h+LORuq0RiMBjYuSeDo5VXdY5HX5BtaOhg6dIo4uIjLN+XvUjgKm6q476lprx1vZsxdkJCNDHR4TQ1ee+Fp1j8za/wB7B5SwaXq254HffVZDJz4ngju/c4jxltloeWdzNSVZ0tHFVK9q91I1Q7f26dnQNMTk6zPH3+DFtAnd8mc+GCzQtP/V4dw2OovT51spFNmzPmfX6bk7uMrq5B3fmtlIogqi4SM2ZporS0hqKSAs33rRFIMVv/cmrPSUxVSzk+5rIX8gwGI8+/dKf1/WOPfIvunlrM5sX4TDlAgMVJQFCdE0wMDTVx//0vWNe8/PKDVpfR+WLr1lwuXmz26kY+PDxGS0vXnGYgd4UQggMHNngd97W6+jorViQSHT2/wh9AZGQYOTkpXLzY7FV5NR7tfA/iAPLz0+jo6GNgwLNQPTU1zZkzDezYMfexpfTYv3+910L1jRvdBAUZSU6On4ee2aMK1UePepeg7OjRGnbuLJi3sARaUlKWIITwSqiWUlJWVjVvYQkc2bWrgFOnrnglVPf3D9PVNUBubuo89MxGXFwMTz55h/X9bbc9icnUSuAJ8+sLKWW5lPKjUsocKWWkZcmWUn5ESlnmQzvCspR6KPcRS7mPeCjXrGmz2WHbDzTbPC0/cKibobdeZ/9fsJTLV8VRKeWglPLXUsp/l1Jul1Iul1KGWD6zfCnlB6WUf3XXboBbiylzH6dOH+En/30QgJjYSO57+N8UMRWt3ZNrt0btFdPmJO5hjCRUa1el3LbCFE5XurCisuxQlVZuNA0SGx9KVGyodU+KVazE6DALMqj1NU3p7sIxGY4w2B+DUPYUGhrMqrXJnD/bZtvghqPlTezem2WJR6ptzrmeLxag7lG+kZUZSxgemqCnZ8TFHm1Cjslk5uTxq+zak2M5JPdWWIoQ5KPA6sZatXBfAUcq6+yEalfc7BxgYmKK9HkW/kCx3t6zJ4/Kcu9CMJ062cjGTSudEu2apcaF20F4np17tq1uwtJooqPDaW66qVPO/myQUlJ6uJqS/Wtmse+Zs2VrJlWX7BOUOSaoAkDA8PA4zc3drFkzfx5tqlgqkRQVr+LwoWp78dTJTV/5futq21m+PI7omFAck0mpJSUmnfXYrVPaN4NuWYHAaFmU73XHznW8/wOKc0tf3yCPP/ptpvHNsvrvjYC7fwAtAUF1Tujiqae+RUeH8vT0ne98I/v375r3XhiNBnbvXkVFhWfXmEOHLs5LIiJXJCbGEh4eSkuL3o3chpqBfOfOhRH+ALZty+PChSYmJ90L1a2tPRiNCyP8AZoEZZ6tFI8cqWHHjvwFEf5AEaqzspK5dMn9DVxNRFRcvDDCHyhCdXt7r0ehemBghI6OPvLy5lf40+KtRfWFC03k5qYSERE6Tz2zJyjIyLZteRw75nnSsZAPKT72sf+PDRuUWMjnzlXz6qs/A3yP/xogQIAArzeklAxNnOVL93zXek+5+8H3kpAYqwipZvskVKp1quOoQnWxt1mmOrirC631p23aqWT1FhiBmNhQ4hPCuH51QFPOvikhwGySVJ3uZN2OZUhLn5TQA1L526Fv3txV1Limqrxlq+QUOAADRtauT6axvpux0SmnxFdauruGGR+fYnm645jR+3udwD5Rjfe1lP/3FedQUdrgKJ85HKPk9Okm1m9YTkioEf2s846WcNJuvdYNWU9k1RNWteOYsLBg1qxZzrmzzW6PTE1EVFwy80REsyUzK4mBgVF6e/Tc6W0MD4/TdLWLNWs9C3+eEo15qu2KvW6FaptkUFfXTmpqPDEx82/YAopQvXt3HpUV2jGjsPt9qX+XldZQXLxq1mNGR4tSd4v2+1maGE1ERAgtmgRlemLq1JSJs6eb2b4zy3NfrP/U35D6OMvTeaGV8exlvcef/BwREUpo+W9/8xdcrjqF2ex7rOIAAf4eCQiqfmeSxsYqXnzxVQBCQoJ5/vn7Fqw3OTkp9PYO09vrOlmxYsEmSEmZ+wzk7iguXkd5+WW3T5yPHp2fDOTuMBoN7NpVQEVFtcsyajzShbL4U0lKiiM0NJhr11y7RqnCX35+2jz2zJnt23M5d67RrVB98WIzOTkp85KIyBVqgjJPQvXBgxc4cGDDgj2kACVBWWbmMqqqXAvVExNTXLzYzNat8xdbSo9Vq5bT2trN4KBrb+vm5ptERISSmDg/sagdMRqNvPLKQ9b3Dz74IgMDzehPKAMECBDg1mHS1Mov/u9PnDymhBLKyUvjo5/5R2siKpNDTFMhHCYZwiZyODvlWtbb3S+1Vp9KabM0K2sErN++jEunblqy09sLCQIl3mT1hS6yVy8hOMSokQ6UuKkGrXcwzhMiz9ap1miq2CWPEmqzFttbIdhdmMHRimYXLSpjxorSq+wttr8P+2x/6mVhrWWrNsZqXHwES5ZE0NjQZWtPgHqPE8DIyDhNjd2sXptmjevpHrWAK9djs6WUswWrqwRWUsKGjSuprW1nbGzS5Z6v1LWTkhI/b4mIXFHsRdzXstIainSEP8U61T2+Wam6PknCwkJYtTrNrVA9PW3i1MlGdu5a2DFjVnYS/X0jDkK1sMZrBmhv60NKSUqqa8MWb4XS2YjYhfvyOVJxxSlBmS1FFJw8fpXNWzM8zG+1vxnHvx3LOWLUXKucr7vLly/jvgc+CoDJZOKuO19kyt5ZJoAGa1zwWS6z74dIFkJ8QAjxihDiiBBiVAghhRCnvagbLIS4RwhxQQgxIoToFUIcEkK8y02dUCHEV4QQrUKICSHEZSHEh92UDxNCNAohbgghomZ6nIudgKDqdzq4++5nraLQXXd9jKys+Y2f6ciBA67FH9Xib//+hRX+QAngXlCwnPPn9eO+DgyM0NY2fxnI3ZGbm0pPz6CLAO5w6VIz2dkLK/ypFBevo6zMdYKyhbT402I0Gtm1q4DKSv24r5OTU1y40MS2bfMbj1SPZcviCAlxLVS3tNwkPHzhhD8tO3bkcfZso8tMoxUVl9m9exVGR9/HecaTUG02m6moqKKoaGGvVUVF23nPe/4BgK6uXh5//D8B95b1AQIECPB6RkozXUMneeTBH1jXPfaVjxMSEoyUipiqHWFok1A5olin6kQBFarcKa3llFclwZLVZlVAEBAWYiRvXQLV51TLL1sPBDA6Os31q4NkrVqClMo+QYnnGiwc0zppkmeh328Ag3AQO3QK2oRKW9m09DgmJqbouqk/Zmys7yZpWRQxsdoxo4+WqTr2tvbrvAsRsGN3JidPtDBt0hfyKsrq2VuUgxAaMdTOinQmwpN9shyJmkxHa4kn7d4jJHv25lFeVmO3f3WsOz1t4uTJRnbtzplBf/zLkiVRxMdH0tjQqbu9o70fs1mSmmYv/LkTSp2ser3+2N1/Rxs3ZVJb0+pCqDZw4ngDW7ZmERw894Yt2u/USeCU0ipUS6me07bjMpvNlJZWU1S8yq1oOjtrX+8IDw+hYFUKF87bjBukJkDK0OAYba195Bcka49eZ9Fs1Txc0PtKlV+6EUEQEGQRUnXiMmvmfrfd8X5WZihedYcPnuJ3v/0zJnPvzA/8Fkb4afED70NJDPoFYDfgldm4ECIMOAR8BVgDNAC9QAnwCyHEMy6q/gi4B4gC6lByEfxACPFpF+UfArKA26WU7s30X8cEBFW/MszBg4f59a//BkBKSiIPPPCpBe4TJCTEEBMTwdWrzlmpq6payMyc3wzk7tiyJZuqqhbGx51v5GoG+oUW/lT279eP+6pkIL/K9u0L+/RWJSIilPz8NM6fv+q07dq1LkJDg0lKipv/jumQm5tKV9cA/f3O19yKimp27y5YcOFPpaRkHeXlzkK12WymvPwyxcVrF6hn9qhCtV6Cst7eIXp7h8nJSVmAnjmTnBxPcLCRGze6nbadO3eVVavSCQ8PWYCe2fOVr9xDWJgSHuGrX/0RV65cACYWtlMBAgQIMEeMTNXw9Rd/TpslJvcb/mELB968RXHzN9vHTTUIxbUe6Um6sY3lnMd1Qudve4HPICB7VTytzYOMjUw5lT57tJ1Nu1OQQpFO7YIIaCxlDVj66xH7KbCrhE1K4it7sUkIwd7ibCpLrzqNGaanzZw5eZ3tuzKd9ua+N+o/izQs1KOxWcaq+7aXUW2qsd7nHhYWzLr1qZw7c91pn50dg5imzaSmxbnsl3J4roUg71EFW8UCTxFZtRaskhUrEhgdneRm14CmliKenTjewKbNGRiDDPaxK2cVc3Tm7Nmbz7Gj9RaLahtqPFK9sAS+iX2+HJvrs8tgEBTuLaCi3HnMODQ0xvXrPaxa5XsoK0fRW2/REztdxd2VSJYsiSIuLpKrVzudjr36cisrVi4lMip0XkRTT2zclEFNdRvj41N2lqkAZYfr2FdcYLkuef7NSKmNj+pg1S3trnToPlhRw4I4nAbh4WE8+5XbrO/vu+dlRifqF+w3E8ArBoG/Ac8A7wYe9LLes0Ah0ASskVJukFLmAG9HmdDcJ4R4m7aCEGIj8B7gApAhpVwPbAXGgMc1CU3V8nko4uufpZQ/n9nhvT5YHMrELYFkevoGt9/+lHXN00/fRXT04rBu3rdvDZWV1Xbu9JOT05w7d3XeM5C7w2AwUFi4mvJye9eY69e7CAmZ/wzk7li6NIaoqHCamuyfOFdW1lgs/hYuLIEjW7fmcOlSi10A94VORKSHq7ivfX3D9PQMkZOzcPFIHYmICCU3N40LF+wtqs+fb6KgYDnh4QsTj1SP3NxUbt7sd8o0eujQRQ4cWL9AvdKnuNg57uv4+CTV1dfYvDl7AXtmIyNjOffc83EApqamuOuuZ1GSxgcIECDArYXJPM7VluN89YVfAErM60ef/RigTOdNlnKqUGm1LBXKdmG33WBZ7AU/fVRbVkvMUs09wWBQ2g8Wgi17Ujh7tN2yRSnT3TXK1JSZpSlR1pYAjDqJqNwJl1rXTH3rVItyrC6WdXYhACx7iIkNIyk5msZ6+weGZ09dZ/2mNDuLP09WpPahEJwPwiayal6ti31h9btQFmXdmnUpNDV2MzqiGjcYkFJSXnqFvSWOcwZtJnIFZ/1ltuKq/b60FqxFJQWUHa6xszYcHhrn2rUeVq22jRnVz0wVXF1aLM6R6BoaGsy69emcPWM/ZqypVoS/6Gh7jzavXP31wiR41XW9+LY2VmYkMjIyQXfXoF2d0sNVFJestvvNOmWrdyGSOvZX799MUITqK3bz26mpac6da2bbds/xSOcLg0GwuzDXEvdV7auk9UYvQcEGliVHe9WO1D0vNA+MrA9WnB/2aB8mueId7yqhqHgLAFcbb/C1r/6YaekiAeDfMYvFQlVK+X0p5RullA9IKX8BtHuqI4RYBnzG8vbjUkprMGIp5W9RrFYBHnGoutfy+rKUss9S/iLwcyARWOVQ/j9RLjCf9/6IXp8EBFW/0cu3v/0TqqquALBt2zo++MG3L3CfbISFhbB27UrOnGm0rlMTES0m4Q8gKyuZwcFRenqUG7kq/C1kIiJXFBWtpbLSFvd1oTKQe0JPqD5//ir5+WmLSvgDJUFZREQYzc02N+qDBy+wf//iEv5AEaovXmy2CtXj45NUVbWwZcviEP5UbEK1zaL66tUOYmIiSEiIWcCeORMZGUZOTgoXLzZb15WVVVFYuMYp8/FCct99nyQtbRkAv//9Yf78578CrmNVBwgQIMDrkeGpczz60A+tLsCf+Pe3kpO3HJMZVGM7o7BZphoMyt+gkWoEWC0nNW3rialay0ubCGZyKqfGQV2WGonZJOm5qcTfltLM6Yo2Nu9JxYxt0qqNHGgn3Wg0SXs7Lu39xoC2lLB7tUVTBTBin6Fde4jbd63gzMnrTE8rH9zw8ATXWvopWJ2MbzhPzdXPyzk2qvaobG7GblsXgj37sqksb7DWr6vtJC09jphod6GsbEKda2HPH9arKmZi48JITIqmvr7dKrQq8UgL7IU/L0Q7X0VXX4XX9RtWUH+lg9FRxaNlamqaM2ea2L5jdmNG+1+VjjWnk/WnREphjYFrC9Vgswou2b+Kw4erkFJJeKQk2jWQnBxn166rhGJ68XDngtDQYNasXc4ZjVB97Gg927dnL2i+DT0yMhIZGhqjp3sY1aK4oryefUWeDZuU801fTLV/dd5qfVjihYInhOD5F++0jreffuJ7tHdcwmx2n4j5743FEkN1hvwzEAI0SCkP62z/luV1sxBCe3FSJ4yOFiSqiGuNcyeEeB/wBuBpKWUjtziLZ3b6usZMb28jX/ziK9Y1r7zy0KKa/ANs3JhJbe0NxsYm6O8fobNzYTOQu+PAgQ0cPKhYKS50BnJ3hIWFsHr1Cs6eVa4VBw9e5MCBDQvcK32yspLp71csPScmprh0qYWtWxc+tpQeRUVrqaiowmw209TUQXR0OEuXLi7hD5QEZXv2rKKiQhGqy8svU1i4etH99kERqsPDQ2lpuYnZbKayspp9+9YsdLd02bYtjwsXmpiYmKK7e5Dh4TEyM5ctdLfsiIyM4Nln77a+v+OOp5maamX2E8QAAQIEWBxMTndTWVHGL/+vAoCEpTHc9eB7MZstQgw2odIgnOOSqhZRithqxODztENgxqwrXKkGogZg295UzlS0I6WkuX6AxJRIwqOCbeUAo8HeOlViP6F12oNVBDZgEEarKCEsB2UvuEoQEueEL/Yz5uBgIxs2p3HmlOJOf6TsKoVFWTZrXSf3XL1PxIidkCpc1dHKvr7P3NOWxzE1ZeZm5xBTUybOnb7Gth0Zbmo4C6W+WUvO/N65c3c2p05cxWQy09bWhxCwLCXaLkyAN4Ket6KrXTRXH4RWIQR7iwooK1Xc6Y8dbdAV/jxbdJp1Fuc+qUKq8zGpx2lbHOPfxsVFkpgYQ0O94k6viNTOYQkWHskGjVA9MDBKR8cAefkpMzrv5wLt91ZUkk/Z4VoAqi62kpWdSESk+/mtN7GJhfZ/rWBnvch5/1ms35DHxz7xDgCGh0d5+KFvME2L1/UDLHp2Wl4r9DZKKVtRQgFoywKoLrmOlqjq+w4AIUQM8CJQjxJa4JZn8c36X4dI2c4HP3gvfX0DAHzgA//Mrl2bFrhXzhgMBvbtW0NpaRWHDi18BnJ3xMdHsWRJFDU11xdFBnJ3bNqURU3NderqbhAZGbYohT+VAweUuK/l5VWLVvgDJYD7qlXpnD3bSEVFNUVFiyMeqR7Z2Sn09Q3T2NjO4OAoWVm+WprMH8XFStzX06cbWLt2JWFhCx+PVA+j0cDu3QVUVlZz6NAF9u9fnA8p3v/+t1mv9bW1V/nGN34E9CxspwIECBDAD0gp6R4+wSc//Lx13QOPfIDo2CjMEtSE1QZhP4HXiqlGgeLij9GNHKC6qKrvjFbR0JsxqsEAUVHBJC+P5GpNH9Xnuli7JcnasraP6jpVZ3BsXVq32+w8DXYCJnZb7Y8BS1nNWp3u569K4npLP00Nyr0iJSXOB9FTFUzsXfS9cyb1bJnqyL7iXMpL6zl5rInN21YQHBTkuZKdHbD0IVmSrY6vImtISBAbNq7g1ImrVJTWsa84X7NVL9mVvRDpi5jrSnB1/P5cWbemLY9nYmKKhoYO2tv6yMlb5uQir+xHDaNg1lkcxFAX3617cdi+jt4Zs3tPHseP13PxQgtZWUlEehD+5hO7qMBCsG9fAeVltRw+VE2JJSyB64cNc4u96K1Y+KpLfHwECQmR1Na0c+niDbZsXellq44Pamy/c4F6MXP8vvWuU959Ho889hliY5WQKT/8we84c/ooJvOIh1p/T+hdc2eyLAiqSXSDmzKqVan2YvpXlBP5XiFEoRAiSgjxYeCtwBWNJerjQArwOSnl30WCicWppryumOSLX3yCP/6xDFASwDzzzN0e6iwcK1cm0d09iJQsigzk7ti7dw3/7/+dZefO/EWTiEgP1Z3+j388s6iFP4AlS6IJCwvhxo2eRS38AWzenM3x43Xk5aUuWuFPZf/+Dfz2tycWZVgCLeHhIWRnp3DqVD0bN2YudHfckpOTSkvLTSIjw4iPX5hY1Fu3vovly/e5XNLTi2hsVJ7ar127ljvvfIaNG/cC0wvS3wABAgTwF+OmJv7pDbfT3mYR/tISeN+H34DJbJN0vHkmbysindbYxFQBGMHB6lNJ8CRdTjvV0gYBG7cmceZIO3nrlhAUbFAsY5GK5ayQirirU9ddp1WZ1Caiqm71Nvd6JVO2ajmqPU59QUtxp8/kz3+spbDIk5eQxhIVYbWUddymX9O2zZXg5onomHASlkZRf+UmefnejhkdxdDZxiT1TmRdvTaVy5daSU2NIzLKUfhzjBkq7ba5tvrUtwLV76X38UCL96/mD787x74SJSyBswWqLeGQtzgn7fKmji32rV6tkJAg1q5Lp7Kijm3bF08oK70HGukrEujtHUaaJYlJNsOW+TAc0hdQnc8zlV2FORz8aw3bdmT4OL+1f7Bj37YnMdV+267tHyJrxVtdLju2fEApKQRr167ljfs/w7RsDCSosiD89M9CuhDiqqtlDrq/xPLa66aMui1eXSGlbEERS5NRrFuHgB+gTHo+C6iJqz4H/K+U8q9qXSFEkhBicQtPs8CbR40B3CBlG0ePnrO+N5lMfPe7fyAvb3G6UkspaWvrxWAQvPZa2aK1UAUladb4+BR/+9sFu9ivi5H+/hEmJqb43/+tIDQ0eKG745bW1h5MJvOi//5NJjPDw+OcOHGFpqabnissICMj40xMTPPLXx4jMtJdfLGFp7Ozj/HxKV57rXxRP6iQUnLz5gB9fcMMDo4tSB+amlrp7e33WC4pKYnc3FyqqqpoaGhB8YpJm+vuBQgQIMCcYDZP0dl7lqZGW6i0gf5Rfv3jaiIiI5QVqjknAvsoorbV+pacvkz81R15lommp81MjE1z7lgHV2v7ba0LYSfKuhKChc5fjtaoEhe9FaBKtPrt2DM0NMHU5DS/+eVFwsIcx4xC87/rXvrGzEWQjvYBpqbM/PynZzAYFu+Y0WyWDAyMcuHCdTo6Bnyo6XhOzuwYfbGEHBubZHLSxB9/d56oKNuY0d9xRn22ztQp3t01yOjoJD/9yVH9mKSLQV8Tlvltax8Gg+CnPzlqP7+Zgz76/l3Zyk9NmRgfn6L0YB3nz173b8fscP39N11tp6+v32MLmzdvxmAwMDIyxs9++jv+9V9TCBKJfuxjgAVAvehMuimjWpaGa1dKKR8RQlwE3gcsRbFk/bqU8oJQfnTfBEaAOwGEEG8DXgEyLe+PoiTCqvXTsSwKAoLqrBji97//M6WlJ+zW/v73f+DEif9blO7UZ882kpubysjIOCkpS8jPX7yT/p///Aif+MSb+POfz/D2t+9YlDFUQUlE9H//V8l731vIX/96nn/5l8JFK1Q2NrZz9WoMS5fGMDU1zfbtngOhLxR//vMZ9u5dzenT9RQVrVu0oRRMJjM//nEpn/3sP/LrXx/nve8tXJS/fYCurgEqKi6zaVMW9fXtvOlNmxa6Sy45fryWNWtW0NnZT25uCpmZ829R/cIL3iVte+Mb/4Ff/erXAIyMjHHx4hnWr0/ANmYJECBAgNcPY9NVvPjsT+jvH7auGx0Z48KlEzz87CeUpFMaPcHq6i9stqY2QdXRNV1rqapxWXVAWtydVetKV4ZR6uqDf2xh5/tSOXmkg10H0omMDrFYqUKQwWaRKrElzbL1yiYFay1ADcJRQLLFTlVkWuWgDZr1yoHbOSTb1Z+aMvHLn53nnZ/cxV//VMO73rPREhbBsbijsOt6XOGYdd3RSlZJLOQLipDceqOfy1VtrMxYQm/PKLsKs+zK2KMnlHsjL88U+xbLS6+weesKrtTdZPOWlSSnxM5x0hdnC2FvBEwpJT997Rif/HQJv/vtWd757q1WoVIv+drs+uZdvxzd51X6+0f4618usWdPPufONfPWf3IeM+olpppLHI9FdXc/f66ZnJxkRkcnWJYcS36+fY4Qs25CJ++xhYbQPprx9rjty/36F2f58Md287e/1PDWt633GEPV2oqlGe/Pa9cFX3wxhYgO995/ISEhbNq0kVdf/QEAD97/Vf7pn0uIjVyCwbC4En7NJ0p4G/9cXCzNXJdSZnko6k/GLa/uTgD1pHSyaJFS/hL4pU6dTwE7gNuklG1CiF3Ar1DisX4aSAC+CBwUQqyRUvbPrPuLj8U5639dYGZi4hp33vmMdU16egoAp09X8d///esF6pdrtBnI9+xZxfHjtUxP++vm7V/UDORLl8ZQVLSOw4cvLXSXXFJWVsXevWtYsiSaxMRY6uvbFrpLuphMZo4cqWHfvjVs3JhJXV2rNdPoYqOra4CRkQlWrkxi//4NdtnpFxunT9ezfn0GMTERrF27ktOn3YWkWTiklBw8qMROzsxMZnBwlO7uwYXuli4jI+M0NLSzfn0G+/atoaKiGrN5doPhmXD69C+5caPc7fK3v73GRz7yz9x//yes9W6//SmkbHfTcoAAAQIsTkzmYarrT/HNr/0WUNx+Qy1WlK9+649crbtuE081izp3N6jbNMKDzU3UWUy1lLDTp6Q15qXr674aXVIC7W0jGAyChGWRbN+bypnKdmsyLPX5pl7AAfvWcCOmCrtFFXGEEJZEWza3bZBYM3ZJZ9Hx5LFmtmxbQWxsOOkrl1BT3WEz9NUVU+0/S71FSrN1cQ454LvYJQApzVRWNFBYlENeQRKtrX0MDY5p9us+rqfjdv9LbrZ99feP0NU1RHZOoiXuax1Smuxik0q7pFH+3b82lqlekijt+4sXrpGTm0xMbDibNmdw8kSjpTV/jnGkwzt3EYxdC0OHD1VTsn8NqWnxmM2SjvZ+5/oLakCi/A4nJqaoqrrB5i0Z7Nqdy8kTjU7zW4PwXfLQuvLbznHt377T3NRNdEwYS5dGs7col/KyK17X9T0zvOs+Hjv5I65e+6Nm+YPT8p//9ST3PvAe3vTmXQDcuHGTF557FTM3fOnELYrw07Ig9Flel7gpo27rc1PGihAiEXgKOA/8p2X1AygH+RYp5bellE8DdwCpwEd97POiJiCozphevvrVH1rcO6GoaDvf//5T1q0PPPAiQ0PDriovCNoM5MHBQWzenM2JE95fyOcLxwzk6elLmZqaorPTq9/0vKJmIM/IUDKQFxau5tixWkymxSdUnz7dwLp1GYSGBiOEoKhoLaWli0+ollJy6NBFDhxQ4pHGx0eRkBCzKIXq0dEJ6uvb2LAhE2BRC9VXrrSxbFk8sbGRgJqg7OIC90qfw4cvUVy8DiEEYWEhrFmzYlGG/ZieNnHy5BV27y7g9ts/TFZWOgCHD5/gV7/6A0p4oQABAgR4/TA8dZ6H7/seU1NKLOj/uPNd3Hb3uwHlmvfI/d+3K6+d4Ctiqo74J+xFJaftSHvxC1uiHSntrVMl2EUpNEvJqcp2tu5NQQJLEsMJCTHQ1TZsneRo5Q89QcIWsMDRCs0eb1LcqNKaNTKl9QAkgwMT3OwcJidXcZndsm0FF8+1MjnlOGbUfjbaz9NXZib8SKDqUhvZ2UuJiAi2ZKfPobzU1wfG9gKrpzikM6X88BWKSvIQQhAVFcqKlfHUVnc47F/bH7NGhJZOi+/oCd1aQVkpMzExSdWlG2zasgKJmYLVKVy/1sPgkCpU+5PZtdfc3EVkZChLl0YDUFyymtLD1bqfz0zEypngbJ2qUFlex+49ebb57ZZMq1DtC97FQvW9VRWz2cyxIw3s2askWk5Li2d62kxnx2IwbrAX+Hq6hxkaHCcjcylfeeF2qxX1C8/9iKaWS5jN4y7aCfA6QBV/3MWnVIMmeysUPYcSb/Uz0uYSsRuok1Jqbxy/0Wy7ZQgIqjNims7OKzz++DcAJSnRyy8/yBvesJt3vvONAHR0dPHUU99ayE7a0dMz6JSBfO3alTQ3dzI8vDDxCV2hl4FcsVK8uOiCYR88aJ+BPCQkiE2bFp9QrQh/rXaJiFasSGRiYoqbN/sXrmM61Ne3kZQUS1ycLRHR3r2qUD3/VoruOHz4EkVF66wTSIPBQFHRWsrKqha4Z/ZMT5s4fryWPXtWWdcpQnU0DQ2LS6ju6OjDZDKxfPlS67pNm7Korb3B2Ji7cD/zz/HjdWzZkkNwcBBhYaG88ML91m133/0s4+PXmElm5QABAgRYCCZNnfztrwf58x9OApCSmsAX7v7/+Pwd7yTVck0+/NezHPzzaae6AsUiVDiKD8L5jXQjQFmtUx2EVBXHK+qVy30sz4ghLNwWi3RrYQpnj6qCGpZ9esJWQs/qTnW5lw7H5CwOqpKqmlFeWldXlDawrzjH2n5QkIGt21dy4liTx975ymwsHicmpqi61M6mLenWdcuSYwgKMtB6o3/GPbK35vQugZMnrrX0EhEZwtKltjHj1u0ZnDt7jSknodpzvxwtfp0FV7263u3jaGU9u/ZkYzRaQl0I2Fe8ivLSGu8O1ifs++bqs3ZcJ6USj/ZIRR179xVY10dHh5G+IoGa6tY56OtMUKxTe3uG6R8YJSsrybpl9eo0rrV0MzxsL/o5Cr9zI6C65vzZ66xanWoXN7moON9iUe3tPn21bPT1WJT2yw7XUbRf+f7z8zP43H+8F4Dx8Qkeuv+rTNPiY7u3Ev5KSbVgVqrHLa+FehuFEGlYYp5qyrpECLEX+BDwbSmlNg5mLOD4tEB9H+dtZ18PBATVGdHJQw+9xNDQCACf+MR72LhRESqef/4+QkKUC+WLL75KY+O1BeulloMHL3LgwAa7dUIISkrWLyp3+rGxCerqWp0ykMfERJCWlkBt7eJxM6ivbyMxMdYpA/m6dStpaupkZGTxPL0rLb1EUdFap0nC/v3rF5VQbTKZOH68jj17VtutDwkJZsOGTE6dWjxCdWdnP1NTU6SnL7Vbv2JFIuPjk9y8ObBAPXPm5MkrbN6cTUiIfdjswsLVHD26eIRqKSWHD1+kpGS93XqDwcC+fWsoK1s816qhoTGuXeti9WrbhPPtbz/AgQOKa1RT0w1eeun7QM8C9TBAgAABvEdKSf/YGR6693vWdV964kNERoYRHh7Kl578sHX9l+//HpOTU1ZrT4OAIMd5oc5cUZVT3U/yvb8fTU6YqL3Uw7rNS63tGwRERgSRkR3Dlcu9enquE0Lzv0EEubdDFdq+awVilxUAuHGtn7CwYJYm2o8Zs3MT6eocYnBAz7jBm/ZdMfNx3YmjTezYuRJjkP00cW9RDkfKZ5vp29Fq1LXY5wmzWXK0soFCi8WfSlCQka3bMzhxzB8JsrV9tXwfuqKq+6W3d4T+vlEys+wT+iSnxGAwCNra+vC/mOfcnqvWtZbj5881U7A6jfBw+xCLO3bmcOZMk65QPddWqq6sUw8frqZkv/2cQQhBkcaiVkqJWZrt4qiqMZrnUkDVMjY2RV1dB+s3ptutj44JI215PHW1HS5qapmNtbr3NDbcZElCJPHxEdZ1D37xkyQmxgPwfz/7K5UVFZjNi8GydqF4Xbv8/waYAnKFECU62z9teT3nYF3qhBAiCPgG0I3i4q+lB8gSwi5+jpq8pdvnXi9iAoKqz4xx5swJvv/9XwAQGxvNE0/cbt2alZXOnXcqYSEmJ6e4++5nF6KTdjQ0tJGQEO0k/AGkpi5BSmhr612AnjlTWlpFUdFa3aQ+u3YVcPLklUUR99VkMnHsWC2FhauctgkhKC5et2jcqTs7+5mYmCI93TkrY2xsJCkpS6irWxxPnE+cuMKmTVlOwh/A+vUZNDS0LwqhWg1LoLVO1qII1RcWhVA9PDxGU1Mna9eudNoWGhrM+vUZnDpVvwA9c6am5gbLly8lJibCadvKlUmMjk7Q1bU4hOpDhy5SUrLO7iGFEIKXXnrAev168slv0tZWC0wvUC8DBAgQwDvGTU18/zu/pq5GyTq9dUc+737vPkCxpHvnu/eybZcy5rla38ar3/yjImBaFjchQFHkEKNdqZneHdV9AZw70cnG7UkYjQbrftVL8rpNiVy53Mv0pElJTCXcTV/VxFna1m2TXv2EUN5Phk1mydEjzezZl+20zWAQ7CvJpeyw3rzVtg9fBEdH61Rf4lz2943S2ztKZrbzmDEiMoTM7KVcvuSPGOFat3g1QIJvwurF8zfIK1hmZ52skpuXREfHoAuhenbMRAYpO1RrtfizF8Yk+4rzqCi94hBr2NPiLY7l9a1UVcFyfHySmupWNm3KcCoXFGRk+/Zsjh+b3zGj8wMO5cfc2NhJfFwES5bY5reqgLosWUlk27pI5reV5VfYU5iDwTEjHrB9ZyZnT7d4sKiejfjm/fliMpk5cayRXXtyLftUrvBxcdE88ti/W8vddccLTJqaFsU8J4BvSCk7AdWN+ntCiHx1mxDibcC9lrePetHcHcBa4B4pZZ/DtsNAIvAflraDgC9rtt0yBARVn5BI2c5ttz1pvYB8+cufJzHRPqbvgw9+muRkZSDy61//jb/97ei891TFZDJz9GgthYWrXZbZv38dhw8vvJXizZsDjI1NsGKF8yAOIDg4iK1bczl2rHaee+bMyZP1bNiQabVGdiQtLQGzWdLevrA3cpvwt95lmd27Czhxom7BhWp3wh/YhOrFYFFdW3uD1NQlusIfKEJ1cnI8V64svFB96NAlJ+FPy4YNmTQ0tC24UD01Nc2pU1fYtavAZZnFEvqjtbUHg0GQkuIcz33dunw+85n3ATAyMsoDD7wIeGN5ECBAgAALg9k8RdvN0zzz2GvWdU89/wmM2ofbQvD4c5+w3ktefPqn9NzsVxI/2VncGBAYEQRp/raXXG2xSB0lWe8QwGD/BH3d46zMigVsVrIGwCjAaBRs3r6Ms8c73U52hKbfSiIqR8shYWdQZBN3hEMbzqhWcJcvtpOTl0i4jvAnJSxNjCI8PJjr17TzUUeLvLl087VRbg1LoL9989Z0qi62MTnp7weFWotVz7FWx8enqKluZ+OmdN3tQgiKivMoOzwHnk0+altNV7uIjQtnyZJIyxqtyCmJjAwlI3Mpl6vmKgSTo0WwayrK69hTmKcr/AHk5afQ1tbHwMCo07b5iaWqPPwwmcwcO3KF3YV5dlao2vNmX3EB5aW1dpapNuZvHNndNcTo6CQrViagZ5kYFGRk89aVnDyuZ1HtWH5u+33mdDPr1qcTGqoattgufh/7+DtZv0ExMDx/rpYfvvorzLeWoaH3OGZlnOky626IdCFEt7oAX7Ns2qBdL4S416HqvcAxFNf+y0KI80KIBuC3QCjwgpTyN572jSKQlkspf6hT5HFgFHhJCHEFuA68E6gBfjSzI16cBARVnxjkf//3Nxw5chaA/PxMPve59zuVio6O4pln7rK+v/32p5ieXhgLpVOnlAzkoaH6wh9AVFQ4GRlJXL68cOEJFOHvglNYAkdWr07n+vVuhoacb+TzxcjIOI2NSgZyd+zfr4RTWEjxp66uldTUJdZERHoEBwexZUsOx4/XzWPPnNEmInLF8uVLmZ420dHh+BBs/tAmInLHnj2rOH58YYVqxfJckpqa4LKMkqBs3YInKDt2rI5t2/Ksge/1iIuLJCkpdkETlEkpKS1VRGpXPPbYF4iPVyb5//3fv+bEiWMoY4oAAQIEWHyMTl/m6cf+m4F+JZTV+z6wny3b8kEo4oWatH7D5hze96EDAAwNjvLMYz92nkgIm3gjLJapyiKs7zWFNX9rBB83wyZ1en+ivI2dRanW9XqS7MqsGAZ6Jxjqn0QIg20eq9uyfQsCgRDCYUwiNGWxHpE7JsanqbncycbNaboCoVp7z75sjlQ0YjZLhy2Of7tHiQXpjDdWqi1NPURHh7EkwdmjTcVoNLBt50qOH/Fn3Ff1nLFP4qT8ry+sHqloYNeebF2PNpXEpGhCw4K4fm02xg1aQct393Cz2czxo43s2auX/8XW1tbtGVw8f30OhGq9fTp/phJJd/cQw8PjrMxIdHmYSrg4xZ1ed7uf3Zi17akxT83SzOnTV1mzbjkhoUEuz5GoqDAyMhOprtIzbpgLd2v1AZGmzxIlHmlJAfbnkT35Bcm0tfYzNKgaN/jbJdzzeTs6MsHVxi7Wrk+zrNFe5wwYjUG89JJNl/vSF7/KwEA9UlewvtVx5cLv6zJrjECCZlEv3kEO6+0sgKSUY0AxcB9QjeKKvxQoA94tpbzbi32/AoQAn9XbKKWstuyjFEgHwoHXgP2W/d8yBARVrzEzOtrCPfd8xbrmpZceICQkRLf0Bz/4drZtUybc1dUNlJefmpdeahkdnaChwZaB3B07duRz5kyDNavrfHPlSivJyfFuhT9QbuRq3M+FwhvhDyA6OpwVKxKprr4+Tz2zx1vhD2DNmhVcu9bF0NDCXN/a23sxmyVpaa6FP5WFjvt67FgtW7fmEhzsHJZAiypUnzixMEK1Go/UnXWySnr6UqamTHR2LoxQPTg4SmtrN6tWLfdYds8eNUHZwgjVVVUtZGYuIyoq3GWZhIR4Hn30P6zvb7vtSczmNubTIiJAgAABvGHaNMSFqqO8+p0/AcpD9i89/iGrCArYSXQPPvJBoqKV69+v/6+Svt5h20ZhS7ThLJ7OHlUQvXFtiIjIYOITwqzzUtWgznFktqs4laPlqqBiK6S0JTT1HaZETkM8rW2qZyFV5djRFnbuWWm1+HMSfizNhIcHk5efxKULrThOz7ydds8mEZUi/DWzuzDTo+FUdk4iPT1KTNC5xebirhXNenqGGR6aICPT85hx775cjlQ0+G3M6KtgeO7MNVavTdMxbLHvj9FoYPvOTI4d8T07vXeogrV2ja0PAkHpoWpKSlarK1yStCyWkJAgrl93jhHvS3gJd0idfyqjo5M01HewfsMKj+1s257F+fMtTkK1zTJ9pv21WbY7W9nb+tpQf5PEpBji4iJwJ8gLIdhXnE/Z4To3fZrtOey+fnnZFfbuy7MPZWXpi3rN21e0lXe/W0nA3dXVxxOPfwszC++N9/eKlLJZSim8WB7RqTsppfyKlHK9lDJCShknpSyWUv7Cy32/S0oZIqW87KbMKSlliZQy3NL+v0kpbzm3vYCg6jVdPPfcd7lxQzkH3vKWfbzlLUUuSxsMBl555SF2797MY489QEnJzvnqqBXHDOTuCAoysnNnPkeOzEWmSfdMT5s4ceKKXQZydyQnx2M0Gmltnf9kL3oZyN2xc2c+p0/XL4hQrc1A7gnlibMS+mG+UYS/S14Jf6AkKEtPX0pNzfwnKBsaGuX69W67RETuWLNmBc3NNxkenn+h+vLla2RkJLkV/rQoQvXCWFSriai8uVaFhASxeXM2J07Mf4Kyyclpzp5tZMeOPI9lP/OZ97F6tWKRcuLEBV577VdA/9x2MECAAAF8QErJ8PQFHrzr25jNithyx73vITklAVV8MUv7afiy5DjuefB9/PM79vLKf36R+CXRygaXlp9YtAeDxeJVK0KoGyXeCBtqBvIzxzrZsSfZ2i9XkxmBIH5JGNExIVxvHkToxkgFg4PwKxzKCDtZwbNIrFq29veNMTQwwYpMW3gYd4Lcxs3LqbncwcT4lF1vvEFqYpHOhAvnWilYvYywMH1DEUf2leRSrhv31R+4jhcqkZQdvsK+kly9ik6ER4SQm7fMIlT7gqPYJmwCvJeMjk5y5Uon6zcsx5uK2TlJdHcN0d8/H0K1PfX1HSQkRhMXrxi2eBoLFhWvoqKsVrecr67/quWpbVEe4bj6rVSU1bJ3X4FXY0aj0cD2HdkcP+o+RrFvuKtnE61NJjOnTlxl5+5svPltLkuOJSjYSOuN+Tdu6OwYZHrKRNryeADsvQrsj/eZZ+8gLCwUgK9/7TWuXDmHSU7Me58XEuGnfwFuDQKCqldMce1aDc8++x0AgoKCePHF+z3W2rVrE5WVr/GGN+zg4sXmOe6jPZ2dfUxNTTtlIHdHXl4aHR19DAyMzGHPnDlxQslA7o3wp1JSorgoz6f44008UkeCgozs2JHP0aPzG/dVLwO5J1JSliCEmHehurr6OitWJBId7Z3wB0qCslOnrsy7UK1+/94+gVeEakWonE+mpqY5c6aBHTvyPRe2EBMTQVraEmpr51eovnGjm6AgI8nJ8V7XWbt2JU1NnfMuVB85Us2uXQUYjZ4n1MHBwbz0ki3h5X33Pc/w8DVw4Y4ZIECAAPPNpKmdP/z2/1FeegGAlRnJ/PsX3o6ahElKe5tHI8rE4XO3vZ1Xf3IPoaGR9PWMKwKig/jkKDqo0iQ4WrGZNSWccXSQrL7QQ05BHKFhQXZlnOvZxK9tu1M4c6zD4k5vL5IKq5WZ/U4FBjsh1bM1m3OIgIrDTewtycK1mGLfnsFgYOeebI5WNroooc9sxdSxsSnq626ybsNyrzWmhIRIoqJDaWmej1wBtnAAjQ03WbIkgrj4cF3rRT02bUnnclUbExNTbsvZcP4QZiJ9VJarFn+O023XVopFJaqVom/98x5ngdpkMnPieAN79tg/LHY3xYqICCU3L5mLF/TDxemJRc7CqSqequev53P4ZucgExNTLE93jmHvipzcZdy8OcCAg1Dtu5WqYwo+x2uePadPNrFh4wrdRLv22NraV5RHRfkVnfmtv+a7OknJpKS8tJaiknw8PzQSZGSmceddHwKU+cbdd72AlAsXNnAhCAiqAbQEBFWv6OC++55nbEyJa/If//EBCgqcM3XqIYRg27Y8Llxo8uFGPjtmIvyB6k6/YV7d6YeHx2hu7mTNGs9uG1oiI8PIykrm0qXmuemYDjU110lPX0p0tH4iIlfk56fR1tY7r0K1XgZyb5hvoXpqaprTp+vZudN74Q8UoXrbtjyOHZs/d/rW1h6MRt+EP4DUVGXQ19Y2f0L1kSM17NiR7zYeqR67dhVw8uSVeYv7qsYjLS52HY9Uj4VIUNbfP8LNmwPk5qZ6LmzhTW8q5G1vKwGgre0mzzzzTaBrjnoYIECAAN5jNpvoGz3PF+/7rnXdE1/5BGFhYagip6NQZRCWiaRFINq1L5Vj5Y4xrbVur9L61rEt2/hEOry6Znximoa6flavT7DuSU0j5YRmZVhYELmrl1B1vtsyjdWKsfYxXe1FFlVsFZpt9jtRRVTH4VZTYy9x8eHExasPi6VTfechmiAjM4HBwXF6e0Z8mHB7N2ZzNSY8WtHIrsIsjEbfxoy792ZxrPKq1bp5rjGZzJw63szOPdpQZvYhAfTEVYPBwM7dWXZCtWv0v2PftA9B180hxsendYQ/99/V0sRoIiJCuNaiN2acjYu6a06fusr6DSsIDjE6fH7u+7p5SyaXLl73OL9VvhWtcOqufW3yLAfxV0rKDtdQXOI60bIeilC9itLDzl6Ywu1n6ujWr1dCvS5o4//CyPAEzU3drF7racxov++IyFCyshOpuqi1qPb3nMy+vSu1HaSmxRMdE+5W8FOu/cr3cM99HyUtLQmAP/6hnD//+S+YzIN+7meAAK8PAoKqR0apqKjgpz/9AwBLl8bz8MOf86kFo9HA7t0FVFbqB/D2N7W1N0hLS3CZgdwdSUmxhIYG09Jycw565oySgdx7iz8t27fncu7cVSYn516oVjKQ17vNQO6K+Y776i4DuSeiosLJzFxGVVXLHPTMmaNHa2ck/AGsWrWc1tZuBgfnPtmPGo/UXSIid+zfv27eEpQNDIzQ0dFHfn6a58IOBAcHsW1bLseOzY9F9cWLzeTkpBAZGeZz3bS0BMxmSXv7fFjHwKFDF3yyTlZ54YX7CQ5WYqc9//z3aWq6DMzPw7UAAQIEcMW4uZ7/+tr/0dykhLLaV7yRt/7zHtQJvsRslQcMQJDOpS82PpT4hDCaGwc0a9X6OpZQTuu0CYhsYpj9ZdYmbp6q7GDrrmXWeKQGywQfi95lSzrlLAasXreUxiu9jI+ZNCKKwZqsytZzVWS1F1qcxVBnEVVFEf6usXPPSktdvZRZem7VFis1H7LT+xo31THJVnfXCKOjU6Sv8H3MGBYWTMHqZC6c08ao9Wy5N1POnr7O2vWplgzkjiEBnOOtagXWzKyl9PeP0dvrzrjBPtSD+wANenWVRRH+1EREvlO4L48jFQ2aBGX+xvZZjYxMcPXqTdasU8aMjkfsbthqNBrYuTuHyopazFJJFiWlVBa3wqkna1RbH7TtXKnrIDk1jphY7z3aVBITYwiPCOFai3NWensrdc9Wq46Co56FeHlpHXuL8zW/N2eh3tU+tmzNsAjVc++FNz1t4szpZrbvzEZx83eWhoTloYJEKt8xksjIcJ58+jZrmbvveoGJqcZ5e7iy8Dg+eJvpEuBWICCousWM2dzG7bc/ZV3z5JN3EBcX43NLOTmp9PQM0dc37LnwLFATEc1E+FMpKVlHefnlORd/lAzkNgs+XzEajezevYrKyrmP+3rsWC3bt7vPQO6OZcviCAkJ5vr1ubVO8yYDuSd27Mjj7NnGOc80OjAwQltb74yEP0DjTj/3QvWlS81kZ89M+ANFqM7ImB+h2tewBI6sWpXO9evdDA3NrVA9OTnFhQtNbNvmOR6pK/bvXz8vQnVLy00iIkJJTIz1uW5ubga33664Rk1MTHLPPc8B7X7uYYAAAQJ4j8k8zrXWM7zw9E8BxYLv6Rc+Y4l96GBJiiJSKpKg5r5icfPfuiuZsyc7MZl04qAKnNpT0c8MrUmSI+zX9faOMzo6zfKV0dYySn+FIgJYKxis9z+t6GEwwPY9aZysbLP03YhBBGMVVq1TIm+mRu7vrxfOtrF6bTIhoUGa8kJfZLbev2xtxi+JYMmSSK42uh8zSi/dpPUQCJCSitJ6ikpyPSaicsX6jcupq+1kfMwx7quee7Rr619Pn+noyCRNjd2sWZei2YeKu8/BVq64JI9yXaFau39hW+OV5uFcqP5KJ8uSY4l1Ev68+67Cw4PJX5XMhfNqUtu5E18qymrZW1RgFcycLcmVVymls2gqJVnZSfT1jdJnEar1rYR9PUedBfnpaROnT11l5y7vPET12LsvnyOVV3SFamG5jgjrtUD/4YC+xG7fXkf7AGYpSU2Nc9qLNxiNBnbsyubYkQantv3NyeNNbN6SQXBwkM6xOQupWv71/W9h5y7FG7autolv/tdPMNM5p/1dHPjL4T8gqt4qBARVtwzwgx/8jLNnleRlGzYU8PGPv3vGrR04sJ6DBy/4q3O6HDtWy7ZtnjOQuyM8PJT8/DTOn7/qx57ZY8tAPnPhDyA3N5WurgH6++dOqFYykPdSUOA5A7k7SkrWUVZWNafijzcZyD1hNBrZtatgzhOUHTp0kQMHZi78gZKgLDjYyI0bzk+c/cXk5BTnzzexfbt3CRBcsWNHHufOXZ3TuK/XrnURGhpMUlLcjNuYL4vqiopqdu8uwGic+W0oOjqcFSsSqa6+7rnwDDGbzZSXV1FUNPNr1Re/+FmSkhQX1V/84v9RWloGzL1ldYAAAQLoMTp9kce+9ANGRpRQVh/75D+yZm2m1dJKYkZaklEp1qkG1fTTuqh37pAQI6vWJXDxbBe+WSY6xnK07FOzWJsRcKyslV37Uq0rDUKxIrWJH6q46nq/y1fEMj5uord7FIMI0ggotj47Wmjpur66ObTR0UmuNvSwZn2y2lPXhR32pP17155sThxrwmRyZ/E1M2swNWRDY0M3iUnROsKf9xgMsLswiyMVjbhOJuXOOsuT4Kp8r5XljezZl2Ppu6svwFlY1ZaMXxJJbFw4TVe77Uo4SxxCo6b6ZmE2PW3i9MkmSyKimbNx0wpqq9sZH5+rMaOks6Of6WkzaWnxOFr3WktJZzHN0fq3uGQVpYf95YXp+PtTOHXiKps2Z8xyfhtCXn6KU9xXPXFL+7t3F/HS0UJciUdaR1Gxq1Bm3olo2TlJ9PYM09c3d2PFocFxbtzoI39VKga9655Ax9pYU0YIXnzpXuv7xx/9Jl3dVzDLgBdWgL8vAoKqS8wMDjbxwAMvWte88spDXiUjcUVCQgzR0eE0Wdyr/I2agXzVKu8TEbli69YcLl1qmbO4r0oG8tkJfyoHDmzg4MG5E38Uiz/f45E6EhERSm5uGhcuNPmpZ/b4koHcE7m5qdy82U9//9zEffWH8KdSXDy3cV8rK2u8TkTkDjVB2VwJ1VJKysqqZmWdrJKcHI/ROHdCdV/fMD09Q+TkeB+P1BU7d+Zz5kzDnAnV585dpaAgnfBw7zIf6xETE8VTT91hfX/bbU9iMrUy15YHAQIECODIlLmPk6cq+cmP/gZAbFwUDzz8IY21o03o0lqlOgoMWgrWLKXl6gCjo8qY0dkd3jEWooMIgXTrWtzcOEDC0nBi4kI0+7cX5BQpNMjSa33LSIFgd9EKjpbeQLVkFcKIGitA+du2Xz0JxZMT+NHyZnbvzbSMGZ1FGl2kg2ALhIYGsXZdKufO6Cd78dXV3x6BySw5fbKFHbszZm0ktWLlEkZHJunuVo0bXAmrnmy79C0Db3YOMWXNQO5adPVGrNpdmM3xo1cxm8Hm4qx9UiC8/Dz0hdXTJ5vYuHklwcHejhn1hVqDwcDuwhyOVNR72Y5vSCkpK62zJCICx+/L24RfAEsSooiNjaDpqqtwcd6eYM4WkgDDQ+Ncu9bDqtWzHzNu2ryS6sutTIxPO8mkM/m9O57ftdXtpK9YQlT0zDzatBTtz6fs0FyE4FJ+M2WldRQVF1gs/bW4tkp1ZOu2NXzwQ28DoL9/iEe//A0kjnG1b0W8feDi2wOZAK9PAoKqS7p44olvcPOmEhT83e9+M0VF22fdalHRWioqquckxshsXX21GAwGCgtXU15e5Yee2WPLQD574Q9g6dIYIiPDaG72v5vB9evdBAcbWbYs3i/tbd2aw8WLzXMiVPuSgdwTNivFC37omT2q8OdrIiJXREaGkZOTwsWLzX5pT0t//zBdXb4lInJHXl4qHR19c5Kg7Pz5q+TnpxEeHuqX9uYyQdnBgxd8TprniqAgI9u353H0qP8HnePjk1y+fI0tW2ZnaQLwkY+8i82b1wBw8WId3/3uT4D+WbcbIECAAN4ipWR48jz33/Vt67oHvvQBEpbGABaTVItQZ5W3hP2Ywl5gsAiVQrCjMI0TFa1ejkG9dwmenjZz7kQnm3cmWfbobKGoJoZSu6S8V61PjSju/YrYGhsbRmJSFE0NvZbtQvPqaKuog5vD67o5zOSkmdTlsZq+esJxYm37e+36NBobuhgdndSpN9N7s9L+6ZPXWL8xzYsM5N6xrySX8sMNDmMGVaCzZXH3JNQ5fQcSykvrKSrO9/Ad6YkUzl9WaGgwq9e6EKqF4xt3VrTOlYeHJ2hp6bUIf7MVUQQrMxIZGhqnp3vEh3reUVfTzvLl8URH25LQKSgJpLwRUrXs2ZvH0SP1uvPbmVlp296VldZQVFww4/mt9jwxGozs2ZNHZUWdfQntNcRLHB9qTE2ZOHe2hW07MmfUT8fWExKiiI4Jo7nJn8YNyjnY2tpHUJCB5OQ4u89Hqh4KPnz/Tz51G1FRSt6W73z751y8eBqzHPNjnxcfM3Pw9/IeE+B1R0BQ1WWS+voLvPzyfwMQGhrCc8/d66GOd4SFhbBmzQrOnPEm06T33LjRPaMM5O7Iykqmv3+Enp4hv7UJSgbynTtnlojIFUVFaykvv+xXoVoR/nzPQO4OJUHZKioqLvutTZhZBnJPJCbGEh4e6vcEZefPN5GXl0ZEhH+EP4Bt2/K4cKHJ70L1wYMX/Sb8gSpUb/B76I+JiSkuXWph69Ycv7UZGRlGdrb/heqmpg6io8NZutT3WNSuyM9Po62t1+9CdXn5ZfbuXY3BMPtbpdFo5JVXHrS+f+ihl+jrawBMs247QIAAAbxh3HSdn/3095w+oYgJ+QXpfOxT/2SJi2ibRAuMFo9nI/YWa2YHMcwmEKWkRTE9LenqdOWiqh/n0pN16sUz3axan0BIiDpm1Lf6FGqCKWsMRE2YAod623et4MzJVqanzba6BKEnWnk7AZYSKg43sbdYFVOcXWj1sIpN0tZL6zYhKNyXQ2WZvZXibK1TR4YnaGnuYdUarfA3O2LjwlmaGMXVBlfij7PFqjdWkHW1naSlxREdo2/x57tcIVi/IZ36K502oVr3I9BPeOX6uKD8cB377BIROVrq2qxqlfNNTX6m7YS6KHWKSvIpO1yjs++Zf29TUybOnmlh+84sN6W8t1AFRahes3Y5Z880uyjhqa+OYqryvq2tDyEEySlxXvXDdeu2MyIjM5HBwTF6u4edrOmdrTX10YtffOJYI1u3Zfp1frtnby7HjjT4fX5bUVZLUdFq62fiy3etRWAgJSWRBx78BKCEybrzjmcxy2vzkog3QIDFwIxmiUKICCHEW4QQXxRC/FII0SKEkJblES/bWCaEeEEIUSeEGBNC9AohKoQQnxBePCJqbGzk05/+NJmZmYSFhZGUlMSb3/xmfvGLX8zkkBzo4K67nmFqShFn7rnn42RkzC5+ppZNm7Korb3B2JjeE2ff8UciIlccOLDBr1aKagbyvLyZJSJyRXh4CKtXr+DsWf/Ffb1woYmcnNQZJyJyRU5OCn19w/T2+k+onmkGck8UF6+jvLzKbzdyRfhr9qvwB6pQXUBlpb9iOEFz800iI8NmlIjIHUlJ/heqy8urKCz0j/CnZfv2XC5caGJy0j9CtdlspqKimqKitX5pT0UIwYED/o372t09yODgKJmZyX5rs7BwK+9731sB6Onp57HH/hOY/XkwNDTEI488wrp164iKiiI2NpZt27bxwgsvMDnpn/tMgAB/D9zK41spzXQPnubRL/7Quu6J5z5JcLDBYpFmu8+rIp9WFnIMB6BMIexFhd1FyzlWfsPtRNqXOfboyBTXmwfJX60aC9hbDQpUwVRPCNWIbRqRxCCCCA4xsm5jCudOt2rKO0twTpKcm2+voa6blNQYomPCdNpyVVEVzlzsD0hbHs/kpImbnYOatTMVKpTPoaKsnr1FuV6LR96yc3cGJ483e4j76igy6sfuBCUe6dnTs7X404qUliAWQlC4L5eKcr0EVZ7QD2fQ3tYHQpKSEuu0zbEvzhabNgHVsW58fCQJCVE0NjiOFWaekOzk8ats2ZrhQfizt1r0hvUbVlB/pUN3fuvZStX596LEI61lX7FviZbdyerC8v3vP7CGw6X6cwbvfhf25/jgwBjtbQPk5i/zqa/62D7vsLBgVq1O5fxZf+UKkFRduk5WVhKRkaGWNbOzdgf4wm0fICtL0UpKD5/i17/6A2b6ZtvZRTy+9cWtf7bW6gEWOzO9k24H/gg8DrwTWOFLZSHEFuAycCeQB0wD0UAh8B3gz0IIl+Zrf/zjH1m/fj3f/va3aW5uJjQ0lJ6eHv7yl7/w7ne/m4997GOzeCoyxF/+8jd+97vDAKSlLeP++z81w7b0MRgM7Nu3hrKyS35pb7YZyN2xZEk08fFRNDb6Jyu1EpZgg9+FP4DNm7OoqbnG+PjsL7ATE1NcvNjMtm2zS0Tkiv37N/hN/JlNBnJPhIeHUFCQzvnz/on7WlFxmT17Vs0qEZErcnJS6ekZoq9v9gnKFOGvyu/Cn0pxsf8sqnt6hujvHyEry3/Cn4qaoKyy0j9xX8+ebWTNmhWEhc08HqkrkpLiCA0N5to191mRvUVJmrbBL21pefbZuwkPV67VX//6j6mtPQ9MzLi9lpYW1q9fz6OPPkpVlZL0bmJigtOnT3P33Xezc+dO+vpmP6gNEODvhFt2fDs2XcNXn/9f2luVUFb/8NbtvOGNWzBjAjsx1YhE2Mlc9pZ66jTQjKOgExkVTHJqJA21vXaZwFWUvx3ve66P50RlOzsKUzRjRj2ZRBWoXE1UtTVs2wpWJ3K9pZ/RkSmr4Kpaqgq9ya6bYatpWnLu9A227VxhqaufWEcfz8LVvpJcykvrLQliZufV0NE+gJSSlNRYy/78N7kPCQli/cblnDmlH/fVhqNw6OhyrPw7ebyZzVt9iUfqDvvjS1+xhMmJKbq6Bl2U9x4pJeVlSjxK33rk3ee+qzCHE8caPQjV3jE0OEZbaz95BZ7GjI7WxJ4xGASFe/OpKHMVgsnV2F//91Jd1UpmZiJRUe7nt9orgr5dssal37I5Pj6SJUuiaGzwPVyc3u+07HAtxfvz52R+u35jOnV1HYyNzd64YXJymksXbrBtmxLKyncxVWALpWI71rCwUJ577i7r+3vveZHx8SaneNm+4M34VgjhP9dcnwgIqgFszEbV6AMOAs8B/wp4lWlJCBEL/B5IAGqBbVLKaCAS+DwwBbwJeEmvflNTE//yL//C6Ogoe/bsoa6ujoGBAQYGBnj44YcBePXVV3nuuedmcEiSqakb3HHH09Y1zzxzF5GRETNoyz0rVyYxOjpBV9fArNrxVwZyd+zdu4YjR2pmfSNvablJWFgISUn+F/5AEar37l1DWdns475WVlaze/fcCH8ACQnRxMZGcPXq7BKU+SMDuSe2bMmmqqpl1kJ1b68idmZnp/ipZ84cOLDeL+70585dZdWqFbNKROSO8PBQCgqW+0WoPnTowpwIfyq5ual0dw/MWqgeG5ukpuYGmza5czGbHapF9WzdjBoa2khIUB4m+ZsVK1K5917FNWp6epo773wG5fbpe59NJhNve9vbaG5uJiUlhb/+9a+MjIwwOjrKT3/6U6Kjozl37hz/9m//5t+DCBDg1uaWG99Om8aov3qcr7/0awCCg4N4/NmPW6466thOK56oIqu6aK9PepNAaS27aXsSF8/eZHrKhNX60BJSwJes9F2do5hNkmWpkZa9GjDg4PbvyjpVkxDKXno12JXZU5TJkbImuyaVRWAQSggBhJ5Yq50MGzhz8gYbtyx3Ify5mzRrhEU3xWJiwklJjeVK3WxyBRgsrr717C3OxVEw8xer1ybT3NTDyIi3Dwpt54421urQ4DitrX3kFSTNoBf635Ul95jFxVtQVFJA2eG6WY8Zqi+3kZGxlMgoV89K7EV2X0WskJAg1m1I58zp5hn3UaXssJKIyrPwp7VIV997Jn1FAuPjU3TddBaqvYsZqZSZnJzm/PkWtm73fszoaBWuiqiuDnVPYR7Hjtbrzm/dW6nal79xvZeQ0CASk/wRysr5czYYBHsKc6ickUW1PceONLJjVxYGo5iBmKqNX21fVwj453eUULJfyTfT1NTKyy/9N2bvbp9OeDu+BX48ox0ECOBHZqoWVUgpl0gp3yClvFdK+VO8N7G5G0gGxoB/lFKeBpBSTkop/xP4sqXcp4QQTlmLHn74YUZGRkhOTub3v/89eXlKkaioKB599FE+9SnFmvTJJ5+cgVVOL9/85mtUVzcAsHPnRt7//rf52Ib3KEl/Ls7qRu6vDOTuCA0NZt26DE6fbphxG4rwd5ni4rmx+FPJyFjG8PAY3d0zf+Jsy0A+d8IfwL59a6msnF2CMn9kIPeEIlSvprx8dnFflUREcyf8ASQkxBAdHU5T08yF6vHxSaqrr7F589wJf+AfobqxsZ24uCiWLIn2Y8+cUSyqZydUl5VdYt++NX4PS6AlIiKU3Ny0WQnVJpOZo0drKSxc7cee2XPvvZ9g+XLFOuRPfyrnj3/8C+C7YP2DH/yAS5cUT4df/OIXvOENbwCU3+x73/tevvWtb1n28SeEEAf80/sAAW5pbsnx7ajpIl9+6FXr/ebTn38b2TmpGmtHRytJZb2UFhd9O+3NFkfVFgrANo4NCjKwfnMi50453oelw6sSC1RvCCyl5Hh5Ozv2qnHhtbEmtV12dq1XhSIhnMVURxEpOTkaKaGjfchWH4HAqKlna8tZEBKMDE1x41o/eQXLsGWcd+imGxxFDVcix/admZw51cz09EwsVJVe1FZ3kL4inqioEJy/D/9YTAkh2FuUQ0XpTHNFWOKRll5hb1EOQniK7+je6suijzuIasqbmNhwliXHUn9l5kL15OQ0F85dY+v2TDd901uv/qWWcU62pmXtujSuNnYx6rVQ7cyNG30EBRtJWuat8Gf/MMVbAa6oZBVlh2t057fa35aC/u/l2NF6tu/IdjJs8RQpV3Xp98ZQNDQ02CJUex8uztFCXEpJRfkV9hble93GTFixMoHR0Um6u2YaLk7Q3zdGT/cw2TlJyrVX52Ga69oGF5+37bclhODFl+61jvOffuo7tLVVY5a+W9Z6O74F3rIQ41ubWD+7JcCtwYxmtlLK2fibfMjy+lMppd6s92soM0sjYGdWI4SIVGNI/fu//ztxcXFOlR944AEABgcH+fWvf+1Dt6bp7q7n4Ye/al3zyisPzenkPy4uiqSkWOrr22ZUv6/PvxnI3bFxYyZXrrQyOjqzG/n5801+zUDujtkm/Tl48AIHDvgvEZErQkODWbt25YyFan9mIPdEZmYyg4OjMxaqFeEvkoSEuRX+QElQVlExc6G6rKyKwsK5Ff5AGRQUFs5cqDaZzBw5UsO+fWv83DNnli6NISoqnKammU06uroGGBmZYOXKmVia+MbWrTlcutQ84wRlp07Vs359BqGhwX7umY2IiHC7RId33PEUk5M38NVa6Ic//CEAJSUl7Nq1y2n7+973PjIzrZO8DzkVCBAggB234vh2ytRDRXkZv/nFEQASk+K4+/73IhGARJvpXotWC5Eu/1ClLrOd5JWdH0d76wjDQxO4s4R0ZU/QWNdPyvJIoqKDrQKJnmDm2Gen+anGJFEIo6WORogRgsLiTI6UXdURf/Qnu/ZSjoGK0gYKi7N1J8feWeSp1qnuhaugYMGmLemcOtHiRZvOPZmaMnH+7HW2bs/Q74MfrVVTUmMxm810dsxszNh6o4+gICPLkmPQfuI23Iu/NlFNOBRzrrdzdzanTzbNUKiG40cb2bYjS8ejzb3I69RfjcWzHkII9u7Lo7xsZlaKUkoqy66wr8jpWY4HfBd9YmMjSEqOpf6KK+MGYX3V/qW+6+8fpevmIDm5y9yKp2jr6n7fnlm/fgUN9Z26FtXexFK9dOEGubnLiIjwh2GL+9+gkqDMV4tq2/lXeriWopJVmn2p125XDy1s7v2OlqlWIdXh8163LpdPf/o9AIyMjPHgA69glr7Hf13841t3bvy+LAFuBeZWMXBACJGPLR7Vn/TKSCmHgQrL2zc5bC4cGxsD4C1veYvuPjIyMli1SrlY/OUvf/Ghdzf58pe/Rn+/cvP/8Iffyfbtcy+q7dmzmuPH6zCZfL+Rz7WrrxYhBMXFaykt9T3u68TEFFVV/s1A7o74+CgSE2cmVDc1dRATE0FCgv8ykLtj48ZM6upaGRvzXaj2ZwZyb1ASlPke99VsVoW/ubVOVgkLC2HNmhWcOeO7dUR39yDDw2NkZvojqLxnsrIUobqnx/cnzqdPN7Bu3dwKf1oUodr3uK9SSks80rm/noKSoGzPnlUzEqpHRsZpaGhjwwY9SxP/8t73/iN79mwG4MqVZr7+9f8GXGVHdmZ0dJQjRxSBxNX9UAjBP/zDP6hvHe+nAQIE8BOLdXwrpWRo8gL332W15uGLj36ImNhIbC69lkQ9FmFVL4O8/rTP0XRVcd2WmEFIdu5L4Vh5q25Nd0xPmbl0rpuN29QHcMLhVflTz1rU1XtnqzgbkVGhpK+Mo676pqWsTSz1NE1qbx3EYDQ4CX/uxB9nFGHbHWoc2/xVy2i93s/Q0LiXbavtw4ljTWzdvtIhEZFzEiR/TfT3FedSXtrgsxeelJIj5Y3sLVLnDO76ou/i7Uv3g4ONbNy8klMnfPdsGegf5WbnIDm5jg+L9T9DT+eF7QGBfpm05fFMT5tmJFRfuniD7JxEIiJ9NWyZmdC+a3cOp05e1RWqheaag85nUna4huKS1R4s+GYuotq1IgT7igooL9PPFaDtm+O1cWJiiqpLN9i0ZeXMO6Bp3RNxcREkJkXTUO9tMlObCNrS3EVUVCgJSyO92K+e4Ge7TugJqVoeeeyzxMcr8+j/+dHvOXHiCCbziJd9DoxvA7z+mFdBFdAqKu4CXarbHH0urfXXrHFtlbV2rVLs8mVvJ9QTXLp0hm9+86cAREVF8PTTd3pZd3aEhASxaVMWJ054/8Sxra2T5uZOoqLCWbp0foQ/gPT0RCYmpujs7PeqvJSStrbOOctA7o7CwlUcO1brtVDd1zfA8PAIFRXV82Lxp2IwGCgqWktpqfdxX9vaOunqGvB7BnJPxMdHkZAQTUOD90J1W1unRfhbOW/CH8CmTVnU1t7QzTSqx9jYOL29/Rw6NPdhCRxRQn94b1Hd0dHF0NAo9fWtbNw498KfiipUnz3rvWtUW1sn9fVtJCXFEhfn/3ikrsjOTqG/f5jeXu+EarPZTHv7TUpLL1FUtG5e3HCEELzyykPWfT322H9y82YDSg4bz9TU1FjFbfWep4dmW7IQYsksuhwgQADXLMrx7aSphf/5wW+puqhctzdsyuH9H3qjZatETSwi3MTGmymJy8IJCjLQ3jpkl2hIRc8Ks72th3OnbrJ+SyJBQdoxo/QooLq7bOuJWFoX1i3bVnDxXCtTUyaP9QAGB0cYHBzjSPlVCouydXrjPbZ+OyYBUgMq2GLPCiHYW5xDRan3nk3t7T30943Q2TFITt4yhJNQ4rh//3z/UdGhpKfHU1fjvWdLW1sXly+1k5m9lIhI1eLPu/64v227V4BWrU7h+rVehoe9E6qnpqa5ebOXssN1FO8vcBgz6O3Le4HdXsx3bquoOJ/y0lqvheqbN5Xjqrp4g81bM7yq4w+Cg4PYuGklp0/qjxkFQU7r2tu6uN7SQ0RECImJMQ6fmaO7tP/6ujw9gakpE52dA879FI6iojq/7eJoZQO79uTMWb4NPXbuzubUiSYPeU3U80bS3z/I0NAoRysbKNznyjpZtToV1lf1+qh94OMcOkOfhIQ4Hn7kM9b3d97xHNPmJq/P2dfD+FYv9MRM/gW4NZhvQVXrm+7usbW6LUYIoZ2FpwLEx8cTEeE6UVRaWhoAbW3eCT9StnP77U9Zf7wPPvgZUlLm3jVVZe3alTQ1dTI8POaxbGdnN6tW/SPvfvcXWLFibpI7uUO1UvTmovirX/2VrKw38K1v/Q/LvI7X4x9CQoLZuDGTkyfrvSp/993PkpPzZlpbm+dV+ANYsSKR8fFJbt50vpE7MjY2zu7d/0px8QdJSnKf9XIuKCxczdGjtV4lKDt16iIrV+7nwQefJz09fh56Z8NgMLBv3xrKyryzqH7uue+Snf1Gjhw5TnT0/H6uS5YoyY8aGto9ljWbzbzjHZ9jw4a3Exo6Ne/xdzZtyqKm5ppXQvW1a23k5LyJD33oHtLT4+a+cw4cOOB96I//+Z/fkp39Rl577ZcsXTp/wu+WLWv56EffBcDAwBBf+tIrgHeTT+39Tb3n6eGwbe7jwwQI8PfJohvfms1TdPad5/Ev/8C67unnP2OJuW/J0iyEJfGSO6u5mbNjbzInKtuR0tE6z7nV0oPn2VzwSV554ScsTQqylBIgXFlQad+5FlsNutap9lZtQUEGtmxP59SxFp2yzrEtn3j0+6zNfy/VtZcIDw+a8cTYUz0lXqP98S9LjsFoNNDW6nnMODVl5h/f9Hn27fkkMbEmDGp8WY/TP/+Iqlt3rODcmetOQrUeNdVNFGT/f9x52/OkLY/CvQgqcRSApZQuQ0h4QgjBvuJ8yg/XeVX+v77xM1blvYM///kgMbHqmNFm7W3X9gzPDpuYZf85RMeEk7Y8nrpaz7kCpJR85INfYuPaf2Ha1D9D4U9f6PeG1WvSaGnp1hWqlSMyWIQ7A103+1i/9l285913Wr5/S7k5EFD1KC5ZTenhat35raPr/29/U0ZBztv51n+9RmKSo8XnTPD+Mw0JCWLDxnROn2zW2ao9V5Q2H7j3FVbnvYtr1xsJDXUWsW01hfW7cLRMtbr3+8CnPv1uClYpRh8nT1ziJ6/9GpPs8aru4h/f+svdP+D2f6sw34KqNoDiqJty2m3Rjn+7G2xqtw8NeWOdNMhvfvNHDh06DkBm5nLuuOMjXtTzH4o7/ToOH/Ys/nzxiy8zODjMmTPnefnlH85D7+yJiYkgNXUJdXXu3bjGxye4665nmJiY5Be/+D0HDx6bpx7aWLcug8bGdkZG3D9xPn36Eq+++ks6O7t49tlv0NvbPz8d1KBaKXoSql944fu0tLRSXV3HU099Y556ZyM0NJj16zM4dcq9UC2l5AtfeJLp6WkOHizjJz/5wzz10MbKlUmMjk7Q1eV+0nH9ejvPPPMd+vsH+c//fJXGxmvz1EMbe/eu4ejRGo9C9Wuv/Y4TJy7Q1HSNRx55ZVYJzWaCkqBsLWVlni2q77vvecbGxjlx4gzf/OZP5qF39ixZEk1cXCSNje6F6qGhYe6/X+nra6/9kuPHz89PBy08+eQdREcrA/LvfOdnnD9/GiWnjXu09zd390SHbXMfxDhAgL9PFt34dtxUx3NP/Q/dlnvgu95TxO7CtYqVEYqQqgprinApNK706naHyZ6Pc7/wiGBWZsVSV9WracDZEnZ62sRD93yXqSkTf/5jKf/vT6ft3Eqlg6jjfb8cJ6zKcSrxVLXbBNm5SXR2DjE4YLv+CiEsYoqtjdqaZr71jV/S093PV1/6IR3t3okEun1z6rd3FqKFRdlUlnl2p//2N39BbU0z9Veu8uTj3551NntfCQoysmXbCk4eb3ZRQhEhpYR77nyFqalpKiqO86Mf/t6ulLuUVLZ2rCeL/noPJKfEIoSgra3fbbmurj6eePTbDA+N8r3v/pTLVY0u9zNbCzTbQwj79rfvzOLs6WaPQvXvf1fOwb+d4Pr1Np54/L+YnJxZbHnHc9JbUVURqldRXlprW4e+q/+XH/5PBgaGOXv2Il/76o/nPWlPTEw4qanx1NU6P6wyS3WsLRgfn+C+e15iYmKSX/3qTxw6eHLe+qiyem0qzU3djAxrw8Vpr6vK93PubC2vfv+33LzZw/Nf+Q49Pf267QmHa6EN6dG93xXBwcE898Jd1vcPPfBVBoevYDZ7frgSGN8GeL0x34LqIsPM+Pg17rrrGeuaF164n7CwuU+c5EhaWgJms6S9vddlmbNnL/O97/0cgJiYKB577Avz1T07du8u4MSJOrcB3F988VWamxXR9cCBXbztbfvnq3tWvBGqpZTcdtuT1kHml7/8eRIS5teaEiA2NpLk5HiuXHEtVN+40cHTT38bAKPRyAsv3D9f3bNjw4ZMGhra3ArVr732O6swVVCQxWc+87556p09ilDt3qJaFf4APv/5fyM/P2u+umdFFapPn3YtVA8Pj3Dffc9b37/88oPzGkZDJSMjiZGRcbdCdUXFaX76U0VEX7o0ni9+8d/nq3t27Nu3liNHatwKz08//W3a27sAePvbD1BSsnO+ugdAcnIiX/rSZwHt9ciz5UmAAAECuMJkHqam7iTf/PqvASVky6NPfdyyVYCOWCrAeq+0yjhWH8+ZCxvrNydRU9XD5IQas9V54v6D7/yZuholccnmrbm8+7377LZrLSodRRZn0cW1dapSVLt/W38MwsDeohzKrdnptftXRCAp4b67v24d/955z7+RljYzjzbXWpE6XnF934qMDCUjK4HqKtcPDLu7B3ni0e9a3z//4h2zEKhm/v3n5ifR0TbI4IB2zGh/Tv3x90c4+DdFmEpfkcwX7ni/VUz02TnWrqBvIQz2FedTUeo+6c8jD/8XAwPDAHz4I29j0+ZVDjv1vzuvY1iOoCAjm7dmcPK46xBMExOT3Hv3S9b3z37lNkJCZuOB55sYr34GqSnKnKq9rV/zmWh+WwIuXKjle9/9JQDR0ZE89vjnZtHPmbNzVy6nTunHfQXlOvS1V35C01VlrlZcspW3/XPRLPfq+0MOJfRHHuWlddh+Sw6WxFJy1x0vWM/lB7/0CZYujdNpTbUStv9NzsQq1a5VYeBNb97NW/9pL6CE83ju2e9h5sbMG10kqPdGvywLfTAB/MJ8z8a1j9TdPYbXbhty/Ht01N3Df9v26GhPDyt6efnlH3D1qjKI279/J+94xxs81Jk79u9fz+HDl3Rv5I7C38MPf45ly5bOdxcBJS7O1q25HD+u7xrT1tbJU08pCRAMBgMvv/zgvLsmqyxfvhSTyURHR5/u9p/+9A8cPXoOgPz8TD73uffPZ/fs2LNnFcePuxaq77//eUZHFcuJz372/axZkzuf3bMihKCoaJ3LBGUjI6NOwl9w8PyGUVCJi4siKcl1grIjR87wk58olhAJCXF8+cufn8/u2bFhQyb19W2MjuonKHvmmW/T1qYEov/nf97PG9+4Zz67Z8eBA+tdJigzm83cfvtT1vdPPHE7cXHzG/JDJTQ0mHXrVnL6tH68uatXr/PCC98HlDAhzz9/33x2z8oXvvAhcnKUpAbl5af4+c9/C7hPOqG9v7m7Jzps8z37WYAAAbxhUY1vx001PHjPt6zjidvu/hfSVyyzWpa5jqMJjq7LijWrnjWltoZBU0+1fDUARoKMRrbsSOH08XaN0GSb/Pf2DPLM469Z23rq+U8qDwulrXXQWsU5WJw69V3bJ80WYTsexzpq2cSkKMLCgrhxvU+nruD//ek4f/uLIvwtT0/ijrv+zeVn4h5PY2LVjd11iS3bVnDxfCuTk3pxtwWPP/Jt+vuVU+wDH3or27Y7xub1ZVw+c8tWIQT7SnKoKK1Hz911YmKS++55xVr+ma98gYhwX0IveXMc3gmrkVGhZGQupbpKf8x48cIVvv/dXwNKvo3HnvwPp/3P7WzH9rnlF6TQ1trH0KC+R8vXvvoTrjYq4lVR8Rbe8a75N2xRKSpeRXmZGvfVYBXrhAApBXfc/hXr/PaLX/o0y5YlLEg/g4ONbN2axfFjtjGjzToV2tu7ePrJ7wHK/Pb5F+9asPltamocZgkd7QPonds//9lfOVJ5HoCc3BV87vPvRfswSWDEYImh7YgQEv1QK96jfG6SZ5+/g+BgJdTASy/8D41N5zGb3Sdifn2Mb1257/u6BLgVmG9BVXuHch0Uw7Zt0JIV1a5+X1+f2x9Ya6vy5Cg11V04jWna22t58sn/AhZe+AOIjg5nxYpEqquvO237v//7E5WVZwDIzc3gP/7jA/PdPTtWr07n2rUuhoacb+QPPPAiIyPK9/Pv//6vrF3rKgj2/ODKSnF0dIx7733O+v6llx4gJCTEsfq8ERwcxJYtOboJyo4dO8ePf/w7AJYsieWRRxZO+ANIT19qCeDuLFQ/++x3aG1V4kD+0z+V8OY3753v7tmxZ89qjh+vc0pQZjabue22J63vn3jiduLj5z8usYo7obqp6TrPP68If8HBQQtmnawSFxdFYqK+UP2DH/ySs2eVhCnr1+fziU+8Z767Z8fGjVnU1bXqCtX33PMVqwvcHXd8xCpqzjehoSG8+KLtO73nnuf+f/buO06q6v7/+Otz78xsp4MIioAC9t5bLNGo0cSoMSbmq8Zek5hI8ktMMaYYe4q9xJJYUZPYSxQVexcVBJXepMOydWbu+f1x7szOLrvLLuyyC7yfedzszG1z5i7OnP3cz/kcamqm09ofgIXfb7nvvOY02db2GeVEpD26Tf82Ey3g6aee49mnfeBv8Cb9+NFPv03TiUfaHyRrrn+cC6LmHjcsPkzpA61Dh/di6eJali2tXek0V/zhfpYu8Zfi+O8dwK57jGryR35LgdDW30PzdUJXfu9Nz7vP/pvz2rgpK0Uz0+ks/2/0dfnnf/rzeZSWrl7N9VX9qeHrgfogoHOu2bcZhgG77zWUN16bttK2jz/6gttu+Q/gA3+//+O5K7dhrfwJ6P999B/Qk1RRklkzl9L0H8CN143hi8994G/f/XbimGNXJ/DX1kzUVU+8tevuw/jwgxkrBaqdc1z0k6vzI15+8avTGTiwMLGl8yeZaTRc3mD/A7bkpRdXTm6ZN28hl/2howN/BTdYWnifLeUTV5SXMHRoPyZOmNMk4d14+KFnGfdy7u/bIVzww65LbAHYcqtBzJq1iMrKmkbBVIBfX3wdVVX+797TzzyG7bZf08SW1Q1a+ov4lQNGNjtBWXV1Lb/8f3/PP7/y6gsL/r4tLGFS+Lvy/11YB9RPLmzPiBFDuOBH3wV81vQvf/53Mkxt9Xj1b2Vds7YDqoWF91qetq1h24SWjm9thtOPP/a7tTZTKszjl7+8lhUrfMf1rLO+w3bbjWpl/7Vjzz1H8e67n5NON3yRV1fXMHp09wn8gQ/+HHjgditlqb355ofcffd/AOjduye/+90FXdC6xioqStl0035MnNg4UH3FFbcxa5YfWnvEEV/h8MPXdNjGmttmmyFMm9Z4grKmgb/f//5H9OnTqwta15gPVDfOqJ42bRZXXuk7cT7w1zUZf4VSqQQ77TR8pUD1XXf9m3ffbQj8nXHG8V3RvEY23bQf9fVpvvxyaaP1o0dfSV2dnwiqKwN/hfbdd2tef/3TRoHq5ctX8ItfXJN//te/XhxPgNJ1fKB625UC1S+88DqPPPIs4IfdX3zx2c0dvtYceeSB+azj6dNnc/XVtwMLWtx/q622ypd8yH3nNadg2zznXMs1ZURkTXSL/q1zjuW1n/CL0Tfl1136pzMKAn8tB1MbMkBXZ6b3hmGjDdmvjf/E2Psrm/DaS42He078ZDp33PoUAKVlxfz60pP8efLDIZsL3TSp59gkk9U/CprUeSw8i2OlqG6BkpIkW4wcwEfjG/99fuP1D/HZZF9jfe99tufYbx/c4jla07agW9tqVm6+RX8WLVjBsqUNfUbnjIsuvDYf+Pv5L05h441bGtHW0X8Gtpx9te/+W/DqOF/3Nfebnf/lYv4UB/7MjKuv/Uk7An8rv0b7YobN/zsPw4Dd9hjOG6990Wj9fx55gZde9IG/4ZtvUhD4W/vzdVucYbjRwF4kEiGzZzVObvjNxdfn/7497Yxvsf0OHZPYknuXK5djWPkK5DLbc9ntu+2+Oe+/N63g71ujpqaWn42+On/MlVdftIZlCdacmXHAgVsz9oXGH9HvvP0Jd9/1KAC9elXw29+taZ9xdQKXjYf3l1cUs+mQvnw6oXHpj2uv/iczZvi/bw/92l4cfsS+8ZYg/7/mMvVXt1WFnHNxzeMo385fXHwqAwb0AeCRh5/npRdfIhstbfEc60L/tqX/Btr7P1k/rNWAqnNuEpCb8eWw5vYxszIgl9L2bJPNr5SUlADw9NNPN/sa06dPZ+LEiQAceuihLbSkhrfffoM77/T1Wnr16tFl9UibSiRCdt99JK+91lDA+6qrbmfGDN+x+9rX9uWII7o+8Aew8cZ9CAJj9mxfkL9p4O93v7ugS+qRNmevvbbk7bc/y3+Rz5gxh8svvxWARCLRKDusK/lAtQ9U5vzzn//l7bf98223HcmZZ36nq5rXSI8epQwe3IdPP234A+lnP7uS2lqfCfjDH57EyJHDuqp5jWy77WZMndoQqG4a+PvLX37Z5YG/nIMO2qFRRvWLL77Jww8/A8BGG/Xj4ou7ph5pU6lUgh13HMZbbzXUff3DH25g/nz/eXDccV/jgAP26KrmNTJkSH/q6tLMn78UgEwm06gswWWX/YSKivIWjl47zIxrr/1F/t/hZZfdwqxZnwLNDef0xfj32ccHYFv6PnTO8cwzz+SeNv0+FZEO0l36t/XZ6dxy44N8NtnfQN5z76059vhcxl/jOowtW7keX+7o5veL8n9AF04eZdAwrteM3n1LKatIMXPacr/VGRePvj0/GeOPRx/HoE36rRQUMwICC/0kWe0Itq1OwCAXBNppl02Y8PFc6ur85+/8+Uv50+8bAn9XXtOewF+TNrQhO7U96/c/cAQvjW34Hn70vy/lA39Dhw3igh91RQ37lYOqJaVFbLHFAD4uCFT/9tc3UVnpA38/OO0b7LBjWwJ/zQxRjn9vq2flf+tbjBjAgvnLWbbUt62mpo6f/6yhLMHlV/6EoqJUtwiJ7P+VUYx7aXL+38e770zgrjv9iLaePcvXIPBX+DtsS7igcW3IphcmkUiw+x6b89qrDckNV191JzNm+GDgIYfuxde/vv9qtrVjbbRRT8IwYPZsH6NzznHhjy/Pb//NJWfTr9/a/vu2+X9pu+0xlPffm5GfoGzmzHlcefldgI8pXHH1TwksgbUwvB9spRs27Zm7zjlH5KL80jiY6vXoUc7v/tDwt8tPL7yK+uyUFj/T1o3+rYb8S4OumJTq7vjnCWY2tJnt5wHlQBa4p3CDc67q2GOPBeDGG29k2bJlKx18+eX+A6+iooKjjz66mdM7nJvDD3/YOPDXr1+f9r6PTjNq1GDmzFnMsmVV+RnIwU9EdO21XVuWoKkDD/RDlJ1z3HPPo7z55ocAbL31Fl02EVFzcoHq11/3gerCwN8FF3y/SyYiasmgQf7f4pw5i+IZyBvu3v7lL78kkUh0VdNWstdeW/LWW5PJZLK89NJbjBnjv/j69++Tn2inO2g6Qdkf/3gjX365EIBjjjmUtT0RUWt69Chl0CAfqM5ms41uUvzpTxfSo0fXBv4KbbfdUL74Yi5VVbV89tk0/vIX34krKkpx5ZU/6+LWNVZY+uPWWx/ko498p37XXbflpJOO7trGxbbZZgTnnuuzXqqra/h//+8qoOUJqk4++WQAxo4dy5tvvrnS9jFjxjBlSn7SirtX2kFEOlKX9m+dyzBn/of8+fe+GWbG5Vf/MF//0/K1TdsSUG3LtsI/hv0f0JYPpsZ/LLrCIdaOPfYZxNuvzSGKHE89/hYvj/V9xiGbbcR5P/oWAf4PfwjJ1WBtaHMzGXD5VwoKnq8cOGjcbW5puH9DECgIAvbcexivvTIFMH73m5tZvrwKgJN/cBQ777xlK9eoZau68q1NhuR3WHlV335llJWlmDFtMbW1af7f6L/lt11+5Y9anWi39Vq6q1IYbMstrf+edtplCJ98NIe6ujTvvTuRu+7wgb8ePcq45NL2BP6a/72tmYZ/p2bGVw7ckhfH+uH0f732HqZP84Hggw7eI56IqOv/FjOMsrIShm8xgI/Hz8Y5H6zK+dVvz6R//7YG/gp/dy2/t6b/zTRkoq76FUaM3Jgvv1zGsmU1zJo1jysu9zcpwjDk6mt+3m3+vnU49j9gS8a95Ccou+/ep3jzDf/3w5ZbDeOss7/dxs/Sll+h7Zr+N9X42EQiZNfdhvLm676vd/H/u46aGv/37TnnfYetthoen6Vp+N8/d/nJ79qXm5oLpDpco/M6sgXnytWCjjjp5K+z085+JPBH4z/n9tvGkInUv5X1w2oHVM2st5n1yy0F5yotXG9mTf/6z/2FWAo8YWa7xOdLmdk5wO/j/W5xzq1UTPLSSy+lrKyMuXPnctRRR/HZZ/6ubFVVFZdeeik33eSHOf3qV7+id+/mvkQquffef+dnIN9qq80555zvru5l6BRmlp/0pekM5FtttXkXt66x8vIShg3biDff/LTbTETUki233ITZsxfx9NOv8MADTwJ+BvLf/KZrZpNszUEH+eDfH/94E/Pm+WG/Rx/9VQ4+eK8ublljyWSC3XYbwSuvfNIk8PcTevZc1aRwa9fgwX2JIsdrr33ItdfeCXTPwB/A3nv7QPXNNz/A+PG+Q7/LLttwyinHdHHLGstlVI8d+xE//emf8xngo0efxtChm3Rx6xrr2bOMgQN789Zbn/LrXzdkmvztb7/KDy3qDi655Px8SY977nmM119/FWh+0omTTz6Z7bbbDuccxx57LM8//zzgRwuMGTOGM844A4DDDz8c59zza6P9Iuu6dbV/W5f9nD9ccjvLlvnA3/dPPoyddhmJmZ9xOZfp6ZdEQYC1YSKphklKcoGyxtk0K9fdbC4gl3u+8h/oqaIEI7bqzXtvzeXXP7+94b3/+TRyGbqF5/HBmrAgGJBra+Hjxu1sflhlYaCoYdhyYXubxnKGDu/LsqU1vPzSh9xxux/qW1FRyu9+f3ZcjqC9AZU1D/y1NPR/n/0257VXp/DXa+9j2lQf+DvgwF35xjc7ekRb2zIWWxrSGoYBe+w1jNde+YKfXnhNPoB88a9Pzw8JXvVrN3mdTorB9etfQWlpirfe/IzLL/M17MMw5Kprfhr/99R97LrrcD4aP4t7//Ukr7/mS7GN2nIo55zbWimrloLpK/93n9Psv782XYjcf2PGgQduw9gXPuaXv/gL1dX+79tzzz2BrbfevK0n61S5uqllZUUMHz6At9/6nIt/0dBnvOrqizrp79vVz2IcMWoj5s5ZynPPvMUD9/uszX79evHrX5/d7PD+3H83EVGTM7Ve7qUwG7Xw30LusQ+mNn+eMExw1TU/yT//3W9uYvHSSTjX/ETMbe3fAk91Tf9WGarSYE1S3d4HmivkNzpecu4CTsk9cc4tM7MjgWeArYF3zKwSKAZyn1DPAhc296LDhg3jwQcf5Nvf/jbjxo1j5MiR9OzZkxUrVuRr+Z1yyimMHj26ucOpqprXKPB37bW/6HaBP4ABA3rxxRdTus0M5K3ZY4+RfOtbP2HuXB/46+oZyFviayluw377NRQ8/+MfL+yyGchbU15eQjLpuOaaO4GunYF8VbbaalOuueZuPvzQZ//utNPW/OAH3Svwl3PQQdtz4IEn5wN/P/3pqQwfvmkXt2plyWSCESMGcsQRDb/zv/714m4V+MsZPLgvt976CI89NhaAQYMG8POfn7GKo7rGPvtsxde/fj6LFi0F4HvfO5K99tqpaxvVRJ8+vfj973/IeeddCsCPfvRH3njjAIKgabDBD6F79NFHOfDAA5k2bRpf/epXKS0tJYoiamv9Hyo77bQT99xzz0rHikiL1rn+rXOO9z94kztvfwLwgb/fXnp6453ykScDXMGPgoBJvm6p/67xfxJHTYaAFv4RvnIwtfVB0I6tt+/POafcxNQpfqjvvl/Znm9+a798dprD5SeYNmu9FE+u3mrTFjR6bs1PTrVSdmoz9j9gCw4+4Nx84O+XvzotPwO5P69rMSCw0iuu4m/nVWan5ndcubnFJUn69k1x7tl3ALmJiC5sU8afrzfbeIhu0z3atr5tw9+Hb96fm274Tz7wN2LkEM45b3Umr+yozNRmzhv/3Hf/URzxtR/mA39nnX0c2267phMRdbwwDNh+h0342iG/ya+78uqf5mdXb6xpFmrrF7Gjixr0H9CTqVOnce89/rOqb99e/OaS3HDw+EOpm9hlt2F857iLmT17PgBHfH1/Dv3a3vntq/5vZ1WafjblzlN4U6rpuVe+RmbG/geM5ID9T8uvu+R359K7d9O/bxtuQBR+3rTld5yrjbr6HPvsuyPf/s4hjHngORYuXMqffn8L11y7AyEr3yBsa//2/fffP3ENGiXSIazNX+BNDzSbRvMdzqbucs6d0szxGwE/B44ENgVq8UX57wL+4ZxretskxwF88cUXXH755Tz33HPMmTOHiooKdt55Z8466yxyw6aa85vf/Jjf/97faSouTnWbGp9NOedYuHAp9fV+IpqePSsoLy/t4lY1L5PJMn/+ovyH80Yb9e1Ww9ILVVXVsHTpcsAHrfr379Nthpg0tXjx0vywjfLy0m6X8ZkTRRHz5y/O/8HXr19vioq6dtK0ltTW1uWDaUEQsNFGfbtlkBJg6dJKqqp8/a6SkqJuMRFZc/xn1RLq69MA9O7dg9LSlYN/3UE6ncnXeDUzNtqob7epnVvIOcf8+YvJZHzgf+jQTZk6dUaL+1dWVnLVVVfxyCOPMHXqVIIgYOTIkXz3u9/lggsuyE1iuNY+6MxsyrBhmw6bMmVch5xv+PD9mDp15lTnXPepzSLrrXWxfxtFEQcctCvjXnofgIoepVRUdGSf0TXzqHmth1MhykYsmL8sP2lS/wG9Wgj8rKk1/8irqaljyWLfZwzDkI026t2oz9h9Qj+wdEllPvBXVlZMr15t7zOuyfto71WOIseCBUvz3299+/ZotSxBV2Zx1denWbDAT/jU0Gfonn3G5ctX5OvRFhWl6NevV9c2qAXOORYtWkpdne8z9uxZ3s3/vl2c//t2wIA+nfRZteb837eVgA9GDhjQu5v9feuvYcM19Wu32WZLPv54YotHtaF/u1bfpO/fbjLssy9e6JDzjdj8IKZOnaX+7TputQOqXWiNGnz66Sdz++0qtSEiIuuGiooKli9fvqanUUBVpHtb7f7tkiVLOOywg3jrrQ86sDkiIiKdp0+fPixatGhNTtFFAdWxHXK+EZsfqIDqeqB73mbpRLfddhevvPIa8+Z92W3viOU456iuriGRSHTbjL+cKIqorKyioqKs22b85aTTGWpr66ioKOvqprTKOZefOKukpLiLW9M65xyVlVWUlpaQSHS/jL9CmUyWqqoaevQo62Z3b1dWV1dPJpOhrKz7f1ZVVdWQSiVJpbpfCZVC2WzEihVV9OhR3u1//5WVKyguLmXTTYd0dVNEpBvr3bs3r732NptvPpTKFSsoLu7Mz+E1S8RwgIscK1bUUFZW0kkZfx332Z5JZ6ipqaOiorTF74zukppSW1uHixylpe3vM65J5nG7X8v5339xcaqNGX9d912dzWZZsWJd6TOmSafT68jft7UkEuE68vdtNRUVpfr7do3FRWQiR2VlNeXlFQwe3L3mWxBZHRtcQBXg008/w9eAimj+S7q50Vjt+RBtenxzhYebKYKUX99cvZQ1Ob65trd0/Np472t6/Lry3tf0+LX53rvbtWvPvxv9N7Pq4/Xvpvk2dfd/N2syi6yIbEjCMMG0abNwLhMPpyysx1f4WdXwvOkotVUHjAo/zxxNB7k1HN/a56Y1+9pte/2CM7XY9ubasHJtQn98w3N/fMv7N30/rV+71l7bz3rdVONar+07vnHbm29v+9pe+Hpr8t474vgmbW8m9NueOp9Nj1+5lm7rNSvX7vGtH7vK483ip64Nr92cVl7fOvK9t/xvffWOX3ld69euSdtde9rezPHNrGv/8Y2163hrx3svfK/WsLXZ411zr92cFt577niLJxvs5jcoWraqWuHtO5es+zbIgKqXm9m0OWuaYde9M/Ra19XvvSuvXVe3Xf9u1k1d/d517dZN63LbRaS7Mkt02sznIrKO0WdB+21I16w973VDui6rpIshDbp37rqIiIiIiIiIiIhIN7IBZ6iKiIiIiIiIiIis2rpbrkA6gwKqIiIiIiIiIiIirVJAVRpoyL+IiIiIiIiIiIhIGylDVURERDrUjBlzGD58vw47l4iIiIhIV5oxYw6bD9+/w84l6751MaCqHGsREZHua2Y2m2Xq1Jkdes6OPJlIN6T+rYiISPel/q2sxJxzXd0GERERERERERERkXWCaqiKiIiIiIiIiIiItNF6G1A1swozu8TMPjKzFWa2zMzeNrOfmlmqq9u3LjGzvmb2AzP7l5lNMLMqM6szs1lm9h8z+1YbzrGRmV1tZpPMrMbMFpvZODM73cw0zC1mZv/PzFxuWcW+uqatMLMeZvZzM3vNzBYU/JsdG3829GrhOF3XZpjZIWb2oJlNN7Pa+NpMMbN7zOwrqzhW17QJMys1s8PN7Fdm9kh8XXP/7V/SxnOs0XU1s83N7GYzmxr/Tueb2TNmduwav0ER6XDq23Ys9W/XHvVvO476tx1L/duOpf6tbHCcc+vdAmwGTAVcvFQBtQXP3wN6d3U715UFSBdcOwfUACuarHsSKG3h+F2AhQX7VjY55zNAUVe/z65egFHxtc1f11b21TVt/VoeCMwruB5pYEmTf7M76rq26VoacFMznwHVTdZd08LxuqbNX5cDmly/wuWSNhy/RtcVOCL+bsztvwzIFjz/B3FZIC1atHT9or5tp1xT9W/XznVW/7bjrqX6tx13LdW/7Zzrqv6tlg1qWe8yVM0sBB4DhgJzgUOcc2VAKXAC/j/KnYB7uqqN66AE8BZwLrC5c67EOVcODANuj/c5HLi56YFm1hN4HOgLfArs5pyrAMqA8/EfkIcC13b2m+jOzCzAX8ti4PVV7Ktr2goz2wd4AtgI+B+wL/6Ltzf+c2BX4I/4L9jC43Rdm3cKcFb8+CFgZPwZUApsCfw33nZh02weXdNVWgI8D1wJfBf/R9Iqrel1NbNhwIP4/x5eBUY553oCPYFL491+AIxerXclIh1KfdtOo/5tJ1P/tuOof9vhTkH9286i/q1sOLo6otvRC3AaDXcg9mpm+3cLth/c1e1dFxbgwFVsL7y7t2mTbb+P11cDw5o59hfx9gz+i6zL328XXeMfxdfhX8AluevZwr66pi1fx1Lgi/j9PwQE7ThW17X56zI2ft+fAYlmticLrvl9uqZtvq5hM+um0YY7+Gt6XYF/xtvnAr2a2X4zDXf1lfGmRUsXL+rbdtp1Vf+286+x+rcdcx3Vv+34a6r+bedcV/VvtWxQy3qXoQqcHP8c65xr7k7o/fghUwAnrZ0mrducc2NXscvtBY93bbItd43vd85NZWV/xw+vCoETV6+F67b4btofgUXAhW04RNe0Zf8HDMcP2TnbORe141hd1+ZtHP/80DmXabrROZcGPoifljfZrGvaAudcdg0OX+3ramZlQK6G1I3OuaXNHH9Z/LMHcPQatFNEOob6tp1A/dvOpf5th1L/tuOpf9sJ1L+VDc16FVA1s1Jgn/jpU83t45xzwNPx00PXRrs2ALUFj8PcAzMbBQyJn7b0+1gBjIufbqi/j1vxQxl+4pxb0NqOuqarlPsi/q9zbmFbD9J1bdWU+OcOZpZoutHMksCO8dN3CtbrmnaCDriu+wIlqzh+GjCxheNFZC1S37ZLqX+7ZtS/7Tjq33Y89W+7EfVvZV21XgVUga1oeE8ft7JfbttAM+vTuU3aIBxQ8PijgsfbFjxuy+9j645q0LrCzM4ADgb+55y7uw2H6Jq2wMyKaMggecnMhpvZ7fHMp3VmNs/M/mtmhzdzuK5ry26Mf24B3GdmW+Q2xJ2fB/FZE1/QuKaRrmnnWNPrWnj8J204fps2tktEOof6tl3ngILH6t+2g/q3HUf9206j/m33ov6trJPWt4DqoILHs1vZr3DboBb3klUys174eiYA45xzkwo2t/f30cPMmg6pWG+Z2WB8se4aGoqir4quacuGAqn48SbAeOBUoD++Fs9GwDeAJ83sxibH6rq2wDn3GH6oXj1wHPCZmVWbWTW+YPwB+E7p7s655QWH6pp2jjW9rrnjlzjnqttwvL4jRbqW+rZdQP3b1af+bYcbivq3HU79225H/VtZJ61vAdWKgset/YdUuK2ixb2kVfHMnf/E16CpAy5osot+H627GT/r4CXOuSmr2jmma9qy3gWPf4GfCfK7QLnzM6AOwdeZAzjbzH5UsL+uayucc38BjgHmx6tKaBhWU4S/Fj2bHKZr2jnW9LpWNLO9teP1OxHpWvosXcvUv11j6t92LPVvO4n6t92K+reyTlrfAqqydv0VODJ+fK5z7sOubMy6xMy+D3wdX+z8mq5tzXojaPL4bOfc/XFReZxzM/EFzN+P9/lVczWTpDEzKzWzB4DHgRn4mkP98JkRh+KH1XwfeMvMtu+yhoqIiHQM9W9Xk/q3nUL9206g/q2IdIT1LaBaWfC4tJX9CrdVtriXtMjMrgLOj59e6Jz7RzO76ffRDDMbAPwFyAJnNDezZCt0TVtW+D5nOuceaLpDPCvq1fHTfsAuzRyr69rYlcDxwGRgf+fcc865Rc65hc6554D94239gOsLjtM17Rxrel0rm9ne2vH6nYh0LX2WrkXq364+9W87jfq3nUP92+5F/VtZJ61vAdU5BY8Ht7Jf4bY5Le4lzTKzK4Cfxk9Hx8MlmtPe38fyePa+9d3lQF/gFuBTMysvXGiok0TB+tw6XdOWFdbb+bSV/SYWPN4s/qnr2gwzqwDOjJ9e55yrabpPvO66+Om+8R9UoGvaWdb0uuaO7x3PHr6q4/UdKdK11LddS9S/XWPq33YO9W87mPq33ZL6t7JOWt8CqhOBKH68bSv75bbNc84t7twmrV/M7EpgdPz0Z865q1rZvXCGvrb8PiasSdvWIcPin+fg7441XX5RsG9u3RXxc13TFsT/Lec6na6VXa3wsPinrmvzRgK5YWNftLLfZwWPc/++dU07x5pe18LjW5vhNHd8azOlikjnU992LVD/tkOof9sJ1L/tFOrfdj/q38o6ab0KqMYzur0aPz2suX3MzICvxU+fXRvtWl/Ew6Auip/+zDl3ZWv7xzOizoiftvT7KAP2i5/q97EKuqarlHu/W8X/rTdnq4LHU0HXtRVRwePNWtzLzzCbUwm6pp2lA67rK/iZl1s7fjMa/jvR70WkC6lv2/nUv+16uqarpP5tx1L/tptR/1bWVetVQDV2V/zzQDPbo5nt3waGx4/vXjtNWvfFnc3cMKiLVtXZLJC7xieY2dBmtp8HlOPrLd2zRo1cRzjnDnDOWUsL8LuCfXPrf1xwCl3Tlt0R/9wU+E7TjfHMvT+Jn84G3ivYrOu6sk9p6Jyc3twkB2YW0jBsagkwqWCzrmnnWO3r6pyrAh6On55jZk1nrwX4efyzEvjPmjZWRNaY+radRP3bjqP+badS/7ZjqX/bPal/K+se59x6teDT98fjhzrMAg6O1wf4DueyeNuTXd3WdWXB10Ry8XJhO4/tCcyNj/0E2CVen8IPCaqLt93Q1e+zuyzAJbnrrWu6WtdvTPz+l+A7ncl4/abAfQX/lk/WdW3T9fxbwTV7Ctgu/jwNgO2BZwq2/0bXtF3Xtjd+soPcMiO+Hlc0WV/ekdcVP2xtRbzPy8CIeH0Z8Bt85obDZ2p1+XXSomVDX1DftrOuq/q3a/d6X4L6t2ty/dS/7djrqf5t511b9W+1bDBLlzegU94UDMUPdch9CFbh70Llnr8H9O7qdq4LCzCk4LplgXmrWC5q5hy7AAsLzrMcqC94/gxQ1NXvtbssrKLDqWu6yutXBrxUcC1qgcUFzx3wO13XNl/PEnxH0zW5prVN1t0LhLqm7bq205pcw5aWOzv6ugJHxN+Nuf2XApmC53cA1tXXSIsWLX5BfduOvp7q3679a35J7tq0so+uacvXRv3bjr2e6t923rWd1uQaqn+rZb1d1sch/zjnpuHvLF2KL1DsgDTwLr5G0p7OuSVd1sB1S9Dk8UarWMqbnsA59y6+OPS1+OLeSfwH3SvAGcDhzrm6znsL6x9d05Y5P+TjQPx1eBl/XcrxQ6DuB/Zxzv22hWN1XZtwfpbTI/BZUP/FZ0fl6nfNxA+vOdI59z3nXLaZ43VNO8GaXlfn3JP478lb8R3fEnyn8zngOOfcD5xzrhPfgoi0g/q2HU79225I17Rl6t92LPVvuyf1b2VdY/r3JCIiIiIiIiIiItI262WGqoiIiIiIiIiIiEhnUEBVREREREREREREpI0UUBURERERERERERFpIwVURURERERERERERNpIAVURERERERERERGRNlJAVURERERERERERKSNFFAVERERERERERERaSMFVEVERERERERERETaSAFVERERERERERERkTZKdHUDRGTdYGYjgW2ATYFywAGVwAzgI+fc1C5snoiIiIhIu6h/KyIiq8ucc13dBhHppsxsB+Bs4BhgwCp2nwmMAW50zn3e2W0TEREREWkv9W9FRKQjKKAqIisxs8HAX4DjVuPwLPAP4OfOuSUd2S4RERERkdWh/q2IiHQkBVRFpBEzOxS4D+jTzOYIWADMBUJgY6AvYM3sOwM4xjn3bic1VURERERkldS/FRGRjqZJqUQkz8yOAR5n5c7me8CZwEbOuYHOuZ2cc9s75/oDmwAXAJ82OWYIMNbM9u7sdouIiIiINEf9WxER6QzKUBURAMxsO+BNoKRgdT0wGrjOORet4vgU8AvgNzS+WTMf2NE5N7djWywiIiIi0jL1b0VEpLMooCoimFkp8A6wVcHqeuBY59zj7TzXacCtNB4m9Txw6Ko6rSIiIiIiHUH9WxER6Uwa8i8iAL+icWcT4IL2djYBnHO3A39ssvpg4NTVbJuIiIiISHupfysiIp1GGaoiGzgzKwdmAr0KVr8EHOhW8wPCzIqAD4FRBasnAVu15ZxmVgbsAGwJ9AZKgVqgMm7r58BnyggQERERkabUvxURkc6W6OoGiEiXO43GnU2AX65uZxPAOVdnZr8D7i1YPQr4BvDflo4zs92A/wd8HShaxcssN7NXgIedc/9Y3baKiIiIyHpH/VsREelUylAV2cCZ2atA4UylE5xz23TAeYuAOTSeUfUe59z3W9j/j/ii/9bc9lZknXO6OSQiIiIigPq3IiLS+VRDVWQDZmYlwK5NVo/piHM75+qAx5qs3q+FdvwS+CUrdzZrgIn42Vnfww+Fqu+I9omIiIjI+kf9WxERWRsUUBXZsO0JpJqse6sDz/92k+dDzGyzwhVmNhD4TZP9Hgf2BSqcc1s75/Z0zu3inBsBlAHbAT8FXgGUZi8iIiIiOerfiohIp9MwApEN25bNrHu3A8/f3Lm2AqYXPD+axvWk7nPOfa+lEzrnMsDH8XKNma3x8C0RERERWW+ofysiIp1OGaoiG7a+TZ5HwPwOPP+8Ztb1afJ8VJPnf2vPCzjnPmlXi0RERERkfab+rYiIdDoFVEU2bE07f8vXZPbTZixtZl3TTm5p0zZ04OuLiIiIyIZF/VsREel0CqiKbNgqmjxf0cHnb+58PZo8n93keYvDoUREREREVkH9WxER6XQKqIps2CqbPC/v4PM3d76md+ifafL8YjO7ycxGdnBbRERERGT9p/6tiIh0OgVURTZsi5s872Fm1oHn79XMukWFT5xzb+JnPS10FjDJzD4ys7+Y2bfNbHAHtktERERE1k/q34qISKdTQFVkw7aoyfMAGNCB5x/YzLqmnVyA7wMvNLN+W+BHwIPALDP7wsxuM7PDzCzRge0UERERkfWD+rciItLpFFAV2bB92sy6XTrw/M2da0LTFc65ZcAhwMnAh62cbzhwGvAUMNnM/q8jGikiIiIi6w31b0VEpNMpoCqyYXsDqG+ybvcOPP9uTZ7PcM7NaG5H51zknLvbObcjsBVwPnA/MLOFcw8D7jazOzp4GJeIiIiIrLvUvxURkU5nzrmuboOIdCEzexXYu2DVBOfcNh1w3iJgDtCnYPU9zrnvr8a5NgUOAI4CvgEUNdnlR865v61mU0VERERkPaL+rYiIdDZlqIrImCbPtzazvZvds32OoXFnE3ytqHZzzs10zv3TOXc8/s798012+fnqnFdERERE1kvq34qISKdShqrIBs7MyvHDjnoVrH4ROMit5gdEfPf+Q2BUwepJwFare84m5+8NzAJKC1aPcM59vqbnFhEREZF1m/q3IiLS2ZShKrKBc86tAG5ssvoA4Iw1OO2vaNzZBLiqIzqbAM65JcBHTVb374hzi4iIiMi6Tf1bERHpbMpQFRHMrBR4B18sP6ceOMY590Q7z3UqcBtQWEj/eeBQ51y0pm0teJ0v8LOi5mzpnJvUUecXERERkXWX+rciItKZlKEqIjjnqoHvALUFq1PAw2Z2gZmt8rPCzFJm9mvgVhp3NhcA/9dSZ9PMbjSzY80sbGt7zewEGnc2FwNT2nq8iIiIiKzf1L8VEZHOpICqiADgnPsI+D6QKVhdBPwNeNvMTjOzvk2PM7ONzew8fE2pS2n8uVIJfMs5N7eVl94LeAiYaWY3mNkRZjaguR3NbAszuwr4V5NNtzvn0qt4iyIiIiKyAVH/VkREOouG/ItII2b2NeA+oHczmyNgPjAXCIGB+NpO1sy+M/GdzXdX8XofADs0s2k+sBBYju/4bkLzdaTGA3s652paex0RERER2TCpfysiIh1NAVURWYmZbQL8BTh2NQ7PAncAP3fOLW7Da31A8x3Otnga+K5zbulqHi8iIiIiGwD1b0VEpCMpoCoiLTKzHYBzgGNY9Syjs4AxwI3Ouc/a8RqbAF8HDgP2BpodDlUgAzwH3OCce7ytryMiIiIiov6tiIh0BAVURWSVzMyAUcA2+KFJ5YDD15CaCYx3znVI0XwzGwqMBDYDegHFQDWwBJgUv1ZlR7yWiIiIiGyY1L8VEZE1oYCqiIiIiIiIiIiISBsFq95FREREREREREREREABVREREREREREREZE2U0BVREREREREREREpI0UUBURERERERERERFpIwVURURERERERERERNpIAVURERERERERERGRNlJAVURERERERERERKSNFFAVERERERERERERaSMFVEVERERERERERETaSAFVERERERERERERkTZSQFVERERERERERESkjRRQFREREREREREREWkjBVRFRERERERERERE2kgBVREREREREREREZE2UkBVREREREREREREpI0UUBURERERERERERFpIwVURURERERERERERNpIAVURERERERERERGRNlJAVURERERERERERKSNFFAVERERERERERERaSMFVEVERERERERERETaSAFVERERERERERERkTZSQFVERERERERERESkjRRQFREREREREREREWkjBVRFRERERERERERE2kgBVRHJM7MKM7vEzD4ysxVmtszM3jazn5pZag3OW2pmh5vZr8zsETObbmYuXi5p4zk2MrOrzWySmdWY2WIzG2dmp5uZteH4zc3sZjObama1ZjbfzJ4xs2NX932JiIiIiIiIyIbHnHNd3QYR6QbMbDPgRWBovKoaCIGi+Pn7wMHOuSWrce4DgLEtbP6dc+6SVRy/C/AM0DdetQIoBhLx82eBbzjn6lo4/ghgDFAar1oOlNNwU+kO4DSnD0QRERERERERWQVlqIoIZhYCj+GDqXOBQ5xzZfgA5AlAJbATcM8avMwS4HngSuC7wLw2tq0n8Dg+mPopsJtzrgIoA84H0sChwLUtHD8MeDB+L68Co5xzPYGewKXxbj8ARq/WuxIRERERERGRDYoCqiICcAqwXfz4WOfc/wCcc5Fz7gHgrHjb4WZ28Gqcf5xzro9z7qvOuZ855+4Hms0mbcZFwECgBjjCOfdO3LZ659z1wG/j/c40s5HNHH8pPvg6DzjSOTc5Pn6Fc+63wC3xfhebWe/VeG8iIiIi0k2ppJWIiHQGBVRFBODk+OdY59zrzWy/H5gaPz6pvSd3zmVXt2EFr3e/c25qM9v/ji8BEAInFm4wszIg16G80Tm3tJnjL4t/9gCOXoN2ioiIiEg3Epe0Go+/Ab8tYPhyVrsCVwFvrMEN9d2BJ4HfA98ChrSzbbsAnwA/AUYCGaAC2Be4FXjazIpaOf4I/Hs7Ez/KrA4/outQ4CEz+0dbgrIiIrJ6FFAV2cCZWSmwT/z0qeb2iWuLPh0/PXRttAvAzEbR0DltqW0rgHHx06Zt2xcoWcXx04CJLRwvIiIiIusglbRSSSsRkc6kgKqIbEXDZ8HHreyX2zbQzPp0bpPytm3m9ZuT27Z1K8d/0objt2lju0RERESkezsFlbRSSSsRkU6SWPUuIrKeG1TweHYr+xVuGwQs7pzmNNLetvUws/I4a7Xw+CXOueo2HD+olX1EpA3M7CVg0w4+7Uzn3Fc6+JwiIrJ+a0tJqz8Cw/Alpp5vz8nXQkmrXwLl+JJWuQBre0panUlDSas71qCtIhs89W+lOetiQNV1dQNE1if33HMPJ57oS49+9tlnH7a037PPPsuhh/oR8a+99tpHa/q6m222GdOnT+e3v/3tbynoJBb64x//yMUXXwxAOp1e1tK5brnlFs4880wA5syZU5lbf8YZZ3DrrbcyePDg3rTy2fHLX/6SP/3pT6RSqb6t7SeyDlubNdQ2xWxYUXHPDjlZXe0ycPrPUtZ7+kcu0oGqq6sJgoAoirjiiisOpJn/xpxznHvuudx4440MHDjwJFZjnoCm2tK/nTRpUv7xgw8++AP80PyV2nbEEUfw1FNPseeee/4G+E1u29NPP81hhx0GwFtvvfU74HfNHb/11lszceJETjjhhH8A/1jDtybS3azt+sDq38pK1sWAqoiIiHRjRcU92eXAczrkXO+OvZG6mqUdci4REdkwTJw4kSiKANh2221b3C+3bd68eSxevJg+fTq/qtXHHzdUsVpV25566ikmTJjQ4vHbbNNytaptt92WiRMn8sknrVW9EpG2Uv9WmlINVZENXEVFRf5xdXXLo+ILtxUe05nWtG25x60dW7h9bb0vkQ2CBR2ziIiItNOcOXPyjwcPHtzifoXbCo/pTO1t2/Lly1mxYkV+fe743r17U1pausrj19b7EtkgqH8rBfSbFNnADRrUUDZ09uyWy5QWbis8pjO1t209evSgvLx8peOXLFnSalA1d/zael8iGwIj6JBFRESkvSor8xWgWg06Fm4rPKYzrWnbco9bO7Zw+9p6XyIbAvVvpZB+kyIbuK222oog8B8FhUOImsptGzhw4FoZDgWNh0G1pW1bb711i8e3Ntwpd3xrw6ZEREREREREREABVZENXmlpKfvssw/gi9w3xznHM888A5CfmGptGDVqFEOGDGm1bVVVVYwbN67Ztu27776UlJS0evz06dOZOHFis8eLyGoyMLMOWdb6lAMiIrLOU0krlbQS6XDq30oTCqiKCCeffDIAY8eO5c0331xp+5gxY5gyZQoAJ520xhOgtkvu9e6//36mTZu20vbrr7+eFStWEIYhJ554YqNtZWVlHHvssQDceOONLFu2bKXjL7/8csB3No8++uiObbzIBsoAs6Bjlq5+MyIiss5RSSuVtBLpaOrfSlMKqIoIJ598Mttttx3OOY499lief/55AKIoYsyYMZxxxhkAHH744Rx88MGr9RpLlixh4cKF+SU382p1dXWj9YVF9wEuuugiBg4cSHV1NV//+td59913Aaivr+fGG2/k17/+NQBnnnkmI0eOXOl1L730UsrKypg7dy5HHXUUn332GeAzWy+99FJuuukmAH71q1/Ru3fv1XpvIiIiItJ9qKSVSlqJiHQ2BVRFhEQiwaOPPsrQoUOZPXs2X/3qVykrK6OsrIzjjz+e5cuXs9NOO3HPPfc0e/zcuXN58MEHW71LvtNOO9G/f//8MnPmTACuvPLKRuvPP//8Rsf17NmTxx9/nL59+zJhwgR23XXX/J36c889l/r6eg499FCuvfbaZl932LBhPPjgg5SWljJu3DhGjhxJr1696NmzJ7/97W9xznHKKacwevTo1bx6ItIc66BFRESkvVTSSiWtRDqD+rdSSAFVEQFg6NChjB8/nt/85jdsu+22mBnJZJJddtmFq666ijfeeKPZDM4vZoxnz7135Tvf+Q77HrA7Xy6e3uFt22WXXfjkk0+48MILGTFiBOl0mrKyMvbdd19uvfVWnnrqKYqKilo8/ogjjmD8+PGcccYZDB06lJqaGnr16sUhhxzCQw89xB133OFr2YhIBzF8F6MjFv23KSIi7aeSVippJdKx1L+Vxsw519VtaK91rsEi66uazGJuvuvPXHj6lfl1j7x8PV/f5wekgpIubJmINLHWem1mNqW4tPew3Q++sEPO99bz11JbvWSqc254h5xQpHtS/1akg2UyGXbeeWc++ugjBg8ezF133cXBBx9MFEU8/PDDnH766SxfvpzDDz+cJ598cqXjFy1aRDabZcCAAS2+xpIlS8hms/nnO++8MzNnzmT06NH87Gc/y68vLi5uVAd12bJlbLnllsybN4+tt96au+++m1122YX6+npuv/12fvzjH1NfX88555zDDTfcsNLrTp06le22246qqir2228/br/9dkaMGEFVVRVXX301l1xyCc45Lr/88kbtEFmPrNWIpPq30hwFVEVktTjnmLb8eY7+6nmMf2dyfv2h39qf6/5xOSN67dmFrRORJtZ+QPWrP+2Q8731v6vV4ZQNgfq3Ip1g2rRpHHjggfks0NLSUqIoora2FvAlqZ5//vmVRmE98fxHfOdbB1JVuZjTf3wZf7vsp5QUJ1Y6/9ChQ5k+fdWjs04++WTuvPPORuveffddvva1r7Fo0SLAZ5PW1taSTqcBP1T/0UcfbXEU1pNPPsm3v/3tfMmtnj17smLFinyA95RTTuEf//iHRmHJ+qprAqrq30oBDfkXkdWyrG4GD973ZD6YGoT+4+TZf7/M8+NeZlndwq5snoiIiIhs4FanpNVTL0/leyedQVXlIsBx+99/wwWX/a/D26aSViIi6zZlqIpIu0Uuy/uz/sPhu53Jgi8XA/Dt049izG2PATBq+83517M3scuAg9WRE+ke1nqG6h6HXNQh53vzuat0B182BOrfinQD6XSWvb59Jx88/TOydYvz6zfe6yfcde2POWSPTbuwdSJSoEsyVNW/lULKUBWRdptXPYEbr30gH0zd72t7cMHvTmPzrYcCMGn8Fzx872PMrZ7WdY0UkS5lBB2yiIiIrC3X3vkuUz9+vFEwFWDhx/dy8a2vEEW69yGyIVP/VgrpNyki7VKfreGDSW/yzxv+A0AimeDs35xGGCY4/3dn5ve76bK7eWfmB2SjTBe1VERERESkbeYvruaWe8exZFpugioj1XsoAOnKeXzxwZPc8NiELmufiIh0Lwqoiki7zK56n2suuYP6Ol8w/7gzvsnAzQZRl4Xt9tqefQ/fB4ClC5Zy6zX38v7CiV3ZXBHpEoZZxyxreUSXiIhsoH55zct8OekhXLYegB4jDqH/nmeQ+x5aMv5h/vrQGyyrqu/CVopI11H/VhpTQFVE2qwqvZAXxr7I84+9BkCf/r047vwTqMtCdcZIR8YJPz+NZCoJwFN3/of/ffAOy9PVXdlsEekKFnTMIiIi0sne+WQeT//vZVbMfQOAIFVO7x2PIzVgc8pHHAiAS9cw7937+eU973ZlU0WkK6l/KwX0mxSRNomiiOnL3uWKi2/NrzvlZydRVlFK2hmZCGoz0HPQQA79wTEAZNMZ7vvzLbww+6OuaraICGZ2lJldb2ZvmNksM6uNayNgAAEAAElEQVQ1sxVm9rGZ/cXMNmvhuGlm5laxFK9mm5JmNtrMPjSzKjNbbGYvmNkxrRxTZGZXmNlsM6szs0/M7ORW9i82sy/i91y+Ou0UEVnfRZFj9F/HsXDS/fl1vbc/jqC0ByQC+ux2ApYsAWDFxOf577g3mTB7WVc1V0REugkFVEWkTRbVTeW+Ox9l8ifTABi5/RYceNwhZLOQiaA+a1SmAzLO+OrpJ9Czfx8Axo99g/899wIzKhd2YetFZG3rZkX7fwqcC+wMZIGPgAXAVsCPgAlmdmgrx38MvNrCErW3MXEQ9gXgCmAb4HNgMXAg8LCZ/bmFQ/8JjAbKgUnACOBOMzurhf0vBoYDP3bOrWhvO0VENgT3PP0pn7z7LHVLpwKQ7LkJFVsdAqHhkgFB77702vM78d6OhS/exo/vew/nNEGVyIamm/VvpYvpNykiq5SJ0kye8w5/v+yf+XWn/uYsshZSmzXqskZdBNVZqM0aqZJSvnnhafl9x/z5Jp6YPl4dT5ENiHXQ0kH+AXwVqHDObeac2805NwwYCbwMlAL3mFlZC8df4Jzbt4VldYrpXQ7sC0wFtnHO7eCc2wL4JlAH/NzMjio8wMx2BL4NfAgMdc5tD+wK1AC/N7Owyf4j8cHXp51zD61GG0VE1ntVNWkuv+M1Fk8Yk1/Xd4+TsZIUriiERADJgB67HEmi9yAA6mZ9wvuvPsvD783qqmaLSBfpZv1b6WIKqIrIKs2p/pgbrryXZYsrATjgm19h2I7bUJ+BuizUZqEmE7C83qhJGxkH23/9IDbdbksA5k+ZwVP3PMKbC6Z35dsQkQ2Uc+5u59zzzrm6Juu/AI6Pn/YD9u/stpjZRsDZ8dPTnHOTCtrzKD5rFeCSJofuF//8i3NuSbz/eOAhoD8+27bQ9YADzu+wxouIrGf+eNe7zP7oP2Rr/RD+0k13pXjIDrhEAGEAgeHCAJJF9D749PxxS8bezh+eHE9tOttVTRcRkS6mgKqItKo6s4K3x7/BA7c/AUBRcRHf+9kPAKiPjLrIqI8CKtOQxahzUJOFdBRwxOhz8ud57oa7eezj96nJpLvkfYjI2mOAWdAxSye31Tn3JX64PfhM1c72DSAFfO6cG9vM9pvjnzub2eYF63vEP+c12X9u/LNnboWZnYDPyL0sDhqLiEgTM+ZVct8Tr7P0s6f9iiBB771PhpIEpEJcMsCl4izVhFEycneKN98FgOyyBUx76UH+/PzkLnwHIrI2rUv9W1k7FFAVkVZNWfY+V/7qNrIZfwf+2LOPpc/AAYAPnNZmjfqsUe/810I2gpqsUZUJ2GTbrdjuiK8CULu8kidvuIv/Tp/U/AuJyHrEOqzD2dkDo8xsK6APvhbq+y3sdraZPW5mz5vZPWZ2tplVrOZL7hn/HNfcRufcbHwpgMJ9Ab6MfzbNRM09nwdgZj2Aa4DP8KUFRESkGaNveoP5H9wLUQaAntsdSbLfoDgr1XwgNfQZqi4wCAN6H3YWBP5P6OUvP8BdL3/AnGU1Xfk2RGStWXf6t7J2KKAqIi1aXDuXZ55+njfHvgdA/0H9+dppx+Ecvm5q1lhaZ1RlIYqMbGRkMLLxFC0RsN/Zp5Es8ZNgf/jvx3jyjTeZU1XZRe9IRATMG2BmxwCPxquvcs5NaeGQ7wBfBw4CvgfcCEwxs0NW4+VHxj8/b2WfXFbpqIJ1z+E/Vn9mZvuaWbmZnRy3a3JBJurvgY2B85qWOBAREW/cR3MZN24s1bPeASAs6UXP3b8NydAP90/5ny4VQqqhlmpy8DDK9/gGAC5dx/wnb2X0E5905VsREZEuooCqiDTLuYjPFr7PX397W37dd352Ki5ZwvIMVGWhNmPURlCfNSIgE0E2a0QYYeCIHBT1688uJ33PnzOKeOkv13Pf5xM0QZXI+syPieqgBYBNzWxKS0ubm2X2fTNz+MDkl8DDQAY40Tn382YOeQ04E58FWgb0Bo7CZ7L2Ax41s53beXX6xD8Xt7JPblvv3Arn3HR8sHQgPru1Ergzbv+58fvbETgPeMA591zu2Dh4nC8JICKyIYsix/+7+TUWvvev/Lpee52IlZb6zNRkgAsaaqhi+KzVIMDh6HnQ/xGU+ios1R+O5flXX+OtmUu66N2IyFrT8f1bWccpoCoizZpZ9Rn33P4IM7+YDcCWu27Djod+hdrIqEoHVNYHLEsbGReQiYf51zvIAglzmEFNxkg7Y6fvfJseGw8EYMY77/P6c88wftGXrby6iKz7gg5aOtR84FXgdWAmPrA6EjjRzDZpurNz7nvOuVudc58656qdc0udc48D+wDvAcW0f1h9cfyzvpV9cpmlJU3acwlwLDAGGAvcBuzunHvezAy4CagCfgJgZkfFAecvgaVm9qqZbdnO9oqIrFf+8fQkJr39BOmlMwFI9d+csu0P8lmoQYBLhpDIDfmPJ6ZKhZAKwEFQXEHPQ0/Jn2/xo9dz4TMTiJQsILIB6Jb9W+ki+k2KyErqs/W8N+U9/nH1fQCYGd+9+CzSBKQjqMtCddYHTwHqI5+tms4EOOcIDTJZqEoH1EdGUXGSg350Zv78L/zlJh6f+jGZKOqCdyci66CZzrnhLS1tPYlz7lnn3L7Oub2dc0OAEfgh/0cAb7Q1i9M5VwNcHD89yMx6teO91MY/U63sUxT/XKkwn3PuEefc8c65g5xzZzjnPow3nQnsAfzaOTfHzPYC/o3/qD4L+CWwI/B8O9srIrLeqKpNc9V9r7Pkw4fy6/ocfCZWnIKiEFecG+If4gLDWVxPNTB/C84BBmV7H0VyY//1k541mU9feIx/jp/dNW9KRES6hAKqIrKST5d+xC2X30VVZTUABxx3CIO3HEE2gnQEtVFA5AwwogjqswHVGaM6C0nzJQBqskbGQYDDgFEH7Mdmu+wAwJLZcxn3r/t5aa4mnxZZX5lZhyydKa6ZehzwCTAYOL8dh78W/wyAzdtxXG5caJ9W9slta9MYUjPrD/wJ+AC4Pl79C/yAssOdc7c45y4DLgQGAT9oR3tFRNYbv/nne8x990GiOl/Pv2zU/hRttrWfeCoRQNLXS22YlAq/DiCXCFCSwBIhvb51Xv68Sx+/hT89/wk16Swisv5aF/q3svYooCoijVTWV/Lq26/x9H3PAlBcVsKRF/wgHyBdkTHSkQ+mGhA5f8M+iL8X0pFRm4XaTEA6MhLxp4wzx5Gjz8bimVHH3nYvL0x8nxVpzZkisj4ygg5ZOptzLgs8FT/dtR2HFg7ZT7TjuMnxzy1a2ScXoJ3cyj6FrsTXWz07fj8AewOTnHOFk1/9t2CbiMgGZercSh58+jWWf+r7uJYootcBp/gh/skQihJ+WH9gPkOgKPRLYOCcz04NG2qqFo/cmZLt9wMgqlzCvKfu4rcvtzbfoIis69aV/q2sHfpNikgjHyx6n+t/e0t+0qgjzvkeYe/e1EVGbdbIRD6wGo94IuP8JFRFgaMo8LVUKzMBKzJGMnA454OsATBg5ObseswRANRX1/Dk3//Bo9MndNl7FRGJJeOf7ekXbVvwuD3jPN+If+7b3EYzGwwMa7Jvi8xsP+Ak4Bbn3JsFm3oCy5vsnnveq62NFRFZX/zoptdY+Mad4HymaY/djyHRp78PmAbms1TNcHHAlIRhiaDhmyEwKEn4oCqAQa9vng2h/wpZ/uJDPDDuHWYtX6lai4iIrIcUUBWRvNlVc3jiP0/zyVsfAzBgs8Hs9p1vsbTOqEpDdcbION+JzGWmAoQ4kqEPstZljayz/MSF9c4vzkFtBvY582SKK8oB+ODRZ3jhtdeYtWLZ2n2jItLJrAPv4HfusCgzSwFHxk/fb8ehP4t/TnDOzWrHcf8F0sAIMzuwme1n5drSJLt0JWaWAG4AFuKH+BdaBAw3s7Bg3cj458J2tFdEZJ33zHuzeOu1F6idMx6AsKI/FXsci0sm/BD/VJyJaj64asmAwM9B5QVGUBxggR+lRTxsN9l/MD0PPt7vk0mz8N838uPnJnbFWxSRTrfu9G9l7VBAVUQAiKKId+a8x+1/uC2/7oifnEENKWqzAbXZgExkRJGRzRrZCFwcOA0DhzlHfWRkMBz+Br7fahQFkCGgLhsQ9ujFPqf/X/41Hr/yBv71+YR8RqyIrB+6S40pM9vVzH5vZiOa2TYSeAw/xH4FcGvBtovM7AIz69vkmL5mdjPw7XjVb5s5755mNi1eNinc5pz7Erg5fnq7mY0qOO4oGgK1v2vD27sQnyk72jnXtN7qWKA/cEF87kRBW8e24dwiIuuFTCbil3e8zqK37s6v633wqQQ9yiDhM1EbT8BtWDzK30dUDcKAIBUShuSXIIBkEnp97UTCnv6ronr8q7zxyou8MnPx2n+jItLpukv/VroHBVRFBICPl0zm3hseYOGcBQCM2nsXhu6zFzWZgOqsUZUxajNGOoL6KB7q73y9/qIQ0i43CZUvJhhavOBIBpCNfIbqinTAqKOPptdmQwD48uMJPPvvJ3h9/pdd9+ZFZH1WDvwKmGxm883sXTN7w8ymA5OAQ4HFwDedc4VD9zcB/gYsMLMpZvammY0H5gFn4pP0f+6ce4iVFQObxUtz9VV/BryOH9r/iZl9YGafA48CRcDVzrn/NnNcnpltig+Qvuycu6uZXX4PVAPXmtlkYCbwLWAi8M/Wzi0isj7562MTmPb2o2SWzwOgaJNtKNl2f0gGuCDAhWF+2D+hESR8sCN/rz8wEikjCGi0hHH+f1hSSt+jz8y/3oIHr+PilyYQKVlARGS9poCqiFCdruW1iW/z35vHABCEAQf+6GyW1ieoiSyeaMqozvqfWSwOqgI4Agf1ka+lmgggFXcwzRxFoS8PUBcZaQIiZ9SRYLfzGmZGfevGm3lgwodkcrOnisi6z6xjljX3IfBD4BFgKX4yqF2AMuAVfLB1lHPuhSbH3Y8PqL6JD3LugM9knYLPZN3FOXfF6jTIOVcDHAD8HJiAH4rfD3gJOM45d1EbTvNXIAWc28JrTIhf40VgU6AEuBc4KH59EZH13qLKWm7875ssee/heI3R+7CzsJIkLhFPOhWSD6ZaPOlUYXaqGfkga+6rKRdQTST8z157H0LxsK0AqJ8zlcnPPMzdn7SnGoyIrBO6T/9WuoH2zEorIuupt+d/zD+vvIO6mjoAdj72GxRtOpSqNP4D30HWwBzUYJQ5RwYjEzlKQsjE3wkJA8MRRX44RBj4534iK8M5yMaTWA3eY3eG7LUnM15/g6oFC3nprnt5ZLPhHD98pVG5IrIOMuse92zjofB/j5f2HPcGbZgUqoVjX2QVxbGcc/XAFfGyOq9xTBv2eRtork6riMgG4eJ/vceXb96HS/v7SGU7HEpyyChcGE82Ffp+bm6YfxAYUZSft8oHThON4x9mPls1t84/DtnohPOZfplPGFjw79v5275f5biRG1Oe0p/cIuuL7tK/le5B/xpENnALahbzwisv89qjvqReSc8ebHvyD6hKh9S6wA/xdwbO/IRTkZ9kKhtBOoKarM9OBQjN4Ry+jqpBwhwZF2e1RkZ91ojwx2Yj2O28c7B4vNTH993PQ++8z5K6uq66FCIiIiKynhg/bTGPvfAqKz71AxAsVUrPQ34AxQlIBpAM/RD/pBGERhg2GepvYLnAaW7BsIB8QDWXrRoEUD5qW3rtfQgA2apKZj78Dy57u9W5BUVEZB2mgKrIBsw5x6tzP+Cff7gpv26300+B8grqIh8ErYt83dQIcOb/rzrjs0wdARkX5DueoUHWxaUAnB/qn4kgjZGJA7PprJF2hsNIbLwZo445FoBsfT2v33ATN0z4dG1fBhHpBNZB/xMREWkv5xw/vfttFr12B/HYfXoe9H3C/v38GM04QGoGFmenQkNwNBc0zQ3pz9dNTUDY5C/owqDqxsefRVBUDMDC5//Lo2+8zRfLVqy19y0inUv9WymkgKrIBmzyshk8OeYJpn78GQD9Nx/KZkd8w2eTOiProC7yAdN01ggwMvh6qJnI38HPRuBcw9dC5Pz6IM5OrY/8UP+abBxUjY+rz0LawagT/4+iXr0AmPXSWJ564WU+Wtx0smoRWZcYhlnQMYs6nSIi0k4PvTGDD197lro5EwFI9BlM+QHHxjVTAyxhBCEkEkYYkl8Kg6OJREF2Kv67LWhS/jAIIMgdHxjF/Qcw8Ojv+41Rlhl3/53fvjEZpwmqRNZ56t9KUwqoimyg0tkMr0z7gAevuiO/brfzzydIhPFkU+R/pp0f3p9xkIl8dmk6zkT1GamOwHwWa4RREjrMfHZqhP+ZxYji+qmR81mq2cjIlvZg5Cmn59sw/oa/c/WHkzQzqoiIiIi0W106yx/GvM3i1+7Or+t19LlYSZIgn21qBAWZqZDLYyW/PpGIs1WBwHzgNcgtccA1kcxlsTbUVR145HdI9R8IwPLxb/P22Gd5ftbCtXcBRERkrVBAVWQD9dr8T3no+ntZttBng27xlX3ov/MuRJEPikYRuPjOWURA2kFNxqiPcjfq4+xTfCZr5CCTNUJzpAKfhZqOywZknK+9mnV+ItUI/ziu98+mhx5BRTwZVeWUz3j93//hkSmz1/IVEZGOpCFRIiLSFf786CfMePUhspU+iFk8YleKt9srHygNAssP48/XQQ2Jv3P8904y2cyk3LlMVfMZqYmEEQa+9mouSzUIIUgVMeSkc/PtmX7XdVzz7qeks9m1fCVEpKOpfyuFFFAV2QAtq6vm5Q/e4bm7/g1AmEyyyznnYEDa+Q/4LI2nqE5HPoAa4YfwuwhqsxBFUJ0JqIuMCEdJCEGcnVofB1PNIIjv+2fjLFdfgxWiyMiQYPiZP8y/1tS7b+a2DydSlc6snQsiIp0g6KBFRESkbeYureGOZ95h2Xu+j0sQ0uuY8whCiwOjRjLOKs0N6feBVWsUMA2ChgiqmVFYCiAIIJmKz5MsqLtasE/fPb9Cj212BKBu3mwm/Ptebvt0xtq/ICLSwdS/lQb6TYpsgJ6Z/REPXHkr2XQagG2PP46yjQf7QCk+SErBvbMogtrIqHeOAEdoUBMZWYyqyOJMVL+vw5cHSEd+WH8uCzUR+A+c6ozPaHWOeOi/D7722nZH+u97IADpZUuZdO/tXD/xi7V2TURERERk3XbR/e+zYNxduEw9AOV7fZPkoKGNJp0qDHz62qnxRhcHS5MNdVItfmAF2axh2FB3NSjMYg38hFWJEMIwYNipP/QnBGaNuZv73v+YxbX1a/V6iIhI51FAVWQDM71yEf97fiyfjH0NgJI+fRj53e+Tjvyw/dB8vdMwVxPVQV3W36VPxZ8Y9Q4yzpcFqM34oGhd5AOjNVljeSYg7Sz/ARM53yGNcnVXI79EEb6OKgY4Rp52NkEq5dv5n4cY+/7bzFpRtbYvkYh0BAs6ZhEREWmDN6cs4oWXx1H96csABKU96XnEKQ3D+uOh/g38cH0zHxgNQp9x2nSof2EGahiaz24NG4b++8ms4pqscYTVAijffAQDDzkKgGxNNZPvupkrPvx87V8YEek46t9KAf0mRTYgzjken/YB//nzzfl1O5xxBkFpGWkX10x1+MH4zsUBT0hjQESx+czSFfWBzzDFqHcGzqjNBixPByyvN2ozfqh/gN+fuCZr2jXUTs04IxPXXw0MUiH02HhjRnz7BN/WbJYPb7yBmz6d3AVXSkTWiOWGSK75ojJTIiKyKs45Rt//HovH3ppf1/PwHxCUVwCFQ/tzWxt/ueSDqk0yWHO1UROhkUz6YGoYNBxD/F3VdNh/Lhi72YmnEZaVA/Dl80/y3GuvM2FxZSdfDRHpFOrfShMKqIpsQN5aMJ3H73iQBVOmAdBn1JYM/uph+eH3mTjzNBn4LNUMfqg/QEno66rWZX1QNBtnmho+aFqVNpbUBSyuD1mRDqjL+ACqc4DzWa1mPgM2E09i5eLgbSJwFAURqcCx9Xe/S0m/fgDMffN13hr7LO8vXNQVl0tERERE1gH3vDmdCa88Tv08Xy4qufFwyvY5Mr89F/SE/Cj8fJJYLviZSDZknDYsPus0l43aKHs1sIKyAY1rrOb2SfXqzWYnnOJfyDk+v/Wv/PnDT3E+40BERNZhia5ugIisHXWZDFffchNP/6Xhzv3W51yAI/BD8oG08/VRceSzUx3mg574Yf11URCfzwjNUZyADEZV2h+TTPjgadYZycCRDCzOb/UB1Ld+dAZ1ixc3apu/SdfQsczW1eUf/+8XF3P47f9gzsRPCUy38kTWFab/XkVEZC2oTWf543/fY8nLd+fX9Tn2fBKpkChqyEp15KeZ8uviuQNyCgOu+azTgiAqFARmrfEx4Pu5gRWe07/OoK8fy9xnHqVm1gyWT/yId59+gme22JTDNh3YoddBRDqf+rdSSAFVkQ3Ef6ZP5p2nX8yNwaesooId07UkJ4z3fcJ4OL/FP108HB98Vik4MlFB2DMu3B/4h2Sy/meQ+44xCHC+41nQuXx5/pfULVva5na7bJYls2fz3KzpfG3Toat/AURkLTKswwbBqOMqIiItu/TJCcx44nqiqqUAlOywHyVb7gw0Hr7vXMulC5NJ/9MCCMzXWm0Iplo++BrGwVSjYaIq/zoN31UuH8T1w7QSqQRbnHEBH/12NABf/ONGbtjvKxw0eACpQANGRdYd6t9KYwqoimwA5lVX8cTrbzD19bfy66oqK3n2o08Y+f2TCfFdvkTgs0zrInDOD/kPgVTgh/pX1fuC/i4erl+c9GHS2jSk4wzVhiFPPi81ERjJuEOaDCEcsBHFyaQPvsZh1sJyVvnH9Wnqq6tJp9Okq2t4YsIHfGXjTShO6GNLRERERGDW0mpuuPoyqj96wa8IAvodc+5Kk0o519DHzI22t4IkAMtnnPpEgIaAakPQIzehlXM+sBrl5h0w450fnUb9kiYjsCjMioUgmSJK15NevJBnTv42t417k3O33ryDr4iIiKwtikyIrOecc9wwYSJPX3Oj7/lBPg31iwfuZZNDD6e0/wAiDIscdVmjLmuYOZKBoziE6oxRW+8PiyJ/ijCMJ5iKIJOBbLZhSSQgkXAEQUBdGrLOUZTw2asH33gLCXP5DNdU4DAgMEcy8MFbq09T8fFE/vv0U3zyxDO4KOKVW27nkc0353tbbNdl11JE2k5DokREpLP9+D8fUTNrQv55GATsU/0ZZcsWAg1B1dxIqebmpAryk0vlylCRPza/ryuc0Grlc7694Evq2zgCK4oi6hcvYsy7H3DC8E3oU1zUxncrIl1N/VsppICqyHrupbkLeerx51jw/jsAlAzYiH6778XMx/9Dtq6WT2+/ha1/9mtKQqjJGnX1kEtZLTYfg01nfQ3UwkJTofk6qem0o76+IdDqnA+qBgFEUUMGqpkjEQdNA3w2bMY5EuaDqamC0ROlU6aRGTGEA4afymdjX6a+uoYP/vMkLx13FAcPGsZGpeWdf+FEZA2pwykiIp3nxSmLGPfyC9RP/TC/LpvJ8MSLLzPo9F+Rr5Uas6BwOL6XSECYgDAwgtwoK4MgtJWyXAGSuWH+zhHgJ1o1IOi/EUWJZMGLNWSo5lc5R1BXS/WKFQB8evsN/HnUCK7YffuOvTAi0onUv5UGCqiKrMfSUcTV705k/E3X59dt/N1z2WjXnZn30gukK5czZ+xzbPz1owm32Y66tJFxDsvmhvY76iMjnW3cJcxlp6bTUFfvA6iu4M59Nut/mkFRERQnoSwBRWHD+igu3G+QnwgrCCBRuYIgk4E+PeiBY59Tv8fY627HRRHPXnMjO+ywHedstafuDoqIiIhsoLJRxC9e+JilT9+cX2dFJbi6Girf/B+1B3+L0i22bZRt6grH38frwhASCcsHTA0fTM1tD4K4n2o+IcAsHlkVr7fI95D3/Put+UlWE7nJCYirqDrIAlt8MZEZPfrwzPmnk162jHkvvcBL48bx0RZD2K5Pr067ViIi0jlUBVtkPfaPidP54MH7qf9yNgClI3fARu1POtWDod8/Nb/f5Jv/zvKqiPoMgOVnKa3NQHU9uCjXA407h3Ewtbns1FxdqmzWF/ivKIKS0JcPIN4eOZ/x6ogzXwFngIso+2IabtRQwLdhz+8dQ+/BGwMw7Z0PGPfk03y8ZF7nXTQR6RhBBy0iIiJN3PLBTKY8/wj1c6cDUDxsK/odc2Z++/z7/k5gUcOkVHE9/6Dg5n4y1UwwNaBRZmpucqnQHEHgO7mh5SZs9bVUkwEUh5AyR1Eu0GoOM0cQLz0qlxFZQLr/Row4+Yx8Oyfe+Feu/PBTolxnWkS6N/VvpYB+lSLrqcW19dz19ofMeeguv8KMim+cT329UVPjqNj3KEqHDAOg8rNPmTf2GbJZh3POB0Ud1GWMmjp/bBQ5oshvz2R8QLW+vuH1/HG+8wi+Q1qUgqIEpEKHxSUC/OyIPpCay3vNxIHW5PzFZHuUQ0mRL/IPJIuSfO0nDR3kZ669mf9N+4hMlO3kKygia6TpX6Sru4iIiBRYVpfmulc/YtFjd+TXDT3tR2z8tW9QNNj3bWumfsry158lkcgN3zfC0E84ZYFfF4aWD7AChKERJvxQ/2TCT6YaGKRCX6oqFUDK4pqrMZ+16stXJQK/n1njP7INx6azpzJnyDACgyGHfZ2K4VsAUPnFZ7z36H94fObcTrxiItJh1L+VAgqoiqynLnvvMybdeStRbTUAFXt/ndQmW2AGtTVQVRUw6Hvn5/ef+a+bSVdVE0U+GJrOQjrtO5XZrCOd9pmofhIqRzbrCjJTGx4nEkZxsVFWahQnCvNac1y+IxqYz4bNOMNFEcUzZ1M/bDBZ/B1/53xdkm0P3Ist9tgRgCVzvuTpO+7nlXmfd97FExEREZFu6ZJXPmfuf/5BtqoSgH5f+RoDtt+aZFGSQSdekN9v7gM34+pqfNYpfnh/EBqplFFcDIlGwdSGLNZEmMtCdQTxEP4QR4KV4yC5p7n5AUKDhEEicCTj7NT+C7+kslcfsskkAY4gDBl19g/z55h85y3c+N4n1GQynXK9RESkcyigKrIemrC4kqfHvc7il58CwErKqDjkFJJJSCT9cPyaKrChu9Bj5/0AyCxbzJxH/pkfth9FuSH8RiYDdXWObJZ8dmougOqzVn2HMpUy/xoJSISOMO7AZuKgaWAuvosfD4fC5WtSlc6YTc3gjckGIZnIkYlrUoWBI8D4+kXnYPGYrLG338fTH39AZX3tWr+2ItJGuoMvIiIdbNLiFfxn3BssGftfAIKiEkb84CyKkj5IuvEeu9B7t4a+7fzH/kkY+oCpWTxEP+nnCsB85qqZEQSWn5AqGfr+Zxg4ShOORODizFMX11CNh3LhcLj8qKrcWjM/YVUygFQ2Q5/5c1mw8WDCOIM1wNF3ux3pv88BAKSXLuGTf93BdROmru3LKSLtpf6tFFBAVWQ945zj0rcmMeX2v+ULmvb86v8RFvXEZR0ucmSzES6KqKvO0uOIM7F4VtL5Tz5IzdzZcUZqQ2exrs7l60ul6312qs9Q9ec3gzBhpFLxbKkhpBL+A8YP6W+oyxrig6xJ/F17gLCujtTSpWQG9icTGekooD4y3+k0qMfovfkwdj3uSN+Gmlqe/Nvt/Gf6xLV2XUVERESk6zjn+MVLn7JgzHXg/J39TY/7PkX9+2MGxSmoKIERp56T79t++cSD1C+YQyJOKggTkEyaz0INfTZqKgXJhCMR+sXimqmJOAEgiJdcDMSIyyDmthNvixMMQnzgNjQYOHsGiwdvCkFAaI4wPkEEDD/tXCyZAmDWow/x6NvvMruqZm1eUhERWQMKqIqsZx6fMp83n3ic6s8+AiAxYBMqdjkSq8/iajJka7JQH0E6gqoMQaIfFfscQ5AqYsuvHoVV9CWddvnh/dmMI5P2d/LTaUddPUTZOIs1G9dKLYLi4tzdf1+fKhdMzdIwCRXk1jtKEo7iOIO159RprBg+lChXXzXOkg1wZLKQzhqZrLH/GSdT3KMCgI+eeI7nX3mV6SuWru1LLCJtYR20iIiIAE9PW8Dbzz9D1YT3ACjqP5Ahx36HwHywtTjhKElC/2GDGHL08QC4TJrZ91zvM1Lx2am54f2ppPOB1MDlJ5ryj12+ZmrTRLKUxRNUFQZSib+uDHwv10vW1lBUU01V374+y9WAOJkgclC60cZsdux343Zm+PTW67hi/KTOunwi0hHUv5UCCqiKrEfS2Yg/vfoxs+65Ib+u92FnYxkDB1FlGmozkI3iIqkZqI/osce3GfTzO9h7vwOw6izLlvoM1XS9o6YGiop9cLW+HnBx3qrzwdQwLuqfSAD44VJh4AOp2cgvofnXz0Y+sJqKh0KF5iipXAYY6R4VZF1DSYBcZ7Q6E/jzOAh79GDfM07Kv7dnr76eByZ/gnMOEelGDFwHLep0iohIOhvxh3ETmD+moY+7+ennkSwuyk8IlQzID9Pf4aTvUdSnDwBL3nmD+vmzCQJIJswP+084SpNQknKUJv2N/qLAkTIfSE2ag9wQ/3iYf0DuuZ+Eyo+0cgSBfxzG8wTkaqn2mzqVhcOG+7JY1hBHyY3aCnAM/873KOrbH4CFb77KKy88zxvzF6/16ysibaD+rTShgKrIeuRv709j8ph/klmyAIDiLXejZMhOPoqZzWL1EVSnsUyEZR3Ete+D4lKsrB/vZgazzaIppOsi6moiamp9R9HMyGQcLmoY0oSDZMr8Xf7cjKYB+dIAuazUgIZhUQ7A4tqqceez99TpLBk+lHRkZOK4qB9C5ajNQn1kpCOjPs5U3f5bR9F32GYAzP3kU1589HHemD9nLV1hEREREVnbbhg/nSlP3E993Ofrsc1ODNz3K/nMz4Q5kvFEUKmEY1DvYvY463Q22X0PTvzVr+kzZBAVxbka/45U4PunReZv9CcCf3wiaKiZGhDXQo2DrKl4fSJXAoCGIf/QsA6gZOkSsqkU6dKSfMA1MBd3oc23OYSwuISRp56Vf5+Tbv4713z4KdlIyQIiIt2dAqoi64mF1XXcPu5dljxzn18RhPTZ7wdY1mFR5If5RxHUZqEuC5mo4WDnsPosS6qSZOqhf81S6pZmyNRFJBI+mAqA+dpTFjTMhJoLoBrma2wXtCnCZ6vm7uYbvlOajQAHibkLqOvdiyiVwgGRMyIgYRFFIVRnQuojo94Z9S7wHdBEggN+fG7+NV6+/lb+/el40lG2066tiKwGDYkSEZEOsLimnrve+JD5j97tV1jAyLN+SCIwQvOZqcWhI2GOskREaRz43OaIQzn4isvov/FG9K9dSkkiIpELnoaOMIwoSvhSAaUJR1Gc5ZqIM1IT5jNVQ/y5Q/P1UXNlAcL88H+Xr7MaGOAi+k6fzuKhm/mMVYvnG4i/0wLzQd2E+azXQQd+lZ5bbgNA1czpjH/kQe6bMrNLrrWIrIL6t1JAAVWR9cRv35zMzPtuwqXrASjf/SiSPQf7WqlZsEwEGBYYVpPB6rNYPMSeLFCdhfos768YwI5VU3FV/jzpjCOT8RNQBXEANZm0eKi/r0UFEAQO53JD9X1HNiR+novHxl8gWWdk6rMUz57H8k0H+85t3FEFP1SqMm1kIsg4Hwf253ZEwJDddmX4vnsDULVwEePu/BePTf+s8y+yiLSdZkEVEZEOcMlbk5n5wC1EtX7CpoFfO4qeW2xOEAc6ExaRsohkEFEcQknC39BPhlCWiHCjNmXonKn0SEb0LnKUJXygtCTOQs1lpoaBI0FEGGeUJuIyVkE8SWpuoqnQ/BD+VEg+oBvSUB6g97w5rBjQH5KJ/B/bhi9fBT7A6gOz/hxBYGx5zg/z7/eLf93Ovz74iOV16bV6nUWkDdS/lQIKqIqsBz5YsIxnXniRFW+/AEBQ2pPeOx7nP6wzWSyTxdLZhilK6+OM1ayPgFo2wuoyWH1EfTpgTnU5W6TnY0BtraOu1gc0w0TDsH5fP7WhDdk4AdacwxeYaagTZbkZquLnWQfF02exYtNNIAjyzcoNlUpnjRVpn5GadYaLb+VFzpcAiID9LjiLwBdu5e17x/DC+HdYVle71q65iIiIiHSuTxYu53+vvsbCl54CIFFWzlannEoicKTCiKRFlCd9PdQeST8SqnA4fknCUVySJOjfk40WzaMsEVEcZCkOHckwyg/vD+NyU0WhLwNQEhKXE2jIQjXz9VLDOMM0oCBLNZ5oNUzXU75gIdWDNvblAAriJhnnn+Qmv0rGwVkDeo7aikFfPczvt2IFE+++nb9OULKAiEh3poCqyDouco7fvjaBWXf/Pb+u5/4nEiRLoT6LxRNQudzYgiiOfNZnIRsP/c9GWF08ZD4TMbG2L1skF2N1aaLqLFEmIoorBASBFQRTjSgygsBwrmF9NusTYyN8LdVMRH44VDYyqKkluaKamr5+soBE3LRchurSdIDDn8PwWao+QzXIlW+l16aD2e2EYwDI1qd57q+38MCUTzrvQotIO3TQ3ftcWruIiGxwnHNc/OokZtz1t/y6Ef/3A3r27UlZwlEcQnnSURxEJM3lJ6UK4x5jwhzFoaMo4SgdsRE9v5zHwFQdfYoieqciysJ4sijzfxQXh45UXA7ATzIVD8/PfSWRG9bv8pmpydD3Y5Nx8HXAjBlUDh2C5Ute+Z5rfdb3icF8b9aAeFuEEQAjf3AmYUkJADOe+C8vvPMWU5avWMtXXURapv6tNKaAqsg67oFJs3nn0f9QN8PfxU4OHE7FFgf6rNTqDGTBRfjxT5lclBP/GR7hU0urM1jkfPA1gmwi5KNgMNvXzoDqTH4yqlzANAwhlfKv7+Ih+eD7hdkMZJ0j64xsnASbjhqCpRn8rKcLhg0jwnxfEv9hZLi4iUYmMmoj8xmtUUMpAZ9x4CgKHXufeiKlvXsBMHnsOMaOfZEvli/p7EsuIm1hHbSIiMgG6clp8/ng2ceomuxvmJduMoRRR3+T4sBRFkaUhll6JCNKk75fGFpDear80Py4vmpxMqB41CBKp86iVyqiIpmlOIwIQx/UDM3XUE3Fw/sTARSFPmibCl0+YBpAXGs1fj18vdUAKKquIqyvp65PL6DhKyyKIBM/S5gjEfpMWofviicC3+6Sfv3Y/IT/yx80/vq/cc3Hk9beBReRVVP/VgoooCqyDltRn+HqVz7myzG35tf12f9ULAp85NI535ssCiHth/1bOuuDp4nAB11r0wSV9fH4fHwxp55FzA570TNTTY+wDgscUZzQ6vL1UH2wM7eAD576ck+WH9qfxd+5T8d1UEuWLiObTFJfWkpuGH/W+WCqATWR/1iqd5afpMoPo/Id12QQxbWuoKisjH3PPS3/3l/4yw08MmU8US7CKyIiIiLrnPpslstf/5g599+UX7fjOedTXhxSkfLD/MtTPpCaoGFIfr5QVG7ip8CRNEdR4CjZuBe2ooa+UTUbFUdUJCPKwoiSMKI49MP5zRxJfJZraA31TkP844Q5UkG8PrfEAdbeU6exdPNhQMNEVOAnXQ0gXwIgIJeQ0BBZMfz72OKY4ygZuDEAiz98n1eefoaX5i1YW5ddRETaQQFVkVYsWrSIO+64g+9///tsvfXWlJWVUVRUxCabbMLRRx/Nv//971We48svv+SnP/0po0aNoqSkhD59+rDffvtx22234XKRyNX0pze+YOpDd5Gt9FmZJVvtS0n/rfwEVFHkxxYFvrdnNWmozfraqWF8WzwLVGcgYTiDqCSBK4pvzUfwbmYwuzATgoB0GurrHZm0n3wqk2loR1DwSeIKbrk5fCmAbARVGaM+C/1mTGPp0M1IhblkWcPwHc9MHAfNFsRDM5HvZAaBzw4oDX1mAECEY8cjv8bGW40AYP5nUxj74L95Y/6az4z63HPPcfzxx7PZZptRXFxMSUkJw4cP58QTT+Sll15q9djO/J2LrDMKpzxek0VERDY4N348jSn//ifpxT6Y2H/3vRi6166kLPIZouYoDhyp0Gd8hgUTSIXmh+73TDlKQkdZ0n+dOKDHdptS/8lMylOOHsmInsmI0tBnnwJgfhi/z1T1gdpk4EiFPj+hJM6ETeQzVv1SunAxUVkp2ZIiLC5QFcWjuLKYLw8QZ846B2nX8P0WxuUKkiGQKmLUGeflt02+9Tr++v4nZJQsINI9qH8rBRRQFWnFwIEDOfXUU7nnnnuYOHEiURSRTCaZPXs2//3vfznmmGM44ogjqK6ubvb4d999l2222YZrrrmGyZMnk0gkqKys5JVXXuGMM87gsMMOo66ubrXaNnVpNWNeeZfF/3vIr0gk6bPTd33A1IGlHRSHvtdWm4FMPGGUQW4cvdVnoD7CpRI4MyhL4kpTPhCbzrK8qJyqoIiN00sAn6UaOchkHNms7ywGgSMMG+7CJxL+9LkJAQDqXUCE0evLuVT27YdLJiHObM1lngLUOf+R5CDfWTUzAoPiuBNqPtYbH2skQuPQn5ybvy4v3nAHj306ntrCiG87OOc4++yzOfTQQxkzZgwzZszAzDAzpk6dyr333ssBBxzAT37yk2aP78zfuci6RWOiRES6o+6eMLCgupZ/vfE+cx+9DwALQ/Y6/2zKkz44mgwcZSkfTE0ZpMxnoULcrwwcpYmI4iDygVHIT35a0quYZGmS7IJl9Ct2DC7L0r84ojThKAkjUnEQNZ99GvdngzhrNT/hFb4sQCqABI7SGbOoGbYJxYEfzh/F319Zt3LsJFNweYK4XEBgjijr66n233s/+uywMwA18+by4QP3c9dn09fomoKSBUQ6hvq30kABVZFWZDIZdt99d2644Qa++OILampqWLFiBVOnTuW00/xQ86eeeoqzzjprpWOXLVvGkUceyaJFi9hyyy15++23qayspKqqiuuuu45kMsmzzz7LhRdeuFpt+8VLk5j7wPW+aCnQY5ejSZYN8Buzkf+vOwwgnSWoSWOZLC53RyxyPls17aBHyhc/NWvIaM04H9U044PkELavngGRyw/vr6uLg6YBJBJGGDTuSIXxJ4sjHtLkwNIZei+cz+KBg0hHRl1kZJ1hcWc17YinnIprVxEPq7IoX40gcr5pvkyAD7SaweAdt2XrQw8AoGbZcsbeegdPzFq9mlN33nknN998MwDHHXcckydPpqamhurqaj799FO++c1vAnDttdeu9AdHZ//ORURERNZUd04YAPjDe5OZeveNRPX1AIw65lgGDduElPm+YdJ84DMZNGSPJoK4Pqn5YGrPlKM0AcUJh4vrnJaGvkRA/+0GsXzCHFIW0Tvl6JPKUpHwmaqpMPKZruZv+Fs8pD8w8pmwiXydVf/6ZTPnUDd4IywRNkxchYv7wA1D+g2Hc454Glgsrtuay15tWG+MOPOC/BCwaQ/czV1vjWdZXf1qXU8lC4iIdA4FVEVa8cILL/Dmm29yzjnnMHz48Pz6oUOHctttt+UDqf/617+YObPxMPOrrrqKefPmUVJSwpNPPsmuu+4KQCqV4rzzzuN3v/sdALfccguTJ09uV7temrGYl5//H1XjXwMgrOhLr22O8hudwzLO10K1ODha53BBfIs+IA6WggvxPbjcsKMsPhgbRVCcAKCegClhP0bVzyUIIF3vyMa31oN4NtXAGu6yRVHDsKpcYDUw2GzOVGYN2oyaKKAuG8+P5RoCpenI8nfzA4Nk6ChNOIoS/u5/NoL6yHxh/3iyqtxwKoCDLzidZHERAO88+CjjP36dxXXtnxn17rvvBmCLLbbgvvvuY8SIEflto0aNYsyYMfl/Cw8++GCjYzvzdy6yTumom/e6iS8i0uG6c8LAewuW8sJLL7PotRcASPXsxd6nn0hpAooSjuIwoiLlJ5pKxcPkk7lAauioSEB5XB6qIbvUZ7UWhT5jtbQkpNdmvan8Yj4VScfAUsegsoj+JRGlIYT4UVK58gHga6vGxawA4vqskMjUkVy8hPqN+8cTuPriV0nwI8aM/GRZWEN2quFIWMOIrkwWP1rM+UBsydAt2Phr3wAgqq1h0p038acPPluta6pkAZEOov6tNKGAqkgrDjzwwFa35zqdAO+8806jbbnA3AknnMCwYcNWOvaCCy6gvLycbDbLPffc0+Y2Rc7xi+c/YeGY6/Preu3zfYJkiX/i8DVREwaZLJaOcLi4GFTgs1QjfA+uNAk1WUgalgh8NLQ67W+94ws/WdrxWbARm9UvJOXSpNNxxYCsI4rLsUY0DPmH+Dlx7VMcpbUrKKqvY2mPPnF2qi8BkI3buzQd+HVxLDeAeMhVwwyoEX6Cqkzkj8tlDfjXdQzetD+HnvZtALKZLP++4mZenftRm69rzty5cwHYYYcdSCQSK21PJpPsuOOOAKxY0Thg21m/c5F1UtBBSwcws6PM7Hoze8PMZplZrZmtMLOPzewvZrZZK8cmzWy0mX1oZlVmttjMXjCzY9awTe0+r5kVmdkVZjbbzOrM7BMzO7mV/YvN7Iv4PZevSXtFZP3RXRMGIuf44zsTmXr73/Lrdj3jVHpUlGMGxYGjOIwnhIqH24f4G/xFgaM4EVGWjChL+nIAycCRNCgJfSZqeQKKQkdRCBuP6k/NrMVYJkNxCD2SvqZqaehrswZxkDaI+6NhAMT9ziBw+cmlUpNnkh05JJ+VmpMb7BVPZeDfX5Sba8AVlLbyj9PxxFWFcZbNTjyNsMx/dM9/4WmeHPsKE5dUtuuagpIFRDpUN+rfStfTr1JkDRQXF+cfZ7PZ/ONJkyYxY8YMAA4//PBmjy0vL2e//fYD4Nlnn23za9724Uw+e/oh0vOmA5AaPIryYfv6np750fsu5cckWVXaF28qSUAqxCUDH53MiZzv5SXiY7MOy8brajKQ9kOTXFHIJxWbsc2yab4jGeZmJ4X6bNxBdHET4vH5YVz/1AIYOmsKs4ds7ju9xLWkHATOsTxtLK/3L5d1Fnc+/V3/QgaEGLVZP8lVYQC3OPRZCUecdhx9Nu4PwEfj3mHc/55jRuWXbb62QL5D+eGHH5Jppg5rOp3mgw8+AMh3KqFzf+cissZ+CpwL7IzPxf8IWABsBfwImGBmhzY9yMyKgReAK4BtgM+BxcCBwMNm9ufVacwanPefwGigHJgEjADuNLOV08i8i4HhwI+dc+1P2ReR9VJ3TBgAePCL2Xz4+L+pmuozMXttvgXbH3WYH3JPRGnSUZLIjY5yJENfL7Uk9BWseiahd8r3CX32qqMkEVGW8gOvEvHkU6nAUZKCLXYZxIz35hAYlCcdfYodG5VmGVSWpSzhM18NXxM114ctTC4Llq/wI8N6VfggrxkhPqEgjR95FcQ1XcHXRw0LhvnnEgMycSZr7sS5kllheS82Of7U/PWZevvfufjVie2uWapkARGRzqGAqsgaePHFF/OPt9tuu/zjjz/+OP942223bfH43LYJEya06fVW1Gf460sfsvixf+TX9d37B5g1FC11CXzEsi4bV8IHEnFmagCWGyMfxj22kqQvCuXw4+qLQqhKx2Pyo/i0xrxET4qyaXpHVSQSRiLhh91nMn63KNvQMQyChgL+fZYtoq6klHRpMWHoSMQZA4YPwC6oSbC8PqQyHVCTLSwBAFE8tD838WoUQSY/2CoOspqjT8pnCmRTxZwwuqHj+a8/3cI7894ncm2fGfWcc84B4PPPP+e73/0un3/+eX7bpEmTOP7445kyZQqbb755o+FNnfU7F1lnda/hUP8AvgpUOOc2c87t5pwbBowEXgZKgXvMrKzJcZcD+wJTgW2cczs457YAvgnUAT83s6NWoz3tPq+Z7Qh8G/gQGOqc2x7YFagBfm9mYZP9R+KDr0875x5ajTaKyAaqKxIGVtRnuPndT5hxz635dfv8+DyKk/6jrTzlh8gbPkM1FTRMlp2I66mWJyLKkxHFoZ9gqjR09Ez57NSiwAcyy5JQmoCyEPoMriBdm6FmaQ1hPJlURcrRK+nrsCYCF09J4IOihX84BziCSTNIj9yMwHz3OTe0P+Ny7WxIEMhNxOrbS/69EEHGGSEuTlDw8wvkEhf6HHQ0xYP8IIqqzz/h7ccf4z+fz2vzdQUlC4h0qO7Vv5UupoCqyGpaunQpl112GQD77bcfo0aNym+bM2dO/vHgwYNbPEdu2/Lly1e6I9ycS179jNn//gdRjd+3bNuDKOq3BeAgE2GRw4LA3y2vSkNguGKfnZrv4QXgUr6wlEsEuNBw8Vh7l418pmp95IOYQQCp0N9pr3V83GsYO66YGmef+uBpNuuXyPl4LJCf36rIIjaZO4P5mwzxd/iDhrv7aQdL64N4GL/PdK3JGOmskYn8kov95ob+1zjLZwhkHRQFEX2LonxZ2IQ59vj6/ozaZWsA5k6dzcN3PMInixuCoqty1FFHce2115JKpXjooYcYMWIEpaWllJaWsuWWW/Liiy9yzjnn8NZbb9GjR4/8cZ31OxdZV7n8FBxrtnRIW5y72zn3vHOursn6L4Dj46f9gP1z28xsI+Ds+OlpzrlJBcc9is8uBbikPW1Zg/PuF//8i3NuSbz/eOAhoD8+27bQ9fiPxvPb0z4RkbWdMADwl4+m8Nn9d5JethSAzQ48gKE7b++HzBNRFPgRTGY+0JkK/ASmofnaqD1Tjt5FUJGEXkWOnimftVqW9PVTE4Ef0ZQMHKHlB3YxdLfBTH17ts8/MB9sLU85+hdHPuO1KKIo9MHUIM40DQyCeQtxfSqgOAXEyQRxIDTr/FCsRDycH3xftjgsCATH5Qoy8cCw3Ndd5CCdtXwZLIIEg77f8DE+74GbuOylj6nJNAS6V0XJAiIdpzv1b6XrKaAqshqiKOL//u//mDt3LkVFRfz9739vtL2ysqG+UWlpaYvnKdxWeExzPl9SxcMvv8Gylx8FwJLF9N75hIYCppk4m9SA2ozvFSYtX2jK4oCrM4OSJC4ZQnEyjoo636PLAjUZLO7ZuaTv9bl4KFJNqpTlRWVsXLs4365cPVXnyC84X0VgwLzZLOw/EBcmfGcxgiz+lnw6gsp03OEMfHS2JmtUZ42qDNRElq9OEAHpbO7Ovp8Ey8zFGQbOl4TF0SPpt53067OwuCbAv/9+D+M+/4DaTNtnRv3xj3/MI488woABAwCoqamhpqYGgLq6OiorK1m2bFmjYzrjdy4inc859yV+uD34TNWcbwAp4HPn3NhmDr05/rmzmW3ejpdc3fPm7uA0TU2aG//smVthZifgM3Ivi4PGIiJt0hUJA9Mrq3j0zXeY89gYAIJUiv3PO5OiwAdQe6RyNUehLJ6s1MxnjpYm/HD90kTka6YGvk5qKvRD+4sC3ydNBY7SpL+5HwZ++FNgjooeKcr7FLN45lI/yRTEJQCgb1GWnsmIVFxiAOIRWFEGps3DDRvks1jzE1f5hIF88DT0wdJ4sFgcEG6Y4AocDiNwEDmfSJCJ/ARV6bQPqIYJKN5yd8p32BuAzNKFfP7vf3Hlm1Pa/DtVsoCISOdQQFVkNfzoRz/i8ccfB+CGG25ghx126PTX/OnzE5h//3UQD1/vucdxJEp6+/HwcWqoC4DIYRmHS4a4VMIP5zd8xmoY+ImozKAo4TNVnfO3w6PI3y/LOlwY+EmtkiGYYWakin091Cn9hrL54pmYi/K1UwGiyJHJuDig6ijO1NNjySK+7DPQB0YdpJ0PcjoH6chIxjWkEoEjEQ/kr4+MehdQnzXqsz6QWps16p0fTpUbOtUj6UiEsCLj7/KVhA1DsTbbegT7HevLIdasqObBa+7ktS8b7rK3prq6mu985zsceeSRDBkyhGeffZaFCxeyYMECnn32WbbZZhv+9a9/sfvuuzN+/Pg1/K2KrMdyYzHXdOlkZrYV0Ad/7+b9gk17xj/HNXecc242fsh+4b5tsbrnzRWEbpqJmns+D8DMegDXAJ/hSwuIiLRJVyQMAPz5g8/54o7rcHF5ge1O+A59Bw0gEThKQkdR6LNKk+byE1LlMkDLExF9ixzlSR/ATAaOVNxfTPopBSgJfeA1cD7PIMfwXzNDdxrIjPFfEkVZgrhvWhI6BpRAn2JHz6SjIunbUhw6wilzsGEDCRJBPs/MFYzUCogDqdbw2OLJVMOCr7a0M8IAMuQmYPUB1WzB/ASZjC8F0O+483x0FVj67P3c9dJ7zF5eu8prm6NkAZEOso70b2XtUEBVpJ0uuugirrvuOgCuvfZaTj311JX2qaioyD+urq5u8VyF2wqPaerpKQt46/lnqP70PQDCXgPpsfXXfc8rMOJp733F/XTkM1P9LXicgavL4swP8SfwGaIuFUBJHGyNnE9tDeIgajLwY56SAUECwqQRhIbhyFjIzF4bs9nCWdTXO7LxXfR02ncAnfNB1UEzpzJn02GEgQ941kVGFp9lmhvKHzeRVAhFcew3E/n4bl1krMj4pSbjg68W16QqCrIkzN/Br42MyPngagbfgY6AY39yMsVlJQC8POZpXn3nDRbVLlv54jYxevRoHnzwQUaOHMnLL7/MIYccQt++fenXrx+HHHIIL7/8MiNHjmThwoWcd955nfY7F1nndeMaU+YNMLNjgEfj1Vc55wpTfkbGP1urGZLL/hzVyj5Nre55n8N/vP3MzPY1s3IzOxn4OjC5IBP198DGwHlNSxyIiLSmKxIGXp23iHHPP8fid14HoKRfP/Y86XhSoZ90qkfK10QtCh1FYfzV4KAkgJ4pR8+UX5+Mh9eb5SYybQhkpuKb92HgM1eTcWAzEXeXi1Ihg7fsy6xPFviRUDRklJYnHOXJiF7JLGWJiGRdLbZ0BbZxH/96+FzTTGTUR77+aTJwDXGT+DxJIMQR4ghwufwIXNaRdX6IfzYyslkrGPXlqK2JJ6jqtwk9DzzOH5Op58uHb+TnYyetdD2bo2QBkQ7Ujfq3ZtbHzP5oZh+Y2Qozqzez2Wb2sJkd2MpxSTMbbWYfmlmVmS02sxfifnFLxxSZ2RXx+evM7JO4H9rS/sVm9oWZzTKz8jV9r92VAqoi7fCzn/2Mq6++GoArr7ySH//4x83uN2jQoPzj2bNnt3i+3LYePXpQXt7850w6G/Hr58ez8KEb8+v67HMSQSLlxxPlPpTDwGen4nK9Nz/834B0hqhHCpcK/Z/juVRPZ7jAcCmfkeqSof+Zu52eMCwMCOOpToLQf/pPL92IftWLCevqfQcwLuMUj7KnuGoFFmWpquhJgJ8fK+38sPxEHP/N1ZAKzTclDCAVP8b5TmV9FFAXBdRlfefZ4TDL0rPId5irs354VDKACMtnyxpGz369+cZ53wN8h/SeP97Ey/M+avF3Af6u+i233ALA+eefT0lJyUr7lJSUcP75vpbVK6+8wvz584GO/Z2LyEo2NbMpLS1tPYmZfd/8WMsIn/H5MP5ezInOuZ832b1P/HMxLctt693WNqzueZ1z0/HB0oH47NZK4E58+8+F/MRV5wEPOOeeyx0bB4/zJQFERJrqioSBTOS44r1PmXp7QybsPueeSY/yEopDR+9kRGmiYRKnXIA0sIiiMEt5wlGR8kP8E3FmadxVJRn6fmVR0FDfNIyDrYkgDmzGAVEDBo7ow6KZy6mvSRPEdVYdvn/aM+kYWOroWxTBpzMp324TisNc7VN/jqxriJH4/rAjijumQdyeXF4D+BJYBtQTzyeAn08A8nPCkk67fLYqQK/Dvk9Q4b8Wqt57ibEvvsirs1r7KvGULCCy/jGzEcBHwC+B7fD92o+BcuAY4AUz+1UzxxUDL+Br9m+Dv8G/GDgQeNjM/tzCS/4TP9lpOTAJGAHcaWZntbD/xcBw4MfOufW2FogCqiJtNHr0aK688koArrjiCi666KIW9y0s3F5Y0L2p3Latt966xX3+/v40pj31IJmFvo5R0ZDtKN1sd595GprvwcW32K0m4zNNc1HK4gRguB7FUJIA53BBfFxgkM367NREiCtN4MqSvkxAcQJKElhgJMI4Phv3ALNZRyYDH/ccxrZLpxJFDXWjggBwjuFzpvDFoM2py0JtnHGaMt/hdbnOZeBrSOVmTQ3xta6KQ1+wNYsf6p91RjKENOYTaHMd0SieiArfgc1m/dCpdOR3CIBDTvoGG23mA52T3v6I/z36FFOWN9SDamry5Mn52U8337zlkogjRozIP5461Y/M7cjfuch6ITdz3ZouHWs+8CrwOjATH1gdCZxoZps02Tc3zXVrBZhzGaAr331p2Wqf1zl3CXAsMAYYC9wG7O6ce9584eibgCrgJwBmdlQccP4SWGpmr5rZlu1oq4hsALoiYQDg3s9nMvG/D1A9y88kP2Dbbdjx8K+QCiOKw4geKT9gKgggGfq6p2aOkoSjX7GfgCpZEBQ14sApDfVKQ/MZnrncAyu4kZ+I66kGAaQCY9Tug/ji7Tl+0ipz+azXkoSvqWpLKkmlAsp6leSzUyGeQCouaxXiu+W+uxuvi7/KEtZ4pG88/QHZCDJZI3INyQHgSNfHlbkinxwQlJTT5xun549fMObv/L//TcwHbpujZAGRDtZ9+rc3AYPwJZ62d85t7pzbGT9R6e/jfS41s6ZDDS4H9sWXl9rGObeDc24L4Jv4/ufPzeyoxm/ZdgS+DXwIDHXObQ/sCtQAvzezsMn+I/HB16edcw91xJvtrhRQFWmDiy66iKuuugrwwdTRo0e3uv+oUaMYMmQIAE8//XSz+1RVVTFunC+hd+ihhza7z4Lqeu549UMWPXm3X2EBffY9FUpSPkCaiRpun9dlfOQSh0sEfkKpZOizVMuTkAhwuRqqJUnfQ6vK+EzWohDXo8iXACgKcHEU1TnAfDA1V8cpm3HU1Di+pILARfSsqySZhIQv68RGyxewvKwHNWExtWmozxjZyA9/SuLvyIfEVQniJQgcYZxBkAggdI6M85GOXBcxG090FQRQkzHqnZEwfxlSYS4zwBodk0qlOOEXZ+Sv5wOX38brsz4gm7vV30QQNHwkTp8+vcXf75dffpl/nLsT31G/c5H1Qu6v2g5ZAJjpnBve0tLWZjnnnnXO7euc29s5NwR/d/1R4AjgjSZZnLnidKlWTlkU/6xpaxvW9LzOuUecc8c75w5yzp3hnPsw3nQmsAfwa+fcHDPbC/g3flDAWfgMhh2B582sVzvaKyLrsa5KGKisT3PXex8y9Z478uu+dtE59C4yypOO3kX+ZruvlRr5YfgGpaGjNGwYxh8UxidyGaihX58MGr5GcrVTQwrqmsavG8RzbvfZqAwXOVYsqvaB17gKVlHCZ8UuGD+HzXfbmJ5F0CMZD+3HkY7Md8fjTNqVwpvWEPCNpxoABxlnmHOknZGJEwJyB+dKaTVVtsdhpDb1lWPSs79g4jP/5tb3Z7Z4nZUsINKBOr5/u3rNMKvAZ5QCXOSc+yS3zTlX75z7DfBB3OLDC47bCDg7fnqac25SwXGP4rNWAS5p8pL7xT//4pxbEu8/HngIH8BtWt//evyn2fmr8fbWKQqoiqzCRRddlL9rf9VVV60ymJpz0kknAXD//fczbdq0lbZff/31rFixgjAMOfHEE5s9x6VvTGbmmFtwdf7v7/IdDyM1aDi5ivwW+aijM6Au8kP2ixJ+wqmSpB+6X56CTBwZLU3iikNcYJBxWCbywddECKUprDxFsvf/Z++9wyW5qqvv3z5V1d03T84ajUY5J0SUAIHJ4IABg20MmGRj4/fDxmAbgzHYBmEbG2NyFCaZLJABkYQCKEujOKM0OedwQ4eqs78/9qnqvpNQGMEwOktPqW9XV52q6u7pXr3O2mvXkT5rRpWm0kMKbaa83eqG5d86fAyn71xeuVgTLThq61pWzVpI4aHdEXKv1ffGuJeKxCYHmKDTkFhAKIUqZ//7nAaHgdAOrtdElFricaL44GD13i5Xsafp3Asfy+nnnwPAtvWb+d6nvsRdO+7Z7/N90kknVTP3n/zkJysC2ouiKKqZ/qlTp07qfnsoXvOIiIhfHkJm6ouAO4H5TCZ+O8LttL3360H52I6DbLM3Dvm4IjIT+BeMPH8orP5bApFW1Y+r6nuAN2Fuhlc9iPONiIg4QvGrMgwA/Nvt93Pv/3yKfMwqQU9+7jM57vTjqae+akTlHAiWkZo4pe480+rKlLqV0JduT+lxfpaT9SlA6U7tEVtdT+SUSBBXnU3uA5z8uPnce916UixrNXW2zca7tzP76BFGBjKGat4yXUPeah7IcinuHsgwOskoEM676ctz1MqNmueh0ev+BpGEKS/sflXtuPST/OcVd7C71dnvMaNZICLiiESdriR7oOircn3Ws+43sQn9+1T18v3s87Fwe46I9M7ADIfbjXttvyHcVoYEEXkp8BvAe3ry/Y9YREE1IuIgeOtb31qJqe9///v5q7/6qwe875vf/GbmzJnD+Pg4z3ve87jpppsAaLfbfOQjH+Htb387AK973es44YQT9tn/1i27+O5Pf8aunxmBkcYgUx7/MhNKQ6t7TRN0IDN7Zi1BM3Om+noClPVNAkUgZf0mtlquaoEO1tD+DAYykppQG0io9Ttc6nAuzKKHMqOy+VSzaeuSBJpJnR0DU5i7ZzPOwaJta1g1bQE5SZX3pGoO1dxDqzCBtiScSY+YWs7Yd8J2ZffWRJQUb2VaoaFVoTZOPTHRtF2INaNS6HgrmSqPnznhj9/+Opy1hOVrH/kqS5bfQLPYtzNqX18fr3mNlVLdfPPNvOAFL+D222/He4/3nttuu43nPve5/PznPwesY2qSdCscHu5rHhFxREEO0fIIQ1UL4Hvh7mN6HipnXo47yO4l2dz/LM3+8UiM+69Y3uqfhOsBeCJwt6r2Nr+6pOexiIiIRzF+lYaBFXvG+P4117Dm+98BIOvr43l/8UoGMyVzynBNqYvxvIEU6k7pSywvteYgFa1cpb0QuiX8gfZVvDOhp9xeenL8g6hqxjOlbzBj+txBNq3YaeMISFGw/p5tLDh1JrVUmVKD6Q3PnH5fOWHLPNbJNVDdOIJSHO02nAJRaHtnBoNyPdBuKZ3966MA1BefQf85Zk7zo7tY9+3P8o4r7t3vttEsEBFxiHEY8FtV3YpFVwE8aZ9TtJzUktNe1/PQ48PtVQcYdx0WBdC7LVh0FOzrRC3vbwzHHQbej8UQXHTQizhCEAXViIgDYPXq1bzvfeZ6d85x0UUXMWfOnAMu5Qx/iZGRES699FKmT5/OXXfdxWMe85gqW+gNb3gD7XabZz7zmfzHf/zHPsdWVf7+Z8tY84l/qdZNecof4KZMrRiiJoLWnbFNJ5Z/2kjRWoIOZaHzU2L21VSsnB+C6liYANtIoOGQmiNJhVpdSGtCow/q9e4Me3MC8hw0RAq44BpIM2HFtKNYuGMd9YlxpoztYuPQrKpEKU0hS5VGCntaQqewZIK8h0za9YYZ+QJaXiipXn9qGVq1xDKwSp0YYCC1P9oFtIJg2/FGZH1w7QrmLDj+pIU8/4+eD0BrosVn3vMZ7ti2/wZVF110Ec9+9rMBm40/44wz6O/vp7+/nzPPPJMf/OAHALzsZS/jbW972yF7zSMijjgcBoTzQaCcve/lRdeG2/P3t4OIzAeO2WvbB4JDOq6IXAD8EfBxVe0lzSPA7r02L+9PeaAnGxERceThV2kYAPinW+7mvk98sOq2dP6rf59Zc6aTOaOrmYSMU0xgbSQmqPanSpooaRJcpdL9mhBCjFTPcYSukLp3lW3pTi2drqUHwYmy+KxZrLx9C5p7Mgf33biRY8+eTZZYbFUiyoC1G6DulJrzJACiFL7rPhWkW5HVc17tYAyYKKSKHvBhn7zj2Y/miYhMcr6OvOBPkMySY/Zc+Q2+duVN3Ld9bJ/9olkgIuIQ4/Dht2/Bfhm/T0ReKyJzRKRfRM4FvgEsBL6uqj/o2af8R3ofB0bpKj2xZ90PsY+pt4jI+SIyKCKvAJ4H3NPjRH03MBf4M1Vt8ShA+qs+gYiIwxW+J2fTez+pFGZ/GB3dt3ndueeey5133slFF13EpZdeypo1axgYGOC0007jFa94BX/8x388qRSnxDfu28iP3/xqOhstpD+ZMpvB855vCqXvyU3tS9FWgfRnpjM2EnOveqDmUOesPr9M3/fmWpWWh+Ea4oRaw5GkkGWC9TRR0hTyjhFMX5Rh+OXzIUZCE6FWE0gTVk49irPX3MHt80+hrOEv3QFJOIVWx1yt6qzJlLgwkx+yUj3QKp2nAn2pZzC1RlWpmDtVgzs1dQV1Z8bctgj//pI3snvbjknfTWVYQUli1Wu4Prj8m5fzO7e9kFvvuJVpjclVt319fXz3u9/l61//Op///Oe56aab2Lx5MyLCUUcdxWMf+1he9apX8bznPW+/74OH+ppHRET8aiAiNeD54e4tPQ9dAvw3cLyIXLif0qiyq+kte7lAfxEO2bgikgIfBrZiJf692AYsFpGkx7VaEumtD+J8IyIijiDszzBw0UUHNhK9+c1vnpSrWk4eP+tZz6omj4eGhmg2m3SCrfJgk8dXbNjCjT+4lO232cftyPx5PO3lv02WGOfrS7tNpWoOGkloTBVcpanrNncqmamjrHwqYwK6SHoolyPMy4txyMR1I6LEdUv3s0xYfOZM7r1lE4tOnMbYriYnPn6+CZ4h/zQTpZbA7P6CtJUykXt2tB1dtgyImqgK5JiBIPeQe6HwQh7iqjoFFF7wXmk2qSIMipDYJXRNCGVUVjp1NkNPexm7L7sYfMG2b36YN51+Ct/5vXP3ec4vuugi7r33Xr7//e9XS71uUd2tVlfzOJhZ4KG+3hEREQfFUaF56H5xsD4BqvplEdkD/APw8b0e3orFWH1kr/XlD9/tBzmn8rGpPcdaJSLvDsfqdbe2gTcAZeOqPwP+V1V/WG4gIrOAlqruOsgxf20RBdWIiANg0aJFVUf6h4PZs2fz/ve/n/e///0PaPtmXvC+q++guW5FtW7mUB+/kdyPlKeTmTApXsGBNgLZSkpG6JFU0AKrIRKx1iZlLZEDmTAB1bXKWXsBBbX/VZv6oixLUnN9iuAmwI3a3yKQ5W0G2+OctPFeNLEWAiWhdRJ6Z2lZThW6rGKlVSXtrUL60UCWdZ9uqGCkN3PKbroTfLvWb2b3zgf3Gb1x9Ubu3L6E8+deWAmtJUSEF73oRbzoRS96UGOWeLCveUTEEYlD08H0YUNEHoN1Lv2cqt6712MnAB/ESuxHgU+Uj6nqJhH5GEZIPyUizynD+0P307eETf9xP8d8PPDlcPd8VV17KMbdD94EnAa8smwS0IPLgZcCbwT+M4iv/9DzWERExKMQv0rDQO49//Dlr3LHRe+u1j3vr15Hf1+NzFlWqqMrnlpWvobmUpbJXwqqApZuJdZUKnFWet+LpIdH+r0EyfJv1e52vgjuUmDB4hGuWbqNWzeNcer5C3p9CVCKuQIjGTjJ2TrhGBWhgzVgdRK2F8V78CECa8I7PEKuQibQUSX35j41bdPMDSKw+p9fR7HLtA3t+f+kG3GgnuZd1/L9P/8tfnDeEp65eMak5yGaBSIiDiEOE34bcCwmkioWAbAjrJuBNSu9GbimZ/tGuG0fZMxylqWvd6WqvlNEbsO45QzMyfrfqnqr2I/pjwJjwF9CxWk/QKi6EpGfY42wlj2kKz1MEQXViIjDDO+/eQX3f+PzaHO8WrdxzUq+c9cW+k94IuQeHQmNo3a2rcHUQIoUCvUEFSFRj08c2vYIHs0ys5mW9faDNZIUGg2hfyDkpSq021AUiq+6itpMedGxBlYkQtpw1GpCrd4VRx+/7lauX3gmR+/awB0LT7X1SchaFaWVd5tNqQpZ0iXFLrFS/txbLlaWKMOZZyTzqNMyAraa8c/EM1xXckJ5l4PhebOQWrbX91s3t6pcr4VnYrTJxMQEeSfnO//7HRa9djFHDS56RF/TiIhHJQ4fvjkI/D3w9yKyBSOcHawkaWHYZjvw4pAd1Yu3AOcCTwDuFJE7wnhlxum/q+ol+zlmAzg6/L0/rvVQx60gIkdhAumVqnrxfjZ5N9Z84D9E5A3AEDAHWAr8z8HGjoiIOHLxqzIMAPzPfau4+xv/iw817VOnTeP4+jAT1yyjLcq4dCfLE6fmSA3fJZPK+/f6fim53t5fOwf6Gtrf1ZcT+72Pje1uMb67zR0/WxtMB93tinBfwpGaBdQLIQ0u1OBTABU80Aj7DCD4ntirIgitqhZ9VZkbgNU7NtHavfNAT+c+yLdv5G/+7zZ+488vxEWzQETEI4NDy2/XHMyFetDTEPkQ5g69FThTVW8P6zPgr4D3AD8RkSep6s1ht7KJSO0gQ9fD7cTeD6jqN7A4gb3xOuBxwP9T1fUi8gTgm1ge6+uB6RgX/7GInKqqOx/whR7miIJqRMRhhI1jLb7ws5vYflkwNjlX5Uvt+OEnaZzwWFx/zZTI8RxNpQqREif40ElKagmaA4kjqycUHrwImiVk/YJ3Qr0BtZpQTih7b2JqkXc7oooIaWql9ihIakJqltlj3sPRoxvYOjiN3cNTKUa3MHV8J7sGp+C9NbEqxdoyfUDVGlslCdRr0AiPl87TgcTTSAoaPd1bXRBUy8tt+xA7ECy7b/7Kf+Pokm4JjQEaiYZ9lIZ4Vv74HtoD47z1D/4egE+992IufN5TmHvsAtIkfhxGRByhuBX4C+CpwOlYM6h+YBdwNfB94GMh4H8SVHVCRJ4K/H/AH2Il823gCuCDqvr1h3JCh2jcD2CE+A0HOMZd4RjvwxoLtIAvAn+lqvuQ5IiIiIhHEjtbbb5w0y1suaEb9bxj+3bu2LCcZ/7B8xlMldRZ89H+VBnMFAc0UstSLdOrSo7nxCbpnSiNpPR1dpGIVsLH3vqxqlS5pb0iabkUQOGVK791H9PnDLDw5OlMmztkfFphNLfMf8tTtb/bHrZOwFjh2NF0dLw1nOoUQtMLuYdx74wHF64SZfd0HK02jI9bA1irDLMTLqbOJk2ySszd26Faop5PMD42Br5gxWVf5cPnHsOfP/GYQ/CqRUREHI4QkTOAP8XSRH63J8MUVe0A7xWRk4BXAP8EPDc8XFYzTc68m4zysb0rnw50LjOBfwGWAB8Kq/8W+wR+ThlfJSLbgI8BrwKOmIyQqCBERBxGeMd1d7Pmix9FO+bCH3z8b5NvWE5zxRKKnZvYc/23GH767xvbG89hSh3EIaJkfY52G5IMfOj6lNYElwCBdEqWkPYZWavXHWn4BMhzJc9LUbUrgpaELkkElwhFmZ/q7LHEFyzcvZGbF59JmgrL5y7mtJV3cPPisyi8oKqTCCqA91o1oWp3IEshFyPMTpRZjaIq9YLQsApIQvmTEyO6Kd1IAYcR2hKq1o9LQkmWeNhy/xZmLJ7G3JNO5PxnP4Grv38N2zfv4H8++EUWvWsxJ0894xF/fSMiHj2QQ1gS9fDGCaXwHwzLQ9m/jYmS73sQ+/yUX3DiD2XcvfZ/4QPY5gbgwocyfkRERMShxPvvuId7Pv0xitbkPiXf/+/PceELnkw6fdC4n1oTqloZHVVGQQUxtZfvifRsFxRVa0qqkz6ByzL9kosmzrJNq/YA9PBOZxP59926lYUnTGXu4ilcd9kKnvSbg4gIeQEdbxsnoaw/V2umNbMPpnpPKrCz7Sg6SrswLmpiqjVohdAHIPDuItfKdGDXZRez+B8+bsKrD/0Mgvmhl1ufOLaWYtsGLn//W0CVXT/+H97/mGfwR+csYLiRERERcShx2PDb88MA9/aKqXvhu5ig+tiedfcAT8LMBQfCsT3bPhD8K5a3+tyezP4nAnfv1QvgEkxQfSJHkKAaw04iIg4T3LJlF5f/5KfsufGnALiBEaae9xKmPPv1lDbS3Vd8iXxsm7GwzAGmJrpE8GVbe1V8bu3u08A6yxL8/kFrJDUyIpWYWhSQ57Z0S/1N+Cwfcw76hxLqdcGJkOc23ql7VrFi5kLU2Ux7O8nYMjyDudvXVyJtnnczqsrYriTpirbt3BpWeQ8jWUEtMYcCBEFXFPBkieJVqTslFa2ytKC35MpIdCJKKp4ERVHyVsHO1TuYdcIMQHn9O15NVrMn4Gsf/ya3LruRiXzfzqgREREPA3KIloiIiIiIX2ss3b6bH159NWt+ZM2mG8NDnP4b5wMwtnM3l374C4hY9NO0RpmZCmko+y/L+QPzreAI8VFhG+dscr7MWU0J1U0yeR/7eqm8qUi5iHHIvJmzbvlOFp0ynUZ/yrxjRli9bBsOpRO02owuD62J4lD6E2UoU4ZTT0OUNFhJvRcTYb1SIJU7tVU4Oh0TTCuNptRr9vr+6+0pUPLqWt7imM4W7p96BgPnPBMAbY6x5buf5q3fvevhvWgRERH7x+HBb4fC7cHyW8qjNHrWXRtuz9/vDiLzCZmnPdse+AAiFwB/BHxcVa/reWgE2L3X5uX9Kb9o3F8nREE1IuIwgKryj9cuZc3FXQPVlAtejmsMUJu9iMHHWgNqbTfZ9d1PdducClWQad6xP4u2WTprDelp9ARpZkJmmgpZzVUl+HmutFuQB1GzNwIgNO+krIZPU8u+R2EgbzLUGWf7yPTK2QqwZvp85mzfhOvkFEUZHVBelVSNrAin7r2JqoX3TG/4avtSMK2JMpyZA2Ek3LpAjstogp5nEhGlnlpjgyw0KNh+x1rmnjkfxJoELDh6Di9+3W8D0GnnfPTdn+D+3bcestczIiIiIiIiIiICvCrvvXUZ93z0v6p1z3jDy3nhW19P1rCovh98/jusv281NfE0An9Lghu1LPNPnJIlXd7nxDL4K95IV3QtRdMydzVxxhvL8VzYuKx8ynoEWRG49doNnP34OWSJRQMsPm0mq5dtp9ksEEzotQargasm4RjBSTtSh6GaZyRT+lOYKBw1B52gb3iFdjAtQHCflpw2uAS6btXyOejeB9vurOZqliQLUBFGnv5KpN4PwOgN3+WSH1zN0k17Dv0LGhERcTigdI+eICIHymB9dri9u2fdJVgfgeNF5ML97PP6cHvLXu7SfRCanX4Y2IqV+PdiG7BYRJKedSeE230itn6dEQXViIjDAF+9bwM3X/I1WmvtcyubcyyDpzzNGk4lwsjTXo7rs4mo8Zt+SHv9Mtxgisss0xS6DlAVIUkgSbtz8M4JaWru1IGBbtl+kpgrtXSOThY/u+uckyoKIE2F/gHhlK33c++c7ud3SfxyL9wz5WiO375yr3xWQgSAbehct0kVIgzVmBTSL1imai1RaglkoozUjDz3JdBIQkwAXeeBAHUHdel2bC12TaCtDtPnDVbnmgq86k0vYfqsqQBc8b1rueLyy9nV3vIwX8mIiAjAfgyKHJIlulQjIiIifn3x3TUbuO17l7B92VIAZi5eyNNe+jxmzpvJ8177YgB84fnsP32c/sxE1JoLjUqdiZ2Z29dlWomt9DpXtRJMSx5Y3mZhSXvcrll4vFeQ3b1tgnYzZ97CQdIg6rpEOOHs2Sy7aROpg4HU+GZ5Hl3GbVw2EWUwhf7MKqUSUUTVmqyGplXNjrPMVK+VgaGETk4smBSdBcapRzqj9PsWG2UE1JP2TWHkgpeWe7D9ux/lTV+6+ZA0IIuIiAg4fPjtZcAmzIj/NRE5tTpFkUxE/hp4ZVh1cfmYqm7Cyu4BPiUiJ/bs9wKsaSrAPz6Ac3gTcBrw1yFeqxeXAzOBN4axU6yRavnYEYMoqEZE/Iox3sn5r2tvZf1XPlWtm/q8P4WBOjpQQ53DDYww8qxXVI/vuOTDSGqqqAudmGp1E0iTTOifklTiqImr1kgqSbqkzNyqXWcpdN2ppaja3y/09Yeyq8SyVLMMZrd20MrqNPsHQ86TVG5T74VtQ9MYaE8wTcYBc7rmeZcMlsdRtaZYtdSTOGVPx7qkmovAGhIkzrKnBjKqbq+uhwx7ynGtJ2rirFTMtlG2LFnD4scuqPJUVY2ADw3282d//8rq2v/j7Z/g7m2ReEZEHDJ07eYPb4mIiIiI+LXERJ7zidvu4O5Pf6xa96K3/gl9tYSGg+e/5neZMW8mAEuuupnrfnQDLpTdl2JoIkom3ZL3csK9kViZveupeE16NAqh60BNCLeBH5Zjl9msZeGXQ7nhqg087snzgG5OvwNmLBxmYleT5u5W5UYVF/JYy+MqdAooEBqJRQAkIgxnvjo/D4y2TFgF6OTd56ukoBr6DZQojQneW5wWCuc2V3CjLLQBw7qhs15AOmUuAK01d3DzT77Ht25e93BewoiIiL1xGPBbVR0Hfh8YBc4GbheRlSJyC+YOLfP5v0G3UVSJtwDXYKX9d4rIEhG5D/g2UAf+XVUvOfhTIEdhAumVqnrxfjZ5NzAO/IeI3AOsAX4HWAr8z4O93sMZUVCNiPgV4wO3L+eeL3+WYs9OAPrOupDGcaejjcSmvwWkJgw9+bdJ5ywCoL1qKWM3/oj+ASNYWWYiZZZBowFp5kh6yqASB1kmVZl/6TpVhVqtW+ZUQpxFBGQ1c6QKkKRClglZCsdsWsmqeccARv5Kt2kpzqrC0pnHcvzG5XhfZrRqFaRfHss5pV6D2f1KKkLLO8Zy64SaOWtpWsaoZj1ib1m2ZSS5zMAKpVd0hdM963bSP62fgaF6dW1d/Vh57kueyilnHw/A/UtX8fXPfYuN48sP4asbEREREREREfHoxMfvXsk9X/ocE9u2AXDSUx7P6eefEwRMZaC/zqv+7tXV9h985yfRvDOpRD8N5e5h7pwkOFYn6xKWgVoKoKWYWrpGYfLflVAqYlmrApkT1i7fzYxZfQwO17rmMTE+6oETHz+fe65bVzlIk55jKdD0kGO8ua2CRxiuFQykPgi5ak2tCgmVZfu6U/eH0Fmg4tkLOlvZJX2MagO8R7yVqTlJmfqUV1X77bjiM7zrazfS6hT7GzYiIuLXGKr6E8wh+p/AXZgj9DRgAvg+8DJV/d2eRlHlfhPAU4G3hv1OAGYAVwAvUtU3P4DDfwCoAW84wLndFY7xU+AooA/4IvC0cPwjBlFQjYj4FWL1nnG+evUNbLns6wBIVmPqb72epD+jPqVOUk9IB1PS/hTSlKm/8+fVvtsv+SQUBerNOZokQq0uDA45sswE0VrdbtNMqgD7Mpu0zG3Kal1BtiSmaQq1msUEpKnQ1y/0NcydetSOdWyeOpsiy8JsOdXYvQ7UZl8/Y2mDqbu34QutogXKZlQikKXQnxbU05CnqkLHO9peSIBWYXR2YK8mpb2TepVjVWxQJThlC8/WuzZy9JlzTITFYgMCLw4E2PGX735dNdbHLvoCd2+4kcLnREREPEzIIVoiIiIiIn7tsGG8ybdvvIl7vvYVAJI05YVveV0llmbOYpye/oLzOe2xVq26Zvl6vvLJS81RSuB2dGOtPCFb1U3+ehCZ3FqgyhvtboFIdycnIM4arYpYXwHN4bYbN3PW42Z3twljdML9qdMa9A/X2LJmd+V+LfsVFB5yDf0LPIx2hJaHwQQGM6p+AM1ccCG/oNma/Jztz7RWCq9FYeNKnnP6xBpu9fNAvfVuVUU65oLtW/xYGgvPBCDfvZmVP/0q/3rp0gf56kVERBwQhxG/VdVVqvomVT1NVQdUNVPV2ar6HFX98kH2a6vq+1T1DFXtV9UpqvpUVf36AzzuC1W1pqp3HmSbG1T1QlXtC+P/gapufCjXeTgjCqoREb9CvPvGe1j+2f+urJ1TnvEy6rNmkzaEWr9Qa0BWd2Q1R+Jg4NTH0H/Gk5h53Mmc8/q34EkZGBAafVCvw8CAOU/LUv9SEE3TrmBaOlMN1iSq0ei6TJMQHZ2m9neWQS2zMRp0mL1zCxtmWilUVZqkUBShU2oQbFVh6ZRFnLBjNVr4fcr90xQaNRis7UV4saD+XW1Hx1u+Ve8HVW8of7lkTkOuqqIILYUtyzYx64SZZLUEUBLU3AiuS8oVOOOxJ/GcFz0VgF079vCxiz7PmrHbD9VLHBHx6IU7REtERERExK8d3n/HPdzz6Q/j22bBvOAPf4cFi4w/puJJRcmckibwF+9+XYiQgk/825fZsXUHqdOqzN+HpTcDtReVRiGT11V/hweEBCcOwRZwOBKEjNtu2cxpZ82knrnqq6eMs1LKCinlxPPmct/NG/HeeGUq1iOgoJv1OpZDEdy0RchQrSce77XKbC1F0l5UfQR6TA6qJqRqcAOc0l7PvW4mOYk5Uwtv+QGhPEuAaRf8sQ0C7Lrx63z2O9excecRZQqLiPjVIfLbiB7ElzIi4leEq9Zv5Yof/JBdt1wLQDp1JsO/8VLSVKg3HP39jv4BR/+AWEZTCo0+mPe6v2fmW/6bc0dSaklBFpyktbrQ13A9TlQTQUthFCaX9UNJ3IQ0s/3TzJayiVWSQKMhNOo2C79o/QpWzVuEhCZVJcpS/l50OtDShJV9szlubD1pyHAtXaq1FGrOHKOFr84IJ0LHC6OFY9w70oSeon72OY5iadylUxUgn+gwtmE3c46bBqEDaz2DLKQo5Aqd4KwVhTe+/RU0+i0W4Kuf+S433vZzJjqxM2pERERERERExIPFzdt28PMrfsLaK68EYHD6VJ79Jy8ldQBKPYFa4hkIrs3TzjyW3/qDZ1Cr13jBi57B0GAfMFkUtRL9nvL/ACGIrJPE1B4LmDiEBKkK9E3NEDEDAiJMjOesWbmHE0+eMalJVYLloaZ0YwaymmP+8VNZfecWMyuEk7AjKrlXOoXgw9FyNelWVXACI3WPQ2k1D/DkCaB27oU34bXkvo2ixYLOdu7zM+3Bwjpcle7UErVpCxk65VkAaN5i4xWf5W8/e8MvfN0iIiIiIh4coqAaEfErQO6V99ywjNUX/3e1bupv/wlpXx+NhpXwgwmQLjECltWELHPU+vtJGynLh+dzWnMtWWb5qAN9dLljD6ksnam23v7oFSWTBOp1YeowNEJjq1rNhNgs635I9E+MkXXa7BicCr43L1Vpt23fWijdb7e7M+8r+ucwb3wrdd9BVaqIABcC/VXKXFOlnph0mnsr/1eFZgGtwibfy1gBa34VXAMaJuGDayEBdt22lrlnz6cIhDTw9+paPNY8qxRyZ8+bwav+wjrNFoXn3972SVbsuSU2qIqIeFg4jGqiIiIiIiJ+KfCqvH/JXSz72Aerdc/7i1cxPDSAwya2M6f0J1BzkIqSivLGt/0R/3fdR3jy+U8mTbvNVRXjdrVESUMDKOiavEoBFCZ/a0gYwEkpntrfThKcJAipOVZJuebKdTzpyUcjzoEklXCbh/ESscn7cvxFJ09nw4qdtCby6tiJQOGFsY6jg1B3MBG48mgutL1dQ5Z4c5we4PkTMV6rQN6xjNUyKuuc5kpudkd1L9jrZNeBVxNZC8+Us1+Eqw8CMLb8an542RXccPfmh/qyRkREVIj8NqKLKKhGRPwK8Jmlq7n96/9La8NqAOqLT6P/nKfhnLlT07TrMu10LAO1FDnrDXOrbp02k2kTu2jkbdLUSqaqkiAml8SXjahEdJ+s0zSBWqokKdRClmoZEZAmIZNUYdG6+7l/3rEA5JPcqd2SpFparlOKXOl0FK9w58giTt6+Eu+1e15YKH+nABREjRQWKhQ4cgWnsLvl2Nl2NHMTXv1eS+VKDaJrc/soTmB45gB5cL9qmMS3nFYrGUPMqZqrrXvZn/w2c4+aBcB1Vyzhe9/9EdtbGx6pt0BExJGPyDcjIiIiHnX45qp13HXp19l+vzX5nH/y8Tzxhb9B5hTnlL7E058q/RkkTq16KFVmzx5m3sKZnHzeHO68bmP1IzXBuF5vJRJQfUckYaWEPFQJAipAIjUcGd2fvKXvtOtW3bRxDBRmzxsKW0gl1npvzbNKMTd1IXYgEU45by733rAeFGoChSq5qnFTrBKq7YV2YSJroUIi4L1DpYzUomoia9fQvby8Y8cH49gjrT0k3rOFIWtE1SuklhuphhgAJU0HmXrGi6uHt95wMW/5xLV4H80CEREPC5HfRvQgCqoREb9k7Gp1+MS1t7H+a5+t1k194Z8zMOgYsInkilDlOVUeqojgnLlR00QYHHSsWLCYk7bcT39mpCsvjJz25i71lub3iqkAWaY06koqAEK9BoN1SBIlSUBRFJi2cytjfQM0a33VcUp4b6Swk/c4T5PQ9Coca2tjCmnRYag1Nuk8cgVPV7SdyIWOD+X4wLgXJgphvIDxwrJVczVhtaBnQt4LEx6ahbLttrXMP2d+6KZq7oCygWpZEOXEds690MyhU0DaqPNn73hVdV0feMcnWbnjFryPnVEjIiIiIiIiIn4RJvKCzy65naWf+VS17iV/+yf0p47UwWDqGakp/anlkWZiDUNLeROB2UcN0RzvsGdnEydKFsTMzBl/S6R0tkImLgigZSaqiaSWkZpCtc4Gd5KRSEoiCYkkODKuuXI15z9lMaWUahw6wWPHqJqfAg7LQHUCsxcMkrcLxneMkwWeXngLG6g5pVUIrUKsJwDBCKAwXghZqvTVjT+XfL0LpSh0kpjqC+XciRXclCwsNykvqTyx7nqvVQnX0PFPJxtZAEBr2/3cec33+cLl9x2iVzsiIiIiIgqqERG/ZLz7unu5/wufxE+MAjD4+OfQOOak4ECdPF2V51Z+bw2mbBGBgX7LA20PDeESYXB0d5fohbxU6RmjFGFLUTZNlUZNaWRGTC3nycjqQN3IYuICDfUFCzetZt3cRYizbNRe92t5TgDNdrlOqnzW8ph3TFvMaTuWV6yxU9ixwdyjLRU6YSkn3dteaHsh947dHceuNkzkwXUawv7T4DQFYWzVdvpmDVPry7qh/whFFcBq1wXdzqwtFZreTuQ3fvNJnPOE0Gl2xQa+8PGvs2ninkP0ykdEPMpQzmo83CUiIiIi4tcCH112H8u/+Cmau3YDcPZznsqJjzkFJ55GUjCYKn1pKH13UEugnkDqumX1icC5589jydXrqYWvgTK/VAil/EFJdC4xEdQlwZma4CTFSZ3U1UgkNecqCQl1LEu1zFTNuGfpNo5aOI2BwUaPYUzQwEXTxM7R2WpzqDqlEZyqpz9hHvdctx5B6Uus+VQqStt3I6taheWneoWON06aJtBXh/6GuVRrte719ZohSqF1UWcrm90Q45p1O2Tt/fXoA3sutHIQSJYw/bxXVJtsv+VLvO9z1zLRyg/tCx8R8WhC5LcRPYiCakTELxF37xjl0quuZftPLwVA6n3M+t3X0GhArSb7cZB2XZ+11LhTmgaxU2x2f/PRx7Bg7XJEYLABjVq4rdt2jYbdlqJnGsqrsiyQ2CAsWgdSZaiuzOhXptY9famyaOtatsyaC6nbh+CVblSwyfDxppHALINGnzXFSlLLhG1mDXY1BpkztrXKxLJrELwKuTpyFbwqddc9RqHlZLuj7R0THRNUSwHZBUFY84Kx5ZuZdcpsggEVr9CX2FLGTJWNUMuKJwU64e9UhL95T7fT7Mf/7cvcs/Zm8uJAnQMiIiIiIiIiIiI2TYxz2Q3Xsewb3wIga9T53b/6Y8tLTZXZfTBcCxmqYre9ZfxCKO13MDKlztQZddat3EUSBNVUIHVCFtylqauZMCrmRHVkODKEDGdBAZjrtMxRdbggqDpJKHLHbbes59zHLgrybILDXKuevGp4WjpSKxlXuv0JhkZqTJnVx6ZVO0nFxNfMQTM3d2qhQq5d4WS8cNScOWwdkIV+BWUDWVWlyM2RWgQ3gBQFJzfXcofOLZsOINLzY6EktxrE1Cr7yx7rm3UK/fPPBaCY2MnaG77Kuz9306F/A0REREQ8ChEF1YiIXyLedvUyVl38QQv1BKY99+VkU6eTplKV9/SW/pTuTxEY7INaJtRSqZotNRLoZDV2jUxj5vaNJIGk9tVCV/sUGnWxcHuFeqb012Ews+0SZ6SwLlB3Sia2DGXKYKoM+DZTd21n+4zZeC/kuY3jnJE+W+xcywZRrZatr9XEmmpV2VawYsbRLN6xBgpPEpptebUGVN7b7H0SxFYN6wov5CEDtV0Io7ljPJxH4kKuqsLosg1MPXE2zjkKbB9RK7tKpGqEWi1FOEYmln2l2DYnnLaYF778mQCMjU7wn+++mDVjt/3y3iQREUcCyl+dh2T5VV9MRERERMQvwseW3snSj30QH2bfn/7HL2H+gpkMZ8YrM1caArTin04UgmCZOBMjk/Cxf/ZjZ3P7DZtxofbdXKXlF4KlnEpYukJpWomntn2KkOIkRcRcrM6Z6Hr9NSt5zOMWkablFVhVlarvCqcSGl+5blWVoct/Tzx3Dvcv2UKnKEgc5k71YvFU2nWddgq7382DtQEamXH1LCv5b3fSH+DUibXclc6jUNd7+JCfhW2ceyQslbgqVC6CaWf9Pji70J13f48vf+ca1mzacwhe9YiIRxkiv43YC1FQjYj4JeH7KzZz3f/9H817lwCQzZzH8Pm/Y93nHTQnbDvnqITLUrzMUhMBRUy4LAor0TdBUtg5bwFzt6yn5q3baElUR/qUzCmCkiYWpp+KIqI4UVwors+cZzCFvlSphXIm52Du6uWsXrCYjhfauVZdRr1XioJq8d6iAIpCaTWh3bYy/3pDqjImEHySsGbafBZtW0MqJiAXPZmoZcZpx1tn1QLIEdoqYZ01rBrPhWYRxNEC2mMt8p2jTFkwhU4os9JgJagl5kBtFcJ4bkTXGqAKglJLjCTnhR238PCGv3s5g8MDAFzyxR/x8xt+zlhnxy/vzRIRcQRA5dAsERERERGHN27btpVrf/xDVl93IwBT5szkWX/8Owxklpk6UrOM/0SUemrcNBGdJI8mYk2rJPDUWj3hpNOnc8ct23BirlJHaqKopOYmrcr8MxNN6W1M5cLjaVXqTyj337O7zeZNezjuuFmAlT2ZmCp42pU4C6GiislOVei6RJPEcfQp01lx2xbyAlreBa5pBohUwKsy4V2IozIObtevuCSIzElpqtDSd0Gj02JmvotVOrXKTRUNHVfz4GAtc7KK4FAFE1J7mg1kA7MZOeHZ4bGcTbd8kTd/6JpD/TaIiHhUIPLbiF5EQTUi4peA3Cv/eOWdbPjKR6p1U573egrNAOi0y1IfE1O973b2dEEcbeXBZVkY+WqEMnbnwCXClvkLmbVuVSht16qEPxGl5pRaWq7zJM6ynpyYg7M/M8E1S4zM5grp7lHEKzv7hi0DKnQbdU5pt01A7XTCrHtvJ1Ig75j4Wma/QrdEasPwLKaN7aTWadFBaPluhZK5U8UaTwVx04fmVU0vtMPsfq6O0dyxvSmMF8L4HWuYfsZRdETIKSfnLf+qU1hmVdtDy5sQ2/GChszYMpfLXK1CW2Fk2givffNL7XpU+be3fZyVu29BJ3cNiIiIiIiIiIh4VCP3ns8tu43rP9TluC99yx9z9PQ60+vKYBaaSoVqqKrRE12TlnPmWhUgoxsFcPJpM1izcg/NCQFJQBISasFx6oA0lPqH+6FJlTlTTUB1YlEAidRJXR0nKVf+9G6eeuEpYcI/QaqIAA37uMoMUBrSUrHKqrKBltCNK5h73FR2rN/Dtt05Xo2jJ2gwKBh3TEMVWFotwfUaKrXSpFv+n2VWXXb2+ApuckcHEk3X7lr1BtDubSWseiTXbtaVE0Rg6sm/RdIYAWB87Q1cdeXl/Oz2DYf8/RARERHxaEIUVCMifgn48JKV3PvNL1Ds2ARA/fhzaJzwePBBQMwhzSQIqkqaQj10/8wyc1OCEblGBn3OV6XspeN0fNp0GuNjDHXGcShTapaBOlTzTGt4RjLPlJpnILUsKyt1V2qYK1WwdWAkcNqKFWw+5hgaqblRk0D0ShG1RG/ua5KYm1bp5quK2H7mVBWS1LF83mIWrl1elTUV3kTgVEpOaJlTHbV8VVWHIngkZGiFeAEvtLfsxmUJjWn9lF4HV10H7OlY+X/Z70sxZ2rquvmxpeOgjA8oFF7y6udx9LHzAVhy3VK+843vs6255hF8l0REHGGIof0RERERRzx+tH4VS772v2xbtRaAE849lWf99vlMqxnfrCcWM9VIlL5gCKiHHNFyYrsmtlSxAJhU6iTh8RccxbVXrg0ZpykiNSvjJyOVBk5qJK5e3U+lj0RqJNRJpUZCjUSCCEvK2tU7aTTqzJw5JcQFpEACaoKqo1blsiaSkriU1CWkLqHuTPgss14TNDR2FY46Zx6blqxDMOG45uxW1Cbt+0IlWH9ipoZ6aMwFIUoLIUuFWs0E0BntXRTq2OEGjKQmgpQkFQDtknA7iUBm9zP5L+CyBtNOf0m1atstX+BvPng13u9n+4iIiAMj8tuIHkRBNSLiEcb2ZpuPXXkz27/3BVvhHFOf83okSUj7E9SriaaFiamlAJkkYQkNktIE0lQZrClTG7YdgaCaIGgNquavWW75p4lnICkYSkxMHa55ZjU8w5kykFrJVX/mGanbDHp/YjPnDujfspX2yAiuXqPuPE6UNBHSRCcJqq6neZSJv6ZM1mqB3wXXapJAGsKnRGBP/zAqwsDobsCyU2s9Y5Uz74pdo/lJzS1qZVJU99vL1lI/aQHtwvwFjfCp5tXK+3Mvlk3L5EZWZcdWgjO2lpgAq1juVZJlvOndr6lex/9852e4f+vN+LIOKyIiIiIiIiLiUYyxTodv334TP/vk5wGbOH/ju17LzD4TEGtBfKwl4W83ubEThEntciFohw4SZ2X68+aNUBTK1s3jJJLhqqzUDBFrOGX5p6XgalmrziUhDkDCOgcqXHXVUp7ylFPxtEA0lPrnKB0QqZq4SshUtfJ/CfqHVueP3Q29oIR8eMgqo3aMUiA0UuOuE4WEawv803X5c9UgteTMYs1c00Q5c2wlN9cXBuHFiO+kMuFCkUK7nVq9IoVHSprqFVFbZx1eYXD+k6hPOQaA9u41LLv+Uj576V2H/H0RERER8WhBFFQjIh5h/OO197Luqx9H29YpfvBxL6A2axEugyQTnBOS1HJJOx0TJdNUyDKhr2YcqJYauWw4qCddJ6egQSAMGanDfbh6xtzxbUyte4Yy66g6UrMMq9R5ElESulEAmQv3Q+m7+ILBdesZWzifvtTKtGYPKIM1TyfvdiJNku41inTdqM4JtZqRW1UjkfW6UKspWaYVaV4+bzFHr11e5USVZLMUipNQElaWVNn5hlgAtZxX1m5GZ0+jqGXk3rKoSq7pPTQLQcWcAQg0UmUoM1eBxzJYQ16/FYb1TBh6hfOf8Rie9HTrjLpp/VYu/tBX2Ti29BF/z0REREREREREHO74yoplXPPxT9McHQfg6S95BqeffRxpmdUvZbm8TYaXXAu6fZOq+3Sr1GXST1ThSU9ZxM+uWImqBNHThZJ/qZpSJVILAqj0KJ7dMUC49daVnHjiPGqN7uOFNzFVyfHq0d7w0SCqBoZamT/LxlmdEEs1llvU1PQz57N1yVq8VxIF760PAJRNrhSnoF6YKMR6B4hlrJbH84Vw9PhG1ten08rqIbRVuyVUIUNVyi6rpXDaKqpc1e7F9UQBKIg4pp/+B9XD25d+g3/77M8Yb3Ye4CseEREREdGLKKhGRDyCuGP7bi674ip2/fwHALj+YUYu+ENUoDGUkqRCVrNt8w406mH2PhA1X3QbUzlRGomnLzFWVBCaRwn0OWU49UyrexqnzGdw5Roy8bY4T5YojUQtIUogS8zZ2kgsHH8gM1EVYHDVWsYWzCNJhUSU/gQGU089BdRC83tn10W6OanOwcBA9/rLHFU7h1IYtoiCTlZn5/BUZmzbQMOsoYiDlG62VhkXVVYylc5UxOM6OW7dVtJjZiMiZZNTfNg/J5DzIJqWDQUyZ05UmFw1hZgLuIqoCuvf/E+vJk1tj8/819dYuuJ62vnEoXqLREQcoThE5VClMyciIiIi4rDC2tHd/Piaq7n1ku8D0DfYx5/+7R/SlyhZQhWtlIn93ftp3pUPeybTy/VCJZbaXWFwsM6CBSPce8/WkIla1hQFAbUaV0NN095QWq0Od9yxmrPPXmRuVKzSydPu2U8n7dOFBLHVTkvVKsvaXmjmwljhzFXbqOFmjtBcs40WQtNLFadVIvfQUvBI5UwtCqkq1Vyes2h0E/cPz6feCO6CcFABI7qdHqHUK7SKntO1QaXwXRtsz6X1TT+ewQWPB8C3R1l/y9f4h49cd6CXOSIiYhIiv42YjCioRkQ8QvCqvG/JUtb8z39V60ae8XKSKSNk/YmV+ehkYTILuUmq9jnbzO1Wgb4EBlKlUCNwDaf0OaXmPH2pZzDzDKSekf4EN2866dpN9GXmaK0FETENuasJJlwmIRSfQOxcq0Vtzx7y2dOoO6gnMFTz9CfmhB1sCLW0e43lhHrpUDWhVapSLueUvhoMZlhelIO+tMzOUjbOWcD8betJfR5K+G280tHQPVDX1VA2r/L3rsUdP59aaj6GHJvp9yV37ImVIjSgqgexunQWQKiC0l7abM9ROCyLT1jA77/2eQA0J9q8/52fYcPEkof79oiIOLIhdGdFHu4S+WZERETEYQVV5XP33sVV//WRqmHnH73pZcybM9WcqD08rix1h9DFvmecipf2VCRJOaMNIfVecWSc+7hFLLlpDXnH9wiyLowre8mfMukewNVX3cUTnng86orqER/cqIrv2br84unKvxqcq6nr6iAdYCwX9nRMGEWNo/cdN4eJ5ZuZaBYUKpUhgFCd3/RCq5CySp/cS8VZiwJO2LGKu0cW4hJnFWGpdJ0AQYGV8jYvbGn77uOKlf3vRxsuza7TT3oxkpijY9eqH/OVb1/B8rU7932hIyIiJiPy24i9EAXViIhHCJet3cAN3/k24/dbiXg2dxGDj3k+rj8lG0zpdMyRWZYSZbUumUoc5AWV81JUyZKCzEGnEDpqXVMHa+YunZIpjRQGM6XlBY6ejduwDfLcSJyYMxSM2CbOkziPiNKX2nqHMnDvSlrHH81AGkgu5ujUMEHeyCx7NXEVp7N9XelUlUn3++u2vxMTdhupib99qaeeeNJM2Dl/AXPWr0bExM285znsdTA4MXFUAR1tohMt0lkjZK5L0EUsN7VZSPAthOyu8EmnwcVaRgoksq8AW5L+Mm4gEfjTt7yUqdOHAfjuN67iqquuYKyz7RC/YyIiIiIiIiIiDn9cs3kj13zv/1h98+0AzDtmHr//2hdUTaVKDVAkRErR5VclkhADsLf06aoNNThVLU+1ltY559yFXH/dcgSHkPbsBdZadH8Qtm/bzY6dYxx9zLRqO68e1Zxy6l1CEyz7edxVI70WKL6SWBXj6B0P7Rzaaly944VCBZcIybFzad6zwcwSzswLIjBRuKrhKuHI3pf8X+lvTzDYGWdDY5qdkwhpJkiaICHvS8qy/gI7iVYg4yErVfYWUieJqXYnrU9jyrHPC9t4ttzxJf7yX688wPMXEREREXEgREE1IuIRwESe84lb7+D+iz9WrZv+oj9neF6DvmFHXnI+1dBdU8u7VXVOJzdh0nulL4PhNHAnoD/tzuYnAlmipGIlU50CVAROWIDevQbpCf33Kt2yd9clt4ko2a5dkDncSB+Z0+AyNRLYUaE/dCTtqwuNWjeKAEwELglhiTRR6pk5UUUsi3UoNMQaSM1xO5x6mjOm0ze2h0ZzonLnluMUwaVQku7yMVm2ivyERZWTNBWoC4Aj90K7oArnSqSbmVqErCsfXK4lyfdMbohVPq/WdRWmjAzyF2/7w+ra3vt3n2Tj2M2VKyMiImJ/kEO0REREREQcLmgXBV9aeidXffDj1bo//4dX06hnABU3E+kasaAnTolycn/fiqTMuSCWllsLCRnl98EJJ81j/bqd7Nk9Yc2oRII7df98TII4+pPL7+SpTzsJepyoSh4CtMqzs1vLYU3CWZbuVa1csOqh5YWJ3FGEI5SNUAln3Z45Fdk9hkw0QyWa0ikkcNBuRVXhxXTQwsY9Zdv93DH12OqJqirBUiBx3aZUqoi3DFVrTFV0xVS/185096nyrIApi59N2jcdgImtd3Ldz3/CZT9bud/nMSIioheR30Z0EQXViIhHAJ+9dwU3f+7zdHaYi3Ho7PMZOOUxZI0E56DIrUQ+z6HT1klCYpbZ+jQNLs9MqbvCOocWQoLScFo1l6onnsyZ2KnazQFlxjC0OrDHGgWUQmEt8dSc7VfmpjqUvvtXk5y4kKGMMA9vTQTaavlXSRBtU1FqWXAgpCY6FkXXsaoKWQpT+kzIdIR4gTCeE6OnmYNGCokTthyzmNmrl5tI3ONErRpTSddF6rbuxPc1cAP1ioQnYg5d7038ze2iLLvLdQm8V2uUVWb0QxCte+5b0wBCI6wu/3zJHz2DE09dBMCdt97PVz7/HXa2Vxz6N09ExJGCQ5YxFRERERFxuOCSVcu55UufZ9fGzQCcfcHZPOXZ5yGiIfO/5FKWpVpWBFW8q6fJaFlhZNIpWLF+V4IVQCRBQ85qIilPvfBkrvjpMls36cz2932h3H//RqZM7WfK1Abl0bx6vHboRgvs/ZNYKgesVOdkTtTxAlq5CajBDmHiKGJ5qF5o+oT8xKOp3bMK0z2FtnavCWyCv+ip1J8xvp2JtM7utA/ocmrvQ6RB6hAnaEK32VRJmHvNub3OhPC0SDmYatVAwCU1pp/0kmqzbUu/zDs+9DM6+YGcvhEREUDktxGTEAXViIhDjE0TE1xy482s/tb/AiBpxvw/+FP6+ixbdGICGo1ueXzRnSzvmaUvS6SstL8uMJ47ml4Yril9iTlH66mR0rSHwJaiZwJw0lGwdDUZgFgzqv4UagkMpJaRKii1DZtxs6eS9qU9pUFGVHMvlRu2EYhxzcFQv9LfgCwTajXbw0hfcMyG8xdn8QFpYmOkQSTNkkDoRNHBAXya0b9rp3kSqvJ7O4eOQjuUQzVWrMEft4BG2p3fy8InWa6E5lOyl2PWRNTyqS4bWJWibXl93byv8Hz27CtJwtve+9rqtfr3d32OVVtuofCxM2pERERERETEkY+drSaX3X4z13/uywC4xPGmd7+GzEnVgAogQcmCQ1V6zFgucEvnurFLJdLgTjWXaGLNp6Qs6++2DJ01a5haLWHt2u10mVp52ytZCp0i55pr7uEJT1rMZH9su+d+dz+7dSFqQAGHkCDiUK/kBYx1hIlc6FSOVKHj7cwLhdFccCgM9+GzFLbtMv2zXKxxAbkXcm98Fe85fvsq7ppyDNrzu6CsACsjtjRJQAR1oGWFW694WroAJpX99/h39zKtDsw9j8a0EwDojG3i/iXf4b++sISIiIiIiAeGKKhGRBxifOiuu7n5ox9GO20AZj77RdTnHIVLoMiN6KSZ4JLQYCkpmzgJjQZ4L9QyI3WN1IS9AiNuNedpuOAUdeZMTZ1Sq5yaJi7WE2vA1DfUwA33oZu3YyKtUngjtJmDRqJkRU6ybjONY2eRidL0Riz7EotmMpFRQwaUR7Bj9mUmzGaZicNlE6o0URqJzdzXgkO1vFXM2dpIbBzouhh2HL2IGatWkYRS/RB1GibdBVQYXL+B9txZJPXESvV7eGTZgCoLAq4TocDOwwdXQLe0XyaV+Zfl/QQHRLrXJ6MPoupjzz+dZ/3mEwHYtmUXH/7XL7C1eeche+9ERBxR+EWVTg90iYiIiIg4LPDF+5Zx48c/TqfZAuC3XvE8Tjx5YTU57cQ4pnNlhr6GyXFrbpoFfho8BZW+l1bZpUFMJUUkCYvDiVRbizgueMqJXH3l3ZMEQpn0pWH3br5pJaeeNp96vdtR1WuB1727NpXjC4qvyvxTqeEkQ0jxKGOFTch7utVThS81TCX31jg2dSAqTCxeSG35Gjo6eeK+g+WolvcX7lzPusFZ5C41N2pwphaFVZ9pEQ7qgFSQ1FljKio7sD2fky5pP1+gMvlWRJhx8suqFTvu/Taf/PLP2bpzYv9vgIiIiMhvIyYhCqoREYcQt23fzk9+/FO2/NyC3bMp05j+/D8CrMS/1YLBoZIwQq0upIHjWYm/IGKsS/DUnHUybXuhQJla89QSqCXmTC3zSMuZfssatW6qaSCztRPm4+9bj/cWEdDBHKcOE0T7Vqxl6KR59GfOnJgYAbYSJiOYiYSYAQf1EC9QT6CWltuamFqvmQtWAFGtxNLye8Mm2K08vy/plmqJgNZT9syYwdDGjRByTotwiypZ0aK+dRty1EwyF/Jk1Wyk5bZpIO/WUMpIaMcH8qtd0dX3jF8+V4Gj7neyv/f2Le96JbWQE/bZj3yHu+6+jk4xeijfRhERRwYi4YyIiIg4YnD/7h1cc80V3PrdnwAwPHWYN7z196uy/rIyqZzUT0IFVSm2ln+LdOXLEolI5QxNxMTUVBo4Mso808DsUPX09zU47rjZ3Hbbmv2eqyCMjbe4796NnH7mAiqxVAWvbZiUnVp+0ZQ/i3tjAEzgVVW8h1ZurtI8bJb7qoIeRZgoXMUpQaGW0ZwxncENGyHw9LLqvpzYd+0Oc3ZvZsXgvOqUJMRpQXCn+p5TE4eW2QqlDr0/VFmqk56YySXHAvXhoxlacIHtkk+w/vav8vcfuPoAg0ZERER+G9GLKKhGRBwieFU+dsddLPnIB6t1c178WlyjH+eMiCVJt5ETTL6fpjYTnWU2Mz2YwUCiIXvUM61e0Eg8dVfQCBmojcTcoqDUQpl94gg5VrbU60Jt0Qzcyg12TNFqDj4dH0fGJ5h61Ag1Zy6CeqL0p7ZNlih9ThmueWqJJxPoS0PCqphrNQHocSPUXOlg1ZCTpeQaJtDDt4fDhNmalDmuJq7umjuXwS2bkLxDImqdULGogWkrVuGPP4qBmnZL8wMxbXsr9y8zWsumWuV2uYdWAUXoxArdkn4huClC4yvnupFUdhDbJhETwRcumsNr3vg7AHQ6Of/8tk+zeeKWQ/AOioiIiIiIiIg4/KCqfOHeO7niPz9UrXvNW36fadMGQ9SSTb5nwX3qnLlSE3py6V3XxVrGf1plVYqQ4CTBkQEJidSxUvte1cGjFCgeEeGcc4/h9ttX02pNjl4qhdArr1jKBU8+MYwRMlDpBPfp5D0m34LDdeMGFNpFi/HCZNg8cFav0PFS9R2YyINQ6s2pmqtDPYzOn0v/pi24PK8aWPVO3B+3eQX3zFiE9GQqdjp2YO8xC6xX6BRIq+gOkDjIUjRN0J591cleYqr0XGIP+a3iAZRpx/42LmS37l53Nd/9/lXcfveWfd8IERERERGTEAXViIhDhB+uW8fVX/8mYyuXA9C36EQGHvvsqpy/yK08viozT7qlPmkK7XZ3NrrmlLrzQSRV+hLP3L6CwVTpS5WRumdmw/5uOMtUrQcym0q3YVWCuTUbC2fC5p2knTZ9iZI5z0Dmad+1hjlnL6CReJzAcKrMqnsGU08j8QykBUOZZyCItVni6UsI52RiaSO1Eq6hutlba6VTQaAm5moNRUnBdWv3UwdDNWUo8/QnSiZKPYVdC49i1upVJo7iqYvSP76bpMhJpg9ST8oJeaUeIrUKTIROpRsvUNLnMtMq9/aDwBogmHhaCxmySRCDE5lMqysKLmDWVVv5p2/6XWbPnQbAj793PT/+0eWMtTc+km+viIhfP8TQ/oiIiIgjAtdsXs9N376ENXfeC8AxJx3NS17xbBIHiFJLjINZs1HjnkkQUSHk2/eM15te6nCBfLmqvN9JGoRQ6XIwjL9paCrlHDz+8cfy85/ds8/5btq0nXYrZ/5RU6p11oiqDfsIqnYeEsKppGKZNuGf+zatQumEPFNFqkZTeU+fp7GOQ4GWF5qFTeDngDhh99FHMXXVats+MM2igProKLWizba+qVWZv/eKL9QavpZlVRMdpJmHJ8/4qJbZCan0asE9na66T7SI69lG9slZTWvDTD3m+dWrs2XZl3nL+36KTsoQiIiIACK/jZiEKKhGRBwCtIqCL912G0s/+6lq3bQXvxFx1rApz835WJKo0j0pIjgnJIkYcVJAPSMNz1Dd058q02qeRUMFIzUTQesJ9IWS+bozUbDuoC+DhrOlL4H+VBnOlMHQfGrktAW4e9YwnHlmNJR02y4agzWGp9Spi5A5ZWrdM7vfM7WuzGx45vV75vR7+msmeo5kntQpQ6mnL/EkwdE6kJroWroTygZZ9cQaFNQDi+5L7X5JzxLMfVp3Jqg2nOJmTqHebtLfGmcgVabUCgbvX40/6WgGMkBLh2uXQ3ZL+RUpJ+1DqVnv4kOkQT0phVftrXqaJKBK73deeOUK7Pj9g3289R9fUb3W//y3n2TL+C2ReEZE9KL7QffwloiIiIiIXxnavuCbS2/lpx/ucty3/vNr6asn1eR2Klh1UNl0iu7Ht4S/neuuU8qopYxEMpzUSMLfQm2/56GVCqgh51RZfOwstm8fY8eOsXCsBFXPTy9fylOednK1r4jgOZCYSk9uannGYa0qLd8KzVG7btQCaIXYKK8w2pFQDSUUWD5qp4BcbfvW1KmkzRaMNi0iQMEXynEblnP3rMVAqFhT6LSpGquqesuuKsv+C8uxklD6b80MEioTbuDBk8RUVSZ1uoIeB2uXt44sfDpZ/2wAmjvv5Zbrf8Q3f3jvfp+viIhHNSK/jehBFFQjIg4BvnD/vVz9qU/T2b0LgMHznk7/cafhklKmoyrlETFHqjVxgnqdKg4AoD+DodRTF8hEGc5yZjY8Qxn0J8pQZiJm2jMxXeY1lUJhTUxYrTlCMymYMmuADE9jdIwFfQW771rPsefOYSCDeuoZSJVpdXO9DqTKrIYyUjMnaoaN0whi6UCq9CWhxD+BqQ1ohP16M0hDJVGVfFWVfWGcsBQtawn0Z3aswVRpn3A0U5avYGrN09i6DR0ZIOmrIYHQInb93dJ/8xY0vTBRlG7UvcL56dJwpyEWQbqlZyXZTxwhrauMItBJHWrLIX/rJU/h7MdYZ9R7lq3hi5/+JqOdSDwjIiIiIiIijhx8Z9W9LPn8xezeugOApz73CTzxKWeShmofq/DRkGNvPKoUT8tGn6lM/slpjtXMyvxDEyokARJc2FaqeqMSZbtSoQxnUuCpF57ET39yVxURcM89G5g7bwpDw/Vqz8LnqE6OBug9hk4Keyr7CHhaRZN2YZpm1XxKIS+CuKrmNB3PhbYKLS9VJX1HpeKXucL6hYuZvWq5aaMeZuzexmhjgPG0Dw3jF4WGBdR3GwGYSdcaAFjrgpLR0s1R6H2uwolKqcz6bnm/5Qh0xygVcHEp0497STXEtnu/xj/9509ptQsiIiIiIvaPKKhGRDxMbB4f42uX/ZD7v/ENAKRWZ+pvvZ5aXajVzHlacpdeAdC5shGVrUsSSBPoy5ThmpW/iyhzB5T+FPocDGcwkHgysZKnckK6LLG30H+LCaiHsROBwZoyUofF581j521r2bJsM/OPn8ZgX1rFKdVTZSAzEbaRKNP7lMEM6ok1kUodTBQSBMaQt+o8dfEkIbM0EZu1Lymph8DvNJTXdwXWAiuRUqWKKqglnnrqqQ/XSQYaDGzbRrJyA/7YedTTLh8sxy58t6wfhcILLe9sMr9n2/I5ryVGfnO6rlRg8oYhT7V02ZpDlZBl2908ccI//9trqr3e9+4vsnHzTXjffvBvooiIIw2lJemQLL/qi4mIiIh4dGJXa4Kf3HY9l3/u6wBktZS3/NMfk5SNSnsaUrnARUsZtLuINV2tmkuZYzRxGa5yp9ZIyEgkpVsvRM8o9Nz2kjZl2rQhhkf6WLlyK51Ozg3XL+dxTzi2u4VqcKey1zilmFoy167Q6H1O7nOaeU6nsOawuUqoJIMJb2PkCjs7wnghtItu09NqJIs+pZU7Rmv9TNT7GNy+nU6uHL1lNStnHW1xXxKiA3oNAW1vC/QMHDYKXVvFVFg0S3oIMd1BlJAj0LO/MvlESzerV/qnnkr/1FPs2lrbWXX7d/i3j1zzi98oERGPFkR+G7EX0l/1CURE/Lrjw9f/nO+/6U2UBG/K015IY+Ysa25kPGdSIyrV7sx9lsH4uN0CDPeby7NQSBX6ywZQagH/NWduAATaheBk3xJzCW4BF8RNwdwCjdTTX6+xe2Y/a5du5fwXn4KKcbVaAoMhQxSsIRYanAVCaBxlWVHmC7BmUf2plfi3vOWYJmINqKCqPMJjIm3pJxCCSKlC7hUXogvSssFV6WI9eT6dy2+FExfSyBxpGFtQHOZCFbEPsTInNRWhUKUVhN80lE/Vku73Vilgey0beNFtmhDcqOXL9bsX/iVbN+/Y9znuue3rqzMx0WJ0T5Mnnv0nrFh3AoO1cx/guyci4ghGJIoRERERv9b42n1LuPRv3knRMZfiy//0tzl60eyKK6USKn4Imall1Q898U4hD1URVNsIjlQaJFJDSKESWoPY2lUFwzjlaGVrUzuCGQsEEcf555/C1792LQuPnsE55ywiy5LSv4nXAtWi56y6LC4ksk46JkBHmzQLaCs0vVRV9wXGmwXjoaMdYTx3Vurvy3L/rm5ZqDBRCBMqtFRYPX8RJyy7ndrQKGunzaOtCert90Kno3hvzVbJC2gViAanqi8F1B6nqWKKbkGXsHtn+4ZL0cJbyT9Y2b8AuG7Jfy9CXMCMhS9i9Y5/Ajw71l/GxV98Eq/9/bOZNXPgwb15IiKOVER+G9GDKKhGRDwM3Lx1C1dfcyNadMthZm9fzQU7b0d2dbczIU965tQVF5Q978EloclScIISHJt9CewUGBWqLvQlyjxQG62L3pzrki+V2aYAe7aNk7cLbvruvXgpySuhWVPXQWqZpEYMi1B11KdCHfu7n5LPSTlRjqpQPhPluZqAql36KpPP2wUiXpXVB67rvYdWB3ffWtyGrXSw83ACZdGWAJ2ewXpF0/KYgj2n5Wap057z2neCsFd0XbdqCzt39LyQB0FRFGzduptbb7+Gx519IqkbfED7RUREREREREQcbli9ZysffevbWL1sBQD1Ro3X/+WLrMI8xEuJdBtQ9YqoUHKrSbZJBIc4F5yp2aSyfie9bav2hiAVO50MRak3EhYvnsWtt63ita+/sHrEe4+nNemMugmu0LUedKXWvOjQLjwdL8GdasJoaQ5tFibsFh42Nx0TPQ2o6DlCEcr4Q9W+GUXJ2DIyg/lb1nH1cY+nKEz/LHIl74AvSXSuaEli8x4RtTqAIh1PRbq9R0UsXqy8zNLuWugkfr32Z+8kb+3ez3PcLe0Sl4G2SZxy/03v5q/ffSoX/9cLDvL6RERERDw6EQXViIiHiNx7PnXnnfzsgx+etH7ZDdfQecGrqS+wciPnrJw/SaxTaVEoeW7l/p029PULaSrMHvRMqfsghiozGwV9fZ5EYCD1DKZKPemKffQIqiVRKrBy/XK9V0CU/sRcp3u2T3D/zRuYvXgqo9snmH3mfGrO00iMQ7ULK2VvF+Y6beUmkDZzGM0d7TBLv7MpuIRqRn6i42h6C+Qv86JK2VJQawAlpYBZOhlsi0SULGTDEgTTzMHEzfeRnnwKxdI1DJ23GK1lNAsrGCv5Y/l8+FBS5SSsQ2kk9lxkTulLjToPpJ5GZs9Pb0xCGVcAk5tULXj/TOp93Y9J2esWrJRsbLTNnj2jAPzdmz/BJd87jSmNpz7o91RExJEEPUw6mIq1in4C8JvA+cDJwDCwE7gFuBj4ou6nq5yIrASO/gWH6FPV5kM4rwz4/4A/BI4DWsAS4L9V9RsH2KcOvBv4A2AGcB/wPlW9+ADbN4A7gTpwkqqOPtjzjIiIePTBe88P19zKpmX3VOtarTaXXnwjC4+eb9VEGN+iR8Pbd5K6K5iWjlLEBXm060ottz4w9uOoDCOUf2/YuIO84/nfL16DS1zYy7o56aQx9mdHKO8qhRaVqaA0DEBXmyxCo6l2QSWmOqCOVJpkTaFPbYwplWPVth0c202qnnNWLqFQZ5EEZak/QOlSDSsEDSZetV/uZSZqo/fcy2ZUe5X17y3EAl+6aget/Qqq+yLPcyDn8suv5ObbHsM5Z8x9QPtFRBzJOFz4bcSBISKPBS7EeP+xwHRgCtAEtgObgBuAK4EfqurOh3qsKKhGRDxEfGPlKn7yua/Q3LAWgGzGHDpbN4J6Nn7xgyz4y/8ImVGgofzfOWi3AIFWE9LMxNa+mqc/85Wrswz3bxfW7KkU/irxDxujlyJpEAOTnniBjOB0zYRCPfdct46Tzj+KxmCN1Uu3MmWszcBIN0e1HL8ikpT5r2KNRJ3SKqBvAPZ0BIfSBJLEoz4hTbpz/h1jhTiRveb/bfxMuttWt0FM9TtHERHq04dITltA8641DJ29GE2MF2Y9TlQf9i87rXY8ZM6ul3D+hQfE00gtViBx3cn6AnBadqQtS8js8W9e8X7AOGziTAiupUz6CfCj76zgrPNm8tyn/SWrVm7iyp/exqXf+REv+Z0TqaWReEY8inH48M2nAT/qub8cWAEcAzwjLC8Tkd9V1dZ+9ge4AziQXX3/baMPgiB0/hAjegUmeg5g5O9CEblIVf9mP7v+D/BiYDdwN3AS8FkRaajqx/az/duAxcCLo5gaERHxQHH7jhX8/KtfZ83967orFb7//e/x+f/7FzInJKHxacnHKh4JiCRYkyiHscUCcKShzN+JQyQpNUxcyE6tpE7Z62NVyzG6zFdIqj3Wrd/Orbes5rTT57Ns2Uae8cxT8eopaAKKqgmrJvFmFLS6Am9YvHbIfUGz6NDMzTTQ8pCrWEm+h9Fc6HhhtC2sHk0oCqETzqHwJpg2C2h5YSIYEZohX7WZO/rH9rBg8xrWTJ3H7G0buXHKCeQdaLdNVPVezc5aUOWlSsu6YokPuQPtHCm3CcGrdr8nGqAVLLF5geSTBdXCTSWp97qBlb21ZSdKmhRMjNvXxuYVX+Gv33UmP/rqHyCxO3nEox3xn8BhiWCgeCnw18BZPQ/1fsINhuVo4LHAnwFjIvJp4F9Vde2DPW5sShUR8RCwq93myzffxj1f+pytEGHOq99BNnMeAGNLb2H0lquAbi68OVO1ylXtdCBNhVoNRurKlJrHY8LdSE0Bywztc1ALrtPEKal4ksSWtGepJZ7+VEnElppTBmtKX2ZxAltX7WJoRh+NoRoemH/ufNbcuJZaKIFXLcXYkFalhOZXRpRr4klD7EDqrFlVfwppiCfwKPXE3KaZU2pijzfCOZf5pOX4ReDK5SRf7iFL7JitO1fTOHUB9UQZnDWEKzz57jErLXNaHdNcrgpixxRRy0YVrSIWNIjEg7Wecn7tmbDXUkAOd6he0ioTLEtM4M6SyWLqulV76B/ImDN3mHe999XVvm9/66fYMX6rxRZERET8qiGYgPr/gNmqeqyqPkZVpwN/hDlDnwf840HGeKOqnn+A5aF0orsIE1NXAKeq6pmqehzwW+F83ioik+orReQsTEy9FVikqmcAjwEmgHeLTK6XFZETMFL5fVX92kM4x4iIiEch2kWHq++/ha994PPVujnzpgNww8/v5LJLflZxpL19/V0PqAuiavenZiIZTlKcpJQfV4KV+guyl0axl2Kxv54BpZCpBVddsYwLnnI8CxZOYWK8zdYte1A6mJha7mvxA56iylct4b2n8G06vkPuzXnaVhNTwcr5m9545VgubGkJ7Z7rLR2mubcGVoUX2oXQKYTc26KqHLv+fu6ds5gt6QiSFwxOjOJDTwAtXaWEOz6Ior3E03vLUS0P6j3S9j2xAIAImgWHgnNo71PplQWPezuLLvw3Fj35X1l0wb+y6PHvZdFj38sx5XLev/CK17yVY8/7F2r98wFoja3m9pu+x5e/eec+r0NERETErxoiMhv4AfAFTEzdO4HmQOUQiiUZvhG4U0Re9mCPHQXViIiHgI8tu4drPvYJ/MQYACPnP5fG4lOZ89I/rbbZ/JUP4zutkAcKnbYyMWGOVBEYGIBaDYZqytR6QeqUTDwN52kk3hoteag5T02gnho3aqRQd9boqRYyrGoOGgnUndq2otRFaTgrt/fes/K2zRx79hxzwSrMnd1HX13YtWm0agdQNo0SzHWQJRKaDliea8OFTvdqmayZg74UBjJldqOgLkqC4pwJqybCeurOxNoEO7/UWTaUEkTU8EnkgM7ardRnjVDrq1Wi6cyzFjB2x1okHNcFkdOFa+8P1z+cqT13wcXrS+FUzZnazbCa3Ni0bJ7VW6ommBDtyhgAus4LMBfBzddu5LFPmovgeMFvPZELnnImACtXbOQjH/wqLX//oX/zRUT8uuBQdUF9+LgeOFFV/0tVN/c+oKr/A7wr3H2tiDzivCiQvj8Jd1+tqnf3nM+3gfeFu+/ca9cLwu1/quqOsP1twNeAmViUQS8+hH3E/fkhO/mIiIgjHjdsvYsffvRidu/YA8BvvvgpvPP9XX773rd/hnazaVn14WPaSU9DKkmwBlLGpMxLWseRAUlwOHZrlGTvn6Nh8n3yCg1VX+X23b4Ed925lqOPmcHAYB2AJ194Ij+9fCmF7wRnalfmFRw+JPH3Rg4UTFDg8WrVYR4zfEIwegaHascLY7mwu+3MuRpG9ioUQYjN1TGWO9paNm21ZdaOzewYmMKE1MlzuG3oGE7ftdyaTmERUiFTwM7Zq4mlvUS16F4LijlTKR/X6tmS8gVJrHRNnZiwWhLfoiLI+9RYHDWrxdadGRMTjhlHvbBav33td/iXf7+MZisnIuJRjcOH30ZQGQiWAE9n7wJeW9YDtwM/A24E7sXMCD0fqIA5V78gIu99MMePgmpExIPE8t17+O5V17Hxx98FwDUGmPabryFJYOQxF9B/8jkAdLZuYMePvlo5UvMc1CviLD+1Vhcr9089UzPFqzCQKjMbPuR/wpQaeKysKglNllJMgKw5E1brif3dl5rY2kiUwRpMbWjYTll7+2YWnTKDWs2C9GsOBlLlzCfM5e4bNiCidPOwTDRtJCakDtaUqQ2lkQaR1dm2hbeYgcwpI5kyr98zUisYygoGUm95pSn0ZSb0Osrs0lB2D4iUjtogcuYFE8s3M/XkOfQnSl9qTttsoEZ92gBu43YguEUdpNhYqbPthlLoS0y4VYJTN7Ey/Tw4DVpeqpz+vaenKgEWu7Y0iL17RykIsOy2bRx30lQajQzFI+J4z7+/Hudsw39/75dYu/4WvD9QBXFExBEOd4iWhwlV3a2qnYNs8r1wOw0TJh9p/CZQA+5T1cv383hZun+OiBzbs3443G7ca/sN4XakXCEiLwV+A3iPqsaZnYiIiAeEna09XH/zNVz6OeO4ff113vKuV/CM55zHBU87G4D1a7bwiQ9+q3KpdlNSJ4uUlEmp4oIr1YXGU6UomuDE9YgL+xMYShWw566Ux1Ga7Ta3LVnLY85bhNVKeYaHa8yY1c/9920NGaqCJfBDQWfS2KpK4TsWD+C7DlPvTSRVtQirZmEl/OO5sKMl5PTwxaCB5t62a+UmvOZeqrgAcs/cLeu4b+QoWi2rWhtzDbZnwyxob7WzqS5VA2n1VZMACY4AKYLAWhQh06rnanom/e3FCWTZfkT0CDhlrECIC+jhw46CExeMs/T+BhSexsBi+kdOt+cu38Oa+y7ln/7tpwd+A0VEPBpwmPDbCBCRmcB3gVk9q1vAV7Cqr1mquiBUgj1ZVR+nqicCQ8DZwN9iAmsoD0CBt4jI/3ug5xBfyoiIBwFV5cN3LeOWD/1XxaSmPf8VpMPTQoWOMOv3/hyCyWnrpf9Dc8sWOm0r9w9aG/W6NajKUnAi7MkdotCfeNIg4KViTag63vKYNMzGl/PCTrrTLk66E8yps33LDvatiQ5b1+1hwQlTcaI4sdL8egKN/ow5i0ZYvWwbYGO0im4Mk8PySmvl9WPX6H0gkxhPG8hMgB2pKQMp9CdaiaiWTYo9P6IgQhpiDMoIpwKLNujcs46hk+YizlmpvXQbRE07eQ7j920k856aC+7U1KIHMmfCqYjSSGypJybUZiEvtQjNBUphtSzzV4xn7v189rpRJ/08EGi1CpbfvZNTz5zZM8konH76sbzqNc8FYHR0gn98+6eZKJY+7PddRETEI4re1h4TB9jmT0TkUhH5sYh8QUT+RESGHuLxHh9ur9rfg6q6DosC6N0WLEAf9nWilvc3AojIMPB+jCBe9BDPMSIi4lEGVeWajbfy5Ys+RhFymf70L1/EvPkzEBH+/j2vJgmzyx/696+zYd3WSY08AVJJTb6ULOSk2t9lpmrP0eg2rOrGN03WVA/k4AoHFOHan9/HeY87hiTpMraCnPOesICbrl9DURRYe1QNGaw+bOVCJJdSaAuP8cNm0Y0vBePDzcLyVNse9rSFidCQtVAxbh44c8tLyEx15Gr7gNAp4KgNq1g+ZQFt7yzmNOiidw0exUmja5GisJ8VQiV2SiDj0ikg90g7qKdeu7bXgz1VXqlKtlIpS6+6LlfVbr5qyGI9ecEYd69oUOTdAafNewGItV3ZteUqPv+lH7N67Y4DvDYRERERv1R8EuuJUH6ZXAacoKovU9VLVXXb/nZSVa+qt6nq+4LA+jqsWW05zr+HqK1fiCioRkQ8CPx80yZ+csn/sXvpbQBksxcw9WkvJElMWCxyaBx1LFOe+psAaKvJlm98gk7bkyQ2Wex6/tU5LFN1Z0dALO/UoPRnpWPSMdoJJUiWL2+z6EWYvK72MPKTJSYulmTwzmvWc9oT5lFz9g8+pSsgeoVFp85kzbLttFtFCN2Xyslpwid0CMdXaOvkKoWErouz5qCRKQOZiayVq1MshiCryrhCBmpwuyaipM0mfvc4tTlTIGSlZkmIN0hgoOGYfsJsJu7dACHbNaHrcE1CRmsm5tQdTKmcrN3SsMmMs8y3LTlsrwnChQxWpDuxn4jDATf9bCPnPWkeieuVWo2u/90/vIKRKYMAfOFzl3HDjT+jU+x8IG+viIgjB4eqHOqXUxZV5iXdqqq7D7DN72E5q08Dfh/4CLBcRJ7xEI53Qri97yDblK7SE3vW/RD7yH+LiJwvIoMi8opwXvf0OFHfDcwF/uwgTbYiIiIiJmHt6EZ+/qMfcP0VSwCYv3AWr33jb1dmqpNPOYpXvs4mjSfGW7znHZ+rxFQr+8+CCzWrJppLjlQ6Vcv7Nnk92ds6GT1uyv1AcOzcMcqWLXs49riZ1baWl1qQZY7Tz5rDkhs2BeG2t/TfyKnXDl7bJqYW0M6hExylHqk48Z62swZVhbC5mQQhNRwP26ZZGG/2ZWV+cLjmObixJgOje1hbn4F6c6eW/DPXhLv75nHKxFoqobg7QNelWrlVw633k58xJ2iZvbD385gmk8usykdCAyu8/cDoSzvMnNphzcZaeB7tKtP6FEZmhcQZLdi85tv81du/u9/XJSLiiMevF789oiEi5wPPp/uF8R+q+lxVXfNgx1LVTwGPw6q+yumn9x10p4AoqEZEPEDk3vOZO+7gzk9+tFo368V/RlLLSJIeyqcw9XmvwvWbeWns+stoLl9KkgjihDS1f/NJYrmoE4Wj8Fa8FBp6dl2VYciOTxjtGNFrFkLTB9KnPSlUahPQYXg8sG3jOApMm9UfBMFQAZTYdj7YMxefPZv7b95IJ1QAldeiakJqs2MdSjshkF+w/R3QlxrpkuCKzYJDVrV0ynqy0LAqdVauL2LnACaEDqbQvHM1/acuxKtY46vAK9MgBCcOpiyaSnPrHvy4tQJIQvSBk6542hcyZglOXCfaI6eGuIEgyBb0llh1q6LUd1+LymEbrnvH1ibj4x0WLBwCyjyvpHrCZswY5m/+/uXVe+Qtb/oQE8VdPU0RIiIeJTi0ZPMoEVl+oOWhn6KcQzfPdH+ZST/HZq1PBgaAqcALgFuAGcC3wxgPBtPC7faDbFM+NrVcoaqrMLF0DuZu3QN8FitceEO4nrOwjqX/q6o/LPcVkVkiUkUCRERERPTCq+eWTbdw8T9/slr3N+9+FY2+erdyx8Ff//3LmDbd+O3XvnwFN1y7DBGHYM2mHCmJqwFJ4Ecmqkpwrpb/uZDe33Wp0nMLe7eo6q7vZqhecfkynnrhSZRd57vCqd076ZQ5rF61kz2jLUwiFVz4zybTO3jySqdsBqG0lF5zhdEcCoTcw56OfdiWtFERigJaudBWIcdcq167zayabTh2/XLumLoYkBD/1Z3QB1hVn8mszi76ilZwXSgSHKPitetWLeiWkHWfkH2eu/1r04EoQ2Up1nKsHMjhrBPGWbJsgK6q2z3OyKwLSVJ73Sd238WVV/6Yn1wV02QiHqWIYurhgjf3/P0VVX3zAbd8AFDVe4FnQZUN8xsicsYv2i8KqhERDxBfun8FV178Rdpbreqy/9THMnDGE0hSjJio8SBV0MYII895ZbXvxLWXUBQmopYz06nrdgR1ouTeZrfbasJg7qFThBJ1YFcHdreN9LVze1ygKu0XMbGyjEbKC+XO69Zz6uPnAYEaqTk4U+lpyqQw/agRRnc2GdvVDpTThMVcYWdbaHojip7S3WrNrvqcJ3UmpoKdQ1KKkdh5DWbQX1Maqe0DSibdpe4g37ob6hkM91FaRcvJdOnlgE6YfdYCdt2+lobrCq5ZOCeHuV6zSTEAwQFb5qkmVE2mhMkZWGXZlgax2YfxrVDNsrauuXIDT3zyAnqdF+FMA8H2vOZPXsAJJx4FwA3XLeUrX7qUtl93KN6GERERhwihOdQ3gQz4pqp+ee9tVPX3VfUTqrpMVcdVdaeqXgo8CbgZiwt4sGX1ZcRA+yDblM7Svr3O553A7wJfBS7HSp0eq6o/FlMVPgqMAX8ZrvEFQXDeBOwUkZ+JyEkP8nwjIiKOcKwevYfLLv4aa1ZYJPNjzz+NZ//WEwECJ7QqoxnThvjbt/9htd/fvflTqCYk0iCVBok0AmPqZqOa0KnhPxvRRNhS4qxVf+/Nq0q2Zv8l1f3Vq7bSP1BnxszhalvjYEW4l5C4Guc/5Th+fuX9+4yndBsrFd6W8u+gZZrzNBc0ZKvubrtqhLLqq+2FTo++WUZLlVx9aM8O2pKwuzYYYk+tMs2XvaQwYfaWgWM4txmSXsoMqm4J2n6MuoHIlo7UsgmA9pSR9WYxlIS5dB+UYmpwUcwYaVN4YeeetHKnqioaTlRcjSlzn10dffu6S/mbf/xWFQ0RERER8cuEiGRY1ZhgvPf/HYpxVfUO4AN0P3Wf94v2iYJqRMQDwM5Wi6/edAv3f+WLtsIlzHjRn5nrNDgaRcqceBNMB574W9SOOZ3znv97HP+yPyPvWFOqdjtQOmfh9VloKjXhhbGOZTG1CmiHjqLtwkqRWnnCzrZYCRGOQs0tUIqDWfjX3FFb1t67kxnzh6j31/ChEVOvM9QHzlXytsXnzWflDWtJS1lQldFOyI0qoPCCql2v5dx7hmqBmqqdV+ogR+kEt2wtiLfmCg1xBKLUEivn70+VgdTTXLqWxikLUIzrlW7dkvN1zZ3K0Ix+kkTIt+8hdVbun4a81HrazZ/tS6wZVuZMnC2zaRGtOGjJQ8uM10K7/LV8Ti1n1Xy5q5bvZvrMBiNT+ip6bz8MZNJkYy1LeM+/dTvi/sPbPsGOPbej2ht4FRFxhKP8R/ZwF8MaVV18oOXBnlpwa34PWAjcBLzyweyvqhPA28Ldp4nIlAexezPc1g6yTT3c7pPpqqrfUNWXqOrTVPW1qnpreOh1WLnS21V1vYg8AROMC+D1wN8BZwE/fpDnGxERcQSjUzS5Y+WNXPyfNqckIvzDe19DGkhNGXtkYqLjla95HqecugiA7dv2sGndbhKX4iQJLlVrPuUIS8jfLJVBJ0nFn7o/Rc33WQqrpbu1ez/BYQ5Y74Wrr7qbC558UtizrBQqqrGEBNWC2XP6UZRNG0ftGCqoerzmdlty7VDqbzqj5aSO5mYkKIBtLan4YZnBn4d9y3gAa2oldAqh3RFaTc9xm1eybPriSY5U6Aqq5U/27ekQijCjtatyoEpwPUgZ+F+Km95XlVeTUAqvVZ6V7iPEavmjITSjsu09Z5wwzq339O9jFSgHVVUaI6eR9ZlJI29t4e47f8gHP3HlvucREXGk49Dy24iHhsdjlWMAl6rqpoNt/CDxqZ6/f+MXbRwF1YiIB4APL72H6z/6MXzbTENTL/wdanMXmaMx5CF5D3mu5GHSW5KUWX/xAcae8UrOHFuNiNJshTl0gU5u5CtRI5CdwjGWW1j9zpZjIhfG87K8v2xGZTPh5Qx6Sc4k5IfaZLPQaikr79zKsWfOQoBmIHn1pFvIU1YMlaVcg1Ma1Adr7Nqwm0KtO+l44Wir0FLZZ4K8L+mKpRJGsuajRpOzREkTrUTczMFgAtMbnoFUaTjPcKp0VmymsWAqSS2txvFYAykhNCHFRNIEE58XnTufLbeuoxbK+VOBTEKeaogVqIX81XqIBUh7P+2EqmFV2cdAtce7IJAEsbZ8jrxXbrlhM+c9fn41iAmp3WeghOJ5+jMfw7Ofa/1k1q/byn/86xdp5vc+yHdeRMSvJxRzeh+S5RCfm4gMAt/HunveCTzrINmpB8PPw60Djn0Q++0It9MOsk352I6DbFMhdDn9F2AJ8KGw+m+xD6fnqOrHVfU9wJuAecCrHsT5RkREHMG4b/dtfPqiixkbtfmb33vFMznljMU9Jshuo1MRRy2r877/+HPe/s5X845/+GsWLlyAI2Ny0ykohc2910HP53qY6Jb9/CTtyqlliqvte+uSFZx08jz6+jJjnOLw+CpaqRRrlRwl50lPPpqfXbEK7ws8HQosAkCxvgBlP4JSTG0WMNGxuKvCC80cxooEj1RVYK1CGO8YR/ZAjjCeh6zV3Er75+3YwMaBGXSSrGqAVf5uKCNKzbGq+KLgpuQozmmuQKx0zQjqpBwuLAqgdK+WpWa9FFS7rlOqjNSe+2AqcI+Yc8z8Fuu31Gi3XDjHItxaIy87xwJVz/CcZ1b77dlyBR/4+GVs3zl2gHdWRMSRh8OZ3z7KsLDn7x8cyoFV9W6gLC096hdtHwXViIhfgHt37uYHP72STVf8CIBkcIQZv/lK0lDqn3eUVstuOy0jIKXjMUkcY66PbekQR7W3UXRAnLlQ88I4UEcto2miEDpe2NZKGS+EsdwC8As1QkfgQ83cOoZ6zMVaNvEUupn1y5ds5JgzZpEkjtyXoqkRYlVz1ObanSEXTFw84dy5rF2ygcIrE2Giv/yw701TKrNIC+26PhOxXNfMwUDaFSZFtHLC1pIgpibecmKLDs2125h6/CzqArVQpl+eUxlTgIYSq9QE08ZAytT5w+xauc3cqU5ppNb4aihVGolSC4Jo2YCroo7azYEtnaqlXFyJrOX66stOuPXGzZxy+gyymjkrzLjRzQdjnx8Nyj9f9HqyzJwZ//X+r3D/ypsp/IGaiEdERDzSEJF+4P+wme17gN84UAfQB4Dekv30gFvti3vC7XEH2aYUaO85yDa9+Fcsb/VPtGuFfyJwt6r2Nr+6pOexiIiIRzlGOzu5/saf8a0v/hiAoZEB/vodf2gcqZxcdiE3X8qSe+WCp5zFW/7u5Sw6Zib33r2lhwtNKuwPk85duOqnZ9hOC7rt7Q/k2AouVREmmm2W3rWWs84+uvuwKlQl/OZW9bQo64v6B2ssWDjMsqWb8HTwmlOor5q7lo2oOoXx6z1tx1jhaGMl/RuaSU8TKctHbRdCG4IBIfQbKGx9XoC0OyzYtYkVw/MoCuPdJSaV+4engLbS7KSsZwqL/Vbj/L1PifdIUZJ39lVinCCud9CepbTTFr77QyGxcrA0UxYf1eLulQ186dwtB9fwA4Kiikio9S+gb+R0e7hosmXVj/m7d196gNctIiIi4hHDzJ6/H4lcvbXYJ+CsX7RhFFQjIg4CVeW/7riTm/77g9W6WS/8YxpThpAgprbbUOSQdzxFN5IJ58AFHnnXwFGcPLGW1PmesUNGUyFM5K7iOrs6jmYuTBSOtreZco+5LsdzYXs7uFR7KoDKBkoKjO9ps3vrODMXjRhBVAClP/zcLx2feztOEcgaCTMXT2Pz3VusUVbYl722z5xRV08ZvG+Mz2FZpYns++GSiVJPoD+BqTUYrim771zP1FPnkSRWtt+fKA3REDtgGbJKyGZNrIy/dJUuPH0W2+7dSs0XNEJsQiNlUnm/uNCUqnJWUOXdEs4/CSJuPYybhB8QvRgbbbN65S5OOHWKNT+QMlXVhadufx+lnmNPmM/r/+y3AWg227zjbz9Gs7hrP9tGRByBkEO0HKrTEWlgguKTgZXA01V148MY8rSevx8Mmbs23J6/vwdFZD5wzF7bHhAicgHwR8DHVfW6nodGgL2dt+X9KQ/0ZCMiIo5MqCr371rCB97+icrd+f/e+lJmzByxifAwed6bYioSis0FHAnnPe5obrlpDZ1Or2WSsMfkeaYyI7XbQd6j+CDgKXs3qOqW8jtE7LGrr7qLJz3pJJzrCrUeK9+3iwJVb87KMD4oZz5mHrcv2Ui73UHVU6jNiLW8OUw7gS+P5cJEKOXPC2FPiL4yTdLK+Y2jmwnCe2gWjtFOQjM3MbXTgWO3rOTeqUfjcZO4drfCTPGFWkxYp4BWgbRylnZmcAKbSYuONaJygV/2OlX3RtVdtXRNhO2toULIESug45GO72ZbJcKpx46zdEUfGqIQzHThUV/gtaDwOYXPJx1uaPaFiMsAGNt+I5dceiV33X0oq20jIg5zHGb89lGK3tisg/UkeKgoexkcLJ4LiIJqRMRBcdna9Vz59UsYvf9uAOoLFjPjac8nSQVEaU0oRW5E0LcUCRn8Vaaq2m1HE5bV5nHKxFoTUgulCKRrrC2MtQGBPbmjXQhjhZCruVPbuTDRMWG15a1BVCsQunYhFIQA/FCyfvd16zjxcfNxIuTYP/K+xITGPExuFz2CYu/kde6FacfPZPvKHWRFjrjSdqWTtjcx2Mhk72R5PQiaJVUuP2ASoJEqmbMZ98yBG51AJ9r0zxnGAalTBlIYyGwp81MbrsxDtfFSQrl+6jj6zNmsu22jjedCpmzP69f9virJernYOoflqaZS5tFabEAp2pbfdddfvYHHnj/XCL12s1N7Ya6LvV2qnrf83R8wY+YUAL759Su58soryP3BmntHRBwhOIy6oIbw+q9jWUhrgaep6tqHOexbwu1dD3KsS7AOoseLyIX7efz14faWvdyl+0BEUuDDwFasxL8X24DFMtkidkK43fogzjciIuIIxLbWOr73zf/jluuWAnDs8fN5xeufC4SJ7DBBnYjgJEUkRXAkkuHC1HeSOM459yhuuG4ligYRMzhUq8/vrjhqUUl7f65720Z6clOlFFddtf3WrbvYs3uCY46ZGUZ1YYLcTPmqiifH0+q6LDGBNUlyznrMbG66bkM3/7QIDakKMze0CxNKJ0KFWMfDlqbDe3OqFmrVZM3QiKpsJjuRm15p/B4aE2P0d5ps7p/WNT54JS/Kcn8TUvOOWqlZ7tFQTubVcUdnNmewocfWagRf9/d1qGqZqlUDAOmWrFk4bCWwSvkDIIiugwMFI0MF67fVQom/RzW3V1ELvM/x2oFJr6eQZFMYmnFBdW471n2PN73tGw/4fRcR8WuPw4jfPoqxpefvOY/A+HOxT9PNv2jDKKhGRBwAzTzn00vuYOlnP1Gtm/eHbyStZXivdNp0lcNQmuMcaCgdKomVquJzZUU2g9mdnfQVrVDuU2Yp2W0rt3L+tofRPAn3YXfHsaWZsLWZ0PGCqDCaO9qBPPlgFVBg2/o9ZPWE4WnWGNoaV1k5vPYIn13/QDhfQjfTAnDC/LPmsWHJWhIx52dSSpCBQDYLE1JbhZEroZtF6rU8m1KUVFLnqYeSsVJk3njLWhacu4BGAkOZMpRBPfWkiTWS6k889cSTJWrNrIJgmvRkos4+egrjOyaY2N3qtjXY6/tJpJvzWkZSSRBNs5ADS+CfWVJVQVV66dZN43ivzJ43AOonkfTuMWwAV/0AKKEMjwzwjne9qlrzN3/1Ycbad1RukIiIiEcWQVD8AvBcYCMmpq54APu9WUTeKCLT91o/XUQ+Brw4rPqH/ez7eBFZGZYFvY+F4PyPhbufEpETe/Z7AV2h9h8fwOW9CXPK/rWq7tjrscuxkqg3hrHTnnO9/AGMHRERcYTCq+fezTfygX/8bLXu7e95NbVaVsU6lc5UQqm/0CVIrmwaJcIJJ85h/fod7N49HlLwhURqYe+EqsnUfgQE7YkIqFyWEhidlMKquTwvv/x2Lny6FQa4wLU8Xfekp4DgTK3GVxMHlYKjjx1i88YJdu1qVzmobW8i6UQhjBWwJzdDgwd2ta2fgJX4Y9mooclrxzsKD83cMZEnZnAooNNRTtqynGUzFofj22UVHnyhqLeeC75QU2HbljsgRXA3eM+61iBTGWfQt2wA1y29msQcgyNCe90OZXesoutEFe+RvDAFObfOW1J4zlo8ypL7Bqk6jpWviPogpnr2heDEMTjjCSTZFABaYyu48YYr+c5ld+73vRYRERHxCKC3wuzxh3LgwPvLhre/sJItCqoREQfA/9x3P9d/7nN0dpqbcPgxT2b4tHNRVdptK/UvOxmJg6TuECc2kSzgC8ArvpUDikuEJQPHcMqOFUFUDGQwuFnH2jYrPpELe5rClgnHznbCeOHoqGM8D+KpqJUmhRL/jg88SOHeGzdw4mPnmbCLiYUNN1niK//23rJZO4EktrxQhEeH5w7RbuY0d05U+atVcpZa86l2YaQyHHoSHU4FGolltg6mMJCUZfQWB7B73U7qIw36hhvUnDKQKkOpia6g1BJlsAZT61APZf5lpqmbXBHGcY+bz33Xr8NJl2butYkJsUFYTaU7Xm+FFFJmhFWrQZUbrt7A4588ryLvig/lZL3uh16Ku/fHasEfvuKZnHGWxSXeduv9fO6z36JdPFxzXETEYYxyBuNQLA9/Ev8ldMXPJvAZEbn6AMvZPfstAP4L2CIiy0XkOhG5DSNXr8N+rr5VVb+2n2M2gKPDsr981bcA12Cl/XeKyBIRuQ/4NlAH/l1VLznYRYnIUZhAeqWqXryfTd4NjAP/ISL3AGuA3wGWAv9zsLEjIiKObGwYX8bnP/S/bFxnZvWnPvNcnvbsxwDG3VzgTc6ZeOoktYUMVzFCKB2kFzzlOK664v7gL02DGCpV1JLIgX9yWoVP2VRqn2l/AO67dx3Tpw8ydWq/8TaUQgtK1VBDjure3MxrG08neGfhvAvmct1V62mbrkirMDPDRA4THWsoVTpYd3YchVqZfyvEcLW8WOaqwri35rG5JzSmhRlj2xnPGoxm/ZWIWhTeHKm5bePzcOBOEDfbReUclY65dW9qzeOcxoZuuVdJTsunsafZlHRLx8zx2tGumFoGv/YmMijMntJivOnYvSdYDsR+l6haAypzphbsDRdeR3EZIz0NqnZtvIy/e/e36XT23Sci4ojC4cVvH824lm6Aye+GSrRDhZfQLTv9+cE2hCioRkTsFxvHx/nm9Tex+ltfBUDSjHm//6egSnMCmqOB+DmomJSCFgoS6JwvLLuoaYqnS4QdtSG8hynN3eS5kufdjp+dHDq5Mt6C0SZsG3fsmBDGOlZ61PIJE7nQzoNCKiaIlmVLa5dtY9YxU6jX0+AStTL6xFWT3lXeal5AM8zEjxfmTC0ntAsP7RxmnHkUG29ZS6tQCpVQeBW+A4Cx3Ai3Vy0v344pNpGeOkhRGs4cpqXmKN6z+Y6NTD9tHmDu2UYKA5nSl1qOat1Zo6ks6clkLVXjsGgo3x+c2qDRn7Jj/Z5JLtXA4cP5Wil/6bjNQjRD6Ux1QWQt9ylNFMvv3sm8BQMMDvZ+RnsQH0qx9hVVnUzuRgvgkoSL/v0N1f13veMzbN6xBL9XLlVExBGFw6ckqt7z9yLgSQdZRnq2/TImqF4XxjgTaxa1HPgEcK6qvu+hnJCqTgBPBd4K3IWV4s8ArgBepKpvfgDDfADLdnrD/h5U1bvCMX6KdSntA76IOXRjd7yIiEcp2nmT2+67lk9/0Mq00zThH97zahKseicNE9CJJAhpKPevGBZQiqXmUlU8M2cPkmWODetGSV0N1+NMTahRNvGUSdPd3VvtzVOtqJ4xy7wouPaae3jSk06ybbV0tRYIgvceTyeM0+VlPpSvgxkBCoWRaX2ktZT1a8fIvTCeOyYKux3NXegLANtbzppU9WiUrdKdWoQs1cDP88KEUi08x21bxT1TF1U5qapBRA3l/r5QtBRQS+doARTeSvLD6e/yDZo+ZU6yh4rUJ3t9H2r4XyWk+m5JP3RF1rIRlSqiitOC044e5fZ7+3syuqRn2AcmijaGT6Y2sAiAor2D1ff9hPd/+KcPaN+IiF9rHD789lGLUJV1PfYJNp9uddfDgohMYXLl2Xd/0T5RUI2I2A8+suwebvjwR9DcCNrM57yEdPo8Wi2lM5FTtHsUyjDL7Js5fiJHJDzW9jCeV7X26i079Zb+RZyyfQWdljWx8h4mJpSJCWV0VBkbU8YnlLFx2D0ujLaF3R2hUyhjHfu7nRtvahVWqjTaLNhw7zbmnzIrlOUbN6q77nY5Nknd9taJdE/Ibh0PWVHtkInaKewYzVoDN9zPzjW7qyimVinIYmKthBwmb3YBQvoAipXt19MuRysbYW29ZzPDi2fg0gSHNatKRUnD+fYHgbXyPjhIxMTTtEcoBarU0uMfM5f7btrQJZGUoq5WOWC1BLJwv5vMVY6jJCjOdcXYouO5Y8lWTn/MrEl+CcCypfDV+n0zwcrRy3UFTzz/NH7nRU8BYOuWnbzvny+mWTzQBt4REREPFar6WVWVB7j8tGe/a1X1/6nqE1R1vqo2VHVAVU9U1dep6pKDHPOnPWOuPMA2bVV9n6qeoar9qjpFVZ+qql9/gNf1QlWtqeoB6yxV9QZVvVBV+8L4f/AwG3FFRET8mmP12C3897s+S3PC+ni84vXP5/gTLZnEEaqBRHAuqZyppTjqyhzUkIvaO3l8/pOP52dXLmdS0U4lvkpwrSZBiLXHHG6vkv/JcUhKwY033MPpZywkqyVdrbASSosgpk4uT1evaBBZfY/OmCuc/Lg53HndBsZyq/iaKIyut7ydVasQRjsmrhbBldr2ZXaq/T1aCO0Q8VVy+YU71rFueDZ5YsYGXxDME9bEVvNQct/qKfPPg9BZnn4Plbx1YhZn1jcZ1/Y9QnPlkihCKb/vLqVzFZDCd8m3VyQ8dty8Jqs21um0pSc2QKsc1QcKEWHKnGdXJ71ny5V86FOXsX3n+AMeIyIiIuJh4OPhVoF3iMhvP5zBRKQf+AYWlyXAfZgp4aCIgmrEYYP3vve9FenaX85SLzZt2sRf/dVfceKJJ9LX18e0adO44IIL+OQnP/mwsylv3rqNH172E7ZedzUA6ZTpTH/eH9JuKxNjSmenETQKqjB5RjvGyLzHt8rOT97ISyBAvvDkLWU0T1krI8zZsYm8UJpNz8S40mwqrSa025a96r3Syi0KYDwXWuoYyx3b2o6JwtHMHXtyE0HXLlnPorPnImJB+WpG2arJZ0fLBp+Wf7qr7djRcrS8MJbbOhNaYXfHRNamh+z4uey5ewPtjqcd8qYmvD02pQa1kG9aCpeGkE8qkIpW5FAUfKvD6LqdDB0zPeSYKrVUSYPgmSbdxlJV7indcn/C/SS4ZBMHjQT6+lPmHTeV1Uu3WmZqaC5V5rZmQuVyLScFe12vTsLxpfvzYMkNmzn9nJkkiWV3+b1yvmxXj9vPe9UF10YvM1Y8//gvr6HRsGaBH/3Qt7hr2c/Ji9GH9X4F2L17NxdddBFPfOITmTlzJvV6nQULFnDhhRfyzne+k507d+53v0fy31FERNfy/TCXiIiIiF9jHC78FmC0s42rr76C737jKgCmTR/m//3N71FyoUSMC6VSx0kNRwbBZyrieppFlU7VciLaMTgwwHHHzeH229ZUblTXk78qVQVlrzu1p9JnkiiqKAV7Rse5//6NnH7GgkpEVcoGSoqnzWQR1rrVl9mqZf+Asoy/XYDLMmYtmsLqpdtpF8Jo2zFWlBIvbJxwdNTySltBkyzHaPmS+gt5brmpXiHLO8wZ3crq4blVE6qisHiwohQ62+EEfBBQ8/28nmLPieSedp6wqjnMcenWrvCqmLO1UyBttaZTpVrc85tDygsPXbfKhlS1xLNwdpP71zSqbbVUhQEnmUU2HAS978O0MZv+qefYet9m65of8Ka3ffOg+z9QRG4bcdgi8tvDBZ8D7sCezRT4XxH5azlYxswBICInY/0Fntqz+m9V9ReWk8qv4QfLr90JR/xi3H333Zx11lk0m81q3YHemzfddBPPetaz2LZtGwCDg4M0m03y3N7vz3zmM/n2t79NvV7f7/4HQ6HKqy//OV955cuZWLMCgNmv/FtGnvgs2k1PsSdHOjnaXzNC1Ftm48TslZ3CGGlJlGoJmgUCGgiPw/Pszl1cMeMMOjh8YZEAIpCkkCRClglJAkkCIwPQn6nljSae2X0F0+veyuSb44zesYaznnFsaIoFWaL0OfDBbdnxSruwYP1WAXtCmVIiNlEe5rIpPEwUrspnFSBfuwVttuk/YR5tD4LQn3kGEwBze+YKKoKGjNRG0q1Oahbd2KcN169k+JjpTJ0zRCPxDKRQT5UsiJuTqHTgh2VkQdHzdkgoHRR2jEKh8Mq137mX8559DH19qZWsSTVRj2evbNXwIyBzlg9bT7ov0eieNlf+YA3PeeHi4MSwx0qZ1Kqj7IeBNV5wPYLyZPiejC8h4Z/f9Vne989fBOBZz3ksX/3WfzKYPf4X/sg6EC6//HJe9rKXsWnTJgDSNGVwcHAS0bzllls466yzJu33SP47ijgs8UujbiKyvG/mvGMu/I9LDsl4l7/pt5jYsn6Fqi7+xVtHRPzaIvLbIxCHC78tj3vXth/wkqe/gbtuWw7Av/znG3j5q59VTTJnzuEkwUk9uFMlcDjjPKVTtVuuasKnkJJIHV8oX/riz3jJ7z2BWi3FegL2fv34ShjtfS604kndCWlF+d53b+H0M45i/oJplMxUtUDxFL5dlfqXPKvwHqVtmanqK/7YDl6HsY5VYu1pKzd9915mn38CTUlNkwRG245NrYS8AK/CuIdcBQ38eCw3h2r7/2fvveMluapr/+8+Vd19852oGU1QGAlloYiEEukRHsYY8zBG74dJxhgb288m22RsMMFEYwwm52QMJtgEA8o5DMoapZnR5Dw3daiqc/bvj3OquvreO0HSKNeaT013V506dbr6dtXuddZeOxOScOjMwnEbV7F++CC2N+YUxanSjpLkZq25usG6ghQtfifYoFZFu2Rr6oIww/GseWu5cMcyEmuQJBCkeb9aCpBzDYOqH1Sa+6kGIlaV046ZYN2WBlt31nz2HBa1vjZAHt9am2Jd1xVmxt+rUFg3qDqybIJtd/4r6joAHHTEa/j1T97LCccevN9/m9NRxbYV9hMPKS1ZxbePPIjI6XgV6QBd+uJm4DPAN1V1fB/7nwH8CfByvI1W3se/q+pL9mcMlUK1wsMO5xyvfvWrabfbnHXWWXttOzY2xu/+7u+yY8cOjjnmGK655homJiaYmpriX/7lX6jVavzyl7/k9a9//f0ay7fvXM8l3/r3gkxtHHoMjROeQTKVYacsZncrzDJbZKLjH9sZ0kqRTorsaiNjSZB5pqHKplexSpIhiUU6Kdpx3BIfzNE7VpPuztBmhmtZbNuSdRzOKS5Pv8kzeBxYFYxAqsJYKmxtGzavXE//8cu5d0rY1PSq1XYa0pcyaGUwmfoU/11tYXfHMNYxtDNhdyJsbxu2tQybmsLmlmFXIkymQRXrwCxZQGfLOM3JlI71gXVMGBOBTMVfeXJlaqjVVQRnqpDsaqKZY/igIRqRYyAOCtcQl+d9kPet3Xg9V6oWyMnUwJBGBmoRHHf6Yu66ZjO1QCQ77VW0FoWtwmMsQU0bRBPBwYCrLt7EGU9ZUgxAw3+5w5df6yvZKjaQqTNGGdBdpyh//cY/ZOmyBQD84mdX88ufX0Dqtt2fP1cuu+wynve857Flyxae+cxncumll9LpdNi1axfNZpNrr72Wt7/97YyOjvbs92B/jypUqFChQoXHOx5J8S3Azs4afvitnxZk6rEnHMb/fdUzMaZcSL5GZPqIpBY84QVDLahLI0Q8SSo98Y73jhcMURRx1tlHcdmlq4r1vRFheZ+ZyHOBHI5Nm3eSppYlS+cGElUDSWqxLsPRmbG/o92jdHWESXeFTuoLqnYcJGqYd+wStty4kVQFq0JihU3NiCTz6f1NJzj1YoOOk0Ktmma+1oBznrccbE0Q2YwdfXPIMkIBKkfatl6R2rFeSRqyx4AiFV+cK8jOrpQ2L2ygqIUbx+dz0tDWoErNkGAZULDFod+CTLWuW4QKiuB6ZDCjr+7YuqselKk+s041V/6GT0YMIl2Vqv9Myh63vQRrFA8yvPCpxetdG3/G697y77N+vvuDKratUKHC/kJVrwX+L/6qmt9ojgc+DewUkZtF5Jsi8ikReZ+I/JOIfF5EfikiO/HFrf4EKBdMuRh42f6OoSJUKzzs+NSnPsVll13GS1/6Up797Gfvte1HPvIRNm/eTH9/P//93//N6aefDkC9Xucv/uIveO973wvA5z73Oe644775U04kKf96xU2s+faXinVznvWnsDtBt7WRrVN+prftkLEOTCbIeAeZ6CCTCTKRIDuayO42ZusUZkcLplIYT5CdbZhIYDKBpoV2xrqpIeYlkww2J9DJBDeVYKcy0raSpZAmIVXIQavjCVKn0HbCeCJeZbp5N1l/H+O1QTZORWycitjZNoynhh0dYUfbsGHSsHYiYu2UYc2UYcOUYWvHsLklbG0ZdnWEsdSwuxOxs2MY63hC1c/Cw6QVOkcewuSt6+moJydT9Q4H7cwrUC3dWqCutCRhpVNly2/Xs/TUpQzWlEYEdaOFv76WF+2qSotpR+kWuopkRiwHeNuAxcuH6TRTJna2C/1DFPbJids83b8m3UJU+RgcsGnDJLW6Yd7C/mlH6H0s1muugnCzqk6MSCnNTRkaHOK9739Nsf3v3vxZJju33CffKoBms8nLX/5yWq0WL3rRi/jFL37BOeecgzH+st7f389pp53G+973Pg4//PCefR/M71GFChUqVKhQ4ZET3wJYl3LPlmv5xPu+Xqz7h3/6E/pqkY+vJCKWPiJpYIhDdo5P0zch1d/bGfkU/jIMccjWARCOOGIxO3dOsbvHS7Mc4+Tqxl51arEei3UpF114G0992jHdfVUAS6YdLB1cKPpUlKhyWdGXU+eFCMGZK7WEolMwkRrGEoMcNId0qkM20SJT2NExpHjuMwlFXzPnfVTT4KOaWS9a8Kn+isscR229h1vnrfCFqZz6+L3lfMGBVLsBsnWB8LSItT7LLfciCFVhJQ370Q2Qt4z3MSApI7S7AbZSpPj3EKm5b6rtBtP5WT35qCl+e+eQf2W72VP5cVAv5PCihai7rThgvkjwW81jXqF/3qlE9fkApK313LjyN3znh9dxX1HFthUqVLivUNWfAM8CNoVVZV+ZY/GE618AbwPeCLwaeCa+EG35x7sCXwCeo6rJ/h6/IlQrPKxYvXo1b3/725k/fz4f//jH99n+a1/7GgDnn3/+jBspwF/91V8xNDSEtZZvfvOb92ks77/6Lm798udxzQkABo9/Gn3znoA0U2QiwUyliLWYVodoVxszlSFbmshYgtndQXa0kanMk6ud4EXkFOlYTDvDTKWYyQTTDrPLkxnXTS7mdF3vo7eWT//RTMmaljT1hvbW5tVJ/bUhc4Ypa0gzqN2zganDlrMrNWzrRIxlviLpVCbsSjypOp4aJjIfPI51InYlhp1tw84kZiyNaLmIZmZoOiFRQ0eFLPhiWQ0e93OGQBUzNulT81WwCBmGDFNoNvPYytCdbO+LlM6GnQwtHGRopEbdeOuCmgkepntIcsw35brP8qNXtPrAToLNQM14D9aTz13CjZdvJMaTpoT2Bu0Wn5J86R7L4YP76y7bzGnnHFysn251U84tKebz1QK+IFn3x0bXBVaohSVGgBef/wzOPOt4AO68Yx2f+8y/07Fr9u8PNeDrX/8699xzD/39/Xz2s58tgs39wYP5PapQoUKFChUe73gkxbcAW1u38LmPfZsd28YAeN7vn825TzkRA8QIkRiM1EKaf+Q9U5FQkKpblEqKZG/wMVgUtvdGSk9/xglc8JuboKRu9BPPrqf4kfdD7V1QuGPVJpYsHWVopB6KgPp9rUuwro3TTtg3wzmLc45M26CB7MPzkla972krExwwkQgToVZAx8HACYcwcdM62lbY3YlIra8rkAUSNVO/tJ1XsLYSwTnJeVEWTmxnd22YSdPAZkqS+DR/TUIRKutTzCSk73v/U9flKDNfLEocoWgURcq+ZBZJvcp15aZ5nLpgeyBlKdL4vVdqSa2a71+OrUVYujBhbDJmsmlQa73iVxUXFKqFClUznMvCblFRkKxL8JY/K1ciVoXhRc8oDjmx5Te8+4M/otNJuS+oYtsKFSrcH4SisicB/wa06F4F92YLUdZV3Qi8SFVfq6oz0x/2gopQrfCw4jWveQ1TU1N87GMfY+HChXttu2rVKu69914Anvvc587aZmhoiPPOOw+AX/7yl/s9jnvGpvjPi69i7JIfAyBxgzlP+v+Q8TYyniC728GsE5jyAZK0MkzqPHmaej8jyRwS/Iroi0F8UjhOvQVAmAyW1CHqGKePjotYJOOQp9E451P/Mw1enZ7crAcCUoGGgcENm0iXHERci7zJvig1Ua9izbzJ/o62YUfHMJkKOzsROxKvXk2cr0yaE6DefqkbCOfkpXMGh/i0+GOXE61ahylmpz1lGAcvJZ9u5Afo0/GVmnEMiGXsjq0cefJBDMfKUA36SxPfuSp1OozppueXU/VrAvWQql8mWYu/gdEGI/P72LR2rNjXoN4WoEhpo1DHlve94+adHHLECP0DcVHpNv+NYHLbgGmUavf/7hpDhAnKDsH7kUXhRwoCIsKHPva6Yp8P/sPX2LT1tzi335NhReD4ghe8gAULFuz3fg/m96hChS5KX94Hujy0FlkVKlSo8IDxSIlvAZJskptvv4qvfuYnADQaNd7zj68kMhAbQ2QaRKYvkGfl623US6aFqW4XfFP9GhOsAXoYPObPH2Z4uI/VqzdTKutUTIbnRKvTsvrRE6edtM1116zm9DMOxWnq16tiaZO4CRxJIAEzHBmZJmTqiTuHFyFk1i8dK7RC1lU79TZXuzuG8VRoZ0La34/t62PbveO0nM8CS5zP0MqXnExNrPdNtdYXnLIdx+E713Hr0HKSROl0HJ224lp+R7EaiFTt9ckKhWtz0tRLaIOMNidSO9YLOloZksFkJ2asVWPJULP4JPwb1p7U/0KlmrnCBsCIcuyhU9x8dz+aZbgs7ZLa2v1s/HnOXytGolCEzP8NeKLaed9VzXBh8fsojaEV1IeO8MPKJthwzwW87+O/uE9/q1VsW+GRjyq+faRCVber6uuAw4C3Aj8DxpiWDEs3MfUmvNfqc1T1FFX9z/tz3IpQrfCw4fOf/zy//vWveeYzn8nLX/7yfba/+eabi+cnnHDCHtvl22699db9HsvfXXo79375n/0sLzB66v+hFo9C2yJTiU/ZUYXUYlqpD3gmEx8AZeqDJrycU4xCI/bXSNuNyAqPJA1VPkPFzZXJYk6KN3nyzuGn0iPj/UFVsU4Lz3lVMOLotx0aO3aSLllIw+RKUE9U7uwYdiaGsaBS3Rk8UydTIbWeIHVIUJX6VCYXLug5QZrX1Mo0pMYL9A3UqS8cobN+B6A+dT4QlOAJTlBqkV/XH8GcBmy9ZQuHnbCQwX5Df+xT/U1YvKJz5u2kHtL0p6f3C56ojSM/zpz0jEsdGOCkMxZx23VbUOdm+KaWn0N4DiQdyx237OS4kxcWx+rqMfLjz9SpSlDzQtm4P6deo6BYLetc/etTTzuaP3rFcwAYG5vi/e/9Mm17277/WIFOp8O1114LwFOf+lTuueceXv3qV7Ns2TIajQaLFy/mBS94AT/72c9m7Ptgfo8qVCgwXd79QJcKFSpUeJTgkRTfAmxu/ZYPvfOLpKlXHv7ZX/8+hx66yCtQi+yZEPFIrkGVkPadRz6ChOKj3Rz23AJgGnzAy3lPOY7LLr0d52yIj0qEXEGuTrc7slx91T2ccvpy4poncL0yNSW1LXzBZd+XU0vmEhKXkbq0SO9PFNoOWk6YTKFphaYVtncMU8GqqmNhMvXOXK0Vy4ju2UCSQicTWhY6zpA6X2+gmQrNROh0FGcVF4pOrdh9L3cOLKFjDWmqZB1FOxaSDMlcieQMaf254tQ674faCb8FwjZShxSeq1nhsxqKKHDzljkcf9AYRm1hHeANYvM2vp3/TUIh0jh62RR3re8jS8NvEAgK1XKBMA2WW7bwqc1hSgWzfcFYDaSq7fYTluGDnk5OL0ztuJIvfvUXbNy0e7/+TqvYtsKjAlV8+4iHqm5T1Y+o6u+q6lxgET71/xzgVDzhOqyqJ6nqX6rqrx7I8SpCtcLDgg0bNvDmN7+Z/v5+/u3f/m2/9tm4cWPxfOnSpXtsl28bHx9ncnJyn/3+evU2rvjxf9G6ayUA8eBCRlc8B2lmmFaKmUzBiFekjnU8ybo78QFL5lWpRfqNdaiIr3Lk1FeFmvLFqAqJaYZnIhXIHEliWJuOciTbfOqOn05GjBSV7lVzX3thuKaMrF5L+4hD6Yv9zHkkICht5yuYTobgbzI1QZUakagQmS47qRCKKfk0+EiUyDhi4zDSTeOpRX79UN0xeNQi2vdsIdaM/kiLtvVIicTRFyl146iJX7TdYXzrBCuOGqXfKI1IC7K0gM9Z8k+BSLop+YLfJmGskdHCS7VuvFK3ZvzYY+mm89fqhiOPn8+qG7b5fgKJmof9UYlYzcnVlVdu4ZQzF1GLwo8HKVGguXeYSFgMQuzVHBJTpLyJ0J34mo6cns0J1oh3/8OrGRryXq1f/sJ/89sbrsK6iX3+za5Zs4Yk8WrW9evX88QnPpEvfelLbNu2jYGBAbZs2cKPf/xjfud3foc///M/79n3wfoeVahQoUKFCo93PJLiW4DJZDO//tWv+fXPrgFg8cHz+Ks3vsgXohIhNg2iPLW7h0ytgZSM5sOTLgGHz8QpyDZheuzT11fjuOOXsvL6tT37ltP/cwfUPOV8bGyKjet3c+TRC3wM6HckdVMkronFlYg/S9q1Hy1sqjIXiNHEx8TNoFAdT4TEGjrO0LaGKRuROMO2rI/JRQfRt2ETHRWcGlwQjbas0Ek9gZplShYKTsXtNgtau7knXkiSeDLVdSzSybqZauUg3qnPTktdUKUqZYNXCfvSDs9zMlVd4ZNqM7h7+xBHzx/vKl5zwjWk+hdq2ODP2qhZFs/rcM+9UfBNJZx3hzoXyG6Hc55EVXVovq70GYEWpDhkRR+9pq4Q1ecyMO/U8Lll7Nz4K/7m7ftXoKqKbStUqPBgIChX71DVK1X1BlVdp6qtA9V/RahWeFjw2te+lrGxMd7znvewYsWK/dpnYqJLNA0MDOyxXXlbeZ/ZYK3jbb+4iU3f+Uyxbt5xf4jJDNLKYCL1QVBiYSpFWinS8Sk4IZfIB0MQzEbVs32q0Mq8DUDIgVdvPhpM4kOwkzokybhjai4rzE5qNoV6nhau1OpCrSbkFkKZQjwxBZklGxnGqTfZz0nD8cSQIkxlhqYztKyn8BSvMjUFSagIzqfCi6M/8ktfpIzWHP0RzG0ocxrKQATz68qAUQbqwryjF9G5Y5P3QhWfft8vjv5YGa4rQzW/jDRgzTUbeeJZS+iLhUbs31rN+LFM1zRIIHXrQeGah+YmnIu6hJT9sK/JCVa6xapMiTg9+vi5bFwzTruZFu87NqXiVEBecmFiV8L47g6HrhjpKljDqPJ0JyHGUCOSBpHUMeKr3hYEay5TnaG2oJeIDYUeDMKixQt4y9v+yO/lHH/7ps/QyvY9a75r167i+Qc+8AFqtRrf/va3mZycZNeuXdx7772cf/75AHz2s5/lk5/8ZNH+wfgeVagwHWFe6IAs1QR+hQoVHi14pMS34Imz9ePX88G3fbFY9873vYLRkf7gk5oXkurNxQlBKF2fKq9SLJscGWJPuhYkqnZ3p9CQctLJh3L77RtottphfZiwDwRq3lqD4vSSi+7i3KetQESLPhI3SaIdH0I7X3AqUyW1PgksJ1M71lte+fBd2J2YkKklbG8Jk06YsMJYKjS9Wxe7EqFpYfv8xQzs2IFtZ6S2W4Sqk0GrDZ22kiaeWO104Lidq7mu73DSxKf+a16pNXXdglC5f2qI9aWVBXWE8yrW0nppZdDxNgHdN+qJUimeK6u39rFktEWDzH88gTgVFwQemeshVE9aMcHK2+qeJHUWdQ7U4VyuMlWstViXYW2K07RLfGvmSVa8tcOeBQO9mbT9856MRF4s0Bm/nV//5gIuvequff69VrFthUcDqvj28QER+bKIrBaRNftqWxGqFR5yfOMb3+C//uu/OPnkk3nDG97wsI7lU7++k7u+/TWy8c0A9M07mqF5p/jgZirx6f5GoJ1idneglfk8IsFHcantLrZErHasL1DVCetrPjiV1JvSS+L8DHTuuZooN2SLOSneiJTsqgQKVakq2EwZuGsNGw9ZQYKwuR0XsVPiDKmTIphspT4gFHwl0lggCrPcNQMDtZCaH3tBbcPAUKz0RTAYK4Oxfz0QWebUlYEajNTh4BVzyHZN0Ug6zGko8/uVuX0wWoeB2Kf690UwsXWKOBYWLR4gFqhLl/SsRZ5cbeQqU/FLbMJFKRfq5ueALhFrBGKjhd1AHHnBr5GuP6r3XxVOO+dgrr9sc1C4QiRCTQw1MdRNRN3EiERccfFGznnKkiJR34R/niT1afu+UEPdK1KDr1Ru1i896lPTG25Ou1tKoHH9c8Pr/uoPWHHEEgAuuei3/OiHvyB1W/b6d+uc63n+2c9+lvPPP59arQbA8uXL+eY3v8kpp5wCwPve9z6yLNvn96FChQOJ3GLjgS4VKlSo8GjAIym+BdjRvotvf+lH3HH7OgBOO+NoXnz+U3zEIhFGPKGaxyV5DGMkppcoldJz6EZoZSKNafGOXy8C55x3NJdefMe0bRm95Jxj44ZdRLGwcNFg0c45S9u2yNS7tlp8mD0ZwnEHpITarlaYymB3ArsTYVfq0/rHU2EiNUwmwlhHGE8MU5mh44StrZjJ1LDbxqxduoJF69bQdr7swUQLpqag3VY6CaQppAmMtMaxDra5AbTjkHaGtNNuCn8gNCWzSNt6snQq9WRqEsQUWZF6FlhhPJmaWwUEK1Ox4U3aQNBmcOPaEU46JK+9QLcYVcliQB2MDrQQlO078WpUdVhngxLVk6ieQM0AGx671QFUtataLT6rLgm+J4iJGVxwbvF6fPOv+Zu3f7cndp0NVWxb4dGCKr59XGARcEhY9oqKUK3wkGLr1q38zd/8DVEU8fnPf544jvd73+Hh4eJ5s9ncY7vytvI+07Fjos0//+BKdl3+3bBGWHjIi7wJ/HgbO970wWE7Q6ZStNWBduJ9jVLrn3cyv0x0fGCkeCXrzpb3WG2mPrDpuG4w1bHQSv3STv3+iWWzG2bQpIy4pncGaDvaHaXVUjptpd1SBrduY1djmN2mj7FE2NkxNC00U8OutvdJ3dGO2NGJ2N4xtK031s/Ux2udEJcJ3aKgwa0AxJOccSA7c4JyqAaaX/wBRVh62jI2Xr+emigN49vXg6+pT8lXVl29kZPPOhiR7v7Q7ScOjz3+pnSzmHxhK0+k1qPu/qZ0A8rD+Px9QK8tzaIlg1jr2LG1SSwmKETDEtSl69aMMzxSZ+78fowYjEiYOYzIi0lFphbM+YXyv5lHZNo2ivVliHiZrAg0Gg3+8UPdAlVv/9vPMN68ueTHOhPlv+vly5fzkpe8ZEYbYwxvfOMbAdi+fTvXXXfdjH0PxPeoQoUKFSpUeLzjkRTfAmQu4c6N1/KJD3SrmX/gI3+CMSZk2Hh1adcDtRxFzYQjT//O1amzeKdO2yPH8kPmMdXssG3bWCmFnFKspKhaLr34Ls59yuHFNlWl4yZ7Rqb4ULons91BmglJUKiOJYZdHcPOdsRk5ouxjmfCeGqYssbzkgo7Ooa2g7YVUgdjg6OYLKM2PkUr8arUdtsrUpMEOh0l6SgnTazh2uhQNPUD0FSLughiQ4HaLPgPpEGNWiZabbAG6Fiko4ijG5SHj0LA24AVvHY3Jtw+0SASx9xG0i16VdiPOVxmUZtwytEtrr8lAg3FpjTDE9nek1ZdVth79f4NeBWwBlZXg5mrSATkf9d7Z4MaI8cTNXxNgqyzlTtuuZjPffXSve5TxbYVKlR4NKIiVCs8pHjrW9/Kjh07+NM//VOOOeYYJicne5bcOweYsW7JkiXFtg0bNuzxGPm2kZERhoaG9tju7d+7ge0/+yKa+TSkkYVnE0cLcc0WdqqJa7V8bNFKYSpBm2205QlUnWjhOpkPgNoJmtmu6XwrQSYTpJl65/tMMe0UM5EgU2lQrjrvj9TRbsyZKdfZpZyWrEU7FtuxtJtKqwWtttKazFi2bT23jh7KVGLY1o5pWUMzi9jWidmVROxKDBOZMJkZJlJPqGYqWBU6zpCRV+mEVCUoML1e0kiPXRNWfRp+WTGq+OH2z+mj3jBMbZsoVKHl0Gr9qp0sOmSY/sFazzkvJwtFkhe0ytWj3WNEQuE0OntKxJ4DuXLhxEjgzPOWcO0lm4LNQemfGNQZrr1yE086ewm5AUCe3t+tbOpH3ktv5in8sy1RD8e655FK6Cfiec8/l6f/r9MAWLtmM5/6xHdI7Oo97ln2hzrmmGP22O7YY48tnq9duxY48N+jChX2CDlAS4UKFSo8wvFIim8BNjdv5NMf/Cbju6cA+MOXPp1Tn3QUPu6IeyeJxaAoIoZIAmEWAjbvES90fTJNKf27hEJulUeKefEi77H5tKcfzYUX3F4i78oFO+GWmzZx+BHzGRj0qlmnirUZWZFy3hUDdNS/UgXrfJyb4BWrbQfNTJjIhI7zqf/NTGinhk4mOPVLaoXxJCK1hsQZrPNFWu88+AgO23A3rabSbkOW+tpPqOIcHNbeygYzStvFXf/TUIRKbCgulWSBMM2QJPNka/A3NYn1g0y1W9srV2YqoaxAN2W/KDaVWwiEQlM33DPCyYePB4/W/PNS1CnqMg45uMPm7TDVJqTte6WptRmZ9apUR1YUn3KBLN9Tan/umZr/bpC9yusMIjB00NOLNZPbLuFD//xjJibbe9yrim0rPGpQxbcVSqgI1QoPKVav9iTRZz7zGYaHh2csH/jAB4q2+bq3vOUtQG/VxnI1x+nItx133HF7bHPj6h389L8vZOJeP1tqoj7mHvw72LRDlrZoNrdjohq0E2y7jUvbfnJYFeccai2iimYOdVqkh4tTTAbi8MGV+tlqkhAEIUUeu+b57LEUZeon6WNc+1ja2RnSfRSXOdKO8oSx9dw9tAQrEc1ESdIQt4VCVE1rmMoi75+aCakKmRMSK2RqyL3k41zpiRLhfEEqA7EoEibQUwtWtaijpXQdDWpG6Y/hiNOXsvb6TXiFQ35fUDSzrLt9B8ee4memVcNbFj+vbaR74Yno3k9y8jQ20IiCLUA+rnKRqnwpZZhJaf9c8epPqzA6UmPJsmHuWrUb8s9JDIaIm1Zu5ejjFtDXVyt6KYpPEZGn5ueEchd7Vo/2ttwbndr1dhBj+OBH/oIo8sf7yAe/wb0bfotzyaz7zps3rwg89xbUllWuebsD+T2qUGFv2POkw31bKlSoUOGRjkdKfAvQseNcf8MVfPtLPwdgYLCPt733ZUA3+wZ645A8IpsxeYz0qFOlRKblTqg+qKUUGs2MkUZG+znooGHuvmtb0W+gSEmSjJtu3Mipp/nMSnWKdW06TOK0G+NpEH3mh8hUyELMmmTe5qpthVaW2195pep4amiGFP9czLmtExVZXM550jSzMK4NdtaGWTi2jSwDaxXnFGsV6aQc1d7IrXYRJIFITYIaNQvpYJZuwanwXKy3+iKxPt2/KCilBZk6g0jNA+/pGfaBr24lEdt211i+sA1OUetw1uJsBrQ56rCEW+4SCESqqiPTFEdSpPbnHfpYMZDgpQJTRQudvi5XJ8/OCuV/H7X+JdSHjvJ92BZb772It73vhzPa56hi2wqPFlTxbYUyKkK1wqMGRx99NIcc4oOtn//857O2mZqa4pJLLgHg2c9+9qxtVJU3f+ZKtv/m8+QBwuiiZ4Opo1isTXyg55S03SJtTZBlHUSCujNLcZphohh1aVdeGUk3XSdP3bH4gCu13tizJhAZNBKfxz5U8+xhPQ6WUt5L9US3AckyyBwuc9SSNguTMTYMHYS1SrMJzbaPxSYTX3yqlUEz8y4DSfDFbztohrpZKkAgT0GpRV3/0ZxkTdUvmfq3E4ZUpOHHojQCoTo0GLP4sDlsXrWjIFNrEdx1/WaOOfUg4ij3Ce2GWzmZWhSFkuCfGmwCasb370N8JRYtUvyF3Bs1kKamawsQG/+Yq1yhG+4Zapx2xlJuun47NvOGB0Zi2m3H3Xfu4PgTFyGSE6neL3X6pTEvnlDWqc6ekt+bArevW2VB2CIcf8KR/MlrXwDA1FSbd7/9c3TcnXvcN//7vu222/ZoD3DbbbcVzw8//HDgwH2PKlSoUKFChQoPHAf6vrxh8no++LYvFJ6U/+/NL2bRwfNR9VRmTo6WIT15SH6NkSjEPV31omG6lcH0SKdXnaoFYaecedYKrrlqNdb2xixXXHYPZ5x5aJhUNjgsVlMyV3LuDFyjF3tqYWXl7ayEDO+huisxTGaGloPxTOgEYUGiPjMrcULTerI1tV6pmllIM+gkSnNKuaV/OUeMr0czi3OhhkGqnNhZx80cjDo8KWoDw5vZkMoffFOLdH/ri9p2Mu+zmrhe0jnEbj61P1ek5mSs7fqm5h6p6k+EADjHbWsHOHpZkwgHzuKyNta2OeGojFvv9qpbVYdzGdZlqM7mNxo+J3V7tZoqt/dEkNkjKZRvBxhceA4EAr+583q++/1fcdfqrXvsvYptK1So8GhDRahWeEhx4YUXFn49sy3vfve7i7b5uk984hPFupe//OUAfOc732HNmjUz+v/0pz/N5OQkURTx0pe+dNYxfP/Ce/jtr39MZ/fdAMT1BQzMPTV4CXmj9v6+eTiX0G5vJ8kmsTYpzNutpiDBPD2XfeZsXj6V7vDrnINWCIr8m0LFyzC1FpbIyylFgMxhM8PdLOA43eIDsbbjtNYabhg+DOsoPFWdhdSqn/C2QicTOqmQpuInvNXP1Hes8TP66olL6/xj8eWXPFOrq/90+DaKFPPY/bF/i/1RV7W67PiFbLxjB2mSYQQ64x0md7VZvmK0ON9xTngWx+k9biSeOBW6RauiUHQqL1xV9IFfYqGgPcsKV+gSthpWRCaiXq9zyulLWXnN1lBIKuaKS9Zy5jmHEplcidrVuk73QM0Dxr0TpNO37t/MY9lz9W3vehVz53pPp29/8xdcdeWlZHZ81v1e9apXAbBu3Tq++93vztjunONjH/sY4NOoTj311GLbgfgeVaiwL1Sm/RUqVHi84JEQ3wJMJJv4+U//hysuvhGAQw5bxJ/+1e+VrqeKkob41YHkPqr51LiHCRPEWlKn5qncZXT3mpkmTs82qNdjnnjyMq69Jn9/jl27ptixfYIVRy7wrYM6NQ2p6Pnke+K8S5YCVr3SFPXPM4VmKkylwlQmvs5TUKqmVuiEpZ15EcLWVkQrE89TBu7SOV+zIMugYw2rGks4enI9WQYucwx2mszPJllvRz2rm6f7+5QuX1AqFI3KpbTSsd0CVOXTEVSoovgsNuuK/jxBG5SuZVVqSc2ar3NWWLW2n2MOm8TaDOcsg/2WhXOVtRvBWkvmUhxZ8EGdjvDpaVaQ3oWnavGzZbZCUko5AvcWWb0wxm+PaqP0zz29+Lx3b76Av3zrt2bp06OKbSs8GlDFtxXKqAjVCo8qvOlNb2Lx4sU0m02e97znFWbkSZLwmc98hne+850A/Omf/ilHHXXUjP3bnYz3f/Eytt/UvUmPLH6W949Si3MZUdTvZ7+zFk4zP7urGVnWJk1aRRq2sx2f6FSr+fz0LJ9FDvJOA2K9ZxKEnCIHGDypOlDzxGojRmODOnxQpcqduoBlupu+LGGBHcepsKs2QppCp+2DP0XpdMoz65AkPiCSEOfEpQt2ZByR8WFQEMoCXXUq+HitZmAgCt6pKBFQF6gbJQ5EZx4eixEOP2Uxq6/fRD2CW67YwMnndH2M8sJTM9ENvuOom+Kfp+sboG6E2BgiIwWBGpmuMrXcNvdNLW5S5EtUxKFHHj2fTRsnmJhI2LFtinYrZfkhc0AMIjWMxF6lWhqxhMJR3VHvTaXa+07v631SMMyfP5e3v/vVxbo3v+GTtO2qWWfpzzvvPP7gD/4AgD//8z/nu9/9LmmaAj4QfelLX8rKlSsBeP/73x+CW48H+j2qUGF/IAdoqVChQoXHOg7EfVlVWbPzGj70ji8V6975j6+ir68+o1038ybEc+FiK+G1CriQLu6n101QrJZp0/IVWgs1aqFOVTeDkDvu+KWsXb2dqck2inLxBXdw3tOPBMA5S0aLbBr5V2TTO29l5dSLQjMnpBY6mSdSd3cMzVRoOaFlPWGaqCdiO86rVcdTLzTIbEjzzyBNHWnqi7+mqZKmcHe0kIPS3fRnHeg4Tk/Wcp1b5uN45z1Q80JThVrV+oFJvnQyJHXdfaxDXFCyFtW0gsw2V6GWzqf/3ZC398WrRENfCmod926IWTAnZaAvBRynHme55iYCkerXzfw7cfSm8bsZ7aZXDpiOXIhBKdNqllYADMw7HRN7v9J0ajVXXH4Rv7jglln7rWLbCo8GVPFthTL2vwRlhQqPAIyOjvLTn/6U5zznOdx6662cfvrpDA8P0263ixvus5/9bD7+8Y/Puv8Hv3E9d/3PJ3DpGACNoSfQGDoS6ywiXosZmwapnSLJJhGpkVeFdy5BcRgX0UnGMVKjXh8C460AJCsFI7XIk6qp9TJLTCBTw6xvn9+ueUUnq4hzYAzOKUQRK1nKKbqOIZdwcf8xpAnkk8BxDO02NBpKFAnOaTFxHUVgjGBCCn7DKJFRBkM2eiPqhlBCV1SbF3+qiVKPNKTR+8f+uhKJ0hecCco3gYXLR9hw2zbW3raDvsEao3P7fN/SLTpVRhQMSfM+AvcM+L5zJWscvExVlUakuDB73k1Gmx1mGiGq6kB86to5Tz2Myy5cS9JxPPPZx1A23y9CR2GaQrUXD+YNUBD+5LUv4Iuf/xG33bKaa6+5je9868e8/GVLqUdLZrT/yle+wtatW7n44os5//zzaTQaDAwMsGvXrqLNu971Ll7xilf07PdAv0cVKlSoUKFChQOHA3Ff3tJcxb/80ze4d81mAM56yok89/lPLmXydJ95Rs5PGivWx4IioBpEBq4gXf0e5UgtjwVN6XX3MffknI2SExHOe+pRXHLRHRxz/CIGh+vMnz/o09LJcC7FqQZ7Ao9W5sUDNgzb4e2pklCiYDL1ROlESsjM8sSrdd5XNcmMV6k6YTwxXpSg3h/VWqXThiQUn8rrQIFwfeNwTmvdw52ykI5G7Hb9BcFJZpE0F1K4oFINBKkNvwkcFFLaPHjNxRU2EJo5d5zP/hc2AMzGhfa2dZ4YXXlbH6cc2+a2e5QkVXaMzb7z7GpT8JKFnGQ1BVm6LwsACan8qoKIndHehN80YmoMLDiXyc0+FX9q20W88R3f5lmX/H0PIZqjim0rVKjwaEJFqFZ41OG0007jlltu4UMf+hA//elPWbduHYODg5xwwgm84hWv4I//+I9nvUFv2jHFZz/7Oaa2rizWjSx+dlG5UoF6PAS4oEL1oZwJck/nUhDFOuhku6lFfdT6BrtVnPI4omZKU/3dYlAi4pWpql1ZaCw+46qT+X0aeLJVlW06xJPMOrbKMO24AU4xQTFpIiFNQnp8FLKLUqjXIQ72AfUokKNG6TMa5pE9MVqGxafdi/giUANx8DQVpR510+vztH2gCHTzt3nMmUu47Id38DuvOA4Nb8uEt9pVeGpPikOuJM1J11rJciDCV5/VoIoAF7xUJZznsj5Civ7LyAO9shp24UFDtJopQ0P9jI4OYgl+uerwVO9sKVG90CAzzo+rmvtJlYjcvdCu5575arZs2bnXY3Q6XgVdr9f58z/9AL/3+09nwchBGNN7yR4cHOSCCy7gS1/6El//+te5+eabmZiYYOnSpZx33nn81V/9FWefffasx7i/36MKFfYL0p0AOhB9VahQocJjHQ/kvpzZDp/454/xuY/9O+Bjznd98E8Ky6K8DJWnQfMcpHzSOZChanyspQLifLyjIGJCLFyOvUrjyPlZusrXrulnuZEfy8FL5nD9dWu46Dd38OL/e6rPCqMJQKpZUbjeabAiDfWdctFmhi8m1QkeqZOpMJEaJlJfkDUnVDMHE6khdUJHhYkkZHipt83KMiVLIUkgzfxBbdYd9o5oCHVwhlvNL93RPsU/C16naSA/Q7q+ZK7kd5o/dz28puT1FZx6yS34+FOlIFLJieQ9cZm5RUBhAaDsGo+wVjjziZafXZrNbrywRzK19AFOS+Xf8yCYtl3YsfqruGxqH+19PO/S3dx29Sf55Oeeyuv/7FkzWlWxbYVHNKr49hEDEfnyg9j9ifs9jv0zoH5E4VE34AqPDLzsPf/DDz/9t0xtv75Yd+bZz+XY458UXgnGxD7Qcgn5/LuEIFLVIRIFGwDFSEwUeQUrZWP2PPc8n6/PWcTCIFRQU2qrYeZa6HqxKgjKQjOJw7AtGkbCPlGuMFUwUX4oT0YagSj2IUtkND8cEsjMSHzxp/L1O1en5sOpFUWhQno9YErFofJ9iucCrYmEzlTK0Jw6ff21nkJSHloQqPl6n2LWk2DfbTvtDqOzvpIZW3oxvWdBFTZtmEBEWLJ0dNqNrKuv2D/sH4E6Hf/vL9/Brl2797s9wJv/9mX8wz+8i0Z85H3ar0KFEh6ysE1E7hlYtPTw53z2xwekv1/82e/R3LJhtaquOCAdVqjwyEQV31a437hn/Br+6AV/yhUX/haAwaFBPvTRdzAw0CgmwHMSVAK52VtQSHoeizgtb7sft5Burs++YjNl185JJiY6LDtkTmjvcKqFrlU1L4gq3R61GzLnnKRT75lqVQrXLZ9hL6FmlIT2XuWaj8w5r4J1tstlQldQmr8Ydi2GtMNmHQ4csZbUpPh0/2JdPsjpp6C0riBOuwRqflb24wRTqF0DgY36MzY0YBkaUDZu01n22d/OlfubjPy9b32c5tTEfdrn4GP/glVXf4Thob77fLwKFQIeUkqyim8fWRARy4P7N6B4vnSvFHqlUK3wuMA1t2zmgl9f0EOmAlxz1YWs3XE8Jh4kjvtBHWnawqnFuhYiMcbUcC7DuQQjdZJsnDjqp1EfoRY1cJpRqw9g4rpnJPvjwFAKGuGfh6pKbrAO/QbF+K9/3fhZbCNQi9BGBB3AwMn9m7mtsZQFpslOM8ja2kLiGvQPCDYIWvMZsiiCKBLqdejvF4bqSkOUmlEGahrmnL1SdW6f8+UHjK+lGgsMxD6Q6o9hMIb+POVfoB75/bzHqY/MIrpKU5dkXPuLe3jGi57AlT9bzXnPX0EjEiQvNGW6Xq5CrnLN1b9RTwqaEBVKCQp1ajnBrLcKaan+a8/nKoUWw5+k/AfBtVduuAoVhwABAABJREFU4LgTDqbVdPT31Tn+iYv954QNcaoLnlP7E4F2VapCXLqc7/1nx8c+uoj+gdreu1ZoNhN27x4D4J8//l1e8arnc+yRSzGmfz/GVqFChQoVKlR4PGAq3cUVV11SkKkAU5NT3HrHdfzte15GPfKT4zVTQ4i94jQ89sQ7AoY48IOKoBhpYCQq2SHlcY8Urz0NGpZCCTm7OhWUVqvFf/7gBo498WDmzOnjyGOHUFXaNvXRnMJ4Siio6j1TVYQ8o75tfWp/MxPambClbWhlXpmaOF+IandimMhM8FIVJkNaf5b6kSUdn3GWplqQqtbSVZA6R73V5n/pHdydzaNpa9yTzvVq1NQvJNYbvOYsL3if1DzdX7VLuDr1bZ32qlYpTsvekateM4da9YV0nQIZcex4+hltVq12tBLlzrU5Kb03VWovfGydZ2nlRcp8P73E++xI7CAm3nfs3FdXmk2vZN218RLe8I7v8vlPvGK/x1mhQoUKjzRUhGqFxzxUlbd86lK2/fYbxbrG4KF0ptbibIvxbRcxuvg5qMuwLvOEmnjfU2Nqwbw9RdXRTLZTi/qI435i04cLZTjVlVKbgimpAsQh7bxQqAIdReqKFlWhoq6JaOoQVQZqKXNNi+sbR7ClpvyvsZtYH8+jEQxQvU+qN9N3Dmox1GreWzUSrzDVEL+q80VHa8bP1nes0B/51P+hyBOmNQP9RkGEmpQ0BqrE0iVT8wJQeWgVG7jpmk2c8KTFDPTHHH7MXO65cRsnnLqw5zOIwljy8NtIFFLyu2pTQ+RZ3ryYQU/g3iVPy4GdkHs8mRK5WlZdSLFMTSasWzvGi15yKKoR3//Obzny6IX0NWoIkVdFCBiNcd7gah9/WQ7FeDKV7lvZV9x56VVf3Ee/cOlFd3PwklG+/KXv8fGPfItOJ+Htf/tpvv29Y+g3+52BUKHCwwqz799gFSpUqFDhAUBVWT2xkg+/7XPFuigyWOv4/Kf+k//vFc/iCUcuLk31huqoeZp/KStI1PhCVCH+Eepdfah2J7e9JZNvkaf3d8nUWRPO6cZjjssuuZuzzjmcRQf38YPv3cwhRx6JGB/F2VDoPglFpVCvPlV8lr1TaAWitGNhZ+rT/q0KmfrnzcwwGcjVTIUk9YVbbQaZ9TUH0syrVG1G17rUhepXziFty8luPTcki9iaDfKsxl3c2xrGdhTJrDdwTR2lgNkXqEpd97niq2eVpbOznZq9f8BdY9ckFPyyKaq22PmJR6XcdEfMui3Kc891rF6vpNn+kKm9pKkXYWlpCZ+xdtvsCQtW/HEPKata7sMXHDvziQ02bplk5aX/BprSHruJH/zoV7zpL57D0U9YvB/jrVDhkYEqvn3EYL81+A8mKhORCo95fOtXd3LL5f9NMrkOgPrAUhYd/krE+KqnzZ3X0mltJsmmULVEpgEocdQPGJzr4Jwjs21UsxB6+lx71awoWoXiyVEbZpEj/KNIyCGSfLodzZlJCHZFPvVf2r4w1mn9m7m2cThxQ8g04rb6Ek6y6wuZZ344a4tin9TrQqMuRFEQvfoj0tGuZWuqSmZ9Cn9NlDhS6gYaxjFah5Ga0oi7Q6tH3k81H2aZTDXA1M4maSvl4OXDGANHHD+PDavHyJKMetS7v+crJXhx1QIRWepNIq9IlbxN7+VJ9nC56vqXCr4KrQEMRmKEqOjn8ovv5ZynHAoiGCM86cmHcNXl9xS9Fz82JMIQ5+94b39aEBQcvWseGMZ2t9i+bZIVRy7gLX/3Sg5aNA+AH/3wIi668GKs2/0Aj1ChwoMPOcBLhQoVKlSYie3te/nx9/6bG69dBcARRy/nla97AQBpkvH+t38ZgMjkGT9dok1zUUBBlU73P/WvHRYXSNMusxra5irIUvCT95P3rKXjbds2xtRUwrLloxBlnHTaQq67YhOJVVILLStMZBJGJmQIKTl36VWo7UC6TmYwlRjvpWohtV6xOpkamplXrbYTYbLplamZ9XFzlobxhKE7B5oFgjSz0LGMJFMMug6bO4O41HFrcwEnxpuQZgLNzBOqJTJVVBFHoSKVLPRlw7pp/KZQSsjaG5yCVTRTny03jUwdHnAMDyobtxmEiBtWwSnH3L+7ZlexnP8N3Oce2NMde3Q4Zt5oxL1bIvrn5VZryviWC3jNG75yf4ZbocLDgiq+feRAVWNVjR6MBfjF/o6jIlT3ExMTE1xyySV87Wtf46Mf/Sjve9/7+OhHP8qXvvQlfvKTn7Bu3bqHe4gVZkG7k/GBr1zOztu/X6xbsOyF1PrmMffgZ4c1yuSWX5VSY/wMOvhCVGnWRNWS2hZx1Ee9NoAxEdaloSBR7OWiNZMblnp486Zu+pCGqp9xaCsU1UC9KhM0EhbWp2hLjfFoABHBOWVNbQGL7DiD2inUqYQZdRNBoyHEUTi0+oJSSBGHEUk+Qe4N+1WFhoGG8STpQOxVrzXjQ9+8eFVPEanQd444gtuu2sgp5ywhDsWrGpFw2tmLuf6yTb54luTuB/68GIkwUsNIXDwXakQSU079zyHBm3Zf6UYigpEakdQx1AMh2t1v6+YpEFi8ZAQf6lsOXTGHHTsm2b2rGY4qPgUO002H6zHnnw3Tfy5MO0n3A5dceDfnPe1IjESMjgzz9+/782LbW97wCdrpHfusvFrh4cdXvvKVnr/f97znPQ/3kCpUqFBhj6ji3EcfrMu4Y8t1fPK9Xy7W/d0/vpq/fOtLWLhoLgA/+8mVXHbhjXTjFT+B7LREkIZtqhanebV2P7ntSdNguVT6B+DUE61ObdGXFoRcrz0AeELw4gvv4rynHUGqTRTL4U8YYfOWKXaPJ7QtdPKw2Ule74nMCokVOhm0M6HlhNTBrnbEZCq0rNCyhkSFyUyYylP/gxK1S6R6RapzYK0GUYIGEjWDToY0M6RtOV3Wcd3UYp/e33asH+9nrmkzSNr1QnUOcQ7JC0WlFskyJAmMb6ahgFT3MyvOyv4oU61DncVlGZqmqM1As56dTzku4bpba0XP67bA6LAwPLivv56uOrUX04tSddvsK/b0IXcExDPi9jNPrHPFDS0AGqNPxMQjAGStDay87mJ+8NPr9jXgCg8zqri2QoXZUaX87wXbtm3jS1/6Et///ve5/vrrcW7vs3UHHXQQz372s3npS1/Ks571LKIo2mv7Cg8+3v/161l/1XexqTdKH5x3Cv0jTwCB0UVPZ3zb5WTJTpLmGjoTdzIwenwgVn0aUyedQBAy2yYydeJoEGPqOOcQcURRnSiKMXHsGcbYKy1Rn/ajdQmBaGA3jYSUIkNhYo+iuRqzJpzcv5XfNI7BNIQscYhAo99w88DhnLD7Hq5beCwQujOBCK151WquXM35WkWoizKVCTWj1CMfEGWlyfIIF7hgT6Bm1vfZZ7SokeXjIimm043AlrW7mTO/n5E5DYIrLLGBpcuHWHXjdnZtb7Fw4QAitUI/6mvLloM1Cb6s5cBrdgKzm4402/cw+JlK19fUn1mHKlx28Vqe87wnFH3nqoxzn3YEF11wBy/4PyeXjh7C3RKR7Aqyffqxy7P5pss+32f4ftat3cnAQI0FC0aQoH19+St+l8999j+4/rrbuemmu/jyF3/Aa1+7jFq05H4eq0KFhwb3++tQoUKFhwRVnPvoxr2Tt/HlT32PrZt3AvC05zyJ8/7XqUQCb3r3y3nr6z4JwDve8gUuvPKTxLGE2CJXleap/15I4NP9FcFnDPnnIWZSwUgeQ7lQU8mF12V5alePmsdSqj5b6+67tjF/QT+Dw4aMJqqCc3Dy2Uu47tKNnPiMw3FIoUOwDhL1x0yAZiI0reBU2N0ROopP6XeecE1UaFtD2wpJ5gnZTqLBHss/eh5UC1KVzHkvgUyRzKsQlrpdjLs6U53YB8XBN3XltvmcOn8bl25Y1FUs5G/VepsArCMvTDs9e36/b4m5B0Hm0DQwwHTPd46DF1qmWsLYhD+IL/QqXH1TxpknRvzqyj1ZV+15JCIG1az0Gd6nkRf3/dw+QNVx8EJhqm3ZNR6sJExE/4JzmNr8MwCa2y7jLe/9Ns9/zknUahU1UeGRjyq+rVBGpVCdBRMTE7z1rW/lkEMO4W//9m+59tpr9xlkAmzdupVvfOMbPPe5z+Xwww/nK1/5yn7tV+HBwbrNE3z9hxczds//ACASM3/5C5B6jJgYVJm35HeL9hNbf02aTuI0xTlLZts4lyBEOE2Jowa12iCRqeGLGDniqA+p1TyTWDKyB0FzeWYeq+Zq1I5FplI/622gkHfWDE8Y2M2aeD5JvYbLFE19gBRFMFYfxmGY2x7z6UkOBoeEgYGQ+h/S/2uhzpU/rAYHAq8aqEcabF1D9VPbraFlxF8QIoG6eCuAmJk3DSMQY7nr+q2ceMYiTAi465HfNxLh7Kcs5+qLNyMaERFhiIJvqgTvVA8f+sV0ixsQAvY9kKqzXrIMUVC9ziwHJay6dRvLDxllcKheWu+DxHnzGwyPNFi7Zkfp/XWLWBVKV8wsfVOsmz5nrzPWzA7fbyCZneGKy9Zw7nlHFf0KYIzhIx97Q7HPe971WXbsui2kfFU4UDj++ON7Zt5FhC9/+cv73rHCnpFL3B/oUqFChQOKKs599KOTNbnxzqv5xr/+EIBaLeZv3/fHxfaXvPTpnHTqkQDcevNavvrFn9OdVC4XkfKTxlYzlAyw4borOM18HYGyijV/rq7rPZr7qIa2rlCz5ussaZpwzVVrOPXMxaQ6GfZT2hZGFvRDHLN902QYnw+ZfUaVr/3UsV592kx9yv/uROikQuq8cnUy82RrMzMkzpOynY4vOIV2laneN1WxifVBcGIhs0FVajFJxonRZm4cm+9Vq5kiiUMyZbwZ084iDmo0vfo0n0/PHJLakI1GIFspXndVqfkJm8a0lqG+LoMmGS7rkqk6/XsmjhOOSrhhVUzXogGMiRifNEy1YMnC+37/3JcKdbbtqvnfQ7lwbO69annSCQ2uvqld2sNQGzycuH8ZAC4bZ8M9l/G+j/3kPo+3wp5RxbUPIqr4tkIJFaE6DStXruSUU07hwx/+MO12e8b2RqPBsmXLOPnkkznppJNYtGjRrDP069at41WvehUveclLHophV5gFb/rUZWy7+usQiKc5S59FrW8B6sBmKeosjeHjujf0dIzx7ZeSpFM4Z0nSSWpxP5ntEEf9xNEAkTGByHIYUyOKa0gUhbx2LWasFaAvVMkMs8xFNdDUpwWBopH44lRxRC12rIh3sWpgGVIzvpIUSq0eAmCFm0YP57hdaxC0KEJlguGpdZ5YjcQrU1FfbCpFGIxgIFbqojTCt75joRGV1Kn44dYiX4SqHkEj9sk75b/wWJR7btjGEcfPo69uiAzUJNTcEojEMGdOP4sWD7P27omelH2h97siYqZpU3NSNa8wyrT25XXe41SY2VZCur/NlJt+u5lTn7S8UFyU2yqWs845nCsuu7v4UehtHIQoeLrmFXH9zH/E7JfNPKrW4vizhaT5GKRn3H48N/52PUcfvYi+/lrPHgBnn3MSf3i+t6jYsWOM9//D50ndmlmOUOH+4IorruDWW2+dsf6LX9x3AbEKFSpUeDShinMfG1gzcSOf/vsv02knAPzRa5/PYUcuLQi8KBL+/p/+pGj/gb//Jrt3TpVS8iFP4yd4oRYFQZWeFP5ewsxiNcORhTYWp4rTXOHajYXKxO31167m+CcuIKrZIj5KLCRhcv+oMw5m1TWbyaySWaFtpQiZJ1PDZOrJ0lSF7W3jU/wRUvUq1pYVJhPvpeosZJmSpqXHkN5vrWJbFtrO+wtY9cpSp5A6jpUt3N0cxaaCZIp0rCdLMwsZ3LhplCcu3IWElH8y64tUpYEszeNd6xDnvVUpL/lvhZx8LW+zDs0stBM0SX1QX6CrBlYcRx+WsXp9RJJO/8sQjIm47lbLqcdGs3A295XEKf+9hDXh7yBfAnsc2tqQ6efFJccdUefOexOStDcqFhEGFpxbjKe961o+88WfsmnL7vs4vgqzoYprK1R46FARqiVccMEFPOUpT+Huu+/uWT9//nze9ra3cfHFFzM5Ocm6detYuXIlv/3tb9m8eTOtVotf//rXvP71r2fp0qU9+27btu2hfAsVAi64fj2XXPArmttvBCCqz2Husud4xs9asnQSEDLbpH/BOeQ39GT3Smy6i8y1ERR1CuKITINa3I8xMS6kwjQaI0hc663U5ELA0MuVFf7uoj6lSDTMTJnuV/Dk+mZu6F+OxBKqjCrGeHUq+LSottTZ1DePQ6e2UG9IT6BkrU/Rzw/ri0EpfUbpix0NA5FRYqOIeE+nwRgakdKIcnpS6Y+gHoaXaxJAMeJ9VbN2ypZ7xzn6+LnUjCde67EQG0NUIkJPe/LBrLx2E1moNJqTnF0IPk2+NOieOG9PKtUyKWkC+VraLt3P4+or7uW0M5ZRq3U/kOm9xn2OY45bzA0r1xfryjPw+XG6+8862FL7rro0J3y7fUC5n/xfu51y++2bOemUQ6bNJnd7ff8H/pL+/gYAn/3X73PbbddiXWvWMVS4b9hTgHnZZZexatWqh3g0jx3kNiQPdKlQocKBQRXnPjYwnmznwosv4H9+fBkA8xaM8to3eWJbBCLjY7onn3UsL3rJUwDYuWOCD73/m0CuKu0qTr061af1ex/5IBLwWyGQol6xar1QwTmcJtiCWAupU0XfFsX3OznZYvXqnaw4ZohMbZHO37LQyvyjadQ46NBRVt+2g7Yrajv5wlIZNDOhE/aZyrxKNbFesdrMDO3MsLsThVR/pd2GNFE6HR8fa0j5dx0b+GMtFY9ySMfS32mzLB7nnp2DXnWQOki8SpWQBZ9kEffuHuCIueNezOs5RLop/orkatI8lCwTqF3b2u660EYzC0na/S0RoM6G7vzO9Zqy/GDLXWujGYpRIwaRiCyLuXu949gVe7qJ7kmJOp081XCM7mPvmyj3o6U+lHoNjlhe47a7e1lfY/wvjqgxn8bo8WHXjLHNl/E37/j2HsZV4b6gimsfXFTxbYUyqo8y4IYbbuAFL3gBk5OTxToR4c1vfjN3330373//+znvvPOI45neLrVajWc84xl87GMf46677uKjH/0oCxcufCiHX6EE6xxv++zlbL/xW8W6BYe9EBM1wDps1gaFNGtjbULcWEBt+BgARJSkuRHn2kRRH1bb1OJBGvUR4qgfVQEiarUhoqhWrrrkyVEjPv3fiI8nohDZ1sOiIfgydFWtKMOmzYCkbO6f7xsEI3vTML0kpMDdQ0s5bHITsWYFJ5sTcFHklaZGHPXIBzYLGo7+CBpGQ0q+V6vObTivZs3VDKKhXpYSR72FB4xA3SiDMdx+1SZOP+dgYiMFl9wlEUNqvEKtZjjx5EWsvGZjOLezEJ97gRHDzEuUDxSlsA/o3V6mS8fH2mzbMskRT5jfPX5BtpaVqo4TTlrMqlWbabY6xf65UtVbAARiVKJgABDNIEgLiW9xRnwqf+4dK8VzYTqte/mld3P2OUcQRXs6J8ry5Yt401teDkCWWd78xk+Q6T17PYcV9o3JyUm++93v7nF7NZt//yEHaKlQocIDRxXnPjagqty163o+9o7PF+v+3ztexvDIoCdTpZtVpMC73/cKBgb8ZOwXPvsT1q/bVtoqIc0/K8V7MRSJ+zmRaguFqgsK1YyETBMcXbVqQa2p9HipXnrR3Zxx7iIckgsx6Vhoh6z71Hk16sJjDmLTnTtptS3WedVpMxShmsqEzAnb2xHjaUTHGnYlhpYzTFmhFTxTrYU09WRqlkGa+scsA80cZF7cgFUktUgrQyY7yHibk2ubuGH7fLDiGV3rkII7DIpU67hryxCHz21Sj1xJYerfrVeu9nxg+/pA0czi0syrUp32kKSqWqh8c5x8TMoNt8deTTxrXQEQMdy+2nH4UkO9SHzanztqryI5J1Dz7Lw9HW82nHFinetuScPvjNmP3TfvTMT4v89kYhU//8VvuOq6u2dtW2H/UMW1Dz6q+LZCGRWhCjSbTV784hczMTFRrKvVanzta1/jwx/+MKOjo/vdV19fH294wxu49dZbedaznvVgDLfCPvCZ/7yVO6/4MenUJgAaIysYWvAk1Dmcc6RpC+eUTroL63y6W33uKdSGjuSFL341g3MOxVDDOYtIRGQaRFEjzNorcdygMTAKtcgvOUmXs5W5tDMKbGdk0NigkUFFfJwpXSJWRTitvoFr+w/3MVuIMLXmixy5EFhJqH/kxHDH6CEcuX0t0BW5NuqeszUC/cGWdbTmqEeOmnEYo16hKo6+CIZrPs7zhUi9PUDd+MdY/MWhJn7J149vb6JOWbx0sMce1hOrfiBdn1ThqGMXsGHdOFOT03OSgjp1xvPprWTaK6HsozrDU7XU/NILV3Pu01YE4rb3tpWTpIVtgDjOOvdwLr9kWhCXB8jl44TPu0yNdvsK/fcQrrO8r5IKdefOKSYnOhx62Pw9ts/x+je+jOWHLAbgf355JT/771+RuV373K/CnvHd7363h2A477zzesj+r33ta2RZNtuuFSpUqPCoQBXnPnawubWa73/jx9xx82oAjn3iCl740mcidIuDGulmLC1ZtoC/edOLOfIJy/n79/81y5YvDNkv5aKaOfxEv8vJtKBO9K0slhRLB0eCI0HJcJrgXIqqC3t3J+NR2LRxF5nNmLeov1CdZg7aVmgHv9PMQSsVWs5w8IkHc+/KTTRtrkr1afyJFcZTmMwIilWvVG2mQjMxtFKfyWUzpdVUOoknU52DLFWc9f4BaoMitZUiUyk0U2hmzHNNamrZ1mwUxKkUafkuKE8Vn+Al3LRplBOXjBUZZT1q1PDe9yrmxPer1qKdzCtT8/MdfFSdy4nM7o5zhh2NOmzdOVOdmsNILsaIuf5Wx+nH77l4XK+dQ1YiUWe0pEu27psnnjMsDPQJm7bnvxR6Y/pcpWqiPvrmnVGsn9p6CX/x1q9V3swPAFVcW6HCQ4uKUAX+8R//kTvvvLNn3cc//nH+6I/+6H73uWDBAn7+85/z13/91w90eBXuA3ZPdPjUdy5n1+0/LNYtPPzFvkJ8lpElU6g6MtcEfFV75xxIg74Fz+DmexxnnzxKFNVxmlKPB2jUh4lMncx1iEwf9cYwEsVoXE5Vl+5i6PooGXxxqkbklyg874uKtKhl8Rhj9DMR9SPW+Sl7q1CLfLq/CibwtgBRDDtGFzCUNOlPWohAo6bUIp+S3zCWRuSIjWWk7j1S+2Kfyt8XKcN16AtDzZPxY4HYeIVqzgvHRqkbpR779XWj3HD5Rk47t1tdvkxVapdeJVdoighnPeUQLr/43p7PaaaydA+EquT9mWltgrdp+V8pWFh/724afTELFg4Wo+t9pKiG6v1OhWXL59BsJmzb1v3Bmf8oyP1Tfevgo1p85qb74RR7TY80e03hy7jogjt46tOPmvX9T8fAQB//+MG/Kl6/+Y0fo53cXQWeDwDTZ+rf8Y538NSnPrV4vWXLFn76058+1MN6TKDy7K9Q4ZGBKs59bMBqxu0br+OzH/xGse5vP/CnxFFUJEQZpl83hf/3xj/gipX/yiGHHMbY7tw3V0P6ft7KZwDlRaS08FXNUNKQ+u9VikWxKnVYtb1K1pL3qnOOSy++h9POXVxwi448bd9bmCYOplKfzp85GFw8Smu8w/iuDm0rJBamUkMzE3Z2IiaSiKY17E5jrDOMZ4ZWEixOAy+ZpJ5YdU5x1hZ1DKSTIZ0M2ilmMvEFYqdSTOY4Ze42Vu6YX6Ti+1T+oEotis52Y7stE30MxinDjaR7Dp2bnTidjsIvNaT5u65Xqrpuca9usa+gWlXl1ONSrr91pop8NgjCpu3Q3xDmjpgZMWjX63R/Bk2pXdkCYHac+cQaV92YluJ4P6LZ0Bg9HlOfC4DtbOG2my/nq9+9fD/GU2E2VHHtg49HYnwrIr8jIj8QkY0i0hGRLSJymYi8T0RmXDREpCYibxaRG0RkSkR2ishvROT/7OUYDRH5sIhsCMe4RUResZf2fSJyt4isF5GhA/VeH2l43BOq27dv5+Mf/3jPumc961m87nWve8B9G2N44Qtf+ID7qbD/eOfnr2Lz1d/BZU0Ahhc9mb7hQ3E2w6ZtrG3jnMVa7z3Z9QOyOG2zYWvCQH+duaOGWjxALR4iiup48lV8IapaHSJB4shHr3H3a5SrUIlMNwUoFr+Eqk0ah31CcHRCfSs32oMBgYnUp8uP1ujr7wY+zttVYSJfaCCK4PaFKzhqs1dUDtSVgdjRiJSD+h0NowwGX9Q4KE7rkaNuoCZKFDnv5yq5r6q/sDeCsrVmlIFQmKoe1q+7azeLlg4xNNS9JnuhbV7Jc6aCFGDx4hFEhM0bx/0+MxId9n5HmS1FPk+lnw3OKVdcupazn3LYPo/RJVV9UHju01Zw8YV3zEi3gtyCIJ9l76pbu337x7xgQC9mDzrvvmsb8+cPMWfuwKzbZ0L5gxc/k7PPORmAu+68l0//yzdxbNrP/SuUcdttt3HFFVcUr5csWcIzn/lMXvnKV/a0q9Kj7gcOULA5i8C8QoUK9wFVnPvYwZrxm/nCR7/Fru1jADznhedx2lnHe3Wq5C5U/jnk7lKGRqNOHEec89RlXH7RutBbiFbUFw/ycCF93+LUlopM5cWl8vblJQOsJ2E1QzULhKty220bWbxskIGhWiHk7GTQzkzwRhWmUmEsNbStIbUwngrDTzyErSvX07awKzFMZMLORNjVMUxlQivzNaA6FjqpkGRClinWOpLEL2nqsIl61rYdilClDloZMpEGvwHvoXpI/wTb2300kwixFrEaGFpXFJuVab6mqLJyzSinHDbeXbcncWdpH+9L4HCZRZPMH6fYXKadvTetc7YgVJcuztixW5hsSsnbdE/IJ/ENV9+snHGiKR1DSyn8ZYJ0fybn931DPuRgw64xZaLpxxdFuVVXb+yeq1RFIgYWnFesb22/gnd/6HtMNTvTu66wD1Rx7UOAR1h8KyKxiHwd+C/ghfiZrxuASeB04O1A37R9+oDfAB8GjgfuAnYCTwf+Q0Q+uIfDfR14MzAErAKeAHxFRF67h/ZvB1YAf6Oqk3to80jFR4FXAa/cV8PHPaH6la98hWaz2bPu7//+72eoyA4kLrzwwh612vSL3L7wla98pWf/97znPQ/KOB9tuG3NTn7080sZX3shABI1mL/s91Cr2FaTJPGFqHxRqW76i3XldHThspVTnH1yP3HURy0eJDINVJV6bRBTr3uGMgpfnfzvxIhXopa5NaueXK0ZiCKIDToQ+/0zRSPh2JHd3OUWkMUxZBZ1YOpC1B8R18Tbsho/6y4CfX1Cve7XJYODJPUGB7d30YiUwVgZCaRqLfilOnxwXTOOwdiTrkM1pc/ASAOGY6VREtoagVh8Aap6pMQSilhlllU3bOOE0xYGz9Zu+y7y8lW9Ck0RwzlPOYzLLl7jU4Sme6nO8qxn+wxVp/g+9sDJ3nLjZo48agH9/bVZGkwPFoWuOziMzuln4UFD3HXX1p73kAetvXYDM4ne7rrZVKq9sNZx9ZWrOeucFXttVx6DBhL8ox9/Y3FO/vEfvsCWrXfgdEap1wr7wPSA8mUvexnGGF70ohcxODhYrP/Zz37Gxo0bH+rhVajwiISI/KOIPOHhHkeF/UMV5z420MqmuP7WK/nuF34CQKOvzt+8+5Wo+lisZop5+8Ibvxuj+Ohs7vx+BodrrF0zhnOeNPUJ7D5uceUCU6XK7WW1JIFY7cZ6fnHOe6taTXGa0Uk63HD9Jk449SCyUK/KOtidCpOZkKgwkQpNKziFpoWJTJiyQtbfhw40GN84zkQaSNdOxEQa0XKGljWkVmhaQ5J4JWqWKVOTvhiVTbSoReCVpa5Qp5p2UKlmDkkdxlmOGdnFLdtHkVQhpVsRK6Ty93xT8hfWMdmJGW/VWDK37b1T94Y8T96BZniz11JM6mO8rsLXE6l5ur/DGMvxR1huvKMcQ89OgPrPsSsGaLYido7B8sX+b2HPaf37g14164yiWAZOOrrG9bf3xqRSiBLKQgSK61BtYDm1gcN8n3aKrWsv550f+MH9HOPjF1Vc+7jEZ4A/wpOoZ6jqclU9Q1WPAOYCLwCmz058CDgXWA0cr6onqeqRpbZvFZHnl3cQkZOBF4fjHKaqT8QTti3gH2RasRQROQpPvv5cVb9/IN/wQwFV/bWqfk1Vv7avto97QnW6afPJJ5/Mk5/85IdpNBUeCN7ymSvYdu3XyW/085b+b+J4FE1TnEtD4AhebepVlrYIWDziqI/dEy2271KOXD5IZGqgYKIaEteQODCcuVcqPqVfY+kamObRbBxyr4wn67TWJWJVoN7nWFqf4K5oATpQ9wFcLPTPrxdkar3hValx7D1S49h3V6sJtbpw7+JDOXTzGvojy0jNsqDPYVD64pCyH8FQpIzWlL4IhmJlIHIM1hx9kU/nj4yWCFK/brDmVal141Wqt16/leNPXUi9ZrrhkEwPi8qGUb2eT4NDdQ45bC6rbu2tBjzdI3VvmJkyL10mOKDTybjtli2cdOrB0/eercdpi8cZZx3GtVetIbMZ04PG2Qpl7anQlE6TKkwPOq+/di0nPnEp9fr+pW650jk95dRjeNUf/z4A4+OTvPud/4KyYb/6qeCRpilf//rXe9a94hU+a2VoaIgXvehFxXprLV/96lcf0vE9FvBITImqcEDwVmCViFwkIq8Qkf6He0AV9owqzn1s4K6xlXzyXV/EBkXjy//y/7Bk2UHF5LYRMKWpXB+nBbJTQ9o4yhlnL+HaKzaQudxDUUsUWcnDUwOVWrx2PoVeXWmdJxydKo4US4rTlMRlXHHlWo49ZSFODKqCVZhKvS9q23of1JYVEit0rDDeEXa2Dbs7XomarljG5O0b2dU2bGzFbO9EJApWhTT4qnbaSpoqSaK0237JWhbtePUpqSdNpZ0hzdQXoGpZJHFeheqU40Z3sWrXKC6Vgnz1ZKo/KQVRWoS4oY31z2++d4jjlk8i6vZgPRrUrtYrXZ21qM18an/wSc3JU+ds8E3VGZ/FcUc4Vq02WEvPeqeuu4S+fH+9ZOtvb4eTjjIYI9yXwlL7g3J8e8KRMbevzphu0VmO4WXa+jyu7pt/dvG8M/Zbvvqt/+be9dsP6Fgfy6ji2ocOj5T4VkSeDvwJsBF4hqpeU96uqk1V/bFqV3UjIouAPwsvX62qq0rtf4xXrQK8Z9rhchn5J1R1V2h/I/B9YCFw7LT2n8ZfFf/y/r27Rw8e14Tqjh07uO6663rWValLj07815X3cs1Fv6C963YA4r4FjC56ug8u0oQkaxKZGFVLZOqIaPCd7IaRQlTMEP/2NsuJR4GJfOARRTWkUQ+FqLqemZpHsSKhK+06tYt4UjVsoi/2DGXNwFCdU+buYGX9UBhuQAxEQjwcE9V9wCPi0/tFoNEH/QNCHAu1mhDHnugcHanhFs9nybZNjNSV2LhQXMqn/deMox4FgtQ4RmreQ3W07tP6+yLoj70/am4NkC85adqcSNi2eYrDjxwtpZRBJL0kYjdVKQ/Ly8oGOO1Jh3DTbzeRdLoEdhkyjZ7dM8peTL3tr7x0LWeefWhII9qffmYqTOv1iBNPXsp119xbrlkL5KqP/b0DdhW709Gc6nDP3ds5/sQlM3fba3/dFK+//4e/ZGTE29F86Ys/ZOXKq3Da3FsHFUr48Y9/zNatW4vXZ5xxBsce240FpiuqvvSlL+0jva5CGcKBCzgrTvURi/OALwObRORzInLGvnao8NCiinMfG9jR2sKvfvlrLvvVtQAsWrKAV/3Vi8I10sdv+d0pV6eC157mxKgLMVm9YTjy6DncfMOOIvrNq7h3PVDL8Zvi/VMpHsvII77AF5JZZefuFls2TXHwYaO0rfdH7VhhRzuilRmaVtjeiWhbYTw17EqE8czQdEIzM0xaYUzrjC9cBPduZrxjmAyp/Ym3QKXZUr80od1WmhMO27Roy3p1atsiHQsd64nUxPoCVEnm1aHWMiAdFvW1uHd7H6SB9Axeqfk9rPQmSyrTbiyQZcI9m/o5asm0+EvVHyf149HM+jT/NEFdBtN8Up3rKoJz9bC3WbD0NVIOXui4e73FBQWwc35R54pxOc2wmoX9XE/M4pzh9tVwwpGz31Hve3wzs31/A5Yvjrhz7R7i/GBBMD3+zmP2qD5KffTE0L1lfMtlvPZNX76P43r8ooprHxo8wuLbN4THf1LVnfu5z+8BdeAuVb1glu3/Fh5PFZEjSutHwuPmae1z37nRfIWInA88E/iAqk6r+HzgICJPfSiWfY3jcU2oXnfddTMuJGeeeebDNJoK9xeZdbzrC5ex44ZvF+sWLH+hJ0jTNkk26RWqLvPZO+qwNkPVYl0W4hCHmDrWtYlMnSge4a57+zj28AQTxRBFSCQUUgBCKFHm9qx2+VljvCLVCCqgjcg/iuAaMXP6O8RG2VYfwfRFSBRh6oa+4Yg4DurUuj9OoyEMDoq3a4188alYlIHYMlSzRIcuYmDrNoY1oW6U/tjRiGCwpgzVuhftwVrO6SqxOGqifgn2APVIg99W92ZRi+C6SzdxxlOWEEXSk+I/g4oMZjDd75SGE+RnwqPIcNoZy7nmyrUP4NOefskKn4UqO3dMMTbW5pDD5vSoJwqbXMVLgwvvU52x5P+OOf4g7l2zk6nJNr1BY1n7ka/RbvA6LRVOcT3nKR/TxRfdyTlPOYLcj2z60jv+kOqPQaFImzrooLm87e2vKfp9/d98COvWVsHRfmJ6WlQ+i5/jaU97Goceemjx+q677uKiiy56SMZW4cBCPM4WkQ+KyKUiskNEUhHZJiK/FJGXyl7yn++Pcf9+juvRWhCgN+cThvEKiStF5CYReYOILDhAx6rwAFDFuY9+OOdYtet6PvmuLxTr/t+7X0n/oLfEiwRfQIkQhmo3V6jr6Z4rVS0Wx1FPnM89q3bTaWeYkKXZY3NU/N9VpLo9+GuWc5JUoemUKy/ZyAlnLaEjhiwoU3d0hEkL4yns7viiVG0LzQwmE6GZCa1UmEyF8Y5hSyti3bwl1LbuYGoyI82gmUCzDa02tFqQpuCskrQc2s4gcUgaik+1gyK17QtP0c6QtvVVsFJFUuWUBTtZuXFOkdqfCyKKm0E5RMyLU1kNb9grTyV1rN7Yx9L5HRq1cI6cepVrKVlLXYbaDMiLfpWKfDlbnEVPpGaUL7OnHQ/X3uJH5mPaPN70PrcuLw6mFrRXLFLG3euEZYsM/Y0D9fO/NwvrSSfUuObmvdtP5aSq/8Xgz7TLC3Gp0ph7MhL5pIdsajWXXHIRF15+2wEa72MbVVz7+ELwQX1OePkjEXmSiPyriPyPiPxYRN4lIstm2TVPUblktn5VdQPeCqDcFmBLeJyuRM1fbw7jGgE+BtyJtxZ4MPEb4IKHYNkrHteE6u233z5j3WmnnfYwjKTCA8E///BmVl/2H2RtnxbSP3IUA6Mn4qwlzRLSrAk4rEsRlCxrk2aTwUvVz+SKxDhNAIjjASKps2ZjncULMvr7o1CAKoJaIEbByzhzKF3BpIBGAjWDq0VoFPLmxZOrbrTOqf1buK52CLUB75Var0P/cES94QtORRGFV2kthv46NGrQX1OG6o6RuuXQwZSFfY7YQOPYpZg71zMUUvUjo9Ry8SyKQemPlP6Q4l+PoBF7wrTPwEDsSVXoxpUCbN0wSa1umL+wvyBbc0FuT/qOeIVvb0q+CbP7XV/RFUfOY9vWScbGWnv4NPd1SZKe54IJaWyOSy5czTlPPWwGodlTIEqmB5kzSVVwiMA5TzmcSy+6Czft37TkuD32kas6pu+/ecsYWWY5eOnojDF0x1wmafMfRJ4E9j9ufL9/8Vfnc+QTDgHg0ktW8u///iOc7tjHOaywfv16fvGLXxSv6/U6559/fk8bEeHlL395z7rKxP++QQ7QcgDwDOAyfKr6OcBuvAeUAZ4FfAP4iYg0ZryH+2/cv1c8gH4fCQUBnhTGvYaZv9qPAz4CbBCR74vIc8XPAlV4GFDFuY9+bJi6m+998YesvWs9ACedcSzPeeFTAG/TlPvlZw5SJ57gDCLKsqAyJ1pV/f3ttLMWcc2lm33Eoq6nzBSESEa12Kfok26fRb+Bw0sdbFg3RRRHDMztJ7VCywrjibC9bRjvGLa1DTs7EZOJYXfHMJYYpqyhbYWJzNC0hp1JxKT1z+8+aAWHbVpNmkCSeDVqu6V0OkqaOJKOxU5mPr2/kwUS1af1086QqQyx6lP88YMX65hfa2It7GrWwxtXX6y1eJPFSeh6ser0ExDOlIMb7x7kiYdPBPWr83UUnEOdT/N3LvVEqvpU/3yyPE/PzyfUmTapPn/UH3P7rlxFmmcrueCVmuE0xWf0hphRNQgIuhP54D/3a292POnEco2BHPd3Mt7vN3+OwRjYunPfdgLGGIrbQpHd5iGmTmPuk4rXre1X8Bdv+XLIKqywJ1Rx7UOLR0h8exJQA6aAPwCuBP4crwx9PvBe4A4RefG0/Y4Kj3ftpe9cVXp0ad3/4L+sbxGRc0VkKEzoPw+4o6RE/QfgYOAvVPWhqiy3vxew8tX9gPX7uA5yN2zo9Rw0xjB//vyHaTQV7g92jrf41+9dxu47fxrWCPMP/wPEgNqE1PoUnML/KRjmg1f6+RhDMaaGupQ46mewbyFRXAeEG+4c4KRjWsHtn+4VsKxMzVnG2PjCU/UI+mJcZGBOw0tDI0FrBh1pcFjfOFtrw7Qb/UR5+n5D6B80NAKh2mh4AnV0SFk07JhTdwzXvBfqaE05qM8xUoOB2NGIHYOLR5EkIZ5qUTOeJG1EEOMYrsNIHYZrSsMo/RE0DNTFOw3ERolMN8Vf1RcIMCjXXraZs85d6i0ApPvWDb2VOv35LKs/DUZqoWVcnDARw7lPW8GlF9xX9b+hXEgsP/U51tyzizlz+5g7r0Fv9dnZroM6bftMIhQci5cM4dSxedPukppger97JlO7xRu6+6laLr3wTs596opiWy9p2iViy6pX0EBLGzRMAqCWWt3w4X96ffHO/u6tn2SyddcM76wKvfjKV77SE5w///nPZ968eTPaTZ/d/4//+A/GxsYe9PE9ViBGDshyIIaCn23/a2CRqh6hqqer6nzg5XgD/ufhg8/puM/G/fuJR21BAFVdqap/p6or8OqFjwPr6L0wxsD/wVedXSsi7xeR/avAV+GAoYpzH91IbcINa6/lix/5VrHu9e97DSDBM7VUE167AsqC7KREkEo3kQpg2SHDtFqW7dsmuwrTacd3pcciytGCf+wKNgPnOJnADVdu4qgzDqblhMTB9pZhaztiezuk9ieGiVSYzGAixT9PYSoTJhJfqGpnYphMDVMdYVM0SpRl1MYnabeVVsuTqklHSVsOnfKp/dLKoJNBEhSqSRaUqr4AlVeMBu/UJOPkg3by2/WjoCVyY8ace4lMzUWkgSyVvC8LqGP7rphYHHP60pJU2GJtgmriiVPnSt60we9UXVCkBpUm3cwlVcdpxyvX3DzNt5byhD7TB+0fRJheXFVV2bbbx/ML5vTG1A+U3jnzxBpX3ZjuV5aUH4sE5fNMe4Da8FGYuk9ycMlO7ll1Bf/2td88oPE91lHFtQ8tDnB8u1xE7tnTspdh5AVDGvhJ7suB08Lro4B/B/qBb4jISaX98j+MvVkE5Nvm5itUdS2eLF2MV7dOAF8BMuB1UMSpfwF8V1X/pzhfIgeJSK4kOpCYfiHc1wLdS/7+tt8nHteE6vj4eM/rkZGRkrquwqMBf/uFq9lyxTdR6ydARg46h0bfYpzNwqxvVrI0jbC2g9OUem2YyDQwElOLBnDOEkcD9NXnE0V1nPPpNjvHa2Bg3mjm0/cB4siTp+CLTAmeSM1J0z6vStX+CG14gtXVIlwjwozUOFa2cOvQIdQHY2p174va3w/1uu+6r64MNmCo4ZjT5xip+yJSgzXLQM0yWg/Fp4zSiLwnaizQd/whNG++l1g8WWqM0jAwaBx9saMvVhoR4dEXn2rESi14pwpdytIIrLp5F4cdMcrAQIxBikUQIonCq6i0xOTXKE+iQq9Pkt+2YOEQ/QM11t27e8bnKbPO2c32neyusxauvXI9Z559yB7+SvK29332/dynHs6lF92N+pKs9Khdiz731W+XOL1j1VaWLBtleKRvln72hPJ1P2/tFaqqjuc+72ye9WyfkXHvvZv5+Ee/SqZVgao9QVX50pe+1LNueoCZ44gjjuDcc88tXrdaLb71rW/N2rbCIxpXA0er6j+r6tbyBlX9OvD34eVrympKuf/G/XvFA+j3EVcQQFWvUdU3qeqheIIYZl50lwJ/B9wpIheIyMuCQrfCg4wqzn10Y9XYzXzhn77G5PgUAM//v8/kuJOf0PvrsUSe5llB+euc1yO0I7wW9fHWWU9ZzJWXbOwhwcpTw9DtO+8jCwRqpgVHSdvCRAZ33rqTRYfNQRo1nHqCdTyBXR3Y1fF+qZOpYTw1jCcRuzoxY2nEWBKxO4mYyCJ2JhHWBb/URMks3Dp3BcftuIcs8UWo0sRh2xY3mcBEgkwlmKkU086Q1Pumeg9V12V/rfokpdRx+OgUG8f66WRRt+hU/uYpvdmCPc7XOcSFC1wxd+6Kk/LbVQOcfHSTvMCUtRnOJViXq0czkHKtAR9b+sNp8LHtigJWLIeNWx3NTq+l1GxFp3ohRR86/Z8q19xsOePE6QVR73uMnGPFMsPm7ZZme3/I1Nzbtbd4LXSFGSJC34KzivWdndfyng99j6nmQyV2e3Shimsft8htnGJgO/A7qnq9qiaqeidwPvBbvF/q20v75fFXspe+8y9bT9FRVX0P8CI8WXsB8AXgDFX9dbDO+ixeMfsGABF5fiCFtwC7ReQyETnmfrzXPeEZ+Myu/V2uoXvh2d99nravQTyuCdVOp/fCPDR0oOzFKjwUuHH1Dn728wuZXH8ZACYaYO7Bz8UlKZplpLaFqp9S9mrUDGvbRaV2EYMxuezUEkf99DX85Im3CQCpxdywZoSTDhsH1JtV5T5K4TeJGk+mEhk0jqBRQxsRzOsHCUWo+mOY08cJ0WZuayxBGhFx3afHGyNEkVCPfVGp4YYy0nDM61cW9ztG6kqfUYYiZU5dGYx8uFsWy1oHDPRhRvrJNu8iMo5G5BiqOxb0K8Mx1IRQsMrzwREh+M6Vp5Kn8itZall1yw5OOGUhlFP7PZ06y6ch4f/wT0zQU5ZlvVKcs7PPW8GVl67GuZnB18z+ZyseVfo7uH4Tx56wiEZjZgqTYDBEXTXtfYwXh4YbLDtkDqtu21asKwem+z+jr6Rpxspr13Pamct7VAP7gglUdt5PuU9QRIR/+ujriSJ/3j78wS9z77obcW5v98nHL37zm9+wevXq4vVBBx3Ec5/73D22nx6UVulR9wGPkJwoVR0vVzidBT8Lj/PwxGSO+2vcvy88ZgoClKGqV5RedoAf4wP2nKMR4KnAV/GFrD4rIqc/FGN7vKKKcx+9GO+MceV1l/GTb/wSgIHBfv78bS8HpVCnFh6UJZWlJ0ClJ0O9rFyFsL8RhkcbzF/Qz733jKMKJiw5USuh3lEe9mbhubWBTFVILLQzYbzpWHf7Tg45biFOIbHCjk7ErsQwlkZMZIYdHcNYKuxOvFp1LBVaFlpWaGdCO4O2FVIrpJmSpGCtMmEa7KoNsWhyO1nqcO0MGU8xOzvIROoNWTvBHzUwvVJIaF3hdUpmicVx5PxJVm0b7qpSy4/Qy1LnG52jqMllXdFvuX5Cqx2xbXfE8kUdrOvgApHaMyFeENwz1aNaYm/jCI5dATfdNU18VWo3XbVaegOl56XMqrC62RY2bVeOXF6nN+XuviOK4IQn1LhhVdbzfqYjzxTM61bMPKZ/bkKdgLhvMfGgv/2pa7N70xW8+b3fpsJMVHHtw4ADG9+uU9UVe1r2Mop26fnnVHWivFH9F+3j4eVzSoKBfL/6XvrOLbBmePSp6g9U9Q9V9Rmq+hpVvSFs+lPgTOCdqrpRRM4Cfoi/cr4WeBtwMvBrEZmzl2PvN1T14vuyUFLl3sf99orHNaHaaPTapU1OHih7sQoPNlSVN376MrZd/bVi3dwlz0GkBurIbBK8hfLfcQbnfLq/J+wsINSiAazrEEf9DPTNQ8RgbYKY2BeWioWWi9k83sehC9ooguYFNeOoq0ptxGhfDAMxGgs6UkdjwcUGF7YNNCwH6STr+g+i0TD09fn0/v5+GKgrc/th0SCM1qE/gqHY+QJSRumLHAM1x0Ds6Kv5QkfGhGRyhU4Y0/CxS2jdsZFhYxk0MKem9IUiVkb8YyyeF45CJBmJlyvExj+vGfjtlVs46cxFmAh6AjOhRKzm5KlgxKtTBSkKHEDk7xczCFmhv7/OE44+iJtv2LiHTzi/y3SN68v/cjSbCffcvZ0TTlwatsThuBGGWtjfPCBy5rQzlnHjyo2kae9sep6Cv7+X0euuWscppy+jFsc972Fv6JLLZfrcIydlVZVjjl3Bn73uDwBotTq84+8+jWXtfh3j8YbpgeNLX/pS4ni6UqOLP/zDP6S/vztBe91113HDDTfssX2FLg5UFdSHAGW1ZDl4vL/G/fvCY6kgwJ7QUdUX4tWpfw2spFfSP4oPvq8ORbn+RkRm5idWeECo4txHJ1SVG3fewL++5/NFGu+rXv8S5h00r5gAz4nSgvyELueGJ1XLatM8az2HU8UpnPSkg/jt1VvJMte1CghkaY+VgPMeqZmDJFepOp+qnzhYde0WVpyyCGeEXR1hR0fY2RHGEsNEIkwmMJkYJlLDZGIYTyOmbEQrM0xlhpY1dNR4ZWqqdNqQpYpzSpYpN/cv55ipdchEAlMWphJohbT+1EJqfZp/Yv1r65DMIplD0vAmMjjhoN3cunUEp9Ir3w3+pT1+qdYVbHJXmRqI1LRLDha+qM5y650xRx3exognU50qzrmweAsoT57aQgTrP7riCarKSUcLN66y/nNxJd9VbImc7BKqzuXKTyn+horF5USsLdbdsMpy7BGGyFD8VtKesbqiz9mWfPtJR8XcuCohs0r3N5cW56Xcj38M5wANOgsf55aV87mIoG/+GRB+UyRjt/CN7/yMe9ZsoUIvqrj2occjJL7dVXq+p8pt+foRuqn++X57i7mmt90rRGQh8I94Reynw+q/w1+Qnquqn1PVDwCvB5YAr9qffh8teFwTqiMjIz2vx8fH98v7pcLDj+9deDc3XvTfdMa8tUitbzHDC8/DuhRrLZltIUAUNajHQ6imOM2Io/6gSoU4qiPGEEV14togtdogIoLiqMV9UK9BXw3ti7ht1xyOXDRJ1CfQiND+2Kf0D8TQF6F1gzZ8Wr/ri9HhOlqLkL4ImdcgnlPjjGwtt88/nOERw/BgSPXvU0YaynCfY6hmGax5ZWlfZBmIlZpxjMSOg/odCxuOuXWvNh2Klbp4tWlEcBgVpb8hDK9YQOvuzQzULPP6Qkp/5NP9Y+OJ1FpQqTYir1xtCMRGiAxMjXUY393hkMNHSmYjXaWpiOm5G3SLURFeexLT5O1mhXDSKUu57dYtdNrZrNspCMs993HZxfdw9nlHYEIFL0/uRojEiEReKVsQvLP1te+7WRwbTj19Gddc2bUILBOiUnwCuc9rfhwpWkyMJ2zcMM5RRx+0x/dSXnI7BSOmOLciYKRspxD2DId557v/jPnz5wDwnW//jCsuvwJ7wGrQPDawa9cufvjDH/as21NaVI6RkRFe+MIX9qyrZvMfc/i/4fEGVS3nSN9f4/594bFYEGBWqOoOVf0XVT0deCK+YNUmesnVE/EE8EYR+XcReY5UeekHBFWc++jE+qkN/OLHP2fl5TcBsPSwg3nxa14A+IKl0CVKs/Bx5naeNmy0JWVpiRfsKlatkjkwtYijTpjPzddtC4Wt/CR95rzos5M/WsiskKiQOr+0rNCxwu5dCZNjbeYsG6WVCjtahq1Nw+aWYWMzYnMrYlsnZiwzNDPDVChW1c58wapmCq0UOh1IE6WTKO22I818in/SdrTbym12IcdP3osZ7/gldYEwDUWoEtdVqrY9yUrqPNlqLUNRhzl9CRt39yMukKdpSWWaG8LmBaemm8RmFk0tLnXBE9WWSEaHdRntNOGWu5Rjj7BYl+Fc5gUe+CJS1mU4zYJ/alhCCr8nTJWhAWX+KKzZmCtRXWiX4lwW7Mxsz9JVrJbX576sNhyzu5+1jpvuSDn5GB8j5++he2kO9gKlglnlRVEG+mHRgoh71idhDL3XlrxORXccXQuCAkJx/OmQaJD66BPz3mhuu5xXv/4L9+9L9RhFFdc+rlGuOtneQ5vy+vwH8R3h8ci99J1nR92xlzZl/BPeb/XPVAtj5LOBVapajnV/VNr2mMHjmlBdunRpz2vnHNu3b3+YRlNhf9HqZLz/K5ez46bvFusWHPoinEsRDI4UMZEnS00Nf7f2N+rINALZFhNHA4jEjAwuZ3juckyjD4nq1PqHkUYd+mN0sO4LTNVibts+yvEHT6ADNRhtwFCo9jRUg5GGXzfSgIUDMFCDwRoyXKM2ELEkmoDY0JkzzNCwMDgA9dgxZ0BZOpxxcL8nS/sjX1BqpO4J0HkNZbShzKk7RuuO4ZpjIHIM1ZSBmjIYQz1SBmLHUA3mN5QnHDeX9uZxRjWlz/jtDeOJ00i6NbQiUfoM9EXQHwt9Ag1juOaSTZz9lKV4l9TcHiH3SS2Thd1t3VJVXqlqpF60n30RIhNx1jkruOLSNfR6sRoMNSJphHT92fvYvnWSNLEsWzYvkJoRhjqGWtGXoRbGXgt95V6vOQEq0EOImmnb/PMjjlrIlk2TjI+XuYnuezHEYfHH7I6jjqHOZRet4bynHeXJ3sJ7drozbSCiiTASB6Vvl5TtJVzjYgY/x9y5I7znva8rXr/h9R/GuXuDH1cFgG984xu0293Y4qSTTuKkk07a537Tg9NvfvObM1JpK8zEgciGKrFq99e0f+9jFDmVrp/pB6dtvl/G/fuBx1JBgP2Gqt6iqm8FluFJ4O/hg/38F3wN7831M6gk9gcCVZz76IN1lms2ruQL7+96Ir7u3X9CHGyNikLzhERu7SpRC16Qbop+EvjFsueJw3tyJOqXQ46ey7q1E4xPpUXWfOqgY4UkkKgdFd8+pPtPJTCWCm0r3HblBg45bRk7OobVkxHbO8LGlmFzK2JnIoynQjMzNLOQ2u8MHSu0EphqC822MDGljE8qzZYyMa6025B0oN1UsvEM3dpiza5BFjPBQKvpCdTMdUnTxHp1aicUpWqm0LTQTn1hqrbllIN3sXLNSGCYSyex8ESgm+5v8yJWDrGZJ1M7GSQpuCzfEXCoy8hsiyybwrmE1esdi+Y7+hq+oKknNFMUT6hal5AXLFXN6BYv9Z/kGSdEXHVTVhqgLS3+mF1iNQ2Pvl1OXvbuN/21X7d6Q8rCucJgf1dd2lsQde/LWSc1uOKGFnmBrK4/KoUaVbV7Dnrn0Ty6BW3L67vP66NPRKJB/91oreeqKy7mJ7+8fi/foMcXqrj24cEBjm/vF0I2Ux4r7cl2Kl/fAXaE51eGx3NnNgcRWQocPq3tHiEi5+ELvH5OVa8qbRoFxqc1z1/P2Ve/jyY8rgnVY46Z6Yl73XXXPQwjqXBf8IFvrWTdld/HdnYDMDDnBPpHnoC6JKSXpETGE1KRqRV+qHHUhzERxkTEUQMRQxz1EfcNETX6EWMwUURUayCNuidTByKoxxAb1ttR5g0kDA6ql3fGJtgCRBDnqf8ROlhDaxE0YmqDMfUaPHFyLWuWrWBkSJg34JWhCweURQOOwVroKpCcsXEMRo4+YxmtK8M1v8ypKyM1X4RqoKb0G4jE2wIMxDBUd0QCA7Fw9BlLWH3dRuJIiYONq5lGqNZMqKmVrzPChnsnGBlpMG/+AJHxVWQlT7sXMFKbRgLmBF85/T1CSuv2thx22AImxtvs3DE1bZ/wKKZEQnYXVLjkwrt4ytOOLtoa4qDoNMF3tNSjeMVsmbTtEpvdcfbe5rqvRQznPu0ILr1gdekvUYHZ3qOUxmTYuGGMWhyxeNHorARqeZ0Jqto9jSMfiwlK4LICWIA/ec0LOeHEJwBw3bW38PWv/Qe6V87m8YXpM/D7msXP8cxnPpNly5YVr3fu3DlDEVBhGkrXmge6POCoc09D9MWhfogn8n6oqt+Z1uR+G/fvA4+lggD3Gerxc1X9v3gl7Z/RDdrzX9zL9rR/hf1HFec++nDLrlX88PP/zuZ1PrX5tPNO5uxnndFzGcx9Tbvp4pLba5YFhl0KTQV1gqoUqfu2JOC0Kpzw5IO57tJNJE7wdZ2kS8XlGfCBa2xaaKuQOWHr+nHiwTpusJ/xRNjRMexODDs7Ebs7EeNJSO23QtsJHby6tZ1AO4U0g3YnEKgJdNpKmihZ6sjaFm1amEyQ8QTTzFi5cz6n9G8JngMWUj8wyTQwx4FBzjm5IN1dNNSm1TGMT0VdD9R8vtkGcjY3hg1SXrEEVho/bVWqou5T6T2xmdoOme1gNS2I06tvtjzpBJhJInpisUw+lrF4gdBJYdd4TqbCdBJSez7o8jEKhrhkCZDvM31y3afgX3FDkzOfWC8da/+weEFEmik7doeiWvk/LVkb5Kn9+4Axe7bPEhPTmPek4nVn51W84R1fJcvsrO0fb6ji2ocBj6z4NleXvaJcVLWEPw6PF6mvsAxeJZoCTxCRp8+yz2vD48pp6tIZEJEY+Fd8Uay/m7Z5B7BCuqmi0M3QekzN7O7ZYONxgNNOO82neJduaFdffTX/+3//74dxVBX2hnu3TPKNH1/O2F2hhohEzF/6ApL2BKBkWRMJKf1GaliboKgnzkyt4J+iqIYxNWLTwAwMwGANmoEii8QznnXxuVVWC9Zx5c4FnDJvG5fuWgpRUAXUDNRiqBt0pA8iqDUMEgm1unBEezM7RubTGKzRFyv1CMAxt+YYrvviUm2V4qI6GCvDdZ/SHwk0jFI33aJROCVTIRYfvDQiv09fDLHxKf6jSwbZePs2xrY3mbdwoDh/Zb0jMu06robrrtjM8154ZCBKTUHuKeI9SXsiesK5ldCnP+9Rz3Vz33jq04/igt+s4oUvOqU7zp4+tPBWyr+rd96xlcUHjzIy0he25eSuhveWt+89lhET+uia8GvJVWy6t6mW/l940BD1RsyGdbtZunwush9Boqpy6cV38vzf2/dscf4+9g+CSO9svlMliiM+9rE38+xn/SkAb/u7T/LC//MsRkfmlrxtH5+YzSNq0aJF/OpXv9qv/U877TTWr19fvP7iF7/I+eeff0DHWGGvWLcPc/77hKDW/BlwCHAd8MpZmj0g4/694AEXBAB+MMs+eUGAv55WEGA1PkCeD7wDXxDgeFXdfR/G/KBAVceAz4vIlXj/rd95mIf0mEIV5z660ErbXH3ndfz7p78HgIkMr3vPnxBHeVTWC1XQEDY4FRAF6RapcirkheobIQRIcxuAEG8Y/PY5i4awN+5g59YmcxcO4MAXpQIcEvw+ffzZTCUUqFLuXbmJQ552JNvahu0dw86wTKRC0wmZih8HYJ143jLzRKrNvPVA0oE09f2lHYsmigSy1DRTZDL10lir7EobMADzG212NOs+dT8LilIoFYlSUIdYEGc5cdk4F90yr8sQ52++zFvmJxXxxy+Rq97D1DdUF1LfncW6DkquJu1i55gf0kHzYOvOvOvygWTGPiJw2nER/3PF9BqKM9vOBtVuvBzW4MnbPe3h2DkmJCksXhCzeXuGID0k6HTSN+//jBP7+eXlU9OO70DMfo62F754cH5+yvGwEg+uIBq/DdvZgkvHWHf3VXzoUz/h7a///ft4lMcWqri2At5G6bV4L/2Pi8ibVTUJE+z/D3g+/uv4gXwHVd0iIv8G/CXwRRF5rqquAj8JD7wlNH3vfhz/9cAJwCtVdde0bRcA5wN/BXwikK/vLm17zOBxTajOnz+f008/nWuuuaZY94Mf/IB3vetdD+pxH6g1WLPZPEAjefThbZ+7kq1Xfh11fpJldOF5mHiENPM3dcUi6kNOwWC1gzExRureO1WEKKoRx32+XX/NV4CKBW145aMKnkiVQKZCQZ7u1n4SHWdhf4utdsjLPBsxRIJrxBALJoK4LhgjNIzlsMnN3Hz0SSwecDQMNK0wt6YM1bx6NLEQi4/r+iJHf+QKb1PBFX6n/j35gCtCaQRla1+k1I2PnmPxpKoIHH/WUlb+eg1PecER/mxI13sLuoG5Cefqxt9u5ajj5tPoi8MPMH/E3CO1G3lqMRhT2LGEc16oK3PIPk23584bZP68QVbfs50VRyyctv/MQC7LLNdds5YXn3/6LMebGZxOh4iAdk+EEBXBo5bfX9Fzt5Nzn7KCn/7oZv7g/DkQ/HYL5WipXf4dv/mmjRxxxEIGh3oLg8zEzEnFvV0ncl+q0hq8IkF42jOexO///jP4z//8DVu27OAD//g5PvDBZTzeBV+z+UO99KUvvd/9/frXv2b16tUcfvjhD2RYj208SMrSBwoRGQJ+DpwC3AI8Z5p3ao48ODxgxv0PVr/7URDgrtBuB/Bv+IIAH78PYz7gCGN+KfAKoDzrdF9/i1fYA6o499GFq7fdxDc//BU6LS9Qf/7LfocVxxzmN8rMu74pyNTwWgXruq1y+89YulYBDsFqN3op24ce++QlXP/rtZz9/CNQCUVYw2fp+UphIidTFTbdtp3Rw+czpnW2tgy7U2Ei8W2aWUSq3nc1U0C98jXNIMsUZ73ANEu9MlVVySbToDJ1aOqKVH1Jsi5papWV2+dxzqIt/GZ8cZchzoUJgVfuVuFSjlzcZO22flKb/z4Agvep5ORrflLzylxF+n++PSgxgyepaop12axkao5rb3Y840zDf1/imKkQnYmjDzOs3mDpJHn6/+yYSXKWCpbq7LFv7vWafze7j4arb+rw7LP7+PEFM2saqLpAdmoxOXPM4XXWbkxpd7TUtwtkrCvyt7oD3OdbxxiDtZ7e98fxwgH/CI35T6a58UcAdHav5JOf/U9e87JncNCCkb13/BhGFdc+jHiExLequk1E/gD4MZ5AfZmI3IkXCyzGX0jeoqoXTtv1LcBpwFnALSJyMzBE1yLgo6r6o70dW0SW4wnSi1X1q7M0+Qfg9/BE7+uA4TCm24Cv39f3+kjG4zrlH+AlL3lJz+sbbriBK6/cp13EA0JfX1/P61brvghbYOvWrQdyOI8aXH7TJi78n98wtdmnq0XxEHOXPIc0m/KpSC7BmFooTFQjzfx5jUzdk6i1fuLGAFFjEFNvIPUaMuA9Un3EGXlJqAhaLwUnJigAYoHIsHJyIScNbwMUjUOafz2C/hiJhHqfoVYXGn1w4tRatixZzpx+aJhQOMo4+iJH3figzyLUjCM2joV9jpF6IE2NMlr3qoLiui1eU9kfKwORYzh2DMfKQKw0jNIIKf5GYHCwxoIlQ6y/c3fXnSjwhVKiDo0Inbbjnjt2cdyJC4r3nZ9HEzxSfXBU9vXMh5Sn+5cJ1ryP/ftsn3zOEVx1xWqszUP80huehmuuWsMppx1CrbY/isvZByChgFVXFZqTonu/JPYP1DniyIXcfOOmsCZPcuoNlFUhSTJuvnEDp55+6H6Mc//GXR7/TGjhUfWhf3oD9br3W/vkJ77B3XffgHt4a9M8rGi1WnzrW986oH2qKl/+8pcPaJ+PNTxCqqBOG5MMAP8FPBlvtv9MVd2xh+YPhnH/g9Xvo6IggIjUROSFIvIjYAO+GFVOpua3pXuB9zwc43ssoopzHx3Y1trJJZdfyoU//A0Aw3OGedkbXkrkQ0/vFC9+wly1FCVoIEtLxGgWljTwgTkZW3CSpRgjtw8FaAzWmLdkiHV37e4mj4eCVrmnaurwBalalh1rdlE/dCFbW4bN7YidHb9M2QgLZE5IgzVAJ/PKVGvxZGqmYQFnHVnH4aZSmEwxUylmKiEa62AmE0+y5gNVpZ1EbJ7q57DB8a71aMjakaIKl4Jz1LAcurDF3RsHvIA3J1CtIsEugDy1P3WodahTX3AqeB0U1e6dxTmHtSmp7aCk7I34bCewbrPyhENm3si6xaz8UovhyOXCrXfP7NPHq10BQW8MKKEAax7XzhxH7mfaPVldohSgk8CaDRnHrpgtaaLkr6pKHCnHrmhw46o2zoXiXKEgVddLNXi55uv2c4pMxBRjMpLXafBvKGosoDYUsoVdwu5NV/L6dz6mOJn7hCqufXjxSIpvVfVX+DjqK3jLp1PwoskfA09X1Y/Msk8LeBrwVuBWfCr+AuAi4A9U9U37cehP4jOtXjfbRlW9NRzjQmA53sLqW8AzwvEfM3jcE6qvfOUrGRgY6Fn3zne+c1ZfmwOF0dHRntdbtmy5T/tfffXVB3I4jwo4p/ztpy5h28ruzXPe8t/DFUpD5z0r8wrvCA5PsMZRH7W4n1pjkNrQCFGjAX01GOlD++po5sJVLZCpefRKmAU2wWy0HqGRkNQarMtGOXJ43JOxkfGEagS1OtRqQhzDHNqMpk2yg+b5FP5IqRlluOboi/0hM4VY/n/2zjtQjqs8+793Zrbept5lqzfLkiy5yN0mBEgIgUBCHOCDBD4gISEJxUAgXyiBECChfOQLJiRgWqgJYEqwKbIty5Jtyeq99657r27b3Zk55/vjTNtyi5oty/PI47s7e86ZM7P3zr77nOd9XpPS35pROAFH6IgmI0adKsQhkCWGTG3LalqDulgZ2whls7ZRq5oEfRPUzLpuDLs2nMT1VRxGSaxqsDABzKrHDnHjrROxLCEO1BIeoonUsNhrNPQgdQLytYZMPYflu1zOYf61E3h6zYGaV6rH6OoqcehgO3PmTkjML4m6hLhBj23mnvQ+DYtUharcaixaMpEtm45SLocr+UEAWaMdWbliDzfdPBXbHug2W3+M/gLixn3robVi6rSJvOOd/wuASsXlPff+M5qDQxn0isT3v/99Ojs7L/q4999/P0oNrjpJcXlARPIYQvEOYB/wG1rrYwN0uajG/Zdq3OdCQQARWSIinweOAP+FSUNziEnUEsYL7EVa66la6488k/O7kpHGuZc/tNY8fnw9X/vofdG+177rdbQNb8GyIEhCirxMw3cuKcRUhGSq8Sh1A89UW0zUFtZa8oIxwiL3URF7bUyTpi8ay95Np3ArGl9LxDWWfOFsRSgrQ6oeWHOYlmsmcbTkcKpkc9YV+jzzuvFnNUSqH6b4u0aR6vsa39fmuatxXYVbUtBZRkoKqfhQ9pGeMM0/YHQ9H/G12TyfbcdbmDmiG8cyMbwgSGj0GstxWTCli437W0JDqIhMRQVkpqfQnm8210NXPLTronwf7buGWNWeIQ7RKFXBUyWG6jm6eZdm1hTzvSD5flOjbF081+Lpre4A6fkhkRpuAfFoDUVcUG01UOuvCrBpZ4VZUzJkanJXTWFXCC/oDfMLrN7ch6/C4lQ6upcY/9Rg7KRtwBAZVcuygjmFSuIwzjU/syOuBzFiAbdrOw/85Fes3bhvSGNfaUjj2hRJaK13aq3/RGs9WWud1VqP1lq/XGv9yAB9KlrrT2qtF2iti1rrYVrru7TW/zXEY74yONbmAdo8pbW+W2tdCMZ/7SBx93MSz3tCdeTIkbzjHe+o2vfLX/6Sf/3Xf73gsZVSDQ2eJ0+eHBhwG6xfvx7fH5q59rFjx/j1r399wXN7ruH+n2xl2+M/pdJtvF5yxck0j7wJrb1o1TaTKZBximSyzfi6gm3nyNgFnGwTVjaPZA35iWNBzkbnbfN1ThEtE2nbQjdn42WjbKBazQSpRDZgCdvKI5mWbcfJaLQjkLOxszaFolGnNjcJ88/spmPaVAqOITptIGuFQ2qzAo4mI5q8o2gJFoZFNAVb05KJU7Ek6FuwNc0Z8zNraRzRWKLNFC1N3janJxKcZsZi1qIxbF1tvsyEC/PJn6dP9lIqeUyc3GLaBIGRFRDTVZDwf2EwF8tn69Sd57jyNn/BRPbsOklvT6ikTCpIDZY/vJPb75pVp9AUpG7fuU4iWt2PNLd2UNzKqjo3y7K46eYprFqxt6p/0ny/o72X06e7AwuDhkdLbNXnMfT5Nrp9xxVa3/O+NzJ+vDn+j364jF/+8tf4+uIHX88F1KZFvf/9769Thwxlc12X0aPj9/TgwYM89NBDz/TpPCcgXLwV/IuxiC8iGQyR90LgEGaF/NDAvS6ecf+lGvdyLgggIuNE5N0ishF4CvhzjI8rxETqOkya2gSt9Wu01r+61PN6viGNcy9/7O48xEPf/yk71xtB+lWzruYlr/mtKBQNf4YK1PAxoQK1JmM9JE+TpX6Urq67ZOgxif4Qw8JWtm0xbcEYdq49HsWKfR6UPCgrKHvQeaqPUsmnu7WFUyWhvWLR7dr0+lZAuAoVX6h4JqXf80ISN0GmeuanX1HQ56FLHlIOtoqHlP1AUmsKTpnnHgTp/8qHLSdauGZsZ5TCX8Ub+orWgkshozjREdguaRVdHK11ojKXmaRWyqhQfRflVwL1Zazu9L0KnirHF3wIUBrWb/e5bk7yNqwSPzVtzYqWJjh8YnAiK4xVLcvC3P4HRvXCSeinHG4qeqw0PL2lzPXXVKvLJRGrtjZbDGux2H+kFFyXhK9sVGQrLqQVxk718+gftu1gWbYhcgO7sTC+t+wCuWGLorZ9p1byp/d++ZIuDl2uSOPaZw+XW3yb4tnH855QBXMTmjlzZtW+d7zjHXzjG9847zFPnz7NS17yEj73uc/VvdbU1MTcuXOj5x0dHTz44INDGvcDH/gA5fLzK3W3p8/ln7+ynNM74hoco6b8Pijj12NbWRy7iCVZxMpEXkiWOCbdP5NFchnIOubGZVvoQsaoVD1iNWrWQheMHyqEaf4WYb6VFgHbNiFI1mGDPYmF1hHIOzS1WuQLkMtBIQ9j+trRuSxWS4GsrchZOigYZdLywYQceQeTvu8EFf+0cRbIWrpKRWpbmpytaHKg6GgKTkycZgIBbTEgU0M/1rCI1aTpbZw+3ktvVyXy4dfEN/LHHz3MLXdMjMjAUKFZS+7FfksmFcfCxhYbCwerxo75fPzTRGxuu2MWyx/dEcyjGkePdGBZwrhxw5K9zLGiLx0X/tEUKlaTx6glQKdMG0lHex/tZ5I+b3FA98jDO7jjrpmDBHlS9Vgu4ier1pqWliIf+4e/jPa96x2fxPP2P+8Cz127dvHoo49W7Xvd6153XmM5jlOXPtvIwyrF5YWAUPwmpuDRMQyZunfgXqC1Po7xGwVj3D87MeaAxv0islRE9gVblYHxhYzbAO/AFAS4t5+CAKMxBQFC8vWDidcuJVqBg8AngWuCfeG3+DMYn9clWuslWuv/py+DAllXMtI49/KFp3xWHlzLtz4Vp9q+6e/eQi5jY0mcbu8TCCtJ0mAGIbkafryHtp9hHOlroaINIeoHRaJ8LcFYJrYJbUOVhtFXt9Fxoofurgqegh7PoqKFsi/0+XBs7WH6ZkzhUE+GMxWbbt8Umyor06YceKX6CYGpUibFv1LRuK5J9fdcZTxSez3EVYY0LftQUvHKv6/NBQh9DLxYUnuks8DwfIWi48btiS/OdVO7WLe3JSHJNSep/YCc9RXomOgPyVOlXJTv4vsunirj+x6e7+LpMkNVpprxjLfowWOa4a1CS1NILIYko5nsTQsyPLGhthDVgCMDtYrVwQLIMPOq1qIqJEU1B465tDZbtDUnYmABCcqR37KoiRVru6n+7cMU6NJmMV9F6lfd4FjnEn8G3zlqVKqZtmsQx/im+qVjbFi7gm/9YOU5jPvcRxrXpkhxeSElVIFiscj3vvc9mpubo32u6/L617+e9773vZw926hWRWOUSiU+85nPMHfuXH7xi1/02+53fud3qp7fe++9dHd3Dzj2pz71Kb785S8PeS5XCj5y3xMcXvN9lGuuT/PIJRRaZgQrtA6WOGQy+cg/1fd7EREyThErlwtYxkRVp2zAQBLkUFmCtgSds6E1C45J7SdjzEi1E5KpEvfP2hzODKNFygzLlMgWLPJ5sxXziinH99E17Wocy9S8EoGMGELVNiIAMpYmg9mXEZAg+HVEkwt8UDM2ZG1Nk2OUqVlb4yR4t3CVzBFtiNVg2plk8SlLWHjrBNatOBIH38GDfbs7GTWmQHNrNlJ5ioSKzGrCrxphWvqFqUJrMXHScFxXceJ4V4IktdBaWP7ILu64a3ZV+4ZHrxJjnf9cIhVAYAdgFKt2RDjfcfcMlj+8q+oYGsXefSdpbskxcqS5n9SqAxpZFVzALBvsM0GsRvGa1/02N9w4H4DNm3fzb1/8Nornlzfdl79crV5YvHhx1Rf9c0Wt4f8DDzzAqVOXXOz3nMRl5DH1auAPgscl4Csi8lg/23U1fd8DrMSk4G8WkXUisgvjTZWjf+P+PHB1sDWSEZ3vuBGGWBCgF1MQYAeG4Pw9LnJBADG4S0TuS+6GyFQ7/Gb9K+A1GDXqX2qt112sOaQYGGmce/li7alt/PC+/6Q9KAV/428uZeFt10X3vpBANbagUhVFJMOLpDLVD5ystAR9CYtDSV3NJXRc9J6Qq0SYecNEtq86zNmKUFFQ8oSygs6DnXgtRc46ObrDNP9AmVrxhFJF8HwJCFWNClPrlVGruhViMrXso3s9xPVNer/ng6sQzzCx4ivE940q1Zi3mn1KR3TbuiPDuG5iR91FmDC8RGevQ0+vBUqhPY32fJTnoV1DpmqlTLZWSADq+EIo7aF0Bc/rw/d7UaqSSGkPt2oVZhJJ4hTgiY0eN15rkyxuBYpJY4XOLk1n97kudp/Lh2OYxTX4MVau7+PmhYXouQ7S2SaPy9Ddo+joqlaaKx3+hiVofkmqUhtfnwFnGylw61WqIjb5kTdFbctnnuR9H/km5fK5ENLPbaRx7bOPyyi+TXEZICVUAyxcuJAf/ehHVcGm1ppPfvKTTJs2jb/9279lxYoVeF59BUTXdVm2bBnvete7mDFjBu985zs5efLkgMd785vfjJMw1NmyZQt33nknK1asqGqnlGLVqlW87GUv4z3vMaKV6dOn83zBgWNdfOeHj9J5yKR/iZVh5NRXorUO1mMFCf2DtI/vl81ecbCcLOI4gSeqbSqWioXOZQzj6KpYgZq3jVw0vLk5Ao5l+lpBSpRtoSxBFzLoZqN4Xds6nZsq+8jlhELBEKCTTh2lZ/Ro7KyDLSYt30FjBbxuEGZQcDT5jCFKEbPoDiad37ECwlU0eUdTsAlS/GPlqS2QC9vYwf5+ruPwUQWcrMWJI92RYgFfsf6pE1x301g0OuEfCoMTfvXBUUzIDuGNrRs9vhXdeddsHn14e/AeGzJzy6YjTJk6iqbmfLQiP7CiM3zh4qgx4wJWBhYWI0Y00zasyP49Z6L9SvmsWrGXW26r+RvVyTlVz/NClKmN0/6DQ2qNZQn//Ol7o30f+uC/cubMTpSuv49difB9n69+tZpnOt9V/BBLly5lxoy4llClUuHrX3/+FkYYCJdRwJlLPJ4C3DrA1pbseJGM++twJRQEEJFbRORzGAuFXwNvofrmq4H9GKXtVK31i7TW39FaVy702CnOHWmce/mh2y3x+OYn+fG/mwwsJ+vwur/534SFQyOEKfzRQrYhRkNRZvg4qWK1Ev2UqnbsjBK+QzI1bKNjS4Dc8ALYNieP9lDxjfK0UtF0bjvGyUlX0eXZVLTQ51tUfKGnIvS6gRDUA9/TKGWy7JUC19XBFpCpnlGnWj0ueArpC1SqnpmIhLJcL5TOKiTyKggmrDSdvRnKrjCm0Bftt5Ri3uRuNu8rmvR+NzRyNdLZsLhUyCibVH9DcBqFqim25Psunl/GU314fh9au4iEV7H6w6meNKx+3tkFPX2K8aMlSpO3BK6bm2HNlgrnTjxKNIe4IJVVtR/sgJwMicnBx+/sVpztUUwaa0fXSKO4/poCT2zsaXDOSRVqSKCqfsnUi6FStQuTsQsTzXheN8f3r+KDnxyS7eNzHmlce3ngMopvn9cQkWXnsgE3nUffhwebR0qoJvCCF7yARx55hGnTplXtP336NB/72Me47bbbaG5u5qqrrmLJkiVcd911jB8/nkKhwAte8AI+/elPc/jw4aq+EyZMaHis6dOn8773va9q39NPP81tt93G2LFjueGGG1i4cCEjR47k5ptv5ic/+QkAL3zhC+v6Xcl452ce4+Smb5mIDBg24TdxnGHg+8YwHm0UjIEJulIVbMmSyTZBIWMYzHwmji+ylvFDVSpmJR1BZwO2MzTizjlox4K8jbYxZKtjQXMm8F61yBYFt6WJUr7AuNIZslkhp8uMaj+ON2mcUZraphiVbcU2rEJQRMqCfPAXGKoDHEuRszH2AEGRqWYntnINb76mkJWp9mpbsXg2uSW9swAW3DyBjauOorVRxG5Yc5K5C0aSSchZ+w1zGt70q0P+oZrODzZwS2uBCROHsWPbMQQLt6LZsP4Q1984Ne416IeQ1Py8OEjaAQgWt9w6gydW7g1+PYUNa48wZ95YcvmaIgGJL0GNZnmBs2qwLw5ql968gNe89qUAnDnTyUc+9K9oDjfoc+XhZz/7GUeOHIme27bNH/3RH13wuK95zWuqnqfpUZc3tNb3a61liNvDDfqfs3G/1vrhxJj7+mnznCwIICKfEpH9wGMYO4Fx4eGCra7AlNb6+VsV7zJCGudeXlh+bD3f/Mf/wK0Ydd1L/+T3mDh1grGACsoohdagyQhLB5nvYeH7SsBPhgpTJwg3whR+r0F4lhwj/FkJSFVfwZmyReuCSRxdd4RuV9On4OS245QnjaWMjauh17MoeRal0NrUE3xf4/k6IlYrFU2lrKkEylQVkqRlZQpPKW3S/BPp/KFC1aTlG5JUai9AKNv1NRsOtXHtxE5TlMqD2eO72XOkgFsRtGeUr9oL/FB9z3yn0CryTA11wDr0/USh8fFVCfAi4lApv8pXNUkcJj1DQ8K0Fms2uyyZl4li2GtmZNi+z8VNvEFDIRxFrAQpUxNXRgKAMLaXIY8b4qmNvSyZl0fEXJNrZ+bYsb9MxY0X8s116s8XOflao+JVQyOPkypVISSFzfnlRtwUnVulcwNf/MpPOHa8Y8jn+FxFGtemSFGFO4A7z2EbTvzFeSjt7wi2AZESqjVYvHgx69at493vfjf5fL7u9XK5zMGDB3n66adZt24dx44da2i0P2PGDL71rW/xzW9+s99jffCDH+QNb3hD3f4TJ06wevVqNmzYQEdHR7T/d3/3d/nhD39YteJ/JePhNYd4fNlD9J7ZBICdHcaw0XejymV8z8VXFWJDdpPibDsFrKzxTBXHQmesKoZRh3nxFZ1I4beNtBRMmZDAQxUbtGOjsw46a6GyNjqfgYwDGYeWYTatrcLhSVOZdmo/jlbMPLaPs1OuxhLI2wo7iABFNJalzM9AtZp1DLGqg+nlLRiW1YZQFfPHaVL/TZ8wZrLEKFhD54Jcg7/iUPSYpPDyBYdJU9vYtfk0pV6XIwe7mTlvOBaCLU5AiJptKF6kOvH/80ctyWgUrjcuncGa1fvxPJ/HV+zgpqXTse2hVDJ9ZhAGrLmcw7xrJrBuzWHKfT47t59kwcLJyZZYEnjSikSr7FERrYvAqPavUtXRe/oPH/9LikVzP/vCF77L5s1rULp04Qe/zFEbEP7Gb/wG48aN66f10FGrBti8eTNPPPFEP62fx6hd5TnfLcXlhndiFK9VFAdpgannBNI49/LA0Z7TPPzrZTz1kFHsDhs1nN//8z8kY5mF+FxgFxXY/GNJvGLhA1oLfsBBhvyiq8z+cAE+qV6F6ttpWJg0tCQ1RKrgKaHLFXo8wbWzZMcPo3PPaXq6XErHz9I+cgyuEnorFh0Vix5PKLlENqUaQQXZ+54f+Kj6pgiV7+n4BddHKuZFKQdp/555jKtD7wFQGgn9CZJ+BeHrnqZSEQ6ezjN9dDd5cRk/vMyewxmouOiKG5CmSX1udBWiSCl+rlDaxfc9tPYiAtWk+nsoZfbrRKp7lVtAP3Gx1j7limLXAY+50xzyWbh6gs32vY0yhgaKrYfyoRgSkdXjxArW/qGUouz6bN9X5tqZeXJZmDYpy5bdpeB3Sg9CpsbnoLQOro0KKsfXF6oajFitVd2G30/s7HAyrUGau/boPrGKt777yrcLSePaywRpfHu5QV+ibUhICdUGaGlp4VOf+hT79+/n4x//OEuWLKmqVtofxo0bx5/8yZ/w61//mh07dnDPPfcMSEw5jsNXvvIV7r//fqZOndpvuxkzZvCVr3yFH/3oRzQ1NZ3XOT3X4PuKv/7Y9zn29BeifSMnvRwRG7QfpCwn07AFERvbzmJlskZhagesI5g/CdsySlNPJ0hWC20JkfM/gC3GHiD0Um3OQFMWhudwmm1yzRatw4SmgpDLCmQdTo4cx9Sju8l5FRjZhlgmGDbcrPE3zdmhRyoUM8ZcThMQrJamkPHJ2YZkNeJZTdbRkZBWxChX8wm1auin2iiFIHycVKrOWTia3VvPsPKRIyy9fQKOOFR5jmoVELyD3eUt4kD03FbWY9SqNWNf1lwmw5IlU3l42VZOnexixozxyDncrmKj/gR5eQkgIixYNIldO0/wyLLt3HLbdGwr9qCV4E2Ijh+qCS76dPob0CglJkwcw/v+5k2ASRd61zs/hdL7L/YkLiscP36cn/70p1X7LjQtKsTMmTO54YYbqvalq/n1SOPN5wXSAlPPQaRx7rMLpRTfe+JBPv/2v4/2vebeN9DSUiQrJqMpDK2Md2pAkAbPfRXq/gRfCQpBKeORGtj/R/1ryVRNzEkqBa4vuL5Q9oSyB30enCnZdFQMqepMG0vv/lOcWXeQrulT6FMWXa5Fj28ZUakHSgm+HxeecoOUf881ylRTiEqhyr6R0rpgdVcQ10fKKiJQxdVxBnmQvi+hDDey6NSIH0hyveBiuIqdh4pMGdXHouldrN9ZhIofpPHHqI5RQ1IwIPowpJ/nlVGqgiFLExcy8dP4wqqARE0SqzGBmFSrGlLWEJDb9npMn+xw08Isqzf1534y0CffQH+n9Z+csfdo8rX6MUJ1bvgGbNldYtqkHLcsKvLkxt4awniosX5SmRq+gaG6d2jEqhEw2MH3g2rVbW7YYrCMo4/Xs5tf/noZT6zZNcS5PfeQxrWXD9L49rLBw8H2yCXcHh5sElf+EvAFYMyYMbzvfe/jfe97H2fPnmXt2rXs3buXU6dOUSqVKBQKDBs2jLFjx7Jo0SImTZo0+KA1EBHe8IY38IY3vIGNGzeyZs0aTp48ied5jBkzhiVLlrBo0aKqPn/8x3/MH//xH1+ck7xM8YX/2sSOR/8N7ZtKr5ZTpHnYdWhl0qI0Po5tPkRFBGzHFA9yAi/UkLkMV7FJqlMVoRmpdhIVnHwM6ynE3qoZC3IOVk7AcXACErVQEAp5AssBODFqLEvXraB90fzIVcDMLahhZemI+LQJfFUtEwc6gG0pRuUNyRoSqNkgKLYIforxUA0FtBAQqoPckUP+TmuwbZg6cxjbNpxi3LiWui9Cxkt1KBVMJWivCCjLeAw9NIVr9UeJBP/Z0Vhz5k7goYc28opXXI8VVBj19WAr4tXjm5XtC1XRDnIUEeYvmMiK5bv4rZcuiL1fRRIkcPjROfTqsOeCW256PcePne5vhoB5X2zb4u67X8DyR5czZ9ZvsmPnBiwZdknm9Gzjq1/9apUXYLFY5Pd+7/cu2vive93reOqpp6Ln3/72t/nMZz7zvCADUjzvoYBlwH8AP0g9UZ+7SOPcZweb2/fzlY/+X/q6egEoNBe545W/gWXpSJGKxCn92QT/5ap4sTyMKqyAbwwTr0LStJ76ElSYX6TBVYKrDanqo+l1LSoKuj3wtYkdPG3jjxmJ3neCrqYW+lwxqf6+4HlCxSUiEn0P3OC57ycEpYrYC9XVSF/FpPm7hmAVzzeeqV7ga+pro1x1qQ4VNYZg1dVUZ3g++45mmXNVH6tOFUyxpNDei+C7Qj+p+CHp5/t9qOB2Fip8o9fDCaARNFqbOE/rZBweFpyK9xkla1J4ADv2e1w3J8MjT53rrXOwRY8kTRO+z/E5V8fmVoLEbBwnb91T4qZriyx7sqeqyFYji69GhKhZBDA1GmLyOWwfPraiuYVjhOKKjv3fQvu9/Ywfz33kyJHMnDmTVU/8nLe+ez5rf/0Pl0xI8WwijWtTpKiG1vo3nu05AMi5Vt67DPCcm3CKc8OZsyVu/l//wc6fvhvtx2nJv/WyNzJhglE4aGoDAwBBLDHFpMzT+LPbCsjR0Kw0ZDejSk5B5BQUoIr2B4SrZRv1pFjg2IJtxelXIpB1y+QqZcQSvOamqPCTYIjTZOBnmykG+4we1BEd8bqI8c6yEuSpSDWRGp1C8HhofxQaCzh5zAQnI0cXyGRidaokH4lU7al7vQ61mtZzCWTiRPgkevsqdHb0YlnC2HFt5gwu6H51qW4dmqNHOgEYMbKZXC65TpW8anVlJi4a3v4Xf0N7e8eg7SZMmMCSJUv48Y9/jG3bnO1+ikJu0RUZeKaowzP2JovInpbxE6e+9ns/uSjjffMPfoeuo4f3aq2nDd46xaWGiFyltT7wbM/jCkQa3z4PUPZdvvz0j7j3RW+kp6Mr2v+mv3wz8xdeE4SfuiputCAKU03UWI2wbVi0NNk3DJuSH/PhOL4GreMCV6FlQFj7CQzHqdq7AegtNqEsGz/o4wdcYTheSKyG+6JjaQ1+sOCutDmQObh5nvQliAcL9sVnLP3FgNrE06NaXdDQ0WVRX1utmoKtjsdCNamX2FP1v6r2VUWSBvtkTYwRNh4z0gT87WcVbj81QhsP21/U398kBog6q+aVbBnvGzvSAQ1nzvpUXF3bucE8+oP08zixt0Ec+v3//Dy9vV0NWlfj1a9+Nb/4xS9ob28nP+oO7vv83/OGewa1PUxxZeAZ/QKTxrcpGiFVqKa47PDBf3uSY09/t4pMBfjVL/6HCbP/2pixW475adto36TSWJlskEtvGeWpF0R1toUuOlBwoM83Ragcy/imZhPMaCAF1RmT6q8KGexiBqc1QyYr5PKCYwstTVDMmsDQEkWL5TNly0bab7ie1m27KE2ZhNVWJCPGB8uy4pAjZ2maHU2TYwhTzxdsWzEmDyIaO1CiNjm6SiSbsXQdoeoExGzonTUYHEuzb1s7k69qZfqs4ax89DC//YoZGCVnsqVg4dR5LcXpQlaipST62FVjDMzRWVXtrJpbkVKab//n47z1z36DZb/ezMKFVzNx0ohAoXp+Kk99TurWoWPXzhOMGdvBousm89CDW3jVHyyp8jWVKvWtgTp3e5YB8elPj6d4LDvAeObIL3nJi/nv//4BYFL//+/nvsa97xmPzYX7L6VIUYUhqOfPZawUlw8GIlNFZAnwAoyJ/3RgBKYIQAljD3ACeAqTQvULrXXnpZ5vihSXEx47to0f/Ms3qshUgJ898FN++y9eSksxExWk8jFxYM42sZ4XCC59HSyyGx2AeW6FvvtBAnsQG5qoTSJCNlSM9vpCn28el32hI1CnGq5TTGGqioV/8BRe8zDOjBjFyL372T5jPn2e4LqacoWo+JTSGhWMF6pTBeOdqvoCNWpFIV0lpNeFio94Gvo8o05NyFmloqLssoizbBTehCfkKSaP6uVk3mLvIYcl1/Tx8FM5qhbsxYIgDgz95UNfT88v46tSTZwoUfGpuE81wkJJIRlYpWgNFJhhPwmssqZOcjh+xmb7XpfbFuf5+WOlKvuyaOy6D9BkTB6qXiXxWmJeIkFafeO4NzzvRoSqeU0xdWKWk2ccNu3s464bm/nxw/Gt2nioasJ4PKlejc8jJpEFO3gfE2rhOoSvxd8Pyn4BcVQ4wQbQzJg+lVOn22lvbweg3L6av/noN/jDVywln882PP8UKS4IaXybogYpoZrissLmfe386MFHOHtoudlh5cjmR1PpPUSldJSu06toG3M7kskk1KYCYoNjh/klREvnGQvtCBQd49EUspA5O07rV5iCVZagLUPG6qyNNGcg75DJWTiO0FSEplx4GE0u8EkdffgI5QnjcDI2fTOvpmnrLtzFc7HtuOiUxqT6tziaoqMjn1RE0+KQ8Do149pCVaq/VXPDtSUuVCDVi+79QKM9xbYNp3jFq2fjODYtrVkO7Ovk6qnDGrXGuHPFJKlJYRqoAFIy/b+RhiKcbzxmNQkbtWD9un3MmTuRQiHLnXfN40c/XM0fveaWQOV5LhjSxTlv+L7iqSf28apXLyabzTBuXBs7d5xg1ux6glIHv2jJ0lQaFT3TF2AHsPLJr5sx+iWNhS2bjtDdXeGtb3sxS294DUop/uFjX+J/vf5ljB83Eksy5338FClSPL8hIq8E3gdcn9idvPk2B9vVwA3A24AeEfl34FNa6yOkSHGF43Spi0eeXsmvvvljADK5LJNnT2HPhh0c3XeEn33tx/zBW1+JiESUWVjcNCQoIUjACkPggDjNJr7kJ82boigoIGR9DT2ucb+q+IIP9HhCt2uIVE+ZXmUFZ7o1Iw8d59j8a2n3MxSzebKn2ulsGk6pZIjTqBiVqnmutfF6dX0Iik/h+kiPawhUH3CVeRxMULSOC1KFGIhIRYOnsTyPWZP7+OWqIspXnO0WJozxOXLCjmm90K8zUqMastRXHr6q1JGpEMe8SjeWkWp80EH0XkUq1qtaNQrHgoWzsvz4kV6UEs50Kq4eb7P/qPFrNUWkQnK21j6rPxIyScIm29VfuPC8B4qLJcj2u25OgQce7sT1FCfOeEyblGPPoXJwHBPJqjoiNT7b6seKZB2EOLVf6vokrQBaJr06QVZrQksFpQ3pawncdvdIfvTrUzjFq/F696P9Xk4ffpIPfPx7/POHX9vveaZIkSLFxUJalCrFZQOtNff+2ypOrv0m4QfriMm/zegpfxi1aT/6IMrvCz7wA1ZSBCuTMWrTMMIs+3GckXeC4Mws6Ws7yJ934g9ybQWFqIqOKUI1Mo/d4pAvCtkstLVAPgN5W5G1FA6ajKVp8ssUO9rxJ4zGFo1dzGINbyF3+jR521BmFhobTdGGvG2Up2GhKAdN3gEwhaksS1NwjHg2Y8dp/skKotX1LgeGUSZochZsePI4i64fg+2YP/sbb5nA6pVHUaqfwKomHajeAqD6eZIcNHNuvJofq1wbnYlQLlfYuuUw1y2eAkBTU47p08eyceNBLEmW2BocZvRLd5tb/dR+FiyaRDZr1qZuunk6q5/ch+eFwXEyHSwu4iXRv+Q1uHTLlJWKy/p1B7n+hiksWjSbN/3vVwLQ1dXD//nA59EcvmTHTvH8hBAuCF34li7gX74QkTYReQD4PoZMrf3Q6I8R0EAR+CtgS0DIpkhxxUJrzYOHNvJfn/g3lG9Io5e99dW8+SNvj0ijb33uW7Sf6jAqz6CyvSMBQQnR4rot8R+ahwl/w5C20dKs0uD5UPag14Vu16LXE/p8oaMinCpZlHyLHlfo9YROVzjeZ9N04BCnJkyiR9l4PuweO4Wrju3DLSs8L1Cm+hrf13iBKtX3Q8ISfE9Br2cI1YpG2suGyfUC0YOn4vT+sMCUl/D8xBDGVWJKFbDCvgYXtKuYO7WX7ftyqICI3bDTYf4MF5GkElOho8JRPlorPFVGabcq1T94txIHrL6Naa3RKrGhUFH2VNyvXtGqWTgny8Zd5egardlcYuGcLCIa3a+/K5AQH5jflWQMnURynkm/UqPGrVeSJntKRKYuml1k0+4yXsAxr9ncw3Vzi7G/bwBLTEyfLATbCPF1V1EcHM6rUevw/QI/6me+7tnBcS3AZuGcJjbv6sP1IDfiRsLvB5WOjfzH137KwcP91RZIkeL8kca3KWqREqopLhv86PH9rHnk55RObwfAyY9m+Jg7KbRMo2WkqTyo/F7aj//C3IFCtjEkR8PPYF+ZG5QIWoCcE7n4a9uCbMBWilGnahuT5l80ilRpzmDnHeyskMlZ5LJCawGasppiBgq2piWraHI0I/buozLjKhwbHNHkbI09cxz2vqPYyse2jJI0Y0HBURQc006CCLE1axSoWVuTtSFvB4WmCLbgcSAYAIJ6W7VBTfSoOvK0MJYDvd0VTp/oY+qMtujmncs7zJo3ko1rT9S8E6Fi0gSKVelKg4g9dSIAbehxKzYWNlbwr+ZVRGD5o9u59bbZQSEqg+tvmMaGdQeoVLw6Infg+cTnc7HR011m/95TzLtmAmFgm8nYXLf4KlY/ua+fo6qaa5qkVm0EK9rOD42D2idW7uWGm6Zg2Saw/dBH/oy2tmYA7r//R6xevRJf95znMVOkaIyLFXCmuDwhIhOBNcDvUC9J0sBhYCOwAlgN7AR6qGYqAFqA74vI+5+BaadI8axgV+dxHnnol2xZsQaAkRPG8NL//SqmXzuD33j1iwDo7erlq5/8GpVAdZqzY2pJkj+DvyAdqFbtGq/+5AK8DrjLbk/oCVL7e30oKeh0hfaySf3v86Dkm3ZdFaHUVcHp6uFU6yhKnoWnoMt1ONQ8lomnDqMCUjAUiobEqlKhDYAy5KmrjRK1z0VKLuLHZKqESlRfIW6gTk2i6q4S9FNEWWja8ylkK4wa7rH/iE0YuXqesPugzZypPnFMawhGpT2U8vFU2bQf0A5KB3G4FUwhJAXjWE4rjVIuSvkopVBaUU+mQlNBmDA6w64DbkQqer5m654KC2ZloiJY9SRjow9BU3Q1tBtI2g6E80w+1oGicyiWWYW8MHmcw459se2a58PGHb0smlNsOJehiAI0fvAe6OqVNa2jrbaHgZl3rbK2KW8xdUKBHfsqiNjY2WFkh10b9ek5sZK3vOvLg55vihTngzS+TZFESqimuCxQdn0+8tWVnFn7zWjfqBl/gFgmDXnkVa9ALOOF03nsUSp9R6PPbrEsIqPScNk+KCZFS676QNEdzKQLKcuQrDpvo9ty6NYc2VaHbA6yWYt8XijkzXAF2wQBOUuTt6Cp5yyOKJwRLdiicWxNwdYUszb5aWORvUewgZxAk6Npzmhs0aGoloylydrKFKQSo3gtOCYwDkW0Qe3LSKHaSJ8pApaVJFHD/oZMzVjw5KNHWHrH+DqSc961o9m94wylvnh1vlplGoZJ1an5/ZOayTSecDOztiXTb7/QZ/T0qS66ukpMmTq66nXbtlh68wxWPLbjnAhVAjXopbjVLX9kJ7ffOavums6dN4GDB87Q3VPqp2cjhYAkfkrN8wtDZ0cfJ050MX3GaDOe1owePYK//bu3mtlozTv++pNofeACi36lSJHi+QIRKQAPAFMTu0vAN4GXAqO01pO11gu11ndorW/SWs8GWoGFwLuAbcT+MBr4qIj80TN5HilSPBPwteIXBzbww0/+W7TvD+59E/liHkHzune/nkJzAYAHv/0Quzbtxg6IJ7+WYwzIVoKfYdZTEiHx6geeqL0+lDzo84SSL/hacH2hu2LR61mUfYsez6Ls27SXbU5XHNr27uPgVdMpa4s+F3p6wXVhd3Ec43tO4XiuIU9945Pqeeax72uU56PLPtLngecjZQ/pKCEhd6mJVaYhUaqJmWICZWp4wr4ft/dDMlWD8rhuTh9rt+WpTXXffdBm0lifXNYQcWYYH8+v4OsKoCK/0Mbp8cnssGQ6elK4YNSpMckaeB7UqmqBmxYUWLmhL3kENJrt+ypMHueQz+q68U1MFnwTEIlqGwzm32gUuBqlQh/Vocd2SxcUWFU1T3OwHfvLTBqbpZiv/m2zJBACiFVXe6HBzECHdHKClE4oVvsnVsPvF+YYNy9sZuX6nmCG5pi5YYsQ2/wdeb37WP7ocn716OYhn3uKFCmeWxCRO5+JbbB5pIRqissC//xfG9i38r/w+kx6RmHEPIrD50fLN05+GMMnvjhorTi5/7/QnorZw5D5Uzr2Q80EhaeUMixlQluvA5KS5gy6LY8eUcBqdnCKFmIJlg3ZLOSykMkYFamxW1U0OZCzfNr27sebNQU7UKYWHZPWX7A0TZNHoE53YZcrZG1N0VbkoxR+TdZStGQ1TQ4UbUOk5u34D1KCYEqiLfjWKYY7roYJMjKBUDckbK2AkD16sJtc3mHU6GI0egjbEm66bSKrlh+qGa3xM9O7PliSQF3Z/yp17f6aVfXgpWW/3sLdL5jXoD/MmDmOU6fO0tHR288xGh1VqlSzFwvHjnWilGb8hOHUnZcl3HHXbB5dtr3f/kmVarhHRILANFSsOsE1teoI7f4QB7Pxe/Howzu4866ZAfEbB7J/9rY/ZPbsKQCsfHwd3/n2AyjOnMNVSJFiYMhF2lJclvg0sIiYDH0QmK61fr3W+uda6/ZGnbTBJq31Z7XW84A/AToT4/yHiEx5Jk4gRYpnCk+c2M0vvv59Tuw39jozr5/PjS+53dzjBIaPHs4f/uVrAEMqfenDX8Sx4hghLDylkpwi1YnfSf4x9DGt+NDjCp1li15f6PUl4i1Pli16fKHsm9R/VwtnXaNidc50UrEzdGabKblCqQLlCvieplIRNjZPYV7HXkPYuho/IEqVjyE6y9qk+pd9pORBr2fUqFobuaPnI34oZVWGaE0QadVkanCyIZPsq4iIHTXcw/WEjrN2Vf9gFNZtc1g0xw3S9H18VSYYLGgTq1PDtHizBSSprlVG1kTIOtQPxyn2OhwrTG/XmtHDLbSCU+2N1LCaVRv6uHFBnpDcjU8ljP2G/kmotUIp75yJVIBRwy0EONkeUvLVRO7K9T3cvKiprt9gKf9V8yNpPWCuX23P0DKi/3k62LZw/LSbOL6FWFnyI5ZG7fpOr+CvPvCNgDhPkeLiIY1vLxv8Glj2DGwDIiVUUzzrOHm2xL//8Ak6thqTfsRi1MxXYykx6fmOYRGHTXwhTm4EAH0dW+lt3xSwhoGXamDuox0bnbWhJWt+w6Myp8Y/VduCtgSVt9GFDLrZQYoOds4mmxUsC7JZoaXFopCDfOBNpYBmx6hB8ydOw/BWJJ/BFk3G1hRsyNkax4acLTTPn4y79QAFB5qzceDb5EBrFoqWUaVmAkWqY4FtGTsAOypaFW+huDZZRVSCqFMIiNaQTCXkjzVrHj/G0tvGB+1qbt8CEye3Uip5nD7Z27hN4nlSeSpBgaUo6IqeJ/uZ57ZU179r9CGya+cxRo5qZvjw+mAtxN0vuIZlv96SIG8Hhg7I6HNTtQ4yptYsf3gnd9w1u2p/aFkAMG58GyLCkSMdA86udtywCEG0IVhRIauhEavJ1K+DB9rJF7KMGt1K8iuX1pps1uGf/vndUb/3vfcz9PbuCbzAUqS4cFjoi7KluLwgIlOBNxLfhO/TWv+21vrouY6ltf4acBNwOhgvD/zdxZprihTPNnq9Mo9se5qf32cysESEP3r/W8nYEsVqWsPL/vh3mTB1AgBbntzMoz9ZERa9jzxUtTaeqSEVZYnJQjIDx/yjDvjMbg96fHAx2fQa6HOhqyL0uhZlD1xl1KplX+ioWPS6wuiD+9g1dip9LvSVNeUSaGUUqho4mRtGVnm0lHsi8tb3QCsFno8ue1glDylVkG4Xq7tivFGDNH/xMVymIk77D08jOlE/rqIVMskqUKpqAMXCWSXWb89F51+Lk+02jq0Z1uLhKzdSbobqzWoCNfRBDQnU/tWrMWrjOIXWnlGqJl5burBW9VmNE2d8LAtGDrMMERsQimEsV11sqp+TpTa9f+gIl9qT6tRatamIcOKMBwhjRtQXMbVEAHuI5GpIqobHrxYZmDg2ehZt4Xt3y6ImHl/XXTWiiZNtsi2zsXNjzFEqZ9izbQWf++JDQ7kMKVIMGWl8e9lhqBezQf7AoG0HRUqopnjWce9XnuLkym+g/QoAbZPuIlucEH8eWwK2YNlZRk15VdTv1P7/QtvKFJrKWEaV6pi25AJFamD8ry1BOUaxqrM2OmfBqCI0ZZBCBssCO7BVzeWFQsEQq8aeVWOJ+ajPWmD5HrmDR7Gmj6fgKFoymmEZU3AqZxulqAayI5qwLMFvP4sT8L2WZWwBmjOaQsYQtVnbjCuY42WkfvUqeqzjByFxakvsw2LWaIPLBmzfdIZps9rIF6oJzVrccudkVjxyqMqwPlZJhkRcTKImCT7j/ZkMnqpX8mu9Ug3rW73H9xWrVu7i1ttmMxBGjWqhpTnHgf1DN5pPaDyG3GcgbNt6jMlXjaS5JZ/YW39Od9w1m+UPb+83jV6jUHVK1WpU+2KFxPXQ1ja1FlYs38ntd8yi/jfKqFRf/Fu38lu/dRsAhw4d51Of/DKac+ZEUqRI8fzCu4HwQ+VXwF9cyGBa6x3Aq4hv168TkQkXMmaKFJcLfnFoCz/5/P2Uus2i9a2vejFTr5kRZBIFv/ICmVyGN/7tm6N+9/39f9DVU8YnsBYNyNQQNoZMDTOafKXxlMb1TV3WvoAkdZUEWfWC68PpksWxPpuyElyMBUAZobMilJQw/NgxTraOpkdn6CtBqaRNSn/gjxpibcs0FnXtRvkaFa7DutoUn+p1jSFrSZmfKkGKejoqQCV+8FpAokqY3u+qiHCNoDXaj0nDaZPKHDruUK4EsV0D71KlFKs3WSye5+HrSpCeb66XUhVDfg6q5IxT0sNF7Zi4bNA69FkNyL/ZU7IcPOrRWxqY5Fy1vo+lC/NEqk2xauY1MA9gjumjVG2BraFh5lUZDp9w6S0F59qAGBURVq3v4ZZFTVR/Q0k+ChWtQ4Bu9KT2m0/1+c64Ksexkx49fdXXMylGKIy6NdpfOvMU//QvP6K9I60TkCLFFQ49hA2qby6DtR0SUkI1xbOKdXtO88tfPkz3gRUAWJkmhk/7XbM6nQ3UeBk7Kl/aNPI68i0zAMgXm/D9XnRkOGpYS+1YkM/E5lEWkHegKYMuOOjmLDTnIGsbAjaxAmw7Qi4nZLMmWTwXKEgzlo40kcX9h2HqeIo5ocmGZkeTs1SkMhXiGHDEwomc3XgYCzNOwSYoTEVUsCqculmtivjjyEPVTuyDSGxr7gaJWCd8HlrKeq7Prq1nuHZR0o+0MQnX2pZj9Ngie3d1YIIhCdSuNqG2NgyuBiqaJGHfkIAViapyDjSD1U/tYcGiq8hmByZ+AW6/cw4rlm9nwIKoNccLiz5dKFxXsXbNQa6/cUo8ftVKeoym5hxTpo5iy+YjA4xYXRUWaEjAVtsBhCR2UrFaH/huWH+Q2XPGUShm60hZcxxD6n7qn9+N45jr/qlPfoUDBzajdGWAOadIMTSE9iMXuqW47PDS4KcG3qUvgvmy1vpR4AfBmA7w4oF7pEhx+eN4byfLn3yclf/9IAD55iKvesfryVpmsT6MGcPb3PUvuJEldy4GYNY1syiXKigtaJ28GZoeZkHe/On5mJC3ooQuV+j2xPCX4ZJ4wGO2ly36lOBpMXWhfKFPCb2e0ONZVEqKkaePs7dtAqU+Q6ZWykG2vV9tK9Ajec7YzUx2gwVuzw8m4SMVhbgK8cyGBjyNeDrwS9VxgakQmoB4pYZH1GhfoX0F2gN8HNtn2qQK2/eZ2gq6QUp36Gva1ety/IzPlAnVpKPSfkCUDv32JdbAi9kq9FINrAAcWzFveo71O0rBnPo/Xm9Jc/Skx4zJRv0Zf/YN5Ctaq0o9N3VqqEx1bLhmRo5128tVrzciVXtLmoPHXGZPzde9ZjKrwu8IA394m9oJ4fjJ9klRRrVAw7Zh4ewCT2/tbThiOIaTH0emeabpqUqcOryCez/0nQHnkyLFuSCNby87rAbuHsL2FPGNZShth4SUUE3xrEFrzbvuf5JTj98f7Rsx4xXYmWaTnq80WkBbFjr6nNWMmvRKJsz6Y/7of72ZTHGYIV7tgGG0LWNGCuBrtAg6YxSpKmej845hNHM22hJwbMSGTNbCcYRi0Xxg20ExJ0NsqsCfVJEp9+F0dpGfOIxmR9OUUTRlNHkHspbxO3WDwCdjaZqbMrROaKVz32kc0WRtk+ZviSFKY3WpjmxekwhvuCGpGsYboUeqJNog8WVwRFjz+DGuXzoOKzmo6MTY1Qe7fukEnn7qGL4fBkQZqkL9fr8z13uhBnsbEK/VbQB6esrs3n2ca6+d3M/41cjns8ydN5H1aw8MqX20HHXBn1zCEyv3cv2NU3AcO9gz8JjX3ziV9WsPUKn0rxgwqU5DnEFCsVqtFJaquZRKLls3H2HR4qsa9jX9zdFnzb6aP/+Le4J+Zd733s+gOTjEGaVI0RjhV6SLsaUx5+UDEZkJTMa8LY9rrTdcxOG/kHj8wos4booUzzi01vzPoU388JP3RYTYy972WkaMHh5lJIUhmQ4EnAK87e//lE9/75P83qteSaGl2RSlCtapQ6rKERPqCoGQU0GfEvpcwU2IQbUKlKlKKHlCh2tR9iUQiQplJbSXbc6UbHoqFpOP7GX3qCm4vklG9T2qbQe0RiltVKlKs7F4FfP6DmH5biBi8LF6XcQ1pJ6UamKfkDT1VRyOKqNKFU8lsuzNQbWv0J4PyoOEHdH8mSU2784Zojka2PQLU/h938f3XHxVYt22CvNnWljioZQbeKkawi7Wn/bzPta92pgUTfqpgvFQvf6aIk9v7cVP2BoMtPy0bluZ+TNzOI5dQ5TqhPI1bh96vZrXz98ndPHcPOu3l6nlpftL31+3vY/5M/JkHEm0MZspTiWIOA37xoOb/zWKYRtDs3hukfXbe/GiP4r+URi5FAK7sUrnJr7/g1+wdcdAAocUKYaGNL69LHFGa/3oYBvExULOpe1gSAnVFM8avrtyHxuW/YTymT0AZFom0nr1ncFdJvjVzJhKTeIGhaV8Ra71Kopjl3LgdBMzxvUEYsiAHrIFsk6woq3RORvVZApPMTwPRSeSe4pjik/ZtpDNQCYrZDISEZDGmlWRd8CxNG1ZTeuefRTmT6Y1K2QD0tUJyVErDHuMutWxNHkbxl8zltM7T5LRHgXbnJIdrk4Bgk7Wy4qQXMGyLY0tZhPRUeiCJMOYeDt7pkTX2QqTp7QmRySi4SLlafixYJPJOMxfOJZ1q49E7ZLQYtLEawPJ/njK/hLda/HIw1u5486550R4LrpuCtu2HqHU5w6h9QWLpwDhbGeJY0c7mDlrbLRvsE9C27a44aZprHp89yDjV1/XwQRf1T6rVrQZFa7FiuU7ufnWGVj1FczqyG+N4m//z1sYNWo4AN/9zoMsX74cpbvr+qZIkeJ5j/mJxxfbmG45UAoeX3ORx06R4hnF1vajLHvgZ+xeswmAMVdP5IWve1mwkG7iPiWxWLPim7hzwtSJzLrpGkbNHMWxLSfCbPjISlQAB4VlaZQm8j/tdoVygqAteSalv8eHsxXhZMnGVYJC8LXJxu8oW3RXhD4XMr295EoljueH4/ugfI3nGWWq52n8ILVfB+QqgIfN9uw4ruk7ZApQ9XpIqFR1/XjSSiEBcYqv4wJVSpl+btwuioWUNiRqDVHYXPRpbVIcPRn7eIaeqL72UcrF8yp4qoSv+wAz9007XRbOzqAThahi6CFQqwNDJ8hUjaK1yWJEm83eQyVqVaP9xXi+gg07KiyakyFM/TdNFXExLRUUngqNaEP/18Fjx3iu8bk2F4XRI2z2Hm4cT5s4sjqWVArWbu1jyTWm2G3tgj2EgoMwk6rBNxUdGopVCz36+y7QVLAYPzrDrgMhGQ5hIBuTuDEBbDnN5Icvjs645+QK/uzerw5yZVKkSJHi3JESqimeFZQqPh/99hO0r/7PaN+oa19riL5QUZl1zG+oH6QLWRpEEMeGrM3uY0UmjyyRtZRh9TI2NGcDVStQzKBbc+jWLLrghFWfjNrVsZCMjVhCJiBTCwWwLIm5XIGsgI1RlbZ1deBkLAojmshYJlXLseJiUU4QGGcsTdGGQpjSb1tctWAshzYcM6Qo8SlaoiNlaRJhPGHUpkFamNSQlw3IVABbhJWPHuHmOyaQVI8aDq222lVIxhlibs680Rzc305PdyUeMZDB1oc+A8GCKGDrv8fxY514ns+kSSOGNGo0uiXcevtsHlu+c9C2VcEa9Ub6AyMknIVHHt7OnXfPISwWNVT+d8bMMZw8cZbOjkYpSvEca4P5waqcRjNMkKsgnDndQ9fZElOnjh20b3j0tmEtfOTv/zza886//gSev3/IwXmKFI0Q3hsvdEtxWWFU4vG2izmw1roC7Md8aIwapHmKFJctfKV4cO86fvzpf4/23fO+t1DIZ6LMpEiISVxISmljL6o0jJg2krNHzuL1uSgNlYhQ1WRs89ik6hvCVGsBbTLvez2jSu1zzWudrtDjGWWqr6DsCT2uxemyIVldV5h2eBdbR0+P+EvXjdWUobWp78dkqiFdFXtkFOO9ToqVElaPa7wFfI30etXhnx8UmCI4WVdBYA0QF58i4AyVSeNPEqxYaA2L5pR4emsuUmdqpVA6SOFXHp5yUbho7ZKcwO6DLuNG2zQVBv5QGSjKNcfsT51avf/W61p4fF0XSpvCV6Gnati/v/hq32HF2BFWMM8g7o7eBx0U1opT+6uKOw0hZqs9v5sXFli5vv+CWf1h7+EKo4c7tBRjKiGORy0sy8a2MliWjSUOglQRpiY7a+iq2lsWNdcUogqveTBe4tqGyLUtRJwWALy+g6x+cjk/+p+nz/lcU6SoRRrfpkgiJVRTPCv4+A82cnDZt/FLHQAUxy+mOGpedTxiE0SX2jCTrkayDtq48KNsi42HWlkwscM8z1roQNGqbQvVnEG35WBEAfKO8YwUQUx+PrYN2RyBZ6pRqoaw0GQtn7yjydiarCjyew7Qcu3koAiAmaoTdTHp/llLkxWjTnUsAkWpYtKUVnrP9FHpLkdEXFhQqtbZM0nUWZhxorbJG7Cu+mHai3BwXxdtw3MMH1GoIf1C8tROpItX380ty+KWO6aw4tF91XNKPK5VqQ4UwGmt6hLak4Hhw8u2cNfd8/rtPxCmTBlNb0+F06culZIyPutDB8+QyzmMHtNaZ20w6Cgi3Hn3HB5eNjD3EAaX1cTquRGaIsIjy7Zy9wuuIfRbjZXI/U9ao3jjm17BgoWzAFi7dhtfvf97KE6d0/FTpKiC6IuzpbickFz9uhQ3iJPBz5GXYOwUKZ4RPH5iJ7/4yrdpP3oCgGtuW8Kiu28IFs81yTBCY3SGWdt8hmtMnKdFmLB4AoeePmx0iD6RtlJp6HOFPg/6PMHVEhCyQkUJfZ5J5+/zLU6Vbc5WbBRGzdrrW3S4Nh0VGz8gSkecPU1vtki3UwjIU42v4ni0NhVca9BhfF5SrFGTWKwPBi9oo05NNI5Uq0qZwlMBgSqV+oG10oEnakwYai1opRg7okxvL5ztwihXtSFTtfZR2sNXLkaxWZ9NBfDE+hJLFxQGff8aqVUH8j+tVb1OGpulr6Q43VkhshbQpspWUoFZGz+HvvirNlS4aUE2cTxVlf6ftAGIr1G4f+DzSmL8aJtyRXOmc+B+jVSqAI+v6+GW65r66RUWpzLkqlhhPFprXxXPq7/vE+NGZfB8zan2evssEWkgPgjGthwKI2+O9vadfpx3/91/4rqNVMopUpwD0vg2RQIpoZriGcfh0z3c/+PH6dzyU7PDchg1/57kIrQpe68xARjEhvMCYltoDLN4ojtPLqNoafJhWA6dtdCFDHpYHj08D00Z48cqGDbSAckIlm2UqI4j5Asm1T9UgDo2tOY0w3OanK0pWprW48fJjB+Ok8uADouS6urgWIynVd4xW9Y2rzcFtgKzb5rA1lWHg5M0qfvhClWU2h8oXR0xlyBrB3RYggtLBuHh2mykXlXw9Kpj3HDzROKiRUFRKcRcN5JjJRWs5rVx41sA4djRs/28gzHtF+2p+kyova3Uqi/Nz23bjjBx0ghaWwcPbvvD3S+Yx8O/HkwklajQKqHidCgwJKTWmsce3cntd8w6bwPxUaNbKBazHNh/eghzVVVE97mIRHfvOs6IEc2MGNEcK2kxBbniQlaNzl8jtsWnP3NvtOdvP/B5Ojt3XpAvV4oUKa44JPNCL6W+4vxKVadI8Syjp1Ji2aan+OV/fBcAy7Z4zfvfQs6WKMYLEXJ0Ts06rQrqNjWNakYpzdmTvfha8JWgMOn8HRWh17MoK8FT0OtDtw/dAcHa50FnxShVPW1sASo+9LrC2YpFjytGZeopppzcz86RU4LwzKT6a1VPH4ZZ+irwQcX1EdejvZJFfM1IqweUNqrTgFwVpY0gwlWmKFVFG2bYa1BIKrAF0EoFhbD8RAxS4dpZZdZvD9Pho15ofHzlJojN2hR7oxA92eHhKxg78tyLlPYXCyldTc6JwE3XtrByQxhDm7gu9kCNidBaUUMYh5/pVLiuZuxIi/p3oWpWVcrX2n+JVg2VtzfOL/DExlLd/qHiTKdPX1kxYXQGsBCxE1usVoXaYlWJGBdddx1qsXRBE6vWNxZPxER37WaQaZqGnR8PgHI7ObT3cT7x+Z+e9zmnSJEiRS1SQjXFM453f3U1px79ijGZB9pmvBinaaz5+BOMiVQYtfmYgMwnYBttaiPStSdGcN2kDnTOQtuCzpvCVDrroM2Sf8BWWliFDJmsRSYjZHPQ1ASOExOWlkDBVjTZPkVbkbUURamQOXKC7LSxRjCLUQ6EWUu2mJ9hmJARYxPgWOZnLiBW20YWyBcznDx8FifwX43IVGIvVom2WvrToLY6YMDpYmOzae1JZl8zkmzOCtfEgzYmsLGk+k9eB8GY0iraNMJtd0xlxaN7YrP7OvVkbWBpxjAFAqqDGpOeZMrDhvOpVDxWP7WHm5ZOrwoGk/MYaAvbt7UVGT26lV07j5tZRAFr9YZOBpjV6oDqLbyi8XlsXH+QGbPGUChmasat7Zvc6udw2+0zWbF8J37wBWHAMUj8ZGjXwvN8Vj6+g1tvm5X4Xal9zwdSqiruvPN6XvUqUwvmxIkzfOyj/4bicD/tU6QYGGlK1BWJ9sTjofqKnAvGYW5SHZdg7BQpLjkePLyRH3/2y7glUzH9Ba/9XSZNNwUiNXF4qxNZ7nYipgOTmh9GK2MWTuTI04eoBIWNXN+Qp2UFJd94r7rKEKZ9FYuSbwpQdbsWHRWLPk/o9owNQJ+yOFOxKHnGGsDzYNKpwxxuG4dvmwI+ShnVashT+b6OhKdKGW9V7WmTrl/ykbKP9Pqs7R7LdU3HoS8oUKUC9YHrm4OFJ+ur4PXgdEOVpVJo30X5bnVauwatPGZd7bHvsIPrVZNxYQGq6hT45IJ/ckEdVm3o46Zrh7KQX6skrR4nRjVROX9GkZ0H+ihXdE2bMMZUQdwfjhMsfAcFlMI47clNFa6fnx1whrFqtT59Phy/PwuDudOy7D3s1syzfxiVapKINvN8YkOJG68t9is4sCyryuPUEhtLYrFHfP0ax6dzp+XZf6RCX7nRPPv3XI1UqiIURt0W7S2dWc3n7nuA02e6BjjbFCkGRhrfpkgiJVRTPKN4bMsxHv7Vr+g9uBoAO9fG8HkvN5+nFia1P+SyKsEKt69JWFkatWmQA6+yFl2ZIu1SZGKuGwoZaM4baWfeeKSiTN6S5YAVpPVbllGNOk4i7UTAsTUjcz4tWU3B0RQdcHYfxpk5EYWFp2LFoFmgDwLNUPcXcL45x3ifFhwd3TBtgbk3jmf7U8fMCr7E4YMdqhNCkjQIrocS5qigfanPZe/uTuZeG2ZKmgDOHMNctJgYrd7ituZfU3OWyVe1sX3LceJZJEhSfEP0Jf4Z0rY6kI3bK5PaoM3jJ1btZPH1U7AcqRojpj1Voq+um2f4XKFYeut0nly1B88LA+r6FepkQa0ohU3HY4SBqAhV/cvlCps3HeG6xVc1GDd8rBps9XPIFzLMmjOGjesPDjKGb7zAEqRq9bWovT7muq1evYf5CyaTzTr1aWQSE+pGJ+Bg4SQUzDH+8ZPvJJczQfzn/+832blzE0qXSZHiXBDeptMqqFcc9iYez76YA4tIDpgcPN13McdOkeKZwJHedh59bDmrf7oMgOZhrbziL16DHXzMKh2XFvKCnzbVWUimSJUE6fug8znyo1s4e6DdZNMroc+zqPiGLDXqU0yqfxAyd7jCGdek/leUsSv1EHpdoc+1qLiC54FVrjCm+xSH2sZHi8peILis5akiO1NfG3Wpp0zjiiFL+zyHo6UmpuY6wwAZqXjQ5yEVHbPJYUiTWLvWxpA1vAJBu5AgVGQzPldN8Ni136mak1YKHV1R4v4DoFTWHDjqMntK/2RlbIgVXoTGY9Z5dmaEWVcX2LizP8/8JHkYxGbhEZP1DsTMc/9hl9lTnH7GSs6v8Rz7uxIZB2ZPybJx57nFdpZIoD61ImLUdYW9hzyumZ6PzcRqMt/MedqRBYCZuRV9N0med/U8hbnT8qzfXn89Q5I2HL9xpGD2OblRZFsDezHtcvbY47z9b75xTueeIkWINL5NUYuUUE3xjMFXmvd85QnOPH5/tG/Eta/GsvLB0new/B0EaeL5iB+oNEVALHQo6czaRn06LAfNGTZ1j+Sa4mkko9ECKjQb1XGVUStjIxG/KhQKEisFtCkm1ZZRFG1NztLmRtfdh+4uwehhJigNohMLExSXlNDjS6C+DGNEjSPGT9VO/IVZAtm8zdUzh7Fvi0n9ToYA1RScHiSMixG+vmr5EW66dXwNKZhEI+KvdiMKThffMIn1647gVjyqfZtC1aoXtI37Vs+qOsBV2kehONvZw+HDZ5g9Z+wA80iSqPVEavK1bNZmwcLJrHlqL/UIVQEhE55UgfoRMdnfCv7KFXtYesu0Kn/d6vMbCuUdt1103WS2bD5KqeQy8Bh+MD+Td1d9LWpVw4qe3hK7dx1nwYLJ8VEbGPQDVUEugBV565qP9qlTJvGud/8JAK7r8Z53fwo4OoTzTJEixfMAq4lvWr99kcf+DSAXPF5zkcdOkeKSQmvNzw+s5wefuC/a93t/9Xqa21pqWkrVp36QQBXFp34QBisFPZ7gAyPmjqNzx3E8V1HyTSxqCFkJCFjDayolVHxjBdDjWrgBAespiz5XONVn47qxJ+rsU3vYNXpqxGRFdaAgUqWGBalAx/LVio/VVzEK1TC9H9h2soVZrZ04Ktjv60SIE8Q7vkKUNl6qSqN9ZcjUMF6RRFp48HjhnArrt+cIInMTAakgBtI68CJMxmmNPT/DNht2lpk7LUtmIK6yDjENIuLEc4xkCxY3LWjlyY3dQbZW7RYTf1IzV8tqbEGweZfLrClOv/OMU+rrz1UPEKPeML/Ams2lIVtKmdoLJpXfEsccM3Fqm3aVmXV1lmyW6rchOYYVKnEDC4CweFWURZW0AzDbDfObWLOlF1U3z2S78D0ZuF5AfvgNYBkSvdK1lZ/+zzI2bDk4tAuQIkWKFAMgJVSvUJw9e5ZPfOIT3HLLLYwePZpcLsekSZO4++67+dCHPkRHR0fDfsePH+dd73oXs2fPplAoMGLECG6//Xb+/d//vV+z8KHiSw9tZ8fyB6h0mA+w3LCpNE++JTCnDyp9Kox6M4wYEwh9ULUlKMcyfqnFDDgWXlnY4Y9kXv4MJp/elEDVlYDkzIrha33IZMwmEgaLGksUxYxPW9an4Jj0e1cL1o79uHOuooKJC40Na6z89LTgKkzcJ+CIwrFMmn9zxqT/i5gpgXk+Y/4oDuxsxy0Zy4P6bBWjao1ChSEsX5060Uel4jFuYlNN/GRG6Y8wbIhAFWk7FktumMRTq/oLODREpGTUdQAYMvCRh7dzx11zBkjTOXdcc+1E9u45RU9Pf6vtYeBd/0qj4lwA7Wd66GjvZeq0i1ds2rIsbr51Go8/tnsIrRPEqdb9xcUALH9kG7fdOcssJlQpfautBJJI2gFITSD73vf+byZONNm8P/7xMh568FfAxU+P+sd//Mc6JUN/uJT3phSXBsa25MK3FJcPtNbdxKTqEhGZcRGHvyfxeNlFHDfFFYbLMcZdf+Ygv/r+jzmweScAk2ZN4Y5Xv6SK3xHRWAEt6IiJKTNWeJ8zS6R+ELEZntJ0tm2LYbPHcmzTUSpK8JSJaQWNq4RSkPLvajhesun1BV8H7RC6AwuAMNseoKXUjaN8OpqGAaEyVhtf1cAn1WzB5MP0/YpCelzjhRr6oGqg4qE92Hq6jfkj2uPCU5F3gGkvSsdKV9/EkfH1CTJpLAuxjKKxrVWRy2pOnDbp4TGZGlLKUE2kVccS1SpJE/NpJazbVmbJvEKsqkz8C8eRiOyTqvGSatJQqTms1aG5aHPouBctUkdboOoUqf3a3X/cYwqUWazd6rJkXpZG+rZ43Jio7S+mDTGsxaa1yeLg8Wqb6rqrkPBDrTvvmn8aWL2lxA3XDGylEF4DKzHvyGNVIGkN0NZiM7zV4cBRr4YsFVPkSqqtBKpVvrVWV2A5RfLDr4/29pxczlve8eUB53s+SOPa5wfS+DZFEimhegVi2bJlzJo1i/e9732sXLmSjo4OisUihw8f5uGHH+bDH/4w+/btq+u3Zs0arrnmGj796U+zY8cOHMehq6uLxx57jDe/+c285CUvoVw+v9Tfs70VPvODJ2lf+91o38gFr0NUUHwqufzo+okgLdBrWoK2LLRjoQsOtOagLWeMSDXoZoc99igm2GfJZTzDQppoE2xBbMv4PgHFJot8IQywTPr/6IJmdF7T5EQZSNinOtD5HFaTCRAsgaylcWyNJZqsBbZocrbpk7ehyYGCDTnLBMpW6JOK6ePY4NjCghvHsemJY1VkqiVEQXYUztV8FjdMaNGaVY8eYekdE8y1IiTfqlsP9uEsCW+kcGV72swRHD/exdnOgU3rLcLl88aqyBCHD53ByViMGduS8Cy9cIgIt985k+WP7BygVbxan0wPShZqShKPjyzbwR13z2o00AVhytRRnD3bx5nTPefULyZHq/efOH4Wt+IzadJwkl5a9WlwjVWrkR1AQqXa3NzEP/7ju6M273znx3Hdw3XjXQi2b9/Ohz/84SG1vZT3phSXCHIRPaYu3tpLiouDrwc/BRjaH/EgEJFZGEJVMD6tP7sY46a48nA5xrgV3+OhXWv5yee+Eu37o/f/KdmMHS1XWmLiO8syi+yWgGOZBXQNgXhTqCgLT5sFeyFI3NKQmTiCntM99PVUjPAzUKm6GspKKGuhqxIQq0FyVrjof7Zi0VORQGkKaM2sE7vZOXZ6bGOlwA98CFRoZxV+5GtMXO4Gnqllo1IVRcjEYvW44CkOdRQYnq/Q5HixQjXYROkgTFE0kB0aCFWk2OK5Luu2FgK1KigVZk0l2keRe9y3nrwMGgfb/iM+I9psWpr6T7xN+q9alhXZJpl/dlW/265r4fG13TWEa6zCTJJrochBaxV4k/aPQ8cVbS0Wbc3VqfEi9WTxYBCEpQvyrFpf6pdEDlWf1eRq7ejVqlhBOHzcp7nJYljLAOcTDC9SXSA1Hl8Tqk1vXdTM4+t6SKp7kwRs3dA1RWerSU3TPtc2HyvTBoBfOsaGdcv53gNPDHjNzgVpXPs8QRrfpqhBSqheYVixYgUvfelLOX78OC984Qt57LHHKJfLtLe309vby+rVq/nABz5AW1tbVb/Ozk5+53d+h9OnTzNnzhyeeuopurq66Onp4V/+5V/IZDI89NBDvOMd7zivef3dd9Zx7NH/RFVMlcbmyTeTHzUzSvOXMK5SCunzgs/UIGLIWFBwoOiYn00ZdMZC5WxDsloCeQdsmzX2VSyRw0ahCuZnwHhms9DcImQyYAe1qmwbChlFS86nOWNIUjMPTXbPQZg1iZwNBQuaAk/VvGUIUysoLOUIWKKxLU3GgoxtiFMTFhjS1ZKgT3Ba469qobfH5eyZUiK8M+oFO1Gsqg4NCNZ9uzoZM76JppaEH5SEK+PSf+e6kczrJnEqDmBuu3Mqjz3cKJ0+7jsUBazWmsce3cXtd8xM7r1oq7ATJg7D9xXHj50dUnsrSGGqu6Yi7N1zirZhBUaMaLooc6vFXXfP5pFl24fYujZtK36steaRZdu48+5aK8MgWK/xpo37JduGEW6Qcyim5x+95qUsXboQgK1bd/OFL3wDOD3EOQ8MpRRvetObKJVK3HzzzQO2vdT3phQpUpwzvgF0Bo//UERuvwhj/l+IVub+Q2vtXoQxU1xhuFxj3GVHtvPQl/6Ts6faAVjyoluZd/Mi41IexRiJ7KNgXyahKSj5Ql9gIeVYgtIak7hllKeeFlrmT6Z9/SEj7lSC61uUPKHXE3orQkfFxlOm+JSvjV3AmZKN70tsSQqM7TpFZ76VPidvCoIqjeeZbH7P15EVVgStwdNREargdAwxWvaRPjdWnPqatYfauG5ie0ygakCrwKdAVw1u0skbqS8tJo31aT9r01syKfZJ/3gzrTDW0QniLMlSVCtMk2pOEFauL3PzwgIhiVi9QZ2dVWJ4sWKybsqELGc6Pc721NpfSaDwrFaURnO27CGJC57Y4HLjgkzDecYKUqtfsjHE5HEOZ3s0nd2184yvudSQv+Fpm2NIRC438i1dta7EzQsbq35rCVoZgE2aNDZDd5+io6vWyiEkY8PH1cSpIacTxHVQJDaOmy1yI5dGr5dOr+Jdf/c1KpUL/7hJ49oUKZ6/SAnVKwi9vb28/vWvp6+vj1e96lU8+OCD3HrrrdHqZ6FQYMmSJXz0ox9l6tSpVX3/6Z/+iWPHjlEoFPjZz37G9debtIhsNsuf//mfRytu//Zv/8aOHTvOaV47j3by3w+u4Oz2XwAgdo4R8++JfvvET0QSZT+osoRJ77fFEKdZGwoOOuegixm0Y0Exa5b5mzJBESqHU4VhCJqRfnckARBLsC3IZC0KBcG2CYpSQUtOMalZ05YJC0NpEE3h8FFk4ihyOZucpcnbhmzNWOZnmMKftw1h2pwJFawmrT+ECGSD/aEiIQyDrr9tAk+vOFJV6a/OqpPkSnT9a76n2LDmJItuGBOFC2FwVT2GNCZow9erKndWE3ijRjeRyzscOtDRb//a6qKNSNJNG48wbfooik1ZGAIBe+4Q7rhrFo8+vGMAkja6SnFqGXFQLCIopVj1+B5uuW36JZijwbDhRUaMaGLP7pPn2DP+EqG1Zse2Y0yYOJyW1oHSrKq/gBCqEXRtYF6tnhCBz372A9G+D37w85w6tRvzjejC8PnPf54VK1bw2te+lhe96EUDtr2U96YUlxa1X2/Pd0txeUFr3Ql8gnD9Db4jImPPdzwR+TvgN4Px2oGPX4x5priycLnGuO3lXpatf5JHvvYDAJxshj+4939jJJnxp2uYfWRJzCl6CsoeuL4YD1QC239lPPrdkKP0BV8JdlsTYtt0n+im2xN6fehT0OcJ3b4Yf1VluMuSBz0VU5jK8w1Z6vsa8T2uPn2AXSMm4/ka34NKxZCqvk8V8WpqQ2lTfKrsBb6oCvEUlh/UOuhzjQVA6Jnqac72Zqi4FmNaEgq7UB0bkqxVqCXcLCzRzJ1WYsvuQmCBbwbozytT62Sco2ueB8cIFaMBIdjZpektaSaOdRqkaQ8tTrUtYfG8JlZv7qs5VkigDpD+rVUN4QeNPgE7uzXdPZqJY+p9RiPr2ei6NJ63JcLieTnWbK5E4/av5K1ReDaYf7Va1Lx2tkfR2aW4avzQzGmlAQ0horj+mjxPbiwl5mBI6VgoItHPJML3vJ5IjVqQKV6FUzD1BrTfw4kDT/Dhf/rhkOY7ENK49vmFNL59zmI3sB5YdzEHTQnVKwhf//rX2bNnD4VCgfvuu2/QNJIkvva1rwFwzz331AWiAG9/+9tpbm7G932++c1vntO83vmNNZx69P4oSmub9zKcphGx636I0EcVTGp/1obmLLTkoGCjMxa66KCLDjRnzJ0oY4NjoW1jB4AlrMlOYYm7D8kIVkawbaHQZFFsEhwHLEvIODAsrxmW0xQdhSMaR5RZda5UyJw4TXHqaPI2FBxNU8aQp6EnqiKozIohWA1RalSqITlqVAjGEiATtkmQo63DcrSNyHFwb2fQPq5AH2JgEhQ2rD7BNYtG4WSsKq/VoStTofY2EPohJXHLHVNY+dj+BkRl2Lf/wk4AlYrHpg2HWXz91Q1fvxgqVRGLlpY8kyYPZ/vWY/20iknFWhIxvNZrnz7IvPnjyeUyFzyngbD01uk8sXIvvl/3zaIBNLXfQDzPZ/VT+7hx6bQh9FbR+5M4437bh+lzN9x4LW94wysA6Og4ywc/+H+BE0OYb//Yu3cvH/jABxg5ciSf+cxnBm1/Ke9NKS4tLlpKVIrLEf8ETA22pcC5eZhU48uJsWZrrdsvfHoprjRcrjHujw9s5qef/hKea1RuL3zDKxk9eXxVZCWB16cGJCBSjSWpSdUvK+PbjxF80uuB0oY+ClP+fW04S2fOJLo3H6LX05Q9oeJblH2LsieUtUVFC64W2is2HSWbchk8V+MHfqhTTh9k//CJeJhUKt83RKrnmlR/iAlfFVbJqvgme8xTSNkzitS+YKIlFYcnKvA0VZr1B1q5dtJZRPmIb4rM1qb6mzi5NkXeRCBzpvax62AO19Vo7SX61BKKOvZMoFqVmEzZr1VUhvtWb65w/TVZLOvcyVQQFswusHVPCc+P9zUiTxudZ3ws4wurVHIBvHoOa7ZUWDzXiWLVRkRtMu29mqK2mD8zx459Lq5HdB10gtCtnfNQ4vJqVbDB6s0lrpubG/SzW5CgUFW1CGT+jDw79pcpV+pj4yryNzG+UVn7wblA/++f6WRUquZxuWMdX/iPBzhy7Pw/dtK49vmHNL69bHB/sD00lMZa67drrRdrrRcPofmHgd8DXjFYw5RQvYIQ3qBf/vKXM2rU0AvpbN++nQMHDgDwW7/1Ww3bNDc3c/vtJqvvoYeG9Dtr2m46xlOP/JK+oxsAsJtG0Tb7t4PUH12lTpWyZz7fcjYUMlDIoHM2OILO2KicMSfVOQedtQ0/m7XRdmAJYBkVZp+d41hmGNPUKZyMkM1BLkeQ6i84geJ0eM6nNePT5ISFpAzx2br3ALm5k8g6YupbhX6oQep+6KdvUvk1hYxCRFFwdNQ+I6ZPVmKiLlwsT8YoC28cx6bVJ9BKYdf8NQ7iZU5vt8vRQz1Mnz0MEbN2GxyJ/lLEG6FuhVjq+xQKGWbMGsWm9bVEZRCUoROpWMFREye6csUebrp5KrYdE7D1/p4DTnOIEG64aQpPrzmA6/qDN69BX2+FnduPs2DhpIsxmQGRyznMv3YCT685cA694ov01BN7WXz91WQy9YrkJAQbCwer6n02Suz+Avdku3/4h3fS3FwE4L77vs3GjWuAgT11B8Kb3/xmenp6+PSnP83o0aMHbHsp700pUqQ4f2itfa31gcTWfQFjHUqMc+pizjPFlYPLMcbd2XmSh3/1KzYtWwlA6+gR/PZb/zCwgQq/ZJnHGhPnlBRUgux3N9hX8aHiG7/TSkiuEipWgwJTGipK6LOz+KNH4B04RckXulyhJyBmlQJXCWfLQsmFsmsIU88zRVlzbpkRvZ0cLo4J4tH4Nc+rIVM9ZZjfPkOmSuCfStkPTiBI9/dUlP4v4Un6mopncfB0nuljeoNUf4IiVAYDhbi5jM/YkRX2HLAx7h+JGEWIFKZxnJwcLRBmRKSqaugfH6Liwu6DHnOnOUG/ev/5JJKFPos5m6snZNm21yhxaxWfIglbggbHT3qpxvOuPyetNa4HOw/4zJte+9W9Pi2+FvmsxdSJGbbtDVW+MfEazSRxnQaacy3i9H8znuvB9r0V5s/MDtyxAXJZYfrkHFt2l+q+Aw28gDJYLGsQztPODiPbek3Q1afr+Ere9t77z3m+IdK4NkWKZwda6zdprd+otR58JePcx35Ca/2A1vqBwdqmhOoVgnK5zOrVqwG488472bNnD29605uYNGkSuVyOcePG8fKXv5z/+Z//qeu7adOm6PH8+fP7PUb42pYtW4Y0J6U07//uGk4/+pVo38jrX4dlZ4Pl9tq8olCZ6kDORudtdM5BWRaqLQ/DC2gn8ERVmBx6ATIW2uTrExpWbW2axJzyUYpZRVOTRTYrZDJCLgMtOc3YojK2rHaQjh927+rG8jzyY1qD1aPQV1WbSqxiSv1kRId2rpGnaqhUFan2VA3jkVBjGH3sa8hkbWbMG8HWtdWp30PRlD65/Ag33T4ex6qmRLWO1YjJy9sItSvD/a3Oaw0LF49n6+bjlMse1R5TUauGoUxHey9nTnczbXptkHFuxO9QIGLhODZLbriaJ1ftHUKPapXq8kd3cevtMxqnZl0CzF8wkT27TtLbWzmHXoqurhKHDrYze864qvc2LLAV+sOaTaJXIfkuhddf1Wya2HNVM278aN7//j81R1aKv/qrj+Krw+d1vl/60pf41a9+xQtf+EJe//rXD9r+Ut2bUjwTMPfLi7FdjHtDihQpnru4LGNcrfnBno387J/ui/a94h1vpNhcjOybovgvCHld3xSQ0ghKmzR8pYQ+36IvIFL9RJaQ1oa77AvS+Xu9oM1VY9GHTtJX9ujzLEoBkeopoewZQrXiCr6vUX481pxTe9g6ahpaAvLVjUWjSoXzAe2ZolP0eUhP2aT1V4JCVJXAYFVppJyM44P/hQSrhp1HikwZ00dGvKqYf7AIa+GcXtZuyxH7BNRDa4UKFImN/DzjiHvweG7rHo/pkx0ce+CF+FobgZsWFnhiQ2/wrN6KIFRMJgnKgUlKTbXKsrrt9n0+Uyfa5LJJsrNaXWoITrtqu3FBjqc2DVbgKPkt5VzmnPzuYH5u2+ty9YQM+dzA175WpXrTtUWe3NQbkPo1xw3fykZv9XkgN3wxYuUB8Hp288tfLuOxVUOtbRAjjWufj0jj2xTVSAnVKwT79u2jUjGkzKFDh1iwYAFf/vKXOXnyJMVikePHj/PAAw/w27/92/zZn/1ZVd8jR45EjydOnNjvMcLXzp49S3f34GKUf/31Tvb88rt4XUbVmBs7h+KkGxDfrGxHhahsMWRqzjHK1LyNDn1Tcza6ORMUnRKT1i+BRDRY8SdrI1kbK2NWSZ0MZAo2u9smMa/3ANksZB0oZDQFW1F0FC2OJheoSjO2cQ5QWpPZsZ/C/KuicwgLTmUsjRN8gFuYx1lLG39Vx9gC5B3THk1UzTVEbUwQqly1hhnzhnNo31n6ehOm6IMECyeO9mBZwuhxxSihp7rzUMnK2n5JVPe3LIubbrmKVY/tpzaikYisq1epPrJsB3fcPauf418KlSrMmj2Wo0c6OXu2r8GryWsTH/DkiS7KJZfJV424OJMYAkSE2+6YwWOP7Dynfo8u286dd82uKQxQvdX/XgR7o8JTtUgSrEEgHez7q79+PVOnGtXusmVP8sMf/hSlO85pzocPH+bee++lUCjwxS9+cUh9LtW9KcWlh3DxUqLSrKgUKZ7fuBxj3OXH9rL8uz/g+O79AFx97WyW/u4LsEL7Jg06uH/52niilnTseekp6FNCtw+ujtuFnJ1SUPGg7BkyteILFSW4SvDFpnPyZGTXEbo9wfMtKsqixxW6yrFfqp/gB4eVzqIROrItAXGqKVcwxai82D9V+woqGin7yNkK5uCBpDZUmKpAVhvCV0hoDxDxgRoUbN5V5NppvVWp/v1Ba82wlhJaK063178eygdir8xGBKjQwKTVvNLPYrnWsHpzhRuvzQ04N639KE1+ZJuFYwvHTsWqz7igU01G0JBJkySJWbsvnKfH9dfYVH8yJkUOQQwY2AGMaLPIZoRjp1SVTYBRfDaiAeL5JhWrterVWph4NB7zqU0lbrw2P+gZh3HpiDaHQt7iyAk3cRwdtRlslIG/zyTmGcxR7By5EddH+/tOreTP33d/YLswNKRx7fMTaXybohYpoXqFoL09jjw+/vGPk8lk+Na3vkV3dzft7e0cOHCAe+65B4D77ruPz33uc1H7rq6u6HGxWOz3GMnXkn0aobO3wmd//CQdq78X7BFGXv96RAvaNyvbOiBItSVGWeoI2hFUNvBDDdP8h+fBElNDpykDFdMfkYB4leiz1LYFyzZq1JNtoxlZ6aaVEs1ZTVNGMTLvMyKrsSxNS04n0uw19okzyLAmMk25SGWasTR2oFINFQdNGShmNHnHqFRtMerUpLZTM3jKfkhd2Zaw+JbxrH7sWJWaoT+I1jz12FGW3j4Bu5YwS6RAJX0zG41Zr06tnV99/6unjqCzs0z7md64X93HQfx8/74zNLfkGDmyuf8Tusgq1TCQvOPuWTy6rBFRWatYMB5cjzw8EPF76TBx0nAqrs+J42eH1P7I4Q5sRxg7vg0ACzvaBiJR+ydSGyHWUmt88vksn/qne6NX7333pyiVGnnq9o+3vvWtdHZ28qEPfYhp0wb3fYVLc29KkSJFihTPLVxuMW6v5/LgtnX86l+/Gu179d/8KVnbBIOijbZSa5Mh7yoxOSAB5+grQ5SWPaHkWeggxd/TUcIWvZ7Qp4Q+3yhP3SDtXwMdFYuzw0Zg9fZBTwlXQcmFXlfoq4ghVIM0e61B+Zo5p/ewZcS0IMVfUyoHBGqglAUI7bgoe6YIVcUUoBJPJ6SsRoUqocelDn1TqRaUBlW3jp3JUsz7tDQFxGM/11RphdY+C2f3sXbrQEWN6omz2sX8+naD4/Bxl2LeYlhr/Vdj428akqkmNrplUZFV6/sIi2g1ir8IZmfI3wQ5qIkfN2yf/BmPAZpjpzT5rDCiLXzNKGNDu4FaX9WlC3I8ubHSr69rY4TfUBKZS4l4r7/YL6lUPXbKJ+sII9sGpxpEhFsWNvH4utCK2xy/oUq1n/4xmZsUGvTTPngt0zIbK2tEFKpyih1bHue+ry4bdL4h0rg2RYoUkBKqVwySK2pKKe677z7uueceMhlTWGfy5Ml885vf5LrrrgPgox/9KJ534dW6+8P7HtjE8V9+Fe0ahWDz7BeQGzE18FjSaAuT2p+1wbbAcdAZB10walTdlEE1Z0zxKQgkn1aUZgQY0jXnYGcsLFuwHSGbNVtzs9DULBy6ahqzj+2mJadocRRFW5O1NbYoMmLIUkEjSmHvO0LLnAnYAg6agmXcBTJi/lB8bfxRHdEUbI1D6DpgvFNDhMSrxdDoQQ2MntCE7ylOn4yJynA1uha7trQzaUoLTU3VRZMaBWbVe3TNK4Ov5tb3t7j9ruksX7a74TGTz5VSrFyxm1tvn9Fw7OpxBw/Uhg4zhzFjWshmbQ4dbCBzqDnmrp0nGDeulba2wgUe+/xw512zeOThHYOeu9aw/JGd3HHnHMCoNRobLZwPiVqLOJjWKF7xit/gBS+4CYC9ew/xuc/ejx5igapvfOMb/PSnP2XRokW8853vPN8JpXiOwbpIW4oUKZ7fuNxi3B/u28avv/g1+s4aouOG33kBMxbNjeeowfONCjUkQv0g3d9V0BOQpa6OIxGlJdpcZVSrhow1BKynBD9I6W+vWPR6wsHJ0xm1by+9nkV3WShVAs7Tj8NlpWBS93FOFEbQSxbla1zXEK6m8FRgCxBkj1HxkM4S0mOun4RcZSCGQGMmlzxZXwXyWgj9DUw2mmm3bnsTi2b3DBKOKK4aX+L4aYtSeTC/zPhxbRp+pGrshyxsVNU+LAa1cn2Jmxc0UlXGHqegmT45y9GTLj19OiiilfRN7U/9qYegtIzPqZpMDElSc7wnN/nceK0TvVb9M8aUiTYn2326ewciQIf6KVvvr9ooZk0qVVeu72Ppwv7j6tBe6uoJDqc7fbp7ar8D6ESrgePj6ve7cWGw6rZmnvmRN0f7y2ee4mOf/h7dPY2y26qRxrXPb6TxbYok0vfyCkFLS0v0ePLkyfzhH/5hXRvLsnjXu94FwKlTp1izZk1d397e3rp+jV5L9qnFpmOd/OgXy+ne8isAJFtg+GIzH9HapPK35qHZCe4oge49qOak8zY6b3xUyQUBQyao9hQGcQUHacng5IRcXshlIR8UnioUMM+zGt3WhDg2o7rbGZYzxahsUbRmNJYV+p2C7D1KbupYnIyNhSFFnYQnKphQquAYm4CiY1wIhJhAhUQ4o+sTjhpxZbbE+xffNp6nlh/FV7qKTE0+Vq7Pto2nWbRkTNU4/RFn9SrT/oKqhrsbBDDC8BFFhg0vsG/P6ZpXqlOQ1q89xNx548nnMzyTSAZRt905kxXLdzU47/i55/k89cR+brp5aKvLlwItrXkmTBjGju3H+2lhzmnzpiNMnT6aYpMx+9cN0tqqCzVcLPiICP/8mfdGhQE+9rEvcuTIVpQe+EvriRMn+Ou//mts2+ZLX/oSjjOQ8qQaF/velOKZhVykLUWKFM9vXE4x7rHebh5Zs4qnvv9jALKFHK981xtNASIMMdrjCxUtVIJQw1Pmua8DD1QFnjZeqjoUfypDnnoK+jyh7Asl36KshAom1b/LtThZtvA1lLXQVyhSyubJnz5jClApk8LvBZalSgGuz9Vnj7C9eZIhT4M2ITyPQE1qGFzpdk3RWBUUjlXxBMU3RqsS+qFG+xLMcBhvJUjX7h6b7l6b8aMb+8UrpRBcZk2psGX34DFj0uqoPg7SiXZWTZ8QjQo7QVeP4sxZxdUTggJVEYkYH8O2hYWzC6zdlvAkbcBpDlhEadAPNkMkVnuTxp+I3b3C6Q7N1eNriUNzTEPqwoJZGdZtS9iJNYCZ52ATCknrpFq1f2I1XNDv6YPjpz2mTcokRqomSC0LFs3JsWZLKfgy0v9cBiNV42JlIV3V/3ghue0UJuA0TTXj+32cOriSez/07QGPk8a1KdL49vKAiHzlmdgGm0dKqF4hSPqwzJkzp992c+fGK+j79+8HYMKECdG+w4f7LzYTvtba2kpzc+MUbq017/jRJs78/IuEH7bDlvwBdrENLaBtoyql6ASMpTFC1Y6gCg66KQfNOUOe5mwTmNmBRNQLApqMIAUHO2eRzVlksxa5vEW+YJHLC20tQs6Btgy0OIrKzKsZvv8AOfGxrUBdahmXARuwyxXkVCctV480xalEk3MCshVDqmqgydEUbFOAKiZRjVeWEJOzhIrVIQstTUDd1JJlzIQm9u7sNF1r7rQ2sO6JEyy6cSy23V+Q2Gj0oe+tbyWJx7FFwNJbp/DkygP4viL8WEimO5X6XHZsP86CRZOGdJyLr1I116dYzDJ9xmg2bejv91qz+sl9LFo8mUzG7qfNM4Mbl05lzVP78bxkzlyoHhAqFcXG9YdYcv0UILhGGqB65f5i+dDWQuNz7bUzeetbXw1AT08fH/jAZ4GBC1S9973v5fTp07zlLW9hzpw5dHd3V22hLx5Qt+9i3ptSpEiRIsVzE5dLjAvwrd2b+dVn70MFpOKL/vc9tI0ZFRWQKgeeqIbQFFSgSi374IY+qEHcF9iVUlZQCkJcVwdKVh3ylEaZ2udBlyf0uDa+snB9C9cXdo+ZwsQj+/EqCt83haaC0ADlw5yz+9nRehU6iIuSvqoqVJeGytReF+lxY1VqkOYvrkLcgHStJIKMqqzwQJmqMCRr6JkaWAJs2llg3vS+oBBLDK01SlW4ZkaFrbszKDUQoZYkT6WBOpW657Up8GZf9fGTfZ7eUmLRnBxiRQxxVd/Fc4ts2F7CVzUEX8NpJ2P1+q/c/SsvG51T9b5123yunWUH4hCpIpkBFs7OsnmXR1VI2S/OhdoJVKO6OlZvTKparN1WYcGsbMJirRoLZ+XYsruM59eeR/j95tyC2qRPbO14te3C9yc/4kYQ8x2g0rmJb33/IXbtPdbvMdK4NkWKywavB97wDGwDIiVUrxCMGDEiCjgHItiSH3hhu2SVwWT1wVqEr82bN6/fNt/fcIR1v/gx5SNbAXCGT6Bl3ksgYyG2QHMWig46Y5noMvg80zkbCnZYBSpmMUXQEvin+pj2LTmyrQ65vEWhKDgO2LbhZpvzQZEoW9Gc8Sk6imLBgrHDyR89QcbSNGXMYexg87cdoGn+ZKzgejgJsaegAz7XqFvzpk5WeDWNklXidVAISdXkWm7y+seP7aq3KXgvloxh69qTuK6KrKrCMXvOVjh9qo8p01qjPkOpRt9IpSpD/NOP+wYBSTBMNpdh3vxxrH/6UOTfmcSKx3Zxy20zsKzB5zfAwS8KrltyFZs2HqFcrlVSarq7yxzYf5q58yY07PtMIpOxWXz91Tz1xD7iZJD4+q1asZsbl07DtgOvroDJD79gxO/x4BVZzw8miP7gh9/GsGHmd/BrX/0RTz65EqX7X2Xfu3cvAF/4whdoaWmp2z7+8Y9HbcN973nPe4CLe29K8czjYpn2XwyIyDgReZ2IfE5EVohIr4hoEVk9SL99QbuBtsErbzQeOyMi94rIehHpEZEzIvJrEXnlAH1yIvJJETksImUR2Swi/QZ7IpIXkd0ickhE0m9lKZ6TuFxi3I1nTvDYgw+ye9XTZl4Tx3LX61+Jq6AvSNOvGBEnmkD0idmnkNidPGjnaaHigx+k+ocp/WUlMfmqwPXhTMWmo2zjYYpZlTzoKkG353CoeSyTO47gBWJE5RslatHro7XSw9H8iIC4DFSrGFWo7yuo+Eivi9XjImUvEXtpE8S6gXdBUqUakK2mEJWOidfw5Nz6GMRTNnsP5Zh1dSna5wfepMWCz8hhioPHhr6wbVlWXQp9Ukk6VNT2cT3YuqfCgpmmQJVKFNNqKliMH51h18GYMBtINRmrP6XftvU2AKEPqBW1SMw2mK/G82HrHsW1M2sFFkIxDxNG2+w+ODTbi6GpVJMIv+GoqEhXrVWBUsYD1fdh484Ki+bUF/wq5IXJ4zLs2B+raGMv2PB3ofY9Hlp8W29nUE+shgWqrEwr2bZrg72KnhMredt77u937DSuTXE5xbcpgKGxBjqxDXXMIbVNCdUrCC960YsA2Lp1a78fOFu3bo0eT506FYDZs2dz1VWmsv3Pf/7zhv16enpYvnx51XFqUXJ9PvKzdbQ/9B/RvuF3vBFpK0LOQWdsU4jKtoJKoYYsxbaMl2rGNsvGWhv1ahjjZCzQYpjMlgx23iKTg2zWkKmWZQjVjKNpK2iKjs+IrGJ0zmd0XlHMgDVlHPbhkzRpF0s0EmyqowsBClHRJI0RKmoERc6GrKVpy2manOpVbSG2BEhm3CdvkJGIkOp9tfdQM67gZGzmXjeaTWtOBLOJlbIrHznCzbdPSHyZGDppljyeSHVqylAUrnUFrBCuXTCR3TtP0dtbncJ1+mQ3vT0Vrrp6xJDn1++Bz7dr4pxs2+Kmm6eycsXuunaPPrydO+6aPSRi+tLCXOXZc8Zx6GA7XV2lqlc72ns5daqbGTPH9mPvEASzF1Xl2wiaUaOG8cEPvS3a846//ke0PnxJjnex7k0pnnkIiXvjhW4XZ0r3AF8H/hK4BThXw+RNwIp+tnP+Fh+QsL8GPglcA+wCzgB3A/8lIv/YT9evA/cCzcB2YCZwv4i8tZ/2HwCmAX+ttU5LBad4zuLZjnGV1nx3xwaWfS6u5v2yd74ZP5OnoiTgGyUodC+GMFWC7wuelmiRXPlQCYpRlTzBU5bhKzX0BlYBYREqIyAVzlQsul1DtFY8oeJBqQJ9feBWYF/zeMZ1ncSqVFB+vLg6v30Pm4dPJSgbhecRkFwa5QXS2IofymdNqr85WROnez5S9iOPVAn5OR2QrZEKlTgk9atvh5J4uudwngljKmQzPkqbKlYan8VzXdZuyTLQ3b42tT/0Pa15l/rt33DMGnVqqBjdvq/CVeMzFHKSGFO4ZVFzUDip3k+0f4Qp6NHTxEMr8XpcUCmMSRun1sfH3XXAZ8JooZDgKkUsbrw2y5ObKg1VsQPN8/wQzyneqgta7T7oMn60Q1Oh+hhLFxRYtaHarzQkhWPhh64jvYeK0HahkUo5Ol5w3rlhixDbFILyevez4rFHefDhDed13IGQxrXPfVyG8e3zGfuCbX/icX8bmEteGkLbfUG7ISElVK8g/Mmf/AkABw8e5Dvf+U7d60opPv3pTwMmfWrx4sXRa69//esB+Pa3v82+ffvq+v6///f/6O7uxrZtXvva1zY8/j8+vJODD34bv+sUAPlpiylMXQJZGx0WlRIBSyNlFatR7cRrgmkb5M9rxwqsAUDnbaSYIVewKeQtssZGEsuCtibN2GZNs6MZlVO0ZhS5sOaVgNgWMnMC1u44vUJrjdp2kLZrJ0f7slasSs3YkLE0WUeTs6NphL1NLS2rwQqTrn9aG2oNtCo1ZeYwThzpoaerYm60Gg4f6CJfdBg+Kn9epFlSaVq9Gj6UW7lUnUGY2m+Jza13zGTFo7uqWj/yyE7uuHvWkOZVf5xzmddgiN+tadNH036mh/YzPdG+o0c6ERHGjW+7iMc8F4Sr5bGCQcTijrtm8+iy7VEbEeGRh7dz1wv6T3M0XwEafbm4NPjTP3s1c+caz9lVq9bzn//5AzQdDds+/PDDNYF29fbBD34wahvu++xnPxvtuxj3phQpgLPAL4F/BH4feP859n+71vq2frbGxoAD4xPAbcBe4Bqt9UKt9Qzg5RhR23tF5GXJDiKyCPgDYD0wRWu9ALge6AP+XmJJT9h+FoZ8/bnW+vvnMccUKS4bPNsx7q8O7+Wxb36H9kNHALh6yQJm33Ub5VCkGRKgGK9UT5u0fU/HqlRXmWJUZW2SrnwM5eTr2D+14gueMkSqp4SSD6fKNl2ejafEEKselEpEHvsKYWvbFOZ17IuONaqvnbKdodNpCq6PUVv6Pvi+QrkKSj5WyUNc3xCmUd0lMyEp+7EY0ddRWj++DtL6iZQDEvZLpJhLbYyqYcOOAtfO6iWMV8aM8Kl40NHV/1fSxipQFRw0ST6eC6pj29pjPLGxj5sWFAjjs/GjM/g+nO7wox5VJF0/h49IvQHjb6kj/aqf18b9fqQKfXKTzw3z41v/6OHGTuJU+7ldj3NVqeq6fwodEOSNykitWl/ipkTBr1HDbUTgxJl6T4LQC9USB6ISv0MlsPtH7K8qxHG3ADZiZciNuDFqWzq9kr/6m69VFcQLkca1KVJcHtBaT9daTxvKluj26BDbPzLUeaSE6hWE22+/nd///d8H4M/+7M/4zne+g+uaNIqDBw/y2te+lrVr1wLwsY99rMow/d3vfjfjxo2jt7eXl770pZGZf6VS4Qtf+AL/5//8HwDe8pa3MGtWPVl2pKvE/cvWcHbFd80Oy2b4i98CTdk4uHIso1BNZqBYhkDVAa+kARwbbYHKCroli85YpkhV1iZfFApFsENlqgWFjKY1p2jJKQq2YlhWU8wEPqYJNI0fBj1lrG6zGqoPn8Ye1YpTMMysoLEtExXaaIq2JucYZWqmZixLkh/HMYzyNfE8+Jn86K8lU2sXTUVgyW3jefqxo6at1qxZeZzrbxuX0KrHoUp/lTbroan5vl137IH6mvaChR2tHE+aNJxyxePkiS4EYfeuk4we3cywYcWhDhzOZIBn54fa1eg7757NIw/vAMw1W/7ITu64M/m7/EwQqvUkai3GjmvDcSyOHOlABPbvO0Vzc46RowbP1q0NYS+VSjWTcfinT7832vO+9/4z3d17AsXJxcWF3ptSPHu4nKqgaq2/rLX+Ta3132it/ws4epGGPmeIyFjgT4Onb9JahysoaK0fwKhWAT5U0/X24OdntdbtQfsNwPeB0cDcmvb/D3Pz/ouLNvkUKZ4lPJsxbo/r8sDGp3n8y98EQCyLF7/zzyhpk3auNfT5hhRVgVo1tCZVOiZKS0EbT8f6PfO6BJ6oFn2+KUjVp6C9Ihzutel1Bd8Hzw+4TpeomJWpE6U5mRtGTrm0VnoQrZnbuZ/NbVMAQ6RWKhrXDQtVGc8BXfLMZMLiUzqYrOsjbvBasD8uUBUQpY1SsLyaGCD5NEgDP9lukXEUbc0uoFk4u8L6bXlqbY6qUX0gpfwovhEsY2V1zplGOjpeI8L2+Gkf24IxIzKI2Cxd0MSqDT1V/atUqgMsaEeZRQ2mWO/v2p+aMvENIFEoKyRORw4zMd8N822e3BiTlOeiUh2obS19OjBilXT472SHh69g7EiTJXfzggKr1vf1P4SEP2LC/PyyyerPqe6aB29Opnkmds4U/VVuBwd2PcFnv/jgeRxzYKRx7XMfl1N8m+LZR/peXmG4//77ueOOO+jo6OCee+6hpaWFESNGcNVVV/Htb5uqhX/3d3/HG97whqp+bW1t/OQnP2HkyJFs2bKF66+/PjLCftvb3kalUuFFL3oRn/nMZxoe9z2/3MaJH34B7RmhTstNv0tm7NVxA41Rm1pg9XnBKrY2jJ5jGTmoJVBwjKQ0a0MhE5iMWEjGws6YAlT5vKleCZrmgmJci6YtA0Ub2rIaW4x6NPlx6VgKx4LcNZNxtx4Ez0ftP07brPEYotGQsBkLHMw08jbkrUDhmjgREU1CRFsFu/azXqLTj8KLwTxTBBg9tojtWJw40sO2jaeZNmsYuZwTjRP5cNWQZ4MTaOdKsFlBL+O7Kg0C3jvumsWjD+/E832eemIfS2+Z1mCcwVDLKp/HEIOMO2JkE62tBfbtPcXWzUe5esoImpqzVDvdXgpSNemH2k9ETRxEiwh33DWb5Y/swPcVjz+2i9vuGGpAFdPt0Z4hE+7nhhe/+BZ+52V3AXDkyAk++YkvAccv+nEu9N6U4tmDJfqibFcgfhfIAru01ssavB7mFC8WkemJ/aGBdm21jJAcDuX2iMg9wAuBj2ut6/1OUqR4DuLZinG/vXsrP/nwJ6j0GgJo0ct/i1Gzpkc8pKskqNdkfFI9JfiBJ6qrhF7PKFOVNjGA0tUq1LDWUyXgM8sKel3hbMXmbMUO+gWZ+L6mUo7Fosms9Y3DpnFtxx6uPnuEQ8XRuFYmKlTlukE7P0jvdxVWxY8HCeNyXyGVgPUNETLDkQFroETV5gIIwbgJArVKnRqq9wKFxbptBa6bW2L65AoHjzmU3UZ+oUHXmrg1SntPLPTH+4eGkIwcrM+qDX3cvLDI3Kk59h1x6SvXtlcXYBkVkqNqwOJO1UjKNFR0LZ7c5HLjfJuZV1kcOq7oK9f0GiKp2qiA09AI1KFh1YY+bro2z8yrshw+4dJbGnhcEUGsMP3//OaQXFipGztQrIoIgo2ITW7kLVGb0pmn+MTn/puzXQMQv+eBNK597iONb1Mk4QzeJMVzCU1NTSxbtowvf/nLfP3rX2fTpk10dXUxceJEbr/9dt7+9rdzyy23NOy7ZMkSNm/ezCc+8Ql+8pOfcPDgQZqampg/fz5veMMbeOMb39jwg+mJw+088NmP0rd1BQBSaKHtN15riNJSsEqasU3g1RtEW46YyNEKiFZHjHo1awNiilgVM2hfI47gZKDQJBSLBGSqMDyvyWeg4GjytjLp+XagMK2KBzTFQJhptxSwmnKo9XsozhqHnTEf07ZoHIxKVSyT6p+xNAW7mkwNiVS77hjm5jpQWKWD005CpLZFTLldf9s4fv3jfQD8zqtnNByvut/Aq7hxQJJYlR9kxbcqRUniY8TzF9raCowb38rPf7aZBQsnks2e622l+neq3qD/QlCd0nXLbdP57+89jdaaP3zNDYl24TWpbn/+x2t0DrXpXf2PUmzKMW3aaH7+0w3MmTuefD4z5Bno6D2uL9hwLrjphj/i2LFTA7bxgvKxIsLHPvZFfvSjX7N+wyYsyZ73cRvhfO9NKVJcRPypiLwb4716DFgOfFNr3XUeYy0Nfi5v9KLW+rCI7AWmBm1DQjRcsZgLJM3XQmXqMQARaQU+DezEWAukSHFF4NmIcQ92d/LZv3k/+54w9evsbIY73vrHKGUhoqgoCRbYBV+bwkyeBq0EhcYizpYHQbTGVYIP0Wto45la9g0RW9Fwos/GDULmUAxaqeiI0/R90KqagOt18nTaBWb2HOahsTcYjtPXVCrEoY0CKgopefHElEbcgKH1/JhA1Rp833inhu10eIIJMhWqUv0hEQmFBYpCtlVDT5/QftZm7rQKP320OVBwSnAudoIlro/Hav00lfYREYbKp4bXSym/4fhJ9PRpTpzxWTwvz7f/5ywidg3hGT8+l3irPpYeas/a+Rqiuq9kceyU5rq5Nt//hRtcD5tYgjHE0XX4rg2uuj0flMqaQ8ddbpif49s/P4ePThHQF/LdID6nswe/i/L6L6Rqmjs4NkycOI4DO7/BX/3tDXzlc2+5gOPXI41rU6S4ciCXJiX0kuI5N+ErGVpr7v76Sh5964vQJZMKc+2Nt3D9S//QBFoqkRIUpBKFCzJaa8OOhiyjY2SlWgTLNgWrwiZigW0LdkBwimjydkhuGs9TUzGv/tcjtGmN5ux6+Ke7yY5tRSwx/YLXzNimEJVIDdWXUKo2MpIeSrW+2o9Gif5XP28Bjh3uIZe3GT4yP8SAq7+QTgZo0eBkqtpWE4G1owG4rs/BA2eYMnXkBQYAl0IhWn1tjxzuwHEsxoxt7af9pcTQz8/zfA7sP83VU0Zi20OvfFt9tPNX/v7FX7yP9jMdQ2p7yy23cOzYMY4ePUx3z1YsmTr0A6V4pvBMeFqYA4nsGTFxwtQP/PyHF2W8j73kFZw5fGRvjQfSBUFE/hj4CrBGa339AO32AVf38/Ip4DVa61+c47GXY/xTP6C1/od+2vwCozD9e6313wX7rgb2ACcwXqrrgFcBX8aoXWcH7T6HKb71onOdW4pnFWl8e5lBa81Hnl7Fv/zR/+LUTrOu4WQyvO2Df0vriOFI4i2zhIjU08QEat2YGK4yjOlCsjQMlzVGveqqRIqTRNymyfgi/Fn/K9Na7ianPU5k2kAk4kWjSWmNRIRpYn/wXMKCU+HYSV9VXdOHRAjbwC81nmP9PNuaPfI5OH7arr9OOvpf1f9J7qlRqsZ9+v8z0tX/S+zvv8/wVptiXjhywhv0D1Si34jGLQcnXaXhwzro6jaCMKxFKBSEYydVzVtRK7/oZ6xG5PUluCUNa7FoKlgcOekNSIInr9XFmYcZ4/v/+S/09g5O5t5+++2cPn2aLVu20Dzplax5+PPMmj7hIswjxUXGMxbbwnMjvk1RDxEZiYmdBfiF1vrFQ+jzICYO11rrAdViqUI1xQXhuzuOsumhByIyFWDj009x6u4/JeOMNJ5LfhAi9rnGPMo3wZzO2MYbteCgRaPzWfNrnnPINjsmcASyWSgWLfIFyOWEjKUZllE0ZYwyNRcoSvO2xqrhnUQULZnk3VZRWb2L3PgJ2DmH5pnjjDo1IE8zFuQcxahcvXo0rJsVWgAkSVoRcGrI3FoC1Ca2CaiiKEXHhG7ihbNnSjz12BFKfT6/+bIpZHODk2pS99M8Msbu4TFMaksVLVo12aAPNlWeVmJUrlZN2r+I8POfbmLKlBEgws23JjNUB0MDdepF/mgMjfsBOjv7+MXPN+N6ipf+7rUUCrVKynNVqNanR9W3OL9z+uVDm7l66kjcis+dd/dfkGrgY1uEOmOofZ8Hxqf/eQLH6q5PPXL5PFOnTuXxxx8H4OFHHuHOO0dhS8t5zTlFin4wWUT29PfiJQxGHwc+hlGTHsCk6t8GfAS4DnhARG7VWj99DmOOCH6eGaBN+NrwcIfWer+I/D3wQarVrRXgbUBYuOrPge8kyVQRGQOUtdad5zDPFCme13j8+DEe+/FPIzIVwHNdHlr+KC/7yN+EuT6AiWY8bYpRhZGBBERo+NPoCgQXExMiRhRa9oVKYBNQ8uBM2cYPiEzLMv36eo061fNBBfFxdao4FMs9XKv3cTA7koJfZnPxKjwvmKJSUPaQbtcIHsp+TKx6Gioe4gUqVd/sF0/Fqf2+b1SsKozrYwVq7LWamIyvjQpU1xcdamnyuW5Omf1HMrQ0+Wzala96XWsd9UsSsiY93lwYpX185ZKM2YzqtP548bXS/SpTY21m/Fprk8Wt1xV4fF+FEW02qzeX4sYJGIWtZSTDNWM0QmNi1fzWNPJUrT6PmDAN09Wbixa3Lc6yfb3LqOE2qzfH2UPmeiUtDpK/M7EX6zNBqDYXhTuvL/LY2j7GjXJ4YkP/RbQbEqo1RPJQEZ+npuznEXtg1W5zcxOTJ1/F8uXmY7bv1Cr+5C//jRU//dC5HThFihSXC5JKnxH9tqpG2G7QFZiUUE1x3ih5Pp9avpH2B/6t+gXPpeOBLzL6Ze8zH3qhBNQPwhXRJr2/6EAhg3YsdM6OvFNzRcsENgK2Dbkc5AuQz4CDpmArihmNJYp80E1EUyviE6EqZd8WjT59lkzOYdT8iRxZto2Wq0eQKZh06owFWUsxPFtPhtaqWOtRQ6bWvBr2DRf+Lcyck3NNwhZ4YvkRbrlrIh1nyjz9xHGW3jH4ymhyFhGJRj0Rq4NZRAFLGEliSLhawjV+GJXCivoeO9qJUpqlt07n+99ZQ3dXieaW6uB4aLj4ZCqYgFMHgfmjy3Zw1wtm09fn8tiju/jNF8+raT24/mCIRz2HFK56nD7VTU93mRe+6Br+67ur6WjvZdjwcy30Zd5nK3Gbj4o4DGFyTzz1rSEd4yc/XsuhQ/v4pqnVwTv/+h95cvX1WPY1F+AtluJKwJWQrKa1fk3Nrl7gJyLyK+AxYDEmrf43z2HY8AZZGaBN6IJXqJnPh0RkA3APMApjB/AvWuv1Yv7g7gN6gHcCiMjLgM8RBJMi8jimENa2c5hvihTPO1R8n29vWc+qL8QxbqZYwO3tY9uDv2LxH7ycidfOM36o2kQHhlMU0KZoKlobNao2MZmrTD0oAbxg/bYUkKkK6PGEPk+qIhHfN76pvm840VoyNSpO5WsWdOxhbet0uqw8d3dsZHtlHB4ZcyBTGcuoUD0/GJiospUhTw2hKn5IrBIeDAl5SFUTJwXkKpZEpKpWGt0PmQpw3Zwya7fl6OqxeMFNFQoHFX3lxCdGIq3fqD7rmbTkYnnUVgSt+1sY1yjlNdgfHsXETKHvvAjcvLDI4+t66ezSzJ2Wpakg9PTp6lAwUOGakF7inckmWkcRuXlVNwwnhQZC35odGj/KHtPaDwpmZXhiQ4X2szB7qkNzUdPdW0u46ypCNbx+50Kanr8nv0bEYumCIo+v6+NMh8+8aTmaC8LZHvM7YiV9XqUfMvc8Q8qkJUTr5FfHs9I6KKZa/bv0opvbWLmlE3Ga0V43fukIa9c8zg//Zw2v+K0l5zeJFFcUroT49nmGNwQ/NXCNiOS01uX+GotIFpiHuesMJH4A0t+HFBeAT63ew+7vfRXV1Q5Aft7NWC2GzO/buoLy/g0mwMpIzBj+f/b+O86O7Cr3h7+7qk7oLKmVc85Zo1GYoNFc4sVgMJhwueRgbF9zMWD7Aj/AXN6LjcHGBAO2sTHYOGIbHHCekUYaxVHOOUut3FLHc07VXu8fu9KJfbrVsuWZevTZqnOq9t61q0511apnP2stC6NMbclA1kEchTTYJgmVrbBSCssPAWBZ4DiKTNaiMQsNaSGb1rSlhZQSmp2ImHRKklAZl3xN2gpUAkIaTf+xK4xZOgnLgvYlk7h3+IqJoYqQsjSNjok8QFFfJnaqbUWq1LL4qaUnZyBPa2WMNduqEAZAwYWz9xg5KkvriAzTZrbSebufe51V/+7LIIAOjIMSwyQwqIrtwUBtYFckYOOxVI0xF/X1wqZTPP3MHJRSPPXMHF7YdKrOUZaoUx8y93bp4h2yDSlGj2lhytRR9PcVuHWz0qRTPQNRsRKti4LcP9hYNz13nA3PGlXqM8/OZ9PzQ+c+BO2XYiXLcODa1buA8Gu//mpWrjRhHA8cOMGHP/RpNLXjryZ4eUMNcwEuicjMauXbeGgAiEgf8Af+12eVUiMG0TyQ5dSSgGf8ZVk2DBH5nIj8pIg8KyK/JiIH/E2/DqwB/lBEriql1gGfx9AirwN+H1gOfGuQ402Q4BWHz54/zY6PfYLuGzcBmLZuDetf/2vh9ufe/T5yeU1fAbpdRb9nklB5Eok6RfwEVGJiphYEPFFoTDKqgmeSVrlakfcsel2LnKvCOKkg5PMmCZVJSFU8RkOkmuX4vtvccxrpshvQotjbMJ3Hes+GhCk5D9XvoQouFLSJiwrg+vFTNX7iKaISZL4Ksm9pTcy0LPpeBK86mTphTIHuXkVXj5m4338sy4oFkVJRdDlRGsDMGcWTfFZCsW0pInieV5NMLd6f+Tx5XIruPk1nlwYFOw72s25ZQ+XmQDxWaXkCVyn+XnHoEtprsR+gpEQK2CAm7fjRFn05zd37Rp2782Cex5c4oZo3UvVK+fdBk6kexRdIpXFWGrcZZ76gud1ZQPDYtr+bdcuzYX2TtKxEifoQIaLR4uL/tYbrx49O4Wq41anJjFoTru+/vYPf/cN/xXWrXUcJXil4CPZtgocI3xb+ZaLTnQV+cYBmv0Akfjg80D4SQjXBkHC1u4+PbdpF57c+Y1Y4KUa9+o20/cAvh3XuPPdBRPnGVs48gCTI5pS1TQKqlA3ZFCprQ8Yh02AuSaUgk1U0NEJLY6AehQbbJItyLCFl+cpUJaQreMM3Oj6RapvkUu7Fm7RMGYmTdVAKmsa1IAUPfb+HlC00OKZNHEEcVbuGHRQQrtVQ6Y9MxZaqpG/xhAO7brBy7bhw5ZqnJ7Jz89XqO6kJY9DEM3Uaw0WXGFMqZgSGctWKd3vt93X8WAdTpo6kqdm8+48f34ZlKa5d7RxgTKrk28N9pGiteHHLaZ58Okru9fTGuWx+/mSV2fa4SteKlfITohgeEjXA6VPXGT2mmREjjCK1fXQzzc0ZLpwfGkEZGejlLwwPAhFh06ZjPLNxIbZt8e6/emu47Q//v7+js/NkqAxOkOBlim3+0gIGE+vkrr+s5XYUbLtbo04IpdQY4M8wcVXf56/+PcwN6wdF5AMi8g7gzcBE4JcGMd4ECV5RuNnXx5f272Pfv30SAMu2Wfem1zPnVa+ifeYMADqOneDgV75J3vfrKaWSPAkIVF8c6pOugVC0oBX9fnFF0V2wKMTc+ZWCfB5cN1CpRuPTWsJ1ngdewWNR7yUON071iVzhpmoBLbTnu6DPMy7+AdsbJJUK2Ni4m7+W4u9ClA/B5wxDcyf+iJdYn1USISklLJ6d49CpyIvpzn0brRXtIwKiqritlHw3pGqx63r59lIys5YtEvhdRW7xlhIeW9TArkMR0Xu706PgCuNHVw6/FZCckW1dLmSoD0XWelEpI0CV5rFFKXbHxnnnnkcurxnfHrSPu/vHr9DhRDF5Wk6oCmuWNLLjQE9Y5+59j95+zaRxKUIy2VeMaomUwsOV60X5ClgRwdMeWiqHfli/rJVt++4D4DROw85OMO3cLi6f286f/fWXhmU8CRIkePhQSv0K8E2Mt1f8pvQupdTGKm2eAt4VW/XiQPtJCNUEQ8Lvbz3B5Y//PXjGAGrZ8JPYoybQtPR7SI83pFXh5nm69n0N8TSqz0OUgsYUNKWQtI002FgtKZRjyKh0FixLYVnKKFMzimxG0ZgWUrYmYwutKUOOZmxQGFI1YxebTkoJaaXJWEJKQVqB4xXovXib9jljw3qOBVNWTebG/iu0pYSsVZxYyhBkEsY9LV4foVStWrq96hx6hXZKweF9N5m3eBSptB32NbI9S1NLiisX6s+KGXVfbOjESdWQ2FUWCqd4tKpYmVoMTaHgsn/vJR57fFrRlqc2zGHL5lMDGEF1nqRhwqEDl5g7b1xRzNS2tgbGjmvl9KkbJQPxiWVVfWARiaqGdeyep9m14yzrnphdtP7Jp+eybetpdKmr3aBQbIw/qJF69OgVpk0bTXOzeTF66qmV/ORPmRjft27d5U//7z+iufJA+0jwXQxl7sXDUR7hKfy4y/5gQiid9Jeza9QJCNqTNerE8ReYeKu/IdFMxnrghIicjtX7z9i2BAkSVMA/nTjJ9r//AF7e/Ikv+onX0Dx5CmI5PPGbbwjr7fzHfyLX2+fbc0Z5ihi3c1dDzo+L6oqiy1WhOlVjwgQYZSrkPJNiQMe4QxGhkPfFngASqC2FwGM/eIzP77vC6ex4XMsxYQFM5+xxprEqfx6VL6Dynk+S+i792tRReV+BWkFgGIkvfZKV+LrYYIMQAa72+bvKr5dzp+U5ezlNwS2+qe8/nmH5vJwJExBDJQVlEErI2GAVElrFRimi0boyuVupvunXYtHsLKcv5CkUimvtPNTP40sqqFTLUDxupVSFIxm4j2IpRMlWEebPSHHmYp6+vKC1RmsPEZedB/t4bHEqjLcaJUj7zjxMF87McO5Kjv588ZHsPNjD44sbw/etuOo1HtJhOMjVoL2WAqWq1AALZjZw/mp/OE6lFJn2tQTnLXd3P3/z/v/gxq17Qx5HgpcBXhn27Xc1lFJPKaVeBD5IpDQ97n8HaAa+qZT6L6XU25RSv6qUeqtS6kvAJiDIXO0BHxtofwmhmmDQ2Hn9Dpu/9S16DxlxjtXaTuvGn0HZYHnCyO9/XVj33qaPorvuG/ay0UEco0wVx0ZlHKysjWUrLFvR2GjhpMB2oLFJkc0q2hqMMrXJEZocQ5LavirVJIiKq0OFwFQdkTbEa9Y2bvVdR64yetEExK/s+ELZptYUraMb6b58D8cWFP4NzrcmHas8BEAxpOa9sHQeO56QKsy8GvQk0N9b4NK5+8xdVC5cemz9ePZsv143qWaFBmX5jHE0k27ckIyrf6mLUu27/K4d51m1eiqOYxftp6k5w7QZozl6uJqi9turTu3vL3Ds6DWWr5xatm3Nuhns3nkB19Ul44qMqaKRPgQSNY49u8+zdNkU0ulibiabTTF/wQQO7Ls45L6rZdodCgoFl717zvH4mmJR3jv//LfJZo1a+X1/90lOnNiPrh6iJsHLHJYanvIIY3Hs82BmD3b4yycrbVRKTSIKoL+jUp2S+k8BPw98QER2xja1AfdLqgffR9Q72AQJXkk4fOcum17YxLnnngcgO2IEy37u5+jzLAqiGLNqFdOeegKAvjt3OPTRj0eNJfKOd0Xh+URpzoOcRNpLjc9BiqLXs+nzLFyfIFV+EqpcfxSuVGufDNK+ENTvSAQyOs+k/B3OZcf52wXJm4H0uRYdbjMz3dtR0qiAPHV1lEzKL4Zo1SF5ajguL2oTR8B9an9AFTiq0GYCMinNlPEuZy6lys55f97i6k2HGZNLGMwKNkucVFOhZ1VploP4g6MaoVpu8wFk0xazp2Y4ejYfHkM4zpxw/kqB+TMGTthZdV9Deqb5dntACmpNyoG509IcOt1PMRMOubxw7nKB+TMi1398e3+oBmxtArs60inF/BlZDp0sT0BVcC1OXcyxaFalvAvxEAURmTpYUtWcL68ooVklpBzFolmN7D9hkiwHilY7PYpUi58YVgrcu7ad3/yDjw5qDAlefngF2LfflVBKrVNKfQPYDKwjurnfweQe+HNMjoLg7P8A8A7gA8A7gf8edOWXvxaRAV2EE0I1waDgac079x7j6sf/Llw38kdeR2ZEo5k9V4rM1EU0Ln4GAN13n3vbPomkLKQhhWQspDEFjQ7pJgvHgXQGmlsUjmMUquk0pFKQTQkNjh/b1NY0OkLaAkdFy4DsDFz/Mza0pQwJ6/iKU7erD7cnR8uEtvCvqsERMram0YY5K8Zx/uB1Y0SqoL8oEVXp/a5YDVuyrYbNpIpuoCZAetwssBTs2trBY09MqOhCnsk6zJ4/gqMHbtf4hSrvO9pRRKqaO4XJEFoaN9Wsq4779/u5fu0es+eOqbj9sdXTOHjgCvl8pVhDJaTtQ36gbNt6inVPzMaySultRTqdYvmKKezZfYG4oRnXAwxXXNSB0NOT49zZmyxaMqni9mUrpnL82DX6+mrlsamNUr3DUGf8t287xeNrZoVkeoCpUyfwlrf+EgCu6/K7v/OXCEMngRMkeMQRxLk4KiKXB9HuP4ECMKeKy1EwK7mvRF1aBqWUA/w9cAvj4h/HbWCmMm/BAeb6yyTIcYIEJdAi/O3hY7z0d5GNu+SXf5lcQysFn1dEYPXrfwMrZYjBg5/6DPeuXA15Sc8vBV+d2uca0tTCKrK0PK3odSMv+cAr3PPAc4V8wX9G+0RoMAkfig19LOs+x4Hm6eH0vhSCmKkuKu9xJD+GefZNbM/1k0+Jr1L1P/sSRiWRajXchdbmTlWWiMpndoPQACUe9ZayCG47gY2xbF6OAyczVDP6jp+xmTOtgGMH3lSVya9ymyUgE4PPteoGR6aMZ5ayCV6FA4+sx5c0sPtwf9l5DnD4VI75M9KkBuGT8ECT8f51IVpixKjm8SVZdh/u9VW4HlrrIgvv8Ok8c6c7pJxYbFelfKIwOubBY3BtVi9q5KUjvWWXUNDXkdP9zJmWIZ2q1q//zuJvrletGsXO9WKxX6u3Wb24mZeOdFNJ0JweuRIsQ6K73Sf50pe/xe79Z2vuP0GCBN8+KKVWK6W+gnHP/28U/7EfBp4UkUMich74FcCl+s1M+e2/TJQroSYSQjXBoPDZc5fZ9++fIX/1PACZGQtpW/c9OGkbQVC2QlIWbd/zy6iUUap1Hfgv8j3XkAYHGlKQslApC9tW2L5rfyZjXP2Vwv8OrRloSWuaHGhxTG4rS0VEqa3AUhpHaSwER0HGElrTfnZVH/cOXGLCyskE1F7WNn1kbHBsSKVtpiwczYWDN4qONW2Vzx6VuenXOFfhfHnVmSjjAhTYBLdv9OK5mvETm6r2OX9pO2dPdNLfXzsoepnJUGHfxTP5UqNuObZsOs2Tz8wGBZUIOtu2WL1mOju2lRocJYmoBt7VA+H27W7u3+tj+ozRsWQGxfFQFyyayIXzt+npjpSUpUZm3IAbrnhOpdiy6QRPbZhbooqNYFmK9U/O5sUt9Sb9qoRKsXMHN+N/714vHdc6mTt3QsXtb3nrLzF5son/+5X/2sJ//dfX8eTe0Iec4LsWapjKdwpKqd9VSr1JKdVesr5dKfV+4LX+qj+u0HatUuq8XybHt4nIdeD9/tcPKaXmxdr9MBFR+yd1DPPNGKXsW0Tkbsm254ExwJv8vp3YWJ+vo+8ECV5R+PLFK+z9wue5c8rMY7TNmsWk7/0hegqKglh4YpJItUyaxJKf/AkAdKHA9r/7R7QYF/+8VuR98WdOmyK+safwSVcX8p6ip2AR8JhiQpZSKAgm0oCgPSL3fj8lQcCpiQht+S5srbnptBklnmfiByhPo/o9cAXxFEdzY1iSvh4pTQs6iqOqY2RqEF8gULAGrvz+OiVErHE8gZVPyhobSfs2RXT3bmt2STmaG7csRGtT4naV1ngCR06nWDw378fTxHdjLy6BG39ZvE1dGis0jsDmi2y/yJ6LiMgRrYrGrOLqzXIbO6jvaWHP0X5WLcxWHJ/W2pCfgSLYJ0OLjyHaVlTi+9JGWanD82muCRHNiFab5kaLy9cDRa+mKLSsCFqEPUdyrF6c9dtVtl8HQ6oqpbCsuA1dG23NFq0tFhc7SpXHxAh32HW4lzVLG2v0FKlVTZvaooDgOjFMf/xaqDzm1mab9rYU568We1QFKlXLzpIZsTJc33drG6//3Q/XGU4iwcsR3+327csFSqkVSqkvAjuB76eYAukC/ghYLSJhdmcR+QSwEeMBVulOfBn4LeDVIvW5WSaEaoK60VUo8NH9h7j8qQ+H6yb87JtoarGxHXAyNqrBhrSFM2osreuNsYn2uPutDyKOZbJL2Ra2A6m0cevPZKJbSiYDtu+q3+R4ZCwhbWkcS7AtQSnzWSlN2tGkbWhwoMmPq9rkFN+g+q/dJd2SIe3HeVRAxhYcJTQ6gUu+MHnuSO5c6SLXax76ToW7XPnkspTFTw3rlrSxytrG1KzKUKu7tlzj8acmFG8rgWUpVq0fx0tbOypXCOoNxkAqJVQpVmiW4vKlTjJphzFjm4kIupIkAiLMmj2GWze76ezsDfdUuueH+TRRKDY/d4JnNi4iCGmglB1TnJqdK6V4+pl5vLD5ZA0VQfHM9nCTqtc77uF5wsRJI2vWmzZ9ND3dOW7f6n6AvZnnxWCyu8bx/HNHeebZRVWIX6GxsYF3/vmbwzVv+Z2/JJ8/h67hapXg5QlLybCU4YBSaopS6lZQgL/1Ny2Lr1dKvTXWbDLwN8BNpdRZpdROpdRBoAP4dcyN4W0i8u8VdpkFpvmlkpbprcB2jGv/EaXUfqXUaeALQAZ4t4j850DHhCFIXxCRf6lQ5U+BXuCvlFIngUvAjwHHgMRnMUGCGPpclw8fOMyhD30oXLfgN36TXkmhUeS0os+LeMYl//NnaRhlwjNd3bOXOx23yXmKnKvIuxY5T1HwLFwxr1omF5TC1Ypuz6YrryJFapAbyjUxUj1PKBQMmap9olVr431vPgvaE1Z0n2Nf0wxDPnoCfTqKMaA1yjUxUy/mW2i3emnS/VBwUYHcVgeK1UBa67/oB+xupce2F68fsMX4BJYusigNz6pZsaCfvcfSFL2zBrELJMpsf/m6YkSLpjEbqAqD+lE2eRGPIIFRoDwME62GdoYqce+W2DLqs3SCed2yBrYfKHdNj1zPTblwNceIFovWZlXWZ9BvYEdG2esj1lR8Irg0+VJAvJo2HsXjjI5n/bImtu3vKRpjMfFqml66XqClyaKtpdjOLxYYDF6palmWTzbWpoXWr2hi+/7eCluK21y5XqCpwWJka+WEXxGi81gkcoid3si9vzwRWTXBwpMrWtm6rzQ6TvFYU60LsFJtAOjcDY4e2sZHPrVlgPEmeLniUbJvX+F4CfghiomMPoztPltE/p+IlLl2isiLIrIeY4P/CPA/gVcBi0Rkqoj8rdSKEVKChFBNUDfef/w0+z78Ibwekxhp5JPfT9v8hTh+Uik7pXAabVSDg8raND/xWuy2saQyWdoWrMDOKuyUZYjUBpN4yrYNgWpZ5rPjKBwbmlIeI9Iax9KG+LTMxZqxjat/o22SSKUCpaoFKSWkY89i0Zr7x64xbulEXAyp2eQIWVvI2sURlyylmLdmIid3mjB4pXFTKz2DKyWVCrf5360K9YrrGoP63Ml7jJvYRHPLwHGZJk1toa/X5c6tcqMv1mvFbyGxGLg8+WchNPYGsKlEhG1bzvLE0zPja6lE0CmleHrjXF54PsipUjKqh0KmRvN+Z8/cZNSoZkaNaiaIs1VqNAYE6oSJI0CEa9c6y3qsRp4OF6kqImx+/gRPb5w3cGVgw7Pz2fTc8YEr1t7rkFSqFy/eIptNMXZsa816P/XTP8j6J5YDcOLEef7+ff+GcHPIo02QYBhgA+2x0uyvd0rWx2Uyn8QYZTsxJOcyTLKos5jA9qtEJJ4JtG6ISB/wDPA24CjGFX80Ju7TT4jI79bRzV8DaeANlTaKyFF/H5uAKZgspx8HnvX3nyBBAh//cPwMBz/6L+Q6jdB73FPP0LxoBQVRYb4lV1sUtCFFJdvI8l9/HXO/7/v5xT/+Y2TkGHJa0S9RvFQPY5EEU7IFDT0u9OaNQlVrKBTwE00J+byQy0EuV5x0Cojc/UUQgam5W9xItdKtMniuT6L2u36GK8+QqX5RrrD3/jhWNl1H5YLEURLFPXUlipuqfcZYE6lT/eMwA3b9DFoxgs40JJqOD45emDLe5XanRW9fJfJRQjI16GnPUYdVCwsldf2hievXL/aMivdn1vlhC5TCCPOryUGjvqdPdLjdqbnfU07ERa72Udm2v4f1yxsrjjMkfsMfMN4+IIZ9ghbPLzp2Liq9x5u+pk1I0dnlcb+7tE5wLoux/UA/a5dmw/1Xs/WqJ6GtjCh8QOU2U8enuN+t6eyqRGyW0w/b9vWwfnl1D70I5b9jQCbrkEitfwJ/yrg0PX2au/cre/5F4guLzKh14fr+27v4o3d+mt6+JE9AggSPCHKYEFizReTNIjLgi6eIXBSRL4vIJ0TkK3El62CQEKoJ6sL5rm6+tG0XV776BQCsTAOT/8evk0qpcJLZcRTKsrAdCywLq62Z9p/7I+b83j/x/etWhlnr02lMAioHvygTMzWjyKSEtowwKiORW74SbMBR2ny2hAYHUnac+DSK0zi6T9+gdWY7TtrGAtJKSNsm/moq1s7yP48Y24SlFH23eoyyNFYqoaqKlFj81Qqka/E6wXM1h/fdZNljY6O1A3BbazZMYOeWqxUNo1J1qtlffBY5RrCGRk2xMWrWlPd9+OA1Zs8ZTUNjOfErpe1FGD26mcamNBcv3Cke00OSphrS1EI8xY5tp3niyblF262i5AXFeHrjPF7YdKJKfK5gWWw0DwepevzYNSZPGUVLS6Wg/OUYMaKR0WOaOX3q+gPuudz1vxZEhC0vnGDDMwsGqolS8FfvfVtoiP7p/30/N28eQ0vtUBUJXj4IlPnDUYbjbiEi50VE1VHeHmuzQ0T+t4isE5FJIpIVkSYRmScivy4i+2vsb1Osz/NV6uRF5F0islREGkVkhIg8IyKfrfOYXiMiaRE5UqPObhHZKCINfv8/KyK1XRwSJHiF4UpPH/+xcw/n/sOIza10mpm/9AbyrirSR1oqipHqimLy934/i9/ye6RbR5K51+WLN1WoRA087A0RauKp3svZ5D1zVyu4Pp/pglsw5KrWlD2ZtYfv0i94HijPY37fFQ5nJqNdD/Ia+jwo+DuMc0oe4Amd/WkKrmJsqickTZX2idTgu6dDpWrE4Pr3YBFDpJbzY2ifPDQmUUTcWZawYGaeI6fLE1FFKD7azvvQl1OMHx2RYsbV3itRncbjpgrFbuhxNSZUeorE7R/LguXzs+w5Wv88U2eXR3evZvK46sdWqoAtDh3lq1lLzlu1nsBcf6sWNrL7cCXVZ2Xc6zbk65TxAbFsxhF5ag2dCqjWh1KwalG1cVZ+ot/v0dy55zJ9Yr0Jv4qJVQn/UgccddE4H1/awo4D1dSpxW2cxknYDVPM3r0ebl7cwR/82WfqHG+ClwseNfs2AXmMyGGOiLxJRK59uweQEKoJ6sLfHT3Bgb//W2PVARN+7H+SHT0GrcEtGGPAtgGJ2WAZm8zsReTHT8ZNp5moO2lsUjQ0KiwrIlMtP4FU2haaHKHN8RiRMuSpiXcqWJamIQVp362/9CaUtovjpnq5PL1X7jB69mijnrWE5rQhXR2r2GSJ04wL107k2M5qRGUxKin1g5usojKZWqnPgy/dYNHyMVgpq9iUqsF1tbZlaB/dwIWzxUZAufNNdHSq5AgsVckLtbpBl8u5HD3cwbKVkytuJzRnIgNSRHjiyTls23oGHaoZhucJEihOLWy/OOEM+94951iydCqZTAqIufir6mRuc3OWadPaOXakNJlfvH75+XkQUrVQ8Ni35wKPr50xqHbrnpjNrh1n8bz6Z+ErITL2o9+r2vEc2H+BuXPH09BQn7G7atUifvEXXw3AvXtd/PEfvg9hMHl7Eny3Qw1TSZAgQYKHif+37yTHPvA+xDWTflNf8zOkRk+I03JYBDmbFJ5WFFxFv6vo9SzOTZ7JhEvnfGLMCA0KotDBNK9Pwvb7qlTBJ05FcF1NPm/UqW6JKjWOeLjGhb2XOZqdhCvKEJx5o0o1xKhG6ShplHJ9xWpBc+D6KJa130ZpQQVsLxhX/8BGC0WV4h85Yb8Uim0OBRRnTy+2kRbMzHPqQhrPq/y6WaqoNDEphf3HLZbN0yifmJWKqs3i0E3RE6NS8qHaT5JlczMcPZvHrUAWG1Qe/65Dvaxe3FjD46p0Ar6ymhXKs9lXwtK5DRw920/BrU68Vpoof+lIPysXZnyPuWKlanTuDIamVDRieQoAAQAASURBVC3uY8mcLCfO58gXyscSV6eWuuC/dKSXlQsaQqHLQAjCPpjiUp+HbrTPxbMbOXm+j1yFcRaPOWqTbV9DcD3k7x3gXz7+VS5eHjhZcIKXFxL79pHBPwPzROQ3ZHDJYYcVCaGaYEBsv36DzV/5Gp0H9wCQHjOBcT/0U1iWMrPqLqTS+MHKTZsg6VQqbWKknhw9ncVdF3DsGInqu/xblpByhKa0Znyjx9hGDUr8pFNm6Sjjr+kgFdzxDfFqICgl3D90mbHLJpH2/e2bHaNMtX0ytdJNzLaguTnF2MktXD55t+Y5UUoqGlCOMiEI6iFTQejtznPjWg/T57aZekVba2PFmrEc2H2jiFSzSo0if+a43NW9/E9fKuw1bpjtePEca9ZNw7ar3zaCmE/xvTU0ppk3fzwH91/yx/UgMI8gVSHBQIDe3hynT19n6dKpvqFX3INV4dgDPPb4DPbvu0ihEFnVpYaiQbFRHCkMBoddO86yavV0HGeguFHFSKcdli6bwp7d5we/0yKI72pW/FuXHksuV+DwocusemwwxK/wp//vTbS0GBeuD37ws+zfvwtP6ldWJEiQIEGCBA8TO2/cYcu3vsmtXdsASLePYdJrfhZPG+WipcyT3lGBI7uiXyt6tVl6Ggp2hs4R7bTf7IiRroQmlSfGxb8vb+xmrfFjpUJ/n1Gnur77vcT4NhHCmKrmM2QKOcZ69zlvjzKKVFejel2z9AQV8y5XBYm874GctrnU1cTs1vuRS7/2CVjws2MRymRNsio/RECumG00ZGq5KjAgBbMZzbh2l/NXU5TaUPFkpsUwfeULivNXFLOne0WTvsUICMFSt/MKgogShi6+74aMYsr4FCfPl4XZC1HNvT1fEE5eyLF4drGHkYTJuUrJ5oFQfXK7IaOYNinN8XO1XMxLbFW/i7wrnDhXYPGckji24fFV996qB3E7OZtWzJyc4eiZ8rBkld494ii4cORsP8vmNtSsF5zf+s5pyRj8cWbSirnTGjh8uj6bNExQlWoj1brQH4hH1/XtvP6t/zzocSRIkODBISK/KiIXvtPjSAjVBDXhas0/HznC4fe/L1w37RfeQKohE86WO46JgQoRWemkFOmMoqFBkckqJJOmo3Uss3quYtuQTinSjqAQUpZJFNXsaJodj7QVGEmGTLUtIWOb76kS3kkpyFhgYfqyFBQ6e5GCR9u4FhwLFEJzyoQPsCh+/AZqUltFfwyzl43l3JFbuHFSrWiv5cmolE/4ltJulZSq8T52b73G6icnhLOfZeZBDVshlbZZsKSdQ3tMiJAShxt/MKUzzb5as8Joo3GWG6937/Ryr7OP6TOLkl1XQRAJKoqJtWzFVI4fu0Z/vzsERjVOEluxUj6LrlC8sPk4Tz89H1UpeO0AcByb1Y/PYMe2MwPUDM5RsRE/GLXq/ft9XL3Sydx54wc9ToBFSyZx7uxNenoePH6T+Ikk4mvi2PrCCdY/ObfspWQgjB8/ht//g18zPYrw22/+C0QuDlv82QSPNuxhKgkSJEjwMKBFeMdLxzn7ob8N1834xd+AtFHJRT4+EuZgynmGHM1ry3frV4iC6+MmMepGB7rgktNGner4AtJeV9GbA9cVtJaQTC3kzVJL4NYfjMt89zzBc33PL7+s6jvHnuwM0MpU7PdD6QRmifZJUs/zFaraqE+1RnmakzdbmdHWTUqZHaq44RlbGjGj+LJcr0RUKcbNH6/q83zF/H72H88CJhzYQN4+8YzpIsLxc8LMyUI6VVU2WoJycrcY8ZwBEdYubWDHwdqu/sUK2GIcPdPPrCkZ0qm4O398H3pYbJ61S5vYebBngFo+kVvh5eHYuRzTJ6XIZkqsvSKlavQ7DVapGtiHa5Y2setQpXGWKGGryHpPnMsxdWKahkzl7eXnd3AI9rt2aSs7D3UNSQyRGbkCZRkS3e05y6ZNm3hh+4PmNkjw3YTEvk0QR0KoJqiJ/7hwnm0f/xT9HcYNumXRCkav34BtR4ZfOqOiz2lIZ8wy5X+2TEhVLo2ayMSuG2SlQMqRMI5p1hYaHaE9o2lKmcdtSpk4p4qIqDSK1WhsxoPbJJlSvjLURug8cImJq6YYEhahJWXIS0W5/rI0lgmA7VjMXT6Wk3sqx6gsNamC745VgTytYYtcv9qDbVu0j20sWj8YleqchSO5fL6L3p5CaPgopbCUjUlAVUqaBgZTOSKjolylumXTaZ56ZvYAo4nDWOjhjK6lWPfELF7ccrJ2s/hYfeI0cOePu/QX14tc+q/fuEc+7zJ5Sm3iV9V4jM2eO47rHfe4dy8ysKvPqgcGfHV1ZzVsfv4EGzbOq2pUDgSlFE9tmMuWTSeG1L4YQqliJDiOO7e76bzXy8yZY6u0rd3vb/7vn2XWLBNz6oXNL/G5z34FofPBh5zgkYdR8j94SZAgQYKHgU+euszh//g0vZeMwKV1/mJGP/W9iBh7zlIBkanIe4qCZ5Y5T0UkJ4CApyyuTJzKxCsXAk4TDeRd6M8r8q5RmHoe5HNCPicUCj5nGXCGKq5QFQJxYyAUHVO4h4fFHdVo2F3XQ+UDlWlEmipXIC+ogo6YWD/BlACHb7SxZOxd4/YfEJl+CACltQlppbVJVhWUQBUYJP6p4Zkzqs1FBG53WjGSMRBe6PCYRIKs9lE8US1BVnvF/uOK5fMpIiQrqTfj7Uvraq1jZG2xvTV6hIVScP22a9qVizeLEFeqxse/81A3a5c212g8mOdY1EdwLKNH2DiOouNWvXHoY/ZcbNc7D/Xx+JJsVEe8ogrFxHFErNaL9jabTFpx9Wb5OItc/QfoZ8eBHtYuK05QFal9B28TRAmrzPUxqs2hqcHi8vXqquRKCI5BWWnSox4L1/ff3sHr3/qhokmBBC9vJPZtgjgSQjVBVXTn83x2/wGOf/xfzQrLYtavvIm0Ez0KHSciXgJeyLYVtu/OH7j3Wxak0hYXJk5nzq3zJpapEpodaElpRqU9GizB9m8wKVtI2RrL0jiWWZe2S29CmmZHSCkxoQCU0HvlLo2jm8g2O9iWxrKEBkew/DaCYPvGShBKwFCMZp3yy8SZrdy71UtvV65ovSn+8RIZ3ClV3D4aY+ViIex58RqrnxxfpmKN315V6YoSKKVY/dQEdm/pwJg9ceVpZBSFpCN2zF2+qKeyZFkBLpy9Q2tbAyNHNTIYqJDCNn1Nmz6a7q5+bt3qrtHKuB1Fx1HN7CqOiwrGYNr03FE2PrtowLFZNTKTKqXYsHEem5+vZ7a5suE8kBrh8uW7pNM2Y8e11rGP6pg4aSSeJ1zvuPdA/UDwklMkQUFEeP75o2x8duGQ+81kMvzlu98Sfn/bW99NX9+FOmNdJUiQIEGCBMOPnoLL+186xIWPfyhcN+NXfxPRUWx/LwpFGpb49+CJGdhPd1rbyfb1kunvQwv0uYqufigUhFwefD6SvJ+AKiBKQ3LWfwyLGOVqXJmKFlb2n2dvZpqJZRokokJ8932fQA2zZunY0idZtSnXuhpoThdocfqj+v62UOUakKiuh2jPJ6S0T3gGhF0psSmIdlk+v599x1LxAyqpF3eFN4SnLlNWKq7dgMas0Nqk0eKhQwJQwn6CPgJSNdgWEbWx/caISq01a5c1sO1Ad9hnQLr5lUMEfRlytjyJ1NUbBbIZxai24dKcRfapiLB2WRPb9g+kTg1bFNugMVO345ZHyoH2NqusflAqqXHrJVXXLW9me8VxlsVqq9nP9dsujq0YPdIOY8sOFSYGr8nSFti5TyxvZevegRJR1UaqeQ5W2og3dP42Z47v4AP/+vwD9ZkgQYLBQSnlKKU+opT6qF9+YBBtRyql3qiU+jel1NeUUv+ulPp/SqmVgx1HQqgmqIqPnT7O9vd/EK/PKPXGf98PM2L27ChOqjKEqYghTNNp891JKdJp4+5v26akHEXKFvKjRtBQyNHU10XK1jSlPEZmPEZnXJrSmrRlFKctjpBWkLUgbUGDIzgKHIwK1UHI+GRpyjJqVrSm83gH05eNo8mBFkdoTfuxU0Pi1Fe2BqpUy8RwtQJ1qV+UUixZP5Ej264Yik9F21Ws2ICtytvXgqXg9NE7TJnZSkMgyY2hyECvo7/xE5oREW5e78OyLKNO9WOHqti4LZUKSUilopnoWk49ooWd2y+w7skZsbV1DAqwVGBYRobQho3zeSEkKsuJX8v/p2L/4ojUqOX7O3HiGhMnjaS1tXbspaiv6re/MWNbyWZTXLp4p6hFdcSVqpHBXskIFBG2bj7JUxvm1TXOgfD0xnlsfv7EsLiUmaOIXmrOnrnBiLZGRo1qfqBeX/XDz/A937sOgPPnr/BX7/lnhBsPPN4Ejy4USRbUBAkSPLr4i/2nOf2xD+L2mEnesc/+AE2zFgDGbgqSRwVe78HTXWuzvUzFiqLgwekJs5l99TSeht5+IZ8zBGqgTi0UzDrPM+7/JmGngBKf6JMiklX7JOzsfAeXnVH0a9tPEOWhXA/lCsrzUAUvJE/xtK9ONaSqcgOi1LcTtLD/YisrJvuxVIOD1Eb1amKrCuJpRHtESaFqyDcBRDNjskvHTZv+nD+pHhKdvvI0Fv8yUqS6YZ1oP6burkOa1UsI9y1hP4HS1SsnTkWK+inu1/Q1Z2qKqzfy9PbFY51qlFI+CRf8HvHtXqwUn4dt+7tZv/xBbKWiIwjLrCkZOm4V6OmrfxI68HwKjiGO7Qf6WLus1E4unlAvTjTlqzIHeBLPmJTi5h2Xrt7yccY9ser1ynpxXzfrQpXqg9q30T6nT0px+16Be90u8URg9drQoUpVWWTa14br83de4o///BP09j14GK4EjzYS+/aRwkbg54CfBV4D7KmnkVLqtcA54G+BnwG+12//e8BLSqlPK6XqVj0lhGqCirjSfZ9vvPgiF772FQCcpmYW/MKv0BJL8J3JgG1DQ4P5bBJN+W7/6SjplG0L6RRkUkJbWtM1azpTLp1jZEqY1VpgaqNHexZaHGiwTQKpjG2I1ZQlpC2h0YasI2QcoSkFrSlhfAM029CYggYH7p/oYNKCMTRnLJocE3c1a/mJovzEAo4yF72tCOOxBgmvgpDsgQkxsr2BbIPD7avdUaxVDHlr++0sZZJkWaq4baUS9K8LHicP32HxijEVQwfU00e82Mph/dPT2bHlKkh8S7yVXdZL4FJfub4xng7svcrCRePJZNKUxi5VvpI0Kk5YLJUqGbnpb+TIJtrbmzlz+obfh+279JtSCXGX/mpPHtf12L3rDGvXzalcoUq/tfDUhrls3XIyZmTV89irotiIGWqHDl5m1pyxNDamGQ60tGSZPGUUx49dG4begpcewfM027ed5Imn5j5wr0op3v2et2Hb5jd+xzs+yJUrx9BSeOC+Ezy6iE/qPEhJkCBBguHEuXs9fHHbDq5+7QsA2A0NTP+51yEaLNvcdwJveC8e21QXL8E88ZXyyVJX0Z1upN/J0nL3Nj09EZkKxh7I5SLStIg4DetERKqf9B7HKzAnf52jqQlGmeoKKu+hAl4vp31CFCj4ZKqWSH0KhiT1l8r16Op16Oq3mdDa57v7C8rV4OLzhYJ4LvWSWaI9HFszZ1qBY2ecAWqbG7uWgEgt6UsET3toXLp6hc4umDK+6tR/feOL1bNtWDI3y75jxbFTDa8cJ36FUuVrNXT1aG7ddZkxaXhsOwDbUiyf38DeY72DmjQvU3XGPvb0CdfvuMyYnCptRbmHVbmooRIsC5bNy7DveI5yamFwVENwznv6PK7dzDN7amZQ7UthvPPMO45lCasWNrPzUBf+X26w13Df9fVpjsnJjsdpMoIT0f3cvbqTt7z9Ew803gTfHUjs20cGPxz7/GERuTlQA6XU9wCfAFqp5moKPwF8QylVl0orIVQTVMS/nT7C5vdGiahm/ewv0tw+IvAAwrHEJ0uNi3/g1u84kM0ahaptQyYFDSlz02iwTdFNDejmBsbcv8nItIlx2uAIWUdocDStaSFrQ6NjyNXmlHHrDxSrKWW2ZWxwbEOO6v4cfTe6mDpvBA0pIesYZatjGTI1UKgq5Y8zIFZ91/9Qsar8WKz+5yVrJ3B01zWUSJi4yi6pa8X7rjUL5dffu+M6yx4fh2OrIpI0Tj8Gf93xFEwBlRmHpVJYyqalJcukKW2cPH4nVjsgOi1slSrqxSC+LB2JRW9vnlMnb7J02RRfNVpa7PCzpeIlCisQ1g3IUGWx7sm57Nx+NooZVhEDk6hx7NxxmlWPzSRVmrVsQFTvvKEhzdy54zl44JKpGctiOjAqG9+5nMvhg5dZuWraIMdZG4+vncG+PRcoFOpN3jAQhD17zrF4yRQymdQwqF81ixbN4jd+46cA6O3t4/d+76+Aqw880gQJEiRIkKBeiAhv33OCMx/+25AZnfwTP4/VOhpUpE4NyU6/XZxEDV6E4y75BS8iQI+Pmsb0jguIq6MQpa6QLwiuG/XvxhJOBfsQKSZxAZblLnIwMxnJB6SpF6lNC0alashT18RULfg78PwwVbH4oCom3jx8qZVFk7tQBMpWQrWqLh3EwGeWRXMKHD2dQkt1WykguCKisvz38c9GuG7fMc2yuaooj4KpW9nmqebqH2Dlggb2H+/zQzhIkUI1isXqIVUI32p46WgPKxY0Msj8nVWxcmEDB070FhHy9aI4lFhxu73H+lk2N41dNs5iYjHyaotkJ5W8x5bPy3DkTA7XK1egDkadWppwau/RXpbNa8QelkgKiuXzmjlyuhfXjZTKcdVyafiDepAZuRp8j7zC/SN89BP/xfmLA3I6CRIkGB48E/v8zwNVVko5wAcpplsEuAXcLFm3Gvj/1TOIhFBNUIY9N6/x9c9/idtHjwDQNGUqs370x7BETOZSgbQ/sWlZwUy6CkMA2LZxozcEo/jEoyZj+/FQ0dhzJtB86QoNSocKTxtDuNqWIWwzvvu/YwnKMktDdgoNJROrV/deYfbjk8hYqthFP46A1IQwLmu5MVGMTNZh6uwRnD1yO+QcowRWA7cvRde9HJ13+pk2c2AVeeDcVIpQH6oiohRg5eqJHNrXUUaqVZpRjuKbqmBFGbZtOcv6p2aEBlBkWJWXOCUcKUqNzEOpiIC1sMim0yxdOpU9L50tGVu8zwFPT4iurj4uXbzNggUT628UHM8ABOmKVVM5evgquVzBbzOYH7zYMBQRtm09xdr1s7AHe+EMAMexWbV6Ort2nB2W/np7c5w+1cHSZVPDdcNBqv7xn7yBUaPaAPi3j32RHTu2oaX3AftN8KgimJx60JIgQYIEw4VvXr7Jrm98lXsH9wKQHTeRCa96rXHlJyI1A5TaI3ECNPjsuoREqecJPW6Kiw1jmdF9FQS0NsrUvt6onusK2tNFsVIhUrMGJkRLoYc23ccVayTkvdC9P0gopfq9kARVBSLX/mIPd0OmBsFf/XWFApzryDJ3XHdRAivRMclsHRDt0dSgGdWquXw9zn5FXlDl9my5TRHYGbqEEHU9OHFeWDRbldStZpdUf3A0NSjGjnI4ezlfsY/icAGDg+vCkdP9LJ8/uJwDlcdpMWFMmtMXc5TakvWgVpx6z4NDp3Ism19JTVtJqVqO4F2kKWsxaVyKUxcCOzlOoNbPhFZKOOVp2H+8l1ULmyo3qhNKKZoabKZPzHDsXF/ZfkpGEm6vRayGSXdTLaTbloZte2/t4Fd+64MPNN4Ejz4S+/Y7D98lfyHmhn9KRPbW0exngalEarKjwCoRGSsi44C5wNf8ugK8SSk1bqBOE0I1QRFc7fG54wfY9g/Rw2Dp6/8X2bSD9v/wbSvyIIobgZalsG2THMrENRXStrkaW9NCxleGjsl4tDXaZKePpf9MRxi+ybFMvFMwRGzGFtrS0ORAgxWQpEKjU2wq9d7qwrZg1LgG49ofU6QGUKEilVCRWqo3rCbHn7tkNBdO3kXnPdJWML0vZTPlqkYfgTp15wtXWfPUxJr7i7sBVHqMK6V8FWgxq+w4Fisem8ienVdLCE+7rH2YqVRJ0UkIDKRbN3rI9btMnjIibBMdZSVNbbmiNAoLUKqMhSVLp3DmzA16enJDIlHj2PT8UZ55duGAM9+VUDquUliWxdr1s3hxy6mhDS4WB+vunR7u3ulhxswxQ+yrNubOG8/VK53cv983cOUBsGXzCZ58ei6oYlL/QUnVUaPaePuf/K/w+5t/6x1ofWlY4r8mePSghqkkSJAgwXAg73n8+a4jnP/nvwvXTfvFN6KcTFUXzEoK0njxtNDbL2FM1N4+8Fw41zyRSb03SekCbiEiUgHEV4+aJFUaz9N4nlGvFs3Fas1jufPsSU+Dfs+sz2vf1V+MEhUMgVoIVKZemGBKBdu0oEoltwrQcPZylsmj86TtiEmWwZCpPim5cmGevccyUCGpaGnSSylRfZYTV+Vk4KmLwpTximwFDrB0sjtuDwZxRIOyblmW7QdqJXh6MHvkxPl+po5P05h9sFfs9cub2b4/nsT1QW2x4janLxWYMMahIVupXnn/lmXFBBTRsa1ZmmXHgf4K+yvOhVDLRq9F/p65lGNce4qmhgc7n+uWNfuJvYJjGAj1nG9zTOm2JSjbkL5e3yV2bH+Brz534IHGm+DRRmLfPhJYQHQaX6izzf/0l4IJcPOjIrI/2CgiZ4AfAV7yVzkYErYmEkI1QRG+dvk0X/vwx+m7adwVxq1Zx8THH8fCxHsKXPtFVBQsX6L1ad8N3/EJxJQy7vpZy8RDzVgeIzJi4p5OHU2uoxO3zyXlJ58K+LhGGxqdoI1ZgpnjTttGXap8v6XLe68wb80kE9tUmZAAgXd23EAOklApJSGxOhAUJobRksfHcWjHtTL3/rLKNXDlQhdNzSlGtmfrJg9LVapxYrOSW82suaO40dFN1/0gKHp1I7Oa0SgibN10hqefmYMK45pWvv2XkqFGhepgYVd0CwrUq5Zl8fSGBbyw+TgPgitX7mDbFuPHjxhSe6UUMsAPN2PmGO519nHndnfNetVhzvMLm06wYaNJRDUYd6J6oZRig5+g6kFw4/p98jmXSZNHIHjlL0IPNG7Nr7/uJ1i0aDYAu3Yd4mMf+xxC5wONOUGCBAkSJBgI7z9ygVOf/yT9103M8bYlKxnx2FNo8ZNLUd3LPbB5A7vSuOYLhbzPZ7pG8RmGBlCKY23TWdh5llxO4wUK0NC13i9BriMC5lYwyaQ0Ewqd9JDins6Y2KdBsikdSzyltVGsFjzf7R/KLJtwX1J0MEqbeKyHTjaybE4PaNBeZVf8WhjX7tGft7jX5QegVZYpBDZsaX+Bh9TgKIWXjmgeWxyECxjYFonIVOPSP360Il8Qbne6g9rvYLFtfzfrlg9dVTl+tIPnCTfvPtg44/Zapet6x8H+CgmqwhZFfQTLiFhVjB1l3hFu3fWKyVNsLMsOf+JqZGq99uT2/d08sWLoCb/GjHSwLcWNO+6AQoqSEVJLrRp68FkpMqNWh+v7b+/gN3/vI3jecIXhSpAgQQXMjn3ePVBlPx7q00QPny/7BGoRxMR6+aPYqqcG6jshVBOEuJfr5yv797D/458CQNk2q17/ehptQaMwIZEMkRk3OgNyMWUbhWmQrClja7KOZlRW4yhoSWna0ka9qgBRioaFU+g9chFbCZZPkloITSmhyTExUFO2KRlbaEkLtjI7VsDds7cZPbmVxiYndPWv9JxUEMZAdSqQodUITgujyJ08rZXe7jz37/SH6+tpH0ILe3dcZ9X68QNULEcoJlBR3FKgokGglOKJDdPYuul81TolLWL/jOr0zKlbjBvfSmtbY6x9YPxGJGoQK9UqSkwVV7LG9lEhHurkyaNwXY/rHffqPxkxiAgvbDrOhmcWDql9bHQD1tnw7PwYUTn4OcXz527S0pplVHsTpeTkcJKrY8e1kk7bXL58d0jtRYTNz59gw8b5EE/KMCyjM3Ach/e89/+E33//9/6Krq7TZS5+Cb7LoYYvC2oyjZ8gQYIHxc3eHB/bdYjLn/2oWWFZTPvF30R8K0BZVOXoRMeI0kCZ6gr5HORyRn1ayBOGDTBconCrYQROvkBLf7cfnlOK9xG44LuG2AwTSWlQnmZZ7iL7rSmQF5QnqFwQHwBTXzBtw/CfGhW4+0Po+q/CoLDBOr+vgml7826KlCOMaHEHdPUvm8xHWDqvwIET2QrGcMgWR2v8/sNM9CX2Ty1b4MYdI9poH1H9wRASqAhaaz/OqiHG1ixpYkdNderwwBB3ijGjBkrOVRlrlzaz/UClSfzBTm6X1in+fuuuh2gYM6oSHSBVievAy+3xpVl2HsoTJ8cVCstywneViqMSQWvPj1M78HHc6nQpuMKE0aWJtOrDuuXNvLi/m6L3kUGh1nk3fTlNM7EyYwHQhXtcPL2dv3zfl4c03gSPOBL79lHB6NjnepJyrMUoTgN8sUbdbwBBTLolA3WcEKoJQnzu/FG+/N4P4uXzAMz+sR9n5LQpofEY/N1rXWJMKUilIONfogrIOkKTY5SpaSWkLE2DHwIgaK0Bq70FJRrd2Y3jX40tqUitGjiWpBSklQkbELrzF1xunrzF3GVjsBVhLNZasBATLD6mXq1Fpsb7W/3URF7aYlQNVkxNOxAUcOzAbWbNH0Em6wzJtb001mjpzH58++ixTaQzDlcvdRX3McCOFRbaVezZdYk162YW9RmS56GrTLHLfy01aq3dPrNxIZuePzokQvHwoUvMmDmGpqYHzABax684alQTI0Y2cvbMzUHGUQWtNdtfPMv6J2cRDwFQWoaLWH1qwzy2bj45pL5OnbjOhIlttLQG51RTyah+UJXqf/tva/iRVz8LwLVrN3nnOz4IdDxAnwkeNSiGz+BM7M0ECRI8KP7fnlOc+/g/ovtNWJxx3/dqslNnAT7HGHi8l7j0B/yeSEyZ6gr5fJQ8yi0IQTgowcRMdV0oFISXMjNY1XfOEJ/BozOQu3qYUppIXoR5hWucs0aT17aJfer65Ks/WLPOQ7n+Nq1RQQytoMS5yWCbFsgHatboWb7/RBPL5naHJFdlRV5g+0WYPc3l4rUU+YKKjk00SODhEhuCjg6yuh1Re3J11yHN44vtsI8ooVSw+/iJjNYvmJnh3JUc/flKXl/Dj237u1m/rLaqMn6eg/Mxf0aWC9fy9OWGYyq7+DesrFLtY+3S2irVSvbfnGkprt7Q9PVD6TuJZZVfJ0W91qkwjmP7gW7WLhu86nfO1AzXbubp7YtdI1BzfJVRWa0azzORbV8b1s517uPd7/s8t+92lfWU4LsbiX37yCB+Q6hHSRTIyIObz45qFcXcpE76ddsH6jghVBMAcObebb763GbOP78JgPSIEaz4hZ8jY0FOlNEnKnCUoCg1sEyiKItArarJ2kKjI2T9GKqjs5qUI7GETobcdJQwZtlk7hy8jNbGFT9tR/FJA8LTsYS2jFGqKmXis3Ycvsas5eNwHCskWSs9nqPHupCy67/ogxsmftu2kRnaRmW4duF+UaWB7LF8v8vZk53MXzLg3+MA44lGbpUGei8Zw/qnprJ968XogV9GyJqIspbvoh+oS/fsPs+yFVNIpUz/pS79ljKJpawqmT4jErW+p0RrawOTJo/i+PHBZXvP513277/A6sdnDapdJQQZZwfC+ifnsGPbaTyvtrFfiv17L7Fw0QSy2fjMepxMhfjJelBitbExzaw5Yzl08PKg2rmux0u7z/H42pkU/yUJgg7DT8iAL0P1QPOuv/gd0n52u/e85yOcPXsILfkH6DNBggQJEiQox8Fb9/jGlm3cfP6rANjNLUz6yV8JeUelDAcYzJeWTrZ72g8zJb6bf5CAyk9CFXj2as9/hmuhUDBJqLrIcpcmpug7oTrUKFFjA/RjqgaK1bSbZ7p7i5OMRuU9496f98JHsyqIUagGBSLCNDAtNIBE6lQthnjNeyjXJ1Njj/G+fpvbnRZTxhXCdcWEY0CaRY1SjjBjUp5TF9Mx9rkWWWY8X7SUhxOC2upUMwKLnj7ouK2ZObkaaWf61DrqK51SzJ+R5dDJ8lifg50krxe9/ZrL1/PMnVZ50t94/xQfb8pRLJyV5cCJWsk6BzsRX3t7X0642FFgzvRq6s/y/diWsHCWw8GThTCMV6BatSyjrgkSv1YWLUiVz9XRnxPOXcmxYGZZ0NeqsG1YOq+RvUd7ia6VKHnu0FCJWDV92ZkxOM1zTDWd5+61Hbz5Dz82xP0kSJBgAMRfGitl2CvFstjnHuDYAPXv+MsB440khGoCtAj/fuYw3/yrfwjXLf6lX6WxpZm8P2MfEKZWiZGplHlgWcrYZo4FI1JC1oKsJVhK05rWZGwxrvb4pCrG9T9rCdnmNE1jWrh/8S4NKbM+DkOgGpWqrcBGKHT103e3n4kzWkOStlJM1PiqwOV/IATJq+Lq1KDd8rXjOLDzOp5XnwGggN0vdrBq3Tgsa/CJlwIneon9i1PEZh/lnTY2Zpk1u50jB68bsjAIKlvSNiDHFIru7hwXL9xh4aIJUdKqIkVqMaEWP8rBkKilWLN2Ni/tPkuhUH+soRe3nmTdujnY9nDdwgYeeCbjsHjJZPbtuUC9t86+vjwnT1xnybLJNWoFbz7By0WxoTwU4nLlqmkcPniZXK7++Fu7d55jxcpppFKl5yIYjxlffOuDkKqzZ0/lf//WzwOQzxd461v/Ergy5P4SPHqwhqkkSJAgwVAhIrx9+wkufuSvw3VTfuqXcVragJitV2lWXozaNIAWozyVgJ90hXzBtA2VrJpQvWrIU4+DzmQWF65gBTLYOI8mYvKDhqSqsCJ3kf1qMhKEAcjrKP6p64VFuQGTq1FFZKr4iaj8NgWBguf3Ex1bAAV4nsfh02kWzMpjWVJGOFkx4jHYtnRuPwdPZtBeYMfElIwV1KkRiRiRUsX7qZSsyozQiGuNrXTwhGbxbBvbrmyDlMZtXb2okT1He9FDN1mGhP3He1kytxGnYrL70ol1eGyRIf90bV7ZR70G98B25MGTORbOTFc9n5GdavpauSDNgeP5onGq8D0j/gdVbYxWSb36cPBEHwtmZkk59bVZtbCJ/cd7KddBDH7f5RAq/YaZkY+BMqSy23WCz/3H1zl09OID7CfBo4jEvn0kEJd/T6yj/jqiG9NeGfglNiBpK2XdK0LyWyZg541LfP0zX+DOKZPJvG3WbGb+4A+ggJxnISjSvqt9QKgG8aSUn3TKsYx6Na080jbYypCojbYwIm2CSjmWkLHBUkKDbUjSjK8YHbFgPPdOXcfydLGWUgGYdoESNuvAxT1XWbRuIo5SNR/ZceowIGoDirB2/TgkVC00ZGzmLh7Fsf036zm1dN7up6/XZeLUlrrqVxpHSJiKNrGJBlCnmoDrFstWTuLYkevkch7FZGp0xuLZN7dsPsXTz8ytOVtfSVHwoB5TqZTNY6tnsnPH6brqd3b2cOvWfWbPGXw82mowLwoD3w6XLJvM6VM36OutT0m59YXTPPHUbKyBYlGEKDbMBnaNqwzbtli7fhbbt56qq353Vz+XLt5h3oLq51TwENHoElJ96KSq5vf/4NcYP96EwPn8577Jc889j5bEPerlgmGLMZUgQYIEQ8TnTndw4OtfpPvUUQAaJk9jzPf8aKhOte3Au8rUD9z6g/xRAYHqeeD5c5RaG3d+t2DWhds9o1jN50EHZKgLrrI4YY1jYeEKZaxemCgK8IS2QjcNkue6bjZKVE8bt34w5KvrJ6GKk6NF4QJAafEJVoG8a4ofc1X5/cRNDfMc9/A8xcnzaRbOLLVxAvsxsklaGl0as5qOm4oyIrSi275Gwmxc5YgnP4q78Zvfo0h2i6fh8GmP5fMGjqnZ1mzR1mJz8Vqhap2HpVL1NOw71suqhZVc1QPfOfOvtdlmVJvDuSu5CnUroVgpWQtx0rpSXa1h//F+Vi3MVhBNxPYomuZGRfsIi/NXo9AQ0X6C8xgILSoyydR+c6sOLbDnSC+rFw/s+t/caDGuPcWZS7mifUVed5afm2I4fvuof8tpJD0iEMIJ/bd38Ou//YFhy5WQ4NFAYt8+EojPVKyuWgtQSs0CpsVWVXX3jyGI0XpvoIoJofoKR851+fKJA7z4jx8O1y19w2+SSdn0esr3SDKmkc9t+iGcjOu/Y0HWMQmjMo7Q6MdRbU4JKUtoTZlkU7Zf17GMW79jG3I0UL5iW4xdMJbrR8rjKGas4rnEzqtdZBsdRrQ3hG5Zlio20Uof1U6JeLLSPSweGRTKicLg67xFo7hw5j59vbXVf0qEnS9cZc3TEyr2Vw1mHMYJv3QA8Qd/uSuN8slUB7CwbYfH101n57YLlB5x1Nasv3b1Hkopxk8YSTn5GkNo9w8HoRZh/vyJXLl8h/v3+was+/xzR9n47KIH3mcp6omlqpTiyafnsOWFkwxkDN662UV/X4EpU0cNYTRxdcXQFKszZ43lzp0e7t4ZOAHD5k0nePqZecalsep4fGWJ6DrOVH1oaWni//3Zb4Xff/vNf47rXkoMzwQJEiRI8MDodz3+8sXDXP74P4brpv/Sm8B3S7Z9vid4pmnfvtVajCu/K6G3fCAuNUSrUCgQuvoHsVQLBSHXL5GENZDwaeEs7UzyOsl6OYJgrSp4norZuXJdHvMusocpkQmQj3nvFLRRmQYJqfy+w0RUmkipqk2MVfKeUajW8LI2iZsMzl9NMW60R0MmIkUjdWpkh6xY0M/eo+WEZiUyVWsXLbVtZvHDAVRG+WT+uSvCuHabpoZSi6RYfbh+eRPb9j/8RFTVcPZyjrHtDs2Nxa/chuQ0mdAU8MSKZl7cN/QJ5Vp2kyGoa9tV56+6tI+waWmyapKq65Zn2H6gv0Z/kWozeO8pCw9mKSxlYSl70GT2hWt5RrbatDZXJmsDrF/ezLb9lRJ7xccUvBw+qFVb/E6Ual2IcoyQxuvvYP/ebXzqP7Y/4D4SJEhQgkOxzz+hlKrl9v/L/jL4Y91Sq2OlVAaYjbk5XBhoIAmh+grH166c4Ev/8FFynZ0ATHj6GcYuW4poRUHMbLRt+TahihJCpW1ozkBTypCVtjIu+ZbCqFQtwbGElC1+wighYxtlahCE2VZGper5n8dMH0nXjW5yPWZm3HiQB8pSn8BVwpm911j0+IQwKVRk5kUopgKlbBYoSHgVoNIfQikBG6kXFKufmMCeF6/VPLcXz95n1OgGWtsydT2qa83XhtlQ4y5SClAB/RoQqcUZRafPbKfzbh937/SGJGqoSg0DClhGnbphXpVxFbv0V1KpPiiUUmGCqlo4f/4mTU0ZRo8enOK3rjHUaVBNnjKKfM7l5o3qhpqIsPn5kzy9ce5wDY+hhAPY8Ox8Nj9/omadq1c7sZRi3Pj6zqmgCXSq4boHUKn+3M//MI89ZgjyQ4dO8sEPfhLh1hD7S/AoQSkZlpIgQYIEQ8F79pzl/Bc+Rv6OeaaMfOwJWpc+7j+/BMuKnmRaBE8b1anrmqI12I7hR8UnVF1XyOdNnNTgOay1UMh75PMeuiB+oimf+NQmGZVSir3WFB7zLqF8N/9IYaqhIEz2OrmrG+jW6VAea3JdCXgeKu+hCl6UTEp8JWoIiYhcV0NOF8VqVbFqgQEd2HRRzHyL/cezLJ/f77eJiCsTH1YzcUyO+90W3b3F1nNlMlVXJFPjdowuSlIkJfWq+7/vOuyyZmnpO3TQXjFlfIr7PZrOroFDSj0slSr4CaqWVw/DN2mcQ0+fx937voxlUDZVsVK1ctvSc1q5/+0H+li3rHqM0gljbPpzEo6zvL9K/ZrzWmZjK4jCkcXDjA2MF/d188SK6udzwpgUBVe43ekW9RlX6kb5E6xhT06mlE1mVCSYy93dzdv+70fJ5+sPw5Xg0UZi337nISKXgdOYG89E4P9WqqeUmgu8iehm0AV8a4DuVxORKicHGktCqL6CcTfXy7f27ubgZz4HgJVOs+TXXkeDDf1iHn1Z/woRTNxT2ydGM7aQtYW0rbEtHRKmtvJoyQhpCxps49LfaEOLAw2Weeg7/tLyZ2UFEy4gZSumPzaZCy9FyXSygXrAJ18vHr3F5FkjSTf417ivUI3fkkovakdVvtAt5cdK9fsvig0bfpIw1EEcEyY34RY0t29UVlSKp9m/+wYr1owt7bAmLCqoTv2BWcrx3f1jBkhYPyBVi2lZheLpjbPZuulMbA92kdL12JGrTJveTlNzPHD+QHFRh8vtO8K48W04js3ly3cqbtda2LrlBE89Pf+B9/Wg2LDREJXVDvv0qRuMHddKW1u1zKn1ojRGU2mhJrna3t5Ma1uW8+cqE5QiwhZfnVofSR7bZ4VrYCjXgWUp3vPe/xN+/+M/+lvu3j0zoJolwaMNhbm/DkdJvKISJEgwWHT05Pi3bfu4/qVPAqAch8k/9waCsKO2RRj3VGvBcyMi1S1EilTP83NGeeB6JtlUIR9tNzmfhEKPB32xBFCFQKVKSJzeslpQIozS3ZGrv68ktVyXxeoah7zxhjh1tXH5B/PozZmEUkVe80Is0VWQJcsvBS9KWEWF+2hclluCO/dsRKB9hOtPpBvSU7RGKY/FcwocOlXb3V5E8DwXLRFJWMle0RI7QUWDG/jOf+eewvVgXLsd7iOAZSkeW9TI7sO1EjyV4uE8bW53euQLwsQxxefMUobMW7OkhZ0Hqk/SDw8Gts/u3tf09gsTxzplKlWlYPXiLLsOBeEE4+EGqp+3IhKzQr14aIDi/A3V++zs8uju8Zg8vvI1uHZpE9sPdNfsw+wvGtNwE+p2wxTsrPFQFLeba+d38fa/+Pdh3UeC7wwS+/aRwr/5SwHeqpT6sFJqqVIqrZQarZT6eWAz0EQ0lfgJERnIJfbVsc97BhpEQqi+gvHFC4f4/F++H/F9lma99qdpmzCBIFSThRSlInIsc8E02kJTSkjbQkoZ0tVRkLaEZl+x6ljG5d+xzPpGR5N1jM2X9uOoNqbMlZ21ocExV3hTeyO2Y9N1o9sPCWD2bwFeweXambvMWjw6vAFZ+LM8gXtJyTFaSsrI0uLtkeo2mCwtI1ercIprnprA7i1Xy0gkBRzac5MFS9pJpe26XP0DOjROZEZxfgIdrjEyTK3iuD9Fn2MqA4CRo5poa2vkwrm7WCFla5b5vMv+fZdYtXp6rK+Bk2dJjNAbTmx4ZgEvbDpWkZjbt+88CxZOoqGhnkR+g4eZra7tQhSgta2BCRPbOHXyRtk21/XYvfM8a9bNGO4hxlCbXI1j/ZNz2P7i6aKMtwGOHr7K9BmjaWxyyrbV3rd5k5QSperQIKxfv5yf+R8/BMDt25383z95H1Ae/iNBggQJEiSoB7/3wnGufeofkILxehr3319LeuwURKKJeBHQnuB5kPdd+ENOMsj35EUcaX+fSThVLAoVvH7Xj3caFJ/49IWXvikLwB57Kqu8i1H8gLwGFxZaNzipx+B6yrQraJT2i+uiCi4qiMsKJk5qoET1fJf+YFkQCOYkI8GmQaBuVSoWm7R8Ynz/iSzL5+Uiaa7PDM+f4XLmooPrFhuKcXWq1hqtPbS4iLgDTLbWysA0sH2x+3CB1YsDuzCqv2ROhpPnc+QL9dsow61UjGPHgW7WLC2P/bl4dhMnL/TRX/AeUKgwUNv6+tt9qJ/Vi7L+30jUZsHMNGcvF8jlIzY/Hue2XpSHKwtCSkQkavw9phrBuvNQD48vbirb98KZWc5dydGfKyWEKw8yCr0AxYHfhoYgOZdSivTI1WF/hXsH+YcP/SfXrnc+UP8JEgwEpdR/V0qJX87XqJdSSr1FKXVAKdWjlLqjlHpOKfWaGm0ySql3KaWuKKVySqkjSqlfqFE/q5Q6o5S6rJSqLisfOt4L3IJQo/eLwH5MIqkbwEeAscFwgF7gT2t1qMwf8WuJbgYvDjSIhFB9heLs/Vt87Wvf4tI2E5M3O3oM83/qZ0hbkBeFrQzx6WFc+ZtswCdV0zZkLON+n7IExxZSliZraVpSGscyCamaHEOuNjiYzwhZR8zSJ2dtn4hNxa7EqasmcmHPFTJKo5SQtsGx4Piua8x7bALKCjSZUvYgLTXvAsK0qE6MQK0WEDq+vlK4AIDm1jTjJjZx/tS9ou19PQUuX+hizsKRlTsvGa8hcuMEarhnAqrVVilDgobGRXkvoaI0XGvUqBY265+czc5t58tItZ3bz7F6zXSceArSOm2JSgrFB0VTU4ZZs8dx6OClovX9/XmOH73CihXTH3gftWANYoZ6zbpZ7Nl9HtctPqd7dl9g+YoppNODISmHikrEdrEKJJtNsWDRRPbtLc4yms+7HNh/kVWrpw1pv4IbkqpFW4Z0HQjveOebaWw0it73ve8THD26Fy0DJlZM8AgjPkH1ICVBggQJBoOXOjrZvHkzd3duAsBpG8n4H/35kEy1/Fcvk1zKkKmlCNWnPofpuZpCQUJBp9mu8fpd6PUMAer55GYB350zcMmPlKZ9pOlQrczwbvnMrZClwETrPmfdESiNSURV8PvSoPo0qlJOJTfqGs+QryEjHPCm4IcXkIhMDeBV9wTpz1lcu+UwfVIuVKdm0sKkcR5nLhXbN3E7QHzFqRQNrjJ0iTt/lCmeAdtG41Rc6vCYNz0aU0PaYubkNEfPDt6GeFiu//154ezlHAtnRS71mbRi7rQsh08HKtpim25448mXvCFV6TtXEM5cyrNwZiReSKdgzrQUR05HycoiYUXtEFRViczgn4oUoioIY1byrlMsFDHXSL4AJ87nWDIn8gRLOYr5M7McPNHn91H+blQJAaFrbI761NG1EIzdSo/AafFDf4lL941dvP4tH3ygvhM8GnhU7VulVAvwj3XUywLPAe8CFmFc5+8AG4HPKqXeWaXpR4G3AM3ACWAO8BGl1Ouq1P8DYCbwWyIy7DJ8EbkP/DyQp/jhES/BH7UGflVErgzQ7Y8AU/y2N0Rk30DjSAjVVyBEhC+eOcB/vfv94bpFv/o6mpsbKMTUqWlLY2Pc921l9J+OAtsy7v6NjqbBNgmnGmwYmdGkLOPmn7HNlZtWhmB1LD++qmXcrNK2uaozdjGZCpBuSDFmahvXTt4JdZk9nf309xQYPyWI8+i74sfaKX99gCBWa6X7VTy7XlAv3Bb7bJdsK+1r+eqxHN57E88n1Sxg99ZrPP7khAoEackYMEHZbRVoU62SEvwLHvTls7QKhaWccJvlE6iWHx81mAXOZBwWLprA/r2GqFRKcf9+PzdvdDF7zpiicdVvwJWTecNh/D22eiYHD1wsijW0ZfNxnnhqHtYjlBIxlbJZsXIaL+06H67r6c5x4fwdFiya8G0eTaQeib5Hhu6SpZM5ebyD3t4oe+yObWdYvWYmdumMw6D2GihVH1ylOnnyON76tl8BwPM8fvu3/5yBn3kJHmUkWVATJEjw7YaI8Pubj9Hxyb8N10366V/DyjaBAssyZGjBd+8vNVu0lrC4nqA9QWtNXwUHQen1Ez4FISU1KNczJKr246RGtYMBckSPZ57uwM4ZlnSlfYW9hYkoz7/huUE8AYH+Qix8QKBO1VEsAsEktNK62AQoG2zJVz1wXNHjZ9PMnprHsTwEzbL5efYfdyizhgNy2XPNUg+kSg1Qrk4dikr00MkC82Y4pBxjI69ekmX34X5EHq0HyMGTfcyfkSWdMuNas7SJXYd6Ytfgg1tTYU9l57/+sExHzuSZPTVNOqUQhMcWZdlzJBeOMxhlGPxJyksxBlB+quh3jwhNReSdV4lcVRw7m2PWlCyZtNn++JIm9hzpDf0rBwMV7qdcQfsgSLctA8uQ027PWb7+zefZsefUsPWf4DuDR9i+/XMMGfgfddR7EjgHLBKRZSIyG+PqngPeppT64XgDpdRyjHLzADBdRJYCjwF9wJ+qIHZHVH8uhnz9qog8tHgXIvI14FXAeao//W4APyEin6yjyxeBBX55rJ4xJITqKxA7b5znSx/7LJ0XjGJt5MJFTH32v2EDrq+YdixN1oKMY/7g+7UKlZ6BojRjg62EtBIaHc8nV42iVSnw8EMD+FeZY5novhn/z81WlMUnNc9TYeaSMVw5edsE4VdwZPsVFq+fFNYLbkQSa1dEfIbhAor7tq3yi960jQioQFUAJiFXceXir07KYvGK0Rx86QYKuHWjF62FsROaqj6OLSwcZZvYSUXGgiopwfhsguDpgcN/6LavnCICtlL7QL26dPlkTp28QX+fMeBfeP4kGzbOLTNeB2NIlKpUhwO2bbFu/Rxe3GqSKd261UVXd47p08cM0HK4UP9tcf7CCVy6eIfuLkNUvrD5FE8/M+ehuo0NjPJwAJYFTzw1m60vnEJE6Lzbw40b95k1u71qHNh69yWh29eDqio0v/O7v8DUqYaM/sbXt/HlL38NLfcfZIAJEiRIkOAVhI8fu8qRL32KvgunAWicMZdRT/1g9KwTo0qtlOvIhAAgfHyKz2OWufkD2tXGXT/vmUoBAeoFHZX27hutWtCiOOqNY4nVwSjVgyWa214jiEZ5Jn4qrkDeQ/W6vuI0ImRxYy79EMVqBVPX351CzPdAauuHGZCAkB0AWhRHz6RZNKefka0uKUe4edf2w/2Y4mnPJ1CNKlVLgXrUpaXq1GIMxoaw0CLsO1Zg1aIMo9psMmnFtZuen3dg8HhYKlUR2H24l8cXNzGy1aYxa3HlRoFiu1OXN6p/D9S2xeqz1UTgpSP9PL44y4gWi5Ymi8vX3So2fzChX33fllXf+SxVlBZ/L31fMuPcdaiXtcuaaGt2GNFic+FaIVYv7JmyF7gK+w5DDygGrF8vlJ0l3bY8/J67vZvf+N0PDrP6OEECUEo9CfwG8HngP2vUG+fXA/gVEQmzF4vIFzCqVYC3lzR9yl++V0Tu+vUPAv8OjMEQkHG8D3NT+F+DPZbBQkS+6e//tRiF7heBrwAfBn4JmCki/1FnX7dE5KRfLg/cIiFUX3Hodwt89eheNv39P4frFr/+TTSmjIOOozQtjibrK0gRMzHuifLtQ/FVqoYMzdhCU1ozNqvxfFpSGU6WrC3hI0zEV4L6KlWLIMZqucoxaxm3jbkrx3HypQ46zt+jZWSWlrYMAeEZkKWOIox6GUnoo7nJYKliy+LkUxFxmvJjwQb9lKpTq2HWvBFcv9JDX1eenS9cY83TE6nkMRLRoBF5Gv9XVNc3IIz61ISujmKfRgccJJiKlKzlfQTHq5Tiiadms+WF01y6eIdsQ4rRY8ozuw/uEV9uXA2HjTBr9jhu3+rm7t0enn/uCBufXfjgndaJwRDKSimefmY+L2w6Sce1e4gI4ye0PcTRDQXmGp8ydST9fXlu3bzH5udPsGHjPH+LRktUBp9cyo+lKqBjMVVNP4MbaUNDhj9/12+H33/3d95FPn8+MTy/S/GoukQlSJDg5YmevMcf/cOHuPpvMXXqz/0mgu2rTo17vy55pBj3faNKDb4HLv/5nElCFawz2wV6XT9eaoWBaCk2hgJDWEwCKgqai3oEo60eHrcusjc3EVyNyutI8Zr3UDkvNjcqUdxUHQxajPDACwcGrlHGqriLfyl3WXoCauBSh83IVs1ji/PsOZIqnq8FjHzC8138B4NahOrg+7nU4TGixeLJlVl2HswB1gMSYw/n4XOpI09rs80zq1vYts94wJaGmyoKoTDkPUW2WNHaOju8csOlqcHi6VUNbDvQV1M8Ebj/l+8r+j5Ykro4n0RxCdSrStlcu+XRkLHY+HgL2/b3YlsOlrJC0UqlhL21ELn/19+m58p/0n3xE0Wl59Kn6b387/Re/ncK9w9Hfbl3OLjlH/jXT70wqPOR4NHCo2bf+i78/wR0YzLa18KPAGngtIg8X2F74MK8Uik1K7a+1V+WJrm45i/DF2Cl1E8D3wO8Q0TO8G2AiORE5HMi8kYR+VEReZWI/JqI/KuIDCY74aDx7Qjyl+ARwucObOXdP/U63H4TV2jq934/4xfOI21pXE9hWybeKUCvZ0jUHtfCUr6LfqAq9R0qMpaQcTS2vz1KJKVpss3j3FJmMj0VU5WmHXBUPAaqYBwuDMlqWTBhehsXjtzi1pUuNvzoHMAkwQpVqcSUqSXP+SJ3flUeRxUM8RonWgHi3kGlN7pqNz6lFGuemsA3v3SBKTNbaGouzzpp3PrLSc/SfkrWoJTlE7DBKK3wo4VTkYithSlTR7Fv7yWe/9ZxXvvTcRW7EDsLsc/1IIijGRDDg21fGRufXcgX/3MPU6aOZuTI8kD+DwuWsvDCBAwDY/yENpSl+PpXj/Ka1658uIN7QDy9cS5f/PwBxk1oYVR7I4LnX5PmDyh+PUnMlT/Kglrpd/WNdsyLX7HHR/m1sGb1z9DRcavmONPpFPl8gcuXbzBn9vdx4eJBVBhTPMF3A8x9d3iI8IRUTZAgQT34sx2nub7pi6H8dMSodp5pcFFXDkTCswr2ifG0KFoB+B71ZYI8Mds9nzSNd1dqRml/hY61VYBtOm0hR5OVZ336QtSv317F4qDGfK2Ll6XErd9HmV1cVCcgumrcnyXqQBCaGzTNTbBueb64msQPbjCQ6m7ZAoPT+1gIxu5uaVS0NhsSsHjCf2jPooc1l9vSaNHWYrNhdWtsbel5LH8nGCrKn6H19dXcoBjR6vDUAKZtdSGCihaxayqO4fBya2pQjGx1eGpVvTlvBprwH9x189lP9NObH5ivmTZtGjNmzGDTpk38nz/9V376x9aTyZS/MyZ4tPGI2rd/BMwD3iQiVwbgA9b6yy2VNvrtzwEz/LoBIXrdXy4AvhprEihTOwCUUq3Ae4BTmNACL3skhOorCDf77vPSqWN037wdrls4pp3Zxw6hVGQ4KGU8hJpQaIF2ImcJ2zImUEBkOr77f69vm4mCXp9UvVqi8Az+to27vsRonAhO3CVfQdedPvJ9Hju+dq6CurTc1qRCnUqmcz03sDJTpkYb0dBxtQfPEzqu9MZsaqk6MzrgEFTtWVVV0YofGHfu9NDd1c+X/vNgDQJ28Hf4MoPqAR8SIsLVq3dxXY8bN+49WGeD3vngjLzOzh7udfbxlS8d+g67+9eGiNDRcY9CweWzn36pQo1q11otVJgIqHEtnD13lbt3Ogcc65w5c5g0aRKbN2/m+vXjjBs3Ckslj6wECRIkSFCOs509fHrLbnqO7g3Xdd65zRePXmTEM6/GtkBZJoZqnFgVEVzXeMDHH99aC7kcYRIqSwFKI92uUZFqXz0auCcTKUJFKI5pFRisBV9NqsAWzfemT3PdbeZMfgQ3ext85ak2Ca4K2qhWA+JWhwMLE0ypeFJMrVFBBvZAvRqYMbF64hZCciuuIJRQghuRSVoLlpXne9cLHbfh3GXFjTtOmNzL0/E8IOVqyGrQNSatpWYogNK6XngcliW8akOWq2cLnLviceWGh9YawUOHcRwGj8GMpx5YFvzosyO4dDrPhat5Ll4zJLWWeBx8CN6Gak9o10KxwCFqb94rimOWVoBofvTZVg6d6uPydY8LVwtmLJUEKhWtRKuob6VU1d/hQUhVpeDVG5s5eLKfazc9zl+JZW8rHVYwUSJGTV16vSqlin5vE8YCogSslcfZ72ZRdu3rRCnF009v4DOf+TQAty/v5g/e8Un+8u0/N9AhJkhQE35s07cAu4C/r6OJnymN0zXqnMEQqvNi676BuUm9VSm1G9gP/DjwQ8DJmBL1T4EJwPeJSI5XAJK301cIRIRvXjnE5/6yOLvgC1//GvN+9adJNTSS1wpRxlbr9ixEoNu1UAiNjpC1jJt+2jKGY0ta05wyqtF+DUqgOa1JW0KTYx7kacvYnSkreIQLLWkxSauMZ1LoOZSyhCYbUr7ALddXYPfXzzNxZpYJ01uZOr01nOAMSN0wD4A2nxWRGjVIglVyJkLFamAQlp8s3+im5Flc0YgwMVF3b+tgzvyRnDx2l1e9ZlZR8iRFpRhO1RJWGRLV2OIpv20JQwzYFM9o1mto9fcX+Py/72PBwvGMn9DGnLnjSg4ueDGobDTVQpg8K9bXg3CLO3ecZu6c8Rw7dpXX/uSaumMwDRc8qZRStxyFgsdnPrmLRYsn0dqWZdHiiQ95ZEPH/r2XmDajndMnr/Mjr1mK4wx8TuOqailaX06kWsqEpjBxyyqrp9/z7ol0NKSpBcuyePLJJ/nYxz6GiPCH/9/f8IEPTgemDjjeBI8OHt2phQQJErycICK89fkTdHzq7yIG1MfNz/0TLY89i93a4qszTWxQvyFCcRPxFZz5vB9PNahniYlnmvOKc0Aq/CRU2hCqlh+aSfs0kfYlo57EXPNhsdPB0cIYrhea2JA5xze6p4eJp0wMVQ8VGMk6HJhPpmrj0h8fdCH2vYpLv3heqEAoJ1PLM1oJLotma46ftbh6U7HxcY+vv2isba9kH4MLzTMcZGqxynbp3BRHz7hcvOby/U9kuXozisWglOWTr995LJvXyJEz/Zy7nONVG9q41JGvopbUxfaXyCBJ1fLfR/kx2eL9lPYbhH1aNi/DsXN9nL6Y54c3tHHpWsEQ4VL8zlFNnWqEOuJ/Vv7nuBRmeLB4doaT5/OcvJDnVRuaudhRKL0FxAblL8UKx+ivCJfmWgmOwTJ/F0rKrrc4mia9Ovxc6ToTgaVzmzh1tZ/+fkOgF+4f4QMf+SK//Rs/xMTxo+o+3gSPBobZvp2ilDpbbaOIzKw6DuMW+CH/669LfTfR4IK7U6NOsG1kbBwXlFJ/CvwxxerWPPAGfzzLgTcCnxKRb8TGORbIici3WSH17UESQ/UVglP3rvGVL3yNC3v2A+BkMgD03rrNno99CksZV3uHKORSzgtIUVNsS3AsI3N3LE2DLWQtyPmEaTYltGeE5hQoZep6QkgJWgrStiFgbWVmaW1lVKkKoTFGpgIc3XGVRWsmsOjxiZx46XpkvCnzECyax7XMfoIYqLYynyMICqmQACvsMoqdakWELfESqxvuF0VPl8uNaz0sXDKWaTPaOHn0bmxr+Z9YPK5pvCewsJSNpVI+mRrLpOqPwQQNKCVo67+tb9t6hnVPzGTt+lns3nke1w3fForqDWW2WMoM5KEbTT09Oc6cuc6KVTNYsGgS+/ZeGHJfQ0d953XHtjOsfnwGj6+dwcH9l8nnBxtHLIKIFMUzLS2VMqnWqh8vfX05jh29ysrHprB0xST2vnSRMENrLMGElP2LthEr8W3BG5+IhxYP7Y+raJxao7Vm+85/4/zFr1cs5y5+jXMXv8YnPvk+fvGXfoCGBnOf+vCHP8eevTvwpEKa5QSPLKxhKgkSJEhQC988f4vt3/om3Yd2AuCMHEPzqmcA8Hruc+uLHwk96rVnOBLx8zIFpGlgsWgtuB54brjCEJQFDb1ejNzE2GXaKEkpBAqBiDQNVaIBmepzMk2SY7Tq5WKhlZy2uNTfwuzMHUOUur5CNRDzxcMFeEaxqjyfWNXauJR5GiWx8cYPKGxuVLXRc1kjWpcoU+NkqqYhqxnbLpy/qsjl4fxVmD3NPOcZIkGpq7QbfKz0yOZsbFBMGmtz+qJLvgCnL7osnOlgWRaKILHrdz5BVUNWMW1CmhPn+skXhOPn+lk6twEIYneWxlIttscHf46K7digfWms/MhO8xDxyGaEGZPSHDubo+AKx871s3Ruxu+xPtu4NFkpBETs8FFRmbRi5uQUx87lcT04eibHMn+cg4Pyf+dIvauUZcKuWbZZKhulHLOe4lJBghP2C4psxmLe9GYOn3FJtQY5ITS9N3fxK7/1gaEdfILvKB4h+/Z3gZXAe0TkQJ1tsv4yX6NOoCxtiK8UkbdjVKmfAZ7HxG19XES+pcwf+D8CPcBvAyilftgni68DnUqpF5VS8+sc53cNkneVVwA8rXnu/EG+8O5InfrE/3odlmMEyts/9mk6r3b4JKVPj3hmmVKaRkeT9clTx3fnb0sLjY5RplpAWgmtjiYVV3b6Hkq2ZchJxxKaUsWkqaVMbNVGx9TDb3vvZi/aE0ZPaCaVsZg+bySnD5m4i0HzUrPC9gnRgLy1/EEoX5UarCuNpxp/vpc4xBR9j26AJhWUjUIpm22bL7Nuw2SUUixbNY6jB29TyFdy9Y9cbAiPJOjJwooTqKXqvthoypNPURfu3O7h/v0+ps8YTSpls3zlFF7aVYOoHBIfKg9o/Bls3nSUDRsWoJRi+fLpHD92hb6+Wvf94YeqaiBFuHevj+sd95g9dxyO47B6zXR2bKs6yVgTIoLGq0BUBia1oCv8q06KFvexbesZ1jwxA2UJ8xaO5uL5O/R090PJfozW2yMiTr0aRfuj8tB4eLhmKQU8yaNxEX9bnJQ1JK9XkTDu7u7n7NnrrF03j//vD18Xnps3/9Y7EbmYJKhKkCBBggQhXC380aaj3Prs+8J1o3/89Yz+iTeiUoZcufvc58hdOR/aNSKC50mYeCqYr/c8IV+AQvAqGRCnBQ/pcY16VEvM/T4oMRI1aAd+YigdKU39bSvTV9hTmAiioCCc6B7JzIZO0p5ryFkdEKbEiFQ/DIAXufxHrv+xE1L6iAyemT6ZGhw/gYu5BM/7eBem3qqFHnuPGlmEoDl+Tpg5RXBs386ITZrWj2p16++jVC34+OI0uw5FNuLxcy4zpzhkUhGJp6wHIfOGhwRct6yZ7Qe6w+9Hz/Qzc3KGbNr0X5qcKrLPhooKRLl/7uLvDgHBHtRbu7SJHQd7wu3HzvYzdWKKhkyg7DaLgcnV6qrO4cCaJVl2HeozyeQETp4vMGV8ioZs7XFZllUh0RVFpGqEiGQNE10pK1Lh+MRr1IcdK2bbumUj2XmoC1CkRyxFWYbP8novsHnzZp5/8eiwn5sE31W4JCIzq5VqjZRSc4C3A+eAPxnE/vr9ZS13wWBmokzJ4id++kkRedZP+hQQub8OrAH+UESuKqXWAZ/HvFS+Dvh9YDnwLaXUiEGM95FHQqi+AvDi9VP8x4c/zb2rJinblMdWsOzHX8VjP/1jAHj5Apv+7oPGZhOFoMijSFvQmoIGyySfspRgK6HB9sja5uJxBdKWpsHWjM4GxCmkbfPXk7ENUZryidPS+WHL77fB9pWqyhhyR3ZcZfE64zqdtmDmonYun+4k319d/WdZRpVq+yUkUq3qplBZ4ikChavCVjYWNraywqRSpQ/Za5e7SWdsRo9pBBSOY7NqzST27LhR9rA2D9vimUzlU7XhbGgYM8mODSi+x+IzOJiM9JufP8GGZ+aG3xcsnMDFC3fo7g7fHMpPxiBRrlIdPDo6OnFdzaTJxiPBshRPPjWfLS8cf+C+BwMr/M2rY/Pzx9mwcV5oSM2aPZabN7vp7BxKMsHA8IwIzmrq0MpFqvZx+3YXXV39TJ0+AsFFKXji6els3Xy2pJ1UGE+14lUoLkLBLy5aPN/9yWQAFlyfZHV9QjaqG5QXNh3lqQ1zQHm84Y0/xew5xs3/xa37+PSn/wMtd0nw3YFgEutBS4IECRJUw/v2nOfMVz5DvuMiANlZS2h+7FmckWMZ9YP/w1TSmo5Pvc9X4cVEnFL81HPdQLHqqza1hrwLeRdV0H598U0ziVz9g8di/H7lu+eHylGfGB2ruiiIzT03a0jSvId4wuG77SxpuxWLm2raKBGjgi3E7CvPJ1Q9fIJVR2rV2L4N6YqJJeqVKkNL1IuxfyC0txXQWnG7M1CVmnO3/xisXMiQUEmdGpF5g+op/DRmpLGbb3XG+4SXjuR5bHHaV6caRBPlg3uwDEds/DEjHWxLcf128XvMjoM9rF1WO5nScAgVivoTCUNbiMRtSBg9wiblKDpuFY9z58E+1iyNxGr1n5FS0v3Bz6WIMKJFkUnD1ZtuZKsr2HGwj7VLG2p3ABXf0aoXK1KnBkRpFS/EUoxqdWhutLh8PQdYKCtLeuSqcHvu9k7e+LYPo6vGKUjwKOIRsW//EaM2ff0gs9gHL1K1Yk0E2+p66VJKjQH+DBNXNZjd/D3MH/wPisgHROQdwJuBicAvDWK8jzwSQvVlju5CjueO7uO5f/oEAMqy2Phbb6A1rXjmV3+WxlEjADj53Atc2LMfz4RtAhSO0mRtTco2iaeU0mRsTWsGGhyh1zNk56iMMDJjiFMH390fo2S1AQQ/XICEKlSzB0OApi1zUwnuK1fPdNI+oYmmlhSWCkhRxdI1Ezi0/RpQHn3JURFNaWFUqajIbLJUBfK05LtNfBxGERqRapY/OxkoRY3z/c6tV1j71CQgiBtpMWP2KG7f6uVeZzBbHhCpKuYiYoeqU2Psxf8UY1JdiknToSZ+OnvmJiNHNTFyVFPs+BVPPzOHLZtOVuxs6EZbcbvB9CMibHr+KBufLbbWp00fTU9Pjls37w9xTENDLWPp0sU7ZLMpxoyNsrQqZbFh41xeeP5k1XbfCbzw/GmeemYWUdRhGD+xFUTouNY1zHuTkDDVFPBwKyhYKpO3HR2deFozYWIrgouThr9492+HtX7vbe+lq/ekn9ggwaMME2JleIp6iAqXBAkSfPfidl+ef9h6kNtf+hezQilG/9SbCAzAUd/30zijTKz4nsO76DqwI3L99znTgFjyPMEtiIkD4PoEZUGjel2T7CmsHDTGJy79pejYuqDEiE4toD2Wp6+xv2+8+Z4LXPjhWl8TLU6BFtu3HUWHJCxu/LMXfldeLDRAOJ8q0cEFKwNXf23c/INtoe9L4P6vBdGC1i4rFnjsOeIn6YkpUa/cEBqzQmtT5dBDVUMSac+EGSgLXeQT1aLrKlE/pqxe7LDzUJ4oRqf5Pa9cd2nIKNp8rlL5KtV4jPdo/5VKdQXuYMItBWXd8iZe3NdVdm6u3MiRTsHINstPTEXZ2JCSQExVwkBV3388Bmg08V58rObz2mVNbNvfXfL7aDpuFXBsRXub5Y/PH2Md/+L7LVLGhtv9c+qHhqpdTJKx9csb2LavJ+zFXGNmnCC0j1B19hf1W+l3rpS4KliGRHsNlfYTK9vYsvc+0Rupwmmeg5VuB0AX7nL2+Db+5oNfq9g+waOHR8i+XYX5w/oXpVRHvAB/7deZElu/3l8XvKTOrtH3rJK6A+EvMPFWf0OiQMLrgRMiEk9+9Z+xbS8bJITqyxz/dekIn/+rD1HoM+rulT/+KibNnYZjabLNjWx4/S+HdTe99x/oL2g8FCkFLSmhwYG0MnFTmxxoSUHGFoLQmxlL0+hAg58jybZMjFTHMm78GvPZUSbOagCjBBXj9B4jVLWrOXPwBvNXjgXMvgVzoY6b3Eyu3+XenWL1eaj7VCbLp2WZPuMJVsO6KipFY8GfLQooT2XHtpeoU5V5kB49dIsZs0fQ0JD2+zPxTS2leOqZ6WzbfBmFE7r0l7nqm47KXMsVlSW1ZerUOmfMPU+zc/s51j0xq2zb+AltoBTXrt5juFxzApfzoeD4satMmdJOS0v57PLGZxfy/PNHh2V2vl5UO8ciwtYtJ3lqw9yy+qNHN9PYlObihduD3VusDB/Onr7FqPYm2kZmy7Y9+cwMXtx87iGd08iADv/VcA8UEbZsOlWkogbhB3/wCb7v+9cBcPFiB3/17o+gufIQxpsgQYIECb6b8IebT9LxHx9C9xk36pZ1P0h26jxEjJ1nZbKMfe1vhPU7Pvl3eIUC2jPu/VqbF1q3APkchqgM3PM9gZxrXOqDZFHaj3EaxE31fJIzUJRCRGi6frbUgNx0NXOcu1zKt5JzLeh3UXkvjIGK57H3+khWjrlt1KiuoPIuFLxoXJ6YbSEbTEyN6o8rJHnxuVRfnRq69sczagVkVhCWx4TzmTVFc/k69OaiMEJaou27DnusXiJ+u6DfyIYs9qyJ7IBSTxejjjTeLfV530hsHcye6nDlhkdff2w/Ir5njGb7gT7WLkv7Y/J/IBW37WrZPqX79deKxI65nnEKc6ZluHIjT3efSyWvo6377vPEiubY7xIfmyG9wxANRbaVpj5PJj9Mk0TH4GmX0t9t1pQM128X6O71KD5uBQjb9nezdvnA6s/yM+mPMWb/xd8TtNbhb1btXIpEhPyMyQ437xS43+MV/d5B2b6/m3XLGur+faK/gzjJXOk3prL9igeUr58xKcudewXud7sEZLLyr7/MqMfDerm7e3nHez9D572esj4SJBgAChhXoQRqHyu2LnDx3+Evn6zYoVKTgBkldasPQKmngJ8HPiAiO2Ob2oBSJVTwfcRA/X43ISFUX8a40tPJN7du5eCXvwlAtrWFZ1738zQ6Qtoy7v3z//v3M3beHABunz7D8S9/BaUga3uk/HipliWghKwtNDlCBsgLZB0Y3SBkbH/WEZOIyrFAWYQzL2YWJk5imnUpv26c9Dy17zpzlo7BcSxsBKXEr2/ar3xyInu3XgURLPxQApYhZy1fpeqED6zyEiAkUQmUsrVilAazkFEH+Zxw4uhtlq4ch/ITSQWxcsBi1OgmmprSXL54v8TlP5rZRFUiWe0SF//gX6n7ef2k2749F1m8ZCKZjFNx+9Mb5rBl86nY7HWEoRNtxXGf6umnUPB46aWzrFlbecJsxIgmxoxp5fSp60Mc09BQye3/4IFLzJ07noaK2eoVTz41h21bz5i4TvXuR0UK5uGC52l277zAmvXTKNd1Q1NzhinTRnDi2M1h22cxAoPfQwa4Jk4c62DylJE0t5QQv0rzrr98M7ZtJhT+4s8/woVLh9H62xtTN8HgUe0+PNiSIEGCBKU4crObL2/dyb0tXwJAZRtpf/WvhqQRYmzR1tUbaZy7FIDC9cvc/dbn0FpCT33XA9cVX5nqk5Guh+ovoPrdkEBVgYt9wMWAITHjj7K4q71gSFjXlLR2mZm+y4mekdBXQPUUIO/FCFzo7k/TnXOY0NxrBqaJiNQgHqsHSjDjCcbrxZaFgGz1B+YWKp6/4olvY/9p8bBUgdnTPI6cjhNN8YOGrh7o7BImj/e3SdSHlJB0huSsEKdV/IMeEHECNiKuHBsWznI4eLL0+KLj6urR3O7UTJtYbP8Ohwt/rXHGj92xhWVzG9l7tKvCdoPuXo8btwvMmmLsn3KbWUJCsnw/lfusND7x49cHhLAJy2Ta2RYsn9/InqPVSb2ePk3HzQIzJzvR9TOI14Qoxn/5lvCTEBKaUQmuKY1taVYuaOKlIz1lbQP09gtXrheYMy2eoKry7zO0cxnrtUqSNcuCxxa1sOtQJS8whZ0dh9Pkc1Y6x92rO/mdt/9b3ftN8J3Fo2DfisgIEVGVCpFL/YXY+k3+uv8ECsAcpdTGCl2/zl/uK1GXVjgPygH+HriFcfGP4zYwUykVV4QFqplbdR3kdwkSQvVlChHhi+cP8aV3/UO47qlf+3maRrQaAlL8cEyWzTO/9Yawzt5/+hD5rvu0pTUZxxCajmXiqKb92KgFIGsLbSmPBluw/TioKKMKdQIhp4KMI6Rt009AJzqBetSv4zve0NeT43ZHN9PmjMBGyNhme0oF8npobc3QPqaBq+fv+2SohGEBlN9fQKwGCBWsFGsAVWx70T2tyh0uCEZuqRQvbb/K6rVTcOxU6Oof9BQQoOuenMbOFy8VkWrmBhqktirejyq5u0YEr12B5K04xDL09uY5c/omi5dOqlqnqTnDtOmjOHbkmr9mqCRqBKlgMg3Eqe7YforVq2fiONUzsa5/Yi47dpzC88rJwYeH4nOfyxU4evgqK1ZNrVhbKYtsQ4p588dzcP/lwe0pFmJiOLB/z2UWLZlAOlO9v5WrJ3Ng71UKhYfhRh+8YAXLyigUPPbuucjqNdMrbp+/YDqvf+NrAejry/EH/+dvcDn/EMabIEGCBAkedYgIb3nuGDc/83ehcTHyB38ep20UgQ0T2IWWpRjzU28KDac7X/oXCvfughjRXyEvRgFaiNlqPQXI6WICVYiIykAJGnuoKYiITTNIPxaqKUsbbnCoZzRSAPIxIlYbMlf5rv2HrrayeGwnVkH76lS/T21CA4RwS4hTiPbtj1c8lygBVT3QLJsvHDyhcXXMrbyMDNXsO+axbJ5Fea6nem3IwdmapSTjigVpDpwohPFwIxQbyHuP9rN8fgarzAwafo+gSli1sIX9x3sYyGx96Ug3K+Y3FYVGq4RApTkoIjNQXRIQlL5SNFCLirBiQSMHT/RSFmo37MPU3Xusl6VzM9iWVLT16xgN5eKNSBk6UCKu5fMbOXyqL/SUrCbY2H+8jyVzstR4pXhg1BKLLJ/fzOHTPRTc6nXSIx8Dn2sq3D/Gpz/7VY6dSjywEjxciMh14P3+1w8ppeYF25RSPwy81f/6J3V092ZgMfAWkbIkF88DY4A3+X07wB/Htr1skBCqL1McunuVr3/uS1w7YhL5jJ4xjRU//ioEwQEKWpHXCkfB+GVLmfs9ZoIid+8eRz/6r0VxTW1l4p+mLOOinxdNa0oYlREaHSFjQdaGtGWIU8sSbP9hmFaRyWKMrsjN3/HjqipfCXtk21VWrJ+IbSkyjqlTSoAqBYtWj+PonusgXujab5S0QTIqsy8bU0p1naXmk13CTtYyr5RyuHenwL3OPDNmjDZBymPxUONoaEwzb8FYDh8oVVSW1g3Uq+VP/UphAgYzs7518ymefHr2gG1WrZ7G/n2XyOcrJP0aIr9a7vpfvaP79/u4euUu8+ZPrNlnOu2wfMV0du86M7RBDQFWiZL4xS2nWLt+Fla5dV6EZSsmc+zoNfr7K6tDqkGp8mtpKOjtyXP2zG0WLhlHrXPvOBarVk9m9/ZLD7zPyjBvo5FbWlyJYD7v2nGOVaun1SDThT/4w1+jvb0NgE998mtse3ELWg93/NcEw4lHJGg/AEqp8Uqp/6mU+mul1ItKqV6llCilXqqjbUop9Ral1AGlVI9S6o5S6jml1GsecEyD7lcplVFKvUspdUUplVNKHVFK/UKN+lml1Bml1GWlVO3MJwkSfJfgC6dusO9bX6Hv5H4AnDGTGLHxNUXcYvCI9jxITZpL6/r/Tqqxmfn//adQDS2A4Abu86GHsxjVaM649qtSEtVXnyoxyaaChFHKd8E3Zq6/LlCVupoW6aPFznEt14TyPFQQd1ULSjTK9ffradyC4tyNRuaM7w7jsyrx94E/FrcCO+cZrzAlflw+rdGFHKHKFCmKu1kKrT2aG4QRLcLFjsjN2bibF8c8BSi4cPK8ZtFshcSGVh6HsnyspYrX+hD129yoaB9hceFqOftX6lHmenDsbJ5lc0vDHqmSZX0o7b8Wmhstxo9OcfpS/4B1XU84eKqXlQuaa9jsEu67HiKzPOFXsTIz6KOpwWLi2BSnLuZKuyB+rCZUABw82ceK+Q2R+/4gvdkCQjiIm1ovGrMWk8enOXG+n4F+A08bUnXlwsZBjW0gFP/+lY+7IWMxfWKWY2cr5wkK+rCcJtJtS8K++m7t4vW/+0/DOt4EDwePkn07RLwV2I5x7T+ilNqvlDoNfAHIAO8Wkf+s1YFSagqGIH1BRP6lQpU/BXqBv1JKnQQuAT8GHAM+OmxH8gggIVRfhvC05r9O7ecbf/uhcN33/s7rcVIOKQU5gZxnEkj5ocV54vW/ip3JMGvRQtb82A/i+FdGkEwqbUODA30ejEhDk2PI1CA2qqOKyUpLga00WccQscbFX0w8VgscJUWJpG5f68F2LNrHNprQASrYf1wWbwyAVMpi/tJ2ju+7GYYCiNseAREczJqXSuvj3+2Sm1mpoRQZNWaktkrx4uZLPP3MHJSysLCLdKnF7v2wZPkETh67RX+/B7FkVMX7DPqPrasSfmAwdt+N6/fJ5z0mTR45YF3HsVm9Zjo7t5+jdPb4wfSq9alUNz1/lGeeXVgXWbx48WTOnb1JT08lw+/hIBj2ndvd3OvsY8bMMQO2sSyLdU/M4sUtNb0lKrdV5lp5kFv0iy+c5YmnZ/phM2pj1tx2rnd0cf/ewEb/0BC80AXuXtGY7t3r5drVe8ydN65mDyNHtvD2/xvFwvudN7+bvD7zbY2pm2BwsIapDBN+GmPA/SYmGH5dgeCUUlngOeBdwCLgNHAH2Ah8Vin1zqEM5gH6/SjwFqAZOAHMAT6ilHpdlfp/AMwEfktEuocy1gQJHiXkPc2fPHeEW5+LPLDaf/wNqFQQgkeh/BtHkBNKBEa8+nVM+OOP88SqZaTEQ3uCBOpPkTBxlNWVQ4Xfg5ikEtZRQUKqIIYqQftYPcFv70FBs7LpOnvujUUVxMTMiie1cn2C1PVQBQ9yLmeupJkyso+0cs1YBD82a7CM7U+bGK8KKeLLtFfw3cQDZaKuakIGKtTHFsOuw5W3VVIWnrygmTrBIpsJJkuLCbbqz+fBecSU9rN2aZqdB2uF/Sk+0pPnC0wZ79CQjdZbVpCxvX6CtFr/1fDEijZe3Fd/MtWT5/uYNC5DY7Y4t0I5ap/fiEitZR8Fxy2sX9HMtn3dA9pTgY1++lKe8WNsmhujJ/RAbaOkU54feiAep7Q+O27d8ia276//MXb2cp6xo5yicQ4H4u8qld5b1q9oY9v++n73VOtilG0SBnt9V9i5Yytf+Oqe4RlogoeGR8y+HTREpA94BngbcBTjij8a2Az8hIj8bh3d/DUmLusbKm0UkaP+PjYBUzB298eBZ/39v2yQEKovQzx/9ST/9YF/o/umSYoz5+l1zFizKnSbz3mKfm0ZNwglZC3BGj2Bn/rXD/Hjf/Z2JniACHZAglqQsbXvWm/UqVlbyDomBIBtmTirWd+9P62EtCU0O0FCKvPdwlev+orUVKAuRTi88yornxiPpXxX/9jxxAnQwJ1/zoKRXL3YRX9vIVSomnADFla8VCQv49+tshinETEaGDRmaZHm4rlO2kZkGTmqiSIKuYo9ZlmKtU9OY+fWS2Ec1CDBVbHatLyxNcQkVGAMlxc2neLpjXPqbjN7zlhuXL/PvXt9JeMZOmFVj0r18qXbpFI248a11dWnUooNzyxg0/NHhzyuwSK4jjY/f4INz86vo4WpP31GO91dOW7fHjyPEb9WBosb17souB4TJrXUty+lePKZGWzddG7Q+6ofkS9inFTdsumUP0Ex0HEKv/yrP8aSpeaa3rvnGB/9l8/jyY2HN+QELyfcB74JvBP4CeD362z355jA/eeARSKyTERmA68GcsDbfBepwWLQ/SqllgOvBQ4A00VkKfAY0Af8aUmcKpRSczHk61dF5N+HMMYECR45vGfXOS585eO4tzsAyM5/jKalxQmDA+WPEJCqgtXYimps5UB2Gst7L6LzvjLV8wlNAfrdGFFJSGQqzyhOVRCnVMRPDqUjN/9AQRr0WTBhBCaku+lxHbpzKSPrDElan5wteKici+r3jGohr8FVHDzbzLIZ3VHIAaLxxIlTJYLowDU/4GkFLYF3TJBMR/uT2hIrJiGQFo8JY6CvHzq7IrIzcsGujpeOeKxeZBf1GaHc5qsWc7I2on4mjLHozwt371cfl/EgKn7F3Xmwn7VLqs2jDc7WssL3g+qYMDpNoaC51VnB86sGtu2/zxMrWqlue5cKFSopguux282FNa7dxvOEm3fdCv3FicPi1tv397BuWUNNIrWURC2+9ganUB47ysTBvXHHLRlX7d9h+/4e1i9vGtS+6kdcdGMwZmQKx1Z03Kod5z94x1SWQ3rUY+H63J1dvPkP/plCYXDXTYIEcYjIR/y4qdNr1MmLyLtEZKmINPoxWZ8Rkc/WuY/XiEhaRI7UqLNbRDaKSIPf/8+KSMcQDumRRkKovsxwP9/HNw7sZtvHzLuT5Tj8t996HSBkbXBR9LvKzGRjjE4t4CjN+CnjSE9oQ3X1YvX3k7ZMm6wtNKeMvTmhUWiwoTntu9n7z5LAdLExJGrGgaYUpH2CtC1tFK4py5S0FRGk54/fYdKMVhobU6aPQJVKRGcGyabSlkmGlbYUa56cyEtbrsVCAqiyB76lLGxfFRqfEVKAHcYxjScDUrHndBDr1JCziM3O7ZdY+8QMs7lI+lr9T2nqtFH0dBe4cyty/VDhf8F+iG2LjojiFnXj5InrTJjYRmtr/dk4lVJs2DiXF54/SXlSqUHtvgjV3LyDzy9sPs6GZxYMqs+Jk0aitdBxrXPoAxsUFGfP3GTEyEZGjRrYMFMq+k2jczrIPSoVM9rrv1WLCFs3neGpDbMYjME6ZmwzmazD5Uudgx3qIBBlxxU0ly/fIZ12GDO2pS6lqW1b/MW73xx+/8M/eB+d909UdClM8J1F4CUwHGXwUwrlEJEPi8j3isjv+cbitYHaKKXGAYEs+ldE5ESsvy9g1KUAbx/MWB6g36f85XuDWFUichD4d0ycqtIb6fswN/L/NZjxJUjwqOJ2b54PbN7P3a/6yVssizGvfWOxYsyKRKVgyNTQY1/DNdVKg5dnhNvru+R74GpUwcPqKycxlECY1B3MDSmIixiQp7FwALge5A0xq7RmScstDt5pj+K0Rvl1DIHb6xq3MY8isvRmZ5q0o2lrKMTUqbrosW7IVInF1TSNPa8SmSNFNrI5J4bsUgjL58Oeo/UZe/Hn9fXbgmPDqDZVtU7pOAaD+PNdKXhsUZrdhweflPLGHQ/LgtEjo3mnSKVqDTpR1UAJRNcta2HbgcGHJbpxp4AIjB+dqVGrfKzl7v31QLFueSvbD9Qz6V+8z1udHq4rjB8dJfyKK5O1DmKiSkmpjCikROWQFOuWN7N9f0/ZOAbC7XseubwwcUzlxLxDQXCelf/+FyUlNqrkrXs76+zJHIvTOB0rM9b07d7n8rldvPNvvjBs400wvHjU7NsE33kkhOrLDF+/cpgvvOcDeHkzM/34z7yGUVMmhT90kDTUxBo18Z5yYtGcFtoymqY0pBZOQR2/SMY2hGizrUkhNNiGTA1UqxGJKqHbv+0Tpg22UaRmbMhapo0hMU29IDGVLnicO3qbpSvGkPKJWNvnEoMEV3bQt69ODdz8J0w0xNatjj6f9lS+S70dc5cOUkQVx6U05KuFpWwsZZtYqMrBVikUjl8UVvCgxOHA3issWDSeTMaJ+vD3WUz/xmHG8OTGWWzdfC6qVnQHrUedWv814Loee3Zf4PG1M+pv5GP0mBayDSkuXbxTsuUBGNUaRtTBgxeZPWccjY21DMfKeGbjQjY9f/Tb4vKttbBj2xnWP1m/4jcQi40Y2cio9mbOnL45pH3byjbXYZ2361MnbjJhUhvNralB72v909PZvuXCQzynxrgWjLvXi1vO8OTTswfRXnhm42P86I9tBOD69du8888+hCsPK/5rggeBpWRYyncQP4JxZzotIs9X2B4E9V+plJr1bei31V+Wzu4H5HAo81dK/TTwPcA7ROTbF3Q6QYKHiN/+5nFufv79SN6Ep2l96kdJT4zbOooglqfW4HkRmSoxF/uXUtNZlT9vNrga1e8acrO4J1SgQI0jYGs9L1KzBjFYXZ9U9T295jXd5VxPKwVtgRvFN1VaowquUabm/eRX2qhk42bi/tMtLJ/dTWhHVeDLpGQCXHseQmV1WzC5bU5FECPVY94M4ewlIVeIdlAt9mngph0nv3Yecnl8cWC31iLNHiz55fwZDmcvu+Tq4FMrqVR3HOhn3dJioUExkVq/UlXVEFMsnNnIuSv99OeGNtm7bf991i9vrVEjblfX495fGQtmNnDhah89FSYS6lGB7jjYw5qlDUXXQqRGrRwmouxIgrY1lLdzp2e43JGnt7/4fNZLgu882MPjSx6WSjXCnGmNXL2Zo6evvt899IxUisyoNeH6fOd+3vsPn+P23SRPwKOKl4F9m2AYkRCqLyNc7LrNt761iePPbQWgadQInvjl/wEYAtMV6PcUadt8V8qsszAJpTIBSdraiJVxsG53krE1zWlI2cIoP567Y8Ufs8Z9PxRrEiSbMvVSNjQ4hljN2GZpyFVDuB7a1cHS1eNwbFWkeFX4ClYrIGAVtlJYlkWUCR3WPjWRHVuuViSAiuOaWqHiz1IWjpXCUkEU2TghWmJYKUOr9vdqTp+6xeKlE8yWig9xFS4D8iugc0eOaKR9dBNnT5cSlZXGXPpnObj5q907z7Ny1VRSqaGltnzy6dm8uOW0Hyi+srJ0sKikUs3lChw6cIlVj80cUp8tLVmmTG3n2NGHnxFz755zLFkyJSTTB4t1T8xk145zeAOlea2Jga8D1/XY99IlVq2ZwlCM64aGFLPmtHP4wMP2xhCOHLzKzNmjyTZGxG+919if/flvksmYeHl/+9ef4NTpfXj6YcV/TfAKxlp/uaXSRhG5gnHZj9d9mP0GGQ5LlajB9w4ApVQr8B7gFCa0QIIE3/XY33GPr2/eSveubwBgNbUy6od/Mdwe2I1xBJxgRKYaZrWbDHdpYHLhNqpg1KkqIE59wQGeLg/1qShWmIJx2/e8UO2qXI3yNFkpMLWhi9P3Wg1p6+oo/mleo3pc494fDrb8mHt7Le7cd5jcnjPj8VWoyi+6iEjzXfilWnx5Yzub2JUu+BObKUczYzIcPxeRX9WfxXGSLPrc0wfXb2umT4z/AJUIsqGrU9MpmD3V4eiZ+l2hS2313n7h6k2X2VOLJ5wD8jUe/qseWKr8NTrlKBbOauDAiZ66x1mK3n7NxWs5FswciAQcul2echSLZzex73g3g08QZtDbr7lwJce86WmCJGb1kKiVURoizMCxYcmcRvYf76VWCIJa6M8LZy/nWDhz8OKNUpRONATvpI5tsXxeM3uODC7EV6BstTPtOM2+aEMK3L++mze+7Z8feLwJEiR4+EgI1ZcJRISvXzzA5971j+G6Z974K2Sam7AQ3wvJuMSbJFFBTCWTPCptCynbEJppBS2LJ1M4cYU2R8j4rv+OJdiWmItGQeA+lIoRrCnLkKYOkLEg5atRLWVoS1sJtmVUp933c9y/m2P67FZDnFrRBWnFFKkBihSmvtHT3JJm0tQWTp/oxFKlqs4oNqoVc/YPFKWBm7+K/Yu3tZTtb7fZtuUs656cUZRwqmhfIREaqGSN0jX+8F+zfjov7bw0IKlWSqgOxmjo6urn8qW7zFswvv5GJWhoSDN33jgOHbjMgylTS1Hc14tbT7Ju/Rxse+i3oTVrZ7NnzzkKhQdTPdRCb2+OUyc7WLZ8GkO9ZabTDouXTmLvSxeG1D6YDIjU15Xx0s6LLF81mVRq6E4ky1dN5NiR6+RyDy9+Uy5X4OjhayxbOYmB4oGVQ5g5cxL/+80/C0A+X+D/vOW9eCH/lOBRgRqm8h3EXH9ZK7NcoP6c923o9xuYN9a3KqWeVEo1K6V+Afgh4GRMifqnwATgjSJV2ZUECb5rICL85peOcPezfxuuG/lDv4TV2BpwjL6Lv6C1KZ4noZs/WpuYpm6gKNUcYhJL9DXsXCFKTBUkgJKinUcu/cGOXBP/VPnfVUEitamvUl3WdosDd9tBfPVCQOjmNSrn+TFYAymtNiRpQLj6Y0SEo2cbmT+9F8uX3pr4qKYgcaWoxvMKJuGPLi+GTPXQvkpU/ParFsLeo1E/OojHKsUq1EB5+P9n773j49jK+//3mdmm3lzkblnuvfdKAl9CSSEEQgjhm0KAEEK7hCR800MIoeWXkAKBAKH3UALkEnDvTe62bMuSLdmyZFldqy0z5/fHmdmZ2aZdaeV7uXc/fo21O3POmTNl5zzzOZ/neZxtpuf7heY4qxbpaAJPn5zFSLNurMXZ//plfs5eiWAY6Y8v3aLsCe8o0nR9lJULgyqPBI57uoIqm87OlxaB7V0MT/tSSjavKufU5UEMM5/jTD3uc9cGWbGwFJ+Vpdd97pzj867P9VxKKdm0spzTl/sTKm6VvMxz4ycWdzvO+VXXs+n6CMsbA/h0Vzt5LOBWp6bagxuWl3L+2gipr075WQeXbo6ypCFEYAL2caaQCkIINiwvp+nGEKYcT/uqTqBmPQhF9seHmvne93/MhSvje28oYnLxHLBviyggioTqcwTnelr5/pe/TdfNFgDqly1i9cteCKjYRsrLSVLmUxLzEh2VJEpAqU9S5lPJo3xCEvCZVJRqVM2rYbS1i5Cu3P19qHo+W2FqkZ6ghkANJwmVrkl8mi1pt8hU3UpihVrOHr7P1t0zENZ3BSeCqJvqVJvSP3rWbpzG5fOPPKRaijEkNDR8lou/PyMp5U1GpfrzqHuYSCTO7DnVY7KbmhWv1f7mflwqUm0m58/cd9VIIoFTvuf3uD10oJndexfnHQsqGWvXz+HqlQeMjsaYDJVqb+8wj3uGaFyYPbP7WPD5dDZvbuTE8ZsTaicbDh28zq49S9W9kffw55RfuWomLbcfMTI8fn4j2/4HB0bpaO9n0dIpTIQI1zSNLdvncuLI5BlxJ4+2sWnrXHRd3Rdmlji7mfCHf/R/mTFjCgDf/c5B/vdHP8Ew+yetz0Xkj0LFmLIwRwjRkmmZpEOotf5mcy2wt9VMdrtSyjYUWVqPUrcOAp8B4lhZVq3EVW8BviKl/JFdVwgxTQiRCAlQRBE/TfhSUwdXfvxtIm3XAfDPmE/5jpcnCFPbpd8wnL9m3PmOTWAaZkItGh81uRmrZZn2MEFiClO54xM1nURTdlxTS50qogbCsEIBxFztGraq1aRaDxMQBl3DIaVaNQwrtqqBGI1BOO60Dap9O2xAEqFrmIKbbUGWzB+xYpDbxKZhKVQVTNMkboQTJJVXSarilqux1bSIVZPKCkkoCPe7DUwZt5SrBuqR4ggvlKu+kdQ5r1I1Hje5dDPGmiW2jNdw9u1xAc8NpouQrCyHijJB+8NYos3URZJ63EkEHRLDhIvNEdYtC+EOYeAun84GkWn34douJVUVGtUVPtruT9RjRk0KnLs6yOYVFYnr5rj32/dBPInkS+5f6n0AJpXlGnXVfu50hLGvlToPqQS0+/iUa76ZsOclJoY0OHNliI0rShhvSAfHtpW43xUqyjSm1vppaY94+jWedxwp4cyVETatLB1XH7OhrESjfkqAW3ftWBT50WW2SlXTSwhUr0msDz86ye+84+OZqhXxDKLA9m0RP+UoEqrPAUSMGAdvnuN/PvaZxLqXvvv3KPELyn0yEbc0qEPQcu336XZsUpMyn0mFz7QSUJlU+SU1QVi6uo7+1sdoRlzFN7Xc9W3FKXiHC58uCWgqnmpAd1SmurBiq9oPEA0e3BukosJP7ZSQ9VCxXflJ6EbdUKrN9IaYz6exduN0zp3sTFKQ2kmldJd61OcoV10z0e56ToIqQAqOHLzNzr2NlhtYdojE/oQr7qWW2Lp8ZT2tLb0MD0dxU8YAySrZlBM8Bu539KHrGtPrs8Vdyg2aprFtxwKOHblN/grCzLAN0gM/ucreFyyfUFs2Fi+ZwYP7vfT3j4xdOE90PewnEokxZ04dkD/B7SbnhRDs3L2Qw4eyidLGas8dt9eLIwdvs3NPI6IAMXnmNdTS3z9K7+PCn9O+3jB9vaM0NNZix29L5yaW/d6QlJeX8jfvf2tizVPv/AiR+G2X0qSIIiYMK9AN2SL22TMkuWcAnEC7Usq/AH4Z+BqwH/gksFlK+WOhBrF/A4aBdwIIIV5uEc4PgT4hxFEhxNI8+lpEEc8owrE4f/W9Jvq+/6nEuupfeitoPpKHiQSZaljKVAnETETMJlINxKgBo3FEOEZLuIqZ+iAhEVNl41IljlKtpTYes4hRqz2ilvI1Zn2OK0J1XV0PTY/qFEkbNRQ/GTEQcanKQoKoFaYVNiB5XzbXapq0dujU18UIBe3xzUSacaVQNdVimBEkccul3yHSbCWlUiDGcUg5k00r4dQlu00jsc0+r6odh1xNhZesa2mPMb1OIxQ0ksbw/MZlNY47dsG2NUGOXxiLpExHdqYSnwAt7TGm1eqUl+Zj02UjKxV2rqvmyLnCTey2tI8ypdZPZZmy+dQpTVWR5htOYdf66pTESZJEoGFnnXTdPxgue81LZLfeH6WmykdlufDcP7kiVRmr2t2xtoJj54fybi8T7nXGqCzXqK7IPyRatoRfO9aVc7xpyAoBYSepGh9b5q9chvCp9zgz8pDLTcf52ndOjKutIooo4smgSKg+B3Cy6yrf/dcvMNI3AMCaF+9lyfrlhHQIaMrN3q+hlKY+pVINCOWuXxWEKUFJqU/FUa0KQKkPyv0G5QFB44YZ3Gt6oMhSXeITju5SWGSpiZNsKmAlpbLjoSZIVRzXf0zJ+RMP2bSjXtGPruyIad3pE6rRzGhcXEfXwxEGB2LYcVEdYtRNnLoGUeHen/CUtdfdvtnDtOkVVFaFyMZuahlcsd3Eqh3PdcfuBRw71IbbtV+VSw1ZkCuklBw+eJNde3JPmjQW5jdMYWAgzOOeYSaiePRC0nKnm4rKEurqygvSohAikaCqkJBSsn//VfbuW+7ZV/6PTec6zppdQzxm8PDhwLj75Xb/t9u+39GP7tOYVl+4oPu79jZw+MCdgrVn4/D+Fnbta3CtcSsecoU67tf82ovZtGkFANeutvCJj38ZMxFmsohnEqJAs/d2EkLgnpRyQaZlkg7DfoMPZCljB2ULP6l2pZTflFK+Skr5AinlG6SUF6xNvwtsAf5USnlfCLEN+BaKKXkj8CfAWuDHQojqPPpbxPMAP/rRj3jVq17FvHnzCIVClJSUsGDBAl772tdy8ODBrHUfPnzIu971LpYsWUJJSQm1tbXs2rWLT37ykxOeOH3fj5q5/+MvYA70AFCycgehxesdd37UeG0YEkNxjI5s1TAgHLOISxdZGo1bXJTg/Gg964KdSnWaUIxKrzrVctcXo3EnbIBd1rRIKNNESJhTOsTjcIDhUV3FTY2biHgcEbMJWUvRCgi77WynyFRu5U03QqxdMqpUgqZDNtqxUE0zqs61pbg0ZXZl6Jx66B2AobBInEO3ulV9jqetmw0nLoyyZZXf44qeP5w6c2f46B80GRgq7GTp8Qthtq3JXa3oTnabDnPrgwwOx+kbLGy4pCPn+tm5vsp1LZPhkKq5/Nbm1IcYDhs87o+laUcRq3ZoCJmiSs7WzwF2rKuwyNHs/XGHhPDGAXYwc5qf0ZhJT3/c8z40UQ+8Y+eH2ba2cCrVGVP8xOKSR33qujt+lpDtvTEV9ruqTrB2U2Jt5PFp3vGnnyYWG9991dPTw6c//Wl+/dd/neXLl1NWVkYwGGT27Nn84i/+It/61rfGbGMyn+8/jZgE+7aIn3IUCdUc8Ww1NHsjQxxpOs2hL30HAH8oyEve/tv4dUVsYv1YheXmH9RJeBYFNEmpz6TUJ/HrEk2TVAagxGdSbb3mTZtdQXQ4SrR/VJGpmrUI5+aRgCaUi3/Qp9So7oeF7lKECuDqxR4WLq2hpCSAJmwi0zkPHpIzsT0bFFG6fc9cjh28lyAvU0oJPWmg87bhkKoKRlxw9vQ9Nm2blyiRaf+QrC51VK/2MShqVWfmrBqMODzsHEppw/s9d1y5fJ+GBVMoK5t4wHU39uxdzMH9NyiUStU0TY4fbWbnrsWJdYUYjKdNryIQ8HHvbs+E27LRfOMBM2bUUFXlNbzyV6l6y+/eu5hDB5onfNx23F4pJUcPtbBzdyPjTSyQDjW1pVRVh2htyeaVnB/a7jymsipETW2yMSsT7mO5qVSVwa5pGh/66LsSa//qzz9Od881THP8LzRF47Nw0Aq0PIPotf7WZiljb+vNUmbS2xVCTAX+FmgC/tla/ceoweTnpJSfkFK+H3gHMBP4zTz6W8RzGFJK3vSmN/GiF72Ir33ta9y9ezdhu9y5c4cvfvGL7N27l3e+851p6589e5YVK1bwkY98hObmZnw+H4ODgxw5coQ3vOENvPjFLyYSGV+om/a+ET77v2cYPPINtUL3U/2Lb3ZITqlcoxU5A2D7/6OIzphpJY2ySEwJGAYi4Zks6THK0DGpFWEnTqoQCEMRngKJMKRSuSb4Rqm2mSoEloqnCpppsKy2jyvd1Yp4jZpKoRq1nvtx00lmZQ8FScO2kNKxCO1YBsDjfh9SSmqrUhWj7qSM0iZDpWmNqV5IqUJxrV4MF65bNVIUjuMbp6SUPOqLYZiSabWO2tVecoHby0QTsG5ZkDNXCp908nG/SSwumTE1dV4rO2mX7v0CNq+u4MTF8U+Wp4OUJj19EcIRk9nTQ9lKkiBEs5xoIWDL6iqOX3BUtA7xTULdLN3xhHO8F/oG4wyNGMyZ7se26Zz23fFnDWsC3bDuz/SE6pbV5RxvGpgwgZqM/iFFzs+t949d2EK2SYGta8o4ccF5n7NFD/Z7YH5Q5fWS2eihmWrfxjDdd0/z1x8e2/ZMh/r6en7rt36LL3zhC1y7dg3TNPH7/XR0dPDtb3+bV7ziFbzkJS9hZCS9R9pkPt9/mvEcsG+LKCCK13IMPJsNTYADDy7xxfd/AjOurMO9//dXqJ0xLbE94fwuIKhL/Dix92sDJlV+8OuqXJVflSvzuclNWLJlFs0nO9Bcd4sQSnEqUQrUkA4hzdmne3EjMhqnpbmPlWumWKpOm/z0DqbqHDutpcv8aLvzayKAQDB1ajnBoI+OewOuNtykZvoZQ+GJe2rvX+fcmXusWTcTvz91u7tuOmVpSghXV+ZQgWDPviUcPXg7YWCmtpF2d2kRjca52NTOhk3zcq+UI2pqy6ipLaPldjeFUKmeP3eXZStmEgzlbsjkij17l3H40PWCEFbxuMGpU7fZtn1hyrZ0WV3HhnNBKypDzJpVzY3rE1NS2kbb1cudNCyopaSs8Od06455nDp+d8xEarnANE1OHrvL1p3p7lPHdc7539qSeFFOVwe2bF3Fr/36SwDo7R3gr//y4xjcHXc/i8ZnES40W39THwQOGpPKPlPtfhAVb/VN0glktx24IaV0xxn5tmtbEUXwmc98ho9/XMXpe+UrX0lzczPhcJiRkRGuX7/OL/zCLwDw0Y9+NGVCqb+/n5e97GX09PSwdOlSTp8+zeDgIMPDw3zsYx/D7/fz9NNP8453vGNcfXvXf12i538+AYZS01XseSW+2pmOQtSUSEMqVaohXWE+bUWp4ahNrRinImL9PEyJsNSs54ans6600yoLwrDVpxYRGzMUQYr01HP6ARgmy2r6uNlTiRHHitdqqD6BUsvaJK09piUNbiJ5sDO8322VqtseM4y4J3alTYqZOEmLkhVzKxbCjVbVPdLY4OOFTTydvDjK5lUh3ImHct+HY2+sXBTgxp0Y4xTnpSDZlj95cZQtq9JHa/HmU3B7B9meSs621YvLuHEnTDRWqPPoVgvDsaY+tq2pHuPdwNmYyQ5etaic5tZhRiNG0rWx31uMhBLWnuaWYymoXTh5cZDNqyoANXkhXf+snpHLvbBiYSm3740yGrXvhdwI41xx+vIIG1aU5vSulW1/yxaEuNMRIRxJV8ZWnGZWNXtKC0dcJIQgWLs5US/af4l/+vg3edjVn7mBDIjH42zevJl/+Zd/4fbt24TDYYaGhrhz5w6//du/DcAPfvAD3vjGN6bUneznexFFPFdQJFTHwLPZ0Lwz2MWPf/i/XDl8GoDq+qnsfP2voGuOOz4opWqpLgnqaj5QACW6pNwn8WvWrDqKcBXCoNTn3U9ZVZDKmhCP2voTN4yGI3lXcVMl6ZK16243DQRnjj5k6/bZ6JovQTCmm0G3oQldGTGJGKjOvI4Te9TB1p1zOHn0Xko7ye71ThtaUjlFfA4PRbnX1suS5ZmSJtmq02RyNvugqQwyHxUVJcyaXcPN64/Q8Ck1hPAavLnixLEWtmxrQE93AQqAbTsaOXn8DoZhv6ko5OpeZCMcjtJ8o5PVa+ZYM9O5KBFzR2lpkEWL67l4Yfxkmo1TJ2+zYUMDfr9v7MLjwKYtDZw700Y8Pr4A/jai0TiXLzxg/cb5FC4sg4Ng0MeyldO5cO7+2IXHwIVzD1i2YjrBYKZzaisWjJT7I/OxqfV//b63UFamXoo+8W/f4PLl05hmPh7YDorGZ+EgRGGWZxB24LKd6TYKIWYBDUlln3i7QohdwG8An5BSnnRtqgKSJVP29+pcO1vEcxv/+Z//CcDChQv50pe+xKJFTuigJUuW8LWvfY0FC1RUja9+9aueuh/60Ifo7OykpKSE73//+2zcuBGAQCDAW97yFv7yL/8SgE984hM0N+cz5wAHb3ZzYP8BwlePAqBV1FL5s6/FifMoLMWpRaTaLvumCVFDxTRF2l7xikyNW8mgDFORl6aqG4756YqVMi/Y77XAJHb+H4tcldZ26agTrPZCWpzp5aO0Pi5VpKthOsSslBCVSW3L7E4lhldhKqUkPCrofKTRMMtIrDOkE4rZtMgwk7iLlFP9tacrQ0GYNR1u3U00bH2Y2MPWNB0bcTQiufsgzpL5fs8zfCxbT7WhEAoK5s/yc/1OtlDT+cNtp49GJa334yxfUEKur8TJtnooIGicE+LK7ZExw5ONBW+SLOdcRaImN9uGWbmwIlttstmBAb9g8bwyLjYPpC3nXJpUEjy9qCUVo1GTG60jrFxUAsJWVycnDsuOgF+wtKGEi83DCHRXv8YmjHNFNCa5cWeUVYuyqX4Te0u71u8TLFsQ4uKN9LamliBSbUFPLqSqE8pLC1Tjr7DCncs4Q92neNO7/z2H/nrxk5/8hJMnT/LmN7858RwHmD9/Pp/85CcTtuznP/957t3zvj9P5vP9px3PAfu2iAKiSKiOgWeroWlKyU9am/jaB5zsfy99x+9QWhbyxDn1CUlQMwnpKjmVIQUBTVLhl5T7nbAAJX5FqJan4ToEsGRjPTfPP0RaxqTi7iSakJToKkYruGbdSb25HveEGRmJM3teTZIbRPrBSsOHVxTvDEzJ7vU2SkoDLFhUy5WLD5O2e4ndlNim9nah9nzkYAs79ixIUra6W0xDxgqRlz26eesCzp+9RzxuOtqBNOrWbOjrG+FR9xCNC6eNXXicCAZ9rFw1k/Nn7+K8mbjJ0NzaOXKomZ27FqFp9uuIRI6jnWxYv6GBS5fuEYkkx4bKHYODo9xte8Sy5bMylhk7DEVS+aQZar9fZ/3GeZw60TrOXiocP9rClm0L0HS3AqWwWLm6npZbPYRHxn9Ow+EYt5ofsWJ1/Rgl3fFUzRzuD2Xwz5o1lT/8o98EwDAM3vXOjxCTLeMyuovGZ+HwHMiC+m0gBiwSQuxLs91m1c8nqUCfWLtCCB/wL8AjlIu/Gz3AAuEJHo4db+VRHv0t4jmMBw8eALBmzRp8vlQj0O/3s3btWgCGhoY822wb+Vd/9VdpaGhIrspb3/pWysvLMQyDL3zhCzn3yTAl7/mvC/T+j2PjVv/cb6MFS11KVJdCNG5a650YpcIwUaSrVC76UQMRUSSnSCOavDpYy9LSx+gYqu244ahM4wYibhGktiLVrXyVsG5GLxce1KgkU1FDrU/kezK9I3QiBqvTCY86NZMqUBpca/GzaH4Mny4xTDvJlMSUyaEAzMQ2qzJI2Lgczl7J+VLkAW+HL92MsHRBAJ9uesbibHE13W1sXhXi1KXCu/on4/LNCEsaggT8aWz9DDkcbKWqQGfrmkpOXhxKS/zlA5khlqiNC82DLFtQlrafrh672nMUnVKabF9TxYmLvVlsbW/oh9SEb+lIWCdOrt3/y7eGWTSvlIAfr8I1R3Nsy6oKTl8eQkr7WJLVrZn7kw+utkRYMDtIKJD5fGbbx6aVZZy9OpIIpZwOmrCSIru8FPNBoHotaCqUW3y4had/tJ9jp/OzHfftS2diOLCFAgBnzpzxbJus5/tzAc8B+7aIAqJIqI6BZ6OhCXDx8W1+8Pmv09XaAcCC9StY/aLd+IRMLAYqkVSlX1IRkBgSgpqKpVruUwpWIVQyqaBmEtBUUqlk6AL8AZ15S+u4c6ULn/UAUPWkSjaleWdblHpVWJ+V0XH80AN27pnvUoiSVZ0qhI9EoiiBpVJ1b081dASwZv0Mrl/pJhKxA4S71akaCM0zC2hXtEnezvuDCKB+RiXJg58dBzU1RECuT0WnLz6fzsZN8zl98k6OdVNxaH8ze/YtHrvgBLFy9Sxu3+pmZMRWCoxNiLvxqHuQ8EiMOXPrXGuTr/3EGVVd19i2fRFHD98YdxsHrERU+cbQyhdLlk6no6OXwYHxvTD09Y7Q0zNEQ2Od1SP7d1XYx7oQgm275nP00Pjv02OHWtm2a16CTM+OVBcv53s6qPVve8evMW++ijm1/8en+O53foghe/Pua9H4LMKGlPIhYDM6nxJCLLG3CSFeDvyh9fUvk+sKIbYKIVqtZXah2k2DdwArgXdLmXLD7wemAm+12vYBf+7aVkQRiYmjCxcuEI+n+lbHYjGampoAEpNEADdu3ODuXSVz/Lmf+7m0bZeXl7Nr1y4Ann766Zz79Imjd2g++F/EHqpxxz97MaXrXmipUHG58FuLnfwpZiriMy5dhCdKsWrHL/XwMzIRX9WQGteHallR2q3KxWWCvBXxZDWpXU+RRXUlEZDweNCvwgNIXEmrTCcZFjhkqWdJGt9Mdyct4sqKLWqagqu3/axYGMWUKhFVJlvaPTEppaSmSqJp0O16UsiEinD8cCtLnXVw/lqE9ctDObpWO32oq9II+ASdjybmxZMJbhvelHDu2iibV5aS2X5KT7bWVfsoCerc74pbZbS8iTNHlToW66hx4mIf29fWZGvNs9gkZ22Vn7JSH/c609mbLsGJp9sy6S8e8jQTASwlnLg4wLY1lSRIfVf4gGw8aHWFTmW5zt0HkSThgl3Je14nSqqevDTMltXZkrmmb7+yXKemUqftfq7q6dzvB7cAQ+hBgtXrEttGe07x5nd/0hNneKIIhRyVrvJEVJjM53sRRTzXUCRUx8Cz0dCMmXGO3jzH9//1i4Aa1F/5njdR4hf4hTIONAFBTbn2l+io3JRCEawh3STkkwiVXpSAz6TEp2KigvAMdrZZIATMXVbLw9YBYuEoOkqVGrKTUOEQtHbCKuU+rz613u6nbkopVdUlVruaNfto78x2wddRcUndQeJlojPumKhOD10QoGmCTdts139H3ZogZ+3FVpS6mpBS4+ghpU5Nbd1d39nh+AOmCxYvred+R/+4SLW21h5Ky4LUTSkf5/5zhxCCnbsXcuTgTWtN7ipVKSUHfnKdPfuWpG5LUSFOnFRtbJxOb+8wjx8PjV04Cfc7etE0Qf2M6qzllCvPeFSq7u8ikaBqPDhokemSyXnhcGPW7CqiUYPurvzP6aPuYSKjcWbPqc6jlkzcG24lc7YwE6FQgL/7+7clvr/n3f9AOHIrazKB8aBofOaOZ1PQfiHEHCHEI3sB/snatMa9Xgjxh0lV/xA4jnLBvyKEaBJC3AK+AwSBD0spv51mlyFgnrWki3Mx3nY9x4QiSA9JKT+bpshfAyPAR4UQzcA94JeAa8DnsrVdxPMHb37zmwG4desWr3nNa7h1yxFF37hxg1e96lW0tLTQ2NjoCVdy+fLlxOeVK1dmbN/edvXq1Zz60x+O8ZEfnKd/v3OL1vz876sx1ISE7SFx4piCQ7LGTMf9P24gDAMRjSt1qUet6SJdpURIyd3RCqb4w5Rixbk2pB1o1KlnmN7kUkjW1vfS1F6l1ruKAt514KhbXfCqV634kyljndNOe6dOTaVBaUjiZGJPD4Gj/ty0Es641KlSmkgmFqA0NaGVg7sP4tRWapSXeo8n+dgUQeSs27qmhOMXxhe2J1e4SdV7nXEqKzRqKtOHJMpk5+9YV86x80N4E+jm5uLtde/PDXcfRCgv9VFTmRoz3078lM5E2rWhlsNnc0kw6lWFOgRoHhJToONhhNKQRm2VfT5zmRyHneuqOHp+II0XmLtfhVOqPuiOEwwI6qr0lG3ZbMcd68o5ej43e9hJUpUP2e6U8VUsRvMrEt2MPuLGleN84nM/zmnfueDAgQOJz6tWrUp8nqzn+3MFzyb7tohnHsVrOQaebYYmwKmuy3z9Hz5DeHAYgG2/9CIWrFyEX0h0oTKO+oSkRDepDCjS0wDKfIpkLfM7UnO/kAQ1pTZ1zVEmPuka+HSbMBWs2DKDKycf4Lfq+IQiax16U6ALTQ0eloFhGnDudCebt81JOhKlXE0dXJKVp9kGy/QD07yGagb6IvQ9DuOQqVrS9GsqOXr96kPmzKuhrDzoMbaUMjVNzNS8uNRkQk0NtHv2LeVgnqSaaUqOH73Nzl3ZcpoUFrNm1xCNGnQ9tEPweVWqmYyaWze7mDa9kqrq5Mzuqt5kYN8LVnDgJ/kN7lJKDh64xp69y3Iqr4tUAyxfTJ9eic+n0dGen5KyrbWH8oog1bXeZArOpEHhH+079zZw5MCdvIxXKSVHDrSwc2+qWnOMmtb/tvJGptucsuIXf2kfu/dsAKDldjv/9I+fJy4nHv/VjaLxmQdEgZbCQAfqXIs9E+VLWu95UEkpw8Be4D3AVZTL/BTgIPBKKeVT4+lMgdr9/4AA8HsZ9nHV2scBYA5QAnwReIG1/yKK4OUvfzkf/ehHCQQCfP3rX2fRokWUlpZSWlrK0qVLOXDgAG9+85s5deoUlZWViXr37zvP1lmzMofIsbcNDAykeHKlw//73hU6f/I5zBFla5Su/RmCc5eBdBGlhgkx231fKtI0bitV7aRRqARUEQMRc6lCLUWqiqkqvUmiTMm5x1NZV9llhQ8wVFtgqVlxXP5R7c2vGqJzMMhoxDXuutWphusxZrpJXYWUR5xpN21au5XIJAWoaUpOXDLYvGrs8VhabcyfJel8BCOjdhumRWSaWUnRieL4hVG2rQmRnQhzyKsFs/08fBxnODw5/XHDbdMfvxBm25pssVS9V6phdoDux3EGR1Tf7RBhCU+4tO83CuNVBQshOHz2Mbs2OCpVe6JZJtSghufcNswuoacvSv9QOuLcrU51PjskKpBIbpZfX4+c62fnuqq029LZkfNmBOkbitM3aGTsVyZMhFQ91jTMtrXZVKpezK73MzRsWP3MHU7ej7ENGzfxKoRGoHZzYlu09yx/9cGvMjQ88SG8r6+P97///QDs2rWLJUsc4ctkPd+fM3h22bdFPMMoEqpj4NlmaPZFBjh48igHv/pDAIJlpfzC2/8vAZ8k5IOQDiU+COiSUp8iPOOWMefT1PqApSjVhElAt1Smadw8/ClEK0yZUYYZNxl4NIImJJpQRKsuhEW6+iyVqeOycOHMQ1asnoY/4BBQAoGu6QihowkdTWgJl35N5J4IKFMsVbAJoFZnplN469nxj2zEopJL5++zfpOX+E1nFNnJq3JHan0b06ZX4vf76Gjvy7m1i033WLK0nlBJ4TO7Z8PufYs4dOCmyxhMVqp6jRrDMDl9soWt2xvJhMlQqdbWlVNVVUpLS1fOda5cbqdhwVTKy3MJUj9epN4zu/Ys4sihWzkft2maHDtym+07G0lnkOca/D5fVFaGmD6jglvNuYdevH2zh6nTy6msGs85ldgpm1PukQwvfkIIPvSRd6Jp6jf//r/5FA86r2Ca44//6kbR+PzphZSyVUopclj+Ik3dqJTy76WUq6WUpVLKainlXinlN7Ls74CrzdYMZfJuN6n+K6SUASllxoiIUsrTUsp9UsoSq/3XSik7c2m/iOcP3v72t/PNb36TadNUPPZwOEw4rF7YI5EIg4OD9Pf3e+oMDg4mPpeWppswTd3mrpMOzQ+H+NaBkwyd/i4Awh+k6kW/lYg5KqSK4y/sGKk2QRmz1aZYsUglIm5CzFDxTONWghzTImENHKIUnCHTgL5YiLipMTUQdtzxE6EGTMdpSoJPxllYN8T1++UpIjwBiGhS7NSkoSuVTHV3SibIsuSKcTNM36DJaBTqp2Q9pYCJrklWNAouW05GioCLu9Sp3vYTJF0OyUfH8gLpGzQZGTWZNU1PS8q53Zd1DVYvDnD+WmSsgyoYbHu8f9BkYNhk3oxAingC8IQZ0zVYt7SUM1eG05QRSeW9bU2UvO4fitPbH2P+zBCmNLBjzntc/S2SXAjJphVVnLzYl/bIXb1KcuW3SXa7XZWZLZ9ktAPDBo/6YjTMsu2/zPU0ARtXlnPy4mAadWomFEapOjRi0vU4zoLZjmdkpntaCNi8soyTl4bTbs8GdV/YYedysdNdKtWSGeilc1XfjDCP2k/ynr/6Ut59cMM0TV73utfx4MEDgsEg//RP/+TZPhnP9yKKeK6iSKjmgGeLoSml5GhnE//5vn9LDBwvefOvUTu1Gg1Ffvo1ScgnVdxTiziNSIdMLfGpgUsIqPRDqU+pWu0peoFUbvsoklVLsgT9GqzfOZOmY/fxCZnkUO9TgdqtRRMa4ZE4d9sGWLYiKWmSsP9o7i9pgr8LMg/CqYpPd73a2jKqqktovfMY78CVPIhpCHycPtHGhs1z8fv0BCmVkrQq0dEMXcoVSfV371nMkUM3czIIRkdjXL/WyZp1yYrfyUdlZQkzZlbRfMOd9CvVELdx5tQd1qybSyCQjSSXKQRZIRJU7dy9hGNHm3OKNRSNxmk638amzZmJ3/TI9xGaeuOUlgVZ0DiFK5dyU1JeON/OsuX1BEKZ963uV30c/cuOTVvncP5MB/FkF8Y0iMdNzp1uZ/O2uePcm/1iYCBJfQFJ/a2oMqvXLOa33/BLAAwNjfBn7/1n4rSOsw8OisZnfijU5H1xEr+IIiYXIyMjvPrVr+ZlL3sZc+fO5emnn+bRo0d0d3fz9NNPs2LFCj7/+c+zefNmLl68OKl9eec3m+j5n08kiMXK3a/GVzlVueSblsrUTvZkq01jDtkp4raCVSKGY4hRpVAFVIzVTEOXxCFnDYOm7jrW1j5S7VkJr4RpJpSswiq/on6A650VmKbrKWUlb8VwyFfVrlfmJ+z17iVd/1zkjkRimDErARWcvWKyfpk25jNy1RK4ckt1CcBMtJlqbDmJhhzyLDu5OrbBdupShA0rgojkC5Ck1FyzNMilm1GM/MR/E4Zt45++HGbDihBCsx1yU0cgITTWLC3lyq1RbzQIFzQXaZZKno3PwLWvC8CJC71sWlWNJlJd4O19SCTrl1Vx6eYg0VjaGytDXRtmmmvuvR/GwqnLg2xYUYGWxhR111+9pIzrLWFicchXnZqpzXxw7uoIa5eWoGvZ21i1qITm1gjR2Pj247xPji3KSb5vgjWbsG36WP8VPv+VH9LSmrtoJBlve9vb+N73vgfAv/zLv7BmzZpxt/V8Q9G+LSIZRUJ1DDybDM07gw/4wbf/hxunlWvplLkzefFv/AIBTcXO14UiTks0Em78o5Y6VcMhHFWMVRO/BkHdin2K9aO2PmuaQ2Xaw4YVYpXycj/TZpbRdqs/kXgKoaFpNhHpGCLHDrWzfdc8NE1LE/80dcBwK0Zx9TlfaPgBja07Gjh1vC1hPCZTo2qdxmB/hK6HQzQumuJqw1bauvsjxt2nbCgtC9K4cBqXcyDVjh6+xbYdjTkm+Ck8Nm9t4OzpNuIeSzJVqTo8FKH1ziOWr5iZQ6tmEqk6cUY1GPSzYuVszp1tHbPssaPNbNnaiK7nGRc1z+EwkwG1fuM8Ll3sIBrNHstsNBzjxvVOVq6ZxVjnaDKUqn6/zup1Mzl7qn3MsudOt7N67Qz8/omGRnApLnK8L/7sL95IVZXy6P7sZ77L2TMnMM38FQVuFI3P/GEnKpzoUkQRRUwe3v3ud/PVr36VxYsXc+jQIV74whdSV1fHlClTeOELX8ihQ4dYvHgxjx494i1veUuiXkVFReLzyMhIxvbd29x1kvE/Vzs5dejHjN4+B4BeNY2Kbb+s1Khxy90evASknZQKqQxhW1EaU+76JEICKGWqrXT1tCFwyEwriVUkLmjvL6WxetBFiqo/ikw1KfXHqC2J0t4bdA7CtMhUU0LURWJlI3Lt9k2vDeUkorJILJR6MGY4rr6RGLTelyyen/lBWVYC02sFrfft3ZhWDgXXQWXsWKpS1f09XTKqdIjGJLfvxljeGPAQVqaLLC4rEcyc6uP2vcJ4lOQLgSAWhxutUVYtCqn3ljShvkpDgjn1AW60jp37QLMUql7bL7dBzSG2U0MyROOSy7f6WbesOmP90pDG/FmlXL2tPGFMaSpF6zjiyjvXLD/bPB6XXL45zLql9u8+tX4oqLFgdogrt0c871y5j/1jTbbn0E8DLt0cZe3SkrR9BAgGBI1zg1y5PX5Xe+cd2D64sUhVR3Sk+SvwV62wtpgMd5/kje/+93H146mnnuJjH/sYAB/96Ef5rd/6rZQyhX6+P9dQtG+LcKNIqI6BZ4uhaZgGh9rO86UPfDKx7tV/9LsJ5Z+t/9MAKZQ7isDygoIEUWpTEhV+tT7VGcUqL71rhJCJNjUNVm+cxuVz3cTjUrntW27xEpu01OjqHEGaMGNWdcbjUl3TsNV07oeLQHiMrdR66Z9Ejou+IBj0sWxFPU1nO9IcqVP+0IFb7Nq7KBFYPr3LyUSefGMbU+s3zufyxQ4ikcykWs+jIYYGI8ybXzeBvkwMfr/O+g1zOX2yNWmL1wg5uP86u/cuyXmGOTlLbSFc/9esmceNGw8YGcnsPtbXN0x31wALF9Xn3b4mUo3tHGqlrNF1jc1bGzh+tCVrzSOHb7F9ZyNCy+3cOBMchSNXlyybSvvdPoYGM5/T4aEI99r6WLJ8WsYy+cFWMXsVFenVE5KpU2v4f3/2hsTad73jQ8Rk9nObDUXjs4giinguYnBwkE984hMA/P7v/z4lJSUpZUpKSvj93/99AI4cOUJXl1JFzZzpTJZ2dHRk3Ie9rbKykvLy9Ek0DVPyx187R+//fCKxruZnfxvNF3Q98t2EKEpxahGowjSVgtU0EKNxpUw1pJWICkVoCkjYKclEppXACiuUgDAlNx5VsaB6EJ9mOlXt+K0mrJvVx/n2amV027DbdXtxGPY+k9Sp9nFY7Urp7ZbVOZxY4mCY0ZQS11okC+cKMjkCbV6pc+qS26bKTqhJj3rVPnm2G7iT3d0m+ZKTR2bC1ZYojXP8BP3SU9/GltUhTlzMP0FrISEQXGuJsmC2n1BAIDTbbd+x27auKePEhcxjvBum51xZ+0gQam7bzGufpYYFSCINkVy5NciC2WWUBFNtSiklO9bVcuTcIyu8g30dZcq+coWXVJVJ6zLj+p0R5s4IUpLGq0pKyfY1FRy/MEiqbZxPPydOqt5sizBrmp/SkvTUyNbVZZy6OFwQD7r8k1QpBKpWI3T1jDZG7nLk8CGe3n8hr33/4R/+IR/+8IcB+OAHP8jb3/72tOUK+XwvoojnOoqEahY8WwxNgHM9N/n2J7/K4/uq/aXb17PxBZsRmnLL92kAEk1TNltAg6hUNGVQV6EABBAQENRNJ3WNVd/tHCxQhKwN3bVdF1K16ddYtW4qTae7rARU1iys1YqUkuOH7rFjb4OL1EkPIQQaKpaqPZMrhMUAZ81gno6kVO77bqxcM4PbNx8xOhJN20b73V5KQn6mTq2w1qQSqhNTpmYLTeBA1zW2bm/kRBZSzc7s/kxjybJ62u/1MjiYbPwqQ6vzQT8SqJ9RlUerYxvk+ULTBLt2L+HwoesZy+z/yVX2vWB53q5FNnKP95QdjQun0tMzRF9vekP90aMhRoYjzJ5bQz4qgURCtgIRqkIIdu5p4MjBOxnLHDnYyo49DeM+p6lQ95Vp/ct+9Grrm37vVSxZOh+A48cu8tUvf4e4mXv8VxtF43P8KEQG1KKRUkQRk4fm5mbicTWJ29iYOeTNokWLEp/v3FHPfnfyPXdSvmTY25YvX56xzMd+fJM7B75OvFfJKINzVlCyeIcTU1S6iCATkLZ7v6U8jZqOKtROTpUgMl07Sh48JFaCKhVzVcRtclXt8kpXFaum9TnKV4sEnVY+SjSu0T/ssjdNmSB4U0jUZDI1qS+mWzGLVEpC0xvuRkppEapJhyCh6bpk/XL301LtZXotxOLQO2DvJ9mmHovIS9kbiiA003qNZCNXpYSzVyJsWhW0TodD7E6r0zFNeNT7hH3900AgOHU5zOZVIXtFYuJ8Wq0PAXQ9jpPNprLVoJnIa69t5D3nucRYtftz9HwPO9dPSVKzmkyrDeDT4X53WJHx41Clpuubs871LQeG8VjTADvW2u8DTptTqn0E/BoPuqMeccL4bMexz9uY/bwwxPY1qXZZbZVOSUijo2sy1NPZSVW3SlVofgI1GxLboo9P8dY//nROoc1AicQ++MEPAvD3f//3PPVU5tyXhXy+PxdRtG+LcKN4LbPg2WJoDsfCHLl+hu//+1cA0HSN1733dwno4BfOEKJZHKQuHFtOFxDUJD5NIoRJuV9S4VPlA5pFlgrFh2qaK3YqgJBoKAJV0yS6JvFpoOsQ0HUWL62js2OE4UGD5Dijzdd7mDWnigorwY83FIAXAi1L4PaxYSeIUkRocsgAgSY0tu9awNFDaYhKU+PYkdvs2L2Q5CRV7jbGD5H0LXtbCxqn8fjxcFpS7fatLqZMKae6JnN8xicFIQS79y7m0IHmpC3KqDt88Aa79yriN6+s8GMkuRoP5s6dQjgco7trIGVbW9sjysqCTJlamaZmbsj3/shWfs++xRzcn3xO1Xk4uL+Z3fsWI7NOMmTYp0cFMXGSc/qMCjQheHA/9Zx2PlBxQetnFFp9aQJxckmGBhK/38cHP/SOxJo/+aN/YnA4t5i6NorG50SRrCoe71JEEUVMBjRXcMO2traM5R4+dOKm28r6JUuWMHeuipH9wx/+MG294eFhDh8+DMCLXvSitGX6R6L843dP0n/sy9YaQe0L3qCmAW1VqEdNasVNtUV3FiFK3EpAFTMdcjMd3CSnHTIgZni5L1O1cX+glMpgjHJfJEGoCmmyamY/F+4ljXH2/qKuMTppuEmMvobTOSmlh/CSZvrnXzZX7Y4uSXkpqEg3zji/YYXOGU/KuuSTkmx35zY+potrnq5MMrHa0RWnNKRRXeklX7esCnHyGVanutHZbRIMaNRWqd+H0ASa0Nm6pozjF1T4IHeSKjdMmWqjTBbud4/i1wVTawKe9bs3TOXQ2UfYPxKJ9RuSjugld/PauUfSeQSlrk/Fw54omgbTau1EuqrNneurOHyuv2DChOS+5vX+IU26H8eRSKbXeYU529eWc/R8YZOIKg83PSUU3ljwlTWiBVR4OjPWR9vNk3z04z8Ys95TTz3Fhz70IUDZs+9+97uzli/U8/25i6J9W4SDIqGaBc8GQxPg2MMrfPWD/0E0rFxs9/3ay1mweC6aUAOG5VCC31Kp+jVJ3JrhDPokZT5JiQZVAfDrijj12fE7SNJ5WuECNCHxCQjoKplVUFMErE+Az3JV0DQf2/fM5dihu059BPG4yaXznWzY7E2a5LhHu8lT9d0OE+BGsgu4Fy49rbDbSYqQ6vo4e041kUic7q4h12aNSxc7WLR4OiUlgdT65DfIpYfI+jUd9rxgaQqpZhgmp060snXHggn2p3CYXl+Jrmvc7+jzrL9+tZM5c2spL3fiiuVu2BRao6qw7wXL2b//qjd2lyk5cug6u3YvnXD7+RuD6W+EurpyyiuCtLX2eNbfvtXN1KnlVFaF0tbLbY+FI1QBduxp4OjBVs85lVJy9OAddu6ZX5B9pIMTTTVZJeEtBZIXvXg7P/eSnQC0tz/kwx/8DCZjx3+FovFZRBFFPPexdOnShPfVJz/5yYSIwA3DMBLeWjU1NSxZsiSx7Td+4zcA+PKXv0xra2tK3X/+539maGgIXdd57Wtfm7YP7/lKE10HPoeMqtiE5atfSGBaY+JdUyQSNllkqmEllxI4pKiUys3fHRIgGdL1146nGjcgmpSsyvK9F9a+z9+vZt2svkSM1cYpw9zrLSEad03AGxaBaxhe8tduj8wjr7RUo/aYJjExTcNjA5umMWa80lOXTDattENemSyeJ7j7wCQSxRo1nfo2cep2Z59o9vlMkEn/jl8YZdsax5ZZPD/A3c4YI5HxqignBycuhNm22hEwLJoXoONhnJEk3tdNqsqkJFtOmXTEdXrS1XEDz81WO3i2m90bnPwPS+ZX0NEVZmjE/VtW596bwyKn5tP2MR0ZNJaNf/R8PzvWOSrVhXNCdD6KMjSSiMdh9WsiNmq6iYj87unjTcNsc6lUG2YFeNQXZ3C48Pen2+U/HTnvlHOpVIUgWLs5sS3Sd56/+4ev0j+QOQzFU089lfC0+tCHPjSmPWujEM/3Iop4PqBIqGbBs8HQ7Bx5zMGjhznx3f0AlFVX8Iq3/ho+TU1oxxH4hEpCBcq93w7dpAFBXeLXIaBJQroTa9Xt0u8eu5RiVSlRA5rEL2RCtaonwgOomVqBzrTp5ei6xoOOwYSb/tmTHazbOAufzxVc3DUQJDIcoqPhs8goe31uCWyEFXvG7odDhArPHzd27VvIkQO3EoNrdNTg2pUHrF0/O3Ee3ImzJu6tnJ861UZdXTkVlSW03nFItbNn2li1ZlYiZu6zBbv2LOLwwZuJcxqLGZw/d5eNm+fhyEccAycXYnUyVKpVVaXU11dxs7kzsa6pqZWly2ZaZPpEka9KNfOjd8euhRw/ejuhpDQMk9MnW9myrWFc6lRvLxPBPibUDkBZeYB5DTVcv+JkGb1xtYs586opc5HphYcVy83lbihd95cD9fkDH3wbPp96rnz4g//JnbaLmGbm+K9QND4LhWRd9HiXIoooYnJQUlLC7/zO7wBw7tw5Xv7yl3Pp0iVM08Q0TS5evMhLXvISjh07BsDb3/52dN2x05566inq6+sZGRnhpS99KWfPngUgGo3yr//6r/zpn/4pAL/7u7/L4sWp4Yqut/fznf89zNClHwEgAqVU7/h1Eowm6mPic0wiDByzImYi4oZy/49Ly+SQioS14R7uElyL9IYFcMOwE0splergiJ/hiE592Qh+3WR+3TA3O8tcbbrUsHFXe5Lkmb5E+zZMM9lGsmOUJo/1ElOmC1vlYGBYMDAEs6dL/D5YPE9w9bY7/ng2iDzVqeNH/1Ccnj6DuTN8+HVYviDAxebsY/IzgaERSXdvnIZZfnw6rFgY5EJzJPHekd6OSj03tju++3uhiGspJUMjce53j7J4Xjk+XbB+WTWnLj9OKSvERBOEZuyFpz+ZMBw2aX8YYfG8EnQN1i0r58yVISvcW6GRn+Xgvj4joyZ3O6MsmR9C02D98jLOXJlYUtNs0JJi9GaGc0x6aBq+MktgY0bof3Cat733P9PWes973pOwZz/ykY/wrne9K+e+TfT5/lxG0b4two0ioZoFz7ShKaXkWOcFvvi+f0us+6U/+A2qqyuUl1KCgpTY3KWGY4YFdEm5T+ITkrKACg8ghKIsMw15uibRhYq56teUotWnO0muNEAIH+7HwLbdczl++C5SSgYHIjx8MMTCxVO8DbuFo5YbfqpSNfnhMlZCqgy3r3CXcVBZGWL6jEpuNXcj0Dl29DZbty+wMnlOxmPNq3bNZxc7di1KkGrDwxHu3H7EipUzx674hFFWFqRhwRSuXFZxz04eb2Hj5vkJAkvBNh7drjjZWk3QYzmWzw3bti/i5MlbxOMGo6NRrl7pYN36+RNvGCeTayEQCvlZuqyeC+eVkvLs6TZWr5mFL1CYx3Uh7/X1m2ZxsekB0ahBLGZwsekB6zfNLlj7mWHrVFVMVZHx3pIsXjKft/z+qwEYHY3w3j/6R+Jk9jgoGp+FQzELahFFPPvxgQ98gBe/+MWAUtevXr2a0tJSSktLWbNmDU8//TQAr3nNa3jve9/rqVtVVcX3vvc96urquHr1Khs3bkzEh/693/s9otEoL3rRi/joRz+asl8pJW/514N0fef9iXXVW1+Nr6xa/fA15wEgQBGntrLUNJWLf8xy8R817EZVGRvu54dNcBqGIlPjSUpWe5shE4mn7O2XOipZOWuAVbP6ufygCulu2DZV40nq1LRxU5ONGRdxKi0dYVKcU9M0MYx4ViLTjv3fdB3WLhGsW6rRdENiSkE2Wxrc6rfJdz+1ycWzV8OsXxZiw4oQTddGMS2SfHJ8lMaP89cirF0SZMPyEBduRFRIX+tiKq87LS2J6I5pmnpeMx+jQ77mRoLb3lGnLz9mw/Iatqyu5ezVXox0EwUTQibSNL/9nLs6yJol5WxaUcGFG0PWT2YyrrlXqZpNzJFuIqHp+ggrF5Wwfnkpl2+GSaOnKig0S2Th/BZTDZ/kBFaBmg0glMgmNnidb3z7RzTffuCpc/fuXf7+7/9e7UPT+MAHPkB9fX3GxfbKsjGR5/tzHUX7tgg3RCHUX08YT7TD4XCYV7ziFR4XzmBQqa8iEWdG9TWveQ2f+9znPIQqwNmzZ/k//+f/0NOj1IYVFRWMjo4Si6nA1i960Yv4zne+k2jTjZaBNv78/e/j83/37wDMXDSfv/qvj1Ee0Bg1BSYCDZMyv6IWAzrETTU0BTUo8Ukq/crsC1nCRjuLqCGlR6UqpbJdgxaRmiBQrR+77jIgAlqy27Hg3OkOSkr8tN3pZePWuUybVu7amolMTDcrKRPxh9RsfLrLLdBEIIUYEsKXVCp1p7GYwTe/0sTPvmg5x47c5hdesdaqW+inWlIiqjwJVYCmc20Ypkn3wwFWrZnNzFnVheteAWEYJl/54mn+z4uXs39/M7/8K+sznE+R8jnzeRdoSYmUCnGNrlxup79/hOHhCAsX1dPQMHXCbdowsiQgSIdMrmGgwhF89UuneeGLl/Pjp6/xyldvQIrCJmvwZn0dP27ffMSD+4MIAdPrK1InUyYN9suEE/tYpL2vBH19Q6xa9st0d/cC8OMD/86eXa9C17yxc+/evcu8efMAZXxOnZr9/njqqadS4qpO5Jk/yXhippsQomX2vBkNP77wlYK09zNrXk1724M7UspnT8yTIoooPJ4xg1xKyTe+8Q0+//nPc/bsWbq6uhBCMG3aNDZv3sxv/uZv8tKXvjRj/YcPH/KBD3yA733ve9y7d49QKMTKlSt5/etfz2/91m95QmjZ+OaJNn791b9M+K6aeBKBUua+8TMQDFmz+dYbp+3yH7Fio5qW6jIuETFTkaD2UGqHA9CFOpv2bg2LhDVJUaUqNapF1MasRFe2uz5CkavAypn9zK0N8/3L9fZJAxMrzqupEmLZMDIQqinqVCNx/qXpeF+4YZoGMSNMJntY/W+57WOwZrFg4TzBN36U3r5Ip5CUMo5hxDLsw1VugreoaTOnCNYuDbGiMcQX/3vAO2ZnEEU8U1i1KMDKRUG+9H0VHz5xDiwCWB2TOtdSSiRGVhWq2/abiFpV7Uu1JaVkzZJKNq2o5ZPfvJOmtIr3at8n47OnM9njqQRgtvZXNJayeVUln/6vzpR+qbrefU0c2fuWSZm9tCHEltVlfPbbPWm3Fxqmi3yXUjLc8S2kkdmNH0CaMUpL/CxevJimC1fY8ZL3cuR7f57Y3traSkNDQ859+PM//3P+4i/+ImX9eJ7vTxBP9EFRtG+LSIcioZrLDp8BQzNuGrz33/6Cv3/L3yTWvfvTf8uaHWsZNQRSAFIQ1E3KfOpp4tcgZrkyVfgl5X5Jqc9KOqWOxDMHLYRj22kSfJokYKlSNdfjySZThQC/CCVm0tQ6tdEwTL702YtMm17G/3mpOyZlJtf5JLIM4XLbxTJIolZf3YOdbrnbJBGWSS4T2QyxG1e7OHakhVf8ynpqakongUwFL6E6vvABpin53KePUlVdkiB+n624fauL/T++wS/80lqmThsrGZFI+pvJ+LJj6zrlJ3qppJR87rOHCYX8vOpXt02ssZS2Tcw8XPKzEaoAd9t6ePqHV3nJy1YxfWY5k/Hok4kETxNoQ0q+8eWLSCl55WvWTNLvKRs0dm15Aw8fel3cRNKn4eEwfX2DzJgxg0ePulm5ahFnz1zx9Pc5bnw+cUL1JwUyOF9QNDiLeH7gp84gHy8iMYPN7/omlz/1JswR59n9C7/9LupmzHJJeKwNNuFpw1YHmA65BTiu98lPO7u8/Tm5kL3OVpbaTjWuXdaVR9AFPBoKWMpP1z7Ttp/cB/fu3J479jE4QWy8XU8lWe3+JwtwQTK1RqBr0NUr8YRdFZ6CSTXtJEWTS6i6259W60PXBQ97Ypjm2MPTM0WwTq3R0X3Q3WMQN9Mdv02Cub9nV6Em1x0vEveLhOl1QXw+jc5H4SSFqvM7Kuw5zNxWNjtwao0fv0/w8HHM1c/k8pN3rUWan306TK3x4fcJuh7HiBdWz5AFiYcI3/jSPzMyMnYirJe+9KVcuHCB9vZ2Sqa/kK/+59/xshetn9xuPrvwjBCqRfu2CDeKhOqzFGe7L/On734vP/jsdxLr3von72ROw1ziUtGJEvAJiWZ5Rtn2nCbUep/m2KMpQ5W1wr78QsgEceop5/4rbNVg+oba7/VTUhKgbmpZSv3MLaeHsuvSqedswyCZLsn9eTo8FOVR9xCz51Tj8z2JmKTje9ZLKWm/95hA0Mf06ePPQv8kEA5Hedg5wKzZNfj9E4mHJNJ8S1UuTATt93rQdY0ZM2sm3pgb43JXy1w+MhrjwYN+ZsyqIhCYrNhXY/cjFzzoGABgxqxn4j4V/MHv/z96e/vGLFlXV8fevXv5xje+QXV1BV09N/Fr0ye/i88OPHFCdf/Fwhic+1YXDc4inhd4Xti3AH/99Qt85O/fT99p7zMiOHM501/zfoRPQ/p1NTcdNZQSNWYibOVpzISokSBUhSkdhYBwLTbZGTGcxFMJw9ZlCBsSonG1nzRk6oyqMPVVUTp6Q8ytHeHMnWqQqFithmmrGVKJX9fuvOrUOG4SU5omklS/YtM0iRnJ8RudXARuSAxmTRPMqRfcaTdZOE9w5JxDxNokV7IiT8p4Yr2jIE3FxNWpzvHNmuancU6AG60RVjSG+MmpYUupmGD+xsSTIFhnTNVZODfAtZYIqxYF+ckppRh0XMgd8tT+rrYZCZGIe5tCunXjhykNZk4NsmJhJRdu9LNheTXfP+zkC7BjpybyV4ikmzsvTEylOr0uwKpFZZy/PsSmlRX88EivVTZZpToZ1za1f5nUqVNrfaxZUsq5q8NsXlXGD48MTEJ/0kMpj02G2r85pkJ1+vSpbN28gf/6r28CIHyVNKx9Pc0nPpriMfscxjNCqBbt2yLceHZluCkCgKFYmCMXT/OjL37fs/6HP/gBf/iFDyM0zfY0ospn4teVitRObFrmM6kISEp08NmJpKzkUgjvRbecTvBrENJBJj2W7OHNJwS6FvIYMO4B78rFh8xrqKH97gB7fmYBVdUlZFYUeh/y6YwiiYkpHfcjgZ4w5lSsKHcDqWrXTJCGxte+fIZXvWYjly/e5yUvX5Wx7PiQbFSMX1V5/ep9Zs+ppbt7gO07FjBl6ljKz2cGUkq+8sXTvObXN3P2dBs//0tr86idqlYF972VrFKdmKF162YnU6dWEg5HWbd+PjMLTKqa0ptJNxckx0tT6yRf/8pZfu11mzl+7Da/8MurJ1X5ORH3//a7fdy41o0QsGjpVObMrS5098bERz48nZJSf9ptblL+BS94Id/97n8DMDQ0Ql//Deqq6tC04lBYRBFFFPEk8KB3hP/479P0n/+WWiE09PI6jMFuIvevEm4+SumyXcqojZoIw4qLavNAcdOKV5qBTJXSccGyyFLh5k2kq5xNuJqmatfLeakmhWTFzEEONE8hbmgsmjZEVShGf9iv9pGciMqFxKjtIllN00xD5Jie7Wq/griRLlmT184UQmBKEyFg7VLBj46ZRGKSZY2C2ip43J+mCbu7Sf0QQktrk0wU9jGpfcDmVaV8//AAkahk1SKYUqPT02diSpWJXkiRbBam9j2LvVIosnXTyhA/PDLMaNREygBTawQPe2wXfy8xqQg6dXMJoSfOo5QT9wTKBoHGznV1fPdQJ+FRA8OQzJxWwoPuUWwb2sFECdxM7zSZlKZe7FhbyQ+PPmZkVBKNSWZODXC/W4V3k8hEX6V1HxQW9g/f+pYlCdv2teX86PgAI2GT8Khk1jQ/HV2xAvcnPdR9JCif/UsZSXdbDPfCn5nK08d60EP1GKOdyPgAHS0n+cA/fY8/efsvPJH+FlFEEcWkVM9KHO68yOc+8EniMTWbW1aliLSb569x4nsHEdYEuJCKTLXtxqip3P4rApKQy23fPYcqpONJpWkq+VRQVzFWk8cuh9KySczk2UiFSCTOtctdrFk/i517Gzh84I4qkXYszNWtw0QZAj5S3OfTkJa5QKBx/txdVqycyZy5tQgheHC/L6e6uSOZGBxfK7GYwbmzbWze2sDefUs5eOBmQbLdTwYuXmhn0eLpzJpdQ2lZkLbWfOIN2XemNwi/M/s/8RifNgzD5MTxW+zYuZg9e5dx8MC1gp/TQhnxN651MntODdNnVjJlSjktt55EDKf8hwPTlBw/0sb23fPZvms+J460YaZ1iZtcHD75CZpbv8mt1m9zq/Xb3G79bmK51fpdbrd9j6d//Gle9xs/x8/87EYA4nGD97/vkxjce+L9fT5AFHgpooginhv4oy+co/voZ5FxlbW+cs1LqNv7hsT2xwc/jRmNqKRTozFENK5UoLayLmJ4TQOPB7V0TApTQtxExE3HHcuOxypdn+NqEaZrnQtLpg/R0lNG3FBj5PnWStbN60eYhuqXXTxTIqrkPiZNuip1qiqgiEdFJpkpqlUVLzy9nWGybIHg9j1JNK52eOqSweaVXgGD2+ZJDTtkqxcn44nr7GdFY4hbdyNEoqovxy+MsH1tqYdEm6gaVqb5l2/9pQ0+bt+LEI4YSGly/MIQW1eXuI7Fa6/aBJ16xxIqPFmS8nIysHJRBbfbRwhbidkOn+9h57o61Zck4t0hKcc3ymZ/p3Gf49SCSxtKaXsQYWRUnadjTQNsW1Pp6tOTtB0z72vxvCAdD6OMhFU/T1wcYvPqsieaQMhOdJvp/hFCsGheCfe7IwyHTYK1W7HPeaT3PB/552/S83jwyXX4eYaifVtEMoqE6rMMD4Z72P+T/Zz90TEAKqfU8Nq/elti+zc//EkGh0eVOjUAAd1yMUHiExDUDfyaM+jZtmFiEt1abydQ9VlLOp2g/UPXhEgxRtzlTh1rZ+PWOei6Rm1dKeXlAe619mU4wuRkTZkg0IRKOqNZZK6WUKZqziKS28vcYngkzu1b3axcPQuA3XsXcfhgoYnK7AZFrjh1ooUNG+fj8+lUVZcybVolt291T7x7BUYkEuPq5Qes2zAHgJ27FnL86O1xkmqpM7F20H33/Tfe63XmdAur18wlGPRTXh5i/vwpXLnSPq62MkEZhhN7rCoy/S4bNs8FTDZvn8+Zk20YxmQqHOyJCm8s4rFw5WInCxdPoaTET6jEz6IlU7h84cHYFQsO9WJoWgk9kp9XhiE5daKFrTsaed/f/QGhkEoI9bF//BI3ms9jmqPPQJ+f4xDO5N2Es6AWLc4iinhO4FRzFz/63/0M3zwCgBaqpGrTqylp2Eho7loAjIFuBk59ExExEJ5cjxJGDYRhIkwTIVWCqoT605RKNACW4WsioqajXrXduBKqVBQhaspE4ilnblfVCfoMZtWM0tJVanVBMhL10TvsZ1bVqJePzGSa2PuXEsOIp5Ca3vioDlMcT4xL6V+9bYLMlAZBP8yfKbhxx8lmPhxWcVTnz8omXEiH1PITIThNVyDXgF+waF6Qy7ecMXc4bHK/K8bCuX5sl3kYv62XCWORrPY6U5r4dcni+QEu3QxjX5ORUcm9hzEWzwsmCFQpvWIALyybSoCTN8JLcE4Ufp9gRWMFF6475Fl41OBOxzArGrOFYPL22SHSC0MDJR+azydYuaiM89cHFUkoBJGopKVjlOWNpWnqT9agn+laWf3UYeWiEs5fc1ztI1HJ7bsRljeWTFKfMsE6z8L+7EDXYN3SCs5dHUIIgR6sw1+xRG2UMQYenuQP/uQ/n3B/n0co2rdFJKFIqD6LYJomB9ub+OL7Pp5Y94vv/C3WvXAnK/dsBqD3YQ8/+tRX8QuTUp9MXEABhHwmNUEniVRClWqVsOOumq6NQlNK1WRoYMVmtShMkeROa+2j93GYvt4wDQvqEpu27pzPiWNtHhcfT6XEt2xPEdsQSTZANNd4nvwkykbP6hw5dIuduxYqIxRBWXmQefPruHqlkATQxJ+MgwNh7nf0snhpfWLd1u2NnDrROqmk2nhw7Mhttm1fkEiyEyrxs2RpPRebJqL6cxuo0pXJ1EG+hvbwcITbtx+yavWcxLpNmxs5f66VaDQ1btlEkBJnOIcabpw6cYcNm+ah+9S9FAjorFo7k3Onn4SSMvfRPTIa59qVh6xZPyOxbvW6GVy/2kVktLDnNDdIII6TaMtxQTt3po1Vq2cTCPhomD+Tdz71OgBisTjveeofiHPnGehvEUUUUcTzB6YpeepTJ+k5+pnEuuotv4oeKkOYktodv5mYJB849Q3i/a5JZCkhZiBsdappEaJu5akbMRMRl0kkKiSGB0PFZMWw6rrVpRaHIaRk9ewBLnRUkRgXLRPsyt0yls0eRrOzYBnS04fEKOrqX9pElDJ5vLKaM+KJsspDzOsl5sRDVQe2YYXGuWumrZlMlLtww2TVQg09YWbYZGVqrFS7TWXPFeYtP9llecvqUk5fHkm5XOevhVm9uARNS47vOrmqRWnbmNK5T6SUbFwZ4syVEdflU2Warg2zclEIn2/sfgnrHUoIlUxXE7bHnXviemKv4VtXV3PqUl9y2F7OXetj1aJK/JYdmZ6gdNbZ6uB0ixfSVdc9CZ88Ge+1JTevrODslUFM00ssX7wxzLIFJQT8hSWax4sNy8touh4m+VXr0s0wS+YHPf2cbGiJmMJainJ8w4oKmq4PEXfFZQ7UbAAtAEBs8Abf+e//5fzF1ifW3yKKeD6jSKg+i3C9v5Xvf/FbdNxsBWDeqsVs/fmfQQC/8p43oPuU687T//F14o8eEtSVPQgQ1CWVfldiKZfXkzVdr0IAJNmXAq8HFFjzqMJWrakGNJE+uPXRg23s2uuNo1xS4mfJsmlcPJ9MVOZ+uwlcBp0ns1ZmUjYbnfqoa4hYNM6s2TWewhs3z+fC+XsFItWS1LLjHHcP7r/Bnn1LPUZFIOBj7bq5nDnVNpEOFhSPHw/T3xdm/oIpnvVr1s3h+rVORkcnEm9Iuv5K18vI+HDo4DV271nmOac+n86WLQs5fuzmBPo5cbgNpYGBMA/u97Nw8VTcLyHLVtTTducxw8PRye2LR6maHcePtrFl+zxPxnpN09i6Yx7Hj7ROXidzgFt9Eh6J0XK7mxWrlDIdAU/94euZNWsaAN//78P8zw//F8Pse4Z6+9zFeDQv+elgiiiiiJ8WfG7/La4d/wHRRy0ABGrnUbn0Z1VCJwMCtXOoWPsSAGQ8Qt+xz6uKGhBHxbWyYZOpRhIZasVDFXHTIV0T5dVHIVDxUk1rMZSy1S5rP3OqSmP4fZJHAwGrDdWAkBIjJrjVUcLSuSOpZK4bLtLXTE4GZRqkxlxXxJQhI5BhLHbbMRKT6goIBaDzESlxIQ0Drtw2Wb04uZ3s9pRw5SaYiDrV3Z/qSp2yEo32h6m2oWHChRth1i8rfSKu1VJKpCkxTVPFvpdKmWpKg+oKQWWZRtuDSIoK1TDh3LVhNi4vG2sXCYgEQ69sJKXQVNdZfR5fDPeqch81VX5a74etNc41lqbOqcv9bFldk2UQ9ZLdmWzszORqtgvlvGVWlvmYUhPgTkdEhUFITBDoSKlz5vIIW1ZVMtmhEcZCeanGtDofLe123GLHCpFScOryCFtWlZOfVTIxq8YWALnrVpT6mDk1yM27o571ml5CsHpdom64+zhvfOpTz9pwcT/tKNq3RbhRJFSfJYgacY63nOOb/+hI9F/73jdR4hMENMnCRbN40et+HoBYJMoXPvAfCVtRQxLUJO6cLPbj0yZYbXVqgkTF8nKSKhSA2x5VoQB0NR8uBLo142XDHlTb7vRRWRWkptbtrqG2rVo7g5s3HjEatg2n3F3z3WVEDo+d5KRFKdulxqEDzezau9jTfwBd19i8tYGTxwutUBvfY7K9vRd/QGfa9FRXneUrZ9LW2sPw0OSSarni4P5m9uxbnLJe0wTbdjRy9PCtCe7BvotVHDGJYTtkkVCu5kC0PuzsJxYzmD27NmXbosX1PHzYT39/9kya+WB8bv/qHj+0/ya79y3Gm0FDtbljzwKOHLxdqG6O0RtbbZD+Pu59PMJg/yjzGmpSts2dX8PgYITHPYU7p/nBcfsXQuPwwWZ27Vnk+d2XlZXwvve/NfH93e/6CKOx20XDs8AoGpxFFFEEwEgkxge+epLeM19MrKvd8hsIA4QVNxRNKVa1kMobMNx8iNEH1xNJqBKxUC0SlLjpIlQV0UncUp7aqlN7Xj6xVwmxOMLEtbhirAq7lGTt3AGa7rlsMXt4MJTx3PowxPSaCCGf4SFVE/tyGdZp1alpiEoVO1WRrIp8Ep5tHjLVUn9uXqVx6lJm76WWdkn9FEFpyK43dtIpta/8QgAlw44Ha2PH2jKOnR/OWP72vSj1U3yUlXjJvUKNy1JaBKqpYqJKrL/SwJQGUsaR0mDrmhKOnB+0JvJdohOLdL19b5QpNT4qSnM7N4oU07HPZTqSLJu9lQm7N9Zy+Gxvuj0CcKd9hLpqP9Xl6RN2jh+pyub0qlb1d+f6Ko6c67fOQSruPohSVa5TVaG2ey/3RK2Dsco723asq+BYU+b7s70zRkWZRk2lnkO/1JJZ9Zvr70r9DjWho2s+NOFj+7oqjjUNJt1HCv7K5Wj+KgCMyEMuXzjGZ796JMd9FZEPivZtEW4UCdVnCU52XeFr//h5hvtUHJxtP7+PJeuXITTwaRK/gFe85TVU1qkH5eHvHeb0sctIKagKQEXA+8N0izoTE/d4dX+asLYpcYAK8yRBt+nXhCGa+pM3Tcnp4/fYumOeZ709jmqasJRqbfYWd6m8zo13wMi3HY2bN7qon1FFZWUobZ3GhVPp6hqkvy+cWj1nTFydKqXkyMFmdu1Zkna7EILde5dw6GDzeDpYUNy53U11dQk1teln6efNr2NoMELPo6GC7VOiDF9be+hWIbrJVTfJKqXkwP6r7N23PG2bQgj2vWA5+39ytWD9hNwmDJL70X7vMYGgzpSp6c/pjJlVSFPysHOgEF3MpVdk+o0d3n+HXfsWpN0GsGvvAg7vb5mkfuUGieRhZz/xuMHMWQ7xaz8Pf/XXXszWbasBuHG9lY//25cxZOcz0dUiiiiiiOc0/vKLTdw//jWMcD8ApfO2UDJtRYIQtUca3V9K9ZZfTdR7fORTyEgMETMdJYBNVCYrUw1TKQTc7vdaIm+4irsaN1N5TUMmc0TMqR2lZzjASMTnlMEiXxP+wIILN8tZs9CxczwjposZMmVyIiojKXaqXUUiiSe8wjKrA5VNNG+G4HEfDIVT1alunLpssGmlnYU+G0HpJXAnlp3eqTt/ZoDH/XEGhrO3d6xpmG1rylL6OF5S1SFRTVeYA/c5MC2b0cCUJnPqffQPxekbjFqT+Bbxak3q21LnI+cH2b6u3GNz2krXdBP9Dvllr7DVquraapqOpvkgQby6XeNTbbG5M0IMDsfpHXDUvpqtenW9jxw++5hdG1LFBElnyXO+xoa3L5nrSGZPDzAyavC4P+65h5MJxmNNw+xcV2F9T9+3zH2ZCF2l7oUZU/1EoiY9fUZKXXc/jzYNs31teUaiNHu4BBtuZbL73XbsPs+YGsA0oLs3lqimYnHa95GPYN22RPnRnpP82d99mfDos0OIU0QRz1UUCdVnAfoigxxtOsGPvvBdAIIlQX71qd9ERylMQ7p65E+dUs7r3/36RL1P/dXH0WWMoC4TiagSQY5RhKnnL94LLnEm903LOyrxLE9M1rtcSFzk1cVzD1iyfBrBoDPzmUwizZlXzfBwlJ5H3oQv45mRUf1Qs/XOv7FhxiVnz7SxeWuDaidNJSEEe/Yu4uD+G+Pomeqd99v45pwuXWynceFUSksDGcvMmFmNlND54EmRaqkwTZMTx++wfWdj1nJ79i3m4P5Ckr/SoyhQRq5hJSJKpBNwPkmTa9c6mDmrhoqKUMZWp06tpKTEz922RwXrqSbye7RKKTl6+BY7di1M4wLoYOeeRo4ceDJKSvUbS02kcOf2Y6prQlTXZA7QX11TQm1dKS23eia9n+mhXqgPHmhm195FpE4nqd/9hz/6rkSNv/6Lj9P16Cqm+UzEf31uomBB+4soooifWtztGuRL3z9G/5X/BkBofmrXv9YVjlF44qJWLH8h/tq5gEqaKgf7LbLUIkxNEm76QuIQpTHpxEhNct9X8a5cSlQbhuEZHtQzR7K0foirHeVWVVcdT5IseDzgRwC1lS43dpvctaolKzWlaaYlUwFMmdv4Y0oTTZOsWiw4f93ISqYC9PSpLk2rHcvV3zljqXkQcoe7rqbB+uUlnLkyttdKT59BLC6ZMUWfkJ1jE6mOu76ZWG9vM0wDU8YTZKkmTDauLOPkRXd29NT4tiB53B9jNGIwc5rPsjut9t3WqDQTi3MsXiWqQ7ypde6QAE7YBeGpJ4SKnXqsKVWdKnBIWiEEvQNxhkfizKnPbAPnj7FIeVz9rOT4hQFEhrBxNvoGDQaGDebUZ37/UW3mRmSmJzcz93nr6lJOXPCqU9ORogNDJn2DBvNmjKX69ZLi3uuZvA8taV+p5Kpdd+vqMo5fGCKbotlfOgdficoXIY1hutpO8P/e/7Ux+ltEvijat0W4USRUn2FIKTnddZEvve/jmNas98vf+Cqm1E9FAjoSn1CDV0CTvOjVP8uC5UoZ1nq1haYfHcWvOa79NjSwOQUEEiEUsSqENaQkxhWBaS0BTdUz7WzZUrVkqwGx/oZHYtxqfszK1TO8O03zYFBKNYcAGi/ZmHEHWUtrnD7ZyroNc/H79az7njK1gpLSAHfbHk+8X+M4xEgkzuWL7azfOH/Msnv2LuHQgeZnzD35/Nl7rFg500Omp0N1TSlTppRz+1ZXgXtgKVMTh28rCNyKD0k8HufsmTts2bpQrUmjYLWxe88yDh++gZkc2f8JQZHp07KS6QDlFUFmz63hxrVCn9NscAw7wzA5feJuijI9HbZsn8uZk/eesURqN64/ZNbsKsorgqRTu0gp2bhpBb/x+pcD0Nc3yF/++b8R5+4z0NvnJsbSjuS6FFFEET+9eNe/n6Ln5OfAcmWvXPFS/BXTnB94XJGlwjARhkRDp2737zBz75v41df9LrpeZiWYyrADmy9LzJnZ9qbru4lDhtobTCsplfshI2HZzCFudJVjSOsVySZn7ZirNiyFbNPNMtYsGkbYRncS35Q6SZqekHJUlLnAZOVCwbUW6UlMkw2nL8XYtDIf9++J2O3OeVqzuISrt0eJ5ThXeeLiMFtWl5FMZo5l83pd+tXFtmOgJrZZbv3qmnjP9eolZVxrCROL53I+JccvDLBldTlCuFWv7sWJver2qEruc1o1q/AScQ7pqrFmcRXXWkaIxlJHyHSqyGMX+ti6unoM8iZflerYCuKVC8tobhshkqM48tSlYTatTBdDN9++5YfljSFa2iOMRp3J9myJsU5fHmHD8lL0jOS3m8j1bnOTp+lc/7MpXZctCNF2P0o4Il0Jq9IjWLcV+96I9F3g05//AXfbCycaKaJo3xbhRZFQfYbxMNzJ/h/+iItHzwMwZdY0Xv47rwCLHgpokoAO5QFFhvp8Oq/7f2+kekoNv/abr+FnX74dXaTamYm5OKESVSUTrkJ4J901rKRVrrYk0nI7klipUAHJiSN32bZzLkIjrYHgRlV1GVOmlhVEqaahkykGTzoMDUbpaO9jydLpasUYT65duxdx7MitPEm1JGNmnI/H40dvsXV7I7o+9k+yvCLE3Hl1XL/65N2TwyNRbt3sYtWaWTmV37pjAadOtBaYVHPI/URmVs9WRayePHGL9Rvno/tFQsXqKef6WlISYMmSGVy40FawXuYaYN8m09dtmEcus/4bNs/hwrl2YrFcX7wmBrdK9cK5+yxfOZ1AcOwkCoGgjxWr6mk6e3/S+5iMWEzSdLaDjVvmADaJDmnedPmr972F8nIVB/qTn/gmly6ewjCfqfivRRRRRBHPHRy8/IAjB3/MyL2zAOgl1VSv/EW1UeC48NvqU2tgLqldQnD+Ph6NljKvTCnHhClVnNREiADpxFN1mRgCi1iySRGJlwi14Y6HZVUMBQymVUa422N5YLgSUZFM6lr1RqM6Dx/7mVc/6ihtLSSrPJU6NXnsVuOrYebGPJnSpCQIs6YJbt3N3Q4IR0zudRosmje2HT12aIAs/XMdc0lQMHdmgOt3IllqeDEakdzpiLJsQTBrvi+7n14S1em3d8x3CE6ZRnUaCmosmB3i6u3cx/7RqOT2vVFWNNp5JJIJ1fT7VxCu7SSts74lkWqaplMa8rN4fhlXbo+4SFYVJkAI3VKCetWLkajJjdZhVi+uyHI0XiIwH6SrE/QLlswv5dLNYee3au0nU/vRmOT6nVFWLUr2fhIZ95MrMim4A37B0oYQF5vDSWphhwB11KZqicUF1+5EWLU45CmrCS2xiFz+CWEldR37mPw+WL4gxMXmUZzzobmutxd6oJpA1Qrr4A2GHp7g9//4c3mdsyKKKCJ3FAnVZxCGNDnz4Dyf/dt/T6x77Xt+m0AoiAEEBAR05fJku+3HDFiwcRWfOvIfvOTVL6CnrT+xzYaOQ3/qInUWxP1dSkWmBmz7ynZRQhGY0mMYmPR0DTE6GmfW3CpAetpNVf+p22vz9vmcPXUXM8dZ9EJAQ+fwgZuJZDS5DMKhEj+Ll07n0oX2PPaUdGbHMdb39g7zuGeIBY3Tcq6zaUsDTefvPTFSzcbhQzfZsWthzkZNIOBj1epZnD1TOKJSwWU0e9SqCoMDYTraey0y3W1EZ8b6DfO5crmd0dHUDLTjgZqMGPs8HT9y0yLTyam8z6ezftMcTp8o9DnNDIEgPGLQcquHFatn5lxv+arp3Lndw8jwk4rfpGb8z568y7qNs9F9mnXlnURmbkgkM2ZM4Y/+5LcA9SL41Ds/TFy2FBNUFQACWZCliCKK+OmDaUr++JMn6DnlJFutXfcadF/QUXImRHwWYWmYLvd+gyvdVSyp7Ue3SUhpkZtSOkSqCR4SS3O92piu/YBLrepSpwqUka0J1s4boKmjmsRYbHOu7risiXZlos0bLSUsnD2K7kooqeKhJttoGVz9zTgphkxGmGxaqXH6cu4T1TahdPlWnKUNPnxZ5kRFmpBFuQfaUv2zsXVNWYordS641BxmWUMIv89L7Lpd9r0u/fb7h6NKTT7XzvtMqk5s+5pKjl8YJP0bU2ZcbB5mSUMJAX+6mKfJOrSE1MX110Wa5nB+t62p5PjFAWxbR9WVSa7jqX25dHOYxfPKCQZ8ibqpbulj7t6D9PtS2LqmipOXBkBquBseax9Xb4/SOCdEKODuV/pjyqOnHiWps+hsXlXO2SthnPOgYtC6FzdRai837sSZPzNASdDlru9Z0nQ3Q/eFhwBPX3jjijLOXgtjSrey1SFu0yFYvQGhqVAPseHb7P/JIQ6feOZzcDxXULRvi3CjSKg+g7jy+BZf/9Q3ud+qFFzLNq1ky8/tTNhoIZ+kxCfxaWDbPoNRQbkPaiv8LFg9jXtXHhGLJRsN6n+JE1vVDZuAFQJ0TbWvCZc56plAtY0VgWmaHD10j+175mGnvXTiVrrjWNrKQdVoIKCzcs1Mzp25N6Hz5czrjTW7Lrjf0Y+ua0yvrySfgXftujlcvfIgR1ItyVUj5714cfAnN9jzgqV51fH5dDZubuDk8Tvj3Gv+6OoaJDIaZ/acmrzqrVg1kzu3HzE8nLtCYWykEmNuHDxwg917lyYRv9mvkKZp7NixmCOHrxeqk4xl+PU+Hqa3d4SGBVNU6Rwt2oWLp9L5YICBgdGxCxcIxw61sH3XgrwUAkIItu+ez9FDrZPXMQ80BgciPLg/wKIlU6116m1aJqaZwHP/SHjr215DwwKluj6w/wzf+a8fYsjxhP8owkYu7xP5LEUUUcRPFz71PzdoPv4dYv0dAATrFlLesNMpELPIU9MhJjElRAxLtSqRUuPG4yqW1/aRyKLqIVMtlarEiqlqbbfdsBLz+64Jb7u+DeshU1cWwZSC3pGAUw7AVOEIvKSst8+mKbh+p4RljWFHj5ikikuNnap2LKXMWZ0KMKVa/e3uzd/zxzThwo0Y65Zmdv1P54ZuIx8vrCk1Oj5d8LAn/7jkpoQzV0fYtLLUJdYwE38dd37DWhxiNRNEgkj0ji5Tqv0E/IIH3XHXeg2BbpFq9l/7s5M4SkqNU5eG2bq6IqXddEtKm27yWqQns23UVvkoCWm0d45ax2vbNN52bDVrsjv68Qv9bF9bRSph7A0p4Fa7Ji9KsqMWge127iUEayr9lJfq3OuMgrVvd9iC9OSms5y8OMyWNeWu82ZfvWSi05e06InFvk4CO9xbMomsUVWuU1Wuc7fTSJzDrEmlki7nqcthtqyeeGxaobmPLfWeqSzTqa3Sabtv35+alcRMXRNN09GEL+XeFHqIUO3mxH7Cj47w+3/0nxOKjVyEQtG+LSIZRUL1GcJoPMLxm2f56j99EVAD4K+/942YUkMDaoOScp+04p5KNE0Sj0uEBgHdJKBLgn6NxrXTuHnOcf3WLWI0oEFIS/rRuifOhFKm+gT4ku8CoUwJr20iuXOrjynTSqisCmBayYBMeyY44R6kFof0VA0sWzGd1pbHDI9bqZb8yMn8CBJS48ihW+zco2Jn5jPrqmka23Y0cuzI7fz7N46nYuudR1RUhqirK8+77qLF0+nsHGCgP5z/jvOElJJDB5rZs29x3nWFEOzas4gjB29ORs+cT9YLQEd7Lz6fbpHpCho6Wg4EeMOCaQz0h+npGUyzNX/oYwTiP7j/Onv2LSFf1zohBLv2LuTwgVsT6F3u6O4aIho1mDW7BscwzQ0zZ1URjxt0PRwau/CEoM71kYMt7NyzIEXxkZj0SaNSDYWCfOCD70is+8N3/wMjo7cwzSerAC+iiCKKeC5geDTGh750nN4mJxlK3abXI4SG1ASpsXqkilEaMyBqQMwiMYG7Q2VMKRmlxBfzhghwkbEJIjWxmF6Boq1UtfaVECpK1AchWTNngAt3K1XZBPlKSiIqTztWciyA9od+aqtilIbiSNOrkFTjTjZ1au7YtFLn9OV8bAbvvu8+MKmtElSUZrGjx+1a7fRr+5oyjjXlr0610XY/SnWFRmWZulCKTHUTqPkSQ+ldr3esq+Do+aGU9cl13Vaju1x7Z4yKMp2aCl+GPeTW5ljYub6Kw2d7SZ8oywuZ9E4G0NEVoazER1213yIMsdSt7ray9yk5mY5IqDYd0nXn+iqOnh9E1/weF/js7TtEZ2ePpCSoMaUmkCANM8UVTepdYvGWTU+W7lhXyrGmcPr2cmC9Oh8Z+H2CuurcQ9FlgrufWlJftyf1M71QxBUawhVywF+xFC1QB4ARfcTNq0f55OcPTbi/RRRRhBdFQvUZwqmuy3zpw58lPKTIsL2vejGzli8kjhrgyn2SkOsZbZowYih1apVfEaa6gGnzqxh4NMLIYDTxzNctotSvW4OeazDQsMhUi0hNDheQGCRRD3ibgIjFTM6f7mTTtll406ialkOtiTfmksfHCiFgx+4Gjhy8laImzBdqmEh/6woEVy8/YP6COsrKguRjrNiY31DHwECYnp5sBJDI8i03mKbJ8aO32LFr0Thqq+uzd98SDu6ffBeOm81d1NdXUlmVObN7NsycVU08bvKwc6CAvUq9j6SUHD7UzO49i3GrAVKRmQDf+4LlHPjJ1YL1MpOi405LN5VVJdTUliSVz804mzqtnEDAR0d730S7mBVSSg7vv8WuvY2AfTx5vgjsWcCRA3cm0Y1e9eV+Rz8+n8bU6ZUZzrv3uZTQ80vJy39+N/t+Rs3mt97p4B//4XMYPPn4r88pFKfviyjieYk//cwZOk99GTOqCLXy+TsJTVmIFHiUo0IIhJQIE6UajSnCVMTdjKfgfFcd66Y8ttz1veNI4hGRzvwEr6t+WiJG0lA3woO+EKOG7ihQDemEIPAWV0jeh5Q0XS9l7dIRi/SzvbUsgtfTY2FVk0hyJ1QXzIYH3RCO5DqWpvHKAE5ejLFldSaVava2s7kZ26EFFs4N8KA7znA4P9LT684vOXxugO3rSpNUqONDOlJu4dwQD3viDIdl2u25LsfOD7NjfUXe9TRPjE67n6k264LZIbofRxkYzl/tq6DsncPnetm5vprU85jQVedyJq0apnXb2wm3JPNnBujtj9M/lK4dm8QVadSvXrLwWFOYbWvSvG/kaDM4bvje7/Yyd4afgWHJwHDqdc9mcyQT5CcujLJtdcmYRPqY/5LCBNh9mT3dz0hY0j+U/v5UMVjdCmOrn4ntOqVTHK+A0ccn+cu//yoj4ScVhus5jKJ9W4QLRUL1GcDjyACHTx7j0DeeBqC0ooxfesdvJLaX+wFhxzFVS0yCJgQhS53quO0Llm6dxfUTHSpeqpAEdfCL5LDkCpo7dodIJVTB/o17b40LZztZtW46fr878JI9+LrIVamMXyejpaMYmDGrEtOQPOwc8IQHyAXJxpsm0h0dxKKSSxfb2bBxnnV+cmo+BXv3LeFQPkTlOPbTdO4uy1bMJBTKJ+uqF1OnVRIM+Wm/1zvuNsZCPG5w9nQbW7YtmFA7u/ct5tCB5kkg1Zz2Ll9sZ0HjVErLQi5TJV0Wzcyt1daWU1Nbzu1bDwvSu3QvHqZpcuLYbbbtWJiyLdO9nQ47dy/g2OHJjfd580Y39TMrqah0XJvUMXlfArKhojLIzFmV3LwxWVlGNaSUHDvUwo7dCzKePaVStV0xk17KheDDH3mnZaDC3/3tf9Bx/ypmHq6YRRRRRBHPd7Q86OdrPzzKQPP/AiB8QWo3vIbEsGECCG+2VNuclBaZKnGIUAl94QCGAVMCIwmBnud9ND1v6HXPt2E67aKBzw+N04a50V3uKFLj0qtqTW7TggCPodk/6CMShak1UVdxt9jAOzqZZu4x23Udli2ASwlnn/G/jfcNSkZGJTOmesdw25ZIJmjSIdm2Ua7EEl2H1YtLOHcttwRPbhLVlHb4MBWmp28wxuBwnFnTdex3jELZO7oGa5aUcPbq+FW0NvqHDPoHDebNCBSgZ15SVdNgw/IKTl/uT1cSyB6mwY2BoTg9fTEaZmUSR6gfYvamXJ5hGKjZEBNNwMYV5Zy+PGzZsan1nLAN2ZfBYYPux3EaZvndtb2iW/c8SdK/RPk0EAI2LA9x+nL+3n3JbQ6HJZ09cRbMHv97XKJfSb8nIWDTyhCnLucS2iszQ+crmYm/TL2/SSNMT8cJ/uivvzrh/hZRRBEOioTqM4DjnRf47N/8W2Lw+4W3vIbK2mpAUqKb+DVrYty2AyXETUGpX1LiU0mqfMKZR6ysK8Ef1OnpGFQUh1APYjflYUX7wacrI0LPok4F76AxPBSlvW2AxctqyXzLSM9nO9O6/U9igjTZuXc+Rw+2uNx1ZMq/dEhHSjnxZpzvJ47dYdOWBnQ9d1IqHaprSqmtLaPldneGEiLD59wQDke5caOT1WvmjKt/buzas4Sjh29NGql25lQra9fNwe+fmFtLRUWIWbNruHG9c+zCOcM55mg0bpHp89GseETJyKSqSMaOnYs5fuwmRrIyZVxINXLOn7vL8hUzCYXSn9NMCuxklJQGaFw4hSsXH0y0k2kRjxucP3OPTVvnpWzLV6m6Yctszp/pIB4vtBu92v/VS500NNrK9MxQMeyMJJc4heUrGnnDG38ZgOHhMH/6J/9EnLYC9/f5g9T8uONbiiiiiJ8evO2fj9Nz8rMJVWb1il/AV1qrNtpDamLokM53QHhc8+0NEkxoelDD2ukul2cpvY9w0w4/5fqeTKbaSalsCMGKGQNc66zElEljmana8Kx1xx8UeA1poZIWNF0PsmZp1HVs6cdIwzAwZTwn201KyerFkks3JYYVimYst3x37NF0OHMlxobl/qTuOX3RtLFtPq9NpequX1bKhRvhFGFvct+8JKqhyLnE4kx6nrg4yOZV5SCcd4RCEKvrlpVy6WYYo0AmyalLw2xYUZpWqJINjkt8+tFu3dJyLjYPEounO16vKtH7OX1HTl3qZ+OKSk/uNi9sUlWOQaxaZa0ya5aWcuX2cIZ+umpItxAnmSF1lrNXR1i7NIgmnERjpkwi1W3dTrp/LoI2ca+ZJisaA1y/EyESNT3rMy3JBG7ye2rT9QirFwfQC2ysrGgMcLMtSjSW/XxqmjsGrfe628cfqtsBVgiySN8FvvCV/+VeR09hO/w8Q9G+LcKN4rV8wrg72M4P/+v73DynXIrrG2bxM7/+84DEJyTVAStBlPX8jEsYsjw8NKRahDXcmQnxJyu2zOD6mQdoSM+kv6qnCFi/5pCs2WgQDWFlKFc4friDbbvnKLcMTwvJsNe5XUecOEcSSWl5gJmzK2m+1uVSIHhHq3TkarrhRBOaZ/1A3yg9j4ZoXKiS0YxXnWpj645GTh6/k4ZUE1m+5YYjh5rZuWsRWr6WVxqUlgZYuHg6ly50TLitZAwNjnK3rZely+sL0t7mrfM5d+YusVghSTV175w4eput2xbi0y2Jtwu5uPK4EQz6WbV6DmfP3Jlw71QsT+dRGw5Hab7RycrVszLWST+7nx5r1s/myuUHRCLjdQXLjLOn7rF2w+yMZLpDqo7dX79fZ+2GmZw52V7YTqIRjcS5cukBazfMzqF8UvK8JPzZX/wuNTUq/u7nP/ffnD51DNOc7Pivz024461NZCmiiCJ+OvD9k/c4ffTHhDsvA+Arm0rV8pc6xqpb3WmTpxZn6oH9uzekipdqmkQigvb+EhprhlOHcncCKodlSUWSPVcWjFNdGqOjP2SZrTLxV0gnPqqzH28fk74ipUEsrnGv08+iuXFld3jGGbddm+r9kKzUA+VRUVYimVINrfe9/fdmP09pLc06B9EYtLQbLG3QrX2nY0DHHtttUlUIQVmJTv0UH7fveY/Nm1hKJZNykkXayZXS9zcak9y4E2b14tIxjylXlJZozJoW4GZb4ZKlxuKS6y2jrF6Sf2is5JjvNkpDGvNmBLl+J52KdqzBMf32WFxy+dYQ65ZWpt3uQF2TschriUkoIGiYGeJaSxgQY5DdbqI2M6EaNySXb46ydmnItd5Ogmx6ic2k3amQEc5i1w8FYMFsP1dvh0klddMTvJ7fo/1okY4dGTckF29GWLs0mJ7YzeOfjaBf0DgnwOVbo54QGO5jSg6PAfajxiKRXb9nX6CSUPV6++ww+PAQb3jnf2a5RkWMhaJ9W4QbRUL1CcIwDU61n+dLH/hUYt2v/fHv4gv4CWpQ4Xd7QAlMUxA3BTFpuy84k+FR16SZT0BZqY85C6ppveqdcbInq4SQiZipQpBCuto/avXHuS0ePhhCANNnlCVIITtDpSfmjyvuo0wxHm3FqjKc1m+eSdO5diKxKLZyNRFjKmFw2/WyhwXQrAyOGj4OHrjJLitp0ngD6rsRDPpYuWom587eTdqSpE7Nc1ePugcZGYkyZ27dRLuYwPoN87hy+X7BSbVDB26ye++igpxPAJ9PZ8OmeZw6MXGi0o2+3hF6eoZYuHBGqtvMOJXKq9fM5dbNTkZGJm5wuwnSwwdtMn3CzQKg6xqbt87nxNHCntOhwQjtd3tZvHRa1nJizCkaB4uXTuV+ez+DA4V6iVEn8eSxVjZtnYdPz11FLZNe4uw4VlOm1PCnf/G7iXLvfPuHiZmtkxpWoYgiiijipx2xuMGfffooPWecF/W6Da9F0wOJR61X7YnDY7hhDyemBMNQBm/MREho7q5gQe0QPmFPylq2o1uJasc/TUaSOlUIWDenn/Pt1WqHyerYZHWrvd7dgPtwXKFkbt4N0TA7ht+XjikWGEaqMtUbGkBZwKal5tu0Ck5fTu6A3Q079mTyODy2h821ljgL5/gIZPBY1nIwVBwbXbB9bRknLnhdqRMqQWlgWn9tUixXgvTK7REWzgkRCgiv2GKc4/L2teUcayr8ROnVllEaZgUJBfO3O9U19J7vHeuqOHK+L1ONpLrp2kuPay3DzJ8ZojSUi9fh2Ni+tpJjTQPgUnlnvza5hW9oboswe7qf0lCyElq6YrcmtWwmE6XOsmV1KScvJZPT7t9dGkLVen+Vyb8n165v34tRP1WnrCT9OZdSIs3Mi0OYqmXTyhCnLg17SFTTNJLUs0YSwWpNUkjvMdmTLqGa9Qi9DIDY8B2OHTnAjw8VLl9EEUU8n1EkVJ8grvY285V/+wo9D5Qb+erdG1m9dzMakqAuKdG9Q4MpIWJa7v1SJaISwrET7Yl9TZNoQOOqKbTd6CUasd2BFIGqiySvpKR+ucdcpU61CFwpLXXqbJIT5aQab0kkYxb4fIK1G2dw9uQ9nLAABqZb0WrPKlokq5tYtY1MZ/ZN597dx5SVBZk6pbxg5B/AytWzaLnVzciwTQCNnSk+G6SUHPjJdfbuW1qQ/tnQNI1tOxZy7MjtgrX54H4fQhPUz6gqWJsAi5dM58H9fgYHcokLlAsEB/c3s3ff8iTjKj9VakqrQrB7z1IOHrhWkF4CdHcNMhqOMXtOTQ6lc388NzTW0dcbprc3t5hlueDwgVvs3NuY0+/Jq1zPXF4Iwc49DRw5WCjyV9DXG+bx4xEaGqeMq4WEQe+azHrjG3+F5ctVzKlTJy/x5S9+B5Oie1S+EAVaiiiiiGc/PvrNy7Sc/g7xoS4AQtOWUzp7k1KFJshKKzFNIo6pV7Xq+dFLiTCkcrs3lUpVSrjaWcnK6f0OcWqbgloSKToGplZFGI3rDIT9qp4VwxXD2p8Nd3takk2ROCxHZiuEQEqNy7dCrFo8am2zkscgkKaJSkSV/ITzdlxtMamvg2gUHvdnJ6G8xCrkMiZLCWevxdiw3JexjBB61jZs1E/xEY9LunsNpBSWgs6wCNQ4VkaxlOPMBVIq1/+tayrGVd+N6XU+pCnp7i28Vw/AiQvDbFtTPq669uS7lJJptX4E8LAnXZxddxKrrC1m3HK0qY/t63KxRUn0KR2m1vjx+QQPHkVBODk03O9qprTJP7fi1SZGk9d7cfzCCNvWlKXZsxM2TnXQvT4VdVU6wYDgQXc8cTxeQtJM6Yv6bj+m3MdjLS6C81jTCFtWhdKGDZDSraxNXZzzYFJdISgJQfvDaIIkzW1xSGHFD7jDAEiE5qNkyrbE+Rh+dJg/+OP/xChUzIvnGYr2bRFuFAnVJ4RwbJTj107znU98HQDdp/Prf/IGdCQ1AUmZz566VwmjkGo+TMMiRHFc9k2X/RnQQMcyUTTBik3TuXjygXNhrZkqN6GayctcJH1qvvaYOfMqKSsP4P3ZWzpWoZSqAq8hlgufuXBxHZ0PhixSzZXUKinuqkOkOttS+is1jh25zc5dqQl+JgohBDt3L+TwoVvptub9NLx1s4tp0yupqi4tSP/caFgwlf6+ML2PJ06qSSk5fPAmu/csKkDPvBBCsHvvIg4eyCPpVxa0tfZQXhGkdkr5uNWomTB7Th3RaJyuh/0Tb0wKDu6/zp4XLCWXF4J8j2X3voUc3l8YQv1BRz+arjG9fiyXMAe5xlSdVl+Bz6fxoGNgQn0U+BAIjhy4ze69C6292vvPHENZWPF1bWVtgjCWzja/P8CHP/LuRJ0/+eN/YGjodgaXyCIyomhxFlHE8wLdfWH+/b9O0nfxW2qFENRufJ3KV2MPd+key/Y2izBN+R43nTYsQvZ+fwmVoTjl/phXXDYWpNt+lKyePcjFjio84QIMmb3NDM8kaToHantzdT7yUV4iqSw3cfw7NSQmtkeE115WZWxi1FbErV+ucfqKgSkdYi0d+ePqTaK9XOzx+10mZSWC6ooMY2aWeJxut+PNq0IcvzCEIzt2q/4mjo6uKCVBjdoqfUIq1a1ryjl2YfLC+Dx4FMPvE9RVZyap08FW8dpQ6tR+Us/9WN9zQ+ejKLoGU2szJdLK7ce1Y10VRxP9tK99skLUpj7dJGK6fcmUe/thTwyJZGqNlob4TCI6U1SbzrJtbSlHzw8lEbrZjjvxMEos0iKGU4lMyaPeOIYpmV6nJ7WRPVZs8rJ9bSnHmiaSKE2o93OhpUyGBMoXowenq6OK9nKn+Sj/38d/NIF9PY9RtG+LcKFIqD4hnH10ic984D+Ijiql48/++suZs3AOIV1Sqit3fMCJj4rAlAKfrmywkE+RoomJeJRyVQjvkFA/t5LhwSgDfaPqdyqkJ1B28gV3G1tuAicWM7h8oZu1G+tJVadmPs5cSSAhBDv2zOPowTbPepk04+bM3DmDmON+oebgLja1sWRpPaGSiWdZTIdZs2uIxQy6HnoNsHyFsIZhcvpkC1u3Nxawd17secFSDu6/MeF2rl55wLyGKZSVZ0/wM15Mm15JwK/T0d47gVYEpgnHj95mx65FStXsiatUGCN+3wtWsH//1Qm7fN+62cX0+koqK0M5lbcnLHJFTW0plVUhWu9MTEkppeTooRZ27sn/PnWIyuxDy47d8zl66E6Gc+qoebwKG4csVZM4grbWx4pMrytL7F9DR0v89SUWNfmjk96ScYx8O0vsz75wGy972R4A7t/v5gN/9ylMJif5VxFFFFHETzPe++kzdJ7+EjKuPE8qFr6AUNXcJPWpcMhLKR2XeikdIhMSHIRIETPadSTn71WxblaftxO2OjV5SWx3Pi6sH6HtcQlRQ/OSuFIi3HFWk4coIVTadVuZkEyIJsYsRdqcvx5k3dJIgiQ1pU2wCOzwWfakXro4mkvmC9ruS0YjMtGmt1PpiCgvyei4kqd/g5fS5MTFKFtWZ7b3UpLdJBSISkG3tMFPa0eUcMSwVHXuPhaONThyfoCd6yoZr323ZH6Iew+ihEcLYx9mwrGmIbavzV2laisY7eNa0lBK+8MII6NmmlAALvnLGC8iY20/cq6Pneuqs/UM97lOttkWzS3hQXeEoZF0cXCzEYfqmM2sia+c8sebhtmWcj5lkoLT/p6KBbODdD2OZ+hnPrAEPp5+O/08cWGYrWtKST7WXDF/VoCe/jiDw+OZvLefIxpC+NCE7gk3Zj87SqbsSKwbfXyaD/7TN+nrnwiBW0QRRRQJ1SeA7vBjfnzwIMe/dxCAippKfv4tryGkSyoDEjvfS1BTJGlcqqFBs5SpmhX/1B33VNMgoCcen0hJgipYt2MWZ4/cT9isHo+qrGQoCQLn7IkHrNs4HZ/P56mUEp/SNZuer3v11Gll+AM699uzKdXsoPUSEkSqE88mPBrl2rVOVq+dA5DVbWQi2LN3CYcO3HC1m7+BeObUHdasnUsgkN+sdT6orS2jurqMOy2Pxt1GNBrnwvl7bNw0r4A9S8XOPYs4cujWOK+VOv8Xzt9j6bIZhEKKTE+e9S7EfVBZWcLMmTXcuDF+Mi0eNywy3Vb85nb/KFLVrajMjm07Gzh5rNWKITU+XLvSybwFtZSVOaoFdx+SFzfxaX9XzxFvnGU3kVlaFmL+gjquXu7C+6KXTsIkUrZr+MAUnDzaxs6di1yEaebzKtL8A+/7vorJ5Zy7v//Qu/D71e/1Ix/+LK2tVzFlaiKRItKjOIFfRBHPfTTdfsR//+QwQ7cOAKAFSqlZ/UpFklp2odQ1pFAiUYlNnloEqZsINVDEpmF6E0gZUm2Lq/ABg6N+hmM69RVWvM6xBGeGo3716ybzpoxwq7vcGQAsUleki72aqU3wZJEVtku/RW4CDA7rDI1ozJgas3ajxg/HPT8z/D5YNFdwtUVa5W1/tCzdkomTSCIhrFTig2xP1MFhSd+AydwZY0/iSovYVa7MBj5dsmxBkIs3UkkZhyTOPdZ6NgwOG3T3xlgw20kABLnZej4frFgYoulG4UIjZcLQiMnDnhiNc8YWJdgxZe2byqcLVi8q49xVR8ThENpaTveOF5nLDocNOroiLJ6XzWsu/bnVNVi7tJyzV4cS+8jd5FbXTmRp342RUZP2zihL5icLEtwTCel/G7oG65aVcvZKIUlDN7HqvHOGI5K796MsbchfjKJpsH5ZCWcuq/tzfOHrrJAqQlixU/EkmQbwhabjr1iijsKM8Pj+cZ7686+MY1/PbxTt2yLcKBKqkwwpJacenuczf/PxxLpfetvrqaupIKgpl30p1Y/Kp4GyM4XSXgoQSEp9ELLYUl2ocj7N+0PUXJPmldVBKqqDdLQOOApW4Sgq3XZn8uAnhGCgP0J31wgNC2tSSZwC//q375rL8cN3xzSGnIlA96yf4NjhW2zd3gCaac2UO4aGOUZcnnxQUVnCjJnVNN/oBPJXpw4PRWi984jlK2dOqB+5YPuuhZw41jJuUu3EsRY2b21A1yf38VBaGmDBwqlcvtiRZ001DI2G49y4bpPpbjLfG6i+ENz61m2LOH3qNvH4+GINnT51m7Xr5+H3uw0bLYdFWESh/TLifSlJJjdDwQDLV8zkwrkHKYSn2809UxuxqMmlpvts2Dg/sd9UVad3UTSqZiWI0xKfdfxo+FFq0lRl6LqNs7l84QHRaDzNsWU2PTRr0ufC+XaWLa8naJHpXjJVpEz2ZF7sOt4bRSBYvKiBP3jb6wCIRKK85w8/BOR7vz5/oYnCLEUUUcSzE1JK3v2JE/Sc/ExiXfXqV6IHKxPxRqX9Q7YVqXGbSMX5m2jQcvOPuVaaNmGJxWNIMEwudVSyckY/YqzkRm6XfmD13AEu3a9SdE5CnZqmiYwEbfJYkUx+eG2vi81BVi6MYJqxlEY96tQkw3LDMp3z15MVfJntWYdM9apWnX2ltyNsnL0WZe1Sf0rCTDuWpKMslJhmHClVLMpNK8s4e3UkbQ6vxLEVkEo4fWWI9cvK0TXTM+6PZedvXFHGuWsjTGC+OS+cvTrM2qWlZDKlHRd/b783rijn/PUhTJl8jrz3SK6EmxDZbfmzVwdYu7QCX3K2Ym9vPf0G2LCigqbrQ8Q9kxD5GNxuterY72lN10dYsTCELwPnnxyO2cbapaVcah5hnOb7GPAeA8CF5jDLF4Tw56mdWbOkhCu3RvPsp2WHW7FSNc2X8t6uiGvvb7+kdgsIZTtHB67w9W89zZXr7fl1+HmOon1bhBtFQnWScXugje99+Xu0XlExOGcvaWD7K19ESFcEqBAQx1Gfht0ZSIGQT6lWbUvSjqnqc5EAAonfiqVqY9Xmei6ffogR944uHjLVXiToLqPk2KF2tu+ejSaSY6MW/pdfUuqnYWEtVy91jVEyNZtjz6MhhoZGmTu/GixXb+VRJq1Yq9JV04mxkz/Jqo5789YFnD3dhhHP3xo7dOAGu/cumZRzmIxg0M+KVbM4f/Ze3nX7+8J0dw/RuHDqJPQsFes3zOXypftEIrkkB3C/CMCRw81s37kQXdM8BHf62EwTg9+vs2HjAk6eSBdLNzuGhkZpbX3EsuUzAZk1HlkqnNlmTThkpe3Ono4kXbl6FreauwiHYy6C0yFNnfqpbZw81sqmLW4yPZ2uM73Sc4yj8BC3INB1nU1b53Hy6N205yPz3jRGwzGabzxk1ZrZY5Ck44H3he+9730j06bVAvCNrz/NgQMHMeXkxV8rYnIghPgLIYQcY3nTONveJ4T4nhCiWwgRFkJcF0L8tRAiXSYNu87LhRBnhRCjQoguIcS/CSEyBi0WQrzf6uMvjaePRRQxGfjaoTtcPPo/RLpvAuCvnEnVwp9V44IukLqmEgBoOEmf3ASne47cNBFx01GJSplw2bIf6cIlwIzFNFp7ylg0dcjVWAZYKtjKkhglAYOHgyFvyAFTIjzuClkO2m1iGG7CUsU9TbYt44agpd3PkvljJ+K0bcTKcigvk3R0JXfEtvTHN8g5yrXU0DyGoXHjjsHKhU74LIdMTY/Kcp2aSp22+9k9N2wbRuC2f8Z3DPG45NLNYdYtK8cb5ikzKso0plT7aO14ch4mhgEXb4ywfnmq+lN5w6SSqeWlOtPq/Ny+p+4VhwQHTcs9BFR+/ZScuzbIxhXZYuZ7JeBlJRozpwa5eTeM+zrm+46Taq9n6acJ56+PsHFF6rBqSneIOKefpSGN2dMDNLdFUuoUDl5S3zAkZ6+F2bgi91wZJUHB3PoAN1q9/XRf//Rwq88tYYE1gWHbw5rQ0TQfuu5LhADQfGWEatYn2hjuPsKbn/pswb07iyji+YIioTqJiBkxTree44sf+kxi3a/+8ZuoCuqJRKGjBsRN5bIfN0g4QADoSEp0ZwbDTkzlcz1bhbJZUx63gYBO4/Jami8+Sihgk5+Tjs7TcsFC0HFvkFDIR93U0idC/gGsWV/P1UtdRDOQau7ZNsdw0jl84Ba79i10lfMqBOwkVt5A6MnqRZmypOsBKFJt/cZ5nDrZktfxdT7oR0rJjJnVedWbCFavmcPN5i7CI/kZjwf332DPvsVP7NrrusaWbQ2cODpWMiVvfx49GmRkOMrceVMy2BpJEwkFMBKWLZvJvXs9DA6G86p3YP9V9u5dZr0JKuSunvSSr5nIWDfdqGsaO3Yt5Nih/BJUDfSFeWSR6ZnI0nz67bxA2bFLU/vbuHAqj3uG6e8bzYmktds5cvgW23c2ToqK2vuckFRWlvO+v317Yvs73v5+DONe0fAcA+n0SBNZCogu4GiGJe+4HkKItwI/Bl4KjALXgPnA/wNOCyFq09R5GfBtYC1wC3WIbwS+L9LIiYQQS4F3Av8tpfxWvn0soojJQCRq8DefPUbP+S8m1tVtfB1C8+GZ/beTA1hIDIXuBFDSIlLdpKvFtSSUqe5Hrqlc9G93lTC3ZoSAbhMzaZ7LLgJ37bwBmtqrvQaxYfUlFyTJMEVKtfTtNLfCnBmSYGAMbyyrXxtXSE5dykY2pbbj2Af25LOTeDHZprO/J4exutEaZ+4MnVAwWZmavt871pVz9HzuE4yOXZAcMii/sfxGa5g504OUhrzHlWlc3r42v34WCjfvRpgxNUBZiTo+W5UqM4Ru2LmukqPnvWHQvDFUxzsaZq936+4I9VOClJfmRtruTCSierK40x5laq2PilLnfjGt/BruI7Tvg+1rJzcBmbU3vL8RQWtHlNoqH5Vlud3X29aWceKiOyRBrlaQvV66XPy1FDW6M3/l/D6CVavRfBUAxMMdnD17hK9/93RO/X2+41ls3xbxDKFIqE4iLj6+xpf++YsM9PQBsOFFO1i+bRUBXdlkcQNG4oKoqSbhR01rFhI1B13ic35oGjKRhEpY1qUQEh1Fsqb8IAUsXF7LvZZ+ImGHqEyEi0oqq5SqgpNHO9i6cxbiCahTbei6xqZtszl5LJO7QbKBJGi59Yi6KWVUVYdcQ5hIY0ylZpu0iVbTQ7g6/7wqVm9rS5fOoL29l8HBsZUGoAb1QwdusHvfkpzKFwpCCHbtWczhQ7krKu+2Paa0LMCUKbkH0i8EFjRO5fHjYfp6M8W18g47UkoO7b/Bnn1LMyoRc1Ut5AMhBHv3LefA/qs513lwvxeA6TOqGK9SNnmG2omPlHmZM7eW0dE4jx4N5Ux+Hjpwkz0vWIKmaRnLjAepqhQvdu9byOH9udynimh91D1EeCTKnLl14+pPcpvel0+wpFK4JVSvf/0vsn79cgAuXmzmk5/8CpKJJf96fkAWaCkofiCl3Jlh+XY+DQkhNgD/YH19IzBXSrkeWACcBZYB/56m6t9Yf18kpVwJzEWRsjuAl6Up/y8o2uet+fSviCImE+//4nnunfkWxshjAEpnraN0xhq1UQikECqAIdIbD1XDq1IFhIlDatqPYDeTpWW+AAC80ElEQVQS/rwSYZoIy+1fonHxfhWrZ2YhdywSdGZNmIFRH0OjvoQqVcVk9ZIxWR85yaSu56uRltCzY402XdNYt2xsRd6s6Sqm6YAn5KNq1zsWpxtXU9dlnvxznq9C2Paz5NSlKJtXBsn0/LXH89n1AYZGTPqH8n9Gp58czs/GONo0wPa17gRV6e2UWdP8hCMmvQOT4vM9Jo67ElTJLGPazGkBIjGTnr5UcYmKg6nl6eHkYCy3f4Aj53rZtb4mSwnV9xlTA8QNSdfjKF7bdHw2YrLIZSwcPT/E9nXu9xQz0Y67rWm1OhJJ9+NcPOAKAcdDSgg42jTM9nXJatpUKm1qjQ9NCB72GIn1wiJFhdDQNB0h9LRq1cweWaodW1DkPFBdwg7NR6hue+L76KNj/PFff8UKw1XE2HhW2rdFPEMoEqqThKHoMMcvnub7n1HvZ76An9e+57epsHhKnwZR9wS5hJiy60CCLpQbv4IiU32uq6UJNfmfHPLGIVytQNw7ZnD+eC6iG8G1S900Lq4mVOIfu3iBMX9BDX29Yfp606n/vAdpGHD6xF02b5/n2Sqz1HFgP8BMvGSrW8GarGR1PfgE7N6zmIP7r+d0XNeuPmDO3FrKy3PL7F5IzJ5TSzQSp7trMLFOzY6bKUvcMDh65BY7djYmAryPP0RC/tizbwkH9zen2ZJ6HVtudTNlaiXVNWO503gHq0L0v76+Gl3X6Oh4PGZZKSUHDlxj777lGdUIuSN/Q3X3vsUc2t+c03HfbeuhtCw4aWS6iqGWPm5bbV0ZZeVB7rZmP6caujVB0czufYsn4NY/xtywTFWpaprGR/7hjxJF/uxP/5G+vttW/LMinsf4U5Qd9Tkp5Sek9WOTUt4HXoMaYF4hhFhtV7Dc+tcAh6SUP7bKh4G/tYrsdu9ACPHrwD7gfVLKO5N8PEUUkRMe9Azzn98+Rv/V76oVQqd2g4o3rVywLHWqlE6OpBRYBKlhOuSkaXrUoko/YJOprmr2NkPS1R8g5DOoCsW8Bez2AE2TrJg1xOWOSqdP4CFVx3y3Nb3tCknC4M7mGq9ip0LXY42AT1JTmX5HUqqwVWsWS85dy8UVOvWFPJV0MbOMlfY+vGNh12MTXYcp1VoKESctO1jXdDavLOPUpZGkNnIfmIVQE672IvJUq3Y9jiEETKv1J9pLt4/Nq8s4efGZy2Le3RvHMCTTagUZfggAbF1dwfELg2m2iKTPk6NSfdQXIxozmTk1W0Ilyfa1Sp1aOKFNfrb5436D0ahk5jR/kg3m/T1sW1vB8aYned29tmXfgMlwWDJregB74t5NlKpFZ9vaMo5fCCfWua+T8+7lfh91w5tzwe6Hl0jNfH79ZQ34SmYBYMYH6Gg5xt985DsTOw1FFPE8RJFQnSSc7bnIZ97/SYyYmul5yW+/gpnz6hOu/uqlXT38dKHc/qMmREyISQhaXhfS2i4sFalAognpUBMZxjM72PGMWWXEoiaPuxVRma68lDAaidN89TEr106xepWk6nwCpNrOvfM5cqAtzRb3PjXOn2ln5ZoZBAI+3K7B3s+539qOmtFRpnljG3mPeXp9Jbou6Gh/bJGP6c9NLGbQdK6NTVsacu5LobF77xIOHmh2iFTrzUYmLRcv3GPRkmkESnyJYAluUsmr3i08yVpbV0ZFZYjWO49ca1MNdMOQnDrZwrYdjWO2qfpfeOzZu5yDB66NeexXr3Qwb94USssCE97neAzXqqoSpk2v5NbN7PGJTVNy7Mhtdu5amLXcRGErVdO582/f1cDxo60ZE6nZdW7f6mbqtAqqqnKPTWW3kNnRJullJfHVO6u/c8c6Xv2rPwfAo0e9/PVf/Qvj8BB/XiHTWc93eTZCCFEOvNj6+onk7VLKm8BPrK+/4tpUYf3tTKpi30xVrn1UAR8CbgAfnGCXiyiiYHjqY8foOvdFpKHIwqqlLyZQOQM0gdQE0hKmphCktjFrml6C0v6YzvXeVrNK6d0uLMLWhPNtlayd3YfHbhPO1yUzhrn1qIy46bINDaVyFR6+wlKuStdib/SoU12fs4zPakxzSJ+zV3U2rEiNnWlj+QK4edck5hKJZYthqrooPXaZ01nHfnfKq7IqqZSJ85T19ufUpShbVocSpI+92OVXLgrS3BYlGrNPgdubxU0MZXuSp6rtHHLVTsaZDRpHzw+xY21Fxv2saCzhVluESBSeqZHFlCZHL/SzbW3mGKUrGktpuTfKaCT1WtvnIRGLfsy4mumRi0r1WFMf29ZWZ9y+fEEZdzrChCP2vWO3PZHzmRqKbSycuDDEllVliNSYGwAsmR/i3oMoI6NPKAMZ4FV8q+t06tIom1eWJjy/krFwrp/7XXFGRr3H4SZSba9UBwLQEcKXtk1TGkjTwDRNSx3vfga4Vc7q91pStwP7Wo72nuVf/+O/6ezqm8iJeF7guWzfFpE/ioTqJKA73MX/Pv0Tzv74BADV0+p45ZtflVCYBlzq1BJd2WKxpGd+snmj4qBKdM0hWLNBs+haAWzaNYNTh++nHaTsG+Ds0U42bJ2OJjRrvwkKLUmtaT/oczoVeaGmtoSKyiBtd/qStjgHGx6O09rSw/KV9UnH4Y37o4ncZ7lzg5dw3b1nEYcPNifFl3ITzyYnjt1iw6b56Lr2xOMt2v2orApSX1/JzeYup49J7gaR0RjXrz5kzboZpA+RYOCNQYvnnsgUHiFfbN/ZyPGjLZimxDvbqiCE4OzpO6xeO4dAILf0mclB/wtxHcrKgixYMI3LlzIn/YrF4pw7d4eNmxsKoE61kf/Qu3X7Ak6fbCWeJW3oxaZ7LFla/0SU6d7EVA5CIT9Ll03j4vn7GerpGIbJmVOtbNnWkIc6Nbv5khrWwNnmPAFteZXk/e9/JyUlSm3+sY99kevXmzBlbuE/no+wPSYmuhQYa4QQXxRC/EQI8W0redSKcbSzDggCEeBUhjKHrb9bXet6ULkolyWVtb+7ida/BaYDvyelfHLZVIooIgtOXX3I/oOHGL57EgAtWEnNqlcAKDd/gZrRN1F3ejIS5oWLIE2Qpk6xBFeSadiWJMjQ4YiPvmE/s6pcz2NrvA/6TWZUj3Knp8wZAiRWIqqkB4zX/HHKuonclBABthosFabpTTIzHBY86hXMnZFKoAQDKs7qjdbc7BRldxkJ4iXV88pto6mwA2BYf939TSZrBMNh6OwxWTBb946RaIT8Go1zgly/E0Ok2Npeki070eot70aiPDqa8KEJv/XXh4qlrojX0Qi0P4yxpMGtqlTtB/wai+aHuHwr0xid3I/CDzbq2piER03a7kdY2lCSUibgFyxdUMKF5lQ1ZbKtpK6BnVhsPMh+jOGISUv7CCsaUxM/+X2CFQvLabo2OIH9j6dXqYhEJbfujbKiMXlyXeLzCVYuKqHpRqYwYvn2LF9qzCEwozHJzbtRVjR6Vb9CCHw+wapFIZquj6ZRoSY/AwRuhatN0HpIV2mHHbFj9Dq2K7jff7xhvPRgHYHK5dauYwx0HuWtf/T58Z6w5w2epfZtEc8QioRqgSGl5PzD83z6bxzBymve/ZuUlpeoOSUBCImGtQhrot7VRlCTCVd+m1i1larJF8z9W3R+mBJNc76XVwSYVl9G2+3/n73zjpPcKPP+t9Q93ZNndmZzns0557yY5ANzR4bjiCZz8HJgbIwNmIwNJtwBBgwGmxxMME5g7M05zubd2Z20aeJOTh1U7x+SWqHVaWY2Gf32o+2RVFUqlaSqp371hDbXOrde6aWzI8S4iYX6IKkLtjahzK6teLW0VlesHse+Xed1Us28H+1efezYWsnqdZNiQoV+5zhaQv9/YAKSJsi4E4W5eUHKJg/l+NFL2KVvTZhtb+umrq6NqdOGx84ZAq2pSWDX9EwdHCs5ZEx71BhEtbdq+cpJ7N9bTTji7hdn5/ZKVqyeiKK4dQfOAd4gVg2S1aa/OqB7yM7OYtbs0Rw+ZPela0Sr7Orqo7qqidlzxmRUrtOXaibVcrpIMO5rydJJlB+uTehraOeOCpYvn4LPf31Hy6wsHwsWjePAvhrX8729YU6eqGP+wnHXpD5ugbYMzF0whjOnG+jtCZvpLeTrgX01zJs/lmAgFfHrNIFykKgOYTIZjIUE400fP34kn7rzPQBEIhE++ckHgIspy/FwQ2EBmjn+RuA1aMGjjgohvi00Fax0MU3/rZVShhOkMSLDxZxoSyl7gS3APCHE3UKIIt0lwFf0JM8ACCGWAB8Efi2lfAEPHm4ASCm58wfbaT70i9ixkgVvQgnk2rtbVcbM7Q0I0DVWo5qZfVTzh2pLZ/kCbUO1uZ5tnrBdC45fzGfWyHYUg4nV5cj549o5crFIyyCxz2KtdXbjNKRRjkVbNaH7AjtU1V0T9WiFwqzJKopiP7d4Fhw4kXwB1kqOGgQpuuzlZIJNOczZcPF10twN2MfF8lNh5kwN4LP4FhNCsGxeNvuPh0AqGsmpWLVYU4+riYhWN8JVxHyHGtfwa4SShdA7fKqbuVNzyfLby10+L4/9x7qxEkjuvuGd84dk++nBLfjU4dOdzJmi1dOK5XML2HesM4Fsam9781cj2DJH6nsrP9XBrMn5LvUsYt/xdv2zGlwlkdgnnaZsBnDkTBfTy3IJZNmDnC2dncehk93O7seBTEnSNO/DoiFu/H3iXB9TxgfIDthlz4UzgpSf7iUS8y8tbPmt9dTef6EvMPj07sjxvePe31jnZMb35gxyll2yFKFo1nShjtP845+b2XswswDMHjz8K8MjVAcZlR1V/OHnf+Hi2VoAJs+fzurXbNC7LEnAp9E7WQrk+jUxJ2Tp9LN9kqBf6mYMhlCkE6qKKdsZBKsBY7FfSl2D1VInAcxfNpxjBxqJRMyLGQ9/z7bLrFo3xrYSaiehjLpYhDeHX5bBIleD2X6mzxrGkUOmko5RYkN9J5GoyqgxumBsuT8rjLFY6adZjFam9dOQrrLD4iUTOHrkoiuptmXzGdZvMPw8OtUdsPwdLwTHUsQEZ+fmNOEySFS3VU2NVFu4aDwH98VrVF5p7qKzo48JE+MCUSeBRWCII1gH8l4I5s4fx5lTdfT0aMpYQpgv89bNp1m7YXrawpZZW2eAquR1MUnUqOXeolg1UHw+heUrprBzR7zf17a2burr25g81SDTBwf91QiYOWsUNdVX6Ozsizu3Y9tZVq2ZjKJcW+JXEQqKw6RPUQQrV5exc7tViNM0Yro6+6ipbmbm7FGOTzpdAdgiyGZ8q9LyXkvuuOPdjBunacg/+8w2nn76OVTZnrSEf1VkotuRxpRmnBCiMtGWRnXqgAfQtEWHAdnAPOCH+mU+junHNB0YnWYy57/GuSGO458E2vXrtQLlwAzgp1LKnUL72B8COvS0AAgh8oQQI8Vgqwd58JAmfvnMKY7veYZQq7ZIFyieQMHkDWYAAKkTqREVEY5qAaSkjMllRKTdp6pN/LGTlUm7aoscYPQTkYhCRUMe04d3xMoekhvCp0iauoJxZG9MA9Y6LiQaRoy0Ud1Zl8UlgOpiki+ljNNONRCNCk5XKcyeosbSDimELL+kvtnMr5VhkqcmgWpdwAdjAd0pM1obVzsGdvcBVrLOclQfK1UpOFYRYcF0cxGzpEghJyioa1JtJI9Gcpq/xhYjRF2sjhLBSrQqik8PyqO7AhACRfHrxKofIXyoUuHQyR4WzzK1FYsLfeTnKlyoT7TW5XY9O8nb3/mDlDJOFgbtszhwopOlc0xf9cUFPgrzfdRejn9XhHV1wVEVoy0yncYbxLRBUru98KqEfcfaWDGvKHasKN9PSVEW1Rd7bHlSE6BpPnPcgq2lkO2kYO/RTlbMM10+FOb5GDrET9XFUNplJSPb+7PplcNUPIJ9x3pYOicn9l3nZsOIUh+VF0KW9PZ339m2AoFQnOWb331imMo+1utgaXPFl0P2kKWxHF0N2/nwnY9ec+vKmwmDLN96uMnhCeWDiIgaZn/Vfn7zHVNV/l2f/QBBv0AIlRyfRpQqQuLTCdNI1LoOJcn1S4K6dqnm118z8w8oFrdOaB+h1bjHhLQFr9ILxp+lMGvBUI7ut/tTrK1qp6g4SNGQoIOwSfaZ6x25jVCzm4APBLPnjeDcmWZ6dE01TXhV2L7lHGvXT46rnUxSV59Ixw9TPOLyuFzC51NYtqKM3Tvsc/nztVcIBrMYNrwgPhMQT6LaVCEcm7MtzYrYCVZQpUTV/3Y+ghmzRnKhtpWuTrtwuXXTWdZuSO2PNDHsWg+G31JV/5fee2EIhQJFEaxeO5XtWyuw+tqsu9yGqkpGjy7uZy3tpKqzKlZNVJNEtbu6cD6LKVNH0tjYTmur3Uxr0wsn2LhxFlrY4usPIQTrNkxl22Y7+dvc3KmT6aXXq2Y4P6xxE4bQ3RWmualLJ1y189u2nGXt+mkJtKitZdmXktxM+fsHVX+7VXJzs/n61z8RO/PJT9xPKFTtCZ4uuJEETinlD6WUd0kp90gpm6SUfVLKo1LKDwF36ck+IYSYmGaRRqTBZKb4xizZZucppSwHFgHfQ/Oz+hfgXcD79CQfBJYA90gp64QQo4QQT6KRsJeBy0KI96ZZTw8eBgU9vWHuf3g7V47+PnasdP5/IUKq5reqL4rojSB6tV+NPFX1TUIkigir2qaqiGhU01Y1tohqaoMiQaraBrrw6zBntwmCmuBT3ZDDqMJespUIIFkwvo3DF4rsaa2Cs2F3aVUIs8LQYNCvIVQXAUsvzEp+qGrySNlVFwUjh0qyg1repXMke49FLQvQVkUG56K7SaCYpKtBsCS6ovuCuwn3nrbqYpQRpQp5Odr55fOC7DlqLnhrBKfVjY8w2xTd66ehfWqxHrH6ZbWSfEavr2kc+hCWf7ExHauvVa286ktRhpX4KcjLQhE+Vi/IZ1d5r1Z+bPHWOqKkHlmSa9EmaOUEZKrZnn2UFmVRmKeRpWsWFrHjkNuCrMPU37W+1mCfSe8Es93tRFoiMrT6Ui9DCrMoKvADgrWLh7DtYCtmG0pLnZKN1JZr47Ns5n76VES8jHehLkxBno/iAq09Vy8sYOfhTksa+3ufmAC9GtBmQ1KqXGwIk5sjKC7Qrrdyfg47D/fo32v8fVn3Y2Szpbkl1kWTDGpkU3CxfwuBotkoWUMAiPbVc/LoDn75x50Zlf+vhBtJvvVw/eERqoOI853H+MW3HqOrTevM17/2JcxeNI2gTyWoSLJ9GvXnF1rQKVVqAag0SIoChk9VO8mm6CQsoHeksSy2oUJF03x1I9QAJk0vpu5CF10dGqkWjUoO76ln6cqRmgmBBanHGCf5JxEGcYa0mUdnCkURLF89jl3bavUjPs6camT0mGLyC4JxgoWS8jXOrMty+mNNNuBOnjKMpqZOWls0Xz1SSnZsO8vadYMZ4CeecFVl1DRFJ6r7eTSegXVlVN+QrF43hW2bz8ZKrTzbRElpHsVDMg3wk6q2pssBaXlD030vxo0vpacnRFOTpmUipWTr5tOs3zA9YZ50amUXPGSMRI3KqCUIl1UT1Zkn/j3YuHEWm144EduvrWkiJyeLocMSkekDRf+67JGjNE2Dy5faYse2bDrD+o3TEmW56hBCxIJPWO9r7YbJbN10Dim1NPV17Ugko0YXYTfnT2XSP7j1NcypVFTe9OZXsmr1QgDOnKnm+9//FZLGwb2gByfOSyknJdoGWPaDwCXAD9yWZh7DMV+yqHOG47Qe5wkp5Tkp5UellLdIKV8rpXxUSimFEMPRzP8PAA8JIbKAZ4FXAPcDtwMXgIeFEG90luvBw9XCl3+yl5p9j6H2aWNz3uhl5BRP0wTZsKqZW4Wi0BuB3qj2d5++3xNGtPdBRy90h6ArDN0R6Ilq0VgNUjaiE64GuWo1tzfIB4G2r7sN0IZqiZCahmt5bSELxrQxvqSbxo4gPSGflj6q51GtJK20uxwQIj5otgE1Xm6xRxnXxixNxkmlGSk4cFxh8awo40dJGq6odHbH+z51I0y0oFLSQqhoWqwasaqmIFXN61vrnAx7joZYPi/AhNE+mltVOrriLxAjHWNkqIUE0jehWIlQ6z8lJtfH9mMamJbqOvk7S5kCwa7DvaxakMP4UVm0tEfp6NJNnBWBUKz+J/2YLgTsW/q0h1uFTDkhGXYcbmfNokLGjwrS1hmhtSPezUOsPZKQforhS9MmQ8XXy5CHTDLV6cfenZTddrCFtYuGMG5kkM6uKC3tET0/sfQmOeokTA3NZCshbr9D5706j1vv340ANfZ3Hupi9aICxowI0NsnudLmbE8zz/VZ+NbmGzsOdbNqQS4jh/oJR6C5NZrwvmK+UR3tYSygxF0h7fsy5pEy9hwNFwBC+MgZuiqWsqd5N/d86Xf09aXW8vbg4V8dHqE6SOiNdLD38A6e/sXTAOTkZvPRz76DUbmSkbmSUbmSooBkaEBSEpAEFYlfkQQVlaCiaa/mZ0UJKio+tFldUNG0VYOKTsIqGmHmQ3cJYCFeVZ1MM/pkqxxjjO1CCJauHcW+7ZcQwPHDjUybXUIg6Cfx4JYuzIjxVgINnCti6WHs+CJ6eyI0NXYRCUsOH7zIkuXxfh7jHLa71F2JEx6SwW2lMDnWb5zGlk2a9t+R8gtMnTaCnNyBR3ZPBFUagaIiSCKY5GU0pkXn/CeRjBhVgBAaqRaNquzbU8PyVRNimpjS8gztm9V3bsa1jdXJmt80VXIJPgVs2DiDzS+cQkrJqROXGTe+hPyCbAYCiUkum+2STBs4NYYOKyQvL0hNdROqKtm29RTr1jtjzdwYWLthGtu2VCCl5NzZBkpL8wedTO8PrNonAEXFOQwfXkDluWakhG1bKli3YTrJvkWTRBXpfLIDgK4xJCTf/vZdMSH4S198iIaG06gyuVbSvxoMRaWBblcbUrOl3aPvprvK0KL/JvOXYpxrSZLGiQeBQuCDUmNMbkNzTfBVKeVnpJSPAC8DOtH8v3rwcNVRc6mNH3z3a3Re3K4dED5KZr1J/xvThCpi0eiM6loDvVHoDkNPRIvIGlJjJKroUzWNVp2MFQYx2xfVSVk9bdgkXIno14nqWqvRqB6MQNOIbW7xoaAye1QHJy7m68elnk4jVUVUNets+Hs1NGJj5mCWTddUtS1vSxUspvnmFo6RmsbCrdMfuyqjNLVo8sjCGVB+OrHvVKcpvxZjIIpp2m9A1cckd1dLprJF+p2qlCrNrSrRKCydk8Whk+4K+XZ/pM75hAsx6rIpimHibzF3tmi7JoR+uqVDJRSSrJiXw8ETIQvRZ9WKVfT6mtcwtF5NclfbTLcFWl6bC4OYtmp6fmOtuNIWobtXZc2iAvYc7XC5nXhz72Q3ryg+fIo/TuPXqHP8ArQbUWw9pqVvaVfp6IqyfskQdh1pi6WPaUOnmBtY52OZmconhptpe1tnlLaOKOuX5LP7SKdbrpTlXn1IOrtVrrSprF+Sy54jPa7PV+tL4vsC47gzoJyba4BkaD//e1qrfk5L5SO0VD5Ca9XPaK36GW3VP6et+jG6GzaD/k4PKQpy4cRP+fz9f+7vTb+ocbPItx6uDdILle0hKaSU1Lbt49O3fz0mwLz3E29i8rhSshQt8JRfN9kXSBDawnyWTxue/IokxwcBnzm8aWn1BXGhXSMWqMohmxi/PkV3E2BJ4xxCho7IRVEEF6rbqT3XzqvfNBmfviorrAX2G0ZHr8Rf37KTTue/ZsMENj1XxciRhSxcPBa/P15wScc0P8UJG+K1U1PnKR2aT35BkLMV9Zw4dpk3/+eStK7VH2gm6UkE7wSRZg2sWT+JZ/52gomTSpg9dySBoIJ7hAX9xXOujqYoX4P5VopYWcbDtwp2Emv5ApMMKyrOZfiIQk6fquPQwRre9NZlaVzXHTYn78K45uAJV2vXzeBPf9zLzNljmD5jNDk5Af2ag3sdMFau+1dufn6QCRNLOH7sEkfLL/L6Ny0a1Lr1F8ZkRkqBikQRPlasmszjvz9AX1+YceNLyc93kumDr4GaDsz3X7Bo8Uze/e7/4JFH/kxbWwef++z/8cMfTQSuTYCvmwM3lbRoMAXpykWGD43xQoisBIGpJjvSJoUQYgPwX8APpJT79cOGyshTRjop5RUhxHbglUKIfCml2yzSg4dBwx3f2kZPo2mNkZOTzYZp3QSCdYmHI6n/p2LRMNWOiUyGMGdaJw9kEyW0PwrywuRkSdZNbQQ9wKhRlnDRNE11XVelgBix5DxstXJJjsICyA3CS1c4ux2DqPI5D6dVbqxBnDyVy/whGYxsRQWCvGyFl61KY2E7QR0l7sftcmD/YNZToSBX8PJVbovFuozpJJpdXzCp/2VKscnb3eluIfkzKirwUZjn49Y1JS5pkyuAxMs+dqIzfTgJ9/i8RQV+ivL83LqmNMm8zXl88MZ96yWT6eUU5ikUFfh4+aqijMu9lsjP9VFcoPDSlXmOM8k6Obfz/cMff91Ld19XynRz580jLy+PXbt28fBjT/HxD76ckcOLB6UOLx7cVPKth6sMj1AdBLSHLvHuN95B9Vkt2nNhcT7v+vC/E/BJ/EIjSlVpmuNHJWQrgE8TlQKKRq769aBTCjJOBjJkUSlBCvOYRUZEURSMoIyGolZUqhoxq5cjUFi+dgx/fOwkL79tEllK0DFIDhYBZCdWY/6PMiihsCibouIczlU0sWJ1WRo5EpeuCEXX7ExMCMZrsaZf29Vrp/DIw9t55a1zkvh57D+klDGz/oEgLz/IqDGFHD1ymXe+d3GK8owXLFE7uEjosX37RMN4/jEXANJMZ5idSP248T6uWDWZn/xoCy+5ZSZ+fybBt/Xa2fyQGceM9zFB1fuBnJwAU6aNZM+us3zgQ7doxQqBkEpS8rv/6D9Ru3jpBH764+2sWDGJQOBG6v61FXYfaFGDgwqz545h+5YKbv/AOmuqaybDrFz2durrmpOmiaqaRlBZWRm//OWTbN9+gGPHj6OIgWlTe7gumKP/Xkgz/UE0EjYILAN2uKRZq//uSlWYbtr/A6AeuMdyypghOh3ttVvOe4Sqh6uG53fXsmPL8/S1VcWO9XR38bfn9lI6642aj1QDhvBp+B4N66b/hmk+aKb5Uamlda7pJiIujaFcARTFTqRaswjIyVFZPqOdc21ZdPb6qGnOMwXosGonc53XM8hWCwGsaaLax3Kpa4mCttBm+rIPYyxGS90nO1LRq2mJ5o1KblCybong5DmV7l6oqDGuYWiXxssPqXyzarCay2uLj6oaRVGsJu2pSSWDGM4JCl6yPMiJsyH6QpLT1anroMZ84aZehHfzS2r1JWvcSyrCMDsoeNnKXI6dCROJwqmqaAItQOe+ZqEUW/ZP0jDWvIZFnlSNuYX5Mpo+be33AJAdENy6tojyUz1I4FiF6REmdbxBC9nrcNVmD0ZmHNM+EuctSd3Szbz/eAQDgtvWD+XQyVYURVB+2n2YETGlHLtG6sAQX1aiegayBK9eX8ShU90EsgSHT8V52Ela7sDrmZ48HsgSvHpdIZtP9pCT7ePgyR5LXmsZhha1SOonVQif/nzTj9fQF81G+FzSW24jKyuLhQsX8ctf/gKAzvpdfPCOX/CXxz6a9nU8ePhXw400o74poapRaloOcvZUbexYX0+IF/54nKKigpjGqFuXCRp5agTti/kzwbpSaJxM3mE7HZNr17QMqpbjob4oob4o2/55noKCJH7/YhWPW393JrD8nWQFsx9jV/3lTiJhlT//vtwS3dBaqlNVIRmSm60n8/GTCqFQhL7eCJteOM3BA7WpM2SEmPOEQSmtob6DUF+EP/3u6DWK7p68XRPR7OFwlJ6eEFs3n+bokXQ5jv4ivXaIq6u+29jQTk9PiN/+Zhc+nykQXz1/Tf0rNxpR6eoMsXtXJRUVDakzXGOImGa7pLmpg67uEH/83QH8cZH2Bhvx7VlVeZmWltaUOYPBIGvWrOGXv/wl586dR1VrEMq0QRLWb270z33MtYcQ4lXAbH33H+nkkVJ2CiH+jmaS/34chKoQYirwEn33j2kUeQcwE3i7lLLVctxg9acCpyzHp6HNpFrSqa8HD/1BJKpy73e30HTyt+ZBoYBUaTv3LIXj1pKVM9wyhFrIT9Vioo91IVPYlEbdYOs5DJJWc6ytS0SWMhzdzILJHRw+l097l5+NC1u40JxNVIp4ftKVvLUfl1LGkakaouYCcSyt4YIpVpBuoht1vdUlcxT2HYtypV3yqrV+qi5EiEQTzRYylSecJJpAI1ozKELHsnkB9h0L0dSi8uoN2VReiBBOwalqsqVhUYPeDta6WMlDF9ne1Yw5fhHfSrIun5vNvmMh6psjvGZjHufO9xCJXdaUIYRjPuUkJuM1N61ptfPWZyEUAdJ+L0JoddN+TaIcYMX8fHYf6aKuKcxrbymmorqXvrDxIlvnce7tklheVUAYmrLSkj4eQgiQPp1UdScFV84vYveRdi429PH6lw7jdFU3vSEnGSdAStuzHLjoY7Sh8zm7Y/ncPPYd6+Z8XYjX3lLMqapeevvc0seXO3Ck/00unZ3L/hPd1F6O8O8vKeRkJfTE1VOQzgKCFmxNT5eQ1I33XVs07i1xNTetxDTif/n8bMrPdaMSAHoIdVawefNWtu95JWuWT037fl/suFnkWw/XBh6hOkA09p7hkYcep+GyqcnU1xdi995tfO17HyWg2BfRI8YKPeAXkoAAv8+QLzP7OA2aTUFBCD+K8LueN2B0mE/88SRve/cC/vlMJf/27zMJBJL7ANI0B02pVcYGav28K+lq3qdGphp/GYRJFMNheSKt0boLHRw/Vs/YcUPo6Ohj2coJ9hVQSxRwrT6p2y+Rybw2nPhs+5k8jj8/foh3vHslz/39BK9+zTyyc7LSz5wEg6WZaqChvoP9e2uZNn04dZfbWL1+4qCUmxwa2Z948LH4rbKojv7tr+W87WUr2PLCaW591Rzy8oNxOWN+yvTJi6llbH51buZcIq2JhTWBVp4i/HHv3JXmTjZvOsGixWVUnLnMy14xL3ZelXa/woOFTFelDfz96WOsWT+F3TsreekrZlFUlJM606DCrdFNTRqf3oe1tXXx92eP8Jo1Uyg/fJ5bXzXPJV9quAukzueq/Trl9W89OJKc3MTfsfEeLFu2kh07dqCqKqGQyh8e/wtvfP178ftK+1XnFws0/1CDI3AOtBghxGzgY2im9OWW4wrwZuCH+qGnpJT7HHm3A2OB70gpv+Mo+kvAq4G3CyF2AA/rgaVGAb9B6/z+Yr1mgvpNQPOHullK+UvH6U3AXcCnhRAvSCm7hBCvBxYAu6SU3Wk1ggcP/cBDvy2n4vDThLvqAMgunkywZBptlc8g1QjNx3/HyCUfddcWNYJVuUERmpZqHD9jyJkO6GSqnshOpFo4pqGFISJRQWuX1nefPp/L7LFdHK3ON9NmMBy7acVJ1Uqa2iqPwIdE1ccHVd+PH6+HFmukXFOrVkb5mSgLZ/rYd8zwHOJWyXQrLuP6TM0vKdiZbBFL734twdBigSKg4YpW/0MnwyyaFWDPEXdfqvFlGvu+GBlpJ7b637mbwScFpUUKfr+g4YpECB+HToZZOifIrvKwyzjkfs1EnJ2ZXUugad/a5TqNoHSvo06tA5LSIh/BgEJdkzYH2nesh+Xz89l2oMtGktnrZS3Ype4xpRwBqg8hpCVYWiL5x3gOCsIR4wCgpMhPbrbChfo+AHaVt7FyfiGb9rUmvP7gkKl6SbF3I/k7X1zgoyDPx/k67X3cXd7Finn5bN4X75vWLDfDuqC4qCel8w2ZKMrXXBLsOByO1XPl/Fxe2Gtq/cY/fzcVIGMubfmGpBKX2hbkypi7O15wpya2EILCfB+lRT72HFHIHbqKrvrnAehs2MJH7/4V+5/7LD5f5haDLzbcSPKthxsDXlCqASCs9nKyZh8PP/h7QPu4cvM1guJPv/onJw6f1YcrrauNWvoyHxKfAj7dAifRSqRbBMDYeeOf8MWRqcZ5azqAc2euMGJUAcVDcliyfCz7d59Pq1OwOr42o3QajttN5+3OLeYg3UGoCXyW+I9GWX5zkz52bqtm9dpJzJg1gvM1LXR19rnen7GXDhThHs0ybtDKoIOrqmyiuDiH0qH5rFw9me3bzqafOQlUqaIScatdvyClZPvmc6xdP5nJU4fS2NBJW1tv6owDhp2Id6kZWpioKKqUqFJy/vwVggEfI0YUsnb9NLZuqbDdhxHkQRJFjQXo0ssgqoecisYCT5mbllYlYvet6ro5A1bJ+H9S5YUXjrN+4wzGTyylo7OXhsa2WOAJ2106AkuYm7RsbufjN2NyZraFJWBFgu3y5VbCkSijxhSxdsNUtmw6bQuQkU4Zybeo437szz8xjAUaJUY+b950kvUbZzJ6TAlqVHL5cmvabWNtT+uzS/pc9YULlQgqYSRRtu95hIrqv+rbXyx//5WK6ieoqH6C3Qd+w5vf8m888M0Pxe7m03d+l86e066RWD1cN2ShaZEeFkI0CyEOCiH2Ak3Ar9GCQG1D81/qxFhgAlDsPKGTr5/Qd38E1AghDgJVwGLgNPC+NOr3f2gL3B92OfcPNFJ1FXBeCHEc+D2avt1n0yjbg4d+4UpbDz/45TZazv41dqx01n9SMvXV+IJFAHTVH6S76YRjnUrqZv7RmPjiKmYKx28quEXxsIl0knmTuzhSVRBLd6ExSGlhmFx/RCdl468nrJsw/7aOtXbEHzNldcUeyEjx6/KwHyMiupSwdI6PfcfMxf3ay5KSIoX8XH0BOqFcriTZhCOvnXE2x2RnkCJnOSaWzwuw54jpxuB8nUpxgUJhXnpBjmLzhligKfPXdE2Q3gvgPifS8i+bF2DvUbOeF+qjFOQJivKt9UxVvkmUJAqaZF2kdbVYsmxCN1EUlvnSygV57DzcbalnmLwchSGF/vjyXO7b9VqOtFq7W9vbrQ3M+Rwu86I1C4vYfrA9lu9iQ4jsoEJJkZselkHuuVY/cQMlhVGn5DTF6oX5bD/UGSvvclOYQJagtDiZvlj8t2MNRmYN7hVTCImbhwuSf0P2e1y1IJ+dh03fpfXNERQFhg7Jslw/7mHG11PvX+zJtHzaXNxez2SBweIgYOX8bHaVa3PtYOEsfMFhAET7Gqk4vp0fPbo1Yat68PCvDI9QHQAudZXznS89RneX5q/lTe9+Jf/96f8EtEH3S59+GINGMtxJGd1jlg+CCrFAU07SzJVAdRngFWH4RHJPp1j88UQiKocPXGbJ8jEowkfZ5BKaGrtpa+1NKEAYZQyeiYR0GUpNulUXBThxrJ6yyUPJzQsiFMHq9ZPYsaVyUGoQ7ytV0/LtD1RVZffOSlat0WKPTJhYSndXH81NA3dpN9j+NytONzJqTBEFhdkIIVi7YQrbN1cN6jXsiBfOE0MjuiQRVBlhx7YK1qybjJSSkaOKkFJy+VKrTjKrMQpWYp3wOMkyO2lm/8ZUnRDNhKw2SFWDnJWcPVdP8ZBciktykais3ziDLZtO6MRt1KGd6kbuuRN87hs478Mgdk3K2Ekea5sqo2zbUsGa9ZORqJQOyyU728/52mYMHd5UZRhtLV22+NV6q3BtbeNEJLZ5tPZ8E4Ggn2HDCwDJug3T2Lb5tMWXVCZEeKaQ+v/Wd8u4rnVfI+a3bT7N2o2TuPVVq3npy5YDUFtzme986zGi8mq7qbgZ4Dbh7s82YFSjaYA+BbQCU9A0PEPAM8DbgY0OU/u0oGutvkwvJw+YBdQAXwWWSCmbkuUXQtyG5jbgQSnlSZfyJfDvwENARK/7IeDVUsrnM62vBw/p4nPf28Wl439GjWgybsGYVWQPKUPx51Ay4w2xdM0nfoM0IlNHVc1vajhqmmQpIvG6cCJeLm4T8XyFDYKyUX1cbA7SF9YDFkjt+KGKfBZM6UxvSLARw8m0U43E1jHPGvndp0WsF37LcY3gmjpecKFepceuI8CeI1GWz8tKWL6V2HHfTPLMJP5sN0R6C/Raminj/VxqUOnutefZfSTE8nmBNMpxwiSfTO3S5CRmOtuksT7qm1W6euyanLuPhC3tmYyIdle0sLWIZdHbppnq8s/ttoUQTBoXpPFKlK5uGSPvQGHnoR5WLcizpe/3kKgTuc55TkISzbgHYRZeNiaHptYI7V12Nw3bD7WzdlGxS0Ws71UmlU6czvpOGPV3ppswOkhLe5T2TtVW3s7DXaxakG9LK4RJ3lsVg2IKQtbNMiONvWexelnTJiberRg3Mov2riitHfY53c7yblYtyI1dP65ZSI8ENZ65ccD2DmYgZo0Z7qerV6W1PRrLnDN0bayo7uadfP27f6W9M5mP2n8l3DDyrYcbAB6h2k/0RFrYs28Hf/2NNp8pLMrjo3e/jbe+91VMnDIGgAO7T/K3P22PTcWNT8cvJD5hlzH7o21ppk0uIBkd8aF9l5i/aBRZAT/GQLFmw0S2b0mPVBuIwGMbEGx9iUQICUKCUEFIQqEwx49eZsGSsbGmGDmqEIC6y+36XTuFhfRbTBFOoi9eaE0Xhw6cZ/ac0QSDphC8buM0tmxKK6hzQji1GweKSCTKof3nWbJ8fOzY0GH55OQEuFDbNqjXMuGYbKTZrsePXGLylGFk5wZidOHq9ZPZtuUMasxHWX8JMxMGSWjXpkyRx0LqRaMR9uw6y6o1U2LHiodomspnK+pjVxk8xJelke6pyGM4c7qeMWOLKCgMxs6vWjeJndsrUdX0yjDo28Rkb//vQ4OKlFG2bz3D2vXTYunyC7IZO24Ip0/WpVFG/5Hu+2mg6lwTQ0pyKR6SA0LywDc/HjOF+sb9j1J74Qiq2peilBcz3Kaa/fs3UKFTStkqpfyKlPLVUsrJUspCKWVASjlSSvlvUspfSunqKBEp5UQppZBS3pek/Of1ckqllNlSyulSynuklClX1qSUf9PL/0ySNB1Syg9LKYdLKYNSyiVSymfTunkPHvqBE2ebeOLZbbSf3wKA8AUpmW6SqAVjVhEsmghAqOMC7bVaOm29SdpMsoQtko80N+e8Mh1yNVYoth2/T2XSqB7OXMiNu05bh59wVDCsOKRrN+gb2IVHadZbVePNoJ2udpwEmlOT0XCHZVVs8PtgRpng2Fkn2QltndDbB6OGibjyrPtpy9quiB+z7XlkrJ6zJ/s5UhGJK7e9E7p6JGNH+Po5D9Cj8g5C3+5TYM7ULI6cjq9nRxe0d0nGjjDuK9XL5U62an5gZYIy4uE2hvl8ggXTgxw+FUJRfAhFQSia5m5Xj6C1XWXimKwYeTfgf4r1g3J/1rF3VX/mQmj1XDK7gH3HO+PSdnarNLaEmTwux6UMu/Zuouulu8XceVnmQtbzPkWwZFYu+4/3xOXt6pE0XIkwZXzQ5VtwkR0TvA7OOWuy78xOuJtytBCa79R9x+I983T3wKWGMJPH+XWLO13GjnVNCa5nrY9VTnKre5pQhGDpnGz2H+2z9HuSrJxRZOVrflNltIeGms3c/aV0XMK/2DFI3+kg9IEebgx4hGo/IKXkQuchvvbph2PHPnzXf1JUWkRWIItPf+X22PGv3vszOru0SbUAfEIS8GlCAOiDhrRrmkkSm9VG9U2N+TTV/AVZzZBVGY3L197ey8Xz7UyZXqLdg66BVTI0h9zcLGqrr8SZ77pd326efFValz07q1m6Yiw+n50sXr1+Etu3nEOVKm5applAWDowBaf5RHpl9HSHqDhTz9z5Y2zHi4tzGTosn3NnMw/6I6UkKqMMtnbq/j21LFg8lqws+72uXjuJXdurdZ9QgwWfvpkQaQ4YvX1hjh+7zLxFI3UT7BCSKPn5WYwdP4TTJ+tTF5I27JqT6b7TUtd8OXSghtlzxxAM2k2LVq6ewt7dlUSjTsL3+gya4XCUw/sv2Mh0gJycLKZMG8axI5f6Va4RCM8ZEC+GtF8pUyA9Wn6BKdOGk5tr135ZuryMgwdqCYcH97uI1SAmmPpIZ1iMRlX27q5hxaqJgNZ/z5g1kfd/8HUAdHf3cu/d3yMs01us8uDBg4cbBVJK7vrmVhqP/xajIx8y6dX4A0VGAoQQDJ39n7E8V07/mWhPu66daiFBnMGnEq3bCf0/kWCzxvyxHtfHjzllXZyoycMIghTjPfXf8rP5zJ3cpRGqFnlHmMyNbY3WVQZz01i1ut9RVVQ1aiFVTG1Gw6R3/nQ4UiGR0t0Uff/xKItnZcXk0ESRzdOH20CcbHDWrjd/ukamJvJcc+BEmEUzs9KWlzU4tUEHTibMn+7n+NkIceKWjoMnwiycmYUSF4jKSVS5sfnWRXSIr7dJwhpayYnYuQXTgxw7G9aDZBkSsXmdAyd6WTgjiDKIs3LDNVsyGBqZBhbOyOfY2S4iEfd3ZN/xThbNzMcXV6zWTk6rr/4HZlVxagNbMW9aDieregknqOeBE93Mn56j19P6fBTNNZTxz9Bkz7Ca7t97fEFzp2ZzurqPUNj9Ozx0spu504IoPjNWRrLgyVcLMydncbY2rAdHM6C9FzklK0F3M9DXdpRf//5ZTp+9fM3r6MHDjQyPUO0HWkPn+dMfnubwPi3g7qSpY3nT7f8WO3/LK5aw/mWLAbh0oYmH//dPgNbYPkMGBKJSF9lEYsPSRMbL5pogGCao5ha/v3NrDSvXjbGsQJvbijXj2L2j1qGpJhP8beJq0EOtLT1cae6mbHJp3N3n5QcYN2EIp080OuqReU0MLVWBz7GSl35Z27ZWsGbdVNdVwBWrJrFnV5ULqZYc6iBoXjrR0d7LxQttTJsxPO5cdk4WU6cN51h5nUvOTOFu4p/q+ViWAtizo4qlK8ZpvoV1/7GGf9RFy8Zx+OBFQuHIoAkcMhap1zySDrq6+zhb0cDceWPjzgUCfubOH8eBfdWDUseBYv+eWhYsGYvfH+9IfsGiMZw8Xkdfb4qQvTHYJ0Om3ykRL7in/SlpCfv6whw7eoFFiyfYzwqB3+9n8dIJ7N199QhK+2p/8onI4QMXmD13FIEYma69wfd87nZKSjRt+t/+5u/s3rkDVXUPjvCvgVQaQeluHjx4uFZ4clMl+3f9k94WTcb15wyjuOyl2klVl0CFILtkKvmjV2iHw520nP6rLVaP7cu1aH9q+yQebq1arDFt0sT9QX5OhMLcCJevBM06OsoPhQQXGwJMGtOri5TS1FYFm9gVVePJVM3U30EWoWo8rKq6+OyOR16upKQIai9r9yF0TTLrfYXCUHUhyoyyqxP4xe473nlOu++8HMHwEoXqi4kXMENhOHs+wqxJmcQ2tj8/k8Q0FjIz6+tzswUjSn1UXkhcz3AEztREmDXFh/XZuBF9Tk1d85mqcecTawPHj1+52Qqjh/s5WxtBWP3rCh8+xY+i+Imqfk5WRpg7Nehop/6Nf4bGoqapmqoMnTjLVhg/KsipqsQm3ZGI5GhFFwtn5sedM1SC+me5FA8zroCdqM0OCiaOCXCy0moBZPczqqo+jpzpY+HMXFtZJvGr2onVNOprkqiJvnd7GcGAYNLYICcqnbEqRCx9VIXy0z0smpmrd3XStiWC27yqP3NhgECWYMq4AMfPmdqp2j1q772SlU+weKGeWqWzYQcfueuxfl3rxQVPvvVgwiNUM4Qqo1Q1HeDbn/957NhdX30fWVmaUGF0sfd89fYYgfHj7zzO5QuNCKEFohI4FsP7O+5IAJEyf92lDhRFMHxknuv57Bw/U2eUcqw8kfbftVst27a5krUbJ7lcX2vZRUvHcOTQBULhiOVspr4wNSh6QC0DmmCUXt7Ghg76eiOMHTfE9Xwg4GfegrEc2FeTdn36G7k9FbZvOcea9ZMTmn/MXzSGUycaMiDVEsE5OIiEA7ypj20uALS2dNHa0kPZ5BKXHCp+v2Dx0rHs21UTK2EwiFVnOem8Stu3nGHNWncyHWDO3DFUVjbS3WUKfObkYfCQSvugo72XyxfbmDY9nkwHLfLvilVl7NqRiqjUSEbtnw8Ff4x6tKVKaW4YV4PYXzu3n2XlqikoLioaQgimTR/F5UtttF/FQGpCCEvgOkP7xI7urhCV55qYPXeU44zKkJJ8PnufGYPok5/4Fn3RigFoadzkSKRtlunmwYOHa4JQOMoX/nczTWf+EDs2dPqbEDLLlScpnfFGhE+zKGi/uJVITyugj3SGXz+H9qcNzq7RSBunQZCYpFkwuZNDZwusoqIuaGMSsqqkojqbslG9ZPmioEqEoamqWsf/RHKYrokak11UVFUibW6IkmPpHNh3zNAcTNyvnaiMMGWcnyz/YI0b9raTMYLa3Leaay+fl8Weo+GUpZ6qilI21kcgK2VSnGOpISPEk5HpY9ncLPYeC6VMd7oqyoRRPoIB+zNKNC47g39akcpU3Qy+ZW4r5gfZczSUQC4yj52piTB+dBY52SbpKuJclGWO1FqqWh1WLyhk5+H2lOWdru5h3IggudluChTu71emML/DeJlyxbw8dh8xA3s53xuDmDxb28foYVnk5YBtpUdLBUibeJH4fZAumuepv/cV83LZe7TLMqcwiVQrzp0PMXKon3zdW0k6dRpMLJubzf7jvbF2M5WrLCR28QKET+MQIt017N65laeeO3zV63ZDw5NvPVjgEaoZoqnnJD/57u+ov9wMwPpXLGXtSxdhdM4KWtClKdPG8vYPvBqA3p4Q3/z8o/gVs8GtXbEEp7yTFEYZ5pqYRCbIL6Vk17YLrFo3FsMMO2biYMHcBSM5c7KJXhupphEKWmRSbbNhkPuB2uoWCgqClJTmJkzj9wsWLxvPnl2VsdVQgwzLdOCxOQHPAFJKtmw+w/qN05Kmmz1nNFWVTXR1pedHUR1kM3+ASxfb8PkVRowsSJgmRqptT5/8tcPdxN/5ethJVOnYYNumStZuLEtyHZXJ00qor+ugvd0k1QZOqjoFo2TlSRoa2ukLRRiTgEwHTUBdu34aW7cMzJduKiQ0t9exbcs51mxITKYDTCgrob2tl5Yrdh9PpvapL+l1hIvAmylarnTR1tpD2eRhCdMoisK6jdPYuvnqtimYE17FEvnVwI6tlaxaOynh/b7nfbcxe7a2KHRg/wl+9Yu/ElEzd//hwYMHD9ca3330AFXHniLSq8VSyymZSW7pApOcBMDUHPUHiige/0qKhk7hJbd9FH9A09BPwB3EIOL+0BEnHDsgLZqlUjJiSB/dfQodXboGYtSp2arlERKkFJw4l8ucKT3mZQ1tMENCcQ1EFY3JL3YNx/RlthFDo/T0QWuHcWtuviEVvWwfB09GWTK7P4GfEsHa0G5ahFp9Rg5VCIUlV9pSy1WqKtl/PMSS2VmumoSJru8cO+0m94Y8mVx7a3iJQlSVNLemJ//tPx5m6Zwskk22tPpHHCbcmcNKpo4o9SERNLfKBG4GwLhXIRT2HQ2xfK7p+zMRsZtWPfT2S+RP1YphJVkoAuqbUxPpADsOt7NqQWHc8URaqv0nBu31HlrsI8svqG9WcQsQZdTC2HaWd7JyfuI5pfHeOutpvMumpmZmWrclRT6CAYVLjdY5deL8Ow93sXJ+HhD/HTnbLtliTKZaqkMKFfKyFS7Uhx33ailTCISSRU7pytixnuadfPLeX181N1wePNxs8AjVDBCK9HD07B4e+b8/A+DP8nPXlzV/qcbQ7xNmo/73nW9mSKk24Pzzmb001bcghH1otJKj6XbV7h2mdB0rTx9vZkJZEbl5AVs+57UURbBs1Vj2bD+PqZk1eJ12Kqiqyu4dNaxcMzFl2slTh9JQ30FbW7dl4NaQjqmECXcNtFSoONPAyJGFFBblJC9dCNaun8q2LRUpy4xK03/OYEFKyY6tlaxZNzll2gllJXR2hLjSHO84PTESCbyO1WLbRMSqQmLeb03VFQqLshlSkljwAa1N12yYyPbN57B+NQMlVU0hUN9PslK9ddNp1m+cnrLMMWOGEIlEqa9rs9R/cLVUTXPBeFy62EaW38fwEYnJdANrN05m66azGFFnrSv/dufpsSubAmy/b8f89rZsOs36jTNS5hgxophA0M+F8639vWjasLsB0f5uqO8gHIkyZmxxwnx+v58HvvXx2P5n7/k+bR2nSBDz6EUOMUibBw8erjaaW3v40S+30VL9tH5EMHT6W1BiDj2xCK1S85UaURky/lZKZ3+K8WOHk58d0b5Ya55kcOP2kqVVzb8FkjkTuzh6Ls9yTpraqQYsQnddU4D83Cj5uVp/rEZVzZxfopv1m4lj8qQLuZaRb1OpsnCG5NBJ5824QwjBxYYIeTmC4oLBmqY5ZWVr/c37WzI7i33HUpNqhkbr5cYoOUEYUhhTCTbNqWNklc8lbzzspJhTFrG3w9I56dXTQH2zSiBLUFLkdh+mRuRgW4otnRNg75HUWrTGvTdcUfH5oLRYsWkNDxSa5qy7/CmEYPWCQnakoZ1qoOFKGCFgeEm8erKblmp/oSnxmT5QVy3IZ1e56ZLAGgzKzZdpU0sUVYURpYldUzg1Qg2T/oEQ66sW5LHrcFfa6Ztbo4QjklFD/ZjfkXZdgYhVYbCloZXzc9h5uNvSfonmz4Ks/Cn4skcCoIZbqa3czYM/+FeOjenJtx5MeIRqBmjoPcg3Pv8z+nq1wfG/PvBqJk4ZA0gUnVdQhGnlVFicz12ffzu3vXYd3/zuvYwclVijDdL7rBTrKm+KHKFQlONHGpm/eEScdql9gVj7qMdPLKazM0RLc28CEoVBIFHcceTQZWbMGk4wO5U/JgWEYO2GyWzbfBZD8FH1f06N1cTEav9e/Ugkyv691Sxf6XRL4I7RY4qJRlXq61IJKoNv6n/iWB1lk0rIzUtPy0Fr06o0S7dGQTVhvDHxJv2JBRNVVdmzs5YVayakdeVhw/MJBv1cON+Ck1TtP7GaXr6KM/WMGl1MYWFyMt3A+o0z2LL5tOM9HNyPxyfifa0ZZPrqdem9pyVD8hlSkkfVueaYWX9yk/6B1tosoKqykaLiXEpK3V2SOLF2/XR2bKtIyzXDQGG4ANAEWoXtWypZuz71AsWGlyzitn9fB0BdXTP3f+2nhNXaq13dGw7pRjlN9c+DBw9XH3c9sJWGM48jo5pVTdGY9QTzRgMSYR0jDf+jERUiaGSHlJSfK2Dh5E4LmZq4k3Y9I10UA6zkqPVXwpQxPdTUBQmHha65qsYFnUKVcUWWn85jwQyD7LCSCBYyVVVBqvpCmL22qos/1WSYOjFK9SUIhYV+m4kWbFXb756jYZbPCzB4E+9EWqpafaaX+ai+FKUvNf+H9f53HwmxfF4wyTWdRJeTdLVvdsQHsZo2wcf5uii96Rl/xbDnSJjlc+O1VLVrRgeVwASYOsHPxfooPX3uBFXsL4fbg71HQyyfOzjaydbxU7Mmip/7TB0f5FJDmK6ezOYhOw61szqBlqrxV+zaCYVGxba51c/4XiaPy6KuOUJnt5XwjH+PndhV3sWKeYnlS1O72lpe/zWUy8YGaGyJ0NGdWXvuPtLFcls9k783iZCuzDR+lJ/m1gitHWG9n7OSqvYg1Ibsnzt0bSx/75U9fPfHT9HS2pnW9V5s8ORbD1Z4hGqa6Ak3sXnLVv7+lx0AlAwt4kN3vAWQ+IWpneoXxoqatv+f734FP3jskwwZUkRTfberdmoMaXxXtmE3hcn6/l2XWLRsJD4two/5+TpWgK2mvWs3lLHdQapl7hcxM/T0hKk43cSc+U6fhIkgKR2WRzA7iwu1LfrgrW3umgSptFYN86LU2L+3moWLxpOVlX7AgHUbNDPlRNePXgXNtVAowrHySyxcMi7tPMVDcikpyaPq3JUUKRO/d/EkamoBtfzgZWbOHkEwmH5wg1VrJ7JrW7VFALYIx/0UglJpqWpkeg3LVkyyZkqKgoJsxo4dwumTZkTMZFql/Ye9vONHL1M2uTQpmW515SFQWLlqMnt3xwdSM/qMwf3+tbJUVWX3znOsXjsl7Zy5uQEmTx3B0SMXGfx2dIcQgorTDYweXUxhYW5a1/3a/R8hoDuX+9/v/JaKc/uJqomDPXjw4MHD9cKR0438/fmtdFzeBYDiz6Vk4mvcTaqiGDEjTUho7/bT3acwYkifGRgKdKE4zYokSmcd5qUk4Isyfngv585na2b+ERUiUhc7pH1zyAgdXT66enyMKO3Vi9MIBFUPaW9oqpqyjBPpEyU+X5RJ4+BUVWIiNVH5HV2Stg7JhFGGvJlKdkh1PpFFF/h9MG2CnxNnM/el39ktaW5VmTDaTS52Wia5kWCpNvP+/H7B9DI/xyqiKe41Hl09koYrKhPHWG0DrUTu4Ck2+H0wc5KfIxVuWrTu9Tbk2a4eSV1zlMnj/AzGND1G2ghD/jTL9Plg7rRsDp3szvha3b0q5+v7mD4xtbWe8W4Km496O6lsujgw8xhtogjJvGnZHDjRjft7lBg9fZLauhDTJ5qkv1Wr1aqdmU55yeBTYOGMHA4cz8TaT0Nvn6TqYoiZk4KxevSXcEuWz3BXsmhGkH3HOknXwbUQPvzZIwgUztLOqiFaLm7n4/f8rl919ODhxQSPUE0DUkoudh7gK3c/HDv28c++nYKivFiXZXTBhnaqAviFxAhEtXztKPZuu2z3i5JhPWzaqcKpcWrvPNta+2hu6mHi5GKMSPba+KUPagkefVFxDiWluVSdu3JVSVQrdm2rZsWaCShKOtcy06xaN4md26tQrdoIumakm8YqGAOJQ+vOUW4idHb0cr62hRmzRqZRTxMFBdmMHTeE0yfr4s6pgyzEGdizo5qlKybg82X2ia9YNZF9u8/HkWomnIKQU1DOzNdQT0+Ys2eamD0vszbNyc1i8tRSjpVfxk0I6p+2qmHaZzli+V737alm4WIHmZ7GK7tsxSQOHqix+Rrqr//exDArEuqLcPzoZRYuHhuXxlw8iV8hDQb9zJ4zmkMHa7W0V0kb3VrgoQO1zJ4zhmAwragWMSxaPIHjRy/S15e+yd9AEIlEObi/lqXLJ2L6VE0+eS2bPIaPfvzNAIRCYT5z1/cIqandf7yY4K3ge/Bwc+CTX9tE46nfxvZLyl6DLysfJ6FFVNcEjaqxU0JVY1/p0doC5kzoRAgZl9UVzmHaOuY4FSotmDtFM/WXiDiyVRNFNE1VYePmDIIVjpzJZvaUHgyZRVXDukZqcpnMIF3TxcKZKodPgZSJNBLtcGpoHjwZZv6MLHyK28Km/W8jOn360IgVkCye5efgiRBRNd389vHv0MkQ86dn4RJT0kVRORWB6iZPatuiGT4Onwpjr2bqMcIg0A6fCjF3qh8hrIoWgy+DL5wZoPx0mFSvi0YiWrUjNZSfCjNnahZ+v5WEHBxYF/UXz8rl8Kkeoiq2IL3p4tDJTuZOzcPvc2oS264Y83Vq/dvp+9T57hvvs5SwcGaQI2d6iEb7R3aWn+5h9uRs/D6TuLbDdFHhRra6bxKnws78GTkcq+gl0k89maNnephZlk2W3058GxhoMF6trlHmT8vmeGUv4UiysvTZsU7CG88st3QlKJrM3td2jL89vYljJy/0u043Kzz51oMVHqGaBlpDlfzmsb9x6qimuTlz3iRe+7ZbAInPQqAqumaqwQvGOEwgLz+LUePyqTzdGis3k8/IOXUXJBKqNOzccp7V68ejiCzbiqCVDIj/mLWBbfnq8ezfk4xUGzw0NXbR0x1m3PjiNFLb7zM3J4sp04Zx7Mgll7RWYUw3YUg0EAm7gJEIW7dUsHZ94sjuybB0+UQOHqiNc+DtJPAGA62tPTQ3dzFpytCM8waCfmbPHcXhA5ddzvqwdxnJNAnSw86t1axcmy6ZbseCxaM5ebyevj5DVWYwtFXd35HOjl4unG9hxsyRzuQp4ff7WLxkInt3VzrODN5AqljM4nbvrGaZjUw3vnsroequYT13/lgqzzbR05OWzV8/YH5nPd0hKs7UM29B+lrUBnw+hRWrJrNrRyXxvePgY+/uahYtGU8g4NcmAGkKQ5/69DsYMbIUgCf+spUXXthEJNpyVet6Y0EM0ubBg4erhT8+e5oj+/9BX7s2RgVyR1E0ar09kZQgVQip8bGYYpqoEA4LauqzmTLKoY2faHHO7ZhTHnAQpkV5EbIDKg0tATNAlaUoAbHjTqlASokqJaGwpPJCFtMmhPSo1lFkzOzbXTbLhHyTUlKQFyEvBy43CuK1UxNNv+zlhyNwujrC7CmmBY9JTpl3bSpZpNNfSttvYT4U5itcqE9fHnX6GI9E4WRlhLlTsyxpDMIs7WIT1FXbCvKguFBwvs65cO9k7hNfMBKF42fDzJumIGU0ppmceIvaCLTkabUtL1tSUiSovhhxsY5zq1u8IBlV4VhFmIUzrC4f+j9lt8oqxnwnP9fP8BI/lRfSkffc2lUQVRUOnuxiyez8WB3jFXZSC8rWxQC7T1RJXo5k1LAsKmp6EpCdVmIznvhUVZVIRGX/iS4Wz87R+wDVZev/XEYIQW62YNyILM7UZOiLwgJVwv4T3Sydk6uR7bFVq8zLcsqnmm/YKDlBwfhRAU5VpmMxFf/cFX8eOUOW6XuSjvrNfPCOX2RewZse11++FRpWCSG+LoTYLoRoFkKEhRCNQoh/CCHeJpKQFkKILCHEp4QQ5UKILiHEFSHEC0KI1yXJExRCPCCEuCiE6BNCHBdCvDNJ+mwhxDkhxAUhRP6AbvgGhkeopkBUDXOubh/f+fIvY8fu/tr78Pt8WMUZzdxf8zMl0DRTDZJVEZqINHfxME4cbiISVuOHpRTflN13qsNvpSPvhdp2cvOyKBmap/m2colUbc8vbEJPIOBn9ryRHD5wMXmlBggpJds3V7FmY3p+Ht06ngWLxlhINZdr6Fqqxv+uooy0zAYS4PKlVoQQjBxVlGZd7fD7fSxeOoG9u6tix1SXaIqDgW2bzrJuY/om1E7MnjuKqnPNdHeHsQtyA9NGdaKpsYu+3ghjxxX3K7+iKCxfNZ7dO2r0IyZ5bkWmpKqblurWzRWs3eBCpqc5Fk6bMZJLF1vpaDcFmMHUUtVIPoXWlm5arnRTNnkohjm/gi/594+w9AGCNeumsH3r1dCktGtZbNt6hrXrp/VbC37S5OG0tnTT2tLDQIWSZOjo6OXihRamzzDJdKO9TXLavW0LCnL54lc+GNu/8xP/S3f4VMZaTh48ePBwNdAXivKl722m+ezjsWNDp74ZofjMYR/9NyJt+wIQ1r5MFwfOXcph/PBeAn5HP+e2cOocnqWmWRoTPeKGdMn8aZ2Un8nXry9t01IRSxVPsKjSIE2jSDXK2RqFsSPDZPlDOnGmaW9JvV6aQmu8FloqaIGsoiyZI9l/3D2N4tA6dPcfquFMdZTxo3xkO9yUmuO2n8zHQBlT2F0xL8AeS+CkdO/T7g5MUFETZewIHzlBoRO8g7sgtnxuFnuPprJKSSSTmsfOnY8wephCTjCK6SM3kSsCK8HnjAngvq1YEGR3ea/tvTECHVnLTkzQRlHVKGdrQwwvVcjNljFXcvY7yuyfFYqisGpBLrvKDdN0o/BEcql1LmB1FyeouhBieEkWhXk+G4knjQ+J+HfK6UrOSlw7n9+qBfnsOmz46Uz0bB3KFNJYPFGRRJBEqbrYTWmRj4I8I49zHmMlZxM0QxKsnJ9nac/+o/ZymCGFPorytWfhNofJeF5jWQzS6pmu31N3kjlYPB8lS/OfG+m5yOGDW3n8yQMZ1cnDoOAlwA7gLmA10AqUo32kLwN+CfxNCBHn5FoIkQ28ADwAzAbOAleAjcDjQoivJ7jmL4BPAfnAaWAq8HMhxAcSpL8HmAR8XEr5onW46xGqKdDYe4zvfeO3XGlqA+CVr13DklWzbRqpPgGKkAR8mP5TtdhJtgb2+RXmLh5G+b6GjESMZL5WnWSEqirs3XGJFavH4xP+hBpUqfyizpozgqpzV+juulqaalB5tplhw/MoKspOM0d8XRVFYcWqiezaXuWS3gpj0DRWQeMFpkTXkFKybUsF69ZPTbOe7pg2fQSXL7XRrpNqMk7NY+Corb5CXn4w7QA/bhBCsGrtJHZurcb0L+smfPQfGpleyZoNZa7n0p3ATCgroa21l5YrViFmoJqqduHz8qU2FEUwcqQLmZ5msUII1m+czpZNp51nGJwJhxY8advmStZvmK6RqYYWi7ATps7NefkxY4cQ6ovS0NDRr3ok0mSworGhg1BfhLHjSvpxDRNam55yaJgPLrm6dfMZ1m2IJ35jbUhyTdX//K9XsHjJTABOHK/kkYf/SES6aYC/2DBYBlGelqoHD1cLD/xkL7Un/ka0rxWAvKHzyR2i+ciLjW+qNId/fWwVRhAoF9FJIjhalc/cSS5zp0SfslPEcF/9ZsywEK0dfrp6fSSyp1alTppKC0ljI2oM+UBw+KSPhTM1K5dYHjWqETGxKPXEXAE45RM3GUUSZewISVsHdHS5aacaTWGSqqm0X/cfD7N0TiLXOAbzbN1P3m8aJOHYkQrtXZK2TjuRlb6/V/2K+ni473iEZfOCadUhE4wertDVK2ntGLgSgqqq7DnSy/L52cRruiba0qznMB+9fZKWdjv5qokP5rPWCNaIC0lrv9bu8l5WzM+OI9wHYvYNMGqoj3BE831rhbFIrCnjWBVykpsq7yrvZPXCAod8Sex+nfdm1yiVJFpMGDnUT1SVNLYk9+2rrcNYFlCIYMbXIHbtHYfbWbOoAHs7W0huoubfGXwDw0s0DfKGK5n7II6HYOfhHlYu0HzTJpqnp3oHZNw9wtAhPhQF6puTLUy4fbd2LWAhBDlD18TO9jTt4FP3/YpIf30d3HS4YeRbAVQB/w8YIaWcLKVcIqUsBd4B9AGvAr7gkvd+YI2ef7aUcr6Ucgrw73q+u4QQt9kuJsQC4I1opO1EKeU8YAnQA3xJOPxRCiGmoZGvz0op/ziQG73R4RGqSRCKdnD4+G5+9aMnAQhmB/jkfe/Cp5OmioUw9SdQXBPCLuZMmFJEY103ne3pE5X2gUtg+ETV3ltjtdAH+Dhe3sCU6aVk5xhmIpZ8GQSXEkKwel0ZO7ZWp13PTBCNquzfc4GlK8f3K7+1TSaUldDe5iTVUiGRwBTfuZ04fpkJE0vJy08UxTTNOgvBuo3T2LqpQtdOHVyoqsquHdWsWhtPUqYLYyV7zNhiwiGVxvoOBoNAdeJcRTPDRuRT6CDTtdXkKNbgYqmI1bUbyti2yUmoxwunmQigxvUNMn3N+gQavxmMg8NHFOLP8nHhQouZXVhVgAaG6qpmCgqyKR2ar0emt77NmZmZrNs4la2bnIHUnPmdwQXcSNT4c5rG72nWb5zRvxu1oKQ0n8KiHKoqGy2uTQbvXb10sRWfT2HEyMKEaYQQensb/bG9HRRF4YFvfSy2/6Uv/IT65nJUdTAE7xsdbu9dfzYPHjwMNi43dvKz32+lrebv2gHho3TyG/W/LQlVCVLVfKVKEIYfw/j5euzvhtYA2QGVwlxHPxdnzi/tAaySQBGSmRO7OVGVp+ezFmOa8drHLYMocRKgGqlV3ywJZEmKC015I6qGUFVr5Gtdm1Bqsolh82T8U6U9jRCSudOg/HRyYtK+EJi8AeqbVfw+QUmRsz+0BlgiLRnf0MRVFFg0M8D+Y04TZXetwnTQeEVFEYKhxVb5wKqtmnl/LgQsmpnFgeP985luJes09w4qjS1RpIRhJebis12rNvMxSAhYMiebvUd6E57Xa2StnbO2tq25NUokIhlRmn4g3HSwdG4Oe470oijafWvvjenT1Jxj2e8/0cJ8c2uUvpBk9DA30t+5CGEsVkQxNX/d37UV86zaqY5SY998VCdCze/TuK4TV9oidPWojB2ROGCrVaM38Tdgb5OV83PZVd6VsMxMIIRCa4dKR5fKuJHJg/UmmtckOr5yfh47E7RnathdLPhzJuDP0Vx2qZEO6qp389XvPtXPsm9G3BDy7V5gupTyf6WUDdYTUspfAF/Ud98nLGaRQogRgGE+d7uU8rQl3xNoWqsA9zmut1b//Y6UskVPfwT4IzAMmOlI/320D/G/M7+1mwseoZoE9d2H+fq9j8RWXN7zsdcxdvzwuGFGCGlzxB7zq+rynQghWLZ2FHu2XbIcS1wHw9Rf6ISoT/hxRkk0Vg/7eqOcOdXE3AUjUeKCVmVy5xpGjSkkElFpqB98De2D+y4wb8EoAoF0hYTkr+raDZPZuulsihK0a8UPkIlXokOhCOWHzrNk2cQ065kcw4cXkBXwceFC86CUZ8WRQ5eYMXM42dmZBfjRpwQ4Td3XbpjC9s3nEq4c9xeRiMqBvRdY5kqmm4KQUa9YPRMQq0NKcikakk115RWX8uI1VdObImjpjh+/xMSyUvLyEpDpGc431q2fxvYtdqLSFOIzhUlsqqpk145zrF47DXORJdWA7SRHza2wMI9Ro4s5c7ohgzLSExjOnmlgxMgiCouSR4ZNF6vWTGXXjrO6Gb2V6B0YMtVMN9eb7X2VRLJ85Sze/NaXAnCluZ2vfekR+v7FAlR58HCzQQiRK4S4VQhxrxDiT0KIGiGE1Lf70ixjhBDiQSHEaSFEj+6jbJsQ4r3JfJsZOHfuHB/4wAcoKysjOzub4cOH84pXvILHH388VdaU+NQ3ttBw6g9IfXGnePxLCeQON7tRiaYFGrGQl4Y5fhpk2+Gz+SyYYizKYpKnBlyITyBh9z19Qg9nL+QQjWqaCnZCxdAgTUzOaJc05QiJdt8HjvtYPEvLJy2kjCojmgm2jOhp3bUXtWU8jWRFSmZOgopqCKXB/ylC0ZshtZy192iY5XOd8p0g3qmtlfhKLAvMnhKgoiZMJGo9bm+7zElVhb1HIyyb64vVw6xn/0iEmZP8nDsfSas9nbCTqfZF9t1Helgxz9D+NI5a5la24Ek+hxJLvJwzc1KAyvMR+sLxbRbv4knilLsSyU97jvaxbK7Tks86B0xuJePEjLIA1RfD9Ibc6mk+H1eLpiTYVd7J8nl26zjDZ7Gp8Wmfa7kHiDLqmU3N5RA9fXbNVvOb758W8e4j7SyfW5BkXixtrZnqG5g2Icj5+jDdvYOxmG++J/uO9bJkdk7K+buNAE44yxFMGR/gcmOIrh63vib+2zSeuV2L2FRWEUIhd+iaWJ6eK/v53o+fpPlKf6zbTHR0dHDfffcxd+5c8vPzKSoqYunSpTz44IOEQlfPavZmhJSyXUqZrGd8Rv8tQSM8DbwGCABnpZSbXPL9SP9dJISYbDluaJc4o2wbZndFxgEhxFuAlwJfk1KeS1LHFwU8QjUBOkN1PPvs82z5x34ARo4eyvs+9rqYdqpf6DKnsGunWvs9gbuINHRYDoGAj7qLyYlKgUBxmOWaPlF9cYPonu3nWb5qHD6fP66c/mLN+jK2b67q10p1InR1hqipamXG7OGDVuaQklyKh+RQVZmIqHT6q3JLEz8o79lVxdLlE1GUxGZbmWLVukns3FY5qG3a2xPmzOkG5i4Yk1Z6k0S1Tj7s9SkoDDJqTCEVpweX/D247wLzF44iK8tOpsdr7RqaJRZt1TgtEw0rVk9g765aF9+UkngzqvRo1b5QmKOHL7J46QQtl9vzyvDTys0LMnnKMI4dydQ/sRvxaQo/hw+dZ+as0Q4yfWArpMtWTOLAvhoiETVlHmPBJ9UWjars31fFilWT4y/YT2RnZzF7zhgOHahNWwM/HRw/eomyyUPJTUSmO2BqqrqRupIvfvX95OZqk6IfP/Rnjp/Yi6oOjkbDjYrBM4ny4OG6YBnwNPAl4LVARiY1QojFwHHgE8A0IAIUoJnZPQw86+bbzMDTTz/NvHnz+PGPf0x1dTXBYJDm5mb+8Y9/8IY3vIH3vOc9/ZYjth+8yKbNW+iq1/ze+QKFDJnwKq3b8lm+OTcLzjT5i65eP22dfsaU9pluA9zy2TgRRwJ9lpIdiDKyNET1pQBS1bXc4vxeJodUVU340zVJY/XsETS3CsaNdKugpp2brNKmRUuUYEAybiRU1KasjgXpLVp39Ujqm1XKxhiyk8+lvvFwjotCCLIDCmVj/JyqcnvAA+tzu3vhUqNk8jgFKWUCYjc9cjWQBZPG+hLUMzkMMlUzsY/gbOfeXjhfF2HahKwYaZW+JZ+deA0GFKZOCHCiMhIjX41NUZxahonu20nialtfSFB7OcqMsqBZZmwuaCFA02jTgF8woyzAsQpTK3mwZKa+kOTc+T5mTc6OEZ/GQofpQsPpK9j9/c3yC2ZNzqb8dLdF+9v5zfcvDkVfSHKmpoc5U3ITpnESk4kCi2X5BXOmZFN+Kp0AT6lhfRbhCJypDjFnSnoyaLJ5jd8vmDcth4MnnJac7s9eI1KtrhjcyHfwZw8lWDRXTxKm7fI2PnLXr9OqrxtqamqYN28eX/jCFzh27BhSSvr6+ti/fz933HEHK1asoKWlpd/lDyZuEvnWuhJjfUlX6L/b3DJJKS+iuQKwpgWo13+dmqjGfh2AEKIQ+BZQgeZa4EWPfhOqL+aVeylVLrYf4Gv3/CR27I4vvJO8vOy4abLAfiBGuKZo2aVrRrF/R11SYdgnDMff1lXReAghaLnSQ1dXiPETi+M/0AF8rwWFQUaPLaTidFP/C3Fgx5Yq1qyfmOEgHtfqcVi5uoy9u2qIRuOFUyVFIC87tGfS1tpDQ307k6eYizqZBCZwLVlKcnP9TJoylONHB8+P4s7tlaxcXYbiphZtvX5M+HcKJWAjnPU2WLJ8Aof2Xxg0vzhdnX2cr2ll+iw7mZ7cf5hdg9b0aWY+i2DQz8w5Izh84FKCMjJ3AbDXINOtk8t+CG9OLFoykaNHztsCqQmRTKsy+TPVyPQ65i8cnxapme4WCPhZtGQC+/dWp0ybbh+zf28VCxZNiCPTB4q588dx5kwdPT3a6rUQVtI5c4RCEY4eucjiJRP6ld/eB2vvzJixw/nkXf8JaC5P7vrk9+iOnhzwwsqNvZqfCamfGeHvwcM1QgvwPPAN4K3Ea2a4QghRBDwJlAKngKVSygIgD838LQy8HPi2W/6qqire9KY30d3dzerVqzl9+jRtbW20tbXxuc99DoCf/exnfOMb38j4hlRV8plvbqbp1G9ix0omvw7Fl619bg5z+hisa6+peExdG/VYZR4zxnejJBk7bV+4dBzQx5f5U7o4cjYfpIiRonEWKEn6UqsfR02T1H7+aIXC7CkqimI/YXUjkCxgjXHtxbPh4ImE1YiDuZCc3nSs/EyEuVP9+GKyXv8siJbNzWb/sUTjg32RPTO5V2vnI2cizJ6s4PfZF8FNctVti8eS2VkcOBFORyHarIGFCErmnxPgyJk+Zk0O4FOcriLcNTQ1E3mH/AMsmR3kwImQrZ4JfecmkKOsZTvHvWMVYWaUZZHld5aj2ORHC83r+m/J7GwOnuizKYq71a8/kFJSfqaL6WXZZGVZCU/tb4nqkN8TP5cls3M5cKKLSNRqxj84MRwAjp3tZtqEHIJZie410XXs6RfPzuXQ6R5cpp39QPw3cLIqxKSxWQQD7vWUUqa1prRwRjblCetpX2zRyk1NVhvfdXbJMoSi8XahjlM8+/cX2H+4JmleN0SjUW677Taqq6sZNWoUzz33HF1dXXR3d/Pb3/6WgoICDh06xNve9raMy746uCnk27fqv+VSynbL8Wn6bzLTXkOrdLrl2HNoH+KdQog1Qoh8IcQ70fy0nrFoon4JGAV8RErp9CnzosRANFRftCv3V/oqePThP1NVoWmRLVw2g9vesE7zmSosflMVEELiF7oHU51MTYcqyMnxM3FKEWeO282UDd0mxUEGmCsZjmN657d9cw1rNkzE+UgHY/VjyfKxHNp/kXB44KRa/eUOpJSMHJ3YJ2E8nNpw7ggE/cyeM5Lygxcc6Z2veXptsmXTadZtnALCNOeK/UsSkCA5tPQLFo3l+NHLhPoG7kexuamL7q4Q4yYMSXBFpzZqIqEknrTOyvKxcPE4DuzJVKPSHdu3VLN6fVmcwOZ0N+AOUxtEyngfR3PmjeRcRTM93YmsH5yzwcSkaltrD02NnZRNGZLm25c+fD6FFasms2uHfRwznP/br5XYXM8QvLdvq2D1mqkpyfT+YMbMUZw/f4WODnefYJmgs7OX2porzJw1ahBqZoeiCNasnca2LWcGpbzdOypZtqIMny+zIVJ7L52a3+Zz+dj/vJnxE0YA8MI/9/PMU88RURv7Xc+baTXfg4ebENuklCVSypdKKe+UUv4WLVhDOrgDGImmFfJvUsr9AFLKkJTy+8Dn9XTvF1rgBhs+97nP0dXVxciRI3nyySeZNk1Lkp+fzxe+8AXe//73A/CVr3wl42/8p388yqnDfyfUoclKwYIJFIxcofVUTsUvw0V/IjjXjiQ2bdRoVHDuYjbTx7v7uBeWX6eCglFWSX4YISTNLT5QJULGywwpZbGYRqpEqlFTjtC3cETlVBXMsbhL1/pzI2CVw8rF5XolReD3QYOb96GEiF/QToZoFI6djTB/ehaZE0ta+pIihWBQUNec7NrJ5MNU19D80x6piDB/ul2L1kqsxsMu5xQXCHKyBZcb02errCbkhq/YZO2kqnD4VB+LZmUnTWerpYMIHVKoUJAnuNSgWsykZVLFjVTm9ELY00gEh06FWTw7mCBfanKmKF+hqEDhfF1k0DTjnH5MpVTZd6yD5XPz3VJbvtv4tjbKys+DkiIfVRd7yUQDPbN6w96jHayYn3wumlhLFQrzfAwb4qfqwuAsXDu1yI167jvWy/I4lw8GmWqZjxoLRY6mysuGEaU+zp0PYaPcLW4orO9t6rY23zUpJYovSHbJstjZzoYtfOiOn2U8P/75z3/O0aNHAXj88cd56Us1N1mKovDmN7+ZH/1Is0J/5plneP755zMq+ybAOCFEZaKtPwUKIRZh+kn9uuO0ERE42WhlnIuRC1LKGjTebySadmsH8HM0Du/D+nUXAB8BfielfM5Sn+H6QvOLEgM1+X/RrdxH1BBnL+7ne1//bezYPfe/D0WImE9UIUz/qMLYV3RCFfAJES8k6vDp6REwe+FQzhy/QqgvGktrxFL0iXiTfiEcZiP6qcqzVygpzaGoONsxSIpBWfzw+30sXDKGA3supE6cBFJKtm+pco3snhzp38TseaM4d7aJ7m5zgHMSquksvtbWXCE3L0DJ0DykzdG5uxCQKXw+hWUrJrJ7Z3XGeZ3X3rrpLGs32IMm2UlU68quUzA1ZkuJacNpM4Zz8UIbnR0DW2Squ6Qtjo0cVRB3D+kKS6bz+fjJlBCCVWsnsmNrlXtmrYS4a7mRqts2a20qhIhR6UbqwcCkycO50txJS4tm8i0wzLv8aH66fK4Cr7GIYghbTY0ddHf3MX5C6aDUK+56QrBu/XS2bjqdOnEKbN10mnUbpg+aeZkT48aX0NsTprFB898Ub2aYHlpbumlq6rRppqeClJKojKLGAqqZ0J6jZmWQkxPky1//UOzc3Z/6Pp19J5NqaiTCzbGaf1Os4Hvw4Aop5UBWkd+h//5WSuk2KP0f0InWOdg+0q6urpil1Yc+9CGKi4vjMt99990AtLe385e//CXtSnV0hfjOT7bSfPbPsWOl096iyUmKsPAXqsY2CWEKTc5P0Ul+gq49ak9WfTmbkaUhggFH3xi3sIp2Ldu6r2T+1E7KK/L7PfxKiysgVdUjgEvVMfZLKi/AqGGQEzSOqLFfYyHdtXz9+JLZsO9Y+vVS9aA8YLRFelOyygsqI4YKcuM5loSwjjHL5wbYc8SQkY1nkGisNGSO9K9loPpilOElgrwcsBJj7hqrpjqJsS2fl8XeI2GXOiW7R61cVTX86aZRz0sRSosVCnLt8ny6sv3yeUF2l5tzDqfFVXrFpB7rztdFKS5QKMyLX1zXtFsTa/oCrJifza7y9BbH432+mjBJVKcJvobay30U5vsoLnCuxFiJbns5Vj/IaxYWsP1QO/3+4NPE+foQeTkKQwoTBX6y35edgIRVC/PYeXjwAlElwqXGCMGAoKTITKO930aAvKilr4p3abZifg67DndhkKjWOYSVSE1HKzX+HdXSB4tmowQ0ji7aV8/JYzt45Neu1uQJ8eijjwKwceNGVq5cGXf+LW95C2VlGn/w2GOPZVT21cGNK98KLejUn4Es4M/6YrAVxuiRbDXAmPTbAl5IKe8DXg/8AdgE/ARYJqV8Xrcu/yHQhaYwiRDiNp0UrgdahRA7hBADj0p8g2EghOqLcuW+sfcI3/7KL+lo1zrJ173tFuYtnGpYHQH6UG8hV4W+b9VOdXZJMVFFoOuhCbIUP0uWj+bQ7nr8QsEX6+icAaX0TtBFgzAaVTmw5yLLVo1D4MyX0a0nxdTpQ7l0sZ2O9v6TaqdPNjJ2fDF5+en5g3FH8pvSSLVJ7NhaqaeOb8tUUFXJzu1nWbN2qoOMNU3P1X5rrIpYmWWTS2m50k1Li7vmRjqoPNvMsGH5FBXnOEhUg3RMRla6awg4iTwhBGvWT2Hb5mREZXJIKdmxtZo16yfGn0vTZE1rW+2+VKLayqxFeJBSMnpsIaG+KI0NyfwTW9vE1FQ1yjlf00JuboDSoYZzfauQ4WizAch7618yiy0vnNbfUfvAKvTOwjD9cjOtl1KyZdMp1m+8uuPSyFFFKD7BpYv913S8fKkVhFbW1cT6l8xgy6ZTCf1dpYMtm86wfmPcsOMKK5GazJ+X9Zt67evXs3rtfADOnb3IQ9/7PSE1I4d7wL/8ar4HDzcshBDTMa22nnFLI6XsxPRf9nLrue3bt9PTo7k7u/XWW12vMXHiRGbO1NyW/eMf/0i7bvd9ZzsXT/0FNayNkfnDl5FTNFnroaSEqKpt1u7MsA+2CcKZLNgLjpzLZ/6UTn0vQVZBnMgyfkQf9S0BevqUGDPllLNSy10WcsvmFNYgSlWQIKVg/3FYMscwxbcTrnFugyzXnTha00ztTtOYI758SL9BFfYejbJsbvII4G6YONpHY0uUzm67DGTViDRnLIplHtG/CcWeo2GWz/NjLuwbm351XW52aluOH+XjSpukozv1tZ3m48ldSLljd3kvKxbkEP9Mkr9b40b6aO1Qae/S/XyqUQuRa9Q7E0Ex+b3uPtLH8nnuEeoTuQsAGDvCT0eXSluH2S6ptVQdCx5xfkwTzzF2HGpn9UI37U+dVCdqUaYwt7EjAnT1qLS0D9x6Lx1sP9TOGtd6mtDmBva2GD0sQG+fypW2wXCH5pT/45/LrvIeVs7Lib1jrsohLosBI0p9hCOSplbV4h7CETg1LSLVqKdlz0bMGgGqNHQ37eLLD/6Vru70OIPu7m527NgBJB7zhBC88pWvBDIb824SnJdSTkq0ZVKQrrT4DJoMcgB4l0syY6Ry70w0GGRNnINgKeWfpJRvklK+REr5PilluX7q/cBy4LNSyktCiJVoxG4U+ADwGWAB8LwQojiT+7rR0W9C9cW4ct8X7WDfgZ384VHtQ83Nz+GTn3sHfmGKFz5HP+ccugyNNu1vS/gYoeX1CR8KPhRdF3V8WRFtrSFaW/pipbkRp/Ekl/Z7+MBlZs8bQSDg5vR88CCEYM2GMrZv6R+pFg5HKT94icXLxvbn6i5/JcaYscWEQhEaGzrTEBjiceTwBabNGEF2ThaKq29LTZBwaq1aib2kvpNiQcV8rNswjW2b+hf8LhpV2b+3hqWrJsRWdpMTqFa4+5hM1FojRhbg9/m4fLE9QYrkOH2igXET4sl090mFO+InQub9WldkV2+YmEYgNXeBW1Ulu3ZUsWrtpLjU0q1tM5hQmt+2NlEZVlpIYWEuNVVN2L1cpbcwcraigWHDCykuTuxYf7CwbsN0tm050y9tbCklW7ecZv2Gq78gWVSUw4iRhZw9Ux87Zk4wUj+smupm8guClA51M1ezQ0ptaSXdwAhC1wYXwsc3vvWxmCD69S//nEt1x1DVzMzGbobV/FS+d9PdPHi4yTDH8ncynUXj3CzbwWNmltmzZye+yBztMsePH0+rUudqWvjT37bTdmEzAELJonTS6xAqGlmpxmuXAha+Q0+jsY+p12ot3W5zWxaKkAwpCFuKcxRg2Q33NhHtrWPquB5OVeVo141Zf6U/Djm1U+PlNUOW02rV1KLd2tAh8cSaRr7aj4b7WlBDjcyeAkcz8jgTLyem1lI1zze3SlRVMrwk9TROSpVouB013MS86VkcPuXmFsl8WCa5mtaNJMWVNkkoLBk51C3KvSlTGXKzGulADTcyf7qPQydVrORuYq0usx0z0Uy1oqVdpadXMnqY4iCm3NNHw51E+xpZODOLgydCLhp+mUyvk5NpVrR1SLp6JGOGu/vhMDV+teurkS6ifQ0snh1k37F4tj/ZHMk0O5cO67DU84zWjihtnVEmjArGytACUkV1n8RaWUabqdEe1FATy+cWsLu8f/OM/qC9M0pzW5iyMYkUfWRMGQpAjfYQ7Wtg2dxcdh/pvzKMFU7NVzd0dksaW6JMHO0nWSdtvLtqtJdITx3L5+aw52iPizsB89mmWUtHneMrmpU7jqw8TeaU0S7qa7bx2a//Na3ST548GQsqbIxrbjDO1dXVceVKRr5VBh03onwrhMgHngUWornVfIXDd6oBQ0OmxOUcjnNpadMIIYYBXwUOA9/XD9+N9vLcKqX8sZTya8D/AKOBd6dT7s2CgZr8Z4wbeeW+rms/X/nMT2If9Uc+9SZGjByi1Vv/z2r9BBK/opn4g6Z5qh21wMiDQacYWqim2v3q9ePYteUS8T4UzWu5dXndXWGqK1uYOWdYwnyDieEj8vH7FS5dbMs4777d51m8dCz+VNG64tC/V3Tdhils23w2Y0213t4wJ09cZsHCcZZciYLbGDSbqhMrdk3HRMRqbEUPQWlpPoVF2dRUZTYwSCQH99cyZ/5IAgFB+mSqu0mQ072EG9asn8yOrdVpmi+ZCIejHDl8mUVL3cj09LQI3IhXqWupGnvGv8LCbEaMzOfsmVSB1OI1VY+WX2Tq9GFk52S51DN+lTrdeV3s28dcUAFYs3Y6O3acifU5br6S3RCNquzbU8mKVZPTq8AAkZcXpGzSMI4fy9yX7onjl5g4cegANdPTx4pVU9i3t8oWSM1uVugOjUw/x+q1UxKmiaWVqoVMTR/Gs523YBrvuv02ADo6uvnCZ39ESK1Iu5ybZzVfDNLmwcNNhdGWv5N1msa5Qn0iBMClS1pwxSFDhpCbm3jBbMyYMbb0qXDn/VtoqPgDhj/RIeNfSVa2PmeSEqIyXowwSFZVQkQmD1YFVoHXckz7Ka/QtVSlClLGf9kWwbWx+vdUH/4STz39DyKRPpDu+n7JCIE402ssgSCTaA8eOA5LZ7v3O3b/2NB2+VnqKh7iyaeeJRxOTz012UJy4nHK2R+q7D0WZukcp6zijs6GzTRXPsqTTz1DKBSvNWZqeCYKEmXIP5m70dl3LMyS2dZ6Gs/FKrdq7dFe/zyNZ3/KU089RTjcZ9NadRsTjOevae5lPibb6nm0lyVzshHC+RHEP6vOxq00Vz3GU08+RU9Pd5yvVjd/mO6IP5fM/Btg//EQi2ZlJZznGS4AQKGraQctNb/hqSefpK8vvfdTIz7V2DuRyN1Z4vwaebq7vI0lc/IRVsUTKZEyrPv81IPESUlf8z46L/yZp5/6Gz29cQpxVxX7jnWyeFYBSsJmF/pUQ9J7ZS8dF/7I008/QU/PYBCqqWN8qKqKqkbZf7yThTOzSeza33xve5v30Hb+dzzz9F/p7rLX00qUJ3+uwvFrz++GnNLVoL+/PS0H+cVv/k51beqg1tYxzBjX3GA9l+64d/VwY8m3Qohc4ClgBXAGeKmUsjlBcmP5L9mEx5hgprtU+A00f6sftChdrgJOSymtQUP+ajn3osE1J1S5QVfu20MXeeIv/2Dvds2EcnzZSN79odfEGkgI08zfgOEzFfTPwrHaYIolOlUq3FcUh5TkUFgUpLbaTlTa/KdaHpWqqhw/WsnOrTWsWjshbvAdLGfjbli9biI7t1anvapVceY8dXWt1F/uYPK0/vh57N+9FBRmM3JUERVnGtJKL6Xk6JEKdm4/x8rVk3VhRIMS527BDZog5wxeZZTtbC9FJ9QBVq+ewp6d1UjV0E50F6gNk/5z52ppqL9CdeUVZs4eTiaCjnsnnl4b5+YFKJtUyslj6bUpwLEj59i7q9aVTFdlJoKvm8aB1HxWxiYnptC2ZMU4Du1LJ5Calq+m+hL1dS2cPtnAvIXug7mrD7UkTSdiX75P/41PnJ2dxazZYzl8qCZm0m9oMSfCkfIz7N9bxfwF4100068eFi+dyJHD5wmF0jPFOn++jsuXmyk/VMuSZWVXuXYmsrJ8LFg4nv17q13OugswR8rPcPhgLTNmjiQ7O/EE1TDxd/Pjmy4M0vyzX3gvhYWaW4nHfv4UBw7uIap2pFVGf1bzhRDJVqI9ePAweLA6Ck8267aei+Xp6ND6gWRkqvW8kT4Z/rm9ml07XqD7iiZD+4NDKB6n6yuoKq5x9Gxrjg6tVGt0aSzHDALWKWoI6An7aGzNYvyI5GagXa0n6G45ipQql88fRqj9kVjQiFuMW4zvrxPJsd29KpcaJJPHJSBV9Xy9nZX0dpxCSpW6i8cyqFniwDwa3MqxyoVaw/f2aX41p01IHDVMSpVQ93n6OjQLk/qLJ5OkdSogOGVRQfxLkhp9Iai+FGV6mVs9Tdkt1F1LX/tJQFJ38YRO6pnuAOw+0Z3XT9c6K0k9w5LK82FmTsqK+fiEeC3VcM8l+tpP6PU8iVMWSEWIJkK62mqhMJytjTBrcnL5L9JbH6tn/aVTtu/BgGndpdXZ/ZtI3q6myzPVlj8ckZys7Gb+dKvFj/nuG2mjfU2EOzU//fWXz7jW82oiHJEcP9fFgul5SVJJon3NhNpPAFB/8fSg1DMZ8W74STW+j0gUjp3tZf6MHBJDEu27Qm/bEQDqLp4C7MHS0jfxB+t3lo5GpS9QRLBogV6VKO11W/nYZ36T8irWMSzZuGc9l864968CIUQ2GlG5DqgGbpFSJotrtFv/XeN2UggxBihzpE12/bVo1uc/llLusZwqApwassZ+capybyZcD0L1hlu5V1WV2pYD3P/ZR2LH7v7yewgGszS5UMS7ixL6f4Z2qk8osfMGjRIXVMpRgrVzWrZ6NPt3X0ZKS6cVS25/TL/91T9ZteQD/N93H0XxhePJl6uo0JObF6BscgknjtanTBsOR3jLG+5l+cJ309p2fsDXzpQoXr5iEgf21cQ01ZKNA3/+0wssXfRffOXL38efFU8YJSLErDBNwk13AAYJClahw76KnZ0TYNbs0ZQfvGh5axSXsrVVyne89T4Wz30HDU2VGZhruIfqTa2XasfCJeM4duQyoVBqk6rn/r6HFYtv5967v4vit5szZ+LjKjnxajFz0VeQJRJ/lsLchaM4uO9CyjaSUuX97/kai+a8jZraUxjBKuKDVbkci+3alk4sJGqi98b8/hcsmMCpk5fo6THbSEkQXGD7tkMsXfSffOy/v0RWwM1s7+pBC6Q2iV070nNR8d8f+hpzZ76OMxXHXCezVxMzZ4+mpqaJzk67Roabpuqhg6dYvuQdvO/2z+HPStymUuq+ewc4aTMwYngJd9/7br1syZ2f+F9C0fS0VPuzmo997L0mEIP0z4MHD/1HNKpyz4ObaDr7+9ix0kmvR/EFgDS0TlXV5KsUEneBlkBSMXJVtf99siqHaeN78AnVQtLKGGslZZSm6j/Eihw6/j8QitO6waph5Q6r9pWmDWeX6xIRA0ZwnKNnJTMnCfwuHKBEJRoN03b52dixwhG3oPhSW2Gks5Acr6Xqpi2q4djZCNPL/PhduDWDPOmsfyF2LH/YWpd6WrVErfUziEvDz7vdogebAkDyfvrE2QjTJvjJSsABSqnSUf9PSz3Xofiy0eQ71SE7W8fhwRmPDRw/F2LqhCwCWartHbFqwnZY2jN36Eq9ngbsbZGpdqqZL/n0/FRVhElj/QQTrP9KKels3GTWs3QFit+cM7vJuIk1FuPrYhKoTus4exnHz3YxaWw22UHF8b1q6VRVpa/Z5GuCxQtt9bxWOFnZw8TR2eQEnQpKWgcgpaS7aTvGvWUPWYzwDayeiZ6x1U+qM2Dpmeo+xo3IIjfb+e6Y/lG7GrdY6rkExZ8f03J1K9OlZmSilepEbunSWNuEOs+yZctmnt+WeCHnZsWNIt8KIbKAx4GXAheAl0gpU0UR/ytaAPipQoiNLuc/oP8ecmiXul3fD/wAaEIz8beiGZgk7MGBjCAVqVWXbyJcD0L1hlu5b+w9zcPf+wMXazXNu1Ub5nPLq5bb1u2ci/MISZb+HQiHeYxLVxTXGTn7pmDQz4xZpRw9FE9UWsm19vYuPn/PT5BSsumFXZw+aQ9mci38zS1YPIbjR+sI9SXXVHv4h3/l9Mkampta+OVjT/XjSv2/F4GiaaotGsf+vTVJy+rp6eXTd/4fADu27+XwofiI5ql9WzmhE326R12n8OKcCMydP5aKM/X09oZ1jVj7czfK/OWjz3D40BlaWtp47Gd/S4NQddMyoN8duc+nsHT5BPbuTB5EJxyOcNcdmguVfXsPs2/PCdv5eLIyGZKRtxZN4FiZ2orutJlDuVDbSmdnX0L3CwB/fnwLO7aV09bWwS8fe4Jo1Goi6LyaafpvLnpYh0Z3ElVYCFSnbzJFEaxZN4OtW07F5bFCVVXu+MSDABw+fIytWw4maZerg8lThtPU2E5ra3JTp2ef2cGzz+ygo6OLxx79M+HwtQkwYEAIwboN09m6Of5b1lMAClLCJ//nW0gpOX78FP98Ln4h1h54auBaMFZ88COvZ+o0zQPOzh3l/PEPzxBVU8sY/VnNxz72XgMMxATKbfPg4aaBVfBMJqxaz8XyFBRon2p3d/J+1jhvpE+Eh355iHNHnybcrcmX2YVTyB+2RDupytTqn1Zr8ERpjPHVEJSd1ty6SwE1IjhdncOssm77eS0zbXVbCPdoijXB/DLyi5fEXyMdWIMUuflLd/GzaSWUVBWOnpHMn+5GJql0t+wn0qfNGbJyRpNTND+Nxdv0F5KTPxTzOqoK5afCLJrhxqxJetuOEulrBMAfHE52USLLPoOkdl7DomJsI1WNNM7z7v21KuHQyTCLZrkzgL1tR4n01uv1HEZO8VzcJDBnQCtn/Qc6XkgJ+4/3sXROtqvc2Nt2nEiv9n76AqVkF821nbf5w8zgum5zt2SkqlbPEEvmuMeU6Ws/RaTnkl7PErKL55t5XWSYdOaO8SRqethV3s6qBYXEv/uSSHcV0T6tPYW/kKzC2Rkoigwudpa3s2qBtS812yTSXUW0V2tPxV9IUG/PgdU1nng3tVITl7urvJsV8w1tWns/EeqqJNJzXq9nATlDFln6NXuZ6fIFGfMKIoucUtOau6thK/9z72+SKlZYx7Bk4571XKpx7+rixpBvdaLyV8C/AXVoZGpVqnxSynrgR/ruT3V3nEaZtwF36rtfSKMa/4Nmff4pKWWL49wmYBjwUb1sP2bg+U28iHA9CNUbCmG1l+NVe3n4238ENMLoM199r41usnVF+n8C0wWANuhJm4lIPHmSuqlnzRvGuYpWenus5IP9Q/vW/b+hoV57X2/79zWs37g4YdqrBZ9PYemKcexJQqo1NbXy1S/+LLb/wLc+ek3IXgMGGTlj5kjO17bQ1ZnYd9B3v/0bamsuA3DLS5fxqlevdU2nEZ2ZfjJWjdUoqsUlgCpV3Tm7RAhYuWYy27dW6OSNGif0tLd3c99nH47t3/+t/8bnS2zqpcGV3s/wHuyYNGUozU3dtLYmbtMfP/QXzpzS3o8VK+fwxrfcEjunxkxYUiM9twAWylqaQSOEgFXrJ7JjS7WZ0iH89PT0cc9dD8X2v/rAhx1m9G4CvdDdxPlQ8KPgcyWorSRqqiafMGEo3d19NDaalhFOLdXHfv43Dh3USNd586fx7tv/PXmhVwFCCNZvnMGWF04lTBMOR/jUJ78V2//a1z9Gbm52wvRXC6NGFQNw+XJr3DmjL3r8j/9k545yAKZMHceH//tNsTSqVC1E6tXRsA0EAjzwzY/H9j/z6f+jveuUrkngwYOHmxRW06jEKuTmuXY9dgAAo0dryuQtLS1JJ5cXL160pXdDe0cf3/vpFlqqn9SPCIZOfpPWB1qDUFlFBZt2qmXHNpYJ209KfsXShZ6vC1BaFCEnGLXli4Y6uXL+ydj+sAlv0Opp0WDVLpW8P5aqqcEosUeAt6RyOWJPV3NZUloM+RYLWym1ADodjVtjx4pGvtLhlzBBvWxkYPJ7MAMLKY65RHy+2jqVIYWCgjxT0NDq2Utn4/bYsfwRL0lC0lk1DOOvYWrNWrVSrb7lU5MEF+pVCvMEhfmOxeJoH50NZnsWxOpp1sX062mal1ufoRmbQrG1XX9wsT5CXq5CsYOviUR66WrcFtvPG7be0Z6Oe05TO7W/U6PLjSo5QcGQQnsBUg3R2bgltp8/fAMigcu5WB7He6sFojJN+FP720yMS40hAn7B0GI7mS7VCKEr+2L72aXLU9bzaqKuKYzPJxg2RKunMd+TaoTe5l2xdNlDV/XbrYMB531atVJTtXPDlQgCGF7i1993H0IIVDVCT5P5fmaXrkLzZ5ouAW7/bgcSNClYOBNfcDgA0VATlad28P1HNidMbx3DjHHNDdZzyca9fyG8CXij/ncv8DMhxPYE20JH3juBXWim/ceFEIeFEGeBJ4Ag8KCU8q/JLi6EGIdGkG6VUj7qkuRLaAqS3xZCnAHOA68FTgK/6Ncd36C4HoTqDbVyX9ddzne+9Cg9XRo59Nb33Mq0WRNsMqZ0bALNf6qK1uFq5JihjWiSZVYT73RILCEEK9aMYfc2q6a22QlWnrvE9//3TwAEAll85f4POfKnvMSgoWxyKVeudNPa4u48/Ctf+Dmtrdr84G1vfwWLl/Ynwnf/bkixmLZrmmpT2brZ3ZT24sUGHvj6zwHw+Xx848H/6beJTnKY2pOSqF1bVTMgY+z4Qrp7QjQ1tuNcTRQIvvG1X8TI9H9/3TrWb3D2jVYYJv6DS6YaWLthCts3uy+CNTW18rUv/Ty2/8C3/tvRpumRU5oAlw6xpH1ztiUQfeFjxMg8hIC6SyZRaRUc//fbv+N8raYVccvLlnLrq1bibHdD61TRA0ppxxN1nemTqE5sfMlsNr9wwu4SQhdw2ts7+ew9348d/+a3PpEGmX51MHRYAXn5QWpr3H2dP/SD33PmdA0Aq1bP501vecW1rJ4Nhpaq2yS3p6ePz9z1f7H9b3zz4wQCWTGNVGkLmnF1oODjlbeu4hWvXAnAhfP1fOubjxFJYa3Tn9V87GPvVYfAubA4gO1aVtyDh4HDGh8gsZNj85zNhMPqFzlZHAAjpkCyeAL33L+FyxV/QY1qMm7hyFVk542zkKmO/s2mgKincfN7BemPcTopa1JtgvLTeSyY3mW7bvP5v6FGNZmyYOhygrkTkKqqkzv6pkbjfDXGw6lFaU/rpp2aKFDU3qMqy+Zax3tJZ+M2pF7P7KI5ZOWMSWqBZB7LdCwRNuImWRl7joZZPtce+KmraVesnsGC6QRy3QKDxiNR0yaWjZ2svMAe3Mq8h71x9YSupl2o0W69ntMI5E2w1EWixTcxyCYzkJGhxGIuorvVr3+jx+7Dvaycb7gc0Ajcnit7UKPaOxvIm0Qgb7wtjz1ae0b6qYnPpCDudh8JsXye3YVDV/Ne1Ig2/8rKKyMrdzzpvHs2FwexBYmBBfoysP1gC2sWDbEdC7cfQ+rt6csZgy/HDAZ8vbRUdxxqZ/XCQqzvbF/bEdSIJj75c8aQlTvRlifzujoI8Ng7nm45gl3lPaycb/f52td6CDWszXV82aPJyitDSqO/tHISqes0MGh+j/OGrY8d6W7ayQPf+SvtHe6cwcyZM2OxS6yxcpwwzo0cOZKSkusXFuAGkm+tH/9EYHWSrciaUUrZA2wA7kKTQaYBQ4EtwBuklHekcf3vAgHgw24npZQn9GtsBsYBOcCv0TRpr230uauM60Go3jAr993hK+zYtZW//U7TOi4qzuejd78VIcBvkR2dnY9t/VZIVF2jMM7CKZbe/k8r073jHD22gL6+KM2N2r0JCzn42U//mFBI8/H30Y+/gbJJ5r1djynn2g2T2La5Mu74saOV/PTHTwCQn5/DF77y/n5eob/3ZDeBGDmqCEURXL7UGpfy3s98n+5ubaLxgQ+9nlmzJyUtWdNSHRiRpb0FES2gEkaAG41WXbthMts2n4t7P85WnOd739X8igWDWXzl6x/B3XeVu3bAYPsiLB2aR15ekNqatrhzX77vZzEy/e3vvJVFS0wyPZNAVDItMjVWMli/L4tgsmrdeHZsrdYWOSzf38ULDTx4/68BTev669/8iE2gFPqkwD4VJFZ+nEaqw5Q/UxQX5zJ8RBFnK0w/4oquFfK1r/yUhoYrALzu9bewfsOSBKVcG6xeO5Ud2ypQVft72tjYwpe/8GNAa48Hv33HNdVMdyI/P5sJE4Zy8ni8L+1vP/gLamu1tn75K1byyn9brU3crwGRCnZS/v5v/D/8urO+B7/xGLXnj6CqoURZ+7Waj33svUZIx9Qpnc2Dh5sHUsrTgGHC80q3NEKIPMAwh/mH9dyaNWvIydHUIp999lncUFNTw8mTml+6l7/85a5pzpxt5omnttJep2kpCl82JWNeo3FTURUi0r5maVv3lKb/U+MztHWJ0r6f7HOVth8AWjv8RCKCoUPCIKGv6yLtDXo9lSAlo18Nqp1EA1XzcWpRVjA2zUegiqpGTAIBNc7MNDEp6S6btHZAbwhGDtW0JCN9jXRfOaDVU2RRMHwDElW/7cSkaiqtWjfEk2mJy2jtkHT3SkYPV/R6XqGn5ZBekJ/84esT5rXW0vy1j4F26zujXoYsbCcS3TXbtP22TklHl2TsCC1PJHSF7pb9ehIfBcM3uNTJrIfxnO0kkd3HqhBalHtTazVxsNdEaO9SaelQGT9K0/CLhlrpaTkYq2fesHX2u8tIY9GuBZhJeic6uyXNrVo9VVUl3NdCd0zrUyHfUU83GK4oDK1p0zXF4MhBUkbp6I5S39zHlPGarpQa6SLUdlRPIQgOWR7XFteDVO3qUbnUEGLaeM2qSo100ddquNcS5JSudn1mmdTVeFfMd9b9u7a6WLAToYLuXsmF+jBTx+tKAOEOeq7sN+s51K2eFlbC1V2GUb/+aqbaB4GsnNEECzRLcqn20nRxG3fe9wfXnLm5uaxevRpIPOZJKfn73/8OJB7zri2uv3wrpfy5lFKkuW12yR+SUj4gpZwnpcyVUhZLKTdIKR9P8/qvk1IGpJQJV36llPuklBullDl6+W+TyQNm3ZS4HoTqDbFyL6Wkrms/X//MT2LH/t89/0lpaWFs33jNbf4O9b99QgtBo0rDS6ZermUzDL2FRc/VSsI4BUIDq9aPZceWC3rnqaXf9PxBnnxiBwAjR5Xwybv+y35D12HOWVKaS35BkNrqltgxKSV3ffJ7MSH2U3f/FyNHlfaj9P5pVioOstN4dms3TGXblgpbO+/ZfZTf/ErruEtKCrn3c+/tRz1TwwhMZZKnxiQhgrRsAIVFQYYNz+dcRaONhP/MXd+P+aH82P+8hYllBqFi7ZCNYEimCfpgE6lWrFpbxu7t1TZS7diRczzy8N8AjUy/78vONs3E1D8zYco5YTGedV5+gPETizh1okH/ErV/n/vMj2Nk+vs/9FpmzpoYK8mARqnGt6G0aKSbxzKqritWrprK7t1nbX5cK89e4P++q0XJDAYDfO2B/zfwCw0QOTkBps8cRflhu9uP+z73EG1tGpn+jne+mkWLZ16P6tmwZNlEDh+qJRw2mYPz5+v4xv0/BzTN9Ae++XGEEMhrQKQasBKq02dM5EMf1qx2enr6uOfu7xGWiYN/9Wc1X0p5ZTDq7cGDh7TwmP77FiHERJfzHwHy0QSCX1lP5OXl8frXvx6Ahx56iLa2+IXL+++/H9C01f/jP/7DtQIf/9w/aKj8PUafVjL2VvyBAm1fBaSqOeGMSpNAVbVj0vjbMLc3yFWbxoChGohpvmVosrqIHc5DR87kMXdqF1KqNNb+MVbekFEvx59VgFOiVqNRkFGt3rFfNabBKGUUqUbNXNIgYc3NbaE21ULvgeMqi2ZqJujtdf+M1TNv6Ep8WYWA1Ile9/xOeSYzokiknWf/8TCLZ2YhBHQ2bMKQt3JLlur1TB9uxLPNeiZGuqTSrnRqrAoOnIiwcEYWQgg66zdj+LvNK1mGL1Acu5bVLYLdJNok2N1Iv5hloIUYsgekNOqTHPuP97BwZjZCSLqatmrvHJBTvDBWT8vd2tomMTKXx5OVJ6Xk4Ik+FkwPoCiSrkZLPYcswBcwtUITBaJKrfHdf1if4b5j7SyaWYBPgVDLgVg9swpmoASK9O/n+mimGhAoHDzRzbzpufh80HtlD0ht7hUonI0vaJ3P9qeuwjLnN4hsK3Fqbmb5xjts/K2dO3yqhzlTg/h9kp7m3SDDej1n4g8OTXB9c9HBWnasdv2aLprkYMzthv53ztDVIDQ3ar2t5fzxL89z5px7UOt3vvOdAGzatIk9e/bEnf/DH/5AZaWmxPWOd7yjPxX14OGq4ZoTqjfKyn1HuJo//f5ZyvefAWDqzPG89T234jO0U/X+wUamGr/C6kEoeYfqpFpNcs0aBd7uUzO/IMCwEblUndWE6Egkyt13mH4e7/vy+ykoMD0iXE+DyJVrJrB7R02MQH3yie1s2aSt5pVNGs1HPvaGa1gbN21NDXl5QSaUDeXEMU1JS1VV7vifb8fOf+6+91NSYtOGT4h0tFStJKpd6Ev2vmhplq4cx/69tUQimubqC//cx1N/0zQ3Ro4q5Y673o5hig6GSXoW1sjy1wLZ2VlMnzmCo4e1hSYpJXdayPQ7P/N2Row0hQ83M7vE6I8PSYlI0L4Ll47m2OE6QiFNMNqz6zi/+40WUbaktIjPfO49aNoWxmY+Q+MrdV4rXuNk4IJgIOBn4cKJ7N1jkml3fupbMTL9E3e8nYkTbwy/QQsWjufUicv09mpC3JHyMzzyk78AkJ+fyxe/8pHrWDsTfr+PpcvK2L3TDFR5793fo6enD4APfeQNzJg58ZrWydl/CCH4zGdvZ+jQYgB+/9t/sGP7NqLRdpfcN89qfqKoppn+8+DhekEIMUQIMdTYMEXBXOtxIUS+I+s30YJE5AJPCSEW6+UFhBAfQvMtBvBjKeUZ53W/+MUvkpeXx+XLl7ntttuoqNDcFnV1dfHFL36RH/7whwDce++9DBkyxJmd7XtqOXTgBXrbtXxZweEUD99o4Sd17dQoEFG1Y1ELYRqJaDyXNdhUjHTFLtIY7gOc1sGxtV5hnXPHjveFFS41BMhT9tPToTWBP1BK0fB4rTpNrjDkAqsmpblJqVv96CSrqqqoMmJWzOGKy1J6QgUHrZ5QfVFlaO45Ql1V2i1lFZFXutySSiJt0eetBGR/TKbjtcZSIRSGs+fDDM+vMevpLyCvdFkG13XWP7lc4yQtk503iMxwRKGiVmVEQS19nWf1euaTO3S5fl03kjTZYqddS9Xq99NKrNr9rNrdKThKQyIJR+BUVYgxJXWEOjWZTPhyySld6rjH/snc/XULYGpka+96OBLlxLk+xpXWE+qs0OuZQ3bJMtsccyD16r+lkXndSFRSfrqDySM7iXTp1o1KkEDxAhLNj66tlqrWOUVVKD/dzZSR7YQ7tX5JKEGyS5YmzZ1eXWWsfzLM8TObO5h9X1SVHDrZw+RRrYQ6Tur1DJBdkvx7ty6EOM3P0yP8nf6KjXpZNMl1i0BfVgE5JYZFnUp73Rbu/cqfXEt95zvfydy5c5FS8vrXv57nn39ey6Wq/OEPf+B973sfALfeeiu33HKLaxnXEp5868GK6xWU6rqu3KsywqXmg3zj84/Gjt37tfeSneVLtLAOSITQCBtFgE8kom5Swc0xgEMoRLJo+QgO7btEOBLlkYef5OSJagAWL53BW9/2MnuR1/F7zM7OYsas4Rw5dJne3j4+c6c1wM+HyM4OJsmdDJnflOJ4nZ3j/5KlEyg/fIFQKMKvf/Us+/ZpGs6zZk/ive9/bYa1czH5sJDl8c85XUgCAYV5C0dxcN95IpEId37yf2Nnv/TVD5JniZBgkKeGf08zyvzAXROkg3kLxnDmVCO9vWGe+Ms2tm4+BMCkyXYyPRP/YZkRr3bEa41qf/t8CouXjWHvrvOoapS7PvG9WJrP3nc7Q4Yk87XsLvAY95TKd1qmmD1nLNVVjXR29vKPf+zkb09sBmDMmOF86q53Dbj8wYKiCFatmcL2rWeQUnLHJx6Mkel333s7I0cmWiG/9pgybQT1de20tfWwc8dhfvsbjYQsLS3ins+aWtTXakFCuLALQ4YU8vkvfiCW5o5PfJuQGu/+w8DNsZp//U2iPHgYIA4BjZbNcPT3Kcfx71kzSSnbgFcDzcAsYL8Qoh3oBH6A5nfsH2gRcuNQVlbG73//e3Jzc9m2bRvTpk2juLiYoqIiPv/5zyOl5F3vehef+tSn3Ctdfp6manPiOnT86xCKJeCis1uJkaUSGdXIR6GqujGNocXqyKcmKMv57Rqaq8ZmURA8Vemj8tQTsZylY/8dRXGPWJ8MdtNZwwVQOGUJaozUsNTexfT4+NkQtRX/jB0rHPEShGs97eW499+ZyQlCKGnJFlJKTpzro7bihdix/OHrUtbTcTXLddOX21JpUTrLOVkZoeaM2Z75w9ejKO4R69OTH51zqXTqadWc1c2w4+rZS83pTbH93KGrELZ62seodLVTM+UnjXJNzV37vZ6u7qPq1PNmPUtXofgymX/Z71u4xGDIdCx2e2dPVXVRc8YMnBQsXoiw1NOZ5Vq6jLLO6ypqe6k8tTm2n12yFMWXPLhqOnU1+6nM5wpu7XnufA/nTm6O7QeHLEHx5cSlsyPRYkK6bS1ihgv2b9NcyLDeY86QxSh+bY4V7q5h795tcSUC+P1+nnjiCSZOnMjFixd56UtfSl5eHnl5ebzpTW+ivb2dhQsX8qtf/co1/7WHJ996MCEGQgAIIYaAjbU5iCZsfgN4wHK81+oHVQhRBJwCRqKZ9L9DSnlACBEAbge+gyZsPiSldDq6lVVVVcydO5euri7Wrl3LT3/6U6ZOnUpXVxcPPvgg9913H1JK7r//fu688864erf2neCee+/jB9/UfHm87FXL+fFv7sFnea/tzSJRLH1PtqLgUyAqU+mnavDFPph0fSxqqxZnTrZw8cIVbn/nPbRc0Rxi/3Pb91i+fLZt0LcOAtfDX6GqSh7/7REqq47wpft+CsD6jYt48u8PDqA+1tcqvTUcw9Q9tu9y7bMVDZytuMwHP/AZ6i43AfD037/HS25JvvLoBqv5mIypZ2h7A4WUkj/97igXL53mM3dpwYiWLpvF89t+gKIYgZEECn73/MbAZtOMuDorvbU1Vzhx7BKf+Ph91FRr2qq/ffzLvPo1a2Jp1DQdrmum9JEB1sgkxWLvg9C+6SceP0lj0zk+8bHvADBrdhk79z2CP8vajm7vm2bup+DDGp1TM2tJ/d5likuXWti39yyfvfernDihkWI/f/QrvPW/rl+Ap0T4y58O0t5+mffd/nkAJk0ey+GjvycYdJ8cXS80NnSwY/sZ7v/6dzh4QFvN/7/v38X7PvA6WzrNj6r7+2qfbElsE6Q0BSNFZCVMGY1GWbH0HRw9omnt/PDhz3L7ez6KXxkWlzYSibBo0SKOHj3KmDFjePTRR7nllltQVZXHH3+c9773vbS3t3Prrbfy9NNPwzWU3IQQlWVlY8vOnP1n6sRpYNqUl1JVdaFKSpncybUHD4MMIUQ1MCGNpI9KKd/lkn8EWtCHV6PJx71orq8eBR6RdjYvrtM5d+4c999/P8899xyXLl2ioKCARYsW8YEPfCCmXOCG/3rnx/nVY9/VK+FH8eWZATBE/JVk3DFjx6Xb6EdPEpdFL16N9qKqmusdIfwIX24/x1AX+cJ5jwOQgaQaQqp9+p4P4cu5DvJ2GjKUGkLG/G8rg1TPwbhP+3hpb09Ff+6DcJlBhlTDjnpej+eeGgOpZ/o8wCDMbdQISOP9FKBkD7qLhMGAVk+jPQUoN+pz72c9XcaAQYG1XGHKxdb3U1H89PR0EQi4zxE6Ojr45je/yZ/+9CeqqqpQFIVp06bx1re+lY9+9KNu+a7pg/HkWw9uGCihWk0/BU3d/OnvgGET3AFkA8ZS6j+A10gZ6ykMSICnn36aN77xjbHAVEVFRXR2dhKNagTXu971Lh555BHXjmXv8d+wdvE7CfWFycry89ze71E2ZbTtizSjRoJAxgZ6Bcj2+wBJJI0AO9r6Z+Zki2HO/cbbvsY/n9NWc978n7fw8M/vsdbSoumUWfmDjQP7K3nFxg/R29uHoijs3P8T5swdSN9gJ63SgZ1cdCevpZS8+Q2f54m/aqawr/n39fz+8QfiE6aJqAzrQrqVUB0cnDxxkY2r30dnp/aOb9r+EEuXz8L0lSrjtFAFIvY6WL9t0+RHxXxfBm80vf2d3+C3v/4rABtvWcwTz3zTvqKepgm/RrwOtB2F1j6xF8AkV2uqG1mz7L20tmqm1H979ltstJDpqd41Bb8LoSoczyHdhZPk+OAHHuAnD2srsctXzGPb9kc1Yrhf7hCuHuout7Bk4VtobGwG4I9/fpDbXrP+OtfKHXd+8iG++x1t0WfuvCns3vcYPl+8JrfhU8/qUzXexYNd08X9kdtGFQQKinBfBDGwZfNBXvHSDwIwfHgJJ04/T2nxAte01dXVbNy4kerqakBzBaCqKr29GkGxcOFCnn/+ecMk+DoQqs+nTpwGpk25xRM4PfwrYNAG5f37D7Bs+Qptwu3BgwcPHjzcAMjPL6C1tcVV9u4nrhOh6sm3HkxcN0JVz5/Jyr2BWIX7u3L/6v/YyFN/3QxAXn42BYV5adyCXufYf/RP9E3zsxdAOByhsaFN2xcwfMSQweyABhUtLR30dGvcd25eNsXFTndimWJg/WOi3JFIlIaGlhjZOGJESSy6dn8gk+wNFG2tnXR1acRITk6QISUFJGuXRGeutgeiaDRKQ73ZpsOGF5OVlZw0So7BqLF7a7S3ddHZ2QNAdnaAktKifpR89cfuaFSloeFKzIR+2LAhBAKZmO1dO3R0dNHe3gVAMJhFaWnxDbmSr6pamxoBv4YOLUqqRXu1vpt03p/m5jZ6e7X+dMSIodTVNSZMm8Fq/jUnVCvOvZA6cRqYOvklnsDp4V8Bg9rtzJ+/kHOVNURVkWbAF4faUhIlVfeDMo3z8apRmhlzFMVwSdDvVpC2vxIrYTnrmd4FDXNrw0rItVjbbQ/+4rXtWtbLSPOEZoobTVxPN8Q1g4g/lmwEibv31Ejanum4OEijWgOBzR5FjSJi1mHx5zOqgxD2FzTRa+I8H0tjf8+tyVVVe+7J33978XZczRmDeTPW9rRf98aSHePrmQiZ1jvddnaW6/ahyQzqmajcdND/ZySE5u0lLzfIxInj2b9/fz+un7j4wSws5cU8+daDCwZEqF4nDKjC0WiU//nkR/i/7/5osOrjwYMHDx48XBUUFha6+grvBzxC1YOHGxs3nUDuwYMHDx48XEd4hKqH646BqJHdlPD5fPzvd37IP597gbq6eoLZ6Wt9Ob9YY5ExhlRLgRlCoC3S9nT3kpM7ED8zg1Gx1GVEIlEikUiSQFSD3EAJyklckukwu6enj+zsAIrVOW5ay+/JdFIzWaJPry0ikSjhcIScHPc2zVxfVbv2YM/aYm0aDKD4NH+j9npkesX+rP27LfnH549Go4RCids0Na5NXEYpJb29fQQCWTesZrqBaFSlry9Ebm5yp/3XG1JK+vpC+P1+/H7NrUqS1Jb/448nzyv0NzFe5STdd6enp49AIJsxY8ammeNGxI2lbeLBgwcPHjx48ODBw8DgybceTPzLEaoGThw/c72r4MGDBw8ePLxIIQZx6WHwBFchxEbgk8ByIB+oAf4AfF1K2XWtyhRC3AbcB8wG2oE/AXdKKdsTpP8a8GngdVLKP/ennh48ePDgwYMHDx4GghtTvvVw/aCkTuLBgwcPHjx48HBzQwjxUeB54FVoPttPAhOBe4F9QoiSa1GmEOLVwF+BBcBZNIn6A8DTQog4uUwIMQP4BPCUR6Z68ODBgwcPHjx48HBjwCNUPXjw4MGDBw9XAWKQtkGoiRCLge/oux8AxkspFwGTgAPATODha1Tml/Xfl0sp5wDj0UjZ1WhBOp34ARAFPppJ/Tx48ODBgwcPHjwMNm4c+dbD9YdHqHrw4MGDBw8eBh1CiEHZBgmfRZN5fiGl/LHUI3JKKS8BbwVU4HVCiHlXs0whRCEwH9gqpXxeT98DfFVPss56ASHEfwEbga9IKasyvGcPHjx48ODBgwcPg4gbTL71cJ3hEaoePHjw4MGDhxcthBD5wCv13R87z0spKwAjZOsbr3KZBfpvnSPLZf23yHKNIuCbwGngG+nUy4MHDx48ePDgwYMHD9cGHqHqwYMHDx48eLgKuGFMohYCQaAP2JsgzTb9d8VVLrMZiKC5A7DC2LcSrV8FRgAfllKG0qyXBw8ePHjw4MGDh6uGG0a+9XADwCNUPXjw4MGDBw+DDjFI/wYB0/TfWillOEGac/rv9KtZppSyF9gCzBNC3C2EKNJdAnxFT/IMgBBiCfBB4NdSyhfw4MGDBw8ePHjwcN1xA8m3Hm4A+K93BTx48ODBgwcPLy7U1l5i0qS1g1YWME4IUZkojZRyUpIiSvTfK0nSGOeGpFmtgZT5SWArmgbqVy3Hfyql3CmEUICHgA49LQBCiDw0lwENUko1zXp6eBGjo6ODBx98kMcff5yqqip8Ph/Tpk3jLW95Cx/96EcJBALXu4o3BZqbm3niiSd4/vnnOXjwIDU1NUQiEYYNG8aSJUt45zvfyWtf+9qkZdTX1/PAAw/w5JNPUltbS05ODrNnz+ad73wnt99+u+cvD/j617/O3XffHdvX3U67wmtPd7S3t/PQQw/x17/+lYqKCtrb2xk2bBhTp05l/fr1fPzjH6e4uDgun9ee8Xjuued4+OGH2bNnD/X19QghGDVqFCtXruT9738/69evT5jXa08T3d3dbNmyhQMHDnDw4EEOHDhAbW0tAJ///Oe57777UpYx0PYUQkwG7gReDowC2oFDwI+llI8P7A7tuAryrYebHCLZYObBgwcPHjx48JAJhBBbgHGDXOwoTD+jcUhGqAohPgt8EdgmpVyXIM1LgOeBqJQy5WLzQMvUhf+PA7PQBP+/AI9JKaUQ4sPA94H/llJ+XwgxCngYuBXNsqgBuEdK+ZNU9fTw4oUQYgKwGZioH+oGfGiuKECbTN4ipWy55pW7ySCECGNXMukFokCe5dgzwBuklN0u+RcDfwdK9UOdQLalzH8Ar5FS9g1y1W8aCCGmA4fR2gUAKaUrS+K1pzuEEBuB36C5ggHNfUwnUGxJtlBKediRz2tPC4TGzj0EfMByuBeQQI7l2LellJ9wye+1pwVCiA3ApgSnvyClvC9F/gG1pxDi34A/ALn6oXYgH9MS+2fA7XIQSK+rJN+el1ImZu893PiQUr5oNzRNjvuAo2gfZxuwD03jI3C963czbWid3LuBXwIngC4033EX0CaCr02jjBHAg2gBNnrQtHe2Ae9FJ/f/1Tfg02gDutQ+T689B9CWhcBdwE6g0fK+btL7hWKvXTNqz5cBvwdq0ATPHqAS+BWwPkVer03t7ZGLRo7dC/xJb1Pju78vzTIG1KbAZOBHQJX+PBvQBNrXX+/2uQrt/Sm9bXcnSXOrnqbjepWp5xkOtAD70SYDWUA5EEbTZn0PcEAv943Xu2297fpsaMTpEf09uAS8VD+uAG9Gm1BK4OnrXdebYdPbag/wIWCS5fhE4CeW/vkXLnmL0BZ7JHASWKIfDwAfAUL6uR9c7/u8ju2rANv1dthJEhnXa8+EbbgabdFEAs/p+4p+LgdYDHwZKPPaM2VbvtvyTf8BmGo5Nx1tXmucf60jr9ee8e25AU0G/SfwAPAWSxvdlyLvgNoTKEPjeKTex0zTj+cDX7A8xzuvdzt524t3u+4VuGo3BhPQJorGh9SFufokgYPAkOtdz5tlQ5vMScvWY+nAjO1pIDdB/sVAkyVth6PMvwPB632f17mNp+vtmpJQ9dozZVtuRAvuYrRHGI2ksL6vC7x2TastBfBDl++/23HsWwnye20a3yYbHG1n3e5LI/+A2hT4N31MNNK3oWljGfuP8CIiutFIZgmcSZLmbXqa2utVpp7nF/qzMCYVr9PL+IIlTYn+zMuvd9t62/XZgNst3+tKl/NvtZy/5XrX90bfgI0pzlvHwHGOc1/Sj3fjILP083fr5yPok/1/tQ34f3ob/BJtQTuhjOu1p2ub5KL55JbAH9GJ1DTzeu0Zf8+b9HuuAPwu57Ms7f0brz1TtqfP5Vg16RGqA2pPXWaSaKRsscv5H2HKuR7v421XZXtRBqUSQviAv6GtLF8GXialzEMbkN6CNhFZiKZZ5SE9+NEiGX8YmCylzJFS/v/27j1Kkro64Pj37gIry/LUICoCGgFXREjwwUHNQUlQQIzhoZID0agYjHp8IUQlgBrzEFRilFUQHxgCEUnggBBFY4yQSALIQ5GHxoVFIAis7iKvwN788at2amq6e3qma2Z2er6fc+p0169+VVNze3em5v7qd2sZZWTojKrPfpQfXONExObARZS7XG8EnpeZm1KmUr2NkgjYF/jETH8T66uqZt4ZlCkO/zlJX+PZR0S8EPga5Q6+bwIvoiSXtqT8DHgu5QEwv2zsZ1y7ez1j06K+Srmg2TgzlwLPBC6otr0rIsYVmTOmfa2mTAc/iZL8uKt/92LYmEbE0yh3Gi8FLgd2zszNKXcJfKjq9seUOzBHxc3V63YRsWGPPr/Z6Dvrx6ymzR0OfCYzr6ya96pev9bpl5n3Ue7EeE5ELBvwfDVaXle9fjszu10znEO5qQDgj2bnlOavzPz2JF3OqL1/bmNbJ77nZOZPmejvKDcgLKYMsiwo1e+cjwD3Au8aYBfjOdERwNMpg9lH5dRqaBvPiZ5UvV6bmY82N2Z50OQ11Wrzd6zxbMjMx4bYfdrxrOrKH1ytrsjMX3TZ/6+q182AVw1xnlJPI5lQpSQAdq3eH5yZ3wTIzHWZ+Y+MJQf2i4h95uD85qOXZuYLMnNFZv76wSCZuTIz38RYIvXwiGjWFjka2IZyIbB/5w/FzHwkMz8NnFD1e3NE7MTC9HbK9J2zKLVi+jGePUTEUuBMyvSn84CXZeblnYvPzHwwM6/KzOO6/OI2rt11LnZ+DByWmbd0NmTmTcChlKn/AK9u7GtMu/tuZm6Vmb+bmcdk5jmUkhSDGDamH6IkX+8CXpGZN1f735+ZJwCnVf0+EBGDPqBpfXc1ZdrYEuD5Pfp0njDQd0Brpo5ZJWVPBf4X+EBt0+bV65rGLmsa27VAVL/nXlitXtKtT2Ym8C/V6r6zcV4j7qHa+8WdN1Vd0O2q1V6fxf2UciywMD+L0ym/c96dmT/v19F49tS5DrsgM+8ZdCfj2VPnmnW3iJhQM736fbx7tXplrd14tqiFeL6IsZq3vfZfSSkl0G1/qRWjmlB15L5ljt7PHEfvW+UofvscyW+Zo/mzq7og/3q1+ubm9ojYEXhptfrVOTrm0cBy4OjG53Jv9bpjo/9OwDrKnc5aWJYzdv3+gz79Otu2iYitZvaURt7etffX194/u/Z+kM/iWW2d0HwQEUcC+wDfzMwzB9jFeDZExBLG/q76TkQ8PSLOiIjbI+LhiLgrIi6IiP267G48u1tRvT4DODsintHZUCX5vkL5W+InjJ/tYzzbNWw86/v/cID9dxnwvKQpGbmEqiP3c8bR++lz9L49juK3z5H89YSj+UPp1Ok6IiLeXD3ll4h4EuWpyYuA8zPz2vpOEXFZRKyMiHe2dcym6mntxwH/lpl/39jcGcz8syohTkQcTPk/d0V2eeK4Rt6Ta+9/1qdffduTe/ZSXxGxBaWOH5TZBTfVNk/1s9hsoZTpiIinUEraPMj4p6n3Yzwn2oHygB6AbSkPo3sD8BuUupNPBF4JXBwRKxr7Gs8uMvNCyg0sjwCHALdExAMR8QCllNLelKTr8zOzPjvEeLZr2Hh29l89ybVQZ39/D2pGjFxCFUfu58retfeO3g/I0fv2OIo/YxzJX384mj9NmfnfwLur1c8Ct0bE1ZTZKnsANwFHdtl1W8pDLrdo8ZhNf0epU/6nXbZ9g5JU3QtYFRE/pPyfewz48wGOrdGzae19vz8i69s27dlLPVX17b9MmanxMKU8U52fRW+fpZQkObFeKmwSxnOieumd91HqpB8GLKueDbAdZeYlwFER8Y5af+PZQ2aeQnno491V08aMDTgvocShWVLHeLZr2Hhu2mV7v/39LDQjRjGh6sj9LHP0fnocvW/dDjiK3zpH8tcrjuYPofoD6vcod+duQkk43wr8JfDcqdzV3tYxI+JA4EDgY5n5o+b2akbN71P+jz1KGdj4PqX+7bemer6SpuRvgVdU7/90srvNVUTE4cABlHJAH5/bs5n3FjXeH5WZ51TllsjMVZTyPt+v+hzXbTaRxkTE0oj4R8oDPm+jzMZ5AuXvhX0pA86HA/8VEc+ZsxOVNC+M4g9cR49m0QyO3t/fygmu3zqj98fOwuj9QohncxR/DWUU/7zM/L/qYWkfBV5LGcW/MTP/tupvXPvIzFMi4hbg88DWjI3iw/iR/Ptq7ca0fcPGdMGP5ldJyIETkZm5Q9vHbOx7IRCT9FlLuXu12x2sWnjW1t4v7dOvvm1tz17qKiJOBt5Wrb4rMz/fpVvzs2g+PK6+rds+IycitgZOodxFf2S32ut9GM+J6t/fqurhyuNk5rqI+Bjw95TE4B7AFRjPXk6iPET1ZuB3MvPB2rZLI+IyymDATsCnGXvApPFs17DxXNtle7/9/Sw0I0bxDlXNLkfvp8HR+xnhKP4McCRfktYrd9TeP6VPv/q2O3r20gQR8VHgPdXqe6s70buZ6mexpqpzPcr+Bng8cBpwY0Qsqy+MzSSi1t5pM54T1Wej3NinX32Gw/bVq/FsiIhNGXuQ5KcayVQAqrZPVasvqgYJwHi2bdh4dvbfsnqGzmT7+3tQM2IUE6qO3M+SaY7e97JgPo+WR+97WTDxrBloFB/4WLXaGcVv7mtcx2uO5F+amfdm5j2ZeSnwO9W2J1BG8juMafuGjamj+dL89yNgXfX+2X36dbbdlZn39emnmog4CXhvtXpMZp7cp3u9lvUgn8UNw5zbPPG06vUtlN8hzeV9tb6dto9W68azofq/20mqZp+u9ZkOnX7Gc6KdGJuh+5M+/W6pve/8mzae7Ro2nvX9+9X87+zf79kB0rSNYkLVkftZ4Oj9UBy9nxmO4rfMkfz1jqP50gJX1T++vFp9ebc+ERHAy6rVb8zGeY2C6kaBo6vVYzLzpH79q2cG3Fat9vosNmFsyrCfRR/Gs6fO97m8+r/dzfLa+5+C8exhXe399j17lecudKwF49m2FuJ5GeU5JP32356x/xt+HpoRo5hQdeR+hjl6PzRH72eAo/gzwpH89Yuj+ZIAvlS9viQiXtBl+6HA06v3Z87OKc1vVTK1c6PA0ZMlU2s68X1tROzQZftbgWWUWUlnDXWS80Bm7p2Z0WsBPljr22l/Z+0QxnOiL1SvTwVe09xYPc/i3dXqz4Cra5uN53g3MpaEe1O30l8RsZixmwlWA/WHLRvPdk07npn5K+C8avUtEbF5l/2PrV7XAucPe7JSNyOXUHXkfmY5ej+3jOekHMVvlyP56xFH8yVVvgRcTxkgPC8i9oGSWImIQ4HTq36XVA9NUx8R8TeMJVPfnZkf69e/4WTgLkqplK9FxB7VMTeKiLcAH676nZaZN7d1ziPMeDZk5neBr1arKyLiNRGxIUD1wNWzgN+qtn+gKm/VYTxrqllVn6tWfxu4MCJ2rX52LqqeBXAxsFfV55TMfKx2COPZRURsGRFP6CyM5ZiW1turmZh1w8bzeOBXlIdjXxgRO1b7bxIRxwNHVf3+IjNXt/X9SuNk5sgtwBspd56tA17QZfurq+0J7DPX5ztfFsoPvU7c3jOF/T5c7fMrYIcu24+ptj8K7DTX3+dcL8CJnTgbzynH7sW1f6Ov7bJ9EWXkPoHbgUXGtW88N6Y88T2Bq4ANuvRZTBnESuA+YLExnVasV1axOHGSfkPFFPhytf0OYPMu20+ttq8BtpzruLi4uHRfgB0og4JZ+5nwYG39av8PDxTH7Woxe4zyx32/5egux9gDuKd2nDXAI7X1rwNL5vp7XR+Wya5xjWfPmGwCfKcWg4eqa66sLR80ngPFcmPgkkbsHqqWets/1K9pjWffmK5sxK7X8sW24wnsX/3+6/T/BeUauLP+BSDmOkYuo7vM+QnMyDdVpqhex1jSZJ+qfRFlGtQvq20Xz/W5zpeFUvez84PpXVPcd3PgzmrfHwJ7VO0bUaa9P1xtO3Wuv8/1YZnsYtN4Thq/c6vvfzVlatSGVftTgbNr/45fZ1wHiucnazG7BNi1+lm6CHhOdaHT2X68MR04rltSHuTVWW6rYvHRRvuyNmNKKclwf9Xn34Edq/ZNKCP966ptx8x1jFxcXPovwKaUKdTXV/+v1wBXUu623Giuz28+LJTE9CCJgM5yYo/jPBH4OOUhjQ9W1yDfBd5EbfB2oS8MkFA1nj1jsqj6/r8D3EtJOt1Oubbdy3hOKZYBHEKZBr6qunZ6qLoW+ypwgPGcUjxXDvjz84szEU/gNynPJvlp9VneQ5lldfBcx8Zl9JfITEZRVYfj25QLJSh3WS0CHletf5+SaF096yc3z0TEdsCt1eo64OeT7HJyNuqqVrfwf53yMCYo04IfB2xYrX8DeGVmPtzKSc9jEXEicAJAlnpT3foYzx6qKc8XU54+D+UX6wOUBFbHhzLzhC77GteGiNgY+CfGTxHvfP9Lam1nA0fk+KlRxrSHiFhJ/zIKHV/KzNc39h0qphGxP2XgofNgql9SalQtrta/CLwhR/UCQZIkSZKGNHI1VDsycyXl7qkPUR7EkcD/UaatHg3saTJ1YIsa7584ydKsj0JmXkV5CMonKA+w2ZBye/5lwJHAfgstoTIM49lbliLlL6HE4d8pcVlGKdR/DvDCbsnUal/j2pCl3tT+lLv7L6DcDdFJ9K+iFIR/RWb+YTOZWu1vTFs2bEwz82LK78fTKXcVbEyZInUpcEhm/rHJVEmSJEnqbWTvUJUkSZIkSZKkto3sHaqSJEmSJEmS1DYTqpIkSZIkSZI0IBOqkiRJkiRJkjQgE6qSJEmSJEmSNCATqpIkSZIkSZI0IBOqkiRJkiRJkjSgDeb6BCRJkiRJmgsRsROwC/BUYBmQwFrgNuD6zPzpHJ6eJGk9FZk51+cgSZIkSdKsiIjdgKOAg4CtJ+m+CjgXWJGZP57pc5MkzQ8mVCVJkiRJIy8ingKcAhwyjd0fAz4PHJuZq9s8L0nS/GNCVZIkSZI00iJiX+BsYKsum9cBPwfuBBYDTwIeD0SXvrcBB2XmVTN0qpKkecCEqqSBWWNKkiRJ801EHAScA2zY2HQ18BngnzPznsY+T6aUBHgr8MzGfmuBl2fmf8zMGUuS1ncmVCX1ZY0pSZIkzVcRsStwBbBxrfkR4L3ApzJz3ST7bwS8DzgeWFTbdDewe2be2e4ZS5LmAxOqkrqyxpQkSZLms4hYClwJLK81PwIcnJkXTfFYbwROZ3wZgG8B+06WlJUkjZ5Fk3eRtNBUNaauo3sydR3wv8A1wPXAPZSp/3WLgSOBayJij5k7U0mSJKmn4xifTAV4+1STqQCZeQbwkUbzPsAbpnlukqR5zDtUJY1jjSlJkiTNdxGxjFKOaota83eAl+Q0/wiOiCXAtcDOteabgOWDHDMiNgF2o1wvbwksBR6iXC+vAn4M3OIdr5K0/jOhKunXrDElSZKkURAR76CUr6p74bCD/BFxGPAPjeZXZeYFffZ5HvBnwAHAkkm+xBrgMuC8zPz8MOcqSZo5JlQlAdaYkiRJ0uiIiMuBvWpNN2TmLi0cdwlwB7BVrfmszDy8R/+PUG44iG7b+3gsMzeY3llKkmaaNVQldVhjSpIkSfNeRGwMPLfRfG4bx87Mh4ELG80v7nEe7wfez8Rk6oPAjygzw66mTPV/pI3zkyTNDu9QlWSNKUmSJI2MiHgJ8K+N5gMy8+KWjv9W4FON5h0y89Zan22AlYyf4n8R8NfA9zLzscYxN6Bc9+4L/AGwZ2Y2n2kgSVpPOIVAEsAbGZ9MBXj/dJOpUEbvI+KDjK8xtTPwSqC1GlMRYY0pSZIk1TUfkgpwVYvH73as5cCttfVXMf5a9uzM/MNeB8zMR4EfVMvHI2Lo8gSSpJnjlH9JAK9urN8wbMH+yj8B9zXaDu3VuaoxdQVwEJMnUwE2A/YHTpvuCUqSJGnkPL6xvo7ykNS23NWlbavG+s6N9U9O5Qtk5g+ndEaSpFllQlVa4KwxJUmSpBHTTG6uGWbmVRe/6NLWTOIubZ5Di19fkjTHTKhK2hPYqNH2Xy0e/78b69tFxPb1hqrG1PGNfhcBLwI2zcxnZeaemblHZu4IbALsCrwHuAywGLQkSZI6Nm2s39/y8bsdb7PG+s8a6z2n+0uS5h8TqpLmqsZU3auYWGPqwMy8vFmwH0qNqcz8QWZ+PDNfDOze2tlKkiRpvlvbWF/W8vG7Ha95B+rXG+sfiIjPRMROLZ+LJGkOmFCVZI0pSZIkjZJmDf/NIqJZVmoYW3Rpu7e+kplXUGZc1f0JcFNEXB8Rp0TEoRHxlBbPS5I0S0yoSrLGlCRJkkbJvY31RcDWLR5/my5tzSQuwOHAv3ZpfzbwDuArwO0R8ZOI+FxEvDwiNmjxPCVJM8SEqiRrTEmSJGmU3NilbY8Wj9/tWDc0GzLzl8DvAa8Dru1zvKcDbwQuAW6OiCPaOElJ0swxoSrJGlOSJEkaJd8DHmm0Pb/F4z+vsX5bZt7WrWNmrsvMMzNzd8pzBN4GnAOs6nHspwFnRsQXWi5TIElqkQlVSdaYkiRJ0sjIzAeBKxvNh7Zx7IhYAhzYaP7ugOd1Y2Z+OjMPy8ztgO2APwLOBR5udH898PYhT1eSNENMqEqyxpQkSZJGzbmN9WdFxF4tHPcgJj6D4CvTOVBmrsrML2fmqyl3pn6r0eXY6RxXkjTzTKhKssaUJEmSRs3nmPhw1I8MMxOrujv1hEbzTcCF0z1mR2beSbmL9oFa85Mj4hnDHluS1D4TqpKsMSVJkqSRkpn3AysazXsDRw5x2OOAnRttJ2dmDnHMX8vM1cD1jebfaOPYkqR2RUs/+yXNYxFxOVCfAnVDZu7SwnGXAHcwflrUWZl5+DSO9VTKRfCBwCuBJY0u78jMT07zVCVJkjRiImIppZbq8lrzI8BBmfm1KR7rDZS7XuuD+N8C9s3MdcOea+3r/IQyI6vjmZl5U1vHlyS1wztUJYE1piRJkjRiMvMB4DXAQ7XmjYDzIuLtETHp38MRsVFE/DlwOuOTqT8HjuiVTI2IFRFxcEQsHvR8I+K1jE+m3gf8z6D7S5Jmj3eoSiIillGm1W9Ra/434KXTncJU3Z16LeOnRd0ELG9jWlREbAncDiytNe+YmT8e9tiSJEkaHRFxMKWMVPOBplcDpwLnZ+a9jX2eRLk54G3AMxv7rQX2y8zL+3zNa4DdgDuB84GLgCsz8+4ufZ8BHAW8E6gnYE/KzGP6f3eSpLlgQlUSABHxl8D7Gs1/kpmnTfN4H6bUmao7MjM/N53j9fga3wNeUGvaKzP/s63jS5IkaTRExMuAs4Etu2xeB9xNSX4uBrah1C7tVqN/FfAHmXnVJF/vGkpCtelu4B5gDaWE1bZ0r5N6HbBnZj7Y7+tIkuaGCVVJgDWmJEmSNNoiYlvgFODgaez+GPAF4NjMvG+Ar3UN3ROqg/gX4LDM/MU095ckzTBrqEoCrDElSZKk0ZaZt2fmIcDuwGcp16iTuR34BKVs1ZGDJFMrr6BM4z+fclfqZB4FLgEOzMz9TKZK0vrNO1QljWONKUmSJC0EERGUev+7UKbeLwOScv26CrguM1sZsI+IHYCdgO0pzy14HPAAsJrynIHrMnNtG19LkjTzTKhKmsAaU5IkSZIkSd2ZUJXUlTWmJEmSJEmSJjKhKqmviNgNeAtlSn+3u0PrbgfOBVZk5i1T+BrbAgcALwf2AraeZJdHgUuBUzPzokG/jiRJkiRJ0rBMqEoaiDWmJEmSJEmSTKhKkiRJkiRJ0sAWzfUJSJIkSZIkSdJ8YUJVkiRJkiRJkgZkQlWSJEmSJEmSBmRCVZIkSZIkSZIGZEJVkiRJkiRJkgZkQlWSJEmSJEmSBmRCVZIkSZIkSZIGZEJVkiRJkiRJkgZkQlWSJEmSJEmSBmRCVZIkSZIkSZIGZEJVkiRJkiRJkgZkQlWSJEmSJEmSBmRCVZIkSZIkSZIGZEJVkiRJkiRJkgZkQlWSJEmSJEmSBmRCVZIkSZIkSZIGZEJVkiRJkiRJkgb0/1hUja0mmh5IAAAAAElFTkSuQmCC\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# set overall style\\n\",\n \"sns.set_context(\\\"notebook\\\", font_scale=1.5, rc={\\\"lines.linewidth\\\": 2.5, \\\"axes.labelsize\\\": \\\"large\\\"})\\n\",\n \"\\n\",\n \"a, b, c = target\\n\",\n \"tmp = pd.DataFrame([{k: v for k, v in s.items()} for s in pred_results.composition], index=pred_results.index)\\n\",\n \"\\n\",\n \"fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(14, 10), dpi=100)\\n\",\n \"\\n\",\n \"# all class\\n\",\n \"plot_tri(\\n\",\n \" tmp[a].values * n,\\n\",\n \" tmp[b].values * n,\\n\",\n \" tmp[c].values * n,\\n\",\n \" pred_results.pred_label.apply(trans).values,\\n\",\n \" a, b, c,\\n\",\n \" cbar_kw=dict(\\n\",\n \" format=fmt,\\n\",\n \" ticks=np.arange(-1, 2)\\n\",\n \" ),\\n\",\n \" cmap=plt.get_cmap(cmap, 3),\\n\",\n \" norm=norm,\\n\",\n \" half_size=False,\\n\",\n \" reduce_ticks=True,\\n\",\n \" ax=ax1\\n\",\n \")\\n\",\n \"\\n\",\n \"\\n\",\n \"# QC\\n\",\n \"plot_tri(\\n\",\n \" tmp[a].values * n,\\n\",\n \" tmp[b].values * n,\\n\",\n \" tmp[c].values * n,\\n\",\n \" pred_results.QC,\\n\",\n \" a, b, c,\\n\",\n \" cbarlabel='QC',\\n\",\n \" cmap=cmap,\\n\",\n \" percentage=True,\\n\",\n \" half_size=False,\\n\",\n \" reduce_ticks=True,\\n\",\n \" ax=ax2\\n\",\n \")\\n\",\n \"\\n\",\n \"# AC\\n\",\n \"plot_tri(\\n\",\n \" tmp[a].values * n,\\n\",\n \" tmp[b].values * n,\\n\",\n \" tmp[c].values * n,\\n\",\n \" pred_results.AC,\\n\",\n \" a, b, c,\\n\",\n \" cbarlabel='AC',\\n\",\n \" cmap=cmap,\\n\",\n \" percentage=True,\\n\",\n \" half_size=False,\\n\",\n \" reduce_ticks=True,\\n\",\n \" ax=ax3\\n\",\n \")\\n\",\n \"\\n\",\n \"# OTHERS\\n\",\n \"plot_tri(\\n\",\n \" tmp[a].values * n,\\n\",\n \" tmp[b].values * n,\\n\",\n \" tmp[c].values * n,\\n\",\n \" pred_results.OTHERS,\\n\",\n \" a, b, c,\\n\",\n \" cbarlabel='OTHERS',\\n\",\n \" cmap=cmap,\\n\",\n \" percentage=True,\\n\",\n \" half_size=False,\\n\",\n \" reduce_ticks=True,\\n\",\n \" ax=ax4\\n\",\n \")\\n\",\n \"\\n\",\n \"# save figure\\n\",\n \"Path('phase_diagram/').mkdir(exist_ok=True, parents=True)\\n\",\n \"plt.savefig(f\\\"phase_diagram/{'_'.join(target)}.png\\\", bbox_inches='tight', pad_inches=0, dpi=300)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Retrain a model with all data\\n\",\n \"\\n\",\n \"Finally, we retrain the model using the optimized hyperparameters with all data and save it.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
RandomForestClassifier(bootstrap=False, criterion='entropy', max_depth=25,\\n\",\n       \"                       max_features='log2', n_estimators=200, random_state=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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\"\n ],\n \"text/plain\": [\n \"RandomForestClassifier(bootstrap=False, criterion='entropy', max_depth=25,\\n\",\n \" max_features='log2', n_estimators=200, random_state=0)\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# prepare the random forest class\\n\",\n \"rfc = RandomForestClassifier(\\n\",\n \" random_state=0,\\n\",\n \" max_depth=25,\\n\",\n \" n_estimators=200,\\n\",\n \" max_features='log2',\\n\",\n \" bootstrap=False,\\n\",\n \" criterion=\\\"entropy\\\",\\n\",\n \")\\n\",\n \"\\n\",\n \"rfc.fit(desc, training_data.label)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['pre_trained_model/classification_model_with_all_data_training.pkl.z']\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"joblib.dump(rfc, 'pre_trained_model/classification_model_with_all_data_training.pkl.z')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3.9.6 ('avalon')\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.6\"\n },\n \"vscode\": {\n \"interpreter\": {\n \"hash\": \"3e3c6009f759c7b6322bb34d4b09bf3ae54292a31a37602d74e0556d4d94cebc\"\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "samples/predict_hypermaterials/tools.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Tools\\n\",\n \"\\n\",\n \"This file provides the helper functions to simplify the demonstration.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"from matplotlib.ticker import FormatStrFormatter, PercentFormatter\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def transform_xy(x, y, z=None):\\n\",\n \" if z is None:\\n\",\n \" z = 100 - x - y\\n\",\n \" h = np.sqrt(3) * 0.5\\n\",\n \" x_ = (z / 100) + (x / 100) / 2\\n\",\n \" y_ = (x / 100) * h\\n\",\n \" \\n\",\n \" return x_, y_\\n\",\n \"\\n\",\n \"def transform_xy_halfsize(x, y, z=None):\\n\",\n \" if z is None:\\n\",\n \" z = 100 - x - y\\n\",\n \" h = np.sqrt(3) * 0.5\\n\",\n \" x_ = (z / 100) * 2 + (x / 100) * 2 / 2 - 0.5\\n\",\n \" y_ = (x / 100) * 2 * h - h\\n\",\n \" \\n\",\n \" xy = np.stack([x_, y_])\\n\",\n \" idx = (xy >= 0).all(axis=0)\\n\",\n \" xy = xy[..., idx]\\n\",\n \" \\n\",\n \" return xy[0], xy[1], idx\\n\",\n \"\\n\",\n \"\\n\",\n \"def plot_tri(x,y,z,d, label_x,label_y,label_z, *additional_points, show_cbar=True, cbarlabel=None, percentage=False, reduce_ticks=False, ax=None, half_size=False, cbar_kw={}, **kwargs):\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" if not ax:\\n\",\n \" fig, ax = plt.subplots(dpi=150)\\n\",\n \" else:\\n\",\n \" fig = ax.get_figure()\\n\",\n \" \\n\",\n \" l = len(d)\\n\",\n \" ax.set_aspect('equal', 'datalim')\\n\",\n \" ax.tick_params(labelbottom=False, labelleft=False, labelright=False, labeltop=False)\\n\",\n \" ax.tick_params(bottom=False, left=False, right=False, top=False)\\n\",\n \" ax.spines['bottom'].set_visible(False)\\n\",\n \" ax.spines['left'].set_visible(False)\\n\",\n \" ax.spines['right'].set_visible(False)\\n\",\n \" ax.spines['top'].set_visible(False)\\n\",\n \" h = np.sqrt(3.0)*0.5\\n\",\n \"\\n\",\n \" # outer-triangle\\n\",\n \" ax.plot([0.0, 1.0],[0.0, 0.0], 'k-', lw=2)\\n\",\n \" ax.plot([0.0, 0.5],[0.0, h], 'k-', lw=2)\\n\",\n \" ax.plot([1.0, 0.5],[0.0, h], 'k-', lw=2)\\n\",\n \"\\n\",\n \"\\n\",\n \" # inner axis\\n\",\n \" for i in range(1,10):\\n\",\n \" ax.plot([i/20.0, 1.0-i/20.0],[h*i/10.0, h*i/10.0], color='gray', lw=0.5,ls=\\\"-\\\")\\n\",\n \" ax.plot([1.0-i/20.0-0.025, 1.0-i/20.0],[h*i/10.0, h*i/10.0], color='black', lw=2,ls=\\\"-\\\")\\n\",\n \"\\n\",\n \" ax.plot([i/20.0, i/10.0],[h*i/10.0, 0.0], color='gray', lw=0.5)\\n\",\n \" ax.plot([i/20.0, i/20+0.05/4],[h*i/10.0, h*i/10-h/10*0.25], color='black', lw=2)\\n\",\n \"\\n\",\n \" ax.plot([0.5+i/20.0, i/10.0],[h*(1.0-i/10.0), 0.0], color='gray', lw=0.5)\\n\",\n \" ax.plot([i/10.0+0.025/2, i/10.0],[h/40, 0.0], color='black', lw=2)\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" # vertex labels\\n\",\n \" ax.text(0.75+0.25/2+0.01, h/2, label_x, fontsize='xx-large', rotation=0)#x\\n\",\n \" ax.text(0.25/2-0.13, h/2, label_y, fontsize='xx-large')#y\\n\",\n \" ax.text(0.5-0.05, -0.17, label_z, fontsize='xx-large', rotation=0)#z\\n\",\n \"\\n\",\n \" # axis labels\\n\",\n \" step = 2 if reduce_ticks else 1\\n\",\n \" interval = 5 if half_size else 10\\n\",\n \" shift = 50 if half_size else 0\\n\",\n \" for i in range(0,11, step):\\n\",\n \" r = i*interval\\n\",\n \" ax.text((10-i)/20.0-0.01, h*(10-i)/10.0, '%d' % r, fontsize='medium', rotation=0,horizontalalignment='right') #x\\n\",\n \" ax.text(0.5+(10-i)/20.0+0.01, h*(1.0-(10-i)/10.0), '%d' % (r + shift), fontsize='medium',horizontalalignment='left')#y\\n\",\n \" ax.text(i/10.0-0.03, -0.07, '%d' % r, fontsize='medium', rotation=0) #z\\n\",\n \" \\n\",\n \" if half_size:\\n\",\n \" X, Y, idx = transform_xy_halfsize(x,y,z)\\n\",\n \" d = d[idx]\\n\",\n \" else:\\n\",\n \" X, Y = transform_xy(x, y, z)\\n\",\n \"\\n\",\n \" sca = ax.scatter(X, Y, c=d, s=5, alpha=0.8, **kwargs)\\n\",\n \" \\n\",\n \" for fn in additional_points:\\n\",\n \" fn(ax)\\n\",\n \"\\n\",\n \" # show colorbar\\n\",\n \" if show_cbar:\\n\",\n \" cbar = ax.figure.colorbar(sca, ax=ax, **cbar_kw)\\n\",\n \" if percentage:\\n\",\n \" cbar.ax.yaxis.set_major_formatter(PercentFormatter(1))\\n\",\n \" if cbarlabel is not None:\\n\",\n \" cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\\\"bottom\\\", fontsize='x-large')\\n\",\n \"\\n\",\n \" fig.tight_layout()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from itertools import product\\n\",\n \"\\n\",\n \"class ConfusionMatrixDisplay:\\n\",\n \" def __init__(self, confusion_matrix, display_labels):\\n\",\n \" self.confusion_matrix = confusion_matrix\\n\",\n \" self.display_labels = display_labels\\n\",\n \"\\n\",\n \" def plot(self, *, include_values=True, cmap='viridis',\\n\",\n \" xticks_rotation='horizontal', values_format=None, ax=None, anno_fontsize=14, tick_fontsize=14, label_fontsize=20):\\n\",\n \"\\n\",\n \" if ax is None:\\n\",\n \" fig, ax = plt.subplots()\\n\",\n \" else:\\n\",\n \" fig = ax.figure\\n\",\n \"\\n\",\n \" cm = self.confusion_matrix\\n\",\n \" n_classes = cm.shape[0]\\n\",\n \" self.im_ = ax.imshow(cm, interpolation='nearest', cmap=cmap)\\n\",\n \" self.text_ = None\\n\",\n \"\\n\",\n \" cmap_min, cmap_max = self.im_.cmap(0), self.im_.cmap(256)\\n\",\n \"\\n\",\n \" if include_values:\\n\",\n \" self.text_ = np.empty_like(cm, dtype=object)\\n\",\n \" if values_format is None:\\n\",\n \" values_format = '.2g'\\n\",\n \"\\n\",\n \" # print text with appropriate color depending on background\\n\",\n \" thresh = (cm.max() - cm.min()) / 2.\\n\",\n \" for i, j in product(range(n_classes), range(n_classes)):\\n\",\n \" color = cmap_max if cm[i, j] < thresh else cmap_min\\n\",\n \" self.text_[i, j] = ax.text(j, i,\\n\",\n \" format(cm[i, j], values_format),\\n\",\n \" ha=\\\"center\\\", va=\\\"center\\\",\\n\",\n \" color=color, fontsize=anno_fontsize)\\n\",\n \"\\n\",\n \" cbar = fig.colorbar(self.im_, ax=ax)\\n\",\n \" cbar.ax.tick_params(labelsize=anno_fontsize) \\n\",\n \" ax.set(xticks=np.arange(n_classes),\\n\",\n \" yticks=np.arange(n_classes),\\n\",\n \" xticklabels=self.display_labels,\\n\",\n \" yticklabels=self.display_labels,\\n\",\n \" ylabel=\\\"True label\\\",\\n\",\n \" xlabel=\\\"Predicted label\\\")\\n\",\n \"\\n\",\n \" ax.set_ylim((n_classes - 0.5, -0.5))\\n\",\n \" plt.setp(ax.get_xticklabels(), rotation=xticks_rotation)\\n\",\n \"\\n\",\n \" ax.xaxis.label.set_size(label_fontsize)\\n\",\n \" ax.yaxis.label.set_size(label_fontsize)\\n\",\n \" ax.tick_params(labelsize=tick_fontsize)\\n\",\n \" \\n\",\n \" self.figure_ = fig\\n\",\n \" self.ax_ = ax\\n\",\n \" return self\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"def make_virtual_compounds(*elements, interval=0.5, pen_size=10, reorder_elements=True, max_proportion=100, **element_ratio):\\n\",\n \" dim = len(elements)\\n\",\n \" \\n\",\n \" # for list type\\n\",\n \" if dim != 0:\\n\",\n \" # elements and element_ratio are mutually exclusive\\n\",\n \" if len(element_ratio) != 0:\\n\",\n \" raise ValueError('elements and element_ratio are mutually exclusive')\\n\",\n \"\\n\",\n \" # sort elements in alphabetical order\\n\",\n \" if reorder_elements:\\n\",\n \" elements = tuple(sorted(tuple(set(elements))))\\n\",\n \" \\n\",\n \" # generate mesh\\n\",\n \" grid = np.arange(0, 100, interval)\\n\",\n \" mesh = np.array(np.meshgrid(*([grid] * dim))).T.reshape(-1, dim)\\n\",\n \"\\n\",\n \" # for dict type\\n\",\n \" else:\\n\",\n \" if len(element_ratio) == 0:\\n\",\n \" return None\\n\",\n \"\\n\",\n \" # sort elements in alphabetical order\\n\",\n \" dim = len(element_ratio)\\n\",\n \" \\n\",\n \" if reorder_elements:\\n\",\n \" elements, ratios = zip(*[(k, element_ratio[k]) for k in sorted(element_ratio.keys())])\\n\",\n \" else:\\n\",\n \" elements, ratios = zip(*element_ratio.items())\\n\",\n \" \\n\",\n \" # generate mesh\\n\",\n \" s = [np.arange(max(r - pen_size, 0), min(r + pen_size, 100), interval) for r in ratios]\\n\",\n \" mesh = np.array(np.meshgrid(*s)).T.reshape(-1, dim)\\n\",\n \" \\n\",\n \" # select the only ratios have sum equal to 100\\n\",\n \" mesh = mesh[mesh.sum(1) == 100]\\n\",\n \" \\n\",\n \" if max_proportion < 100:\\n\",\n \" mesh = mesh[mesh[..., 0] >= max_proportion]\\n\",\n \"\\n\",\n \" # return as pandas.DataFrame\\n\",\n \" comps = [{e:f for e, f in zip(elements, frac_compositon) } for frac_compositon in mesh]\\n\",\n \" return pd.DataFrame({'composition': comps, 'elements': [elements] * mesh.shape[0]})\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3.9.6 ('avalon')\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.6\"\n },\n \"vscode\": {\n \"interpreter\": {\n \"hash\": \"3e3c6009f759c7b6322bb34d4b09bf3ae54292a31a37602d74e0556d4d94cebc\"\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "samples/predict_hypermaterials/training_data.csv", "content": ",composition,elements,label,for_test\nmp-1077980,\"{'Sr': 1.0, 'Ag': 4.0, 'Sb': 2.0}\",\"('Ag', 'Sb', 'Sr')\",OTHERS,False\nmp-30079,\"{'Li': 1.0, 'Mg': 2.0, 'Ge': 1.0}\",\"('Ge', 'Li', 'Mg')\",OTHERS,False\nmvc-2967,\"{'Ba': 2.0, 'Tl': 1.0, 'Fe': 2.0, 'O': 7.0}\",\"('Ba', 'Fe', 'O', 'Tl')\",OTHERS,False\nmp-1202279,\"{'K': 18.0, 'Ho': 18.0, 'F': 72.0}\",\"('F', 'Ho', 'K')\",OTHERS,False\nmp-34421,\"{'Bi': 3.0, 'Cd': 1.0, 'O': 7.0}\",\"('Bi', 'Cd', 'O')\",OTHERS,False\nmp-976583,\"{'K': 6.0, 'Co': 2.0}\",\"('Co', 'K')\",OTHERS,False\nmp-626550,\"{'Ti': 6.0, 'H': 4.0, 'O': 14.0}\",\"('H', 'O', 'Ti')\",OTHERS,False\nmp-555012,\"{'La': 24.0, 'S': 16.0, 'N': 12.0, 'Cl': 4.0}\",\"('Cl', 'La', 'N', 'S')\",OTHERS,False\nmp-755743,\"{'Li': 4.0, 'Ti': 2.0, 'Cr': 2.0, 'O': 8.0}\",\"('Cr', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-766253,\"{'La': 2.0, 'Th': 11.0, 'O': 26.0}\",\"('La', 'O', 'Th')\",OTHERS,False\nmp-753335,\"{'Li': 4.0, 'V': 4.0, 'F': 14.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-1176451,\"{'Mn': 5.0, 'V': 1.0, 'O': 12.0}\",\"('Mn', 'O', 'V')\",OTHERS,False\nmp-21893,\"{'Mn': 4.0, 'Te': 8.0}\",\"('Mn', 'Te')\",OTHERS,False\nmp-568695,\"{'Ca': 3.0, 'Cd': 3.0, 'Sn': 3.0}\",\"('Ca', 'Cd', 'Sn')\",OTHERS,False\nmp-753169,\"{'Mn': 4.0, 'F': 10.0}\",\"('F', 'Mn')\",OTHERS,False\nmp-1040366,\"{'K': 1.0, 'Li': 1.0, 'Mg': 30.0, 'O': 31.0}\",\"('K', 'Li', 'Mg', 'O')\",OTHERS,False\nmp-1208310,\"{'Tb': 4.0, 'Si': 4.0, 'Pd': 8.0}\",\"('Pd', 'Si', 'Tb')\",OTHERS,False\nmp-1215696,\"{'Zn': 2.0, 'In': 1.0, 'Cu': 1.0, 'S': 4.0}\",\"('Cu', 'In', 'S', 'Zn')\",OTHERS,False\nmp-1187611,\"{'Tm': 1.0, 'Lu': 1.0, 'Ag': 2.0}\",\"('Ag', 'Lu', 'Tm')\",OTHERS,False\nmp-1194239,\"{'Pu': 4.0, 'Si': 2.0, 'O': 20.0}\",\"('O', 'Pu', 'Si')\",OTHERS,False\nmp-1097220,\"{'Sc': 2.0, 'Zn': 1.0, 'Hg': 1.0}\",\"('Hg', 'Sc', 'Zn')\",OTHERS,False\nmp-1096426,\"{'Cs': 1.0, 'Na': 2.0, 'Bi': 1.0}\",\"('Bi', 'Cs', 'Na')\",OTHERS,False\nmp-19725,\"{'Fe': 6.0, 'Ge': 6.0, 'Hf': 1.0}\",\"('Fe', 'Ge', 'Hf')\",OTHERS,False\nmvc-9863,\"{'La': 2.0, 'Ti': 2.0, 'Zn': 2.0, 'Cr': 2.0, 'O': 12.0}\",\"('Cr', 'La', 'O', 'Ti', 'Zn')\",OTHERS,False\nmp-1112161,\"{'Cs': 2.0, 'Rb': 1.0, 'Bi': 1.0, 'I': 6.0}\",\"('Bi', 'Cs', 'I', 'Rb')\",OTHERS,False\nmp-1245490,\"{'Ba': 8.0, 'V': 2.0, 'N': 8.0}\",\"('Ba', 'N', 'V')\",OTHERS,False\nmp-559407,\"{'Na': 3.0, 'Gd': 3.0, 'H': 6.0, 'S': 6.0, 'O': 27.0}\",\"('Gd', 'H', 'Na', 'O', 'S')\",OTHERS,False\nmp-1207970,\"{'Tm': 10.0, 'Bi': 2.0, 'Pt': 4.0}\",\"('Bi', 'Pt', 'Tm')\",OTHERS,False\nmp-1246472,\"{'Mg': 2.0, 'In': 1.0, 'Mo': 3.0, 'S': 8.0}\",\"('In', 'Mg', 'Mo', 'S')\",OTHERS,False\nmp-571474,\"{'Sm': 8.0, 'Sn': 6.0}\",\"('Sm', 'Sn')\",OTHERS,False\nmp-1016058,\"{'Cr': 4.0, 'N': 8.0}\",\"('Cr', 'N')\",OTHERS,False\nmp-772720,\"{'Cr': 12.0, 'Co': 8.0, 'O': 48.0}\",\"('Co', 'Cr', 'O')\",OTHERS,False\nmp-1039782,\"{'Na': 1.0, 'Mg': 30.0, 'W': 1.0, 'O': 32.0}\",\"('Mg', 'Na', 'O', 'W')\",OTHERS,False\nmp-1198768,\"{'Ca': 4.0, 'Si': 4.0, 'B': 4.0, 'O': 20.0}\",\"('B', 'Ca', 'O', 'Si')\",OTHERS,False\nmp-675857,\"{'Cd': 4.0, 'Sn': 2.0, 'O': 8.0}\",\"('Cd', 'O', 'Sn')\",OTHERS,False\nmvc-16729,\"{'Y': 2.0, 'Mn': 4.0, 'S': 8.0}\",\"('Mn', 'S', 'Y')\",OTHERS,False\nmp-5808,\"{'Rb': 1.0, 'Te': 2.0, 'Nd': 1.0}\",\"('Nd', 'Rb', 'Te')\",OTHERS,False\nmp-677726,\"{'Al': 12.0, 'Si': 12.0, 'Ag': 16.0, 'S': 5.0, 'O': 48.0}\",\"('Ag', 'Al', 'O', 'S', 'Si')\",OTHERS,False\nmp-1093949,\"{'Ba': 2.0, 'Tl': 1.0, 'Ag': 1.0}\",\"('Ag', 'Ba', 'Tl')\",OTHERS,False\nmp-21604,\"{'S': 24.0, 'In': 4.0, 'Sm': 12.0}\",\"('In', 'S', 'Sm')\",OTHERS,False\nmp-1225810,\"{'Cu': 2.0, 'Si': 1.0, 'Te': 3.0}\",\"('Cu', 'Si', 'Te')\",OTHERS,False\nmp-540651,\"{'Ba': 8.0, 'Mn': 8.0, 'Sn': 8.0, 'S': 32.0}\",\"('Ba', 'Mn', 'S', 'Sn')\",OTHERS,False\nmp-1194912,\"{'Y': 4.0, 'V': 4.0, 'Te': 8.0, 'O': 32.0}\",\"('O', 'Te', 'V', 'Y')\",OTHERS,False\nmp-1176954,\"{'Li': 6.0, 'Nb': 1.0, 'Ni': 3.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'Nb', 'Ni', 'O', 'P')\",OTHERS,False\nmp-1183146,\"{'B': 1.0, 'Au': 3.0}\",\"('Au', 'B')\",OTHERS,False\nmp-16521,\"{'Al': 3.0, 'Os': 2.0}\",\"('Al', 'Os')\",OTHERS,False\nmp-690550,\"{'Co': 14.0, 'Ru': 4.0, 'O': 24.0}\",\"('Co', 'O', 'Ru')\",OTHERS,False\nmp-9201,\"{'K': 4.0, 'Ge': 2.0, 'Au': 7.0}\",\"('Au', 'Ge', 'K')\",OTHERS,False\nmp-1201395,\"{'Tb': 16.0, 'Te': 12.0, 'N': 8.0}\",\"('N', 'Tb', 'Te')\",OTHERS,False\nmp-645338,\"{'Sm': 16.0, 'B': 48.0, 'O': 96.0}\",\"('B', 'O', 'Sm')\",OTHERS,False\nmp-1205720,\"{'Ho': 2.0, 'Mn': 4.0, 'Si': 2.0, 'C': 2.0}\",\"('C', 'Ho', 'Mn', 'Si')\",OTHERS,False\nmp-29507,\"{'O': 26.0, 'V': 6.0, 'Cu': 11.0}\",\"('Cu', 'O', 'V')\",OTHERS,False\nmp-1199783,\"{'Re': 12.0, 'H': 12.0, 'C': 48.0, 'O': 48.0}\",\"('C', 'H', 'O', 'Re')\",OTHERS,False\nmp-1176347,\"{'Na': 10.0, 'Fe': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Fe', 'Na', 'O', 'P')\",OTHERS,False\nmp-1183114,\"{'Ac': 2.0, 'Cu': 6.0}\",\"('Ac', 'Cu')\",OTHERS,False\nmp-531201,\"{'Cd': 10.0, 'Ho': 20.0, 'Se': 40.0}\",\"('Cd', 'Ho', 'Se')\",OTHERS,False\nmp-766427,\"{'K': 4.0, 'Na': 2.0, 'Zn': 4.0, 'H': 10.0, 'C': 8.0, 'O': 28.0}\",\"('C', 'H', 'K', 'Na', 'O', 'Zn')\",OTHERS,False\nmp-1093703,\"{'Zr': 2.0, 'Zn': 1.0, 'Cd': 1.0}\",\"('Cd', 'Zn', 'Zr')\",OTHERS,False\nmp-1216881,\"{'U': 1.0, 'Al': 9.0, 'Fe': 3.0}\",\"('Al', 'Fe', 'U')\",OTHERS,False\nmp-865403,\"{'Tm': 4.0, 'Mg': 2.0, 'Ge': 4.0}\",\"('Ge', 'Mg', 'Tm')\",OTHERS,False\nmp-28601,\"{'Br': 40.0, 'Nb': 8.0}\",\"('Br', 'Nb')\",OTHERS,False\nmp-1209795,\"{'Pr': 4.0, 'Al': 18.0, 'Ir': 6.0}\",\"('Al', 'Ir', 'Pr')\",OTHERS,False\nmp-571165,\"{'Dy': 8.0, 'Fe': 56.0, 'C': 4.0}\",\"('C', 'Dy', 'Fe')\",OTHERS,False\nmp-8689,\"{'V': 4.0, 'Ge': 1.0, 'Se': 8.0}\",\"('Ge', 'Se', 'V')\",OTHERS,False\nmp-772938,\"{'Li': 4.0, 'Ta': 8.0, 'O': 22.0}\",\"('Li', 'O', 'Ta')\",OTHERS,False\nmp-862621,\"{'Ga': 2.0, 'Pt': 6.0}\",\"('Ga', 'Pt')\",OTHERS,False\nmp-754167,\"{'Mn': 5.0, 'O': 1.0, 'F': 11.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-1217987,\"{'Ta': 3.0, 'Co': 2.0, 'Mo': 1.0}\",\"('Co', 'Mo', 'Ta')\",OTHERS,False\nmp-505750,\"{'Rb': 8.0, 'Sn': 16.0, 'Se': 32.0}\",\"('Rb', 'Se', 'Sn')\",OTHERS,False\nmp-1020647,\"{'Sr': 8.0, 'Li': 4.0, 'Be': 4.0, 'B': 12.0, 'O': 32.0}\",\"('B', 'Be', 'Li', 'O', 'Sr')\",OTHERS,False\nmp-1238141,\"{'V': 4.0, 'Ni': 2.0, 'H': 16.0, 'O': 20.0}\",\"('H', 'Ni', 'O', 'V')\",OTHERS,False\nmp-1203000,\"{'Nd': 4.0, 'H': 44.0, 'S': 12.0, 'O': 64.0}\",\"('H', 'Nd', 'O', 'S')\",OTHERS,False\nmp-867847,\"{'La': 2.0, 'Zn': 1.0, 'Ru': 1.0}\",\"('La', 'Ru', 'Zn')\",OTHERS,False\nmp-1196071,\"{'Ba': 28.0, 'Fe': 28.0, 'O': 70.0}\",\"('Ba', 'Fe', 'O')\",OTHERS,False\nmp-30747,\"{'Zn': 22.0, 'Ir': 4.0}\",\"('Ir', 'Zn')\",OTHERS,False\nmp-1190441,\"{'Li': 4.0, 'Sb': 4.0, 'F': 16.0}\",\"('F', 'Li', 'Sb')\",OTHERS,False\nmp-1275,\"{'Si': 2.0, 'Mo': 6.0}\",\"('Mo', 'Si')\",OTHERS,False\nmp-676556,\"{'Sm': 2.0, 'Zr': 8.0, 'O': 19.0}\",\"('O', 'Sm', 'Zr')\",OTHERS,False\nmp-20441,\"{'Mn': 2.0, 'Co': 2.0, 'C': 1.0}\",\"('C', 'Co', 'Mn')\",OTHERS,False\nmp-1187045,\"{'Sn': 3.0, 'Mo': 1.0}\",\"('Mo', 'Sn')\",OTHERS,False\nmp-22401,\"{'Si': 20.0, 'Nd': 8.0, 'Re': 12.0}\",\"('Nd', 'Re', 'Si')\",OTHERS,False\nmp-559537,\"{'Ca': 8.0, 'P': 12.0, 'O': 38.0}\",\"('Ca', 'O', 'P')\",OTHERS,False\nmp-1228000,\"{'Ca': 2.0, 'Mn': 7.0, 'Si': 10.0, 'H': 12.0, 'O': 35.0}\",\"('Ca', 'H', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1207109,\"{'Sm': 1.0, 'Hg': 2.0}\",\"('Hg', 'Sm')\",OTHERS,False\nmp-13498,\"{'Mg': 1.0, 'Tb': 2.0}\",\"('Mg', 'Tb')\",OTHERS,False\nmp-768358,\"{'Sc': 2.0, 'In': 2.0, 'O': 6.0}\",\"('In', 'O', 'Sc')\",OTHERS,False\nmp-753061,\"{'Ti': 1.0, 'Co': 3.0, 'O': 8.0}\",\"('Co', 'O', 'Ti')\",OTHERS,False\nmp-1184981,\"{'Li': 2.0, 'Nd': 1.0, 'Pb': 1.0}\",\"('Li', 'Nd', 'Pb')\",OTHERS,False\nmvc-11754,\"{'V': 4.0, 'S': 10.0}\",\"('S', 'V')\",OTHERS,False\nmp-1096757,\"{'Nb': 1.0, 'Cr': 2.0, 'Re': 1.0}\",\"('Cr', 'Nb', 'Re')\",OTHERS,False\nmp-772337,\"{'Cr': 12.0, 'Cu': 8.0, 'O': 48.0}\",\"('Cr', 'Cu', 'O')\",OTHERS,False\nmp-1218557,\"{'Sr': 6.0, 'Co': 2.0, 'Sb': 4.0, 'O': 18.0}\",\"('Co', 'O', 'Sb', 'Sr')\",OTHERS,False\nmp-1247120,\"{'Cd': 22.0, 'C': 4.0, 'N': 20.0}\",\"('C', 'Cd', 'N')\",OTHERS,False\nmp-868632,\"{'Mn': 2.0, 'O': 2.0, 'F': 2.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-1187278,\"{'Tb': 3.0, 'Dy': 1.0}\",\"('Dy', 'Tb')\",OTHERS,False\nmp-772269,\"{'Mn': 6.0, 'P': 6.0, 'O': 24.0}\",\"('Mn', 'O', 'P')\",OTHERS,False\nmp-1073410,\"{'Mg': 4.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-19858,{'Sr': 6.0},\"('Sr',)\",OTHERS,False\nmp-1211266,\"{'Li': 16.0, 'Mo': 12.0, 'O': 48.0}\",\"('Li', 'Mo', 'O')\",OTHERS,False\nmp-554598,\"{'B': 4.0, 'H': 20.0, 'C': 4.0, 'N': 2.0, 'Cl': 2.0, 'O': 6.0}\",\"('B', 'C', 'Cl', 'H', 'N', 'O')\",OTHERS,False\nmp-1095988,\"{'Sr': 2.0, 'Ag': 1.0, 'Hg': 1.0}\",\"('Ag', 'Hg', 'Sr')\",OTHERS,False\nmp-1064647,\"{'K': 2.0, 'N': 2.0}\",\"('K', 'N')\",OTHERS,False\nmp-1184199,\"{'Eu': 2.0, 'Ag': 1.0, 'Pb': 1.0}\",\"('Ag', 'Eu', 'Pb')\",OTHERS,False\nmp-1213927,\"{'Ce': 12.0, 'P': 4.0, 'Br': 12.0}\",\"('Br', 'Ce', 'P')\",OTHERS,False\nmp-1174745,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1210027,\"{'Rb': 36.0, 'Co': 8.0, 'S': 28.0}\",\"('Co', 'Rb', 'S')\",OTHERS,False\nmp-757597,\"{'Li': 4.0, 'Sn': 4.0, 'P': 16.0, 'O': 48.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,False\nmp-1197135,\"{'La': 2.0, 'N': 6.0, 'O': 26.0}\",\"('La', 'N', 'O')\",OTHERS,False\nmp-759865,\"{'Li': 3.0, 'V': 3.0, 'Cr': 3.0, 'P': 6.0, 'H': 6.0, 'O': 30.0}\",\"('Cr', 'H', 'Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1228512,\"{'Ba': 3.0, 'Fe': 4.0, 'P': 8.0, 'Pb': 1.0, 'O': 28.0}\",\"('Ba', 'Fe', 'O', 'P', 'Pb')\",OTHERS,False\nmp-1210921,\"{'Li': 2.0, 'Zn': 8.0, 'Sn': 2.0, 'O': 16.0}\",\"('Li', 'O', 'Sn', 'Zn')\",OTHERS,False\nmp-1232186,\"{'Ce': 8.0, 'Mg': 4.0, 'S': 16.0}\",\"('Ce', 'Mg', 'S')\",OTHERS,False\nmp-1202615,\"{'Ho': 40.0, 'Ir': 8.0, 'Br': 72.0}\",\"('Br', 'Ho', 'Ir')\",OTHERS,False\nmp-12797,\"{'Be': 4.0, 'O': 14.0, 'Si': 4.0, 'Ba': 2.0}\",\"('Ba', 'Be', 'O', 'Si')\",OTHERS,False\nmp-1220054,\"{'Ni': 1.0, 'Rh': 1.0}\",\"('Ni', 'Rh')\",OTHERS,False\nmp-1239257,\"{'Zr': 4.0, 'Cr': 4.0, 'Cu': 4.0, 'S': 16.0}\",\"('Cr', 'Cu', 'S', 'Zr')\",OTHERS,False\nmp-13903,\"{'F': 6.0, 'Zn': 1.0, 'Sn': 1.0}\",\"('F', 'Sn', 'Zn')\",OTHERS,False\nmp-765733,\"{'Mn': 8.0, 'O': 14.0, 'F': 2.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-1246065,\"{'Ti': 4.0, 'Mo': 4.0, 'N': 12.0}\",\"('Mo', 'N', 'Ti')\",OTHERS,False\nmp-26035,\"{'Li': 18.0, 'O': 58.0, 'P': 16.0, 'Mn': 6.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-754685,\"{'Li': 4.0, 'Cr': 1.0, 'Fe': 1.0, 'W': 2.0, 'O': 12.0}\",\"('Cr', 'Fe', 'Li', 'O', 'W')\",OTHERS,False\nmp-1200101,\"{'Er': 12.0, 'P': 36.0, 'O': 108.0}\",\"('Er', 'O', 'P')\",OTHERS,False\nmp-23227,\"{'Cu': 2.0, 'Br': 2.0}\",\"('Br', 'Cu')\",OTHERS,False\nmp-1193242,\"{'Cs': 8.0, 'Co': 4.0, 'Cl': 16.0}\",\"('Cl', 'Co', 'Cs')\",OTHERS,False\nmp-1201134,\"{'Cu': 4.0, 'C': 8.0, 'O': 24.0}\",\"('C', 'Cu', 'O')\",OTHERS,False\nmp-864624,\"{'Ho': 1.0, 'Zn': 1.0, 'Rh': 2.0}\",\"('Ho', 'Rh', 'Zn')\",OTHERS,False\nmp-20596,\"{'Si': 2.0, 'Ni': 2.0, 'U': 1.0}\",\"('Ni', 'Si', 'U')\",OTHERS,False\nmp-31352,\"{'O': 18.0, 'Se': 6.0, 'Er': 4.0}\",\"('Er', 'O', 'Se')\",OTHERS,False\nmp-1019524,\"{'Ba': 3.0, 'P': 6.0, 'N': 8.0, 'O': 6.0}\",\"('Ba', 'N', 'O', 'P')\",OTHERS,False\nmp-1221447,\"{'Na': 2.0, 'Al': 1.0, 'Ge': 7.0, 'O': 16.0}\",\"('Al', 'Ge', 'Na', 'O')\",OTHERS,False\nmp-6391,\"{'Na': 4.0, 'Zn': 2.0, 'Si': 2.0, 'O': 8.0}\",\"('Na', 'O', 'Si', 'Zn')\",OTHERS,False\nmp-643586,\"{'Cs': 2.0, 'H': 2.0, 'S': 4.0, 'O': 12.0, 'F': 4.0}\",\"('Cs', 'F', 'H', 'O', 'S')\",OTHERS,False\nmp-715438,\"{'Fe': 6.0, 'O': 8.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-754430,\"{'Mn': 7.0, 'O': 12.0}\",\"('Mn', 'O')\",OTHERS,False\nmp-306,\"{'B': 6.0, 'O': 9.0}\",\"('B', 'O')\",OTHERS,False\nmvc-10016,\"{'La': 1.0, 'V': 1.0, 'Cr': 1.0, 'O': 6.0}\",\"('Cr', 'La', 'O', 'V')\",OTHERS,False\nmp-1183975,\"{'Ga': 1.0, 'Ge': 3.0}\",\"('Ga', 'Ge')\",OTHERS,False\nmp-1180190,\"{'Mo': 2.0, 'O': 10.0}\",\"('Mo', 'O')\",OTHERS,False\nmp-1174068,\"{'Li': 6.0, 'Mn': 4.0, 'O': 10.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-756691,\"{'Li': 6.0, 'Co': 1.0, 'Ni': 1.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Co', 'Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-764826,\"{'Li': 24.0, 'Mn': 12.0, 'F': 60.0}\",\"('F', 'Li', 'Mn')\",OTHERS,False\nmp-771487,\"{'Na': 10.0, 'Mn': 4.0, 'P': 2.0, 'C': 8.0, 'O': 32.0}\",\"('C', 'Mn', 'Na', 'O', 'P')\",OTHERS,False\nmp-1177323,\"{'Li': 4.0, 'Nb': 1.0, 'V': 3.0, 'Cr': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Cr', 'Li', 'Nb', 'O', 'P', 'V')\",OTHERS,False\nmp-1216790,\"{'V': 2.0, 'Mo': 1.0, 'O': 8.0}\",\"('Mo', 'O', 'V')\",OTHERS,False\nmp-1190399,\"{'Na': 4.0, 'Zn': 2.0, 'Ge': 4.0, 'S': 12.0}\",\"('Ge', 'Na', 'S', 'Zn')\",OTHERS,False\nmp-571347,\"{'Pr': 6.0, 'Cu': 2.0, 'Ge': 2.0, 'Se': 14.0}\",\"('Cu', 'Ge', 'Pr', 'Se')\",OTHERS,False\nmp-753420,\"{'Li': 2.0, 'Mn': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-650442,\"{'Tl': 12.0, 'Ag': 4.0, 'Te': 8.0}\",\"('Ag', 'Te', 'Tl')\",OTHERS,False\nmp-756392,\"{'Li': 2.0, 'Cu': 2.0, 'Si': 4.0, 'O': 10.0}\",\"('Cu', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1104788,\"{'Co': 2.0, 'B': 4.0, 'O': 8.0}\",\"('B', 'Co', 'O')\",OTHERS,False\nmp-722571,\"{'Re': 20.0, 'H': 20.0, 'C': 80.0, 'O': 80.0}\",\"('C', 'H', 'O', 'Re')\",OTHERS,False\nmp-567459,\"{'Ag': 4.0, 'C': 8.0, 'N': 12.0}\",\"('Ag', 'C', 'N')\",OTHERS,False\nmp-753624,\"{'Li': 2.0, 'Mn': 2.0, 'P': 2.0, 'O': 8.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-19997,\"{'Ni': 2.0, 'In': 4.0, 'Eu': 2.0}\",\"('Eu', 'In', 'Ni')\",OTHERS,False\nmp-980944,\"{'Yb': 6.0, 'Y': 2.0}\",\"('Y', 'Yb')\",OTHERS,False\nmp-864655,\"{'Pa': 1.0, 'Co': 3.0}\",\"('Co', 'Pa')\",OTHERS,False\nmp-753040,\"{'Li': 1.0, 'Mn': 1.0, 'P': 3.0, 'H': 1.0, 'O': 10.0}\",\"('H', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1239310,\"{'Sr': 4.0, 'Y': 2.0, 'Cr': 2.0, 'Cu': 4.0, 'O': 14.0}\",\"('Cr', 'Cu', 'O', 'Sr', 'Y')\",OTHERS,False\nmp-19237,\"{'O': 4.0, 'Sr': 2.0, 'Mo': 1.0}\",\"('Mo', 'O', 'Sr')\",OTHERS,False\nmp-1174721,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1208970,\"{'Sm': 3.0, 'P': 6.0, 'Pd': 20.0}\",\"('P', 'Pd', 'Sm')\",OTHERS,False\nmp-1196827,\"{'K': 16.0, 'Li': 4.0, 'H': 12.0, 'S': 16.0, 'O': 64.0}\",\"('H', 'K', 'Li', 'O', 'S')\",OTHERS,False\nmp-570286,\"{'Bi': 2.0, 'Se': 2.0}\",\"('Bi', 'Se')\",OTHERS,False\nmp-754127,\"{'Li': 2.0, 'Co': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Co', 'Li', 'O', 'P')\",OTHERS,False\nmp-662575,\"{'Eu': 2.0, 'Sr': 4.0, 'Ga': 2.0, 'Cu': 4.0, 'O': 14.0}\",\"('Cu', 'Eu', 'Ga', 'O', 'Sr')\",OTHERS,False\nmp-14901,\"{'O': 48.0, 'Na': 12.0, 'Zn': 12.0, 'As': 12.0}\",\"('As', 'Na', 'O', 'Zn')\",OTHERS,False\nmp-568989,\"{'Li': 56.0, 'Ni': 8.0, 'N': 32.0}\",\"('Li', 'N', 'Ni')\",OTHERS,False\nmp-760071,\"{'Li': 10.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1106340,\"{'La': 10.0, 'Mn': 2.0, 'Pb': 6.0}\",\"('La', 'Mn', 'Pb')\",OTHERS,False\nmp-776447,\"{'Li': 4.0, 'Mn': 6.0, 'B': 6.0, 'O': 18.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1244597,\"{'Mg': 4.0, 'Ta': 4.0, 'Bi': 4.0, 'O': 20.0}\",\"('Bi', 'Mg', 'O', 'Ta')\",OTHERS,False\nmp-1199645,\"{'Mn': 8.0, 'As': 8.0, 'O': 36.0}\",\"('As', 'Mn', 'O')\",OTHERS,False\nmp-1184926,\"{'Ho': 1.0, 'Np': 3.0}\",\"('Ho', 'Np')\",OTHERS,False\nmp-1200738,\"{'Pr': 10.0, 'Sn': 20.0, 'Rh': 8.0}\",\"('Pr', 'Rh', 'Sn')\",OTHERS,False\nmp-780653,\"{'Li': 20.0, 'Mn': 2.0, 'Si': 4.0, 'O': 20.0}\",\"('Li', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1073360,\"{'Mg': 4.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-863354,\"{'Co': 2.0, 'Sn': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Co', 'O', 'P', 'Sn')\",OTHERS,False\nmp-755639,\"{'Li': 4.0, 'Mn': 1.0, 'Fe': 3.0, 'B': 4.0, 'O': 12.0}\",\"('B', 'Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1190642,\"{'Er': 2.0, 'Fe': 12.0, 'P': 7.0}\",\"('Er', 'Fe', 'P')\",OTHERS,False\nmp-1200403,\"{'Nb': 8.0, 'Ag': 4.0, 'O': 22.0}\",\"('Ag', 'Nb', 'O')\",OTHERS,False\nmp-30136,\"{'O': 14.0, 'Mo': 4.0, 'Hg': 4.0}\",\"('Hg', 'Mo', 'O')\",OTHERS,False\nmp-1184848,\"{'K': 3.0, 'Mo': 1.0}\",\"('K', 'Mo')\",OTHERS,False\nmp-1210839,\"{'Lu': 4.0, 'Be': 4.0, 'Ge': 2.0, 'O': 14.0}\",\"('Be', 'Ge', 'Lu', 'O')\",OTHERS,False\nmp-1182249,\"{'Fe': 24.0, 'O': 32.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-1103642,\"{'Ag': 1.0, 'Mo': 6.0, 'Se': 8.0}\",\"('Ag', 'Mo', 'Se')\",OTHERS,False\nmp-753891,\"{'Zr': 4.0, 'Ti': 4.0, 'O': 16.0}\",\"('O', 'Ti', 'Zr')\",OTHERS,False\nmp-1195911,\"{'Sr': 2.0, 'Yb': 8.0, 'Si': 10.0, 'O': 34.0}\",\"('O', 'Si', 'Sr', 'Yb')\",OTHERS,False\nmp-560815,\"{'Al': 18.0, 'P': 18.0, 'O': 72.0}\",\"('Al', 'O', 'P')\",OTHERS,False\nmp-26393,\"{'Li': 4.0, 'O': 48.0, 'P': 12.0, 'Mn': 16.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-759338,\"{'Mn': 6.0, 'O': 8.0, 'F': 4.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-1203491,\"{'Ho': 12.0, 'In': 4.0, 'S': 24.0}\",\"('Ho', 'In', 'S')\",OTHERS,False\nmp-1193222,\"{'Li': 3.0, 'Mg': 3.0, 'Al': 3.0, 'F': 18.0}\",\"('Al', 'F', 'Li', 'Mg')\",OTHERS,False\nmp-571057,\"{'K': 4.0, 'Y': 4.0, 'P': 8.0, 'Se': 24.0}\",\"('K', 'P', 'Se', 'Y')\",OTHERS,False\nmvc-7568,\"{'Mg': 4.0, 'Ti': 6.0, 'O': 16.0}\",\"('Mg', 'O', 'Ti')\",OTHERS,False\nmp-1219110,\"{'Sm': 2.0, 'Al': 2.0, 'Zn': 2.0}\",\"('Al', 'Sm', 'Zn')\",OTHERS,False\nmp-1025324,\"{'Si': 1.0, 'Sn': 1.0, 'Pt': 5.0}\",\"('Pt', 'Si', 'Sn')\",OTHERS,False\nmp-1229121,\"{'Ag': 1.0, 'Bi': 1.0, 'Pb': 1.0, 'S': 3.0}\",\"('Ag', 'Bi', 'Pb', 'S')\",OTHERS,False\nmp-27513,\"{'Cl': 10.0, 'Sc': 7.0}\",\"('Cl', 'Sc')\",OTHERS,False\nmp-16866,\"{'N': 6.0, 'O': 22.0, 'K': 2.0, 'Np': 2.0}\",\"('K', 'N', 'Np', 'O')\",OTHERS,False\nmp-1208837,\"{'Sr': 1.0, 'In': 2.0, 'Cu': 2.0}\",\"('Cu', 'In', 'Sr')\",OTHERS,False\nmp-1212460,\"{'H': 2.0, 'I': 2.0, 'N': 4.0}\",\"('H', 'I', 'N')\",OTHERS,False\nmp-1101509,\"{'Li': 1.0, 'Mo': 1.0, 'P': 2.0, 'O': 8.0}\",\"('Li', 'Mo', 'O', 'P')\",OTHERS,False\nmp-1182212,\"{'Ca': 16.0, 'Si': 8.0, 'O': 40.0}\",\"('Ca', 'O', 'Si')\",OTHERS,False\nmp-505731,\"{'O': 32.0, 'P': 8.0, 'Co': 4.0, 'Ba': 8.0}\",\"('Ba', 'Co', 'O', 'P')\",OTHERS,False\nmvc-13307,\"{'Y': 2.0, 'Mg': 1.0, 'Mn': 2.0, 'S': 2.0, 'O': 5.0}\",\"('Mg', 'Mn', 'O', 'S', 'Y')\",OTHERS,False\nmp-1219174,\"{'Sm': 1.0, 'Zn': 2.0, 'Ga': 2.0}\",\"('Ga', 'Sm', 'Zn')\",OTHERS,False\nmp-19058,\"{'Ba': 6.0, 'Co': 2.0, 'Ru': 4.0, 'O': 18.0}\",\"('Ba', 'Co', 'O', 'Ru')\",OTHERS,False\nmp-1228920,\"{'Cs': 2.0, 'Fe': 2.0, 'Cu': 2.0, 'F': 12.0}\",\"('Cs', 'Cu', 'F', 'Fe')\",OTHERS,False\nmp-32530,\"{'Li': 12.0, 'V': 8.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1008920,\"{'Mn': 2.0, 'Ge': 1.0}\",\"('Ge', 'Mn')\",OTHERS,False\nmp-27410,\"{'P': 3.0, 'Sn': 4.0}\",\"('P', 'Sn')\",OTHERS,False\nmp-1095969,\"{'Mn': 1.0, 'Nb': 2.0, 'Cr': 1.0}\",\"('Cr', 'Mn', 'Nb')\",OTHERS,False\nmp-1225885,\"{'Cu': 2.0, 'Ni': 1.0, 'Sn': 1.0, 'Se': 4.0}\",\"('Cu', 'Ni', 'Se', 'Sn')\",OTHERS,False\nmp-1247345,\"{'Ba': 6.0, 'Fe': 4.0, 'N': 8.0}\",\"('Ba', 'Fe', 'N')\",OTHERS,False\nmp-21065,\"{'Si': 4.0, 'P': 8.0}\",\"('P', 'Si')\",OTHERS,False\nmp-1103571,\"{'Al': 2.0, 'Si': 2.0, 'O': 11.0}\",\"('Al', 'O', 'Si')\",OTHERS,False\nmp-1095419,\"{'Ta': 4.0, 'Cr': 4.0, 'P': 4.0}\",\"('Cr', 'P', 'Ta')\",OTHERS,False\nmp-7574,\"{'B': 2.0, 'Al': 2.0, 'Mo': 2.0}\",\"('Al', 'B', 'Mo')\",OTHERS,False\nmp-26875,\"{'Li': 8.0, 'O': 16.0, 'P': 4.0, 'Cu': 4.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-1103857,\"{'U': 2.0, 'Mn': 8.0, 'P': 4.0}\",\"('Mn', 'P', 'U')\",OTHERS,False\nmp-1217081,\"{'Ti': 3.0, 'Al': 5.0}\",\"('Al', 'Ti')\",OTHERS,False\nmp-758997,\"{'Li': 3.0, 'Cu': 3.0, 'C': 3.0, 'O': 9.0}\",\"('C', 'Cu', 'Li', 'O')\",OTHERS,False\nmp-1182130,\"{'C': 6.0, 'I': 2.0, 'N': 2.0}\",\"('C', 'I', 'N')\",OTHERS,False\nmp-1226793,\"{'Ce': 4.0, 'Ni': 8.0, 'B': 12.0}\",\"('B', 'Ce', 'Ni')\",OTHERS,False\nmp-542181,\"{'Eu': 2.0, 'K': 6.0, 'V': 4.0, 'O': 16.0}\",\"('Eu', 'K', 'O', 'V')\",OTHERS,False\nmp-864991,\"{'Ca': 2.0, 'Ag': 1.0, 'Pb': 1.0}\",\"('Ag', 'Ca', 'Pb')\",OTHERS,False\nmp-504495,\"{'Na': 4.0, 'Zn': 4.0, 'Mo': 4.0, 'O': 20.0, 'H': 4.0}\",\"('H', 'Mo', 'Na', 'O', 'Zn')\",OTHERS,False\nmp-653559,\"{'P': 16.0, 'Se': 12.0, 'I': 8.0}\",\"('I', 'P', 'Se')\",OTHERS,False\nmp-23996,\"{'H': 16.0, 'Be': 2.0, 'O': 16.0, 'S': 2.0}\",\"('Be', 'H', 'O', 'S')\",OTHERS,False\nmp-570527,\"{'Yb': 20.0, 'Au': 16.0}\",\"('Au', 'Yb')\",OTHERS,False\nmp-1249385,\"{'Ba': 2.0, 'Ti': 2.0, 'Al': 1.0, 'Tl': 1.0, 'O': 7.0}\",\"('Al', 'Ba', 'O', 'Ti', 'Tl')\",OTHERS,False\nmp-1206002,\"{'Er': 6.0, 'Fe': 1.0, 'Bi': 2.0}\",\"('Bi', 'Er', 'Fe')\",OTHERS,False\nmp-581681,\"{'Ca': 42.0, 'Mn': 8.0, 'Sb': 36.0}\",\"('Ca', 'Mn', 'Sb')\",OTHERS,False\nmp-1206973,\"{'Fe': 2.0, 'Co': 2.0, 'Ge': 2.0}\",\"('Co', 'Fe', 'Ge')\",OTHERS,False\nmp-1218764,\"{'Sr': 2.0, 'Tb': 1.0, 'Cu': 2.0, 'Ir': 1.0, 'O': 8.0}\",\"('Cu', 'Ir', 'O', 'Sr', 'Tb')\",OTHERS,False\nmp-554747,\"{'Li': 20.0, 'La': 12.0, 'Nb': 8.0, 'O': 48.0}\",\"('La', 'Li', 'Nb', 'O')\",OTHERS,False\nmp-1187264,\"{'Tb': 2.0, 'Ni': 1.0, 'Ir': 1.0}\",\"('Ir', 'Ni', 'Tb')\",OTHERS,False\nmvc-16518,\"{'Ca': 4.0, 'Ta': 4.0, 'Cr': 2.0, 'O': 16.0}\",\"('Ca', 'Cr', 'O', 'Ta')\",OTHERS,False\nmp-1227730,\"{'Ca': 4.0, 'Fe': 3.0, 'As': 8.0, 'Pt': 1.0}\",\"('As', 'Ca', 'Fe', 'Pt')\",OTHERS,False\nmp-1184192,\"{'Er': 6.0, 'Tm': 2.0}\",\"('Er', 'Tm')\",OTHERS,False\nmp-505262,\"{'Gd': 2.0, 'Se': 2.0, 'Cu': 2.0, 'O': 2.0}\",\"('Cu', 'Gd', 'O', 'Se')\",OTHERS,False\nmp-1174519,\"{'Li': 5.0, 'Mn': 3.0, 'O': 8.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-569696,\"{'La': 1.0, 'Al': 2.0, 'Ga': 2.0}\",\"('Al', 'Ga', 'La')\",OTHERS,False\nmp-1184692,\"{'Ho': 2.0, 'Zn': 1.0, 'Pt': 1.0}\",\"('Ho', 'Pt', 'Zn')\",OTHERS,False\nmp-772617,\"{'Zn': 4.0, 'N': 8.0, 'O': 24.0}\",\"('N', 'O', 'Zn')\",OTHERS,False\nmp-1210330,\"{'Na': 8.0, 'Zr': 4.0, 'Ge': 10.0, 'H': 2.0, 'O': 32.0}\",\"('Ge', 'H', 'Na', 'O', 'Zr')\",OTHERS,False\nmp-29743,\"{'O': 56.0, 'Sr': 20.0, 'U': 12.0}\",\"('O', 'Sr', 'U')\",OTHERS,False\nmp-1210278,\"{'Pu': 8.0, 'Mo': 16.0, 'O': 64.0}\",\"('Mo', 'O', 'Pu')\",OTHERS,False\nmp-1203156,\"{'Y': 8.0, 'Cd': 8.0, 'Bi': 8.0, 'O': 32.0}\",\"('Bi', 'Cd', 'O', 'Y')\",OTHERS,False\nmp-1205834,\"{'Er': 2.0, 'Al': 4.0, 'Ni': 1.0, 'Ge': 2.0}\",\"('Al', 'Er', 'Ge', 'Ni')\",OTHERS,False\nmp-4007,\"{'Si': 2.0, 'Ni': 2.0, 'Nd': 1.0}\",\"('Nd', 'Ni', 'Si')\",OTHERS,False\nmp-1018900,\"{'Pr': 2.0, 'Cu': 2.0, 'Pb': 2.0}\",\"('Cu', 'Pb', 'Pr')\",OTHERS,False\nmp-1228832,\"{'Al': 1.0, 'Ni': 6.0, 'Ge': 1.0}\",\"('Al', 'Ge', 'Ni')\",OTHERS,False\nmp-1205555,\"{'U': 2.0, 'Si': 2.0, 'Os': 4.0, 'C': 2.0}\",\"('C', 'Os', 'Si', 'U')\",OTHERS,False\nmp-1178538,\"{'Ba': 4.0, 'Ca': 4.0, 'I': 16.0}\",\"('Ba', 'Ca', 'I')\",OTHERS,False\nmp-754942,\"{'Sr': 2.0, 'Sm': 4.0, 'O': 8.0}\",\"('O', 'Sm', 'Sr')\",OTHERS,False\nmp-652101,\"{'Zn': 20.0, 'Fe': 2.0, 'B': 16.0, 'Rh': 36.0}\",\"('B', 'Fe', 'Rh', 'Zn')\",OTHERS,False\nmp-972472,\"{'Sn': 1.0, 'As': 3.0}\",\"('As', 'Sn')\",OTHERS,False\nmp-769626,\"{'Li': 4.0, 'Mn': 2.0, 'Nb': 3.0, 'Fe': 3.0, 'O': 16.0}\",\"('Fe', 'Li', 'Mn', 'Nb', 'O')\",OTHERS,False\nmp-573051,\"{'Re': 6.0, 'O': 18.0, 'F': 6.0}\",\"('F', 'O', 'Re')\",OTHERS,False\nmp-1186693,\"{'Pr': 2.0, 'Mg': 1.0, 'Cd': 1.0}\",\"('Cd', 'Mg', 'Pr')\",OTHERS,False\nmp-2503,\"{'Se': 8.0, 'Pd': 14.0}\",\"('Pd', 'Se')\",OTHERS,False\nmp-1186346,\"{'Np': 1.0, 'Sn': 1.0, 'O': 3.0}\",\"('Np', 'O', 'Sn')\",OTHERS,False\nmp-1196261,\"{'Sb': 8.0, 'Xe': 4.0, 'F': 38.0}\",\"('F', 'Sb', 'Xe')\",OTHERS,False\nmp-1103729,\"{'Na': 1.0, 'Ge': 6.0, 'As': 6.0}\",\"('As', 'Ge', 'Na')\",OTHERS,False\nmp-510728,\"{'Co': 1.0, 'Mn': 1.0, 'N': 12.0, 'H': 18.0, 'C': 6.0}\",\"('C', 'Co', 'H', 'Mn', 'N')\",OTHERS,False\nmp-765725,\"{'Na': 3.0, 'Li': 5.0, 'Mn': 5.0, 'O': 9.0}\",\"('Li', 'Mn', 'Na', 'O')\",OTHERS,False\nZn 58 Mg 40 Dy 2,\"{'Zn': 58.0, 'Mg': 40.0, 'Dy': 2.0}\",\"('Dy', 'Mg', 'Zn')\",QC,False\nmp-756075,\"{'Cr': 1.0, 'Te': 1.0, 'W': 2.0, 'O': 12.0}\",\"('Cr', 'O', 'Te', 'W')\",OTHERS,False\nmp-722344,\"{'K': 1.0, 'Ca': 4.0, 'B': 22.0, 'H': 18.0, 'Cl': 1.0, 'O': 46.0}\",\"('B', 'Ca', 'Cl', 'H', 'K', 'O')\",OTHERS,False\nmp-362,\"{'Ni': 6.0, 'S': 4.0}\",\"('Ni', 'S')\",OTHERS,False\nmp-1181143,\"{'K': 4.0, 'Tb': 4.0, 'N': 16.0, 'O': 56.0}\",\"('K', 'N', 'O', 'Tb')\",OTHERS,False\nmp-1094552,\"{'Mg': 6.0, 'Sb': 6.0}\",\"('Mg', 'Sb')\",OTHERS,False\nmp-1219321,\"{'Sm': 4.0, 'Cr': 1.0, 'Fe': 33.0}\",\"('Cr', 'Fe', 'Sm')\",OTHERS,False\nmp-1178360,\"{'Dy': 4.0, 'In': 4.0, 'O': 12.0}\",\"('Dy', 'In', 'O')\",OTHERS,False\nmp-1218644,\"{'Sr': 5.0, 'Ca': 1.0, 'Cu': 4.0, 'Bi': 4.0, 'O': 8.0}\",\"('Bi', 'Ca', 'Cu', 'O', 'Sr')\",OTHERS,False\nmp-1029125,\"{'Te': 2.0, 'Mo': 1.0, 'W': 3.0, 'Se': 2.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-753120,\"{'W': 4.0, 'O': 4.0, 'F': 16.0}\",\"('F', 'O', 'W')\",OTHERS,False\nmp-29151,\"{'Li': 5.0, 'N': 1.0, 'Cl': 2.0}\",\"('Cl', 'Li', 'N')\",OTHERS,False\nmp-759214,\"{'Co': 4.0, 'P': 12.0, 'O': 36.0}\",\"('Co', 'O', 'P')\",OTHERS,False\nmp-569913,\"{'Ce': 6.0, 'Si': 2.0, 'Pt': 10.0}\",\"('Ce', 'Pt', 'Si')\",OTHERS,False\nmp-1220045,\"{'Os': 3.0, 'W': 3.0, 'C': 2.0}\",\"('C', 'Os', 'W')\",OTHERS,False\nmp-1245610,\"{'Si': 12.0, 'Sb': 20.0, 'N': 36.0}\",\"('N', 'Sb', 'Si')\",OTHERS,False\nmp-1225095,\"{'H': 4.0, 'Ru': 4.0, 'Rh': 4.0, 'C': 24.0, 'O': 24.0}\",\"('C', 'H', 'O', 'Rh', 'Ru')\",OTHERS,False\nmp-541870,\"{'K': 4.0, 'Al': 8.0, 'P': 8.0, 'O': 44.0, 'H': 20.0}\",\"('Al', 'H', 'K', 'O', 'P')\",OTHERS,False\nmp-714885,\"{'V': 2.0, 'O': 2.0}\",\"('O', 'V')\",OTHERS,False\nmp-1038709,\"{'Cs': 1.0, 'Mg': 30.0, 'Ga': 1.0, 'O': 32.0}\",\"('Cs', 'Ga', 'Mg', 'O')\",OTHERS,False\nmp-743603,\"{'Li': 3.0, 'Ti': 3.0, 'Fe': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Fe', 'Li', 'O', 'P', 'Ti')\",OTHERS,False\nmp-1100586,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1179515,\"{'Sc': 6.0, 'Ru': 2.0, 'C': 8.0}\",\"('C', 'Ru', 'Sc')\",OTHERS,False\nmp-1223486,\"{'La': 18.0, 'Al': 10.0, 'Se': 42.0}\",\"('Al', 'La', 'Se')\",OTHERS,False\nmp-770831,\"{'Na': 6.0, 'Co': 2.0, 'B': 2.0, 'S': 2.0, 'O': 14.0}\",\"('B', 'Co', 'Na', 'O', 'S')\",OTHERS,False\nmp-11734,\"{'O': 6.0, 'Mg': 1.0, 'Sr': 2.0, 'Re': 1.0}\",\"('Mg', 'O', 'Re', 'Sr')\",OTHERS,False\nmp-1193559,\"{'Rb': 4.0, 'S': 4.0, 'N': 4.0, 'O': 16.0}\",\"('N', 'O', 'Rb', 'S')\",OTHERS,False\nmp-764980,\"{'Li': 4.0, 'Mn': 4.0, 'F': 20.0}\",\"('F', 'Li', 'Mn')\",OTHERS,False\nmp-778364,\"{'Na': 12.0, 'Ti': 4.0, 'O': 14.0}\",\"('Na', 'O', 'Ti')\",OTHERS,False\nmp-8176,\"{'O': 2.0, 'Rb': 1.0, 'Tl': 1.0}\",\"('O', 'Rb', 'Tl')\",OTHERS,False\nmp-1196125,\"{'Si': 14.0, 'H': 84.0, 'C': 28.0}\",\"('C', 'H', 'Si')\",OTHERS,False\nmp-690435,\"{'Li': 7.0, 'Mn': 11.0, 'O': 24.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-5900,\"{'F': 32.0, 'Zr': 4.0, 'Ba': 8.0}\",\"('Ba', 'F', 'Zr')\",OTHERS,False\nmp-775804,\"{'Li': 12.0, 'Sb': 4.0, 'S': 12.0}\",\"('Li', 'S', 'Sb')\",OTHERS,False\nmp-29598,\"{'N': 14.0, 'Na': 2.0, 'P': 8.0}\",\"('N', 'Na', 'P')\",OTHERS,False\nmp-1080277,\"{'Ce': 6.0, 'Se': 12.0}\",\"('Ce', 'Se')\",OTHERS,False\nmp-1185851,\"{'Mg': 2.0, 'C': 2.0}\",\"('C', 'Mg')\",OTHERS,False\nmp-761745,\"{'Sr': 2.0, 'Cd': 4.0, 'H': 32.0, 'Br': 12.0, 'O': 16.0}\",\"('Br', 'Cd', 'H', 'O', 'Sr')\",OTHERS,False\nmp-2201,\"{'Se': 1.0, 'Pb': 1.0}\",\"('Pb', 'Se')\",OTHERS,False\nmp-1227444,\"{'Bi': 1.0, 'Pb': 1.0, 'Br': 1.0, 'O': 2.0}\",\"('Bi', 'Br', 'O', 'Pb')\",OTHERS,False\nmp-755409,\"{'Li': 5.0, 'Mn': 2.0, 'Cu': 3.0, 'O': 10.0}\",\"('Cu', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-510262,\"{'O': 2.0, 'Cs': 6.0}\",\"('Cs', 'O')\",OTHERS,False\nmp-1195553,\"{'Zr': 2.0, 'Zn': 40.0, 'Fe': 4.0}\",\"('Fe', 'Zn', 'Zr')\",OTHERS,False\nmp-1226753,\"{'Cd': 1.0, 'Bi': 1.0, 'I': 1.0, 'O': 2.0}\",\"('Bi', 'Cd', 'I', 'O')\",OTHERS,False\nmp-693655,\"{'Ba': 4.0, 'Sm': 7.0, 'Si': 12.0, 'B': 1.0, 'N': 27.0}\",\"('B', 'Ba', 'N', 'Si', 'Sm')\",OTHERS,False\nmp-760261,\"{'Li': 12.0, 'Cr': 4.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Cr', 'Li', 'O')\",OTHERS,False\nmp-1215547,\"{'Zn': 3.0, 'Cr': 8.0, 'Fe': 1.0, 'S': 16.0}\",\"('Cr', 'Fe', 'S', 'Zn')\",OTHERS,False\nmp-540610,\"{'K': 4.0, 'U': 14.0, 'O': 44.0}\",\"('K', 'O', 'U')\",OTHERS,False\nmp-24055,\"{'H': 24.0, 'N': 24.0, 'O': 32.0, 'K': 4.0, 'Co': 4.0}\",\"('Co', 'H', 'K', 'N', 'O')\",OTHERS,False\nmp-22711,\"{'Al': 14.0, 'Gd': 2.0, 'Au': 6.0}\",\"('Al', 'Au', 'Gd')\",OTHERS,False\nmp-1205588,\"{'Mn': 6.0, 'Ge': 2.0, 'N': 2.0}\",\"('Ge', 'Mn', 'N')\",OTHERS,False\nmp-627291,\"{'V': 6.0, 'H': 12.0, 'O': 20.0}\",\"('H', 'O', 'V')\",OTHERS,False\nmp-1237686,\"{'Rb': 8.0, 'Al': 4.0, 'H': 16.0, 'N': 8.0}\",\"('Al', 'H', 'N', 'Rb')\",OTHERS,False\nmp-1079888,\"{'Ti': 4.0, 'Se': 4.0}\",\"('Se', 'Ti')\",OTHERS,False\nmp-29412,\"{'Br': 28.0, 'Ta': 12.0}\",\"('Br', 'Ta')\",OTHERS,False\nmp-4736,\"{'O': 56.0, 'P': 20.0, 'Nd': 4.0}\",\"('Nd', 'O', 'P')\",OTHERS,False\nmp-18998,\"{'Sr': 16.0, 'Mn': 12.0, 'O': 40.0}\",\"('Mn', 'O', 'Sr')\",OTHERS,False\nmp-4495,\"{'Li': 2.0, 'K': 2.0, 'Te': 2.0}\",\"('K', 'Li', 'Te')\",OTHERS,False\nmp-774722,\"{'Li': 1.0, 'La': 20.0, 'Cu': 9.0, 'O': 40.0}\",\"('Cu', 'La', 'Li', 'O')\",OTHERS,False\nmp-1187727,\"{'Yb': 1.0, 'Eu': 1.0, 'Zn': 2.0}\",\"('Eu', 'Yb', 'Zn')\",OTHERS,False\nmp-1210713,\"{'Mn': 13.0, 'Fe': 1.0, 'Si': 2.0, 'O': 22.0}\",\"('Fe', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-980049,\"{'Yb': 3.0, 'Re': 1.0}\",\"('Re', 'Yb')\",OTHERS,False\nmp-18282,\"{'O': 16.0, 'Sc': 2.0, 'Zn': 2.0, 'Mo': 6.0}\",\"('Mo', 'O', 'Sc', 'Zn')\",OTHERS,False\nmp-531213,\"{'Ca': 13.0, 'F': 43.0, 'Y': 6.0}\",\"('Ca', 'F', 'Y')\",OTHERS,False\nmp-757595,\"{'H': 24.0, 'S': 8.0, 'N': 8.0, 'O': 24.0}\",\"('H', 'N', 'O', 'S')\",OTHERS,False\nmp-1008865,\"{'Nd': 1.0, 'Cd': 2.0}\",\"('Cd', 'Nd')\",OTHERS,False\nmp-1097314,\"{'Mn': 1.0, 'Ga': 1.0, 'Fe': 2.0}\",\"('Fe', 'Ga', 'Mn')\",OTHERS,False\nmp-1221406,\"{'Na': 3.0, 'Eu': 3.0, 'F': 12.0}\",\"('Eu', 'F', 'Na')\",OTHERS,False\nmp-1201218,\"{'Si': 18.0, 'C': 16.0, 'N': 2.0, 'O': 38.0}\",\"('C', 'N', 'O', 'Si')\",OTHERS,False\nmp-1187561,\"{'Yb': 1.0, 'Ga': 1.0, 'O': 3.0}\",\"('Ga', 'O', 'Yb')\",OTHERS,False\nmp-1072492,\"{'Ca': 2.0, 'C': 4.0}\",\"('C', 'Ca')\",OTHERS,False\nmp-1205778,\"{'Ho': 3.0, 'Cd': 3.0, 'Au': 3.0}\",\"('Au', 'Cd', 'Ho')\",OTHERS,False\nmp-776576,\"{'Li': 2.0, 'Ti': 2.0, 'Mn': 1.0, 'Cr': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Cr', 'Li', 'Mn', 'O', 'P', 'Ti')\",OTHERS,False\nmp-1223695,\"{'In': 1.0, 'Sn': 1.0, 'Te': 2.0}\",\"('In', 'Sn', 'Te')\",OTHERS,False\nmp-1001835,\"{'Li': 2.0, 'B': 2.0}\",\"('B', 'Li')\",OTHERS,False\nmp-973895,\"{'Ni': 3.0, 'H': 1.0}\",\"('H', 'Ni')\",OTHERS,False\nmp-999263,\"{'Rh': 2.0, 'N': 2.0}\",\"('N', 'Rh')\",OTHERS,False\nmp-1114653,\"{'K': 1.0, 'Rb': 2.0, 'Al': 1.0, 'Cl': 6.0}\",\"('Al', 'Cl', 'K', 'Rb')\",OTHERS,False\nmp-1094554,\"{'Mg': 8.0, 'Sb': 4.0}\",\"('Mg', 'Sb')\",OTHERS,False\nmp-34501,\"{'Fe': 4.0, 'Co': 2.0, 'O': 8.0}\",\"('Co', 'Fe', 'O')\",OTHERS,False\nmp-1025347,\"{'Ti': 2.0, 'Co': 1.0, 'Se': 4.0}\",\"('Co', 'Se', 'Ti')\",OTHERS,False\nmp-556458,\"{'Ba': 3.0, 'Ca': 3.0, 'C': 6.0, 'O': 18.0}\",\"('Ba', 'C', 'Ca', 'O')\",OTHERS,False\nAl 88.6 Cu 19.4 Li 50.3,\"{'Al': 88.6, 'Cu': 19.4, 'Li': 50.3}\",\"('Al', 'Cu', 'Li')\",AC,False\nmp-757826,\"{'Cs': 1.0, 'Ca': 1.0, 'N': 3.0, 'O': 6.0}\",\"('Ca', 'Cs', 'N', 'O')\",OTHERS,False\nmp-1223419,\"{'K': 2.0, 'Si': 10.0, 'B': 2.0, 'O': 26.0}\",\"('B', 'K', 'O', 'Si')\",OTHERS,False\nmp-1096027,\"{'Ti': 2.0, 'Zn': 1.0, 'Rh': 1.0}\",\"('Rh', 'Ti', 'Zn')\",OTHERS,False\nmp-1200006,\"{'Ag': 16.0, 'H': 128.0, 'C': 48.0, 'S': 32.0, 'N': 16.0, 'O': 48.0}\",\"('Ag', 'C', 'H', 'N', 'O', 'S')\",OTHERS,False\nmp-1210341,\"{'Na': 8.0, 'Al': 6.0, 'Si': 6.0, 'N': 2.0, 'O': 24.0}\",\"('Al', 'N', 'Na', 'O', 'Si')\",OTHERS,False\nmp-30843,\"{'Sr': 28.0, 'Pt': 12.0}\",\"('Pt', 'Sr')\",OTHERS,False\nmp-556201,\"{'Ag': 2.0, 'Sb': 2.0, 'C': 4.0, 'N': 4.0, 'Cl': 4.0, 'F': 12.0}\",\"('Ag', 'C', 'Cl', 'F', 'N', 'Sb')\",OTHERS,False\nmvc-15475,\"{'Al': 1.0, 'Ag': 1.0, 'O': 3.0}\",\"('Ag', 'Al', 'O')\",OTHERS,False\nmp-1208552,\"{'Tb': 4.0, 'Al': 18.0, 'Ir': 6.0}\",\"('Al', 'Ir', 'Tb')\",OTHERS,False\nmp-665747,\"{'Eu': 4.0, 'Ag': 4.0}\",\"('Ag', 'Eu')\",OTHERS,False\nmp-6425,\"{'Li': 6.0, 'O': 24.0, 'P': 6.0, 'In': 4.0}\",\"('In', 'Li', 'O', 'P')\",OTHERS,False\nmp-759823,\"{'Li': 12.0, 'Cu': 12.0, 'P': 12.0, 'O': 48.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-27556,\"{'O': 24.0, 'Nb': 8.0, 'Cs': 8.0}\",\"('Cs', 'Nb', 'O')\",OTHERS,False\nmp-27760,\"{'Pr': 2.0, 'Si': 4.0}\",\"('Pr', 'Si')\",OTHERS,False\nmp-28645,\"{'N': 2.0, 'Ca': 2.0, 'Ni': 2.0}\",\"('Ca', 'N', 'Ni')\",OTHERS,False\nmp-753239,\"{'Li': 2.0, 'Sn': 2.0, 'P': 2.0, 'H': 2.0, 'O': 10.0}\",\"('H', 'Li', 'O', 'P', 'Sn')\",OTHERS,False\nmp-755944,\"{'Li': 3.0, 'Fe': 2.0, 'Co': 2.0, 'O': 8.0}\",\"('Co', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-646609,\"{'U': 4.0, 'In': 2.0, 'Rh': 4.0}\",\"('In', 'Rh', 'U')\",OTHERS,False\nmp-973738,\"{'Ge': 1.0, 'B': 1.0, 'O': 3.0}\",\"('B', 'Ge', 'O')\",OTHERS,False\nmp-1209298,\"{'Rb': 2.0, 'P': 30.0}\",\"('P', 'Rb')\",OTHERS,False\nmp-684642,\"{'S': 18.0, 'N': 18.0}\",\"('N', 'S')\",OTHERS,False\nmp-10209,\"{'Al': 3.0, 'Ni': 1.0, 'Ge': 2.0, 'Y': 3.0}\",\"('Al', 'Ge', 'Ni', 'Y')\",OTHERS,False\nmp-12569,\"{'B': 16.0, 'Pr': 4.0}\",\"('B', 'Pr')\",OTHERS,False\nmp-1223708,\"{'K': 2.0, 'Mo': 8.0, 'O': 13.0}\",\"('K', 'Mo', 'O')\",OTHERS,False\nmp-560060,\"{'Nd': 2.0, 'Sn': 4.0, 'P': 20.0, 'Cl': 74.0, 'O': 24.0}\",\"('Cl', 'Nd', 'O', 'P', 'Sn')\",OTHERS,False\nmp-571079,\"{'Tl': 2.0, 'Cl': 2.0}\",\"('Cl', 'Tl')\",OTHERS,False\nmp-1199182,\"{'K': 8.0, 'Cu': 16.0, 'Sb': 8.0, 'Se': 24.0}\",\"('Cu', 'K', 'Sb', 'Se')\",OTHERS,False\nmp-1076681,\"{'Ba': 1.0, 'Sr': 7.0, 'Co': 6.0, 'Cu': 2.0, 'O': 24.0}\",\"('Ba', 'Co', 'Cu', 'O', 'Sr')\",OTHERS,False\nmp-1019091,\"{'La': 2.0, 'Se': 4.0}\",\"('La', 'Se')\",OTHERS,False\nmp-645158,\"{'La': 4.0, 'Mn': 4.0, 'O': 12.0}\",\"('La', 'Mn', 'O')\",OTHERS,False\nmvc-5781,\"{'Zn': 4.0, 'Ir': 2.0, 'W': 2.0, 'O': 12.0}\",\"('Ir', 'O', 'W', 'Zn')\",OTHERS,False\nmp-974417,\"{'Re': 3.0, 'Sb': 1.0}\",\"('Re', 'Sb')\",OTHERS,False\nmp-12651,\"{'Mg': 4.0, 'Pt': 4.0}\",\"('Mg', 'Pt')\",OTHERS,False\nmp-1038767,\"{'Mg': 4.0, 'Cd': 4.0}\",\"('Cd', 'Mg')\",OTHERS,False\nmp-1179617,\"{'W': 17.0, 'O': 47.0}\",\"('O', 'W')\",OTHERS,False\nmvc-10584,\"{'Ca': 2.0, 'V': 6.0, 'P': 6.0, 'O': 26.0}\",\"('Ca', 'O', 'P', 'V')\",OTHERS,False\nmvc-9655,\"{'Pr': 2.0, 'W': 4.0, 'O': 12.0}\",\"('O', 'Pr', 'W')\",OTHERS,False\nmp-635469,\"{'Ce': 2.0, 'Cu': 2.0, 'S': 2.0, 'O': 2.0}\",\"('Ce', 'Cu', 'O', 'S')\",OTHERS,False\nmp-770815,\"{'Cs': 12.0, 'Y': 4.0, 'O': 12.0}\",\"('Cs', 'O', 'Y')\",OTHERS,False\nmp-752655,\"{'Li': 4.0, 'Nb': 2.0, 'Fe': 3.0, 'Ni': 3.0, 'O': 16.0}\",\"('Fe', 'Li', 'Nb', 'Ni', 'O')\",OTHERS,False\nmp-1225750,\"{'Er': 2.0, 'Si': 3.0}\",\"('Er', 'Si')\",OTHERS,False\nmp-669435,\"{'K': 4.0, 'La': 4.0, 'C': 4.0, 'O': 16.0}\",\"('C', 'K', 'La', 'O')\",OTHERS,False\nmp-555835,\"{'Gd': 12.0, 'Sc': 8.0, 'Ga': 12.0, 'O': 48.0}\",\"('Ga', 'Gd', 'O', 'Sc')\",OTHERS,False\nmp-1077593,\"{'Sm': 2.0, 'Sn': 4.0}\",\"('Sm', 'Sn')\",OTHERS,False\nmp-849295,\"{'Fe': 2.0, 'B': 2.0, 'O': 6.0}\",\"('B', 'Fe', 'O')\",OTHERS,False\nmp-6331,\"{'B': 8.0, 'O': 28.0, 'Na': 8.0, 'Y': 8.0}\",\"('B', 'Na', 'O', 'Y')\",OTHERS,False\nmp-1185369,\"{'Li': 1.0, 'Lu': 1.0, 'In': 2.0}\",\"('In', 'Li', 'Lu')\",OTHERS,False\nmp-1213040,\"{'Er': 1.0, 'Mn': 4.0, 'Cu': 3.0, 'O': 12.0}\",\"('Cu', 'Er', 'Mn', 'O')\",OTHERS,False\nmp-555273,\"{'Ca': 12.0, 'Ge': 4.0, 'Cl': 8.0, 'O': 16.0}\",\"('Ca', 'Cl', 'Ge', 'O')\",OTHERS,False\nmp-1201508,\"{'K': 16.0, 'Si': 16.0}\",\"('K', 'Si')\",OTHERS,False\nmp-684969,\"{'Sc': 4.0, 'S': 6.0}\",\"('S', 'Sc')\",OTHERS,False\nmp-758790,\"{'Li': 4.0, 'Fe': 4.0, 'P': 16.0, 'O': 48.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-1208093,\"{'V': 2.0, 'Cu': 3.0, 'O': 11.0}\",\"('Cu', 'O', 'V')\",OTHERS,False\nmp-540482,\"{'Li': 2.0, 'Cr': 2.0, 'P': 4.0, 'O': 14.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-684606,\"{'Cu': 27.0, 'Se': 20.0}\",\"('Cu', 'Se')\",OTHERS,False\nmp-773139,\"{'Li': 4.0, 'V': 3.0, 'Sb': 5.0, 'O': 16.0}\",\"('Li', 'O', 'Sb', 'V')\",OTHERS,False\nmp-1006330,\"{'Ce': 3.0, 'Cu': 1.0}\",\"('Ce', 'Cu')\",OTHERS,False\nmp-626644,\"{'Ca': 1.0, 'H': 2.0, 'O': 2.0}\",\"('Ca', 'H', 'O')\",OTHERS,False\nmp-1094383,\"{'Mg': 4.0, 'Ti': 4.0}\",\"('Mg', 'Ti')\",OTHERS,False\nmp-1074645,\"{'Mg': 8.0, 'Si': 4.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1193092,\"{'Er': 2.0, 'Be': 26.0}\",\"('Be', 'Er')\",OTHERS,False\nmp-977534,\"{'Zr': 1.0, 'Nb': 1.0, 'Tc': 2.0}\",\"('Nb', 'Tc', 'Zr')\",OTHERS,False\nmp-1192121,\"{'Tb': 4.0, 'Re': 4.0, 'B': 16.0}\",\"('B', 'Re', 'Tb')\",OTHERS,False\nmp-977012,\"{'H': 6.0, 'Pb': 1.0, 'C': 1.0, 'Br': 3.0, 'N': 1.0}\",\"('Br', 'C', 'H', 'N', 'Pb')\",OTHERS,False\nmp-542314,\"{'Nd': 2.0, 'Cu': 2.0, 'S': 2.0, 'O': 2.0}\",\"('Cu', 'Nd', 'O', 'S')\",OTHERS,False\nmp-1174327,\"{'Li': 8.0, 'Co': 6.0, 'O': 14.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-1100768,\"{'Tb': 1.0, 'Cd': 1.0, 'Hg': 2.0}\",\"('Cd', 'Hg', 'Tb')\",OTHERS,False\nmp-1018737,\"{'K': 2.0, 'Mg': 2.0, 'P': 2.0}\",\"('K', 'Mg', 'P')\",OTHERS,False\nmp-1222593,\"{'Lu': 3.0, 'Mn': 1.0, 'Fe': 3.0, 'O': 10.0}\",\"('Fe', 'Lu', 'Mn', 'O')\",OTHERS,False\nmp-1247078,\"{'Mg': 2.0, 'Ga': 1.0, 'W': 3.0, 'S': 8.0}\",\"('Ga', 'Mg', 'S', 'W')\",OTHERS,False\nmp-1183230,\"{'Ac': 1.0, 'Sm': 3.0}\",\"('Ac', 'Sm')\",OTHERS,False\nmp-1193901,\"{'Ho': 4.0, 'Mo': 4.0, 'Br': 4.0, 'O': 16.0}\",\"('Br', 'Ho', 'Mo', 'O')\",OTHERS,False\nmp-753231,\"{'Li': 2.0, 'V': 4.0, 'O': 3.0, 'F': 8.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-20106,\"{'Li': 4.0, 'Sb': 4.0, 'Nd': 2.0}\",\"('Li', 'Nd', 'Sb')\",OTHERS,False\nmp-977550,\"{'Zr': 1.0, 'Sc': 1.0, 'Os': 2.0}\",\"('Os', 'Sc', 'Zr')\",OTHERS,False\nmp-29665,\"{'Sb': 3.0, 'Au': 1.0}\",\"('Au', 'Sb')\",OTHERS,False\nmp-1219165,\"{'Sm': 4.0, 'Fe': 3.0, 'Co': 1.0, 'As': 4.0, 'O': 4.0}\",\"('As', 'Co', 'Fe', 'O', 'Sm')\",OTHERS,False\nmp-639160,\"{'Gd': 4.0, 'In': 2.0, 'Ge': 4.0}\",\"('Gd', 'Ge', 'In')\",OTHERS,False\nmp-1186935,\"{'Sc': 2.0, 'Cd': 1.0, 'Os': 1.0}\",\"('Cd', 'Os', 'Sc')\",OTHERS,False\nmp-1246245,\"{'Ba': 8.0, 'Fe': 3.0, 'N': 8.0}\",\"('Ba', 'Fe', 'N')\",OTHERS,False\nmp-1193953,\"{'U': 4.0, 'N': 4.0, 'F': 20.0}\",\"('F', 'N', 'U')\",OTHERS,False\nmp-22339,\"{'Co': 3.0, 'Sn': 3.0, 'Th': 3.0}\",\"('Co', 'Sn', 'Th')\",OTHERS,False\nmp-1096227,\"{'Ca': 1.0, 'Cd': 2.0, 'In': 1.0}\",\"('Ca', 'Cd', 'In')\",OTHERS,False\nmp-1215860,\"{'Yb': 1.0, 'Lu': 2.0, 'Se': 4.0}\",\"('Lu', 'Se', 'Yb')\",OTHERS,False\nmp-1213479,\"{'Dy': 4.0, 'Cu': 2.0, 'W': 8.0, 'O': 32.0}\",\"('Cu', 'Dy', 'O', 'W')\",OTHERS,False\nmp-17623,\"{'Si': 4.0, 'Co': 18.0, 'Sm': 2.0}\",\"('Co', 'Si', 'Sm')\",OTHERS,False\nmp-1218040,\"{'Sr': 2.0, 'Nd': 2.0, 'Sc': 2.0, 'O': 8.0}\",\"('Nd', 'O', 'Sc', 'Sr')\",OTHERS,False\nmp-1225871,\"{'Cu': 8.0, 'Ge': 4.0, 'S': 12.0}\",\"('Cu', 'Ge', 'S')\",OTHERS,False\nmp-758355,\"{'Li': 8.0, 'V': 8.0, 'P': 8.0, 'O': 40.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmvc-3621,\"{'Mn': 4.0, 'Zn': 6.0, 'O': 14.0}\",\"('Mn', 'O', 'Zn')\",OTHERS,False\nmp-541582,\"{'Re': 4.0, 'Se': 8.0}\",\"('Re', 'Se')\",OTHERS,False\nmp-1188815,\"{'U': 4.0, 'Co': 2.0, 'S': 10.0}\",\"('Co', 'S', 'U')\",OTHERS,False\nmp-1176443,\"{'Mn': 4.0, 'Fe': 4.0, 'B': 4.0, 'O': 16.0}\",\"('B', 'Fe', 'Mn', 'O')\",OTHERS,False\nmp-570596,\"{'Nd': 4.0, 'Ni': 34.0}\",\"('Nd', 'Ni')\",OTHERS,False\nmp-1220390,\"{'Nb': 1.0, 'Au': 4.0}\",\"('Au', 'Nb')\",OTHERS,False\nmp-560570,\"{'Rb': 2.0, 'Na': 1.0, 'Al': 6.0, 'F': 21.0}\",\"('Al', 'F', 'Na', 'Rb')\",OTHERS,False\nmp-17891,\"{'O': 16.0, 'Na': 4.0, 'Si': 4.0, 'Y': 4.0}\",\"('Na', 'O', 'Si', 'Y')\",OTHERS,False\nmp-1226322,\"{'Cr': 4.0, 'In': 1.0, 'Ag': 1.0, 'Se': 8.0}\",\"('Ag', 'Cr', 'In', 'Se')\",OTHERS,False\nmp-1255465,\"{'Zn': 2.0, 'Cu': 2.0, 'Si': 4.0, 'O': 12.0}\",\"('Cu', 'O', 'Si', 'Zn')\",OTHERS,False\nmp-978101,\"{'Pr': 2.0, 'Fe': 2.0, 'Ge': 4.0}\",\"('Fe', 'Ge', 'Pr')\",OTHERS,False\nmp-14206,\"{'K': 3.0, 'As': 2.0, 'Ag': 3.0}\",\"('Ag', 'As', 'K')\",OTHERS,False\nmp-22747,\"{'C': 4.0, 'O': 8.0, 'Pb': 2.0}\",\"('C', 'O', 'Pb')\",OTHERS,False\nmp-532178,\"{'O': 48.0, 'Yb': 16.0, 'Zr': 12.0}\",\"('O', 'Yb', 'Zr')\",OTHERS,False\nmp-560189,\"{'Na': 8.0, 'Li': 16.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Li', 'Na', 'O')\",OTHERS,False\nmp-622930,\"{'Ba': 8.0, 'In': 12.0, 'O': 26.0}\",\"('Ba', 'In', 'O')\",OTHERS,False\nmp-1221141,\"{'Na': 5.0, 'Br': 4.0, 'Cl': 1.0}\",\"('Br', 'Cl', 'Na')\",OTHERS,False\nmp-1210580,\"{'Na': 2.0, 'Lu': 2.0, 'Re': 8.0, 'O': 40.0}\",\"('Lu', 'Na', 'O', 'Re')\",OTHERS,False\nmp-672315,\"{'P': 6.0, 'C': 12.0, 'S': 12.0, 'N': 18.0}\",\"('C', 'N', 'P', 'S')\",OTHERS,False\nmp-22136,\"{'O': 12.0, 'Co': 4.0, 'Ge': 4.0}\",\"('Co', 'Ge', 'O')\",OTHERS,False\nmp-30696,\"{'Cu': 8.0, 'Cd': 20.0}\",\"('Cd', 'Cu')\",OTHERS,False\nmp-1253602,\"{'Ba': 6.0, 'Al': 3.0, 'W': 6.0, 'F': 33.0}\",\"('Al', 'Ba', 'F', 'W')\",OTHERS,False\nmp-11558,\"{'Sc': 5.0, 'Re': 24.0}\",\"('Re', 'Sc')\",OTHERS,False\nmp-22652,\"{'Fe': 12.0, 'S': 12.0}\",\"('Fe', 'S')\",OTHERS,False\nmp-558911,\"{'Cs': 4.0, 'Co': 6.0, 'S': 8.0}\",\"('Co', 'Cs', 'S')\",OTHERS,False\nmp-759893,\"{'Mn': 12.0, 'O': 7.0, 'F': 17.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-558861,\"{'K': 4.0, 'I': 4.0, 'O': 8.0, 'F': 8.0}\",\"('F', 'I', 'K', 'O')\",OTHERS,False\nmp-1216890,\"{'Ti': 3.0, 'Ni': 3.0}\",\"('Ni', 'Ti')\",OTHERS,False\nmp-1199416,\"{'Eu': 8.0, 'Co': 4.0, 'Si': 12.0, 'O': 40.0}\",\"('Co', 'Eu', 'O', 'Si')\",OTHERS,False\nGa 43 Co 47 Cu 10,\"{'Ga': 43.0, 'Co': 47.0, 'Cu': 10.0}\",\"('Co', 'Cu', 'Ga')\",QC,False\nmvc-5860,\"{'Bi': 16.0, 'O': 32.0}\",\"('Bi', 'O')\",OTHERS,False\nmp-555559,\"{'Sm': 12.0, 'Nb': 4.0, 'Se': 12.0, 'O': 16.0}\",\"('Nb', 'O', 'Se', 'Sm')\",OTHERS,False\nmp-1174544,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-16488,\"{'Al': 18.0, 'Co': 4.0}\",\"('Al', 'Co')\",OTHERS,False\nmp-753298,\"{'Pr': 1.0, 'Y': 1.0, 'O': 2.0}\",\"('O', 'Pr', 'Y')\",OTHERS,False\nmp-1246650,\"{'Mn': 24.0, 'Se': 16.0, 'N': 16.0}\",\"('Mn', 'N', 'Se')\",OTHERS,False\nmp-1105794,\"{'Pu': 4.0, 'Si': 10.0, 'Pt': 6.0}\",\"('Pt', 'Pu', 'Si')\",OTHERS,False\nmp-849270,\"{'Ti': 34.0, 'N': 12.0, 'O': 48.0}\",\"('N', 'O', 'Ti')\",OTHERS,False\nmp-1187876,\"{'Y': 1.0, 'Rh': 2.0, 'Pb': 1.0}\",\"('Pb', 'Rh', 'Y')\",OTHERS,False\nmp-555282,\"{'Ga': 8.0, 'S': 20.0, 'N': 20.0, 'Cl': 28.0}\",\"('Cl', 'Ga', 'N', 'S')\",OTHERS,False\nmp-777339,\"{'Li': 2.0, 'V': 6.0, 'O': 2.0, 'F': 22.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-1114521,\"{'Rb': 2.0, 'Al': 1.0, 'In': 1.0, 'I': 6.0}\",\"('Al', 'I', 'In', 'Rb')\",OTHERS,False\nmp-1190610,\"{'Ho': 14.0, 'Te': 4.0, 'Au': 4.0}\",\"('Au', 'Ho', 'Te')\",OTHERS,False\nmp-569348,\"{'Tl': 8.0, 'Ga': 8.0, 'Br': 32.0}\",\"('Br', 'Ga', 'Tl')\",OTHERS,False\nmp-1028409,\"{'Mo': 3.0, 'W': 1.0, 'Se': 8.0}\",\"('Mo', 'Se', 'W')\",OTHERS,False\nmp-1264019,\"{'Mn': 8.0, 'Zn': 12.0, 'Si': 12.0, 'O': 48.0}\",\"('Mn', 'O', 'Si', 'Zn')\",OTHERS,False\nmp-1185613,\"{'Mg': 1.0, 'Tl': 1.0, 'O': 3.0}\",\"('Mg', 'O', 'Tl')\",OTHERS,False\nmp-1245870,\"{'Sc': 6.0, 'Si': 12.0, 'N': 22.0}\",\"('N', 'Sc', 'Si')\",OTHERS,False\nmp-1194659,\"{'Cs': 4.0, 'Nb': 4.0, 'V': 8.0, 'O': 32.0}\",\"('Cs', 'Nb', 'O', 'V')\",OTHERS,False\nmp-1206701,\"{'Sr': 2.0, 'Yb': 1.0, 'U': 1.0, 'O': 6.0}\",\"('O', 'Sr', 'U', 'Yb')\",OTHERS,False\nmp-567379,{'Bi': 4.0},\"('Bi',)\",OTHERS,False\nmp-1039255,\"{'Mg': 8.0, 'Cd': 4.0}\",\"('Cd', 'Mg')\",OTHERS,False\nmp-21083,\"{'O': 20.0, 'Co': 4.0, 'Ba': 4.0, 'Yb': 8.0}\",\"('Ba', 'Co', 'O', 'Yb')\",OTHERS,False\nmp-975395,\"{'Nd': 1.0, 'Mg': 16.0, 'Al': 12.0}\",\"('Al', 'Mg', 'Nd')\",OTHERS,False\nmp-6113,\"{'Li': 4.0, 'O': 20.0, 'Ti': 4.0, 'As': 4.0}\",\"('As', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-1216536,\"{'Tm': 1.0, 'Pa': 1.0, 'O': 4.0}\",\"('O', 'Pa', 'Tm')\",OTHERS,False\nmp-549389,\"{'Li': 4.0, 'O': 4.0, 'C': 1.0}\",\"('C', 'Li', 'O')\",OTHERS,False\nmp-1093564,\"{'Li': 1.0, 'Mg': 1.0, 'Ga': 2.0}\",\"('Ga', 'Li', 'Mg')\",OTHERS,False\nmp-557653,\"{'Si': 4.0, 'O': 8.0}\",\"('O', 'Si')\",OTHERS,False\nmp-1078874,\"{'Ba': 2.0, 'Ce': 1.0, 'Ta': 1.0, 'O': 6.0}\",\"('Ba', 'Ce', 'O', 'Ta')\",OTHERS,False\nmp-1194887,\"{'Ca': 32.0, 'As': 24.0}\",\"('As', 'Ca')\",OTHERS,False\nmp-1849,\"{'P': 3.0, 'Mn': 6.0}\",\"('Mn', 'P')\",OTHERS,False\nmp-1216722,\"{'Ti': 1.0, 'Mo': 1.0, 'O': 4.0}\",\"('Mo', 'O', 'Ti')\",OTHERS,False\nmp-998308,\"{'Cs': 1.0, 'Sc': 1.0, 'Br': 3.0}\",\"('Br', 'Cs', 'Sc')\",OTHERS,False\nmp-756463,\"{'Mn': 3.0, 'Sb': 1.0, 'P': 4.0, 'O': 16.0}\",\"('Mn', 'O', 'P', 'Sb')\",OTHERS,False\nmp-849268,\"{'Ni': 6.0, 'O': 2.0, 'F': 10.0}\",\"('F', 'Ni', 'O')\",OTHERS,False\nmp-975190,\"{'Rb': 1.0, 'Te': 1.0, 'O': 3.0}\",\"('O', 'Rb', 'Te')\",OTHERS,False\nmp-7219,\"{'Na': 4.0, 'Se': 6.0, 'Zr': 2.0}\",\"('Na', 'Se', 'Zr')\",OTHERS,False\nmp-1246886,\"{'Ta': 2.0, 'C': 12.0, 'N': 18.0}\",\"('C', 'N', 'Ta')\",OTHERS,False\nmp-1213497,\"{'Gd': 4.0, 'Ga': 18.0, 'Ir': 6.0}\",\"('Ga', 'Gd', 'Ir')\",OTHERS,False\nmp-1216457,\"{'V': 6.0, 'Co': 1.0, 'Rh': 1.0}\",\"('Co', 'Rh', 'V')\",OTHERS,False\nmp-757767,\"{'Li': 32.0, 'Mn': 8.0, 'P': 16.0, 'O': 72.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1227151,\"{'Ca': 2.0, 'Sm': 2.0, 'Mn': 4.0, 'O': 12.0}\",\"('Ca', 'Mn', 'O', 'Sm')\",OTHERS,False\nmp-1080041,\"{'Cs': 2.0, 'Na': 1.0, 'Bi': 1.0, 'Cl': 6.0}\",\"('Bi', 'Cl', 'Cs', 'Na')\",OTHERS,False\nmp-1197020,{'Ge': 34.0},\"('Ge',)\",OTHERS,False\nmp-738654,\"{'U': 4.0, 'H': 64.0, 'C': 16.0, 'N': 16.0, 'O': 48.0}\",\"('C', 'H', 'N', 'O', 'U')\",OTHERS,False\nmp-752507,\"{'Li': 4.0, 'Cu': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-441,\"{'Rb': 2.0, 'Te': 1.0}\",\"('Rb', 'Te')\",OTHERS,False\nmp-1181880,\"{'Fe': 6.0, 'S': 6.0, 'O': 60.0}\",\"('Fe', 'O', 'S')\",OTHERS,False\nmp-676060,\"{'Gd': 4.0, 'Cu': 2.0, 'O': 8.0}\",\"('Cu', 'Gd', 'O')\",OTHERS,False\nmp-1217020,\"{'Ti': 1.0, 'Al': 1.0, 'Fe': 2.0}\",\"('Al', 'Fe', 'Ti')\",OTHERS,False\nmp-757486,\"{'Li': 9.0, 'Cr': 3.0, 'P': 6.0, 'O': 24.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-1245936,\"{'Ba': 28.0, 'Zr': 4.0, 'N': 24.0}\",\"('Ba', 'N', 'Zr')\",OTHERS,False\nmp-22579,\"{'O': 16.0, 'Mn': 4.0, 'Se': 4.0}\",\"('Mn', 'O', 'Se')\",OTHERS,False\nmp-758978,\"{'Li': 3.0, 'Ni': 4.0, 'O': 8.0}\",\"('Li', 'Ni', 'O')\",OTHERS,False\nmp-631335,\"{'Mg': 1.0, 'Ta': 1.0, 'Zn': 1.0}\",\"('Mg', 'Ta', 'Zn')\",OTHERS,False\nmp-776457,\"{'Li': 1.0, 'V': 4.0, 'O': 1.0, 'F': 11.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-976147,\"{'Pr': 1.0, 'Er': 3.0}\",\"('Er', 'Pr')\",OTHERS,False\nmp-392,\"{'Sb': 8.0, 'U': 6.0}\",\"('Sb', 'U')\",OTHERS,False\nmp-975652,\"{'Pr': 2.0, 'Te': 4.0}\",\"('Pr', 'Te')\",OTHERS,False\nmp-1058581,{'Ba': 2.0},\"('Ba',)\",OTHERS,False\nmp-640073,\"{'Fe': 2.0, 'Cu': 2.0, 'S': 4.0}\",\"('Cu', 'Fe', 'S')\",OTHERS,False\nmp-1226362,\"{'Cu': 6.0, 'Te': 2.0, 'Mo': 2.0, 'H': 2.0, 'Cl': 4.0, 'O': 15.0}\",\"('Cl', 'Cu', 'H', 'Mo', 'O', 'Te')\",OTHERS,False\nmp-757153,\"{'Li': 1.0, 'Fe': 5.0, 'O': 8.0}\",\"('Fe', 'Li', 'O')\",OTHERS,False\nmp-28810,\"{'C': 2.0, 'Na': 4.0, 'S': 6.0}\",\"('C', 'Na', 'S')\",OTHERS,False\nmp-1174704,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1178410,\"{'Cu': 4.0, 'Ni': 2.0, 'O': 8.0}\",\"('Cu', 'Ni', 'O')\",OTHERS,False\nmp-1012103,\"{'V': 16.0, 'Ni': 8.0, 'O': 48.0}\",\"('Ni', 'O', 'V')\",OTHERS,False\nmp-1226313,\"{'Cr': 1.0, 'Mo': 1.0, 'O': 3.0}\",\"('Cr', 'Mo', 'O')\",OTHERS,False\nmp-1195013,\"{'Na': 4.0, 'Pd': 2.0, 'S': 16.0, 'O': 52.0}\",\"('Na', 'O', 'Pd', 'S')\",OTHERS,False\nmp-1181862,\"{'Cd': 2.0, 'S': 2.0}\",\"('Cd', 'S')\",OTHERS,False\nmp-640345,\"{'Cs': 2.0, 'Mn': 1.0, 'Fe': 1.0, 'C': 6.0, 'N': 6.0}\",\"('C', 'Cs', 'Fe', 'Mn', 'N')\",OTHERS,False\nmp-1187675,\"{'Yb': 1.0, 'Ho': 3.0}\",\"('Ho', 'Yb')\",OTHERS,False\nmp-19292,\"{'O': 6.0, 'Co': 1.0, 'As': 2.0}\",\"('As', 'Co', 'O')\",OTHERS,False\nmp-1105218,\"{'Na': 4.0, 'Os': 4.0, 'O': 12.0}\",\"('Na', 'O', 'Os')\",OTHERS,False\nmp-1245863,\"{'Mg': 1.0, 'Fe': 10.0, 'N': 8.0}\",\"('Fe', 'Mg', 'N')\",OTHERS,False\nZn 58 Mg 40 Tm 2,\"{'Zn': 58.0, 'Mg': 40.0, 'Tm': 2.0}\",\"('Mg', 'Tm', 'Zn')\",QC,False\nmp-776506,\"{'Li': 6.0, 'Mn': 3.0, 'V': 3.0, 'P': 6.0, 'H': 6.0, 'O': 30.0}\",\"('H', 'Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-1180521,\"{'Na': 16.0, 'W': 8.0, 'O': 48.0}\",\"('Na', 'O', 'W')\",OTHERS,False\nmp-768613,\"{'Zr': 1.0, 'Mn': 6.0, 'Sn': 6.0}\",\"('Mn', 'Sn', 'Zr')\",OTHERS,False\nmp-1197056,\"{'Sm': 8.0, 'Ga': 16.0, 'Se': 32.0}\",\"('Ga', 'Se', 'Sm')\",OTHERS,False\nmp-1217973,\"{'Sr': 1.0, 'Pr': 1.0, 'V': 1.0, 'O': 4.0}\",\"('O', 'Pr', 'Sr', 'V')\",OTHERS,False\nmp-625367,\"{'Lu': 2.0, 'H': 2.0, 'O': 4.0}\",\"('H', 'Lu', 'O')\",OTHERS,False\nmp-1191988,\"{'Y': 4.0, 'Mn': 4.0, 'B': 16.0}\",\"('B', 'Mn', 'Y')\",OTHERS,False\nmp-648788,\"{'Te': 14.0, 'Os': 2.0, 'O': 10.0, 'F': 64.0}\",\"('F', 'O', 'Os', 'Te')\",OTHERS,False\nmp-1018779,\"{'Li': 3.0, 'Pr': 1.0, 'Sb': 2.0}\",\"('Li', 'Pr', 'Sb')\",OTHERS,False\nmp-10846,\"{'Ni': 4.0, 'As': 4.0, 'Se': 4.0}\",\"('As', 'Ni', 'Se')\",OTHERS,False\nmp-753323,\"{'Li': 4.0, 'Mn': 2.0, 'O': 1.0, 'F': 7.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1206244,\"{'Lu': 2.0, 'O': 4.0}\",\"('Lu', 'O')\",OTHERS,False\nmp-1104583,\"{'Pt': 1.0, 'S': 2.0, 'N': 4.0, 'O': 8.0}\",\"('N', 'O', 'Pt', 'S')\",OTHERS,False\nmp-1827,\"{'Ga': 4.0, 'Sr': 1.0}\",\"('Ga', 'Sr')\",OTHERS,False\nmp-1178188,\"{'Ho': 10.0, 'Ti': 10.0, 'O': 34.0}\",\"('Ho', 'O', 'Ti')\",OTHERS,False\nmp-1101948,\"{'Nb': 4.0, 'P': 4.0, 'Rh': 4.0}\",\"('Nb', 'P', 'Rh')\",OTHERS,False\nmp-1188137,\"{'Cd': 4.0, 'Hg': 4.0, 'S': 4.0, 'Br': 4.0}\",\"('Br', 'Cd', 'Hg', 'S')\",OTHERS,False\nmp-998928,\"{'Tl': 1.0, 'Cd': 1.0, 'S': 2.0}\",\"('Cd', 'S', 'Tl')\",OTHERS,False\nmp-761797,\"{'Li': 6.0, 'Mn': 15.0, 'O': 32.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-1058171,\"{'Na': 1.0, 'S': 1.0}\",\"('Na', 'S')\",OTHERS,False\nmp-1220172,\"{'Nd': 1.0, 'Al': 2.0, 'Pt': 3.0}\",\"('Al', 'Nd', 'Pt')\",OTHERS,False\nmp-1072645,\"{'Si': 4.0, 'Os': 2.0}\",\"('Os', 'Si')\",OTHERS,False\nmp-20313,\"{'O': 16.0, 'Si': 4.0, 'Fe': 8.0}\",\"('Fe', 'O', 'Si')\",OTHERS,False\nmp-1226506,\"{'Ce': 1.0, 'Th': 2.0, 'O': 6.0}\",\"('Ce', 'O', 'Th')\",OTHERS,False\nmp-997002,\"{'K': 2.0, 'Au': 2.0, 'O': 4.0}\",\"('Au', 'K', 'O')\",OTHERS,False\nmp-756149,\"{'Li': 3.0, 'Nb': 4.0, 'Fe': 1.0, 'O': 12.0}\",\"('Fe', 'Li', 'Nb', 'O')\",OTHERS,False\nmp-1186176,\"{'Na': 1.0, 'Sn': 3.0}\",\"('Na', 'Sn')\",OTHERS,False\nmp-675293,\"{'Yb': 2.0, 'Y': 4.0, 'S': 8.0}\",\"('S', 'Y', 'Yb')\",OTHERS,False\nmp-8308,\"{'B': 2.0, 'Ca': 3.0, 'Ni': 7.0}\",\"('B', 'Ca', 'Ni')\",OTHERS,False\nmp-530985,\"{'La': 20.0, 'Mn': 18.0, 'O': 60.0}\",\"('La', 'Mn', 'O')\",OTHERS,False\nmp-11436,\"{'Er': 1.0, 'V': 2.0, 'Ga': 4.0}\",\"('Er', 'Ga', 'V')\",OTHERS,False\nmp-1185050,\"{'La': 2.0, 'Tl': 1.0, 'Ag': 1.0}\",\"('Ag', 'La', 'Tl')\",OTHERS,False\nmp-1225980,\"{'Cu': 19.0, 'Sn': 16.0}\",\"('Cu', 'Sn')\",OTHERS,False\nmp-557407,\"{'K': 6.0, 'Na': 2.0, 'U': 2.0, 'C': 6.0, 'O': 22.0}\",\"('C', 'K', 'Na', 'O', 'U')\",OTHERS,False\nmp-768091,\"{'Li': 8.0, 'Ni': 14.0, 'O': 22.0}\",\"('Li', 'Ni', 'O')\",OTHERS,False\nmvc-10602,\"{'Cr': 6.0, 'P': 6.0, 'O': 26.0}\",\"('Cr', 'O', 'P')\",OTHERS,False\nmp-570451,\"{'Nb': 4.0, 'Te': 6.0}\",\"('Nb', 'Te')\",OTHERS,False\nmp-756258,\"{'Li': 3.0, 'Co': 4.0, 'Cu': 1.0, 'O': 8.0}\",\"('Co', 'Cu', 'Li', 'O')\",OTHERS,False\nmp-541695,\"{'W': 4.0, 'C': 24.0, 'O': 24.0}\",\"('C', 'O', 'W')\",OTHERS,False\nmp-1197831,\"{'Li': 8.0, 'Br': 8.0, 'O': 24.0}\",\"('Br', 'Li', 'O')\",OTHERS,False\nmp-1037722,\"{'Hf': 1.0, 'Mg': 30.0, 'Al': 1.0, 'O': 32.0}\",\"('Al', 'Hf', 'Mg', 'O')\",OTHERS,False\nmp-1199697,\"{'Ce': 12.0, 'Al': 24.0, 'Pt': 16.0}\",\"('Al', 'Ce', 'Pt')\",OTHERS,False\nmp-776417,\"{'Li': 1.0, 'V': 6.0, 'O': 7.0, 'F': 5.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-540632,\"{'Rb': 2.0, 'Ta': 2.0, 'Ge': 6.0, 'O': 18.0}\",\"('Ge', 'O', 'Rb', 'Ta')\",OTHERS,False\nmp-1114397,\"{'Rb': 2.0, 'Y': 1.0, 'In': 1.0, 'F': 6.0}\",\"('F', 'In', 'Rb', 'Y')\",OTHERS,False\nmp-973283,\"{'Ho': 1.0, 'Lu': 1.0, 'Ag': 2.0}\",\"('Ag', 'Ho', 'Lu')\",OTHERS,False\nmp-26407,\"{'Li': 8.0, 'O': 28.0, 'P': 8.0, 'Sn': 4.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,False\nmp-676762,\"{'Yb': 2.0, 'Ce': 4.0, 'S': 8.0}\",\"('Ce', 'S', 'Yb')\",OTHERS,False\nmp-1212695,\"{'Ga': 2.0, 'Hg': 5.0, 'Se': 8.0}\",\"('Ga', 'Hg', 'Se')\",OTHERS,False\nmp-582540,\"{'Eu': 4.0, 'Mo': 4.0, 'O': 16.0, 'F': 4.0}\",\"('Eu', 'F', 'Mo', 'O')\",OTHERS,False\nmp-1227974,\"{'Ba': 1.0, 'Bi': 2.0, 'Br': 2.0, 'O': 2.0}\",\"('Ba', 'Bi', 'Br', 'O')\",OTHERS,False\nmp-1215344,\"{'Zr': 8.0, 'Ag': 1.0, 'Pd': 3.0}\",\"('Ag', 'Pd', 'Zr')\",OTHERS,False\nmp-542292,\"{'Ag': 2.0, 'Mn': 6.0, 'As': 6.0, 'O': 24.0, 'H': 4.0}\",\"('Ag', 'As', 'H', 'Mn', 'O')\",OTHERS,False\nmp-770653,\"{'Sr': 12.0, 'W': 8.0, 'O': 36.0}\",\"('O', 'Sr', 'W')\",OTHERS,False\nmp-1239256,\"{'Zr': 4.0, 'Cr': 4.0, 'Cu': 4.0, 'S': 16.0}\",\"('Cr', 'Cu', 'S', 'Zr')\",OTHERS,False\nmp-775905,\"{'Li': 8.0, 'Mn': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nCd 65 Mg 20 Lu 15,\"{'Cd': 65.0, 'Mg': 20.0, 'Lu': 15.0}\",\"('Cd', 'Lu', 'Mg')\",QC,False\nmp-572621,\"{'Ni': 4.0, 'P': 4.0, 'C': 12.0, 'O': 18.0}\",\"('C', 'Ni', 'O', 'P')\",OTHERS,False\nmp-1209523,\"{'Rb': 6.0, 'Sc': 4.0, 'Br': 18.0}\",\"('Br', 'Rb', 'Sc')\",OTHERS,False\nmp-27941,\"{'O': 12.0, 'Cl': 4.0, 'Ag': 4.0}\",\"('Ag', 'Cl', 'O')\",OTHERS,False\nmp-556282,\"{'Ba': 8.0, 'Al': 4.0, 'In': 4.0, 'O': 20.0}\",\"('Al', 'Ba', 'In', 'O')\",OTHERS,False\nmp-559809,\"{'Rb': 8.0, 'Fe': 4.0, 'O': 10.0}\",\"('Fe', 'O', 'Rb')\",OTHERS,False\nmp-779351,\"{'Li': 4.0, 'Mn': 1.0, 'P': 6.0, 'H': 8.0, 'O': 22.0}\",\"('H', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-755421,\"{'Ca': 6.0, 'Hf': 1.0, 'O': 8.0}\",\"('Ca', 'Hf', 'O')\",OTHERS,False\nmp-1213505,\"{'Er': 8.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Er', 'O')\",OTHERS,False\nmp-1197097,\"{'K': 8.0, 'P': 8.0, 'H': 24.0, 'O': 32.0}\",\"('H', 'K', 'O', 'P')\",OTHERS,False\nmp-1223001,\"{'La': 4.0, 'Th': 4.0, 'U': 4.0, 'S': 20.0}\",\"('La', 'S', 'Th', 'U')\",OTHERS,False\nmp-1226829,\"{'Cd': 4.0, 'In': 1.0, 'Te': 4.0, 'As': 1.0}\",\"('As', 'Cd', 'In', 'Te')\",OTHERS,False\nmvc-11797,\"{'Ca': 2.0, 'Cu': 4.0, 'O': 8.0}\",\"('Ca', 'Cu', 'O')\",OTHERS,False\nmvc-1659,\"{'Zn': 2.0, 'Sb': 4.0, 'O': 12.0}\",\"('O', 'Sb', 'Zn')\",OTHERS,False\nmp-1036104,\"{'Mg': 14.0, 'Mn': 1.0, 'Cr': 1.0, 'O': 16.0}\",\"('Cr', 'Mg', 'Mn', 'O')\",OTHERS,False\nmp-1187900,\"{'Yb': 4.0, 'Mg': 2.0}\",\"('Mg', 'Yb')\",OTHERS,False\nmp-698096,\"{'La': 5.0, 'Fe': 1.0, 'Re': 3.0, 'O': 16.0}\",\"('Fe', 'La', 'O', 'Re')\",OTHERS,False\nmp-1246482,\"{'Mg': 2.0, 'Ni': 2.0, 'W': 2.0, 'S': 8.0}\",\"('Mg', 'Ni', 'S', 'W')\",OTHERS,False\nmp-1191803,\"{'Ag': 10.0, 'Te': 2.0, 'P': 2.0, 'O': 8.0}\",\"('Ag', 'O', 'P', 'Te')\",OTHERS,False\nmp-683867,\"{'Tb': 16.0, 'B': 48.0, 'O': 96.0}\",\"('B', 'O', 'Tb')\",OTHERS,False\nmp-1206680,\"{'Li': 2.0, 'Sn': 1.0, 'Pd': 1.0}\",\"('Li', 'Pd', 'Sn')\",OTHERS,False\nmp-2375,\"{'Pd': 3.0, 'Np': 1.0}\",\"('Np', 'Pd')\",OTHERS,False\nmp-1199954,\"{'Mn': 8.0, 'B': 16.0, 'O': 32.0}\",\"('B', 'Mn', 'O')\",OTHERS,False\nmp-1204230,\"{'Sr': 64.0, 'Ga': 32.0, 'O': 112.0}\",\"('Ga', 'O', 'Sr')\",OTHERS,False\nmp-778241,\"{'Li': 2.0, 'V': 3.0, 'F': 8.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-1174542,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1205833,\"{'Ba': 2.0, 'Nb': 1.0, 'Rh': 1.0, 'O': 6.0}\",\"('Ba', 'Nb', 'O', 'Rh')\",OTHERS,False\nmp-1184670,\"{'Hf': 6.0, 'Al': 2.0}\",\"('Al', 'Hf')\",OTHERS,False\nmp-19719,\"{'Mn': 2.0, 'Se': 8.0, 'Yb': 4.0}\",\"('Mn', 'Se', 'Yb')\",OTHERS,False\nmvc-2232,\"{'Ba': 4.0, 'Mg': 2.0, 'Fe': 4.0, 'Cu': 2.0, 'F': 28.0}\",\"('Ba', 'Cu', 'F', 'Fe', 'Mg')\",OTHERS,False\nmp-6027,\"{'O': 12.0, 'Cu': 2.0, 'Ba': 4.0, 'Tl': 4.0}\",\"('Ba', 'Cu', 'O', 'Tl')\",OTHERS,False\nmp-1204777,\"{'Zn': 4.0, 'H': 48.0, 'C': 24.0, 'S': 16.0, 'N': 8.0}\",\"('C', 'H', 'N', 'S', 'Zn')\",OTHERS,False\nmp-1221554,\"{'Mn': 2.0, 'Si': 4.0, 'Ni': 2.0}\",\"('Mn', 'Ni', 'Si')\",OTHERS,False\nmp-775748,\"{'Li': 4.0, 'Cr': 3.0, 'Fe': 2.0, 'Sn': 3.0, 'O': 16.0}\",\"('Cr', 'Fe', 'Li', 'O', 'Sn')\",OTHERS,False\nmp-1211634,\"{'K': 2.0, 'Pr': 2.0, 'Cl': 8.0, 'O': 8.0}\",\"('Cl', 'K', 'O', 'Pr')\",OTHERS,False\nmp-768490,\"{'Bi': 8.0, 'S': 12.0, 'O': 48.0}\",\"('Bi', 'O', 'S')\",OTHERS,False\nmp-1198194,\"{'Cs': 2.0, 'P': 4.0, 'H': 10.0, 'O': 16.0}\",\"('Cs', 'H', 'O', 'P')\",OTHERS,False\nmp-557324,\"{'Cs': 2.0, 'Cr': 1.0, 'F': 6.0}\",\"('Cr', 'Cs', 'F')\",OTHERS,False\nmp-1216744,\"{'U': 1.0, 'Al': 8.0, 'Cu': 4.0}\",\"('Al', 'Cu', 'U')\",OTHERS,False\nmp-764273,\"{'Na': 4.0, 'Li': 2.0, 'Mn': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Li', 'Mn', 'Na', 'O', 'P')\",OTHERS,False\nmp-8713,\"{'S': 4.0, 'K': 4.0, 'Mn': 2.0}\",\"('K', 'Mn', 'S')\",OTHERS,False\nmp-766800,\"{'Li': 6.0, 'Fe': 12.0, 'P': 12.0, 'O': 48.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-1095778,\"{'Sr': 1.0, 'Pb': 1.0, 'Au': 2.0}\",\"('Au', 'Pb', 'Sr')\",OTHERS,False\nmp-1106187,\"{'Er': 7.0, 'Fe': 1.0, 'I': 12.0}\",\"('Er', 'Fe', 'I')\",OTHERS,False\nmp-1188147,\"{'B': 16.0, 'Os': 4.0}\",\"('B', 'Os')\",OTHERS,False\nmp-1185744,\"{'Mg': 17.0, 'Al': 11.0, 'Rh': 1.0}\",\"('Al', 'Mg', 'Rh')\",OTHERS,False\nmp-757889,\"{'Li': 4.0, 'Sn': 4.0, 'P': 12.0, 'O': 36.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,False\nmp-1216438,\"{'V': 2.0, 'Co': 6.0, 'As': 2.0, 'O': 16.0}\",\"('As', 'Co', 'O', 'V')\",OTHERS,False\nmp-1064529,\"{'Rb': 2.0, 'N': 2.0}\",\"('N', 'Rb')\",OTHERS,False\nmp-1222213,\"{'Mg': 4.0, 'Al': 4.0, 'Si': 2.0, 'O': 18.0}\",\"('Al', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-1247324,\"{'Re': 8.0, 'Pb': 12.0, 'N': 16.0}\",\"('N', 'Pb', 'Re')\",OTHERS,False\nAl 39 Pd 21 Fe 2,\"{'Al': 39.0, 'Pd': 21.0, 'Fe': 2.0}\",\"('Al', 'Fe', 'Pd')\",AC,False\nmp-1087238,\"{'Y': 2.0, 'Cu': 4.0, 'Sn': 4.0}\",\"('Cu', 'Sn', 'Y')\",OTHERS,False\nmp-1180425,\"{'Mg': 2.0, 'N': 2.0, 'Cl': 6.0, 'O': 12.0}\",\"('Cl', 'Mg', 'N', 'O')\",OTHERS,False\nmp-756689,\"{'La': 4.0, 'Sb': 2.0, 'O': 12.0}\",\"('La', 'O', 'Sb')\",OTHERS,False\nmp-31467,\"{'Ga': 3.0, 'Pd': 7.0}\",\"('Ga', 'Pd')\",OTHERS,False\nmp-22756,\"{'O': 6.0, 'K': 4.0, 'Pb': 2.0}\",\"('K', 'O', 'Pb')\",OTHERS,False\nmp-1094138,\"{'Na': 4.0, 'Co': 8.0, 'P': 8.0, 'O': 36.0}\",\"('Co', 'Na', 'O', 'P')\",OTHERS,False\nmp-675912,\"{'Yb': 4.0, 'Sm': 2.0, 'S': 8.0}\",\"('S', 'Sm', 'Yb')\",OTHERS,False\nmp-1206552,\"{'Ba': 2.0, 'Bi': 2.0, 'Br': 2.0, 'O': 4.0}\",\"('Ba', 'Bi', 'Br', 'O')\",OTHERS,False\nmp-1216581,\"{'V': 4.0, 'Fe': 2.0, 'Si': 2.0}\",\"('Fe', 'Si', 'V')\",OTHERS,False\nmp-772579,\"{'La': 8.0, 'W': 4.0, 'O': 24.0}\",\"('La', 'O', 'W')\",OTHERS,False\nmp-5284,\"{'Si': 2.0, 'Cr': 2.0, 'U': 1.0}\",\"('Cr', 'Si', 'U')\",OTHERS,False\nmp-1174579,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmvc-13134,\"{'Cr': 4.0, 'O': 12.0}\",\"('Cr', 'O')\",OTHERS,False\nmp-504957,\"{'Rb': 6.0, 'Re': 6.0, 'Cl': 24.0}\",\"('Cl', 'Rb', 'Re')\",OTHERS,False\nmp-995200,\"{'H': 2.0, 'C': 6.0}\",\"('C', 'H')\",OTHERS,False\nmp-755052,\"{'Li': 2.0, 'V': 4.0, 'O': 8.0}\",\"('Li', 'O', 'V')\",OTHERS,False\nmp-758539,\"{'Li': 2.0, 'Co': 3.0, 'O': 6.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-767672,\"{'Li': 4.0, 'V': 4.0, 'F': 20.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-9167,\"{'C': 4.0, 'N': 8.0, 'Sr': 4.0}\",\"('C', 'N', 'Sr')\",OTHERS,False\nmp-1221664,\"{'Mn': 1.0, 'Cd': 2.0, 'Te': 3.0}\",\"('Cd', 'Mn', 'Te')\",OTHERS,False\nmp-770170,\"{'Li': 6.0, 'V': 6.0, 'B': 4.0, 'O': 20.0}\",\"('B', 'Li', 'O', 'V')\",OTHERS,False\nmp-679630,\"{'Ba': 4.0, 'Be': 2.0, 'S': 2.0, 'O': 8.0, 'F': 8.0}\",\"('Ba', 'Be', 'F', 'O', 'S')\",OTHERS,False\nmp-758652,\"{'Zn': 2.0, 'Co': 2.0, 'O': 6.0}\",\"('Co', 'O', 'Zn')\",OTHERS,False\nmp-1096845,\"{'Ba': 2.0, 'Ag': 2.0, 'O': 4.0}\",\"('Ag', 'Ba', 'O')\",OTHERS,False\nmp-1223371,\"{'La': 2.0, 'Al': 3.0, 'Ga': 1.0, 'Pd': 4.0}\",\"('Al', 'Ga', 'La', 'Pd')\",OTHERS,False\nmp-1093586,\"{'Hf': 2.0, 'V': 1.0, 'Tc': 1.0}\",\"('Hf', 'Tc', 'V')\",OTHERS,False\nmp-1199260,\"{'B': 4.0, 'P': 8.0, 'Pb': 8.0, 'O': 36.0}\",\"('B', 'O', 'P', 'Pb')\",OTHERS,False\nmp-24234,\"{'H': 16.0, 'N': 4.0, 'O': 4.0, 'S': 4.0, 'Mo': 2.0}\",\"('H', 'Mo', 'N', 'O', 'S')\",OTHERS,False\nmp-1207223,\"{'Zr': 3.0, 'Zn': 3.0, 'Ge': 3.0}\",\"('Ge', 'Zn', 'Zr')\",OTHERS,False\nmp-29209,\"{'Mg': 2.0, 'Ba': 1.0, 'Bi': 2.0}\",\"('Ba', 'Bi', 'Mg')\",OTHERS,False\nmp-559781,\"{'K': 2.0, 'Y': 2.0, 'C': 16.0, 'O': 16.0}\",\"('C', 'K', 'O', 'Y')\",OTHERS,False\nmp-774906,\"{'Li': 8.0, 'Mn': 4.0, 'Ni': 12.0, 'O': 32.0}\",\"('Li', 'Mn', 'Ni', 'O')\",OTHERS,False\nmp-761277,\"{'Li': 4.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1209334,\"{'Rb': 2.0, 'Ho': 2.0, 'W': 4.0, 'O': 16.0}\",\"('Ho', 'O', 'Rb', 'W')\",OTHERS,False\nmp-1229327,\"{'Ba': 8.0, 'Gd': 4.0, 'Cu': 12.0, 'O': 27.0}\",\"('Ba', 'Cu', 'Gd', 'O')\",OTHERS,False\nmp-1244565,\"{'Ca': 2.0, 'Sn': 4.0, 'O': 10.0}\",\"('Ca', 'O', 'Sn')\",OTHERS,False\nmp-780263,\"{'Ba': 24.0, 'Ge': 12.0, 'O': 48.0}\",\"('Ba', 'Ge', 'O')\",OTHERS,False\nmp-1095164,\"{'Gd': 2.0, 'B': 4.0, 'Ru': 4.0}\",\"('B', 'Gd', 'Ru')\",OTHERS,False\nmp-1096534,\"{'Y': 1.0, 'Zr': 1.0, 'Pd': 2.0}\",\"('Pd', 'Y', 'Zr')\",OTHERS,False\nmp-11301,\"{'Cd': 2.0, 'Ho': 1.0}\",\"('Cd', 'Ho')\",OTHERS,False\nmp-676083,\"{'Ce': 4.0, 'U': 2.0, 'Se': 8.0}\",\"('Ce', 'Se', 'U')\",OTHERS,False\nmp-26155,\"{'Li': 2.0, 'O': 24.0, 'P': 8.0, 'Mn': 2.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1223424,\"{'La': 4.0, 'Pr': 1.0, 'Co': 10.0, 'P': 10.0}\",\"('Co', 'La', 'P', 'Pr')\",OTHERS,False\nmp-1180326,\"{'Na': 6.0, 'Np': 2.0, 'O': 12.0}\",\"('Na', 'Np', 'O')\",OTHERS,False\nmp-31433,\"{'Ni': 2.0, 'Sn': 8.0, 'Lu': 2.0}\",\"('Lu', 'Ni', 'Sn')\",OTHERS,False\nmp-541991,\"{'Cu': 6.0, 'Mo': 6.0, 'O': 24.0}\",\"('Cu', 'Mo', 'O')\",OTHERS,False\nmp-5878,\"{'F': 3.0, 'K': 1.0, 'Zn': 1.0}\",\"('F', 'K', 'Zn')\",OTHERS,False\nmp-777593,\"{'Li': 32.0, 'Ti': 3.0, 'Cr': 13.0, 'O': 48.0}\",\"('Cr', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-752793,\"{'Li': 8.0, 'Ag': 4.0, 'F': 12.0}\",\"('Ag', 'F', 'Li')\",OTHERS,False\nmp-989564,\"{'La': 1.0, 'Os': 1.0, 'N': 3.0}\",\"('La', 'N', 'Os')\",OTHERS,False\nmp-760468,\"{'Li': 2.0, 'Si': 4.0, 'Bi': 2.0, 'O': 12.0}\",\"('Bi', 'Li', 'O', 'Si')\",OTHERS,False\nmp-3035,\"{'Si': 2.0, 'Fe': 2.0, 'Ce': 1.0}\",\"('Ce', 'Fe', 'Si')\",OTHERS,False\nmp-36946,\"{'Tl': 2.0, 'Bi': 2.0, 'S': 4.0}\",\"('Bi', 'S', 'Tl')\",OTHERS,False\nmp-28592,\"{'Li': 7.0, 'O': 2.0, 'Br': 3.0}\",\"('Br', 'Li', 'O')\",OTHERS,False\nmp-1188906,\"{'Sm': 2.0, 'Ga': 2.0, 'Co': 15.0}\",\"('Co', 'Ga', 'Sm')\",OTHERS,False\nmp-10247,\"{'N': 3.0, 'Cr': 1.0, 'U': 2.0}\",\"('Cr', 'N', 'U')\",OTHERS,False\nmp-865902,\"{'Yb': 4.0, 'B': 16.0, 'Os': 4.0}\",\"('B', 'Os', 'Yb')\",OTHERS,False\nmp-1206794,\"{'Li': 1.0, 'Au': 1.0}\",\"('Au', 'Li')\",OTHERS,False\nmp-1037802,\"{'Ca': 1.0, 'Mg': 30.0, 'Mn': 1.0, 'O': 32.0}\",\"('Ca', 'Mg', 'Mn', 'O')\",OTHERS,False\nmp-570414,\"{'Tm': 8.0, 'Fe': 56.0, 'C': 4.0}\",\"('C', 'Fe', 'Tm')\",OTHERS,False\nmp-21792,\"{'O': 32.0, 'Na': 40.0, 'Co': 8.0}\",\"('Co', 'Na', 'O')\",OTHERS,False\nmvc-3232,\"{'Zn': 2.0, 'Si': 2.0, 'Ni': 2.0, 'O': 10.0}\",\"('Ni', 'O', 'Si', 'Zn')\",OTHERS,False\nmp-1078033,\"{'U': 1.0, 'Ga': 5.0, 'Rh': 1.0}\",\"('Ga', 'Rh', 'U')\",OTHERS,False\nmp-31337,\"{'Pd': 3.0, 'In': 1.0}\",\"('In', 'Pd')\",OTHERS,False\nmp-20132,\"{'In': 1.0, 'Hg': 1.0}\",\"('Hg', 'In')\",OTHERS,False\nmp-1097671,\"{'Ta': 1.0, 'Be': 1.0, 'Rh': 2.0}\",\"('Be', 'Rh', 'Ta')\",OTHERS,False\nmp-989405,\"{'Cs': 2.0, 'Li': 1.0, 'In': 1.0, 'Br': 6.0}\",\"('Br', 'Cs', 'In', 'Li')\",OTHERS,False\nmp-863669,\"{'Pm': 2.0, 'Cu': 1.0, 'Pt': 1.0}\",\"('Cu', 'Pm', 'Pt')\",OTHERS,False\nmp-26317,\"{'Li': 2.0, 'Cr': 2.0, 'P': 2.0, 'O': 8.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-1096445,\"{'Cs': 1.0, 'Rb': 2.0, 'Bi': 1.0}\",\"('Bi', 'Cs', 'Rb')\",OTHERS,False\nmp-834,\"{'N': 1.0, 'Th': 1.0}\",\"('N', 'Th')\",OTHERS,False\nmp-560714,\"{'Ba': 4.0, 'V': 16.0, 'As': 16.0, 'O': 76.0}\",\"('As', 'Ba', 'O', 'V')\",OTHERS,False\nmp-601286,\"{'Tb': 4.0, 'Cu': 4.0, 'Pb': 4.0, 'Se': 12.0}\",\"('Cu', 'Pb', 'Se', 'Tb')\",OTHERS,False\nmp-1174046,\"{'Li': 6.0, 'Mn': 2.0, 'Co': 2.0, 'O': 10.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nAu 60.7 Sn 25.2 Eu 14.1,\"{'Au': 60.7, 'Sn': 25.2, 'Eu': 14.1}\",\"('Au', 'Eu', 'Sn')\",AC,False\nmp-11247,\"{'Li': 3.0, 'Au': 1.0}\",\"('Au', 'Li')\",OTHERS,False\nAl 70 Pd 21 Tc 9,\"{'Al': 70.0, 'Pd': 21.0, 'Tc': 9.0}\",\"('Al', 'Pd', 'Tc')\",QC,False\nmp-6504,\"{'C': 2.0, 'O': 6.0, 'Mg': 1.0, 'Ba': 1.0}\",\"('Ba', 'C', 'Mg', 'O')\",OTHERS,False\nmp-680690,\"{'La': 1.0, 'Cu': 3.0, 'Ru': 4.0, 'O': 12.0}\",\"('Cu', 'La', 'O', 'Ru')\",OTHERS,False\nmp-1189013,\"{'K': 2.0, 'Pt': 1.0, 'N': 4.0, 'O': 10.0}\",\"('K', 'N', 'O', 'Pt')\",OTHERS,False\nmp-779341,\"{'Li': 9.0, 'Mn': 12.0, 'B': 12.0, 'O': 36.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1209044,\"{'Sc': 20.0, 'Sb': 12.0}\",\"('Sb', 'Sc')\",OTHERS,False\nmp-1196608,\"{'Nd': 12.0, 'Tl': 2.0, 'Fe': 26.0}\",\"('Fe', 'Nd', 'Tl')\",OTHERS,False\nmp-559994,\"{'La': 24.0, 'S': 16.0, 'Br': 4.0, 'N': 12.0}\",\"('Br', 'La', 'N', 'S')\",OTHERS,False\nmp-510357,\"{'La': 8.0, 'Br': 10.0, 'B': 8.0}\",\"('B', 'Br', 'La')\",OTHERS,False\nmp-1019108,\"{'Sm': 2.0, 'Co': 2.0, 'Si': 2.0}\",\"('Co', 'Si', 'Sm')\",OTHERS,False\nmp-1191536,\"{'Eu': 2.0, 'Zn': 22.0}\",\"('Eu', 'Zn')\",OTHERS,False\nmp-19039,\"{'O': 8.0, 'Mo': 2.0, 'Cd': 2.0}\",\"('Cd', 'Mo', 'O')\",OTHERS,False\nmp-1066,\"{'N': 1.0, 'Yb': 1.0}\",\"('N', 'Yb')\",OTHERS,False\nmp-1211215,\"{'Li': 8.0, 'Nd': 4.0, 'N': 20.0, 'O': 60.0}\",\"('Li', 'N', 'Nd', 'O')\",OTHERS,False\nmp-19356,\"{'O': 18.0, 'Mn': 6.0, 'Yb': 6.0}\",\"('Mn', 'O', 'Yb')\",OTHERS,False\nmp-1173397,\"{'Sc': 6.0, 'Nb': 2.0, 'O': 14.0}\",\"('Nb', 'O', 'Sc')\",OTHERS,False\nmp-972439,\"{'Zn': 6.0, 'P': 4.0, 'O': 16.0}\",\"('O', 'P', 'Zn')\",OTHERS,False\nmp-1227705,\"{'Ba': 1.0, 'Sr': 1.0, 'Ca': 1.0, 'Mg': 1.0, 'Si': 2.0, 'O': 8.0}\",\"('Ba', 'Ca', 'Mg', 'O', 'Si', 'Sr')\",OTHERS,False\nmp-6574,\"{'C': 1.0, 'N': 2.0, 'O': 2.0, 'Er': 2.0}\",\"('C', 'Er', 'N', 'O')\",OTHERS,False\nmvc-657,\"{'Fe': 2.0, 'W': 4.0, 'O': 16.0}\",\"('Fe', 'O', 'W')\",OTHERS,False\nmp-675374,\"{'Al': 4.0, 'Pb': 2.0, 'Se': 8.0}\",\"('Al', 'Pb', 'Se')\",OTHERS,False\nmp-1214906,\"{'Ag': 2.0, 'Mo': 2.0, 'O': 8.0}\",\"('Ag', 'Mo', 'O')\",OTHERS,False\nmp-13283,\"{'S': 12.0, 'Sm': 2.0, 'Er': 6.0}\",\"('Er', 'S', 'Sm')\",OTHERS,False\nmp-1179608,\"{'S': 2.0, 'O': 8.0}\",\"('O', 'S')\",OTHERS,False\nmp-616615,\"{'Hf': 16.0, 'Mn': 2.0, 'Te': 12.0}\",\"('Hf', 'Mn', 'Te')\",OTHERS,False\nmp-1189383,\"{'Li': 4.0, 'Cd': 2.0, 'Ge': 2.0, 'S': 8.0}\",\"('Cd', 'Ge', 'Li', 'S')\",OTHERS,False\nmp-1222873,\"{'Li': 2.0, 'Al': 1.0, 'Ni': 3.0, 'O': 6.0}\",\"('Al', 'Li', 'Ni', 'O')\",OTHERS,False\nAl 70.5 Pd 21 Mn 8.5,\"{'Al': 70.5, 'Pd': 21.0, 'Mn': 8.5}\",\"('Al', 'Mn', 'Pd')\",QC,False\nmp-625211,\"{'Li': 2.0, 'H': 6.0, 'O': 4.0}\",\"('H', 'Li', 'O')\",OTHERS,False\nmp-1245835,\"{'Pd': 2.0, 'C': 4.0, 'N': 4.0}\",\"('C', 'N', 'Pd')\",OTHERS,False\nmp-1200792,\"{'Fe': 4.0, 'P': 16.0, 'C': 16.0, 'O': 40.0}\",\"('C', 'Fe', 'O', 'P')\",OTHERS,False\nmp-1175669,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-22417,\"{'Li': 4.0, 'O': 16.0, 'P': 4.0, 'Ni': 4.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-631267,\"{'Ta': 1.0, 'Fe': 1.0, 'Sb': 1.0}\",\"('Fe', 'Sb', 'Ta')\",OTHERS,False\nmp-1222349,\"{'Li': 2.0, 'Ge': 2.0, 'Rh': 2.0, 'O': 8.0}\",\"('Ge', 'Li', 'O', 'Rh')\",OTHERS,False\nmp-1076897,\"{'Ca': 28.0, 'Mg': 4.0, 'Mn': 32.0, 'O': 80.0}\",\"('Ca', 'Mg', 'Mn', 'O')\",OTHERS,False\nmp-619577,\"{'K': 16.0, 'V': 16.0, 'P': 32.0, 'O': 120.0}\",\"('K', 'O', 'P', 'V')\",OTHERS,False\nmp-1227547,\"{'Cu': 6.0, 'Pb': 2.0, 'Se': 6.0, 'N': 2.0, 'O': 27.0}\",\"('Cu', 'N', 'O', 'Pb', 'Se')\",OTHERS,False\nmp-1147764,\"{'Na': 4.0, 'Cu': 2.0, 'I': 4.0, 'Cl': 4.0}\",\"('Cl', 'Cu', 'I', 'Na')\",OTHERS,False\nmp-983388,\"{'K': 3.0, 'Hg': 1.0}\",\"('Hg', 'K')\",OTHERS,False\nmp-722979,\"{'K': 4.0, 'Zn': 2.0, 'H': 16.0, 'N': 8.0}\",\"('H', 'K', 'N', 'Zn')\",OTHERS,False\nmp-1176187,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-560223,\"{'K': 8.0, 'Se': 4.0, 'S': 8.0, 'O': 24.0}\",\"('K', 'O', 'S', 'Se')\",OTHERS,False\nmp-1217234,\"{'Ti': 8.0, 'Co': 3.0, 'Ni': 1.0, 'Sn': 4.0}\",\"('Co', 'Ni', 'Sn', 'Ti')\",OTHERS,False\nmp-1093568,\"{'Y': 1.0, 'Zn': 2.0, 'Pd': 1.0}\",\"('Pd', 'Y', 'Zn')\",OTHERS,False\nmp-1040315,\"{'K': 1.0, 'Mg': 30.0, 'B': 1.0, 'O': 31.0}\",\"('B', 'K', 'Mg', 'O')\",OTHERS,False\nmp-771282,\"{'Li': 24.0, 'Ti': 7.0, 'Cr': 5.0, 'O': 36.0}\",\"('Cr', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-758332,\"{'Ca': 4.0, 'Gd': 16.0, 'O': 28.0}\",\"('Ca', 'Gd', 'O')\",OTHERS,False\nmvc-8343,\"{'Ca': 4.0, 'Cr': 4.0, 'Ni': 4.0, 'P': 8.0, 'O': 36.0}\",\"('Ca', 'Cr', 'Ni', 'O', 'P')\",OTHERS,False\nmp-1097023,\"{'K': 4.0, 'Fe': 4.0, 'O': 12.0}\",\"('Fe', 'K', 'O')\",OTHERS,False\nmp-752931,\"{'Li': 2.0, 'Mn': 1.0, 'F': 4.0}\",\"('F', 'Li', 'Mn')\",OTHERS,False\nmp-774311,\"{'Li': 12.0, 'Mn': 4.0, 'V': 4.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-1189104,\"{'Eu': 6.0, 'Ga': 4.0, 'P': 8.0}\",\"('Eu', 'Ga', 'P')\",OTHERS,False\nmp-570759,\"{'Tb': 8.0, 'Bi': 6.0}\",\"('Bi', 'Tb')\",OTHERS,False\nmp-582084,\"{'Cd': 18.0, 'I': 36.0}\",\"('Cd', 'I')\",OTHERS,False\nmp-1198634,\"{'Yb': 2.0, 'P': 6.0, 'H': 12.0, 'O': 12.0}\",\"('H', 'O', 'P', 'Yb')\",OTHERS,False\nmp-1214425,\"{'C': 96.0, 'Br': 8.0, 'F': 72.0}\",\"('Br', 'C', 'F')\",OTHERS,False\nmp-758373,\"{'Li': 8.0, 'Co': 8.0, 'P': 8.0, 'O': 32.0}\",\"('Co', 'Li', 'O', 'P')\",OTHERS,False\nmp-1218226,\"{'Sr': 2.0, 'La': 2.0, 'Ga': 2.0, 'Cu': 2.0, 'O': 10.0}\",\"('Cu', 'Ga', 'La', 'O', 'Sr')\",OTHERS,False\nmp-1095715,\"{'Ca': 1.0, 'La': 1.0, 'Rh': 2.0}\",\"('Ca', 'La', 'Rh')\",OTHERS,False\nmp-14883,\"{'S': 10.0, 'Zr': 3.0, 'Ba': 4.0}\",\"('Ba', 'S', 'Zr')\",OTHERS,False\nmp-24664,\"{'H': 16.0, 'C': 4.0, 'N': 8.0, 'O': 4.0, 'Cl': 4.0, 'Zn': 2.0}\",\"('C', 'Cl', 'H', 'N', 'O', 'Zn')\",OTHERS,False\nmp-850538,\"{'Mn': 2.0, 'Fe': 3.0, 'Sn': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Fe', 'Mn', 'O', 'P', 'Sn')\",OTHERS,False\nmp-1225646,\"{'Dy': 12.0, 'Cu': 4.0, 'Se': 24.0}\",\"('Cu', 'Dy', 'Se')\",OTHERS,False\nmp-1196142,\"{'Te': 8.0, 'N': 4.0, 'O': 24.0}\",\"('N', 'O', 'Te')\",OTHERS,False\nmp-1239184,\"{'Cr': 8.0, 'Hg': 4.0, 'S': 16.0}\",\"('Cr', 'Hg', 'S')\",OTHERS,False\nmp-8720,\"{'Mn': 2.0, 'Rb': 4.0, 'Te': 4.0}\",\"('Mn', 'Rb', 'Te')\",OTHERS,False\nmp-677349,\"{'Li': 22.0, 'Mn': 2.0, 'As': 12.0}\",\"('As', 'Li', 'Mn')\",OTHERS,False\nmp-1217575,\"{'Th': 14.0, 'Ag': 51.0}\",\"('Ag', 'Th')\",OTHERS,False\nmp-1205783,\"{'Na': 1.0, 'Sc': 1.0, 'N': 2.0, 'F': 6.0}\",\"('F', 'N', 'Na', 'Sc')\",OTHERS,False\nmp-675711,\"{'Li': 2.0, 'Mo': 12.0, 'S': 16.0}\",\"('Li', 'Mo', 'S')\",OTHERS,False\nmp-770619,\"{'Li': 24.0, 'Mn': 11.0, 'Cr': 1.0, 'O': 36.0}\",\"('Cr', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1025084,\"{'Ho': 2.0, 'B': 2.0, 'C': 2.0}\",\"('B', 'C', 'Ho')\",OTHERS,False\nmvc-8947,\"{'Ca': 2.0, 'La': 2.0, 'Fe': 2.0, 'Ni': 2.0, 'O': 12.0}\",\"('Ca', 'Fe', 'La', 'Ni', 'O')\",OTHERS,False\nmp-1186661,\"{'Pu': 1.0, 'Cl': 1.0}\",\"('Cl', 'Pu')\",OTHERS,False\nmp-685369,\"{'Ti': 6.0, 'Fe': 14.0, 'O': 30.0}\",\"('Fe', 'O', 'Ti')\",OTHERS,False\nmp-7921,\"{'F': 6.0, 'Mg': 1.0, 'Pd': 1.0}\",\"('F', 'Mg', 'Pd')\",OTHERS,False\nmp-541047,\"{'Al': 1.0, 'Pd': 2.0, 'Zr': 1.0}\",\"('Al', 'Pd', 'Zr')\",OTHERS,False\nmp-1246515,\"{'Ca': 12.0, 'Te': 6.0, 'N': 4.0}\",\"('Ca', 'N', 'Te')\",OTHERS,False\nmp-28550,\"{'F': 14.0, 'Ag': 3.0, 'Hf': 2.0}\",\"('Ag', 'F', 'Hf')\",OTHERS,False\nmp-23194,\"{'La': 1.0, 'I': 2.0}\",\"('I', 'La')\",OTHERS,False\nmp-1201679,\"{'Ba': 4.0, 'La': 8.0, 'W': 4.0, 'O': 28.0}\",\"('Ba', 'La', 'O', 'W')\",OTHERS,False\nmp-753188,\"{'Li': 5.0, 'Mn': 2.0, 'Co': 5.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-19112,\"{'O': 8.0, 'S': 2.0, 'Fe': 2.0}\",\"('Fe', 'O', 'S')\",OTHERS,False\nmp-21895,\"{'Na': 30.0, 'Pb': 8.0}\",\"('Na', 'Pb')\",OTHERS,False\nmp-982243,\"{'Y': 6.0, 'Sn': 2.0}\",\"('Sn', 'Y')\",OTHERS,False\nmp-12466,\"{'Fe': 2.0, 'Se': 4.0, 'Pd': 4.0}\",\"('Fe', 'Pd', 'Se')\",OTHERS,False\nmp-669913,\"{'Sr': 4.0, 'Pb': 6.0}\",\"('Pb', 'Sr')\",OTHERS,False\nmvc-13624,\"{'Ca': 1.0, 'La': 2.0, 'Ti': 1.0, 'O': 6.0}\",\"('Ca', 'La', 'O', 'Ti')\",OTHERS,False\nmp-759047,\"{'Li': 12.0, 'Cr': 3.0, 'P': 6.0, 'O': 24.0, 'F': 6.0}\",\"('Cr', 'F', 'Li', 'O', 'P')\",OTHERS,False\nmp-1199246,\"{'Ba': 10.0, 'B': 6.0, 'Br': 2.0, 'O': 18.0}\",\"('B', 'Ba', 'Br', 'O')\",OTHERS,False\nmp-775072,\"{'Li': 4.0, 'Mn': 3.0, 'Cu': 2.0, 'Sb': 3.0, 'O': 16.0}\",\"('Cu', 'Li', 'Mn', 'O', 'Sb')\",OTHERS,False\nmp-1188841,\"{'Ba': 1.0, 'Nb': 2.0, 'V': 2.0, 'O': 11.0}\",\"('Ba', 'Nb', 'O', 'V')\",OTHERS,False\nmp-3782,\"{'O': 24.0, 'Sb': 10.0, 'Nd': 6.0}\",\"('Nd', 'O', 'Sb')\",OTHERS,False\nmp-1112894,\"{'Cs': 2.0, 'In': 1.0, 'As': 1.0, 'Cl': 6.0}\",\"('As', 'Cl', 'Cs', 'In')\",OTHERS,False\nmp-675755,\"{'Ti': 1.0, 'Cu': 3.0, 'O': 4.0}\",\"('Cu', 'O', 'Ti')\",OTHERS,False\nmp-752935,\"{'Li': 2.0, 'Fe': 4.0, 'O': 8.0}\",\"('Fe', 'Li', 'O')\",OTHERS,False\nmp-1079395,\"{'Ce': 4.0, 'Ge': 4.0}\",\"('Ce', 'Ge')\",OTHERS,False\nmp-777583,\"{'Li': 8.0, 'Mn': 3.0, 'Fe': 5.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-812,\"{'O': 24.0, 'Ho': 16.0}\",\"('Ho', 'O')\",OTHERS,False\nmp-24798,\"{'H': 12.0, 'B': 12.0, 'Cl': 1.0, 'Rb': 3.0}\",\"('B', 'Cl', 'H', 'Rb')\",OTHERS,False\nmvc-2321,\"{'Ba': 4.0, 'Mg': 2.0, 'Co': 4.0, 'Cu': 2.0, 'F': 28.0}\",\"('Ba', 'Co', 'Cu', 'F', 'Mg')\",OTHERS,False\nmp-1176570,\"{'Mn': 3.0, 'Ni': 1.0, 'Sn': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Mn', 'Ni', 'O', 'P', 'Sn')\",OTHERS,False\nmp-1183235,\"{'Ac': 1.0, 'Nd': 3.0}\",\"('Ac', 'Nd')\",OTHERS,False\nmp-6073,\"{'O': 48.0, 'Mg': 12.0, 'Al': 8.0, 'Si': 12.0}\",\"('Al', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-1209845,\"{'Nd': 4.0, 'Si': 4.0, 'Pd': 8.0}\",\"('Nd', 'Pd', 'Si')\",OTHERS,False\nmp-758399,\"{'Li': 6.0, 'V': 3.0, 'Cr': 3.0, 'P': 6.0, 'H': 6.0, 'O': 30.0}\",\"('Cr', 'H', 'Li', 'O', 'P', 'V')\",OTHERS,False\nZn 76 Yb 14 Mg 10,\"{'Zn': 76.0, 'Yb': 14.0, 'Mg': 10.0}\",\"('Mg', 'Yb', 'Zn')\",QC,False\nmp-1024052,\"{'Na': 8.0, 'Zn': 4.0, 'Se': 8.0}\",\"('Na', 'Se', 'Zn')\",OTHERS,False\nmp-570807,\"{'Sr': 4.0, 'In': 13.0, 'Au': 9.0}\",\"('Au', 'In', 'Sr')\",OTHERS,False\nmp-1782,\"{'Sc': 1.0, 'Se': 1.0}\",\"('Sc', 'Se')\",OTHERS,False\nmp-1222754,\"{'La': 1.0, 'Y': 1.0, 'Mg': 6.0}\",\"('La', 'Mg', 'Y')\",OTHERS,False\nmp-1178224,\"{'Fe': 4.0, 'O': 4.0, 'F': 4.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-11327,\"{'Se': 1.0, 'Rb': 2.0}\",\"('Rb', 'Se')\",OTHERS,False\nmp-1184476,\"{'Gd': 2.0, 'In': 1.0, 'Hg': 1.0}\",\"('Gd', 'Hg', 'In')\",OTHERS,False\nmp-558982,\"{'Hg': 4.0, 'C': 8.0, 'O': 8.0, 'F': 12.0}\",\"('C', 'F', 'Hg', 'O')\",OTHERS,False\nmp-1245091,\"{'Cr': 32.0, 'O': 48.0}\",\"('Cr', 'O')\",OTHERS,False\nmp-7795,\"{'Rb': 1.0, 'Sn': 1.0, 'S': 2.0}\",\"('Rb', 'S', 'Sn')\",OTHERS,False\nmp-723002,\"{'Ag': 4.0, 'H': 24.0, 'S': 2.0, 'N': 8.0, 'O': 8.0}\",\"('Ag', 'H', 'N', 'O', 'S')\",OTHERS,False\nmp-1080016,\"{'Tl': 6.0, 'Ir': 2.0}\",\"('Ir', 'Tl')\",OTHERS,False\nmp-20353,\"{'Ga': 2.0, 'Gd': 2.0}\",\"('Ga', 'Gd')\",OTHERS,False\nmp-574486,\"{'Ag': 8.0, 'C': 4.0, 'N': 8.0}\",\"('Ag', 'C', 'N')\",OTHERS,False\nmp-1203864,\"{'B': 8.0, 'P': 24.0, 'Pb': 24.0, 'O': 96.0}\",\"('B', 'O', 'P', 'Pb')\",OTHERS,False\nmp-768846,\"{'Rb': 2.0, 'Fe': 4.0, 'O': 7.0}\",\"('Fe', 'O', 'Rb')\",OTHERS,False\nmvc-6932,\"{'Ca': 2.0, 'Cr': 4.0, 'O': 8.0}\",\"('Ca', 'Cr', 'O')\",OTHERS,False\nmp-9771,\"{'O': 6.0, 'Ti': 2.0, 'U': 1.0}\",\"('O', 'Ti', 'U')\",OTHERS,False\nmp-774237,\"{'Li': 5.0, 'Cr': 2.0, 'Ni': 5.0, 'O': 12.0}\",\"('Cr', 'Li', 'Ni', 'O')\",OTHERS,False\nmp-1203988,\"{'H': 30.0, 'Pd': 2.0, 'Ru': 2.0, 'N': 18.0, 'O': 22.0}\",\"('H', 'N', 'O', 'Pd', 'Ru')\",OTHERS,False\nmp-757668,\"{'Li': 4.0, 'Mn': 3.0, 'Ni': 2.0, 'Sb': 3.0, 'O': 16.0}\",\"('Li', 'Mn', 'Ni', 'O', 'Sb')\",OTHERS,False\nmp-1177871,\"{'Li': 2.0, 'Nb': 1.0, 'Co': 3.0, 'O': 8.0}\",\"('Co', 'Li', 'Nb', 'O')\",OTHERS,False\nmp-1209024,\"{'Sc': 12.0, 'Sn': 10.0}\",\"('Sc', 'Sn')\",OTHERS,False\nmp-19803,\"{'O': 8.0, 'Cd': 2.0, 'In': 4.0}\",\"('Cd', 'In', 'O')\",OTHERS,False\nmp-752820,\"{'Li': 4.0, 'V': 2.0, 'P': 4.0, 'O': 14.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1228657,\"{'B': 4.0, 'Ru': 2.0, 'W': 2.0}\",\"('B', 'Ru', 'W')\",OTHERS,False\nmp-1101695,\"{'Na': 2.0, 'Fe': 24.0, 'O': 38.0}\",\"('Fe', 'Na', 'O')\",OTHERS,False\nmp-1175904,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-779498,\"{'Ba': 24.0, 'Cl': 16.0, 'O': 16.0}\",\"('Ba', 'Cl', 'O')\",OTHERS,False\nmp-1192618,\"{'Nb': 8.0, 'Fe': 8.0, 'Si': 14.0}\",\"('Fe', 'Nb', 'Si')\",OTHERS,False\nmp-19875,\"{'Si': 2.0, 'Ru': 2.0, 'Gd': 2.0}\",\"('Gd', 'Ru', 'Si')\",OTHERS,False\nmp-1104829,\"{'Nb': 5.0, 'Sn': 1.0, 'Se': 8.0}\",\"('Nb', 'Se', 'Sn')\",OTHERS,False\nmp-1185170,\"{'La': 3.0, 'Lu': 1.0}\",\"('La', 'Lu')\",OTHERS,False\nmp-865429,\"{'Yb': 1.0, 'Y': 1.0, 'Rh': 2.0}\",\"('Rh', 'Y', 'Yb')\",OTHERS,False\nmp-1184327,\"{'Eu': 3.0, 'V': 1.0}\",\"('Eu', 'V')\",OTHERS,False\nmp-1173284,\"{'Sr': 24.0, 'Fe': 12.0, 'Mo': 12.0, 'O': 72.0}\",\"('Fe', 'Mo', 'O', 'Sr')\",OTHERS,False\nmp-995122,\"{'Nb': 1.0, 'S': 2.0}\",\"('Nb', 'S')\",OTHERS,False\nmp-1193749,\"{'Rb': 4.0, 'Mg': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Mg', 'O', 'P', 'Rb')\",OTHERS,False\nmp-1100345,\"{'Ca': 2.0, 'Sn': 2.0, 'S': 6.0}\",\"('Ca', 'S', 'Sn')\",OTHERS,False\nmp-3779,\"{'Si': 2.0, 'Cr': 2.0, 'Te': 6.0}\",\"('Cr', 'Si', 'Te')\",OTHERS,False\nmp-1223321,\"{'K': 4.0, 'Ta': 4.0, 'Si': 8.0, 'O': 28.0}\",\"('K', 'O', 'Si', 'Ta')\",OTHERS,False\nmp-760022,\"{'Mn': 12.0, 'O': 10.0, 'F': 14.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-1206382,\"{'Tm': 2.0, 'Ni': 2.0, 'Pb': 1.0}\",\"('Ni', 'Pb', 'Tm')\",OTHERS,False\nmp-561712,\"{'Cs': 4.0, 'Er': 4.0, 'P': 16.0, 'O': 48.0}\",\"('Cs', 'Er', 'O', 'P')\",OTHERS,False\nmp-1066131,\"{'Y': 1.0, 'Tl': 1.0, 'S': 2.0}\",\"('S', 'Tl', 'Y')\",OTHERS,False\nmp-865751,\"{'Yb': 1.0, 'Dy': 1.0, 'Rh': 2.0}\",\"('Dy', 'Rh', 'Yb')\",OTHERS,False\nmp-1184505,\"{'Gd': 1.0, 'Pa': 1.0, 'Tc': 2.0}\",\"('Gd', 'Pa', 'Tc')\",OTHERS,False\nmp-1215634,\"{'Zr': 4.0, 'Te': 6.0}\",\"('Te', 'Zr')\",OTHERS,False\nmp-554461,\"{'K': 2.0, 'Os': 4.0, 'O': 12.0}\",\"('K', 'O', 'Os')\",OTHERS,False\nmp-755927,\"{'Li': 2.0, 'Cr': 3.0, 'Fe': 1.0, 'O': 8.0}\",\"('Cr', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-861894,\"{'Li': 2.0, 'La': 1.0, 'Sn': 1.0}\",\"('La', 'Li', 'Sn')\",OTHERS,False\nmp-1222347,\"{'Li': 2.0, 'Ti': 2.0, 'Cr': 2.0, 'O': 8.0}\",\"('Cr', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-1216437,\"{'V': 6.0, 'Ge': 1.0, 'Os': 1.0}\",\"('Ge', 'Os', 'V')\",OTHERS,False\nmp-1221259,\"{'Na': 3.0, 'Ca': 1.0, 'Mg': 1.0, 'Cr': 3.0, 'Si': 8.0, 'O': 24.0}\",\"('Ca', 'Cr', 'Mg', 'Na', 'O', 'Si')\",OTHERS,False\nmp-559871,\"{'Al': 6.0, 'F': 18.0}\",\"('Al', 'F')\",OTHERS,False\nmp-35031,\"{'Cu': 1.0, 'Pr': 5.0, 'Se': 8.0}\",\"('Cu', 'Pr', 'Se')\",OTHERS,False\nmp-600029,\"{'Si': 24.0, 'O': 48.0}\",\"('O', 'Si')\",OTHERS,False\nmp-757062,\"{'Li': 4.0, 'Nb': 3.0, 'V': 3.0, 'Sn': 2.0, 'O': 16.0}\",\"('Li', 'Nb', 'O', 'Sn', 'V')\",OTHERS,False\nmp-1212579,\"{'H': 24.0, 'C': 24.0, 'N': 40.0}\",\"('C', 'H', 'N')\",OTHERS,False\nmp-1175048,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-556289,\"{'Na': 8.0, 'Er': 4.0, 'Mo': 4.0, 'P': 4.0, 'O': 32.0}\",\"('Er', 'Mo', 'Na', 'O', 'P')\",OTHERS,False\nmp-1246047,\"{'Te': 2.0, 'C': 2.0, 'N': 4.0}\",\"('C', 'N', 'Te')\",OTHERS,False\nmp-2176,\"{'Zn': 1.0, 'Te': 1.0}\",\"('Te', 'Zn')\",OTHERS,False\nmp-1202079,\"{'Ti': 42.0, 'Mn': 50.0}\",\"('Mn', 'Ti')\",OTHERS,False\nmvc-5090,\"{'Ca': 4.0, 'Ti': 2.0, 'W': 2.0, 'O': 12.0}\",\"('Ca', 'O', 'Ti', 'W')\",OTHERS,False\nmp-984716,\"{'Ca': 1.0, 'Sn': 4.0, 'P': 6.0, 'O': 24.0}\",\"('Ca', 'O', 'P', 'Sn')\",OTHERS,False\nmp-1175920,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-566602,\"{'La': 4.0, 'Mn': 4.0, 'O': 12.0}\",\"('La', 'Mn', 'O')\",OTHERS,False\nmp-1079810,\"{'Hf': 3.0, 'Sn': 6.0}\",\"('Hf', 'Sn')\",OTHERS,False\nmp-505042,\"{'Bi': 8.0, 'Cu': 4.0, 'O': 16.0}\",\"('Bi', 'Cu', 'O')\",OTHERS,False\nmp-36526,\"{'Ac': 1.0, 'F': 1.0, 'O': 1.0}\",\"('Ac', 'F', 'O')\",OTHERS,False\nmp-683950,\"{'Ca': 20.0, 'Pr': 8.0, 'Cu': 48.0, 'O': 82.0}\",\"('Ca', 'Cu', 'O', 'Pr')\",OTHERS,False\nmp-777883,\"{'Ba': 24.0, 'Y': 4.0, 'Cl': 60.0}\",\"('Ba', 'Cl', 'Y')\",OTHERS,False\nAl 70 Ni 20 Rh 10,\"{'Al': 70.0, 'Ni': 20.0, 'Rh': 10.0}\",\"('Al', 'Ni', 'Rh')\",QC,False\nmp-558048,\"{'Na': 2.0, 'V': 2.0, 'Ge': 4.0, 'O': 12.0}\",\"('Ge', 'Na', 'O', 'V')\",OTHERS,False\nmp-1204281,\"{'Hf': 12.0, 'Ge': 24.0, 'Ru': 12.0}\",\"('Ge', 'Hf', 'Ru')\",OTHERS,False\nmp-1216310,\"{'V': 2.0, 'Mo': 2.0, 'As': 4.0}\",\"('As', 'Mo', 'V')\",OTHERS,False\nmp-1102373,\"{'Y': 4.0, 'Ge': 4.0, 'Ir': 4.0}\",\"('Ge', 'Ir', 'Y')\",OTHERS,False\nmp-975051,\"{'Rb': 3.0, 'Ag': 1.0}\",\"('Ag', 'Rb')\",OTHERS,False\nmp-1183999,\"{'Cs': 1.0, 'Yb': 3.0}\",\"('Cs', 'Yb')\",OTHERS,False\nmp-1222787,\"{'La': 2.0, 'Ti': 2.0, 'Nb': 2.0, 'O': 12.0}\",\"('La', 'Nb', 'O', 'Ti')\",OTHERS,False\nmp-1195884,\"{'K': 4.0, 'Nd': 2.0, 'N': 10.0, 'O': 34.0}\",\"('K', 'N', 'Nd', 'O')\",OTHERS,False\nmp-1185611,\"{'Lu': 3.0, 'Pu': 1.0}\",\"('Lu', 'Pu')\",OTHERS,False\nmp-771523,\"{'Li': 4.0, 'Cr': 8.0, 'O': 16.0}\",\"('Cr', 'Li', 'O')\",OTHERS,False\nmp-541997,\"{'K': 12.0, 'Fe': 4.0, 'Se': 12.0}\",\"('Fe', 'K', 'Se')\",OTHERS,False\nmp-16037,\"{'O': 2.0, 'Te': 1.0, 'Dy': 2.0}\",\"('Dy', 'O', 'Te')\",OTHERS,False\nmp-1190394,\"{'Nd': 8.0, 'Fe': 2.0, 'S': 12.0, 'O': 2.0}\",\"('Fe', 'Nd', 'O', 'S')\",OTHERS,False\nmp-1099080,\"{'Mg': 14.0, 'Fe': 1.0, 'Co': 1.0}\",\"('Co', 'Fe', 'Mg')\",OTHERS,False\nmp-1245707,\"{'Sb': 2.0, 'Te': 2.0, 'N': 2.0}\",\"('N', 'Sb', 'Te')\",OTHERS,False\nmp-705526,\"{'H': 64.0, 'Au': 4.0, 'C': 24.0, 'S': 8.0, 'N': 16.0, 'Cl': 4.0, 'O': 16.0}\",\"('Au', 'C', 'Cl', 'H', 'N', 'O', 'S')\",OTHERS,False\nmp-31378,\"{'O': 52.0, 'Cr': 16.0, 'Cs': 8.0}\",\"('Cr', 'Cs', 'O')\",OTHERS,False\nmp-267,\"{'Ru': 2.0, 'Te': 4.0}\",\"('Ru', 'Te')\",OTHERS,False\nmp-504357,\"{'Cr': 16.0, 'P': 12.0, 'O': 48.0}\",\"('Cr', 'O', 'P')\",OTHERS,False\nmp-1224618,\"{'Ho': 5.0, 'Co': 19.0, 'P': 12.0}\",\"('Co', 'Ho', 'P')\",OTHERS,False\nmp-1190699,\"{'C': 8.0, 'O': 16.0}\",\"('C', 'O')\",OTHERS,False\nmp-1217824,\"{'Ta': 1.0, 'V': 1.0, 'C': 1.0, 'N': 1.0}\",\"('C', 'N', 'Ta', 'V')\",OTHERS,False\nmp-21683,\"{'In': 2.0, 'Ni': 21.0, 'P': 6.0}\",\"('In', 'Ni', 'P')\",OTHERS,False\nmp-754078,\"{'Li': 1.0, 'Fe': 2.0, 'P': 2.0, 'O': 8.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-1226274,\"{'Cs': 2.0, 'K': 1.0, 'Ti': 1.0, 'O': 2.0, 'F': 3.0}\",\"('Cs', 'F', 'K', 'O', 'Ti')\",OTHERS,False\nmp-560022,\"{'Ba': 18.0, 'Nb': 4.0, 'C': 2.0, 'N': 20.0, 'O': 2.0}\",\"('Ba', 'C', 'N', 'Nb', 'O')\",OTHERS,False\nmp-1187717,{'Y': 4.0},\"('Y',)\",OTHERS,False\nmp-1103473,\"{'Cs': 1.0, 'Cr': 1.0, 'P': 2.0, 'S': 7.0}\",\"('Cr', 'Cs', 'P', 'S')\",OTHERS,False\nmp-24066,\"{'H': 2.0, 'O': 2.0, 'Cl': 2.0, 'Sr': 2.0}\",\"('Cl', 'H', 'O', 'Sr')\",OTHERS,False\nmp-557825,\"{'H': 8.0, 'C': 2.0, 'N': 4.0, 'O': 2.0}\",\"('C', 'H', 'N', 'O')\",OTHERS,False\nmp-974469,\"{'Re': 2.0, 'Te': 2.0}\",\"('Re', 'Te')\",OTHERS,False\nmp-1189057,\"{'Lu': 4.0, 'Ge': 4.0, 'Pd': 8.0}\",\"('Ge', 'Lu', 'Pd')\",OTHERS,False\nmp-1235846,\"{'Sr': 2.0, 'Li': 1.0, 'O': 12.0}\",\"('Li', 'O', 'Sr')\",OTHERS,False\nmp-556459,\"{'V': 2.0, 'P': 2.0, 'O': 10.0}\",\"('O', 'P', 'V')\",OTHERS,False\nmp-1245656,\"{'K': 2.0, 'Pd': 2.0, 'N': 2.0}\",\"('K', 'N', 'Pd')\",OTHERS,False\nmp-1220381,\"{'Nb': 6.0, 'Ga': 1.0, 'Ge': 1.0}\",\"('Ga', 'Ge', 'Nb')\",OTHERS,False\nmp-675743,\"{'Mg': 2.0, 'Mo': 12.0, 'Se': 16.0}\",\"('Mg', 'Mo', 'Se')\",OTHERS,False\nmp-1184233,\"{'Er': 1.0, 'Mg': 1.0, 'Ag': 2.0}\",\"('Ag', 'Er', 'Mg')\",OTHERS,False\nmp-1094804,\"{'Mg': 4.0, 'Cd': 8.0}\",\"('Cd', 'Mg')\",OTHERS,False\nmp-560090,\"{'Mn': 4.0, 'Ni': 12.0, 'Te': 6.0, 'Pb': 2.0, 'O': 36.0}\",\"('Mn', 'Ni', 'O', 'Pb', 'Te')\",OTHERS,False\nmp-4788,\"{'O': 12.0, 'K': 2.0, 'Os': 4.0}\",\"('K', 'O', 'Os')\",OTHERS,False\nmp-734561,\"{'Rb': 6.0, 'P': 6.0, 'H': 8.0, 'O': 24.0}\",\"('H', 'O', 'P', 'Rb')\",OTHERS,False\nmp-755567,\"{'Fe': 6.0, 'O': 8.0, 'F': 4.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-1104131,\"{'Nb': 2.0, 'P': 2.0, 'O': 10.0}\",\"('Nb', 'O', 'P')\",OTHERS,False\nmp-707290,\"{'Nb': 20.0, 'Pb': 28.0, 'O': 78.0}\",\"('Nb', 'O', 'Pb')\",OTHERS,False\nmp-1219465,\"{'Sm': 4.0, 'Re': 12.0, 'O': 60.0}\",\"('O', 'Re', 'Sm')\",OTHERS,False\nmp-1212327,\"{'Ho': 16.0, 'Al': 8.0, 'O': 36.0}\",\"('Al', 'Ho', 'O')\",OTHERS,False\nmp-752651,\"{'Li': 4.0, 'Ti': 2.0, 'Nb': 3.0, 'Cu': 3.0, 'O': 16.0}\",\"('Cu', 'Li', 'Nb', 'O', 'Ti')\",OTHERS,False\nmp-1194276,\"{'Ca': 4.0, 'Ho': 8.0, 'S': 16.0}\",\"('Ca', 'Ho', 'S')\",OTHERS,False\nmp-1225693,\"{'Dy': 1.0, 'Mn': 2.0, 'Si': 1.0, 'Ge': 1.0}\",\"('Dy', 'Ge', 'Mn', 'Si')\",OTHERS,False\nmp-11873,\"{'As': 1.0, 'Pd': 5.0, 'Tl': 1.0}\",\"('As', 'Pd', 'Tl')\",OTHERS,False\nmp-778809,\"{'Fe': 10.0, 'O': 14.0, 'F': 6.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-8219,\"{'P': 2.0, 'Cu': 2.0, 'Zr': 1.0}\",\"('Cu', 'P', 'Zr')\",OTHERS,False\nmp-1191259,\"{'Cu': 3.0, 'Bi': 2.0, 'P': 2.0, 'O': 14.0}\",\"('Bi', 'Cu', 'O', 'P')\",OTHERS,False\nmp-11523,\"{'Ni': 3.0, 'Sn': 1.0}\",\"('Ni', 'Sn')\",OTHERS,False\nmp-631495,\"{'Ba': 1.0, 'Y': 1.0, 'Sc': 1.0}\",\"('Ba', 'Sc', 'Y')\",OTHERS,False\nmp-21620,\"{'Si': 20.0, 'Tc': 12.0, 'U': 8.0}\",\"('Si', 'Tc', 'U')\",OTHERS,False\nmp-1182246,\"{'Ba': 2.0, 'Ge': 6.0, 'O': 16.0}\",\"('Ba', 'Ge', 'O')\",OTHERS,False\nmp-752890,\"{'Li': 2.0, 'Cu': 4.0, 'C': 4.0, 'O': 14.0}\",\"('C', 'Cu', 'Li', 'O')\",OTHERS,False\nCd 6 Yb 1,\"{'Cd': 6.0, 'Yb': 1.0}\",\"('Cd', 'Yb')\",AC,False\nmp-23405,\"{'Br': 6.0, 'Te': 1.0, 'Cs': 2.0}\",\"('Br', 'Cs', 'Te')\",OTHERS,False\nmp-1198640,\"{'Mg': 1.0, 'Hg': 6.0, 'N': 6.0, 'O': 26.0}\",\"('Hg', 'Mg', 'N', 'O')\",OTHERS,False\nmp-1185830,\"{'Mg': 5.0, 'In': 1.0}\",\"('In', 'Mg')\",OTHERS,False\nmp-765020,\"{'Sr': 24.0, 'Fe': 16.0, 'O': 53.0}\",\"('Fe', 'O', 'Sr')\",OTHERS,False\nmp-752865,\"{'Li': 4.0, 'Mn': 5.0, 'Cu': 3.0, 'O': 16.0}\",\"('Cu', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-767518,\"{'Na': 4.0, 'Sc': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Na', 'O', 'P', 'Sc')\",OTHERS,False\nmp-774463,\"{'Li': 6.0, 'Cr': 4.0, 'P': 6.0, 'O': 24.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-676663,\"{'Zn': 6.0, 'Sn': 6.0, 'Sb': 12.0}\",\"('Sb', 'Sn', 'Zn')\",OTHERS,False\nmp-555509,\"{'Ho': 6.0, 'Cu': 2.0, 'Ge': 2.0, 'S': 14.0}\",\"('Cu', 'Ge', 'Ho', 'S')\",OTHERS,False\nmp-1219565,\"{'Rb': 1.0, 'O': 2.0}\",\"('O', 'Rb')\",OTHERS,False\nmp-1218104,\"{'Sr': 1.0, 'Pr': 3.0, 'Fe': 4.0, 'O': 12.0}\",\"('Fe', 'O', 'Pr', 'Sr')\",OTHERS,False\nmp-1114631,\"{'Rb': 3.0, 'Sb': 1.0, 'Cl': 6.0}\",\"('Cl', 'Rb', 'Sb')\",OTHERS,False\nmp-765146,\"{'Li': 20.0, 'V': 4.0, 'C': 16.0, 'O': 48.0}\",\"('C', 'Li', 'O', 'V')\",OTHERS,False\nmp-768438,\"{'Li': 16.0, 'Ti': 4.0, 'Cr': 4.0, 'O': 24.0}\",\"('Cr', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-557969,\"{'Li': 2.0, 'La': 4.0, 'S': 4.0, 'O': 16.0, 'F': 6.0}\",\"('F', 'La', 'Li', 'O', 'S')\",OTHERS,False\nmp-754659,\"{'Zr': 6.0, 'N': 4.0, 'O': 6.0}\",\"('N', 'O', 'Zr')\",OTHERS,False\nmp-677732,\"{'Nd': 2.0, 'Ti': 4.0, 'Cd': 2.0, 'O': 12.0, 'F': 2.0}\",\"('Cd', 'F', 'Nd', 'O', 'Ti')\",OTHERS,False\nmvc-5764,\"{'Ca': 8.0, 'Mn': 16.0, 'O': 32.0}\",\"('Ca', 'Mn', 'O')\",OTHERS,False\nmp-1222544,\"{'Li': 4.0, 'Co': 1.0, 'Ru': 1.0, 'O': 6.0}\",\"('Co', 'Li', 'O', 'Ru')\",OTHERS,False\nmp-1029975,\"{'Al': 4.0, 'C': 6.0, 'N': 12.0}\",\"('Al', 'C', 'N')\",OTHERS,False\nmp-1199077,\"{'Ba': 4.0, 'B': 12.0, 'H': 8.0, 'O': 26.0}\",\"('B', 'Ba', 'H', 'O')\",OTHERS,False\nmp-770175,\"{'Li': 4.0, 'Mn': 6.0, 'Fe': 2.0, 'O': 16.0}\",\"('Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-865402,\"{'Tm': 3.0, 'Al': 3.0, 'Cu': 3.0}\",\"('Al', 'Cu', 'Tm')\",OTHERS,False\nmp-505802,\"{'Sn': 2.0, 'Mo': 8.0, 'O': 12.0}\",\"('Mo', 'O', 'Sn')\",OTHERS,False\nmp-1209222,\"{'Rb': 2.0, 'Lu': 2.0, 'Be': 2.0, 'F': 12.0}\",\"('Be', 'F', 'Lu', 'Rb')\",OTHERS,False\nmp-1186131,\"{'Na': 1.0, 'Dy': 3.0}\",\"('Dy', 'Na')\",OTHERS,False\nmp-24356,\"{'H': 16.0, 'N': 4.0, 'Cl': 12.0, 'Cd': 4.0}\",\"('Cd', 'Cl', 'H', 'N')\",OTHERS,False\nmp-1216388,\"{'Zr': 18.0, 'Co': 5.0, 'Sn': 4.0}\",\"('Co', 'Sn', 'Zr')\",OTHERS,False\nmp-561525,\"{'K': 6.0, 'Ga': 6.0, 'B': 6.0, 'O': 21.0}\",\"('B', 'Ga', 'K', 'O')\",OTHERS,False\nmp-555073,\"{'La': 12.0, 'As': 8.0, 'Cl': 4.0, 'O': 28.0}\",\"('As', 'Cl', 'La', 'O')\",OTHERS,False\nmp-554131,\"{'Sr': 4.0, 'Li': 4.0, 'Co': 4.0, 'F': 24.0}\",\"('Co', 'F', 'Li', 'Sr')\",OTHERS,False\nmp-762505,\"{'Ti': 3.0, 'Co': 3.0, 'Sb': 2.0, 'O': 16.0}\",\"('Co', 'O', 'Sb', 'Ti')\",OTHERS,False\nmp-1101,\"{'As': 1.0, 'Tm': 1.0}\",\"('As', 'Tm')\",OTHERS,False\nmp-27570,\"{'Cl': 16.0, 'Fe': 4.0, 'Cs': 4.0}\",\"('Cl', 'Cs', 'Fe')\",OTHERS,False\nmp-1187310,\"{'Tb': 1.0, 'Be': 1.0, 'O': 3.0}\",\"('Be', 'O', 'Tb')\",OTHERS,False\nmp-1212001,\"{'K': 12.0, 'Si': 24.0, 'H': 24.0, 'N': 44.0}\",\"('H', 'K', 'N', 'Si')\",OTHERS,False\nmp-1218980,\"{'Sm': 2.0, 'Ga': 2.0, 'Si': 2.0}\",\"('Ga', 'Si', 'Sm')\",OTHERS,False\nmp-13136,\"{'C': 1.0, 'W': 1.0}\",\"('C', 'W')\",OTHERS,False\nmvc-16045,\"{'Sr': 4.0, 'Ca': 1.0, 'Cr': 2.0, 'S': 2.0, 'O': 6.0}\",\"('Ca', 'Cr', 'O', 'S', 'Sr')\",OTHERS,False\nmp-582227,\"{'Eu': 1.0, 'Mn': 2.0, 'Sb': 2.0}\",\"('Eu', 'Mn', 'Sb')\",OTHERS,False\nAg 42.5 Ga 42.5 Yb 15,\"{'Ag': 42.5, 'Ga': 42.5, 'Yb': 15.0}\",\"('Ag', 'Ga', 'Yb')\",AC,False\nmp-1237680,\"{'Li': 4.0, 'Cr': 4.0, 'O': 18.0}\",\"('Cr', 'Li', 'O')\",OTHERS,False\nmp-757440,\"{'Cr': 3.0, 'S': 6.0, 'O': 24.0}\",\"('Cr', 'O', 'S')\",OTHERS,False\nmp-530787,\"{'O': 53.0, 'Y': 6.0, 'Zr': 22.0}\",\"('O', 'Y', 'Zr')\",OTHERS,False\nmp-21153,\"{'Er': 4.0, 'Ge': 4.0, 'Ir': 4.0}\",\"('Er', 'Ge', 'Ir')\",OTHERS,False\nmp-1101284,\"{'Ti': 6.0, 'Nb': 2.0, 'O': 16.0}\",\"('Nb', 'O', 'Ti')\",OTHERS,False\nmp-1203703,\"{'Tm': 4.0, 'N': 12.0, 'O': 36.0}\",\"('N', 'O', 'Tm')\",OTHERS,False\nmp-1193375,\"{'Yb': 2.0, 'Ni': 21.0, 'B': 6.0}\",\"('B', 'Ni', 'Yb')\",OTHERS,False\nmp-25992,\"{'Ni': 6.0, 'P': 8.0, 'O': 28.0}\",\"('Ni', 'O', 'P')\",OTHERS,False\nmp-1174245,\"{'Li': 7.0, 'Co': 5.0, 'O': 12.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nAl 68 Pd 20 Ru 12,\"{'Al': 68.0, 'Pd': 20.0, 'Ru': 12.0}\",\"('Al', 'Pd', 'Ru')\",AC,False\nmp-840,\"{'Rh': 4.0, 'Sm': 2.0}\",\"('Rh', 'Sm')\",OTHERS,False\nmp-766870,\"{'Mn': 5.0, 'O': 9.0, 'F': 1.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-1178581,\"{'Ba': 16.0, 'Ti': 12.0, 'O': 40.0}\",\"('Ba', 'O', 'Ti')\",OTHERS,False\nmp-707839,\"{'Si': 2.0, 'H': 28.0, 'O': 8.0, 'F': 12.0}\",\"('F', 'H', 'O', 'Si')\",OTHERS,False\nmp-4689,\"{'Al': 28.0, 'Fe': 4.0, 'Cu': 8.0}\",\"('Al', 'Cu', 'Fe')\",OTHERS,False\nmvc-10589,\"{'Ba': 4.0, 'Mg': 4.0, 'Mo': 4.0, 'F': 28.0}\",\"('Ba', 'F', 'Mg', 'Mo')\",OTHERS,False\nmp-675372,\"{'Ca': 2.0, 'Zr': 8.0, 'O': 18.0}\",\"('Ca', 'O', 'Zr')\",OTHERS,False\nmp-755243,\"{'Li': 5.0, 'Cu': 1.0, 'S': 1.0, 'O': 2.0}\",\"('Cu', 'Li', 'O', 'S')\",OTHERS,False\nmp-1178259,\"{'Fe': 4.0, 'Co': 12.0, 'O': 32.0}\",\"('Co', 'Fe', 'O')\",OTHERS,False\nmp-1008823,\"{'Os': 1.0, 'N': 2.0}\",\"('N', 'Os')\",OTHERS,False\nmp-1189981,\"{'Zr': 10.0, 'Zn': 2.0, 'Sn': 6.0}\",\"('Sn', 'Zn', 'Zr')\",OTHERS,False\nmp-1227768,\"{'Ba': 1.0, 'Sr': 2.0, 'P': 2.0, 'O': 8.0}\",\"('Ba', 'O', 'P', 'Sr')\",OTHERS,False\nmp-653838,\"{'Zr': 4.0, 'Fe': 48.0, 'Si': 8.0, 'B': 4.0}\",\"('B', 'Fe', 'Si', 'Zr')\",OTHERS,False\nmp-1182142,\"{'Ba': 2.0, 'Al': 4.0, 'O': 12.0}\",\"('Al', 'Ba', 'O')\",OTHERS,False\nmp-32308,\"{'O': 12.0, 'Ni': 2.0, 'Ta': 4.0}\",\"('Ni', 'O', 'Ta')\",OTHERS,False\nmp-684911,\"{'Sm': 16.0, 'Te': 24.0}\",\"('Sm', 'Te')\",OTHERS,False\nmp-1219038,\"{'Sm': 1.0, 'U': 1.0, 'Te': 6.0}\",\"('Sm', 'Te', 'U')\",OTHERS,False\nmp-1183962,\"{'Eu': 2.0, 'Cd': 1.0, 'Pb': 1.0}\",\"('Cd', 'Eu', 'Pb')\",OTHERS,False\nmp-1097234,\"{'V': 1.0, 'Cr': 1.0, 'W': 2.0}\",\"('Cr', 'V', 'W')\",OTHERS,False\nmp-1183799,\"{'Dy': 1.0, 'Ho': 1.0, 'Zn': 2.0}\",\"('Dy', 'Ho', 'Zn')\",OTHERS,False\nmp-760939,\"{'Cu': 6.0, 'O': 1.0, 'F': 11.0}\",\"('Cu', 'F', 'O')\",OTHERS,False\nmp-1232092,\"{'Sm': 8.0, 'Mg': 4.0, 'S': 16.0}\",\"('Mg', 'S', 'Sm')\",OTHERS,False\nmvc-5962,\"{'Ca': 2.0, 'Ta': 1.0, 'W': 1.0, 'O': 6.0}\",\"('Ca', 'O', 'Ta', 'W')\",OTHERS,False\nmp-13323,\"{'O': 6.0, 'Nb': 1.0, 'Ba': 2.0, 'Eu': 1.0}\",\"('Ba', 'Eu', 'Nb', 'O')\",OTHERS,False\nmp-540879,\"{'Eu': 4.0, 'B': 8.0, 'O': 16.0}\",\"('B', 'Eu', 'O')\",OTHERS,False\nmp-10208,\"{'P': 4.0, 'Ni': 2.0, 'Mo': 2.0}\",\"('Mo', 'Ni', 'P')\",OTHERS,False\nmp-1193690,\"{'Ge': 7.0, 'H': 1.0, 'O': 20.0}\",\"('Ge', 'H', 'O')\",OTHERS,False\nmp-1201385,\"{'Ba': 6.0, 'Fe': 52.0, 'O': 82.0}\",\"('Ba', 'Fe', 'O')\",OTHERS,False\nmp-1105702,\"{'Zr': 8.0, 'Nb': 2.0, 'Ga': 6.0}\",\"('Ga', 'Nb', 'Zr')\",OTHERS,False\nmp-1097724,\"{'Na': 4.0, 'W': 4.0, 'O': 10.0}\",\"('Na', 'O', 'W')\",OTHERS,False\nmp-766541,\"{'P': 10.0, 'W': 8.0, 'O': 48.0}\",\"('O', 'P', 'W')\",OTHERS,False\nmp-1245342,\"{'Li': 2.0, 'Lu': 2.0, 'S': 4.0, 'O': 16.0}\",\"('Li', 'Lu', 'O', 'S')\",OTHERS,False\nmp-560656,\"{'Ca': 20.0, 'Te': 20.0, 'O': 60.0}\",\"('Ca', 'O', 'Te')\",OTHERS,False\nmp-721094,\"{'Na': 5.0, 'Ca': 2.0, 'Ce': 3.0, 'Ti': 8.0, 'Nb': 2.0, 'O': 30.0}\",\"('Ca', 'Ce', 'Na', 'Nb', 'O', 'Ti')\",OTHERS,False\nmp-534778,\"{'Ca': 4.0, 'Na': 2.0, 'O': 36.0, 'Sc': 2.0, 'Si': 12.0, 'Zn': 4.0}\",\"('Ca', 'Na', 'O', 'Sc', 'Si', 'Zn')\",OTHERS,False\nmp-542654,\"{'In': 9.0, 'S': 15.0, 'Rb': 3.0}\",\"('In', 'Rb', 'S')\",OTHERS,False\nmp-2977,\"{'Cu': 1.0, 'Ag': 1.0, 'Te': 2.0}\",\"('Ag', 'Cu', 'Te')\",OTHERS,False\nmp-1218356,\"{'Sr': 1.0, 'Al': 1.0, 'Ga': 1.0}\",\"('Al', 'Ga', 'Sr')\",OTHERS,False\nmp-4586,\"{'Li': 2.0, 'Al': 2.0, 'Te': 4.0}\",\"('Al', 'Li', 'Te')\",OTHERS,False\nmp-27376,\"{'O': 23.0, 'Si': 10.0, 'Rb': 6.0}\",\"('O', 'Rb', 'Si')\",OTHERS,False\nmp-706622,\"{'P': 4.0, 'H': 24.0, 'N': 8.0, 'O': 8.0}\",\"('H', 'N', 'O', 'P')\",OTHERS,False\nmp-1171010,\"{'Ca': 1.0, 'Fe': 2.0, 'Si': 4.0, 'O': 12.0}\",\"('Ca', 'Fe', 'O', 'Si')\",OTHERS,False\nmp-1176721,\"{'Li': 1.0, 'Fe': 5.0, 'O': 5.0, 'F': 1.0}\",\"('F', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1186598,\"{'Pm': 1.0, 'Ho': 1.0, 'Ir': 2.0}\",\"('Ho', 'Ir', 'Pm')\",OTHERS,False\nmp-1207138,\"{'Sm': 1.0, 'Eu': 2.0, 'Ta': 1.0, 'O': 6.0}\",\"('Eu', 'O', 'Sm', 'Ta')\",OTHERS,False\nmp-605406,\"{'Rb': 2.0, 'U': 4.0, 'H': 10.0, 'C': 8.0, 'O': 30.0}\",\"('C', 'H', 'O', 'Rb', 'U')\",OTHERS,False\nmp-1229046,\"{'Al': 1.0, 'In': 1.0, 'Cu': 2.0, 'Se': 4.0}\",\"('Al', 'Cu', 'In', 'Se')\",OTHERS,False\nmp-1077939,\"{'Cr': 1.0, 'Te': 4.0, 'Rh': 2.0}\",\"('Cr', 'Rh', 'Te')\",OTHERS,False\nmp-1220441,\"{'Nb': 4.0, 'Rh': 1.0}\",\"('Nb', 'Rh')\",OTHERS,False\nmp-1080124,\"{'Cd': 2.0, 'Cl': 8.0}\",\"('Cd', 'Cl')\",OTHERS,False\nmp-27887,\"{'Si': 8.0, 'P': 12.0, 'Ba': 6.0}\",\"('Ba', 'P', 'Si')\",OTHERS,False\nmp-1199218,\"{'Li': 12.0, 'As': 28.0, 'H': 156.0, 'N': 52.0}\",\"('As', 'H', 'Li', 'N')\",OTHERS,False\nmp-1187266,\"{'Tb': 3.0, 'Au': 1.0}\",\"('Au', 'Tb')\",OTHERS,False\nmp-625282,\"{'Fe': 4.0, 'H': 4.0, 'O': 8.0}\",\"('Fe', 'H', 'O')\",OTHERS,False\nmp-1114539,\"{'K': 1.0, 'Rb': 2.0, 'In': 1.0, 'Br': 6.0}\",\"('Br', 'In', 'K', 'Rb')\",OTHERS,False\nmp-746820,\"{'Ni': 1.0, 'H': 6.0, 'S': 2.0, 'O': 12.0}\",\"('H', 'Ni', 'O', 'S')\",OTHERS,False\nmp-958,\"{'Cr': 6.0, 'Rh': 2.0}\",\"('Cr', 'Rh')\",OTHERS,False\nmp-753885,\"{'Li': 4.0, 'Cr': 2.0, 'Co': 3.0, 'Sb': 3.0, 'O': 16.0}\",\"('Co', 'Cr', 'Li', 'O', 'Sb')\",OTHERS,False\nmp-1183424,\"{'Be': 3.0, 'Ge': 1.0}\",\"('Be', 'Ge')\",OTHERS,False\nmp-1222801,\"{'La': 1.0, 'Nd': 1.0, 'Cu': 1.0, 'O': 4.0}\",\"('Cu', 'La', 'Nd', 'O')\",OTHERS,False\nmp-1194348,\"{'Eu': 12.0, 'Ga': 4.0, 'P': 12.0}\",\"('Eu', 'Ga', 'P')\",OTHERS,False\nmp-772241,\"{'Li': 6.0, 'Mn': 2.0, 'Al': 4.0, 'O': 12.0}\",\"('Al', 'Li', 'Mn', 'O')\",OTHERS,False\nmvc-14316,\"{'Ca': 2.0, 'Fe': 2.0, 'F': 10.0}\",\"('Ca', 'F', 'Fe')\",OTHERS,False\nmp-3808,\"{'O': 4.0, 'K': 2.0, 'U': 1.0}\",\"('K', 'O', 'U')\",OTHERS,False\nmp-1190793,\"{'P': 4.0, 'N': 4.0, 'O': 16.0}\",\"('N', 'O', 'P')\",OTHERS,False\nmp-1214797,\"{'Ba': 4.0, 'Er': 2.0, 'Re': 2.0, 'O': 12.0}\",\"('Ba', 'Er', 'O', 'Re')\",OTHERS,False\nmp-1197539,\"{'K': 12.0, 'As': 4.0, 'Se': 16.0}\",\"('As', 'K', 'Se')\",OTHERS,False\nmp-1227902,\"{'Ca': 12.0, 'Al': 4.0, 'Fe': 4.0, 'Si': 12.0, 'O': 48.0}\",\"('Al', 'Ca', 'Fe', 'O', 'Si')\",OTHERS,False\nmp-540439,\"{'Mn': 2.0, 'P': 6.0, 'O': 18.0}\",\"('Mn', 'O', 'P')\",OTHERS,False\nmp-510240,\"{'Rb': 4.0, 'Ag': 8.0, 'S': 6.0}\",\"('Ag', 'Rb', 'S')\",OTHERS,False\nmvc-10945,\"{'Ca': 4.0, 'V': 2.0, 'N': 6.0}\",\"('Ca', 'N', 'V')\",OTHERS,False\nmvc-4438,\"{'Mg': 12.0, 'Co': 8.0, 'Ge': 12.0, 'O': 48.0}\",\"('Co', 'Ge', 'Mg', 'O')\",OTHERS,False\nmp-767412,\"{'Li': 3.0, 'Co': 4.0, 'S': 8.0}\",\"('Co', 'Li', 'S')\",OTHERS,False\nmp-1210026,\"{'Nd': 10.0, 'Ge': 6.0, 'O': 26.0}\",\"('Ge', 'Nd', 'O')\",OTHERS,False\nmp-865184,\"{'Li': 1.0, 'Sc': 1.0, 'Pd': 2.0}\",\"('Li', 'Pd', 'Sc')\",OTHERS,False\nmp-769030,\"{'Bi': 20.0, 'B': 4.0, 'O': 40.0}\",\"('B', 'Bi', 'O')\",OTHERS,False\nmp-608469,\"{'Co': 8.0, 'C': 32.0, 'O': 32.0}\",\"('C', 'Co', 'O')\",OTHERS,False\nmp-1212594,\"{'In': 12.0, 'Te': 12.0, 'Br': 4.0}\",\"('Br', 'In', 'Te')\",OTHERS,False\nmp-1184962,\"{'K': 1.0, 'Cd': 3.0}\",\"('Cd', 'K')\",OTHERS,False\nmp-1200758,\"{'Ba': 12.0, 'V': 8.0, 'Se': 16.0, 'O': 64.0}\",\"('Ba', 'O', 'Se', 'V')\",OTHERS,False\nmp-560043,\"{'K': 1.0, 'Sb': 4.0, 'F': 13.0}\",\"('F', 'K', 'Sb')\",OTHERS,False\nmp-645189,\"{'Cs': 2.0, 'K': 2.0, 'Pt': 12.0, 'S': 12.0, 'O': 56.0}\",\"('Cs', 'K', 'O', 'Pt', 'S')\",OTHERS,False\nmp-1184478,{'In': 1.0},\"('In',)\",OTHERS,False\nmp-1039462,\"{'Ca': 4.0, 'Mg': 2.0}\",\"('Ca', 'Mg')\",OTHERS,False\nmp-1222929,\"{'La': 1.0, 'Al': 1.0, 'Ni': 4.0}\",\"('Al', 'La', 'Ni')\",OTHERS,False\nmvc-5816,\"{'Co': 8.0, 'O': 16.0}\",\"('Co', 'O')\",OTHERS,False\nmp-1226531,\"{'Ce': 1.0, 'Si': 1.0, 'B': 1.0, 'Ir': 3.0}\",\"('B', 'Ce', 'Ir', 'Si')\",OTHERS,False\nmp-1096308,\"{'Al': 1.0, 'Fe': 1.0, 'Ru': 2.0}\",\"('Al', 'Fe', 'Ru')\",OTHERS,False\nmp-1185441,\"{'Li': 1.0, 'Sm': 2.0, 'Ga': 1.0}\",\"('Ga', 'Li', 'Sm')\",OTHERS,False\nmp-1208525,\"{'Sr': 2.0, 'Y': 1.0, 'Cu': 2.0, 'Pb': 1.0, 'O': 7.0}\",\"('Cu', 'O', 'Pb', 'Sr', 'Y')\",OTHERS,False\nmvc-16754,\"{'Y': 2.0, 'Ti': 4.0, 'O': 8.0}\",\"('O', 'Ti', 'Y')\",OTHERS,False\nmp-1218581,\"{'Sr': 1.0, 'Ca': 3.0, 'V': 4.0, 'O': 12.0}\",\"('Ca', 'O', 'Sr', 'V')\",OTHERS,False\nmp-34355,\"{'As': 13.0, 'Li': 31.0, 'Zr': 2.0}\",\"('As', 'Li', 'Zr')\",OTHERS,False\nmp-867673,\"{'Na': 3.0, 'Pt': 12.0, 'O': 16.0}\",\"('Na', 'O', 'Pt')\",OTHERS,False\nmp-1187640,\"{'Tm': 5.0, 'Mg': 1.0}\",\"('Mg', 'Tm')\",OTHERS,False\nmp-1246700,\"{'Ca': 4.0, 'Ni': 4.0, 'S': 2.0, 'F': 12.0}\",\"('Ca', 'F', 'Ni', 'S')\",OTHERS,False\nmp-985477,\"{'Al': 8.0, 'Tl': 8.0, 'S': 16.0}\",\"('Al', 'S', 'Tl')\",OTHERS,False\nmp-1111618,\"{'K': 2.0, 'Na': 1.0, 'Sc': 1.0, 'I': 6.0}\",\"('I', 'K', 'Na', 'Sc')\",OTHERS,False\nmp-1147536,\"{'Ca': 2.0, 'Cu': 1.0, 'S': 2.0, 'O': 2.0}\",\"('Ca', 'Cu', 'O', 'S')\",OTHERS,False\nmp-1535,\"{'Si': 2.0, 'Tb': 1.0}\",\"('Si', 'Tb')\",OTHERS,False\nmp-13863,\"{'O': 12.0, 'Ge': 4.0, 'Ba': 4.0}\",\"('Ba', 'Ge', 'O')\",OTHERS,False\nmp-1187683,\"{'U': 3.0, 'Hg': 1.0}\",\"('Hg', 'U')\",OTHERS,False\nmp-1221701,\"{'Mn': 1.0, 'V': 1.0, 'S': 2.0}\",\"('Mn', 'S', 'V')\",OTHERS,False\nmp-1100530,\"{'Li': 9.0, 'Mn': 7.0, 'O': 16.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-30828,\"{'Sr': 8.0, 'Pb': 4.0}\",\"('Pb', 'Sr')\",OTHERS,False\nmp-861974,\"{'Pa': 1.0, 'Cd': 1.0, 'Pt': 2.0}\",\"('Cd', 'Pa', 'Pt')\",OTHERS,False\nmp-1239191,\"{'Cs': 4.0, 'Cr': 8.0, 'S': 16.0}\",\"('Cr', 'Cs', 'S')\",OTHERS,False\nmp-1199915,\"{'Lu': 8.0, 'B': 24.0, 'Os': 4.0}\",\"('B', 'Lu', 'Os')\",OTHERS,False\nmp-898,\"{'Al': 15.0, 'Ho': 5.0}\",\"('Al', 'Ho')\",OTHERS,False\nmp-685503,\"{'K': 4.0, 'Zr': 48.0, 'I': 112.0}\",\"('I', 'K', 'Zr')\",OTHERS,False\nmp-11489,\"{'Li': 3.0, 'Pd': 1.0}\",\"('Li', 'Pd')\",OTHERS,False\nmp-1175220,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-545436,\"{'O': 6.0, 'Bi': 1.0, 'Ba': 2.0, 'Yb': 1.0}\",\"('Ba', 'Bi', 'O', 'Yb')\",OTHERS,False\nmp-22394,\"{'C': 1.0, 'Ru': 3.0, 'Th': 1.0}\",\"('C', 'Ru', 'Th')\",OTHERS,False\nmp-675748,\"{'Zn': 2.0, 'Ge': 1.0, 'S': 4.0}\",\"('Ge', 'S', 'Zn')\",OTHERS,False\nmp-1211431,\"{'La': 4.0, 'Cu': 20.0, 'Sn': 4.0}\",\"('Cu', 'La', 'Sn')\",OTHERS,False\nmp-581345,\"{'Nd': 24.0, 'Ge': 24.0, 'O': 84.0}\",\"('Ge', 'Nd', 'O')\",OTHERS,False\nmp-1187948,\"{'Zn': 6.0, 'Ni': 2.0}\",\"('Ni', 'Zn')\",OTHERS,False\nmvc-12644,\"{'Ca': 2.0, 'Ni': 4.0, 'O': 8.0}\",\"('Ca', 'Ni', 'O')\",OTHERS,False\nmp-4449,\"{'O': 16.0, 'Se': 4.0, 'Tl': 8.0}\",\"('O', 'Se', 'Tl')\",OTHERS,False\nmp-1183967,\"{'Ga': 1.0, 'Ag': 3.0}\",\"('Ag', 'Ga')\",OTHERS,False\nmp-1228179,\"{'Ba': 4.0, 'Pr': 1.0, 'Dy': 1.0, 'Cu': 6.0, 'O': 14.0}\",\"('Ba', 'Cu', 'Dy', 'O', 'Pr')\",OTHERS,False\nmp-1221933,\"{'Mn': 3.0, 'Zn': 9.0, 'P': 8.0, 'O': 32.0}\",\"('Mn', 'O', 'P', 'Zn')\",OTHERS,False\nmp-709031,\"{'Al': 8.0, 'P': 8.0, 'H': 22.0, 'C': 6.0, 'N': 2.0, 'O': 36.0}\",\"('Al', 'C', 'H', 'N', 'O', 'P')\",OTHERS,False\nmp-26618,\"{'Li': 4.0, 'O': 16.0, 'P': 4.0, 'Cr': 2.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-1178754,\"{'W': 4.0, 'O': 14.0}\",\"('O', 'W')\",OTHERS,False\nmp-23179,\"{'Ni': 4.0, 'Bi': 12.0}\",\"('Bi', 'Ni')\",OTHERS,False\nmp-1187262,\"{'Sr': 3.0, 'U': 1.0}\",\"('Sr', 'U')\",OTHERS,False\nmp-11407,\"{'Ga': 1.0, 'Pt': 3.0}\",\"('Ga', 'Pt')\",OTHERS,False\nmp-1210237,\"{'Na': 1.0, 'Eu': 1.0, 'Cu': 2.0, 'F': 8.0}\",\"('Cu', 'Eu', 'F', 'Na')\",OTHERS,False\nmp-554002,\"{'Al': 4.0, 'H': 12.0, 'O': 12.0}\",\"('Al', 'H', 'O')\",OTHERS,False\nmp-1207916,\"{'Tm': 12.0, 'Sc': 8.0, 'Al': 12.0, 'O': 48.0}\",\"('Al', 'O', 'Sc', 'Tm')\",OTHERS,False\nmp-1216500,\"{'Zr': 2.0, 'Mn': 12.0, 'Ga': 1.0, 'Sn': 11.0}\",\"('Ga', 'Mn', 'Sn', 'Zr')\",OTHERS,False\nmp-1217476,\"{'Tb': 1.0, 'Si': 2.0, 'Ir': 1.0, 'Rh': 1.0}\",\"('Ir', 'Rh', 'Si', 'Tb')\",OTHERS,False\nmp-755045,\"{'Li': 1.0, 'Ni': 2.0, 'O': 4.0}\",\"('Li', 'Ni', 'O')\",OTHERS,False\nmp-1195190,\"{'Ba': 16.0, 'Fe': 12.0, 'Se': 24.0, 'F': 16.0}\",\"('Ba', 'F', 'Fe', 'Se')\",OTHERS,False\nmp-10714,\"{'C': 1.0, 'Rh': 3.0, 'Tm': 1.0}\",\"('C', 'Rh', 'Tm')\",OTHERS,False\nmp-1104672,\"{'Rb': 1.0, 'Hg': 3.0, 'N': 1.0, 'Cl': 8.0, 'O': 2.0}\",\"('Cl', 'Hg', 'N', 'O', 'Rb')\",OTHERS,False\nmp-1077241,\"{'Y': 2.0, 'Cu': 4.0}\",\"('Cu', 'Y')\",OTHERS,False\nmp-1200258,\"{'Hg': 4.0, 'S': 8.0, 'O': 28.0}\",\"('Hg', 'O', 'S')\",OTHERS,False\nmp-1080723,\"{'Hf': 2.0, 'Cu': 2.0, 'Ge': 2.0, 'As': 2.0}\",\"('As', 'Cu', 'Ge', 'Hf')\",OTHERS,False\nmp-1112618,\"{'Cs': 2.0, 'Sm': 1.0, 'Cu': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cs', 'Cu', 'Sm')\",OTHERS,False\nmp-1208097,\"{'V': 8.0, 'S': 12.0, 'N': 8.0, 'O': 48.0}\",\"('N', 'O', 'S', 'V')\",OTHERS,False\nmp-4041,\"{'Al': 2.0, 'Ge': 2.0, 'Yb': 1.0}\",\"('Al', 'Ge', 'Yb')\",OTHERS,False\nmp-754467,\"{'Li': 1.0, 'V': 2.0, 'O': 3.0, 'F': 3.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-6603,\"{'O': 12.0, 'Sr': 4.0, 'Ce': 2.0, 'Ir': 2.0}\",\"('Ce', 'Ir', 'O', 'Sr')\",OTHERS,False\nmp-559961,\"{'Tl': 8.0, 'Se': 4.0, 'O': 16.0}\",\"('O', 'Se', 'Tl')\",OTHERS,False\nmp-1196648,\"{'Nd': 12.0, 'Zn': 2.0, 'Fe': 26.0}\",\"('Fe', 'Nd', 'Zn')\",OTHERS,False\nmp-1209139,\"{'Sb': 4.0, 'Te': 7.0, 'Pb': 1.0}\",\"('Pb', 'Sb', 'Te')\",OTHERS,False\nmp-1178445,\"{'Cr': 8.0, 'P': 16.0, 'O': 48.0}\",\"('Cr', 'O', 'P')\",OTHERS,False\nmp-1208558,\"{'Te': 2.0, 'Br': 12.0, 'O': 16.0}\",\"('Br', 'O', 'Te')\",OTHERS,False\nmp-998592,\"{'Rb': 4.0, 'Ca': 4.0, 'I': 12.0}\",\"('Ca', 'I', 'Rb')\",OTHERS,False\nmp-606691,\"{'K': 4.0, 'U': 8.0, 'P': 12.0, 'O': 56.0}\",\"('K', 'O', 'P', 'U')\",OTHERS,False\nmp-768598,\"{'Na': 4.0, 'Ni': 2.0, 'C': 2.0, 'S': 2.0, 'O': 14.0}\",\"('C', 'Na', 'Ni', 'O', 'S')\",OTHERS,False\nmp-10108,\"{'P': 1.0, 'Np': 1.0}\",\"('Np', 'P')\",OTHERS,False\nmp-1191394,\"{'Li': 8.0, 'Nd': 7.0, 'Ge': 10.0}\",\"('Ge', 'Li', 'Nd')\",OTHERS,False\nmp-27805,\"{'Si': 4.0, 'S': 20.0, 'Ba': 12.0}\",\"('Ba', 'S', 'Si')\",OTHERS,False\nmp-4187,\"{'N': 4.0, 'O': 2.0, 'Ge': 4.0}\",\"('Ge', 'N', 'O')\",OTHERS,False\nmp-1069477,\"{'Gd': 1.0, 'Si': 3.0, 'Ir': 1.0}\",\"('Gd', 'Ir', 'Si')\",OTHERS,False\nmp-754751,\"{'Li': 3.0, 'Nb': 3.0, 'Te': 1.0, 'O': 12.0}\",\"('Li', 'Nb', 'O', 'Te')\",OTHERS,False\nmp-510006,\"{'Pu': 4.0, 'P': 8.0, 'K': 12.0, 'S': 32.0}\",\"('K', 'P', 'Pu', 'S')\",OTHERS,False\nmp-756583,\"{'Li': 4.0, 'V': 5.0, 'Cu': 1.0, 'Cl': 1.0, 'O': 15.0}\",\"('Cl', 'Cu', 'Li', 'O', 'V')\",OTHERS,False\nmp-22569,\"{'O': 16.0, 'P': 4.0, 'In': 4.0}\",\"('In', 'O', 'P')\",OTHERS,False\nmp-1180367,\"{'Nd': 4.0, 'C': 12.0, 'O': 40.0}\",\"('C', 'Nd', 'O')\",OTHERS,False\nmp-973114,\"{'Ho': 1.0, 'Lu': 1.0, 'Ru': 2.0}\",\"('Ho', 'Lu', 'Ru')\",OTHERS,False\nmp-504532,\"{'O': 44.0, 'As': 8.0, 'W': 8.0}\",\"('As', 'O', 'W')\",OTHERS,False\nmp-1214361,\"{'Ca': 4.0, 'Mn': 6.0, 'Si': 6.0, 'H': 6.0, 'O': 28.0}\",\"('Ca', 'H', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1185110,\"{'K': 3.0, 'Rh': 1.0}\",\"('K', 'Rh')\",OTHERS,False\nmp-768883,\"{'Sn': 4.0, 'S': 6.0, 'O': 24.0}\",\"('O', 'S', 'Sn')\",OTHERS,False\nmp-1223761,\"{'La': 2.0, 'Ga': 7.0, 'Au': 1.0}\",\"('Au', 'Ga', 'La')\",OTHERS,False\nmp-1189317,\"{'Mg': 4.0, 'Cr': 2.0, 'B': 4.0, 'Ir': 10.0}\",\"('B', 'Cr', 'Ir', 'Mg')\",OTHERS,False\nmp-865202,\"{'Tm': 1.0, 'Cd': 1.0, 'Pt': 2.0}\",\"('Cd', 'Pt', 'Tm')\",OTHERS,False\nmp-2497,\"{'Zn': 1.0, 'Gd': 1.0}\",\"('Gd', 'Zn')\",OTHERS,False\nmp-29970,\"{'O': 56.0, 'Sb': 20.0, 'Cs': 12.0}\",\"('Cs', 'O', 'Sb')\",OTHERS,False\nmp-1214469,\"{'Ca': 3.0, 'Cd': 3.0, 'N': 12.0, 'O': 36.0}\",\"('Ca', 'Cd', 'N', 'O')\",OTHERS,False\nmp-1206320,\"{'Rb': 2.0, 'Sc': 2.0, 'Cl': 6.0}\",\"('Cl', 'Rb', 'Sc')\",OTHERS,False\nmp-27714,\"{'H': 12.0, 'O': 4.0, 'F': 4.0}\",\"('F', 'H', 'O')\",OTHERS,False\nmp-644443,\"{'Ca': 2.0, 'P': 2.0, 'H': 4.0, 'O': 12.0}\",\"('Ca', 'H', 'O', 'P')\",OTHERS,False\nmp-1034881,\"{'Cs': 1.0, 'Na': 1.0, 'Mg': 14.0, 'O': 15.0}\",\"('Cs', 'Mg', 'Na', 'O')\",OTHERS,False\nmp-757241,\"{'V': 10.0, 'W': 6.0, 'O': 40.0}\",\"('O', 'V', 'W')\",OTHERS,False\nmp-1245853,\"{'Ba': 6.0, 'Y': 2.0, 'N': 6.0}\",\"('Ba', 'N', 'Y')\",OTHERS,False\nmp-1103677,\"{'Tb': 3.0, 'Ga': 9.0, 'Pt': 2.0}\",\"('Ga', 'Pt', 'Tb')\",OTHERS,False\nmp-1246559,\"{'Dy': 2.0, 'Mg': 2.0, 'Cr': 2.0, 'S': 8.0}\",\"('Cr', 'Dy', 'Mg', 'S')\",OTHERS,False\nmp-865483,\"{'Li': 2.0, 'Gd': 1.0, 'In': 1.0}\",\"('Gd', 'In', 'Li')\",OTHERS,False\nmp-1183768,\"{'Ce': 1.0, 'Nd': 1.0, 'Hg': 2.0}\",\"('Ce', 'Hg', 'Nd')\",OTHERS,False\nmp-779893,\"{'Dy': 4.0, 'P': 20.0, 'O': 56.0}\",\"('Dy', 'O', 'P')\",OTHERS,False\nmp-1099956,\"{'K': 4.0, 'Na': 28.0, 'V': 24.0, 'Mo': 8.0, 'O': 80.0}\",\"('K', 'Mo', 'Na', 'O', 'V')\",OTHERS,False\nmp-756525,\"{'Mn': 3.0, 'Cu': 1.0, 'O': 8.0}\",\"('Cu', 'Mn', 'O')\",OTHERS,False\nmp-1096407,\"{'Al': 1.0, 'Ni': 1.0, 'Pt': 2.0}\",\"('Al', 'Ni', 'Pt')\",OTHERS,False\nmp-680331,\"{'Cd': 4.0, 'Hg': 12.0, 'C': 24.0, 'S': 24.0, 'N': 24.0, 'Cl': 8.0}\",\"('C', 'Cd', 'Cl', 'Hg', 'N', 'S')\",OTHERS,False\nmp-557634,\"{'Na': 2.0, 'V': 6.0, 'P': 6.0, 'O': 24.0}\",\"('Na', 'O', 'P', 'V')\",OTHERS,False\nmvc-545,\"{'Ba': 1.0, 'Ca': 1.0, 'Ag': 4.0, 'O': 8.0}\",\"('Ag', 'Ba', 'Ca', 'O')\",OTHERS,False\nmp-1173947,\"{'Li': 6.0, 'Mn': 4.0, 'O': 10.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-1245883,\"{'Sc': 16.0, 'Te': 12.0, 'N': 8.0}\",\"('N', 'Sc', 'Te')\",OTHERS,False\nmp-1185192,\"{'Li': 3.0, 'La': 1.0}\",\"('La', 'Li')\",OTHERS,False\nmp-1215156,\"{'Al': 18.0, 'P': 18.0, 'H': 42.0, 'O': 72.0}\",\"('Al', 'H', 'O', 'P')\",OTHERS,False\nmp-774293,\"{'Li': 8.0, 'Ti': 8.0, 'Mn': 8.0, 'Co': 2.0, 'O': 36.0}\",\"('Co', 'Li', 'Mn', 'O', 'Ti')\",OTHERS,False\nmp-561261,\"{'Ce': 8.0, 'P': 8.0, 'S': 32.0}\",\"('Ce', 'P', 'S')\",OTHERS,False\nmp-623261,\"{'Pr': 4.0, 'Ga': 4.0, 'Co': 4.0}\",\"('Co', 'Ga', 'Pr')\",OTHERS,False\nmp-777233,\"{'Ba': 4.0, 'Y': 4.0, 'F': 20.0}\",\"('Ba', 'F', 'Y')\",OTHERS,False\nmp-755337,\"{'Li': 4.0, 'Nb': 1.0, 'Fe': 3.0, 'O': 8.0}\",\"('Fe', 'Li', 'Nb', 'O')\",OTHERS,False\nmp-974606,\"{'Re': 2.0, 'H': 12.0, 'C': 2.0, 'N': 6.0, 'O': 8.0}\",\"('C', 'H', 'N', 'O', 'Re')\",OTHERS,False\nmp-19764,\"{'In': 1.0, 'Pr': 3.0}\",\"('In', 'Pr')\",OTHERS,False\nmp-1112421,\"{'K': 2.0, 'Pr': 1.0, 'Au': 1.0, 'Cl': 6.0}\",\"('Au', 'Cl', 'K', 'Pr')\",OTHERS,False\nmp-758453,\"{'Na': 8.0, 'P': 8.0, 'H': 16.0, 'N': 8.0, 'O': 20.0}\",\"('H', 'N', 'Na', 'O', 'P')\",OTHERS,False\nmp-1215853,\"{'Yb': 1.0, 'Tm': 2.0, 'Se': 4.0}\",\"('Se', 'Tm', 'Yb')\",OTHERS,False\nmp-1176482,\"{'Mn': 3.0, 'Cr': 2.0, 'Fe': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Cr', 'Fe', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1245073,\"{'Cr': 8.0, 'Fe': 24.0, 'O': 48.0}\",\"('Cr', 'Fe', 'O')\",OTHERS,False\nmp-1245737,\"{'Na': 2.0, 'Cr': 2.0, 'N': 2.0}\",\"('Cr', 'N', 'Na')\",OTHERS,False\nmp-849745,\"{'Li': 12.0, 'Mn': 8.0, 'P': 16.0, 'O': 56.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-672187,\"{'Pr': 4.0, 'Pd': 4.0, 'Pb': 2.0}\",\"('Pb', 'Pd', 'Pr')\",OTHERS,False\nmp-1185197,\"{'Li': 6.0, 'Ho': 2.0}\",\"('Ho', 'Li')\",OTHERS,False\nmp-1014005,\"{'Mn': 2.0, 'Si': 1.0, 'Rh': 1.0}\",\"('Mn', 'Rh', 'Si')\",OTHERS,False\nmp-4989,\"{'Cr': 3.0, 'Ni': 3.0, 'As': 3.0}\",\"('As', 'Cr', 'Ni')\",OTHERS,False\nmp-1078429,\"{'Eu': 2.0, 'Ga': 6.0, 'Pd': 2.0}\",\"('Eu', 'Ga', 'Pd')\",OTHERS,False\nmp-766132,\"{'Li': 8.0, 'Zr': 4.0, 'Fe': 4.0, 'P': 12.0, 'O': 48.0}\",\"('Fe', 'Li', 'O', 'P', 'Zr')\",OTHERS,False\nmp-21660,\"{'Co': 10.0, 'Ga': 20.0, 'Hf': 10.0}\",\"('Co', 'Ga', 'Hf')\",OTHERS,False\nmp-1078398,\"{'Ce': 2.0, 'As': 2.0, 'O': 6.0}\",\"('As', 'Ce', 'O')\",OTHERS,False\nmp-20961,\"{'S': 8.0, 'Pd': 6.0, 'Eu': 2.0}\",\"('Eu', 'Pd', 'S')\",OTHERS,False\nmp-1033751,\"{'Mg': 14.0, 'Bi': 1.0, 'C': 1.0, 'O': 16.0}\",\"('Bi', 'C', 'Mg', 'O')\",OTHERS,False\nmp-1658,\"{'O': 24.0, 'Tl': 16.0}\",\"('O', 'Tl')\",OTHERS,False\nmp-767562,\"{'Na': 4.0, 'Lu': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Lu', 'Na', 'O', 'P')\",OTHERS,False\nmp-30626,\"{'Zn': 12.0, 'Dy': 4.0}\",\"('Dy', 'Zn')\",OTHERS,False\nmp-1209407,\"{'Pr': 4.0, 'Re': 6.0, 'O': 19.0}\",\"('O', 'Pr', 'Re')\",OTHERS,False\nmp-1188269,\"{'Pb': 4.0, 'S': 8.0, 'N': 8.0}\",\"('N', 'Pb', 'S')\",OTHERS,False\nmp-7979,\"{'F': 6.0, 'K': 2.0, 'Pd': 1.0}\",\"('F', 'K', 'Pd')\",OTHERS,False\nmp-849092,\"{'Ni': 2.0, 'S': 4.0}\",\"('Ni', 'S')\",OTHERS,False\nmp-554852,\"{'Y': 4.0, 'Te': 10.0, 'O': 26.0}\",\"('O', 'Te', 'Y')\",OTHERS,False\nmp-1223138,\"{'La': 4.0, 'Ti': 2.0, 'Mn': 2.0, 'O': 12.0}\",\"('La', 'Mn', 'O', 'Ti')\",OTHERS,False\nmp-1245321,\"{'Ti': 30.0, 'O': 60.0}\",\"('O', 'Ti')\",OTHERS,False\nmp-697789,\"{'Li': 2.0, 'Cr': 2.0, 'P': 4.0, 'O': 14.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-1190173,\"{'Cu': 2.0, 'Pb': 6.0, 'Cl': 4.0, 'O': 8.0}\",\"('Cl', 'Cu', 'O', 'Pb')\",OTHERS,False\nmp-1173113,\"{'Tb': 10.0, 'Yb': 1.0, 'Se': 16.0}\",\"('Se', 'Tb', 'Yb')\",OTHERS,False\nmp-643004,\"{'Sr': 1.0, 'Mg': 2.0, 'Fe': 1.0, 'H': 8.0}\",\"('Fe', 'H', 'Mg', 'Sr')\",OTHERS,False\nmp-1223901,\"{'In': 1.0, 'Bi': 3.0}\",\"('Bi', 'In')\",OTHERS,False\nmp-1214016,\"{'Ca': 10.0, 'Sn': 6.0}\",\"('Ca', 'Sn')\",OTHERS,False\nmp-1198305,\"{'Ce': 40.0, 'Se': 56.0, 'O': 4.0}\",\"('Ce', 'O', 'Se')\",OTHERS,False\nmp-1196696,\"{'Na': 28.0, 'U': 4.0, 'H': 16.0, 'S': 16.0, 'Cl': 4.0, 'O': 80.0}\",\"('Cl', 'H', 'Na', 'O', 'S', 'U')\",OTHERS,False\nmp-756452,\"{'Sm': 4.0, 'Dy': 4.0, 'O': 12.0}\",\"('Dy', 'O', 'Sm')\",OTHERS,False\nmp-1114497,\"{'Rb': 3.0, 'Ga': 1.0, 'Br': 6.0}\",\"('Br', 'Ga', 'Rb')\",OTHERS,False\nmp-1208173,\"{'V': 4.0, 'Pb': 4.0, 'O': 8.0}\",\"('O', 'Pb', 'V')\",OTHERS,False\nmp-1177282,\"{'Li': 4.0, 'Ti': 2.0, 'Mn': 3.0, 'V': 3.0, 'O': 16.0}\",\"('Li', 'Mn', 'O', 'Ti', 'V')\",OTHERS,False\nmp-1070498,\"{'Rb': 2.0, 'Te': 2.0, 'Pt': 1.0}\",\"('Pt', 'Rb', 'Te')\",OTHERS,False\nmp-555688,\"{'W': 4.0, 'S': 8.0, 'N': 12.0, 'Cl': 12.0}\",\"('Cl', 'N', 'S', 'W')\",OTHERS,False\nmp-1194130,\"{'Zr': 16.0, 'Pd': 8.0, 'N': 4.0}\",\"('N', 'Pd', 'Zr')\",OTHERS,False\nmp-1239263,\"{'Hf': 4.0, 'Cr': 4.0, 'Cu': 4.0, 'S': 16.0}\",\"('Cr', 'Cu', 'Hf', 'S')\",OTHERS,False\nmp-779467,\"{'Li': 9.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1095823,\"{'Zn': 2.0, 'Ag': 1.0, 'Ir': 1.0}\",\"('Ag', 'Ir', 'Zn')\",OTHERS,False\nmp-33554,\"{'Cl': 6.0, 'Gd': 1.0, 'Na': 3.0}\",\"('Cl', 'Gd', 'Na')\",OTHERS,False\nmp-985301,\"{'Ac': 1.0, 'Dy': 3.0}\",\"('Ac', 'Dy')\",OTHERS,False\nmp-22017,\"{'In': 4.0, 'La': 2.0, 'Ir': 2.0}\",\"('In', 'Ir', 'La')\",OTHERS,False\nmp-1223441,\"{'K': 1.0, 'Ba': 1.0, 'Li': 1.0, 'Zn': 1.0, 'F': 6.0}\",\"('Ba', 'F', 'K', 'Li', 'Zn')\",OTHERS,False\nmp-1178389,\"{'Fe': 2.0, 'Co': 3.0, 'Sb': 3.0, 'O': 16.0}\",\"('Co', 'Fe', 'O', 'Sb')\",OTHERS,False\nmp-31169,\"{'Al': 1.0, 'Sc': 1.0, 'Ag': 2.0}\",\"('Ag', 'Al', 'Sc')\",OTHERS,False\nmp-570463,\"{'Ta': 8.0, 'Co': 16.0}\",\"('Co', 'Ta')\",OTHERS,False\nmp-14718,\"{'Na': 12.0, 'Mn': 6.0, 'Cr': 6.0, 'F': 42.0}\",\"('Cr', 'F', 'Mn', 'Na')\",OTHERS,False\nmp-1225248,\"{'Fe': 36.0, 'B': 4.0, 'P': 8.0}\",\"('B', 'Fe', 'P')\",OTHERS,False\nmp-1260870,\"{'Ba': 2.0, 'Ti': 3.0, 'Al': 1.0, 'O': 7.0}\",\"('Al', 'Ba', 'O', 'Ti')\",OTHERS,False\nmp-1189173,\"{'La': 6.0, 'Bi': 8.0, 'Pt': 6.0}\",\"('Bi', 'La', 'Pt')\",OTHERS,False\nmp-778476,\"{'Li': 6.0, 'Mn': 5.0, 'Fe': 1.0, 'B': 6.0, 'O': 18.0}\",\"('B', 'Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-698362,\"{'H': 88.0, 'Ir': 4.0, 'C': 12.0, 'N': 24.0, 'O': 32.0}\",\"('C', 'H', 'Ir', 'N', 'O')\",OTHERS,False\nmp-1095898,\"{'Sc': 2.0, 'Ge': 1.0, 'Pd': 1.0}\",\"('Ge', 'Pd', 'Sc')\",OTHERS,False\nmp-985557,\"{'Ce': 3.0, 'Dy': 1.0}\",\"('Ce', 'Dy')\",OTHERS,False\nmp-567241,\"{'Pr': 3.0, 'Ag': 4.0, 'Sn': 4.0}\",\"('Ag', 'Pr', 'Sn')\",OTHERS,False\nmp-7187,\"{'Al': 1.0, 'Ti': 1.0, 'Ni': 2.0}\",\"('Al', 'Ni', 'Ti')\",OTHERS,False\nmp-11441,\"{'Ga': 6.0, 'Hf': 4.0}\",\"('Ga', 'Hf')\",OTHERS,False\nmp-1202551,\"{'Cu': 8.0, 'As': 8.0, 'H': 24.0, 'O': 40.0}\",\"('As', 'Cu', 'H', 'O')\",OTHERS,False\nmp-1213357,\"{'K': 12.0, 'Sm': 8.0, 'N': 36.0, 'O': 108.0}\",\"('K', 'N', 'O', 'Sm')\",OTHERS,False\nmp-752642,\"{'V': 4.0, 'As': 4.0, 'O': 16.0}\",\"('As', 'O', 'V')\",OTHERS,False\nmp-570360,\"{'Rb': 4.0, 'Nd': 8.0, 'I': 20.0}\",\"('I', 'Nd', 'Rb')\",OTHERS,False\nmp-774496,\"{'Li': 30.0, 'Mn': 6.0, 'Si': 24.0, 'O': 72.0}\",\"('Li', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1038896,\"{'Mg': 4.0, 'Bi': 8.0}\",\"('Bi', 'Mg')\",OTHERS,False\nmp-10669,\"{'Al': 2.0, 'Ge': 2.0, 'Ba': 3.0}\",\"('Al', 'Ba', 'Ge')\",OTHERS,False\nmp-10963,\"{'Se': 8.0, 'Ag': 4.0, 'Sn': 2.0, 'Hg': 2.0}\",\"('Ag', 'Hg', 'Se', 'Sn')\",OTHERS,False\nmp-1201365,\"{'Pu': 8.0, 'Re': 4.0, 'B': 24.0}\",\"('B', 'Pu', 'Re')\",OTHERS,False\nmp-1223006,\"{'La': 1.0, 'Al': 2.0, 'Ag': 3.0}\",\"('Ag', 'Al', 'La')\",OTHERS,False\nmp-1247428,\"{'Ca': 6.0, 'Re': 2.0, 'N': 6.0}\",\"('Ca', 'N', 'Re')\",OTHERS,False\nmp-1207311,\"{'Nd': 2.0, 'Ag': 1.0, 'Sb': 3.0}\",\"('Ag', 'Nd', 'Sb')\",OTHERS,False\nmp-27725,\"{'I': 4.0, 'Au': 4.0}\",\"('Au', 'I')\",OTHERS,False\nmp-1208738,\"{'Tb': 12.0, 'Ni': 6.0, 'Pb': 1.0}\",\"('Ni', 'Pb', 'Tb')\",OTHERS,False\nmp-861886,\"{'Li': 1.0, 'Dy': 1.0, 'Hg': 2.0}\",\"('Dy', 'Hg', 'Li')\",OTHERS,False\nmp-560920,\"{'Si': 32.0, 'O': 64.0}\",\"('O', 'Si')\",OTHERS,False\nmp-1227645,\"{'Ba': 1.0, 'Sr': 1.0, 'Hf': 2.0, 'O': 6.0}\",\"('Ba', 'Hf', 'O', 'Sr')\",OTHERS,False\nmp-568476,\"{'Bi': 4.0, 'Pd': 8.0, 'Pb': 4.0}\",\"('Bi', 'Pb', 'Pd')\",OTHERS,False\nmp-867627,\"{'Ba': 12.0, 'Pt': 9.0, 'O': 27.0}\",\"('Ba', 'O', 'Pt')\",OTHERS,False\nmp-758743,\"{'Li': 8.0, 'Cu': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Cu', 'Li', 'O', 'Si')\",OTHERS,False\nmp-555318,\"{'K': 4.0, 'Zn': 2.0, 'Si': 4.0, 'O': 12.0}\",\"('K', 'O', 'Si', 'Zn')\",OTHERS,False\nmp-25372,\"{'Li': 1.0, 'Cu': 1.0, 'O': 2.0}\",\"('Cu', 'Li', 'O')\",OTHERS,False\nmp-1080177,\"{'Pu': 3.0, 'Al': 3.0, 'Ni': 3.0}\",\"('Al', 'Ni', 'Pu')\",OTHERS,False\nmp-864904,\"{'Ho': 1.0, 'Cd': 1.0, 'Ag': 2.0}\",\"('Ag', 'Cd', 'Ho')\",OTHERS,False\nmp-505271,\"{'O': 12.0, 'Ni': 2.0, 'Sb': 4.0}\",\"('Ni', 'O', 'Sb')\",OTHERS,False\nmp-1019783,\"{'K': 16.0, 'S': 16.0, 'O': 24.0}\",\"('K', 'O', 'S')\",OTHERS,False\nmp-1095608,\"{'K': 2.0, 'P': 2.0, 'O': 8.0}\",\"('K', 'O', 'P')\",OTHERS,False\nmp-1095957,\"{'Mg': 1.0, 'Os': 2.0, 'W': 1.0}\",\"('Mg', 'Os', 'W')\",OTHERS,False\nmp-758324,\"{'Ti': 10.0, 'Nb': 4.0, 'O': 28.0}\",\"('Nb', 'O', 'Ti')\",OTHERS,False\nmp-1106407,\"{'Hf': 3.0, 'Co': 9.0, 'B': 6.0}\",\"('B', 'Co', 'Hf')\",OTHERS,False\nmp-18547,\"{'O': 16.0, 'Na': 4.0, 'Ge': 4.0, 'Gd': 4.0}\",\"('Gd', 'Ge', 'Na', 'O')\",OTHERS,False\nmp-1264900,\"{'Mg': 8.0, 'Mn': 8.0, 'Si': 12.0, 'O': 48.0}\",\"('Mg', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1204063,\"{'Li': 88.0, 'Ge': 20.0}\",\"('Ge', 'Li')\",OTHERS,False\nmp-1174644,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-8348,\"{'Tl': 1.0, 'Sb': 1.0, 'F': 6.0}\",\"('F', 'Sb', 'Tl')\",OTHERS,False\nmp-1194936,\"{'Mg': 64.0, 'Si': 32.0, 'Bi': 1.0}\",\"('Bi', 'Mg', 'Si')\",OTHERS,False\nmp-1197529,\"{'Sr': 2.0, 'Au': 4.0, 'S': 8.0, 'O': 32.0}\",\"('Au', 'O', 'S', 'Sr')\",OTHERS,False\nmp-5156,\"{'O': 28.0, 'P': 8.0, 'Th': 4.0}\",\"('O', 'P', 'Th')\",OTHERS,False\nmp-640915,\"{'Na': 8.0, 'La': 4.0, 'P': 4.0, 'S': 8.0, 'O': 28.0}\",\"('La', 'Na', 'O', 'P', 'S')\",OTHERS,False\nmp-1113880,\"{'Rb': 2.0, 'Al': 1.0, 'Cu': 1.0, 'F': 6.0}\",\"('Al', 'Cu', 'F', 'Rb')\",OTHERS,False\nmp-1205230,\"{'Si': 8.0, 'H': 64.0, 'C': 32.0, 'I': 32.0}\",\"('C', 'H', 'I', 'Si')\",OTHERS,False\nmp-863732,\"{'Pm': 2.0, 'Pt': 1.0, 'Rh': 1.0}\",\"('Pm', 'Pt', 'Rh')\",OTHERS,False\nmp-1175245,\"{'Li': 7.0, 'Mn': 4.0, 'Co': 1.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-558862,\"{'Na': 2.0, 'Ge': 8.0, 'P': 6.0, 'O': 24.0}\",\"('Ge', 'Na', 'O', 'P')\",OTHERS,False\nmp-1102925,\"{'Nb': 4.0, 'Fe': 4.0, 'Si': 4.0}\",\"('Fe', 'Nb', 'Si')\",OTHERS,False\nAl 73 Ir 14.5 Os 12.5,\"{'Al': 73.0, 'Ir': 14.5, 'Os': 12.5}\",\"('Al', 'Ir', 'Os')\",QC,False\nmp-29443,\"{'P': 2.0, 'I': 4.0}\",\"('I', 'P')\",OTHERS,False\nmp-1078343,\"{'U': 1.0, 'Ga': 5.0, 'Ru': 1.0}\",\"('Ga', 'Ru', 'U')\",OTHERS,False\nmp-1262565,\"{'Mg': 8.0, 'Ti': 8.0, 'Si': 16.0, 'O': 48.0}\",\"('Mg', 'O', 'Si', 'Ti')\",OTHERS,False\nmp-756010,\"{'Fe': 6.0, 'O': 10.0, 'F': 2.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-1186353,\"{'Nd': 6.0, 'Np': 2.0}\",\"('Nd', 'Np')\",OTHERS,False\nmvc-6707,\"{'Mg': 2.0, 'Sn': 2.0, 'P': 4.0, 'O': 14.0}\",\"('Mg', 'O', 'P', 'Sn')\",OTHERS,False\nmp-769713,\"{'Na': 8.0, 'B': 8.0, 'Sb': 4.0, 'S': 2.0, 'O': 32.0}\",\"('B', 'Na', 'O', 'S', 'Sb')\",OTHERS,False\nmp-1173939,\"{'Li': 4.0, 'Mn': 2.0, 'O': 6.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-1189044,\"{'Ti': 12.0, 'Ge': 4.0}\",\"('Ge', 'Ti')\",OTHERS,False\nmp-771043,\"{'Y': 8.0, 'Zr': 8.0, 'O': 28.0}\",\"('O', 'Y', 'Zr')\",OTHERS,False\nmp-775289,\"{'Mn': 3.0, 'Cr': 1.0, 'Cu': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Cr', 'Cu', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1245323,\"{'Al': 40.0, 'O': 60.0}\",\"('Al', 'O')\",OTHERS,False\nmp-733936,\"{'Cs': 8.0, 'Mg': 4.0, 'H': 32.0, 'C': 8.0, 'O': 40.0}\",\"('C', 'Cs', 'H', 'Mg', 'O')\",OTHERS,False\nmp-557035,\"{'K': 6.0, 'S': 2.0, 'O': 8.0, 'F': 2.0}\",\"('F', 'K', 'O', 'S')\",OTHERS,False\nmp-771232,\"{'Li': 5.0, 'Mn': 2.0, 'Ni': 5.0, 'O': 12.0}\",\"('Li', 'Mn', 'Ni', 'O')\",OTHERS,False\nmp-606102,\"{'Ce': 3.0, 'In': 3.0, 'Ru': 2.0}\",\"('Ce', 'In', 'Ru')\",OTHERS,False\nmp-1218851,\"{'Sr': 2.0, 'Ca': 1.0, 'Ti': 3.0, 'O': 9.0}\",\"('Ca', 'O', 'Sr', 'Ti')\",OTHERS,False\nmp-755552,\"{'Li': 1.0, 'Y': 1.0, 'S': 2.0}\",\"('Li', 'S', 'Y')\",OTHERS,False\nmp-19453,\"{'O': 28.0, 'Mo': 4.0, 'Te': 8.0}\",\"('Mo', 'O', 'Te')\",OTHERS,False\nmp-605735,\"{'Sr': 6.0, 'In': 8.0, 'Pb': 2.0}\",\"('In', 'Pb', 'Sr')\",OTHERS,False\nmp-25382,\"{'O': 8.0, 'F': 2.0, 'P': 2.0, 'Fe': 2.0}\",\"('F', 'Fe', 'O', 'P')\",OTHERS,False\nmp-560247,\"{'Li': 4.0, 'Ca': 4.0, 'Si': 10.0, 'O': 26.0}\",\"('Ca', 'Li', 'O', 'Si')\",OTHERS,False\nmp-558997,\"{'Sm': 2.0, 'Mo': 2.0, 'Cl': 2.0, 'O': 8.0}\",\"('Cl', 'Mo', 'O', 'Sm')\",OTHERS,False\nmp-1215148,\"{'Ce': 4.0, 'W': 8.0, 'S': 6.0, 'O': 28.0}\",\"('Ce', 'O', 'S', 'W')\",OTHERS,False\nmp-1225405,\"{'Dy': 2.0, 'In': 3.0, 'Cu': 1.0}\",\"('Cu', 'Dy', 'In')\",OTHERS,False\nmp-18340,\"{'O': 24.0, 'Mo': 4.0, 'Tb': 8.0}\",\"('Mo', 'O', 'Tb')\",OTHERS,False\nmp-504927,\"{'O': 8.0, 'Cr': 2.0, 'Cu': 2.0}\",\"('Cr', 'Cu', 'O')\",OTHERS,False\nmp-1106252,\"{'Nd': 10.0, 'Ga': 6.0}\",\"('Ga', 'Nd')\",OTHERS,False\nmp-3107,\"{'O': 7.0, 'P': 1.0, 'Ga': 3.0}\",\"('Ga', 'O', 'P')\",OTHERS,False\nmp-1212904,\"{'Dy': 10.0, 'Co': 4.0, 'Bi': 2.0}\",\"('Bi', 'Co', 'Dy')\",OTHERS,False\nmp-1181689,\"{'Cs': 6.0, 'Ba': 8.0, 'C': 6.0, 'O': 18.0, 'F': 10.0}\",\"('Ba', 'C', 'Cs', 'F', 'O')\",OTHERS,False\nmp-567114,\"{'Sr': 2.0, 'Bi': 4.0, 'Pd': 4.0}\",\"('Bi', 'Pd', 'Sr')\",OTHERS,False\nAu 49.7 In 35.4 Ce 14.9,\"{'Au': 49.7, 'In': 35.4, 'Ce': 14.9}\",\"('Au', 'Ce', 'In')\",AC,False\nmp-1181604,\"{'Fe': 4.0, 'H': 4.0, 'O': 8.0}\",\"('Fe', 'H', 'O')\",OTHERS,False\nmp-654008,\"{'K': 3.0, 'Cr': 11.0, 'S': 18.0}\",\"('Cr', 'K', 'S')\",OTHERS,False\nmp-675532,\"{'La': 2.0, 'Ti': 12.0, 'O': 24.0}\",\"('La', 'O', 'Ti')\",OTHERS,False\nmp-1179601,\"{'Sb': 8.0, 'N': 12.0, 'Cl': 28.0, 'O': 4.0}\",\"('Cl', 'N', 'O', 'Sb')\",OTHERS,False\nmp-1093852,\"{'Y': 1.0, 'Sc': 1.0, 'Pt': 2.0}\",\"('Pt', 'Sc', 'Y')\",OTHERS,False\nmp-21294,\"{'Y': 4.0, 'In': 2.0}\",\"('In', 'Y')\",OTHERS,False\nmp-1216675,\"{'Ti': 1.0, 'Mo': 2.0}\",\"('Mo', 'Ti')\",OTHERS,False\nmp-1291,\"{'In': 3.0, 'Er': 1.0}\",\"('Er', 'In')\",OTHERS,False\nmp-557432,\"{'Ge': 4.0, 'Cl': 4.0, 'O': 8.0, 'F': 20.0}\",\"('Cl', 'F', 'Ge', 'O')\",OTHERS,False\nmp-1096958,\"{'Dy': 2.0, 'Mn': 4.0, 'O': 8.0}\",\"('Dy', 'Mn', 'O')\",OTHERS,False\nmp-1226961,\"{'Ce': 2.0, 'Pr': 4.0, 'S': 8.0}\",\"('Ce', 'Pr', 'S')\",OTHERS,False\nmp-649944,\"{'Na': 8.0, 'Cu': 12.0, 'Si': 16.0, 'O': 48.0}\",\"('Cu', 'Na', 'O', 'Si')\",OTHERS,False\nmp-569879,\"{'Cs': 2.0, 'Ta': 6.0, 'Pb': 1.0, 'Cl': 18.0}\",\"('Cl', 'Cs', 'Pb', 'Ta')\",OTHERS,False\nmp-1246896,\"{'Mn': 6.0, 'Al': 2.0, 'N': 6.0}\",\"('Al', 'Mn', 'N')\",OTHERS,False\nmp-631582,\"{'Al': 1.0, 'Tc': 2.0, 'Pb': 1.0}\",\"('Al', 'Pb', 'Tc')\",OTHERS,False\nmp-1074259,\"{'Mg': 8.0, 'Si': 6.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1227844,\"{'Ba': 1.0, 'La': 1.0, 'Mn': 2.0, 'O': 6.0}\",\"('Ba', 'La', 'Mn', 'O')\",OTHERS,False\nmp-775903,\"{'Li': 4.0, 'Mn': 2.0, 'P': 4.0, 'O': 14.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1095340,\"{'Ba': 2.0, 'Tb': 2.0, 'Ag': 2.0, 'S': 6.0}\",\"('Ag', 'Ba', 'S', 'Tb')\",OTHERS,False\nmp-556062,\"{'Na': 2.0, 'Nb': 8.0, 'Tl': 6.0, 'P': 4.0, 'O': 34.0}\",\"('Na', 'Nb', 'O', 'P', 'Tl')\",OTHERS,False\nmp-677029,\"{'Ca': 4.0, 'Mg': 2.0, 'Al': 4.0, 'Si': 6.0, 'O': 24.0}\",\"('Al', 'Ca', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-1095985,\"{'Mg': 1.0, 'Ti': 1.0, 'Au': 2.0}\",\"('Au', 'Mg', 'Ti')\",OTHERS,False\nmp-1223342,\"{'La': 4.0, 'Mn': 2.0, 'Cr': 2.0, 'O': 12.0}\",\"('Cr', 'La', 'Mn', 'O')\",OTHERS,False\nmp-1030704,\"{'Na': 30.0, 'W': 14.0, 'N': 38.0}\",\"('N', 'Na', 'W')\",OTHERS,False\nmp-552113,\"{'V': 4.0, 'O': 6.0}\",\"('O', 'V')\",OTHERS,False\nmp-1196520,\"{'V': 8.0, 'P': 8.0, 'N': 8.0, 'O': 48.0}\",\"('N', 'O', 'P', 'V')\",OTHERS,False\nmp-863767,\"{'Fe': 26.0, 'Sn': 4.0, 'O': 40.0}\",\"('Fe', 'O', 'Sn')\",OTHERS,False\nmp-779587,\"{'Ti': 6.0, 'P': 6.0, 'O': 24.0}\",\"('O', 'P', 'Ti')\",OTHERS,False\nmp-38284,\"{'Ce': 8.0, 'Nd': 8.0, 'O': 28.0}\",\"('Ce', 'Nd', 'O')\",OTHERS,False\nmp-756490,\"{'Li': 12.0, 'Mn': 2.0, 'S': 8.0}\",\"('Li', 'Mn', 'S')\",OTHERS,False\nmp-1112437,\"{'Rb': 2.0, 'Pr': 1.0, 'Cu': 1.0, 'I': 6.0}\",\"('Cu', 'I', 'Pr', 'Rb')\",OTHERS,False\nmp-1203006,\"{'Lu': 6.0, 'Ge': 26.0, 'Ru': 8.0}\",\"('Ge', 'Lu', 'Ru')\",OTHERS,False\nmp-695119,\"{'Na': 6.0, 'Zr': 4.0, 'Si': 4.0, 'P': 2.0, 'O': 24.0}\",\"('Na', 'O', 'P', 'Si', 'Zr')\",OTHERS,False\nmp-1101512,\"{'Li': 4.0, 'Fe': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-22025,\"{'O': 7.0, 'Co': 2.0, 'As': 2.0}\",\"('As', 'Co', 'O')\",OTHERS,False\nmp-27359,\"{'Cl': 6.0, 'Sc': 2.0, 'Cs': 2.0}\",\"('Cl', 'Cs', 'Sc')\",OTHERS,False\nmp-972448,\"{'Sm': 4.0, 'B': 4.0, 'S': 12.0}\",\"('B', 'S', 'Sm')\",OTHERS,False\nmp-674423,\"{'Sr': 20.0, 'Ta': 8.0, 'O': 39.0}\",\"('O', 'Sr', 'Ta')\",OTHERS,False\nmp-775128,\"{'Fe': 3.0, 'Sb': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Fe', 'O', 'P', 'Sb')\",OTHERS,False\nmp-978490,\"{'Si': 1.0, 'Pd': 1.0, 'O': 3.0}\",\"('O', 'Pd', 'Si')\",OTHERS,False\nmp-569943,\"{'K': 8.0, 'Ag': 4.0, 'I': 12.0}\",\"('Ag', 'I', 'K')\",OTHERS,False\nmp-1096843,\"{'Ba': 1.0, 'Cu': 1.0, 'S': 2.0}\",\"('Ba', 'Cu', 'S')\",OTHERS,False\nmp-640950,\"{'Na': 6.0, 'B': 54.0, 'Se': 48.0}\",\"('B', 'Na', 'Se')\",OTHERS,False\nmp-19137,\"{'O': 6.0, 'K': 2.0, 'Mn': 1.0, 'Se': 2.0}\",\"('K', 'Mn', 'O', 'Se')\",OTHERS,False\nmp-1215786,\"{'Zn': 2.0, 'Cd': 6.0, 'Ge': 2.0, 'S': 12.0}\",\"('Cd', 'Ge', 'S', 'Zn')\",OTHERS,False\nmp-571580,\"{'Er': 56.0, 'In': 12.0, 'Pd': 12.0}\",\"('Er', 'In', 'Pd')\",OTHERS,False\nmp-556481,\"{'Sr': 2.0, 'U': 2.0, 'Se': 4.0, 'O': 16.0}\",\"('O', 'Se', 'Sr', 'U')\",OTHERS,False\nmp-759603,\"{'H': 36.0, 'C': 4.0, 'N': 20.0, 'Cl': 8.0}\",\"('C', 'Cl', 'H', 'N')\",OTHERS,False\nmp-1184082,\"{'Er': 2.0, 'Zn': 1.0, 'Hg': 1.0}\",\"('Er', 'Hg', 'Zn')\",OTHERS,False\nmp-16342,\"{'Ge': 2.0, 'Ho': 2.0}\",\"('Ge', 'Ho')\",OTHERS,False\nmp-1188076,\"{'Th': 1.0, 'Mg': 149.0}\",\"('Mg', 'Th')\",OTHERS,False\nmp-1147741,\"{'Li': 32.0, 'Zn': 8.0, 'P': 16.0, 'S': 64.0}\",\"('Li', 'P', 'S', 'Zn')\",OTHERS,False\nmp-11604,\"{'S': 8.0, 'K': 2.0, 'Cu': 2.0, 'Sm': 4.0}\",\"('Cu', 'K', 'S', 'Sm')\",OTHERS,False\nmp-1079860,\"{'Hf': 3.0, 'Ga': 3.0, 'Ni': 3.0}\",\"('Ga', 'Hf', 'Ni')\",OTHERS,False\nmp-1194612,\"{'Y': 4.0, 'Nb': 4.0, 'Te': 8.0, 'O': 32.0}\",\"('Nb', 'O', 'Te', 'Y')\",OTHERS,False\nmp-11275,\"{'Be': 8.0, 'Re': 4.0}\",\"('Be', 'Re')\",OTHERS,False\nmp-554805,\"{'Sr': 12.0, 'Se': 12.0, 'Cl': 8.0, 'O': 32.0}\",\"('Cl', 'O', 'Se', 'Sr')\",OTHERS,False\nmp-560833,\"{'B': 12.0, 'Pb': 24.0, 'Cl': 4.0, 'O': 40.0}\",\"('B', 'Cl', 'O', 'Pb')\",OTHERS,False\nmp-1074408,\"{'Mg': 8.0, 'Si': 6.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-17395,\"{'B': 10.0, 'O': 20.0, 'Cu': 2.0, 'Tm': 2.0}\",\"('B', 'Cu', 'O', 'Tm')\",OTHERS,False\nAl 65 Cu 20 Ru 15,\"{'Al': 65.0, 'Cu': 20.0, 'Ru': 15.0}\",\"('Al', 'Cu', 'Ru')\",QC,False\nmp-10003,\"{'Si': 2.0, 'Co': 2.0, 'Nb': 8.0}\",\"('Co', 'Nb', 'Si')\",OTHERS,False\nmp-219,\"{'P': 1.0, 'Mo': 1.0}\",\"('Mo', 'P')\",OTHERS,False\nmp-1019253,\"{'Sm': 2.0, 'Se': 4.0}\",\"('Se', 'Sm')\",OTHERS,False\nmp-1250551,\"{'Si': 112.0, 'O': 224.0}\",\"('O', 'Si')\",OTHERS,False\nmp-27474,\"{'Pa': 2.0, 'Br': 8.0}\",\"('Br', 'Pa')\",OTHERS,False\nmp-616507,\"{'Cs': 4.0, 'Na': 4.0, 'Ir': 2.0, 'O': 8.0}\",\"('Cs', 'Ir', 'Na', 'O')\",OTHERS,False\nmp-1208821,\"{'Sr': 2.0, 'Zn': 2.0, 'O': 10.0}\",\"('O', 'Sr', 'Zn')\",OTHERS,False\nmvc-2541,\"{'Sr': 2.0, 'Y': 1.0, 'Tl': 1.0, 'V': 2.0, 'O': 7.0}\",\"('O', 'Sr', 'Tl', 'V', 'Y')\",OTHERS,False\nmp-1112505,\"{'Cs': 2.0, 'Al': 1.0, 'Tl': 1.0, 'Cl': 6.0}\",\"('Al', 'Cl', 'Cs', 'Tl')\",OTHERS,False\nmp-567511,\"{'H': 32.0, 'Pt': 4.0, 'C': 4.0, 'N': 32.0}\",\"('C', 'H', 'N', 'Pt')\",OTHERS,False\nmp-17314,\"{'Li': 2.0, 'O': 32.0, 'P': 6.0, 'Mo': 6.0}\",\"('Li', 'Mo', 'O', 'P')\",OTHERS,False\nmp-776165,\"{'Ag': 4.0, 'Au': 4.0, 'O': 12.0}\",\"('Ag', 'Au', 'O')\",OTHERS,False\nmp-1211437,\"{'La': 4.0, 'W': 4.0, 'O': 8.0}\",\"('La', 'O', 'W')\",OTHERS,False\nmp-569543,\"{'Cs': 2.0, 'La': 2.0, 'Zr': 12.0, 'Fe': 2.0, 'Cl': 36.0}\",\"('Cl', 'Cs', 'Fe', 'La', 'Zr')\",OTHERS,False\nZn 80 Sc 15 Mg 5,\"{'Zn': 80.0, 'Sc': 15.0, 'Mg': 5.0}\",\"('Mg', 'Sc', 'Zn')\",QC,False\nmp-707055,\"{'Na': 16.0, 'Ge': 8.0, 'H': 96.0, 'S': 16.0, 'O': 56.0}\",\"('Ge', 'H', 'Na', 'O', 'S')\",OTHERS,False\nmp-1219928,\"{'Pd': 1.0, 'Pt': 1.0, 'Au': 1.0}\",\"('Au', 'Pd', 'Pt')\",OTHERS,False\nmp-1200211,\"{'Ca': 6.0, 'Sn': 26.0, 'Ir': 8.0}\",\"('Ca', 'Ir', 'Sn')\",OTHERS,False\nmp-1102043,\"{'La': 4.0, 'Os': 8.0}\",\"('La', 'Os')\",OTHERS,False\nmp-760329,\"{'Li': 6.0, 'V': 4.0, 'F': 24.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-1208095,\"{'Yb': 3.0, 'N': 21.0, 'O': 45.0}\",\"('N', 'O', 'Yb')\",OTHERS,False\nmp-1102036,\"{'Ni': 6.0, 'P': 6.0}\",\"('Ni', 'P')\",OTHERS,False\nmp-1201500,\"{'Ca': 2.0, 'Fe': 10.0, 'P': 10.0, 'O': 44.0}\",\"('Ca', 'Fe', 'O', 'P')\",OTHERS,False\nmp-11595,\"{'Ge': 6.0, 'Yb': 10.0}\",\"('Ge', 'Yb')\",OTHERS,False\nmp-1080202,\"{'Fe': 4.0, 'N': 8.0}\",\"('Fe', 'N')\",OTHERS,False\nmp-1191502,\"{'V': 2.0, 'In': 2.0, 'Se': 4.0, 'O': 16.0}\",\"('In', 'O', 'Se', 'V')\",OTHERS,False\nmp-1025223,\"{'Hf': 4.0, 'Al': 3.0}\",\"('Al', 'Hf')\",OTHERS,False\nmp-631436,\"{'V': 1.0, 'Os': 1.0, 'Cl': 1.0}\",\"('Cl', 'Os', 'V')\",OTHERS,False\nmp-1101293,\"{'Ti': 3.0, 'V': 1.0, 'Ni': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Ni', 'O', 'P', 'Ti', 'V')\",OTHERS,False\nmp-973391,\"{'Li': 4.0, 'Si': 4.0, 'B': 24.0}\",\"('B', 'Li', 'Si')\",OTHERS,False\nmp-778744,\"{'Li': 4.0, 'Mn': 2.0, 'Ni': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Li', 'Mn', 'Ni', 'O', 'P')\",OTHERS,False\nmp-570491,\"{'Ta': 1.0, 'Ni': 3.0}\",\"('Ni', 'Ta')\",OTHERS,False\nmp-1198791,\"{'Sb': 8.0, 'Te': 12.0, 'W': 4.0, 'C': 16.0, 'O': 16.0, 'F': 48.0}\",\"('C', 'F', 'O', 'Sb', 'Te', 'W')\",OTHERS,False\nmp-1249484,\"{'K': 2.0, 'Mg': 6.0, 'Al': 2.0, 'Si': 6.0, 'O': 20.0, 'F': 4.0}\",\"('Al', 'F', 'K', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-542658,\"{'K': 4.0, 'Mo': 8.0, 'O': 26.0}\",\"('K', 'Mo', 'O')\",OTHERS,False\nmp-764498,\"{'Mn': 12.0, 'O': 7.0, 'F': 17.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-1225451,\"{'Dy': 12.0, 'Co': 9.0}\",\"('Co', 'Dy')\",OTHERS,False\nmp-1205629,\"{'Cs': 3.0, 'Fe': 1.0, 'F': 6.0}\",\"('Cs', 'F', 'Fe')\",OTHERS,False\nmp-1096801,\"{'Ba': 2.0, 'Zr': 2.0, 'P': 4.0}\",\"('Ba', 'P', 'Zr')\",OTHERS,False\nmp-1079720,\"{'Ag': 4.0, 'O': 4.0}\",\"('Ag', 'O')\",OTHERS,False\nmp-1216030,\"{'Yb': 4.0, 'Fe': 1.0, 'S': 7.0}\",\"('Fe', 'S', 'Yb')\",OTHERS,False\nmp-997048,\"{'Ag': 2.0, 'Br': 2.0, 'O': 4.0}\",\"('Ag', 'Br', 'O')\",OTHERS,False\nmp-1185542,\"{'Cs': 1.0, 'K': 2.0, 'Rb': 1.0}\",\"('Cs', 'K', 'Rb')\",OTHERS,False\nmp-30029,\"{'Ni': 39.0, 'Ga': 9.0, 'Ge': 18.0}\",\"('Ga', 'Ge', 'Ni')\",OTHERS,False\nmp-1095337,\"{'Sm': 2.0, 'V': 2.0, 'O': 8.0}\",\"('O', 'Sm', 'V')\",OTHERS,False\nmp-8179,\"{'Li': 2.0, 'O': 4.0, 'Tl': 2.0}\",\"('Li', 'O', 'Tl')\",OTHERS,False\nmp-20373,\"{'B': 4.0, 'Co': 12.0}\",\"('B', 'Co')\",OTHERS,False\nGa 35 V 45 Ni 6 Si 14,\"{'Ga': 35.0, 'V': 45.0, 'Ni': 6.0, 'Si': 14.0}\",\"('Ga', 'Ni', 'Si', 'V')\",QC,False\nmp-756672,\"{'Li': 16.0, 'Si': 16.0, 'Bi': 8.0, 'O': 52.0}\",\"('Bi', 'Li', 'O', 'Si')\",OTHERS,False\nmp-755081,\"{'Li': 8.0, 'Ni': 5.0, 'O': 10.0}\",\"('Li', 'Ni', 'O')\",OTHERS,False\nmp-19271,\"{'O': 12.0, 'Cr': 4.0, 'Ba': 4.0}\",\"('Ba', 'Cr', 'O')\",OTHERS,False\nmvc-4661,\"{'Zn': 2.0, 'Sb': 4.0, 'O': 8.0}\",\"('O', 'Sb', 'Zn')\",OTHERS,False\nmp-772819,\"{'K': 6.0, 'Cu': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Cu', 'K', 'O', 'P')\",OTHERS,False\nmp-1096787,\"{'Al': 2.0, 'Zn': 2.0, 'S': 5.0}\",\"('Al', 'S', 'Zn')\",OTHERS,False\nmp-4165,\"{'N': 4.0, 'O': 4.0, 'Ta': 4.0}\",\"('N', 'O', 'Ta')\",OTHERS,False\nmp-510569,\"{'Cs': 2.0, 'Ce': 2.0, 'Cu': 2.0, 'S': 6.0}\",\"('Ce', 'Cs', 'Cu', 'S')\",OTHERS,False\nmp-11563,\"{'Ti': 1.0, 'Rh': 1.0}\",\"('Rh', 'Ti')\",OTHERS,False\nmp-554115,\"{'Zn': 24.0, 'S': 24.0}\",\"('S', 'Zn')\",OTHERS,False\nmp-10200,\"{'Be': 2.0, 'Si': 2.0, 'Zr': 2.0}\",\"('Be', 'Si', 'Zr')\",OTHERS,False\nmp-554142,\"{'Ge': 2.0, 'Te': 4.0, 'O': 12.0}\",\"('Ge', 'O', 'Te')\",OTHERS,False\nmp-1216888,\"{'U': 3.0, 'Al': 3.0, 'Co': 1.0, 'Rh': 2.0}\",\"('Al', 'Co', 'Rh', 'U')\",OTHERS,False\nmp-755936,\"{'Lu': 2.0, 'W': 2.0, 'O': 8.0}\",\"('Lu', 'O', 'W')\",OTHERS,False\nmp-1224789,\"{'Ga': 2.0, 'As': 1.0, 'P': 1.0}\",\"('As', 'Ga', 'P')\",OTHERS,False\nmp-29516,\"{'Br': 32.0, 'Rh': 4.0, 'Bi': 28.0}\",\"('Bi', 'Br', 'Rh')\",OTHERS,False\nmp-1216452,\"{'V': 6.0, 'Si': 1.0, 'C': 1.0}\",\"('C', 'Si', 'V')\",OTHERS,False\nmp-1220245,\"{'Nd': 2.0, 'Zn': 1.0, 'Sb': 4.0}\",\"('Nd', 'Sb', 'Zn')\",OTHERS,False\nmp-38077,\"{'Ce': 5.0, 'K': 1.0, 'S': 8.0}\",\"('Ce', 'K', 'S')\",OTHERS,False\nmp-27167,\"{'F': 6.0, 'Sn': 4.0, 'I': 2.0}\",\"('F', 'I', 'Sn')\",OTHERS,False\nmp-780803,\"{'Li': 16.0, 'Fe': 16.0, 'F': 48.0}\",\"('F', 'Fe', 'Li')\",OTHERS,False\nmp-759065,\"{'Li': 12.0, 'V': 8.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1191432,\"{'Ce': 6.0, 'Al': 2.0, 'Cd': 2.0, 'S': 14.0}\",\"('Al', 'Cd', 'Ce', 'S')\",OTHERS,False\nmp-978255,\"{'Mg': 2.0, 'Ti': 6.0}\",\"('Mg', 'Ti')\",OTHERS,False\nmp-546707,\"{'Na': 4.0, 'O': 4.0, 'C': 1.0}\",\"('C', 'Na', 'O')\",OTHERS,False\nmp-20699,\"{'Y': 4.0, 'Mn': 4.0, 'O': 12.0}\",\"('Mn', 'O', 'Y')\",OTHERS,False\nmp-1216095,\"{'Y': 2.0, 'Mn': 3.0, 'Fe': 1.0}\",\"('Fe', 'Mn', 'Y')\",OTHERS,False\nmp-973198,{'Na': 3.0},\"('Na',)\",OTHERS,False\nmp-639904,\"{'Ce': 12.0, 'Mo': 4.0, 'O': 28.0}\",\"('Ce', 'Mo', 'O')\",OTHERS,False\nmp-1220251,\"{'Nd': 4.0, 'Mn': 1.0, 'Ni': 3.0, 'O': 12.0}\",\"('Mn', 'Nd', 'Ni', 'O')\",OTHERS,False\nmp-1219193,\"{'Si': 1.0, 'Ni': 6.0, 'Ge': 1.0}\",\"('Ge', 'Ni', 'Si')\",OTHERS,False\nmp-31070,\"{'S': 18.0, 'As': 16.0}\",\"('As', 'S')\",OTHERS,False\nmp-9066,\"{'Li': 4.0, 'O': 8.0, 'Na': 2.0, 'As': 2.0}\",\"('As', 'Li', 'Na', 'O')\",OTHERS,False\nmp-9899,\"{'P': 2.0, 'Ag': 2.0, 'Ba': 2.0}\",\"('Ag', 'Ba', 'P')\",OTHERS,False\nmp-1188605,\"{'V': 10.0, 'P': 6.0}\",\"('P', 'V')\",OTHERS,False\nAl 71.5 Co 28.5,\"{'Al': 71.5, 'Co': 28.5}\",\"('Al', 'Co')\",AC,False\nmp-6651,\"{'O': 12.0, 'F': 24.0, 'K': 8.0, 'Ta': 8.0}\",\"('F', 'K', 'O', 'Ta')\",OTHERS,False\nmp-1208632,\"{'Zn': 12.0, 'N': 72.0}\",\"('N', 'Zn')\",OTHERS,False\nmp-1222245,\"{'Mn': 2.0, 'Fe': 8.0, 'Bi': 10.0, 'O': 30.0}\",\"('Bi', 'Fe', 'Mn', 'O')\",OTHERS,False\nmp-604496,\"{'Ce': 2.0, 'Si': 2.0, 'H': 2.0, 'Ru': 2.0}\",\"('Ce', 'H', 'Ru', 'Si')\",OTHERS,False\nmp-2965,\"{'Si': 2.0, 'Mn': 2.0, 'Ce': 1.0}\",\"('Ce', 'Mn', 'Si')\",OTHERS,False\nmp-1205100,\"{'Sn': 2.0, 'S': 12.0, 'N': 4.0, 'O': 42.0}\",\"('N', 'O', 'S', 'Sn')\",OTHERS,False\nmvc-9486,\"{'Ti': 8.0, 'Zn': 4.0, 'P': 8.0, 'O': 32.0}\",\"('O', 'P', 'Ti', 'Zn')\",OTHERS,False\nmp-1224432,\"{'Hf': 1.0, 'U': 3.0, 'S': 3.0}\",\"('Hf', 'S', 'U')\",OTHERS,False\nmp-1179922,\"{'Pt': 4.0, 'N': 8.0, 'O': 16.0}\",\"('N', 'O', 'Pt')\",OTHERS,False\nmp-1197024,\"{'Fe': 4.0, 'Bi': 4.0, 'Se': 12.0, 'O': 36.0}\",\"('Bi', 'Fe', 'O', 'Se')\",OTHERS,False\nmp-1029965,\"{'K': 30.0, 'Os': 14.0, 'N': 38.0}\",\"('K', 'N', 'Os')\",OTHERS,False\nmp-1226910,\"{'Cs': 1.0, 'Nb': 2.0, 'Si': 8.0, 'O': 24.0}\",\"('Cs', 'Nb', 'O', 'Si')\",OTHERS,False\nmp-1097178,\"{'Ta': 1.0, 'Si': 1.0, 'Tc': 2.0}\",\"('Si', 'Ta', 'Tc')\",OTHERS,False\nmp-18520,\"{'O': 24.0, 'P': 4.0, 'V': 4.0, 'Ba': 4.0}\",\"('Ba', 'O', 'P', 'V')\",OTHERS,False\nmp-1093946,\"{'Be': 1.0, 'Ge': 1.0, 'Ir': 2.0}\",\"('Be', 'Ge', 'Ir')\",OTHERS,False\nmp-1220311,\"{'Nd': 1.0, 'Er': 3.0, 'Fe': 34.0}\",\"('Er', 'Fe', 'Nd')\",OTHERS,False\nTa 181 Te 112,\"{'Ta': 181.0, 'Te': 112.0}\",\"('Ta', 'Te')\",AC,False\nmp-761240,\"{'Li': 4.0, 'Mn': 2.0, 'V': 2.0, 'P': 8.0, 'O': 28.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-2591,\"{'O': 4.0, 'Ti': 12.0}\",\"('O', 'Ti')\",OTHERS,False\nmp-758932,\"{'Li': 40.0, 'Fe': 8.0, 'H': 8.0, 'O': 32.0}\",\"('Fe', 'H', 'Li', 'O')\",OTHERS,False\nmp-643392,\"{'Ca': 4.0, 'H': 8.0, 'O': 8.0}\",\"('Ca', 'H', 'O')\",OTHERS,False\nmp-1245618,\"{'Ge': 4.0, 'Te': 2.0, 'N': 4.0}\",\"('Ge', 'N', 'Te')\",OTHERS,False\nmp-779534,\"{'Li': 9.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-19388,\"{'O': 8.0, 'Ni': 2.0, 'As': 2.0, 'Ba': 1.0}\",\"('As', 'Ba', 'Ni', 'O')\",OTHERS,False\nmp-715811,\"{'Fe': 12.0, 'O': 16.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-1177898,\"{'Li': 4.0, 'Mn': 2.0, 'Fe': 2.0, 'O': 8.0}\",\"('Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-765572,\"{'Co': 4.0, 'B': 4.0, 'O': 12.0}\",\"('B', 'Co', 'O')\",OTHERS,False\nmp-1216756,\"{'Tm': 2.0, 'B': 6.0, 'Rh': 9.0}\",\"('B', 'Rh', 'Tm')\",OTHERS,False\nmp-1192271,\"{'Cs': 4.0, 'U': 2.0, 'Pd': 6.0, 'S': 12.0}\",\"('Cs', 'Pd', 'S', 'U')\",OTHERS,False\nmp-770464,\"{'La': 4.0, 'Bi': 4.0, 'O': 16.0}\",\"('Bi', 'La', 'O')\",OTHERS,False\nmp-1094041,\"{'Sr': 1.0, 'Ca': 3.0, 'Mn': 4.0, 'O': 12.0}\",\"('Ca', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-1096616,\"{'Mg': 1.0, 'Zr': 1.0, 'Cd': 2.0}\",\"('Cd', 'Mg', 'Zr')\",OTHERS,False\nmp-31082,\"{'Li': 4.0, 'Ge': 2.0, 'Zr': 1.0}\",\"('Ge', 'Li', 'Zr')\",OTHERS,False\nmp-752784,\"{'Mn': 8.0, 'O': 8.0, 'F': 8.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-510366,\"{'K': 12.0, 'Co': 4.0, 'O': 10.0}\",\"('Co', 'K', 'O')\",OTHERS,False\nmp-1078409,\"{'Ba': 2.0, 'La': 1.0, 'Bi': 1.0, 'O': 6.0}\",\"('Ba', 'Bi', 'La', 'O')\",OTHERS,False\nmp-849471,\"{'Li': 4.0, 'Mn': 3.0, 'Cr': 2.0, 'Fe': 3.0, 'O': 16.0}\",\"('Cr', 'Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1222442,\"{'Li': 5.0, 'Au': 1.0, 'O': 4.0}\",\"('Au', 'Li', 'O')\",OTHERS,False\nmp-1076208,\"{'K': 20.0, 'Na': 12.0, 'Nb': 16.0, 'W': 16.0, 'O': 80.0}\",\"('K', 'Na', 'Nb', 'O', 'W')\",OTHERS,False\nmp-961681,\"{'Zr': 1.0, 'Ge': 1.0, 'Pt': 1.0}\",\"('Ge', 'Pt', 'Zr')\",OTHERS,False\nmp-1188003,\"{'Zr': 2.0, 'Tc': 1.0, 'Ru': 1.0}\",\"('Ru', 'Tc', 'Zr')\",OTHERS,False\nmp-1220432,\"{'Nd': 4.0, 'Ti': 2.0, 'Cu': 2.0, 'O': 12.0}\",\"('Cu', 'Nd', 'O', 'Ti')\",OTHERS,False\nmp-1025290,\"{'La': 2.0, 'Fe': 2.0, 'Si': 2.0, 'C': 1.0}\",\"('C', 'Fe', 'La', 'Si')\",OTHERS,False\nmp-776648,\"{'Li': 4.0, 'Fe': 6.0, 'F': 16.0}\",\"('F', 'Fe', 'Li')\",OTHERS,False\nmp-1093709,\"{'Sc': 1.0, 'Cu': 2.0, 'Ag': 1.0}\",\"('Ag', 'Cu', 'Sc')\",OTHERS,False\nmp-1074189,\"{'Mg': 8.0, 'Si': 14.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-612405,\"{'Fe': 6.0, 'O': 8.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-1188851,\"{'Ho': 10.0, 'Bi': 2.0, 'Au': 4.0}\",\"('Au', 'Bi', 'Ho')\",OTHERS,False\nmp-1096783,\"{'Ba': 8.0, 'V': 4.0, 'Fe': 4.0, 'O': 24.0}\",\"('Ba', 'Fe', 'O', 'V')\",OTHERS,False\nmp-1229004,\"{'Al': 1.0, 'Cr': 4.0, 'Cu': 1.0, 'Se': 8.0}\",\"('Al', 'Cr', 'Cu', 'Se')\",OTHERS,False\nmp-757110,\"{'Na': 6.0, 'Ni': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Na', 'Ni', 'O', 'P')\",OTHERS,False\nmvc-8188,\"{'Ca': 2.0, 'Fe': 4.0, 'O': 8.0}\",\"('Ca', 'Fe', 'O')\",OTHERS,False\nmp-1097499,\"{'Nb': 1.0, 'Cr': 2.0, 'W': 1.0}\",\"('Cr', 'Nb', 'W')\",OTHERS,False\nmp-29538,\"{'Cl': 10.0, 'Ba': 2.0, 'Gd': 2.0}\",\"('Ba', 'Cl', 'Gd')\",OTHERS,False\nmp-29759,\"{'V': 4.0, 'Xe': 8.0, 'F': 68.0}\",\"('F', 'V', 'Xe')\",OTHERS,False\nmp-560071,\"{'F': 6.0, 'K': 1.0, 'Co': 1.0, 'Rb': 2.0}\",\"('Co', 'F', 'K', 'Rb')\",OTHERS,False\nmp-1189962,\"{'Pr': 4.0, 'Sc': 4.0, 'S': 12.0}\",\"('Pr', 'S', 'Sc')\",OTHERS,False\nmvc-16379,\"{'Y': 4.0, 'Co': 12.0, 'O': 24.0}\",\"('Co', 'O', 'Y')\",OTHERS,False\nmp-1211841,\"{'K': 2.0, 'H': 16.0, 'W': 2.0, 'N': 4.0, 'F': 12.0}\",\"('F', 'H', 'K', 'N', 'W')\",OTHERS,False\nmp-568922,\"{'Ce': 14.0, 'C': 6.0, 'I': 20.0}\",\"('C', 'Ce', 'I')\",OTHERS,False\nmp-768144,\"{'Na': 4.0, 'Sb': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Na', 'O', 'P', 'Sb')\",OTHERS,False\nmp-1039728,\"{'Na': 1.0, 'Hf': 1.0, 'Mg': 30.0, 'O': 31.0}\",\"('Hf', 'Mg', 'Na', 'O')\",OTHERS,False\nmp-11718,\"{'F': 1.0, 'Rb': 1.0}\",\"('F', 'Rb')\",OTHERS,False\nmp-1228334,\"{'Ba': 4.0, 'Eu': 2.0, 'Cu': 6.0, 'O': 13.0}\",\"('Ba', 'Cu', 'Eu', 'O')\",OTHERS,False\nmp-675423,\"{'Sr': 3.0, 'Cr': 2.0, 'O': 7.0}\",\"('Cr', 'O', 'Sr')\",OTHERS,False\nmp-1178173,\"{'Ho': 2.0, 'Te': 1.0, 'O': 2.0}\",\"('Ho', 'O', 'Te')\",OTHERS,False\nmp-9964,\"{'N': 1.0, 'Ti': 3.0, 'Tl': 1.0}\",\"('N', 'Ti', 'Tl')\",OTHERS,False\nmp-2824,\"{'Al': 4.0, 'Pd': 8.0}\",\"('Al', 'Pd')\",OTHERS,False\nmp-1227522,\"{'Ca': 12.0, 'Al': 2.0, 'Cr': 6.0, 'Si': 12.0, 'O': 48.0}\",\"('Al', 'Ca', 'Cr', 'O', 'Si')\",OTHERS,False\nmp-1215420,\"{'Zr': 3.0, 'Ta': 1.0, 'Fe': 8.0}\",\"('Fe', 'Ta', 'Zr')\",OTHERS,False\nmp-1184147,\"{'Cu': 2.0, 'Ge': 6.0}\",\"('Cu', 'Ge')\",OTHERS,False\nmp-773043,\"{'Li': 8.0, 'Fe': 16.0, 'O': 32.0}\",\"('Fe', 'Li', 'O')\",OTHERS,False\nmp-1176270,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-976008,\"{'Nd': 1.0, 'Tm': 1.0, 'Mg': 2.0}\",\"('Mg', 'Nd', 'Tm')\",OTHERS,False\nmp-1218804,\"{'Sr': 4.0, 'In': 17.0, 'Au': 15.0}\",\"('Au', 'In', 'Sr')\",OTHERS,False\nmp-1020620,\"{'Rb': 5.0, 'Li': 6.0, 'B': 11.0, 'O': 22.0}\",\"('B', 'Li', 'O', 'Rb')\",OTHERS,False\nmp-23915,\"{'H': 4.0, 'O': 52.0, 'Al': 12.0, 'Si': 12.0, 'Ca': 8.0}\",\"('Al', 'Ca', 'H', 'O', 'Si')\",OTHERS,False\nmp-23414,\"{'N': 2.0, 'Cl': 12.0, 'Sc': 8.0}\",\"('Cl', 'N', 'Sc')\",OTHERS,False\nmp-1192042,\"{'Tm': 6.0, 'Al': 2.0, 'Ni': 16.0}\",\"('Al', 'Ni', 'Tm')\",OTHERS,False\nmp-532537,\"{'Ag': 4.0, 'Ca': 8.0, 'Mn': 8.0, 'O': 48.0, 'V': 12.0}\",\"('Ag', 'Ca', 'Mn', 'O', 'V')\",OTHERS,False\nmp-1175273,\"{'Li': 7.0, 'Mn': 4.0, 'Co': 1.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1179346,\"{'Te': 2.0, 'C': 8.0, 'S': 8.0, 'N': 16.0, 'F': 8.0}\",\"('C', 'F', 'N', 'S', 'Te')\",OTHERS,False\nmp-1187253,\"{'Ta': 1.0, 'Ti': 3.0}\",\"('Ta', 'Ti')\",OTHERS,False\nmp-1221943,\"{'Mg': 1.0, 'Ge': 1.0, 'P': 2.0}\",\"('Ge', 'Mg', 'P')\",OTHERS,False\nmp-720985,\"{'Ca': 1.0, 'Er': 15.0, 'Si': 8.0, 'Se': 11.0, 'Cl': 1.0, 'O': 28.0}\",\"('Ca', 'Cl', 'Er', 'O', 'Se', 'Si')\",OTHERS,False\nmp-1192759,\"{'Ir': 3.0, 'N': 10.0, 'Cl': 10.0}\",\"('Cl', 'Ir', 'N')\",OTHERS,False\nmp-1213589,\"{'Eu': 9.0, 'Ni': 26.0, 'P': 12.0}\",\"('Eu', 'Ni', 'P')\",OTHERS,False\nmp-556748,\"{'Ca': 2.0, 'Cr': 2.0, 'F': 10.0}\",\"('Ca', 'Cr', 'F')\",OTHERS,False\nmp-29974,\"{'Li': 28.0, 'Ge': 32.0, 'Rb': 4.0}\",\"('Ge', 'Li', 'Rb')\",OTHERS,False\nmp-505131,\"{'Li': 2.0, 'O': 8.0, 'V': 2.0, 'Cu': 2.0}\",\"('Cu', 'Li', 'O', 'V')\",OTHERS,False\nmp-31214,\"{'P': 8.0, 'Ti': 24.0}\",\"('P', 'Ti')\",OTHERS,False\nmp-1093948,\"{'Sr': 1.0, 'La': 1.0, 'Ag': 2.0}\",\"('Ag', 'La', 'Sr')\",OTHERS,False\nmp-1228713,\"{'Al': 4.0, 'Ni': 15.0}\",\"('Al', 'Ni')\",OTHERS,False\nmp-1208398,\"{'Tl': 8.0, 'Se': 12.0, 'O': 48.0}\",\"('O', 'Se', 'Tl')\",OTHERS,False\nmp-765857,\"{'Li': 3.0, 'Cr': 3.0, 'Si': 6.0, 'O': 18.0}\",\"('Cr', 'Li', 'O', 'Si')\",OTHERS,False\nmp-773065,\"{'Li': 5.0, 'V': 5.0, 'Cr': 2.0, 'O': 12.0}\",\"('Cr', 'Li', 'O', 'V')\",OTHERS,False\nmp-773108,\"{'Li': 10.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-753626,\"{'Li': 4.0, 'Nd': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Li', 'Nd', 'O', 'P')\",OTHERS,False\nmp-570806,\"{'Mg': 60.0, 'Al': 48.0, 'Ag': 38.0}\",\"('Ag', 'Al', 'Mg')\",OTHERS,False\nmvc-4994,\"{'Ca': 4.0, 'Mo': 2.0, 'W': 2.0, 'O': 12.0}\",\"('Ca', 'Mo', 'O', 'W')\",OTHERS,False\nmp-1219106,\"{'Sm': 1.0, 'Y': 1.0, 'Mn': 4.0, 'Ge': 4.0}\",\"('Ge', 'Mn', 'Sm', 'Y')\",OTHERS,False\nmp-1226593,\"{'Ce': 2.0, 'Y': 2.0, 'Ga': 4.0, 'Pd': 8.0}\",\"('Ce', 'Ga', 'Pd', 'Y')\",OTHERS,False\nmp-19034,\"{'O': 16.0, 'Mg': 6.0, 'V': 4.0}\",\"('Mg', 'O', 'V')\",OTHERS,False\nmp-1186495,\"{'Pm': 3.0, 'Er': 1.0}\",\"('Er', 'Pm')\",OTHERS,False\nmp-7928,\"{'S': 2.0, 'K': 2.0, 'Pt': 1.0}\",\"('K', 'Pt', 'S')\",OTHERS,False\nmp-1201038,\"{'K': 4.0, 'Na': 4.0, 'Al': 24.0, 'Si': 24.0, 'H': 16.0, 'O': 96.0}\",\"('Al', 'H', 'K', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1180271,\"{'Mg': 1.0, 'O': 2.0}\",\"('Mg', 'O')\",OTHERS,False\nmp-18378,\"{'Al': 4.0, 'K': 12.0, 'Te': 12.0}\",\"('Al', 'K', 'Te')\",OTHERS,False\nmp-21634,\"{'Na': 8.0, 'W': 8.0, 'O': 28.0}\",\"('Na', 'O', 'W')\",OTHERS,False\nmp-1221985,\"{'Mg': 1.0, 'Cd': 1.0, 'Ag': 2.0}\",\"('Ag', 'Cd', 'Mg')\",OTHERS,False\nmp-762116,\"{'Li': 4.0, 'Cu': 16.0, 'P': 12.0, 'O': 48.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nCd 65 Mg 20 Y 15,\"{'Cd': 65.0, 'Mg': 20.0, 'Y': 15.0}\",\"('Cd', 'Mg', 'Y')\",QC,False\nmp-772390,\"{'Li': 4.0, 'Al': 4.0, 'Fe': 4.0, 'O': 16.0}\",\"('Al', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-864953,\"{'Mn': 1.0, 'V': 2.0, 'Cr': 1.0}\",\"('Cr', 'Mn', 'V')\",OTHERS,False\nmp-1177443,\"{'Li': 4.0, 'Fe': 2.0, 'Cu': 3.0, 'Sn': 3.0, 'O': 16.0}\",\"('Cu', 'Fe', 'Li', 'O', 'Sn')\",OTHERS,False\nmp-1211512,\"{'K': 4.0, 'Y': 2.0, 'Sb': 2.0, 'O': 14.0}\",\"('K', 'O', 'Sb', 'Y')\",OTHERS,False\nmp-1936,\"{'As': 2.0, 'Ta': 2.0}\",\"('As', 'Ta')\",OTHERS,False\nmp-1187199,\"{'Ta': 3.0, 'Bi': 1.0}\",\"('Bi', 'Ta')\",OTHERS,False\nmp-1245498,\"{'Sr': 8.0, 'Ir': 2.0, 'N': 8.0}\",\"('Ir', 'N', 'Sr')\",OTHERS,False\nmp-570798,\"{'Er': 3.0, 'P': 6.0, 'Pd': 20.0}\",\"('Er', 'P', 'Pd')\",OTHERS,False\nmp-1247357,\"{'Y': 8.0, 'In': 4.0, 'N': 12.0}\",\"('In', 'N', 'Y')\",OTHERS,False\nmp-867291,\"{'Tb': 2.0, 'Ir': 1.0, 'Ru': 1.0}\",\"('Ir', 'Ru', 'Tb')\",OTHERS,False\nmp-1245767,\"{'Ca': 8.0, 'Ir': 4.0, 'N': 12.0}\",\"('Ca', 'Ir', 'N')\",OTHERS,False\nmp-22643,\"{'Co': 2.0, 'Ge': 4.0, 'Tb': 3.0}\",\"('Co', 'Ge', 'Tb')\",OTHERS,False\nmp-1205556,\"{'Ba': 2.0, 'Nd': 1.0, 'Re': 1.0, 'O': 6.0}\",\"('Ba', 'Nd', 'O', 'Re')\",OTHERS,False\nmp-1080127,\"{'Yb': 3.0, 'Cd': 3.0, 'Pb': 3.0}\",\"('Cd', 'Pb', 'Yb')\",OTHERS,False\nAu 61.2 Sn 23.9 Dy 15.2,\"{'Au': 61.2, 'Sn': 23.9, 'Dy': 15.2}\",\"('Au', 'Dy', 'Sn')\",AC,False\nmp-780575,\"{'Na': 8.0, 'Fe': 4.0, 'Si': 24.0, 'O': 60.0}\",\"('Fe', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1832,\"{'La': 1.0, 'Pt': 5.0}\",\"('La', 'Pt')\",OTHERS,False\nmp-542501,\"{'U': 3.0, 'Te': 2.0, 'Rb': 2.0, 'O': 14.0}\",\"('O', 'Rb', 'Te', 'U')\",OTHERS,False\nmp-1186401,\"{'Pa': 1.0, 'Si': 1.0, 'O': 3.0}\",\"('O', 'Pa', 'Si')\",OTHERS,False\nmp-505478,\"{'Na': 2.0, 'Fe': 2.0, 'B': 2.0, 'P': 4.0, 'O': 20.0, 'H': 6.0}\",\"('B', 'Fe', 'H', 'Na', 'O', 'P')\",OTHERS,False\nmp-1025510,\"{'Fe': 1.0, 'Cu': 2.0, 'Si': 1.0, 'Se': 4.0}\",\"('Cu', 'Fe', 'Se', 'Si')\",OTHERS,False\nmp-767157,\"{'Ti': 16.0, 'Cu': 1.0, 'S': 32.0}\",\"('Cu', 'S', 'Ti')\",OTHERS,False\nmp-1246884,\"{'Y': 3.0, 'Mg': 2.0, 'V': 1.0, 'S': 8.0}\",\"('Mg', 'S', 'V', 'Y')\",OTHERS,False\nmp-1469,\"{'P': 16.0, 'S': 12.0}\",\"('P', 'S')\",OTHERS,False\nmp-11714,\"{'C': 4.0, 'Si': 4.0}\",\"('C', 'Si')\",OTHERS,False\nmp-1198442,\"{'Cu': 8.0, 'P': 4.0, 'C': 8.0, 'S': 4.0, 'O': 52.0}\",\"('C', 'Cu', 'O', 'P', 'S')\",OTHERS,False\nmp-766048,\"{'Li': 8.0, 'V': 4.0, 'Si': 2.0, 'Ge': 2.0, 'O': 20.0}\",\"('Ge', 'Li', 'O', 'Si', 'V')\",OTHERS,False\nmp-1094475,\"{'Mg': 6.0, 'Zn': 6.0}\",\"('Mg', 'Zn')\",OTHERS,False\nmp-541868,\"{'Te': 6.0, 'Mo': 2.0, 'Cl': 32.0}\",\"('Cl', 'Mo', 'Te')\",OTHERS,False\nmp-1094493,\"{'Mg': 6.0, 'Zn': 6.0}\",\"('Mg', 'Zn')\",OTHERS,False\nmp-975738,\"{'Pr': 2.0, 'B': 4.0, 'Os': 4.0}\",\"('B', 'Os', 'Pr')\",OTHERS,False\nmp-1186483,\"{'Pm': 4.0, 'Mg': 2.0}\",\"('Mg', 'Pm')\",OTHERS,False\nmp-1176410,\"{'Na': 8.0, 'Ni': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Na', 'Ni', 'O', 'P')\",OTHERS,False\nmp-690644,\"{'Tl': 1.0, 'In': 1.0, 'S': 2.0}\",\"('In', 'S', 'Tl')\",OTHERS,False\nmp-752502,\"{'Li': 7.0, 'Cr': 3.0, 'Si': 2.0, 'O': 12.0}\",\"('Cr', 'Li', 'O', 'Si')\",OTHERS,False\nmp-30170,\"{'Ge': 40.0, 'Pd': 6.0, 'Ba': 8.0}\",\"('Ba', 'Ge', 'Pd')\",OTHERS,False\nmp-1203462,\"{'Na': 4.0, 'Ho': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Ho', 'Na', 'O', 'P')\",OTHERS,False\nmp-1175073,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-14222,\"{'O': 4.0, 'Ga': 2.0, 'Tl': 2.0}\",\"('Ga', 'O', 'Tl')\",OTHERS,False\nmp-583476,\"{'Nb': 28.0, 'S': 8.0, 'I': 76.0}\",\"('I', 'Nb', 'S')\",OTHERS,False\nmp-35555,\"{'F': 1.0, 'La': 1.0, 'O': 1.0}\",\"('F', 'La', 'O')\",OTHERS,False\nmp-1078621,\"{'Tl': 2.0, 'In': 2.0, 'S': 4.0}\",\"('In', 'S', 'Tl')\",OTHERS,False\nmp-1215536,\"{'Zr': 4.0, 'Si': 2.0, 'Mo': 6.0}\",\"('Mo', 'Si', 'Zr')\",OTHERS,False\nmp-19981,{'Gd': 4.0},\"('Gd',)\",OTHERS,False\nmp-9835,\"{'Co': 2.0, 'Sb': 4.0}\",\"('Co', 'Sb')\",OTHERS,False\nmp-1209369,\"{'Rb': 5.0, 'Dy': 3.0, 'I': 12.0}\",\"('Dy', 'I', 'Rb')\",OTHERS,False\nmp-16690,\"{'P': 8.0, 'S': 28.0, 'K': 8.0, 'Nd': 4.0}\",\"('K', 'Nd', 'P', 'S')\",OTHERS,False\nmp-1096411,\"{'Zr': 2.0, 'In': 1.0, 'Tc': 1.0}\",\"('In', 'Tc', 'Zr')\",OTHERS,False\nmp-570883,\"{'Ca': 42.0, 'Zn': 72.0, 'Ni': 4.0}\",\"('Ca', 'Ni', 'Zn')\",OTHERS,False\nmp-16496,\"{'Al': 2.0, 'Cu': 4.0, 'Re': 4.0}\",\"('Al', 'Cu', 'Re')\",OTHERS,False\nmp-1210367,\"{'Na': 6.0, 'P': 6.0, 'N': 6.0, 'O': 14.0}\",\"('N', 'Na', 'O', 'P')\",OTHERS,False\nmp-1113333,\"{'Tb': 1.0, 'Cs': 2.0, 'Cu': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cs', 'Cu', 'Tb')\",OTHERS,False\nmp-675073,\"{'Na': 8.0, 'Pt': 2.0, 'O': 8.0}\",\"('Na', 'O', 'Pt')\",OTHERS,False\nmp-1245354,\"{'Li': 56.0, 'Cr': 8.0, 'N': 32.0}\",\"('Cr', 'Li', 'N')\",OTHERS,False\nmp-23202,\"{'In': 2.0, 'I': 2.0}\",\"('I', 'In')\",OTHERS,False\nmp-753471,\"{'Li': 4.0, 'Cu': 4.0, 'B': 4.0, 'O': 12.0}\",\"('B', 'Cu', 'Li', 'O')\",OTHERS,False\nmp-29216,\"{'Cl': 8.0, 'Cs': 4.0, 'Pt': 2.0}\",\"('Cl', 'Cs', 'Pt')\",OTHERS,False\nmp-1207427,\"{'Zr': 18.0, 'Mo': 8.0, 'O': 6.0}\",\"('Mo', 'O', 'Zr')\",OTHERS,False\nmp-1077262,\"{'Tb': 1.0, 'Cu': 5.0}\",\"('Cu', 'Tb')\",OTHERS,False\nmp-1024977,\"{'Yb': 1.0, 'Ni': 4.0, 'Au': 1.0}\",\"('Au', 'Ni', 'Yb')\",OTHERS,False\nmp-778047,\"{'Na': 32.0, 'Ni': 8.0, 'O': 32.0}\",\"('Na', 'Ni', 'O')\",OTHERS,False\nmp-1222759,\"{'La': 1.0, 'U': 3.0, 'C': 8.0}\",\"('C', 'La', 'U')\",OTHERS,False\nmp-1195560,\"{'C': 24.0, 'F': 40.0}\",\"('C', 'F')\",OTHERS,False\nmp-865778,\"{'Lu': 10.0, 'Rh': 6.0}\",\"('Lu', 'Rh')\",OTHERS,False\nmp-28313,\"{'Nd': 4.0, 'Cl': 8.0}\",\"('Cl', 'Nd')\",OTHERS,False\nmp-1032034,\"{'Sr': 1.0, 'Mg': 6.0, 'Zn': 1.0, 'O': 8.0}\",\"('Mg', 'O', 'Sr', 'Zn')\",OTHERS,False\nmp-542606,{'Pu': 17.0},\"('Pu',)\",OTHERS,False\nmp-1186636,\"{'Pm': 2.0, 'Rh': 6.0}\",\"('Pm', 'Rh')\",OTHERS,False\nmp-995196,\"{'C': 2.0, 'O': 6.0}\",\"('C', 'O')\",OTHERS,False\nmp-4651,\"{'O': 6.0, 'Ti': 2.0, 'Sr': 2.0}\",\"('O', 'Sr', 'Ti')\",OTHERS,False\nmp-771566,\"{'Lu': 8.0, 'Te': 4.0, 'O': 24.0}\",\"('Lu', 'O', 'Te')\",OTHERS,False\nmp-1228701,\"{'Al': 4.0, 'Ge': 2.0, 'W': 3.0}\",\"('Al', 'Ge', 'W')\",OTHERS,False\nmp-753560,\"{'Li': 2.0, 'V': 2.0, 'P': 2.0, 'H': 2.0, 'O': 10.0}\",\"('H', 'Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1226828,\"{'Ce': 2.0, 'Pd': 3.0, 'Rh': 3.0}\",\"('Ce', 'Pd', 'Rh')\",OTHERS,False\nmp-27080,\"{'Li': 4.0, 'Ni': 12.0, 'P': 16.0, 'O': 60.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-8948,\"{'As': 2.0, 'Pd': 2.0, 'Ce': 2.0}\",\"('As', 'Ce', 'Pd')\",OTHERS,False\nmp-1188397,\"{'Pr': 4.0, 'Mg': 2.0, 'Ir': 2.0, 'O': 12.0}\",\"('Ir', 'Mg', 'O', 'Pr')\",OTHERS,False\nmp-861906,\"{'Ba': 1.0, 'Na': 2.0, 'Mg': 1.0, 'P': 2.0, 'O': 8.0}\",\"('Ba', 'Mg', 'Na', 'O', 'P')\",OTHERS,False\nmp-1232293,\"{'Mg': 1.0, 'Al': 2.0, 'Sn': 2.0}\",\"('Al', 'Mg', 'Sn')\",OTHERS,False\nmp-1208510,\"{'Sr': 2.0, 'Y': 1.0, 'Fe': 3.0, 'O': 8.0}\",\"('Fe', 'O', 'Sr', 'Y')\",OTHERS,False\nmp-541075,\"{'Rb': 4.0, 'Mo': 6.0, 'O': 20.0}\",\"('Mo', 'O', 'Rb')\",OTHERS,False\nmp-1210000,\"{'Nd': 6.0, 'Cd': 4.0, 'Si': 6.0, 'O': 26.0}\",\"('Cd', 'Nd', 'O', 'Si')\",OTHERS,False\nmp-1019740,\"{'Ga': 1.0, 'B': 3.0, 'N': 4.0}\",\"('B', 'Ga', 'N')\",OTHERS,False\nmp-1897,\"{'Rh': 4.0, 'Pu': 2.0}\",\"('Pu', 'Rh')\",OTHERS,False\nmp-683598,\"{'P': 4.0, 'W': 4.0, 'C': 20.0, 'Br': 12.0, 'O': 20.0}\",\"('Br', 'C', 'O', 'P', 'W')\",OTHERS,False\nmp-1216917,\"{'Ti': 1.0, 'In': 1.0, 'Ni': 2.0}\",\"('In', 'Ni', 'Ti')\",OTHERS,False\nmp-703350,\"{'La': 4.0, 'Ge': 6.0, 'O': 18.0}\",\"('Ge', 'La', 'O')\",OTHERS,False\nmp-504891,\"{'La': 6.0, 'Ga': 2.0, 'Mn': 2.0, 'S': 14.0}\",\"('Ga', 'La', 'Mn', 'S')\",OTHERS,False\nmp-1185469,\"{'Li': 1.0, 'Yb': 3.0}\",\"('Li', 'Yb')\",OTHERS,False\nmp-1076679,\"{'La': 7.0, 'Sm': 1.0, 'V': 7.0, 'Cr': 1.0, 'O': 24.0}\",\"('Cr', 'La', 'O', 'Sm', 'V')\",OTHERS,False\nmp-1194017,\"{'Th': 4.0, 'P': 4.0, 'O': 20.0}\",\"('O', 'P', 'Th')\",OTHERS,False\nmp-1652,\"{'Zn': 2.0, 'Pd': 2.0}\",\"('Pd', 'Zn')\",OTHERS,False\nmp-1190316,\"{'Yb': 1.0, 'Fe': 4.0, 'Cu': 3.0, 'O': 12.0}\",\"('Cu', 'Fe', 'O', 'Yb')\",OTHERS,False\nmp-542333,\"{'S': 5.0, 'Cu': 9.0}\",\"('Cu', 'S')\",OTHERS,False\nmp-604537,\"{'Mn': 10.0, 'Ga': 10.0, 'Ni': 20.0}\",\"('Ga', 'Mn', 'Ni')\",OTHERS,False\nmp-1080537,\"{'K': 2.0, 'Na': 1.0, 'V': 1.0, 'F': 6.0}\",\"('F', 'K', 'Na', 'V')\",OTHERS,False\nmp-1226956,\"{'Cd': 10.0, 'P': 6.0, 'O': 26.0}\",\"('Cd', 'O', 'P')\",OTHERS,False\nmp-1105011,\"{'W': 2.0, 'S': 4.0, 'N': 4.0, 'O': 4.0}\",\"('N', 'O', 'S', 'W')\",OTHERS,False\nmp-1074542,\"{'Mg': 16.0, 'Si': 12.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-556523,\"{'La': 2.0, 'Pb': 12.0, 'Cl': 2.0, 'O': 14.0}\",\"('Cl', 'La', 'O', 'Pb')\",OTHERS,False\nmp-1097575,\"{'Sc': 2.0, 'Al': 1.0, 'Au': 1.0}\",\"('Al', 'Au', 'Sc')\",OTHERS,False\nmp-505108,\"{'Fe': 1.0, 'Sn': 1.0, 'F': 6.0, 'O': 6.0, 'H': 12.0}\",\"('F', 'Fe', 'H', 'O', 'Sn')\",OTHERS,False\nmp-558356,\"{'Na': 4.0, 'Cr': 2.0, 'Ni': 2.0, 'F': 14.0}\",\"('Cr', 'F', 'Na', 'Ni')\",OTHERS,False\nmp-1154732,\"{'Ca': 4.0, 'Si': 16.0, 'W': 4.0, 'O': 40.0}\",\"('Ca', 'O', 'Si', 'W')\",OTHERS,False\nmp-640715,\"{'Yb': 16.0, 'Rb': 24.0, 'P': 24.0, 'O': 96.0}\",\"('O', 'P', 'Rb', 'Yb')\",OTHERS,False\nmp-1227967,\"{'Ba': 1.0, 'Ga': 3.0, 'Sn': 1.0}\",\"('Ba', 'Ga', 'Sn')\",OTHERS,False\nmp-772830,\"{'Li': 4.0, 'V': 2.0, 'Si': 1.0, 'Ge': 1.0, 'O': 10.0}\",\"('Ge', 'Li', 'O', 'Si', 'V')\",OTHERS,False\nmp-8675,\"{'Pd': 4.0, 'Hg': 4.0}\",\"('Hg', 'Pd')\",OTHERS,False\nmp-1096979,\"{'Na': 24.0, 'V': 24.0, 'Zn': 24.0, 'O': 96.0}\",\"('Na', 'O', 'V', 'Zn')\",OTHERS,False\nmp-1211010,\"{'Ni': 2.0, 'P': 2.0, 'N': 2.0, 'O': 20.0}\",\"('N', 'Ni', 'O', 'P')\",OTHERS,False\nmp-18820,\"{'O': 7.0, 'Fe': 2.0, 'Sr': 3.0}\",\"('Fe', 'O', 'Sr')\",OTHERS,False\nmp-759523,\"{'Li': 4.0, 'V': 2.0, 'Fe': 2.0, 'P': 8.0, 'O': 28.0}\",\"('Fe', 'Li', 'O', 'P', 'V')\",OTHERS,False\nmp-20979,\"{'Mg': 3.0, 'In': 3.0, 'Tm': 3.0}\",\"('In', 'Mg', 'Tm')\",OTHERS,False\nmp-1245514,\"{'Sb': 2.0, 'C': 3.0, 'N': 6.0}\",\"('C', 'N', 'Sb')\",OTHERS,False\nmp-775023,\"{'Li': 2.0, 'Fe': 3.0, 'Sn': 1.0, 'O': 8.0}\",\"('Fe', 'Li', 'O', 'Sn')\",OTHERS,False\nmp-1244965,\"{'Si': 34.0, 'O': 68.0}\",\"('O', 'Si')\",OTHERS,False\nmp-849952,\"{'Li': 2.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1183938,\"{'Cs': 1.0, 'K': 2.0, 'As': 1.0}\",\"('As', 'Cs', 'K')\",OTHERS,False\nmp-1239391,\"{'Zn': 4.0, 'Co': 8.0, 'Cu': 8.0, 'O': 32.0}\",\"('Co', 'Cu', 'O', 'Zn')\",OTHERS,False\nmp-1178487,\"{'Bi': 10.0, 'O': 14.0, 'F': 2.0}\",\"('Bi', 'F', 'O')\",OTHERS,False\nmp-1246778,\"{'Li': 4.0, 'Cd': 4.0, 'N': 4.0}\",\"('Cd', 'Li', 'N')\",OTHERS,False\nmp-976380,\"{'Nd': 2.0, 'Ni': 4.0, 'Sb': 4.0}\",\"('Nd', 'Ni', 'Sb')\",OTHERS,False\nmp-776246,\"{'Zr': 16.0, 'N': 16.0, 'O': 8.0}\",\"('N', 'O', 'Zr')\",OTHERS,False\nmp-33569,\"{'La': 4.0, 'Se': 8.0, 'Sr': 2.0}\",\"('La', 'Se', 'Sr')\",OTHERS,False\nmp-1213705,\"{'Cs': 4.0, 'Na': 2.0, 'Ga': 2.0, 'P': 4.0}\",\"('Cs', 'Ga', 'Na', 'P')\",OTHERS,False\nmp-23060,\"{'I': 6.0, 'Cs': 2.0, 'Pt': 1.0}\",\"('Cs', 'I', 'Pt')\",OTHERS,False\nmp-1180066,\"{'Ni': 1.0, 'I': 2.0, 'O': 6.0}\",\"('I', 'Ni', 'O')\",OTHERS,False\nmp-744841,\"{'Si': 2.0, 'Mo': 24.0, 'H': 48.0, 'C': 12.0, 'N': 6.0, 'O': 80.0}\",\"('C', 'H', 'Mo', 'N', 'O', 'Si')\",OTHERS,False\nmp-1078818,\"{'Mg': 4.0, 'Ni': 2.0, 'H': 4.0}\",\"('H', 'Mg', 'Ni')\",OTHERS,False\nmp-1184504,\"{'Eu': 6.0, 'W': 2.0}\",\"('Eu', 'W')\",OTHERS,False\nmp-849361,\"{'Li': 4.0, 'V': 4.0, 'F': 20.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-1212011,\"{'In': 4.0, 'Sb': 4.0, 'Se': 12.0}\",\"('In', 'Sb', 'Se')\",OTHERS,False\nmp-1384,\"{'La': 1.0, 'Ir': 5.0}\",\"('Ir', 'La')\",OTHERS,False\nmp-773918,\"{'Zn': 1.0, 'Bi': 38.0, 'O': 60.0}\",\"('Bi', 'O', 'Zn')\",OTHERS,False\nmp-759435,\"{'Li': 4.0, 'Cu': 4.0, 'P': 12.0, 'O': 36.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-1220982,\"{'Na': 1.0, 'Li': 1.0, 'Ga': 2.0, 'Si': 4.0, 'O': 12.0}\",\"('Ga', 'Li', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1225162,\"{'Eu': 1.0, 'Si': 3.0, 'Ni': 1.0}\",\"('Eu', 'Ni', 'Si')\",OTHERS,False\nmp-1205433,\"{'Dy': 2.0, 'In': 8.0, 'Pt': 8.0}\",\"('Dy', 'In', 'Pt')\",OTHERS,False\nmp-752904,\"{'Li': 3.0, 'Fe': 3.0, 'Si': 1.0, 'O': 8.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-758846,\"{'Li': 3.0, 'Mn': 2.0, 'Ni': 5.0, 'O': 12.0}\",\"('Li', 'Mn', 'Ni', 'O')\",OTHERS,False\nmp-6805,\"{'O': 48.0, 'P': 12.0, 'K': 12.0, 'Ga': 8.0}\",\"('Ga', 'K', 'O', 'P')\",OTHERS,False\nmp-1185485,\"{'Lu': 1.0, 'Al': 1.0, 'O': 3.0}\",\"('Al', 'Lu', 'O')\",OTHERS,False\nmp-1184799,\"{'In': 1.0, 'Os': 1.0, 'O': 3.0}\",\"('In', 'O', 'Os')\",OTHERS,False\nmp-1186244,\"{'Pm': 1.0, 'Nd': 3.0}\",\"('Nd', 'Pm')\",OTHERS,False\nmp-768042,\"{'Na': 8.0, 'Fe': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Fe', 'Na', 'O', 'P')\",OTHERS,False\nmp-1225429,\"{'Eu': 2.0, 'Al': 3.0, 'Ag': 1.0}\",\"('Ag', 'Al', 'Eu')\",OTHERS,False\nmp-1187603,\"{'Tm': 1.0, 'Sb': 1.0, 'Rh': 2.0}\",\"('Rh', 'Sb', 'Tm')\",OTHERS,False\nmp-1190160,\"{'K': 4.0, 'Se': 4.0, 'O': 14.0}\",\"('K', 'O', 'Se')\",OTHERS,False\nmp-1219871,\"{'Pd': 4.0, 'Se': 4.0, 'S': 4.0}\",\"('Pd', 'S', 'Se')\",OTHERS,False\nmp-1078780,\"{'Zn': 1.0, 'Cd': 3.0, 'S': 4.0}\",\"('Cd', 'S', 'Zn')\",OTHERS,False\nmp-984454,\"{'K': 1.0, 'Tl': 1.0, 'O': 3.0}\",\"('K', 'O', 'Tl')\",OTHERS,False\nmp-1201660,\"{'Ho': 10.0, 'Ge': 20.0, 'Os': 8.0}\",\"('Ge', 'Ho', 'Os')\",OTHERS,False\nmp-15963,\"{'P': 3.0, 'Ru': 3.0, 'Hf': 3.0}\",\"('Hf', 'P', 'Ru')\",OTHERS,False\nmp-1096584,\"{'Li': 1.0, 'Ga': 2.0, 'Tc': 1.0}\",\"('Ga', 'Li', 'Tc')\",OTHERS,False\nmp-696490,\"{'Ca': 8.0, 'H': 8.0, 'S': 8.0, 'O': 28.0}\",\"('Ca', 'H', 'O', 'S')\",OTHERS,False\nmp-1238814,\"{'H': 4.0, 'N': 1.0}\",\"('H', 'N')\",OTHERS,False\nmp-1246350,\"{'Sr': 8.0, 'V': 4.0, 'N': 8.0}\",\"('N', 'Sr', 'V')\",OTHERS,False\nmp-11143,\"{'Si': 8.0, 'Cu': 18.0, 'Ba': 2.0}\",\"('Ba', 'Cu', 'Si')\",OTHERS,False\nmp-631358,\"{'Sr': 1.0, 'Ta': 2.0, 'Mn': 1.0}\",\"('Mn', 'Sr', 'Ta')\",OTHERS,False\nmp-1221508,\"{'Na': 11.0, 'Ti': 1.0, 'Nb': 2.0, 'Si': 4.0, 'P': 2.0, 'O': 25.0, 'F': 1.0}\",\"('F', 'Na', 'Nb', 'O', 'P', 'Si', 'Ti')\",OTHERS,False\nmp-1215195,\"{'Zr': 1.0, 'U': 1.0, 'C': 2.0}\",\"('C', 'U', 'Zr')\",OTHERS,False\nmp-1224102,\"{'In': 2.0, 'Cu': 2.0, 'Sn': 2.0, 'S': 8.0}\",\"('Cu', 'In', 'S', 'Sn')\",OTHERS,False\nmp-1217816,\"{'Ta': 1.0, 'V': 1.0, 'H': 4.0}\",\"('H', 'Ta', 'V')\",OTHERS,False\nmp-676020,\"{'Ba': 2.0, 'C': 2.0, 'O': 6.0}\",\"('Ba', 'C', 'O')\",OTHERS,False\nmp-764781,\"{'Li': 3.0, 'Co': 4.0, 'O': 8.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-561352,\"{'Ba': 2.0, 'Na': 4.0, 'Ti': 4.0, 'Si': 8.0, 'O': 28.0}\",\"('Ba', 'Na', 'O', 'Si', 'Ti')\",OTHERS,False\nmp-753245,\"{'Li': 2.0, 'Mn': 4.0, 'O': 6.0, 'F': 2.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-20701,\"{'K': 2.0, 'Ba': 1.0, 'Co': 1.0, 'N': 6.0, 'O': 12.0}\",\"('Ba', 'Co', 'K', 'N', 'O')\",OTHERS,False\nmp-16509,\"{'Al': 8.0, 'Ni': 2.0, 'Lu': 2.0}\",\"('Al', 'Lu', 'Ni')\",OTHERS,False\nmp-1800,\"{'C': 12.0, 'Nd': 8.0}\",\"('C', 'Nd')\",OTHERS,False\nmp-1221192,\"{'Na': 8.0, 'Li': 2.0, 'Nb': 10.0, 'O': 30.0}\",\"('Li', 'Na', 'Nb', 'O')\",OTHERS,False\nmp-694915,\"{'Ca': 2.0, 'La': 2.0, 'Mn': 2.0, 'Sn': 2.0, 'O': 12.0}\",\"('Ca', 'La', 'Mn', 'O', 'Sn')\",OTHERS,False\nmp-753828,\"{'Li': 4.0, 'Co': 2.0, 'Si': 2.0, 'O': 8.0}\",\"('Co', 'Li', 'O', 'Si')\",OTHERS,False\nmp-759326,\"{'Li': 6.0, 'Mn': 11.0, 'O': 2.0, 'F': 24.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1025261,\"{'Mg': 1.0, 'P': 1.0, 'Pd': 5.0}\",\"('Mg', 'P', 'Pd')\",OTHERS,False\nmp-1036235,\"{'Mg': 14.0, 'Mn': 1.0, 'Co': 1.0, 'O': 16.0}\",\"('Co', 'Mg', 'Mn', 'O')\",OTHERS,False\nmp-865554,\"{'Mn': 2.0, 'V': 1.0, 'Ge': 1.0}\",\"('Ge', 'Mn', 'V')\",OTHERS,False\nmp-1175727,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-770910,\"{'Li': 12.0, 'Mn': 4.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1198784,\"{'Cs': 4.0, 'U': 2.0, 'Nb': 12.0, 'Cl': 30.0, 'O': 6.0}\",\"('Cl', 'Cs', 'Nb', 'O', 'U')\",OTHERS,False\nmp-1104791,\"{'Li': 1.0, 'Mg': 8.0, 'Si': 4.0}\",\"('Li', 'Mg', 'Si')\",OTHERS,False\nmp-27493,\"{'O': 32.0, 'P': 8.0, 'Sn': 12.0}\",\"('O', 'P', 'Sn')\",OTHERS,False\nmp-761260,\"{'Li': 4.0, 'V': 2.0, 'O': 4.0, 'F': 4.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-31474,\"{'Rb': 2.0, 'Hg': 7.0}\",\"('Hg', 'Rb')\",OTHERS,False\nmp-1221556,\"{'Na': 4.0, 'Mg': 1.0, 'V': 10.0, 'H': 44.0, 'O': 52.0}\",\"('H', 'Mg', 'Na', 'O', 'V')\",OTHERS,False\nmp-553949,\"{'Ag': 4.0, 'Hg': 4.0, 'Te': 6.0, 'O': 24.0}\",\"('Ag', 'Hg', 'O', 'Te')\",OTHERS,False\nmp-867164,\"{'Pm': 3.0, 'Sn': 1.0}\",\"('Pm', 'Sn')\",OTHERS,False\nmp-31476,\"{'H': 6.0, 'C': 12.0, 'Cs': 6.0}\",\"('C', 'Cs', 'H')\",OTHERS,False\nmp-603940,\"{'Fe': 2.0, 'H': 24.0, 'C': 8.0, 'N': 2.0, 'Cl': 8.0}\",\"('C', 'Cl', 'Fe', 'H', 'N')\",OTHERS,False\nmp-756542,\"{'Mn': 2.0, 'V': 2.0, 'O': 8.0}\",\"('Mn', 'O', 'V')\",OTHERS,False\nmp-31447,\"{'Tb': 2.0, 'Ag': 2.0, 'Pb': 2.0}\",\"('Ag', 'Pb', 'Tb')\",OTHERS,False\nmp-1027480,\"{'Te': 2.0, 'Mo': 2.0, 'W': 2.0, 'Se': 2.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-1198290,\"{'Na': 4.0, 'U': 4.0, 'H': 36.0, 'Se': 8.0, 'O': 48.0}\",\"('H', 'Na', 'O', 'Se', 'U')\",OTHERS,False\nmp-1177564,\"{'Li': 12.0, 'Mn': 4.0, 'V': 4.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-1206924,\"{'Ba': 1.0, 'Zn': 2.0, 'Ge': 2.0}\",\"('Ba', 'Ge', 'Zn')\",OTHERS,False\nmp-780704,\"{'V': 6.0, 'O': 8.0, 'F': 4.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-8312,\"{'As': 4.0, 'Ta': 5.0}\",\"('As', 'Ta')\",OTHERS,False\nmp-851099,\"{'Li': 9.0, 'Mn': 7.0, 'Cr': 12.0, 'O': 48.0}\",\"('Cr', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-21013,\"{'Li': 1.0, 'C': 6.0, 'N': 6.0, 'Fe': 1.0, 'Cs': 2.0}\",\"('C', 'Cs', 'Fe', 'Li', 'N')\",OTHERS,False\nmp-1070789,\"{'Cr': 1.0, 'Au': 4.0}\",\"('Au', 'Cr')\",OTHERS,False\nmp-1224477,\"{'H': 11.0, 'I': 1.0, 'N': 2.0, 'O': 6.0}\",\"('H', 'I', 'N', 'O')\",OTHERS,False\nmp-684784,\"{'Sr': 1.0, 'Nd': 7.0, 'Fe': 8.0, 'As': 8.0, 'O': 8.0}\",\"('As', 'Fe', 'Nd', 'O', 'Sr')\",OTHERS,False\nmp-1218452,\"{'Sr': 5.0, 'Fe': 4.0, 'Co': 1.0, 'O': 10.0}\",\"('Co', 'Fe', 'O', 'Sr')\",OTHERS,False\nmp-9548,\"{'Ge': 6.0, 'As': 6.0}\",\"('As', 'Ge')\",OTHERS,False\nAg 42.9 In 43.6 Eu 13.5,\"{'Ag': 42.9, 'In': 43.6, 'Eu': 13.5}\",\"('Ag', 'Eu', 'In')\",AC,False\nmp-683616,\"{'Li': 5.0, 'Ti': 7.0, 'In': 1.0, 'P': 12.0, 'O': 48.0}\",\"('In', 'Li', 'O', 'P', 'Ti')\",OTHERS,False\nmp-30495,\"{'Sm': 1.0, 'Cd': 2.0}\",\"('Cd', 'Sm')\",OTHERS,False\nmp-769486,\"{'Na': 6.0, 'Ni': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Na', 'Ni', 'O', 'P')\",OTHERS,False\nmp-28318,\"{'Hg': 4.0, 'Te': 4.0, 'O': 12.0}\",\"('Hg', 'O', 'Te')\",OTHERS,False\nmp-1175750,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-27491,\"{'Ca': 2.0, 'Fe': 8.0, 'O': 12.0}\",\"('Ca', 'Fe', 'O')\",OTHERS,False\nmp-1227175,\"{'Ca': 2.0, 'Nd': 2.0, 'Cr': 2.0, 'O': 8.0}\",\"('Ca', 'Cr', 'Nd', 'O')\",OTHERS,False\nmp-1188236,\"{'Gd': 4.0, 'Mg': 2.0, 'Ir': 2.0, 'O': 12.0}\",\"('Gd', 'Ir', 'Mg', 'O')\",OTHERS,False\nmvc-601,\"{'Y': 1.0, 'Sn': 1.0, 'W': 2.0, 'O': 8.0}\",\"('O', 'Sn', 'W', 'Y')\",OTHERS,False\nmp-562541,\"{'Cs': 8.0, 'Na': 12.0, 'Tl': 4.0, 'O': 16.0}\",\"('Cs', 'Na', 'O', 'Tl')\",OTHERS,False\nmp-1238482,\"{'Mn': 2.0, 'Al': 4.0, 'P': 4.0, 'H': 40.0, 'O': 32.0}\",\"('Al', 'H', 'Mn', 'O', 'P')\",OTHERS,False\nmp-643432,\"{'H': 12.0, 'N': 4.0}\",\"('H', 'N')\",OTHERS,False\nmp-1099868,\"{'K': 8.0, 'Na': 24.0, 'V': 16.0, 'Mo': 16.0, 'O': 80.0}\",\"('K', 'Mo', 'Na', 'O', 'V')\",OTHERS,False\nmp-1247030,\"{'Lu': 1.0, 'Mg': 2.0, 'Mo': 3.0, 'S': 8.0}\",\"('Lu', 'Mg', 'Mo', 'S')\",OTHERS,False\nmp-1018809,\"{'Mo': 2.0, 'S': 4.0}\",\"('Mo', 'S')\",OTHERS,False\nmp-1206203,\"{'Yb': 2.0, 'Se': 2.0, 'I': 2.0}\",\"('I', 'Se', 'Yb')\",OTHERS,False\nmvc-16572,\"{'Ca': 4.0, 'Mo': 4.0, 'O': 12.0}\",\"('Ca', 'Mo', 'O')\",OTHERS,False\nmp-30599,\"{'Ti': 2.0, 'Cu': 3.0}\",\"('Cu', 'Ti')\",OTHERS,False\nmp-1204662,\"{'Sr': 18.0, 'W': 6.0, 'O': 36.0}\",\"('O', 'Sr', 'W')\",OTHERS,False\nmp-755970,\"{'Li': 6.0, 'Mn': 2.0, 'Si': 2.0, 'B': 2.0, 'O': 14.0}\",\"('B', 'Li', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1221713,\"{'Mn': 2.0, 'Fe': 8.0, 'Si': 6.0}\",\"('Fe', 'Mn', 'Si')\",OTHERS,False\nmp-1206795,\"{'Sm': 2.0, 'Fe': 4.0, 'Si': 2.0, 'C': 2.0}\",\"('C', 'Fe', 'Si', 'Sm')\",OTHERS,False\nmp-1095591,\"{'Tm': 3.0, 'Cu': 4.0, 'Sn': 4.0}\",\"('Cu', 'Sn', 'Tm')\",OTHERS,False\nmp-1228562,\"{'Al': 9.0, 'Ge': 3.0, 'W': 6.0}\",\"('Al', 'Ge', 'W')\",OTHERS,False\nmp-1214122,\"{'Ca': 2.0, 'Ce': 2.0, 'Al': 2.0, 'Fe': 4.0, 'Si': 6.0, 'O': 26.0}\",\"('Al', 'Ca', 'Ce', 'Fe', 'O', 'Si')\",OTHERS,False\nmp-772977,\"{'Ba': 10.0, 'Li': 1.0, 'W': 7.0, 'O': 30.0}\",\"('Ba', 'Li', 'O', 'W')\",OTHERS,False\nmp-697008,\"{'Cu': 2.0, 'H': 24.0, 'N': 4.0, 'O': 4.0, 'F': 8.0}\",\"('Cu', 'F', 'H', 'N', 'O')\",OTHERS,False\nmp-1209449,\"{'Rb': 12.0, 'Lu': 4.0, 'Cl': 24.0}\",\"('Cl', 'Lu', 'Rb')\",OTHERS,False\nmp-1214127,\"{'Ca': 2.0, 'Al': 4.0, 'Si': 12.0, 'H': 20.0, 'O': 42.0}\",\"('Al', 'Ca', 'H', 'O', 'Si')\",OTHERS,False\nmp-1218750,\"{'Sr': 2.0, 'Nb': 1.0, 'In': 1.0, 'O': 6.0}\",\"('In', 'Nb', 'O', 'Sr')\",OTHERS,False\nmp-760101,\"{'Li': 6.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1072320,\"{'Hg': 2.0, 'S': 2.0, 'Br': 2.0}\",\"('Br', 'Hg', 'S')\",OTHERS,False\nmp-1070456,\"{'Be': 3.0, 'N': 2.0}\",\"('Be', 'N')\",OTHERS,False\nmp-769464,\"{'Li': 4.0, 'La': 12.0, 'Mn': 4.0, 'O': 28.0}\",\"('La', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1199385,\"{'Er': 20.0, 'In': 40.0, 'Ni': 18.0}\",\"('Er', 'In', 'Ni')\",OTHERS,False\nmp-1235,\"{'Ti': 2.0, 'Ir': 2.0}\",\"('Ir', 'Ti')\",OTHERS,False\nmp-1179195,\"{'Si': 20.0, 'O': 30.0}\",\"('O', 'Si')\",OTHERS,False\nmp-1223968,\"{'Ho': 2.0, 'Si': 1.0, 'Ge': 1.0}\",\"('Ge', 'Ho', 'Si')\",OTHERS,False\nmp-70,{'Rb': 1.0},\"('Rb',)\",OTHERS,False\nmp-1226388,\"{'Cr': 4.0, 'Cd': 2.0, 'Se': 6.0, 'S': 2.0}\",\"('Cd', 'Cr', 'S', 'Se')\",OTHERS,False\nmp-19253,\"{'O': 3.0, 'V': 1.0, 'Nd': 1.0}\",\"('Nd', 'O', 'V')\",OTHERS,False\nmp-11307,\"{'Mg': 2.0, 'Cd': 2.0}\",\"('Cd', 'Mg')\",OTHERS,False\nmp-1177924,\"{'Li': 2.0, 'Mn': 3.0, 'W': 1.0, 'O': 8.0}\",\"('Li', 'Mn', 'O', 'W')\",OTHERS,False\nmp-1205739,\"{'Li': 2.0, 'Sn': 1.0, 'F': 6.0}\",\"('F', 'Li', 'Sn')\",OTHERS,False\nmp-1184083,\"{'Er': 2.0, 'Zn': 1.0, 'Ag': 1.0}\",\"('Ag', 'Er', 'Zn')\",OTHERS,False\nmp-1176364,\"{'Na': 6.0, 'Li': 6.0, 'Fe': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Fe', 'Li', 'Na', 'O', 'P')\",OTHERS,False\nmp-865050,\"{'Hf': 1.0, 'Mg': 1.0, 'Rh': 2.0}\",\"('Hf', 'Mg', 'Rh')\",OTHERS,False\nmp-1216330,\"{'V': 2.0, 'Fe': 2.0, 'Sb': 2.0, 'O': 12.0}\",\"('Fe', 'O', 'Sb', 'V')\",OTHERS,False\nmp-694910,\"{'Fe': 35.0, 'O': 36.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-849757,\"{'Li': 4.0, 'Mn': 4.0, 'Si': 16.0, 'O': 40.0}\",\"('Li', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1184359,\"{'Eu': 1.0, 'Hg': 1.0, 'O': 3.0}\",\"('Eu', 'Hg', 'O')\",OTHERS,False\nmp-12867,\"{'O': 6.0, 'Sr': 2.0, 'Sn': 2.0}\",\"('O', 'Sn', 'Sr')\",OTHERS,False\nmp-1222296,\"{'Li': 1.0, 'Sm': 1.0, 'S': 2.0}\",\"('Li', 'S', 'Sm')\",OTHERS,False\nmp-1232422,\"{'Yb': 3.0, 'Hf': 1.0}\",\"('Hf', 'Yb')\",OTHERS,False\nmp-1183580,\"{'Cd': 3.0, 'B': 1.0}\",\"('B', 'Cd')\",OTHERS,False\nmp-17189,\"{'K': 6.0, 'Ce': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Ce', 'K', 'O', 'P')\",OTHERS,False\nmp-561051,\"{'Rb': 4.0, 'Sb': 8.0, 'S': 14.0}\",\"('Rb', 'S', 'Sb')\",OTHERS,False\nmp-759536,\"{'V': 8.0, 'O': 6.0, 'F': 18.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-1226037,\"{'Co': 3.0, 'Ni': 3.0, 'P': 3.0}\",\"('Co', 'Ni', 'P')\",OTHERS,False\nmp-1215636,\"{'Zn': 1.0, 'Ni': 4.0, 'O': 5.0}\",\"('Ni', 'O', 'Zn')\",OTHERS,False\nmp-1198572,\"{'Yb': 2.0, 'Al': 2.0, 'B': 28.0}\",\"('Al', 'B', 'Yb')\",OTHERS,False\nmp-753486,\"{'Ba': 2.0, 'Ca': 2.0, 'I': 8.0}\",\"('Ba', 'Ca', 'I')\",OTHERS,False\nmp-1100520,\"{'Li': 9.0, 'Mn': 7.0, 'O': 16.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-22350,\"{'N': 1.0, 'Sn': 1.0, 'Nd': 3.0}\",\"('N', 'Nd', 'Sn')\",OTHERS,False\nmvc-9204,\"{'Ti': 2.0, 'Cr': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Cr', 'O', 'P', 'Ti')\",OTHERS,False\nmp-1227996,\"{'Ca': 6.0, 'Fe': 14.0, 'Si': 12.0, 'O': 48.0}\",\"('Ca', 'Fe', 'O', 'Si')\",OTHERS,False\nmp-504372,\"{'Ni': 12.0, 'P': 14.0, 'O': 48.0}\",\"('Ni', 'O', 'P')\",OTHERS,False\nmp-1104792,\"{'Mn': 1.0, 'Be': 12.0}\",\"('Be', 'Mn')\",OTHERS,False\nmp-1224812,\"{'Ga': 1.0, 'Co': 4.0}\",\"('Co', 'Ga')\",OTHERS,False\nmp-1037110,\"{'Mg': 30.0, 'Zn': 1.0, 'Bi': 1.0, 'O': 32.0}\",\"('Bi', 'Mg', 'O', 'Zn')\",OTHERS,False\nmp-862555,\"{'Li': 1.0, 'Y': 2.0, 'Al': 1.0}\",\"('Al', 'Li', 'Y')\",OTHERS,False\nmp-697813,\"{'Li': 4.0, 'Cr': 8.0, 'P': 12.0, 'O': 48.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-694534,\"{'Li': 2.0, 'Fe': 2.0, 'P': 4.0, 'O': 14.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-772498,\"{'Na': 6.0, 'Li': 6.0, 'Mn': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Li', 'Mn', 'Na', 'O', 'P')\",OTHERS,False\nmp-16373,\"{'Ge': 4.0, 'U': 2.0}\",\"('Ge', 'U')\",OTHERS,False\nmp-763099,\"{'Li': 2.0, 'V': 1.0, 'O': 2.0, 'F': 1.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-705201,\"{'Fe': 16.0, 'P': 16.0, 'O': 64.0}\",\"('Fe', 'O', 'P')\",OTHERS,False\nmp-1075482,\"{'Mg': 12.0, 'Si': 16.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1219995,\"{'Pr': 12.0, 'Al': 6.0, 'Fe': 22.0}\",\"('Al', 'Fe', 'Pr')\",OTHERS,False\nmvc-1666,\"{'Ta': 4.0, 'Zn': 2.0, 'O': 12.0}\",\"('O', 'Ta', 'Zn')\",OTHERS,False\nmp-778347,\"{'Li': 4.0, 'Fe': 4.0, 'F': 16.0}\",\"('F', 'Fe', 'Li')\",OTHERS,False\nmp-1198871,\"{'Zn': 6.0, 'P': 12.0, 'H': 12.0, 'O': 38.0}\",\"('H', 'O', 'P', 'Zn')\",OTHERS,False\nmp-705845,\"{'Ba': 4.0, 'Fe': 4.0, 'O': 12.0}\",\"('Ba', 'Fe', 'O')\",OTHERS,False\nmp-1113297,\"{'Eu': 1.0, 'Rb': 2.0, 'Cu': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cu', 'Eu', 'Rb')\",OTHERS,False\nmp-12170,\"{'S': 10.0, 'Sn': 2.0, 'La': 4.0}\",\"('La', 'S', 'Sn')\",OTHERS,False\nmp-655613,\"{'Tb': 6.0, 'Cs': 2.0, 'Te': 7.0, 'N': 2.0}\",\"('Cs', 'N', 'Tb', 'Te')\",OTHERS,False\nmp-1183141,\"{'Ag': 2.0, 'C': 2.0}\",\"('Ag', 'C')\",OTHERS,False\nmvc-1471,\"{'Y': 2.0, 'Ti': 6.0, 'Se': 4.0, 'Cl': 2.0, 'O': 16.0}\",\"('Cl', 'O', 'Se', 'Ti', 'Y')\",OTHERS,False\nmp-3465,\"{'B': 2.0, 'Rh': 3.0, 'La': 1.0}\",\"('B', 'La', 'Rh')\",OTHERS,False\nmp-1203539,\"{'Gd': 4.0, 'Sb': 4.0, 'Mo': 8.0, 'O': 36.0}\",\"('Gd', 'Mo', 'O', 'Sb')\",OTHERS,False\nmp-1194954,\"{'Na': 4.0, 'Nb': 8.0, 'H': 28.0, 'O': 36.0}\",\"('H', 'Na', 'Nb', 'O')\",OTHERS,False\nmp-569580,\"{'Na': 4.0, 'La': 16.0, 'I': 28.0, 'N': 8.0}\",\"('I', 'La', 'N', 'Na')\",OTHERS,False\nmp-1075132,\"{'Mg': 6.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-979427,\"{'Y': 1.0, 'Ho': 1.0, 'Zn': 2.0}\",\"('Ho', 'Y', 'Zn')\",OTHERS,False\nmp-1079845,\"{'Mn': 3.0, 'Ni': 3.0, 'As': 3.0}\",\"('As', 'Mn', 'Ni')\",OTHERS,False\nmp-1209115,\"{'Sb': 2.0, 'H': 1.0, 'F': 13.0}\",\"('F', 'H', 'Sb')\",OTHERS,False\nmp-780393,\"{'Ho': 12.0, 'B': 12.0, 'O': 36.0}\",\"('B', 'Ho', 'O')\",OTHERS,False\nmp-8769,\"{'Co': 2.0, 'Se': 4.0, 'Rb': 4.0}\",\"('Co', 'Rb', 'Se')\",OTHERS,False\nmp-754502,\"{'Sm': 1.0, 'Y': 1.0, 'O': 2.0}\",\"('O', 'Sm', 'Y')\",OTHERS,False\nmp-779727,\"{'Ti': 34.0, 'N': 12.0, 'O': 48.0}\",\"('N', 'O', 'Ti')\",OTHERS,False\nmp-570113,\"{'Bi': 2.0, 'Au': 4.0}\",\"('Au', 'Bi')\",OTHERS,False\nmp-1019802,\"{'K': 2.0, 'Al': 2.0, 'P': 8.0, 'O': 24.0}\",\"('Al', 'K', 'O', 'P')\",OTHERS,False\nmp-1093563,\"{'Cs': 1.0, 'Rb': 1.0, 'Au': 2.0}\",\"('Au', 'Cs', 'Rb')\",OTHERS,False\nmp-1217793,\"{'Sr': 1.0, 'Ti': 1.0, 'Fe': 3.0, 'Bi': 3.0, 'O': 12.0}\",\"('Bi', 'Fe', 'O', 'Sr', 'Ti')\",OTHERS,False\nmp-1209862,\"{'P': 4.0, 'N': 4.0, 'F': 24.0}\",\"('F', 'N', 'P')\",OTHERS,False\nmp-559832,\"{'Hg': 12.0, 'S': 8.0, 'Br': 8.0}\",\"('Br', 'Hg', 'S')\",OTHERS,False\nmp-556908,\"{'Nd': 10.0, 'As': 8.0, 'Cl': 6.0, 'O': 24.0}\",\"('As', 'Cl', 'Nd', 'O')\",OTHERS,False\nmvc-12627,\"{'Mg': 2.0, 'V': 4.0, 'O': 8.0}\",\"('Mg', 'O', 'V')\",OTHERS,False\nmp-776547,\"{'Li': 4.0, 'Fe': 2.0, 'B': 4.0, 'O': 12.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1223477,\"{'K': 1.0, 'As': 2.0, 'Rh': 1.0}\",\"('As', 'K', 'Rh')\",OTHERS,False\nmp-758261,\"{'Li': 8.0, 'Cr': 4.0, 'Si': 16.0, 'O': 40.0}\",\"('Cr', 'Li', 'O', 'Si')\",OTHERS,False\nmp-676586,\"{'Na': 1.0, 'V': 2.0, 'S': 4.0}\",\"('Na', 'S', 'V')\",OTHERS,False\nmp-531340,\"{'Al': 28.0, 'Mg': 14.0, 'O': 56.0}\",\"('Al', 'Mg', 'O')\",OTHERS,False\nmp-642625,\"{'Hg': 36.0, 'Sb': 3.0, 'Br': 3.0, 'Cl': 6.0, 'O': 18.0}\",\"('Br', 'Cl', 'Hg', 'O', 'Sb')\",OTHERS,False\nmp-773084,\"{'Ba': 6.0, 'Li': 2.0, 'Ti': 7.0, 'Nb': 9.0, 'O': 42.0}\",\"('Ba', 'Li', 'Nb', 'O', 'Ti')\",OTHERS,False\nmp-557180,\"{'Ca': 2.0, 'As': 4.0, 'Xe': 8.0, 'F': 40.0}\",\"('As', 'Ca', 'F', 'Xe')\",OTHERS,False\nmp-1202141,\"{'Tm': 24.0, 'Ga': 16.0}\",\"('Ga', 'Tm')\",OTHERS,False\nmp-1195366,\"{'Cu': 8.0, 'Pb': 16.0, 'Cl': 24.0, 'O': 16.0}\",\"('Cl', 'Cu', 'O', 'Pb')\",OTHERS,False\nmp-754279,\"{'Li': 4.0, 'Cr': 3.0, 'Ni': 1.0, 'O': 8.0}\",\"('Cr', 'Li', 'Ni', 'O')\",OTHERS,False\nmp-998988,\"{'Ti': 3.0, 'Ga': 1.0}\",\"('Ga', 'Ti')\",OTHERS,False\nmp-1238924,\"{'Tl': 3.0, 'As': 1.0, 'F': 6.0}\",\"('As', 'F', 'Tl')\",OTHERS,False\nmp-504555,\"{'U': 8.0, 'K': 4.0, 'F': 36.0}\",\"('F', 'K', 'U')\",OTHERS,False\nmp-775263,\"{'Li': 6.0, 'Co': 1.0, 'Ni': 9.0, 'O': 20.0}\",\"('Co', 'Li', 'Ni', 'O')\",OTHERS,False\nmp-1183555,\"{'Ca': 1.0, 'Nd': 1.0, 'Ag': 2.0}\",\"('Ag', 'Ca', 'Nd')\",OTHERS,False\nmp-772665,\"{'Be': 4.0, 'Cr': 4.0, 'O': 16.0}\",\"('Be', 'Cr', 'O')\",OTHERS,False\nmp-581443,\"{'Cs': 24.0, 'U': 12.0, 'Mo': 12.0, 'O': 84.0}\",\"('Cs', 'Mo', 'O', 'U')\",OTHERS,False\nmp-26796,\"{'Cu': 10.0, 'P': 6.0, 'O': 26.0}\",\"('Cu', 'O', 'P')\",OTHERS,False\nmp-1176952,\"{'Li': 12.0, 'Mn': 2.0, 'V': 6.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-1196989,\"{'Zn': 2.0, 'P': 4.0, 'H': 16.0, 'O': 20.0}\",\"('H', 'O', 'P', 'Zn')\",OTHERS,False\nmp-42981,\"{'F': 4.0, 'Gd': 4.0, 'Na': 4.0, 'Nb': 4.0, 'O': 24.0, 'Ti': 4.0}\",\"('F', 'Gd', 'Na', 'Nb', 'O', 'Ti')\",OTHERS,False\nmp-1101151,\"{'Na': 1.0, 'Ho': 1.0, 'O': 2.0}\",\"('Ho', 'Na', 'O')\",OTHERS,False\nmp-1074455,\"{'Mg': 8.0, 'Si': 6.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-5401,\"{'B': 8.0, 'O': 20.0, 'Sr': 8.0}\",\"('B', 'O', 'Sr')\",OTHERS,False\nmp-1228760,\"{'Ba': 6.0, 'Ca': 6.0, 'Cu': 9.0, 'Hg': 3.0, 'O': 25.0}\",\"('Ba', 'Ca', 'Cu', 'Hg', 'O')\",OTHERS,False\nmp-1195488,\"{'Sr': 2.0, 'Fe': 2.0, 'Ni': 4.0, 'P': 6.0, 'O': 24.0}\",\"('Fe', 'Ni', 'O', 'P', 'Sr')\",OTHERS,False\nmp-560410,\"{'Sr': 4.0, 'U': 2.0, 'Ni': 2.0, 'O': 12.0}\",\"('Ni', 'O', 'Sr', 'U')\",OTHERS,False\nmp-999126,\"{'Tb': 1.0, 'Na': 1.0, 'S': 2.0}\",\"('Na', 'S', 'Tb')\",OTHERS,False\nmp-1232041,\"{'La': 8.0, 'Mg': 4.0, 'Se': 16.0}\",\"('La', 'Mg', 'Se')\",OTHERS,False\nmp-1268088,\"{'Ba': 1.0, 'Al': 1.0, 'Cu': 1.0, 'Ag': 1.0, 'O': 5.0}\",\"('Ag', 'Al', 'Ba', 'Cu', 'O')\",OTHERS,False\nmp-1095972,\"{'Sr': 2.0, 'Hg': 1.0, 'Ge': 1.0}\",\"('Ge', 'Hg', 'Sr')\",OTHERS,False\nmp-4124,\"{'C': 1.0, 'N': 2.0, 'Ca': 1.0}\",\"('C', 'Ca', 'N')\",OTHERS,False\nmp-570175,\"{'Ce': 10.0, 'Si': 6.0}\",\"('Ce', 'Si')\",OTHERS,False\nmp-14970,\"{'B': 2.0, 'O': 10.0, 'Cu': 2.0, 'Ba': 2.0, 'La': 2.0}\",\"('B', 'Ba', 'Cu', 'La', 'O')\",OTHERS,False\nmp-1096469,\"{'Hf': 2.0, 'Re': 1.0, 'Pt': 1.0}\",\"('Hf', 'Pt', 'Re')\",OTHERS,False\nmp-773055,\"{'Li': 6.0, 'Mg': 6.0, 'Ti': 12.0, 'O': 32.0}\",\"('Li', 'Mg', 'O', 'Ti')\",OTHERS,False\nmp-679985,\"{'Ba': 6.0, 'In': 8.0, 'Cu': 6.0, 'O': 24.0}\",\"('Ba', 'Cu', 'In', 'O')\",OTHERS,False\nmp-569136,\"{'K': 6.0, 'Cu': 22.0, 'Te': 32.0}\",\"('Cu', 'K', 'Te')\",OTHERS,False\nmp-17885,\"{'Ge': 8.0, 'Te': 20.0, 'Ba': 8.0}\",\"('Ba', 'Ge', 'Te')\",OTHERS,False\nmp-1214682,\"{'Ba': 12.0, 'Gd': 4.0, 'Al': 8.0, 'O': 30.0}\",\"('Al', 'Ba', 'Gd', 'O')\",OTHERS,False\nmp-1248937,\"{'Y': 4.0, 'Si': 2.0, 'Sb': 2.0, 'W': 13.0, 'O': 28.0}\",\"('O', 'Sb', 'Si', 'W', 'Y')\",OTHERS,False\nmp-977549,\"{'Zr': 2.0, 'Os': 1.0, 'Ru': 1.0}\",\"('Os', 'Ru', 'Zr')\",OTHERS,False\nmp-23776,\"{'H': 32.0, 'Be': 4.0, 'O': 16.0, 'Cl': 8.0}\",\"('Be', 'Cl', 'H', 'O')\",OTHERS,False\nGa 33 Fe 46 Cu 3 Si 18,\"{'Ga': 33.0, 'Fe': 46.0, 'Cu': 3.0, 'Si': 18.0}\",\"('Cu', 'Fe', 'Ga', 'Si')\",QC,False\nmp-5690,\"{'O': 12.0, 'Sc': 4.0, 'Gd': 4.0}\",\"('Gd', 'O', 'Sc')\",OTHERS,False\nmp-1197552,\"{'U': 8.0, 'Pb': 4.0, 'Se': 20.0}\",\"('Pb', 'Se', 'U')\",OTHERS,False\nmp-17646,\"{'O': 24.0, 'Yb': 8.0, 'W': 4.0}\",\"('O', 'W', 'Yb')\",OTHERS,False\nmp-34508,\"{'S': 8.0, 'Sm': 4.0, 'Sr': 2.0}\",\"('S', 'Sm', 'Sr')\",OTHERS,False\nmp-756639,\"{'Li': 6.0, 'Nb': 3.0, 'O': 3.0, 'F': 15.0}\",\"('F', 'Li', 'Nb', 'O')\",OTHERS,False\nmp-754549,\"{'Na': 4.0, 'Cu': 2.0, 'O': 4.0}\",\"('Cu', 'Na', 'O')\",OTHERS,False\nmp-567908,\"{'Lu': 4.0, 'B': 3.0, 'C': 4.0}\",\"('B', 'C', 'Lu')\",OTHERS,False\nmp-1184869,\"{'Ho': 1.0, 'Lu': 1.0, 'Hg': 2.0}\",\"('Hg', 'Ho', 'Lu')\",OTHERS,False\nmp-1114593,\"{'Rb': 2.0, 'Na': 1.0, 'Nd': 1.0, 'Cl': 6.0}\",\"('Cl', 'Na', 'Nd', 'Rb')\",OTHERS,False\nmp-1073501,\"{'Mg': 4.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-560029,\"{'Y': 6.0, 'Br': 2.0, 'O': 8.0}\",\"('Br', 'O', 'Y')\",OTHERS,False\nmp-1096815,\"{'Ba': 6.0, 'Ta': 8.0, 'Nb': 2.0, 'O': 30.0}\",\"('Ba', 'Nb', 'O', 'Ta')\",OTHERS,False\nmp-24150,\"{'H': 1.0, 'Li': 1.0, 'O': 3.0, 'Nd': 2.0}\",\"('H', 'Li', 'Nd', 'O')\",OTHERS,False\nmvc-10122,\"{'Mg': 6.0, 'Ni': 12.0, 'O': 24.0}\",\"('Mg', 'Ni', 'O')\",OTHERS,False\nmvc-9680,\"{'Pr': 2.0, 'Zn': 2.0, 'W': 4.0, 'O': 12.0}\",\"('O', 'Pr', 'W', 'Zn')\",OTHERS,False\nmp-4227,\"{'S': 6.0, 'V': 2.0, 'Ba': 2.0}\",\"('Ba', 'S', 'V')\",OTHERS,False\nmp-1227384,\"{'Ca': 2.0, 'Fe': 6.0, 'Si': 8.0, 'O': 24.0}\",\"('Ca', 'Fe', 'O', 'Si')\",OTHERS,False\nmp-3882,\"{'Si': 2.0, 'Rh': 2.0, 'Sm': 1.0}\",\"('Rh', 'Si', 'Sm')\",OTHERS,False\nmp-1184828,\"{'Ho': 1.0, 'Tm': 1.0, 'In': 2.0}\",\"('Ho', 'In', 'Tm')\",OTHERS,False\nmp-1246713,\"{'Sr': 2.0, 'Zn': 4.0, 'N': 4.0}\",\"('N', 'Sr', 'Zn')\",OTHERS,False\nmp-1246665,\"{'Li': 2.0, 'Cr': 4.0, 'N': 6.0}\",\"('Cr', 'Li', 'N')\",OTHERS,False\nmp-1113314,\"{'Na': 2.0, 'Cu': 1.0, 'Sb': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cu', 'Na', 'Sb')\",OTHERS,False\nmp-20497,\"{'Sr': 2.0, 'In': 4.0, 'Ir': 2.0}\",\"('In', 'Ir', 'Sr')\",OTHERS,False\nmp-28749,\"{'Cr': 4.0, 'Xe': 4.0, 'F': 24.0}\",\"('Cr', 'F', 'Xe')\",OTHERS,False\nmp-856,\"{'O': 4.0, 'Sn': 2.0}\",\"('O', 'Sn')\",OTHERS,False\nmp-1186611,\"{'Pm': 1.0, 'Nd': 1.0, 'Ga': 2.0}\",\"('Ga', 'Nd', 'Pm')\",OTHERS,False\nmp-1382,\"{'S': 8.0, 'Ce': 6.0}\",\"('Ce', 'S')\",OTHERS,False\nmp-4756,\"{'Zn': 2.0, 'Sn': 2.0, 'Sb': 4.0}\",\"('Sb', 'Sn', 'Zn')\",OTHERS,False\nmp-1207452,\"{'Zr': 6.0, 'U': 4.0, 'Ge': 8.0}\",\"('Ge', 'U', 'Zr')\",OTHERS,False\nmp-755658,\"{'Ce': 2.0, 'Pr': 2.0, 'O': 4.0}\",\"('Ce', 'O', 'Pr')\",OTHERS,False\nmp-867235,\"{'Li': 1.0, 'Y': 2.0, 'Ir': 1.0}\",\"('Ir', 'Li', 'Y')\",OTHERS,False\nmp-1176835,\"{'Li': 8.0, 'Fe': 7.0, 'P': 12.0, 'W': 1.0, 'O': 48.0}\",\"('Fe', 'Li', 'O', 'P', 'W')\",OTHERS,False\nmvc-12108,\"{'Ca': 2.0, 'Mn': 4.0, 'O': 8.0}\",\"('Ca', 'Mn', 'O')\",OTHERS,False\nmp-3042,\"{'F': 6.0, 'Si': 1.0, 'K': 2.0}\",\"('F', 'K', 'Si')\",OTHERS,False\nmp-1003403,\"{'Mn': 16.0, 'H': 2.0, 'O': 32.0}\",\"('H', 'Mn', 'O')\",OTHERS,False\nmp-774794,\"{'Li': 20.0, 'Sm': 12.0, 'Sb': 8.0, 'O': 48.0}\",\"('Li', 'O', 'Sb', 'Sm')\",OTHERS,False\nmp-1194649,\"{'Rb': 2.0, 'Na': 4.0, 'Mn': 6.0, 'P': 8.0, 'Cl': 2.0, 'O': 28.0}\",\"('Cl', 'Mn', 'Na', 'O', 'P', 'Rb')\",OTHERS,False\nmp-1178641,\"{'Zn': 1.0, 'Cu': 3.0, 'Ni': 1.0, 'Se': 4.0}\",\"('Cu', 'Ni', 'Se', 'Zn')\",OTHERS,False\nAl 76 Co 24,\"{'Al': 76.0, 'Co': 24.0}\",\"('Al', 'Co')\",AC,False\nmp-1039529,\"{'Ca': 6.0, 'Mg': 12.0}\",\"('Ca', 'Mg')\",OTHERS,False\nmp-12107,\"{'Ti': 1.0, 'Pt': 3.0}\",\"('Pt', 'Ti')\",OTHERS,False\nmp-1186622,\"{'Pm': 1.0, 'P': 3.0}\",\"('P', 'Pm')\",OTHERS,False\nmp-1211637,\"{'K': 2.0, 'Ca': 2.0, 'P': 2.0, 'H': 2.0, 'O': 8.0}\",\"('Ca', 'H', 'K', 'O', 'P')\",OTHERS,False\nmp-542709,\"{'Eu': 2.0, 'Si': 12.0}\",\"('Eu', 'Si')\",OTHERS,False\nmp-638,\"{'Zn': 5.0, 'Sr': 1.0}\",\"('Sr', 'Zn')\",OTHERS,False\nmp-1183254,\"{'Ag': 6.0, 'S': 2.0}\",\"('Ag', 'S')\",OTHERS,False\nmp-571333,\"{'Ca': 10.0, 'Si': 4.0, 'N': 12.0}\",\"('Ca', 'N', 'Si')\",OTHERS,False\nmp-1064861,\"{'Ga': 1.0, 'C': 3.0}\",\"('C', 'Ga')\",OTHERS,False\nmp-866187,\"{'Ti': 2.0, 'Zn': 1.0, 'Tc': 1.0}\",\"('Tc', 'Ti', 'Zn')\",OTHERS,False\nmp-1224473,\"{'Hf': 2.0, 'Ni': 1.0, 'S': 4.0}\",\"('Hf', 'Ni', 'S')\",OTHERS,False\nmp-556074,\"{'K': 2.0, 'Cu': 3.0, 'Se': 4.0, 'O': 12.0}\",\"('Cu', 'K', 'O', 'Se')\",OTHERS,False\nmp-568827,\"{'Be': 12.0, 'W': 1.0}\",\"('Be', 'W')\",OTHERS,False\nmp-9664,\"{'B': 2.0, 'P': 4.0, 'K': 6.0}\",\"('B', 'K', 'P')\",OTHERS,False\nmvc-12125,\"{'Fe': 4.0, 'O': 8.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-22424,\"{'O': 8.0, 'Na': 4.0, 'S': 2.0}\",\"('Na', 'O', 'S')\",OTHERS,False\nmp-1111572,\"{'K': 2.0, 'Tl': 1.0, 'Pd': 1.0, 'F': 6.0}\",\"('F', 'K', 'Pd', 'Tl')\",OTHERS,False\nmp-1194940,\"{'Na': 16.0, 'Fe': 8.0, 'P': 8.0, 'O': 32.0, 'F': 8.0}\",\"('F', 'Fe', 'Na', 'O', 'P')\",OTHERS,False\nmp-13082,\"{'Si': 1.0, 'Mn': 2.0, 'Co': 1.0}\",\"('Co', 'Mn', 'Si')\",OTHERS,False\nmp-997004,\"{'Ca': 2.0, 'Ag': 2.0, 'O': 4.0}\",\"('Ag', 'Ca', 'O')\",OTHERS,False\nmp-757284,\"{'Mn': 3.0, 'Cr': 1.0, 'Co': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Co', 'Cr', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1213119,\"{'Dy': 2.0, 'Cu': 1.0, 'Si': 4.0, 'O': 12.0}\",\"('Cu', 'Dy', 'O', 'Si')\",OTHERS,False\nmp-530539,\"{'O': 36.0, 'Sr': 16.0, 'U': 8.0}\",\"('O', 'Sr', 'U')\",OTHERS,False\nmp-510662,\"{'Cs': 2.0, 'U': 2.0, 'Ag': 2.0, 'Se': 6.0}\",\"('Ag', 'Cs', 'Se', 'U')\",OTHERS,False\nmp-630391,\"{'Cs': 4.0, 'Zn': 4.0, 'Ag': 4.0, 'C': 16.0, 'S': 16.0, 'N': 16.0}\",\"('Ag', 'C', 'Cs', 'N', 'S', 'Zn')\",OTHERS,False\nmp-1104531,\"{'Sm': 4.0, 'Te': 10.0}\",\"('Sm', 'Te')\",OTHERS,False\nmp-974744,\"{'Nd': 1.0, 'Zn': 2.0, 'Ag': 1.0}\",\"('Ag', 'Nd', 'Zn')\",OTHERS,False\nmp-1198063,\"{'Na': 4.0, 'Ti': 4.0, 'Si': 4.0, 'O': 22.0}\",\"('Na', 'O', 'Si', 'Ti')\",OTHERS,False\nmp-542934,\"{'Cu': 18.0, 'Ce': 2.0, 'Mg': 4.0}\",\"('Ce', 'Cu', 'Mg')\",OTHERS,False\nmp-583193,\"{'Cs': 12.0, 'P': 4.0, 'Se': 16.0}\",\"('Cs', 'P', 'Se')\",OTHERS,False\nmp-686717,\"{'Fe': 2.0, 'Ni': 1.0, 'Se': 4.0}\",\"('Fe', 'Ni', 'Se')\",OTHERS,False\nmp-29398,\"{'I': 6.0, 'Cs': 2.0, 'Hf': 1.0}\",\"('Cs', 'Hf', 'I')\",OTHERS,False\nmp-12666,\"{'Be': 12.0, 'Pd': 1.0}\",\"('Be', 'Pd')\",OTHERS,False\nmp-1184880,\"{'K': 3.0, 'Hf': 1.0}\",\"('Hf', 'K')\",OTHERS,False\nmp-1229278,\"{'Al': 4.0, 'H': 50.0, 'S': 7.0, 'O': 52.0}\",\"('Al', 'H', 'O', 'S')\",OTHERS,False\nmp-562965,\"{'Cs': 4.0, 'Cr': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Cr', 'Cs', 'O', 'P')\",OTHERS,False\nmp-1203391,\"{'K': 64.0, 'Sr': 16.0, 'Si': 48.0, 'O': 144.0}\",\"('K', 'O', 'Si', 'Sr')\",OTHERS,False\nmp-1215290,\"{'Zr': 2.0, 'Nb': 2.0, 'Fe': 8.0}\",\"('Fe', 'Nb', 'Zr')\",OTHERS,False\nmp-1216039,\"{'Zr': 2.0, 'Ti': 3.0, 'Pb': 5.0, 'O': 15.0}\",\"('O', 'Pb', 'Ti', 'Zr')\",OTHERS,False\nmp-1185930,\"{'Mn': 6.0, 'Sb': 2.0}\",\"('Mn', 'Sb')\",OTHERS,False\nmp-13525,\"{'In': 3.0, 'Ho': 3.0, 'Ir': 3.0}\",\"('Ho', 'In', 'Ir')\",OTHERS,False\nmp-1227614,\"{'Ca': 4.0, 'Mg': 3.0, 'Si': 8.0, 'Ni': 1.0, 'O': 24.0}\",\"('Ca', 'Mg', 'Ni', 'O', 'Si')\",OTHERS,False\nmp-1183913,\"{'Cs': 1.0, 'Pd': 1.0, 'O': 3.0}\",\"('Cs', 'O', 'Pd')\",OTHERS,False\nmp-1232353,\"{'Sr': 6.0, 'Nd': 2.0, 'Ta': 2.0, 'Al': 6.0, 'O': 24.0}\",\"('Al', 'Nd', 'O', 'Sr', 'Ta')\",OTHERS,False\nmp-1196488,\"{'Zn': 8.0, 'H': 40.0, 'Cl': 16.0, 'O': 20.0}\",\"('Cl', 'H', 'O', 'Zn')\",OTHERS,False\nmp-746730,\"{'Fe': 4.0, 'P': 16.0, 'O': 44.0}\",\"('Fe', 'O', 'P')\",OTHERS,False\nmp-755959,\"{'Na': 4.0, 'Li': 4.0, 'O': 4.0}\",\"('Li', 'Na', 'O')\",OTHERS,False\nmp-800,\"{'B': 2.0, 'Tm': 1.0}\",\"('B', 'Tm')\",OTHERS,False\nmp-1202414,\"{'Nd': 40.0, 'Se': 56.0, 'O': 4.0}\",\"('Nd', 'O', 'Se')\",OTHERS,False\nmp-1213254,\"{'Cs': 2.0, 'Gd': 2.0, 'W': 4.0, 'O': 16.0}\",\"('Cs', 'Gd', 'O', 'W')\",OTHERS,False\nmp-24179,\"{'H': 24.0, 'C': 8.0, 'N': 4.0, 'O': 16.0, 'S': 8.0, 'K': 4.0}\",\"('C', 'H', 'K', 'N', 'O', 'S')\",OTHERS,False\nmp-1078860,\"{'Sr': 2.0, 'Ge': 2.0, 'Au': 6.0}\",\"('Au', 'Ge', 'Sr')\",OTHERS,False\nmp-1180133,\"{'Na': 1.0, 'Ca': 1.0, 'H': 6.0, 'Ir': 1.0}\",\"('Ca', 'H', 'Ir', 'Na')\",OTHERS,False\nmp-759404,\"{'Li': 6.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1207695,\"{'Tm': 4.0, 'S': 4.0, 'I': 4.0}\",\"('I', 'S', 'Tm')\",OTHERS,False\nmp-1103788,\"{'Sr': 2.0, 'Ni': 4.0, 'P': 8.0}\",\"('Ni', 'P', 'Sr')\",OTHERS,False\nmp-1255862,\"{'Mg': 8.0, 'Si': 14.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1219137,\"{'Sm': 2.0, 'Ga': 3.0, 'Cu': 1.0}\",\"('Cu', 'Ga', 'Sm')\",OTHERS,False\nmp-1222005,\"{'Mn': 3.0, 'Ge': 1.0}\",\"('Ge', 'Mn')\",OTHERS,False\nmp-849328,\"{'Li': 4.0, 'Mn': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1080815,\"{'Zr': 3.0, 'Sn': 3.0, 'Rh': 3.0}\",\"('Rh', 'Sn', 'Zr')\",OTHERS,False\nmp-1211549,\"{'La': 12.0, 'Sb': 4.0, 'O': 28.0}\",\"('La', 'O', 'Sb')\",OTHERS,False\nmp-1079542,\"{'Co': 3.0, 'Ni': 3.0, 'As': 3.0}\",\"('As', 'Co', 'Ni')\",OTHERS,False\nAg 47.7 In 38.7 Ce 14.2,\"{'Ag': 47.7, 'In': 38.7, 'Ce': 14.2}\",\"('Ag', 'Ce', 'In')\",AC,False\nmp-676893,\"{'Ca': 4.0, 'Th': 1.0, 'F': 12.0}\",\"('Ca', 'F', 'Th')\",OTHERS,False\nmp-1182283,\"{'Ca': 4.0, 'Mn': 2.0, 'As': 4.0, 'O': 20.0}\",\"('As', 'Ca', 'Mn', 'O')\",OTHERS,False\nmp-1207424,\"{'Zr': 8.0, 'In': 4.0, 'Au': 8.0}\",\"('Au', 'In', 'Zr')\",OTHERS,False\nmp-774920,\"{'Li': 2.0, 'Fe': 2.0, 'Sb': 2.0, 'O': 8.0}\",\"('Fe', 'Li', 'O', 'Sb')\",OTHERS,False\nmp-1214265,\"{'Bi': 12.0, 'Br': 5.0, 'O': 16.0}\",\"('Bi', 'Br', 'O')\",OTHERS,False\nmp-758147,\"{'Li': 4.0, 'Cu': 4.0, 'P': 12.0, 'O': 36.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-557981,\"{'K': 24.0, 'Co': 24.0, 'P': 24.0, 'O': 96.0}\",\"('Co', 'K', 'O', 'P')\",OTHERS,False\nmp-1176362,\"{'Na': 6.0, 'Li': 6.0, 'Fe': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Fe', 'Li', 'Na', 'O', 'P')\",OTHERS,False\nmp-961655,\"{'Y': 1.0, 'Cd': 1.0, 'Ga': 1.0}\",\"('Cd', 'Ga', 'Y')\",OTHERS,False\nmp-1215709,\"{'Y': 1.0, 'Zr': 1.0, 'Fe': 4.0}\",\"('Fe', 'Y', 'Zr')\",OTHERS,False\nmp-1214488,\"{'Ba': 8.0, 'As': 6.0}\",\"('As', 'Ba')\",OTHERS,False\nmp-505669,\"{'La': 8.0, 'Co': 8.0, 'O': 20.0}\",\"('Co', 'La', 'O')\",OTHERS,False\nmp-28575,\"{'S': 16.0, 'Cl': 12.0, 'W': 2.0}\",\"('Cl', 'S', 'W')\",OTHERS,False\nmp-17702,\"{'Yb': 8.0, 'Si': 4.0, 'O': 20.0}\",\"('O', 'Si', 'Yb')\",OTHERS,False\nmp-565518,\"{'Na': 2.0, 'V': 6.0, 'Fe': 6.0, 'O': 24.0}\",\"('Fe', 'Na', 'O', 'V')\",OTHERS,False\nmp-1004373,\"{'Li': 4.0, 'Mn': 4.0, 'O': 8.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-1174854,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1104073,\"{'C': 11.0, 'N': 4.0}\",\"('C', 'N')\",OTHERS,False\nmp-1029176,\"{'Te': 2.0, 'Mo': 2.0, 'W': 2.0, 'Se': 4.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-766923,\"{'Li': 4.0, 'V': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Li', 'O', 'Si', 'V')\",OTHERS,False\nmp-1194852,\"{'Na': 8.0, 'Cd': 8.0, 'P': 24.0, 'H': 64.0, 'O': 80.0}\",\"('Cd', 'H', 'Na', 'O', 'P')\",OTHERS,False\nmp-27581,\"{'Pb': 7.0, 'Cl': 2.0, 'F': 12.0}\",\"('Cl', 'F', 'Pb')\",OTHERS,False\nmp-652036,\"{'Gd': 4.0, 'Fe': 20.0, 'P': 12.0}\",\"('Fe', 'Gd', 'P')\",OTHERS,False\nmp-29034,\"{'Ge': 8.0, 'Te': 24.0, 'Tl': 16.0}\",\"('Ge', 'Te', 'Tl')\",OTHERS,False\nmp-1009830,\"{'Be': 2.0, 'Zn': 1.0}\",\"('Be', 'Zn')\",OTHERS,False\nmp-1219533,\"{'Re': 1.0, 'Ir': 1.0}\",\"('Ir', 'Re')\",OTHERS,False\nmp-1227624,\"{'Fe': 4.0, 'Cu': 26.0, 'Ge': 4.0, 'S': 32.0}\",\"('Cu', 'Fe', 'Ge', 'S')\",OTHERS,False\nmp-1076445,\"{'Sr': 8.0, 'Mn': 1.0, 'Fe': 7.0, 'O': 24.0}\",\"('Fe', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-694881,\"{'Na': 2.0, 'Nd': 6.0, 'Ti': 6.0, 'Mn': 2.0, 'O': 24.0}\",\"('Mn', 'Na', 'Nd', 'O', 'Ti')\",OTHERS,False\nmp-636950,\"{'Mo': 8.0, 'P': 8.0, 'O': 44.0}\",\"('Mo', 'O', 'P')\",OTHERS,False\nmp-1210917,\"{'Lu': 4.0, 'Sn': 4.0, 'F': 28.0}\",\"('F', 'Lu', 'Sn')\",OTHERS,False\nmp-1175736,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1186819,\"{'Rb': 4.0, 'Mg': 2.0}\",\"('Mg', 'Rb')\",OTHERS,False\nmp-5673,\"{'P': 16.0, 'Ni': 2.0, 'Mo': 2.0}\",\"('Mo', 'Ni', 'P')\",OTHERS,False\nmp-1019781,\"{'K': 4.0, 'Na': 6.0, 'P': 6.0, 'O': 20.0}\",\"('K', 'Na', 'O', 'P')\",OTHERS,False\nmp-1226346,\"{'Cr': 3.0, 'Te': 2.0, 'Se': 2.0}\",\"('Cr', 'Se', 'Te')\",OTHERS,False\nmp-554930,\"{'Nb': 8.0, 'Ag': 4.0, 'P': 4.0, 'S': 40.0}\",\"('Ag', 'Nb', 'P', 'S')\",OTHERS,False\nmp-704492,\"{'Mn': 16.0, 'P': 16.0, 'O': 64.0}\",\"('Mn', 'O', 'P')\",OTHERS,False\nmp-1239130,\"{'Nb': 2.0, 'Cr': 6.0, 'Cu': 4.0, 'S': 16.0}\",\"('Cr', 'Cu', 'Nb', 'S')\",OTHERS,False\nmp-1096686,\"{'Tl': 1.0, 'Bi': 1.0, 'Pb': 2.0}\",\"('Bi', 'Pb', 'Tl')\",OTHERS,False\nmp-1222926,\"{'La': 1.0, 'Cu': 1.0, 'Si': 2.0, 'Ni': 1.0}\",\"('Cu', 'La', 'Ni', 'Si')\",OTHERS,False\nmp-28137,\"{'O': 8.0, 'Cl': 2.0, 'Co': 1.0}\",\"('Cl', 'Co', 'O')\",OTHERS,False\nmp-867175,\"{'Sm': 4.0, 'Ag': 4.0, 'As': 8.0}\",\"('Ag', 'As', 'Sm')\",OTHERS,False\nmp-22829,\"{'B': 32.0, 'O': 56.0, 'Co': 8.0}\",\"('B', 'Co', 'O')\",OTHERS,False\nmp-571055,\"{'Fe': 11.0, 'Mo': 6.0, 'C': 5.0}\",\"('C', 'Fe', 'Mo')\",OTHERS,False\nmp-1219723,\"{'Pr': 1.0, 'Th': 1.0, 'C': 1.0, 'N': 1.0}\",\"('C', 'N', 'Pr', 'Th')\",OTHERS,False\nmp-1183940,\"{'Cs': 6.0, 'K': 2.0}\",\"('Cs', 'K')\",OTHERS,False\nmp-756426,\"{'Zr': 2.0, 'Nb': 2.0, 'O': 8.0}\",\"('Nb', 'O', 'Zr')\",OTHERS,False\nmp-1097154,\"{'Y': 2.0, 'Hg': 1.0, 'Au': 1.0}\",\"('Au', 'Hg', 'Y')\",OTHERS,False\nmp-760798,\"{'Li': 8.0, 'Cu': 4.0, 'F': 20.0}\",\"('Cu', 'F', 'Li')\",OTHERS,False\nmp-1076037,\"{'Eu': 4.0, 'Ti': 4.0, 'O': 10.0}\",\"('Eu', 'O', 'Ti')\",OTHERS,False\nmp-1020020,\"{'Li': 8.0, 'N': 1.0, 'O': 3.0}\",\"('Li', 'N', 'O')\",OTHERS,False\nmp-27957,\"{'O': 8.0, 'Ni': 2.0, 'Ba': 6.0}\",\"('Ba', 'Ni', 'O')\",OTHERS,False\nmp-1224124,\"{'In': 4.0, 'P': 6.0, 'Pb': 2.0, 'O': 23.0}\",\"('In', 'O', 'P', 'Pb')\",OTHERS,False\nmp-1212113,\"{'I': 8.0, 'N': 12.0}\",\"('I', 'N')\",OTHERS,False\nmp-1193402,\"{'Eu': 6.0, 'Ga': 8.0, 'Ge': 12.0}\",\"('Eu', 'Ga', 'Ge')\",OTHERS,False\nmp-1222094,\"{'Mg': 4.0, 'Te': 3.0, 'Se': 1.0}\",\"('Mg', 'Se', 'Te')\",OTHERS,False\nmp-1939,\"{'Ni': 4.0, 'Er': 2.0}\",\"('Er', 'Ni')\",OTHERS,False\nmp-542817,\"{'U': 8.0, 'Pt': 8.0}\",\"('Pt', 'U')\",OTHERS,False\nmp-1037497,\"{'Mg': 30.0, 'Zn': 1.0, 'Co': 1.0, 'O': 32.0}\",\"('Co', 'Mg', 'O', 'Zn')\",OTHERS,False\nmp-1174772,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-541111,\"{'Ga': 4.0, 'Al': 4.0, 'Cl': 16.0}\",\"('Al', 'Cl', 'Ga')\",OTHERS,False\nmp-973631,\"{'Lu': 2.0, 'Al': 1.0, 'Ru': 1.0}\",\"('Al', 'Lu', 'Ru')\",OTHERS,False\nZn 77 Sc 7 Tm 9 Fe 7,\"{'Zn': 77.0, 'Sc': 7.0, 'Tm': 9.0, 'Fe': 7.0}\",\"('Fe', 'Sc', 'Tm', 'Zn')\",QC,False\nmp-1220274,\"{'Nd': 2.0, 'Y': 1.0}\",\"('Nd', 'Y')\",OTHERS,False\nmp-1176630,\"{'Li': 4.0, 'Mn': 4.0, 'F': 12.0}\",\"('F', 'Li', 'Mn')\",OTHERS,False\nmp-1174881,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1246137,\"{'Sr': 32.0, 'Mn': 16.0, 'N': 32.0}\",\"('Mn', 'N', 'Sr')\",OTHERS,False\nmp-1035244,\"{'Mg': 14.0, 'Cu': 1.0, 'C': 1.0, 'O': 16.0}\",\"('C', 'Cu', 'Mg', 'O')\",OTHERS,False\nmp-1176247,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-695948,\"{'P': 12.0, 'H': 48.0, 'S': 12.0, 'N': 12.0, 'O': 24.0}\",\"('H', 'N', 'O', 'P', 'S')\",OTHERS,False\nmp-556303,\"{'Cu': 2.0, 'Bi': 4.0, 'Se': 8.0, 'O': 24.0}\",\"('Bi', 'Cu', 'O', 'Se')\",OTHERS,False\nmp-504263,\"{'Li': 14.0, 'Co': 9.0, 'P': 16.0, 'O': 56.0}\",\"('Co', 'Li', 'O', 'P')\",OTHERS,False\nmp-779434,\"{'Li': 20.0, 'La': 12.0, 'Nb': 8.0, 'O': 48.0}\",\"('La', 'Li', 'Nb', 'O')\",OTHERS,False\nmp-757136,\"{'Li': 2.0, 'Co': 3.0, 'Sn': 1.0, 'O': 8.0}\",\"('Co', 'Li', 'O', 'Sn')\",OTHERS,False\nmp-1032467,\"{'Li': 1.0, 'Mg': 6.0, 'Fe': 1.0, 'O': 8.0}\",\"('Fe', 'Li', 'Mg', 'O')\",OTHERS,False\nmp-1184081,\"{'Er': 2.0, 'Ru': 1.0, 'Au': 1.0}\",\"('Au', 'Er', 'Ru')\",OTHERS,False\nmp-780624,\"{'V': 6.0, 'O': 5.0, 'F': 19.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-570584,\"{'Ta': 2.0, 'Si': 4.0, 'H': 70.0, 'C': 24.0, 'N': 8.0}\",\"('C', 'H', 'N', 'Si', 'Ta')\",OTHERS,False\nmp-18560,\"{'O': 16.0, 'F': 4.0, 'P': 4.0, 'Np': 4.0}\",\"('F', 'Np', 'O', 'P')\",OTHERS,False\nmp-1245128,\"{'Mg': 40.0, 'O': 40.0}\",\"('Mg', 'O')\",OTHERS,False\nmp-1154546,\"{'Ca': 4.0, 'Si': 16.0, 'Sn': 4.0, 'O': 40.0}\",\"('Ca', 'O', 'Si', 'Sn')\",OTHERS,False\nmp-1223844,\"{'In': 2.0, 'Sb': 1.0, 'As': 1.0}\",\"('As', 'In', 'Sb')\",OTHERS,False\nmvc-8814,\"{'Ti': 4.0, 'Zn': 2.0, 'P': 6.0, 'O': 24.0}\",\"('O', 'P', 'Ti', 'Zn')\",OTHERS,False\nmp-9972,\"{'Si': 2.0, 'Y': 2.0}\",\"('Si', 'Y')\",OTHERS,False\nmp-1073692,\"{'Mg': 6.0, 'Si': 6.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1030535,\"{'Mo': 2.0, 'W': 2.0, 'Se': 6.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-30977,\"{'H': 48.0, 'B': 40.0, 'Br': 8.0}\",\"('B', 'Br', 'H')\",OTHERS,False\nmp-554909,\"{'Ba': 6.0, 'Mg': 2.0, 'Ir': 4.0, 'O': 18.0}\",\"('Ba', 'Ir', 'Mg', 'O')\",OTHERS,False\nmp-1216804,\"{'Tl': 2.0, 'V': 6.0, 'Cd': 8.0, 'O': 24.0}\",\"('Cd', 'O', 'Tl', 'V')\",OTHERS,False\nmp-1184464,\"{'Eu': 2.0, 'Zn': 1.0, 'In': 1.0}\",\"('Eu', 'In', 'Zn')\",OTHERS,False\nmp-20726,\"{'Ca': 4.0, 'Sr': 4.0, 'Sn': 4.0}\",\"('Ca', 'Sn', 'Sr')\",OTHERS,False\nmp-560710,\"{'Cs': 2.0, 'Tl': 1.0, 'Mo': 1.0, 'F': 6.0}\",\"('Cs', 'F', 'Mo', 'Tl')\",OTHERS,False\nmp-5893,\"{'O': 3.0, 'Si': 1.0, 'Ca': 1.0}\",\"('Ca', 'O', 'Si')\",OTHERS,False\nmp-759285,\"{'Li': 4.0, 'Fe': 7.0, 'O': 2.0, 'F': 14.0}\",\"('F', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-755738,\"{'Rb': 8.0, 'O': 4.0}\",\"('O', 'Rb')\",OTHERS,False\nmp-772636,\"{'Na': 2.0, 'Li': 10.0, 'Fe': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Fe', 'Li', 'Na', 'O', 'P')\",OTHERS,False\nmp-774649,\"{'V': 3.0, 'Ni': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Ni', 'O', 'P', 'V')\",OTHERS,False\nmp-553968,\"{'Mo': 8.0, 'P': 8.0, 'Cl': 40.0, 'O': 24.0}\",\"('Cl', 'Mo', 'O', 'P')\",OTHERS,False\nmp-42255,\"{'Ca': 2.0, 'F': 2.0, 'Na': 2.0, 'Nb': 4.0, 'O': 12.0}\",\"('Ca', 'F', 'Na', 'Nb', 'O')\",OTHERS,False\nmp-1094096,\"{'Ni': 4.0, 'O': 3.0}\",\"('Ni', 'O')\",OTHERS,False\nmp-1217321,\"{'Th': 2.0, 'Fe': 1.0, 'Si': 3.0}\",\"('Fe', 'Si', 'Th')\",OTHERS,False\nmp-1202377,\"{'Zr': 4.0, 'Te': 8.0, 'Br': 4.0, 'O': 22.0}\",\"('Br', 'O', 'Te', 'Zr')\",OTHERS,False\nmp-1094407,\"{'Y': 6.0, 'Mg': 6.0}\",\"('Mg', 'Y')\",OTHERS,False\nmp-762515,\"{'Li': 12.0, 'Cu': 18.0, 'C': 18.0, 'O': 54.0}\",\"('C', 'Cu', 'Li', 'O')\",OTHERS,False\nmp-1196505,\"{'Na': 18.0, 'Fe': 2.0, 'Mo': 12.0, 'O': 48.0}\",\"('Fe', 'Mo', 'Na', 'O')\",OTHERS,False\nmp-866517,\"{'Ce': 6.0, 'Mg': 2.0, 'Al': 2.0, 'S': 14.0}\",\"('Al', 'Ce', 'Mg', 'S')\",OTHERS,False\nAg 42 In 45 Ca 13,\"{'Ag': 42.0, 'In': 45.0, 'Ca': 13.0}\",\"('Ag', 'Ca', 'In')\",AC,False\nmp-13573,\"{'Si': 7.0, 'Sc': 5.0, 'Pt': 9.0}\",\"('Pt', 'Sc', 'Si')\",OTHERS,False\nmp-1222550,\"{'Mg': 4.0, 'Fe': 14.0, 'O': 24.0}\",\"('Fe', 'Mg', 'O')\",OTHERS,False\nmp-1227581,\"{'Cd': 2.0, 'Cu': 10.0, 'Sb': 4.0, 'S': 13.0}\",\"('Cd', 'Cu', 'S', 'Sb')\",OTHERS,False\nmp-1009652,\"{'Rh': 1.0, 'C': 1.0}\",\"('C', 'Rh')\",OTHERS,False\nmp-1180318,\"{'Na': 8.0, 'P': 8.0, 'O': 28.0}\",\"('Na', 'O', 'P')\",OTHERS,False\nmp-1226733,\"{'Cd': 1.0, 'Hg': 1.0, 'Se': 2.0}\",\"('Cd', 'Hg', 'Se')\",OTHERS,False\nmp-616173,\"{'Ce': 20.0, 'Ni': 4.0, 'Ge': 8.0}\",\"('Ce', 'Ge', 'Ni')\",OTHERS,False\nmp-628,\"{'Co': 2.0, 'Zr': 4.0}\",\"('Co', 'Zr')\",OTHERS,False\nmp-1078567,\"{'Sr': 2.0, 'Bi': 4.0, 'Pd': 4.0}\",\"('Bi', 'Pd', 'Sr')\",OTHERS,False\nmp-765523,\"{'Ca': 7.0, 'Cu': 17.0, 'O': 24.0}\",\"('Ca', 'Cu', 'O')\",OTHERS,False\nmp-17561,\"{'Mn': 2.0, 'Nb': 4.0, 'Zn': 4.0, 'O': 16.0}\",\"('Mn', 'Nb', 'O', 'Zn')\",OTHERS,False\nmp-1175424,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-31249,\"{'O': 22.0, 'Te': 8.0, 'La': 4.0}\",\"('La', 'O', 'Te')\",OTHERS,False\nZn 40 Mg 39.5 Ga 25,\"{'Zn': 40.0, 'Mg': 39.5, 'Ga': 25.0}\",\"('Ga', 'Mg', 'Zn')\",QC,False\nmp-561259,\"{'Na': 12.0, 'Nb': 4.0, 'O': 4.0, 'F': 24.0}\",\"('F', 'Na', 'Nb', 'O')\",OTHERS,False\nmp-1238863,\"{'Ti': 4.0, 'Cr': 4.0, 'Ag': 4.0, 'S': 16.0}\",\"('Ag', 'Cr', 'S', 'Ti')\",OTHERS,False\nmp-1104388,\"{'Y': 9.0, 'Pd': 6.0}\",\"('Pd', 'Y')\",OTHERS,False\nAl 70.5 Mn 16.5 Pd 13,\"{'Al': 70.5, 'Mn': 16.5, 'Pd': 13.0}\",\"('Al', 'Mn', 'Pd')\",QC,False\nmp-6227,\"{'O': 14.0, 'Si': 4.0, 'Ca': 4.0, 'Zn': 2.0}\",\"('Ca', 'O', 'Si', 'Zn')\",OTHERS,False\nmp-979986,\"{'Yb': 2.0, 'Ba': 1.0, 'Ca': 1.0}\",\"('Ba', 'Ca', 'Yb')\",OTHERS,False\nmp-1215708,\"{'Y': 1.0, 'U': 1.0, 'Sb': 2.0}\",\"('Sb', 'U', 'Y')\",OTHERS,False\nmp-1182880,\"{'Al': 1.0, 'Cu': 3.0, 'Hg': 1.0, 'Se': 4.0}\",\"('Al', 'Cu', 'Hg', 'Se')\",OTHERS,False\nmp-1224397,\"{'K': 3.0, 'Ge': 7.0, 'H': 1.0, 'O': 20.0}\",\"('Ge', 'H', 'K', 'O')\",OTHERS,False\nmp-1215485,\"{'Zn': 1.0, 'In': 2.0, 'Ge': 1.0, 'As': 4.0}\",\"('As', 'Ge', 'In', 'Zn')\",OTHERS,False\nmp-1183620,\"{'Ca': 1.0, 'Eu': 3.0}\",\"('Ca', 'Eu')\",OTHERS,False\nmp-1177363,\"{'Li': 4.0, 'Mn': 3.0, 'Sn': 3.0, 'Sb': 2.0, 'O': 16.0}\",\"('Li', 'Mn', 'O', 'Sb', 'Sn')\",OTHERS,False\nmp-1214367,\"{'Ba': 2.0, 'Tb': 2.0, 'Fe': 8.0, 'O': 14.0}\",\"('Ba', 'Fe', 'O', 'Tb')\",OTHERS,False\nmp-1187718,\"{'Y': 2.0, 'Cu': 1.0, 'Os': 1.0}\",\"('Cu', 'Os', 'Y')\",OTHERS,False\nmp-675430,\"{'Sm': 8.0, 'Ag': 4.0, 'Se': 14.0}\",\"('Ag', 'Se', 'Sm')\",OTHERS,False\nmp-1112048,\"{'K': 2.0, 'Ce': 1.0, 'Cu': 1.0, 'I': 6.0}\",\"('Ce', 'Cu', 'I', 'K')\",OTHERS,False\nmp-849291,\"{'Li': 20.0, 'Fe': 12.0, 'F': 56.0}\",\"('F', 'Fe', 'Li')\",OTHERS,False\nmp-20478,\"{'Si': 2.0, 'Ti': 2.0, 'Lu': 2.0}\",\"('Lu', 'Si', 'Ti')\",OTHERS,False\nmp-1197173,\"{'Pr': 12.0, 'Fe': 26.0, 'Sn': 2.0}\",\"('Fe', 'Pr', 'Sn')\",OTHERS,False\nmp-21099,\"{'O': 8.0, 'S': 2.0, 'K': 2.0, 'Pb': 1.0}\",\"('K', 'O', 'Pb', 'S')\",OTHERS,False\nmp-22433,\"{'Si': 4.0, 'Ir': 4.0, 'Np': 2.0}\",\"('Ir', 'Np', 'Si')\",OTHERS,False\nmp-5517,\"{'P': 2.0, 'Ni': 2.0, 'Tb': 1.0}\",\"('Ni', 'P', 'Tb')\",OTHERS,False\nmp-864931,\"{'Mg': 4.0, 'Co': 8.0}\",\"('Co', 'Mg')\",OTHERS,False\nmp-776654,\"{'Li': 7.0, 'V': 5.0, 'O': 12.0}\",\"('Li', 'O', 'V')\",OTHERS,False\nmp-1190013,\"{'Ti': 8.0, 'Ni': 8.0}\",\"('Ni', 'Ti')\",OTHERS,False\nmp-1176150,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1225792,\"{'Er': 5.0, 'P': 12.0, 'Ir': 19.0}\",\"('Er', 'Ir', 'P')\",OTHERS,False\nmp-1192413,\"{'Cs': 2.0, 'In': 2.0, 'Te': 6.0, 'O': 16.0}\",\"('Cs', 'In', 'O', 'Te')\",OTHERS,False\nmp-780338,\"{'Li': 8.0, 'Cr': 8.0, 'S': 16.0, 'O': 64.0}\",\"('Cr', 'Li', 'O', 'S')\",OTHERS,False\nmp-1104185,\"{'Ti': 1.0, 'Tl': 1.0, 'V': 4.0, 'S': 8.0}\",\"('S', 'Ti', 'Tl', 'V')\",OTHERS,False\nmp-1026910,\"{'Mo': 2.0, 'W': 2.0, 'Se': 4.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-744711,\"{'Zn': 2.0, 'Cr': 2.0, 'H': 56.0, 'C': 12.0, 'N': 24.0, 'Cl': 10.0, 'O': 16.0}\",\"('C', 'Cl', 'Cr', 'H', 'N', 'O', 'Zn')\",OTHERS,False\nmp-1020645,\"{'Na': 8.0, 'N': 1.0, 'O': 3.0}\",\"('N', 'Na', 'O')\",OTHERS,False\nmp-1079819,\"{'Lu': 3.0, 'Al': 3.0, 'Ni': 3.0}\",\"('Al', 'Lu', 'Ni')\",OTHERS,False\nmp-1223512,\"{'K': 1.0, 'Mg': 3.0, 'Al': 1.0, 'Si': 3.0, 'O': 10.0, 'F': 2.0}\",\"('Al', 'F', 'K', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-26785,\"{'Li': 4.0, 'O': 28.0, 'P': 8.0, 'Ni': 4.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-767961,\"{'Ti': 3.0, 'Mn': 2.0, 'P': 6.0, 'W': 1.0, 'O': 24.0}\",\"('Mn', 'O', 'P', 'Ti', 'W')\",OTHERS,False\nmp-752400,\"{'Ce': 4.0, 'Sm': 4.0, 'O': 14.0}\",\"('Ce', 'O', 'Sm')\",OTHERS,False\nmp-611448,{'C': 12.0},\"('C',)\",OTHERS,False\nmp-1186527,\"{'Pm': 6.0, 'Nd': 2.0}\",\"('Nd', 'Pm')\",OTHERS,False\nmp-12532,\"{'P': 1.0, 'S': 4.0, 'K': 1.0, 'Ag': 2.0}\",\"('Ag', 'K', 'P', 'S')\",OTHERS,False\nMn 82 Si 15 Al 3,\"{'Mn': 82.0, 'Si': 15.0, 'Al': 3.0}\",\"('Al', 'Mn', 'Si')\",QC,False\nmp-1225978,\"{'La': 2.0, 'In': 3.0, 'Sn': 3.0}\",\"('In', 'La', 'Sn')\",OTHERS,False\nmp-10837,\"{'F': 28.0, 'Na': 8.0, 'Mg': 4.0, 'In': 4.0}\",\"('F', 'In', 'Mg', 'Na')\",OTHERS,False\nmp-1211485,\"{'La': 12.0, 'Au': 4.0, 'I': 12.0}\",\"('Au', 'I', 'La')\",OTHERS,False\nmp-30051,\"{'C': 4.0, 'Si': 12.0, 'Cl': 28.0}\",\"('C', 'Cl', 'Si')\",OTHERS,False\nmp-767722,\"{'V': 1.0, 'Fe': 1.0, 'P': 4.0, 'O': 14.0}\",\"('Fe', 'O', 'P', 'V')\",OTHERS,False\nAl 65 Cu 20 Fe 15,\"{'Al': 65.0, 'Cu': 20.0, 'Fe': 15.0}\",\"('Al', 'Cu', 'Fe')\",QC,False\nmp-1076424,\"{'Sr': 6.0, 'Ca': 2.0, 'Fe': 8.0, 'O': 24.0}\",\"('Ca', 'Fe', 'O', 'Sr')\",OTHERS,False\nmp-1222533,\"{'Li': 4.0, 'Si': 1.0, 'Ge': 3.0, 'O': 10.0}\",\"('Ge', 'Li', 'O', 'Si')\",OTHERS,False\nmp-861664,\"{'Li': 1.0, 'Tm': 1.0, 'Au': 2.0}\",\"('Au', 'Li', 'Tm')\",OTHERS,False\nmp-1201999,\"{'Y': 40.0, 'Ir': 8.0, 'Br': 72.0}\",\"('Br', 'Ir', 'Y')\",OTHERS,False\nmp-699473,\"{'Li': 4.0, 'B': 24.0, 'H': 52.0, 'O': 14.0}\",\"('B', 'H', 'Li', 'O')\",OTHERS,False\nmp-1147612,\"{'Mg': 1.0, 'Cu': 3.0, 'N': 3.0}\",\"('Cu', 'Mg', 'N')\",OTHERS,False\nmp-778259,\"{'Pr': 8.0, 'Hf': 8.0, 'O': 32.0}\",\"('Hf', 'O', 'Pr')\",OTHERS,False\nmvc-8266,\"{'Ca': 4.0, 'Fe': 4.0, 'Ni': 4.0, 'P': 8.0, 'O': 36.0}\",\"('Ca', 'Fe', 'Ni', 'O', 'P')\",OTHERS,False\nmp-1220987,\"{'Na': 1.0, 'Li': 1.0, 'Mn': 1.0, 'S': 2.0}\",\"('Li', 'Mn', 'Na', 'S')\",OTHERS,False\nmp-1218309,\"{'Sr': 1.0, 'La': 2.0, 'Ce': 1.0, 'Mn': 2.0, 'Cr': 2.0, 'O': 12.0}\",\"('Ce', 'Cr', 'La', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-686311,\"{'Na': 7.0, 'Bi': 3.0, 'P': 12.0, 'Pb': 10.0, 'O': 48.0}\",\"('Bi', 'Na', 'O', 'P', 'Pb')\",OTHERS,False\nmvc-13997,\"{'Al': 2.0, 'V': 2.0, 'O': 6.0}\",\"('Al', 'O', 'V')\",OTHERS,False\nmp-1203517,\"{'Cs': 4.0, 'H': 24.0, 'C': 8.0, 'S': 8.0, 'N': 4.0, 'O': 16.0}\",\"('C', 'Cs', 'H', 'N', 'O', 'S')\",OTHERS,False\nmp-1180715,\"{'Na': 12.0, 'As': 4.0, 'H': 64.0, 'S': 16.0, 'O': 32.0}\",\"('As', 'H', 'Na', 'O', 'S')\",OTHERS,False\nmp-1205702,\"{'Co': 1.0, 'Re': 1.0, 'Pb': 2.0, 'O': 6.0}\",\"('Co', 'O', 'Pb', 'Re')\",OTHERS,False\nmp-1197745,\"{'Cs': 2.0, 'Ti': 4.0, 'P': 6.0, 'O': 20.0, 'F': 8.0}\",\"('Cs', 'F', 'O', 'P', 'Ti')\",OTHERS,False\nmp-600190,\"{'La': 4.0, 'H': 96.0, 'C': 12.0, 'N': 12.0, 'Cl': 24.0, 'O': 12.0}\",\"('C', 'Cl', 'H', 'La', 'N', 'O')\",OTHERS,False\nmp-978513,\"{'Sm': 1.0, 'Er': 1.0, 'Mg': 2.0}\",\"('Er', 'Mg', 'Sm')\",OTHERS,False\nmp-771347,\"{'Li': 11.0, 'Ti': 4.0, 'Fe': 9.0, 'O': 32.0}\",\"('Fe', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-669445,\"{'Cu': 2.0, 'Bi': 8.0, 'Pb': 6.0, 'S': 19.0}\",\"('Bi', 'Cu', 'Pb', 'S')\",OTHERS,False\nmp-1174194,\"{'Li': 6.0, 'Mn': 2.0, 'Co': 2.0, 'O': 10.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-541913,\"{'Hg': 1.0, 'Fe': 1.0, 'S': 4.0, 'C': 4.0, 'N': 4.0}\",\"('C', 'Fe', 'Hg', 'N', 'S')\",OTHERS,False\nmp-553919,\"{'Ag': 6.0, 'Mo': 2.0, 'Cl': 1.0, 'O': 7.0, 'F': 3.0}\",\"('Ag', 'Cl', 'F', 'Mo', 'O')\",OTHERS,False\nmp-20922,\"{'C': 1.0, 'Ga': 1.0, 'Dy': 3.0}\",\"('C', 'Dy', 'Ga')\",OTHERS,False\nmp-546079,\"{'C': 2.0, 'O': 6.0, 'Mg': 2.0}\",\"('C', 'Mg', 'O')\",OTHERS,False\nmp-1226134,\"{'Cs': 4.0, 'Tl': 1.0, 'Sb': 1.0, 'Cl': 12.0}\",\"('Cl', 'Cs', 'Sb', 'Tl')\",OTHERS,False\nmp-758100,\"{'Li': 4.0, 'Cu': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-1080525,\"{'Fe': 4.0, 'B': 4.0}\",\"('B', 'Fe')\",OTHERS,False\nmp-1216180,\"{'Yb': 29.0, 'Se': 32.0}\",\"('Se', 'Yb')\",OTHERS,False\nmp-1183876,\"{'Cd': 2.0, 'Hg': 4.0, 'Se': 2.0, 'O': 12.0}\",\"('Cd', 'Hg', 'O', 'Se')\",OTHERS,False\nmp-1186897,\"{'Rb': 1.0, 'Rh': 1.0, 'O': 3.0}\",\"('O', 'Rb', 'Rh')\",OTHERS,False\nmp-1189166,\"{'Tm': 4.0, 'Ge': 4.0, 'Pd': 8.0}\",\"('Ge', 'Pd', 'Tm')\",OTHERS,False\nmp-1211495,\"{'La': 12.0, 'Pt': 4.0, 'I': 12.0}\",\"('I', 'La', 'Pt')\",OTHERS,False\nmp-866282,\"{'Ca': 1.0, 'Th': 1.0, 'Rh': 2.0}\",\"('Ca', 'Rh', 'Th')\",OTHERS,False\nmp-1105483,\"{'Ce': 6.0, 'Sn': 12.0, 'Ru': 2.0}\",\"('Ce', 'Ru', 'Sn')\",OTHERS,False\nmp-1210221,\"{'Na': 4.0, 'W': 4.0, 'O': 15.0}\",\"('Na', 'O', 'W')\",OTHERS,False\nmp-1095484,\"{'Pr': 2.0, 'Al': 8.0, 'Ni': 2.0}\",\"('Al', 'Ni', 'Pr')\",OTHERS,False\nmp-1215302,\"{'Zr': 4.0, 'Al': 4.0, 'Pd': 4.0}\",\"('Al', 'Pd', 'Zr')\",OTHERS,False\nmp-8620,\"{'Si': 10.0, 'Rh': 6.0, 'La': 4.0}\",\"('La', 'Rh', 'Si')\",OTHERS,False\nmp-13494,\"{'Fe': 29.0, 'Nd': 3.0}\",\"('Fe', 'Nd')\",OTHERS,False\nmp-647075,\"{'Zn': 64.0, 'S': 64.0}\",\"('S', 'Zn')\",OTHERS,False\nmp-1112784,\"{'Cs': 2.0, 'K': 1.0, 'Ce': 1.0, 'I': 6.0}\",\"('Ce', 'Cs', 'I', 'K')\",OTHERS,False\nTa 1.6 Te 1,\"{'Ta': 1.6, 'Te': 1.0}\",\"('Ta', 'Te')\",QC,False\nmp-1219622,\"{'Rb': 2.0, 'Li': 2.0, 'Mg': 2.0, 'B': 8.0, 'H': 32.0}\",\"('B', 'H', 'Li', 'Mg', 'Rb')\",OTHERS,False\nmp-1095573,\"{'Nb': 4.0, 'Co': 4.0, 'Si': 4.0}\",\"('Co', 'Nb', 'Si')\",OTHERS,False\nmp-763324,\"{'Li': 2.0, 'Mn': 2.0, 'P': 4.0, 'H': 3.0, 'O': 16.0}\",\"('H', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-697408,\"{'H': 20.0, 'C': 4.0, 'N': 4.0, 'O': 10.0}\",\"('C', 'H', 'N', 'O')\",OTHERS,False\nmp-1222651,\"{'Li': 2.0, 'Mg': 1.0, 'Ge': 1.0}\",\"('Ge', 'Li', 'Mg')\",OTHERS,False\nmp-11713,\"{'C': 5.0, 'Si': 5.0}\",\"('C', 'Si')\",OTHERS,False\nmp-1184847,\"{'Ho': 1.0, 'Lu': 1.0, 'Ir': 2.0}\",\"('Ho', 'Ir', 'Lu')\",OTHERS,False\nmp-1020627,\"{'Sr': 4.0, 'Li': 4.0, 'Al': 12.0, 'N': 16.0}\",\"('Al', 'Li', 'N', 'Sr')\",OTHERS,False\nmp-1210310,\"{'Rb': 4.0, 'Pr': 4.0, 'S': 8.0, 'O': 48.0}\",\"('O', 'Pr', 'Rb', 'S')\",OTHERS,False\nmp-1220011,\"{'P': 6.0, 'N': 6.0, 'O': 6.0}\",\"('N', 'O', 'P')\",OTHERS,False\nmp-755266,\"{'Li': 4.0, 'Ti': 3.0, 'O': 8.0}\",\"('Li', 'O', 'Ti')\",OTHERS,False\nmp-1247036,\"{'Mg': 2.0, 'Ti': 3.0, 'Cr': 1.0, 'S': 8.0}\",\"('Cr', 'Mg', 'S', 'Ti')\",OTHERS,False\nmp-1246298,\"{'Sr': 8.0, 'Ru': 2.0, 'N': 8.0}\",\"('N', 'Ru', 'Sr')\",OTHERS,False\nmp-7007,\"{'Se': 2.0, 'Nb': 1.0}\",\"('Nb', 'Se')\",OTHERS,False\nmp-1215917,\"{'Zr': 16.0, 'Ni': 4.0, 'Mo': 4.0}\",\"('Mo', 'Ni', 'Zr')\",OTHERS,False\nmp-1175812,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-30971,\"{'Cl': 4.0, 'Cu': 4.0, 'Te': 8.0}\",\"('Cl', 'Cu', 'Te')\",OTHERS,False\nmp-1221774,\"{'Mn': 2.0, 'Cr': 2.0, 'Te': 4.0}\",\"('Cr', 'Mn', 'Te')\",OTHERS,False\nmp-1204384,\"{'Ag': 4.0, 'H': 40.0, 'S': 32.0, 'N': 32.0, 'Cl': 4.0, 'O': 24.0}\",\"('Ag', 'Cl', 'H', 'N', 'O', 'S')\",OTHERS,False\nmp-866141,\"{'Ti': 1.0, 'Fe': 2.0, 'Si': 1.0}\",\"('Fe', 'Si', 'Ti')\",OTHERS,False\nmp-1238823,\"{'Cs': 4.0, 'Cr': 8.0, 'S': 16.0}\",\"('Cr', 'Cs', 'S')\",OTHERS,False\nmp-1096050,\"{'Mg': 1.0, 'Al': 1.0, 'Pt': 2.0}\",\"('Al', 'Mg', 'Pt')\",OTHERS,False\nmp-27211,\"{'F': 13.0, 'K': 1.0, 'Sb': 4.0}\",\"('F', 'K', 'Sb')\",OTHERS,False\nmp-30542,\"{'P': 32.0, 'Ni': 8.0, 'W': 2.0}\",\"('Ni', 'P', 'W')\",OTHERS,False\nmp-27734,\"{'Cr': 6.0, 'Br': 18.0}\",\"('Br', 'Cr')\",OTHERS,False\nmp-1196152,\"{'Y': 4.0, 'B': 12.0, 'H': 96.0, 'N': 16.0}\",\"('B', 'H', 'N', 'Y')\",OTHERS,False\nmp-768492,\"{'Li': 8.0, 'Ti': 1.0, 'Mn': 3.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'Mn', 'O', 'P', 'Ti')\",OTHERS,False\nCd 65 Mg 20 Tm 15,\"{'Cd': 65.0, 'Mg': 20.0, 'Tm': 15.0}\",\"('Cd', 'Mg', 'Tm')\",QC,False\nmp-1214538,\"{'Ba': 8.0, 'Na': 8.0, 'Ga': 8.0, 'F': 48.0}\",\"('Ba', 'F', 'Ga', 'Na')\",OTHERS,False\nmvc-932,\"{'Ba': 1.0, 'Zn': 1.0, 'Cr': 4.0, 'O': 8.0}\",\"('Ba', 'Cr', 'O', 'Zn')\",OTHERS,False\nmp-1013713,\"{'Ba': 3.0, 'Bi': 1.0, 'P': 1.0}\",\"('Ba', 'Bi', 'P')\",OTHERS,False\nmp-1211986,\"{'K': 8.0, 'Hg': 12.0, 'S': 16.0}\",\"('Hg', 'K', 'S')\",OTHERS,False\nmp-1270,\"{'S': 12.0, 'Dy': 8.0}\",\"('Dy', 'S')\",OTHERS,False\nmp-570598,\"{'Sr': 2.0, 'Si': 14.0, 'N': 20.0}\",\"('N', 'Si', 'Sr')\",OTHERS,False\nmp-8258,\"{'Al': 8.0, 'S': 14.0, 'Ba': 2.0}\",\"('Al', 'Ba', 'S')\",OTHERS,False\nmp-1017631,\"{'Sc': 1.0, 'B': 1.0, 'Pd': 3.0}\",\"('B', 'Pd', 'Sc')\",OTHERS,False\nmp-1202872,\"{'K': 8.0, 'B': 8.0, 'C': 8.0, 'N': 8.0, 'F': 24.0}\",\"('B', 'C', 'F', 'K', 'N')\",OTHERS,False\nCd 84 Yb 16,\"{'Cd': 84.0, 'Yb': 16.0}\",\"('Cd', 'Yb')\",QC,False\nmp-1174179,\"{'Li': 8.0, 'Co': 6.0, 'O': 14.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-24473,\"{'H': 16.0, 'Be': 4.0, 'N': 4.0, 'O': 16.0, 'P': 4.0}\",\"('Be', 'H', 'N', 'O', 'P')\",OTHERS,False\nmvc-4358,\"{'Mg': 4.0, 'Ta': 2.0, 'Cr': 2.0, 'O': 12.0}\",\"('Cr', 'Mg', 'O', 'Ta')\",OTHERS,False\nmp-22916,\"{'Na': 1.0, 'Br': 1.0}\",\"('Br', 'Na')\",OTHERS,False\nmp-760269,\"{'Li': 10.0, 'Fe': 4.0, 'B': 4.0, 'O': 16.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-582819,{'Pu': 8.0},\"('Pu',)\",OTHERS,False\nmp-1193528,\"{'Ca': 2.0, 'I': 4.0, 'O': 24.0}\",\"('Ca', 'I', 'O')\",OTHERS,False\nmp-557821,\"{'Ca': 6.0, 'Cr': 6.0, 'P': 8.0, 'O': 32.0}\",\"('Ca', 'Cr', 'O', 'P')\",OTHERS,False\nmp-1188244,\"{'Lu': 8.0, 'Re': 4.0, 'C': 8.0}\",\"('C', 'Lu', 'Re')\",OTHERS,False\nmp-720765,\"{'H': 12.0, 'S': 4.0, 'N': 4.0, 'O': 16.0}\",\"('H', 'N', 'O', 'S')\",OTHERS,False\nmp-1211808,\"{'K': 6.0, 'Na': 14.0, 'Tl': 18.0, 'Hg': 1.0}\",\"('Hg', 'K', 'Na', 'Tl')\",OTHERS,False\nmp-1094575,\"{'Li': 6.0, 'Mg': 6.0}\",\"('Li', 'Mg')\",OTHERS,False\nmp-1238861,\"{'Ti': 2.0, 'Cr': 2.0, 'Cu': 2.0, 'S': 8.0}\",\"('Cr', 'Cu', 'S', 'Ti')\",OTHERS,False\nmp-1195212,\"{'Rb': 16.0, 'Si': 40.0, 'Ni': 8.0, 'O': 96.0}\",\"('Ni', 'O', 'Rb', 'Si')\",OTHERS,False\nmp-1211727,\"{'K': 2.0, 'Rb': 1.0, 'Yb': 1.0, 'V': 2.0, 'O': 8.0}\",\"('K', 'O', 'Rb', 'V', 'Yb')\",OTHERS,False\nmp-18150,\"{'O': 24.0, 'P': 6.0, 'K': 9.0, 'Lu': 3.0}\",\"('K', 'Lu', 'O', 'P')\",OTHERS,False\nmp-758196,\"{'Mg': 9.0, 'As': 6.0, 'O': 24.0}\",\"('As', 'Mg', 'O')\",OTHERS,False\nmp-1208360,\"{'Tb': 2.0, 'Tl': 2.0, 'W': 4.0, 'O': 16.0}\",\"('O', 'Tb', 'Tl', 'W')\",OTHERS,False\nmp-829,\"{'Al': 1.0, 'Pd': 1.0}\",\"('Al', 'Pd')\",OTHERS,False\nmp-1185776,\"{'Mg': 2.0, 'Ir': 2.0, 'O': 5.0}\",\"('Ir', 'Mg', 'O')\",OTHERS,False\nmp-1106254,\"{'La': 2.0, 'Lu': 6.0, 'S': 12.0}\",\"('La', 'Lu', 'S')\",OTHERS,False\nmp-510713,\"{'O': 16.0, 'K': 6.0, 'Mn': 4.0}\",\"('K', 'Mn', 'O')\",OTHERS,False\nmvc-3064,\"{'Ba': 2.0, 'Tl': 2.0, 'Zn': 2.0, 'Fe': 3.0, 'O': 10.0}\",\"('Ba', 'Fe', 'O', 'Tl', 'Zn')\",OTHERS,False\nmp-1208140,\"{'Tl': 6.0, 'Ge': 2.0, 'F': 14.0}\",\"('F', 'Ge', 'Tl')\",OTHERS,False\nmp-23149,\"{'K': 8.0, 'Al': 6.0, 'Si': 6.0, 'Cl': 2.0, 'O': 24.0}\",\"('Al', 'Cl', 'K', 'O', 'Si')\",OTHERS,False\nmp-1079801,\"{'Nb': 1.0, 'Ni': 3.0, 'H': 2.0, 'C': 2.0}\",\"('C', 'H', 'Nb', 'Ni')\",OTHERS,False\nmp-1210861,\"{'Na': 8.0, 'Si': 24.0, 'O': 48.0}\",\"('Na', 'O', 'Si')\",OTHERS,False\nmp-756093,\"{'Na': 12.0, 'Nb': 1.0, 'O': 7.0}\",\"('Na', 'Nb', 'O')\",OTHERS,False\nmp-1105556,\"{'La': 1.0, 'As': 12.0, 'Os': 4.0}\",\"('As', 'La', 'Os')\",OTHERS,False\nmp-9266,\"{'Na': 12.0, 'S': 8.0, 'Fe': 2.0}\",\"('Fe', 'Na', 'S')\",OTHERS,False\nmp-754157,\"{'Al': 2.0, 'In': 2.0, 'O': 6.0}\",\"('Al', 'In', 'O')\",OTHERS,False\nmp-1205879,\"{'Pr': 3.0, 'Cd': 3.0, 'Au': 3.0}\",\"('Au', 'Cd', 'Pr')\",OTHERS,False\nmp-1175375,\"{'Li': 7.0, 'Mn': 5.0, 'O': 12.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-769567,\"{'Li': 10.0, 'Ti': 11.0, 'Cr': 9.0, 'O': 40.0}\",\"('Cr', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-3312,\"{'Se': 8.0, 'Sb': 4.0, 'Cs': 2.0}\",\"('Cs', 'Sb', 'Se')\",OTHERS,False\nmp-554736,\"{'Ca': 2.0, 'B': 4.0, 'H': 24.0, 'O': 20.0}\",\"('B', 'Ca', 'H', 'O')\",OTHERS,False\nmp-1099277,\"{'Li': 1.0, 'Mg': 6.0, 'Fe': 1.0}\",\"('Fe', 'Li', 'Mg')\",OTHERS,False\nmp-850900,\"{'Li': 6.0, 'V': 4.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1796,\"{'Te': 6.0, 'Tl': 10.0}\",\"('Te', 'Tl')\",OTHERS,False\nmp-568709,\"{'Dy': 8.0, 'Ni': 28.0, 'Sn': 12.0}\",\"('Dy', 'Ni', 'Sn')\",OTHERS,False\nmp-1097565,\"{'Hf': 2.0, 'Re': 1.0, 'Ru': 1.0}\",\"('Hf', 'Re', 'Ru')\",OTHERS,False\nmp-1216037,\"{'Zn': 3.0, 'Rh': 2.0, 'C': 12.0, 'N': 12.0}\",\"('C', 'N', 'Rh', 'Zn')\",OTHERS,False\nmp-1182211,\"{'Ba': 2.0, 'Yb': 2.0, 'Co': 8.0, 'O': 14.0}\",\"('Ba', 'Co', 'O', 'Yb')\",OTHERS,False\nmp-10992,\"{'Cu': 2.0, 'As': 4.0, 'Dy': 2.0}\",\"('As', 'Cu', 'Dy')\",OTHERS,False\nmp-532650,\"{'Ca': 8.0, 'Mn': 6.0, 'B': 6.0, 'C': 2.0, 'O': 30.0}\",\"('B', 'C', 'Ca', 'Mn', 'O')\",OTHERS,False\nmp-31508,\"{'K': 4.0, 'Br': 20.0, 'Pb': 8.0}\",\"('Br', 'K', 'Pb')\",OTHERS,False\nmp-554370,\"{'Ti': 2.0, 'S': 2.0, 'Cl': 12.0, 'O': 2.0}\",\"('Cl', 'O', 'S', 'Ti')\",OTHERS,False\nmp-1228875,\"{'Cs': 2.0, 'Ni': 2.0, 'P': 2.0}\",\"('Cs', 'Ni', 'P')\",OTHERS,False\nmp-1147714,\"{'P': 8.0, 'S': 12.0}\",\"('P', 'S')\",OTHERS,False\nmp-568135,\"{'Cs': 8.0, 'Sc': 12.0, 'C': 2.0, 'Cl': 26.0}\",\"('C', 'Cl', 'Cs', 'Sc')\",OTHERS,False\nmp-762236,\"{'Al': 6.0, 'Cr': 2.0, 'O': 12.0}\",\"('Al', 'Cr', 'O')\",OTHERS,False\nmp-1078913,\"{'Rb': 3.0, 'Ce': 1.0, 'F': 6.0}\",\"('Ce', 'F', 'Rb')\",OTHERS,False\nmp-558346,\"{'Nd': 4.0, 'I': 12.0, 'O': 36.0}\",\"('I', 'Nd', 'O')\",OTHERS,False\nmp-761234,\"{'Li': 3.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1226091,\"{'Cr': 8.0, 'Cu': 3.0, 'Ni': 1.0, 'S': 16.0}\",\"('Cr', 'Cu', 'Ni', 'S')\",OTHERS,False\nmp-976818,\"{'Ni': 3.0, 'Au': 1.0}\",\"('Au', 'Ni')\",OTHERS,False\nmvc-3244,\"{'Mg': 2.0, 'Cr': 2.0, 'P': 2.0, 'O': 10.0}\",\"('Cr', 'Mg', 'O', 'P')\",OTHERS,False\nmp-1213043,\"{'Cs': 1.0, 'Mg': 1.0, 'As': 1.0, 'H': 6.0, 'O': 4.0}\",\"('As', 'Cs', 'H', 'Mg', 'O')\",OTHERS,False\nmp-1076751,\"{'K': 4.0, 'Na': 4.0, 'Nb': 1.0, 'W': 7.0, 'O': 24.0}\",\"('K', 'Na', 'Nb', 'O', 'W')\",OTHERS,False\nmp-571225,\"{'Cd': 7.0, 'I': 14.0}\",\"('Cd', 'I')\",OTHERS,False\nmp-707816,\"{'H': 10.0, 'C': 2.0, 'S': 2.0, 'N': 4.0, 'Cl': 2.0, 'O': 8.0}\",\"('C', 'Cl', 'H', 'N', 'O', 'S')\",OTHERS,False\nmp-768682,\"{'Rb': 10.0, 'Fe': 2.0, 'O': 8.0}\",\"('Fe', 'O', 'Rb')\",OTHERS,False\nmp-1205657,\"{'Gd': 4.0, 'Mg': 2.0, 'Ni': 4.0}\",\"('Gd', 'Mg', 'Ni')\",OTHERS,False\nmp-570037,\"{'Co': 6.0, 'Au': 12.0, 'C': 24.0, 'N': 24.0}\",\"('Au', 'C', 'Co', 'N')\",OTHERS,False\nmp-1178244,\"{'Fe': 18.0, 'O': 18.0, 'F': 18.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-776035,\"{'Li': 18.0, 'Cu': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-973921,\"{'Lu': 2.0, 'Ni': 12.0, 'As': 7.0}\",\"('As', 'Lu', 'Ni')\",OTHERS,False\nmp-866121,\"{'V': 3.0, 'Os': 1.0}\",\"('Os', 'V')\",OTHERS,False\nmp-1213994,\"{'Ca': 2.0, 'H': 12.0, 'Pt': 2.0, 'O': 12.0}\",\"('Ca', 'H', 'O', 'Pt')\",OTHERS,False\nmp-611378,\"{'Ba': 1.0, 'Lu': 1.0, 'Fe': 1.0, 'Cu': 1.0, 'O': 5.0}\",\"('Ba', 'Cu', 'Fe', 'Lu', 'O')\",OTHERS,False\nmp-709921,\"{'Cs': 4.0, 'Na': 8.0, 'Cu': 4.0, 'Si': 24.0, 'H': 8.0, 'O': 62.0}\",\"('Cs', 'Cu', 'H', 'Na', 'O', 'Si')\",OTHERS,False\nmp-561257,\"{'Li': 2.0, 'Pr': 4.0, 'C': 4.0, 'N': 8.0, 'F': 6.0}\",\"('C', 'F', 'Li', 'N', 'Pr')\",OTHERS,False\nmp-1186256,\"{'Nd': 2.0, 'Tl': 1.0, 'Ag': 1.0}\",\"('Ag', 'Nd', 'Tl')\",OTHERS,False\nmp-1199071,\"{'Pr': 2.0, 'Cd': 40.0, 'Pd': 4.0}\",\"('Cd', 'Pd', 'Pr')\",OTHERS,False\nmp-1209430,\"{'Pr': 3.0, 'P': 6.0, 'Pd': 20.0}\",\"('P', 'Pd', 'Pr')\",OTHERS,False\nmp-1219697,\"{'Rb': 3.0, 'Ag': 9.0, 'Sb': 4.0, 'S': 12.0}\",\"('Ag', 'Rb', 'S', 'Sb')\",OTHERS,False\nmp-755205,\"{'Zr': 8.0, 'N': 8.0, 'O': 4.0}\",\"('N', 'O', 'Zr')\",OTHERS,False\nmp-1228011,\"{'Ba': 1.0, 'La': 2.0, 'Fe': 2.0, 'O': 5.0, 'F': 4.0}\",\"('Ba', 'F', 'Fe', 'La', 'O')\",OTHERS,False\nmp-1703,\"{'Zn': 1.0, 'Yb': 1.0}\",\"('Yb', 'Zn')\",OTHERS,False\nmp-754903,\"{'Y': 2.0, 'B': 6.0, 'O': 12.0}\",\"('B', 'O', 'Y')\",OTHERS,False\nAl 73.2 Co 26.8,\"{'Al': 73.2, 'Co': 26.8}\",\"('Al', 'Co')\",AC,False\nmp-761223,\"{'Mn': 6.0, 'O': 2.0, 'F': 16.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-698125,\"{'Np': 2.0, 'H': 32.0, 'C': 8.0, 'N': 16.0, 'Cl': 2.0, 'O': 12.0}\",\"('C', 'Cl', 'H', 'N', 'Np', 'O')\",OTHERS,False\nmp-1213423,\"{'Eu': 16.0, 'As': 24.0}\",\"('As', 'Eu')\",OTHERS,False\nmp-776063,\"{'Li': 16.0, 'Mn': 8.0, 'P': 8.0, 'O': 32.0, 'F': 8.0}\",\"('F', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1182253,\"{'Ca': 2.0, 'N': 4.0, 'O': 16.0}\",\"('Ca', 'N', 'O')\",OTHERS,False\nmp-1225717,\"{'Cu': 3.0, 'N': 1.0}\",\"('Cu', 'N')\",OTHERS,False\nmp-1186395,\"{'Pa': 1.0, 'Hg': 1.0, 'O': 3.0}\",\"('Hg', 'O', 'Pa')\",OTHERS,False\nmp-1095090,\"{'Ba': 1.0, 'Mn': 2.0, 'O': 5.0}\",\"('Ba', 'Mn', 'O')\",OTHERS,False\nmp-1207267,\"{'Pr': 4.0, 'Co': 1.0, 'I': 5.0}\",\"('Co', 'I', 'Pr')\",OTHERS,False\nmp-1225403,\"{'Dy': 2.0, 'Cu': 6.0, 'Se': 6.0}\",\"('Cu', 'Dy', 'Se')\",OTHERS,False\nmp-555983,\"{'Er': 8.0, 'Si': 4.0, 'O': 20.0}\",\"('Er', 'O', 'Si')\",OTHERS,False\nmp-1215741,\"{'Zn': 2.0, 'Cr': 3.0, 'Fe': 1.0, 'O': 8.0}\",\"('Cr', 'Fe', 'O', 'Zn')\",OTHERS,False\nmvc-9983,\"{'Mg': 2.0, 'Mo': 4.0, 'O': 8.0}\",\"('Mg', 'Mo', 'O')\",OTHERS,False\nmp-21625,\"{'Co': 6.0, 'Ga': 18.0, 'Y': 4.0}\",\"('Co', 'Ga', 'Y')\",OTHERS,False\nmp-759513,\"{'Li': 14.0, 'Ni': 2.0, 'O': 8.0, 'F': 4.0}\",\"('F', 'Li', 'Ni', 'O')\",OTHERS,False\nmp-1211804,\"{'K': 6.0, 'Na': 14.0, 'Tl': 18.0, 'Cd': 1.0}\",\"('Cd', 'K', 'Na', 'Tl')\",OTHERS,False\nmp-27684,\"{'O': 6.0, 'Tl': 8.0}\",\"('O', 'Tl')\",OTHERS,False\nmp-1226714,\"{'Ce': 1.0, 'Er': 4.0, 'S': 7.0}\",\"('Ce', 'Er', 'S')\",OTHERS,False\nmp-21011,\"{'As': 6.0, 'Ce': 8.0}\",\"('As', 'Ce')\",OTHERS,False\nmp-680415,\"{'Os': 6.0, 'C': 20.0, 'Br': 4.0, 'O': 20.0}\",\"('Br', 'C', 'O', 'Os')\",OTHERS,False\nmp-759653,\"{'Li': 4.0, 'Bi': 8.0, 'P': 4.0, 'O': 24.0}\",\"('Bi', 'Li', 'O', 'P')\",OTHERS,False\nmp-768795,\"{'Li': 8.0, 'Bi': 8.0, 'B': 16.0, 'O': 40.0}\",\"('B', 'Bi', 'Li', 'O')\",OTHERS,False\nmp-780308,\"{'Li': 5.0, 'Mn': 6.0, 'B': 6.0, 'O': 18.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-12557,\"{'Sr': 2.0, 'Cu': 2.0, 'As': 2.0}\",\"('As', 'Cu', 'Sr')\",OTHERS,False\nmp-707519,\"{'Mg': 16.0, 'Si': 8.0, 'H': 1.0, 'O': 32.0}\",\"('H', 'Mg', 'O', 'Si')\",OTHERS,False\nmvc-11654,\"{'Mn': 4.0, 'Zn': 1.0, 'O': 8.0}\",\"('Mn', 'O', 'Zn')\",OTHERS,False\nmp-1096340,\"{'Na': 1.0, 'Hg': 2.0, 'Bi': 1.0}\",\"('Bi', 'Hg', 'Na')\",OTHERS,False\nmp-1207971,\"{'Tm': 2.0, 'Co': 8.0, 'Ge': 4.0}\",\"('Co', 'Ge', 'Tm')\",OTHERS,False\nmp-1179218,\"{'Sr': 2.0, 'Br': 2.0, 'O': 10.0}\",\"('Br', 'O', 'Sr')\",OTHERS,False\nmp-1212562,\"{'Na': 4.0, 'Cr': 4.0, 'H': 48.0, 'S': 8.0, 'O': 32.0}\",\"('Cr', 'H', 'Na', 'O', 'S')\",OTHERS,False\nmp-1229210,\"{'Ba': 10.0, 'Er': 5.0, 'Cu': 15.0, 'O': 34.0}\",\"('Ba', 'Cu', 'Er', 'O')\",OTHERS,False\nmp-1246127,\"{'Ca': 6.0, 'Ir': 4.0, 'N': 8.0}\",\"('Ca', 'Ir', 'N')\",OTHERS,False\nmp-850215,\"{'Li': 2.0, 'Ti': 2.0, 'Mn': 2.0, 'O': 8.0}\",\"('Li', 'Mn', 'O', 'Ti')\",OTHERS,False\nmp-1245746,\"{'Na': 1.0, 'Sb': 1.0, 'N': 2.0}\",\"('N', 'Na', 'Sb')\",OTHERS,False\nmp-20138,\"{'Pd': 3.0, 'In': 3.0, 'La': 3.0}\",\"('In', 'La', 'Pd')\",OTHERS,False\nmp-1176673,\"{'Na': 10.0, 'Ni': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Na', 'Ni', 'O', 'P')\",OTHERS,False\nmp-1201474,\"{'K': 16.0, 'S': 16.0, 'O': 64.0}\",\"('K', 'O', 'S')\",OTHERS,False\nmp-19917,\"{'Si': 2.0, 'Zr': 2.0, 'Te': 2.0}\",\"('Si', 'Te', 'Zr')\",OTHERS,False\nmp-1180621,\"{'K': 1.0, 'S': 1.0}\",\"('K', 'S')\",OTHERS,False\nmp-607371,\"{'Ru': 2.0, 'N': 4.0}\",\"('N', 'Ru')\",OTHERS,False\nmp-662599,\"{'Ag': 36.0, 'As': 12.0, 'Se': 36.0}\",\"('Ag', 'As', 'Se')\",OTHERS,False\nmp-1097065,\"{'Li': 2.0, 'Hf': 1.0, 'N': 2.0}\",\"('Hf', 'Li', 'N')\",OTHERS,False\nmp-685374,\"{'Ti': 6.0, 'Fe': 10.0, 'O': 24.0}\",\"('Fe', 'O', 'Ti')\",OTHERS,False\nmp-33255,\"{'Ni': 15.0, 'O': 16.0}\",\"('Ni', 'O')\",OTHERS,False\nmp-753451,\"{'Li': 2.0, 'Fe': 4.0, 'P': 4.0, 'H': 2.0, 'O': 16.0}\",\"('Fe', 'H', 'Li', 'O', 'P')\",OTHERS,False\nmp-974843,\"{'Rb': 3.0, 'Os': 1.0}\",\"('Os', 'Rb')\",OTHERS,False\nmp-998536,\"{'Rb': 4.0, 'Ca': 4.0, 'Br': 12.0}\",\"('Br', 'Ca', 'Rb')\",OTHERS,False\nmp-1211668,\"{'La': 2.0, 'Nb': 16.0, 'O': 28.0}\",\"('La', 'Nb', 'O')\",OTHERS,False\nmp-6239,\"{'O': 48.0, 'Si': 12.0, 'Mn': 12.0, 'Fe': 8.0}\",\"('Fe', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1207622,\"{'Yb': 10.0, 'Pt': 18.0}\",\"('Pt', 'Yb')\",OTHERS,False\nmp-705468,\"{'Li': 6.0, 'Mn': 6.0, 'P': 8.0, 'O': 32.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1186813,\"{'Pu': 3.0, 'Br': 1.0}\",\"('Br', 'Pu')\",OTHERS,False\nmp-1096395,\"{'Li': 1.0, 'Mg': 1.0, 'Pb': 2.0}\",\"('Li', 'Mg', 'Pb')\",OTHERS,False\nmp-1199323,\"{'Th': 4.0, 'P': 8.0, 'H': 8.0, 'C': 4.0, 'O': 32.0}\",\"('C', 'H', 'O', 'P', 'Th')\",OTHERS,False\nmp-28444,\"{'O': 16.0, 'Si': 4.0, 'Fe': 6.0}\",\"('Fe', 'O', 'Si')\",OTHERS,False\nmvc-6475,\"{'Mg': 2.0, 'Ti': 4.0, 'O': 8.0}\",\"('Mg', 'O', 'Ti')\",OTHERS,False\nmp-780146,\"{'Fe': 6.0, 'O': 4.0, 'F': 8.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-1219095,\"{'Sm': 2.0, 'Bi': 2.0, 'Ru': 4.0, 'O': 14.0}\",\"('Bi', 'O', 'Ru', 'Sm')\",OTHERS,False\nmp-1175110,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-3569,\"{'Sb': 12.0, 'Nd': 1.0, 'Os': 4.0}\",\"('Nd', 'Os', 'Sb')\",OTHERS,False\nmp-755642,\"{'Li': 6.0, 'V': 2.0, 'S': 8.0}\",\"('Li', 'S', 'V')\",OTHERS,False\nmp-1209956,\"{'Na': 1.0, 'Tb': 1.0, 'Pd': 6.0, 'O': 8.0}\",\"('Na', 'O', 'Pd', 'Tb')\",OTHERS,False\nmp-1220313,\"{'Nd': 2.0, 'Sb': 1.0, 'O': 2.0}\",\"('Nd', 'O', 'Sb')\",OTHERS,False\nmp-2501,\"{'B': 2.0, 'Mo': 4.0}\",\"('B', 'Mo')\",OTHERS,False\nmp-771834,\"{'Tb': 12.0, 'Nb': 4.0, 'O': 28.0}\",\"('Nb', 'O', 'Tb')\",OTHERS,False\nmp-669325,\"{'Te': 2.0, 'W': 2.0, 'Cl': 18.0}\",\"('Cl', 'Te', 'W')\",OTHERS,False\nmp-1079552,\"{'Tb': 2.0, 'Ga': 4.0, 'Ni': 2.0}\",\"('Ga', 'Ni', 'Tb')\",OTHERS,False\nmp-7022,\"{'V': 10.0, 'As': 6.0}\",\"('As', 'V')\",OTHERS,False\nmp-9930,\"{'P': 2.0, 'Se': 2.0, 'U': 2.0}\",\"('P', 'Se', 'U')\",OTHERS,False\nmp-12540,\"{'O': 48.0, 'P': 8.0, 'Zr': 8.0, 'Mo': 4.0}\",\"('Mo', 'O', 'P', 'Zr')\",OTHERS,False\nmp-558786,\"{'Ag': 12.0, 'Ge': 2.0, 'S': 2.0, 'O': 16.0}\",\"('Ag', 'Ge', 'O', 'S')\",OTHERS,False\nmp-25923,\"{'Li': 6.0, 'O': 32.0, 'P': 8.0, 'Mn': 9.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1074028,\"{'Mg': 18.0, 'Si': 10.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1196619,\"{'Ir': 8.0, 'Se': 36.0, 'Cl': 24.0}\",\"('Cl', 'Ir', 'Se')\",OTHERS,False\nmp-1113353,\"{'Cs': 2.0, 'Cu': 1.0, 'Sb': 1.0, 'I': 6.0}\",\"('Cs', 'Cu', 'I', 'Sb')\",OTHERS,False\nmp-8111,\"{'La': 2.0, 'O': 2.0, 'F': 2.0}\",\"('F', 'La', 'O')\",OTHERS,False\nmp-998435,\"{'Cs': 4.0, 'Sc': 4.0, 'Br': 12.0}\",\"('Br', 'Cs', 'Sc')\",OTHERS,False\nmp-504962,\"{'In': 4.0, 'Na': 12.0, 'K': 8.0, 'O': 16.0}\",\"('In', 'K', 'Na', 'O')\",OTHERS,False\nmp-769138,\"{'Dy': 12.0, 'Sb': 12.0, 'O': 42.0}\",\"('Dy', 'O', 'Sb')\",OTHERS,False\nmp-1245714,\"{'Sr': 16.0, 'C': 8.0, 'N': 20.0}\",\"('C', 'N', 'Sr')\",OTHERS,False\nmp-1019796,\"{'K': 8.0, 'P': 4.0, 'O': 14.0}\",\"('K', 'O', 'P')\",OTHERS,False\nmp-1069771,\"{'Rb': 2.0, 'Pd': 1.0, 'Se': 2.0}\",\"('Pd', 'Rb', 'Se')\",OTHERS,False\nmp-2568,\"{'Si': 2.0, 'Y': 1.0}\",\"('Si', 'Y')\",OTHERS,False\nmp-568801,\"{'Pr': 6.0, 'Cu': 2.0, 'Si': 2.0, 'Se': 14.0}\",\"('Cu', 'Pr', 'Se', 'Si')\",OTHERS,False\nmp-755361,\"{'Na': 6.0, 'V': 2.0, 'B': 2.0, 'As': 2.0, 'O': 14.0}\",\"('As', 'B', 'Na', 'O', 'V')\",OTHERS,False\nmp-36329,\"{'O': 4.0, 'U': 1.0, 'Sr': 1.0}\",\"('O', 'Sr', 'U')\",OTHERS,False\nmp-974358,\"{'Ir': 3.0, 'Ru': 1.0}\",\"('Ir', 'Ru')\",OTHERS,False\nmp-766544,\"{'Li': 4.0, 'Mn': 4.0, 'Si': 6.0, 'O': 20.0}\",\"('Li', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-26194,\"{'O': 52.0, 'P': 12.0, 'Mo': 8.0}\",\"('Mo', 'O', 'P')\",OTHERS,False\nmp-1030390,\"{'Te': 2.0, 'Mo': 2.0, 'W': 2.0, 'Se': 2.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-1193254,\"{'Sr': 4.0, 'C': 8.0, 'O': 16.0}\",\"('C', 'O', 'Sr')\",OTHERS,False\nmp-556213,\"{'K': 16.0, 'Mn': 16.0, 'V': 16.0, 'O': 64.0}\",\"('K', 'Mn', 'O', 'V')\",OTHERS,False\nmp-559,\"{'Y': 1.0, 'Pd': 3.0}\",\"('Pd', 'Y')\",OTHERS,False\nmp-867195,\"{'Sr': 1.0, 'Os': 1.0, 'O': 3.0}\",\"('O', 'Os', 'Sr')\",OTHERS,False\nmp-780722,\"{'Y': 6.0, 'N': 2.0, 'O': 5.0}\",\"('N', 'O', 'Y')\",OTHERS,False\nmvc-1326,\"{'Ba': 2.0, 'Y': 2.0, 'W': 8.0, 'O': 14.0}\",\"('Ba', 'O', 'W', 'Y')\",OTHERS,False\nmp-1093905,\"{'Li': 1.0, 'La': 2.0, 'Tl': 1.0}\",\"('La', 'Li', 'Tl')\",OTHERS,False\nmp-1692,\"{'Cu': 2.0, 'O': 2.0}\",\"('Cu', 'O')\",OTHERS,False\nmp-1211502,\"{'La': 10.0, 'B': 6.0, 'O': 26.0}\",\"('B', 'La', 'O')\",OTHERS,False\nmp-1209181,\"{'Rb': 1.0, 'N': 1.0}\",\"('N', 'Rb')\",OTHERS,False\nmp-1223843,\"{'Ho': 1.0, 'Bi': 1.0, 'Te': 3.0}\",\"('Bi', 'Ho', 'Te')\",OTHERS,False\nmp-1204607,\"{'K': 8.0, 'I': 12.0, 'N': 4.0, 'O': 48.0}\",\"('I', 'K', 'N', 'O')\",OTHERS,False\nmp-1074288,\"{'Mg': 8.0, 'Si': 6.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-9571,\"{'Ca': 1.0, 'Zn': 2.0, 'As': 2.0}\",\"('As', 'Ca', 'Zn')\",OTHERS,False\nmp-1112124,\"{'Cs': 2.0, 'Rb': 1.0, 'Pr': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cs', 'Pr', 'Rb')\",OTHERS,False\nmp-1113404,\"{'Rb': 2.0, 'In': 1.0, 'Cu': 1.0, 'Br': 6.0}\",\"('Br', 'Cu', 'In', 'Rb')\",OTHERS,False\nmp-1217818,\"{'Ta': 1.0, 'V': 1.0, 'B': 4.0}\",\"('B', 'Ta', 'V')\",OTHERS,False\nmp-1209668,\"{'Sr': 6.0, 'H': 10.0, 'Os': 4.0, 'O': 16.0}\",\"('H', 'O', 'Os', 'Sr')\",OTHERS,False\nmp-849432,\"{'Ni': 1.0, 'H': 12.0, 'S': 2.0, 'N': 2.0, 'O': 10.0}\",\"('H', 'N', 'Ni', 'O', 'S')\",OTHERS,False\nmvc-14736,\"{'Y': 4.0, 'W': 4.0, 'O': 12.0}\",\"('O', 'W', 'Y')\",OTHERS,False\nmp-1187413,\"{'Th': 2.0, 'Ag': 6.0}\",\"('Ag', 'Th')\",OTHERS,False\nmp-764769,\"{'Li': 8.0, 'V': 2.0, 'F': 14.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-973498,\"{'In': 3.0, 'Au': 1.0}\",\"('Au', 'In')\",OTHERS,False\nmp-1219700,\"{'Sc': 2.0, 'Mn': 12.0, 'Ga': 1.0, 'Ge': 11.0}\",\"('Ga', 'Ge', 'Mn', 'Sc')\",OTHERS,False\nmp-1183082,\"{'Ac': 3.0, 'Zr': 1.0}\",\"('Ac', 'Zr')\",OTHERS,False\nmp-1193798,\"{'La': 6.0, 'Fe': 2.0, 'W': 2.0, 'S': 6.0, 'O': 12.0}\",\"('Fe', 'La', 'O', 'S', 'W')\",OTHERS,False\nCd 65 Mg 20 Dy 15,\"{'Cd': 65.0, 'Mg': 20.0, 'Dy': 15.0}\",\"('Cd', 'Dy', 'Mg')\",QC,False\nmp-557531,\"{'Pb': 4.0, 'S': 2.0, 'O': 8.0, 'F': 4.0}\",\"('F', 'O', 'Pb', 'S')\",OTHERS,False\nmp-1209816,\"{'Np': 4.0, 'F': 24.0}\",\"('F', 'Np')\",OTHERS,False\nmp-7400,\"{'Na': 2.0, 'Al': 2.0, 'Ge': 2.0}\",\"('Al', 'Ge', 'Na')\",OTHERS,False\nmp-1220782,\"{'Nb': 12.0, 'Ga': 3.0, 'Ge': 1.0}\",\"('Ga', 'Ge', 'Nb')\",OTHERS,False\nmp-1021328,\"{'H': 4.0, 'C': 1.0}\",\"('C', 'H')\",OTHERS,False\nmp-20103,\"{'Tm': 3.0, 'In': 3.0, 'Pt': 3.0}\",\"('In', 'Pt', 'Tm')\",OTHERS,False\nmp-23623,\"{'B': 20.0, 'O': 36.0, 'Br': 4.0, 'Pb': 8.0}\",\"('B', 'Br', 'O', 'Pb')\",OTHERS,False\nmp-1226559,\"{'Co': 3.0, 'Ni': 1.0}\",\"('Co', 'Ni')\",OTHERS,False\nmp-1196645,\"{'Cd': 8.0, 'S': 4.0, 'O': 24.0}\",\"('Cd', 'O', 'S')\",OTHERS,False\nmp-1211992,\"{'K': 18.0, 'Nd': 2.0, 'P': 8.0, 'S': 32.0}\",\"('K', 'Nd', 'P', 'S')\",OTHERS,False\nmp-626112,\"{'H': 28.0, 'N': 4.0, 'O': 24.0}\",\"('H', 'N', 'O')\",OTHERS,False\nmp-1213882,\"{'Ce': 4.0, 'Ta': 4.0, 'O': 18.0}\",\"('Ce', 'O', 'Ta')\",OTHERS,False\nmp-1096146,\"{'Y': 2.0, 'Rh': 1.0, 'Pb': 1.0}\",\"('Pb', 'Rh', 'Y')\",OTHERS,False\nmp-1222785,\"{'La': 1.0, 'Nd': 1.0, 'Al': 4.0}\",\"('Al', 'La', 'Nd')\",OTHERS,False\nmp-557769,\"{'Ca': 8.0, 'H': 16.0, 'C': 16.0, 'O': 40.0}\",\"('C', 'Ca', 'H', 'O')\",OTHERS,False\nmp-1080594,\"{'Dy': 3.0, 'In': 3.0, 'Cu': 3.0}\",\"('Cu', 'Dy', 'In')\",OTHERS,False\nmp-567471,\"{'Hg': 8.0, 'I': 16.0}\",\"('Hg', 'I')\",OTHERS,False\nmp-716814,\"{'Fe': 12.0, 'O': 18.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-759251,\"{'Li': 4.0, 'Sb': 4.0, 'P': 16.0, 'O': 48.0}\",\"('Li', 'O', 'P', 'Sb')\",OTHERS,False\nmp-561315,\"{'Cs': 6.0, 'Al': 2.0, 'Ge': 4.0, 'O': 14.0}\",\"('Al', 'Cs', 'Ge', 'O')\",OTHERS,False\nmp-865928,\"{'Ti': 2.0, 'Tc': 1.0, 'Pt': 1.0}\",\"('Pt', 'Tc', 'Ti')\",OTHERS,False\nmp-35589,\"{'F': 7.0, 'Na': 3.0, 'U': 1.0}\",\"('F', 'Na', 'U')\",OTHERS,False\nmp-1225373,\"{'Dy': 2.0, 'P': 7.0, 'Rh': 12.0}\",\"('Dy', 'P', 'Rh')\",OTHERS,False\nmp-12022,\"{'B': 8.0, 'O': 14.0, 'Hg': 2.0}\",\"('B', 'Hg', 'O')\",OTHERS,False\nmp-27658,\"{'Li': 5.0, 'B': 4.0}\",\"('B', 'Li')\",OTHERS,False\nmp-752549,\"{'Li': 1.0, 'Ag': 1.0, 'F': 2.0}\",\"('Ag', 'F', 'Li')\",OTHERS,False\nmp-9665,\"{'B': 2.0, 'K': 6.0, 'As': 4.0}\",\"('As', 'B', 'K')\",OTHERS,False\nmp-505396,\"{'Pb': 4.0, 'Cs': 4.0, 'Na': 12.0, 'O': 16.0}\",\"('Cs', 'Na', 'O', 'Pb')\",OTHERS,False\nmp-1217591,\"{'Yb': 12.0, 'Cd': 72.0}\",\"('Cd', 'Yb')\",OTHERS,False\nmp-742801,\"{'V': 4.0, 'Cu': 2.0, 'H': 12.0, 'N': 4.0, 'O': 12.0}\",\"('Cu', 'H', 'N', 'O', 'V')\",OTHERS,False\nmp-1185360,\"{'Li': 1.0, 'Mn': 1.0, 'Ir': 2.0}\",\"('Ir', 'Li', 'Mn')\",OTHERS,False\nmp-863436,\"{'Co': 8.0, 'Te': 16.0, 'O': 40.0}\",\"('Co', 'O', 'Te')\",OTHERS,False\nmp-1094352,\"{'Sr': 8.0, 'Mg': 4.0}\",\"('Mg', 'Sr')\",OTHERS,False\nmp-757997,\"{'Li': 4.0, 'Sn': 4.0, 'P': 16.0, 'O': 48.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,False\nmp-1224936,\"{'Fe': 15.0, 'O': 16.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-7591,\"{'Ge': 1.0, 'As': 1.0}\",\"('As', 'Ge')\",OTHERS,False\nmp-557865,\"{'N': 6.0, 'O': 12.0}\",\"('N', 'O')\",OTHERS,False\nmp-651122,\"{'Mn': 12.0, 'Bi': 4.0, 'C': 60.0, 'O': 60.0}\",\"('Bi', 'C', 'Mn', 'O')\",OTHERS,False\nmp-1095889,\"{'Ta': 1.0, 'Nb': 1.0, 'Os': 2.0}\",\"('Nb', 'Os', 'Ta')\",OTHERS,False\nmp-1184534,\"{'Gd': 1.0, 'Lu': 1.0, 'Cd': 2.0}\",\"('Cd', 'Gd', 'Lu')\",OTHERS,False\nmp-1172894,\"{'Ca': 2.0, 'Ga': 4.0}\",\"('Ca', 'Ga')\",OTHERS,False\nmp-1183430,\"{'Ca': 2.0, 'Mg': 4.0}\",\"('Ca', 'Mg')\",OTHERS,False\nmp-1217436,\"{'Th': 4.0, 'U': 8.0, 'S': 20.0}\",\"('S', 'Th', 'U')\",OTHERS,False\nmp-7851,\"{'Co': 9.0, 'Ta': 3.0}\",\"('Co', 'Ta')\",OTHERS,False\nmp-768986,\"{'Li': 40.0, 'Bi': 8.0, 'O': 32.0}\",\"('Bi', 'Li', 'O')\",OTHERS,False\nmp-867551,\"{'Li': 12.0, 'Ni': 12.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-1214566,\"{'Ba': 6.0, 'Co': 2.0, 'O': 10.0}\",\"('Ba', 'Co', 'O')\",OTHERS,False\nmp-770367,\"{'Li': 2.0, 'V': 4.0, 'S': 6.0, 'O': 24.0}\",\"('Li', 'O', 'S', 'V')\",OTHERS,False\nmp-768430,\"{'Li': 24.0, 'Ti': 7.0, 'Cr': 5.0, 'O': 36.0}\",\"('Cr', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-976280,\"{'Li': 2.0, 'Br': 2.0}\",\"('Br', 'Li')\",OTHERS,False\nmp-13230,\"{'F': 12.0, 'K': 6.0, 'Y': 2.0}\",\"('F', 'K', 'Y')\",OTHERS,False\nAu 44.2 In 41.7 Ca 14.1,\"{'Au': 44.2, 'In': 41.7, 'Ca': 14.1}\",\"('Au', 'Ca', 'In')\",QC,False\nmp-27539,\"{'O': 10.0, 'La': 4.0, 'Re': 2.0}\",\"('La', 'O', 'Re')\",OTHERS,False\nmp-30011,\"{'F': 28.0, 'Kr': 4.0, 'Sb': 4.0}\",\"('F', 'Kr', 'Sb')\",OTHERS,False\nmp-1100775,\"{'Sr': 1.0, 'Eu': 1.0, 'O': 3.0}\",\"('Eu', 'O', 'Sr')\",OTHERS,False\nmp-25420,\"{'Li': 2.0, 'Ti': 2.0, 'P': 2.0, 'O': 8.0, 'F': 2.0}\",\"('F', 'Li', 'O', 'P', 'Ti')\",OTHERS,False\nmp-1095730,\"{'Tl': 2.0, 'Cd': 1.0, 'In': 1.0}\",\"('Cd', 'In', 'Tl')\",OTHERS,False\nmp-774912,\"{'Li': 4.0, 'Nb': 2.0, 'Co': 3.0, 'Sb': 3.0, 'O': 16.0}\",\"('Co', 'Li', 'Nb', 'O', 'Sb')\",OTHERS,False\nmp-1095377,\"{'Er': 4.0, 'Sn': 4.0, 'Pd': 4.0}\",\"('Er', 'Pd', 'Sn')\",OTHERS,False\nmp-510122,\"{'Ce': 2.0, 'Co': 6.0, 'Pu': 8.0}\",\"('Ce', 'Co', 'Pu')\",OTHERS,False\nmvc-4004,\"{'Al': 4.0, 'W': 4.0, 'O': 12.0}\",\"('Al', 'O', 'W')\",OTHERS,False\nmp-1218918,\"{'Sr': 10.0, 'P': 6.0, 'Br': 1.0, 'O': 24.0, 'F': 1.0}\",\"('Br', 'F', 'O', 'P', 'Sr')\",OTHERS,False\nmp-1181006,\"{'Li': 4.0, 'V': 4.0, 'Te': 4.0, 'O': 20.0}\",\"('Li', 'O', 'Te', 'V')\",OTHERS,False\nmp-28866,\"{'Cl': 12.0, 'Te': 2.0, 'Os': 1.0}\",\"('Cl', 'Os', 'Te')\",OTHERS,False\nmp-556235,\"{'Ca': 4.0, 'C': 4.0, 'O': 12.0}\",\"('C', 'Ca', 'O')\",OTHERS,False\nmp-1185435,\"{'Li': 1.0, 'Yb': 1.0, 'Au': 2.0}\",\"('Au', 'Li', 'Yb')\",OTHERS,False\nmp-1220063,\"{'Pr': 7.0, 'In': 6.0, 'Ni': 5.0, 'Ge': 3.0}\",\"('Ge', 'In', 'Ni', 'Pr')\",OTHERS,False\nmp-1228917,\"{'Al': 1.0, 'In': 1.0, 'Ga': 1.0, 'Sb': 3.0}\",\"('Al', 'Ga', 'In', 'Sb')\",OTHERS,False\nmp-1214309,\"{'C': 16.0, 'N': 16.0, 'O': 32.0}\",\"('C', 'N', 'O')\",OTHERS,False\nmp-1212306,\"{'Ho': 16.0, 'Cd': 4.0, 'Pt': 4.0}\",\"('Cd', 'Ho', 'Pt')\",OTHERS,False\nmp-1215589,\"{'Zr': 3.0, 'Al': 1.0, 'N': 4.0}\",\"('Al', 'N', 'Zr')\",OTHERS,False\nmp-30792,\"{'Na': 16.0, 'Pb': 16.0}\",\"('Na', 'Pb')\",OTHERS,False\nZn 75 Sc 15 Ag 10,\"{'Zn': 75.0, 'Sc': 15.0, 'Ag': 10.0}\",\"('Ag', 'Sc', 'Zn')\",QC,False\nmp-743872,\"{'V': 4.0, 'P': 4.0, 'H': 20.0, 'N': 4.0, 'O': 24.0}\",\"('H', 'N', 'O', 'P', 'V')\",OTHERS,False\nmp-1221881,\"{'Mn': 2.0, 'Al': 1.0, 'Cu': 3.0}\",\"('Al', 'Cu', 'Mn')\",OTHERS,False\nmp-1218661,\"{'Sr': 6.0, 'Ti': 2.0, 'Nb': 8.0, 'O': 30.0}\",\"('Nb', 'O', 'Sr', 'Ti')\",OTHERS,False\nmp-1246203,\"{'Ca': 6.0, 'Ni': 2.0, 'N': 6.0}\",\"('Ca', 'N', 'Ni')\",OTHERS,False\nmp-1209473,\"{'Rb': 2.0, 'Na': 6.0, 'Fe': 14.0, 'P': 12.0, 'O': 48.0}\",\"('Fe', 'Na', 'O', 'P', 'Rb')\",OTHERS,False\nmp-777271,\"{'Li': 9.0, 'Mn': 12.0, 'B': 12.0, 'O': 36.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1035964,\"{'Cs': 1.0, 'K': 1.0, 'Mg': 14.0, 'O': 15.0}\",\"('Cs', 'K', 'Mg', 'O')\",OTHERS,False\nmp-1101681,\"{'Nd': 2.0, 'Bi': 7.0, 'O': 14.0}\",\"('Bi', 'Nd', 'O')\",OTHERS,False\nmp-1207153,\"{'Gd': 3.0, 'Ga': 1.0, 'C': 1.0}\",\"('C', 'Ga', 'Gd')\",OTHERS,False\nmp-1210743,\"{'Li': 2.0, 'C': 1.0}\",\"('C', 'Li')\",OTHERS,False\nmp-1079313,\"{'V': 6.0, 'Ge': 2.0, 'C': 2.0}\",\"('C', 'Ge', 'V')\",OTHERS,False\nmp-1198817,\"{'Gd': 8.0, 'Si': 6.0, 'S': 24.0}\",\"('Gd', 'S', 'Si')\",OTHERS,False\nmp-1189433,\"{'La': 10.0, 'Sn': 6.0, 'C': 2.0}\",\"('C', 'La', 'Sn')\",OTHERS,False\nmp-1223414,\"{'La': 4.0, 'Te': 12.0, 'W': 2.0, 'O': 36.0}\",\"('La', 'O', 'Te', 'W')\",OTHERS,False\nmp-1080709,\"{'Nd': 4.0, 'Au': 4.0}\",\"('Au', 'Nd')\",OTHERS,False\nmp-762322,\"{'Li': 2.0, 'Ti': 4.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'O', 'P', 'Ti')\",OTHERS,False\nmp-775202,\"{'Cr': 1.0, 'Sb': 3.0, 'P': 6.0, 'O': 24.0}\",\"('Cr', 'O', 'P', 'Sb')\",OTHERS,False\nmp-1038009,\"{'Sr': 1.0, 'Ca': 1.0, 'Mg': 30.0, 'O': 32.0}\",\"('Ca', 'Mg', 'O', 'Sr')\",OTHERS,False\nmp-13065,\"{'O': 3.0, 'Ho': 2.0}\",\"('Ho', 'O')\",OTHERS,False\nmp-541487,\"{'Tl': 2.0, 'Pt': 4.0, 'Se': 6.0}\",\"('Pt', 'Se', 'Tl')\",OTHERS,False\nmp-9517,\"{'N': 4.0, 'Ti': 2.0, 'Sr': 2.0}\",\"('N', 'Sr', 'Ti')\",OTHERS,False\nmp-1188299,\"{'Tm': 4.0, 'Si': 4.0, 'Pd': 8.0}\",\"('Pd', 'Si', 'Tm')\",OTHERS,False\nmp-721365,\"{'V': 4.0, 'H': 40.0, 'S': 4.0, 'O': 40.0}\",\"('H', 'O', 'S', 'V')\",OTHERS,False\nmp-17746,\"{'Hf': 6.0, 'Ge': 6.0, 'Pd': 6.0}\",\"('Ge', 'Hf', 'Pd')\",OTHERS,False\nmvc-12592,\"{'Mg': 2.0, 'Cr': 4.0, 'O': 8.0}\",\"('Cr', 'Mg', 'O')\",OTHERS,False\nmp-1187112,\"{'Sr': 6.0, 'Dy': 2.0}\",\"('Dy', 'Sr')\",OTHERS,False\nmp-1078810,\"{'Sc': 4.0, 'Sn': 2.0, 'Au': 4.0}\",\"('Au', 'Sc', 'Sn')\",OTHERS,False\nmp-1180312,\"{'Mg': 8.0, 'O': 8.0}\",\"('Mg', 'O')\",OTHERS,False\nmp-1225422,\"{'Fe': 20.0, 'Bi': 16.0, 'O': 52.0, 'F': 4.0}\",\"('Bi', 'F', 'Fe', 'O')\",OTHERS,False\nmp-1187318,\"{'Tb': 6.0, 'Pr': 2.0}\",\"('Pr', 'Tb')\",OTHERS,False\nmp-1224843,\"{'Ga': 1.0, 'Mo': 4.0, 'Se': 4.0, 'S': 4.0}\",\"('Ga', 'Mo', 'S', 'Se')\",OTHERS,False\nmp-1216295,\"{'V': 1.0, 'Re': 11.0, 'B': 4.0}\",\"('B', 'Re', 'V')\",OTHERS,False\nmp-1217611,\"{'Ti': 2.0, 'Fe': 4.0, 'Bi': 4.0, 'Pb': 2.0, 'O': 18.0}\",\"('Bi', 'Fe', 'O', 'Pb', 'Ti')\",OTHERS,False\nmp-1186280,\"{'Nd': 3.0, 'Ti': 1.0}\",\"('Nd', 'Ti')\",OTHERS,False\nmp-1192484,\"{'Y': 20.0, 'Cd': 6.0, 'Ru': 2.0}\",\"('Cd', 'Ru', 'Y')\",OTHERS,False\nmp-1201180,\"{'Mn': 2.0, 'Fe': 4.0, 'P': 4.0, 'O': 32.0}\",\"('Fe', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1191469,\"{'Cd': 5.0, 'N': 2.0, 'O': 16.0}\",\"('Cd', 'N', 'O')\",OTHERS,False\nmp-1199635,\"{'Cu': 4.0, 'C': 32.0, 'N': 8.0, 'Cl': 16.0}\",\"('C', 'Cl', 'Cu', 'N')\",OTHERS,False\nmp-27252,\"{'Cd': 4.0, 'I': 12.0, 'Tl': 4.0}\",\"('Cd', 'I', 'Tl')\",OTHERS,False\nmp-1076508,\"{'La': 4.0, 'Cr': 4.0, 'O': 10.0}\",\"('Cr', 'La', 'O')\",OTHERS,False\nmp-17283,\"{'K': 12.0, 'Se': 16.0, 'Nb': 4.0}\",\"('K', 'Nb', 'Se')\",OTHERS,False\nmp-485,\"{'Ni': 6.0, 'Zr': 2.0}\",\"('Ni', 'Zr')\",OTHERS,False\nmvc-2260,\"{'Cr': 9.0, 'O': 13.0}\",\"('Cr', 'O')\",OTHERS,False\nmp-1029726,\"{'Li': 12.0, 'Ir': 2.0, 'N': 8.0}\",\"('Ir', 'Li', 'N')\",OTHERS,False\nmp-30403,\"{'Li': 2.0, 'Au': 1.0, 'Pb': 1.0}\",\"('Au', 'Li', 'Pb')\",OTHERS,False\nmp-1212145,\"{'K': 12.0, 'Be': 12.0, 'B': 12.0, 'P': 24.0, 'O': 100.0}\",\"('B', 'Be', 'K', 'O', 'P')\",OTHERS,False\nmp-1207603,\"{'Y': 1.0, 'Ga': 3.0, 'B': 4.0, 'O': 12.0}\",\"('B', 'Ga', 'O', 'Y')\",OTHERS,False\nmp-1113032,\"{'Cs': 2.0, 'K': 1.0, 'Ta': 1.0, 'F': 6.0}\",\"('Cs', 'F', 'K', 'Ta')\",OTHERS,False\nmvc-10179,\"{'Ca': 4.0, 'P': 8.0, 'W': 8.0, 'O': 32.0}\",\"('Ca', 'O', 'P', 'W')\",OTHERS,False\nmp-862657,\"{'Li': 1.0, 'Cu': 1.0, 'Pd': 2.0}\",\"('Cu', 'Li', 'Pd')\",OTHERS,False\nmp-1078573,\"{'Ge': 2.0, 'H': 8.0}\",\"('Ge', 'H')\",OTHERS,False\nmvc-10236,\"{'Mg': 6.0, 'Ni': 12.0, 'O': 24.0}\",\"('Mg', 'Ni', 'O')\",OTHERS,False\nmp-1201338,\"{'V': 12.0, 'O': 26.0}\",\"('O', 'V')\",OTHERS,False\nmp-1196235,\"{'Sm': 8.0, 'U': 4.0, 'Se': 20.0}\",\"('Se', 'Sm', 'U')\",OTHERS,False\nmp-1247301,\"{'Co': 3.0, 'C': 6.0, 'N': 9.0}\",\"('C', 'Co', 'N')\",OTHERS,False\nmp-770332,\"{'Na': 2.0, 'Bi': 2.0, 'C': 2.0, 'S': 2.0, 'O': 14.0}\",\"('Bi', 'C', 'Na', 'O', 'S')\",OTHERS,False\nmp-31080,\"{'Al': 2.0, 'Si': 2.0, 'Y': 2.0}\",\"('Al', 'Si', 'Y')\",OTHERS,False\nmp-1202849,\"{'Sr': 2.0, 'H': 36.0, 'Cl': 4.0, 'O': 34.0}\",\"('Cl', 'H', 'O', 'Sr')\",OTHERS,False\nmp-556573,\"{'K': 8.0, 'W': 4.0, 'C': 8.0, 'O': 36.0}\",\"('C', 'K', 'O', 'W')\",OTHERS,False\nmp-1248182,\"{'Na': 4.0, 'Al': 11.0, 'Si': 13.0, 'Ag': 7.0, 'O': 48.0}\",\"('Ag', 'Al', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1209156,\"{'Sb': 4.0, 'C': 8.0, 'O': 20.0}\",\"('C', 'O', 'Sb')\",OTHERS,False\nmp-1228559,\"{'Al': 5.0, 'Ni': 41.0, 'B': 12.0}\",\"('Al', 'B', 'Ni')\",OTHERS,False\nmp-766112,\"{'Li': 8.0, 'Mn': 2.0, 'V': 6.0, 'P': 16.0, 'O': 56.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-1247552,\"{'Na': 2.0, 'Ti': 1.0, 'N': 2.0}\",\"('N', 'Na', 'Ti')\",OTHERS,False\nmp-1040029,\"{'Rb': 1.0, 'La': 1.0, 'Mg': 30.0, 'O': 32.0}\",\"('La', 'Mg', 'O', 'Rb')\",OTHERS,False\nmp-1204691,\"{'Pr': 4.0, 'B': 20.0, 'O': 40.0}\",\"('B', 'O', 'Pr')\",OTHERS,False\nmp-13126,\"{'N': 2.0, 'Zr': 2.0}\",\"('N', 'Zr')\",OTHERS,False\nmp-559631,\"{'Bi': 12.0, 'Rh': 24.0, 'O': 58.0}\",\"('Bi', 'O', 'Rh')\",OTHERS,False\nmp-1039945,\"{'Rb': 1.0, 'Mg': 30.0, 'Si': 1.0, 'O': 32.0}\",\"('Mg', 'O', 'Rb', 'Si')\",OTHERS,False\nCd 25 Eu 4,\"{'Cd': 25.0, 'Eu': 4.0}\",\"('Cd', 'Eu')\",AC,False\nmp-1025175,\"{'Lu': 2.0, 'Cr': 2.0, 'C': 3.0}\",\"('C', 'Cr', 'Lu')\",OTHERS,False\nmp-772255,\"{'Cu': 6.0, 'P': 6.0, 'O': 18.0}\",\"('Cu', 'O', 'P')\",OTHERS,False\nmp-1227177,\"{'Ca': 1.0, 'Eu': 1.0, 'Si': 4.0}\",\"('Ca', 'Eu', 'Si')\",OTHERS,False\nmp-777003,\"{'Fe': 7.0, 'Te': 1.0, 'P': 12.0, 'O': 48.0}\",\"('Fe', 'O', 'P', 'Te')\",OTHERS,False\nmp-1217422,\"{'Tc': 1.0, 'Rh': 1.0}\",\"('Rh', 'Tc')\",OTHERS,False\nmp-1215887,\"{'Y': 1.0, 'U': 1.0, 'Cu': 4.0, 'Si': 4.0}\",\"('Cu', 'Si', 'U', 'Y')\",OTHERS,False\nmp-765294,\"{'Sr': 2.0, 'P': 4.0, 'H': 8.0, 'O': 12.0}\",\"('H', 'O', 'P', 'Sr')\",OTHERS,False\nmp-1223068,\"{'Li': 14.0, 'Eu': 16.0, 'Si': 8.0, 'Cl': 14.0, 'O': 32.0}\",\"('Cl', 'Eu', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1176274,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1038357,\"{'Hf': 1.0, 'Mg': 30.0, 'Bi': 1.0, 'O': 32.0}\",\"('Bi', 'Hf', 'Mg', 'O')\",OTHERS,False\nmp-1211511,\"{'K': 4.0, 'Tl': 8.0}\",\"('K', 'Tl')\",OTHERS,False\nmp-1213389,\"{'Cs': 2.0, 'Lu': 2.0, 'Ta': 12.0, 'Br': 36.0}\",\"('Br', 'Cs', 'Lu', 'Ta')\",OTHERS,False\nmp-755385,\"{'Fe': 6.0, 'O': 2.0, 'F': 10.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-704268,\"{'Mn': 36.0, 'Ag': 48.0, 'O': 96.0}\",\"('Ag', 'Mn', 'O')\",OTHERS,False\nmp-35822,\"{'Li': 8.0, 'O': 8.0, 'Si': 2.0}\",\"('Li', 'O', 'Si')\",OTHERS,False\nmp-1192080,\"{'Nd': 2.0, 'Mn': 3.0, 'Sb': 4.0, 'S': 12.0}\",\"('Mn', 'Nd', 'S', 'Sb')\",OTHERS,False\nmp-684493,\"{'Li': 4.0, 'Sn': 8.0, 'P': 12.0, 'O': 40.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,False\nmp-1193479,\"{'U': 6.0, 'O': 22.0}\",\"('O', 'U')\",OTHERS,False\nmp-1200133,\"{'Mg': 4.0, 'Cu': 4.0, 'C': 4.0, 'O': 20.0}\",\"('C', 'Cu', 'Mg', 'O')\",OTHERS,False\nmp-30247,\"{'H': 2.0, 'O': 2.0, 'Mg': 1.0}\",\"('H', 'Mg', 'O')\",OTHERS,False\nmp-1079587,\"{'Yb': 4.0, 'Pd': 4.0}\",\"('Pd', 'Yb')\",OTHERS,False\nmp-14578,\"{'S': 44.0, 'Cs': 16.0, 'Ta': 8.0}\",\"('Cs', 'S', 'Ta')\",OTHERS,False\nZn 58 Mg 40 Er 2,\"{'Zn': 58.0, 'Mg': 40.0, 'Er': 2.0}\",\"('Er', 'Mg', 'Zn')\",QC,False\nmp-12565,\"{'Mn': 1.0, 'Au': 4.0}\",\"('Au', 'Mn')\",OTHERS,False\nmp-766028,\"{'K': 4.0, 'Li': 5.0, 'Ti': 8.0, 'P': 8.0, 'O': 40.0}\",\"('K', 'Li', 'O', 'P', 'Ti')\",OTHERS,False\nmp-1096760,\"{'Ti': 1.0, 'Al': 1.0, 'Ir': 2.0}\",\"('Al', 'Ir', 'Ti')\",OTHERS,False\nmp-561108,\"{'Y': 4.0, 'I': 12.0, 'O': 36.0}\",\"('I', 'O', 'Y')\",OTHERS,False\nmvc-1192,\"{'Ba': 2.0, 'Sn': 3.0, 'O': 7.0}\",\"('Ba', 'O', 'Sn')\",OTHERS,False\nmp-849518,\"{'Mn': 12.0, 'O': 7.0, 'F': 17.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-1175125,\"{'Li': 7.0, 'Mn': 4.0, 'Co': 1.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-561008,\"{'Ca': 8.0, 'P': 16.0, 'O': 48.0}\",\"('Ca', 'O', 'P')\",OTHERS,False\nmp-1190821,\"{'Y': 2.0, 'Fe': 17.0, 'C': 3.0}\",\"('C', 'Fe', 'Y')\",OTHERS,False\nmp-11473,\"{'Hg': 1.0, 'Tl': 1.0}\",\"('Hg', 'Tl')\",OTHERS,False\nmp-2242,\"{'S': 4.0, 'Ge': 4.0}\",\"('Ge', 'S')\",OTHERS,False\nmp-772590,\"{'Li': 12.0, 'V': 4.0, 'B': 8.0, 'S': 2.0, 'O': 32.0}\",\"('B', 'Li', 'O', 'S', 'V')\",OTHERS,False\nmp-759535,\"{'Na': 12.0, 'Cu': 20.0, 'O': 16.0}\",\"('Cu', 'Na', 'O')\",OTHERS,False\nmp-504735,\"{'Ni': 12.0, 'Ti': 12.0, 'O': 4.0}\",\"('Ni', 'O', 'Ti')\",OTHERS,False\nmp-755794,\"{'Rb': 6.0, 'Lu': 2.0, 'O': 6.0}\",\"('Lu', 'O', 'Rb')\",OTHERS,False\nmp-972866,\"{'Sc': 2.0, 'Pd': 1.0, 'Au': 1.0}\",\"('Au', 'Pd', 'Sc')\",OTHERS,False\nmp-13240,\"{'Sc': 1.0, 'Cu': 4.0, 'In': 1.0}\",\"('Cu', 'In', 'Sc')\",OTHERS,False\nmp-1198692,\"{'Ti': 20.0, 'Ge': 16.0}\",\"('Ge', 'Ti')\",OTHERS,False\nmp-770173,\"{'Mg': 4.0, 'Mn': 6.0, 'O': 16.0}\",\"('Mg', 'Mn', 'O')\",OTHERS,False\nmp-1211262,\"{'La': 2.0, 'B': 2.0, 'Pt': 8.0}\",\"('B', 'La', 'Pt')\",OTHERS,False\nmp-8806,\"{'Sr': 4.0, 'Cu': 4.0, 'O': 6.0}\",\"('Cu', 'O', 'Sr')\",OTHERS,False\nmp-1198034,\"{'Cd': 4.0, 'H': 20.0, 'Br': 12.0, 'N': 8.0}\",\"('Br', 'Cd', 'H', 'N')\",OTHERS,False\nmp-1185732,\"{'Mg': 16.0, 'Ta': 1.0, 'Al': 12.0}\",\"('Al', 'Mg', 'Ta')\",OTHERS,False\nmp-865025,\"{'Hf': 1.0, 'Ta': 1.0, 'Re': 2.0}\",\"('Hf', 'Re', 'Ta')\",OTHERS,False\nmp-734875,\"{'H': 4.0, 'Pb': 8.0, 'C': 2.0, 'Cl': 12.0, 'O': 8.0}\",\"('C', 'Cl', 'H', 'O', 'Pb')\",OTHERS,False\nmp-1195066,\"{'Cs': 14.0, 'Fe': 8.0, 'S': 16.0}\",\"('Cs', 'Fe', 'S')\",OTHERS,False\nmp-1210767,\"{'Pr': 12.0, 'In': 1.0, 'Co': 6.0}\",\"('Co', 'In', 'Pr')\",OTHERS,False\nmp-758993,\"{'Li': 8.0, 'Cr': 8.0, 'P': 8.0, 'O': 28.0, 'F': 8.0}\",\"('Cr', 'F', 'Li', 'O', 'P')\",OTHERS,False\nmp-504815,\"{'Re': 10.0, 'Ni': 4.0, 'As': 24.0}\",\"('As', 'Ni', 'Re')\",OTHERS,False\nmp-30532,\"{'Zn': 2.0, 'I': 8.0, 'Tl': 4.0}\",\"('I', 'Tl', 'Zn')\",OTHERS,False\nmp-6578,\"{'B': 4.0, 'O': 48.0, 'P': 12.0, 'Ba': 12.0}\",\"('B', 'Ba', 'O', 'P')\",OTHERS,False\nmp-1227336,\"{'Ba': 1.0, 'Sr': 1.0, 'Ti': 2.0, 'O': 6.0}\",\"('Ba', 'O', 'Sr', 'Ti')\",OTHERS,False\nmp-1102559,\"{'Nb': 4.0, 'Se': 8.0}\",\"('Nb', 'Se')\",OTHERS,False\nmp-1174192,\"{'Li': 4.0, 'Mn': 3.0, 'Co': 1.0, 'O': 8.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-20064,\"{'Ga': 2.0, 'Dy': 1.0}\",\"('Dy', 'Ga')\",OTHERS,False\nmp-1222723,\"{'La': 1.0, 'U': 1.0, 'O': 4.0}\",\"('La', 'O', 'U')\",OTHERS,False\nmp-744386,\"{'Al': 9.0, 'Fe': 2.0, 'Si': 4.0, 'H': 1.0, 'O': 24.0}\",\"('Al', 'Fe', 'H', 'O', 'Si')\",OTHERS,False\nmp-865032,\"{'Mn': 2.0, 'Pt': 1.0, 'Rh': 1.0}\",\"('Mn', 'Pt', 'Rh')\",OTHERS,False\nmp-643801,\"{'K': 2.0, 'H': 6.0, 'Pb': 1.0, 'O': 6.0}\",\"('H', 'K', 'O', 'Pb')\",OTHERS,False\nmp-1071252,\"{'La': 1.0, 'Ga': 2.0, 'Rh': 3.0}\",\"('Ga', 'La', 'Rh')\",OTHERS,False\nmp-1202411,\"{'Fe': 8.0, 'P': 4.0, 'H': 4.0, 'O': 12.0, 'F': 8.0}\",\"('F', 'Fe', 'H', 'O', 'P')\",OTHERS,False\nmp-20787,\"{'Fe': 4.0, 'B': 4.0}\",\"('B', 'Fe')\",OTHERS,False\nmp-556050,\"{'P': 32.0, 'S': 8.0, 'N': 48.0, 'F': 48.0}\",\"('F', 'N', 'P', 'S')\",OTHERS,False\nmp-1104424,\"{'Ba': 8.0, 'Sb': 4.0, 'H': 2.0, 'O': 1.0}\",\"('Ba', 'H', 'O', 'Sb')\",OTHERS,False\nmp-19918,\"{'Ge': 2.0, 'Gd': 2.0}\",\"('Gd', 'Ge')\",OTHERS,False\nmp-1245079,\"{'Be': 50.0, 'O': 50.0}\",\"('Be', 'O')\",OTHERS,False\nmp-1228533,\"{'Ba': 2.0, 'Sr': 1.0, 'Ca': 3.0, 'Mg': 2.0, 'Si': 4.0, 'O': 16.0}\",\"('Ba', 'Ca', 'Mg', 'O', 'Si', 'Sr')\",OTHERS,False\nmp-1186660,\"{'Pu': 1.0, 'Cd': 1.0, 'Hg': 2.0}\",\"('Cd', 'Hg', 'Pu')\",OTHERS,False\nmp-979137,\"{'Sr': 1.0, 'Ga': 1.0, 'Si': 1.0, 'H': 1.0}\",\"('Ga', 'H', 'Si', 'Sr')\",OTHERS,False\nmp-754756,\"{'Li': 2.0, 'Ni': 1.0, 'O': 2.0}\",\"('Li', 'Ni', 'O')\",OTHERS,False\nmp-1217486,\"{'Tb': 2.0, 'Mn': 2.0, 'Fe': 2.0}\",\"('Fe', 'Mn', 'Tb')\",OTHERS,False\nmp-29912,\"{'Sb': 16.0, 'Mo': 8.0, 'Se': 8.0}\",\"('Mo', 'Sb', 'Se')\",OTHERS,False\nmp-1226515,\"{'Ce': 1.0, 'Mn': 1.0, 'Si': 2.0, 'Pd': 1.0}\",\"('Ce', 'Mn', 'Pd', 'Si')\",OTHERS,False\nmp-24420,\"{'H': 2.0, 'Se': 1.0}\",\"('H', 'Se')\",OTHERS,False\nmp-1219026,\"{'Sm': 1.0, 'Y': 1.0, 'Fe': 17.0}\",\"('Fe', 'Sm', 'Y')\",OTHERS,False\nmp-753172,\"{'Li': 4.0, 'Cr': 5.0, 'O': 10.0}\",\"('Cr', 'Li', 'O')\",OTHERS,False\nmp-1184970,\"{'Li': 2.0, 'Hg': 1.0, 'Bi': 1.0}\",\"('Bi', 'Hg', 'Li')\",OTHERS,False\nmp-974061,\"{'K': 1.0, 'Si': 1.0, 'O': 3.0}\",\"('K', 'O', 'Si')\",OTHERS,False\nmp-1097667,\"{'Y': 1.0, 'Sc': 1.0, 'Tl': 2.0}\",\"('Sc', 'Tl', 'Y')\",OTHERS,False\nmp-26304,\"{'O': 32.0, 'P': 8.0, 'Cu': 9.0}\",\"('Cu', 'O', 'P')\",OTHERS,False\nmp-775479,\"{'Li': 8.0, 'Fe': 12.0, 'Cu': 4.0, 'O': 32.0}\",\"('Cu', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1019797,\"{'K': 20.0, 'B': 4.0, 'S': 16.0, 'O': 64.0}\",\"('B', 'K', 'O', 'S')\",OTHERS,False\nmp-1218425,\"{'Sr': 3.0, 'Ta': 1.0, 'Co': 1.0, 'O': 7.0}\",\"('Co', 'O', 'Sr', 'Ta')\",OTHERS,False\nmp-1208499,\"{'Tb': 12.0, 'Nb': 8.0, 'Al': 86.0}\",\"('Al', 'Nb', 'Tb')\",OTHERS,False\nmp-1025834,\"{'Te': 2.0, 'Mo': 1.0, 'W': 2.0, 'Se': 4.0}\",\"('Mo', 'Se', 'Te', 'W')\",OTHERS,False\nmp-1207401,\"{'Zn': 2.0, 'Se': 2.0, 'O': 10.0}\",\"('O', 'Se', 'Zn')\",OTHERS,False\nmp-11560,\"{'Si': 6.0, 'Ti': 6.0, 'Ru': 6.0}\",\"('Ru', 'Si', 'Ti')\",OTHERS,False\nAu 50.5 Ga 35.9 Ca 13.6,\"{'Au': 50.5, 'Ga': 35.9, 'Ca': 13.6}\",\"('Au', 'Ca', 'Ga')\",AC,False\nmp-770905,\"{'Li': 2.0, 'Mn': 2.0, 'As': 2.0, 'C': 2.0, 'O': 14.0}\",\"('As', 'C', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-754642,\"{'Mn': 4.0, 'Fe': 2.0, 'O': 8.0}\",\"('Fe', 'Mn', 'O')\",OTHERS,False\nmp-753461,\"{'Li': 4.0, 'Mn': 3.0, 'O': 1.0, 'F': 11.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1192180,\"{'Tm': 8.0, 'P': 8.0, 'S': 8.0}\",\"('P', 'S', 'Tm')\",OTHERS,False\nmp-1185721,\"{'Mg': 16.0, 'Al': 12.0, 'Zn': 1.0}\",\"('Al', 'Mg', 'Zn')\",OTHERS,False\nmp-1182691,\"{'Mg': 16.0, 'Si': 24.0, 'O': 92.0}\",\"('Mg', 'O', 'Si')\",OTHERS,False\nmp-555117,\"{'Cs': 16.0, 'Bi': 32.0, 'O': 56.0}\",\"('Bi', 'Cs', 'O')\",OTHERS,False\nmp-772482,\"{'Mn': 3.0, 'Sn': 3.0, 'Te': 2.0, 'O': 16.0}\",\"('Mn', 'O', 'Sn', 'Te')\",OTHERS,False\nmp-1266421,\"{'Si': 12.0, 'Bi': 18.0, 'O': 52.0}\",\"('Bi', 'O', 'Si')\",OTHERS,False\nmp-1245456,\"{'Cu': 16.0, 'Ge': 16.0, 'N': 32.0}\",\"('Cu', 'Ge', 'N')\",OTHERS,False\nmp-757620,\"{'Li': 4.0, 'V': 4.0, 'O': 4.0, 'F': 12.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-771660,\"{'Mn': 2.0, 'P': 4.0, 'O': 14.0}\",\"('Mn', 'O', 'P')\",OTHERS,False\nmp-24199,\"{'H': 1.0, 'Li': 1.0, 'F': 2.0}\",\"('F', 'H', 'Li')\",OTHERS,False\nmvc-10039,\"{'La': 1.0, 'Cr': 1.0, 'Ni': 1.0, 'O': 6.0}\",\"('Cr', 'La', 'Ni', 'O')\",OTHERS,False\nmp-1201811,\"{'Sr': 4.0, 'O': 40.0}\",\"('O', 'Sr')\",OTHERS,False\nmp-4248,\"{'C': 2.0, 'Co': 1.0, 'Y': 1.0}\",\"('C', 'Co', 'Y')\",OTHERS,False\nmp-1025194,\"{'Pr': 2.0, 'Fe': 2.0, 'Si': 2.0, 'C': 1.0}\",\"('C', 'Fe', 'Pr', 'Si')\",OTHERS,False\nmp-998883,{'Ge': 1.0},\"('Ge',)\",OTHERS,False\nmp-9312,\"{'C': 2.0, 'Ni': 1.0, 'Pr': 1.0}\",\"('C', 'Ni', 'Pr')\",OTHERS,False\nmp-1227930,\"{'Ba': 2.0, 'La': 2.0, 'Sm': 2.0, 'Mn': 6.0, 'O': 18.0}\",\"('Ba', 'La', 'Mn', 'O', 'Sm')\",OTHERS,False\nmp-1027002,\"{'Te': 2.0, 'Mo': 1.0, 'W': 3.0, 'Se': 2.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-24672,\"{'H': 20.0, 'Ni': 2.0, 'Zr': 12.0, 'Sn': 4.0}\",\"('H', 'Ni', 'Sn', 'Zr')\",OTHERS,False\nmp-977368,\"{'K': 2.0, 'Dy': 4.0, 'Cu': 4.0, 'Se': 9.0}\",\"('Cu', 'Dy', 'K', 'Se')\",OTHERS,False\nAg 2 In 4 Yb 1,\"{'Ag': 2.0, 'In': 4.0, 'Yb': 1.0}\",\"('Ag', 'In', 'Yb')\",AC,False\nmp-1407,\"{'Se': 8.0, 'Rh': 3.0}\",\"('Rh', 'Se')\",OTHERS,False\nmp-29593,\"{'P': 12.0, 'Cl': 96.0, 'Re': 8.0}\",\"('Cl', 'P', 'Re')\",OTHERS,False\nmp-1216835,\"{'Tl': 1.0, 'V': 12.0, 'S': 16.0}\",\"('S', 'Tl', 'V')\",OTHERS,False\nmp-1218367,\"{'Sr': 3.0, 'Ca': 1.0, 'S': 4.0}\",\"('Ca', 'S', 'Sr')\",OTHERS,False\nmp-1245115,\"{'Sn': 40.0, 'O': 40.0}\",\"('O', 'Sn')\",OTHERS,False\nmp-758707,\"{'Li': 8.0, 'Mn': 12.0, 'W': 4.0, 'O': 32.0}\",\"('Li', 'Mn', 'O', 'W')\",OTHERS,False\nmp-14322,\"{'O': 9.0, 'Ba': 3.0, 'Tm': 4.0}\",\"('Ba', 'O', 'Tm')\",OTHERS,False\nmp-672344,\"{'Ce': 4.0, 'Al': 20.0, 'Pt': 12.0}\",\"('Al', 'Ce', 'Pt')\",OTHERS,False\nmp-1192281,\"{'Cs': 1.0, 'B': 12.0, 'O': 12.0}\",\"('B', 'Cs', 'O')\",OTHERS,False\nmp-1178459,\"{'Cr': 12.0, 'Fe': 7.0, 'O': 48.0}\",\"('Cr', 'Fe', 'O')\",OTHERS,False\nmp-568619,\"{'Si': 5.0, 'C': 5.0}\",\"('C', 'Si')\",OTHERS,False\nmp-1071438,\"{'Gd': 1.0, 'Ni': 4.0, 'Au': 1.0}\",\"('Au', 'Gd', 'Ni')\",OTHERS,False\nmp-1209830,\"{'P': 1.0, 'Br': 2.0}\",\"('Br', 'P')\",OTHERS,False\nmp-764879,\"{'V': 8.0, 'O': 6.0, 'F': 18.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-22919,\"{'Ag': 1.0, 'I': 1.0}\",\"('Ag', 'I')\",OTHERS,False\nmp-16830,\"{'B': 6.0, 'Ni': 12.0, 'Sr': 1.0}\",\"('B', 'Ni', 'Sr')\",OTHERS,False\nmp-1102848,\"{'La': 3.0, 'Ir': 9.0}\",\"('Ir', 'La')\",OTHERS,False\nmp-1096160,\"{'Sr': 2.0, 'In': 1.0, 'Hg': 1.0}\",\"('Hg', 'In', 'Sr')\",OTHERS,False\nmp-704317,\"{'K': 8.0, 'Cu': 12.0, 'Mo': 16.0, 'O': 64.0}\",\"('Cu', 'K', 'Mo', 'O')\",OTHERS,False\nmp-1093901,\"{'Zr': 1.0, 'Sb': 1.0, 'Ru': 2.0}\",\"('Ru', 'Sb', 'Zr')\",OTHERS,False\nmp-1205061,\"{'Zr': 5.0, 'Tl': 20.0, 'Se': 20.0}\",\"('Se', 'Tl', 'Zr')\",OTHERS,False\nmp-1183178,\"{'Ag': 2.0, 'As': 2.0}\",\"('Ag', 'As')\",OTHERS,False\nmp-19123,\"{'Li': 2.0, 'O': 8.0, 'V': 2.0, 'Rb': 4.0}\",\"('Li', 'O', 'Rb', 'V')\",OTHERS,False\nmp-752757,\"{'V': 4.0, 'O': 2.0, 'F': 10.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-1215929,\"{'Y': 2.0, 'Mg': 2.0, 'Al': 8.0}\",\"('Al', 'Mg', 'Y')\",OTHERS,False\nmp-669564,\"{'Cs': 4.0, 'Re': 24.0, 'S': 32.0, 'Br': 12.0}\",\"('Br', 'Cs', 'Re', 'S')\",OTHERS,False\nmp-1080131,\"{'Cu': 2.0, 'Si': 1.0, 'Hg': 1.0, 'Te': 4.0}\",\"('Cu', 'Hg', 'Si', 'Te')\",OTHERS,False\nmp-1091371,\"{'Bi': 4.0, 'O': 6.0}\",\"('Bi', 'O')\",OTHERS,False\nmp-7342,\"{'Sm': 12.0, 'Ir': 4.0}\",\"('Ir', 'Sm')\",OTHERS,False\nmp-1019596,\"{'Ce': 2.0, 'Zr': 2.0, 'O': 8.0}\",\"('Ce', 'O', 'Zr')\",OTHERS,False\nmp-1216527,\"{'Tl': 1.0, 'In': 1.0}\",\"('In', 'Tl')\",OTHERS,False\nmp-766254,\"{'Cu': 4.0, 'P': 12.0, 'O': 36.0}\",\"('Cu', 'O', 'P')\",OTHERS,False\nmp-849611,\"{'Li': 8.0, 'Mn': 12.0, 'B': 12.0, 'O': 36.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1216714,\"{'Ti': 2.0, 'Nb': 2.0, 'Al': 2.0, 'C': 2.0}\",\"('Al', 'C', 'Nb', 'Ti')\",OTHERS,False\nmp-1210996,\"{'Mg': 4.0, 'Al': 6.0, 'Si': 12.0, 'O': 36.0}\",\"('Al', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-30340,\"{'Ag': 2.0, 'In': 1.0, 'Er': 1.0}\",\"('Ag', 'Er', 'In')\",OTHERS,False\nmp-1215622,\"{'Zn': 1.0, 'Cu': 2.0, 'Sn': 1.0, 'Se': 3.0, 'S': 1.0}\",\"('Cu', 'S', 'Se', 'Sn', 'Zn')\",OTHERS,False\nmp-1185959,\"{'Mg': 2.0, 'S': 4.0}\",\"('Mg', 'S')\",OTHERS,False\nmp-8860,\"{'Na': 3.0, 'As': 1.0}\",\"('As', 'Na')\",OTHERS,False\nmp-770833,\"{'Li': 8.0, 'Sn': 4.0, 'B': 4.0, 'O': 16.0}\",\"('B', 'Li', 'O', 'Sn')\",OTHERS,False\nmp-631388,\"{'Cd': 1.0, 'Ir': 1.0, 'Ru': 1.0}\",\"('Cd', 'Ir', 'Ru')\",OTHERS,False\nmp-867352,\"{'Tc': 2.0, 'Rh': 6.0}\",\"('Rh', 'Tc')\",OTHERS,False\nmp-777902,\"{'Ti': 3.0, 'Mn': 2.0, 'Cu': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Cu', 'Mn', 'O', 'P', 'Ti')\",OTHERS,False\nmp-772144,\"{'Mn': 5.0, 'V': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Mn', 'O', 'P', 'V')\",OTHERS,False\nmvc-11476,\"{'Mg': 1.0, 'V': 4.0, 'S': 8.0}\",\"('Mg', 'S', 'V')\",OTHERS,False\nmp-505603,\"{'Pb': 20.0, 'S': 4.0, 'O': 32.0}\",\"('O', 'Pb', 'S')\",OTHERS,False\nmp-780319,\"{'Li': 6.0, 'Mn': 2.0, 'P': 4.0, 'H': 4.0, 'O': 18.0}\",\"('H', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-752550,\"{'Li': 3.0, 'Ni': 1.0, 'O': 1.0, 'F': 3.0}\",\"('F', 'Li', 'Ni', 'O')\",OTHERS,False\nmp-1207436,\"{'Zr': 9.0, 'Pd': 11.0}\",\"('Pd', 'Zr')\",OTHERS,False\nmp-4431,\"{'S': 6.0, 'As': 2.0, 'Ag': 6.0}\",\"('Ag', 'As', 'S')\",OTHERS,False\nmp-555707,\"{'S': 16.0, 'N': 8.0, 'O': 12.0, 'F': 4.0}\",\"('F', 'N', 'O', 'S')\",OTHERS,False\nmp-13998,\"{'O': 32.0, 'Na': 40.0, 'Al': 8.0}\",\"('Al', 'Na', 'O')\",OTHERS,False\nmp-863295,\"{'Cs': 4.0, 'V': 4.0, 'Ga': 4.0, 'P': 8.0, 'O': 40.0}\",\"('Cs', 'Ga', 'O', 'P', 'V')\",OTHERS,False\nmp-504714,\"{'O': 26.0, 'I': 2.0, 'Ni': 6.0, 'B': 14.0}\",\"('B', 'I', 'Ni', 'O')\",OTHERS,False\nmp-773135,\"{'Li': 6.0, 'Cu': 6.0, 'P': 4.0, 'O': 16.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-1101665,\"{'Na': 2.0, 'Li': 10.0, 'Fe': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Fe', 'Li', 'Na', 'O', 'P')\",OTHERS,False\nmp-1226052,\"{'Co': 1.0, 'Cu': 2.0, 'Ge': 1.0, 'Se': 4.0}\",\"('Co', 'Cu', 'Ge', 'Se')\",OTHERS,False\nmp-19090,\"{'O': 12.0, 'Mn': 2.0, 'Mo': 2.0, 'Te': 2.0}\",\"('Mn', 'Mo', 'O', 'Te')\",OTHERS,False\nmp-1207317,\"{'U': 2.0, 'P': 3.0}\",\"('P', 'U')\",OTHERS,False\nmp-1184294,\"{'Fe': 2.0, 'Ag': 6.0}\",\"('Ag', 'Fe')\",OTHERS,False\nmp-13236,\"{'Si': 6.0, 'Ho': 10.0}\",\"('Ho', 'Si')\",OTHERS,False\nmp-862543,\"{'Sc': 2.0, 'Al': 1.0, 'Tc': 1.0}\",\"('Al', 'Sc', 'Tc')\",OTHERS,False\nmp-762282,\"{'Li': 3.0, 'Cr': 5.0, 'O': 10.0}\",\"('Cr', 'Li', 'O')\",OTHERS,False\nmp-1187636,\"{'Tm': 3.0, 'Hg': 1.0}\",\"('Hg', 'Tm')\",OTHERS,False\nmp-1080467,\"{'K': 1.0, 'Cd': 4.0, 'P': 3.0}\",\"('Cd', 'K', 'P')\",OTHERS,False\nmp-866031,\"{'Al': 2.0, 'Fe': 1.0, 'Ir': 1.0}\",\"('Al', 'Fe', 'Ir')\",OTHERS,False\nmp-1020636,\"{'Zn': 16.0, 'Si': 8.0, 'O': 32.0}\",\"('O', 'Si', 'Zn')\",OTHERS,False\nmp-24105,\"{'H': 2.0, 'O': 2.0, 'Co': 1.0}\",\"('Co', 'H', 'O')\",OTHERS,False\nmp-1103596,\"{'Ce': 1.0, 'Ge': 4.0, 'Rh': 6.0}\",\"('Ce', 'Ge', 'Rh')\",OTHERS,False\nmp-1186751,\"{'Pr': 1.0, 'Ho': 1.0, 'Tl': 2.0}\",\"('Ho', 'Pr', 'Tl')\",OTHERS,False\nmvc-12394,\"{'Mg': 4.0, 'Ti': 8.0, 'O': 16.0}\",\"('Mg', 'O', 'Ti')\",OTHERS,False\nmp-1198811,\"{'Ca': 4.0, 'Hg': 4.0, 'C': 16.0, 'S': 16.0, 'N': 16.0, 'O': 12.0}\",\"('C', 'Ca', 'Hg', 'N', 'O', 'S')\",OTHERS,False\nmp-1185886,\"{'Mg': 2.0, 'Hg': 4.0}\",\"('Hg', 'Mg')\",OTHERS,False\nmp-1247072,\"{'Ba': 12.0, 'Ru': 8.0, 'N': 16.0}\",\"('Ba', 'N', 'Ru')\",OTHERS,False\nmp-1147572,\"{'La': 2.0, 'Pd': 1.0, 'O': 2.0, 'F': 2.0}\",\"('F', 'La', 'O', 'Pd')\",OTHERS,False\nmp-1113362,\"{'Cs': 2.0, 'Ce': 1.0, 'Cu': 1.0, 'I': 6.0}\",\"('Ce', 'Cs', 'Cu', 'I')\",OTHERS,False\nmp-1018707,\"{'Gd': 2.0, 'Se': 4.0}\",\"('Gd', 'Se')\",OTHERS,False\nmp-765988,\"{'Li': 1.0, 'V': 1.0, 'Fe': 1.0, 'P': 2.0, 'H': 2.0, 'O': 10.0}\",\"('Fe', 'H', 'Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1039915,\"{'Na': 1.0, 'Mg': 30.0, 'Si': 1.0, 'O': 32.0}\",\"('Mg', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1219577,\"{'Rb': 2.0, 'Ta': 4.0, 'O': 12.0}\",\"('O', 'Rb', 'Ta')\",OTHERS,False\nmp-11507,\"{'Ni': 4.0, 'Mo': 1.0}\",\"('Mo', 'Ni')\",OTHERS,False\nmp-1220452,\"{'Nb': 1.0, 'Cu': 3.0, 'Se': 2.0, 'S': 2.0}\",\"('Cu', 'Nb', 'S', 'Se')\",OTHERS,False\nmp-1183920,\"{'Cs': 1.0, 'Pa': 1.0, 'O': 3.0}\",\"('Cs', 'O', 'Pa')\",OTHERS,False\nmp-542884,\"{'S': 8.0, 'Rb': 8.0, 'Be': 4.0, 'O': 40.0, 'H': 16.0}\",\"('Be', 'H', 'O', 'Rb', 'S')\",OTHERS,False\nmp-754224,\"{'Ba': 2.0, 'Sr': 4.0, 'I': 12.0}\",\"('Ba', 'I', 'Sr')\",OTHERS,False\nmp-975422,\"{'Rb': 2.0, 'F': 6.0}\",\"('F', 'Rb')\",OTHERS,False\nmp-767435,\"{'P': 12.0, 'W': 8.0, 'O': 48.0}\",\"('O', 'P', 'W')\",OTHERS,False\nmp-1184260,\"{'Fe': 3.0, 'Cu': 1.0}\",\"('Cu', 'Fe')\",OTHERS,False\nmp-1202260,\"{'Lu': 4.0, 'Ni': 34.0}\",\"('Lu', 'Ni')\",OTHERS,False\nmp-759330,\"{'Rb': 16.0, 'Ge': 36.0, 'H': 60.0, 'N': 20.0}\",\"('Ge', 'H', 'N', 'Rb')\",OTHERS,False\nmp-674982,\"{'Li': 19.0, 'Mn': 5.0, 'N': 9.0}\",\"('Li', 'Mn', 'N')\",OTHERS,False\nmp-9295,\"{'Cu': 3.0, 'Te': 4.0, 'Ta': 1.0}\",\"('Cu', 'Ta', 'Te')\",OTHERS,False\nmp-31950,\"{'Li': 2.0, 'O': 24.0, 'P': 8.0, 'Mn': 2.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1197228,\"{'Ni': 2.0, 'P': 8.0, 'H': 36.0, 'C': 4.0, 'N': 4.0, 'O': 30.0}\",\"('C', 'H', 'N', 'Ni', 'O', 'P')\",OTHERS,False\nmp-18199,\"{'O': 4.0, 'As': 12.0, 'Sr': 12.0, 'Ta': 4.0}\",\"('As', 'O', 'Sr', 'Ta')\",OTHERS,False\nmp-1245716,\"{'Mg': 4.0, 'Re': 4.0, 'N': 8.0}\",\"('Mg', 'N', 'Re')\",OTHERS,False\nmp-685928,\"{'Rb': 10.0, 'Tc': 6.0, 'S': 14.0}\",\"('Rb', 'S', 'Tc')\",OTHERS,False\nmp-1218958,\"{'Sn': 1.0, 'Pb': 4.0, 'Se': 5.0}\",\"('Pb', 'Se', 'Sn')\",OTHERS,False\nmp-1103838,\"{'Pd': 4.0, 'Pb': 2.0, 'O': 8.0}\",\"('O', 'Pb', 'Pd')\",OTHERS,False\nmp-1013560,\"{'Ba': 3.0, 'As': 2.0}\",\"('As', 'Ba')\",OTHERS,False\nmp-1188045,\"{'Zr': 2.0, 'Mn': 6.0}\",\"('Mn', 'Zr')\",OTHERS,False\nmp-1227286,\"{'Ba': 1.0, 'Y': 1.0, 'Mn': 1.0, 'Co': 1.0, 'O': 5.0}\",\"('Ba', 'Co', 'Mn', 'O', 'Y')\",OTHERS,False\nmp-1227505,\"{'Ca': 1.0, 'V': 4.0, 'Co': 1.0, 'Cu': 2.0, 'O': 12.0}\",\"('Ca', 'Co', 'Cu', 'O', 'V')\",OTHERS,False\nmp-771121,\"{'La': 8.0, 'Hf': 4.0, 'O': 20.0}\",\"('Hf', 'La', 'O')\",OTHERS,False\nmp-767737,\"{'Co': 9.0, 'Cu': 3.0, 'O': 16.0}\",\"('Co', 'Cu', 'O')\",OTHERS,False\nmp-1245668,\"{'Cu': 2.0, 'As': 3.0}\",\"('As', 'Cu')\",OTHERS,False\nmp-1113444,\"{'Cs': 2.0, 'Tl': 1.0, 'Ag': 1.0, 'Br': 6.0}\",\"('Ag', 'Br', 'Cs', 'Tl')\",OTHERS,False\nmp-1196028,\"{'Lu': 8.0, 'B': 8.0, 'N': 8.0, 'O': 52.0}\",\"('B', 'Lu', 'N', 'O')\",OTHERS,False\nmp-1072114,\"{'Ho': 2.0, 'O': 2.0, 'F': 2.0}\",\"('F', 'Ho', 'O')\",OTHERS,False\nmp-1094261,\"{'Zr': 4.0, 'Sn': 4.0}\",\"('Sn', 'Zr')\",OTHERS,False\nmp-1217340,\"{'Th': 2.0, 'U': 2.0, 'B': 16.0}\",\"('B', 'Th', 'U')\",OTHERS,False\nmp-861594,\"{'Pr': 2.0, 'Tl': 1.0, 'Cd': 1.0}\",\"('Cd', 'Pr', 'Tl')\",OTHERS,False\nmp-32780,\"{'Au': 4.0, 'Cl': 4.0}\",\"('Au', 'Cl')\",OTHERS,False\nmp-1220382,\"{'Nb': 5.0, 'Se': 2.0, 'S': 2.0}\",\"('Nb', 'S', 'Se')\",OTHERS,False\nmp-1030467,\"{'Te': 4.0, 'Mo': 3.0, 'W': 1.0, 'S': 4.0}\",\"('Mo', 'S', 'Te', 'W')\",OTHERS,False\nmp-1216859,\"{'Ti': 2.0, 'Cr': 2.0, 'Sb': 2.0, 'O': 12.0}\",\"('Cr', 'O', 'Sb', 'Ti')\",OTHERS,False\nmp-1219677,\"{'Si': 18.0, 'C': 20.0, 'N': 2.0, 'O': 36.0}\",\"('C', 'N', 'O', 'Si')\",OTHERS,False\nmp-1196428,\"{'Rb': 4.0, 'B': 8.0, 'Se': 12.0, 'O': 40.0}\",\"('B', 'O', 'Rb', 'Se')\",OTHERS,False\nmp-763313,\"{'Li': 2.0, 'Co': 2.0, 'Si': 2.0, 'O': 8.0}\",\"('Co', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1223233,\"{'La': 4.0, 'V': 2.0, 'Fe': 2.0, 'O': 12.0}\",\"('Fe', 'La', 'O', 'V')\",OTHERS,False\nmp-622785,\"{'Fe': 16.0, 'S': 16.0, 'N': 16.0, 'O': 16.0}\",\"('Fe', 'N', 'O', 'S')\",OTHERS,False\nmp-768944,\"{'Li': 8.0, 'V': 8.0, 'B': 16.0, 'O': 40.0}\",\"('B', 'Li', 'O', 'V')\",OTHERS,False\nmp-1203099,\"{'Si': 12.0, 'O': 26.0}\",\"('O', 'Si')\",OTHERS,False\nmvc-10022,\"{'W': 4.0, 'O': 8.0}\",\"('O', 'W')\",OTHERS,False\nmp-768648,\"{'Li': 8.0, 'Fe': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-756332,\"{'Li': 2.0, 'Cr': 4.0, 'O': 6.0}\",\"('Cr', 'Li', 'O')\",OTHERS,False\nmp-546292,\"{'O': 2.0, 'Cl': 2.0, 'Yb': 2.0}\",\"('Cl', 'O', 'Yb')\",OTHERS,False\nmp-569673,\"{'Pr': 4.0, 'I': 8.0}\",\"('I', 'Pr')\",OTHERS,False\nmp-772624,\"{'Al': 8.0, 'C': 4.0, 'O': 4.0}\",\"('Al', 'C', 'O')\",OTHERS,False\nmp-21319,\"{'C': 1.0, 'Ce': 3.0, 'Tl': 1.0}\",\"('C', 'Ce', 'Tl')\",OTHERS,False\nmp-755771,\"{'Sr': 6.0, 'As': 4.0, 'O': 16.0}\",\"('As', 'O', 'Sr')\",OTHERS,False\nmp-1191945,\"{'Tb': 2.0, 'Fe': 17.0, 'N': 3.0}\",\"('Fe', 'N', 'Tb')\",OTHERS,False\nmp-10871,\"{'Al': 1.0, 'Au': 1.0}\",\"('Al', 'Au')\",OTHERS,False\nmp-1191664,\"{'Ta': 20.0, 'Ni': 4.0}\",\"('Ni', 'Ta')\",OTHERS,False\nmp-1481,\"{'Sn': 1.0, 'Te': 1.0}\",\"('Sn', 'Te')\",OTHERS,False\nmp-22175,\"{'C': 4.0, 'N': 4.0, 'S': 4.0, 'Eu': 2.0}\",\"('C', 'Eu', 'N', 'S')\",OTHERS,False\nmp-560038,\"{'Mn': 6.0, 'B': 14.0, 'I': 2.0, 'O': 26.0}\",\"('B', 'I', 'Mn', 'O')\",OTHERS,False\nmp-1208346,\"{'Tb': 4.0, 'Fe': 4.0, 'B': 16.0}\",\"('B', 'Fe', 'Tb')\",OTHERS,False\nmp-767279,\"{'Li': 4.0, 'Dy': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Dy', 'Li', 'O', 'P')\",OTHERS,False\nmp-1174794,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-640747,\"{'Sr': 12.0, 'Nb': 8.0, 'Co': 4.0, 'O': 36.0}\",\"('Co', 'Nb', 'O', 'Sr')\",OTHERS,False\nZn 58 Mg 40 Y 2,\"{'Zn': 58.0, 'Mg': 40.0, 'Y': 2.0}\",\"('Mg', 'Y', 'Zn')\",QC,False\nmp-560642,\"{'Na': 12.0, 'Sb': 4.0, 'P': 8.0, 'O': 36.0}\",\"('Na', 'O', 'P', 'Sb')\",OTHERS,False\nmp-644389,\"{'Li': 2.0, 'H': 2.0, 'Pd': 1.0}\",\"('H', 'Li', 'Pd')\",OTHERS,False\nmp-1079726,\"{'Zr': 4.0, 'Se': 2.0, 'N': 4.0}\",\"('N', 'Se', 'Zr')\",OTHERS,False\nZn 56.8 Er 8.7 Mg 34.6,\"{'Zn': 56.8, 'Er': 8.7, 'Mg': 34.6}\",\"('Er', 'Mg', 'Zn')\",QC,False\nmp-551283,\"{'O': 6.0, 'Cu': 2.0, 'Na': 2.0, 'Te': 1.0}\",\"('Cu', 'Na', 'O', 'Te')\",OTHERS,False\nmp-6501,\"{'O': 20.0, 'Zn': 4.0, 'Ba': 4.0, 'Ho': 8.0}\",\"('Ba', 'Ho', 'O', 'Zn')\",OTHERS,False\nmp-1195624,\"{'Ba': 12.0, 'Sb': 8.0, 'S': 28.0}\",\"('Ba', 'S', 'Sb')\",OTHERS,False\nmp-30943,\"{'Mg': 2.0, 'P': 2.0, 'Se': 6.0}\",\"('Mg', 'P', 'Se')\",OTHERS,False\nmp-1208029,\"{'Tm': 16.0, 'Mg': 4.0, 'Ni': 4.0}\",\"('Mg', 'Ni', 'Tm')\",OTHERS,False\nmp-699888,\"{'Na': 8.0, 'Ti': 4.0, 'Fe': 4.0, 'P': 12.0, 'O': 48.0}\",\"('Fe', 'Na', 'O', 'P', 'Ti')\",OTHERS,False\nmp-764848,\"{'Li': 28.0, 'Mn': 10.0, 'F': 48.0}\",\"('F', 'Li', 'Mn')\",OTHERS,False\nmp-1182901,\"{'Al': 12.0, 'B': 10.0, 'O': 36.0}\",\"('Al', 'B', 'O')\",OTHERS,False\nmp-1201991,\"{'Yb': 2.0, 'N': 6.0, 'O': 28.0}\",\"('N', 'O', 'Yb')\",OTHERS,False\nmp-980949,\"{'W': 2.0, 'Cl': 8.0}\",\"('Cl', 'W')\",OTHERS,False\nCd 76 Ca 13,\"{'Cd': 76.0, 'Ca': 13.0}\",\"('Ca', 'Cd')\",AC,False\nmp-14391,\"{'O': 92.0, 'Na': 12.0, 'P': 32.0, 'V': 4.0}\",\"('Na', 'O', 'P', 'V')\",OTHERS,False\nmp-1245788,\"{'Ba': 2.0, 'Zn': 4.0, 'N': 4.0}\",\"('Ba', 'N', 'Zn')\",OTHERS,False\nmp-773007,\"{'Li': 2.0, 'Cu': 10.0, 'P': 4.0, 'O': 16.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-866811,\"{'Ca': 4.0, 'Sn': 4.0, 'S': 12.0}\",\"('Ca', 'S', 'Sn')\",OTHERS,False\nmp-1184593,\"{'Ho': 2.0, 'Ir': 1.0, 'Pd': 1.0}\",\"('Ho', 'Ir', 'Pd')\",OTHERS,False\nmp-1185011,\"{'La': 3.0, 'Zr': 1.0}\",\"('La', 'Zr')\",OTHERS,False\nmp-1183543,\"{'Ca': 1.0, 'In': 1.0, 'O': 3.0}\",\"('Ca', 'In', 'O')\",OTHERS,False\nmp-1210770,\"{'Lu': 2.0, 'Ta': 10.0, 'Ag': 4.0, 'O': 30.0}\",\"('Ag', 'Lu', 'O', 'Ta')\",OTHERS,False\nmp-9029,\"{'N': 6.0, 'Ca': 6.0, 'V': 2.0}\",\"('Ca', 'N', 'V')\",OTHERS,False\nmp-1183442,\"{'Bi': 1.0, 'Pd': 2.0, 'Au': 1.0}\",\"('Au', 'Bi', 'Pd')\",OTHERS,False\nmp-703684,\"{'Mn': 1.0, 'H': 12.0, 'N': 2.0, 'Cl': 4.0, 'O': 2.0}\",\"('Cl', 'H', 'Mn', 'N', 'O')\",OTHERS,False\nmp-1205507,\"{'Eu': 2.0, 'Mg': 1.0, 'W': 1.0, 'O': 6.0}\",\"('Eu', 'Mg', 'O', 'W')\",OTHERS,False\nmp-1183201,\"{'Au': 3.0, 'Cl': 1.0}\",\"('Au', 'Cl')\",OTHERS,False\nmp-769008,\"{'Li': 8.0, 'Bi': 6.0, 'O': 16.0}\",\"('Bi', 'Li', 'O')\",OTHERS,False\nmp-1204674,\"{'Ni': 18.0, 'Te': 10.0, 'Pb': 12.0, 'O': 60.0}\",\"('Ni', 'O', 'Pb', 'Te')\",OTHERS,False\nmp-582159,\"{'Si': 8.0, 'Ni': 28.0, 'P': 20.0}\",\"('Ni', 'P', 'Si')\",OTHERS,False\nmp-17668,\"{'Ga': 14.0, 'Yb': 2.0, 'Au': 6.0}\",\"('Au', 'Ga', 'Yb')\",OTHERS,False\nmp-1213326,\"{'Cs': 2.0, 'Tm': 2.0, 'Nb': 12.0, 'Br': 36.0}\",\"('Br', 'Cs', 'Nb', 'Tm')\",OTHERS,False\nmp-30930,\"{'Al': 4.0, 'I': 12.0}\",\"('Al', 'I')\",OTHERS,False\nmp-757220,\"{'Lu': 2.0, 'H': 12.0, 'Cl': 6.0, 'O': 30.0}\",\"('Cl', 'H', 'Lu', 'O')\",OTHERS,False\nmp-1219669,\"{'Pu': 2.0, 'O': 3.0}\",\"('O', 'Pu')\",OTHERS,False\nmp-1213575,\"{'Er': 2.0, 'Zr': 12.0, 'P': 18.0, 'O': 72.0}\",\"('Er', 'O', 'P', 'Zr')\",OTHERS,False\nmp-972912,\"{'La': 3.0, 'Er': 1.0}\",\"('Er', 'La')\",OTHERS,False\nmp-720379,\"{'K': 6.0, 'Na': 2.0, 'P': 8.0, 'H': 8.0, 'O': 28.0}\",\"('H', 'K', 'Na', 'O', 'P')\",OTHERS,False\nmp-1247479,\"{'Zn': 10.0, 'Fe': 2.0, 'N': 8.0}\",\"('Fe', 'N', 'Zn')\",OTHERS,False\nmp-1097438,\"{'Hf': 2.0, 'Fe': 1.0, 'Co': 1.0}\",\"('Co', 'Fe', 'Hf')\",OTHERS,False\nmp-754496,\"{'Li': 20.0, 'Sb': 4.0, 'S': 4.0}\",\"('Li', 'S', 'Sb')\",OTHERS,False\nmp-730460,\"{'Na': 10.0, 'H': 6.0, 'C': 8.0, 'O': 24.0}\",\"('C', 'H', 'Na', 'O')\",OTHERS,False\nmp-695259,\"{'Ba': 6.0, 'Li': 2.0, 'Ti': 10.0, 'Sb': 6.0, 'O': 42.0}\",\"('Ba', 'Li', 'O', 'Sb', 'Ti')\",OTHERS,False\nmp-1213861,\"{'Co': 2.0, 'P': 4.0, 'O': 20.0}\",\"('Co', 'O', 'P')\",OTHERS,False\nmp-1182052,\"{'Ca': 10.0, 'P': 6.0, 'O': 24.0}\",\"('Ca', 'O', 'P')\",OTHERS,False\nmp-1025029,\"{'Pr': 2.0, 'H': 2.0, 'Se': 2.0}\",\"('H', 'Pr', 'Se')\",OTHERS,False\nmp-1094085,\"{'Nd': 3.0, 'Sn': 1.0, 'N': 1.0}\",\"('N', 'Nd', 'Sn')\",OTHERS,False\nmp-1186282,\"{'Nd': 3.0, 'Ti': 1.0}\",\"('Nd', 'Ti')\",OTHERS,False\nmp-676932,\"{'Eu': 2.0, 'Sr': 2.0, 'Li': 2.0, 'Te': 2.0, 'O': 12.0}\",\"('Eu', 'Li', 'O', 'Sr', 'Te')\",OTHERS,False\nmp-1194454,\"{'Si': 2.0, 'N': 8.0, 'O': 6.0, 'F': 12.0}\",\"('F', 'N', 'O', 'Si')\",OTHERS,False\nmp-1186932,\"{'Sc': 2.0, 'Ag': 1.0, 'Pt': 1.0}\",\"('Ag', 'Pt', 'Sc')\",OTHERS,False\nmp-779057,\"{'Li': 8.0, 'Fe': 8.0, 'B': 8.0, 'O': 28.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmvc-10387,\"{'Ba': 4.0, 'Zn': 4.0, 'Sn': 4.0, 'F': 28.0}\",\"('Ba', 'F', 'Sn', 'Zn')\",OTHERS,False\nmp-558446,\"{'Ag': 4.0, 'Hg': 4.0, 'S': 4.0, 'I': 4.0}\",\"('Ag', 'Hg', 'I', 'S')\",OTHERS,False\nmp-1228501,\"{'Ba': 3.0, 'La': 3.0, 'Cu': 6.0, 'O': 14.0}\",\"('Ba', 'Cu', 'La', 'O')\",OTHERS,False\nmp-23108,\"{'Na': 1.0, 'Cl': 6.0, 'Cs': 2.0, 'U': 1.0}\",\"('Cl', 'Cs', 'Na', 'U')\",OTHERS,False\nmp-715572,\"{'Fe': 16.0, 'O': 24.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-1078767,\"{'Hf': 3.0, 'Al': 3.0, 'Ni': 3.0}\",\"('Al', 'Hf', 'Ni')\",OTHERS,False\nmp-5438,\"{'B': 1.0, 'Rh': 3.0, 'Tm': 1.0}\",\"('B', 'Rh', 'Tm')\",OTHERS,False\nmp-647818,\"{'F': 12.0, 'Ni': 4.0, 'Cs': 2.0}\",\"('Cs', 'F', 'Ni')\",OTHERS,False\nmp-1212653,\"{'K': 12.0, 'Fe': 4.0, 'H': 12.0, 'C': 24.0, 'O': 48.0}\",\"('C', 'Fe', 'H', 'K', 'O')\",OTHERS,False\nmp-1066856,\"{'Ag': 2.0, 'O': 2.0}\",\"('Ag', 'O')\",OTHERS,False\nmp-1194844,\"{'Mg': 2.0, 'Bi': 4.0, 'I': 16.0, 'O': 16.0}\",\"('Bi', 'I', 'Mg', 'O')\",OTHERS,False\nmp-1106245,\"{'Zr': 10.0, 'Al': 2.0, 'Sb': 6.0}\",\"('Al', 'Sb', 'Zr')\",OTHERS,False\nmp-559713,\"{'Fe': 12.0, 'P': 16.0, 'O': 56.0}\",\"('Fe', 'O', 'P')\",OTHERS,False\nmp-1215198,\"{'Zr': 1.0, 'U': 1.0, 'B': 24.0}\",\"('B', 'U', 'Zr')\",OTHERS,False\nmp-1185235,\"{'Li': 3.0, 'Rh': 1.0}\",\"('Li', 'Rh')\",OTHERS,False\nmp-1252085,\"{'Al': 1.0, 'W': 3.0, 'O': 8.0}\",\"('Al', 'O', 'W')\",OTHERS,False\nmp-1226663,\"{'Er': 18.0, 'B': 10.0, 'S': 42.0}\",\"('B', 'Er', 'S')\",OTHERS,False\nmp-765686,\"{'Li': 4.0, 'Co': 8.0, 'O': 2.0, 'F': 16.0}\",\"('Co', 'F', 'Li', 'O')\",OTHERS,False\nmp-753532,\"{'Nb': 4.0, 'O': 6.0, 'F': 4.0}\",\"('F', 'Nb', 'O')\",OTHERS,False\nmp-572,\"{'Cd': 1.0, 'Sm': 1.0}\",\"('Cd', 'Sm')\",OTHERS,False\nmp-1211442,\"{'Rb': 4.0, 'Pr': 4.0, 'Se': 8.0, 'O': 52.0}\",\"('O', 'Pr', 'Rb', 'Se')\",OTHERS,False\nmp-1228150,\"{'Ba': 3.0, 'La': 1.0, 'Nb': 3.0, 'O': 12.0}\",\"('Ba', 'La', 'Nb', 'O')\",OTHERS,False\nmp-754647,\"{'Li': 3.0, 'Al': 3.0, 'V': 3.0, 'O': 12.0}\",\"('Al', 'Li', 'O', 'V')\",OTHERS,False\nmp-999068,\"{'Ti': 2.0, 'Al': 1.0, 'V': 1.0}\",\"('Al', 'Ti', 'V')\",OTHERS,False\nmp-1202245,\"{'Yb': 24.0, 'Sn': 16.0, 'Pd': 16.0}\",\"('Pd', 'Sn', 'Yb')\",OTHERS,False\nZn 73.6 Mg 2.5 Sc 11.2,\"{'Zn': 73.6, 'Mg': 2.5, 'Sc': 11.2}\",\"('Mg', 'Sc', 'Zn')\",AC,False\nmp-8479,\"{'Si': 2.0, 'S': 8.0, 'Tl': 8.0}\",\"('S', 'Si', 'Tl')\",OTHERS,False\nmp-758188,\"{'Li': 8.0, 'Cr': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Cr', 'Li', 'O', 'Si')\",OTHERS,False\nV 15 Ni 10 Si 1,\"{'V': 15.0, 'Ni': 10.0, 'Si': 1.0}\",\"('Ni', 'Si', 'V')\",QC,False\nmp-980757,\"{'Sm': 10.0, 'Sb': 6.0}\",\"('Sb', 'Sm')\",OTHERS,False\nmp-12494,\"{'Cd': 2.0, 'Te': 6.0, 'Cs': 2.0, 'Pr': 2.0}\",\"('Cd', 'Cs', 'Pr', 'Te')\",OTHERS,False\nmp-1100865,\"{'Yb': 10.0, 'Ti': 6.0, 'O': 27.0}\",\"('O', 'Ti', 'Yb')\",OTHERS,False\nmp-1181630,\"{'Er': 34.0, 'Te': 6.0, 'Ru': 12.0}\",\"('Er', 'Ru', 'Te')\",OTHERS,False\nmp-695295,\"{'H': 22.0, 'Ru': 1.0, 'N': 7.0, 'Cl': 4.0, 'O': 8.0}\",\"('Cl', 'H', 'N', 'O', 'Ru')\",OTHERS,False\nmp-755975,\"{'Li': 2.0, 'Ti': 1.0, 'V': 3.0, 'O': 8.0}\",\"('Li', 'O', 'Ti', 'V')\",OTHERS,False\nmp-1187453,\"{'Ti': 2.0, 'Al': 1.0, 'Ni': 1.0}\",\"('Al', 'Ni', 'Ti')\",OTHERS,False\nmp-772995,\"{'Li': 7.0, 'Ti': 12.0, 'Fe': 5.0, 'O': 32.0}\",\"('Fe', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-763535,\"{'Li': 1.0, 'P': 5.0, 'W': 3.0, 'O': 19.0}\",\"('Li', 'O', 'P', 'W')\",OTHERS,False\nmp-1198459,\"{'Nd': 8.0, 'S': 12.0, 'O': 64.0}\",\"('Nd', 'O', 'S')\",OTHERS,False\nmp-764838,\"{'K': 8.0, 'Co': 4.0, 'P': 16.0, 'H': 40.0, 'O': 68.0}\",\"('Co', 'H', 'K', 'O', 'P')\",OTHERS,False\nmp-561166,\"{'Na': 4.0, 'Zr': 2.0, 'Ge': 4.0, 'O': 14.0}\",\"('Ge', 'Na', 'O', 'Zr')\",OTHERS,False\nmp-753835,\"{'Li': 4.0, 'Mn': 8.0, 'O': 12.0, 'F': 4.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1224716,\"{'Fe': 2.0, 'Co': 1.0, 'Se': 4.0}\",\"('Co', 'Fe', 'Se')\",OTHERS,False\nmp-1027135,\"{'Te': 2.0, 'Mo': 2.0, 'W': 2.0, 'Se': 4.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-1247400,\"{'Sr': 2.0, 'Os': 14.0, 'N': 20.0}\",\"('N', 'Os', 'Sr')\",OTHERS,False\nmvc-11660,\"{'Ca': 1.0, 'Co': 4.0, 'O': 8.0}\",\"('Ca', 'Co', 'O')\",OTHERS,False\nmp-30021,\"{'K': 5.0, 'Sb': 4.0}\",\"('K', 'Sb')\",OTHERS,False\nmp-757214,\"{'Li': 3.0, 'Ti': 1.0, 'Co': 3.0, 'O': 8.0}\",\"('Co', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-1076683,\"{'Ba': 12.0, 'Sr': 20.0, 'Co': 16.0, 'Cu': 16.0, 'O': 80.0}\",\"('Ba', 'Co', 'Cu', 'O', 'Sr')\",OTHERS,False\nmvc-10594,\"{'Ca': 2.0, 'Cr': 6.0, 'P': 6.0, 'O': 26.0}\",\"('Ca', 'Cr', 'O', 'P')\",OTHERS,False\nmp-554160,\"{'K': 8.0, 'Cu': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Cu', 'K', 'O', 'P')\",OTHERS,False\nmp-1221439,\"{'Na': 4.0, 'Li': 5.0, 'W': 10.0, 'O': 30.0}\",\"('Li', 'Na', 'O', 'W')\",OTHERS,False\nmp-1177825,\"{'Li': 8.0, 'V': 12.0, 'W': 4.0, 'O': 32.0}\",\"('Li', 'O', 'V', 'W')\",OTHERS,False\nmp-1225817,\"{'Dy': 2.0, 'Fe': 4.0, 'Co': 4.0, 'B': 2.0}\",\"('B', 'Co', 'Dy', 'Fe')\",OTHERS,False\nmp-753274,\"{'Fe': 8.0, 'O': 8.0, 'F': 8.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-571584,\"{'Li': 1.0, 'Mg': 1.0, 'Sb': 1.0, 'Pt': 1.0}\",\"('Li', 'Mg', 'Pt', 'Sb')\",OTHERS,False\nmp-1106202,\"{'Ce': 4.0, 'Zn': 10.0, 'Sn': 2.0}\",\"('Ce', 'Sn', 'Zn')\",OTHERS,False\nmp-1218525,\"{'Sr': 3.0, 'La': 1.0, 'Ti': 2.0, 'Mn': 2.0, 'O': 12.0}\",\"('La', 'Mn', 'O', 'Sr', 'Ti')\",OTHERS,False\nmp-1225692,\"{'Cu': 4.0, 'Br': 1.0, 'Cl': 3.0}\",\"('Br', 'Cl', 'Cu')\",OTHERS,False\nmp-581601,\"{'Zn': 12.0, 'S': 12.0}\",\"('S', 'Zn')\",OTHERS,False\nmp-603128,\"{'O': 28.0, 'Fe': 8.0, 'Sr': 4.0, 'Eu': 8.0}\",\"('Eu', 'Fe', 'O', 'Sr')\",OTHERS,False\nmp-556103,\"{'Cs': 18.0, 'Ga': 12.0, 'F': 54.0}\",\"('Cs', 'F', 'Ga')\",OTHERS,False\nmp-1095382,\"{'Fe': 5.0, 'O': 7.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-23602,\"{'Ta': 4.0, 'Bi': 16.0, 'Cl': 4.0, 'O': 32.0}\",\"('Bi', 'Cl', 'O', 'Ta')\",OTHERS,False\nmp-865797,\"{'Lu': 1.0, 'Co': 2.0, 'Sn': 1.0}\",\"('Co', 'Lu', 'Sn')\",OTHERS,False\nmp-756834,\"{'Ho': 4.0, 'Cr': 4.0, 'O': 12.0}\",\"('Cr', 'Ho', 'O')\",OTHERS,False\nmp-1216633,\"{'U': 1.0, 'Ru': 2.0, 'Rh': 1.0}\",\"('Rh', 'Ru', 'U')\",OTHERS,False\nmp-1227870,\"{'Ce': 4.0, 'Zr': 2.0, 'Co': 32.0}\",\"('Ce', 'Co', 'Zr')\",OTHERS,False\nmp-1101492,\"{'Li': 2.0, 'V': 4.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-977552,\"{'Be': 3.0, 'Tc': 1.0}\",\"('Be', 'Tc')\",OTHERS,False\nmp-1200581,\"{'Bi': 16.0, 'B': 24.0, 'H': 8.0, 'O': 64.0}\",\"('B', 'Bi', 'H', 'O')\",OTHERS,False\nmp-998610,\"{'Tl': 1.0, 'Ag': 1.0, 'Cl': 3.0}\",\"('Ag', 'Cl', 'Tl')\",OTHERS,False\nmp-754833,\"{'Hf': 8.0, 'N': 8.0, 'O': 4.0}\",\"('Hf', 'N', 'O')\",OTHERS,False\nmp-1027286,\"{'Te': 2.0, 'Mo': 2.0, 'W': 2.0, 'Se': 2.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-1228939,\"{'Al': 6.0, 'N': 2.0, 'O': 6.0}\",\"('Al', 'N', 'O')\",OTHERS,False\nmp-761589,\"{'Nb': 2.0, 'Co': 6.0, 'O': 16.0}\",\"('Co', 'Nb', 'O')\",OTHERS,False\nmp-567891,\"{'Nd': 1.0, 'Ag': 2.0}\",\"('Ag', 'Nd')\",OTHERS,False\nmp-17383,\"{'Ni': 8.0, 'Ge': 4.0}\",\"('Ge', 'Ni')\",OTHERS,False\nmp-541636,\"{'Er': 4.0, 'P': 16.0, 'K': 4.0, 'O': 48.0}\",\"('Er', 'K', 'O', 'P')\",OTHERS,False\nmp-1238486,\"{'Mn': 2.0, 'H': 6.0, 'C': 10.0, 'N': 2.0, 'O': 12.0}\",\"('C', 'H', 'Mn', 'N', 'O')\",OTHERS,False\nmp-1192886,\"{'Ni': 6.0, 'N': 6.0, 'F': 18.0}\",\"('F', 'N', 'Ni')\",OTHERS,False\nmp-674420,\"{'Li': 8.0, 'Ni': 4.0, 'O': 12.0}\",\"('Li', 'Ni', 'O')\",OTHERS,False\nmp-9630,\"{'S': 2.0, 'Cr': 1.0, 'Tl': 1.0}\",\"('Cr', 'S', 'Tl')\",OTHERS,False\nmp-865519,\"{'Y': 1.0, 'Ta': 1.0, 'Ru': 2.0}\",\"('Ru', 'Ta', 'Y')\",OTHERS,False\nmp-1245297,\"{'B': 50.0, 'N': 50.0}\",\"('B', 'N')\",OTHERS,False\nmp-21677,\"{'Ca': 6.0, 'Ni': 16.0, 'In': 8.0}\",\"('Ca', 'In', 'Ni')\",OTHERS,False\nmp-753593,\"{'Ti': 10.0, 'O': 13.0}\",\"('O', 'Ti')\",OTHERS,False\nmp-1244998,\"{'Ga': 50.0, 'N': 50.0}\",\"('Ga', 'N')\",OTHERS,False\nmp-771945,\"{'Sn': 1.0, 'P': 6.0, 'W': 3.0, 'O': 24.0}\",\"('O', 'P', 'Sn', 'W')\",OTHERS,False\nmp-761141,\"{'Li': 11.0, 'V': 12.0, 'Co': 4.0, 'O': 32.0}\",\"('Co', 'Li', 'O', 'V')\",OTHERS,False\nmp-559369,\"{'Zr': 4.0, 'H': 24.0, 'N': 20.0, 'O': 70.0}\",\"('H', 'N', 'O', 'Zr')\",OTHERS,False\nmp-3629,\"{'O': 48.0, 'Y': 8.0, 'W': 12.0}\",\"('O', 'W', 'Y')\",OTHERS,False\nmp-1095424,\"{'Pr': 4.0, 'Mn': 2.0, 'As': 5.0}\",\"('As', 'Mn', 'Pr')\",OTHERS,False\nmp-559676,\"{'Sn': 2.0, 'S': 2.0}\",\"('S', 'Sn')\",OTHERS,False\nMn 4 Si 1,\"{'Mn': 4.0, 'Si': 1.0}\",\"('Mn', 'Si')\",QC,False\nmp-862618,\"{'Li': 1.0, 'Er': 1.0, 'Hg': 2.0}\",\"('Er', 'Hg', 'Li')\",OTHERS,False\nmp-1206490,\"{'Nb': 4.0, 'B': 4.0, 'Mo': 2.0}\",\"('B', 'Mo', 'Nb')\",OTHERS,False\nmp-865877,\"{'Ti': 2.0, 'Re': 1.0, 'Pt': 1.0}\",\"('Pt', 'Re', 'Ti')\",OTHERS,False\nmp-1182555,\"{'Cd': 4.0, 'C': 28.0, 'S': 12.0, 'N': 16.0}\",\"('C', 'Cd', 'N', 'S')\",OTHERS,False\nmvc-1239,\"{'Zn': 4.0, 'Ni': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Ni', 'O', 'P', 'Zn')\",OTHERS,False\nmp-13654,\"{'Y': 6.0, 'Sb': 8.0, 'Au': 6.0}\",\"('Au', 'Sb', 'Y')\",OTHERS,False\nmp-1209357,\"{'Rb': 3.0, 'Y': 1.0, 'P': 2.0, 'O': 8.0}\",\"('O', 'P', 'Rb', 'Y')\",OTHERS,False\nmp-1186269,\"{'Nd': 3.0, 'Hf': 1.0}\",\"('Hf', 'Nd')\",OTHERS,False\nmp-1183324,\"{'Ba': 1.0, 'Sr': 3.0}\",\"('Ba', 'Sr')\",OTHERS,False\nmp-1222962,\"{'La': 1.0, 'Nd': 3.0, 'Mn': 4.0, 'O': 12.0}\",\"('La', 'Mn', 'Nd', 'O')\",OTHERS,False\nmp-1179999,\"{'Pb': 20.0, 'S': 2.0, 'O': 26.0, 'F': 4.0}\",\"('F', 'O', 'Pb', 'S')\",OTHERS,False\nmp-1177365,\"{'Li': 4.0, 'Nb': 3.0, 'Ni': 3.0, 'Te': 2.0, 'O': 16.0}\",\"('Li', 'Nb', 'Ni', 'O', 'Te')\",OTHERS,False\nmp-1224422,\"{'Hf': 6.0, 'Ga': 6.0, 'Pd': 4.0, 'Pt': 2.0}\",\"('Ga', 'Hf', 'Pd', 'Pt')\",OTHERS,False\nmp-755487,\"{'Li': 4.0, 'Bi': 2.0, 'O': 6.0}\",\"('Bi', 'Li', 'O')\",OTHERS,False\nmp-1200162,\"{'C': 164.0, 'F': 56.0}\",\"('C', 'F')\",OTHERS,False\nmp-1207159,\"{'Cs': 3.0, 'Eu': 1.0, 'F': 6.0}\",\"('Cs', 'Eu', 'F')\",OTHERS,False\nmp-1206697,\"{'Eu': 1.0, 'Ni': 1.0, 'P': 1.0}\",\"('Eu', 'Ni', 'P')\",OTHERS,False\nmp-1197353,\"{'Ba': 8.0, 'Li': 2.0, 'Ga': 10.0, 'Se': 24.0}\",\"('Ba', 'Ga', 'Li', 'Se')\",OTHERS,False\nmp-1191101,\"{'K': 4.0, 'Mg': 4.0, 'B': 4.0, 'O': 12.0}\",\"('B', 'K', 'Mg', 'O')\",OTHERS,False\nmp-759946,\"{'Sc': 16.0, 'O': 15.0}\",\"('O', 'Sc')\",OTHERS,False\nmp-11500,\"{'Mn': 1.0, 'Ni': 1.0}\",\"('Mn', 'Ni')\",OTHERS,False\nmp-752802,\"{'Fe': 5.0, 'O': 4.0, 'F': 8.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-1101055,\"{'Ta': 2.0, 'In': 2.0, 'S': 4.0}\",\"('In', 'S', 'Ta')\",OTHERS,False\nmp-721246,\"{'Sr': 12.0, 'Co': 2.0, 'C': 4.0, 'N': 14.0}\",\"('C', 'Co', 'N', 'Sr')\",OTHERS,False\nmp-989525,\"{'Cs': 2.0, 'Rb': 1.0, 'Pb': 1.0, 'F': 6.0}\",\"('Cs', 'F', 'Pb', 'Rb')\",OTHERS,False\nmp-696633,\"{'Ca': 10.0, 'Ta': 1.0, 'Ti': 8.0, 'Al': 1.0, 'Si': 10.0, 'O': 50.0}\",\"('Al', 'Ca', 'O', 'Si', 'Ta', 'Ti')\",OTHERS,False\nmp-775969,\"{'V': 3.0, 'Cr': 3.0, 'W': 2.0, 'O': 16.0}\",\"('Cr', 'O', 'V', 'W')\",OTHERS,False\nmp-5752,\"{'Si': 2.0, 'Tb': 1.0, 'Ir': 2.0}\",\"('Ir', 'Si', 'Tb')\",OTHERS,False\nmp-1208504,\"{'Tb': 3.0, 'Fe': 2.0, 'Si': 7.0}\",\"('Fe', 'Si', 'Tb')\",OTHERS,False\nmp-1223852,\"{'In': 1.0, 'Ga': 1.0, 'Ag': 2.0, 'S': 4.0}\",\"('Ag', 'Ga', 'In', 'S')\",OTHERS,False\nmp-1201586,\"{'Zr': 4.0, 'P': 8.0, 'O': 32.0}\",\"('O', 'P', 'Zr')\",OTHERS,False\nmp-12846,\"{'Ga': 4.0, 'Ge': 4.0, 'Yb': 4.0}\",\"('Ga', 'Ge', 'Yb')\",OTHERS,False\nmp-864800,\"{'Yb': 2.0, 'Pd': 1.0, 'Au': 1.0}\",\"('Au', 'Pd', 'Yb')\",OTHERS,False\nmp-1247225,\"{'Mg': 16.0, 'Ge': 4.0, 'N': 16.0}\",\"('Ge', 'Mg', 'N')\",OTHERS,False\nmp-761137,\"{'Li': 10.0, 'Ti': 3.0, 'Mn': 5.0, 'O': 16.0}\",\"('Li', 'Mn', 'O', 'Ti')\",OTHERS,False\nmp-1027091,\"{'Mo': 2.0, 'W': 2.0, 'Se': 4.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-780936,\"{'Li': 5.0, 'V': 6.0, 'O': 5.0, 'F': 19.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-1075363,\"{'Mg': 10.0, 'Si': 12.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-754998,\"{'Ce': 2.0, 'Se': 1.0, 'O': 2.0}\",\"('Ce', 'O', 'Se')\",OTHERS,False\nmp-743613,\"{'K': 1.0, 'Mn': 1.0, 'H': 5.0, 'S': 2.0, 'O': 10.0}\",\"('H', 'K', 'Mn', 'O', 'S')\",OTHERS,False\nmp-1202713,\"{'Ir': 8.0, 'Cl': 8.0, 'O': 8.0, 'F': 48.0}\",\"('Cl', 'F', 'Ir', 'O')\",OTHERS,False\nmp-22512,\"{'Sr': 4.0, 'In': 4.0, 'O': 10.0}\",\"('In', 'O', 'Sr')\",OTHERS,False\nmp-1210571,\"{'Na': 4.0, 'Cd': 6.0, 'P': 8.0, 'N': 2.0, 'Cl': 2.0, 'O': 28.0}\",\"('Cd', 'Cl', 'N', 'Na', 'O', 'P')\",OTHERS,False\nmp-676104,\"{'Tm': 4.0, 'Cu': 2.0, 'O': 8.0}\",\"('Cu', 'O', 'Tm')\",OTHERS,False\nmp-1207090,\"{'Cu': 1.0, 'Sn': 1.0, 'Pd': 2.0}\",\"('Cu', 'Pd', 'Sn')\",OTHERS,False\nmp-1211794,\"{'K': 2.0, 'Na': 1.0, 'O': 2.0}\",\"('K', 'Na', 'O')\",OTHERS,False\nmp-542121,\"{'Rb': 4.0, 'Nb': 2.0, 'Si': 8.0, 'O': 23.0}\",\"('Nb', 'O', 'Rb', 'Si')\",OTHERS,False\nmp-1214990,\"{'Ba': 4.0, 'C': 8.0, 'O': 24.0}\",\"('Ba', 'C', 'O')\",OTHERS,False\nmp-24333,\"{'H': 24.0, 'O': 12.0, 'Cl': 6.0, 'U': 2.0}\",\"('Cl', 'H', 'O', 'U')\",OTHERS,False\nmp-1102232,\"{'H': 4.0, 'C': 4.0, 'N': 4.0}\",\"('C', 'H', 'N')\",OTHERS,False\nmp-1215852,\"{'Yb': 1.0, 'Eu': 1.0, 'Si': 4.0, 'Au': 4.0}\",\"('Au', 'Eu', 'Si', 'Yb')\",OTHERS,False\nmp-1201518,\"{'K': 6.0, 'Fe': 10.0, 'F': 30.0}\",\"('F', 'Fe', 'K')\",OTHERS,False\nGa 3.22 Ni 2.78 Zr 1,\"{'Ga': 3.22, 'Ni': 2.78, 'Zr': 1.0}\",\"('Ga', 'Ni', 'Zr')\",AC,False\nZn 77 Sc 8 Ho 8 Fe 7,\"{'Zn': 77.0, 'Sc': 8.0, 'Ho': 8.0, 'Fe': 7.0}\",\"('Fe', 'Ho', 'Sc', 'Zn')\",QC,False\nmp-1193079,\"{'Nd': 4.0, 'Cu': 24.0}\",\"('Cu', 'Nd')\",OTHERS,False\nmp-1177831,\"{'Li': 4.0, 'V': 4.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-16855,\"{'O': 26.0, 'Si': 4.0, 'K': 6.0, 'Ta': 6.0}\",\"('K', 'O', 'Si', 'Ta')\",OTHERS,False\nmp-774356,\"{'Li': 6.0, 'Mn': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Li', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1095397,\"{'Sn': 4.0, 'S': 8.0}\",\"('S', 'Sn')\",OTHERS,False\nmp-560925,\"{'Na': 10.0, 'Fe': 6.0, 'F': 28.0}\",\"('F', 'Fe', 'Na')\",OTHERS,False\nmp-1215427,\"{'Zn': 1.0, 'Cd': 1.0, 'Se': 2.0}\",\"('Cd', 'Se', 'Zn')\",OTHERS,False\nmp-19864,\"{'O': 12.0, 'Ca': 4.0, 'Fe': 2.0, 'W': 2.0}\",\"('Ca', 'Fe', 'O', 'W')\",OTHERS,False\nmp-766206,\"{'Sr': 1.0, 'Li': 4.0, 'La': 15.0, 'Co': 4.0, 'O': 32.0}\",\"('Co', 'La', 'Li', 'O', 'Sr')\",OTHERS,False\nmp-1101173,\"{'W': 2.0, 'N': 4.0}\",\"('N', 'W')\",OTHERS,False\nmp-644369,\"{'Ba': 2.0, 'Bi': 2.0, 'O': 6.0}\",\"('Ba', 'Bi', 'O')\",OTHERS,False\nmp-1201230,\"{'Bi': 4.0, 'Pb': 12.0, 'S': 18.0}\",\"('Bi', 'Pb', 'S')\",OTHERS,False\nmp-1190421,\"{'Cs': 2.0, 'Dy': 4.0, 'Ag': 6.0, 'Te': 10.0}\",\"('Ag', 'Cs', 'Dy', 'Te')\",OTHERS,False\nmp-1174284,\"{'Li': 6.0, 'Mn': 2.0, 'Co': 2.0, 'O': 10.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-9575,\"{'Li': 2.0, 'Be': 2.0, 'Sb': 2.0}\",\"('Be', 'Li', 'Sb')\",OTHERS,False\nmp-22317,\"{'Ga': 4.0, 'Eu': 2.0}\",\"('Eu', 'Ga')\",OTHERS,False\nmp-1223853,\"{'K': 2.0, 'Zr': 1.0, 'P': 2.0, 'O': 8.0}\",\"('K', 'O', 'P', 'Zr')\",OTHERS,False\nmp-1232235,\"{'Pm': 1.0, 'S': 1.0}\",\"('Pm', 'S')\",OTHERS,False\nmp-530036,\"{'Tb': 22.0, 'S': 32.0}\",\"('S', 'Tb')\",OTHERS,False\nmp-1184742,\"{'Gd': 1.0, 'Zr': 1.0, 'Ru': 2.0}\",\"('Gd', 'Ru', 'Zr')\",OTHERS,False\nmp-1056027,{'K': 1.0},\"('K',)\",OTHERS,False\nmp-541971,\"{'Tm': 2.0, 'Ni': 12.0, 'P': 7.0}\",\"('Ni', 'P', 'Tm')\",OTHERS,False\nmp-30622,\"{'Ru': 4.0, 'Dy': 10.0}\",\"('Dy', 'Ru')\",OTHERS,False\nmp-558122,\"{'Gd': 4.0, 'S': 4.0, 'Cl': 4.0, 'O': 16.0}\",\"('Cl', 'Gd', 'O', 'S')\",OTHERS,False\nmp-640922,\"{'Ce': 3.0, 'In': 3.0, 'Pt': 3.0}\",\"('Ce', 'In', 'Pt')\",OTHERS,False\nmp-867315,\"{'Ca': 1.0, 'Gd': 1.0, 'Hg': 2.0}\",\"('Ca', 'Gd', 'Hg')\",OTHERS,False\nmp-12574,\"{'C': 1.0, 'Dy': 2.0}\",\"('C', 'Dy')\",OTHERS,False\nmp-758011,\"{'Li': 4.0, 'Fe': 8.0, 'Si': 8.0, 'O': 26.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-685153,\"{'Fe': 64.0, 'O': 96.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-1201969,\"{'Ce': 2.0, 'N': 16.0, 'O': 36.0}\",\"('Ce', 'N', 'O')\",OTHERS,False\nmp-30334,\"{'Al': 14.0, 'Th': 4.0}\",\"('Al', 'Th')\",OTHERS,False\nmp-505148,\"{'Th': 2.0, 'Pb': 2.0, 'I': 12.0}\",\"('I', 'Pb', 'Th')\",OTHERS,False\nmp-686004,\"{'Li': 12.0, 'Sc': 4.0, 'Cl': 24.0}\",\"('Cl', 'Li', 'Sc')\",OTHERS,False\nmp-30675,\"{'Ga': 8.0, 'Ti': 10.0}\",\"('Ga', 'Ti')\",OTHERS,False\nmp-1208788,\"{'Sr': 6.0, 'Ti': 2.0, 'Si': 8.0, 'H': 4.0, 'O': 26.0}\",\"('H', 'O', 'Si', 'Sr', 'Ti')\",OTHERS,False\nmp-677127,\"{'Y': 1.0, 'Al': 6.0, 'Si': 18.0, 'N': 30.0, 'O': 2.0}\",\"('Al', 'N', 'O', 'Si', 'Y')\",OTHERS,False\nmp-557824,\"{'Rb': 8.0, 'Re': 12.0, 'S': 24.0}\",\"('Rb', 'Re', 'S')\",OTHERS,False\nmp-581276,\"{'Cu': 4.0, 'Te': 4.0, 'Cl': 4.0, 'O': 10.0}\",\"('Cl', 'Cu', 'O', 'Te')\",OTHERS,False\nmp-1212761,\"{'Gd': 1.0, 'Fe': 4.0, 'P': 12.0}\",\"('Fe', 'Gd', 'P')\",OTHERS,False\nmp-1105942,\"{'Sm': 10.0, 'Ga': 6.0}\",\"('Ga', 'Sm')\",OTHERS,False\nmp-1219003,\"{'Sn': 8.0, 'Se': 4.0, 'S': 8.0}\",\"('S', 'Se', 'Sn')\",OTHERS,False\nmp-1226283,\"{'Cr': 1.0, 'W': 1.0, 'O': 3.0}\",\"('Cr', 'O', 'W')\",OTHERS,False\nmvc-9917,\"{'Mg': 6.0, 'Fe': 12.0, 'O': 24.0}\",\"('Fe', 'Mg', 'O')\",OTHERS,False\nZn 84 Ti 8 Mg 8,\"{'Zn': 84.0, 'Ti': 8.0, 'Mg': 8.0}\",\"('Mg', 'Ti', 'Zn')\",QC,False\nmp-976254,\"{'Li': 3.0, 'Mg': 1.0}\",\"('Li', 'Mg')\",OTHERS,False\nmp-1185144,\"{'K': 2.0, 'Th': 6.0}\",\"('K', 'Th')\",OTHERS,False\nmp-1208894,\"{'Sm': 4.0, 'Si': 10.0, 'Pd': 6.0}\",\"('Pd', 'Si', 'Sm')\",OTHERS,False\nmp-559162,\"{'Si': 4.0, 'B': 44.0, 'H': 40.0, 'C': 16.0, 'F': 44.0}\",\"('B', 'C', 'F', 'H', 'Si')\",OTHERS,False\nmp-777342,\"{'V': 8.0, 'O': 6.0, 'F': 18.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-972205,\"{'V': 2.0, 'Mo': 1.0, 'Os': 1.0}\",\"('Mo', 'Os', 'V')\",OTHERS,False\nmp-1205601,\"{'Yb': 4.0, 'Sn': 2.0, 'Pd': 4.0}\",\"('Pd', 'Sn', 'Yb')\",OTHERS,False\nmp-510400,\"{'Gd': 1.0, 'Ir': 3.0}\",\"('Gd', 'Ir')\",OTHERS,False\nmp-765840,\"{'Li': 4.0, 'V': 3.0, 'Co': 1.0, 'P': 4.0, 'O': 16.0}\",\"('Co', 'Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1228163,\"{'Ca': 1.0, 'Mn': 1.0, 'Zn': 1.0, 'As': 2.0, 'H': 3.0, 'O': 10.0}\",\"('As', 'Ca', 'H', 'Mn', 'O', 'Zn')\",OTHERS,False\nmp-759878,\"{'Rb': 2.0, 'Mg': 2.0, 'H': 24.0, 'Cl': 6.0, 'O': 12.0}\",\"('Cl', 'H', 'Mg', 'O', 'Rb')\",OTHERS,False\nmp-753083,\"{'Li': 3.0, 'Fe': 4.0, 'O': 8.0}\",\"('Fe', 'Li', 'O')\",OTHERS,False\nmp-1195598,\"{'Ba': 2.0, 'Er': 8.0, 'Si': 10.0, 'O': 34.0}\",\"('Ba', 'Er', 'O', 'Si')\",OTHERS,False\nmp-1177942,\"{'Li': 4.0, 'Hf': 4.0, 'O': 10.0}\",\"('Hf', 'Li', 'O')\",OTHERS,False\nmp-1095822,\"{'Zr': 2.0, 'Pt': 1.0, 'Rh': 1.0}\",\"('Pt', 'Rh', 'Zr')\",OTHERS,False\nmp-1199886,\"{'Sn': 4.0, 'Mo': 30.0, 'Se': 38.0}\",\"('Mo', 'Se', 'Sn')\",OTHERS,False\nmp-1072085,\"{'Ce': 1.0, 'Si': 5.0}\",\"('Ce', 'Si')\",OTHERS,False\nmp-6080,\"{'S': 10.0, 'Co': 2.0, 'Ba': 2.0, 'Nd': 4.0}\",\"('Ba', 'Co', 'Nd', 'S')\",OTHERS,False\nmp-567197,\"{'Y': 2.0, 'Sn': 2.0, 'Au': 2.0}\",\"('Au', 'Sn', 'Y')\",OTHERS,False\nmp-1104194,\"{'Ca': 1.0, 'Mo': 6.0, 'S': 8.0}\",\"('Ca', 'Mo', 'S')\",OTHERS,False\nmp-540391,\"{'O': 16.0, 'P': 4.0, 'Co': 4.0}\",\"('Co', 'O', 'P')\",OTHERS,False\nmp-1229080,\"{'Ce': 5.0, 'Cu': 19.0, 'P': 12.0}\",\"('Ce', 'Cu', 'P')\",OTHERS,False\nmp-1020638,\"{'Na': 8.0, 'P': 1.0, 'O': 3.0}\",\"('Na', 'O', 'P')\",OTHERS,False\nmp-542327,\"{'Ce': 5.0, 'Co': 19.0}\",\"('Ce', 'Co')\",OTHERS,False\nmp-1178789,\"{'V': 2.0, 'Zn': 1.0, 'O': 6.0}\",\"('O', 'V', 'Zn')\",OTHERS,False\nmp-614951,\"{'Rb': 9.0, 'N': 9.0, 'O': 27.0}\",\"('N', 'O', 'Rb')\",OTHERS,False\nmp-1247652,\"{'Ca': 32.0, 'Mn': 24.0, 'Cr': 8.0, 'O': 84.0}\",\"('Ca', 'Cr', 'Mn', 'O')\",OTHERS,False\nmp-614502,{'Gd': 1.0},\"('Gd',)\",OTHERS,False\nmp-557785,\"{'Mn': 2.0, 'H': 42.0, 'C': 14.0, 'S': 6.0, 'N': 2.0}\",\"('C', 'H', 'Mn', 'N', 'S')\",OTHERS,False\nmp-973633,\"{'Lu': 2.0, 'Si': 4.0, 'Ni': 2.0}\",\"('Lu', 'Ni', 'Si')\",OTHERS,False\nmp-1388,\"{'Rh': 4.0, 'Dy': 2.0}\",\"('Dy', 'Rh')\",OTHERS,False\nmp-9580,\"{'Ga': 2.0, 'Se': 4.0, 'Tl': 2.0}\",\"('Ga', 'Se', 'Tl')\",OTHERS,False\nmp-562517,\"{'Ca': 2.0, 'Mg': 2.0, 'Si': 4.0, 'O': 12.0}\",\"('Ca', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-982558,\"{'Ho': 1.0, 'Ge': 1.0, 'O': 3.0}\",\"('Ge', 'Ho', 'O')\",OTHERS,False\nmp-1218474,\"{'Sr': 3.0, 'Fe': 2.0, 'Ru': 1.0, 'O': 9.0}\",\"('Fe', 'O', 'Ru', 'Sr')\",OTHERS,False\nmp-1247135,\"{'Si': 14.0, 'Ge': 2.0, 'N': 20.0}\",\"('Ge', 'N', 'Si')\",OTHERS,False\nmp-30922,\"{'Co': 1.0, 'Ho': 6.0, 'Bi': 2.0}\",\"('Bi', 'Co', 'Ho')\",OTHERS,False\nmp-18562,\"{'Na': 8.0, 'Si': 8.0, 'Se': 20.0}\",\"('Na', 'Se', 'Si')\",OTHERS,False\nmvc-15567,\"{'Mg': 4.0, 'Fe': 4.0, 'O': 8.0}\",\"('Fe', 'Mg', 'O')\",OTHERS,False\nmp-1016146,\"{'Mg': 4.0, 'Mn': 16.0, 'O': 32.0}\",\"('Mg', 'Mn', 'O')\",OTHERS,False\nmp-571222,\"{'Cs': 1.0, 'Br': 1.0}\",\"('Br', 'Cs')\",OTHERS,False\nmp-510494,\"{'La': 10.0, 'Sn': 6.0}\",\"('La', 'Sn')\",OTHERS,False\nmp-998428,\"{'Cs': 4.0, 'Ca': 4.0, 'I': 12.0}\",\"('Ca', 'Cs', 'I')\",OTHERS,False\nmp-698214,\"{'K': 1.0, 'Mn': 1.0, 'H': 24.0, 'C': 14.0, 'N': 8.0}\",\"('C', 'H', 'K', 'Mn', 'N')\",OTHERS,False\nmvc-11373,\"{'Ca': 2.0, 'Mn': 2.0, 'W': 2.0, 'O': 12.0}\",\"('Ca', 'Mn', 'O', 'W')\",OTHERS,False\nmp-1223474,\"{'K': 2.0, 'H': 2.0, 'C': 2.0, 'O': 6.0}\",\"('C', 'H', 'K', 'O')\",OTHERS,False\nmp-1199576,\"{'U': 12.0, 'Ag': 8.0, 'Ge': 8.0, 'O': 64.0}\",\"('Ag', 'Ge', 'O', 'U')\",OTHERS,False\nmp-1186222,\"{'Nb': 1.0, 'Ga': 1.0, 'Os': 2.0}\",\"('Ga', 'Nb', 'Os')\",OTHERS,False\nmvc-3251,\"{'Zn': 2.0, 'Cr': 2.0, 'P': 2.0, 'O': 10.0}\",\"('Cr', 'O', 'P', 'Zn')\",OTHERS,False\nmp-1039906,\"{'Sr': 1.0, 'Mg': 30.0, 'Si': 1.0, 'O': 32.0}\",\"('Mg', 'O', 'Si', 'Sr')\",OTHERS,False\nmp-705842,\"{'Fe': 9.0, 'Cu': 3.0, 'O': 16.0}\",\"('Cu', 'Fe', 'O')\",OTHERS,False\nmp-1068732,\"{'Th': 1.0, 'Si': 3.0, 'Ru': 1.0}\",\"('Ru', 'Si', 'Th')\",OTHERS,False\nmp-649616,\"{'Pd': 4.0, 'Xe': 8.0, 'F': 64.0}\",\"('F', 'Pd', 'Xe')\",OTHERS,False\nmp-532701,\"{'In': 2.0, 'Mg': 6.0, 'Na': 6.0, 'O': 48.0, 'S': 12.0}\",\"('In', 'Mg', 'Na', 'O', 'S')\",OTHERS,False\nmp-559328,\"{'Fe': 4.0, 'H': 16.0, 'C': 12.0, 'O': 20.0}\",\"('C', 'Fe', 'H', 'O')\",OTHERS,False\nmp-1097636,\"{'Ti': 1.0, 'Zn': 2.0, 'Pt': 1.0}\",\"('Pt', 'Ti', 'Zn')\",OTHERS,False\nmvc-3773,\"{'Y': 6.0, 'Ni': 6.0, 'O': 18.0}\",\"('Ni', 'O', 'Y')\",OTHERS,False\nmp-758950,\"{'V': 4.0, 'O': 5.0, 'F': 7.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-677288,\"{'Mg': 5.0, 'Al': 1.0, 'H': 21.0, 'O': 17.0}\",\"('Al', 'H', 'Mg', 'O')\",OTHERS,False\nmp-1183610,\"{'Ca': 2.0, 'Sm': 6.0}\",\"('Ca', 'Sm')\",OTHERS,False\nmp-976596,\"{'Li': 1.0, 'Lu': 2.0, 'Ga': 1.0}\",\"('Ga', 'Li', 'Lu')\",OTHERS,False\nmp-775441,\"{'Li': 4.0, 'V': 3.0, 'Fe': 3.0, 'Sb': 2.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'Sb', 'V')\",OTHERS,False\nmvc-12661,\"{'Zn': 4.0, 'Fe': 8.0, 'O': 16.0}\",\"('Fe', 'O', 'Zn')\",OTHERS,False\nmp-1228165,\"{'Al': 5.0, 'Se': 8.0}\",\"('Al', 'Se')\",OTHERS,False\nmp-1032984,\"{'Ca': 1.0, 'Mg': 6.0, 'Ni': 1.0, 'O': 8.0}\",\"('Ca', 'Mg', 'Ni', 'O')\",OTHERS,False\nmp-532676,\"{'Al': 6.0, 'Li': 2.0, 'Mg': 2.0, 'O': 48.0, 'Se': 12.0}\",\"('Al', 'Li', 'Mg', 'O', 'Se')\",OTHERS,False\nmp-1210779,\"{'Mo': 2.0, 'C': 8.0, 'O': 8.0, 'F': 12.0}\",\"('C', 'F', 'Mo', 'O')\",OTHERS,False\nmp-1193682,\"{'Mo': 4.0, 'S': 16.0, 'N': 8.0}\",\"('Mo', 'N', 'S')\",OTHERS,False\nmp-559427,\"{'Ba': 2.0, 'Sr': 8.0, 'U': 6.0, 'O': 28.0}\",\"('Ba', 'O', 'Sr', 'U')\",OTHERS,False\nmp-774931,\"{'Li': 2.0, 'La': 28.0, 'Cu': 12.0, 'O': 56.0}\",\"('Cu', 'La', 'Li', 'O')\",OTHERS,False\nmp-677056,\"{'Na': 6.0, 'U': 3.0, 'I': 18.0}\",\"('I', 'Na', 'U')\",OTHERS,False\nmp-559091,\"{'Si': 4.0, 'O': 8.0}\",\"('O', 'Si')\",OTHERS,False\nmp-1226939,\"{'Cd': 2.0, 'Te': 1.0, 'Se': 1.0}\",\"('Cd', 'Se', 'Te')\",OTHERS,False\nmp-849316,\"{'Co': 6.0, 'O': 2.0, 'F': 10.0}\",\"('Co', 'F', 'O')\",OTHERS,False\nmp-765543,\"{'Sr': 3.0, 'Rh': 16.0, 'O': 32.0}\",\"('O', 'Rh', 'Sr')\",OTHERS,False\nmp-772890,\"{'Np': 4.0, 'Se': 8.0, 'O': 24.0}\",\"('Np', 'O', 'Se')\",OTHERS,False\nmp-775275,\"{'Mn': 3.0, 'Sb': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Mn', 'O', 'P', 'Sb')\",OTHERS,False\nmp-1185726,\"{'Mg': 16.0, 'Sc': 1.0, 'Al': 12.0}\",\"('Al', 'Mg', 'Sc')\",OTHERS,False\nmp-863365,\"{'Li': 4.0, 'Sn': 2.0, 'P': 8.0, 'O': 24.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,False\nmp-28611,\"{'O': 14.0, 'Ta': 4.0, 'Th': 2.0}\",\"('O', 'Ta', 'Th')\",OTHERS,False\nmp-1212398,\"{'Li': 8.0, 'Eu': 20.0, 'Sn': 28.0}\",\"('Eu', 'Li', 'Sn')\",OTHERS,False\nmp-1199190,\"{'Ba': 12.0, 'Bi': 3.0, 'Ir': 9.0, 'O': 36.0}\",\"('Ba', 'Bi', 'Ir', 'O')\",OTHERS,False\nmp-1071163,\"{'Ti': 3.0, 'O': 3.0}\",\"('O', 'Ti')\",OTHERS,False\nmp-1079725,\"{'Er': 6.0, 'Co': 1.0, 'Te': 2.0}\",\"('Co', 'Er', 'Te')\",OTHERS,False\nmp-21109,\"{'La': 3.0, 'Al': 12.0}\",\"('Al', 'La')\",OTHERS,False\nmp-1202524,\"{'K': 4.0, 'Cu': 2.0, 'H': 28.0, 'C': 8.0, 'N': 12.0, 'O': 16.0}\",\"('C', 'Cu', 'H', 'K', 'N', 'O')\",OTHERS,False\nmp-867863,\"{'Sm': 1.0, 'Cu': 4.0, 'Ag': 1.0}\",\"('Ag', 'Cu', 'Sm')\",OTHERS,False\nmp-1001612,\"{'Lu': 2.0, 'Si': 2.0}\",\"('Lu', 'Si')\",OTHERS,False\nmp-559235,\"{'B': 2.0, 'H': 22.0, 'C': 8.0, 'N': 2.0, 'Cl': 2.0, 'F': 8.0}\",\"('B', 'C', 'Cl', 'F', 'H', 'N')\",OTHERS,False\nmp-768605,\"{'Ho': 8.0, 'Ti': 4.0, 'O': 20.0}\",\"('Ho', 'O', 'Ti')\",OTHERS,False\nmp-6977,\"{'Be': 6.0, 'N': 4.0}\",\"('Be', 'N')\",OTHERS,False\nAl 70 Pd 23 Mn 6 Si 1,\"{'Al': 70.0, 'Pd': 23.0, 'Mn': 6.0, 'Si': 1.0}\",\"('Al', 'Mn', 'Pd', 'Si')\",AC,False\nmp-755442,\"{'Mn': 4.0, 'Co': 1.0, 'O': 8.0}\",\"('Co', 'Mn', 'O')\",OTHERS,False\nmp-1215570,\"{'Zr': 3.0, 'Sn': 4.0, 'Sb': 2.0}\",\"('Sb', 'Sn', 'Zr')\",OTHERS,False\nmp-1177309,\"{'Li': 8.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-772840,\"{'Sm': 8.0, 'Ge': 8.0, 'O': 28.0}\",\"('Ge', 'O', 'Sm')\",OTHERS,False\nmp-5709,\"{'O': 8.0, 'P': 2.0, 'Tl': 6.0}\",\"('O', 'P', 'Tl')\",OTHERS,False\nmp-1232230,\"{'Eu': 2.0, 'Se': 4.0}\",\"('Eu', 'Se')\",OTHERS,False\nmp-705895,\"{'Ba': 2.0, 'Nb': 4.0, 'Fe': 4.0, 'P': 12.0, 'O': 48.0}\",\"('Ba', 'Fe', 'Nb', 'O', 'P')\",OTHERS,False\nmp-619575,\"{'Cl': 8.0, 'F': 24.0}\",\"('Cl', 'F')\",OTHERS,False\nmp-697144,\"{'Rb': 2.0, 'H': 4.0, 'N': 2.0}\",\"('H', 'N', 'Rb')\",OTHERS,False\nmp-1223497,\"{'La': 2.0, 'Ge': 4.0, 'Pd': 3.0, 'Pt': 1.0}\",\"('Ge', 'La', 'Pd', 'Pt')\",OTHERS,False\nmp-1222541,\"{'Li': 4.0, 'Fe': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1076543,\"{'Ca': 7.0, 'Mg': 1.0, 'Ti': 1.0, 'Mn': 7.0, 'O': 24.0}\",\"('Ca', 'Mg', 'Mn', 'O', 'Ti')\",OTHERS,False\nmp-20989,\"{'Ba': 1.0, 'Gd': 1.0, 'Fe': 2.0, 'O': 5.0}\",\"('Ba', 'Fe', 'Gd', 'O')\",OTHERS,False\nmp-862840,\"{'Pr': 11.0, 'In': 9.0, 'Ni': 4.0}\",\"('In', 'Ni', 'Pr')\",OTHERS,False\nAu 47.2 In 37.2 Gd 15.6,\"{'Au': 47.2, 'In': 37.2, 'Gd': 15.6}\",\"('Au', 'Gd', 'In')\",AC,False\nmp-18343,\"{'O': 28.0, 'Mg': 4.0, 'P': 8.0, 'Ba': 4.0}\",\"('Ba', 'Mg', 'O', 'P')\",OTHERS,False\nmp-754365,\"{'Li': 4.0, 'Cr': 4.0, 'O': 14.0}\",\"('Cr', 'Li', 'O')\",OTHERS,False\nmp-1104364,\"{'Cd': 2.0, 'Br': 4.0, 'O': 8.0}\",\"('Br', 'Cd', 'O')\",OTHERS,False\nmp-1178324,\"{'Fe': 6.0, 'O': 2.0, 'F': 14.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-11102,\"{'Rh': 4.0, 'In': 4.0, 'Yb': 4.0}\",\"('In', 'Rh', 'Yb')\",OTHERS,False\nmp-1232260,\"{'Mg': 4.0, 'In': 8.0, 'S': 16.0}\",\"('In', 'Mg', 'S')\",OTHERS,False\nmp-1209414,\"{'Sm': 8.0, 'Mn': 2.0, 'S': 14.0}\",\"('Mn', 'S', 'Sm')\",OTHERS,False\nmp-510570,\"{'Ca': 1.0, 'Mn': 4.0, 'Cu': 3.0, 'O': 12.0}\",\"('Ca', 'Cu', 'Mn', 'O')\",OTHERS,False\nmp-1192361,\"{'Zn': 22.0, 'Co': 4.0}\",\"('Co', 'Zn')\",OTHERS,False\nGa 3.64 Ni 2.36 Sc 1,\"{'Ga': 3.64, 'Ni': 2.36, 'Sc': 1.0}\",\"('Ga', 'Ni', 'Sc')\",AC,False\nmp-1200625,\"{'Ba': 2.0, 'Ti': 2.0, 'Mn': 4.0, 'Si': 4.0, 'O': 20.0}\",\"('Ba', 'Mn', 'O', 'Si', 'Ti')\",OTHERS,False\nmp-1033061,\"{'Ba': 1.0, 'Mg': 6.0, 'Al': 1.0, 'O': 8.0}\",\"('Al', 'Ba', 'Mg', 'O')\",OTHERS,False\nmp-756259,\"{'Li': 4.0, 'Mn': 5.0, 'Nb': 1.0, 'O': 12.0}\",\"('Li', 'Mn', 'Nb', 'O')\",OTHERS,False\nmp-1187499,\"{'Ti': 1.0, 'Mn': 1.0, 'Ir': 2.0}\",\"('Ir', 'Mn', 'Ti')\",OTHERS,False\nmp-1176872,\"{'Li': 14.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1193772,\"{'Tl': 8.0, 'Cr': 4.0, 'O': 16.0}\",\"('Cr', 'O', 'Tl')\",OTHERS,False\nmp-1212777,\"{'K': 24.0, 'Ge': 48.0, 'O': 96.0}\",\"('Ge', 'K', 'O')\",OTHERS,False\nmp-17562,\"{'B': 6.0, 'O': 18.0, 'Sc': 2.0, 'Sr': 6.0}\",\"('B', 'O', 'Sc', 'Sr')\",OTHERS,False\nmp-1213601,\"{'Dy': 22.0, 'Ge': 36.0}\",\"('Dy', 'Ge')\",OTHERS,False\nmp-557611,\"{'Mn': 4.0, 'Si': 4.0, 'Pb': 4.0, 'O': 18.0}\",\"('Mn', 'O', 'Pb', 'Si')\",OTHERS,False\nmp-1038735,\"{'Cs': 1.0, 'Mg': 30.0, 'Bi': 1.0, 'O': 32.0}\",\"('Bi', 'Cs', 'Mg', 'O')\",OTHERS,False\nmp-1216009,\"{'Y': 2.0, 'Lu': 2.0, 'Ga': 12.0}\",\"('Ga', 'Lu', 'Y')\",OTHERS,False\nmp-10334,\"{'P': 6.0, 'Cd': 6.0, 'Pr': 2.0}\",\"('Cd', 'P', 'Pr')\",OTHERS,False\nmp-1179679,\"{'Rb': 2.0, 'P': 2.0}\",\"('P', 'Rb')\",OTHERS,False\nmp-1203086,\"{'Sr': 18.0, 'W': 6.0, 'O': 36.0}\",\"('O', 'Sr', 'W')\",OTHERS,False\nmp-559644,\"{'K': 8.0, 'Cu': 4.0, 'P': 12.0, 'S': 36.0}\",\"('Cu', 'K', 'P', 'S')\",OTHERS,False\nmp-648893,\"{'Na': 32.0, 'V': 16.0, 'O': 56.0}\",\"('Na', 'O', 'V')\",OTHERS,False\nmp-1214545,\"{'Ba': 10.0, 'As': 6.0, 'S': 2.0, 'O': 24.0}\",\"('As', 'Ba', 'O', 'S')\",OTHERS,False\nmp-1186437,\"{'Pd': 3.0, 'W': 1.0}\",\"('Pd', 'W')\",OTHERS,False\nmp-93,{'U': 30.0},\"('U',)\",OTHERS,False\nmp-759155,\"{'Li': 8.0, 'Fe': 8.0, 'C': 8.0, 'O': 28.0}\",\"('C', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1195788,\"{'K': 8.0, 'Mo': 8.0, 'Se': 8.0, 'O': 44.0}\",\"('K', 'Mo', 'O', 'Se')\",OTHERS,False\nmp-740930,\"{'La': 2.0, 'P': 6.0, 'H': 12.0, 'O': 12.0}\",\"('H', 'La', 'O', 'P')\",OTHERS,False\nmp-1195859,\"{'Na': 2.0, 'B': 2.0, 'H': 68.0, 'C': 24.0, 'N': 8.0}\",\"('B', 'C', 'H', 'N', 'Na')\",OTHERS,False\nmp-550745,\"{'O': 6.0, 'Fe': 1.0, 'Bi': 2.0, 'Ti': 1.0}\",\"('Bi', 'Fe', 'O', 'Ti')\",OTHERS,False\nmp-1200868,\"{'La': 8.0, 'P': 18.0, 'Rh': 16.0}\",\"('La', 'P', 'Rh')\",OTHERS,False\nmp-4856,\"{'Co': 1.0, 'Ga': 5.0, 'Ho': 1.0}\",\"('Co', 'Ga', 'Ho')\",OTHERS,False\nmp-1091398,\"{'Y': 3.0, 'Si': 3.0, 'Ag': 3.0}\",\"('Ag', 'Si', 'Y')\",OTHERS,False\nmp-775999,\"{'Li': 4.0, 'Ti': 3.0, 'Nb': 2.0, 'Fe': 3.0, 'O': 16.0}\",\"('Fe', 'Li', 'Nb', 'O', 'Ti')\",OTHERS,False\nmp-1246548,\"{'In': 1.0, 'Fe': 2.0, 'N': 2.0}\",\"('Fe', 'In', 'N')\",OTHERS,False\nmp-768082,\"{'Na': 10.0, 'Ni': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Na', 'Ni', 'O', 'P')\",OTHERS,False\nmp-1195803,\"{'Na': 12.0, 'Nb': 12.0, 'O': 36.0}\",\"('Na', 'Nb', 'O')\",OTHERS,False\nmp-1106224,\"{'Mn': 4.0, 'V': 4.0, 'O': 12.0}\",\"('Mn', 'O', 'V')\",OTHERS,False\nmp-1212897,\"{'Er': 8.0, 'Si': 12.0, 'Pd': 4.0}\",\"('Er', 'Pd', 'Si')\",OTHERS,False\nmp-1026016,\"{'Te': 2.0, 'Mo': 2.0, 'W': 1.0, 'Se': 2.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-1036160,\"{'Sr': 1.0, 'Mg': 14.0, 'Mn': 1.0, 'O': 16.0}\",\"('Mg', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-676338,\"{'Mg': 2.0, 'In': 4.0, 'O': 8.0}\",\"('In', 'Mg', 'O')\",OTHERS,False\nmp-1180166,\"{'Na': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Na', 'O', 'P')\",OTHERS,False\nmp-754322,\"{'Sr': 2.0, 'Cu': 1.0, 'O': 4.0}\",\"('Cu', 'O', 'Sr')\",OTHERS,False\nmp-1211441,\"{'Nd': 46.0, 'Mg': 8.0, 'Ni': 14.0}\",\"('Mg', 'Nd', 'Ni')\",OTHERS,False\nmvc-4829,\"{'Zn': 2.0, 'Mo': 4.0, 'O': 8.0}\",\"('Mo', 'O', 'Zn')\",OTHERS,False\nmp-639789,\"{'Fe': 8.0, 'Re': 12.0}\",\"('Fe', 'Re')\",OTHERS,False\nmp-974950,\"{'Rb': 3.0, 'Li': 1.0}\",\"('Li', 'Rb')\",OTHERS,False\nmvc-2902,\"{'Ca': 8.0, 'Ti': 10.0, 'Te': 6.0, 'O': 36.0}\",\"('Ca', 'O', 'Te', 'Ti')\",OTHERS,False\nmp-1177125,\"{'Li': 5.0, 'Fe': 3.0, 'O': 2.0, 'F': 10.0}\",\"('F', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-755735,\"{'Li': 2.0, 'Co': 4.0, 'O': 3.0, 'F': 8.0}\",\"('Co', 'F', 'Li', 'O')\",OTHERS,False\nmp-758939,\"{'Li': 4.0, 'Fe': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1224991,\"{'K': 2.0, 'Na': 2.0, 'Ca': 4.0, 'Mg': 15.0, 'Si': 24.0, 'O': 72.0}\",\"('Ca', 'K', 'Mg', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1185553,\"{'Cs': 3.0, 'Hg': 1.0}\",\"('Cs', 'Hg')\",OTHERS,False\nmp-1191623,\"{'Ce': 2.0, 'Ni': 12.0, 'As': 7.0}\",\"('As', 'Ce', 'Ni')\",OTHERS,False\nmp-1094363,\"{'Y': 3.0, 'Mg': 3.0}\",\"('Mg', 'Y')\",OTHERS,False\nmp-3141,\"{'Rh': 3.0, 'In': 3.0, 'Er': 3.0}\",\"('Er', 'In', 'Rh')\",OTHERS,False\nmp-12789,\"{'Al': 20.0, 'Fe': 4.0, 'Yb': 2.0}\",\"('Al', 'Fe', 'Yb')\",OTHERS,False\nMg 43 Al 42 Pd 15,\"{'Mg': 43.0, 'Al': 42.0, 'Pd': 15.0}\",\"('Al', 'Mg', 'Pd')\",QC,False\nmp-541318,\"{'Cs': 2.0, 'Ti': 6.0, 'P': 12.0, 'Si': 4.0, 'O': 50.0}\",\"('Cs', 'O', 'P', 'Si', 'Ti')\",OTHERS,False\nmp-1182147,\"{'C': 4.0, 'S': 4.0, 'N': 8.0}\",\"('C', 'N', 'S')\",OTHERS,False\nmp-1184781,\"{'In': 1.0, 'Ge': 3.0}\",\"('Ge', 'In')\",OTHERS,False\nmp-864926,\"{'Hf': 1.0, 'Pa': 1.0, 'Tc': 2.0}\",\"('Hf', 'Pa', 'Tc')\",OTHERS,False\nmp-38950,\"{'Ce': 4.0, 'O': 14.0, 'Y': 4.0}\",\"('Ce', 'O', 'Y')\",OTHERS,False\nmvc-34,\"{'Mn': 4.0, 'S': 8.0}\",\"('Mn', 'S')\",OTHERS,False\nmp-1177986,\"{'Li': 2.0, 'Fe': 1.0, 'S': 2.0}\",\"('Fe', 'Li', 'S')\",OTHERS,False\nmp-980189,\"{'Yb': 3.0, 'Na': 1.0}\",\"('Na', 'Yb')\",OTHERS,False\nmp-1184097,\"{'Er': 2.0, 'Cu': 1.0, 'Tc': 1.0}\",\"('Cu', 'Er', 'Tc')\",OTHERS,False\nmp-1220765,\"{'Na': 2.0, 'Hf': 6.0, 'N': 6.0, 'Cl': 6.0}\",\"('Cl', 'Hf', 'N', 'Na')\",OTHERS,False\nmp-2398,\"{'Sc': 4.0, 'Sn': 8.0}\",\"('Sc', 'Sn')\",OTHERS,False\nmp-8249,\"{'P': 2.0, 'S': 6.0, 'Tl': 2.0}\",\"('P', 'S', 'Tl')\",OTHERS,False\nmp-1252404,\"{'Ba': 2.0, 'Al': 2.0, 'Ni': 8.0, 'O': 14.0}\",\"('Al', 'Ba', 'Ni', 'O')\",OTHERS,False\nmp-698580,\"{'Co': 7.0, 'Re': 17.0, 'O': 48.0}\",\"('Co', 'O', 'Re')\",OTHERS,False\nmp-1095669,\"{'Hf': 4.0, 'Tc': 8.0}\",\"('Hf', 'Tc')\",OTHERS,False\nmp-1076439,\"{'Ba': 4.0, 'Co': 4.0, 'O': 10.0}\",\"('Ba', 'Co', 'O')\",OTHERS,False\nmp-1205088,\"{'La': 26.0, 'Hg': 116.0}\",\"('Hg', 'La')\",OTHERS,False\nmp-11140,\"{'Sb': 20.0, 'Ho': 8.0}\",\"('Ho', 'Sb')\",OTHERS,False\nmp-1182477,\"{'As': 1.0, 'N': 1.0, 'O': 2.0, 'F': 6.0}\",\"('As', 'F', 'N', 'O')\",OTHERS,False\nmp-768437,\"{'Li': 8.0, 'Bi': 4.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Bi', 'Li', 'O')\",OTHERS,False\nmp-554911,\"{'K': 2.0, 'Mn': 2.0, 'Zn': 4.0, 'Si': 4.0, 'O': 15.0}\",\"('K', 'Mn', 'O', 'Si', 'Zn')\",OTHERS,False\nmp-1079272,\"{'Sr': 2.0, 'Zr': 1.0, 'V': 1.0, 'O': 6.0}\",\"('O', 'Sr', 'V', 'Zr')\",OTHERS,False\nmp-1095833,\"{'Tl': 2.0, 'Hg': 1.0, 'Te': 1.0}\",\"('Hg', 'Te', 'Tl')\",OTHERS,False\nmp-1027058,\"{'Te': 4.0, 'Mo': 2.0, 'W': 2.0, 'S': 4.0}\",\"('Mo', 'S', 'Te', 'W')\",OTHERS,False\nmp-1029665,\"{'Sr': 6.0, 'B': 2.0, 'N': 6.0}\",\"('B', 'N', 'Sr')\",OTHERS,False\nmp-1188711,\"{'U': 4.0, 'V': 4.0, 'S': 12.0}\",\"('S', 'U', 'V')\",OTHERS,False\nmp-753374,\"{'Li': 4.0, 'V': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Li', 'O', 'Si', 'V')\",OTHERS,False\nmp-607450,\"{'In': 12.0, 'Ru': 4.0}\",\"('In', 'Ru')\",OTHERS,False\nmp-1199496,\"{'Cd': 48.0, 'As': 32.0}\",\"('As', 'Cd')\",OTHERS,False\nmp-1097684,\"{'Mn': 1.0, 'V': 2.0, 'Mo': 1.0}\",\"('Mn', 'Mo', 'V')\",OTHERS,False\nmp-864894,\"{'Be': 6.0, 'Rh': 2.0}\",\"('Be', 'Rh')\",OTHERS,False\nmp-1113012,\"{'Cs': 2.0, 'Li': 1.0, 'Mo': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cs', 'Li', 'Mo')\",OTHERS,False\nmp-1246262,\"{'Zn': 4.0, 'Ir': 2.0, 'N': 6.0}\",\"('Ir', 'N', 'Zn')\",OTHERS,False\nmp-1226937,\"{'Ce': 3.0, 'Th': 3.0, 'Si': 4.0}\",\"('Ce', 'Si', 'Th')\",OTHERS,False\nmp-1220898,\"{'Pr': 32.0, 'In': 3.0, 'Rh': 19.0}\",\"('In', 'Pr', 'Rh')\",OTHERS,False\nmp-1185664,\"{'Mg': 16.0, 'Al': 12.0, 'Ga': 1.0}\",\"('Al', 'Ga', 'Mg')\",OTHERS,False\nmp-1187970,\"{'Yb': 3.0, 'Cr': 1.0}\",\"('Cr', 'Yb')\",OTHERS,False\nmp-23082,\"{'O': 12.0, 'Cl': 4.0, 'K': 4.0, 'Cr': 4.0}\",\"('Cl', 'Cr', 'K', 'O')\",OTHERS,False\nmp-759825,\"{'Bi': 4.0, 'O': 4.0, 'F': 4.0}\",\"('Bi', 'F', 'O')\",OTHERS,False\nmp-1185607,\"{'La': 1.0, 'Eu': 1.0, 'Hg': 2.0}\",\"('Eu', 'Hg', 'La')\",OTHERS,False\nmp-720868,\"{'Ca': 4.0, 'Cd': 2.0, 'H': 48.0, 'Cl': 12.0, 'O': 24.0}\",\"('Ca', 'Cd', 'Cl', 'H', 'O')\",OTHERS,False\nmp-1188308,\"{'Er': 10.0, 'Sb': 2.0, 'Au': 4.0}\",\"('Au', 'Er', 'Sb')\",OTHERS,False\nmp-1120802,\"{'Li': 12.0, 'Mn': 1.0, 'Co': 1.0, 'Ni': 10.0, 'O': 24.0}\",\"('Co', 'Li', 'Mn', 'Ni', 'O')\",OTHERS,False\nmp-1096508,\"{'K': 2.0, 'Hg': 1.0, 'Au': 1.0}\",\"('Au', 'Hg', 'K')\",OTHERS,False\nmp-675142,\"{'Yb': 12.0, 'Dy': 4.0, 'Sb': 12.0}\",\"('Dy', 'Sb', 'Yb')\",OTHERS,False\nmp-571189,\"{'Na': 2.0, 'Hf': 4.0, 'Cu': 2.0, 'Se': 10.0}\",\"('Cu', 'Hf', 'Na', 'Se')\",OTHERS,False\nmp-567352,\"{'Ga': 8.0, 'Sb': 32.0, 'Br': 32.0}\",\"('Br', 'Ga', 'Sb')\",OTHERS,False\nmp-754595,\"{'Li': 2.0, 'Ti': 3.0, 'Fe': 1.0, 'O': 8.0}\",\"('Fe', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-675535,\"{'La': 4.0, 'Sn': 2.0, 'Sb': 8.0}\",\"('La', 'Sb', 'Sn')\",OTHERS,False\nmp-1211930,\"{'K': 4.0, 'Ho': 4.0, 'S': 8.0, 'O': 40.0}\",\"('Ho', 'K', 'O', 'S')\",OTHERS,False\nmp-1226457,\"{'Cs': 16.0, 'Mn': 12.0, 'O': 24.0}\",\"('Cs', 'Mn', 'O')\",OTHERS,False\nmp-1179495,\"{'Si': 1.0, 'Hg': 2.0, 'O': 4.0, 'F': 6.0}\",\"('F', 'Hg', 'O', 'Si')\",OTHERS,False\nmp-605006,\"{'Li': 8.0, 'H': 64.0, 'C': 16.0, 'S': 16.0, 'N': 8.0, 'O': 40.0}\",\"('C', 'H', 'Li', 'N', 'O', 'S')\",OTHERS,False\nmp-1228565,\"{'Ba': 3.0, 'Sr': 3.0, 'Np': 2.0}\",\"('Ba', 'Np', 'Sr')\",OTHERS,False\nmp-31186,\"{'Al': 1.0, 'Fe': 2.0, 'Ni': 1.0}\",\"('Al', 'Fe', 'Ni')\",OTHERS,False\nmp-1202543,\"{'Na': 4.0, 'H': 48.0, 'Ru': 4.0, 'C': 16.0, 'S': 8.0, 'Cl': 16.0, 'O': 8.0}\",\"('C', 'Cl', 'H', 'Na', 'O', 'Ru', 'S')\",OTHERS,False\nmp-1222201,\"{'Mg': 6.0, 'Fe': 2.0, 'Si': 4.0, 'O': 16.0}\",\"('Fe', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-1173915,\"{'Li': 4.0, 'Mn': 2.0, 'Co': 2.0, 'O': 8.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1215285,\"{'Zr': 2.0, 'Cr': 2.0, 'Cu': 2.0, 'Se': 8.0}\",\"('Cr', 'Cu', 'Se', 'Zr')\",OTHERS,False\nmp-1100673,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-2921,\"{'O': 48.0, 'Ga': 20.0, 'Gd': 12.0}\",\"('Ga', 'Gd', 'O')\",OTHERS,False\nmvc-8002,\"{'Zn': 4.0, 'Co': 6.0, 'O': 16.0}\",\"('Co', 'O', 'Zn')\",OTHERS,False\nmp-1211408,\"{'Lu': 6.0, 'Al': 60.0, 'Re': 12.0}\",\"('Al', 'Lu', 'Re')\",OTHERS,False\nmp-1246181,\"{'Mn': 16.0, 'Si': 40.0, 'N': 64.0}\",\"('Mn', 'N', 'Si')\",OTHERS,False\nmp-11572,\"{'Tc': 1.0, 'Ta': 1.0}\",\"('Ta', 'Tc')\",OTHERS,False\nmp-761916,\"{'Na': 4.0, 'H': 16.0, 'Au': 4.0, 'Br': 16.0, 'O': 8.0}\",\"('Au', 'Br', 'H', 'Na', 'O')\",OTHERS,False\nmp-27842,\"{'Cr': 2.0, 'Br': 2.0, 'O': 2.0}\",\"('Br', 'Cr', 'O')\",OTHERS,False\nmp-1186466,\"{'Pr': 1.0, 'Nd': 1.0, 'Ir': 2.0}\",\"('Ir', 'Nd', 'Pr')\",OTHERS,False\nmp-1188989,\"{'Tb': 6.0, 'Co': 4.0, 'Ge': 6.0}\",\"('Co', 'Ge', 'Tb')\",OTHERS,False\nmp-449,\"{'Si': 6.0, 'Fe': 10.0}\",\"('Fe', 'Si')\",OTHERS,False\nmp-1095201,\"{'Zr': 2.0, 'Cu': 2.0, 'Ge': 4.0}\",\"('Cu', 'Ge', 'Zr')\",OTHERS,False\nmp-765566,\"{'Li': 3.0, 'Ag': 6.0, 'F': 18.0}\",\"('Ag', 'F', 'Li')\",OTHERS,False\nmp-1218787,\"{'Sr': 4.0, 'Ce': 1.0, 'Y': 3.0, 'Cu': 4.0, 'Ru': 2.0, 'O': 20.0}\",\"('Ce', 'Cu', 'O', 'Ru', 'Sr', 'Y')\",OTHERS,False\nmp-1244586,\"{'Zn': 4.0, 'Cr': 4.0, 'Bi': 4.0, 'O': 20.0}\",\"('Bi', 'Cr', 'O', 'Zn')\",OTHERS,False\nmp-22904,\"{'Cl': 4.0, 'Ca': 2.0}\",\"('Ca', 'Cl')\",OTHERS,False\nmp-1175376,\"{'Li': 9.0, 'Co': 7.0, 'O': 16.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-1178362,\"{'Dy': 2.0, 'Cl': 2.0, 'O': 2.0}\",\"('Cl', 'Dy', 'O')\",OTHERS,False\nAu 62.3 Sn 23.1 Gd 14.6,\"{'Au': 62.3, 'Sn': 23.1, 'Gd': 14.6}\",\"('Au', 'Gd', 'Sn')\",AC,False\nmp-1184860,\"{'Ho': 2.0, 'Mg': 4.0}\",\"('Ho', 'Mg')\",OTHERS,False\nmp-1178916,\"{'V': 4.0, 'P': 4.0, 'H': 44.0, 'O': 36.0}\",\"('H', 'O', 'P', 'V')\",OTHERS,False\nmp-1227166,\"{'Ca': 2.0, 'Nd': 2.0, 'Mn': 4.0, 'O': 12.0}\",\"('Ca', 'Mn', 'Nd', 'O')\",OTHERS,False\nmp-971855,\"{'Tm': 4.0, 'Co': 6.0, 'Si': 10.0}\",\"('Co', 'Si', 'Tm')\",OTHERS,False\nmp-780133,\"{'Mn': 12.0, 'O': 14.0, 'F': 10.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-654,\"{'Fe': 17.0, 'Ce': 2.0}\",\"('Ce', 'Fe')\",OTHERS,False\nmp-755514,\"{'Li': 5.0, 'Mn': 2.0, 'Fe': 3.0, 'O': 10.0}\",\"('Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1013531,\"{'Sr': 3.0, 'Bi': 1.0, 'N': 1.0}\",\"('Bi', 'N', 'Sr')\",OTHERS,False\nmvc-4609,\"{'Mg': 2.0, 'Cu': 4.0, 'O': 8.0}\",\"('Cu', 'Mg', 'O')\",OTHERS,False\nmp-1185395,\"{'Li': 1.0, 'Pm': 3.0}\",\"('Li', 'Pm')\",OTHERS,False\nmvc-4854,\"{'Mg': 4.0, 'Cr': 4.0, 'O': 8.0}\",\"('Cr', 'Mg', 'O')\",OTHERS,False\nmp-27907,\"{'S': 26.0, 'Sb': 12.0, 'Pb': 8.0}\",\"('Pb', 'S', 'Sb')\",OTHERS,False\nmp-1185168,\"{'K': 2.0, 'Rb': 6.0}\",\"('K', 'Rb')\",OTHERS,False\nmp-780535,\"{'Ba': 2.0, 'Lu': 4.0, 'O': 8.0}\",\"('Ba', 'Lu', 'O')\",OTHERS,False\nmp-1224792,\"{'Ga': 1.0, 'Ag': 1.0, 'Se': 2.0}\",\"('Ag', 'Ga', 'Se')\",OTHERS,False\nmp-1093681,\"{'Ca': 1.0, 'In': 1.0, 'Ag': 2.0}\",\"('Ag', 'Ca', 'In')\",OTHERS,False\nmp-555785,\"{'Nd': 4.0, 'Ti': 4.0, 'O': 14.0}\",\"('Nd', 'O', 'Ti')\",OTHERS,False\nmp-2136,\"{'O': 12.0, 'Sb': 8.0}\",\"('O', 'Sb')\",OTHERS,False\nmp-568348,{'P': 84.0},\"('P',)\",OTHERS,False\nmp-555941,\"{'Al': 6.0, 'P': 6.0, 'O': 24.0}\",\"('Al', 'O', 'P')\",OTHERS,False\nmp-1184424,\"{'Dy': 1.0, 'Y': 1.0, 'In': 2.0}\",\"('Dy', 'In', 'Y')\",OTHERS,False\nmp-1208770,\"{'Sr': 6.0, 'Li': 12.0, 'Nb': 4.0, 'O': 22.0}\",\"('Li', 'Nb', 'O', 'Sr')\",OTHERS,False\nmp-764402,\"{'Mn': 2.0, 'Fe': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Fe', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1199399,\"{'Ti': 2.0, 'Si': 14.0, 'H': 96.0, 'C': 32.0, 'O': 4.0}\",\"('C', 'H', 'O', 'Si', 'Ti')\",OTHERS,False\nmp-755678,\"{'Ca': 2.0, 'La': 4.0, 'I': 16.0}\",\"('Ca', 'I', 'La')\",OTHERS,False\nmp-5915,\"{'N': 8.0, 'O': 24.0, 'Tl': 8.0}\",\"('N', 'O', 'Tl')\",OTHERS,False\nmp-1184636,\"{'Hf': 1.0, 'Th': 1.0, 'Tc': 2.0}\",\"('Hf', 'Tc', 'Th')\",OTHERS,False\nmp-1112957,\"{'Cs': 2.0, 'Hg': 1.0, 'Sb': 1.0, 'F': 6.0}\",\"('Cs', 'F', 'Hg', 'Sb')\",OTHERS,False\nmp-1190722,\"{'Mg': 1.0, 'U': 2.0, 'Si': 2.0, 'O': 16.0}\",\"('Mg', 'O', 'Si', 'U')\",OTHERS,False\nmp-561093,\"{'Rb': 8.0, 'Cr': 8.0, 'O': 28.0}\",\"('Cr', 'O', 'Rb')\",OTHERS,False\nmp-867167,\"{'Li': 1.0, 'Ca': 2.0, 'Tl': 1.0}\",\"('Ca', 'Li', 'Tl')\",OTHERS,False\nmp-1194999,\"{'Na': 2.0, 'Co': 4.0, 'As': 6.0, 'O': 20.0}\",\"('As', 'Co', 'Na', 'O')\",OTHERS,False\nmp-1013719,\"{'Ba': 3.0, 'Bi': 1.0, 'Sb': 1.0}\",\"('Ba', 'Bi', 'Sb')\",OTHERS,False\nmp-1184539,\"{'Eu': 2.0, 'Th': 6.0}\",\"('Eu', 'Th')\",OTHERS,False\nmp-18787,\"{'O': 2.0, 'Mn': 3.0, 'Sb': 2.0, 'Ba': 2.0}\",\"('Ba', 'Mn', 'O', 'Sb')\",OTHERS,False\nmp-764587,\"{'Li': 4.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-755126,\"{'Mn': 6.0, 'O': 11.0, 'F': 1.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-973066,\"{'Sc': 1.0, 'Ge': 1.0, 'Rh': 2.0}\",\"('Ge', 'Rh', 'Sc')\",OTHERS,False\nmp-550566,\"{'O': 4.0, 'Bi': 2.0, 'Eu': 1.0, 'Cl': 1.0}\",\"('Bi', 'Cl', 'Eu', 'O')\",OTHERS,False\nmp-755519,\"{'Li': 3.0, 'Mn': 1.0, 'Cr': 3.0, 'O': 8.0}\",\"('Cr', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-607479,\"{'Eu': 4.0, 'Rb': 4.0, 'Si': 4.0, 'S': 16.0}\",\"('Eu', 'Rb', 'S', 'Si')\",OTHERS,False\nmp-1224835,\"{'Fe': 2.0, 'Pb': 3.0, 'W': 1.0, 'O': 9.0}\",\"('Fe', 'O', 'Pb', 'W')\",OTHERS,False\nmp-17642,\"{'O': 24.0, 'Cr': 4.0, 'Rb': 4.0, 'U': 4.0}\",\"('Cr', 'O', 'Rb', 'U')\",OTHERS,False\nmvc-11925,\"{'Mg': 2.0, 'Bi': 2.0, 'P': 2.0, 'O': 12.0}\",\"('Bi', 'Mg', 'O', 'P')\",OTHERS,False\nmp-780570,\"{'Ti': 8.0, 'S': 16.0, 'O': 64.0}\",\"('O', 'S', 'Ti')\",OTHERS,False\nmp-1094067,\"{'Sc': 4.0, 'F': 12.0}\",\"('F', 'Sc')\",OTHERS,False\nmp-18444,\"{'O': 12.0, 'Cu': 2.0, 'Sr': 6.0, 'Rh': 2.0}\",\"('Cu', 'O', 'Rh', 'Sr')\",OTHERS,False\nmp-849415,\"{'Li': 8.0, 'Fe': 8.0, 'P': 16.0, 'O': 56.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-4840,\"{'Cu': 2.0, 'Ga': 2.0, 'Se': 4.0}\",\"('Cu', 'Ga', 'Se')\",OTHERS,False\nmp-1220322,\"{'Nb': 1.0, 'Re': 1.0}\",\"('Nb', 'Re')\",OTHERS,False\nmvc-15235,\"{'Y': 4.0, 'Cr': 12.0, 'O': 36.0}\",\"('Cr', 'O', 'Y')\",OTHERS,False\nmp-26453,\"{'Li': 6.0, 'Mn': 4.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-9848,\"{'P': 10.0, 'Pr': 2.0}\",\"('P', 'Pr')\",OTHERS,False\nmp-1197740,\"{'Ce': 8.0, 'Si': 4.0, 'S': 20.0}\",\"('Ce', 'S', 'Si')\",OTHERS,False\nCd 65 Mg 20 Ca 15,\"{'Cd': 65.0, 'Mg': 20.0, 'Ca': 15.0}\",\"('Ca', 'Cd', 'Mg')\",QC,False\nmp-1075184,\"{'Mg': 6.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1214076,\"{'Ca': 2.0, 'Al': 4.0, 'Si': 8.0, 'H': 4.0, 'O': 24.0}\",\"('Al', 'Ca', 'H', 'O', 'Si')\",OTHERS,False\nmp-1036621,\"{'Y': 1.0, 'Mg': 14.0, 'Al': 1.0, 'O': 16.0}\",\"('Al', 'Mg', 'O', 'Y')\",OTHERS,False\nmp-1223672,\"{'K': 2.0, 'Ba': 1.0, 'Bi': 3.0, 'O': 9.0}\",\"('Ba', 'Bi', 'K', 'O')\",OTHERS,False\nmp-2633,\"{'O': 8.0, 'Ge': 4.0}\",\"('Ge', 'O')\",OTHERS,False\nmvc-5236,\"{'Sn': 12.0, 'O': 24.0}\",\"('O', 'Sn')\",OTHERS,False\nmp-1216036,\"{'Zn': 6.0, 'Co': 8.0, 'Sb': 4.0, 'O': 24.0}\",\"('Co', 'O', 'Sb', 'Zn')\",OTHERS,False\nmp-569341,\"{'Nd': 3.0, 'B': 3.0, 'Pt': 6.0}\",\"('B', 'Nd', 'Pt')\",OTHERS,False\nmp-1194728,\"{'Ba': 6.0, 'Na': 2.0, 'Gd': 6.0, 'Si': 12.0, 'O': 40.0}\",\"('Ba', 'Gd', 'Na', 'O', 'Si')\",OTHERS,False\nmp-558498,\"{'Zn': 8.0, 'Ga': 8.0, 'N': 8.0, 'O': 8.0}\",\"('Ga', 'N', 'O', 'Zn')\",OTHERS,False\nmp-18737,\"{'Nd': 4.0, 'Ni': 2.0, 'O': 8.0}\",\"('Nd', 'Ni', 'O')\",OTHERS,False\nmp-17167,\"{'Be': 6.0, 'O': 24.0, 'Si': 6.0, 'Cd': 8.0, 'Te': 2.0}\",\"('Be', 'Cd', 'O', 'Si', 'Te')\",OTHERS,False\nmp-530524,\"{'F': 60.0, 'Tl': 4.0, 'Zr': 12.0}\",\"('F', 'Tl', 'Zr')\",OTHERS,False\nmp-1196849,\"{'Fe': 6.0, 'W': 20.0, 'C': 6.0}\",\"('C', 'Fe', 'W')\",OTHERS,False\nmp-17540,\"{'O': 18.0, 'Co': 2.0, 'Se': 6.0, 'Sr': 4.0}\",\"('Co', 'O', 'Se', 'Sr')\",OTHERS,False\nmp-541929,\"{'Fe': 4.0, 'P': 12.0, 'Si': 4.0, 'O': 44.0}\",\"('Fe', 'O', 'P', 'Si')\",OTHERS,False\nmp-600006,\"{'Si': 20.0, 'O': 40.0}\",\"('O', 'Si')\",OTHERS,False\nmp-1222757,\"{'Li': 2.0, 'Mg': 1.0, 'Ti': 9.0, 'O': 20.0}\",\"('Li', 'Mg', 'O', 'Ti')\",OTHERS,False\nmp-1189806,\"{'Yb': 4.0, 'Cu': 2.0, 'Ge': 12.0}\",\"('Cu', 'Ge', 'Yb')\",OTHERS,False\nAg 41 In 44 Yb 15,\"{'Ag': 41.0, 'In': 44.0, 'Yb': 15.0}\",\"('Ag', 'In', 'Yb')\",AC,False\nmp-768139,\"{'Li': 16.0, 'Sb': 4.0, 'S': 2.0}\",\"('Li', 'S', 'Sb')\",OTHERS,False\nmp-772646,\"{'Li': 12.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-15753,\"{'Li': 4.0, 'As': 4.0, 'Ba': 3.0}\",\"('As', 'Ba', 'Li')\",OTHERS,False\nmp-755859,\"{'Li': 4.0, 'Mn': 1.0, 'Fe': 3.0, 'B': 4.0, 'O': 12.0}\",\"('B', 'Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1180419,\"{'Na': 8.0, 'Ag': 8.0, 'I': 16.0, 'O': 24.0}\",\"('Ag', 'I', 'Na', 'O')\",OTHERS,False\nmp-867913,\"{'Li': 1.0, 'Ho': 2.0, 'Ir': 1.0}\",\"('Ho', 'Ir', 'Li')\",OTHERS,False\nmp-26552,\"{'O': 44.0, 'P': 12.0, 'Co': 10.0}\",\"('Co', 'O', 'P')\",OTHERS,False\nmp-998762,\"{'Mn': 1.0, 'Tl': 1.0, 'F': 3.0}\",\"('F', 'Mn', 'Tl')\",OTHERS,False\nmp-863389,\"{'Nb': 16.0, 'Tl': 16.0, 'O': 53.0}\",\"('Nb', 'O', 'Tl')\",OTHERS,False\nmp-772663,\"{'Mn': 2.0, 'H': 8.0, 'S': 4.0, 'O': 20.0}\",\"('H', 'Mn', 'O', 'S')\",OTHERS,False\nmvc-8085,\"{'Zn': 2.0, 'Cu': 2.0, 'Ge': 4.0, 'O': 12.0}\",\"('Cu', 'Ge', 'O', 'Zn')\",OTHERS,False\nmp-1080559,\"{'In': 2.0, 'Ga': 4.0, 'Au': 4.0}\",\"('Au', 'Ga', 'In')\",OTHERS,False\nmp-1246818,\"{'Sr': 40.0, 'Cd': 8.0, 'N': 32.0}\",\"('Cd', 'N', 'Sr')\",OTHERS,False\nmp-6872,\"{'N': 2.0, 'O': 6.0, 'F': 12.0, 'Si': 2.0, 'K': 6.0}\",\"('F', 'K', 'N', 'O', 'Si')\",OTHERS,False\nmp-774354,\"{'Li': 4.0, 'Mn': 8.0, 'F': 20.0}\",\"('F', 'Li', 'Mn')\",OTHERS,False\nmp-1226833,\"{'Ce': 2.0, 'Zn': 3.0, 'Cu': 7.0}\",\"('Ce', 'Cu', 'Zn')\",OTHERS,False\nmp-1203228,\"{'La': 6.0, 'Rh': 8.0, 'Pb': 26.0}\",\"('La', 'Pb', 'Rh')\",OTHERS,False\nmp-1038195,\"{'Mg': 30.0, 'Al': 1.0, 'C': 1.0, 'O': 32.0}\",\"('Al', 'C', 'Mg', 'O')\",OTHERS,False\nmp-778583,\"{'Li': 3.0, 'Mn': 2.0, 'Co': 2.0, 'O': 8.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-667447,\"{'Te': 8.0, 'As': 8.0, 'Se': 16.0, 'F': 48.0}\",\"('As', 'F', 'Se', 'Te')\",OTHERS,False\nmp-1189078,\"{'Na': 2.0, 'Al': 2.0, 'C': 2.0, 'O': 10.0}\",\"('Al', 'C', 'Na', 'O')\",OTHERS,False\nmp-1181438,\"{'Er': 10.0, 'Bi': 2.0, 'Au': 4.0}\",\"('Au', 'Bi', 'Er')\",OTHERS,False\nmp-22253,\"{'S': 4.0, 'Zn': 1.0, 'In': 2.0}\",\"('In', 'S', 'Zn')\",OTHERS,False\nmp-758848,\"{'Ti': 5.0, 'Nb': 1.0, 'O': 12.0}\",\"('Nb', 'O', 'Ti')\",OTHERS,False\nmp-25466,\"{'Li': 2.0, 'C': 2.0, 'O': 14.0, 'P': 2.0, 'Cu': 2.0}\",\"('C', 'Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-1096900,\"{'Cr': 4.0, 'N': 8.0}\",\"('Cr', 'N')\",OTHERS,False\nmp-865409,\"{'U': 1.0, 'Tc': 2.0, 'Sn': 1.0}\",\"('Sn', 'Tc', 'U')\",OTHERS,False\nmp-11243,\"{'Er': 2.0, 'Au': 2.0}\",\"('Au', 'Er')\",OTHERS,False\nmp-864637,\"{'Nd': 8.0, 'Al': 8.0}\",\"('Al', 'Nd')\",OTHERS,False\nmp-1080454,\"{'Dy': 3.0, 'Pd': 3.0, 'Pb': 3.0}\",\"('Dy', 'Pb', 'Pd')\",OTHERS,False\nmvc-9392,\"{'Pr': 2.0, 'Mg': 2.0, 'Mo': 4.0, 'O': 12.0}\",\"('Mg', 'Mo', 'O', 'Pr')\",OTHERS,False\nmp-1203188,\"{'Ca': 8.0, 'As': 4.0, 'H': 16.0, 'F': 52.0}\",\"('As', 'Ca', 'F', 'H')\",OTHERS,False\nmp-1246325,\"{'Ta': 2.0, 'Mn': 4.0, 'N': 6.0}\",\"('Mn', 'N', 'Ta')\",OTHERS,False\nmp-754598,\"{'Hg': 9.0, 'Se': 3.0, 'O': 18.0}\",\"('Hg', 'O', 'Se')\",OTHERS,False\nmp-1210098,\"{'Rb': 8.0, 'Ce': 4.0, 'Cl': 20.0}\",\"('Ce', 'Cl', 'Rb')\",OTHERS,False\nmp-865876,\"{'Ti': 2.0, 'Tc': 1.0, 'Os': 1.0}\",\"('Os', 'Tc', 'Ti')\",OTHERS,False\nmp-505765,\"{'Ba': 20.0, 'Co': 20.0, 'N': 20.0}\",\"('Ba', 'Co', 'N')\",OTHERS,False\nmp-697563,\"{'Ca': 4.0, 'Mg': 1.0, 'B': 4.0, 'H': 6.0, 'C': 2.0, 'O': 18.0}\",\"('B', 'C', 'Ca', 'H', 'Mg', 'O')\",OTHERS,False\nmp-756308,\"{'Li': 4.0, 'Ti': 3.0, 'Fe': 3.0, 'Sn': 2.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'Sn', 'Ti')\",OTHERS,False\nmp-1173971,\"{'Li': 6.0, 'Mn': 2.0, 'Co': 2.0, 'O': 10.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1105758,\"{'Al': 2.0, 'Tl': 4.0, 'O': 2.0, 'F': 10.0}\",\"('Al', 'F', 'O', 'Tl')\",OTHERS,False\nmp-28538,\"{'H': 12.0, 'Si': 4.0, 'I': 4.0}\",\"('H', 'I', 'Si')\",OTHERS,False\nmp-569557,\"{'Dy': 6.0, 'Si': 2.0, 'Cu': 2.0, 'Se': 14.0}\",\"('Cu', 'Dy', 'Se', 'Si')\",OTHERS,False\nmp-3255,\"{'O': 6.0, 'Cu': 4.0, 'Sr': 2.0}\",\"('Cu', 'O', 'Sr')\",OTHERS,False\nmp-17620,\"{'B': 6.0, 'O': 18.0, 'Tm': 6.0}\",\"('B', 'O', 'Tm')\",OTHERS,False\nmp-1185759,\"{'Mg': 17.0, 'Al': 11.0, 'Tl': 1.0}\",\"('Al', 'Mg', 'Tl')\",OTHERS,False\nmp-735563,\"{'Cs': 1.0, 'Co': 1.0, 'H': 18.0, 'N': 6.0, 'Cl': 4.0, 'O': 8.0}\",\"('Cl', 'Co', 'Cs', 'H', 'N', 'O')\",OTHERS,False\nmp-8371,\"{'C': 2.0, 'Lu': 1.0}\",\"('C', 'Lu')\",OTHERS,False\nmp-1205435,\"{'K': 8.0, 'Fe': 8.0, 'P': 8.0, 'O': 28.0, 'F': 8.0}\",\"('F', 'Fe', 'K', 'O', 'P')\",OTHERS,False\nmp-504722,\"{'Ni': 4.0, 'N': 8.0, 'O': 24.0}\",\"('N', 'Ni', 'O')\",OTHERS,False\nmp-7237,\"{'O': 4.0, 'Cu': 2.0, 'Ag': 2.0}\",\"('Ag', 'Cu', 'O')\",OTHERS,False\nmp-1078200,\"{'V': 2.0, 'Co': 1.0, 'Se': 4.0}\",\"('Co', 'Se', 'V')\",OTHERS,False\nmp-1177476,\"{'Li': 3.0, 'V': 5.0, 'O': 12.0}\",\"('Li', 'O', 'V')\",OTHERS,False\nmp-1187133,\"{'Sr': 6.0, 'Fe': 2.0}\",\"('Fe', 'Sr')\",OTHERS,False\nmp-720332,\"{'Na': 5.0, 'Ca': 1.0, 'Al': 5.0, 'Fe': 1.0, 'Si': 12.0, 'O': 36.0}\",\"('Al', 'Ca', 'Fe', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1219396,\"{'Sc': 2.0, 'Te': 3.0}\",\"('Sc', 'Te')\",OTHERS,False\nmvc-1364,\"{'Ba': 2.0, 'V': 3.0, 'O': 7.0}\",\"('Ba', 'O', 'V')\",OTHERS,False\nmp-760095,\"{'Li': 6.0, 'Mn': 6.0, 'F': 18.0}\",\"('F', 'Li', 'Mn')\",OTHERS,False\nmp-761214,\"{'Li': 3.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1024991,\"{'Er': 2.0, 'Cu': 4.0}\",\"('Cu', 'Er')\",OTHERS,False\nmp-1175447,\"{'Li': 9.0, 'Co': 7.0, 'O': 16.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-1225379,\"{'Fe': 30.0, 'Si': 6.0, 'P': 6.0}\",\"('Fe', 'P', 'Si')\",OTHERS,False\nmp-20294,\"{'Rh': 1.0, 'In': 5.0, 'Ce': 1.0}\",\"('Ce', 'In', 'Rh')\",OTHERS,False\nmp-977207,\"{'Li': 4.0, 'Mg': 2.0}\",\"('Li', 'Mg')\",OTHERS,False\nmp-1199265,\"{'Nb': 4.0, 'Pb': 4.0, 'Se': 8.0, 'O': 30.0}\",\"('Nb', 'O', 'Pb', 'Se')\",OTHERS,False\nmp-1181157,\"{'K': 2.0, 'La': 2.0, 'C': 8.0, 'O': 22.0}\",\"('C', 'K', 'La', 'O')\",OTHERS,False\nmp-774219,\"{'Li': 3.0, 'Mn': 1.0, 'Co': 3.0, 'O': 8.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-17217,\"{'Ge': 4.0, 'Te': 12.0, 'Tl': 12.0}\",\"('Ge', 'Te', 'Tl')\",OTHERS,False\nmp-1096250,\"{'Ti': 1.0, 'V': 1.0, 'Mo': 2.0}\",\"('Mo', 'Ti', 'V')\",OTHERS,False\nmp-1222270,\"{'Li': 1.0, 'Mg': 1.0}\",\"('Li', 'Mg')\",OTHERS,False\nmp-1221952,\"{'Mg': 1.0, 'Ti': 2.0, 'Ni': 1.0, 'O': 6.0}\",\"('Mg', 'Ni', 'O', 'Ti')\",OTHERS,False\nmp-1215969,\"{'Yb': 20.0, 'Cd': 3.0, 'Au': 9.0}\",\"('Au', 'Cd', 'Yb')\",OTHERS,False\nmp-1174093,\"{'Li': 4.0, 'Mn': 1.0, 'Co': 3.0, 'O': 8.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-672653,\"{'Nb': 6.0, 'Ni': 16.0, 'Ge': 7.0}\",\"('Ge', 'Nb', 'Ni')\",OTHERS,False\nmp-1215993,\"{'Y': 1.0, 'Bi': 3.0, 'Ru': 4.0, 'O': 14.0}\",\"('Bi', 'O', 'Ru', 'Y')\",OTHERS,False\nmp-26716,\"{'O': 28.0, 'P': 8.0, 'Co': 4.0}\",\"('Co', 'O', 'P')\",OTHERS,False\nmp-15317,\"{'F': 6.0, 'Na': 1.0, 'Rb': 2.0, 'Ho': 1.0}\",\"('F', 'Ho', 'Na', 'Rb')\",OTHERS,False\nmp-862287,\"{'Be': 1.0, 'Al': 1.0, 'Rh': 2.0}\",\"('Al', 'Be', 'Rh')\",OTHERS,False\nmp-30187,\"{'O': 11.0, 'Ga': 2.0, 'Te': 4.0}\",\"('Ga', 'O', 'Te')\",OTHERS,False\nmp-560092,\"{'Rb': 2.0, 'Na': 10.0, 'Be': 16.0, 'O': 22.0}\",\"('Be', 'Na', 'O', 'Rb')\",OTHERS,False\nmp-1097242,\"{'Y': 1.0, 'Sc': 1.0, 'Au': 2.0}\",\"('Au', 'Sc', 'Y')\",OTHERS,False\nmp-698218,\"{'La': 2.0, 'H': 26.0, 'C': 6.0, 'S': 6.0, 'O': 22.0}\",\"('C', 'H', 'La', 'O', 'S')\",OTHERS,False\nmp-1080742,\"{'Sr': 2.0, 'Zr': 1.0, 'Cr': 1.0, 'O': 6.0}\",\"('Cr', 'O', 'Sr', 'Zr')\",OTHERS,False\nmp-560791,\"{'Cs': 2.0, 'Na': 2.0, 'Ti': 2.0, 'O': 6.0}\",\"('Cs', 'Na', 'O', 'Ti')\",OTHERS,False\nmp-698183,\"{'Mg': 3.0, 'P': 2.0, 'H': 44.0, 'O': 30.0}\",\"('H', 'Mg', 'O', 'P')\",OTHERS,False\nmp-11765,\"{'Ti': 5.0, 'Se': 4.0}\",\"('Se', 'Ti')\",OTHERS,False\nmp-29062,\"{'F': 24.0, 'Zr': 4.0, 'Th': 2.0}\",\"('F', 'Th', 'Zr')\",OTHERS,False\nmp-554144,\"{'F': 7.0, 'K': 3.0, 'Mn': 2.0}\",\"('F', 'K', 'Mn')\",OTHERS,False\nmp-1111199,\"{'K': 2.0, 'Al': 1.0, 'Tl': 1.0, 'I': 6.0}\",\"('Al', 'I', 'K', 'Tl')\",OTHERS,False\nmp-733610,\"{'H': 14.0, 'C': 6.0, 'N': 6.0, 'O': 10.0}\",\"('C', 'H', 'N', 'O')\",OTHERS,False\nmp-19241,\"{'O': 6.0, 'Ni': 2.0, 'Ba': 2.0}\",\"('Ba', 'Ni', 'O')\",OTHERS,False\nmp-1207591,\"{'Yb': 2.0, 'Eu': 2.0, 'Cu': 2.0, 'S': 6.0}\",\"('Cu', 'Eu', 'S', 'Yb')\",OTHERS,False\nmp-757117,\"{'Li': 4.0, 'Fe': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1246919,\"{'Mn': 10.0, 'Ge': 4.0, 'N': 12.0}\",\"('Ge', 'Mn', 'N')\",OTHERS,False\nmp-768303,\"{'Dy': 2.0, 'Nb': 2.0, 'O': 8.0}\",\"('Dy', 'Nb', 'O')\",OTHERS,False\nmp-1080375,\"{'Ce': 24.0, 'Se': 48.0}\",\"('Ce', 'Se')\",OTHERS,False\nmvc-2270,\"{'Ca': 2.0, 'Mn': 9.0, 'O': 13.0}\",\"('Ca', 'Mn', 'O')\",OTHERS,False\nmp-1080314,\"{'Ce': 6.0, 'Se': 12.0}\",\"('Ce', 'Se')\",OTHERS,False\nmp-979963,\"{'Yb': 3.0, 'Ti': 1.0}\",\"('Ti', 'Yb')\",OTHERS,False\nmp-21172,\"{'Pb': 3.0, 'Pu': 1.0}\",\"('Pb', 'Pu')\",OTHERS,False\nmp-1185438,\"{'Lu': 3.0, 'Hg': 1.0}\",\"('Hg', 'Lu')\",OTHERS,False\nmvc-16196,\"{'Zn': 4.0, 'Bi': 4.0, 'O': 10.0}\",\"('Bi', 'O', 'Zn')\",OTHERS,False\nmp-1229223,\"{'As': 14.0, 'H': 6.0, 'O': 31.0}\",\"('As', 'H', 'O')\",OTHERS,False\nmp-12716,\"{'Li': 1.0, 'Pd': 2.0, 'Tl': 1.0}\",\"('Li', 'Pd', 'Tl')\",OTHERS,False\nmp-1095753,\"{'La': 2.0, 'Tl': 1.0, 'Ag': 1.0}\",\"('Ag', 'La', 'Tl')\",OTHERS,False\nmp-849302,\"{'Li': 6.0, 'Fe': 8.0, 'F': 38.0}\",\"('F', 'Fe', 'Li')\",OTHERS,False\nmp-1192181,\"{'Au': 2.0, 'C': 8.0, 'N': 8.0, 'O': 4.0}\",\"('Au', 'C', 'N', 'O')\",OTHERS,False\nmp-567601,\"{'Sr': 16.0, 'C': 4.0, 'N': 16.0}\",\"('C', 'N', 'Sr')\",OTHERS,False\nmp-1220026,\"{'Pr': 1.0, 'Er': 1.0, 'Co': 17.0}\",\"('Co', 'Er', 'Pr')\",OTHERS,False\nmp-8351,\"{'O': 16.0, 'Na': 4.0, 'Al': 4.0, 'Si': 4.0}\",\"('Al', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1079201,\"{'B': 2.0, 'C': 4.0, 'N': 2.0}\",\"('B', 'C', 'N')\",OTHERS,False\nmp-641661,\"{'Xe': 16.0, 'F': 96.0}\",\"('F', 'Xe')\",OTHERS,False\nmp-1222046,\"{'Mg': 1.0, 'Fe': 4.0, 'Cu': 1.0, 'O': 8.0}\",\"('Cu', 'Fe', 'Mg', 'O')\",OTHERS,False\nmp-761163,\"{'Na': 2.0, 'Ni': 7.0, 'O': 14.0}\",\"('Na', 'Ni', 'O')\",OTHERS,False\nmp-755855,\"{'Li': 2.0, 'Fe': 3.0, 'Bi': 1.0, 'O': 8.0}\",\"('Bi', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1193346,\"{'Zr': 16.0, 'Co': 8.0, 'N': 4.0}\",\"('Co', 'N', 'Zr')\",OTHERS,False\nmp-9069,\"{'Na': 2.0, 'Al': 2.0, 'K': 4.0, 'As': 4.0}\",\"('Al', 'As', 'K', 'Na')\",OTHERS,False\nmp-1176348,\"{'Na': 10.0, 'Fe': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Fe', 'Na', 'O', 'P')\",OTHERS,False\nmp-1226096,\"{'Co': 7.0, 'Ge': 4.0}\",\"('Co', 'Ge')\",OTHERS,False\nmp-1246494,\"{'Sr': 12.0, 'Rh': 8.0, 'N': 16.0}\",\"('N', 'Rh', 'Sr')\",OTHERS,False\nmp-1204289,\"{'Cs': 32.0, 'Bi': 8.0, 'As': 24.0, 'Se': 56.0}\",\"('As', 'Bi', 'Cs', 'Se')\",OTHERS,False\nmp-685863,\"{'Li': 24.0, 'Si': 6.0, 'O': 24.0}\",\"('Li', 'O', 'Si')\",OTHERS,False\nmp-743584,\"{'H': 12.0, 'W': 1.0, 'N': 3.0, 'O': 3.0, 'F': 3.0}\",\"('F', 'H', 'N', 'O', 'W')\",OTHERS,False\nmp-20318,\"{'B': 2.0, 'Mn': 4.0}\",\"('B', 'Mn')\",OTHERS,False\nmvc-10529,\"{'P': 8.0, 'W': 8.0, 'O': 32.0}\",\"('O', 'P', 'W')\",OTHERS,False\nmp-756442,\"{'Mg': 2.0, 'Co': 4.0, 'O': 8.0}\",\"('Co', 'Mg', 'O')\",OTHERS,False\nmp-753010,\"{'Li': 8.0, 'Co': 2.0, 'O': 8.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-1221833,\"{'Mn': 2.0, 'Ni': 1.0, 'Sb': 2.0, 'Pt': 1.0}\",\"('Mn', 'Ni', 'Pt', 'Sb')\",OTHERS,False\nmp-23870,\"{'H': 1.0, 'Na': 1.0}\",\"('H', 'Na')\",OTHERS,False\nmp-1182867,\"{'Al': 2.0, 'W': 2.0, 'O': 8.0}\",\"('Al', 'O', 'W')\",OTHERS,False\nmp-1218922,\"{'Sn': 1.0, 'Te': 1.0, 'Pb': 4.0, 'S': 4.0}\",\"('Pb', 'S', 'Sn', 'Te')\",OTHERS,False\nmp-777761,\"{'Li': 6.0, 'Mn': 1.0, 'V': 3.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-1208574,\"{'Ta': 16.0, 'Se': 32.0, 'S': 4.0, 'I': 32.0}\",\"('I', 'S', 'Se', 'Ta')\",OTHERS,False\nmp-1071985,\"{'Ce': 2.0, 'Sn': 2.0, 'Au': 2.0}\",\"('Au', 'Ce', 'Sn')\",OTHERS,False\nmp-1185215,\"{'Li': 6.0, 'Fe': 2.0}\",\"('Fe', 'Li')\",OTHERS,False\nmp-1080356,\"{'Ce': 18.0, 'Se': 36.0}\",\"('Ce', 'Se')\",OTHERS,False\nmp-1112935,\"{'Cs': 2.0, 'Ta': 1.0, 'Ag': 1.0, 'I': 6.0}\",\"('Ag', 'Cs', 'I', 'Ta')\",OTHERS,False\nmp-22291,\"{'Se': 6.0, 'Cs': 2.0, 'Gd': 2.0, 'Hg': 2.0}\",\"('Cs', 'Gd', 'Hg', 'Se')\",OTHERS,False\nAl 74.8 Co 17.0 Ni 8.2,\"{'Al': 74.8, 'Co': 17.0, 'Ni': 8.2}\",\"('Al', 'Co', 'Ni')\",AC,False\nmp-505089,\"{'Li': 16.0, 'Tb': 4.0, 'F': 32.0}\",\"('F', 'Li', 'Tb')\",OTHERS,False\nmp-1237354,\"{'H': 14.0, 'C': 4.0, 'N': 14.0, 'O': 12.0}\",\"('C', 'H', 'N', 'O')\",OTHERS,False\nmp-1185407,\"{'Li': 1.0, 'Pr': 1.0, 'Au': 2.0}\",\"('Au', 'Li', 'Pr')\",OTHERS,False\nmp-28204,\"{'H': 28.0, 'O': 16.0, 'Cs': 4.0}\",\"('Cs', 'H', 'O')\",OTHERS,False\nmp-1245424,\"{'Ge': 4.0, 'Pb': 28.0, 'N': 24.0}\",\"('Ge', 'N', 'Pb')\",OTHERS,False\nmp-1212627,\"{'Mn': 2.0, 'C': 12.0, 'O': 12.0}\",\"('C', 'Mn', 'O')\",OTHERS,False\nmp-510564,\"{'Nd': 1.0, 'Rh': 1.0, 'In': 5.0}\",\"('In', 'Nd', 'Rh')\",OTHERS,False\nmvc-9167,\"{'Mg': 4.0, 'Co': 12.0, 'O': 28.0}\",\"('Co', 'Mg', 'O')\",OTHERS,False\nmp-1210992,\"{'Li': 2.0, 'Sm': 2.0, 'S': 4.0, 'O': 16.0}\",\"('Li', 'O', 'S', 'Sm')\",OTHERS,False\nmp-891,\"{'Ni': 6.0, 'Ta': 2.0}\",\"('Ni', 'Ta')\",OTHERS,False\nmp-1216097,\"{'Y': 3.0, 'Bi': 1.0, 'Ru': 4.0, 'O': 14.0}\",\"('Bi', 'O', 'Ru', 'Y')\",OTHERS,False\nmp-1104382,\"{'Cr': 4.0, 'Ga': 2.0, 'S': 8.0}\",\"('Cr', 'Ga', 'S')\",OTHERS,False\nmp-1224071,\"{'K': 10.0, 'Cu': 2.0, 'C': 2.0, 'O': 10.0}\",\"('C', 'Cu', 'K', 'O')\",OTHERS,False\nmp-1196392,\"{'Li': 8.0, 'B': 8.0, 'H': 40.0, 'N': 8.0}\",\"('B', 'H', 'Li', 'N')\",OTHERS,False\nmp-1076766,\"{'La': 20.0, 'Sm': 12.0, 'V': 4.0, 'Cr': 28.0, 'O': 80.0}\",\"('Cr', 'La', 'O', 'Sm', 'V')\",OTHERS,False\nmp-779989,\"{'Gd': 8.0, 'P': 16.0, 'O': 52.0}\",\"('Gd', 'O', 'P')\",OTHERS,False\nmp-1184280,\"{'Fe': 6.0, 'W': 2.0}\",\"('Fe', 'W')\",OTHERS,False\nmp-1246053,\"{'Ba': 6.0, 'Nb': 4.0, 'N': 8.0}\",\"('Ba', 'N', 'Nb')\",OTHERS,False\nmp-1193310,\"{'Cr': 22.0, 'W': 1.0, 'C': 6.0}\",\"('C', 'Cr', 'W')\",OTHERS,False\nmp-772781,\"{'Lu': 4.0, 'B': 8.0, 'O': 18.0}\",\"('B', 'Lu', 'O')\",OTHERS,False\nmp-17639,\"{'Zn': 17.0, 'Y': 2.0}\",\"('Y', 'Zn')\",OTHERS,False\nmp-1025934,\"{'Mo': 2.0, 'W': 1.0, 'Se': 2.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-776574,\"{'V': 6.0, 'O': 9.0, 'F': 9.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-17712,\"{'O': 22.0, 'Si': 4.0, 'V': 5.0, 'Pr': 4.0}\",\"('O', 'Pr', 'Si', 'V')\",OTHERS,False\nmp-1183407,\"{'Be': 3.0, 'Si': 1.0}\",\"('Be', 'Si')\",OTHERS,False\nmp-1105280,\"{'Li': 4.0, 'Ta': 4.0, 'O': 12.0}\",\"('Li', 'O', 'Ta')\",OTHERS,False\nmp-1202587,\"{'K': 4.0, 'Ho': 8.0, 'F': 28.0}\",\"('F', 'Ho', 'K')\",OTHERS,False\nmp-1185267,\"{'Li': 1.0, 'Np': 1.0, 'Ru': 2.0}\",\"('Li', 'Np', 'Ru')\",OTHERS,False\nmp-1217134,\"{'Ti': 3.0, 'V': 1.0, 'S': 4.0}\",\"('S', 'Ti', 'V')\",OTHERS,False\nmp-1222159,\"{'Mg': 4.0, 'Fe': 1.0, 'O': 5.0}\",\"('Fe', 'Mg', 'O')\",OTHERS,False\nmp-1020683,\"{'Na': 2.0, 'Ca': 2.0, 'P': 6.0, 'O': 18.0}\",\"('Ca', 'Na', 'O', 'P')\",OTHERS,False\nmp-1097672,\"{'Cs': 2.0, 'Hg': 1.0, 'Te': 1.0}\",\"('Cs', 'Hg', 'Te')\",OTHERS,False\nmp-758647,\"{'Li': 4.0, 'V': 11.0, 'O': 22.0}\",\"('Li', 'O', 'V')\",OTHERS,False\nmp-1189298,\"{'Yb': 4.0, 'B': 16.0}\",\"('B', 'Yb')\",OTHERS,False\nmp-1213811,\"{'Cs': 4.0, 'Co': 2.0, 'S': 4.0, 'O': 28.0}\",\"('Co', 'Cs', 'O', 'S')\",OTHERS,False\nmp-620058,\"{'Pb': 12.0, 'N': 72.0}\",\"('N', 'Pb')\",OTHERS,False\nmp-672394,\"{'Yb': 2.0, 'In': 8.0, 'Ni': 2.0}\",\"('In', 'Ni', 'Yb')\",OTHERS,False\nmp-1197699,\"{'Pt': 8.0, 'Cl': 32.0, 'O': 28.0}\",\"('Cl', 'O', 'Pt')\",OTHERS,False\nmp-1229219,\"{'Ag': 3.0, 'Te': 32.0, 'Au': 13.0}\",\"('Ag', 'Au', 'Te')\",OTHERS,False\nmp-1175016,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmvc-15019,\"{'V': 4.0, 'P': 6.0, 'O': 24.0}\",\"('O', 'P', 'V')\",OTHERS,False\nAl 72 Pd 17 Os 11,\"{'Al': 72.0, 'Pd': 17.0, 'Os': 11.0}\",\"('Al', 'Os', 'Pd')\",QC,False\nmp-695887,\"{'K': 3.0, 'Cu': 2.0, 'P': 4.0, 'H': 1.0, 'O': 14.0}\",\"('Cu', 'H', 'K', 'O', 'P')\",OTHERS,False\nmp-1174763,\"{'Li': 5.0, 'Mn': 3.0, 'O': 8.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-774530,\"{'Ba': 8.0, 'Ti': 22.0, 'O': 52.0}\",\"('Ba', 'O', 'Ti')\",OTHERS,False\nmp-753484,\"{'Cr': 4.0, 'O': 4.0, 'F': 4.0}\",\"('Cr', 'F', 'O')\",OTHERS,False\nmp-1204490,\"{'Nb': 8.0, 'Cu': 4.0, 'O': 24.0}\",\"('Cu', 'Nb', 'O')\",OTHERS,False\nmp-556136,\"{'K': 4.0, 'Mo': 4.0, 'I': 4.0, 'O': 24.0}\",\"('I', 'K', 'Mo', 'O')\",OTHERS,False\nmp-1217894,\"{'Ta': 1.0, 'Re': 1.0}\",\"('Re', 'Ta')\",OTHERS,False\nmp-1208931,\"{'Sr': 2.0, 'Er': 1.0, 'Cu': 2.0, 'Bi': 2.0, 'O': 8.0}\",\"('Bi', 'Cu', 'Er', 'O', 'Sr')\",OTHERS,False\nmp-1185751,\"{'Mg': 17.0, 'Al': 11.0, 'Si': 1.0}\",\"('Al', 'Mg', 'Si')\",OTHERS,False\nmp-775168,\"{'Mn': 1.0, 'Cu': 3.0, 'P': 4.0, 'O': 16.0}\",\"('Cu', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1038690,\"{'Ba': 1.0, 'Mg': 30.0, 'Cr': 1.0, 'O': 32.0}\",\"('Ba', 'Cr', 'Mg', 'O')\",OTHERS,False\nmp-1196002,\"{'Mg': 16.0, 'Si': 16.0, 'O': 48.0}\",\"('Mg', 'O', 'Si')\",OTHERS,False\nmp-684265,\"{'Mg': 4.0, 'Al': 8.0, 'Si': 10.0, 'O': 36.0}\",\"('Al', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-769592,\"{'Na': 6.0, 'V': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Na', 'O', 'P', 'V')\",OTHERS,False\nmvc-10706,\"{'Mg': 1.0, 'Sb': 4.0, 'O': 8.0}\",\"('Mg', 'O', 'Sb')\",OTHERS,False\nmp-865174,\"{'Hf': 1.0, 'Cd': 1.0, 'Rh': 2.0}\",\"('Cd', 'Hf', 'Rh')\",OTHERS,False\nmp-675307,\"{'Fe': 10.0, 'Ni': 14.0, 'S': 32.0}\",\"('Fe', 'Ni', 'S')\",OTHERS,False\nmp-867993,\"{'V': 12.0, 'Co': 4.0, 'O': 32.0}\",\"('Co', 'O', 'V')\",OTHERS,False\nmvc-8797,\"{'Ca': 1.0, 'Sn': 4.0, 'O': 8.0}\",\"('Ca', 'O', 'Sn')\",OTHERS,False\nmp-557309,\"{'Na': 6.0, 'Li': 2.0, 'W': 2.0, 'O': 10.0}\",\"('Li', 'Na', 'O', 'W')\",OTHERS,False\nmp-1220427,\"{'Nb': 6.0, 'Ag': 1.0, 'Se': 8.0}\",\"('Ag', 'Nb', 'Se')\",OTHERS,False\nmp-1198112,\"{'Nb': 16.0, 'P': 16.0, 'Cl': 96.0, 'O': 32.0}\",\"('Cl', 'Nb', 'O', 'P')\",OTHERS,False\nmp-1195102,\"{'Rb': 8.0, 'Ag': 40.0, 'P': 16.0, 'S': 64.0}\",\"('Ag', 'P', 'Rb', 'S')\",OTHERS,False\nmvc-7422,\"{'Mg': 8.0, 'Ti': 8.0, 'Bi': 8.0, 'O': 40.0}\",\"('Bi', 'Mg', 'O', 'Ti')\",OTHERS,False\nmp-867328,\"{'K': 2.0, 'Y': 2.0, 'Si': 2.0, 'S': 8.0}\",\"('K', 'S', 'Si', 'Y')\",OTHERS,False\nmp-777110,\"{'Li': 32.0, 'Ti': 13.0, 'Cr': 3.0, 'O': 48.0}\",\"('Cr', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-581963,\"{'K': 16.0, 'Hf': 12.0, 'Te': 68.0}\",\"('Hf', 'K', 'Te')\",OTHERS,False\nmp-979274,\"{'U': 1.0, 'Ag': 3.0}\",\"('Ag', 'U')\",OTHERS,False\nmp-772992,\"{'Li': 9.0, 'Fe': 3.0, 'W': 7.0, 'O': 28.0}\",\"('Fe', 'Li', 'O', 'W')\",OTHERS,False\nmp-866716,\"{'Rb': 4.0, 'Li': 4.0, 'H': 48.0, 'Se': 12.0, 'N': 16.0}\",\"('H', 'Li', 'N', 'Rb', 'Se')\",OTHERS,False\nmvc-9232,\"{'Zn': 4.0, 'Fe': 4.0, 'Bi': 4.0, 'O': 20.0}\",\"('Bi', 'Fe', 'O', 'Zn')\",OTHERS,False\nmp-770492,\"{'Li': 24.0, 'Mn': 11.0, 'Cr': 1.0, 'O': 36.0}\",\"('Cr', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-757637,\"{'Li': 14.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1063280,\"{'La': 1.0, 'Hg': 2.0}\",\"('Hg', 'La')\",OTHERS,False\nmp-761246,\"{'Li': 4.0, 'Mn': 2.0, 'Fe': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Fe', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-6246,\"{'C': 4.0, 'O': 12.0, 'F': 8.0, 'Ca': 8.0}\",\"('C', 'Ca', 'F', 'O')\",OTHERS,False\nmp-1192187,\"{'Na': 12.0, 'Tl': 12.0}\",\"('Na', 'Tl')\",OTHERS,False\nmp-31480,\"{'Ca': 12.0, 'Ga': 8.0, 'Pd': 8.0}\",\"('Ca', 'Ga', 'Pd')\",OTHERS,False\nmp-757837,\"{'Li': 4.0, 'Co': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Co', 'Li', 'O', 'P')\",OTHERS,False\nmp-768418,\"{'Li': 12.0, 'Mn': 4.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-760024,\"{'Na': 4.0, 'Ti': 20.0, 'O': 40.0}\",\"('Na', 'O', 'Ti')\",OTHERS,False\nmp-694955,\"{'Na': 1.0, 'Li': 2.0, 'La': 3.0, 'Ti': 6.0, 'O': 18.0}\",\"('La', 'Li', 'Na', 'O', 'Ti')\",OTHERS,False\nmp-1016841,\"{'Hf': 1.0, 'Hg': 1.0, 'O': 3.0}\",\"('Hf', 'Hg', 'O')\",OTHERS,False\nmp-1102535,\"{'Pt': 1.0, 'C': 4.0, 'S': 2.0, 'I': 2.0, 'O': 2.0}\",\"('C', 'I', 'O', 'Pt', 'S')\",OTHERS,False\nmp-1184306,\"{'Eu': 1.0, 'Bi': 1.0, 'Pd': 2.0}\",\"('Bi', 'Eu', 'Pd')\",OTHERS,False\nmp-1195356,\"{'Ge': 8.0, 'Bi': 8.0, 'N': 8.0, 'O': 56.0}\",\"('Bi', 'Ge', 'N', 'O')\",OTHERS,False\nmp-1226325,\"{'Cr': 4.0, 'Cd': 1.0, 'Fe': 1.0, 'S': 8.0}\",\"('Cd', 'Cr', 'Fe', 'S')\",OTHERS,False\nmp-1112892,\"{'Cs': 2.0, 'In': 1.0, 'Bi': 1.0, 'Br': 6.0}\",\"('Bi', 'Br', 'Cs', 'In')\",OTHERS,False\nmp-17599,\"{'O': 12.0, 'Ca': 1.0, 'Cu': 3.0, 'Ge': 4.0}\",\"('Ca', 'Cu', 'Ge', 'O')\",OTHERS,False\nmp-708035,\"{'Na': 2.0, 'Zn': 2.0, 'H': 18.0, 'O': 12.0}\",\"('H', 'Na', 'O', 'Zn')\",OTHERS,False\nmp-23581,\"{'B': 14.0, 'O': 26.0, 'Cr': 6.0, 'Br': 2.0}\",\"('B', 'Br', 'Cr', 'O')\",OTHERS,False\nmp-772563,\"{'Li': 8.0, 'V': 4.0, 'O': 8.0, 'F': 12.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-1214142,\"{'Ca': 11.0, 'Si': 4.0, 'Cl': 1.0, 'O': 18.0}\",\"('Ca', 'Cl', 'O', 'Si')\",OTHERS,False\nmp-496,\"{'Si': 8.0, 'Sr': 4.0}\",\"('Si', 'Sr')\",OTHERS,False\nmp-753869,\"{'Li': 2.0, 'V': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1266944,\"{'Al': 2.0, 'Mo': 6.0, 'Se': 4.0, 'Cl': 2.0, 'O': 16.0}\",\"('Al', 'Cl', 'Mo', 'O', 'Se')\",OTHERS,False\nmp-1033843,\"{'Mg': 14.0, 'Cr': 1.0, 'Bi': 1.0, 'O': 16.0}\",\"('Bi', 'Cr', 'Mg', 'O')\",OTHERS,False\nmp-669554,\"{'Mn': 12.0, 'Si': 4.0, 'Ni': 8.0}\",\"('Mn', 'Ni', 'Si')\",OTHERS,False\nmp-1191291,\"{'Hg': 4.0, 'S': 2.0, 'Br': 8.0, 'N': 4.0, 'O': 6.0}\",\"('Br', 'Hg', 'N', 'O', 'S')\",OTHERS,False\nmp-1228072,\"{'Ba': 4.0, 'Ca': 1.0, 'Zr': 5.0, 'O': 15.0}\",\"('Ba', 'Ca', 'O', 'Zr')\",OTHERS,False\nmp-1228271,\"{'Ba': 8.0, 'Cu': 4.0, 'Hg': 4.0, 'O': 17.0}\",\"('Ba', 'Cu', 'Hg', 'O')\",OTHERS,False\nmp-1219237,\"{'Si': 4.0, 'Mo': 1.0, 'W': 1.0}\",\"('Mo', 'Si', 'W')\",OTHERS,False\nmp-1246099,\"{'Ca': 12.0, 'In': 8.0, 'N': 16.0}\",\"('Ca', 'In', 'N')\",OTHERS,False\nmp-1095433,\"{'Tm': 2.0, 'Ga': 8.0, 'Ni': 2.0}\",\"('Ga', 'Ni', 'Tm')\",OTHERS,False\nmp-1201686,\"{'Tl': 4.0, 'Mo': 6.0, 'Se': 2.0, 'O': 24.0}\",\"('Mo', 'O', 'Se', 'Tl')\",OTHERS,False\nmp-697818,\"{'Sr': 4.0, 'Pr': 4.0, 'Mn': 2.0, 'Cu': 2.0, 'O': 16.0}\",\"('Cu', 'Mn', 'O', 'Pr', 'Sr')\",OTHERS,False\nmp-1198628,\"{'Na': 12.0, 'Ru': 4.0, 'O': 16.0}\",\"('Na', 'O', 'Ru')\",OTHERS,False\nmp-762239,\"{'Mn': 2.0, 'C': 4.0, 'O': 12.0}\",\"('C', 'Mn', 'O')\",OTHERS,False\nmp-1105173,\"{'Li': 4.0, 'Fe': 2.0, 'Sn': 2.0, 'S': 8.0}\",\"('Fe', 'Li', 'S', 'Sn')\",OTHERS,False\nmp-1143551,\"{'Si': 12.0, 'W': 8.0, 'O': 48.0}\",\"('O', 'Si', 'W')\",OTHERS,False\nmp-27181,\"{'Cl': 16.0, 'K': 4.0, 'Au': 4.0}\",\"('Au', 'Cl', 'K')\",OTHERS,False\nmp-568646,\"{'Ta': 24.0, 'Si': 8.0}\",\"('Si', 'Ta')\",OTHERS,False\nmp-1227655,\"{'Ce': 4.0, 'V': 2.0, 'Co': 32.0}\",\"('Ce', 'Co', 'V')\",OTHERS,False\nmp-1084749,\"{'Ti': 1.0, 'Zn': 1.0, 'Bi': 2.0, 'O': 6.0}\",\"('Bi', 'O', 'Ti', 'Zn')\",OTHERS,False\nmp-676095,\"{'Na': 3.0, 'Pr': 5.0, 'Cl': 18.0}\",\"('Cl', 'Na', 'Pr')\",OTHERS,False\nmp-1036771,\"{'Mg': 30.0, 'V': 1.0, 'Sn': 1.0, 'O': 32.0}\",\"('Mg', 'O', 'Sn', 'V')\",OTHERS,False\nmp-1188091,\"{'Ce': 4.0, 'Ge': 10.0, 'Ir': 6.0}\",\"('Ce', 'Ge', 'Ir')\",OTHERS,False\nmp-8180,\"{'Li': 2.0, 'N': 2.0, 'O': 6.0}\",\"('Li', 'N', 'O')\",OTHERS,False\nmp-553924,\"{'Ag': 16.0, 'P': 4.0, 'I': 4.0, 'O': 16.0}\",\"('Ag', 'I', 'O', 'P')\",OTHERS,False\nmp-1185571,\"{'Cs': 2.0, 'As': 2.0, 'H': 4.0, 'O': 8.0}\",\"('As', 'Cs', 'H', 'O')\",OTHERS,False\nmp-1079086,\"{'U': 3.0, 'Al': 3.0, 'Ni': 3.0}\",\"('Al', 'Ni', 'U')\",OTHERS,False\nmp-770773,\"{'Rb': 8.0, 'S': 8.0, 'O': 28.0}\",\"('O', 'Rb', 'S')\",OTHERS,False\nmp-1174181,\"{'Li': 6.0, 'Co': 4.0, 'O': 10.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-1006888,\"{'K': 1.0, 'Y': 1.0, 'S': 2.0}\",\"('K', 'S', 'Y')\",OTHERS,False\nCd 85 Ca 15,\"{'Cd': 85.0, 'Ca': 15.0}\",\"('Ca', 'Cd')\",QC,False\nmvc-6200,\"{'Ca': 6.0, 'Ti': 6.0, 'As': 8.0, 'O': 32.0}\",\"('As', 'Ca', 'O', 'Ti')\",OTHERS,False\nmp-560084,\"{'Ni': 4.0, 'As': 8.0, 'S': 24.0, 'N': 16.0, 'O': 32.0, 'F': 64.0}\",\"('As', 'F', 'N', 'Ni', 'O', 'S')\",OTHERS,False\nmp-1218778,\"{'Sr': 8.0, 'Li': 4.0, 'Si': 4.0, 'N': 12.0}\",\"('Li', 'N', 'Si', 'Sr')\",OTHERS,False\nmp-10739,\"{'P': 3.0, 'Ti': 3.0, 'Ru': 3.0}\",\"('P', 'Ru', 'Ti')\",OTHERS,False\nmp-772810,\"{'V': 4.0, 'Sb': 2.0, 'P': 6.0, 'O': 24.0}\",\"('O', 'P', 'Sb', 'V')\",OTHERS,False\nmp-720245,\"{'Cs': 2.0, 'K': 2.0, 'Na': 2.0, 'Li': 26.0, 'Si': 8.0, 'O': 32.0}\",\"('Cs', 'K', 'Li', 'Na', 'O', 'Si')\",OTHERS,False\nmp-754578,\"{'Tm': 4.0, 'Ga': 4.0, 'O': 12.0}\",\"('Ga', 'O', 'Tm')\",OTHERS,False\nmp-17894,\"{'P': 4.0, 'S': 16.0, 'Rb': 6.0, 'Sm': 2.0}\",\"('P', 'Rb', 'S', 'Sm')\",OTHERS,False\nmp-1232089,\"{'Tm': 16.0, 'Mg': 8.0, 'Se': 32.0}\",\"('Mg', 'Se', 'Tm')\",OTHERS,False\nmp-1216070,\"{'Y': 2.0, 'Mn': 3.0, 'Co': 1.0}\",\"('Co', 'Mn', 'Y')\",OTHERS,False\nAl 71 Pd 21 Re 8,\"{'Al': 71.0, 'Pd': 21.0, 'Re': 8.0}\",\"('Al', 'Pd', 'Re')\",QC,False\nmp-557704,\"{'Tl': 9.0, 'N': 9.0, 'O': 27.0}\",\"('N', 'O', 'Tl')\",OTHERS,False\nmp-6873,\"{'O': 8.0, 'F': 2.0, 'Al': 2.0, 'Si': 2.0, 'Ca': 2.0}\",\"('Al', 'Ca', 'F', 'O', 'Si')\",OTHERS,False\nmp-554023,\"{'Be': 4.0, 'B': 2.0, 'O': 6.0, 'F': 2.0}\",\"('B', 'Be', 'F', 'O')\",OTHERS,False\nmp-772513,\"{'Li': 4.0, 'Cr': 12.0, 'O': 32.0}\",\"('Cr', 'Li', 'O')\",OTHERS,False\nmp-865629,\"{'Li': 2.0, 'Ce': 1.0, 'Al': 1.0}\",\"('Al', 'Ce', 'Li')\",OTHERS,False\nmp-1009007,\"{'Lu': 1.0, 'Sb': 1.0}\",\"('Lu', 'Sb')\",OTHERS,False\nV 3 Ni 2,\"{'V': 3.0, 'Ni': 2.0}\",\"('Ni', 'V')\",QC,False\nmp-552131,\"{'Cl': 2.0, 'O': 4.0, 'Cs': 2.0}\",\"('Cl', 'Cs', 'O')\",OTHERS,False\nmp-1227047,\"{'Ca': 1.0, 'Yb': 3.0}\",\"('Ca', 'Yb')\",OTHERS,False\nmp-1201292,\"{'K': 8.0, 'Zr': 4.0, 'Si': 12.0, 'O': 40.0}\",\"('K', 'O', 'Si', 'Zr')\",OTHERS,False\nmp-1216314,\"{'V': 1.0, 'Ni': 3.0}\",\"('Ni', 'V')\",OTHERS,False\nmp-1247339,\"{'Ba': 12.0, 'Mo': 8.0, 'N': 16.0}\",\"('Ba', 'Mo', 'N')\",OTHERS,False\nmp-1201907,\"{'Ba': 20.0, 'Hg': 103.0}\",\"('Ba', 'Hg')\",OTHERS,False\nmp-20550,\"{'C': 1.0, 'Ce': 3.0, 'Pb': 1.0}\",\"('C', 'Ce', 'Pb')\",OTHERS,False\nmp-1223187,\"{'La': 6.0, 'Co': 1.0, 'Si': 2.0, 'S': 14.0}\",\"('Co', 'La', 'S', 'Si')\",OTHERS,False\nmp-1018732,\"{'Ho': 2.0, 'Te': 4.0}\",\"('Ho', 'Te')\",OTHERS,False\nmp-1225908,\"{'Cs': 1.0, 'Ti': 3.0, 'Al': 1.0, 'O': 8.0}\",\"('Al', 'Cs', 'O', 'Ti')\",OTHERS,False\nmp-2556,\"{'Sn': 1.0, 'Tl': 1.0}\",\"('Sn', 'Tl')\",OTHERS,False\nmp-1224342,\"{'Hf': 1.0, 'Sc': 3.0, 'Si': 8.0}\",\"('Hf', 'Sc', 'Si')\",OTHERS,False\nmp-1078473,\"{'Mn': 2.0, 'C': 2.0, 'O': 6.0}\",\"('C', 'Mn', 'O')\",OTHERS,False\nmp-570245,\"{'Ba': 4.0, 'Ga': 2.0, 'Ge': 2.0, 'N': 2.0}\",\"('Ba', 'Ga', 'Ge', 'N')\",OTHERS,False\nmp-28431,\"{'I': 4.0, 'Sb': 2.0, 'F': 12.0}\",\"('F', 'I', 'Sb')\",OTHERS,False\nmp-1096004,\"{'Hf': 2.0, 'Nb': 1.0, 'In': 1.0}\",\"('Hf', 'In', 'Nb')\",OTHERS,False\nmp-1111122,\"{'K': 2.0, 'Y': 1.0, 'Tl': 1.0, 'F': 6.0}\",\"('F', 'K', 'Tl', 'Y')\",OTHERS,False\nmp-754333,\"{'Ti': 2.0, 'O': 2.0}\",\"('O', 'Ti')\",OTHERS,False\nmp-1192761,\"{'Cs': 8.0, 'Hg': 4.0, 'Cl': 16.0}\",\"('Cl', 'Cs', 'Hg')\",OTHERS,False\nmp-1225182,\"{'Gd': 4.0, 'Nb': 2.0, 'Ga': 2.0, 'O': 14.0}\",\"('Ga', 'Gd', 'Nb', 'O')\",OTHERS,False\nmp-21819,\"{'P': 19.0, 'Cr': 30.0, 'U': 2.0}\",\"('Cr', 'P', 'U')\",OTHERS,False\nmp-1059160,\"{'Br': 1.0, 'N': 1.0}\",\"('Br', 'N')\",OTHERS,False\nmp-1103670,\"{'Tb': 4.0, 'Ga': 4.0, 'Ni': 4.0}\",\"('Ga', 'Ni', 'Tb')\",OTHERS,False\nmp-556562,\"{'Li': 2.0, 'As': 2.0, 'H': 12.0, 'O': 6.0, 'F': 12.0}\",\"('As', 'F', 'H', 'Li', 'O')\",OTHERS,False\nmp-1039188,\"{'Mg': 2.0, 'Bi': 2.0}\",\"('Bi', 'Mg')\",OTHERS,False\nmp-1096899,\"{'Cr': 4.0, 'N': 8.0}\",\"('Cr', 'N')\",OTHERS,False\nmp-1245841,\"{'K': 12.0, 'Mo': 2.0, 'N': 8.0}\",\"('K', 'Mo', 'N')\",OTHERS,False\nmvc-6139,\"{'Zn': 2.0, 'Ni': 4.0, 'O': 8.0}\",\"('Ni', 'O', 'Zn')\",OTHERS,False\nmp-1221104,\"{'Na': 2.0, 'Ca': 2.0, 'Al': 2.0, 'Si': 4.0, 'O': 14.0}\",\"('Al', 'Ca', 'Na', 'O', 'Si')\",OTHERS,False\nmp-770555,\"{'Mn': 8.0, 'P': 8.0, 'O': 32.0}\",\"('Mn', 'O', 'P')\",OTHERS,False\nmvc-8355,\"{'Ca': 2.0, 'Co': 2.0, 'Ge': 4.0, 'O': 12.0}\",\"('Ca', 'Co', 'Ge', 'O')\",OTHERS,False\nmp-1245831,\"{'Ba': 6.0, 'Sc': 2.0, 'N': 6.0}\",\"('Ba', 'N', 'Sc')\",OTHERS,False\nmp-1030853,\"{'Rb': 1.0, 'Mg': 6.0, 'Si': 1.0, 'O': 8.0}\",\"('Mg', 'O', 'Rb', 'Si')\",OTHERS,False\nmp-558712,\"{'Ag': 8.0, 'Bi': 4.0, 'O': 12.0}\",\"('Ag', 'Bi', 'O')\",OTHERS,False\nmp-1196766,\"{'Ce': 2.0, 'Ni': 4.0, 'N': 34.0, 'O': 48.0}\",\"('Ce', 'N', 'Ni', 'O')\",OTHERS,False\nmp-1213621,\"{'Dy': 4.0, 'H': 52.0, 'C': 32.0, 'N': 24.0, 'O': 52.0}\",\"('C', 'Dy', 'H', 'N', 'O')\",OTHERS,False\nmp-1200692,\"{'Si': 17.0, 'C': 17.0}\",\"('C', 'Si')\",OTHERS,False\nmp-1185311,\"{'Li': 1.0, 'Cr': 1.0, 'O': 3.0}\",\"('Cr', 'Li', 'O')\",OTHERS,False\nmp-556202,\"{'Cu': 2.0, 'C': 8.0, 'O': 8.0}\",\"('C', 'Cu', 'O')\",OTHERS,False\nmp-1093705,\"{'K': 2.0, 'Hg': 1.0, 'Se': 1.0}\",\"('Hg', 'K', 'Se')\",OTHERS,False\nmp-757023,\"{'V': 4.0, 'Cr': 4.0, 'O': 12.0}\",\"('Cr', 'O', 'V')\",OTHERS,False\nmp-760026,\"{'Li': 6.0, 'Mn': 5.0, 'Sb': 1.0, 'O': 12.0}\",\"('Li', 'Mn', 'O', 'Sb')\",OTHERS,False\nmp-1238525,\"{'Al': 4.0, 'S': 8.0, 'N': 4.0, 'O': 80.0}\",\"('Al', 'N', 'O', 'S')\",OTHERS,False\nmp-25359,\"{'O': 32.0, 'P': 6.0, 'Mo': 6.0}\",\"('Mo', 'O', 'P')\",OTHERS,False\nmp-16852,\"{'Ni': 6.0, 'Ga': 14.0}\",\"('Ga', 'Ni')\",OTHERS,False\nmp-744260,\"{'Li': 8.0, 'La': 8.0, 'Mo': 16.0, 'O': 64.0}\",\"('La', 'Li', 'Mo', 'O')\",OTHERS,False\nmp-1219975,\"{'Rb': 8.0, 'Mo': 12.0, 'O': 44.0}\",\"('Mo', 'O', 'Rb')\",OTHERS,False\nmp-1018941,\"{'Pr': 2.0, 'Te': 2.0, 'Cl': 2.0}\",\"('Cl', 'Pr', 'Te')\",OTHERS,False\nmp-1253012,\"{'Al': 8.0, 'Fe': 6.0, 'O': 24.0}\",\"('Al', 'Fe', 'O')\",OTHERS,False\nCd 65 Mg 20 Tb 15,\"{'Cd': 65.0, 'Mg': 20.0, 'Tb': 15.0}\",\"('Cd', 'Mg', 'Tb')\",QC,False\nmp-1507,\"{'Te': 2.0, 'Hg': 2.0}\",\"('Hg', 'Te')\",OTHERS,False\nmvc-6155,\"{'Ti': 6.0, 'As': 8.0, 'O': 32.0}\",\"('As', 'O', 'Ti')\",OTHERS,False\nmp-1076878,\"{'La': 28.0, 'Sm': 4.0, 'V': 32.0, 'O': 80.0}\",\"('La', 'O', 'Sm', 'V')\",OTHERS,False\nmp-1200699,\"{'P': 8.0, 'O': 12.0, 'F': 16.0}\",\"('F', 'O', 'P')\",OTHERS,False\nmp-766885,\"{'Sb': 18.0, 'P': 6.0, 'O': 42.0}\",\"('O', 'P', 'Sb')\",OTHERS,False\nAg 2 In 4 Ca 1,\"{'Ag': 2.0, 'In': 4.0, 'Ca': 1.0}\",\"('Ag', 'Ca', 'In')\",AC,False\nmp-973433,\"{'Lu': 1.0, 'Sc': 1.0, 'Ru': 2.0}\",\"('Lu', 'Ru', 'Sc')\",OTHERS,False\nmp-103,{'Hf': 2.0},\"('Hf',)\",OTHERS,False\nmp-1238169,\"{'Hg': 12.0, 'H': 16.0, 'I': 8.0, 'O': 48.0}\",\"('H', 'Hg', 'I', 'O')\",OTHERS,False\nmp-556697,\"{'Ca': 2.0, 'Ta': 4.0, 'Bi': 4.0, 'O': 18.0}\",\"('Bi', 'Ca', 'O', 'Ta')\",OTHERS,False\nmp-1019052,\"{'Re': 3.0, 'N': 3.0}\",\"('N', 'Re')\",OTHERS,False\nmp-1213132,\"{'Er': 4.0, 'Mg': 4.0, 'B': 20.0, 'O': 40.0}\",\"('B', 'Er', 'Mg', 'O')\",OTHERS,False\nmp-567969,\"{'Ho': 1.0, 'B': 2.0, 'Rh': 2.0, 'C': 1.0}\",\"('B', 'C', 'Ho', 'Rh')\",OTHERS,False\nmp-705520,\"{'Sn': 12.0, 'H': 16.0, 'N': 8.0, 'O': 40.0}\",\"('H', 'N', 'O', 'Sn')\",OTHERS,False\nmp-1075152,\"{'Mg': 6.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-13941,\"{'O': 6.0, 'Sr': 2.0, 'Yb': 1.0, 'Re': 1.0}\",\"('O', 'Re', 'Sr', 'Yb')\",OTHERS,False\nmp-510659,\"{'Ag': 4.0, 'Cu': 4.0, 'V': 4.0, 'O': 16.0}\",\"('Ag', 'Cu', 'O', 'V')\",OTHERS,False\nmp-7343,\"{'Y': 12.0, 'Pt': 4.0}\",\"('Pt', 'Y')\",OTHERS,False\nmp-706241,\"{'Rb': 8.0, 'W': 13.0, 'O': 43.0}\",\"('O', 'Rb', 'W')\",OTHERS,False\nmp-3345,\"{'S': 8.0, 'Cu': 6.0, 'As': 2.0}\",\"('As', 'Cu', 'S')\",OTHERS,False\nmp-10636,\"{'Sr': 2.0, 'Sb': 4.0}\",\"('Sb', 'Sr')\",OTHERS,False\nmp-770547,\"{'Mn': 8.0, 'P': 4.0, 'O': 20.0}\",\"('Mn', 'O', 'P')\",OTHERS,False\nmp-1019610,\"{'Cs': 16.0, 'Mg': 8.0, 'Si': 40.0, 'O': 96.0}\",\"('Cs', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-1196867,\"{'La': 8.0, 'N': 24.0}\",\"('La', 'N')\",OTHERS,False\nmp-559938,\"{'Li': 2.0, 'V': 6.0, 'Te': 4.0, 'O': 24.0}\",\"('Li', 'O', 'Te', 'V')\",OTHERS,False\nmp-505682,\"{'Dy': 24.0, 'Te': 12.0}\",\"('Dy', 'Te')\",OTHERS,False\nmp-31763,\"{'O': 12.0, 'Ca': 4.0, 'Fe': 2.0, 'Re': 2.0}\",\"('Ca', 'Fe', 'O', 'Re')\",OTHERS,False\nmp-1191401,\"{'Hf': 8.0, 'Fe': 16.0}\",\"('Fe', 'Hf')\",OTHERS,False\nmp-560879,\"{'C': 4.0, 'S': 8.0, 'N': 12.0, 'Cl': 12.0}\",\"('C', 'Cl', 'N', 'S')\",OTHERS,False\nmp-972869,\"{'Sc': 2.0, 'Ga': 1.0, 'Pt': 1.0}\",\"('Ga', 'Pt', 'Sc')\",OTHERS,False\nmp-1203456,\"{'Hg': 2.0, 'H': 32.0, 'Br': 12.0, 'N': 8.0}\",\"('Br', 'H', 'Hg', 'N')\",OTHERS,False\nmvc-5646,\"{'Ti': 2.0, 'Zn': 4.0, 'Ir': 2.0, 'O': 12.0}\",\"('Ir', 'O', 'Ti', 'Zn')\",OTHERS,False\nAl 40 Mn 10.1 Si 7.4,\"{'Al': 40.0, 'Mn': 10.1, 'Si': 7.4}\",\"('Al', 'Mn', 'Si')\",AC,False\nmp-775268,\"{'Li': 8.0, 'Mn': 7.0, 'Fe': 1.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1180695,\"{'Rb': 24.0, 'V': 8.0, 'O': 64.0}\",\"('O', 'Rb', 'V')\",OTHERS,False\nmp-1205898,\"{'Sc': 6.0, 'Te': 2.0, 'Ru': 1.0}\",\"('Ru', 'Sc', 'Te')\",OTHERS,False\nmp-763417,\"{'Co': 6.0, 'O': 2.0, 'F': 10.0}\",\"('Co', 'F', 'O')\",OTHERS,False\nmp-1221769,\"{'Mn': 8.0, 'B': 2.0}\",\"('B', 'Mn')\",OTHERS,False\nmp-568069,\"{'Ti': 4.0, 'Bi': 2.0}\",\"('Bi', 'Ti')\",OTHERS,False\nmp-1223482,\"{'Li': 6.0, 'Mo': 30.0, 'Se': 38.0}\",\"('Li', 'Mo', 'Se')\",OTHERS,False\nmp-1099867,\"{'K': 2.0, 'Na': 6.0, 'V': 4.0, 'Mo': 4.0, 'O': 24.0}\",\"('K', 'Mo', 'Na', 'O', 'V')\",OTHERS,False\nmp-16517,\"{'Al': 4.0, 'Ni': 2.0, 'Tm': 2.0}\",\"('Al', 'Ni', 'Tm')\",OTHERS,False\nmp-531051,\"{'Ca': 16.0, 'Nb': 8.0, 'O': 36.0}\",\"('Ca', 'Nb', 'O')\",OTHERS,False\nmp-773026,\"{'Ti': 12.0, 'Fe': 5.0, 'O': 32.0}\",\"('Fe', 'O', 'Ti')\",OTHERS,False\nmp-1086664,\"{'Rb': 1.0, 'Cd': 4.0, 'As': 3.0}\",\"('As', 'Cd', 'Rb')\",OTHERS,False\nmp-27479,\"{'O': 36.0, 'Pr': 8.0, 'W': 8.0}\",\"('O', 'Pr', 'W')\",OTHERS,False\nmp-1175629,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1210353,\"{'Na': 8.0, 'Al': 6.0, 'Si': 6.0, 'S': 2.0, 'O': 32.0}\",\"('Al', 'Na', 'O', 'S', 'Si')\",OTHERS,False\nmp-1210174,\"{'Nb': 8.0, 'Co': 8.0, 'Si': 12.0}\",\"('Co', 'Nb', 'Si')\",OTHERS,False\nmp-1224049,\"{'K': 6.0, 'H': 2.0, 'Se': 4.0, 'O': 16.0}\",\"('H', 'K', 'O', 'Se')\",OTHERS,False\nmp-1189619,\"{'Eu': 10.0, 'Al': 10.0}\",\"('Al', 'Eu')\",OTHERS,False\nmp-505248,\"{'Cr': 2.0, 'Rb': 6.0, 'O': 12.0, 'H': 12.0}\",\"('Cr', 'H', 'O', 'Rb')\",OTHERS,False\nmp-13274,\"{'C': 1.0, 'O': 4.0, 'Na': 4.0}\",\"('C', 'Na', 'O')\",OTHERS,False\nmp-864949,\"{'Mn': 1.0, 'Ga': 1.0, 'Rh': 2.0}\",\"('Ga', 'Mn', 'Rh')\",OTHERS,False\nmp-1214720,\"{'Ba': 2.0, 'Gd': 1.0, 'Cu': 4.0, 'O': 8.0}\",\"('Ba', 'Cu', 'Gd', 'O')\",OTHERS,False\nAg 42.2 In 42.6 Tm 15.2,\"{'Ag': 42.2, 'In': 42.6, 'Tm': 15.2}\",\"('Ag', 'In', 'Tm')\",AC,False\nmp-1198331,\"{'Rb': 16.0, 'Li': 4.0, 'H': 12.0, 'S': 16.0, 'O': 64.0}\",\"('H', 'Li', 'O', 'Rb', 'S')\",OTHERS,False\nmp-1177172,\"{'Li': 8.0, 'V': 10.0, 'Cr': 8.0, 'O': 36.0}\",\"('Cr', 'Li', 'O', 'V')\",OTHERS,False\nmp-695254,\"{'Ca': 10.0, 'La': 6.0, 'Mg': 3.0, 'Ti': 13.0, 'O': 48.0}\",\"('Ca', 'La', 'Mg', 'O', 'Ti')\",OTHERS,False\nmp-1228048,\"{'Ba': 4.0, 'Ta': 2.0, 'Ti': 1.0, 'Zn': 1.0, 'O': 12.0}\",\"('Ba', 'O', 'Ta', 'Ti', 'Zn')\",OTHERS,False\nmp-7386,\"{'F': 8.0, 'Na': 2.0, 'Ag': 2.0}\",\"('Ag', 'F', 'Na')\",OTHERS,False\nmp-978785,\"{'Si': 16.0, 'Mo': 4.0, 'Pt': 12.0}\",\"('Mo', 'Pt', 'Si')\",OTHERS,False\nmp-763763,\"{'Li': 4.0, 'Cr': 3.0, 'Fe': 2.0, 'Co': 3.0, 'O': 16.0}\",\"('Co', 'Cr', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-758488,\"{'Li': 2.0, 'P': 6.0, 'W': 4.0, 'O': 26.0}\",\"('Li', 'O', 'P', 'W')\",OTHERS,False\nmp-3193,\"{'O': 20.0, 'Na': 8.0, 'Si': 8.0}\",\"('Na', 'O', 'Si')\",OTHERS,False\nAu 61.2 Sn 24.3 Ca 14.5,\"{'Au': 61.2, 'Sn': 24.3, 'Ca': 14.5}\",\"('Au', 'Ca', 'Sn')\",AC,False\nmp-757655,\"{'Li': 17.0, 'Ni': 11.0, 'O': 28.0}\",\"('Li', 'Ni', 'O')\",OTHERS,False\nmp-1225167,\"{'Gd': 3.0, 'Y': 3.0, 'Fe': 23.0}\",\"('Fe', 'Gd', 'Y')\",OTHERS,False\nmp-867574,\"{'Li': 4.0, 'Mn': 8.0, 'Si': 8.0, 'O': 26.0}\",\"('Li', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-574176,\"{'Ba': 4.0, 'In': 8.0, 'Au': 8.0}\",\"('Au', 'Ba', 'In')\",OTHERS,False\nmp-1204738,\"{'Fe': 4.0, 'As': 8.0, 'S': 16.0, 'O': 32.0, 'F': 48.0}\",\"('As', 'F', 'Fe', 'O', 'S')\",OTHERS,False\nmp-631631,\"{'Mn': 5.0, 'V': 2.0, 'Pb': 3.0, 'O': 16.0}\",\"('Mn', 'O', 'Pb', 'V')\",OTHERS,False\nmp-1222471,\"{'Mg': 7.0, 'Ti': 1.0, 'Al': 6.0, 'Si': 8.0, 'H': 8.0, 'O': 38.0}\",\"('Al', 'H', 'Mg', 'O', 'Si', 'Ti')\",OTHERS,False\nmp-561037,\"{'K': 8.0, 'U': 1.0, 'Cr': 4.0, 'N': 2.0, 'O': 24.0}\",\"('Cr', 'K', 'N', 'O', 'U')\",OTHERS,False\nmp-759405,\"{'Bi': 12.0, 'O': 8.0, 'F': 20.0}\",\"('Bi', 'F', 'O')\",OTHERS,False\nmp-569576,\"{'La': 20.0, 'Si': 12.0, 'N': 36.0}\",\"('La', 'N', 'Si')\",OTHERS,False\nmp-1106150,\"{'Ce': 1.0, 'Mn': 4.0, 'Cu': 3.0, 'O': 12.0}\",\"('Ce', 'Cu', 'Mn', 'O')\",OTHERS,False\nmp-770899,\"{'Li': 4.0, 'V': 6.0, 'O': 16.0}\",\"('Li', 'O', 'V')\",OTHERS,False\nmp-569456,\"{'Ti': 4.0, 'Hg': 24.0, 'As': 16.0, 'Br': 28.0}\",\"('As', 'Br', 'Hg', 'Ti')\",OTHERS,False\nmp-850091,\"{'Li': 9.0, 'Nb': 10.0, 'Cr': 6.0, 'P': 24.0, 'O': 96.0}\",\"('Cr', 'Li', 'Nb', 'O', 'P')\",OTHERS,False\nmp-18807,\"{'O': 7.0, 'V': 2.0, 'Zn': 2.0}\",\"('O', 'V', 'Zn')\",OTHERS,False\nmp-683975,\"{'Yb': 12.0, 'K': 24.0, 'P': 20.0, 'S': 80.0}\",\"('K', 'P', 'S', 'Yb')\",OTHERS,False\nmvc-16255,\"{'Ba': 1.0, 'Al': 1.0, 'Cu': 1.0, 'W': 1.0, 'O': 5.0}\",\"('Al', 'Ba', 'Cu', 'O', 'W')\",OTHERS,False\nmp-504876,\"{'Hg': 19.0, 'Br': 18.0, 'As': 10.0}\",\"('As', 'Br', 'Hg')\",OTHERS,False\nmp-1251156,\"{'Sr': 4.0, 'Al': 2.0, 'Cu': 6.0, 'O': 14.0}\",\"('Al', 'Cu', 'O', 'Sr')\",OTHERS,False\nmp-1187229,\"{'Sr': 1.0, 'La': 3.0}\",\"('La', 'Sr')\",OTHERS,False\nmp-8914,\"{'Ga': 4.0, 'Sr': 4.0, 'Sn': 4.0}\",\"('Ga', 'Sn', 'Sr')\",OTHERS,False\nmp-1208283,\"{'Ti': 10.0, 'Co': 2.0, 'Sn': 6.0}\",\"('Co', 'Sn', 'Ti')\",OTHERS,False\nmp-1104706,\"{'Na': 2.0, 'Ca': 2.0, 'Si': 2.0, 'O': 8.0}\",\"('Ca', 'Na', 'O', 'Si')\",OTHERS,False\nmp-777669,\"{'Li': 16.0, 'Fe': 8.0, 'F': 32.0}\",\"('F', 'Fe', 'Li')\",OTHERS,False\nmp-757690,\"{'Li': 2.0, 'Nb': 2.0, 'P': 8.0, 'O': 24.0}\",\"('Li', 'Nb', 'O', 'P')\",OTHERS,False\nmp-26005,\"{'Li': 2.0, 'O': 48.0, 'P': 14.0, 'Mn': 12.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-627,\"{'Se': 1.0, 'Np': 1.0}\",\"('Np', 'Se')\",OTHERS,False\nmp-3262,\"{'Si': 2.0, 'Co': 2.0, 'Tm': 1.0}\",\"('Co', 'Si', 'Tm')\",OTHERS,False\nmp-1195876,\"{'U': 8.0, 'B': 24.0, 'Mo': 4.0}\",\"('B', 'Mo', 'U')\",OTHERS,False\nmp-1186882,\"{'Rb': 3.0, 'Zr': 1.0}\",\"('Rb', 'Zr')\",OTHERS,False\nmp-1245820,\"{'Sr': 2.0, 'C': 14.0, 'N': 20.0}\",\"('C', 'N', 'Sr')\",OTHERS,False\nmp-1066655,\"{'K': 3.0, 'Rh': 1.0}\",\"('K', 'Rh')\",OTHERS,False\nmp-20433,\"{'Ti': 6.0, 'In': 8.0}\",\"('In', 'Ti')\",OTHERS,False\nmp-6121,\"{'O': 20.0, 'Zn': 4.0, 'Y': 8.0, 'Ba': 4.0}\",\"('Ba', 'O', 'Y', 'Zn')\",OTHERS,False\nmp-1027109,\"{'Mo': 3.0, 'W': 1.0, 'Se': 6.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-624762,\"{'Fe': 16.0, 'Te': 8.0, 'C': 44.0, 'O': 44.0}\",\"('C', 'Fe', 'O', 'Te')\",OTHERS,False\nmp-745021,\"{'Sr': 8.0, 'Mg': 8.0, 'V': 12.0, 'O': 56.0}\",\"('Mg', 'O', 'Sr', 'V')\",OTHERS,False\nmp-759173,\"{'Li': 1.0, 'V': 3.0, 'O': 8.0}\",\"('Li', 'O', 'V')\",OTHERS,False\nmp-19910,\"{'Mg': 2.0, 'Ni': 4.0, 'Ce': 4.0}\",\"('Ce', 'Mg', 'Ni')\",OTHERS,False\nmp-1112949,\"{'Cs': 3.0, 'Tl': 1.0, 'I': 6.0}\",\"('Cs', 'I', 'Tl')\",OTHERS,False\nmp-1111166,\"{'K': 3.0, 'Sb': 1.0, 'Br': 6.0}\",\"('Br', 'K', 'Sb')\",OTHERS,False\nmp-552623,\"{'O': 4.0, 'Na': 4.0, 'C': 1.0}\",\"('C', 'Na', 'O')\",OTHERS,False\nmp-1205678,\"{'Ho': 4.0, 'Mg': 2.0, 'Si': 4.0}\",\"('Ho', 'Mg', 'Si')\",OTHERS,False\nmp-776169,\"{'Li': 8.0, 'Mn': 4.0, 'F': 20.0}\",\"('F', 'Li', 'Mn')\",OTHERS,False\nmp-849764,\"{'H': 112.0, 'Pt': 8.0, 'Br': 20.0, 'N': 36.0, 'O': 8.0}\",\"('Br', 'H', 'N', 'O', 'Pt')\",OTHERS,False\nmvc-7907,\"{'Mg': 3.0, 'Ta': 6.0, 'Fe': 4.0, 'O': 24.0}\",\"('Fe', 'Mg', 'O', 'Ta')\",OTHERS,False\nmp-5014,\"{'S': 4.0, 'Cu': 4.0, 'Ag': 4.0}\",\"('Ag', 'Cu', 'S')\",OTHERS,False\nmp-974332,\"{'S': 3.0, 'Br': 1.0}\",\"('Br', 'S')\",OTHERS,False\nmp-1097379,\"{'Ti': 1.0, 'Nb': 2.0, 'Mo': 1.0}\",\"('Mo', 'Nb', 'Ti')\",OTHERS,False\nmp-676749,\"{'Li': 6.0, 'U': 3.0, 'Cl': 18.0}\",\"('Cl', 'Li', 'U')\",OTHERS,False\nmp-673703,\"{'Rb': 3.0, 'Bi': 7.0, 'Pb': 3.0, 'I': 10.0, 'O': 10.0}\",\"('Bi', 'I', 'O', 'Pb', 'Rb')\",OTHERS,False\nmp-757001,\"{'Y': 15.0, 'Tm': 1.0, 'O': 24.0}\",\"('O', 'Tm', 'Y')\",OTHERS,False\nmp-1013549,\"{'Ba': 3.0, 'Bi': 1.0, 'N': 1.0}\",\"('Ba', 'Bi', 'N')\",OTHERS,False\nmp-1232160,\"{'Tb': 4.0, 'Mg': 4.0, 'S': 12.0}\",\"('Mg', 'S', 'Tb')\",OTHERS,False\nmp-559642,\"{'Ca': 2.0, 'Zr': 2.0, 'Al': 18.0, 'B': 2.0, 'O': 36.0}\",\"('Al', 'B', 'Ca', 'O', 'Zr')\",OTHERS,False\nmp-1183505,\"{'Bi': 6.0, 'As': 2.0}\",\"('As', 'Bi')\",OTHERS,False\nmp-1186550,\"{'Pm': 1.0, 'Cd': 1.0, 'Cu': 2.0}\",\"('Cd', 'Cu', 'Pm')\",OTHERS,False\nmp-1204045,\"{'U': 4.0, 'Fe': 30.0, 'Ge': 4.0}\",\"('Fe', 'Ge', 'U')\",OTHERS,False\nmp-640286,\"{'Ce': 4.0, 'Cu': 16.0, 'Sn': 4.0}\",\"('Ce', 'Cu', 'Sn')\",OTHERS,False\nmp-1097589,\"{'Ta': 1.0, 'V': 1.0, 'Mo': 2.0}\",\"('Mo', 'Ta', 'V')\",OTHERS,False\nmp-1100379,\"{'Ca': 12.0, 'Sn': 12.0, 'S': 36.0}\",\"('Ca', 'S', 'Sn')\",OTHERS,False\nmp-30820,\"{'Li': 2.0, 'Al': 1.0, 'Rh': 1.0}\",\"('Al', 'Li', 'Rh')\",OTHERS,False\nmp-22903,\"{'Rb': 1.0, 'I': 1.0}\",\"('I', 'Rb')\",OTHERS,False\nmp-510607,\"{'Sr': 4.0, 'Cr': 4.0, 'O': 16.0}\",\"('Cr', 'O', 'Sr')\",OTHERS,False\nZn 65 Mg 26 Ho 9,\"{'Zn': 65.0, 'Mg': 26.0, 'Ho': 9.0}\",\"('Ho', 'Mg', 'Zn')\",QC,False\nmp-10193,\"{'P': 1.0, 'Lu': 1.0, 'Pt': 1.0}\",\"('Lu', 'P', 'Pt')\",OTHERS,False\nmp-31049,\"{'O': 4.0, 'Nd': 2.0}\",\"('Nd', 'O')\",OTHERS,False\nmp-768225,\"{'Cu': 4.0, 'Ni': 2.0, 'O': 8.0}\",\"('Cu', 'Ni', 'O')\",OTHERS,False\nmp-772825,\"{'Ta': 8.0, 'Be': 4.0, 'O': 24.0}\",\"('Be', 'O', 'Ta')\",OTHERS,False\nmp-1027112,\"{'Mo': 1.0, 'W': 3.0, 'Se': 4.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-416,\"{'Cr': 6.0, 'Os': 2.0}\",\"('Cr', 'Os')\",OTHERS,False\nmp-754992,\"{'Lu': 1.0, 'Tl': 1.0, 'O': 2.0}\",\"('Lu', 'O', 'Tl')\",OTHERS,False\nmp-1194289,\"{'Ga': 2.0, 'Co': 21.0, 'B': 6.0}\",\"('B', 'Co', 'Ga')\",OTHERS,False\nmp-865343,\"{'Tm': 2.0, 'Ir': 1.0, 'Os': 1.0}\",\"('Ir', 'Os', 'Tm')\",OTHERS,False\nmp-1995,\"{'C': 2.0, 'Pr': 1.0}\",\"('C', 'Pr')\",OTHERS,False\nmp-22958,\"{'O': 3.0, 'K': 1.0, 'Br': 1.0}\",\"('Br', 'K', 'O')\",OTHERS,False\nmp-1203528,\"{'K': 8.0, 'Ba': 2.0, 'V': 12.0, 'O': 36.0}\",\"('Ba', 'K', 'O', 'V')\",OTHERS,False\nmp-1245213,\"{'Zn': 50.0, 'S': 50.0}\",\"('S', 'Zn')\",OTHERS,False\nmp-1222070,\"{'Mg': 1.0, 'Ti': 4.0, 'P': 6.0, 'O': 24.0}\",\"('Mg', 'O', 'P', 'Ti')\",OTHERS,False\nmp-1037934,\"{'Ca': 1.0, 'Mg': 30.0, 'Cd': 1.0, 'O': 32.0}\",\"('Ca', 'Cd', 'Mg', 'O')\",OTHERS,False\nmp-4991,\"{'O': 14.0, 'Sn': 4.0, 'Tb': 4.0}\",\"('O', 'Sn', 'Tb')\",OTHERS,False\nmp-505824,\"{'Cs': 2.0, 'Pd': 1.0, 'C': 2.0}\",\"('C', 'Cs', 'Pd')\",OTHERS,False\nmp-662527,\"{'Ce': 8.0, 'Si': 8.0, 'O': 28.0}\",\"('Ce', 'O', 'Si')\",OTHERS,False\nmp-542566,\"{'Pr': 6.0, 'Pd': 12.0, 'Sb': 10.0}\",\"('Pd', 'Pr', 'Sb')\",OTHERS,False\nmp-1228668,\"{'Ba': 2.0, 'Cu': 3.0, 'O': 6.0}\",\"('Ba', 'Cu', 'O')\",OTHERS,False\nmp-1187370,\"{'Tb': 1.0, 'Pm': 1.0, 'Ir': 2.0}\",\"('Ir', 'Pm', 'Tb')\",OTHERS,False\nmp-754919,\"{'V': 2.0, 'O': 2.0, 'F': 6.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-1221791,\"{'Mn': 2.0, 'Sb': 2.0, 'Pt': 1.0, 'Au': 1.0}\",\"('Au', 'Mn', 'Pt', 'Sb')\",OTHERS,False\nmp-1147685,\"{'Li': 20.0, 'Zn': 2.0, 'P': 8.0, 'S': 32.0}\",\"('Li', 'P', 'S', 'Zn')\",OTHERS,False\nmp-19108,\"{'Ca': 4.0, 'Mn': 2.0, 'W': 2.0, 'O': 12.0}\",\"('Ca', 'Mn', 'O', 'W')\",OTHERS,False\nmp-29982,\"{'B': 6.0, 'C': 3.0, 'Nb': 7.0}\",\"('B', 'C', 'Nb')\",OTHERS,False\nmvc-16311,\"{'Y': 1.0, 'Sb': 1.0, 'W': 2.0, 'O': 8.0}\",\"('O', 'Sb', 'W', 'Y')\",OTHERS,False\nmp-1187312,\"{'Tb': 1.0, 'As': 3.0}\",\"('As', 'Tb')\",OTHERS,False\nmp-1106223,\"{'Nd': 4.0, 'Ge': 8.0, 'Pt': 4.0}\",\"('Ge', 'Nd', 'Pt')\",OTHERS,False\nmp-1232246,\"{'Ce': 32.0, 'Mg': 16.0, 'S': 64.0}\",\"('Ce', 'Mg', 'S')\",OTHERS,False\nmp-1208599,\"{'Sr': 2.0, 'Li': 2.0, 'Ti': 2.0, 'F': 12.0}\",\"('F', 'Li', 'Sr', 'Ti')\",OTHERS,False\nmp-753127,\"{'Na': 3.0, 'Li': 4.0, 'Mn': 5.0, 'O': 9.0}\",\"('Li', 'Mn', 'Na', 'O')\",OTHERS,False\nmp-27276,\"{'Si': 12.0, 'Ni': 31.0}\",\"('Ni', 'Si')\",OTHERS,False\nmp-766414,\"{'Mn': 2.0, 'Fe': 6.0, 'P': 8.0, 'O': 32.0}\",\"('Fe', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1178773,\"{'Y': 10.0, 'Fe': 20.0}\",\"('Fe', 'Y')\",OTHERS,False\nmp-1247451,\"{'Mg': 6.0, 'Ga': 6.0, 'N': 10.0}\",\"('Ga', 'Mg', 'N')\",OTHERS,False\nmp-1278,\"{'Si': 2.0, 'Zr': 4.0}\",\"('Si', 'Zr')\",OTHERS,False\nmp-1227648,\"{'Ca': 1.0, 'In': 6.0, 'Cu': 6.0}\",\"('Ca', 'Cu', 'In')\",OTHERS,False\nmp-1056985,\"{'Ti': 1.0, 'Pd': 1.0}\",\"('Pd', 'Ti')\",OTHERS,False\nmp-1190582,\"{'Mg': 6.0, 'Nb': 1.0, 'H': 16.0}\",\"('H', 'Mg', 'Nb')\",OTHERS,False\nmp-1214524,\"{'Ba': 8.0, 'Dy': 2.0, 'Cu': 6.0, 'O': 18.0}\",\"('Ba', 'Cu', 'Dy', 'O')\",OTHERS,False\nmp-4068,\"{'Na': 8.0, 'S': 12.0, 'Ge': 4.0}\",\"('Ge', 'Na', 'S')\",OTHERS,False\nmp-771164,\"{'Na': 4.0, 'Mn': 2.0, 'B': 2.0, 'As': 2.0, 'O': 14.0}\",\"('As', 'B', 'Mn', 'Na', 'O')\",OTHERS,False\nmp-767576,\"{'Li': 12.0, 'Mn': 2.0, 'O': 8.0, 'F': 2.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-703595,\"{'Cd': 13.0, 'I': 28.0}\",\"('Cd', 'I')\",OTHERS,False\nmp-676552,\"{'Nd': 2.0, 'Th': 8.0, 'O': 19.0}\",\"('Nd', 'O', 'Th')\",OTHERS,False\nmp-510342,\"{'Dy': 4.0, 'Te': 8.0, 'O': 22.0}\",\"('Dy', 'O', 'Te')\",OTHERS,False\nmp-1215613,\"{'Yb': 7.0, 'Mg': 1.0, 'Pt': 4.0}\",\"('Mg', 'Pt', 'Yb')\",OTHERS,False\nmp-1190998,\"{'Pr': 8.0, 'P': 8.0, 'S': 8.0}\",\"('P', 'Pr', 'S')\",OTHERS,False\nmp-1207084,\"{'Al': 1.0, 'Ni': 3.0, 'C': 1.0}\",\"('Al', 'C', 'Ni')\",OTHERS,False\nmp-755470,\"{'Li': 2.0, 'Bi': 1.0, 'S': 2.0}\",\"('Bi', 'Li', 'S')\",OTHERS,False\nmp-756054,\"{'Zr': 8.0, 'N': 8.0, 'O': 4.0}\",\"('N', 'O', 'Zr')\",OTHERS,False\nmp-626467,\"{'Al': 8.0, 'H': 24.0, 'O': 24.0}\",\"('Al', 'H', 'O')\",OTHERS,False\nmp-1080178,\"{'Gd': 3.0, 'Zn': 3.0, 'Pd': 3.0}\",\"('Gd', 'Pd', 'Zn')\",OTHERS,False\nmp-1209792,\"{'Rb': 1.0, 'Cu': 2.0, 'O': 10.0}\",\"('Cu', 'O', 'Rb')\",OTHERS,False\nmp-601846,\"{'Yb': 2.0, 'In': 8.0, 'Ir': 1.0}\",\"('In', 'Ir', 'Yb')\",OTHERS,False\nmp-558775,\"{'Al': 6.0, 'Pb': 10.0, 'F': 38.0}\",\"('Al', 'F', 'Pb')\",OTHERS,False\nmp-972924,\"{'La': 1.0, 'Mg': 16.0, 'Al': 12.0}\",\"('Al', 'La', 'Mg')\",OTHERS,False\nmp-1176372,\"{'Na': 6.0, 'Mn': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Mn', 'Na', 'O', 'P')\",OTHERS,False\nmp-557463,\"{'Na': 12.0, 'Sc': 4.0, 'Si': 8.0, 'O': 28.0}\",\"('Na', 'O', 'Sc', 'Si')\",OTHERS,False\nmp-1094255,\"{'Zr': 6.0, 'Sn': 2.0}\",\"('Sn', 'Zr')\",OTHERS,False\nmp-865439,\"{'Th': 6.0, 'O': 4.0}\",\"('O', 'Th')\",OTHERS,False\nmp-1178904,\"{'V': 4.0, 'Cu': 2.0, 'P': 4.0, 'O': 30.0}\",\"('Cu', 'O', 'P', 'V')\",OTHERS,False\nmp-9351,\"{'Ge': 2.0, 'Lu': 2.0, 'Au': 2.0}\",\"('Au', 'Ge', 'Lu')\",OTHERS,False\nmp-565679,\"{'Hg': 16.0, 'Se': 16.0, 'O': 36.0}\",\"('Hg', 'O', 'Se')\",OTHERS,False\nmp-1187402,\"{'Th': 1.0, 'Al': 1.0, 'Cu': 2.0}\",\"('Al', 'Cu', 'Th')\",OTHERS,False\nmp-14017,\"{'K': 6.0, 'Sb': 2.0}\",\"('K', 'Sb')\",OTHERS,False\nmp-758528,\"{'Li': 6.0, 'Mn': 10.0, 'O': 4.0, 'F': 18.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-778474,\"{'Li': 12.0, 'Co': 4.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Co', 'Li', 'O')\",OTHERS,False\nmp-1179605,{'Sb': 2.0},\"('Sb',)\",OTHERS,False\nmp-29973,\"{'N': 4.0, 'Sr': 4.0}\",\"('N', 'Sr')\",OTHERS,False\nmp-1024968,\"{'Pu': 1.0, 'B': 2.0, 'Rh': 3.0}\",\"('B', 'Pu', 'Rh')\",OTHERS,False\nmp-8613,\"{'P': 2.0, 'S': 6.0, 'Mn': 2.0}\",\"('Mn', 'P', 'S')\",OTHERS,False\nmp-974364,\"{'Ru': 1.0, 'Au': 3.0}\",\"('Au', 'Ru')\",OTHERS,False\nmp-775876,\"{'Na': 2.0, 'Li': 2.0, 'Mn': 2.0, 'P': 4.0, 'O': 14.0}\",\"('Li', 'Mn', 'Na', 'O', 'P')\",OTHERS,False\nmvc-15619,\"{'Mg': 4.0, 'Co': 2.0, 'Sb': 2.0, 'O': 12.0}\",\"('Co', 'Mg', 'O', 'Sb')\",OTHERS,False\nmp-1104601,\"{'Co': 1.0, 'N': 2.0, 'O': 10.0}\",\"('Co', 'N', 'O')\",OTHERS,False\nmp-673798,\"{'K': 6.0, 'Cl': 2.0, 'O': 2.0}\",\"('Cl', 'K', 'O')\",OTHERS,False\nmp-1095641,\"{'Tb': 5.0, 'S': 7.0}\",\"('S', 'Tb')\",OTHERS,False\nmp-1209090,\"{'Rb': 2.0, 'Tm': 2.0, 'Be': 2.0, 'F': 12.0}\",\"('Be', 'F', 'Rb', 'Tm')\",OTHERS,False\nmp-9721,\"{'O': 1.0, 'Ca': 3.0, 'Ge': 1.0}\",\"('Ca', 'Ge', 'O')\",OTHERS,False\nmp-32509,\"{'V': 2.0, 'P': 8.0, 'O': 24.0}\",\"('O', 'P', 'V')\",OTHERS,False\nmp-1070152,\"{'Ca': 1.0, 'Co': 2.0, 'Si': 2.0}\",\"('Ca', 'Co', 'Si')\",OTHERS,False\nmp-22588,\"{'O': 12.0, 'Fe': 4.0, 'Lu': 4.0}\",\"('Fe', 'Lu', 'O')\",OTHERS,False\nmp-1227199,\"{'Ca': 1.0, 'Pr': 3.0, 'Co': 4.0, 'O': 12.0}\",\"('Ca', 'Co', 'O', 'Pr')\",OTHERS,False\nmp-1187114,\"{'Sr': 3.0, 'Cr': 1.0}\",\"('Cr', 'Sr')\",OTHERS,False\nmp-1073927,\"{'Mg': 12.0, 'Si': 10.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1246426,\"{'Ge': 4.0, 'C': 4.0, 'N': 8.0}\",\"('C', 'Ge', 'N')\",OTHERS,False\nmp-6652,\"{'B': 6.0, 'O': 18.0, 'K': 2.0, 'Zn': 8.0}\",\"('B', 'K', 'O', 'Zn')\",OTHERS,False\nmp-867782,\"{'Mn': 2.0, 'O': 2.0, 'F': 2.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-14214,\"{'Si': 8.0, 'P': 32.0, 'Ba': 32.0}\",\"('Ba', 'P', 'Si')\",OTHERS,False\nmp-850284,\"{'Li': 4.0, 'Co': 2.0, 'O': 2.0, 'F': 6.0}\",\"('Co', 'F', 'Li', 'O')\",OTHERS,False\nmp-697264,\"{'Cd': 4.0, 'H': 8.0, 'Se': 4.0, 'O': 16.0}\",\"('Cd', 'H', 'O', 'Se')\",OTHERS,False\nmp-1188370,\"{'La': 6.0, 'Sn': 10.0}\",\"('La', 'Sn')\",OTHERS,False\nmp-1031330,\"{'K': 1.0, 'La': 1.0, 'Mg': 6.0, 'O': 8.0}\",\"('K', 'La', 'Mg', 'O')\",OTHERS,False\nmp-21974,\"{'O': 48.0, 'Fe': 20.0, 'Dy': 12.0}\",\"('Dy', 'Fe', 'O')\",OTHERS,False\nmp-1176791,\"{'Li': 18.0, 'Mn': 2.0, 'Si': 4.0, 'O': 20.0}\",\"('Li', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-705429,\"{'Li': 12.0, 'Co': 10.0, 'P': 16.0, 'O': 56.0}\",\"('Co', 'Li', 'O', 'P')\",OTHERS,False\nmp-1214619,\"{'C': 40.0, 'Cl': 36.0, 'O': 2.0}\",\"('C', 'Cl', 'O')\",OTHERS,False\nmp-1105966,\"{'Gd': 8.0, 'Te': 12.0}\",\"('Gd', 'Te')\",OTHERS,False\nmp-1173997,\"{'Li': 5.0, 'Mn': 1.0, 'Co': 2.0, 'O': 8.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-28473,\"{'C': 2.0, 'F': 6.0, 'Cl': 2.0}\",\"('C', 'Cl', 'F')\",OTHERS,False\nmp-1203464,\"{'Bi': 8.0, 'As': 4.0, 'O': 24.0}\",\"('As', 'Bi', 'O')\",OTHERS,False\nmp-1192255,\"{'Sm': 2.0, 'Mn': 3.0, 'Sb': 4.0, 'S': 12.0}\",\"('Mn', 'S', 'Sb', 'Sm')\",OTHERS,False\nmp-5391,\"{'O': 16.0, 'Mg': 8.0, 'Si': 4.0}\",\"('Mg', 'O', 'Si')\",OTHERS,False\nmp-1213756,\"{'Cr': 7.0, 'Te': 8.0}\",\"('Cr', 'Te')\",OTHERS,False\nmp-756866,\"{'Li': 2.0, 'Ti': 1.0, 'Mn': 3.0, 'O': 8.0}\",\"('Li', 'Mn', 'O', 'Ti')\",OTHERS,False\nmp-1224328,\"{'Hf': 1.0, 'Nb': 1.0, 'B': 4.0}\",\"('B', 'Hf', 'Nb')\",OTHERS,False\nmp-1205810,\"{'Eu': 2.0, 'Br': 6.0}\",\"('Br', 'Eu')\",OTHERS,False\nmp-1111564,\"{'Li': 2.0, 'Al': 1.0, 'Ag': 1.0, 'F': 6.0}\",\"('Ag', 'Al', 'F', 'Li')\",OTHERS,False\nmp-1210883,\"{'Nd': 28.0, 'V': 8.0, 'B': 4.0, 'O': 68.0}\",\"('B', 'Nd', 'O', 'V')\",OTHERS,False\nmp-1211154,\"{'Na': 6.0, 'Ca': 8.0, 'Ti': 2.0, 'Si': 8.0, 'O': 32.0, 'F': 4.0}\",\"('Ca', 'F', 'Na', 'O', 'Si', 'Ti')\",OTHERS,False\nmp-1206671,\"{'Lu': 2.0, 'Se': 2.0, 'I': 2.0}\",\"('I', 'Lu', 'Se')\",OTHERS,False\nmp-780706,\"{'Li': 2.0, 'Mn': 2.0, 'P': 4.0, 'O': 14.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1183219,\"{'Ba': 2.0, 'Mg': 1.0, 'Tl': 1.0}\",\"('Ba', 'Mg', 'Tl')\",OTHERS,False\nmp-5479,\"{'F': 4.0, 'Al': 1.0, 'Rb': 1.0}\",\"('Al', 'F', 'Rb')\",OTHERS,False\nmp-568808,\"{'Tb': 6.0, 'Se': 8.0}\",\"('Se', 'Tb')\",OTHERS,False\nmp-1183933,\"{'Cs': 1.0, 'Np': 1.0, 'O': 3.0}\",\"('Cs', 'Np', 'O')\",OTHERS,False\nmp-1033732,\"{'Mg': 14.0, 'B': 1.0, 'Sb': 1.0, 'O': 16.0}\",\"('B', 'Mg', 'O', 'Sb')\",OTHERS,False\nmp-705432,\"{'Li': 12.0, 'Ni': 10.0, 'P': 16.0, 'O': 56.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-975933,\"{'Nd': 1.0, 'Ho': 1.0, 'In': 2.0}\",\"('Ho', 'In', 'Nd')\",OTHERS,False\nmp-1198649,\"{'K': 9.0, 'Y': 3.0, 'Si': 12.0, 'O': 32.0, 'F': 2.0}\",\"('F', 'K', 'O', 'Si', 'Y')\",OTHERS,False\nmp-1176194,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-642649,\"{'Ba': 2.0, 'H': 4.0, 'O': 6.0}\",\"('Ba', 'H', 'O')\",OTHERS,False\nmp-1215921,\"{'Y': 1.0, 'Ho': 1.0, 'C': 4.0}\",\"('C', 'Ho', 'Y')\",OTHERS,False\nmp-754057,\"{'Li': 4.0, 'Fe': 2.0, 'Cu': 3.0, 'Sb': 3.0, 'O': 16.0}\",\"('Cu', 'Fe', 'Li', 'O', 'Sb')\",OTHERS,False\nmp-779706,\"{'Ag': 4.0, 'Ge': 4.0, 'O': 12.0}\",\"('Ag', 'Ge', 'O')\",OTHERS,False\nmp-1178561,\"{'Ag': 1.0, 'Cl': 1.0, 'O': 3.0}\",\"('Ag', 'Cl', 'O')\",OTHERS,False\nmp-571467,\"{'Cs': 2.0, 'Na': 1.0, 'Y': 1.0, 'Br': 6.0}\",\"('Br', 'Cs', 'Na', 'Y')\",OTHERS,False\nmp-1220708,\"{'Na': 1.0, 'Ho': 1.0, 'F': 4.0}\",\"('F', 'Ho', 'Na')\",OTHERS,False\nmp-23280,\"{'Cl': 12.0, 'As': 4.0}\",\"('As', 'Cl')\",OTHERS,False\nmp-1028947,\"{'Mo': 1.0, 'W': 3.0, 'Se': 4.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-753719,\"{'Sr': 1.0, 'Li': 1.0, 'Nb': 2.0, 'O': 6.0, 'F': 1.0}\",\"('F', 'Li', 'Nb', 'O', 'Sr')\",OTHERS,False\nmp-985297,\"{'Ag': 3.0, 'Rh': 1.0}\",\"('Ag', 'Rh')\",OTHERS,False\nmp-757169,\"{'Li': 4.0, 'Mn': 2.0, 'Si': 4.0, 'O': 12.0}\",\"('Li', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1211819,\"{'K': 6.0, 'Hf': 8.0, 'Ag': 4.0, 'F': 46.0}\",\"('Ag', 'F', 'Hf', 'K')\",OTHERS,False\nmp-25823,\"{'Li': 2.0, 'Ni': 4.0, 'P': 10.0, 'O': 30.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-542132,\"{'Yb': 2.0, 'As': 4.0, 'Cu': 2.0}\",\"('As', 'Cu', 'Yb')\",OTHERS,False\nmp-849955,\"{'Li': 9.0, 'Mn': 15.0, 'Fe': 6.0, 'O': 36.0}\",\"('Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-7151,\"{'Cu': 2.0, 'Se': 6.0, 'Cs': 2.0, 'U': 2.0}\",\"('Cs', 'Cu', 'Se', 'U')\",OTHERS,False\nmp-20269,\"{'Mn': 1.0, 'Ga': 1.0, 'Pt': 1.0}\",\"('Ga', 'Mn', 'Pt')\",OTHERS,False\nmp-1095694,\"{'Ag': 8.0, 'S': 4.0}\",\"('Ag', 'S')\",OTHERS,False\nmp-1177438,\"{'Li': 4.0, 'Mn': 2.0, 'Cr': 3.0, 'Co': 3.0, 'O': 16.0}\",\"('Co', 'Cr', 'Li', 'Mn', 'O')\",OTHERS,False\nmvc-671,\"{'W': 3.0, 'O': 8.0}\",\"('O', 'W')\",OTHERS,False\nmp-1478,\"{'Cu': 8.0, 'O': 6.0}\",\"('Cu', 'O')\",OTHERS,False\nmp-1183679,\"{'Cr': 1.0, 'H': 3.0}\",\"('Cr', 'H')\",OTHERS,False\nmp-601396,\"{'Cu': 4.0, 'H': 40.0, 'C': 8.0, 'N': 16.0, 'O': 40.0}\",\"('C', 'Cu', 'H', 'N', 'O')\",OTHERS,False\nmp-1187595,\"{'Yb': 1.0, 'Ni': 1.0, 'O': 3.0}\",\"('Ni', 'O', 'Yb')\",OTHERS,False\nmp-1179005,\"{'V': 4.0, 'Re': 4.0, 'O': 22.0}\",\"('O', 'Re', 'V')\",OTHERS,False\nmp-1196075,\"{'Si': 20.0, 'H': 114.0, 'C': 38.0}\",\"('C', 'H', 'Si')\",OTHERS,False\nmp-556377,\"{'Ba': 17.0, 'Dy': 16.0, 'Zn': 8.0, 'Pt': 4.0, 'O': 57.0}\",\"('Ba', 'Dy', 'O', 'Pt', 'Zn')\",OTHERS,False\nmp-3787,\"{'S': 6.0, 'Fe': 4.0, 'Rb': 2.0}\",\"('Fe', 'Rb', 'S')\",OTHERS,False\nmp-1247046,\"{'Fe': 1.0, 'Co': 10.0, 'N': 8.0}\",\"('Co', 'Fe', 'N')\",OTHERS,False\nmp-1211487,\"{'K': 8.0, 'Yb': 4.0, 'F': 20.0}\",\"('F', 'K', 'Yb')\",OTHERS,False\nmp-1196069,\"{'Nd': 12.0, 'Al': 86.0, 'W': 8.0}\",\"('Al', 'Nd', 'W')\",OTHERS,False\nmp-1224629,\"{'Gd': 2.0, 'Fe': 4.0, 'Bi': 2.0, 'O': 12.0}\",\"('Bi', 'Fe', 'Gd', 'O')\",OTHERS,False\nmp-975812,\"{'Pr': 1.0, 'Sm': 3.0}\",\"('Pr', 'Sm')\",OTHERS,False\nmp-1183972,\"{'Cs': 1.0, 'Sr': 1.0, 'O': 3.0}\",\"('Cs', 'O', 'Sr')\",OTHERS,False\nmp-10106,\"{'Ca': 4.0, 'As': 2.0}\",\"('As', 'Ca')\",OTHERS,False\nmp-1218394,\"{'Sr': 3.0, 'Be': 2.0, 'B': 5.0, 'H': 1.0, 'O': 13.0}\",\"('B', 'Be', 'H', 'O', 'Sr')\",OTHERS,False\nmp-1214083,\"{'Ca': 12.0, 'Si': 4.0, 'O': 16.0, 'F': 4.0}\",\"('Ca', 'F', 'O', 'Si')\",OTHERS,False\nmp-567939,\"{'Cs': 1.0, 'Sm': 1.0, 'Cl': 3.0}\",\"('Cl', 'Cs', 'Sm')\",OTHERS,False\nmp-1245720,\"{'Zn': 1.0, 'Pb': 2.0, 'N': 2.0}\",\"('N', 'Pb', 'Zn')\",OTHERS,False\nmp-1225560,\"{'Er': 2.0, 'O': 3.0}\",\"('Er', 'O')\",OTHERS,False\nmp-773502,\"{'K': 2.0, 'Co': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Co', 'K', 'O', 'P')\",OTHERS,False\nmp-1094985,\"{'Mg': 8.0, 'Cd': 4.0}\",\"('Cd', 'Mg')\",OTHERS,False\nmp-1095452,\"{'U': 3.0, 'Fe': 2.0, 'Ge': 7.0}\",\"('Fe', 'Ge', 'U')\",OTHERS,False\nmp-1183556,\"{'Ca': 1.0, 'Y': 1.0, 'Cd': 2.0}\",\"('Ca', 'Cd', 'Y')\",OTHERS,False\nmp-30216,\"{'K': 4.0, 'I': 12.0, 'Re': 2.0}\",\"('I', 'K', 'Re')\",OTHERS,False\nmp-1210455,\"{'Na': 3.0, 'La': 1.0, 'As': 2.0, 'O': 8.0}\",\"('As', 'La', 'Na', 'O')\",OTHERS,False\nmp-1186570,\"{'Pr': 1.0, 'Dy': 1.0, 'In': 2.0}\",\"('Dy', 'In', 'Pr')\",OTHERS,False\nmp-755576,\"{'Zr': 8.0, 'N': 8.0, 'O': 4.0}\",\"('N', 'O', 'Zr')\",OTHERS,False\nmp-1226984,\"{'La': 20.0, 'Ga': 6.0, 'Co': 14.0}\",\"('Co', 'Ga', 'La')\",OTHERS,False\nmp-1018740,\"{'La': 2.0, 'Co': 2.0, 'Si': 2.0}\",\"('Co', 'La', 'Si')\",OTHERS,False\nmp-1005761,\"{'Sm': 2.0, 'Gd': 6.0}\",\"('Gd', 'Sm')\",OTHERS,False\nmp-1186674,\"{'Pm': 1.0, 'Zn': 2.0, 'Hg': 1.0}\",\"('Hg', 'Pm', 'Zn')\",OTHERS,False\nmp-162,{'Yb': 1.0},\"('Yb',)\",OTHERS,False\nmp-1191851,\"{'Yb': 6.0, 'Si': 4.0, 'Ni': 12.0}\",\"('Ni', 'Si', 'Yb')\",OTHERS,False\nmp-977446,\"{'Nd': 2.0, 'V': 2.0, 'Ge': 6.0}\",\"('Ge', 'Nd', 'V')\",OTHERS,False\nmp-1207829,\"{'Yb': 12.0, 'Mn': 8.0, 'Al': 86.0}\",\"('Al', 'Mn', 'Yb')\",OTHERS,False\nmp-1207780,\"{'Y': 12.0, 'Rh': 4.0}\",\"('Rh', 'Y')\",OTHERS,False\nmp-770370,\"{'V': 12.0, 'B': 4.0, 'O': 24.0}\",\"('B', 'O', 'V')\",OTHERS,False\nmp-1217198,\"{'V': 4.0, 'Cd': 4.0, 'Ni': 4.0, 'P': 8.0, 'O': 52.0}\",\"('Cd', 'Ni', 'O', 'P', 'V')\",OTHERS,False\nmp-565711,\"{'P': 24.0, 'O': 24.0, 'F': 36.0}\",\"('F', 'O', 'P')\",OTHERS,False\nmp-1099622,\"{'K': 5.0, 'Na': 3.0, 'Nb': 7.0, 'W': 1.0, 'O': 24.0}\",\"('K', 'Na', 'Nb', 'O', 'W')\",OTHERS,False\nmp-1177697,\"{'Li': 3.0, 'Cu': 4.0, 'Sb': 1.0, 'O': 8.0}\",\"('Cu', 'Li', 'O', 'Sb')\",OTHERS,False\nmp-1246934,\"{'Mg': 2.0, 'Ti': 3.0, 'Ni': 1.0, 'S': 8.0}\",\"('Mg', 'Ni', 'S', 'Ti')\",OTHERS,False\nmp-1178865,\"{'W': 4.0, 'O': 20.0}\",\"('O', 'W')\",OTHERS,False\nmp-1195268,\"{'Co': 8.0, 'Bi': 16.0, 'S': 8.0, 'O': 56.0}\",\"('Bi', 'Co', 'O', 'S')\",OTHERS,False\nmp-685478,\"{'Na': 32.0, 'Pd': 16.0, 'O': 48.0}\",\"('Na', 'O', 'Pd')\",OTHERS,False\nmp-28238,\"{'Se': 44.0, 'Sb': 16.0, 'Ba': 16.0}\",\"('Ba', 'Sb', 'Se')\",OTHERS,False\nmp-780897,\"{'Li': 4.0, 'Mn': 4.0, 'P': 8.0, 'H': 12.0, 'O': 34.0}\",\"('H', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-557295,\"{'V': 8.0, 'Se': 16.0, 'O': 48.0}\",\"('O', 'Se', 'V')\",OTHERS,False\nmp-29357,\"{'C': 2.0, 'Cl': 28.0, 'Zr': 12.0}\",\"('C', 'Cl', 'Zr')\",OTHERS,False\nmp-1205990,\"{'Nb': 1.0, 'N': 2.0, 'Cl': 6.0}\",\"('Cl', 'N', 'Nb')\",OTHERS,False\nmp-1185618,\"{'Mg': 1.0, 'V': 1.0, 'Rh': 2.0}\",\"('Mg', 'Rh', 'V')\",OTHERS,False\nmp-973926,\"{'K': 2.0, 'Co': 2.0, 'H': 6.0, 'C': 6.0, 'O': 12.0}\",\"('C', 'Co', 'H', 'K', 'O')\",OTHERS,False\nmp-1091374,\"{'Nb': 2.0, 'Cr': 2.0, 'N': 4.0}\",\"('Cr', 'N', 'Nb')\",OTHERS,False\nmp-18190,\"{'O': 12.0, 'Ca': 1.0, 'Cu': 3.0, 'Sn': 4.0}\",\"('Ca', 'Cu', 'O', 'Sn')\",OTHERS,False\nmp-697873,\"{'Sn': 4.0, 'H': 4.0, 'C': 8.0, 'O': 12.0}\",\"('C', 'H', 'O', 'Sn')\",OTHERS,False\nmp-13456,\"{'S': 5.0, 'Zn': 5.0}\",\"('S', 'Zn')\",OTHERS,False\nmp-3654,\"{'F': 3.0, 'Ca': 1.0, 'Rb': 1.0}\",\"('Ca', 'F', 'Rb')\",OTHERS,False\nmp-1101241,\"{'V': 14.0, 'Fe': 5.0, 'O': 32.0}\",\"('Fe', 'O', 'V')\",OTHERS,False\nmp-1176633,\"{'Li': 8.0, 'Ni': 8.0, 'P': 8.0, 'O': 32.0, 'F': 8.0}\",\"('F', 'Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-7935,\"{'Sb': 2.0, 'Te': 2.0, 'U': 2.0}\",\"('Sb', 'Te', 'U')\",OTHERS,False\nmp-23408,\"{'S': 20.0, 'Tl': 16.0, 'Bi': 8.0}\",\"('Bi', 'S', 'Tl')\",OTHERS,False\nmp-977358,\"{'Ho': 2.0, 'Ag': 1.0, 'Ir': 1.0}\",\"('Ag', 'Ho', 'Ir')\",OTHERS,False\nmp-31084,\"{'Zr': 12.0, 'Ge': 24.0, 'Rh': 12.0}\",\"('Ge', 'Rh', 'Zr')\",OTHERS,False\nmp-766402,\"{'Li': 8.0, 'V': 4.0, 'Si': 12.0, 'O': 32.0}\",\"('Li', 'O', 'Si', 'V')\",OTHERS,False\nmp-556291,\"{'Ba': 12.0, 'Sn': 8.0, 'S': 28.0}\",\"('Ba', 'S', 'Sn')\",OTHERS,False\nmp-1099087,\"{'Mg': 14.0, 'Co': 1.0, 'Sn': 1.0}\",\"('Co', 'Mg', 'Sn')\",OTHERS,False\nmp-555891,\"{'Si': 4.0, 'O': 8.0}\",\"('O', 'Si')\",OTHERS,False\nmp-1207733,\"{'Y': 4.0, 'Cr': 4.0, 'Ge': 4.0, 'O': 20.0}\",\"('Cr', 'Ge', 'O', 'Y')\",OTHERS,False\nmp-1039117,\"{'Ca': 1.0, 'Mg': 3.0}\",\"('Ca', 'Mg')\",OTHERS,False\nmp-505212,\"{'Cs': 6.0, 'Au': 2.0, 'O': 2.0}\",\"('Au', 'Cs', 'O')\",OTHERS,False\nmp-2976,\"{'B': 1.0, 'Pd': 3.0, 'Pr': 1.0}\",\"('B', 'Pd', 'Pr')\",OTHERS,False\nmp-765596,\"{'Li': 4.0, 'V': 8.0, 'O': 20.0, 'F': 4.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-1112603,\"{'Cs': 2.0, 'Hg': 1.0, 'As': 1.0, 'F': 6.0}\",\"('As', 'Cs', 'F', 'Hg')\",OTHERS,False\nmp-643743,\"{'Cu': 4.0, 'H': 4.0, 'Cl': 4.0, 'O': 4.0}\",\"('Cl', 'Cu', 'H', 'O')\",OTHERS,False\nmp-1197511,\"{'Ti': 18.0, 'Mn': 4.0, 'B': 16.0, 'Ru': 36.0}\",\"('B', 'Mn', 'Ru', 'Ti')\",OTHERS,False\nmp-1197271,\"{'Eu': 4.0, 'V': 8.0, 'I': 4.0, 'O': 36.0}\",\"('Eu', 'I', 'O', 'V')\",OTHERS,False\nmp-557784,\"{'La': 4.0, 'Nb': 4.0, 'Te': 4.0, 'O': 24.0}\",\"('La', 'Nb', 'O', 'Te')\",OTHERS,False\nmp-1203752,\"{'Tb': 4.0, 'Fe': 20.0, 'P': 12.0}\",\"('Fe', 'P', 'Tb')\",OTHERS,False\nmp-1244920,\"{'V': 30.0, 'O': 75.0}\",\"('O', 'V')\",OTHERS,False\nmp-1197808,\"{'Ca': 4.0, 'Sn': 4.0, 'O': 24.0}\",\"('Ca', 'O', 'Sn')\",OTHERS,False\nmp-769159,\"{'Li': 4.0, 'Cu': 4.0, 'P': 4.0, 'H': 8.0, 'O': 20.0}\",\"('Cu', 'H', 'Li', 'O', 'P')\",OTHERS,False\nmp-556638,\"{'K': 4.0, 'Th': 2.0, 'Mo': 6.0, 'O': 24.0}\",\"('K', 'Mo', 'O', 'Th')\",OTHERS,False\nmp-1195226,\"{'Pb': 8.0, 'Br': 6.0, 'N': 2.0, 'O': 32.0}\",\"('Br', 'N', 'O', 'Pb')\",OTHERS,False\nmp-1919,\"{'Cr': 8.0, 'Zr': 4.0}\",\"('Cr', 'Zr')\",OTHERS,False\nmp-774106,\"{'Li': 6.0, 'Fe': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-759331,\"{'Fe': 18.0, 'O': 18.0, 'F': 18.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-1176549,\"{'Li': 1.0, 'V': 3.0, 'O': 6.0}\",\"('Li', 'O', 'V')\",OTHERS,False\nmp-1016825,\"{'Hf': 1.0, 'Mg': 1.0, 'O': 3.0}\",\"('Hf', 'Mg', 'O')\",OTHERS,False\nmp-30304,\"{'O': 14.0, 'Sb': 2.0, 'Bi': 6.0}\",\"('Bi', 'O', 'Sb')\",OTHERS,False\nmp-753099,\"{'Li': 3.0, 'Co': 2.0, 'Si': 2.0, 'O': 8.0}\",\"('Co', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1197179,\"{'Er': 22.0, 'In': 12.0, 'Ge': 8.0}\",\"('Er', 'Ge', 'In')\",OTHERS,False\nmp-1223689,\"{'K': 2.0, 'Cd': 3.0, 'Sn': 1.0, 'As': 4.0}\",\"('As', 'Cd', 'K', 'Sn')\",OTHERS,False\nmp-766530,\"{'Li': 4.0, 'Fe': 4.0, 'Si': 6.0, 'O': 20.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1251718,\"{'Ba': 8.0, 'Al': 16.0, 'O': 48.0}\",\"('Al', 'Ba', 'O')\",OTHERS,False\nmp-1197754,\"{'Np': 2.0, 'U': 2.0, 'P': 8.0, 'H': 8.0, 'C': 4.0, 'O': 32.0}\",\"('C', 'H', 'Np', 'O', 'P', 'U')\",OTHERS,False\nmp-1080076,\"{'Sr': 2.0, 'N': 1.0, 'O': 6.0}\",\"('N', 'O', 'Sr')\",OTHERS,False\nmp-1176923,\"{'Li': 12.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-505401,\"{'K': 4.0, 'Mn': 4.0, 'F': 16.0, 'O': 4.0, 'H': 8.0}\",\"('F', 'H', 'K', 'Mn', 'O')\",OTHERS,False\nmp-1221649,\"{'Mn': 1.0, 'Co': 1.0, 'Ni': 1.0, 'Sn': 1.0}\",\"('Co', 'Mn', 'Ni', 'Sn')\",OTHERS,False\nmp-1252068,\"{'K': 2.0, 'Al': 22.0, 'O': 34.0}\",\"('Al', 'K', 'O')\",OTHERS,False\nmp-1213734,\"{'Cs': 6.0, 'Nb': 12.0, 'S': 2.0, 'Br': 34.0}\",\"('Br', 'Cs', 'Nb', 'S')\",OTHERS,False\nmvc-7738,\"{'Ca': 4.0, 'Bi': 6.0, 'O': 16.0}\",\"('Bi', 'Ca', 'O')\",OTHERS,False\nmp-11131,\"{'S': 8.0, 'K': 2.0, 'Ge': 2.0, 'Hg': 3.0}\",\"('Ge', 'Hg', 'K', 'S')\",OTHERS,False\nmp-25259,\"{'O': 20.0, 'Fe': 2.0, 'Se': 8.0}\",\"('Fe', 'O', 'Se')\",OTHERS,False\nmp-1095161,\"{'Sm': 2.0, 'Mn': 2.0, 'Ge': 4.0}\",\"('Ge', 'Mn', 'Sm')\",OTHERS,False\nmp-1025575,\"{'Mo': 1.0, 'W': 2.0, 'Se': 6.0}\",\"('Mo', 'Se', 'W')\",OTHERS,False\nmp-685267,\"{'Cr': 36.0, 'Ga': 12.0, 'S': 72.0}\",\"('Cr', 'Ga', 'S')\",OTHERS,False\nmp-610787,\"{'Gd': 1.0, 'Cr': 2.0, 'Si': 2.0}\",\"('Cr', 'Gd', 'Si')\",OTHERS,False\nmp-752911,\"{'Li': 4.0, 'V': 4.0, 'F': 12.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-1220519,\"{'Nb': 4.0, 'N': 3.0}\",\"('N', 'Nb')\",OTHERS,False\nmp-1180517,\"{'Mg': 1.0, 'S': 1.0, 'O': 9.0}\",\"('Mg', 'O', 'S')\",OTHERS,False\nmp-778,\"{'P': 3.0, 'Fe': 6.0}\",\"('Fe', 'P')\",OTHERS,False\nmp-1074528,\"{'Mg': 16.0, 'Si': 12.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1039344,\"{'Sn': 6.0, 'Bi': 2.0}\",\"('Bi', 'Sn')\",OTHERS,False\nmp-766465,\"{'Sr': 8.0, 'Mn': 4.0, 'C': 4.0, 'O': 12.0, 'F': 20.0}\",\"('C', 'F', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-1215189,\"{'Zr': 1.0, 'Ti': 1.0, 'S': 4.0}\",\"('S', 'Ti', 'Zr')\",OTHERS,False\nmp-1223667,\"{'K': 2.0, 'Ba': 1.0, 'Sn': 1.0, 'Te': 4.0}\",\"('Ba', 'K', 'Sn', 'Te')\",OTHERS,False\nmp-1197995,\"{'Na': 4.0, 'Mg': 2.0, 'P': 8.0, 'H': 24.0, 'O': 36.0}\",\"('H', 'Mg', 'Na', 'O', 'P')\",OTHERS,False\nmp-1206770,\"{'Np': 1.0, 'Ga': 5.0, 'Rh': 1.0}\",\"('Ga', 'Np', 'Rh')\",OTHERS,False\nmp-1080154,\"{'Te': 2.0, 'Mo': 3.0, 'W': 1.0, 'Se': 4.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-28702,\"{'Gd': 2.0, 'B': 3.0, 'C': 2.0}\",\"('B', 'C', 'Gd')\",OTHERS,False\nmp-9457,\"{'O': 12.0, 'Ca': 6.0, 'Re': 2.0}\",\"('Ca', 'O', 'Re')\",OTHERS,False\nmp-1218722,\"{'Sr': 2.0, 'Pr': 2.0, 'Cr': 1.0, 'Ni': 1.0, 'O': 8.0}\",\"('Cr', 'Ni', 'O', 'Pr', 'Sr')\",OTHERS,False\nmp-568621,\"{'Mg': 16.0, 'B': 2.0, 'Rh': 8.0}\",\"('B', 'Mg', 'Rh')\",OTHERS,False\nmp-1099088,\"{'K': 1.0, 'Mg': 14.0, 'Co': 1.0}\",\"('Co', 'K', 'Mg')\",OTHERS,False\nmp-1019808,\"{'K': 6.0, 'Be': 12.0, 'B': 18.0, 'O': 42.0}\",\"('B', 'Be', 'K', 'O')\",OTHERS,False\nmp-675710,\"{'K': 4.0, 'U': 4.0, 'O': 14.0}\",\"('K', 'O', 'U')\",OTHERS,False\nmp-1190482,\"{'Mn': 4.0, 'N': 4.0, 'Cl': 12.0}\",\"('Cl', 'Mn', 'N')\",OTHERS,False\nmp-560812,\"{'K': 2.0, 'U': 4.0, 'P': 6.0, 'O': 24.0}\",\"('K', 'O', 'P', 'U')\",OTHERS,False\nmp-1246012,\"{'Ni': 2.0, 'C': 12.0, 'N': 18.0}\",\"('C', 'N', 'Ni')\",OTHERS,False\nmp-1227951,\"{'Ba': 1.0, 'La': 1.0, 'Co': 2.0, 'O': 6.0}\",\"('Ba', 'Co', 'La', 'O')\",OTHERS,False\nmp-22188,\"{'Cu': 2.0, 'Sn': 2.0, 'Dy': 2.0}\",\"('Cu', 'Dy', 'Sn')\",OTHERS,False\nmp-1221057,\"{'Na': 2.0, 'Eu': 2.0, 'Ti': 2.0, 'Nb': 2.0, 'O': 12.0, 'F': 2.0}\",\"('Eu', 'F', 'Na', 'Nb', 'O', 'Ti')\",OTHERS,False\nmp-865524,\"{'Y': 1.0, 'Cd': 1.0, 'Pt': 2.0}\",\"('Cd', 'Pt', 'Y')\",OTHERS,False\nmp-5746,\"{'O': 20.0, 'Mg': 4.0, 'Te': 8.0}\",\"('Mg', 'O', 'Te')\",OTHERS,False\nmp-17388,\"{'Cu': 6.0, 'Sn': 5.0, 'Yb': 3.0}\",\"('Cu', 'Sn', 'Yb')\",OTHERS,False\nmp-781626,\"{'Li': 4.0, 'Nb': 2.0, 'O': 6.0}\",\"('Li', 'Nb', 'O')\",OTHERS,False\nmp-26742,\"{'O': 24.0, 'P': 6.0, 'Cu': 8.0}\",\"('Cu', 'O', 'P')\",OTHERS,False\nmp-1078838,\"{'Ti': 2.0, 'Fe': 2.0, 'H': 4.0}\",\"('Fe', 'H', 'Ti')\",OTHERS,False\nmp-568791,\"{'Ca': 3.0, 'Si': 1.0, 'Br': 2.0}\",\"('Br', 'Ca', 'Si')\",OTHERS,False\nmp-865406,\"{'Lu': 2.0, 'Zn': 1.0, 'Au': 1.0}\",\"('Au', 'Lu', 'Zn')\",OTHERS,False\nmp-776483,\"{'Li': 8.0, 'Mn': 4.0, 'P': 4.0, 'O': 16.0, 'F': 4.0}\",\"('F', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-622508,\"{'Gd': 1.0, 'Si': 2.0, 'Rh': 3.0}\",\"('Gd', 'Rh', 'Si')\",OTHERS,False\nmp-1028539,\"{'Te': 4.0, 'Mo': 2.0, 'W': 2.0, 'Se': 4.0}\",\"('Mo', 'Se', 'Te', 'W')\",OTHERS,False\nmp-1223170,\"{'La': 2.0, 'Ti': 3.0, 'Ni': 1.0, 'O': 10.0}\",\"('La', 'Ni', 'O', 'Ti')\",OTHERS,False\nmp-769557,\"{'Li': 2.0, 'Mn': 2.0, 'Nb': 2.0, 'O': 8.0}\",\"('Li', 'Mn', 'Nb', 'O')\",OTHERS,False\nmp-1226264,\"{'Cr': 4.0, 'Ga': 1.0, 'S': 8.0}\",\"('Cr', 'Ga', 'S')\",OTHERS,False\nmp-1204207,\"{'Nb': 30.0, 'Ge': 21.0}\",\"('Ge', 'Nb')\",OTHERS,False\nmp-1247458,\"{'Sr': 24.0, 'Pb': 3.0, 'N': 18.0}\",\"('N', 'Pb', 'Sr')\",OTHERS,False\nmp-757855,\"{'V': 8.0, 'Co': 4.0, 'H': 16.0, 'O': 32.0}\",\"('Co', 'H', 'O', 'V')\",OTHERS,False\nmp-1080793,\"{'Pu': 2.0, 'Te': 6.0}\",\"('Pu', 'Te')\",OTHERS,False\nmp-1179633,\"{'S': 4.0, 'N': 2.0, 'O': 18.0}\",\"('N', 'O', 'S')\",OTHERS,False\nmp-768954,\"{'Ba': 8.0, 'Y': 4.0, 'Cl': 28.0}\",\"('Ba', 'Cl', 'Y')\",OTHERS,False\nmp-1023124,\"{'Co': 2.0, 'Mo': 1.0, 'S': 4.0}\",\"('Co', 'Mo', 'S')\",OTHERS,False\nmp-29646,\"{'As': 8.0, 'Hf': 4.0}\",\"('As', 'Hf')\",OTHERS,False\nmp-654301,\"{'Nb': 10.0, 'P': 14.0, 'O': 60.0}\",\"('Nb', 'O', 'P')\",OTHERS,False\nmp-754116,\"{'Ba': 2.0, 'Y': 4.0, 'Br': 16.0}\",\"('Ba', 'Br', 'Y')\",OTHERS,False\nmp-1093757,\"{'Zr': 1.0, 'Ti': 1.0, 'Au': 2.0}\",\"('Au', 'Ti', 'Zr')\",OTHERS,False\nmp-768865,\"{'Lu': 8.0, 'Ge': 4.0, 'O': 20.0}\",\"('Ge', 'Lu', 'O')\",OTHERS,False\nmp-34322,\"{'Dy': 4.0, 'O': 14.0, 'Ti': 4.0}\",\"('Dy', 'O', 'Ti')\",OTHERS,False\nmp-756266,\"{'Li': 4.0, 'Cr': 3.0, 'Ga': 1.0, 'O': 8.0}\",\"('Cr', 'Ga', 'Li', 'O')\",OTHERS,False\nmvc-3477,\"{'Mn': 2.0, 'Zn': 2.0, 'F': 8.0}\",\"('F', 'Mn', 'Zn')\",OTHERS,False\nmp-582278,\"{'Tm': 16.0, 'In': 4.0, 'Rh': 4.0}\",\"('In', 'Rh', 'Tm')\",OTHERS,False\nmp-753874,\"{'Li': 4.0, 'Si': 4.0, 'Ni': 2.0, 'O': 12.0}\",\"('Li', 'Ni', 'O', 'Si')\",OTHERS,False\nmp-756160,\"{'Er': 6.0, 'Ta': 2.0, 'O': 14.0}\",\"('Er', 'O', 'Ta')\",OTHERS,False\nmp-1038781,\"{'Mg': 8.0, 'Bi': 4.0}\",\"('Bi', 'Mg')\",OTHERS,False\nmp-779091,\"{'Li': 2.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1225460,\"{'Fe': 4.0, 'H': 36.0, 'S': 8.0, 'O': 48.0}\",\"('Fe', 'H', 'O', 'S')\",OTHERS,False\nmp-1095497,\"{'Y': 3.0, 'Ir': 9.0}\",\"('Ir', 'Y')\",OTHERS,False\nmvc-2762,\"{'Ba': 2.0, 'Tl': 2.0, 'Zn': 2.0, 'Sn': 3.0, 'O': 10.0}\",\"('Ba', 'O', 'Sn', 'Tl', 'Zn')\",OTHERS,False\nmp-1224741,\"{'Gd': 4.0, 'Ti': 2.0, 'Cu': 2.0, 'O': 12.0}\",\"('Cu', 'Gd', 'O', 'Ti')\",OTHERS,False\nmvc-2773,\"{'Sr': 2.0, 'Sn': 2.0, 'P': 4.0, 'O': 16.0}\",\"('O', 'P', 'Sn', 'Sr')\",OTHERS,False\nmp-1200788,\"{'Cu': 12.0, 'P': 32.0, 'Se': 24.0, 'Br': 12.0}\",\"('Br', 'Cu', 'P', 'Se')\",OTHERS,False\nmvc-2768,\"{'Ba': 2.0, 'Ca': 2.0, 'Tl': 2.0, 'Sn': 3.0, 'O': 10.0}\",\"('Ba', 'Ca', 'O', 'Sn', 'Tl')\",OTHERS,False\nmp-675462,\"{'Mo': 8.0, 'Ru': 4.0, 'Se': 16.0}\",\"('Mo', 'Ru', 'Se')\",OTHERS,False\nmp-1216820,\"{'U': 2.0, 'Cu': 1.0, 'Si': 3.0}\",\"('Cu', 'Si', 'U')\",OTHERS,False\nmp-1209821,\"{'Np': 4.0, 'Ge': 4.0, 'O': 8.0}\",\"('Ge', 'Np', 'O')\",OTHERS,False\nmp-865762,\"{'Tl': 1.0, 'W': 2.0, 'Cl': 6.0, 'O': 2.0}\",\"('Cl', 'O', 'Tl', 'W')\",OTHERS,False\nmp-22502,\"{'N': 1.0, 'Mn': 3.0, 'Ir': 1.0}\",\"('Ir', 'Mn', 'N')\",OTHERS,False\nmp-776754,\"{'Li': 2.0, 'Fe': 3.0, 'F': 8.0}\",\"('F', 'Fe', 'Li')\",OTHERS,False\nmp-1189473,\"{'Fe': 1.0, 'Sb': 1.0, 'Cl': 8.0, 'O': 8.0}\",\"('Cl', 'Fe', 'O', 'Sb')\",OTHERS,False\nmp-628185,\"{'K': 6.0, 'Na': 2.0, 'Sn': 6.0, 'Se': 16.0}\",\"('K', 'Na', 'Se', 'Sn')\",OTHERS,False\nmp-30186,\"{'Si': 46.0, 'Ba': 6.0}\",\"('Ba', 'Si')\",OTHERS,False\nmp-636380,\"{'Li': 2.0, 'O': 8.0, 'Mo': 4.0}\",\"('Li', 'Mo', 'O')\",OTHERS,False\nmp-1221707,\"{'Mn': 9.0, 'Al': 4.0, 'V': 3.0}\",\"('Al', 'Mn', 'V')\",OTHERS,False\nmp-1204916,\"{'K': 8.0, 'Fe': 4.0, 'F': 20.0}\",\"('F', 'Fe', 'K')\",OTHERS,False\nmp-579552,\"{'La': 24.0, 'C': 12.0, 'O': 60.0}\",\"('C', 'La', 'O')\",OTHERS,False\nmp-1213172,\"{'Cs': 2.0, 'Tm': 2.0, 'Mo': 4.0, 'O': 16.0}\",\"('Cs', 'Mo', 'O', 'Tm')\",OTHERS,False\nmp-1206129,\"{'Rb': 1.0, 'As': 2.0, 'Ru': 2.0}\",\"('As', 'Rb', 'Ru')\",OTHERS,False\nmp-1199646,\"{'Li': 4.0, 'Zn': 8.0, 'B': 20.0, 'H': 80.0}\",\"('B', 'H', 'Li', 'Zn')\",OTHERS,False\nmp-1185264,\"{'La': 3.0, 'Eu': 1.0}\",\"('Eu', 'La')\",OTHERS,False\nmp-776286,\"{'Ti': 3.0, 'Cr': 1.0, 'Sn': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Cr', 'O', 'P', 'Sn', 'Ti')\",OTHERS,False\nmp-1195771,\"{'Zn': 8.0, 'As': 8.0, 'O': 36.0}\",\"('As', 'O', 'Zn')\",OTHERS,False\nmp-867169,\"{'Sr': 2.0, 'Sn': 1.0, 'Hg': 1.0}\",\"('Hg', 'Sn', 'Sr')\",OTHERS,False\nmp-1226470,\"{'Ce': 1.0, 'Y': 1.0, 'Co': 4.0}\",\"('Ce', 'Co', 'Y')\",OTHERS,False\nmp-1181758,\"{'Mn': 2.0, 'B': 8.0, 'O': 32.0}\",\"('B', 'Mn', 'O')\",OTHERS,False\nmp-1213420,\"{'Eu': 2.0, 'Sb': 3.0, 'Pd': 3.0}\",\"('Eu', 'Pd', 'Sb')\",OTHERS,False\nmp-1016823,\"{'Ba': 1.0, 'Ge': 1.0, 'O': 3.0}\",\"('Ba', 'Ge', 'O')\",OTHERS,False\nmp-557748,\"{'Sb': 8.0, 'Ir': 8.0, 'C': 16.0, 'O': 16.0, 'F': 48.0}\",\"('C', 'F', 'Ir', 'O', 'Sb')\",OTHERS,False\nmvc-11241,\"{'V': 1.0, 'S': 2.0}\",\"('S', 'V')\",OTHERS,False\nmp-30741,\"{'Ir': 3.0, 'Pa': 1.0}\",\"('Ir', 'Pa')\",OTHERS,False\nmp-769344,\"{'Ba': 16.0, 'Ta': 8.0, 'O': 36.0}\",\"('Ba', 'O', 'Ta')\",OTHERS,False\nmp-771042,\"{'Li': 6.0, 'Nb': 3.0, 'Fe': 3.0, 'O': 16.0}\",\"('Fe', 'Li', 'Nb', 'O')\",OTHERS,False\nmp-1175222,\"{'Li': 7.0, 'Mn': 4.0, 'Co': 1.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-770212,\"{'Li': 24.0, 'Ti': 8.0, 'S': 24.0, 'O': 4.0}\",\"('Li', 'O', 'S', 'Ti')\",OTHERS,False\nmp-1073254,\"{'Mg': 4.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1222420,\"{'Li': 1.0, 'Fe': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-722485,\"{'Sr': 2.0, 'P': 4.0, 'H': 12.0, 'O': 18.0}\",\"('H', 'O', 'P', 'Sr')\",OTHERS,False\nmp-1183498,\"{'Ca': 3.0, 'Ho': 1.0}\",\"('Ca', 'Ho')\",OTHERS,False\nmp-1025183,\"{'Ho': 2.0, 'B': 4.0, 'C': 1.0}\",\"('B', 'C', 'Ho')\",OTHERS,False\nmp-1176918,\"{'Li': 12.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmvc-13706,\"{'Cr': 3.0, 'O': 8.0}\",\"('Cr', 'O')\",OTHERS,False\nmp-1210164,\"{'Ni': 2.0, 'N': 8.0, 'O': 16.0}\",\"('N', 'Ni', 'O')\",OTHERS,False\nmp-561551,\"{'Y': 4.0, 'Si': 4.0, 'O': 14.0}\",\"('O', 'Si', 'Y')\",OTHERS,False\nmp-29238,\"{'Se': 4.0, 'Ag': 4.0, 'Tl': 4.0}\",\"('Ag', 'Se', 'Tl')\",OTHERS,False\nmp-1191807,\"{'Cu': 8.0, 'Cl': 4.0, 'O': 12.0}\",\"('Cl', 'Cu', 'O')\",OTHERS,False\nmp-683983,\"{'Ta': 16.0, 'O': 32.0}\",\"('O', 'Ta')\",OTHERS,False\nmp-1204030,\"{'Si': 12.0, 'C': 16.0, 'N': 4.0, 'O': 38.0}\",\"('C', 'N', 'O', 'Si')\",OTHERS,False\nmp-1198773,\"{'Yb': 8.0, 'Cr': 8.0, 'O': 40.0}\",\"('Cr', 'O', 'Yb')\",OTHERS,False\nmp-7224,\"{'C': 4.0, 'Th': 2.0}\",\"('C', 'Th')\",OTHERS,False\nmp-1213192,\"{'Dy': 4.0, 'Fe': 34.0, 'C': 6.0}\",\"('C', 'Dy', 'Fe')\",OTHERS,False\nmp-867296,\"{'Re': 4.0, 'P': 4.0, 'O': 20.0}\",\"('O', 'P', 'Re')\",OTHERS,False\nmp-777558,\"{'Li': 32.0, 'Ti': 3.0, 'Cr': 13.0, 'O': 48.0}\",\"('Cr', 'Li', 'O', 'Ti')\",OTHERS,False\nAu 64.2 Sn 21.3 Pr 14.5,\"{'Au': 64.2, 'Sn': 21.3, 'Pr': 14.5}\",\"('Au', 'Pr', 'Sn')\",AC,False\nmp-1188832,\"{'Nd': 4.0, 'Ga': 4.0, 'Pd': 8.0}\",\"('Ga', 'Nd', 'Pd')\",OTHERS,False\nmp-1215656,\"{'Zn': 1.0, 'Fe': 4.0, 'Cu': 1.0, 'O': 8.0}\",\"('Cu', 'Fe', 'O', 'Zn')\",OTHERS,False\nmp-2226,\"{'Pd': 1.0, 'Dy': 1.0}\",\"('Dy', 'Pd')\",OTHERS,False\nmp-1184190,\"{'Er': 1.0, 'Mg': 16.0, 'Al': 12.0}\",\"('Al', 'Er', 'Mg')\",OTHERS,False\nmp-1183917,\"{'Cs': 1.0, 'Mo': 1.0, 'O': 3.0}\",\"('Cs', 'Mo', 'O')\",OTHERS,False\nmp-1205020,\"{'Ca': 1.0, 'Cu': 4.0, 'As': 4.0, 'H': 2.0, 'O': 26.0}\",\"('As', 'Ca', 'Cu', 'H', 'O')\",OTHERS,False\nmp-1178405,\"{'Cs': 4.0, 'Hf': 2.0, 'O': 6.0}\",\"('Cs', 'Hf', 'O')\",OTHERS,False\nmp-1218070,\"{'Ta': 6.0, 'Cr': 1.0, 'C': 3.0, 'S': 6.0}\",\"('C', 'Cr', 'S', 'Ta')\",OTHERS,False\nmp-1213412,\"{'Gd': 16.0, 'Si': 8.0, 'Ni': 8.0, 'Bi': 4.0}\",\"('Bi', 'Gd', 'Ni', 'Si')\",OTHERS,False\nmp-541897,\"{'Cd': 2.0, 'Rb': 4.0, 'Se': 12.0, 'P': 4.0}\",\"('Cd', 'P', 'Rb', 'Se')\",OTHERS,False\nmp-1206747,\"{'Lu': 1.0, 'Tl': 1.0}\",\"('Lu', 'Tl')\",OTHERS,False\nmp-973892,\"{'Eu': 1.0, 'Al': 1.0, 'O': 3.0}\",\"('Al', 'Eu', 'O')\",OTHERS,False\nmp-1198386,\"{'Al': 16.0, 'Mo': 24.0}\",\"('Al', 'Mo')\",OTHERS,False\nmp-1100777,\"{'V': 1.0, 'Te': 1.0, 'O': 3.0}\",\"('O', 'Te', 'V')\",OTHERS,False\nmp-1201340,\"{'U': 4.0, 'P': 8.0, 'H': 8.0, 'C': 4.0, 'O': 52.0}\",\"('C', 'H', 'O', 'P', 'U')\",OTHERS,False\nmp-570480,\"{'Tc': 8.0, 'Br': 32.0}\",\"('Br', 'Tc')\",OTHERS,False\nmp-1093552,\"{'Mg': 2.0, 'Cd': 1.0, 'Pt': 1.0}\",\"('Cd', 'Mg', 'Pt')\",OTHERS,False\nmp-1056955,\"{'Ag': 1.0, 'N': 1.0}\",\"('Ag', 'N')\",OTHERS,False\nmp-710,\"{'P': 1.0, 'Sm': 1.0}\",\"('P', 'Sm')\",OTHERS,False\nmp-1086666,\"{'Sr': 2.0, 'Ta': 1.0, 'In': 1.0, 'O': 6.0}\",\"('In', 'O', 'Sr', 'Ta')\",OTHERS,False\nmp-16281,\"{'O': 14.0, 'Cd': 4.0, 'Sb': 4.0}\",\"('Cd', 'O', 'Sb')\",OTHERS,False\nmp-624417,\"{'Cd': 11.0, 'I': 22.0}\",\"('Cd', 'I')\",OTHERS,False\nmp-1222567,\"{'Li': 2.0, 'Nb': 2.0, 'W': 2.0, 'O': 14.0}\",\"('Li', 'Nb', 'O', 'W')\",OTHERS,False\nmp-1029595,\"{'Na': 12.0, 'Os': 4.0, 'N': 8.0}\",\"('N', 'Na', 'Os')\",OTHERS,False\nmp-1094963,\"{'Ce': 2.0, 'Mg': 2.0}\",\"('Ce', 'Mg')\",OTHERS,False\nmp-1021490,\"{'Sn': 4.0, 'S': 1.0, 'F': 6.0}\",\"('F', 'S', 'Sn')\",OTHERS,False\nmp-1111200,\"{'K': 2.0, 'Tl': 1.0, 'As': 1.0, 'I': 6.0}\",\"('As', 'I', 'K', 'Tl')\",OTHERS,False\nmp-695314,\"{'Ca': 10.0, 'Si': 4.0, 'H': 2.0, 'O': 18.0, 'F': 2.0}\",\"('Ca', 'F', 'H', 'O', 'Si')\",OTHERS,False\nmp-1176983,\"{'Li': 12.0, 'Mn': 2.0, 'V': 6.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-1099574,\"{'Ce': 16.0, 'Se': 32.0}\",\"('Ce', 'Se')\",OTHERS,False\nmp-1214495,\"{'Ba': 6.0, 'Ti': 2.0, 'O': 10.0}\",\"('Ba', 'O', 'Ti')\",OTHERS,False\nmp-1182967,\"{'Al': 2.0, 'Cu': 4.0, 'As': 2.0, 'O': 24.0}\",\"('Al', 'As', 'Cu', 'O')\",OTHERS,False\nmp-867369,\"{'Tc': 2.0, 'F': 6.0}\",\"('F', 'Tc')\",OTHERS,False\nmp-755692,\"{'Nb': 2.0, 'V': 2.0, 'O': 10.0}\",\"('Nb', 'O', 'V')\",OTHERS,False\nmp-1219459,\"{'Sb': 1.0, 'Te': 1.0}\",\"('Sb', 'Te')\",OTHERS,False\nmp-768407,\"{'Li': 24.0, 'Ti': 5.0, 'Cr': 7.0, 'O': 36.0}\",\"('Cr', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-1035190,\"{'Mg': 14.0, 'Zn': 1.0, 'Co': 1.0, 'O': 16.0}\",\"('Co', 'Mg', 'O', 'Zn')\",OTHERS,False\nmp-1203220,\"{'In': 8.0, 'Rh': 8.0, 'O': 24.0}\",\"('In', 'O', 'Rh')\",OTHERS,False\nmp-866013,\"{'Dy': 1.0, 'Tm': 1.0, 'Mg': 2.0}\",\"('Dy', 'Mg', 'Tm')\",OTHERS,False\nmp-12553,\"{'Al': 6.0, 'Pr': 2.0}\",\"('Al', 'Pr')\",OTHERS,False\nmvc-1258,\"{'Mg': 4.0, 'Cr': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Cr', 'Mg', 'O', 'P')\",OTHERS,False\nmp-1210149,\"{'Sm': 46.0, 'Cd': 8.0, 'Rh': 14.0}\",\"('Cd', 'Rh', 'Sm')\",OTHERS,False\nmp-722501,\"{'Sn': 4.0, 'H': 12.0, 'S': 16.0, 'N': 16.0, 'Cl': 20.0, 'O': 4.0}\",\"('Cl', 'H', 'N', 'O', 'S', 'Sn')\",OTHERS,False\nmp-560687,\"{'Nb': 12.0, 'Te': 32.0, 'I': 28.0, 'O': 4.0}\",\"('I', 'Nb', 'O', 'Te')\",OTHERS,False\nmp-1008498,{'Ca': 4.0},\"('Ca',)\",OTHERS,False\nmp-1186803,\"{'Pu': 1.0, 'S': 3.0}\",\"('Pu', 'S')\",OTHERS,False\nmp-22965,\"{'Te': 1.0, 'I': 1.0, 'Bi': 1.0}\",\"('Bi', 'I', 'Te')\",OTHERS,False\nmp-1208543,\"{'Tb': 4.0, 'Co': 2.0, 'Pt': 2.0, 'O': 12.0}\",\"('Co', 'O', 'Pt', 'Tb')\",OTHERS,False\nmp-1079779,\"{'Tb': 4.0, 'In': 2.0, 'Cu': 4.0}\",\"('Cu', 'In', 'Tb')\",OTHERS,False\nmp-1078217,\"{'Na': 2.0, 'Fe': 2.0, 'F': 6.0}\",\"('F', 'Fe', 'Na')\",OTHERS,False\nmp-1193698,\"{'Zr': 12.0, 'Fe': 12.0, 'C': 4.0}\",\"('C', 'Fe', 'Zr')\",OTHERS,False\nmp-1192827,\"{'Eu': 16.0, 'In': 6.0}\",\"('Eu', 'In')\",OTHERS,False\nmp-761164,\"{'Li': 4.0, 'Nb': 4.0, 'F': 20.0}\",\"('F', 'Li', 'Nb')\",OTHERS,False\nmp-754117,\"{'Y': 2.0, 'H': 2.0, 'O': 4.0}\",\"('H', 'O', 'Y')\",OTHERS,False\nmp-555560,\"{'Cd': 2.0, 'Te': 2.0, 'Mo': 2.0, 'O': 12.0}\",\"('Cd', 'Mo', 'O', 'Te')\",OTHERS,False\nmp-13501,\"{'C': 2.0, 'Co': 1.0, 'Er': 1.0}\",\"('C', 'Co', 'Er')\",OTHERS,False\nmp-13992,\"{'S': 4.0, 'Cs': 2.0, 'Pt': 3.0}\",\"('Cs', 'Pt', 'S')\",OTHERS,False\nmp-19814,\"{'Ni': 2.0, 'As': 4.0}\",\"('As', 'Ni')\",OTHERS,False\nmp-20828,\"{'Pt': 3.0, 'Pb': 1.0}\",\"('Pb', 'Pt')\",OTHERS,False\nmp-35876,\"{'Ca': 2.0, 'Nd': 4.0, 'S': 8.0}\",\"('Ca', 'Nd', 'S')\",OTHERS,False\nmp-1076151,\"{'Ca': 4.0, 'Ti': 4.0, 'O': 10.0}\",\"('Ca', 'O', 'Ti')\",OTHERS,False\nmp-1099116,\"{'Ce': 1.0, 'Mg': 14.0, 'C': 1.0}\",\"('C', 'Ce', 'Mg')\",OTHERS,False\nmp-23415,\"{'Tl': 4.0, 'Fe': 4.0, 'I': 12.0}\",\"('Fe', 'I', 'Tl')\",OTHERS,False\nmp-1246175,\"{'Ca': 6.0, 'Zr': 4.0, 'N': 8.0}\",\"('Ca', 'N', 'Zr')\",OTHERS,False\nmp-1181826,\"{'Co': 1.0, 'Cu': 1.0, 'P': 2.0, 'O': 7.0}\",\"('Co', 'Cu', 'O', 'P')\",OTHERS,False\nmp-1075724,\"{'Mg': 10.0, 'Si': 18.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-541787,\"{'Na': 32.0, 'Hg': 12.0}\",\"('Hg', 'Na')\",OTHERS,False\nmp-765350,\"{'La': 8.0, 'H': 8.0, 'Se': 24.0, 'O': 80.0}\",\"('H', 'La', 'O', 'Se')\",OTHERS,False\nmp-1184176,\"{'Dy': 4.0, 'Sc': 4.0, 'S': 12.0}\",\"('Dy', 'S', 'Sc')\",OTHERS,False\nmp-1212202,\"{'Mn': 8.0, 'Cr': 12.0, 'N': 8.0, 'O': 48.0}\",\"('Cr', 'Mn', 'N', 'O')\",OTHERS,False\nmp-1200365,\"{'Cs': 4.0, 'Mo': 10.0, 'O': 32.0}\",\"('Cs', 'Mo', 'O')\",OTHERS,False\nmp-1104964,\"{'Lu': 2.0, 'Ni': 8.0, 'As': 4.0}\",\"('As', 'Lu', 'Ni')\",OTHERS,False\nmp-1183595,\"{'Ca': 1.0, 'Yb': 1.0, 'Tl': 2.0}\",\"('Ca', 'Tl', 'Yb')\",OTHERS,False\nmp-1210069,\"{'Na': 1.0, 'Dy': 1.0, 'Pd': 6.0, 'O': 8.0}\",\"('Dy', 'Na', 'O', 'Pd')\",OTHERS,False\nmp-20082,\"{'As': 4.0, 'Rh': 6.0, 'Yb': 1.0}\",\"('As', 'Rh', 'Yb')\",OTHERS,False\nmp-1183340,\"{'Ba': 3.0, 'Ho': 1.0}\",\"('Ba', 'Ho')\",OTHERS,False\nmp-1214186,\"{'C': 12.0, 'I': 4.0, 'F': 8.0}\",\"('C', 'F', 'I')\",OTHERS,False\nmp-543094,\"{'Li': 4.0, 'Fe': 8.0, 'P': 12.0, 'O': 48.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-1190199,\"{'Cu': 1.0, 'Sb': 6.0, 'S': 2.0, 'O': 16.0}\",\"('Cu', 'O', 'S', 'Sb')\",OTHERS,False\nmp-19467,\"{'O': 32.0, 'P': 6.0, 'Mo': 6.0, 'Ag': 2.0}\",\"('Ag', 'Mo', 'O', 'P')\",OTHERS,False\nmp-504353,\"{'Li': 2.0, 'O': 24.0, 'P': 8.0, 'Sb': 2.0}\",\"('Li', 'O', 'P', 'Sb')\",OTHERS,False\nmp-1096596,\"{'Ti': 1.0, 'Cu': 1.0, 'Au': 2.0}\",\"('Au', 'Cu', 'Ti')\",OTHERS,False\nmp-23662,\"{'Li': 8.0, 'B': 8.0, 'H': 32.0, 'O': 32.0}\",\"('B', 'H', 'Li', 'O')\",OTHERS,False\nmp-764989,\"{'Li': 8.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1207893,\"{'V': 5.0, 'Se': 8.0}\",\"('Se', 'V')\",OTHERS,False\nmp-554756,\"{'P': 12.0, 'H': 72.0, 'C': 12.0, 'N': 12.0, 'O': 36.0}\",\"('C', 'H', 'N', 'O', 'P')\",OTHERS,False\nmp-1246486,\"{'Ca': 14.0, 'Re': 2.0, 'N': 12.0}\",\"('Ca', 'N', 'Re')\",OTHERS,False\nmp-849498,\"{'Li': 10.0, 'Mn': 12.0, 'B': 12.0, 'O': 36.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-29922,\"{'O': 40.0, 'P': 4.0, 'Sb': 20.0}\",\"('O', 'P', 'Sb')\",OTHERS,False\nmp-1216294,\"{'U': 2.0, 'Al': 2.0, 'Co': 2.0}\",\"('Al', 'Co', 'U')\",OTHERS,False\nmp-1106389,\"{'K': 2.0, 'Bi': 2.0, 'F': 12.0}\",\"('Bi', 'F', 'K')\",OTHERS,False\nmp-1175781,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-753920,\"{'Tm': 2.0, 'Se': 1.0, 'O': 2.0}\",\"('O', 'Se', 'Tm')\",OTHERS,False\nmp-8341,\"{'O': 40.0, 'Na': 8.0, 'Ge': 8.0, 'Sb': 8.0}\",\"('Ge', 'Na', 'O', 'Sb')\",OTHERS,False\nmp-757511,\"{'Li': 8.0, 'Ni': 4.0, 'P': 16.0, 'O': 48.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-29487,\"{'Cl': 12.0, 'Pd': 6.0}\",\"('Cl', 'Pd')\",OTHERS,False\nmp-22005,\"{'O': 28.0, 'V': 8.0, 'Pb': 8.0}\",\"('O', 'Pb', 'V')\",OTHERS,False\nmp-677233,\"{'Na': 4.0, 'Al': 12.0, 'Tl': 8.0, 'Si': 12.0, 'O': 48.0}\",\"('Al', 'Na', 'O', 'Si', 'Tl')\",OTHERS,False\nmp-1102542,\"{'Hf': 8.0, 'Ni': 2.0, 'P': 2.0}\",\"('Hf', 'Ni', 'P')\",OTHERS,False\nmp-1225482,\"{'La': 1.0, 'Ce': 1.0, 'Al': 2.0, 'O': 6.0}\",\"('Al', 'Ce', 'La', 'O')\",OTHERS,False\nmvc-16792,\"{'Y': 2.0, 'Mn': 4.0, 'S': 8.0}\",\"('Mn', 'S', 'Y')\",OTHERS,False\nmp-770680,\"{'Li': 36.0, 'Mn': 8.0, 'Al': 4.0, 'O': 32.0}\",\"('Al', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-12497,\"{'Cd': 2.0, 'Te': 6.0, 'Cs': 2.0, 'Sm': 2.0}\",\"('Cd', 'Cs', 'Sm', 'Te')\",OTHERS,False\nmp-1205553,\"{'Sr': 2.0, 'Lu': 1.0, 'Ta': 1.0, 'O': 6.0}\",\"('Lu', 'O', 'Sr', 'Ta')\",OTHERS,False\nmp-985288,\"{'As': 6.0, 'W': 2.0}\",\"('As', 'W')\",OTHERS,False\nmp-556631,\"{'K': 4.0, 'Zr': 4.0, 'Sn': 4.0, 'F': 28.0}\",\"('F', 'K', 'Sn', 'Zr')\",OTHERS,False\nCd 6 Gd 1,\"{'Cd': 6.0, 'Gd': 1.0}\",\"('Cd', 'Gd')\",AC,False\nmp-690794,\"{'Sr': 2.0, 'H': 1.0, 'N': 1.0}\",\"('H', 'N', 'Sr')\",OTHERS,False\nmp-1007636,\"{'K': 1.0, 'Lu': 1.0, 'S': 2.0}\",\"('K', 'Lu', 'S')\",OTHERS,False\nmp-1176171,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-755601,\"{'Fe': 6.0, 'O': 8.0, 'F': 4.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-559638,\"{'K': 12.0, 'Np': 4.0, 'O': 8.0, 'F': 20.0}\",\"('F', 'K', 'Np', 'O')\",OTHERS,False\nmp-1186475,\"{'Pm': 2.0, 'Mg': 1.0, 'Ge': 1.0}\",\"('Ge', 'Mg', 'Pm')\",OTHERS,False\nmp-1185635,\"{'Mg': 149.0, 'Tl': 1.0}\",\"('Mg', 'Tl')\",OTHERS,False\nmp-1225147,\"{'Fe': 4.0, 'Ni': 4.0, 'Pd': 1.0, 'S': 8.0}\",\"('Fe', 'Ni', 'Pd', 'S')\",OTHERS,False\nmp-4322,\"{'B': 2.0, 'Co': 2.0, 'Pr': 1.0}\",\"('B', 'Co', 'Pr')\",OTHERS,False\nmp-696989,\"{'Na': 2.0, 'P': 2.0, 'H': 10.0, 'C': 4.0, 'S': 2.0, 'N': 6.0, 'O': 8.0}\",\"('C', 'H', 'N', 'Na', 'O', 'P', 'S')\",OTHERS,False\nmp-1113667,\"{'Rb': 2.0, 'Bi': 1.0, 'Au': 1.0, 'Cl': 6.0}\",\"('Au', 'Bi', 'Cl', 'Rb')\",OTHERS,False\nmp-1093629,\"{'Mn': 1.0, 'Al': 2.0, 'Fe': 1.0}\",\"('Al', 'Fe', 'Mn')\",OTHERS,False\nmp-707845,\"{'K': 12.0, 'Zr': 4.0, 'H': 4.0, 'S': 4.0, 'O': 20.0, 'F': 20.0}\",\"('F', 'H', 'K', 'O', 'S', 'Zr')\",OTHERS,False\nmp-32502,\"{'Li': 2.0, 'V': 2.0, 'P': 8.0, 'O': 24.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-978544,\"{'Sm': 1.0, 'Lu': 1.0, 'Tl': 2.0}\",\"('Lu', 'Sm', 'Tl')\",OTHERS,False\nmp-1184930,\"{'Ho': 2.0, 'Lu': 6.0}\",\"('Ho', 'Lu')\",OTHERS,False\nmp-1173714,\"{'Na': 8.0, 'Al': 6.0, 'Si': 6.0, 'N': 2.0, 'O': 28.0}\",\"('Al', 'N', 'Na', 'O', 'Si')\",OTHERS,False\nmp-11467,\"{'Nd': 1.0, 'Hg': 1.0}\",\"('Hg', 'Nd')\",OTHERS,False\nmp-1202582,\"{'Mo': 4.0, 'Rh': 4.0, 'N': 20.0, 'Cl': 4.0, 'O': 16.0}\",\"('Cl', 'Mo', 'N', 'O', 'Rh')\",OTHERS,False\nmp-1246734,\"{'Fe': 16.0, 'Si': 16.0, 'N': 32.0}\",\"('Fe', 'N', 'Si')\",OTHERS,False\nmp-559764,\"{'Nd': 12.0, 'Si': 8.0, 'Cl': 4.0, 'O': 32.0}\",\"('Cl', 'Nd', 'O', 'Si')\",OTHERS,False\nmp-1102114,\"{'Ta': 4.0, 'N': 8.0}\",\"('N', 'Ta')\",OTHERS,False\nmp-1112617,\"{'Cs': 2.0, 'Pr': 1.0, 'Cu': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cs', 'Cu', 'Pr')\",OTHERS,False\nmp-1037960,\"{'Ca': 1.0, 'Mg': 30.0, 'Cr': 1.0, 'O': 32.0}\",\"('Ca', 'Cr', 'Mg', 'O')\",OTHERS,False\nmp-850786,\"{'Li': 6.0, 'Mn': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-20547,\"{'Ca': 12.0, 'Fe': 24.0, 'O': 48.0}\",\"('Ca', 'Fe', 'O')\",OTHERS,False\nmp-1247673,\"{'Ca': 8.0, 'Ti': 3.0, 'Mn': 5.0, 'O': 21.0}\",\"('Ca', 'Mn', 'O', 'Ti')\",OTHERS,False\nmp-1173025,\"{'Ag': 6.0, 'S': 2.0, 'I': 2.0}\",\"('Ag', 'I', 'S')\",OTHERS,False\nmp-1074466,\"{'Mg': 8.0, 'Si': 6.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1188666,\"{'Sm': 6.0, 'Cu': 6.0, 'Sb': 8.0}\",\"('Cu', 'Sb', 'Sm')\",OTHERS,False\nmp-2363,\"{'Mo': 4.0, 'Hf': 2.0}\",\"('Hf', 'Mo')\",OTHERS,False\nmp-973662,\"{'Lu': 2.0, 'Zn': 1.0, 'Cu': 1.0}\",\"('Cu', 'Lu', 'Zn')\",OTHERS,False\nmp-1232329,\"{'K': 2.0, 'Zn': 2.0, 'Cu': 1.0, 'S': 2.0, 'O': 2.0}\",\"('Cu', 'K', 'O', 'S', 'Zn')\",OTHERS,False\nmp-1102396,\"{'Ba': 8.0, 'In': 4.0}\",\"('Ba', 'In')\",OTHERS,False\nmp-1221960,\"{'Mn': 1.0, 'Fe': 2.0, 'Pb': 2.0, 'O': 3.0, 'F': 12.0}\",\"('F', 'Fe', 'Mn', 'O', 'Pb')\",OTHERS,False\nmp-1210466,\"{'Na': 4.0, 'Mg': 2.0, 'Sc': 2.0, 'F': 14.0}\",\"('F', 'Mg', 'Na', 'Sc')\",OTHERS,False\nmp-1227504,\"{'Bi': 8.0, 'Se': 8.0, 'S': 4.0}\",\"('Bi', 'S', 'Se')\",OTHERS,False\nmvc-20,\"{'Nb': 4.0, 'Te': 2.0, 'O': 16.0}\",\"('Nb', 'O', 'Te')\",OTHERS,False\nmp-758588,\"{'K': 8.0, 'Li': 6.0, 'Fe': 4.0, 'O': 16.0}\",\"('Fe', 'K', 'Li', 'O')\",OTHERS,False\nmp-769110,\"{'Li': 4.0, 'Co': 8.0, 'S': 12.0, 'O': 48.0}\",\"('Co', 'Li', 'O', 'S')\",OTHERS,False\nmp-1246819,\"{'In': 1.0, 'C': 1.0, 'N': 2.0}\",\"('C', 'In', 'N')\",OTHERS,False\nmp-1040297,\"{'K': 1.0, 'Mg': 30.0, 'B': 1.0, 'O': 31.0}\",\"('B', 'K', 'Mg', 'O')\",OTHERS,False\nmp-12602,\"{'Zn': 5.0, 'Pr': 1.0}\",\"('Pr', 'Zn')\",OTHERS,False\nmp-1208877,\"{'Sr': 2.0, 'Eu': 1.0, 'Ta': 1.0, 'Cu': 2.0, 'O': 8.0}\",\"('Cu', 'Eu', 'O', 'Sr', 'Ta')\",OTHERS,False\nmp-1188001,\"{'Zr': 2.0, 'Tc': 1.0, 'Ir': 1.0}\",\"('Ir', 'Tc', 'Zr')\",OTHERS,False\nmp-27628,\"{'Cl': 8.0, 'Te': 12.0}\",\"('Cl', 'Te')\",OTHERS,False\nmp-1111912,\"{'K': 2.0, 'Rb': 1.0, 'Ir': 1.0, 'F': 6.0}\",\"('F', 'Ir', 'K', 'Rb')\",OTHERS,False\nmp-11291,\"{'Cd': 1.0, 'Ce': 1.0}\",\"('Cd', 'Ce')\",OTHERS,False\nmp-1220232,\"{'Ni': 10.0, 'Ge': 2.0, 'B': 4.0, 'O': 20.0}\",\"('B', 'Ge', 'Ni', 'O')\",OTHERS,False\nmp-4393,\"{'O': 68.0, 'Cs': 16.0, 'U': 20.0}\",\"('Cs', 'O', 'U')\",OTHERS,False\nmp-862635,\"{'Sc': 1.0, 'Sb': 1.0, 'Rh': 2.0}\",\"('Rh', 'Sb', 'Sc')\",OTHERS,False\nmp-1259193,\"{'Sr': 4.0, 'Y': 2.0, 'Mn': 4.0, 'Al': 2.0, 'O': 14.0}\",\"('Al', 'Mn', 'O', 'Sr', 'Y')\",OTHERS,False\nmp-8387,\"{'O': 6.0, 'Te': 1.0, 'Tl': 6.0}\",\"('O', 'Te', 'Tl')\",OTHERS,False\nmp-31245,\"{'O': 22.0, 'Te': 8.0, 'Tb': 4.0}\",\"('O', 'Tb', 'Te')\",OTHERS,False\nmp-558153,\"{'Ba': 24.0, 'Nb': 12.0, 'O': 18.0, 'F': 72.0}\",\"('Ba', 'F', 'Nb', 'O')\",OTHERS,False\nmp-31147,\"{'Si': 2.0, 'Zn': 2.0, 'Ba': 2.0}\",\"('Ba', 'Si', 'Zn')\",OTHERS,False\nmp-1027143,\"{'Mo': 2.0, 'W': 2.0, 'Se': 2.0, 'S': 6.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-1079307,\"{'Dy': 3.0, 'Co': 2.0, 'Ge': 4.0}\",\"('Co', 'Dy', 'Ge')\",OTHERS,False\nmp-849522,\"{'Li': 16.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1215607,\"{'Zr': 2.0, 'Ti': 1.0, 'Sn': 1.0, 'O': 8.0}\",\"('O', 'Sn', 'Ti', 'Zr')\",OTHERS,False\nmp-1211000,\"{'Li': 2.0, 'Sm': 2.0, 'W': 4.0, 'O': 16.0}\",\"('Li', 'O', 'Sm', 'W')\",OTHERS,False\nmp-1078813,\"{'Y': 3.0, 'Zn': 3.0, 'Pd': 3.0}\",\"('Pd', 'Y', 'Zn')\",OTHERS,False\nmp-740746,\"{'Na': 4.0, 'H': 8.0, 'Br': 4.0, 'O': 20.0}\",\"('Br', 'H', 'Na', 'O')\",OTHERS,False\nmp-1072004,\"{'U': 2.0, 'Co': 2.0, 'Ge': 2.0}\",\"('Co', 'Ge', 'U')\",OTHERS,False\nmp-673681,\"{'Ta': 22.0, 'O': 4.0}\",\"('O', 'Ta')\",OTHERS,False\nmp-735521,\"{'Mn': 4.0, 'H': 24.0, 'O': 12.0, 'F': 12.0}\",\"('F', 'H', 'Mn', 'O')\",OTHERS,False\nmp-581635,\"{'Cs': 6.0, 'Te': 34.0, 'Mo': 30.0}\",\"('Cs', 'Mo', 'Te')\",OTHERS,False\nmp-1077288,\"{'Na': 4.0, 'Cl': 2.0}\",\"('Cl', 'Na')\",OTHERS,False\nmp-758298,\"{'Li': 8.0, 'Ni': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-1209465,\"{'Rb': 4.0, 'Mo': 2.0, 'O': 4.0, 'F': 8.0}\",\"('F', 'Mo', 'O', 'Rb')\",OTHERS,False\nmp-1187161,\"{'Sr': 6.0, 'Pm': 2.0}\",\"('Pm', 'Sr')\",OTHERS,False\nmp-1174413,\"{'Li': 6.0, 'Mn': 2.0, 'Co': 2.0, 'O': 10.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-3775,\"{'F': 18.0, 'Na': 6.0, 'Si': 3.0}\",\"('F', 'Na', 'Si')\",OTHERS,False\nmp-510083,\"{'Tb': 8.0, 'Ti': 12.0, 'Si': 16.0}\",\"('Si', 'Tb', 'Ti')\",OTHERS,False\nmp-866159,\"{'Y': 1.0, 'Zr': 1.0, 'Ru': 2.0}\",\"('Ru', 'Y', 'Zr')\",OTHERS,False\nmp-686466,\"{'Pr': 32.0, 'Si': 32.0, 'N': 54.0, 'Cl': 6.0, 'O': 28.0}\",\"('Cl', 'N', 'O', 'Pr', 'Si')\",OTHERS,False\nmp-746692,\"{'K': 4.0, 'Co': 6.0, 'P': 8.0, 'H': 8.0, 'O': 32.0}\",\"('Co', 'H', 'K', 'O', 'P')\",OTHERS,False\nmp-1114468,\"{'Rb': 2.0, 'Al': 1.0, 'Tl': 1.0, 'F': 6.0}\",\"('Al', 'F', 'Rb', 'Tl')\",OTHERS,False\nmp-1182659,\"{'Cu': 4.0, 'H': 28.0, 'C': 12.0, 'S': 4.0, 'N': 12.0, 'O': 16.0}\",\"('C', 'Cu', 'H', 'N', 'O', 'S')\",OTHERS,False\nmp-1213233,\"{'Dy': 8.0, 'Ni': 2.0}\",\"('Dy', 'Ni')\",OTHERS,False\nmp-972868,\"{'Si': 2.0, 'Au': 6.0}\",\"('Au', 'Si')\",OTHERS,False\nmp-1181940,\"{'Cd': 1.0, 'Cl': 2.0}\",\"('Cd', 'Cl')\",OTHERS,False\nmp-1106156,\"{'Cs': 4.0, 'Hg': 6.0, 'Se': 8.0}\",\"('Cs', 'Hg', 'Se')\",OTHERS,False\nmp-778232,\"{'Li': 8.0, 'V': 12.0, 'Cr': 8.0, 'O': 48.0}\",\"('Cr', 'Li', 'O', 'V')\",OTHERS,False\nmp-849641,\"{'V': 8.0, 'O': 12.0, 'F': 4.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-1112651,\"{'Cs': 3.0, 'In': 1.0, 'Br': 6.0}\",\"('Br', 'Cs', 'In')\",OTHERS,False\nmp-685168,\"{'Be': 16.0, 'F': 32.0}\",\"('Be', 'F')\",OTHERS,False\nmp-1220473,\"{'Nb': 2.0, 'Tl': 2.0, 'Te': 2.0, 'O': 12.0}\",\"('Nb', 'O', 'Te', 'Tl')\",OTHERS,False\nmp-530184,\"{'Al': 16.0, 'Ni': 8.0, 'O': 32.0}\",\"('Al', 'Ni', 'O')\",OTHERS,False\nmp-2970,\"{'O': 8.0, 'Na': 8.0, 'Ge': 2.0}\",\"('Ge', 'Na', 'O')\",OTHERS,False\nmp-556705,\"{'As': 4.0, 'S': 4.0, 'Cl': 12.0, 'F': 24.0}\",\"('As', 'Cl', 'F', 'S')\",OTHERS,False\nmp-1218571,\"{'Sr': 3.0, 'Li': 4.0, 'Nb': 6.0, 'O': 20.0}\",\"('Li', 'Nb', 'O', 'Sr')\",OTHERS,False\nmp-754976,\"{'Cs': 2.0, 'Be': 1.0, 'O': 2.0}\",\"('Be', 'Cs', 'O')\",OTHERS,False\nmp-777532,\"{'Li': 32.0, 'Ti': 5.0, 'Cr': 11.0, 'O': 48.0}\",\"('Cr', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-865753,\"{'Yb': 1.0, 'Er': 1.0, 'Pd': 2.0}\",\"('Er', 'Pd', 'Yb')\",OTHERS,False\nmp-1180739,\"{'La': 4.0, 'Ga': 4.0, 'O': 12.0}\",\"('Ga', 'La', 'O')\",OTHERS,False\nmp-1228871,\"{'Cd': 1.0, 'Fe': 4.0, 'Cu': 10.0, 'Sn': 5.0, 'Se': 20.0}\",\"('Cd', 'Cu', 'Fe', 'Se', 'Sn')\",OTHERS,False\nmp-557597,\"{'K': 2.0, 'Ca': 1.0, 'N': 4.0, 'O': 8.0}\",\"('Ca', 'K', 'N', 'O')\",OTHERS,False\nmp-752398,\"{'Ba': 1.0, 'Cu': 1.0, 'O': 2.0}\",\"('Ba', 'Cu', 'O')\",OTHERS,False\nmp-755103,\"{'Li': 4.0, 'Fe': 4.0, 'F': 12.0}\",\"('F', 'Fe', 'Li')\",OTHERS,False\nmp-21608,\"{'B': 6.0, 'Ni': 21.0, 'Tm': 2.0}\",\"('B', 'Ni', 'Tm')\",OTHERS,False\nmvc-10576,\"{'Ba': 2.0, 'Mg': 3.0, 'Tl': 2.0, 'Sn': 4.0, 'O': 12.0}\",\"('Ba', 'Mg', 'O', 'Sn', 'Tl')\",OTHERS,False\nAg 43.4 In 42.8 Eu 13.8,\"{'Ag': 43.4, 'In': 42.8, 'Eu': 13.8}\",\"('Ag', 'Eu', 'In')\",AC,False\nmp-1006328,\"{'Li': 8.0, 'Ce': 8.0, 'Sn': 8.0}\",\"('Ce', 'Li', 'Sn')\",OTHERS,False\nmp-27713,\"{'O': 1.0, 'V': 1.0, 'Br': 2.0}\",\"('Br', 'O', 'V')\",OTHERS,False\nmp-1244982,\"{'Al': 40.0, 'O': 60.0}\",\"('Al', 'O')\",OTHERS,False\nmp-29618,\"{'P': 6.0, 'Ni': 2.0, 'W': 4.0}\",\"('Ni', 'P', 'W')\",OTHERS,False\nmp-675419,\"{'Li': 2.0, 'Pr': 2.0, 'S': 4.0}\",\"('Li', 'Pr', 'S')\",OTHERS,False\nmvc-3060,\"{'Ba': 2.0, 'Mn': 3.0, 'Tl': 2.0, 'Zn': 2.0, 'O': 10.0}\",\"('Ba', 'Mn', 'O', 'Tl', 'Zn')\",OTHERS,False\nmp-1100672,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-768293,\"{'Lu': 8.0, 'Se': 8.0, 'O': 24.0}\",\"('Lu', 'O', 'Se')\",OTHERS,False\nmp-1189443,\"{'H': 4.0, 'N': 4.0, 'F': 8.0}\",\"('F', 'H', 'N')\",OTHERS,False\nmp-754041,\"{'Gd': 4.0, 'Hf': 2.0, 'O': 10.0}\",\"('Gd', 'Hf', 'O')\",OTHERS,False\nmp-1196472,\"{'Pr': 8.0, 'U': 4.0, 'S': 20.0}\",\"('Pr', 'S', 'U')\",OTHERS,False\nmp-907,\"{'O': 49.0, 'W': 18.0}\",\"('O', 'W')\",OTHERS,False\nmp-1247466,\"{'Mg': 2.0, 'Sc': 3.0, 'Cr': 1.0, 'S': 8.0}\",\"('Cr', 'Mg', 'S', 'Sc')\",OTHERS,False\nmp-561179,\"{'Ba': 8.0, 'Cu': 4.0, 'I': 4.0, 'O': 8.0}\",\"('Ba', 'Cu', 'I', 'O')\",OTHERS,False\nmp-1702,\"{'Rh': 4.0, 'La': 2.0}\",\"('La', 'Rh')\",OTHERS,False\nmp-850985,\"{'Li': 4.0, 'Fe': 4.0, 'P': 8.0, 'H': 4.0, 'O': 28.0}\",\"('Fe', 'H', 'Li', 'O', 'P')\",OTHERS,False\nmp-1226856,\"{'Ce': 3.0, 'Nd': 1.0, 'C': 8.0}\",\"('C', 'Ce', 'Nd')\",OTHERS,False\nmp-541785,\"{'Ge': 2.0, 'Pd': 2.0, 'S': 6.0}\",\"('Ge', 'Pd', 'S')\",OTHERS,False\nmp-1001784,\"{'Li': 1.0, 'Ti': 1.0, 'S': 2.0}\",\"('Li', 'S', 'Ti')\",OTHERS,False\nmp-1203368,\"{'Zn': 48.0, 'As': 32.0}\",\"('As', 'Zn')\",OTHERS,False\nmp-27077,\"{'Li': 14.0, 'O': 64.0, 'P': 18.0, 'Cr': 8.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-1198461,\"{'Na': 80.0, 'Fe': 16.0, 'P': 32.0, 'O': 128.0, 'F': 32.0}\",\"('F', 'Fe', 'Na', 'O', 'P')\",OTHERS,False\nmp-1228042,\"{'Ca': 4.0, 'Eu': 2.0, 'Cu': 4.0, 'O': 12.0}\",\"('Ca', 'Cu', 'Eu', 'O')\",OTHERS,False\nmp-580329,\"{'Pu': 3.0, 'Sn': 1.0}\",\"('Pu', 'Sn')\",OTHERS,False\nmp-743619,\"{'Co': 2.0, 'H': 12.0, 'C': 4.0, 'S': 4.0, 'N': 4.0, 'O': 6.0}\",\"('C', 'Co', 'H', 'N', 'O', 'S')\",OTHERS,False\nmp-768232,\"{'Rb': 12.0, 'Sb': 4.0, 'O': 12.0}\",\"('O', 'Rb', 'Sb')\",OTHERS,False\nmp-705194,\"{'Mn': 16.0, 'Sn': 8.0, 'C': 80.0, 'Br': 8.0, 'O': 80.0}\",\"('Br', 'C', 'Mn', 'O', 'Sn')\",OTHERS,False\nmp-1216288,\"{'W': 12.0, 'N': 3.0, 'O': 36.0}\",\"('N', 'O', 'W')\",OTHERS,False\nmp-705871,\"{'Ca': 14.0, 'Al': 6.0, 'Si': 20.0, 'N': 42.0}\",\"('Al', 'Ca', 'N', 'Si')\",OTHERS,False\nmp-774348,\"{'Li': 6.0, 'Mn': 2.0, 'Fe': 4.0, 'B': 6.0, 'O': 18.0}\",\"('B', 'Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1245231,\"{'B': 50.0, 'N': 50.0}\",\"('B', 'N')\",OTHERS,False\nmp-1174645,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-29584,\"{'Be': 1.0, 'K': 4.0, 'As': 2.0}\",\"('As', 'Be', 'K')\",OTHERS,False\nmp-1198503,\"{'U': 2.0, 'Zn': 40.0, 'Rh': 4.0}\",\"('Rh', 'U', 'Zn')\",OTHERS,False\nmvc-10098,\"{'Zn': 6.0, 'Cr': 12.0, 'O': 24.0}\",\"('Cr', 'O', 'Zn')\",OTHERS,False\nmp-1222879,\"{'La': 1.0, 'Mn': 1.0, 'Ni': 4.0}\",\"('La', 'Mn', 'Ni')\",OTHERS,False\nmp-634378,\"{'Na': 4.0, 'H': 4.0, 'S': 4.0, 'O': 16.0}\",\"('H', 'Na', 'O', 'S')\",OTHERS,False\nmp-558281,\"{'F': 8.0, 'Cr': 2.0, 'Sr': 2.0}\",\"('Cr', 'F', 'Sr')\",OTHERS,False\nmp-1178661,\"{'Yb': 2.0, 'Mo': 2.0, 'Cl': 2.0, 'O': 8.0}\",\"('Cl', 'Mo', 'O', 'Yb')\",OTHERS,False\nmp-1174818,\"{'Li': 6.0, 'Mn': 3.0, 'Co': 1.0, 'O': 10.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-19844,\"{'Na': 1.0, 'Fe': 4.0, 'Sb': 12.0}\",\"('Fe', 'Na', 'Sb')\",OTHERS,False\nmp-1191530,\"{'Er': 6.0, 'Ga': 2.0, 'Co': 2.0, 'S': 14.0}\",\"('Co', 'Er', 'Ga', 'S')\",OTHERS,False\nmp-1186326,\"{'Nd': 1.0, 'Re': 1.0, 'O': 3.0}\",\"('Nd', 'O', 'Re')\",OTHERS,False\nmp-554243,\"{'Si': 6.0, 'O': 12.0}\",\"('O', 'Si')\",OTHERS,False\nmp-1178513,\"{'Ba': 2.0, 'Sn': 2.0, 'O': 6.0}\",\"('Ba', 'O', 'Sn')\",OTHERS,False\nmp-775155,\"{'Li': 12.0, 'Fe': 3.0, 'O': 3.0, 'F': 15.0}\",\"('F', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-773119,\"{'Na': 4.0, 'Mn': 2.0, 'As': 2.0, 'C': 2.0, 'O': 14.0}\",\"('As', 'C', 'Mn', 'Na', 'O')\",OTHERS,False\nmp-30094,\"{'H': 16.0, 'C': 8.0, 'N': 16.0}\",\"('C', 'H', 'N')\",OTHERS,False\nmp-1219720,\"{'Pt': 1.0, 'Se': 1.0, 'S': 1.0}\",\"('Pt', 'S', 'Se')\",OTHERS,False\nmvc-10215,\"{'Ho': 2.0, 'Mn': 4.0, 'Zn': 2.0, 'O': 12.0}\",\"('Ho', 'Mn', 'O', 'Zn')\",OTHERS,False\nmp-763331,\"{'Li': 2.0, 'Mn': 4.0, 'F': 10.0}\",\"('F', 'Li', 'Mn')\",OTHERS,False\nmp-11417,\"{'Ga': 2.0, 'Tb': 2.0}\",\"('Ga', 'Tb')\",OTHERS,False\nmp-776391,\"{'Li': 4.0, 'Fe': 3.0, 'Co': 3.0, 'Cu': 2.0, 'O': 16.0}\",\"('Co', 'Cu', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-866163,\"{'Y': 1.0, 'In': 1.0, 'Rh': 2.0}\",\"('In', 'Rh', 'Y')\",OTHERS,False\nmp-1208590,\"{'Ta': 1.0, 'In': 1.0, 'Se': 2.0}\",\"('In', 'Se', 'Ta')\",OTHERS,False\nmp-556600,\"{'Cs': 4.0, 'S': 8.0, 'N': 12.0, 'O': 16.0}\",\"('Cs', 'N', 'O', 'S')\",OTHERS,False\nmp-20529,\"{'O': 18.0, 'Na': 2.0, 'Ba': 6.0, 'Ir': 4.0}\",\"('Ba', 'Ir', 'Na', 'O')\",OTHERS,False\nmp-1210859,\"{'Li': 1.0, 'Ti': 1.0, 'S': 2.0, 'O': 1.0}\",\"('Li', 'O', 'S', 'Ti')\",OTHERS,False\nmp-777833,\"{'Fe': 10.0, 'O': 4.0, 'F': 16.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-761192,\"{'Li': 4.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-8066,\"{'Cu': 18.0, 'As': 6.0}\",\"('As', 'Cu')\",OTHERS,False\nmp-1174255,\"{'Li': 6.0, 'Mn': 2.0, 'Co': 2.0, 'O': 10.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-757590,\"{'Li': 8.0, 'Sn': 8.0, 'P': 8.0, 'O': 32.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,False\nmp-29749,\"{'Ni': 10.0, 'Ga': 6.0, 'Ge': 4.0}\",\"('Ga', 'Ge', 'Ni')\",OTHERS,False\nmp-771330,\"{'Mn': 4.0, 'I': 8.0, 'O': 24.0}\",\"('I', 'Mn', 'O')\",OTHERS,False\nmp-1224395,\"{'Hf': 1.0, 'Al': 6.0, 'Fe': 6.0}\",\"('Al', 'Fe', 'Hf')\",OTHERS,False\nmp-1246997,\"{'Nb': 2.0, 'Cr': 6.0, 'Ag': 4.0, 'S': 16.0}\",\"('Ag', 'Cr', 'Nb', 'S')\",OTHERS,False\nmp-1068510,\"{'In': 2.0, 'Te': 3.0}\",\"('In', 'Te')\",OTHERS,False\nmp-1025448,\"{'Y': 4.0, 'Pt': 4.0}\",\"('Pt', 'Y')\",OTHERS,False\nmp-1177560,\"{'Li': 12.0, 'Mn': 4.0, 'V': 4.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-1079891,\"{'Ba': 4.0, 'Br': 4.0, 'O': 2.0}\",\"('Ba', 'Br', 'O')\",OTHERS,False\nmp-1039895,\"{'Rb': 1.0, 'Mg': 30.0, 'B': 1.0, 'O': 31.0}\",\"('B', 'Mg', 'O', 'Rb')\",OTHERS,False\nmp-775126,\"{'Mn': 12.0, 'O': 7.0, 'F': 17.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-560939,\"{'Na': 4.0, 'La': 4.0, 'P': 8.0, 'O': 28.0}\",\"('La', 'Na', 'O', 'P')\",OTHERS,False\nmp-743952,\"{'Mo': 8.0, 'H': 12.0, 'C': 16.0, 'O': 32.0}\",\"('C', 'H', 'Mo', 'O')\",OTHERS,False\nmvc-13320,\"{'Sr': 4.0, 'Y': 2.0, 'Cr': 4.0, 'O': 14.0}\",\"('Cr', 'O', 'Sr', 'Y')\",OTHERS,False\nmp-755907,\"{'Co': 8.0, 'O': 4.0, 'F': 12.0}\",\"('Co', 'F', 'O')\",OTHERS,False\nmp-16695,\"{'O': 28.0, 'P': 8.0, 'K': 8.0, 'Zn': 4.0}\",\"('K', 'O', 'P', 'Zn')\",OTHERS,False\nmp-28673,\"{'O': 24.0, 'P': 16.0, 'S': 4.0}\",\"('O', 'P', 'S')\",OTHERS,False\nmp-759278,\"{'Li': 8.0, 'Cu': 4.0, 'P': 16.0, 'O': 48.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-1220225,\"{'Nd': 2.0, 'Cu': 2.0, 'Ag': 2.0}\",\"('Ag', 'Cu', 'Nd')\",OTHERS,False\nmp-504900,\"{'Eu': 2.0, 'Sb': 4.0, 'Pd': 4.0}\",\"('Eu', 'Pd', 'Sb')\",OTHERS,False\nmp-1177698,\"{'Li': 12.0, 'Mn': 8.0, 'P': 16.0, 'O': 56.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-675063,\"{'K': 6.0, 'Ba': 12.0, 'As': 30.0}\",\"('As', 'Ba', 'K')\",OTHERS,False\nmp-608777,\"{'Eu': 2.0, 'In': 4.0, 'As': 4.0}\",\"('As', 'Eu', 'In')\",OTHERS,False\nmp-1200525,\"{'Cs': 4.0, 'Fe': 4.0, 'P': 6.0, 'O': 16.0, 'F': 14.0}\",\"('Cs', 'F', 'Fe', 'O', 'P')\",OTHERS,False\nmp-1076062,\"{'Sr': 7.0, 'Ca': 1.0, 'Fe': 5.0, 'Co': 3.0, 'O': 24.0}\",\"('Ca', 'Co', 'Fe', 'O', 'Sr')\",OTHERS,False\nmp-1096118,\"{'Sc': 1.0, 'Cd': 2.0, 'Ag': 1.0}\",\"('Ag', 'Cd', 'Sc')\",OTHERS,False\nmp-22514,\"{'Mn': 4.0, 'Sn': 2.0}\",\"('Mn', 'Sn')\",OTHERS,False\nmp-1209976,\"{'Nd': 16.0, 'Cd': 4.0, 'Pd': 4.0}\",\"('Cd', 'Nd', 'Pd')\",OTHERS,False\nmp-1226201,\"{'Cu': 7.0, 'Te': 2.0, 'S': 2.0, 'O': 20.0}\",\"('Cu', 'O', 'S', 'Te')\",OTHERS,False\nmp-560322,\"{'K': 4.0, 'Ru': 2.0, 'N': 6.0, 'Cl': 6.0, 'O': 10.0}\",\"('Cl', 'K', 'N', 'O', 'Ru')\",OTHERS,False\nmp-9675,\"{'B': 2.0, 'P': 4.0, 'Cs': 6.0}\",\"('B', 'Cs', 'P')\",OTHERS,False\nmp-1215605,\"{'Yb': 2.0, 'Ag': 2.0, 'Sn': 2.0}\",\"('Ag', 'Sn', 'Yb')\",OTHERS,False\nmp-1078811,\"{'Nb': 4.0, 'Ge': 2.0, 'C': 2.0}\",\"('C', 'Ge', 'Nb')\",OTHERS,False\nmp-14780,\"{'Na': 12.0, 'Mn': 2.0, 'Se': 8.0}\",\"('Mn', 'Na', 'Se')\",OTHERS,False\nmp-1218153,\"{'Sr': 1.0, 'Li': 1.0, 'Al': 1.0, 'Sb': 2.0}\",\"('Al', 'Li', 'Sb', 'Sr')\",OTHERS,False\nmp-540914,\"{'K': 12.0, 'Mn': 4.0, 'O': 12.0}\",\"('K', 'Mn', 'O')\",OTHERS,False\nmp-18099,\"{'F': 20.0, 'Sb': 4.0, 'Ba': 4.0}\",\"('Ba', 'F', 'Sb')\",OTHERS,False\nmp-1201031,\"{'Zn': 4.0, 'As': 8.0, 'S': 16.0, 'O': 32.0, 'F': 48.0}\",\"('As', 'F', 'O', 'S', 'Zn')\",OTHERS,False\nmp-694629,\"{'V': 9.0, 'P': 12.0, 'O': 48.0}\",\"('O', 'P', 'V')\",OTHERS,False\nmp-1224233,\"{'Ho': 3.0, 'Mn': 3.0, 'Ga': 2.0, 'Ge': 1.0}\",\"('Ga', 'Ge', 'Ho', 'Mn')\",OTHERS,False\nmp-1189135,\"{'Dy': 6.0, 'Cu': 6.0, 'Sb': 8.0}\",\"('Cu', 'Dy', 'Sb')\",OTHERS,False\nmp-557482,\"{'Ca': 10.0, 'As': 6.0, 'O': 24.0, 'F': 2.0}\",\"('As', 'Ca', 'F', 'O')\",OTHERS,False\nmp-1027147,\"{'Mo': 2.0, 'W': 2.0, 'Se': 4.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-1218062,\"{'Ta': 3.0, 'W': 1.0, 'S': 8.0}\",\"('S', 'Ta', 'W')\",OTHERS,False\nmp-1224078,\"{'In': 2.0, 'Fe': 2.0, 'Co': 2.0, 'O': 8.0}\",\"('Co', 'Fe', 'In', 'O')\",OTHERS,False\nmvc-157,\"{'Mg': 1.0, 'W': 4.0, 'O': 8.0}\",\"('Mg', 'O', 'W')\",OTHERS,False\nmp-21416,\"{'Al': 4.0, 'Pu': 2.0}\",\"('Al', 'Pu')\",OTHERS,False\nmp-9842,\"{'C': 4.0, 'O': 12.0, 'Rb': 2.0, 'Sm': 2.0}\",\"('C', 'O', 'Rb', 'Sm')\",OTHERS,False\nmp-16338,\"{'Si': 1.0, 'S': 8.0, 'Ga': 1.0, 'Mo': 4.0}\",\"('Ga', 'Mo', 'S', 'Si')\",OTHERS,False\nmp-568598,\"{'Cu': 2.0, 'Hg': 1.0, 'I': 4.0}\",\"('Cu', 'Hg', 'I')\",OTHERS,False\nmp-1080556,\"{'Eu': 2.0, 'Ni': 2.0, 'Ge': 4.0}\",\"('Eu', 'Ge', 'Ni')\",OTHERS,False\nmp-1034626,\"{'Na': 1.0, 'Mg': 14.0, 'B': 1.0, 'O': 16.0}\",\"('B', 'Mg', 'Na', 'O')\",OTHERS,False\nmp-21833,\"{'P': 24.0, 'Mo': 32.0}\",\"('Mo', 'P')\",OTHERS,False\nmp-540923,\"{'Ba': 12.0, 'Sn': 4.0, 'P': 12.0}\",\"('Ba', 'P', 'Sn')\",OTHERS,False\nmp-1218331,\"{'Sr': 3.0, 'Ca': 1.0, 'Si': 8.0}\",\"('Ca', 'Si', 'Sr')\",OTHERS,False\nmp-1216976,\"{'Tl': 5.0, 'Sb': 10.0, 'As': 3.0, 'S': 22.0}\",\"('As', 'S', 'Sb', 'Tl')\",OTHERS,False\nmp-1214523,\"{'Be': 52.0, 'Mo': 4.0}\",\"('Be', 'Mo')\",OTHERS,False\nmp-779427,\"{'Sm': 4.0, 'V': 4.0, 'O': 14.0}\",\"('O', 'Sm', 'V')\",OTHERS,False\nmp-1193529,\"{'Pr': 2.0, 'Be': 26.0}\",\"('Be', 'Pr')\",OTHERS,False\nmp-1218633,\"{'Sr': 3.0, 'Nb': 2.0, 'Cu': 1.0, 'O': 9.0}\",\"('Cu', 'Nb', 'O', 'Sr')\",OTHERS,False\nmp-30737,\"{'Mg': 3.0, 'Y': 3.0, 'Ag': 3.0}\",\"('Ag', 'Mg', 'Y')\",OTHERS,False\nmp-1178817,\"{'V': 1.0, 'H': 6.0, 'O': 3.0, 'F': 3.0}\",\"('F', 'H', 'O', 'V')\",OTHERS,False\nmp-1111218,\"{'K': 2.0, 'Na': 1.0, 'Y': 1.0, 'Cl': 6.0}\",\"('Cl', 'K', 'Na', 'Y')\",OTHERS,False\nmp-1186111,\"{'Na': 1.0, 'Ac': 1.0, 'In': 2.0}\",\"('Ac', 'In', 'Na')\",OTHERS,False\nmp-753262,\"{'Li': 4.0, 'Co': 2.0, 'Si': 6.0, 'O': 16.0}\",\"('Co', 'Li', 'O', 'Si')\",OTHERS,False\nmp-555419,\"{'Te': 12.0, 'As': 8.0, 'C': 6.0, 'N': 6.0, 'F': 48.0}\",\"('As', 'C', 'F', 'N', 'Te')\",OTHERS,False\nmp-23416,\"{'Li': 8.0, 'Cl': 16.0, 'Zn': 4.0}\",\"('Cl', 'Li', 'Zn')\",OTHERS,False\nmp-1212666,\"{'Ga': 5.0, 'Cu': 1.0, 'Se': 8.0}\",\"('Cu', 'Ga', 'Se')\",OTHERS,False\nmp-1246876,\"{'Mg': 2.0, 'Cr': 2.0, 'Ga': 2.0, 'S': 8.0}\",\"('Cr', 'Ga', 'Mg', 'S')\",OTHERS,False\nmp-754558,\"{'Fe': 2.0, 'Ni': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Fe', 'Ni', 'O', 'P')\",OTHERS,False\nmp-1192810,\"{'Mo': 6.0, 'N': 4.0, 'O': 20.0}\",\"('Mo', 'N', 'O')\",OTHERS,False\nmp-1215551,\"{'Yb': 2.0, 'Zn': 2.0, 'Ga': 2.0}\",\"('Ga', 'Yb', 'Zn')\",OTHERS,False\nmp-867186,\"{'Ce': 1.0, 'Zn': 2.0, 'Ag': 1.0}\",\"('Ag', 'Ce', 'Zn')\",OTHERS,False\nmp-1222821,\"{'Li': 1.0, 'Ni': 3.0, 'O': 6.0}\",\"('Li', 'Ni', 'O')\",OTHERS,False\nmp-867688,\"{'Li': 10.0, 'Fe': 2.0, 'P': 8.0, 'O': 28.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-1097084,\"{'Fe': 2.0, 'Ir': 1.0, 'Rh': 1.0}\",\"('Fe', 'Ir', 'Rh')\",OTHERS,False\nmp-1112975,\"{'Cs': 3.0, 'Er': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cs', 'Er')\",OTHERS,False\nmp-754764,\"{'Fe': 4.0, 'O': 4.0, 'F': 4.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-555580,\"{'Sr': 1.0, 'Zr': 4.0, 'P': 6.0, 'O': 24.0}\",\"('O', 'P', 'Sr', 'Zr')\",OTHERS,False\nmp-757526,\"{'Li': 4.0, 'V': 3.0, 'Co': 3.0, 'W': 2.0, 'O': 16.0}\",\"('Co', 'Li', 'O', 'V', 'W')\",OTHERS,False\nmp-1220034,\"{'P': 2.0, 'H': 2.0, 'Pb': 2.0, 'O': 8.0}\",\"('H', 'O', 'P', 'Pb')\",OTHERS,False\nmp-29514,\"{'H': 4.0, 'F': 16.0, 'P': 4.0}\",\"('F', 'H', 'P')\",OTHERS,False\nmp-1187049,\"{'Th': 3.0, 'Pb': 1.0}\",\"('Pb', 'Th')\",OTHERS,False\nmp-676121,\"{'Ag': 6.0, 'S': 2.0, 'I': 2.0}\",\"('Ag', 'I', 'S')\",OTHERS,False\nmvc-2200,\"{'Ca': 2.0, 'Mn': 2.0, 'P': 8.0, 'O': 24.0}\",\"('Ca', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1213873,\"{'Ce': 10.0, 'Sn': 6.0, 'Au': 2.0}\",\"('Au', 'Ce', 'Sn')\",OTHERS,False\nmp-1076602,\"{'Sr': 3.0, 'Ca': 5.0, 'Fe': 4.0, 'Co': 4.0, 'O': 24.0}\",\"('Ca', 'Co', 'Fe', 'O', 'Sr')\",OTHERS,False\nmp-764645,\"{'Li': 6.0, 'Mn': 2.0, 'F': 14.0}\",\"('F', 'Li', 'Mn')\",OTHERS,False\nmp-1193618,\"{'Rb': 8.0, 'Sn': 2.0, 'S': 8.0, 'O': 8.0}\",\"('O', 'Rb', 'S', 'Sn')\",OTHERS,False\nmp-1203867,\"{'Ce': 4.0, 'Co': 20.0, 'P': 12.0}\",\"('Ce', 'Co', 'P')\",OTHERS,False\nmp-1213449,\"{'K': 8.0, 'Te': 4.0, 'Mo': 12.0, 'O': 60.0}\",\"('K', 'Mo', 'O', 'Te')\",OTHERS,False\nmp-17954,\"{'S': 12.0, 'Cu': 6.0, 'Sn': 3.0, 'Ba': 3.0}\",\"('Ba', 'Cu', 'S', 'Sn')\",OTHERS,False\nmp-1211351,\"{'La': 6.0, 'Be': 2.0, 'Al': 2.0, 'S': 14.0}\",\"('Al', 'Be', 'La', 'S')\",OTHERS,False\nmp-1181590,\"{'Mg': 2.0, 'I': 4.0, 'O': 32.0}\",\"('I', 'Mg', 'O')\",OTHERS,False\nmp-1101836,\"{'Gd': 2.0, 'H': 4.0, 'Cl': 2.0, 'O': 4.0}\",\"('Cl', 'Gd', 'H', 'O')\",OTHERS,False\nmp-778822,\"{'Mn': 4.0, 'S': 6.0, 'O': 24.0}\",\"('Mn', 'O', 'S')\",OTHERS,False\nmp-557311,\"{'Sr': 16.0, 'Ge': 8.0, 'O': 32.0}\",\"('Ge', 'O', 'Sr')\",OTHERS,False\nmp-851030,\"{'Li': 8.0, 'Fe': 4.0, 'P': 4.0, 'H': 4.0, 'O': 20.0}\",\"('Fe', 'H', 'Li', 'O', 'P')\",OTHERS,False\nmp-18685,\"{'S': 12.0, 'Cu': 6.0, 'Ge': 3.0, 'Sr': 3.0}\",\"('Cu', 'Ge', 'S', 'Sr')\",OTHERS,False\nmvc-13442,\"{'Cu': 4.0, 'S': 2.0}\",\"('Cu', 'S')\",OTHERS,False\nmp-1196732,\"{'Rb': 12.0, 'H': 24.0, 'I': 36.0, 'O': 108.0}\",\"('H', 'I', 'O', 'Rb')\",OTHERS,False\nmp-776293,\"{'Na': 8.0, 'Sb': 4.0, 'C': 8.0, 'S': 2.0, 'O': 32.0}\",\"('C', 'Na', 'O', 'S', 'Sb')\",OTHERS,False\nmp-558546,\"{'K': 6.0, 'S': 6.0, 'O': 18.0}\",\"('K', 'O', 'S')\",OTHERS,False\nmp-1199891,\"{'K': 8.0, 'P': 4.0, 'H': 16.0, 'S': 12.0, 'O': 8.0}\",\"('H', 'K', 'O', 'P', 'S')\",OTHERS,False\nmp-556776,\"{'Sr': 4.0, 'P': 8.0, 'O': 24.0}\",\"('O', 'P', 'Sr')\",OTHERS,False\nmp-1114561,\"{'Rb': 2.0, 'Li': 1.0, 'Bi': 1.0, 'Br': 6.0}\",\"('Bi', 'Br', 'Li', 'Rb')\",OTHERS,False\nmp-1193946,\"{'Mn': 4.0, 'Nb': 8.0, 'S': 16.0}\",\"('Mn', 'Nb', 'S')\",OTHERS,False\nmp-1232043,\"{'La': 8.0, 'Mg': 4.0, 'Se': 16.0}\",\"('La', 'Mg', 'Se')\",OTHERS,False\nmp-1097457,\"{'Sr': 2.0, 'Li': 1.0, 'Al': 1.0}\",\"('Al', 'Li', 'Sr')\",OTHERS,False\nmp-570756,\"{'Cs': 3.0, 'Li': 2.0, 'Cl': 5.0}\",\"('Cl', 'Cs', 'Li')\",OTHERS,False\nmp-1080857,\"{'Ce': 16.0, 'Se': 32.0}\",\"('Ce', 'Se')\",OTHERS,False\nmp-707788,\"{'Na': 4.0, 'Ca': 4.0, 'B': 20.0, 'H': 40.0, 'O': 56.0}\",\"('B', 'Ca', 'H', 'Na', 'O')\",OTHERS,False\nmp-1215942,\"{'Y': 1.0, 'Sc': 1.0, 'Fe': 4.0}\",\"('Fe', 'Sc', 'Y')\",OTHERS,False\nmp-1225255,\"{'Gd': 2.0, 'Co': 1.0, 'Ni': 3.0}\",\"('Co', 'Gd', 'Ni')\",OTHERS,False\nmp-1192708,\"{'U': 8.0, 'Co': 1.0, 'S': 17.0}\",\"('Co', 'S', 'U')\",OTHERS,False\nmp-1186284,\"{'Nd': 3.0, 'Pa': 1.0}\",\"('Nd', 'Pa')\",OTHERS,False\nmp-1197420,\"{'Mg': 2.0, 'Cr': 2.0, 'H': 44.0, 'O': 30.0}\",\"('Cr', 'H', 'Mg', 'O')\",OTHERS,False\nmp-984806,\"{'Ba': 4.0, 'Pr': 2.0, 'Ru': 2.0, 'O': 12.0}\",\"('Ba', 'O', 'Pr', 'Ru')\",OTHERS,False\nmp-758226,\"{'Cr': 1.0, 'Fe': 3.0, 'Co': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Co', 'Cr', 'Fe', 'O', 'P')\",OTHERS,False\nmp-10577,\"{'N': 20.0, 'Sr': 20.0, 'Nb': 4.0}\",\"('N', 'Nb', 'Sr')\",OTHERS,False\nmp-1033692,\"{'Hf': 1.0, 'Mg': 14.0, 'Cd': 1.0, 'O': 16.0}\",\"('Cd', 'Hf', 'Mg', 'O')\",OTHERS,False\nmp-17756,\"{'S': 12.0, 'Rb': 3.0, 'Ag': 6.0, 'Sb': 3.0}\",\"('Ag', 'Rb', 'S', 'Sb')\",OTHERS,False\nmp-540635,\"{'Ba': 2.0, 'Sn': 2.0, 'Ge': 6.0, 'O': 18.0}\",\"('Ba', 'Ge', 'O', 'Sn')\",OTHERS,False\nmp-567768,\"{'Yb': 2.0, 'Cu': 2.0, 'Ge': 2.0}\",\"('Cu', 'Ge', 'Yb')\",OTHERS,False\nmp-1245612,\"{'Mn': 28.0, 'Si': 4.0, 'N': 24.0}\",\"('Mn', 'N', 'Si')\",OTHERS,False\nmvc-684,\"{'Al': 2.0, 'Cr': 2.0, 'W': 4.0, 'O': 16.0}\",\"('Al', 'Cr', 'O', 'W')\",OTHERS,False\nmp-1211961,\"{'K': 2.0, 'Si': 4.0, 'O': 8.0}\",\"('K', 'O', 'Si')\",OTHERS,False\nmp-28691,\"{'Te': 8.0, 'I': 2.0, 'Ta': 2.0}\",\"('I', 'Ta', 'Te')\",OTHERS,False\nmp-3433,\"{'Mg': 1.0, 'Ni': 4.0, 'Y': 1.0}\",\"('Mg', 'Ni', 'Y')\",OTHERS,False\nmp-30223,\"{'Fe': 4.0, 'I': 2.0, 'La': 4.0}\",\"('Fe', 'I', 'La')\",OTHERS,False\nmp-1218090,\"{'Ta': 3.0, 'Al': 1.0, 'Fe': 8.0}\",\"('Al', 'Fe', 'Ta')\",OTHERS,False\nmp-766351,\"{'Ba': 8.0, 'Ca': 4.0, 'I': 24.0}\",\"('Ba', 'Ca', 'I')\",OTHERS,False\nmp-1246792,\"{'Mg': 4.0, 'Cu': 4.0, 'N': 4.0}\",\"('Cu', 'Mg', 'N')\",OTHERS,False\nmp-1223097,\"{'Li': 16.0, 'H': 8.0, 'N': 8.0}\",\"('H', 'Li', 'N')\",OTHERS,False\nmp-618964,\"{'Ba': 10.0, 'Fe': 8.0, 'S': 22.0}\",\"('Ba', 'Fe', 'S')\",OTHERS,False\nmp-861934,\"{'Ho': 1.0, 'Al': 1.0, 'Ag': 2.0}\",\"('Ag', 'Al', 'Ho')\",OTHERS,False\nmp-1195152,\"{'Ba': 12.0, 'V': 16.0, 'Zn': 4.0, 'O': 56.0}\",\"('Ba', 'O', 'V', 'Zn')\",OTHERS,False\nmvc-3104,\"{'Ba': 2.0, 'Ca': 2.0, 'Tl': 2.0, 'W': 3.0, 'O': 10.0}\",\"('Ba', 'Ca', 'O', 'Tl', 'W')\",OTHERS,False\nmp-31308,\"{'S': 40.0, 'K': 8.0, 'Ta': 8.0}\",\"('K', 'S', 'Ta')\",OTHERS,False\nmp-771914,\"{'Ba': 6.0, 'La': 4.0, 'Cl': 24.0}\",\"('Ba', 'Cl', 'La')\",OTHERS,False\nmp-1016155,\"{'Na': 1.0, 'Mn': 4.0, 'O': 8.0}\",\"('Mn', 'Na', 'O')\",OTHERS,False\nmp-1227871,\"{'Ca': 6.0, 'Cd': 4.0, 'Ga': 8.0}\",\"('Ca', 'Cd', 'Ga')\",OTHERS,False\nmp-675479,\"{'Pu': 2.0, 'Pa': 2.0, 'O': 8.0}\",\"('O', 'Pa', 'Pu')\",OTHERS,False\nmvc-5756,\"{'Ca': 1.0, 'W': 1.0, 'O': 3.0}\",\"('Ca', 'O', 'W')\",OTHERS,False\nmp-7917,\"{'Ge': 2.0, 'Se': 2.0, 'Hf': 2.0}\",\"('Ge', 'Hf', 'Se')\",OTHERS,False\nmvc-16087,\"{'Y': 4.0, 'Ti': 12.0, 'O': 24.0}\",\"('O', 'Ti', 'Y')\",OTHERS,False\nmp-1221205,\"{'Na': 3.0, 'P': 1.0, 'O': 4.0}\",\"('Na', 'O', 'P')\",OTHERS,False\nmp-1212260,\"{'Ho': 6.0, 'Al': 24.0, 'Ru': 8.0}\",\"('Al', 'Ho', 'Ru')\",OTHERS,False\nmp-1181699,\"{'Cl': 2.0, 'O': 6.0}\",\"('Cl', 'O')\",OTHERS,False\nmp-980060,\"{'Tb': 1.0, 'Ag': 3.0}\",\"('Ag', 'Tb')\",OTHERS,False\nmp-1215532,\"{'Zn': 4.0, 'Fe': 1.0, 'Se': 5.0}\",\"('Fe', 'Se', 'Zn')\",OTHERS,False\nmp-1213005,\"{'Er': 4.0, 'Si': 4.0, 'Ni': 4.0}\",\"('Er', 'Ni', 'Si')\",OTHERS,False\nmp-23059,\"{'Cl': 6.0, 'Rb': 2.0, 'Sn': 1.0}\",\"('Cl', 'Rb', 'Sn')\",OTHERS,False\nmp-754334,\"{'Na': 2.0, 'Tm': 2.0, 'O': 4.0}\",\"('Na', 'O', 'Tm')\",OTHERS,False\nmp-1181627,\"{'Cu': 2.0, 'H': 28.0, 'C': 8.0, 'N': 8.0, 'O': 16.0}\",\"('C', 'Cu', 'H', 'N', 'O')\",OTHERS,False\nmp-757010,\"{'Li': 3.0, 'Fe': 1.0, 'Ni': 4.0, 'O': 8.0}\",\"('Fe', 'Li', 'Ni', 'O')\",OTHERS,False\nmvc-6842,\"{'Zn': 4.0, 'Ag': 8.0, 'O': 16.0}\",\"('Ag', 'O', 'Zn')\",OTHERS,False\nmp-1203168,\"{'Sn': 8.0, 'H': 48.0, 'C': 16.0, 'Cl': 8.0, 'O': 4.0}\",\"('C', 'Cl', 'H', 'O', 'Sn')\",OTHERS,False\nmp-1036763,\"{'Mg': 30.0, 'V': 1.0, 'Sn': 1.0, 'O': 32.0}\",\"('Mg', 'O', 'Sn', 'V')\",OTHERS,False\nmp-5622,\"{'P': 2.0, 'Co': 2.0, 'Nd': 1.0}\",\"('Co', 'Nd', 'P')\",OTHERS,False\nmp-568758,\"{'Bi': 8.0, 'Br': 8.0}\",\"('Bi', 'Br')\",OTHERS,False\nmp-1206153,\"{'Eu': 2.0, 'Zr': 1.0, 'O': 4.0}\",\"('Eu', 'O', 'Zr')\",OTHERS,False\nmp-753293,\"{'Li': 4.0, 'Ni': 2.0, 'P': 4.0, 'O': 14.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-1006112,\"{'Na': 3.0, 'Co': 1.0}\",\"('Co', 'Na')\",OTHERS,False\nmp-753722,\"{'Li': 1.0, 'Cu': 1.0, 'S': 2.0}\",\"('Cu', 'Li', 'S')\",OTHERS,False\nmp-758573,\"{'H': 16.0, 'Ru': 3.0, 'C': 6.0, 'Cl': 6.0, 'O': 14.0}\",\"('C', 'Cl', 'H', 'O', 'Ru')\",OTHERS,False\nmp-1087537,\"{'Cr': 3.0, 'P': 3.0, 'Pd': 3.0}\",\"('Cr', 'P', 'Pd')\",OTHERS,False\nmp-755178,\"{'Mn': 6.0, 'O': 4.0, 'F': 8.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-1094248,\"{'Y': 2.0, 'Sn': 6.0}\",\"('Sn', 'Y')\",OTHERS,False\nmp-675744,\"{'Yb': 8.0, 'Zr': 6.0, 'O': 24.0}\",\"('O', 'Yb', 'Zr')\",OTHERS,False\nmp-1173749,\"{'Na': 8.0, 'Al': 6.0, 'Si': 6.0, 'O': 25.0}\",\"('Al', 'Na', 'O', 'Si')\",OTHERS,False\nmp-568342,\"{'Tb': 2.0, 'Cl': 2.0}\",\"('Cl', 'Tb')\",OTHERS,False\nmp-696457,\"{'Sc': 2.0, 'P': 6.0, 'H': 12.0, 'O': 24.0}\",\"('H', 'O', 'P', 'Sc')\",OTHERS,False\nmp-1105105,\"{'I': 10.0, 'N': 4.0}\",\"('I', 'N')\",OTHERS,False\nmp-776954,\"{'Li': 4.0, 'Mn': 2.0, 'P': 2.0, 'H': 2.0, 'O': 10.0}\",\"('H', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1208318,\"{'Tb': 4.0, 'Si': 4.0, 'Pt': 8.0}\",\"('Pt', 'Si', 'Tb')\",OTHERS,False\nmp-1094231,\"{'Mg': 2.0, 'Sn': 10.0}\",\"('Mg', 'Sn')\",OTHERS,False\nmp-568031,\"{'Ga': 8.0, 'Hg': 16.0, 'Sb': 8.0, 'Cl': 32.0}\",\"('Cl', 'Ga', 'Hg', 'Sb')\",OTHERS,False\nmp-758669,\"{'Li': 2.0, 'V': 4.0, 'C': 8.0, 'O': 24.0}\",\"('C', 'Li', 'O', 'V')\",OTHERS,False\nmp-554955,\"{'Mg': 1.0, 'Cr': 1.0, 'F': 6.0}\",\"('Cr', 'F', 'Mg')\",OTHERS,False\nmp-772496,\"{'Mn': 3.0, 'Cu': 1.0, 'Ni': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Cu', 'Mn', 'Ni', 'O', 'P')\",OTHERS,False\nmp-676110,\"{'Tb': 14.0, 'Pb': 3.0, 'Se': 24.0}\",\"('Pb', 'Se', 'Tb')\",OTHERS,False\nmp-1247846,\"{'Al': 16.0, 'O': 24.0}\",\"('Al', 'O')\",OTHERS,False\nmp-754649,\"{'Mn': 2.0, 'C': 2.0, 'S': 2.0, 'O': 14.0}\",\"('C', 'Mn', 'O', 'S')\",OTHERS,False\nmp-1099669,\"{'K': 4.0, 'Na': 28.0, 'V': 28.0, 'Mo': 4.0, 'O': 80.0}\",\"('K', 'Mo', 'Na', 'O', 'V')\",OTHERS,False\nmp-1219595,\"{'Rb': 2.0, 'Hg': 2.0, 'Sb': 2.0, 'Te': 6.0}\",\"('Hg', 'Rb', 'Sb', 'Te')\",OTHERS,False\nmp-10179,\"{'Li': 1.0, 'Mg': 1.0, 'Pd': 1.0, 'Sb': 1.0}\",\"('Li', 'Mg', 'Pd', 'Sb')\",OTHERS,False\nmp-1247446,\"{'Sr': 6.0, 'Zr': 6.0, 'N': 10.0}\",\"('N', 'Sr', 'Zr')\",OTHERS,False\nmp-1196501,\"{'Al': 4.0, 'Si': 6.0, 'N': 4.0, 'O': 20.0}\",\"('Al', 'N', 'O', 'Si')\",OTHERS,False\nmp-1073302,\"{'Mg': 4.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-21653,\"{'Ba': 12.0, 'Ni': 12.0, 'N': 12.0}\",\"('Ba', 'N', 'Ni')\",OTHERS,False\nmp-1218180,\"{'Sr': 1.0, 'Nd': 2.0, 'Fe': 2.0, 'O': 7.0}\",\"('Fe', 'Nd', 'O', 'Sr')\",OTHERS,False\nmp-5170,\"{'Si': 4.0, 'Co': 2.0, 'Nd': 2.0}\",\"('Co', 'Nd', 'Si')\",OTHERS,False\nmp-1025876,\"{'Te': 2.0, 'Mo': 3.0, 'Se': 4.0}\",\"('Mo', 'Se', 'Te')\",OTHERS,False\nmp-1246783,\"{'Hf': 2.0, 'Cr': 2.0, 'Ag': 2.0, 'S': 8.0}\",\"('Ag', 'Cr', 'Hf', 'S')\",OTHERS,False\nmp-767204,\"{'Li': 4.0, 'V': 2.0, 'Si': 12.0, 'O': 30.0}\",\"('Li', 'O', 'Si', 'V')\",OTHERS,False\nmp-1203166,\"{'Er': 4.0, 'Mo': 6.0, 'H': 4.0, 'Se': 4.0, 'O': 34.0}\",\"('Er', 'H', 'Mo', 'O', 'Se')\",OTHERS,False\nmp-669546,\"{'Al': 13.0, 'Pd': 13.0}\",\"('Al', 'Pd')\",OTHERS,False\nmp-554396,\"{'Mg': 4.0, 'Si': 2.0, 'O': 8.0}\",\"('Mg', 'O', 'Si')\",OTHERS,False\nmp-1246732,\"{'Mg': 2.0, 'Co': 2.0, 'W': 2.0, 'S': 8.0}\",\"('Co', 'Mg', 'S', 'W')\",OTHERS,False\nmp-1206309,\"{'Er': 1.0, 'Cr': 2.0, 'Si': 2.0, 'C': 1.0}\",\"('C', 'Cr', 'Er', 'Si')\",OTHERS,False\nmp-976264,\"{'Li': 3.0, 'Nd': 1.0}\",\"('Li', 'Nd')\",OTHERS,False\nmp-974325,\"{'Nd': 2.0, 'Dy': 6.0}\",\"('Dy', 'Nd')\",OTHERS,False\nmp-1022965,\"{'K': 6.0, 'V': 4.0, 'P': 6.0, 'O': 24.0}\",\"('K', 'O', 'P', 'V')\",OTHERS,False\nmp-1194676,\"{'Zn': 2.0, 'H': 48.0, 'Au': 4.0, 'C': 24.0, 'S': 16.0, 'N': 8.0, 'Cl': 8.0}\",\"('Au', 'C', 'Cl', 'H', 'N', 'S', 'Zn')\",OTHERS,False\nmp-20646,\"{'Mn': 2.0, 'Ge': 2.0, 'Sm': 1.0}\",\"('Ge', 'Mn', 'Sm')\",OTHERS,False\nmp-754682,\"{'Li': 2.0, 'Ti': 1.0, 'Co': 3.0, 'O': 8.0}\",\"('Co', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-21903,\"{'S': 8.0, 'K': 2.0, 'Ge': 2.0, 'Eu': 2.0}\",\"('Eu', 'Ge', 'K', 'S')\",OTHERS,False\nmp-756024,\"{'Cr': 2.0, 'Sb': 4.0, 'O': 12.0}\",\"('Cr', 'O', 'Sb')\",OTHERS,False\nmp-865513,\"{'Y': 1.0, 'Sb': 1.0, 'Pd': 2.0}\",\"('Pd', 'Sb', 'Y')\",OTHERS,False\nmp-863426,\"{'Li': 6.0, 'Mn': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1200045,\"{'Bi': 24.0, 'N': 20.0, 'O': 104.0}\",\"('Bi', 'N', 'O')\",OTHERS,False\nmp-29381,\"{'Na': 16.0, 'As': 8.0, 'Te': 16.0}\",\"('As', 'Na', 'Te')\",OTHERS,False\nmp-546152,\"{'O': 4.0, 'Bi': 2.0, 'Sm': 1.0, 'Cl': 1.0}\",\"('Bi', 'Cl', 'O', 'Sm')\",OTHERS,False\nmp-695154,\"{'Na': 7.0, 'Bi': 3.0, 'P': 12.0, 'Pb': 10.0, 'O': 48.0}\",\"('Bi', 'Na', 'O', 'P', 'Pb')\",OTHERS,False\nmp-3200,\"{'O': 14.0, 'Ti': 4.0, 'Lu': 4.0}\",\"('Lu', 'O', 'Ti')\",OTHERS,False\nmp-755104,\"{'Ba': 4.0, 'Y': 2.0, 'Cl': 14.0}\",\"('Ba', 'Cl', 'Y')\",OTHERS,False\nmp-1094629,\"{'Mg': 2.0, 'Ga': 4.0}\",\"('Ga', 'Mg')\",OTHERS,False\nmp-5169,\"{'Cu': 8.0, 'P': 4.0, 'O': 18.0}\",\"('Cu', 'O', 'P')\",OTHERS,False\nmp-40802,\"{'Ba': 1.0, 'Bi': 1.0, 'La': 1.0, 'O': 6.0, 'Sr': 1.0}\",\"('Ba', 'Bi', 'La', 'O', 'Sr')\",OTHERS,False\nmp-1175840,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-30859,\"{'Zr': 9.0, 'Pt': 11.0}\",\"('Pt', 'Zr')\",OTHERS,False\nmvc-8434,\"{'Cr': 4.0, 'Ge': 8.0, 'O': 24.0}\",\"('Cr', 'Ge', 'O')\",OTHERS,False\nmp-1183368,\"{'Ba': 6.0, 'Pr': 2.0}\",\"('Ba', 'Pr')\",OTHERS,False\nmp-1114554,\"{'Tb': 1.0, 'K': 1.0, 'Rb': 2.0, 'Cl': 6.0}\",\"('Cl', 'K', 'Rb', 'Tb')\",OTHERS,False\nmp-1217705,\"{'Tb': 2.0, 'Ge': 3.0}\",\"('Ge', 'Tb')\",OTHERS,False\nmp-1183070,\"{'Ac': 2.0, 'Tl': 1.0, 'Sn': 1.0}\",\"('Ac', 'Sn', 'Tl')\",OTHERS,False\nmp-1207257,\"{'K': 2.0, 'Cd': 1.0, 'Sb': 2.0}\",\"('Cd', 'K', 'Sb')\",OTHERS,False\nmp-1201169,\"{'P': 16.0, 'Se': 12.0, 'Br': 8.0}\",\"('Br', 'P', 'Se')\",OTHERS,False\nmp-777779,\"{'Li': 4.0, 'Mn': 1.0, 'Te': 3.0, 'O': 12.0}\",\"('Li', 'Mn', 'O', 'Te')\",OTHERS,False\nmp-1203277,\"{'K': 12.0, 'Ho': 4.0, 'Si': 12.0, 'O': 40.0}\",\"('Ho', 'K', 'O', 'Si')\",OTHERS,False\nmp-28760,\"{'S': 4.0, 'K': 4.0, 'Rb': 4.0}\",\"('K', 'Rb', 'S')\",OTHERS,False\nmp-1104486,\"{'Tm': 6.0, 'Ge': 8.0}\",\"('Ge', 'Tm')\",OTHERS,False\nmp-866804,\"{'Ti': 1.0, 'Mn': 1.0, 'H': 12.0, 'O': 6.0, 'F': 6.0}\",\"('F', 'H', 'Mn', 'O', 'Ti')\",OTHERS,False\nmp-1222497,\"{'Lu': 1.0, 'Al': 8.0, 'Ni': 3.0}\",\"('Al', 'Lu', 'Ni')\",OTHERS,False\nmp-556725,\"{'Cu': 2.0, 'H': 16.0, 'C': 4.0, 'Br': 6.0, 'N': 2.0, 'O': 2.0}\",\"('Br', 'C', 'Cu', 'H', 'N', 'O')\",OTHERS,False\nmp-755170,\"{'Fe': 8.0, 'O': 2.0, 'F': 14.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-510062,\"{'Sm': 2.0, 'Cs': 2.0, 'Cd': 2.0, 'Se': 6.0}\",\"('Cd', 'Cs', 'Se', 'Sm')\",OTHERS,False\nmp-571212,\"{'Yb': 10.0, 'Ag': 6.0}\",\"('Ag', 'Yb')\",OTHERS,False\nmvc-1887,\"{'Ba': 4.0, 'Mg': 2.0, 'Cu': 2.0, 'Sb': 4.0, 'F': 28.0}\",\"('Ba', 'Cu', 'F', 'Mg', 'Sb')\",OTHERS,False\nmp-1219789,\"{'Rb': 4.0, 'Ba': 2.0, 'Cl': 8.0}\",\"('Ba', 'Cl', 'Rb')\",OTHERS,False\nmp-1106192,\"{'Eu': 4.0, 'Zr': 4.0, 'Se': 12.0}\",\"('Eu', 'Se', 'Zr')\",OTHERS,False\nmp-1227727,\"{'Ba': 1.0, 'Sr': 4.0, 'Fe': 5.0, 'O': 15.0}\",\"('Ba', 'Fe', 'O', 'Sr')\",OTHERS,False\nmp-980066,\"{'Sm': 8.0, 'Cu': 12.0, 'As': 12.0}\",\"('As', 'Cu', 'Sm')\",OTHERS,False\nmp-1190681,\"{'Zr': 8.0, 'Fe': 16.0}\",\"('Fe', 'Zr')\",OTHERS,False\nmp-1215652,\"{'Zn': 4.0, 'Cd': 1.0, 'Se': 5.0}\",\"('Cd', 'Se', 'Zn')\",OTHERS,False\nmp-5351,\"{'Ge': 2.0, 'Pd': 2.0, 'Tb': 1.0}\",\"('Ge', 'Pd', 'Tb')\",OTHERS,False\nmp-971911,\"{'Zn': 2.0, 'N': 2.0}\",\"('N', 'Zn')\",OTHERS,False\nmp-1244700,\"{'Cr': 12.0, 'O': 28.0}\",\"('Cr', 'O')\",OTHERS,False\nmp-9194,\"{'O': 2.0, 'Cu': 2.0, 'Se': 2.0, 'Sm': 2.0}\",\"('Cu', 'O', 'Se', 'Sm')\",OTHERS,False\nmp-754161,\"{'Rb': 6.0, 'Ho': 2.0, 'O': 6.0}\",\"('Ho', 'O', 'Rb')\",OTHERS,False\nmp-754801,\"{'Li': 3.0, 'Ti': 6.0, 'O': 13.0}\",\"('Li', 'O', 'Ti')\",OTHERS,False\nmp-1199509,\"{'Mg': 8.0, 'As': 4.0, 'O': 20.0}\",\"('As', 'Mg', 'O')\",OTHERS,False\nmp-698941,\"{'Nd': 10.0, 'Fe': 10.0, 'As': 10.0, 'O': 8.0, 'F': 2.0}\",\"('As', 'F', 'Fe', 'Nd', 'O')\",OTHERS,False\nmp-13925,\"{'F': 6.0, 'Na': 1.0, 'Y': 1.0, 'Cs': 2.0}\",\"('Cs', 'F', 'Na', 'Y')\",OTHERS,False\nmp-1221346,\"{'Na': 4.0, 'Nd': 8.0, 'N': 2.0, 'Cl': 18.0, 'O': 2.0}\",\"('Cl', 'N', 'Na', 'Nd', 'O')\",OTHERS,False\nmp-13196,\"{'O': 16.0, 'Sb': 4.0, 'Sm': 4.0}\",\"('O', 'Sb', 'Sm')\",OTHERS,False\nmp-21170,\"{'K': 4.0, 'Pb': 8.0}\",\"('K', 'Pb')\",OTHERS,False\nmp-570981,\"{'Ce': 2.0, 'Al': 16.0, 'Pt': 9.0}\",\"('Al', 'Ce', 'Pt')\",OTHERS,False\nmp-1132757,\"{'Mg': 2.0, 'V': 2.0, 'Si': 4.0, 'O': 12.0}\",\"('Mg', 'O', 'Si', 'V')\",OTHERS,False\nmp-1177267,\"{'Li': 4.0, 'V': 3.0, 'Cu': 1.0, 'P': 4.0, 'O': 16.0}\",\"('Cu', 'Li', 'O', 'P', 'V')\",OTHERS,False\nmp-867842,\"{'Sc': 1.0, 'In': 1.0, 'Rh': 2.0}\",\"('In', 'Rh', 'Sc')\",OTHERS,False\nmp-1076669,\"{'La': 7.0, 'Sm': 1.0, 'Mn': 6.0, 'Fe': 2.0, 'O': 24.0}\",\"('Fe', 'La', 'Mn', 'O', 'Sm')\",OTHERS,False\nmp-1174421,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-637194,\"{'Cs': 6.0, 'Sc': 4.0, 'Cl': 18.0}\",\"('Cl', 'Cs', 'Sc')\",OTHERS,False\nmp-31075,\"{'Se': 8.0, 'Br': 8.0, 'Hg': 12.0}\",\"('Br', 'Hg', 'Se')\",OTHERS,False\nmp-1194742,\"{'Ba': 6.0, 'B': 6.0, 'Pb': 4.0, 'Br': 2.0, 'O': 18.0}\",\"('B', 'Ba', 'Br', 'O', 'Pb')\",OTHERS,False\nmp-1097647,\"{'Y': 2.0, 'Pd': 1.0, 'Pt': 1.0}\",\"('Pd', 'Pt', 'Y')\",OTHERS,False\nmp-769605,\"{'Li': 8.0, 'V': 2.0, 'Cr': 6.0, 'O': 16.0}\",\"('Cr', 'Li', 'O', 'V')\",OTHERS,False\nmp-20773,\"{'As': 1.0, 'Pu': 1.0}\",\"('As', 'Pu')\",OTHERS,False\nmp-1225195,\"{'Fe': 2.0, 'C': 6.0, 'N': 6.0, 'O': 4.0}\",\"('C', 'Fe', 'N', 'O')\",OTHERS,False\nmp-30756,\"{'Ru': 8.0, 'Sn': 24.0, 'La': 8.0}\",\"('La', 'Ru', 'Sn')\",OTHERS,False\nmp-1191680,\"{'Ta': 6.0, 'Se': 18.0}\",\"('Se', 'Ta')\",OTHERS,False\nmp-1187021,\"{'Sm': 1.0, 'Mg': 5.0}\",\"('Mg', 'Sm')\",OTHERS,False\nmp-1212458,\"{'Ho': 12.0, 'Mn': 8.0, 'Al': 86.0}\",\"('Al', 'Ho', 'Mn')\",OTHERS,False\nmp-639876,\"{'U': 4.0, 'Sn': 2.0, 'Rh': 4.0}\",\"('Rh', 'Sn', 'U')\",OTHERS,False\nmp-1079393,\"{'Co': 6.0, 'Si': 2.0}\",\"('Co', 'Si')\",OTHERS,False\nmp-569505,\"{'Tb': 2.0, 'Re': 4.0, 'Si': 2.0, 'C': 2.0}\",\"('C', 'Re', 'Si', 'Tb')\",OTHERS,False\nmp-11621,\"{'Ni': 1.0, 'Zn': 1.0, 'Sb': 1.0}\",\"('Ni', 'Sb', 'Zn')\",OTHERS,False\nmp-1204729,\"{'Yb': 12.0, 'Al': 86.0, 'W': 8.0}\",\"('Al', 'W', 'Yb')\",OTHERS,False\nmp-18041,\"{'O': 16.0, 'Sm': 2.0, 'W': 4.0, 'Tl': 2.0}\",\"('O', 'Sm', 'Tl', 'W')\",OTHERS,False\nmp-1245963,\"{'Ba': 2.0, 'Fe': 4.0, 'N': 4.0}\",\"('Ba', 'Fe', 'N')\",OTHERS,False\nmp-1228860,\"{'Ba': 4.0, 'Cr': 13.0, 'O': 28.0}\",\"('Ba', 'Cr', 'O')\",OTHERS,False\nmp-758664,\"{'Fe': 18.0, 'O': 18.0, 'F': 18.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-555837,\"{'Rb': 6.0, 'Si': 10.0, 'O': 23.0}\",\"('O', 'Rb', 'Si')\",OTHERS,False\nmp-1104225,\"{'Lu': 3.0, 'Ga': 9.0, 'Pd': 2.0}\",\"('Ga', 'Lu', 'Pd')\",OTHERS,False\nmp-1203797,\"{'Nd': 26.0, 'B': 8.0, 'O': 52.0}\",\"('B', 'Nd', 'O')\",OTHERS,False\nmp-866873,\"{'Ca': 6.0, 'Sn': 4.0, 'S': 14.0}\",\"('Ca', 'S', 'Sn')\",OTHERS,False\nmp-1018136,\"{'La': 1.0, 'Bi': 1.0, 'Pt': 1.0}\",\"('Bi', 'La', 'Pt')\",OTHERS,False\nmp-1224692,\"{'Fe': 2.0, 'Pt': 1.0}\",\"('Fe', 'Pt')\",OTHERS,False\nmp-20509,\"{'Ba': 2.0, 'Y': 1.0, 'Cu': 4.0, 'O': 8.0}\",\"('Ba', 'Cu', 'O', 'Y')\",OTHERS,False\nmvc-13704,\"{'Zn': 4.0, 'Sb': 2.0, 'Mo': 2.0, 'O': 12.0}\",\"('Mo', 'O', 'Sb', 'Zn')\",OTHERS,False\nmp-778408,\"{'Hg': 2.0, 'Bi': 2.0, 'O': 6.0}\",\"('Bi', 'Hg', 'O')\",OTHERS,False\nmp-771151,\"{'Li': 4.0, 'Mn': 2.0, 'As': 2.0, 'C': 2.0, 'O': 14.0}\",\"('As', 'C', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-973451,\"{'Nd': 2.0, 'Bi': 1.0, 'O': 2.0}\",\"('Bi', 'Nd', 'O')\",OTHERS,False\nmp-768200,\"{'Li': 4.0, 'Ho': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Ho', 'Li', 'O', 'P')\",OTHERS,False\nmp-753777,\"{'Li': 6.0, 'Fe': 1.0, 'Co': 1.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Co', 'Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-3411,\"{'Se': 6.0, 'Mo': 6.0, 'Tl': 2.0}\",\"('Mo', 'Se', 'Tl')\",OTHERS,False\nmp-12174,\"{'Ni': 30.0, 'Hf': 10.0}\",\"('Hf', 'Ni')\",OTHERS,False\nmp-977455,\"{'Pa': 1.0, 'Ag': 1.0, 'O': 3.0}\",\"('Ag', 'O', 'Pa')\",OTHERS,False\nmp-1181167,\"{'Na': 8.0, 'B': 16.0, 'O': 44.0}\",\"('B', 'Na', 'O')\",OTHERS,False\nmp-10872,\"{'Al': 1.0, 'Hf': 1.0, 'Au': 2.0}\",\"('Al', 'Au', 'Hf')\",OTHERS,False\nmp-755125,\"{'Li': 2.0, 'Mn': 2.0, 'Si': 2.0, 'O': 8.0}\",\"('Li', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1099332,\"{'Sr': 1.0, 'Ce': 1.0, 'Mg': 6.0}\",\"('Ce', 'Mg', 'Sr')\",OTHERS,False\nmp-1103088,\"{'Dy': 3.0, 'Al': 9.0}\",\"('Al', 'Dy')\",OTHERS,False\nmp-760954,\"{'Li': 5.0, 'Ti': 2.0, 'V': 3.0, 'O': 10.0}\",\"('Li', 'O', 'Ti', 'V')\",OTHERS,False\nmp-1180456,\"{'Li': 3.0, 'V': 3.0, 'O': 6.0}\",\"('Li', 'O', 'V')\",OTHERS,False\nmp-1094709,\"{'Mg': 3.0, 'Sb': 1.0}\",\"('Mg', 'Sb')\",OTHERS,False\nmp-22461,\"{'Fe': 3.0, 'Sn': 1.0}\",\"('Fe', 'Sn')\",OTHERS,False\nmp-1095709,\"{'Tc': 2.0, 'Ge': 1.0, 'W': 1.0}\",\"('Ge', 'Tc', 'W')\",OTHERS,False\nmp-773100,\"{'La': 4.0, 'Bi': 2.0, 'O': 10.0}\",\"('Bi', 'La', 'O')\",OTHERS,False\nmp-1214654,\"{'Ba': 4.0, 'La': 2.0, 'Nb': 2.0, 'Cu': 4.0, 'O': 16.0}\",\"('Ba', 'Cu', 'La', 'Nb', 'O')\",OTHERS,False\nmp-1184074,\"{'Dy': 1.0, 'Tm': 1.0, 'Ru': 2.0}\",\"('Dy', 'Ru', 'Tm')\",OTHERS,False\nmp-1221048,\"{'Na': 2.0, 'Li': 2.0, 'V': 4.0, 'O': 12.0}\",\"('Li', 'Na', 'O', 'V')\",OTHERS,False\nmp-1215695,\"{'Zn': 6.0, 'Ga': 2.0, 'Ag': 2.0, 'S': 10.0}\",\"('Ag', 'Ga', 'S', 'Zn')\",OTHERS,False\nmp-1207493,\"{'Yb': 4.0, 'Si': 4.0, 'Pd': 4.0}\",\"('Pd', 'Si', 'Yb')\",OTHERS,False\nmp-1210775,\"{'Lu': 4.0, 'O': 4.0, 'F': 4.0}\",\"('F', 'Lu', 'O')\",OTHERS,False\nmp-23626,\"{'Rb': 4.0, 'Li': 8.0, 'I': 12.0, 'O': 36.0}\",\"('I', 'Li', 'O', 'Rb')\",OTHERS,False\nmp-722891,\"{'Na': 4.0, 'Ni': 2.0, 'H': 28.0, 'N': 12.0}\",\"('H', 'N', 'Na', 'Ni')\",OTHERS,False\nmp-1226724,\"{'Cd': 1.0, 'Sb': 1.0}\",\"('Cd', 'Sb')\",OTHERS,False\nmp-1245570,\"{'Mn': 16.0, 'Se': 12.0, 'N': 8.0}\",\"('Mn', 'N', 'Se')\",OTHERS,False\nmp-25827,\"{'Li': 2.0, 'O': 22.0, 'P': 6.0, 'Mo': 4.0}\",\"('Li', 'Mo', 'O', 'P')\",OTHERS,False\nmp-1184919,\"{'K': 6.0, 'Na': 2.0}\",\"('K', 'Na')\",OTHERS,False\nmp-540619,\"{'K': 12.0, 'Sn': 4.0, 'Te': 12.0}\",\"('K', 'Sn', 'Te')\",OTHERS,False\nmp-768960,\"{'Li': 40.0, 'B': 8.0, 'O': 32.0}\",\"('B', 'Li', 'O')\",OTHERS,False\nmp-1221608,\"{'Mo': 6.0, 'Pb': 1.0, 'Se': 4.0, 'S': 4.0}\",\"('Mo', 'Pb', 'S', 'Se')\",OTHERS,False\nmp-1173753,\"{'Na': 4.0, 'Ga': 4.0, 'Se': 6.0}\",\"('Ga', 'Na', 'Se')\",OTHERS,False\nAl 66.6 Rh 26.1 Si 7.3,\"{'Al': 66.6, 'Rh': 26.1, 'Si': 7.3}\",\"('Al', 'Rh', 'Si')\",AC,False\nmp-1186717,\"{'Pr': 3.0, 'Hf': 1.0}\",\"('Hf', 'Pr')\",OTHERS,False\nmp-1120736,\"{'Na': 10.0, 'Ti': 6.0, 'F': 28.0}\",\"('F', 'Na', 'Ti')\",OTHERS,False\nmp-1217173,\"{'Ti': 16.0, 'Mn': 8.0, 'O': 4.0}\",\"('Mn', 'O', 'Ti')\",OTHERS,False\nmp-28513,\"{'O': 12.0, 'Cl': 4.0, 'Pb': 2.0}\",\"('Cl', 'O', 'Pb')\",OTHERS,False\nmp-1179787,\"{'Rb': 1.0, 'Cd': 6.0, 'C': 12.0, 'Br': 1.0, 'O': 26.0}\",\"('Br', 'C', 'Cd', 'O', 'Rb')\",OTHERS,False\nmp-1206284,\"{'La': 2.0, 'C': 2.0, 'Br': 2.0}\",\"('Br', 'C', 'La')\",OTHERS,False\nmvc-13426,\"{'Ca': 1.0, 'Cu': 2.0, 'N': 2.0}\",\"('Ca', 'Cu', 'N')\",OTHERS,False\nmp-1095427,\"{'Hf': 4.0, 'Co': 4.0, 'P': 4.0}\",\"('Co', 'Hf', 'P')\",OTHERS,False\nmp-556346,\"{'Pr': 4.0, 'I': 12.0, 'O': 36.0}\",\"('I', 'O', 'Pr')\",OTHERS,False\nmp-1225036,\"{'Gd': 1.0, 'Al': 6.0, 'Cu': 6.0}\",\"('Al', 'Cu', 'Gd')\",OTHERS,False\nmp-15796,\"{'Li': 1.0, 'Se': 2.0, 'Ho': 1.0}\",\"('Ho', 'Li', 'Se')\",OTHERS,False\nmp-1105352,\"{'Ce': 1.0, 'Fe': 4.0, 'Cu': 3.0, 'O': 12.0}\",\"('Ce', 'Cu', 'Fe', 'O')\",OTHERS,False\nmp-1247144,\"{'La': 2.0, 'Mg': 2.0, 'Mo': 2.0, 'S': 8.0}\",\"('La', 'Mg', 'Mo', 'S')\",OTHERS,False\nmp-762249,\"{'V': 2.0, 'C': 4.0, 'O': 12.0}\",\"('C', 'O', 'V')\",OTHERS,False\nmp-775938,\"{'Ti': 12.0, 'O': 24.0}\",\"('O', 'Ti')\",OTHERS,False\nmp-765521,\"{'Li': 4.0, 'V': 4.0, 'O': 4.0, 'F': 12.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-1232150,\"{'Mg': 4.0, 'Sc': 4.0, 'Se': 12.0}\",\"('Mg', 'Sc', 'Se')\",OTHERS,False\nmp-560262,\"{'Zn': 2.0, 'In': 4.0, 'S': 8.0}\",\"('In', 'S', 'Zn')\",OTHERS,False\nmp-867247,\"{'Tb': 2.0, 'Br': 6.0}\",\"('Br', 'Tb')\",OTHERS,False\nmp-765086,\"{'Li': 10.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1186042,\"{'Na': 6.0, 'Ca': 2.0}\",\"('Ca', 'Na')\",OTHERS,False\nmp-1217247,\"{'Ti': 16.0, 'Co': 4.0, 'Ni': 4.0}\",\"('Co', 'Ni', 'Ti')\",OTHERS,False\nmp-1017629,\"{'Mg': 1.0, 'Ni': 1.0, 'H': 3.0}\",\"('H', 'Mg', 'Ni')\",OTHERS,False\nmp-1190932,\"{'Co': 3.0, 'Te': 2.0, 'P': 2.0, 'O': 14.0}\",\"('Co', 'O', 'P', 'Te')\",OTHERS,False\nmp-1239190,\"{'Ta': 2.0, 'Cr': 6.0, 'Ag': 4.0, 'S': 16.0}\",\"('Ag', 'Cr', 'S', 'Ta')\",OTHERS,False\nmp-1205523,\"{'Sc': 4.0, 'Co': 2.0, 'Ge': 4.0}\",\"('Co', 'Ge', 'Sc')\",OTHERS,False\nmp-1206590,\"{'Co': 1.0, 'Au': 3.0}\",\"('Au', 'Co')\",OTHERS,False\nmp-36577,\"{'As': 2.0, 'S': 4.0, 'Sr': 1.0}\",\"('As', 'S', 'Sr')\",OTHERS,False\nmp-1037480,\"{'Y': 1.0, 'Mg': 30.0, 'Cr': 1.0, 'O': 32.0}\",\"('Cr', 'Mg', 'O', 'Y')\",OTHERS,False\nmp-1100897,\"{'Y': 2.0, 'Zr': 8.0, 'O': 19.0}\",\"('O', 'Y', 'Zr')\",OTHERS,False\nmp-8885,\"{'S': 8.0, 'Cd': 4.0, 'Ba': 4.0}\",\"('Ba', 'Cd', 'S')\",OTHERS,False\nmp-1097227,\"{'Sr': 2.0, 'Hg': 1.0, 'Sb': 1.0}\",\"('Hg', 'Sb', 'Sr')\",OTHERS,False\nmp-1220301,\"{'Nb': 2.0, 'Tl': 2.0, 'W': 2.0, 'O': 12.0}\",\"('Nb', 'O', 'Tl', 'W')\",OTHERS,False\nmp-757780,\"{'Li': 24.0, 'Co': 24.0, 'P': 24.0, 'O': 96.0}\",\"('Co', 'Li', 'O', 'P')\",OTHERS,False\nmp-1188749,\"{'Tb': 4.0, 'Ga': 4.0, 'Pd': 8.0}\",\"('Ga', 'Pd', 'Tb')\",OTHERS,False\nmp-1218281,\"{'Sr': 1.0, 'Eu': 1.0, 'Ni': 1.0, 'O': 4.0}\",\"('Eu', 'Ni', 'O', 'Sr')\",OTHERS,False\nmp-1076317,\"{'Mg': 1.0, 'Cu': 1.0, 'O': 3.0}\",\"('Cu', 'Mg', 'O')\",OTHERS,False\nmp-1214856,\"{'Al': 4.0, 'S': 4.0, 'N': 4.0, 'Cl': 12.0}\",\"('Al', 'Cl', 'N', 'S')\",OTHERS,False\nmp-722280,\"{'Li': 4.0, 'Cu': 4.0, 'H': 16.0, 'Cl': 12.0, 'O': 8.0}\",\"('Cl', 'Cu', 'H', 'Li', 'O')\",OTHERS,False\nmp-1176501,\"{'Lu': 2.0, 'Bi': 2.0, 'O': 6.0}\",\"('Bi', 'Lu', 'O')\",OTHERS,False\nmp-1181934,\"{'Mg': 2.0, 'H': 32.0, 'C': 8.0, 'S': 8.0, 'N': 4.0, 'O': 32.0, 'F': 24.0}\",\"('C', 'F', 'H', 'Mg', 'N', 'O', 'S')\",OTHERS,False\nmp-1018064,\"{'Er': 1.0, 'Fe': 1.0, 'C': 2.0}\",\"('C', 'Er', 'Fe')\",OTHERS,False\nmp-1245216,\"{'Zn': 50.0, 'S': 50.0}\",\"('S', 'Zn')\",OTHERS,False\nmp-1202863,\"{'Na': 8.0, 'Mg': 8.0, 'P': 8.0, 'C': 16.0, 'O': 48.0}\",\"('C', 'Mg', 'Na', 'O', 'P')\",OTHERS,False\nmp-767927,\"{'Li': 8.0, 'Ti': 2.0, 'V': 8.0, 'Cr': 8.0, 'O': 36.0}\",\"('Cr', 'Li', 'O', 'Ti', 'V')\",OTHERS,False\nmp-18825,\"{'Y': 2.0, 'Cr': 2.0, 'O': 8.0}\",\"('Cr', 'O', 'Y')\",OTHERS,False\nmp-8073,\"{'As': 4.0, 'Pd': 8.0}\",\"('As', 'Pd')\",OTHERS,False\nmp-27568,\"{'Al': 6.0, 'Ge': 14.0, 'Ba': 20.0}\",\"('Al', 'Ba', 'Ge')\",OTHERS,False\nmp-1201050,\"{'Sm': 4.0, 'Nb': 4.0, 'S': 14.0, 'O': 60.0}\",\"('Nb', 'O', 'S', 'Sm')\",OTHERS,False\nmp-1219618,\"{'Rb': 4.0, 'H': 16.0, 'O': 12.0}\",\"('H', 'O', 'Rb')\",OTHERS,False\nmp-1181620,\"{'Co': 1.0, 'I': 2.0, 'O': 6.0}\",\"('Co', 'I', 'O')\",OTHERS,False\nmp-1190024,\"{'Ba': 6.0, 'Na': 2.0, 'Sb': 2.0, 'O': 12.0}\",\"('Ba', 'Na', 'O', 'Sb')\",OTHERS,False\nmp-1102309,\"{'Yb': 8.0, 'Ga': 4.0}\",\"('Ga', 'Yb')\",OTHERS,False\nmp-780850,\"{'Li': 6.0, 'Mn': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1174348,\"{'Li': 8.0, 'Mn': 6.0, 'O': 14.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-755848,\"{'Li': 6.0, 'Cr': 3.0, 'Fe': 3.0, 'O': 12.0}\",\"('Cr', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1246916,\"{'Mg': 2.0, 'Ni': 10.0, 'N': 8.0}\",\"('Mg', 'N', 'Ni')\",OTHERS,False\nmp-1246348,\"{'Fe': 3.0, 'Co': 8.0, 'N': 8.0}\",\"('Co', 'Fe', 'N')\",OTHERS,False\nmp-1227523,\"{'Ce': 4.0, 'U': 8.0, 'S': 20.0}\",\"('Ce', 'S', 'U')\",OTHERS,False\nmp-1221600,\"{'Mn': 2.0, 'Re': 2.0, 'B': 4.0}\",\"('B', 'Mn', 'Re')\",OTHERS,False\nAl 67 Pd 11 Mn 14 Si 7,\"{'Al': 67.0, 'Pd': 11.0, 'Mn': 14.0, 'Si': 7.0}\",\"('Al', 'Mn', 'Pd', 'Si')\",AC,False\nmp-1206101,\"{'Ag': 1.0, 'Pb': 1.0, 'F': 6.0}\",\"('Ag', 'F', 'Pb')\",OTHERS,False\nmp-3627,\"{'S': 8.0, 'Mo': 6.0, 'Eu': 1.0}\",\"('Eu', 'Mo', 'S')\",OTHERS,False\nmp-1101817,\"{'Sm': 4.0, 'Ni': 4.0, 'Ge': 4.0}\",\"('Ge', 'Ni', 'Sm')\",OTHERS,False\nmp-757453,\"{'Fe': 6.0, 'O': 6.0, 'F': 6.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-1009494,\"{'Np': 1.0, 'N': 1.0}\",\"('N', 'Np')\",OTHERS,False\nmp-1208255,\"{'Th': 2.0, 'N': 8.0, 'O': 34.0}\",\"('N', 'O', 'Th')\",OTHERS,False\nmp-1246406,\"{'Mg': 2.0, 'V': 1.0, 'Mo': 3.0, 'S': 8.0}\",\"('Mg', 'Mo', 'S', 'V')\",OTHERS,False\nmp-1187388,\"{'Tb': 1.0, 'Tm': 1.0, 'Ir': 2.0}\",\"('Ir', 'Tb', 'Tm')\",OTHERS,False\nmp-1080058,\"{'K': 2.0, 'I': 2.0, 'O': 6.0}\",\"('I', 'K', 'O')\",OTHERS,False\nmp-756968,\"{'K': 6.0, 'Ca': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Ca', 'K', 'O', 'P')\",OTHERS,False\nmp-864661,\"{'Zn': 1.0, 'Cu': 1.0, 'Pd': 2.0}\",\"('Cu', 'Pd', 'Zn')\",OTHERS,False\nmp-761143,\"{'K': 3.0, 'Li': 3.0, 'Sb': 3.0, 'P': 6.0, 'O': 24.0}\",\"('K', 'Li', 'O', 'P', 'Sb')\",OTHERS,False\nmp-1225300,\"{'Dy': 2.0, 'Al': 3.0, 'Co': 1.0}\",\"('Al', 'Co', 'Dy')\",OTHERS,False\nmp-1213415,\"{'Cu': 6.0, 'Hg': 18.0, 'As': 12.0, 'Cl': 18.0}\",\"('As', 'Cl', 'Cu', 'Hg')\",OTHERS,False\nmp-1187213,\"{'Ta': 2.0, 'Re': 1.0, 'Os': 1.0}\",\"('Os', 'Re', 'Ta')\",OTHERS,False\nmp-6407,\"{'F': 160.0, 'Se': 32.0, 'Sb': 32.0, 'Te': 16.0}\",\"('F', 'Sb', 'Se', 'Te')\",OTHERS,False\nmp-29419,\"{'Cl': 6.0, 'Te': 4.0, 'Hf': 1.0}\",\"('Cl', 'Hf', 'Te')\",OTHERS,False\nmp-1198750,\"{'Mg': 2.0, 'H': 44.0, 'S': 2.0, 'O': 30.0}\",\"('H', 'Mg', 'O', 'S')\",OTHERS,False\nmp-555732,\"{'Yb': 8.0, 'P': 8.0, 'S': 32.0}\",\"('P', 'S', 'Yb')\",OTHERS,False\nmp-505647,\"{'Gd': 2.0, 'W': 4.0, 'K': 2.0, 'O': 16.0}\",\"('Gd', 'K', 'O', 'W')\",OTHERS,False\nmp-1208134,\"{'Tl': 1.0, 'Cu': 2.0, 'H': 1.0, 'Se': 2.0, 'O': 10.0}\",\"('Cu', 'H', 'O', 'Se', 'Tl')\",OTHERS,False\nmp-581156,\"{'Cs': 14.0, 'U': 16.0, 'V': 4.0, 'Cl': 2.0, 'O': 64.0}\",\"('Cl', 'Cs', 'O', 'U', 'V')\",OTHERS,False\nmp-1096373,\"{'Mg': 1.0, 'Sc': 1.0, 'Tl': 2.0}\",\"('Mg', 'Sc', 'Tl')\",OTHERS,False\nmp-1245419,\"{'Li': 12.0, 'Mn': 4.0, 'N': 8.0}\",\"('Li', 'Mn', 'N')\",OTHERS,False\nmp-758457,\"{'Li': 4.0, 'Ti': 4.0, 'P': 16.0, 'O': 48.0}\",\"('Li', 'O', 'P', 'Ti')\",OTHERS,False\nmp-505186,\"{'Cs': 8.0, 'Pr': 12.0, 'I': 26.0, 'C': 4.0}\",\"('C', 'Cs', 'I', 'Pr')\",OTHERS,False\nmp-1119132,\"{'Cs': 20.0, 'Os': 20.0, 'N': 40.0}\",\"('Cs', 'N', 'Os')\",OTHERS,False\nmp-567076,\"{'Li': 2.0, 'Bi': 1.0, 'Au': 1.0}\",\"('Au', 'Bi', 'Li')\",OTHERS,False\nmp-983229,\"{'Pm': 2.0, 'Zn': 1.0, 'Rh': 1.0}\",\"('Pm', 'Rh', 'Zn')\",OTHERS,False\nmp-604910,\"{'Mn': 2.0, 'Se': 2.0}\",\"('Mn', 'Se')\",OTHERS,False\nmp-766086,\"{'Li': 4.0, 'La': 8.0, 'Ti': 8.0, 'Cr': 4.0, 'O': 36.0}\",\"('Cr', 'La', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-1192303,\"{'Pr': 2.0, 'Mn': 3.0, 'Sb': 4.0, 'S': 12.0}\",\"('Mn', 'Pr', 'S', 'Sb')\",OTHERS,False\nmp-561125,\"{'Ba': 10.0, 'P': 6.0, 'Br': 2.0, 'O': 24.0}\",\"('Ba', 'Br', 'O', 'P')\",OTHERS,False\nmp-1114171,\"{'K': 2.0, 'Na': 1.0, 'Ru': 1.0, 'F': 6.0}\",\"('F', 'K', 'Na', 'Ru')\",OTHERS,False\nmp-558557,\"{'Ba': 1.0, 'La': 4.0, 'Cu': 5.0, 'O': 12.0}\",\"('Ba', 'Cu', 'La', 'O')\",OTHERS,False\nmp-759528,\"{'Li': 3.0, 'V': 3.0, 'Fe': 3.0, 'P': 6.0, 'H': 6.0, 'O': 30.0}\",\"('Fe', 'H', 'Li', 'O', 'P', 'V')\",OTHERS,False\nmp-8632,{'V': 1.0},\"('V',)\",OTHERS,False\nmp-1224665,\"{'Lu': 12.0, 'Co': 31.0, 'Sn': 16.0}\",\"('Co', 'Lu', 'Sn')\",OTHERS,False\nmp-753508,\"{'Li': 4.0, 'Cu': 4.0, 'S': 4.0}\",\"('Cu', 'Li', 'S')\",OTHERS,False\nmp-7739,\"{'F': 6.0, 'Zn': 1.0, 'Ba': 2.0}\",\"('Ba', 'F', 'Zn')\",OTHERS,False\nmp-1194394,\"{'Na': 6.0, 'C': 4.0, 'O': 16.0}\",\"('C', 'Na', 'O')\",OTHERS,False\nmp-705355,\"{'Li': 4.0, 'Mn': 2.0, 'P': 10.0, 'O': 30.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1206453,\"{'Sm': 4.0, 'Cd': 2.0, 'Cu': 4.0}\",\"('Cd', 'Cu', 'Sm')\",OTHERS,False\nmp-1213807,\"{'Ce': 8.0, 'Si': 8.0, 'Pd': 8.0}\",\"('Ce', 'Pd', 'Si')\",OTHERS,False\nmp-978929,\"{'Sr': 1.0, 'Ac': 1.0, 'Zn': 2.0}\",\"('Ac', 'Sr', 'Zn')\",OTHERS,False\nmp-555331,\"{'K': 2.0, 'Gd': 2.0, 'Pd': 2.0, 'O': 6.0}\",\"('Gd', 'K', 'O', 'Pd')\",OTHERS,False\nmp-1210918,\"{'Mg': 12.0, 'Si': 8.0, 'O': 36.0}\",\"('Mg', 'O', 'Si')\",OTHERS,False\nmp-14568,\"{'F': 8.0, 'Mg': 2.0, 'Ba': 2.0}\",\"('Ba', 'F', 'Mg')\",OTHERS,False\nmp-567808,\"{'Tm': 1.0, 'Cu': 2.0, 'Ge': 2.0}\",\"('Cu', 'Ge', 'Tm')\",OTHERS,False\nmp-758847,\"{'Li': 12.0, 'Fe': 4.0, 'P': 8.0, 'O': 32.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-1232213,\"{'Y': 2.0, 'Se': 4.0}\",\"('Se', 'Y')\",OTHERS,False\nmp-1197266,\"{'Nd': 14.0, 'Se': 8.0, 'Cl': 6.0, 'O': 34.0}\",\"('Cl', 'Nd', 'O', 'Se')\",OTHERS,False\nmp-1218397,\"{'Sr': 5.0, 'Zn': 1.0, 'Cd': 4.0}\",\"('Cd', 'Sr', 'Zn')\",OTHERS,False\nmp-30313,\"{'O': 28.0, 'Se': 8.0, 'Tb': 8.0}\",\"('O', 'Se', 'Tb')\",OTHERS,False\nmp-1018651,\"{'Ba': 2.0, 'H': 3.0, 'I': 1.0}\",\"('Ba', 'H', 'I')\",OTHERS,False\nmp-31362,\"{'Na': 6.0, 'Cl': 12.0, 'Y': 2.0}\",\"('Cl', 'Na', 'Y')\",OTHERS,False\nmp-27421,\"{'F': 6.0, 'Rb': 1.0, 'Bi': 1.0}\",\"('Bi', 'F', 'Rb')\",OTHERS,False\nmp-17048,\"{'S': 16.0, 'Rb': 8.0, 'W': 4.0}\",\"('Rb', 'S', 'W')\",OTHERS,False\nmp-849672,\"{'Mn': 12.0, 'O': 2.0, 'F': 22.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-866308,\"{'Ca': 4.0, 'B': 8.0, 'H': 56.0, 'N': 8.0}\",\"('B', 'Ca', 'H', 'N')\",OTHERS,False\nmp-600219,\"{'Mn': 4.0, 'H': 48.0, 'C': 8.0, 'N': 8.0, 'Cl': 16.0}\",\"('C', 'Cl', 'H', 'Mn', 'N')\",OTHERS,False\nmp-21298,\"{'Si': 4.0, 'Np': 2.0}\",\"('Np', 'Si')\",OTHERS,False\nmvc-5849,\"{'Ca': 6.0, 'Mn': 12.0, 'O': 24.0}\",\"('Ca', 'Mn', 'O')\",OTHERS,False\nmp-1094476,\"{'Mg': 2.0, 'Zn': 6.0}\",\"('Mg', 'Zn')\",OTHERS,False\nmp-867721,\"{'Mn': 6.0, 'Si': 6.0, 'O': 20.0}\",\"('Mn', 'O', 'Si')\",OTHERS,False\nmp-753711,\"{'Ca': 3.0, 'P': 2.0, 'O': 8.0}\",\"('Ca', 'O', 'P')\",OTHERS,False\nmp-554311,\"{'F': 6.0, 'Cr': 1.0, 'Rb': 1.0}\",\"('Cr', 'F', 'Rb')\",OTHERS,False\nmp-1228510,\"{'Ba': 2.0, 'Sr': 3.0, 'Ca': 1.0, 'Mg': 2.0, 'Si': 4.0, 'O': 16.0}\",\"('Ba', 'Ca', 'Mg', 'O', 'Si', 'Sr')\",OTHERS,False\nmp-773098,\"{'Li': 4.0, 'Mn': 3.0, 'V': 1.0, 'O': 8.0}\",\"('Li', 'Mn', 'O', 'V')\",OTHERS,False\nmp-867634,\"{'Li': 8.0, 'Mn': 8.0, 'P': 8.0, 'O': 32.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-975031,\"{'Rb': 4.0, 'Be': 4.0, 'H': 12.0}\",\"('Be', 'H', 'Rb')\",OTHERS,False\nmp-1245220,\"{'Zn': 50.0, 'S': 50.0}\",\"('S', 'Zn')\",OTHERS,False\nmp-30328,\"{'Fe': 1.0, 'Ho': 6.0, 'Bi': 2.0}\",\"('Bi', 'Fe', 'Ho')\",OTHERS,False\nmp-1077378,\"{'Cs': 2.0, 'Mn': 2.0, 'P': 2.0}\",\"('Cs', 'Mn', 'P')\",OTHERS,False\nmp-758284,\"{'Zn': 4.0, 'P': 8.0, 'H': 28.0, 'N': 4.0, 'O': 32.0}\",\"('H', 'N', 'O', 'P', 'Zn')\",OTHERS,False\nmp-1099865,\"{'La': 28.0, 'Sm': 4.0, 'Mn': 32.0, 'O': 80.0}\",\"('La', 'Mn', 'O', 'Sm')\",OTHERS,False\nmp-1187703,\"{'Y': 2.0, 'Ir': 1.0, 'Ru': 1.0}\",\"('Ir', 'Ru', 'Y')\",OTHERS,False\nmp-20720,\"{'N': 6.0, 'O': 12.0, 'Ni': 1.0, 'Pb': 2.0}\",\"('N', 'Ni', 'O', 'Pb')\",OTHERS,False\nmp-1186262,\"{'Nd': 3.0, 'Dy': 1.0}\",\"('Dy', 'Nd')\",OTHERS,False\nmp-1200604,\"{'Si': 1.0, 'P': 4.0, 'H': 28.0, 'C': 10.0, 'Cl': 4.0}\",\"('C', 'Cl', 'H', 'P', 'Si')\",OTHERS,False\nmp-1176406,\"{'Na': 8.0, 'Bi': 4.0, 'O': 12.0}\",\"('Bi', 'Na', 'O')\",OTHERS,False\nmp-767798,\"{'Ni': 17.0, 'As': 12.0, 'O': 48.0}\",\"('As', 'Ni', 'O')\",OTHERS,False\nmp-756058,\"{'Li': 2.0, 'Cr': 3.0, 'Fe': 1.0, 'O': 8.0}\",\"('Cr', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1175878,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1175965,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1072586,\"{'Sm': 1.0, 'Cd': 1.0, 'Ni': 4.0}\",\"('Cd', 'Ni', 'Sm')\",OTHERS,False\nmp-1039919,\"{'La': 1.0, 'Hf': 1.0, 'Mg': 30.0, 'O': 32.0}\",\"('Hf', 'La', 'Mg', 'O')\",OTHERS,False\nmp-756748,\"{'Sc': 4.0, 'H': 12.0, 'Cl': 8.0, 'O': 40.0}\",\"('Cl', 'H', 'O', 'Sc')\",OTHERS,False\nmp-758780,\"{'Li': 4.0, 'Bi': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Bi', 'Li', 'O', 'P')\",OTHERS,False\nmp-1101069,\"{'Cr': 2.0, 'P': 2.0, 'Se': 6.0}\",\"('Cr', 'P', 'Se')\",OTHERS,False\nmp-1227088,\"{'Ca': 6.0, 'Ag': 3.0, 'Ge': 5.0}\",\"('Ag', 'Ca', 'Ge')\",OTHERS,False\nmp-759159,\"{'Li': 5.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1095204,\"{'Tm': 4.0, 'Ni': 4.0, 'Sn': 2.0}\",\"('Ni', 'Sn', 'Tm')\",OTHERS,False\nmp-1220795,\"{'Nb': 12.0, 'Sn': 3.0, 'Sb': 1.0}\",\"('Nb', 'Sb', 'Sn')\",OTHERS,False\nZn 61.4 Mg 24.5 Er 14.1,\"{'Zn': 61.4, 'Mg': 24.5, 'Er': 14.1}\",\"('Er', 'Mg', 'Zn')\",AC,False\nmp-862965,\"{'Pm': 1.0, 'Tl': 1.0, 'Au': 2.0}\",\"('Au', 'Pm', 'Tl')\",OTHERS,False\nmp-27617,\"{'F': 4.0, 'Se': 1.0, 'Ce': 2.0}\",\"('Ce', 'F', 'Se')\",OTHERS,False\nmp-641587,\"{'Fe': 21.0, 'Mo': 2.0, 'C': 6.0}\",\"('C', 'Fe', 'Mo')\",OTHERS,False\nmp-641924,\"{'Bi': 8.0, 'Pb': 4.0, 'S': 16.0}\",\"('Bi', 'Pb', 'S')\",OTHERS,False\nmp-1225439,\"{'Fe': 2.0, 'Ni': 6.0, 'Mo': 12.0, 'N': 4.0}\",\"('Fe', 'Mo', 'N', 'Ni')\",OTHERS,False\nmp-1183803,\"{'Ce': 1.0, 'Dy': 1.0, 'Mg': 2.0}\",\"('Ce', 'Dy', 'Mg')\",OTHERS,False\nmp-1182869,\"{'Al': 4.0, 'O': 8.0}\",\"('Al', 'O')\",OTHERS,False\nmp-1106325,\"{'Ca': 1.0, 'Cu': 3.0, 'Pt': 4.0, 'O': 12.0}\",\"('Ca', 'Cu', 'O', 'Pt')\",OTHERS,False\nmp-862978,\"{'Er': 2.0, 'Ag': 1.0, 'Ru': 1.0}\",\"('Ag', 'Er', 'Ru')\",OTHERS,False\nmp-1224851,\"{'Ga': 1.0, 'Cu': 1.0, 'Ge': 1.0, 'Se': 4.0}\",\"('Cu', 'Ga', 'Ge', 'Se')\",OTHERS,False\nmp-1071036,\"{'Tb': 2.0, 'Sc': 2.0, 'Sb': 2.0}\",\"('Sb', 'Sc', 'Tb')\",OTHERS,False\nmp-1182891,\"{'Al': 2.0, 'C': 2.0, 'N': 2.0, 'O': 10.0}\",\"('Al', 'C', 'N', 'O')\",OTHERS,False\nmp-1213382,\"{'Cs': 2.0, 'Pr': 2.0, 'Nb': 12.0, 'Br': 36.0}\",\"('Br', 'Cs', 'Nb', 'Pr')\",OTHERS,False\nmp-22915,\"{'Ag': 1.0, 'I': 1.0}\",\"('Ag', 'I')\",OTHERS,False\nmp-1210351,\"{'Na': 6.0, 'Yb': 2.0, 'Br': 12.0}\",\"('Br', 'Na', 'Yb')\",OTHERS,False\nmp-1205905,\"{'Dy': 4.0, 'Ge': 4.0, 'Ru': 2.0}\",\"('Dy', 'Ge', 'Ru')\",OTHERS,False\nmvc-6278,\"{'Zn': 8.0, 'Mo': 16.0, 'O': 32.0}\",\"('Mo', 'O', 'Zn')\",OTHERS,False\nmp-767219,\"{'Li': 8.0, 'Ti': 8.0, 'Mn': 2.0, 'Co': 8.0, 'O': 36.0}\",\"('Co', 'Li', 'Mn', 'O', 'Ti')\",OTHERS,False\nmp-1113723,\"{'Rb': 2.0, 'Ag': 1.0, 'Sb': 1.0, 'I': 6.0}\",\"('Ag', 'I', 'Rb', 'Sb')\",OTHERS,False\nmp-770191,\"{'Li': 8.0, 'V': 8.0, 'S': 12.0, 'O': 48.0}\",\"('Li', 'O', 'S', 'V')\",OTHERS,False\nmp-996997,\"{'Li': 2.0, 'Ag': 2.0, 'O': 4.0}\",\"('Ag', 'Li', 'O')\",OTHERS,False\nmp-541090,\"{'Ce': 4.0, 'Se': 8.0, 'O': 24.0}\",\"('Ce', 'O', 'Se')\",OTHERS,False\nmp-23058,\"{'O': 2.0, 'Cl': 2.0, 'Nd': 2.0}\",\"('Cl', 'Nd', 'O')\",OTHERS,False\nmp-1227957,\"{'Ba': 1.0, 'Fe': 1.0, 'As': 2.0, 'Ru': 1.0}\",\"('As', 'Ba', 'Fe', 'Ru')\",OTHERS,False\nmp-1222669,\"{'Li': 2.0, 'I': 1.0, 'Br': 1.0}\",\"('Br', 'I', 'Li')\",OTHERS,False\nmp-1219556,\"{'Sb': 8.0, 'Te': 16.0, 'Pd': 12.0}\",\"('Pd', 'Sb', 'Te')\",OTHERS,False\nmp-1187158,\"{'Sr': 2.0, 'Br': 4.0}\",\"('Br', 'Sr')\",OTHERS,False\nmp-1209311,\"{'Rb': 2.0, 'Gd': 2.0, 'Mo': 4.0, 'O': 16.0}\",\"('Gd', 'Mo', 'O', 'Rb')\",OTHERS,False\nmp-753738,\"{'V': 3.0, 'Co': 1.0, 'O': 8.0}\",\"('Co', 'O', 'V')\",OTHERS,False\nmp-3407,\"{'Cr': 5.0, 'Se': 8.0, 'Tl': 1.0}\",\"('Cr', 'Se', 'Tl')\",OTHERS,False\nmp-21208,\"{'F': 9.0, 'Rh': 3.0}\",\"('F', 'Rh')\",OTHERS,False\nmp-755700,\"{'Fe': 6.0, 'O': 8.0, 'F': 4.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-1112490,\"{'Cs': 2.0, 'Tl': 1.0, 'In': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cs', 'In', 'Tl')\",OTHERS,False\nmp-1224400,\"{'In': 2.0, 'Cu': 2.0, 'Se': 2.0, 'S': 2.0}\",\"('Cu', 'In', 'S', 'Se')\",OTHERS,False\nmp-1229240,\"{'Ca': 12.0, 'Ti': 12.0, 'Si': 8.0, 'Ge': 4.0, 'O': 60.0}\",\"('Ca', 'Ge', 'O', 'Si', 'Ti')\",OTHERS,False\nmp-27301,\"{'Cl': 16.0, 'Rb': 6.0, 'Au': 6.0}\",\"('Au', 'Cl', 'Rb')\",OTHERS,False\nmp-1229313,\"{'Ca': 2.0, 'Eu': 4.0, 'Ge': 8.0, 'O': 24.0}\",\"('Ca', 'Eu', 'Ge', 'O')\",OTHERS,False\nmp-1105201,\"{'Ba': 5.0, 'In': 4.0, 'Te': 4.0, 'S': 7.0}\",\"('Ba', 'In', 'S', 'Te')\",OTHERS,False\nmp-555948,\"{'C': 74.0, 'F': 42.0}\",\"('C', 'F')\",OTHERS,False\nmp-510232,\"{'Nd': 12.0, 'Co': 6.0, 'Sn': 1.0}\",\"('Co', 'Nd', 'Sn')\",OTHERS,False\nmp-1207440,\"{'Zr': 14.0, 'Pt': 20.0}\",\"('Pt', 'Zr')\",OTHERS,False\nmp-7238,\"{'B': 4.0, 'O': 12.0, 'Nd': 4.0}\",\"('B', 'Nd', 'O')\",OTHERS,False\nmp-1175576,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-623837,\"{'As': 2.0, 'C': 6.0, 'N': 6.0}\",\"('As', 'C', 'N')\",OTHERS,False\nmp-867529,\"{'Li': 2.0, 'Ni': 4.0, 'P': 6.0, 'O': 20.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-8215,\"{'S': 6.0, 'La': 2.0, 'Yb': 2.0}\",\"('La', 'S', 'Yb')\",OTHERS,False\nmp-1114337,\"{'Na': 2.0, 'Li': 1.0, 'Ir': 1.0, 'F': 6.0}\",\"('F', 'Ir', 'Li', 'Na')\",OTHERS,False\nmp-6691,\"{'O': 8.0, 'Cu': 4.0, 'Ba': 2.0, 'Dy': 1.0}\",\"('Ba', 'Cu', 'Dy', 'O')\",OTHERS,False\nmp-1246459,\"{'Ca': 10.0, 'Fe': 2.0, 'N': 8.0}\",\"('Ca', 'Fe', 'N')\",OTHERS,False\nmp-768749,\"{'Li': 8.0, 'Al': 2.0, 'Cr': 6.0, 'O': 16.0}\",\"('Al', 'Cr', 'Li', 'O')\",OTHERS,False\nmp-1226293,\"{'Cr': 4.0, 'In': 1.0, 'Ag': 1.0, 'S': 8.0}\",\"('Ag', 'Cr', 'In', 'S')\",OTHERS,False\nmp-1113491,\"{'Cs': 3.0, 'Sc': 1.0, 'I': 6.0}\",\"('Cs', 'I', 'Sc')\",OTHERS,False\nmp-723036,\"{'Fe': 4.0, 'H': 64.0, 'C': 16.0, 'S': 16.0, 'N': 32.0, 'Cl': 8.0}\",\"('C', 'Cl', 'Fe', 'H', 'N', 'S')\",OTHERS,False\nmp-1211285,\"{'K': 1.0, 'Ta': 1.0, 'P': 2.0, 'O': 8.0}\",\"('K', 'O', 'P', 'Ta')\",OTHERS,False\nmp-16955,\"{'Li': 12.0, 'O': 16.0, 'K': 8.0, 'Fe': 4.0}\",\"('Fe', 'K', 'Li', 'O')\",OTHERS,False\nmp-1104460,\"{'Zn': 2.0, 'P': 4.0, 'O': 8.0}\",\"('O', 'P', 'Zn')\",OTHERS,False\nmvc-15145,\"{'Zn': 3.0, 'Cu': 10.0, 'Te': 6.0, 'O': 36.0}\",\"('Cu', 'O', 'Te', 'Zn')\",OTHERS,False\nmp-17056,\"{'Li': 6.0, 'O': 24.0, 'P': 6.0, 'Zn': 6.0}\",\"('Li', 'O', 'P', 'Zn')\",OTHERS,False\nmp-554259,\"{'Sr': 4.0, 'Ag': 4.0, 'B': 28.0, 'O': 48.0}\",\"('Ag', 'B', 'O', 'Sr')\",OTHERS,False\nmp-1204743,\"{'Rb': 8.0, 'Cd': 4.0, 'C': 8.0, 'Cl': 8.0, 'O': 20.0}\",\"('C', 'Cd', 'Cl', 'O', 'Rb')\",OTHERS,False\nmp-1176538,\"{'Li': 4.0, 'V': 4.0, 'F': 16.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-567769,\"{'Ca': 1.0, 'Si': 2.0, 'Pd': 2.0}\",\"('Ca', 'Pd', 'Si')\",OTHERS,False\nmp-1187241,\"{'Ta': 2.0, 'Ir': 6.0}\",\"('Ir', 'Ta')\",OTHERS,False\nmp-27422,\"{'F': 6.0, 'Cs': 1.0, 'Bi': 1.0}\",\"('Bi', 'Cs', 'F')\",OTHERS,False\nmp-1019270,\"{'Ta': 2.0, 'Ti': 1.0, 'N': 3.0}\",\"('N', 'Ta', 'Ti')\",OTHERS,False\nmp-1222465,\"{'Lu': 12.0, 'Co': 9.0}\",\"('Co', 'Lu')\",OTHERS,False\nmp-1096564,\"{'Sr': 2.0, 'Zn': 1.0, 'Cd': 1.0}\",\"('Cd', 'Sr', 'Zn')\",OTHERS,False\nmp-560547,\"{'K': 2.0, 'Cr': 2.0, 'F': 6.0}\",\"('Cr', 'F', 'K')\",OTHERS,False\nmp-1218965,\"{'Sm': 1.0, 'U': 1.0, 'N': 2.0}\",\"('N', 'Sm', 'U')\",OTHERS,False\nmp-1184811,\"{'In': 2.0, 'Ga': 6.0}\",\"('Ga', 'In')\",OTHERS,False\nmp-25929,\"{'Li': 6.0, 'O': 24.0, 'P': 6.0, 'Bi': 4.0}\",\"('Bi', 'Li', 'O', 'P')\",OTHERS,False\nmp-1111388,\"{'K': 3.0, 'Pr': 1.0, 'Br': 6.0}\",\"('Br', 'K', 'Pr')\",OTHERS,False\nmp-1194097,\"{'In': 1.0, 'Co': 22.0, 'B': 6.0}\",\"('B', 'Co', 'In')\",OTHERS,False\nmp-1080849,\"{'K': 16.0, 'Re': 16.0, 'N': 32.0}\",\"('K', 'N', 'Re')\",OTHERS,False\nmvc-10961,\"{'V': 8.0, 'O': 18.0}\",\"('O', 'V')\",OTHERS,False\nmp-582399,\"{'Pu': 6.0, 'Pd': 10.0}\",\"('Pd', 'Pu')\",OTHERS,False\nmp-554529,\"{'Ba': 24.0, 'Ti': 12.0, 'O': 48.0}\",\"('Ba', 'O', 'Ti')\",OTHERS,False\nmp-1147675,\"{'Rh': 6.0, 'Cl': 8.0}\",\"('Cl', 'Rh')\",OTHERS,False\nmp-557176,\"{'Ba': 10.0, 'B': 8.0, 'O': 20.0, 'F': 4.0}\",\"('B', 'Ba', 'F', 'O')\",OTHERS,False\nmp-1028528,\"{'Te': 2.0, 'Mo': 2.0, 'W': 2.0, 'Se': 4.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-1194044,\"{'V': 16.0, 'Co': 8.0, 'N': 4.0}\",\"('Co', 'N', 'V')\",OTHERS,False\nmp-761151,\"{'Zr': 6.0, 'O': 11.0}\",\"('O', 'Zr')\",OTHERS,False\nmp-1099932,\"{'K': 1.0, 'Na': 7.0, 'V': 6.0, 'Cr': 2.0, 'O': 24.0}\",\"('Cr', 'K', 'Na', 'O', 'V')\",OTHERS,False\nmp-1220910,\"{'Na': 2.0, 'Ta': 2.0, 'W': 2.0, 'O': 14.0}\",\"('Na', 'O', 'Ta', 'W')\",OTHERS,False\nmp-780186,\"{'Li': 9.0, 'Mn': 10.0, 'O': 20.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-1218235,\"{'Sr': 1.0, 'Gd': 1.0, 'Ni': 1.0, 'O': 4.0}\",\"('Gd', 'Ni', 'O', 'Sr')\",OTHERS,False\nmp-752542,\"{'Li': 2.0, 'Ti': 2.0, 'Mn': 3.0, 'O': 10.0}\",\"('Li', 'Mn', 'O', 'Ti')\",OTHERS,False\nmp-758792,\"{'Li': 18.0, 'Cr': 6.0, 'Si': 12.0, 'O': 42.0}\",\"('Cr', 'Li', 'O', 'Si')\",OTHERS,False\nmp-7280,\"{'P': 8.0, 'S': 8.0, 'Pd': 8.0}\",\"('P', 'Pd', 'S')\",OTHERS,False\nmp-614572,\"{'Ca': 12.0, 'In': 4.0, 'P': 12.0}\",\"('Ca', 'In', 'P')\",OTHERS,False\nmp-1218689,\"{'Sr': 2.0, 'V': 11.0, 'Ni': 1.0, 'O': 22.0}\",\"('Ni', 'O', 'Sr', 'V')\",OTHERS,False\nmp-978285,\"{'Mg': 3.0, 'Mn': 1.0}\",\"('Mg', 'Mn')\",OTHERS,False\nmp-1346,\"{'B': 12.0, 'O': 2.0}\",\"('B', 'O')\",OTHERS,False\nmp-1203746,\"{'As': 4.0, 'S': 4.0, 'Xe': 4.0, 'N': 4.0, 'F': 40.0}\",\"('As', 'F', 'N', 'S', 'Xe')\",OTHERS,False\nGa 3.85 Ni 2.15 Hf 1,\"{'Ga': 3.85, 'Ni': 2.15, 'Hf': 1.0}\",\"('Ga', 'Hf', 'Ni')\",AC,False\nmp-1084775,\"{'Sc': 3.0, 'Sn': 3.0, 'Pt': 3.0}\",\"('Pt', 'Sc', 'Sn')\",OTHERS,False\nmp-1186318,\"{'Nd': 2.0, 'Lu': 6.0}\",\"('Lu', 'Nd')\",OTHERS,False\nmp-1080851,\"{'Ce': 12.0, 'Se': 24.0}\",\"('Ce', 'Se')\",OTHERS,False\nmp-1219139,\"{'Sm': 1.0, 'Er': 3.0, 'Fe': 34.0}\",\"('Er', 'Fe', 'Sm')\",OTHERS,False\nmp-1080018,\"{'Th': 2.0, 'Co': 4.0, 'Sn': 4.0}\",\"('Co', 'Sn', 'Th')\",OTHERS,False\nmp-1246063,\"{'V': 2.0, 'Co': 8.0, 'N': 8.0}\",\"('Co', 'N', 'V')\",OTHERS,False\nmp-1184754,\"{'K': 2.0, 'Rb': 1.0, 'Bi': 1.0}\",\"('Bi', 'K', 'Rb')\",OTHERS,False\nmp-759818,\"{'W': 8.0, 'O': 8.0, 'F': 32.0}\",\"('F', 'O', 'W')\",OTHERS,False\nmp-752429,\"{'Sc': 4.0, 'Tl': 4.0, 'O': 12.0}\",\"('O', 'Sc', 'Tl')\",OTHERS,False\nmp-684604,\"{'Cu': 2.0, 'S': 4.0}\",\"('Cu', 'S')\",OTHERS,False\nmvc-9199,\"{'Mg': 4.0, 'Fe': 12.0, 'O': 28.0}\",\"('Fe', 'Mg', 'O')\",OTHERS,False\nmp-19061,\"{'Li': 4.0, 'O': 24.0, 'Si': 8.0, 'Fe': 4.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1232039,\"{'La': 16.0, 'Mg': 8.0, 'Se': 32.0}\",\"('La', 'Mg', 'Se')\",OTHERS,False\nmp-1186392,\"{'Pa': 1.0, 'Be': 1.0, 'O': 3.0}\",\"('Be', 'O', 'Pa')\",OTHERS,False\nmp-7541,\"{'P': 6.0, 'Sn': 2.0}\",\"('P', 'Sn')\",OTHERS,False\nmp-31090,\"{'Pd': 4.0, 'Er': 4.0, 'Pb': 2.0}\",\"('Er', 'Pb', 'Pd')\",OTHERS,False\nmp-1492,\"{'Cd': 3.0, 'Te': 3.0}\",\"('Cd', 'Te')\",OTHERS,False\nmp-1191414,\"{'Y': 6.0, 'Cl': 4.0, 'O': 12.0}\",\"('Cl', 'O', 'Y')\",OTHERS,False\nmp-1205603,\"{'Tb': 2.0, 'Si': 2.0, 'Ru': 4.0, 'C': 2.0}\",\"('C', 'Ru', 'Si', 'Tb')\",OTHERS,False\nmp-559405,\"{'Rb': 1.0, 'U': 2.0, 'Sb': 1.0, 'S': 8.0}\",\"('Rb', 'S', 'Sb', 'U')\",OTHERS,False\nmp-1039962,\"{'Ce': 1.0, 'Mg': 30.0, 'B': 1.0, 'O': 32.0}\",\"('B', 'Ce', 'Mg', 'O')\",OTHERS,False\nmp-758334,\"{'Li': 6.0, 'Cr': 3.0, 'Sb': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Cr', 'Li', 'O', 'P', 'Sb')\",OTHERS,False\nmp-1245578,\"{'Li': 1.0, 'Pt': 1.0, 'N': 1.0}\",\"('Li', 'N', 'Pt')\",OTHERS,False\nmp-568666,\"{'Rb': 2.0, 'Au': 2.0, 'I': 6.0}\",\"('Au', 'I', 'Rb')\",OTHERS,False\nmp-758498,\"{'Li': 4.0, 'Cu': 2.0, 'Si': 2.0, 'O': 8.0}\",\"('Cu', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1225306,\"{'Dy': 2.0, 'Si': 3.0, 'Ni': 1.0}\",\"('Dy', 'Ni', 'Si')\",OTHERS,False\nmp-865764,\"{'Yb': 1.0, 'Gd': 1.0, 'Rh': 2.0}\",\"('Gd', 'Rh', 'Yb')\",OTHERS,False\nmp-1216863,\"{'Ti': 2.0, 'Nb': 2.0, 'Bi': 6.0, 'O': 18.0}\",\"('Bi', 'Nb', 'O', 'Ti')\",OTHERS,False\nmp-1094619,\"{'Mg': 8.0, 'Ga': 4.0}\",\"('Ga', 'Mg')\",OTHERS,False\nmp-1183990,\"{'Ga': 2.0, 'O': 2.0}\",\"('Ga', 'O')\",OTHERS,False\nmp-26585,\"{'Li': 10.0, 'O': 36.0, 'P': 10.0, 'Sb': 4.0}\",\"('Li', 'O', 'P', 'Sb')\",OTHERS,False\nmp-2563,\"{'Se': 1.0, 'Ce': 1.0}\",\"('Ce', 'Se')\",OTHERS,False\nmp-1218906,\"{'Sr': 10.0, 'Mg': 5.0, 'Mo': 3.0, 'W': 2.0, 'O': 30.0}\",\"('Mg', 'Mo', 'O', 'Sr', 'W')\",OTHERS,False\nmp-974627,\"{'K': 3.0, 'Tl': 1.0}\",\"('K', 'Tl')\",OTHERS,False\nmp-1174401,\"{'Li': 7.0, 'Co': 5.0, 'O': 12.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-23565,\"{'O': 14.0, 'Cr': 4.0, 'Ag': 1.0, 'Bi': 1.0}\",\"('Ag', 'Bi', 'Cr', 'O')\",OTHERS,False\nmp-631535,\"{'Be': 1.0, 'W': 2.0, 'Cl': 1.0}\",\"('Be', 'Cl', 'W')\",OTHERS,False\nmp-768536,\"{'Cs': 12.0, 'La': 4.0, 'O': 12.0}\",\"('Cs', 'La', 'O')\",OTHERS,False\nmp-1212318,\"{'Na': 1.0, 'Yb': 3.0, 'Se': 6.0}\",\"('Na', 'Se', 'Yb')\",OTHERS,False\nmp-1232409,\"{'Nb': 16.0, 'Si': 4.0, 'Te': 16.0}\",\"('Nb', 'Si', 'Te')\",OTHERS,False\nmp-1210642,\"{'Mn': 4.0, 'Te': 4.0, 'O': 12.0}\",\"('Mn', 'O', 'Te')\",OTHERS,False\nmp-9348,\"{'F': 6.0, 'K': 1.0, 'Sc': 1.0, 'Rb': 2.0}\",\"('F', 'K', 'Rb', 'Sc')\",OTHERS,False\nmp-694992,\"{'Co': 1.0, 'P': 2.0, 'H': 18.0, 'N': 6.0, 'F': 12.0}\",\"('Co', 'F', 'H', 'N', 'P')\",OTHERS,False\nmp-23548,\"{'Ag': 2.0, 'Bi': 2.0, 'O': 6.0}\",\"('Ag', 'Bi', 'O')\",OTHERS,False\nmp-1076893,\"{'Ba': 8.0, 'Sr': 24.0, 'Co': 20.0, 'Cu': 12.0, 'O': 80.0}\",\"('Ba', 'Co', 'Cu', 'O', 'Sr')\",OTHERS,False\nmp-1247384,\"{'Mg': 2.0, 'Mn': 1.0, 'W': 3.0, 'S': 8.0}\",\"('Mg', 'Mn', 'S', 'W')\",OTHERS,False\nmp-2607,\"{'Ge': 3.0, 'U': 1.0}\",\"('Ge', 'U')\",OTHERS,False\nmp-1095886,\"{'Hf': 2.0, 'Ni': 1.0, 'Rh': 1.0}\",\"('Hf', 'Ni', 'Rh')\",OTHERS,False\nmp-1219653,\"{'Pu': 1.0, 'Zr': 1.0}\",\"('Pu', 'Zr')\",OTHERS,False\nmp-1185355,\"{'Li': 2.0, 'Eu': 6.0}\",\"('Eu', 'Li')\",OTHERS,False\nmp-865615,\"{'Ga': 1.0, 'Si': 1.0, 'Ru': 2.0}\",\"('Ga', 'Ru', 'Si')\",OTHERS,False\nmp-971975,\"{'Sr': 1.0, 'Hg': 1.0, 'O': 3.0}\",\"('Hg', 'O', 'Sr')\",OTHERS,False\nmp-762514,\"{'Li': 4.0, 'Mn': 12.0, 'O': 24.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-1246222,\"{'Mn': 6.0, 'Si': 12.0, 'N': 22.0}\",\"('Mn', 'N', 'Si')\",OTHERS,False\nmp-504450,\"{'Tl': 4.0, 'F': 20.0, 'Be': 8.0}\",\"('Be', 'F', 'Tl')\",OTHERS,False\nmp-1183144,{'Al': 4.0},\"('Al',)\",OTHERS,False\nmp-1073115,\"{'Mg': 8.0, 'Si': 12.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1211663,\"{'K': 4.0, 'Gd': 8.0, 'Cl': 28.0}\",\"('Cl', 'Gd', 'K')\",OTHERS,False\nmp-1218860,\"{'Sr': 6.0, 'Tl': 3.0, 'Cu': 3.0, 'O': 16.0}\",\"('Cu', 'O', 'Sr', 'Tl')\",OTHERS,False\nmp-672695,\"{'K': 4.0, 'Ag': 4.0, 'C': 4.0, 'O': 12.0}\",\"('Ag', 'C', 'K', 'O')\",OTHERS,False\nmp-1195181,\"{'Co': 16.0, 'Se': 12.0, 'Cl': 8.0, 'O': 36.0}\",\"('Cl', 'Co', 'O', 'Se')\",OTHERS,False\nmp-1225645,\"{'Er': 4.0, 'Fe': 1.0, 'S': 7.0}\",\"('Er', 'Fe', 'S')\",OTHERS,False\nmp-1199833,\"{'Cs': 40.0, 'Tl': 24.0, 'Si': 4.0, 'O': 16.0}\",\"('Cs', 'O', 'Si', 'Tl')\",OTHERS,False\nmp-571634,\"{'Yb': 10.0, 'Pb': 6.0}\",\"('Pb', 'Yb')\",OTHERS,False\nmp-1079538,\"{'V': 2.0, 'H': 2.0, 'O': 5.0}\",\"('H', 'O', 'V')\",OTHERS,False\nmp-1200466,\"{'Rb': 8.0, 'Sb': 8.0, 'S': 4.0, 'O': 20.0, 'F': 24.0}\",\"('F', 'O', 'Rb', 'S', 'Sb')\",OTHERS,False\nmp-1080734,\"{'Ce': 2.0, 'B': 4.0, 'Os': 4.0}\",\"('B', 'Ce', 'Os')\",OTHERS,False\nmp-867704,\"{'Li': 2.0, 'Ni': 2.0, 'P': 6.0, 'O': 18.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-13570,\"{'Mg': 2.0, 'Sc': 4.0, 'Ga': 4.0}\",\"('Ga', 'Mg', 'Sc')\",OTHERS,False\nmp-1228844,\"{'Er': 20.0, 'Cu': 6.0, 'Pb': 9.0, 'S': 42.0}\",\"('Cu', 'Er', 'Pb', 'S')\",OTHERS,False\nmp-1025238,\"{'Tm': 1.0, 'Ti': 2.0, 'Ga': 4.0}\",\"('Ga', 'Ti', 'Tm')\",OTHERS,False\nmp-981387,\"{'Hg': 3.0, 'Bi': 1.0}\",\"('Bi', 'Hg')\",OTHERS,False\nmp-1096807,\"{'Al': 1.0, 'O': 3.0, 'F': 3.0}\",\"('Al', 'F', 'O')\",OTHERS,False\nmp-752849,\"{'Y': 4.0, 'Pb': 4.0, 'O': 14.0}\",\"('O', 'Pb', 'Y')\",OTHERS,False\nmp-1101094,\"{'Cs': 2.0, 'Ce': 1.0, 'Cl': 6.0}\",\"('Ce', 'Cl', 'Cs')\",OTHERS,False\nmp-1226220,\"{'Cr': 1.0, 'Ga': 1.0, 'Fe': 2.0}\",\"('Cr', 'Fe', 'Ga')\",OTHERS,False\nmp-542120,\"{'Ba': 8.0, 'Fe': 4.0, 'O': 16.0}\",\"('Ba', 'Fe', 'O')\",OTHERS,False\nmp-1227295,\"{'Be': 4.0, 'Ni': 1.0, 'Ge': 1.0}\",\"('Be', 'Ge', 'Ni')\",OTHERS,False\nmp-1197847,\"{'Pr': 5.0, 'Mg': 41.0}\",\"('Mg', 'Pr')\",OTHERS,False\nmp-1211742,\"{'K': 4.0, 'Tb': 4.0, 'H': 4.0, 'S': 8.0, 'O': 32.0}\",\"('H', 'K', 'O', 'S', 'Tb')\",OTHERS,False\nmp-1215799,\"{'Yb': 5.0, 'Se': 7.0}\",\"('Se', 'Yb')\",OTHERS,False\nmp-1229104,\"{'Al': 2.0, 'H': 30.0, 'O': 12.0, 'F': 12.0}\",\"('Al', 'F', 'H', 'O')\",OTHERS,False\nmp-1200518,\"{'In': 2.0, 'Re': 8.0, 'C': 28.0, 'I': 6.0, 'O': 28.0}\",\"('C', 'I', 'In', 'O', 'Re')\",OTHERS,False\nmp-1186950,\"{'Sc': 2.0, 'In': 1.0, 'Hg': 1.0}\",\"('Hg', 'In', 'Sc')\",OTHERS,False\nmp-26887,\"{'Li': 4.0, 'V': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1093847,\"{'Ba': 2.0, 'Pd': 1.0, 'Au': 1.0}\",\"('Au', 'Ba', 'Pd')\",OTHERS,False\nmp-981208,\"{'Y': 1.0, 'Tm': 1.0, 'Hg': 2.0}\",\"('Hg', 'Tm', 'Y')\",OTHERS,False\nmp-26855,\"{'Li': 4.0, 'O': 24.0, 'P': 8.0, 'Sn': 2.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,False\nmp-1178425,\"{'Co': 3.0, 'F': 9.0}\",\"('Co', 'F')\",OTHERS,False\nmp-1216851,\"{'V': 32.0, 'N': 3.0}\",\"('N', 'V')\",OTHERS,False\nmp-8842,\"{'Si': 4.0, 'Ca': 4.0, 'Pd': 4.0}\",\"('Ca', 'Pd', 'Si')\",OTHERS,False\nmp-1030405,\"{'Te': 4.0, 'Mo': 2.0, 'W': 2.0, 'Se': 2.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-979420,\"{'Y': 1.0, 'Er': 1.0, 'Ir': 2.0}\",\"('Er', 'Ir', 'Y')\",OTHERS,False\nmp-759667,\"{'Li': 8.0, 'Sn': 4.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Li', 'O', 'Sn')\",OTHERS,False\nmvc-15702,\"{'Zn': 1.0, 'Sn': 2.0, 'N': 2.0}\",\"('N', 'Sn', 'Zn')\",OTHERS,False\nmp-1105934,\"{'Li': 2.0, 'Mn': 2.0, 'As': 2.0, 'O': 10.0}\",\"('As', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1019532,\"{'Ba': 4.0, 'Al': 16.0, 'O': 28.0}\",\"('Al', 'Ba', 'O')\",OTHERS,False\nmp-769266,\"{'Ca': 12.0, 'Ta': 8.0, 'O': 32.0}\",\"('Ca', 'O', 'Ta')\",OTHERS,False\nmp-645962,\"{'La': 8.0, 'B': 28.0, 'O': 54.0}\",\"('B', 'La', 'O')\",OTHERS,False\nmp-753668,\"{'Ba': 2.0, 'Gd': 1.0, 'Sb': 1.0, 'O': 6.0}\",\"('Ba', 'Gd', 'O', 'Sb')\",OTHERS,False\nmp-753816,\"{'Na': 12.0, 'Mg': 2.0, 'O': 8.0}\",\"('Mg', 'Na', 'O')\",OTHERS,False\nmp-14116,\"{'O': 2.0, 'Cu': 1.0, 'Rh': 1.0}\",\"('Cu', 'O', 'Rh')\",OTHERS,False\nmp-1070603,\"{'Sm': 1.0, 'Si': 2.0, 'Ir': 2.0}\",\"('Ir', 'Si', 'Sm')\",OTHERS,False\nmp-646420,\"{'Nd': 4.0, 'In': 2.0, 'Rh': 4.0}\",\"('In', 'Nd', 'Rh')\",OTHERS,False\nmp-1038779,\"{'Mg': 3.0, 'Al': 3.0}\",\"('Al', 'Mg')\",OTHERS,False\nmp-771143,\"{'Na': 6.0, 'Ni': 2.0, 'B': 2.0, 'S': 2.0, 'O': 14.0}\",\"('B', 'Na', 'Ni', 'O', 'S')\",OTHERS,False\nmp-780697,\"{'Mn': 10.0, 'O': 5.0, 'F': 15.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-695452,\"{'K': 2.0, 'Al': 11.0, 'Si': 13.0, 'Ag': 9.0, 'O': 48.0}\",\"('Ag', 'Al', 'K', 'O', 'Si')\",OTHERS,False\nmp-1227292,\"{'Be': 4.0, 'Cu': 1.0, 'Ge': 1.0}\",\"('Be', 'Cu', 'Ge')\",OTHERS,False\nmp-554763,\"{'Np': 4.0, 'Ag': 4.0, 'Se': 4.0, 'O': 20.0}\",\"('Ag', 'Np', 'O', 'Se')\",OTHERS,False\nmp-642821,\"{'K': 6.0, 'H': 10.0, 'Pt': 2.0}\",\"('H', 'K', 'Pt')\",OTHERS,False\nmp-1186245,\"{'Nd': 2.0, 'Ho': 2.0, 'O': 5.0}\",\"('Ho', 'Nd', 'O')\",OTHERS,False\nmp-1094517,\"{'Mg': 4.0, 'Sn': 2.0}\",\"('Mg', 'Sn')\",OTHERS,False\nmp-615760,\"{'Ba': 10.0, 'In': 4.0, 'Sb': 12.0}\",\"('Ba', 'In', 'Sb')\",OTHERS,False\nmp-1020711,\"{'Rb': 4.0, 'Si': 2.0, 'S': 12.0, 'O': 42.0}\",\"('O', 'Rb', 'S', 'Si')\",OTHERS,False\nmp-23237,\"{'F': 12.0, 'Bi': 4.0}\",\"('Bi', 'F')\",OTHERS,False\nmp-1079460,\"{'Ti': 6.0, 'Sn': 2.0}\",\"('Sn', 'Ti')\",OTHERS,False\nmp-23811,\"{'H': 6.0, 'B': 2.0, 'O': 20.0, 'P': 4.0, 'Ca': 2.0, 'Ni': 2.0}\",\"('B', 'Ca', 'H', 'Ni', 'O', 'P')\",OTHERS,False\nmp-757227,\"{'Li': 8.0, 'Fe': 8.0, 'P': 8.0, 'O': 32.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-1097440,\"{'Ca': 1.0, 'Sc': 1.0, 'Rh': 2.0}\",\"('Ca', 'Rh', 'Sc')\",OTHERS,False\nmp-1114417,\"{'Rb': 2.0, 'Li': 1.0, 'Ce': 1.0, 'Cl': 6.0}\",\"('Ce', 'Cl', 'Li', 'Rb')\",OTHERS,False\nmp-1114491,\"{'Rb': 3.0, 'Au': 1.0, 'Br': 6.0}\",\"('Au', 'Br', 'Rb')\",OTHERS,False\nmp-547069,\"{'Fe': 1.0, 'Bi': 1.0, 'O': 3.0}\",\"('Bi', 'Fe', 'O')\",OTHERS,False\nmp-1207406,\"{'Zr': 10.0, 'Al': 6.0, 'N': 2.0}\",\"('Al', 'N', 'Zr')\",OTHERS,False\nmp-1028492,\"{'Te': 2.0, 'W': 4.0, 'S': 6.0}\",\"('S', 'Te', 'W')\",OTHERS,False\nmp-1183621,\"{'Cd': 3.0, 'Pd': 1.0}\",\"('Cd', 'Pd')\",OTHERS,False\nmp-675287,\"{'Cs': 2.0, 'Cu': 2.0, 'Cl': 6.0}\",\"('Cl', 'Cs', 'Cu')\",OTHERS,False\nmp-1256151,\"{'Zr': 10.0, 'Al': 2.0, 'Ni': 8.0}\",\"('Al', 'Ni', 'Zr')\",OTHERS,False\nmp-976804,\"{'Ni': 1.0, 'Ag': 1.0, 'Te': 2.0}\",\"('Ag', 'Ni', 'Te')\",OTHERS,False\nmp-1183266,\"{'Ac': 2.0, 'Pr': 6.0}\",\"('Ac', 'Pr')\",OTHERS,False\nmp-556938,\"{'Pr': 30.0, 'Ti': 24.0, 'Se': 58.0, 'I': 8.0, 'O': 25.0}\",\"('I', 'O', 'Pr', 'Se', 'Ti')\",OTHERS,False\nmp-1184616,\"{'Ho': 2.0, 'Mg': 1.0, 'Os': 1.0}\",\"('Ho', 'Mg', 'Os')\",OTHERS,False\nAl 75 Os 10 Pd 15,\"{'Al': 75.0, 'Os': 10.0, 'Pd': 15.0}\",\"('Al', 'Os', 'Pd')\",QC,False\nmp-1210013,\"{'Nb': 4.0, 'Fe': 8.0, 'O': 18.0}\",\"('Fe', 'Nb', 'O')\",OTHERS,False\nmp-647218,\"{'Re': 8.0, 'P': 8.0, 'O': 44.0}\",\"('O', 'P', 'Re')\",OTHERS,False\nmp-1036617,\"{'Mg': 14.0, 'Al': 1.0, 'V': 1.0, 'O': 16.0}\",\"('Al', 'Mg', 'O', 'V')\",OTHERS,False\nmp-1214317,\"{'Ca': 12.0, 'Ga': 8.0, 'Sn': 12.0, 'O': 48.0}\",\"('Ca', 'Ga', 'O', 'Sn')\",OTHERS,False\nmvc-4610,\"{'Y': 2.0, 'Co': 4.0, 'O': 8.0}\",\"('Co', 'O', 'Y')\",OTHERS,False\nmp-22036,\"{'Zr': 4.0, 'Pd': 4.0, 'Sb': 4.0}\",\"('Pd', 'Sb', 'Zr')\",OTHERS,False\nmp-680450,\"{'K': 32.0, 'Li': 16.0, 'Sn': 64.0}\",\"('K', 'Li', 'Sn')\",OTHERS,False\nmp-769627,\"{'V': 2.0, 'Cr': 1.0, 'Fe': 3.0, 'P': 6.0, 'O': 24.0}\",\"('Cr', 'Fe', 'O', 'P', 'V')\",OTHERS,False\nmp-667315,\"{'Sc': 4.0, 'Nb': 24.0, 'Cl': 52.0, 'O': 12.0}\",\"('Cl', 'Nb', 'O', 'Sc')\",OTHERS,False\nmp-768602,\"{'Li': 4.0, 'Fe': 5.0, 'Sb': 3.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'Sb')\",OTHERS,False\nmp-1226309,\"{'Cr': 2.0, 'Cu': 2.0, 'S': 8.0}\",\"('Cr', 'Cu', 'S')\",OTHERS,False\nmp-1218614,\"{'Sr': 4.0, 'Li': 1.0, 'Ru': 3.0, 'O': 12.0}\",\"('Li', 'O', 'Ru', 'Sr')\",OTHERS,False\nmp-28558,\"{'Ge': 4.0, 'Br': 12.0, 'Rb': 4.0}\",\"('Br', 'Ge', 'Rb')\",OTHERS,False\nmp-1237663,\"{'Tl': 8.0, 'P': 4.0, 'O': 16.0}\",\"('O', 'P', 'Tl')\",OTHERS,False\nmp-555869,\"{'Li': 2.0, 'V': 4.0, 'O': 10.0}\",\"('Li', 'O', 'V')\",OTHERS,False\nmp-1221599,\"{'Mn': 1.0, 'V': 1.0, 'Co': 4.0, 'Si': 2.0}\",\"('Co', 'Mn', 'Si', 'V')\",OTHERS,False\nmp-12628,{'Rb': 8.0},\"('Rb',)\",OTHERS,False\nmp-559019,\"{'Nd': 24.0, 'S': 16.0, 'Br': 4.0, 'N': 12.0}\",\"('Br', 'N', 'Nd', 'S')\",OTHERS,False\nmp-22707,\"{'Ag': 2.0, 'Sb': 2.0, 'Eu': 2.0}\",\"('Ag', 'Eu', 'Sb')\",OTHERS,False\nmp-626307,\"{'Fe': 4.0, 'H': 4.0, 'O': 8.0}\",\"('Fe', 'H', 'O')\",OTHERS,False\nmp-1221059,\"{'Na': 2.0, 'Sb': 2.0, 'W': 2.0, 'O': 12.0}\",\"('Na', 'O', 'Sb', 'W')\",OTHERS,False\nmp-1211359,\"{'La': 1.0, 'Mn': 3.0, 'Cr': 4.0, 'O': 12.0}\",\"('Cr', 'La', 'Mn', 'O')\",OTHERS,False\nmp-1218554,\"{'Sr': 7.0, 'Y': 6.0, 'O': 1.0, 'F': 30.0}\",\"('F', 'O', 'Sr', 'Y')\",OTHERS,False\nmp-1009770,\"{'Ru': 1.0, 'N': 1.0}\",\"('N', 'Ru')\",OTHERS,False\nmp-972486,\"{'Sm': 3.0, 'Ti': 1.0}\",\"('Sm', 'Ti')\",OTHERS,False\nmp-1187986,\"{'Yb': 6.0, 'Sc': 2.0}\",\"('Sc', 'Yb')\",OTHERS,False\nmp-1097707,\"{'Cu': 96.0, 'Se': 48.0}\",\"('Cu', 'Se')\",OTHERS,False\nmp-1225740,\"{'Er': 2.0, 'Mn': 3.0, 'Sb': 3.0, 'O': 14.0}\",\"('Er', 'Mn', 'O', 'Sb')\",OTHERS,False\nmp-1221596,\"{'Mn': 2.0, 'Mo': 2.0, 'As': 4.0}\",\"('As', 'Mn', 'Mo')\",OTHERS,False\nmp-621852,\"{'La': 4.0, 'Ge': 10.0, 'Ru': 6.0}\",\"('Ge', 'La', 'Ru')\",OTHERS,False\nmp-21635,\"{'O': 24.0, 'Mn': 4.0, 'Ge': 8.0, 'Ce': 2.0}\",\"('Ce', 'Ge', 'Mn', 'O')\",OTHERS,False\nmp-626553,\"{'Ca': 2.0, 'H': 32.0, 'O': 20.0}\",\"('Ca', 'H', 'O')\",OTHERS,False\nmp-765076,\"{'Li': 4.0, 'Mn': 4.0, 'O': 4.0, 'F': 8.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1224583,\"{'Hf': 4.0, 'Ga': 4.0, 'Co': 4.0}\",\"('Co', 'Ga', 'Hf')\",OTHERS,False\nmp-690136,\"{'Mg': 4.0, 'Al': 3.0, 'H': 17.0, 'O': 17.0}\",\"('Al', 'H', 'Mg', 'O')\",OTHERS,False\nmp-1213827,\"{'Ce': 8.0, 'Ge': 1.0, 'Pd': 24.0}\",\"('Ce', 'Ge', 'Pd')\",OTHERS,False\nmp-251,\"{'N': 1.0, 'Ta': 1.0}\",\"('N', 'Ta')\",OTHERS,False\nmp-981396,\"{'Y': 2.0, 'B': 8.0, 'Ir': 8.0}\",\"('B', 'Ir', 'Y')\",OTHERS,False\nmp-1184569,{'Hg': 2.0},\"('Hg',)\",OTHERS,False\nmp-1203078,\"{'Sm': 4.0, 'Er': 11.0, 'S': 22.0}\",\"('Er', 'S', 'Sm')\",OTHERS,False\nmp-977548,\"{'Nd': 4.0, 'Mg': 2.0, 'Ge': 4.0}\",\"('Ge', 'Mg', 'Nd')\",OTHERS,False\nmp-1182250,\"{'Cd': 8.0, 'Se': 8.0, 'O': 30.0}\",\"('Cd', 'O', 'Se')\",OTHERS,False\nAg 42 In 42 Yb 16,\"{'Ag': 42.0, 'In': 42.0, 'Yb': 16.0}\",\"('Ag', 'In', 'Yb')\",QC,False\nmp-568570,\"{'Cs': 4.0, 'Sn': 4.0, 'I': 12.0}\",\"('Cs', 'I', 'Sn')\",OTHERS,False\nmp-1213056,\"{'Dy': 2.0, 'Al': 20.0, 'Fe': 4.0}\",\"('Al', 'Dy', 'Fe')\",OTHERS,False\nmp-20792,\"{'Ca': 4.0, 'Pd': 4.0, 'In': 2.0}\",\"('Ca', 'In', 'Pd')\",OTHERS,False\nmp-1106250,\"{'Gd': 10.0, 'Pb': 6.0}\",\"('Gd', 'Pb')\",OTHERS,False\nmp-1102945,\"{'Hf': 4.0, 'Ge': 4.0, 'Rh': 4.0}\",\"('Ge', 'Hf', 'Rh')\",OTHERS,False\nmp-1199032,\"{'Ni': 6.0, 'B': 6.0, 'P': 6.0, 'O': 36.0}\",\"('B', 'Ni', 'O', 'P')\",OTHERS,False\nmp-504616,\"{'Ce': 4.0, 'Co': 14.0, 'B': 6.0}\",\"('B', 'Ce', 'Co')\",OTHERS,False\nmp-1187954,\"{'Zn': 1.0, 'In': 3.0}\",\"('In', 'Zn')\",OTHERS,False\nmp-557250,\"{'Hg': 4.0, 'I': 4.0, 'N': 4.0, 'O': 12.0}\",\"('Hg', 'I', 'N', 'O')\",OTHERS,False\nmp-1206322,\"{'Ce': 2.0, 'Ag': 4.0}\",\"('Ag', 'Ce')\",OTHERS,False\nmp-117,{'Sn': 2.0},\"('Sn',)\",OTHERS,False\nmp-1215507,\"{'Yb': 1.0, 'Zn': 1.0, 'Cu': 1.0, 'As': 2.0}\",\"('As', 'Cu', 'Yb', 'Zn')\",OTHERS,False\nmp-34234,\"{'Al': 12.0, 'Ni': 6.0, 'O': 24.0}\",\"('Al', 'Ni', 'O')\",OTHERS,False\nmp-1228896,\"{'Ba': 10.0, 'Bi': 2.0, 'Pb': 8.0, 'O': 30.0}\",\"('Ba', 'Bi', 'O', 'Pb')\",OTHERS,False\nmp-555988,\"{'Na': 8.0, 'U': 2.0, 'Cr': 6.0, 'O': 28.0}\",\"('Cr', 'Na', 'O', 'U')\",OTHERS,False\nmp-1184036,\"{'Ga': 2.0, 'Ag': 6.0}\",\"('Ag', 'Ga')\",OTHERS,False\nmp-18297,\"{'N': 16.0, 'Ga': 8.0, 'Ba': 12.0}\",\"('Ba', 'Ga', 'N')\",OTHERS,False\nmp-1239251,\"{'Ta': 2.0, 'Cr': 6.0, 'Cu': 4.0, 'S': 16.0}\",\"('Cr', 'Cu', 'S', 'Ta')\",OTHERS,False\nmp-867819,\"{'Li': 3.0, 'Cr': 3.0, 'S': 6.0, 'O': 24.0}\",\"('Cr', 'Li', 'O', 'S')\",OTHERS,False\nmp-26675,\"{'Li': 4.0, 'O': 48.0, 'P': 16.0, 'Sb': 4.0}\",\"('Li', 'O', 'P', 'Sb')\",OTHERS,False\nmp-22097,\"{'O': 12.0, 'Mo': 2.0, 'Rh': 4.0}\",\"('Mo', 'O', 'Rh')\",OTHERS,False\nmp-24681,\"{'H': 18.0, 'N': 6.0, 'O': 8.0, 'Cl': 4.0, 'Cr': 1.0, 'Cs': 1.0}\",\"('Cl', 'Cr', 'Cs', 'H', 'N', 'O')\",OTHERS,False\nmp-605437,\"{'Fe': 4.0, 'H': 4.0, 'O': 8.0}\",\"('Fe', 'H', 'O')\",OTHERS,False\nmp-580,\"{'B': 2.0, 'Pu': 1.0}\",\"('B', 'Pu')\",OTHERS,False\nmp-1187938,\"{'Yb': 1.0, 'Ag': 1.0, 'Au': 2.0}\",\"('Ag', 'Au', 'Yb')\",OTHERS,False\nmp-569302,\"{'Gd': 1.0, 'Si': 2.0, 'Ru': 2.0}\",\"('Gd', 'Ru', 'Si')\",OTHERS,False\nmp-768149,\"{'Dy': 6.0, 'In': 6.0, 'O': 18.0}\",\"('Dy', 'In', 'O')\",OTHERS,False\nmp-14139,\"{'O': 48.0, 'Fe': 20.0, 'Sm': 12.0}\",\"('Fe', 'O', 'Sm')\",OTHERS,False\nmp-23084,\"{'O': 4.0, 'Cl': 2.0, 'Pb': 2.0, 'Bi': 2.0}\",\"('Bi', 'Cl', 'O', 'Pb')\",OTHERS,False\nmp-1225289,\"{'Dy': 2.0, 'Te': 1.0, 'S': 2.0}\",\"('Dy', 'S', 'Te')\",OTHERS,False\nmp-559355,\"{'Hg': 6.0, 'As': 2.0, 'S': 8.0, 'Cl': 2.0}\",\"('As', 'Cl', 'Hg', 'S')\",OTHERS,False\nmp-573514,\"{'Cs': 8.0, 'Sb': 8.0}\",\"('Cs', 'Sb')\",OTHERS,False\nmp-640447,\"{'Ce': 10.0, 'Ni': 2.0, 'Pb': 6.0}\",\"('Ce', 'Ni', 'Pb')\",OTHERS,False\nmp-771054,\"{'V': 12.0, 'P': 8.0, 'O': 32.0}\",\"('O', 'P', 'V')\",OTHERS,False\nmp-540287,\"{'Li': 4.0, 'Cr': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-757008,\"{'Mg': 1.0, 'Fe': 10.0, 'O': 16.0}\",\"('Fe', 'Mg', 'O')\",OTHERS,False\nmvc-4454,\"{'Ti': 4.0, 'Al': 2.0, 'O': 8.0}\",\"('Al', 'O', 'Ti')\",OTHERS,False\nmp-1209091,\"{'Rb': 2.0, 'Tb': 6.0, 'F': 20.0}\",\"('F', 'Rb', 'Tb')\",OTHERS,False\nmp-1213177,\"{'Cs': 4.0, 'Tm': 4.0, 'I': 12.0}\",\"('Cs', 'I', 'Tm')\",OTHERS,False\nmp-780199,\"{'Li': 5.0, 'Mn': 6.0, 'B': 6.0, 'O': 18.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1218665,\"{'Sr': 6.0, 'Ta': 8.0, 'Ti': 8.0, 'O': 42.0}\",\"('O', 'Sr', 'Ta', 'Ti')\",OTHERS,False\nmp-4972,\"{'Cu': 2.0, 'In': 1.0, 'Lu': 1.0}\",\"('Cu', 'In', 'Lu')\",OTHERS,False\nmp-1223169,\"{'La': 3.0, 'Y': 1.0, 'Hf': 4.0, 'O': 14.0}\",\"('Hf', 'La', 'O', 'Y')\",OTHERS,False\nmp-1203935,\"{'Fe': 4.0, 'S': 4.0, 'O': 32.0}\",\"('Fe', 'O', 'S')\",OTHERS,False\nmp-1183874,\"{'Ce': 6.0, 'Sc': 2.0}\",\"('Ce', 'Sc')\",OTHERS,False\nmp-223,\"{'O': 6.0, 'Ge': 3.0}\",\"('Ge', 'O')\",OTHERS,False\nmp-755172,\"{'Li': 5.0, 'V': 3.0, 'Fe': 2.0, 'O': 10.0}\",\"('Fe', 'Li', 'O', 'V')\",OTHERS,False\nmp-1194158,\"{'Er': 4.0, 'Mo': 4.0, 'Br': 4.0, 'O': 16.0}\",\"('Br', 'Er', 'Mo', 'O')\",OTHERS,False\nmp-1074738,\"{'Mg': 8.0, 'Si': 4.0}\",\"('Mg', 'Si')\",OTHERS,False\nmvc-1288,\"{'Ca': 4.0, 'V': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Ca', 'O', 'P', 'V')\",OTHERS,False\nmp-1076570,\"{'Sr': 7.0, 'Ca': 1.0, 'Ti': 7.0, 'Mn': 1.0, 'O': 24.0}\",\"('Ca', 'Mn', 'O', 'Sr', 'Ti')\",OTHERS,False\nmp-1186604,\"{'Pm': 1.0, 'Lu': 1.0, 'Al': 2.0}\",\"('Al', 'Lu', 'Pm')\",OTHERS,False\nmp-1013705,\"{'Sr': 3.0, 'Bi': 1.0, 'As': 1.0}\",\"('As', 'Bi', 'Sr')\",OTHERS,False\nmp-866079,\"{'Mg': 1.0, 'Sc': 2.0, 'Ru': 1.0}\",\"('Mg', 'Ru', 'Sc')\",OTHERS,False\nmp-1190646,\"{'K': 4.0, 'Cu': 2.0, 'Te': 2.0, 'O': 14.0}\",\"('Cu', 'K', 'O', 'Te')\",OTHERS,False\nmp-1224855,\"{'Fe': 1.0, 'O': 2.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-1198448,\"{'Na': 4.0, 'Al': 12.0, 'Si': 12.0, 'H': 8.0, 'O': 48.0}\",\"('Al', 'H', 'Na', 'O', 'Si')\",OTHERS,False\nmp-30815,\"{'Al': 3.0, 'Pd': 2.0, 'La': 1.0}\",\"('Al', 'La', 'Pd')\",OTHERS,False\nmp-1106110,\"{'Eu': 8.0, 'S': 12.0}\",\"('Eu', 'S')\",OTHERS,False\nmp-862805,\"{'Pr': 1.0, 'Sn': 1.0, 'Au': 2.0}\",\"('Au', 'Pr', 'Sn')\",OTHERS,False\nZn 77 Mg 18 Hf 5,\"{'Zn': 77.0, 'Mg': 18.0, 'Hf': 5.0}\",\"('Hf', 'Mg', 'Zn')\",AC,False\nmp-1079890,\"{'Al': 4.0, 'Cu': 2.0, 'Ir': 2.0}\",\"('Al', 'Cu', 'Ir')\",OTHERS,False\nmp-1214638,\"{'Ba': 6.0, 'Fe': 2.0, 'Ru': 4.0, 'O': 18.0}\",\"('Ba', 'Fe', 'O', 'Ru')\",OTHERS,False\nmp-849734,\"{'Li': 2.0, 'V': 2.0, 'F': 8.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-1183973,\"{'Ga': 3.0, 'Si': 1.0}\",\"('Ga', 'Si')\",OTHERS,False\nmp-1208086,\"{'Tm': 3.0, 'P': 6.0, 'Pd': 20.0}\",\"('P', 'Pd', 'Tm')\",OTHERS,False\nmp-561224,\"{'Te': 4.0, 'O': 8.0}\",\"('O', 'Te')\",OTHERS,False\nmp-3346,\"{'Zr': 2.0, 'Sb': 2.0, 'Ho': 2.0}\",\"('Ho', 'Sb', 'Zr')\",OTHERS,False\nmp-23918,\"{'H': 8.0, 'Na': 2.0, 'Ga': 2.0}\",\"('Ga', 'H', 'Na')\",OTHERS,False\nmp-1093912,\"{'Ca': 1.0, 'Hg': 2.0, 'Bi': 1.0}\",\"('Bi', 'Ca', 'Hg')\",OTHERS,False\nmp-763659,\"{'Li': 3.0, 'V': 2.0, 'Fe': 2.0, 'O': 8.0}\",\"('Fe', 'Li', 'O', 'V')\",OTHERS,False\nmp-1220867,\"{'Na': 2.0, 'Tl': 2.0, 'O': 2.0}\",\"('Na', 'O', 'Tl')\",OTHERS,False\nmp-24034,\"{'H': 12.0, 'Al': 2.0, 'K': 6.0}\",\"('Al', 'H', 'K')\",OTHERS,False\nmp-977324,\"{'Ga': 3.0, 'Ni': 20.0, 'B': 6.0}\",\"('B', 'Ga', 'Ni')\",OTHERS,False\nmp-1176897,\"{'Li': 14.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1181173,\"{'K': 16.0, 'Ag': 8.0, 'Sn': 12.0, 'S': 36.0, 'O': 8.0}\",\"('Ag', 'K', 'O', 'S', 'Sn')\",OTHERS,False\nmp-555885,\"{'Sr': 2.0, 'V': 8.0, 'Ag': 4.0, 'O': 24.0}\",\"('Ag', 'O', 'Sr', 'V')\",OTHERS,False\nmp-1197960,\"{'Bi': 24.0, 'S': 24.0, 'O': 108.0}\",\"('Bi', 'O', 'S')\",OTHERS,False\nmp-1025917,\"{'Mo': 1.0, 'W': 2.0, 'Se': 2.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-2562,\"{'Mn': 2.0, 'S': 2.0}\",\"('Mn', 'S')\",OTHERS,False\nmp-1216361,\"{'V': 4.0, 'P': 8.0, 'Pb': 4.0, 'O': 32.0}\",\"('O', 'P', 'Pb', 'V')\",OTHERS,False\nmp-1220506,\"{'Nd': 2.0, 'Fe': 15.0, 'Si': 2.0, 'H': 2.0}\",\"('Fe', 'H', 'Nd', 'Si')\",OTHERS,False\nmvc-14571,\"{'Ti': 2.0, 'Zn': 4.0, 'Sb': 2.0, 'O': 12.0}\",\"('O', 'Sb', 'Ti', 'Zn')\",OTHERS,False\nmp-1111985,\"{'K': 2.0, 'In': 1.0, 'Ag': 1.0, 'Cl': 6.0}\",\"('Ag', 'Cl', 'In', 'K')\",OTHERS,False\nmp-6809,\"{'O': 10.0, 'Si': 2.0, 'Ca': 2.0, 'Sn': 2.0}\",\"('Ca', 'O', 'Si', 'Sn')\",OTHERS,False\nmp-1018806,\"{'Mo': 2.0, 'Se': 2.0, 'S': 2.0}\",\"('Mo', 'S', 'Se')\",OTHERS,False\nAu 61.1 Ga 25.0 Ca 13.9,\"{'Au': 61.1, 'Ga': 25.0, 'Ca': 13.9}\",\"('Au', 'Ca', 'Ga')\",AC,False\nmp-21370,\"{'O': 6.0, 'Sb': 1.0, 'Ba': 2.0, 'Eu': 1.0}\",\"('Ba', 'Eu', 'O', 'Sb')\",OTHERS,False\nmp-9770,\"{'S': 24.0, 'Ge': 4.0, 'Ag': 32.0}\",\"('Ag', 'Ge', 'S')\",OTHERS,False\nmvc-8878,\"{'Mg': 2.0, 'Ti': 2.0, 'Cu': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Cu', 'Mg', 'O', 'P', 'Ti')\",OTHERS,False\nmp-7310,\"{'F': 12.0, 'Zr': 2.0, 'Pb': 2.0}\",\"('F', 'Pb', 'Zr')\",OTHERS,False\nmp-1016875,\"{'Cd': 1.0, 'Rh': 1.0, 'O': 3.0}\",\"('Cd', 'O', 'Rh')\",OTHERS,False\nmp-1186973,\"{'Sc': 1.0, 'Ag': 3.0}\",\"('Ag', 'Sc')\",OTHERS,False\nmp-862677,\"{'Li': 1.0, 'Ho': 1.0, 'Tl': 2.0}\",\"('Ho', 'Li', 'Tl')\",OTHERS,False\nmp-1224110,\"{'Ho': 6.0, 'Mn': 1.0, 'Ge': 2.0, 'S': 14.0}\",\"('Ge', 'Ho', 'Mn', 'S')\",OTHERS,False\nmp-850999,\"{'Na': 4.0, 'Cr': 16.0, 'O': 48.0}\",\"('Cr', 'Na', 'O')\",OTHERS,False\nmp-1245407,\"{'Y': 6.0, 'Ge': 12.0, 'N': 22.0}\",\"('Ge', 'N', 'Y')\",OTHERS,False\nmp-11869,\"{'Pd': 1.0, 'Sn': 1.0, 'Hf': 1.0}\",\"('Hf', 'Pd', 'Sn')\",OTHERS,False\nmp-704988,\"{'Ni': 4.0, 'P': 10.0, 'O': 30.0}\",\"('Ni', 'O', 'P')\",OTHERS,False\nmp-1105787,\"{'Pb': 8.0, 'O': 4.0, 'F': 8.0}\",\"('F', 'O', 'Pb')\",OTHERS,False\nmp-570017,\"{'Mg': 20.0, 'Au': 60.0}\",\"('Au', 'Mg')\",OTHERS,False\nmp-30474,\"{'Ca': 22.0, 'Ga': 14.0}\",\"('Ca', 'Ga')\",OTHERS,False\nmp-4198,\"{'O': 8.0, 'P': 2.0, 'Ag': 6.0}\",\"('Ag', 'O', 'P')\",OTHERS,False\nmvc-321,\"{'Mg': 2.0, 'Ti': 8.0, 'O': 12.0}\",\"('Mg', 'O', 'Ti')\",OTHERS,False\nmvc-12619,\"{'Mg': 4.0, 'Co': 4.0, 'O': 8.0}\",\"('Co', 'Mg', 'O')\",OTHERS,False\nmp-1104560,\"{'Dy': 4.0, 'Cd': 2.0, 'Te': 8.0}\",\"('Cd', 'Dy', 'Te')\",OTHERS,False\nmp-1223315,\"{'K': 1.0, 'Y': 1.0, 'Nb': 6.0, 'Cl': 18.0}\",\"('Cl', 'K', 'Nb', 'Y')\",OTHERS,False\nmp-685779,\"{'Sn': 5.0, 'Mo': 36.0, 'S': 48.0}\",\"('Mo', 'S', 'Sn')\",OTHERS,False\nmp-12894,\"{'O': 2.0, 'S': 1.0, 'Y': 2.0}\",\"('O', 'S', 'Y')\",OTHERS,False\nmp-764484,\"{'Li': 3.0, 'Mn': 3.0, 'V': 3.0, 'P': 12.0, 'O': 42.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-1187398,\"{'Tc': 6.0, 'W': 2.0}\",\"('Tc', 'W')\",OTHERS,False\nmp-1174246,\"{'Li': 7.0, 'Co': 5.0, 'O': 12.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-1102672,\"{'Sr': 8.0, 'Al': 4.0}\",\"('Al', 'Sr')\",OTHERS,False\nmp-1218475,\"{'Sr': 4.0, 'Mg': 1.0, 'Ti': 1.0, 'Fe': 2.0, 'As': 2.0, 'O': 6.0}\",\"('As', 'Fe', 'Mg', 'O', 'Sr', 'Ti')\",OTHERS,False\nmp-1101621,\"{'As': 8.0, 'Pd': 10.0, 'Pb': 2.0}\",\"('As', 'Pb', 'Pd')\",OTHERS,False\nmp-775225,\"{'Nb': 3.0, 'Ni': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Nb', 'Ni', 'O', 'P')\",OTHERS,False\nmp-361,\"{'Cu': 4.0, 'O': 2.0}\",\"('Cu', 'O')\",OTHERS,False\nmp-755752,\"{'Rb': 8.0, 'Be': 8.0, 'O': 12.0}\",\"('Be', 'O', 'Rb')\",OTHERS,False\nmp-20627,\"{'Co': 1.0, 'Ga': 5.0, 'Yb': 1.0}\",\"('Co', 'Ga', 'Yb')\",OTHERS,False\nmp-27346,\"{'Al': 8.0, 'Cl': 32.0, 'Hg': 12.0}\",\"('Al', 'Cl', 'Hg')\",OTHERS,False\nmp-24816,\"{'H': 4.0, 'O': 10.0, 'K': 2.0, 'Cr': 2.0, 'Cd': 1.0}\",\"('Cd', 'Cr', 'H', 'K', 'O')\",OTHERS,False\nmp-1202708,\"{'Yb': 8.0, 'Ni': 2.0, 'B': 26.0}\",\"('B', 'Ni', 'Yb')\",OTHERS,False\nmp-1197925,\"{'Sr': 8.0, 'Ga': 4.0, 'P': 8.0, 'O': 32.0, 'F': 8.0}\",\"('F', 'Ga', 'O', 'P', 'Sr')\",OTHERS,False\nmp-1194266,\"{'K': 12.0, 'Sb': 4.0, 'S': 12.0}\",\"('K', 'S', 'Sb')\",OTHERS,False\nmp-8911,\"{'S': 2.0, 'Cu': 2.0, 'Ag': 2.0}\",\"('Ag', 'Cu', 'S')\",OTHERS,False\nmp-568790,\"{'Ca': 56.0, 'Ga': 4.0, 'As': 44.0}\",\"('As', 'Ca', 'Ga')\",OTHERS,False\nmp-4434,\"{'O': 12.0, 'Al': 4.0, 'Tb': 4.0}\",\"('Al', 'O', 'Tb')\",OTHERS,False\nmp-1039108,\"{'Ca': 4.0, 'Mg': 8.0}\",\"('Ca', 'Mg')\",OTHERS,False\nmp-1181373,\"{'H': 12.0, 'N': 8.0, 'O': 18.0}\",\"('H', 'N', 'O')\",OTHERS,False\nmp-1189560,\"{'Y': 4.0, 'Si': 12.0}\",\"('Si', 'Y')\",OTHERS,False\nmp-1175317,\"{'Li': 7.0, 'Mn': 5.0, 'O': 12.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nZn 55 Mg 40 Nd 5,\"{'Zn': 55.0, 'Mg': 40.0, 'Nd': 5.0}\",\"('Mg', 'Nd', 'Zn')\",QC,False\nmvc-10699,\"{'Ca': 1.0, 'Sb': 4.0, 'O': 8.0}\",\"('Ca', 'O', 'Sb')\",OTHERS,False\nmp-756318,\"{'Al': 4.0, 'Co': 2.0, 'Cl': 16.0}\",\"('Al', 'Cl', 'Co')\",OTHERS,False\nmp-767521,\"{'Na': 4.0, 'In': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'In', 'Na', 'O', 'P')\",OTHERS,False\nmp-861884,\"{'Ac': 1.0, 'Ga': 1.0, 'Te': 2.0}\",\"('Ac', 'Ga', 'Te')\",OTHERS,False\nmp-1077942,\"{'Rb': 2.0, 'Te': 1.0, 'Se': 4.0}\",\"('Rb', 'Se', 'Te')\",OTHERS,False\nmp-759877,\"{'Li': 4.0, 'Cu': 1.0, 'F': 7.0}\",\"('Cu', 'F', 'Li')\",OTHERS,False\nmp-1214815,\"{'Co': 2.0, 'N': 8.0, 'F': 24.0}\",\"('Co', 'F', 'N')\",OTHERS,False\nmp-1211272,\"{'La': 6.0, 'Nb': 2.0, 'Cl': 6.0, 'O': 12.0}\",\"('Cl', 'La', 'Nb', 'O')\",OTHERS,False\nmp-1185724,\"{'Mg': 16.0, 'Mn': 1.0, 'Al': 12.0}\",\"('Al', 'Mg', 'Mn')\",OTHERS,False\nmp-6265,\"{'O': 6.0, 'Sb': 1.0, 'Ba': 2.0, 'Tb': 1.0}\",\"('Ba', 'O', 'Sb', 'Tb')\",OTHERS,False\nmp-22749,\"{'C': 4.0, 'Mn': 10.0}\",\"('C', 'Mn')\",OTHERS,False\nmp-1098218,\"{'Mg': 30.0, 'Cu': 1.0, 'Si': 1.0, 'O': 32.0}\",\"('Cu', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-1216181,\"{'Y': 2.0, 'B': 6.0, 'Rh': 9.0}\",\"('B', 'Rh', 'Y')\",OTHERS,False\nmp-1207848,\"{'Y': 12.0, 'Ge': 8.0, 'Rh': 8.0}\",\"('Ge', 'Rh', 'Y')\",OTHERS,False\nmp-549490,\"{'O': 5.0, 'Nb': 4.0, 'K': 1.0, 'F': 1.0}\",\"('F', 'K', 'Nb', 'O')\",OTHERS,False\nmp-1184572,\"{'Hf': 1.0, 'Mg': 1.0, 'Pt': 2.0}\",\"('Hf', 'Mg', 'Pt')\",OTHERS,False\nmp-761175,\"{'Na': 2.0, 'Ni': 8.0, 'H': 18.0, 'C': 6.0, 'O': 30.0}\",\"('C', 'H', 'Na', 'Ni', 'O')\",OTHERS,False\nmp-567849,\"{'Er': 4.0, 'Al': 6.0, 'Co': 2.0}\",\"('Al', 'Co', 'Er')\",OTHERS,False\nmp-1038570,\"{'Mg': 30.0, 'Cr': 1.0, 'Cd': 1.0, 'O': 32.0}\",\"('Cd', 'Cr', 'Mg', 'O')\",OTHERS,False\nmp-567787,\"{'Li': 1.0, 'In': 1.0, 'Ag': 2.0}\",\"('Ag', 'In', 'Li')\",OTHERS,False\nmp-15919,\"{'Be': 12.0, 'O': 48.0, 'P': 12.0, 'Ag': 12.0}\",\"('Ag', 'Be', 'O', 'P')\",OTHERS,False\nmp-1208371,\"{'Tl': 4.0, 'N': 8.0, 'Cl': 20.0, 'O': 4.0}\",\"('Cl', 'N', 'O', 'Tl')\",OTHERS,False\nmp-631449,\"{'Li': 1.0, 'Ta': 1.0, 'Be': 1.0}\",\"('Be', 'Li', 'Ta')\",OTHERS,False\nmp-561848,\"{'C': 24.0, 'O': 16.0}\",\"('C', 'O')\",OTHERS,False\nmp-21278,\"{'In': 4.0, 'Ce': 2.0, 'Ir': 2.0}\",\"('Ce', 'In', 'Ir')\",OTHERS,False\nmp-1208045,\"{'Tl': 1.0, 'Fe': 1.0, 'S': 2.0, 'O': 8.0}\",\"('Fe', 'O', 'S', 'Tl')\",OTHERS,False\nmp-1079141,\"{'Pr': 2.0, 'Ge': 4.0, 'Ir': 4.0}\",\"('Ge', 'Ir', 'Pr')\",OTHERS,False\nmp-754151,\"{'V': 2.0, 'O': 2.0, 'F': 2.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-979073,\"{'Tm': 1.0, 'Mg': 2.0, 'Sc': 1.0}\",\"('Mg', 'Sc', 'Tm')\",OTHERS,False\nmp-29505,\"{'O': 12.0, 'Cs': 12.0, 'Bi': 4.0}\",\"('Bi', 'Cs', 'O')\",OTHERS,False\nmp-1175732,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmvc-1912,\"{'Y': 1.0, 'Cu': 3.0, 'Bi': 4.0, 'O': 12.0}\",\"('Bi', 'Cu', 'O', 'Y')\",OTHERS,False\nmp-27552,\"{'I': 6.0, 'Tl': 2.0, 'Pb': 2.0}\",\"('I', 'Pb', 'Tl')\",OTHERS,False\nmp-672254,\"{'Ca': 3.0, 'Pd': 3.0, 'Pb': 3.0}\",\"('Ca', 'Pb', 'Pd')\",OTHERS,False\nmp-1218038,\"{'Ta': 3.0, 'V': 7.0, 'Si': 6.0}\",\"('Si', 'Ta', 'V')\",OTHERS,False\nmp-778109,\"{'Li': 12.0, 'Nb': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'Nb', 'O', 'P')\",OTHERS,False\nmp-1019087,\"{'Ca': 2.0, 'Sn': 2.0, 'Hg': 2.0}\",\"('Ca', 'Hg', 'Sn')\",OTHERS,False\nmp-1246920,\"{'Pb': 2.0, 'C': 16.0, 'N': 12.0}\",\"('C', 'N', 'Pb')\",OTHERS,False\nmp-1212980,\"{'Eu': 6.0, 'In': 6.0, 'O': 18.0}\",\"('Eu', 'In', 'O')\",OTHERS,False\nmp-772294,\"{'Na': 10.0, 'Li': 2.0, 'Fe': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Fe', 'Li', 'Na', 'O', 'P')\",OTHERS,False\nmp-1074753,\"{'Mg': 8.0, 'Si': 4.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1247069,\"{'Cd': 6.0, 'Ga': 4.0, 'N': 8.0}\",\"('Cd', 'Ga', 'N')\",OTHERS,False\nmp-1112710,\"{'Cs': 2.0, 'K': 1.0, 'Sc': 1.0, 'I': 6.0}\",\"('Cs', 'I', 'K', 'Sc')\",OTHERS,False\nmp-862720,\"{'Sr': 2.0, 'Cd': 1.0, 'In': 1.0}\",\"('Cd', 'In', 'Sr')\",OTHERS,False\nmp-1095596,\"{'Ca': 2.0, 'B': 1.0, 'As': 1.0, 'O': 8.0}\",\"('As', 'B', 'Ca', 'O')\",OTHERS,False\nmp-2333,\"{'Pd': 3.0, 'Nd': 1.0}\",\"('Nd', 'Pd')\",OTHERS,False\nmvc-2075,\"{'Mg': 2.0, 'Sn': 9.0, 'O': 13.0}\",\"('Mg', 'O', 'Sn')\",OTHERS,False\nmp-1199234,\"{'Tl': 4.0, 'Mo': 30.0, 'Se': 38.0}\",\"('Mo', 'Se', 'Tl')\",OTHERS,False\nmp-1214395,\"{'Ba': 8.0, 'In': 8.0, 'F': 40.0}\",\"('Ba', 'F', 'In')\",OTHERS,False\nmp-1203378,\"{'Cs': 14.0, 'In': 6.0, 'As': 12.0}\",\"('As', 'Cs', 'In')\",OTHERS,False\nAl 57.3 Cu 31.4 Ru 11.3,\"{'Al': 57.3, 'Cu': 31.4, 'Ru': 11.3}\",\"('Al', 'Cu', 'Ru')\",AC,False\nmp-1186671,\"{'Pm': 1.0, 'Y': 3.0}\",\"('Pm', 'Y')\",OTHERS,False\nmp-1197938,\"{'Co': 6.0, 'B': 14.0, 'O': 26.0, 'F': 2.0}\",\"('B', 'Co', 'F', 'O')\",OTHERS,False\nmp-1173940,\"{'Li': 3.0, 'Mn': 1.0, 'Co': 2.0, 'O': 6.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1028535,\"{'Te': 2.0, 'Mo': 1.0, 'W': 3.0, 'Se': 2.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-973626,\"{'Hg': 6.0, 'Au': 2.0}\",\"('Au', 'Hg')\",OTHERS,False\nmp-758112,\"{'Co': 2.0, 'Ni': 3.0, 'Sb': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Co', 'Ni', 'O', 'P', 'Sb')\",OTHERS,False\nmp-722865,\"{'H': 32.0, 'Se': 8.0, 'N': 8.0, 'O': 20.0}\",\"('H', 'N', 'O', 'Se')\",OTHERS,False\nmp-1222794,\"{'La': 1.0, 'Ga': 3.0, 'Pt': 1.0}\",\"('Ga', 'La', 'Pt')\",OTHERS,False\nmp-567348,\"{'Tm': 1.0, 'In': 1.0, 'Pd': 2.0}\",\"('In', 'Pd', 'Tm')\",OTHERS,False\nmp-1202721,\"{'Ag': 4.0, 'H': 24.0, 'C': 8.0, 'N': 20.0, 'O': 16.0}\",\"('Ag', 'C', 'H', 'N', 'O')\",OTHERS,False\nmp-6562,\"{'O': 4.0, 'Cu': 1.0, 'Ba': 2.0, 'Hg': 1.0}\",\"('Ba', 'Cu', 'Hg', 'O')\",OTHERS,False\nmp-669389,\"{'Sr': 6.0, 'In': 2.0, 'Rh': 2.0, 'O': 12.0}\",\"('In', 'O', 'Rh', 'Sr')\",OTHERS,False\nmp-1095566,\"{'La': 2.0, 'Co': 8.0, 'B': 2.0}\",\"('B', 'Co', 'La')\",OTHERS,False\nmp-571514,\"{'Cd': 8.0, 'I': 16.0}\",\"('Cd', 'I')\",OTHERS,False\nmp-1213682,\"{'Cs': 1.0, 'Er': 1.0, 'Mo': 2.0, 'O': 8.0}\",\"('Cs', 'Er', 'Mo', 'O')\",OTHERS,False\nmp-973958,\"{'K': 1.0, 'Ru': 1.0, 'O': 3.0}\",\"('K', 'O', 'Ru')\",OTHERS,False\nmp-773087,\"{'Li': 2.0, 'Mg': 1.0, 'Cu': 2.0, 'Si': 4.0, 'O': 12.0}\",\"('Cu', 'Li', 'Mg', 'O', 'Si')\",OTHERS,False\nCd 65 Mg 20 Er 15,\"{'Cd': 65.0, 'Mg': 20.0, 'Er': 15.0}\",\"('Cd', 'Er', 'Mg')\",QC,False\nmvc-16726,\"{'Al': 2.0, 'Co': 4.0, 'S': 8.0}\",\"('Al', 'Co', 'S')\",OTHERS,False\nmp-1228149,\"{'Ba': 3.0, 'Sr': 9.0, 'Al': 4.0, 'O': 16.0, 'F': 4.0}\",\"('Al', 'Ba', 'F', 'O', 'Sr')\",OTHERS,False\nmp-1228037,\"{'Ba': 1.0, 'Ca': 1.0, 'Dy': 2.0, 'F': 10.0}\",\"('Ba', 'Ca', 'Dy', 'F')\",OTHERS,False\nmp-1195436,\"{'Cd': 12.0, 'B': 4.0, 'P': 4.0, 'O': 28.0}\",\"('B', 'Cd', 'O', 'P')\",OTHERS,False\nmp-389,\"{'Si': 4.0, 'Pd': 4.0}\",\"('Pd', 'Si')\",OTHERS,False\nmp-1200220,\"{'Al': 24.0, 'Ir': 8.0}\",\"('Al', 'Ir')\",OTHERS,False\nmp-1073599,\"{'Mg': 8.0, 'Si': 16.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-862360,\"{'Sc': 2.0, 'Ni': 1.0, 'Ir': 1.0}\",\"('Ir', 'Ni', 'Sc')\",OTHERS,False\nmp-569435,\"{'K': 4.0, 'Bi': 4.0, 'P': 8.0, 'Se': 24.0}\",\"('Bi', 'K', 'P', 'Se')\",OTHERS,False\nmp-1113276,\"{'Rb': 2.0, 'Bi': 1.0, 'Au': 1.0, 'F': 6.0}\",\"('Au', 'Bi', 'F', 'Rb')\",OTHERS,False\nmp-1029750,\"{'Sr': 6.0, 'Ru': 2.0, 'N': 6.0}\",\"('N', 'Ru', 'Sr')\",OTHERS,False\nmp-1173734,\"{'Na': 1.0, 'Ca': 3.0, 'Fe': 4.0, 'Si': 8.0, 'O': 24.0}\",\"('Ca', 'Fe', 'Na', 'O', 'Si')\",OTHERS,False\nmp-558475,\"{'K': 16.0, 'Nb': 8.0, 'S': 40.0, 'O': 4.0}\",\"('K', 'Nb', 'O', 'S')\",OTHERS,False\nmp-11366,\"{'Cu': 2.0, 'Zn': 1.0, 'Zr': 1.0}\",\"('Cu', 'Zn', 'Zr')\",OTHERS,False\nmp-756488,\"{'Co': 4.0, 'O': 8.0}\",\"('Co', 'O')\",OTHERS,False\nmp-22392,\"{'F': 2.0, 'Cu': 2.0, 'Se': 2.0, 'Sm': 2.0}\",\"('Cu', 'F', 'Se', 'Sm')\",OTHERS,False\nmp-1201992,\"{'La': 2.0, 'V': 2.0, 'H': 4.0, 'I': 8.0, 'O': 30.0}\",\"('H', 'I', 'La', 'O', 'V')\",OTHERS,False\nmp-1215891,\"{'Y': 1.0, 'Th': 1.0}\",\"('Th', 'Y')\",OTHERS,False\nmp-774859,\"{'Li': 4.0, 'Nb': 4.0, 'P': 4.0, 'O': 20.0}\",\"('Li', 'Nb', 'O', 'P')\",OTHERS,False\nmp-690667,\"{'Ag': 7.0, 'N': 1.0, 'O': 8.0}\",\"('Ag', 'N', 'O')\",OTHERS,False\nmp-867293,\"{'Li': 1.0, 'Co': 2.0, 'Si': 1.0}\",\"('Co', 'Li', 'Si')\",OTHERS,False\nmp-690540,\"{'V': 2.0, 'Bi': 4.0, 'O': 11.0}\",\"('Bi', 'O', 'V')\",OTHERS,False\nmp-654900,\"{'Cu': 4.0, 'H': 36.0, 'C': 16.0, 'N': 4.0, 'O': 24.0}\",\"('C', 'Cu', 'H', 'N', 'O')\",OTHERS,False\nmp-540631,\"{'K': 2.0, 'Ta': 2.0, 'Ge': 6.0, 'O': 18.0}\",\"('Ge', 'K', 'O', 'Ta')\",OTHERS,False\nmp-1077413,\"{'Na': 2.0, 'P': 2.0, 'O': 2.0}\",\"('Na', 'O', 'P')\",OTHERS,False\nmp-21437,\"{'O': 12.0, 'Fe': 4.0, 'Te': 2.0}\",\"('Fe', 'O', 'Te')\",OTHERS,False\nmp-1219732,\"{'Pt': 4.0, 'Se': 1.0, 'S': 3.0}\",\"('Pt', 'S', 'Se')\",OTHERS,False\nmp-582119,\"{'Tb': 16.0, 'In': 4.0, 'Rh': 4.0}\",\"('In', 'Rh', 'Tb')\",OTHERS,False\nmp-1219236,\"{'Re': 6.0, 'Br': 16.0, 'Cl': 2.0}\",\"('Br', 'Cl', 'Re')\",OTHERS,False\nmp-22428,\"{'Sr': 4.0, 'Ce': 4.0, 'O': 12.0}\",\"('Ce', 'O', 'Sr')\",OTHERS,False\nmp-867873,\"{'Li': 1.0, 'Tm': 2.0, 'Al': 1.0}\",\"('Al', 'Li', 'Tm')\",OTHERS,False\nmp-21827,\"{'Cu': 8.0, 'Se': 32.0, 'In': 18.0}\",\"('Cu', 'In', 'Se')\",OTHERS,False\nmp-1027137,\"{'Te': 2.0, 'Mo': 3.0, 'W': 1.0, 'Se': 4.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-675325,\"{'Al': 1.0, 'Ag': 9.0, 'S': 6.0}\",\"('Ag', 'Al', 'S')\",OTHERS,False\nmp-972119,\"{'Sr': 1.0, 'Ca': 1.0, 'Tl': 2.0}\",\"('Ca', 'Sr', 'Tl')\",OTHERS,False\nmp-1174250,\"{'Li': 8.0, 'Co': 6.0, 'O': 14.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-557509,\"{'Mn': 4.0, 'H': 44.0, 'C': 20.0, 'N': 4.0, 'O': 24.0}\",\"('C', 'H', 'Mn', 'N', 'O')\",OTHERS,False\nmp-626791,\"{'V': 4.0, 'H': 4.0, 'O': 8.0}\",\"('H', 'O', 'V')\",OTHERS,False\nmp-1232167,\"{'Mg': 8.0, 'Sc': 16.0, 'Se': 32.0}\",\"('Mg', 'Sc', 'Se')\",OTHERS,False\nmp-1098705,\"{'Ce': 4.0, 'Se': 8.0}\",\"('Ce', 'Se')\",OTHERS,False\nmp-1184689,\"{'Ho': 2.0, 'Ni': 1.0, 'Ru': 1.0}\",\"('Ho', 'Ni', 'Ru')\",OTHERS,False\nmp-1208379,\"{'Tb': 4.0, 'W': 4.0, 'O': 20.0}\",\"('O', 'Tb', 'W')\",OTHERS,False\nmp-778265,\"{'Li': 4.0, 'Mn': 3.0, 'Fe': 1.0, 'B': 4.0, 'O': 12.0}\",\"('B', 'Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-27017,\"{'Li': 8.0, 'O': 32.0, 'P': 8.0, 'Sn': 8.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,False\nmp-1184920,\"{'Ho': 2.0, 'Os': 6.0}\",\"('Ho', 'Os')\",OTHERS,False\nmp-18854,\"{'O': 4.0, 'Cr': 1.0, 'Sr': 2.0}\",\"('Cr', 'O', 'Sr')\",OTHERS,False\nmp-768869,\"{'Li': 8.0, 'V': 4.0, 'B': 4.0, 'O': 16.0}\",\"('B', 'Li', 'O', 'V')\",OTHERS,False\nmvc-5152,\"{'Mg': 6.0, 'Cu': 12.0, 'O': 24.0}\",\"('Cu', 'Mg', 'O')\",OTHERS,False\nmp-570419,\"{'Zr': 6.0, 'Al': 6.0, 'C': 10.0}\",\"('Al', 'C', 'Zr')\",OTHERS,False\nmp-639511,\"{'Si': 32.0, 'O': 64.0}\",\"('O', 'Si')\",OTHERS,False\nmp-1213744,\"{'Cr': 2.0, 'Cu': 2.0, 'W': 4.0, 'O': 16.0}\",\"('Cr', 'Cu', 'O', 'W')\",OTHERS,False\nmp-770065,\"{'Gd': 8.0, 'Ti': 4.0, 'O': 20.0}\",\"('Gd', 'O', 'Ti')\",OTHERS,False\nmp-1227586,\"{'Ca': 4.0, 'Fe': 4.0, 'O': 10.0}\",\"('Ca', 'Fe', 'O')\",OTHERS,False\nmp-1073454,\"{'Mg': 8.0, 'Si': 16.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1018683,\"{'Dy': 2.0, 'Se': 4.0}\",\"('Dy', 'Se')\",OTHERS,False\nAl 71.8 Co 21.1 Ni 7.1,\"{'Al': 71.8, 'Co': 21.1, 'Ni': 7.1}\",\"('Al', 'Co', 'Ni')\",AC,False\nmp-772993,\"{'Li': 2.0, 'Ti': 7.0, 'Nb': 6.0, 'O': 30.0}\",\"('Li', 'Nb', 'O', 'Ti')\",OTHERS,False\nmp-1246702,\"{'Ca': 10.0, 'Co': 4.0, 'N': 12.0}\",\"('Ca', 'Co', 'N')\",OTHERS,False\nmp-1215756,\"{'Zr': 6.0, 'Bi': 2.0, 'F': 30.0}\",\"('Bi', 'F', 'Zr')\",OTHERS,False\nmp-1198330,\"{'Cu': 1.0, 'C': 32.0, 'N': 8.0, 'F': 16.0}\",\"('C', 'Cu', 'F', 'N')\",OTHERS,False\nmp-1186050,\"{'Na': 3.0, 'Hg': 1.0}\",\"('Hg', 'Na')\",OTHERS,False\nmp-571638,\"{'Rb': 4.0, 'Cu': 2.0, 'Br': 4.0, 'Cl': 4.0}\",\"('Br', 'Cl', 'Cu', 'Rb')\",OTHERS,False\nmp-13361,\"{'O': 8.0, 'P': 2.0, 'Cu': 1.0, 'Cd': 2.0}\",\"('Cd', 'Cu', 'O', 'P')\",OTHERS,False\nmp-1224834,\"{'Ga': 1.0, 'Si': 1.0, 'Ni': 6.0}\",\"('Ga', 'Ni', 'Si')\",OTHERS,False\nmp-861982,\"{'Pa': 1.0, 'Ga': 3.0}\",\"('Ga', 'Pa')\",OTHERS,False\nmp-29987,\"{'Al': 4.0, 'I': 4.0, 'La': 6.0}\",\"('Al', 'I', 'La')\",OTHERS,False\nmp-695492,\"{'Ca': 2.0, 'Fe': 3.0, 'Sb': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Ca', 'Fe', 'O', 'P', 'Sb')\",OTHERS,False\nmp-1174526,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1193030,\"{'Ce': 8.0, 'Co': 2.0, 'S': 14.0}\",\"('Ce', 'Co', 'S')\",OTHERS,False\nmp-677743,\"{'Na': 12.0, 'Ca': 8.0, 'Ta': 12.0, 'Ti': 8.0, 'O': 60.0}\",\"('Ca', 'Na', 'O', 'Ta', 'Ti')\",OTHERS,False\nmvc-10720,\"{'Mg': 1.0, 'Bi': 4.0, 'O': 8.0}\",\"('Bi', 'Mg', 'O')\",OTHERS,False\nmvc-10393,\"{'Ho': 2.0, 'W': 4.0, 'O': 12.0}\",\"('Ho', 'O', 'W')\",OTHERS,False\nmp-1213571,\"{'Cs': 4.0, 'Np': 4.0, 'Mo': 4.0, 'O': 24.0}\",\"('Cs', 'Mo', 'Np', 'O')\",OTHERS,False\nmp-1111173,\"{'K': 3.0, 'In': 1.0, 'Cl': 6.0}\",\"('Cl', 'In', 'K')\",OTHERS,False\nmp-1193370,\"{'Nb': 8.0, 'Cu': 4.0, 'S': 16.0}\",\"('Cu', 'Nb', 'S')\",OTHERS,False\nmp-1174287,\"{'Li': 8.0, 'Mn': 6.0, 'O': 14.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-1222993,\"{'La': 1.0, 'Eu': 1.0, 'Pd': 6.0}\",\"('Eu', 'La', 'Pd')\",OTHERS,False\nmp-768940,\"{'Cr': 8.0, 'S': 12.0, 'O': 48.0}\",\"('Cr', 'O', 'S')\",OTHERS,False\nmp-1245461,\"{'Cd': 8.0, 'Ru': 2.0, 'N': 8.0}\",\"('Cd', 'N', 'Ru')\",OTHERS,False\nmp-758200,\"{'Li': 8.0, 'Ni': 8.0, 'P': 8.0, 'O': 32.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-772973,\"{'Li': 4.0, 'P': 20.0, 'W': 4.0, 'O': 60.0}\",\"('Li', 'O', 'P', 'W')\",OTHERS,False\nmp-780099,\"{'Fe': 8.0, 'O': 4.0, 'F': 12.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-723120,\"{'Ba': 2.0, 'H': 12.0, 'C': 8.0, 'O': 14.0}\",\"('Ba', 'C', 'H', 'O')\",OTHERS,False\nmp-20512,\"{'Fe': 2.0, 'Sn': 2.0}\",\"('Fe', 'Sn')\",OTHERS,False\nmp-1014339,\"{'Cr': 12.0, 'N': 24.0}\",\"('Cr', 'N')\",OTHERS,False\nmp-1223899,\"{'Ho': 1.0, 'Al': 1.0, 'Ni': 4.0}\",\"('Al', 'Ho', 'Ni')\",OTHERS,False\nmp-1105724,\"{'Ba': 4.0, 'Er': 2.0, 'Ga': 2.0, 'Te': 10.0}\",\"('Ba', 'Er', 'Ga', 'Te')\",OTHERS,False\nmp-1207967,\"{'Y': 12.0, 'Cr': 8.0, 'Ga': 12.0, 'O': 48.0}\",\"('Cr', 'Ga', 'O', 'Y')\",OTHERS,False\nmp-14500,\"{'As': 12.0, 'Sr': 2.0, 'Pt': 8.0}\",\"('As', 'Pt', 'Sr')\",OTHERS,False\nmp-1215907,\"{'Y': 1.0, 'Ag': 1.0, 'Te': 2.0}\",\"('Ag', 'Te', 'Y')\",OTHERS,False\nmp-1209388,\"{'Sc': 8.0, 'In': 4.0, 'Au': 8.0}\",\"('Au', 'In', 'Sc')\",OTHERS,False\nmp-761102,\"{'Na': 3.0, 'Mn': 20.0, 'O': 40.0}\",\"('Mn', 'Na', 'O')\",OTHERS,False\nmp-760078,\"{'Ba': 4.0, 'Ti': 11.0, 'O': 26.0}\",\"('Ba', 'O', 'Ti')\",OTHERS,False\nmp-1211068,\"{'Mn': 2.0, 'P': 4.0, 'O': 20.0}\",\"('Mn', 'O', 'P')\",OTHERS,False\nmp-21272,\"{'Ni': 1.0, 'Sb': 1.0, 'Er': 1.0}\",\"('Er', 'Ni', 'Sb')\",OTHERS,False\nmp-1247291,\"{'Mg': 2.0, 'V': 3.0, 'In': 1.0, 'S': 8.0}\",\"('In', 'Mg', 'S', 'V')\",OTHERS,False\nmp-708135,\"{'Y': 1.0, 'H': 16.0, 'C': 4.0, 'N': 9.0, 'O': 16.0}\",\"('C', 'H', 'N', 'O', 'Y')\",OTHERS,False\nmp-623006,\"{'U': 1.0, 'Ga': 5.0, 'Ir': 1.0}\",\"('Ga', 'Ir', 'U')\",OTHERS,False\nmp-973850,\"{'Ho': 5.0, 'Ga': 15.0}\",\"('Ga', 'Ho')\",OTHERS,False\nmp-1200889,\"{'Ti': 8.0, 'Bi': 8.0, 'O': 28.0}\",\"('Bi', 'O', 'Ti')\",OTHERS,False\nmp-23057,\"{'Li': 2.0, 'Br': 4.0, 'Cs': 2.0}\",\"('Br', 'Cs', 'Li')\",OTHERS,False\nmp-7531,\"{'O': 13.0, 'Al': 6.0, 'Ca': 4.0}\",\"('Al', 'Ca', 'O')\",OTHERS,False\nmp-1196701,\"{'Na': 4.0, 'Al': 4.0, 'Si': 6.0, 'O': 28.0}\",\"('Al', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1093886,\"{'Ta': 2.0, 'Mn': 1.0, 'Ru': 1.0}\",\"('Mn', 'Ru', 'Ta')\",OTHERS,False\nmp-1218927,\"{'Sr': 10.0, 'Cu': 5.0, 'Mo': 1.0, 'W': 4.0, 'O': 30.0}\",\"('Cu', 'Mo', 'O', 'Sr', 'W')\",OTHERS,False\nmp-1102537,\"{'Nb': 4.0, 'V': 4.0, 'P': 4.0}\",\"('Nb', 'P', 'V')\",OTHERS,False\nmp-755111,\"{'Li': 1.0, 'V': 4.0, 'O': 1.0, 'F': 11.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-863971,\"{'K': 2.0, 'Mn': 3.0, 'P': 4.0, 'H': 4.0, 'O': 12.0}\",\"('H', 'K', 'Mn', 'O', 'P')\",OTHERS,False\nmp-541338,\"{'Al': 12.0, 'P': 12.0, 'O': 48.0}\",\"('Al', 'O', 'P')\",OTHERS,False\nmp-759426,\"{'Li': 4.0, 'V': 1.0, 'P': 6.0, 'O': 18.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-864807,\"{'Hf': 1.0, 'Zn': 1.0, 'Ni': 2.0}\",\"('Hf', 'Ni', 'Zn')\",OTHERS,False\nmp-1221286,\"{'Na': 3.0, 'Mn': 2.0, 'B': 1.0, 'O': 6.0}\",\"('B', 'Mn', 'Na', 'O')\",OTHERS,False\nmp-1261478,\"{'Al': 4.0, 'Ni': 12.0, 'O': 24.0}\",\"('Al', 'Ni', 'O')\",OTHERS,False\nmp-1181996,{'C': 2.0},\"('C',)\",OTHERS,False\nmp-1218849,\"{'Sr': 2.0, 'Gd': 1.0, 'Cu': 2.0, 'Ir': 1.0, 'O': 8.0}\",\"('Cu', 'Gd', 'Ir', 'O', 'Sr')\",OTHERS,False\nZn 75 Sc 15 Co 10,\"{'Zn': 75.0, 'Sc': 15.0, 'Co': 10.0}\",\"('Co', 'Sc', 'Zn')\",QC,False\nmp-1174059,\"{'Li': 5.0, 'Mn': 1.0, 'Co': 2.0, 'O': 8.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-755159,\"{'Tl': 4.0, 'Ge': 4.0, 'O': 14.0}\",\"('Ge', 'O', 'Tl')\",OTHERS,False\nmp-18819,\"{'O': 7.0, 'P': 2.0, 'Ni': 2.0}\",\"('Ni', 'O', 'P')\",OTHERS,False\nmp-766000,\"{'Li': 1.0, 'V': 1.0, 'Cr': 1.0, 'P': 4.0, 'O': 14.0}\",\"('Cr', 'Li', 'O', 'P', 'V')\",OTHERS,False\nmp-18987,\"{'Li': 2.0, 'O': 14.0, 'P': 4.0, 'Mo': 2.0}\",\"('Li', 'Mo', 'O', 'P')\",OTHERS,False\nmp-755273,\"{'Li': 4.0, 'Fe': 8.0, 'O': 4.0, 'F': 12.0}\",\"('F', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-643923,\"{'Zr': 8.0, 'Co': 4.0, 'H': 20.0}\",\"('Co', 'H', 'Zr')\",OTHERS,False\nmp-1175059,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmvc-4380,\"{'Ca': 4.0, 'Ta': 2.0, 'Ni': 2.0, 'O': 12.0}\",\"('Ca', 'Ni', 'O', 'Ta')\",OTHERS,False\nmp-1181255,\"{'Li': 16.0, 'P': 16.0, 'O': 68.0}\",\"('Li', 'O', 'P')\",OTHERS,False\nmp-753794,\"{'Hf': 4.0, 'P': 4.0, 'O': 18.0}\",\"('Hf', 'O', 'P')\",OTHERS,False\nmp-559287,\"{'Ho': 4.0, 'Sn': 6.0, 'Pb': 6.0, 'S': 24.0}\",\"('Ho', 'Pb', 'S', 'Sn')\",OTHERS,False\nAl 65 Cu 20 Ir 15,\"{'Al': 65.0, 'Cu': 20.0, 'Ir': 15.0}\",\"('Al', 'Cu', 'Ir')\",QC,False\nmp-761140,\"{'Na': 4.0, 'Ti': 7.0, 'O': 16.0}\",\"('Na', 'O', 'Ti')\",OTHERS,False\nmp-622086,\"{'La': 8.0, 'Ge': 4.0, 'S': 20.0}\",\"('Ge', 'La', 'S')\",OTHERS,False\nmvc-5715,\"{'Mg': 4.0, 'Cu': 2.0, 'Ir': 2.0, 'O': 12.0}\",\"('Cu', 'Ir', 'Mg', 'O')\",OTHERS,False\nmp-18084,\"{'O': 20.0, 'Zn': 4.0, 'Ba': 4.0, 'Tb': 8.0}\",\"('Ba', 'O', 'Tb', 'Zn')\",OTHERS,False\nmp-1208803,\"{'Sr': 2.0, 'La': 1.0, 'Cu': 2.0, 'Hg': 1.0, 'O': 6.0}\",\"('Cu', 'Hg', 'La', 'O', 'Sr')\",OTHERS,False\nmp-758428,\"{'Ti': 6.0, 'Nb': 2.0, 'O': 16.0}\",\"('Nb', 'O', 'Ti')\",OTHERS,False\nmp-1101460,\"{'Nb': 4.0, 'Cr': 4.0, 'O': 16.0}\",\"('Cr', 'Nb', 'O')\",OTHERS,False\nmp-1177320,\"{'Li': 8.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-625762,\"{'Al': 8.0, 'H': 24.0, 'O': 24.0}\",\"('Al', 'H', 'O')\",OTHERS,False\nmp-1207411,\"{'Zr': 12.0, 'Ga': 4.0, 'Ni': 2.0, 'H': 20.0}\",\"('Ga', 'H', 'Ni', 'Zr')\",OTHERS,False\nmp-1183475,\"{'Ca': 2.0, 'Br': 2.0}\",\"('Br', 'Ca')\",OTHERS,False\nmp-979751,\"{'Ta': 1.0, 'Ti': 1.0, 'Fe': 2.0}\",\"('Fe', 'Ta', 'Ti')\",OTHERS,False\nmp-1222066,\"{'Mg': 1.0, 'Ti': 4.0, 'Mn': 1.0, 'Zn': 1.0, 'Ni': 1.0, 'O': 12.0}\",\"('Mg', 'Mn', 'Ni', 'O', 'Ti', 'Zn')\",OTHERS,False\nmp-1214618,\"{'Ba': 6.0, 'Cd': 2.0, 'Ir': 4.0, 'O': 18.0}\",\"('Ba', 'Cd', 'Ir', 'O')\",OTHERS,False\nmp-1227456,\"{'Ca': 10.0, 'Ge': 6.0, 'F': 1.0}\",\"('Ca', 'F', 'Ge')\",OTHERS,False\nmp-1246475,\"{'Ga': 4.0, 'Fe': 4.0, 'N': 8.0}\",\"('Fe', 'Ga', 'N')\",OTHERS,False\nmp-20745,{'Pb': 2.0},\"('Pb',)\",OTHERS,False\nmp-1185981,\"{'Mn': 2.0, 'Ir': 1.0, 'Rh': 1.0}\",\"('Ir', 'Mn', 'Rh')\",OTHERS,False\nmp-28274,\"{'F': 24.0, 'In': 4.0, 'Ba': 6.0}\",\"('Ba', 'F', 'In')\",OTHERS,False\nmp-1216875,\"{'Tm': 2.0, 'Cu': 6.0, 'Te': 6.0}\",\"('Cu', 'Te', 'Tm')\",OTHERS,False\nmp-755620,\"{'Mn': 1.0, 'Co': 5.0, 'O': 12.0}\",\"('Co', 'Mn', 'O')\",OTHERS,False\nmp-540469,\"{'Li': 2.0, 'Co': 3.0, 'P': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'O', 'P')\",OTHERS,False\nmp-20725,\"{'Pb': 2.0, 'O': 4.0}\",\"('O', 'Pb')\",OTHERS,False\nmp-40374,\"{'Na': 6.0, 'Ti': 1.0, 'Mn': 1.0, 'Si': 6.0, 'O': 18.0}\",\"('Mn', 'Na', 'O', 'Si', 'Ti')\",OTHERS,False\nmp-1080096,\"{'Pt': 1.0, 'S': 6.0, 'N': 2.0}\",\"('N', 'Pt', 'S')\",OTHERS,False\nmp-1208658,\"{'Ta': 2.0, 'P': 8.0, 'S': 20.0, 'Cl': 10.0}\",\"('Cl', 'P', 'S', 'Ta')\",OTHERS,False\nmp-8013,\"{'F': 6.0, 'K': 1.0, 'Ru': 1.0}\",\"('F', 'K', 'Ru')\",OTHERS,False\nmp-21879,\"{'O': 6.0, 'Ba': 2.0, 'Ce': 1.0, 'Pt': 1.0}\",\"('Ba', 'Ce', 'O', 'Pt')\",OTHERS,False\nmp-1225453,\"{'Er': 14.0, 'Ag': 51.0}\",\"('Ag', 'Er')\",OTHERS,False\nmp-1217994,\"{'Ta': 2.0, 'Zn': 1.0, 'S': 4.0}\",\"('S', 'Ta', 'Zn')\",OTHERS,False\nmp-1246036,\"{'Ba': 4.0, 'Sn': 4.0, 'N': 8.0}\",\"('Ba', 'N', 'Sn')\",OTHERS,False\nmp-1199458,\"{'Co': 12.0, 'B': 6.0, 'P': 24.0, 'H': 12.0, 'Pb': 24.0, 'Cl': 6.0, 'O': 108.0}\",\"('B', 'Cl', 'Co', 'H', 'O', 'P', 'Pb')\",OTHERS,False\nmp-978960,\"{'Sm': 3.0, 'Tm': 1.0}\",\"('Sm', 'Tm')\",OTHERS,False\nmp-1029059,\"{'Te': 4.0, 'Mo': 1.0, 'W': 3.0, 'Se': 2.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-626785,\"{'K': 2.0, 'H': 2.0, 'O': 2.0}\",\"('H', 'K', 'O')\",OTHERS,False\nmp-1183547,\"{'Ca': 1.0, 'Sb': 1.0, 'Pd': 2.0}\",\"('Ca', 'Pd', 'Sb')\",OTHERS,False\nmp-7074,\"{'O': 12.0, 'Na': 2.0, 'Si': 4.0, 'Sc': 2.0}\",\"('Na', 'O', 'Sc', 'Si')\",OTHERS,False\nmp-1213477,\"{'Cs': 4.0, 'Hf': 24.0, 'C': 4.0, 'Cl': 60.0}\",\"('C', 'Cl', 'Cs', 'Hf')\",OTHERS,False\nmp-651051,\"{'Te': 8.0, 'W': 12.0, 'C': 60.0, 'O': 60.0}\",\"('C', 'O', 'Te', 'W')\",OTHERS,False\nmp-24142,\"{'H': 8.0, 'O': 4.0, 'F': 10.0, 'Mg': 2.0, 'Al': 2.0}\",\"('Al', 'F', 'H', 'Mg', 'O')\",OTHERS,False\nmp-772808,\"{'K': 4.0, 'Bi': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('Bi', 'C', 'K', 'O', 'P')\",OTHERS,False\nmp-1191331,\"{'Ti': 16.0, 'Co': 8.0}\",\"('Co', 'Ti')\",OTHERS,False\nmp-698397,\"{'Sn': 1.0, 'H': 24.0, 'C': 6.0, 'N': 2.0, 'O': 2.0, 'F': 6.0}\",\"('C', 'F', 'H', 'N', 'O', 'Sn')\",OTHERS,False\nmp-780421,\"{'La': 6.0, 'Al': 6.0, 'O': 18.0}\",\"('Al', 'La', 'O')\",OTHERS,False\nmp-1210288,\"{'Na': 6.0, 'Mo': 2.0, 'O': 8.0}\",\"('Mo', 'Na', 'O')\",OTHERS,False\nmp-531773,\"{'Li': 28.0, 'Mg': 22.0, 'N': 25.0}\",\"('Li', 'Mg', 'N')\",OTHERS,False\nmp-1040443,\"{'K': 2.0, 'Ba': 2.0, 'Bi': 2.0, 'Te': 2.0, 'O': 12.0}\",\"('Ba', 'Bi', 'K', 'O', 'Te')\",OTHERS,False\nmp-625175,\"{'H': 12.0, 'Cl': 4.0, 'O': 20.0}\",\"('Cl', 'H', 'O')\",OTHERS,False\nmp-1215110,\"{'Co': 4.0, 'Mo': 12.0, 'O': 72.0}\",\"('Co', 'Mo', 'O')\",OTHERS,False\nmp-769122,\"{'Li': 8.0, 'Sb': 4.0, 'H': 12.0, 'O': 6.0, 'F': 20.0}\",\"('F', 'H', 'Li', 'O', 'Sb')\",OTHERS,False\nmp-867753,\"{'Cu': 1.0, 'Sb': 1.0, 'Rh': 2.0}\",\"('Cu', 'Rh', 'Sb')\",OTHERS,False\nmp-1200385,\"{'Eu': 12.0, 'Sb': 16.0, 'Se': 36.0}\",\"('Eu', 'Sb', 'Se')\",OTHERS,False\nmp-733877,\"{'Ca': 4.0, 'B': 12.0, 'H': 52.0, 'O': 48.0}\",\"('B', 'Ca', 'H', 'O')\",OTHERS,False\nmp-1211998,\"{'La': 2.0, 'In': 4.0, 'Cu': 18.0}\",\"('Cu', 'In', 'La')\",OTHERS,False\nmp-1188828,\"{'Ba': 4.0, 'Br': 8.0, 'O': 4.0}\",\"('Ba', 'Br', 'O')\",OTHERS,False\nmp-1200777,\"{'Er': 10.0, 'Ge': 20.0, 'Rh': 8.0}\",\"('Er', 'Ge', 'Rh')\",OTHERS,False\nmp-1195619,\"{'Pb': 16.0, 'N': 8.0, 'O': 40.0}\",\"('N', 'O', 'Pb')\",OTHERS,False\nmp-784631,\"{'Cr': 1.0, 'Ni': 2.0}\",\"('Cr', 'Ni')\",OTHERS,False\nmp-849556,\"{'Li': 6.0, 'Mn': 9.0, 'Si': 6.0, 'O': 24.0}\",\"('Li', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1210612,\"{'P': 4.0, 'N': 8.0, 'O': 16.0}\",\"('N', 'O', 'P')\",OTHERS,False\nmp-13610,\"{'F': 6.0, 'Zn': 1.0, 'Pb': 1.0}\",\"('F', 'Pb', 'Zn')\",OTHERS,False\nmp-776594,\"{'Mn': 8.0, 'O': 13.0, 'F': 3.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-1120780,\"{'Au': 4.0, 'Cl': 4.0}\",\"('Au', 'Cl')\",OTHERS,False\nmp-549,\"{'Sc': 1.0, 'Sb': 1.0}\",\"('Sb', 'Sc')\",OTHERS,False\nmp-1184898,\"{'K': 3.0, 'Ga': 1.0}\",\"('Ga', 'K')\",OTHERS,False\nmp-1205959,\"{'Lu': 3.0, 'Mg': 3.0, 'Tl': 3.0}\",\"('Lu', 'Mg', 'Tl')\",OTHERS,False\nmp-555515,\"{'Cu': 2.0, 'Ag': 8.0, 'Te': 2.0, 'O': 12.0}\",\"('Ag', 'Cu', 'O', 'Te')\",OTHERS,False\nmp-1232078,\"{'Er': 8.0, 'Mg': 4.0, 'Se': 16.0}\",\"('Er', 'Mg', 'Se')\",OTHERS,False\nmp-766058,\"{'Na': 2.0, 'Fe': 16.0, 'O': 24.0}\",\"('Fe', 'Na', 'O')\",OTHERS,False\nmp-1096055,\"{'Y': 2.0, 'Zn': 1.0, 'Pd': 1.0}\",\"('Pd', 'Y', 'Zn')\",OTHERS,False\nmp-1206018,\"{'Mn': 2.0, 'Ge': 2.0, 'As': 4.0}\",\"('As', 'Ge', 'Mn')\",OTHERS,False\nmvc-649,\"{'Ba': 2.0, 'Y': 1.0, 'Sn': 3.0, 'O': 8.0}\",\"('Ba', 'O', 'Sn', 'Y')\",OTHERS,False\nmp-1215982,\"{'Yb': 3.0, 'Fe': 3.0, 'S': 8.0}\",\"('Fe', 'S', 'Yb')\",OTHERS,False\nmp-561529,\"{'Eu': 8.0, 'K': 4.0, 'Si': 16.0, 'O': 40.0, 'F': 4.0}\",\"('Eu', 'F', 'K', 'O', 'Si')\",OTHERS,False\nmp-1183203,\"{'Al': 1.0, 'Ge': 3.0}\",\"('Al', 'Ge')\",OTHERS,False\nmp-1147737,\"{'Li': 4.0, 'Zn': 1.0, 'P': 2.0, 'S': 8.0}\",\"('Li', 'P', 'S', 'Zn')\",OTHERS,False\nmp-29391,\"{'Sb': 4.0, 'Cl': 12.0, 'F': 8.0}\",\"('Cl', 'F', 'Sb')\",OTHERS,False\nmp-762632,\"{'Li': 2.0, 'Fe': 2.0, 'Si': 2.0, 'O': 8.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1245632,\"{'Re': 2.0, 'Ni': 2.0, 'N': 4.0}\",\"('N', 'Ni', 'Re')\",OTHERS,False\nmp-1175401,\"{'Li': 9.0, 'Co': 7.0, 'O': 16.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmvc-4436,\"{'Zn': 12.0, 'Co': 8.0, 'Ge': 12.0, 'O': 48.0}\",\"('Co', 'Ge', 'O', 'Zn')\",OTHERS,False\nmp-18473,\"{'O': 16.0, 'Rb': 2.0, 'Mo': 4.0, 'La': 2.0}\",\"('La', 'Mo', 'O', 'Rb')\",OTHERS,False\nmp-1215594,\"{'Yb': 1.0, 'Gd': 3.0, 'F': 12.0}\",\"('F', 'Gd', 'Yb')\",OTHERS,False\nmp-758890,\"{'Li': 2.0, 'Ag': 4.0, 'F': 8.0}\",\"('Ag', 'F', 'Li')\",OTHERS,False\nmp-764009,\"{'Mn': 6.0, 'O': 7.0, 'F': 5.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-20771,\"{'Zn': 2.0, 'Ge': 2.0, 'Eu': 1.0}\",\"('Eu', 'Ge', 'Zn')\",OTHERS,False\nmp-17138,\"{'O': 24.0, 'P': 4.0, 'K': 4.0, 'Mo': 4.0}\",\"('K', 'Mo', 'O', 'P')\",OTHERS,False\nmp-1097593,\"{'Cu': 1.0, 'Sb': 1.0, 'Pd': 2.0}\",\"('Cu', 'Pd', 'Sb')\",OTHERS,False\nmp-1184788,\"{'Ge': 2.0, 'Rh': 6.0}\",\"('Ge', 'Rh')\",OTHERS,False\nmp-776410,\"{'Mg': 8.0, 'N': 16.0, 'O': 48.0}\",\"('Mg', 'N', 'O')\",OTHERS,False\nmp-5077,\"{'Li': 2.0, 'Na': 1.0, 'Sb': 1.0}\",\"('Li', 'Na', 'Sb')\",OTHERS,False\nmp-1112164,\"{'K': 2.0, 'Er': 1.0, 'Cu': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cu', 'Er', 'K')\",OTHERS,False\nmp-978534,\"{'Si': 2.0, 'Ge': 2.0}\",\"('Ge', 'Si')\",OTHERS,False\nmp-758776,\"{'Li': 6.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1223668,\"{'K': 1.0, 'Mn': 1.0, 'Sn': 3.0, 'O': 8.0}\",\"('K', 'Mn', 'O', 'Sn')\",OTHERS,False\nmp-1204107,\"{'Al': 8.0, 'Fe': 4.0, 'Si': 10.0, 'O': 36.0}\",\"('Al', 'Fe', 'O', 'Si')\",OTHERS,False\nmp-1228111,\"{'Ba': 1.0, 'Nb': 2.0, 'Fe': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Ba', 'Fe', 'Nb', 'O', 'P')\",OTHERS,False\nmp-1222388,\"{'Li': 1.0, 'Mo': 6.0, 'Se': 6.0, 'S': 2.0}\",\"('Li', 'Mo', 'S', 'Se')\",OTHERS,False\nmp-778867,\"{'Li': 9.0, 'Mn': 12.0, 'B': 12.0, 'O': 36.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-557961,\"{'Sb': 6.0, 'C': 6.0, 'N': 6.0, 'Cl': 24.0, 'O': 6.0}\",\"('C', 'Cl', 'N', 'O', 'Sb')\",OTHERS,False\nmp-1079874,\"{'Er': 3.0, 'Sn': 3.0, 'Pd': 3.0}\",\"('Er', 'Pd', 'Sn')\",OTHERS,False\nmp-1199988,\"{'Al': 2.0, 'Pb': 6.0, 'S': 2.0, 'O': 22.0}\",\"('Al', 'O', 'Pb', 'S')\",OTHERS,False\nmp-1097485,\"{'Ca': 1.0, 'La': 1.0, 'Au': 2.0}\",\"('Au', 'Ca', 'La')\",OTHERS,False\nmp-27642,\"{'Br': 20.0, 'Pa': 4.0}\",\"('Br', 'Pa')\",OTHERS,False\nmvc-3734,\"{'Zn': 4.0, 'Cr': 4.0, 'F': 20.0}\",\"('Cr', 'F', 'Zn')\",OTHERS,False\nmp-730585,\"{'Li': 8.0, 'B': 8.0, 'H': 16.0, 'O': 8.0, 'F': 32.0}\",\"('B', 'F', 'H', 'Li', 'O')\",OTHERS,False\nmp-1094069,\"{'Sr': 2.0, 'Cd': 2.0, 'Cu': 1.0, 'S': 2.0, 'O': 2.0}\",\"('Cd', 'Cu', 'O', 'S', 'Sr')\",OTHERS,False\nmp-759591,\"{'Sr': 20.0, 'V': 15.0, 'O': 49.0}\",\"('O', 'Sr', 'V')\",OTHERS,False\nmp-865430,\"{'Y': 1.0, 'Te': 1.0}\",\"('Te', 'Y')\",OTHERS,False\nmp-1178550,\"{'Al': 4.0, 'In': 4.0, 'O': 12.0}\",\"('Al', 'In', 'O')\",OTHERS,False\nmp-1214951,\"{'Al': 2.0, 'Zn': 20.0, 'B': 16.0, 'Rh': 36.0}\",\"('Al', 'B', 'Rh', 'Zn')\",OTHERS,False\nmp-774064,\"{'Li': 2.0, 'Sb': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Li', 'O', 'P', 'Sb')\",OTHERS,False\nmp-1228974,\"{'Al': 4.0, 'V': 4.0, 'Co': 4.0}\",\"('Al', 'Co', 'V')\",OTHERS,False\nmp-770431,\"{'Ho': 8.0, 'Se': 12.0, 'O': 48.0}\",\"('Ho', 'O', 'Se')\",OTHERS,False\nmp-1194267,\"{'Cu': 6.0, 'Te': 2.0, 'Pb': 2.0, 'O': 16.0}\",\"('Cu', 'O', 'Pb', 'Te')\",OTHERS,False\nmp-849741,\"{'Ni': 8.0, 'P': 24.0, 'O': 72.0}\",\"('Ni', 'O', 'P')\",OTHERS,False\nmp-1193985,\"{'Ta': 2.0, 'Co': 21.0, 'B': 6.0}\",\"('B', 'Co', 'Ta')\",OTHERS,False\nmp-1019738,\"{'Eu': 8.0, 'Ba': 4.0, 'O': 16.0}\",\"('Ba', 'Eu', 'O')\",OTHERS,False\nmp-1208033,\"{'Tl': 2.0, 'Pt': 6.0, 'O': 8.0}\",\"('O', 'Pt', 'Tl')\",OTHERS,False\nmp-677716,\"{'Rb': 4.0, 'Al': 12.0, 'Cd': 4.0, 'Si': 12.0, 'O': 48.0}\",\"('Al', 'Cd', 'O', 'Rb', 'Si')\",OTHERS,False\nAu 37 In 39.6 Ca 12.6,\"{'Au': 37.0, 'In': 39.6, 'Ca': 12.6}\",\"('Au', 'Ca', 'In')\",AC,False\nmp-1224061,\"{'In': 2.0, 'Te': 1.0, 'As': 1.0}\",\"('As', 'In', 'Te')\",OTHERS,False\nmp-1215182,\"{'Zr': 1.0, 'Ti': 1.0, 'C': 2.0}\",\"('C', 'Ti', 'Zr')\",OTHERS,False\nmp-1223990,\"{'In': 4.0, 'Bi': 1.0}\",\"('Bi', 'In')\",OTHERS,False\nmp-690672,\"{'K': 6.0, 'Na': 2.0, 'Fe': 2.0, 'Cl': 12.0}\",\"('Cl', 'Fe', 'K', 'Na')\",OTHERS,False\nmp-1222492,\"{'Li': 3.0, 'Er': 1.0, 'Br': 6.0}\",\"('Br', 'Er', 'Li')\",OTHERS,False\nmp-1227726,\"{'Ca': 8.0, 'Mn': 4.0, 'Al': 4.0, 'O': 22.0}\",\"('Al', 'Ca', 'Mn', 'O')\",OTHERS,False\nmp-7818,\"{'Se': 2.0, 'Pd': 8.0}\",\"('Pd', 'Se')\",OTHERS,False\nmp-6563,\"{'Al': 36.0, 'Si': 24.0, 'Fe': 16.0, 'Tb': 4.0}\",\"('Al', 'Fe', 'Si', 'Tb')\",OTHERS,False\nmp-1181047,\"{'Ho': 10.0, 'Sb': 2.0, 'Au': 4.0}\",\"('Au', 'Ho', 'Sb')\",OTHERS,False\nmp-733612,\"{'Rb': 4.0, 'H': 12.0, 'S': 8.0, 'O': 32.0}\",\"('H', 'O', 'Rb', 'S')\",OTHERS,False\nmp-1203791,\"{'B': 8.0, 'P': 4.0, 'H': 80.0, 'C': 20.0, 'N': 4.0}\",\"('B', 'C', 'H', 'N', 'P')\",OTHERS,False\nmp-6782,\"{'Li': 2.0, 'O': 6.0, 'Zr': 1.0, 'Te': 1.0}\",\"('Li', 'O', 'Te', 'Zr')\",OTHERS,False\nmp-865274,\"{'Tl': 8.0, 'In': 8.0, 'S': 16.0}\",\"('In', 'S', 'Tl')\",OTHERS,False\nmp-1185024,\"{'La': 1.0, 'Ag': 3.0}\",\"('Ag', 'La')\",OTHERS,False\nmp-1211967,\"{'K': 4.0, 'Sr': 24.0, 'Sc': 4.0, 'Si': 16.0, 'O': 64.0}\",\"('K', 'O', 'Sc', 'Si', 'Sr')\",OTHERS,False\nmp-24013,\"{'H': 144.0, 'N': 48.0, 'Cl': 40.0, 'Cr': 8.0, 'Cu': 8.0}\",\"('Cl', 'Cr', 'Cu', 'H', 'N')\",OTHERS,False\nmp-30053,\"{'C': 2.0, 'N': 2.0, 'Na': 2.0}\",\"('C', 'N', 'Na')\",OTHERS,False\nmp-1401,\"{'Zn': 4.0, 'Zr': 2.0}\",\"('Zn', 'Zr')\",OTHERS,False\nmp-1185522,\"{'Lu': 6.0, 'Tl': 2.0}\",\"('Lu', 'Tl')\",OTHERS,False\nmp-697924,\"{'Mg': 1.0, 'Co': 1.0, 'H': 16.0, 'Cl': 4.0, 'O': 8.0}\",\"('Cl', 'Co', 'H', 'Mg', 'O')\",OTHERS,False\nmp-1213915,\"{'Ce': 6.0, 'Zn': 6.0, 'Co': 12.0}\",\"('Ce', 'Co', 'Zn')\",OTHERS,False\nmp-1074264,\"{'Mg': 8.0, 'Si': 6.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1211564,\"{'Li': 4.0, 'Er': 16.0, 'Ge': 16.0}\",\"('Er', 'Ge', 'Li')\",OTHERS,False\nmp-30557,\"{'Co': 9.0, 'Ga': 6.0, 'Y': 3.0}\",\"('Co', 'Ga', 'Y')\",OTHERS,False\nmp-978522,\"{'Sm': 1.0, 'Y': 1.0, 'Ir': 2.0}\",\"('Ir', 'Sm', 'Y')\",OTHERS,False\nmp-755210,\"{'Y': 4.0, 'Cu': 2.0, 'O': 7.0}\",\"('Cu', 'O', 'Y')\",OTHERS,False\nmp-1228930,\"{'Ba': 6.0, 'La': 2.0, 'V': 6.0, 'O': 24.0}\",\"('Ba', 'La', 'O', 'V')\",OTHERS,False\nmp-1238874,\"{'Ti': 4.0, 'Cr': 4.0, 'Ag': 4.0, 'S': 16.0}\",\"('Ag', 'Cr', 'S', 'Ti')\",OTHERS,False\nmp-1223292,\"{'La': 2.0, 'Cu': 5.0, 'Ni': 5.0}\",\"('Cu', 'La', 'Ni')\",OTHERS,False\nmp-672689,\"{'Hf': 6.0, 'Co': 16.0, 'Si': 7.0}\",\"('Co', 'Hf', 'Si')\",OTHERS,False\nmp-781060,\"{'Mn': 8.0, 'O': 4.0, 'F': 12.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-12024,\"{'V': 4.0, 'Se': 8.0, 'Rb': 4.0}\",\"('Rb', 'Se', 'V')\",OTHERS,False\nmp-1206169,\"{'Sr': 2.0, 'Cu': 2.0, 'Si': 2.0}\",\"('Cu', 'Si', 'Sr')\",OTHERS,False\nmp-1219552,\"{'Rb': 2.0, 'Ti': 6.0, 'Nb': 2.0, 'O': 18.0}\",\"('Nb', 'O', 'Rb', 'Ti')\",OTHERS,False\nmp-685632,\"{'Ba': 7.0, 'U': 7.0, 'O': 24.0}\",\"('Ba', 'O', 'U')\",OTHERS,False\nmp-1190037,\"{'Nb': 6.0, 'Fe': 2.0, 'Se': 12.0}\",\"('Fe', 'Nb', 'Se')\",OTHERS,False\nmp-754634,\"{'Li': 6.0, 'W': 1.0, 'O': 6.0}\",\"('Li', 'O', 'W')\",OTHERS,False\nmp-1029236,\"{'Te': 6.0, 'Mo': 2.0, 'W': 2.0, 'Se': 2.0}\",\"('Mo', 'Se', 'Te', 'W')\",OTHERS,False\nmp-631405,\"{'Zr': 1.0, 'Tc': 1.0, 'Cl': 1.0}\",\"('Cl', 'Tc', 'Zr')\",OTHERS,False\nmp-1181340,\"{'Fe': 6.0, 'O': 8.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-1187300,\"{'Tb': 3.0, 'Pm': 1.0}\",\"('Pm', 'Tb')\",OTHERS,False\nZn 77 Sc 8 Er 8 Fe 7,\"{'Zn': 77.0, 'Sc': 8.0, 'Er': 8.0, 'Fe': 7.0}\",\"('Er', 'Fe', 'Sc', 'Zn')\",QC,False\nmp-1245945,\"{'K': 2.0, 'Sn': 1.0, 'N': 2.0}\",\"('K', 'N', 'Sn')\",OTHERS,False\nmp-1201836,\"{'Ti': 20.0, 'As': 12.0}\",\"('As', 'Ti')\",OTHERS,False\nmp-642651,\"{'Yb': 8.0, 'Ge': 16.0, 'Ru': 12.0}\",\"('Ge', 'Ru', 'Yb')\",OTHERS,False\nmp-625465,\"{'Tm': 2.0, 'H': 6.0, 'O': 6.0}\",\"('H', 'O', 'Tm')\",OTHERS,False\nmp-30364,\"{'Ba': 1.0, 'Au': 5.0}\",\"('Au', 'Ba')\",OTHERS,False\nmp-1074924,\"{'Mg': 6.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-865329,\"{'Lu': 2.0, 'Mg': 1.0, 'Tc': 1.0}\",\"('Lu', 'Mg', 'Tc')\",OTHERS,False\nmp-759096,\"{'Na': 7.0, 'W': 12.0, 'O': 36.0}\",\"('Na', 'O', 'W')\",OTHERS,False\nmp-1207492,\"{'Zn': 3.0, 'P': 2.0, 'H': 4.0, 'O': 8.0}\",\"('H', 'O', 'P', 'Zn')\",OTHERS,False\nmp-867669,\"{'Li': 8.0, 'V': 2.0, 'F': 12.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-772826,\"{'K': 8.0, 'Li': 4.0, 'Ti': 8.0, 'As': 8.0, 'O': 40.0}\",\"('As', 'K', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-1207673,\"{'Yb': 8.0, 'Sn': 20.0, 'Pd': 12.0}\",\"('Pd', 'Sn', 'Yb')\",OTHERS,False\nmp-862331,\"{'La': 2.0, 'Cu': 1.0, 'Ru': 1.0}\",\"('Cu', 'La', 'Ru')\",OTHERS,False\nmp-1096982,\"{'H': 2.0, 'C': 2.0, 'S': 1.0}\",\"('C', 'H', 'S')\",OTHERS,False\nmp-867815,\"{'Ac': 2.0, 'Ga': 6.0}\",\"('Ac', 'Ga')\",OTHERS,False\nmp-1184248,\"{'Er': 1.0, 'Tm': 1.0, 'Pd': 2.0}\",\"('Er', 'Pd', 'Tm')\",OTHERS,False\nmp-23130,\"{'O': 2.0, 'Cl': 2.0, 'Co': 1.0, 'Sr': 2.0}\",\"('Cl', 'Co', 'O', 'Sr')\",OTHERS,False\nmvc-277,\"{'Sr': 4.0, 'Cu': 4.0, 'Sn': 2.0, 'O': 14.0}\",\"('Cu', 'O', 'Sn', 'Sr')\",OTHERS,False\nmp-683792,\"{'Fe': 8.0, 'Os': 4.0, 'C': 48.0, 'O': 48.0}\",\"('C', 'Fe', 'O', 'Os')\",OTHERS,False\nmp-557145,\"{'Cr': 4.0, 'Sb': 4.0, 'F': 40.0}\",\"('Cr', 'F', 'Sb')\",OTHERS,False\nmp-1212754,\"{'Gd': 2.0, 'Cl': 6.0, 'O': 24.0}\",\"('Cl', 'Gd', 'O')\",OTHERS,False\nmp-1211805,\"{'Pr': 16.0, 'Sb': 28.0, 'Rh': 34.0}\",\"('Pr', 'Rh', 'Sb')\",OTHERS,False\nmp-510709,\"{'Sr': 8.0, 'As': 8.0, 'O': 40.0, 'H': 24.0}\",\"('As', 'H', 'O', 'Sr')\",OTHERS,False\nmp-18493,\"{'O': 24.0, 'Na': 8.0, 'P': 6.0, 'Fe': 4.0}\",\"('Fe', 'Na', 'O', 'P')\",OTHERS,False\nmp-684000,\"{'Eu': 8.0, 'Si': 12.0, 'Ni': 32.0}\",\"('Eu', 'Ni', 'Si')\",OTHERS,False\nmp-1352,\"{'N': 1.0, 'Zr': 1.0}\",\"('N', 'Zr')\",OTHERS,False\nmp-1187999,\"{'Yb': 6.0, 'Lu': 2.0}\",\"('Lu', 'Yb')\",OTHERS,False\nmp-1190145,\"{'Na': 4.0, 'In': 4.0, 'Ge': 2.0, 'S': 12.0}\",\"('Ge', 'In', 'Na', 'S')\",OTHERS,False\nmp-24366,\"{'K': 4.0, 'Zn': 2.0, 'H': 24.0, 'S': 4.0, 'O': 28.0}\",\"('H', 'K', 'O', 'S', 'Zn')\",OTHERS,False\nmp-1025266,\"{'Tb': 2.0, 'Cr': 2.0, 'C': 3.0}\",\"('C', 'Cr', 'Tb')\",OTHERS,False\nmp-9529,\"{'Ge': 4.0, 'Rh': 4.0, 'Dy': 4.0}\",\"('Dy', 'Ge', 'Rh')\",OTHERS,False\nmp-754050,\"{'Mn': 2.0, 'O': 3.0, 'F': 1.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-1036237,\"{'Mg': 14.0, 'Mn': 1.0, 'Fe': 1.0, 'O': 16.0}\",\"('Fe', 'Mg', 'Mn', 'O')\",OTHERS,False\nmp-1211532,\"{'K': 4.0, 'Tm': 4.0, 'Mo': 8.0, 'O': 32.0}\",\"('K', 'Mo', 'O', 'Tm')\",OTHERS,False\nmp-556939,\"{'Np': 2.0, 'I': 2.0, 'O': 2.0}\",\"('I', 'Np', 'O')\",OTHERS,False\nmp-978543,\"{'Sm': 1.0, 'Er': 1.0, 'Tl': 2.0}\",\"('Er', 'Sm', 'Tl')\",OTHERS,False\nmp-1182571,\"{'Ca': 8.0, 'Be': 16.0, 'P': 12.0, 'O': 80.0}\",\"('Be', 'Ca', 'O', 'P')\",OTHERS,False\nmp-1173794,\"{'Na': 18.0, 'O': 3.0}\",\"('Na', 'O')\",OTHERS,False\nmp-961711,\"{'Zr': 1.0, 'Si': 1.0, 'Pt': 1.0}\",\"('Pt', 'Si', 'Zr')\",OTHERS,False\nmp-1211960,\"{'K': 4.0, 'Ge': 4.0, 'O': 6.0}\",\"('Ge', 'K', 'O')\",OTHERS,False\nmp-36539,\"{'Bi': 2.0, 'K': 2.0, 'Se': 4.0}\",\"('Bi', 'K', 'Se')\",OTHERS,False\nmp-20231,\"{'Pd': 4.0, 'In': 2.0, 'Ce': 2.0}\",\"('Ce', 'In', 'Pd')\",OTHERS,False\nmp-1183867,\"{'Ce': 2.0, 'Cd': 1.0, 'In': 1.0}\",\"('Cd', 'Ce', 'In')\",OTHERS,False\nmp-1096521,\"{'Al': 2.0, 'Fe': 1.0, 'Cu': 1.0}\",\"('Al', 'Cu', 'Fe')\",OTHERS,False\nmp-999576,\"{'Mn': 2.0, 'Si': 1.0, 'Ru': 1.0}\",\"('Mn', 'Ru', 'Si')\",OTHERS,False\nmp-1209037,\"{'Sr': 8.0, 'Cr': 8.0, 'F': 40.0}\",\"('Cr', 'F', 'Sr')\",OTHERS,False\nmp-8766,\"{'S': 4.0, 'Co': 2.0, 'Rb': 4.0}\",\"('Co', 'Rb', 'S')\",OTHERS,False\nmp-1191014,\"{'Ce': 12.0, 'Ni': 4.0, 'Ge': 8.0}\",\"('Ce', 'Ge', 'Ni')\",OTHERS,False\nmp-755271,\"{'Li': 3.0, 'Ni': 3.0, 'B': 3.0, 'O': 9.0}\",\"('B', 'Li', 'Ni', 'O')\",OTHERS,False\nmp-978,\"{'Sr': 8.0, 'Sn': 4.0}\",\"('Sn', 'Sr')\",OTHERS,False\nmp-867908,\"{'La': 1.0, 'Bi': 1.0, 'Au': 2.0}\",\"('Au', 'Bi', 'La')\",OTHERS,False\nmp-8895,\"{'B': 4.0, 'Ca': 2.0, 'Rh': 5.0}\",\"('B', 'Ca', 'Rh')\",OTHERS,False\nmp-753482,\"{'Li': 2.0, 'Mn': 1.0, 'Co': 1.0, 'O': 4.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-759117,\"{'Li': 8.0, 'Sb': 4.0, 'P': 12.0, 'O': 40.0}\",\"('Li', 'O', 'P', 'Sb')\",OTHERS,False\nmp-1248957,\"{'Si': 32.0, 'O': 64.0}\",\"('O', 'Si')\",OTHERS,False\nmp-1205991,\"{'La': 1.0, 'Bi': 2.0, 'Cl': 1.0, 'O': 4.0}\",\"('Bi', 'Cl', 'La', 'O')\",OTHERS,False\nmp-863250,\"{'Ta': 1.0, 'Mn': 2.0, 'Ge': 1.0}\",\"('Ge', 'Mn', 'Ta')\",OTHERS,False\nmp-510085,\"{'Er': 8.0, 'Ti': 12.0, 'Si': 16.0}\",\"('Er', 'Si', 'Ti')\",OTHERS,False\nmp-1209592,\"{'P': 8.0, 'O': 24.0}\",\"('O', 'P')\",OTHERS,False\nmp-1077164,\"{'Li': 1.0, 'Ti': 1.0, 'S': 2.0, 'O': 2.0}\",\"('Li', 'O', 'S', 'Ti')\",OTHERS,False\nmp-1246108,\"{'Mn': 10.0, 'Zn': 1.0, 'N': 8.0}\",\"('Mn', 'N', 'Zn')\",OTHERS,False\nmp-1203960,\"{'U': 4.0, 'H': 24.0, 'Br': 8.0, 'O': 20.0}\",\"('Br', 'H', 'O', 'U')\",OTHERS,False\nmp-763558,\"{'Li': 1.0, 'V': 2.0, 'C': 4.0, 'O': 12.0}\",\"('C', 'Li', 'O', 'V')\",OTHERS,False\nmp-1206944,\"{'La': 3.0, 'Mg': 3.0, 'Cu': 3.0}\",\"('Cu', 'La', 'Mg')\",OTHERS,False\nmp-29903,\"{'F': 20.0, 'Te': 4.0, 'Tl': 4.0}\",\"('F', 'Te', 'Tl')\",OTHERS,False\nmp-1062310,\"{'Lu': 1.0, 'Cd': 2.0}\",\"('Cd', 'Lu')\",OTHERS,False\nmp-695900,\"{'Sb': 4.0, 'H': 4.0, 'C': 4.0, 'S': 4.0, 'O': 12.0, 'F': 32.0}\",\"('C', 'F', 'H', 'O', 'S', 'Sb')\",OTHERS,False\nmp-30145,\"{'Se': 26.0, 'Rb': 4.0, 'Bi': 16.0}\",\"('Bi', 'Rb', 'Se')\",OTHERS,False\nmp-1221327,\"{'Na': 2.0, 'Mn': 1.0, 'Ni': 1.0, 'O': 4.0}\",\"('Mn', 'Na', 'Ni', 'O')\",OTHERS,False\nmp-1237078,\"{'Rb': 2.0, 'U': 1.0, 'Br': 4.0, 'O': 4.0}\",\"('Br', 'O', 'Rb', 'U')\",OTHERS,False\nmp-1212239,\"{'Hg': 6.0, 'Pt': 2.0, 'N': 4.0, 'Cl': 16.0}\",\"('Cl', 'Hg', 'N', 'Pt')\",OTHERS,False\nmp-1182773,\"{'Ba': 4.0, 'Gd': 8.0, 'Te': 16.0}\",\"('Ba', 'Gd', 'Te')\",OTHERS,False\nmp-1102758,\"{'Lu': 4.0, 'Ga': 4.0, 'Ni': 4.0}\",\"('Ga', 'Lu', 'Ni')\",OTHERS,False\nmp-30823,\"{'Os': 6.0, 'Pu': 10.0}\",\"('Os', 'Pu')\",OTHERS,False\nmp-1436,\"{'Ag': 4.0, 'Eu': 2.0}\",\"('Ag', 'Eu')\",OTHERS,False\nmp-973964,\"{'Lu': 2.0, 'Ga': 1.0, 'Ag': 1.0}\",\"('Ag', 'Ga', 'Lu')\",OTHERS,False\nmp-541751,\"{'Bi': 24.0, 'Ni': 8.0, 'I': 6.0}\",\"('Bi', 'I', 'Ni')\",OTHERS,False\nmp-1199681,\"{'Al': 4.0, 'Sn': 4.0, 'P': 4.0, 'H': 72.0, 'C': 24.0, 'Cl': 16.0}\",\"('Al', 'C', 'Cl', 'H', 'P', 'Sn')\",OTHERS,False\nmp-23253,\"{'Rb': 2.0, 'Bi': 4.0}\",\"('Bi', 'Rb')\",OTHERS,False\nmp-616565,\"{'Rb': 8.0, 'Cr': 16.0, 'O': 52.0}\",\"('Cr', 'O', 'Rb')\",OTHERS,False\nmp-851093,\"{'Ni': 6.0, 'P': 8.0, 'O': 28.0}\",\"('Ni', 'O', 'P')\",OTHERS,False\nmp-1114365,\"{'Rb': 2.0, 'Na': 1.0, 'Ir': 1.0, 'F': 6.0}\",\"('F', 'Ir', 'Na', 'Rb')\",OTHERS,False\nmp-755892,\"{'Sr': 2.0, 'La': 4.0, 'Cl': 16.0}\",\"('Cl', 'La', 'Sr')\",OTHERS,False\nmp-1225227,\"{'Fe': 3.0, 'Co': 3.0, 'Si': 2.0}\",\"('Co', 'Fe', 'Si')\",OTHERS,False\nmvc-14364,\"{'Mg': 4.0, 'Sb': 4.0, 'F': 20.0}\",\"('F', 'Mg', 'Sb')\",OTHERS,False\nmp-1245791,\"{'K': 4.0, 'Ni': 4.0, 'N': 4.0}\",\"('K', 'N', 'Ni')\",OTHERS,False\nmp-2819,\"{'In': 1.0, 'Te': 1.0}\",\"('In', 'Te')\",OTHERS,False\nmvc-14220,\"{'Mg': 2.0, 'Mo': 2.0, 'F': 10.0}\",\"('F', 'Mg', 'Mo')\",OTHERS,False\nmp-850908,\"{'Li': 2.0, 'Mg': 11.0, 'W': 12.0, 'O': 48.0}\",\"('Li', 'Mg', 'O', 'W')\",OTHERS,False\nmp-778284,\"{'Li': 4.0, 'V': 3.0, 'Co': 2.0, 'Sn': 3.0, 'O': 16.0}\",\"('Co', 'Li', 'O', 'Sn', 'V')\",OTHERS,False\nmp-1218618,\"{'Sr': 12.0, 'Ca': 2.0, 'Mn': 8.0, 'O': 26.0}\",\"('Ca', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-1246706,\"{'Na': 16.0, 'Ir': 16.0, 'N': 32.0}\",\"('Ir', 'N', 'Na')\",OTHERS,False\nmp-1245695,\"{'Sr': 28.0, 'Re': 4.0, 'N': 24.0}\",\"('N', 'Re', 'Sr')\",OTHERS,False\nmp-1221056,\"{'Na': 2.0, 'Dy': 2.0, 'Ti': 4.0, 'O': 12.0}\",\"('Dy', 'Na', 'O', 'Ti')\",OTHERS,False\nmp-1867,\"{'Ni': 2.0, 'Ta': 4.0}\",\"('Ni', 'Ta')\",OTHERS,False\nmp-555993,\"{'Cr': 2.0, 'Ag': 6.0, 'Cl': 2.0, 'O': 8.0}\",\"('Ag', 'Cl', 'Cr', 'O')\",OTHERS,False\nmp-1114513,\"{'Rb': 2.0, 'Na': 1.0, 'Ce': 1.0, 'I': 6.0}\",\"('Ce', 'I', 'Na', 'Rb')\",OTHERS,False\nmp-762019,\"{'Li': 1.0, 'V': 1.0, 'C': 2.0, 'O': 6.0}\",\"('C', 'Li', 'O', 'V')\",OTHERS,False\nmp-3155,\"{'C': 4.0, 'Sc': 3.0, 'Fe': 1.0}\",\"('C', 'Fe', 'Sc')\",OTHERS,False\nmp-778904,\"{'Mn': 12.0, 'O': 14.0, 'F': 10.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-1106022,\"{'Y': 4.0, 'Ge': 10.0, 'Rh': 6.0}\",\"('Ge', 'Rh', 'Y')\",OTHERS,False\nmp-1018639,\"{'Ti': 1.0, 'Sn': 1.0, 'O': 3.0}\",\"('O', 'Sn', 'Ti')\",OTHERS,False\nmp-553898,\"{'Ge': 12.0, 'Pb': 12.0, 'O': 36.0}\",\"('Ge', 'O', 'Pb')\",OTHERS,False\nmp-1215539,\"{'Yb': 1.0, 'Mn': 2.0, 'Bi': 1.0, 'Sb': 1.0}\",\"('Bi', 'Mn', 'Sb', 'Yb')\",OTHERS,False\nmp-698074,\"{'Ca': 6.0, 'H': 6.0, 'S': 6.0, 'O': 27.0}\",\"('Ca', 'H', 'O', 'S')\",OTHERS,False\nmp-690772,\"{'V': 10.0, 'S': 16.0}\",\"('S', 'V')\",OTHERS,False\nmp-1222856,\"{'La': 2.0, 'Ge': 4.0, 'Pd': 2.0, 'Rh': 2.0}\",\"('Ge', 'La', 'Pd', 'Rh')\",OTHERS,False\nmp-1184657,{'Hg': 8.0},\"('Hg',)\",OTHERS,False\nmp-1223244,\"{'La': 2.0, 'Al': 3.0, 'Ge': 1.0}\",\"('Al', 'Ge', 'La')\",OTHERS,False\nmp-1016883,\"{'Zr': 1.0, 'Zn': 1.0, 'O': 3.0}\",\"('O', 'Zn', 'Zr')\",OTHERS,False\nmp-1025068,\"{'Nd': 1.0, 'B': 2.0, 'Ir': 3.0}\",\"('B', 'Ir', 'Nd')\",OTHERS,False\nmp-865746,\"{'Ga': 1.0, 'Tc': 2.0, 'W': 1.0}\",\"('Ga', 'Tc', 'W')\",OTHERS,False\nmp-15397,\"{'O': 15.0, 'Al': 2.0, 'Ti': 7.0}\",\"('Al', 'O', 'Ti')\",OTHERS,False\nmp-1142857,\"{'Si': 12.0, 'Mo': 12.0, 'O': 48.0}\",\"('Mo', 'O', 'Si')\",OTHERS,False\nmp-769734,\"{'Sc': 4.0, 'Fe': 4.0, 'O': 12.0}\",\"('Fe', 'O', 'Sc')\",OTHERS,False\nmp-1188757,\"{'Y': 12.0, 'Os': 4.0}\",\"('Os', 'Y')\",OTHERS,False\nmp-505225,\"{'U': 4.0, 'P': 4.0, 'O': 20.0}\",\"('O', 'P', 'U')\",OTHERS,False\nmp-1205693,\"{'Ba': 2.0, 'Dy': 1.0, 'U': 1.0, 'O': 6.0}\",\"('Ba', 'Dy', 'O', 'U')\",OTHERS,False\nmp-29564,\"{'Mg': 6.0, 'Si': 8.0, 'Ba': 4.0}\",\"('Ba', 'Mg', 'Si')\",OTHERS,False\nmp-760432,\"{'Cu': 4.0, 'O': 6.0}\",\"('Cu', 'O')\",OTHERS,False\nmp-558954,\"{'Na': 8.0, 'Sr': 8.0, 'Al': 8.0, 'P': 4.0, 'O': 16.0, 'F': 36.0}\",\"('Al', 'F', 'Na', 'O', 'P', 'Sr')\",OTHERS,False\nmvc-10142,\"{'Mg': 6.0, 'Fe': 12.0, 'O': 24.0}\",\"('Fe', 'Mg', 'O')\",OTHERS,False\nmp-1097204,\"{'Al': 1.0, 'Ga': 1.0, 'Ir': 2.0}\",\"('Al', 'Ga', 'Ir')\",OTHERS,False\nmp-849457,\"{'Li': 3.0, 'Ni': 1.0, 'Sb': 4.0, 'O': 12.0}\",\"('Li', 'Ni', 'O', 'Sb')\",OTHERS,False\nmp-779007,\"{'Li': 10.0, 'Fe': 2.0, 'Co': 3.0, 'Ni': 3.0, 'O': 16.0}\",\"('Co', 'Fe', 'Li', 'Ni', 'O')\",OTHERS,False\nmp-1219869,\"{'Pr': 2.0, 'Fe': 1.0, 'Si': 3.0}\",\"('Fe', 'Pr', 'Si')\",OTHERS,False\nmvc-1093,\"{'Ca': 4.0, 'W': 4.0, 'O': 8.0}\",\"('Ca', 'O', 'W')\",OTHERS,False\nmp-29477,\"{'C': 4.0, 'S': 14.0, 'Cl': 12.0}\",\"('C', 'Cl', 'S')\",OTHERS,False\nmp-29519,\"{'K': 4.0, 'I': 16.0, 'Au': 4.0}\",\"('Au', 'I', 'K')\",OTHERS,False\nmp-1209818,\"{'Ni': 7.0, 'Mo': 9.0, 'P': 6.0}\",\"('Mo', 'Ni', 'P')\",OTHERS,False\nmp-2847,\"{'B': 16.0, 'Er': 4.0}\",\"('B', 'Er')\",OTHERS,False\nmp-1176980,\"{'Li': 12.0, 'Mn': 2.0, 'V': 6.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-849732,\"{'Li': 1.0, 'Sb': 1.0, 'Te': 3.0, 'O': 12.0}\",\"('Li', 'O', 'Sb', 'Te')\",OTHERS,False\nmp-1211672,\"{'Nd': 6.0, 'N': 8.0, 'O': 33.0}\",\"('N', 'Nd', 'O')\",OTHERS,False\nmp-774389,\"{'Li': 4.0, 'Mn': 1.0, 'V': 3.0, 'P': 8.0, 'O': 28.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-27133,\"{'P': 8.0, 'S': 32.0, 'Bi': 8.0}\",\"('Bi', 'P', 'S')\",OTHERS,False\nmp-1191148,\"{'Sr': 2.0, 'Cu': 4.0, 'B': 4.0, 'O': 12.0}\",\"('B', 'Cu', 'O', 'Sr')\",OTHERS,False\nmp-1223195,\"{'La': 4.0, 'U': 2.0, 'Te': 10.0}\",\"('La', 'Te', 'U')\",OTHERS,False\nmp-1221325,\"{'Na': 2.0, 'Pt': 1.0, 'O': 3.0}\",\"('Na', 'O', 'Pt')\",OTHERS,False\nmp-28166,\"{'K': 3.0, 'O': 1.0, 'Br': 1.0}\",\"('Br', 'K', 'O')\",OTHERS,False\nmp-1211737,\"{'K': 8.0, 'Tb': 4.0, 'Cl': 20.0}\",\"('Cl', 'K', 'Tb')\",OTHERS,False\nmvc-6095,\"{'Zn': 2.0, 'Fe': 1.0, 'W': 1.0, 'O': 6.0}\",\"('Fe', 'O', 'W', 'Zn')\",OTHERS,False\nmp-569330,\"{'Ce': 8.0, 'C': 8.0, 'Br': 6.0}\",\"('Br', 'C', 'Ce')\",OTHERS,False\nmp-1222923,\"{'La': 1.0, 'Pr': 3.0, 'Mn': 4.0, 'O': 12.0}\",\"('La', 'Mn', 'O', 'Pr')\",OTHERS,False\nmp-759298,\"{'Li': 10.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1113420,\"{'Eu': 1.0, 'Cs': 2.0, 'Au': 1.0, 'Cl': 6.0}\",\"('Au', 'Cl', 'Cs', 'Eu')\",OTHERS,False\nmp-2514,\"{'Mg': 24.0, 'P': 16.0}\",\"('Mg', 'P')\",OTHERS,False\nZn 58 Mg 40 Lu 2,\"{'Zn': 58.0, 'Mg': 40.0, 'Lu': 2.0}\",\"('Lu', 'Mg', 'Zn')\",QC,False\nCu 48 Sc 15 Ga 34 Mg 3,\"{'Cu': 48.0, 'Sc': 15.0, 'Ga': 34.0, 'Mg': 3.0}\",\"('Cu', 'Ga', 'Mg', 'Sc')\",QC,False\nmp-26691,\"{'Li': 6.0, 'Ni': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-753178,\"{'Li': 1.0, 'Fe': 2.0, 'C': 3.0, 'O': 9.0}\",\"('C', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-680322,\"{'Bi': 17.0, 'P': 5.0, 'Pb': 5.0, 'O': 43.0}\",\"('Bi', 'O', 'P', 'Pb')\",OTHERS,False\nmp-865015,\"{'Mn': 1.0, 'Si': 1.0, 'Rh': 2.0}\",\"('Mn', 'Rh', 'Si')\",OTHERS,False\nmp-1232361,\"{'Cs': 1.0, 'In': 1.0, 'Cu': 6.0, 'O': 8.0}\",\"('Cs', 'Cu', 'In', 'O')\",OTHERS,False\nmp-1226713,\"{'Ce': 1.0, 'Dy': 4.0, 'S': 7.0}\",\"('Ce', 'Dy', 'S')\",OTHERS,False\nmp-631661,\"{'Y': 2.0, 'Fe': 1.0, 'B': 1.0}\",\"('B', 'Fe', 'Y')\",OTHERS,False\nmp-300,\"{'Yb': 1.0, 'Pt': 3.0}\",\"('Pt', 'Yb')\",OTHERS,False\nmp-1111512,\"{'Na': 2.0, 'Sb': 1.0, 'Au': 1.0, 'F': 6.0}\",\"('Au', 'F', 'Na', 'Sb')\",OTHERS,False\nmp-568580,\"{'Na': 40.0, 'Ga': 24.0, 'Sn': 12.0}\",\"('Ga', 'Na', 'Sn')\",OTHERS,False\nmp-643074,\"{'Cs': 4.0, 'P': 4.0, 'H': 4.0, 'O': 14.0}\",\"('Cs', 'H', 'O', 'P')\",OTHERS,False\nmp-753270,\"{'Li': 2.0, 'V': 3.0, 'P': 4.0, 'O': 14.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-975390,\"{'Rb': 1.0, 'Ca': 3.0}\",\"('Ca', 'Rb')\",OTHERS,False\nmp-560801,\"{'Gd': 3.0, 'Al': 9.0, 'B': 12.0, 'O': 36.0}\",\"('Al', 'B', 'Gd', 'O')\",OTHERS,False\nmp-1186463,\"{'Pd': 1.0, 'S': 3.0}\",\"('Pd', 'S')\",OTHERS,False\nmp-1176417,\"{'Mn': 4.0, 'Te': 4.0, 'O': 12.0}\",\"('Mn', 'O', 'Te')\",OTHERS,False\nmp-555085,\"{'K': 2.0, 'Au': 2.0, 'N': 8.0, 'O': 24.0}\",\"('Au', 'K', 'N', 'O')\",OTHERS,False\nmp-779318,\"{'Li': 2.0, 'Cr': 4.0, 'P': 6.0, 'O': 26.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-1223222,\"{'La': 4.0, 'C': 2.0, 'I': 5.0}\",\"('C', 'I', 'La')\",OTHERS,False\nmp-568716,\"{'Sr': 20.0, 'Ag': 20.0}\",\"('Ag', 'Sr')\",OTHERS,False\nmp-557693,\"{'Cs': 8.0, 'Zn': 8.0, 'P': 8.0, 'O': 32.0}\",\"('Cs', 'O', 'P', 'Zn')\",OTHERS,False\nZn 74 Mg 15 Ho 11,\"{'Zn': 74.0, 'Mg': 15.0, 'Ho': 11.0}\",\"('Ho', 'Mg', 'Zn')\",QC,False\nmp-22687,\"{'In': 4.0, 'Ba': 1.0}\",\"('Ba', 'In')\",OTHERS,False\nmp-28341,\"{'Li': 4.0, 'Cl': 16.0, 'Ga': 4.0}\",\"('Cl', 'Ga', 'Li')\",OTHERS,False\nmvc-8344,\"{'Mg': 2.0, 'Mn': 4.0, 'O': 10.0}\",\"('Mg', 'Mn', 'O')\",OTHERS,False\nmp-1186320,\"{'Np': 1.0, 'Hg': 1.0, 'O': 3.0}\",\"('Hg', 'Np', 'O')\",OTHERS,False\nmp-1214561,\"{'Ba': 2.0, 'Tb': 1.0, 'W': 1.0, 'O': 6.0}\",\"('Ba', 'O', 'Tb', 'W')\",OTHERS,False\nmp-752663,\"{'Li': 8.0, 'Fe': 4.0, 'B': 4.0, 'O': 16.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1073794,\"{'Mg': 6.0, 'Si': 6.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-984353,\"{'As': 2.0, 'Au': 6.0}\",\"('As', 'Au')\",OTHERS,False\nmp-1194892,\"{'Si': 2.0, 'P': 8.0, 'Pd': 2.0, 'Pb': 2.0, 'O': 28.0}\",\"('O', 'P', 'Pb', 'Pd', 'Si')\",OTHERS,False\nmp-707342,\"{'Zn': 2.0, 'H': 36.0, 'Ru': 4.0, 'Br': 14.0, 'N': 12.0}\",\"('Br', 'H', 'N', 'Ru', 'Zn')\",OTHERS,False\nmp-1093916,\"{'Hf': 1.0, 'Zr': 1.0, 'Co': 2.0}\",\"('Co', 'Hf', 'Zr')\",OTHERS,False\nmp-680695,\"{'Ce': 6.0, 'B': 4.0, 'Cl': 6.0, 'O': 12.0}\",\"('B', 'Ce', 'Cl', 'O')\",OTHERS,False\nmp-686526,\"{'Na': 4.0, 'Ca': 4.0, 'Al': 4.0, 'Si': 8.0, 'O': 28.0}\",\"('Al', 'Ca', 'Na', 'O', 'Si')\",OTHERS,False\nmp-16932,\"{'Th': 2.0, 'Co': 17.0}\",\"('Co', 'Th')\",OTHERS,False\nmp-696993,\"{'Sn': 8.0, 'H': 8.0, 'S': 4.0, 'O': 20.0, 'F': 8.0}\",\"('F', 'H', 'O', 'S', 'Sn')\",OTHERS,False\nmp-1245621,\"{'Fe': 10.0, 'Ge': 4.0, 'N': 12.0}\",\"('Fe', 'Ge', 'N')\",OTHERS,False\nmvc-1072,\"{'Ca': 4.0, 'Cu': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Ca', 'Cu', 'O', 'P')\",OTHERS,False\nmp-764057,\"{'V': 6.0, 'O': 12.0, 'F': 6.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-13385,\"{'P': 4.0, 'Se': 12.0, 'Ag': 2.0, 'Tm': 2.0}\",\"('Ag', 'P', 'Se', 'Tm')\",OTHERS,False\nmp-773515,\"{'Te': 3.0, 'W': 1.0, 'O': 12.0}\",\"('O', 'Te', 'W')\",OTHERS,False\nmp-13918,\"{'Ge': 2.0, 'Sr': 1.0, 'Au': 2.0}\",\"('Au', 'Ge', 'Sr')\",OTHERS,False\nmvc-1098,\"{'Ba': 2.0, 'V': 3.0, 'O': 8.0}\",\"('Ba', 'O', 'V')\",OTHERS,False\nmp-28989,\"{'Li': 10.0, 'N': 3.0, 'Br': 1.0}\",\"('Br', 'Li', 'N')\",OTHERS,False\nmp-1103877,\"{'Nd': 6.0, 'Ga': 2.0, 'Co': 6.0}\",\"('Co', 'Ga', 'Nd')\",OTHERS,False\nmp-6345,\"{'O': 18.0, 'Ru': 4.0, 'Ba': 6.0, 'Tb': 2.0}\",\"('Ba', 'O', 'Ru', 'Tb')\",OTHERS,False\nmp-510113,\"{'Bi': 8.0, 'Mn': 10.0, 'Ni': 4.0}\",\"('Bi', 'Mn', 'Ni')\",OTHERS,False\nmvc-2664,\"{'Mg': 4.0, 'Ta': 4.0, 'Fe': 2.0, 'O': 16.0}\",\"('Fe', 'Mg', 'O', 'Ta')\",OTHERS,False\nmp-1207719,\"{'Yb': 12.0, 'Ta': 8.0, 'Al': 86.0}\",\"('Al', 'Ta', 'Yb')\",OTHERS,False\nmp-682548,\"{'Cs': 5.0, 'Te': 3.0, 'H': 4.0, 'N': 1.0, 'Cl': 18.0}\",\"('Cl', 'Cs', 'H', 'N', 'Te')\",OTHERS,False\nmp-1190813,\"{'Cs': 4.0, 'U': 2.0, 'Pt': 6.0, 'Se': 12.0}\",\"('Cs', 'Pt', 'Se', 'U')\",OTHERS,False\nmp-1096504,\"{'Sr': 2.0, 'Li': 1.0, 'Pd': 1.0}\",\"('Li', 'Pd', 'Sr')\",OTHERS,False\nmvc-4008,\"{'Zn': 4.0, 'Ni': 4.0, 'O': 12.0}\",\"('Ni', 'O', 'Zn')\",OTHERS,False\nmp-766243,\"{'Y': 8.0, 'Zr': 8.0, 'O': 24.0}\",\"('O', 'Y', 'Zr')\",OTHERS,False\nmp-26692,\"{'Li': 4.0, 'Ni': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-759092,\"{'Li': 12.0, 'Fe': 12.0, 'P': 12.0, 'O': 48.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-1104197,\"{'Ba': 1.0, 'Yb': 1.0, 'Fe': 4.0, 'O': 7.0}\",\"('Ba', 'Fe', 'O', 'Yb')\",OTHERS,False\nmp-21515,\"{'S': 8.0, 'Fe': 6.0}\",\"('Fe', 'S')\",OTHERS,False\nmp-1006256,\"{'Ce': 1.0, 'Y': 1.0, 'Tl': 2.0}\",\"('Ce', 'Tl', 'Y')\",OTHERS,False\nmp-1188061,\"{'Zr': 6.0, 'Sn': 2.0}\",\"('Sn', 'Zr')\",OTHERS,False\nmp-1205648,\"{'Rb': 4.0, 'Ni': 2.0, 'As': 4.0}\",\"('As', 'Ni', 'Rb')\",OTHERS,False\nmp-30097,\"{'Cl': 16.0, 'Te': 14.0, 'Bi': 4.0}\",\"('Bi', 'Cl', 'Te')\",OTHERS,False\nmp-768522,\"{'La': 8.0, 'Ce': 8.0, 'O': 28.0}\",\"('Ce', 'La', 'O')\",OTHERS,False\nmp-1177753,\"{'Li': 6.0, 'Fe': 4.0, 'O': 12.0}\",\"('Fe', 'Li', 'O')\",OTHERS,False\nmp-1244909,\"{'Al': 40.0, 'O': 60.0}\",\"('Al', 'O')\",OTHERS,False\nmp-1189350,\"{'La': 10.0, 'As': 2.0, 'Pb': 6.0}\",\"('As', 'La', 'Pb')\",OTHERS,False\nmp-1080340,\"{'Ce': 2.0, 'Se': 4.0}\",\"('Ce', 'Se')\",OTHERS,False\nmp-780005,\"{'Li': 8.0, 'Mn': 2.0, 'V': 6.0, 'P': 16.0, 'O': 56.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-771026,\"{'Na': 4.0, 'B': 2.0, 'Sb': 2.0, 'S': 2.0, 'O': 14.0}\",\"('B', 'Na', 'O', 'S', 'Sb')\",OTHERS,False\nmp-1075098,\"{'Mg': 6.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-626219,\"{'H': 20.0, 'Cl': 4.0, 'O': 24.0}\",\"('Cl', 'H', 'O')\",OTHERS,False\nmp-1097373,\"{'Sc': 1.0, 'In': 1.0, 'Hg': 2.0}\",\"('Hg', 'In', 'Sc')\",OTHERS,False\nmp-9942,\"{'S': 4.0, 'Ti': 2.0, 'Fe': 1.0}\",\"('Fe', 'S', 'Ti')\",OTHERS,False\nmp-1185069,\"{'La': 1.0, 'Lu': 1.0, 'Tl': 2.0}\",\"('La', 'Lu', 'Tl')\",OTHERS,False\nmp-1184253,\"{'Er': 1.0, 'Tm': 1.0, 'Ag': 2.0}\",\"('Ag', 'Er', 'Tm')\",OTHERS,False\nmp-1212971,\"{'Dy': 2.0, 'Be': 2.0, 'N': 2.0, 'F': 12.0}\",\"('Be', 'Dy', 'F', 'N')\",OTHERS,False\nmp-1074201,\"{'Mg': 8.0, 'Si': 14.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1095140,\"{'Er': 4.0, 'Co': 2.0, 'Ge': 4.0}\",\"('Co', 'Er', 'Ge')\",OTHERS,False\nmp-1200829,\"{'Na': 4.0, 'Sb': 4.0, 'F': 24.0}\",\"('F', 'Na', 'Sb')\",OTHERS,False\nmp-1197782,\"{'K': 6.0, 'V': 10.0, 'Te': 8.0, 'H': 16.0, 'O': 48.0}\",\"('H', 'K', 'O', 'Te', 'V')\",OTHERS,False\nmp-764723,\"{'Li': 2.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1095131,\"{'Sr': 1.0, 'Ta': 2.0, 'O': 7.0}\",\"('O', 'Sr', 'Ta')\",OTHERS,False\nmp-1097388,\"{'Y': 1.0, 'Zn': 1.0, 'Pd': 2.0}\",\"('Pd', 'Y', 'Zn')\",OTHERS,False\nmp-1672,\"{'S': 1.0, 'Ca': 1.0}\",\"('Ca', 'S')\",OTHERS,False\nmp-1175066,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-23297,\"{'F': 6.0, 'Br': 2.0}\",\"('Br', 'F')\",OTHERS,False\nmp-1197684,\"{'Na': 20.0, 'Al': 4.0, 'H': 32.0, 'O': 32.0}\",\"('Al', 'H', 'Na', 'O')\",OTHERS,False\nmp-16455,\"{'S': 10.0, 'Zn': 2.0, 'Ba': 2.0, 'Nd': 4.0}\",\"('Ba', 'Nd', 'S', 'Zn')\",OTHERS,False\nmp-1198676,\"{'Cs': 16.0, 'Fe': 4.0, 'O': 14.0}\",\"('Cs', 'Fe', 'O')\",OTHERS,False\nmp-770745,\"{'Li': 8.0, 'Cr': 8.0, 'O': 28.0}\",\"('Cr', 'Li', 'O')\",OTHERS,False\nmp-760396,\"{'Ta': 2.0, 'Al': 2.0, 'O': 8.0}\",\"('Al', 'O', 'Ta')\",OTHERS,False\nmp-757516,\"{'Li': 4.0, 'Ni': 16.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-1193512,\"{'Cu': 3.0, 'I': 6.0, 'O': 20.0}\",\"('Cu', 'I', 'O')\",OTHERS,False\nmp-1246055,\"{'Fe': 2.0, 'C': 12.0, 'N': 18.0}\",\"('C', 'Fe', 'N')\",OTHERS,False\nmp-20205,\"{'Yb': 8.0, 'Cu': 8.0, 'O': 20.0}\",\"('Cu', 'O', 'Yb')\",OTHERS,False\nmp-1095701,\"{'Mg': 30.0, 'Sn': 1.0, 'C': 1.0, 'O': 32.0}\",\"('C', 'Mg', 'O', 'Sn')\",OTHERS,False\nmp-1209321,\"{'Rb': 8.0, 'O': 16.0, 'F': 8.0}\",\"('F', 'O', 'Rb')\",OTHERS,False\nmp-1194932,\"{'Sm': 12.0, 'Se': 6.0, 'O': 6.0, 'F': 12.0}\",\"('F', 'O', 'Se', 'Sm')\",OTHERS,False\nmp-1079462,\"{'Sm': 2.0, 'Ge': 4.0, 'Pt': 4.0}\",\"('Ge', 'Pt', 'Sm')\",OTHERS,False\nmp-976339,\"{'Li': 1.0, 'Tm': 2.0, 'In': 1.0}\",\"('In', 'Li', 'Tm')\",OTHERS,False\nmp-862762,\"{'Pr': 12.0, 'Co': 4.0}\",\"('Co', 'Pr')\",OTHERS,False\nmp-760823,\"{'Li': 22.0, 'V': 8.0, 'F': 48.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-1187498,\"{'Tl': 3.0, 'Zn': 1.0}\",\"('Tl', 'Zn')\",OTHERS,False\nmp-569898,\"{'Ce': 3.0, 'Ga': 1.0, 'Br': 3.0}\",\"('Br', 'Ce', 'Ga')\",OTHERS,False\nmp-1229238,\"{'Ba': 10.0, 'Gd': 4.0, 'Y': 1.0, 'Cu': 15.0, 'O': 35.0}\",\"('Ba', 'Cu', 'Gd', 'O', 'Y')\",OTHERS,False\nmp-1245543,\"{'Ti': 24.0, 'C': 24.0, 'N': 56.0}\",\"('C', 'N', 'Ti')\",OTHERS,False\nmp-984772,\"{'Ce': 3.0, 'Zn': 1.0}\",\"('Ce', 'Zn')\",OTHERS,False\nmp-1185862,\"{'Mg': 1.0, 'F': 3.0}\",\"('F', 'Mg')\",OTHERS,False\nmp-570900,\"{'K': 16.0, 'Sn': 36.0}\",\"('K', 'Sn')\",OTHERS,False\nmp-1094375,\"{'Y': 10.0, 'Mg': 2.0}\",\"('Mg', 'Y')\",OTHERS,False\nmp-1213845,\"{'Co': 2.0, 'Cu': 4.0, 'Ge': 2.0, 'Se': 8.0}\",\"('Co', 'Cu', 'Ge', 'Se')\",OTHERS,False\nmp-1246723,\"{'Sm': 2.0, 'Mg': 2.0, 'Cr': 2.0, 'S': 8.0}\",\"('Cr', 'Mg', 'S', 'Sm')\",OTHERS,False\nmvc-5049,\"{'Mg': 4.0, 'V': 2.0, 'W': 2.0, 'O': 12.0}\",\"('Mg', 'O', 'V', 'W')\",OTHERS,False\nmp-1186143,\"{'Na': 1.0, 'Lu': 3.0}\",\"('Lu', 'Na')\",OTHERS,False\nmp-867761,\"{'Sc': 2.0, 'Co': 1.0, 'Ru': 1.0}\",\"('Co', 'Ru', 'Sc')\",OTHERS,False\nmp-1215347,\"{'Zr': 6.0, 'Co': 2.0, 'O': 1.0}\",\"('Co', 'O', 'Zr')\",OTHERS,False\nmp-1006367,\"{'Ce': 8.0, 'Hf': 4.0, 'Se': 20.0}\",\"('Ce', 'Hf', 'Se')\",OTHERS,False\nmp-1005829,\"{'Sm': 2.0, 'Mn': 12.0, 'P': 7.0}\",\"('Mn', 'P', 'Sm')\",OTHERS,False\nmp-1207518,\"{'Yb': 4.0, 'Rh': 4.0, 'O': 12.0}\",\"('O', 'Rh', 'Yb')\",OTHERS,False\nmp-601820,\"{'Fe': 3.0, 'Co': 1.0}\",\"('Co', 'Fe')\",OTHERS,False\nmp-1079700,\"{'Dy': 6.0, 'Co': 1.0, 'Te': 2.0}\",\"('Co', 'Dy', 'Te')\",OTHERS,False\nmp-1175544,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-758091,\"{'Li': 2.0, 'V': 2.0, 'F': 8.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-1076488,\"{'Sr': 24.0, 'Ca': 8.0, 'Fe': 12.0, 'Co': 20.0, 'O': 80.0}\",\"('Ca', 'Co', 'Fe', 'O', 'Sr')\",OTHERS,False\nmvc-6059,\"{'Zn': 2.0, 'Cu': 4.0, 'O': 8.0}\",\"('Cu', 'O', 'Zn')\",OTHERS,False\nmp-1177982,\"{'Li': 2.0, 'Fe': 1.0, 'B': 2.0, 'O': 6.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-680614,\"{'Yb': 32.0, 'Li': 4.0, 'Ge': 52.0}\",\"('Ge', 'Li', 'Yb')\",OTHERS,False\nmp-460,\"{'Zn': 1.0, 'Pr': 1.0}\",\"('Pr', 'Zn')\",OTHERS,False\nmp-722342,\"{'Na': 4.0, 'Ca': 8.0, 'B': 36.0, 'H': 32.0, 'O': 80.0}\",\"('B', 'Ca', 'H', 'Na', 'O')\",OTHERS,False\nmp-735491,\"{'Ca': 1.0, 'Mg': 2.0, 'H': 24.0, 'Cl': 6.0, 'O': 12.0}\",\"('Ca', 'Cl', 'H', 'Mg', 'O')\",OTHERS,False\nmp-1223246,\"{'La': 2.0, 'Ga': 1.0, 'Ni': 1.0, 'O': 6.0}\",\"('Ga', 'La', 'Ni', 'O')\",OTHERS,False\nmp-1219249,\"{'Si': 3.0, 'P': 3.0, 'Ir': 1.0}\",\"('Ir', 'P', 'Si')\",OTHERS,False\nmp-1185514,\"{'Lu': 1.0, 'Ge': 1.0, 'O': 3.0}\",\"('Ge', 'Lu', 'O')\",OTHERS,False\nmp-1187545,\"{'Tl': 3.0, 'Ag': 1.0}\",\"('Ag', 'Tl')\",OTHERS,False\nmp-695708,\"{'Ba': 14.0, 'Si': 2.0, 'B': 6.0, 'N': 2.0, 'O': 26.0}\",\"('B', 'Ba', 'N', 'O', 'Si')\",OTHERS,False\nmp-780778,\"{'Li': 4.0, 'Mn': 14.0, 'P': 16.0, 'O': 56.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-667288,\"{'K': 16.0, 'P': 32.0, 'Pb': 8.0, 'O': 96.0}\",\"('K', 'O', 'P', 'Pb')\",OTHERS,False\nmp-753723,\"{'Li': 4.0, 'Mn': 4.0, 'O': 2.0, 'F': 12.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1013561,\"{'Sr': 3.0, 'Sb': 1.0, 'As': 1.0}\",\"('As', 'Sb', 'Sr')\",OTHERS,False\nmp-1220780,\"{'Na': 1.0, 'La': 1.0, 'Nb': 4.0, 'O': 12.0}\",\"('La', 'Na', 'Nb', 'O')\",OTHERS,False\nmp-1220298,\"{'Ni': 17.0, 'Ge': 4.0, 'Se': 4.0}\",\"('Ge', 'Ni', 'Se')\",OTHERS,False\nmp-1189374,\"{'Ho': 12.0, 'Ga': 2.0, 'Ni': 4.0}\",\"('Ga', 'Ho', 'Ni')\",OTHERS,False\nmp-17849,\"{'O': 1.0, 'Fe': 16.0, 'Ho': 6.0}\",\"('Fe', 'Ho', 'O')\",OTHERS,False\nmp-1205212,\"{'B': 128.0, 'F': 192.0}\",\"('B', 'F')\",OTHERS,False\nmp-755821,\"{'Li': 5.0, 'Cu': 1.0, 'P': 2.0, 'O': 8.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-757073,\"{'Ba': 4.0, 'Li': 1.0, 'Y': 2.0, 'Cu': 5.0, 'O': 14.0}\",\"('Ba', 'Cu', 'Li', 'O', 'Y')\",OTHERS,False\nmp-1207212,\"{'Li': 1.0, 'Hf': 1.0, 'S': 2.0}\",\"('Hf', 'Li', 'S')\",OTHERS,False\nmp-1219172,\"{'Sm': 2.0, 'Sb': 1.0, 'S': 1.0}\",\"('S', 'Sb', 'Sm')\",OTHERS,False\nmp-569207,\"{'K': 4.0, 'Os': 2.0, 'N': 2.0, 'Cl': 10.0}\",\"('Cl', 'K', 'N', 'Os')\",OTHERS,False\nmp-1220985,\"{'Nd': 10.0, 'Ta': 30.0, 'O': 90.0}\",\"('Nd', 'O', 'Ta')\",OTHERS,False\nmp-780275,\"{'Ag': 2.0, 'B': 2.0, 'O': 6.0}\",\"('Ag', 'B', 'O')\",OTHERS,False\nmp-1213452,\"{'H': 20.0, 'Pb': 4.0, 'C': 8.0, 'S': 4.0, 'I': 4.0, 'N': 24.0}\",\"('C', 'H', 'I', 'N', 'Pb', 'S')\",OTHERS,False\nmp-8001,\"{'Li': 1.0, 'O': 2.0, 'Al': 1.0}\",\"('Al', 'Li', 'O')\",OTHERS,False\nmp-1173595,\"{'Sr': 14.0, 'Nb': 6.0, 'O': 29.0}\",\"('Nb', 'O', 'Sr')\",OTHERS,False\nmp-505343,\"{'U': 2.0, 'Co': 8.0, 'B': 8.0}\",\"('B', 'Co', 'U')\",OTHERS,False\nmp-1071904,\"{'Pr': 2.0, 'Cu': 4.0}\",\"('Cu', 'Pr')\",OTHERS,False\nmp-761193,\"{'Li': 2.0, 'Cu': 8.0, 'P': 6.0, 'O': 24.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-865234,\"{'Ta': 1.0, 'Zn': 1.0, 'Ru': 2.0}\",\"('Ru', 'Ta', 'Zn')\",OTHERS,False\nmp-1035359,\"{'Mg': 14.0, 'Nb': 1.0, 'Cu': 1.0, 'O': 16.0}\",\"('Cu', 'Mg', 'Nb', 'O')\",OTHERS,False\nmp-1178636,\"{'Zr': 8.0, 'N': 16.0, 'F': 48.0}\",\"('F', 'N', 'Zr')\",OTHERS,False\nmp-1229224,\"{'Al': 2.0, 'B': 4.0, 'Pb': 12.0, 'O': 14.0, 'F': 14.0}\",\"('Al', 'B', 'F', 'O', 'Pb')\",OTHERS,False\nmp-36641,\"{'Cd': 1.0, 'Ga': 6.0, 'Te': 10.0}\",\"('Cd', 'Ga', 'Te')\",OTHERS,False\nAl 6 Cu 1 Li 3,\"{'Al': 6.0, 'Cu': 1.0, 'Li': 3.0}\",\"('Al', 'Cu', 'Li')\",QC,False\nmp-676296,\"{'U': 1.0, 'Bi': 4.0, 'O': 8.0}\",\"('Bi', 'O', 'U')\",OTHERS,False\nmp-1212634,\"{'Er': 4.0, 'Te': 4.0, 'As': 4.0}\",\"('As', 'Er', 'Te')\",OTHERS,False\nmp-24416,\"{'H': 2.0, 'Mn': 2.0}\",\"('H', 'Mn')\",OTHERS,False\nmp-760378,\"{'Li': 2.0, 'Ti': 4.0, 'O': 8.0}\",\"('Li', 'O', 'Ti')\",OTHERS,False\nmp-18474,\"{'Ge': 2.0, 'Se': 12.0, 'Ag': 16.0}\",\"('Ag', 'Ge', 'Se')\",OTHERS,False\nmp-755773,\"{'Mn': 6.0, 'O': 7.0, 'F': 5.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-865361,\"{'Tm': 2.0, 'Mg': 1.0, 'Os': 1.0}\",\"('Mg', 'Os', 'Tm')\",OTHERS,False\nmp-1111384,\"{'K': 3.0, 'Pr': 1.0, 'Cl': 6.0}\",\"('Cl', 'K', 'Pr')\",OTHERS,False\nmp-861905,\"{'Li': 1.0, 'Bi': 1.0, 'Pd': 2.0}\",\"('Bi', 'Li', 'Pd')\",OTHERS,False\nmp-1217074,\"{'Ti': 2.0, 'H': 1.0, 'C': 1.0}\",\"('C', 'H', 'Ti')\",OTHERS,False\nmp-1250217,\"{'Zn': 6.0, 'Cu': 4.0, 'Si': 8.0, 'O': 28.0}\",\"('Cu', 'O', 'Si', 'Zn')\",OTHERS,False\nmp-1219194,\"{'Sm': 12.0, 'Ta': 4.0, 'Ti': 8.0, 'O': 42.0}\",\"('O', 'Sm', 'Ta', 'Ti')\",OTHERS,False\nmp-1075975,\"{'Eu': 1.0, 'Co': 1.0, 'O': 3.0}\",\"('Co', 'Eu', 'O')\",OTHERS,False\nmp-1228231,\"{'Ba': 6.0, 'Ti': 2.0, 'Fe': 4.0, 'O': 18.0}\",\"('Ba', 'Fe', 'O', 'Ti')\",OTHERS,False\nmp-1014910,\"{'Cr': 4.0, 'N': 8.0}\",\"('Cr', 'N')\",OTHERS,False\nmp-736122,\"{'Ba': 18.0, 'Ca': 6.0, 'Zr': 6.0, 'W': 6.0, 'O': 54.0}\",\"('Ba', 'Ca', 'O', 'W', 'Zr')\",OTHERS,False\nmp-1080442,\"{'U': 3.0, 'Ga': 3.0, 'Pd': 3.0}\",\"('Ga', 'Pd', 'U')\",OTHERS,False\nmp-1228558,\"{'Al': 1.0, 'Si': 5.0, 'C': 2.0, 'O': 12.0}\",\"('Al', 'C', 'O', 'Si')\",OTHERS,False\nmp-558203,\"{'Si': 32.0, 'O': 64.0}\",\"('O', 'Si')\",OTHERS,False\nmp-744517,\"{'Li': 8.0, 'Mo': 8.0, 'H': 48.0, 'N': 8.0, 'O': 40.0}\",\"('H', 'Li', 'Mo', 'N', 'O')\",OTHERS,False\nmp-1220796,\"{'Na': 10.0, 'Sr': 2.0, 'Nb': 2.0, 'As': 8.0}\",\"('As', 'Na', 'Nb', 'Sr')\",OTHERS,False\nmp-1110992,\"{'Na': 2.0, 'Tl': 1.0, 'Cu': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cu', 'Na', 'Tl')\",OTHERS,False\nmp-1200509,\"{'Mn': 1.0, 'H': 36.0, 'C': 60.0, 'N': 12.0}\",\"('C', 'H', 'Mn', 'N')\",OTHERS,False\nmp-1187007,\"{'Si': 1.0, 'Pb': 3.0}\",\"('Pb', 'Si')\",OTHERS,False\nmp-1224945,\"{'Fe': 1.0, 'Cu': 1.0}\",\"('Cu', 'Fe')\",OTHERS,False\nmp-1075570,\"{'Mg': 10.0, 'Si': 12.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-545512,\"{'O': 2.0, 'Ca': 2.0}\",\"('Ca', 'O')\",OTHERS,False\nmp-1176011,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1103472,\"{'Na': 2.0, 'U': 1.0, 'F': 8.0}\",\"('F', 'Na', 'U')\",OTHERS,False\nmp-1014230,\"{'Ti': 1.0, 'Zn': 1.0}\",\"('Ti', 'Zn')\",OTHERS,False\nmp-555962,\"{'Li': 4.0, 'Mo': 4.0, 'As': 4.0, 'O': 24.0}\",\"('As', 'Li', 'Mo', 'O')\",OTHERS,False\nmp-774712,\"{'Li': 2.0, 'Cu': 2.0, 'S': 2.0}\",\"('Cu', 'Li', 'S')\",OTHERS,False\nmp-29276,\"{'O': 34.0, 'P': 12.0, 'Cd': 4.0}\",\"('Cd', 'O', 'P')\",OTHERS,False\nmp-27439,\"{'O': 2.0, 'I': 2.0, 'Tm': 2.0}\",\"('I', 'O', 'Tm')\",OTHERS,False\nmvc-7669,\"{'Ba': 1.0, 'Y': 1.0, 'Mn': 1.0, 'Cu': 1.0, 'O': 5.0}\",\"('Ba', 'Cu', 'Mn', 'O', 'Y')\",OTHERS,False\nmp-540948,\"{'Zn': 2.0, 'Ni': 2.0, 'P': 8.0, 'O': 24.0}\",\"('Ni', 'O', 'P', 'Zn')\",OTHERS,False\nmp-6075,\"{'Li': 2.0, 'B': 10.0, 'O': 20.0, 'Ba': 4.0}\",\"('B', 'Ba', 'Li', 'O')\",OTHERS,False\nmvc-13558,\"{'Fe': 4.0, 'S': 8.0}\",\"('Fe', 'S')\",OTHERS,False\nmp-18348,\"{'O': 28.0, 'P': 8.0, 'K': 4.0, 'Ga': 4.0}\",\"('Ga', 'K', 'O', 'P')\",OTHERS,False\nAl 72 Pd 17 Ru 11,\"{'Al': 72.0, 'Pd': 17.0, 'Ru': 11.0}\",\"('Al', 'Pd', 'Ru')\",QC,False\nmp-555439,\"{'Cs': 2.0, 'Li': 6.0, 'F': 8.0}\",\"('Cs', 'F', 'Li')\",OTHERS,False\nmp-1245934,\"{'Cd': 2.0, 'C': 16.0, 'N': 12.0}\",\"('C', 'Cd', 'N')\",OTHERS,False\nmp-1092278,\"{'Mn': 2.0, 'B': 4.0, 'Mo': 4.0}\",\"('B', 'Mn', 'Mo')\",OTHERS,False\nmp-7719,\"{'O': 16.0, 'Na': 2.0, 'S': 4.0, 'Tm': 2.0}\",\"('Na', 'O', 'S', 'Tm')\",OTHERS,False\nmp-18962,\"{'O': 6.0, 'Ca': 1.0, 'Sr': 2.0, 'Mo': 1.0}\",\"('Ca', 'Mo', 'O', 'Sr')\",OTHERS,False\nmp-1238791,\"{'Cr': 4.0, 'Cu': 2.0, 'S': 8.0}\",\"('Cr', 'Cu', 'S')\",OTHERS,False\nmp-1176421,\"{'Mn': 2.0, 'Zn': 8.0, 'O': 10.0}\",\"('Mn', 'O', 'Zn')\",OTHERS,False\nmp-1253743,\"{'Ca': 4.0, 'Ti': 2.0, 'Al': 2.0, 'O': 10.0}\",\"('Al', 'Ca', 'O', 'Ti')\",OTHERS,False\nmp-1245401,\"{'Mn': 1.0, 'Cr': 1.0, 'N': 2.0}\",\"('Cr', 'Mn', 'N')\",OTHERS,False\nmp-772613,\"{'Li': 4.0, 'Cr': 6.0, 'Ni': 2.0, 'O': 16.0}\",\"('Cr', 'Li', 'Ni', 'O')\",OTHERS,False\nmp-1114453,\"{'Rb': 2.0, 'Na': 1.0, 'Pr': 1.0, 'I': 6.0}\",\"('I', 'Na', 'Pr', 'Rb')\",OTHERS,False\nmp-1245337,\"{'Si': 40.0, 'Ni': 16.0, 'N': 64.0}\",\"('N', 'Ni', 'Si')\",OTHERS,False\nmp-753509,\"{'Li': 3.0, 'Mn': 1.0, 'O': 1.0, 'F': 4.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1112902,\"{'Cs': 2.0, 'Hg': 1.0, 'Sb': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cs', 'Hg', 'Sb')\",OTHERS,False\nmp-1032862,\"{'Mg': 6.0, 'Mn': 1.0, 'Fe': 1.0, 'O': 8.0}\",\"('Fe', 'Mg', 'Mn', 'O')\",OTHERS,False\nmp-10740,{'Pa': 1.0},\"('Pa',)\",OTHERS,False\nmp-685705,\"{'Mn': 17.0, 'Nb': 31.0, 'N': 48.0}\",\"('Mn', 'N', 'Nb')\",OTHERS,False\nmp-768177,\"{'Li': 8.0, 'V': 4.0, 'B': 4.0, 'O': 16.0}\",\"('B', 'Li', 'O', 'V')\",OTHERS,False\nmp-1096162,\"{'Mg': 2.0, 'Cd': 1.0, 'Ga': 1.0}\",\"('Cd', 'Ga', 'Mg')\",OTHERS,False\nmp-560409,\"{'P': 24.0, 'S': 24.0, 'N': 48.0, 'F': 40.0}\",\"('F', 'N', 'P', 'S')\",OTHERS,False\nmp-1025034,\"{'Th': 1.0, 'Ni': 2.0, 'B': 2.0, 'C': 1.0}\",\"('B', 'C', 'Ni', 'Th')\",OTHERS,False\nmp-1226011,\"{'Co': 1.0, 'Si': 4.0, 'Ni': 3.0}\",\"('Co', 'Ni', 'Si')\",OTHERS,False\nmvc-9211,\"{'Ti': 2.0, 'Zn': 2.0, 'Ni': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Ni', 'O', 'P', 'Ti', 'Zn')\",OTHERS,False\nmp-1229165,\"{'Ag': 3.0, 'Bi': 7.0, 'S': 12.0}\",\"('Ag', 'Bi', 'S')\",OTHERS,False\nmp-13156,\"{'Ag': 2.0, 'Hf': 2.0}\",\"('Ag', 'Hf')\",OTHERS,False\nmp-1173858,\"{'Na': 7.0, 'Sr': 6.0, 'Nd': 7.0, 'Ti': 20.0, 'O': 60.0}\",\"('Na', 'Nd', 'O', 'Sr', 'Ti')\",OTHERS,False\nmp-1147677,\"{'Ca': 1.0, 'Cu': 6.0, 'O': 8.0}\",\"('Ca', 'Cu', 'O')\",OTHERS,False\nZn 47.3 Mg 27 Al 10.7,\"{'Zn': 47.3, 'Mg': 27.0, 'Al': 10.7}\",\"('Al', 'Mg', 'Zn')\",AC,False\nmp-1103519,\"{'K': 4.0, 'Cu': 4.0, 'O': 4.0}\",\"('Cu', 'K', 'O')\",OTHERS,False\nmp-1104636,\"{'Er': 2.0, 'Al': 6.0, 'C': 6.0}\",\"('Al', 'C', 'Er')\",OTHERS,False\nmp-772436,\"{'Li': 16.0, 'Al': 8.0, 'Fe': 8.0, 'O': 32.0}\",\"('Al', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1078698,\"{'Zr': 2.0, 'Cu': 2.0, 'Ge': 2.0, 'As': 2.0}\",\"('As', 'Cu', 'Ge', 'Zr')\",OTHERS,False\nmp-1211200,\"{'Mg': 2.0, 'U': 4.0, 'P': 4.0, 'H': 40.0, 'O': 44.0}\",\"('H', 'Mg', 'O', 'P', 'U')\",OTHERS,False\nmvc-6262,\"{'Mg': 4.0, 'Bi': 8.0, 'O': 16.0}\",\"('Bi', 'Mg', 'O')\",OTHERS,False\nmvc-4222,\"{'Ge': 12.0, 'Te': 8.0, 'O': 48.0}\",\"('Ge', 'O', 'Te')\",OTHERS,False\nmp-733,\"{'O': 6.0, 'Ge': 3.0}\",\"('Ge', 'O')\",OTHERS,False\nmp-1194955,\"{'Na': 6.0, 'Gd': 2.0, 'C': 6.0, 'O': 18.0}\",\"('C', 'Gd', 'Na', 'O')\",OTHERS,False\nmp-780360,\"{'Na': 32.0, 'Gd': 8.0, 'O': 28.0}\",\"('Gd', 'Na', 'O')\",OTHERS,False\nmp-3419,\"{'F': 8.0, 'Rb': 2.0, 'Au': 2.0}\",\"('Au', 'F', 'Rb')\",OTHERS,False\nmp-775805,\"{'Zr': 8.0, 'N': 8.0, 'O': 4.0}\",\"('N', 'O', 'Zr')\",OTHERS,False\nmp-781594,\"{'Li': 2.0, 'Mn': 6.0, 'B': 6.0, 'O': 18.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1065668,\"{'Gd': 1.0, 'Ni': 1.0, 'C': 2.0}\",\"('C', 'Gd', 'Ni')\",OTHERS,False\nmp-33581,\"{'Ba': 9.0, 'Nb': 10.0, 'O': 30.0}\",\"('Ba', 'Nb', 'O')\",OTHERS,False\nmp-1221677,\"{'Mn': 1.0, 'H': 6.0, 'N': 2.0, 'Cl': 2.0}\",\"('Cl', 'H', 'Mn', 'N')\",OTHERS,False\nmp-12838,\"{'Si': 6.0, 'Fe': 2.0, 'Tm': 6.0}\",\"('Fe', 'Si', 'Tm')\",OTHERS,False\nmp-580539,\"{'Gd': 3.0, 'Al': 1.0, 'C': 1.0}\",\"('Al', 'C', 'Gd')\",OTHERS,False\nmp-1225045,\"{'Er': 2.0, 'Al': 3.0, 'Co': 1.0}\",\"('Al', 'Co', 'Er')\",OTHERS,False\nmp-12243,\"{'F': 14.0, 'Si': 2.0, 'Rb': 6.0}\",\"('F', 'Rb', 'Si')\",OTHERS,False\nZn 17 Yb 3,\"{'Zn': 17.0, 'Yb': 3.0}\",\"('Yb', 'Zn')\",AC,False\nmp-1194349,\"{'Nb': 12.0, 'Ni': 12.0, 'C': 4.0}\",\"('C', 'Nb', 'Ni')\",OTHERS,False\nmp-975079,\"{'Mn': 2.0, 'Zn': 6.0}\",\"('Mn', 'Zn')\",OTHERS,False\nmp-1192447,\"{'Nd': 6.0, 'Mg': 23.0, 'C': 1.0}\",\"('C', 'Mg', 'Nd')\",OTHERS,False\nmp-14091,\"{'Al': 2.0, 'Se': 4.0, 'Ag': 2.0}\",\"('Ag', 'Al', 'Se')\",OTHERS,False\nmp-1186120,\"{'Np': 1.0, 'Co': 3.0}\",\"('Co', 'Np')\",OTHERS,False\nmp-768399,\"{'Ba': 6.0, 'Y': 4.0, 'Br': 24.0}\",\"('Ba', 'Br', 'Y')\",OTHERS,False\nmp-759281,\"{'Li': 12.0, 'Fe': 12.0, 'Si': 12.0, 'O': 48.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-722369,\"{'Na': 12.0, 'B': 20.0, 'H': 8.0, 'O': 40.0}\",\"('B', 'H', 'Na', 'O')\",OTHERS,False\nmp-540550,\"{'Na': 4.0, 'Cl': 4.0, 'O': 20.0, 'H': 8.0}\",\"('Cl', 'H', 'Na', 'O')\",OTHERS,False\nmp-1228892,\"{'Cs': 2.0, 'Cr': 2.0, 'Cu': 2.0, 'F': 12.0}\",\"('Cr', 'Cs', 'Cu', 'F')\",OTHERS,False\nmp-759320,\"{'Li': 9.0, 'Mn': 9.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1205384,\"{'La': 4.0, 'Te': 8.0}\",\"('La', 'Te')\",OTHERS,False\nmp-510547,\"{'Cu': 8.0, 'Cl': 4.0, 'O': 12.0, 'H': 12.0}\",\"('Cl', 'Cu', 'H', 'O')\",OTHERS,False\nmp-8248,\"{'O': 6.0, 'Rb': 4.0, 'Te': 2.0}\",\"('O', 'Rb', 'Te')\",OTHERS,False\nmp-1120786,\"{'V': 2.0, 'Bi': 4.0}\",\"('Bi', 'V')\",OTHERS,False\nmp-1209066,\"{'Sb': 4.0, 'As': 2.0, 'S': 4.0}\",\"('As', 'S', 'Sb')\",OTHERS,False\nmp-866114,\"{'Hf': 1.0, 'Tc': 2.0, 'W': 1.0}\",\"('Hf', 'Tc', 'W')\",OTHERS,False\nmp-1183173,\"{'Al': 1.0, 'Ga': 1.0, 'Ni': 2.0}\",\"('Al', 'Ga', 'Ni')\",OTHERS,False\nmp-12071,\"{'Ti': 6.0, 'As': 2.0}\",\"('As', 'Ti')\",OTHERS,False\nmp-1100700,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1208320,\"{'Tb': 4.0, 'Ni': 4.0, 'P': 4.0}\",\"('Ni', 'P', 'Tb')\",OTHERS,False\nmp-1238871,\"{'Ti': 4.0, 'Cr': 4.0, 'Ag': 4.0, 'S': 16.0}\",\"('Ag', 'Cr', 'S', 'Ti')\",OTHERS,False\nmp-556006,\"{'Os': 4.0, 'Cl': 16.0, 'O': 4.0}\",\"('Cl', 'O', 'Os')\",OTHERS,False\nmp-1195604,\"{'Mg': 1.0, 'H': 30.0, 'C': 2.0, 'S': 2.0, 'O': 18.0}\",\"('C', 'H', 'Mg', 'O', 'S')\",OTHERS,False\nmp-19771,\"{'Co': 2.0, 'Ge': 2.0, 'Dy': 1.0}\",\"('Co', 'Dy', 'Ge')\",OTHERS,False\nmp-6452,\"{'O': 20.0, 'Zn': 4.0, 'Ba': 4.0, 'Sm': 8.0}\",\"('Ba', 'O', 'Sm', 'Zn')\",OTHERS,False\nmp-3922,\"{'S': 8.0, 'Ag': 4.0, 'Sb': 4.0}\",\"('Ag', 'S', 'Sb')\",OTHERS,False\nmvc-8489,\"{'Ta': 4.0, 'Ni': 4.0, 'P': 8.0, 'O': 36.0}\",\"('Ni', 'O', 'P', 'Ta')\",OTHERS,False\nmp-1221218,\"{'Na': 8.0, 'Eu': 4.0, 'Sn': 2.0, 'As': 8.0}\",\"('As', 'Eu', 'Na', 'Sn')\",OTHERS,False\nmp-1020018,\"{'Li': 20.0, 'B': 4.0, 'S': 16.0, 'O': 64.0}\",\"('B', 'Li', 'O', 'S')\",OTHERS,False\nmp-1201589,\"{'Fe': 6.0, 'Se': 6.0, 'O': 20.0}\",\"('Fe', 'O', 'Se')\",OTHERS,False\nmp-1214100,\"{'Ca': 2.0, 'H': 1.0, 'I': 2.0}\",\"('Ca', 'H', 'I')\",OTHERS,False\nmp-24653,\"{'H': 32.0, 'N': 8.0, 'F': 20.0, 'Ti': 4.0}\",\"('F', 'H', 'N', 'Ti')\",OTHERS,False\nmp-1226695,\"{'Co': 2.0, 'H': 30.0, 'Br': 4.0, 'N': 12.0, 'O': 4.0}\",\"('Br', 'Co', 'H', 'N', 'O')\",OTHERS,False\nmp-1225584,\"{'Er': 1.0, 'Al': 1.0, 'Cu': 4.0}\",\"('Al', 'Cu', 'Er')\",OTHERS,False\nmp-1222566,\"{'Li': 9.0, 'Mn': 3.0, 'N': 4.0}\",\"('Li', 'Mn', 'N')\",OTHERS,False\nmp-1079120,\"{'Nb': 2.0, 'B': 4.0, 'Mo': 4.0}\",\"('B', 'Mo', 'Nb')\",OTHERS,False\nmp-569132,\"{'K': 12.0, 'In': 4.0, 'Cl': 24.0}\",\"('Cl', 'In', 'K')\",OTHERS,False\nmp-978527,\"{'Sm': 1.0, 'Dy': 1.0, 'Tl': 2.0}\",\"('Dy', 'Sm', 'Tl')\",OTHERS,False\nmp-22750,\"{'O': 8.0, 'P': 2.0, 'Pb': 3.0}\",\"('O', 'P', 'Pb')\",OTHERS,False\nmp-12697,\"{'Sc': 1.0, 'Mn': 6.0, 'Sn': 6.0}\",\"('Mn', 'Sc', 'Sn')\",OTHERS,False\nmp-1178571,\"{'Ag': 16.0, 'Ge': 16.0, 'O': 48.0}\",\"('Ag', 'Ge', 'O')\",OTHERS,False\nmp-1197685,\"{'Os': 16.0, 'C': 24.0}\",\"('C', 'Os')\",OTHERS,False\nmp-1069658,\"{'Eu': 1.0, 'Ge': 3.0, 'Rh': 1.0}\",\"('Eu', 'Ge', 'Rh')\",OTHERS,False\nmvc-11047,\"{'Mn': 2.0, 'S': 4.0}\",\"('Mn', 'S')\",OTHERS,False\nmp-735063,\"{'H': 28.0, 'C': 4.0, 'S': 4.0, 'N': 12.0, 'O': 16.0}\",\"('C', 'H', 'N', 'O', 'S')\",OTHERS,False\nmp-775750,\"{'Li': 4.0, 'Fe': 4.0, 'Si': 24.0, 'O': 60.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1216427,\"{'V': 6.0, 'Si': 1.0, 'Os': 1.0}\",\"('Os', 'Si', 'V')\",OTHERS,False\nmp-1227066,\"{'Ce': 1.0, 'Pr': 3.0, 'Fe': 34.0}\",\"('Ce', 'Fe', 'Pr')\",OTHERS,False\nmp-22627,\"{'Ga': 18.0, 'Ru': 6.0, 'Dy': 4.0}\",\"('Dy', 'Ga', 'Ru')\",OTHERS,False\nmp-765639,\"{'Sm': 11.0, 'Y': 5.0, 'O': 24.0}\",\"('O', 'Sm', 'Y')\",OTHERS,False\nmp-21447,\"{'P': 2.0, 'Co': 2.0, 'Nb': 8.0}\",\"('Co', 'Nb', 'P')\",OTHERS,False\nmvc-81,\"{'Ca': 4.0, 'Nb': 4.0, 'Sn': 2.0, 'O': 16.0}\",\"('Ca', 'Nb', 'O', 'Sn')\",OTHERS,False\nmp-1186542,\"{'Pm': 6.0, 'Tm': 2.0}\",\"('Pm', 'Tm')\",OTHERS,False\nmp-757811,\"{'Li': 6.0, 'V': 6.0, 'P': 8.0, 'O': 32.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1266684,\"{'Li': 8.0, 'Si': 8.0, 'Bi': 8.0, 'O': 32.0}\",\"('Bi', 'Li', 'O', 'Si')\",OTHERS,False\nmp-705305,\"{'V': 4.0, 'P': 12.0, 'O': 36.0}\",\"('O', 'P', 'V')\",OTHERS,False\nmp-1228382,\"{'Ba': 2.0, 'Nb': 1.0, 'Co': 1.0, 'O': 6.0}\",\"('Ba', 'Co', 'Nb', 'O')\",OTHERS,False\nmp-1181041,\"{'Ho': 4.0, 'W': 4.0, 'C': 8.0}\",\"('C', 'Ho', 'W')\",OTHERS,False\nmp-545488,\"{'Si': 4.0, 'O': 8.0}\",\"('O', 'Si')\",OTHERS,False\nmp-1078877,\"{'Mg': 1.0, 'Si': 1.0, 'H': 2.0, 'O': 4.0}\",\"('H', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-1194939,\"{'Na': 12.0, 'Mn': 3.0, 'S': 6.0, 'O': 24.0, 'F': 8.0}\",\"('F', 'Mn', 'Na', 'O', 'S')\",OTHERS,False\nmp-1216840,\"{'U': 5.0, 'P': 1.0, 'S': 4.0}\",\"('P', 'S', 'U')\",OTHERS,False\nmp-611062,\"{'Eu': 2.0, 'Sb': 4.0}\",\"('Eu', 'Sb')\",OTHERS,False\nmvc-3987,\"{'Mg': 4.0, 'Cr': 4.0, 'O': 12.0}\",\"('Cr', 'Mg', 'O')\",OTHERS,False\nmp-1213227,\"{'Dy': 4.0, 'Si': 12.0, 'Ni': 20.0}\",\"('Dy', 'Ni', 'Si')\",OTHERS,False\nmp-1233677,\"{'Ca': 1.0, 'Al': 2.0, 'Si': 6.0, 'O': 16.0}\",\"('Al', 'Ca', 'O', 'Si')\",OTHERS,False\nmp-864930,\"{'Li': 1.0, 'Ti': 1.0, 'Ir': 2.0}\",\"('Ir', 'Li', 'Ti')\",OTHERS,False\nmp-12722,\"{'Al': 4.0, 'Pd': 4.0, 'Tb': 4.0}\",\"('Al', 'Pd', 'Tb')\",OTHERS,False\nmp-1093670,\"{'Li': 1.0, 'Hg': 2.0, 'Rh': 1.0}\",\"('Hg', 'Li', 'Rh')\",OTHERS,False\nmp-1219105,\"{'Sm': 1.0, 'Al': 3.0, 'Cu': 1.0}\",\"('Al', 'Cu', 'Sm')\",OTHERS,False\nmvc-4456,\"{'Y': 2.0, 'Sn': 4.0, 'O': 8.0}\",\"('O', 'Sn', 'Y')\",OTHERS,False\nmp-1185219,\"{'La': 1.0, 'Ce': 1.0, 'Zn': 2.0}\",\"('Ce', 'La', 'Zn')\",OTHERS,False\nmp-758121,\"{'Fe': 4.0, 'C': 6.0, 'O': 18.0}\",\"('C', 'Fe', 'O')\",OTHERS,False\nmvc-3034,\"{'Mg': 8.0, 'Co': 10.0, 'Te': 6.0, 'O': 36.0}\",\"('Co', 'Mg', 'O', 'Te')\",OTHERS,False\nmp-1228574,\"{'Ba': 6.0, 'Ca': 6.0, 'Mg': 1.0, 'C': 13.0, 'O': 39.0}\",\"('Ba', 'C', 'Ca', 'Mg', 'O')\",OTHERS,False\nmp-1111171,\"{'K': 3.0, 'Pr': 1.0, 'I': 6.0}\",\"('I', 'K', 'Pr')\",OTHERS,False\nmp-676799,\"{'Ag': 8.0, 'Ge': 1.0, 'Te': 6.0}\",\"('Ag', 'Ge', 'Te')\",OTHERS,False\nmp-3285,\"{'O': 22.0, 'Na': 4.0, 'Ta': 8.0}\",\"('Na', 'O', 'Ta')\",OTHERS,False\nmp-1214095,\"{'Ca': 2.0, 'La': 1.0, 'Mn': 2.0, 'O': 7.0}\",\"('Ca', 'La', 'Mn', 'O')\",OTHERS,False\nmp-753089,\"{'Li': 2.0, 'Cu': 4.0, 'F': 12.0}\",\"('Cu', 'F', 'Li')\",OTHERS,False\nmp-561531,\"{'Y': 4.0, 'Si': 4.0, 'O': 14.0}\",\"('O', 'Si', 'Y')\",OTHERS,False\nmp-753724,\"{'Li': 3.0, 'Ni': 5.0, 'O': 1.0, 'F': 11.0}\",\"('F', 'Li', 'Ni', 'O')\",OTHERS,False\nmp-771823,\"{'Li': 4.0, 'V': 4.0, 'Ni': 4.0, 'O': 16.0}\",\"('Li', 'Ni', 'O', 'V')\",OTHERS,False\nmp-1227032,\"{'Ca': 1.0, 'Mn': 1.0, 'O': 2.0}\",\"('Ca', 'Mn', 'O')\",OTHERS,False\nmp-1201953,\"{'Ba': 6.0, 'Al': 4.0, 'B': 20.0, 'H': 8.0, 'O': 46.0}\",\"('Al', 'B', 'Ba', 'H', 'O')\",OTHERS,False\nmp-1223115,\"{'La': 8.0, 'Sb': 4.0, 'Pb': 2.0}\",\"('La', 'Pb', 'Sb')\",OTHERS,False\nmvc-8890,\"{'Zn': 4.0, 'Bi': 8.0, 'O': 20.0}\",\"('Bi', 'O', 'Zn')\",OTHERS,False\nmp-756912,\"{'Li': 4.0, 'Zn': 4.0, 'B': 4.0, 'O': 12.0}\",\"('B', 'Li', 'O', 'Zn')\",OTHERS,False\nmp-1202407,\"{'La': 4.0, 'B': 20.0, 'H': 20.0, 'N': 4.0, 'O': 56.0}\",\"('B', 'H', 'La', 'N', 'O')\",OTHERS,False\nmp-1079661,\"{'B': 1.0, 'C': 7.0}\",\"('B', 'C')\",OTHERS,False\nmp-1175277,\"{'Li': 7.0, 'Mn': 4.0, 'Co': 1.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1106184,\"{'Mn': 4.0, 'B': 16.0}\",\"('B', 'Mn')\",OTHERS,False\nmp-1212495,\"{'Ge': 4.0, 'P': 8.0}\",\"('Ge', 'P')\",OTHERS,False\nmp-978802,\"{'Sm': 3.0, 'Pd': 1.0}\",\"('Pd', 'Sm')\",OTHERS,False\nmp-1196282,\"{'Cs': 8.0, 'H': 16.0, 'F': 24.0}\",\"('Cs', 'F', 'H')\",OTHERS,False\nmp-12237,\"{'O': 56.0, 'P': 20.0, 'Ho': 4.0}\",\"('Ho', 'O', 'P')\",OTHERS,False\nmp-865091,\"{'Hf': 1.0, 'Mn': 1.0, 'Rh': 2.0}\",\"('Hf', 'Mn', 'Rh')\",OTHERS,False\nmp-775910,\"{'Zr': 10.0, 'O': 20.0}\",\"('O', 'Zr')\",OTHERS,False\nmp-1187292,\"{'Tb': 6.0, 'Ge': 10.0}\",\"('Ge', 'Tb')\",OTHERS,False\nmp-573196,\"{'Cs': 6.0, 'Li': 4.0, 'Ga': 2.0, 'Mo': 8.0, 'O': 32.0}\",\"('Cs', 'Ga', 'Li', 'Mo', 'O')\",OTHERS,False\nmp-1204227,\"{'Al': 4.0, 'N': 12.0, 'O': 72.0}\",\"('Al', 'N', 'O')\",OTHERS,False\nmp-1207421,\"{'Zr': 12.0, 'Zn': 8.0}\",\"('Zn', 'Zr')\",OTHERS,False\nmp-570183,\"{'Pr': 1.0, 'As': 2.0, 'Pd': 2.0}\",\"('As', 'Pd', 'Pr')\",OTHERS,False\nmp-1222685,\"{'La': 1.0, 'Yb': 1.0, 'Al': 4.0}\",\"('Al', 'La', 'Yb')\",OTHERS,False\nmp-1223719,\"{'K': 4.0, 'Co': 4.0, 'Se': 6.0, 'O': 18.0}\",\"('Co', 'K', 'O', 'Se')\",OTHERS,False\nmp-1134210,\"{'Al': 4.0, 'Co': 13.0, 'Si': 2.0, 'Sb': 2.0, 'O': 28.0}\",\"('Al', 'Co', 'O', 'Sb', 'Si')\",OTHERS,False\nmp-1186070,\"{'Na': 3.0, 'Pr': 1.0}\",\"('Na', 'Pr')\",OTHERS,False\nmp-684766,\"{'Na': 1.0, 'Co': 2.0, 'H': 3.0, 'S': 2.0, 'O': 10.0}\",\"('Co', 'H', 'Na', 'O', 'S')\",OTHERS,False\nmp-1216708,\"{'V': 18.0, 'Ni': 12.0}\",\"('Ni', 'V')\",OTHERS,False\nmp-1188041,\"{'Zr': 3.0, 'Mn': 1.0}\",\"('Mn', 'Zr')\",OTHERS,False\nmp-1220691,\"{'Nb': 2.0, 'Cu': 1.0, 'S': 4.0}\",\"('Cu', 'Nb', 'S')\",OTHERS,False\nmp-1219682,\"{'Rb': 4.0, 'Nb': 12.0, 'P': 12.0, 'O': 60.0}\",\"('Nb', 'O', 'P', 'Rb')\",OTHERS,False\nmp-1185020,\"{'La': 1.0, 'Ho': 1.0, 'Tl': 2.0}\",\"('Ho', 'La', 'Tl')\",OTHERS,False\nmp-1066791,\"{'Tm': 1.0, 'Tl': 1.0, 'S': 2.0}\",\"('S', 'Tl', 'Tm')\",OTHERS,False\nmp-1213189,\"{'Cs': 2.0, 'Lu': 2.0, 'Mo': 4.0, 'O': 16.0}\",\"('Cs', 'Lu', 'Mo', 'O')\",OTHERS,False\nmp-772024,\"{'Ba': 12.0, 'La': 4.0, 'Br': 36.0}\",\"('Ba', 'Br', 'La')\",OTHERS,False\nmp-981214,\"{'Sr': 1.0, 'Mg': 5.0}\",\"('Mg', 'Sr')\",OTHERS,False\nmp-1203905,\"{'K': 8.0, 'In': 4.0, 'Cl': 20.0, 'O': 4.0}\",\"('Cl', 'In', 'K', 'O')\",OTHERS,False\nmp-1206823,\"{'Pr': 1.0, 'I': 6.0}\",\"('I', 'Pr')\",OTHERS,False\nmp-1212903,\"{'Er': 4.0, 'Zn': 4.0, 'Rh': 4.0}\",\"('Er', 'Rh', 'Zn')\",OTHERS,False\nmp-720860,\"{'H': 36.0, 'C': 4.0, 'S': 4.0, 'O': 28.0, 'F': 12.0}\",\"('C', 'F', 'H', 'O', 'S')\",OTHERS,False\nmp-1174936,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nAg 41.7 In 43.2 Yb 15.1,\"{'Ag': 41.7, 'In': 43.2, 'Yb': 15.1}\",\"('Ag', 'In', 'Yb')\",AC,False\nmp-1365,\"{'Ti': 12.0, 'Ge': 10.0}\",\"('Ge', 'Ti')\",OTHERS,False\nmp-1190931,\"{'Na': 2.0, 'Fe': 2.0, 'Mo': 4.0, 'O': 16.0}\",\"('Fe', 'Mo', 'Na', 'O')\",OTHERS,False\nmp-779863,\"{'Hf': 16.0, 'N': 16.0, 'O': 8.0}\",\"('Hf', 'N', 'O')\",OTHERS,False\nmp-569158,\"{'Dy': 4.0, 'Co': 14.0, 'B': 6.0}\",\"('B', 'Co', 'Dy')\",OTHERS,False\nmp-1217432,\"{'V': 20.0, 'Ga': 1.0, 'Ge': 4.0, 'S': 40.0}\",\"('Ga', 'Ge', 'S', 'V')\",OTHERS,False\nmp-1198712,\"{'Y': 20.0, 'Ir': 12.0}\",\"('Ir', 'Y')\",OTHERS,False\nmp-559420,\"{'Sb': 16.0, 'Pt': 4.0, 'C': 16.0, 'O': 16.0, 'F': 88.0}\",\"('C', 'F', 'O', 'Pt', 'Sb')\",OTHERS,False\nmp-2235,\"{'Rh': 4.0, 'Yb': 2.0}\",\"('Rh', 'Yb')\",OTHERS,False\nmp-1008734,\"{'Th': 1.0, 'H': 2.0}\",\"('H', 'Th')\",OTHERS,False\nmp-11694,\"{'Cs': 2.0, 'Pd': 3.0, 'Se': 4.0}\",\"('Cs', 'Pd', 'Se')\",OTHERS,False\nmp-774897,\"{'Li': 3.0, 'Mn': 2.0, 'Cr': 1.0, 'O': 6.0}\",\"('Cr', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1201880,\"{'Tb': 6.0, 'Co': 8.0, 'Sn': 26.0}\",\"('Co', 'Sn', 'Tb')\",OTHERS,False\nmp-971769,{'Tm': 4.0},\"('Tm',)\",OTHERS,False\nmp-1245979,\"{'Zn': 4.0, 'Co': 4.0, 'N': 8.0}\",\"('Co', 'N', 'Zn')\",OTHERS,False\nmp-654051,\"{'Nb': 12.0, 'S': 2.0, 'I': 18.0}\",\"('I', 'Nb', 'S')\",OTHERS,False\nmp-755038,\"{'Li': 5.0, 'Fe': 1.0, 'O': 3.0, 'F': 2.0}\",\"('F', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1195541,\"{'P': 8.0, 'Pb': 8.0, 'O': 24.0}\",\"('O', 'P', 'Pb')\",OTHERS,False\nmp-1339,\"{'Nb': 1.0, 'Ir': 3.0}\",\"('Ir', 'Nb')\",OTHERS,False\nmp-1209461,\"{'Rb': 4.0, 'W': 6.0, 'Se': 2.0, 'O': 24.0}\",\"('O', 'Rb', 'Se', 'W')\",OTHERS,False\nmp-677094,\"{'Mg': 1.0, 'Ti': 3.0, 'Nb': 2.0, 'Pb': 6.0, 'O': 18.0}\",\"('Mg', 'Nb', 'O', 'Pb', 'Ti')\",OTHERS,False\nmp-30260,\"{'Ni': 21.0, 'Zr': 8.0}\",\"('Ni', 'Zr')\",OTHERS,False\nmp-1227122,\"{'Ca': 1.0, 'Ta': 4.0, 'O': 14.0}\",\"('Ca', 'O', 'Ta')\",OTHERS,False\nmp-1096097,\"{'Sr': 2.0, 'Li': 1.0, 'Ir': 1.0}\",\"('Ir', 'Li', 'Sr')\",OTHERS,False\nmp-774592,\"{'Li': 6.0, 'Co': 3.0, 'Sb': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Co', 'Li', 'O', 'P', 'Sb')\",OTHERS,False\nmp-1105624,\"{'Na': 6.0, 'La': 2.0, 'N': 12.0}\",\"('La', 'N', 'Na')\",OTHERS,False\nmp-772076,\"{'Sr': 4.0, 'Li': 4.0, 'Nb': 5.0, 'Fe': 1.0, 'O': 20.0}\",\"('Fe', 'Li', 'Nb', 'O', 'Sr')\",OTHERS,False\nmp-779424,\"{'Gd': 6.0, 'Ta': 2.0, 'O': 14.0}\",\"('Gd', 'O', 'Ta')\",OTHERS,False\nmp-770822,\"{'Li': 6.0, 'Fe': 2.0, 'Si': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1211449,\"{'K': 2.0, 'Ho': 2.0, 'Mo': 4.0, 'O': 16.0}\",\"('Ho', 'K', 'Mo', 'O')\",OTHERS,False\nmp-1229226,\"{'Al': 8.0, 'Cu': 4.0, 'Si': 8.0, 'O': 32.0, 'F': 4.0}\",\"('Al', 'Cu', 'F', 'O', 'Si')\",OTHERS,False\nmp-1175560,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-757167,\"{'Li': 6.0, 'Si': 3.0, 'Ni': 3.0, 'O': 12.0}\",\"('Li', 'Ni', 'O', 'Si')\",OTHERS,False\nmp-672314,\"{'Mg': 12.0, 'In': 4.0}\",\"('In', 'Mg')\",OTHERS,False\nmp-1207310,\"{'Sm': 2.0, 'Cd': 1.0, 'Sb': 3.0}\",\"('Cd', 'Sb', 'Sm')\",OTHERS,False\nmp-643471,\"{'Ba': 2.0, 'Y': 1.0, 'Cu': 4.0, 'O': 6.0}\",\"('Ba', 'Cu', 'O', 'Y')\",OTHERS,False\nmvc-5821,\"{'Mn': 2.0, 'Zn': 4.0, 'Ir': 2.0, 'O': 12.0}\",\"('Ir', 'Mn', 'O', 'Zn')\",OTHERS,False\nmp-10379,\"{'Cu': 4.0, 'Ge': 2.0, 'Hf': 3.0}\",\"('Cu', 'Ge', 'Hf')\",OTHERS,False\nmp-16253,\"{'Si': 4.0, 'Ca': 4.0, 'Ba': 4.0}\",\"('Ba', 'Ca', 'Si')\",OTHERS,False\nmp-706664,\"{'B': 80.0, 'H': 104.0}\",\"('B', 'H')\",OTHERS,False\nmp-1211478,\"{'K': 2.0, 'Ho': 6.0, 'F': 20.0}\",\"('F', 'Ho', 'K')\",OTHERS,False\nmp-779241,\"{'Li': 2.0, 'Cu': 10.0, 'P': 4.0, 'O': 16.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-1112629,\"{'Cs': 2.0, 'Al': 1.0, 'Hg': 1.0, 'Br': 6.0}\",\"('Al', 'Br', 'Cs', 'Hg')\",OTHERS,False\nmp-1183976,\"{'Cs': 1.0, 'Te': 1.0, 'O': 3.0}\",\"('Cs', 'O', 'Te')\",OTHERS,False\nmp-696275,\"{'Sn': 1.0, 'H': 8.0, 'N': 2.0, 'Cl': 6.0}\",\"('Cl', 'H', 'N', 'Sn')\",OTHERS,False\nCd 65 Mg 20 Yb 15,\"{'Cd': 65.0, 'Mg': 20.0, 'Yb': 15.0}\",\"('Cd', 'Mg', 'Yb')\",QC,False\nmp-771195,\"{'Na': 10.0, 'Ni': 4.0, 'As': 2.0, 'C': 8.0, 'O': 32.0}\",\"('As', 'C', 'Na', 'Ni', 'O')\",OTHERS,False\nmp-697038,\"{'Ba': 2.0, 'H': 6.0, 'Ru': 1.0}\",\"('Ba', 'H', 'Ru')\",OTHERS,False\nmp-1221115,\"{'Na': 1.0, 'Cr': 12.0, 'Si': 8.0, 'Br': 1.0, 'O': 28.0}\",\"('Br', 'Cr', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1100459,\"{'Mg': 8.0, 'Si': 16.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1182139,\"{'Ba': 4.0, 'I': 8.0, 'O': 4.0}\",\"('Ba', 'I', 'O')\",OTHERS,False\nmp-1217550,\"{'Tb': 4.0, 'Nd': 4.0, 'Co': 56.0, 'B': 4.0}\",\"('B', 'Co', 'Nd', 'Tb')\",OTHERS,False\nmp-1100158,\"{'Ba': 1.0, 'Hf': 1.0, 'Mg': 6.0}\",\"('Ba', 'Hf', 'Mg')\",OTHERS,False\nmp-863292,\"{'Na': 6.0, 'V': 2.0, 'P': 4.0, 'O': 18.0}\",\"('Na', 'O', 'P', 'V')\",OTHERS,False\nmp-735553,\"{'V': 12.0, 'P': 8.0, 'H': 48.0, 'C': 12.0, 'N': 8.0, 'O': 60.0}\",\"('C', 'H', 'N', 'O', 'P', 'V')\",OTHERS,False\nmp-1175354,\"{'Li': 7.0, 'Mn': 5.0, 'O': 12.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-1224321,\"{'Hf': 2.0, 'Al': 2.0, 'Zn': 2.0}\",\"('Al', 'Hf', 'Zn')\",OTHERS,False\nmp-892,\"{'Sc': 1.0, 'Pt': 1.0}\",\"('Pt', 'Sc')\",OTHERS,False\nmp-8958,\"{'F': 10.0, 'Rb': 2.0, 'Pd': 4.0}\",\"('F', 'Pd', 'Rb')\",OTHERS,False\nmp-754336,\"{'Li': 2.0, 'Mn': 1.0, 'Fe': 1.0, 'O': 4.0}\",\"('Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1101034,\"{'Ti': 1.0, 'H': 12.0, 'N': 3.0, 'F': 7.0}\",\"('F', 'H', 'N', 'Ti')\",OTHERS,False\nmp-1076939,\"{'Gd': 2.0, 'Mn': 4.0}\",\"('Gd', 'Mn')\",OTHERS,False\nmp-989626,\"{'La': 2.0, 'W': 2.0, 'N': 6.0}\",\"('La', 'N', 'W')\",OTHERS,False\nmp-1039741,\"{'Na': 1.0, 'Hf': 1.0, 'Mg': 30.0, 'O': 31.0}\",\"('Hf', 'Mg', 'Na', 'O')\",OTHERS,False\nmp-867872,\"{'Li': 1.0, 'Cd': 1.0, 'Ag': 2.0}\",\"('Ag', 'Cd', 'Li')\",OTHERS,False\nmp-20223,\"{'O': 16.0, 'Mn': 8.0, 'Ge': 4.0}\",\"('Ge', 'Mn', 'O')\",OTHERS,False\nmvc-7973,\"{'Ca': 4.0, 'Mn': 4.0, 'As': 8.0, 'O': 28.0}\",\"('As', 'Ca', 'Mn', 'O')\",OTHERS,False\nmp-31387,\"{'Ga': 4.0, 'Pd': 4.0, 'Er': 4.0}\",\"('Er', 'Ga', 'Pd')\",OTHERS,False\nmp-1076601,\"{'Sr': 20.0, 'Ca': 12.0, 'Mn': 24.0, 'Fe': 8.0, 'O': 80.0}\",\"('Ca', 'Fe', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-1199267,\"{'Tb': 8.0, 'Fe': 8.0, 'Sb': 24.0}\",\"('Fe', 'Sb', 'Tb')\",OTHERS,False\nmp-555030,\"{'Na': 4.0, 'Y': 4.0, 'P': 16.0, 'O': 48.0}\",\"('Na', 'O', 'P', 'Y')\",OTHERS,False\nmp-1174529,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1178335,\"{'Ga': 2.0, 'Pt': 2.0, 'O': 4.0}\",\"('Ga', 'O', 'Pt')\",OTHERS,False\nmp-1070573,\"{'La': 1.0, 'Al': 3.0, 'Cu': 1.0}\",\"('Al', 'Cu', 'La')\",OTHERS,False\nmp-1111664,\"{'K': 2.0, 'Li': 1.0, 'Sb': 1.0, 'Cl': 6.0}\",\"('Cl', 'K', 'Li', 'Sb')\",OTHERS,False\nmp-779330,\"{'Li': 8.0, 'Mn': 4.0, 'H': 32.0, 'S': 8.0, 'O': 48.0}\",\"('H', 'Li', 'Mn', 'O', 'S')\",OTHERS,False\nmp-1073003,\"{'Mg': 8.0, 'Si': 12.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-31285,\"{'Se': 32.0, 'Pd': 4.0, 'Cs': 8.0}\",\"('Cs', 'Pd', 'Se')\",OTHERS,False\nmp-862791,\"{'Ga': 1.0, 'Cu': 1.0, 'Pt': 2.0}\",\"('Cu', 'Ga', 'Pt')\",OTHERS,False\nmp-1017519,\"{'Ir': 1.0, 'C': 1.0}\",\"('C', 'Ir')\",OTHERS,False\nmp-1220361,\"{'Nb': 1.0, 'O': 3.0}\",\"('Nb', 'O')\",OTHERS,False\nmp-707483,\"{'Ti': 4.0, 'P': 8.0, 'H': 16.0, 'O': 36.0}\",\"('H', 'O', 'P', 'Ti')\",OTHERS,False\nmp-1179847,\"{'Pt': 4.0, 'N': 8.0, 'Cl': 8.0}\",\"('Cl', 'N', 'Pt')\",OTHERS,False\nmp-771773,\"{'Na': 4.0, 'Bi': 2.0, 'B': 2.0, 'P': 2.0, 'O': 14.0}\",\"('B', 'Bi', 'Na', 'O', 'P')\",OTHERS,False\nmp-757324,\"{'Ce': 2.0, 'Zr': 12.0, 'O': 28.0}\",\"('Ce', 'O', 'Zr')\",OTHERS,False\nmp-23893,\"{'H': 20.0, 'N': 4.0, 'O': 4.0, 'Br': 4.0}\",\"('Br', 'H', 'N', 'O')\",OTHERS,False\nmp-942702,\"{'Li': 2.0, 'Mn': 2.0, 'S': 2.0, 'O': 8.0, 'F': 2.0}\",\"('F', 'Li', 'Mn', 'O', 'S')\",OTHERS,False\nmp-605196,\"{'Cs': 2.0, 'Cu': 6.0, 'As': 16.0, 'H': 48.0, 'C': 16.0, 'I': 8.0, 'O': 16.0}\",\"('As', 'C', 'Cs', 'Cu', 'H', 'I', 'O')\",OTHERS,False\nmp-1062228,\"{'Ca': 1.0, 'O': 2.0}\",\"('Ca', 'O')\",OTHERS,False\nmp-1213281,\"{'Eu': 8.0, 'Mo': 4.0, 'O': 24.0}\",\"('Eu', 'Mo', 'O')\",OTHERS,False\nmp-770630,\"{'Rb': 2.0, 'Li': 4.0, 'Cr': 4.0, 'B': 6.0, 'O': 18.0}\",\"('B', 'Cr', 'Li', 'O', 'Rb')\",OTHERS,False\nmp-1208725,\"{'Sr': 3.0, 'Fe': 2.0, 'Ag': 2.0, 'S': 2.0, 'O': 5.0}\",\"('Ag', 'Fe', 'O', 'S', 'Sr')\",OTHERS,False\nmp-1147772,\"{'Ba': 2.0, 'Na': 2.0, 'Cu': 2.0, 'Br': 2.0, 'O': 4.0}\",\"('Ba', 'Br', 'Cu', 'Na', 'O')\",OTHERS,False\nmp-1206431,\"{'In': 2.0, 'Au': 3.0}\",\"('Au', 'In')\",OTHERS,False\nmp-1196267,\"{'Be': 4.0, 'Al': 4.0, 'Si': 4.0, 'O': 20.0}\",\"('Al', 'Be', 'O', 'Si')\",OTHERS,False\nmp-1095714,\"{'La': 1.0, 'Sb': 1.0, 'Pd': 2.0}\",\"('La', 'Pd', 'Sb')\",OTHERS,False\nmp-542664,\"{'Ce': 8.0, 'Se': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Ce', 'O', 'Se', 'Si')\",OTHERS,False\nmp-1248251,\"{'Al': 4.0, 'V': 4.0, 'O': 12.0}\",\"('Al', 'O', 'V')\",OTHERS,False\nmp-1209733,\"{'Sm': 2.0, 'Zr': 12.0, 'P': 18.0, 'O': 72.0}\",\"('O', 'P', 'Sm', 'Zr')\",OTHERS,False\nmp-687239,\"{'K': 10.0, 'Y': 2.0, 'Ni': 4.0, 'N': 24.0, 'O': 48.0}\",\"('K', 'N', 'Ni', 'O', 'Y')\",OTHERS,False\nmp-1199878,\"{'Ba': 4.0, 'U': 8.0, 'S': 20.0}\",\"('Ba', 'S', 'U')\",OTHERS,False\nmp-865014,\"{'Mg': 4.0, 'Cu': 2.0}\",\"('Cu', 'Mg')\",OTHERS,False\nmp-1245839,\"{'Sr': 6.0, 'Ni': 6.0, 'N': 10.0}\",\"('N', 'Ni', 'Sr')\",OTHERS,False\nmp-1174374,\"{'Li': 7.0, 'Co': 5.0, 'O': 12.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-1181299,\"{'Mg': 2.0, 'U': 2.0, 'S': 4.0, 'O': 42.0}\",\"('Mg', 'O', 'S', 'U')\",OTHERS,False\nmp-21048,\"{'P': 4.0, 'Cr': 4.0}\",\"('Cr', 'P')\",OTHERS,False\nmp-1076487,\"{'Sr': 28.0, 'Ca': 4.0, 'Mn': 4.0, 'Fe': 28.0, 'O': 80.0}\",\"('Ca', 'Fe', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-1095465,\"{'Y': 4.0, 'Ge': 4.0, 'Rh': 4.0}\",\"('Ge', 'Rh', 'Y')\",OTHERS,False\nmp-30432,\"{'Rh': 6.0, 'Ba': 2.0, 'Pb': 12.0}\",\"('Ba', 'Pb', 'Rh')\",OTHERS,False\nmp-561248,\"{'Sm': 8.0, 'Cu': 8.0, 'Te': 8.0, 'S': 8.0}\",\"('Cu', 'S', 'Sm', 'Te')\",OTHERS,False\nmp-35612,\"{'F': 1.0, 'O': 1.0, 'Pu': 1.0}\",\"('F', 'O', 'Pu')\",OTHERS,False\nmp-1214392,\"{'Ca': 3.0, 'S': 6.0, 'O': 30.0}\",\"('Ca', 'O', 'S')\",OTHERS,False\nmp-978270,\"{'Mg': 2.0, 'Zr': 6.0}\",\"('Mg', 'Zr')\",OTHERS,False\nmp-1192374,\"{'K': 4.0, 'Ge': 12.0, 'As': 12.0}\",\"('As', 'Ge', 'K')\",OTHERS,False\nmp-764985,\"{'Li': 8.0, 'Fe': 3.0, 'Co': 7.0, 'O': 20.0}\",\"('Co', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1223490,\"{'K': 4.0, 'C': 4.0, 'N': 4.0}\",\"('C', 'K', 'N')\",OTHERS,False\nmp-18673,\"{'O': 16.0, 'P': 4.0, 'Zn': 4.0, 'Cs': 4.0}\",\"('Cs', 'O', 'P', 'Zn')\",OTHERS,False\nmp-1185331,\"{'Li': 1.0, 'Dy': 2.0, 'Rh': 1.0}\",\"('Dy', 'Li', 'Rh')\",OTHERS,False\nmp-1208793,\"{'Sm': 1.0, 'Mg': 5.0, 'Sb': 4.0}\",\"('Mg', 'Sb', 'Sm')\",OTHERS,False\nmp-866536,\"{'Eu': 2.0, 'Na': 1.0, 'Sn': 1.0}\",\"('Eu', 'Na', 'Sn')\",OTHERS,False\nmp-753609,\"{'Li': 2.0, 'Mn': 1.0, 'P': 2.0, 'O': 8.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1247106,\"{'Mn': 4.0, 'Sn': 4.0, 'N': 8.0}\",\"('Mn', 'N', 'Sn')\",OTHERS,False\nmp-1078472,\"{'Ti': 2.0, 'Cu': 2.0, 'Ge': 2.0, 'As': 2.0}\",\"('As', 'Cu', 'Ge', 'Ti')\",OTHERS,False\nmp-1225239,\"{'Fe': 3.0, 'Ge': 2.0}\",\"('Fe', 'Ge')\",OTHERS,False\nmp-1232202,\"{'Ce': 16.0, 'Mg': 8.0, 'S': 32.0}\",\"('Ce', 'Mg', 'S')\",OTHERS,False\nmp-560929,\"{'F': 12.0, 'Na': 6.0, 'Cr': 2.0}\",\"('Cr', 'F', 'Na')\",OTHERS,False\nmp-16623,\"{'Al': 14.0, 'Dy': 2.0, 'Au': 6.0}\",\"('Al', 'Au', 'Dy')\",OTHERS,False\nmp-685124,\"{'Ni': 19.0, 'Se': 20.0}\",\"('Ni', 'Se')\",OTHERS,False\nZn 64 Mg 25 Y 11,\"{'Zn': 64.0, 'Mg': 25.0, 'Y': 11.0}\",\"('Mg', 'Y', 'Zn')\",QC,False\nmvc-5154,\"{'Ca': 4.0, 'Al': 2.0, 'Ag': 2.0, 'O': 10.0}\",\"('Ag', 'Al', 'Ca', 'O')\",OTHERS,False\nmp-1174756,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmvc-2876,\"{'Zn': 1.0, 'Sb': 4.0, 'O': 8.0}\",\"('O', 'Sb', 'Zn')\",OTHERS,False\nmp-7359,\"{'As': 2.0, 'Ag': 2.0, 'Ba': 2.0}\",\"('Ag', 'As', 'Ba')\",OTHERS,False\nmp-1075486,\"{'Mg': 10.0, 'Si': 12.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1370,\"{'Si': 32.0, 'Cs': 32.0}\",\"('Cs', 'Si')\",OTHERS,False\nmp-36733,\"{'Cd': 2.0, 'La': 4.0, 'Se': 8.0}\",\"('Cd', 'La', 'Se')\",OTHERS,False\nmp-559434,\"{'Tb': 4.0, 'B': 12.0, 'O': 24.0}\",\"('B', 'O', 'Tb')\",OTHERS,False\nmvc-8440,\"{'Mg': 4.0, 'Ti': 4.0, 'Si': 16.0, 'O': 40.0}\",\"('Mg', 'O', 'Si', 'Ti')\",OTHERS,False\nmp-989584,\"{'Ca': 6.0, 'Sn': 2.0, 'N': 1.0, 'O': 1.0}\",\"('Ca', 'N', 'O', 'Sn')\",OTHERS,False\nmp-6531,\"{'O': 48.0, 'S': 12.0, 'K': 8.0, 'Mn': 8.0}\",\"('K', 'Mn', 'O', 'S')\",OTHERS,False\nmp-1216268,\"{'Y': 9.0, 'Lu': 3.0, 'Al': 20.0, 'O': 48.0}\",\"('Al', 'Lu', 'O', 'Y')\",OTHERS,False\nmp-759604,\"{'Mn': 12.0, 'O': 4.0, 'F': 32.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-862332,\"{'La': 2.0, 'Hg': 6.0}\",\"('Hg', 'La')\",OTHERS,False\nmp-1212221,\"{'Ho': 10.0, 'Bi': 2.0, 'Pd': 4.0}\",\"('Bi', 'Ho', 'Pd')\",OTHERS,False\nmp-975322,\"{'Rb': 1.0, 'Fe': 1.0, 'O': 3.0}\",\"('Fe', 'O', 'Rb')\",OTHERS,False\nmp-1225359,\"{'Fe': 4.0, 'Cu': 8.0, 'B': 4.0, 'O': 20.0}\",\"('B', 'Cu', 'Fe', 'O')\",OTHERS,False\nmp-568746,\"{'Bi': 4.0, 'Pd': 2.0}\",\"('Bi', 'Pd')\",OTHERS,False\nmp-758740,\"{'Li': 2.0, 'Fe': 8.0, 'P': 6.0, 'O': 24.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-1025296,\"{'Sn': 1.0, 'P': 1.0, 'Pd': 5.0}\",\"('P', 'Pd', 'Sn')\",OTHERS,False\nmp-1177021,\"{'Li': 12.0, 'Mn': 2.0, 'V': 6.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-1202959,\"{'Zn': 18.0, 'S': 18.0}\",\"('S', 'Zn')\",OTHERS,False\nmp-3742,\"{'O': 38.0, 'Fe': 24.0, 'Sr': 2.0}\",\"('Fe', 'O', 'Sr')\",OTHERS,False\nmp-600167,\"{'Cu': 4.0, 'H': 24.0, 'C': 16.0, 'O': 32.0}\",\"('C', 'Cu', 'H', 'O')\",OTHERS,False\nmp-1211979,\"{'K': 4.0, 'Nb': 12.0, 'Cu': 4.0, 'O': 36.0}\",\"('Cu', 'K', 'Nb', 'O')\",OTHERS,False\nmp-1025470,\"{'Lu': 2.0, 'Te': 6.0}\",\"('Lu', 'Te')\",OTHERS,False\nmp-622171,\"{'V': 4.0, 'Sb': 8.0, 'O': 20.0}\",\"('O', 'Sb', 'V')\",OTHERS,False\nmp-1180870,\"{'K': 6.0, 'Na': 2.0, 'Cr': 4.0, 'O': 16.0}\",\"('Cr', 'K', 'Na', 'O')\",OTHERS,False\nmp-25393,\"{'Li': 4.0, 'O': 8.0, 'F': 2.0, 'P': 2.0, 'Fe': 2.0}\",\"('F', 'Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-1220523,\"{'Nb': 12.0, 'Zn': 4.0, 'Au': 4.0}\",\"('Au', 'Nb', 'Zn')\",OTHERS,False\nmp-1102657,\"{'Y': 8.0, 'Pt': 4.0}\",\"('Pt', 'Y')\",OTHERS,False\nmp-1223480,\"{'K': 1.0, 'Nd': 9.0, 'Si': 6.0, 'O': 26.0}\",\"('K', 'Nd', 'O', 'Si')\",OTHERS,False\nmp-915,\"{'Y': 1.0, 'Cd': 1.0}\",\"('Cd', 'Y')\",OTHERS,False\nmp-643367,\"{'Y': 2.0, 'C': 2.0, 'Br': 2.0}\",\"('Br', 'C', 'Y')\",OTHERS,False\nmp-865353,\"{'Tm': 2.0, 'I': 6.0}\",\"('I', 'Tm')\",OTHERS,False\nmp-23453,\"{'Br': 28.0, 'Sb': 20.0, 'Hg': 24.0}\",\"('Br', 'Hg', 'Sb')\",OTHERS,False\nmp-1113725,\"{'Rb': 2.0, 'Er': 1.0, 'Ag': 1.0, 'Cl': 6.0}\",\"('Ag', 'Cl', 'Er', 'Rb')\",OTHERS,False\nmp-1112106,\"{'Cs': 2.0, 'Y': 1.0, 'Tl': 1.0, 'Br': 6.0}\",\"('Br', 'Cs', 'Tl', 'Y')\",OTHERS,False\nmp-997589,\"{'La': 8.0, 'Al': 7.0, 'In': 1.0, 'O': 24.0}\",\"('Al', 'In', 'La', 'O')\",OTHERS,False\nmp-1097494,\"{'Mg': 1.0, 'Pd': 2.0, 'Pb': 1.0}\",\"('Mg', 'Pb', 'Pd')\",OTHERS,False\nmp-27722,\"{'N': 8.0, 'S': 24.0, 'Pd': 4.0}\",\"('N', 'Pd', 'S')\",OTHERS,False\nmp-1212103,\"{'K': 8.0, 'Fe': 8.0, 'P': 12.0, 'O': 48.0}\",\"('Fe', 'K', 'O', 'P')\",OTHERS,False\nmp-773014,\"{'Mn': 3.0, 'S': 6.0, 'O': 24.0}\",\"('Mn', 'O', 'S')\",OTHERS,False\nmp-626547,\"{'Ti': 6.0, 'H': 4.0, 'O': 14.0}\",\"('H', 'O', 'Ti')\",OTHERS,False\nmp-1205255,\"{'U': 4.0, 'N': 4.0, 'O': 32.0}\",\"('N', 'O', 'U')\",OTHERS,False\nmp-1073904,\"{'Mg': 12.0, 'Si': 10.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-11654,\"{'B': 3.0, 'O': 12.0, 'As': 3.0}\",\"('As', 'B', 'O')\",OTHERS,False\nmp-1228556,\"{'Al': 6.0, 'Cu': 2.0, 'B': 4.0, 'O': 17.0}\",\"('Al', 'B', 'Cu', 'O')\",OTHERS,False\nmp-1029282,\"{'Te': 2.0, 'Mo': 2.0, 'W': 2.0, 'Se': 2.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-4136,\"{'O': 16.0, 'P': 4.0, 'Ce': 4.0}\",\"('Ce', 'O', 'P')\",OTHERS,False\nmp-1172967,\"{'Li': 2.0, 'Ni': 4.0, 'O': 8.0}\",\"('Li', 'Ni', 'O')\",OTHERS,False\nmp-1222642,\"{'Li': 2.0, 'Ti': 3.0, 'Zn': 1.0, 'O': 8.0}\",\"('Li', 'O', 'Ti', 'Zn')\",OTHERS,False\nmp-1203386,\"{'K': 8.0, 'H': 12.0, 'Ir': 4.0, 'N': 4.0, 'Cl': 20.0}\",\"('Cl', 'H', 'Ir', 'K', 'N')\",OTHERS,False\nmp-556059,\"{'K': 4.0, 'Ni': 2.0, 'N': 8.0, 'O': 24.0}\",\"('K', 'N', 'Ni', 'O')\",OTHERS,False\nmp-774245,\"{'Li': 5.0, 'Mn': 5.0, 'Ni': 2.0, 'O': 12.0}\",\"('Li', 'Mn', 'Ni', 'O')\",OTHERS,False\nmp-10906,\"{'Al': 2.0, 'Zr': 2.0, 'Pt': 4.0}\",\"('Al', 'Pt', 'Zr')\",OTHERS,False\nmp-685418,\"{'Fe': 16.0, 'Cu': 8.0, 'O': 32.0}\",\"('Cu', 'Fe', 'O')\",OTHERS,False\nmvc-5883,\"{'Ca': 2.0, 'Cu': 4.0, 'O': 8.0}\",\"('Ca', 'Cu', 'O')\",OTHERS,False\nmp-757238,\"{'Gd': 2.0, 'As': 2.0, 'O': 8.0}\",\"('As', 'Gd', 'O')\",OTHERS,False\nmp-1175769,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1195843,\"{'Fe': 4.0, 'H': 32.0, 'S': 4.0, 'O': 32.0}\",\"('Fe', 'H', 'O', 'S')\",OTHERS,False\nmp-1038234,\"{'K': 1.0, 'Mg': 30.0, 'Al': 1.0, 'O': 32.0}\",\"('Al', 'K', 'Mg', 'O')\",OTHERS,False\nmp-862960,\"{'Pm': 1.0, 'Sn': 3.0}\",\"('Pm', 'Sn')\",OTHERS,False\nmp-763524,\"{'Li': 1.0, 'Fe': 2.0, 'C': 4.0, 'O': 12.0}\",\"('C', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1217676,\"{'Tb': 24.0, 'Se': 45.0}\",\"('Se', 'Tb')\",OTHERS,False\nmp-1228524,\"{'Al': 4.0, 'Te': 6.0}\",\"('Al', 'Te')\",OTHERS,False\nmp-3390,\"{'Si': 2.0, 'Cu': 2.0, 'Y': 1.0}\",\"('Cu', 'Si', 'Y')\",OTHERS,False\nmp-1225279,\"{'Dy': 1.0, 'Cu': 1.0, 'Ge': 1.0}\",\"('Cu', 'Dy', 'Ge')\",OTHERS,False\nmp-998421,\"{'K': 2.0, 'Ca': 2.0, 'Cl': 6.0}\",\"('Ca', 'Cl', 'K')\",OTHERS,False\nmp-12075,\"{'B': 2.0, 'Fe': 2.0, 'Tb': 1.0}\",\"('B', 'Fe', 'Tb')\",OTHERS,False\nmp-1185183,\"{'La': 1.0, 'Ce': 1.0, 'Hg': 2.0}\",\"('Ce', 'Hg', 'La')\",OTHERS,False\nmp-1247638,\"{'Sr': 4.0, 'Ca': 28.0, 'Mn': 32.0, 'O': 96.0}\",\"('Ca', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-1197458,\"{'K': 8.0, 'Th': 2.0, 'C': 16.0, 'O': 40.0}\",\"('C', 'K', 'O', 'Th')\",OTHERS,False\nmp-1178447,\"{'Fe': 8.0, 'O': 4.0, 'F': 12.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-1175152,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1194972,\"{'S': 8.0, 'Xe': 4.0, 'O': 24.0, 'F': 8.0}\",\"('F', 'O', 'S', 'Xe')\",OTHERS,False\nmp-562263,\"{'Cs': 8.0, 'Pb': 4.0, 'O': 8.0}\",\"('Cs', 'O', 'Pb')\",OTHERS,False\nmp-531661,\"{'Nd': 10.0, 'O': 39.0, 'Ti': 12.0}\",\"('Nd', 'O', 'Ti')\",OTHERS,False\nmp-1223123,\"{'Li': 2.0, 'Ta': 2.0, 'W': 2.0, 'O': 13.0}\",\"('Li', 'O', 'Ta', 'W')\",OTHERS,False\nmp-1222841,\"{'La': 3.0, 'Si': 3.0, 'B': 3.0, 'O': 15.0}\",\"('B', 'La', 'O', 'Si')\",OTHERS,False\nmp-1192792,\"{'Sr': 4.0, 'Ru': 4.0, 'O': 12.0}\",\"('O', 'Ru', 'Sr')\",OTHERS,False\nmp-1221033,\"{'Na': 1.0, 'Sr': 12.0, 'Ni': 7.0, 'O': 23.0}\",\"('Na', 'Ni', 'O', 'Sr')\",OTHERS,False\nmp-3682,\"{'F': 6.0, 'K': 2.0, 'Cu': 2.0}\",\"('Cu', 'F', 'K')\",OTHERS,False\nmp-15994,\"{'O': 48.0, 'P': 16.0, 'Hg': 8.0}\",\"('Hg', 'O', 'P')\",OTHERS,False\nmp-1106129,\"{'Bi': 4.0, 'Te': 2.0, 'Br': 2.0, 'O': 9.0}\",\"('Bi', 'Br', 'O', 'Te')\",OTHERS,False\nmp-1187893,\"{'Y': 1.0, 'Np': 3.0}\",\"('Np', 'Y')\",OTHERS,False\nmp-867159,\"{'Sm': 1.0, 'Dy': 1.0, 'Mg': 2.0}\",\"('Dy', 'Mg', 'Sm')\",OTHERS,False\nmp-768959,\"{'Na': 8.0, 'Sb': 4.0, 'C': 8.0, 'S': 2.0, 'O': 32.0}\",\"('C', 'Na', 'O', 'S', 'Sb')\",OTHERS,False\nmp-759658,\"{'Li': 4.0, 'Ag': 4.0, 'F': 8.0}\",\"('Ag', 'F', 'Li')\",OTHERS,False\nmp-1196928,\"{'Mg': 2.0, 'Zr': 2.0, 'P': 4.0, 'H': 16.0, 'O': 24.0}\",\"('H', 'Mg', 'O', 'P', 'Zr')\",OTHERS,False\nmp-1201244,\"{'Rb': 12.0, 'Ir': 4.0, 'Br': 24.0, 'O': 4.0}\",\"('Br', 'Ir', 'O', 'Rb')\",OTHERS,False\nmp-1183984,\"{'Cu': 3.0, 'Te': 1.0}\",\"('Cu', 'Te')\",OTHERS,False\nmp-1186207,\"{'Nb': 2.0, 'Re': 1.0, 'Ru': 1.0}\",\"('Nb', 'Re', 'Ru')\",OTHERS,False\nmp-1195614,\"{'La': 4.0, 'Tl': 4.0, 'P': 8.0, 'S': 24.0}\",\"('La', 'P', 'S', 'Tl')\",OTHERS,False\nmp-1174064,\"{'Li': 6.0, 'Mn': 2.0, 'Co': 2.0, 'O': 10.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-510377,\"{'B': 1.0, 'Eu': 1.0, 'Rh': 3.0}\",\"('B', 'Eu', 'Rh')\",OTHERS,False\nmp-570506,\"{'Zr': 4.0, 'I': 8.0}\",\"('I', 'Zr')\",OTHERS,False\nmp-1191016,\"{'Tm': 6.0, 'B': 14.0, 'W': 2.0}\",\"('B', 'Tm', 'W')\",OTHERS,False\nmp-756977,\"{'Ni': 1.0, 'H': 12.0, 'S': 1.0, 'O': 9.0}\",\"('H', 'Ni', 'O', 'S')\",OTHERS,False\nmp-1202032,\"{'Fe': 4.0, 'Sn': 4.0, 'O': 24.0}\",\"('Fe', 'O', 'Sn')\",OTHERS,False\nmp-1039058,\"{'Mg': 4.0, 'Bi': 8.0}\",\"('Bi', 'Mg')\",OTHERS,False\nmp-1072824,\"{'Dy': 2.0, 'Cu': 2.0, 'Sn': 2.0}\",\"('Cu', 'Dy', 'Sn')\",OTHERS,False\nmp-1246430,\"{'Zn': 6.0, 'Mo': 2.0, 'N': 8.0}\",\"('Mo', 'N', 'Zn')\",OTHERS,False\nmp-1203025,\"{'Np': 16.0, 'N': 24.0}\",\"('N', 'Np')\",OTHERS,False\nmp-12990,\"{'C': 2.0, 'Al': 2.0, 'Ti': 4.0}\",\"('Al', 'C', 'Ti')\",OTHERS,False\nmp-1220641,\"{'Nb': 3.0, 'B': 4.0, 'W': 3.0}\",\"('B', 'Nb', 'W')\",OTHERS,False\nmp-554497,\"{'Cu': 1.0, 'Sb': 2.0, 'Xe': 4.0, 'F': 20.0}\",\"('Cu', 'F', 'Sb', 'Xe')\",OTHERS,False\nmp-1024954,\"{'Ho': 2.0, 'Cu': 2.0, 'Sn': 2.0}\",\"('Cu', 'Ho', 'Sn')\",OTHERS,False\nmp-1232305,\"{'Y': 4.0, 'Mg': 4.0, 'Se': 12.0}\",\"('Mg', 'Se', 'Y')\",OTHERS,False\nmp-29815,\"{'As': 16.0, 'La': 8.0}\",\"('As', 'La')\",OTHERS,False\nmp-850399,\"{'Li': 2.0, 'V': 2.0, 'P': 2.0, 'H': 2.0, 'O': 10.0}\",\"('H', 'Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1103416,\"{'Na': 2.0, 'B': 2.0, 'H': 8.0}\",\"('B', 'H', 'Na')\",OTHERS,False\nmp-1175120,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1219621,\"{'Rb': 1.0, 'Cu': 2.0, 'H': 3.0, 'S': 2.0, 'O': 10.0}\",\"('Cu', 'H', 'O', 'Rb', 'S')\",OTHERS,False\nmp-1080109,\"{'Cu': 4.0, 'S': 3.0, 'N': 1.0}\",\"('Cu', 'N', 'S')\",OTHERS,False\nmp-1197395,\"{'K': 4.0, 'Mn': 6.0, 'H': 12.0, 'S': 6.0, 'O': 32.0}\",\"('H', 'K', 'Mn', 'O', 'S')\",OTHERS,False\nmp-1073181,\"{'Mg': 8.0, 'Si': 12.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1187060,\"{'Th': 6.0, 'V': 2.0}\",\"('Th', 'V')\",OTHERS,False\nmp-1178327,\"{'Fe': 8.0, 'O': 14.0, 'F': 2.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-756365,\"{'Ca': 4.0, 'Ce': 4.0, 'O': 12.0}\",\"('Ca', 'Ce', 'O')\",OTHERS,False\nmp-1187802,\"{'Y': 6.0, 'Ge': 2.0}\",\"('Ge', 'Y')\",OTHERS,False\nmvc-9587,\"{'Ca': 2.0, 'Bi': 4.0, 'O': 8.0}\",\"('Bi', 'Ca', 'O')\",OTHERS,False\nmp-1037923,\"{'Mg': 30.0, 'Mn': 1.0, 'Fe': 1.0, 'O': 32.0}\",\"('Fe', 'Mg', 'Mn', 'O')\",OTHERS,False\nmp-1099625,\"{'Sm': 4.0, 'Ni': 4.0, 'O': 10.0}\",\"('Ni', 'O', 'Sm')\",OTHERS,False\nmp-572159,\"{'K': 2.0, 'Fe': 2.0, 'C': 12.0, 'N': 6.0, 'O': 6.0}\",\"('C', 'Fe', 'K', 'N', 'O')\",OTHERS,False\nmp-1178781,\"{'Zn': 4.0, 'P': 6.0, 'O': 32.0}\",\"('O', 'P', 'Zn')\",OTHERS,False\nmp-1101036,\"{'Tl': 90.0, 'Sb': 10.0, 'Te': 60.0}\",\"('Sb', 'Te', 'Tl')\",OTHERS,False\nmp-1113520,\"{'Cs': 2.0, 'Sc': 1.0, 'In': 1.0, 'Br': 6.0}\",\"('Br', 'Cs', 'In', 'Sc')\",OTHERS,False\nmp-672244,\"{'Gd': 1.0, 'Al': 4.0, 'Ge': 2.0, 'Au': 1.0}\",\"('Al', 'Au', 'Gd', 'Ge')\",OTHERS,False\nmp-865562,\"{'Be': 3.0, 'Ru': 1.0}\",\"('Be', 'Ru')\",OTHERS,False\nmp-22541,\"{'Mn': 1.0, 'Ni': 1.0, 'Sb': 1.0}\",\"('Mn', 'Ni', 'Sb')\",OTHERS,False\nmp-1179643,{'S': 4.0},\"('S',)\",OTHERS,False\nZn 77 Mg 17.5 Ti 5.5,\"{'Zn': 77.0, 'Mg': 17.5, 'Ti': 5.5}\",\"('Mg', 'Ti', 'Zn')\",AC,False\nmp-685931,\"{'Ba': 20.0, 'Nb': 19.0, 'Se': 60.0}\",\"('Ba', 'Nb', 'Se')\",OTHERS,False\nmp-1194498,\"{'Ba': 2.0, 'Fe': 6.0, 'P': 6.0, 'O': 24.0}\",\"('Ba', 'Fe', 'O', 'P')\",OTHERS,False\nmp-1101339,\"{'Tb': 2.0, 'Ga': 2.0, 'O': 6.0}\",\"('Ga', 'O', 'Tb')\",OTHERS,False\nmp-1073623,\"{'Mg': 4.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-530399,\"{'Al': 20.0, 'Li': 4.0, 'O': 32.0}\",\"('Al', 'Li', 'O')\",OTHERS,False\nmp-1216133,\"{'Yb': 4.0, 'Zr': 1.0, 'Co': 33.0}\",\"('Co', 'Yb', 'Zr')\",OTHERS,False\nmp-1187680,\"{'Yb': 2.0, 'Nb': 6.0}\",\"('Nb', 'Yb')\",OTHERS,False\nmp-1246820,\"{'Re': 2.0, 'Te': 2.0, 'N': 2.0}\",\"('N', 'Re', 'Te')\",OTHERS,False\nmp-12337,\"{'O': 12.0, 'Na': 2.0, 'La': 4.0, 'Os': 2.0}\",\"('La', 'Na', 'O', 'Os')\",OTHERS,False\nmp-1224966,\"{'Fe': 1.0, 'Co': 1.0, 'Se': 2.0}\",\"('Co', 'Fe', 'Se')\",OTHERS,False\nmp-1198056,\"{'Na': 8.0, 'Al': 8.0, 'Si': 8.0, 'O': 32.0}\",\"('Al', 'Na', 'O', 'Si')\",OTHERS,False\nmvc-10213,\"{'Zn': 6.0, 'Co': 12.0, 'O': 24.0}\",\"('Co', 'O', 'Zn')\",OTHERS,False\nmp-20257,\"{'As': 2.0, 'Pd': 6.0, 'Pb': 4.0}\",\"('As', 'Pb', 'Pd')\",OTHERS,False\nmp-1219023,\"{'Sm': 1.0, 'Ga': 3.0, 'Ni': 1.0}\",\"('Ga', 'Ni', 'Sm')\",OTHERS,False\nmvc-9088,\"{'Zn': 2.0, 'Ni': 4.0, 'P': 4.0, 'O': 20.0}\",\"('Ni', 'O', 'P', 'Zn')\",OTHERS,False\nmp-778187,\"{'Li': 8.0, 'Co': 6.0, 'P': 8.0, 'H': 36.0, 'O': 48.0}\",\"('Co', 'H', 'Li', 'O', 'P')\",OTHERS,False\nmp-567199,\"{'Pb': 5.0, 'I': 10.0}\",\"('I', 'Pb')\",OTHERS,False\nmp-555101,\"{'K': 3.0, 'Co': 2.0, 'F': 7.0}\",\"('Co', 'F', 'K')\",OTHERS,False\nmp-1028769,\"{'W': 4.0, 'Se': 6.0, 'S': 2.0}\",\"('S', 'Se', 'W')\",OTHERS,False\nmp-1028795,\"{'Te': 2.0, 'Mo': 1.0, 'W': 3.0, 'Se': 2.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-1214583,\"{'Ba': 2.0, 'Y': 1.0, 'Cu': 2.0, 'Hg': 1.0, 'O': 7.0}\",\"('Ba', 'Cu', 'Hg', 'O', 'Y')\",OTHERS,False\nmp-1190719,\"{'Bi': 16.0, 'Mo': 6.0}\",\"('Bi', 'Mo')\",OTHERS,False\nmp-677627,\"{'Li': 12.0, 'Nd': 12.0, 'Te': 8.0, 'O': 48.0}\",\"('Li', 'Nd', 'O', 'Te')\",OTHERS,False\nmp-755418,\"{'Li': 4.0, 'Mn': 3.0, 'Nb': 3.0, 'Sb': 2.0, 'O': 16.0}\",\"('Li', 'Mn', 'Nb', 'O', 'Sb')\",OTHERS,False\nmp-1099747,\"{'Eu': 4.0, 'Mn': 4.0, 'O': 10.0}\",\"('Eu', 'Mn', 'O')\",OTHERS,False\nmp-754393,\"{'Li': 5.0, 'Mn': 7.0, 'O': 12.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-3526,\"{'In': 3.0, 'Er': 3.0, 'Pt': 3.0}\",\"('Er', 'In', 'Pt')\",OTHERS,False\nmp-9939,\"{'Ge': 2.0, 'Hf': 4.0}\",\"('Ge', 'Hf')\",OTHERS,False\nmp-705147,\"{'Fe': 8.0, 'Ge': 4.0, 'Se': 16.0}\",\"('Fe', 'Ge', 'Se')\",OTHERS,False\nmp-24390,\"{'H': 4.0, 'O': 16.0, 'P': 4.0, 'Ca': 4.0}\",\"('Ca', 'H', 'O', 'P')\",OTHERS,False\nmp-555271,\"{'Ba': 4.0, 'Zn': 4.0, 'Cl': 2.0, 'F': 14.0}\",\"('Ba', 'Cl', 'F', 'Zn')\",OTHERS,False\nmp-1215723,\"{'Zr': 1.0, 'Al': 6.0, 'Fe': 6.0}\",\"('Al', 'Fe', 'Zr')\",OTHERS,False\nmp-867677,\"{'Li': 4.0, 'Ag': 4.0, 'F': 24.0}\",\"('Ag', 'F', 'Li')\",OTHERS,False\nmp-765480,\"{'Li': 4.0, 'V': 4.0, 'F': 20.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-558298,\"{'Zn': 3.0, 'Te': 1.0, 'As': 2.0, 'Pb': 3.0, 'O': 14.0}\",\"('As', 'O', 'Pb', 'Te', 'Zn')\",OTHERS,False\nmp-1190355,\"{'Li': 2.0, 'Al': 2.0, 'Si': 4.0, 'O': 14.0}\",\"('Al', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1213036,\"{'Er': 3.0, 'Ga': 9.0, 'Os': 3.0}\",\"('Er', 'Ga', 'Os')\",OTHERS,False\nCd 6 Ca 1,\"{'Cd': 6.0, 'Ca': 1.0}\",\"('Ca', 'Cd')\",AC,False\nmp-1192146,\"{'Nb': 4.0, 'Te': 16.0, 'Os': 4.0}\",\"('Nb', 'Os', 'Te')\",OTHERS,False\nmp-213,\"{'Ca': 1.0, 'Pd': 1.0}\",\"('Ca', 'Pd')\",OTHERS,False\nmp-570293,\"{'Nb': 10.0, 'Ga': 6.0}\",\"('Ga', 'Nb')\",OTHERS,False\nmp-972285,\"{'Sr': 3.0, 'Li': 1.0}\",\"('Li', 'Sr')\",OTHERS,False\nmp-21309,\"{'O': 6.0, 'Mg': 1.0, 'Te': 1.0, 'Pb': 2.0}\",\"('Mg', 'O', 'Pb', 'Te')\",OTHERS,False\nmp-1195937,\"{'Na': 8.0, 'Be': 8.0, 'Si': 24.0, 'O': 64.0}\",\"('Be', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1194721,\"{'Sc': 6.0, 'Mn': 6.0, 'Al': 14.0, 'Si': 10.0}\",\"('Al', 'Mn', 'Sc', 'Si')\",OTHERS,False\nmp-1216668,\"{'V': 6.0, 'Zn': 4.0, 'Fe': 2.0, 'O': 22.0}\",\"('Fe', 'O', 'V', 'Zn')\",OTHERS,False\nmp-1183346,\"{'Ba': 2.0, 'Ca': 6.0}\",\"('Ba', 'Ca')\",OTHERS,False\nmp-3886,\"{'C': 2.0, 'Al': 2.0, 'Zr': 4.0}\",\"('Al', 'C', 'Zr')\",OTHERS,False\nmp-10960,\"{'O': 5.0, 'S': 2.0, 'Ti': 2.0, 'Tb': 2.0}\",\"('O', 'S', 'Tb', 'Ti')\",OTHERS,False\nmp-31268,\"{'Al': 2.0, 'Br': 12.0, 'Bi': 2.0}\",\"('Al', 'Bi', 'Br')\",OTHERS,False\nmp-1187431,\"{'Ti': 2.0, 'Cu': 1.0, 'Ir': 1.0}\",\"('Cu', 'Ir', 'Ti')\",OTHERS,False\nmvc-8819,\"{'Si': 16.0, 'Ni': 4.0, 'O': 40.0}\",\"('Ni', 'O', 'Si')\",OTHERS,False\nmp-5836,\"{'O': 8.0, 'Si': 2.0, 'Th': 2.0}\",\"('O', 'Si', 'Th')\",OTHERS,False\nmvc-11655,\"{'Mg': 1.0, 'Mn': 4.0, 'O': 8.0}\",\"('Mg', 'Mn', 'O')\",OTHERS,False\nmp-1096338,\"{'Na': 1.0, 'Li': 1.0, 'Hg': 2.0}\",\"('Hg', 'Li', 'Na')\",OTHERS,False\nmp-8261,\"{'O': 9.0, 'Ba': 3.0, 'Yb': 4.0}\",\"('Ba', 'O', 'Yb')\",OTHERS,False\nmp-1105554,\"{'Tm': 8.0, 'Re': 4.0, 'C': 8.0}\",\"('C', 'Re', 'Tm')\",OTHERS,False\nmp-1175203,\"{'Li': 7.0, 'Mn': 4.0, 'Co': 1.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-754990,\"{'Li': 4.0, 'V': 2.0, 'P': 4.0, 'H': 2.0, 'O': 16.0}\",\"('H', 'Li', 'O', 'P', 'V')\",OTHERS,False\nmp-625477,\"{'Eu': 2.0, 'H': 6.0, 'O': 6.0}\",\"('Eu', 'H', 'O')\",OTHERS,False\nmp-1100664,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1202854,\"{'Pr': 20.0, 'Mo': 12.0, 'O': 64.0}\",\"('Mo', 'O', 'Pr')\",OTHERS,False\nmp-22896,\"{'Cl': 6.0, 'La': 2.0}\",\"('Cl', 'La')\",OTHERS,False\nmp-706995,\"{'Zn': 4.0, 'Co': 4.0, 'H': 72.0, 'N': 24.0, 'Cl': 20.0}\",\"('Cl', 'Co', 'H', 'N', 'Zn')\",OTHERS,False\nmp-1247556,\"{'K': 1.0, 'Ba': 1.0, 'N': 1.0}\",\"('Ba', 'K', 'N')\",OTHERS,False\nmp-753781,\"{'Eu': 1.0, 'Hf': 1.0, 'O': 3.0}\",\"('Eu', 'Hf', 'O')\",OTHERS,False\nmp-21554,\"{'P': 6.0, 'Pb': 10.0, 'O': 24.0, 'F': 2.0}\",\"('F', 'O', 'P', 'Pb')\",OTHERS,False\nmp-1184239,\"{'Er': 1.0, 'Sc': 1.0, 'Zn': 2.0}\",\"('Er', 'Sc', 'Zn')\",OTHERS,False\nmp-861915,\"{'Li': 1.0, 'Dy': 2.0, 'Ir': 1.0}\",\"('Dy', 'Ir', 'Li')\",OTHERS,False\nmp-21708,\"{'Cu': 24.0, 'Ce': 4.0}\",\"('Ce', 'Cu')\",OTHERS,False\nmp-1220197,\"{'Nd': 1.0, 'Al': 1.0, 'Cu': 4.0}\",\"('Al', 'Cu', 'Nd')\",OTHERS,False\nmp-6183,\"{'O': 12.0, 'Ru': 2.0, 'Ba': 4.0, 'Pr': 2.0}\",\"('Ba', 'O', 'Pr', 'Ru')\",OTHERS,False\nmp-1224097,\"{'K': 4.0, 'N': 2.0, 'O': 5.0}\",\"('K', 'N', 'O')\",OTHERS,False\nmp-1198084,\"{'Ca': 6.0, 'S': 6.0, 'O': 27.0}\",\"('Ca', 'O', 'S')\",OTHERS,False\nmp-1206315,\"{'Sm': 3.0, 'Cd': 3.0, 'Au': 3.0}\",\"('Au', 'Cd', 'Sm')\",OTHERS,False\nmp-560014,\"{'Pr': 6.0, 'Cu': 2.0, 'Sn': 2.0, 'S': 14.0}\",\"('Cu', 'Pr', 'S', 'Sn')\",OTHERS,False\nmp-1218470,\"{'Sr': 1.0, 'Ca': 1.0, 'Ni': 8.0, 'Sn': 4.0}\",\"('Ca', 'Ni', 'Sn', 'Sr')\",OTHERS,False\nmp-7109,\"{'Si': 2.0, 'Lu': 1.0, 'Pt': 2.0}\",\"('Lu', 'Pt', 'Si')\",OTHERS,False\nmp-555276,\"{'Sm': 8.0, 'U': 4.0, 'S': 20.0}\",\"('S', 'Sm', 'U')\",OTHERS,False\nmp-1244551,\"{'Cr': 8.0, 'Bi': 8.0, 'O': 40.0}\",\"('Bi', 'Cr', 'O')\",OTHERS,False\nmp-581967,\"{'Nb': 28.0, 'O': 70.0}\",\"('Nb', 'O')\",OTHERS,False\nmp-1217922,\"{'Ta': 1.0, 'Re': 1.0, 'Se': 4.0}\",\"('Re', 'Se', 'Ta')\",OTHERS,False\nmp-1211736,\"{'K': 2.0, 'Rb': 1.0, 'Pr': 1.0, 'V': 2.0, 'O': 8.0}\",\"('K', 'O', 'Pr', 'Rb', 'V')\",OTHERS,False\nmvc-7646,\"{'Mg': 4.0, 'Sn': 6.0, 'O': 16.0}\",\"('Mg', 'O', 'Sn')\",OTHERS,False\nmp-1093680,\"{'Mg': 1.0, 'Cr': 1.0, 'Pt': 2.0}\",\"('Cr', 'Mg', 'Pt')\",OTHERS,False\nmp-21742,\"{'Ba': 20.0, 'Al': 4.0, 'Ir': 8.0, 'O': 44.0}\",\"('Al', 'Ba', 'Ir', 'O')\",OTHERS,False\nmp-722351,\"{'H': 12.0, 'Pb': 4.0, 'S': 8.0, 'N': 12.0}\",\"('H', 'N', 'Pb', 'S')\",OTHERS,False\nmp-1006891,\"{'K': 1.0, 'Sm': 1.0, 'Se': 2.0}\",\"('K', 'Se', 'Sm')\",OTHERS,False\nmp-766589,\"{'Li': 10.0, 'Co': 16.0, 'O': 32.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-1246592,\"{'Mg': 2.0, 'Fe': 10.0, 'N': 8.0}\",\"('Fe', 'Mg', 'N')\",OTHERS,False\nmp-626879,\"{'V': 12.0, 'H': 8.0, 'O': 32.0}\",\"('H', 'O', 'V')\",OTHERS,False\nmp-1078876,\"{'Er': 3.0, 'Sn': 3.0, 'Ir': 3.0}\",\"('Er', 'Ir', 'Sn')\",OTHERS,False\nmp-1187242,\"{'Ta': 1.0, 'Nb': 1.0, 'Re': 2.0}\",\"('Nb', 'Re', 'Ta')\",OTHERS,False\nmp-1027894,\"{'Mo': 3.0, 'W': 1.0, 'Se': 4.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-1252808,\"{'Al': 8.0, 'B': 24.0, 'H': 112.0, 'N': 8.0}\",\"('Al', 'B', 'H', 'N')\",OTHERS,False\nmp-766215,\"{'Na': 5.0, 'Li': 1.0, 'Fe': 6.0, 'Si': 12.0, 'O': 36.0}\",\"('Fe', 'Li', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1246474,\"{'Zr': 4.0, 'Cr': 4.0, 'Ag': 4.0, 'S': 16.0}\",\"('Ag', 'Cr', 'S', 'Zr')\",OTHERS,False\nmp-1018692,\"{'Er': 2.0, 'Te': 4.0}\",\"('Er', 'Te')\",OTHERS,False\nmp-11447,\"{'Te': 1.0, 'U': 1.0}\",\"('Te', 'U')\",OTHERS,False\nmp-20750,\"{'C': 1.0, 'Sn': 1.0, 'Tb': 3.0}\",\"('C', 'Sn', 'Tb')\",OTHERS,False\nmp-558544,\"{'Mo': 2.0, 'S': 4.0}\",\"('Mo', 'S')\",OTHERS,False\nmp-1216639,\"{'U': 2.0, 'Al': 3.0, 'Si': 3.0}\",\"('Al', 'Si', 'U')\",OTHERS,False\nmp-759027,\"{'Fe': 12.0, 'O': 12.0, 'F': 12.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-1221042,\"{'Na': 2.0, 'Ga': 12.0, 'Ag': 2.0, 'Te': 20.0}\",\"('Ag', 'Ga', 'Na', 'Te')\",OTHERS,False\nmp-677152,\"{'Ho': 10.0, 'Te': 7.0, 'S': 10.0}\",\"('Ho', 'S', 'Te')\",OTHERS,False\nmp-645044,\"{'Mn': 8.0, 'Hg': 16.0, 'C': 48.0, 'S': 48.0, 'N': 48.0}\",\"('C', 'Hg', 'Mn', 'N', 'S')\",OTHERS,False\nmp-640118,\"{'Ce': 4.0, 'Zn': 12.0}\",\"('Ce', 'Zn')\",OTHERS,False\nmp-1094364,\"{'Mg': 12.0, 'Ti': 12.0}\",\"('Mg', 'Ti')\",OTHERS,False\nmvc-2412,\"{'Zn': 2.0, 'Cr': 9.0, 'O': 13.0}\",\"('Cr', 'O', 'Zn')\",OTHERS,False\nmp-359,\"{'P': 6.0, 'Cu': 18.0}\",\"('Cu', 'P')\",OTHERS,False\nmp-1104235,\"{'Hg': 1.0, 'H': 4.0, 'C': 2.0, 'N': 4.0, 'Cl': 2.0}\",\"('C', 'Cl', 'H', 'Hg', 'N')\",OTHERS,False\nmp-1214830,\"{'B': 6.0, 'H': 12.0, 'C': 12.0}\",\"('B', 'C', 'H')\",OTHERS,False\nmp-1220068,\"{'Rb': 4.0, 'U': 12.0, 'Pt': 8.0, 'Se': 34.0}\",\"('Pt', 'Rb', 'Se', 'U')\",OTHERS,False\nmp-1213030,\"{'Er': 8.0, 'Fe': 2.0}\",\"('Er', 'Fe')\",OTHERS,False\nmp-1175581,\"{'Li': 7.0, 'Mn': 5.0, 'O': 12.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-13008,\"{'Cr': 2.0, 'Ge': 6.0, 'Nd': 2.0}\",\"('Cr', 'Ge', 'Nd')\",OTHERS,False\nmp-30934,\"{'S': 24.0, 'As': 1.0, 'I': 3.0}\",\"('As', 'I', 'S')\",OTHERS,False\nmvc-6779,\"{'Zn': 4.0, 'Mo': 8.0, 'O': 16.0}\",\"('Mo', 'O', 'Zn')\",OTHERS,False\nmp-1112650,\"{'Cs': 3.0, 'Sm': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cs', 'Sm')\",OTHERS,False\nmvc-3788,\"{'Ti': 4.0, 'Zn': 4.0, 'O': 12.0}\",\"('O', 'Ti', 'Zn')\",OTHERS,False\nmp-1183941,\"{'Ce': 4.0, 'Mg': 2.0}\",\"('Ce', 'Mg')\",OTHERS,False\nmp-1215958,\"{'Y': 2.0, 'Fe': 2.0, 'Co': 2.0}\",\"('Co', 'Fe', 'Y')\",OTHERS,False\nmp-1196079,\"{'Mg': 24.0, 'Bi': 16.0}\",\"('Bi', 'Mg')\",OTHERS,False\nmvc-5228,\"{'Mg': 6.0, 'Mn': 12.0, 'O': 24.0}\",\"('Mg', 'Mn', 'O')\",OTHERS,False\nmp-1078566,\"{'Ce': 2.0, 'Mn': 2.0, 'Se': 2.0, 'O': 3.0}\",\"('Ce', 'Mn', 'O', 'Se')\",OTHERS,False\nmp-1204595,\"{'Gd': 16.0, 'Si': 8.0, 'S': 12.0, 'O': 28.0}\",\"('Gd', 'O', 'S', 'Si')\",OTHERS,False\nmp-26612,\"{'Fe': 6.0, 'O': 24.0, 'P': 6.0}\",\"('Fe', 'O', 'P')\",OTHERS,False\nmvc-7248,\"{'Ba': 1.0, 'Cu': 1.0, 'Re': 1.0, 'O': 5.0}\",\"('Ba', 'Cu', 'O', 'Re')\",OTHERS,False\nmp-779704,\"{'Si': 2.0, 'Hg': 4.0, 'O': 8.0}\",\"('Hg', 'O', 'Si')\",OTHERS,False\nmp-556063,\"{'Pr': 1.0, 'P': 3.0, 'H': 6.0, 'O': 12.0}\",\"('H', 'O', 'P', 'Pr')\",OTHERS,False\nmp-1062510,\"{'Nd': 1.0, 'Pd': 2.0}\",\"('Nd', 'Pd')\",OTHERS,False\nmp-1099583,\"{'Sm': 1.0, 'Ti': 1.0, 'O': 3.0}\",\"('O', 'Sm', 'Ti')\",OTHERS,False\nmvc-10170,\"{'Mg': 4.0, 'Cu': 8.0, 'Te': 8.0, 'O': 32.0}\",\"('Cu', 'Mg', 'O', 'Te')\",OTHERS,False\nmp-1225530,\"{'Dy': 1.0, 'Ho': 1.0, 'Mn': 4.0}\",\"('Dy', 'Ho', 'Mn')\",OTHERS,False\nmp-1079825,\"{'Ca': 1.0, 'Ti': 2.0, 'O': 6.0}\",\"('Ca', 'O', 'Ti')\",OTHERS,False\nmp-21071,\"{'Eu': 2.0, 'S': 1.0, 'O': 2.0}\",\"('Eu', 'O', 'S')\",OTHERS,False\nmp-31040,\"{'Cl': 8.0, 'Nb': 2.0}\",\"('Cl', 'Nb')\",OTHERS,False\nAl 65 Cu 15 Rh 20,\"{'Al': 65.0, 'Cu': 15.0, 'Rh': 20.0}\",\"('Al', 'Cu', 'Rh')\",QC,False\nmp-1030745,\"{'Mo': 2.0, 'W': 2.0, 'Se': 2.0, 'S': 6.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-6328,\"{'O': 1.0, 'Na': 2.0, 'Ti': 2.0, 'Sb': 2.0}\",\"('Na', 'O', 'Sb', 'Ti')\",OTHERS,False\nmp-1185443,\"{'Li': 1.0, 'Yb': 1.0, 'Pb': 2.0}\",\"('Li', 'Pb', 'Yb')\",OTHERS,False\nZn 58 Mg 40 Ho 2,\"{'Zn': 58.0, 'Mg': 40.0, 'Ho': 2.0}\",\"('Ho', 'Mg', 'Zn')\",QC,False\nmp-1029679,\"{'Ba': 2.0, 'Ge': 14.0, 'N': 20.0}\",\"('Ba', 'Ge', 'N')\",OTHERS,False\nmp-1184015,\"{'Ga': 2.0, 'C': 2.0}\",\"('C', 'Ga')\",OTHERS,False\nmp-1205204,\"{'Zn': 8.0, 'Ni': 4.0, 'P': 8.0, 'H': 32.0, 'O': 48.0}\",\"('H', 'Ni', 'O', 'P', 'Zn')\",OTHERS,False\nmp-1096661,\"{'Hf': 2.0, 'Nb': 1.0, 'Ir': 1.0}\",\"('Hf', 'Ir', 'Nb')\",OTHERS,False\nmp-1208206,\"{'Tm': 10.0, 'In': 8.0}\",\"('In', 'Tm')\",OTHERS,False\nmp-865138,\"{'Hf': 2.0, 'Mn': 1.0, 'Ir': 1.0}\",\"('Hf', 'Ir', 'Mn')\",OTHERS,False\nmp-864789,\"{'Yb': 2.0, 'Sn': 1.0, 'Hg': 1.0}\",\"('Hg', 'Sn', 'Yb')\",OTHERS,False\nmp-704745,\"{'Cu': 8.0, 'O': 1.0}\",\"('Cu', 'O')\",OTHERS,False\nmp-1218378,\"{'Sr': 4.0, 'Br': 1.0, 'N': 2.0, 'Cl': 1.0}\",\"('Br', 'Cl', 'N', 'Sr')\",OTHERS,False\nmp-1226014,\"{'Co': 4.0, 'P': 4.0, 'Se': 4.0}\",\"('Co', 'P', 'Se')\",OTHERS,False\nmp-16479,\"{'S': 4.0, 'Yb': 2.0}\",\"('S', 'Yb')\",OTHERS,False\nmp-766711,\"{'Li': 16.0, 'Si': 8.0, 'Ni': 8.0, 'O': 32.0}\",\"('Li', 'Ni', 'O', 'Si')\",OTHERS,False\nmp-560894,\"{'Li': 8.0, 'Be': 6.0, 'P': 6.0, 'Cl': 2.0, 'O': 24.0}\",\"('Be', 'Cl', 'Li', 'O', 'P')\",OTHERS,False\nmp-1027358,\"{'Te': 4.0, 'Mo': 4.0, 'S': 4.0}\",\"('Mo', 'S', 'Te')\",OTHERS,False\nmp-1222061,\"{'Mg': 1.0, 'Al': 1.0, 'Cr': 3.0, 'Fe': 1.0, 'O': 8.0}\",\"('Al', 'Cr', 'Fe', 'Mg', 'O')\",OTHERS,False\nmp-1195450,\"{'Li': 8.0, 'Ca': 4.0, 'B': 32.0, 'H': 28.0, 'O': 70.0}\",\"('B', 'Ca', 'H', 'Li', 'O')\",OTHERS,False\nmp-1245994,\"{'Al': 2.0, 'Cr': 4.0, 'N': 6.0}\",\"('Al', 'Cr', 'N')\",OTHERS,False\nmp-1226126,\"{'Co': 1.0, 'Re': 2.0, 'Mo': 2.0, 'S': 8.0}\",\"('Co', 'Mo', 'Re', 'S')\",OTHERS,False\nmp-707110,\"{'Co': 12.0, 'C': 28.0, 'S': 8.0, 'O': 28.0}\",\"('C', 'Co', 'O', 'S')\",OTHERS,False\nmp-20543,\"{'C': 1.0, 'Sn': 1.0, 'Pr': 3.0}\",\"('C', 'Pr', 'Sn')\",OTHERS,False\nmp-765036,\"{'Li': 4.0, 'Mn': 2.0, 'O': 2.0, 'F': 6.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-14665,\"{'O': 32.0, 'S': 8.0, 'K': 4.0, 'Nd': 4.0}\",\"('K', 'Nd', 'O', 'S')\",OTHERS,False\nmp-1175158,\"{'Li': 7.0, 'Mn': 4.0, 'Co': 1.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1186532,\"{'Pm': 3.0, 'Ti': 1.0}\",\"('Pm', 'Ti')\",OTHERS,False\nmp-1175088,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-571448,\"{'Cs': 2.0, 'Pu': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cs', 'Pu')\",OTHERS,False\nmp-5518,\"{'Ga': 2.0, 'Se': 4.0, 'Ag': 2.0}\",\"('Ag', 'Ga', 'Se')\",OTHERS,False\nmp-849423,\"{'Li': 6.0, 'Mn': 3.0, 'V': 3.0, 'P': 6.0, 'H': 6.0, 'O': 30.0}\",\"('H', 'Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-761204,\"{'Li': 12.0, 'Mn': 2.0, 'V': 6.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-1113566,\"{'Eu': 1.0, 'Cs': 2.0, 'Ag': 1.0, 'Cl': 6.0}\",\"('Ag', 'Cl', 'Cs', 'Eu')\",OTHERS,False\nmp-1190904,\"{'Tb': 12.0, 'Si': 8.0, 'Ni': 4.0}\",\"('Ni', 'Si', 'Tb')\",OTHERS,False\nmp-760091,\"{'Nb': 8.0, 'S': 16.0, 'O': 64.0}\",\"('Nb', 'O', 'S')\",OTHERS,False\nmp-1225825,\"{'La': 3.0, 'Gd': 1.0, 'Br': 4.0, 'O': 4.0}\",\"('Br', 'Gd', 'La', 'O')\",OTHERS,False\nmp-551135,\"{'O': 5.0, 'B': 2.0, 'Cu': 1.0, 'Ba': 1.0}\",\"('B', 'Ba', 'Cu', 'O')\",OTHERS,False\nCd 6 Dy 1,\"{'Cd': 6.0, 'Dy': 1.0}\",\"('Cd', 'Dy')\",AC,False\nmp-1079059,\"{'Er': 4.0, 'In': 2.0, 'Au': 4.0}\",\"('Au', 'Er', 'In')\",OTHERS,False\nmp-69,{'Sm': 4.0},\"('Sm',)\",OTHERS,False\nmp-779428,\"{'Sr': 8.0, 'Hf': 2.0, 'O': 12.0}\",\"('Hf', 'O', 'Sr')\",OTHERS,False\nmp-1245132,\"{'Ga': 50.0, 'N': 50.0}\",\"('Ga', 'N')\",OTHERS,False\nmp-1095025,\"{'Pr': 1.0, 'Cr': 2.0, 'B': 6.0}\",\"('B', 'Cr', 'Pr')\",OTHERS,False\nmp-24,{'C': 8.0},\"('C',)\",OTHERS,False\nmp-1093899,\"{'Zr': 1.0, 'Cd': 1.0, 'Pt': 2.0}\",\"('Cd', 'Pt', 'Zr')\",OTHERS,False\nCd 6 Sr 1,\"{'Cd': 6.0, 'Sr': 1.0}\",\"('Cd', 'Sr')\",AC,False\nmp-778102,\"{'Ba': 4.0, 'Y': 4.0, 'F': 20.0}\",\"('Ba', 'F', 'Y')\",OTHERS,False\nmp-758280,\"{'Li': 2.0, 'V': 3.0, 'C': 6.0, 'O': 18.0}\",\"('C', 'Li', 'O', 'V')\",OTHERS,False\nmvc-6533,\"{'Ca': 2.0, 'W': 4.0, 'O': 8.0}\",\"('Ca', 'O', 'W')\",OTHERS,False\nmp-1078100,\"{'Cd': 1.0, 'P': 1.0, 'Pd': 5.0}\",\"('Cd', 'P', 'Pd')\",OTHERS,False\nmp-690014,\"{'Ni': 2.0, 'P': 4.0, 'O': 14.0}\",\"('Ni', 'O', 'P')\",OTHERS,False\nmp-1096129,\"{'La': 2.0, 'Hg': 1.0, 'Ru': 1.0}\",\"('Hg', 'La', 'Ru')\",OTHERS,False\nmp-1080452,\"{'K': 1.0, 'Cd': 4.0, 'As': 3.0}\",\"('As', 'Cd', 'K')\",OTHERS,False\nmp-629397,\"{'Fe': 1.0, 'Ni': 1.0, 'N': 1.0}\",\"('Fe', 'N', 'Ni')\",OTHERS,False\nmp-1193017,\"{'Gd': 6.0, 'Zn': 23.0}\",\"('Gd', 'Zn')\",OTHERS,False\nmp-771304,\"{'Li': 6.0, 'Si': 2.0, 'Sb': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Li', 'O', 'Sb', 'Si')\",OTHERS,False\nmp-1206263,\"{'Tm': 3.0, 'Mg': 3.0, 'Ag': 3.0}\",\"('Ag', 'Mg', 'Tm')\",OTHERS,False\nmp-1027051,\"{'Mo': 3.0, 'W': 1.0, 'Se': 4.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-1194116,\"{'Ce': 4.0, 'Mo': 4.0, 'Br': 4.0, 'O': 16.0}\",\"('Br', 'Ce', 'Mo', 'O')\",OTHERS,False\nmp-1200004,\"{'Ho': 18.0, 'C': 18.0, 'O': 72.0}\",\"('C', 'Ho', 'O')\",OTHERS,False\nmp-31426,\"{'Ni': 3.0, 'In': 3.0, 'Ho': 3.0}\",\"('Ho', 'In', 'Ni')\",OTHERS,False\nmp-11334,{'W': 8.0},\"('W',)\",OTHERS,False\nmp-674343,\"{'Ti': 10.0, 'Cu': 7.0, 'S': 20.0}\",\"('Cu', 'S', 'Ti')\",OTHERS,False\nmp-984744,\"{'Ca': 6.0, 'Tl': 2.0}\",\"('Ca', 'Tl')\",OTHERS,False\nmp-1072798,\"{'Er': 2.0, 'Al': 2.0, 'Zn': 2.0}\",\"('Al', 'Er', 'Zn')\",OTHERS,False\nmp-553972,\"{'Cu': 8.0, 'C': 8.0, 'S': 8.0, 'N': 8.0}\",\"('C', 'Cu', 'N', 'S')\",OTHERS,False\nmp-1179159,\"{'Tl': 2.0, 'C': 8.0, 'S': 8.0, 'N': 2.0}\",\"('C', 'N', 'S', 'Tl')\",OTHERS,False\nmp-7723,\"{'F': 4.0, 'Na': 1.0, 'Al': 1.0}\",\"('Al', 'F', 'Na')\",OTHERS,False\nmp-1102615,\"{'Ce': 4.0, 'Te': 4.0, 'Cl': 4.0}\",\"('Ce', 'Cl', 'Te')\",OTHERS,False\nmp-754254,\"{'P': 4.0, 'W': 2.0, 'O': 16.0}\",\"('O', 'P', 'W')\",OTHERS,False\nmp-505611,\"{'O': 2.0, 'S': 2.0, 'Ni': 1.0, 'Cu': 2.0, 'Sr': 2.0}\",\"('Cu', 'Ni', 'O', 'S', 'Sr')\",OTHERS,False\nmp-1114383,\"{'Rb': 2.0, 'Tl': 2.0, 'I': 6.0}\",\"('I', 'Rb', 'Tl')\",OTHERS,False\nmp-1215213,\"{'Zr': 1.0, 'Ta': 1.0, 'Cr': 4.0}\",\"('Cr', 'Ta', 'Zr')\",OTHERS,False\nmvc-16046,\"{'Sr': 3.0, 'Mg': 1.0, 'Mn': 2.0, 'S': 2.0, 'O': 5.0}\",\"('Mg', 'Mn', 'O', 'S', 'Sr')\",OTHERS,False\nmp-1207675,\"{'Tm': 4.0, 'Si': 4.0, 'Ir': 4.0}\",\"('Ir', 'Si', 'Tm')\",OTHERS,False\nmp-777475,\"{'Li': 6.0, 'Fe': 2.0, 'F': 12.0}\",\"('F', 'Fe', 'Li')\",OTHERS,False\nmp-1007685,\"{'In': 1.0, 'Ag': 1.0, 'Se': 2.0}\",\"('Ag', 'In', 'Se')\",OTHERS,False\nmp-26745,\"{'Li': 2.0, 'Sb': 2.0, 'P': 4.0, 'O': 14.0}\",\"('Li', 'O', 'P', 'Sb')\",OTHERS,False\nmp-755301,\"{'Ni': 6.0, 'O': 1.0, 'F': 11.0}\",\"('F', 'Ni', 'O')\",OTHERS,False\nmp-28241,\"{'Zr': 6.0, 'I': 12.0}\",\"('I', 'Zr')\",OTHERS,False\nmp-1191093,\"{'Gd': 2.0, 'Fe': 17.0, 'H': 3.0}\",\"('Fe', 'Gd', 'H')\",OTHERS,False\nmp-1026440,\"{'Hf': 1.0, 'Mg': 14.0, 'Cd': 1.0}\",\"('Cd', 'Hf', 'Mg')\",OTHERS,False\nmp-753398,\"{'W': 4.0, 'O': 4.0, 'F': 12.0}\",\"('F', 'O', 'W')\",OTHERS,False\nmp-1001918,\"{'Hf': 1.0, 'C': 1.0}\",\"('C', 'Hf')\",OTHERS,False\nmp-28828,\"{'Li': 6.0, 'Cl': 8.0, 'Fe': 1.0}\",\"('Cl', 'Fe', 'Li')\",OTHERS,False\nmp-546007,\"{'La': 2.0, 'Mo': 1.0, 'O': 6.0}\",\"('La', 'Mo', 'O')\",OTHERS,False\nmp-1038045,\"{'Ca': 1.0, 'Mg': 30.0, 'Fe': 1.0, 'O': 32.0}\",\"('Ca', 'Fe', 'Mg', 'O')\",OTHERS,False\nmp-972423,\"{'Tb': 2.0, 'Ga': 4.0, 'Pd': 2.0}\",\"('Ga', 'Pd', 'Tb')\",OTHERS,False\nmp-753334,\"{'V': 4.0, 'O': 7.0, 'F': 5.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-1229107,\"{'Ag': 3.0, 'Sn': 1.0}\",\"('Ag', 'Sn')\",OTHERS,False\nmp-1210906,\"{'Na': 2.0, 'Mn': 1.0, 'Sb': 2.0}\",\"('Mn', 'Na', 'Sb')\",OTHERS,False\nmp-20836,\"{'Ni': 4.0, 'As': 4.0, 'Nd': 2.0}\",\"('As', 'Nd', 'Ni')\",OTHERS,False\nmp-1098064,\"{'Cs': 1.0, 'Hf': 1.0, 'Mg': 14.0, 'O': 15.0}\",\"('Cs', 'Hf', 'Mg', 'O')\",OTHERS,False\nmp-20857,\"{'B': 4.0, 'Co': 4.0}\",\"('B', 'Co')\",OTHERS,False\nmp-1186267,\"{'Nd': 6.0, 'Er': 2.0}\",\"('Er', 'Nd')\",OTHERS,False\nmp-29997,\"{'As': 4.0, 'I': 12.0, 'La': 12.0}\",\"('As', 'I', 'La')\",OTHERS,False\nmp-41701,\"{'Li': 2.0, 'Co': 4.0, 'P': 8.0, 'H': 6.0, 'O': 28.0}\",\"('Co', 'H', 'Li', 'O', 'P')\",OTHERS,False\nmp-23965,\"{'H': 16.0, 'O': 8.0, 'Co': 2.0, 'Br': 4.0}\",\"('Br', 'Co', 'H', 'O')\",OTHERS,False\nmp-569819,\"{'Th': 14.0, 'Os': 6.0}\",\"('Os', 'Th')\",OTHERS,False\nmp-21547,\"{'Na': 24.0, 'In': 8.0, 'Sb': 16.0}\",\"('In', 'Na', 'Sb')\",OTHERS,False\nmp-1192391,\"{'Rb': 2.0, 'Li': 4.0, 'Cd': 2.0, 'C': 4.0, 'O': 12.0, 'F': 2.0}\",\"('C', 'Cd', 'F', 'Li', 'O', 'Rb')\",OTHERS,False\nmp-1183106,\"{'Ac': 2.0, 'Zn': 1.0, 'In': 1.0}\",\"('Ac', 'In', 'Zn')\",OTHERS,False\nmp-1207531,\"{'Zn': 12.0, 'B': 28.0, 'I': 4.0, 'O': 52.0}\",\"('B', 'I', 'O', 'Zn')\",OTHERS,False\nmp-771902,\"{'Li': 4.0, 'Ti': 3.0, 'Cr': 2.0, 'Fe': 3.0, 'O': 16.0}\",\"('Cr', 'Fe', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-751967,\"{'V': 8.0, 'Ni': 4.0, 'P': 8.0, 'H': 24.0, 'O': 52.0}\",\"('H', 'Ni', 'O', 'P', 'V')\",OTHERS,False\nmp-13002,\"{'F': 12.0, 'Si': 2.0, 'K': 4.0}\",\"('F', 'K', 'Si')\",OTHERS,False\nmp-1222646,\"{'Li': 2.0, 'Mg': 1.0, 'Hg': 1.0}\",\"('Hg', 'Li', 'Mg')\",OTHERS,False\nmp-1105961,\"{'Th': 4.0, 'Ta': 4.0, 'N': 12.0}\",\"('N', 'Ta', 'Th')\",OTHERS,False\nmp-775811,\"{'Li': 4.0, 'V': 4.0, 'Fe': 4.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'V')\",OTHERS,False\nmvc-4608,\"{'Y': 2.0, 'Cr': 4.0, 'O': 8.0}\",\"('Cr', 'O', 'Y')\",OTHERS,False\nmp-555458,\"{'Ba': 4.0, 'Fe': 2.0, 'S': 4.0, 'I': 5.0}\",\"('Ba', 'Fe', 'I', 'S')\",OTHERS,False\nmp-7167,\"{'C': 1.0, 'Rh': 3.0, 'Sm': 1.0}\",\"('C', 'Rh', 'Sm')\",OTHERS,False\nmp-755965,\"{'Li': 2.0, 'Ti': 2.0, 'Fe': 2.0, 'O': 8.0}\",\"('Fe', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-982730,\"{'Ho': 2.0, 'Zn': 1.0, 'Ir': 1.0}\",\"('Ho', 'Ir', 'Zn')\",OTHERS,False\nmp-561398,\"{'Na': 2.0, 'Al': 2.0, 'Mo': 4.0, 'O': 16.0}\",\"('Al', 'Mo', 'Na', 'O')\",OTHERS,False\nmp-766887,\"{'Li': 13.0, 'Co': 15.0, 'O': 28.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-755278,\"{'Li': 2.0, 'V': 1.0, 'Cr': 1.0, 'O': 4.0}\",\"('Cr', 'Li', 'O', 'V')\",OTHERS,False\nmp-777897,\"{'Li': 4.0, 'Ti': 2.0, 'V': 3.0, 'Cu': 3.0, 'O': 16.0}\",\"('Cu', 'Li', 'O', 'Ti', 'V')\",OTHERS,False\nmp-1205148,\"{'Pr': 2.0, 'H': 40.0, 'S': 8.0, 'N': 10.0, 'O': 32.0}\",\"('H', 'N', 'O', 'Pr', 'S')\",OTHERS,False\nmp-560577,\"{'F': 28.0, 'Ca': 4.0, 'Cr': 4.0, 'Ba': 4.0}\",\"('Ba', 'Ca', 'Cr', 'F')\",OTHERS,False\nmp-754198,\"{'Ba': 1.0, 'La': 2.0, 'I': 8.0}\",\"('Ba', 'I', 'La')\",OTHERS,False\nmp-777464,\"{'Li': 4.0, 'Ti': 5.0, 'Cr': 3.0, 'O': 16.0}\",\"('Cr', 'Li', 'O', 'Ti')\",OTHERS,False\nmp-755995,\"{'P': 4.0, 'W': 2.0, 'O': 14.0}\",\"('O', 'P', 'W')\",OTHERS,False\nmp-1221200,\"{'Na': 8.0, 'Li': 8.0, 'Mn': 2.0, 'Fe': 6.0, 'P': 8.0, 'O': 32.0, 'F': 8.0}\",\"('F', 'Fe', 'Li', 'Mn', 'Na', 'O', 'P')\",OTHERS,False\nmp-1029396,\"{'Sr': 2.0, 'Hf': 2.0, 'N': 4.0}\",\"('Hf', 'N', 'Sr')\",OTHERS,False\nmvc-15700,\"{'Te': 1.0, 'F': 6.0}\",\"('F', 'Te')\",OTHERS,False\nmp-10141,\"{'B': 2.0, 'Sm': 1.0}\",\"('B', 'Sm')\",OTHERS,False\nmp-1225916,\"{'Cs': 2.0, 'C': 2.0, 'N': 2.0, 'O': 2.0}\",\"('C', 'Cs', 'N', 'O')\",OTHERS,False\nmp-570369,\"{'Ce': 32.0, 'Ru': 18.0}\",\"('Ce', 'Ru')\",OTHERS,False\nmp-673174,\"{'Fe': 24.0, 'N': 9.0}\",\"('Fe', 'N')\",OTHERS,False\nmp-771034,\"{'Li': 6.0, 'Mn': 2.0, 'As': 2.0, 'C': 2.0, 'O': 14.0}\",\"('As', 'C', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-983556,\"{'Er': 2.0, 'Mg': 1.0, 'Tl': 1.0}\",\"('Er', 'Mg', 'Tl')\",OTHERS,False\nmp-1238813,\"{'Li': 2.0, 'Cr': 4.0, 'S': 8.0}\",\"('Cr', 'Li', 'S')\",OTHERS,False\nmp-669471,\"{'Hg': 4.0, 'S': 16.0, 'N': 12.0, 'Cl': 12.0}\",\"('Cl', 'Hg', 'N', 'S')\",OTHERS,False\nmp-752571,\"{'Li': 5.0, 'Co': 7.0, 'O': 3.0, 'F': 13.0}\",\"('Co', 'F', 'Li', 'O')\",OTHERS,False\nmp-532220,\"{'Cr': 48.0, 'Dy': 16.0, 'S': 96.0}\",\"('Cr', 'Dy', 'S')\",OTHERS,False\nmp-1029256,\"{'Te': 8.0, 'Mo': 2.0, 'W': 2.0}\",\"('Mo', 'Te', 'W')\",OTHERS,False\nmp-554665,\"{'Si': 8.0, 'O': 16.0}\",\"('O', 'Si')\",OTHERS,False\nmp-1184254,\"{'Ga': 1.0, 'Pd': 3.0}\",\"('Ga', 'Pd')\",OTHERS,False\nmp-1192847,\"{'Ho': 6.0, 'Zn': 23.0}\",\"('Ho', 'Zn')\",OTHERS,False\nmp-5788,\"{'Na': 4.0, 'U': 4.0, 'O': 12.0}\",\"('Na', 'O', 'U')\",OTHERS,False\nmp-19805,\"{'Ge': 2.0, 'Pt': 2.0, 'U': 1.0}\",\"('Ge', 'Pt', 'U')\",OTHERS,False\nmp-558592,\"{'Sb': 32.0, 'Br': 8.0, 'O': 44.0}\",\"('Br', 'O', 'Sb')\",OTHERS,False\nmp-1206332,\"{'Sr': 2.0, 'La': 1.0, 'Sb': 1.0, 'O': 6.0}\",\"('La', 'O', 'Sb', 'Sr')\",OTHERS,False\nmp-1070394,\"{'Ce': 1.0, 'Si': 3.0, 'Rh': 1.0}\",\"('Ce', 'Rh', 'Si')\",OTHERS,False\nmp-1214183,\"{'Ca': 12.0, 'Al': 86.0, 'W': 8.0}\",\"('Al', 'Ca', 'W')\",OTHERS,False\nmp-1204837,\"{'Na': 4.0, 'Fe': 8.0, 'Si': 24.0, 'O': 60.0}\",\"('Fe', 'Na', 'O', 'Si')\",OTHERS,False\nmp-684496,\"{'Sb': 4.0, 'P': 8.0, 'O': 28.0}\",\"('O', 'P', 'Sb')\",OTHERS,False\nmp-1246291,\"{'Dy': 2.0, 'Mg': 2.0, 'W': 2.0, 'S': 8.0}\",\"('Dy', 'Mg', 'S', 'W')\",OTHERS,False\nmp-1038514,\"{'Hf': 1.0, 'Mg': 30.0, 'Cd': 1.0, 'O': 32.0}\",\"('Cd', 'Hf', 'Mg', 'O')\",OTHERS,False\nmp-850751,\"{'Li': 4.0, 'V': 4.0, 'P': 8.0, 'H': 8.0, 'O': 32.0}\",\"('H', 'Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1102068,\"{'Dy': 4.0, 'As': 4.0, 'Pd': 4.0}\",\"('As', 'Dy', 'Pd')\",OTHERS,False\nmp-981262,\"{'Yb': 2.0, 'F': 2.0}\",\"('F', 'Yb')\",OTHERS,False\nmp-1184403,\"{'Gd': 2.0, 'Al': 1.0, 'Cd': 1.0}\",\"('Al', 'Cd', 'Gd')\",OTHERS,False\nmvc-3750,\"{'Mg': 4.0, 'Mo': 4.0, 'F': 20.0}\",\"('F', 'Mg', 'Mo')\",OTHERS,False\nmp-756919,\"{'Mg': 6.0, 'As': 4.0, 'O': 16.0}\",\"('As', 'Mg', 'O')\",OTHERS,False\nmp-1099096,\"{'Ce': 1.0, 'Mg': 14.0, 'Cd': 1.0}\",\"('Cd', 'Ce', 'Mg')\",OTHERS,False\nmp-866108,\"{'Hf': 2.0, 'Zn': 6.0}\",\"('Hf', 'Zn')\",OTHERS,False\nmp-1232334,\"{'Sr': 3.0, 'Cu': 2.0, 'O': 4.0, 'F': 4.0}\",\"('Cu', 'F', 'O', 'Sr')\",OTHERS,False\nmp-21372,\"{'O': 2.0, 'P': 2.0, 'Ru': 2.0, 'Ce': 2.0}\",\"('Ce', 'O', 'P', 'Ru')\",OTHERS,False\nmp-9518,\"{'O': 2.0, 'P': 2.0, 'Zn': 2.0, 'Nd': 2.0}\",\"('Nd', 'O', 'P', 'Zn')\",OTHERS,False\nmp-1219419,\"{'Sc': 5.0, 'P': 12.0, 'Ir': 19.0}\",\"('Ir', 'P', 'Sc')\",OTHERS,False\nmp-863697,\"{'Pm': 2.0, 'Li': 1.0, 'Si': 1.0}\",\"('Li', 'Pm', 'Si')\",OTHERS,False\nmp-755334,\"{'Co': 6.0, 'O': 4.0, 'F': 8.0}\",\"('Co', 'F', 'O')\",OTHERS,False\nmp-769649,\"{'Gd': 2.0, 'Ta': 6.0, 'O': 18.0}\",\"('Gd', 'O', 'Ta')\",OTHERS,False\nmp-1211869,\"{'La': 12.0, 'Sc': 8.0, 'Ga': 12.0, 'O': 48.0}\",\"('Ga', 'La', 'O', 'Sc')\",OTHERS,False\nmp-1216689,\"{'Tl': 1.0, 'Cr': 5.0, 'Te': 8.0}\",\"('Cr', 'Te', 'Tl')\",OTHERS,False\nmp-1236984,\"{'Na': 2.0, 'Mg': 4.0, 'S': 6.0, 'O': 24.0}\",\"('Mg', 'Na', 'O', 'S')\",OTHERS,False\nmp-1251411,\"{'Na': 4.0, 'Al': 4.0, 'H': 32.0, 'N': 16.0}\",\"('Al', 'H', 'N', 'Na')\",OTHERS,False\nCr 5 Ni 3 Si 2,\"{'Cr': 5.0, 'Ni': 3.0, 'Si': 2.0}\",\"('Cr', 'Ni', 'Si')\",QC,False\nmp-762052,\"{'Na': 4.0, 'Ga': 22.0, 'O': 34.0}\",\"('Ga', 'Na', 'O')\",OTHERS,False\nmp-1214838,\"{'Al': 18.0, 'Cr': 6.0, 'Si': 2.0}\",\"('Al', 'Cr', 'Si')\",OTHERS,False\nmp-1247387,\"{'Li': 12.0, 'C': 4.0, 'N': 8.0}\",\"('C', 'Li', 'N')\",OTHERS,False\nmp-1014296,\"{'C': 6.0, 'N': 2.0}\",\"('C', 'N')\",OTHERS,False\nmp-1247156,\"{'Co': 1.0, 'Ni': 2.0, 'N': 2.0}\",\"('Co', 'N', 'Ni')\",OTHERS,False\nmp-1079296,\"{'Hf': 2.0, 'Au': 6.0}\",\"('Au', 'Hf')\",OTHERS,False\nCd 6 Sm 1,\"{'Cd': 6.0, 'Sm': 1.0}\",\"('Cd', 'Sm')\",AC,False\nmp-569698,\"{'U': 1.0, 'Al': 2.0, 'Pd': 5.0}\",\"('Al', 'Pd', 'U')\",OTHERS,False\nmp-31456,\"{'Ru': 1.0, 'Sb': 1.0, 'Hf': 1.0}\",\"('Hf', 'Ru', 'Sb')\",OTHERS,False\nmp-1014993,\"{'Cr': 4.0, 'N': 8.0}\",\"('Cr', 'N')\",OTHERS,False\nmp-1173467,\"{'Rb': 2.0, 'Na': 2.0, 'Mg': 10.0, 'Si': 24.0, 'O': 60.0}\",\"('Mg', 'Na', 'O', 'Rb', 'Si')\",OTHERS,False\nmp-780172,\"{'Tm': 8.0, 'Te': 4.0, 'O': 24.0}\",\"('O', 'Te', 'Tm')\",OTHERS,False\nmp-753051,\"{'Li': 2.0, 'Cr': 2.0, 'P': 2.0, 'H': 2.0, 'O': 10.0}\",\"('Cr', 'H', 'Li', 'O', 'P')\",OTHERS,False\nmp-677074,\"{'Pb': 2.0, 'O': 2.0}\",\"('O', 'Pb')\",OTHERS,False\nmp-26739,\"{'Cr': 8.0, 'Li': 12.0, 'O': 48.0, 'P': 12.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-1232447,\"{'Li': 7.0, 'Mn': 4.0, 'Co': 1.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1245775,\"{'Sr': 12.0, 'Ta': 8.0, 'N': 16.0}\",\"('N', 'Sr', 'Ta')\",OTHERS,False\nmp-26532,\"{'O': 20.0, 'P': 6.0, 'Cu': 4.0}\",\"('Cu', 'O', 'P')\",OTHERS,False\nmp-1523,\"{'P': 8.0, 'Zr': 4.0}\",\"('P', 'Zr')\",OTHERS,False\nmp-21268,\"{'Si': 4.0, 'As': 8.0}\",\"('As', 'Si')\",OTHERS,False\nmp-1228516,\"{'Ba': 2.0, 'Nd': 2.0, 'Fe': 1.0, 'Co': 3.0, 'O': 12.0}\",\"('Ba', 'Co', 'Fe', 'Nd', 'O')\",OTHERS,False\nmp-17546,\"{'O': 16.0, 'Ru': 4.0, 'Cs': 8.0}\",\"('Cs', 'O', 'Ru')\",OTHERS,False\nmp-30978,\"{'Al': 8.0, 'K': 4.0, 'Br': 28.0}\",\"('Al', 'Br', 'K')\",OTHERS,False\nmp-1246166,\"{'Zr': 1.0, 'Fe': 2.0, 'N': 2.0}\",\"('Fe', 'N', 'Zr')\",OTHERS,False\nmvc-2922,\"{'Ca': 4.0, 'Mn': 4.0, 'O': 8.0}\",\"('Ca', 'Mn', 'O')\",OTHERS,False\nmp-1194311,\"{'Sc': 3.0, 'Ni': 20.0, 'B': 6.0}\",\"('B', 'Ni', 'Sc')\",OTHERS,False\nmp-1183956,\"{'Cs': 2.0, 'La': 2.0}\",\"('Cs', 'La')\",OTHERS,False\nmp-510471,\"{'Cu': 4.0, 'Gd': 4.0, 'S': 8.0}\",\"('Cu', 'Gd', 'S')\",OTHERS,False\nmp-560637,\"{'Ga': 8.0, 'S': 40.0, 'N': 40.0, 'Cl': 32.0}\",\"('Cl', 'Ga', 'N', 'S')\",OTHERS,False\nmp-1201689,\"{'La': 8.0, 'Ni': 24.0, 'B': 8.0}\",\"('B', 'La', 'Ni')\",OTHERS,False\nAl 70 Pd 20 Re 10,\"{'Al': 70.0, 'Pd': 20.0, 'Re': 10.0}\",\"('Al', 'Pd', 'Re')\",QC,False\nmp-1078916,\"{'Tm': 4.0, 'In': 2.0, 'Cu': 4.0}\",\"('Cu', 'In', 'Tm')\",OTHERS,False\nmp-1098545,\"{'Sr': 1.0, 'Hf': 1.0, 'Mg': 30.0, 'O': 32.0}\",\"('Hf', 'Mg', 'O', 'Sr')\",OTHERS,False\nmp-1201234,\"{'Cu': 4.0, 'H': 32.0, 'S': 12.0, 'O': 40.0}\",\"('Cu', 'H', 'O', 'S')\",OTHERS,False\nmp-644245,\"{'Na': 4.0, 'P': 2.0, 'H': 6.0, 'O': 10.0}\",\"('H', 'Na', 'O', 'P')\",OTHERS,False\nmp-1196828,\"{'Na': 4.0, 'Li': 4.0, 'Mg': 4.0, 'P': 4.0, 'O': 16.0, 'F': 4.0}\",\"('F', 'Li', 'Mg', 'Na', 'O', 'P')\",OTHERS,False\nmp-772972,\"{'Li': 8.0, 'Ti': 8.0, 'O': 20.0}\",\"('Li', 'O', 'Ti')\",OTHERS,False\nmp-752698,\"{'Li': 4.0, 'Cr': 2.0, 'Cu': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Cr', 'Cu', 'Li', 'O', 'P')\",OTHERS,False\nmp-24118,\"{'H': 20.0, 'O': 16.0, 'S': 2.0}\",\"('H', 'O', 'S')\",OTHERS,False\nmp-1079367,\"{'U': 3.0, 'Al': 3.0, 'Co': 3.0}\",\"('Al', 'Co', 'U')\",OTHERS,False\nmp-1182361,\"{'Ba': 2.0, 'B': 10.0, 'O': 20.0}\",\"('B', 'Ba', 'O')\",OTHERS,False\nmp-12464,\"{'Co': 2.0, 'Se': 4.0, 'Pd': 4.0}\",\"('Co', 'Pd', 'Se')\",OTHERS,False\nmp-1223550,\"{'K': 1.0, 'P': 4.0, 'N': 3.0, 'O': 16.0}\",\"('K', 'N', 'O', 'P')\",OTHERS,False\nmp-765515,\"{'Li': 1.0, 'Co': 15.0, 'O': 16.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-1183449,\"{'Be': 1.0, 'V': 1.0, 'O': 3.0}\",\"('Be', 'O', 'V')\",OTHERS,False\nmp-237,\"{'Te': 1.0, 'Tm': 1.0}\",\"('Te', 'Tm')\",OTHERS,False\nmp-1103859,\"{'Tl': 1.0, 'Mo': 6.0, 'S': 8.0}\",\"('Mo', 'S', 'Tl')\",OTHERS,False\nmp-15257,\"{'Se': 12.0, 'Tb': 8.0}\",\"('Se', 'Tb')\",OTHERS,False\nmp-1185847,\"{'Mg': 1.0, 'Bi': 1.0, 'O': 3.0}\",\"('Bi', 'Mg', 'O')\",OTHERS,False\nmvc-353,\"{'Ba': 2.0, 'Tl': 2.0, 'Co': 4.0, 'O': 12.0}\",\"('Ba', 'Co', 'O', 'Tl')\",OTHERS,False\nmp-637220,\"{'Gd': 2.0, 'Si': 4.0, 'Pt': 4.0}\",\"('Gd', 'Pt', 'Si')\",OTHERS,False\nmp-29536,\"{'C': 4.0, 'F': 16.0, 'Ag': 20.0}\",\"('Ag', 'C', 'F')\",OTHERS,False\nmp-27100,\"{'Cr': 8.0, 'O': 52.0, 'P': 12.0}\",\"('Cr', 'O', 'P')\",OTHERS,False\nmp-1211688,\"{'K': 2.0, 'Li': 2.0, 'Ho': 4.0, 'Mo': 8.0, 'O': 32.0}\",\"('Ho', 'K', 'Li', 'Mo', 'O')\",OTHERS,False\nmp-29979,\"{'B': 6.0, 'C': 2.0, 'Nb': 6.0}\",\"('B', 'C', 'Nb')\",OTHERS,False\nmp-1247586,\"{'Sr': 1.0, 'Ca': 7.0, 'Mn': 6.0, 'Cr': 2.0, 'O': 21.0}\",\"('Ca', 'Cr', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-21526,\"{'K': 16.0, 'Pb': 16.0}\",\"('K', 'Pb')\",OTHERS,False\nmp-1080388,\"{'Pu': 1.0, 'Pt': 5.0}\",\"('Pt', 'Pu')\",OTHERS,False\nmp-770537,\"{'Li': 4.0, 'Mn': 8.0, 'O': 12.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-707040,\"{'Ca': 2.0, 'Al': 4.0, 'H': 32.0, 'N': 18.0}\",\"('Al', 'Ca', 'H', 'N')\",OTHERS,False\nmp-568685,\"{'Tm': 4.0, 'Al': 4.0, 'Rh': 4.0}\",\"('Al', 'Rh', 'Tm')\",OTHERS,False\nmp-779987,\"{'Fe': 6.0, 'O': 2.0, 'F': 10.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-8456,\"{'As': 2.0, 'Sr': 2.0, 'Pt': 2.0}\",\"('As', 'Pt', 'Sr')\",OTHERS,False\nmp-775274,\"{'K': 8.0, 'Cr': 4.0, 'O': 16.0}\",\"('Cr', 'K', 'O')\",OTHERS,False\nmp-753971,\"{'Li': 5.0, 'Fe': 1.0, 'Ni': 4.0, 'O': 10.0}\",\"('Fe', 'Li', 'Ni', 'O')\",OTHERS,False\nmp-504748,\"{'Pb': 10.0, 'P': 6.0, 'O': 24.0, 'Cl': 2.0}\",\"('Cl', 'O', 'P', 'Pb')\",OTHERS,False\nmp-1228616,\"{'Ba': 4.0, 'Pb': 8.0, 'S': 12.0, 'O': 48.0}\",\"('Ba', 'O', 'Pb', 'S')\",OTHERS,False\nmp-1210400,\"{'Na': 6.0, 'Li': 2.0, 'Ti': 4.0, 'F': 24.0}\",\"('F', 'Li', 'Na', 'Ti')\",OTHERS,False\nmp-22784,\"{'Ni': 3.0, 'In': 1.0}\",\"('In', 'Ni')\",OTHERS,False\nmp-780690,\"{'Co': 6.0, 'O': 8.0, 'F': 4.0}\",\"('Co', 'F', 'O')\",OTHERS,False\nmp-744713,\"{'Na': 4.0, 'Fe': 12.0, 'P': 8.0, 'H': 32.0, 'O': 56.0}\",\"('Fe', 'H', 'Na', 'O', 'P')\",OTHERS,False\nmp-770328,\"{'Li': 8.0, 'Cr': 6.0, 'Fe': 2.0, 'O': 16.0}\",\"('Cr', 'Fe', 'Li', 'O')\",OTHERS,False\nmvc-6159,\"{'Ca': 4.0, 'Y': 2.0, 'Sb': 2.0, 'O': 10.0}\",\"('Ca', 'O', 'Sb', 'Y')\",OTHERS,False\nmp-1099308,\"{'Ba': 1.0, 'Li': 1.0, 'Mg': 6.0}\",\"('Ba', 'Li', 'Mg')\",OTHERS,False\nmp-552185,\"{'O': 6.0, 'N': 2.0, 'Ag': 2.0}\",\"('Ag', 'N', 'O')\",OTHERS,False\nmp-1175655,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-866021,\"{'Eu': 1.0, 'In': 1.0, 'Au': 2.0}\",\"('Au', 'Eu', 'In')\",OTHERS,False\nmp-1100441,\"{'Cr': 1.0, 'Cu': 1.0, 'B': 1.0}\",\"('B', 'Cr', 'Cu')\",OTHERS,False\nmp-1190799,\"{'Ca': 2.0, 'P': 4.0, 'O': 18.0}\",\"('Ca', 'O', 'P')\",OTHERS,False\nmp-1177411,\"{'Li': 4.0, 'Fe': 3.0, 'Ni': 2.0, 'Sb': 3.0, 'O': 16.0}\",\"('Fe', 'Li', 'Ni', 'O', 'Sb')\",OTHERS,False\nmp-1225961,\"{'Cs': 2.0, 'Sc': 2.0, 'Cu': 2.0, 'F': 12.0}\",\"('Cs', 'Cu', 'F', 'Sc')\",OTHERS,False\nmp-1183321,\"{'Ba': 1.0, 'Sr': 2.0, 'Ca': 1.0}\",\"('Ba', 'Ca', 'Sr')\",OTHERS,False\nmp-755996,\"{'Li': 5.0, 'Fe': 3.0, 'Cu': 2.0, 'O': 10.0}\",\"('Cu', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1207480,\"{'Y': 4.0, 'Mn': 2.0, 'C': 8.0}\",\"('C', 'Mn', 'Y')\",OTHERS,False\nmp-672215,\"{'Gd': 1.0, 'P': 2.0, 'Ru': 2.0}\",\"('Gd', 'P', 'Ru')\",OTHERS,False\nmp-755893,\"{'Ca': 4.0, 'Bi': 4.0, 'O': 12.0}\",\"('Bi', 'Ca', 'O')\",OTHERS,False\nmp-1238269,\"{'Tl': 8.0, 'C': 2.0, 'O': 10.0}\",\"('C', 'O', 'Tl')\",OTHERS,False\nmp-983565,\"{'Cu': 2.0, 'B': 2.0, 'Se': 4.0}\",\"('B', 'Cu', 'Se')\",OTHERS,False\nmp-353,\"{'O': 2.0, 'Ag': 4.0}\",\"('Ag', 'O')\",OTHERS,False\nmp-1196047,\"{'Cs': 4.0, 'V': 8.0, 'Sb': 4.0, 'O': 32.0}\",\"('Cs', 'O', 'Sb', 'V')\",OTHERS,False\nAl 72.5 Pd 27.5,\"{'Al': 72.5, 'Pd': 27.5}\",\"('Al', 'Pd')\",AC,False\nmp-556666,\"{'Ca': 4.0, 'Bi': 4.0, 'C': 4.0, 'O': 16.0, 'F': 4.0}\",\"('Bi', 'C', 'Ca', 'F', 'O')\",OTHERS,False\nmvc-6574,\"{'Y': 2.0, 'Fe': 6.0, 'F': 30.0}\",\"('F', 'Fe', 'Y')\",OTHERS,False\nmp-17033,\"{'F': 24.0, 'Al': 4.0, 'Pd': 4.0, 'Cs': 4.0}\",\"('Al', 'Cs', 'F', 'Pd')\",OTHERS,False\nmp-774402,\"{'V': 1.0, 'Cr': 3.0, 'P': 6.0, 'O': 24.0}\",\"('Cr', 'O', 'P', 'V')\",OTHERS,False\nmp-1078086,\"{'In': 2.0, 'Ni': 3.0, 'Se': 2.0}\",\"('In', 'Ni', 'Se')\",OTHERS,False\nmp-4057,\"{'B': 4.0, 'F': 16.0, 'Cs': 4.0}\",\"('B', 'Cs', 'F')\",OTHERS,False\nmp-1228779,\"{'Ba': 8.0, 'Ca': 5.0, 'Mn': 3.0, 'Fe': 8.0, 'F': 56.0}\",\"('Ba', 'Ca', 'F', 'Fe', 'Mn')\",OTHERS,False\nmp-984356,\"{'Cs': 1.0, 'Ir': 1.0, 'O': 3.0}\",\"('Cs', 'Ir', 'O')\",OTHERS,False\nmp-1005693,\"{'Sm': 2.0, 'In': 1.0, 'Hg': 1.0}\",\"('Hg', 'In', 'Sm')\",OTHERS,False\nmp-1186252,\"{'Nd': 4.0, 'Mg': 2.0}\",\"('Mg', 'Nd')\",OTHERS,False\nmp-754349,\"{'Gd': 2.0, 'O': 2.0, 'F': 2.0}\",\"('F', 'Gd', 'O')\",OTHERS,False\nmp-1223232,\"{'La': 4.0, 'Ce': 1.0, 'Zr': 3.0, 'O': 14.0}\",\"('Ce', 'La', 'O', 'Zr')\",OTHERS,False\nmp-1224476,\"{'Ho': 6.0, 'Ni': 4.0, 'Ru': 8.0}\",\"('Ho', 'Ni', 'Ru')\",OTHERS,False\nmp-1079997,\"{'V': 1.0, 'Pt': 8.0}\",\"('Pt', 'V')\",OTHERS,False\nmp-1207693,\"{'Yb': 12.0, 'Al': 12.0, 'Cr': 8.0, 'O': 48.0}\",\"('Al', 'Cr', 'O', 'Yb')\",OTHERS,False\nmp-25969,\"{'Li': 2.0, 'O': 24.0, 'P': 6.0, 'Mn': 8.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1223665,\"{'Ir': 1.0, 'Pt': 1.0}\",\"('Ir', 'Pt')\",OTHERS,False\nmp-1075406,\"{'Mg': 10.0, 'Si': 12.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-553880,\"{'Zn': 26.0, 'S': 26.0}\",\"('S', 'Zn')\",OTHERS,False\nmp-8578,\"{'C': 1.0, 'Sc': 3.0, 'In': 1.0}\",\"('C', 'In', 'Sc')\",OTHERS,False\nmp-1079654,\"{'Sm': 2.0, 'Mn': 1.0, 'Ga': 6.0}\",\"('Ga', 'Mn', 'Sm')\",OTHERS,False\nmp-849527,\"{'Rb': 12.0, 'In': 4.0, 'O': 12.0}\",\"('In', 'O', 'Rb')\",OTHERS,False\nmp-1246754,\"{'Li': 24.0, 'Ir': 8.0, 'N': 16.0}\",\"('Ir', 'Li', 'N')\",OTHERS,False\nmp-1986,\"{'Zn': 1.0, 'O': 1.0}\",\"('O', 'Zn')\",OTHERS,False\nmp-15886,\"{'Na': 4.0, 'S': 6.0, 'U': 2.0}\",\"('Na', 'S', 'U')\",OTHERS,False\nmp-571479,\"{'Yb': 8.0, 'Ba': 8.0, 'Sn': 24.0}\",\"('Ba', 'Sn', 'Yb')\",OTHERS,False\nmp-1218879,\"{'Sr': 4.0, 'Al': 4.0, 'Si': 2.0, 'O': 14.0}\",\"('Al', 'O', 'Si', 'Sr')\",OTHERS,False\nmp-677780,\"{'Ca': 4.0, 'Mg': 2.0, 'Si': 4.0, 'O': 14.0}\",\"('Ca', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-1228432,\"{'Ba': 8.0, 'Ta': 2.0, 'Ru': 3.0, 'Br': 2.0, 'O': 18.0}\",\"('Ba', 'Br', 'O', 'Ru', 'Ta')\",OTHERS,False\nmp-1102399,\"{'Fe': 2.0, 'P': 2.0, 'O': 7.0}\",\"('Fe', 'O', 'P')\",OTHERS,False\nmp-775340,\"{'Li': 4.0, 'Cr': 2.0, 'Si': 12.0, 'O': 30.0}\",\"('Cr', 'Li', 'O', 'Si')\",OTHERS,False\nmp-556884,\"{'Ca': 12.0, 'Si': 24.0, 'N': 24.0, 'O': 24.0}\",\"('Ca', 'N', 'O', 'Si')\",OTHERS,False\nmp-6250,\"{'O': 20.0, 'Zn': 4.0, 'Ba': 4.0, 'Tm': 8.0}\",\"('Ba', 'O', 'Tm', 'Zn')\",OTHERS,False\nmp-1194202,\"{'Ba': 2.0, 'Ho': 2.0, 'Fe': 8.0, 'O': 14.0}\",\"('Ba', 'Fe', 'Ho', 'O')\",OTHERS,False\nmp-1101361,\"{'V': 4.0, 'Co': 1.0, 'Cu': 1.0, 'O': 12.0}\",\"('Co', 'Cu', 'O', 'V')\",OTHERS,False\nmp-631308,\"{'Sn': 1.0, 'Ir': 1.0, 'Se': 2.0}\",\"('Ir', 'Se', 'Sn')\",OTHERS,False\nmp-866207,\"{'Lu': 1.0, 'Cd': 1.0, 'Pd': 2.0}\",\"('Cd', 'Lu', 'Pd')\",OTHERS,False\nmvc-4607,\"{'Mo': 4.0, 'O': 10.0}\",\"('Mo', 'O')\",OTHERS,False\nmp-773785,\"{'K': 8.0, 'Te': 4.0, 'O': 16.0}\",\"('K', 'O', 'Te')\",OTHERS,False\nmp-1105840,\"{'Nb': 6.0, 'V': 2.0, 'Se': 12.0}\",\"('Nb', 'Se', 'V')\",OTHERS,False\nmp-694564,\"{'Li': 2.0, 'Fe': 1.0, 'P': 8.0, 'O': 24.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-772609,\"{'La': 8.0, 'Zn': 8.0, 'O': 20.0}\",\"('La', 'O', 'Zn')\",OTHERS,False\nmp-29644,\"{'Ge': 1.0, 'Bi': 4.0, 'Te': 7.0}\",\"('Bi', 'Ge', 'Te')\",OTHERS,False\nmp-1100537,\"{'Li': 9.0, 'Mn': 7.0, 'O': 16.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-649632,\"{'K': 4.0, 'Co': 4.0, 'C': 16.0, 'O': 16.0}\",\"('C', 'Co', 'K', 'O')\",OTHERS,False\nmp-983509,\"{'Na': 6.0, 'Cd': 2.0}\",\"('Cd', 'Na')\",OTHERS,False\nmp-850185,\"{'Li': 6.0, 'Bi': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Bi', 'Li', 'O', 'P')\",OTHERS,False\nmp-755593,\"{'Li': 8.0, 'Co': 2.0, 'O': 6.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-755992,\"{'Fe': 6.0, 'O': 8.0, 'F': 4.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-767409,\"{'Li': 5.0, 'Sb': 1.0, 'S': 1.0}\",\"('Li', 'S', 'Sb')\",OTHERS,False\nmp-1176896,\"{'Li': 12.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1020631,\"{'Zn': 2.0, 'Ge': 2.0, 'O': 6.0}\",\"('Ge', 'O', 'Zn')\",OTHERS,False\nmp-1184567,\"{'Hf': 2.0, 'Tc': 1.0, 'Ir': 1.0}\",\"('Hf', 'Ir', 'Tc')\",OTHERS,False\nmp-1184119,\"{'Cu': 1.0, 'Pd': 3.0}\",\"('Cu', 'Pd')\",OTHERS,False\nmp-675212,\"{'Li': 3.0, 'W': 8.0, 'O': 24.0}\",\"('Li', 'O', 'W')\",OTHERS,False\nmp-1178257,\"{'Fe': 7.0, 'P': 8.0, 'O': 32.0}\",\"('Fe', 'O', 'P')\",OTHERS,False\nmp-862870,\"{'Li': 1.0, 'Tc': 1.0, 'O': 3.0}\",\"('Li', 'O', 'Tc')\",OTHERS,False\nmp-554411,\"{'Cs': 4.0, 'Mn': 4.0, 'F': 16.0}\",\"('Cs', 'F', 'Mn')\",OTHERS,False\nmp-4962,\"{'S': 2.0, 'Co': 2.0, 'Sb': 2.0}\",\"('Co', 'S', 'Sb')\",OTHERS,False\nmp-18761,\"{'O': 8.0, 'K': 3.0, 'V': 3.0}\",\"('K', 'O', 'V')\",OTHERS,False\nmp-1177877,\"{'Li': 2.0, 'Mn': 1.0, 'V': 1.0, 'P': 2.0, 'O': 8.0, 'F': 2.0}\",\"('F', 'Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-1173697,\"{'Na': 2.0, 'La': 4.0, 'Ti': 4.0, 'Mn': 2.0, 'O': 18.0}\",\"('La', 'Mn', 'Na', 'O', 'Ti')\",OTHERS,False\nmp-989511,\"{'Cs': 2.0, 'As': 1.0, 'Br': 1.0, 'Cl': 6.0}\",\"('As', 'Br', 'Cl', 'Cs')\",OTHERS,False\nmp-1222477,\"{'Li': 6.0, 'V': 6.0, 'Zn': 6.0, 'O': 24.0}\",\"('Li', 'O', 'V', 'Zn')\",OTHERS,False\nmp-720295,\"{'U': 4.0, 'H': 40.0, 'N': 8.0, 'O': 28.0}\",\"('H', 'N', 'O', 'U')\",OTHERS,False\nmp-1193284,\"{'Nd': 4.0, 'Mo': 4.0, 'Br': 4.0, 'O': 16.0}\",\"('Br', 'Mo', 'Nd', 'O')\",OTHERS,False\nmp-1246767,\"{'Ga': 20.0, 'Ge': 12.0, 'N': 36.0}\",\"('Ga', 'Ge', 'N')\",OTHERS,False\nmp-972095,\"{'Zn': 6.0, 'Hg': 2.0}\",\"('Hg', 'Zn')\",OTHERS,False\nmp-753268,\"{'Li': 6.0, 'Cu': 1.0, 'F': 8.0}\",\"('Cu', 'F', 'Li')\",OTHERS,False\nmp-1211810,\"{'K': 2.0, 'Li': 2.0, 'Tm': 4.0, 'Mo': 8.0, 'O': 32.0}\",\"('K', 'Li', 'Mo', 'O', 'Tm')\",OTHERS,False\nmp-1217538,\"{'Te': 2.0, 'W': 2.0, 'N': 2.0, 'O': 15.0}\",\"('N', 'O', 'Te', 'W')\",OTHERS,False\nmp-1018140,\"{'Mg': 1.0, 'Ni': 1.0}\",\"('Mg', 'Ni')\",OTHERS,False\nmp-1218068,\"{'Sr': 2.0, 'Pr': 2.0, 'Zn': 2.0, 'Ru': 2.0, 'O': 12.0}\",\"('O', 'Pr', 'Ru', 'Sr', 'Zn')\",OTHERS,False\nmp-1192619,{'C': 29.0},\"('C',)\",OTHERS,False\nmp-1229303,\"{'Ba': 10.0, 'Gd': 1.0, 'Y': 4.0, 'Cu': 15.0, 'O': 35.0}\",\"('Ba', 'Cu', 'Gd', 'O', 'Y')\",OTHERS,False\nmp-1194450,\"{'Ti': 6.0, 'Al': 16.0, 'Rh': 7.0}\",\"('Al', 'Rh', 'Ti')\",OTHERS,False\nmp-1211025,\"{'Na': 6.0, 'Fe': 6.0, 'B': 6.0, 'P': 12.0, 'O': 66.0}\",\"('B', 'Fe', 'Na', 'O', 'P')\",OTHERS,False\nmp-755878,\"{'Cu': 6.0, 'O': 1.0, 'F': 11.0}\",\"('Cu', 'F', 'O')\",OTHERS,False\nmp-19954,\"{'Al': 16.0, 'Cr': 10.0}\",\"('Al', 'Cr')\",OTHERS,False\nmp-20991,\"{'O': 6.0, 'Ba': 2.0, 'Pb': 2.0}\",\"('Ba', 'O', 'Pb')\",OTHERS,False\nmp-1223258,\"{'La': 2.0, 'Si': 3.0, 'Ni': 1.0}\",\"('La', 'Ni', 'Si')\",OTHERS,False\nmp-756391,\"{'Mn': 3.0, 'Ni': 1.0, 'P': 4.0, 'O': 16.0}\",\"('Mn', 'Ni', 'O', 'P')\",OTHERS,False\nmp-982013,\"{'Sr': 6.0, 'Tm': 2.0}\",\"('Sr', 'Tm')\",OTHERS,False\nmp-780658,\"{'Li': 2.0, 'Mn': 6.0, 'P': 12.0, 'O': 40.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-755619,\"{'Sn': 4.0, 'P': 4.0, 'O': 14.0}\",\"('O', 'P', 'Sn')\",OTHERS,False\nmp-1195322,\"{'Rb': 2.0, 'Pd': 1.0, 'S': 8.0, 'O': 26.0}\",\"('O', 'Pd', 'Rb', 'S')\",OTHERS,False\nmp-20424,\"{'C': 1.0, 'Mg': 1.0, 'Co': 3.0}\",\"('C', 'Co', 'Mg')\",OTHERS,False\nmp-861937,\"{'Ba': 2.0, 'As': 1.0, 'Au': 1.0}\",\"('As', 'Au', 'Ba')\",OTHERS,False\nmp-3211,\"{'O': 2.0, 'S': 1.0, 'Nd': 2.0}\",\"('Nd', 'O', 'S')\",OTHERS,False\nmp-1194001,\"{'Pr': 8.0, 'P': 2.0, 'Cl': 2.0, 'O': 16.0}\",\"('Cl', 'O', 'P', 'Pr')\",OTHERS,False\nmp-698063,\"{'Na': 8.0, 'P': 8.0, 'H': 8.0, 'O': 28.0}\",\"('H', 'Na', 'O', 'P')\",OTHERS,False\nmp-1147614,\"{'Li': 24.0, 'Zn': 12.0, 'P': 16.0, 'S': 64.0}\",\"('Li', 'P', 'S', 'Zn')\",OTHERS,False\nmp-1187062,\"{'Th': 6.0, 'Se': 2.0}\",\"('Se', 'Th')\",OTHERS,False\nmp-1208652,\"{'Sr': 2.0, 'Ge': 2.0, 'O': 8.0}\",\"('Ge', 'O', 'Sr')\",OTHERS,False\nmp-1112033,\"{'K': 2.0, 'Hg': 1.0, 'Bi': 1.0, 'Cl': 6.0}\",\"('Bi', 'Cl', 'Hg', 'K')\",OTHERS,False\nmp-1114287,\"{'K': 2.0, 'Ta': 1.0, 'Ag': 1.0, 'F': 6.0}\",\"('Ag', 'F', 'K', 'Ta')\",OTHERS,False\nmp-1246307,\"{'Mg': 22.0, 'C': 4.0, 'N': 20.0}\",\"('C', 'Mg', 'N')\",OTHERS,False\nmvc-13239,\"{'Ti': 2.0, 'F': 8.0}\",\"('F', 'Ti')\",OTHERS,False\nmp-29403,\"{'Cu': 1.0, 'Ga': 1.0, 'I': 4.0}\",\"('Cu', 'Ga', 'I')\",OTHERS,False\nmp-758047,\"{'Mn': 7.0, 'Co': 1.0, 'P': 12.0, 'O': 48.0}\",\"('Co', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1213387,\"{'Dy': 30.0, 'C': 40.0}\",\"('C', 'Dy')\",OTHERS,False\nmp-1184761,\"{'Ir': 3.0, 'Os': 1.0}\",\"('Ir', 'Os')\",OTHERS,False\nmp-757647,\"{'Li': 8.0, 'Mn': 8.0, 'Si': 24.0, 'O': 60.0}\",\"('Li', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1025495,\"{'Nd': 2.0, 'Si': 4.0, 'Rh': 2.0}\",\"('Nd', 'Rh', 'Si')\",OTHERS,False\nmp-1096039,\"{'Mn': 1.0, 'Ag': 1.0, 'Pd': 2.0}\",\"('Ag', 'Mn', 'Pd')\",OTHERS,False\nmp-1176979,\"{'Li': 6.0, 'Fe': 1.0, 'S': 4.0}\",\"('Fe', 'Li', 'S')\",OTHERS,False\nmp-567329,\"{'Cr': 4.0, 'Hg': 24.0, 'As': 16.0, 'Br': 28.0}\",\"('As', 'Br', 'Cr', 'Hg')\",OTHERS,False\nmp-1218483,\"{'Sr': 4.0, 'Zn': 1.0, 'Cu': 1.0, 'W': 2.0, 'O': 12.0}\",\"('Cu', 'O', 'Sr', 'W', 'Zn')\",OTHERS,False\nmp-1179593,\"{'Sr': 12.0, 'S': 24.0, 'O': 120.0}\",\"('O', 'S', 'Sr')\",OTHERS,False\nmp-1176833,\"{'Li': 8.0, 'Mn': 1.0, 'Fe': 7.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1219260,\"{'Sm': 2.0, 'Fe': 17.0, 'C': 1.0, 'N': 1.0}\",\"('C', 'Fe', 'N', 'Sm')\",OTHERS,False\nmvc-2597,\"{'Ba': 2.0, 'Tl': 1.0, 'Bi': 2.0, 'O': 7.0}\",\"('Ba', 'Bi', 'O', 'Tl')\",OTHERS,False\nmp-1100539,\"{'Li': 9.0, 'Mn': 7.0, 'O': 16.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-754605,\"{'Li': 2.0, 'Lu': 2.0, 'O': 4.0}\",\"('Li', 'Lu', 'O')\",OTHERS,False\nmp-779011,\"{'Li': 6.0, 'Mn': 1.0, 'Fe': 5.0, 'B': 6.0, 'O': 18.0}\",\"('B', 'Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1178835,\"{'V': 4.0, 'Re': 4.0, 'O': 22.0}\",\"('O', 'Re', 'V')\",OTHERS,False\nmp-779877,\"{'Li': 6.0, 'Mn': 1.0, 'Fe': 5.0, 'B': 6.0, 'O': 18.0}\",\"('B', 'Fe', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1084832,\"{'Fe': 5.0, 'Co': 3.0}\",\"('Co', 'Fe')\",OTHERS,False\nmp-554174,\"{'Ca': 8.0, 'P': 8.0, 'H': 32.0, 'O': 44.0}\",\"('Ca', 'H', 'O', 'P')\",OTHERS,False\nmp-775320,\"{'Li': 4.0, 'V': 3.0, 'O': 8.0}\",\"('Li', 'O', 'V')\",OTHERS,False\nmp-655591,\"{'Li': 2.0, 'B': 26.0, 'C': 4.0}\",\"('B', 'C', 'Li')\",OTHERS,False\nmp-976055,\"{'Li': 3.0, 'In': 1.0}\",\"('In', 'Li')\",OTHERS,False\nmp-768905,\"{'Li': 24.0, 'Mn': 5.0, 'Cr': 7.0, 'O': 36.0}\",\"('Cr', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1175170,\"{'Li': 7.0, 'Mn': 4.0, 'Co': 1.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-23737,\"{'H': 3.0, 'Mg': 1.0, 'K': 1.0}\",\"('H', 'K', 'Mg')\",OTHERS,False\nmp-26750,\"{'Fe': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Fe', 'O', 'P')\",OTHERS,False\nmp-1188053,\"{'Zr': 6.0, 'Ta': 2.0}\",\"('Ta', 'Zr')\",OTHERS,False\nAu 42 In 42 Yb 16,\"{'Au': 42.0, 'In': 42.0, 'Yb': 16.0}\",\"('Au', 'In', 'Yb')\",AC,False\nmp-753638,\"{'Li': 3.0, 'Nb': 2.0, 'Fe': 3.0, 'O': 10.0}\",\"('Fe', 'Li', 'Nb', 'O')\",OTHERS,False\nmp-770852,\"{'Li': 4.0, 'Ti': 2.0, 'Ni': 4.0, 'O': 10.0}\",\"('Li', 'Ni', 'O', 'Ti')\",OTHERS,False\nmp-567705,\"{'Ti': 4.0, 'Al': 8.0}\",\"('Al', 'Ti')\",OTHERS,False\nmp-15793,\"{'Li': 1.0, 'Se': 2.0, 'Tb': 1.0}\",\"('Li', 'Se', 'Tb')\",OTHERS,False\nmvc-7052,\"{'Mo': 2.0, 'S': 4.0}\",\"('Mo', 'S')\",OTHERS,False\nmp-1206907,\"{'Tl': 1.0, 'N': 1.0}\",\"('N', 'Tl')\",OTHERS,False\nmp-1093731,\"{'Ti': 2.0, 'Sn': 1.0, 'Mo': 1.0}\",\"('Mo', 'Sn', 'Ti')\",OTHERS,False\nmp-759907,\"{'Li': 2.0, 'Fe': 3.0, 'Cu': 1.0, 'O': 8.0}\",\"('Cu', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1039319,\"{'Mg': 8.0, 'Cd': 4.0}\",\"('Cd', 'Mg')\",OTHERS,False\nmp-673633,\"{'In': 4.0, 'S': 6.0}\",\"('In', 'S')\",OTHERS,False\nmp-763247,\"{'Li': 2.0, 'V': 2.0, 'Si': 2.0, 'O': 10.0}\",\"('Li', 'O', 'Si', 'V')\",OTHERS,False\nmp-1247354,\"{'Re': 2.0, 'C': 12.0, 'N': 18.0}\",\"('C', 'N', 'Re')\",OTHERS,False\nmp-1224733,\"{'Gd': 2.0, 'Zn': 1.0, 'Ag': 1.0}\",\"('Ag', 'Gd', 'Zn')\",OTHERS,False\nmp-1177532,\"{'Li': 6.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1226389,\"{'Cr': 1.0, 'O': 3.0, 'F': 3.0}\",\"('Cr', 'F', 'O')\",OTHERS,False\nmp-1184550,\"{'Ge': 2.0, 'C': 2.0}\",\"('C', 'Ge')\",OTHERS,False\nmp-756763,\"{'Li': 3.0, 'Cr': 1.0, 'Co': 4.0, 'O': 8.0}\",\"('Co', 'Cr', 'Li', 'O')\",OTHERS,False\nmp-1220146,\"{'Ni': 16.0, 'Bi': 16.0, 'S': 2.0, 'I': 4.0}\",\"('Bi', 'I', 'Ni', 'S')\",OTHERS,False\nmp-767597,\"{'Li': 4.0, 'Mn': 4.0, 'F': 20.0}\",\"('F', 'Li', 'Mn')\",OTHERS,False\nmp-1199056,\"{'Rb': 16.0, 'Ge': 16.0}\",\"('Ge', 'Rb')\",OTHERS,False\nmp-865681,\"{'Ti': 1.0, 'Si': 1.0, 'Ru': 2.0}\",\"('Ru', 'Si', 'Ti')\",OTHERS,False\nmp-1093540,\"{'Li': 2.0, 'Ga': 1.0, 'Bi': 1.0}\",\"('Bi', 'Ga', 'Li')\",OTHERS,False\nmp-1183738,\"{'Ce': 1.0, 'Sb': 3.0}\",\"('Ce', 'Sb')\",OTHERS,False\nmp-1226019,\"{'Co': 5.0, 'Ni': 1.0, 'S': 8.0}\",\"('Co', 'Ni', 'S')\",OTHERS,False\nmp-653887,\"{'Co': 16.0, 'Pb': 4.0, 'C': 64.0, 'O': 64.0}\",\"('C', 'Co', 'O', 'Pb')\",OTHERS,False\nmp-759662,\"{'V': 6.0, 'O': 9.0, 'F': 3.0}\",\"('F', 'O', 'V')\",OTHERS,False\nmp-1183523,\"{'Ca': 6.0, 'Dy': 2.0}\",\"('Ca', 'Dy')\",OTHERS,False\nmp-1208294,\"{'U': 4.0, 'Cl': 8.0}\",\"('Cl', 'U')\",OTHERS,False\nmp-1025231,\"{'Pr': 2.0, 'Cr': 1.0, 'S': 4.0}\",\"('Cr', 'Pr', 'S')\",OTHERS,False\nmp-1205187,\"{'Sr': 2.0, 'H': 16.0, 'Cl': 4.0, 'O': 24.0}\",\"('Cl', 'H', 'O', 'Sr')\",OTHERS,False\nZn 75 Sc 15 Fe 10,\"{'Zn': 75.0, 'Sc': 15.0, 'Fe': 10.0}\",\"('Fe', 'Sc', 'Zn')\",QC,False\nmp-778861,\"{'Li': 4.0, 'V': 8.0, 'O': 4.0, 'F': 20.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-626216,\"{'Sr': 1.0, 'H': 16.0, 'O': 10.0}\",\"('H', 'O', 'Sr')\",OTHERS,False\nmp-1196708,\"{'Ge': 14.0, 'N': 4.0, 'O': 30.0}\",\"('Ge', 'N', 'O')\",OTHERS,False\nmp-1036480,\"{'Rb': 1.0, 'Mg': 14.0, 'Al': 1.0, 'O': 16.0}\",\"('Al', 'Mg', 'O', 'Rb')\",OTHERS,False\nmp-775774,\"{'Ag': 2.0, 'Bi': 2.0, 'O': 6.0}\",\"('Ag', 'Bi', 'O')\",OTHERS,False\nmp-568998,\"{'Cu': 6.0, 'Se': 6.0}\",\"('Cu', 'Se')\",OTHERS,False\nmp-1102248,\"{'Tm': 4.0, 'Al': 4.0, 'Au': 4.0}\",\"('Al', 'Au', 'Tm')\",OTHERS,False\nmp-15559,\"{'S': 32.0, 'K': 4.0, 'Sb': 20.0}\",\"('K', 'S', 'Sb')\",OTHERS,False\nmp-780798,\"{'Li': 1.0, 'Mn': 2.0, 'Cr': 2.0, 'O': 8.0}\",\"('Cr', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-696416,\"{'Na': 2.0, 'Si': 6.0, 'B': 2.0, 'O': 16.0}\",\"('B', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1033646,\"{'Rb': 1.0, 'Hf': 1.0, 'Mg': 6.0, 'O': 7.0}\",\"('Hf', 'Mg', 'O', 'Rb')\",OTHERS,False\nmp-1207390,\"{'Zn': 2.0, 'Pt': 6.0, 'O': 8.0}\",\"('O', 'Pt', 'Zn')\",OTHERS,False\nmp-756050,\"{'Ta': 4.0, 'Cu': 4.0, 'O': 12.0}\",\"('Cu', 'O', 'Ta')\",OTHERS,False\nmp-558579,\"{'Tl': 4.0, 'H': 4.0, 'C': 4.0, 'O': 8.0}\",\"('C', 'H', 'O', 'Tl')\",OTHERS,False\nmp-1029952,\"{'Te': 6.0, 'Mo': 2.0, 'W': 2.0, 'S': 2.0}\",\"('Mo', 'S', 'Te', 'W')\",OTHERS,False\nmp-1183784,\"{'Ce': 3.0, 'Cl': 1.0}\",\"('Ce', 'Cl')\",OTHERS,False\nmp-768257,\"{'Na': 8.0, 'Bi': 4.0, 'C': 8.0, 'S': 2.0, 'O': 32.0}\",\"('Bi', 'C', 'Na', 'O', 'S')\",OTHERS,False\nmp-763176,\"{'Mn': 6.0, 'O': 8.0, 'F': 4.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-1036441,\"{'Cs': 1.0, 'Mg': 14.0, 'Mn': 1.0, 'O': 16.0}\",\"('Cs', 'Mg', 'Mn', 'O')\",OTHERS,False\nmp-604147,\"{'Na': 8.0, 'Li': 4.0, 'H': 24.0, 'N': 12.0}\",\"('H', 'Li', 'N', 'Na')\",OTHERS,False\nmp-559918,\"{'Ti': 8.0, 'Cu': 4.0, 'S': 16.0}\",\"('Cu', 'S', 'Ti')\",OTHERS,False\nmp-1096643,\"{'Li': 1.0, 'Tl': 2.0, 'Pd': 1.0}\",\"('Li', 'Pd', 'Tl')\",OTHERS,False\nmp-1244813,\"{'Y': 4.0, 'V': 13.0, 'Si': 2.0, 'Sb': 2.0, 'O': 28.0}\",\"('O', 'Sb', 'Si', 'V', 'Y')\",OTHERS,False\nmp-560159,\"{'B': 12.0, 'H': 42.0, 'C': 10.0, 'N': 4.0, 'O': 2.0}\",\"('B', 'C', 'H', 'N', 'O')\",OTHERS,False\nmp-1196513,\"{'Sc': 12.0, 'Ge': 24.0, 'Ru': 12.0}\",\"('Ge', 'Ru', 'Sc')\",OTHERS,False\nmp-541041,\"{'Nd': 4.0, 'Ag': 4.0, 'P': 16.0, 'O': 48.0}\",\"('Ag', 'Nd', 'O', 'P')\",OTHERS,False\nmp-1019586,\"{'Ca': 2.0, 'Al': 4.0, 'Si': 2.0, 'O': 12.0}\",\"('Al', 'Ca', 'O', 'Si')\",OTHERS,False\nmp-557299,\"{'Nd': 12.0, 'Mo': 4.0, 'O': 28.0}\",\"('Mo', 'Nd', 'O')\",OTHERS,False\nmp-1095875,\"{'Sc': 1.0, 'Zn': 1.0, 'Ir': 2.0}\",\"('Ir', 'Sc', 'Zn')\",OTHERS,False\nmp-504627,\"{'Er': 8.0, 'Ni': 2.0, 'B': 26.0}\",\"('B', 'Er', 'Ni')\",OTHERS,False\nmp-779250,\"{'Li': 16.0, 'Fe': 8.0, 'B': 8.0, 'O': 32.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-757415,\"{'Na': 8.0, 'Fe': 4.0, 'O': 8.0}\",\"('Fe', 'Na', 'O')\",OTHERS,False\nmp-849411,\"{'Li': 8.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-27943,\"{'W': 6.0, 'N': 3.0}\",\"('N', 'W')\",OTHERS,False\nmp-758806,\"{'B': 80.0, 'P': 16.0, 'H': 64.0, 'Cl': 16.0}\",\"('B', 'Cl', 'H', 'P')\",OTHERS,False\nmp-23037,\"{'Cs': 1.0, 'Pb': 1.0, 'Cl': 3.0}\",\"('Cl', 'Cs', 'Pb')\",OTHERS,False\nmp-775921,\"{'Li': 6.0, 'V': 4.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1213696,\"{'Cs': 4.0, 'Co': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Co', 'Cs', 'O', 'P')\",OTHERS,False\nmp-861913,\"{'Li': 1.0, 'Dy': 2.0, 'Al': 1.0}\",\"('Al', 'Dy', 'Li')\",OTHERS,False\nmp-27897,\"{'N': 8.0, 'O': 16.0, 'Ba': 4.0}\",\"('Ba', 'N', 'O')\",OTHERS,False\nmp-1217682,\"{'Tb': 2.0, 'Hf': 1.0, 'Al': 9.0}\",\"('Al', 'Hf', 'Tb')\",OTHERS,False\nmp-1208703,\"{'Sr': 4.0, 'Cr': 3.0, 'O': 10.0}\",\"('Cr', 'O', 'Sr')\",OTHERS,False\nmp-7806,\"{'P': 4.0, 'Cr': 12.0}\",\"('Cr', 'P')\",OTHERS,False\nmp-1029155,\"{'Te': 2.0, 'Mo': 2.0, 'W': 2.0, 'Se': 2.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-759735,\"{'Li': 4.0, 'Bi': 2.0, 'P': 10.0, 'O': 30.0}\",\"('Bi', 'Li', 'O', 'P')\",OTHERS,False\nmp-568357,\"{'K': 8.0, 'La': 12.0, 'Os': 2.0, 'I': 28.0}\",\"('I', 'K', 'La', 'Os')\",OTHERS,False\nmp-764248,\"{'Li': 4.0, 'V': 2.0, 'Cr': 2.0, 'P': 8.0, 'O': 28.0}\",\"('Cr', 'Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1105271,\"{'Mn': 4.0, 'Bi': 4.0, 'O': 12.0}\",\"('Bi', 'Mn', 'O')\",OTHERS,False\nmvc-2146,\"{'Zn': 4.0, 'Co': 2.0, 'N': 4.0}\",\"('Co', 'N', 'Zn')\",OTHERS,False\nmp-1101240,\"{'V': 3.0, 'Co': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Co', 'O', 'P', 'V')\",OTHERS,False\nmp-5473,\"{'Mg': 4.0, 'Si': 4.0, 'Ca': 4.0}\",\"('Ca', 'Mg', 'Si')\",OTHERS,False\nmp-977415,\"{'Ho': 1.0, 'Cd': 1.0, 'Au': 2.0}\",\"('Au', 'Cd', 'Ho')\",OTHERS,False\nmp-1209209,\"{'Sb': 4.0, 'N': 1.0, 'F': 13.0}\",\"('F', 'N', 'Sb')\",OTHERS,False\nmp-1218178,\"{'Sr': 2.0, 'La': 2.0, 'Ni': 2.0, 'O': 8.0}\",\"('La', 'Ni', 'O', 'Sr')\",OTHERS,False\nmp-559227,\"{'Rb': 4.0, 'Ge': 4.0, 'Bi': 4.0, 'S': 16.0}\",\"('Bi', 'Ge', 'Rb', 'S')\",OTHERS,False\nmp-1222934,\"{'La': 2.0, 'Ge': 2.0, 'Pd': 2.0}\",\"('Ge', 'La', 'Pd')\",OTHERS,False\nmp-1093902,\"{'Ba': 1.0, 'Mg': 1.0, 'Tl': 2.0}\",\"('Ba', 'Mg', 'Tl')\",OTHERS,False\nmp-15988,\"{'Li': 2.0, 'Cu': 1.0, 'Sb': 1.0}\",\"('Cu', 'Li', 'Sb')\",OTHERS,False\nmp-1247705,\"{'Sr': 4.0, 'Ca': 28.0, 'Mn': 24.0, 'Al': 8.0, 'O': 92.0}\",\"('Al', 'Ca', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-1111896,\"{'Na': 3.0, 'Er': 1.0, 'Cl': 6.0}\",\"('Cl', 'Er', 'Na')\",OTHERS,False\nmp-1246758,\"{'Fe': 2.0, 'Cu': 1.0, 'N': 2.0}\",\"('Cu', 'Fe', 'N')\",OTHERS,False\nmp-27351,\"{'N': 12.0, 'Cl': 16.0, 'Sb': 4.0}\",\"('Cl', 'N', 'Sb')\",OTHERS,False\nmp-684013,\"{'Ba': 4.0, 'In': 26.0, 'Ir': 8.0}\",\"('Ba', 'In', 'Ir')\",OTHERS,False\nmp-780525,\"{'Mn': 12.0, 'O': 2.0, 'F': 22.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmvc-5035,\"{'Zn': 4.0, 'Te': 2.0, 'W': 2.0, 'O': 12.0}\",\"('O', 'Te', 'W', 'Zn')\",OTHERS,False\nmp-752975,\"{'Fe': 4.0, 'O': 4.0, 'F': 4.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-1093607,\"{'Hf': 2.0, 'Mn': 1.0, 'Co': 1.0}\",\"('Co', 'Hf', 'Mn')\",OTHERS,False\nmp-1186211,\"{'Na': 2.0, 'Zn': 6.0}\",\"('Na', 'Zn')\",OTHERS,False\nmp-1113281,\"{'Cs': 2.0, 'Al': 1.0, 'In': 1.0, 'Br': 6.0}\",\"('Al', 'Br', 'Cs', 'In')\",OTHERS,False\nmp-9952,\"{'Zn': 2.0, 'Sb': 6.0, 'Hf': 10.0}\",\"('Hf', 'Sb', 'Zn')\",OTHERS,False\nmp-669497,\"{'Dy': 20.0, 'Cl': 44.0}\",\"('Cl', 'Dy')\",OTHERS,False\nmp-10436,\"{'Al': 2.0, 'Si': 2.0, 'Tb': 1.0}\",\"('Al', 'Si', 'Tb')\",OTHERS,False\nmp-1113018,\"{'Cs': 2.0, 'Li': 1.0, 'Dy': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cs', 'Dy', 'Li')\",OTHERS,False\nmp-1195438,\"{'Al': 8.0, 'B': 56.0, 'Pd': 120.0}\",\"('Al', 'B', 'Pd')\",OTHERS,False\nmp-1227605,\"{'Bi': 12.0, 'I': 6.0, 'Br': 6.0}\",\"('Bi', 'Br', 'I')\",OTHERS,False\nmp-1225536,\"{'Dy': 1.0, 'Tm': 1.0, 'Mn': 4.0}\",\"('Dy', 'Mn', 'Tm')\",OTHERS,False\nmp-19752,\"{'O': 12.0, 'P': 3.0, 'Fe': 3.0}\",\"('Fe', 'O', 'P')\",OTHERS,False\nmvc-11245,\"{'Zn': 2.0, 'Fe': 4.0, 'S': 8.0}\",\"('Fe', 'S', 'Zn')\",OTHERS,False\nmp-3415,\"{'Si': 2.0, 'Ru': 2.0, 'Yb': 1.0}\",\"('Ru', 'Si', 'Yb')\",OTHERS,False\nmp-1101531,\"{'Li': 18.0, 'Co': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Co', 'Li', 'O', 'P')\",OTHERS,False\nmp-570019,\"{'Cd': 8.0, 'I': 16.0}\",\"('Cd', 'I')\",OTHERS,False\nmp-1096127,\"{'Y': 2.0, 'Cu': 1.0, 'Ag': 1.0}\",\"('Ag', 'Cu', 'Y')\",OTHERS,False\nmp-721699,\"{'Ca': 2.0, 'H': 32.0, 'C': 8.0, 'N': 20.0, 'O': 20.0}\",\"('C', 'Ca', 'H', 'N', 'O')\",OTHERS,False\nmp-1226086,\"{'Cr': 10.0, 'Te': 12.0}\",\"('Cr', 'Te')\",OTHERS,False\nmp-624243,\"{'Fe': 12.0, 'W': 12.0, 'C': 2.0}\",\"('C', 'Fe', 'W')\",OTHERS,False\nmp-635413,\"{'Cs': 3.0, 'Bi': 1.0}\",\"('Bi', 'Cs')\",OTHERS,False\nmp-1228266,\"{'Al': 6.0, 'C': 1.0, 'O': 7.0}\",\"('Al', 'C', 'O')\",OTHERS,False\nmp-18837,\"{'O': 8.0, 'K': 1.0, 'Sc': 1.0, 'Mo': 2.0}\",\"('K', 'Mo', 'O', 'Sc')\",OTHERS,False\nmp-1181356,\"{'Ga': 3.0, 'S': 2.0, 'O': 15.0}\",\"('Ga', 'O', 'S')\",OTHERS,False\nmp-20092,\"{'O': 6.0, 'Eu': 1.0, 'Ta': 2.0}\",\"('Eu', 'O', 'Ta')\",OTHERS,False\nmp-29351,\"{'Ba': 4.0, 'Mo': 24.0, 'O': 40.0}\",\"('Ba', 'Mo', 'O')\",OTHERS,False\nmp-24577,\"{'H': 12.0, 'O': 6.0, 'F': 6.0, 'Ni': 1.0, 'Sn': 1.0}\",\"('F', 'H', 'Ni', 'O', 'Sn')\",OTHERS,False\nmp-1195802,\"{'Sm': 8.0, 'Ge': 6.0, 'S': 24.0}\",\"('Ge', 'S', 'Sm')\",OTHERS,False\nmp-1104856,\"{'La': 6.0, 'Pt': 8.0}\",\"('La', 'Pt')\",OTHERS,False\nmp-756641,\"{'Li': 4.0, 'B': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('B', 'C', 'Li', 'O', 'P')\",OTHERS,False\nmp-1195554,\"{'Th': 4.0, 'Te': 8.0, 'Mo': 4.0, 'O': 36.0}\",\"('Mo', 'O', 'Te', 'Th')\",OTHERS,False\nmp-706986,\"{'P': 8.0, 'H': 32.0, 'N': 8.0, 'O': 24.0}\",\"('H', 'N', 'O', 'P')\",OTHERS,False\nmp-983410,\"{'Ho': 2.0, 'Cu': 1.0, 'Pd': 1.0}\",\"('Cu', 'Ho', 'Pd')\",OTHERS,False\nmp-2840,\"{'Ir': 4.0, 'Pu': 2.0}\",\"('Ir', 'Pu')\",OTHERS,False\nmvc-3757,\"{'Zn': 4.0, 'Mo': 4.0, 'F': 20.0}\",\"('F', 'Mo', 'Zn')\",OTHERS,False\nmp-558428,\"{'Ba': 9.0, 'Sc': 2.0, 'Si': 6.0, 'O': 24.0}\",\"('Ba', 'O', 'Sc', 'Si')\",OTHERS,False\nmp-771568,\"{'Sr': 4.0, 'Ca': 8.0, 'I': 24.0}\",\"('Ca', 'I', 'Sr')\",OTHERS,False\nmp-775995,\"{'Mn': 3.0, 'Cr': 1.0, 'Sn': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Cr', 'Mn', 'O', 'P', 'Sn')\",OTHERS,False\nmp-1247545,\"{'Nb': 4.0, 'Mo': 4.0, 'N': 12.0}\",\"('Mo', 'N', 'Nb')\",OTHERS,False\nmp-774235,\"{'Li': 1.0, 'Cr': 3.0, 'O': 4.0}\",\"('Cr', 'Li', 'O')\",OTHERS,False\nmp-1102313,\"{'Yb': 4.0, 'Sb': 4.0, 'Ir': 4.0}\",\"('Ir', 'Sb', 'Yb')\",OTHERS,False\nmp-1076680,\"{'Sr': 16.0, 'Ca': 16.0, 'Ti': 8.0, 'Mn': 24.0, 'O': 80.0}\",\"('Ca', 'Mn', 'O', 'Sr', 'Ti')\",OTHERS,False\nmp-669550,\"{'Ce': 3.0, 'Mn': 1.0, 'Al': 24.0, 'Cu': 8.0}\",\"('Al', 'Ce', 'Cu', 'Mn')\",OTHERS,False\nmp-16721,\"{'Al': 8.0, 'Tm': 8.0}\",\"('Al', 'Tm')\",OTHERS,False\nmp-555726,\"{'Na': 4.0, 'Cl': 4.0, 'O': 12.0}\",\"('Cl', 'Na', 'O')\",OTHERS,False\nmp-1187918,\"{'Zn': 3.0, 'P': 1.0}\",\"('P', 'Zn')\",OTHERS,False\nmp-1221653,\"{'Mn': 1.0, 'Nb': 4.0, 'C': 2.0, 'S': 4.0}\",\"('C', 'Mn', 'Nb', 'S')\",OTHERS,False\nmp-1224283,\"{'Hf': 1.0, 'Ta': 1.0, 'B': 4.0}\",\"('B', 'Hf', 'Ta')\",OTHERS,False\nmp-752670,\"{'Li': 6.0, 'Fe': 2.0, 'S': 6.0}\",\"('Fe', 'Li', 'S')\",OTHERS,False\nmp-989546,\"{'Sr': 2.0, 'Tc': 2.0, 'N': 6.0}\",\"('N', 'Sr', 'Tc')\",OTHERS,False\nmp-1224413,\"{'H': 3.0, 'Pd': 4.0}\",\"('H', 'Pd')\",OTHERS,False\nmp-756396,\"{'Fe': 8.0, 'O': 12.0, 'F': 4.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-1221052,\"{'Na': 14.0, 'Fe': 14.0, 'P': 12.0, 'O': 48.0, 'F': 6.0}\",\"('F', 'Fe', 'Na', 'O', 'P')\",OTHERS,False\nmp-759775,\"{'Li': 16.0, 'Mn': 18.0, 'O': 36.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-1099889,\"{'Sr': 28.0, 'Ca': 4.0, 'Ti': 28.0, 'Mn': 4.0, 'O': 80.0}\",\"('Ca', 'Mn', 'O', 'Sr', 'Ti')\",OTHERS,False\nmp-22402,\"{'O': 14.0, 'Co': 8.0, 'In': 2.0, 'Ba': 2.0}\",\"('Ba', 'Co', 'In', 'O')\",OTHERS,False\nmp-21431,\"{'Ho': 1.0, 'In': 3.0}\",\"('Ho', 'In')\",OTHERS,False\nmp-31015,\"{'S': 1.0, 'Br': 7.0, 'Ta': 3.0}\",\"('Br', 'S', 'Ta')\",OTHERS,False\nAg 46.4 In 39.7 Gd 13.9,\"{'Ag': 46.4, 'In': 39.7, 'Gd': 13.9}\",\"('Ag', 'Gd', 'In')\",AC,False\nBe 17 Ru 3,\"{'Be': 17.0, 'Ru': 3.0}\",\"('Be', 'Ru')\",AC,False\nmp-1202437,\"{'Cs': 4.0, 'H': 8.0, 'S': 4.0, 'N': 4.0, 'O': 12.0}\",\"('Cs', 'H', 'N', 'O', 'S')\",OTHERS,False\nmp-1222178,\"{'Mn': 2.0, 'Fe': 8.0, 'Bi': 10.0, 'O': 30.0}\",\"('Bi', 'Fe', 'Mn', 'O')\",OTHERS,False\nmp-1206808,\"{'Cs': 2.0, 'Li': 1.0, 'Al': 1.0, 'F': 6.0}\",\"('Al', 'Cs', 'F', 'Li')\",OTHERS,False\nmp-510657,\"{'O': 4.0, 'V': 1.0, 'Cu': 3.0}\",\"('Cu', 'O', 'V')\",OTHERS,False\nmp-1208612,\"{'Sr': 1.0, 'Li': 1.0, 'Si': 1.0}\",\"('Li', 'Si', 'Sr')\",OTHERS,False\nmp-1212738,\"{'Gd': 12.0, 'Si': 4.0, 'Br': 12.0}\",\"('Br', 'Gd', 'Si')\",OTHERS,False\nmp-646548,\"{'Eu': 4.0, 'K': 4.0, 'As': 4.0, 'S': 12.0}\",\"('As', 'Eu', 'K', 'S')\",OTHERS,False\nmp-999439,\"{'Nb': 3.0, 'Cr': 1.0}\",\"('Cr', 'Nb')\",OTHERS,False\nmp-753930,\"{'Li': 4.0, 'V': 2.0, 'Fe': 2.0, 'O': 10.0}\",\"('Fe', 'Li', 'O', 'V')\",OTHERS,False\nmp-1105411,\"{'Li': 2.0, 'Cr': 2.0, 'Ge': 4.0, 'O': 12.0}\",\"('Cr', 'Ge', 'Li', 'O')\",OTHERS,False\nmp-30818,\"{'Li': 2.0, 'Al': 1.0, 'Pt': 1.0}\",\"('Al', 'Li', 'Pt')\",OTHERS,False\nmp-777308,\"{'Mn': 8.0, 'O': 14.0, 'F': 2.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-771140,\"{'Li': 5.0, 'Mn': 5.0, 'Sn': 2.0, 'O': 12.0}\",\"('Li', 'Mn', 'O', 'Sn')\",OTHERS,False\nmp-571438,\"{'Be': 12.0, 'V': 1.0}\",\"('Be', 'V')\",OTHERS,False\nmp-978967,\"{'Sr': 1.0, 'Dy': 3.0}\",\"('Dy', 'Sr')\",OTHERS,False\nmp-1246060,\"{'Zr': 6.0, 'Ag': 3.0, 'N': 9.0}\",\"('Ag', 'N', 'Zr')\",OTHERS,False\nmp-29655,\"{'H': 32.0, 'B': 16.0, 'C': 8.0}\",\"('B', 'C', 'H')\",OTHERS,False\nmp-5456,\"{'O': 3.0, 'Cu': 1.0, 'Sr': 2.0}\",\"('Cu', 'O', 'Sr')\",OTHERS,False\nmp-1074413,\"{'Mg': 8.0, 'Si': 6.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-29107,\"{'Rb': 8.0, 'Te': 16.0, 'Hg': 12.0}\",\"('Hg', 'Rb', 'Te')\",OTHERS,False\nmp-542232,\"{'Ca': 4.0, 'Fe': 4.0, 'F': 20.0}\",\"('Ca', 'F', 'Fe')\",OTHERS,False\nmp-1174631,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1185380,\"{'Li': 2.0, 'Mg': 2.0}\",\"('Li', 'Mg')\",OTHERS,False\nmp-8121,\"{'Si': 2.0, 'Cu': 2.0, 'Sm': 2.0}\",\"('Cu', 'Si', 'Sm')\",OTHERS,False\nmp-1204381,\"{'Ce': 8.0, 'Ni': 28.0}\",\"('Ce', 'Ni')\",OTHERS,False\nmp-1204517,\"{'Er': 4.0, 'Fe': 28.0, 'Si': 6.0}\",\"('Er', 'Fe', 'Si')\",OTHERS,False\nmp-765008,\"{'Li': 6.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-4093,\"{'O': 8.0, 'P': 2.0, 'Cu': 3.0}\",\"('Cu', 'O', 'P')\",OTHERS,False\nmp-772423,\"{'Ni': 3.0, 'S': 6.0, 'O': 24.0}\",\"('Ni', 'O', 'S')\",OTHERS,False\nmp-974882,\"{'Rb': 3.0, 'Mo': 1.0}\",\"('Mo', 'Rb')\",OTHERS,False\nmp-23612,\"{'B': 6.0, 'O': 10.0, 'K': 3.0, 'Br': 1.0}\",\"('B', 'Br', 'K', 'O')\",OTHERS,False\nmp-1193894,\"{'Cs': 12.0, 'Sb': 4.0, 'S': 12.0}\",\"('Cs', 'S', 'Sb')\",OTHERS,False\nmp-625619,\"{'H': 4.0, 'N': 2.0, 'O': 3.0}\",\"('H', 'N', 'O')\",OTHERS,False\nmp-571248,\"{'Cs': 4.0, 'Na': 2.0, 'Au': 6.0, 'C': 12.0, 'N': 12.0}\",\"('Au', 'C', 'Cs', 'N', 'Na')\",OTHERS,False\nmp-1245647,\"{'Zn': 4.0, 'Ag': 4.0, 'N': 4.0}\",\"('Ag', 'N', 'Zn')\",OTHERS,False\nmp-556171,\"{'Cd': 4.0, 'P': 8.0, 'Xe': 20.0, 'F': 88.0}\",\"('Cd', 'F', 'P', 'Xe')\",OTHERS,False\nmp-1227506,\"{'Ba': 4.0, 'Sr': 4.0, 'Si': 16.0}\",\"('Ba', 'Si', 'Sr')\",OTHERS,False\nmp-1019717,\"{'Cs': 4.0, 'B': 4.0, 'S': 12.0, 'O': 44.0}\",\"('B', 'Cs', 'O', 'S')\",OTHERS,False\nmvc-2247,\"{'Ba': 4.0, 'Ca': 2.0, 'Mn': 4.0, 'Cu': 2.0, 'F': 28.0}\",\"('Ba', 'Ca', 'Cu', 'F', 'Mn')\",OTHERS,False\nmp-694908,\"{'Sr': 3.0, 'La': 7.0, 'Ti': 3.0, 'Mn': 7.0, 'O': 30.0}\",\"('La', 'Mn', 'O', 'Sr', 'Ti')\",OTHERS,False\nmp-1200512,\"{'Er': 12.0, 'Al': 86.0, 'W': 8.0}\",\"('Al', 'Er', 'W')\",OTHERS,False\nAl 65 Cu 20 Co 15,\"{'Al': 65.0, 'Cu': 20.0, 'Co': 15.0}\",\"('Al', 'Co', 'Cu')\",QC,False\nmp-754517,\"{'Sr': 1.0, 'Ca': 2.0, 'I': 6.0}\",\"('Ca', 'I', 'Sr')\",OTHERS,False\nmp-675833,\"{'Mg': 2.0, 'Mo': 12.0, 'Se': 16.0}\",\"('Mg', 'Mo', 'Se')\",OTHERS,False\nmp-990083,\"{'Mo': 18.0, 'S': 36.0}\",\"('Mo', 'S')\",OTHERS,False\nmp-1245166,\"{'Cr': 4.0, 'Fe': 28.0, 'O': 48.0}\",\"('Cr', 'Fe', 'O')\",OTHERS,False\nmp-865538,\"{'Ti': 2.0, 'Re': 1.0, 'Pd': 1.0}\",\"('Pd', 'Re', 'Ti')\",OTHERS,False\nmp-16000,\"{'Ti': 12.0, 'Se': 54.0, 'Ag': 2.0, 'Cs': 10.0}\",\"('Ag', 'Cs', 'Se', 'Ti')\",OTHERS,False\nmp-1185817,\"{'Mg': 5.0, 'In': 1.0}\",\"('In', 'Mg')\",OTHERS,False\nmp-1208259,\"{'Ti': 6.0, 'Co': 2.0, 'S': 12.0}\",\"('Co', 'S', 'Ti')\",OTHERS,False\nmp-1212693,\"{'Nd': 8.0, 'In': 24.0, 'Pt': 8.0}\",\"('In', 'Nd', 'Pt')\",OTHERS,False\nmp-567747,\"{'Co': 7.0, 'Mo': 6.0}\",\"('Co', 'Mo')\",OTHERS,False\nmp-778508,\"{'Li': 12.0, 'Mn': 4.0, 'V': 4.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,False\nmp-867809,\"{'V': 6.0, 'P': 6.0, 'O': 24.0}\",\"('O', 'P', 'V')\",OTHERS,False\nmp-672388,\"{'Th': 2.0, 'Pb': 2.0}\",\"('Pb', 'Th')\",OTHERS,False\nmp-758778,\"{'Li': 2.0, 'Fe': 8.0, 'P': 6.0, 'O': 24.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-24229,\"{'H': 20.0, 'C': 4.0, 'O': 22.0, 'Na': 4.0, 'Ca': 2.0}\",\"('C', 'Ca', 'H', 'Na', 'O')\",OTHERS,False\nmp-1206175,\"{'Eu': 2.0, 'Al': 2.0, 'Ge': 2.0}\",\"('Al', 'Eu', 'Ge')\",OTHERS,False\nmp-676089,\"{'Fe': 2.0, 'Co': 1.0, 'Se': 4.0}\",\"('Co', 'Fe', 'Se')\",OTHERS,False\nmp-706400,\"{'Ba': 4.0, 'P': 8.0, 'H': 16.0, 'O': 32.0}\",\"('Ba', 'H', 'O', 'P')\",OTHERS,False\nmp-632672,\"{'Li': 1.0, 'H': 1.0, 'F': 2.0}\",\"('F', 'H', 'Li')\",OTHERS,False\nmp-1218676,\"{'Sr': 4.0, 'La': 1.0, 'Fe': 2.0, 'Co': 3.0, 'O': 15.0}\",\"('Co', 'Fe', 'La', 'O', 'Sr')\",OTHERS,False\nmp-1210772,\"{'Li': 2.0, 'H': 6.0, 'Pt': 1.0, 'O': 6.0}\",\"('H', 'Li', 'O', 'Pt')\",OTHERS,False\nmp-542533,\"{'Cs': 4.0, 'Na': 4.0, 'O': 52.0, 'B': 16.0, 'H': 48.0}\",\"('B', 'Cs', 'H', 'Na', 'O')\",OTHERS,False\nmp-774411,\"{'Li': 2.0, 'V': 1.0, 'O': 2.0}\",\"('Li', 'O', 'V')\",OTHERS,False\nmp-1208008,\"{'Tm': 8.0, 'Si': 12.0, 'Pd': 4.0}\",\"('Pd', 'Si', 'Tm')\",OTHERS,False\nmp-1216020,\"{'Zn': 35.0, 'Cu': 17.0}\",\"('Cu', 'Zn')\",OTHERS,False\nmvc-4363,\"{'Al': 8.0, 'Si': 12.0, 'W': 12.0, 'O': 48.0}\",\"('Al', 'O', 'Si', 'W')\",OTHERS,False\nmp-973261,\"{'Mg': 4.0, 'Sn': 2.0, 'O': 8.0}\",\"('Mg', 'O', 'Sn')\",OTHERS,False\nmp-1197032,\"{'Ag': 28.0, 'P': 4.0, 'S': 24.0}\",\"('Ag', 'P', 'S')\",OTHERS,False\nmvc-14714,\"{'Zn': 1.0, 'Mo': 1.0, 'F': 6.0}\",\"('F', 'Mo', 'Zn')\",OTHERS,False\nmp-1029354,\"{'Mn': 2.0, 'Zn': 4.0, 'N': 6.0}\",\"('Mn', 'N', 'Zn')\",OTHERS,False\nmp-13182,\"{'Li': 4.0, 'O': 10.0, 'Ti': 2.0, 'Ge': 2.0}\",\"('Ge', 'Li', 'O', 'Ti')\",OTHERS,False\nmvc-8331,\"{'V': 4.0, 'Zn': 1.0, 'Cu': 3.0, 'O': 12.0}\",\"('Cu', 'O', 'V', 'Zn')\",OTHERS,False\nmp-761118,\"{'Ti': 7.0, 'Sn': 3.0, 'O': 20.0}\",\"('O', 'Sn', 'Ti')\",OTHERS,False\nmp-9612,\"{'O': 44.0, 'La': 12.0, 'Ir': 12.0}\",\"('Ir', 'La', 'O')\",OTHERS,False\nmp-768854,\"{'Li': 10.0, 'Mn': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-780461,\"{'Mn': 12.0, 'O': 10.0, 'F': 14.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmp-768350,\"{'Ba': 8.0, 'Y': 4.0, 'F': 28.0}\",\"('Ba', 'F', 'Y')\",OTHERS,False\nZn 56.8 Mg 34.6 Tb 8.7,\"{'Zn': 56.8, 'Mg': 34.6, 'Tb': 8.7}\",\"('Mg', 'Tb', 'Zn')\",QC,False\nmp-1228377,\"{'Ba': 3.0, 'Sr': 1.0, 'Co': 4.0, 'O': 12.0}\",\"('Ba', 'Co', 'O', 'Sr')\",OTHERS,False\nmp-1216661,\"{'Ti': 1.0, 'Zn': 1.0, 'Cu': 2.0}\",\"('Cu', 'Ti', 'Zn')\",OTHERS,False\nmp-1095821,\"{'Ti': 2.0, 'Cr': 1.0, 'Co': 1.0}\",\"('Co', 'Cr', 'Ti')\",OTHERS,False\nmp-557121,\"{'K': 4.0, 'Sn': 2.0, 'Au': 4.0, 'S': 8.0}\",\"('Au', 'K', 'S', 'Sn')\",OTHERS,False\nmp-616577,\"{'Sr': 12.0, 'B': 4.0, 'C': 4.0, 'N': 4.0, 'Cl': 8.0}\",\"('B', 'C', 'Cl', 'N', 'Sr')\",OTHERS,False\nmp-867860,\"{'Sm': 1.0, 'Zn': 1.0, 'Au': 2.0}\",\"('Au', 'Sm', 'Zn')\",OTHERS,False\nmp-1245973,\"{'Mn': 16.0, 'Ge': 4.0, 'N': 16.0}\",\"('Ge', 'Mn', 'N')\",OTHERS,False\nmp-570613,\"{'Cd': 6.0, 'I': 12.0}\",\"('Cd', 'I')\",OTHERS,False\nmp-1220922,\"{'Na': 4.0, 'Ti': 4.0, 'Fe': 4.0, 'O': 16.0}\",\"('Fe', 'Na', 'O', 'Ti')\",OTHERS,False\nmp-1208192,\"{'Tm': 12.0, 'In': 2.0, 'Fe': 3.0}\",\"('Fe', 'In', 'Tm')\",OTHERS,False\nmp-1094126,\"{'Sm': 2.0, 'Fe': 2.0, 'O': 5.0}\",\"('Fe', 'O', 'Sm')\",OTHERS,False\nmp-977590,\"{'Nd': 1.0, 'In': 1.0, 'Pd': 2.0}\",\"('In', 'Nd', 'Pd')\",OTHERS,False\nmp-1096261,\"{'Mg': 1.0, 'Ga': 1.0, 'Pt': 2.0}\",\"('Ga', 'Mg', 'Pt')\",OTHERS,False\nmp-560209,\"{'Li': 18.0, 'Al': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Al', 'Li', 'O', 'P')\",OTHERS,False\nmp-1179413,\"{'Ta': 6.0, 'S': 12.0}\",\"('S', 'Ta')\",OTHERS,False\nmp-675037,\"{'Eu': 2.0, 'Sm': 4.0, 'S': 8.0}\",\"('Eu', 'S', 'Sm')\",OTHERS,False\nmp-677292,\"{'Cs': 6.0, 'Ca': 3.0, 'Al': 12.0, 'Si': 12.0, 'O': 48.0}\",\"('Al', 'Ca', 'Cs', 'O', 'Si')\",OTHERS,False\nmp-1035858,\"{'Y': 1.0, 'Mg': 14.0, 'B': 1.0, 'O': 16.0}\",\"('B', 'Mg', 'O', 'Y')\",OTHERS,False\nmp-1206849,\"{'Sr': 2.0, 'P': 2.0, 'Au': 2.0}\",\"('Au', 'P', 'Sr')\",OTHERS,False\nmvc-7989,\"{'Zn': 4.0, 'Cr': 4.0, 'As': 8.0, 'O': 28.0}\",\"('As', 'Cr', 'O', 'Zn')\",OTHERS,False\nmp-973675,\"{'Hf': 18.0, 'As': 2.0, 'W': 8.0}\",\"('As', 'Hf', 'W')\",OTHERS,False\nmp-699393,\"{'Yb': 2.0, 'Si': 12.0, 'H': 108.0, 'C': 36.0, 'N': 6.0}\",\"('C', 'H', 'N', 'Si', 'Yb')\",OTHERS,False\nmp-780578,\"{'Li': 8.0, 'Mn': 2.0, 'Si': 6.0, 'O': 18.0}\",\"('Li', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1204346,\"{'Fe': 4.0, 'S': 4.0, 'O': 34.0}\",\"('Fe', 'O', 'S')\",OTHERS,False\nmp-1218440,\"{'Sr': 3.0, 'Fe': 2.0, 'Te': 1.0, 'O': 9.0}\",\"('Fe', 'O', 'Sr', 'Te')\",OTHERS,False\nmp-683971,\"{'Sm': 15.0, 'Ga': 44.0, 'Ni': 52.0}\",\"('Ga', 'Ni', 'Sm')\",OTHERS,False\nmp-554094,\"{'Ca': 2.0, 'Mg': 4.0, 'S': 6.0, 'O': 24.0}\",\"('Ca', 'Mg', 'O', 'S')\",OTHERS,False\nmvc-10399,\"{'Ho': 2.0, 'Zn': 2.0, 'W': 4.0, 'O': 12.0}\",\"('Ho', 'O', 'W', 'Zn')\",OTHERS,False\nmp-603327,\"{'H': 24.0, 'S': 8.0, 'N': 8.0, 'O': 24.0}\",\"('H', 'N', 'O', 'S')\",OTHERS,False\nmp-1208698,\"{'Sm': 2.0, 'P': 6.0, 'O': 18.0}\",\"('O', 'P', 'Sm')\",OTHERS,False\nmp-1187496,\"{'Tl': 3.0, 'Si': 1.0}\",\"('Si', 'Tl')\",OTHERS,False\nmp-34144,\"{'Al': 12.0, 'Mg': 6.0, 'O': 24.0}\",\"('Al', 'Mg', 'O')\",OTHERS,False\nmp-1245031,\"{'Ti': 39.0, 'O': 65.0}\",\"('O', 'Ti')\",OTHERS,False\nmp-695597,\"{'Na': 4.0, 'Mg': 12.0, 'Si': 16.0, 'O': 44.0, 'F': 4.0}\",\"('F', 'Mg', 'Na', 'O', 'Si')\",OTHERS,False\nmp-733998,\"{'Tb': 4.0, 'H': 48.0, 'S': 8.0, 'N': 4.0, 'O': 48.0}\",\"('H', 'N', 'O', 'S', 'Tb')\",OTHERS,False\nmp-1225133,\"{'Fe': 3.0, 'Cu': 1.0, 'As': 2.0}\",\"('As', 'Cu', 'Fe')\",OTHERS,False\nmp-780871,\"{'Li': 6.0, 'Fe': 12.0, 'Si': 12.0, 'O': 48.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-541353,\"{'K': 8.0, 'Tc': 12.0, 'S': 24.0}\",\"('K', 'S', 'Tc')\",OTHERS,False\nmp-24809,\"{'H': 4.0, 'Ca': 2.0}\",\"('Ca', 'H')\",OTHERS,False\nmp-757703,\"{'Mn': 3.0, 'Co': 1.0, 'Sn': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Co', 'Mn', 'O', 'P', 'Sn')\",OTHERS,False\nmp-560584,\"{'Si': 6.0, 'Ag': 18.0, 'O': 21.0}\",\"('Ag', 'O', 'Si')\",OTHERS,False\nmp-27821,\"{'P': 11.0, 'Ag': 3.0}\",\"('Ag', 'P')\",OTHERS,False\nmp-976355,\"{'Lu': 3.0, 'Si': 1.0}\",\"('Lu', 'Si')\",OTHERS,False\nmp-1208963,\"{'Sm': 16.0, 'Mg': 4.0, 'Pd': 4.0}\",\"('Mg', 'Pd', 'Sm')\",OTHERS,False\nmp-1220924,\"{'Na': 2.0, 'Pr': 2.0, 'Ti': 4.0, 'O': 12.0}\",\"('Na', 'O', 'Pr', 'Ti')\",OTHERS,False\nmvc-13319,\"{'Mg': 4.0, 'Nb': 4.0, 'Fe': 2.0, 'O': 16.0}\",\"('Fe', 'Mg', 'Nb', 'O')\",OTHERS,False\nmp-1093588,\"{'Mg': 1.0, 'Sn': 1.0, 'Pt': 2.0}\",\"('Mg', 'Pt', 'Sn')\",OTHERS,False\nmp-1001021,\"{'Y': 4.0, 'Zn': 2.0, 'Se': 8.0}\",\"('Se', 'Y', 'Zn')\",OTHERS,False\nmp-1228040,\"{'Ca': 1.0, 'U': 2.0, 'P': 2.0, 'O': 18.0}\",\"('Ca', 'O', 'P', 'U')\",OTHERS,False\nmp-754297,\"{'Ti': 2.0, 'P': 2.0, 'H': 2.0, 'O': 10.0}\",\"('H', 'O', 'P', 'Ti')\",OTHERS,False\nmp-1186351,\"{'Nd': 1.0, 'Sm': 1.0, 'In': 2.0}\",\"('In', 'Nd', 'Sm')\",OTHERS,False\nmp-1227127,\"{'Ca': 4.0, 'Mg': 4.0, 'Cd': 4.0}\",\"('Ca', 'Cd', 'Mg')\",OTHERS,False\nmp-1038146,\"{'K': 1.0, 'Mg': 30.0, 'Mn': 1.0, 'O': 32.0}\",\"('K', 'Mg', 'Mn', 'O')\",OTHERS,False\nmp-543028,\"{'Tl': 16.0, 'Te': 8.0, 'O': 24.0}\",\"('O', 'Te', 'Tl')\",OTHERS,False\nmp-865270,\"{'Tm': 1.0, 'Ta': 1.0, 'Os': 2.0}\",\"('Os', 'Ta', 'Tm')\",OTHERS,False\nmp-1394,\"{'O': 1.0, 'Rb': 2.0}\",\"('O', 'Rb')\",OTHERS,False\nmp-1110633,\"{'K': 1.0, 'Rb': 2.0, 'Sc': 1.0, 'I': 6.0}\",\"('I', 'K', 'Rb', 'Sc')\",OTHERS,False\nmp-1103367,\"{'C': 4.0, 'N': 4.0, 'O': 4.0}\",\"('C', 'N', 'O')\",OTHERS,False\nmp-759637,\"{'Na': 8.0, 'Sr': 4.0, 'Li': 4.0, 'V': 4.0, 'P': 8.0, 'O': 36.0}\",\"('Li', 'Na', 'O', 'P', 'Sr', 'V')\",OTHERS,False\nmp-1217722,\"{'Tb': 4.0, 'Gd': 8.0, 'Ga': 20.0, 'O': 48.0}\",\"('Ga', 'Gd', 'O', 'Tb')\",OTHERS,False\nmp-1220518,\"{'Nb': 4.0, 'Ni': 1.0, 'C': 2.0, 'S': 4.0}\",\"('C', 'Nb', 'Ni', 'S')\",OTHERS,False\nmp-675980,\"{'Cr': 4.0, 'P': 12.0, 'S': 36.0}\",\"('Cr', 'P', 'S')\",OTHERS,False\nmp-862962,\"{'Ca': 1.0, 'La': 1.0, 'Zn': 2.0}\",\"('Ca', 'La', 'Zn')\",OTHERS,False\nmp-1211671,\"{'La': 6.0, 'Ge': 10.0}\",\"('Ge', 'La')\",OTHERS,False\nmp-1104114,\"{'Eu': 1.0, 'Mo': 6.0, 'Se': 8.0}\",\"('Eu', 'Mo', 'Se')\",OTHERS,False\nmp-626529,\"{'H': 28.0, 'N': 4.0, 'O': 24.0}\",\"('H', 'N', 'O')\",OTHERS,False\nmp-1213891,\"{'Cs': 12.0, 'Pr': 4.0, 'Cl': 24.0}\",\"('Cl', 'Cs', 'Pr')\",OTHERS,False\nmp-1176710,\"{'Li': 8.0, 'Mn': 8.0, 'P': 16.0, 'O': 56.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1206860,\"{'Nd': 3.0, 'Mg': 3.0, 'In': 3.0}\",\"('In', 'Mg', 'Nd')\",OTHERS,False\nmp-1214940,\"{'Al': 4.0, 'P': 4.0, 'H': 8.0, 'O': 16.0}\",\"('Al', 'H', 'O', 'P')\",OTHERS,False\nmp-1187907,\"{'Yb': 1.0, 'Br': 1.0}\",\"('Br', 'Yb')\",OTHERS,False\nmp-1076638,\"{'Rb': 1.0, 'V': 1.0, 'O': 3.0}\",\"('O', 'Rb', 'V')\",OTHERS,False\nmp-1185055,\"{'Hf': 8.0, 'In': 4.0, 'Ni': 8.0}\",\"('Hf', 'In', 'Ni')\",OTHERS,False\nmp-1221134,\"{'Na': 1.0, 'Ca': 2.0, 'Mg': 5.0, 'Al': 1.0, 'Si': 7.0, 'O': 22.0, 'F': 2.0}\",\"('Al', 'Ca', 'F', 'Mg', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1021466,\"{'Cs': 1.0, 'P': 1.0, 'Pb': 1.0, 'I': 2.0, 'F': 6.0}\",\"('Cs', 'F', 'I', 'P', 'Pb')\",OTHERS,False\nmp-1203434,\"{'Na': 16.0, 'Fe': 16.0, 'F': 56.0}\",\"('F', 'Fe', 'Na')\",OTHERS,False\nmp-1183797,\"{'Ce': 4.0, 'Si': 10.0, 'Ni': 6.0}\",\"('Ce', 'Ni', 'Si')\",OTHERS,False\nmp-720118,\"{'Sr': 3.0, 'Nd': 10.0, 'Al': 12.0, 'Si': 18.0, 'N': 36.0, 'O': 18.0}\",\"('Al', 'N', 'Nd', 'O', 'Si', 'Sr')\",OTHERS,False\nmp-775190,\"{'Li': 2.0, 'V': 3.0, 'Cu': 1.0, 'O': 8.0}\",\"('Cu', 'Li', 'O', 'V')\",OTHERS,False\nmp-1079706,\"{'Ti': 5.0, 'Al': 2.0, 'C': 3.0}\",\"('Al', 'C', 'Ti')\",OTHERS,False\nmvc-15532,\"{'Zn': 1.0, 'Cr': 2.0, 'N': 2.0}\",\"('Cr', 'N', 'Zn')\",OTHERS,False\nmp-1186461,\"{'Pm': 2.0, 'Ga': 1.0, 'Hg': 1.0}\",\"('Ga', 'Hg', 'Pm')\",OTHERS,False\nmp-22304,\"{'Se': 4.0, 'Cd': 1.0, 'In': 2.0}\",\"('Cd', 'In', 'Se')\",OTHERS,False\nmp-556569,\"{'Sr': 2.0, 'Ta': 4.0, 'Bi': 4.0, 'O': 18.0}\",\"('Bi', 'O', 'Sr', 'Ta')\",OTHERS,False\nmp-556665,\"{'Tl': 4.0, 'Bi': 4.0, 'P': 8.0, 'S': 28.0}\",\"('Bi', 'P', 'S', 'Tl')\",OTHERS,False\nmp-11567,\"{'Sc': 1.0, 'Zn': 12.0}\",\"('Sc', 'Zn')\",OTHERS,False\nmp-1194580,\"{'Rb': 4.0, 'Na': 8.0, 'B': 24.0, 'Cl': 4.0, 'O': 40.0}\",\"('B', 'Cl', 'Na', 'O', 'Rb')\",OTHERS,False\nmp-1174913,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-850403,\"{'Li': 4.0, 'Fe': 4.0, 'P': 4.0, 'H': 8.0, 'O': 20.0}\",\"('Fe', 'H', 'Li', 'O', 'P')\",OTHERS,False\nmp-1214494,\"{'Ba': 10.0, 'Y': 4.0, 'Zr': 2.0, 'Al': 4.0, 'O': 26.0}\",\"('Al', 'Ba', 'O', 'Y', 'Zr')\",OTHERS,False\nmp-1084796,\"{'Ca': 1.0, 'Zn': 3.0, 'Se': 4.0}\",\"('Ca', 'Se', 'Zn')\",OTHERS,False\nmp-977592,\"{'Bi': 4.0, 'Rh': 6.0, 'S': 4.0}\",\"('Bi', 'Rh', 'S')\",OTHERS,False\nmp-754860,\"{'Co': 10.0, 'O': 5.0, 'F': 15.0}\",\"('Co', 'F', 'O')\",OTHERS,False\nmvc-15097,\"{'Y': 2.0, 'Cu': 3.0, 'W': 6.0, 'O': 24.0}\",\"('Cu', 'O', 'W', 'Y')\",OTHERS,False\nmp-10224,\"{'Na': 1.0, 'S': 2.0, 'V': 1.0}\",\"('Na', 'S', 'V')\",OTHERS,False\nmp-1102017,\"{'Cd': 4.0, 'Br': 4.0, 'O': 4.0}\",\"('Br', 'Cd', 'O')\",OTHERS,False\nmp-1017983,\"{'Ti': 1.0, 'Mo': 3.0}\",\"('Mo', 'Ti')\",OTHERS,False\nmp-1037677,\"{'Li': 1.0, 'Mg': 30.0, 'Fe': 1.0, 'O': 32.0}\",\"('Fe', 'Li', 'Mg', 'O')\",OTHERS,False\nmp-1076858,\"{'Sr': 7.0, 'Ca': 1.0, 'Fe': 7.0, 'Co': 1.0, 'O': 24.0}\",\"('Ca', 'Co', 'Fe', 'O', 'Sr')\",OTHERS,False\nmp-973779,\"{'Nd': 2.0, 'Cd': 1.0, 'Hg': 1.0}\",\"('Cd', 'Hg', 'Nd')\",OTHERS,False\nmp-560164,\"{'Li': 3.0, 'Al': 1.0, 'Mo': 2.0, 'As': 2.0, 'O': 14.0}\",\"('Al', 'As', 'Li', 'Mo', 'O')\",OTHERS,False\nmp-758877,\"{'Li': 14.0, 'Mn': 14.0, 'P': 12.0, 'O': 48.0, 'F': 6.0}\",\"('F', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1250185,\"{'Ca': 8.0, 'Mn': 8.0, 'Si': 12.0, 'O': 48.0}\",\"('Ca', 'Mn', 'O', 'Si')\",OTHERS,False\nmp-1114466,\"{'Rb': 2.0, 'Al': 1.0, 'Tl': 1.0, 'Br': 6.0}\",\"('Al', 'Br', 'Rb', 'Tl')\",OTHERS,False\nmp-1223817,\"{'K': 4.0, 'Mo': 1.0, 'W': 1.0, 'O': 8.0}\",\"('K', 'Mo', 'O', 'W')\",OTHERS,False\nmp-1247276,\"{'Mg': 2.0, 'Ti': 1.0, 'Mn': 3.0, 'S': 8.0}\",\"('Mg', 'Mn', 'S', 'Ti')\",OTHERS,False\nmp-770456,\"{'Li': 6.0, 'Ti': 2.0, 'O': 7.0}\",\"('Li', 'O', 'Ti')\",OTHERS,False\nmp-570629,\"{'Ce': 8.0, 'Ru': 6.0}\",\"('Ce', 'Ru')\",OTHERS,False\nmp-13003,\"{'O': 12.0, 'Ge': 4.0, 'Cd': 4.0}\",\"('Cd', 'Ge', 'O')\",OTHERS,False\nmp-772842,\"{'Na': 8.0, 'Ge': 8.0, 'O': 20.0}\",\"('Ge', 'Na', 'O')\",OTHERS,False\nmp-865386,\"{'Gd': 2.0, 'Ga': 6.0}\",\"('Ga', 'Gd')\",OTHERS,False\nmp-1202680,\"{'Co': 12.0, 'Te': 8.0, 'O': 32.0}\",\"('Co', 'O', 'Te')\",OTHERS,False\nmp-978512,\"{'Sm': 1.0, 'Tm': 1.0, 'Mg': 2.0}\",\"('Mg', 'Sm', 'Tm')\",OTHERS,False\nmp-505014,\"{'Cs': 8.0, 'Sn': 4.0, 'Te': 14.0}\",\"('Cs', 'Sn', 'Te')\",OTHERS,False\nmp-569940,\"{'K': 6.0, 'Bi': 2.0}\",\"('Bi', 'K')\",OTHERS,False\nmp-677659,\"{'Sr': 7.0, 'La': 7.0, 'Cu': 7.0, 'Ru': 7.0, 'O': 42.0}\",\"('Cu', 'La', 'O', 'Ru', 'Sr')\",OTHERS,False\nmp-1196332,\"{'Fe': 8.0, 'H': 40.0, 'S': 8.0, 'O': 44.0}\",\"('Fe', 'H', 'O', 'S')\",OTHERS,False\nmp-6008,\"{'O': 48.0, 'Al': 8.0, 'Si': 12.0, 'Ca': 12.0}\",\"('Al', 'Ca', 'O', 'Si')\",OTHERS,False\nmp-504944,\"{'P': 8.0, 'Na': 8.0, 'O': 32.0, 'H': 16.0}\",\"('H', 'Na', 'O', 'P')\",OTHERS,False\nmp-1095460,\"{'Hg': 4.0, 'Sb': 1.0, 'H': 1.0, 'O': 6.0}\",\"('H', 'Hg', 'O', 'Sb')\",OTHERS,False\nmp-1192354,\"{'Eu': 4.0, 'Ga': 4.0, 'Ge': 2.0, 'S': 14.0}\",\"('Eu', 'Ga', 'Ge', 'S')\",OTHERS,False\nmp-1180149,\"{'Na': 2.0, 'V': 4.0, 'O': 10.0}\",\"('Na', 'O', 'V')\",OTHERS,False\nmp-1213703,\"{'Cs': 3.0, 'Ca': 2.0, 'Cl': 7.0}\",\"('Ca', 'Cl', 'Cs')\",OTHERS,False\nmp-1186408,\"{'Pa': 2.0, 'Si': 6.0}\",\"('Pa', 'Si')\",OTHERS,False\nmp-567614,\"{'Tb': 4.0, 'Co': 4.0, 'I': 2.0}\",\"('Co', 'I', 'Tb')\",OTHERS,False\nmp-849366,\"{'Li': 2.0, 'Co': 6.0, 'O': 2.0, 'F': 10.0}\",\"('Co', 'F', 'Li', 'O')\",OTHERS,False\nAl 71.5 Co 23.6 Cu 4.9,\"{'Al': 71.5, 'Co': 23.6, 'Cu': 4.9}\",\"('Al', 'Co', 'Cu')\",AC,False\nmp-769001,\"{'Li': 12.0, 'Sn': 4.0, 'B': 8.0, 'S': 2.0, 'O': 32.0}\",\"('B', 'Li', 'O', 'S', 'Sn')\",OTHERS,False\nmp-774516,\"{'Li': 8.0, 'Mg': 3.0, 'Cu': 9.0, 'Si': 16.0, 'O': 48.0}\",\"('Cu', 'Li', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-1180667,\"{'La': 6.0, 'Fe': 2.0, 'O': 12.0}\",\"('Fe', 'La', 'O')\",OTHERS,False\nmp-1197627,\"{'Pu': 4.0, 'Te': 8.0, 'Cl': 4.0, 'O': 22.0}\",\"('Cl', 'O', 'Pu', 'Te')\",OTHERS,False\nmp-1187466,\"{'Ti': 3.0, 'Cd': 1.0}\",\"('Cd', 'Ti')\",OTHERS,False\nmp-673029,\"{'Li': 2.0, 'Sn': 4.0, 'P': 10.0, 'O': 32.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,False\nmp-1205209,\"{'Fe': 8.0, 'S': 8.0, 'O': 56.0}\",\"('Fe', 'O', 'S')\",OTHERS,False\nmp-1080029,\"{'Ba': 2.0, 'Mn': 2.0, 'Se': 2.0, 'O': 1.0, 'F': 2.0}\",\"('Ba', 'F', 'Mn', 'O', 'Se')\",OTHERS,False\nmp-1196548,\"{'H': 36.0, 'C': 32.0, 'N': 4.0, 'O': 36.0}\",\"('C', 'H', 'N', 'O')\",OTHERS,False\nmp-707443,\"{'Na': 7.0, 'Al': 1.0, 'H': 2.0, 'C': 4.0, 'O': 12.0, 'F': 4.0}\",\"('Al', 'C', 'F', 'H', 'Na', 'O')\",OTHERS,False\nmp-1227803,\"{'Ba': 4.0, 'Sn': 8.0, 'Bi': 4.0}\",\"('Ba', 'Bi', 'Sn')\",OTHERS,False\nmp-22239,\"{'Ge': 4.0, 'Rh': 4.0}\",\"('Ge', 'Rh')\",OTHERS,False\nmp-1222518,\"{'Li': 4.0, 'Te': 4.0, 'H': 20.0, 'O': 24.0}\",\"('H', 'Li', 'O', 'Te')\",OTHERS,False\nmp-973538,\"{'Lu': 2.0, 'Al': 1.0, 'Zn': 1.0}\",\"('Al', 'Lu', 'Zn')\",OTHERS,False\nmp-756535,\"{'Li': 2.0, 'Fe': 2.0, 'F': 8.0}\",\"('F', 'Fe', 'Li')\",OTHERS,False\nmp-768369,\"{'Ba': 8.0, 'Y': 4.0, 'F': 28.0}\",\"('Ba', 'F', 'Y')\",OTHERS,False\nmvc-6817,\"{'Zn': 4.0, 'Cr': 8.0, 'O': 16.0}\",\"('Cr', 'O', 'Zn')\",OTHERS,False\nmp-1202237,\"{'Mg': 4.0, 'B': 8.0, 'H': 52.0, 'N': 12.0}\",\"('B', 'H', 'Mg', 'N')\",OTHERS,False\nmp-849419,\"{'Fe': 10.0, 'O': 9.0, 'F': 11.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmp-1208841,\"{'Sr': 2.0, 'Fe': 1.0, 'As': 2.0, 'F': 1.0}\",\"('As', 'F', 'Fe', 'Sr')\",OTHERS,False\nmp-862676,\"{'Li': 1.0, 'Ho': 1.0, 'In': 2.0}\",\"('Ho', 'In', 'Li')\",OTHERS,False\nmp-1180387,\"{'Mg': 1.0, 'Mn': 2.0, 'Br': 6.0, 'O': 12.0}\",\"('Br', 'Mg', 'Mn', 'O')\",OTHERS,False\nmp-1196485,\"{'Sm': 12.0, 'In': 4.0, 'S': 24.0}\",\"('In', 'S', 'Sm')\",OTHERS,False\nmp-1028607,\"{'Te': 4.0, 'Mo': 1.0, 'W': 3.0, 'Se': 4.0}\",\"('Mo', 'Se', 'Te', 'W')\",OTHERS,False\nmp-773118,\"{'Ba': 6.0, 'Li': 2.0, 'Ta': 9.0, 'Ti': 7.0, 'O': 42.0}\",\"('Ba', 'Li', 'O', 'Ta', 'Ti')\",OTHERS,False\nmp-1096115,\"{'Li': 1.0, 'Pd': 2.0, 'Au': 1.0}\",\"('Au', 'Li', 'Pd')\",OTHERS,False\nmp-1218186,\"{'Sr': 1.0, 'Mn': 1.0, 'Te': 1.0, 'O': 6.0}\",\"('Mn', 'O', 'Sr', 'Te')\",OTHERS,False\nmvc-6562,\"{'Y': 2.0, 'W': 6.0, 'F': 30.0}\",\"('F', 'W', 'Y')\",OTHERS,False\nmp-1213751,\"{'Cs': 8.0, 'B': 48.0, 'H': 4.0, 'F': 48.0}\",\"('B', 'Cs', 'F', 'H')\",OTHERS,False\nmp-684950,\"{'K': 1.0, 'Na': 1.0, 'Zr': 2.0, 'Be': 1.0, 'P': 4.0, 'O': 16.0}\",\"('Be', 'K', 'Na', 'O', 'P', 'Zr')\",OTHERS,False\nmp-1213907,\"{'Ce': 4.0, 'Ga': 18.0, 'Rh': 6.0}\",\"('Ce', 'Ga', 'Rh')\",OTHERS,False\nmp-1112422,\"{'K': 2.0, 'Nd': 1.0, 'Au': 1.0, 'Cl': 6.0}\",\"('Au', 'Cl', 'K', 'Nd')\",OTHERS,False\nmp-28642,\"{'F': 72.0, 'Al': 8.0, 'Ba': 24.0}\",\"('Al', 'Ba', 'F')\",OTHERS,False\nmp-16591,\"{'O': 28.0, 'Si': 8.0, 'S': 12.0, 'Tm': 16.0}\",\"('O', 'S', 'Si', 'Tm')\",OTHERS,False\nmp-28143,\"{'H': 32.0, 'N': 8.0, 'S': 20.0}\",\"('H', 'N', 'S')\",OTHERS,False\nmp-1080155,\"{'Te': 4.0, 'Mo': 2.0, 'W': 2.0, 'Se': 2.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-690522,\"{'Mo': 2.0, 'Cl': 4.0, 'O': 1.0}\",\"('Cl', 'Mo', 'O')\",OTHERS,False\nmvc-6418,\"{'Ca': 2.0, 'Cr': 4.0, 'O': 8.0}\",\"('Ca', 'Cr', 'O')\",OTHERS,False\nmp-758463,\"{'Li': 1.0, 'Ni': 1.0, 'Sn': 1.0, 'O': 4.0}\",\"('Li', 'Ni', 'O', 'Sn')\",OTHERS,False\nmp-1203016,\"{'Ba': 18.0, 'Al': 18.0, 'As': 30.0}\",\"('Al', 'As', 'Ba')\",OTHERS,False\nmp-556591,\"{'Si': 18.0, 'O': 36.0}\",\"('O', 'Si')\",OTHERS,False\nmp-1011376,\"{'Ce': 1.0, 'Se': 2.0}\",\"('Ce', 'Se')\",OTHERS,False\nmp-23486,\"{'Cl': 6.0, 'Ni': 2.0, 'Rb': 2.0}\",\"('Cl', 'Ni', 'Rb')\",OTHERS,False\nmp-867583,\"{'Ag': 6.0, 'Sb': 6.0, 'O': 18.0}\",\"('Ag', 'O', 'Sb')\",OTHERS,False\nmp-9205,\"{'O': 4.0, 'F': 2.0, 'K': 2.0, 'Se': 2.0}\",\"('F', 'K', 'O', 'Se')\",OTHERS,False\nmp-758923,\"{'Sb': 4.0, 'As': 4.0, 'Au': 4.0, 'F': 36.0}\",\"('As', 'Au', 'F', 'Sb')\",OTHERS,False\nmp-18852,\"{'O': 8.0, 'Na': 4.0, 'Mo': 2.0}\",\"('Mo', 'Na', 'O')\",OTHERS,False\nmp-1101165,\"{'Yb': 2.0, 'Bi': 2.0, 'O': 6.0}\",\"('Bi', 'O', 'Yb')\",OTHERS,False\nmp-1203225,\"{'Al': 4.0, 'Se': 6.0, 'O': 24.0}\",\"('Al', 'O', 'Se')\",OTHERS,False\nmp-1212687,\"{'Fe': 2.0, 'Ni': 2.0, 'Ag': 4.0, 'F': 14.0}\",\"('Ag', 'F', 'Fe', 'Ni')\",OTHERS,False\nmp-757269,\"{'Li': 6.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,False\nCd 65 Mg 20 Gd 15,\"{'Cd': 65.0, 'Mg': 20.0, 'Gd': 15.0}\",\"('Cd', 'Gd', 'Mg')\",QC,False\nmp-867161,\"{'Li': 1.0, 'In': 3.0}\",\"('In', 'Li')\",OTHERS,False\nmp-865533,\"{'Ti': 2.0, 'Re': 1.0, 'Os': 1.0}\",\"('Os', 'Re', 'Ti')\",OTHERS,False\nmp-1226930,\"{'Cr': 19.0, 'S': 30.0}\",\"('Cr', 'S')\",OTHERS,False\nmp-966801,\"{'Li': 4.0, 'Ca': 4.0, 'B': 4.0, 'O': 12.0}\",\"('B', 'Ca', 'Li', 'O')\",OTHERS,False\nmp-1184131,\"{'Er': 2.0, 'Ag': 1.0, 'Os': 1.0}\",\"('Ag', 'Er', 'Os')\",OTHERS,False\nmp-1222558,\"{'Li': 3.0, 'Ag': 3.0, 'Ge': 2.0}\",\"('Ag', 'Ge', 'Li')\",OTHERS,False\nmp-695325,\"{'Ti': 5.0, 'Zn': 1.0, 'Bi': 2.0, 'Pb': 4.0, 'O': 18.0}\",\"('Bi', 'O', 'Pb', 'Ti', 'Zn')\",OTHERS,False\nmp-675045,\"{'Li': 14.0, 'V': 2.0, 'P': 8.0}\",\"('Li', 'P', 'V')\",OTHERS,False\nmp-758947,\"{'Li': 14.0, 'Mn': 14.0, 'P': 12.0, 'O': 48.0, 'F': 6.0}\",\"('F', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1200662,\"{'Hg': 8.0, 'S': 8.0, 'O': 32.0}\",\"('Hg', 'O', 'S')\",OTHERS,False\nmp-1217221,\"{'Ti': 4.0, 'Fe': 1.0, 'Co': 1.0, 'S': 8.0}\",\"('Co', 'Fe', 'S', 'Ti')\",OTHERS,False\nmp-10647,\"{'Se': 1.0, 'Tl': 1.0}\",\"('Se', 'Tl')\",OTHERS,False\nmp-15960,\"{'Li': 40.0, 'O': 32.0, 'Al': 8.0}\",\"('Al', 'Li', 'O')\",OTHERS,False\nmp-9847,\"{'P': 10.0, 'Yb': 2.0}\",\"('P', 'Yb')\",OTHERS,False\nmvc-1915,\"{'Ca': 2.0, 'Ti': 9.0, 'O': 13.0}\",\"('Ca', 'O', 'Ti')\",OTHERS,False\nmvc-9330,\"{'Pr': 2.0, 'Zn': 2.0, 'Cu': 4.0, 'O': 12.0}\",\"('Cu', 'O', 'Pr', 'Zn')\",OTHERS,False\nmp-1210714,\"{'V': 4.0, 'H': 48.0, 'S': 4.0, 'O': 40.0}\",\"('H', 'O', 'S', 'V')\",OTHERS,False\nmp-1079433,\"{'Ba': 1.0, 'Mg': 4.0, 'Si': 3.0}\",\"('Ba', 'Mg', 'Si')\",OTHERS,False\nmp-1224891,\"{'Ga': 3.0, 'Cu': 3.0, 'Si': 1.0, 'Se': 8.0}\",\"('Cu', 'Ga', 'Se', 'Si')\",OTHERS,False\nmvc-7066,\"{'Mg': 4.0, 'Mo': 8.0, 'O': 16.0}\",\"('Mg', 'Mo', 'O')\",OTHERS,False\nmp-1183046,\"{'Zr': 2.0, 'Ti': 6.0}\",\"('Ti', 'Zr')\",OTHERS,False\nmp-1114452,\"{'Rb': 2.0, 'Na': 1.0, 'Pr': 1.0, 'F': 6.0}\",\"('F', 'Na', 'Pr', 'Rb')\",OTHERS,False\nmp-861726,\"{'Pu': 1.0, 'Bi': 1.0, 'Pd': 2.0}\",\"('Bi', 'Pd', 'Pu')\",OTHERS,False\nmp-1225302,\"{'Fe': 10.0, 'P': 6.0, 'O': 34.0}\",\"('Fe', 'O', 'P')\",OTHERS,False\nmp-12304,\"{'Sc': 22.0, 'Ir': 8.0}\",\"('Ir', 'Sc')\",OTHERS,False\nmp-676275,\"{'Nd': 2.0, 'Pb': 5.0, 'F': 16.0}\",\"('F', 'Nd', 'Pb')\",OTHERS,False\nmp-1080472,\"{'Pt': 1.0, 'N': 2.0, 'Cl': 6.0}\",\"('Cl', 'N', 'Pt')\",OTHERS,False\nmp-1211232,\"{'La': 2.0, 'Al': 6.0, 'Ni': 4.0}\",\"('Al', 'La', 'Ni')\",OTHERS,False\nmp-510581,\"{'Pr': 4.0, 'Ni': 4.0, 'Sn': 4.0, 'H': 8.0}\",\"('H', 'Ni', 'Pr', 'Sn')\",OTHERS,False\nmp-1079846,\"{'Th': 3.0, 'Al': 3.0, 'Ir': 3.0}\",\"('Al', 'Ir', 'Th')\",OTHERS,False\nmp-752636,\"{'Li': 4.0, 'Sb': 4.0, 'O': 12.0}\",\"('Li', 'O', 'Sb')\",OTHERS,False\nmp-1201161,\"{'Pr': 16.0, 'As': 4.0, 'Br': 4.0, 'O': 32.0}\",\"('As', 'Br', 'O', 'Pr')\",OTHERS,False\nmp-546790,\"{'O': 2.0, 'Te': 2.0, 'La': 2.0, 'Cu': 2.0}\",\"('Cu', 'La', 'O', 'Te')\",OTHERS,False\nmp-540771,\"{'Ba': 4.0, 'Zr': 4.0, 'S': 12.0}\",\"('Ba', 'S', 'Zr')\",OTHERS,False\nmp-1103880,\"{'Tm': 3.0, 'Ga': 9.0, 'Pt': 2.0}\",\"('Ga', 'Pt', 'Tm')\",OTHERS,False\nmp-1030340,\"{'Te': 6.0, 'Mo': 3.0, 'W': 1.0, 'S': 2.0}\",\"('Mo', 'S', 'Te', 'W')\",OTHERS,False\nmp-1097176,\"{'Sr': 1.0, 'Li': 1.0, 'Tl': 2.0}\",\"('Li', 'Sr', 'Tl')\",OTHERS,False\nmp-30718,\"{'K': 20.0, 'Hg': 28.0}\",\"('Hg', 'K')\",OTHERS,False\nmp-1221880,\"{'Mn': 2.0, 'Cu': 1.0, 'Sb': 2.0, 'Pd': 1.0}\",\"('Cu', 'Mn', 'Pd', 'Sb')\",OTHERS,False\nmp-705479,\"{'Sn': 4.0, 'H': 24.0, 'Cl': 16.0, 'O': 12.0}\",\"('Cl', 'H', 'O', 'Sn')\",OTHERS,False\nmp-1018735,\"{'Ba': 1.0, 'Ti': 2.0, 'As': 2.0, 'O': 1.0}\",\"('As', 'Ba', 'O', 'Ti')\",OTHERS,False\nmp-1195997,\"{'Y': 4.0, 'B': 12.0, 'H': 120.0, 'N': 24.0}\",\"('B', 'H', 'N', 'Y')\",OTHERS,False\nmp-1097659,\"{'Y': 2.0, 'Ga': 1.0, 'Pd': 1.0}\",\"('Ga', 'Pd', 'Y')\",OTHERS,False\nmp-768514,\"{'Li': 9.0, 'Ti': 1.0, 'Mn': 3.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'Mn', 'O', 'P', 'Ti')\",OTHERS,False\nmp-1205964,\"{'Ca': 2.0, 'Fe': 1.0, 'H': 6.0}\",\"('Ca', 'Fe', 'H')\",OTHERS,False\nmp-977584,\"{'Zr': 2.0, 'Zn': 1.0, 'Ag': 2.0, 'F': 14.0}\",\"('Ag', 'F', 'Zn', 'Zr')\",OTHERS,False\nmp-675539,\"{'Na': 7.0, 'Cu': 3.0, 'O': 8.0}\",\"('Cu', 'Na', 'O')\",OTHERS,False\nmp-1114444,\"{'Rb': 2.0, 'Na': 1.0, 'Lu': 1.0, 'Cl': 6.0}\",\"('Cl', 'Lu', 'Na', 'Rb')\",OTHERS,False\nmp-16194,\"{'O': 36.0, 'Si': 12.0, 'Rb': 24.0}\",\"('O', 'Rb', 'Si')\",OTHERS,False\nmp-560091,\"{'Cs': 2.0, 'Zr': 4.0, 'P': 6.0, 'O': 24.0}\",\"('Cs', 'O', 'P', 'Zr')\",OTHERS,False\nmp-559422,\"{'Bi': 32.0, 'Au': 16.0, 'O': 72.0}\",\"('Au', 'Bi', 'O')\",OTHERS,False\nmp-1184411,\"{'Eu': 1.0, 'Mo': 1.0, 'O': 3.0}\",\"('Eu', 'Mo', 'O')\",OTHERS,False\nmp-1105153,\"{'Sr': 4.0, 'Os': 4.0, 'O': 12.0}\",\"('O', 'Os', 'Sr')\",OTHERS,False\nmp-559025,\"{'Ba': 2.0, 'Er': 2.0, 'Ag': 2.0, 'S': 6.0}\",\"('Ag', 'Ba', 'Er', 'S')\",OTHERS,False\nmp-985,\"{'Cu': 1.0, 'Tm': 1.0}\",\"('Cu', 'Tm')\",OTHERS,False\nmp-774791,\"{'Li': 4.0, 'Mn': 3.0, 'Cr': 3.0, 'Te': 2.0, 'O': 16.0}\",\"('Cr', 'Li', 'Mn', 'O', 'Te')\",OTHERS,False\nmp-1076134,\"{'Sr': 12.0, 'Ca': 20.0, 'Ti': 12.0, 'Mn': 20.0, 'O': 80.0}\",\"('Ca', 'Mn', 'O', 'Sr', 'Ti')\",OTHERS,False\nmp-1190342,\"{'Lu': 10.0, 'Pt': 6.0}\",\"('Lu', 'Pt')\",OTHERS,False\nmp-866101,\"{'Ac': 1.0, 'Cr': 1.0, 'O': 3.0}\",\"('Ac', 'Cr', 'O')\",OTHERS,False\nmp-1193367,\"{'Na': 2.0, 'Ca': 4.0, 'Si': 6.0, 'O': 18.0}\",\"('Ca', 'Na', 'O', 'Si')\",OTHERS,False\nmp-1076247,\"{'Ba': 6.0, 'Sr': 2.0, 'Co': 1.0, 'Cu': 7.0, 'O': 24.0}\",\"('Ba', 'Co', 'Cu', 'O', 'Sr')\",OTHERS,False\nmp-5036,\"{'P': 8.0, 'S': 32.0, 'Er': 8.0}\",\"('Er', 'P', 'S')\",OTHERS,False\nmp-1225500,\"{'Dy': 1.0, 'Ho': 1.0, 'Al': 4.0}\",\"('Al', 'Dy', 'Ho')\",OTHERS,False\nmp-1101457,\"{'Ni': 2.0, 'Bi': 4.0, 'O': 12.0}\",\"('Bi', 'Ni', 'O')\",OTHERS,False\nmp-1229089,\"{'Ag': 1.0, 'Bi': 1.0, 'Se': 1.0, 'S': 1.0}\",\"('Ag', 'Bi', 'S', 'Se')\",OTHERS,False\nmvc-1114,\"{'Ba': 2.0, 'Y': 2.0, 'Ti': 8.0, 'O': 14.0}\",\"('Ba', 'O', 'Ti', 'Y')\",OTHERS,False\nmp-1199390,\"{'Ag': 4.0, 'H': 80.0, 'S': 24.0, 'N': 20.0, 'O': 36.0}\",\"('Ag', 'H', 'N', 'O', 'S')\",OTHERS,False\nmp-1598,\"{'Na': 6.0, 'P': 2.0}\",\"('Na', 'P')\",OTHERS,False\nmp-1036503,\"{'Cs': 1.0, 'Mg': 14.0, 'Al': 1.0, 'O': 16.0}\",\"('Al', 'Cs', 'Mg', 'O')\",OTHERS,False\nmp-555629,\"{'Sb': 4.0, 'Te': 4.0, 'Cl': 12.0, 'F': 24.0}\",\"('Cl', 'F', 'Sb', 'Te')\",OTHERS,False\nmp-1076184,\"{'Sr': 8.0, 'Ca': 24.0, 'Mn': 32.0, 'O': 80.0}\",\"('Ca', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-1112900,\"{'Cs': 2.0, 'Tl': 1.0, 'Hg': 1.0, 'Br': 6.0}\",\"('Br', 'Cs', 'Hg', 'Tl')\",OTHERS,False\nmp-540079,\"{'Ni': 4.0, 'P': 16.0, 'O': 48.0}\",\"('Ni', 'O', 'P')\",OTHERS,False\nmp-1572,\"{'Ga': 4.0, 'Se': 4.0}\",\"('Ga', 'Se')\",OTHERS,False\nmp-510751,\"{'Cu': 16.0, 'O': 16.0}\",\"('Cu', 'O')\",OTHERS,False\nmp-8925,\"{'Ag': 3.0, 'Te': 2.0, 'Tl': 1.0}\",\"('Ag', 'Te', 'Tl')\",OTHERS,False\nmp-568537,\"{'Yb': 1.0, 'In': 1.0, 'Au': 2.0}\",\"('Au', 'In', 'Yb')\",OTHERS,False\nmp-1183402,\"{'Ba': 2.0, 'Li': 2.0, 'B': 18.0, 'O': 30.0}\",\"('B', 'Ba', 'Li', 'O')\",OTHERS,False\nmp-1894,\"{'C': 1.0, 'W': 1.0}\",\"('C', 'W')\",OTHERS,False\nmp-30006,\"{'N': 8.0, 'O': 24.0, 'Co': 4.0}\",\"('Co', 'N', 'O')\",OTHERS,False\nmp-22978,\"{'Rb': 2.0, 'Mn': 1.0, 'Cl': 4.0}\",\"('Cl', 'Mn', 'Rb')\",OTHERS,False\nmp-1174104,\"{'Li': 5.0, 'Co': 3.0, 'O': 8.0}\",\"('Co', 'Li', 'O')\",OTHERS,False\nmp-1113426,\"{'Cs': 1.0, 'Rb': 2.0, 'As': 1.0, 'Br': 6.0}\",\"('As', 'Br', 'Cs', 'Rb')\",OTHERS,False\nmp-543019,\"{'Gd': 2.0, 'Ni': 4.0}\",\"('Gd', 'Ni')\",OTHERS,False\nmp-569340,\"{'Rb': 8.0, 'Zn': 4.0, 'N': 48.0}\",\"('N', 'Rb', 'Zn')\",OTHERS,False\nmp-1221426,\"{'Mo': 1.0, 'Ir': 4.0}\",\"('Ir', 'Mo')\",OTHERS,False\nmp-674513,\"{'Dy': 6.0, 'Ta': 2.0, 'O': 14.0}\",\"('Dy', 'O', 'Ta')\",OTHERS,False\nmp-1245567,\"{'Fe': 1.0, 'Co': 2.0, 'N': 2.0}\",\"('Co', 'Fe', 'N')\",OTHERS,False\nmp-571265,\"{'Rb': 4.0, 'U': 2.0, 'Br': 12.0}\",\"('Br', 'Rb', 'U')\",OTHERS,False\nmp-20331,\"{'Cu': 4.0, 'Se': 8.0, 'Sb': 4.0}\",\"('Cu', 'Sb', 'Se')\",OTHERS,False\nmp-1037241,\"{'Y': 1.0, 'Mg': 30.0, 'C': 1.0, 'O': 32.0}\",\"('C', 'Mg', 'O', 'Y')\",OTHERS,False\nmp-1029153,\"{'Te': 2.0, 'Mo': 2.0, 'W': 2.0, 'Se': 4.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-1219452,\"{'Sc': 2.0, 'Tl': 4.0, 'Mo': 30.0, 'S': 38.0}\",\"('Mo', 'S', 'Sc', 'Tl')\",OTHERS,False\nmp-694855,\"{'Li': 4.0, 'Mo': 2.0, 'O': 6.0}\",\"('Li', 'Mo', 'O')\",OTHERS,False\nmp-21862,\"{'C': 1.0, 'V': 1.0, 'Ru': 3.0}\",\"('C', 'Ru', 'V')\",OTHERS,False\nmp-570009,\"{'Np': 1.0, 'Mn': 2.0, 'Si': 2.0}\",\"('Mn', 'Np', 'Si')\",OTHERS,False\nmp-569894,\"{'La': 4.0, 'B': 4.0, 'Cl': 5.0}\",\"('B', 'Cl', 'La')\",OTHERS,False\nmp-973601,\"{'Hg': 1.0, 'I': 3.0}\",\"('Hg', 'I')\",OTHERS,False\nmp-1112225,\"{'Rb': 2.0, 'In': 1.0, 'Hg': 1.0, 'F': 6.0}\",\"('F', 'Hg', 'In', 'Rb')\",OTHERS,False\nmp-510567,\"{'Ce': 6.0, 'Cu': 2.0, 'Sn': 2.0, 'S': 14.0}\",\"('Ce', 'Cu', 'S', 'Sn')\",OTHERS,False\nmp-758596,\"{'Li': 4.0, 'Fe': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,False\nmp-36484,\"{'V': 16.0, 'Ni': 4.0, 'O': 30.0}\",\"('Ni', 'O', 'V')\",OTHERS,False\nmvc-4401,\"{'Ge': 12.0, 'W': 8.0, 'O': 48.0}\",\"('Ge', 'O', 'W')\",OTHERS,False\nmp-557635,\"{'Sb': 16.0, 'Te': 8.0, 'Se': 8.0, 'F': 80.0}\",\"('F', 'Sb', 'Se', 'Te')\",OTHERS,False\nmp-1222222,\"{'Mn': 4.0, 'Ga': 3.0, 'Co': 8.0, 'Ge': 1.0}\",\"('Co', 'Ga', 'Ge', 'Mn')\",OTHERS,False\nmp-1099847,\"{'Sr': 4.0, 'Ca': 28.0, 'Fe': 4.0, 'Co': 28.0, 'O': 80.0}\",\"('Ca', 'Co', 'Fe', 'O', 'Sr')\",OTHERS,False\nmp-849364,\"{'Li': 4.0, 'Mn': 4.0, 'P': 4.0, 'H': 24.0, 'O': 30.0}\",\"('H', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1229221,\"{'As': 8.0, 'Se': 12.0, 'S': 12.0, 'N': 16.0, 'F': 48.0}\",\"('As', 'F', 'N', 'S', 'Se')\",OTHERS,False\nmp-1218095,\"{'Sr': 1.0, 'Nd': 1.0, 'Ga': 1.0, 'O': 4.0}\",\"('Ga', 'Nd', 'O', 'Sr')\",OTHERS,False\nmp-1025937,\"{'Te': 4.0, 'Mo': 3.0, 'S': 2.0}\",\"('Mo', 'S', 'Te')\",OTHERS,False\nmp-1023939,\"{'Mo': 2.0, 'S': 4.0}\",\"('Mo', 'S')\",OTHERS,False\nmp-1063133,\"{'Ca': 1.0, 'Pd': 2.0}\",\"('Ca', 'Pd')\",OTHERS,False\nmp-568627,\"{'Rb': 8.0, 'Tm': 2.0, 'I': 12.0}\",\"('I', 'Rb', 'Tm')\",OTHERS,False\nmp-769301,\"{'Ca': 16.0, 'Ta': 8.0, 'O': 36.0}\",\"('Ca', 'O', 'Ta')\",OTHERS,False\nmp-1210989,\"{'Li': 12.0, 'Er': 12.0, 'Te': 8.0, 'O': 48.0}\",\"('Er', 'Li', 'O', 'Te')\",OTHERS,False\nmp-642729,\"{'La': 1.0, 'H': 3.0, 'C': 3.0, 'O': 6.0}\",\"('C', 'H', 'La', 'O')\",OTHERS,False\nmp-763252,\"{'Li': 2.0, 'Sb': 2.0, 'W': 2.0, 'O': 12.0}\",\"('Li', 'O', 'Sb', 'W')\",OTHERS,False\nmp-1202794,\"{'Si': 20.0, 'H': 114.0, 'C': 38.0}\",\"('C', 'H', 'Si')\",OTHERS,False\nmp-759168,\"{'Li': 6.0, 'V': 2.0, 'O': 4.0, 'F': 4.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-1198240,\"{'La': 10.0, 'Sn': 20.0, 'Ir': 8.0}\",\"('Ir', 'La', 'Sn')\",OTHERS,False\nmp-1079156,\"{'Ag': 2.0, 'Cl': 2.0, 'O': 4.0}\",\"('Ag', 'Cl', 'O')\",OTHERS,False\nmp-1475,\"{'Be': 12.0, 'Mo': 1.0}\",\"('Be', 'Mo')\",OTHERS,False\nmp-542610,\"{'Mo': 16.0, 'O': 44.0}\",\"('Mo', 'O')\",OTHERS,False\nmp-1195057,\"{'K': 6.0, 'Ba': 6.0, 'Li': 4.0, 'Al': 8.0, 'B': 12.0, 'O': 40.0, 'F': 2.0}\",\"('Al', 'B', 'Ba', 'F', 'K', 'Li', 'O')\",OTHERS,False\nmp-19391,\"{'Na': 1.0, 'V': 1.0, 'O': 2.0}\",\"('Na', 'O', 'V')\",OTHERS,False\nmp-1096653,\"{'Y': 2.0, 'Ag': 1.0, 'Pb': 1.0}\",\"('Ag', 'Pb', 'Y')\",OTHERS,False\nmp-1217891,\"{'Ta': 1.0, 'Ti': 1.0, 'Al': 6.0}\",\"('Al', 'Ta', 'Ti')\",OTHERS,False\nmp-850747,\"{'Li': 4.0, 'Fe': 8.0, 'P': 8.0, 'H': 4.0, 'O': 32.0}\",\"('Fe', 'H', 'Li', 'O', 'P')\",OTHERS,False\nmp-761592,\"{'Li': 3.0, 'Fe': 2.0, 'Co': 1.0, 'O': 6.0}\",\"('Co', 'Fe', 'Li', 'O')\",OTHERS,False\nmp-1174578,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-13452,\"{'Be': 1.0, 'Pd': 2.0}\",\"('Be', 'Pd')\",OTHERS,False\nmp-979273,\"{'Te': 1.0, 'Pd': 3.0}\",\"('Pd', 'Te')\",OTHERS,False\nmp-1180197,\"{'Mo': 2.0, 'O': 10.0}\",\"('Mo', 'O')\",OTHERS,False\nmp-1182486,\"{'Ba': 2.0, 'H': 32.0, 'O': 20.0}\",\"('Ba', 'H', 'O')\",OTHERS,False\nmp-850763,\"{'Li': 4.0, 'Cr': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-961724,\"{'Zr': 1.0, 'Bi': 1.0, 'Rh': 1.0}\",\"('Bi', 'Rh', 'Zr')\",OTHERS,False\nmp-1183367,\"{'Ba': 1.0, 'Eu': 3.0}\",\"('Ba', 'Eu')\",OTHERS,False\nmp-1173098,\"{'Ba': 6.0, 'Ti': 2.0, 'Nb': 10.0, 'Si': 8.0, 'O': 51.0}\",\"('Ba', 'Nb', 'O', 'Si', 'Ti')\",OTHERS,False\nmp-1224687,\"{'Fe': 2.0, 'Cu': 1.0, 'S': 3.0}\",\"('Cu', 'Fe', 'S')\",OTHERS,False\nmp-636253,\"{'Gd': 1.0, 'Cu': 5.0}\",\"('Cu', 'Gd')\",OTHERS,False\nmp-1073122,\"{'Mg': 8.0, 'Si': 12.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1211854,\"{'La': 4.0, 'Mo': 4.0, 'O': 20.0}\",\"('La', 'Mo', 'O')\",OTHERS,False\nmp-569603,\"{'Ba': 4.0, 'Ca': 16.0, 'Cu': 8.0, 'N': 16.0}\",\"('Ba', 'Ca', 'Cu', 'N')\",OTHERS,False\nmp-1229286,\"{'Ba': 51.0, 'Pd': 24.0, 'O': 10.0}\",\"('Ba', 'O', 'Pd')\",OTHERS,False\nmp-758291,\"{'Li': 6.0, 'Sn': 6.0, 'P': 8.0, 'O': 32.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,False\nmp-1190320,\"{'Al': 12.0, 'Ge': 10.0}\",\"('Al', 'Ge')\",OTHERS,False\nmp-1097596,\"{'Sc': 2.0, 'Zn': 1.0, 'Rh': 1.0}\",\"('Rh', 'Sc', 'Zn')\",OTHERS,False\nmp-630227,{'C': 60.0},\"('C',)\",OTHERS,False\nmp-1111483,\"{'Rb': 2.0, 'Cu': 1.0, 'Pd': 1.0, 'F': 6.0}\",\"('Cu', 'F', 'Pd', 'Rb')\",OTHERS,False\nmp-2484,\"{'Sn': 3.0, 'Sm': 1.0}\",\"('Sm', 'Sn')\",OTHERS,False\nmp-552488,\"{'O': 2.0, 'La': 2.0, 'Cu': 2.0, 'Se': 2.0}\",\"('Cu', 'La', 'O', 'Se')\",OTHERS,False\nmp-7394,\"{'S': 4.0, 'Ge': 1.0, 'Ag': 2.0, 'Ba': 1.0}\",\"('Ag', 'Ba', 'Ge', 'S')\",OTHERS,False\nmp-1017533,\"{'Y': 2.0, 'N': 2.0}\",\"('N', 'Y')\",OTHERS,False\nmp-1227818,\"{'Ba': 1.0, 'Sr': 11.0, 'Al': 4.0, 'O': 16.0, 'F': 4.0}\",\"('Al', 'Ba', 'F', 'O', 'Sr')\",OTHERS,False\nAl 55 Cu 25.5 Fe 12.5 Si 7,\"{'Al': 55.0, 'Cu': 25.5, 'Fe': 12.5, 'Si': 7.0}\",\"('Al', 'Cu', 'Fe', 'Si')\",AC,False\nmp-866216,\"{'Ca': 1.0, 'Dy': 1.0, 'Rh': 2.0}\",\"('Ca', 'Dy', 'Rh')\",OTHERS,False\nmp-15237,\"{'C': 10.0, 'Tb': 8.0}\",\"('C', 'Tb')\",OTHERS,False\nmp-985575,\"{'Li': 2.0, 'Ag': 2.0, 'C': 4.0, 'O': 8.0}\",\"('Ag', 'C', 'Li', 'O')\",OTHERS,False\nmp-619775,\"{'Y': 14.0, 'C': 4.0, 'I': 24.0, 'N': 2.0}\",\"('C', 'I', 'N', 'Y')\",OTHERS,False\nmp-1181787,\"{'Fe': 4.0, 'C': 8.0, 'Cl': 4.0, 'O': 18.0}\",\"('C', 'Cl', 'Fe', 'O')\",OTHERS,False\nmp-1247688,\"{'Ca': 8.0, 'Ti': 2.0, 'Mn': 6.0, 'O': 22.0}\",\"('Ca', 'Mn', 'O', 'Ti')\",OTHERS,False\nmvc-10123,\"{'Ho': 2.0, 'Zn': 2.0, 'Mo': 4.0, 'O': 12.0}\",\"('Ho', 'Mo', 'O', 'Zn')\",OTHERS,False\nmp-1202829,\"{'Ba': 8.0, 'Ga': 4.0, 'Bi': 4.0, 'Te': 20.0}\",\"('Ba', 'Bi', 'Ga', 'Te')\",OTHERS,False\nmp-1185600,\"{'Mg': 149.0, 'Os': 1.0}\",\"('Mg', 'Os')\",OTHERS,False\nmp-985594,\"{'Na': 2.0, 'Ag': 2.0, 'C': 4.0, 'O': 8.0}\",\"('Ag', 'C', 'Na', 'O')\",OTHERS,False\nmp-776355,\"{'Li': 1.0, 'V': 6.0, 'O': 7.0, 'F': 5.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-985540,\"{'Ac': 6.0, 'La': 2.0}\",\"('Ac', 'La')\",OTHERS,False\nmp-768377,\"{'Fe': 3.0, 'Cu': 3.0, 'P': 6.0, 'O': 24.0}\",\"('Cu', 'Fe', 'O', 'P')\",OTHERS,False\nmp-756230,\"{'Mn': 2.0, 'P': 2.0, 'H': 2.0, 'O': 10.0}\",\"('H', 'Mn', 'O', 'P')\",OTHERS,False\nmp-776335,\"{'Zr': 16.0, 'N': 16.0, 'O': 8.0}\",\"('N', 'O', 'Zr')\",OTHERS,False\nmp-1111497,\"{'K': 3.0, 'Bi': 1.0, 'Cl': 6.0}\",\"('Bi', 'Cl', 'K')\",OTHERS,False\nmp-1211590,\"{'La': 5.0, 'Mg': 41.0}\",\"('La', 'Mg')\",OTHERS,False\nmp-755480,\"{'K': 2.0, 'Rb': 2.0, 'O': 2.0}\",\"('K', 'O', 'Rb')\",OTHERS,False\nmp-639496,\"{'Eu': 2.0, 'In': 4.0, 'Au': 2.0}\",\"('Au', 'Eu', 'In')\",OTHERS,False\nmp-28606,\"{'K': 6.0, 'Se': 26.0, 'Au': 2.0}\",\"('Au', 'K', 'Se')\",OTHERS,False\nmp-17673,\"{'Lu': 12.0, 'O': 8.0, 'F': 20.0}\",\"('F', 'Lu', 'O')\",OTHERS,False\nmp-1223171,\"{'Li': 19.0, 'V': 12.0, 'O': 32.0}\",\"('Li', 'O', 'V')\",OTHERS,False\nmp-1100171,\"{'Cs': 1.0, 'Mg': 6.0, 'Mo': 1.0}\",\"('Cs', 'Mg', 'Mo')\",OTHERS,False\nmp-22211,\"{'V': 6.0, 'Sn': 2.0}\",\"('Sn', 'V')\",OTHERS,False\nmp-1096622,\"{'Li': 2.0, 'Ca': 1.0, 'Al': 1.0}\",\"('Al', 'Ca', 'Li')\",OTHERS,False\nmp-773466,\"{'Na': 4.0, 'Cu': 12.0, 'O': 16.0}\",\"('Cu', 'Na', 'O')\",OTHERS,False\nmvc-10947,\"{'Al': 2.0, 'Fe': 4.0, 'O': 8.0}\",\"('Al', 'Fe', 'O')\",OTHERS,False\nmp-530048,\"{'Fe': 21.0, 'O': 32.0}\",\"('Fe', 'O')\",OTHERS,False\nmp-1236246,\"{'Li': 1.0, 'Ti': 1.0, 'O': 2.0}\",\"('Li', 'O', 'Ti')\",OTHERS,False\nmp-772237,\"{'Na': 2.0, 'Li': 10.0, 'Fe': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Fe', 'Li', 'Na', 'O', 'P')\",OTHERS,False\nmp-23745,\"{'H': 16.0, 'N': 4.0, 'O': 8.0, 'Se': 2.0}\",\"('H', 'N', 'O', 'Se')\",OTHERS,False\nZn 75 Sc 15 Mn 10,\"{'Zn': 75.0, 'Sc': 15.0, 'Mn': 10.0}\",\"('Mn', 'Sc', 'Zn')\",QC,False\nmp-769502,\"{'Li': 4.0, 'Nb': 2.0, 'Fe': 3.0, 'O': 10.0}\",\"('Fe', 'Li', 'Nb', 'O')\",OTHERS,False\nmp-1187598,\"{'Tm': 2.0, 'Mg': 1.0, 'Cd': 1.0}\",\"('Cd', 'Mg', 'Tm')\",OTHERS,False\nmp-19,{'Te': 3.0},\"('Te',)\",OTHERS,False\nmp-40685,\"{'Na': 2.0, 'O': 28.0, 'Sb': 4.0, 'Sm': 6.0, 'Ti': 4.0}\",\"('Na', 'O', 'Sb', 'Sm', 'Ti')\",OTHERS,False\nmp-1196623,\"{'Cu': 6.0, 'Ag': 4.0, 'Bi': 14.0, 'Pb': 6.0, 'S': 32.0}\",\"('Ag', 'Bi', 'Cu', 'Pb', 'S')\",OTHERS,False\nmvc-9221,\"{'Mg': 4.0, 'Fe': 4.0, 'Bi': 4.0, 'O': 20.0}\",\"('Bi', 'Fe', 'Mg', 'O')\",OTHERS,False\nmp-6067,\"{'O': 48.0, 'S': 12.0, 'Cd': 8.0, 'Tl': 8.0}\",\"('Cd', 'O', 'S', 'Tl')\",OTHERS,False\nmp-1180390,\"{'Mg': 4.0, 'Te': 4.0, 'I': 24.0, 'O': 24.0}\",\"('I', 'Mg', 'O', 'Te')\",OTHERS,False\nmp-1217196,\"{'Ti': 5.0, 'Mn': 1.0, 'S': 10.0}\",\"('Mn', 'S', 'Ti')\",OTHERS,False\nmp-581868,\"{'Na': 4.0, 'Y': 4.0, 'I': 16.0, 'O': 48.0}\",\"('I', 'Na', 'O', 'Y')\",OTHERS,False\nmp-1205022,\"{'Cs': 16.0, 'Te': 112.0}\",\"('Cs', 'Te')\",OTHERS,False\nmp-1226853,\"{'Ce': 5.0, 'Bi': 1.0, 'Sb': 4.0}\",\"('Bi', 'Ce', 'Sb')\",OTHERS,False\nmp-542459,\"{'Ba': 4.0, 'Ge': 8.0, 'Ga': 8.0, 'O': 32.0}\",\"('Ba', 'Ga', 'Ge', 'O')\",OTHERS,False\nmp-849731,\"{'Li': 1.0, 'Ti': 1.0, 'V': 2.0, 'O': 6.0}\",\"('Li', 'O', 'Ti', 'V')\",OTHERS,False\nmp-504149,\"{'Fe': 12.0, 'P': 12.0, 'O': 48.0}\",\"('Fe', 'O', 'P')\",OTHERS,False\nmp-1187702,\"{'V': 1.0, 'W': 3.0}\",\"('V', 'W')\",OTHERS,False\nmp-556046,\"{'Sr': 2.0, 'Ga': 4.0, 'B': 4.0, 'O': 14.0}\",\"('B', 'Ga', 'O', 'Sr')\",OTHERS,False\nmp-1222250,\"{'Mg': 4.0, 'Al': 2.0, 'H': 12.0, 'C': 1.0, 'O': 18.0}\",\"('Al', 'C', 'H', 'Mg', 'O')\",OTHERS,False\nmvc-10619,\"{'V': 6.0, 'P': 6.0, 'O': 26.0}\",\"('O', 'P', 'V')\",OTHERS,False\nmp-22765,\"{'Si': 6.0, 'Pd': 2.0, 'Eu': 4.0}\",\"('Eu', 'Pd', 'Si')\",OTHERS,False\nmp-1009746,\"{'Sc': 1.0, 'P': 1.0}\",\"('P', 'Sc')\",OTHERS,False\nmp-546810,\"{'O': 4.0, 'C': 4.0, 'Rb': 2.0}\",\"('C', 'O', 'Rb')\",OTHERS,False\nmp-685544,\"{'Ga': 24.0, 'Cu': 12.0, 'O': 48.0}\",\"('Cu', 'Ga', 'O')\",OTHERS,False\nmp-28168,\"{'C': 2.0, 'Br': 28.0, 'Th': 12.0}\",\"('Br', 'C', 'Th')\",OTHERS,False\nmp-1226584,\"{'Ce': 1.0, 'Si': 1.0, 'B': 1.0, 'Rh': 3.0}\",\"('B', 'Ce', 'Rh', 'Si')\",OTHERS,False\nmp-1246633,\"{'Mg': 2.0, 'V': 2.0, 'Ga': 2.0, 'S': 8.0}\",\"('Ga', 'Mg', 'S', 'V')\",OTHERS,False\nmp-1178260,\"{'Fe': 10.0, 'O': 14.0, 'F': 6.0}\",\"('F', 'Fe', 'O')\",OTHERS,False\nmvc-3347,\"{'Mg': 4.0, 'Cr': 4.0, 'O': 8.0}\",\"('Cr', 'Mg', 'O')\",OTHERS,False\nmp-1029730,\"{'Ca': 8.0, 'Hf': 2.0, 'N': 8.0}\",\"('Ca', 'Hf', 'N')\",OTHERS,False\nmp-1032466,\"{'Sr': 1.0, 'Mg': 6.0, 'Mn': 1.0, 'O': 8.0}\",\"('Mg', 'Mn', 'O', 'Sr')\",OTHERS,False\nmvc-7046,\"{'Si': 16.0, 'Mo': 8.0, 'O': 48.0}\",\"('Mo', 'O', 'Si')\",OTHERS,False\nZn 76 Mg 17 Hf 7,\"{'Zn': 76.0, 'Mg': 17.0, 'Hf': 7.0}\",\"('Hf', 'Mg', 'Zn')\",QC,False\nmp-1245175,\"{'Ta': 50.0, 'N': 50.0}\",\"('N', 'Ta')\",OTHERS,False\nmp-755110,\"{'Cs': 4.0, 'Mn': 2.0, 'O': 8.0}\",\"('Cs', 'Mn', 'O')\",OTHERS,False\nmp-1036013,\"{'Y': 1.0, 'Mg': 14.0, 'Cu': 1.0, 'O': 16.0}\",\"('Cu', 'Mg', 'O', 'Y')\",OTHERS,False\nmp-1200491,\"{'K': 8.0, 'Al': 8.0, 'P': 8.0, 'O': 40.0}\",\"('Al', 'K', 'O', 'P')\",OTHERS,False\nmp-9596,\"{'B': 8.0, 'La': 2.0, 'Ir': 8.0}\",\"('B', 'Ir', 'La')\",OTHERS,False\nmp-865779,\"{'Ga': 2.0, 'Co': 1.0, 'Ru': 1.0}\",\"('Co', 'Ga', 'Ru')\",OTHERS,False\nmp-1032523,\"{'Mg': 6.0, 'Fe': 1.0, 'Si': 1.0, 'O': 8.0}\",\"('Fe', 'Mg', 'O', 'Si')\",OTHERS,False\nmp-1216131,\"{'Y': 2.0, 'O': 3.0, 'F': 3.0}\",\"('F', 'O', 'Y')\",OTHERS,False\nmp-1747,\"{'K': 2.0, 'Te': 1.0}\",\"('K', 'Te')\",OTHERS,False\nmp-1214225,\"{'C': 16.0, 'S': 24.0, 'I': 8.0}\",\"('C', 'I', 'S')\",OTHERS,False\nmp-1246689,\"{'Mg': 2.0, 'Ga': 2.0, 'Mo': 2.0, 'S': 8.0}\",\"('Ga', 'Mg', 'Mo', 'S')\",OTHERS,False\nmp-637263,\"{'Zr': 10.0, 'Al': 2.0, 'Ni': 8.0}\",\"('Al', 'Ni', 'Zr')\",OTHERS,False\nmp-1191489,\"{'Ca': 2.0, 'Al': 4.0, 'O': 8.0, 'F': 8.0}\",\"('Al', 'Ca', 'F', 'O')\",OTHERS,False\nmp-757982,\"{'In': 4.0, 'H': 12.0, 'O': 12.0}\",\"('H', 'In', 'O')\",OTHERS,False\nmp-765894,\"{'Li': 5.0, 'V': 1.0, 'F': 8.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-541221,\"{'Ba': 6.0, 'N': 12.0, 'O': 30.0, 'H': 12.0}\",\"('Ba', 'H', 'N', 'O')\",OTHERS,False\nmp-2723,\"{'O': 4.0, 'Ir': 2.0}\",\"('Ir', 'O')\",OTHERS,False\nmp-562631,\"{'K': 4.0, 'S': 2.0, 'O': 8.0}\",\"('K', 'O', 'S')\",OTHERS,False\nmp-1111490,\"{'K': 3.0, 'In': 1.0, 'Br': 6.0}\",\"('Br', 'In', 'K')\",OTHERS,False\nmp-571350,\"{'Ba': 2.0, 'As': 4.0, 'Pd': 4.0}\",\"('As', 'Ba', 'Pd')\",OTHERS,False\nmp-1184672,\"{'Hf': 2.0, 'Ti': 6.0}\",\"('Hf', 'Ti')\",OTHERS,False\nmp-632692,\"{'Li': 1.0, 'Fe': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-1205470,\"{'Ca': 4.0, 'Hg': 4.0, 'H': 64.0, 'Br': 16.0, 'O': 32.0}\",\"('Br', 'Ca', 'H', 'Hg', 'O')\",OTHERS,False\nmp-41742,\"{'Ca': 2.0, 'Nd': 2.0, 'Ti': 2.0, 'Mn': 2.0, 'O': 12.0}\",\"('Ca', 'Mn', 'Nd', 'O', 'Ti')\",OTHERS,False\nmp-1818,\"{'F': 4.0, 'Si': 1.0}\",\"('F', 'Si')\",OTHERS,False\nmp-2075,\"{'N': 8.0, 'Si': 6.0}\",\"('N', 'Si')\",OTHERS,False\nmp-18649,\"{'Mn': 6.0, 'Ge': 6.0, 'Rh': 6.0}\",\"('Ge', 'Mn', 'Rh')\",OTHERS,False\nmp-1079867,\"{'Li': 4.0, 'Pr': 2.0, 'Ge': 2.0}\",\"('Ge', 'Li', 'Pr')\",OTHERS,False\nmp-30564,\"{'Co': 2.0, 'Th': 2.0}\",\"('Co', 'Th')\",OTHERS,False\nmp-1219972,\"{'Pr': 2.0, 'Ga': 2.0, 'Si': 2.0}\",\"('Ga', 'Pr', 'Si')\",OTHERS,False\nmp-753276,\"{'Li': 2.0, 'Co': 1.0, 'Ni': 1.0, 'O': 4.0}\",\"('Co', 'Li', 'Ni', 'O')\",OTHERS,False\nmp-23201,\"{'Ba': 1.0, 'Bi': 3.0}\",\"('Ba', 'Bi')\",OTHERS,False\nmp-569935,\"{'La': 3.0, 'B': 2.0, 'N': 4.0}\",\"('B', 'La', 'N')\",OTHERS,False\nmp-504732,\"{'Mn': 12.0, 'Ti': 12.0, 'O': 4.0}\",\"('Mn', 'O', 'Ti')\",OTHERS,False\nmp-1076782,\"{'Ba': 1.0, 'Co': 1.0, 'O': 3.0}\",\"('Ba', 'Co', 'O')\",OTHERS,False\nmp-757068,\"{'Li': 4.0, 'Cr': 1.0, 'Ni': 3.0, 'P': 4.0, 'O': 16.0}\",\"('Cr', 'Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-770610,\"{'Li': 8.0, 'Al': 4.0, 'Co': 4.0, 'O': 16.0}\",\"('Al', 'Co', 'Li', 'O')\",OTHERS,False\nmp-1263325,\"{'Al': 1.0, 'Ni': 1.0, 'F': 5.0}\",\"('Al', 'F', 'Ni')\",OTHERS,False\nmp-698018,\"{'Li': 2.0, 'Al': 2.0, 'Si': 4.0, 'H': 4.0, 'O': 14.0}\",\"('Al', 'H', 'Li', 'O', 'Si')\",OTHERS,False\nmp-1216900,\"{'Tl': 1.0, 'V': 2.0, 'Cr': 3.0, 'S': 8.0}\",\"('Cr', 'S', 'Tl', 'V')\",OTHERS,False\nmp-760794,\"{'Li': 8.0, 'V': 8.0, 'F': 32.0}\",\"('F', 'Li', 'V')\",OTHERS,False\nmp-1229173,\"{'Ba': 12.0, 'Sm': 4.0, 'Al': 3.0, 'Co': 5.0, 'O': 30.0}\",\"('Al', 'Ba', 'Co', 'O', 'Sm')\",OTHERS,False\nmp-1221360,\"{'Na': 2.0, 'Nb': 2.0, 'W': 2.0, 'O': 13.0}\",\"('Na', 'Nb', 'O', 'W')\",OTHERS,False\nmp-756651,\"{'Li': 6.0, 'Mn': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Li', 'Mn', 'O', 'P')\",OTHERS,False\nmp-1217172,\"{'Ti': 5.0, 'S': 8.0}\",\"('S', 'Ti')\",OTHERS,False\nmp-1228335,\"{'Ba': 4.0, 'Ti': 1.0, 'Al': 3.0, 'P': 4.0, 'O': 20.0, 'F': 4.0}\",\"('Al', 'Ba', 'F', 'O', 'P', 'Ti')\",OTHERS,False\nmp-12570,\"{'Th': 1.0, 'B': 12.0}\",\"('B', 'Th')\",OTHERS,False\nmp-1086668,\"{'La': 2.0, 'Sn': 4.0, 'Rh': 2.0}\",\"('La', 'Rh', 'Sn')\",OTHERS,False\nmp-1244593,\"{'Ca': 2.0, 'Ti': 2.0, 'Bi': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Bi', 'Ca', 'O', 'P', 'Ti')\",OTHERS,False\nmp-1211455,\"{'K': 2.0, 'Gd': 2.0, 'Mo': 4.0, 'O': 16.0}\",\"('Gd', 'K', 'Mo', 'O')\",OTHERS,False\nmp-1209439,\"{'Rb': 8.0, 'Ta': 6.0, 'O': 19.0}\",\"('O', 'Rb', 'Ta')\",OTHERS,False\nmp-1034435,\"{'Ba': 1.0, 'Mg': 14.0, 'Cr': 1.0, 'O': 16.0}\",\"('Ba', 'Cr', 'Mg', 'O')\",OTHERS,False\nmp-644841,\"{'K': 8.0, 'Pd': 4.0, 'N': 16.0, 'O': 32.0}\",\"('K', 'N', 'O', 'Pd')\",OTHERS,False\nmp-1228840,\"{'Al': 1.0, 'V': 1.0, 'Ni': 2.0}\",\"('Al', 'Ni', 'V')\",OTHERS,False\nmp-20024,\"{'Cu': 2.0, 'Sn': 2.0, 'La': 2.0}\",\"('Cu', 'La', 'Sn')\",OTHERS,False\nmp-707897,\"{'Ca': 4.0, 'Al': 2.0, 'H': 22.0, 'C': 1.0, 'O': 20.0}\",\"('Al', 'C', 'Ca', 'H', 'O')\",OTHERS,False\nmp-1221811,\"{'Mn': 3.0, 'Ni': 1.0, 'O': 4.0}\",\"('Mn', 'Ni', 'O')\",OTHERS,False\nmp-776476,\"{'Cd': 4.0, 'Fe': 4.0, 'O': 12.0}\",\"('Cd', 'Fe', 'O')\",OTHERS,False\nmp-1192549,\"{'Ba': 4.0, 'U': 2.0, 'Cu': 8.0, 'Se': 12.0}\",\"('Ba', 'Cu', 'Se', 'U')\",OTHERS,False\nmp-14324,\"{'Be': 4.0, 'O': 10.0, 'Na': 8.0, 'K': 4.0}\",\"('Be', 'K', 'Na', 'O')\",OTHERS,False\nAl 75 Ru 10 Pd 15,\"{'Al': 75.0, 'Ru': 10.0, 'Pd': 15.0}\",\"('Al', 'Pd', 'Ru')\",QC,False\nmp-1218002,\"{'Ta': 2.0, 'Mo': 1.0, 'S': 6.0}\",\"('Mo', 'S', 'Ta')\",OTHERS,False\nmp-561500,\"{'Cs': 4.0, 'Na': 2.0, 'V': 2.0, 'F': 12.0}\",\"('Cs', 'F', 'Na', 'V')\",OTHERS,False\nmp-530214,\"{'Fe': 16.0, 'Ni': 8.0, 'O': 32.0}\",\"('Fe', 'Ni', 'O')\",OTHERS,False\nmp-1224887,\"{'Fe': 6.0, 'Ni': 6.0, 'B': 4.0}\",\"('B', 'Fe', 'Ni')\",OTHERS,False\nmp-643378,\"{'Cu': 1.0, 'H': 4.0, 'Pb': 2.0, 'Cl': 2.0, 'O': 4.0}\",\"('Cl', 'Cu', 'H', 'O', 'Pb')\",OTHERS,False\nmp-1185057,\"{'K': 3.0, 'Yb': 1.0}\",\"('K', 'Yb')\",OTHERS,False\nmp-698602,\"{'Sr': 5.0, 'La': 5.0, 'Mn': 9.0, 'Cu': 1.0, 'O': 30.0}\",\"('Cu', 'La', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-1095643,\"{'Nb': 4.0, 'Cr': 8.0}\",\"('Cr', 'Nb')\",OTHERS,False\nmp-586092,\"{'Li': 12.0, 'Fe': 20.0, 'O': 32.0}\",\"('Fe', 'Li', 'O')\",OTHERS,False\nmp-760397,\"{'Li': 1.0, 'Co': 1.0, 'Cu': 1.0, 'O': 4.0}\",\"('Co', 'Cu', 'Li', 'O')\",OTHERS,False\nmp-1104271,\"{'Ag': 6.0, 'Sb': 2.0, 'S': 6.0}\",\"('Ag', 'S', 'Sb')\",OTHERS,False\nmp-866192,\"{'Yb': 1.0, 'Li': 2.0, 'Sn': 1.0}\",\"('Li', 'Sn', 'Yb')\",OTHERS,False\nmp-1105780,\"{'Bi': 8.0, 'O': 12.0}\",\"('Bi', 'O')\",OTHERS,False\nmp-26944,\"{'O': 20.0, 'P': 6.0, 'Sn': 4.0}\",\"('O', 'P', 'Sn')\",OTHERS,False\nmp-753438,\"{'Li': 8.0, 'V': 4.0, 'O': 8.0, 'F': 4.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,False\nmp-554272,\"{'Cu': 24.0, 'Sb': 8.0, 'S': 24.0}\",\"('Cu', 'S', 'Sb')\",OTHERS,False\nmp-1103730,\"{'Na': 4.0, 'Mg': 1.0, 'Ge': 2.0, 'Se': 6.0}\",\"('Ge', 'Mg', 'Na', 'Se')\",OTHERS,False\nmp-15889,\"{'O': 12.0, 'Ba': 4.0, 'La': 2.0, 'Ir': 2.0}\",\"('Ba', 'Ir', 'La', 'O')\",OTHERS,False\nmp-1209233,\"{'Sc': 22.0, 'Ga': 20.0}\",\"('Ga', 'Sc')\",OTHERS,False\nmp-1214747,\"{'Ba': 2.0, 'Ca': 2.0, 'Nd': 1.0, 'Ti': 2.0, 'Cu': 2.0, 'O': 12.0}\",\"('Ba', 'Ca', 'Cu', 'Nd', 'O', 'Ti')\",OTHERS,False\nmp-17968,\"{'O': 24.0, 'Na': 4.0, 'Ca': 2.0, 'V': 8.0}\",\"('Ca', 'Na', 'O', 'V')\",OTHERS,False\nmp-3928,\"{'O': 20.0, 'Na': 10.0, 'P': 6.0}\",\"('Na', 'O', 'P')\",OTHERS,False\nmp-559826,\"{'Er': 4.0, 'Cu': 4.0, 'S': 8.0}\",\"('Cu', 'Er', 'S')\",OTHERS,False\nmp-772060,\"{'Li': 3.0, 'Nb': 1.0, 'Fe': 3.0, 'O': 8.0}\",\"('Fe', 'Li', 'Nb', 'O')\",OTHERS,False\nmp-644278,\"{'Y': 2.0, 'H': 2.0, 'C': 1.0}\",\"('C', 'H', 'Y')\",OTHERS,False\nmp-1186783,\"{'Pt': 3.0, 'Cl': 1.0}\",\"('Cl', 'Pt')\",OTHERS,False\nmp-762271,\"{'Li': 2.0, 'V': 3.0, 'O': 6.0}\",\"('Li', 'O', 'V')\",OTHERS,False\nmp-1097261,\"{'Y': 1.0, 'Hf': 1.0, 'Rh': 2.0}\",\"('Hf', 'Rh', 'Y')\",OTHERS,False\nmp-1213717,\"{'Cs': 8.0, 'Sb': 4.0, 'F': 20.0}\",\"('Cs', 'F', 'Sb')\",OTHERS,False\nmp-622258,\"{'Rb': 4.0, 'V': 8.0, 'Sb': 4.0, 'O': 32.0}\",\"('O', 'Rb', 'Sb', 'V')\",OTHERS,False\nmp-555387,\"{'Ba': 4.0, 'Si': 8.0, 'B': 8.0, 'O': 32.0}\",\"('B', 'Ba', 'O', 'Si')\",OTHERS,False\nmp-554084,\"{'Ba': 1.0, 'Bi': 6.0, 'P': 4.0, 'O': 20.0}\",\"('Ba', 'Bi', 'O', 'P')\",OTHERS,False\nmp-771949,\"{'Ti': 1.0, 'Mn': 3.0, 'P': 4.0, 'O': 16.0}\",\"('Mn', 'O', 'P', 'Ti')\",OTHERS,False\nmp-1112417,\"{'K': 2.0, 'Ta': 1.0, 'Au': 1.0, 'F': 6.0}\",\"('Au', 'F', 'K', 'Ta')\",OTHERS,False\nmp-5400,\"{'Si': 2.0, 'Cr': 2.0, 'Th': 1.0}\",\"('Cr', 'Si', 'Th')\",OTHERS,False\nmp-756644,\"{'Li': 8.0, 'Fe': 7.0, 'O': 15.0}\",\"('Fe', 'Li', 'O')\",OTHERS,False\nmp-1223910,\"{'Ho': 2.0, 'Co': 2.0, 'Ni': 2.0}\",\"('Co', 'Ho', 'Ni')\",OTHERS,False\nmp-1188063,\"{'Yb': 3.0, 'Ce': 1.0}\",\"('Ce', 'Yb')\",OTHERS,False\nmp-4533,\"{'O': 6.0, 'Na': 4.0, 'Si': 2.0}\",\"('Na', 'O', 'Si')\",OTHERS,False\nmp-621981,\"{'Tl': 8.0, 'Ag': 8.0, 'C': 16.0, 'N': 16.0}\",\"('Ag', 'C', 'N', 'Tl')\",OTHERS,False\nmp-559985,\"{'Ca': 4.0, 'P': 16.0, 'O': 44.0}\",\"('Ca', 'O', 'P')\",OTHERS,False\nmp-1206259,\"{'Tm': 3.0, 'Mg': 3.0, 'Au': 3.0}\",\"('Au', 'Mg', 'Tm')\",OTHERS,False\nmp-1033791,\"{'Mg': 14.0, 'Cr': 1.0, 'C': 1.0, 'O': 16.0}\",\"('C', 'Cr', 'Mg', 'O')\",OTHERS,False\nmp-12962,\"{'Fe': 1.0, 'Zr': 6.0, 'Sb': 2.0}\",\"('Fe', 'Sb', 'Zr')\",OTHERS,False\nmp-27659,\"{'Pu': 1.0, 'Pt': 4.0}\",\"('Pt', 'Pu')\",OTHERS,False\nmp-976972,\"{'Ho': 1.0, 'Tm': 1.0, 'Cu': 2.0}\",\"('Cu', 'Ho', 'Tm')\",OTHERS,False\nmp-1175361,\"{'Li': 7.0, 'Mn': 4.0, 'Co': 1.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-865258,\"{'Tb': 1.0, 'Yb': 1.0, 'Rh': 2.0}\",\"('Rh', 'Tb', 'Yb')\",OTHERS,False\nmp-12664,\"{'Be': 12.0, 'Au': 1.0}\",\"('Au', 'Be')\",OTHERS,False\nmp-1077045,\"{'Ti': 4.0, 'H': 2.0}\",\"('H', 'Ti')\",OTHERS,False\nmp-1217848,\"{'Tb': 2.0, 'Cu': 6.0, 'S': 6.0}\",\"('Cu', 'S', 'Tb')\",OTHERS,False\nmp-1078823,\"{'Cs': 2.0, 'Zr': 1.0, 'Ag': 2.0, 'Te': 4.0}\",\"('Ag', 'Cs', 'Te', 'Zr')\",OTHERS,False\nmp-977372,\"{'Pm': 2.0, 'Sn': 1.0, 'Hg': 1.0}\",\"('Hg', 'Pm', 'Sn')\",OTHERS,False\nmp-1189442,\"{'Tm': 12.0, 'Pt': 4.0}\",\"('Pt', 'Tm')\",OTHERS,False\nmp-13771,\"{'O': 48.0, 'Sc': 8.0, 'Ge': 12.0, 'Cd': 12.0}\",\"('Cd', 'Ge', 'O', 'Sc')\",OTHERS,False\nmp-1104709,\"{'Sm': 1.0, 'Mn': 6.0, 'Sn': 6.0}\",\"('Mn', 'Sm', 'Sn')\",OTHERS,False\nmp-1111060,\"{'K': 1.0, 'Na': 2.0, 'Al': 1.0, 'F': 6.0}\",\"('Al', 'F', 'K', 'Na')\",OTHERS,False\nmp-560464,\"{'U': 4.0, 'Tl': 8.0, 'Te': 8.0, 'O': 32.0}\",\"('O', 'Te', 'Tl', 'U')\",OTHERS,False\nmp-1077866,\"{'Ba': 1.0, 'Zn': 1.0, 'B': 1.0, 'O': 3.0, 'F': 1.0}\",\"('B', 'Ba', 'F', 'O', 'Zn')\",OTHERS,False\nmp-990091,\"{'Ag': 8.0, 'W': 4.0, 'S': 16.0}\",\"('Ag', 'S', 'W')\",OTHERS,False\nmp-978096,\"{'Pu': 3.0, 'Au': 1.0}\",\"('Au', 'Pu')\",OTHERS,False\nmp-647867,\"{'La': 16.0, 'Mo': 28.0, 'O': 108.0}\",\"('La', 'Mo', 'O')\",OTHERS,False\nmp-1218971,\"{'Sm': 1.0, 'Th': 1.0, 'N': 2.0}\",\"('N', 'Sm', 'Th')\",OTHERS,False\nmp-30774,\"{'Mg': 1.0, 'Ni': 2.0, 'Sn': 1.0}\",\"('Mg', 'Ni', 'Sn')\",OTHERS,False\nmp-15359,\"{'B': 4.0, 'O': 12.0, 'Al': 3.0, 'Gd': 1.0}\",\"('Al', 'B', 'Gd', 'O')\",OTHERS,False\nmp-773158,\"{'Li': 12.0, 'Fe': 8.0, 'P': 12.0, 'O': 48.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,False\nmp-600032,\"{'Si': 48.0, 'O': 96.0}\",\"('O', 'Si')\",OTHERS,False\nmp-541628,\"{'Cs': 4.0, 'Te': 4.0, 'F': 20.0}\",\"('Cs', 'F', 'Te')\",OTHERS,False\nmp-753795,\"{'Li': 12.0, 'Bi': 4.0, 'O': 12.0}\",\"('Bi', 'Li', 'O')\",OTHERS,False\nmp-1073575,\"{'Mg': 4.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-685196,\"{'Tl': 4.0, 'Ag': 32.0, 'Te': 22.0}\",\"('Ag', 'Te', 'Tl')\",OTHERS,False\nmp-1078889,\"{'La': 2.0, 'As': 4.0, 'Ir': 4.0}\",\"('As', 'Ir', 'La')\",OTHERS,False\nmp-1177019,\"{'Li': 7.0, 'Fe': 1.0, 'Ni': 3.0, 'P': 6.0, 'O': 24.0}\",\"('Fe', 'Li', 'Ni', 'O', 'P')\",OTHERS,False\nmp-774797,\"{'Li': 8.0, 'Ti': 12.0, 'Zn': 4.0, 'O': 32.0}\",\"('Li', 'O', 'Ti', 'Zn')\",OTHERS,False\nmp-1227409,\"{'Bi': 1.0, 'As': 1.0, 'Pd': 6.0}\",\"('As', 'Bi', 'Pd')\",OTHERS,False\nmp-1008631,\"{'V': 1.0, 'Co': 1.0, 'Te': 1.0}\",\"('Co', 'Te', 'V')\",OTHERS,False\nmp-1114589,\"{'Eu': 1.0, 'Rb': 2.0, 'Na': 1.0, 'Cl': 6.0}\",\"('Cl', 'Eu', 'Na', 'Rb')\",OTHERS,False\nmp-1095431,\"{'Yb': 4.0, 'Ga': 4.0, 'Ni': 4.0}\",\"('Ga', 'Ni', 'Yb')\",OTHERS,False\nmp-1246312,\"{'Hf': 4.0, 'Cr': 4.0, 'Cu': 4.0, 'S': 16.0}\",\"('Cr', 'Cu', 'Hf', 'S')\",OTHERS,False\nmp-17092,\"{'Sc': 6.0, 'Pd': 6.0, 'Sn': 6.0}\",\"('Pd', 'Sc', 'Sn')\",OTHERS,False\nmp-1211829,\"{'K': 2.0, 'Ce': 2.0, 'S': 4.0, 'O': 16.0}\",\"('Ce', 'K', 'O', 'S')\",OTHERS,False\nmp-1226015,\"{'Co': 2.0, 'Ni': 2.0, 'Ge': 2.0}\",\"('Co', 'Ge', 'Ni')\",OTHERS,False\nmp-1112922,\"{'Cs': 2.0, 'Sb': 1.0, 'Au': 1.0, 'I': 6.0}\",\"('Au', 'Cs', 'I', 'Sb')\",OTHERS,False\nmp-1184372,\"{'Eu': 2.0, 'Sb': 1.0, 'Au': 1.0}\",\"('Au', 'Eu', 'Sb')\",OTHERS,False\nmp-866009,\"{'Dy': 1.0, 'Zr': 1.0, 'Ru': 2.0}\",\"('Dy', 'Ru', 'Zr')\",OTHERS,False\nmp-1202819,\"{'Sr': 8.0, 'B': 20.0, 'H': 4.0, 'O': 40.0}\",\"('B', 'H', 'O', 'Sr')\",OTHERS,False\nmp-776264,\"{'Li': 2.0, 'Fe': 2.0, 'F': 8.0}\",\"('F', 'Fe', 'Li')\",OTHERS,False\nmp-755520,\"{'Na': 1.0, 'Mn': 1.0, 'O': 2.0}\",\"('Mn', 'Na', 'O')\",OTHERS,False\nmp-2041,\"{'Zn': 4.0, 'Ho': 2.0}\",\"('Ho', 'Zn')\",OTHERS,False\nmp-866106,\"{'Ba': 2.0, 'Hg': 1.0, 'Pb': 1.0}\",\"('Ba', 'Hg', 'Pb')\",OTHERS,False\nmp-1094098,\"{'Na': 2.0, 'Ga': 2.0, 'Te': 4.0}\",\"('Ga', 'Na', 'Te')\",OTHERS,False\nmp-865141,\"{'Lu': 4.0, 'Ni': 13.0, 'C': 4.0}\",\"('C', 'Lu', 'Ni')\",OTHERS,False\nmp-1196551,\"{'Rb': 7.0, 'Sm': 1.0, 'Fe': 6.0, 'P': 8.0, 'O': 34.0}\",\"('Fe', 'O', 'P', 'Rb', 'Sm')\",OTHERS,False\nmp-8678,\"{'O': 8.0, 'F': 2.0, 'Na': 2.0, 'Al': 2.0, 'P': 2.0}\",\"('Al', 'F', 'Na', 'O', 'P')\",OTHERS,False\nmp-12160,\"{'Li': 24.0, 'B': 12.0, 'O': 36.0, 'Ho': 4.0}\",\"('B', 'Ho', 'Li', 'O')\",OTHERS,False\nmp-33333,\"{'Na': 2.0, 'Sb': 2.0, 'Se': 4.0}\",\"('Na', 'Sb', 'Se')\",OTHERS,False\nmp-555319,\"{'Rb': 12.0, 'Ti': 12.0, 'P': 20.0, 'O': 80.0}\",\"('O', 'P', 'Rb', 'Ti')\",OTHERS,False\nmp-4473,\"{'Cu': 8.0, 'Se': 8.0, 'Ba': 4.0}\",\"('Ba', 'Cu', 'Se')\",OTHERS,False\nmp-1232136,\"{'Pr': 4.0, 'Mg': 4.0, 'Se': 12.0}\",\"('Mg', 'Pr', 'Se')\",OTHERS,False\nmp-9947,\"{'C': 7.0, 'Si': 7.0}\",\"('C', 'Si')\",OTHERS,False\nmp-760113,\"{'Li': 8.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1208544,\"{'Tb': 8.0, 'Si': 16.0, 'C': 4.0, 'N': 24.0}\",\"('C', 'N', 'Si', 'Tb')\",OTHERS,False\nmp-18622,\"{'P': 8.0, 'Hf': 14.0}\",\"('Hf', 'P')\",OTHERS,False\nmp-19187,\"{'O': 14.0, 'Na': 2.0, 'P': 4.0, 'Co': 1.0, 'Zr': 1.0}\",\"('Co', 'Na', 'O', 'P', 'Zr')\",OTHERS,False\nmp-1206826,\"{'La': 4.0, 'Si': 1.0, 'I': 5.0}\",\"('I', 'La', 'Si')\",OTHERS,False\nmp-1023934,\"{'Mo': 1.0, 'Se': 2.0}\",\"('Mo', 'Se')\",OTHERS,False\nmp-644758,\"{'Fe': 8.0, 'Mo': 16.0, 'N': 4.0}\",\"('Fe', 'Mo', 'N')\",OTHERS,False\nmp-7152,\"{'Cu': 2.0, 'Se': 6.0, 'Zr': 2.0, 'Cs': 2.0}\",\"('Cs', 'Cu', 'Se', 'Zr')\",OTHERS,False\nmp-559307,\"{'Cu': 12.0, 'P': 16.0, 'S': 12.0, 'I': 12.0}\",\"('Cu', 'I', 'P', 'S')\",OTHERS,False\nmp-776250,\"{'Zr': 4.0, 'N': 4.0, 'O': 2.0}\",\"('N', 'O', 'Zr')\",OTHERS,False\nmp-1224088,\"{'Ho': 1.0, 'Al': 6.0, 'Fe': 6.0}\",\"('Al', 'Fe', 'Ho')\",OTHERS,False\nmp-757698,\"{'Mn': 23.0, 'Fe': 1.0, 'O': 32.0}\",\"('Fe', 'Mn', 'O')\",OTHERS,False\nmp-1211074,\"{'Li': 3.0, 'Pt': 3.0, 'O': 6.0}\",\"('Li', 'O', 'Pt')\",OTHERS,False\nmp-1069042,\"{'Eu': 1.0, 'Zn': 2.0, 'Sb': 2.0}\",\"('Eu', 'Sb', 'Zn')\",OTHERS,False\nmp-1225649,\"{'Er': 4.0, 'Al': 4.0, 'Fe': 4.0}\",\"('Al', 'Er', 'Fe')\",OTHERS,False\nmp-1198347,\"{'Tb': 12.0, 'Al': 86.0, 'Cr': 8.0}\",\"('Al', 'Cr', 'Tb')\",OTHERS,False\nmp-773011,\"{'Dy': 8.0, 'Ge': 8.0, 'O': 28.0}\",\"('Dy', 'Ge', 'O')\",OTHERS,False\nmvc-5939,\"{'Mg': 2.0, 'Ti': 1.0, 'W': 1.0, 'O': 6.0}\",\"('Mg', 'O', 'Ti', 'W')\",OTHERS,False\nmp-581877,\"{'Cs': 6.0, 'Zr': 3.0, 'Si': 18.0, 'O': 45.0}\",\"('Cs', 'O', 'Si', 'Zr')\",OTHERS,False\nmp-1185250,\"{'Er': 1.0, 'Mg': 149.0}\",\"('Er', 'Mg')\",OTHERS,False\nmp-26173,\"{'Li': 1.0, 'Bi': 3.0, 'P': 2.0, 'O': 10.0}\",\"('Bi', 'Li', 'O', 'P')\",OTHERS,False\nmp-1217056,\"{'U': 7.0, 'V': 5.0, 'Ag': 3.0, 'O': 35.0}\",\"('Ag', 'O', 'U', 'V')\",OTHERS,False\nmp-1199352,\"{'Sb': 4.0, 'Xe': 4.0, 'F': 28.0}\",\"('F', 'Sb', 'Xe')\",OTHERS,False\nmp-1094395,\"{'Mg': 6.0, 'Ti': 2.0}\",\"('Mg', 'Ti')\",OTHERS,False\nmp-1215095,\"{'B': 8.0, 'N': 4.0, 'O': 22.0}\",\"('B', 'N', 'O')\",OTHERS,False\nmp-1174569,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1208866,\"{'Si': 2.0, 'P': 4.0, 'Ir': 1.0}\",\"('Ir', 'P', 'Si')\",OTHERS,False\nmp-1201801,\"{'Np': 4.0, 'Co': 2.0, 'O': 16.0, 'F': 20.0}\",\"('Co', 'F', 'Np', 'O')\",OTHERS,False\nmp-1173004,\"{'Mo': 6.0, 'O': 16.0}\",\"('Mo', 'O')\",OTHERS,False\nmp-1186313,\"{'Nd': 1.0, 'Ho': 1.0, 'Zn': 2.0}\",\"('Ho', 'Nd', 'Zn')\",OTHERS,False\nmp-1227010,\"{'Ce': 6.0, 'Al': 3.0, 'Ga': 3.0, 'Ni': 6.0}\",\"('Al', 'Ce', 'Ga', 'Ni')\",OTHERS,False\nmp-1218506,\"{'Sr': 4.0, 'Cu': 3.0, 'Ni': 1.0, 'O': 8.0}\",\"('Cu', 'Ni', 'O', 'Sr')\",OTHERS,False\nmp-573471,\"{'Li': 85.0, 'Sn': 20.0}\",\"('Li', 'Sn')\",OTHERS,False\nmp-1213686,\"{'Cs': 1.0, 'Cr': 1.0, 'Mo': 2.0, 'O': 8.0}\",\"('Cr', 'Cs', 'Mo', 'O')\",OTHERS,False\nmp-6198,\"{'O': 32.0, 'Si': 8.0, 'Ga': 8.0, 'Sr': 4.0}\",\"('Ga', 'O', 'Si', 'Sr')\",OTHERS,False\nmp-722382,\"{'P': 4.0, 'H': 44.0, 'C': 4.0, 'N': 8.0, 'O': 16.0}\",\"('C', 'H', 'N', 'O', 'P')\",OTHERS,False\nmp-21007,\"{'Sn': 3.0, 'Sm': 3.0, 'Pt': 3.0}\",\"('Pt', 'Sm', 'Sn')\",OTHERS,False\nmp-1244820,\"{'Sr': 4.0, 'Y': 2.0, 'Bi': 4.0, 'O': 14.0}\",\"('Bi', 'O', 'Sr', 'Y')\",OTHERS,False\nmp-558801,\"{'K': 4.0, 'Pr': 8.0, 'Cl': 18.0, 'O': 4.0}\",\"('Cl', 'K', 'O', 'Pr')\",OTHERS,False\nmp-1215660,\"{'Zn': 1.0, 'Hg': 4.0, 'Te': 5.0}\",\"('Hg', 'Te', 'Zn')\",OTHERS,False\nmp-530409,\"{'Bi': 20.0, 'Cl': 36.0, 'Hf': 6.0}\",\"('Bi', 'Cl', 'Hf')\",OTHERS,False\nmp-1977,\"{'Sn': 3.0, 'Nd': 1.0}\",\"('Nd', 'Sn')\",OTHERS,False\nmp-559588,\"{'Cd': 2.0, 'Sb': 2.0, 'S': 4.0, 'Br': 2.0}\",\"('Br', 'Cd', 'S', 'Sb')\",OTHERS,False\nmp-1025283,\"{'Pr': 1.0, 'In': 5.0, 'Rh': 1.0}\",\"('In', 'Pr', 'Rh')\",OTHERS,False\nmp-764927,\"{'Li': 12.0, 'Cr': 3.0, 'P': 9.0, 'O': 36.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-1225944,\"{'Cs': 2.0, 'Sb': 2.0, 'W': 2.0, 'O': 12.0}\",\"('Cs', 'O', 'Sb', 'W')\",OTHERS,False\nmp-1096132,\"{'Y': 2.0, 'Hg': 1.0, 'Pd': 1.0}\",\"('Hg', 'Pd', 'Y')\",OTHERS,False\nmp-36890,\"{'Bi': 3.0, 'O': 7.0, 'Ta': 1.0}\",\"('Bi', 'O', 'Ta')\",OTHERS,False\nmp-27398,\"{'Tl': 8.0, 'Br': 16.0}\",\"('Br', 'Tl')\",OTHERS,False\nmp-555092,\"{'K': 12.0, 'W': 4.0, 'S': 12.0, 'Cl': 4.0, 'O': 4.0}\",\"('Cl', 'K', 'O', 'S', 'W')\",OTHERS,False\nmp-1174835,\"{'Li': 6.0, 'Mn': 3.0, 'Co': 1.0, 'O': 10.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-767627,\"{'Mn': 6.0, 'O': 4.0, 'F': 12.0}\",\"('F', 'Mn', 'O')\",OTHERS,False\nmvc-16348,\"{'Zn': 6.0, 'Sb': 12.0, 'O': 24.0}\",\"('O', 'Sb', 'Zn')\",OTHERS,False\nAu 12.2 In 6.3 Ca 3,\"{'Au': 12.2, 'In': 6.3, 'Ca': 3.0}\",\"('Au', 'Ca', 'In')\",AC,False\nmp-683660,\"{'Mn': 8.0, 'C': 32.0, 'Br': 8.0, 'O': 32.0}\",\"('Br', 'C', 'Mn', 'O')\",OTHERS,False\nmp-1175864,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-27008,\"{'Fe': 4.0, 'P': 6.0, 'O': 20.0}\",\"('Fe', 'O', 'P')\",OTHERS,False\nmp-567655,\"{'Sr': 2.0, 'C': 1.0, 'N': 2.0, 'Cl': 2.0}\",\"('C', 'Cl', 'N', 'Sr')\",OTHERS,False\nmp-531021,\"{'Li': 11.0, 'Mn': 13.0, 'O': 32.0}\",\"('Li', 'Mn', 'O')\",OTHERS,False\nmp-1247615,\"{'Ca': 16.0, 'Mn': 12.0, 'Cr': 4.0, 'O': 44.0}\",\"('Ca', 'Cr', 'Mn', 'O')\",OTHERS,False\nmp-570163,\"{'Tm': 8.0, 'Mn': 8.0, 'Sn': 14.0}\",\"('Mn', 'Sn', 'Tm')\",OTHERS,False\nmp-1073495,\"{'Mg': 4.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1218624,\"{'Sr': 5.0, 'Ca': 5.0, 'P': 6.0, 'Cl': 2.0, 'O': 24.0}\",\"('Ca', 'Cl', 'O', 'P', 'Sr')\",OTHERS,False\nmp-1183774,\"{'Co': 6.0, 'Sn': 2.0}\",\"('Co', 'Sn')\",OTHERS,False\nmp-1237806,\"{'H': 16.0, 'C': 4.0, 'N': 12.0, 'O': 14.0}\",\"('C', 'H', 'N', 'O')\",OTHERS,False\nmp-758769,\"{'Li': 4.0, 'Ni': 4.0, 'C': 8.0, 'O': 24.0}\",\"('C', 'Li', 'Ni', 'O')\",OTHERS,False\nmp-861891,\"{'Se': 2.0, 'Br': 4.0}\",\"('Br', 'Se')\",OTHERS,False\nmvc-525,\"{'Ca': 2.0, 'Cu': 6.0, 'P': 8.0, 'O': 28.0}\",\"('Ca', 'Cu', 'O', 'P')\",OTHERS,False\nmp-849509,\"{'Li': 16.0, 'V': 8.0, 'P': 8.0, 'O': 32.0, 'F': 8.0}\",\"('F', 'Li', 'O', 'P', 'V')\",OTHERS,False\nmp-1208619,\"{'Sr': 2.0, 'Li': 2.0, 'Ga': 2.0, 'F': 12.0}\",\"('F', 'Ga', 'Li', 'Sr')\",OTHERS,False\nmp-1194315,\"{'Ni': 14.0, 'P': 14.0}\",\"('Ni', 'P')\",OTHERS,False\nmp-1074011,\"{'Mg': 12.0, 'Si': 10.0}\",\"('Mg', 'Si')\",OTHERS,False\nmp-1245956,\"{'Al': 2.0, 'Pb': 6.0, 'N': 6.0}\",\"('Al', 'N', 'Pb')\",OTHERS,False\nmp-1219254,\"{'Sc': 1.0, 'In': 1.0, 'P': 2.0}\",\"('In', 'P', 'Sc')\",OTHERS,False\nmp-1227158,\"{'Ce': 2.0, 'Ta': 14.0, 'O': 38.0}\",\"('Ce', 'O', 'Ta')\",OTHERS,False\nmp-1037735,\"{'Y': 1.0, 'Mg': 30.0, 'V': 1.0, 'O': 32.0}\",\"('Mg', 'O', 'V', 'Y')\",OTHERS,False\nmp-1076123,\"{'Eu': 4.0, 'Co': 4.0, 'O': 10.0}\",\"('Co', 'Eu', 'O')\",OTHERS,False\nmp-1198344,\"{'Ir': 4.0, 'Rh': 4.0, 'Br': 24.0, 'N': 20.0, 'Cl': 4.0}\",\"('Br', 'Cl', 'Ir', 'N', 'Rh')\",OTHERS,False\nmp-1184519,\"{'In': 6.0, 'Cl': 2.0}\",\"('Cl', 'In')\",OTHERS,False\nAl 55.1 Cu 14.6 Ru 20.2 Si 10.1,\"{'Al': 55.1, 'Cu': 14.6, 'Ru': 20.2, 'Si': 10.1}\",\"('Al', 'Cu', 'Ru', 'Si')\",AC,False\nmp-720100,\"{'Na': 2.0, 'H': 36.0, 'C': 18.0, 'I': 2.0, 'N': 6.0, 'O': 6.0}\",\"('C', 'H', 'I', 'N', 'Na', 'O')\",OTHERS,False\nmp-1212858,\"{'Dy': 10.0, 'Sb': 2.0, 'Pt': 4.0}\",\"('Dy', 'Pt', 'Sb')\",OTHERS,False\nmp-11493,\"{'Lu': 1.0, 'Pb': 2.0}\",\"('Lu', 'Pb')\",OTHERS,False\nmp-675192,\"{'Ce': 3.0, 'Dy': 2.0, 'O': 9.0}\",\"('Ce', 'Dy', 'O')\",OTHERS,False\nmp-759552,\"{'Li': 4.0, 'Cr': 2.0, 'P': 8.0, 'H': 32.0, 'O': 40.0}\",\"('Cr', 'H', 'Li', 'O', 'P')\",OTHERS,False\nmvc-10489,\"{'Ca': 4.0, 'Co': 8.0, 'Cu': 8.0, 'O': 32.0}\",\"('Ca', 'Co', 'Cu', 'O')\",OTHERS,False\nmp-1245357,\"{'Ba': 12.0, 'Mn': 24.0, 'N': 48.0}\",\"('Ba', 'Mn', 'N')\",OTHERS,False\nmp-1027293,\"{'Mo': 2.0, 'W': 2.0, 'Se': 2.0, 'S': 6.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,False\nmp-25380,\"{'O': 8.0, 'F': 2.0, 'P': 2.0, 'Cu': 2.0}\",\"('Cu', 'F', 'O', 'P')\",OTHERS,False\nmp-1229007,\"{'Ag': 1.0, 'Sb': 1.0, 'Te': 1.0, 'Se': 1.0}\",\"('Ag', 'Sb', 'Se', 'Te')\",OTHERS,False\nmp-1182617,\"{'Co': 6.0, 'P': 6.0, 'H': 18.0, 'O': 30.0}\",\"('Co', 'H', 'O', 'P')\",OTHERS,False\nmp-1200195,\"{'Sc': 12.0, 'Mn': 12.0, 'Ge': 24.0}\",\"('Ge', 'Mn', 'Sc')\",OTHERS,False\nZn 75 Sc 15 Au 10,\"{'Zn': 75.0, 'Sc': 15.0, 'Au': 10.0}\",\"('Au', 'Sc', 'Zn')\",QC,False\nmp-760595,\"{'Cu': 4.0, 'C': 4.0, 'O': 12.0}\",\"('C', 'Cu', 'O')\",OTHERS,False\nmp-1223475,\"{'K': 2.0, 'Na': 2.0, 'Ca': 2.0, 'Th': 2.0, 'Si': 16.0, 'O': 40.0}\",\"('Ca', 'K', 'Na', 'O', 'Si', 'Th')\",OTHERS,False\nmp-10478,\"{'As': 4.0, 'Rh': 4.0, 'Ce': 4.0}\",\"('As', 'Ce', 'Rh')\",OTHERS,False\nmp-1025282,\"{'Ti': 1.0, 'V': 2.0, 'Te': 4.0}\",\"('Te', 'Ti', 'V')\",OTHERS,False\nmp-556146,\"{'Sn': 4.0, 'C': 4.0, 'S': 4.0, 'N': 4.0, 'F': 4.0}\",\"('C', 'F', 'N', 'S', 'Sn')\",OTHERS,False\nmp-641962,\"{'K': 4.0, 'Au': 4.0, 'C': 8.0, 'S': 8.0, 'N': 8.0}\",\"('Au', 'C', 'K', 'N', 'S')\",OTHERS,False\nmp-1215061,\"{'Ba': 4.0, 'Na': 2.0, 'Ti': 4.0, 'Mn': 2.0, 'Re': 4.0, 'Si': 16.0, 'H': 2.0, 'O': 52.0, 'F': 2.0}\",\"('Ba', 'F', 'H', 'Mn', 'Na', 'O', 'Re', 'Si', 'Ti')\",OTHERS,False\nmp-1101555,\"{'Li': 4.0, 'Mo': 2.0, 'P': 8.0, 'O': 26.0}\",\"('Li', 'Mo', 'O', 'P')\",OTHERS,False\nmp-1094254,\"{'Zr': 5.0, 'Sn': 1.0}\",\"('Sn', 'Zr')\",OTHERS,False\nmp-865913,\"{'Li': 2.0, 'Ac': 1.0, 'Sn': 1.0}\",\"('Ac', 'Li', 'Sn')\",OTHERS,False\nmp-1222706,\"{'La': 1.0, 'Zn': 1.0, 'Ag': 1.0, 'As': 2.0}\",\"('Ag', 'As', 'La', 'Zn')\",OTHERS,False\nmp-510484,\"{'Sn': 4.0, 'Ni': 8.0, 'Ca': 2.0}\",\"('Ca', 'Ni', 'Sn')\",OTHERS,False\nmp-1219530,\"{'Sb': 28.0, 'Mo': 9.0, 'Ru': 3.0}\",\"('Mo', 'Ru', 'Sb')\",OTHERS,False\nmp-1207948,\"{'V': 8.0, 'As': 8.0, 'N': 8.0, 'O': 32.0, 'F': 8.0}\",\"('As', 'F', 'N', 'O', 'V')\",OTHERS,False\nmp-753741,\"{'Li': 12.0, 'Cu': 4.0, 'S': 8.0}\",\"('Cu', 'Li', 'S')\",OTHERS,False\nmp-1094171,\"{'La': 6.0, 'Mg': 2.0}\",\"('La', 'Mg')\",OTHERS,False\nCd 19 Pr 3,\"{'Cd': 19.0, 'Pr': 3.0}\",\"('Cd', 'Pr')\",AC,False\nmvc-12664,\"{'Mg': 4.0, 'Ni': 4.0, 'O': 8.0}\",\"('Mg', 'Ni', 'O')\",OTHERS,False\nmvc-9385,\"{'Pr': 2.0, 'Mg': 2.0, 'Cr': 4.0, 'O': 12.0}\",\"('Cr', 'Mg', 'O', 'Pr')\",OTHERS,False\nmp-4240,\"{'Rb': 1.0, 'Te': 2.0, 'Ce': 1.0}\",\"('Ce', 'Rb', 'Te')\",OTHERS,False\nmp-865080,\"{'Na': 1.0, 'Ce': 1.0, 'Au': 2.0}\",\"('Au', 'Ce', 'Na')\",OTHERS,False\nmp-1216971,\"{'Ti': 3.0, 'Nb': 6.0, 'O': 21.0}\",\"('Nb', 'O', 'Ti')\",OTHERS,False\nmp-1211760,\"{'K': 4.0, 'Eu': 8.0, 'Cl': 28.0}\",\"('Cl', 'Eu', 'K')\",OTHERS,False\nmp-1176051,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,False\nmp-1177350,\"{'Li': 4.0, 'Mn': 5.0, 'Sb': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'Mn', 'O', 'P', 'Sb')\",OTHERS,False\nmp-1183370,\"{'Ba': 4.0, 'Sn': 4.0, 'S': 12.0}\",\"('Ba', 'S', 'Sn')\",OTHERS,False\nmp-1095603,\"{'Y': 4.0, 'As': 4.0, 'Se': 4.0}\",\"('As', 'Se', 'Y')\",OTHERS,False\nmp-1182493,\"{'Ca': 4.0, 'V': 4.0, 'Si': 16.0, 'O': 60.0}\",\"('Ca', 'O', 'Si', 'V')\",OTHERS,False\nmp-1190638,\"{'Dy': 8.0, 'Ge': 8.0, 'Pd': 8.0}\",\"('Dy', 'Ge', 'Pd')\",OTHERS,False\nmp-1223896,\"{'Hg': 1.0, 'Br': 1.0, 'N': 1.0}\",\"('Br', 'Hg', 'N')\",OTHERS,False\nmp-680232,\"{'K': 16.0, 'Ag': 8.0, 'C': 24.0, 'S': 24.0, 'N': 24.0}\",\"('Ag', 'C', 'K', 'N', 'S')\",OTHERS,False\nmp-1226025,\"{'K': 3.0, 'Zr': 4.0, 'Si': 12.0, 'H': 1.0, 'O': 44.0}\",\"('H', 'K', 'O', 'Si', 'Zr')\",OTHERS,False\nmp-759399,\"{'Na': 4.0, 'Co': 12.0, 'O': 16.0}\",\"('Co', 'Na', 'O')\",OTHERS,False\nmp-1001790,\"{'Li': 1.0, 'O': 3.0}\",\"('Li', 'O')\",OTHERS,False\nmvc-5807,\"{'Zn': 4.0, 'Cr': 2.0, 'Ir': 2.0, 'O': 12.0}\",\"('Cr', 'Ir', 'O', 'Zn')\",OTHERS,False\nmp-778251,\"{'Li': 4.0, 'Ti': 1.0, 'Co': 2.0, 'Ni': 3.0, 'P': 6.0, 'O': 24.0}\",\"('Co', 'Li', 'Ni', 'O', 'P', 'Ti')\",OTHERS,False\nmp-1188179,\"{'Er': 4.0, 'Al': 6.0, 'Ge': 8.0}\",\"('Al', 'Er', 'Ge')\",OTHERS,False\nmp-540702,\"{'Fe': 21.0, 'Se': 24.0}\",\"('Fe', 'Se')\",OTHERS,False\nmp-572674,\"{'Re': 3.0, 'C': 6.0, 'I': 6.0, 'O': 6.0}\",\"('C', 'I', 'O', 'Re')\",OTHERS,False\nmp-1227758,\"{'Ca': 4.0, 'Ti': 2.0, 'Cr': 2.0, 'O': 12.0}\",\"('Ca', 'Cr', 'O', 'Ti')\",OTHERS,False\nmp-1201100,\"{'Ce': 3.0, 'Ag': 3.0, 'S': 6.0, 'O': 27.0}\",\"('Ag', 'Ce', 'O', 'S')\",OTHERS,False\nmp-1226632,\"{'Cr': 24.0, 'P': 11.0, 'C': 1.0}\",\"('C', 'Cr', 'P')\",OTHERS,False\nmvc-5754,\"{'Mg': 4.0, 'Bi': 8.0, 'O': 16.0}\",\"('Bi', 'Mg', 'O')\",OTHERS,False\nmp-1224896,\"{'Fe': 2.0, 'Sb': 12.0, 'Pd': 2.0}\",\"('Fe', 'Pd', 'Sb')\",OTHERS,False\nmp-1096989,\"{'H': 24.0, 'C': 4.0, 'N': 4.0, 'Cl': 4.0}\",\"('C', 'Cl', 'H', 'N')\",OTHERS,False\nmp-867537,\"{'Li': 4.0, 'Co': 3.0, 'Ni': 1.0, 'O': 8.0}\",\"('Co', 'Li', 'Ni', 'O')\",OTHERS,False\nmp-1199851,\"{'Er': 4.0, 'C': 12.0, 'O': 32.0}\",\"('C', 'Er', 'O')\",OTHERS,False\nmp-14046,\"{'O': 48.0, 'Si': 12.0, 'V': 8.0, 'Mn': 12.0}\",\"('Mn', 'O', 'Si', 'V')\",OTHERS,False\nmp-2199,\"{'Si': 1.0, 'Fe': 3.0}\",\"('Fe', 'Si')\",OTHERS,False\nmp-29422,\"{'Cl': 8.0, 'Hf': 2.0}\",\"('Cl', 'Hf')\",OTHERS,False\nmp-556044,\"{'Si': 12.0, 'O': 24.0}\",\"('O', 'Si')\",OTHERS,False\nmp-705154,\"{'Li': 2.0, 'Cr': 8.0, 'P': 14.0, 'O': 48.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,False\nmp-1247606,\"{'Sr': 4.0, 'Ca': 28.0, 'Mn': 28.0, 'Cr': 4.0, 'O': 88.0}\",\"('Ca', 'Cr', 'Mn', 'O', 'Sr')\",OTHERS,False\nmp-1094704,\"{'Mg': 10.0, 'Cd': 2.0}\",\"('Cd', 'Mg')\",OTHERS,False\nmp-1027049,\"{'Te': 2.0, 'Mo': 2.0, 'W': 2.0, 'Se': 4.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,False\nmp-772659,\"{'Li': 12.0, 'Cr': 4.0, 'O': 16.0}\",\"('Cr', 'Li', 'O')\",OTHERS,False\nmp-755444,\"{'Li': 1.0, 'Zn': 1.0, 'Fe': 4.0, 'O': 8.0}\",\"('Fe', 'Li', 'O', 'Zn')\",OTHERS,False\nmp-1187775,\"{'Yb': 2.0, 'Pu': 6.0}\",\"('Pu', 'Yb')\",OTHERS,False\nmp-1214188,\"{'Ce': 8.0, 'Ta': 12.0, 'Se': 8.0, 'O': 32.0}\",\"('Ce', 'O', 'Se', 'Ta')\",OTHERS,False\nmp-7432,\"{'Sr': 1.0, 'Cd': 2.0, 'Sb': 2.0}\",\"('Cd', 'Sb', 'Sr')\",OTHERS,False\nmp-752797,\"{'Co': 6.0, 'O': 1.0, 'F': 11.0}\",\"('Co', 'F', 'O')\",OTHERS,False\nmp-2544,\"{'Be': 17.0, 'Zr': 2.0}\",\"('Be', 'Zr')\",OTHERS,False\nmp-7463,\"{'P': 6.0, 'Cu': 18.0}\",\"('Cu', 'P')\",OTHERS,True\nmp-1195659,\"{'Th': 4.0, 'Se': 8.0, 'O': 24.0}\",\"('O', 'Se', 'Th')\",OTHERS,True\nmp-1018786,\"{'Li': 2.0, 'Mn': 2.0, 'Sb': 2.0}\",\"('Li', 'Mn', 'Sb')\",OTHERS,True\nmp-651173,\"{'K': 6.0, 'W': 2.0, 'C': 8.0, 'N': 8.0, 'O': 2.0, 'F': 2.0}\",\"('C', 'F', 'K', 'N', 'O', 'W')\",OTHERS,True\nmp-1095230,\"{'Ca': 2.0, 'Se': 2.0, 'O': 8.0}\",\"('Ca', 'O', 'Se')\",OTHERS,True\nmp-1218639,\"{'Sr': 5.0, 'Mn': 4.0, 'C': 1.0, 'O': 13.0}\",\"('C', 'Mn', 'O', 'Sr')\",OTHERS,True\nmp-21313,\"{'C': 1.0, 'Mn': 3.0, 'Ga': 1.0}\",\"('C', 'Ga', 'Mn')\",OTHERS,True\nmp-768659,\"{'Li': 4.0, 'Co': 4.0, 'S': 6.0, 'O': 24.0}\",\"('Co', 'Li', 'O', 'S')\",OTHERS,True\nmp-1210938,\"{'Sm': 10.0, 'In': 20.0, 'Pd': 10.0}\",\"('In', 'Pd', 'Sm')\",OTHERS,True\nmvc-2827,\"{'Ca': 4.0, 'Ta': 4.0, 'Fe': 2.0, 'O': 16.0}\",\"('Ca', 'Fe', 'O', 'Ta')\",OTHERS,True\nmp-762265,\"{'Li': 3.0, 'V': 5.0, 'O': 10.0}\",\"('Li', 'O', 'V')\",OTHERS,True\nmp-779031,\"{'Na': 4.0, 'V': 4.0, 'P': 8.0, 'O': 32.0}\",\"('Na', 'O', 'P', 'V')\",OTHERS,True\nZn 72 Sc 16 Cu 12,\"{'Zn': 72.0, 'Sc': 16.0, 'Cu': 12.0}\",\"('Cu', 'Sc', 'Zn')\",QC,True\nmp-1246289,\"{'Mg': 6.0, 'Bi': 6.0, 'N': 10.0}\",\"('Bi', 'Mg', 'N')\",OTHERS,True\nmp-1200207,\"{'Cs': 16.0, 'Ga': 22.0}\",\"('Cs', 'Ga')\",OTHERS,True\nmp-1111691,\"{'K': 3.0, 'Tl': 1.0, 'F': 6.0}\",\"('F', 'K', 'Tl')\",OTHERS,True\nmp-9820,\"{'F': 6.0, 'As': 1.0, 'Cs': 1.0}\",\"('As', 'Cs', 'F')\",OTHERS,True\nmp-22724,\"{'F': 14.0, 'Zr': 2.0, 'Ce': 2.0}\",\"('Ce', 'F', 'Zr')\",OTHERS,True\nmp-1207797,\"{'Y': 3.0, 'Ga': 9.0, 'Cu': 2.0}\",\"('Cu', 'Ga', 'Y')\",OTHERS,True\nmp-753198,\"{'Mg': 2.0, 'Fe': 4.0, 'O': 8.0}\",\"('Fe', 'Mg', 'O')\",OTHERS,True\nmp-1029165,\"{'Te': 2.0, 'Mo': 2.0, 'W': 2.0, 'Se': 6.0}\",\"('Mo', 'Se', 'Te', 'W')\",OTHERS,True\nmp-684769,\"{'Sr': 2.0, 'La': 6.0, 'Ti': 1.0, 'Cu': 3.0, 'O': 16.0}\",\"('Cu', 'La', 'O', 'Sr', 'Ti')\",OTHERS,True\nmp-541923,\"{'Tl': 4.0, 'Cu': 20.0, 'Se': 12.0}\",\"('Cu', 'Se', 'Tl')\",OTHERS,True\nmp-1188122,\"{'Ba': 4.0, 'Er': 2.0, 'In': 2.0, 'Se': 10.0}\",\"('Ba', 'Er', 'In', 'Se')\",OTHERS,True\nmp-1112401,\"{'Cs': 1.0, 'Rb': 2.0, 'Ir': 1.0, 'F': 6.0}\",\"('Cs', 'F', 'Ir', 'Rb')\",OTHERS,True\nmp-1020010,\"{'Li': 8.0, 'Ca': 12.0, 'N': 24.0}\",\"('Ca', 'Li', 'N')\",OTHERS,True\nmp-863410,\"{'Li': 4.0, 'Nb': 2.0, 'P': 10.0, 'O': 30.0}\",\"('Li', 'Nb', 'O', 'P')\",OTHERS,True\nmp-558487,\"{'Gd': 4.0, 'P': 20.0, 'O': 56.0}\",\"('Gd', 'O', 'P')\",OTHERS,True\nmp-8184,\"{'Li': 4.0, 'O': 8.0, 'Zn': 2.0, 'Ge': 2.0}\",\"('Ge', 'Li', 'O', 'Zn')\",OTHERS,True\nmp-29776,\"{'Ge': 18.0, 'Rh': 26.0, 'Ce': 8.0}\",\"('Ce', 'Ge', 'Rh')\",OTHERS,True\nmp-582282,\"{'Gd': 2.0, 'Ag': 2.0, 'Se': 4.0}\",\"('Ag', 'Gd', 'Se')\",OTHERS,True\nmp-985626,\"{'Na': 3.0, 'Mn': 2.0, 'Sb': 1.0, 'O': 6.0}\",\"('Mn', 'Na', 'O', 'Sb')\",OTHERS,True\nmp-1025513,\"{'Zr': 4.0, 'Ge': 4.0}\",\"('Ge', 'Zr')\",OTHERS,True\nmp-1209864,\"{'Nd': 6.0, 'Zr': 2.0, 'Sb': 10.0}\",\"('Nd', 'Sb', 'Zr')\",OTHERS,True\nmp-1217847,\"{'Sr': 2.0, 'Ti': 8.0, 'Bi': 8.0, 'O': 30.0}\",\"('Bi', 'O', 'Sr', 'Ti')\",OTHERS,True\nmp-9705,\"{'B': 3.0, 'N': 6.0, 'Na': 1.0, 'Ba': 4.0}\",\"('B', 'Ba', 'N', 'Na')\",OTHERS,True\nmp-1036805,\"{'Ba': 1.0, 'Mg': 30.0, 'V': 1.0, 'O': 32.0}\",\"('Ba', 'Mg', 'O', 'V')\",OTHERS,True\nmp-557328,\"{'Li': 3.0, 'Zn': 2.0, 'Sb': 1.0, 'O': 6.0}\",\"('Li', 'O', 'Sb', 'Zn')\",OTHERS,True\nmp-26684,\"{'Li': 6.0, 'O': 36.0, 'P': 10.0, 'Cr': 4.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,True\nmp-554873,\"{'Ag': 8.0, 'B': 32.0, 'O': 52.0}\",\"('Ag', 'B', 'O')\",OTHERS,True\nmp-10609,\"{'C': 1.0, 'Sn': 1.0, 'Lu': 3.0}\",\"('C', 'Lu', 'Sn')\",OTHERS,True\nmp-1009213,\"{'Mn': 1.0, 'Sb': 1.0}\",\"('Mn', 'Sb')\",OTHERS,True\nmp-1187601,\"{'Tm': 2.0, 'Tl': 1.0, 'Zn': 1.0}\",\"('Tl', 'Tm', 'Zn')\",OTHERS,True\nmp-1188132,\"{'Ho': 4.0, 'Mo': 4.0, 'C': 8.0}\",\"('C', 'Ho', 'Mo')\",OTHERS,True\nmvc-14545,\"{'Ca': 1.0, 'La': 2.0, 'Sb': 1.0, 'O': 6.0}\",\"('Ca', 'La', 'O', 'Sb')\",OTHERS,True\nmp-1222495,\"{'Li': 3.0, 'Cd': 1.0}\",\"('Cd', 'Li')\",OTHERS,True\nmp-27331,\"{'K': 12.0, 'Ge': 4.0, 'Te': 12.0}\",\"('Ge', 'K', 'Te')\",OTHERS,True\nmp-1226404,\"{'Cu': 4.0, 'Si': 4.0, 'H': 72.0, 'C': 28.0, 'S': 12.0, 'I': 4.0}\",\"('C', 'Cu', 'H', 'I', 'S', 'Si')\",OTHERS,True\nmp-569020,\"{'In': 2.0, 'Sb': 2.0}\",\"('In', 'Sb')\",OTHERS,True\nmvc-4646,\"{'Ca': 8.0, 'Bi': 16.0, 'O': 32.0}\",\"('Bi', 'Ca', 'O')\",OTHERS,True\nmp-28319,\"{'I': 8.0, 'Pt': 4.0}\",\"('I', 'Pt')\",OTHERS,True\nmp-1093914,\"{'Mn': 1.0, 'Be': 2.0, 'Ru': 1.0}\",\"('Be', 'Mn', 'Ru')\",OTHERS,True\nmp-1222654,\"{'Li': 2.0, 'Ga': 1.0, 'Si': 1.0}\",\"('Ga', 'Li', 'Si')\",OTHERS,True\nmp-780712,\"{'Ce': 4.0, 'Cr': 4.0, 'O': 12.0}\",\"('Ce', 'Cr', 'O')\",OTHERS,True\nmp-26399,\"{'Li': 6.0, 'O': 16.0, 'P': 4.0, 'V': 2.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-559813,\"{'Ba': 9.0, 'Cu': 7.0, 'Cl': 2.0, 'O': 15.0}\",\"('Ba', 'Cl', 'Cu', 'O')\",OTHERS,True\nmvc-5780,\"{'Mg': 4.0, 'Fe': 2.0, 'Ir': 2.0, 'O': 12.0}\",\"('Fe', 'Ir', 'Mg', 'O')\",OTHERS,True\nmp-862846,\"{'Ca': 1.0, 'La': 1.0, 'Hg': 2.0}\",\"('Ca', 'Hg', 'La')\",OTHERS,True\nmp-29844,\"{'Al': 9.0, 'Cl': 36.0, 'Tb': 3.0}\",\"('Al', 'Cl', 'Tb')\",OTHERS,True\nmp-31145,\"{'Cu': 2.0, 'Ba': 2.0, 'Bi': 2.0}\",\"('Ba', 'Bi', 'Cu')\",OTHERS,True\nmp-20519,\"{'S': 2.0, 'Cd': 1.0, 'In': 1.0}\",\"('Cd', 'In', 'S')\",OTHERS,True\nmp-1033291,\"{'Mg': 6.0, 'B': 1.0, 'C': 1.0, 'O': 8.0}\",\"('B', 'C', 'Mg', 'O')\",OTHERS,True\nmp-541441,\"{'Zn': 2.0, 'Te': 2.0}\",\"('Te', 'Zn')\",OTHERS,True\nmp-1095711,\"{'Mg': 1.0, 'Sc': 1.0, 'Au': 2.0}\",\"('Au', 'Mg', 'Sc')\",OTHERS,True\nmp-771838,\"{'La': 8.0, 'Al': 12.0, 'O': 30.0}\",\"('Al', 'La', 'O')\",OTHERS,True\nmp-758006,\"{'Ca': 8.0, 'Si': 4.0, 'O': 16.0}\",\"('Ca', 'O', 'Si')\",OTHERS,True\nmp-706611,\"{'Li': 4.0, 'Be': 4.0, 'H': 16.0, 'N': 4.0, 'F': 16.0}\",\"('Be', 'F', 'H', 'Li', 'N')\",OTHERS,True\nmp-680205,\"{'Pb': 15.0, 'I': 30.0}\",\"('I', 'Pb')\",OTHERS,True\nmp-5577,\"{'Ge': 2.0, 'Rh': 2.0, 'Ce': 1.0}\",\"('Ce', 'Ge', 'Rh')\",OTHERS,True\nmp-1197388,\"{'Gd': 8.0, 'Fe': 56.0, 'C': 4.0}\",\"('C', 'Fe', 'Gd')\",OTHERS,True\nmp-546266,\"{'O': 4.0, 'Bi': 2.0, 'Dy': 1.0, 'I': 1.0}\",\"('Bi', 'Dy', 'I', 'O')\",OTHERS,True\nmp-574430,\"{'Ba': 2.0, 'U': 2.0, 'I': 12.0}\",\"('Ba', 'I', 'U')\",OTHERS,True\nmp-30782,\"{'Mg': 23.0, 'Sr': 6.0}\",\"('Mg', 'Sr')\",OTHERS,True\nmp-21657,\"{'Se': 12.0, 'Tl': 20.0}\",\"('Se', 'Tl')\",OTHERS,True\nmp-1208739,\"{'Sr': 4.0, 'Al': 4.0, 'H': 4.0, 'O': 4.0, 'F': 16.0}\",\"('Al', 'F', 'H', 'O', 'Sr')\",OTHERS,True\nmp-755999,\"{'Rb': 4.0, 'Be': 4.0, 'O': 6.0}\",\"('Be', 'O', 'Rb')\",OTHERS,True\nmp-561414,\"{'K': 12.0, 'Hg': 12.0, 'S': 8.0, 'Br': 20.0, 'O': 24.0}\",\"('Br', 'Hg', 'K', 'O', 'S')\",OTHERS,True\nmp-1227126,\"{'Ca': 2.0, 'Y': 2.0, 'Mn': 4.0, 'O': 12.0}\",\"('Ca', 'Mn', 'O', 'Y')\",OTHERS,True\nmp-6065,\"{'O': 6.0, 'Ga': 1.0, 'Sr': 2.0, 'Sb': 1.0}\",\"('Ga', 'O', 'Sb', 'Sr')\",OTHERS,True\nmp-555139,\"{'Zr': 4.0, 'Cu': 6.0, 'H': 64.0, 'O': 32.0, 'F': 28.0}\",\"('Cu', 'F', 'H', 'O', 'Zr')\",OTHERS,True\nmp-1095804,\"{'Nb': 1.0, 'Zn': 1.0, 'Ni': 2.0}\",\"('Nb', 'Ni', 'Zn')\",OTHERS,True\nmp-780528,\"{'Na': 12.0, 'Cr': 4.0, 'C': 8.0, 'S': 2.0, 'O': 32.0}\",\"('C', 'Cr', 'Na', 'O', 'S')\",OTHERS,True\nmp-31024,\"{'C': 1.0, 'Cl': 8.0, 'Sc': 5.0}\",\"('C', 'Cl', 'Sc')\",OTHERS,True\nmp-756507,\"{'Li': 2.0, 'Fe': 2.0, 'P': 2.0, 'H': 2.0, 'O': 10.0}\",\"('Fe', 'H', 'Li', 'O', 'P')\",OTHERS,True\nmp-1246688,\"{'Zr': 2.0, 'Sn': 2.0, 'N': 4.0}\",\"('N', 'Sn', 'Zr')\",OTHERS,True\nmp-30301,\"{'Li': 4.0, 'O': 16.0, 'Cl': 4.0}\",\"('Cl', 'Li', 'O')\",OTHERS,True\nmp-757560,\"{'Li': 6.0, 'Sn': 4.0, 'P': 14.0, 'O': 42.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,True\nmp-1094591,\"{'Li': 6.0, 'Mg': 2.0}\",\"('Li', 'Mg')\",OTHERS,True\nmvc-10249,\"{'Mg': 2.0, 'Sb': 6.0, 'P': 6.0, 'O': 26.0}\",\"('Mg', 'O', 'P', 'Sb')\",OTHERS,True\nmp-22243,\"{'Li': 4.0, 'O': 16.0, 'Ni': 4.0, 'As': 4.0}\",\"('As', 'Li', 'Ni', 'O')\",OTHERS,True\nmp-697108,\"{'K': 6.0, 'H': 2.0, 'Rh': 2.0, 'C': 10.0, 'N': 10.0, 'O': 2.0}\",\"('C', 'H', 'K', 'N', 'O', 'Rh')\",OTHERS,True\nmp-1265272,\"{'Li': 2.0, 'Mg': 2.0, 'Al': 6.0, 'S': 12.0, 'O': 48.0}\",\"('Al', 'Li', 'Mg', 'O', 'S')\",OTHERS,True\nmp-601275,\"{'K': 12.0, 'Al': 4.0, 'H': 24.0}\",\"('Al', 'H', 'K')\",OTHERS,True\nmp-29281,\"{'P': 132.0, 'Th': 24.0}\",\"('P', 'Th')\",OTHERS,True\nmp-1079861,\"{'Tm': 2.0, 'Co': 2.0, 'Ge': 4.0}\",\"('Co', 'Ge', 'Tm')\",OTHERS,True\nmp-1200003,\"{'Fe': 8.0, 'As': 8.0, 'N': 8.0, 'O': 32.0, 'F': 8.0}\",\"('As', 'F', 'Fe', 'N', 'O')\",OTHERS,True\nmp-1218092,\"{'Ta': 4.0, 'Nb': 2.0, 'Te': 2.0, 'I': 14.0}\",\"('I', 'Nb', 'Ta', 'Te')\",OTHERS,True\nmp-1245479,\"{'Ca': 10.0, 'Hf': 4.0, 'N': 12.0}\",\"('Ca', 'Hf', 'N')\",OTHERS,True\nmp-865758,\"{'Yb': 1.0, 'Ge': 1.0, 'O': 3.0}\",\"('Ge', 'O', 'Yb')\",OTHERS,True\nmp-26576,\"{'Li': 4.0, 'Fe': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,True\nmp-757666,\"{'Ca': 4.0, 'B': 20.0, 'H': 12.0, 'O': 40.0}\",\"('B', 'Ca', 'H', 'O')\",OTHERS,True\nmp-1213960,\"{'Ce': 4.0, 'Ni': 6.0, 'Ge': 10.0}\",\"('Ce', 'Ge', 'Ni')\",OTHERS,True\nmp-566150,\"{'In': 4.0, 'Cu': 6.0, 'P': 8.0, 'O': 32.0}\",\"('Cu', 'In', 'O', 'P')\",OTHERS,True\nmp-19811,\"{'Ru': 3.0, 'Sn': 3.0, 'U': 3.0}\",\"('Ru', 'Sn', 'U')\",OTHERS,True\nmp-20079,\"{'O': 12.0, 'Ca': 4.0, 'Pb': 4.0}\",\"('Ca', 'O', 'Pb')\",OTHERS,True\nmp-862362,\"{'Sc': 2.0, 'Tc': 1.0, 'Pt': 1.0}\",\"('Pt', 'Sc', 'Tc')\",OTHERS,True\nmp-1228819,\"{'As': 4.0, 'S': 2.0}\",\"('As', 'S')\",OTHERS,True\nmp-849339,\"{'Mn': 6.0, 'O': 10.0, 'F': 2.0}\",\"('F', 'Mn', 'O')\",OTHERS,True\nmp-18909,\"{'O': 8.0, 'P': 2.0, 'Ni': 2.0, 'Ba': 1.0}\",\"('Ba', 'Ni', 'O', 'P')\",OTHERS,True\nmp-1192640,\"{'Na': 2.0, 'Nb': 4.0, 'Se': 4.0, 'O': 19.0}\",\"('Na', 'Nb', 'O', 'Se')\",OTHERS,True\nmp-1096678,\"{'Nb': 2.0, 'Tc': 1.0, 'Ru': 1.0}\",\"('Nb', 'Ru', 'Tc')\",OTHERS,True\nmp-998152,\"{'Tl': 1.0, 'Ag': 1.0, 'F': 3.0}\",\"('Ag', 'F', 'Tl')\",OTHERS,True\nmp-758113,\"{'Li': 3.0, 'Fe': 6.0, 'Si': 6.0, 'O': 24.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,True\nmp-753571,\"{'Li': 1.0, 'V': 1.0, 'F': 6.0}\",\"('F', 'Li', 'V')\",OTHERS,True\nmp-867971,\"{'Mn': 15.0, 'O': 32.0}\",\"('Mn', 'O')\",OTHERS,True\nmp-757177,\"{'Li': 12.0, 'Cu': 2.0, 'S': 8.0}\",\"('Cu', 'Li', 'S')\",OTHERS,True\nmp-19739,\"{'Sc': 1.0, 'Fe': 6.0, 'Ge': 6.0}\",\"('Fe', 'Ge', 'Sc')\",OTHERS,True\nmp-27721,\"{'H': 12.0, 'As': 4.0}\",\"('As', 'H')\",OTHERS,True\nmp-1221702,\"{'Mn': 1.0, 'Al': 1.0, 'Rh': 2.0}\",\"('Al', 'Mn', 'Rh')\",OTHERS,True\nmp-504703,\"{'Eu': 4.0, 'Fe': 4.0, 'O': 12.0}\",\"('Eu', 'Fe', 'O')\",OTHERS,True\nmp-15821,\"{'Li': 3.0, 'Ge': 3.0, 'Nd': 3.0}\",\"('Ge', 'Li', 'Nd')\",OTHERS,True\nmp-756603,\"{'Li': 4.0, 'Al': 2.0, 'Ni': 2.0, 'O': 8.0}\",\"('Al', 'Li', 'Ni', 'O')\",OTHERS,True\nmp-38125,\"{'Cd': 2.0, 'Se': 8.0, 'Sm': 4.0}\",\"('Cd', 'Se', 'Sm')\",OTHERS,True\nAu 42.9 In 41.9 Yb 15.2,\"{'Au': 42.9, 'In': 41.9, 'Yb': 15.2}\",\"('Au', 'In', 'Yb')\",AC,True\nmp-42022,\"{'Cs': 2.0, 'F': 5.0, 'O': 1.0, 'Rb': 1.0, 'Zr': 1.0}\",\"('Cs', 'F', 'O', 'Rb', 'Zr')\",OTHERS,True\nmp-776555,\"{'Na': 4.0, 'V': 12.0, 'P': 8.0, 'O': 52.0}\",\"('Na', 'O', 'P', 'V')\",OTHERS,True\nmp-760888,\"{'V': 8.0, 'O': 2.0, 'F': 22.0}\",\"('F', 'O', 'V')\",OTHERS,True\nmp-1030319,\"{'Te': 8.0, 'Mo': 4.0}\",\"('Mo', 'Te')\",OTHERS,True\nmp-862811,\"{'Cs': 2.0, 'K': 1.0, 'U': 2.0, 'Si': 4.0, 'O': 14.0}\",\"('Cs', 'K', 'O', 'Si', 'U')\",OTHERS,True\nmp-1183639,\"{'Cd': 1.0, 'Hg': 3.0}\",\"('Cd', 'Hg')\",OTHERS,True\nmp-1185021,\"{'La': 1.0, 'Pm': 1.0, 'Ru': 2.0}\",\"('La', 'Pm', 'Ru')\",OTHERS,True\nmp-556288,\"{'Li': 4.0, 'Re': 2.0, 'O': 6.0}\",\"('Li', 'O', 'Re')\",OTHERS,True\nmp-770413,\"{'La': 4.0, 'Ho': 4.0, 'O': 12.0}\",\"('Ho', 'La', 'O')\",OTHERS,True\nmp-1200278,\"{'Sn': 4.0, 'H': 28.0, 'Cl': 12.0, 'O': 16.0}\",\"('Cl', 'H', 'O', 'Sn')\",OTHERS,True\nmp-698483,\"{'U': 4.0, 'Cu': 2.0, 'As': 4.0, 'H': 48.0, 'O': 48.0}\",\"('As', 'Cu', 'H', 'O', 'U')\",OTHERS,True\nmp-1232173,\"{'Ho': 8.0, 'Mg': 4.0, 'Se': 16.0}\",\"('Ho', 'Mg', 'Se')\",OTHERS,True\nmp-778584,\"{'Li': 1.0, 'Mn': 2.0, 'Co': 2.0, 'O': 8.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmvc-8898,\"{'Mg': 4.0, 'Ti': 12.0, 'O': 28.0}\",\"('Mg', 'O', 'Ti')\",OTHERS,True\nmp-1094218,\"{'La': 15.0, 'Mg': 3.0}\",\"('La', 'Mg')\",OTHERS,True\nmp-2061,\"{'Tl': 3.0, 'Th': 1.0}\",\"('Th', 'Tl')\",OTHERS,True\nmp-1114645,\"{'Rb': 2.0, 'Tl': 1.0, 'Hg': 1.0, 'Br': 6.0}\",\"('Br', 'Hg', 'Rb', 'Tl')\",OTHERS,True\nmp-569338,\"{'Yb': 4.0, 'Al': 4.0, 'Pd': 4.0}\",\"('Al', 'Pd', 'Yb')\",OTHERS,True\nmp-1186681,\"{'Pm': 1.0, 'Y': 1.0, 'Ir': 2.0}\",\"('Ir', 'Pm', 'Y')\",OTHERS,True\nmp-1221085,\"{'Na': 1.0, 'Ce': 1.0, 'Mo': 2.0, 'O': 8.0}\",\"('Ce', 'Mo', 'Na', 'O')\",OTHERS,True\nmp-1199784,\"{'Zr': 4.0, 'C': 12.0, 'N': 12.0, 'O': 48.0}\",\"('C', 'N', 'O', 'Zr')\",OTHERS,True\nmp-1355,\"{'Mn': 23.0, 'Y': 6.0}\",\"('Mn', 'Y')\",OTHERS,True\nmp-1221209,\"{'Na': 3.0, 'W': 2.0, 'Cl': 4.0, 'O': 4.0}\",\"('Cl', 'Na', 'O', 'W')\",OTHERS,True\nmp-862694,\"{'Al': 2.0, 'Ir': 1.0, 'Rh': 1.0}\",\"('Al', 'Ir', 'Rh')\",OTHERS,True\nmp-1039936,\"{'Na': 1.0, 'Ce': 1.0, 'Mg': 30.0, 'O': 32.0}\",\"('Ce', 'Mg', 'Na', 'O')\",OTHERS,True\nmp-1245109,\"{'Zn': 40.0, 'O': 40.0}\",\"('O', 'Zn')\",OTHERS,True\nmp-1227830,\"{'Ba': 2.0, 'Sr': 4.0, 'Sn': 6.0, 'O': 18.0}\",\"('Ba', 'O', 'Sn', 'Sr')\",OTHERS,True\nmp-1179597,\"{'Sb': 8.0, 'F': 48.0}\",\"('F', 'Sb')\",OTHERS,True\nmp-1099098,\"{'Ce': 1.0, 'Mg': 14.0, 'Bi': 1.0}\",\"('Bi', 'Ce', 'Mg')\",OTHERS,True\nmp-1100995,\"{'Tl': 1.0, 'Cu': 7.0, 'S': 4.0}\",\"('Cu', 'S', 'Tl')\",OTHERS,True\nmp-1226373,\"{'Cr': 12.0, 'Sb': 4.0, 'As': 8.0}\",\"('As', 'Cr', 'Sb')\",OTHERS,True\nmvc-940,\"{'Ba': 1.0, 'Ca': 1.0, 'Mn': 4.0, 'O': 8.0}\",\"('Ba', 'Ca', 'Mn', 'O')\",OTHERS,True\nmp-723406,\"{'La': 2.0, 'P': 6.0, 'H': 6.0, 'O': 20.0}\",\"('H', 'La', 'O', 'P')\",OTHERS,True\nmp-1190729,\"{'Ba': 4.0, 'Ge': 4.0, 'O': 16.0}\",\"('Ba', 'Ge', 'O')\",OTHERS,True\nmp-1093633,\"{'K': 1.0, 'Na': 2.0, 'As': 1.0}\",\"('As', 'K', 'Na')\",OTHERS,True\nmp-1147571,\"{'K': 1.0, 'Cu': 2.0, 'Ge': 1.0, 'S': 4.0}\",\"('Cu', 'Ge', 'K', 'S')\",OTHERS,True\nmp-1232324,\"{'Cd': 1.0, 'Pd': 2.0, 'O': 3.0}\",\"('Cd', 'O', 'Pd')\",OTHERS,True\nmp-414,\"{'Se': 1.0, 'Lu': 1.0}\",\"('Lu', 'Se')\",OTHERS,True\nmp-1029850,\"{'K': 30.0, 'Cr': 14.0, 'N': 38.0}\",\"('Cr', 'K', 'N')\",OTHERS,True\nmp-1186105,\"{'Na': 1.0, 'Ac': 3.0}\",\"('Ac', 'Na')\",OTHERS,True\nmp-628726,\"{'Sr': 2.0, 'Al': 4.0, 'Cl': 16.0}\",\"('Al', 'Cl', 'Sr')\",OTHERS,True\nmp-557082,\"{'H': 24.0, 'O': 12.0}\",\"('H', 'O')\",OTHERS,True\nmp-18256,\"{'B': 8.0, 'O': 20.0, 'Mg': 8.0}\",\"('B', 'Mg', 'O')\",OTHERS,True\nmp-1186581,\"{'Pm': 2.0, 'Ge': 6.0}\",\"('Ge', 'Pm')\",OTHERS,True\nmp-1101443,\"{'Nd': 2.0, 'Ta': 6.0, 'O': 18.0}\",\"('Nd', 'O', 'Ta')\",OTHERS,True\nmp-989517,\"{'Cs': 2.0, 'S': 1.0, 'Br': 1.0, 'Cl': 6.0}\",\"('Br', 'Cl', 'Cs', 'S')\",OTHERS,True\nmp-760336,\"{'Li': 15.0, 'Mn': 15.0, 'Co': 3.0, 'O': 36.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-583553,\"{'Mo': 24.0, 'Pb': 4.0, 'Cl': 56.0}\",\"('Cl', 'Mo', 'Pb')\",OTHERS,True\nmp-637249,\"{'Eu': 8.0, 'Li': 4.0, 'Ge': 12.0}\",\"('Eu', 'Ge', 'Li')\",OTHERS,True\nmp-776405,\"{'Li': 12.0, 'Sb': 4.0, 'S': 14.0}\",\"('Li', 'S', 'Sb')\",OTHERS,True\nmp-704203,\"{'Li': 4.0, 'V': 2.0, 'P': 8.0, 'O': 26.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-972877,\"{'Sc': 2.0, 'Al': 1.0, 'Ir': 1.0}\",\"('Al', 'Ir', 'Sc')\",OTHERS,True\nmp-1202899,\"{'Pt': 4.0, 'N': 24.0, 'O': 24.0}\",\"('N', 'O', 'Pt')\",OTHERS,True\nmp-982596,\"{'Tb': 1.0, 'Sc': 1.0}\",\"('Sc', 'Tb')\",OTHERS,True\nmp-510582,\"{'Nd': 4.0, 'Ni': 4.0, 'Sn': 4.0, 'H': 8.0}\",\"('H', 'Nd', 'Ni', 'Sn')\",OTHERS,True\nmp-685218,\"{'Fe': 24.0, 'Co': 12.0, 'O': 48.0}\",\"('Co', 'Fe', 'O')\",OTHERS,True\nmp-554824,\"{'La': 12.0, 'Ru': 4.0, 'O': 28.0}\",\"('La', 'O', 'Ru')\",OTHERS,True\nmp-1025346,\"{'Ga': 1.0, 'As': 1.0, 'Pd': 5.0}\",\"('As', 'Ga', 'Pd')\",OTHERS,True\nmp-867806,\"{'Li': 1.0, 'La': 2.0, 'Ir': 1.0}\",\"('Ir', 'La', 'Li')\",OTHERS,True\nmp-1112759,\"{'Cs': 2.0, 'K': 1.0, 'In': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cs', 'In', 'K')\",OTHERS,True\nmp-25838,\"{'Li': 2.0, 'O': 24.0, 'P': 6.0, 'Cr': 4.0}\",\"('Cr', 'Li', 'O', 'P')\",OTHERS,True\nmp-1198575,\"{'Rb': 18.0, 'La': 6.0, 'Cl': 36.0, 'O': 12.0}\",\"('Cl', 'La', 'O', 'Rb')\",OTHERS,True\nmp-15362,\"{'Li': 12.0, 'B': 12.0, 'O': 36.0, 'Nd': 8.0}\",\"('B', 'Li', 'Nd', 'O')\",OTHERS,True\nmp-1216465,\"{'V': 2.0, 'As': 4.0, 'H': 8.0, 'O': 18.0}\",\"('As', 'H', 'O', 'V')\",OTHERS,True\nmp-647265,\"{'V': 16.0, 'Bi': 56.0, 'O': 120.0}\",\"('Bi', 'O', 'V')\",OTHERS,True\nmp-673024,\"{'Li': 2.0, 'Sn': 8.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,True\nmp-1218050,\"{'Sr': 2.0, 'Pr': 2.0, 'Ga': 6.0, 'O': 14.0}\",\"('Ga', 'O', 'Pr', 'Sr')\",OTHERS,True\nmp-1202122,\"{'Pd': 3.0, 'N': 16.0, 'O': 16.0}\",\"('N', 'O', 'Pd')\",OTHERS,True\nmvc-1338,\"{'Ca': 4.0, 'Cr': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Ca', 'Cr', 'O', 'P')\",OTHERS,True\nmp-865144,\"{'Cd': 2.0, 'Au': 6.0}\",\"('Au', 'Cd')\",OTHERS,True\nmp-756600,\"{'Li': 7.0, 'Mn': 5.0, 'O': 12.0}\",\"('Li', 'Mn', 'O')\",OTHERS,True\nmp-1040512,\"{'Nb': 3.0, 'Si': 1.0, 'O': 10.0}\",\"('Nb', 'O', 'Si')\",OTHERS,True\nmp-1186198,\"{'Na': 1.0, 'Zn': 1.0, 'O': 3.0}\",\"('Na', 'O', 'Zn')\",OTHERS,True\nmp-1076534,\"{'Rb': 1.0, 'Ta': 1.0, 'O': 3.0}\",\"('O', 'Rb', 'Ta')\",OTHERS,True\nmp-1199244,\"{'Cs': 4.0, 'Cu': 4.0, 'S': 24.0}\",\"('Cs', 'Cu', 'S')\",OTHERS,True\nmp-1184390,\"{'Gd': 2.0, 'Tl': 1.0, 'Zn': 1.0}\",\"('Gd', 'Tl', 'Zn')\",OTHERS,True\nmp-770785,\"{'Na': 12.0, 'Mn': 4.0, 'B': 8.0, 'As': 2.0, 'O': 32.0}\",\"('As', 'B', 'Mn', 'Na', 'O')\",OTHERS,True\nmp-1246233,\"{'Na': 24.0, 'Ga': 8.0, 'N': 16.0}\",\"('Ga', 'N', 'Na')\",OTHERS,True\nmp-1219019,\"{'Sm': 1.0, 'Ga': 3.0, 'Pt': 1.0}\",\"('Ga', 'Pt', 'Sm')\",OTHERS,True\nmp-1208977,\"{'Sm': 12.0, 'Si': 8.0, 'Rh': 8.0}\",\"('Rh', 'Si', 'Sm')\",OTHERS,True\nmp-571660,\"{'Cd': 12.0, 'I': 24.0}\",\"('Cd', 'I')\",OTHERS,True\nmp-5510,\"{'Si': 2.0, 'Au': 2.0, 'U': 1.0}\",\"('Au', 'Si', 'U')\",OTHERS,True\nZn 75 Sc 15 Ni 10,\"{'Zn': 75.0, 'Sc': 15.0, 'Ni': 10.0}\",\"('Ni', 'Sc', 'Zn')\",QC,True\nmp-1177102,\"{'Li': 5.0, 'Fe': 1.0, 'S': 4.0}\",\"('Fe', 'Li', 'S')\",OTHERS,True\nmp-758309,\"{'Li': 4.0, 'Co': 8.0, 'P': 12.0, 'O': 48.0}\",\"('Co', 'Li', 'O', 'P')\",OTHERS,True\nGa 2.6 Cu 3.4 Lu 1,\"{'Ga': 2.6, 'Cu': 3.4, 'Lu': 1.0}\",\"('Cu', 'Ga', 'Lu')\",AC,True\nmp-1094334,\"{'Sr': 2.0, 'Mg': 4.0}\",\"('Mg', 'Sr')\",OTHERS,True\nmp-540639,\"{'Fe': 1.0, 'S': 4.0, 'N': 6.0, 'C': 4.0, 'H': 8.0}\",\"('C', 'Fe', 'H', 'N', 'S')\",OTHERS,True\nmp-569299,\"{'Be': 4.0, 'B': 8.0, 'C': 8.0}\",\"('B', 'Be', 'C')\",OTHERS,True\nmp-977128,\"{'Na': 1.0, 'Ru': 3.0}\",\"('Na', 'Ru')\",OTHERS,True\nmp-1225216,\"{'Fe': 2.0, 'Co': 2.0, 'Bi': 4.0, 'As': 4.0, 'O': 24.0}\",\"('As', 'Bi', 'Co', 'Fe', 'O')\",OTHERS,True\nmp-1199772,\"{'Sb': 16.0, 'Xe': 8.0, 'Cl': 8.0, 'F': 88.0}\",\"('Cl', 'F', 'Sb', 'Xe')\",OTHERS,True\nmp-740750,\"{'Na': 8.0, 'P': 8.0, 'H': 12.0, 'O': 30.0}\",\"('H', 'Na', 'O', 'P')\",OTHERS,True\nmp-761105,\"{'Li': 2.0, 'Co': 4.0, 'O': 2.0, 'F': 6.0}\",\"('Co', 'F', 'Li', 'O')\",OTHERS,True\nmp-562987,\"{'V': 1.0, 'Cu': 1.0, 'O': 3.0}\",\"('Cu', 'O', 'V')\",OTHERS,True\nAl 70 Ni 20 Ru 10,\"{'Al': 70.0, 'Ni': 20.0, 'Ru': 10.0}\",\"('Al', 'Ni', 'Ru')\",QC,True\nmp-567368,\"{'Zr': 24.0, 'V': 16.0, 'Ga': 32.0}\",\"('Ga', 'V', 'Zr')\",OTHERS,True\nmvc-4147,\"{'Mg': 4.0, 'Ta': 2.0, 'Cu': 2.0, 'O': 12.0}\",\"('Cu', 'Mg', 'O', 'Ta')\",OTHERS,True\nmp-1227560,\"{'Ce': 4.0, 'Ga': 2.0, 'S': 5.0, 'O': 4.0}\",\"('Ce', 'Ga', 'O', 'S')\",OTHERS,True\nmp-1185990,\"{'Mg': 1.0, 'Tl': 3.0}\",\"('Mg', 'Tl')\",OTHERS,True\nmp-1112047,\"{'K': 2.0, 'Cu': 1.0, 'Mo': 1.0, 'Br': 6.0}\",\"('Br', 'Cu', 'K', 'Mo')\",OTHERS,True\nmp-1210173,\"{'Pr': 12.0, 'Co': 4.0, 'Br': 12.0}\",\"('Br', 'Co', 'Pr')\",OTHERS,True\nmp-981247,\"{'Zn': 7.0, 'Sb': 8.0, 'Ru': 9.0}\",\"('Ru', 'Sb', 'Zn')\",OTHERS,True\nmp-4300,\"{'O': 7.0, 'Si': 2.0, 'Yb': 2.0}\",\"('O', 'Si', 'Yb')\",OTHERS,True\nmp-1100617,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1228596,\"{'Al': 1.0, 'H': 12.0, 'C': 4.0, 'N': 1.0, 'F': 4.0}\",\"('Al', 'C', 'F', 'H', 'N')\",OTHERS,True\nmp-754693,\"{'Lu': 2.0, 'Bi': 6.0, 'O': 12.0}\",\"('Bi', 'Lu', 'O')\",OTHERS,True\nmp-2496,\"{'Sn': 46.0, 'Cs': 8.0}\",\"('Cs', 'Sn')\",OTHERS,True\nmp-1074941,\"{'Mg': 6.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,True\nmp-23541,\"{'K': 2.0, 'Br': 10.0, 'Sn': 4.0}\",\"('Br', 'K', 'Sn')\",OTHERS,True\nmp-975199,\"{'Rb': 1.0, 'Sr': 3.0}\",\"('Rb', 'Sr')\",OTHERS,True\nmp-1212246,\"{'Ho': 10.0, 'In': 8.0, 'Pt': 4.0}\",\"('Ho', 'In', 'Pt')\",OTHERS,True\nmp-1027335,\"{'Mo': 2.0, 'W': 2.0, 'S': 8.0}\",\"('Mo', 'S', 'W')\",OTHERS,True\nmp-706616,\"{'Zn': 4.0, 'H': 32.0, 'O': 16.0, 'F': 8.0}\",\"('F', 'H', 'O', 'Zn')\",OTHERS,True\nmp-769544,\"{'Mg': 1.0, 'Cr': 3.0, 'Se': 2.0, 'S': 4.0, 'O': 24.0}\",\"('Cr', 'Mg', 'O', 'S', 'Se')\",OTHERS,True\nmp-1209887,\"{'Nb': 4.0, 'Fe': 4.0, 'Si': 4.0}\",\"('Fe', 'Nb', 'Si')\",OTHERS,True\nmp-1186906,\"{'Rb': 3.0, 'Gd': 1.0}\",\"('Gd', 'Rb')\",OTHERS,True\nmp-1212910,\"{'Fe': 2.0, 'Bi': 12.0, 'P': 4.0, 'O': 28.0, 'F': 4.0}\",\"('Bi', 'F', 'Fe', 'O', 'P')\",OTHERS,True\nmp-761212,\"{'Li': 2.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,True\nmvc-2643,\"{'Zn': 2.0, 'W': 10.0, 'O': 14.0}\",\"('O', 'W', 'Zn')\",OTHERS,True\nmp-1198558,\"{'Cd': 8.0, 'C': 8.0, 'O': 24.0}\",\"('C', 'Cd', 'O')\",OTHERS,True\nmp-773138,\"{'Na': 2.0, 'Cr': 2.0, 'As': 2.0, 'C': 2.0, 'O': 14.0}\",\"('As', 'C', 'Cr', 'Na', 'O')\",OTHERS,True\nmp-23310,\"{'Yb': 8.0, 'Bi': 6.0}\",\"('Bi', 'Yb')\",OTHERS,True\nmp-756869,\"{'Tb': 2.0, 'K': 4.0, 'O': 6.0}\",\"('K', 'O', 'Tb')\",OTHERS,True\nmp-559347,\"{'Si': 16.0, 'O': 32.0}\",\"('O', 'Si')\",OTHERS,True\nmp-581476,\"{'Zn': 12.0, 'S': 12.0}\",\"('S', 'Zn')\",OTHERS,True\nmp-1224906,\"{'Ge': 4.0, 'Pb': 10.0, 'S': 2.0, 'O': 24.0}\",\"('Ge', 'O', 'Pb', 'S')\",OTHERS,True\nmp-1215422,\"{'Zr': 2.0, 'S': 1.0, 'O': 3.0}\",\"('O', 'S', 'Zr')\",OTHERS,True\nmp-753630,\"{'Li': 9.0, 'V': 6.0, 'P': 12.0, 'H': 6.0, 'O': 48.0}\",\"('H', 'Li', 'O', 'P', 'V')\",OTHERS,True\nmp-17439,\"{'O': 24.0, 'P': 8.0, 'K': 2.0, 'Sm': 2.0}\",\"('K', 'O', 'P', 'Sm')\",OTHERS,True\nmp-780843,\"{'Li': 4.0, 'Fe': 2.0, 'Ni': 3.0, 'Sn': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Fe', 'Li', 'Ni', 'O', 'P', 'Sn')\",OTHERS,True\nmp-1212582,\"{'Eu': 4.0, 'Al': 6.0, 'O': 16.0}\",\"('Al', 'Eu', 'O')\",OTHERS,True\nmp-28837,\"{'Cl': 24.0, 'K': 12.0, 'Rh': 4.0}\",\"('Cl', 'K', 'Rh')\",OTHERS,True\nZn 75 Sc 15 Pd 10,\"{'Zn': 75.0, 'Sc': 15.0, 'Pd': 10.0}\",\"('Pd', 'Sc', 'Zn')\",QC,True\nmp-532510,\"{'Al': 28.0, 'Ni': 14.0, 'O': 56.0}\",\"('Al', 'Ni', 'O')\",OTHERS,True\nmp-568793,\"{'Ca': 28.0, 'Ga': 11.0}\",\"('Ca', 'Ga')\",OTHERS,True\nmp-39888,\"{'Nd': 1.0, 'Ti': 3.0, 'Fe': 1.0, 'Cu': 3.0, 'O': 12.0}\",\"('Cu', 'Fe', 'Nd', 'O', 'Ti')\",OTHERS,True\nmp-772334,\"{'Li': 3.0, 'Fe': 3.0, 'Te': 1.0, 'O': 8.0}\",\"('Fe', 'Li', 'O', 'Te')\",OTHERS,True\nmp-1199106,\"{'Tm': 20.0, 'Pt': 16.0}\",\"('Pt', 'Tm')\",OTHERS,True\nmp-567313,{'Te': 3.0},\"('Te',)\",OTHERS,True\nmp-12866,\"{'O': 6.0, 'Sr': 2.0, 'Sn': 2.0}\",\"('O', 'Sn', 'Sr')\",OTHERS,True\nmp-755087,\"{'Li': 3.0, 'Fe': 1.0, 'F': 6.0}\",\"('F', 'Fe', 'Li')\",OTHERS,True\nmp-1184072,\"{'Dy': 2.0, 'Pd': 1.0, 'Rh': 1.0}\",\"('Dy', 'Pd', 'Rh')\",OTHERS,True\nmp-26134,\"{'Cr': 4.0, 'P': 10.0, 'O': 32.0}\",\"('Cr', 'O', 'P')\",OTHERS,True\nmp-2331,\"{'B': 4.0, 'Mo': 2.0}\",\"('B', 'Mo')\",OTHERS,True\nmp-1217053,\"{'U': 1.0, 'Fe': 10.0, 'Si': 2.0}\",\"('Fe', 'Si', 'U')\",OTHERS,True\nmp-769253,\"{'Dy': 2.0, 'Se': 1.0, 'O': 2.0}\",\"('Dy', 'O', 'Se')\",OTHERS,True\nmp-1078684,\"{'Er': 3.0, 'Cd': 3.0, 'Ga': 3.0}\",\"('Cd', 'Er', 'Ga')\",OTHERS,True\nmp-567858,\"{'Dy': 24.0, 'C': 12.0, 'I': 34.0}\",\"('C', 'Dy', 'I')\",OTHERS,True\nmp-1099903,\"{'K': 20.0, 'Na': 12.0, 'Ta': 24.0, 'Nb': 8.0, 'O': 80.0}\",\"('K', 'Na', 'Nb', 'O', 'Ta')\",OTHERS,True\nmp-1246529,\"{'Fe': 16.0, 'Ge': 16.0, 'N': 32.0}\",\"('Fe', 'Ge', 'N')\",OTHERS,True\nmp-766079,\"{'Sr': 8.0, 'Cu': 8.0, 'O': 17.0}\",\"('Cu', 'O', 'Sr')\",OTHERS,True\nmp-1220481,\"{'Nb': 4.0, 'Al': 1.0}\",\"('Al', 'Nb')\",OTHERS,True\nmp-555202,\"{'K': 2.0, 'Te': 2.0, 'H': 2.0, 'O': 2.0, 'F': 8.0}\",\"('F', 'H', 'K', 'O', 'Te')\",OTHERS,True\nmp-1211077,\"{'Li': 12.0, 'I': 6.0, 'O': 36.0}\",\"('I', 'Li', 'O')\",OTHERS,True\nmp-1212611,\"{'Hf': 4.0, 'As': 8.0, 'O': 36.0}\",\"('As', 'Hf', 'O')\",OTHERS,True\nmp-1219538,\"{'Sb': 10.0, 'O': 28.0}\",\"('O', 'Sb')\",OTHERS,True\nmp-979952,\"{'Yb': 3.0, 'Ti': 1.0}\",\"('Ti', 'Yb')\",OTHERS,True\nmp-1207397,\"{'Zn': 1.0, 'Os': 2.0, 'N': 4.0, 'O': 8.0}\",\"('N', 'O', 'Os', 'Zn')\",OTHERS,True\nmp-1185774,\"{'Mg': 4.0, 'Sc': 2.0}\",\"('Mg', 'Sc')\",OTHERS,True\nmp-1245359,\"{'In': 20.0, 'Si': 12.0, 'N': 36.0}\",\"('In', 'N', 'Si')\",OTHERS,True\nmp-5151,\"{'S': 12.0, 'Ge': 2.0, 'Cd': 8.0}\",\"('Cd', 'Ge', 'S')\",OTHERS,True\nmp-975337,\"{'Rb': 1.0, 'F': 3.0}\",\"('F', 'Rb')\",OTHERS,True\nmp-1220161,\"{'Nd': 2.0, 'Al': 2.0, 'Si': 2.0}\",\"('Al', 'Nd', 'Si')\",OTHERS,True\nmp-504549,\"{'U': 4.0, 'Fe': 4.0, 'O': 16.0}\",\"('Fe', 'O', 'U')\",OTHERS,True\nmp-1099703,\"{'Sr': 6.0, 'Ca': 2.0, 'Ti': 6.0, 'Mn': 2.0, 'O': 24.0}\",\"('Ca', 'Mn', 'O', 'Sr', 'Ti')\",OTHERS,True\nmp-761011,\"{'Li': 6.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-1025028,\"{'Pr': 2.0, 'Si': 2.0, 'Ag': 2.0}\",\"('Ag', 'Pr', 'Si')\",OTHERS,True\nmp-1208914,\"{'Sm': 6.0, 'Al': 24.0, 'Ru': 8.0}\",\"('Al', 'Ru', 'Sm')\",OTHERS,True\nmp-10746,\"{'Mg': 4.0, 'Si': 2.0, 'Cu': 6.0}\",\"('Cu', 'Mg', 'Si')\",OTHERS,True\nmp-556799,\"{'Li': 12.0, 'In': 4.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'In', 'Li', 'O')\",OTHERS,True\nmp-776050,\"{'Li': 2.0, 'Fe': 4.0, 'F': 18.0}\",\"('F', 'Fe', 'Li')\",OTHERS,True\nmp-680168,\"{'K': 8.0, 'Re': 12.0, 'C': 8.0, 'Se': 20.0, 'N': 8.0}\",\"('C', 'K', 'N', 'Re', 'Se')\",OTHERS,True\nmp-3062,\"{'N': 2.0, 'S': 3.0, 'Sm': 4.0}\",\"('N', 'S', 'Sm')\",OTHERS,True\nmp-698396,\"{'Cu': 2.0, 'H': 8.0, 'C': 4.0, 'N': 2.0, 'Cl': 6.0, 'O': 2.0}\",\"('C', 'Cl', 'Cu', 'H', 'N', 'O')\",OTHERS,True\nmp-754557,\"{'Eu': 2.0, 'Y': 4.0, 'O': 8.0}\",\"('Eu', 'O', 'Y')\",OTHERS,True\nmp-1185693,\"{'Mg': 1.0, 'B': 1.0, 'O': 3.0}\",\"('B', 'Mg', 'O')\",OTHERS,True\nmp-778080,\"{'Ba': 2.0, 'Y': 6.0, 'F': 22.0}\",\"('Ba', 'F', 'Y')\",OTHERS,True\nmp-738686,\"{'Sb': 2.0, 'H': 48.0, 'C': 12.0, 'N': 6.0, 'Cl': 12.0}\",\"('C', 'Cl', 'H', 'N', 'Sb')\",OTHERS,True\nmp-766205,\"{'Sr': 1.0, 'Li': 1.0, 'La': 15.0, 'Co': 4.0, 'O': 32.0}\",\"('Co', 'La', 'Li', 'O', 'Sr')\",OTHERS,True\nmp-1223863,\"{'In': 1.0, 'Cu': 1.0, 'Te': 2.0}\",\"('Cu', 'In', 'Te')\",OTHERS,True\nmp-697313,\"{'Al': 24.0, 'Tl': 10.0, 'Cd': 7.0, 'Si': 24.0, 'O': 96.0}\",\"('Al', 'Cd', 'O', 'Si', 'Tl')\",OTHERS,True\nmp-1187237,\"{'Sr': 6.0, 'Ce': 2.0}\",\"('Ce', 'Sr')\",OTHERS,True\nmp-763345,\"{'Li': 8.0, 'V': 4.0, 'O': 8.0, 'F': 4.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,True\nmvc-6576,\"{'Ca': 2.0, 'Sn': 4.0, 'O': 8.0}\",\"('Ca', 'O', 'Sn')\",OTHERS,True\nmp-561942,\"{'Cs': 3.0, 'Ta': 1.0, 'O': 8.0}\",\"('Cs', 'O', 'Ta')\",OTHERS,True\nmp-1222633,\"{'Mg': 4.0, 'Fe': 1.0, 'Cl': 1.0, 'O': 13.0}\",\"('Cl', 'Fe', 'Mg', 'O')\",OTHERS,True\nmp-867271,\"{'Be': 2.0, 'Co': 1.0, 'Ni': 1.0}\",\"('Be', 'Co', 'Ni')\",OTHERS,True\nmvc-513,\"{'Ba': 1.0, 'Zn': 1.0, 'Cu': 4.0, 'O': 8.0}\",\"('Ba', 'Cu', 'O', 'Zn')\",OTHERS,True\nmp-554810,\"{'Ba': 4.0, 'V': 2.0, 'P': 4.0, 'O': 18.0}\",\"('Ba', 'O', 'P', 'V')\",OTHERS,True\nmp-1100068,\"{'Ba': 4.0, 'Sr': 28.0, 'Co': 20.0, 'Cu': 12.0, 'O': 80.0}\",\"('Ba', 'Co', 'Cu', 'O', 'Sr')\",OTHERS,True\nmp-1238596,\"{'Mg': 2.0, 'V': 10.0, 'H': 40.0, 'N': 2.0, 'O': 44.0}\",\"('H', 'Mg', 'N', 'O', 'V')\",OTHERS,True\nmp-21281,\"{'Mn': 2.0, 'Ge': 2.0, 'Th': 1.0}\",\"('Ge', 'Mn', 'Th')\",OTHERS,True\nmp-770400,\"{'Li': 8.0, 'Al': 4.0, 'Ni': 4.0, 'O': 16.0}\",\"('Al', 'Li', 'Ni', 'O')\",OTHERS,True\nmp-685216,\"{'Nb': 24.0, 'Tl': 2.0, 'Te': 32.0}\",\"('Nb', 'Te', 'Tl')\",OTHERS,True\nmp-31157,\"{'N': 1.0, 'Ca': 3.0, 'Sb': 1.0}\",\"('Ca', 'N', 'Sb')\",OTHERS,True\nmp-1221080,\"{'Na': 1.0, 'Ce': 1.0, 'Ti': 2.0, 'O': 6.0}\",\"('Ce', 'Na', 'O', 'Ti')\",OTHERS,True\nmp-1208022,\"{'Tl': 1.0, 'S': 2.0, 'N': 1.0, 'O': 8.0}\",\"('N', 'O', 'S', 'Tl')\",OTHERS,True\nmp-1018687,\"{'Er': 2.0, 'S': 4.0}\",\"('Er', 'S')\",OTHERS,True\nmp-1219596,\"{'Rb': 5.0, 'Bi': 4.0}\",\"('Bi', 'Rb')\",OTHERS,True\nmp-1237926,\"{'Lu': 4.0, 'Mg': 4.0, 'Se': 12.0}\",\"('Lu', 'Mg', 'Se')\",OTHERS,True\nmp-777676,\"{'Li': 4.0, 'V': 4.0, 'F': 12.0}\",\"('F', 'Li', 'V')\",OTHERS,True\nmp-12221,\"{'O': 16.0, 'Ca': 4.0, 'Te': 4.0}\",\"('Ca', 'O', 'Te')\",OTHERS,True\nmp-1212003,\"{'La': 12.0, 'Fe': 20.0, 'O': 48.0}\",\"('Fe', 'La', 'O')\",OTHERS,True\nmp-1176989,\"{'Li': 12.0, 'Mn': 2.0, 'V': 6.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,True\nmp-22753,\"{'C': 4.0, 'Fe': 2.0, 'U': 2.0}\",\"('C', 'Fe', 'U')\",OTHERS,True\nmp-756780,\"{'Ti': 3.0, 'Mn': 3.0, 'Cr': 2.0, 'O': 16.0}\",\"('Cr', 'Mn', 'O', 'Ti')\",OTHERS,True\nmp-3804,\"{'P': 2.0, 'Sr': 1.0, 'Ru': 2.0}\",\"('P', 'Ru', 'Sr')\",OTHERS,True\nmp-1190833,\"{'Nd': 2.0, 'Si': 4.0, 'Ni': 18.0}\",\"('Nd', 'Ni', 'Si')\",OTHERS,True\nmp-653248,\"{'Cr': 24.0, 'B': 56.0, 'Cl': 8.0, 'O': 104.0}\",\"('B', 'Cl', 'Cr', 'O')\",OTHERS,True\nmp-705411,\"{'W': 6.0, 'O': 18.0}\",\"('O', 'W')\",OTHERS,True\nmp-1220084,\"{'Nd': 2.0, 'Ti': 2.0, 'Cd': 2.0, 'Sb': 2.0, 'O': 14.0}\",\"('Cd', 'Nd', 'O', 'Sb', 'Ti')\",OTHERS,True\nmp-849636,\"{'Sr': 4.0, 'Ta': 4.0, 'N': 4.0, 'O': 8.0}\",\"('N', 'O', 'Sr', 'Ta')\",OTHERS,True\nmp-19166,\"{'O': 12.0, 'Ni': 2.0, 'Sr': 6.0, 'Pt': 2.0}\",\"('Ni', 'O', 'Pt', 'Sr')\",OTHERS,True\nmp-1093897,\"{'Li': 2.0, 'La': 1.0, 'Al': 1.0}\",\"('Al', 'La', 'Li')\",OTHERS,True\nmp-707274,\"{'H': 26.0, 'Se': 4.0, 'N': 6.0, 'O': 16.0}\",\"('H', 'N', 'O', 'Se')\",OTHERS,True\nmp-18973,\"{'O': 20.0, 'Co': 4.0, 'Se': 8.0}\",\"('Co', 'O', 'Se')\",OTHERS,True\nmp-557364,\"{'Cd': 8.0, 'B': 16.0, 'F': 64.0}\",\"('B', 'Cd', 'F')\",OTHERS,True\nmp-540592,\"{'Fe': 8.0, 'Te': 12.0, 'O': 36.0}\",\"('Fe', 'O', 'Te')\",OTHERS,True\nmp-5568,\"{'B': 4.0, 'Co': 11.0, 'Nd': 3.0}\",\"('B', 'Co', 'Nd')\",OTHERS,True\nmp-19435,\"{'O': 8.0, 'Co': 2.0, 'Mo': 2.0}\",\"('Co', 'Mo', 'O')\",OTHERS,True\nmp-1227989,\"{'Ba': 1.0, 'Sr': 1.0, 'Al': 20.0, 'Cu': 2.0, 'O': 34.0}\",\"('Al', 'Ba', 'Cu', 'O', 'Sr')\",OTHERS,True\nmp-504578,\"{'Mg': 4.0, 'Cr': 4.0, 'O': 16.0}\",\"('Cr', 'Mg', 'O')\",OTHERS,True\nmp-1178644,\"{'Zn': 1.0, 'Ga': 1.0, 'Cu': 3.0, 'Se': 4.0}\",\"('Cu', 'Ga', 'Se', 'Zn')\",OTHERS,True\nmp-28980,\"{'K': 12.0, 'Bi': 4.0, 'Se': 12.0}\",\"('Bi', 'K', 'Se')\",OTHERS,True\nmp-1197535,\"{'Y': 8.0, 'Nb': 12.0, 'Ge': 16.0}\",\"('Ge', 'Nb', 'Y')\",OTHERS,True\nmp-540258,\"{'O': 24.0, 'P': 8.0, 'Ti': 2.0}\",\"('O', 'P', 'Ti')\",OTHERS,True\nmp-989643,\"{'La': 2.0, 'Co': 2.0, 'N': 6.0}\",\"('Co', 'La', 'N')\",OTHERS,True\nmp-752900,\"{'Li': 4.0, 'Fe': 2.0, 'Si': 2.0, 'O': 8.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,True\nmp-33320,\"{'Ca': 4.0, 'U': 4.0, 'S': 8.0}\",\"('Ca', 'S', 'U')\",OTHERS,True\nmp-1174359,\"{'Li': 8.0, 'Mn': 6.0, 'O': 14.0}\",\"('Li', 'Mn', 'O')\",OTHERS,True\nmp-1207174,\"{'Li': 1.0, 'Al': 2.0, 'Ge': 1.0}\",\"('Al', 'Ge', 'Li')\",OTHERS,True\nmp-1178290,\"{'Fe': 8.0, 'O': 2.0, 'F': 14.0}\",\"('F', 'Fe', 'O')\",OTHERS,True\nmp-672654,\"{'Zr': 6.0, 'Co': 16.0, 'Ge': 7.0}\",\"('Co', 'Ge', 'Zr')\",OTHERS,True\nmp-567353,\"{'Mn': 2.0, 'Si': 2.0, 'Ni': 2.0}\",\"('Mn', 'Ni', 'Si')\",OTHERS,True\nmp-1198694,\"{'Cs': 4.0, 'V': 4.0, 'P': 4.0, 'O': 20.0}\",\"('Cs', 'O', 'P', 'V')\",OTHERS,True\nmp-1009113,\"{'Ho': 1.0, 'Hg': 2.0}\",\"('Hg', 'Ho')\",OTHERS,True\nmp-1175674,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-19911,\"{'Co': 1.0, 'In': 5.0, 'Tm': 1.0}\",\"('Co', 'In', 'Tm')\",OTHERS,True\nmp-1207835,\"{'Tm': 6.0, 'Sb': 2.0, 'O': 14.0}\",\"('O', 'Sb', 'Tm')\",OTHERS,True\nmp-1208811,\"{'Sr': 2.0, 'Lu': 1.0, 'Cu': 2.0, 'Bi': 2.0, 'O': 8.0}\",\"('Bi', 'Cu', 'Lu', 'O', 'Sr')\",OTHERS,True\nmp-562762,\"{'Ba': 4.0, 'Co': 2.0, 'Se': 4.0, 'Cl': 4.0, 'O': 12.0}\",\"('Ba', 'Cl', 'Co', 'O', 'Se')\",OTHERS,True\nmp-1208733,\"{'Sr': 2.0, 'Ta': 1.0, 'Co': 1.0, 'O': 6.0}\",\"('Co', 'O', 'Sr', 'Ta')\",OTHERS,True\nmp-1189703,\"{'Yb': 4.0, 'Ge': 8.0, 'Pd': 4.0}\",\"('Ge', 'Pd', 'Yb')\",OTHERS,True\nmp-1175349,\"{'Li': 7.0, 'Mn': 5.0, 'O': 12.0}\",\"('Li', 'Mn', 'O')\",OTHERS,True\nmp-1025271,\"{'Al': 1.0, 'P': 1.0, 'Pt': 5.0}\",\"('Al', 'P', 'Pt')\",OTHERS,True\nmp-29154,\"{'P': 2.0, 'Co': 2.0, 'Hf': 4.0}\",\"('Co', 'Hf', 'P')\",OTHERS,True\nmp-1193044,\"{'Bi': 2.0, 'P': 6.0, 'N': 2.0, 'O': 20.0}\",\"('Bi', 'N', 'O', 'P')\",OTHERS,True\nmp-744576,\"{'Co': 4.0, 'H': 32.0, 'S': 4.0, 'O': 32.0}\",\"('Co', 'H', 'O', 'S')\",OTHERS,True\nmp-13938,\"{'O': 6.0, 'Sr': 2.0, 'Dy': 1.0, 'Re': 1.0}\",\"('Dy', 'O', 'Re', 'Sr')\",OTHERS,True\nmp-1204722,\"{'Ba': 4.0, 'Zr': 2.0, 'C': 16.0, 'O': 38.0}\",\"('Ba', 'C', 'O', 'Zr')\",OTHERS,True\nmp-763560,\"{'Li': 1.0, 'Mn': 2.0, 'C': 4.0, 'O': 12.0}\",\"('C', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-21479,\"{'Rh': 1.0, 'In': 5.0, 'La': 1.0}\",\"('In', 'La', 'Rh')\",OTHERS,True\nmp-569098,\"{'Rb': 4.0, 'Na': 44.0, 'W': 16.0, 'N': 48.0}\",\"('N', 'Na', 'Rb', 'W')\",OTHERS,True\nmp-976135,\"{'Pr': 1.0, 'Er': 3.0}\",\"('Er', 'Pr')\",OTHERS,True\nmp-752428,\"{'Rb': 8.0, 'Pr': 4.0, 'O': 12.0}\",\"('O', 'Pr', 'Rb')\",OTHERS,True\nmp-773438,\"{'Li': 10.0, 'Cu': 2.0, 'P': 4.0, 'O': 16.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,True\nmp-22998,\"{'S': 2.0, 'Cr': 2.0, 'Br': 2.0}\",\"('Br', 'Cr', 'S')\",OTHERS,True\nmp-568225,\"{'Tb': 4.0, 'B': 16.0}\",\"('B', 'Tb')\",OTHERS,True\nmp-1101228,\"{'V': 4.0, 'F': 18.0}\",\"('F', 'V')\",OTHERS,True\nmp-849589,\"{'Li': 5.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,True\nmp-204,\"{'Fe': 4.0, 'Ce': 2.0}\",\"('Ce', 'Fe')\",OTHERS,True\nmp-851103,\"{'Li': 4.0, 'V': 4.0, 'P': 8.0, 'H': 8.0, 'O': 32.0}\",\"('H', 'Li', 'O', 'P', 'V')\",OTHERS,True\nmp-1198901,\"{'Cs': 4.0, 'Cu': 2.0, 'H': 16.0, 'Se': 4.0, 'O': 24.0}\",\"('Cs', 'Cu', 'H', 'O', 'Se')\",OTHERS,True\nmp-1102476,\"{'Tb': 4.0, 'As': 4.0, 'Se': 4.0}\",\"('As', 'Se', 'Tb')\",OTHERS,True\nmp-1209123,\"{'Rh': 4.0, 'W': 2.0, 'O': 12.0}\",\"('O', 'Rh', 'W')\",OTHERS,True\nmp-1211067,\"{'Li': 2.0, 'Tm': 4.0, 'Br': 10.0}\",\"('Br', 'Li', 'Tm')\",OTHERS,True\nmp-569273,\"{'Mg': 88.0, 'Ir': 14.0}\",\"('Ir', 'Mg')\",OTHERS,True\nmp-5259,\"{'F': 8.0, 'Al': 2.0, 'Rb': 2.0}\",\"('Al', 'F', 'Rb')\",OTHERS,True\nmp-11644,\"{'Li': 4.0, 'Ca': 2.0}\",\"('Ca', 'Li')\",OTHERS,True\nmp-1246153,\"{'Ti': 6.0, 'N': 4.0}\",\"('N', 'Ti')\",OTHERS,True\nmp-757163,\"{'Li': 1.0, 'V': 1.0, 'F': 6.0}\",\"('F', 'Li', 'V')\",OTHERS,True\nmp-28177,\"{'Na': 2.0, 'Cl': 12.0, 'Sb': 2.0}\",\"('Cl', 'Na', 'Sb')\",OTHERS,True\nmp-756074,\"{'Bi': 4.0, 'P': 2.0, 'O': 12.0}\",\"('Bi', 'O', 'P')\",OTHERS,True\nmp-1189145,\"{'Dy': 4.0, 'Si': 8.0, 'Mo': 6.0}\",\"('Dy', 'Mo', 'Si')\",OTHERS,True\nmp-1104602,\"{'Cu': 2.0, 'Ag': 1.0, 'S': 2.0, 'O': 10.0}\",\"('Ag', 'Cu', 'O', 'S')\",OTHERS,True\nmp-1210034,\"{'Nd': 12.0, 'Ti': 4.0, 'Cl': 20.0, 'O': 16.0}\",\"('Cl', 'Nd', 'O', 'Ti')\",OTHERS,True\nmp-1224293,\"{'Ho': 4.0, 'Fe': 2.0, 'Mo': 2.0, 'O': 14.0}\",\"('Fe', 'Ho', 'Mo', 'O')\",OTHERS,True\nmp-1211602,\"{'Li': 4.0, 'Lu': 16.0, 'Ge': 16.0}\",\"('Ge', 'Li', 'Lu')\",OTHERS,True\nmp-3222,\"{'O': 14.0, 'Ru': 2.0, 'La': 6.0}\",\"('La', 'O', 'Ru')\",OTHERS,True\nmp-1177550,\"{'Li': 8.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-14763,\"{'N': 6.0, 'Ca': 6.0, 'Mn': 2.0}\",\"('Ca', 'Mn', 'N')\",OTHERS,True\nmp-1179693,\"{'Rb': 2.0, 'Mn': 2.0, 'As': 2.0}\",\"('As', 'Mn', 'Rb')\",OTHERS,True\nmp-754054,\"{'Li': 2.0, 'V': 1.0, 'Cr': 1.0, 'O': 4.0}\",\"('Cr', 'Li', 'O', 'V')\",OTHERS,True\nmp-1211839,\"{'K': 2.0, 'Al': 4.0, 'Si': 8.0, 'H': 4.0, 'O': 24.0}\",\"('Al', 'H', 'K', 'O', 'Si')\",OTHERS,True\nmp-758542,\"{'Li': 24.0, 'Cu': 24.0, 'P': 24.0, 'O': 96.0}\",\"('Cu', 'Li', 'O', 'P')\",OTHERS,True\nmp-754447,\"{'Cd': 6.0, 'Co': 5.0, 'O': 15.0}\",\"('Cd', 'Co', 'O')\",OTHERS,True\nmp-2776,\"{'Ga': 2.0, 'Au': 1.0}\",\"('Au', 'Ga')\",OTHERS,True\nmp-1214847,\"{'B': 8.0, 'H': 16.0, 'O': 16.0}\",\"('B', 'H', 'O')\",OTHERS,True\nmp-1199853,\"{'In': 2.0, 'S': 6.0, 'N': 6.0, 'O': 24.0}\",\"('In', 'N', 'O', 'S')\",OTHERS,True\nmp-759232,\"{'Li': 1.0, 'Mn': 1.0, 'V': 1.0, 'P': 4.0, 'O': 14.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,True\nmvc-7769,\"{'Mg': 2.0, 'Ni': 4.0, 'O': 8.0}\",\"('Mg', 'Ni', 'O')\",OTHERS,True\nmp-17165,\"{'C': 12.0, 'N': 12.0, 'Na': 2.0, 'Cr': 2.0, 'Cs': 4.0}\",\"('C', 'Cr', 'Cs', 'N', 'Na')\",OTHERS,True\nmp-772650,\"{'Li': 8.0, 'Cr': 20.0, 'O': 48.0}\",\"('Cr', 'Li', 'O')\",OTHERS,True\nmp-862934,\"{'Pm': 1.0, 'Li': 2.0, 'Tl': 1.0}\",\"('Li', 'Pm', 'Tl')\",OTHERS,True\nmp-20061,\"{'Ge': 3.0, 'Pt': 6.0}\",\"('Ge', 'Pt')\",OTHERS,True\nmvc-12291,\"{'Ca': 2.0, 'V': 4.0, 'Ni': 4.0, 'P': 8.0, 'O': 36.0}\",\"('Ca', 'Ni', 'O', 'P', 'V')\",OTHERS,True\nmp-766441,\"{'Li': 4.0, 'V': 2.0, 'C': 4.0, 'O': 12.0}\",\"('C', 'Li', 'O', 'V')\",OTHERS,True\nmp-1217015,\"{'Ti': 1.0, 'Al': 2.0, 'V': 1.0}\",\"('Al', 'Ti', 'V')\",OTHERS,True\nAl 65 Cu 20 Os 15,\"{'Al': 65.0, 'Cu': 20.0, 'Os': 15.0}\",\"('Al', 'Cu', 'Os')\",QC,True\nmp-6853,\"{'O': 14.0, 'Si': 2.0, 'Ca': 3.0, 'Ga': 3.0, 'Ta': 1.0}\",\"('Ca', 'Ga', 'O', 'Si', 'Ta')\",OTHERS,True\nmp-752601,\"{'Na': 1.0, 'Mn': 3.0, 'O': 4.0}\",\"('Mn', 'Na', 'O')\",OTHERS,True\nmp-780053,\"{'Na': 16.0, 'Ge': 16.0, 'O': 40.0}\",\"('Ge', 'Na', 'O')\",OTHERS,True\nmp-680260,\"{'Ti': 45.0, 'Se': 16.0}\",\"('Se', 'Ti')\",OTHERS,True\nmp-17230,\"{'Ga': 2.0, 'Nb': 10.0, 'Sn': 4.0}\",\"('Ga', 'Nb', 'Sn')\",OTHERS,True\nmp-1228976,\"{'Ba': 9.0, 'Bi': 18.0, 'S': 36.0}\",\"('Ba', 'Bi', 'S')\",OTHERS,True\nmp-1180189,\"{'Na': 1.0, 'Al': 3.0, 'Cr': 2.0, 'O': 14.0}\",\"('Al', 'Cr', 'Na', 'O')\",OTHERS,True\nmp-1017467,\"{'Ca': 1.0, 'Mn': 1.0, 'O': 3.0}\",\"('Ca', 'Mn', 'O')\",OTHERS,True\nmp-675771,\"{'Al': 12.0, 'P': 12.0, 'O': 48.0}\",\"('Al', 'O', 'P')\",OTHERS,True\nmp-1196864,\"{'K': 4.0, 'In': 4.0, 'P': 8.0, 'O': 32.0}\",\"('In', 'K', 'O', 'P')\",OTHERS,True\nmp-30960,\"{'O': 24.0, 'Dy': 4.0, 'W': 6.0}\",\"('Dy', 'O', 'W')\",OTHERS,True\nmp-1096377,\"{'Ti': 2.0, 'Ga': 1.0, 'Tc': 1.0}\",\"('Ga', 'Tc', 'Ti')\",OTHERS,True\nmp-1020025,\"{'Li': 16.0, 'B': 48.0, 'O': 80.0}\",\"('B', 'Li', 'O')\",OTHERS,True\nmp-760690,\"{'Li': 8.0, 'Te': 1.0, 'O': 6.0}\",\"('Li', 'O', 'Te')\",OTHERS,True\nmp-1206098,\"{'Zr': 1.0, 'Mn': 1.0, 'F': 6.0}\",\"('F', 'Mn', 'Zr')\",OTHERS,True\nmp-1175530,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1204808,\"{'Na': 4.0, 'Li': 4.0, 'Ni': 2.0, 'P': 12.0, 'H': 48.0, 'O': 60.0}\",\"('H', 'Li', 'Na', 'Ni', 'O', 'P')\",OTHERS,True\nmp-999145,\"{'Sm': 2.0, 'Ni': 2.0}\",\"('Ni', 'Sm')\",OTHERS,True\nmp-1177080,\"{'Li': 10.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-861904,\"{'La': 6.0, 'Al': 2.0, 'Cr': 2.0, 'S': 14.0}\",\"('Al', 'Cr', 'La', 'S')\",OTHERS,True\nmp-557322,\"{'Bi': 4.0, 'Te': 2.0, 'Br': 2.0, 'O': 9.0}\",\"('Bi', 'Br', 'O', 'Te')\",OTHERS,True\nmp-707958,\"{'P': 2.0, 'H': 18.0, 'C': 4.0, 'N': 8.0, 'O': 10.0}\",\"('C', 'H', 'N', 'O', 'P')\",OTHERS,True\nmp-28838,\"{'C': 2.0, 'Si': 2.0, 'U': 3.0}\",\"('C', 'Si', 'U')\",OTHERS,True\nmp-1261917,\"{'Ba': 2.0, 'Al': 2.0, 'W': 8.0, 'O': 14.0}\",\"('Al', 'Ba', 'O', 'W')\",OTHERS,True\nmp-3849,\"{'S': 4.0, 'Fe': 2.0, 'Tl': 2.0}\",\"('Fe', 'S', 'Tl')\",OTHERS,True\nmp-26903,\"{'Li': 6.0, 'V': 4.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-684690,\"{'Sc': 4.0, 'Se': 6.0}\",\"('Sc', 'Se')\",OTHERS,True\nmvc-8750,\"{'Zn': 1.0, 'Sn': 4.0, 'O': 8.0}\",\"('O', 'Sn', 'Zn')\",OTHERS,True\nmp-645796,\"{'Tl': 4.0, 'Fe': 4.0, 'Mo': 8.0, 'O': 32.0}\",\"('Fe', 'Mo', 'O', 'Tl')\",OTHERS,True\nmp-759038,\"{'Li': 4.0, 'Ti': 2.0, 'Fe': 3.0, 'Co': 3.0, 'O': 16.0}\",\"('Co', 'Fe', 'Li', 'O', 'Ti')\",OTHERS,True\nmp-614444,\"{'Zr': 1.0, 'Zn': 1.0}\",\"('Zn', 'Zr')\",OTHERS,True\nmp-1192359,\"{'Cs': 4.0, 'Mn': 2.0, 'Te': 4.0, 'S': 12.0}\",\"('Cs', 'Mn', 'S', 'Te')\",OTHERS,True\nmp-22623,\"{'Cu': 4.0, 'Ge': 4.0, 'Ce': 3.0}\",\"('Ce', 'Cu', 'Ge')\",OTHERS,True\nmp-22399,\"{'O': 4.0, 'S': 8.0, 'Yb': 8.0}\",\"('O', 'S', 'Yb')\",OTHERS,True\nmp-1211411,\"{'Li': 24.0, 'Sn': 24.0, 'Pt': 16.0}\",\"('Li', 'Pt', 'Sn')\",OTHERS,True\nmp-1181733,\"{'K': 4.0, 'Ba': 4.0, 'V': 20.0, 'O': 72.0}\",\"('Ba', 'K', 'O', 'V')\",OTHERS,True\nmp-30656,\"{'Ti': 3.0, 'Ni': 3.0, 'Ga': 3.0}\",\"('Ga', 'Ni', 'Ti')\",OTHERS,True\nmp-1245020,\"{'W': 34.0, 'O': 68.0}\",\"('O', 'W')\",OTHERS,True\nmp-1207202,\"{'La': 1.0, 'P': 2.0, 'Pd': 2.0}\",\"('La', 'P', 'Pd')\",OTHERS,True\nmp-1209593,\"{'Rb': 4.0, 'Cu': 2.0, 'Cl': 8.0, 'O': 4.0}\",\"('Cl', 'Cu', 'O', 'Rb')\",OTHERS,True\nmp-764374,\"{'Li': 4.0, 'Mn': 8.0, 'O': 2.0, 'F': 16.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1247221,\"{'Lu': 3.0, 'Mg': 2.0, 'Mn': 1.0, 'S': 8.0}\",\"('Lu', 'Mg', 'Mn', 'S')\",OTHERS,True\nmp-4865,\"{'Ni': 2.0, 'Ge': 2.0, 'Er': 1.0}\",\"('Er', 'Ge', 'Ni')\",OTHERS,True\nmp-3071,\"{'B': 2.0, 'Ni': 13.0, 'Nd': 3.0}\",\"('B', 'Nd', 'Ni')\",OTHERS,True\nmp-1100553,\"{'Li': 9.0, 'Mn': 7.0, 'O': 16.0}\",\"('Li', 'Mn', 'O')\",OTHERS,True\nmp-1199487,\"{'Mo': 4.0, 'H': 32.0, 'S': 16.0, 'N': 8.0}\",\"('H', 'Mo', 'N', 'S')\",OTHERS,True\nmp-570525,\"{'La': 8.0, 'Zn': 8.0, 'Rh': 8.0}\",\"('La', 'Rh', 'Zn')\",OTHERS,True\nmp-1246782,\"{'Al': 4.0, 'Ni': 2.0, 'N': 6.0}\",\"('Al', 'N', 'Ni')\",OTHERS,True\nmp-1180,\"{'Mn': 1.0, 'Pt': 3.0}\",\"('Mn', 'Pt')\",OTHERS,True\nmp-1217101,\"{'Ti': 3.0, 'S': 4.0}\",\"('S', 'Ti')\",OTHERS,True\nmp-772728,\"{'Na': 10.0, 'Li': 2.0, 'Mn': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Li', 'Mn', 'Na', 'O', 'P')\",OTHERS,True\nmp-1037039,\"{'Mg': 30.0, 'Fe': 1.0, 'Co': 1.0, 'O': 32.0}\",\"('Co', 'Fe', 'Mg', 'O')\",OTHERS,True\nmp-1200531,\"{'Mn': 1.0, 'H': 44.0, 'C': 12.0, 'N': 8.0, 'Cl': 2.0, 'O': 10.0}\",\"('C', 'Cl', 'H', 'Mn', 'N', 'O')\",OTHERS,True\nmp-1205455,\"{'Sn': 4.0, 'Se': 4.0}\",\"('Se', 'Sn')\",OTHERS,True\nmp-1178831,\"{'V': 8.0, 'O': 14.0}\",\"('O', 'V')\",OTHERS,True\nmp-1040135,\"{'Li': 1.0, 'Mg': 30.0, 'B': 1.0, 'O': 32.0}\",\"('B', 'Li', 'Mg', 'O')\",OTHERS,True\nmp-1222656,\"{'Li': 2.0, 'Ti': 1.0, 'O': 3.0}\",\"('Li', 'O', 'Ti')\",OTHERS,True\nmp-1084826,\"{'Nd': 4.0, 'Co': 6.0}\",\"('Co', 'Nd')\",OTHERS,True\nmp-1077232,\"{'W': 2.0, 'N': 4.0}\",\"('N', 'W')\",OTHERS,True\nmp-867795,\"{'Sc': 1.0, 'Nb': 1.0, 'Tc': 2.0}\",\"('Nb', 'Sc', 'Tc')\",OTHERS,True\nGa 2.3 Cu 3.7 Sc 1,\"{'Ga': 2.3, 'Cu': 3.7, 'Sc': 1.0}\",\"('Cu', 'Ga', 'Sc')\",AC,True\nmp-1183996,\"{'Cu': 6.0, 'F': 2.0}\",\"('Cu', 'F')\",OTHERS,True\nmp-1185104,\"{'K': 1.0, 'In': 3.0}\",\"('In', 'K')\",OTHERS,True\nmp-758310,\"{'Li': 12.0, 'Ti': 8.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'O', 'P', 'Ti')\",OTHERS,True\nmp-1184494,\"{'Gd': 1.0, 'Cd': 1.0, 'Pt': 2.0}\",\"('Cd', 'Gd', 'Pt')\",OTHERS,True\nmp-757851,\"{'Li': 8.0, 'Co': 12.0, 'Ni': 4.0, 'O': 32.0}\",\"('Co', 'Li', 'Ni', 'O')\",OTHERS,True\nmp-775258,\"{'Li': 8.0, 'Mn': 2.0, 'Fe': 6.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1073477,\"{'Mg': 4.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,True\nmp-979057,\"{'Sm': 2.0, 'Zn': 1.0, 'Ag': 1.0}\",\"('Ag', 'Sm', 'Zn')\",OTHERS,True\nmp-21005,\"{'In': 1.0, 'Gd': 1.0}\",\"('Gd', 'In')\",OTHERS,True\nmp-772075,\"{'Li': 12.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-1114575,\"{'Rb': 2.0, 'Li': 1.0, 'Sb': 1.0, 'Br': 6.0}\",\"('Br', 'Li', 'Rb', 'Sb')\",OTHERS,True\nmp-684620,\"{'Nb': 25.0, 'S': 48.0}\",\"('Nb', 'S')\",OTHERS,True\nmp-1183381,\"{'Ba': 2.0, 'Zn': 1.0, 'In': 1.0}\",\"('Ba', 'In', 'Zn')\",OTHERS,True\nmp-1093817,\"{'Cs': 2.0, 'Hg': 1.0, 'Au': 1.0}\",\"('Au', 'Cs', 'Hg')\",OTHERS,True\nmp-1147544,\"{'Ba': 2.0, 'Tl': 1.0, 'Cu': 3.0, 'O': 7.0}\",\"('Ba', 'Cu', 'O', 'Tl')\",OTHERS,True\nmp-1177993,\"{'Li': 8.0, 'Mn': 12.0, 'Sn': 4.0, 'O': 32.0}\",\"('Li', 'Mn', 'O', 'Sn')\",OTHERS,True\nmp-1178068,\"{'Li': 24.0, 'Ti': 1.0, 'Cr': 11.0, 'O': 36.0}\",\"('Cr', 'Li', 'O', 'Ti')\",OTHERS,True\nmp-645504,\"{'Yb': 46.0, 'Mg': 8.0, 'Cu': 14.0}\",\"('Cu', 'Mg', 'Yb')\",OTHERS,True\nmp-1245152,{'Al': 100.0},\"('Al',)\",OTHERS,True\nmp-571607,\"{'Cd': 12.0, 'I': 24.0}\",\"('Cd', 'I')\",OTHERS,True\nmp-1197275,\"{'Tb': 8.0, 'Fe': 56.0, 'C': 4.0}\",\"('C', 'Fe', 'Tb')\",OTHERS,True\nmp-1207321,\"{'Pr': 2.0, 'Cu': 1.0, 'Sb': 3.0}\",\"('Cu', 'Pr', 'Sb')\",OTHERS,True\nmp-1266446,\"{'Li': 20.0, 'Cu': 2.0, 'Si': 4.0, 'O': 20.0}\",\"('Cu', 'Li', 'O', 'Si')\",OTHERS,True\nmp-1193638,\"{'Nb': 8.0, 'Fe': 2.0, 'Se': 16.0}\",\"('Fe', 'Nb', 'Se')\",OTHERS,True\nmp-757601,\"{'Ba': 8.0, 'Fe': 8.0, 'O': 21.0}\",\"('Ba', 'Fe', 'O')\",OTHERS,True\nmp-1028604,\"{'Te': 4.0, 'W': 4.0, 'S': 4.0}\",\"('S', 'Te', 'W')\",OTHERS,True\nmp-1078906,\"{'Co': 1.0, 'Te': 1.0, 'Pb': 2.0, 'O': 6.0}\",\"('Co', 'O', 'Pb', 'Te')\",OTHERS,True\nmp-1080715,\"{'Sc': 3.0, 'Si': 3.0, 'Ru': 3.0}\",\"('Ru', 'Sc', 'Si')\",OTHERS,True\nmp-1246994,\"{'Zr': 4.0, 'Cr': 4.0, 'Ag': 4.0, 'S': 16.0}\",\"('Ag', 'Cr', 'S', 'Zr')\",OTHERS,True\nmp-1026034,\"{'Mo': 1.0, 'W': 2.0, 'S': 6.0}\",\"('Mo', 'S', 'W')\",OTHERS,True\nmp-999508,\"{'Mn': 1.0, 'Si': 1.0, 'Ni': 2.0}\",\"('Mn', 'Ni', 'Si')\",OTHERS,True\nmp-1912,\"{'Zn': 1.0, 'Ag': 1.0}\",\"('Ag', 'Zn')\",OTHERS,True\nmp-1212351,\"{'Na': 3.0, 'Cr': 1.0, 'O': 6.0}\",\"('Cr', 'Na', 'O')\",OTHERS,True\nAg 46.9 In 38.7 Pr 14.4,\"{'Ag': 46.9, 'In': 38.7, 'Pr': 14.4}\",\"('Ag', 'In', 'Pr')\",AC,True\nmp-559931,\"{'F': 6.0, 'V': 2.0}\",\"('F', 'V')\",OTHERS,True\nmp-744914,\"{'Ca': 8.0, 'Mg': 1.0, 'Al': 6.0, 'Si': 5.0, 'O': 28.0}\",\"('Al', 'Ca', 'Mg', 'O', 'Si')\",OTHERS,True\nmp-25886,\"{'Cu': 2.0, 'P': 2.0, 'O': 8.0}\",\"('Cu', 'O', 'P')\",OTHERS,True\nmp-690515,\"{'K': 2.0, 'Co': 1.0, 'H': 2.0, 'Se': 2.0, 'O': 10.0}\",\"('Co', 'H', 'K', 'O', 'Se')\",OTHERS,True\nmp-17745,\"{'F': 12.0, 'S': 2.0, 'Mn': 4.0, 'Hg': 4.0}\",\"('F', 'Hg', 'Mn', 'S')\",OTHERS,True\nmvc-5910,\"{'Mg': 2.0, 'Mo': 1.0, 'W': 1.0, 'O': 6.0}\",\"('Mg', 'Mo', 'O', 'W')\",OTHERS,True\nmp-567772,{'Na': 8.0},\"('Na',)\",OTHERS,True\nmp-21497,\"{'O': 10.0, 'S': 2.0, 'Pb': 4.0}\",\"('O', 'Pb', 'S')\",OTHERS,True\nmp-1221041,\"{'Na': 8.0, 'Zr': 3.0, 'Si': 16.0, 'Sn': 1.0, 'H': 16.0, 'O': 52.0}\",\"('H', 'Na', 'O', 'Si', 'Sn', 'Zr')\",OTHERS,True\nmp-29000,\"{'B': 40.0, 'Na': 24.0, 'S': 72.0}\",\"('B', 'Na', 'S')\",OTHERS,True\nmp-756921,\"{'Li': 1.0, 'Zn': 1.0, 'Fe': 10.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'Zn')\",OTHERS,True\nmp-1194642,\"{'Fe': 2.0, 'H': 8.0, 'C': 6.0, 'O': 16.0}\",\"('C', 'Fe', 'H', 'O')\",OTHERS,True\nmp-1228195,\"{'Ba': 7.0, 'Nb': 4.0, 'Mo': 1.0, 'O': 20.0}\",\"('Ba', 'Mo', 'Nb', 'O')\",OTHERS,True\nmp-1214519,\"{'Ba': 12.0, 'Y': 4.0, 'Al': 8.0, 'O': 30.0}\",\"('Al', 'Ba', 'O', 'Y')\",OTHERS,True\nmp-558759,\"{'Cd': 8.0, 'Te': 12.0, 'Cl': 12.0, 'O': 26.0}\",\"('Cd', 'Cl', 'O', 'Te')\",OTHERS,True\nmp-510459,\"{'O': 8.0, 'V': 2.0, 'Mn': 2.0, 'Cu': 2.0}\",\"('Cu', 'Mn', 'O', 'V')\",OTHERS,True\nmp-610706,\"{'F': 6.0, 'Co': 1.0, 'Cs': 2.0}\",\"('Co', 'Cs', 'F')\",OTHERS,True\nmp-1033754,\"{'Hf': 1.0, 'Mg': 14.0, 'Sb': 1.0, 'O': 16.0}\",\"('Hf', 'Mg', 'O', 'Sb')\",OTHERS,True\nmp-32688,\"{'Nd': 16.0, 'Se': 24.0}\",\"('Nd', 'Se')\",OTHERS,True\nmp-1180728,\"{'Mg': 2.0, 'As': 2.0, 'N': 2.0, 'O': 20.0}\",\"('As', 'Mg', 'N', 'O')\",OTHERS,True\nmp-1208711,\"{'Sm': 4.0, 'Br': 8.0}\",\"('Br', 'Sm')\",OTHERS,True\nmp-974948,\"{'Rb': 3.0, 'In': 1.0}\",\"('In', 'Rb')\",OTHERS,True\nmp-1227254,\"{'Ca': 1.0, 'Mn': 1.0, 'Cu': 3.0, 'Ru': 3.0, 'O': 12.0}\",\"('Ca', 'Cu', 'Mn', 'O', 'Ru')\",OTHERS,True\nmp-1211085,\"{'Li': 2.0, 'Ca': 4.0, 'Fe': 2.0, 'N': 4.0}\",\"('Ca', 'Fe', 'Li', 'N')\",OTHERS,True\nmp-622213,\"{'Ge': 16.0, 'S': 32.0}\",\"('Ge', 'S')\",OTHERS,True\nmp-12275,\"{'Zn': 4.0, 'Sr': 4.0, 'Sb': 8.0}\",\"('Sb', 'Sr', 'Zn')\",OTHERS,True\nmp-706227,\"{'B': 6.0, 'H': 30.0, 'C': 24.0, 'N': 24.0, 'O': 12.0}\",\"('B', 'C', 'H', 'N', 'O')\",OTHERS,True\nmp-1215704,\"{'Zr': 3.0, 'Mn': 8.0, 'Al': 1.0}\",\"('Al', 'Mn', 'Zr')\",OTHERS,True\nmp-753013,\"{'Co': 3.0, 'Te': 1.0, 'O': 8.0}\",\"('Co', 'O', 'Te')\",OTHERS,True\nmp-982981,\"{'Na': 6.0, 'Rh': 2.0}\",\"('Na', 'Rh')\",OTHERS,True\nmp-4476,\"{'Si': 2.0, 'Cu': 2.0, 'Ho': 2.0}\",\"('Cu', 'Ho', 'Si')\",OTHERS,True\nmp-1195797,\"{'Hg': 4.0, 'Sb': 14.0, 'H': 4.0, 'Xe': 6.0, 'F': 118.0}\",\"('F', 'H', 'Hg', 'Sb', 'Xe')\",OTHERS,True\nmp-1079559,\"{'Cd': 2.0, 'P': 2.0, 'Se': 6.0}\",\"('Cd', 'P', 'Se')\",OTHERS,True\nmp-1225419,\"{'Fe': 16.0, 'Ni': 20.0, 'P': 12.0}\",\"('Fe', 'Ni', 'P')\",OTHERS,True\nmp-28630,\"{'B': 4.0, 'N': 8.0, 'Na': 12.0}\",\"('B', 'N', 'Na')\",OTHERS,True\nmp-9566,\"{'Mg': 2.0, 'Sr': 1.0, 'Sb': 2.0}\",\"('Mg', 'Sb', 'Sr')\",OTHERS,True\nmp-755597,\"{'Ca': 4.0, 'Ce': 2.0, 'O': 8.0}\",\"('Ca', 'Ce', 'O')\",OTHERS,True\nmp-541218,\"{'Gd': 4.0, 'P': 8.0, 'O': 40.0, 'H': 36.0}\",\"('Gd', 'H', 'O', 'P')\",OTHERS,True\nmp-1178106,\"{'Li': 4.0, 'Fe': 8.0, 'O': 16.0}\",\"('Fe', 'Li', 'O')\",OTHERS,True\nmp-1112148,\"{'Cs': 2.0, 'Na': 1.0, 'Mo': 1.0, 'I': 6.0}\",\"('Cs', 'I', 'Mo', 'Na')\",OTHERS,True\nmp-19879,\"{'P': 4.0, 'Pd': 12.0}\",\"('P', 'Pd')\",OTHERS,True\nmp-1222057,\"{'Mg': 1.0, 'Fe': 4.0, 'Co': 1.0, 'O': 8.0}\",\"('Co', 'Fe', 'Mg', 'O')\",OTHERS,True\nmp-768809,\"{'Li': 12.0, 'Bi': 4.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Bi', 'Li', 'O')\",OTHERS,True\nmp-27814,\"{'C': 1.0, 'S': 14.0}\",\"('C', 'S')\",OTHERS,True\nmp-685044,\"{'Yb': 16.0, 'S': 29.0}\",\"('S', 'Yb')\",OTHERS,True\nmp-8832,\"{'Si': 2.0, 'Cr': 2.0, 'Tb': 1.0}\",\"('Cr', 'Si', 'Tb')\",OTHERS,True\nmp-1096036,\"{'Sc': 1.0, 'Ga': 1.0, 'Ag': 2.0}\",\"('Ag', 'Ga', 'Sc')\",OTHERS,True\nmp-1114505,\"{'Rb': 2.0, 'Tl': 2.0, 'Br': 6.0}\",\"('Br', 'Rb', 'Tl')\",OTHERS,True\nmp-1218981,\"{'Sm': 1.0, 'Pa': 1.0, 'O': 4.0}\",\"('O', 'Pa', 'Sm')\",OTHERS,True\nZn 34.6 Mg 40 Al 25.4,\"{'Zn': 34.6, 'Mg': 40.0, 'Al': 25.4}\",\"('Al', 'Mg', 'Zn')\",AC,True\nmp-1188079,\"{'Zr': 8.0, 'In': 4.0, 'Ni': 8.0}\",\"('In', 'Ni', 'Zr')\",OTHERS,True\nmp-1182698,\"{'Fe': 16.0, 'H': 20.0, 'O': 34.0}\",\"('Fe', 'H', 'O')\",OTHERS,True\nmp-721058,\"{'Co': 8.0, 'P': 8.0, 'H': 24.0, 'O': 32.0}\",\"('Co', 'H', 'O', 'P')\",OTHERS,True\nmp-1206729,\"{'Nb': 1.0, 'Ga': 1.0, 'Pb': 2.0, 'O': 6.0}\",\"('Ga', 'Nb', 'O', 'Pb')\",OTHERS,True\nmp-3788,\"{'O': 14.0, 'Al': 8.0, 'Sr': 2.0}\",\"('Al', 'O', 'Sr')\",OTHERS,True\nmp-758930,\"{'Na': 40.0, 'Fe': 8.0, 'H': 8.0, 'O': 32.0}\",\"('Fe', 'H', 'Na', 'O')\",OTHERS,True\nmp-981384,\"{'Sc': 1.0, 'Os': 3.0}\",\"('Os', 'Sc')\",OTHERS,True\nmp-989537,\"{'Cs': 2.0, 'Tl': 1.0, 'In': 1.0, 'F': 6.0}\",\"('Cs', 'F', 'In', 'Tl')\",OTHERS,True\nmp-757613,\"{'Li': 8.0, 'Co': 12.0, 'Sb': 4.0, 'O': 32.0}\",\"('Co', 'Li', 'O', 'Sb')\",OTHERS,True\nmp-715517,\"{'V': 16.0, 'O': 32.0}\",\"('O', 'V')\",OTHERS,True\nmp-1019543,\"{'Ba': 8.0, 'Ca': 8.0, 'P': 16.0, 'O': 56.0}\",\"('Ba', 'Ca', 'O', 'P')\",OTHERS,True\nmp-557659,\"{'K': 2.0, 'V': 8.0, 'P': 14.0, 'O': 48.0}\",\"('K', 'O', 'P', 'V')\",OTHERS,True\nmp-1009752,\"{'Sc': 1.0, 'Si': 1.0}\",\"('Sc', 'Si')\",OTHERS,True\nmp-5149,\"{'Al': 1.0, 'Co': 2.0, 'Nb': 1.0}\",\"('Al', 'Co', 'Nb')\",OTHERS,True\nmp-684829,\"{'Co': 27.0, 'Se': 32.0}\",\"('Co', 'Se')\",OTHERS,True\nAu 65 Sn 20 Ce 15,\"{'Au': 65.0, 'Sn': 20.0, 'Ce': 15.0}\",\"('Au', 'Ce', 'Sn')\",AC,True\nmp-1198456,\"{'Na': 4.0, 'Zn': 2.0, 'P': 8.0, 'H': 32.0, 'O': 40.0}\",\"('H', 'Na', 'O', 'P', 'Zn')\",OTHERS,True\nmp-1191981,\"{'Ta': 7.0, 'B': 8.0, 'Ru': 6.0}\",\"('B', 'Ru', 'Ta')\",OTHERS,True\nmp-1093890,\"{'Li': 2.0, 'Tl': 1.0, 'Cd': 1.0}\",\"('Cd', 'Li', 'Tl')\",OTHERS,True\nmp-756466,\"{'Nb': 1.0, 'Co': 3.0, 'P': 4.0, 'O': 16.0}\",\"('Co', 'Nb', 'O', 'P')\",OTHERS,True\nmp-20276,\"{'F': 18.0, 'U': 4.0}\",\"('F', 'U')\",OTHERS,True\nmp-1102055,\"{'La': 4.0, 'As': 8.0}\",\"('As', 'La')\",OTHERS,True\nmp-1076181,\"{'Ba': 4.0, 'Sr': 28.0, 'Mn': 8.0, 'Fe': 24.0, 'O': 80.0}\",\"('Ba', 'Fe', 'Mn', 'O', 'Sr')\",OTHERS,True\nmp-1184025,\"{'Eu': 2.0, 'Mg': 1.0, 'In': 1.0}\",\"('Eu', 'In', 'Mg')\",OTHERS,True\nmp-1197569,\"{'Zn': 2.0, 'Ni': 21.0, 'B': 20.0}\",\"('B', 'Ni', 'Zn')\",OTHERS,True\nmp-1177426,\"{'Li': 4.0, 'Fe': 3.0, 'Cu': 2.0, 'Sn': 3.0, 'O': 16.0}\",\"('Cu', 'Fe', 'Li', 'O', 'Sn')\",OTHERS,True\nmp-769176,\"{'K': 40.0, 'Y': 8.0, 'O': 32.0}\",\"('K', 'O', 'Y')\",OTHERS,True\nmp-1113946,\"{'K': 1.0, 'Na': 2.0, 'Sb': 1.0, 'F': 6.0}\",\"('F', 'K', 'Na', 'Sb')\",OTHERS,True\nmp-1212862,\"{'Gd': 4.0, 'In': 8.0, 'Cl': 20.0}\",\"('Cl', 'Gd', 'In')\",OTHERS,True\nmp-1212745,\"{'Eu': 4.0, 'Er': 8.0, 'O': 16.0}\",\"('Er', 'Eu', 'O')\",OTHERS,True\nmp-1114541,\"{'K': 1.0, 'Rb': 2.0, 'Lu': 1.0, 'Cl': 6.0}\",\"('Cl', 'K', 'Lu', 'Rb')\",OTHERS,True\nmp-1218730,\"{'Sr': 2.0, 'Si': 3.0, 'Ni': 1.0}\",\"('Ni', 'Si', 'Sr')\",OTHERS,True\nmp-1173503,\"{'Na': 4.0, 'Pr': 8.0, 'Ti': 8.0, 'Mn': 4.0, 'O': 36.0}\",\"('Mn', 'Na', 'O', 'Pr', 'Ti')\",OTHERS,True\nmp-540783,\"{'Os': 2.0, 'O': 8.0}\",\"('O', 'Os')\",OTHERS,True\nmp-1218535,\"{'Sr': 1.0, 'Al': 3.0, 'P': 1.0, 'S': 1.0, 'O': 14.0}\",\"('Al', 'O', 'P', 'S', 'Sr')\",OTHERS,True\nmp-1206061,\"{'K': 3.0, 'U': 1.0, 'F': 6.0}\",\"('F', 'K', 'U')\",OTHERS,True\nmp-2573,\"{'Al': 3.0, 'Np': 1.0}\",\"('Al', 'Np')\",OTHERS,True\nmp-753708,\"{'Ti': 4.0, 'O': 4.0, 'F': 4.0}\",\"('F', 'O', 'Ti')\",OTHERS,True\nmp-1211125,\"{'Nd': 8.0, 'Co': 2.0, 'S': 14.0}\",\"('Co', 'Nd', 'S')\",OTHERS,True\nmp-8825,\"{'O': 44.0, 'Pr': 24.0}\",\"('O', 'Pr')\",OTHERS,True\nmp-772554,\"{'Li': 4.0, 'Nb': 3.0, 'V': 5.0, 'O': 16.0}\",\"('Li', 'Nb', 'O', 'V')\",OTHERS,True\nmp-849735,\"{'Li': 4.0, 'Cr': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Cr', 'Li', 'O', 'Si')\",OTHERS,True\nmp-1147542,\"{'Ba': 2.0, 'Cu': 1.0, 'S': 2.0, 'Br': 2.0}\",\"('Ba', 'Br', 'Cu', 'S')\",OTHERS,True\nmp-763923,\"{'Co': 6.0, 'O': 4.0, 'F': 8.0}\",\"('Co', 'F', 'O')\",OTHERS,True\nmp-976806,\"{'Ni': 2.0, 'Au': 6.0}\",\"('Au', 'Ni')\",OTHERS,True\nmp-1095172,\"{'Cs': 2.0, 'Nd': 2.0, 'S': 4.0}\",\"('Cs', 'Nd', 'S')\",OTHERS,True\nmp-644223,\"{'Li': 2.0, 'B': 2.0, 'H': 8.0}\",\"('B', 'H', 'Li')\",OTHERS,True\nmp-998429,\"{'Ni': 2.0, 'Ag': 2.0, 'F': 6.0}\",\"('Ag', 'F', 'Ni')\",OTHERS,True\nmp-1101595,\"{'Mo': 4.0, 'P': 6.0, 'O': 24.0}\",\"('Mo', 'O', 'P')\",OTHERS,True\nmp-7524,\"{'P': 2.0, 'Se': 2.0, 'Nb': 2.0}\",\"('Nb', 'P', 'Se')\",OTHERS,True\nmp-643651,\"{'Rb': 6.0, 'Zn': 2.0, 'H': 10.0}\",\"('H', 'Rb', 'Zn')\",OTHERS,True\nmvc-16787,\"{'Zn': 2.0, 'Cr': 4.0, 'S': 8.0}\",\"('Cr', 'S', 'Zn')\",OTHERS,True\nmp-36480,\"{'Cu': 2.0, 'La': 4.0, 'O': 8.0}\",\"('Cu', 'La', 'O')\",OTHERS,True\nmp-1029262,\"{'V': 2.0, 'Zn': 4.0, 'N': 6.0}\",\"('N', 'V', 'Zn')\",OTHERS,True\nmp-774239,\"{'Li': 4.0, 'Co': 8.0, 'O': 16.0}\",\"('Co', 'Li', 'O')\",OTHERS,True\nmp-1018712,\"{'Hf': 1.0, 'Ga': 4.0, 'Co': 1.0}\",\"('Co', 'Ga', 'Hf')\",OTHERS,True\nmp-7999,\"{'O': 14.0, 'Si': 4.0, 'Y': 4.0}\",\"('O', 'Si', 'Y')\",OTHERS,True\nmp-673637,\"{'Ti': 27.0, 'S': 30.0}\",\"('S', 'Ti')\",OTHERS,True\nmp-1227962,\"{'Ba': 1.0, 'Cd': 3.0, 'Ga': 1.0}\",\"('Ba', 'Cd', 'Ga')\",OTHERS,True\nmp-31703,\"{'O': 4.0, 'F': 12.0, 'Cr': 4.0}\",\"('Cr', 'F', 'O')\",OTHERS,True\nmp-19314,\"{'Be': 6.0, 'O': 24.0, 'Si': 6.0, 'S': 2.0, 'Mn': 8.0}\",\"('Be', 'Mn', 'O', 'S', 'Si')\",OTHERS,True\nmp-1196301,\"{'La': 16.0, 'Mn': 8.0, 'Se': 16.0, 'O': 16.0}\",\"('La', 'Mn', 'O', 'Se')\",OTHERS,True\nmp-16750,\"{'Cu': 2.0, 'Ho': 2.0, 'Pb': 2.0}\",\"('Cu', 'Ho', 'Pb')\",OTHERS,True\nmp-753393,\"{'Na': 1.0, 'V': 1.0, 'O': 2.0}\",\"('Na', 'O', 'V')\",OTHERS,True\nmp-864742,\"{'Na': 3.0, 'Sn': 1.0}\",\"('Na', 'Sn')\",OTHERS,True\nmp-985829,\"{'Hf': 1.0, 'S': 2.0}\",\"('Hf', 'S')\",OTHERS,True\nmp-532329,\"{'Cu': 8.0, 'Fe': 12.0, 'S': 40.0}\",\"('Cu', 'Fe', 'S')\",OTHERS,True\nmp-1200744,\"{'Rb': 4.0, 'Cd': 6.0, 'B': 32.0, 'O': 56.0}\",\"('B', 'Cd', 'O', 'Rb')\",OTHERS,True\nmp-26765,\"{'Li': 2.0, 'O': 48.0, 'P': 14.0, 'V': 12.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-558193,\"{'K': 2.0, 'Cu': 3.0, 'As': 4.0, 'O': 12.0}\",\"('As', 'Cu', 'K', 'O')\",OTHERS,True\nmp-983539,\"{'Be': 1.0, 'Au': 3.0}\",\"('Au', 'Be')\",OTHERS,True\nmp-761153,\"{'U': 6.0, 'O': 11.0}\",\"('O', 'U')\",OTHERS,True\nmp-30454,\"{'Er': 1.0, 'Pt': 1.0, 'Bi': 1.0}\",\"('Bi', 'Er', 'Pt')\",OTHERS,True\nmp-1094692,\"{'Mg': 48.0, 'Al': 39.0}\",\"('Al', 'Mg')\",OTHERS,True\nmp-569015,\"{'Mg': 8.0, 'Cu': 8.0, 'As': 8.0}\",\"('As', 'Cu', 'Mg')\",OTHERS,True\nmp-1247341,\"{'La': 1.0, 'Mg': 2.0, 'V': 3.0, 'S': 8.0}\",\"('La', 'Mg', 'S', 'V')\",OTHERS,True\nmp-1187044,\"{'Sn': 6.0, 'P': 2.0}\",\"('P', 'Sn')\",OTHERS,True\nmvc-13836,\"{'Ta': 1.0, 'F': 5.0}\",\"('F', 'Ta')\",OTHERS,True\nmp-12855,\"{'Cu': 2.0, 'Ge': 1.0, 'Se': 4.0, 'Hg': 1.0}\",\"('Cu', 'Ge', 'Hg', 'Se')\",OTHERS,True\nmp-1186285,\"{'Nd': 6.0, 'Pa': 2.0}\",\"('Nd', 'Pa')\",OTHERS,True\nmp-1077377,\"{'Yb': 2.0, 'Cu': 2.0, 'Pb': 2.0}\",\"('Cu', 'Pb', 'Yb')\",OTHERS,True\nmp-1623,\"{'S': 1.0, 'Er': 1.0}\",\"('Er', 'S')\",OTHERS,True\nmp-1120785,\"{'Na': 16.0, 'V': 16.0, 'F': 56.0}\",\"('F', 'Na', 'V')\",OTHERS,True\nmp-2192,\"{'B': 2.0, 'Pt': 2.0}\",\"('B', 'Pt')\",OTHERS,True\nmp-1213829,\"{'Ce': 8.0, 'Al': 1.0, 'Pd': 24.0}\",\"('Al', 'Ce', 'Pd')\",OTHERS,True\nmp-2371,\"{'Ga': 2.0, 'Te': 5.0}\",\"('Ga', 'Te')\",OTHERS,True\nmp-1199808,\"{'Tl': 4.0, 'Ni': 2.0, 'H': 24.0, 'S': 4.0, 'O': 28.0}\",\"('H', 'Ni', 'O', 'S', 'Tl')\",OTHERS,True\nmp-1208539,\"{'Tb': 4.0, 'Cu': 2.0, 'Ge': 4.0, 'O': 16.0}\",\"('Cu', 'Ge', 'O', 'Tb')\",OTHERS,True\nmp-1207144,\"{'Sm': 4.0, 'Mg': 2.0, 'Pd': 4.0}\",\"('Mg', 'Pd', 'Sm')\",OTHERS,True\nmp-1018055,\"{'Hf': 2.0, 'Ir': 2.0}\",\"('Hf', 'Ir')\",OTHERS,True\nmp-5124,\"{'Mn': 1.0, 'Ni': 2.0, 'Sb': 1.0}\",\"('Mn', 'Ni', 'Sb')\",OTHERS,True\nmp-12834,\"{'Th': 4.0, 'Cu': 24.0}\",\"('Cu', 'Th')\",OTHERS,True\nmp-1076253,\"{'La': 6.0, 'Sm': 2.0, 'V': 5.0, 'Cr': 3.0, 'O': 24.0}\",\"('Cr', 'La', 'O', 'Sm', 'V')\",OTHERS,True\nmp-1222826,\"{'Li': 2.0, 'Zr': 6.0, 'Mn': 1.0, 'Cl': 15.0}\",\"('Cl', 'Li', 'Mn', 'Zr')\",OTHERS,True\nmp-861643,\"{'Ca': 1.0, 'La': 1.0, 'Mg': 2.0}\",\"('Ca', 'La', 'Mg')\",OTHERS,True\nmp-1216480,\"{'V': 3.0, 'Ga': 1.0}\",\"('Ga', 'V')\",OTHERS,True\nmp-753113,\"{'Li': 4.0, 'V': 1.0, 'F': 7.0}\",\"('F', 'Li', 'V')\",OTHERS,True\nmp-693284,\"{'Ca': 12.0, 'Hf': 8.0, 'Fe': 8.0, 'Si': 4.0, 'O': 48.0}\",\"('Ca', 'Fe', 'Hf', 'O', 'Si')\",OTHERS,True\nmp-1184775,\"{'Fe': 2.0, 'Sn': 6.0}\",\"('Fe', 'Sn')\",OTHERS,True\nmp-22993,\"{'O': 4.0, 'Cl': 1.0, 'Ag': 1.0}\",\"('Ag', 'Cl', 'O')\",OTHERS,True\nmp-935267,\"{'Li': 16.0, 'U': 4.0, 'C': 12.0, 'O': 44.0}\",\"('C', 'Li', 'O', 'U')\",OTHERS,True\nmp-558945,\"{'Hg': 12.0, 'As': 4.0, 'Br': 4.0, 'O': 16.0}\",\"('As', 'Br', 'Hg', 'O')\",OTHERS,True\nmp-1095315,\"{'Ba': 2.0, 'W': 2.0, 'O': 8.0}\",\"('Ba', 'O', 'W')\",OTHERS,True\nmp-754407,\"{'Li': 2.0, 'Fe': 1.0, 'S': 2.0}\",\"('Fe', 'Li', 'S')\",OTHERS,True\nmp-1111091,\"{'Na': 2.0, 'Nb': 1.0, 'Ag': 1.0, 'F': 6.0}\",\"('Ag', 'F', 'Na', 'Nb')\",OTHERS,True\nmp-1209247,\"{'Rb': 2.0, 'Nd': 2.0, 'Mo': 4.0, 'O': 16.0}\",\"('Mo', 'Nd', 'O', 'Rb')\",OTHERS,True\nmvc-16806,\"{'Li': 2.0, 'Cr': 4.0, 'O': 8.0}\",\"('Cr', 'Li', 'O')\",OTHERS,True\nmp-754690,\"{'Mn': 6.0, 'O': 5.0, 'F': 7.0}\",\"('F', 'Mn', 'O')\",OTHERS,True\nmp-677269,\"{'Ca': 16.0, 'Zr': 9.0, 'Ta': 7.0, 'N': 7.0, 'O': 41.0}\",\"('Ca', 'N', 'O', 'Ta', 'Zr')\",OTHERS,True\nmvc-3735,\"{'Ca': 4.0, 'Mo': 4.0, 'F': 20.0}\",\"('Ca', 'F', 'Mo')\",OTHERS,True\nmp-1245817,\"{'Li': 1.0, 'Fe': 1.0, 'N': 1.0}\",\"('Fe', 'Li', 'N')\",OTHERS,True\nmp-973732,\"{'Li': 1.0, 'Ho': 3.0}\",\"('Ho', 'Li')\",OTHERS,True\nmp-1213158,\"{'Er': 10.0, 'Si': 4.0, 'Sb': 4.0}\",\"('Er', 'Sb', 'Si')\",OTHERS,True\nmp-1019105,\"{'K': 2.0, 'Mg': 2.0, 'Bi': 2.0}\",\"('Bi', 'K', 'Mg')\",OTHERS,True\nmp-1111507,\"{'Eu': 1.0, 'Na': 2.0, 'Cu': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cu', 'Eu', 'Na')\",OTHERS,True\nmp-754411,\"{'Ta': 4.0, 'Fe': 4.0, 'O': 16.0}\",\"('Fe', 'O', 'Ta')\",OTHERS,True\nmp-1216534,\"{'U': 2.0, 'Se': 1.0, 'S': 1.0}\",\"('S', 'Se', 'U')\",OTHERS,True\nmp-1246058,\"{'Ca': 8.0, 'Re': 2.0, 'N': 8.0}\",\"('Ca', 'N', 'Re')\",OTHERS,True\nmp-1191181,\"{'Ho': 2.0, 'Fe': 12.0, 'P': 7.0}\",\"('Fe', 'Ho', 'P')\",OTHERS,True\nmp-778008,\"{'Mn': 14.0, 'P': 16.0, 'O': 56.0}\",\"('Mn', 'O', 'P')\",OTHERS,True\nmp-567626,\"{'Mg': 2.0, 'Zn': 4.0}\",\"('Mg', 'Zn')\",OTHERS,True\nmp-11506,\"{'Ni': 6.0, 'Mo': 2.0}\",\"('Mo', 'Ni')\",OTHERS,True\nmp-567618,\"{'Si': 20.0, 'Pt': 48.0}\",\"('Pt', 'Si')\",OTHERS,True\nmp-685281,\"{'Ti': 1.0, 'Zn': 1.0, 'H': 12.0, 'O': 6.0, 'F': 6.0}\",\"('F', 'H', 'O', 'Ti', 'Zn')\",OTHERS,True\nmp-25082,\"{'Mo': 4.0, 'C': 24.0, 'O': 24.0}\",\"('C', 'Mo', 'O')\",OTHERS,True\nmp-1219434,\"{'Sc': 4.0, 'Fe': 6.0, 'Ru': 2.0}\",\"('Fe', 'Ru', 'Sc')\",OTHERS,True\nmp-772176,\"{'Li': 6.0, 'Nb': 14.0, 'O': 38.0}\",\"('Li', 'Nb', 'O')\",OTHERS,True\nmp-3952,\"{'O': 16.0, 'Y': 8.0, 'Ba': 4.0}\",\"('Ba', 'O', 'Y')\",OTHERS,True\nmp-24853,\"{'O': 5.0, 'Co': 2.0, 'Ba': 1.0, 'Nd': 1.0}\",\"('Ba', 'Co', 'Nd', 'O')\",OTHERS,True\nmp-1219863,\"{'Pr': 6.0, 'Mn': 1.0, 'Si': 2.0, 'S': 14.0}\",\"('Mn', 'Pr', 'S', 'Si')\",OTHERS,True\nmp-989189,\"{'Sb': 2.0, 'Br': 2.0, 'O': 2.0}\",\"('Br', 'O', 'Sb')\",OTHERS,True\nmp-780881,\"{'Li': 4.0, 'Mn': 3.0, 'Cr': 3.0, 'O': 12.0}\",\"('Cr', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1176929,\"{'Li': 12.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nCd 6 Nd 1,\"{'Cd': 6.0, 'Nd': 1.0}\",\"('Cd', 'Nd')\",AC,True\nZn 75 Sc 15 Pt 10,\"{'Zn': 75.0, 'Sc': 15.0, 'Pt': 10.0}\",\"('Pt', 'Sc', 'Zn')\",QC,True\nmp-541721,\"{'Ba': 6.0, 'Er': 2.0, 'Ru': 4.0, 'O': 18.0}\",\"('Ba', 'Er', 'O', 'Ru')\",OTHERS,True\nmp-1208035,\"{'U': 10.0, 'V': 2.0, 'Sb': 6.0}\",\"('Sb', 'U', 'V')\",OTHERS,True\nmp-1147716,\"{'Cs': 2.0, 'Mo': 2.0, 'C': 12.0}\",\"('C', 'Cs', 'Mo')\",OTHERS,True\nmp-1226784,\"{'Ce': 2.0, 'Mn': 1.0, 'Se': 2.0, 'O': 2.0}\",\"('Ce', 'Mn', 'O', 'Se')\",OTHERS,True\nmp-865600,\"{'Y': 2.0, 'Ir': 1.0, 'Pd': 1.0}\",\"('Ir', 'Pd', 'Y')\",OTHERS,True\nmp-761910,\"{'Li': 6.0, 'Cu': 4.0, 'F': 16.0}\",\"('Cu', 'F', 'Li')\",OTHERS,True\nmp-758217,\"{'Li': 6.0, 'V': 4.0, 'H': 8.0, 'O': 4.0, 'F': 20.0}\",\"('F', 'H', 'Li', 'O', 'V')\",OTHERS,True\nmp-773576,\"{'Li': 1.0, 'Zn': 2.0, 'Fe': 9.0, 'O': 16.0}\",\"('Fe', 'Li', 'O', 'Zn')\",OTHERS,True\nmp-1228885,\"{'Ba': 2.0, 'V': 2.0, 'Cu': 1.0, 'F': 12.0}\",\"('Ba', 'Cu', 'F', 'V')\",OTHERS,True\nmp-624486,\"{'K': 12.0, 'Sn': 6.0, 'Ge': 18.0, 'O': 54.0}\",\"('Ge', 'K', 'O', 'Sn')\",OTHERS,True\nmp-29849,\"{'P': 14.0, 'Ag': 6.0, 'Sn': 2.0}\",\"('Ag', 'P', 'Sn')\",OTHERS,True\nmvc-10776,\"{'Ca': 4.0, 'Ti': 8.0, 'Be': 12.0, 'Si': 12.0, 'O': 48.0}\",\"('Be', 'Ca', 'O', 'Si', 'Ti')\",OTHERS,True\nmp-1179365,\"{'Sr': 2.0, 'H': 32.0, 'O': 20.0}\",\"('H', 'O', 'Sr')\",OTHERS,True\nmp-14623,\"{'K': 10.0, 'Cu': 2.0, 'As': 4.0}\",\"('As', 'Cu', 'K')\",OTHERS,True\nmp-1174776,\"{'Li': 8.0, 'Mn': 2.0, 'Co': 4.0, 'O': 14.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-778479,\"{'Mn': 12.0, 'O': 5.0, 'F': 19.0}\",\"('F', 'Mn', 'O')\",OTHERS,True\nmp-1073705,\"{'Mg': 2.0, 'Si': 2.0}\",\"('Mg', 'Si')\",OTHERS,True\nmp-558335,\"{'Na': 8.0, 'Pd': 4.0, 'N': 16.0, 'O': 32.0}\",\"('N', 'Na', 'O', 'Pd')\",OTHERS,True\nmp-1219132,\"{'Re': 4.0, 'Mo': 2.0, 'Se': 8.0}\",\"('Mo', 'Re', 'Se')\",OTHERS,True\nmp-1227371,\"{'Ba': 1.0, 'Sr': 1.0, 'La': 1.0, 'Bi': 1.0, 'O': 6.0}\",\"('Ba', 'Bi', 'La', 'O', 'Sr')\",OTHERS,True\nmvc-5445,\"{'Ca': 4.0, 'Y': 2.0, 'Bi': 2.0, 'O': 10.0}\",\"('Bi', 'Ca', 'O', 'Y')\",OTHERS,True\nmp-763192,\"{'V': 6.0, 'O': 3.0, 'F': 15.0}\",\"('F', 'O', 'V')\",OTHERS,True\nmp-695,\"{'Ti': 2.0, 'Co': 4.0}\",\"('Co', 'Ti')\",OTHERS,True\nmp-1208695,\"{'Ta': 9.0, 'V': 2.0, 'O': 25.0}\",\"('O', 'Ta', 'V')\",OTHERS,True\nmp-1175910,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-31116,\"{'La': 4.0, 'Sc': 4.0, 'O': 12.0}\",\"('La', 'O', 'Sc')\",OTHERS,True\nmp-1178537,\"{'Co': 6.0, 'C': 6.0, 'O': 21.0}\",\"('C', 'Co', 'O')\",OTHERS,True\nmp-1175280,\"{'Li': 7.0, 'Mn': 4.0, 'Co': 1.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1834,\"{'Nd': 1.0, 'Al': 4.0}\",\"('Al', 'Nd')\",OTHERS,True\nmp-1095291,\"{'Li': 4.0, 'Sn': 2.0, 'Se': 6.0}\",\"('Li', 'Se', 'Sn')\",OTHERS,True\nmp-1208409,\"{'Ta': 6.0, 'Co': 2.0, 'S': 12.0}\",\"('Co', 'S', 'Ta')\",OTHERS,True\nmp-768597,\"{'Sr': 4.0, 'La': 4.0, 'Cl': 20.0}\",\"('Cl', 'La', 'Sr')\",OTHERS,True\nmp-867338,\"{'Ac': 1.0, 'Cd': 1.0, 'Ag': 2.0}\",\"('Ac', 'Ag', 'Cd')\",OTHERS,True\nmp-1190707,\"{'Tm': 2.0, 'Fe': 12.0, 'P': 7.0}\",\"('Fe', 'P', 'Tm')\",OTHERS,True\nmp-1113366,\"{'Cs': 1.0, 'Rb': 2.0, 'Pd': 1.0, 'F': 6.0}\",\"('Cs', 'F', 'Pd', 'Rb')\",OTHERS,True\nmp-849468,\"{'Li': 4.0, 'Fe': 3.0, 'Co': 3.0, 'Te': 2.0, 'O': 16.0}\",\"('Co', 'Fe', 'Li', 'O', 'Te')\",OTHERS,True\nmp-754020,\"{'Li': 2.0, 'Co': 2.0, 'Cu': 2.0, 'O': 8.0}\",\"('Co', 'Cu', 'Li', 'O')\",OTHERS,True\nmp-1218284,\"{'Sr': 2.0, 'Dy': 2.0, 'Co': 2.0, 'O': 8.0}\",\"('Co', 'Dy', 'O', 'Sr')\",OTHERS,True\nmp-1221971,\"{'Mg': 1.0, 'Zn': 3.0, 'S': 4.0}\",\"('Mg', 'S', 'Zn')\",OTHERS,True\nmp-1114421,\"{'Rb': 2.0, 'Li': 1.0, 'Lu': 1.0, 'Cl': 6.0}\",\"('Cl', 'Li', 'Lu', 'Rb')\",OTHERS,True\nmp-9765,\"{'O': 14.0, 'Nd': 4.0, 'Pt': 4.0}\",\"('Nd', 'O', 'Pt')\",OTHERS,True\nZn 40 Mg 39.5 Ga 16.4 Al 4.1,\"{'Zn': 40.0, 'Mg': 39.5, 'Ga': 16.4, 'Al': 4.1}\",\"('Al', 'Ga', 'Mg', 'Zn')\",AC,True\nmp-1074405,\"{'Mg': 8.0, 'Si': 6.0}\",\"('Mg', 'Si')\",OTHERS,True\nmp-720104,\"{'Na': 8.0, 'Al': 6.0, 'Ge': 6.0, 'N': 2.0, 'O': 28.0}\",\"('Al', 'Ge', 'N', 'Na', 'O')\",OTHERS,True\nmp-1185546,\"{'Cs': 1.0, 'Rb': 2.0, 'Bi': 1.0}\",\"('Bi', 'Cs', 'Rb')\",OTHERS,True\nmp-1181383,\"{'K': 2.0, 'Co': 1.0, 'B': 12.0, 'O': 30.0}\",\"('B', 'Co', 'K', 'O')\",OTHERS,True\nmp-766168,\"{'Ti': 8.0, 'Sn': 2.0, 'O': 20.0}\",\"('O', 'Sn', 'Ti')\",OTHERS,True\nmp-1078736,\"{'Pr': 2.0, 'Ni': 4.0, 'Sb': 4.0}\",\"('Ni', 'Pr', 'Sb')\",OTHERS,True\nmp-1195793,\"{'Ba': 4.0, 'Fe': 4.0, 'P': 4.0, 'H': 4.0, 'O': 20.0}\",\"('Ba', 'Fe', 'H', 'O', 'P')\",OTHERS,True\nmp-542824,\"{'Ce': 3.0, 'Ag': 4.0, 'Sn': 4.0}\",\"('Ag', 'Ce', 'Sn')\",OTHERS,True\nZn 37 Mg 46 Al 17,\"{'Zn': 37.0, 'Mg': 46.0, 'Al': 17.0}\",\"('Al', 'Mg', 'Zn')\",AC,True\nmp-683793,\"{'Fe': 16.0, 'Te': 8.0, 'Mo': 4.0, 'C': 56.0, 'S': 8.0, 'O': 56.0}\",\"('C', 'Fe', 'Mo', 'O', 'S', 'Te')\",OTHERS,True\nmp-1187005,\"{'Si': 2.0, 'Ag': 6.0}\",\"('Ag', 'Si')\",OTHERS,True\nmp-1096299,\"{'Li': 2.0, 'Hg': 1.0, 'Sb': 1.0}\",\"('Hg', 'Li', 'Sb')\",OTHERS,True\nmp-1207770,\"{'Y': 10.0, 'Bi': 2.0, 'Pt': 4.0}\",\"('Bi', 'Pt', 'Y')\",OTHERS,True\nmp-778385,\"{'B': 24.0, 'H': 24.0, 'Se': 8.0, 'O': 72.0}\",\"('B', 'H', 'O', 'Se')\",OTHERS,True\nmp-979286,{'Xe': 1.0},\"('Xe',)\",OTHERS,True\nmp-1228771,\"{'Ba': 2.0, 'Fe': 10.0, 'Bi': 8.0, 'O': 30.0}\",\"('Ba', 'Bi', 'Fe', 'O')\",OTHERS,True\nmp-541879,\"{'Rb': 8.0, 'Ge': 8.0, 'S': 20.0}\",\"('Ge', 'Rb', 'S')\",OTHERS,True\nmp-1187731,\"{'Y': 2.0, 'Zn': 1.0, 'Hg': 1.0}\",\"('Hg', 'Y', 'Zn')\",OTHERS,True\nmp-1206803,\"{'Y': 2.0, 'C': 1.0, 'I': 2.0}\",\"('C', 'I', 'Y')\",OTHERS,True\nmp-1190708,\"{'Al': 12.0, 'Fe': 8.0, 'Si': 4.0}\",\"('Al', 'Fe', 'Si')\",OTHERS,True\nmp-1219347,\"{'Si': 1.0, 'Pb': 7.0, 'Cl': 2.0, 'O': 8.0}\",\"('Cl', 'O', 'Pb', 'Si')\",OTHERS,True\nmp-1246452,\"{'B': 24.0, 'C': 24.0, 'N': 56.0}\",\"('B', 'C', 'N')\",OTHERS,True\nmvc-11253,\"{'Ca': 2.0, 'Cr': 4.0, 'S': 8.0}\",\"('Ca', 'Cr', 'S')\",OTHERS,True\nmp-1093630,\"{'Li': 1.0, 'Ge': 2.0, 'Rh': 1.0}\",\"('Ge', 'Li', 'Rh')\",OTHERS,True\nmp-735556,\"{'Na': 4.0, 'Co': 2.0, 'H': 16.0, 'C': 4.0, 'O': 20.0}\",\"('C', 'Co', 'H', 'Na', 'O')\",OTHERS,True\nmp-1224984,\"{'Fe': 2.0, 'Co': 2.0, 'B': 2.0}\",\"('B', 'Co', 'Fe')\",OTHERS,True\nZn 17 Sc 3,\"{'Zn': 17.0, 'Sc': 3.0}\",\"('Sc', 'Zn')\",AC,True\nmp-1219453,\"{'Sr': 12.0, 'Ti': 4.0, 'Si': 16.0, 'O': 61.0}\",\"('O', 'Si', 'Sr', 'Ti')\",OTHERS,True\nmp-27989,\"{'C': 2.0, 'N': 2.0, 'Br': 2.0}\",\"('Br', 'C', 'N')\",OTHERS,True\nmp-1209723,\"{'Np': 2.0, 'Tl': 4.0, 'F': 12.0}\",\"('F', 'Np', 'Tl')\",OTHERS,True\nmp-30704,\"{'Ga': 6.0, 'Nb': 10.0}\",\"('Ga', 'Nb')\",OTHERS,True\nmp-1095964,\"{'Zn': 2.0, 'Ag': 1.0, 'Pd': 1.0}\",\"('Ag', 'Pd', 'Zn')\",OTHERS,True\nmp-554291,\"{'Cu': 8.0, 'Sb': 16.0, 'Cl': 8.0, 'O': 24.0}\",\"('Cl', 'Cu', 'O', 'Sb')\",OTHERS,True\nmp-1185389,\"{'Li': 1.0, 'Nd': 2.0, 'Rh': 1.0}\",\"('Li', 'Nd', 'Rh')\",OTHERS,True\nmp-1221814,\"{'Mn': 1.0, 'Fe': 5.0, 'O': 8.0}\",\"('Fe', 'Mn', 'O')\",OTHERS,True\nmp-1228492,\"{'Ca': 8.0, 'Fe': 16.0, 'Cu': 8.0, 'O': 40.0}\",\"('Ca', 'Cu', 'Fe', 'O')\",OTHERS,True\nmp-849273,\"{'Li': 4.0, 'Co': 4.0, 'O': 8.0}\",\"('Co', 'Li', 'O')\",OTHERS,True\nmp-19838,\"{'Si': 2.0, 'Cr': 2.0, 'Pu': 1.0}\",\"('Cr', 'Pu', 'Si')\",OTHERS,True\nmvc-12951,\"{'Mn': 4.0, 'Zn': 1.0, 'S': 8.0}\",\"('Mn', 'S', 'Zn')\",OTHERS,True\nmp-1177254,\"{'Li': 4.0, 'V': 2.0, 'Co': 3.0, 'Cu': 3.0, 'O': 16.0}\",\"('Co', 'Cu', 'Li', 'O', 'V')\",OTHERS,True\nmp-26241,\"{'Li': 4.0, 'O': 28.0, 'P': 8.0, 'V': 6.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmvc-8408,\"{'V': 4.0, 'Zn': 4.0, 'Ge': 8.0, 'O': 24.0}\",\"('Ge', 'O', 'V', 'Zn')\",OTHERS,True\nmp-582024,\"{'Cs': 12.0, 'Cu': 8.0, 'Cl': 20.0}\",\"('Cl', 'Cs', 'Cu')\",OTHERS,True\nmp-709066,\"{'Rb': 16.0, 'Li': 4.0, 'H': 12.0, 'S': 16.0, 'O': 64.0}\",\"('H', 'Li', 'O', 'Rb', 'S')\",OTHERS,True\nmp-972032,\"{'Tl': 2.0, 'I': 6.0, 'O': 18.0}\",\"('I', 'O', 'Tl')\",OTHERS,True\nmp-997032,\"{'Mg': 2.0, 'Au': 2.0, 'O': 4.0}\",\"('Au', 'Mg', 'O')\",OTHERS,True\nmp-1225357,\"{'Dy': 1.0, 'Al': 7.0, 'Fe': 5.0}\",\"('Al', 'Dy', 'Fe')\",OTHERS,True\nmp-1014212,{'Si': 1.0},\"('Si',)\",OTHERS,True\nmp-962057,\"{'Sr': 1.0, 'Ca': 1.0, 'Si': 1.0}\",\"('Ca', 'Si', 'Sr')\",OTHERS,True\nmp-1219143,\"{'Sm': 6.0, 'Mn': 1.0, 'Si': 2.0, 'S': 14.0}\",\"('Mn', 'S', 'Si', 'Sm')\",OTHERS,True\nmp-1186417,\"{'Pa': 1.0, 'Te': 2.0, 'Pd': 1.0}\",\"('Pa', 'Pd', 'Te')\",OTHERS,True\nmp-769720,\"{'La': 4.0, 'Ti': 6.0, 'O': 18.0}\",\"('La', 'O', 'Ti')\",OTHERS,True\nmp-757305,\"{'Li': 8.0, 'Ti': 4.0, 'P': 12.0, 'O': 40.0}\",\"('Li', 'O', 'P', 'Ti')\",OTHERS,True\nmp-684096,\"{'Bi': 4.0, 'P': 16.0, 'O': 48.0}\",\"('Bi', 'O', 'P')\",OTHERS,True\nmp-19245,\"{'O': 6.0, 'Mn': 2.0, 'Ba': 1.0, 'La': 1.0}\",\"('Ba', 'La', 'Mn', 'O')\",OTHERS,True\nmp-767791,\"{'Mg': 5.0, 'Co': 19.0, 'O': 32.0}\",\"('Co', 'Mg', 'O')\",OTHERS,True\nmp-650178,\"{'Se': 40.0, 'S': 8.0, 'O': 24.0, 'F': 8.0}\",\"('F', 'O', 'S', 'Se')\",OTHERS,True\nmp-1212099,\"{'K': 4.0, 'La': 2.0, 'Ta': 12.0, 'Br': 30.0, 'O': 6.0}\",\"('Br', 'K', 'La', 'O', 'Ta')\",OTHERS,True\nmp-771326,\"{'Nb': 1.0, 'Fe': 5.0, 'P': 6.0, 'O': 24.0}\",\"('Fe', 'Nb', 'O', 'P')\",OTHERS,True\nmp-1186187,\"{'Na': 1.0, 'Tl': 2.0, 'Sn': 1.0}\",\"('Na', 'Sn', 'Tl')\",OTHERS,True\nmp-13396,\"{'Li': 1.0, 'Rh': 1.0, 'In': 2.0}\",\"('In', 'Li', 'Rh')\",OTHERS,True\nmp-756829,\"{'Li': 4.0, 'Fe': 3.0, 'Co': 1.0, 'O': 8.0}\",\"('Co', 'Fe', 'Li', 'O')\",OTHERS,True\nmp-11469,\"{'Pr': 1.0, 'Hg': 1.0}\",\"('Hg', 'Pr')\",OTHERS,True\nmp-861878,\"{'Cd': 2.0, 'Ag': 1.0, 'Rh': 1.0}\",\"('Ag', 'Cd', 'Rh')\",OTHERS,True\nmp-12879,\"{'Si': 10.0, 'Rh': 6.0, 'Tb': 4.0}\",\"('Rh', 'Si', 'Tb')\",OTHERS,True\nmp-530546,\"{'O': 108.0, 'Si': 54.0}\",\"('O', 'Si')\",OTHERS,True\nmp-1184988,\"{'Li': 2.0, 'Sm': 1.0, 'Al': 1.0}\",\"('Al', 'Li', 'Sm')\",OTHERS,True\nmp-569496,\"{'Ce': 1.0, 'Si': 2.0, 'Au': 4.0}\",\"('Au', 'Ce', 'Si')\",OTHERS,True\nmp-18279,\"{'Cu': 2.0, 'Ge': 8.0, 'Zr': 4.0}\",\"('Cu', 'Ge', 'Zr')\",OTHERS,True\nmp-1200277,\"{'Cs': 4.0, 'Mg': 2.0, 'Se': 4.0, 'O': 28.0}\",\"('Cs', 'Mg', 'O', 'Se')\",OTHERS,True\nmp-20826,\"{'Cu': 2.0, 'Te': 2.0}\",\"('Cu', 'Te')\",OTHERS,True\nmp-20063,\"{'Li': 3.0, 'Ge': 4.0, 'Ce': 5.0}\",\"('Ce', 'Ge', 'Li')\",OTHERS,True\nmp-755697,\"{'V': 8.0, 'O': 12.0, 'F': 4.0}\",\"('F', 'O', 'V')\",OTHERS,True\nmp-540050,\"{'Fe': 12.0, 'P': 12.0, 'O': 44.0}\",\"('Fe', 'O', 'P')\",OTHERS,True\nmvc-13015,\"{'Cu': 3.0, 'Sn': 4.0, 'O': 12.0}\",\"('Cu', 'O', 'Sn')\",OTHERS,True\nmp-1220425,\"{'Nb': 6.0, 'Bi': 1.0, 'Se': 8.0}\",\"('Bi', 'Nb', 'Se')\",OTHERS,True\nmp-1219917,\"{'Pd': 1.0, 'Rh': 1.0, 'Pb': 4.0}\",\"('Pb', 'Pd', 'Rh')\",OTHERS,True\nmp-1071078,\"{'Ni': 2.0, 'Se': 4.0}\",\"('Ni', 'Se')\",OTHERS,True\nmp-1114690,\"{'Rb': 3.0, 'Ir': 1.0, 'F': 6.0}\",\"('F', 'Ir', 'Rb')\",OTHERS,True\nmp-677308,\"{'Na': 2.0, 'Pr': 4.0, 'Cl': 12.0}\",\"('Cl', 'Na', 'Pr')\",OTHERS,True\nmp-1213319,\"{'Cs': 2.0, 'Pr': 2.0, 'W': 4.0, 'O': 16.0}\",\"('Cs', 'O', 'Pr', 'W')\",OTHERS,True\nmvc-6921,\"{'Mg': 4.0, 'Mn': 8.0, 'O': 16.0}\",\"('Mg', 'Mn', 'O')\",OTHERS,True\nmp-1213830,\"{'Ce': 6.0, 'W': 2.0, 'Cl': 6.0, 'O': 12.0}\",\"('Ce', 'Cl', 'O', 'W')\",OTHERS,True\nmp-758538,\"{'Li': 8.0, 'V': 14.0, 'O': 24.0}\",\"('Li', 'O', 'V')\",OTHERS,True\nmp-1095082,\"{'In': 2.0, 'Ru': 6.0}\",\"('In', 'Ru')\",OTHERS,True\nmp-1220828,\"{'Nb': 16.0, 'Pb': 12.0, 'O': 48.0, 'F': 8.0}\",\"('F', 'Nb', 'O', 'Pb')\",OTHERS,True\nmp-1206296,\"{'Y': 4.0, 'Co': 2.0, 'Si': 4.0}\",\"('Co', 'Si', 'Y')\",OTHERS,True\nAg 42 In 42 Ca 16,\"{'Ag': 42.0, 'In': 42.0, 'Ca': 16.0}\",\"('Ag', 'Ca', 'In')\",QC,True\nmp-1210619,\"{'Na': 24.0, 'Nd': 8.0, 'P': 16.0, 'O': 64.0}\",\"('Na', 'Nd', 'O', 'P')\",OTHERS,True\nmp-768497,\"{'Li': 6.0, 'Mn': 12.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,True\nmp-697962,\"{'Eu': 6.0, 'Mg': 7.0, 'H': 26.0}\",\"('Eu', 'H', 'Mg')\",OTHERS,True\nmp-744703,\"{'Mn': 4.0, 'Cu': 4.0, 'H': 40.0, 'Se': 8.0, 'Cl': 8.0, 'O': 40.0}\",\"('Cl', 'Cu', 'H', 'Mn', 'O', 'Se')\",OTHERS,True\nmp-1016846,\"{'Ti': 1.0, 'Cd': 1.0, 'O': 3.0}\",\"('Cd', 'O', 'Ti')\",OTHERS,True\nmp-773192,\"{'Gd': 4.0, 'W': 6.0, 'O': 24.0}\",\"('Gd', 'O', 'W')\",OTHERS,True\nmp-30042,\"{'Li': 4.0, 'Ge': 2.0, 'Ce': 2.0}\",\"('Ce', 'Ge', 'Li')\",OTHERS,True\nmp-1246423,\"{'B': 4.0, 'Mo': 4.0, 'N': 12.0}\",\"('B', 'Mo', 'N')\",OTHERS,True\nmp-753148,\"{'Li': 2.0, 'Cu': 7.0, 'F': 16.0}\",\"('Cu', 'F', 'Li')\",OTHERS,True\nmp-1183854,\"{'Ce': 1.0, 'Pm': 3.0}\",\"('Ce', 'Pm')\",OTHERS,True\nmp-1196825,\"{'Zr': 4.0, 'H': 48.0, 'N': 20.0, 'F': 16.0}\",\"('F', 'H', 'N', 'Zr')\",OTHERS,True\nmp-1105795,\"{'Cd': 4.0, 'N': 4.0, 'F': 12.0}\",\"('Cd', 'F', 'N')\",OTHERS,True\nmp-17252,\"{'Cu': 6.0, 'Ge': 3.0, 'Se': 12.0, 'Ba': 3.0}\",\"('Ba', 'Cu', 'Ge', 'Se')\",OTHERS,True\nmp-1176130,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-655580,\"{'Ce': 4.0, 'Cu': 16.0, 'Sn': 4.0}\",\"('Ce', 'Cu', 'Sn')\",OTHERS,True\nmp-1100342,\"{'Ca': 2.0, 'Sn': 3.0, 'S': 8.0}\",\"('Ca', 'S', 'Sn')\",OTHERS,True\nmp-22037,\"{'O': 8.0, 'Cu': 6.0, 'Pb': 1.0}\",\"('Cu', 'O', 'Pb')\",OTHERS,True\nmp-635393,\"{'Ca': 2.0, 'P': 1.0, 'I': 1.0}\",\"('Ca', 'I', 'P')\",OTHERS,True\nmp-567365,{'Kr': 2.0},\"('Kr',)\",OTHERS,True\nmp-1229330,\"{'Bi': 1.0, 'W': 18.0, 'O': 60.0}\",\"('Bi', 'O', 'W')\",OTHERS,True\nmp-17146,\"{'S': 20.0, 'K': 8.0}\",\"('K', 'S')\",OTHERS,True\nmp-1071448,\"{'Ce': 2.0, 'Ga': 2.0, 'Co': 2.0}\",\"('Ce', 'Co', 'Ga')\",OTHERS,True\nmp-1209227,\"{'Rb': 1.0, 'Ca': 1.0, 'Br': 3.0}\",\"('Br', 'Ca', 'Rb')\",OTHERS,True\nmvc-8943,\"{'Zn': 4.0, 'Bi': 12.0, 'O': 28.0}\",\"('Bi', 'O', 'Zn')\",OTHERS,True\nmp-1185748,\"{'Mg': 4.0, 'Ag': 2.0}\",\"('Ag', 'Mg')\",OTHERS,True\nmp-3960,\"{'O': 4.0, 'Ca': 1.0, 'U': 1.0}\",\"('Ca', 'O', 'U')\",OTHERS,True\nmp-1112199,\"{'K': 2.0, 'In': 1.0, 'Hg': 1.0, 'F': 6.0}\",\"('F', 'Hg', 'In', 'K')\",OTHERS,True\nmp-1246024,\"{'Sr': 28.0, 'Hf': 4.0, 'N': 24.0}\",\"('Hf', 'N', 'Sr')\",OTHERS,True\nAu 61.2 Sn 24.5 Eu 14.3,\"{'Au': 61.2, 'Sn': 24.5, 'Eu': 14.3}\",\"('Au', 'Eu', 'Sn')\",AC,True\nmvc-13995,\"{'Y': 2.0, 'Ti': 2.0, 'O': 6.0}\",\"('O', 'Ti', 'Y')\",OTHERS,True\nmp-1222406,\"{'Li': 1.0, 'Zr': 2.0, 'Pb': 1.0}\",\"('Li', 'Pb', 'Zr')\",OTHERS,True\nmp-1106139,\"{'In': 8.0, 'Bi': 6.0, 'Pb': 2.0}\",\"('Bi', 'In', 'Pb')\",OTHERS,True\nmp-1214547,\"{'Ba': 4.0, 'Mn': 4.0, 'V': 4.0, 'F': 28.0}\",\"('Ba', 'F', 'Mn', 'V')\",OTHERS,True\nmp-1208575,\"{'Tb': 2.0, 'Cl': 6.0, 'O': 24.0}\",\"('Cl', 'O', 'Tb')\",OTHERS,True\nmp-561973,\"{'K': 4.0, 'Te': 8.0, 'O': 24.0}\",\"('K', 'O', 'Te')\",OTHERS,True\nmp-1218693,\"{'Sr': 4.0, 'V': 6.0, 'Bi': 6.0, 'O': 28.0}\",\"('Bi', 'O', 'Sr', 'V')\",OTHERS,True\nmp-1207120,\"{'Ba': 2.0, 'Li': 1.0, 'I': 1.0, 'O': 6.0}\",\"('Ba', 'I', 'Li', 'O')\",OTHERS,True\nmp-1186295,\"{'Nd': 1.0, 'Cd': 1.0, 'Ag': 2.0}\",\"('Ag', 'Cd', 'Nd')\",OTHERS,True\nmp-2360,\"{'Ca': 2.0, 'Ge': 2.0}\",\"('Ca', 'Ge')\",OTHERS,True\nmvc-13461,\"{'Cr': 2.0, 'N': 4.0}\",\"('Cr', 'N')\",OTHERS,True\nmp-30893,\"{'Sr': 8.0, 'Bi': 6.0}\",\"('Bi', 'Sr')\",OTHERS,True\nmp-1102441,\"{'Re': 4.0, 'N': 8.0}\",\"('N', 'Re')\",OTHERS,True\nmp-1252764,\"{'Al': 2.0, 'F': 6.0}\",\"('Al', 'F')\",OTHERS,True\nmp-1020616,\"{'Rb': 12.0, 'Mg': 12.0, 'P': 12.0, 'O': 48.0}\",\"('Mg', 'O', 'P', 'Rb')\",OTHERS,True\nmp-1214060,\"{'Ca': 2.0, 'Mg': 10.0, 'P': 6.0, 'H': 2.0, 'C': 2.0, 'O': 32.0}\",\"('C', 'Ca', 'H', 'Mg', 'O', 'P')\",OTHERS,True\nmp-765485,\"{'Zr': 15.0, 'V': 1.0, 'O': 32.0}\",\"('O', 'V', 'Zr')\",OTHERS,True\nmp-1214042,\"{'Ca': 6.0, 'Ge': 8.0, 'Au': 14.0}\",\"('Au', 'Ca', 'Ge')\",OTHERS,True\nmp-9018,\"{'Li': 4.0, 'O': 16.0, 'P': 4.0, 'Cd': 4.0}\",\"('Cd', 'Li', 'O', 'P')\",OTHERS,True\nmp-1096047,\"{'Sc': 1.0, 'Cu': 1.0, 'Au': 2.0}\",\"('Au', 'Cu', 'Sc')\",OTHERS,True\nmp-29704,\"{'O': 32.0, 'S': 16.0, 'Cs': 8.0}\",\"('Cs', 'O', 'S')\",OTHERS,True\nmp-1029118,\"{'Te': 2.0, 'Mo': 1.0, 'W': 3.0, 'S': 6.0}\",\"('Mo', 'S', 'Te', 'W')\",OTHERS,True\nmp-1228478,\"{'Ba': 12.0, 'Ti': 9.0, 'Pt': 3.0, 'O': 36.0}\",\"('Ba', 'O', 'Pt', 'Ti')\",OTHERS,True\nmvc-928,\"{'Ba': 1.0, 'Ca': 1.0, 'V': 4.0, 'O': 8.0}\",\"('Ba', 'Ca', 'O', 'V')\",OTHERS,True\nmp-766488,\"{'Li': 6.0, 'Mn': 6.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-626315,\"{'Sr': 2.0, 'H': 32.0, 'O': 20.0}\",\"('H', 'O', 'Sr')\",OTHERS,True\nmp-758372,\"{'Li': 4.0, 'Co': 2.0, 'Si': 2.0, 'O': 8.0}\",\"('Co', 'Li', 'O', 'Si')\",OTHERS,True\nmp-554335,\"{'K': 4.0, 'Cu': 2.0, 'H': 24.0, 'Se': 4.0, 'O': 28.0}\",\"('Cu', 'H', 'K', 'O', 'Se')\",OTHERS,True\nmp-1094512,\"{'Mg': 8.0, 'Sb': 4.0}\",\"('Mg', 'Sb')\",OTHERS,True\nmp-1014226,\"{'Zr': 6.0, 'Zn': 2.0, 'N': 2.0}\",\"('N', 'Zn', 'Zr')\",OTHERS,True\nmp-850798,\"{'Li': 8.0, 'Mn': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Li', 'Mn', 'O', 'Si')\",OTHERS,True\nmp-867571,\"{'Li': 8.0, 'Mn': 8.0, 'P': 8.0, 'O': 36.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,True\nmp-540011,\"{'Li': 4.0, 'Ni': 2.0, 'P': 10.0, 'O': 30.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,True\nmp-534870,\"{'B': 2.0, 'Ga': 6.0, 'H': 8.0, 'Na': 8.0, 'O': 24.0, 'Si': 6.0}\",\"('B', 'Ga', 'H', 'Na', 'O', 'Si')\",OTHERS,True\nmp-1104102,\"{'Yb': 2.0, 'Ni': 8.0, 'As': 4.0}\",\"('As', 'Ni', 'Yb')\",OTHERS,True\nmp-745163,\"{'Mn': 2.0, 'Cu': 5.0, 'P': 4.0, 'H': 2.0, 'O': 18.0}\",\"('Cu', 'H', 'Mn', 'O', 'P')\",OTHERS,True\nmp-1226098,\"{'Co': 1.0, 'Ni': 1.0, 'P': 2.0, 'S': 6.0}\",\"('Co', 'Ni', 'P', 'S')\",OTHERS,True\nmp-1186958,\"{'Sc': 2.0, 'Cu': 1.0, 'Rh': 1.0}\",\"('Cu', 'Rh', 'Sc')\",OTHERS,True\nmp-758910,\"{'Li': 12.0, 'Fe': 6.0, 'Si': 12.0, 'O': 36.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,True\nmp-1194253,\"{'Hf': 18.0, 'W': 8.0, 'Se': 2.0}\",\"('Hf', 'Se', 'W')\",OTHERS,True\nmvc-2883,\"{'Sb': 10.0, 'Te': 6.0, 'O': 36.0}\",\"('O', 'Sb', 'Te')\",OTHERS,True\nmp-1226198,\"{'Co': 8.0, 'Cu': 6.0, 'Ni': 4.0, 'Se': 24.0}\",\"('Co', 'Cu', 'Ni', 'Se')\",OTHERS,True\nmp-22649,\"{'O': 12.0, 'V': 4.0, 'Cd': 4.0}\",\"('Cd', 'O', 'V')\",OTHERS,True\nmp-1077741,\"{'Yb': 1.0, 'Cu': 4.0, 'Ag': 1.0}\",\"('Ag', 'Cu', 'Yb')\",OTHERS,True\nmp-765189,\"{'Li': 3.0, 'V': 3.0, 'O': 5.0, 'F': 3.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,True\nmp-769096,\"{'Mn': 6.0, 'P': 24.0}\",\"('Mn', 'P')\",OTHERS,True\nmp-637614,\"{'Zn': 3.0, 'In': 2.0, 'S': 6.0}\",\"('In', 'S', 'Zn')\",OTHERS,True\nmp-554275,\"{'Na': 16.0, 'V': 4.0, 'H': 4.0, 'O': 20.0}\",\"('H', 'Na', 'O', 'V')\",OTHERS,True\nmp-766558,\"{'Na': 4.0, 'Mn': 12.0, 'O': 24.0}\",\"('Mn', 'Na', 'O')\",OTHERS,True\nmvc-13560,\"{'Ca': 1.0, 'V': 4.0, 'S': 8.0}\",\"('Ca', 'S', 'V')\",OTHERS,True\nmp-2961,\"{'Mg': 2.0, 'Si': 2.0, 'P': 4.0}\",\"('Mg', 'P', 'Si')\",OTHERS,True\nmp-1195435,\"{'Na': 4.0, 'Er': 4.0, 'B': 4.0, 'Te': 2.0, 'O': 20.0}\",\"('B', 'Er', 'Na', 'O', 'Te')\",OTHERS,True\nmp-974368,\"{'Rh': 3.0, 'Pb': 1.0}\",\"('Pb', 'Rh')\",OTHERS,True\nmp-1189706,\"{'Cd': 4.0, 'Tc': 4.0, 'O': 12.0}\",\"('Cd', 'O', 'Tc')\",OTHERS,True\nmp-1207495,\"{'Zr': 12.0, 'Ge': 2.0, 'I': 28.0}\",\"('Ge', 'I', 'Zr')\",OTHERS,True\nmp-1093645,\"{'Ti': 2.0, 'Al': 1.0, 'W': 1.0}\",\"('Al', 'Ti', 'W')\",OTHERS,True\nmp-560282,\"{'K': 8.0, 'Ba': 4.0, 'N': 16.0, 'O': 32.0}\",\"('Ba', 'K', 'N', 'O')\",OTHERS,True\nmvc-16790,\"{'Y': 2.0, 'Ti': 4.0, 'S': 8.0}\",\"('S', 'Ti', 'Y')\",OTHERS,True\nmp-1205595,\"{'Ba': 2.0, 'Tm': 1.0, 'Mo': 1.0, 'O': 6.0}\",\"('Ba', 'Mo', 'O', 'Tm')\",OTHERS,True\nmp-1079224,\"{'V': 6.0, 'As': 2.0, 'N': 2.0}\",\"('As', 'N', 'V')\",OTHERS,True\nmp-996960,\"{'Ag': 2.0, 'N': 2.0, 'O': 4.0}\",\"('Ag', 'N', 'O')\",OTHERS,True\nmp-1221014,\"{'Nb': 24.0, 'I': 42.0, 'Br': 2.0}\",\"('Br', 'I', 'Nb')\",OTHERS,True\nmp-758248,\"{'Li': 4.0, 'Co': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Co', 'Li', 'O', 'P')\",OTHERS,True\nmp-16066,\"{'P': 4.0, 'Ni': 8.0, 'Yb': 2.0}\",\"('Ni', 'P', 'Yb')\",OTHERS,True\nmp-1174032,\"{'Li': 5.0, 'Mn': 1.0, 'Co': 2.0, 'O': 8.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1210319,\"{'Nd': 4.0, 'Fe': 4.0, 'Ge': 8.0, 'O': 28.0}\",\"('Fe', 'Ge', 'Nd', 'O')\",OTHERS,True\nmvc-10716,\"{'Sr': 4.0, 'Y': 2.0, 'Ti': 4.0, 'Ga': 2.0, 'O': 14.0}\",\"('Ga', 'O', 'Sr', 'Ti', 'Y')\",OTHERS,True\nmp-23358,\"{'Hg': 10.0, 'Cl': 4.0, 'O': 8.0}\",\"('Cl', 'Hg', 'O')\",OTHERS,True\nmp-772072,\"{'Li': 4.0, 'Fe': 2.0, 'Si': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Fe', 'Li', 'O', 'Si')\",OTHERS,True\nmp-1239271,\"{'Nb': 1.0, 'Cr': 3.0, 'Ag': 2.0, 'S': 8.0}\",\"('Ag', 'Cr', 'Nb', 'S')\",OTHERS,True\nmp-1212105,\"{'Ho': 1.0, 'Al': 3.0, 'B': 4.0, 'O': 12.0}\",\"('Al', 'B', 'Ho', 'O')\",OTHERS,True\nmp-558956,\"{'K': 8.0, 'V': 4.0, 'P': 4.0, 'O': 24.0}\",\"('K', 'O', 'P', 'V')\",OTHERS,True\nmp-650598,\"{'U': 8.0, 'Pb': 24.0, 'O': 48.0}\",\"('O', 'Pb', 'U')\",OTHERS,True\nmp-1218724,\"{'Sr': 6.0, 'La': 2.0, 'V': 6.0, 'O': 24.0}\",\"('La', 'O', 'Sr', 'V')\",OTHERS,True\nmp-989616,\"{'Ca': 2.0, 'W': 2.0, 'N': 6.0}\",\"('Ca', 'N', 'W')\",OTHERS,True\nmp-1198803,\"{'Sc': 20.0, 'Ge': 16.0}\",\"('Ge', 'Sc')\",OTHERS,True\nmp-1111567,\"{'K': 2.0, 'Sc': 1.0, 'Tl': 1.0, 'F': 6.0}\",\"('F', 'K', 'Sc', 'Tl')\",OTHERS,True\nmp-979028,\"{'Sm': 2.0, 'Mg': 1.0}\",\"('Mg', 'Sm')\",OTHERS,True\nmp-768638,\"{'Dy': 6.0, 'Sb': 10.0, 'O': 24.0}\",\"('Dy', 'O', 'Sb')\",OTHERS,True\nmp-778808,\"{'Li': 24.0, 'La': 12.0, 'Nb': 8.0, 'O': 48.0}\",\"('La', 'Li', 'Nb', 'O')\",OTHERS,True\nmp-867110,\"{'Sc': 2.0, 'Co': 1.0, 'Os': 1.0}\",\"('Co', 'Os', 'Sc')\",OTHERS,True\nmp-973203,\"{'Sb': 3.0, 'Mo': 1.0}\",\"('Mo', 'Sb')\",OTHERS,True\nmp-761257,\"{'Li': 4.0, 'Fe': 8.0, 'O': 2.0, 'F': 16.0}\",\"('F', 'Fe', 'Li', 'O')\",OTHERS,True\nmp-24103,\"{'H': 24.0, 'N': 4.0, 'O': 4.0, 'Cu': 2.0, 'Br': 8.0}\",\"('Br', 'Cu', 'H', 'N', 'O')\",OTHERS,True\nmp-756131,\"{'Sr': 4.0, 'Ca': 2.0, 'I': 12.0}\",\"('Ca', 'I', 'Sr')\",OTHERS,True\nmp-1223416,\"{'La': 2.0, 'Mn': 1.0, 'Pb': 1.0, 'O': 9.0}\",\"('La', 'Mn', 'O', 'Pb')\",OTHERS,True\nmp-772850,\"{'La': 8.0, 'Hf': 8.0, 'O': 28.0}\",\"('Hf', 'La', 'O')\",OTHERS,True\nmp-760076,\"{'Li': 10.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-1198260,\"{'Ce': 6.0, 'Ge': 26.0, 'Ir': 8.0}\",\"('Ce', 'Ge', 'Ir')\",OTHERS,True\nmp-1194197,\"{'La': 12.0, 'Si': 4.0, 'N': 16.0, 'F': 4.0}\",\"('F', 'La', 'N', 'Si')\",OTHERS,True\nmp-760293,\"{'Li': 2.0, 'Mn': 4.0, 'F': 10.0}\",\"('F', 'Li', 'Mn')\",OTHERS,True\nmp-753455,\"{'Bi': 4.0, 'O': 4.0, 'F': 12.0}\",\"('Bi', 'F', 'O')\",OTHERS,True\nmp-1205237,\"{'Ti': 12.0, 'C': 240.0, 'Cl': 48.0}\",\"('C', 'Cl', 'Ti')\",OTHERS,True\nmp-863421,\"{'Li': 12.0, 'V': 6.0, 'Si': 12.0, 'O': 36.0}\",\"('Li', 'O', 'Si', 'V')\",OTHERS,True\nmvc-5503,\"{'Ca': 6.0, 'Co': 12.0, 'O': 24.0}\",\"('Ca', 'Co', 'O')\",OTHERS,True\nmp-1070925,\"{'La': 2.0, 'P': 2.0, 'Rh': 2.0}\",\"('La', 'P', 'Rh')\",OTHERS,True\nmp-752795,\"{'Co': 6.0, 'O': 5.0, 'F': 7.0}\",\"('Co', 'F', 'O')\",OTHERS,True\nmp-766500,\"{'Li': 10.0, 'Mn': 6.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-567967,\"{'Li': 4.0, 'Ga': 4.0, 'I': 16.0}\",\"('Ga', 'I', 'Li')\",OTHERS,True\nmp-32959,\"{'H': 4.0, 'O': 2.0}\",\"('H', 'O')\",OTHERS,True\nmvc-8032,\"{'Zn': 3.0, 'Fe': 4.0, 'Mo': 6.0, 'O': 24.0}\",\"('Fe', 'Mo', 'O', 'Zn')\",OTHERS,True\nmp-36150,\"{'Sm': 4.0, 'Tm': 2.0, 'S': 8.0}\",\"('S', 'Sm', 'Tm')\",OTHERS,True\nmp-1111893,\"{'Na': 3.0, 'Ga': 1.0, 'Cl': 6.0}\",\"('Cl', 'Ga', 'Na')\",OTHERS,True\nmp-675048,\"{'Sr': 6.0, 'Nd': 8.0, 'O': 18.0}\",\"('Nd', 'O', 'Sr')\",OTHERS,True\nmp-603930,\"{'Mg': 4.0, 'Si': 4.0, 'O': 12.0}\",\"('Mg', 'O', 'Si')\",OTHERS,True\nmp-9468,\"{'O': 56.0, 'Mg': 2.0, 'P': 14.0, 'K': 2.0, 'Ca': 18.0}\",\"('Ca', 'K', 'Mg', 'O', 'P')\",OTHERS,True\nmp-1187592,\"{'Tc': 6.0, 'Pb': 2.0}\",\"('Pb', 'Tc')\",OTHERS,True\nmp-4343,\"{'Mn': 2.0, 'As': 2.0, 'Sr': 1.0}\",\"('As', 'Mn', 'Sr')\",OTHERS,True\nmp-1097284,\"{'Ag': 1.0, 'Sn': 1.0, 'Rh': 2.0}\",\"('Ag', 'Rh', 'Sn')\",OTHERS,True\nmp-1114648,\"{'Rb': 2.0, 'In': 1.0, 'As': 1.0, 'Cl': 6.0}\",\"('As', 'Cl', 'In', 'Rb')\",OTHERS,True\nmp-1218046,\"{'Ta': 2.0, 'Co': 8.0, 'Si': 2.0}\",\"('Co', 'Si', 'Ta')\",OTHERS,True\nmp-975109,\"{'Rb': 2.0, 'Na': 6.0}\",\"('Na', 'Rb')\",OTHERS,True\nmp-755904,\"{'Li': 4.0, 'V': 4.0, 'F': 16.0}\",\"('F', 'Li', 'V')\",OTHERS,True\nmp-774589,\"{'Ti': 3.0, 'Mn': 2.0, 'V': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Mn', 'O', 'P', 'Ti', 'V')\",OTHERS,True\nmp-1102662,\"{'Ca': 4.0, 'Mg': 4.0, 'Pd': 4.0}\",\"('Ca', 'Mg', 'Pd')\",OTHERS,True\nmp-615789,\"{'Ba': 8.0, 'Cu': 12.0, 'O': 24.0}\",\"('Ba', 'Cu', 'O')\",OTHERS,True\nmp-974690,\"{'Rb': 2.0, 'Cu': 1.0, 'Cl': 4.0}\",\"('Cl', 'Cu', 'Rb')\",OTHERS,True\nmp-1180949,\"{'K': 1.0, 'Fe': 3.0, 'S': 2.0, 'O': 14.0}\",\"('Fe', 'K', 'O', 'S')\",OTHERS,True\nmp-31502,\"{'Sc': 2.0, 'Cd': 14.0}\",\"('Cd', 'Sc')\",OTHERS,True\nmp-1183604,\"{'Ca': 2.0, 'Eu': 6.0}\",\"('Ca', 'Eu')\",OTHERS,True\nmp-571598,\"{'Ga': 6.0, 'Au': 21.0}\",\"('Au', 'Ga')\",OTHERS,True\nmp-17558,\"{'Ca': 10.0, 'Ga': 4.0, 'As': 12.0}\",\"('As', 'Ca', 'Ga')\",OTHERS,True\nmp-1012879,\"{'Li': 12.0, 'Cr': 8.0, 'Ge': 12.0, 'O': 48.0}\",\"('Cr', 'Ge', 'Li', 'O')\",OTHERS,True\nmp-695285,\"{'Sr': 2.0, 'Al': 2.0, 'Si': 10.0, 'N': 14.0, 'O': 4.0}\",\"('Al', 'N', 'O', 'Si', 'Sr')\",OTHERS,True\nmp-1191492,\"{'La': 8.0, 'P': 8.0, 'S': 8.0}\",\"('La', 'P', 'S')\",OTHERS,True\nmp-979265,\"{'Ta': 2.0, 'C': 1.0, 'N': 3.0}\",\"('C', 'N', 'Ta')\",OTHERS,True\nmp-7881,\"{'Ge': 2.0, 'Sr': 1.0, 'Cd': 2.0}\",\"('Cd', 'Ge', 'Sr')\",OTHERS,True\nmp-1094293,\"{'Sr': 6.0, 'Mg': 2.0}\",\"('Mg', 'Sr')\",OTHERS,True\nmp-1211984,\"{'La': 12.0, 'Ni': 6.0, 'Pb': 1.0}\",\"('La', 'Ni', 'Pb')\",OTHERS,True\nmp-774353,\"{'Li': 3.0, 'Ti': 1.0, 'Ni': 2.0, 'O': 6.0}\",\"('Li', 'Ni', 'O', 'Ti')\",OTHERS,True\nmp-696291,\"{'Zn': 2.0, 'H': 8.0, 'N': 4.0, 'O': 16.0}\",\"('H', 'N', 'O', 'Zn')\",OTHERS,True\nmp-772177,\"{'Li': 2.0, 'Fe': 3.0, 'Cu': 1.0, 'O': 8.0}\",\"('Cu', 'Fe', 'Li', 'O')\",OTHERS,True\nmp-1075761,\"{'Mg': 10.0, 'Si': 18.0}\",\"('Mg', 'Si')\",OTHERS,True\nmp-978847,\"{'Sr': 1.0, 'Ga': 1.0, 'Ge': 1.0, 'H': 1.0}\",\"('Ga', 'Ge', 'H', 'Sr')\",OTHERS,True\nmp-1192592,\"{'Ce': 6.0, 'Mg': 23.0, 'Sb': 1.0}\",\"('Ce', 'Mg', 'Sb')\",OTHERS,True\nmp-765724,\"{'V': 6.0, 'F': 24.0}\",\"('F', 'V')\",OTHERS,True\nmp-865334,\"{'Lu': 2.0, 'Ni': 1.0, 'Os': 1.0}\",\"('Lu', 'Ni', 'Os')\",OTHERS,True\nmp-4103,\"{'O': 14.0, 'Sr': 4.0, 'Sb': 4.0}\",\"('O', 'Sb', 'Sr')\",OTHERS,True\nCd 6 Y 1,\"{'Cd': 6.0, 'Y': 1.0}\",\"('Cd', 'Y')\",AC,True\nmp-773170,\"{'Cr': 6.0, 'Sb': 2.0, 'O': 16.0}\",\"('Cr', 'O', 'Sb')\",OTHERS,True\nmp-756710,\"{'Cr': 3.0, 'Fe': 2.0, 'O': 8.0}\",\"('Cr', 'Fe', 'O')\",OTHERS,True\nmp-17394,\"{'O': 16.0, 'Na': 4.0, 'Ge': 4.0, 'Y': 4.0}\",\"('Ge', 'Na', 'O', 'Y')\",OTHERS,True\nmp-26198,\"{'Co': 4.0, 'P': 8.0, 'O': 28.0}\",\"('Co', 'O', 'P')\",OTHERS,True\nmvc-13133,\"{'Cu': 2.0, 'Sn': 1.0, 'Se': 4.0}\",\"('Cu', 'Se', 'Sn')\",OTHERS,True\nmp-759215,\"{'Li': 6.0, 'Mg': 1.0, 'Ni': 12.0, 'O': 26.0}\",\"('Li', 'Mg', 'Ni', 'O')\",OTHERS,True\nmp-1183186,\"{'As': 1.0, 'Pt': 3.0}\",\"('As', 'Pt')\",OTHERS,True\nmp-1078898,\"{'Gd': 2.0, 'B': 4.0, 'C': 4.0}\",\"('B', 'C', 'Gd')\",OTHERS,True\nmp-25208,\"{'O': 8.0, 'Bi': 4.0}\",\"('Bi', 'O')\",OTHERS,True\nmp-561490,\"{'Pd': 2.0, 'Se': 2.0, 'O': 8.0}\",\"('O', 'Pd', 'Se')\",OTHERS,True\nmp-777668,\"{'Li': 4.0, 'Ti': 3.0, 'Co': 3.0, 'Sn': 2.0, 'O': 16.0}\",\"('Co', 'Li', 'O', 'Sn', 'Ti')\",OTHERS,True\nmp-779064,\"{'Li': 8.0, 'Fe': 2.0, 'O': 2.0, 'F': 10.0}\",\"('F', 'Fe', 'Li', 'O')\",OTHERS,True\nmp-1077889,\"{'Ir': 2.0, 'C': 4.0}\",\"('C', 'Ir')\",OTHERS,True\nmp-568313,\"{'Si': 2.0, 'Se': 4.0}\",\"('Se', 'Si')\",OTHERS,True\nmp-1112080,\"{'K': 2.0, 'Ta': 1.0, 'Ag': 1.0, 'I': 6.0}\",\"('Ag', 'I', 'K', 'Ta')\",OTHERS,True\nmp-1218943,\"{'Sr': 10.0, 'Cu': 5.0, 'Mo': 3.0, 'W': 2.0, 'O': 30.0}\",\"('Cu', 'Mo', 'O', 'Sr', 'W')\",OTHERS,True\nmp-1266342,\"{'Na': 11.0, 'Zr': 8.0, 'Si': 7.0, 'P': 5.0, 'O': 48.0}\",\"('Na', 'O', 'P', 'Si', 'Zr')\",OTHERS,True\nmp-1193542,\"{'Li': 3.0, 'Mn': 3.0, 'V': 3.0, 'F': 18.0}\",\"('F', 'Li', 'Mn', 'V')\",OTHERS,True\nmp-1197580,\"{'Li': 12.0, 'Ca': 34.0, 'Hg': 18.0}\",\"('Ca', 'Hg', 'Li')\",OTHERS,True\nmp-1017555,\"{'Cs': 1.0, 'Be': 1.0, 'F': 3.0}\",\"('Be', 'Cs', 'F')\",OTHERS,True\nZn 84 Mg 7 Zr 9,\"{'Zn': 84.0, 'Mg': 7.0, 'Zr': 9.0}\",\"('Mg', 'Zn', 'Zr')\",QC,True\nmp-765010,\"{'Li': 6.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-772568,\"{'Li': 6.0, 'Cr': 12.0, 'O': 39.0}\",\"('Cr', 'Li', 'O')\",OTHERS,True\nmp-1193543,\"{'Nb': 8.0, 'Ni': 2.0, 'Se': 16.0}\",\"('Nb', 'Ni', 'Se')\",OTHERS,True\nmp-1104326,\"{'Ho': 2.0, 'V': 2.0, 'O': 8.0}\",\"('Ho', 'O', 'V')\",OTHERS,True\nmp-559663,\"{'Li': 2.0, 'F': 12.0, 'Ni': 2.0, 'Sr': 2.0}\",\"('F', 'Li', 'Ni', 'Sr')\",OTHERS,True\nmp-1187073,{'Sr': 3.0},\"('Sr',)\",OTHERS,True\nmp-1222755,\"{'La': 1.0, 'Y': 1.0, 'Mn': 4.0, 'Si': 4.0}\",\"('La', 'Mn', 'Si', 'Y')\",OTHERS,True\nmp-1244901,\"{'V': 30.0, 'O': 75.0}\",\"('O', 'V')\",OTHERS,True\nmp-1210151,\"{'Nd': 6.0, 'Re': 4.0, 'O': 18.0}\",\"('Nd', 'O', 'Re')\",OTHERS,True\nmp-774451,\"{'Li': 4.0, 'Mn': 3.0, 'Fe': 2.0, 'Co': 3.0, 'O': 16.0}\",\"('Co', 'Fe', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1069825,\"{'Pr': 1.0, 'Al': 3.0, 'Cu': 1.0}\",\"('Al', 'Cu', 'Pr')\",OTHERS,True\nmp-1247468,\"{'Mg': 10.0, 'Co': 2.0, 'N': 8.0}\",\"('Co', 'Mg', 'N')\",OTHERS,True\nmp-4895,\"{'As': 2.0, 'Te': 2.0, 'U': 2.0}\",\"('As', 'Te', 'U')\",OTHERS,True\nmp-1224472,\"{'Ho': 30.0, 'Si': 18.0, 'C': 2.0}\",\"('C', 'Ho', 'Si')\",OTHERS,True\nmp-757838,\"{'Li': 4.0, 'Sn': 4.0, 'P': 12.0, 'O': 36.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,True\nmp-543029,\"{'La': 2.0, 'I': 2.0, 'Te': 1.0}\",\"('I', 'La', 'Te')\",OTHERS,True\nmp-1068559,\"{'La': 1.0, 'Co': 1.0, 'Si': 3.0}\",\"('Co', 'La', 'Si')\",OTHERS,True\nmp-1180580,\"{'Li': 1.0, 'H': 1.0, 'F': 2.0}\",\"('F', 'H', 'Li')\",OTHERS,True\nmp-1187698,\"{'V': 1.0, 'Zn': 1.0, 'Co': 2.0}\",\"('Co', 'V', 'Zn')\",OTHERS,True\nmp-7130,\"{'C': 1.0, 'Sc': 1.0, 'Ru': 3.0}\",\"('C', 'Ru', 'Sc')\",OTHERS,True\nmp-696152,\"{'Cu': 1.0, 'Sn': 1.0, 'H': 12.0, 'N': 2.0, 'O': 6.0}\",\"('Cu', 'H', 'N', 'O', 'Sn')\",OTHERS,True\nmp-1183208,\"{'Al': 1.0, 'Ga': 3.0}\",\"('Al', 'Ga')\",OTHERS,True\nmp-1206182,\"{'La': 2.0, 'C': 2.0, 'I': 2.0}\",\"('C', 'I', 'La')\",OTHERS,True\nmp-1216113,\"{'Y': 4.0, 'Mn': 1.0, 'S': 7.0}\",\"('Mn', 'S', 'Y')\",OTHERS,True\nmp-1094665,\"{'Li': 1.0, 'Mg': 3.0}\",\"('Li', 'Mg')\",OTHERS,True\nmp-1186627,\"{'Pm': 1.0, 'Pr': 1.0, 'Hg': 2.0}\",\"('Hg', 'Pm', 'Pr')\",OTHERS,True\nmp-972370,\"{'Yb': 2.0, 'In': 1.0, 'Hg': 1.0}\",\"('Hg', 'In', 'Yb')\",OTHERS,True\nmp-1206494,\"{'Lu': 3.0, 'In': 3.0, 'Ni': 3.0}\",\"('In', 'Lu', 'Ni')\",OTHERS,True\nmp-30142,\"{'Be': 12.0, 'N': 18.0, 'O': 57.0}\",\"('Be', 'N', 'O')\",OTHERS,True\nmp-19932,\"{'Se': 12.0, 'In': 16.0}\",\"('In', 'Se')\",OTHERS,True\nmp-1212719,\"{'Fe': 20.0, 'Si': 4.0, 'C': 4.0}\",\"('C', 'Fe', 'Si')\",OTHERS,True\nmp-978539,\"{'Sm': 1.0, 'Tm': 1.0, 'Ru': 2.0}\",\"('Ru', 'Sm', 'Tm')\",OTHERS,True\nmp-1186053,\"{'Na': 3.0, 'Nb': 1.0}\",\"('Na', 'Nb')\",OTHERS,True\nmp-1210663,\"{'Mg': 14.0, 'Ir': 6.0}\",\"('Ir', 'Mg')\",OTHERS,True\nmp-1209975,\"{'Nd': 12.0, 'Se': 12.0, 'N': 4.0}\",\"('N', 'Nd', 'Se')\",OTHERS,True\nmp-1207889,\"{'W': 12.0, 'I': 24.0}\",\"('I', 'W')\",OTHERS,True\nmp-865176,\"{'Hf': 1.0, 'Cu': 3.0}\",\"('Cu', 'Hf')\",OTHERS,True\nmp-21684,\"{'P': 12.0, 'Cu': 19.0, 'Ce': 5.0}\",\"('Ce', 'Cu', 'P')\",OTHERS,True\nmp-680092,\"{'Cd': 10.0, 'I': 20.0}\",\"('Cd', 'I')\",OTHERS,True\nmp-973924,\"{'Li': 1.0, 'Mg': 16.0, 'Al': 12.0}\",\"('Al', 'Li', 'Mg')\",OTHERS,True\nmp-769010,\"{'Li': 12.0, 'Bi': 10.0, 'O': 28.0}\",\"('Bi', 'Li', 'O')\",OTHERS,True\nmp-23621,\"{'O': 48.0, 'Na': 4.0, 'P': 16.0, 'Bi': 4.0}\",\"('Bi', 'Na', 'O', 'P')\",OTHERS,True\nmp-1064227,{'Ca': 4.0},\"('Ca',)\",OTHERS,True\nmp-1076754,\"{'K': 16.0, 'Na': 16.0, 'Mo': 28.0, 'W': 4.0, 'O': 80.0}\",\"('K', 'Mo', 'Na', 'O', 'W')\",OTHERS,True\nmp-28337,\"{'O': 13.0, 'V': 4.0, 'As': 2.0}\",\"('As', 'O', 'V')\",OTHERS,True\nmp-558376,\"{'Na': 8.0, 'Mn': 8.0, 'O': 12.0}\",\"('Mn', 'Na', 'O')\",OTHERS,True\nmp-1199525,\"{'U': 4.0, 'P': 16.0, 'O': 48.0}\",\"('O', 'P', 'U')\",OTHERS,True\nmp-1120721,\"{'Rb': 2.0, 'Ge': 2.0, 'Br': 6.0}\",\"('Br', 'Ge', 'Rb')\",OTHERS,True\nmvc-1323,\"{'Ba': 2.0, 'Y': 2.0, 'Fe': 8.0, 'O': 14.0}\",\"('Ba', 'Fe', 'O', 'Y')\",OTHERS,True\nmp-1228975,\"{'Al': 2.0, 'Cr': 3.0, 'Cu': 1.0, 'S': 8.0}\",\"('Al', 'Cr', 'Cu', 'S')\",OTHERS,True\nmp-1105355,\"{'Ca': 1.0, 'Mn': 7.0, 'O': 12.0}\",\"('Ca', 'Mn', 'O')\",OTHERS,True\nmp-760126,\"{'Na': 8.0, 'V': 8.0, 'P': 12.0, 'O': 48.0}\",\"('Na', 'O', 'P', 'V')\",OTHERS,True\nmp-554085,\"{'Te': 6.0, 'C': 12.0, 'F': 48.0}\",\"('C', 'F', 'Te')\",OTHERS,True\nmp-1096168,\"{'Ca': 2.0, 'Zn': 1.0, 'Ag': 1.0}\",\"('Ag', 'Ca', 'Zn')\",OTHERS,True\nmp-683902,\"{'Rb': 6.0, 'Nb': 4.0, 'As': 2.0, 'Se': 22.0}\",\"('As', 'Nb', 'Rb', 'Se')\",OTHERS,True\nmp-669347,\"{'K': 8.0, 'Cu': 8.0, 'F': 24.0}\",\"('Cu', 'F', 'K')\",OTHERS,True\nmp-1027946,\"{'Y': 1.0, 'Mg': 14.0, 'Sb': 1.0}\",\"('Mg', 'Sb', 'Y')\",OTHERS,True\nmp-1192712,\"{'K': 4.0, 'I': 4.0, 'Cl': 16.0, 'O': 4.0}\",\"('Cl', 'I', 'K', 'O')\",OTHERS,True\nmp-1182074,\"{'Ca': 2.0, 'Co': 1.0, 'O': 3.0}\",\"('Ca', 'Co', 'O')\",OTHERS,True\nmp-27342,\"{'F': 24.0, 'Ge': 10.0}\",\"('F', 'Ge')\",OTHERS,True\nmp-1113456,\"{'Rb': 2.0, 'Sb': 1.0, 'Au': 1.0, 'Cl': 6.0}\",\"('Au', 'Cl', 'Rb', 'Sb')\",OTHERS,True\nmp-690725,\"{'Tl': 2.0, 'Cu': 2.0, 'H': 2.0, 'S': 2.0, 'O': 10.0}\",\"('Cu', 'H', 'O', 'S', 'Tl')\",OTHERS,True\nmp-1193418,\"{'Y': 2.0, 'I': 6.0, 'O': 22.0}\",\"('I', 'O', 'Y')\",OTHERS,True\nmp-766575,\"{'Li': 32.0, 'Ti': 4.0, 'S': 24.0}\",\"('Li', 'S', 'Ti')\",OTHERS,True\nmp-555076,\"{'K': 4.0, 'Na': 4.0, 'Sn': 4.0, 'F': 24.0}\",\"('F', 'K', 'Na', 'Sn')\",OTHERS,True\nmp-5563,\"{'O': 16.0, 'Ge': 4.0, 'Ag': 20.0}\",\"('Ag', 'Ge', 'O')\",OTHERS,True\nmp-1183288,\"{'Ba': 1.0, 'In': 1.0, 'O': 3.0}\",\"('Ba', 'In', 'O')\",OTHERS,True\nmp-1210101,\"{'Na': 1.0, 'Be': 2.0, 'Tl': 3.0, 'F': 8.0}\",\"('Be', 'F', 'Na', 'Tl')\",OTHERS,True\nmp-6980,\"{'S': 2.0, 'Sc': 1.0, 'Cu': 1.0}\",\"('Cu', 'S', 'Sc')\",OTHERS,True\nCd 65 Mg 20 Ho 15,\"{'Cd': 65.0, 'Mg': 20.0, 'Ho': 15.0}\",\"('Cd', 'Ho', 'Mg')\",QC,True\nmp-1226365,\"{'Cs': 2.0, 'Cu': 3.0, 'Ni': 1.0, 'F': 10.0}\",\"('Cs', 'Cu', 'F', 'Ni')\",OTHERS,True\nmp-1025691,\"{'Te': 4.0, 'Mo': 1.0, 'W': 2.0, 'S': 2.0}\",\"('Mo', 'S', 'Te', 'W')\",OTHERS,True\nmp-571136,\"{'Cd': 7.0, 'I': 14.0}\",\"('Cd', 'I')\",OTHERS,True\nmp-1221207,\"{'Na': 8.0, 'Ca': 4.0, 'Si': 2.0, 'P': 8.0}\",\"('Ca', 'Na', 'P', 'Si')\",OTHERS,True\nmp-1217328,\"{'Th': 1.0, 'Mn': 3.0, 'Al': 2.0}\",\"('Al', 'Mn', 'Th')\",OTHERS,True\nmp-1080104,\"{'Pr': 2.0, 'B': 4.0, 'Ir': 4.0}\",\"('B', 'Ir', 'Pr')\",OTHERS,True\nmp-1205147,\"{'Sm': 2.0, 'Ni': 24.0, 'B': 12.0}\",\"('B', 'Ni', 'Sm')\",OTHERS,True\nmp-640818,\"{'Mo': 36.0, 'O': 104.0}\",\"('Mo', 'O')\",OTHERS,True\nmp-1021492,\"{'Cd': 4.0, 'S': 1.0, 'F': 6.0}\",\"('Cd', 'F', 'S')\",OTHERS,True\nmp-7405,\"{'O': 12.0, 'Al': 4.0, 'Sm': 4.0}\",\"('Al', 'O', 'Sm')\",OTHERS,True\nmp-755923,\"{'Fe': 6.0, 'O': 7.0, 'F': 5.0}\",\"('F', 'Fe', 'O')\",OTHERS,True\nmp-1211934,\"{'La': 12.0, 'Al': 2.0, 'Fe': 26.0}\",\"('Al', 'Fe', 'La')\",OTHERS,True\nmp-1226892,\"{'Ce': 2.0, 'Co': 5.0, 'Cu': 5.0}\",\"('Ce', 'Co', 'Cu')\",OTHERS,True\nZn 56.8 Mg 34.6 Dy 8.7,\"{'Zn': 56.8, 'Mg': 34.6, 'Dy': 8.7}\",\"('Dy', 'Mg', 'Zn')\",QC,True\nmp-19276,\"{'O': 8.0, 'Ba': 2.0, 'Mo': 2.0}\",\"('Ba', 'Mo', 'O')\",OTHERS,True\nmp-1198608,\"{'V': 8.0, 'Zn': 4.0, 'P': 12.0, 'N': 4.0, 'O': 60.0}\",\"('N', 'O', 'P', 'V', 'Zn')\",OTHERS,True\nmp-766389,\"{'Li': 12.0, 'Mn': 3.0, 'O': 3.0, 'F': 15.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-759728,\"{'Sb': 14.0, 'O': 12.0, 'F': 18.0}\",\"('F', 'O', 'Sb')\",OTHERS,True\nmp-557650,\"{'Sr': 3.0, 'P': 6.0, 'N': 8.0, 'O': 6.0}\",\"('N', 'O', 'P', 'Sr')\",OTHERS,True\nmp-5308,\"{'O': 36.0, 'La': 4.0, 'Ta': 12.0}\",\"('La', 'O', 'Ta')\",OTHERS,True\nmp-4954,\"{'Si': 2.0, 'Pd': 2.0, 'La': 1.0}\",\"('La', 'Pd', 'Si')\",OTHERS,True\nmp-1111928,\"{'K': 2.0, 'Li': 1.0, 'As': 1.0, 'F': 6.0}\",\"('As', 'F', 'K', 'Li')\",OTHERS,True\nmp-697721,\"{'H': 32.0, 'C': 8.0, 'N': 4.0, 'Cl': 4.0}\",\"('C', 'Cl', 'H', 'N')\",OTHERS,True\nmp-752897,\"{'Li': 3.0, 'Fe': 7.0, 'O': 12.0}\",\"('Fe', 'Li', 'O')\",OTHERS,True\nmp-17876,\"{'O': 16.0, 'Na': 4.0, 'Mn': 4.0, 'As': 4.0}\",\"('As', 'Mn', 'Na', 'O')\",OTHERS,True\nmp-23041,\"{'S': 4.0, 'Sb': 4.0, 'I': 4.0}\",\"('I', 'S', 'Sb')\",OTHERS,True\nmp-1227498,\"{'Ba': 1.0, 'Sr': 1.0, 'Nd': 1.0, 'Cu': 3.0, 'O': 7.0}\",\"('Ba', 'Cu', 'Nd', 'O', 'Sr')\",OTHERS,True\nmp-1028791,\"{'Te': 4.0, 'Mo': 1.0, 'W': 3.0, 'Se': 2.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,True\nmp-1205359,\"{'Ba': 2.0, 'U': 1.0, 'Cu': 1.0, 'O': 6.0}\",\"('Ba', 'Cu', 'O', 'U')\",OTHERS,True\nmp-1197910,\"{'K': 8.0, 'Si': 32.0, 'H': 216.0, 'C': 72.0, 'O': 24.0}\",\"('C', 'H', 'K', 'O', 'Si')\",OTHERS,True\nmp-15626,\"{'F': 6.0, 'Na': 1.0, 'Ti': 1.0, 'Rb': 2.0}\",\"('F', 'Na', 'Rb', 'Ti')\",OTHERS,True\nmp-561499,\"{'Dy': 6.0, 'Cu': 2.0, 'Sn': 2.0, 'S': 14.0}\",\"('Cu', 'Dy', 'S', 'Sn')\",OTHERS,True\nmp-1210171,\"{'Pu': 8.0, 'Ni': 2.0}\",\"('Ni', 'Pu')\",OTHERS,True\nmp-570847,\"{'Co': 1.0, 'H': 3.0, 'C': 6.0, 'N': 6.0}\",\"('C', 'Co', 'H', 'N')\",OTHERS,True\nmp-767404,\"{'Gd': 4.0, 'As': 8.0, 'O': 22.0}\",\"('As', 'Gd', 'O')\",OTHERS,True\nmp-721047,\"{'Ca': 2.0, 'H': 16.0, 'Cl': 4.0, 'O': 8.0}\",\"('Ca', 'Cl', 'H', 'O')\",OTHERS,True\nmp-560756,\"{'Th': 9.0, 'Mo': 18.0, 'O': 72.0}\",\"('Mo', 'O', 'Th')\",OTHERS,True\nmp-867750,\"{'Ti': 3.0, 'P': 1.0, 'O': 7.0}\",\"('O', 'P', 'Ti')\",OTHERS,True\nmp-694318,\"{'In': 16.0, 'W': 24.0, 'O': 96.0}\",\"('In', 'O', 'W')\",OTHERS,True\nmp-10895,\"{'Al': 1.0, 'V': 1.0, 'Mn': 2.0}\",\"('Al', 'Mn', 'V')\",OTHERS,True\nmp-605166,\"{'K': 4.0, 'Dy': 4.0, 'H': 8.0, 'C': 8.0, 'S': 4.0, 'O': 36.0}\",\"('C', 'Dy', 'H', 'K', 'O', 'S')\",OTHERS,True\nmp-1213975,\"{'Ca': 4.0, 'B': 16.0}\",\"('B', 'Ca')\",OTHERS,True\nmp-1215913,\"{'Y': 1.0, 'Co': 1.0, 'C': 1.0}\",\"('C', 'Co', 'Y')\",OTHERS,True\nmp-765473,\"{'Co': 4.0, 'Te': 8.0, 'O': 20.0}\",\"('Co', 'O', 'Te')\",OTHERS,True\nmp-754099,\"{'Li': 4.0, 'Ti': 2.0, 'Fe': 4.0, 'O': 10.0}\",\"('Fe', 'Li', 'O', 'Ti')\",OTHERS,True\nmp-531110,\"{'Ca': 18.0, 'F': 40.0, 'Pr': 2.0}\",\"('Ca', 'F', 'Pr')\",OTHERS,True\nmp-1064933,{'S': 4.0},\"('S',)\",OTHERS,True\nmp-11565,\"{'Y': 1.0, 'Rh': 5.0}\",\"('Rh', 'Y')\",OTHERS,True\nmp-759999,\"{'Mn': 12.0, 'O': 14.0, 'F': 10.0}\",\"('F', 'Mn', 'O')\",OTHERS,True\nmp-23259,\"{'Li': 1.0, 'Br': 1.0}\",\"('Br', 'Li')\",OTHERS,True\nmp-9873,\"{'Zn': 2.0, 'Ge': 2.0, 'Eu': 2.0}\",\"('Eu', 'Ge', 'Zn')\",OTHERS,True\nmp-768432,\"{'Mn': 2.0, 'Tl': 2.0, 'O': 6.0}\",\"('Mn', 'O', 'Tl')\",OTHERS,True\nmp-12984,\"{'Cu': 4.0, 'Se': 8.0, 'Tb': 4.0}\",\"('Cu', 'Se', 'Tb')\",OTHERS,True\nmp-759261,\"{'Li': 4.0, 'Co': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Co', 'Li', 'O', 'Si')\",OTHERS,True\nmp-1198465,\"{'Er': 8.0, 'B': 24.0, 'Ru': 4.0}\",\"('B', 'Er', 'Ru')\",OTHERS,True\nmp-559672,\"{'K': 2.0, 'Ni': 2.0, 'As': 2.0, 'O': 8.0}\",\"('As', 'K', 'Ni', 'O')\",OTHERS,True\nmp-770540,\"{'Mn': 8.0, 'P': 4.0, 'O': 20.0}\",\"('Mn', 'O', 'P')\",OTHERS,True\nmp-1210604,\"{'Na': 3.0, 'Eu': 1.0, 'V': 2.0, 'O': 8.0}\",\"('Eu', 'Na', 'O', 'V')\",OTHERS,True\nmp-571033,\"{'Ni': 2.0, 'Se': 2.0}\",\"('Ni', 'Se')\",OTHERS,True\nmp-1223557,\"{'K': 2.0, 'U': 4.0, 'Sb': 2.0, 'Se': 16.0}\",\"('K', 'Sb', 'Se', 'U')\",OTHERS,True\nmp-676117,\"{'Li': 4.0, 'Cu': 2.0, 'Ge': 2.0}\",\"('Cu', 'Ge', 'Li')\",OTHERS,True\nmp-771335,\"{'Na': 6.0, 'Sn': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Na', 'O', 'P', 'Sn')\",OTHERS,True\nmp-759841,\"{'Li': 8.0, 'Mn': 16.0, 'O': 4.0, 'F': 32.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-761267,\"{'Li': 4.0, 'V': 2.0, 'O': 2.0, 'F': 8.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,True\nmp-22988,\"{'Cl': 3.0, 'Ge': 1.0, 'Cs': 1.0}\",\"('Cl', 'Cs', 'Ge')\",OTHERS,True\nmp-21105,\"{'Si': 4.0, 'Pu': 2.0}\",\"('Pu', 'Si')\",OTHERS,True\nmp-1215070,\"{'Ca': 8.0, 'Al': 16.0, 'Si': 16.0, 'W': 16.0, 'O': 64.0}\",\"('Al', 'Ca', 'O', 'Si', 'W')\",OTHERS,True\nmp-865531,\"{'Ti': 1.0, 'Mn': 2.0, 'Al': 1.0}\",\"('Al', 'Mn', 'Ti')\",OTHERS,True\nmp-1202107,\"{'Hg': 4.0, 'C': 32.0, 'N': 8.0, 'Cl': 16.0}\",\"('C', 'Cl', 'Hg', 'N')\",OTHERS,True\nmp-561743,\"{'Li': 8.0, 'Co': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Co', 'Li', 'O', 'Si')\",OTHERS,True\nmp-1112506,\"{'Cs': 2.0, 'Al': 1.0, 'Tl': 1.0, 'Br': 6.0}\",\"('Al', 'Br', 'Cs', 'Tl')\",OTHERS,True\nmp-10096,\"{'Na': 6.0, 'P': 8.0, 'Ga': 2.0, 'Sr': 6.0}\",\"('Ga', 'Na', 'P', 'Sr')\",OTHERS,True\nmp-631314,\"{'K': 1.0, 'Ta': 1.0, 'Pt': 1.0}\",\"('K', 'Pt', 'Ta')\",OTHERS,True\nmp-1176603,\"{'Na': 8.0, 'Fe': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Fe', 'Na', 'O', 'P')\",OTHERS,True\nmp-644281,\"{'Mg': 7.0, 'Ti': 1.0, 'H': 16.0}\",\"('H', 'Mg', 'Ti')\",OTHERS,True\nmp-1027772,\"{'Te': 2.0, 'Mo': 3.0, 'W': 1.0, 'Se': 2.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,True\nmvc-1665,\"{'Ba': 4.0, 'Mg': 2.0, 'Cu': 6.0, 'F': 28.0}\",\"('Ba', 'Cu', 'F', 'Mg')\",OTHERS,True\nmp-1078544,\"{'Sc': 4.0, 'In': 2.0, 'Cu': 4.0}\",\"('Cu', 'In', 'Sc')\",OTHERS,True\nmp-1061865,\"{'Te': 1.0, 'N': 2.0}\",\"('N', 'Te')\",OTHERS,True\nmp-1199711,\"{'Si': 72.0, 'O': 116.0}\",\"('O', 'Si')\",OTHERS,True\nmp-4730,\"{'Ga': 2.0, 'Se': 4.0, 'Hg': 1.0}\",\"('Ga', 'Hg', 'Se')\",OTHERS,True\nmp-1177524,\"{'Li': 12.0, 'Ti': 8.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'O', 'P', 'Ti')\",OTHERS,True\nmp-766596,\"{'Ti': 8.0, 'Si': 16.0, 'O': 48.0}\",\"('O', 'Si', 'Ti')\",OTHERS,True\nmp-30527,\"{'Ta': 6.0, 'S': 18.0}\",\"('S', 'Ta')\",OTHERS,True\nmp-1101268,\"{'V': 1.0, 'Fe': 7.0, 'P': 12.0, 'O': 48.0}\",\"('Fe', 'O', 'P', 'V')\",OTHERS,True\nmp-676881,\"{'Ho': 8.0, 'Zr': 6.0, 'O': 24.0}\",\"('Ho', 'O', 'Zr')\",OTHERS,True\nmp-675633,\"{'Ag': 4.0, 'Au': 1.0, 'S': 2.0}\",\"('Ag', 'Au', 'S')\",OTHERS,True\nmp-684705,\"{'Ca': 2.0, 'La': 2.0, 'Mn': 2.0, 'Mo': 2.0, 'O': 12.0}\",\"('Ca', 'La', 'Mn', 'Mo', 'O')\",OTHERS,True\nmp-215,\"{'Y': 1.0, 'Sb': 1.0}\",\"('Sb', 'Y')\",OTHERS,True\nmp-611836,{'O': 2.0},\"('O',)\",OTHERS,True\nmp-779159,\"{'Na': 32.0, 'Cu': 8.0, 'O': 28.0}\",\"('Cu', 'Na', 'O')\",OTHERS,True\nmp-27557,\"{'Na': 32.0, 'Fe': 8.0, 'O': 28.0}\",\"('Fe', 'Na', 'O')\",OTHERS,True\nmp-1244816,\"{'Sr': 2.0, 'Y': 1.0, 'Tl': 1.0, 'Ni': 2.0, 'O': 7.0}\",\"('Ni', 'O', 'Sr', 'Tl', 'Y')\",OTHERS,True\nmp-585476,\"{'Cs': 14.0, 'Np': 6.0, 'Cl': 24.0, 'O': 12.0}\",\"('Cl', 'Cs', 'Np', 'O')\",OTHERS,True\nmp-1214575,\"{'Ba': 10.0, 'Mn': 6.0, 'O': 24.0, 'F': 2.0}\",\"('Ba', 'F', 'Mn', 'O')\",OTHERS,True\nmp-865482,\"{'V': 1.0, 'Tc': 2.0, 'Ge': 1.0}\",\"('Ge', 'Tc', 'V')\",OTHERS,True\nmp-27841,\"{'O': 2.0, 'V': 2.0, 'Br': 2.0}\",\"('Br', 'O', 'V')\",OTHERS,True\nmp-774749,\"{'Rb': 1.0, 'Na': 3.0, 'Li': 12.0, 'Ti': 4.0, 'O': 16.0}\",\"('Li', 'Na', 'O', 'Rb', 'Ti')\",OTHERS,True\nmp-1208535,\"{'Tb': 4.0, 'Ga': 18.0, 'Ir': 6.0}\",\"('Ga', 'Ir', 'Tb')\",OTHERS,True\nmp-617632,\"{'La': 6.0, 'Ag': 2.0, 'Ge': 2.0, 'S': 14.0}\",\"('Ag', 'Ge', 'La', 'S')\",OTHERS,True\nmp-1176994,\"{'Li': 7.0, 'V': 3.0, 'Si': 2.0, 'O': 12.0}\",\"('Li', 'O', 'Si', 'V')\",OTHERS,True\nmp-1194802,\"{'V': 8.0, 'Co': 4.0, 'O': 32.0}\",\"('Co', 'O', 'V')\",OTHERS,True\nmp-1114386,\"{'Rb': 2.0, 'Al': 1.0, 'In': 1.0, 'F': 6.0}\",\"('Al', 'F', 'In', 'Rb')\",OTHERS,True\nmp-754908,\"{'Nb': 1.0, 'Cr': 1.0, 'O': 4.0}\",\"('Cr', 'Nb', 'O')\",OTHERS,True\nmvc-8086,\"{'Sn': 2.0, 'Ge': 4.0, 'O': 12.0}\",\"('Ge', 'O', 'Sn')\",OTHERS,True\nmp-754409,\"{'Ho': 2.0, 'B': 2.0, 'O': 6.0}\",\"('B', 'Ho', 'O')\",OTHERS,True\nmp-10274,\"{'C': 1.0, 'Ga': 1.0, 'Er': 3.0}\",\"('C', 'Er', 'Ga')\",OTHERS,True\nmp-1017579,\"{'In': 2.0, 'Au': 3.0}\",\"('Au', 'In')\",OTHERS,True\nmvc-3996,\"{'Ca': 4.0, 'W': 4.0, 'O': 12.0}\",\"('Ca', 'O', 'W')\",OTHERS,True\nmp-756246,\"{'Nd': 4.0, 'Zr': 4.0, 'O': 12.0}\",\"('Nd', 'O', 'Zr')\",OTHERS,True\nmp-4499,\"{'Mg': 4.0, 'Al': 4.0, 'Si': 4.0}\",\"('Al', 'Mg', 'Si')\",OTHERS,True\nmp-1191015,\"{'Li': 8.0, 'Nd': 8.0, 'Sn': 8.0}\",\"('Li', 'Nd', 'Sn')\",OTHERS,True\nmp-775302,\"{'Li': 8.0, 'Cr': 4.0, 'Si': 24.0, 'O': 60.0}\",\"('Cr', 'Li', 'O', 'Si')\",OTHERS,True\nmp-29266,\"{'S': 2.0, 'Cs': 2.0}\",\"('Cs', 'S')\",OTHERS,True\nmp-650280,\"{'Cs': 16.0, 'As': 64.0, 'S': 104.0}\",\"('As', 'Cs', 'S')\",OTHERS,True\nmp-976784,\"{'Ni': 1.0, 'Au': 3.0}\",\"('Au', 'Ni')\",OTHERS,True\nmp-775189,\"{'Li': 12.0, 'Mn': 6.0, 'Fe': 6.0, 'P': 12.0, 'O': 48.0}\",\"('Fe', 'Li', 'Mn', 'O', 'P')\",OTHERS,True\nmp-23828,\"{'H': 12.0, 'Br': 2.0, 'Sr': 7.0}\",\"('Br', 'H', 'Sr')\",OTHERS,True\nmp-1239164,\"{'Nb': 2.0, 'Cr': 6.0, 'Cu': 4.0, 'S': 16.0}\",\"('Cr', 'Cu', 'Nb', 'S')\",OTHERS,True\nmp-1207192,\"{'Nd': 2.0, 'H': 2.0, 'Se': 2.0}\",\"('H', 'Nd', 'Se')\",OTHERS,True\nmp-757653,\"{'Li': 16.0, 'Fe': 16.0, 'P': 16.0, 'O': 64.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,True\nmp-1094377,\"{'Mg': 12.0, 'Ti': 6.0}\",\"('Mg', 'Ti')\",OTHERS,True\nmp-5431,\"{'O': 6.0, 'Te': 2.0, 'Ba': 2.0}\",\"('Ba', 'O', 'Te')\",OTHERS,True\nmvc-4372,\"{'Ca': 4.0, 'Ta': 2.0, 'Co': 2.0, 'O': 12.0}\",\"('Ca', 'Co', 'O', 'Ta')\",OTHERS,True\nmp-778376,\"{'Li': 32.0, 'Mn': 5.0, 'Cr': 11.0, 'O': 48.0}\",\"('Cr', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1096710,\"{'Ti': 1.0, 'V': 2.0, 'W': 1.0}\",\"('Ti', 'V', 'W')\",OTHERS,True\nmp-6105,\"{'O': 32.0, 'Se': 8.0, 'Pr': 8.0, 'Ta': 12.0}\",\"('O', 'Pr', 'Se', 'Ta')\",OTHERS,True\nmp-752880,\"{'Fe': 8.0, 'O': 8.0, 'F': 8.0}\",\"('F', 'Fe', 'O')\",OTHERS,True\nmp-1187765,\"{'Y': 2.0, 'Al': 1.0, 'Ru': 1.0}\",\"('Al', 'Ru', 'Y')\",OTHERS,True\nmp-1076884,\"{'Sr': 24.0, 'Ca': 8.0, 'Fe': 28.0, 'Co': 4.0, 'O': 80.0}\",\"('Ca', 'Co', 'Fe', 'O', 'Sr')\",OTHERS,True\nmp-642648,\"{'K': 2.0, 'P': 2.0, 'H': 4.0, 'O': 4.0}\",\"('H', 'K', 'O', 'P')\",OTHERS,True\nmp-730972,\"{'U': 4.0, 'P': 12.0, 'H': 40.0, 'Cl': 4.0, 'O': 32.0}\",\"('Cl', 'H', 'O', 'P', 'U')\",OTHERS,True\nmp-505629,\"{'Cr': 12.0, 'Ni': 8.0, 'Si': 4.0}\",\"('Cr', 'Ni', 'Si')\",OTHERS,True\nmp-230,\"{'Sb': 8.0, 'O': 16.0}\",\"('O', 'Sb')\",OTHERS,True\nmp-1211816,\"{'K': 4.0, 'In': 4.0, 'P': 8.0, 'Se': 24.0}\",\"('In', 'K', 'P', 'Se')\",OTHERS,True\nmp-21473,\"{'P': 2.0, 'Ni': 2.0, 'Gd': 1.0}\",\"('Gd', 'Ni', 'P')\",OTHERS,True\nmp-1198684,\"{'K': 12.0, 'Mo': 14.0, 'O': 68.0}\",\"('K', 'Mo', 'O')\",OTHERS,True\nmp-1214277,\"{'Ce': 6.0, 'N': 8.0, 'O': 33.0}\",\"('Ce', 'N', 'O')\",OTHERS,True\nmp-867313,\"{'Li': 1.0, 'Tm': 1.0, 'Pt': 2.0}\",\"('Li', 'Pt', 'Tm')\",OTHERS,True\nmp-9694,\"{'Ge': 4.0, 'Pd': 4.0, 'Tm': 3.0}\",\"('Ge', 'Pd', 'Tm')\",OTHERS,True\nmp-568405,\"{'K': 4.0, 'Nb': 2.0, 'Cl': 12.0}\",\"('Cl', 'K', 'Nb')\",OTHERS,True\nmp-21650,\"{'Li': 8.0, 'O': 32.0, 'Ni': 4.0, 'Ge': 12.0}\",\"('Ge', 'Li', 'Ni', 'O')\",OTHERS,True\nmp-994,\"{'P': 1.0, 'Y': 1.0}\",\"('P', 'Y')\",OTHERS,True\nmp-680167,\"{'Li': 2.0, 'Mo': 12.0, 'Cl': 26.0}\",\"('Cl', 'Li', 'Mo')\",OTHERS,True\nmp-1247016,\"{'Si': 12.0, 'Sb': 6.0, 'N': 22.0}\",\"('N', 'Sb', 'Si')\",OTHERS,True\nmp-1180215,\"{'N': 6.0, 'O': 3.0}\",\"('N', 'O')\",OTHERS,True\nmp-1228626,\"{'Ba': 2.0, 'Si': 3.0, 'Ge': 5.0, 'O': 18.0}\",\"('Ba', 'Ge', 'O', 'Si')\",OTHERS,True\nmp-1098076,\"{'Ce': 1.0, 'Mg': 6.0, 'B': 1.0}\",\"('B', 'Ce', 'Mg')\",OTHERS,True\nmp-757960,\"{'Li': 4.0, 'Ni': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,True\nmp-15779,\"{'Mg': 2.0, 'Si': 1.0, 'Ni': 3.0}\",\"('Mg', 'Ni', 'Si')\",OTHERS,True\nmp-1114642,\"{'Rb': 2.0, 'Li': 1.0, 'Ga': 1.0, 'F': 6.0}\",\"('F', 'Ga', 'Li', 'Rb')\",OTHERS,True\nmp-1019361,\"{'Th': 2.0, 'Sb': 2.0, 'Te': 2.0}\",\"('Sb', 'Te', 'Th')\",OTHERS,True\nmp-1179094,\"{'Sr': 4.0, 'H': 8.0}\",\"('H', 'Sr')\",OTHERS,True\nmp-1096013,\"{'Ta': 2.0, 'Mn': 1.0, 'Fe': 1.0}\",\"('Fe', 'Mn', 'Ta')\",OTHERS,True\nmp-18732,\"{'O': 6.0, 'Ti': 2.0, 'Ni': 2.0}\",\"('Ni', 'O', 'Ti')\",OTHERS,True\nmp-4135,\"{'O': 12.0, 'P': 4.0, 'Rb': 4.0}\",\"('O', 'P', 'Rb')\",OTHERS,True\nmp-20821,\"{'Ga': 3.0, 'Np': 1.0}\",\"('Ga', 'Np')\",OTHERS,True\nmp-18838,\"{'O': 16.0, 'F': 12.0, 'S': 4.0, 'K': 8.0, 'Mn': 4.0}\",\"('F', 'K', 'Mn', 'O', 'S')\",OTHERS,True\nmp-1103779,\"{'Mg': 2.0, 'Cr': 4.0, 'O': 8.0}\",\"('Cr', 'Mg', 'O')\",OTHERS,True\nmp-22966,\"{'C': 2.0, 'F': 4.0, 'Cl': 4.0}\",\"('C', 'Cl', 'F')\",OTHERS,True\nmp-30560,\"{'Co': 1.0, 'Nb': 1.0, 'Sn': 1.0}\",\"('Co', 'Nb', 'Sn')\",OTHERS,True\nmp-19377,\"{'Li': 2.0, 'O': 12.0, 'Mo': 2.0, 'La': 4.0}\",\"('La', 'Li', 'Mo', 'O')\",OTHERS,True\nmp-865508,\"{'U': 2.0, 'Ni': 12.0, 'As': 7.0}\",\"('As', 'Ni', 'U')\",OTHERS,True\nmp-1224129,\"{'In': 1.0, 'Cu': 1.0, 'Sn': 1.0, 'Se': 4.0}\",\"('Cu', 'In', 'Se', 'Sn')\",OTHERS,True\nmp-17153,\"{'Te': 16.0, 'Ba': 4.0, 'Tb': 8.0}\",\"('Ba', 'Tb', 'Te')\",OTHERS,True\nmp-1223740,\"{'K': 4.0, 'H': 16.0, 'Pb': 4.0, 'I': 12.0, 'O': 8.0}\",\"('H', 'I', 'K', 'O', 'Pb')\",OTHERS,True\nmp-1075430,\"{'Mg': 10.0, 'Si': 12.0}\",\"('Mg', 'Si')\",OTHERS,True\nmp-1228981,\"{'Ba': 10.0, 'Fe': 8.0, 'Pt': 2.0, 'Cl': 2.0, 'O': 25.0}\",\"('Ba', 'Cl', 'Fe', 'O', 'Pt')\",OTHERS,True\nmp-1190043,\"{'Tl': 2.0, 'Cr': 6.0, 'Se': 10.0}\",\"('Cr', 'Se', 'Tl')\",OTHERS,True\nmp-2233,\"{'Ag': 2.0, 'Th': 4.0}\",\"('Ag', 'Th')\",OTHERS,True\nmp-990086,\"{'Mo': 18.0, 'H': 2.0, 'S': 36.0}\",\"('H', 'Mo', 'S')\",OTHERS,True\nmp-1012835,\"{'Sm': 2.0, 'Cu': 2.0, 'W': 4.0, 'O': 16.0}\",\"('Cu', 'O', 'Sm', 'W')\",OTHERS,True\nmvc-9555,\"{'Mg': 4.0, 'Ti': 8.0, 'P': 8.0, 'O': 32.0}\",\"('Mg', 'O', 'P', 'Ti')\",OTHERS,True\nmp-1198511,\"{'Tb': 6.0, 'Co': 8.0, 'Ge': 26.0}\",\"('Co', 'Ge', 'Tb')\",OTHERS,True\nmp-1202667,\"{'Ni': 4.0, 'P': 8.0, 'H': 104.0, 'C': 32.0, 'S': 16.0, 'N': 8.0, 'O': 8.0}\",\"('C', 'H', 'N', 'Ni', 'O', 'P', 'S')\",OTHERS,True\nmp-1194543,\"{'K': 8.0, 'Ge': 2.0, 'Se': 8.0, 'O': 8.0}\",\"('Ge', 'K', 'O', 'Se')\",OTHERS,True\nmp-1111493,\"{'K': 3.0, 'Er': 1.0, 'Cl': 6.0}\",\"('Cl', 'Er', 'K')\",OTHERS,True\nmp-1186864,\"{'Rb': 3.0, 'Pm': 1.0}\",\"('Pm', 'Rb')\",OTHERS,True\nmp-695941,\"{'Ag': 12.0, 'H': 52.0, 'Se': 4.0, 'N': 16.0, 'O': 20.0}\",\"('Ag', 'H', 'N', 'O', 'Se')\",OTHERS,True\nAl 71.5 Cu 8.5 Ru 20,\"{'Al': 71.5, 'Cu': 8.5, 'Ru': 20.0}\",\"('Al', 'Cu', 'Ru')\",AC,True\nmp-1186732,\"{'Pr': 6.0, 'Pa': 2.0}\",\"('Pa', 'Pr')\",OTHERS,True\nmp-772170,\"{'Ca': 4.0, 'La': 4.0, 'I': 20.0}\",\"('Ca', 'I', 'La')\",OTHERS,True\nmp-1214216,\"{'C': 36.0, 'Br': 2.0, 'F': 26.0}\",\"('Br', 'C', 'F')\",OTHERS,True\nmp-760034,\"{'Li': 6.0, 'V': 3.0, 'Fe': 3.0, 'P': 6.0, 'H': 6.0, 'O': 30.0}\",\"('Fe', 'H', 'Li', 'O', 'P', 'V')\",OTHERS,True\nmp-1186185,\"{'Na': 1.0, 'Tl': 2.0, 'In': 1.0}\",\"('In', 'Na', 'Tl')\",OTHERS,True\nmp-764566,\"{'Li': 1.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,True\nmp-1181558,\"{'Cs': 1.0, 'Zr': 1.0, 'Cu': 3.0, 'Se': 4.0}\",\"('Cs', 'Cu', 'Se', 'Zr')\",OTHERS,True\nmp-1220728,\"{'Na': 1.0, 'Li': 1.0, 'As': 2.0, 'S': 4.0}\",\"('As', 'Li', 'Na', 'S')\",OTHERS,True\nmp-1221452,\"{'Na': 1.0, 'Fe': 6.0, 'H': 12.0, 'S': 4.0, 'O': 29.0}\",\"('Fe', 'H', 'Na', 'O', 'S')\",OTHERS,True\nmp-1118832,\"{'Ca': 4.0, 'W': 2.0, 'N': 4.0}\",\"('Ca', 'N', 'W')\",OTHERS,True\nmp-1200080,\"{'Pr': 6.0, 'Rh': 8.0, 'Pb': 26.0}\",\"('Pb', 'Pr', 'Rh')\",OTHERS,True\nmp-570644,\"{'Ba': 14.0, 'Na': 21.0, 'Ca': 1.0, 'N': 6.0}\",\"('Ba', 'Ca', 'N', 'Na')\",OTHERS,True\nmp-1074263,\"{'Mg': 16.0, 'Si': 12.0}\",\"('Mg', 'Si')\",OTHERS,True\nmp-1096345,\"{'Al': 1.0, 'Ga': 1.0, 'Ru': 2.0}\",\"('Al', 'Ga', 'Ru')\",OTHERS,True\nmp-556053,\"{'Te': 4.0, 'C': 32.0, 'O': 16.0, 'F': 48.0}\",\"('C', 'F', 'O', 'Te')\",OTHERS,True\nmp-1246774,\"{'Si': 14.0, 'Ni': 2.0, 'N': 20.0}\",\"('N', 'Ni', 'Si')\",OTHERS,True\nmp-975275,\"{'Rb': 1.0, 'Na': 2.0, 'Sb': 1.0}\",\"('Na', 'Rb', 'Sb')\",OTHERS,True\nmp-632490,\"{'Eu': 8.0, 'Sn': 4.0, 'S': 16.0}\",\"('Eu', 'S', 'Sn')\",OTHERS,True\nmp-3523,\"{'O': 16.0, 'Sr': 4.0, 'Gd': 8.0}\",\"('Gd', 'O', 'Sr')\",OTHERS,True\nmp-31104,\"{'K': 16.0, 'Bi': 16.0}\",\"('Bi', 'K')\",OTHERS,True\nmp-1224521,\"{'Ho': 12.0, 'Si': 5.0, 'S': 28.0}\",\"('Ho', 'S', 'Si')\",OTHERS,True\nmp-1195463,\"{'U': 4.0, 'Si': 24.0, 'H': 216.0, 'C': 72.0, 'N': 12.0, 'F': 4.0}\",\"('C', 'F', 'H', 'N', 'Si', 'U')\",OTHERS,True\nmp-1197109,\"{'Fe': 4.0, 'S': 4.0, 'O': 44.0}\",\"('Fe', 'O', 'S')\",OTHERS,True\nmp-1215271,\"{'Zr': 2.0, 'Fe': 2.0, 'Co': 2.0}\",\"('Co', 'Fe', 'Zr')\",OTHERS,True\nmp-1215747,\"{'Yb': 4.0, 'S': 7.0}\",\"('S', 'Yb')\",OTHERS,True\nmp-866140,\"{'Ti': 1.0, 'Ga': 1.0, 'Pd': 2.0}\",\"('Ga', 'Pd', 'Ti')\",OTHERS,True\nmp-28432,\"{'Cd': 4.0, 'I': 14.0, 'Tl': 6.0}\",\"('Cd', 'I', 'Tl')\",OTHERS,True\nmp-1095001,\"{'Ca': 3.0, 'Zn': 3.0}\",\"('Ca', 'Zn')\",OTHERS,True\nmp-556418,\"{'Al': 2.0, 'S': 2.0, 'Cl': 6.0, 'O': 4.0}\",\"('Al', 'Cl', 'O', 'S')\",OTHERS,True\nmp-1039958,\"{'Ce': 1.0, 'Mg': 30.0, 'B': 1.0, 'O': 32.0}\",\"('B', 'Ce', 'Mg', 'O')\",OTHERS,True\nAl 75 Mn 20 Pd 5,\"{'Al': 75.0, 'Mn': 20.0, 'Pd': 5.0}\",\"('Al', 'Mn', 'Pd')\",AC,True\nmp-1218486,\"{'Sr': 2.0, 'Y': 1.0, 'Tl': 1.0, 'Cu': 2.0, 'O': 7.0}\",\"('Cu', 'O', 'Sr', 'Tl', 'Y')\",OTHERS,True\nmp-7163,{'Tb': 1.0},\"('Tb',)\",OTHERS,True\nmp-1112936,\"{'Cs': 2.0, 'Ta': 1.0, 'Ag': 1.0, 'Br': 6.0}\",\"('Ag', 'Br', 'Cs', 'Ta')\",OTHERS,True\nmp-769391,\"{'Ba': 8.0, 'Zr': 2.0, 'O': 12.0}\",\"('Ba', 'O', 'Zr')\",OTHERS,True\nmp-752857,\"{'V': 3.0, 'O': 1.0, 'F': 11.0}\",\"('F', 'O', 'V')\",OTHERS,True\nmp-774838,\"{'Li': 2.0, 'Y': 14.0, 'Ti': 14.0, 'S': 14.0, 'O': 35.0}\",\"('Li', 'O', 'S', 'Ti', 'Y')\",OTHERS,True\nmp-3998,\"{'O': 8.0, 'La': 2.0, 'Ta': 2.0}\",\"('La', 'O', 'Ta')\",OTHERS,True\nmp-1223620,\"{'La': 12.0, 'Ge': 5.0, 'S': 28.0}\",\"('Ge', 'La', 'S')\",OTHERS,True\nmp-551131,\"{'Co': 4.0, 'As': 2.0, 'Cl': 2.0, 'O': 8.0}\",\"('As', 'Cl', 'Co', 'O')\",OTHERS,True\nmp-640381,\"{'Yb': 4.0, 'Cu': 4.0, 'S': 8.0}\",\"('Cu', 'S', 'Yb')\",OTHERS,True\nmp-1101091,\"{'Dy': 3.0, 'In': 4.0, 'Co': 2.0}\",\"('Co', 'Dy', 'In')\",OTHERS,True\nmp-1459,\"{'N': 1.0, 'Ta': 1.0}\",\"('N', 'Ta')\",OTHERS,True\nmp-342,\"{'Te': 1.0, 'Sm': 1.0}\",\"('Sm', 'Te')\",OTHERS,True\nmp-1245418,\"{'Cd': 4.0, 'Sn': 4.0, 'N': 8.0}\",\"('Cd', 'N', 'Sn')\",OTHERS,True\nmp-1209320,\"{'Rb': 4.0, 'Ag': 4.0, 'Te': 4.0, 'S': 12.0}\",\"('Ag', 'Rb', 'S', 'Te')\",OTHERS,True\nmp-865908,\"{'Yb': 2.0, 'Hg': 1.0, 'Pb': 1.0}\",\"('Hg', 'Pb', 'Yb')\",OTHERS,True\nmp-766123,\"{'Ce': 8.0, 'Th': 2.0, 'O': 20.0}\",\"('Ce', 'O', 'Th')\",OTHERS,True\nmp-973834,\"{'Pd': 1.0, 'Au': 3.0}\",\"('Au', 'Pd')\",OTHERS,True\nmp-772199,\"{'Rb': 18.0, 'Cl': 6.0, 'O': 6.0}\",\"('Cl', 'O', 'Rb')\",OTHERS,True\nmp-1200191,\"{'Ga': 42.0, 'Rh': 8.0}\",\"('Ga', 'Rh')\",OTHERS,True\nmp-7711,\"{'O': 1.0, 'Ag': 2.0}\",\"('Ag', 'O')\",OTHERS,True\nmvc-12018,\"{'Ta': 4.0, 'Mn': 2.0, 'Zn': 3.0, 'O': 16.0}\",\"('Mn', 'O', 'Ta', 'Zn')\",OTHERS,True\nmp-604594,\"{'Ce': 4.0, 'In': 10.0, 'Pd': 8.0}\",\"('Ce', 'In', 'Pd')\",OTHERS,True\nmp-779538,\"{'Fe': 10.0, 'O': 14.0, 'F': 6.0}\",\"('F', 'Fe', 'O')\",OTHERS,True\nmp-1210680,\"{'Mg': 4.0, 'Pb': 8.0}\",\"('Mg', 'Pb')\",OTHERS,True\nmp-605809,\"{'Ba': 22.0, 'In': 12.0, 'O': 6.0}\",\"('Ba', 'In', 'O')\",OTHERS,True\nmp-631322,\"{'K': 1.0, 'Sn': 1.0, 'Os': 1.0}\",\"('K', 'Os', 'Sn')\",OTHERS,True\nmp-1211509,\"{'K': 4.0, 'Sm': 8.0, 'I': 20.0}\",\"('I', 'K', 'Sm')\",OTHERS,True\nmp-1196241,\"{'Sr': 8.0, 'Be': 8.0, 'H': 32.0, 'O': 32.0}\",\"('Be', 'H', 'O', 'Sr')\",OTHERS,True\nmp-755545,\"{'Li': 6.0, 'Fe': 1.0, 'O': 4.0, 'F': 2.0}\",\"('F', 'Fe', 'Li', 'O')\",OTHERS,True\nmp-1222405,\"{'Li': 1.0, 'Cd': 3.0}\",\"('Cd', 'Li')\",OTHERS,True\nmp-1194603,\"{'Mn': 8.0, 'Bi': 8.0, 'O': 24.0}\",\"('Bi', 'Mn', 'O')\",OTHERS,True\nmp-697126,\"{'Ca': 4.0, 'H': 12.0, 'Rh': 3.0}\",\"('Ca', 'H', 'Rh')\",OTHERS,True\nmp-1204339,\"{'U': 2.0, 'Cr': 8.0, 'H': 36.0, 'O': 48.0}\",\"('Cr', 'H', 'O', 'U')\",OTHERS,True\nmp-19872,\"{'Al': 2.0, 'Cu': 1.0, 'U': 1.0}\",\"('Al', 'Cu', 'U')\",OTHERS,True\nmp-1095163,\"{'Pt': 1.0, 'Cl': 6.0, 'O': 2.0}\",\"('Cl', 'O', 'Pt')\",OTHERS,True\nmp-19766,\"{'Pd': 3.0, 'Sn': 3.0, 'Ce': 3.0}\",\"('Ce', 'Pd', 'Sn')\",OTHERS,True\nmp-1219205,\"{'Sm': 6.0, 'S': 2.0, 'O': 2.0, 'F': 10.0}\",\"('F', 'O', 'S', 'Sm')\",OTHERS,True\nmp-1104836,\"{'Re': 2.0, 'Cl': 6.0, 'O': 6.0}\",\"('Cl', 'O', 'Re')\",OTHERS,True\nmp-1079374,\"{'Th': 2.0, 'Ta': 2.0, 'N': 6.0}\",\"('N', 'Ta', 'Th')\",OTHERS,True\nmp-1215234,\"{'Zr': 2.0, 'Mn': 2.0, 'Ni': 2.0}\",\"('Mn', 'Ni', 'Zr')\",OTHERS,True\nmvc-4357,\"{'Ca': 4.0, 'Ta': 2.0, 'V': 2.0, 'O': 12.0}\",\"('Ca', 'O', 'Ta', 'V')\",OTHERS,True\nmp-761156,\"{'Li': 3.0, 'La': 5.0, 'Ti': 6.0, 'Nb': 2.0, 'O': 26.0}\",\"('La', 'Li', 'Nb', 'O', 'Ti')\",OTHERS,True\nmp-1185991,\"{'Mn': 1.0, 'Cd': 3.0}\",\"('Cd', 'Mn')\",OTHERS,True\nmp-504922,\"{'U': 4.0, 'Pb': 4.0, 'O': 16.0}\",\"('O', 'Pb', 'U')\",OTHERS,True\nmp-559335,\"{'Zn': 2.0, 'Ag': 4.0, 'C': 8.0, 'S': 8.0, 'N': 8.0}\",\"('Ag', 'C', 'N', 'S', 'Zn')\",OTHERS,True\nmp-1221638,\"{'Mn': 1.0, 'Cr': 3.0, 'Te': 4.0}\",\"('Cr', 'Mn', 'Te')\",OTHERS,True\nmp-19217,\"{'O': 18.0, 'Mn': 6.0, 'Er': 6.0}\",\"('Er', 'Mn', 'O')\",OTHERS,True\nmp-707494,\"{'B': 44.0, 'H': 36.0, 'C': 4.0, 'Br': 24.0, 'O': 16.0}\",\"('B', 'Br', 'C', 'H', 'O')\",OTHERS,True\nmp-1186219,\"{'Nb': 6.0, 'Se': 2.0}\",\"('Nb', 'Se')\",OTHERS,True\nmp-568591,\"{'La': 6.0, 'C': 2.0, 'I': 10.0}\",\"('C', 'I', 'La')\",OTHERS,True\nmp-1097181,\"{'Zr': 1.0, 'Sc': 1.0, 'Zn': 2.0}\",\"('Sc', 'Zn', 'Zr')\",OTHERS,True\nmp-604368,\"{'K': 4.0, 'Na': 2.0, 'V': 10.0, 'H': 20.0, 'O': 38.0}\",\"('H', 'K', 'Na', 'O', 'V')\",OTHERS,True\nmp-1202855,\"{'C': 20.0, 'I': 6.0, 'N': 8.0}\",\"('C', 'I', 'N')\",OTHERS,True\nmp-1094064,\"{'Sb': 1.0, 'H': 3.0}\",\"('H', 'Sb')\",OTHERS,True\nTa 97 Te 60,\"{'Ta': 97.0, 'Te': 60.0}\",\"('Ta', 'Te')\",AC,True\nmp-1097218,\"{'Li': 1.0, 'Cd': 2.0, 'In': 1.0}\",\"('Cd', 'In', 'Li')\",OTHERS,True\nmp-759611,\"{'Ba': 4.0, 'Li': 4.0, 'Ca': 4.0, 'V': 4.0, 'P': 8.0, 'O': 36.0}\",\"('Ba', 'Ca', 'Li', 'O', 'P', 'V')\",OTHERS,True\nmp-1227942,\"{'Ba': 1.0, 'Ga': 1.0, 'Ge': 1.0}\",\"('Ba', 'Ga', 'Ge')\",OTHERS,True\nmp-1175397,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1102504,\"{'Co': 4.0, 'P': 4.0, 'W': 4.0}\",\"('Co', 'P', 'W')\",OTHERS,True\nmp-559366,\"{'P': 6.0, 'Br': 6.0, 'N': 6.0, 'F': 6.0}\",\"('Br', 'F', 'N', 'P')\",OTHERS,True\nmp-1204560,\"{'Rb': 4.0, 'Ce': 2.0, 'N': 10.0, 'O': 38.0}\",\"('Ce', 'N', 'O', 'Rb')\",OTHERS,True\nmp-1214450,\"{'Ba': 1.0, 'Nb': 6.0, 'H': 1.0, 'O': 16.0}\",\"('Ba', 'H', 'Nb', 'O')\",OTHERS,True\nmp-554732,\"{'Rb': 4.0, 'Sb': 4.0, 'S': 4.0, 'O': 16.0, 'F': 8.0}\",\"('F', 'O', 'Rb', 'S', 'Sb')\",OTHERS,True\nmp-1202662,\"{'Sc': 8.0, 'Si': 16.0, 'W': 12.0}\",\"('Sc', 'Si', 'W')\",OTHERS,True\nmp-1029702,\"{'Mg': 4.0, 'Ti': 4.0, 'N': 8.0}\",\"('Mg', 'N', 'Ti')\",OTHERS,True\nmp-1207402,\"{'Zr': 12.0, 'Al': 4.0, 'Fe': 2.0, 'H': 20.0}\",\"('Al', 'Fe', 'H', 'Zr')\",OTHERS,True\nmp-1211034,\"{'Mg': 4.0, 'Si': 8.0, 'O': 22.0}\",\"('Mg', 'O', 'Si')\",OTHERS,True\nmp-1247565,\"{'Sb': 16.0, 'Te': 12.0, 'N': 8.0}\",\"('N', 'Sb', 'Te')\",OTHERS,True\nmp-773137,\"{'K': 2.0, 'Ca': 2.0, 'Bi': 2.0}\",\"('Bi', 'Ca', 'K')\",OTHERS,True\nmp-1079270,\"{'Tm': 2.0, 'Ni': 1.0, 'Sn': 6.0}\",\"('Ni', 'Sn', 'Tm')\",OTHERS,True\nmp-31143,\"{'Er': 8.0, 'Sn': 4.0, 'Au': 8.0}\",\"('Au', 'Er', 'Sn')\",OTHERS,True\nmp-554673,\"{'K': 4.0, 'Zn': 4.0, 'B': 4.0, 'P': 8.0, 'O': 32.0}\",\"('B', 'K', 'O', 'P', 'Zn')\",OTHERS,True\nmp-1208274,\"{'Th': 6.0, 'Si': 24.0, 'Ru': 8.0}\",\"('Ru', 'Si', 'Th')\",OTHERS,True\nmp-1193963,\"{'Mg': 8.0, 'Si': 4.0, 'S': 16.0}\",\"('Mg', 'S', 'Si')\",OTHERS,True\nmp-761209,\"{'Li': 6.0, 'Fe': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,True\nmp-24211,\"{'Co': 2.0, 'H': 16.0, 'I': 4.0, 'O': 20.0}\",\"('Co', 'H', 'I', 'O')\",OTHERS,True\nmp-1179383,\"{'Ti': 4.0, 'Si': 8.0, 'C': 12.0, 'N': 8.0, 'Cl': 20.0}\",\"('C', 'Cl', 'N', 'Si', 'Ti')\",OTHERS,True\nmp-1027053,\"{'Te': 4.0, 'Mo': 3.0, 'W': 1.0, 'Se': 2.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,True\nmp-972591,\"{'Sm': 1.0, 'Mg': 16.0, 'Al': 12.0}\",\"('Al', 'Mg', 'Sm')\",OTHERS,True\nmp-1177772,\"{'Li': 3.0, 'Cr': 2.0, 'Fe': 3.0, 'O': 10.0}\",\"('Cr', 'Fe', 'Li', 'O')\",OTHERS,True\nmp-1224003,\"{'Ho': 3.0, 'Mn': 3.0, 'Ga': 2.0, 'Si': 1.0}\",\"('Ga', 'Ho', 'Mn', 'Si')\",OTHERS,True\nmp-1219252,\"{'Sc': 1.0, 'Tl': 1.0, 'F': 6.0}\",\"('F', 'Sc', 'Tl')\",OTHERS,True\nmp-559879,\"{'P': 4.0, 'S': 8.0, 'N': 4.0, 'Cl': 24.0, 'O': 16.0}\",\"('Cl', 'N', 'O', 'P', 'S')\",OTHERS,True\nmp-1210257,\"{'Na': 6.0, 'Ti': 2.0, 'F': 12.0}\",\"('F', 'Na', 'Ti')\",OTHERS,True\nZn 77 Mg 18 Zr 5,\"{'Zn': 77.0, 'Mg': 18.0, 'Zr': 5.0}\",\"('Mg', 'Zn', 'Zr')\",AC,True\nmp-1075111,\"{'Mg': 6.0, 'Si': 8.0}\",\"('Mg', 'Si')\",OTHERS,True\nmp-675211,\"{'W': 7.0, 'O': 21.0}\",\"('O', 'W')\",OTHERS,True\nmp-1225635,\"{'Fe': 8.0, 'Co': 2.0, 'Bi': 10.0, 'O': 30.0}\",\"('Bi', 'Co', 'Fe', 'O')\",OTHERS,True\nmp-1112599,\"{'Cs': 2.0, 'K': 1.0, 'Au': 1.0, 'F': 6.0}\",\"('Au', 'Cs', 'F', 'K')\",OTHERS,True\nmp-777673,\"{'Li': 8.0, 'Fe': 4.0, 'F': 16.0}\",\"('F', 'Fe', 'Li')\",OTHERS,True\nmp-758671,\"{'Li': 4.0, 'Mn': 4.0, 'P': 12.0, 'O': 36.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,True\nmp-1095043,\"{'Sc': 3.0, 'Os': 1.0, 'C': 4.0}\",\"('C', 'Os', 'Sc')\",OTHERS,True\nmp-1245772,\"{'Na': 4.0, 'Mg': 4.0, 'N': 4.0}\",\"('Mg', 'N', 'Na')\",OTHERS,True\nmp-36216,\"{'Ag': 4.0, 'S': 2.0}\",\"('Ag', 'S')\",OTHERS,True\nmp-1246241,\"{'Fe': 8.0, 'Ru': 2.0, 'N': 8.0}\",\"('Fe', 'N', 'Ru')\",OTHERS,True\nmp-771359,\"{'Cu': 16.0, 'O': 24.0}\",\"('Cu', 'O')\",OTHERS,True\nmp-642281,\"{'Sr': 21.0, 'In': 8.0, 'Pb': 7.0}\",\"('In', 'Pb', 'Sr')\",OTHERS,True\nmp-32833,\"{'Ho': 24.0, 'Se': 44.0}\",\"('Ho', 'Se')\",OTHERS,True\nmp-861600,\"{'Ho': 2.0, 'Zn': 1.0, 'In': 1.0}\",\"('Ho', 'In', 'Zn')\",OTHERS,True\nmp-1008733,{'Ge': 2.0},\"('Ge',)\",OTHERS,True\nmp-1076337,\"{'Ba': 2.0, 'Sr': 6.0, 'Ti': 7.0, 'Mn': 1.0, 'O': 24.0}\",\"('Ba', 'Mn', 'O', 'Sr', 'Ti')\",OTHERS,True\nmp-1198309,\"{'Sc': 40.0, 'Os': 8.0, 'Cl': 72.0}\",\"('Cl', 'Os', 'Sc')\",OTHERS,True\nmp-1025314,\"{'Mn': 1.0, 'Al': 2.0, 'S': 4.0}\",\"('Al', 'Mn', 'S')\",OTHERS,True\nmp-28918,\"{'W': 2.0, 'Sb': 4.0, 'O': 12.0}\",\"('O', 'Sb', 'W')\",OTHERS,True\nmp-1213579,\"{'Gd': 8.0, 'Sc': 12.0, 'Si': 16.0}\",\"('Gd', 'Sc', 'Si')\",OTHERS,True\nmp-766514,\"{'Li': 8.0, 'Fe': 6.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Fe', 'Li', 'O')\",OTHERS,True\nmp-1183872,\"{'Ce': 3.0, 'Cu': 1.0}\",\"('Ce', 'Cu')\",OTHERS,True\nmp-18890,\"{'Ca': 2.0, 'Fe': 2.0, 'Si': 4.0, 'O': 12.0}\",\"('Ca', 'Fe', 'O', 'Si')\",OTHERS,True\nmp-1188603,\"{'La': 1.0, 'Sb': 12.0, 'Ru': 4.0}\",\"('La', 'Ru', 'Sb')\",OTHERS,True\nmp-1211019,\"{'Na': 6.0, 'Mg': 10.0, 'Si': 16.0, 'H': 4.0, 'O': 48.0}\",\"('H', 'Mg', 'Na', 'O', 'Si')\",OTHERS,True\nmp-697593,\"{'Te': 28.0, 'W': 4.0, 'Cl': 20.0, 'O': 4.0}\",\"('Cl', 'O', 'Te', 'W')\",OTHERS,True\nmp-24065,\"{'H': 20.0, 'B': 4.0, 'O': 18.0, 'Ca': 2.0}\",\"('B', 'Ca', 'H', 'O')\",OTHERS,True\nmp-760183,\"{'Li': 10.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-865280,\"{'Nb': 1.0, 'Al': 1.0, 'Fe': 2.0}\",\"('Al', 'Fe', 'Nb')\",OTHERS,True\nmp-866712,\"{'Rb': 2.0, 'Mn': 2.0, 'P': 6.0, 'H': 2.0, 'O': 20.0}\",\"('H', 'Mn', 'O', 'P', 'Rb')\",OTHERS,True\nmp-775429,\"{'Ba': 4.0, 'Li': 1.0, 'Bi': 3.0, 'O': 11.0}\",\"('Ba', 'Bi', 'Li', 'O')\",OTHERS,True\nmp-2102,\"{'Ge': 4.0, 'Pr': 4.0}\",\"('Ge', 'Pr')\",OTHERS,True\nmp-1225821,\"{'Cu': 2.0, 'P': 2.0, 'O': 7.0}\",\"('Cu', 'O', 'P')\",OTHERS,True\nmp-622570,\"{'Ti': 4.0, 'Cu': 6.0}\",\"('Cu', 'Ti')\",OTHERS,True\nmp-757962,\"{'Li': 6.0, 'Co': 6.0, 'P': 6.0, 'O': 24.0}\",\"('Co', 'Li', 'O', 'P')\",OTHERS,True\nmp-10180,\"{'Li': 2.0, 'Pd': 1.0, 'Sb': 1.0}\",\"('Li', 'Pd', 'Sb')\",OTHERS,True\nmp-1078870,\"{'U': 3.0, 'Ga': 3.0, 'Rh': 3.0}\",\"('Ga', 'Rh', 'U')\",OTHERS,True\nmp-1104125,\"{'Y': 4.0, 'Cd': 2.0, 'Se': 8.0}\",\"('Cd', 'Se', 'Y')\",OTHERS,True\nmp-1247481,\"{'Li': 4.0, 'Mn': 4.0, 'N': 4.0}\",\"('Li', 'Mn', 'N')\",OTHERS,True\nmp-768385,\"{'Ba': 8.0, 'Y': 4.0, 'Br': 28.0}\",\"('Ba', 'Br', 'Y')\",OTHERS,True\nmp-1179389,\"{'Te': 2.0, 'C': 8.0, 'S': 8.0, 'N': 16.0, 'Cl': 4.0, 'O': 4.0}\",\"('C', 'Cl', 'N', 'O', 'S', 'Te')\",OTHERS,True\nmp-2850,\"{'B': 4.0, 'Os': 2.0}\",\"('B', 'Os')\",OTHERS,True\nmp-9275,\"{'O': 38.0, 'Ta': 12.0, 'Th': 4.0}\",\"('O', 'Ta', 'Th')\",OTHERS,True\nmp-1190622,\"{'Ba': 4.0, 'Cr': 4.0, 'O': 16.0}\",\"('Ba', 'Cr', 'O')\",OTHERS,True\nmp-972300,\"{'Sr': 3.0, 'Lu': 1.0}\",\"('Lu', 'Sr')\",OTHERS,True\nmp-1190981,\"{'Sr': 8.0, 'Zn': 4.0, 'S': 12.0}\",\"('S', 'Sr', 'Zn')\",OTHERS,True\nmp-30677,\"{'Ga': 20.0, 'Tm': 12.0}\",\"('Ga', 'Tm')\",OTHERS,True\nmp-557755,\"{'Ba': 6.0, 'Cr': 4.0, 'Mo': 2.0, 'O': 18.0}\",\"('Ba', 'Cr', 'Mo', 'O')\",OTHERS,True\nmp-642834,\"{'Ba': 2.0, 'H': 10.0, 'Cl': 2.0, 'O': 6.0}\",\"('Ba', 'Cl', 'H', 'O')\",OTHERS,True\nmp-754462,\"{'Sb': 2.0, 'Te': 4.0, 'F': 12.0}\",\"('F', 'Sb', 'Te')\",OTHERS,True\nmp-1101328,\"{'V': 18.0, 'O': 44.0}\",\"('O', 'V')\",OTHERS,True\nmp-1192903,\"{'Bi': 6.0, 'As': 4.0, 'O': 20.0}\",\"('As', 'Bi', 'O')\",OTHERS,True\nmp-976405,\"{'Na': 1.0, 'Er': 3.0}\",\"('Er', 'Na')\",OTHERS,True\nmp-510638,\"{'Ga': 2.0, 'Mn': 2.0, 'F': 10.0, 'O': 4.0, 'H': 8.0}\",\"('F', 'Ga', 'H', 'Mn', 'O')\",OTHERS,True\nmp-1112215,\"{'K': 2.0, 'Tm': 1.0, 'Cu': 1.0, 'Cl': 6.0}\",\"('Cl', 'Cu', 'K', 'Tm')\",OTHERS,True\nmp-1029458,\"{'Zn': 4.0, 'Cr': 2.0, 'N': 6.0}\",\"('Cr', 'N', 'Zn')\",OTHERS,True\nmp-3772,\"{'Ga': 2.0, 'Se': 4.0, 'Cd': 1.0}\",\"('Cd', 'Ga', 'Se')\",OTHERS,True\nmp-10444,\"{'O': 10.0, 'Al': 4.0, 'Ca': 4.0}\",\"('Al', 'Ca', 'O')\",OTHERS,True\nmp-773707,\"{'Li': 4.0, 'Ti': 3.0, 'Mn': 2.0, 'V': 3.0, 'O': 16.0}\",\"('Li', 'Mn', 'O', 'Ti', 'V')\",OTHERS,True\nmp-1177635,\"{'Li': 3.0, 'Mn': 2.0, 'Sb': 1.0, 'O': 6.0}\",\"('Li', 'Mn', 'O', 'Sb')\",OTHERS,True\nmp-756151,\"{'Li': 4.0, 'Cr': 5.0, 'W': 1.0, 'O': 12.0}\",\"('Cr', 'Li', 'O', 'W')\",OTHERS,True\nmp-1220278,\"{'Nd': 4.0, 'Fe': 4.0, 'Co': 12.0, 'B': 3.0, 'C': 1.0}\",\"('B', 'C', 'Co', 'Fe', 'Nd')\",OTHERS,True\nmp-1213404,\"{'Dy': 2.0, 'N': 6.0, 'O': 28.0}\",\"('Dy', 'N', 'O')\",OTHERS,True\nmp-1206434,\"{'Nd': 1.0, 'Ga': 2.0, 'Cu': 2.0}\",\"('Cu', 'Ga', 'Nd')\",OTHERS,True\nmp-1029069,\"{'Mo': 2.0, 'W': 2.0, 'Se': 4.0, 'S': 4.0}\",\"('Mo', 'S', 'Se', 'W')\",OTHERS,True\nmp-1225998,\"{'Co': 1.0, 'Pt': 1.0}\",\"('Co', 'Pt')\",OTHERS,True\nmp-977129,\"{'Hg': 2.0, 'Pd': 6.0}\",\"('Hg', 'Pd')\",OTHERS,True\nmp-1212660,\"{'Ga': 2.0, 'Cu': 2.0, 'Br': 8.0}\",\"('Br', 'Cu', 'Ga')\",OTHERS,True\nmp-722270,\"{'Zr': 4.0, 'H': 28.0, 'C': 4.0, 'N': 16.0, 'F': 20.0}\",\"('C', 'F', 'H', 'N', 'Zr')\",OTHERS,True\nmp-976588,\"{'Ho': 1.0, 'Th': 3.0}\",\"('Ho', 'Th')\",OTHERS,True\nmp-769616,\"{'Na': 4.0, 'Cr': 2.0, 'P': 2.0, 'C': 2.0, 'O': 14.0}\",\"('C', 'Cr', 'Na', 'O', 'P')\",OTHERS,True\nmp-1095493,\"{'Cd': 4.0, 'Se': 8.0}\",\"('Cd', 'Se')\",OTHERS,True\nmp-1223773,\"{'K': 5.0, 'Bi': 4.0}\",\"('Bi', 'K')\",OTHERS,True\nmp-982557,\"{'Ho': 2.0, 'Ga': 6.0}\",\"('Ga', 'Ho')\",OTHERS,True\nmp-1190171,{'C': 16.0},\"('C',)\",OTHERS,True\nmp-673096,\"{'Li': 3.0, 'Sn': 1.0, 'P': 6.0, 'O': 18.0}\",\"('Li', 'O', 'P', 'Sn')\",OTHERS,True\nmp-1189833,\"{'Yb': 4.0, 'Si': 4.0, 'Pd': 8.0}\",\"('Pd', 'Si', 'Yb')\",OTHERS,True\nmp-1103785,\"{'Pr': 3.0, 'Al': 11.0}\",\"('Al', 'Pr')\",OTHERS,True\nmp-1245450,\"{'Ca': 32.0, 'Mn': 12.0, 'N': 36.0}\",\"('Ca', 'Mn', 'N')\",OTHERS,True\nmp-1002223,\"{'Dy': 1.0, 'P': 1.0}\",\"('Dy', 'P')\",OTHERS,True\nmp-772087,\"{'Er': 6.0, 'Sb': 10.0, 'O': 24.0}\",\"('Er', 'O', 'Sb')\",OTHERS,True\nmp-862599,\"{'Li': 1.0, 'Er': 1.0, 'Au': 2.0}\",\"('Au', 'Er', 'Li')\",OTHERS,True\nmp-1225237,\"{'Ga': 2.0, 'P': 4.0, 'C': 6.0, 'N': 4.0, 'O': 16.0, 'F': 2.0}\",\"('C', 'F', 'Ga', 'N', 'O', 'P')\",OTHERS,True\nmp-1219858,\"{'Pr': 2.0, 'Si': 3.0, 'Ni': 1.0}\",\"('Ni', 'Pr', 'Si')\",OTHERS,True\nmp-1217980,\"{'Ta': 2.0, 'Be': 8.0, 'Mo': 2.0}\",\"('Be', 'Mo', 'Ta')\",OTHERS,True\nmp-1097071,\"{'Li': 1.0, 'Y': 2.0, 'Co': 1.0}\",\"('Co', 'Li', 'Y')\",OTHERS,True\nmp-1019575,\"{'Ca': 16.0, 'Al': 24.0, 'S': 4.0, 'O': 64.0}\",\"('Al', 'Ca', 'O', 'S')\",OTHERS,True\nNi 70.6 Cr 29.4,\"{'Ni': 70.6, 'Cr': 29.4}\",\"('Cr', 'Ni')\",QC,True\nmp-757131,\"{'Sn': 2.0, 'P': 8.0, 'O': 24.0}\",\"('O', 'P', 'Sn')\",OTHERS,True\nmp-774845,\"{'Li': 4.0, 'Ti': 2.0, 'Co': 3.0, 'Sn': 3.0, 'O': 16.0}\",\"('Co', 'Li', 'O', 'Sn', 'Ti')\",OTHERS,True\nmp-569471,\"{'U': 2.0, 'B': 4.0, 'C': 2.0}\",\"('B', 'C', 'U')\",OTHERS,True\nmp-1180514,\"{'Li': 1.0, 'Ti': 1.0, 'S': 2.0, 'O': 2.0}\",\"('Li', 'O', 'S', 'Ti')\",OTHERS,True\nmp-761229,\"{'Na': 8.0, 'Mn': 4.0, 'O': 12.0}\",\"('Mn', 'Na', 'O')\",OTHERS,True\nmp-18468,\"{'C': 4.0, 'Si': 4.0, 'Mn': 20.0}\",\"('C', 'Mn', 'Si')\",OTHERS,True\nmp-1219558,\"{'Sb': 2.0, 'Pb': 4.0, 'S': 4.0, 'I': 6.0}\",\"('I', 'Pb', 'S', 'Sb')\",OTHERS,True\nmp-778813,\"{'Li': 7.0, 'Mn': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1094397,\"{'Y': 6.0, 'Mg': 2.0}\",\"('Mg', 'Y')\",OTHERS,True\nmp-722900,\"{'Ca': 16.0, 'H': 48.0, 'N': 16.0, 'O': 80.0}\",\"('Ca', 'H', 'N', 'O')\",OTHERS,True\nmp-1246336,\"{'Li': 12.0, 'Rh': 4.0, 'N': 8.0}\",\"('Li', 'N', 'Rh')\",OTHERS,True\nmp-683875,\"{'Te': 4.0, 'S': 28.0, 'Br': 8.0}\",\"('Br', 'S', 'Te')\",OTHERS,True\nmp-510475,\"{'Y': 8.0, 'Cu': 8.0, 'O': 20.0}\",\"('Cu', 'O', 'Y')\",OTHERS,True\nmp-1176777,\"{'Li': 3.0, 'Co': 3.0, 'B': 3.0, 'O': 9.0}\",\"('B', 'Co', 'Li', 'O')\",OTHERS,True\nmp-781617,\"{'Li': 5.0, 'Mn': 6.0, 'B': 6.0, 'O': 18.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1215056,\"{'Ba': 3.0, 'Mn': 15.0, 'S': 18.0, 'O': 72.0}\",\"('Ba', 'Mn', 'O', 'S')\",OTHERS,True\nmp-11790,\"{'Cu': 4.0, 'Se': 8.0, 'La': 4.0}\",\"('Cu', 'La', 'Se')\",OTHERS,True\nmp-703476,\"{'P': 4.0, 'H': 32.0, 'N': 8.0, 'O': 14.0}\",\"('H', 'N', 'O', 'P')\",OTHERS,True\nmp-676923,\"{'Cd': 4.0, 'Bi': 12.0, 'O': 22.0}\",\"('Bi', 'Cd', 'O')\",OTHERS,True\nmp-677490,\"{'Li': 16.0, 'Nd': 12.0, 'Sb': 4.0, 'Te': 4.0, 'O': 48.0}\",\"('Li', 'Nd', 'O', 'Sb', 'Te')\",OTHERS,True\nmp-685969,\"{'Ag': 8.0, 'Ge': 1.0, 'Te': 6.0}\",\"('Ag', 'Ge', 'Te')\",OTHERS,True\nmp-1204057,\"{'Si': 72.0, 'O': 144.0}\",\"('O', 'Si')\",OTHERS,True\nmp-18628,\"{'B': 8.0, 'C': 20.0, 'Dy': 20.0}\",\"('B', 'C', 'Dy')\",OTHERS,True\nmp-1216793,\"{'U': 3.0, 'Ni': 3.0, 'P': 6.0}\",\"('Ni', 'P', 'U')\",OTHERS,True\nmp-557104,\"{'Li': 4.0, 'Al': 4.0, 'B': 8.0, 'O': 20.0}\",\"('Al', 'B', 'Li', 'O')\",OTHERS,True\nmp-636284,\"{'W': 4.0, 'O': 12.0}\",\"('O', 'W')\",OTHERS,True\nmp-757790,\"{'Li': 1.0, 'P': 2.0, 'W': 1.0, 'O': 8.0}\",\"('Li', 'O', 'P', 'W')\",OTHERS,True\nmp-1202256,\"{'K': 8.0, 'Cd': 6.0, 'P': 12.0, 'O': 48.0}\",\"('Cd', 'K', 'O', 'P')\",OTHERS,True\nmp-766510,\"{'Li': 12.0, 'Mn': 1.0, 'Fe': 3.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Fe', 'Li', 'Mn', 'O', 'P')\",OTHERS,True\nmp-1212595,\"{'Ho': 4.0, 'Hf': 4.0, 'Mo': 14.0, 'O': 56.0}\",\"('Hf', 'Ho', 'Mo', 'O')\",OTHERS,True\nmp-1203775,\"{'Er': 6.0, 'Ge': 26.0, 'Ir': 8.0}\",\"('Er', 'Ge', 'Ir')\",OTHERS,True\nmp-1200490,\"{'Tb': 16.0, 'Al': 8.0, 'O': 36.0}\",\"('Al', 'O', 'Tb')\",OTHERS,True\nmp-20290,\"{'Tb': 2.0, 'Cu': 2.0, 'Pb': 2.0}\",\"('Cu', 'Pb', 'Tb')\",OTHERS,True\nmp-560402,\"{'U': 6.0, 'O': 16.0}\",\"('O', 'U')\",OTHERS,True\nmp-698171,\"{'Cu': 8.0, 'H': 14.0, 'S': 2.0, 'O': 22.0}\",\"('Cu', 'H', 'O', 'S')\",OTHERS,True\nmvc-13555,\"{'Cr': 1.0, 'S': 2.0}\",\"('Cr', 'S')\",OTHERS,True\nmp-1103228,\"{'Bi': 4.0, 'Ir': 4.0, 'Se': 4.0}\",\"('Bi', 'Ir', 'Se')\",OTHERS,True\nmvc-9427,\"{'Ca': 4.0, 'Ti': 8.0, 'P': 8.0, 'O': 32.0}\",\"('Ca', 'O', 'P', 'Ti')\",OTHERS,True\nmp-1176119,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-2656,\"{'S': 8.0, 'Nd': 6.0}\",\"('Nd', 'S')\",OTHERS,True\nmvc-13985,\"{'Sn': 2.0, 'F': 8.0}\",\"('F', 'Sn')\",OTHERS,True\nmp-1199220,\"{'Eu': 6.0, 'Rh': 8.0, 'Pb': 26.0}\",\"('Eu', 'Pb', 'Rh')\",OTHERS,True\nmp-697834,\"{'Ca': 4.0, 'La': 4.0, 'Mn': 7.0, 'Ni': 1.0, 'O': 24.0}\",\"('Ca', 'La', 'Mn', 'Ni', 'O')\",OTHERS,True\nmp-24595,\"{'H': 2.0, 'B': 5.0, 'O': 10.0, 'Cl': 1.0, 'Ca': 2.0}\",\"('B', 'Ca', 'Cl', 'H', 'O')\",OTHERS,True\nmp-1195483,\"{'Ba': 4.0, 'Th': 8.0, 'S': 20.0}\",\"('Ba', 'S', 'Th')\",OTHERS,True\nmp-24510,\"{'H': 4.0, 'O': 14.0, 'I': 4.0, 'Ba': 2.0}\",\"('Ba', 'H', 'I', 'O')\",OTHERS,True\nAl 40 Mn 25 Fe 15 Ge 20,\"{'Al': 40.0, 'Mn': 25.0, 'Fe': 15.0, 'Ge': 20.0}\",\"('Al', 'Fe', 'Ge', 'Mn')\",QC,True\nmp-1105605,\"{'Tb': 7.0, 'Fe': 1.0, 'I': 12.0}\",\"('Fe', 'I', 'Tb')\",OTHERS,True\nmp-18903,\"{'O': 6.0, 'Sr': 2.0, 'Cd': 1.0, 'W': 1.0}\",\"('Cd', 'O', 'Sr', 'W')\",OTHERS,True\nmp-1198571,\"{'Ba': 8.0, 'Eu': 7.0, 'Cl': 34.0}\",\"('Ba', 'Cl', 'Eu')\",OTHERS,True\nmp-16223,\"{'Te': 8.0, 'Tl': 8.0}\",\"('Te', 'Tl')\",OTHERS,True\nmvc-3948,\"{'Mg': 4.0, 'Cr': 4.0, 'O': 10.0}\",\"('Cr', 'Mg', 'O')\",OTHERS,True\nmp-1184410,\"{'Eu': 2.0, 'Mg': 2.0}\",\"('Eu', 'Mg')\",OTHERS,True\nmp-1102773,\"{'K': 8.0, 'Bi': 1.0, 'O': 3.0}\",\"('Bi', 'K', 'O')\",OTHERS,True\nmp-1191873,\"{'Sm': 6.0, 'Mn': 2.0, 'Ga': 2.0, 'S': 14.0}\",\"('Ga', 'Mn', 'S', 'Sm')\",OTHERS,True\nmp-27358,\"{'O': 20.0, 'Se': 8.0}\",\"('O', 'Se')\",OTHERS,True\nmp-31088,\"{'Pd': 4.0, 'Dy': 4.0, 'Pb': 2.0}\",\"('Dy', 'Pb', 'Pd')\",OTHERS,True\nmp-672671,\"{'Pb': 3.0, 'I': 6.0}\",\"('I', 'Pb')\",OTHERS,True\nmp-755419,\"{'Bi': 2.0, 'Te': 1.0, 'O': 2.0}\",\"('Bi', 'O', 'Te')\",OTHERS,True\nmp-1185263,\"{'Li': 1.0, 'Pa': 1.0, 'O': 3.0}\",\"('Li', 'O', 'Pa')\",OTHERS,True\nmp-1183100,\"{'Ac': 6.0, 'Cd': 2.0}\",\"('Ac', 'Cd')\",OTHERS,True\nmp-1094181,\"{'Hf': 3.0, 'Mg': 3.0}\",\"('Hf', 'Mg')\",OTHERS,True\nmvc-11366,\"{'Co': 2.0, 'Si': 4.0, 'O': 12.0}\",\"('Co', 'O', 'Si')\",OTHERS,True\nmp-649676,\"{'Ca': 12.0, 'Si': 12.0, 'O': 36.0}\",\"('Ca', 'O', 'Si')\",OTHERS,True\nmp-1227184,\"{'Ca': 1.0, 'Nd': 3.0, 'Cr': 4.0, 'O': 16.0}\",\"('Ca', 'Cr', 'Nd', 'O')\",OTHERS,True\nmp-1226017,\"{'Co': 1.0, 'Ni': 1.0, 'Te': 2.0}\",\"('Co', 'Ni', 'Te')\",OTHERS,True\nmp-705416,\"{'Fe': 24.0, 'O': 32.0}\",\"('Fe', 'O')\",OTHERS,True\nmp-765089,\"{'Ti': 3.0, 'V': 2.0, 'Cu': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Cu', 'O', 'P', 'Ti', 'V')\",OTHERS,True\nmp-1226182,\"{'Cs': 2.0, 'Al': 1.0, 'Te': 3.0, 'O': 12.0}\",\"('Al', 'Cs', 'O', 'Te')\",OTHERS,True\nmp-1072256,\"{'Hf': 4.0, 'Ge': 2.0}\",\"('Ge', 'Hf')\",OTHERS,True\nmp-14653,\"{'F': 12.0, 'Ag': 1.0, 'Sb': 2.0}\",\"('Ag', 'F', 'Sb')\",OTHERS,True\nmp-863703,\"{'Pm': 2.0, 'Mg': 1.0, 'Sn': 1.0}\",\"('Mg', 'Pm', 'Sn')\",OTHERS,True\nmp-1199443,\"{'Sn': 8.0, 'H': 48.0, 'C': 8.0, 'N': 24.0, 'Cl': 24.0}\",\"('C', 'Cl', 'H', 'N', 'Sn')\",OTHERS,True\nmp-1175604,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1094416,\"{'Mg': 8.0, 'Ti': 8.0}\",\"('Mg', 'Ti')\",OTHERS,True\nmp-942701,\"{'Li': 2.0, 'Co': 2.0, 'S': 2.0, 'O': 8.0, 'F': 2.0}\",\"('Co', 'F', 'Li', 'O', 'S')\",OTHERS,True\nmp-16867,\"{'O': 16.0, 'Cu': 2.0, 'Yb': 2.0, 'W': 4.0}\",\"('Cu', 'O', 'W', 'Yb')\",OTHERS,True\nmvc-5180,\"{'Ca': 4.0, 'Cr': 4.0, 'O': 8.0}\",\"('Ca', 'Cr', 'O')\",OTHERS,True\nmp-755433,\"{'Li': 2.0, 'Co': 4.0, 'O': 4.0, 'F': 6.0}\",\"('Co', 'F', 'Li', 'O')\",OTHERS,True\nmp-1079075,\"{'Y': 2.0, 'Ga': 4.0, 'Pd': 2.0}\",\"('Ga', 'Pd', 'Y')\",OTHERS,True\nmp-671913,\"{'Mo': 8.0, 'Se': 16.0, 'Cl': 32.0, 'O': 8.0}\",\"('Cl', 'Mo', 'O', 'Se')\",OTHERS,True\nmp-759864,\"{'Li': 2.0, 'V': 2.0, 'Cr': 2.0, 'P': 4.0, 'H': 4.0, 'O': 20.0}\",\"('Cr', 'H', 'Li', 'O', 'P', 'V')\",OTHERS,True\nmp-972176,\"{'Zr': 10.0, 'Al': 6.0, 'C': 2.0}\",\"('Al', 'C', 'Zr')\",OTHERS,True\nmp-1177874,\"{'Li': 8.0, 'Ni': 12.0, 'W': 4.0, 'O': 32.0}\",\"('Li', 'Ni', 'O', 'W')\",OTHERS,True\nmp-759672,\"{'Li': 2.0, 'V': 2.0, 'O': 2.0, 'F': 6.0}\",\"('F', 'Li', 'O', 'V')\",OTHERS,True\nmp-1101598,\"{'Li': 2.0, 'Ni': 2.0, 'P': 4.0, 'O': 14.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,True\nmp-1226589,\"{'Ce': 1.0, 'Nd': 1.0, 'Al': 2.0, 'O': 6.0}\",\"('Al', 'Ce', 'Nd', 'O')\",OTHERS,True\nmp-1184159,\"{'Er': 1.0, 'Hf': 1.0, 'Ru': 2.0}\",\"('Er', 'Hf', 'Ru')\",OTHERS,True\nmp-1023942,\"{'Te': 4.0, 'W': 2.0}\",\"('Te', 'W')\",OTHERS,True\nmp-761063,\"{'Li': 6.0, 'Fe': 2.0, 'Ni': 6.0, 'O': 16.0}\",\"('Fe', 'Li', 'Ni', 'O')\",OTHERS,True\nmp-1036224,\"{'Mg': 14.0, 'Mn': 1.0, 'Sn': 1.0, 'O': 16.0}\",\"('Mg', 'Mn', 'O', 'Sn')\",OTHERS,True\nmp-1188434,\"{'Y': 10.0, 'Tl': 6.0}\",\"('Tl', 'Y')\",OTHERS,True\nmp-961650,\"{'Al': 1.0, 'V': 1.0, 'Ni': 1.0}\",\"('Al', 'Ni', 'V')\",OTHERS,True\nmp-1185128,\"{'La': 6.0, 'Hf': 2.0}\",\"('Hf', 'La')\",OTHERS,True\nmp-780086,\"{'Li': 8.0, 'Mn': 1.0, 'O': 4.0, 'F': 2.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1100580,\"{'Li': 9.0, 'Mn': 7.0, 'O': 16.0}\",\"('Li', 'Mn', 'O')\",OTHERS,True\nmp-1201749,\"{'Sr': 16.0, 'As': 16.0, 'O': 56.0}\",\"('As', 'O', 'Sr')\",OTHERS,True\nmp-703370,\"{'Ca': 13.0, 'Si': 10.0, 'H': 12.0, 'O': 34.0, 'F': 10.0}\",\"('Ca', 'F', 'H', 'O', 'Si')\",OTHERS,True\nmp-865757,\"{'Yb': 1.0, 'Ga': 1.0, 'Rh': 2.0}\",\"('Ga', 'Rh', 'Yb')\",OTHERS,True\nmp-862319,\"{'Ac': 2.0, 'Cd': 1.0, 'Sn': 1.0}\",\"('Ac', 'Cd', 'Sn')\",OTHERS,True\nmp-1178498,\"{'Cd': 2.0, 'Co': 4.0, 'O': 8.0}\",\"('Cd', 'Co', 'O')\",OTHERS,True\nmp-24123,\"{'H': 12.0, 'B': 12.0, 'Rb': 2.0}\",\"('B', 'H', 'Rb')\",OTHERS,True\nmp-758074,\"{'Li': 4.0, 'V': 4.0, 'P': 4.0, 'O': 20.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-761579,\"{'Fe': 2.0, 'Co': 6.0, 'O': 16.0}\",\"('Co', 'Fe', 'O')\",OTHERS,True\nmp-778885,\"{'Li': 10.0, 'Mn': 3.0, 'Cr': 2.0, 'Ni': 3.0, 'O': 16.0}\",\"('Cr', 'Li', 'Mn', 'Ni', 'O')\",OTHERS,True\nmp-24110,\"{'H': 18.0, 'N': 6.0, 'O': 8.0, 'Cl': 4.0, 'Ru': 1.0, 'Cs': 1.0}\",\"('Cl', 'Cs', 'H', 'N', 'O', 'Ru')\",OTHERS,True\nmp-775136,\"{'Li': 4.0, 'Cr': 3.0, 'Co': 3.0, 'Te': 2.0, 'O': 16.0}\",\"('Co', 'Cr', 'Li', 'O', 'Te')\",OTHERS,True\nmp-1069346,\"{'Pr': 1.0, 'V': 1.0, 'O': 3.0}\",\"('O', 'Pr', 'V')\",OTHERS,True\nmp-1209336,\"{'Pr': 8.0, 'Sn': 12.0}\",\"('Pr', 'Sn')\",OTHERS,True\nmp-1106064,\"{'Ho': 4.0, 'Ga': 12.0, 'Ni': 1.0}\",\"('Ga', 'Ho', 'Ni')\",OTHERS,True\nmp-559557,\"{'Cs': 4.0, 'Na': 4.0, 'B': 20.0, 'O': 34.0}\",\"('B', 'Cs', 'Na', 'O')\",OTHERS,True\nmp-1227976,\"{'Ba': 6.0, 'Br': 2.0, 'N': 3.0, 'Cl': 1.0}\",\"('Ba', 'Br', 'Cl', 'N')\",OTHERS,True\nmp-569472,\"{'Gd': 4.0, 'Al': 18.0, 'Ir': 6.0}\",\"('Al', 'Gd', 'Ir')\",OTHERS,True\nmp-22383,\"{'Si': 2.0, 'Co': 2.0, 'Pu': 1.0}\",\"('Co', 'Pu', 'Si')\",OTHERS,True\nmp-541312,\"{'Ge': 3.0, 'Te': 6.0, 'As': 2.0}\",\"('As', 'Ge', 'Te')\",OTHERS,True\nmp-56,{'Ba': 2.0},\"('Ba',)\",OTHERS,True\nmp-759124,\"{'Li': 6.0, 'Co': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Co', 'Li', 'O', 'Si')\",OTHERS,True\nmp-1204519,\"{'Al': 8.0, 'P': 4.0, 'O': 32.0}\",\"('Al', 'O', 'P')\",OTHERS,True\nmp-1176039,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1111297,\"{'K': 3.0, 'Y': 1.0, 'F': 6.0}\",\"('F', 'K', 'Y')\",OTHERS,True\nmp-14704,\"{'Li': 24.0, 'B': 12.0, 'O': 36.0, 'Y': 4.0}\",\"('B', 'Li', 'O', 'Y')\",OTHERS,True\nmp-738615,\"{'Hg': 4.0, 'H': 12.0, 'C': 4.0, 'S': 4.0, 'O': 12.0}\",\"('C', 'H', 'Hg', 'O', 'S')\",OTHERS,True\nmp-771303,\"{'Li': 4.0, 'Mn': 4.0, 'B': 8.0, 'O': 20.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-775861,\"{'Zr': 16.0, 'N': 16.0, 'O': 8.0}\",\"('N', 'O', 'Zr')\",OTHERS,True\nmp-758129,\"{'Li': 4.0, 'Mn': 2.0, 'Cr': 3.0, 'Co': 3.0, 'O': 16.0}\",\"('Co', 'Cr', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1003322,\"{'Ca': 2.0, 'Mn': 2.0, 'O': 4.0}\",\"('Ca', 'Mn', 'O')\",OTHERS,True\nmp-1203536,\"{'Ho': 20.0, 'Co': 24.0, 'Sn': 72.0}\",\"('Co', 'Ho', 'Sn')\",OTHERS,True\nmp-558301,\"{'Si': 16.0, 'O': 32.0}\",\"('O', 'Si')\",OTHERS,True\nmvc-14231,\"{'Ca': 2.0, 'Fe': 2.0, 'F': 8.0}\",\"('Ca', 'F', 'Fe')\",OTHERS,True\nmp-2294,\"{'Al': 6.0, 'Ir': 2.0}\",\"('Al', 'Ir')\",OTHERS,True\nmp-776437,\"{'La': 4.0, 'Ta': 8.0, 'N': 4.0, 'O': 20.0}\",\"('La', 'N', 'O', 'Ta')\",OTHERS,True\nmp-1191365,\"{'Gd': 8.0, 'P': 8.0, 'S': 8.0}\",\"('Gd', 'P', 'S')\",OTHERS,True\nmp-758077,\"{'Li': 2.0, 'Fe': 16.0, 'O': 24.0}\",\"('Fe', 'Li', 'O')\",OTHERS,True\nmp-979940,\"{'Yb': 3.0, 'Zr': 1.0}\",\"('Yb', 'Zr')\",OTHERS,True\nmp-2124,\"{'Sb': 6.0, 'Ho': 8.0}\",\"('Ho', 'Sb')\",OTHERS,True\nmp-556155,\"{'Zn': 20.0, 'S': 20.0}\",\"('S', 'Zn')\",OTHERS,True\nmp-779108,\"{'Be': 16.0, 'P': 16.0, 'O': 56.0}\",\"('Be', 'O', 'P')\",OTHERS,True\nmp-753503,\"{'Li': 2.0, 'Mn': 4.0, 'F': 14.0}\",\"('F', 'Li', 'Mn')\",OTHERS,True\nmp-559417,\"{'La': 2.0, 'B': 4.0, 'Cl': 2.0, 'O': 8.0}\",\"('B', 'Cl', 'La', 'O')\",OTHERS,True\nmp-680400,\"{'K': 12.0, 'Ta': 8.0, 'S': 44.0}\",\"('K', 'S', 'Ta')\",OTHERS,True\nmp-1227917,\"{'Ba': 1.0, 'La': 2.0, 'Fe': 2.0, 'O': 7.0}\",\"('Ba', 'Fe', 'La', 'O')\",OTHERS,True\nmp-1077112,\"{'Eu': 2.0, 'Zn': 2.0, 'Ge': 2.0}\",\"('Eu', 'Ge', 'Zn')\",OTHERS,True\nmp-1073061,\"{'Mg': 8.0, 'Si': 12.0}\",\"('Mg', 'Si')\",OTHERS,True\nmp-752643,\"{'Na': 2.0, 'Ti': 4.0, 'O': 8.0}\",\"('Na', 'O', 'Ti')\",OTHERS,True\nmvc-11563,\"{'Ca': 2.0, 'V': 4.0, 'O': 8.0}\",\"('Ca', 'O', 'V')\",OTHERS,True\nmp-1220094,\"{'Nd': 1.0, 'Sm': 1.0, 'Fe': 17.0, 'N': 3.0}\",\"('Fe', 'N', 'Nd', 'Sm')\",OTHERS,True\nmp-1100344,\"{'Ca': 8.0, 'Sn': 8.0, 'S': 24.0}\",\"('Ca', 'S', 'Sn')\",OTHERS,True\nmp-1184208,\"{'Er': 1.0, 'Tl': 1.0, 'Rh': 2.0}\",\"('Er', 'Rh', 'Tl')\",OTHERS,True\nmp-1246527,\"{'Mn': 4.0, 'S': 3.0, 'N': 2.0}\",\"('Mn', 'N', 'S')\",OTHERS,True\nmp-540915,\"{'Eu': 6.0, 'O': 8.0, 'Br': 2.0}\",\"('Br', 'Eu', 'O')\",OTHERS,True\nmp-560563,\"{'K': 6.0, 'Pd': 4.0, 'O': 8.0}\",\"('K', 'O', 'Pd')\",OTHERS,True\nmp-558250,\"{'Mo': 4.0, 'H': 8.0, 'C': 8.0, 'O': 16.0}\",\"('C', 'H', 'Mo', 'O')\",OTHERS,True\nmp-1100166,\"{'Y': 1.0, 'Mg': 3.0}\",\"('Mg', 'Y')\",OTHERS,True\nmp-1198990,\"{'Na': 8.0, 'Pr': 4.0, 'Ge': 4.0, 'O': 20.0}\",\"('Ge', 'Na', 'O', 'Pr')\",OTHERS,True\nmp-780171,\"{'Hg': 4.0, 'H': 40.0, 'N': 8.0, 'Cl': 16.0, 'O': 4.0}\",\"('Cl', 'H', 'Hg', 'N', 'O')\",OTHERS,True\nmp-23443,\"{'Mn': 4.0, 'I': 12.0, 'Tl': 4.0}\",\"('I', 'Mn', 'Tl')\",OTHERS,True\nmp-1223876,\"{'K': 2.0, 'Th': 1.0, 'F': 6.0}\",\"('F', 'K', 'Th')\",OTHERS,True\nmp-1209305,\"{'Rb': 3.0, 'Yb': 1.0, 'P': 2.0, 'O': 8.0}\",\"('O', 'P', 'Rb', 'Yb')\",OTHERS,True\nmp-768754,\"{'Li': 4.0, 'Bi': 8.0, 'S': 12.0, 'O': 48.0}\",\"('Bi', 'Li', 'O', 'S')\",OTHERS,True\nmp-680674,\"{'Cs': 6.0, 'Bi': 4.0, 'Br': 18.0}\",\"('Bi', 'Br', 'Cs')\",OTHERS,True\nmp-1246838,\"{'V': 4.0, 'Co': 6.0, 'N': 8.0}\",\"('Co', 'N', 'V')\",OTHERS,True\nmp-1100156,\"{'Rb': 1.0, 'Mg': 6.0, 'Sb': 1.0}\",\"('Mg', 'Rb', 'Sb')\",OTHERS,True\nmp-1247033,\"{'Mg': 2.0, 'Ti': 3.0, 'Ga': 1.0, 'S': 8.0}\",\"('Ga', 'Mg', 'S', 'Ti')\",OTHERS,True\nmp-1218551,\"{'Sr': 3.0, 'Y': 2.0, 'Fe': 1.0, 'Cu': 3.0, 'Bi': 1.0, 'O': 12.0}\",\"('Bi', 'Cu', 'Fe', 'O', 'Sr', 'Y')\",OTHERS,True\nmp-1216284,\"{'V': 1.0, 'Re': 1.0, 'O': 4.0}\",\"('O', 'Re', 'V')\",OTHERS,True\nmvc-13322,\"{'Sr': 4.0, 'Y': 4.0, 'Mn': 4.0, 'O': 14.0}\",\"('Mn', 'O', 'Sr', 'Y')\",OTHERS,True\nmp-17770,\"{'O': 24.0, 'P': 8.0, 'Cs': 2.0, 'Pr': 2.0}\",\"('Cs', 'O', 'P', 'Pr')\",OTHERS,True\nmp-17542,\"{'O': 24.0, 'P': 4.0, 'Mo': 4.0, 'Tl': 4.0}\",\"('Mo', 'O', 'P', 'Tl')\",OTHERS,True\nmp-1217298,\"{'Th': 5.0, 'C': 1.0}\",\"('C', 'Th')\",OTHERS,True\nmp-1001917,\"{'Ho': 1.0, 'As': 1.0}\",\"('As', 'Ho')\",OTHERS,True\nmp-849665,\"{'Mn': 12.0, 'O': 4.0, 'F': 32.0}\",\"('F', 'Mn', 'O')\",OTHERS,True\nmp-651688,\"{'Si': 8.0, 'Ag': 20.0, 'O': 26.0}\",\"('Ag', 'O', 'Si')\",OTHERS,True\nmp-1214757,\"{'Ba': 2.0, 'Ca': 1.0, 'Sm': 2.0, 'Ti': 3.0, 'Cu': 2.0, 'O': 14.0}\",\"('Ba', 'Ca', 'Cu', 'O', 'Sm', 'Ti')\",OTHERS,True\nmp-640051,\"{'Pu': 4.0, 'Ni': 4.0, 'Sn': 2.0}\",\"('Ni', 'Pu', 'Sn')\",OTHERS,True\nmp-760107,\"{'V': 28.0, 'O': 24.0, 'F': 36.0}\",\"('F', 'O', 'V')\",OTHERS,True\nmp-1245615,\"{'Nb': 2.0, 'Te': 2.0, 'N': 2.0}\",\"('N', 'Nb', 'Te')\",OTHERS,True\nmp-1101620,\"{'Dy': 6.0, 'Nb': 2.0, 'O': 14.0}\",\"('Dy', 'Nb', 'O')\",OTHERS,True\nmp-1190414,\"{'Yb': 7.0, 'In': 1.0, 'Ni': 4.0, 'Ge': 12.0}\",\"('Ge', 'In', 'Ni', 'Yb')\",OTHERS,True\nmp-1174208,\"{'Li': 6.0, 'Mn': 2.0, 'Co': 2.0, 'O': 10.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1245525,\"{'Ge': 4.0, 'Pb': 4.0, 'N': 8.0}\",\"('Ge', 'N', 'Pb')\",OTHERS,True\nmp-546142,\"{'O': 4.0, 'U': 1.0, 'Na': 2.0}\",\"('Na', 'O', 'U')\",OTHERS,True\nmp-27837,\"{'Na': 1.0, 'H': 1.0, 'F': 2.0}\",\"('F', 'H', 'Na')\",OTHERS,True\nmp-760093,\"{'Li': 2.0, 'Ti': 16.0, 'O': 32.0}\",\"('Li', 'O', 'Ti')\",OTHERS,True\nmp-1223957,\"{'K': 4.0, 'Mg': 1.0, 'Cu': 3.0, 'F': 12.0}\",\"('Cu', 'F', 'K', 'Mg')\",OTHERS,True\nmp-1186167,\"{'Na': 1.0, 'Si': 1.0, 'O': 3.0}\",\"('Na', 'O', 'Si')\",OTHERS,True\nmp-777800,\"{'Fe': 4.0, 'O': 6.0, 'F': 2.0}\",\"('F', 'Fe', 'O')\",OTHERS,True\nmp-1096592,\"{'Hf': 2.0, 'Zn': 1.0, 'Fe': 1.0}\",\"('Fe', 'Hf', 'Zn')\",OTHERS,True\nmp-795607,\"{'V': 8.0, 'N': 16.0, 'O': 44.0}\",\"('N', 'O', 'V')\",OTHERS,True\nmp-1204701,\"{'Cd': 4.0, 'N': 8.0, 'O': 24.0}\",\"('Cd', 'N', 'O')\",OTHERS,True\nmp-675199,\"{'Na': 2.0, 'Pr': 2.0, 'S': 4.0}\",\"('Na', 'Pr', 'S')\",OTHERS,True\nmp-552191,\"{'O': 6.0, 'Ba': 2.0, 'Tb': 1.0, 'Bi': 1.0}\",\"('Ba', 'Bi', 'O', 'Tb')\",OTHERS,True\nmp-615444,\"{'Cr': 2.0, 'P': 2.0, 'C': 10.0, 'Br': 6.0, 'O': 10.0}\",\"('Br', 'C', 'Cr', 'O', 'P')\",OTHERS,True\nmp-753155,\"{'Fe': 8.0, 'O': 14.0, 'F': 2.0}\",\"('F', 'Fe', 'O')\",OTHERS,True\nmp-1183521,\"{'Ca': 12.0, 'Si': 4.0, 'O': 4.0}\",\"('Ca', 'O', 'Si')\",OTHERS,True\nmp-754905,\"{'Ni': 6.0, 'O': 2.0, 'F': 10.0}\",\"('F', 'Ni', 'O')\",OTHERS,True\nmp-759157,\"{'V': 6.0, 'O': 4.0, 'F': 12.0}\",\"('F', 'O', 'V')\",OTHERS,True\nmp-674976,\"{'Zr': 9.0, 'Sc': 1.0, 'O': 20.0}\",\"('O', 'Sc', 'Zr')\",OTHERS,True\nmp-1030461,\"{'Te': 4.0, 'Mo': 4.0, 'S': 4.0}\",\"('Mo', 'S', 'Te')\",OTHERS,True\nmp-10533,\"{'S': 8.0, 'Cu': 4.0, 'Y': 4.0}\",\"('Cu', 'S', 'Y')\",OTHERS,True\nmp-640315,\"{'Y': 2.0, 'Zn': 40.0, 'Ru': 4.0}\",\"('Ru', 'Y', 'Zn')\",OTHERS,True\nmp-976378,\"{'La': 6.0, 'Sn': 8.0}\",\"('La', 'Sn')\",OTHERS,True\nmp-14652,\"{'P': 6.0, 'Cs': 4.0}\",\"('Cs', 'P')\",OTHERS,True\nmp-1019055,\"{'Re': 2.0, 'N': 4.0}\",\"('N', 'Re')\",OTHERS,True\nmp-1079174,\"{'Pu': 4.0, 'Pd': 4.0}\",\"('Pd', 'Pu')\",OTHERS,True\nmp-764759,\"{'Li': 6.0, 'Mn': 5.0, 'O': 12.0}\",\"('Li', 'Mn', 'O')\",OTHERS,True\nmp-1208624,\"{'Sr': 1.0, 'P': 4.0, 'N': 2.0, 'O': 12.0}\",\"('N', 'O', 'P', 'Sr')\",OTHERS,True\nmp-1224902,\"{'Gd': 2.0, 'Er': 2.0, 'Al': 12.0}\",\"('Al', 'Er', 'Gd')\",OTHERS,True\nmp-1194897,\"{'Na': 8.0, 'Lu': 4.0, 'C': 8.0, 'O': 24.0, 'F': 4.0}\",\"('C', 'F', 'Lu', 'Na', 'O')\",OTHERS,True\nmp-1174322,\"{'Li': 8.0, 'Mn': 6.0, 'O': 14.0}\",\"('Li', 'Mn', 'O')\",OTHERS,True\nmp-1104360,\"{'Er': 3.0, 'Ga': 8.0, 'Ir': 3.0}\",\"('Er', 'Ga', 'Ir')\",OTHERS,True\nmp-1222773,\"{'Li': 2.0, 'Fe': 1.0, 'P': 2.0, 'S': 6.0}\",\"('Fe', 'Li', 'P', 'S')\",OTHERS,True\nmp-1209861,\"{'Nd': 6.0, 'Hf': 2.0, 'Sb': 10.0}\",\"('Hf', 'Nd', 'Sb')\",OTHERS,True\nmp-1218687,\"{'Sr': 4.0, 'La': 1.0, 'Co': 5.0, 'O': 15.0}\",\"('Co', 'La', 'O', 'Sr')\",OTHERS,True\nmp-1245701,\"{'Fe': 2.0, 'Ge': 14.0, 'N': 20.0}\",\"('Fe', 'Ge', 'N')\",OTHERS,True\nmp-981390,\"{'Hg': 1.0, 'Bi': 3.0}\",\"('Bi', 'Hg')\",OTHERS,True\nmp-1210236,\"{'Nd': 2.0, 'Fe': 21.0, 'B': 6.0}\",\"('B', 'Fe', 'Nd')\",OTHERS,True\nmp-17926,\"{'Ni': 6.0, 'Zr': 6.0, 'Sb': 8.0}\",\"('Ni', 'Sb', 'Zr')\",OTHERS,True\nmp-559476,\"{'Dy': 4.0, 'I': 12.0, 'O': 36.0}\",\"('Dy', 'I', 'O')\",OTHERS,True\nmp-768941,\"{'Sr': 6.0, 'Te': 2.0, 'O': 12.0}\",\"('O', 'Sr', 'Te')\",OTHERS,True\nmp-504908,\"{'In': 6.0, 'Te': 3.0, 'O': 18.0}\",\"('In', 'O', 'Te')\",OTHERS,True\nmp-1114094,\"{'Rb': 2.0, 'In': 1.0, 'Ag': 1.0, 'F': 6.0}\",\"('Ag', 'F', 'In', 'Rb')\",OTHERS,True\nmp-998911,\"{'Tm': 2.0, 'Ge': 2.0}\",\"('Ge', 'Tm')\",OTHERS,True\nmp-1076202,\"{'Sr': 28.0, 'Ca': 4.0, 'Ti': 20.0, 'Mn': 12.0, 'O': 80.0}\",\"('Ca', 'Mn', 'O', 'Sr', 'Ti')\",OTHERS,True\nmp-1213341,\"{'Gd': 14.0, 'Br': 6.0, 'O': 36.0}\",\"('Br', 'Gd', 'O')\",OTHERS,True\nmp-7131,\"{'Te': 1.0, 'Pr': 1.0}\",\"('Pr', 'Te')\",OTHERS,True\nmp-1185293,\"{'Li': 1.0, 'Ac': 2.0, 'Tl': 1.0}\",\"('Ac', 'Li', 'Tl')\",OTHERS,True\nmp-1201128,\"{'Na': 4.0, 'Ni': 1.0, 'P': 6.0, 'O': 24.0}\",\"('Na', 'Ni', 'O', 'P')\",OTHERS,True\nmp-1027492,\"{'Mo': 4.0, 'Se': 4.0, 'S': 4.0}\",\"('Mo', 'S', 'Se')\",OTHERS,True\nmp-17578,\"{'Ge': 6.0, 'Yb': 4.0, 'Ir': 7.0}\",\"('Ge', 'Ir', 'Yb')\",OTHERS,True\nmp-1217419,\"{'Tc': 3.0, 'Mo': 1.0}\",\"('Mo', 'Tc')\",OTHERS,True\nmvc-16583,\"{'Mg': 2.0, 'Cr': 2.0, 'F': 8.0}\",\"('Cr', 'F', 'Mg')\",OTHERS,True\nmp-557498,\"{'Na': 4.0, 'Bi': 8.0, 'Au': 4.0, 'O': 20.0}\",\"('Au', 'Bi', 'Na', 'O')\",OTHERS,True\nmp-1221047,\"{'Na': 1.0, 'Er': 1.0, 'Mo': 2.0, 'O': 8.0}\",\"('Er', 'Mo', 'Na', 'O')\",OTHERS,True\nmp-1228324,\"{'Ba': 12.0, 'Na': 8.0, 'Sb': 16.0}\",\"('Ba', 'Na', 'Sb')\",OTHERS,True\nmp-753321,\"{'Li': 2.0, 'Fe': 2.0, 'P': 2.0, 'O': 8.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,True\nmp-1093982,\"{'Ti': 1.0, 'Cd': 1.0, 'Pd': 2.0}\",\"('Cd', 'Pd', 'Ti')\",OTHERS,True\nmp-760983,\"{'Li': 10.0, 'V': 5.0, 'Cr': 3.0, 'O': 16.0}\",\"('Cr', 'Li', 'O', 'V')\",OTHERS,True\nmp-775157,\"{'Li': 12.0, 'Mn': 2.0, 'V': 6.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,True\nmp-9442,\"{'Si': 6.0, 'Fe': 4.0, 'Dy': 6.0}\",\"('Dy', 'Fe', 'Si')\",OTHERS,True\nmp-1203729,\"{'Ba': 2.0, 'Cu': 1.0, 'C': 12.0, 'O': 18.0}\",\"('Ba', 'C', 'Cu', 'O')\",OTHERS,True\nmp-1208576,\"{'Ta': 4.0, 'Co': 4.0, 'As': 4.0}\",\"('As', 'Co', 'Ta')\",OTHERS,True\nmp-849278,\"{'Fe': 1.0, 'Ni': 3.0, 'Sn': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Fe', 'Ni', 'O', 'P', 'Sn')\",OTHERS,True\nmp-1199794,\"{'Tm': 20.0, 'In': 40.0, 'Ni': 18.0}\",\"('In', 'Ni', 'Tm')\",OTHERS,True\nmp-772660,\"{'Nb': 4.0, 'Cr': 4.0, 'O': 16.0}\",\"('Cr', 'Nb', 'O')\",OTHERS,True\nmp-752401,\"{'Rb': 4.0, 'Zr': 2.0, 'O': 6.0}\",\"('O', 'Rb', 'Zr')\",OTHERS,True\nmp-776544,\"{'Li': 4.0, 'Fe': 8.0, 'O': 4.0, 'F': 20.0}\",\"('F', 'Fe', 'Li', 'O')\",OTHERS,True\nmp-1221673,\"{'Mn': 2.0, 'Cr': 2.0, 'P': 4.0}\",\"('Cr', 'Mn', 'P')\",OTHERS,True\nmp-11201,\"{'Sc': 6.0, 'Fe': 1.0, 'Sb': 2.0}\",\"('Fe', 'Sb', 'Sc')\",OTHERS,True\nmp-1183066,\"{'Ac': 2.0, 'Zn': 1.0, 'Au': 1.0}\",\"('Ac', 'Au', 'Zn')\",OTHERS,True\nmp-763588,\"{'Li': 2.0, 'Fe': 2.0, 'C': 4.0, 'O': 12.0}\",\"('C', 'Fe', 'Li', 'O')\",OTHERS,True\nmp-27441,\"{'O': 22.0, 'Se': 8.0, 'Au': 4.0}\",\"('Au', 'O', 'Se')\",OTHERS,True\nmp-28085,\"{'F': 28.0, 'K': 4.0, 'As': 8.0}\",\"('As', 'F', 'K')\",OTHERS,True\nmp-1176394,\"{'Na': 8.0, 'Mn': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Mn', 'Na', 'O', 'P')\",OTHERS,True\nmp-4051,\"{'O': 8.0, 'Al': 2.0, 'P': 2.0}\",\"('Al', 'O', 'P')\",OTHERS,True\nmp-1099978,\"{'La': 24.0, 'Sm': 8.0, 'Cr': 20.0, 'Fe': 12.0, 'O': 80.0}\",\"('Cr', 'Fe', 'La', 'O', 'Sm')\",OTHERS,True\nmp-1210094,\"{'Na': 1.0, 'As': 4.0, 'I': 1.0, 'O': 6.0}\",\"('As', 'I', 'Na', 'O')\",OTHERS,True\nmp-1220949,\"{'Na': 14.0, 'Fe': 8.0, 'P': 18.0, 'O': 64.0}\",\"('Fe', 'Na', 'O', 'P')\",OTHERS,True\nmp-18162,\"{'Li': 4.0, 'O': 8.0, 'Cu': 8.0}\",\"('Cu', 'Li', 'O')\",OTHERS,True\nmp-759605,\"{'Li': 4.0, 'Fe': 2.0, 'C': 4.0, 'O': 12.0}\",\"('C', 'Fe', 'Li', 'O')\",OTHERS,True\nmp-1223118,\"{'La': 4.0, 'Nb': 1.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'La', 'Nb', 'O')\",OTHERS,True\nmp-1196614,\"{'Cs': 8.0, 'Tl': 4.0, 'Cl': 20.0, 'O': 4.0}\",\"('Cl', 'Cs', 'O', 'Tl')\",OTHERS,True\nAl 75 Pd 13 Ru 12,\"{'Al': 75.0, 'Pd': 13.0, 'Ru': 12.0}\",\"('Al', 'Pd', 'Ru')\",AC,True\nmp-758595,\"{'Li': 8.0, 'Cr': 4.0, 'Si': 4.0, 'O': 16.0}\",\"('Cr', 'Li', 'O', 'Si')\",OTHERS,True\nmp-1097274,\"{'K': 2.0, 'Rb': 1.0, 'As': 1.0}\",\"('As', 'K', 'Rb')\",OTHERS,True\nmp-1197565,\"{'Ce': 2.0, 'Cd': 40.0, 'Pd': 4.0}\",\"('Cd', 'Ce', 'Pd')\",OTHERS,True\nmp-1095993,\"{'Zr': 1.0, 'In': 1.0, 'Ag': 2.0}\",\"('Ag', 'In', 'Zr')\",OTHERS,True\nmp-1247445,\"{'Mg': 2.0, 'In': 1.0, 'N': 2.0}\",\"('In', 'Mg', 'N')\",OTHERS,True\nmvc-2250,\"{'Y': 1.0, 'Cu': 3.0, 'Ni': 4.0, 'O': 12.0}\",\"('Cu', 'Ni', 'O', 'Y')\",OTHERS,True\nmp-8850,\"{'S': 12.0, 'Dy': 8.0}\",\"('Dy', 'S')\",OTHERS,True\nmp-29136,\"{'N': 10.0, 'Cu': 6.0, 'Sr': 12.0}\",\"('Cu', 'N', 'Sr')\",OTHERS,True\nmp-1220559,\"{'Nb': 4.0, 'H': 3.0, 'S': 8.0}\",\"('H', 'Nb', 'S')\",OTHERS,True\nmp-681,\"{'Zn': 4.0, 'Eu': 2.0}\",\"('Eu', 'Zn')\",OTHERS,True\nmp-568036,\"{'La': 8.0, 'Ga': 4.0, 'N': 12.0}\",\"('Ga', 'La', 'N')\",OTHERS,True\nmp-1209055,\"{'Sb': 1.0, 'Br': 6.0, 'N': 2.0}\",\"('Br', 'N', 'Sb')\",OTHERS,True\nmp-867896,\"{'Sc': 1.0, 'Zn': 1.0, 'Cu': 2.0}\",\"('Cu', 'Sc', 'Zn')\",OTHERS,True\nmp-1196269,\"{'In': 4.0, 'Re': 8.0, 'C': 36.0, 'O': 36.0}\",\"('C', 'In', 'O', 'Re')\",OTHERS,True\nmp-1245099,\"{'Co': 30.0, 'O': 60.0}\",\"('Co', 'O')\",OTHERS,True\nmp-20491,\"{'Cu': 2.0, 'In': 1.0, 'La': 1.0}\",\"('Cu', 'In', 'La')\",OTHERS,True\nmp-1198754,\"{'Zn': 4.0, 'Sn': 4.0, 'P': 56.0}\",\"('P', 'Sn', 'Zn')\",OTHERS,True\nmp-1197223,\"{'Na': 4.0, 'Zn': 1.0, 'P': 4.0, 'H': 16.0, 'C': 2.0, 'N': 2.0, 'O': 16.0}\",\"('C', 'H', 'N', 'Na', 'O', 'P', 'Zn')\",OTHERS,True\nmp-38994,\"{'Na': 1.0, 'Al': 1.0, 'Si': 4.0}\",\"('Al', 'Na', 'Si')\",OTHERS,True\nmp-1037599,\"{'K': 1.0, 'Mg': 30.0, 'Fe': 1.0, 'O': 32.0}\",\"('Fe', 'K', 'Mg', 'O')\",OTHERS,True\nmp-25829,\"{'Li': 2.0, 'Mn': 4.0, 'P': 6.0, 'O': 22.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,True\nmp-1079449,\"{'Nd': 2.0, 'Si': 4.0, 'Ir': 4.0}\",\"('Ir', 'Nd', 'Si')\",OTHERS,True\nmp-1225580,\"{'La': 1.0, 'Ce': 1.0, 'Cu': 4.0, 'Si': 4.0}\",\"('Ce', 'Cu', 'La', 'Si')\",OTHERS,True\nmp-1182301,\"{'Co': 12.0, 'B': 28.0, 'I': 4.0, 'O': 52.0}\",\"('B', 'Co', 'I', 'O')\",OTHERS,True\nmp-1199982,\"{'Rb': 8.0, 'U': 4.0, 'Se': 8.0, 'O': 48.0}\",\"('O', 'Rb', 'Se', 'U')\",OTHERS,True\nmp-1027663,\"{'Te': 4.0, 'Mo': 3.0, 'W': 1.0, 'Se': 4.0}\",\"('Mo', 'Se', 'Te', 'W')\",OTHERS,True\nmp-19987,\"{'Pa': 6.0, 'Sb': 8.0}\",\"('Pa', 'Sb')\",OTHERS,True\nmp-626014,\"{'H': 20.0, 'I': 4.0, 'O': 24.0}\",\"('H', 'I', 'O')\",OTHERS,True\nmp-580354,\"{'Ce': 6.0, 'Ni': 18.0}\",\"('Ce', 'Ni')\",OTHERS,True\nmp-974395,\"{'Pt': 3.0, 'Rh': 1.0}\",\"('Pt', 'Rh')\",OTHERS,True\nmp-21320,\"{'Ni': 3.0, 'Ga': 3.0, 'U': 3.0}\",\"('Ga', 'Ni', 'U')\",OTHERS,True\nmp-941,\"{'Ni': 30.0, 'As': 12.0}\",\"('As', 'Ni')\",OTHERS,True\nmp-504457,\"{'Ba': 2.0, 'Ti': 12.0, 'O': 26.0}\",\"('Ba', 'O', 'Ti')\",OTHERS,True\nmp-19479,\"{'O': 32.0, 'Na': 10.0, 'Mo': 8.0, 'La': 2.0}\",\"('La', 'Mo', 'Na', 'O')\",OTHERS,True\nmp-1097603,\"{'Ba': 2.0, 'Mg': 1.0, 'Zn': 1.0}\",\"('Ba', 'Mg', 'Zn')\",OTHERS,True\nmp-1176477,\"{'Mn': 4.0, 'O': 6.0, 'F': 2.0}\",\"('F', 'Mn', 'O')\",OTHERS,True\nmp-1187143,\"{'Sr': 1.0, 'Be': 1.0, 'O': 3.0}\",\"('Be', 'O', 'Sr')\",OTHERS,True\nmp-20874,\"{'B': 6.0, 'Eu': 1.0}\",\"('B', 'Eu')\",OTHERS,True\nmp-623836,\"{'Th': 8.0, 'Ni': 8.0}\",\"('Ni', 'Th')\",OTHERS,True\nmp-27192,\"{'Cs': 4.0, 'Be': 8.0, 'F': 20.0}\",\"('Be', 'Cs', 'F')\",OTHERS,True\nmp-1245215,\"{'Al': 36.0, 'O': 18.0}\",\"('Al', 'O')\",OTHERS,True\nmp-1226501,\"{'Ce': 1.0, 'Sc': 1.0, 'Pd': 6.0}\",\"('Ce', 'Pd', 'Sc')\",OTHERS,True\nmp-568190,\"{'La': 16.0, 'C': 16.0, 'Cl': 10.0}\",\"('C', 'Cl', 'La')\",OTHERS,True\nmp-631254,\"{'K': 1.0, 'Mn': 1.0, 'Ru': 1.0}\",\"('K', 'Mn', 'Ru')\",OTHERS,True\nmp-1187796,\"{'U': 4.0, 'B': 16.0, 'Mo': 4.0}\",\"('B', 'Mo', 'U')\",OTHERS,True\nmp-695772,\"{'Mo': 2.0, 'P': 8.0, 'O': 24.0}\",\"('Mo', 'O', 'P')\",OTHERS,True\nmp-29305,\"{'O': 24.0, 'Re': 6.0, 'Pb': 3.0}\",\"('O', 'Pb', 'Re')\",OTHERS,True\nmp-1244872,\"{'B': 50.0, 'N': 50.0}\",\"('B', 'N')\",OTHERS,True\nmp-774573,\"{'Li': 4.0, 'Mn': 4.0, 'Ni': 2.0, 'P': 6.0, 'O': 24.0}\",\"('Li', 'Mn', 'Ni', 'O', 'P')\",OTHERS,True\nmp-1074562,\"{'Mg': 8.0, 'Si': 6.0}\",\"('Mg', 'Si')\",OTHERS,True\nmp-1207270,\"{'Tl': 2.0, 'Cu': 1.0, 'Te': 2.0}\",\"('Cu', 'Te', 'Tl')\",OTHERS,True\nmp-629560,\"{'Fe': 6.0, 'C': 18.0, 'Se': 4.0, 'O': 18.0}\",\"('C', 'Fe', 'O', 'Se')\",OTHERS,True\nmp-649470,\"{'K': 12.0, 'Nd': 8.0, 'N': 36.0, 'O': 108.0}\",\"('K', 'N', 'Nd', 'O')\",OTHERS,True\nmvc-680,\"{'Y': 2.0, 'Ni': 2.0, 'W': 4.0, 'O': 16.0}\",\"('Ni', 'O', 'W', 'Y')\",OTHERS,True\nmp-2400,\"{'Na': 4.0, 'S': 4.0}\",\"('Na', 'S')\",OTHERS,True\nmp-1097288,\"{'Sc': 2.0, 'Pt': 1.0, 'Rh': 1.0}\",\"('Pt', 'Rh', 'Sc')\",OTHERS,True\nmp-20692,\"{'N': 8.0, 'Mn': 4.0, 'Ge': 4.0}\",\"('Ge', 'Mn', 'N')\",OTHERS,True\nmp-1177526,\"{'Li': 6.0, 'V': 6.0, 'P': 16.0, 'O': 58.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-772822,\"{'Li': 3.0, 'Cu': 3.0, 'Te': 1.0, 'O': 8.0}\",\"('Cu', 'Li', 'O', 'Te')\",OTHERS,True\nmp-738670,\"{'H': 40.0, 'C': 16.0, 'N': 8.0, 'O': 24.0}\",\"('C', 'H', 'N', 'O')\",OTHERS,True\nmp-1185577,\"{'Cs': 2.0, 'Hg': 6.0}\",\"('Cs', 'Hg')\",OTHERS,True\nmp-866287,\"{'Dy': 1.0, 'Sn': 1.0, 'Au': 2.0}\",\"('Au', 'Dy', 'Sn')\",OTHERS,True\nmp-770495,\"{'Li': 4.0, 'Ti': 2.0, 'Mn': 3.0, 'Fe': 3.0, 'O': 16.0}\",\"('Fe', 'Li', 'Mn', 'O', 'Ti')\",OTHERS,True\nAu 60.3 Sn 24.6 Yb 15.1,\"{'Au': 60.3, 'Sn': 24.6, 'Yb': 15.1}\",\"('Au', 'Sn', 'Yb')\",AC,True\nmp-1186561,\"{'Pm': 2.0, 'Cd': 6.0}\",\"('Cd', 'Pm')\",OTHERS,True\nmp-1094981,\"{'Mg': 4.0, 'Bi': 8.0}\",\"('Bi', 'Mg')\",OTHERS,True\nmp-760102,\"{'W': 8.0, 'O': 4.0, 'F': 32.0}\",\"('F', 'O', 'W')\",OTHERS,True\nmp-8869,\"{'O': 4.0, 'S': 8.0, 'Y': 8.0}\",\"('O', 'S', 'Y')\",OTHERS,True\nmp-1226212,\"{'Cr': 1.0, 'Ni': 1.0, 'Sb': 2.0}\",\"('Cr', 'Ni', 'Sb')\",OTHERS,True\nmp-781628,\"{'Li': 7.0, 'Mn': 8.0, 'B': 8.0, 'O': 24.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-974613,\"{'Nd': 1.0, 'P': 3.0}\",\"('Nd', 'P')\",OTHERS,True\nmp-1185501,\"{'Lu': 1.0, 'Mg': 1.0, 'Tl': 2.0}\",\"('Lu', 'Mg', 'Tl')\",OTHERS,True\nmp-971720,\"{'Zn': 1.0, 'Ga': 3.0}\",\"('Ga', 'Zn')\",OTHERS,True\nmp-1204398,\"{'Co': 4.0, 'Mo': 4.0, 'H': 96.0, 'N': 24.0, 'Cl': 4.0, 'O': 28.0}\",\"('Cl', 'Co', 'H', 'Mo', 'N', 'O')\",OTHERS,True\nmp-1226498,\"{'Ce': 1.0, 'Pr': 1.0, 'Al': 4.0}\",\"('Al', 'Ce', 'Pr')\",OTHERS,True\nmp-1106292,\"{'C': 8.0, 'Se': 4.0, 'N': 8.0}\",\"('C', 'N', 'Se')\",OTHERS,True\nmp-753740,\"{'Cr': 1.0, 'O': 3.0}\",\"('Cr', 'O')\",OTHERS,True\nmp-613327,\"{'Fe': 2.0, 'B': 4.0, 'W': 4.0}\",\"('B', 'Fe', 'W')\",OTHERS,True\nmp-11352,\"{'Al': 5.0, 'Ni': 2.0, 'Pr': 1.0}\",\"('Al', 'Ni', 'Pr')\",OTHERS,True\nmp-1195,\"{'P': 28.0, 'Ba': 6.0}\",\"('Ba', 'P')\",OTHERS,True\nmp-759743,\"{'Sb': 20.0, 'O': 28.0, 'F': 4.0}\",\"('F', 'O', 'Sb')\",OTHERS,True\nmp-28024,\"{'Si': 4.0, 'Y': 4.0, 'Pd': 8.0}\",\"('Pd', 'Si', 'Y')\",OTHERS,True\nmp-624929,\"{'Cd': 1.0, 'In': 1.0, 'Ga': 1.0, 'S': 4.0}\",\"('Cd', 'Ga', 'In', 'S')\",OTHERS,True\nmp-1183403,\"{'Be': 1.0, 'Bi': 1.0, 'O': 3.0}\",\"('Be', 'Bi', 'O')\",OTHERS,True\nmp-7046,\"{'S': 2.0, 'Rb': 1.0, 'Dy': 1.0}\",\"('Dy', 'Rb', 'S')\",OTHERS,True\nmp-1030570,\"{'Te': 4.0, 'Mo': 2.0, 'W': 2.0, 'Se': 2.0, 'S': 2.0}\",\"('Mo', 'S', 'Se', 'Te', 'W')\",OTHERS,True\nmp-697807,\"{'Li': 2.0, 'Mn': 8.0, 'P': 14.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P')\",OTHERS,True\nmp-23983,\"{'H': 8.0, 'O': 14.0, 'P': 2.0, 'V': 2.0, 'Ni': 1.0}\",\"('H', 'Ni', 'O', 'P', 'V')\",OTHERS,True\nmp-567157,\"{'Ba': 14.0, 'Na': 10.0, 'Li': 6.0, 'N': 6.0}\",\"('Ba', 'Li', 'N', 'Na')\",OTHERS,True\nmp-559431,\"{'K': 4.0, 'V': 4.0, 'P': 4.0, 'O': 20.0}\",\"('K', 'O', 'P', 'V')\",OTHERS,True\nmp-1094557,\"{'Mg': 6.0, 'Sb': 6.0}\",\"('Mg', 'Sb')\",OTHERS,True\nmp-755434,\"{'Li': 4.0, 'Mn': 2.0, 'F': 8.0}\",\"('F', 'Li', 'Mn')\",OTHERS,True\nmp-743704,\"{'Ca': 8.0, 'Y': 4.0, 'Al': 14.0, 'Cr': 4.0, 'Si': 2.0, 'O': 48.0}\",\"('Al', 'Ca', 'Cr', 'O', 'Si', 'Y')\",OTHERS,True\nmp-772093,\"{'Li': 2.0, 'Fe': 2.0, 'P': 2.0, 'O': 8.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,True\nmp-17350,\"{'O': 24.0, 'Te': 4.0, 'La': 8.0}\",\"('La', 'O', 'Te')\",OTHERS,True\nmp-1106039,\"{'Sm': 4.0, 'As': 8.0, 'Au': 4.0}\",\"('As', 'Au', 'Sm')\",OTHERS,True\nmp-1226133,\"{'Co': 1.0, 'Mo': 4.0, 'N': 5.0}\",\"('Co', 'Mo', 'N')\",OTHERS,True\nmp-1187097,\"{'Sr': 2.0, 'Pd': 1.0, 'Pt': 1.0}\",\"('Pd', 'Pt', 'Sr')\",OTHERS,True\nmp-1211576,\"{'Nd': 8.0, 'Co': 2.0, 'Si': 8.0, 'C': 8.0, 'O': 44.0}\",\"('C', 'Co', 'Nd', 'O', 'Si')\",OTHERS,True\nmp-762029,\"{'Li': 4.0, 'Ag': 8.0, 'F': 16.0}\",\"('Ag', 'F', 'Li')\",OTHERS,True\nmp-1034331,\"{'Hf': 1.0, 'Mg': 14.0, 'V': 1.0, 'O': 16.0}\",\"('Hf', 'Mg', 'O', 'V')\",OTHERS,True\nmp-722246,\"{'Si': 2.0, 'H': 20.0, 'O': 8.0, 'F': 12.0}\",\"('F', 'H', 'O', 'Si')\",OTHERS,True\nmp-755290,\"{'V': 6.0, 'O': 8.0, 'F': 4.0}\",\"('F', 'O', 'V')\",OTHERS,True\nmp-1205997,\"{'Na': 1.0, 'U': 1.0, 'F': 6.0}\",\"('F', 'Na', 'U')\",OTHERS,True\nmp-1120819,\"{'Na': 8.0, 'Co': 16.0, 'Bi': 8.0, 'O': 48.0}\",\"('Bi', 'Co', 'Na', 'O')\",OTHERS,True\nmp-755473,\"{'Li': 3.0, 'V': 1.0, 'Cr': 3.0, 'O': 8.0}\",\"('Cr', 'Li', 'O', 'V')\",OTHERS,True\nmvc-15452,\"{'Y': 1.0, 'Mo': 1.0, 'O': 3.0}\",\"('Mo', 'O', 'Y')\",OTHERS,True\nmp-571636,\"{'In': 2.0, 'Cl': 2.0}\",\"('Cl', 'In')\",OTHERS,True\nmp-989628,\"{'Y': 4.0, 'Re': 4.0, 'N': 12.0}\",\"('N', 'Re', 'Y')\",OTHERS,True\nmp-1181813,\"{'Fe': 24.0, 'O': 32.0}\",\"('Fe', 'O')\",OTHERS,True\nmp-1177398,\"{'Li': 4.0, 'Mn': 3.0, 'V': 1.0, 'O': 8.0}\",\"('Li', 'Mn', 'O', 'V')\",OTHERS,True\nmp-1247002,\"{'Zr': 2.0, 'Cr': 2.0, 'Ag': 2.0, 'S': 8.0}\",\"('Ag', 'Cr', 'S', 'Zr')\",OTHERS,True\nmp-1093601,\"{'Li': 1.0, 'Ca': 2.0, 'Zn': 1.0}\",\"('Ca', 'Li', 'Zn')\",OTHERS,True\nmp-1212628,\"{'Ho': 12.0, 'Ga': 60.0, 'Ru': 16.0}\",\"('Ga', 'Ho', 'Ru')\",OTHERS,True\nmp-1189414,\"{'Yb': 4.0, 'Si': 12.0}\",\"('Si', 'Yb')\",OTHERS,True\nmp-1096646,\"{'Li': 2.0, 'Y': 1.0, 'Ga': 1.0}\",\"('Ga', 'Li', 'Y')\",OTHERS,True\nmp-1080612,\"{'Gd': 3.0, 'Al': 3.0, 'Cu': 3.0}\",\"('Al', 'Cu', 'Gd')\",OTHERS,True\nmp-5733,\"{'O': 36.0, 'Si': 12.0, 'Ca': 12.0}\",\"('Ca', 'O', 'Si')\",OTHERS,True\nmp-504650,\"{'La': 6.0, 'Cu': 2.0, 'Si': 2.0, 'S': 14.0}\",\"('Cu', 'La', 'S', 'Si')\",OTHERS,True\nmp-542280,\"{'Ca': 2.0, 'P': 12.0, 'Na': 8.0, 'O': 36.0}\",\"('Ca', 'Na', 'O', 'P')\",OTHERS,True\nmp-1181830,\"{'Ca': 4.0, 'C': 8.0, 'O': 16.0}\",\"('C', 'Ca', 'O')\",OTHERS,True\nmp-758336,\"{'Li': 6.0, 'V': 2.0, 'P': 6.0, 'O': 22.0}\",\"('Li', 'O', 'P', 'V')\",OTHERS,True\nmp-1183482,\"{'Ca': 2.0, 'Cu': 1.0, 'Rh': 1.0}\",\"('Ca', 'Cu', 'Rh')\",OTHERS,True\nmp-755241,\"{'Mn': 8.0, 'O': 8.0, 'F': 8.0}\",\"('F', 'Mn', 'O')\",OTHERS,True\nmp-1209892,\"{'Nd': 1.0, 'Fe': 4.0, 'As': 12.0}\",\"('As', 'Fe', 'Nd')\",OTHERS,True\nmp-1232437,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1184722,\"{'Ho': 6.0, 'Th': 2.0}\",\"('Ho', 'Th')\",OTHERS,True\nmp-1025066,\"{'Er': 1.0, 'In': 1.0, 'Co': 4.0}\",\"('Co', 'Er', 'In')\",OTHERS,True\nmp-754962,\"{'Mn': 2.0, 'Fe': 1.0, 'O': 6.0}\",\"('Fe', 'Mn', 'O')\",OTHERS,True\nmp-1217477,\"{'Tb': 1.0, 'Pr': 1.0, 'Fe': 4.0}\",\"('Fe', 'Pr', 'Tb')\",OTHERS,True\nCu 46 Sc 16 Al 38,\"{'Cu': 46.0, 'Sc': 16.0, 'Al': 38.0}\",\"('Al', 'Cu', 'Sc')\",QC,True\nmp-26387,\"{'Li': 4.0, 'Ni': 4.0, 'P': 4.0, 'O': 16.0}\",\"('Li', 'Ni', 'O', 'P')\",OTHERS,True\nmp-766091,\"{'Li': 4.0, 'La': 5.0, 'Ti': 6.0, 'Nb': 2.0, 'O': 26.0}\",\"('La', 'Li', 'Nb', 'O', 'Ti')\",OTHERS,True\nmp-632329,{'C': 2.0},\"('C',)\",OTHERS,True\nmp-774775,\"{'Na': 4.0, 'Mn': 4.0, 'P': 4.0, 'C': 4.0, 'O': 28.0}\",\"('C', 'Mn', 'Na', 'O', 'P')\",OTHERS,True\nmp-759689,\"{'V': 6.0, 'O': 11.0, 'F': 1.0}\",\"('F', 'O', 'V')\",OTHERS,True\nmp-754103,\"{'Ca': 2.0, 'Ni': 2.0, 'O': 6.0}\",\"('Ca', 'Ni', 'O')\",OTHERS,True\nmp-1177257,\"{'Li': 4.0, 'Ti': 3.0, 'V': 3.0, 'Ni': 2.0, 'O': 16.0}\",\"('Li', 'Ni', 'O', 'Ti', 'V')\",OTHERS,True\nmp-1110640,\"{'K': 2.0, 'In': 1.0, 'As': 1.0, 'Br': 6.0}\",\"('As', 'Br', 'In', 'K')\",OTHERS,True\nmp-510057,\"{'Gd': 12.0, 'Nb': 4.0, 'S': 12.0, 'O': 16.0}\",\"('Gd', 'Nb', 'O', 'S')\",OTHERS,True\nmp-17173,\"{'S': 12.0, 'Rh': 8.0}\",\"('Rh', 'S')\",OTHERS,True\nmvc-10598,\"{'Ba': 4.0, 'Mg': 4.0, 'Co': 4.0, 'F': 28.0}\",\"('Ba', 'Co', 'F', 'Mg')\",OTHERS,True\nmp-974065,\"{'Lu': 1.0, 'Sc': 1.0, 'Pd': 2.0}\",\"('Lu', 'Pd', 'Sc')\",OTHERS,True\nmp-864876,\"{'Tb': 1.0, 'In': 1.0, 'Rh': 2.0}\",\"('In', 'Rh', 'Tb')\",OTHERS,True\nmp-1225861,\"{'Dy': 14.0, 'Au': 51.0}\",\"('Au', 'Dy')\",OTHERS,True\nmp-1094626,\"{'Mg': 10.0, 'Ga': 2.0}\",\"('Ga', 'Mg')\",OTHERS,True\nmp-1205196,\"{'Li': 10.0, 'Ce': 10.0, 'Si': 8.0, 'N': 24.0}\",\"('Ce', 'Li', 'N', 'Si')\",OTHERS,True\nmp-1196242,\"{'Dy': 6.0, 'Al': 4.0, 'Ni': 12.0, 'H': 18.0}\",\"('Al', 'Dy', 'H', 'Ni')\",OTHERS,True\nmp-637600,\"{'Gd': 6.0, 'Co': 1.0, 'Br': 10.0}\",\"('Br', 'Co', 'Gd')\",OTHERS,True\nmp-771717,\"{'Mn': 16.0, 'O': 24.0}\",\"('Mn', 'O')\",OTHERS,True\nmp-1226382,\"{'Cr': 4.0, 'Cd': 2.0, 'Se': 2.0, 'S': 6.0}\",\"('Cd', 'Cr', 'S', 'Se')\",OTHERS,True\nmp-755393,\"{'Sr': 2.0, 'La': 2.0, 'I': 10.0}\",\"('I', 'La', 'Sr')\",OTHERS,True\nmp-1187488,\"{'Ti': 1.0, 'Te': 1.0, 'O': 3.0}\",\"('O', 'Te', 'Ti')\",OTHERS,True\nmp-1099244,\"{'Mg': 3.0, 'W': 1.0, 'O': 4.0}\",\"('Mg', 'O', 'W')\",OTHERS,True\nmp-1246277,\"{'Na': 12.0, 'Re': 2.0, 'N': 8.0}\",\"('N', 'Na', 'Re')\",OTHERS,True\nmp-1245017,\"{'Y': 40.0, 'O': 60.0}\",\"('O', 'Y')\",OTHERS,True\nmp-540806,\"{'Ir': 3.0, 'Ca': 6.0, 'O': 12.0}\",\"('Ca', 'Ir', 'O')\",OTHERS,True\nmp-1214640,\"{'Ba': 6.0, 'Ce': 2.0, 'Ir': 4.0, 'O': 18.0}\",\"('Ba', 'Ce', 'Ir', 'O')\",OTHERS,True\nmp-771957,\"{'Ta': 8.0, 'Pb': 4.0, 'O': 24.0}\",\"('O', 'Pb', 'Ta')\",OTHERS,True\nmp-560268,\"{'K': 4.0, 'Mo': 6.0, 'As': 8.0, 'O': 40.0}\",\"('As', 'K', 'Mo', 'O')\",OTHERS,True\nmp-1094724,\"{'Mg': 3.0, 'Sb': 1.0}\",\"('Mg', 'Sb')\",OTHERS,True\nmvc-5466,\"{'Co': 16.0, 'O': 32.0}\",\"('Co', 'O')\",OTHERS,True\nmp-768579,\"{'Na': 4.0, 'Mn': 2.0, 'B': 2.0, 'P': 2.0, 'O': 14.0}\",\"('B', 'Mn', 'Na', 'O', 'P')\",OTHERS,True\nmp-1205479,\"{'K': 44.0, 'Sb': 22.0, 'F': 110.0}\",\"('F', 'K', 'Sb')\",OTHERS,True\nmp-1214564,\"{'Ba': 8.0, 'Na': 8.0, 'Cr': 8.0, 'F': 48.0}\",\"('Ba', 'Cr', 'F', 'Na')\",OTHERS,True\nmp-777874,\"{'Li': 12.0, 'Mn': 2.0, 'V': 6.0, 'P': 12.0, 'O': 48.0}\",\"('Li', 'Mn', 'O', 'P', 'V')\",OTHERS,True\nmp-19155,\"{'Cr': 4.0, 'Hg': 4.0, 'O': 16.0}\",\"('Cr', 'Hg', 'O')\",OTHERS,True\nmp-1094437,\"{'Mg': 8.0, 'Zn': 4.0}\",\"('Mg', 'Zn')\",OTHERS,True\nmp-676260,\"{'Ag': 9.0, 'Te': 4.0, 'Br': 3.0}\",\"('Ag', 'Br', 'Te')\",OTHERS,True\nmp-1025268,\"{'Pu': 2.0, 'W': 2.0, 'C': 3.0}\",\"('C', 'Pu', 'W')\",OTHERS,True\nmp-1102875,\"{'Er': 8.0, 'Al': 4.0}\",\"('Al', 'Er')\",OTHERS,True\nmp-720995,\"{'Sr': 8.0, 'C': 8.0, 'N': 12.0}\",\"('C', 'N', 'Sr')\",OTHERS,True\nmp-1219640,\"{'Rb': 3.0, 'I': 2.0, 'Br': 1.0}\",\"('Br', 'I', 'Rb')\",OTHERS,True\nmp-1079481,\"{'Al': 2.0, 'S': 2.0, 'Br': 6.0}\",\"('Al', 'Br', 'S')\",OTHERS,True\nmp-559134,\"{'Eu': 6.0, 'As': 4.0, 'S': 16.0}\",\"('As', 'Eu', 'S')\",OTHERS,True\nmp-1213307,\"{'Cs': 2.0, 'Nd': 2.0, 'Mo': 4.0, 'O': 16.0}\",\"('Cs', 'Mo', 'Nd', 'O')\",OTHERS,True\nmp-1205613,\"{'Rb': 3.0, 'Sm': 1.0, 'F': 6.0}\",\"('F', 'Rb', 'Sm')\",OTHERS,True\nmp-11666,\"{'Zn': 6.0, 'Ge': 3.0, 'Pr': 2.0}\",\"('Ge', 'Pr', 'Zn')\",OTHERS,True\nmp-1199580,\"{'Zn': 2.0, 'Cu': 2.0, 'C': 8.0, 'O': 24.0}\",\"('C', 'Cu', 'O', 'Zn')\",OTHERS,True\nmp-770731,\"{'Li': 4.0, 'Zr': 8.0, 'O': 16.0}\",\"('Li', 'O', 'Zr')\",OTHERS,True\nmp-1106337,\"{'Mn': 4.0, 'Pb': 4.0, 'O': 12.0}\",\"('Mn', 'O', 'Pb')\",OTHERS,True\nmp-1245403,\"{'Pd': 3.0, 'C': 24.0, 'N': 18.0}\",\"('C', 'N', 'Pd')\",OTHERS,True\nmp-1224874,\"{'Gd': 2.0, 'Si': 3.0}\",\"('Gd', 'Si')\",OTHERS,True\nmp-636934,\"{'Bi': 12.0, 'O': 18.0}\",\"('Bi', 'O')\",OTHERS,True\nmp-14643,\"{'Te': 12.0, 'Er': 8.0}\",\"('Er', 'Te')\",OTHERS,True\nmp-562050,\"{'Cs': 6.0, 'Na': 3.0, 'Ti': 3.0, 'F': 18.0}\",\"('Cs', 'F', 'Na', 'Ti')\",OTHERS,True\nmp-1654,\"{'Ni': 4.0, 'Ce': 2.0}\",\"('Ce', 'Ni')\",OTHERS,True\nmp-754085,\"{'Li': 4.0, 'Bi': 2.0, 'S': 4.0}\",\"('Bi', 'Li', 'S')\",OTHERS,True\nmp-1213293,\"{'Cs': 2.0, 'Tb': 2.0, 'Mo': 4.0, 'O': 16.0}\",\"('Cs', 'Mo', 'O', 'Tb')\",OTHERS,True\nmp-1202486,\"{'La': 28.0, 'Ru': 12.0}\",\"('La', 'Ru')\",OTHERS,True\nmp-1246113,\"{'Cr': 4.0, 'Fe': 6.0, 'N': 8.0}\",\"('Cr', 'Fe', 'N')\",OTHERS,True\nmp-1015582,\"{'Cr': 4.0, 'N': 8.0}\",\"('Cr', 'N')\",OTHERS,True\nmp-1245702,\"{'Ca': 10.0, 'Ir': 4.0, 'N': 12.0}\",\"('Ca', 'Ir', 'N')\",OTHERS,True\nmp-1217895,\"{'Ta': 1.0, 'Mo': 1.0}\",\"('Mo', 'Ta')\",OTHERS,True\nmp-3205,\"{'C': 2.0, 'O': 6.0, 'Ca': 2.0}\",\"('C', 'Ca', 'O')\",OTHERS,True\nmp-974341,\"{'Ru': 2.0, 'Rh': 6.0}\",\"('Rh', 'Ru')\",OTHERS,True\nmp-779357,\"{'V': 6.0, 'O': 2.0, 'F': 22.0}\",\"('F', 'O', 'V')\",OTHERS,True\nmvc-2670,\"{'Ta': 4.0, 'Zn': 4.0, 'Ni': 2.0, 'O': 16.0}\",\"('Ni', 'O', 'Ta', 'Zn')\",OTHERS,True\nmp-569952,\"{'K': 8.0, 'Cu': 4.0, 'Cl': 12.0}\",\"('Cl', 'Cu', 'K')\",OTHERS,True\nmp-1104205,\"{'Ba': 1.0, 'Er': 1.0, 'Fe': 4.0, 'O': 7.0}\",\"('Ba', 'Er', 'Fe', 'O')\",OTHERS,True\nmp-1184577,\"{'Ho': 2.0, 'Os': 1.0, 'Rh': 1.0}\",\"('Ho', 'Os', 'Rh')\",OTHERS,True\nmp-21410,\"{'Fe': 4.0, 'S': 4.0}\",\"('Fe', 'S')\",OTHERS,True\nmp-25813,\"{'Li': 6.0, 'O': 32.0, 'P': 8.0, 'Fe': 6.0}\",\"('Fe', 'Li', 'O', 'P')\",OTHERS,True\nmp-1135,\"{'Nb': 1.0, 'Pd': 3.0}\",\"('Nb', 'Pd')\",OTHERS,True\nmp-780462,\"{'Na': 12.0, 'Mn': 4.0, 'P': 2.0, 'C': 8.0, 'O': 32.0}\",\"('C', 'Mn', 'Na', 'O', 'P')\",OTHERS,True\nmp-1175970,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1203397,\"{'Zr': 4.0, 'Cd': 4.0, 'H': 40.0, 'C': 24.0, 'O': 68.0}\",\"('C', 'Cd', 'H', 'O', 'Zr')\",OTHERS,True\nmp-557661,\"{'Sb': 16.0, 'Pd': 4.0, 'C': 16.0, 'O': 16.0, 'F': 88.0}\",\"('C', 'F', 'O', 'Pd', 'Sb')\",OTHERS,True\nmp-1101545,\"{'Li': 4.0, 'Co': 4.0, 'P': 16.0, 'O': 48.0}\",\"('Co', 'Li', 'O', 'P')\",OTHERS,True\nmp-555392,\"{'K': 12.0, 'Na': 2.0, 'Au': 4.0, 'I': 2.0, 'O': 16.0}\",\"('Au', 'I', 'K', 'Na', 'O')\",OTHERS,True\nmp-1191428,\"{'Sc': 2.0, 'Co': 12.0, 'P': 7.0}\",\"('Co', 'P', 'Sc')\",OTHERS,True\nmp-1173448,\"{'Re': 12.0, 'Se': 8.0, 'Cl': 20.0}\",\"('Cl', 'Re', 'Se')\",OTHERS,True\nmp-753441,\"{'Li': 4.0, 'Mn': 4.0, 'O': 2.0, 'F': 12.0}\",\"('F', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-15318,\"{'F': 6.0, 'Na': 1.0, 'Rb': 2.0, 'Ho': 1.0}\",\"('F', 'Ho', 'Na', 'Rb')\",OTHERS,True\nmp-567446,\"{'La': 46.0, 'Cd': 8.0, 'Pt': 14.0}\",\"('Cd', 'La', 'Pt')\",OTHERS,True\nmp-1175046,\"{'Li': 7.0, 'Mn': 2.0, 'Co': 3.0, 'O': 12.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-985283,\"{'Ag': 3.0, 'Ge': 1.0}\",\"('Ag', 'Ge')\",OTHERS,True\nmp-984726,\"{'Dy': 1.0, 'Al': 8.0, 'Cu': 4.0}\",\"('Al', 'Cu', 'Dy')\",OTHERS,True\nmp-1196275,\"{'Ba': 8.0, 'Fe': 8.0, 'O': 8.0, 'F': 40.0}\",\"('Ba', 'F', 'Fe', 'O')\",OTHERS,True\nmp-1104171,\"{'Yb': 2.0, 'Ga': 4.0, 'Se': 8.0}\",\"('Ga', 'Se', 'Yb')\",OTHERS,True\nmp-15108,\"{'O': 40.0, 'As': 8.0, 'Rb': 8.0, 'Sn': 8.0}\",\"('As', 'O', 'Rb', 'Sn')\",OTHERS,True\nmp-17491,\"{'Li': 4.0, 'Ge': 12.0, 'Ba': 8.0}\",\"('Ba', 'Ge', 'Li')\",OTHERS,True\nmp-1205361,\"{'Te': 12.0, 'Rh': 2.0, 'I': 6.0}\",\"('I', 'Rh', 'Te')\",OTHERS,True\nmp-1178273,\"{'Fe': 10.0, 'O': 4.0, 'F': 16.0}\",\"('F', 'Fe', 'O')\",OTHERS,True\nmvc-12970,\"{'Mg': 2.0, 'Fe': 4.0, 'S': 8.0}\",\"('Fe', 'Mg', 'S')\",OTHERS,True\nmp-865156,\"{'Dy': 2.0, 'Zn': 1.0, 'Ga': 1.0}\",\"('Dy', 'Ga', 'Zn')\",OTHERS,True\nmp-1038168,\"{'Mg': 30.0, 'Mn': 1.0, 'V': 1.0, 'O': 32.0}\",\"('Mg', 'Mn', 'O', 'V')\",OTHERS,True\nmp-20704,\"{'U': 1.0, 'Mn': 4.0, 'Al': 8.0}\",\"('Al', 'Mn', 'U')\",OTHERS,True\nmp-980205,\"{'Y': 4.0, 'B': 16.0, 'Os': 4.0}\",\"('B', 'Os', 'Y')\",OTHERS,True\nmp-4669,\"{'O': 64.0, 'Mo': 16.0, 'Th': 8.0}\",\"('Mo', 'O', 'Th')\",OTHERS,True\nmp-33737,\"{'Mn': 3.0, 'Cu': 3.0, 'O': 8.0}\",\"('Cu', 'Mn', 'O')\",OTHERS,True\nmvc-14333,\"{'V': 2.0, 'Zn': 2.0, 'F': 10.0}\",\"('F', 'V', 'Zn')\",OTHERS,True\nmp-1178743,\"{'Y': 4.0, 'Al': 1.0, 'Si': 2.0, 'O': 10.0, 'F': 5.0}\",\"('Al', 'F', 'O', 'Si', 'Y')\",OTHERS,True\nmp-674804,\"{'Sm': 4.0, 'Pb': 2.0, 'Se': 8.0}\",\"('Pb', 'Se', 'Sm')\",OTHERS,True\nmp-8710,\"{'O': 8.0, 'Ca': 2.0, 'Pt': 3.0}\",\"('Ca', 'O', 'Pt')\",OTHERS,True\nmp-1193652,\"{'Re': 4.0, 'Te': 8.0, 'Cl': 16.0}\",\"('Cl', 'Re', 'Te')\",OTHERS,True\nmp-7138,\"{'Sb': 4.0, 'Yb': 2.0}\",\"('Sb', 'Yb')\",OTHERS,True\nmp-1225677,\"{'Cu': 1.0, 'Au': 1.0}\",\"('Au', 'Cu')\",OTHERS,True\nmvc-867,\"{'Mg': 2.0, 'Co': 6.0, 'P': 8.0, 'O': 28.0}\",\"('Co', 'Mg', 'O', 'P')\",OTHERS,True\nmp-1078956,\"{'Pt': 1.0, 'N': 4.0, 'Cl': 2.0, 'O': 1.0}\",\"('Cl', 'N', 'O', 'Pt')\",OTHERS,True\nmp-1232372,\"{'Sc': 2.0, 'B': 2.0, 'C': 2.0}\",\"('B', 'C', 'Sc')\",OTHERS,True\nmp-18772,\"{'N': 4.0, 'O': 4.0, 'Na': 8.0, 'Mo': 2.0}\",\"('Mo', 'N', 'Na', 'O')\",OTHERS,True\nmp-696878,\"{'K': 3.0, 'Ca': 1.0, 'P': 2.0, 'H': 1.0, 'O': 8.0}\",\"('Ca', 'H', 'K', 'O', 'P')\",OTHERS,True\nmp-3884,\"{'O': 14.0, 'Sn': 4.0, 'Ho': 4.0}\",\"('Ho', 'O', 'Sn')\",OTHERS,True\nmp-764043,\"{'Li': 6.0, 'Mn': 6.0, 'Ni': 2.0, 'O': 16.0}\",\"('Li', 'Mn', 'Ni', 'O')\",OTHERS,True\nmp-1038154,\"{'Mg': 30.0, 'Mn': 1.0, 'Sn': 1.0, 'O': 32.0}\",\"('Mg', 'Mn', 'O', 'Sn')\",OTHERS,True\nmp-1228077,\"{'Ba': 4.0, 'Na': 1.0, 'Ir': 3.0, 'O': 12.0}\",\"('Ba', 'Ir', 'Na', 'O')\",OTHERS,True\nmp-20038,\"{'P': 2.0, 'Co': 2.0, 'Eu': 1.0}\",\"('Co', 'Eu', 'P')\",OTHERS,True\nmp-3890,\"{'Cr': 2.0, 'Fe': 15.0, 'Sm': 2.0}\",\"('Cr', 'Fe', 'Sm')\",OTHERS,True\nmp-1247009,\"{'V': 8.0, 'Fe': 3.0, 'N': 8.0}\",\"('Fe', 'N', 'V')\",OTHERS,True\nmp-542081,\"{'La': 4.0, 'P': 8.0, 'S': 28.0, 'K': 8.0}\",\"('K', 'La', 'P', 'S')\",OTHERS,True\nmp-1039999,\"{'Na': 1.0, 'La': 1.0, 'Mg': 30.0, 'O': 32.0}\",\"('La', 'Mg', 'Na', 'O')\",OTHERS,True\nmp-1185684,\"{'Mg': 16.0, 'Al': 12.0, 'O': 1.0}\",\"('Al', 'Mg', 'O')\",OTHERS,True\nAl 70 Ni 15 Fe 15,\"{'Al': 70.0, 'Ni': 15.0, 'Fe': 15.0}\",\"('Al', 'Fe', 'Ni')\",QC,True\nmp-29088,\"{'N': 4.0, 'Ge': 4.0, 'Sr': 6.0}\",\"('Ge', 'N', 'Sr')\",OTHERS,True\nmp-1201569,\"{'Sc': 10.0, 'V': 5.0, 'O': 25.0}\",\"('O', 'Sc', 'V')\",OTHERS,True\nmp-778169,\"{'Li': 2.0, 'Cu': 4.0, 'S': 6.0, 'O': 24.0}\",\"('Cu', 'Li', 'O', 'S')\",OTHERS,True\nmp-1076592,\"{'Ba': 1.0, 'Sr': 7.0, 'Mn': 1.0, 'Fe': 7.0, 'O': 24.0}\",\"('Ba', 'Fe', 'Mn', 'O', 'Sr')\",OTHERS,True\nmp-755993,\"{'Mn': 3.0, 'Nb': 2.0, 'Fe': 3.0, 'O': 16.0}\",\"('Fe', 'Mn', 'Nb', 'O')\",OTHERS,True\nmp-754889,\"{'Li': 2.0, 'Mn': 4.0, 'B': 4.0, 'O': 12.0}\",\"('B', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-1210156,\"{'Re': 2.0, 'Pd': 1.0, 'N': 4.0, 'O': 8.0}\",\"('N', 'O', 'Pd', 'Re')\",OTHERS,True\nmp-1182808,\"{'Cr': 4.0, 'P': 8.0, 'H': 64.0, 'N': 12.0, 'O': 40.0}\",\"('Cr', 'H', 'N', 'O', 'P')\",OTHERS,True\nmp-602355,\"{'Cr': 6.0, 'H': 2.0, 'O': 16.0}\",\"('Cr', 'H', 'O')\",OTHERS,True\nmp-979416,\"{'Ta': 2.0, 'Be': 2.0, 'O': 5.0}\",\"('Be', 'O', 'Ta')\",OTHERS,True\nmp-1079250,\"{'Sm': 2.0, 'Ni': 2.0, 'Sb': 4.0}\",\"('Ni', 'Sb', 'Sm')\",OTHERS,True\nmp-22875,\"{'Br': 1.0, 'Tl': 1.0}\",\"('Br', 'Tl')\",OTHERS,True\nmp-1184421,\"{'Gd': 6.0, 'Cd': 2.0}\",\"('Cd', 'Gd')\",OTHERS,True\nmp-1100745,\"{'Li': 9.0, 'Mn': 2.0, 'Co': 5.0, 'O': 16.0}\",\"('Co', 'Li', 'Mn', 'O')\",OTHERS,True\nmp-24111,\"{'H': 24.0, 'O': 28.0, 'S': 4.0, 'Co': 2.0, 'Rb': 4.0}\",\"('Co', 'H', 'O', 'Rb', 'S')\",OTHERS,True\nmp-1180144,\"{'Mn': 1.0, 'Mo': 3.0}\",\"('Mn', 'Mo')\",OTHERS,True\nmp-556745,\"{'Tl': 4.0, 'C': 4.0, 'O': 8.0}\",\"('C', 'O', 'Tl')\",OTHERS,True\nmp-1215244,\"{'Zr': 1.0, 'Nb': 2.0, 'Pd': 9.0}\",\"('Nb', 'Pd', 'Zr')\",OTHERS,True\nmp-41191,\"{'F': 1.0, 'Na': 3.0, 'O': 10.0, 'P': 2.0, 'Ti': 2.0}\",\"('F', 'Na', 'O', 'P', 'Ti')\",OTHERS,True\nmp-1207921,\"{'U': 2.0, 'Fe': 8.0, 'B': 2.0}\",\"('B', 'Fe', 'U')\",OTHERS,True\nmp-1184948,\"{'K': 1.0, 'Mn': 1.0, 'O': 3.0}\",\"('K', 'Mn', 'O')\",OTHERS,True\nmvc-8328,\"{'Zn': 1.0, 'Fe': 4.0, 'Cu': 3.0, 'O': 12.0}\",\"('Cu', 'Fe', 'O', 'Zn')\",OTHERS,True\nmp-985587,\"{'Al': 6.0, 'O': 9.0}\",\"('Al', 'O')\",OTHERS,True\nmp-1223322,\"{'K': 1.0, 'Rb': 1.0, 'Mn': 1.0, 'F': 6.0}\",\"('F', 'K', 'Mn', 'Rb')\",OTHERS,True\nmp-1216066,\"{'Y': 2.0, 'Co': 1.0, 'Ru': 3.0}\",\"('Co', 'Ru', 'Y')\",OTHERS,True\nmp-1027645,\"{'Mo': 3.0, 'W': 1.0, 'S': 8.0}\",\"('Mo', 'S', 'W')\",OTHERS,True\nmp-1178643,\"{'Zn': 1.0, 'Cr': 1.0, 'Cu': 3.0, 'Se': 4.0}\",\"('Cr', 'Cu', 'Se', 'Zn')\",OTHERS,True\nmp-783904,\"{'Ni': 2.0, 'H': 40.0, 'S': 4.0, 'N': 4.0, 'O': 28.0}\",\"('H', 'N', 'Ni', 'O', 'S')\",OTHERS,True\nmp-683897,\"{'Pr': 6.0, 'Sn': 26.0, 'Rh': 8.0}\",\"('Pr', 'Rh', 'Sn')\",OTHERS,True\nmp-778793,\"{'Li': 4.0, 'Fe': 3.0, 'Si': 3.0, 'O': 12.0}\",\"('Fe', 'Li', 'O', 'Si')\",OTHERS,True\nmp-766308,\"{'Ba': 4.0, 'Ca': 4.0, 'I': 16.0}\",\"('Ba', 'Ca', 'I')\",OTHERS,True\nmp-686280,\"{'Nd': 12.0, 'Al': 3.0, 'Si': 15.0, 'N': 21.0, 'O': 21.0}\",\"('Al', 'N', 'Nd', 'O', 'Si')\",OTHERS,True\nmp-1216446,\"{'V': 9.0, 'Te': 8.0}\",\"('Te', 'V')\",OTHERS,True\nmp-626872,\"{'V': 12.0, 'H': 8.0, 'O': 32.0}\",\"('H', 'O', 'V')\",OTHERS,True\nmp-5960,\"{'O': 12.0, 'Ca': 6.0, 'U': 2.0}\",\"('Ca', 'O', 'U')\",OTHERS,True\nmp-1198997,\"{'C': 16.0, 'S': 8.0, 'N': 8.0, 'O': 8.0, 'F': 8.0}\",\"('C', 'F', 'N', 'O', 'S')\",OTHERS,True\nmp-1219422,\"{'Sc': 28.0, 'As': 12.0}\",\"('As', 'Sc')\",OTHERS,True\nmp-757593,\"{'Li': 4.0, 'Co': 5.0, 'Sb': 1.0, 'O': 12.0}\",\"('Co', 'Li', 'O', 'Sb')\",OTHERS,True\nmp-1245874,\"{'Ca': 6.0, 'B': 6.0, 'N': 10.0}\",\"('B', 'Ca', 'N')\",OTHERS,True\n" }, { "path": "samples/process_dataset_usgs.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4ef1599e\",\n \"metadata\": {},\n \"source\": [\n \"## Obtain the compact csv file of USGS dataset\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"e316660c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# python packages used in the sample codes\\n\",\n \"\\n\",\n \"import pandas as pd\\n\",\n \"import os\\n\",\n \"import numpy as np\\n\",\n \"import csv\\n\",\n \"import glob\\n\",\n \"import codecs\\n\",\n \"import cirpy\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5f32ca10\",\n \"metadata\": {},\n \"source\": [\n \"#### Download spectrum data and CAS registry number and transform to SMILES\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"026675e0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"input_file = sorted(glob.glob(\\\"usgs_splib07/ASCIIdata/ASCIIdata_splib07a/ChapterO_OrganicCompounds/*AREF.txt\\\"))\\n\",\n \"smiles_list = []\\n\",\n \"spectrum_list = []\\n\",\n \"smiles_error_list = []\\n\",\n \"\\n\",\n \"for i, file_name in enumerate(input_file):\\n\",\n \" file_html = file_name.replace('txt', 'html')\\\\\\n\",\n \" .replace('ASCIIdata_splib07a/ChapterO_OrganicCompounds', '')\\\\\\n\",\n \" .replace('splib07a_', '')\\\\\\n\",\n \" .replace('ASCIIdata/', 'HTMLmetadata')\\n\",\n \" with codecs.open(file_html, 'r', 'utf-8', 'ignore') as fileobj:\\n\",\n \" lines = fileobj.readlines()\\n\",\n \" line_cas = [line for line in lines if 'CAS' in line]\\n\",\n \" cas = ''.join(line_cas) \\n\",\n \" cas = cas[7:]\\n\",\n \" if line_cas!=[]:\\n\",\n \" to_smiles = cirpy.resolve(cas, \\\"smiles\\\")\\n\",\n \" if to_smiles == None: \\n\",\n \" smiles_error_list.append(file_html)\\n\",\n \" elif to_smiles != None:\\n\",\n \" smiles_list = np.append(smiles_list,to_smiles)\\n\",\n \" spectrum_data = pd.read_table(file_name)\\n\",\n \" spectrum_data = spectrum_data.astype('float32')\\n\",\n \" spectrum_list = np.append(spectrum_list,spectrum_data)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a2fac7a4\",\n \"metadata\": {},\n \"source\": [\n \"#### Remove duplicate data and converting reflectance to absorbance\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"44fd683c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"spectrum_list = spectrum_list.reshape(len(smiles_list),-1)\\n\",\n \"spectrum_list = np.array(spectrum_list, dtype='float32')\\n\",\n \"\\n\",\n \"duplicate_list = [1, 2, 4, 6, 8, 10, 12, 14, 16, 18, 21, 26, 28, 31, 32,\\\\\\n\",\n \" 33, 36, 37, 40, 42, 43, 48, 53, 55, 57, 59, 61, 63, 66,\\\\\\n\",\n \" 68, 70, 73, 75, 77, 79, 80, 82, 83, 84, 86, 87, 93, 96,\\\\\\n\",\n \" 97, 98, 104, 105, 106, 110, 111, 113, 117]\\n\",\n \"smiles_list_nodupli = np.delete(smiles_list,[duplicate_list])\\n\",\n \"spectrum_list_nodupli = np.delete(spectrum_list,[duplicate_list],0)\\n\",\n \"spectrum_list_nodupli = 2 - np.log10(spectrum_list_nodupli)\\n\",\n \"spectrum_list_nodupli = np.array(spectrum_list_nodupli, dtype='float32')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"60e3e847\",\n \"metadata\": {},\n \"source\": [\n \"#### Make Dataset_USGS.csv\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"1b0c37c4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df = pd.DataFrame(spectrum_list_nodupli)\\n\",\n \"df.insert(0, 'SMILES', smiles_list_nodupli)\\n\",\n \"df = df.sample(frac=1,random_state=1)\\n\",\n \"df.to_csv('Dataset_USGS.csv')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"edbbbbb4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "samples/random_nn_model_and_training.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"-\"\n }\n },\n \"source\": [\n \"# Random NN modes\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This tutorial shows how to build neural network models.\\n\",\n \"We will learn how to calculate compositional descriptors using `xenonpy.descriptor.Compositions` calculator and train our model using `xenonpy.model.training` modules.\\n\",\n \"\\n\",\n \"In this tutorial, we will use some inorganic sample data from materials project. \\n\",\n \"If you don't have it, please see https://github.com/yoshida-lab/XenonPy/blob/master/samples/build_sample_data.ipynb.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### useful functions\\n\",\n \"\\n\",\n \"Running the following cell will load some commonly used packages, such as `numpy`, `pandas`, and so on. It will also import some in-house functions used in this tutorial. See `samples/tools.ipynb` to check what will be imported.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%run tools.ipynb\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Sequential linear model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We will use `xenonpy.model.SequentialLinear` to build a sequential linear model. The basic layer in `SequentialLinear` model is `xenonpy.model.LinearLayer`\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\\u001b[0;31mInit signature:\\u001b[0m\\n\",\n \"\\u001b[0mSequentialLinear\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0min_features\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mint\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mout_features\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mint\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mbias\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mbool\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mh_neurons\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mTuple\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mfloat\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m...\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mTuple\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mint\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m...\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mh_bias\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mbool\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mTuple\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mbool\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m...\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mh_dropouts\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mfloat\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mTuple\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mfloat\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m...\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;36m0.1\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mh_normalizers\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mfloat\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mNoneType\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mTuple\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mfloat\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mNoneType\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m...\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;36m0.1\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mh_activation_funcs\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mCallable\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mNoneType\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mTuple\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mCallable\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mNoneType\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m...\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mReLU\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mDocstring:\\u001b[0m \\n\",\n \"Sequential model with linear layers and configurable other hype-parameters.\\n\",\n \"e.g. ``dropout``, ``hidden layers``\\n\",\n \"\\u001b[0;31mInit docstring:\\u001b[0m\\n\",\n \"Parameters\\n\",\n \"----------\\n\",\n \"in_features\\n\",\n \" Size of input.\\n\",\n \"out_features\\n\",\n \" Size of output.\\n\",\n \"bias\\n\",\n \" Enable ``bias`` in input layer.\\n\",\n \"h_neurons\\n\",\n \" Number of neurons in hidden layers.\\n\",\n \" Can be a tuple of floats. In that case,\\n\",\n \" all these numbers will be used to calculate the neuron numbers.\\n\",\n \" e.g. (0.5, 0.4, ...) will be expanded as (in_features * 0.5, in_features * 0.4, ...)\\n\",\n \"h_bias\\n\",\n \" ``bias`` in hidden layers.\\n\",\n \"h_dropouts\\n\",\n \" Probabilities of dropout in hidden layers.\\n\",\n \"h_normalizers\\n\",\n \" Momentum of batched normalizers in hidden layers.\\n\",\n \"h_activation_funcs\\n\",\n \" Activation functions in hidden layers.\\n\",\n \"\\u001b[0;31mFile:\\u001b[0m ~/projects/XenonPy/xenonpy/model/sequential.py\\n\",\n \"\\u001b[0;31mType:\\u001b[0m type\\n\",\n \"\\u001b[0;31mSubclasses:\\u001b[0m \\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from xenonpy.model import SequentialLinear, LinearLayer\\n\",\n \"SequentialLinear?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\\u001b[0;31mInit signature:\\u001b[0m\\n\",\n \"\\u001b[0mLinearLayer\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0min_features\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mint\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mout_features\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mint\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mbias\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mbool\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mdropout\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mfloat\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;36m0.0\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mactivation_func\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mCallable\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mReLU\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mnormalizer\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mfloat\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mNoneType\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;36m0.1\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mDocstring:\\u001b[0m \\n\",\n \"Base NN layer. This is a wrap around PyTorch.\\n\",\n \"See here for details: http://pytorch.org/docs/master/nn.html#\\n\",\n \"\\u001b[0;31mInit docstring:\\u001b[0m\\n\",\n \"Parameters\\n\",\n \"----------\\n\",\n \"in_features:\\n\",\n \" Size of each input sample.\\n\",\n \"out_features:\\n\",\n \" Size of each output sample\\n\",\n \"dropout: float\\n\",\n \" Probability of an element to be zeroed. Default: 0.5\\n\",\n \"activation_func: func\\n\",\n \" Activation function.\\n\",\n \"normalizer: func\\n\",\n \" Normalization layers\\n\",\n \"\\u001b[0;31mFile:\\u001b[0m ~/projects/XenonPy/xenonpy/model/sequential.py\\n\",\n \"\\u001b[0;31mType:\\u001b[0m type\\n\",\n \"\\u001b[0;31mSubclasses:\\u001b[0m \\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"LinearLayer?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Following the official suggestion from PyTorch, `SequentialLinear` is a python class that inherit the `torch.nn.Module` class. Users can specify the hyperparameters to get a fully customized model object. For example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"SequentialLinear(\\n\",\n \" (layer_0): LinearLayer(\\n\",\n \" (linear): Linear(in_features=290, out_features=232, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(232, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_1): LinearLayer(\\n\",\n \" (linear): Linear(in_features=232, out_features=203, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(203, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_2): LinearLayer(\\n\",\n \" (linear): Linear(in_features=203, out_features=174, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(174, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (output): Linear(in_features=174, out_features=1, bias=True)\\n\",\n \")\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model = SequentialLinear(290, 1, h_neurons=(0.8, 0.7, 0.6))\\n\",\n \"model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### fully randomized model generation\\n\",\n \"\\n\",\n \"Process of random model generation:\\n\",\n \"\\n\",\n \"1. using a random parameter generator to generate a set of parameter.\\n\",\n \"2. using the generated parameter set to setup a model object.\\n\",\n \"3. loop step 1 and 2 as many times as needed.\\n\",\n \"\\n\",\n \"We provided a general parameter generator `xenonpy.utils.ParameterGenerator` to do all the 3 steps for any callable object.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\\u001b[0;31mInit signature:\\u001b[0m\\n\",\n \"\\u001b[0mParameterGenerator\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mseed\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mint\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mNoneType\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mAny\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mSequence\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mCallable\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mDict\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mDocstring:\\u001b[0m Generator for parameter set generating.\\n\",\n \"\\u001b[0;31mInit docstring:\\u001b[0m\\n\",\n \"Parameters\\n\",\n \"----------\\n\",\n \"seed\\n\",\n \" Numpy random seed.\\n\",\n \"kwargs\\n\",\n \" Parameter candidate.\\n\",\n \"\\u001b[0;31mFile:\\u001b[0m ~/projects/XenonPy/xenonpy/utils/parameter_gen.py\\n\",\n \"\\u001b[0;31mType:\\u001b[0m type\\n\",\n \"\\u001b[0;31mSubclasses:\\u001b[0m \\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from xenonpy.utils import ParameterGenerator\\n\",\n \"ParameterGenerator?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Calling an instance of `ParameterGenerator` will return a generator.\\n\",\n \"This generator can randomly select parameters from parameter candidates and yield them as a dict. This is what we want in step 1.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from math import ceil\\n\",\n \"from random import uniform\\n\",\n \"\\n\",\n \"generator = ParameterGenerator(\\n\",\n \" in_features=290,\\n\",\n \" out_features=1,\\n\",\n \" h_neurons=dict(\\n\",\n \" data=[ceil(uniform(0.1, 1.2) * 290) for _ in range(100)], \\n\",\n \" repeat=(2, 3)\\n\",\n \" )\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Because `generator` is a generator, `for ... in ...` statement can be applied. \\n\",\n \"For example, we can use `for parameters in generator(num_of_models)` to loop all these generated parameter sets.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{'in_features': 290, 'out_features': 1, 'h_neurons': (70, 109)}\\n\",\n \"{'in_features': 290, 'out_features': 1, 'h_neurons': (135, 45)}\\n\",\n \"{'in_features': 290, 'out_features': 1, 'h_neurons': (216, 261, 79)}\\n\",\n \"{'in_features': 290, 'out_features': 1, 'h_neurons': (111, 88, 49)}\\n\",\n \"{'in_features': 290, 'out_features': 1, 'h_neurons': (216, 79, 47)}\\n\",\n \"{'in_features': 290, 'out_features': 1, 'h_neurons': (161, 45)}\\n\",\n \"{'in_features': 290, 'out_features': 1, 'h_neurons': (193, 161, 78)}\\n\",\n \"{'in_features': 290, 'out_features': 1, 'h_neurons': (90, 36, 234)}\\n\",\n \"{'in_features': 290, 'out_features': 1, 'h_neurons': (230, 83, 295)}\\n\",\n \"{'in_features': 290, 'out_features': 1, 'h_neurons': (171, 247)}\\n\"\n ]\n }\n ],\n \"source\": [\n \"for parameters in generator(num=10):\\n\",\n \" print(parameters)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For step 2, we can give a model class to the `factory` parameter as a factory function.\\n\",\n \"If `factory` parameter is given, generator will feed generated parameters to the factory function automatically and yield the result as a second return in each loop. For example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"parameters: {'in_features': 290, 'out_features': 1, 'h_neurons': (182, 335, 204)}\\n\",\n \"SequentialLinear(\\n\",\n \" (layer_0): LinearLayer(\\n\",\n \" (linear): Linear(in_features=290, out_features=182, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(182, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_1): LinearLayer(\\n\",\n \" (linear): Linear(in_features=182, out_features=335, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(335, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_2): LinearLayer(\\n\",\n \" (linear): Linear(in_features=335, out_features=204, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(204, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (output): Linear(in_features=204, out_features=1, bias=True)\\n\",\n \") \\n\",\n \"\\n\",\n \"parameters: {'in_features': 290, 'out_features': 1, 'h_neurons': (108, 255)}\\n\",\n \"SequentialLinear(\\n\",\n \" (layer_0): LinearLayer(\\n\",\n \" (linear): Linear(in_features=290, out_features=108, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(108, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_1): LinearLayer(\\n\",\n \" (linear): Linear(in_features=108, out_features=255, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(255, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (output): Linear(in_features=255, out_features=1, bias=True)\\n\",\n \") \\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"for parameters, model in generator(2, factory=SequentialLinear):\\n\",\n \" print('parameters: ', parameters)\\n\",\n \" print(model, '\\\\n')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### scheduled random model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We also enable users to generate parameters in a functional way by given a function as candidate parameters. In this case, the function accept an int number as vector length and a vector of generated parameters. For example, generate a `n` length vector with float numbers sorted in ascending.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"generator = ParameterGenerator(\\n\",\n \" in_features=290,\\n\",\n \" out_features=1,\\n\",\n \" h_neurons=dict(\\n\",\n \" data=lambda n: sorted(np.random.uniform(0.2, 0.8, size=n), reverse=True), \\n\",\n \" repeat=(2, 3)\\n\",\n \" )\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"parameters: {'in_features': 290, 'out_features': 1, 'h_neurons': (0.7954011465554711, 0.6993610045591458)}\\n\",\n \"SequentialLinear(\\n\",\n \" (layer_0): LinearLayer(\\n\",\n \" (linear): Linear(in_features=290, out_features=231, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(231, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_1): LinearLayer(\\n\",\n \" (linear): Linear(in_features=231, out_features=203, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(203, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (output): Linear(in_features=203, out_features=1, bias=True)\\n\",\n \") \\n\",\n \"\\n\",\n \"parameters: {'in_features': 290, 'out_features': 1, 'h_neurons': (0.798810246990777, 0.38584535382368323, 0.29569841893337045)}\\n\",\n \"SequentialLinear(\\n\",\n \" (layer_0): LinearLayer(\\n\",\n \" (linear): Linear(in_features=290, out_features=232, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(232, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_1): LinearLayer(\\n\",\n \" (linear): Linear(in_features=232, out_features=112, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(112, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_2): LinearLayer(\\n\",\n \" (linear): Linear(in_features=112, out_features=86, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(86, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (output): Linear(in_features=86, out_features=1, bias=True)\\n\",\n \") \\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"for parameters, model in generator(2, factory=SequentialLinear):\\n\",\n \" print('parameters: ', parameters)\\n\",\n \" print(model, '\\\\n')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Model training\\n\",\n \"\\n\",\n \"We provide a general and extendable model training system for neural network models.\\n\",\n \"By customizing the extensions, users can fully control their model training process, and save the training results following the *XenonPy.MDL* format automatically.\\n\",\n \"\\n\",\n \"All these modules and extensions are under the `xenonpy.model.training`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import torch\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from pymatgen import Structure\\n\",\n \"\\n\",\n \"from xenonpy.model.training import Trainer, SGD, MSELoss, Adam, ReduceLROnPlateau, ExponentialLR, ClipValue\\n\",\n \"\\n\",\n \"from xenonpy.datatools import preset, Splitter\\n\",\n \"from xenonpy.descriptor import Compositions\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### prepare training/testing data\\n\",\n \"\\n\",\n \"If you followed the tutorial in https://github.com/yoshida-lab/XenonPy/blob/master/samples/build_sample_data.ipynb,\\n\",\n \"the sample data should be save at `~/.xenonpy/userdata` with name `mp_samples.pd.xz`.\\n\",\n \"You can use `pd.read_pickle` to load this file but we suggest that you use our `xenonpy.datatools.preset` module.\\n\",\n \"\\n\",\n \"We chose `volume` as the example to demostrate how to use our training system. We only use the data which `volume` are smaller than 2,500 to avoid the disparate data.\\n\",\n \"we select data in `pandas.DataFrame` and calculate the descriptors using `xenonpy.descriptor.Compositions` calculator.\\n\",\n \"It is noticed that the input for a neuron network model in pytorch must has shape (N x M x ...), which mean if the input is a 1-D vector, it should be reshaped to 2-D, e.g. (N) -> (N x 1).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
band_gapcompositiondensitye_above_hullefermielementsfinal_energy_per_atomformation_energy_per_atompretty_formulastructurevolume
mp-10088070.0000{'Rb': 1.0, 'Cu': 1.0, 'O': 1.0}4.7846340.9963721.100617[Rb, Cu, O]-3.302762-0.186408RbCuO[[-3.05935361 -3.05935361 -3.05935361] Rb, [0....57.268924
mp-10096400.0000{'Pr': 1.0, 'N': 1.0}8.1457770.7593935.213442[Pr, N]-7.082624-0.714336PrN[[0. 0. 0.] Pr, [1.57925232 1.57925232 1.58276...31.579717
mp-10168250.7745{'Hf': 1.0, 'Mg': 1.0, 'O': 3.0}6.1658880.5895502.424570[Hf, Mg, O]-7.911723-3.060060HfMgO3[[2.03622802 2.03622802 2.03622802] Hf, [0. 0....67.541269
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" band_gap composition density \\\\\\n\",\n \"mp-1008807 0.0000 {'Rb': 1.0, 'Cu': 1.0, 'O': 1.0} 4.784634 \\n\",\n \"mp-1009640 0.0000 {'Pr': 1.0, 'N': 1.0} 8.145777 \\n\",\n \"mp-1016825 0.7745 {'Hf': 1.0, 'Mg': 1.0, 'O': 3.0} 6.165888 \\n\",\n \"\\n\",\n \" e_above_hull efermi elements final_energy_per_atom \\\\\\n\",\n \"mp-1008807 0.996372 1.100617 [Rb, Cu, O] -3.302762 \\n\",\n \"mp-1009640 0.759393 5.213442 [Pr, N] -7.082624 \\n\",\n \"mp-1016825 0.589550 2.424570 [Hf, Mg, O] -7.911723 \\n\",\n \"\\n\",\n \" formation_energy_per_atom pretty_formula \\\\\\n\",\n \"mp-1008807 -0.186408 RbCuO \\n\",\n \"mp-1009640 -0.714336 PrN \\n\",\n \"mp-1016825 -3.060060 HfMgO3 \\n\",\n \"\\n\",\n \" structure volume \\n\",\n \"mp-1008807 [[-3.05935361 -3.05935361 -3.05935361] Rb, [0.... 57.268924 \\n\",\n \"mp-1009640 [[0. 0. 0.] Pr, [1.57925232 1.57925232 1.58276... 31.579717 \\n\",\n \"mp-1016825 [[2.03622802 2.03622802 2.03622802] Hf, [0. 0.... 67.541269 \"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# if you have not have the samples data\\n\",\n \"# preset.build('mp_samples', api_key=)\\n\",\n \"\\n\",\n \"from xenonpy.datatools import preset\\n\",\n \"\\n\",\n \"data = preset.mp_samples\\n\",\n \"data.head(3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In case of the system did not automatically download some descriptor data in XenonPy, please run the following code to sync the data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"fetching dataset `elements` from https://github.com/yoshida-lab/dataset/releases/download/v0.1.3/elements.pd.xz.\\n\",\n \"fetching dataset `elements_completed` from https://github.com/yoshida-lab/dataset/releases/download/v0.1.3/elements_completed.pd.xz.\\n\"\n ]\n }\n ],\n \"source\": [\n \"from xenonpy.datatools import preset\\n\",\n \"preset.sync('elements')\\n\",\n \"preset.sync('elements_completed')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
ave:atomic_numberave:atomic_radiusave:atomic_radius_rahmave:atomic_volumeave:atomic_weightave:boiling_pointave:bulk_modulusave:c6_gbave:covalent_radius_corderoave:covalent_radius_pyykko...min:num_s_valencemin:periodmin:specific_heatmin:thermal_conductivitymin:vdw_radiusmin:vdw_radius_alvarezmin:vdw_radius_mm3min:vdw_radius_uffmin:sound_velocitymin:Polarizability
mp-100880724.666667174.067140209.33333325.66666755.0042671297.06333372.8686801646.90139.333333128.333333...1.02.00.3600.02658152.0150.0182.0349.5317.50.802
mp-100964033.000000137.000000232.50000019.05000077.4573301931.20000043.1824411892.85137.000000123.500000...2.02.00.1920.02583155.0166.0193.0360.6333.61.100
mp-101682521.600000153.120852203.40000013.92000050.1584001420.71400076.663625343.82102.80000096.000000...2.02.00.1460.02658152.0150.0182.0302.1317.50.802
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\"\n ],\n \"text/plain\": [\n \" ave:atomic_number ave:atomic_radius ave:atomic_radius_rahm \\\\\\n\",\n \"mp-1008807 24.666667 174.067140 209.333333 \\n\",\n \"mp-1009640 33.000000 137.000000 232.500000 \\n\",\n \"mp-1016825 21.600000 153.120852 203.400000 \\n\",\n \"\\n\",\n \" ave:atomic_volume ave:atomic_weight ave:boiling_point \\\\\\n\",\n \"mp-1008807 25.666667 55.004267 1297.063333 \\n\",\n \"mp-1009640 19.050000 77.457330 1931.200000 \\n\",\n \"mp-1016825 13.920000 50.158400 1420.714000 \\n\",\n \"\\n\",\n \" ave:bulk_modulus ave:c6_gb ave:covalent_radius_cordero \\\\\\n\",\n \"mp-1008807 72.868680 1646.90 139.333333 \\n\",\n \"mp-1009640 43.182441 1892.85 137.000000 \\n\",\n \"mp-1016825 76.663625 343.82 102.800000 \\n\",\n \"\\n\",\n \" ave:covalent_radius_pyykko ... min:num_s_valence min:period \\\\\\n\",\n \"mp-1008807 128.333333 ... 1.0 2.0 \\n\",\n \"mp-1009640 123.500000 ... 2.0 2.0 \\n\",\n \"mp-1016825 96.000000 ... 2.0 2.0 \\n\",\n \"\\n\",\n \" min:specific_heat min:thermal_conductivity min:vdw_radius \\\\\\n\",\n \"mp-1008807 0.360 0.02658 152.0 \\n\",\n \"mp-1009640 0.192 0.02583 155.0 \\n\",\n \"mp-1016825 0.146 0.02658 152.0 \\n\",\n \"\\n\",\n \" min:vdw_radius_alvarez min:vdw_radius_mm3 min:vdw_radius_uff \\\\\\n\",\n \"mp-1008807 150.0 182.0 349.5 \\n\",\n \"mp-1009640 166.0 193.0 360.6 \\n\",\n \"mp-1016825 150.0 182.0 302.1 \\n\",\n \"\\n\",\n \" min:sound_velocity min:Polarizability \\n\",\n \"mp-1008807 317.5 0.802 \\n\",\n \"mp-1009640 333.6 1.100 \\n\",\n \"mp-1016825 317.5 0.802 \\n\",\n \"\\n\",\n \"[3 rows x 290 columns]\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
volume
mp-100880757.268924
mp-100964031.579717
mp-101682567.541269
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\"\n ],\n \"text/plain\": [\n \" volume\\n\",\n \"mp-1008807 57.268924\\n\",\n \"mp-1009640 31.579717\\n\",\n \"mp-1016825 67.541269\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"prop = data[data.volume <= 2500]['volume'].to_frame() # reshape to 2-D\\n\",\n \"desc = Compositions(featurizers='classic').transform(data.loc[prop.index]['composition'])\\n\",\n \"\\n\",\n \"desc.head(3)\\n\",\n \"prop.head(3)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from xenonpy.datatools import preset\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Use the `xenonpy.datatools.Splitter` to split data into training and test sets.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\\u001b[0;31mInit signature:\\u001b[0m\\n\",\n \"\\u001b[0mSplitter\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0msize\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mint\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mtest_size\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mfloat\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mint\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;36m0.2\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mk_fold\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mint\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mIterable\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mNoneType\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mrandom_state\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mint\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mNoneType\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mshuffle\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mbool\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mDocstring:\\u001b[0m Data splitter for train and test\\n\",\n \"\\u001b[0;31mInit docstring:\\u001b[0m\\n\",\n \"Parameters\\n\",\n \"----------\\n\",\n \"size\\n\",\n \" Total sample size.\\n\",\n \" All data must have same length of their first dim,\\n\",\n \"test_size\\n\",\n \" If float, should be between ``0.0`` and ``1.0`` and represent the proportion\\n\",\n \" of the dataset to include in the test split. If int, represents the\\n\",\n \" absolute number of test samples. Can be ``0`` if cv is ``None``.\\n\",\n \" In this case, :meth:`~Splitter.cv` will yield a tuple only contains ``training`` and ``validation``\\n\",\n \" on each step. By default, the value is set to 0.2.\\n\",\n \"k_fold\\n\",\n \" Number of k-folds.\\n\",\n \" If ``int``, Must be at least 2.\\n\",\n \" If ``Iterable``, it should provide label for each element which will be used for group cv.\\n\",\n \" In this case, the input of :meth:`~Splitter.cv` must be a :class:`pandas.DataFrame` object.\\n\",\n \" Default value is None to specify no cv.\\n\",\n \"random_state\\n\",\n \" If int, random_state is the seed used by the random number generator;\\n\",\n \" Default is None.\\n\",\n \"shuffle\\n\",\n \" Whether or not to shuffle the data before splitting.\\n\",\n \"\\u001b[0;31mFile:\\u001b[0m ~/projects/XenonPy/xenonpy/datatools/splitter.py\\n\",\n \"\\u001b[0;31mType:\\u001b[0m type\\n\",\n \"\\u001b[0;31mSubclasses:\\u001b[0m \\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"Splitter?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(738, 290)\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"(738, 1)\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"(185, 290)\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"(185, 1)\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sp = Splitter(prop.shape[0])\\n\",\n \"x_train, x_val, y_train, y_val = sp.split(desc, prop)\\n\",\n \"\\n\",\n \"x_train.shape\\n\",\n \"y_train.shape\\n\",\n \"x_val.shape\\n\",\n \"y_val.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### model training\\n\",\n \"\\n\",\n \"Model training in pytorch is very flexible.\\n\",\n \"The [official document](https://pytorch.org/tutorials/beginner/pytorch_with_examples.html) explains the concept with examples.\\n\",\n \"In short, training a neuron network model includes:\\n\",\n \"1. calculating loss of training data for the model.\\n\",\n \"2. executing the backpropagation to update the weights between each neuron.\\n\",\n \"3. looping over step 1 and 2 until convergence.\\n\",\n \"\\n\",\n \"In step 1, a calculator, often called `loss function`, is needed to calculate the loss.\\n\",\n \"In step 2, an optimizer will be used.\\n\",\n \"`xenonpy.model.training.Trainer` covers step 3.\\n\",\n \"\\n\",\n \"Here is how you will do it in XenonPy:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"SequentialLinear(\\n\",\n \" (layer_0): LinearLayer(\\n\",\n \" (linear): Linear(in_features=290, out_features=232, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(232, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_1): LinearLayer(\\n\",\n \" (linear): Linear(in_features=232, out_features=174, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(174, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_2): LinearLayer(\\n\",\n \" (linear): Linear(in_features=174, out_features=116, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(116, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_3): LinearLayer(\\n\",\n \" (linear): Linear(in_features=116, out_features=58, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(58, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (output): Linear(in_features=58, out_features=1, bias=True)\\n\",\n \")\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model = SequentialLinear(290, 1, h_neurons=(0.8, 0.6, 0.4, 0.2))\\n\",\n \"model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Trainer(clip_grad=None, cuda=None, epochs=200, loss_func=MSELoss(),\\n\",\n \" lr_scheduler=None,\\n\",\n \" model=SequentialLinear(\\n\",\n \" (layer_0): LinearLayer(\\n\",\n \" (linear): Linear(in_features=290, out_features=232, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(232, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_1): LinearLayer(\\n\",\n \" (linear): Li...\\n\",\n \" (linear): Linear(in_features=116, out_features=58, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(58, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (output): Linear(in_features=58, out_features=1, bias=True)\\n\",\n \"),\\n\",\n \" non_blocking=False,\\n\",\n \" optimizer=Adam (\\n\",\n \"Parameter Group 0\\n\",\n \" amsgrad: False\\n\",\n \" betas: (0.9, 0.999)\\n\",\n \" eps: 1e-08\\n\",\n \" lr: 0.01\\n\",\n \" weight_decay: 0\\n\",\n \"))\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"trainer = Trainer(\\n\",\n \" model=model,\\n\",\n \" optimizer=Adam(lr=0.01),\\n\",\n \" loss_func=MSELoss()\\n\",\n \")\\n\",\n \"trainer\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 100%|██████████| 400/400 [00:06<00:00, 60.86it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"trainer.fit(\\n\",\n \" x_train=torch.tensor(x_train.values, dtype=torch.float),\\n\",\n \" y_train=torch.tensor(y_train.values, dtype=torch.float),\\n\",\n \" epochs=400\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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total_itersi_epochi_batchtrain_mse_loss
39739739813684.717041
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\"\n ],\n \"text/plain\": [\n \" total_iters i_epoch i_batch train_mse_loss\\n\",\n \"397 397 398 1 3684.717041\\n\",\n \"398 398 399 1 3058.761719\\n\",\n \"399 399 400 1 3039.683350\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"y_pred = trainer.predict(x_in=torch.tensor(x_val.values, dtype=torch.float)).detach().numpy().flatten()\\n\",\n \"y_true = y_val.values.flatten()\\n\",\n \"\\n\",\n \"y_fit_pred = trainer.predict(x_in=torch.tensor(x_train.values, dtype=torch.float)).detach().numpy().flatten()\\n\",\n \"y_fit_true = y_train.values.flatten()\\n\",\n \"\\n\",\n \"draw(y_true, y_pred, y_fit_true, y_fit_pred, prop_name='Volume ($\\\\AA^3$)')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can see that although `trainer` can keep training info automatically for us, we still need to convert `DataFrame` to `torch.Tensor` and convert dtype from `np.double` to `torch.float` during training, and eventually convert them back during prediction ourselves. Also, overfitting could be observed in the **prediction vs observation** plot. One possible caused is using all training data in each epoch of the training process. We can use a technique called mini-batch to avoid overfitting, but that will require more coding.\\n\",\n \"\\n\",\n \"To skip these tedious coding steps, we provide an extension system.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, we use `ArrayDataset` to wrap our data, and use `torch.utils.data.DataLoader` to a build mini-batch loader.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from xenonpy.model.training.dataset import ArrayDataset\\n\",\n \"from torch.utils.data import DataLoader\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"train_dataset = DataLoader(ArrayDataset(x_train, y_train), shuffle=True, batch_size=100)\\n\",\n \"val_dataset = DataLoader(ArrayDataset(x_val, y_val), batch_size=1000)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Second, we use `TensorConverter` extension to automatically convert data between `numpy` and `torch.Tensor`.\\n\",\n \"Additionally, we want to trace some stopping criterion and use early stopping when these criterion stop improving.\\n\",\n \"This can be done by using `Validator`.\\n\",\n \"\\n\",\n \"At last, to save our model for latter use, just add `Persist` to the trainer and saving will be done in slient.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from xenonpy.model.training.extension import Validator, TensorConverter, Persist\\n\",\n \"from xenonpy.model.training.dataset import ArrayDataset\\n\",\n \"from xenonpy.model.utils import regression_metrics\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We use `trainer.extend` method to stack up extensions. Note that extensions will be executed in the order of insertion, so `TensorConverter` should always go first and `Persist` be the last.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"trainer = Trainer(\\n\",\n \" optimizer=Adam(lr=0.01),\\n\",\n \" loss_func=MSELoss(),\\n\",\n \").extend(\\n\",\n \" TensorConverter(),\\n\",\n \" Validator(metrics_func=regression_metrics, early_stopping=30, trace_order=5, mae=0.0, pearsonr=1.0),\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\\u001b[0;31mInit signature:\\u001b[0m\\n\",\n \"\\u001b[0mPersist\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mpath\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mpathlib\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mPath\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mstr\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;34m'.'\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mmodel_class\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mCallable\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mmodel_params\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mUnion\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mtuple\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mdict\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m<\\u001b[0m\\u001b[0mbuilt\\u001b[0m\\u001b[0;34m-\\u001b[0m\\u001b[0;32min\\u001b[0m \\u001b[0mfunction\\u001b[0m \\u001b[0many\\u001b[0m\\u001b[0;34m>\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0mincrement\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mFalse\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0msync_training_step\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mFalse\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mdescribe\\u001b[0m\\u001b[0;34m:\\u001b[0m \\u001b[0mAny\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\n\",\n \"\\u001b[0;34m\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mDocstring:\\u001b[0m Trainer extension for data persistence\\n\",\n \"\\u001b[0;31mInit docstring:\\u001b[0m\\n\",\n \"Parameters\\n\",\n \"----------\\n\",\n \"path\\n\",\n \" Path for model saving.\\n\",\n \"model_class\\n\",\n \" A factory function for model reconstructing.\\n\",\n \" In most case this is the model class inherits from :class:`torch.nn.Module`\\n\",\n \"model_params\\n\",\n \" The parameters for model reconstructing.\\n\",\n \" This can be anything but in general this is a dict which can be used as kwargs parameters.\\n\",\n \"increment\\n\",\n \" If ``True``, dir name of path will be decorated with a auto increment number,\\n\",\n \" e.g. use ``model_dir@1`` for ``model_dir``.\\n\",\n \"sync_training_step\\n\",\n \" If ``True``, will save ``trainer.training_info`` at each iteration.\\n\",\n \" Default is ``False``, only save ``trainer.training_info`` at each epoch.\\n\",\n \"describe:\\n\",\n \" Any other information to describe this model.\\n\",\n \" These information will be saved under model dir by name ``describe.pkl.z``.\\n\",\n \"\\u001b[0;31mFile:\\u001b[0m ~/projects/XenonPy/xenonpy/model/training/extension/persist.py\\n\",\n \"\\u001b[0;31mType:\\u001b[0m type\\n\",\n \"\\u001b[0;31mSubclasses:\\u001b[0m \\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"Persist?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"`Persist` need a path to save model. For convenience, we prepare the path name by concatenating the number of neurons in a model.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def make_name(model):\\n\",\n \" name = []\\n\",\n \" for n, m in model.named_children():\\n\",\n \" if 'layer_' in n:\\n\",\n \" name.append(str(m.linear.in_features))\\n\",\n \" else:\\n\",\n \" name.append(str(m.in_features))\\n\",\n \" name.append(str(m.out_features))\\n\",\n \" return '-'.join(name)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"SequentialLinear(\\n\",\n \" (layer_0): LinearLayer(\\n\",\n \" (linear): Linear(in_features=290, out_features=232, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(232, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_1): LinearLayer(\\n\",\n \" (linear): Linear(in_features=232, out_features=174, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(174, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_2): LinearLayer(\\n\",\n \" (linear): Linear(in_features=174, out_features=116, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(116, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_3): LinearLayer(\\n\",\n \" (linear): Linear(in_features=116, out_features=58, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(58, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (output): Linear(in_features=58, out_features=1, bias=True)\\n\",\n \")\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": []\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 12%|█▏ | 47/400 [00:15<02:06, 2.78it/s]\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 31 iterations, finish training at iteration 372\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"model = SequentialLinear(290, 1, h_neurons=(0.8, 0.6, 0.4, 0.2))\\n\",\n \"model\\n\",\n \"\\n\",\n \"model_name = make_name(model)\\n\",\n \"persist = Persist(\\n\",\n \" f'trained_models/{model_name}', \\n\",\n \" # -^- required -^-\\n\",\n \"\\n\",\n \" # -v- optional -v-\\n\",\n \" increment=False, \\n\",\n \" sync_training_step=True,\\n\",\n \" author='Chang Liu',\\n\",\n \" email='liu.chang.1865@gmail.com',\\n\",\n \" dataset='materials project',\\n\",\n \")\\n\",\n \"_ = trainer.extend(persist)\\n\",\n \"trainer.reset(to=model)\\n\",\n \"\\n\",\n \"trainer.fit(training_dataset=train_dataset, validation_dataset=val_dataset, epochs=400)\\n\",\n \"persist(splitter=sp, data_indices=prop.index.tolist()) # <-- calling of this method only after the model training\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"_, ax = plt.subplots(figsize=(10, 5), dpi=150)\\n\",\n \"trainer.training_info.plot(y=['train_mse_loss', 'val_mse'], ax=ax)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Missing directory and/or file name information!\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"y_pred, y_true = trainer.predict(dataset=val_dataset, checkpoint='pearsonr_1')\\n\",\n \"y_fit_pred, y_fit_true = trainer.predict(dataset=train_dataset, checkpoint='pearsonr_1')\\n\",\n \"draw(y_true, y_pred, y_fit_true, y_fit_pred, prop_name='Volume ($\\\\AA^3$)')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Combin random model generating and training\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"generator = ParameterGenerator(\\n\",\n \" in_features=290,\\n\",\n \" out_features=1,\\n\",\n \" h_neurons=dict(\\n\",\n \" data=lambda n: sorted(np.random.uniform(0.2, 0.8, size=n), reverse=True), \\n\",\n \" repeat=(3, 4)\\n\",\n \" )\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 0%| | 0/400 [00:00 ABC DEF Gxx\\n\",\n \" def grouper(iterable, n, fillvalue=None):\\n\",\n \" \\\"\\\"\\\"Collect data into fixed-length chunks or blocks\\\"\\\"\\\"\\n\",\n \" args = [iter(iterable)] * n\\n\",\n \" return zip_longest(fillvalue=fillvalue, *args)\\n\",\n \"\\n\",\n \" # the following props will be fetched\\n\",\n \" mp_props = [\\n\",\n \" 'band_gap',\\n\",\n \" 'density',\\n\",\n \" 'volume',\\n\",\n \" 'material_id',\\n\",\n \" 'pretty_formula',\\n\",\n \" 'elements',\\n\",\n \" 'efermi',\\n\",\n \" 'e_above_hull',\\n\",\n \" 'formation_energy_per_atom',\\n\",\n \" 'final_energy_per_atom',\\n\",\n \" 'unit_cell_formula',\\n\",\n \" 'structure'\\n\",\n \" ]\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" entries = []\\n\",\n \" mpid_groups = [g for g in grouper(mp_ids, 10)]\\n\",\n \"\\n\",\n \" with MPRester(api_key) as mpr:\\n\",\n \" for group in tqdm(mpid_groups):\\n\",\n \" mpid_list = [id for id in filter(None, group)]\\n\",\n \" chunk = mpr.query({\\\"material_id\\\": {\\\"$in\\\": mpid_list}}, mp_props)\\n\",\n \" entries.extend(chunk)\\n\",\n \"\\n\",\n \"\\n\",\n \" df = pd.DataFrame(entries, index=[e['material_id'] for e in entries])\\n\",\n \" df = df.drop('material_id', axis=1)\\n\",\n \" df = df.rename(columns={'unit_cell_formula': 'composition'})\\n\",\n \" df = df.reindex(columns=sorted(df.columns))\\n\",\n \"\\n\",\n \" return df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"100%|██████████| 100/100 [01:06<00:00, 1.63it/s]\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
band_gapcompositiondensitye_above_hullefermielementsfinal_energy_per_atomformation_energy_per_atompretty_formulastructurevolume
mp-208660.1849{'Ge': 4.0, 'Rh': 4.0}9.7555320.039943NaN[Ge, Rh]-6.496651-0.506775GeRh[[0.80283811 1.66009496 3.26577118] Ge, [1.660...119.521991
mp-307590.0000{'Li': 1.0, 'Mg': 2.0, 'Tl': 1.0}5.0229100.0272784.641088[Li, Mg, Tl]-1.970913-0.099951LiMg2Tl[[2.85976352 2.02215817 4.95325571] Li, [1.429...85.932461
mp-34166.7145{'F': 12.0, 'Na': 6.0, 'Al': 2.0}2.8440980.000000-1.602530[Al, F, Na]-5.035544-3.414627Na3AlF6[[3.6307153 1.31968004 3.40159567] F, [4.5775...245.150290
mp-5054122.0103{'K': 8.0, 'In': 8.0, 'S': 16.0}3.0809230.0000001.459451[In, K, S]-3.971909-1.274151KInS2[[ 6.09352925 1.20936514 14.43416479] K, [ 6....940.171218
mp-6846526.8002{'Be': 3.0, 'F': 6.0}1.2469780.223526-5.588439[Be, F]-5.541302-3.346732BeF2[[3.76534884 1.64321 7.5707047 ] Be, [1.460...187.798605
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" band_gap composition density \\\\\\n\",\n \"mp-20866 0.1849 {'Ge': 4.0, 'Rh': 4.0} 9.755532 \\n\",\n \"mp-30759 0.0000 {'Li': 1.0, 'Mg': 2.0, 'Tl': 1.0} 5.022910 \\n\",\n \"mp-3416 6.7145 {'F': 12.0, 'Na': 6.0, 'Al': 2.0} 2.844098 \\n\",\n \"mp-505412 2.0103 {'K': 8.0, 'In': 8.0, 'S': 16.0} 3.080923 \\n\",\n \"mp-684652 6.8002 {'Be': 3.0, 'F': 6.0} 1.246978 \\n\",\n \"\\n\",\n \" e_above_hull efermi elements final_energy_per_atom \\\\\\n\",\n \"mp-20866 0.039943 NaN [Ge, Rh] -6.496651 \\n\",\n \"mp-30759 0.027278 4.641088 [Li, Mg, Tl] -1.970913 \\n\",\n \"mp-3416 0.000000 -1.602530 [Al, F, Na] -5.035544 \\n\",\n \"mp-505412 0.000000 1.459451 [In, K, S] -3.971909 \\n\",\n \"mp-684652 0.223526 -5.588439 [Be, F] -5.541302 \\n\",\n \"\\n\",\n \" formation_energy_per_atom pretty_formula \\\\\\n\",\n \"mp-20866 -0.506775 GeRh \\n\",\n \"mp-30759 -0.099951 LiMg2Tl \\n\",\n \"mp-3416 -3.414627 Na3AlF6 \\n\",\n \"mp-505412 -1.274151 KInS2 \\n\",\n \"mp-684652 -3.346732 BeF2 \\n\",\n \"\\n\",\n \" structure volume \\n\",\n \"mp-20866 [[0.80283811 1.66009496 3.26577118] Ge, [1.660... 119.521991 \\n\",\n \"mp-30759 [[2.85976352 2.02215817 4.95325571] Li, [1.429... 85.932461 \\n\",\n \"mp-3416 [[3.6307153 1.31968004 3.40159567] F, [4.5775... 245.150290 \\n\",\n \"mp-505412 [[ 6.09352925 1.20936514 14.43416479] K, [ 6.... 940.171218 \\n\",\n \"mp-684652 [[3.76534884 1.64321 7.5707047 ] Be, [1.460... 187.798605 \"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# read ids\\n\",\n \"mp_ids = [s.decode('utf-8') for s in np.loadtxt('mp_ids.txt', 'S20')]\\n\",\n \"\\n\",\n \"# fetch data as pandas.DataFrame\\n\",\n \"df = data_fetcher(api_key, mp_ids)\\n\",\n \"df.head(5)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "samples/set1/data1.csv", "content": ",0,1\n0,1,2\n1,3,4\n" }, { "path": "samples/set2/data2.csv", "content": ",0,1\n0,1,2\n1,3,4\n" }, { "path": "samples/tools.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Tools\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import warnings\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\", message=\\\"numpy.dtype size changed\\\")\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\", message=\\\"numpy.ufunc size changed\\\")\\n\",\n \"\\n\",\n \"# basic uses\\n\",\n \"import pprint\\n\",\n \"import re\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import pickle as pk\\n\",\n \"import joblib\\n\",\n \"from itertools import zip_longest\\n\",\n \"from pathlib import Path\\n\",\n \"from math import ceil\\n\",\n \"from random import uniform\\n\",\n \"import seaborn as sb\\n\",\n \"\\n\",\n \"# plotting figures\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from matplotlib.patches import Rectangle\\n\",\n \"\\n\",\n \"# RDKit molecule conversion and drawing\\n\",\n \"from rdkit import Chem\\n\",\n \"from rdkit.Chem import Draw\\n\",\n \"from rdkit.Chem.Draw import rdMolDraw2D\\n\",\n \"\\n\",\n \"# modeling\\n\",\n \"import torch\\n\",\n \"from sklearn.metrics import mean_absolute_error\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"\\n\",\n \"# user-friendly print\\n\",\n \"from IPython.core.interactiveshell import InteractiveShell\\n\",\n \"InteractiveShell.ast_node_interactivity = \\\"all\\\"\\n\",\n \"\\n\",\n \"sb.set()\\n\",\n \"sb.set_context(\\\"notebook\\\", font_scale=1.0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \" # prediction vs. observation plots (single test trial)\\n\",\n \"def draw(y_true, y_pred, y_true_fit=None, y_pred_fit=None, *, prop_name, log_scale=False, file_dir=None, file_name=None):\\n\",\n \"\\n\",\n \" mask = ~np.isnan(y_pred)\\n\",\n \" y_true = y_true[mask]\\n\",\n \" y_pred = y_pred[mask]\\n\",\n \" \\n\",\n \" data = pd.DataFrame(dict(Observation=y_true, Prediction=y_pred, dataset=['test'] * len(y_true)))\\n\",\n \" scores = metrics(data['Observation'], data['Prediction'])\\n\",\n \" \\n\",\n \" if y_true_fit is not None and y_pred_fit is not None:\\n\",\n \" mask = ~np.isnan(y_pred_fit)\\n\",\n \" y_true_fit = y_true_fit[mask]\\n\",\n \" y_pred_fit = y_pred_fit[mask]\\n\",\n \" train_ = pd.DataFrame(dict(Observation=y_true_fit, Prediction=y_pred_fit, dataset=['train'] * len(y_true_fit)))\\n\",\n \" data = pd.concat([train_, data])\\n\",\n \"\\n\",\n \" if log_scale:\\n\",\n \" data = data.apply(lambda c: np.log(c.values) if c.dtype.type is not np.object_ else c, axis=0)\\n\",\n \"# test_ = test_.apply(lambda c: np.log(c.values) if c.dtype.type is not np.object_ else c, axis=0)\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# with sb.set(font_scale=2.5):\\n\",\n \" g = sb.lmplot(x=\\\"Prediction\\\", y=\\\"Observation\\\", hue=\\\"dataset\\\", ci=None,\\n\",\n \" data=data, palette=\\\"Set1\\\", height=10, legend=False, markers=[\\\".\\\", \\\"o\\\"],\\n\",\n \" scatter_kws={'s': 25, 'alpha': 0.7}, hue_order=['train', 'test'])\\n\",\n \" \\n\",\n \" ax = plt.gca()\\n\",\n \" tmp = [data[\\\"Prediction\\\"].max(), data[\\\"Prediction\\\"].min(), data[\\\"Observation\\\"].max(), data[\\\"Observation\\\"].max()]\\n\",\n \" min_, max_ = np.min(tmp), np.max(tmp)\\n\",\n \" margin = (max_- min_) / 15\\n\",\n \" min_ = min_ - margin\\n\",\n \" max_ = max_ + margin\\n\",\n \" ax.set_xlim(min_, max_)\\n\",\n \" ax.set_ylim(min_, max_)\\n\",\n \" ax.set_xlabel(ax.get_xlabel(), fontsize='xx-large')\\n\",\n \" ax.set_ylabel(ax.get_ylabel(), fontsize='xx-large')\\n\",\n \" ax.tick_params(axis='both', which='major', labelsize='xx-large')\\n\",\n \" ax.plot((min_, max_), (min_, max_), ':', color='gray')\\n\",\n \" ax.set_title(prop_name, fontsize='xx-large')\\n\",\n \" if log_scale:\\n\",\n \" ax.set_title(prop_name + ' (log scale)', fontsize='xx-large')\\n\",\n \" ax.text(0.98, 0.03,\\n\",\n \" 'MAE: %.5f\\\\nRMSE: %.5f\\\\nPearsonR: %.5f\\\\nSpearmanR: %.5f' % (scores['mae'], scores['rmse'], scores['pearsonr'], scores['spearmanr']),\\n\",\n \" transform=ax.transAxes, horizontalalignment='right', fontsize='xx-large')\\n\",\n \"\\n\",\n \" ax.legend(loc='upper left', markerscale=2, fancybox=True, shadow=True, frameon=True, facecolor='w', fontsize=18)\\n\",\n \"\\n\",\n \" plt.tight_layout()\\n\",\n \" if file_dir and file_name:\\n\",\n \" if log_scale:\\n\",\n \" plt.savefig(file_dir + '/' + file_name + '_log_scale.png', dpi=300, bbox_inches='tight')\\n\",\n \" else:\\n\",\n \" plt.savefig(file_dir + '/' + file_name + '.png', dip=300, bbox_inches='tight')\\n\",\n \" else:\\n\",\n \" print('Missing directory and/or file name information!')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Prediction vs. observation plots (cross-validation: list of test trials)\\n\",\n \"def draw_cv(y_trues, y_preds, y_trues_fit, y_preds_fit, *, prop_name, log_scale=False, file_dir=None, file_name=None):\\n\",\n \" cv=len(y_trues)\\n\",\n \" \\n\",\n \" y_true = np.concatenate(y_trues)\\n\",\n \" y_pred = np.concatenate(y_preds)\\n\",\n \" y_true_fit = np.concatenate(y_trues_fit)\\n\",\n \" y_pred_fit = np.concatenate(y_preds_fit)\\n\",\n \" \\n\",\n \" mask = ~np.isnan(y_pred_fit)\\n\",\n \" y_true_fit = y_true_fit[mask]\\n\",\n \" y_pred_fit = y_pred_fit[mask]\\n\",\n \"\\n\",\n \" mask = ~np.isnan(y_pred)\\n\",\n \" y_true = y_true[mask]\\n\",\n \" y_pred = y_pred[mask]\\n\",\n \" \\n\",\n \" test_ = pd.DataFrame(dict(Observation=y_true, Prediction=y_pred, dataset=['test'] * len(y_true)))\\n\",\n \" train_ = pd.DataFrame(dict(Observation=y_true_fit, Prediction=y_pred_fit, dataset=['train'] * len(y_true_fit)))\\n\",\n \" data = pd.concat([train_, test_])\\n\",\n \" if log_scale:\\n\",\n \" data = data.apply(lambda c: np.log(c.values) if c.dtype.type is not np.object_ else c, axis=0)\\n\",\n \" test_ = test_.apply(lambda c: np.log(c.values) if c.dtype.type is not np.object_ else c, axis=0)\\n\",\n \"\\n\",\n \" scores = metrics(test_['Observation'], test_['Prediction'])\\n\",\n \"\\n\",\n \"# sb.set(font_scale=2.5)\\n\",\n \" _, ax = plt.subplots(figsize=(8, 8), dpi=150)\\n\",\n \" ax = sb.lmplot(x=\\\"Prediction\\\", y=\\\"Observation\\\", hue=\\\"dataset\\\", ci=None,\\n\",\n \" data=data, palette=\\\"Set1\\\", height=10, legend=False, markers=[\\\".\\\", \\\"o\\\"],\\n\",\n \" scatter_kws={'s': 25, 'alpha': 0.7}, hue_order=['train', 'test'], ax=ax)\\n\",\n \" \\n\",\n \"# ax = plt.gca()\\n\",\n \" tmp = [data[\\\"Prediction\\\"].max(), data[\\\"Prediction\\\"].min(), data[\\\"Observation\\\"].max(), data[\\\"Observation\\\"].max()]\\n\",\n \" min_, max_ = np.min(tmp), np.max(tmp)\\n\",\n \" margin = (max_- min_) / 15\\n\",\n \" min_ = min_ - margin\\n\",\n \" max_ = max_ + margin\\n\",\n \" ax.set_xlim(min_, max_)\\n\",\n \" ax.set_ylim(min_, max_)\\n\",\n \" ax.set_xlabel(ax.get_xlabel(), fontsize='xx-large')\\n\",\n \" ax.set_ylabel(ax.get_ylabel(), fontsize='xx-large')\\n\",\n \" ax.tick_params(axis='both', which='major', labelsize='xx-large')\\n\",\n \" ax.plot((min_, max_), (min_, max_), ':', color='gray')\\n\",\n \" ax.set_title(prop_name, fontsize='xx-large')\\n\",\n \" if log_scale:\\n\",\n \" ax.set_title(prop_name + ' (log scale)', fontsize='xx-large')\\n\",\n \" ax.text(0.98, 0.03,\\n\",\n \" '$%d-fold$ CV\\\\nmae: %.5f\\\\nrmse: %.5f\\\\npearsonr: %.5f\\\\nspearmanr: %.5f' % (cv, scores['mae'], scores['rmse'], scores['pearsonr'], scores['spearmanr']),\\n\",\n \" transform=ax.transAxes, horizontalalignment='right', fontsize='xx-large')\\n\",\n \" ax.legend(loc='upper left', markerscale=2, fancybox=True, shadow=True, frameon=True, facecolor='w')\\n\",\n \" plt.tight_layout()\\n\",\n \" if file_dir and file_name:\\n\",\n \" if log_scale:\\n\",\n \" plt.savefig(file_dir + '/' + file_name + '_log_scale.png', dpi=300, bbox_inches='tight')\\n\",\n \" else:\\n\",\n \" plt.savefig(file_dir + '/' + file_name + '.png', dip=300, bbox_inches='tight')\\n\",\n \" else:\\n\",\n \" print('Missing directory and/or file name information!')\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# calculating basic statistics for predictions\\n\",\n \"def metrics(y_true, y_pred, ignore_nan=True):\\n\",\n \" from sklearn.metrics import mean_absolute_error, r2_score, mean_squared_error\\n\",\n \" from scipy.stats import pearsonr, spearmanr\\n\",\n \" \\n\",\n \" if ignore_nan:\\n\",\n \" mask = ~np.isnan(y_pred)\\n\",\n \" y_true = y_true[mask]\\n\",\n \" y_pred = y_pred[mask]\\n\",\n \" \\n\",\n \" mae = mean_absolute_error(y_true, y_pred)\\n\",\n \" rmse = np.sqrt(mean_squared_error(y_true, y_pred))\\n\",\n \" r2 = r2_score(y_true, y_pred)\\n\",\n \" pr, p_val = pearsonr(y_true, y_pred)\\n\",\n \" sr, _ = spearmanr(y_true, y_pred)\\n\",\n \" return dict(\\n\",\n \" mae=mae,\\n\",\n \" rmse=rmse,\\n\",\n \" r2=r2,\\n\",\n \" pearsonr=pr,\\n\",\n \" spearmanr=sr,\\n\",\n \" p_value=p_val\\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"\\n\",\n \"def quicksort(x):\\n\",\n \" if x==[]: return []\\n\",\n \"\\n\",\n \" smallerSorted = quicksort([a for a in x[1:] if os.path.getmtime(str(a)) <= os.path.getmtime(str(x[0]))])\\n\",\n \" biggerSorted = quicksort([a for a in x[1:] if os.path.getmtime(str(a)) > os.path.getmtime(str(x[0]))])\\n\",\n \"\\n\",\n \" return(smallerSorted+[x[0]]+biggerSorted)\\n\",\n \"\\n\",\n \"def retrieve_nn_models_from_with_cv(*props, score='pearsonr', cv=10):\\n\",\n \" for prop in props:\\n\",\n \" p = Path(prop)\\n\",\n \" models = [x for x in p.iterdir() if x.is_dir() and x.name != '.ipynb_checkpoints']\\n\",\n \" models = quicksort(models)\\n\",\n \" return [Checker.load(x.name, x.parent) for x in models[:10]], p\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "samples/transfer_learning.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Transfer Learning\\n\",\n \"\\n\",\n \"There are various methods for transfer learning such as **fine tuning** and **frozen feature extraction**. In this tutorial, we will demonstrate how to perform a **frozen feature extraction** type of transfer learning in XenonPy.\\n\",\n \"\\n\",\n \"This tutorial will use **Refractive Index** data, which are collected from [Polymer Genome](https://www.polymergenome.org). We do not provide these data directly in this tutorial. If you want to rerun this notebook locally, you must collect these data yourself.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### frozen feature extraction\\n\",\n \"\\n\",\n \"A frozen feature extraction type of transfer learing can be splitted into 2 steps:\\n\",\n \"\\n\",\n \"1. you need to have pre-trained model(s) as source model(s). This can be done by accessing **XenonPy.MDL**.\\n\",\n \"2. you need a feature extractor to generate \\\"neural descriptors\\\" from the source model(s).\\n\",\n \"Here, we would like to introduce you to our feature extractor, ``xenonpy.descriptor.FrozenFeaturizer``.\\n\",\n \"\\n\",\n \"The following codes show a case study of transfer learning between **Refractive Index** of inorganic and organic materials. In this example, the source models will be trained on inorganic compounds and the target will be polymers.\\n\",\n \"\\n\",\n \"Let's do this transfer learning step-by-setp.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### useful functions\\n\",\n \"\\n\",\n \"Running the following cell will load some commonly used packages, such as [NumPy](https://numpy.org/), [pandas](https://pandas.pydata.org/), and so on. It will also import some in-house functions used in this tutorial. See *'tools.ipynb'* file to check what will be imported.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%run tools.ipynb\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### access pre-trained models with MDL class\\n\",\n \"\\n\",\n \"We prepared a wide range of APIs to let you query and download our models.\\n\",\n \"These APIs can be accessed via any HTTP requests.\\n\",\n \"For convenience, we implemented some of the most popular APIs and wrapped them into XenonPy.\\n\",\n \"All these functions can be accessed using `xenonpy.mdl.MDL`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"MDL(api_key='anonymous.user.key', endpoint='http://xenon.ism.ac.jp/api')\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"'0.1.1'\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from xenonpy.mdl import MDL\\n\",\n \"\\n\",\n \"mdl = MDL()\\n\",\n \"mdl\\n\",\n \"\\n\",\n \"mdl.version\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 1. query **Refractive Index** models \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"QueryModelDetailsWith(api_key='anonymous.user.key', endpoint='http://xenon.ism.ac.jp/api', variables={'modelset_has': 'Stable', 'property_has': 'refractive'})\\n\",\n \"Queryable: \\n\",\n \" id\\n\",\n \" transferred\\n\",\n \" succeed\\n\",\n \" isRegression\\n\",\n \" deprecated\\n\",\n \" modelset\\n\",\n \" method\\n\",\n \" property\\n\",\n \" descriptor\\n\",\n \" lang\\n\",\n \" accuracy\\n\",\n \" precision\\n\",\n \" recall\\n\",\n \" f1\\n\",\n \" sensitivity\\n\",\n \" prevalence\\n\",\n \" specificity\\n\",\n \" ppv\\n\",\n \" npv\\n\",\n \" meanAbsError\\n\",\n \" maxAbsError\\n\",\n \" meanSquareError\\n\",\n \" rootMeanSquareError\\n\",\n \" r2\\n\",\n \" pValue\\n\",\n \" spearmanCorr\\n\",\n \" pearsonCorr\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"query = mdl(modelset_has=\\\"Stable\\\", property_has=\\\"refractive\\\")\\n\",\n \"query\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
idmodelsetmeanAbsErrorpearsonCorr
3112949Stable inorganic compounds in materials project0.2824340.873065
8474017Stable inorganic compounds in materials project0.2903820.827995
11894702Stable inorganic compounds in materials project0.2931350.876289
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" id modelset meanAbsError \\\\\\n\",\n \"311 2949 Stable inorganic compounds in materials project 0.282434 \\n\",\n \"847 4017 Stable inorganic compounds in materials project 0.290382 \\n\",\n \"1189 4702 Stable inorganic compounds in materials project 0.293135 \\n\",\n \"\\n\",\n \" pearsonCorr \\n\",\n \"311 0.873065 \\n\",\n \"847 0.827995 \\n\",\n \"1189 0.876289 \"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"summary = query('id', 'modelset', 'meanAbsError', 'pearsonCorr').sort_values('meanAbsError')\\n\",\n \"summary.head(3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 2. download the best performing model based on MAE\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"100%|██████████| 1/1 [00:00<00:00, 1.26it/s]\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
idmodel
02335/Users/liuchang/Google 云端硬盘/postdoctoral/tutor...
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" id model\\n\",\n \"0 2335 /Users/liuchang/Google 云端硬盘/postdoctoral/tutor...\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"results = mdl.pull(summary.id[0].item())\\n\",\n \"results\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 3. load **Refractive Index** data from Polymer Genome and calculate the ``Composition`` descriptors\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
SmilesNatomsNtypesVolume of Cell($\\\\AA^3$)Band Gap, PBE(eV)Band Gap, HSE06(eV)Dielectric ConstantDielectric Constant, ElectronicDielectric Constant, IonicAtomization Energy(eV/atom)Density(g/cm$^3$)Refractive IndexIonization Energy(eV)Electron Affinity(eV)Cohesive Energy(eV/atom)compositionFormula
ID_name
MOL1[C@H]([CH]O)(O[C@H]1[C@H](CO)O[C@@H]([CH][C@@H...843572.425.627.483.782.850.93-5.481.881.696.870.83-0.63{'O': 20.0, 'H': 40.0, 'C': 24.0}H40C24O20
MOL2[CH][C@H](C[C@@H](C[C@H](C[CH][C]=[CH])C(=[CH]...12821258.303.944.832.722.640.08-5.901.101.623.561.56-0.63{'C': 64.0, 'H': 64.0}H64C64
MOL3C[C@H](C[CH][CH][CH]C)[CH2].C[C@@H](C[CH][CH][...1082762.106.327.702.612.590.02-5.141.101.616.190.43-0.51{'C': 36.0, 'H': 72.0}H72C36
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Smiles Natoms Ntypes \\\\\\n\",\n \"ID_name \\n\",\n \"MOL1 [C@H]([CH]O)(O[C@H]1[C@H](CO)O[C@@H]([CH][C@@H... 84 3 \\n\",\n \"MOL2 [CH][C@H](C[C@@H](C[C@H](C[CH][C]=[CH])C(=[CH]... 128 2 \\n\",\n \"MOL3 C[C@H](C[CH][CH][CH]C)[CH2].C[C@@H](C[CH][CH][... 108 2 \\n\",\n \"\\n\",\n \" Volume of Cell($\\\\AA^3$) Band Gap, PBE(eV) Band Gap, HSE06(eV) \\\\\\n\",\n \"ID_name \\n\",\n \"MOL1 572.42 5.62 7.48 \\n\",\n \"MOL2 1258.30 3.94 4.83 \\n\",\n \"MOL3 762.10 6.32 7.70 \\n\",\n \"\\n\",\n \" Dielectric Constant Dielectric Constant, Electronic \\\\\\n\",\n \"ID_name \\n\",\n \"MOL1 3.78 2.85 \\n\",\n \"MOL2 2.72 2.64 \\n\",\n \"MOL3 2.61 2.59 \\n\",\n \"\\n\",\n \" Dielectric Constant, Ionic Atomization Energy(eV/atom) \\\\\\n\",\n \"ID_name \\n\",\n \"MOL1 0.93 -5.48 \\n\",\n \"MOL2 0.08 -5.90 \\n\",\n \"MOL3 0.02 -5.14 \\n\",\n \"\\n\",\n \" Density(g/cm$^3$) Refractive Index Ionization Energy(eV) \\\\\\n\",\n \"ID_name \\n\",\n \"MOL1 1.88 1.69 6.87 \\n\",\n \"MOL2 1.10 1.62 3.56 \\n\",\n \"MOL3 1.10 1.61 6.19 \\n\",\n \"\\n\",\n \" Electron Affinity(eV) Cohesive Energy(eV/atom) \\\\\\n\",\n \"ID_name \\n\",\n \"MOL1 0.83 -0.63 \\n\",\n \"MOL2 1.56 -0.63 \\n\",\n \"MOL3 0.43 -0.51 \\n\",\n \"\\n\",\n \" composition Formula \\n\",\n \"ID_name \\n\",\n \"MOL1 {'O': 20.0, 'H': 40.0, 'C': 24.0} H40C24O20 \\n\",\n \"MOL2 {'C': 64.0, 'H': 64.0} H64C64 \\n\",\n \"MOL3 {'C': 36.0, 'H': 72.0} H72C36 \"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pg = \\n\",\n \"pg.head(3)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
ave:atomic_numberave:atomic_radiusave:atomic_radius_rahmave:atomic_volumeave:atomic_weightave:boiling_pointave:bulk_modulusave:c6_gbave:covalent_radius_corderoave:covalent_radius_pyykko...min:num_s_valencemin:periodmin:specific_heatmin:thermal_conductivitymin:vdw_radiusmin:vdw_radius_alvarezmin:vdw_radius_mm3min:vdw_radius_uffmin:sound_velocitymin:Polarizability
ID_name
MOL14.09523898.42891168.33333311.5619057.7210001488.2738154.59650520.76190551.33333351.666667...1.01.00.7110.02658110.0120.0162.0288.6317.50.666793
MOL23.50000085.00000172.0000009.7000006.5095002560.1400044.89982027.20500052.00000053.500000...1.01.00.7110.18050110.0120.0162.0288.61270.00.666793
MOL32.66666783.00000166.00000011.1666674.6756671713.5200048.86642620.30666745.00000046.333333...1.01.00.7110.18050110.0120.0162.0288.61270.00.666793
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3 rows × 290 columns

\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" ave:atomic_number ave:atomic_radius ave:atomic_radius_rahm \\\\\\n\",\n \"ID_name \\n\",\n \"MOL1 4.095238 98.42891 168.333333 \\n\",\n \"MOL2 3.500000 85.00000 172.000000 \\n\",\n \"MOL3 2.666667 83.00000 166.000000 \\n\",\n \"\\n\",\n \" ave:atomic_volume ave:atomic_weight ave:boiling_point \\\\\\n\",\n \"ID_name \\n\",\n \"MOL1 11.561905 7.721000 1488.27381 \\n\",\n \"MOL2 9.700000 6.509500 2560.14000 \\n\",\n \"MOL3 11.166667 4.675667 1713.52000 \\n\",\n \"\\n\",\n \" ave:bulk_modulus ave:c6_gb ave:covalent_radius_cordero \\\\\\n\",\n \"ID_name \\n\",\n \"MOL1 54.596505 20.761905 51.333333 \\n\",\n \"MOL2 44.899820 27.205000 52.000000 \\n\",\n \"MOL3 48.866426 20.306667 45.000000 \\n\",\n \"\\n\",\n \" ave:covalent_radius_pyykko ... min:num_s_valence min:period \\\\\\n\",\n \"ID_name ... \\n\",\n \"MOL1 51.666667 ... 1.0 1.0 \\n\",\n \"MOL2 53.500000 ... 1.0 1.0 \\n\",\n \"MOL3 46.333333 ... 1.0 1.0 \\n\",\n \"\\n\",\n \" min:specific_heat min:thermal_conductivity min:vdw_radius \\\\\\n\",\n \"ID_name \\n\",\n \"MOL1 0.711 0.02658 110.0 \\n\",\n \"MOL2 0.711 0.18050 110.0 \\n\",\n \"MOL3 0.711 0.18050 110.0 \\n\",\n \"\\n\",\n \" min:vdw_radius_alvarez min:vdw_radius_mm3 min:vdw_radius_uff \\\\\\n\",\n \"ID_name \\n\",\n \"MOL1 120.0 162.0 288.6 \\n\",\n \"MOL2 120.0 162.0 288.6 \\n\",\n \"MOL3 120.0 162.0 288.6 \\n\",\n \"\\n\",\n \" min:sound_velocity min:Polarizability \\n\",\n \"ID_name \\n\",\n \"MOL1 317.5 0.666793 \\n\",\n \"MOL2 1270.0 0.666793 \\n\",\n \"MOL3 1270.0 0.666793 \\n\",\n \"\\n\",\n \"[3 rows x 290 columns]\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from xenonpy.descriptor import Compositions\\n\",\n \"\\n\",\n \"pg_desc = Compositions().transform(pg['composition'])\\n\",\n \"pg_desc.head(3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 4. predict Polymer Genomer **Refractive Index** directly from a model trained on inorganic compounds\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \" includes:\\n\",\n \"\\\"mse_3\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/mse_3.pth.s\\n\",\n \"\\\"mse_1\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/mse_1.pth.s\\n\",\n \"\\\"mae_2\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/mae_2.pth.s\\n\",\n \"\\\"r2_5\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/r2_5.pth.s\\n\",\n \"\\\"mse_5\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/mse_5.pth.s\\n\",\n \"\\\"r2_1\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/r2_1.pth.s\\n\",\n \"\\\"mae_4\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/mae_4.pth.s\\n\",\n \"\\\"r2_3\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/r2_3.pth.s\\n\",\n \"\\\"mae_3\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/mae_3.pth.s\\n\",\n \"\\\"r2_4\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/r2_4.pth.s\\n\",\n \"\\\"mse_2\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/mse_2.pth.s\\n\",\n \"\\\"mae_1\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/mae_1.pth.s\\n\",\n \"\\\"mae_5\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/mae_5.pth.s\\n\",\n \"\\\"r2_2\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/r2_2.pth.s\\n\",\n \"\\\"mse_4\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/mse_4.pth.s\\n\",\n \"\\\"pearsonr_5\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/pearsonr_5.pth.s\\n\",\n \"\\\"pearsonr_1\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/pearsonr_1.pth.s\\n\",\n \"\\\"pearsonr_3\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/pearsonr_3.pth.s\\n\",\n \"\\\"pearsonr_4\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/pearsonr_4.pth.s\\n\",\n \"\\\"pearsonr_2\\\": /Users/liuchang/Google 云端硬盘/postdoctoral/tutorial/xenonpy_hands-on_20190925 2/inorganic.crystal.refractive_index/xenonpy.compositions/pytorch.nn.neural_network/290-187-151-110-102-1-$wHSyTrhF/checkpoints/pearsonr_2.pth.s\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from xenonpy.model.training import Checker\\n\",\n \"\\n\",\n \"checker = Checker(results.model[0])\\n\",\n \"checker.checkpoints\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Trainer(clip_grad=None, cuda=None, epochs=200, loss_func=None,\\n\",\n \" lr_scheduler=None,\\n\",\n \" model=SequentialLinear(\\n\",\n \" (layer_0): LinearLayer(\\n\",\n \" (linear): Linear(in_features=290, out_features=187, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(187, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_1): LinearLayer(\\n\",\n \" (linear): Linear(...\\n\",\n \" (normalizer): BatchNorm1d(110, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_3): LinearLayer(\\n\",\n \" (linear): Linear(in_features=110, out_features=102, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(102, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (output): Linear(in_features=102, out_features=1, bias=True)\\n\",\n \"),\\n\",\n \" non_blocking=False, optimizer=None)\"\n ]\n },\n \"execution_count\": 35,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# --- pre-trained model for prediction\\n\",\n \"from xenonpy.model.training import Trainer\\n\",\n \"\\n\",\n \"trainer = Trainer.load(from_=checker)\\n\",\n \"trainer\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Missing directory and/or file name information!\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"trainer.reset(to='mae_1')\\n\",\n \"\\n\",\n \"y_pred = trainer.predict(x_in=torch.tensor(pg_desc.values, dtype=torch.float)).detach().numpy().flatten()\\n\",\n \"y_true = pg['Refractive Index'].values\\n\",\n \"\\n\",\n \"draw(y_true, y_pred, prop_name='Refractive Index')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 5. frozen feature extraction\\n\",\n \"\\n\",\n \"``FrozenFeaturizer`` accepts a [Pytorch](https://pytorch.org) model as its input.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"FrozenFeaturizer(cuda=False, depth=None,\\n\",\n \" model=SequentialLinear(\\n\",\n \" (layer_0): LinearLayer(\\n\",\n \" (linear): Linear(in_features=290, out_features=187, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(187, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_1): LinearLayer(\\n\",\n \" (linear): Linear(in_features=187, out_features=151, bias=...\\n\",\n \" (normalizer): BatchNorm1d(110, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (layer_3): LinearLayer(\\n\",\n \" (linear): Linear(in_features=110, out_features=102, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1)\\n\",\n \" (normalizer): BatchNorm1d(102, eps=0.1, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (activation): ReLU()\\n\",\n \" )\\n\",\n \" (output): Linear(in_features=102, out_features=1, bias=True)\\n\",\n \"),\\n\",\n \" on_errors='raise', return_type='any')\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from xenonpy.descriptor import FrozenFeaturizer\\n\",\n \"\\n\",\n \"# --- init FrozenFeaturizer with NN model\\n\",\n \"ff = FrozenFeaturizer(model=trainer.model)\\n\",\n \"ff\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The following code will generate new \\\"neural descriptors\\\" from the corresponding neural network model.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"neural_descriptors = ff.transform(pg_desc, depth=2 ,return_type='df')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here, ``depth=x`` means that the last x hidden layer from the output neuron(s) will be concatenated and used as the neural descriptor.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
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MOL1-1.4994971.254353-1.071701-2.697761-1.807236-2.01011-1.944547-1.533609-1.380140-0.416400...-0.146019-0.599903-0.1688260.1351780.6077790.700811-1.405137-0.209901-0.2971320.071797
MOL2-1.2951681.335794-1.657156-3.456876-1.372355-2.39925-1.761726-1.736894-1.464569-1.349594...0.026329-0.161609-0.5422760.2460881.0470520.067110-1.963629-0.109732-0.3286460.103320
MOL3-1.5146081.205772-1.270560-2.831598-1.677628-2.02982-1.933016-1.568646-1.363735-0.656209...-0.118927-0.526747-0.1727020.1540830.6568890.607651-1.418730-0.193771-0.2783430.091769
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3 rows × 212 columns

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\"\n ],\n \"text/plain\": [\n \" L(-2)_1 L(-2)_2 L(-2)_3 L(-2)_4 L(-2)_5 L(-2)_6 L(-2)_7 \\\\\\n\",\n \"ID_name \\n\",\n \"MOL1 -1.499497 1.254353 -1.071701 -2.697761 -1.807236 -2.01011 -1.944547 \\n\",\n \"MOL2 -1.295168 1.335794 -1.657156 -3.456876 -1.372355 -2.39925 -1.761726 \\n\",\n \"MOL3 -1.514608 1.205772 -1.270560 -2.831598 -1.677628 -2.02982 -1.933016 \\n\",\n \"\\n\",\n \" L(-2)_8 L(-2)_9 L(-2)_10 ... L(-1)_93 L(-1)_94 L(-1)_95 \\\\\\n\",\n \"ID_name ... \\n\",\n \"MOL1 -1.533609 -1.380140 -0.416400 ... -0.146019 -0.599903 -0.168826 \\n\",\n \"MOL2 -1.736894 -1.464569 -1.349594 ... 0.026329 -0.161609 -0.542276 \\n\",\n \"MOL3 -1.568646 -1.363735 -0.656209 ... -0.118927 -0.526747 -0.172702 \\n\",\n \"\\n\",\n \" L(-1)_96 L(-1)_97 L(-1)_98 L(-1)_99 L(-1)_100 L(-1)_101 \\\\\\n\",\n \"ID_name \\n\",\n \"MOL1 0.135178 0.607779 0.700811 -1.405137 -0.209901 -0.297132 \\n\",\n \"MOL2 0.246088 1.047052 0.067110 -1.963629 -0.109732 -0.328646 \\n\",\n \"MOL3 0.154083 0.656889 0.607651 -1.418730 -0.193771 -0.278343 \\n\",\n \"\\n\",\n \" L(-1)_102 \\n\",\n \"ID_name \\n\",\n \"MOL1 0.071797 \\n\",\n \"MOL2 0.103320 \\n\",\n \"MOL3 0.091769 \\n\",\n \"\\n\",\n \"[3 rows x 212 columns]\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"neural_descriptors.head(3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As an example, **-1** in the column names denotes the last layer.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"DescriptorHeatmap(bc=True, col_cluster=True, col_colors=None, col_linkage=None,\\n\",\n \" figsize=(70, 10), mask=None, method='average',\\n\",\n \" metric='euclidean', pivot_kws=None, row_cluster=False,\\n\",\n \" row_colors=None, row_linkage=None, save=None)\"\n ]\n },\n \"execution_count\": 41,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from xenonpy.visualization import DescriptorHeatmap\\n\",\n \"\\n\",\n \"sorted_prop = pg['Refractive Index'].sort_values()\\n\",\n \"sorted_desc = neural_descriptors.loc[sorted_prop.index]\\n\",\n \"\\n\",\n \"heatmap = DescriptorHeatmap( \\n\",\n \" bc=True, # use box-cox transform \\n\",\n \"# save=dict(fname='heatmap_density', dpi=150, bbox_inches='tight'), # save fingure to file\\n\",\n \" figsize=(70, 10))\\n\",\n \"heatmap.fit(sorted_desc)\\n\",\n \"heatmap.draw(sorted_prop)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 6. use neural descriptors to train new models.\\n\",\n \"\\n\",\n \"In this case, **Random Forest** model and **Bayesian Ridge Linear** model will be trained.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# split data\\n\",\n \"from xenonpy.datatools import Splitter\\n\",\n \"\\n\",\n \"y = pg['Refractive Index']\\n\",\n \"splitter = Splitter(len(y), test_size=0.2)\\n\",\n \"\\n\",\n \"X_train, X_test, y_train, y_test = splitter.split(neural_descriptors, y.values.reshape(-1, 1))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\\n\",\n \" max_features='auto', max_leaf_nodes=None,\\n\",\n \" min_impurity_decrease=0.0, min_impurity_split=None,\\n\",\n \" min_samples_leaf=1, min_samples_split=2,\\n\",\n \" min_weight_fraction_leaf=0.0, n_estimators=100,\\n\",\n \" n_jobs=None, oob_score=False, random_state=None,\\n\",\n \" verbose=0, warm_start=False)\"\n ]\n },\n \"execution_count\": 43,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# random forest\\n\",\n \"from sklearn.ensemble import RandomForestRegressor\\n\",\n \"\\n\",\n \"rf = RandomForestRegressor(n_estimators=100)\\n\",\n \"rf.fit(X_train, y_train.ravel())\\n\",\n \"y_pred = rf.predict(X_test)\\n\",\n \"y_fit_pred = rf.predict(X_train)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Missing directory and/or file name information!\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"draw(y_test.ravel(), y_pred, y_train.ravel(), y_fit_pred, prop_name='refractive index')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, compute_score=False, copy_X=True,\\n\",\n \" fit_intercept=True, lambda_1=1e-06, lambda_2=1e-06, n_iter=300,\\n\",\n \" normalize=False, tol=0.001, verbose=False)\"\n ]\n },\n \"execution_count\": 45,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# bayesian linear\\n\",\n \"from sklearn.linear_model import BayesianRidge\\n\",\n \"\\n\",\n \"br = BayesianRidge()\\n\",\n \"br.fit(X_train, y_train.ravel())\\n\",\n \"y_pred = br.predict(X_test)\\n\",\n \"y_fit_pred = br.predict(X_train)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Missing directory and/or file name information!\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"draw(y_test.ravel(), y_pred, y_train.ravel(), y_fit_pred, prop_name='refractive index')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "samples/visualization.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Visualization\\n\",\n \"\\n\",\n \"Descriptors on a set of given materials could be displayed as a heatmap.\\n\",\n \"By using this plotting, the descriptor-property relationships can be understand.\\n\",\n \"This tutorial will show how to draw a heatmap.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### useful functions\\n\",\n \"\\n\",\n \"Run this cell will load some well-used packages such as `numpy`, `pandas`, and so on.\\n\",\n \"The running will also import some valuable functions which are written by ourselves.\\n\",\n \"There is no magic, see `samples/tools.ipynb` to know what will be imported.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%run tools.ipynb\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### draw elemental descriptors heatmap\\n\",\n \"\\n\",\n \"We will use ``Composition`` descriptors and property ``density`` to demonstrate heatmap drawing step-by-step.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 1. calculate descriptors\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" ave:atomic_number ave:atomic_radius ave:atomic_radius_rahm \\\\\\n\",\n \"mp-1008807 24.666667 174.067140 209.333333 \\n\",\n \"mp-1009640 33.000000 137.000000 232.500000 \\n\",\n \"mp-1016825 21.600000 153.120852 203.400000 \\n\",\n \"mp-1017582 59.400000 139.000000 232.800000 \\n\",\n \"mp-1021511 32.000000 140.500000 226.000000 \\n\",\n \"\\n\",\n \" ave:atomic_volume ave:atomic_weight ave:boiling_point \\\\\\n\",\n \"mp-1008807 25.666667 55.004267 1297.063333 \\n\",\n \"mp-1009640 19.050000 77.457330 1931.200000 \\n\",\n \"mp-1016825 13.920000 50.158400 1420.714000 \\n\",\n \"mp-1017582 11.020000 147.233694 4226.000000 \\n\",\n \"mp-1021511 14.300000 72.237000 877.912000 \\n\",\n \"\\n\",\n \" ave:bulk_modulus ave:c6_gb ave:covalent_radius_cordero \\\\\\n\",\n \"mp-1008807 72.868680 1646.90 139.333333 \\n\",\n \"mp-1009640 43.182441 1892.85 137.000000 \\n\",\n \"mp-1016825 76.663625 343.82 102.800000 \\n\",\n \"mp-1017582 150.200000 1037.58 137.600000 \\n\",\n \"mp-1021511 24.850000 272.50 124.500000 \\n\",\n \"\\n\",\n \" ave:covalent_radius_pyykko ... min:num_s_valence min:period \\\\\\n\",\n \"mp-1008807 128.333333 ... 1.0 2.0 \\n\",\n \"mp-1009640 123.500000 ... 2.0 2.0 \\n\",\n \"mp-1016825 96.000000 ... 2.0 2.0 \\n\",\n \"mp-1017582 124.800000 ... 1.0 2.0 \\n\",\n \"mp-1021511 119.500000 ... 2.0 3.0 \\n\",\n \"\\n\",\n \" min:specific_heat min:thermal_conductivity min:vdw_radius \\\\\\n\",\n \"mp-1008807 0.360 0.02658 152.0 \\n\",\n \"mp-1009640 0.192 0.02583 155.0 \\n\",\n \"mp-1016825 0.146 0.02658 152.0 \\n\",\n \"mp-1017582 0.133 13.00000 170.0 \\n\",\n \"mp-1021511 0.232 0.20500 180.0 \\n\",\n \"\\n\",\n \" min:vdw_radius_alvarez min:vdw_radius_mm3 min:vdw_radius_uff \\\\\\n\",\n \"mp-1008807 150.0 182.0 349.5 \\n\",\n \"mp-1009640 166.0 193.0 360.6 \\n\",\n \"mp-1016825 150.0 182.0 302.1 \\n\",\n \"mp-1017582 177.0 204.0 275.4 \\n\",\n \"mp-1021511 189.0 215.0 284.8 \\n\",\n \"\\n\",\n \" min:sound_velocity min:Polarizability \\n\",\n \"mp-1008807 317.5 0.802 \\n\",\n \"mp-1009640 333.6 1.100 \\n\",\n \"mp-1016825 317.5 0.802 \\n\",\n \"mp-1017582 2475.0 1.670 \\n\",\n \"mp-1021511 2310.0 2.900 \\n\",\n \"\\n\",\n \"[5 rows x 290 columns]\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# 1. calculate descriptors\\n\",\n \"\\n\",\n \"from xenonpy.datatools import preset\\n\",\n \"from xenonpy.descriptor import Compositions\\n\",\n \"\\n\",\n \"samples = preset.mp_samples\\n\",\n \"cal = Compositions()\\n\",\n \"\\n\",\n \"descriptor = cal.transform(samples['composition'])\\n\",\n \"descriptor.head(5)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 2. sort descriptors by property values\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"mp-754417 0.242136\\n\",\n \"mp-707454 0.584481\\n\",\n \"mp-569787 0.618365\\n\",\n \"Name: density, dtype: float64\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" ave:atomic_number ave:atomic_radius ave:atomic_radius_rahm \\\\\\n\",\n \"mp-754417 1.000000 79.000000 154.000000 \\n\",\n \"mp-707454 2.529412 86.529412 163.764706 \\n\",\n \"mp-569787 2.500000 86.562500 168.875000 \\n\",\n \"\\n\",\n \" ave:atomic_volume ave:atomic_weight ave:boiling_point \\\\\\n\",\n \"mp-754417 14.100000 1.008000 20.280000 \\n\",\n \"mp-707454 14.794118 4.415529 98.300588 \\n\",\n \"mp-569787 12.062500 4.469221 923.522500 \\n\",\n \"\\n\",\n \" ave:bulk_modulus ave:c6_gb ave:covalent_radius_cordero \\\\\\n\",\n \"mp-754417 56.799640 6.510000 31.000000 \\n\",\n \"mp-707454 54.238542 93.583529 46.117647 \\n\",\n \"mp-569787 107.349730 55.982500 46.562500 \\n\",\n \"\\n\",\n \" ave:covalent_radius_pyykko ... min:num_s_valence min:period \\\\\\n\",\n \"mp-754417 32.000000 ... 1.0 1.0 \\n\",\n \"mp-707454 47.117647 ... 1.0 1.0 \\n\",\n \"mp-569787 47.812500 ... 1.0 1.0 \\n\",\n \"\\n\",\n \" min:specific_heat min:thermal_conductivity min:vdw_radius \\\\\\n\",\n \"mp-754417 1.122728 0.18050 110.0 \\n\",\n \"mp-707454 0.772948 0.02583 110.0 \\n\",\n \"mp-569787 0.900000 0.18050 110.0 \\n\",\n \"\\n\",\n \" min:vdw_radius_alvarez min:vdw_radius_mm3 min:vdw_radius_uff \\\\\\n\",\n \"mp-754417 120.0 162.0 288.6 \\n\",\n \"mp-707454 120.0 162.0 245.1 \\n\",\n \"mp-569787 120.0 162.0 288.6 \\n\",\n \"\\n\",\n \" min:sound_velocity min:Polarizability \\n\",\n \"mp-754417 1270.0 0.666793 \\n\",\n \"mp-707454 333.6 0.666793 \\n\",\n \"mp-569787 1270.0 0.666793 \\n\",\n \"\\n\",\n \"[3 rows x 290 columns]\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# use formation energy as our target\\n\",\n \"property_ = 'density'\\n\",\n \"\\n\",\n \"# --- sort property \\n\",\n \"prop = samples[property_].dropna()\\n\",\n \"sorted_prop = prop.sort_values()\\n\",\n \"sorted_prop.head(3)\\n\",\n \"\\n\",\n \"# --- sort descriptors\\n\",\n \"sorted_desc = descriptor.loc[sorted_prop.index]\\n\",\n \"sorted_desc.head(3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 3. draw the heatmap\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# --- import necessary libraries\\n\",\n \"\\n\",\n \"from xenonpy.visualization import DescriptorHeatmap\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"DescriptorHeatmap(bc=True, col_cluster=True, col_colors=None,\\n\",\n \" col_linkage=None, figsize=(70, 10), mask=None, method='average',\\n\",\n \" metric='euclidean', pivot_kws=None, row_cluster=False,\\n\",\n \" row_colors=None, row_linkage=None,\\n\",\n \" save={'fname': 'heatmap_density', 'dpi': 200, 'bbox_inches': 'tight'})\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"heatmap = DescriptorHeatmap( \\n\",\n \" bc=True, # use box-cox transform \\n\",\n \" save=dict(fname='heatmap_density', dpi=200, bbox_inches='tight'), # save fingure to file\\n\",\n \" figsize=(70, 10))\\n\",\n \"heatmap.fit(sorted_desc)\\n\",\n \"heatmap.draw(sorted_prop)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "setup.cfg", "content": "[aliases]\ntest = pytest\n\n[yapf]\nbased_on_style = google\n\n# The column limit.\ncolumn_limit=120\n" }, { "path": "setup.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\n# -*- coding: utf-8 -*-\n\n# uncomment this for compatibility with py27\n# from __future__ import print_function\n\nimport os\nfrom pathlib import Path\n\nfrom ruamel.yaml import YAML\nfrom setuptools import setup, find_packages\n\n# YOUR PACKAGE NAME\n__package__ = 'xenonpy'\n\n\nclass PackageInfo(object):\n \"\"\"\n Appendix\n --------\n classifiers: https://pypi.python.org/pypi?%3Aaction=list_classifiers\n \"\"\"\n\n def __init__(self, conf_file):\n yaml = YAML(typ='safe')\n yaml.indent(mapping=2, sequence=4, offset=2)\n with open(conf_file, 'r') as f:\n self._info = yaml.load(f)\n self.name = __package__\n\n @staticmethod\n def requirements(filename=\"requirements.txt\"):\n \"\"\"\n Return requirements list from a text file.\n\n Parameters\n ----------\n filename: str\n Name of requirement file.\n\n Returns\n -------\n str-list\n \"\"\"\n try:\n require = list()\n f = open(filename, \"rb\")\n for line in f.read().decode(\"utf-8\").split(\"\\n\"):\n line = line.strip()\n if \"#\" in line:\n line = line[:line.find(\"#\")].strip()\n if line:\n require.append(line)\n except IOError:\n print(\"'{}' not found!\".format(filename))\n require = list()\n\n return require\n\n def __getattr__(self, item: str):\n try:\n return self._info[item]\n except KeyError:\n return None\n\n\nif __name__ == \"__main__\":\n # --- Automatically generate setup parameters ---\n cwd = Path(__file__).parent / __package__ / 'conf.yml'\n package = PackageInfo(str(cwd))\n\n # Your package name\n PKG_NAME = package.name\n\n # Your GitHub user name\n GITHUB_USERNAME = package.github_username\n\n # Short description will be the description on PyPI\n SHORT_DESCRIPTION = package.short_description # GitHub Short Description\n\n # Long description will be the body of content on PyPI page\n LONG_DESCRIPTION = package.long_description\n\n # Version number, VERY IMPORTANT!\n VERSION = package.version + package.release\n\n # Author and Maintainer\n AUTHOR = package.author\n\n # Author email\n AUTHOR_EMAIL = package.author_email\n\n MAINTAINER = package.maintainer\n\n MAINTAINER_EMAIL = package.maintainer_email\n\n PACKAGES, INCLUDE_PACKAGE_DATA, PACKAGE_DATA, PY_MODULES = (\n None,\n None,\n None,\n None,\n )\n\n # It's a directory style package\n if os.path.exists(__file__[:-8] + PKG_NAME):\n # Include all sub packages in package directory\n PACKAGES = find_packages(exclude=[\"*.tests\", \"*.tests.*\", \"tests.*\", \"tests\"])\n\n # Include everything in package directory\n INCLUDE_PACKAGE_DATA = True\n PACKAGE_DATA = {\n \"\": [\"*.*\"],\n }\n\n # It's a single script style package\n elif os.path.exists(__file__[:-8] + PKG_NAME + \".py\"):\n PY_MODULES = [\n PKG_NAME,\n ]\n\n # Project Url\n GITHUB_URL = \"https://github.com/{0}/{1}\".format(GITHUB_USERNAME, PKG_NAME)\n # Use todays date as GitHub release tag\n RELEASE_TAG = 'v' + VERSION\n # Source code download url\n DOWNLOAD_URL = \"https://github.com/{0}/{1}/archive/{2}.tar.gz\".format(GITHUB_USERNAME, PKG_NAME, RELEASE_TAG)\n\n LICENSE = package.license or \"'__license__' not found in '%s.__init__.py'!\" % PKG_NAME\n\n PLATFORMS = [\n \"Windows\",\n \"MacOS\",\n \"Unix\",\n ]\n\n CLASSIFIERS = [\n \"Development Status :: 4 - Beta\",\n \"Intended Audience :: Science/Research\",\n \"License :: OSI Approved :: BSD License\",\n \"Natural Language :: English\",\n \"Operating System :: Microsoft :: Windows\",\n \"Operating System :: MacOS\",\n \"Operating System :: Unix\",\n \"Programming Language :: Python :: 3.7\",\n \"Programming Language :: Python :: 3.8\",\n \"Programming Language :: Python :: 3.9\",\n ]\n\n # Read requirements.txt, ignore comments\n INSTALL_REQUIRES = PackageInfo.requirements()\n SETUP_REQUIRES = ['pytest-runner', 'ruamel.yaml']\n TESTS_REQUIRE = ['pytest']\n setup(python_requires='~=3.7',\n name=PKG_NAME,\n description=SHORT_DESCRIPTION,\n long_description=LONG_DESCRIPTION,\n version=VERSION,\n author=AUTHOR,\n author_email=AUTHOR_EMAIL,\n maintainer=MAINTAINER,\n maintainer_email=MAINTAINER_EMAIL,\n packages=PACKAGES,\n include_package_data=INCLUDE_PACKAGE_DATA,\n package_data=PACKAGE_DATA,\n py_modules=PY_MODULES,\n url=GITHUB_URL,\n download_url=DOWNLOAD_URL,\n classifiers=CLASSIFIERS,\n platforms=PLATFORMS,\n license=LICENSE,\n setup_requires=SETUP_REQUIRES,\n install_requires=INSTALL_REQUIRES,\n tests_require=TESTS_REQUIRE)\n" }, { "path": "tests/contrib/descriptor/test_mordred.py", "content": "# Copyright (c) 2021. TsumiNa. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport pandas as pd\nimport pytest\nfrom mordred._base.pandas_module import MordredDataFrame\nfrom rdkit import Chem\n\nfrom xenonpy.contrib.extend_descriptors.descriptor import Mordred2DDescriptor\n\n\n@pytest.fixture(scope='module')\ndef data():\n # ignore numpy warning\n import warnings\n print('ignore NumPy RuntimeWarning\\n')\n warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n warnings.filterwarnings(\"ignore\", message=\"numpy.ndarray size changed\")\n\n smis = ['C(C(O)C1(O))C(CO)OC1O',\n 'CC(C1=CC=CC=C1)CC(C2=CC=CC=C2)CC(C3=CC=CC=C3)CC(C4=CC=CC=C4)',\n ' CC(C)CC(C)CC(C)',\n 'C(F)C(F)(F)']\n\n mols = [Chem.MolFromSmiles(s) for s in smis]\n\n err_smis = ['C(C(O)C1(O))C(CO)OC1O',\n 'CC(C1=CC=CC=C1)CC(C2=CC=CC=C2)CC(C3=CC=',\n 'Ccccccc',\n 'C(F)C(F)(F)']\n yield dict(smis=smis, mols=mols, err_smis=err_smis)\n\n print('test over')\n\n\ndef test_mordred_1(data):\n mordred = Mordred2DDescriptor()\n desc = mordred.transform(data['smis'])\n assert isinstance(desc, MordredDataFrame)\n\n mordred = Mordred2DDescriptor(return_type='df')\n desc = mordred.transform(data['smis'])\n assert isinstance(desc, pd.DataFrame)\n\n\ndef test_mordred_2(data):\n mordred = Mordred2DDescriptor()\n desc = mordred.transform(data['mols'])\n assert isinstance(desc, MordredDataFrame)\n\n\ndef test_mordred_3(data):\n mordred = Mordred2DDescriptor()\n with pytest.raises(ValueError):\n mordred.transform(data['err_smis'])\n\n\nif __name__ == \"__main__\":\n pytest.main()\n" }, { "path": "tests/contrib/ismd/test_reactant_pool.py", "content": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Dec 1 00:39:20 2020\n\n@author: qiz\n\"\"\"\n\nimport pytest\nimport pandas as pd\nfrom xenonpy.contrib.ismd import ReactantPool\nfrom xenonpy.contrib.ismd.reactant_pool import NotSquareError, SimPoolnotmatchError, ReactantNotInPoolError, NoSampleError\n\n\ndef test_NotSquareError():\n\n reactant_pool = pd.DataFrame({\n \"id\": [0, 1, 2, 3, 4],\n \"SMILES\": [\n \"O=C(Cl)Oc1ccc(Cc2ccc(C(F)(F)F)cc2)cc1\", \"CC(NC(=O)OCc1ccccc1)C(C)NC(=O)c1ccccc1O\", \"OC[C@H]1NCC[C@@H]1O\",\n \"C#CCCN1C(=O)c2ccccc2C1=O\", \"CC(=O)OCCS(=O)(=O)Cl\"\n ]\n })\n sim_df = pd.DataFrame(data=[[1, 2, 3], [4, 5, 6]], columns=['0', '1', '2'])\n with pytest.raises(NotSquareError):\n ReactantPool(pool_df=reactant_pool, sim_df=sim_df, pool_smiles_col='SMILES')\n\n\ndef test_SimPoolnotmatchError():\n\n reactant_pool = pd.DataFrame({\n \"id\": [0, 1, 2, 3, 4],\n \"SMILES\": [\n \"O=C(Cl)Oc1ccc(Cc2ccc(C(F)(F)F)cc2)cc1\", \"CC(NC(=O)OCc1ccccc1)C(C)NC(=O)c1ccccc1O\", \"OC[C@H]1NCC[C@@H]1O\",\n \"C#CCCN1C(=O)c2ccccc2C1=O\", \"CC(=O)OCCS(=O)(=O)Cl\"\n ]\n })\n sim_df = pd.DataFrame(data=[[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=['0', '1', '2'])\n with pytest.raises(SimPoolnotmatchError):\n ReactantPool(pool_df=reactant_pool, sim_df=sim_df, pool_smiles_col='SMILES')\n\n\ndef test_ReactantNotInPoolError():\n reactant_pool = pd.DataFrame({\n \"id\": [0, 1, 2],\n \"SMILES\": [\n \"O=C(Cl)Oc1ccc(Cc2ccc(C(F)(F)F)cc2)cc1\", \"CC(NC(=O)OCc1ccccc1)C(C)NC(=O)c1ccccc1O\", \"OC[C@H]1NCC[C@@H]1O\"\n ]\n })\n sim_df = pd.DataFrame(data=[[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=['0', '1', '2'])\n reactant_pool = ReactantPool(pool_df=reactant_pool, sim_df=sim_df, pool_smiles_col='SMILES')\n with pytest.raises(ReactantNotInPoolError):\n reactant_pool.index2sim(5)\n\n\ndef test_NoSampleError():\n reactant_pool = pd.DataFrame({\n \"id\": [0, 1, 2],\n \"SMILES\": [\n \"O=C(Cl)Oc1ccc(Cc2ccc(C(F)(F)F)cc2)cc1\", \"CC(NC(=O)OCc1ccccc1)C(C)NC(=O)c1ccccc1O\", \"OC[C@H]1NCC[C@@H]1O\"\n ]\n })\n sim_df = pd.DataFrame(data=[[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=['0', '1', '2'])\n reactant_pool = ReactantPool(pool_df=reactant_pool, sim_df=sim_df, pool_smiles_col='SMILES')\n init_samples = pd.DataFrame({'reactant_idx': [], 'reactant_smiles': [], 'product_smiles': []})\n with pytest.raises(NoSampleError):\n reactant_pool.proposal(init_samples)\n\n\nclass Reactor():\n\n def __init__(self):\n \"\"\"\n A chemical reaction prediction model\n ----------\n Parameters:\n model : A molecular transformer model for reaction prediction\n \"\"\"\n\n def react(self, reactant_list) -> list:\n \"\"\"\n Tokenize a SMILES molecule or reaction\n ----------\n Parameters:\n reactant_list : list of reactant \n Returns:\n product_list: all_predictions is a list of `batch_size` lists of `n_best` predictions\n \"\"\"\n product_list = [r.replace('.', '') for r in reactant_list]\n return product_list\n\n\ndef test_poolAssignment():\n\n reactant_pool = pd.DataFrame({\"id\": [0, 1, 2], \"SMILES\": [\"C\", \"O\", \"N\"]})\n sim_df = pd.DataFrame(data=[[1, 0.7, 0.2], [0.7, 1, 0], [0.2, 0, 1]], columns=[0, 1, 2])\n fake_reactor = Reactor()\n test_pool = ReactantPool(pool_df=reactant_pool, sim_df=sim_df, reactor=fake_reactor, pool_smiles_col='SMILES')\n assert test_pool._pool_smiles_col in test_pool._pool_df\n\n\ndef test_sampleAssignment():\n\n reactant_pool = pd.DataFrame({\"id\": [0, 1, 2], \"SMILES\": [\"C\", \"O\", \"N\"]})\n sim_df = pd.DataFrame(data=[[1, 0.7, 0.2], [0.7, 1, 0], [0.2, 0, 1]], columns=[0, 1, 2])\n fake_reactor = Reactor()\n test_pool = ReactantPool(pool_df=reactant_pool,\n sim_df=sim_df,\n reactor=fake_reactor,\n pool_smiles_col='SMILES',\n sample_reactant_idx_col='reactant_idx',\n sample_reactant_smiles_col='reactant_smiles',\n sample_product_smiles_col='product_smiles')\n sample_df = pd.DataFrame({\n 'reactant_idx': [[2, 1], [0, 1], [1, 0], [0, 2]],\n 'reactant_smiles': [\"N.O\", \"C.O\", \"O.C\", \"C.N\"],\n 'product_smiles': [\"NO\", \"CO\", \"OC\", \"CN\"]\n })\n\n assert isinstance(test_pool._pool_df, pd.DataFrame)\n assert isinstance(test_pool._sim_df, pd.DataFrame)\n assert test_pool._sample_reactant_idx_col in sample_df\n assert test_pool._sample_reactant_smiles_col in sample_df\n assert test_pool._sample_product_smiles_col in sample_df\n\n\ndef test_singleProposal():\n reactant_pool = pd.DataFrame({\"id\": [0, 1, 2], \"SMILES\": [\"C\", \"O\", \"N\"]})\n sim_df = pd.DataFrame(data=[[1, 0.7, 0.2], [0.7, 1, 0], [0.2, 0, 1]], columns=[0, 1, 2])\n fake_reactor = Reactor()\n test_pool = ReactantPool(pool_df=reactant_pool,\n sim_df=sim_df,\n reactor=fake_reactor,\n pool_smiles_col='SMILES',\n sample_reactant_idx_col='reactant_idx',\n sample_reactant_smiles_col='reactant_smiles',\n sample_product_smiles_col='product_smiles')\n sample_df = pd.DataFrame({\n 'reactant_idx': [[2, 1], [0], [1, 0, 2], [0, 2]],\n 'reactant_smiles': [\"N.O\", \"C\", \"O.C.N\", \"C.N\"],\n 'product_smiles': [\"NO\", \"C\", \"OCN\", \"CN\"]\n })\n\n old_list = [[2, 1], [0], [1, 0, 2], [0, 2]]\n new_list = [test_pool.single_proposal(reactant) for reactant in sample_df[test_pool._sample_reactant_idx_col]]\n\n assert len(old_list) == len(new_list)\n assert all([len(o) == len(n) for o, n in zip(old_list, new_list)])\n\n def list_only_one_diff(list1, list2):\n return len(set(list1) - set(list2)) == 1\n\n assert all([list_only_one_diff(o, n) for o, n in zip(old_list, new_list)])\n\n\ndef test_reactant_id2smiles():\n reactant_pool = pd.DataFrame({\"id\": [0, 1, 2], \"SMILES\": [\"C\", \"O\", \"N\"]})\n sim_df = pd.DataFrame(data=[[1, 0.7, 0.2], [0.7, 1, 0], [0.2, 0, 1]], columns=[0, 1, 2])\n fake_reactor = Reactor()\n test_pool = ReactantPool(pool_df=reactant_pool,\n sim_df=sim_df,\n reactor=fake_reactor,\n pool_smiles_col='SMILES',\n sample_reactant_idx_col='reactant_idx',\n sample_reactant_smiles_col='reactant_smiles',\n sample_product_smiles_col='product_smiles')\n sample_df = pd.DataFrame({\n 'reactant_idx': [[2, 1], [0], [1, 0, 2], [0, 2]],\n 'reactant_smiles': [\"\", \"\", \"\", \"\"],\n 'product_smiles': [\"\", \"\", \"\", \"\"]\n })\n sample_df[test_pool._sample_reactant_smiles_col] = [\n test_pool.single_index2reactant(id_str) for id_str in sample_df[test_pool._sample_reactant_idx_col]\n ]\n\n assert len(sample_df[test_pool._sample_reactant_idx_col]) == len(sample_df[test_pool._sample_reactant_smiles_col])\n assert all([\n len(idx) == len(smi.split(test_pool._splitter)) for idx, smi in zip(\n sample_df[test_pool._sample_reactant_idx_col], sample_df[test_pool._sample_reactant_smiles_col])\n ])\n\n\ndef test_reactant2product():\n reactant_pool = pd.DataFrame({\"id\": [0, 1, 2], \"SMILES\": [\"C\", \"O\", \"N\"]})\n sim_df = pd.DataFrame(data=[[1, 0.7, 0.2], [0.7, 1, 0], [0.2, 0, 1]], columns=[0, 1, 2])\n fake_reactor = Reactor()\n test_pool = ReactantPool(pool_df=reactant_pool,\n sim_df=sim_df,\n reactor=fake_reactor,\n pool_smiles_col='SMILES',\n sample_reactant_idx_col='reactant_idx',\n sample_reactant_smiles_col='reactant_smiles',\n sample_product_smiles_col='product_smiles')\n sample_df = pd.DataFrame({\n 'reactant_idx': [[2, 1], [0], [1, 0, 2], [0, 2]],\n 'reactant_smiles': [\"N.O\", \"C\", \"O.C.N\", \"C.N\"],\n 'product_smiles': [\"\", \"\", \"\", \"\"]\n })\n sample_df[test_pool._sample_product_smiles_col] = test_pool._reactor.react(\n sample_df[test_pool._sample_reactant_smiles_col])\n\n assert len(sample_df[test_pool._sample_product_smiles_col]) == len(sample_df[test_pool._sample_reactant_smiles_col])\n\n\ndef test_proposal():\n reactant_pool = pd.DataFrame({\"id\": [0, 1, 2], \"SMILES\": [\"C\", \"O\", \"N\"]})\n sim_df = pd.DataFrame(data=[[1, 0.7, 0.2], [0.7, 1, 0], [0.2, 0, 1]], columns=[0, 1, 2])\n fake_reactor = Reactor()\n test_pool = ReactantPool(pool_df=reactant_pool,\n sim_df=sim_df,\n reactor=fake_reactor,\n pool_smiles_col='SMILES',\n sample_reactant_idx_col='reactant_idx',\n sample_reactant_smiles_col='reactant_smiles',\n sample_product_smiles_col='product_smiles')\n old_df = pd.DataFrame({\n 'reactant_idx': [[2, 1], [0], [1, 0, 2], [0, 2]],\n 'reactant_smiles': [\"\", \"\", \"\", \"\"],\n 'product_smiles': [\"\", \"\", \"\", \"\"]\n })\n new_df = test_pool.proposal(old_df)\n\n assert len(old_df) == len(new_df)\n assert list(old_df) == list(new_df)\n assert all([\n oidx != nidx\n for oidx, nidx in zip(old_df[test_pool._sample_reactant_idx_col], new_df[test_pool._sample_reactant_idx_col])\n ])\n assert not new_df.isnull().values.any()\n" }, { "path": "tests/contrib/ismd/test_reactor.py", "content": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Dec 1 00:39:20 2020\n\n@author: qiz\n\"\"\"\n\nimport pytest\nfrom xenonpy.contrib.ismd.reactor import smi_tokenizer, SMILESInvalidError\n\n\ndef test_SMILESInvalidError():\n with pytest.raises(SMILESInvalidError):\n smi_tokenizer(\"ABCDE\")\n" }, { "path": "tests/datatools/ids.txt", "content": "mp-862776\nmp-30759\nmp-768076\nmp-9996\nmvc-2470\n" }, { "path": "tests/datatools/test_dataset.py", "content": "# Copyright (c) 2021. TsumiNa. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom os import remove\nfrom pathlib import Path\n\nimport joblib\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nfrom xenonpy.datatools import Dataset\n\n\n@pytest.fixture(scope='module')\ndef test_data():\n # ignore numpy warning\n import warnings\n print('ignore NumPy RuntimeWarning\\n')\n warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n warnings.filterwarnings(\"ignore\", message=\"numpy.ndarray size changed\")\n\n file_path = Path(__file__).parent\n file_name = 'rename.txt'\n file_url = 'https://raw.githubusercontent.com/yoshida-lab/XenonPy/master/.github/fetch_test.txt'\n\n # create data\n ary = [[1, 2], [3, 4]]\n df = pd.DataFrame(ary)\n\n pkl_path = str(file_path / 'test.pkl.z')\n df_path = str(file_path / 'test.pd.xz')\n csv_path = str(file_path / 'test.csv')\n\n joblib.dump(ary, pkl_path)\n df.to_csv(csv_path)\n df.to_pickle(df_path)\n\n yield file_name, file_url, file_path\n\n tmp = file_path / file_name\n\n if tmp.exists():\n remove(str(tmp))\n\n tmp = file_path / 'fetch_test.txt'\n if tmp.exists():\n remove(str(tmp))\n\n tmp = file_path / 'test.pd'\n if tmp.exists():\n remove(str(tmp))\n\n tmp = file_path / 'test.str'\n if tmp.exists():\n remove(str(tmp))\n\n tmp = file_path / 'test.pkl'\n if tmp.exists():\n remove(str(tmp))\n\n remove(pkl_path)\n remove(df_path)\n remove(csv_path)\n\n print('test over')\n\n\ndef test_dataset_1(test_data):\n path = Path(__file__).parents[0]\n\n ds = Dataset()\n assert ds._backend == 'pandas'\n assert ds._paths == ('.',)\n assert ds._prefix == ()\n\n with pytest.warns(RuntimeWarning):\n Dataset(str(path), str(path))\n\n with pytest.raises(RuntimeError):\n Dataset('no_exist_dir')\n\n ds = Dataset(str(path), backend='pickle', prefix=('datatools',))\n assert hasattr(ds, 'datatools_test')\n tmp = '%s' % ds\n assert 'Dataset' in tmp\n\n\ndef test_dataset_2(test_data):\n path = Path(__file__).parents[0]\n\n ds = Dataset(str(path), backend='pickle')\n assert hasattr(ds, 'test')\n\n tmp = ds.test\n assert isinstance(tmp, list)\n assert tmp == [[1, 2], [3, 4]]\n\n tmp = ds.csv\n assert hasattr(tmp, 'test')\n tmp = tmp.test\n assert isinstance(tmp, pd.DataFrame)\n assert np.all(np.array([[0, 1, 2], [1, 3, 4]]) == tmp.values)\n\n tmp = ds.csv(str(path / 'test.csv'))\n assert np.all(np.array([[0, 1, 2], [1, 3, 4]]) == tmp.values)\n\n tmp = ds.pandas\n assert hasattr(tmp, 'test')\n tmp = tmp.test\n assert isinstance(tmp, pd.DataFrame)\n assert np.all(np.array([[1, 2], [3, 4]]) == tmp.values)\n\n tmp = ds.pandas(str(path / 'test.pd.xz'))\n assert np.all(np.array([[1, 2], [3, 4]]) == tmp.values)\n\n tmp = ds.pickle\n assert hasattr(tmp, 'test')\n tmp = tmp.test\n assert isinstance(tmp, list)\n assert [[1, 2], [3, 4]] == tmp\n\n tmp = ds.pickle(str(path / 'test.pkl.z'))\n assert [[1, 2], [3, 4]] == tmp\n\n\ndef test_dataset_3(test_data):\n with pytest.raises(RuntimeError, match='is not a legal path'):\n Dataset.from_http(test_data[1], 'not_exist')\n\n tmp = Dataset.from_http(test_data[1], save_to=test_data[2])\n assert tmp == str(test_data[2] / 'fetch_test.txt')\n assert Path(tmp).exists()\n with open(tmp, 'r') as f:\n assert f.readline() == 'Test xenonpy.utils.Loader._fetch_data'\n\n tmp = Dataset.from_http(test_data[1], save_to=test_data[2], filename=test_data[0])\n assert tmp == str(test_data[2] / 'rename.txt')\n assert Path(tmp).exists()\n with open(tmp, 'r') as f:\n assert f.readline() == 'Test xenonpy.utils.Loader._fetch_data'\n\n\ndef test_dataset_4(test_data):\n file_path = test_data[2]\n data = pd.DataFrame([[1, 2], [3, 4]])\n\n file = file_path / 'test.pd'\n Dataset.to(data, file)\n assert file.exists()\n\n file = file_path / 'test.str'\n Dataset.to(data, str(file))\n assert file.exists()\n\n file = file_path / 'test.pkl'\n Dataset.to(data.values, file)\n assert file.exists()\n\n\nif __name__ == \"__main__\":\n pytest.main()\n" }, { "path": "tests/datatools/test_preset.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom os import remove, getenv\nfrom pathlib import Path\n\nimport pytest\n\nfrom xenonpy import __cfg_root__\nfrom xenonpy.datatools import Preset, preset\n\n\n@pytest.fixture(scope='module')\ndef setup():\n # ignore numpy warning\n import warnings\n print('ignore NumPy RuntimeWarning\\n')\n warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n warnings.filterwarnings(\"ignore\", message=\"numpy.ndarray size changed\")\n\n yield\n\n print('test over')\n\n\ndef test_preset_1():\n path = Path(__cfg_root__) / 'dataset' / 'elements.pd.xz'\n if path.exists():\n remove(str(path))\n\n with pytest.raises(RuntimeError, match=\"data elements not exist\"):\n preset.elements\n\n preset.sync('elements')\n preset.elements\n\n path = Path(__cfg_root__) / 'dataset' / 'elements_completed.pd.xz'\n if path.exists():\n remove(str(path))\n\n with pytest.raises(RuntimeError, match=\"data elements_completed not exist\"):\n preset.elements_completed\n\n preset.sync('elements_completed')\n preset.elements_completed\n\n path = Path(__cfg_root__) / 'dataset' / 'atom_init.pd.xz'\n if path.exists():\n remove(str(path))\n\n with pytest.raises(RuntimeError, match=\"data atom_init not exist\"):\n preset.atom_init\n\n preset.sync('atom_init')\n preset.atom_init\n\n\ndef test_preset_2():\n assert Preset() is preset\n e = preset.elements\n assert 118 == e.shape[0], 'should have 118 elements'\n assert 74 == e.shape[1], 'should have 74 features'\n\n e = preset.elements_completed\n assert 94 == e.shape[0], 'should have 94 completed elements'\n assert 58 == e.shape[1], 'should have 58 completed features'\n\n\ndef test_preset_3():\n assert hasattr(preset, 'dataset_elements')\n e = preset.dataset_elements\n assert 118 == e.shape[0], 'should have 118 elements'\n assert 74 == e.shape[1], 'should have 74 features'\n\n assert hasattr(preset, 'dataset_elements_completed')\n e = preset.dataset_elements_completed\n assert 94 == e.shape[0], 'should have 94 completed elements'\n assert 58 == e.shape[1], 'should have 58 completed features'\n\n\n@pytest.mark.skip(reason=\"fetching test is no need\")\ndef test_preset_4():\n ids = Path(__file__).parent / 'ids.txt'\n samples = Path(__cfg_root__) / 'userdata' / 'mp_samples.pd.xz'\n save_to = Path(__file__).parent / 'tmp.pd.xz'\n\n with pytest.raises(ValueError):\n preset.build('no_exist')\n\n with pytest.raises(RuntimeError):\n preset.build('mp_samples')\n\n with pytest.raises(RuntimeError):\n preset.build('mp_samples')\n\n key = getenv('api_key')\n with pytest.raises(ValueError, match='mp_ids'):\n preset.build('mp_samples', api_key=key, mp_ids=10)\n\n preset.build('mp_samples', api_key=key, mp_ids=str(ids))\n assert samples.exists()\n\n remove(str(samples))\n preset.build('mp_samples',\n api_key=key,\n mp_ids=['mp-862776', 'mp-30759', 'mp-768076', 'mp-9996', 'mvc-2470'])\n assert samples.exists()\n remove(str(samples))\n\n preset.build('mp_samples', api_key=key, save_to=str(save_to), mp_ids=str(ids))\n assert save_to.exists()\n\n remove(str(save_to))\n preset.build('mp_samples',\n api_key=key,\n save_to=str(save_to),\n mp_ids=['mp-862776', 'mp-30759', 'mp-768076', 'mp-9996', 'mvc-2470'])\n assert save_to.exists()\n remove(str(save_to))\n\n\nif __name__ == '__main__':\n pytest.main()\n" }, { "path": "tests/datatools/test_scaler.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nfrom scipy.stats import boxcox\nfrom sklearn.preprocessing import StandardScaler\n\nfrom xenonpy.datatools.transform import Scaler\n\n\n@pytest.fixture(scope='module')\ndef data():\n # ignore numpy warning\n import warnings\n print('ignore NumPy RuntimeWarning\\n')\n warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n warnings.filterwarnings(\"ignore\", message=\"numpy.ndarray size changed\")\n\n # prepare test data\n raw = np.array([1., 2., 3., 4.])\n a = raw.reshape(-1, 1)\n raw_4x1 = a\n raw_4x4 = np.concatenate((a, a, a, a), axis=1)\n\n # raw_shift = raw - raw.min() + 1e-9\n a_, _ = boxcox(raw)\n a_ = a_.reshape(-1, 1)\n trans_4x1 = a_\n trans_4x4 = np.concatenate((a_, a_, a_, a_), axis=1)\n\n raw_err = np.array([1., 1., 1., 1.])\n a = raw_err.reshape(-1, 1)\n raw_err_4x1 = a\n raw_err_4x4 = np.concatenate((a, a, a, a), axis=1)\n\n a_ = np.log(raw_err)\n a_ = a_.reshape(-1, 1)\n trans_err_4x1 = a_\n trans_err_4x4 = np.concatenate((a_, a_, a_, a_), axis=1)\n yield raw_4x1, raw_4x4, trans_4x1, trans_4x4, raw_err_4x1, raw_err_4x4, trans_err_4x1, trans_err_4x4\n\n print('test over')\n\n\ndef test_scaler_1(data):\n scaler = Scaler()\n assert scaler.transform([1, 2, 3, 4]) == [1, 2, 3, 4]\n\n raw_4x4 = pd.DataFrame(data[1], index=list('abcd'), columns=list('jklm'))\n ret = scaler.min_max().fit_transform(raw_4x4)\n assert isinstance(ret, pd.DataFrame)\n assert ret.index.tolist() == list('abcd')\n assert ret.columns.tolist() == list('jklm')\n\n\ndef test_scaler_2(data):\n bc = Scaler().box_cox()\n std = Scaler().standard()\n bc_std = Scaler().box_cox().standard()\n bc_trans = bc.fit_transform(data[0])\n std_trans = std.fit_transform(bc_trans)\n bc_std_trans = bc_std.fit_transform(data[0])\n assert np.allclose(bc_trans, data[2])\n assert np.allclose(std_trans, StandardScaler().fit_transform(bc_trans))\n assert np.allclose(bc_std_trans, std_trans)\n inverse = bc_std.inverse_transform(bc_std_trans)\n assert np.allclose(inverse, data[0])\n\n\nif __name__ == \"__main__\":\n pytest.main()\n" }, { "path": "tests/datatools/test_splitter.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nfrom pandas import DataFrame, Series\n\nfrom xenonpy.datatools import Splitter\n\n\n@pytest.fixture(scope='module')\ndef data():\n # ignore numpy warning\n import warnings\n print('ignore NumPy RuntimeWarning\\n')\n warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n warnings.filterwarnings(\"ignore\", message=\"numpy.ndarray size changed\")\n\n # prepare test data\n l = list(range(10))\n array = np.arange(10)\n matrix = np.arange(100).reshape(10, 10)\n df = DataFrame(matrix)\n se = Series(array)\n flag = list('abcba') * 2\n\n yield array, matrix, df, se, flag, l\n\n print('test over')\n\n\ndef test_init_1():\n with pytest.raises(RuntimeError, match=' can be zero only if is not none'):\n Splitter(10, test_size=0, k_fold=None)\n\n\ndef test_roll_1():\n sp = Splitter(10, test_size=0.3, random_state=123456)\n train, test = sp.split()\n assert train.size == 7\n assert test.size == 3\n train_, test_ = sp.split()\n assert train_.size == 7\n assert test_.size == 3\n assert np.array_equal(train_, train)\n assert np.array_equal(test_, test)\n\n sp.roll(random_state=123456)\n train_, test_ = sp.split()\n assert train_.size == 7\n assert test_.size == 3\n assert np.array_equal(train_, train)\n assert np.array_equal(test_, test)\n\n sp.roll()\n train_, test_ = sp.split()\n assert not np.array_equal(train_, train)\n assert not np.array_equal(test_, test)\n\n\ndef test_split_1(data):\n sp = Splitter(10)\n with pytest.raises(RuntimeError, match='parameter must be set'):\n for _ in sp.cv():\n pass\n\n assert sp.size == 10\n train, test = sp.split()\n assert train.size == 8\n assert test.size == 2\n\n train, test = sp.split(data[0])\n for d in train:\n assert d in data[0]\n for d in test:\n assert d in data[0]\n\n\ndef test_split_2(data):\n sp = Splitter(10, test_size=0.1)\n with pytest.raises(ValueError, match='parameters must have size 10 for dim 0'):\n sp.split(data[1][1:])\n _, test = sp.split(data[1])\n assert test[0] in data[1]\n\n\ndef test_split_3(data):\n sp = Splitter(10)\n sp.split(data[0])\n sp.split(data[1])\n sp.split(data[2])\n sp.split(data[3])\n sp.split(data[5])\n\n with pytest.raises(\n TypeError,\n match=\n \" must be list, numpy.ndarray, pandas.DataFrame, or pandas.Series but got \"\n ):\n sp.split('illegal data')\n\n\ndef test_split_4(data):\n sp = Splitter(10)\n x, x_, y, y_, z, z_ = sp.split(data[1], data[2], data[3])\n assert isinstance(x, np.ndarray)\n assert isinstance(x_, np.ndarray)\n assert isinstance(y, pd.DataFrame)\n assert isinstance(y_, pd.DataFrame)\n assert isinstance(z, pd.Series)\n assert isinstance(z_, pd.Series)\n\n\ndef test_cv_1(data):\n sp = Splitter(10, test_size=0, k_fold=5, random_state=123456)\n with pytest.raises(RuntimeError, match='split action is illegal because `test_size` is none'):\n sp.split()\n\n tmp = []\n for i, (x, x_) in enumerate(sp.cv()):\n assert x.size == 8\n assert x_.size == 2\n assert isinstance(x, np.ndarray)\n assert isinstance(x_, np.ndarray)\n tmp.append(x_)\n assert i == 4\n tmp = np.concatenate(tmp)\n assert not np.array_equal(tmp, data[0])\n tmp = np.sort(tmp)\n assert np.array_equal(tmp, data[0])\n\n tmp = []\n for x, x_ in sp.cv(less_for_train=True):\n assert x.size == 2\n assert x_.size == 8\n tmp.append(x)\n tmp = np.concatenate(tmp)\n tmp = np.sort(tmp)\n assert np.array_equal(tmp, data[0])\n\n\ndef test_cv_2(data):\n sp = Splitter(10, test_size=0.2, k_fold=4)\n tmp = []\n tmp_x_ = []\n for x, x_, _x_ in sp.cv():\n assert x.size == 6\n assert x_.size == 2\n assert _x_.size == 2\n assert isinstance(x, np.ndarray)\n assert isinstance(x_, np.ndarray)\n assert isinstance(_x_, np.ndarray)\n tmp_x_.append(_x_)\n tmp.append(x_)\n assert np.array_equal(tmp_x_[0], tmp_x_[1])\n assert np.array_equal(tmp_x_[0], tmp_x_[2])\n assert np.array_equal(tmp_x_[0], tmp_x_[3])\n tmp = np.concatenate(tmp)\n assert tmp.size == 8\n tmp = np.concatenate([tmp, tmp_x_[0]])\n tmp = np.sort(tmp)\n assert np.array_equal(tmp, data[0])\n\n\ndef test_cv_3(data):\n np.random.seed(123456)\n sp = Splitter(10, test_size=0, k_fold=data[4])\n tmp = []\n for x, x_ in sp.cv():\n assert isinstance(x, np.ndarray)\n assert isinstance(x_, np.ndarray)\n assert x.size + x_.size == 10\n tmp.append(x_)\n sizes = np.sort([x.size for x in tmp])\n assert np.array_equal(sizes, [2, 4, 4])\n tmp = np.concatenate(tmp)\n tmp = np.sort(tmp)\n assert np.array_equal(tmp, data[0])\n\n\ndef test_cv_4(data):\n sp = Splitter(10, test_size=0, k_fold=5, random_state=123456)\n tmp = []\n for _, x_ in sp.cv():\n tmp.append(x_)\n tmp = np.concatenate(tmp)\n\n tmp_ = []\n for _, x_ in sp.cv():\n tmp_.append(x_)\n tmp_ = np.concatenate(tmp_)\n assert np.array_equal(tmp, tmp_)\n\n tmp_ = []\n sp.roll()\n for _, x_ in sp.cv():\n tmp_.append(x_)\n tmp_ = np.concatenate(tmp_)\n assert not np.array_equal(tmp, tmp_)\n\n\ndef test_cv_5(data):\n sp = Splitter(10, test_size=0, k_fold=5)\n for _, x_, _, y_, _, z_ in sp.cv(data[1], data[2], data[3]):\n assert isinstance(x_, np.ndarray)\n assert isinstance(y_, pd.DataFrame)\n assert isinstance(z_, pd.Series)\n assert x_.size == 20\n assert y_.size == 20\n assert z_.size == 2\n\n\nif __name__ == \"__main__\":\n pytest.main()\n" }, { "path": "tests/descriptor/1.cif", "content": "# generated using pymatgen\ndata_LiP\n_symmetry_space_group_name_H-M P2_1/c\n_cell_length_a 5.59387161\n_cell_length_b 4.97038519\n_cell_length_c 10.25075070\n_cell_angle_alpha 90.00000000\n_cell_angle_beta 118.15551780\n_cell_angle_gamma 90.00000000\n_symmetry_Int_Tables_number 14\n_chemical_formula_structural LiP\n_chemical_formula_sum 'Li8 P8'\n_cell_volume 251.28369181\n_cell_formula_units_Z 8\nloop_\n _symmetry_equiv_pos_site_id\n _symmetry_equiv_pos_as_xyz\n 1 'x, y, z'\n 2 '-x, -y, -z'\n 3 '-x, y+1/2, -z+1/2'\n 4 'x, -y+1/2, z+1/2'\nloop_\n _atom_site_type_symbol\n _atom_site_label\n _atom_site_symmetry_multiplicity\n _atom_site_fract_x\n _atom_site_fract_y\n _atom_site_fract_z\n _atom_site_occupancy\n Li Li1 4 0.274232 0.658316 0.970214 1\n Li Li2 4 0.282992 0.111753 0.170700 1\n P P3 4 0.183355 0.605096 0.207084 1\n P P4 4 0.196818 0.156027 0.888258 1\n" }, { "path": "tests/descriptor/2.cif", "content": "# generated using pymatgen\ndata_P2S5\n_symmetry_space_group_name_H-M P-1\n_cell_length_a 9.67888928\n_cell_length_b 9.82382054\n_cell_length_c 9.82486748\n_cell_angle_alpha 92.89432746\n_cell_angle_beta 109.71183722\n_cell_angle_gamma 100.66057747\n_symmetry_Int_Tables_number 2\n_chemical_formula_structural P2S5\n_chemical_formula_sum 'P8 S20'\n_cell_volume 857.80483511\n_cell_formula_units_Z 4\nloop_\n _symmetry_equiv_pos_site_id\n _symmetry_equiv_pos_as_xyz\n 1 'x, y, z'\n 2 '-x, -y, -z'\nloop_\n _atom_site_type_symbol\n _atom_site_label\n _atom_site_symmetry_multiplicity\n _atom_site_fract_x\n _atom_site_fract_y\n _atom_site_fract_z\n _atom_site_occupancy\n P P1 2 0.077394 0.281371 0.836935 1\n P P2 2 0.177140 0.025568 0.341075 1\n P P3 2 0.255828 0.812219 0.097467 1\n P P4 2 0.261669 0.705713 0.429975 1\n S S5 2 0.024578 0.716419 0.003194 1\n S S6 2 0.030568 0.608994 0.334269 1\n S S7 2 0.053651 0.071717 0.753780 1\n S S8 2 0.198430 0.215693 0.416323 1\n S S9 2 0.278972 0.021633 0.181198 1\n S S10 2 0.283009 0.915060 0.512339 1\n S S11 2 0.287214 0.367994 0.923295 1\n S S12 2 0.346487 0.808814 0.951137 1\n S S13 2 0.357745 0.607022 0.586473 1\n S S14 2 0.363210 0.703362 0.269659 1\n" }, { "path": "tests/descriptor/test_base_desc.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom multiprocessing import cpu_count\n\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nfrom xenonpy.descriptor.base import BaseDescriptor, BaseFeaturizer\n\n\n@pytest.fixture(scope='module')\ndef data():\n # ignore numpy warning\n import warnings\n print('ignore NumPy RuntimeWarning\\n')\n warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n warnings.filterwarnings(\"ignore\", message=\"numpy.ndarray size changed\")\n\n class _FakeFeaturier1(BaseFeaturizer):\n\n def __init__(self, n_jobs=1):\n super().__init__(n_jobs=n_jobs)\n\n def featurize(self, *x, **kwargs):\n return x[0]\n\n @property\n def feature_labels(self):\n return ['label1']\n\n class _FakeFeaturier2(BaseFeaturizer):\n\n def __init__(self, n_jobs=1):\n super().__init__(n_jobs=n_jobs)\n\n def featurize(self, *x, **kwargs):\n return x[0]\n\n @property\n def feature_labels(self):\n return ['label2']\n\n class _FakeFeaturier3(BaseFeaturizer):\n\n def __init__(self, n_jobs=1):\n super().__init__(n_jobs=n_jobs)\n\n def featurize(self, *x, **kwargs):\n return x[0]\n\n @property\n def feature_labels(self):\n return ['label3']\n\n class _FakeDescriptor(BaseDescriptor):\n\n def __init__(self, featurizers='all', on_errors='raise'):\n super().__init__(featurizers=featurizers, on_errors=on_errors)\n self.g1 = _FakeFeaturier1()\n self.g1 = _FakeFeaturier2()\n self.g2 = _FakeFeaturier3()\n\n # prepare test data\n yield dict(featurizer=_FakeFeaturier1, descriptor=_FakeDescriptor)\n\n print('test over')\n\n\ndef test_base_feature_props():\n\n class _FakeFeaturier(BaseFeaturizer):\n\n def __init__(self):\n super().__init__()\n\n def featurize(self, *x, **kwargs):\n return x[0]\n\n @property\n def feature_labels(self):\n return ['labels']\n\n bf = _FakeFeaturier()\n with pytest.raises(ValueError, match='`on_errors`'):\n bf.on_errors = 'illegal'\n\n with pytest.raises(ValueError, match='`return_type`'):\n bf.return_type = 'illegal'\n\n # test n_jobs\n assert bf.n_jobs == cpu_count()\n bf.n_jobs = 1\n assert bf.n_jobs == 1\n bf.n_jobs = 100\n assert bf.n_jobs == cpu_count()\n\n # test citation, author\n assert bf.citations == 'No citations'\n assert bf.authors == 'anonymous'\n\n\ndef test_base_feature_1(data):\n featurizer = data['featurizer']()\n assert isinstance(featurizer, BaseFeaturizer)\n assert featurizer.n_jobs == 1\n assert featurizer.featurize(10) == 10\n assert featurizer.feature_labels == ['label1']\n with pytest.raises(TypeError):\n featurizer.fit_transform(56)\n with pytest.raises(RuntimeError):\n featurizer.fit_transform(pd.DataFrame([[0, 1], [1, 2]]))\n\n\ndef test_base_feature_2(data):\n featurizer = data['featurizer']()\n ret = featurizer.fit_transform([1, 2, 3, 4])\n assert isinstance(ret, list)\n ret = featurizer.fit_transform([1, 2, 3, 4], return_type='array')\n assert isinstance(ret, np.ndarray)\n ret = featurizer.fit_transform([1, 2, 3, 4], return_type='df')\n assert isinstance(ret, pd.DataFrame)\n ret = featurizer.fit_transform(np.array([1, 2, 3, 4]))\n assert isinstance(ret, np.ndarray)\n assert (ret == np.array([1, 2, 3, 4])).all()\n ret = featurizer.fit_transform(pd.Series([1, 2, 3, 4]))\n assert isinstance(ret, pd.DataFrame)\n assert (ret.values.ravel() == np.array([1, 2, 3, 4])).all()\n\n\ndef test_base_feature_para(data):\n featurizer = data['featurizer'](n_jobs=2)\n featurizer.parallel_verbose = 1\n ret = featurizer.fit_transform([1, 2, 3, 4])\n assert isinstance(ret, list)\n ret = featurizer.fit_transform([1, 2, 3, 4], return_type='array')\n assert isinstance(ret, np.ndarray)\n ret = featurizer.fit_transform([1, 2, 3, 4], return_type='df')\n assert isinstance(ret, pd.DataFrame)\n ret = featurizer.fit_transform(np.array([1, 2, 3, 4]))\n assert isinstance(ret, np.ndarray)\n assert (ret == np.array([1, 2, 3, 4])).all()\n ret = featurizer.fit_transform(pd.Series([1, 2, 3, 4]))\n assert isinstance(ret, pd.DataFrame)\n assert (ret.values.ravel() == np.array([1, 2, 3, 4])).all()\n\n\ndef test_base_feature_3(data):\n\n class _ErrorFeaturier(BaseFeaturizer):\n\n def __init__(self, n_jobs=1, on_errors='raise'):\n super().__init__(n_jobs=n_jobs, on_errors=on_errors)\n\n def featurize(self, *x):\n raise ValueError()\n\n @property\n def feature_labels(self):\n return ['labels']\n\n featurizer = _ErrorFeaturier()\n assert isinstance(featurizer, BaseFeaturizer)\n with pytest.raises(ValueError):\n featurizer.fit_transform([1, 2, 3, 4])\n\n featurizer = _ErrorFeaturier(on_errors='keep')\n tmp = featurizer.fit_transform([1, 2, 3, 4])\n assert np.alltrue([isinstance(e[0], ValueError) for e in tmp])\n\n featurizer = _ErrorFeaturier(on_errors='nan')\n tmp = featurizer.fit_transform([1, 2, 3, 4])\n assert np.alltrue([np.isnan(e[0]) for e in tmp])\n\n\ndef test_base_feature_4(data):\n featurizer = data['featurizer']()\n tmp_in = pd.DataFrame([[11, 12], [21, 22], [31, 32], [41, 42]])\n ret = featurizer.transform(tmp_in, target_col=0)\n assert isinstance(ret, pd.DataFrame)\n assert (ret.values.ravel() == np.array([11, 21, 31, 41])).all()\n ret = featurizer.transform(tmp_in, return_type='array', target_col=0)\n assert isinstance(ret, np.ndarray)\n ret = featurizer.transform(tmp_in, return_type='df', target_col=0)\n assert isinstance(ret, pd.DataFrame)\n ret = featurizer.transform(tmp_in, target_col=1)\n assert isinstance(ret, pd.DataFrame)\n assert (ret.values.ravel() == np.array([12, 22, 32, 42])).all()\n\n\ndef test_base_descriptor_1(data):\n bd = BaseDescriptor()\n\n # test n_jobs\n assert bd.elapsed == 0\n\n with pytest.raises(NotImplementedError, match='no featurizers'):\n bd.feature_labels\n\n # test featurizers list\n assert hasattr(bd, '__featurizers__')\n assert not bd.__featurizer_sets__\n\n with pytest.raises(ValueError, match='`on_errors`'):\n bd.on_errors = 'illegal'\n\n\ndef test_base_descriptor_2(data):\n bd = data['descriptor']()\n assert len(bd.all_featurizers) == 3\n assert 'g1' in bd.__featurizer_sets__\n assert 'g2' in bd.__featurizer_sets__\n assert bd.feature_labels == (('g1', ['label1', 'label2']), ('g2', [\n 'label3',\n ]))\n\n assert bd.on_errors == 'raise'\n assert bd.__featurizer_sets__['g1'][0].on_errors == 'raise'\n bd.set_params(on_errors='nan')\n assert bd.on_errors == 'nan'\n assert bd.__featurizer_sets__['g1'][0].on_errors == 'nan'\n\n\ndef test_base_descriptor_3(data):\n bd = data['descriptor']()\n with pytest.raises(TypeError):\n bd.fit([1, 2, 3, 4]),\n\n\ndef test_base_descriptor_4(data):\n ff = data['featurizer']\n\n class FakeDescriptor(BaseDescriptor):\n\n def __init__(self):\n super().__init__()\n self.g1 = ff()\n self.g1 = ff()\n\n with pytest.raises(RuntimeError):\n FakeDescriptor()\n\n class FakeDescriptor(BaseDescriptor):\n\n def __init__(self):\n super().__init__()\n self.g1 = ff()\n\n bd = FakeDescriptor()\n assert bd.feature_labels == ['label1']\n\n\ndef test_base_descriptor_5(data):\n bd = data['descriptor']()\n x = pd.DataFrame({'g1': [1, 2, 3, 4], 'g3': [10, 20, 30, 40]})\n tmp = bd.fit_transform(x)\n assert isinstance(tmp, pd.DataFrame)\n assert np.all(tmp.values == np.array([[1, 1], [2, 2], [3, 3], [4, 4]]))\n\n x = pd.Series([1, 2, 3, 4], name='g1')\n tmp = bd.transform(x)\n assert isinstance(tmp, pd.DataFrame)\n assert np.all(tmp.values == np.array([[1, 1], [2, 2], [3, 3], [4, 4]]))\n\n\ndef test_base_descriptor_6(data):\n bd = data['descriptor']()\n x = pd.DataFrame({'g3': [1, 2, 3, 4], 'g4': [1, 2, 3, 4]})\n with pytest.warns(UserWarning):\n bd.fit(x)\n with pytest.warns(UserWarning):\n bd.transform(x, target_col='g3')\n bd.fit(x, g1='g3', g2='g4')\n bd.transform(x)\n\n x = pd.DataFrame({'g1': [1, 2, 3, 4], 'g2': [1, 2, 3, 4]})\n with pytest.warns(UserWarning):\n bd.transform(x, target_col='g1')\n bd.transform(x, g3='g1', g4='g2')\n with pytest.warns(UserWarning):\n bd.transform(x, target_col='g2')\n bd.fit_transform(x, g3='g1', g4='g2')\n\n\ndef test_base_descriptor_7(data):\n # after parallel function in BaseFeaturizer is updated to handle pd.DataFrame (loop rows),\n # target_col parameters can be deleted in this test\n bd = data['descriptor']()\n x = pd.Series([1, 2, 3, 4], name='g3')\n with pytest.warns(UserWarning):\n bd.fit(x)\n with pytest.warns(UserWarning):\n bd.transform(x, target_col='g3')\n bd.fit(x, g1='g3')\n bd.transform(x, target_col='g3')\n\n x = pd.Series([1, 2, 3, 4], name='g1')\n print(type(x))\n with pytest.warns(UserWarning):\n bd.transform(x, target_col='g1')\n bd.transform(x, g3='g1')\n with pytest.warns(UserWarning):\n bd.transform(x, target_col='g1')\n bd.fit_transform(x, g3='g1')\n\n\ndef test_base_descriptor_8(data):\n x = pd.DataFrame({'g1': [1, 2, 3, 4], 'g2': [10, 20, 30, 40]})\n\n bd = data['descriptor'](featurizers='_FakeFeaturier1')\n tmp = bd.transform(x)\n assert bd.featurizers == ('_FakeFeaturier1',)\n assert tmp.shape == (4, 1)\n assert tmp.columns == ['label1']\n assert np.all(tmp.values == np.array([[1], [2], [3], [4]]))\n\n bd = data['descriptor'](featurizers=['_FakeFeaturier3'])\n tmp = bd.transform(x)\n assert bd.featurizers == ('_FakeFeaturier3',)\n assert tmp.shape == (4, 1)\n assert tmp.columns == ['label3']\n assert np.all(tmp.values == np.array([[10], [20], [30], [40]]))\n\n bd = data['descriptor'](featurizers=['_FakeFeaturier1', '_FakeFeaturier3'])\n tmp = bd.transform(x)\n assert tmp.shape == (4, 2)\n assert tmp.columns.tolist() == ['label1', 'label3']\n assert bd.featurizers == ('_FakeFeaturier1', '_FakeFeaturier3')\n assert np.all(tmp.values == np.array([[1, 10], [2, 20], [3, 30], [4, 40]]))\n\n bd = data['descriptor']()\n # use all featurizers by default\n tmp = bd.fit_transform(x)\n assert tmp.shape == (4, 3)\n assert tmp.columns.tolist() == ['label1', 'label2', 'label3']\n assert np.all(tmp.values == np.array([[1, 1, 10], [2, 2, 20], [3, 3, 30], [4, 4, 40]]))\n\n\nif __name__ == \"__main__\":\n pytest.main()\n" }, { "path": "tests/descriptor/test_crystal_graph.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom os import getenv\nfrom pathlib import Path\n\nimport pandas as pd\nimport pytest\nimport torch\nfrom pymatgen.core import Structure as pmg_S\n\nfrom xenonpy.descriptor import CrystalGraphFeaturizer\nfrom xenonpy.datatools import preset\n\npytestmark = pytest.mark.skipif(getenv('TRAVIS_OS_NAME') == 'osx',\n reason=\"no way of currently testing this, skipping tests\")\n\n\n@pytest.fixture(scope='module')\ndef data():\n preset.sync('atom_init')\n\n # prepare path\n pwd = Path(__file__).parent\n cif1 = pmg_S.from_file(str(pwd / '1.cif'))\n cif2 = pmg_S.from_file(str(pwd / '2.cif'))\n\n # ignore numpy warning\n import warnings\n print('ignore NumPy RuntimeWarning\\n')\n warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n warnings.filterwarnings(\"ignore\", message=\"numpy.ndarray size changed\")\n\n cifs = pd.Series([cif1, cif2], name='structure')\n yield cifs\n\n print('test over')\n\n\ndef test_crystal_1(data):\n cg = CrystalGraphFeaturizer()\n assert cg.feature_labels == ['atom_feature', 'neighbor_feature', 'neighbor_idx']\n\n tmp = cg.node_features(data[0])\n assert isinstance(tmp, torch.Tensor)\n assert tmp.shape == (16, 92)\n\n edges, ids = cg.edge_features(data[0])\n assert isinstance(edges, torch.Tensor)\n assert edges.shape == (16, 12, 41)\n\n assert isinstance(ids, torch.Tensor)\n assert ids.shape == (16, 12)\n\n\n#\n# def test_crystal_2(data):\n# cg = CrystalGraphFeaturizer(max_num_nbr=15, radius=10, atom_feature='elements')\n#\n# tmp = cg.node_features(data[0])\n# assert isinstance(tmp, torch.Tensor)\n# assert tmp.shape == (16, 58)\n#\n# edges, ids = cg.edge_features(data[0])\n# assert isinstance(edges, torch.Tensor)\n# assert edges.shape == (16, 15, 51)\n#\n# assert isinstance(ids, torch.Tensor)\n# assert ids.shape == (16, 15)\n#\n#\n# def test_crystal_3(data):\n# cg = CrystalGraphFeaturizer()\n# tmp = cg.transform(data)\n#\n# assert isinstance(tmp, pd.DataFrame)\n# assert tmp.shape == (2, 3)\n# assert tmp.values[0, 0].shape == (16, 92)\n# assert tmp.values[0, 1].shape == (16, 12, 41)\n# assert tmp.values[0, 2].shape == (16, 12)\n#\n#\n# def test_crystal_4(data):\n# cg = CrystalGraphFeaturizer(atom_feature=lambda s: [1, 2, 3, 4], n_jobs=1)\n# tmp = cg.transform(data)\n#\n# assert isinstance(tmp, pd.DataFrame)\n# assert tmp.shape == (2, 3)\n# assert tmp.values[0, 0][0].numpy().tolist() == [1, 2, 3, 4]\n\nif __name__ == \"__main__\":\n pytest.main()\n" }, { "path": "tests/descriptor/test_elemental.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nfrom xenonpy.descriptor import Compositions, Counting\nfrom xenonpy.descriptor.base import BaseCompositionFeaturizer\n\n\ndef test_compositional_feature_1():\n\n class FakeFeaturizer(BaseCompositionFeaturizer):\n\n @property\n def feature_labels(self):\n return ['min:' + s for s in self._elements]\n\n def mix_function(self, elems, nums):\n elems_ = self._elements.loc[elems, :]\n w_ = nums / np.sum(nums)\n return w_.dot(elems_)\n\n desc = FakeFeaturizer(n_jobs=1)\n tmp = desc.fit_transform([{'H': 2}])\n\n assert isinstance(tmp, list)\n\n with pytest.raises(KeyError):\n desc.fit_transform([{'Bl': 2}])\n\n desc = FakeFeaturizer(n_jobs=1, on_errors='nan')\n tmp = desc.fit_transform([{'Bl': 2}])\n assert np.all(np.isnan(tmp[0]))\n\n\ndef test_counting_feature_1():\n comps = [{'H': 2, 'He': 1}, {'Li': 1}]\n counting = Counting(return_type='array')\n tmp = counting.transform(comps)\n assert tmp.shape == (2, 94)\n\n assert np.all(tmp[0, :2] == np.array([2, 1]))\n assert np.all(tmp[0, 2:] == np.zeros(92))\n assert np.all(tmp[1, :3] == np.array([0, 0, 1]))\n\n ohv = Counting(return_type='array', one_hot_vec=True)\n tmp = ohv.transform(comps)\n assert tmp.shape == (2, 94)\n\n assert np.all(tmp[0, :2] == np.array([1, 1]))\n assert np.all(tmp[0, 2:] == np.zeros(92))\n assert np.all(tmp[1, :3] == np.array([0, 0, 1]))\n\n\ndef test_comp_descriptor_1():\n desc = Compositions(n_jobs=1)\n\n desc.fit_transform(pd.Series([{'H': 2}], name='composition'))\n desc.fit_transform(pd.Series([{'H': 2}], name='other'))\n\n tmp1 = desc.fit_transform(pd.Series([{'H': 2}], name='other'), composition='other')\n tmp2 = desc.fit_transform([{'H': 2}])\n\n assert tmp1.shape == (1, 290)\n assert isinstance(tmp1, pd.DataFrame)\n assert isinstance(tmp2, pd.DataFrame)\n\n assert np.all(tmp1.values == tmp2.values)\n\n\nif __name__ == \"__main__\":\n pytest.main()\n" }, { "path": "tests/descriptor/test_fingerprint.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nfrom rdkit import Chem\n\nfrom xenonpy.descriptor import ECFP, FCFP, AtomPairFP, RDKitFP, MACCS, Fingerprints\n\n\n@pytest.fixture(scope='module')\ndef data():\n # ignore numpy warning\n import warnings\n print('ignore NumPy RuntimeWarning\\n')\n warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n warnings.filterwarnings(\"ignore\", message=\"numpy.ndarray size changed\")\n\n smis = ['C(C(O)C1(O))C(CO)OC1O',\n 'CC(C1=CC=CC=C1)CC(C2=CC=CC=C2)CC(C3=CC=CC=C3)CC(C4=CC=CC=C4)',\n ' CC(C)CC(C)CC(C)',\n 'C(F)C(F)(F)']\n\n mols = [Chem.MolFromSmiles(s) for s in smis]\n\n err_smis = ['C(C(O)C1(O))C(CO)OC1O',\n 'CC(C1=CC=CC=C1)CC(C2=CC=CC=C2)CC(C3=CC=',\n 'Ccccccc',\n 'C(F)C(F)(F)']\n yield dict(smis=smis, mols=mols, err_smis=err_smis)\n\n print('test over')\n\n\ndef test_ecfp_1(data):\n fps = ECFP(n_jobs=1)\n fps.transform(data['mols'])\n\n with pytest.raises(TypeError):\n fps.transform(data['smis'])\n\n\ndef test_ecfp_2(data):\n fps = ECFP(n_jobs=1, input_type='smiles')\n with pytest.raises(TypeError):\n fps.transform(data['mols'])\n\n fps.transform(data['smis'])\n\n\ndef test_ecfp_3(data):\n fps = ECFP(n_jobs=1, input_type='any')\n fps.transform(data['mols'] + data['smis'])\n\n\ndef test_ecfp_4(data):\n fps = ECFP(n_jobs=1, input_type='any')\n with pytest.raises(ValueError):\n fps.transform(data['err_smis'])\n\n fps = ECFP(n_jobs=1, input_type='any', on_errors='nan')\n ret = fps.transform(data['err_smis'])\n assert pd.DataFrame(data=ret).shape == (4, 2048)\n assert np.isnan(ret[1][10])\n assert np.isnan(ret[2][20])\n\n\ndef test_fcfp_1(data):\n fps = FCFP(n_jobs=1)\n fps.transform(data['mols'])\n\n with pytest.raises(TypeError):\n fps.transform(data['smis'])\n\n\ndef test_fcfp_2(data):\n fps = FCFP(n_jobs=1, input_type='smiles')\n with pytest.raises(TypeError):\n fps.transform(data['mols'])\n\n fps.transform(data['smis'])\n\n\ndef test_fcfp_3(data):\n fps = FCFP(n_jobs=1, input_type='any')\n fps.transform(data['mols'] + data['smis'])\n\n\ndef test_fcfp_4(data):\n fps = FCFP(n_jobs=1, input_type='any')\n with pytest.raises(ValueError):\n fps.transform(data['err_smis'])\n\n fps = FCFP(n_jobs=1, input_type='any', on_errors='nan')\n ret = fps.transform(data['err_smis'])\n assert pd.DataFrame(data=ret).shape == (4, 2048)\n assert np.isnan(ret[1][10])\n assert np.isnan(ret[2][20])\n\n\ndef test_apfp_1(data):\n fps = AtomPairFP(n_jobs=1)\n fps.transform(data['mols'])\n\n with pytest.raises(TypeError):\n fps.transform(data['smis'])\n\n\ndef test_apfp_2(data):\n fps = AtomPairFP(n_jobs=1, input_type='smiles')\n with pytest.raises(TypeError):\n fps.transform(data['mols'])\n\n fps.transform(data['smis'])\n\n\ndef test_apfp_3(data):\n fps = AtomPairFP(n_jobs=1, input_type='any')\n fps.transform(data['mols'] + data['smis'])\n\n\ndef test_apfp_4(data):\n fps = AtomPairFP(n_jobs=1, input_type='any')\n with pytest.raises(ValueError):\n fps.transform(data['err_smis'])\n\n fps = AtomPairFP(n_jobs=1, input_type='any', on_errors='nan')\n ret = fps.transform(data['err_smis'])\n assert pd.DataFrame(data=ret).shape == (4, 2048)\n assert np.isnan(ret[1][10])\n assert np.isnan(ret[2][20])\n\n\ndef test_rdfp_1(data):\n fps = RDKitFP(n_jobs=1)\n fps.transform(data['mols'])\n\n with pytest.raises(TypeError):\n fps.transform(data['smis'])\n\n\ndef test_rdfp_2(data):\n fps = RDKitFP(n_jobs=1, input_type='smiles')\n with pytest.raises(TypeError):\n fps.transform(data['mols'])\n\n fps.transform(data['smis'])\n\n\ndef test_rdfp_3(data):\n fps = RDKitFP(n_jobs=1, input_type='any')\n fps.transform(data['mols'] + data['smis'])\n\n\ndef test_rdfp_4(data):\n fps = RDKitFP(n_jobs=1, input_type='any')\n with pytest.raises(ValueError):\n fps.transform(data['err_smis'])\n\n fps = RDKitFP(n_jobs=1, input_type='any', on_errors='nan')\n ret = fps.transform(data['err_smis'])\n assert pd.DataFrame(data=ret).shape == (4, 2048)\n assert np.isnan(ret[1][10])\n assert np.isnan(ret[2][20])\n\n\ndef test_maccs_1(data):\n fps = MACCS(n_jobs=1)\n fps.transform(data['mols'])\n\n with pytest.raises(TypeError):\n fps.transform(data['smis'])\n\n\ndef test_maccs_2(data):\n fps = MACCS(n_jobs=1, input_type='smiles')\n with pytest.raises(TypeError):\n fps.transform(data['mols'])\n\n fps.transform(data['smis'])\n\n\ndef test_maccs_3(data):\n fps = MACCS(n_jobs=1, input_type='any')\n fps.transform(data['mols'] + data['smis'])\n\n\ndef test_maccs4(data):\n fps = MACCS(n_jobs=1, input_type='any')\n with pytest.raises(ValueError):\n fps.transform(data['err_smis'])\n\n fps = MACCS(n_jobs=1, input_type='any', on_errors='nan')\n ret = fps.transform(data['err_smis'])\n assert pd.DataFrame(data=ret).shape == (4, 167)\n assert np.isnan(ret[1][10])\n assert np.isnan(ret[2][20])\n\n\ndef test_fps_1(data):\n fps = Fingerprints(n_jobs=1)\n fps.transform(data['mols'])\n\n with pytest.raises(TypeError):\n fps.transform(data['smis'])\n\n\ndef test_fps_2(data):\n fps = Fingerprints(n_jobs=1, input_type='smiles')\n with pytest.raises(TypeError):\n fps.transform(data['mols'])\n\n fps.transform(data['smis'])\n\n\ndef test_fps_3(data):\n fps = Fingerprints(n_jobs=1, input_type='any')\n fps.transform(data['mols'] + data['smis'])\n\n\ndef test_fps_4(data):\n fps = Fingerprints(n_jobs=1, input_type='any')\n with pytest.raises(ValueError):\n fps.transform(data['err_smis'])\n\n fps = Fingerprints(n_jobs=1, input_type='any', on_errors='nan')\n ret = fps.transform(data['err_smis'])\n assert isinstance(ret, pd.DataFrame)\n assert ret.shape == (4, 16759)\n assert np.isnan(ret.values[1][10])\n assert np.isnan(ret.values[2][20])\n\n\ndef test_fps_5(data):\n fps = Fingerprints(featurizers='ECFP', n_jobs=1, input_type='any', on_errors='nan', n_bits=100)\n ret = fps.transform(data['err_smis'])\n assert isinstance(ret, pd.DataFrame)\n assert ret.shape == (4, 100)\n assert np.isnan(ret.values[1][10])\n assert np.isnan(ret.values[2][20])\n\n\ndef test_fps_6(data):\n fps = Fingerprints(n_jobs=1, input_type='any', on_errors='nan', counting=True)\n ret = fps.transform(data['smis'])\n assert isinstance(ret, pd.DataFrame)\n assert ret.shape == (4, 16759)\n\n\nif __name__ == \"__main__\":\n pytest.main()\n" }, { "path": "tests/descriptor/test_frozen_feature.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\n\nfrom collections import OrderedDict\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nfrom torch import nn\n\nfrom xenonpy.descriptor import FrozenFeaturizer\nfrom xenonpy.model import SequentialLinear\n\n\n@pytest.fixture(scope='module')\ndef data():\n # prepare path\n model1 = nn.Sequential(\n nn.Sequential(OrderedDict(layer=nn.Linear(10, 7), act_func=nn.ReLU())),\n nn.Sequential(OrderedDict(layer=nn.Linear(7, 4), act_func=nn.ReLU())),\n nn.Sequential(OrderedDict(layer=nn.Linear(4, 1))),\n )\n model2 = SequentialLinear(10, 1, h_neurons=(7, 4))\n descriptor1 = np.random.rand(10, 10)\n descriptor2 = np.random.rand(20, 10)\n index = ['id_' + str(i) for i in range(10)]\n df = pd.DataFrame(descriptor1, index=index)\n\n # ignore numpy warning\n import warnings\n print('ignore NumPy RuntimeWarning\\n')\n warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n warnings.filterwarnings(\"ignore\", message=\"numpy.ndarray size changed\")\n\n yield model1, descriptor1, df, index, descriptor2, model2\n\n print('test over')\n\n\ndef test_frozen_featurizer_1(data):\n ff = FrozenFeaturizer(data[0])\n ff_features = ff.fit_transform(data[1])\n assert ff_features.shape == (10, 11)\n ff_features = ff.fit_transform(data[1], depth=1)\n assert ff_features.shape == (10, 4)\n ff_features = ff.fit_transform(data[1])\n assert ff_features.shape == (10, 11)\n\n ff = FrozenFeaturizer(data[5])\n ff_features = ff.fit_transform(data[1])\n assert ff_features.shape == (10, 11)\n ff_features = ff.fit_transform(data[1], depth=1)\n assert ff_features.shape == (10, 4)\n ff_features = ff.fit_transform(data[1])\n assert ff_features.shape == (10, 11)\n\n\ndef test_frozen_featurizer_2(data):\n ff = FrozenFeaturizer(data[0])\n ff_features = ff.fit_transform(data[2])\n assert ff_features.shape == (10, 11)\n assert isinstance(ff_features, pd.DataFrame)\n assert ff_features.index.tolist() == list(data[3])\n\n _hlayers = [7, 4]\n labels = ['L(' + str(i - len(_hlayers)) + ')_' + str(j + 1)\n for i in range(2)\n for j in range(_hlayers[i])]\n assert ff_features.columns.tolist() == list(labels)\n\n ff = FrozenFeaturizer(data[5])\n ff_features = ff.fit_transform(data[2])\n assert ff_features.shape == (10, 11)\n assert isinstance(ff_features, pd.DataFrame)\n assert ff_features.index.tolist() == list(data[3])\n\n _hlayers = [7, 4]\n labels = ['L(' + str(i - len(_hlayers)) + ')_' + str(j + 1)\n for i in range(2)\n for j in range(_hlayers[i])]\n assert ff_features.columns.tolist() == list(labels)\n\n\ndef test_frozen_featurizer_3(data):\n ff = FrozenFeaturizer(data[0])\n with pytest.raises(ValueError):\n ff.feature_labels\n ff.fit_transform(data[2])\n ff.feature_labels\n\n ff = FrozenFeaturizer(data[5])\n with pytest.raises(ValueError):\n ff.feature_labels\n ff.fit_transform(data[2])\n ff.feature_labels\n\n\ndef test_frozen_featurizer_4(data):\n ff = FrozenFeaturizer(data[0])\n ff_features = ff.fit_transform(data[1])\n assert ff_features.shape == (10, 11)\n ff_features = ff.fit_transform(data[4])\n assert ff_features.shape == (20, 11)\n\n ff = FrozenFeaturizer(data[5])\n ff_features = ff.fit_transform(data[1])\n assert ff_features.shape == (10, 11)\n ff_features = ff.fit_transform(data[4])\n assert ff_features.shape == (20, 11)\n\n\ndef test_frozen_featurizer_5(data):\n ff = FrozenFeaturizer(data[0])\n\n with pytest.warns(UserWarning, match='is over the max depth of hidden layers starting at the given'):\n ff_features = ff.fit_transform(data[1], depth=2, n_layer=3)\n assert ff_features.shape == (10, 11)\n with pytest.warns(UserWarning, match='is greater than the max depth of hidden layers'):\n ff_features = ff.fit_transform(data[4], depth=3, n_layer=2)\n assert ff_features.shape == (20, 11)\n\n ff = FrozenFeaturizer(data[5])\n ff_features = ff.transform(data[1], depth=2, return_type='df')\n\n desc_ori = []\n for i in [1, 2]:\n desc_ori.append(ff_features[ff_features.columns[[f'L(-{i})' in s for s in ff_features.columns.values]]])\n\n desc_new = []\n for i in [1, 2]:\n for j in [1, 2]:\n desc_new.append(ff.transform(data[1], depth=j, n_layer=i, return_type='df'))\n\n for i in range(2):\n tmp = desc_new[i] == desc_ori[i]\n assert all(tmp.all())\n\n\nif __name__ == \"__main__\":\n pytest.main()\n" }, { "path": "tests/descriptor/test_structures.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom pathlib import Path\n\nimport pandas as pd\nimport pytest\nfrom pymatgen.core import Structure as pmg_S\n\nfrom xenonpy.descriptor import RadialDistributionFunction, Structures, OrbitalFieldMatrix\n\n\n@pytest.fixture(scope='module')\ndef data():\n # prepare path\n pwd = Path(__file__).parent\n cif1 = pmg_S.from_file(str(pwd / '1.cif'))\n cif2 = pmg_S.from_file(str(pwd / '2.cif'))\n\n # ignore numpy warning\n import warnings\n print('ignore NumPy RuntimeWarning\\n')\n warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n warnings.filterwarnings(\"ignore\", message=\"numpy.ndarray size changed\")\n\n cifs = pd.Series([cif1, cif2], name='structure')\n yield cifs\n\n print('test over')\n\n\ndef test_rdf(data):\n RadialDistributionFunction().fit_transform(data)\n assert True\n\n\ndef test_ofm(data):\n OrbitalFieldMatrix().fit_transform(data)\n assert True\n\n\ndef test_structure(data):\n Structures().fit_transform(data)\n assert True\n\n\nif __name__ == \"__main__\":\n pytest.main()\n" }, { "path": "tests/inverse/polymer_test_data.csv", "content": ",smiles,bandgap,refractive_index,density,glass_transition_temperature\n0,C([*])C([*])(SCCC),5.2,1.74,1.04,259\n1,C([*])C([*])(C(=O)OCCSCCC#N),5.63,1.73,1.31,232\n2,O([*])C(=O)OC(C=C1)=CC=C1C(C=C2)=CC=C2CC(C=C3)=CC=C3C(C=C4)=CC=C4([*]),3.49,1.88,1.21,413\n3,O([*])CCOCCOCCOCCOC(=O)NCCCCCCNC([*])(=O),6.47,1.66,1.16,265\n4,C([*])C([*])(C(=O)OCC(F)(F)C(F)(F)OC(F)(F)F),6.95,1.61,1.62,225\n5,CC(S1)=CC=C1C(=S)O,2.7,2.01,1.59,416\n6,C([*])(CC)=CCC([*]),5.45,1.65,0.91,222\n7,NC(=O)C1=CC=C(S1)C(=O),2.73,2.06,1.58,375\n8,C([*])C([*])(C)(C(=O)OCCCCCC),5.61,1.66,1.02,265\n9,C(C=C1)=CC=C1C(C=C2)=CC=C2C(C=C3)=CC=C3O,2.97,1.98,1.16,439\n10,C(=O)C1=CC=C(S1)C(C=C2)=CC=C2C(=S),1.45,2.39,1.48,432\n11,C([*])C([*])(C)(C(=O)OC(C)C),5.73,1.67,1.08,348\n12,C([*])C([*])(C)(C(=O)OCCCC),5.62,1.66,1.06,291\n13,CC(C=C1)=CC=C1C(S2)=CC=C2C(=S),2.17,2.12,1.35,433\n14,C([*])C([*])(C(=O)NCCCCCCCC),6.45,1.66,0.98,214\n15,C([*])C([*])(c1ccc(C(C)=O)cc1),3.83,1.79,1.12,373\n16,C([*])(C=C1)=CC=C1C(=O)OCCCCCCOC([*])(=O),4.82,1.77,1.19,301\n17,C([*])C([*])(c1ccc(OCC)cc1),4.51,1.74,1.05,350\n18,C([*])C([*])(c1cc(C)ccc1F),5.1,1.75,1.03,392\n19,C([*])C([*])(c1ccc(Br)cc1),3.16,1.92,1.24,387\n20,C([*])C([*])([Cl])(OC(=O)C),4.97,1.71,1.43,406\n21,C(F)(F)CC(F)(F)C(=S),2.61,1.8,1.75,340\n22,CC(C=C1)=CC=C1C(=S)C2=CC=C(S2),2.58,2.03,1.35,432\n23,CC(=S)NO,2.97,1.91,1.59,332\n24,CC(=O)C1=CC=C(S1)C(=O),2.91,1.94,1.51,367\n25,NC(=O)C(C=C1)=CC=C1O,3.78,1.87,1.34,457\n26,C([*])C([*])(c1ccc(CCCC)cc1),4.69,1.72,0.96,309\n27,C([*])C([*])(C(C)(C)C),7.33,1.62,0.87,334\n28,NC(=S)NC(C=C1)=CC=C1NC(=S)NC(C)COCCOC(C)COCC(C),4.33,1.79,1.22,350\n29,C([*])C([*])(CCC),7.2,1.65,0.87,251\n30,C([*])C([*])(C(=O)N(C)C),5.33,1.67,1.1,368\n31,C([*])C([*])(N3C1=CC=CC=C1C2=CC=CC=C23),3.04,2.15,1.23,466\n32,C(=O)C1=CC=C(S1)OC(=S),1.9,2.14,1.71,420\n33,C([*])C([*])(C1=CC=C(C)C=C1),4.82,1.75,1.0,381\n34,NC(C=C1)=CC=C1C(=O)C2=CC=C(S2),2.34,2.09,1.45,473\n35,C(=O)C1=CC=C(S1)OC2=CC=C(S2),2.7,2.0,1.53,423\n36,NC(=S)C(=O)C(=S),1.36,2.31,1.62,334\n37,N(C1(=O))C(=O)C(=C2)C1=CC(C3(=O))=C2C(=O)N3CCCN(C4(=O))C(=O)C(=C5)C4=CC(C6(=O))=C5C(=O)N6C(C)COC(C),3.33,1.93,1.38,337\n38,C(C=C1)=CC=C1C(S2)=CC=C2OC(=S),2.09,2.22,1.44,434\n39,C(=NC1=C2)OC1=CC(O3)=C2N=C3C(C=C4)=CC=C4,2.53,2.27,1.42,455\n40,C([*])C([*])(C1=C(OCCCC)C=CC(Br)C1),4.59,1.68,1.08,313\n41,N([*])C(=O)CCCCCCCCCCC([*]),6.65,1.65,0.98,316\n42,C([*])C([*])(C(=O)N(C)c1ccccc1),4.02,1.8,1.19,434\n43,COCOCCCO,7.29,1.66,1.14,188\n44,CC(=O)OCC(=O)O,6.26,1.72,1.48,315\n45,CC(=O)C(=S)C(C=C1)=CC=C1,2.32,2.0,1.39,353\n46,C([*])C([*])(C#N)(C(=O)OCCCCCCCC),6.66,1.65,1.1,321\n47,C(S1)=CC=C1C(S2)=CC=C2C(=S)C(S3)=CC=C3C(=O)NC(=O)N,1.9,2.3,1.63,440\n48,C([*])(=O)C(C=C1)=CC=C1C(=O)OC(C=C2)=CC=C2C(C)(C)C(C=C3)=CC=C3O([*]),3.86,1.83,1.22,422\n49,C(=O)C(C=C1)=CC=C1C2=CC=C(S2)O,2.62,2.07,1.37,420\n50,C([*])C([*])(C)(C(=O)OCCC(C)CC(C)(C)C),5.83,1.64,0.99,303\n51,O([*])C(=O)C(C=C1)=CC=C1CCCCC(C=C2)=CC=C2C([*])(=O),4.04,1.81,1.19,346\n52,C([*])CCCOCO([*]),7.42,1.66,1.06,184\n53,C([*])C([*])(C1=C(OCCC)C=CC(Br)C1),4.46,1.7,1.1,322\n54,CCC(=O)C(=S),2.22,1.94,1.58,345\n55,C([*])C([*])(C)(C(=O)OCCC1=CC=CC=C1),4.44,1.73,1.11,327\n56,C([*])C([*])(C(=C)OCC(C#N)CCCCC),6.6,1.64,1.05,348\n57,CC(=S)CC(=S),2.22,1.95,1.7,334\n58,CCC(C=C1)=CC=C1O,4.28,1.78,1.11,339\n59,C([*])([Cl])(F)C([*])(F)(F),3.28,1.77,2.0,357\n60,C(=O)C(C=C1)=CC=C1C(=O)C2=CC=C(S2),2.47,2.06,1.41,416\n61,C([*])C([*])(C)(C(=O)OCC(O)C),5.86,1.67,1.15,363\n62,C([*])C([*])(C(=O)OCCCCSC),4.44,1.73,1.14,205\n63,CCOCCO,6.36,1.66,1.1,207\n64,C([*])C([*])(C(=O)OCCCCCC),5.78,1.66,1.04,203\n65,NC(=O)OC(C=C1)=CC=C1,3.72,1.87,1.43,460\n66,C(C=C1)=CC=C1OC(S2)=CC=C2O,4.01,1.85,1.33,390\n67,C(C=C1)=CC=C1OC(=O)CC(=O)CCO,4.12,1.75,1.29,275\n68,C([*])C([*])(CCCCCCCCCCCCCC),7.71,1.65,0.87,275\n69,C([*])C([*])(C)(C(=O)OCCCCCCCCCCCC),6.54,1.65,0.97,233\n70,C([*])=CC([*])(C1=CC=CC=C1),4.83,1.76,1.06,341\n71,C([*])C([*])(C1=C(F)C=CC(F)=C1),4.94,1.76,1.07,379\n72,C(C=C1)=CC=C1NC(=O)N,3.38,1.98,1.45,525\n73,NC(=O)OC(=O),4.67,1.76,1.66,351\n74,C([*])C([*])(Cl)(C(=O)OC),4.77,1.73,1.43,404\n75,C([*])C([*])(C(=O)OCCSCCCC#N),5.66,1.72,1.26,214\n76,C(C=C1)=CC=C1C(C=C2)=CC=C2C(S3)=CC=C3C(S4)=CC=C4,1.72,2.43,1.23,452\n77,COC(=O)O,6.84,1.72,1.56,347\n78,CC(C=C1)=CC=C1C(S2)=CC=C2C(C=C3)=CC=C3,2.84,2.0,1.16,449\n79,C(F)(F)CC(F)(F)C(C=C1)=CC=C1,4.47,1.73,1.4,326\n80,C(C=C1)=CC=C1C(=S)C(S2)=CC=C2O,2.36,2.07,1.43,431\n81,C(C=C1)=CC=C1C(C=C2)=CC=C2C(=S)O,2.37,2.06,1.28,426\n82,C([*])C([*])(OC(=O)C[Cl]),5.26,1.69,1.43,314\n83,C([*])C([*])(C(=O)OCF),6.16,1.68,1.31,297\n84,N([*])CCCCCCCCC([*])(=O),6.55,1.66,1.01,319\n85,C([*])(C=C1)=CC=C1C(=O)O([*]),3.54,1.87,1.35,414\n86,C([*])C([*])(C(=O)OCCOCC(F)(F)C(F)(F)C(F)(F)F),6.86,1.63,1.6,232\n87,C([*])C([*])(C(=O)Oc1ccccc1C(C)(C)C),4.97,1.7,1.09,352\n88,C([*])C([*])(OCCCCCCCC),6.77,1.65,0.93,202\n89,C([*])(C)=CCC([*]),5.64,1.67,0.92,213\n90,C([*])C([*])(c1ccc(I)cc1),2.72,1.94,1.33,416\n91,O([*])C(C(C2=CC=CC=C2)=C1)=C(C3=CC=CC=C3)C=C1([*]),3.12,1.98,1.16,483\n92,CC(=O)NC(C=C1)=CC=C1,3.94,1.85,1.29,436\n93,C([*])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}, { "path": "tests/inverse/test_base_inverse.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nfrom xenonpy.inverse.base import BaseLogLikelihood, BaseProposal, BaseResample, BaseSMC\n\n\n@pytest.fixture(scope='module')\ndef data():\n # ignore numpy warning\n import warnings\n print('ignore NumPy RuntimeWarning\\n')\n warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n warnings.filterwarnings(\"ignore\", message=\"numpy.ndarray size changed\")\n\n class LLH(BaseLogLikelihood):\n\n def log_likelihood(self, X, **target):\n return pd.DataFrame(np.asanyarray(X))\n\n class Proposal(BaseProposal):\n\n def proposal(self, X):\n return X\n\n class Resample(BaseResample):\n\n def resample(self, X, freq, size, p):\n return np.random.choice(X, size, p=p)\n\n class SMC(BaseSMC):\n\n def __init__(self):\n self._log_likelihood = LLH()\n self._proposal = Proposal()\n self._resample = Resample()\n\n # prepare test data\n yield dict(llh=LLH, prop=Proposal, smc=SMC, resample=Resample)\n\n print('test over')\n\n\ndef test_base_loglikelihood_1(data):\n llh = data['llh']()\n X = np.array([1, 2, 3, 3, 4, 4])\n ll = llh(X)\n print(ll)\n assert np.all(ll.values.flatten() == X)\n\n\ndef test_base_proposer_1(data):\n proposer = data['prop']()\n X = np.array([1, 2, 3, 4, 5])\n x = proposer(X)\n assert np.all(x == X)\n\n\ndef test_base_resammple_1(data):\n res = data['resample']()\n X = np.array([1, 2, 3, 4, 5])\n x = res(X, np.repeat(1, len(X)), 4, p=[0, 0, 1, 0, 0])\n print(x)\n assert np.all(x == [3, 3, 3, 3])\n\n\ndef test_base_smc_1():\n samples = [1, 2, 3, 4, 5]\n beta = np.linspace(0.05, 1, 5)\n\n class SMC(BaseSMC):\n pass\n\n smc = SMC()\n with pytest.raises(NotImplementedError):\n for s in smc(samples, beta=beta):\n assert s\n\n\ndef test_base_smc_2(data):\n class SMC(BaseSMC):\n pass\n\n smc = SMC()\n llh = data['llh']()\n proposer = data['prop']()\n with pytest.raises(TypeError):\n smc._log_likelihood = 1\n smc._log_likelihood = llh\n\n with pytest.raises(TypeError):\n smc._proposal = 1\n smc._proposal = proposer\n\n\ndef test_base_smc_3(data):\n smc = data['smc']()\n\n samples = [1, 2, 100, 4, 5]\n beta = np.linspace(0.05, 1, 5)\n for s, ll, p, f in smc(samples, beta=beta, yield_lpf=True):\n pass\n assert s == 100\n assert np.all(ll == np.array(100))\n assert p == 1.0\n assert f == 5\n\n\ndef test_not_implement():\n base = BaseLogLikelihood()\n with pytest.raises(NotImplementedError):\n base.log_likelihood([1, 2], a=1, b=1)\n\n base = BaseResample()\n with pytest.raises(NotImplementedError):\n base.resample([1, 2], freq=[5, 5], size=10, p=[.5, .5])\n\n base = BaseProposal()\n with pytest.raises(NotImplementedError):\n base.proposal([1, 2])\n\n\nif __name__ == \"__main__\":\n pytest.main()\n" }, { "path": "tests/inverse/test_iqspr.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport types\nfrom copy import deepcopy\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nfrom sklearn.linear_model import BayesianRidge\n\nfrom xenonpy.descriptor import ECFP, RDKitFP\nfrom xenonpy.inverse.base import BaseLogLikelihoodSet\nfrom xenonpy.inverse.iqspr import (IQSPR, IQSPR4DF, GaussianLogLikelihood, GetProbError, MolConvertError, NGram)\n\n\n@pytest.fixture(scope='module')\ndef data():\n # ignore numpy warning\n import warnings\n print('ignore NumPy RuntimeWarning\\n')\n warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n warnings.filterwarnings(\"ignore\", message=\"numpy.ndarray size changed\")\n\n pwd = Path(__file__).parent\n pg_data = pd.read_csv(str(pwd / 'polymer_test_data.csv'))\n\n X = pg_data['smiles']\n y = pg_data.drop(['smiles', 'Unnamed: 0'], axis=1)\n ecfp = ECFP(n_jobs=1, input_type='smiles', target_col=0)\n rdkitfp = RDKitFP(n_jobs=1, input_type='smiles', target_col=0)\n bre = GaussianLogLikelihood(descriptor=ecfp)\n bre2 = GaussianLogLikelihood(descriptor=rdkitfp)\n bre.fit(X, y[['bandgap', 'glass_transition_temperature']])\n bre2.fit(X, y[['density', 'refractive_index']])\n bre.update_targets(bandgap=(1, 2), glass_transition_temperature=(200, 300))\n bre2.update_targets(refractive_index=(2, 3), density=(0.9, 1.2))\n\n class MyLogLikelihood(BaseLogLikelihoodSet):\n\n def __init__(self):\n super().__init__()\n\n self.loglike = bre\n self.loglike = bre2\n\n like_mdl = MyLogLikelihood()\n ngram = NGram()\n ngram.fit(X[0:20], train_order=5)\n iqspr = IQSPR(estimator=bre, modifier=ngram)\n # prepare test data\n yield dict(ecfp=ecfp, rdkitfp=rdkitfp, bre=bre, bre2=bre2, like_mdl=like_mdl, ngram=ngram, iqspr=iqspr, pg=(X, y))\n\n print('test over')\n\n\ndef test_gaussian_ll_1(data):\n bre = deepcopy(data['bre'])\n bre2 = data['bre2']\n X, _ = data['pg']\n\n assert 'bandgap' in bre._mdl\n assert 'glass_transition_temperature' in bre._mdl\n assert 'refractive_index' in bre2._mdl\n assert 'density' in bre2._mdl\n\n ll = bre.log_likelihood(X.sample(10), bandgap=(7, 8), glass_transition_temperature=(300, 400))\n assert ll.shape == (10, 2)\n assert isinstance(bre['bandgap'], BayesianRidge)\n assert isinstance(bre['glass_transition_temperature'], BayesianRidge)\n\n with pytest.raises(KeyError):\n bre['other']\n\n with pytest.raises(TypeError):\n bre['other'] = 1\n bre['other'] = BayesianRidge()\n\n bre.remove_estimator()\n assert bre._mdl == {}\n\n\ndef test_gaussian_ll_2(data):\n bre = deepcopy(data['bre'])\n X, y = data['pg']\n\n x = X.sample(10)\n tmp = bre.predict(x)\n assert isinstance(tmp, pd.DataFrame)\n assert tmp.shape == (10, 4)\n\n x[66666] = 'CCd'\n tmp = bre.predict(x)\n assert isinstance(tmp, pd.DataFrame)\n assert tmp.shape == (11, 4)\n print(tmp)\n tmp = tmp.loc[66666]\n\n assert tmp.isna().all()\n\n\ndef test_gaussian_ll_3(data):\n like_mdl = data['like_mdl']\n X, y = data['pg']\n ll = like_mdl.log_likelihood(X.sample(10))\n\n assert ll.shape == (10, 4)\n\n\ndef test_gaussian_ll_4(data):\n # check if training of NaN data and pd.Series input are ok\n ecfp = data['ecfp']\n bre = GaussianLogLikelihood(descriptor=ecfp)\n train_data = pd.DataFrame({\n 'x': ['C', 'CC', 'CCC', 'CCCC', 'CCCCC'],\n 'a': [np.nan, np.nan, 3, 4, 5],\n 'b': [1, 2, 3, np.nan, np.nan]\n })\n bre.fit(train_data['x'], train_data['a'])\n\n bre.remove_estimator()\n bre.fit(train_data['x'], train_data[['a', 'b']])\n\n\n# @pytest.mark.skip(\n# reason=\n# \"Sklean.BassClass requires all params passed to __ini__ can be accessed by attribute/property on Python 3.8\"\n# )\ndef test_ngram_1(data):\n ngram = NGram()\n assert ngram.ngram_table is None\n assert ngram.max_len == 1000\n assert ngram.del_range == (1, 10)\n assert ngram.reorder_prob == 0\n assert ngram.sample_order == (1, 10)\n assert ngram._train_order is None\n\n ngram.set_params(max_len=500, reorder_prob=0.2)\n\n assert ngram.max_len == 500\n assert ngram.del_range == (1, 10)\n assert ngram.reorder_prob == 0.2\n\n\ndef test_ngram_2(data):\n ngram = NGram()\n\n with pytest.warns(RuntimeWarning, match=''):\n ngram.fit(data['pg'][0][:20], train_order=5)\n\n assert ngram._train_order == (1, 5)\n assert ngram.sample_order == (1, 5)\n assert ngram.ngram_table is not None\n\n np.random.seed(123456)\n with pytest.warns(RuntimeWarning, match='can not convert'):\n old_smis = ['CC(=S)C([*])(C)=CCC([*])']\n tmp = ngram.proposal(old_smis)\n assert tmp == old_smis\n\n np.random.seed(654321)\n with pytest.warns(RuntimeWarning, match='get_prob: '):\n old_smis = ['C([*])C([*])(C1=C(OCCC)C=CC(Br)C1)']\n tmp = ngram.proposal(old_smis)\n assert tmp == old_smis\n\n\ndef test_ngram_3(data):\n ngram = NGram(sample_order=5)\n ngram.fit(data['pg'][0][:20], train_order=5)\n\n def on_errors(self, error):\n if isinstance(error, MolConvertError):\n raise error\n else:\n return error.old_smi\n\n np.random.seed(123456)\n ngram.on_errors = types.MethodType(on_errors, ngram)\n with pytest.raises(MolConvertError):\n old_smis = ['CC(=S)C([*])(C)=CCC([*])']\n ngram.proposal(old_smis)\n\n def on_errors(self, error):\n if isinstance(error, GetProbError):\n raise error\n else:\n return error.old_smi\n\n np.random.seed(654321)\n ngram.on_errors = types.MethodType(on_errors, ngram)\n with pytest.raises(GetProbError):\n old_smis = ['C([*])C([*])(C1=C(OCCC)C=CC(Br)C1)']\n ngram.proposal(old_smis)\n\n\ndef test_ngram_4():\n smis1 = ['CCCc1ccccc1', 'CC(CCc1ccccc1)CC', 'Cc1ccccc1CC', 'C(CC(C))CC', 'CCCC']\n n_gram1 = NGram() # base case\n n_gram1.fit(smis1, train_order=3)\n assert n_gram1._train_order == (1, 3)\n\n tmp_tab = n_gram1._table\n assert (len(tmp_tab), len(tmp_tab[0][0])) == (3, 2)\n\n check_close = [[(4, 5), (2, 2)], [(6, 5), (3, 2)], [(8, 4), (4, 2)]]\n check_open = [[(5, 5), (2, 2)], [(6, 5), (3, 2)], [(6, 5), (4, 2)]]\n\n for ii, x in enumerate(tmp_tab):\n for i in range(len(x[0])):\n assert x[0][i].shape == check_close[ii][i]\n assert x[1][i].shape == check_open[ii][i]\n\n\ndef test_ngram_5():\n smis1 = ['CCCc1ccccc1', 'CC(CCc1ccccc1)CC', 'Cc1ccccc1CC', 'C(CC(C))CC', 'CCCC']\n smis2 = ['C(F)(F)C', 'CCCF', 'C(F)C=C']\n smis3 = ['c123c(cc(ccc(N)ccc3)cccc2)cccc1', 'c1cncc1', 'CC(=O)CN']\n n_gram1 = NGram() # base case\n n_gram1.fit(smis1, train_order=3)\n n_gram2 = NGram() # higher order but lower num of rings\n n_gram2.fit(smis2, train_order=4)\n n_gram3 = NGram() # lower order but higher num of rings\n n_gram3.fit(smis3, train_order=2)\n\n tmp_ngram = n_gram1.merge_table(n_gram2, weight=1, overwrite=False)\n assert tmp_ngram._train_order == (1, 4)\n tmp_tab = tmp_ngram._table\n assert (len(tmp_tab), len(tmp_tab[0][0])) == (4, 2)\n\n tmp_ngram = n_gram1.merge_table(n_gram3, weight=1, overwrite=False)\n assert tmp_ngram._train_order == (1, 3)\n tmp_tab = tmp_ngram._table\n assert (len(tmp_tab), len(tmp_tab[0][0])) == (3, 4)\n\n n_gram1.merge_table(n_gram2, n_gram3, weight=[0.5, 1])\n tmp_tab = n_gram1._table\n assert n_gram1._train_order == (1, 4)\n assert (len(tmp_tab), len(tmp_tab[0][0])) == (4, 4)\n assert tmp_tab[0][0][0].loc[\"['C']\", \"C\"] == 11.0\n assert tmp_tab[0][1][0].loc[\"['(']\", \"C\"] == 3.0\n\n\ndef test_ngram_6(data):\n smis0 = ['CCCc1ccccc1', 'CC(CCc1ccccc1)CC', 'Cc1ccccc1CC', 'C(CC(C))CC', 'CCCC']\n n_gram0 = NGram() # base case\n n_gram0.fit(smis0, train_order=5)\n\n n_gram1, n_gram2 = n_gram0.split_table(cut_order=2)\n assert n_gram1._train_order == (1, 2)\n assert n_gram1.min_len == 1\n assert n_gram2._train_order == (3, 5)\n assert n_gram2.min_len == 3\n\n n_gram3 = n_gram2.merge_table(n_gram1, weight=1, overwrite=False)\n assert n_gram3._train_order == (1, 5)\n assert n_gram3.min_len == 3\n assert np.all(n_gram3._table[3][0][1] == n_gram0._table[3][0][1])\n assert np.all(n_gram3._table[2][1][0] == n_gram0._table[2][1][0])\n n_gram1.merge_table(n_gram2, weight=1)\n assert n_gram1._train_order == (1, 5)\n assert n_gram1.min_len == 1\n assert np.all(n_gram1._table[3][0][1] == n_gram0._table[3][0][1])\n assert np.all(n_gram1._table[2][1][0] == n_gram0._table[2][1][0])\n\n\ndef test_iqspr_1(data):\n np.random.seed(0)\n ecfp = data['ecfp']\n bre = GaussianLogLikelihood(descriptor=ecfp)\n ngram = NGram()\n iqspr = IQSPR(estimator=bre, modifier=ngram)\n X, y = data['pg']\n bre.fit(X, y)\n bre.update_targets(reset=True, bandgap=(0.1, 0.2), density=(0.9, 1.2))\n ngram.fit(data['pg'][0][0:20], train_order=10)\n beta = np.linspace(0.05, 1, 10)\n for s, ll, p, f in iqspr(data['pg'][0][:5], beta, yield_lpf=True):\n assert np.abs(np.sum(p) - 1.0) < 1e-5\n assert np.sum(f) == 5\n\n\ndef test_iqspr_2(data):\n np.random.seed(0)\n like_mdl = data['like_mdl']\n ngram = data['ngram']\n iqspr = IQSPR(estimator=like_mdl, modifier=ngram)\n\n beta1 = np.linspace(0.05, 1, 10)\n beta2 = np.linspace(0.01, 1, 10)\n beta = pd.DataFrame({\n 'bandgap': beta1,\n 'glass_transition_temperature': beta2,\n 'density': beta1,\n 'refractive_index': beta2\n })\n for s, ll, p, f in iqspr(data['pg'][0][:5], beta, yield_lpf=True):\n assert np.abs(np.sum(p) - 1.0) < 1e-5\n assert np.sum(f) == 5\n\n\ndef test_iqspr_resample1(data):\n # not sure if this test can be fully reliable by only fixing the random seed\n like_mdl = data['like_mdl']\n ngram = data['ngram']\n beta = np.linspace(0.1, 1, 2)\n\n np.random.seed(0)\n iqspr = IQSPR(estimator=like_mdl, modifier=ngram, r_ESS=0)\n soln1 = [\n [\n 'C([*])C([*])(C(=O)OCCSCCC#N)', 'C([*])C([*])(SCCC)',\n 'O([*])C(=O)OC(C=C1)=CC=C1C(C=C2)=CC=C2CC(C=C3)=CC=C3C(C=C4)=CC=C4([*])'\n ],\n [\n 'C([*])C([*])(C(=O)OCC(F)(F)C(F)(F)OC(F)(F)OC(F)(F)C(F)(F)OC(F)(F)C(F)(F)F)',\n 'C([*])C([*])(CC)(C(=O)OCC(F)(F)F)',\n 'O([*])C(=O)OC(C=C1)=CC=C1C(C=C1)=CC=C1CC(C=C1)=CC=C1C(C=C1)=CC=C1C(C=C1)=CC=C1C(C=C1)=CC=C1C(C=C1)=CC=C1C(=S)'\n ]\n ]\n c0 = 0\n for s, ll, p, f in iqspr(data['pg'][0][:3], beta, yield_lpf=True):\n assert np.abs(np.sum(p) - 1.0) < 1e-5\n assert np.sum(f) == 3\n assert np.all(np.sort(s) == np.array(soln1[c0]))\n c0 += 1\n\n np.random.seed(0)\n iqspr = IQSPR(estimator=like_mdl, modifier=ngram, r_ESS=1)\n soln2 = [[\n 'C([*])C([*])(C(=O)OCCSCCC#N)', 'C([*])C([*])(SCCC)',\n 'O([*])C(=O)OC(C=C1)=CC=C1C(C=C2)=CC=C2CC(C=C3)=CC=C3C(C=C4)=CC=C4([*])'\n ],\n [\n 'O([*])C(=O)OC(C=C1)=CC=C1C(C=C1)=CC=C1CC(C=C1)=CC=C1C(C=C1)=CC=C1([*])',\n 'O([*])C(=O)OC(C=C1)=CC=C1C(C=C1)=CC=C1CC(C=C1)=CC=C1C(C=C1)=CC=C1C(=S)'\n ]]\n c0 = 0\n for s, ll, p, f in iqspr(data['pg'][0][:3], beta, yield_lpf=True):\n assert np.abs(np.sum(p) - 1.0) < 1e-5\n assert np.sum(f) == 3\n assert np.all(np.sort(s) == np.array(soln2[c0]))\n c0 += 1\n\n\ndef test_iqspr4df_unique1(data):\n # not sure if this test can be fully reliable by only fixing the random seed\n like_mdl = data['like_mdl']\n ngram = data['ngram']\n beta = np.linspace(0.1, 1, 1)\n samples = pd.DataFrame([data['pg'][0][:2].values.repeat(2), [0, 1, 2, 3]]).T\n\n np.random.seed(0)\n iqspr = IQSPR4DF(estimator=like_mdl, modifier=ngram, r_ESS=0, sample_col=0)\n soln = pd.DataFrame([['C([*])C([*])(SCCC)', 'C([*])C([*])(C(=O)OCCSCCC#N)'], [0, 2]]).T\n for s, ll, p, f in iqspr(samples, beta, yield_lpf=True):\n assert np.abs(np.sum(p) - 1.0) < 1e-5\n assert np.sum(f) == 4\n assert (s == soln).all().all()\n\n\n@pytest.fixture\ndef test_df():\n input = pd.DataFrame([[0, 1], [3, 3], [1, 3], [1, 2], [1, 3]], columns=['a', 'b'])\n return input\n\n\ndef test_iqspr4df_two_col(data, test_df):\n like_mdl = data['like_mdl']\n ngram = data['ngram']\n sample_col1 = ['a', 'b']\n iqspr = IQSPR4DF(estimator=like_mdl, modifier=ngram, r_ESS=0, sample_col=sample_col1)\n\n soln1 = pd.DataFrame([[0, 1], [3, 3], [1, 3], [1, 2]], columns=['a', 'b'])\n freq1 = np.array([1, 1, 2, 1])\n\n uni, f = iqspr.unique(test_df)\n assert (uni == soln1).all().all()\n assert np.all(f == freq1)\n\n\ndef test_iqspr4df_first_col(data, test_df):\n like_mdl = data['like_mdl']\n ngram = data['ngram']\n sample_col2 = ['a']\n iqspr = IQSPR4DF(estimator=like_mdl, modifier=ngram, r_ESS=0, sample_col=sample_col2)\n\n soln2 = pd.DataFrame([[0, 1], [3, 3], [1, 3]], columns=['a', 'b'])\n freq2 = np.array([1, 1, 3])\n\n uni, f = iqspr.unique(test_df)\n assert (uni == soln2).all().all()\n assert np.all(f == freq2)\n\n\ndef test_iqspr4df_second_col(data, test_df):\n like_mdl = data['like_mdl']\n ngram = data['ngram']\n sample_col3 = ['b']\n iqspr = IQSPR4DF(estimator=like_mdl, modifier=ngram, r_ESS=0, sample_col=sample_col3)\n\n soln3 = pd.DataFrame([[0, 1], [3, 3], [1, 2]], columns=['a', 'b'])\n freq3 = np.array([1, 3, 1])\n\n uni, f = iqspr.unique(test_df)\n assert (uni == soln3).all().all()\n assert np.all(f == freq3)\n\n\nif __name__ == \"__main__\":\n pytest.main()\n" }, { "path": "tests/mdl/test_mdl.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom json import JSONDecodeError\nfrom pathlib import Path\nfrom shutil import rmtree\n\nimport pandas as pd\nimport pytest\nfrom requests import HTTPError\n\nfrom xenonpy.mdl import MDL\n\npytestmark = pytest.mark.skipif(True, reason=\"no way of currently testing this, skipping tests\")\n\n\n@pytest.fixture(scope='module')\ndef mdl():\n # ignore numpy warning\n import warnings\n print('ignore NumPy RuntimeWarning\\n')\n warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n warnings.filterwarnings(\"ignore\", message=\"numpy.ndarray size changed\")\n\n yield MDL()\n\n print('test over')\n\n\ndef test_query_properties(mdl):\n ret = mdl.query_properties('test')\n assert isinstance(ret, list)\n\n\ndef test_query_models_1(mdl):\n ret = mdl('test')\n assert isinstance(ret, pd.DataFrame)\n assert ret.shape[0] == 1\n assert ret.index[0] == 'M00000'\n\n ret = mdl('no exists model set')\n assert ret is None\n\n\ndef test_pull_1(mdl):\n ret = mdl('Stable inorganic compounds', property_has='volume')\n urls = ret['url'].iloc[:1]\n paths = mdl.pull(model_ids=urls)\n\n assert Path(paths[0]).exists()\n assert Path(paths[0]).is_dir()\n\n rmtree('S1')\n\n\ndef test_return_nothing(mdl, monkeypatch):\n\n class Request_Dummy(object):\n\n def __init__(self, **_):\n self.status_code = 999\n\n def json(self):\n raise JSONDecodeError(\"error\", \"error\", 0)\n\n monkeypatch.setattr(\"requests.post\", Request_Dummy)\n with pytest.raises(HTTPError) as excinfo:\n mdl(\"test\")\n exc_msg = excinfo.value.args[0]\n assert exc_msg == \"status_code: 999, Server did not responce.\"\n\n\nif __name__ == \"__main__\":\n pytest.main()\n" }, { "path": "tests/models/test_base_runner.py", "content": "# Copyright (c) 2021. TsumiNa. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom collections import OrderedDict\n\nimport pytest\n\nfrom xenonpy.model.training.base import BaseRunner, BaseExtension\n\n\n@pytest.fixture(scope='module')\ndef data():\n # ignore numpy warning\n import warnings\n print('ignore NumPy RuntimeWarning\\n')\n warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n warnings.filterwarnings(\"ignore\", message=\"numpy.ndarray size changed\")\n\n class Ext1(BaseExtension):\n def __init__(self):\n super().__init__()\n self.before = None\n self.after = None\n\n def before_proc(self) -> None:\n self.before = 'ext1'\n\n def after_proc(self) -> None:\n self.after = 'ext1'\n\n def step_forward(self, step_info: OrderedDict) -> None:\n step_info['ext1'] = 'ext1'\n\n def input_proc(self, x_in, y_in, **_):\n if y_in is None:\n return x_in * 10, y_in\n return x_in * 10, y_in * 10\n\n def output_proc(self, y_pred, y_true, **_):\n return y_pred * 10, y_true\n\n class Ext2(BaseExtension):\n def __init__(self):\n super().__init__()\n self.before = None\n self.after = None\n\n def before_proc(self, ext1) -> None:\n self.before = ext1.before + '_ext2'\n\n def after_proc(self, ext1) -> None:\n self.after = ext1.after + '_ext2'\n\n def step_forward(self, step_info: OrderedDict, non_exist='you can not see me!') -> None:\n step_info['ext2'] = step_info['ext1'] + '_ext2'\n step_info['non_exist'] = non_exist\n\n def input_proc(self, x_in, y_in=None, **_):\n if y_in is None:\n return x_in * 2, y_in\n return x_in * 2, y_in * 2\n\n def output_proc(self, y_pred, y_true=None, **_):\n return y_pred * 2, y_true\n\n yield Ext1, Ext2\n print('test over')\n\n\ndef test_base_runner_1(data):\n assert BaseRunner.check_device(False).type == 'cpu'\n assert BaseRunner.check_device('cpu').type == 'cpu'\n\n with pytest.raises(RuntimeError, match='could not use CUDA on this machine'):\n BaseRunner.check_device(True)\n\n with pytest.raises(RuntimeError, match='could not use CUDA on this machine'):\n BaseRunner.check_device('cuda')\n\n with pytest.raises(RuntimeError, match='wrong device identifier'):\n BaseRunner.check_device('other illegal')\n\n\ndef test_base_runner_2(data):\n x, y = 1, 2\n runner = BaseRunner()\n assert runner.input_proc(x, y, ) == (x, y)\n\n\ndef test_base_runner_3(data):\n x, y = 1, 2\n ext1, ext2 = data[0](), data[1]()\n runner = BaseRunner()\n runner.extend(ext1, ext2)\n assert {'ext1', 'ext2'} == set(runner._extensions.keys())\n\n runner._before_proc()\n assert ext1.before == 'ext1'\n assert ext2.before == 'ext1_ext2'\n\n runner._after_proc()\n assert ext1.after == 'ext1'\n assert ext2.after == 'ext1_ext2'\n\n step_info = OrderedDict()\n runner._step_forward(step_info=step_info)\n assert step_info['ext1'] == 'ext1'\n assert step_info['ext2'] == 'ext1_ext2'\n assert step_info['non_exist'] == 'you can not see me!'\n\n assert runner.input_proc(x, y, ) == (x * 10 * 2, y * 10 * 2)\n assert runner.input_proc(x, ) == (x * 10 * 2, None)\n assert runner.output_proc(y, ) == (y * 10 * 2, None)\n\n\nif __name__ == \"__main__\":\n pytest.main()\n" }, { "path": "tests/models/test_checker.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport os\nfrom collections import OrderedDict\nfrom pathlib import Path\nfrom shutil import rmtree\n\nimport numpy as np\nimport pandas as pd\nimport pytest\n\nfrom xenonpy.model import LinearLayer\nfrom xenonpy.model.training import Checker\n\n\n@pytest.fixture(scope='module')\ndef setup():\n # prepare path\n name = 'checker'\n dir_ = os.path.dirname(os.path.abspath(__file__))\n dot = Path()\n test = dict(name=name,\n dir_=dir_,\n cp=dict(model_state=1, b=2),\n path=f'{dir_}/test/{name}',\n dot=str(dot),\n model=LinearLayer(10, 1),\n np=np.ones((2, 3)),\n df=pd.DataFrame(np.ones((2, 3))))\n\n # ignore numpy warning\n import warnings\n print('ignore NumPy RuntimeWarning\\n')\n warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n warnings.filterwarnings(\"ignore\", message=\"numpy.ndarray size changed\")\n\n yield test\n\n rmtree(f'{dir_}/test')\n\n print('test over')\n\n\ndef test_checker_path(setup):\n checker = Checker()\n assert checker.path == str(Path('.').resolve() / Path.cwd().name)\n assert checker.model_name == Path(os.getcwd()).name\n\n checker = Checker(setup['path'])\n assert checker.path == str(Path(setup['path']))\n assert checker.model_name == setup['name']\n\n checker = Checker(setup['path'], increment=True)\n assert checker.path == str(Path(setup['path'] + '@1'))\n assert checker.model_name == setup['name'] + '@1'\n\n\ndef test_checker_model_1(setup):\n checker = Checker(setup['path'])\n\n assert checker.model is None\n assert checker.describe is None\n assert checker.model_structure is None\n with pytest.raises(TypeError):\n checker.model = None\n\n checker.model = setup['model']\n assert isinstance(checker.model, LinearLayer)\n assert isinstance(checker.model_structure, str)\n assert 'LinearLayer' in checker.model_structure\n assert str(checker.model) == str(setup['model'])\n\n assert checker.init_state is not None\n with pytest.raises(TypeError):\n checker.init_state = None\n with pytest.raises(TypeError):\n checker.init_state = OrderedDict(a=1)\n assert isinstance(checker.init_state, OrderedDict)\n\n assert checker.final_state is None\n with pytest.raises(TypeError):\n checker.final_state = None\n with pytest.raises(TypeError):\n checker.init_state = OrderedDict(a=1)\n\n checker.final_state = setup['model'].state_dict()\n assert isinstance(checker.final_state, OrderedDict)\n\n\ndef test_checker_call(setup):\n checker = Checker(setup['path'])\n assert checker['no exists'] is None\n checker.set_checkpoint(**setup['cp'])\n assert (Path(checker.path) / 'checkpoints').exists()\n\n\ndef test_checker_from_cp(setup):\n checker = Checker(setup['path'])\n path = checker.path\n checker.set_checkpoint(test_cp=setup['cp'])\n checker(a=1)\n checker2 = Checker.load(path)\n assert checker2['a'] == 1\n\n cp = checker2.checkpoints['test_cp']\n assert 'b' in cp\n assert cp['model_state'] == setup['cp']['model_state']\n\n\nif __name__ == \"__main__\":\n pytest.main()\n" }, { "path": "tests/models/test_extension.py", "content": "# Copyright (c) 2021. TsumiNa. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom collections import OrderedDict\nfrom pathlib import Path\nfrom scipy.special import softmax\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nfrom shutil import rmtree\n\nimport torch\nimport os\n\nfrom xenonpy.model import SequentialLinear\nfrom xenonpy.model.training import Trainer\nfrom xenonpy.model.training.base import BaseExtension, BaseRunner\nfrom xenonpy.model.training.extension import TensorConverter, Validator, Persist\nfrom xenonpy.model.utils import regression_metrics, classification_metrics\n\n\n@pytest.fixture(scope='module')\ndef data():\n # ignore numpy warning\n import warnings\n print('ignore NumPy RuntimeWarning\\n')\n warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n warnings.filterwarnings(\"ignore\", message=\"numpy.ndarray size changed\")\n\n dir_ = os.path.dirname(os.path.abspath(__file__))\n\n yield\n\n try:\n rmtree(str(Path('.').resolve() / 'test_model'))\n except:\n pass\n try:\n rmtree(str(Path('.').resolve() / 'test_model@1'))\n except:\n pass\n try:\n rmtree(str(Path('.').resolve() / 'test_model_1'))\n except:\n pass\n try:\n rmtree(str(Path('.').resolve() / 'test_model_2'))\n except:\n pass\n try:\n rmtree(str(Path('.').resolve() / 'test_model_3'))\n except:\n pass\n try:\n rmtree(str(Path('.').resolve() / Path(os.getcwd()).name))\n except:\n pass\n\n print('test over')\n\n\ndef test_base_runner_1():\n ext = BaseExtension()\n x, y = 1, 2\n assert ext.input_proc(x, y) == (x, y)\n assert ext.output_proc(y, None) == (y, None)\n\n x, y = (1,), 2\n assert ext.input_proc(x, y) == (x, y)\n assert ext.output_proc(y, None) == (y, None)\n\n x, y = (1,), (2,)\n assert ext.input_proc(x, y) == (x, y)\n assert ext.output_proc(y, y) == (y, y)\n\n\ndef test_tensor_converter_1():\n\n class _Trainer(BaseRunner):\n\n def __init__(self):\n super().__init__()\n self.non_blocking = False\n\n def predict(self, x_, y_): # noqa\n return x_, y_\n\n trainer = _Trainer()\n arr_1 = [1, 2, 3]\n np_1 = np.asarray(arr_1)\n se_1 = pd.Series(arr_1)\n pd_1 = pd.DataFrame(arr_1)\n np_ = np.asarray([arr_1, arr_1])\n pd_ = pd.DataFrame(np_)\n tensor_ = torch.Tensor(np_)\n\n # test auto reshape; #189\n converter = TensorConverter(auto_reshape=False)\n x, y = converter.input_proc(np_1, None, trainer=trainer) # noqa\n assert isinstance(x, torch.Tensor)\n assert x.shape == (3,)\n\n x, y = converter.input_proc(se_1, None, trainer=trainer) # noqa\n assert isinstance(x, torch.Tensor)\n assert x.shape == (3,)\n\n x, y = converter.input_proc(pd_1, None, trainer=trainer) # noqa\n assert isinstance(x, torch.Tensor)\n assert x.shape == (3, 1)\n\n converter = TensorConverter()\n x, y = converter.input_proc(np_1, None, trainer=trainer) # noqa\n assert isinstance(x, torch.Tensor)\n assert x.shape == (3, 1)\n\n x, y = converter.input_proc(se_1, None, trainer=trainer) # noqa\n assert isinstance(x, torch.Tensor)\n assert x.shape == (3, 1)\n\n x, y = converter.input_proc(pd_1, None, trainer=trainer) # noqa\n assert isinstance(x, torch.Tensor)\n assert x.shape == (3, 1)\n\n # normal tests\n x, y = converter.input_proc(np_, None, trainer=trainer) # noqa\n assert isinstance(x, torch.Tensor)\n assert x.shape == (2, 3)\n assert torch.equal(x, tensor_)\n assert y is None\n\n x, y = converter.input_proc(pd_, None, trainer=trainer) # noqa\n assert isinstance(x, torch.Tensor)\n assert x.shape == (2, 3)\n assert torch.equal(x, tensor_)\n assert y is None\n\n x, y = converter.input_proc(tensor_, None, trainer=trainer) # noqa\n assert isinstance(x, torch.Tensor)\n assert x.shape == (2, 3)\n assert torch.equal(x, tensor_)\n assert y is None\n\n x, y = converter.input_proc(np_, np_, trainer=trainer) # noqa\n assert isinstance(x, torch.Tensor)\n assert x.shape == (2, 3)\n assert torch.equal(x, tensor_)\n assert torch.equal(y, tensor_)\n\n x, y = converter.input_proc(pd_, pd_, trainer=trainer) # noqa\n assert isinstance(x, torch.Tensor)\n assert x.shape == (2, 3)\n assert torch.equal(x, tensor_)\n assert torch.equal(y, tensor_)\n\n x, y = converter.input_proc(tensor_, tensor_, trainer=trainer) # noqa\n assert isinstance(x, torch.Tensor)\n assert x.shape == (2, 3)\n assert torch.equal(x, tensor_)\n assert torch.equal(y, tensor_)\n\n converter = TensorConverter(x_dtype=torch.long)\n x, y = converter.input_proc((np_, np_), np_, trainer=trainer) # noqa\n assert isinstance(x, tuple)\n assert len(x) == 2\n assert x[0].dtype == torch.long\n assert x[1].dtype == torch.long\n\n converter = TensorConverter(x_dtype=(torch.long, torch.float32), y_dtype=torch.long)\n x, y = converter.input_proc((np_, np_), np_, trainer=trainer) # noqa\n assert isinstance(x, tuple)\n assert len(x) == 2\n assert x[0].dtype == torch.long\n assert x[1].dtype == torch.float32\n assert y.dtype == torch.long\n\n converter = TensorConverter(x_dtype=(torch.long, torch.float32))\n x, y = converter.input_proc((pd_, pd_), pd_, trainer=trainer) # noqa\n assert isinstance(x, tuple)\n assert len(x) == 2\n assert x[0].dtype == torch.long\n assert x[1].dtype == torch.float32\n\n # for tensor input, dtype change will never be executed\n converter = TensorConverter(x_dtype=(torch.long, torch.long))\n x, y = converter.input_proc((tensor_, tensor_), tensor_, trainer=trainer) # noqa\n assert isinstance(x, tuple)\n assert len(x) == 2\n assert x[0].dtype == torch.float32\n assert x[1].dtype == torch.float32\n\n\ndef test_tensor_converter_2():\n\n class _Trainer(BaseRunner):\n\n def __init__(self):\n super().__init__()\n self.non_blocking = False\n\n def predict(self, x_, y_):\n return x_, y_\n\n trainer = _Trainer()\n converter = TensorConverter()\n np_ = np.asarray([[1, 2, 3], [4, 5, 6]])\n pd_ = pd.DataFrame(np_)\n tensor_ = torch.Tensor(np_) # noqa\n\n x, y = converter.input_proc(np_, np_[0], trainer=trainer) # noqa\n assert isinstance(y, torch.Tensor)\n assert y.shape == (3, 1)\n assert torch.equal(y, tensor_[0].unsqueeze(-1))\n\n x, y = converter.input_proc(pd_, pd_.iloc[0], trainer=trainer) # noqa\n assert isinstance(y, torch.Tensor)\n assert y.shape == (3, 1)\n assert torch.equal(y, tensor_[0].unsqueeze(-1))\n\n x, y = converter.input_proc(tensor_, tensor_[0], trainer=trainer) # noqa\n assert isinstance(y, torch.Tensor)\n assert y.shape == (3,)\n assert torch.equal(y, tensor_[0])\n\n\ndef test_tensor_converter_3():\n np_ = np.asarray([[1, 2, 3], [4, 5, 6]])\n tensor_ = torch.from_numpy(np_)\n\n converter = TensorConverter()\n y, y_ = converter.output_proc(tensor_, None, is_training=True)\n assert y_ is None\n assert isinstance(y, torch.Tensor)\n assert y.shape == (2, 3)\n assert torch.equal(y, tensor_)\n\n y, y_ = converter.output_proc(tensor_, tensor_, is_training=True)\n assert isinstance(y, torch.Tensor)\n assert isinstance(y_, torch.Tensor)\n assert y.equal(y_)\n assert y.shape == (2, 3)\n assert torch.equal(y, tensor_)\n\n y, _ = converter.output_proc((tensor_,), None, is_training=True)\n assert isinstance(y, tuple)\n assert isinstance(y[0], torch.Tensor)\n assert torch.equal(y[0], tensor_)\n\n y, y_ = converter.output_proc(tensor_, tensor_, is_training=False)\n assert isinstance(y, np.ndarray)\n assert isinstance(y_, np.ndarray)\n assert np.all(y == y_)\n assert y.shape == (2, 3)\n assert np.all(y == tensor_.numpy())\n\n y, _ = converter.output_proc((tensor_,), None, is_training=False)\n assert isinstance(y, tuple)\n assert isinstance(y[0], np.ndarray)\n assert np.all(y[0] == tensor_.numpy())\n\n converter = TensorConverter(argmax=True)\n y, y_ = converter.output_proc(tensor_, tensor_, is_training=False)\n assert isinstance(y, np.ndarray)\n assert isinstance(y_, np.ndarray)\n assert y.shape == (2,)\n assert y_.shape == (2, 3)\n assert np.all(y == np.argmax(np_, 1))\n\n y, y_ = converter.output_proc((tensor_, tensor_), None, is_training=False)\n assert isinstance(y, tuple)\n assert y_ is None\n assert y[0].shape == (2,)\n assert y[0].shape == y[1].shape\n assert np.all(y[0] == np.argmax(np_, 1))\n\n converter = TensorConverter(probability=True)\n y, y_ = converter.output_proc(tensor_, tensor_, is_training=False)\n assert isinstance(y, np.ndarray)\n assert isinstance(y_, np.ndarray)\n assert y.shape == (2, 3)\n assert y_.shape == (2, 3)\n assert np.all(y == softmax(np_, 1))\n\n y, y_ = converter.output_proc((tensor_, tensor_), None, is_training=False)\n assert isinstance(y, tuple)\n assert y_ is None\n assert y[0].shape == (2, 3)\n assert y[0].shape == y[1].shape\n assert np.all(y[0] == softmax(np_, 1))\n\n\ndef test_validator_1():\n x = np.random.randn(100) # input\n y = x + np.random.rand() * 0.001 # true values\n\n class _Trainer(BaseRunner):\n\n def __init__(self):\n super().__init__()\n self.x_val = x\n self.y_val = y\n self.loss_type = 'train_loss'\n\n def predict(self, x_, y_):\n return x_, y_\n\n val = Validator('regress', each_iteration=False)\n\n step_info = OrderedDict(train_loss=0, i_epoch=0)\n val.step_forward(trainer=_Trainer(), step_info=step_info) # noqa\n assert 'val_mae' not in step_info\n\n step_info = OrderedDict(train_loss=0, i_epoch=1)\n val.step_forward(trainer=_Trainer(), step_info=step_info) # noqa\n assert step_info['val_mae'] == regression_metrics(y, x)['mae']\n assert set(step_info.keys()) == {\n 'i_epoch', 'val_mae', 'val_mse', 'val_rmse', 'val_r2', 'val_pearsonr', 'val_spearmanr', 'val_p_value',\n 'val_max_ae', 'train_loss'\n }\n\n\ndef test_validator_2():\n y = np.random.randint(3, size=10) # true labels\n x = np.zeros((10, 3)) # input\n for i, j in enumerate(y):\n x[i, j] = 1\n\n class _Trainer(BaseRunner):\n\n def __init__(self):\n super().__init__()\n self.x_val = x\n self.y_val = y\n self.loss_type = 'train_loss'\n\n def predict(self, x_, y_): # noqa\n return x_, y_\n\n val = Validator('classify', each_iteration=False)\n\n step_info = OrderedDict(train_loss=0, i_epoch=0)\n val.step_forward(trainer=_Trainer(), step_info=step_info) # noqa\n assert 'val_f1' not in step_info\n\n step_info = OrderedDict(train_loss=0, i_epoch=1)\n val.step_forward(trainer=_Trainer(), step_info=step_info) # noqa\n assert step_info['val_accuracy'] == classification_metrics(y, x)['accuracy']\n assert set(step_info.keys()) == {\n 'i_epoch', 'val_accuracy', 'val_f1', 'val_precision', 'val_recall', 'val_macro_f1', 'val_macro_precision',\n 'val_macro_recall', 'val_weighted_f1', 'val_weighted_precision', 'val_weighted_recall', 'val_micro_f1',\n 'val_micro_precision', 'val_micro_recall', 'train_loss'\n }\n\n\ndef test_persist_1(data):\n\n class _Trainer(BaseRunner):\n\n def __init__(self):\n super().__init__()\n self.model = SequentialLinear(50, 2)\n\n def predict(self, x_, y_): # noqa\n return x_, y_\n\n p = Persist()\n\n with pytest.raises(ValueError, match='can not access property `path` before training'):\n p.path\n\n p.before_proc(trainer=_Trainer())\n assert p.path == str(Path('.').resolve() / Path(os.getcwd()).name)\n with pytest.raises(ValueError, match='can not reset property `path` after training'):\n p.path = 'aa'\n\n p = Persist('test_model')\n p.before_proc(trainer=_Trainer())\n assert p.path == str(Path('.').resolve() / 'test_model')\n assert (Path('.').resolve() / 'test_model' / 'describe.pkl.z').exists()\n assert (Path('.').resolve() / 'test_model' / 'init_state.pth.s').exists()\n assert (Path('.').resolve() / 'test_model' / 'model.pth.m').exists()\n assert (Path('.').resolve() / 'test_model' / 'model_structure.pkl.z').exists()\n\n p = Persist('test_model', increment=True)\n p.before_proc(trainer=_Trainer())\n assert p.path == str(Path('.').resolve() / 'test_model@1')\n assert (Path('.').resolve() / 'test_model@1' / 'describe.pkl.z').exists()\n assert (Path('.').resolve() / 'test_model@1' / 'init_state.pth.s').exists()\n assert (Path('.').resolve() / 'test_model@1' / 'model.pth.m').exists()\n assert (Path('.').resolve() / 'test_model@1' / 'model_structure.pkl.z').exists()\n\n\ndef test_persist_save_checkpoints(data):\n\n class _Trainer(BaseRunner):\n\n def __init__(self):\n super().__init__()\n self.model = SequentialLinear(50, 2)\n\n def predict(self, x_, y_): # noqa\n return x_, y_\n\n cp_1 = Trainer.checkpoint_tuple(\n id='cp_1',\n iterations=111,\n model_state=SequentialLinear(50, 2).state_dict(),\n )\n cp_2 = Trainer.checkpoint_tuple(\n id='cp_2',\n iterations=111,\n model_state=SequentialLinear(50, 2).state_dict(),\n )\n\n # save checkpoint\n p = Persist('test_model_1', increment=False, only_best_states=False)\n p.before_proc(trainer=_Trainer())\n p.on_checkpoint(cp_1)\n p.on_checkpoint(cp_2)\n assert (Path('.').resolve() / 'test_model_1' / 'checkpoints' / 'cp_1.pth.s').exists()\n assert (Path('.').resolve() / 'test_model_1' / 'checkpoints' / 'cp_2.pth.s').exists()\n\n # reduced save checkpoint\n p = Persist('test_model_2', increment=False, only_best_states=True)\n p.before_proc(trainer=_Trainer())\n p.on_checkpoint(cp_1)\n p.on_checkpoint(cp_2)\n assert (Path('.').resolve() / 'test_model_2' / 'checkpoints' / 'cp.pth.s').exists()\n assert not (Path('.').resolve() / 'test_model_2' / 'checkpoints' / 'cp_1.pth.s').exists()\n assert not (Path('.').resolve() / 'test_model_2' / 'checkpoints' / 'cp_2.pth.s').exists()\n\n # no checkpoint will be saved\n p = Persist('test_model_3', increment=False, only_best_states=True)\n p.before_proc(trainer=_Trainer())\n p.on_checkpoint(cp_2)\n assert not (Path('.').resolve() / 'test_model_3' / 'checkpoints' / 'cp.pth.s').exists()\n assert not (Path('.').resolve() / 'test_model_3' / 'checkpoints' / 'cp_2.pth.s').exists()\n\n\nif __name__ == \"__main__\":\n pytest.main()\n" }, { "path": "tests/models/test_sequential.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport pytest\nimport torch\n\nfrom xenonpy.model import LinearLayer, SequentialLinear\n\n\n@pytest.fixture(scope='module')\ndef data():\n # ignore numpy warning\n import warnings\n print('ignore NumPy RuntimeWarning\\n')\n warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n warnings.filterwarnings(\"ignore\", message=\"numpy.ndarray size changed\")\n\n x = torch.randn(10, 10)\n\n yield (x,)\n print('test over')\n\n\ndef test_layer_1(data):\n layer = LinearLayer(10, 1)\n assert layer.linear.in_features == 10\n assert layer.linear.out_features == 1\n assert layer.linear.bias is not None\n assert layer.dropout.p == 0\n assert layer.activation.__class__.__name__ == 'ReLU'\n assert layer.normalizer.__class__.__name__ == 'BatchNorm1d'\n\n x = layer(data[0])\n assert x.size() == (10, 1)\n\n\ndef test_layer_2(data):\n layer = LinearLayer(10, 5, bias=False, dropout=0.5, normalizer=None, activation_func=torch.nn.Tanh())\n assert layer.linear.in_features == 10\n assert layer.linear.out_features == 5\n assert layer.linear.bias is None\n assert layer.dropout.p == 0.5\n assert layer.activation.__class__.__name__ == 'Tanh'\n assert layer.normalizer is None\n\n x = layer(data[0])\n assert x.size() == (10, 5)\n\n\ndef test_sequential_1(data):\n m = SequentialLinear(10, 5)\n assert isinstance(m, torch.nn.Module)\n assert m.output.in_features == 10\n assert m.output.out_features == 5\n\n x = m(data[0])\n assert x.size() == (10, 5)\n\n\ndef test_sequential_2(data):\n m = SequentialLinear(10, 1, bias=False, h_neurons=(7, 4))\n assert isinstance(m.layer_0, LinearLayer)\n assert isinstance(m.layer_1, LinearLayer)\n assert m.layer_0.linear.in_features == 10\n assert m.layer_0.linear.out_features == 7\n assert m.layer_0.linear.bias is None\n assert m.layer_1.linear.in_features == 7\n assert m.layer_1.linear.out_features == 4\n assert m.layer_1.linear.bias is not None\n assert m.output.in_features == 4\n assert m.output.out_features == 1\n assert m.output.bias is not None\n\n x = m(data[0])\n assert x.size() == (10, 1)\n\n\ndef test_sequential_3():\n m = SequentialLinear(10, 1, bias=False, h_neurons=(0.7, 0.5))\n assert m.layer_0.linear.in_features == 10\n assert m.layer_0.linear.out_features == 7\n assert m.layer_0.linear.bias is None\n assert m.layer_1.linear.in_features == 7\n assert m.layer_1.linear.out_features == 5\n assert m.layer_1.linear.bias is not None\n assert m.output.in_features == 5\n assert m.output.out_features == 1\n assert m.output.bias is not None\n\n\ndef test_sequential_4():\n with pytest.raises(RuntimeError, match='illegal parameter type of '):\n SequentialLinear(10, 5, h_neurons=('NaN',))\n\n with pytest.raises(RuntimeError, match='number of parameter not consistent with number of layers'):\n SequentialLinear(10, 5, h_neurons=(4,), h_dropouts=(1, 5))\n\n m = SequentialLinear(10, 1, h_neurons=(3, 4))\n tmp = (1, 2)\n assert id(m._check_input(tmp)) == id(tmp)\n\n\nif __name__ == \"__main__\":\n pytest.main()\n" }, { "path": "tests/models/test_trainer.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport shutil\nfrom copy import deepcopy\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pytest\nimport torch\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader, TensorDataset\n\nfrom xenonpy.model.training import Trainer, MSELoss, CrossEntropyLoss, Adam, ExponentialLR, SGD, ClipValue, ReduceLROnPlateau, ClipNorm\nfrom xenonpy.model.training.extension import TensorConverter, Persist, Validator\nfrom xenonpy.model.training.dataset import ArrayDataset\n\n\nclass _Net(torch.nn.Module):\n\n def __init__(self, n_feature, n_hidden, n_output):\n super().__init__()\n self.hidden = torch.nn.Linear(n_feature, n_hidden) # hidden layer\n self.predict = torch.nn.Linear(n_hidden, n_output) # output layer\n\n def forward(self, x_):\n x_ = F.relu(self.hidden(x_)) # activation function for hidden layer\n x_ = self.predict(x_) # linear output\n return x_\n\n\n@pytest.fixture(scope='module')\ndef data():\n # ignore numpy warning\n import warnings\n print('ignore NumPy RuntimeWarning\\n')\n warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n warnings.filterwarnings(\"ignore\", message=\"numpy.ndarray size changed\")\n\n model_dir: Path = Path(__file__).parent / 'model_dir'\n\n torch.manual_seed(0)\n np.random.seed(0)\n x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1) # x data (tensor), shape=(100, 1)\n y = x.pow(2) + 0.2 * torch.rand(x.size()) # noisy y data (tensor), shape=(100, 1)\n\n net = _Net(n_feature=1, n_hidden=10, n_output=1)\n\n yield net, (x, y)\n\n if model_dir.exists():\n shutil.rmtree(str(model_dir.resolve()))\n\n print('test over')\n\n\ndef test_trainer_1(data):\n trainer = Trainer()\n assert trainer.device == torch.device('cpu')\n assert trainer.model is None\n assert trainer.optimizer is None\n assert trainer.lr_scheduler is None\n assert trainer.x_val is None\n assert trainer.y_val is None\n assert trainer.validate_dataset is None\n assert trainer._init_states is None\n assert trainer._optimizer_state is None\n assert trainer.total_epochs == 0\n assert trainer.total_iterations == 0\n assert trainer.training_info is None\n assert trainer.loss_type is None\n assert trainer.loss_func is None\n\n trainer = Trainer(optimizer=Adam(),\n loss_func=MSELoss(),\n lr_scheduler=ExponentialLR(gamma=0.99),\n clip_grad=ClipValue(clip_value=0.1))\n assert isinstance(trainer._scheduler, ExponentialLR)\n assert isinstance(trainer._optim, Adam)\n assert isinstance(trainer.clip_grad, ClipValue)\n assert isinstance(trainer.loss_func, MSELoss)\n\n\ndef test_trainer_2(data):\n trainer = Trainer()\n with pytest.raises(RuntimeError, match='no model for training'):\n trainer.fit(*data[1])\n\n with pytest.raises(TypeError, match='parameter `m` must be a instance of '):\n trainer.model = {}\n\n trainer.model = data[0]\n assert isinstance(trainer.model, torch.nn.Module)\n with pytest.raises(RuntimeError, match='no loss function for training'):\n trainer.fit(*data[1])\n\n trainer.loss_func = MSELoss()\n assert trainer.loss_type == 'train_mse_loss'\n assert trainer.loss_func.__class__ == MSELoss\n with pytest.raises(RuntimeError, match='no optimizer for training'):\n trainer.fit(*data[1])\n\n trainer.optimizer = Adam()\n assert isinstance(trainer.optimizer, torch.optim.Adam)\n assert isinstance(trainer._optimizer_state, dict)\n assert isinstance(trainer._init_states, dict)\n\n trainer.lr_scheduler = ExponentialLR(gamma=0.99)\n assert isinstance(trainer.lr_scheduler, torch.optim.lr_scheduler.ExponentialLR)\n\n\ndef test_trainer_3(data):\n model = data[0]\n trainer = Trainer(model=model, optimizer=Adam(), loss_func=MSELoss())\n assert isinstance(trainer.model, torch.nn.Module)\n assert isinstance(trainer.optimizer, torch.optim.Adam)\n assert isinstance(trainer._optimizer_state, dict)\n assert isinstance(trainer._init_states, dict)\n assert trainer.clip_grad is None\n assert trainer.lr_scheduler is None\n\n trainer.lr_scheduler = ExponentialLR(gamma=0.1)\n assert isinstance(trainer.lr_scheduler, torch.optim.lr_scheduler.ExponentialLR)\n\n trainer.optimizer = SGD()\n assert isinstance(trainer.optimizer, torch.optim.SGD)\n\n trainer.clip_grad = ClipNorm(max_norm=0.4)\n assert isinstance(trainer.clip_grad, ClipNorm)\n\n\ndef test_trainer_fit_1(data):\n model = deepcopy(data[0])\n trainer = Trainer(model=model, optimizer=Adam(), loss_func=MSELoss())\n trainer.fit(*data[1])\n assert trainer.total_iterations == 200\n assert trainer.total_epochs == 200\n\n trainer.fit(*data[1], epochs=20)\n assert trainer.total_iterations == 220\n assert trainer.total_epochs == 220\n\n trainer.reset()\n assert trainer.total_iterations == 0\n assert trainer.total_epochs == 0\n\n trainer.fit(*data[1], epochs=20)\n assert trainer.total_iterations == 20\n assert trainer.total_epochs == 20\n\n assert isinstance(trainer.training_info, pd.DataFrame)\n assert 'i_epoch' in trainer.training_info.columns\n\n ret = trainer.to_namedtuple()\n assert isinstance(ret, trainer.results_tuple)\n\n train_set = DataLoader(TensorDataset(*data[1]))\n with pytest.raises(RuntimeError, match='parameter is exclusive of and '):\n trainer.fit(*data[1], training_dataset=train_set)\n\n with pytest.raises(RuntimeError, match='missing parameter or '):\n trainer.fit(data[1][0])\n\n\ndef test_trainer_fit_2(data):\n model = deepcopy(data[0])\n trainer = Trainer(model=model, optimizer=Adam(), loss_func=MSELoss(), epochs=20)\n trainer.fit(*data[1], *data[1])\n assert trainer.total_iterations == 20\n assert trainer.total_epochs == 20\n assert (trainer.x_val, trainer.y_val) == data[1]\n\n train_set = DataLoader(TensorDataset(*data[1]))\n val_set = DataLoader(TensorDataset(*data[1]))\n trainer.fit(training_dataset=train_set, validation_dataset=val_set)\n assert trainer.total_iterations == 2020\n assert trainer.total_epochs == 40\n assert isinstance(trainer.validate_dataset, DataLoader)\n\n\ndef test_trainer_fit_3(data):\n model = deepcopy(data[0])\n trainer = Trainer(model=model, optimizer=Adam(), loss_func=MSELoss(), epochs=5)\n trainer.fit(*data[1])\n assert len(trainer.checkpoints.keys()) == 0\n\n trainer.reset()\n assert trainer.total_iterations == 0\n assert trainer.total_epochs == 0\n assert len(trainer.get_checkpoint()) == 0\n\n trainer.fit(*data[1], checkpoint=True)\n assert len(trainer.get_checkpoint()) == 5\n assert isinstance(trainer.get_checkpoint(2), trainer.checkpoint_tuple)\n assert isinstance(trainer.get_checkpoint('cp_2'), trainer.checkpoint_tuple)\n\n with pytest.raises(TypeError, match='parameter must be str or int'):\n trainer.get_checkpoint([])\n\n trainer.reset(to=3, remove_checkpoints=False)\n assert len(trainer.get_checkpoint()) == 5\n assert isinstance(trainer.get_checkpoint(2), trainer.checkpoint_tuple)\n assert isinstance(trainer.get_checkpoint('cp_2'), trainer.checkpoint_tuple)\n\n trainer.reset(to='cp_3')\n assert trainer.total_iterations == 0\n assert trainer.total_epochs == 0\n assert len(trainer.get_checkpoint()) == 0\n\n with pytest.raises(TypeError, match='parameter must be torch.nnModule, int, or str'):\n trainer.reset(to=[])\n\n # todo: need a real testing\n trainer.fit(*data[1], checkpoint=True)\n trainer.predict(*data[1], checkpoint=3)\n\n trainer.reset()\n trainer.fit(*data[1], checkpoint=lambda i: (True, f'new:{i}'))\n assert len(trainer.get_checkpoint()) == 5\n assert trainer.get_checkpoint() == list([f'new:{i + 1}' for i in range(5)])\n\n\ndef test_trainer_fit_4(data):\n model = deepcopy(data[0])\n trainer = Trainer(model=model,\n optimizer=Adam(),\n loss_func=MSELoss(),\n clip_grad=ClipValue(0.1),\n lr_scheduler=ReduceLROnPlateau(),\n epochs=10)\n\n count = 1\n for i in trainer(*data[1]):\n assert isinstance(i, dict)\n assert i['i_epoch'] == count\n if count == 3:\n trainer.early_stop('stop')\n count += 1\n\n assert trainer.total_epochs == 3\n assert trainer._early_stopping == (True, 'stop')\n\n trainer.reset()\n train_set = DataLoader(TensorDataset(*data[1]))\n count = 1\n for i in trainer(training_dataset=train_set):\n assert isinstance(i, dict)\n assert i['i_batch'] == count\n if count == 3:\n trainer.early_stop('stop!!!')\n count += 1\n assert trainer.total_iterations == 3\n assert trainer._early_stopping == (True, 'stop!!!')\n\n\ndef test_validator_1(data):\n model = deepcopy(data[0])\n trainer = Trainer(model=model, optimizer=Adam(lr=0.1), loss_func=MSELoss(), epochs=20)\n trainer.extend(TensorConverter(), Validator('regress', early_stop=30, trace_order=1, warming_up=0, mae=0))\n trainer.fit(*data[1], *data[1])\n assert trainer.get_checkpoint() == ['mae']\n\n model = deepcopy(data[0])\n trainer = Trainer(model=model, optimizer=Adam(lr=0.1), loss_func=MSELoss(), epochs=20)\n trainer.extend(TensorConverter(), Validator('regress', early_stop=30, trace_order=5, warming_up=50, mae=0))\n trainer.fit(*data[1], *data[1])\n assert trainer.get_checkpoint() == []\n\n\ndef test_persist_1(data):\n model = deepcopy(data[0])\n trainer = Trainer(model=model, optimizer=Adam(lr=0.1), loss_func=MSELoss(), epochs=200)\n trainer.extend(TensorConverter(), Persist('model_dir'))\n trainer.fit(*data[1], *data[1])\n\n persist = trainer['persist']\n checker = persist._checker\n assert isinstance(persist, Persist)\n assert isinstance(checker.model, torch.nn.Module)\n assert isinstance(checker.describe, dict)\n assert isinstance(checker.files, list)\n assert set(checker.files) == {'model', 'init_state', 'model_structure', 'describe', 'training_info', 'final_state'}\n\n trainer = Trainer.load(checker)\n assert isinstance(trainer.training_info, pd.DataFrame)\n assert isinstance(trainer.model, torch.nn.Module)\n assert isinstance(trainer._training_info, list)\n assert trainer.optimizer is None\n assert trainer.lr_scheduler is None\n assert trainer.x_val is None\n assert trainer.y_val is None\n assert trainer.validate_dataset is None\n assert trainer._optimizer_state is None\n assert trainer.total_epochs == 0\n assert trainer.total_iterations == 0\n assert trainer.loss_type is None\n assert trainer.loss_func is None\n\n trainer = Trainer.load(from_=checker.path,\n optimizer=Adam(),\n loss_func=MSELoss(),\n lr_scheduler=ExponentialLR(gamma=0.99),\n clip_grad=ClipValue(clip_value=0.1))\n assert isinstance(trainer._scheduler, ExponentialLR)\n assert isinstance(trainer._optim, Adam)\n assert isinstance(trainer.clip_grad, ClipValue)\n assert isinstance(trainer.loss_func, MSELoss)\n\n\ndef test_trainer_prediction_1(data):\n model = deepcopy(data[0])\n trainer = Trainer(model=model, optimizer=Adam(lr=0.1), loss_func=MSELoss(), epochs=200)\n trainer.extend(TensorConverter())\n trainer.fit(*data[1], *data[1])\n\n trainer = Trainer(model=model).extend(TensorConverter(probability=True))\n y_p = trainer.predict(data[1][0])\n assert np.any(np.not_equal(y_p, data[1][1].numpy()))\n assert np.equal(y_p, np.array([1.] * y_p.shape[0]).reshape(-1, 1)).all()\n\n trainer = Trainer(model=model).extend(TensorConverter(probability=False))\n y_p = trainer.predict(data[1][0])\n assert np.any(np.not_equal(y_p, data[1][1].numpy()))\n assert np.allclose(y_p, data[1][1].numpy(), rtol=0, atol=0.2)\n\n y_p, y_t = trainer.predict(*data[1])\n assert np.any(np.not_equal(y_p, y_t))\n assert np.allclose(y_p, y_t, rtol=0, atol=0.2)\n\n val_set = DataLoader(TensorDataset(*data[1]), batch_size=50)\n y_p, y_t = trainer.predict(dataset=val_set)\n assert np.any(np.not_equal(y_p, y_t))\n assert np.allclose(y_p, y_t, rtol=0, atol=0.2)\n\n with pytest.raises(RuntimeError, match='parameters and are mutually exclusive'):\n trainer.predict(*data[1], dataset='not none')\n\n\ndef test_trainer_prediction_2():\n model = _Net(n_feature=2, n_hidden=10, n_output=2)\n\n n_data = np.ones((100, 2))\n x0 = np.random.normal(2 * n_data, 1)\n y0 = np.zeros(100)\n x1 = np.random.normal(-2 * n_data, 1)\n y1 = np.ones(100)\n\n x = np.vstack((x0, x1))\n y = np.concatenate((y0, y1))\n s = np.arange(x.shape[0])\n np.random.shuffle(s)\n x, y = x[s], y[s]\n\n trainer = Trainer(model=model, optimizer=Adam(lr=0.1), loss_func=CrossEntropyLoss(), epochs=200)\n trainer.extend(TensorConverter(x_dtype=torch.float32, y_dtype=torch.long, argmax=True))\n trainer.fit(x, y)\n\n y_p, y_t = trainer.predict(x, y)\n assert y_p.shape == (200,)\n assert np.all(y_p == y_t)\n\n # trainer.reset()\n val_set = DataLoader(ArrayDataset(x, y, dtypes=(torch.float, torch.long)), batch_size=20)\n trainer.extend(TensorConverter(x_dtype=torch.float32, y_dtype=torch.long, auto_reshape=False))\n y_p, y_t = trainer.predict(dataset=val_set)\n assert y_p.shape == (200, 2)\n\n y_p = np.argmax(y_p, 1)\n assert np.all(y_p == y_t)\n\n\nif __name__ == \"__main__\":\n pytest.main()\n" }, { "path": "tests/models/test_utils.py", "content": "# Copyright (c) 2021. TsumiNa. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport numpy as np\nimport pytest\n\nfrom xenonpy.model.utils import regression_metrics\n\n\n@pytest.fixture(scope='module')\ndef data():\n # ignore numpy warning\n import warnings\n print('ignore NumPy RuntimeWarning\\n')\n warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n warnings.filterwarnings(\"ignore\", message=\"numpy.ndarray size changed\")\n\n noise = 0.001\n x = np.random.randn(100)\n y = x + np.random.rand() * noise\n\n yield x, y, noise\n print('test over')\n\n\ndef test_regression_metrics_1(data):\n x = data[0]\n y = data[1]\n noise = data[2]\n metric = regression_metrics(x, y)\n assert isinstance(metric, dict)\n assert set(metric.keys()) == {'mae', 'mse', 'rmse', 'r2', 'pearsonr', 'spearmanr', 'p_value', 'max_ae'}\n assert metric['mae'] < noise\n assert metric['mse'] < noise ** 2\n assert metric['rmse'] < noise\n assert metric['r2'] > 0.9999\n assert metric['pearsonr'] > 0.9999\n assert metric['spearmanr'] > 0.9999\n assert np.isclose(metric['p_value'], 0, 1e-4)\n assert metric['max_ae'] < noise\n\n assert metric['mae'] == regression_metrics(x.reshape(-1, 1), y)['mae']\n assert metric['rmse'] == regression_metrics(x.reshape(-1, 1), y.reshape(-1, 1))['rmse']\n assert metric['max_ae'] == regression_metrics(x, y.reshape(-1, 1))['max_ae']\n\n\nif __name__ == \"__main__\":\n pytest.main()\n" }, { "path": "tests/models/test_wrapped.py", "content": "# Copyright (c) 2021. TsumiNa. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\n\nimport pytest\n\n\ndef test_import_loss():\n try:\n import xenonpy.model.training.loss\n except ImportError:\n assert \"should not raise ImportError\"\n \n\ndef test_import_lr_scheduler():\n try:\n import xenonpy.model.training.lr_scheduler\n except ImportError:\n assert \"should not raise ImportError\"\n\ndef test_import_optimizer():\n try:\n import xenonpy.model.training.optimizer\n except ImportError:\n assert \"should not raise ImportError\"\n\n\nif __name__ == \"__main__\":\n pytest.main()\n" }, { "path": "tests/utils/test_gadget.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\n\nimport os\nfrom pathlib import Path\n\nimport pytest\n\nfrom xenonpy.utils import set_env, absolute_path, config, camel_to_snake\n\n\ndef test_camel_to_snake_1():\n assert 'camel_to_snake' == camel_to_snake('CamelToSnake')\n\n\ndef test_set_env_1():\n import os\n with set_env(test_env='test'):\n assert os.getenv('test_env') == 'test', 'should got \"test\"'\n assert os.getenv('test_env') is None, 'no test_env'\n\n\ndef test_set_env_2():\n import os\n os.environ['test_env'] = 'foo'\n with set_env(test_env='bar'):\n assert os.getenv('test_env') == 'bar', 'should got \"bar\"'\n assert os.getenv('foo') is None, 'foo'\n\n\ndef test_absolute_path_1():\n path = absolute_path('.')\n path = Path(path)\n cwd = Path(os.getcwd())\n assert path.parent.name == cwd.parent.name\n assert path.name == cwd.name\n\n\ndef test_absolute_path_2():\n path = absolute_path('../')\n path = Path(path)\n cwd = Path(os.getcwd())\n assert path.name == cwd.parent.name\n\n\ndef test_absolute_path_3():\n path = absolute_path('../other')\n path = Path(path)\n cwd = Path(os.getcwd())\n assert path.name == 'other'\n assert path.parent.name == cwd.parent.name\n\n\ndef test_config_1():\n tmp = config('name')\n assert tmp == 'xenonpy'\n\n with pytest.raises(RuntimeError):\n config('no_exist')\n\n tmp = config(new_key='test')\n assert tmp is None\n\n tmp = config('new_key')\n assert tmp == 'test'\n\n tmp = config('github_username', other_key='other')\n assert tmp == 'yoshida-lab'\n\n tmp = config('other_key')\n assert tmp == 'other'\n\n\nif __name__ == \"__main__\":\n pytest.main()\n" }, { "path": "tests/utils/test_parameter_gen.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport numpy as np\nimport pytest\n\nfrom xenonpy.utils import ParameterGenerator\n\n\n@pytest.fixture(scope='module')\ndef data():\n # ignore numpy warning\n import warnings\n print('ignore NumPy RuntimeWarning\\n')\n warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n warnings.filterwarnings(\"ignore\", message=\"numpy.ndarray size changed\")\n\n yield dict(\n int=5,\n tuple=(0, 1, 2, 3),\n func=lambda: np.random.randint(4),\n tuple_dict_1=dict(data=(0, 1, 2, 3), repeat=(1, 2, 3)),\n func_dict_1=dict(data=lambda i: np.random.randint(4, size=i), repeat=(1, 2, 3)),\n tuple_dict_2=dict(data=(0, 1, 2, 3), repeat=2),\n func_dict_2=dict(data=lambda i: np.random.randint(4, size=i), repeat='tuple_dict_1'),\n )\n print('test over')\n\n\ndef test_gen_1():\n with pytest.raises(RuntimeError, match='need parameter candidate'):\n ParameterGenerator()\n\n test_data = (1, 2, 3)\n pg = ParameterGenerator(a=test_data)\n i_ = 0\n for i, t in enumerate(pg(5)):\n assert i_ == i\n i_ += 1\n\n for t, p in pg(5, factory=lambda a: a + 1):\n assert t['a'] + 1 == p\n\n\ndef test_gen_2(data):\n pg = ParameterGenerator(name=data['tuple'])\n for t in pg(10):\n assert 'name' in t\n assert t['name'] in data['tuple']\n assert 0 <= t['name'] < 4\n\n\ndef test_gen_3(data):\n pg = ParameterGenerator(name=data['func'])\n for t in pg(10):\n assert 0 <= t['name'] < 4\n\n\ndef test_gen_4(data):\n pg = ParameterGenerator(name=data['tuple_dict_1'])\n for t in pg(10):\n assert isinstance(t['name'], tuple)\n assert len(t['name']) <= 3\n for i in t['name']:\n assert 0 <= i <= 3\n\n\ndef test_gen_5(data):\n pg = ParameterGenerator(name=data['func_dict_1'])\n for t in pg(10):\n assert len(t['name']) <= 3\n for i in t['name']:\n assert 0 <= i <= 3\n\n\ndef test_gen_6(data):\n pg = ParameterGenerator(name=data['tuple_dict_2'])\n for t in pg(10):\n assert len(t['name']) == 2\n for i in t['name']:\n assert 0 <= i <= 3\n\n\ndef test_gen_7(data):\n pg = ParameterGenerator(name=data['int'])\n for t in pg(10):\n assert t['name'] == 5\n\n\ndef test_gen_8(data):\n pg = ParameterGenerator(name=data['func_dict_2'])\n with pytest.raises(KeyError):\n for _ in pg(10):\n pass\n\n pg = ParameterGenerator(name=data['func_dict_2'], tuple_dict_1=data['tuple_dict_1'])\n for t in pg(10):\n assert 'tuple_dict_1' in t\n assert len(t['name']) == len(t['tuple_dict_1'])\n for i in t['name']:\n assert 0 <= i <= 3\n for i in t['name']:\n assert 0 <= i <= 3\n\n pg = ParameterGenerator(tuple_dict_1=data['tuple_dict_1'], name=data['func_dict_2'])\n for t in pg(10):\n assert 'tuple_dict_1' in t\n assert len(t['name']) == len(t['tuple_dict_1'])\n for i in t['name']:\n assert 0 <= i <= 3\n for i in t['name']:\n assert 0 <= i <= 3\n\n\nif __name__ == \"__main__\":\n pytest.main()\n" }, { "path": "tests/utils/test_product.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\n\nfrom itertools import product\n\nimport pytest\n\nfrom xenonpy.utils.math import Product\n\n\n@pytest.fixture(scope='module')\ndef data():\n # ignore numpy warning\n import warnings\n print('ignore NumPy RuntimeWarning\\n')\n warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n warnings.filterwarnings(\"ignore\", message=\"numpy.ndarray size changed\")\n\n # prepare test data\n a = ['a1', 'a2', 'a3']\n b = ['b1', 'b2']\n c = ['c1', 'c2']\n d = ['d1', 'd2', 'd3']\n yield (a, b, c, d)\n\n print('test over')\n\n\ndef test_product_1(data):\n abcd = list(product(*data))\n p = Product(*data)\n assert p.size == len(abcd), 'should have same length'\n assert p[0] == abcd[0]\n assert p[1] == abcd[1]\n assert p[3] != abcd[4]\n assert p[10] == abcd[10]\n assert p[22] == abcd[22]\n assert p[33] == abcd[33]\n\n with pytest.raises(IndexError):\n p[36]\n\n\ndef test_product_2(data):\n with pytest.raises(ValueError):\n Product(data[0], repeat=1.5)\n\n\ndef test_product_3(data):\n abcd = list(product(data[0], repeat=1))\n p = Product(data[0], repeat=1)\n assert p.size == len(abcd), 'should have same length'\n assert p[0] == abcd[0]\n assert p[1] == abcd[1]\n assert p[2] == abcd[2]\n\n\ndef test_product_4(data):\n abcd = list(product(data[0], repeat=3))\n p = Product(data[0], repeat=3)\n assert p.size == len(abcd), 'should have same length'\n assert p[0] == abcd[0]\n assert p[1] == abcd[1]\n assert p[3] != abcd[4]\n assert p[5] == abcd[5]\n print(p.paras)\n\n\ndef test_product_5(data):\n abcd = list(product(product(*data), repeat=2))\n p = Product(Product(*data), repeat=2)\n assert p.size == len(abcd), 'should have same length'\n assert p[0] == abcd[0]\n assert p[1] == abcd[1]\n assert p[50] != abcd[55]\n assert p[333] == abcd[333]\n assert p[555] == abcd[555]\n assert p[666] != abcd[777]\n\n\nif __name__ == \"__main__\":\n pytest.main()\n" }, { "path": "xenonpy/__doc__.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n" }, { "path": "xenonpy/__init__.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\n# change version in there, conf.yml, setup.py\n\nfrom ._conf import *\n\n\ndef __init(force=False):\n \"\"\"\n Create config file is not exist at ~/.xenonpy/conf.yml\n\n .. warning::\n\n Set ``force=True`` will reset all which under the ``~/.xenonpy`` dir.\n\n Args\n ----\n force: bool\n force reset ``conf.yml`` to default and empty all dirs under ``~/.xenonpy``.\n \"\"\"\n from shutil import rmtree, copyfile\n from ruamel.yaml import YAML\n from sys import version_info\n from pathlib import Path\n\n if version_info[0] != 3 or version_info[1] < 6:\n raise SystemError(\"Python version must be greater than or equal to 3.6\")\n\n yaml = YAML(typ='safe')\n yaml.indent(mapping=2, sequence=4, offset=2)\n root_dir = Path(__cfg_root__)\n root_dir.mkdir(parents=True, exist_ok=True)\n user_cfg_file = root_dir / 'conf.yml'\n\n if force:\n rmtree(str(root_dir))\n\n # copy default conf.yml to ~/.xenonpy\n if not user_cfg_file.exists() or force:\n copyfile(str(Path(__file__).parent / 'conf.yml'), str(user_cfg_file))\n else:\n user_cfg = yaml.load(user_cfg_file)\n if 'version' not in user_cfg or user_cfg['version'] != __version__:\n with open(str(Path(__file__).parent / 'conf.yml'), 'r') as f:\n pack_cfg = yaml.load(f)\n pack_cfg['userdata'] = user_cfg['userdata']\n pack_cfg['usermodel'] = user_cfg['usermodel']\n yaml.dump(pack_cfg, user_cfg_file)\n\n # init dirs\n user_cfg = yaml.load(user_cfg_file)\n dataset_dir = root_dir / 'dataset'\n cached_dir = root_dir / 'cached'\n user_data_dir = Path(user_cfg['userdata']).expanduser().absolute()\n user_model_dir = Path(user_cfg['usermodel']).expanduser().absolute()\n\n # create dirs\n dataset_dir.mkdir(parents=True, exist_ok=True)\n cached_dir.mkdir(parents=True, exist_ok=True)\n user_data_dir.mkdir(parents=True, exist_ok=True)\n user_model_dir.mkdir(parents=True, exist_ok=True)\n\n\n__init()\n" }, { "path": "xenonpy/__main__.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport argparse\nfrom os import remove, rename\nfrom pathlib import Path\n\nfrom tqdm import tqdm\n\nfrom .utils import config\n\n\ndef migrate(args_):\n data_path = Path('~/.xenonpy/dataset').expanduser().absolute()\n user_path = Path(config('userdata')).expanduser().absolute()\n\n def migrate_(f):\n path = data_path / f\n if not path.exists():\n return\n if args_.keep:\n path_ = user_path / f\n rename(str(path), str(path_))\n else:\n remove(str(path))\n\n for file in tqdm(['mp_inorganic.pkl.pd_', 'mp_structure.pkl.pd_', 'oqmd_inorganic.pkl.pd_',\n 'oqmd_structure.pkl.pd_'], desc='migrating'):\n migrate_(file)\n\n\nparser = argparse.ArgumentParser(\n prog='XenonPy',\n description='''\n XenonPy is a Python library that implements a comprehensive set of\n machine learning tools for materials informatics.\n ''')\nsubparsers = parser.add_subparsers()\n\nparser_migrate = subparsers.add_parser('migrate', help='see `migrate -h`')\nparser_migrate.add_argument(\n '-k',\n '--keep',\n action='store_true',\n help=\n 'Keep files fetched from `yoshida-lab/dataset`. These files will be moved to `userdata` dir.'\n)\nparser_migrate.set_defaults(handler=migrate)\n\nargs = parser.parse_args()\nif hasattr(args, 'handler'):\n args.handler(args)\nelse:\n parser.print_help()\n" }, { "path": "xenonpy/_conf.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom pathlib import Path\n\nfrom ruamel.yaml import YAML\n\n__all__ = [\n '__pkg_name__',\n '__version__',\n '__db_version__',\n '__release__',\n '__short_description__',\n '__license__',\n '__author__',\n '__author_email__',\n '__maintainer__',\n '__maintainer_email__',\n '__github_username__',\n '__cfg_root__',\n]\n\n\nclass __PackageInfo(object):\n\n def __init__(self):\n yaml = YAML(typ='safe')\n yaml.indent(mapping=2, sequence=4, offset=2)\n cwd = Path(__file__).parent / 'conf.yml'\n with open(str(cwd), 'r') as f:\n info = yaml.load(f)\n self._info = info\n\n def __getattr__(self, item):\n try:\n return self._info[item]\n except KeyError:\n return None\n\n\npackage_info = __PackageInfo()\n\n__pkg_name__ = package_info.name\n__version__ = package_info.version\n__db_version__ = package_info.db_version\n__release__ = package_info.release\n__short_description__ = package_info.short_description\n__license__ = package_info.license\n__author__ = package_info.author\n__author_email__ = package_info.author_email\n__maintainer__ = package_info.maintainer\n__maintainer_email__ = package_info.maintainer_email\n__github_username__ = package_info.github_username\n__cfg_root__ = str(Path.home().resolve() / ('.' + __pkg_name__))\n" }, { "path": "xenonpy/conf.yml", "content": "###############\n# package info\n###############\nname: \"xenonpy\"\nversion: \"0.6.7\"\ndb_version: \"0.1.3\"\nrelease: \"\"\nshort_description: \"material descriptor library\"\nlicense: \"BSD (3-clause)\"\nauthor:\n - \"TsumiNa\"\n - \"stewu5\"\nauthor_email: \"liu.chang.1865@gmail.com\"\nmaintainer:\n - \"TsumiNa\"\n - \"stewu5\"\nmaintainer_email: \"liu.chang.1865@gmail.com\"\ngithub_username: \"yoshida-lab\"\n\n# sha256 of built-in dataset\natom_init: \"ac362e8bbce4b66987913b39e64b5554cf372e8f55dfc0cd76d6b2354059f84e\"\nelements: \"e9007e45786773c72d2e454ad17dc69f2cd8edf42e66313dc0d343b2b272eb45\"\nelements_completed: \"9a885ac06c99dd79e63bbb431f46d8131f078239a42df028edfe1052a45a2888\"\n\n#################################\n# user custom\n################################\n\n# user-defined path\nuserdata: \"~/.xenonpy/userdata\"\nusermodel: \"~/.xenonpy/usermodel\"\next_data: []\n" }, { "path": "xenonpy/contrib/README.md", "content": "# XenonPy contrib\n\nAny code in this directory is not officially supported and may change or be removed at any time without notice.\n\nThe contrib directory contains project/source code directories from contributors.\nThese contributions/features may eventually be merged into the XenonPy package.\nXenonPy is meant to be a community based project.\nWe encourage users to share their codes with everyone to help enhancing the functionality of XenonPy. \nWhile these codes may still undergo changes, updates, or extra testings,\nusers may create a new folder for their contributions if they are relatively complete.\nIf the contribution is still in the form of code fragments,\nwe still encourage users to share them inside the `sample_codes` folder,\nwhere code contributions are not expected to be complete.\n\nWhen adding a project, please stick to the following directory structure:\n\n1. Create a project directory in `contrib/` with your project name, e.g., `contrib/my_project/`.\n2. Provide a `README.md` under the root of the project directory, e.g., `contrib/my_project/README.md`.\n3. Add unit test code (using pytest) under the `$ROOT/tests/contrib/my_project/`.\n\nFor example, if you create a project named `foo` with source file `foo.py` and the testing file `foo_test.py`. \nIn `contrib/`, they are part of the project `foo`, and their full path is `$ROOT/xenonpy/contrib/foo/foo.py`,\nand in `tests`, the full path is `$ROOT/tests/contrib/foo/test_foo.py`.\n\nWe prepared the `sample_codes` folder for contributors who want to share codes,\nwhich are not complete as a stand-alone project or test codes are not available.\n" }, { "path": "xenonpy/contrib/__init__.py", "content": "# Copyright (c) 2021. TsumiNa. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\n# Add projects here\n" }, { "path": "xenonpy/contrib/extend_descriptors/README.md", "content": "# Extend Descriptors\n\n## FrozenFeaturizerDescriptor\n\nThis is a sample code for creating artificial descriptor based on a trained neural network.\nThis code creates a BaseFeaturizer object in XenonPy that can be used as input for training models.\nThe input is in the same format as the input of the descriptor used in the neural network.\n\nBy passing both the XenonPy descriptor object and XenonPy frozen featurizer object into this class when creating the Base Featurizer, the output will be a dataframe same as other typical XenonPy descriptors, while the number of columns is the number of neurons in the chosen hidden layers.\n\n\n## Mordred2DDescriptor\n\nThis is a sample code for calculating the 2D Mordred descriptor:\nhttps://github.com/mordred-descriptor/mordred\n\nThis code creates a BaseFeaturizer object in XenonPy that can be used as input for training models.\n\n## OrganicCompDescriptor\n\nThis is a sample code for calculating the XenonPy compositional descriptors for organic molecules in SMILES or RDKit MOL format:\nhttps://xenonpy.readthedocs.io/en/latest/features.html#compositional-descriptors\n\nThis code creates a BaseFeaturizer object in XenonPy that can be used as input for training models.\n\n-----------\nwritten by Stephen Wu, 2019.05.31\nupdated by Stephen Wu, 2019.07.11\n" }, { "path": "xenonpy/contrib/extend_descriptors/__init__.py", "content": "# Copyright (c) 2021. stewu5. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n" }, { "path": "xenonpy/contrib/extend_descriptors/descriptor/__init__.py", "content": "# Copyright (c) 2021. stewu5. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom .frozen_featurizer_descriptor import FrozenFeaturizerDescriptor\nfrom .mordred_descriptor import Mordred2DDescriptor\n" }, { "path": "xenonpy/contrib/extend_descriptors/descriptor/frozen_featurizer_descriptor.py", "content": "# Copyright (c) 2021. stewu5. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom typing import Union\n\nfrom xenonpy.descriptor import FrozenFeaturizer\nfrom xenonpy.descriptor.base import BaseFeaturizer, BaseDescriptor\n\n\nclass FrozenFeaturizerDescriptor(BaseFeaturizer):\n\n def __init__(self, descriptor_calculator: Union[BaseDescriptor, BaseFeaturizer],\n frozen_featurizer: FrozenFeaturizer, *,\n on_errors='raise',\n return_type='any'):\n \"\"\"\n A featurizer for extracting artificial descriptors from neural networks\n\n Parameters\n ----------\n descriptor_calculator : BaseFeaturizer or BaseDescriptor\n Convert input data into descriptors to keep consistency with the pre-trained model.\n frozen_featurizer : FrozenFeaturizer\n Extracting artificial descriptors from neural networks\n \"\"\"\n\n # fix n_jobs to be 0 to skip automatic wrapper in XenonPy BaseFeaturizer class\n super().__init__(n_jobs=0, on_errors=on_errors, return_type=return_type)\n self.FP = descriptor_calculator\n self.FP.on_errors = on_errors\n self.FP.return_type = return_type\n self.ff = frozen_featurizer\n self.output = None\n self.__authors__ = ['Stephen Wu', 'TsumiNa']\n\n def featurize(self, x, *, depth=1):\n # transform input to descriptor dataframe\n tmp_df = self.FP.transform(x)\n # convert descriptor dataframe to hidden layer dataframe\n self.output = self.ff.transform(tmp_df, depth=depth, return_type='df')\n return self.output\n\n @property\n def feature_labels(self):\n # column names based on xenonpy frozen featurizer setting\n return self.output.columns\n" }, { "path": "xenonpy/contrib/extend_descriptors/descriptor/mordred_descriptor.py", "content": "# Copyright (c) 2021. stewu5. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport pandas as pd\nfrom mordred import Calculator, descriptors\nfrom rdkit import Chem\nfrom xenonpy.descriptor.base import BaseFeaturizer\n\n\nclass Mordred2DDescriptor(BaseFeaturizer):\n\n def __init__(self, *, on_errors='raise', return_type='any'):\n # fix n_jobs to be 0 to skip automatic wrapper in XenonPy BaseFeaturizer class\n super().__init__(n_jobs=0, on_errors=on_errors, return_type=return_type)\n self.output = None\n self.__authors__ = ['Stephen Wu', 'TsumiNa']\n\n def featurize(self, x):\n # check if type(x) = list\n if isinstance(x, pd.Series):\n x = x.tolist()\n if not isinstance(x, list):\n x = [x]\n # check input format, assume SMILES if not RDKit-MOL\n if not isinstance(x[0], Chem.rdchem.Mol):\n x_mol = []\n for z in x:\n x_mol.append(Chem.MolFromSmiles(z))\n if x_mol[-1] is None:\n raise ValueError('can not convert Mol from SMILES %s' % z)\n else:\n x_mol = x\n\n calc = Calculator(descriptors, ignore_3D=True)\n self.output = calc.pandas(x_mol)\n return self.output\n\n @property\n def feature_labels(self):\n return self.output.columns\n" }, { "path": "xenonpy/contrib/extend_descriptors/descriptor/organic_comp_descriptor.py", "content": "# Copyright (c) 2021. stewu5. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.e\n\nfrom collections import Counter\n\nimport pandas as pd\nfrom rdkit import Chem\nfrom xenonpy.descriptor import Compositions\nfrom xenonpy.descriptor.base import BaseFeaturizer\n\n\nclass OrganicCompDescriptor(BaseFeaturizer):\n\n def __init__(self, n_jobs=-1, *, featurizers='all', on_errors='raise', return_type='any'):\n \"\"\"\n A featurizer for extracting XenonPy compositional descriptors from SMILES or MOL\n \"\"\"\n\n # fix n_jobs to be 0 to skip automatic wrapper in XenonPy BaseFeaturizer class\n super().__init__(n_jobs=0, on_errors=on_errors, return_type=return_type)\n self._cal = Compositions(n_jobs=n_jobs, featurizers=featurizers, on_errors=on_errors)\n\n def featurize(self, x):\n # check if type(x) = list\n if isinstance(x, pd.Series):\n x = x.tolist()\n if not isinstance(x, list):\n x = [x]\n # check input format, assume SMILES if not RDKit-MOL\n if not isinstance(x[0], Chem.rdchem.Mol):\n x_mol = []\n for z in x:\n x_mol.append(Chem.MolFromSmiles(z))\n if x_mol[-1] is None:\n raise ValueError('can not convert Mol from SMILES %s' % z)\n else:\n x_mol = x\n\n # convert to counting dictionary\n mol = [Chem.AddHs(z) for z in x_mol]\n d_list = [dict(Counter([atom.GetSymbol() for atom in z.GetAtoms()])) for z in mol]\n\n self.output = self._cal.transform(d_list)\n\n return self.output\n\n @property\n def feature_labels(self):\n return self.output.columns\n" }, { "path": "xenonpy/contrib/ismd/README.md", "content": "# New BaseProposal class\n\n## reactant_pool\n\nThis is a sample code of a new class of BaseProposal, called ReactantPool.\nReactantPool allows generation of molecules based on virtual reaction of reactants in a predefined pool of reactants. Initialization of this class requires a pretrained reactor, a ``pandas.DataFrame`` of reactant candidates, and a pre-calculated similarity matrix between all pairs of reactant candidates. Based on an initial set of reactant pairs, new reactant pairs are randomly sampled based on the similarity matrix and then reacted with the reactor to create a list of products, which are added to the input ``pandas.DataFrame``.\n\n\n## reactor\nThis is the molecular transformer model used for reaction prediction.\nThis class is used to load the pre-trained molecular transformer model and create a Reactor instance as the reactor for ReactantPool.\n\n\n-----------\nwritten by Qi Zhang, 2021.01.16 updated by Stephen Wu, 2021.01.16\n" }, { "path": "xenonpy/contrib/ismd/__init__.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\n__all__ = ['Reactor', 'load_reactor', 'ReactantPool']\nfrom .reactant_pool import ReactantPool\nfrom .reactor import Reactor, load_reactor\n" }, { "path": "xenonpy/contrib/ismd/reactant_pool.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n# -*- coding: utf-8 -*-\n\nfrom xenonpy.inverse.base import BaseProposal, ProposalError\nimport random\nimport pandas as pd\n\n\nclass ReactantNotInPoolError(ProposalError):\n\n def __init__(self, r_id):\n super().__init__(\"reactant id {} is not in the reactant pool\".format(r_id))\n\n\nclass NotSquareError(ProposalError):\n\n def __init__(self, n_row=0, n_col=0):\n super().__init__(\"dataframe of shape {} * {} is not square\".format(n_row, n_col))\n\n\nclass SimPoolnotmatchError(ProposalError):\n\n def __init__(self, n_pool=0, n_sim=0):\n super().__init__(\n \"reactant pool with length {} dose not match the size of similarity matrix of size {} * {}\"\n .format(n_pool, n_sim, n_sim))\n\n\nclass NoSampleError(ProposalError):\n\n def __init__(self):\n super().__init__(\"sample is empty\")\n\n\nclass ReactantPool(BaseProposal):\n\n def __init__(self,\n *,\n pool_df=None,\n sim_df=None,\n reactor=None,\n pool_smiles_col='SMILES',\n sim_id_in_pool=None,\n sample_reactant_idx_col='reactant_idx',\n sample_reactant_smiles_col='reactant_smiles',\n sample_product_smiles_col='product_smiles',\n splitter='.'):\n \"\"\"\n A module consists the reactant pool for proposal\n\n Parameters\n ----------\n pool_df: [pandas.DataFrame]\n a dataframe contains the all usable reactants,\n sim_df: [pandas.DataFrame]\n a matrix of similarity between each pair of reactant in pool_df, size len(pool_df)*len(pool_df).\n reactor: [xenonpy.contrib.ismd.Reactor]\n reaction prediction model.\n pool_smiles_col: [str]\n the column name of reactant SMILES in the pool_df,\n sim_id_in_pool: [str]\n the column name of id in pool_df corresponding to sim_df, if None, use index.\n sample_reactant_idx_col: [str]\n the column name of reactant id in sample dataframe.\n #sample_reactant_idx_old_col: [str]\n the column name of old reactant id in sample dataframe, copied from sample_reactant_idx_col before proposal.\n sample_reactant_smiles_col: [str]\n the column name of reactant SMILES in sample dataframe\n sample_product_smiles_col: [str]\n the column name of product SMILES in sample dataframe\n splitter: [str]\n string used for concatenating reactant in a reactant set.\n \"\"\"\n if sim_df.shape[0] != sim_df.shape[1]:\n raise NotSquareError(sim_df.shape[0], sim_df.shape[1])\n\n if pool_df.shape[0] != sim_df.shape[0]:\n raise SimPoolnotmatchError(pool_df.shape[0], sim_df.shape[0])\n\n if sim_id_in_pool is not None:\n self._pool_df = pool_df.set_index(sim_id_in_pool)\n else:\n self._pool_df = pool_df\n self._sim_df = sim_df\n self._reactor = reactor\n self._pool_smiles_col = pool_smiles_col\n self._sample_reactant_idx_col = sample_reactant_idx_col\n self._sample_reactant_smiles_col = sample_reactant_smiles_col\n self._sample_product_smiles_col = sample_product_smiles_col\n self._splitter = splitter\n\n def single_index2reactant(self, reactant) -> str:\n \"\"\"\n convert a list of index to string concatenated by the splitter.\n\n Parameters\n ----------\n reactant: [list]\n list of reactant id (e.g. [45011, 29392])\n\n Returns\n -------\n reactant_smiles: [str]\n reactant SMILES concatenated by the splitter (e.g. CC(C)Br.COc1ccc(C=O)cc1O)\n \"\"\"\n r_list = [self._pool_df.iloc[r][self._pool_smiles_col] for r in reactant]\n return self._splitter.join(r_list)\n\n def index2sim(self, r_idx_old):\n \"\"\"\n substitute the reactant id by a similar one.\n\n Parameters\n ----------\n r_idx_old: [int]\n reactant id to be substituted (e.g. 29392)\n\n Returns\n -------\n r_idx_new: [int]\n an id of reactant similar to r_idx_old (e.g. 9980)\n \"\"\"\n if r_idx_old not in self._pool_df.index:\n raise ReactantNotInPoolError(r_idx_old)\n sim_col = self._sim_df[[r_idx_old]].drop(r_idx_old)\n sim_col = sim_col.loc[sim_col[r_idx_old] != 0]\n if len(sim_col) > 0:\n r_idx_new = random.choices(population=sim_col.index, weights=sim_col[r_idx_old], k=1)[0]\n else:\n r_idx_new = random.choices(population=self._sim_df[[r_idx_old]].drop(r_idx_old).index,\n k=1)[0]\n return r_idx_new\n\n def single_proposal(self, reactant):\n \"\"\"\n substitute a randomly selected reactant id in reactant by a similar one.\n\n Parameters\n ----------\n reactant: [list]\n list of id (e.g. [45011, 29392])\n\n Returns\n -------\n r_idx_new: [list]\n list of id (e.g. [45011, 9980])\n \"\"\"\n modify_idx = random.choice(list(range(len(reactant))))\n r_idx_old = reactant[modify_idx]\n r_idx_new = self.index2sim(r_idx_old)\n new_reactant = reactant[:]\n new_reactant[modify_idx] = r_idx_new\n return new_reactant\n\n def proposal(self, sample_df):\n \"\"\"\n 1, perform single_propopsal to all of the reactant list,\n 2, convert reactant id to reactant SMILES\n 3, perform reactant prediction\n\n Parameters\n ----------\n sample_df: [pandas.DataFrame]\n sample dataframe\n\n Returns\n -------\n sample_df: [pandas.DataFrame]\n modified sample dataframe \n \"\"\"\n if len(sample_df) == 0:\n raise NoSampleError()\n new_sample_df = pd.DataFrame(columns=sample_df.columns)\n old_list = [list(r) for r in sample_df[self._sample_reactant_idx_col]]\n new_sample_df[self._sample_reactant_idx_col] = [\n self.single_proposal(reactant) for reactant in old_list\n ]\n new_sample_df[self._sample_reactant_smiles_col] = [\n self.single_index2reactant(id_str)\n for id_str in new_sample_df[self._sample_reactant_idx_col]\n ]\n new_sample_df[self._sample_product_smiles_col] = self._reactor.react(\n new_sample_df[self._sample_reactant_smiles_col])\n return new_sample_df\n" }, { "path": "xenonpy/contrib/ismd/reactor.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport argparse\nimport warnings\n\ntry:\n from onmt.translate.translator import build_translator\n from onmt.translate.translator import Translator\n import onmt.inputters\n import onmt.translate\n import onmt\n import onmt.model_builder\n import onmt.modules\nexcept ImportError as e:\n warnings.warn(\n f\"Can not import package {e}, the ismd is unavailable. To install onmt, see: https://github.com/OpenNMT/OpenNMT-py\"\n )\n\n\nclass SMILESInvalidError(Exception):\n\n def __init__(self, smi):\n super().__init__(\"SMILES {} is not tokenizable.\".format(smi))\n\n\ndef smi_tokenizer(smi) -> str:\n \"\"\"\n Tokenize a SMILES molecule or reaction\n\n Parameters\n ----------\n smi: [str]\n SMILES\n\n Returns\n -------\n tokenized SMILES\n \"\"\"\n import re\n pattern = \"(\\[[^\\]]+]|Br?|Cl?|N|O|S|P|F|I|b|c|n|o|s|p|\\(|\\)|\\.|=|#|-|\\+|\\\\\\\\|\\/|:|~|@|\\?|>|\\*|\\$|\\%[0-9]{2}|[0-9])\"\n regex = re.compile(pattern)\n tokens = [token for token in regex.findall(smi)]\n if smi != ''.join(tokens):\n raise SMILESInvalidError(smi)\n return ' '.join(tokens)\n\n\nclass Reactor():\n\n def __init__(self, model: Translator):\n \"\"\"\n A chemical reaction prediction model\n\n Parameters\n ----------\n model: [onmt.translate.translator.Translator]\n A molecular transformer model for reaction prediction\n \"\"\"\n self._model = model\n\n def react(self, reactant_list, *, batch_size=64) -> list:\n \"\"\"\n Tokenize a SMILES molecule or reaction\n\n Parameters\n ----------\n reactant_list: [list]\n list of reactant \n\n Returns\n -------\n product_list: [list]\n all_predictions is a list of `batch_size` lists of `n_best` predictions\n \"\"\"\n reactant_token_list = [smi_tokenizer(s) for s in reactant_list]\n _, product_list = self._model.translate(src=reactant_token_list,\n src_dir='',\n batch_size=batch_size,\n attn_debug=False)\n product_list = [s[0].replace(' ', '') for s in product_list]\n\n return product_list\n\n\ndef load_reactor(max_length=200, *, device_id=-1, model_path='') -> Reactor:\n \"\"\"\n Loader of reaction prediction model\n\n Parameters\n ----------\n max_length: [int]\n maxmal length of product SMILES\n device_id: [int]\n cpu: -1; gpu: 0,1,2,3...\n model_path: [str]\n path of transformer file.\n\n Returns\n -------\n Reactor : A chemical reaction prediction model\n \"\"\"\n parser = argparse.ArgumentParser(description='translate.py',\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n onmt.opts.translate_opts(parser)\n opt = parser.parse_args([\n '-src', 'dummy_src', '-model', model_path, '-replace_unk', '-max_length',\n str(max_length), '-gpu',\n str(device_id)\n ])\n translator = build_translator(opt, report_score=False)\n\n return Reactor(translator)\n" }, { "path": "xenonpy/contrib/sample_codes/Binary_likelihood/binary_likelihood.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Filter-based likelihood\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"2019.12.24\\n\",\n \"Stephen Wu\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This sample code demonstrates how to construct likelihood used for filtering molecules based on fragments or other specific physical properties. The created likelihood can be combined with other likelihood through the BaseLogLikelihoodSet class and be used in the iQSPR inverse design loop.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# import basic libraries\\n\",\n \"\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# examples of molecules used for testing\\n\",\n \"\\n\",\n \"smis = ['CC(=O)OC(CC(=O)[O-])C[N+](C)(C)C',\\n\",\n \"'CC(=O)OC(CC(=O)O)C[N+](C)(C)C',\\n\",\n \"'C1=CC(C(C(=C1)C(=O)O)O)O',\\n\",\n \"'CC(CN)O',\\n\",\n \"'C(C(=O)COP(=O)(O)O)N',\\n\",\n \"'C1=CC(=C(C=C1[N+](=O)[O-])[N+](=O)[O-])Cl',\\n\",\n \"'CCN1C=NC2=C1N=CN=C2N',\\n\",\n \"'CCC(C)(C(C(=O)O)O)O',\\n\",\n \"'C1(C(C(C(C(C1O)O)OP(=O)(O)O)O)O)O',\\n\",\n \"'C1C(NC2=C(N1)NC(=NC2=O)N)CN(C=O)C3=CC=C(C=C3)C(=O)NC(CCC(=O)O)C(=O)O',\\n\",\n \"'C(CCl)Cl',\\n\",\n \"'C1=C(C=C(C(=C1O)O)O)O',\\n\",\n \"'C1=CC(=C(C=C1Cl)Cl)Cl',\\n\",\n \"'CCCCCC(=O)C=CC1C(CC(=O)C1CCCCCCC(=O)O)O',\\n\",\n \"'CC12CCC(=O)CC1CCC3C2CCC4(C3CCC4O)C',\\n\",\n \"'C1CCC(=O)NCCCCCC(=O)NCC1',\\n\",\n \"'C1C=CC(=NC1C(=O)O)C(=O)O',\\n\",\n \"'C(C(C(C(=O)C(=O)C(=O)O)O)O)O',\\n\",\n \"'C1=CC(=C(C(=C1)O)O)C(=O)O',\\n\",\n \"'C1=CC(=C(C(=C1)O)O)CCC(=O)O']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Due to the flexibility of XenonPy, there are many ways to achieve our goal. Here, we show only one possible way that may be easier for re-using in different projects or problem setup. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For example, assume that we want to pick out molecules with a benzene ring and a carbonyl group while the total molecular weight is less than 200. First, we can make corresponding BaseFeaturizers classes such that when the conditions are met, the featurizer will return 1 (True), and 0 (False) otherwise. Considering reusability, we make separated featurizer for checking molecular weight and fragments, then we combine them with the BaseDescriptor class in XenonPy. Second, we will make a BaseLogLikelihood to convert the False values to negative infinity (or a very large negative value) and the True values to 0.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, let us try to make the featurizers.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# BaseFeaturizer for checking molecular weight\\n\",\n \"\\n\",\n \"from rdkit import Chem\\n\",\n \"from rdkit.Chem import rdMolDescriptors as rdMol\\n\",\n \"from xenonpy.descriptor.base import BaseFeaturizer\\n\",\n \"\\n\",\n \"class MolWeight_binary(BaseFeaturizer):\\n\",\n \"\\n\",\n \" def __init__(self,\\n\",\n \" n_jobs=-1,\\n\",\n \" *,\\n\",\n \" target=None,\\n\",\n \" input_type='any',\\n\",\n \" on_errors='raise',\\n\",\n \" return_type='any'):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" RDKit fingerprint.\\n\",\n \" Parameters\\n\",\n \" ----------\\n\",\n \" n_jobs: int\\n\",\n \" The number of jobs to run in parallel for both fit and predict.\\n\",\n \" Can be -1 or # of cups. Set -1 to use all cpu cores (default).\\n\",\n \" target: (float, float)\\n\",\n \" Lower and upper bound of the molecular weight\\n\",\n \" input_type: string\\n\",\n \" Set the specific type of transform input.\\n\",\n \" Set to ``mol`` (default) to ``rdkit.Chem.rdchem.Mol`` objects as input.\\n\",\n \" When set to ``smlies``, ``transform`` method can use a SMILES list as input.\\n\",\n \" Set to ``any`` to use both.\\n\",\n \" If input is SMILES, ``Chem.MolFromSmiles`` function will be used inside.\\n\",\n \" for ``None`` returns, a ``ValueError`` exception will be raised.\\n\",\n \" on_errors: string\\n\",\n \" How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\\n\",\n \" When 'nan', return a column with ``np.nan``.\\n\",\n \" The length of column corresponding to the number of feature labs.\\n\",\n \" When 'keep', return a column with exception objects.\\n\",\n \" The default is 'raise' which will raise up the exception.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" super().__init__(n_jobs=n_jobs, on_errors=on_errors, return_type=return_type)\\n\",\n \" self.input_type = input_type\\n\",\n \" if target is None:\\n\",\n \" raise RuntimeError(' is empty')\\n\",\n \" else:\\n\",\n \" self.target = target\\n\",\n \" self.__authors__ = ['Stephen Wu']\\n\",\n \"\\n\",\n \" def featurize(self, x):\\n\",\n \" if self.input_type == 'smiles':\\n\",\n \" x_ = x\\n\",\n \" x = Chem.MolFromSmiles(x)\\n\",\n \" if x is None:\\n\",\n \" raise ValueError('can not convert Mol from SMILES %s' % x_)\\n\",\n \" if self.input_type == 'any':\\n\",\n \" if not isinstance(x, Chem.rdchem.Mol):\\n\",\n \" x_ = x\\n\",\n \" x = Chem.MolFromSmiles(x)\\n\",\n \" if x is None:\\n\",\n \" raise ValueError('can not convert Mol from SMILES %s' % x_)\\n\",\n \" \\n\",\n \" tmp = rdMol.CalcExactMolWt(x)\\n\",\n \" \\n\",\n \" return (tmp > self.target[0]) and (tmp < self.target[1])\\n\",\n \"\\n\",\n \" @property\\n\",\n \" def feature_labels(self):\\n\",\n \" return [\\\"MolW_bin\\\"]\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
MolW_bin
0False
1False
2True
3True
4True
5False
6True
7True
8False
9False
10True
11True
12True
13False
14False
15False
16True
17True
18True
19True
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" MolW_bin\\n\",\n \"0 False\\n\",\n \"1 False\\n\",\n \"2 True\\n\",\n \"3 True\\n\",\n \"4 True\\n\",\n \"5 False\\n\",\n \"6 True\\n\",\n \"7 True\\n\",\n \"8 False\\n\",\n \"9 False\\n\",\n \"10 True\\n\",\n \"11 True\\n\",\n \"12 True\\n\",\n \"13 False\\n\",\n \"14 False\\n\",\n \"15 False\\n\",\n \"16 True\\n\",\n \"17 True\\n\",\n \"18 True\\n\",\n \"19 True\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# testing if it works\\n\",\n \"\\n\",\n \"fea = MolWeight_binary(target=(-np.inf,200), on_errors='nan', return_type='df')\\n\",\n \"fea.transform(smis)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# BaseFeaturizer for checking existence of fragments\\n\",\n \"\\n\",\n \"from rdkit import Chem\\n\",\n \"from xenonpy.descriptor.base import BaseFeaturizer\\n\",\n \"\\n\",\n \"class Frag_binary(BaseFeaturizer):\\n\",\n \"\\n\",\n \" def __init__(self,\\n\",\n \" n_jobs=-1,\\n\",\n \" *,\\n\",\n \" target=None,\\n\",\n \" input_type='any',\\n\",\n \" on_errors='raise',\\n\",\n \" return_type='any'):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" RDKit fingerprint.\\n\",\n \" Parameters\\n\",\n \" ----------\\n\",\n \" n_jobs: int\\n\",\n \" The number of jobs to run in parallel for both fit and predict.\\n\",\n \" Can be -1 or # of cups. Set -1 to use all cpu cores (default).\\n\",\n \" target: list(str)\\n\",\n \" List of fragment in SMARTS.\\n\",\n \" input_type: string\\n\",\n \" Set the specific type of transform input.\\n\",\n \" Set to ``mol`` (default) to ``rdkit.Chem.rdchem.Mol`` objects as input.\\n\",\n \" When set to ``smlies``, ``transform`` method can use a SMILES list as input.\\n\",\n \" Set to ``any`` to use both.\\n\",\n \" If input is SMILES, ``Chem.MolFromSmiles`` function will be used inside.\\n\",\n \" for ``None`` returns, a ``ValueError`` exception will be raised.\\n\",\n \" on_errors: string\\n\",\n \" How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\\n\",\n \" When 'nan', return a column with ``np.nan``.\\n\",\n \" The length of column corresponding to the number of feature labs.\\n\",\n \" When 'keep', return a column with exception objects.\\n\",\n \" The default is 'raise' which will raise up the exception.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" super().__init__(n_jobs=n_jobs, on_errors=on_errors, return_type=return_type)\\n\",\n \" self.input_type = input_type\\n\",\n \" if target is None:\\n\",\n \" raise RuntimeError(' is empty')\\n\",\n \" elif isinstance(target, str):\\n\",\n \" self.target = [target]\\n\",\n \" else:\\n\",\n \" self.target = target\\n\",\n \" self.mol = [Chem.MolFromSmarts(x) for x in self.target]\\n\",\n \" if any([x is None for x in self.mol]):\\n\",\n \" raise RuntimeError('At least one is invalid SMARTS')\\n\",\n \" self.__authors__ = ['Stephen Wu']\\n\",\n \"\\n\",\n \" def featurize(self, x):\\n\",\n \" if self.input_type == 'smiles':\\n\",\n \" x_ = x\\n\",\n \" x = Chem.MolFromSmiles(x)\\n\",\n \" if x is None:\\n\",\n \" raise ValueError('can not convert Mol from SMILES %s' % x_)\\n\",\n \" if self.input_type == 'any':\\n\",\n \" if not isinstance(x, Chem.rdchem.Mol):\\n\",\n \" x_ = x\\n\",\n \" x = Chem.MolFromSmiles(x)\\n\",\n \" if x is None:\\n\",\n \" raise ValueError('can not convert Mol from SMILES %s' % x_)\\n\",\n \" \\n\",\n \" return np.array([x.HasSubstructMatch(z) for z in self.mol])\\n\",\n \"\\n\",\n \" @property\\n\",\n \" def feature_labels(self):\\n\",\n \" return self.target\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
c1ccccc1C=O
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\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" c1ccccc1 C=O\\n\",\n \"0 False True\\n\",\n \"1 False True\\n\",\n \"2 False True\\n\",\n \"3 False False\\n\",\n \"4 False True\\n\",\n \"5 True False\\n\",\n \"6 False False\\n\",\n \"7 False True\\n\",\n \"8 False False\\n\",\n \"9 True True\\n\",\n \"10 False False\\n\",\n \"11 True False\\n\",\n \"12 True False\\n\",\n \"13 False True\\n\",\n \"14 False True\\n\",\n \"15 False True\\n\",\n \"16 False True\\n\",\n \"17 False True\\n\",\n \"18 True True\\n\",\n \"19 True True\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# testing if it works\\n\",\n \"\\n\",\n \"fea_frag = Frag_binary(target=['c1ccccc1','C=O'], on_errors='nan', return_type='df')\\n\",\n \"fea_frag.transform(smis)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# BaseDescriptor that combines the two featurizer we made\\n\",\n \"\\n\",\n \"from xenonpy.descriptor.base import BaseDescriptor\\n\",\n \"\\n\",\n \"class custom_binary(BaseDescriptor):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Collect custom made binary descriptors of organic molecules.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" def __init__(self,\\n\",\n \" n_jobs=-1,\\n\",\n \" *,\\n\",\n \" featurizers='all',\\n\",\n \" on_errors='raise',\\n\",\n \" return_type='any'):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Parameters\\n\",\n \" ----------\\n\",\n \" n_jobs: int\\n\",\n \" The number of jobs to run in parallel for both fit and predict.\\n\",\n \" Can be -1 or # of cpus. Set -1 to use all cpu cores (default).\\n\",\n \" input_type: string\\n\",\n \" Set the specific type of transform input.\\n\",\n \" Set to ``mol`` (default) to ``rdkit.Chem.rdchem.Mol`` objects as input.\\n\",\n \" When set to ``smlies``, ``transform`` method can use a SMILES list as input.\\n\",\n \" Set to ``any`` to use both.\\n\",\n \" If input is SMILES, ``Chem.MolFromSmiles`` function will be used inside.\\n\",\n \" for ``None`` returns, a ``ValueError`` exception will be raised.\\n\",\n \" on_errors: string\\n\",\n \" How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\\n\",\n \" When 'nan', return a column with ``np.nan``.\\n\",\n \" The length of column corresponding to the number of feature labs.\\n\",\n \" When 'keep', return a column with exception objects.\\n\",\n \" The default is 'raise' which will raise up the exception.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" super().__init__(featurizers=featurizers)\\n\",\n \" self.n_jobs = n_jobs\\n\",\n \"\\n\",\n \" self.mol = MolWeight_binary(target=(-np.inf,200), on_errors=on_errors, return_type=return_type)\\n\",\n \" self.mol = Frag_binary(target=['c1ccccc1','C=O'], on_errors=on_errors, return_type=return_type)\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
MolW_binc1ccccc1C=O
0FalseFalseTrue
1FalseFalseTrue
2TrueFalseTrue
3TrueFalseFalse
4TrueFalseTrue
5FalseTrueFalse
6TrueFalseFalse
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\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" MolW_bin c1ccccc1 C=O\\n\",\n \"0 False False True\\n\",\n \"1 False False True\\n\",\n \"2 True False True\\n\",\n \"3 True False False\\n\",\n \"4 True False True\\n\",\n \"5 False True False\\n\",\n \"6 True False False\\n\",\n \"7 True False True\\n\",\n \"8 False False False\\n\",\n \"9 False True True\\n\",\n \"10 True False False\\n\",\n \"11 True True False\\n\",\n \"12 True True False\\n\",\n \"13 False False True\\n\",\n \"14 False False True\\n\",\n \"15 False False True\\n\",\n \"16 True False True\\n\",\n \"17 True False True\\n\",\n \"18 True True True\\n\",\n \"19 True True True\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# testing if it works\\n\",\n \"\\n\",\n \"fea_custom = custom_binary(on_errors='nan', return_type='df')\\n\",\n \"fea_custom.transform(smis)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Second, we will make a BaseLogLikelihood to handle the binary featurizers.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from typing import Union\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"from xenonpy.descriptor.base import BaseDescriptor, BaseFeaturizer\\n\",\n \"from xenonpy.inverse.base import BaseLogLikelihood\\n\",\n \"\\n\",\n \"class BinaryLogLikelihood(BaseLogLikelihood):\\n\",\n \" def __init__(self, descriptor: Union[BaseFeaturizer, BaseDescriptor], *, log_0=-1000.0):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Binary loglikelihood.\\n\",\n \" Parameters\\n\",\n \" ----------\\n\",\n \" descriptor: BaseFeaturizer or BaseDescriptor\\n\",\n \" assume the descriptor always return 1 or 0 (True or False), where NaN is considered 0.\\n\",\n \" log_0: float\\n\",\n \" value to take for log_0 probability instead of -inf.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" self._log_0 = log_0\\n\",\n \" \\n\",\n \" if not isinstance(descriptor, (BaseFeaturizer, BaseDescriptor)):\\n\",\n \" raise TypeError(' must be a subclass of or ')\\n\",\n \" self._descriptor = descriptor\\n\",\n \" self._descriptor.on_errors = 'nan'\\n\",\n \"\\n\",\n \" def predict(self, x, **kwargs):\\n\",\n \" return self._descriptor.transform(x, return_type='df')\\n\",\n \"\\n\",\n \" # log_likelihood returns a dataframe of log-likelihood values of each property & sample\\n\",\n \" def log_likelihood(self, x, *, log_0=None):\\n\",\n \"\\n\",\n \" if log_0 is None:\\n\",\n \" log_0 = self._log_0\\n\",\n \" \\n\",\n \" ll = self.predict(x)\\n\",\n \" check = ll == 1\\n\",\n \" ll[~check] = log_0\\n\",\n \" ll[check] = 0\\n\",\n \"\\n\",\n \" return ll\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
MolW_binc1ccccc1C=O
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1-1000.0-1000.00.0
20.0-1000.00.0
30.0-1000.0-1000.0
40.0-1000.00.0
5-1000.00.0-1000.0
60.0-1000.0-1000.0
70.0-1000.00.0
8-1000.0-1000.0-1000.0
9-1000.00.00.0
100.0-1000.0-1000.0
110.00.0-1000.0
120.00.0-1000.0
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14-1000.0-1000.00.0
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\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" MolW_bin c1ccccc1 C=O\\n\",\n \"0 -1000.0 -1000.0 0.0\\n\",\n \"1 -1000.0 -1000.0 0.0\\n\",\n \"2 0.0 -1000.0 0.0\\n\",\n \"3 0.0 -1000.0 -1000.0\\n\",\n \"4 0.0 -1000.0 0.0\\n\",\n \"5 -1000.0 0.0 -1000.0\\n\",\n \"6 0.0 -1000.0 -1000.0\\n\",\n \"7 0.0 -1000.0 0.0\\n\",\n \"8 -1000.0 -1000.0 -1000.0\\n\",\n \"9 -1000.0 0.0 0.0\\n\",\n \"10 0.0 -1000.0 -1000.0\\n\",\n \"11 0.0 0.0 -1000.0\\n\",\n \"12 0.0 0.0 -1000.0\\n\",\n \"13 -1000.0 -1000.0 0.0\\n\",\n \"14 -1000.0 -1000.0 0.0\\n\",\n \"15 -1000.0 -1000.0 0.0\\n\",\n \"16 0.0 -1000.0 0.0\\n\",\n \"17 0.0 -1000.0 0.0\\n\",\n \"18 0.0 0.0 0.0\\n\",\n \"19 0.0 0.0 0.0\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# testing if it works\\n\",\n \"\\n\",\n \"prd_mdl = BinaryLogLikelihood(descriptor=fea_custom)\\n\",\n \"prd_mdl(smis)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now, we have succeeded to make a log-likelihood class that will return 0 when desired conditions are met (controlled by the featurizers) and -1000.0 otherwise. These are the molecules that met our conditions.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['C1=CC(=C(C(=C1)O)O)C(=O)O', 'C1=CC(=C(C(=C1)O)O)CCC(=O)O']\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"tmp = prd_mdl(smis).sum(axis=1) == 0\\n\",\n \"[x for i, x in enumerate(smis) if tmp[i]]\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"xepy37_test\",\n \"language\": \"python\",\n \"name\": \"xepy37_test\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "xenonpy/contrib/sample_codes/Fragment_Ngram/Fragment_Ngram.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Fragment NGram \\n\",\n \"2019.10.21 \\n\",\n \"yoh Noguchi (edited by Stephen Wu on 2019.10.24)\\n\",\n \"\\n\",\n \"__Do not use too many data to begin with!!!__\\n\",\n \"\\n\",\n \"Goal of this script: Create new molecules by modifying substructures in a list of initial molecules based on a pre-trained fragment NGram generator.\\n\",\n \"\\n\",\n \"Steps:\\n\",\n \"1. Prepare a list of initial molecules (in SMILES format)\\n\",\n \"2. Fragmentation using RECAP in RDKit\\n\",\n \"3. Keep bigger part as base structures, and extract only the smaller parts\\n\",\n \"4. Modify the extracted small fragments using a pre-trained NGram\\n\",\n \"5. Exhaustive combination of all new small fragments generated above with the base structures\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from xenonpy.descriptor import Fingerprints\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from xenonpy.inverse.iqspr import GaussianLogLikelihood\\n\",\n \"from xenonpy.inverse.iqspr import NGram\\n\",\n \"from xenonpy.inverse.iqspr import IQSPR\\n\",\n \"import csv\\n\",\n \"import math\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import pickle as pk\\n\",\n \"from rdkit import Chem, DataStructs\\n\",\n \"from rdkit.Chem import Draw, Recap\\n\",\n \"from rdkit.Chem.rdMolDescriptors import GetMorganFingerprint\\n\",\n \"import os\\n\",\n \"from xenonpy.descriptor import FrozenFeaturizer\\n\",\n \"from xenonpy.descriptor.base import BaseFeaturizer\\n\",\n \"from xenonpy.descriptor.base import BaseDescriptor\\n\",\n \"from xenonpy.descriptor import RDKitFP, MACCS, ECFP, AtomPairFP, TopologicalTorsionFP, FCFP\\n\",\n \"from bayes_opt import BayesianOptimization\\n\",\n \"from rdkit.Chem import BRICS\\n\",\n \"from collections import Counter\\n\",\n \"from scipy.special import comb\\n\",\n \"from joblib import Parallel, delayed\\n\",\n \"from tqdm import tqdm\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### User parameters\\n\",\n \"- `CSV_PATH`: directory of csv file to be imported\\n\",\n \"- `NGRAM_PATH`: directory of saved NGram model\\n\",\n \"- `FRAGMENT_LENGTH`: max length of SMILES to be considered a small fragment\\n\",\n \"- `BASE_LENGTH`: max length of SMILES to be considered a base structure\\n\",\n \"- `CREATED_FRAGMENTS_NUMBER`: number of modified fragments per each extracted small fragment\\n\",\n \"※ `len(smis_Fragment)` * `CREATED_FRAGMENTS_NUMBER` = total of number of new fragments\\n\",\n \"- `OUTPUT_FILENAME`: file name of output csv\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"CSV_PATH = 'XXXXX.csv'\\n\",\n \"NGRAM_PATH = 'ngram_reorder_12_O20_peter.obj'\\n\",\n \"FRAGMENT_LENGTH = 30\\n\",\n \"BASE_LENGTH = 50\\n\",\n \"CREATED_FRAGMENTS_NUMBER = 5\\n\",\n \"OUTPUT_FILENAME = 'test.csv'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Read in data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"data = pd.read_csv(CSV_PATH).reset_index(drop=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Verify convertability of SMILES to RDKit MOL\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"check = []\\n\",\n \"for i, smiles in enumerate(data['SMILES']):\\n\",\n \" mol = Chem.MolFromSmiles(smiles)\\n\",\n \" if mol is None:\\n\",\n \" check.append(False)\\n\",\n \" if mol is not None:\\n\",\n \" if '.' in smiles:\\n\",\n \" check.append(False)\\n\",\n \" else:\\n\",\n \" check.append(True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### List of SMILES\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"smiles_list = list(data['SMILES'][check].unique())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Fragmentation（RECAP）\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"frag_list = []\\n\",\n \"for i in range(len(smiles_list)):\\n\",\n \" mol_fu = (Chem.MolFromSmiles(smiles_list[i]))\\n\",\n \" decomp = Chem.Recap.RecapDecompose(mol_fu)\\n\",\n \" first_gen = [node.mol for node in decomp.children.values()]\\n\",\n \" for j in range(len(first_gen)):\\n\",\n \" smiles = Chem.MolToSmiles(first_gen[j])\\n\",\n \" frag_list.append(smiles)\\n\",\n \" \\n\",\n \"frag_list = list(set(frag_list)) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Read in NGram model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"## Ngram model loding for Fragments\\n\",\n \"with open(NGRAM_PATH, 'rb') as f:\\n\",\n \" n_gram = pk.load(f)\\n\",\n \"\\n\",\n \"setattr(n_gram,'min_len',2)\\n\",\n \"n_gram.sample_order = (1, 20)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Categorize fragments\\n\",\n \"- `smis_Fragment`: small fragments\\n\",\n \"- `smis_Base`: base structures\\n\",\n \"- `smis_Large`: big structures to be filtered out\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"smis_Fragment = []\\n\",\n \"smis_Base = []\\n\",\n \"smis_Large = []\\n\",\n \"for smi in frag_list:\\n\",\n \" if len(smi) < FRAGMENT_LENGTH:\\n\",\n \" smis_Fragment.append(smi)\\n\",\n \" elif len(smi) < BASE_LENGTH:\\n\",\n \" smis_Base.append(smi)\\n\",\n \" else:\\n\",\n \" smis_Large.append(smi)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 【Function】combining fragment with base structure\\n\",\n \"__\\\\*A B\\\\* -> BA__\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def combi_smiles(smis_frag, smis_base):\\n\",\n \" smis_frag = smis_frag\\n\",\n \" smis_base = smis_base\\n\",\n \"\\n\",\n \" # prepare NGram object for use of ext. SMILES\\n\",\n \" from xenonpy.inverse.iqspr import NGram\\n\",\n \" ngram = NGram()\\n\",\n \"\\n\",\n \" # check position of '*'\\n\",\n \" mols_base = Chem.MolFromSmiles(smis_base)\\n\",\n \" idx_base = [i for i in range(mols_base.GetNumAtoms()) if mols_base.GetAtomWithIdx(i).GetSymbol() == '*']\\n\",\n \"\\n\",\n \" # rearrange base SMILES to avoid 1st char = '*'\\n\",\n \" if idx_base[0] == 0:\\n\",\n \" smis_base_head = Chem.MolToSmiles(mols_base,rootedAtAtom=1)\\n\",\n \" else:\\n\",\n \" smis_base_head = Chem.MolToSmiles(mols_base,rootedAtAtom=0)\\n\",\n \"\\n\",\n \" # converge base to ext. SMILES and pick insertion location\\n\",\n \" esmi_base = ngram.smi2esmi(smis_base_head)\\n\",\n \" esmi_base = esmi_base[:-1]\\n\",\n \" idx_base = esmi_base.index[esmi_base['esmi'] == '*'].tolist()\\n\",\n \"\\n\",\n \" # rearrange fragment to have 1st char = '*' and convert to ext. SMILES\\n\",\n \" mols_frag = Chem.MolFromSmiles(smis_frag)\\n\",\n \" idx_frag = [i for i in range(mols_frag.GetNumAtoms()) if mols_frag.GetAtomWithIdx(i).GetSymbol() == '*']\\n\",\n \" smis_frag_head = Chem.MolToSmiles(mols_frag,rootedAtAtom=idx_frag[0])\\n\",\n \" esmi_frag = ngram.smi2esmi(smis_frag_head)\\n\",\n \"\\n\",\n \" # remove leading '*' and last '!'\\n\",\n \" esmi_frag = esmi_frag[1:-1]\\n\",\n \"\\n\",\n \" # check open rings of base SMILES\\n\",\n \" nRing_base = esmi_base['n_ring'].loc[idx_base[0]]\\n\",\n \"\\n\",\n \" # re-number rings in fragment SMILES\\n\",\n \" esmi_frag['n_ring'] = esmi_frag['n_ring'] + nRing_base\\n\",\n \"\\n\",\n \" # delete '*' at the insertion location\\n\",\n \" esmi_base = esmi_base.drop(idx_base[0]).reset_index(drop=True)\\n\",\n \"\\n\",\n \" # combine base with the fragment\\n\",\n \" ext_smi = pd.concat([esmi_base.iloc[:idx_base[0]], esmi_frag, esmi_base.iloc[idx_base[0]:]]).reset_index(drop=True)\\n\",\n \" new_pd_row = {'esmi': '!', 'n_br': 0, 'n_ring': 0, 'substr': ['!']}\\n\",\n \" ext_smi.append(new_pd_row, ignore_index=True)\\n\",\n \"\\n\",\n \" fin_smi = ngram.esmi2smi(ext_smi)\\n\",\n \" mol_fin = Chem.MolFromSmiles(fin_smi)\\n\",\n \" #return mol_fin\\n\",\n \" return fin_smi\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 【Function】loop `combi_smiles` over all the base structures and list of fragments\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def loop_frag(base_list, frag_list):\\n\",\n \" comb_smi_list = []\\n\",\n \" for smi in tqdm(base_list):\\n\",\n \" results_list = Parallel(n_jobs=-1)([delayed(combi_smiles)(smi,s) for s in frag_list])\\n\",\n \" comb_smi_list.extend(results_list)\\n\",\n \" return comb_smi_list\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 【Function】Generating new fragments based on an initial fragment using pre-trained NGram\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def create_Fragments(smi, N_frag, n_gram, max_iter = 100):\\n\",\n \" f_list = []\\n\",\n \" len_smi = len(smi)\\n\",\n \" num_min = int(len_smi/10)\\n\",\n \" num_max = (len_smi - 1)\\n\",\n \" n_gram.set_params(del_range=[num_min,num_max],max_len=1500, reorder_prob=0)\\n\",\n \" \\n\",\n \" for _ in range(max_iter):\\n\",\n \" smis_Ngram = n_gram.proposal([smi for _ in range(N_frag-len(f_list))])\\n\",\n \" f_list += [x for x in list(set(smis_Ngram)) if x.count('*') == 1]\\n\",\n \" if len(f_list) == N_frag:\\n\",\n \" break\\n\",\n \" \\n\",\n \" return f_list\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Generate new fragments\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"Frangments_list = []\\n\",\n \"escape_num = 0\\n\",\n \"#smi_list.append(smis_Fragment[0])\\n\",\n \"results_list = Parallel(n_jobs=-1)([delayed(create_Fragments)(s, CREATED_FRAGMENTS_NUMBER, n_gram) for s in smis_Fragment])\\n\",\n \"\\n\",\n \"for res in results_list:\\n\",\n \" Frangments_list.extend(res) \\n\",\n \"#Frangments_list = list(set(Frangments_list))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Combine list of fragments with list of base structures\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"100%|██████████| 5/5 [00:03<00:00, 1.34it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"res = loop_frag(smis_Base, Frangments_list)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Output csv file\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"s_result = pd.Series(res, name='SMILES')\\n\",\n \"s_result.to_csv(OUTPUT_FILENAME, index=False, header='SMILES')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.7\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "xenonpy/contrib/sample_codes/README.md", "content": "# XenonPy contrib/sample_codes\n\nAny code in this directory is not officially supported and may change or be removed at any time without notice.\n\nThe contrib/sample_codes directory contains source code directories from contributors that may not be revised by the XenonPy developers.\nIt is meant to share different examples of XenonPy usage for different applications.\nUser with a similar problem setup can reference these codes and use them as a starting point to develop new codes.\n\nWhen sharing sample codes, please stick to the following rules:\n1. Create a new directory in `contrib/sample_codes`.\n2. Either provide a `README.md` under the root of the project directory, e.g `contrib/my_project/README.md`, \nor include some headline information in the beginning of the Python file/jupyter notebook.\n3. Include a brief explanation of the purpose of the code. (Sharing contact info is recommended in case of any enquiry from other users)\n" }, { "path": "xenonpy/contrib/sample_codes/Random_NN_structure/randNN_structures.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Generating random NN models with different baseline structures\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"2019.11.1\\n\",\n \"Stephen Wu\\n\",\n \"\\n\",\n \"Goal of this script: \\n\",\n \"sample codes of generating random neural network models based on a few different baseline structures other than the basic ones shown in the XenonPy official tutorial.\\n\",\n \" 1. fixing the range of number of neurons in the first and last hidden layer with linear reduction of the range from the first to the last layer.\\n\",\n \" 2. fixing the range of number of neurons in the first and last hidden layer with log reduction of the range from the first to the last layer.\\n\",\n \" 3. randomly picking a subset of descriptors from the full descriptors as input for each neural network\\n\",\n \" \\n\",\n \"Data from Pubchem (a subset selected by Ikebata) is used as an example.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Preparation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"import pickle as pk\\n\",\n \"\\n\",\n \"# user-friendly print\\n\",\n \"from IPython.core.interactiveshell import InteractiveShell\\n\",\n \"InteractiveShell.ast_node_interactivity = \\\"all\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"##### Load files\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can download the in-house data at https://github.com/yoshida-lab/XenonPy/releases/download/v0.4.1/iQSPR_sample_data.csv\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Index(['Unnamed: 0', 'SMILES', 'E', 'HOMO-LUMO gap'], dtype='object')\\n\",\n \"(16674, 4)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
Unnamed: 0SMILESEHOMO-LUMO gap
01CC(=O)OC(CC(=O)[O-])C[N+](C)(C)C177.5774.352512
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\"\n ],\n \"text/plain\": [\n \" Unnamed: 0 SMILES E HOMO-LUMO gap\\n\",\n \"0 1 CC(=O)OC(CC(=O)[O-])C[N+](C)(C)C 177.577 4.352512\\n\",\n \"1 2 CC(=O)OC(CC(=O)O)C[N+](C)(C)C 185.735 7.513497\\n\",\n \"2 3 C1=CC(C(C(=C1)C(=O)O)O)O 98.605 4.581348\\n\",\n \"3 4 CC(CN)O 83.445 8.034841\\n\",\n \"4 5 C(C(=O)COP(=O)(O)O)N 90.877 5.741310\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# load QM9 data from csv file\\n\",\n \"data = pd.read_csv('./iQSPR_sample_data.csv')\\n\",\n \"\\n\",\n \"# take a look at the data\\n\",\n \"print(data.columns)\\n\",\n \"print(data.shape)\\n\",\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"##### Fingerprints\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from xenonpy.descriptor import Fingerprints\\n\",\n \"\\n\",\n \"FPs_ECFP_MACCS = Fingerprints(featurizers=['ECFP','MACCS'], input_type='smiles', on_errors='nan')\\n\",\n \"FPs_All = Fingerprints(featurizers=['RDKitFP','AtomPairFP','TopologicalTorsionFP','ECFP','FCFP','MACCS'], input_type='smiles', on_errors='nan')\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Train DNN models\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"##### 1. fixing the range of number of neurons in the first and last hidden layer with linear reduction of the range from the first to the last layer.\\n\",\n \"##### 2. fixing the range of number of neurons in the first and last hidden layer with log reduction of the range from the first to the last layer.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"roughly takes 1min\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 9.18 s, sys: 764 ms, total: 9.94 s\\n\",\n \"Wall time: 16.5 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"# pick a property as output\\n\",\n \"prop = data['HOMO-LUMO gap'].to_frame()\\n\",\n \"# pre-calculate all descriptors\\n\",\n \"desc = FPs_ECFP_MACCS.transform(data['SMILES'])\\n\",\n \"# remove NaN row\\n\",\n \"prop = prop[~desc.isnull().any(axis=1)]\\n\",\n \"desc = desc[~desc.isnull().any(axis=1)]\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"prepare a function to generate random number of neurons uniformly from a given range at each hidden layer for a given total number of layer, for which the max and min values of the ranges are linearly decaying with a pair of fixed max and min values at the first and the last hidden layer.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# the fixed max and min values at the first and the last hidden layer \\n\",\n \"# are determined empirically based on the length of the descriptor\\n\",\n \"\\n\",\n \"def neuron_vector_lin(nL):\\n\",\n \" max_vec = np.linspace(1500,100,nL)\\n\",\n \" min_vec = np.linspace(1000,20,nL)\\n\",\n \" return sorted([np.random.randint(min_vec[i], max_vec[i]) for i in range(nL)], reverse=True)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[1201, 933, 564, 274, 89]\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[1113, 939, 682, 291, 70]\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[1121, 1008, 590, 448, 30]\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[1104, 1041, 792, 436, 66]\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[1235, 974, 793, 298, 65]\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[1433, 885, 534, 424, 89]\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[1319, 787, 595, 400, 32]\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[1476, 846, 799, 316, 56]\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[1019, 1015, 628, 397, 69]\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[1353, 871, 513, 307, 43]\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# test the function\\n\",\n \"for _ in range(10):\\n\",\n \" neuron_vector_lin(5)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"prepare a function to generate random number of neurons uniformly from a given range at each hidden layer for a given total number of layer, for which the max and min values of the ranges are decaying in log-scale with a pair of fixed max and min values at the first and the last hidden layer.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# the fixed max and min values at the first and the last hidden layer \\n\",\n \"# are determined empirically based on the length of the descriptor\\n\",\n \"\\n\",\n \"def neuron_vector_log(nL):\\n\",\n \" log_base = 2\\n\",\n \" max_vec = np.round(np.logspace(np.log2(100),np.log2(1500),nL,base=log_base))\\n\",\n \" min_vec = np.round(np.logspace(np.log2(20),np.log2(1000),nL,base=log_base))\\n\",\n \" return sorted([np.random.randint(min_vec[i], max_vec[i]) for i in range(nL)], reverse=True)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[1454, 723, 255, 101, 41]\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[1142, 617, 257, 80, 80]\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[1235, 513, 299, 160, 85]\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[1397, 540, 184, 183, 74]\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[1188, 410, 193, 127, 81]\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[1109, 669, 358, 115, 42]\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[1475, 467, 325, 119, 24]\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[1018, 565, 232, 139, 62]\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[1419, 524, 263, 85, 61]\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[1180, 731, 212, 151, 79]\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# test the function\\n\",\n \"for _ in range(10):\\n\",\n \" neuron_vector_log(5)\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"prepare a generator with a selected \\\"neuron_vector\\\" function (we use LeakyReLu in this example)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from torch.nn import LeakyReLU\\n\",\n \"from xenonpy.utils import ParameterGenerator\\n\",\n \"\\n\",\n \"generator = ParameterGenerator(\\n\",\n \" in_features=desc.shape[1],\\n\",\n \" out_features=1,\\n\",\n \" h_neurons=dict(\\n\",\n \" data=neuron_vector_lin, \\n\",\n \" repeat=(3, 4, 5)\\n\",\n \" ),\\n\",\n \" h_activation_funcs=(LeakyReLU(),)\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{'in_features': 2215, 'out_features': 1, 'h_activation_funcs': LeakyReLU(negative_slope=0.01), 'h_neurons': (1106, 944, 457, 57)}\\n\",\n \"{'in_features': 2215, 'out_features': 1, 'h_activation_funcs': LeakyReLU(negative_slope=0.01), 'h_neurons': (1092, 1016, 483, 91)}\\n\",\n \"{'in_features': 2215, 'out_features': 1, 'h_activation_funcs': LeakyReLU(negative_slope=0.01), 'h_neurons': (1365, 739, 401, 42)}\\n\",\n \"{'in_features': 2215, 'out_features': 1, 'h_activation_funcs': LeakyReLU(negative_slope=0.01), 'h_neurons': (1243, 846, 616, 439, 73)}\\n\",\n \"{'in_features': 2215, 'out_features': 1, 'h_activation_funcs': LeakyReLU(negative_slope=0.01), 'h_neurons': (1433, 1071, 611, 287, 32)}\\n\"\n ]\n }\n ],\n \"source\": [\n \"# test the function\\n\",\n \"\\n\",\n \"for parameters in generator(num=5):\\n\",\n \" print(parameters)\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"prepare a function to automatically generate names for each model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def make_name(model):\\n\",\n \" name = ['HLgap']\\n\",\n \" for n, m in model.named_children():\\n\",\n \" if 'layer_' in n:\\n\",\n \" name.append(str(m.linear.in_features))\\n\",\n \" else:\\n\",\n \" name.append(str(m.in_features))\\n\",\n \" name.append(str(m.out_features))\\n\",\n \" return '-'.join(name)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"HLgap-2215-1272-917-651-278-77-1\\n\",\n \"HLgap-2215-1026-902-507-49-1\\n\",\n \"HLgap-2215-1320-825-759-305-55-1\\n\"\n ]\n }\n ],\n \"source\": [\n \"# test the function\\n\",\n \"from xenonpy.model import SequentialLinear, LinearLayer\\n\",\n \"\\n\",\n \"for paras, model in generator(num=3, factory=SequentialLinear):\\n\",\n \" print(make_name(model))\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"training models\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# import libraries\\n\",\n \"import torch\\n\",\n \"from torch.utils.data import DataLoader\\n\",\n \"\\n\",\n \"from xenonpy.datatools import preset, Splitter\\n\",\n \"from xenonpy.descriptor import Compositions\\n\",\n \"\\n\",\n \"from xenonpy.model import SequentialLinear, LinearLayer\\n\",\n \"from xenonpy.model.training import Trainer, SGD, MSELoss, Adam, ReduceLROnPlateau, ExponentialLR, ClipValue\\n\",\n \"from xenonpy.model.training.extension import Validator, TensorConverter, Persist\\n\",\n \"from xenonpy.model.training.dataset import ArrayDataset\\n\",\n \"from xenonpy.model.utils import regression_metrics\\n\",\n \"\\n\",\n \"from collections import OrderedDict\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# prepare a trainer\\n\",\n \"trainer = Trainer(\\n\",\n \" optimizer=Adam(lr=0.01),\\n\",\n \" loss_func=MSELoss(),\\n\",\n \" cuda=False,\\n\",\n \").extend(\\n\",\n \" TensorConverter(),\\n\",\n \" Validator(metrics_func=regression_metrics, early_stopping=50, trace_order=3, mae=0.0, pearsonr=1.0),\\n\",\n \")\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 8%|▊ | 15/200 [02:03<25:27, 8.26s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 391\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 12%|█▎ | 25/200 [03:01<21:08, 7.25s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 664\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 4%|▍ | 8/200 [00:55<22:16, 6.96s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 197\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 5%|▌ | 10/200 [01:07<21:22, 6.75s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 262\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 10%|▉ | 19/200 [03:29<33:17, 11.04s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 508\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 12%|█▏ | 23/200 [03:15<25:05, 8.51s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 610\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 6%|▌ | 11/200 [01:43<29:32, 9.38s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 291\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 4%|▎ | 7/200 [00:54<25:13, 7.84s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 179\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 11%|█ | 22/200 [03:02<24:37, 8.30s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 588\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 6%|▋ | 13/200 [01:46<25:25, 8.16s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 337\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 13%|█▎ | 26/200 [04:11<28:04, 9.68s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 690\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 10%|▉ | 19/200 [02:13<21:14, 7.04s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 509\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 3%|▎ | 6/200 [01:09<37:24, 11.57s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 158\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 4%|▍ | 8/200 [01:01<24:24, 7.63s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 207\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 7%|▋ | 14/200 [02:02<27:08, 8.75s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 367\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 4%|▍ | 9/200 [01:31<32:11, 10.11s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 235\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 6%|▌ | 12/200 [02:07<33:23, 10.66s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 312\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 10%|█ | 21/200 [02:25<20:42, 6.94s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 554\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 9%|▉ | 18/200 [02:46<28:02, 9.24s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 472\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 9%|▉ | 18/200 [02:33<25:51, 8.53s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 478\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 4%|▍ | 9/200 [01:16<27:11, 8.54s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 239\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 8%|▊ | 15/200 [01:35<19:39, 6.38s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 395\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 8%|▊ | 17/200 [02:12<23:46, 7.80s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 450\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 4%|▍ | 9/200 [01:08<24:11, 7.60s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 240\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 5%|▌ | 10/200 [01:01<19:35, 6.19s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 265\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 11%|█ | 22/200 [03:12<26:01, 8.77s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 589\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 7%|▋ | 14/200 [01:42<22:43, 7.33s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 363\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 5%|▌ | 10/200 [01:07<21:17, 6.72s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 253\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 4%|▍ | 9/200 [01:09<24:41, 7.75s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 238\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 10%|█ | 21/200 [02:57<25:11, 8.45s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 558\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 4%|▍ | 8/200 [00:56<22:40, 7.08s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 195\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 10%|█ | 20/200 [02:31<22:47, 7.60s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 538\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 10%|█ | 20/200 [02:05<18:46, 6.26s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 534\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 6%|▌ | 11/200 [01:39<28:28, 9.04s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 293\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 8%|▊ | 16/200 [01:55<22:12, 7.24s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 427\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 8%|▊ | 17/200 [02:14<24:02, 7.88s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 454\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 4%|▍ | 8/200 [01:00<24:12, 7.56s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 215\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 6%|▋ | 13/200 [01:51<26:36, 8.54s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 346\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 12%|█▏ | 24/200 [03:23<24:53, 8.48s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 639\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 8%|▊ | 17/200 [02:01<21:49, 7.16s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 454\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 8%|▊ | 15/200 [02:00<24:44, 8.02s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 398\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 6%|▌ | 12/200 [01:52<29:16, 9.34s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 315\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 8%|▊ | 15/200 [02:11<27:02, 8.77s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 398\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 10%|█ | 21/200 [02:28<21:09, 7.09s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 562\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 10%|█ | 20/200 [02:48<25:18, 8.43s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 532\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 10%|█ | 21/200 [03:10<27:02, 9.06s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Early stopping is applied: no improvement for ['mae', 'pearsonr'] since the last 51 iterations, finish training at iteration 562\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training: 6%|▌ | 11/200 [01:14<22:50, 7.25s/it]\"\n ]\n }\n ],\n \"source\": [\n \"summary = []\\n\",\n \"save_folder = 'Test_HLgap_MACCSECFP'\\n\",\n \"N_train = int(prop.shape[0]*0.8)\\n\",\n \"train_batch_size = 512 # based on total number data\\n\",\n \"test_batch_size = 1024 # based on total number data\\n\",\n \"max_epochs = 200\\n\",\n \"\\n\",\n \"for paras, model in generator(num=100, factory=SequentialLinear):\\n\",\n \" sp = Splitter(prop.shape[0], test_size=prop.shape[0]-N_train)\\n\",\n \" x_train, x_val, y_train, y_val = sp.split(desc, prop)\\n\",\n \" \\n\",\n \" train_dataset = DataLoader(ArrayDataset(x_train, y_train), shuffle=True, batch_size=train_batch_size)\\n\",\n \" val_dataset = DataLoader(ArrayDataset(x_val, y_val), batch_size=test_batch_size)\\n\",\n \" \\n\",\n \" model_name = make_name(model)\\n\",\n \" persist = Persist(\\n\",\n \" f'{save_folder}/{model_name}', \\n\",\n \" # -^- required -^-\\n\",\n \" \\n\",\n \" # -v- optional -v-\\n\",\n \" increment=True, \\n\",\n \" sync_training_step=True,\\n\",\n \" model_class=SequentialLinear,\\n\",\n \" model_params=paras,\\n\",\n \" author='Someone',\\n\",\n \" email='someone@email.com',\\n\",\n \" dataset='Pubchem_ikebata',\\n\",\n \" )\\n\",\n \" _ = trainer.extend(persist)\\n\",\n \" trainer.reset(to=model)\\n\",\n \" \\n\",\n \" trainer.fit(training_dataset=train_dataset, validation_dataset=val_dataset, epochs=max_epochs)\\n\",\n \" persist(splitter=sp, data_indices=prop.index.tolist()) # <-- calling of this method only after the model training\\n\",\n \" \\n\",\n \" training_info = trainer.training_info\\n\",\n \" \\n\",\n \" summary.append(OrderedDict(\\n\",\n \" id=model_name,\\n\",\n \" mae=training_info['val_mae'].min(),\\n\",\n \" mse=training_info['val_mse'].min(),\\n\",\n \" r2=training_info['val_r2'].max(),\\n\",\n \" corr=training_info['val_pearsonr'].max(),\\n\",\n \" spearman_corr=training_info['val_spearmanr'].max(),\\n\",\n \" ))\\n\",\n \" \\n\",\n \"# record the summary of the results\\n\",\n \"summary = pd.DataFrame(summary)\\n\",\n \"summary.to_pickle(f'{save_folder}/summary.pd.xz')\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"plot training results\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# prepare target and plotting folders, and load models\\n\",\n \"import os\\n\",\n \"from xenonpy.model.training import Checker\\n\",\n \"\\n\",\n \"target_folder = 'Test_HLgap_MACCSECFP'\\n\",\n \"save_plots = 'Plots_' + target_folder\\n\",\n \"\\n\",\n \"if not os.path.exists(save_plots):\\n\",\n \" os.makedirs(save_plots)\\n\",\n \"\\n\",\n \"checkers = []\\n\",\n \"model_list = []\\n\",\n \"tmp_file = os.listdir(target_folder)\\n\",\n \"for tmp in tmp_file:\\n\",\n \" if os.path.isdir(f'{target_folder}/{tmp}'):\\n\",\n \" model_list.append(tmp)\\n\",\n \" checkers.append(Checker(f'{target_folder}/{tmp}'))\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# prediction vs. observation plots (single test trial)\\n\",\n \"import seaborn as sb\\n\",\n \"\\n\",\n \"def draw(y_true, y_pred, y_true_fit=None, y_pred_fit=None, *, prop_name, log_scale=False, file_dir=None, file_name=None):\\n\",\n \"\\n\",\n \" mask = ~np.isnan(y_pred)\\n\",\n \" y_true = y_true[mask]\\n\",\n \" y_pred = y_pred[mask]\\n\",\n \" \\n\",\n \" data = pd.DataFrame(dict(Observation=y_true, Prediction=y_pred, dataset=['test'] * len(y_true)))\\n\",\n \" scores = metrics(data['Observation'], data['Prediction'])\\n\",\n \" \\n\",\n \" if y_true_fit is not None and y_pred_fit is not None:\\n\",\n \" mask = ~np.isnan(y_pred_fit)\\n\",\n \" y_true_fit = y_true_fit[mask]\\n\",\n \" y_pred_fit = y_pred_fit[mask]\\n\",\n \" train_ = pd.DataFrame(dict(Observation=y_true_fit, Prediction=y_pred_fit, dataset=['train'] * len(y_true_fit)))\\n\",\n \" data = pd.concat([train_, data])\\n\",\n \"\\n\",\n \" if log_scale:\\n\",\n \" data = data.apply(lambda c: np.log(c.values) if c.dtype.type is not np.object_ else c, axis=0)\\n\",\n \"# test_ = test_.apply(lambda c: np.log(c.values) if c.dtype.type is not np.object_ else c, axis=0)\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"# with sb.set(font_scale=2.5):\\n\",\n \" g = sb.lmplot(x=\\\"Prediction\\\", y=\\\"Observation\\\", hue=\\\"dataset\\\", ci=None,\\n\",\n \" data=data, palette=\\\"Set1\\\", height=10, legend=False, markers=[\\\".\\\", \\\"o\\\"],\\n\",\n \" scatter_kws={'s': 25, 'alpha': 0.7}, hue_order=['train', 'test'])\\n\",\n \" \\n\",\n \" ax = plt.gca()\\n\",\n \" tmp = [data[\\\"Prediction\\\"].max(), data[\\\"Prediction\\\"].min(), data[\\\"Observation\\\"].max(), data[\\\"Observation\\\"].max()]\\n\",\n \" min_, max_ = np.min(tmp), np.max(tmp)\\n\",\n \" margin = (max_- min_) / 15\\n\",\n \" min_ = min_ - margin\\n\",\n \" max_ = max_ + margin\\n\",\n \" ax.set_xlim(min_, max_)\\n\",\n \" ax.set_ylim(min_, max_)\\n\",\n \" ax.set_xlabel(ax.get_xlabel(), fontsize='xx-large')\\n\",\n \" ax.set_ylabel(ax.get_ylabel(), fontsize='xx-large')\\n\",\n \" ax.tick_params(axis='both', which='major', labelsize='xx-large')\\n\",\n \" ax.plot((min_, max_), (min_, max_), ':', color='gray')\\n\",\n \" ax.set_title(prop_name, fontsize='xx-large')\\n\",\n \" if log_scale:\\n\",\n \" ax.set_title(prop_name + ' (log scale)', fontsize='xx-large')\\n\",\n \" ax.text(0.98, 0.03,\\n\",\n \" 'MAE: %.5f\\\\nRMSE: %.5f\\\\nPearsonR: %.5f\\\\nSpearmanR: %.5f' % (scores['mae'], scores['rmse'], scores['pearsonr'], scores['spearmanr']),\\n\",\n \" transform=ax.transAxes, horizontalalignment='right', fontsize='xx-large')\\n\",\n \"\\n\",\n \" ax.legend(loc='upper left', markerscale=2, fancybox=True, shadow=True, frameon=True, facecolor='w', fontsize=18)\\n\",\n \"\\n\",\n \" plt.tight_layout()\\n\",\n \" if file_dir and file_name:\\n\",\n \" if log_scale:\\n\",\n \" plt.savefig(file_dir + '/' + file_name + '_log_scale.png', dpi=300, bbox_inches='tight')\\n\",\n \" else:\\n\",\n \" plt.savefig(file_dir + '/' + file_name + '.png', dip=300, bbox_inches='tight')\\n\",\n \" else:\\n\",\n \" print('Missing directory and/or file name information!')\\n\",\n \" \\n\",\n \"# calculating basic statistics for predictions\\n\",\n \"def metrics(y_true, y_pred, ignore_nan=True):\\n\",\n \" from sklearn.metrics import mean_absolute_error, r2_score, mean_squared_error\\n\",\n \" from scipy.stats import pearsonr, spearmanr\\n\",\n \" \\n\",\n \" if ignore_nan:\\n\",\n \" mask = ~np.isnan(y_pred)\\n\",\n \" y_true = y_true[mask]\\n\",\n \" y_pred = y_pred[mask]\\n\",\n \" \\n\",\n \" mae = mean_absolute_error(y_true, y_pred)\\n\",\n \" rmse = np.sqrt(mean_squared_error(y_true, y_pred))\\n\",\n \" r2 = r2_score(y_true, y_pred)\\n\",\n \" pr, p_val = pearsonr(y_true, y_pred)\\n\",\n \" sr, _ = spearmanr(y_true, y_pred)\\n\",\n \" return dict(\\n\",\n \" mae=mae,\\n\",\n \" rmse=rmse,\\n\",\n \" r2=r2,\\n\",\n \" pearsonr=pr,\\n\",\n \" spearmanr=sr,\\n\",\n \" p_value=p_val\\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"RuntimeError\",\n \"evalue\": \"size mismatch, m1: [1024 x 10407], m2: [567 x 502] at /Users/distiller/project/conda/conda-bld/pytorch_1556653464916/work/aten/src/TH/generic/THTensorMath.cpp:961\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mRuntimeError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m\\u001b[0m\\n\\u001b[1;32m 22\\u001b[0m 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104\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_timer\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mstop\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mfn\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m__name__\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m~/anaconda/envs/xepy37_test/lib/python3.7/site-packages/xenonpy/model/training/trainer.py\\u001b[0m in \\u001b[0;36mpredict\\u001b[0;34m(self, x_in, y_true, dataset, checkpoint, **model_params)\\u001b[0m\\n\\u001b[1;32m 581\\u001b[0m \\u001b[0my_trues\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;34m[\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 582\\u001b[0m \\u001b[0;32mfor\\u001b[0m \\u001b[0mx_in\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0my_true\\u001b[0m \\u001b[0;32min\\u001b[0m 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\\u001b[0;36mforward\\u001b[0;34m(self, input)\\u001b[0m\\n\\u001b[1;32m 90\\u001b[0m \\u001b[0;34m@\\u001b[0m\\u001b[0mweak_script_method\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 91\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0mforward\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0minput\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 92\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mF\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mlinear\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0minput\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mweight\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mbias\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 93\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 94\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0mextra_repr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m~/anaconda/envs/xepy37_test/lib/python3.7/site-packages/torch/nn/functional.py\\u001b[0m in \\u001b[0;36mlinear\\u001b[0;34m(input, weight, bias)\\u001b[0m\\n\\u001b[1;32m 1404\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0minput\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdim\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;34m==\\u001b[0m \\u001b[0;36m2\\u001b[0m \\u001b[0;32mand\\u001b[0m \\u001b[0mbias\\u001b[0m \\u001b[0;32mis\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1405\\u001b[0m \\u001b[0;31m# fused op is marginally faster\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 1406\\u001b[0;31m \\u001b[0mret\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mtorch\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0maddmm\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mbias\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0minput\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mweight\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mt\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 1407\\u001b[0m \\u001b[0;32melse\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1408\\u001b[0m \\u001b[0moutput\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0minput\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mmatmul\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mweight\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mt\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mRuntimeError\\u001b[0m: size mismatch, m1: [1024 x 10407], m2: [567 x 502] at /Users/distiller/project/conda/conda-bld/pytorch_1556653464916/work/aten/src/TH/generic/THTensorMath.cpp:961\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# plot training loss (based on the best mae among the three traced models)\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from xenonpy.model.training import Trainer\\n\",\n \"from xenonpy.model.training.dataset import ArrayDataset\\n\",\n \"from torch.utils.data import DataLoader\\n\",\n \"from xenonpy.model.training.extension import TensorConverter\\n\",\n \"\\n\",\n \"train_batch_size = 512 # based on total number data\\n\",\n \"test_batch_size = 1024 # based on total number data\\n\",\n \"\\n\",\n \"for i, checker in enumerate(checkers):\\n\",\n \" # plot training loss\\n\",\n \" fig, ax = plt.subplots(figsize=(10, 5), dpi=150)\\n\",\n \" trainer = Trainer.load(from_=checker).extend(TensorConverter())\\n\",\n \" _ = trainer.training_info.plot(y=['train_mse_loss', 'val_mse'], ax=ax)\\n\",\n \" fig.savefig(f'{save_plots}/Train_{model_list[i]}')\\n\",\n \" \\n\",\n \" # plot observation vs. prediction (based on the best mae among the three traced models)\\n\",\n \" sp = checker['splitter']\\n\",\n \" x_train, x_val, y_train, y_val = sp.split(desc, prop)\\n\",\n \" \\n\",\n \" train_dataset = DataLoader(ArrayDataset(x_train, y_train), shuffle=True, batch_size=train_batch_size)\\n\",\n \" val_dataset = DataLoader(ArrayDataset(x_val, y_val), batch_size=test_batch_size)\\n\",\n \" \\n\",\n \" y_pred, y_true = trainer.predict(dataset=val_dataset, checkpoint='mae_1')\\n\",\n \" y_fit_pred, y_fit_true = trainer.predict(dataset=train_dataset, checkpoint='mae_1')\\n\",\n \" draw(y_true, y_pred, y_fit_true, y_fit_pred, prop_name='HL gap',file_dir=save_plots, file_name='P2O_'+model_list[i])\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"##### 3. randomly picking a subset of descriptors from the full descriptors as input for each neural network\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"roughly takes 2-3mins\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 56.4 s, sys: 6.77 s, total: 1min 3s\\n\",\n \"Wall time: 2min 20s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"# pick a property as output\\n\",\n \"prop = data['HOMO-LUMO gap'].to_frame()\\n\",\n \"# pre-calculate all descriptors\\n\",\n \"desc = FPs_All.transform(data['SMILES'])\\n\",\n \"# remove NaN row\\n\",\n \"prop = prop[~desc.isnull().any(axis=1)]\\n\",\n \"desc = desc[~desc.isnull().any(axis=1)]\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"prepare a function to generate random number of neurons uniformly from a given range at each hidden layer for a given total number of layer, for which the max and min values of the ranges are linearly decaying with a pair of fixed max and min values at the first and the last hidden layer determined based on fractions of the input dimension.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# the fixed max and min ratio at the first and the last hidden layer \\n\",\n \"# are determined empirically based on the length of the descriptor\\n\",\n \"\\n\",\n \"def neuron_vector(nL, in_neu):\\n\",\n \" max_vec = np.linspace(int(in_neu*0.9),100,nL)\\n\",\n \" min_vec = np.linspace(int(in_neu*0.7),10,nL)\\n\",\n \" return sorted([np.random.randint(min_vec[i], max_vec[i]) for i in range(nL)], reverse=True)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[502, 385, 247, 201, 31]\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[454, 363, 280, 124, 56]\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[458, 424, 229, 173, 96]\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[423, 361, 282, 201, 62]\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[486, 332, 312, 121, 30]\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[529, 379, 278, 117, 13]\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[471, 348, 226, 165, 13]\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[470, 364, 280, 134, 83]\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[480, 334, 245, 169, 37]\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[520, 337, 263, 167, 52]\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# test the function\\n\",\n \"for _ in range(10):\\n\",\n \" neuron_vector(5, 600)\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"prepare a function to automatically generate names for each model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def make_name(model):\\n\",\n \" name = ['HLgap']\\n\",\n \" for n, m in model.named_children():\\n\",\n \" if 'layer_' in n:\\n\",\n \" name.append(str(m.linear.in_features))\\n\",\n \" else:\\n\",\n \" name.append(str(m.in_features))\\n\",\n \" name.append(str(m.out_features))\\n\",\n \" return '-'.join(name)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"training models (this time the generator is greated inside the loop)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# import libraries\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"import torch\\n\",\n \"from torch.utils.data import DataLoader\\n\",\n \"\\n\",\n \"from xenonpy.datatools import preset, Splitter\\n\",\n \"from xenonpy.descriptor import Compositions\\n\",\n \"\\n\",\n \"from torch.nn import LeakyReLU\\n\",\n \"from xenonpy.utils import ParameterGenerator\\n\",\n \"\\n\",\n \"from xenonpy.model import SequentialLinear, LinearLayer\\n\",\n \"from xenonpy.model.training import Trainer, SGD, MSELoss, Adam, ReduceLROnPlateau, ExponentialLR, ClipValue\\n\",\n \"from xenonpy.model.training.extension import Validator, TensorConverter, Persist\\n\",\n \"from xenonpy.model.training.dataset import ArrayDataset\\n\",\n \"from xenonpy.model.utils import regression_metrics\\n\",\n \"\\n\",\n \"from collections import OrderedDict\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"trainer = Trainer(\\n\",\n \" optimizer=Adam(lr=0.01),\\n\",\n \" loss_func=MSELoss(),\\n\",\n \" cuda=False,\\n\",\n \").extend(\\n\",\n \" TensorConverter(),\\n\",\n \" Validator(metrics_func=regression_metrics, early_stopping=50, trace_order=3, mae=0.0, pearsonr=1.0),\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\r\",\n \"Training: 0%| | 0/500 [00:00\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# plot training loss (based on the best mae among the three traced models)\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from xenonpy.model.training import Trainer\\n\",\n \"from xenonpy.model.training.dataset import ArrayDataset\\n\",\n \"from torch.utils.data import DataLoader\\n\",\n \"from xenonpy.model.training.extension import TensorConverter\\n\",\n \"\\n\",\n \"train_batch_size = 512 # based on total number data\\n\",\n \"test_batch_size = 1024 # based on total number data\\n\",\n \"\\n\",\n \"for i, checker in enumerate(checkers):\\n\",\n \" # plot training loss\\n\",\n \" fig, ax = plt.subplots(figsize=(10, 5), dpi=150)\\n\",\n \" trainer = Trainer.load(from_=checker).extend(TensorConverter())\\n\",\n \" _ = trainer.training_info.plot(y=['train_mse_loss', 'val_mse'], ax=ax)\\n\",\n \" fig.savefig(f'{save_plots}/Train_{model_list[i]}')\\n\",\n \" \\n\",\n \" # plot observation vs. prediction (based on the best mae among the three traced models)\\n\",\n \" sp = checker['splitter']\\n\",\n \" x_train, x_val, y_train, y_val = sp.split(desc, prop)\\n\",\n \" \\n\",\n \" x_colnames = checker['x_colnames']\\n\",\n \" x_train = x_train[x_colnames]\\n\",\n \" x_val = x_val[x_colnames]\\n\",\n \" \\n\",\n \" train_dataset = DataLoader(ArrayDataset(x_train, y_train), shuffle=True, batch_size=train_batch_size)\\n\",\n \" val_dataset = DataLoader(ArrayDataset(x_val, y_val), batch_size=test_batch_size)\\n\",\n \" \\n\",\n \" y_pred, y_true = trainer.predict(dataset=val_dataset, checkpoint='mae_1')\\n\",\n \" y_fit_pred, y_fit_true = trainer.predict(dataset=train_dataset, checkpoint='mae_1')\\n\",\n \" draw(y_true, y_pred, y_fit_true, y_fit_pred, prop_name='HL gap',file_dir=save_plots, file_name='P2O_'+model_list[i])\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"xepy37_test\",\n \"language\": \"python\",\n \"name\": \"xepy37_test\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "xenonpy/contrib/sample_codes/combine_fragments/combine_fragments.py", "content": "# Copyright (c) 2021. stewu5. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport pandas as pd\nfrom rdkit import Chem\nfrom xenonpy.inverse.iqspr import NGram\n\ndef combine_fragments(smis_base, smis_frag):\n \"\"\"\n combine two SMILES strings with '*' as connection points, note that no proper treatment for '-*', '=*', or '#*' yet.\n\n Parameters\n ----------\n smis_base: str\n SMILES for combining.\n If no '*', assume connection point at the end.\n If more than one '*', the first will be picked if it's not the 1st character.\n smis_frag: str\n SMILES for combining.\n If no '*', assume connection point at the front.\n If more than one '*', the first will be picked.\n \"\"\"\n\n # prepare NGram object for use of ext. SMILES\n ngram = NGram()\n\n # check position of '*'\n mols_base = Chem.MolFromSmiles(smis_base)\n if mols_base is None:\n raise RuntimeError('Invalid base SMILES!')\n idx_base = [i for i in range(mols_base.GetNumAtoms()) if mols_base.GetAtomWithIdx(i).GetSymbol() == '*']\n\n # rearrange base SMILES to avoid 1st char = '*' (assume no '**')\n if len(idx_base) == 1 and idx_base[0] == 0:\n smis_base_head = Chem.MolToSmiles(mols_base,rootedAtAtom=1)\n elif len(idx_base) == 0:\n smis_base_head = smis_base + '*'\n else:\n smis_base_head = smis_base\n\n # converge base to ext. SMILES and pick insertion location\n esmi_base = ngram.smi2esmi(smis_base_head)\n esmi_base = esmi_base[:-1]\n idx_base = esmi_base.index[esmi_base['esmi'] == '*'].tolist()\n if idx_base[0] == 0:\n if len(idx_base) == 1:\n # put treatment here\n raise RuntimeError('Probably -*, =*, and/or #* exist')\n else:\n idx_base = idx_base[1]\n else:\n idx_base = idx_base[0]\n\n # rearrange fragment to have 1st char = '*' and convert to ext. SMILES\n mols_frag = Chem.MolFromSmiles(smis_frag)\n if mols_frag is None:\n raise RuntimeError('Invalid frag SMILES!')\n idx_frag = [i for i in range(mols_frag.GetNumAtoms()) if mols_frag.GetAtomWithIdx(i).GetSymbol() == '*']\n if len(idx_frag) == 0: # if -*, =*, and/or #* exist, not counted as * right now\n esmi_frag = ngram.smi2esmi(smis_frag)\n # remove last '!'\n esmi_frag = esmi_frag[:-1]\n else:\n esmi_frag = ngram.smi2esmi(Chem.MolToSmiles(mols_frag,rootedAtAtom=idx_frag[0]))\n # remove leading '*' and last '!'\n esmi_frag = esmi_frag[1:-1]\n\n # check open rings of base SMILES\n nRing_base = esmi_base['n_ring'].loc[idx_base]\n\n # re-number rings in fragment SMILES\n esmi_frag['n_ring'] = esmi_frag['n_ring'] + nRing_base\n\n # delete '*' at the insertion location\n esmi_base = esmi_base.drop(idx_base).reset_index(drop=True)\n\n # combine base with the fragment\n ext_smi = pd.concat([esmi_base.iloc[:idx_base], esmi_frag, esmi_base.iloc[idx_base:]]).reset_index(drop=True)\n new_pd_row = {'esmi': '!', 'n_br': 0, 'n_ring': 0, 'substr': ['!']}\n ext_smi.append(new_pd_row, ignore_index=True)\n\n return ngram.esmi2smi(ext_smi)\n\n\n" }, { "path": "xenonpy/contrib/sample_codes/iQSPR_V/iQSPR_F.py", "content": "# IQSPR with focus on molecule variety: bring in new initial molecules from reservoir in every step of SMC\n\nimport numpy as np\nimport pandas as pd\n\nfrom rdkit import Chem\nfrom xenonpy.inverse.iqspr import NGram\nfrom xenonpy.inverse.base import BaseSMC, BaseProposal, BaseLogLikelihood, SMCError\n\n# class SMCError(Exception):\n# \"\"\"Base exception for SMC classes\"\"\"\n# pass\n\nclass IQSPR_F(BaseSMC):\n\n def __init__(self, *, estimator, modifier):\n \"\"\"\n SMC iqspr runner.\n Parameters\n ----------\n estimator : BaseLogLikelihood or BaseLogLikelihoodSet\n Log likelihood estimator for given SMILES.\n modifier : BaseProposal\n Modify given SMILES to new ones.\n \"\"\"\n self._proposal = modifier\n self._log_likelihood = estimator\n\n def resample(self, sims, size, p):\n return np.random.choice(sims, size=size, p=p)\n\n @property\n def modifier(self):\n return self._proposal\n\n @modifier.setter\n def modifier(self, value):\n self._proposal = value\n\n @property\n def estimator(self):\n return self._log_likelihood\n\n @estimator.setter\n def estimator(self, value):\n self._log_likelihood = value\n \n def fragmenting(self, smis):\n frag_list = []\n \n for smi in smis:\n decomp = Chem.Recap.RecapDecompose(Chem.MolFromSmiles(smi))\n first_gen = [node.mol for node in decomp.children.values()]\n frag_list += [Chem.MolToSmiles(x) for x in first_gen]\n \n return list(set(frag_list))\n \n def combine_fragments(self, smis_base, smis_frag):\n \"\"\"\n combine two SMILES strings with '*' as connection points\n Parameters\n ----------\n smis_base: str\n SMILES for combining.\n If no '*', assume connection point at the end.\n If more than one '*', the first will be picked if it's not the 1st character.\n smis_frag: str\n SMILES for combining.\n If no '*', assume connection point at the front.\n If more than one '*', the first will be picked.\n \"\"\"\n\n # prepare NGram object for use of ext. SMILES\n ngram = NGram()\n\n # check position of '*'\n mols_base = Chem.MolFromSmiles(smis_base)\n if mols_base is None:\n raise RuntimeError('Invalid base SMILES!')\n idx_base = [i for i in range(mols_base.GetNumAtoms()) if mols_base.GetAtomWithIdx(i).GetSymbol() == '*']\n\n # rearrange base SMILES to avoid 1st char = '*'\n if len(idx_base) == 1 and idx_base[0] == 0:\n smis_base_head = Chem.MolToSmiles(mols_base,rootedAtAtom=1)\n elif len(idx_base) == 0:\n smis_base_head = smis_base + '*'\n else:\n smis_base_head = smis_base\n\n # converge base to ext. SMILES and pick insertion location\n esmi_base = ngram.smi2esmi(smis_base_head)\n esmi_base = esmi_base[:-1]\n idx_base = esmi_base.index[esmi_base['esmi'] == '*'].tolist()\n if idx_base[0] == 0:\n idx_base = idx_base[1]\n else:\n idx_base = idx_base[0]\n\n # rearrange fragment to have 1st char = '*' and convert to ext. SMILES\n mols_frag = Chem.MolFromSmiles(smis_frag)\n if mols_frag is None:\n raise RuntimeError('Invalid frag SMILES!')\n idx_frag = [i for i in range(mols_frag.GetNumAtoms()) if mols_frag.GetAtomWithIdx(i).GetSymbol() == '*']\n if len(idx_frag) == 0:\n esmi_frag = ngram.smi2esmi(smis_frag)\n # remove last '!'\n esmi_frag = esmi_frag[:-1]\n else:\n esmi_frag = ngram.smi2esmi(Chem.MolToSmiles(mols_frag,rootedAtAtom=idx_frag[0]))\n # remove leading '*' and last '!'\n esmi_frag = esmi_frag[1:-1]\n\n # check open rings of base SMILES\n nRing_base = esmi_base['n_ring'].loc[idx_base]\n\n # re-number rings in fragment SMILES\n esmi_frag['n_ring'] = esmi_frag['n_ring'] + nRing_base\n\n # delete '*' at the insertion location\n esmi_base = esmi_base.drop(idx_base).reset_index(drop=True)\n\n # combine base with the fragment\n ext_smi = pd.concat([esmi_base.iloc[:idx_base], esmi_frag, esmi_base.iloc[idx_base:]]).reset_index(drop=True)\n new_pd_row = {'esmi': '!', 'n_br': 0, 'n_ring': 0, 'substr': ['!']}\n ext_smi.append(new_pd_row, ignore_index=True)\n\n return ngram.esmi2smi(ext_smi)\n \n def __call__(self, samples, beta, *, size=None, p_frag=0.1, yield_lpf=False):\n \"\"\"\n Run SMC\n Parameters\n ----------\n samples: list of object\n Initial samples.\n beta: list/1D-numpy of float or pd.Dataframe\n Annealing parameters for each step.\n If pd.Dataframe, column names should follow keys of mdl in BaseLogLikeihood or BaseLogLikelihoodSet\n size: int\n Sample size for each draw.\n p_frag: float\n Probability of performing a fragment based mutation\n yield_lpf : bool\n Yield estimated log likelihood, probability and frequency of each samples. Default is ``False``.\n Yields\n -------\n samples: list of object\n New samples in each SMC iteration.\n llh: np.ndarray float\n Estimated values of log-likelihood of each samples.\n Only yield when ``yield_lpf=Ture``.\n p: np.ndarray of float\n Estimated probabilities of each samples.\n Only yield when ``yield_lpf=Ture``.\n freq: np.ndarray of float\n The number of unique samples in original samples.\n Only yield when ``yield_lpf=Ture``.\n \"\"\"\n\n # sample size will be set to the length of init_samples if None\n if size is None:\n size = len(samples)\n\n try:\n unique, frequency = self.unique(samples)\n ll = self.log_likelihood(unique)\n\n if isinstance(beta, pd.DataFrame):\n beta = beta[ll.columns].values\n else:\n # assume only one row for beta (1-D list or numpy vector)\n beta = np.transpose(np.repeat([beta], ll.shape[1], axis=0))\n\n w = np.dot(ll.values, beta[0]) + np.log(frequency)\n w_sum = np.log(np.sum(np.exp(w - np.max(w)))) + np.max(w) # avoid underflow\n p = np.exp(w - w_sum)\n if yield_lpf:\n yield unique, ll, p, frequency\n else:\n yield unique\n\n # beta = np.delete(beta,0,0) #remove first \"row\"\n\n except SMCError as e:\n self.on_errors(0, samples, e)\n except Exception as e:\n raise e\n\n # translate between input representation and execute environment representation.\n # make sure beta is not changed (np.delete will return the deleted version, without changing original vector)\n for i, step in enumerate(np.delete(beta, 0, 0)):\n try:\n re_samples = self.resample(unique, size, p)\n if np.random.random() < p_frag:\n frag_list = self.fragmenting(re_samples)\n samples = [self.combine_fragments(np.random.choice(frag_list), np.random.choice(frag_list)) for _ in range(size)]\n else:\n samples = self.proposal(re_samples)\n \n unique, frequency = self.unique(samples)\n\n # annealed likelihood in log - adjust with copy counts\n ll = self.log_likelihood(unique)\n w = np.dot(ll.values, step) + np.log(frequency)\n w_sum = np.log(np.sum(np.exp(w - np.max(w)))) + np.max(w) # avoid underflow\n p = np.exp(w - w_sum)\n if yield_lpf:\n yield unique, ll, p, frequency\n else:\n yield unique\n\n except SMCError as e:\n self.on_errors(i + 1, samples, e)\n except Exception as e:\n raise e\n \n" }, { "path": "xenonpy/contrib/sample_codes/iQSPR_V/iQSPR_V.py", "content": "# IQSPR with focus on molecule variety: bring in new initial molecules from reservoir in every step of SMC\n\nimport numpy as np\nimport pandas as pd\n\nfrom xenonpy.inverse.base import BaseSMC, BaseProposal, BaseLogLikelihood, SMCError\n\n#class SMCError(Exception):\n# \"\"\"Base exception for SMC classes\"\"\"\n# pass\n\nclass IQSPR_V(BaseSMC):\n\n def __init__(self, *, estimator, modifier):\n \"\"\"\n SMC iqspr runner.\n Parameters\n ----------\n estimator : BaseLogLikelihood or BaseLogLikelihoodSet\n Log likelihood estimator for given SMILES.\n modifier : BaseProposal\n Modify given SMILES to new ones.\n \"\"\"\n self._proposal = modifier\n self._log_likelihood = estimator\n\n def resample(self, sims, size, p):\n return np.random.choice(sims, size=size, p=p)\n\n @property\n def modifier(self):\n return self._proposal\n\n @modifier.setter\n def modifier(self, value):\n self._proposal = value\n\n @property\n def estimator(self):\n return self._log_likelihood\n\n @estimator.setter\n def estimator(self, value):\n self._log_likelihood = value\n \n def __call__(self, reservoir, beta, size=100, *, samples=None, ratio=0.5, yield_lpf=False):\n \"\"\"\n Run SMC\n Parameters\n ----------\n reservoir: list of object\n Samples to be drawn as new initial molecules in each step of SMC\n beta: list/1D-numpy of float or pd.Dataframe\n Annealing parameters for each step.\n If pd.Dataframe, column names should follow keys of mdl in BaseLogLikeihood or BaseLogLikelihoodSet\n size: int\n Sample size for each draw.\n samples: list of object\n Initial samples.\n ratio: float\n ratio of molecules to be replaced from reservoir in each step of SMC\n yield_lpf : bool\n Yield estimated log likelihood, probability and frequency of each samples. Default is ``False``.\n Yields\n -------\n samples: list of object\n New samples in each SMC iteration.\n llh: np.ndarray float\n Estimated values of log-likelihood of each samples.\n Only yield when ``yield_lpf=Ture``.\n p: np.ndarray of float\n Estimated probabilities of each samples.\n Only yield when ``yield_lpf=Ture``.\n freq: np.ndarray of float\n The number of unique samples in original samples.\n Only yield when ``yield_lpf=Ture``.\n \"\"\"\n\n # initial samples will be randomly picked from resevoir if samples are not provided\n if samples is None:\n samples = np.random.choice(reservoir,size=size).tolist()\n # refill samples if len(samples) not equals given size\n elif len(samples) < size:\n samples = np.concatenate([np.array(samples), np.random.choice(reservoir,size=size-len(samples))])\n \n res_size = int(size*ratio)\n smc_size = size - res_size\n\n try:\n unique, frequency = self.unique(samples)\n ll = self.log_likelihood(unique)\n\n if isinstance(beta, pd.DataFrame):\n beta = beta[ll.columns].values\n else:\n # assume only one row for beta (1-D list or numpy vector)\n beta = np.transpose(np.repeat([beta], ll.shape[1], axis=0))\n\n w = np.dot(ll.values, beta[0]) + np.log(frequency)\n w_sum = np.log(np.sum(np.exp(w - np.max(w)))) + np.max(w) # avoid underflow\n p = np.exp(w - w_sum)\n if yield_lpf:\n yield unique, ll, p, frequency\n else:\n yield unique\n\n # beta = np.delete(beta,0,0) #remove first \"row\"\n\n except SMCError as e:\n self.on_errors(0, samples, e)\n except Exception as e:\n raise e\n\n # translate between input representation and execute environment representation.\n # make sure beta is not changed (np.delete will return the deleted version, without changing original vector)\n for i, step in enumerate(np.delete(beta, 0, 0)):\n try:\n re_samples = self.resample(unique, smc_size, p)\n samples = self.proposal(np.concatenate([re_samples, np.random.choice(reservoir,size=res_size)]))\n\n unique, frequency = self.unique(samples)\n\n # annealed likelihood in log - adjust with copy counts\n ll = self.log_likelihood(unique)\n w = np.dot(ll.values, step) + np.log(frequency)\n w_sum = np.log(np.sum(np.exp(w - np.max(w)))) + np.max(w) # avoid underflow\n p = np.exp(w - w_sum)\n if yield_lpf:\n yield unique, ll, p, frequency\n else:\n yield unique\n\n except SMCError as e:\n self.on_errors(i + 1, samples, e)\n except Exception as e:\n raise e\n \n" }, { "path": "xenonpy/contrib/sample_codes/iQSPR_V/iQSPR_VF.py", "content": "# IQSPR with focus on molecule variety: bring in new initial molecules from reservoir in every step of SMC and allow mutation based on fragmentation\n\nimport numpy as np\nimport pandas as pd\n\nfrom rdkit import Chem\nfrom xenonpy.inverse.iqspr import NGram\nfrom xenonpy.inverse.base import BaseSMC, BaseProposal, BaseLogLikelihood, SMCError\n\n# class SMCError(Exception):\n# \"\"\"Base exception for SMC classes\"\"\"\n# pass\n\nclass IQSPR_VF(BaseSMC):\n\n def __init__(self, *, estimator, modifier):\n \"\"\"\n SMC iqspr runner.\n Parameters\n ----------\n estimator : BaseLogLikelihood or BaseLogLikelihoodSet\n Log likelihood estimator for given SMILES.\n modifier : BaseProposal\n Modify given SMILES to new ones.\n \"\"\"\n self._proposal = modifier\n self._log_likelihood = estimator\n\n def resample(self, sims, size, p):\n return np.random.choice(sims, size=size, p=p)\n\n @property\n def modifier(self):\n return self._proposal\n\n @modifier.setter\n def modifier(self, value):\n self._proposal = value\n\n @property\n def estimator(self):\n return self._log_likelihood\n\n @estimator.setter\n def estimator(self, value):\n self._log_likelihood = value\n \n def fragmenting(self, smis):\n frag_list = []\n \n for smi in smis:\n decomp = Chem.Recap.RecapDecompose(Chem.MolFromSmiles(smi))\n first_gen = [node.mol for node in decomp.children.values()]\n frag_list += [Chem.MolToSmiles(x) for x in first_gen]\n \n return list(set(frag_list))\n \n def combine_fragments(self, smis_base, smis_frag):\n \"\"\"\n combine two SMILES strings with '*' as connection points\n Parameters\n ----------\n smis_base: str\n SMILES for combining.\n If no '*', assume connection point at the end.\n If more than one '*', the first will be picked if it's not the 1st character.\n smis_frag: str\n SMILES for combining.\n If no '*', assume connection point at the front.\n If more than one '*', the first will be picked.\n \"\"\"\n\n # prepare NGram object for use of ext. SMILES\n ngram = NGram()\n\n # check position of '*'\n mols_base = Chem.MolFromSmiles(smis_base)\n if mols_base is None:\n raise RuntimeError('Invalid base SMILES!')\n idx_base = [i for i in range(mols_base.GetNumAtoms()) if mols_base.GetAtomWithIdx(i).GetSymbol() == '*']\n\n # rearrange base SMILES to avoid 1st char = '*'\n if len(idx_base) == 1 and idx_base[0] == 0:\n smis_base_head = Chem.MolToSmiles(mols_base,rootedAtAtom=1)\n elif len(idx_base) == 0:\n smis_base_head = smis_base + '*'\n else:\n smis_base_head = smis_base\n\n # converge base to ext. SMILES and pick insertion location\n esmi_base = ngram.smi2esmi(smis_base_head)\n esmi_base = esmi_base[:-1]\n idx_base = esmi_base.index[esmi_base['esmi'] == '*'].tolist()\n if idx_base[0] == 0:\n idx_base = idx_base[1]\n else:\n idx_base = idx_base[0]\n\n # rearrange fragment to have 1st char = '*' and convert to ext. SMILES\n mols_frag = Chem.MolFromSmiles(smis_frag)\n if mols_frag is None:\n raise RuntimeError('Invalid frag SMILES!')\n idx_frag = [i for i in range(mols_frag.GetNumAtoms()) if mols_frag.GetAtomWithIdx(i).GetSymbol() == '*']\n if len(idx_frag) == 0:\n esmi_frag = ngram.smi2esmi(smis_frag)\n # remove last '!'\n esmi_frag = esmi_frag[:-1]\n else:\n esmi_frag = ngram.smi2esmi(Chem.MolToSmiles(mols_frag,rootedAtAtom=idx_frag[0]))\n # remove leading '*' and last '!'\n esmi_frag = esmi_frag[1:-1]\n\n # check open rings of base SMILES\n nRing_base = esmi_base['n_ring'].loc[idx_base]\n\n # re-number rings in fragment SMILES\n esmi_frag['n_ring'] = esmi_frag['n_ring'] + nRing_base\n\n # delete '*' at the insertion location\n esmi_base = esmi_base.drop(idx_base).reset_index(drop=True)\n\n # combine base with the fragment\n ext_smi = pd.concat([esmi_base.iloc[:idx_base], esmi_frag, esmi_base.iloc[idx_base:]]).reset_index(drop=True)\n new_pd_row = {'esmi': '!', 'n_br': 0, 'n_ring': 0, 'substr': ['!']}\n ext_smi.append(new_pd_row, ignore_index=True)\n\n return ngram.esmi2smi(ext_smi)\n\n def __call__(self, reservoir, beta, size=100, *, samples=None, ratio=0.5, p_frag=0.1, yield_lpf=False):\n \"\"\"\n Run SMC\n Parameters\n ----------\n reservoir: list of object\n Samples to be drawn as new initial molecules in each step of SMC\n beta: list/1D-numpy of float or pd.Dataframe\n Annealing parameters for each step.\n If pd.Dataframe, column names should follow keys of mdl in BaseLogLikeihood or BaseLogLikelihoodSet\n size: int\n Sample size for each draw.\n samples: list of object\n Initial samples.\n ratio: float\n Ratio of molecules to be replaced from reservoir in each step of SMC\n p_frag: float\n Probability of performing a fragment based mutation\n yield_lpf : bool\n Yield estimated log likelihood, probability and frequency of each samples. Default is ``False``.\n Yields\n -------\n samples: list of object\n New samples in each SMC iteration.\n llh: np.ndarray float\n Estimated values of log-likelihood of each samples.\n Only yield when ``yield_lpf=Ture``.\n p: np.ndarray of float\n Estimated probabilities of each samples.\n Only yield when ``yield_lpf=Ture``.\n freq: np.ndarray of float\n The number of unique samples in original samples.\n Only yield when ``yield_lpf=Ture``.\n \"\"\"\n\n # initial samples will be randomly picked from resevoir if samples are provided\n if samples is None:\n samples = np.random.choice(reservoir,size=size).tolist()\n # refill samples if len(samples) not equals given size\n elif len(samples) < size:\n# samples = samples + np.random.choice(reservoir,size=size-len(samples)).tolist()\n samples = np.concatenate([np.array(samples), np.random.choice(reservoir,size=size-len(samples))])\n \n res_size = int(size*ratio)\n smc_size = size - res_size\n\n try:\n unique, frequency = self.unique(samples)\n ll = self.log_likelihood(unique)\n\n if isinstance(beta, pd.DataFrame):\n beta = beta[ll.columns].values\n else:\n # assume only one row for beta (1-D list or numpy vector)\n beta = np.transpose(np.repeat([beta], ll.shape[1], axis=0))\n\n w = np.dot(ll.values, beta[0]) + np.log(frequency)\n w_sum = np.log(np.sum(np.exp(w - np.max(w)))) + np.max(w) # avoid underflow\n p = np.exp(w - w_sum)\n if yield_lpf:\n yield unique, ll, p, frequency\n else:\n yield unique\n\n # beta = np.delete(beta,0,0) #remove first \"row\"\n\n except SMCError as e:\n self.on_errors(0, samples, e)\n except Exception as e:\n raise e\n\n # translate between input representation and execute environment representation.\n # make sure beta is not changed (np.delete will return the deleted version, without changing original vector)\n for i, step in enumerate(np.delete(beta, 0, 0)):\n try:\n re_samples = self.resample(unique, smc_size, p)\n if np.random.random() < p_frag:\n frag_list = self.fragmenting(np.concatenate([re_samples, np.random.choice(reservoir,size=res_size)]))\n samples = [self.combine_fragments(np.random.choice(frag_list), np.random.choice(frag_list)) for _ in range(size)]\n else:\n samples = self.proposal(np.concatenate([re_samples, np.random.choice(reservoir,size=res_size)])) \n\n unique, frequency = self.unique(samples)\n\n # annealed likelihood in log - adjust with copy counts\n ll = self.log_likelihood(unique)\n w = np.dot(ll.values, step) + np.log(frequency)\n w_sum = np.log(np.sum(np.exp(w - np.max(w)))) + np.max(w) # avoid underflow\n p = np.exp(w - w_sum)\n if yield_lpf:\n yield unique, ll, p, frequency\n else:\n yield unique\n\n except SMCError as e:\n self.on_errors(i + 1, samples, e)\n except Exception as e:\n raise e\n \n" }, { "path": "xenonpy/datatools/__init__.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\n\nfrom .dataset import *\nfrom .preset import *\nfrom .splitter import *\nfrom .transform import *\n" }, { "path": "xenonpy/datatools/dataset.py", "content": "# Copyright (c) 2021. TsumiNa. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport os\nimport re\nfrom collections import defaultdict\nfrom pathlib import Path\nfrom warnings import warn\n\nimport joblib\nimport pandas as pd\nimport requests\n\n__all__ = ['Dataset']\n\n\nclass Dataset(object):\n __extension__ = dict(\n pandas=(r'.*\\.pd($|\\.\\w+$)', pd.read_pickle),\n csv=(r'csv', pd.read_csv),\n excel=(r'(xlsx|xls)', pd.read_excel),\n pickle=(r'.*\\.pkl($|\\.\\w+$)', joblib.load))\n\n __re__ = re.compile(r'[\\s\\-.]')\n\n def __init__(self, *paths, backend='pandas', prefix=None):\n self._backend = backend\n self._files = None\n\n if len(paths) == 0:\n self._paths = ('.',)\n else:\n self._paths = paths\n\n if not prefix:\n prefix = ()\n self._prefix = prefix\n\n self._make_index(prefix=prefix)\n\n def _make_index(self, *, prefix):\n\n def make(path_):\n def _nest(_f):\n f_ = _f\n return lambda s: s.__extension__[s._backend][1](f_)\n\n patten = re.compile(self.__extension__[self._backend][0])\n for f in os.listdir(str(path_)):\n if patten.search(f):\n fn = f.split('.')[0]\n fn = self.__re__.sub('_', fn)\n fp = str(path_ / f)\n\n # select data\n parent = re.split(r'[\\\\/]', str(path_))[-1]\n # parent = str(f.parent).split('/')[-1]\n if parent in prefix:\n fn = '_'.join([parent, fn])\n\n if fn in self._files:\n warn(\n \"file %s with name %s already bind to %s and will be ignored\" %\n (fp, fn, self._files[fn]), RuntimeWarning)\n else:\n self._files[fn] = fp\n setattr(self.__class__, fn, property(_nest(fp)))\n\n self._files = defaultdict(str)\n for path in self._paths:\n path = Path(path).expanduser().absolute()\n if not path.exists():\n raise RuntimeError('%s not exists' % str(path))\n make(path)\n\n @classmethod\n def to(cls, obj, path, *, force_pkl=False):\n if isinstance(path, Path):\n path = str(path)\n if not force_pkl and isinstance(obj, (pd.DataFrame, pd.Series)):\n return obj.to_pickle(path)\n return joblib.dump(obj, path)\n\n @classmethod\n def from_http(cls, url, save_to, *, filename=None, chunk_size=256 * 1024, params=None,\n **kwargs):\n \"\"\"\n Get file object via a http request.\n\n Parameters\n ----------\n url: str\n The resource url.\n save_to: str\n The path of a dir to save the downloaded object into it.\n filename: str, optional\n Specific the file name when saving.\n Set to ``None`` (default) to use a inferred name from http header.\n chunk_size: int, optional\n Chunk size.\n params: any, optional\n Parameters will be passed to ``requests.get`` function.\n See Also: `requests `_\n kwargs: dict, optional\n Pass to ``requests.get`` function as the ``kwargs`` parameters.\n\n Returns\n -------\n str\n File path contains file name.\n \"\"\"\n r = requests.get(url, params, **kwargs)\n r.raise_for_status()\n\n if not filename:\n if 'filename' in r.headers:\n filename = r.headers['filename']\n else:\n filename = url.split('/')[-1]\n\n if isinstance(save_to, str):\n save_to = Path(save_to)\n if not isinstance(save_to, Path) or not save_to.is_dir():\n raise RuntimeError('%s is not a legal path or not point to a dir' % save_to)\n\n file_ = str(save_to / filename)\n with open(file_, 'wb') as f:\n for chunk in r.iter_content(chunk_size=chunk_size):\n if chunk: # filter out keep-alive new chunks\n f.write(chunk)\n\n return file_\n\n def __repr__(self):\n cont_ls = ['<{}> includes:'.format(self.__class__.__name__)]\n\n for k, v in self._files.items():\n cont_ls.append('\"{}\": {}'.format(k, v))\n\n return '\\n'.join(cont_ls)\n\n @property\n def csv(self):\n return Dataset(*self._paths, backend='csv', prefix=self._prefix)\n\n @property\n def pandas(self):\n return Dataset(*self._paths, backend='pandas', prefix=self._prefix)\n\n @property\n def pickle(self):\n return Dataset(*self._paths, backend='pickle', prefix=self._prefix)\n\n @property\n def excel(self):\n return Dataset(*self._paths, backend='excel', prefix=self._prefix)\n\n def __call__(self, *args, **kwargs):\n return self.__extension__[self._backend][1](*args, **kwargs)\n\n def __getattr__(self, name):\n \"\"\"\n Returns sub-dataset.\n\n Parameters\n ----------\n name: str\n Dataset name.\n\n Returns\n -------\n self\n \"\"\"\n if name in self.__extension__:\n return self.__class__(*self._paths, backend=name, prefix=self._prefix)\n raise AttributeError(\"'%s' object has no attribute '%s'\" % (self.__class__.__name__, name))\n" }, { "path": "xenonpy/datatools/mp_ids.txt", "content": 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}, { "path": "xenonpy/datatools/preset.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom itertools import zip_longest\nfrom pathlib import Path\nfrom warnings import warn\n\nimport numpy as np\nimport pandas as pd\nfrom pymatgen.ext.matproj import MPRester\nfrom ruamel.yaml import YAML\nfrom tqdm import tqdm\n\nfrom xenonpy._conf import __cfg_root__\nfrom xenonpy.datatools.dataset import Dataset\nfrom xenonpy.utils import config, get_dataset_url, get_sha256, Singleton\n\n__all__ = ['Preset', 'preset']\n\n\nclass Preset(Dataset, metaclass=Singleton):\n \"\"\"\n Load data from embed dataset in XenonPy's or user create data saved in ``~/.xenonpy/cached`` dir.\n Also can fetch data by http request.\n\n This is sample to demonstration how to use is. Also see parameters documents for details.\n\n ::\n\n >>> from xenonpy.datatools import preset\n >>> elements = preset.elements\n >>> elements.info()\n \n Index: 118 entries, H to Og\n Data columns (total 74 columns):\n atomic_number 118 non-null int64\n atomic_radius 88 non-null float64\n atomic_radius_rahm 96 non-null float64\n atomic_volume 91 non-null float64\n atomic_weight 118 non-null float64\n boiling_point 96 non-null float64\n brinell_hardness 59 non-null float64\n bulk_modulus 69 non-null float64\n ...\n \"\"\"\n\n __dataset__ = ('elements', 'elements_completed', 'atom_init')\n __builder__ = ('mp_samples',)\n\n # set to check params\n\n def __init__(self):\n self._dataset = Path(__cfg_root__) / 'dataset'\n self._ext_data = config('ext_data')\n super().__init__(\n str(self._dataset),\n config('userdata'),\n *self._ext_data,\n backend='pandas',\n prefix=('dataset',))\n\n yaml = YAML(typ='safe')\n yaml.indent(mapping=2, sequence=4, offset=2)\n\n self._yaml = yaml\n\n def sync(self, data, to=None):\n \"\"\"\n load data.\n\n .. note::\n Try to load data from local at ``~/.xenonpy/dataset``.\n If no data, try to fetch them from remote repository.\n\n Args\n -----------\n data: str\n name of data.\n to: str\n The version of repository.\n See Also: https://github.com/yoshida-lab/dataset/releases\n\n Returns\n ------\n ret:DataFrame or Saver or local file path.\n \"\"\"\n\n dataset = self._dataset / (data + '.pd.xz')\n sha256_file = self._dataset / 'sha256.yml'\n\n # check sha256 value\n # make sure sha256_file file exist.\n sha256_file.touch()\n sha256 = self._yaml.load(sha256_file)\n if sha256 is None:\n sha256 = {}\n\n # fetch data from source if not in local\n if not to:\n url = get_dataset_url(data)\n else:\n url = get_dataset_url(data, to)\n print('fetching dataset `{0}` from {1}.'.format(data, url))\n self.from_http(url, save_to=str(self._dataset))\n\n sha256_ = get_sha256(str(dataset))\n sha256[data] = sha256_\n self._yaml.dump(sha256, sha256_file)\n\n self._make_index(prefix=['dataset'])\n\n def build(self, *keys, save_to=None, **kwargs):\n\n # build materials project dataset\n def mp_builder(api_key, mp_ids):\n\n # print('Will fetch %s inorganic compounds from Materials Project' % len(mp_ids))\n\n # split requests into fixed number groups\n # eg: grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx\n def grouper(iterable, n, fillvalue=None):\n \"\"\"Collect data into fixed-length chunks or blocks\"\"\"\n args = [iter(iterable)] * max(n, 1)\n return zip_longest(fillvalue=fillvalue, *args)\n\n # the following props will be fetched\n mp_props = [\n 'band_gap',\n 'density',\n 'volume',\n 'material_id',\n 'pretty_formula',\n 'elements',\n 'efermi',\n 'e_above_hull',\n 'formation_energy_per_atom',\n 'final_energy_per_atom',\n 'unit_cell_formula',\n 'structure'\n ]\n\n entries = []\n mpid_groups = [g for g in grouper(mp_ids, len(mp_ids) // 10)]\n\n with MPRester(api_key) as mpr:\n for group in tqdm(mpid_groups):\n mpid_list = [id for id in filter(None, group)]\n chunk = mpr.query({\"material_id\": {\"$in\": mpid_list}}, mp_props)\n entries.extend(chunk)\n\n df = pd.DataFrame(entries, index=[e['material_id'] for e in entries])\n df = df.drop('material_id', axis=1)\n df = df.rename(columns={'unit_cell_formula': 'composition'})\n df = df.reindex(columns=sorted(df.columns))\n\n return df\n\n for key in keys:\n if key is 'mp_samples':\n if 'api_key' not in kwargs:\n raise RuntimeError('api key of materials projects database is needed')\n if 'mp_ids' in kwargs:\n ids = kwargs['mp_ids']\n if isinstance(ids, (list, tuple)):\n mp_ids = ids\n elif isinstance(ids, str):\n mp_ids = [s.decode('utf-8') for s in np.loadtxt(ids, 'S20')]\n else:\n raise ValueError(\n 'parameter `mp_ids` can only be a str to specific the ids file path'\n 'or a list-like object contain the ids')\n else:\n ids = Path(__file__).absolute().parent / 'mp_ids.txt'\n mp_ids = [s.decode('utf-8') for s in np.loadtxt(str(ids), 'S20')]\n data = mp_builder(kwargs['api_key'], mp_ids)\n if not save_to:\n save_to = Path(config('userdata')) / 'mp_samples.pd.xz'\n save_to = save_to.expanduser().absolute()\n data.to_pickle(save_to)\n self._make_index(prefix=['dataset'])\n return\n\n raise ValueError('no available key(s) in %s, these can only be %s' % (keys, self.__builder__))\n\n def _check(self, data):\n\n dataset = self._dataset / (data + '.pd.xz')\n sha256_file = self._dataset / 'sha256.yml'\n\n # fetch data from source if not in local\n if not dataset.exists():\n raise RuntimeError(\n \"data {0} not exist, please run to download from the repository\".format(data),\n 'See also: https://xenonpy.readthedocs.io/en/latest/tutorials/1-dataset.html'\n )\n\n # check sha256 value\n sha256_file.touch() # make sure sha256_file file exist.\n sha256 = self._yaml.load(sha256_file)\n if sha256 is None:\n sha256 = {}\n if data not in sha256:\n sha256_ = get_sha256(str(dataset))\n sha256[data] = sha256_\n self._yaml.dump(sha256, sha256_file)\n else:\n sha256_ = sha256[data]\n\n if sha256_ != config(data):\n warn(\n \"local version {0} is different from the latest version {1}.\"\n \"you can use to fix it.\".format(data, config('db_version')),\n RuntimeWarning)\n\n @property\n def elements(self):\n \"\"\"\n Element properties from embed dataset.\n These properties are summarized from `mendeleev`_, `pymatgen`_, `CRC Handbook`_ and `magpie`_.\n\n See Also: :doc:`features`\n\n .. _mendeleev: https://mendeleev.readthedocs.io\n .. _pymatgen: http://pymatgen.org/\n .. _CRC Handbook: http://hbcponline.com/faces/contents/ContentsSearch.xhtml\n .. _magpie: https://bitbucket.org/wolverton/magpie\n\n Returns\n -------\n DataFrame:\n element properties in pd.DataFrame\n \"\"\"\n self._check('elements')\n return self.dataset_elements\n\n @property\n def atom_init(self):\n \"\"\"\n The initialization vector for each element.\n\n See Also: https://github.com/txie-93/cgcnn#usage\n \"\"\"\n self._check('atom_init')\n return self.dataset_atom_init\n\n @property\n def elements_completed(self):\n \"\"\"\n Completed element properties. [MICE]_ imputation used\n\n .. [MICE] `Int J Methods Psychiatr Res. 2011 Mar 1; 20(1): 40–49.`__\n doi: `10.1002/mpr.329 <10.1002/mpr.329>`_\n\n .. __: https://www.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&retmode=ref&cmd=prlinks&id=21499542\n\n See Also: :doc:`features`\n\n Returns\n -------\n imputed element properties in pd.DataFrame\n \"\"\"\n self._check('elements_completed')\n return self.dataset_elements_completed\n\n\npreset = Preset()\n" }, { "path": "xenonpy/datatools/splitter.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom typing import Union, Tuple, Iterable, List\n\nimport numpy as np\nimport pandas as pd\nfrom pandas import DataFrame, Series\nfrom sklearn import utils\nfrom sklearn.base import BaseEstimator\nfrom sklearn.model_selection import train_test_split, KFold\n\n__all__ = ['Splitter']\n\n\nclass Splitter(BaseEstimator):\n \"\"\"\n Data splitter for train and test\n \"\"\"\n\n def __init__(self,\n size: int,\n *,\n test_size: Union[float, int] = 0.2,\n k_fold: Union[int, Iterable, None] = None,\n random_state: Union[int, None] = None,\n shuffle: bool = True):\n \"\"\"\n Parameters\n ----------\n size\n Total sample size.\n All data must have same length of their first dim,\n test_size\n If float, should be between ``0.0`` and ``1.0`` and represent the proportion\n of the dataset to include in the test split. If int, represents the\n absolute number of test samples. Can be ``0`` if cv is ``None``.\n In this case, :meth:`~Splitter.cv` will yield a tuple only contains ``training`` and ``validation``\n on each step. By default, the value is set to 0.2.\n k_fold\n Number of k-folds.\n If ``int``, Must be at least 2.\n If ``Iterable``, it should provide label for each element which will be used for group cv.\n In this case, the input of :meth:`~Splitter.cv` must be a :class:`pandas.DataFrame` object.\n Default value is None to specify no cv.\n random_state\n If int, random_state is the seed used by the random number generator;\n Default is None.\n shuffle\n Whether or not to shuffle the data before splitting.\n \"\"\"\n if k_fold is None and test_size == 0:\n raise RuntimeError(' can be zero only if is not none')\n self._k_fold = k_fold\n self._shuffle = shuffle\n self._test_size = test_size\n\n self._sample_size = np.arange(size)\n self._test: Union[np.ndarray, None] = None\n self._train: Union[np.ndarray, None] = None\n self._cv_indices: List[Tuple[np.ndarray, np.ndarray]] = []\n self._random_state = random_state\n self.roll(random_state)\n\n @property\n def size(self):\n return self._sample_size.size\n\n @property\n def shuffle(self):\n return self._shuffle\n\n @property\n def test_size(self):\n return self._test_size\n\n @property\n def k_fold(self):\n return self._k_fold\n\n @property\n def random_state(self):\n return self._random_state\n\n def roll(self, random_state: int = None):\n\n if self._test_size == 0:\n if self._shuffle:\n self._train = utils.shuffle(self._sample_size)\n else:\n self._train = self._sample_size\n else:\n if isinstance(self._k_fold, Iterable):\n k_fold_labels: pd.Series = pd.Series(self._k_fold).reset_index(drop=True)\n unique_labels = k_fold_labels.unique()\n test_size = round(unique_labels.size * self._test_size) if isinstance(self._test_size, float) else round(unique_labels.size * (self._test_size / self.size))\n test_lables: pd.Series = pd.Series(unique_labels).sample(test_size, random_state=random_state)\n self._train, self._test = k_fold_labels[~k_fold_labels.isin(test_lables)].index.values, k_fold_labels[k_fold_labels.isin(test_lables)].index.values\n else:\n self._train, self._test = train_test_split(self._sample_size,\n test_size=self._test_size,\n random_state=random_state,\n shuffle=self._shuffle)\n\n if isinstance(self._k_fold, int):\n cv = KFold(n_splits=self._k_fold, shuffle=self._shuffle, random_state=random_state)\n for train, val in cv.split(self._train):\n self._cv_indices.append((self._train[train], self._train[val]))\n elif isinstance(self._k_fold, Iterable):\n tmp: pd.Series = pd.Series(self._k_fold).reset_index(drop=True).iloc[self._train]\n for g in set(tmp):\n val = tmp[tmp == g].index.values\n train = tmp[tmp != g].index.values\n self._cv_indices.append((train, val))\n\n def _check_input(self, array):\n if isinstance(array, (list, tuple)):\n array = np.asarray(array)\n if not isinstance(array, (np.ndarray, pd.DataFrame, pd.Series)):\n raise TypeError(\n f' must be list, numpy.ndarray, pandas.DataFrame, or pandas.Series but got {array.__class__}.'\n )\n if array.shape[0] != self.size:\n raise ValueError(\n f'parameters must have size {self.size} for dim 0 but got {array.shape[0]}'\n )\n return array\n\n @staticmethod\n def _split(array, *idx):\n\n # all to np.array\n if isinstance(array, np.ndarray):\n return [array[i] for i in idx]\n\n if isinstance(array, (DataFrame, Series)):\n return [array.iloc[i] for i in idx]\n\n def cv(self, *arrays, less_for_train=False):\n \"\"\"\n Split data with cross-validation.\n\n Parameters\n ----------\n *arrays: DataFrame, Series, ndarray, list\n Data for split. Must be a Sequence of indexables with same length / shape[0].\n If None, return the split indices.\n less_for_train: bool\n If true, use less data set for train.\n E.g. ``[1, 2, 3, 4, 5, 6, 7, 8, 9, 0]`` with 5 cv will be split into\n ``[1, 2]`` and ``[3, 4, 5, 6, 7, 8, 9, 0]``. Usually, ``[1, 2]`` (less one)\n will be used for val. With ``less_for_train=True``, ``[1, 2]`` will be\n used for train. Default is ``False``.\n\n Yields\n -------\n tuple\n list containing split of inputs with cv. if inputs are None, only return\n the indices of split. if ``test_size`` is 0, test data/index will\n not return.\n \"\"\"\n\n if self._k_fold is None:\n raise RuntimeError('parameter must be set')\n\n for train, val in self._cv_indices:\n if less_for_train:\n tmp = train\n train = val\n val = tmp\n\n if len(arrays) == 0:\n if self._test is not None:\n yield train, val, self._test\n else:\n yield train, val\n else:\n ret = []\n for array in arrays:\n array = self._check_input(array)\n if self._test is not None:\n ret.extend(self._split(array, train, val, self._test))\n else:\n ret.extend(self._split(array, train, val))\n yield tuple(ret)\n return\n\n def split(self, *arrays: Union[np.ndarray, pd.DataFrame, pd.Series]):\n \"\"\"\n Split data.\n\n Parameters\n ----------\n *arrays\n Dataset for split.\n Size of dim 0 must be equal to :meth:`~Splitter.size`.\n If None, return the split indices.\n\n Returns\n -------\n tuple\n List containing split of inputs. if inputs are None, only return\n the indices of splits. if ``test_size`` is 0, test data/index will\n not return.\n \"\"\"\n if self._test is None:\n raise RuntimeError('split action is illegal because `test_size` is none')\n\n if len(arrays) == 0:\n return self._train, self._test\n\n ret = []\n for array in arrays:\n array = self._check_input(array)\n ret.extend(self._split(array, self._train, self._test))\n return tuple(ret)\n" }, { "path": "xenonpy/datatools/transform.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport numpy as np\nimport pandas as pd\nfrom pandas import DataFrame, Series\nfrom sklearn.base import BaseEstimator, TransformerMixin\nfrom sklearn.preprocessing import MinMaxScaler, StandardScaler\nfrom sklearn.preprocessing import PowerTransformer as PT\n\nfrom xenonpy.utils import Switch\n\n__all__ = ['PowerTransformer', 'Scaler']\n\n\nclass PowerTransformer(BaseEstimator, TransformerMixin):\n \"\"\"\n Box-cox transform.\n References\n ----------\n G.E.P. Box and D.R. Cox, “An Analysis of Transformations”,\n Journal of the Royal Statistical Society B, 26, 211-252 (1964).\n \"\"\"\n\n def __init__(self,\n *,\n method='yeo-johnson',\n standardize=False,\n lmd=None,\n tolerance=(-np.inf, np.inf),\n on_err=None):\n \"\"\"\n\n Parameters\n ----------\n method: 'yeo-johnson' or 'box-cox'\n ‘yeo-johnson’ works with positive and negative values\n ‘box-cox’ only works with strictly positive values\n standardize: boolean\n Normalize to standard normal or not.\n Recommend using a sepearate `standard` function instead of using this option.\n lmd: list or 1-dim ndarray\n You might assign each input xs with a specific lmd yourself.\n Leave None(default) to use a inferred value.\n See `PowerTransformer` for detials.\n tolerance: tuple\n Tolerance of lmd. Set None to accept any.\n Default is **(-np.inf, np.inf)** but recommend **(-2, 2)** for Box-cox transform\n on_err: None or str\n Error handle when try to inference lambda. Can be None or **log**, **nan** or **raise** by string.\n **log** will return the logarithmic transform of xs that have a min shift to 1.\n **nan** return ``ndarray`` with shape xs.shape filled with``np.nan``.\n **raise** raise a FloatingPointError. You can catch it yourself.\n Default(None) will return the input series without scale transform.\n .. _PowerTransformer:\n https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PowerTransformer.html#sklearn.preprocessing.PowerTransformer\n \"\"\"\n self._tolerance = tolerance\n self._pt = PT(method=method, standardize=standardize)\n self._lmd = lmd\n self._shape = None\n self._on_err = on_err\n\n def _check_type(self, x):\n if isinstance(x, list):\n x = np.array(x, dtype=np.float)\n elif isinstance(x, (DataFrame, Series)):\n x = x.values\n if not isinstance(x, np.ndarray):\n raise TypeError(\n 'parameter `X` should be a `DataFrame`, `Series`, `ndarray` or list object '\n 'but got {}'.format(type(x)))\n if len(x.shape) == 1:\n x = x.reshape(-1, 1)\n return x\n\n def fit(self, x):\n \"\"\"\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n The data used to compute the per-feature transformation\n\n Returns\n -------\n self : object\n Fitted scaler.\n \"\"\"\n\n x = self._pt._check_input(self._check_type(x), in_fit=True)\n\n # forcing constant column vectors to have no transformation (lambda=1)\n idx = []\n for i, col in enumerate(x.T):\n if np.all(col == col[0]):\n idx.append(i)\n\n if self._lmd is not None:\n if isinstance(self._lmd, float):\n self._pt.lambdas_ = np.array([self._lmd] * x.shape[1])\n elif x.shape[1] != len(self._lmd):\n raise ValueError('shape[1] of parameter `X` should be {} but got {}'.format(\n x.shape[1], len(self._lmd)))\n else:\n self._pt.lambdas_ = np.array(self._lmd)\n else:\n self._pt.fit(x)\n\n if len(idx) > 0:\n self._pt.lambdas_[idx] = 1.\n\n return self\n\n def transform(self, x):\n ret = self._pt.transform(self._check_type(x))\n if isinstance(x, pd.DataFrame):\n return pd.DataFrame(ret, index=x.index, columns=x.columns)\n return ret\n\n def inverse_transform(self, x):\n ret = self._pt.inverse_transform(self._check_type(x))\n if isinstance(x, pd.DataFrame):\n return pd.DataFrame(ret, index=x.index, columns=x.columns)\n return ret\n\n\nclass Scaler(BaseEstimator, TransformerMixin):\n \"\"\"\n A value-matrix container for data transform.\n\n \"\"\"\n\n def __init__(self):\n \"\"\"\n\n Parameters\n ----------\n value: DataFrame\n Inner data.\n \"\"\"\n self._scalers = []\n\n def power_transformer(self, *args, **kwargs):\n return self._scale(PowerTransformer, *args, **kwargs)\n\n def box_cox(self, *args, **kwargs):\n return self._scale(PowerTransformer, method='box-cox', *args, **kwargs)\n\n def yeo_johnson(self, *args, **kwargs):\n return self._scale(PowerTransformer, method='yeo-johnson', *args, **kwargs)\n\n def min_max(self, *args, **kwargs):\n return self._scale(MinMaxScaler, *args, **kwargs)\n\n def standard(self, *args, **kwargs):\n return self._scale(StandardScaler, *args, **kwargs)\n\n def log(self):\n return self._scale(PowerTransformer, method='box-cox', lmd=0.)\n\n def _scale(self, scaler, *args, **kwargs):\n self._scalers.append(scaler(*args, **kwargs))\n return self\n\n def fit(self, x):\n \"\"\"\n Compute the minimum and maximum to be used for later scaling.\n Parameters\n ----------\n x: array-like, shape [n_samples, n_features]\n The data used to compute the per-feature minimum and maximum\n used for later scaling along the features axis.\n \"\"\"\n if len(self._scalers) == 0:\n return x\n\n for s in self._scalers:\n x = s.fit_transform(x)\n return self\n\n def fit_transform(self, x, y=None, **fit_params):\n if len(self._scalers) == 0:\n return x\n\n x_ = x\n for s in self._scalers:\n x_ = s.fit_transform(x_)\n\n if isinstance(x, pd.DataFrame):\n return pd.DataFrame(x_, index=x.index, columns=x.columns)\n return x_\n\n def transform(self, x):\n \"\"\"Scaling features of X according to feature_range.\n Parameters\n ----------\n x : array-like, shape [n_samples, n_features]\n Input data that will be transformed.\n \"\"\"\n if len(self._scalers) == 0:\n return x\n\n x_ = x\n for s in self._scalers:\n x_ = s.transform(x_)\n\n if isinstance(x, pd.DataFrame):\n return pd.DataFrame(x_, index=x.index, columns=x.columns)\n return x_\n\n def inverse_transform(self, x):\n x_ = x\n for s in self._scalers[::-1]:\n x_ = s.inverse_transform(x_)\n if isinstance(x, pd.DataFrame):\n return pd.DataFrame(x_, index=x.index, columns=x.columns)\n return x_\n\n def reset(self):\n \"\"\"\n Reset internal data-dependent state of the scaler, if necessary.\n __init__ parameters are not touched.\n \"\"\"\n self._scalers = []\n" }, { "path": "xenonpy/descriptor/__init__.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom .cgcnn import *\nfrom .compositions import *\nfrom .fingerprint import *\nfrom .frozen_featurizer import *\nfrom .structure import *\n" }, { "path": "xenonpy/descriptor/base.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom abc import ABCMeta, abstractmethod\nfrom collections import defaultdict\nfrom collections.abc import Iterable\nimport itertools\nimport warnings\nfrom multiprocessing import cpu_count\nfrom typing import DefaultDict, List, Sequence, Union, Set\nfrom joblib import Parallel, delayed\n\nimport numpy as np\nimport pandas as pd\nfrom pymatgen.core.composition import Composition as PMGComp\nfrom sklearn.base import TransformerMixin, BaseEstimator\n\nfrom xenonpy.datatools.preset import preset\nfrom xenonpy.utils import TimedMetaClass, Switch\n\n\nclass BaseFeaturizer(BaseEstimator, TransformerMixin, metaclass=ABCMeta):\n \"\"\"\n Abstract class to calculate features from :class:`pandas.Series` input data.\n Each entry can be any format such a compound formula or a pymatgen crystal structure\n dependent on the featurizer implementation.\n\n This class have similar structure with `matminer BaseFeaturizer`_ but follow more strict convention.\n That means you can embed this feature directly into `matminer BaseFeaturizer`_ class implement.::\n\n class MatFeature(BaseFeaturizer):\n def featurize(self, *x):\n return .featurize(*x)\n\n .. _matminer BaseFeaturizer: https://github.com/hackingmaterials/matminer/blob/master/matminer/featurizers/base_smc.py\n\n **Using a BaseFeaturizer Class**\n\n :meth:`BaseFeaturizer` implement :class:`sklearn.base.BaseEstimator` and :class:`sklearn.base.TransformerMixin`\n that means you can use it in a scikit-learn way.::\n\n featurizer = SomeFeaturizer()\n features = featurizer.fit_transform(X)\n\n You can also employ the featurizer as part of a ScikitLearn Pipeline object.\n You would then provide your input data as an array to the Pipeline, which would\n output the featurers as an :class:`pandas.DataFrame`.\n\n :class:`BaseFeaturizer` also provide you to retrieving proper references for a featurizer.\n The ``__citations__`` returns a list of papers that should be cited.\n The ``__authors__`` returns a list of people who wrote the featurizer.\n Also can be accessed from property ``citations`` and ``citations``.\n\n **Implementing a New BaseFeaturizer Class**\n\n These operations must be implemented for each new featurizer:\n\n - ``featurize`` - Takes a single material as input, returns the features of that material.\n - ``feature_labels`` - Generates a human-meaningful name for each of the features. **Implement this as property**.\n\n Also suggest to implement these two **properties**:\n\n - ``citations`` - Returns a list of citations in BibTeX format.\n - ``implementors`` - Returns a list of people who contributed writing a paper.\n\n All options of the featurizer must be set by the ``__init__`` function. All\n options must be listed as keyword arguments with default values, and the\n value must be saved as a class attribute with the same name or as a property\n (e.g., argument `n` should be stored in `self.n`).\n These requirements are necessary for\n compatibility with the ``get_params`` and ``set_params`` methods of ``BaseEstimator``,\n which enable easy interoperability with scikit-learn.\n :meth:`featurize` must return a list of features in :class:`numpy.ndarray`.\n\n .. note::\n\n None of these operations should change the state of the featurizer. I.e.,\n running each method twice should no produce different results, no class\n attributes should be changed, running one operation should not affect the\n output of another.\n\n \"\"\"\n\n __authors__ = ['anonymous']\n __citations__ = ['No citations']\n\n def __init__(\n self,\n n_jobs: int = -1,\n *,\n on_errors: str = 'raise',\n return_type: str = 'any',\n target_col: Union[List[str], str, None] = None,\n parallel_verbose: int = 0,\n ):\n \"\"\"\n Parameters\n ----------\n n_jobs\n The number of jobs to run in parallel for both fit and predict. Set -1 to use all cpu cores (default).\n Inputs ``X`` will be split into some blocks then run on each cpu cores.\n When set to 0, input X will be treated as a block and pass to ``Featurizer.featurize`` directly.\n This default parallel implementation does not support pd.DataFrame input,\n so please make sure you set n_jobs=0 if the input will be pd.DataFrame.\n on_errors\n How to handle the exceptions in a feature calculations. Can be 'nan', 'keep', 'raise'.\n When 'nan', return a column with ``np.nan``.\n The length of column corresponding to the number of feature labs.\n When 'keep', return a column with exception objects.\n The default is 'raise' which will raise up the exception.\n return_type\n Specify the return type.\n Can be ``any``, ``custom``, ``array`` and ``df``.\n ``array`` and ``df`` force return type to ``np.ndarray`` and ``pd.DataFrame`` respectively.\n If ``any`` or ``custom``, the return type depends on multiple factors (see transform function).\n Default is ``any``\n target_col\n Only relevant when input is pd.DataFrame, otherwise ignored.\n Specify a single column to be used for transformation.\n If ``None``, all columns of the pd.DataFrame is used.\n Default is None.\n parallel_verbose\n The verbosity level: if non zero, progress messages are printed. Above 50, the output is sent to stdout.\n The frequency of the messages increases with the verbosity level.\n If it more than 10, all iterations are reported. Default ``0``.\n \"\"\"\n self.return_type = return_type\n self.target_col = target_col\n self.n_jobs = n_jobs\n self.on_errors = on_errors\n self.parallel_verbose = parallel_verbose\n self._kwargs = {}\n\n @property\n def return_type(self):\n return self._return_type\n\n @return_type.setter\n def return_type(self, val):\n if val not in {'any', 'array', 'df', 'custom'}:\n raise ValueError('`return_type` must be `any`, `custom`, `array` or `df`')\n self._return_type = val\n\n @property\n def on_errors(self):\n return self._on_errors\n\n @on_errors.setter\n def on_errors(self, val):\n if val not in {'nan', 'keep', 'raise'}:\n raise ValueError('`on_errors` must be `nan`, `keep` or `raise`')\n self._on_errors = val\n\n @property\n def parallel_verbose(self):\n return self._parallel_verbose\n\n @parallel_verbose.setter\n def parallel_verbose(self, val):\n if not isinstance(val, int):\n raise ValueError('`parallel_verbose` must be int')\n self._parallel_verbose = val\n\n @property\n def n_jobs(self):\n return self._n_jobs\n\n @n_jobs.setter\n def n_jobs(self, n_jobs):\n \"\"\"Set the number of threads for this \"\"\"\n if n_jobs < -1:\n n_jobs = -1\n if n_jobs > cpu_count() or n_jobs == -1:\n self._n_jobs = cpu_count()\n else:\n self._n_jobs = n_jobs\n\n def fit(self, X, y=None, **fit_kwargs):\n \"\"\"Update the parameters of this featurizer based on available data\n Args:\n X - [list of tuples], training data\n Returns:\n self\n \"\"\"\n return self\n\n # todo: Dose fit_transform need to pass paras to transform?\n def fit_transform(self, X, y=None, **fit_params):\n \"\"\"Fit to data, then transform it.\n\n Fits transformer to X and y with optional parameters fit_params\n and returns a transformed version of X.\n\n Parameters\n ----------\n X : numpy array of shape [n_samples, n_features]\n Training set.\n\n y : numpy array of shape [n_samples]\n Target values.\n\n Returns\n -------\n X_new : numpy array of shape [n_samples, n_features_new]\n Transformed array.\n\n \"\"\"\n # non-optimized default implementation; override when a better\n # method is possible for a given clustering algorithm\n if y is None:\n # fit method of arity 1 (unsupervised transformation)\n return self.fit(X, **fit_params).transform(X, **fit_params)\n else:\n # fit method of arity 2 (supervised transformation)\n return self.fit(X, y, **fit_params).transform(X, **fit_params)\n\n def transform(self, entries: Sequence, *, return_type=None, target_col=None, **kwargs):\n \"\"\"\n Featurize a list of entries.\n If `featurize` takes multiple inputs, supply inputs as a list of tuples,\n or use pd.DataFrame with parameter ``target_col`` to specify the column name(s).\n \n Args\n ----\n entries: list-like or pd.DataFrame\n A list of entries to be featurized or pd.DataFrame with one specified column.\n See detail of target_col if entries is pd.DataFrame.\n Also, make sure n_jobs=0 for pd.DataFrame.\n return_type: str\n Specify the return type.\n Can be ``any``, ``custom``, ``array`` or ``df``.\n ``array`` or ``df`` forces return type to ``np.ndarray`` or ``pd.DataFrame``, respectively.\n If ``any``, the return type follow prefixed rules:\n (1) if input type is pd.Series or pd.DataFrame, returns pd.DataFrame;\n (2) else if input type is np.array, returns np.array;\n (3) else if other input type and n_jobs=0, follows the featurize function return;\n (4) otherwise, return a list of objects (output of featurize function).\n If ``custom``, the return type depends on the featurize function if n_jobs=0,\n or the return type is a list of objects (output of featurize function) for other n_jobs values.\n This is a one-time change that only have effect in the current transformation.\n Default is ``None`` for using the setting at initialization step.\n target_col\n Only relevant when input is pd.DataFrame, otherwise ignored.\n Specify a single column to be used for transformation.\n Default is ``None`` for using the setting at initialization step.\n (see __init__ for more information)\n\n Returns\n -------\n DataFrame\n features for each entry.\n \"\"\"\n self._kwargs = kwargs\n\n # Check inputs\n if not isinstance(entries, Iterable):\n raise TypeError('parameter \"entries\" must be a iterable object')\n\n # Extract relevant columns for pd.DataFrame input\n if isinstance(entries, pd.DataFrame):\n if target_col is None:\n target_col = self.target_col\n if target_col is None:\n target_col = entries.columns.values\n entries = entries[target_col]\n\n # Special case: Empty list\n if len(entries) is 0:\n return []\n\n # Check outputs\n if return_type not in {None, 'any', 'array', 'df', 'custom'}:\n raise ValueError('`return_type` must be None, `any`, `custom`, `array` or `df`')\n\n for c in Switch(self._n_jobs):\n if c(0):\n # Run the actual featurization\n ret = self.featurize(entries, **kwargs)\n break\n if isinstance(entries, pd.DataFrame):\n raise RuntimeError(\"Auto-parallel can not be used when`entries` is `pandas.DataFrame`. \"\n \"Please set `n_jobs` to 0 and implements your algorithm in the `featurize` method\")\n if c(1):\n ret = [self._wrapper(x) for x in entries]\n break\n if c():\n ret = Parallel(n_jobs=self._n_jobs,\n verbose=self._parallel_verbose)(delayed(self._wrapper)(x) for x in entries)\n\n try:\n labels = self.feature_labels\n except NotImplementedError:\n labels = None\n\n if return_type is None:\n return_type = self.return_type\n\n if return_type == 'any':\n if isinstance(entries, (pd.Series, pd.DataFrame)):\n tmp = pd.DataFrame(ret, index=entries.index, columns=labels)\n return tmp\n if isinstance(entries, np.ndarray):\n return np.array(ret)\n return ret\n\n if return_type == 'array':\n return np.array(ret)\n\n if return_type == 'df':\n if isinstance(entries, (pd.Series, pd.DataFrame)):\n return pd.DataFrame(ret, index=entries.index, columns=labels)\n return pd.DataFrame(ret, columns=labels)\n\n if return_type == 'custom':\n return ret\n\n def _wrapper(self, x):\n \"\"\"\n An exception wrapper for featurize, used in featurize_many and\n featurize_dataframe. featurize_wrapper changes the behavior of featurize\n when ignore_errors is True in featurize_many/dataframe.\n Args:\n x: input data to featurize (type depends on featurizer).\n Returns:\n (list) one or more features.\n \"\"\"\n try:\n # Successful featurization returns nan for an error.\n if not isinstance(x, (tuple, list, np.ndarray)):\n return self.featurize(x, **self._kwargs)\n return self.featurize(*x, **self._kwargs)\n except Exception as e:\n if self._on_errors == 'nan':\n return [np.nan] * len(self.feature_labels)\n elif self._on_errors == 'keep':\n return [e] * len(self.feature_labels)\n else:\n raise e\n\n @abstractmethod\n def featurize(self, *x, **kwargs):\n \"\"\"\n Main featurizer function, which has to be implemented\n in any derived featurizer subclass.\n\n Args\n ====\n x: depends on featurizer\n input data to featurize.\n\n Returns\n =======\n any: numpy.ndarray\n one or more features.\n \"\"\"\n\n @property\n @abstractmethod\n def feature_labels(self):\n \"\"\"\n Generate attribute names.\n Returns:\n ([str]) attribute labels.\n \"\"\"\n\n @property\n def citations(self):\n \"\"\"\n Citation(s) and reference(s) for this feature.\n Returns:\n (list) each element should be a string citation,\n ideally in BibTeX format.\n \"\"\"\n return '\\n'.join(self.__citations__)\n\n @property\n def authors(self):\n \"\"\"\n List of implementors of the feature.\n Returns:\n (list) each element should either be a string with author name (e.g.,\n \"Anubhav Jain\") or a dictionary with required key \"name\" and other\n keys like \"email\" or \"institution\" (e.g., {\"name\": \"Anubhav\n Jain\", \"email\": \"ajain@lbl.gov\", \"institution\": \"LBNL\"}).\n \"\"\"\n\n return '\\n'.join(self.__authors__)\n\n\nclass BaseDescriptor(BaseEstimator, TransformerMixin, metaclass=TimedMetaClass):\n \"\"\"\n Abstract class to organize featurizers.\n This class can take list-like[object] or pd.DataFrame as input for transformation or fitting.\n For pd.DataFrame, if any column name matches any group name,\n the matched group(s) will be calculated with corresponding column(s);\n otherwise, the pd.DataFrame will be passed on as is.\n\n Examples\n --------\n\n .. code::\n\n\n class MyDescriptor(BaseDescriptor):\n\n def __init__(self, n_jobs=-1):\n self.descriptor = SomeFeature1(n_jobs)\n self.descriptor = SomeFeature2(n_jobs)\n self.descriptor = SomeFeature3(n_jobs)\n self.descriptor = SomeFeature4(n_jobs)\n\n \"\"\"\n\n def __init__(self, *, featurizers: Union[List[str], str] = 'all', on_errors: str = 'raise'):\n \"\"\"\n\n Parameters\n ----------\n featurizers\n Specify which Featurizer(s) will be used.\n Default is 'all'.\n on_errors\n How to handle the exceptions in a feature calculations. Can be 'nan', 'keep', 'raise'.\n When 'nan', return a column with ``np.nan``.\n The length of column corresponding to the number of feature labs.\n When 'keep', return a column with exception objects.\n The default is 'raise' which will raise up the exception.\n \"\"\"\n self.__featurizers__: Set[str] = set() # featurizers' names\n self.__featurizer_sets__: DefaultDict[str, List[BaseFeaturizer]] = defaultdict(list)\n self.featurizers = featurizers\n self.on_errors = on_errors\n\n @property\n def on_errors(self):\n return self._on_errors\n\n @on_errors.setter\n def on_errors(self, val):\n if val not in {'nan', 'keep', 'raise'}:\n raise ValueError('`on_errors` must be `nan`, `keep` or `raise`')\n self._on_errors = val\n for fea_set in self.__featurizer_sets__.values():\n for fea in fea_set:\n fea.on_errors = val\n\n @property\n def featurizers(self):\n return self._featurizers\n\n @featurizers.setter\n def featurizers(self, val):\n if isinstance(val, str):\n if val != 'all':\n self._featurizers = (val,)\n else:\n self._featurizers = val\n elif isinstance(val, (tuple, List)):\n self._featurizers = tuple(val)\n else:\n raise ValueError('parameter `featurizers` must be `all`, name of featurizer, or list of name of featurizer')\n\n @property\n def elapsed(self):\n return self._timer.elapsed\n\n def __setattr__(self, key, value):\n\n if key == '__featurizer_sets__':\n if not isinstance(value, defaultdict):\n raise RuntimeError('Can not set \"self.__featurizer_sets__\" by yourself')\n super().__setattr__(key, value)\n if isinstance(value, BaseFeaturizer):\n if value.__class__.__name__ in self.__featurizers__:\n raise RuntimeError('Duplicated featurizer <%s>' % value.__class__.__name__)\n self.__featurizer_sets__[key].append(value)\n self.__featurizers__.add(value.__class__.__name__)\n else:\n super().__setattr__(key, value)\n\n def __repr__(self):\n return self.__class__.__name__ + ':\\n' + \\\n '\\n'.join(\n [' |- %s:\\n | |- %s' % (k, '\\n | |- '.join(map(lambda s: s.__class__.__name__, v))) for k, v in\n self.__featurizer_sets__.items()])\n\n def _check_input(self, X, y=None, **kwargs):\n\n def _reformat(x):\n if x is None:\n return x\n\n keys = list(self.__featurizer_sets__.keys())\n if len(keys) == 1:\n if isinstance(x, list):\n return pd.DataFrame(pd.Series(x), columns=keys)\n\n if isinstance(x, np.ndarray):\n if len(x.shape) == 1:\n return pd.DataFrame(x, columns=keys)\n\n if isinstance(x, pd.Series):\n return pd.DataFrame(x.values, columns=keys, index=x.index)\n\n if isinstance(x, pd.Series):\n x = pd.DataFrame(x)\n\n if isinstance(x, pd.DataFrame):\n tmp = set(x.columns) | set(kwargs.keys())\n if set(keys).isdisjoint(tmp):\n # raise KeyError('name of columns do not match any feature set')\n warnings.warn(\n 'name of columns do not match any feature set, '\n 'the whole dataframe is applied to all feature sets', UserWarning)\n # allow type check later for this special case\n return [x]\n return x\n\n raise TypeError('you can not ues a array-like input '\n 'because there are multiple feature sets or the dim of input is not 1')\n\n return _reformat(X), _reformat(y)\n\n def _rename(self, **fit_params):\n for k, v in fit_params.items():\n if k in self.__featurizer_sets__:\n self.__featurizer_sets__[v] = self.__featurizer_sets__.pop(k)\n\n @property\n def all_featurizers(self):\n return list(self.__featurizers__)\n\n def fit(self, X, y=None, **kwargs):\n if not isinstance(X, Iterable):\n raise TypeError('parameter \"entries\" must be a iterable object')\n\n self._rename(**kwargs)\n\n # assume y is in same format of X (do not cover other cases now)\n X, y = self._check_input(X, y)\n if isinstance(X, list):\n for k, features in self.__featurizer_sets__.items():\n for f in features:\n if self._featurizers != 'all' and f.__class__.__name__ not in self._featurizers:\n continue\n # assume y is in same format of X\n if y is not None:\n f.fit(X[0], y[0], **kwargs)\n else:\n f.fit(X[0], **kwargs)\n else:\n for k, features in self.__featurizer_sets__.items():\n if k in X:\n for f in features:\n if self._featurizers != 'all' and f.__class__.__name__ not in self._featurizers:\n continue\n if y is not None and k in y:\n f.fit(X[k], y[k], **kwargs)\n else:\n f.fit(X[k], **kwargs)\n\n return self\n\n def transform(self, X, **kwargs):\n if not isinstance(X, Iterable):\n raise TypeError('parameter \"entries\" must be a iterable object')\n\n if len(X) is 0:\n return None\n\n if 'return_type' in kwargs:\n del kwargs['return_type']\n\n results = []\n\n X, _ = self._check_input(X, **kwargs)\n if isinstance(X, list):\n for k, features in self.__featurizer_sets__.items():\n # if k in kwargs:\n # k = kwargs[k]\n for f in features:\n if self._featurizers != 'all' and f.__class__.__name__ not in self._featurizers:\n continue\n ret = f.transform(X[0], return_type='df', **kwargs)\n results.append(ret)\n else:\n for k, features in self.__featurizer_sets__.items():\n if k in kwargs:\n k = kwargs[k]\n if k in X:\n for f in features:\n if self._featurizers != 'all' and f.__class__.__name__ not in self._featurizers:\n continue\n ret = f.transform(X[k], return_type='df', **kwargs)\n results.append(ret)\n\n return pd.concat(results, axis=1)\n\n @property\n def feature_labels(self):\n \"\"\"\n Generate attribute names.\n Returns:\n ([str]) attribute labels.\n \"\"\"\n\n if len(self.__featurizers__) == 0:\n raise NotImplementedError(\"no featurizers\")\n\n ret = ()\n for k, features in self.__featurizer_sets__.items():\n ret += ((k, list(itertools.chain.from_iterable([f.feature_labels for f in features]))),)\n\n if len(ret) == 1:\n return ret[0][1]\n return ret\n\n\nclass BaseCompositionFeaturizer(BaseFeaturizer, metaclass=ABCMeta):\n\n def __init__(self,\n *,\n elemental_info: Union[pd.DataFrame, None] = None,\n n_jobs: int = -1,\n on_errors: str = 'raise',\n return_type: str = 'any',\n target_col: Union[List[str], str, None] = None):\n \"\"\"\n Base class for composition feature.\n \"\"\"\n\n super().__init__(n_jobs=n_jobs, on_errors=on_errors, return_type=return_type, target_col=target_col)\n\n if elemental_info is None:\n self.elements = preset.elements_completed.copy()\n else:\n self.elements = elemental_info\n self.__authors__ = ['TsumiNa']\n\n\n def featurize(self, comp):\n elems_, nums_ = [], []\n if isinstance(comp, PMGComp):\n comp = comp.as_dict()\n for e, n in comp.items():\n elems_.append(e)\n nums_.append(n)\n return self.mix_function(elems_, nums_)\n\n @abstractmethod\n def mix_function(self, elems, nums):\n \"\"\"\n\n Parameters\n ----------\n elems: list\n Elements in compound.\n nums: list\n Number of each element.\n\n Returns\n -------\n descriptor: numpy.ndarray\n \"\"\"\n" }, { "path": "xenonpy/descriptor/cgcnn.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport warnings\n\nimport numpy as np\nimport torch\nfrom pymatgen.core.structure import Structure\n\nfrom xenonpy.datatools import preset\nfrom xenonpy.descriptor.base import BaseFeaturizer\n\n__all__ = ['CrystalGraphFeaturizer']\n\n\nclass CrystalGraphFeaturizer(BaseFeaturizer):\n\n def __init__(self, *, max_num_nbr=12, radius=8, atom_feature='origin', n_jobs=-1, on_errors='raise',\n return_type='any'):\n \"\"\"\n This featurizer is a port of the original paper [CGCNN]_.\n\n .. [CGCNN] `Crystal Graph Convolutional Neural Networks for an Accurate and\n Interpretable Prediction of Material Properties`__\n __ https://doi.org/10.1103/PhysRevLett.120.145301\n\n Parameters\n ----------\n n_jobs: int\n The number of jobs to run in parallel for both fit and predict.\n Set -1 to use all cpu cores (default).\n Inputs ``X`` will be split into some blocks then run on each cpu cores.\n on_errors: string\n How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\n When 'nan', return a column with ``np.nan``.\n The length of column corresponding to the number of feature labs.\n When 'keep', return a column with exception objects.\n The default is 'raise' which will raise up the exception.\n return_type: str\n Specific the return type.\n Can be ``any``, ``array`` and ``df``.\n ``array`` and ``df`` force return type to ``numpy.ndarray`` and ``pandas.DataFrame`` respectively.\n If ``any``, the return type dependent on the input type.\n Default is ``any``\n \"\"\"\n\n super().__init__(n_jobs=n_jobs, on_errors=on_errors, return_type=return_type)\n self.atom_feature = atom_feature\n self.radius = radius\n self.max_num_nbr = max_num_nbr\n self.__implement__ = ['TsumiNa']\n\n def _atom_feature(self, atom_symbol: str):\n if self.atom_feature == 'origin':\n return preset.atom_init.loc[atom_symbol]\n elif self.atom_feature == 'elements':\n return preset.elements_completed.loc[atom_symbol]\n elif callable(self.atom_feature):\n return self.atom_feature(atom_symbol)\n else:\n raise TypeError('bad `atom feature` parameter')\n\n def edge_features(self, structure: Structure, **kwargs):\n def expand_distance(distances, dmin=0, step=0.2, var=None):\n \"\"\"\n Parameters\n ----------\n \n dmin: float\n Minimum interatomic distance\n dmax: float\n Maximum interatomic distance\n step: float\n Step size for the Gaussian filter\n \"\"\"\n filter_ = np.arange(dmin, self.radius + step, step)\n if var is None:\n var = step\n\n return np.exp(-(distances[..., np.newaxis] - filter_) ** 2 / var ** 2)\n\n all_nbrs = structure.get_all_neighbors(self.radius, include_index=True)\n all_nbrs = [sorted(nbrs, key=lambda x: x[1]) for nbrs in all_nbrs]\n nbr_fea_idx, nbr_fea = [], []\n for nbr in all_nbrs:\n if len(nbr) < self.max_num_nbr:\n warnings.warn('can not find enough neighbors to build graph. '\n 'If it happens frequently, consider increase '\n 'radius.')\n nbr_fea_idx.append(list(map(lambda x: x[2], nbr)) +\n [0] * (self.max_num_nbr - len(nbr)))\n nbr_fea.append(list(map(lambda x: x[1], nbr)) +\n [self.radius + 1.] * (self.max_num_nbr -\n len(nbr)))\n else:\n nbr_fea_idx.append(list(map(lambda x: x[2],\n nbr[:self.max_num_nbr])))\n nbr_fea.append(list(map(lambda x: x[1],\n nbr[:self.max_num_nbr])))\n nbr_fea = np.array(nbr_fea)\n nbr_fea = expand_distance(nbr_fea)\n nbr_fea = torch.Tensor(nbr_fea)\n\n nbr_fea_idx = torch.LongTensor(nbr_fea_idx)\n\n return nbr_fea, nbr_fea_idx\n\n def node_features(self, structure: Structure):\n atom_features = np.vstack([self._atom_feature(s.name) for s in structure.species])\n return torch.Tensor(atom_features)\n\n def featurize(self, structure: Structure):\n return [self.node_features(structure), *self.edge_features(structure)]\n\n @property\n def feature_labels(self):\n return ['atom_feature', 'neighbor_feature', 'neighbor_idx']\n" }, { "path": "xenonpy/descriptor/compositions.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom typing import Union, List\n\nimport numpy as np\nimport pandas as pd\n\nfrom xenonpy.descriptor.base import BaseDescriptor, BaseCompositionFeaturizer\n\n__all__ = [\n 'Compositions', 'Counting', 'WeightedAverage', 'WeightedSum', 'WeightedVariance',\n 'HarmonicMean', 'GeometricMean', 'MaxPooling', 'MinPooling'\n]\n\n\nclass Counting(BaseCompositionFeaturizer):\n\n def __init__(self,\n *,\n one_hot_vec=False,\n n_jobs=-1,\n on_errors='raise',\n return_type='any',\n target_col=None):\n \"\"\"\n\n Parameters\n ----------\n one_hot_vec : bool\n Set ``true`` to using one-hot-vector encoding.\n n_jobs: int\n The number of jobs to run in parallel for both fit and predict.\n Set -1 to use all cpu cores (default).\n Inputs ``X`` will be split into some blocks then run on each cpu cores.\n on_errors: string\n How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\n When 'nan', return a column with ``np.nan``.\n The length of column corresponding to the number of feature labs.\n When 'keep', return a column with exception objects.\n The default is 'raise' which will raise up the exception.\n return_type: str\n Specific the return type.\n Can be ``any``, ``array`` and ``df``.\n ``array`` and ``df`` force return type to ``np.ndarray`` and ``pd.DataFrame`` respectively.\n If ``any``, the return type dependent on the input type.\n Default is ``any``\n target_col\n Only relevant when input is pd.DataFrame, otherwise ignored.\n Specify a single column to be used for transformation.\n If ``None``, all columns of the pd.DataFrame is used.\n Default is None.\n \"\"\"\n\n super().__init__(n_jobs=n_jobs,\n on_errors=on_errors,\n return_type=return_type,\n target_col=target_col)\n self.one_hot_vec = one_hot_vec\n self._elems = self.elements.index.tolist()\n self.__authors__ = ['TsumiNa']\n\n def mix_function(self, elems, nums):\n vec = np.zeros(len(self._elems), dtype=np.int)\n for i, e in enumerate(elems):\n if self.one_hot_vec:\n vec[self._elems.index(e)] = 1\n else:\n vec[self._elems.index(e)] = nums[i]\n\n return vec\n\n @property\n def feature_labels(self):\n return self._elems\n\n\nclass WeightedAverage(BaseCompositionFeaturizer):\n \"\"\"\n\n Parameters\n ----------\n elemental_info\n Elemental level information for each element. For example, the ``atomic number``,\n ``atomic radius``, and etc. By default (``None``), will use the XenonPy embedded information.\n n_jobs: int\n The number of jobs to run in parallel for both fit and predict.\n Set -1 to use all cpu cores (default).\n Inputs ``X`` will be split into some blocks then run on each cpu cores.\n on_errors: string\n How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\n When 'nan', return a column with ``np.nan``.\n The length of column corresponding to the number of feature labs.\n When 'keep', return a column with exception objects.\n The default is 'raise' which will raise up the exception.\n return_type: str\n Specific the return type.\n Can be ``any``, ``array`` and ``df``.\n ``array`` and ``df`` force return type to ``np.ndarray`` and ``pd.DataFrame`` respectively.\n If ``any``, the return type dependent on the input type.\n Default is ``any``\n target_col\n Only relevant when input is pd.DataFrame, otherwise ignored.\n Specify a single column to be used for transformation.\n If ``None``, all columns of the pd.DataFrame is used.\n Default is None.\n \"\"\"\n\n def mix_function(self, elems, nums):\n elems_ = self.elements.loc[elems, :].values\n w_ = nums / np.sum(nums)\n return w_.dot(elems_)\n\n @property\n def feature_labels(self):\n return ['ave:' + s for s in self.elements]\n\n\nclass WeightedSum(BaseCompositionFeaturizer):\n \"\"\"\n\n Parameters\n ----------\n elemental_info\n Elemental level information for each element. For example, the ``atomic number``,\n ``atomic radius``, and etc. By default (``None``), will use the XenonPy embedded information.\n n_jobs: int\n The number of jobs to run in parallel for both fit and predict.\n Set -1 to use all cpu cores (default).\n Inputs ``X`` will be split into some blocks then run on each cpu cores.\n on_errors: string\n How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\n When 'nan', return a column with ``np.nan``.\n The length of column corresponding to the number of feature labs.\n When 'keep', return a column with exception objects.\n The default is 'raise' which will raise up the exception.\n return_type: str\n Specific the return type.\n Can be ``any``, ``array`` and ``df``.\n ``array`` and ``df`` force return type to ``np.ndarray`` and ``pd.DataFrame`` respectively.\n If ``any``, the return type dependent on the input type.\n Default is ``any``\n target_col\n Only relevant when input is pd.DataFrame, otherwise ignored.\n Specify a single column to be used for transformation.\n If ``None``, all columns of the pd.DataFrame is used.\n Default is None.\n \"\"\"\n\n def mix_function(self, elems, nums):\n elems_ = self.elements.loc[elems, :].values\n w_ = np.array(nums)\n return w_.dot(elems_)\n\n @property\n def feature_labels(self):\n return ['sum:' + s for s in self.elements]\n\n\nclass GeometricMean(BaseCompositionFeaturizer):\n \"\"\"\n\n Parameters\n ----------\n elemental_info\n Elemental level information for each element. For example, the ``atomic number``,\n ``atomic radius``, and etc. By default (``None``), will use the XenonPy embedded information.\n n_jobs: int\n The number of jobs to run in parallel for both fit and predict.\n Set -1 to use all cpu cores (default).\n Inputs ``X`` will be split into some blocks then run on each cpu cores.\n on_errors: string\n How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\n When 'nan', return a column with ``np.nan``.\n The length of column corresponding to the number of feature labs.\n When 'keep', return a column with exception objects.\n The default is 'raise' which will raise up the exception.\n return_type: str\n Specific the return type.\n Can be ``any``, ``array`` and ``df``.\n ``array`` and ``df`` force return type to ``np.ndarray`` and ``pd.DataFrame`` respectively.\n If ``any``, the return type dependent on the input type.\n Default is ``any``\n target_col\n Only relevant when input is pd.DataFrame, otherwise ignored.\n Specify a single column to be used for transformation.\n If ``None``, all columns of the pd.DataFrame is used.\n Default is None.\n \"\"\"\n\n def mix_function(self, elems, nums):\n elems_ = self.elements.loc[elems, :].values\n w_ = np.array(nums).reshape(-1, 1)\n tmp = elems_**w_\n return np.power(tmp.prod(axis=0), 1 / sum(w_))\n\n @property\n def feature_labels(self):\n return ['gmean:' + s for s in self.elements]\n\n\nclass HarmonicMean(BaseCompositionFeaturizer):\n \"\"\"\n\n Parameters\n ----------\n elemental_info\n Elemental level information for each element. For example, the ``atomic number``,\n ``atomic radius``, and etc. By default (``None``), will use the XenonPy embedded information.\n n_jobs: int\n The number of jobs to run in parallel for both fit and predict.\n Set -1 to use all cpu cores (default).\n Inputs ``X`` will be split into some blocks then run on each cpu cores.\n on_errors: string\n How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\n When 'nan', return a column with ``np.nan``.\n The length of column corresponding to the number of feature labs.\n When 'keep', return a column with exception objects.\n The default is 'raise' which will raise up the exception.\n return_type: str\n Specific the return type.\n Can be ``any``, ``array`` and ``df``.\n ``array`` and ``df`` force return type to ``np.ndarray`` and ``pd.DataFrame`` respectively.\n If ``any``, the return type dependent on the input type.\n Default is ``any``\n target_col\n Only relevant when input is pd.DataFrame, otherwise ignored.\n Specify a single column to be used for transformation.\n If ``None``, all columns of the pd.DataFrame is used.\n Default is None.\n \"\"\"\n\n def mix_function(self, elems, nums):\n elems_ = 1 / self.elements.loc[elems, :].values\n w_ = np.array(nums)\n tmp = w_.dot(elems_)\n\n return sum(w_) / tmp\n\n @property\n def feature_labels(self):\n return ['hmean:' + s for s in self.elements]\n\n\nclass WeightedVariance(BaseCompositionFeaturizer):\n \"\"\"\n\n Parameters\n ----------\n elemental_info\n Elemental level information for each element. For example, the ``atomic number``,\n ``atomic radius``, and etc. By default (``None``), will use the XenonPy embedded information.\n n_jobs: int\n The number of jobs to run in parallel for both fit and predict.\n Set -1 to use all cpu cores (default).\n Inputs ``X`` will be split into some blocks then run on each cpu cores.\n on_errors: string\n How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\n When 'nan', return a column with ``np.nan``.\n The length of column corresponding to the number of feature labs.\n When 'keep', return a column with exception objects.\n The default is 'raise' which will raise up the exception.\n return_type: str\n Specific the return type.\n Can be ``any``, ``array`` and ``df``.\n ``array`` and ``df`` force return type to ``np.ndarray`` and ``pd.DataFrame`` respectively.\n If ``any``, the return type dependent on the input type.\n Default is ``any``\n target_col\n Only relevant when input is pd.DataFrame, otherwise ignored.\n Specify a single column to be used for transformation.\n If ``None``, all columns of the pd.DataFrame is used.\n Default is None.\n \"\"\"\n\n def mix_function(self, elems, nums):\n elems_ = self.elements.loc[elems, :].values\n w_ = nums / np.sum(nums)\n mean_ = w_.dot(elems_)\n var_ = elems_ - mean_\n return w_.dot(var_**2)\n\n @property\n def feature_labels(self):\n return ['var:' + s for s in self.elements]\n\n\nclass MaxPooling(BaseCompositionFeaturizer):\n \"\"\"\n\n Parameters\n ----------\n elemental_info\n Elemental level information for each element. For example, the ``atomic number``,\n ``atomic radius``, and etc. By default (``None``), will use the XenonPy embedded information.\n n_jobs: int\n The number of jobs to run in parallel for both fit and predict.\n Set -1 to use all cpu cores (default).\n Inputs ``X`` will be split into some blocks then run on each cpu cores.\n on_errors: string\n How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\n When 'nan', return a column with ``np.nan``.\n The length of column corresponding to the number of feature labs.\n When 'keep', return a column with exception objects.\n The default is 'raise' which will raise up the exception.\n return_type: str\n Specific the return type.\n Can be ``any``, ``array`` and ``df``.\n ``array`` and ``df`` force return type to ``np.ndarray`` and ``pd.DataFrame`` respectively.\n If ``any``, the return type dependent on the input type.\n Default is ``any``\n target_col\n Only relevant when input is pd.DataFrame, otherwise ignored.\n Specify a single column to be used for transformation.\n If ``None``, all columns of the pd.DataFrame is used.\n Default is None.\n \"\"\"\n\n def mix_function(self, elems, _):\n elems_ = self.elements.loc[elems, :]\n return elems_.max().values\n\n @property\n def feature_labels(self):\n return ['max:' + s for s in self.elements]\n\n\nclass MinPooling(BaseCompositionFeaturizer):\n \"\"\"\n\n Parameters\n ----------\n elemental_info\n Elemental level information for each element. For example, the ``atomic number``,\n ``atomic radius``, and etc. By default (``None``), will use the XenonPy embedded information.\n n_jobs: int\n The number of jobs to run in parallel for both fit and predict.\n Set -1 to use all cpu cores (default).\n Inputs ``X`` will be split into some blocks then run on each cpu cores.\n on_errors: string\n How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\n When 'nan', return a column with ``np.nan``.\n The length of column corresponding to the number of feature labs.\n When 'keep', return a column with exception objects.\n The default is 'raise' which will raise up the exception.\n return_type: str\n Specific the return type.\n Can be ``any``, ``array`` and ``df``.\n ``array`` and ``df`` force return type to ``np.ndarray`` and ``pd.DataFrame`` respectively.\n If ``any``, the return type dependent on the input type.\n Default is ``any``\n target_col\n Only relevant when input is pd.DataFrame, otherwise ignored.\n Specify a single column to be used for transformation.\n If ``None``, all columns of the pd.DataFrame is used.\n Default is None.\n \"\"\"\n\n def mix_function(self, elems, _):\n elems_ = self.elements.loc[elems, :]\n return elems_.min().values\n\n @property\n def feature_labels(self):\n return ['min:' + s for s in self.elements]\n\n\nclass Compositions(BaseDescriptor):\n \"\"\"\n Calculate elemental descriptors from compound's composition.\n \"\"\"\n\n classic = ['WeightedAverage', 'WeightedSum', 'WeightedVariance', 'MaxPooling', 'MinPooling']\n\n def __init__(self,\n *,\n elemental_info: Union[pd.DataFrame, None] = None,\n n_jobs: int = -1,\n featurizers: Union[str, List[str]] = 'classic',\n on_errors: str = 'nan'):\n \"\"\"\n\n Parameters\n ----------\n elemental_info\n Elemental level information for each element. For example, the ``atomic number``,\n ``atomic radius``, and etc. By default (``None``), will use the XenonPy embedded information.\n n_jobs: int\n The number of jobs to run in parallel for both fit and predict.\n Set -1 to use all cpu cores (default).\n Inputs ``X`` will be split into some blocks then run on each cpu cores.\n featurizers: Union[str, List[str]]\n Name of featurizers that will be used.\n Set to `classic` to be compatible with the old version.\n This is equal to set ``featurizers=['WeightedAverage', 'WeightedSum',\n 'WeightedVariance', 'MaxPooling', 'MinPooling']``.\n Default is 'all'.\n on_errors: string\n How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\n When 'nan', return a column with ``np.nan``.\n The length of column corresponding to the number of feature labs.\n When 'keep', return a column with exception objects.\n The default is 'nan' which will raise up the exception.\n \"\"\"\n\n if featurizers == 'classic':\n super().__init__(featurizers=self.classic)\n else:\n super().__init__(featurizers=featurizers)\n\n self.composition = Counting(n_jobs=n_jobs, on_errors=on_errors)\n self.composition = WeightedAverage(n_jobs=n_jobs,\n on_errors=on_errors,\n elemental_info=elemental_info)\n self.composition = WeightedSum(n_jobs=n_jobs,\n on_errors=on_errors,\n elemental_info=elemental_info)\n self.composition = WeightedVariance(n_jobs=n_jobs,\n on_errors=on_errors,\n elemental_info=elemental_info)\n self.composition = GeometricMean(n_jobs=n_jobs,\n on_errors=on_errors,\n elemental_info=elemental_info)\n self.composition = HarmonicMean(n_jobs=n_jobs,\n on_errors=on_errors,\n elemental_info=elemental_info)\n self.composition = MaxPooling(n_jobs=n_jobs,\n on_errors=on_errors,\n elemental_info=elemental_info)\n self.composition = MinPooling(n_jobs=n_jobs,\n on_errors=on_errors,\n elemental_info=elemental_info)\n" }, { "path": "xenonpy/descriptor/fingerprint.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport numpy as np\nfrom rdkit import Chem\nfrom rdkit.Chem import Descriptors as ChemDesc\nfrom rdkit.Chem import MACCSkeys as MAC\nfrom rdkit.Chem import rdMolDescriptors as rdMol\nfrom rdkit.Chem import rdmolops as rdm\nfrom rdkit.Chem.rdMHFPFingerprint import MHFPEncoder\nfrom rdkit.ML.Descriptors import MoleculeDescriptors\n\nfrom scipy.sparse import coo_matrix\n\nfrom xenonpy.descriptor.base import BaseDescriptor, BaseFeaturizer\n\n__all__ = ['RDKitFP', 'AtomPairFP', 'TopologicalTorsionFP', 'MACCS', 'FCFP', 'ECFP', 'PatternFP', 'LayeredFP',\n 'MHFP', 'DescriptorFeature', 'Fingerprints']\n\n\ndef count_fp(fp, dim=2**10):\n tmp = fp.GetNonzeroElements()\n return coo_matrix((list(tmp.values()), (np.repeat(0, len(tmp)), [i % dim for i in tmp.keys()])),\n shape=(1, dim)).toarray().flatten()\n\n\nclass RDKitFP(BaseFeaturizer):\n\n def __init__(self, n_jobs=-1, *, n_bits=2048, bit_per_entry=None, counting=False,\n input_type='mol', on_errors='raise', return_type='any', target_col=None):\n \"\"\"\n RDKit fingerprint.\n\n Parameters\n ----------\n n_jobs: int\n The number of jobs to run in parallel for both fit and predict.\n Can be -1 or # of cups. Set -1 to use all cpu cores (default).\n n_bits: int\n Fingerprint size.\n bit_per_entry: int\n Number of bits used to represent a single entry (only for non-counting case).\n Default value follows rdkit default.\n counting: boolean\n Record counts of the entries instead of bits only.\n input_type: string\n Set the specific type of transform input.\n Set to ``mol`` (default) to ``rdkit.Chem.rdchem.Mol`` objects as input.\n When set to ``smlies``, ``transform`` method can use a SMILES list as input.\n Set to ``any`` to use both.\n If input is SMILES, ``Chem.MolFromSmiles`` function will be used inside.\n for ``None`` returns, a ``ValueError`` exception will be raised.\n on_errors: string\n How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\n When 'nan', return a column with ``np.nan``.\n The length of column corresponding to the number of feature labs.\n When 'keep', return a column with exception objects.\n The default is 'raise' which will raise up the exception.\n target_col\n Only relevant when input is pd.DataFrame, otherwise ignored.\n Specify a single column to be used for transformation.\n If ``None``, all columns of the pd.DataFrame is used.\n Default is None.\n \"\"\"\n super().__init__(n_jobs=n_jobs, on_errors=on_errors, return_type=return_type, target_col=target_col)\n self.input_type = input_type\n self.n_bits = n_bits\n if bit_per_entry is None:\n self.bit_per_entry = 2\n else:\n self.bit_per_entry = bit_per_entry\n self.counting = counting\n self.__authors__ = ['Stephen Wu', 'TsumiNa']\n\n def featurize(self, x):\n if self.input_type == 'smiles':\n x_ = x\n x = Chem.MolFromSmiles(x)\n if x is None:\n raise ValueError('cannot convert Mol from SMILES %s' % x_)\n if self.input_type == 'any':\n if not isinstance(x, Chem.rdchem.Mol):\n x_ = x\n x = Chem.MolFromSmiles(x)\n if x is None:\n raise ValueError('cannot convert Mol from SMILES %s' % x_)\n\n if self.counting:\n return count_fp(rdm.UnfoldedRDKFingerprintCountBased(x), dim=self.n_bits)\n else:\n return list(Chem.RDKFingerprint(x, fpSize=self.n_bits, nBitsPerHash=self.bit_per_entry))\n\n @property\n def feature_labels(self):\n if self.counting:\n return [\"rdkit_c:\" + str(i) for i in range(self.n_bits)]\n else:\n return [\"rdkit:\" + str(i) for i in range(self.n_bits)]\n\n\nclass AtomPairFP(BaseFeaturizer):\n\n def __init__(self, n_jobs=-1, *, n_bits=2048, bit_per_entry=None, counting=False,\n input_type='mol', on_errors='raise', return_type='any', target_col=None):\n \"\"\"\n Atom Pair fingerprints.\n Returns the atom-pair fingerprint for a molecule.The algorithm used is described here:\n R.E. Carhart, D.H. Smith, R. Venkataraghavan;\n \"Atom Pairs as Molecular Features in Structure-Activity Studies: Definition and Applications\"\n JCICS 25, 64-73 (1985).\n This is currently just in binary bits with fixed length after folding.\n\n Parameters\n ----------\n n_jobs: int\n The number of jobs to run in parallel for both fit and predict.\n Can be -1 or # of cups. Set -1 to use all cpu cores (default).\n n_bits: int\n Fixed bit length based on folding.\n bit_per_entry: int\n Number of bits used to represent a single entry (only for non-counting case).\n Default value follows rdkit default.\n counting: boolean\n Record counts of the entries instead of bits only.\n input_type: string\n Set the specific type of transform input.\n Set to ``mol`` (default) to ``rdkit.Chem.rdchem.Mol`` objects as input.\n When set to ``smlies``, ``transform`` method can use a SMILES list as input.\n Set to ``any`` to use both.\n If input is SMILES, ``Chem.MolFromSmiles`` function will be used inside.\n for ``None`` returns, a ``ValueError`` exception will be raised.\n on_errors: string\n How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\n When 'nan', return a column with ``np.nan``.\n The length of column corresponding to the number of feature labs.\n When 'keep', return a column with exception objects.\n The default is 'raise' which will raise up the exception.\n target_col\n Only relevant when input is pd.DataFrame, otherwise ignored.\n Specify a single column to be used for transformation.\n If ``None``, all columns of the pd.DataFrame is used.\n Default is None.\n \"\"\"\n super().__init__(n_jobs=n_jobs, on_errors=on_errors, return_type=return_type, target_col=target_col)\n self.input_type = input_type\n self.n_bits = n_bits\n if bit_per_entry is None:\n self.bit_per_entry = 4\n else:\n self.bit_per_entry = bit_per_entry\n self.counting = counting\n self.__authors__ = ['Stephen Wu', 'TsumiNa']\n\n def featurize(self, x):\n if self.input_type == 'smiles':\n x_ = x\n x = Chem.MolFromSmiles(x)\n if x is None:\n raise ValueError('cannot convert Mol from SMILES %s' % x_)\n if self.input_type == 'any':\n if not isinstance(x, Chem.rdchem.Mol):\n x_ = x\n x = Chem.MolFromSmiles(x)\n if x is None:\n raise ValueError('cannot convert Mol from SMILES %s' % x_)\n if self.counting:\n return count_fp(rdMol.GetHashedAtomPairFingerprint(x, nBits=self.n_bits), dim=self.n_bits)\n else:\n return list(rdMol.GetHashedAtomPairFingerprintAsBitVect(x, nBits=self.n_bits,\n nBitsPerEntry=self.bit_per_entry))\n\n @property\n def feature_labels(self):\n if self.counting:\n return ['apfp_c:' + str(i) for i in range(self.n_bits)]\n else:\n return ['apfp:' + str(i) for i in range(self.n_bits)]\n\n\nclass TopologicalTorsionFP(BaseFeaturizer):\n\n def __init__(self, n_jobs=-1, *, n_bits=2048, bit_per_entry=None, counting=False,\n input_type='mol', on_errors='raise', return_type='any', target_col=None):\n \"\"\"\n Topological Torsion fingerprints.\n Returns the topological-torsion fingerprint for a molecule.\n This is currently just in binary bits with fixed length after folding.\n\n Parameters\n ----------\n n_jobs: int\n The number of jobs to run in parallel for both fit and predict.\n Can be -1 or # of cups. Set -1 to use all cpu cores (default).\n n_bits: int\n Fixed bit length based on folding.\n bit_per_entry: int\n Number of bits used to represent a single entry (only for non-counting case).\n Default value follows rdkit default.\n counting: boolean\n Record counts of the entries instead of bits only.\n input_type: string\n Set the specific type of transform input.\n Set to ``mol`` (default) to ``rdkit.Chem.rdchem.Mol`` objects as input.\n When set to ``smlies``, ``transform`` method can use a SMILES list as input.\n Set to ``any`` to use both.\n If input is SMILES, ``Chem.MolFromSmiles`` function will be used inside.\n for ``None`` returns, a ``ValueError`` exception will be raised.\n on_errors: string\n How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\n When 'nan', return a column with ``np.nan``.\n The length of column corresponding to the number of feature labs.\n When 'keep', return a column with exception objects.\n The default is 'raise' which will raise up the exception.\n target_col\n Only relevant when input is pd.DataFrame, otherwise ignored.\n Specify a single column to be used for transformation.\n If ``None``, all columns of the pd.DataFrame is used.\n Default is None.\n \"\"\"\n super().__init__(n_jobs=n_jobs, on_errors=on_errors, return_type=return_type, target_col=target_col)\n self.input_type = input_type\n self.n_bits = n_bits\n if bit_per_entry is None:\n self.bit_per_entry = 4\n else:\n self.bit_per_entry = bit_per_entry\n self.counting = counting\n self.__authors__ = ['Stephen Wu', 'TsumiNa']\n\n def featurize(self, x):\n if self.input_type == 'smiles':\n x_ = x\n x = Chem.MolFromSmiles(x)\n if x is None:\n raise ValueError('cannot convert Mol from SMILES %s' % x_)\n if self.input_type == 'any':\n if not isinstance(x, Chem.rdchem.Mol):\n x_ = x\n x = Chem.MolFromSmiles(x)\n if x is None:\n raise ValueError('cannot convert Mol from SMILES %s' % x_)\n if self.counting:\n return count_fp(rdMol.GetHashedTopologicalTorsionFingerprint(x, nBits=self.n_bits), dim=self.n_bits)\n else:\n return list(rdMol.GetHashedTopologicalTorsionFingerprintAsBitVect(x, nBits=self.n_bits,\n nBitsPerEntry=self.bit_per_entry))\n\n @property\n def feature_labels(self):\n if self.counting:\n return ['ttfp_c:' + str(i) for i in range(self.n_bits)]\n else:\n return ['ttfp:' + str(i) for i in range(self.n_bits)]\n\n\nclass MACCS(BaseFeaturizer):\n\n def __init__(self, n_jobs=-1,\n *, input_type='mol', on_errors='raise', return_type='any', target_col=None):\n \"\"\"\n The MACCS keys for a molecule. The result is a 167-bit vector. There are 166 public keys,\n but to maintain consistency with other software packages they are numbered from 1.\n\n Parameters\n ----------\n n_jobs: int\n The number of jobs to run in parallel for both fit and predict.\n Can be -1 or # of cups. Set -1 to use all cpu cores (default).\n input_type: string\n Set the specific type of transform input.\n Set to ``mol`` (default) to ``rdkit.Chem.rdchem.Mol`` objects as input.\n When set to ``smlies``, ``transform`` method can use a SMILES list as input.\n Set to ``any`` to use both.\n If input is SMILES, ``Chem.MolFromSmiles`` function will be used inside.\n for ``None`` returns, a ``ValueError`` exception will be raised.\n on_errors: string\n How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\n When 'nan', return a column with ``np.nan``.\n The length of column corresponding to the number of feature labs.\n When 'keep', return a column with exception objects.\n The default is 'raise' which will raise up the exception.\n target_col\n Only relevant when input is pd.DataFrame, otherwise ignored.\n Specify a single column to be used for transformation.\n If ``None``, all columns of the pd.DataFrame is used.\n Default is None.\n \"\"\"\n super().__init__(n_jobs=n_jobs, on_errors=on_errors, return_type=return_type, target_col=target_col)\n self.input_type = input_type\n self.__authors__ = ['Stephen Wu', 'TsumiNa']\n\n def featurize(self, x):\n if self.input_type == 'smiles':\n x_ = x\n x = Chem.MolFromSmiles(x)\n if x is None:\n raise ValueError('cannot convert Mol from SMILES %s' % x_)\n if self.input_type == 'any':\n if not isinstance(x, Chem.rdchem.Mol):\n x_ = x\n x = Chem.MolFromSmiles(x)\n if x is None:\n raise ValueError('cannot convert Mol from SMILES %s' % x_)\n return list(MAC.GenMACCSKeys(x))\n\n @property\n def feature_labels(self):\n return ['maccs:' + str(i) for i in range(167)]\n\n\nclass FCFP(BaseFeaturizer):\n\n def __init__(self, n_jobs=-1, *, radius=3, n_bits=2048, counting=False,\n input_type='mol', on_errors='raise', return_type='any', target_col=None):\n \"\"\"\n Morgan (Circular) fingerprints + feature-based (FCFP)\n The algorithm used is described in the paper Rogers, D. & Hahn, M. Extended-Connectivity Fingerprints.\n JCIM 50:742-54 (2010)\n\n Parameters\n ----------\n n_jobs: int\n The number of jobs to run in parallel for both fit and predict.\n Can be -1 or # of cups. Set -1 to use all cpu cores (default).\n radius: int\n The radius parameter in the Morgan fingerprints, which is roughly half of the diameter parameter in FCFP,\n i.e., radius=2 is roughly equivalent to FCFP4.\n n_bits: int\n Fixed bit length based on folding.\n counting: boolean\n Record counts of the entries instead of bits only.\n input_type: string\n Set the specific type of transform input.\n Set to ``mol`` (default) to ``rdkit.Chem.rdchem.Mol`` objects as input.\n When set to ``smlies``, ``transform`` method can use a SMILES list as input.\n Set to ``any`` to use both.\n If input is SMILES, ``Chem.MolFromSmiles`` function will be used inside.\n for ``None`` returns, a ``ValueError`` exception will be raised.\n on_errors: string\n How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\n When 'nan', return a column with ``np.nan``.\n The length of column corresponding to the number of feature labs.\n When 'keep', return a column with exception objects.\n The default is 'raise' which will raise up the exception.\n target_col\n Only relevant when input is pd.DataFrame, otherwise ignored.\n Specify a single column to be used for transformation.\n If ``None``, all columns of the pd.DataFrame is used.\n Default is None.\n \"\"\"\n super().__init__(n_jobs=n_jobs, on_errors=on_errors, return_type=return_type, target_col=target_col)\n self.input_type = input_type\n self.radius = radius\n self.n_bits = n_bits\n self.counting = counting\n self.__authors__ = ['Stephen Wu', 'TsumiNa']\n # self.arg = arg # arg[0] = radius, arg[1] = bit length\n\n def featurize(self, x):\n if self.input_type == 'smiles':\n x_ = x\n x = Chem.MolFromSmiles(x)\n if x is None:\n raise ValueError('cannot convert Mol from SMILES %s' % x_)\n if self.input_type == 'any':\n if not isinstance(x, Chem.rdchem.Mol):\n x_ = x\n x = Chem.MolFromSmiles(x)\n if x is None:\n raise ValueError('cannot convert Mol from SMILES %s' % x_)\n if self.counting:\n return count_fp(rdMol.GetHashedMorganFingerprint(\n x, radius=self.radius, nBits=self.n_bits, useFeatures=True), dim=self.n_bits)\n else:\n return list(rdMol.GetMorganFingerprintAsBitVect(\n x, radius=self.radius, nBits=self.n_bits, useFeatures=True))\n\n @property\n def feature_labels(self):\n if self.counting:\n return [f'fcfp{self.radius * 2}_c:' + str(i) for i in range(self.n_bits)]\n else:\n return [f'fcfp{self.radius * 2}:' + str(i) for i in range(self.n_bits)]\n\n\nclass ECFP(BaseFeaturizer):\n\n def __init__(self, n_jobs=-1, *, radius=3, n_bits=2048, counting=False,\n input_type='mol', on_errors='raise', return_type='any', target_col=None):\n \"\"\"\n Morgan (Circular) fingerprints (ECFP)\n The algorithm used is described in the paper Rogers, D. & Hahn, M. Extended-Connectivity Fingerprints.\n JCIM 50:742-54 (2010)\n\n Parameters\n ----------\n n_jobs: int\n The number of jobs to run in parallel for both fit and predict.\n Can be -1 or # of cups. Set -1 to use all cpu cores (default).\n radius: int\n The radius parameter in the Morgan fingerprints, which is roughly half of the diameter parameter in ECFP,\n i.e., radius=2 is roughly equivalent to ECFP4.\n n_bits: int\n Fixed bit length based on folding.\n counting: boolean\n Record counts of the entries instead of bits only.\n input_type: string\n Set the specific type of transform input.\n Set to ``mol`` (default) to ``rdkit.Chem.rdchem.Mol`` objects as input.\n When set to ``smlies``, ``transform`` method can use a SMILES list as input.\n Set to ``any`` to use both.\n If input is SMILES, ``Chem.MolFromSmiles`` function will be used inside.\n for ``None`` returns, a ``ValueError`` exception will be raised.\n on_errors: string\n How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\n When 'nan', return a column with ``np.nan``.\n The length of column corresponding to the number of feature labs.\n When 'keep', return a column with exception objects.\n The default is 'raise' which will raise up the exception.\n target_col\n Only relevant when input is pd.DataFrame, otherwise ignored.\n Specify a single column to be used for transformation.\n If ``None``, all columns of the pd.DataFrame is used.\n Default is None.\n \"\"\"\n super().__init__(n_jobs=n_jobs, on_errors=on_errors, return_type=return_type, target_col=target_col)\n self.input_type = input_type\n self.radius = radius\n self.n_bits = n_bits\n self.counting = counting\n self.__authors__ = ['Stephen Wu', 'TsumiNa']\n # self.arg = arg # arg[0] = radius, arg[1] = bit length\n\n def featurize(self, x):\n if self.input_type == 'smiles':\n x_ = x\n x = Chem.MolFromSmiles(x)\n if x is None:\n raise ValueError('cannot convert Mol from SMILES %s' % x_)\n if self.input_type == 'any':\n if not isinstance(x, Chem.rdchem.Mol):\n x_ = x\n x = Chem.MolFromSmiles(x)\n if x is None:\n raise ValueError('cannot convert Mol from SMILES %s' % x_)\n if self.counting:\n return count_fp(rdMol.GetHashedMorganFingerprint(x, radius=self.radius,\n nBits=self.n_bits), dim=self.n_bits)\n else:\n return list(rdMol.GetMorganFingerprintAsBitVect(x, radius=self.radius, nBits=self.n_bits))\n\n @property\n def feature_labels(self):\n if self.counting:\n return [f'ecfp{self.radius * 2}_c:' + str(i) for i in range(self.n_bits)]\n else:\n return [f'ecfp{self.radius * 2}:' + str(i) for i in range(self.n_bits)]\n\n\nclass PatternFP(BaseFeaturizer):\n\n def __init__(self, n_jobs=-1, *, n_bits=2048,\n input_type='mol', on_errors='raise', return_type='any', target_col=None):\n \"\"\"\n A fingerprint designed to be used in substructure screening using SMARTS patterns (unique in RDKit).\n\n Parameters\n ----------\n n_jobs: int\n The number of jobs to run in parallel for both fit and predict.\n Can be -1 or # of cups. Set -1 to use all cpu cores (default).\n n_bits: int\n Fixed bit length based on folding.\n input_type: string\n Set the specific type of transform input.\n Set to ``mol`` (default) to ``rdkit.Chem.rdchem.Mol`` objects as input.\n When set to ``smlies``, ``transform`` method can use a SMILES list as input.\n Set to ``any`` to use both.\n If input is SMILES, ``Chem.MolFromSmiles`` function will be used inside.\n for ``None`` returns, a ``ValueError`` exception will be raised.\n on_errors: string\n How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\n When 'nan', return a column with ``np.nan``.\n The length of column corresponding to the number of feature labs.\n When 'keep', return a column with exception objects.\n The default is 'raise' which will raise up the exception.\n target_col\n Only relevant when input is pd.DataFrame, otherwise ignored.\n Specify a single column to be used for transformation.\n If ``None``, all columns of the pd.DataFrame is used.\n Default is None.\n \"\"\"\n super().__init__(n_jobs=n_jobs, on_errors=on_errors, return_type=return_type, target_col=target_col)\n self.input_type = input_type\n self.n_bits = n_bits\n self.__authors__ = ['Stephen Wu', 'TsumiNa']\n\n def featurize(self, x):\n if self.input_type == 'smiles':\n x_ = x\n x = Chem.MolFromSmiles(x)\n if x is None:\n raise ValueError('cannot convert Mol from SMILES %s' % x_)\n if self.input_type == 'any':\n if not isinstance(x, Chem.rdchem.Mol):\n x_ = x\n x = Chem.MolFromSmiles(x)\n if x is None:\n raise ValueError('cannot convert Mol from SMILES %s' % x_)\n return list(rdm.PatternFingerprint(x, fpSize=self.n_bits))\n\n @property\n def feature_labels(self):\n return ['patfp:' + str(i) for i in range(self.n_bits)]\n\n\nclass LayeredFP(BaseFeaturizer):\n\n def __init__(self, n_jobs=-1, *, n_bits=2048,\n input_type='mol', on_errors='raise', return_type='any', target_col=None):\n \"\"\"\n A substructure fingerprint that is more complex than PatternFP (unique in RDKit).\n\n Parameters\n ----------\n n_jobs: int\n The number of jobs to run in parallel for both fit and predict.\n Can be -1 or # of cups. Set -1 to use all cpu cores (default).\n n_bits: int\n Fixed bit length based on folding.\n input_type: string\n Set the specific type of transform input.\n Set to ``mol`` (default) to ``rdkit.Chem.rdchem.Mol`` objects as input.\n When set to ``smlies``, ``transform`` method can use a SMILES list as input.\n Set to ``any`` to use both.\n If input is SMILES, ``Chem.MolFromSmiles`` function will be used inside.\n for ``None`` returns, a ``ValueError`` exception will be raised.\n on_errors: string\n How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\n When 'nan', return a column with ``np.nan``.\n The length of column corresponding to the number of feature labs.\n When 'keep', return a column with exception objects.\n The default is 'raise' which will raise up the exception.\n target_col\n Only relevant when input is pd.DataFrame, otherwise ignored.\n Specify a single column to be used for transformation.\n If ``None``, all columns of the pd.DataFrame is used.\n Default is None.\n \"\"\"\n super().__init__(n_jobs=n_jobs, on_errors=on_errors, return_type=return_type, target_col=target_col)\n self.input_type = input_type\n self.n_bits = n_bits\n self.__authors__ = ['Stephen Wu', 'TsumiNa']\n\n def featurize(self, x):\n if self.input_type == 'smiles':\n x_ = x\n x = Chem.MolFromSmiles(x)\n if x is None:\n raise ValueError('cannot convert Mol from SMILES %s' % x_)\n if self.input_type == 'any':\n if not isinstance(x, Chem.rdchem.Mol):\n x_ = x\n x = Chem.MolFromSmiles(x)\n if x is None:\n raise ValueError('cannot convert Mol from SMILES %s' % x_)\n return list(rdm.LayeredFingerprint(x, fpSize=self.n_bits))\n\n @property\n def feature_labels(self):\n return ['layfp:' + str(i) for i in range(self.n_bits)]\n\n\nclass MHFP(BaseFeaturizer):\n\n def __init__(self, n_jobs=1, *, radius=3, n_bits=2048,\n input_type='mol', on_errors='raise', return_type='any', target_col=None):\n \"\"\"\n Variation from the MinHash fingerprint, which is based on ECFP with\n locality sensitive hashing to increase compactness of information during hashing.\n The algorithm used is described in the paper\n Probst, D. & Reymond, J.-L., A probabilistic molecular fingerprint for big data settings.\n Journal of Cheminformatics, 10:66 (2018)\n\n Note that MHFP currently does not support parallel computing, so please fix n_jobs to 1.\n\n Parameters\n ----------\n n_jobs: int\n The number of jobs to run in parallel for both fit and predict.\n Can be -1 or # of cups. Set -1 to use all cpu cores (default).\n radius: int\n The radius parameter in the SECFP(RDKit version) fingerprints,\n which is roughly half of the diameter parameter in ECFP,\n i.e., radius=2 is roughly equivalent to ECFP4.\n n_bits: int\n Fixed bit length based on folding.\n input_type: string\n Set the specific type of transform input.\n Set to ``mol`` (default) to ``rdkit.Chem.rdchem.Mol`` objects as input.\n When set to ``smlies``, ``transform`` method can use a SMILES list as input.\n Set to ``any`` to use both.\n If input is SMILES, ``Chem.MolFromSmiles`` function will be used inside.\n for ``None`` returns, a ``ValueError`` exception will be raised.\n on_errors: string\n How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\n When 'nan', return a column with ``np.nan``.\n The length of column corresponding to the number of feature labs.\n When 'keep', return a column with exception objects.\n The default is 'raise' which will raise up the exception.\n target_col\n Only relevant when input is pd.DataFrame, otherwise ignored.\n Specify a single column to be used for transformation.\n If ``None``, all columns of the pd.DataFrame is used.\n Default is None.\n \"\"\"\n super().__init__(n_jobs=n_jobs, on_errors=on_errors, return_type=return_type, target_col=target_col)\n self.input_type = input_type\n self.radius = radius\n self.n_bits = n_bits\n self.mhfp = MHFPEncoder()\n self.__authors__ = ['Stephen Wu', 'TsumiNa']\n\n def featurize(self, x):\n if self.input_type == 'smiles':\n x_ = x\n x = Chem.MolFromSmiles(x)\n if x is None:\n raise ValueError('cannot convert Mol from SMILES %s' % x_)\n if self.input_type == 'any':\n if not isinstance(x, Chem.rdchem.Mol):\n x_ = x\n x = Chem.MolFromSmiles(x)\n if x is None:\n raise ValueError('cannot convert Mol from SMILES %s' % x_)\n return list(self.mhfp.EncodeSECFPMol(x, radius=self.radius, length=self.n_bits))\n\n @property\n def feature_labels(self):\n return [f'secfp{self.radius * 2}:' + str(i) for i in range(self.n_bits)]\n\n\nclass DescriptorFeature(BaseFeaturizer):\n\n classic = ['MaxEStateIndex', 'MinEStateIndex', 'MaxAbsEStateIndex', 'MinAbsEStateIndex', 'qed', 'MolWt',\n 'HeavyAtomMolWt', 'ExactMolWt', 'NumValenceElectrons', 'NumRadicalElectrons', 'MaxPartialCharge',\n 'MinPartialCharge', 'MaxAbsPartialCharge', 'MinAbsPartialCharge', 'FpDensityMorgan1', 'FpDensityMorgan2',\n 'FpDensityMorgan3', 'BalabanJ', 'BertzCT', 'Chi0', 'Chi0n', 'Chi0v', 'Chi1', 'Chi1n', 'Chi1v', 'Chi2n',\n 'Chi2v', 'Chi3n', 'Chi3v', 'Chi4n', 'Chi4v', 'HallKierAlpha', 'Ipc', 'Kappa1', 'Kappa2', 'Kappa3',\n 'LabuteASA', 'PEOE_VSA1', 'PEOE_VSA10', 'PEOE_VSA11', 'PEOE_VSA12', 'PEOE_VSA13', 'PEOE_VSA14',\n 'PEOE_VSA2', 'PEOE_VSA3', 'PEOE_VSA4', 'PEOE_VSA5', 'PEOE_VSA6', 'PEOE_VSA7', 'PEOE_VSA8', 'PEOE_VSA9',\n 'SMR_VSA1', 'SMR_VSA10', 'SMR_VSA2', 'SMR_VSA3', 'SMR_VSA4', 'SMR_VSA5', 'SMR_VSA6', 'SMR_VSA7',\n 'SMR_VSA8', 'SMR_VSA9', 'SlogP_VSA1', 'SlogP_VSA10', 'SlogP_VSA11', 'SlogP_VSA12', 'SlogP_VSA2',\n 'SlogP_VSA3', 'SlogP_VSA4', 'SlogP_VSA5', 'SlogP_VSA6', 'SlogP_VSA7', 'SlogP_VSA8', 'SlogP_VSA9', 'TPSA',\n 'EState_VSA1', 'EState_VSA10', 'EState_VSA11', 'EState_VSA2', 'EState_VSA3', 'EState_VSA4',\n 'EState_VSA5', 'EState_VSA6', 'EState_VSA7', 'EState_VSA8', 'EState_VSA9', 'VSA_EState1', 'VSA_EState10',\n 'VSA_EState2', 'VSA_EState3', 'VSA_EState4', 'VSA_EState5', 'VSA_EState6', 'VSA_EState7', 'VSA_EState8',\n 'VSA_EState9', 'FractionCSP3', 'HeavyAtomCount', 'NHOHCount', 'NOCount', 'NumAliphaticCarbocycles',\n 'NumAliphaticHeterocycles', 'NumAliphaticRings', 'NumAromaticCarbocycles', 'NumAromaticHeterocycles',\n 'NumAromaticRings', 'NumHAcceptors', 'NumHDonors', 'NumHeteroatoms', 'NumRotatableBonds',\n 'NumSaturatedCarbocycles', 'NumSaturatedHeterocycles', 'NumSaturatedRings', 'RingCount', 'MolLogP',\n 'MolMR', 'fr_Al_COO', 'fr_Al_OH', 'fr_Al_OH_noTert', 'fr_ArN', 'fr_Ar_COO', 'fr_Ar_N', 'fr_Ar_NH',\n 'fr_Ar_OH', 'fr_COO', 'fr_COO2', 'fr_C_O', 'fr_C_O_noCOO', 'fr_C_S', 'fr_HOCCN', 'fr_Imine', 'fr_NH0',\n 'fr_NH1', 'fr_NH2', 'fr_N_O', 'fr_Ndealkylation1', 'fr_Ndealkylation2', 'fr_Nhpyrrole', 'fr_SH',\n 'fr_aldehyde', 'fr_alkyl_carbamate', 'fr_alkyl_halide', 'fr_allylic_oxid', 'fr_amide', 'fr_amidine',\n 'fr_aniline', 'fr_aryl_methyl', 'fr_azide', 'fr_azo', 'fr_barbitur', 'fr_benzene', 'fr_benzodiazepine',\n 'fr_bicyclic', 'fr_diazo', 'fr_dihydropyridine', 'fr_epoxide', 'fr_ester', 'fr_ether', 'fr_furan',\n 'fr_guanido', 'fr_halogen', 'fr_hdrzine', 'fr_hdrzone', 'fr_imidazole', 'fr_imide', 'fr_isocyan',\n 'fr_isothiocyan', 'fr_ketone', 'fr_ketone_Topliss', 'fr_lactam', 'fr_lactone', 'fr_methoxy',\n 'fr_morpholine', 'fr_nitrile', 'fr_nitro', 'fr_nitro_arom', 'fr_nitro_arom_nonortho', 'fr_nitroso',\n 'fr_oxazole', 'fr_oxime', 'fr_para_hydroxylation', 'fr_phenol', 'fr_phenol_noOrthoHbond', 'fr_phos_acid',\n 'fr_phos_ester', 'fr_piperdine', 'fr_piperzine', 'fr_priamide', 'fr_prisulfonamd', 'fr_pyridine',\n 'fr_quatN', 'fr_sulfide', 'fr_sulfonamd', 'fr_sulfone', 'fr_term_acetylene', 'fr_tetrazole',\n 'fr_thiazole', 'fr_thiocyan', 'fr_thiophene', 'fr_unbrch_alkane', 'fr_urea']\n\n def __init__(self, n_jobs=-1,\n *, input_type='mol', on_errors='raise', return_type='any', target_col=None, desc_list='all', add_Hs=False):\n \"\"\"\n All descriptors in RDKit (length = 200) [may include NaN]\n see https://www.rdkit.org/docs/GettingStartedInPython.html#list-of-available-descriptors for the full list\n\n Parameters\n ----------\n n_jobs: int\n The number of jobs to run in parallel for both fit and predict.\n Can be -1 or # of cups. Set -1 to use all cpu cores (default).\n input_type: string\n Set the specific type of transform input.\n Set to ``mol`` (default) to ``rdkit.Chem.rdchem.Mol`` objects as input.\n When set to ``smlies``, ``transform`` method can use a SMILES list as input.\n Set to ``any`` to use both.\n If input is SMILES, ``Chem.MolFromSmiles`` function will be used inside.\n for ``None`` returns, a ``ValueError`` exception will be raised.\n on_errors: string\n How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\n When 'nan', return a column with ``np.nan``.\n The length of column corresponding to the number of feature labs.\n When 'keep', return a column with exception objects.\n The default is 'raise' which will raise up the exception.\n target_col\n Only relevant when input is pd.DataFrame, otherwise ignored.\n Specify a single column to be used for transformation.\n If ``None``, all columns of the pd.DataFrame is used.\n Default is None.\n desc_list: string or list\n List of descriptor names to be called in rdkit to calculate molecule descriptors.\n If ``classic``, the full list of rdkit v.2020.03.xx is used. (length = 200)\n Default is to use the latest list available in the rdkit. (length = 208 in rdkit v.2020.09.xx)\n add_Hs: boolean\n Add hydrogen atoms to the mol format in RDKit or not.\n This may affect a few physical descriptors (e.g., charge related ones).\n \"\"\"\n # self.arg = arg # arg[0] = radius, arg[1] = bit length\n super().__init__(n_jobs=n_jobs, on_errors=on_errors, return_type=return_type, target_col=target_col)\n self.input_type = input_type\n self.add_Hs = add_Hs\n if desc_list == 'all':\n self.nms = [x[0] for x in ChemDesc._descList]\n elif desc_list == 'classic':\n self.nms = self.classic\n else:\n self.nms = desc_list\n self.calc = MoleculeDescriptors.MolecularDescriptorCalculator(self.nms)\n self.__authors__ = ['Stephen Wu', 'TsumiNa']\n\n def featurize(self, x):\n if self.input_type == 'smiles':\n x_ = x\n x = Chem.MolFromSmiles(x)\n if x is None:\n raise ValueError('cannot convert Mol from SMILES %s' % x_)\n if self.add_Hs:\n x = Chem.AddHs(x)\n if x is None:\n raise ValueError('cannot add Hs to Mol for %s' % x_)\n if self.input_type == 'any':\n if not isinstance(x, Chem.rdchem.Mol):\n x_ = x\n x = Chem.MolFromSmiles(x)\n if x is None:\n raise ValueError('cannot convert Mol from SMILES %s' % x_)\n if self.add_Hs:\n x = Chem.AddHs(x)\n if x is None:\n raise ValueError('cannot add Hs to Mol')\n return self.calc.CalcDescriptors(x)\n\n @property\n def feature_labels(self):\n return self.nms\n\n\nclass Fingerprints(BaseDescriptor):\n \"\"\"\n Calculate fingerprints or descriptors of organic molecules.\n Note that MHFP currently does not support parallel computing, so n_jobs is fixed to 1.\n \"\"\"\n\n def __init__(self,\n n_jobs=-1,\n *,\n radius=3,\n n_bits=2048,\n bit_per_entry=None,\n counting=False,\n input_type='mol',\n featurizers='all',\n on_errors='raise',\n target_col=None,\n desc_list='all',\n add_Hs=False):\n \"\"\"\n\n Parameters\n ----------\n n_jobs: int\n The number of jobs to run in parallel for both fit and predict.\n Can be -1 or # of cpus. Set -1 to use all cpu cores (default).\n radius: int\n The radius parameter in the Morgan fingerprints,\n which is roughly half of the diameter parameter in ECFP/FCFP,\n i.e., radius=2 is roughly equivalent to ECFP4/FCFP4.\n n_bits: int\n Fixed bit length based on folding.\n bit_per_entry: int\n Number of bits used to represent a single entry (only for non-counting case)\n in RDKitFP, AtomPairFP, and TopologicalTorsionFP.\n Default value follows rdkit default.\n counting: boolean\n Record counts of the entries instead of bits only.\n featurizers: list[str] or str or 'all'\n Featurizer(s) that will be used.\n Default is 'all'.\n input_type: string\n Set the specific type of transform input.\n Set to ``mol`` (default) to ``rdkit.Chem.rdchem.Mol`` objects as input.\n When set to ``smlies``, ``transform`` method can use a SMILES list as input.\n Set to ``any`` to use both.\n If input is SMILES, ``Chem.MolFromSmiles`` function will be used inside.\n for ``None`` returns, a ``ValueError`` exception will be raised.\n on_errors: string\n How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\n When 'nan', return a column with ``np.nan``.\n The length of column corresponding to the number of feature labs.\n When 'keep', return a column with exception objects.\n The default is 'raise' which will raise up the exception.\n target_col\n Only relevant when input is pd.DataFrame, otherwise ignored.\n Specify a single column to be used for transformation.\n If ``None``, all columns of the pd.DataFrame is used.\n Default is None.\n desc_list: string or list\n List of descriptor names to be called in rdkit to calculate molecule descriptors.\n If ``classic``, the full list of rdkit v.2020.03.xx is used. (length = 200)\n Default is to use the latest list available in the rdkit. (length = 208 in rdkit v.2020.09.xx)\n add_Hs: boolean\n Add hydrogen atoms to the mol format in RDKit or not.\n This may affect a few physical descriptors (e.g., charge related ones) and currently no effect to fingerprints.\n \"\"\"\n\n super().__init__(featurizers=featurizers)\n\n self.mol = RDKitFP(n_jobs, n_bits=n_bits, bit_per_entry=bit_per_entry, counting=counting,\n input_type=input_type, on_errors=on_errors, target_col=target_col)\n self.mol = AtomPairFP(n_jobs, n_bits=n_bits, bit_per_entry=bit_per_entry, counting=counting,\n input_type=input_type, on_errors=on_errors, target_col=target_col)\n self.mol = TopologicalTorsionFP(n_jobs, n_bits=n_bits, input_type=input_type, bit_per_entry=bit_per_entry,\n counting=counting, on_errors=on_errors, target_col=target_col)\n self.mol = MACCS(n_jobs, input_type=input_type, on_errors=on_errors, target_col=target_col)\n self.mol = ECFP(n_jobs, radius=radius, n_bits=n_bits, input_type=input_type, counting=counting,\n on_errors=on_errors, target_col=target_col)\n self.mol = FCFP(n_jobs, radius=radius, n_bits=n_bits, input_type=input_type, counting=counting,\n on_errors=on_errors, target_col=target_col)\n self.mol = PatternFP(n_jobs, n_bits=n_bits, input_type=input_type, on_errors=on_errors, target_col=target_col)\n self.mol = LayeredFP(n_jobs, n_bits=n_bits, input_type=input_type, on_errors=on_errors, target_col=target_col)\n # self.mol = SECFP(n_jobs, radius=radius, n_bits=n_bits, input_type=input_type, on_errors=on_errors)\n self.mol = MHFP(1, radius=radius, n_bits=n_bits,\n input_type=input_type, on_errors=on_errors, target_col=target_col)\n self.mol = DescriptorFeature(n_jobs, input_type=input_type,\n on_errors=on_errors, target_col=target_col, desc_list=desc_list, add_Hs=add_Hs)\n" }, { "path": "xenonpy/descriptor/frozen_featurizer.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport warnings\n\nimport numpy as np\nimport pandas as pd\nimport torch\n\nfrom xenonpy.descriptor.base import BaseFeaturizer\nfrom xenonpy.model import SequentialLinear\n\n__all__ = ['FrozenFeaturizer']\n\n\nclass FrozenFeaturizer(BaseFeaturizer):\n \"\"\"\n A Featurizer to extract hidden layers a from NN model.\n \"\"\"\n\n def __init__(self, model: torch.nn.Module = None, *,\n cuda: bool = False, depth=None, n_layer=None, \n on_errors='raise', return_type='any', target_col=None):\n \"\"\"\n Parameters\n ----------\n model: torch.nn.Module\n Source model.\n cuda: bool\n If ``true``, run on GPU.\n depth: int\n The depth will be retrieved from NN model.\n n_layer: int\n Number of layer to be retrieved starting from the given depth.\n on_errors: string\n How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\n When 'nan', return a column with ``np.nan``.\n The length of column corresponding to the number of feature labs.\n When 'keep', return a column with exception objects.\n The default is 'raise' which will raise up the exception.\n return_type: str\n Specific the return type.\n Can be ``any``, ``array`` and ``df``.\n ``array`` and ``df`` force return type to ``np.ndarray`` and ``pd.DataFrame`` respectively.\n If ``any``, the return type dependent on the input type.\n Default is ``any``\n target_col\n Only relevant when input is pd.DataFrame, otherwise ignored.\n Specify a single column to be used for transformation.\n If ``None``, all columns of the pd.DataFrame is used.\n Default is None.\n \"\"\"\n super().__init__(n_jobs=0, on_errors=on_errors, return_type=return_type, target_col=target_col)\n self.depth = depth\n self.n_layer = n_layer\n self.model = model\n self.cuda = cuda\n self._ret = []\n self.__authors__ = ['TsumiNa']\n self.model.eval()\n self._depth = 0\n\n def featurize(self, descriptor, *, depth=None, n_layer=None):\n if not isinstance(self.model, torch.nn.Module):\n raise TypeError(' must be a instance of ')\n hlayers = []\n if isinstance(descriptor, pd.DataFrame):\n descriptor = descriptor.values\n x_ = torch.from_numpy(descriptor).float()\n if self.cuda:\n x_.cuda()\n self.model.cuda()\n else:\n x_.cpu()\n self.model.cpu()\n\n if isinstance(self.model, SequentialLinear):\n for n, m in self.model.named_children():\n if 'layer_' in n:\n hlayers.append(m.linear(x_).data)\n x_ = m(x_)\n else:\n for m in self.model[:-1]:\n hlayers.append(m.layer(x_).data)\n x_ = m(x_)\n \n # get predefined values when depth/n_layer is not given initially\n if depth is None:\n depth = self.depth\n if n_layer is None:\n n_layer = self.n_layer\n\n # check updated depth values\n if depth is None: # self.depth must be None\n self.depth = len(hlayers) # update self.depth\n self._depth = len(hlayers)\n elif depth > len(hlayers):\n warnings.warn(' is greater than the max depth of hidden layers')\n self._depth = len(hlayers)\n else:\n self._depth = depth\n\n if n_layer is None:\n l_end = 0\n else:\n l_end = n_layer-self._depth\n\n if l_end > -1:\n if l_end > 0:\n warnings.warn(' is over the max depth of hidden layers starting at the given ')\n ret = hlayers[-self._depth:]\n else:\n ret = hlayers[-self._depth:l_end]\n \n if self.cuda:\n ret = [l.cpu().numpy() for l in ret]\n else:\n ret = [l.numpy() for l in ret]\n self._ret = ret\n return np.concatenate(ret, axis=1)\n\n @property\n def feature_labels(self):\n if self._depth == 0:\n raise ValueError('Can not generate labels before transform.')\n return ['L(' + str(i - self._depth) + ')_' + str(j + 1)\n for i in range(len(self._ret))\n for j in range(self._ret[i].shape[1])]\n" }, { "path": "xenonpy/descriptor/structure.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport re\n\nimport numpy as np\nfrom pymatgen.core import Element\nfrom pymatgen.analysis.local_env import VoronoiNN\n\nfrom xenonpy.descriptor.base import BaseDescriptor, BaseFeaturizer\n\n__all__ = ['RadialDistributionFunction', 'OrbitalFieldMatrix', 'Structures']\n\n\nclass RadialDistributionFunction(BaseFeaturizer):\n \"\"\"\n Calculate pair distribution descriptor for machine learning.\n\n \"\"\"\n\n @property\n def feature_labels(self):\n return [str(d) for d in self._interval[1:]]\n\n def __init__(self, n_bins=201, r_max=20.0, *, n_jobs=-1, on_errors='raise', return_type='any', target_col=None):\n \"\"\"\n \n Parameters\n ----------\n n_bins: int\n Number of radial grid points.\n r_max: float\n Maximum of radial grid (the minimum is always set zero).\n n_jobs: int\n The number of jobs to run in parallel for both fit and predict. Set -1 to use all cpu cores (default).\n on_errors: string\n How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\n When 'nan', return a column with ``np.nan``.\n The length of column corresponding to the number of feature labs.\n When 'keep', return a column with exception objects.\n The default is 'raise' which will raise up the exception.\n return_type: str\n Specific the return type.\n Can be ``any``, ``array`` and ``df``.\n ``array`` and ``df`` force return type to ``np.ndarray`` and ``pd.DataFrame`` respectively.\n If ``any``, the return type dependent on the input type.\n Default is ``any``\n target_col\n Only relevant when input is pd.DataFrame, otherwise ignored.\n Specify a single column to be used for transformation.\n If ``None``, all columns of the pd.DataFrame is used.\n Default is None.\n \"\"\"\n\n super().__init__(n_jobs=n_jobs, on_errors=on_errors, return_type=return_type, target_col=target_col)\n assert n_bins >= 1, \"n_bins should be greater than 1!\"\n assert r_max > 0, \"r_max should be greater than 0!\"\n\n self.n_bins = n_bins\n self.r_max = r_max\n self.dr = r_max / (n_bins - 1)\n self._interval = np.arange(0.0, r_max + self.dr, self.dr)\n self.__authors__ = ['TsumiNa']\n\n def featurize(self, structure):\n \"\"\"\n Get RDF of the input structure.\n Args:\n structure: Pymatgen Structure object.\n Returns:\n rdf, dist: (tuple of arrays) the first element is the\n normalized RDF, whereas the second element is\n the inner radius of the RDF bin.\n \"\"\"\n if not structure.is_ordered:\n raise ValueError(\"Disordered structure support not built yet\")\n\n # Get the distances between all atoms\n neighbors_lst = structure.get_all_neighbors(self.r_max)\n all_distances = np.concatenate(tuple(map(lambda x: [e[1] for e in x], neighbors_lst)))\n\n # Compute a histogram\n dist_hist, dist_bins = np.histogram(all_distances, bins=self._interval, density=False)\n\n # Normalize counts\n shell_vol = 4.0 / 3.0 * np.pi * (np.power(dist_bins[1:], 3) - np.power(dist_bins[:-1], 3))\n number_density = structure.num_sites / structure.volume\n return dist_hist / shell_vol / number_density\n\n\nclass OrbitalFieldMatrix(BaseFeaturizer):\n \"\"\"\n Representation based on the valence shell electrons of neighboring atoms.\n\n Each atom is described by a 32-element vector uniquely representing the\n valence subshell. A 32x32 (39x39) matrix is formed by multiplying two\n atomic vectors. An OFM for an atomic environment is the sum of these\n matrices for each atom the center atom coordinates with multiplied by a\n distance function (In this case, 1/r times the weight of the coordinating\n atom in the Voronoi.\n\n \"\"\"\n\n def __init__(self, including_d=True, *, n_jobs=-1, on_errors='raise', return_type='any', target_col=None):\n \"\"\"\n\n Parameters\n ----------\n including_d: bool\n If true, add distance information.\n n_jobs: int\n The number of jobs to run in parallel for both fit and predict. Set -1 to use all cpu cores (default).\n on_errors: string\n How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\n When 'nan', return a column with ``np.nan``.\n The length of column corresponding to the number of feature labs.\n When 'keep', return a column with exception objects.\n The default is 'raise' which will raise up the exception.\n return_type: str\n Specific the return type.\n Can be ``any``, ``array`` and ``df``.\n ``array`` and ``df`` force return type to ``np.ndarray`` and ``pd.DataFrame`` respectively.\n If ``any``, the return type dependent on the input type.\n Default is ``any``\n target_col\n Only relevant when input is pd.DataFrame, otherwise ignored.\n Specify a single column to be used for transformation.\n If ``None``, all columns of the pd.DataFrame is used.\n Default is None.\n \"\"\"\n super().__init__(n_jobs=n_jobs, on_errors=on_errors, return_type=return_type, target_col=target_col)\n self._including_d = including_d\n self.__authors__ = ['TsumiNa']\n self.__citations__ = [\n '''\n @article{LamPham2017,\n archivePrefix = {arXiv},\n arxivId = {1705.01043},\n author = {{Lam Pham}, Tien and Kino, Hiori and Terakura, Kiyoyuki and Miyake, Takashi and Tsuda, Koji and Takigawa, Ichigaku and {Chi Dam}, Hieu},\n doi = {10.1080/14686996.2017.1378060},\n eprint = {1705.01043},\n issn = {18785514},\n journal = {Science and Technology of Advanced Materials},\n keywords = {Material descriptor,data mining,machine learning,magnetic materials,material informatics},\n number = {1},\n pages = {756--765},\n pmid = {29152012},\n publisher = {Taylor {\\&} Francis},\n title = {{Machine learning reveals orbital interaction in materials}},\n url = {https://doi.org/10.1080/14686996.2017.1378060},\n volume = {18},\n year = {2017}\n }\n '''\n ]\n\n @staticmethod\n def get_element_representation(name):\n \"\"\"\n generate one-hot representation for a element, e.g, si = [0.0, 1.0, 0.0, 0.0, ...]\n\n Parameters\n ----------\n name: string\n element symbol\n \"\"\"\n element = Element(name)\n general_element_electronic = {\n 's1': 0.0,\n 's2': 0.0,\n 'p1': 0.0,\n 'p2': 0.0,\n 'p3': 0.0,\n 'p4': 0.0,\n 'p5': 0.0,\n 'p6': 0.0,\n 'd1': 0.0,\n 'd2': 0.0,\n 'd3': 0.0,\n 'd4': 0.0,\n 'd5': 0.0,\n 'd6': 0.0,\n 'd7': 0.0,\n 'd8': 0.0,\n 'd9': 0.0,\n 'd10': 0.0,\n 'f1': 0.0,\n 'f2': 0.0,\n 'f3': 0.0,\n 'f4': 0.0,\n 'f5': 0.0,\n 'f6': 0.0,\n 'f7': 0.0,\n 'f8': 0.0,\n 'f9': 0.0,\n 'f10': 0.0,\n 'f11': 0.0,\n 'f12': 0.0,\n 'f13': 0.0,\n 'f14': 0.0\n }\n\n general_electron_subshells = [\n 's1', 's2', 'p1', 'p2', 'p3', 'p4', 'p5', 'p6', 'd1', 'd2', 'd3', 'd4', 'd5', 'd6', 'd7', 'd8', 'd9', 'd10',\n 'f1', 'f2', 'f3', 'f4', 'f5', 'f6', 'f7', 'f8', 'f9', 'f10', 'f11', 'f12', 'f13', 'f14'\n ]\n\n if name == 'H':\n element_electronic_structure = ['s1']\n elif name == 'He':\n element_electronic_structure = ['s2']\n else:\n element_electronic_structure = [\n ''.join(pair) for pair in re.findall(r\"\\.\\d(\\w+)(\\d+)\", element.electronic_structure)\n ]\n for eletron_subshell in element_electronic_structure:\n general_element_electronic[eletron_subshell] = 1.0\n\n return np.array([general_element_electronic[key] for key in general_electron_subshells])\n\n def featurize(self, structure, is_including_d=True):\n \"\"\"\n Generate OFM descriptor\n\n Parameters\n ----------\n structure: pymatgen.Structure\n The input structure for OFM calculation.\n \"\"\"\n\n atoms = np.array([site.species_string for site in structure])\n coordinator_finder = VoronoiNN(cutoff=10.0)\n\n local_orbital_field_matrices = []\n for i_atom, atom in enumerate(atoms):\n neighbors = coordinator_finder.get_nn_info(structure=structure, n=i_atom)\n\n site = structure[i_atom]\n center_vector = self.get_element_representation(atom)\n env_vector = np.zeros(32)\n\n for nn in neighbors:\n site_x = nn['site']\n w = nn['weight']\n site_x_label = site_x.species_string\n neigh_vector = self.get_element_representation(site_x_label)\n d = np.sqrt(np.sum((site.coords - site_x.coords)**2))\n if self._including_d:\n env_vector += neigh_vector * w / d\n else:\n env_vector += neigh_vector * w\n\n local_matrix = center_vector[None, :] * env_vector[:, None]\n local_matrix = np.ravel(local_matrix)\n local_orbital_field_matrices.append(local_matrix)\n\n return np.array(local_orbital_field_matrices).mean(axis=0)\n\n @property\n def feature_labels(self):\n labels = np.array([\n 's1', 's2', 'p1', 'p2', 'p3', 'p4', 'p5', 'p6', 'd1', 'd2', 'd3', 'd4', 'd5', 'd6', 'd7', 'd8', 'd9', 'd10',\n 'f1', 'f2', 'f3', 'f4', 'f5', 'f6', 'f7', 'f8', 'f9', 'f10', 'f11', 'f12', 'f13', 'f14'\n ])\n\n return [i + '_' + j for i in labels for j in labels]\n\n\nclass Structures(BaseDescriptor):\n \"\"\"\n Calculate structure descriptors from compound's structure.\n \"\"\"\n\n def __init__(self,\n n_bins=201,\n r_max=20.0,\n including_d=True,\n *,\n n_jobs=-1,\n featurizers='all',\n on_errors='raise',\n target_col=None):\n \"\"\"\n\n Parameters\n ----------\n n_bins: int\n Number of radial grid points.\n r_max: float\n Maximum of radial grid (the minimum is always set zero).\n including_d: bool\n If true, add distance information.\n n_jobs: int\n The number of jobs to run in parallel for both fit and predict. Set -1 to use all cpu cores (default).\n featurizers: list[str] or 'all'\n Featurizers that will be used.\n Default is 'all'.\n on_errors: string\n How to handle exceptions in feature calculations. Can be 'nan', 'keep', 'raise'.\n When 'nan', return a column with ``np.nan``.\n The length of column corresponding to the number of feature labs.\n When 'keep', return a column with exception objects.\n The default is 'raise' which will raise up the exception.\n target_col\n Only relevant when input is pd.DataFrame, otherwise ignored.\n Specify a single column to be used for transformation.\n If ``None``, all columns of the pd.DataFrame is used.\n Default is None.\n \"\"\"\n super().__init__(featurizers=featurizers)\n self.n_jobs = n_jobs\n\n self.structure = RadialDistributionFunction(n_bins,\n r_max,\n n_jobs=n_jobs,\n on_errors=on_errors,\n target_col=target_col)\n self.structure = OrbitalFieldMatrix(including_d, n_jobs=n_jobs, on_errors=on_errors, target_col=target_col)\n" }, { "path": "xenonpy/inverse/__init__.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\n__all__ = ['iqspr']\n\nfrom . import iqspr\n" }, { "path": "xenonpy/inverse/base.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\n\nfrom collections import defaultdict\nfrom collections.abc import Iterable\n\nimport warnings\nimport numpy as np\nimport pandas as pd\nfrom sklearn.base import BaseEstimator\n\nfrom xenonpy.utils import TimedMetaClass\n\n\nclass LogLikelihoodError(Exception):\n \"\"\"Base exception for LogLikelihood classes\"\"\"\n pass\n\n\nclass ResampleError(Exception):\n \"\"\"Base exception for Resample classes\"\"\"\n pass\n\n\nclass ProposalError(Exception):\n \"\"\"Base exception for Proposal classes\"\"\"\n old_smi = None\n\n\nclass SMCError(Exception):\n \"\"\"Base exception for SMC classes\"\"\"\n pass\n\n\nclass BaseLogLikelihood(BaseEstimator, metaclass=TimedMetaClass):\n \"\"\"\n Abstract class to calculate likelihood of candidates from :class:`pandas.DataFrame` descriptor data\n of the candidates generated by BaseFeaturizer or BaseDescriptor.\n\n **Using a BaseLogLikelihood Class**\n\n :meth:`BaseLogLikelihood` requires user to define only the log_likelihood function.\n Use of log values is recommended in order to avoid typical underflow\n that may appear for small probability values.\n\n \"\"\"\n\n\n def fit(self, X, y, **kwargs):\n return self\n\n def __call__(self, X, **targets):\n return self.log_likelihood(X, **targets)\n\n def log_likelihood(self, X, **targets):\n \"\"\"\n Log likelihood\n\n Parameters\n ----------\n X: list[object]\n Input samples for likelihood calculation.\n targets: tuple[float, float]\n Target area.\n Should be a tuple which have down and up boundary.\n e.g: ``target1=(10, 20)`` equal to ``target1 should in range [10, 20]``.\n\n Returns\n -------\n log_likelihood: pd.Dataframe of float (col - properties, row - samples)\n Estimated log-likelihood of each sample's property values.\n Cannot be pd.Series!\n \"\"\"\n # raise NotImplementedError(' have no implementation')\n raise NotImplementedError(' method must be implemented')\n\n\nclass BaseLogLikelihoodSet(BaseEstimator, metaclass=TimedMetaClass):\n \"\"\"\n Abstract class to organize log-likelihoods.\n \n Examples\n --------\n .. code::\n\n class MyLogLikelihood(BaseLogLikelihoodSet):\n def __init__(self):\n super().__init__()\n \n self.loglike1 = SomeFeature1()\n self.loglike1 = SomeFeature2()\n self.loglike2 = SomeFeature3()\n self.loglike2 = SomeFeature4()\n\n \"\"\"\n\n def __init__(self, *, loglikelihoods='all'):\n \"\"\"\n \n Parameters\n ----------\n loglikelihoods: list[str] or 'all'\n log-likelihoods that will be used.\n Default is 'all'.\n \"\"\"\n self.loglikelihoods = loglikelihoods\n self.__loglikelihoods__ = set()\n self.__loglikelihood_sets__ = defaultdict(list)\n\n @property\n def elapsed(self):\n return self._timer.elapsed\n\n def __setattr__(self, key, value):\n\n if key == '__loglikelihood_sets__':\n if not isinstance(value, defaultdict):\n raise RuntimeError('Can not set \"self.__loglikelihood_sets__\" by yourself')\n super().__setattr__(key, value)\n if isinstance(value, BaseLogLikelihood):\n # if value.__class__.__name__ in self.__loglikelihoods__:\n # raise RuntimeError('Duplicated log-likelihood <%s>' % value.__class__.__name__)\n self.__loglikelihood_sets__[key].append(value)\n self.__loglikelihoods__.add(value.__class__.__name__)\n else:\n super().__setattr__(key, value)\n\n @property\n def all_loglikelihoods(self):\n return list(self.__loglikelihoods__)\n\n def _check_input(self, X, y=None, **kwargs):\n def _reformat(x):\n if x is None:\n return x\n\n keys = list(self.__loglikelihood_sets__.keys())\n if len(keys) == 1:\n if isinstance(x, list):\n return pd.DataFrame(pd.Series(x), columns=keys)\n\n if isinstance(x, np.ndarray):\n if len(x.shape) == 1:\n return pd.DataFrame(x, columns=keys)\n\n if isinstance(x, pd.Series):\n return pd.DataFrame(x.values, columns=keys, index=x.index)\n\n if isinstance(x, pd.Series):\n x = pd.DataFrame(x)\n\n if isinstance(x, pd.DataFrame):\n tmp = set(x.columns) | set(kwargs.keys())\n if set(keys).isdisjoint(tmp):\n # raise KeyError('name of columns do not match any log-likelihood set')\n warnings.warn('name of columns do not match any log-likelihood set, '\n 'the whole dataframe is applied to all log-likelihood sets', UserWarning)\n # allow type check later for this special case\n return [x]\n return x\n\n raise TypeError(\n 'you can not ues a array-like input'\n 'because there are multiply log-likelihood sets or the dim of input is not 1')\n\n return _reformat(X), _reformat(y)\n\n def __call__(self, X, **kwargs):\n return self.log_likelihood(X, **kwargs)\n\n def log_likelihood(self, X, **kwargs):\n \"\"\"\n Log likelihood\n \n Parameters\n ----------\n X: list-like[object] or pd.DataFrame\n Input samples for likelihood calculation.\n For pd.DataFrame, if any column name matches any group name,\n the matched group(s) will be calculated with corresponding column(s);\n otherwise, the pd.DataFrame will be passed on as is.\n kwargs: list[string]\n specified BaseLogLikelihood.\n \n Returns\n -------\n log_likelihood: pd.Dataframe of float (col - properties, row - samples)\n Estimated log-likelihood of each sample's property values.\n \"\"\"\n # raise NotImplementedError(' have no implementation')\n # raise NotImplementedError(' method must be implemented')\n\n if not isinstance(X, Iterable):\n raise TypeError('parameter \"entries\" must be a iterable object')\n\n if len(X) is 0:\n return None\n\n if 'loglikelihoods' in kwargs:\n loglikelihoods = kwargs['loglikelihoods']\n if loglikelihoods != 'all' and not isinstance(loglikelihoods, list):\n raise TypeError('parameter \"loglikelihoods\" must be a list')\n else:\n loglikelihoods = self.loglikelihoods\n\n # if 'return_type' in kwargs:\n # del kwargs['return_type']\n\n results = []\n\n X, _ = self._check_input(X, **kwargs)\n if isinstance(X, list):\n for k, lls in self.__loglikelihood_sets__.items():\n # if k in kwargs:\n # k = kwargs[k] # what is this for? even if k not in kwargs... nothing happen right?\n for f in lls:\n if loglikelihoods != 'all' and f.__class__.__name__ not in loglikelihoods:\n continue\n ret = f.log_likelihood(X[0])\n results.append(ret)\n else:\n for k, lls in self.__loglikelihood_sets__.items():\n if k in kwargs:\n k = kwargs[k] # what is this for? even if k not in kwargs... nothing happen right?\n if k in X:\n for f in lls:\n if loglikelihoods != 'all' and f.__class__.__name__ not in loglikelihoods:\n continue\n ret = f.log_likelihood(X[k])\n results.append(ret)\n\n return pd.concat(results, axis=1)\n\n\nclass BaseResample(BaseEstimator, metaclass=TimedMetaClass):\n \"\"\"\n Abstract class to resample candidates.\n\n **Using a BaseResample Class**\n\n :meth:`BaseResample` requires user to define only the resample function.\n Use of this function appears in BaseSMC, but can often be skipped,\n as BaseSMC may have its direct implementation inside the class, too.\n\n \"\"\"\n\n\n def fit(self, X, y=None, **kwargs):\n return self\n\n def __call__(self, X, freq, size, p):\n return self.resample(X, freq, size, p)\n\n def resample(self, X, freq, size, p):\n \"\"\"\n Re-sample from given samples.\n\n Parameters\n ----------\n X: list of object\n Input samples for likelihood calculation.\n freq: list or np.array of int\n Frequency of each input sample\n size: int\n Resample size.\n p: np.ndarray of float\n The probabilities associated with each entry in X.\n If not given the sample assumes a uniform distribution over all entries.\n\n Returns\n -------\n new_sample: list of object\n Re-sampling result.\n \"\"\"\n raise NotImplementedError(' method must be implemented')\n\n\nclass BaseProposal(BaseEstimator, metaclass=TimedMetaClass):\n def fit(self, X, y, **kwargs):\n return self\n\n def __call__(self, X):\n return self.proposal(X)\n\n def on_errors(self, error):\n raise error\n\n def proposal(self, X):\n \"\"\"\n Proposal new samples based on the input samples.\n\n Parameters\n ----------\n X: list of object\n Samples for generate next samples\n\n Returns\n -------\n samples: list of object\n Generated samples from input samples.\n \"\"\"\n raise NotImplementedError(' method must be implemented')\n\n\nclass BaseSMC(BaseEstimator, metaclass=TimedMetaClass):\n \"\"\"\n Abstract class to iteratively generate and pick up high likelihood candidates based on\n BaseProposal, BaseLogLikelihood, and/or BaseResample classes.\n\n **Using a BaseSMC Class**\n\n :meth:`BaseSMC` provides a basic looping structure for user to implement algorithms\n that are in the form of sequential Monte Carlo or genetic algorithm.\n To avoid repeated calculation of log-likelihood of the same candidates,\n a unique function is required and implemented in the loop to pick up unique candidates,\n which may need to be able to adjust for different input type of the candidates.\n The default unique function of BaseSMC assumes candidates to be list or np.array.\n\n The set of candidates is assumed to be list-like, but other data types are allowed\n if they are compatible with other components (modifier, estimator, resample and unique functions).\n\n \"\"\"\n\n _log_likelihood = None\n _proposal = None\n _resample = None\n # _targets = None\n\n def log_likelihood(self, X):\n \"\"\"\n Log likelihood\n\n Parameters\n ----------\n X: list of object\n Input samples for likelihood calculation.\n Can be changed to accept other data types.\n\n Returns\n -------\n log_likelihood: np.ndarray of float\n Estimated likelihood of each samples.\n \"\"\"\n if self._log_likelihood is None:\n raise NotImplementedError('user need to implement method or'\n 'set to a instance of ')\n # return self._log_likelihood(X, **self._targets)\n return self._log_likelihood(X)\n\n def resample(self, X, freq, size, p):\n \"\"\"\n Re-sample from given samples.\n\n Parameters\n ----------\n X: list[object]\n Input samples for likelihood calculation.\n Can be changed to accept other data types.\n freq: list[int]\n Frequency of each input sample.\n size: int\n Resample size.\n p: numpy.ndarray[float]\n The probabilities associated with each entry in X.\n If not given the sample assumes a uniform distribution over all entries.\n\n Returns\n -------\n re-sample: list of object\n Re-sampling result.\n \"\"\"\n if self._resample is None:\n raise NotImplementedError('user need to implement method or'\n 'set to a instance of ')\n return self._resample(X, freq, size, p)\n\n def proposal(self, X):\n \"\"\"\n Proposal new samples based on the input samples.\n\n Parameters\n ----------\n X: list of object\n Samples for generate next samples.\n Can be changed to accept other data types.\n\n Returns\n -------\n samples: list of object\n Generated samples from input samples.\n \"\"\"\n if self._proposal is None:\n raise NotImplementedError('user need to implement method or'\n 'set to a instance of ')\n return self._proposal(X)\n\n def on_errors(self, ite, samples, error):\n raise error\n\n def unique(self, X):\n \"\"\"\n\n Parameters\n ----------\n X: list of object\n Input samples.\n Can be changed to accept other data types.\n\n Returns\n -------\n unique: list of object\n The sorted unique values.\n unique_counts: np.ndarray of int\n The number of times each of the unique values comes up in the original array\n \"\"\"\n return np.unique(X, return_counts=True)\n\n def __setattr__(self, key, value):\n if key is '_log_likelihood' and not isinstance(value, (BaseLogLikelihood, BaseLogLikelihoodSet)):\n raise TypeError(\n ' must be a subClass of or ')\n if key is '_proposal' and not isinstance(value, BaseProposal):\n raise TypeError(' must be a subClass of ')\n if key is '_resample' and not isinstance(value, BaseResample):\n raise TypeError(' must be a subClass of ')\n object.__setattr__(self, key, value)\n\n def __call__(self, samples, beta, *, size=None, yield_lpf=False):\n \"\"\"\n Run SMC\n\n Parameters\n ----------\n samples: list of object\n Initial samples.\n This variable can take other data type as long as it matches with other components,\n such as estimator, modifier, resample and unique functions.\n beta: list/1D-numpy of float or pd.Dataframe\n Annealing parameters for each step.\n If pd.Dataframe, column names should follow keys of mdl in BaseLogLikeihood or BaseLogLikelihoodSet\n size: int\n Sample size for each draw.\n Default is None, which means sample size will be the same as the length of samples\n yield_lpf : bool\n Yield estimated log likelihood, probability and frequency of each samples. Default is ``False``.\n\n Yields\n -------\n samples: list of object\n New samples in each SMC iteration.\n This variable can also be other data type, consistent with the input samples.\n llh: np.ndarray float\n Estimated values of log-likelihood of each samples.\n Only yield when ``yield_lpf=Ture``.\n p: np.ndarray of float\n Estimated probabilities of each samples.\n Only yield when ``yield_lpf=Ture``.\n freq: np.ndarray of float\n The number of unique samples in original samples.\n Only yield when ``yield_lpf=Ture``.\n \"\"\"\n\n # sample size will be set to the length of init_samples if None\n if size is None:\n size = len(samples)\n\n # if targets:\n # self.update_targets(**targets)\n # else:\n # if not self._targets:\n # raise ValueError('must set targets area')\n\n try:\n unique, frequency = self.unique(samples)\n ll = self.log_likelihood(unique)\n\n if isinstance(beta, pd.DataFrame):\n beta = beta[ll.columns].values\n else:\n # assume only one row for beta (1-D list or numpy vector)\n beta = np.transpose(np.repeat([beta], ll.shape[1], axis=0))\n\n w = np.dot(ll.values, beta[0]) + np.log(frequency)\n w_sum = np.log(np.sum(np.exp(w - np.max(w)))) + np.max(w) # avoid underflow\n p = np.exp(w - w_sum)\n if yield_lpf:\n yield unique, ll, p, frequency\n else:\n yield unique\n\n # beta = np.delete(beta,0,0) #remove first \"row\"\n\n except SMCError as e:\n self.on_errors(0, samples, e)\n except Exception as e:\n raise e\n\n # translate between input representation and execute environment representation.\n # make sure beta is not changed (np.delete will return the deleted version, without changing original vector)\n for i, step in enumerate(np.delete(beta, 0, 0)):\n try:\n re_samples = self.resample(unique, frequency, size, p)\n samples = self.proposal(re_samples)\n\n unique, frequency = self.unique(samples)\n\n # annealed likelihood in log - adjust with copy counts\n ll = self.log_likelihood(unique)\n w = np.dot(ll.values, step) + np.log(frequency)\n w_sum = np.log(np.sum(np.exp(w - np.max(w)))) + np.max(w) # avoid underflow\n p = np.exp(w - w_sum)\n if yield_lpf:\n yield unique, ll, p, frequency\n else:\n yield unique\n\n except SMCError as e:\n self.on_errors(i + 1, samples, e)\n except Exception as e:\n raise e\n" }, { "path": "xenonpy/inverse/iqspr/__init__.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\n__all__ = ['IQSPR', 'IQSPR4DF', 'GaussianLogLikelihood', 'NGram', 'GetProbError', 'MolConvertError', 'NGramTrainingError']\n\nfrom .estimator import GaussianLogLikelihood\nfrom .iqspr import IQSPR\nfrom .iqspr4df import IQSPR4DF\nfrom .modifier import NGram, GetProbError, MolConvertError, NGramTrainingError\n" }, { "path": "xenonpy/inverse/iqspr/estimator.py", "content": "# Copyright (c) 2022. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom copy import deepcopy\nfrom types import MethodType\nfrom typing import Union\n\nimport numpy as np\nimport pandas as pd\nfrom scipy.stats import norm\nfrom sklearn.base import BaseEstimator\nfrom sklearn.linear_model import BayesianRidge\n\nfrom xenonpy.descriptor.base import BaseDescriptor, BaseFeaturizer\nfrom xenonpy.inverse.base import BaseLogLikelihood\n\n\nclass GaussianLogLikelihood(BaseLogLikelihood):\n def __init__(self, descriptor: Union[BaseFeaturizer, BaseDescriptor], *, targets={}, **estimators: BaseEstimator):\n \"\"\"\n Gaussian loglikelihood.\n\n Parameters\n ----------\n descriptor: BaseFeaturizer or BaseDescriptor\n Descriptor calculator.\n estimators: BaseEstimator\n Gaussian estimators follow the scikit-learn style.\n These estimators must provide a method named ``predict`` which\n accesses descriptors as input and returns ``(mean, std)`` in order.\n By default, BayesianRidge_ will be used.\n\n .. _BayesianRidge: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.BayesianRidge.html#sklearn-linear-model-bayesianridge\n targets: dictionary\n Upper and lower bounds for each property to calculate the Gaussian CDF probability\n \"\"\"\n if estimators:\n self._mdl = deepcopy(estimators)\n else:\n self._mdl = {}\n\n if not isinstance(descriptor, (BaseFeaturizer, BaseDescriptor)):\n raise TypeError(' must be a subclass of or ')\n self.descriptor = descriptor\n self.descriptor.on_errors = 'nan'\n\n self.targets = deepcopy(targets)\n\n def __getitem__(self, item):\n return self._mdl[item]\n\n def __setitem__(self, key, value):\n if not (hasattr(value, 'predict') and isinstance(value.predict, MethodType)):\n raise TypeError('estimator must be a regressor in scikit-learn style')\n self._mdl[key] = deepcopy(value)\n\n def update_targets(self, *, reset=False, **targets):\n \"\"\"\n Update/set the target area.\n\n Parameters\n ----------\n reset: bool\n If ``true``, reset target area.\n targets: tuple[float, float]\n Target area.\n Should be a tuple which have down and up boundary.\n e.g: ``target1=(10, 20)`` equal to ``target1 should in range [10, 20]``.\n \"\"\"\n if reset:\n self.targets = {}\n for k, v in targets.items():\n if not isinstance(v, tuple) or len(v) != 2 or v[1] <= v[0]:\n raise ValueError('must be a tuple with (low, up) boundary')\n self.targets[k] = v\n\n def remove_estimator(self, *properties: str):\n \"\"\"\n Remove estimators from estimator set.\n\n Parameters\n ----------\n properties : str\n The name of properties will be removed from estimator set.\n \"\"\"\n if not properties:\n self._mdl = {}\n self.targets = {}\n else:\n for p in properties:\n del self._mdl[p]\n del self.targets[p]\n\n def predict(self, smiles, **kwargs):\n fps = self.descriptor.transform(smiles, return_type='df')\n fps_ = fps.dropna()\n tmp = {}\n for k, v in self._mdl.items():\n if isinstance(v, BayesianRidge):\n tmp[k + ': mean'], tmp[k + ': std'] = v.predict(fps_, return_std=True)\n else:\n tmp[k + ': mean'], tmp[k + ': std'] = v.predict(fps_, **kwargs)\n\n tmp = pd.DataFrame(data=tmp, index=fps_.index)\n return pd.DataFrame(data=tmp, index=fps.index)\n\n # todo: implement scale function\n def fit(self, smiles, y=None, *, X_scaler=None, y_scaler=None, **kwargs):\n \"\"\"\n Default - automatically remove NaN data rows\n \n Parameters\n ----------\n smiles: list[str]\n SMILES for training.\n y: pandas.DataFrame\n Target properties for training.\n X_scaler: Scaler (optional, not implement)\n Scaler for transform X.\n y_scaler: Scaler (optional, not implement)\n Scaler for transform y.\n kwargs: dict\n Parameters pass to BayesianRidge initialization.\n \"\"\"\n\n if self._mdl:\n raise RuntimeError('estimators have been set.'\n 'If you want to re-train these estimators,'\n 'please use `remove_estimator()` method first.')\n\n if not isinstance(y, (pd.DataFrame, pd.Series)):\n raise TypeError('please package all properties into a pd.DataFrame or pd.Series')\n\n # remove NaN from X\n desc = self.descriptor.transform(smiles, return_type='df').reset_index(drop=True)\n y = y.reset_index(drop=True)\n desc.dropna(inplace=True)\n y = pd.DataFrame(y.loc[desc.index])\n\n for c in y:\n y_ = y[c] # get target property.\n # remove NaN from y_\n y_.dropna(inplace=True)\n desc_ = desc.loc[y_.index]\n desc_ = desc_.values\n\n mdl = BayesianRidge(compute_score=True, **kwargs)\n mdl.fit(desc_, y_)\n self._mdl[c] = mdl\n\n # log_likelihood returns a dataframe of log-likelihood values of each property & sample\n def log_likelihood(self, smis, *, log_0=-1000.0, **targets):\n def _avoid_overflow(ll_):\n # log(exp(log(UP) - log(C)) - exp(log(LOW) - log(C))) + log(C)\n # where C = max(log(UP), max(LOW))\n ll_c = np.max(ll_)\n ll_ = np.log(np.exp(ll_[1] - ll_c) - np.exp(ll_[0] - ll_c)) + ll_c\n return ll_\n\n # self.update_targets(reset=False, **targets):\n for k, v in targets.items():\n if not isinstance(v, tuple) or len(v) != 2 or v[1] <= v[0]:\n raise ValueError('must be a tuple with (low, up) boundary')\n self.targets[k] = v\n\n if not self.targets:\n raise RuntimeError(' is empty')\n\n if isinstance(smis, (pd.Series, pd.DataFrame)):\n ll = pd.DataFrame(np.full((len(smis), len(self._mdl)), log_0), index=smis.index, columns=self._mdl.keys())\n else:\n ll = pd.DataFrame(np.full((len(smis), len(self._mdl)), log_0), columns=self._mdl.keys())\n\n # 1. apply prediction on given sims\n # 2. reset returns' index to [0, 1, ..., len(smis) - 1], this should be consistent with ll's index\n # 3. drop all rows which have NaN value(s)\n pred = self.predict(smis).reset_index(drop=True).dropna(axis='index', how='any')\n\n # because pred only contains available data\n # 'pred.index.values' should eq to the previous implementation\n idx = pred.index.values\n\n # calculate likelihood\n for k, (low, up) in self.targets.items(): # k: target; v: (low, up)\n\n # predict mean, std for all smiles\n mean, std = pred[k + ': mean'], pred[k + ': std']\n\n # calculate low likelihood\n low_ll = norm.logcdf(low, loc=np.asarray(mean), scale=np.asarray(std))\n\n # calculate up likelihood\n up_ll = norm.logcdf(up, loc=np.asarray(mean), scale=np.asarray(std))\n\n # zip low and up likelihood to a 1-dim array then save it.\n # like: [(tar_low_smi1, tar_up_smi1), (tar_low_smi2, tar_up_smi2), ..., (tar_low_smiN, tar_up_smiN)]\n lls = zip(low_ll, up_ll)\n ll[k].iloc[idx] = np.array([*map(_avoid_overflow, list(lls))])\n\n return ll\n" }, { "path": "xenonpy/inverse/iqspr/iqspr.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\n# import necessary libraries\n\nimport numpy as np\n\nfrom xenonpy.inverse.base import BaseSMC, BaseProposal, BaseLogLikelihood\n\n\nclass IQSPR(BaseSMC):\n\n def __init__(self, *, estimator, modifier, r_ESS=1):\n \"\"\"\n SMC iqspr runner (assume data type of samples = list or np.array).\n\n Parameters\n ----------\n estimator : BaseLogLikelihood or BaseLogLikelihoodSet\n Log likelihood estimator for given input samples.\n modifier : BaseProposal\n Modify given input samples to new ones.\n r_ESS : float\n r_ESS*sample_size = Upper threshold of ESS (effective sample size) using in SMC resampling.\n Resample will happen only if calculated ESS is smaller or equal to the upper threshold.\n As 1 <= ESS <= sample_size, picking any r_ESS < 1/sample_size will lead to never resample;\n picking any r_ESS >= 1 will lead to always resample.\n Default is 1, i.e., resample at each step of SMC.\n \"\"\"\n self._proposal = modifier\n self._log_likelihood = estimator\n self._r_ESS = r_ESS\n\n def resample(self, sims, freq, size, p):\n if np.sum(np.power(p, 2)) <= (self._r_ESS*np.sum(freq)):\n return np.random.choice(sims, size=size, p=p)\n else:\n return [item for item, count in zip(sims, freq) for i in range(count)]\n\n @property\n def modifier(self):\n return self._proposal\n\n @modifier.setter\n def modifier(self, value):\n self._proposal = value\n\n @property\n def estimator(self):\n return self._log_likelihood\n\n @estimator.setter\n def estimator(self, value):\n self._log_likelihood = value\n" }, { "path": "xenonpy/inverse/iqspr/iqspr4df.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\n# import necessary libraries\n\nimport numpy as np\nimport pandas as pd\nfrom xenonpy.inverse.base import BaseSMC, BaseProposal, BaseLogLikelihood\n\n\nclass IQSPR4DF(BaseSMC):\n\n def __init__(self, *, estimator, modifier, r_ESS=1, sample_col=None):\n \"\"\"\n SMC iqspr runner (assume data type of samples = pd.DataFrame).\n\n Parameters\n ----------\n estimator : BaseLogLikelihood or BaseLogLikelihoodSet\n Log likelihood estimator for given input samples.\n modifier : BaseProposal\n Modify given input samples to new ones.\n r_ESS : float\n r_ESS*sample_size = Upper threshold of ESS (effective sample size) using in SMC resampling.\n Resample will happen only if calculated ESS is smaller or equal to the upper threshold.\n As 1 <= ESS <= sample_size, picking any r_ESS < 1/sample_size will lead to never resample;\n picking any r_ESS >= 1 will lead to always resample.\n Default is 1, i.e., resample at each step of SMC.\n sample_col : list or str\n Name(s) of columns that will be used to extract unique samples in the unique function.\n Default is None, which means all columns are used.\n \"\"\"\n self._proposal = modifier\n self._log_likelihood = estimator\n self._r_ESS = r_ESS\n if isinstance(sample_col, str):\n self.sample_col = [sample_col]\n elif hasattr(sample_col, '__len__'):\n self.sample_col = sample_col\n else:\n self.sample_col = [sample_col]\n\n def resample(self, sims, freq, size, p):\n if np.sum(np.power(p, 2)) <= (self._r_ESS*np.sum(freq)):\n return sims.sample(n=size, replace=True, weights=p).reset_index(drop=True)\n else:\n return sims.loc[sims.index.repeat(freq), :].reset_index(drop=True)\n\n def unique(self, x):\n \"\"\"\n\n Parameters\n ----------\n X: pd.DataFrame\n Input samples.\n\n Returns\n -------\n unique: pd.DataFrame\n The sorted unique samples.\n unique_counts: np.ndarray of int\n The number of times each of the unique values comes up in the original array\n \"\"\"\n\n if self.sample_col is None:\n sample_col = x.columns.values\n else:\n sample_col = self.sample_col\n uni = x.drop_duplicates(subset=sample_col).reset_index(drop = True)\n freq = []\n for index,row in uni.iterrows():\n tar = row[sample_col]\n x_ = x\n for c,t in zip(sample_col,tar):\n x_ = x_.loc[x_[c] == t]\n freq.append(len(x_))\n return uni, freq\n\n @property\n def modifier(self):\n return self._proposal\n\n @modifier.setter\n def modifier(self, value):\n self._proposal = value\n\n @property\n def estimator(self):\n return self._log_likelihood\n\n @estimator.setter\n def estimator(self, value):\n self._log_likelihood = value\n" }, { "path": "xenonpy/inverse/iqspr/modifier.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport re\nimport warnings\nfrom copy import deepcopy\n\nimport numpy as np\nimport pandas as pd\nfrom rdkit import Chem\nfrom tqdm import tqdm\n\nfrom xenonpy.inverse.base import BaseProposal, ProposalError\n\n\nclass GetProbError(ProposalError):\n\n def __init__(self, tmp_str, i_b, i_r):\n self.tmp_str = tmp_str\n self.iB = i_b\n self.iR = i_r\n self.old_smi = None\n\n super().__init__('get_prob: %s not found in NGram, iB=%i, iR=%i' % (tmp_str, i_b, i_r))\n\n\nclass MolConvertError(ProposalError):\n\n def __init__(self, new_smi):\n self.new_smi = new_smi\n self.old_smi = None\n\n super().__init__('can not convert %s to Mol' % new_smi)\n\n\nclass NGramTrainingError(ProposalError):\n\n def __init__(self, error, smi):\n self.old_smi = smi\n\n super().__init__('training failed for %s, because of <%s>: %s' %\n (smi, error.__class__.__name__, error))\n\n\nclass NGram(BaseProposal):\n\n def __init__(self,\n *,\n ngram_table=None,\n sample_order=(1, 10),\n del_range=(1, 10),\n min_len=1,\n max_len=1000,\n reorder_prob=0):\n \"\"\"\n N-Garm\n\n Parameters\n ----------\n ngram_table: NGram table\n NGram table for modify SMILES.\n sample_order: tuple[int, int] or int\n range of order of ngram table used during proposal,\n when given int, sample_order = (1, int)\n del_range: tuple[int, int] or int\n range of random deletion of SMILES string during proposal,\n when given int, del_range = (1, int)\n min_len: int\n minimum length of the extended SMILES,\n shall be smaller than the lower bound of the sample_order\n max_len: int\n max length of the extended SMILES to be terminated from continuing modification\n reorder_prob: float\n probability of the SMILES being reordered during proposal\n \"\"\"\n\n self.sample_order = sample_order\n self.reorder_prob = reorder_prob\n self.min_len = min_len\n self.max_len = max_len\n self.del_range = del_range\n\n if ngram_table is None:\n self._table = None\n self._train_order = None\n else:\n self._table = deepcopy(ngram_table)\n self._train_order = (1, len(ngram_table))\n\n self._fit_sample_order()\n self._fit_min_len()\n\n @property\n def sample_order(self):\n return self._sample_order\n\n @sample_order.setter\n def sample_order(self, val):\n if isinstance(val, int):\n self._sample_order = (1, val)\n elif isinstance(val, tuple):\n self._sample_order = val\n elif isinstance(val, (list, np.array, pd.Series)):\n self._sample_order = (val[0], val[1])\n else:\n raise TypeError(\n 'please input a of two or a single for sample_order')\n if self._sample_order[0] < 1:\n raise RuntimeError('Min sample_order must be greater than 0')\n if self._sample_order[1] < self._sample_order[0]:\n raise RuntimeError('Min sample_order must not be smaller than max sample_order')\n\n @property\n def reorder_prob(self):\n return self._reorder_prob\n\n @reorder_prob.setter\n def reorder_prob(self, val):\n if isinstance(val, (int, float)):\n self._reorder_prob = val\n else:\n raise TypeError('please input a for reorder_prob')\n\n @property\n def min_len(self):\n return self._min_len\n\n @min_len.setter\n def min_len(self, val):\n if isinstance(val, int):\n self._min_len = val\n else:\n raise TypeError('please input a for min_len')\n\n @property\n def max_len(self):\n return self._max_len\n\n @max_len.setter\n def max_len(self, val):\n if isinstance(val, int):\n self._max_len = val\n else:\n raise TypeError('please input a for max_len')\n\n @property\n def del_range(self):\n return self._del_range\n\n @del_range.setter\n def del_range(self, val):\n if isinstance(val, int):\n self._del_range = (1, val)\n elif isinstance(val, tuple):\n self._del_range = val\n elif isinstance(val, (list, np.array, pd.Series)):\n self._del_range = (val[0], val[1])\n else:\n raise TypeError('please input a of two or a single for del_range')\n if self._del_range[1] < self._del_range[0]:\n raise RuntimeError('Min del_range must not be smaller than max del_range')\n\n def _fit_sample_order(self):\n if self._train_order and self._train_order[1] < self.sample_order[1]:\n warnings.warn(\n 'max : %s is greater than max : %s,'\n 'max will be reduced to max ' %\n (self.sample_order[1], self._train_order[1]), RuntimeWarning)\n self.sample_order = (self.sample_order[0], self._train_order[1])\n if self._train_order and self._train_order[0] > self.sample_order[0]:\n if self._train_order[0] > self.sample_order[1]:\n warnings.warn(\n 'max : %s is smaller than min : %s,'\n ' will be replaced by the values of ' %\n (self.sample_order[1], self._train_order[0]), RuntimeWarning)\n self.sample_order = (self._train_order[0], self._train_order[1])\n else:\n warnings.warn(\n 'min : %s is smaller than min : %s,'\n 'min will be increased to min ' %\n (self.sample_order[0], self._train_order[0]), RuntimeWarning)\n self.sample_order = (self._train_order[0], self.sample_order[1])\n\n def _fit_min_len(self):\n if self.sample_order[0] > self.min_len:\n warnings.warn(\n 'min : %s is greater than min_len: %s,'\n 'min_len will be increased to min ' %\n (self.sample_order[0], self.min_len), RuntimeWarning)\n self.min_len = self.sample_order[0]\n\n def on_errors(self, error):\n \"\"\"\n\n Parameters\n ----------\n error: ProposalError\n Error object.\n Returns\n -------\n\n \"\"\"\n if isinstance(error, GetProbError):\n return error.old_smi\n if isinstance(error, MolConvertError):\n return error.old_smi\n if isinstance(error, NGramTrainingError):\n pass\n\n @property\n def ngram_table(self):\n return deepcopy(self._table)\n\n @ngram_table.setter\n def ngram_table(self, value):\n self._table = deepcopy(value)\n\n def modify(self, ext_smi):\n # reorder for a given probability\n if np.random.random() < self.reorder_prob:\n ext_smi = self.reorder_esmi(ext_smi)\n # number of deletion (randomly pick from given range)\n n_del = np.random.randint(self.del_range[0], self.del_range[1] + 1)\n # first delete then add\n ext_smi = self.del_char(ext_smi,\n min(n_del + 1,\n len(ext_smi) - self.min_len)) # at least leave min_len char\n # add until reaching '!' or a given max value\n for i in range(self.max_len - len(ext_smi)):\n ext_smi, _ = self.sample_next_char(ext_smi)\n if ext_smi['esmi'].iloc[-1] == '!':\n return ext_smi # stop when hitting '!', assume must be valid SMILES\n # check incomplete esmi\n ext_smi = self.validator(ext_smi)\n # fill in the '!'\n new_pd_row = {\n 'esmi': '!',\n 'n_br': 0,\n 'n_ring': 0,\n 'substr': ext_smi['substr'].iloc[-1] + ['!']\n }\n\n warnings.warn('Extended SMILES hits max length', RuntimeWarning)\n\n return ext_smi.append(new_pd_row, ignore_index=True)\n\n @classmethod\n def smi2list(cls, smiles):\n # smi_pat = r'(-\\[.*?\\]|=\\[.*?\\]|#\\[.*?\\]|\\[.*?\\]|-Br|=Br|#Br|-Cl|=Cl|#Cl|Br|Cl|-.|=.|#.|\\%[0-9][0-9]|\\w|\\W)'\n # smi_pat = r'(=\\[.*?\\]|#\\[.*?\\]|\\[.*?\\]|=Br|#Br|=Cl|#Cl|Br|Cl|=.|#.|\\%[0-9][0-9]|\\w|\\W)'\n # smi_pat = r'(\\[.*?\\]|Br|Cl|\\%[0-9][0-9]|\\w|\\W)'\n smi_pat = r'(\\[.*?\\]|Br|Cl|(?<=%)[0-9][0-9]|\\w|\\W)'\n\n # smi_list = list(filter(None, re.split(smi_pat, smiles)))\n smi_list = list(filter(lambda x: not ((x == \"\") or (x == \"%\")), re.split(smi_pat, smiles)))\n\n # combine bond with next token only if the next token isn't a number\n # assume SMILES does not end with a bonding character!\n tmp_idx = [\n i for i, x in enumerate(smi_list) if ((x in \"-=#\") and (not smi_list[i + 1].isdigit()))\n ]\n if len(tmp_idx) > 0:\n for i in tmp_idx:\n smi_list[i + 1] = smi_list[i] + smi_list[i + 1]\n smi_list = np.delete(smi_list, tmp_idx).tolist()\n\n return smi_list\n\n @classmethod\n def smi2esmi(cls, smi):\n smi_list = cls.smi2list(smi)\n\n esmi_list = smi_list + ['!']\n substr_list = [] # list of all contracted substrings (include current char.)\n\n # list of whether open branch exist at current character position (include current char.)\n br_list = []\n\n # list of number of open ring at current character position (include current char.)\n ring_list = []\n v_substr = [] # list of temporary contracted substrings\n v_ringn = [] # list of numbering of open rings\n c_br = 0 # tracking open branch steps for recording contracted substrings\n n_br = 0 # tracking number of open branches\n tmp_ss = [] # list of current contracted substring\n for i in range(len(esmi_list)):\n if c_br == 2:\n v_substr.append(deepcopy(tmp_ss)) # contracted substring added w/o ')'\n c_br = 0\n elif c_br == 1:\n c_br = 2\n\n if esmi_list[i] == '(':\n c_br = 1\n n_br += 1\n elif esmi_list[i] == ')':\n tmp_ss = deepcopy(v_substr[-1]) # retrieve contracted substring added w/o ')'\n v_substr.pop()\n n_br -= 1\n elif esmi_list[i].isdigit():\n esmi_list[i] = int(esmi_list[i])\n if esmi_list[i] in v_ringn:\n esmi_list[i] = v_ringn.index(esmi_list[i])\n v_ringn.pop(esmi_list[i])\n else:\n v_ringn.insert(0, esmi_list[i])\n esmi_list[i] = '&'\n\n tmp_ss.append(esmi_list[i])\n substr_list.append(deepcopy(tmp_ss))\n br_list.append(n_br)\n ring_list.append(len(v_ringn))\n\n return pd.DataFrame({\n 'esmi': esmi_list,\n 'n_br': br_list,\n 'n_ring': ring_list,\n 'substr': substr_list\n })\n\n # may add error check here in the future?\n @classmethod\n def esmi2smi(cls, ext_smi):\n smi_list = ext_smi['esmi'].tolist()\n num_open = []\n num_unused = list(range(99, 0, -1))\n for i in range(len(smi_list)):\n if smi_list[i] == '&':\n if num_unused[-1] > 9:\n smi_list[i] = ''.join(['%', str(num_unused[-1])])\n else:\n smi_list[i] = str(num_unused[-1])\n num_open.insert(0, num_unused[-1])\n num_unused.pop()\n elif isinstance(smi_list[i], int):\n tmp = int(smi_list[i])\n if num_open[tmp] > 9:\n smi_list[i] = ''.join(['%', str(num_open[tmp])])\n else:\n smi_list[i] = str(num_open[tmp])\n num_unused.append(num_open[tmp])\n num_open.pop(tmp)\n if smi_list[-1] == \"!\": # cover cases of incomplete esmi_pd\n smi_list.pop() # remove the final '!'\n return ''.join(smi_list)\n\n def remove_table(self, max_order=None):\n \"\"\"\n Remove estimators from estimator set.\n\n Parameters\n ----------\n max_order: int\n max order to be left in the table, the rest is removed.\n \"\"\"\n if max_order:\n tmp = self._train_order[1] - max_order\n if tmp < 1:\n warnings.warn('Nothing removed', RuntimeWarning)\n else:\n self._table = self._table[:-tmp]\n self._train_order = (self._train_order[0], max_order)\n else:\n self._table = None\n self._train_order = None\n\n def fit(self, smiles, *, train_order=(1, 10)):\n \"\"\"\n\n Parameters\n ----------\n smiles: list[str]\n SMILES for training.\n train_order: tuple[int, int] or int\n range of order when train a NGram table,\n when given int, train_order = (1, int),\n and train_order[0] must be > 0\n\n Returns\n -------\n\n \"\"\"\n\n def _fit_one(ext_smi):\n for iB in [0, 1]:\n # index for open/closed branches char. position, remove last row for '!'\n idx_B = ext_smi.iloc[:-1].index[(ext_smi['n_br'].iloc[:-1] > 0) == iB]\n list_R = ext_smi['n_ring'][idx_B].unique().tolist()\n if len(list_R) > 0:\n # expand list of dataframe for max. num-of-ring + 1\n if len(self._table[0][iB]) < (max(list_R) + 1):\n for ii in range(len(self._table)):\n self._table[ii][iB].extend([\n pd.DataFrame()\n for i in range((max(list_R) + 1) - len(self._table[ii][iB]))\n ])\n for iR in list_R:\n # index for num-of-open-ring char. pos.\n idx_R = idx_B[ext_smi['n_ring'][idx_B] == iR]\n\n # shift one down for 'next character given substring'\n tar_char = ext_smi['esmi'][idx_R + 1].tolist()\n tar_substr = ext_smi['substr'][idx_R].tolist()\n\n for iO in range(self._train_order[0] - 1, self._train_order[1]):\n # index for char with substring length not less than order\n idx_O = [x for x in range(len(tar_substr)) if len(tar_substr[x]) > iO]\n for iC in idx_O:\n if not tar_char[iC] in self._table[iO][iB][iR].columns.tolist():\n self._table[iO][iB][iR][tar_char[iC]] = 0\n tmp_row = str(tar_substr[iC][-(iO + 1):])\n if tmp_row not in self._table[iO][iB][iR].index.tolist():\n self._table[iO][iB][iR].loc[tmp_row] = 0\n\n # somehow 'at' not ok with mixed char and int column names\n self._table[iO][iB][iR].loc[tmp_row, tar_char[iC]] += 1\n\n if self._table:\n raise RuntimeError('NGram table existed.'\n 'If you want to re-train the table,'\n 'please use `remove_table()` method first.')\n\n if isinstance(train_order, int):\n tmp_train_order = (1, train_order)\n elif isinstance(train_order, tuple):\n tmp_train_order = train_order\n elif isinstance(train_order, (list, np.array, pd.Series)):\n tmp_train_order = (train_order[0], train_order[1])\n else:\n raise TypeError('please input a of two or a single for train_order')\n\n if tmp_train_order[0] < 1:\n raise RuntimeError('Min train_order must be greater than 0')\n if tmp_train_order[1] < tmp_train_order[0]:\n raise RuntimeError('Min train_order must not be smaller than max train_order')\n\n self._train_order = tmp_train_order\n self._table = [[[], []] for _ in range(self._train_order[1])]\n\n self._fit_sample_order()\n self._fit_min_len()\n for smi in tqdm(smiles):\n try:\n _fit_one(self.smi2esmi(smi))\n except Exception as e:\n warnings.warn('NGram training failed for %s' % smi, RuntimeWarning)\n e = NGramTrainingError(e, smi)\n self.on_errors(e)\n\n return self\n\n # get probability vector for sampling next character, return character list and corresponding probability in numpy.array (normalized)\n # may cause error if empty string list is fed into 'tmp_str'\n # Warning: maybe can reduce the input of iB and iR - directly input the reduced list of self._ngram_tab (?)\n # Warning: may need to update this function with bisection search for faster speed (?)\n # Warning: may need to add worst case that no pattern found at all?\n\n def get_prob(self, tmp_str, iB, iR):\n # right now we use back-off method, an alternative is Kneser–Nay smoothing\n cand_char = []\n cand_prob = 1\n iB = int(iB)\n for iO in range(self.sample_order[1] - 1, self.sample_order[0] - 2, -1):\n # if (len(tmp_str) > iO) & (str(tmp_str[-(iO + 1):]) in self._table[iO][iB][iR].index.tolist()):\n if len(tmp_str) > iO and str(\n tmp_str[-(iO + 1):]) in self._table[iO][iB][iR].index.tolist():\n cand_char = self._table[iO][iB][iR].columns.tolist()\n cand_prob = np.array(self._table[iO][iB][iR].loc[str(tmp_str[-(iO + 1):])])\n break\n if len(cand_char) == 0:\n warnings.warn('get_prob: %s not found in NGram, iB=%i, iR=%i' % (tmp_str, iB, iR),\n RuntimeWarning)\n raise GetProbError(tmp_str, iB, iR)\n return cand_char, cand_prob / np.sum(cand_prob)\n\n # get the next character, return the probability value\n def sample_next_char(self, ext_smi):\n iB = ext_smi['n_br'].iloc[-1] > 0\n iR = ext_smi['n_ring'].iloc[-1]\n cand_char, cand_prob = self.get_prob(ext_smi['substr'].iloc[-1], iB, iR)\n # here we assume cand_char is not empty\n idx = np.random.choice(range(len(cand_char)), p=cand_prob)\n next_char = cand_char[idx]\n ext_smi = self.add_char(ext_smi, next_char)\n return ext_smi, cand_prob[idx]\n\n @classmethod\n def add_char(cls, ext_smi, next_char):\n new_pd_row = ext_smi.iloc[-1]\n new_pd_row.at['substr'] = new_pd_row['substr'] + [next_char]\n new_pd_row.at['esmi'] = next_char\n if next_char == '(':\n new_pd_row.at['n_br'] += 1\n elif next_char == ')':\n new_pd_row.at['n_br'] -= 1\n # assume '(' must exist before if the extended SMILES is valid! (will fail if violated)\n # idx = next((x for x in range(len(new_pd_row['substr'])-1,-1,-1) if new_pd_row['substr'][x] == '('), None)\n # find index of the last unclosed '('\n tmp_c = 1\n for x in range(len(new_pd_row['substr']) - 2, -1,\n -1): # exclude the already added \"next_char\"\n if new_pd_row['substr'][x] == '(':\n tmp_c -= 1\n elif new_pd_row['substr'][x] == ')':\n tmp_c += 1\n if tmp_c == 0:\n idx = x\n break\n # assume no '()' and '((' pattern that is not valid/possible in SMILES\n new_pd_row.at['substr'] = new_pd_row['substr'][:(idx + 2)] + [')']\n elif next_char == '&':\n new_pd_row.at['n_ring'] += 1\n elif isinstance(next_char, int):\n new_pd_row.at['n_ring'] -= 1\n return ext_smi.append(new_pd_row, ignore_index=True)\n\n @classmethod\n def del_char(cls, ext_smi, n_char):\n if n_char > 0:\n return ext_smi[:-n_char]\n else:\n return ext_smi\n\n # need to make sure esmi_pd is a completed SMILES to use this function\n # todo: kekuleSmiles?\n @classmethod\n def reorder_esmi(cls, ext_smi):\n # convert back to SMILES first, then to rdkit MOL\n mol = Chem.MolFromSmiles(cls.esmi2smi(ext_smi))\n idx = np.random.choice(range(mol.GetNumAtoms())).item()\n # currently assume kekuleSmiles=True, i.e., no small letters but with ':' for aromatic rings\n ext_smi = cls.smi2esmi(Chem.MolToSmiles(mol, rootedAtAtom=idx))\n\n return ext_smi\n\n def validator(self, ext_smi):\n # delete all ending '(' or '&'\n for i in range(len(ext_smi)):\n if not ((ext_smi['esmi'].iloc[-1] == '(') or (ext_smi['esmi'].iloc[-1] == '&')):\n break\n ext_smi = self.del_char(ext_smi, 1)\n # delete or fill in ring closing\n flag_ring = ext_smi['n_ring'].iloc[-1] > 0\n for i in range(len(ext_smi)): # max to double the length of current SMILES\n if flag_ring and (np.random.random() < 0.7): # 50/50 for adding two new char.\n # add a character\n ext_smi, _ = self.sample_next_char(ext_smi)\n flag_ring = ext_smi['n_ring'].iloc[-1] > 0\n else:\n break\n if flag_ring:\n # prepare for delete (1st letter shall not be '&')\n tmp_idx = ext_smi.iloc[1:].index\n tmp_count = np.array(ext_smi['n_ring'].iloc[1:]) - np.array(ext_smi['n_ring'].iloc[:-1])\n num_open = tmp_idx[tmp_count == 1].values.tolist()\n num_open.reverse()\n num_close = tmp_idx[tmp_count == -1].values.tolist()\n idx_pop = []\n for i in num_close:\n idx_pop.append(ext_smi['esmi'][i])\n for ii, i in enumerate(idx_pop):\n ext_smi['esmi'][num_close[ii]] += sum([x < i for x in idx_pop[ii + 1:]]) - i\n num_open.pop(i)\n # delete all irrelevant rows and reconstruct esmi\n ext_smi = self.smi2esmi(\n self.esmi2smi(ext_smi.drop(ext_smi.index[num_open]).reset_index(drop=True)))\n ext_smi = ext_smi.iloc[:-1] # remove the '!'\n\n # delete ':' that are not inside a ring\n # tmp_idx = esmi_pd.index[(esmi_pd['esmi'] == ':') & (esmi_pd['n_ring'] < 1)]\n # if len(tmp_idx) > 0:\n # esmi_pd = smi2esmi(esmi2smi(esmi_pd.drop(tmp_idx).reset_index(drop=True)))\n # esmi_pd = esmi_pd.iloc[:-1] # remove the '!'\n # fill in branch closing (last letter shall not be '(')\n for i in range(ext_smi['n_br'].iloc[-1]):\n ext_smi = self.add_char(ext_smi, ')')\n\n return ext_smi\n\n def proposal(self, smiles):\n \"\"\"\n Propose new SMILES based on the given SMILES.\n Make sure you always check the train_order against sample_order before using the proposal!\n\n Parameters\n ----------\n smiles: list of SMILES\n Given SMILES for modification.\n\n Returns\n -------\n new_smiles: list of SMILES\n The proposed SMILES from the given SMILES.\n \"\"\"\n new_smis = []\n for i, smi in enumerate(smiles):\n ext_smi = self.smi2esmi(smi)\n try:\n new_ext_smi = self.modify(ext_smi)\n new_smi = self.esmi2smi(new_ext_smi)\n if Chem.MolFromSmiles(new_smi) is None:\n warnings.warn('can not convert %s to Mol' % new_smi, RuntimeWarning)\n raise MolConvertError(new_smi)\n new_smis.append(new_smi)\n\n except ProposalError as e:\n e.old_smi = smi\n new_smi = self.on_errors(e)\n new_smis.append(new_smi)\n\n except Exception as e:\n raise e\n\n return new_smis\n\n def _merge_table(self, ngram_tab, weight=1):\n \"\"\"\n Merge with a given NGram table\n\n Parameters\n ----------\n ngram_tab: NGram\n the table in the given NGram class variable will be merged to the table in self\n weight: double\n a scalar to scale the frequency in the given NGram table\n\n Returns\n -------\n tmp_n_gram: NGram\n merged NGram tables\n \"\"\"\n\n self._train_order = (min(self._train_order[0], ngram_tab._train_order[0]),\n max(self._train_order[1], ngram_tab._train_order[1]))\n self._fit_sample_order()\n self._fit_min_len()\n\n n_gram_tab1 = self._table # do not use deepcopy here\n n_gram_tab2 = ngram_tab.ngram_table # default deepcopy used\n w = weight\n\n ord1 = len(n_gram_tab1)\n ord2 = len(n_gram_tab2)\n Bc1 = len(n_gram_tab1[0][0])\n Bc2 = len(n_gram_tab2[0][0])\n Bo1 = len(n_gram_tab1[0][1])\n Bo2 = len(n_gram_tab2[0][1])\n\n # fix the number of ring mis-match first\n if Bc1 < Bc2:\n for ii in range(ord1):\n n_gram_tab1[ii][0].extend([pd.DataFrame() for _ in range(Bc2 - Bc1)])\n elif Bc1 > Bc2:\n for ii in range(ord2):\n n_gram_tab2[ii][0].extend([pd.DataFrame() for _ in range(Bc1 - Bc2)])\n if Bo1 < Bo2:\n for ii in range(ord1):\n n_gram_tab1[ii][1].extend([pd.DataFrame() for _ in range(Bo2 - Bo1)])\n elif Bo1 > Bo2:\n for ii in range(ord2):\n n_gram_tab2[ii][1].extend([pd.DataFrame() for _ in range(Bo1 - Bo2)])\n\n # fix order mis-match\n if ord2 > ord1:\n n_gram_tab1.extend(n_gram_tab2[ord1:])\n\n # combine overlapped order (weighted on tab2)\n for i in range(min(ord1, ord2)):\n for j in range(len(n_gram_tab1[i])):\n for k in range(len(n_gram_tab1[i][j])):\n n_gram_tab1[i][j][k] = n_gram_tab1[i][j][k].add(w * n_gram_tab2[i][j][k],\n fill_value=0).fillna(0)\n\n def merge_table(self, *ngram_tab: 'NGram', weight=1, overwrite=True):\n \"\"\"\n Merge with a given NGram table\n\n Parameters\n ----------\n ngram_tab\n the table(s) in the given NGram class variable(s) will be merged to the table in self\n weight: int/float or list/tuple/np.array/pd.Series[int/float]\n a scalar/vector to scale the frequency in the given NGram table to be merged,\n must have the same length as ngram_tab\n overwrite: boolean\n overwrite the original table (self) or not,\n do not recommend to be False (may have memory issue)\n\n Returns\n -------\n tmp_n_gram: NGram\n merged NGram tables\n \"\"\"\n\n if not np.all([isinstance(x, NGram) for x in ngram_tab]):\n raise TypeError('each element in the input must be ')\n\n if isinstance(weight, (int, float)):\n weight = np.repeat(weight, len(ngram_tab))\n elif isinstance(weight, (tuple, list, np.array, pd.Series)):\n if not np.all([isinstance(x, (int, float)) for x in weight]):\n raise TypeError('each element in weight must be or ')\n else:\n raise TypeError('weight must be or or a list of them')\n\n if overwrite:\n tmp_n_gram = self # do not use deepcopy here\n else:\n tmp_n_gram = deepcopy(self)\n\n for i, tab in enumerate(ngram_tab):\n tmp_n_gram._merge_table(ngram_tab=tab, weight=weight[i])\n\n return tmp_n_gram\n\n def split_table(self, cut_order):\n \"\"\"\n Split NGram table into two\n\n Parameters\n ----------\n cut_order: int\n split NGram table between cut_order and cut_order+1\n\n Returns\n -------\n n_gram1: NGram\n n_gram2: NGram\n \"\"\"\n\n n_gram1 = deepcopy(self)\n n_gram1.remove_table(max_order=cut_order)\n n_gram1._fit_sample_order()\n n_gram1._fit_min_len()\n\n n_gram2 = deepcopy(self)\n for iB in [0, 1]:\n for ii in range(cut_order):\n n_gram2._table[ii][iB] = [\n pd.DataFrame() for _ in range(len(n_gram2._table[ii][iB]))\n ]\n n_gram2._train_order = (cut_order + 1, self._train_order[1])\n n_gram2._fit_sample_order()\n n_gram2._fit_min_len()\n\n return n_gram1, n_gram2\n" }, { "path": "xenonpy/mdl/__init__.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom .mdl import *\n" }, { "path": "xenonpy/mdl/base.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport json\nfrom os import remove\nfrom pathlib import Path\nfrom shutil import make_archive\n\nimport pandas as pd\nimport requests\nfrom requests import HTTPError\nfrom sklearn.base import BaseEstimator\n\nfrom xenonpy.utils import TimedMetaClass\n\n\nclass BaseQuery(BaseEstimator, metaclass=TimedMetaClass):\n queryable = None\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key', endpoint: str = 'http://xenon.ism.ac.jp/api'):\n if self.queryable is None:\n raise RuntimeError('Query class must give a queryable field in list of string')\n\n self._results = None\n self._return_json = False\n self._endpoint = endpoint\n self._api_key = api_key\n self._variables = variables\n\n @property\n def api_key(self):\n return self._api_key\n\n @property\n def endpoint(self):\n return self._endpoint\n\n @property\n def variables(self):\n return self._variables\n\n @property\n def results(self):\n return self._results\n\n def gql(self, *query_vars: str):\n raise NotImplementedError()\n\n @staticmethod\n def _post(ret, return_json):\n if return_json:\n return ret\n\n if not isinstance(ret, list):\n ret = [ret]\n ret = pd.DataFrame(ret)\n return ret\n\n def check_query_vars(self, *query_vars: str):\n if not set(query_vars) <= set(self.queryable):\n raise RuntimeError(f'`query_vars` contains illegal variables, '\n f'available querying variables are: {self.queryable}')\n return query_vars\n\n def __call__(self, *querying_vars, file=None, return_json=None):\n if len(querying_vars) == 0:\n query = self.gql(*self.queryable)\n else:\n query = self.gql(*self.check_query_vars(*querying_vars))\n\n payload = json.dumps({'query': query, 'variables': self._variables})\n\n if file is None:\n ret = requests.post(url=self._endpoint,\n data=payload,\n headers={\n \"content-type\": \"application/json\",\n 'api_key': self._api_key\n })\n else:\n file = Path(file).resolve()\n file = make_archive(str(file), 'gztar', str(file))\n operations = ('operations', payload)\n maps = ('map', json.dumps({0: ['variables.model']}))\n payload_tuples = (operations, maps)\n files = {'0': open(file, 'rb')}\n try:\n ret = requests.post(url=self._endpoint,\n data=payload_tuples,\n headers={'api_key': self._api_key},\n files=files)\n finally:\n files['0'].close()\n remove(file)\n\n if ret.status_code != 200:\n try:\n message = ret.json()\n except json.JSONDecodeError:\n message = \"Server did not responce.\"\n\n raise HTTPError('status_code: %s, %s' % (ret.status_code, message))\n ret = ret.json()\n if 'errors' in ret:\n raise ValueError(ret['errors'][0]['message'])\n query_name = self.__class__.__name__\n ret = ret['data'][query_name[0].lower() + query_name[1:]]\n\n if not ret:\n return None\n\n if return_json is None:\n return_json = self._return_json\n\n ret = self._post(ret, return_json)\n self._results = ret\n return ret\n\n def __repr__(self, N_CHAR_MAX=700):\n queryable = '\\n '.join(self.queryable)\n return f'{super().__repr__(N_CHAR_MAX)}\\nQueryable: \\n {queryable}'\n" }, { "path": "xenonpy/mdl/descriptor.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom xenonpy.mdl.base import BaseQuery\n\n\nclass QueryDescriptorsWith(BaseQuery):\n queryable = [\n 'name',\n 'fullName',\n 'describe',\n ]\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n\n def gql(self, *query_vars: str):\n return f'''\n query (\n $name_has: [String!]\n $fullName_has: [String!]\n $describe_has: [String!]\n ) {{\n queryDescriptorsWith(\n name_has: $name_has\n fullName_has: $fullName_has\n describe_has: $describe_has\n ) {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n\n\nclass QueryDescriptors(BaseQuery):\n queryable = [\n 'name',\n 'fullName',\n 'describe',\n ]\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n\n def gql(self, *query_vars: str):\n return f'''\n query ($query: [String!]!) {{\n queryDescriptors(query: $query) {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n\n\nclass GetDescriptorDetail(BaseQuery):\n queryable = [\n 'name',\n 'fullName',\n 'describe',\n 'count'\n ]\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n self._return_json = True\n\n def gql(self, *query_vars: str):\n return f'''\n query ($name: String!) {{\n getDescriptorDetail(name: $name) {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n\n\nclass ListDescriptors(BaseQuery):\n queryable = [\n 'name',\n 'fullName',\n 'describe',\n ]\n\n def __init__(self, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables={}, api_key=api_key, endpoint=endpoint)\n\n def gql(self, *query_vars: str):\n return f'''\n query {{\n listDescriptors {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n\n\nclass CreateDescriptor(BaseQuery):\n queryable = [\n 'name',\n 'fullName',\n 'describe',\n ]\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n self._return_json = True\n\n def gql(self, *query_vars: str):\n return f'''\n mutation ($with_: CreateDescriptorInput!) {{\n createDescriptor (with_: $with_) {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n\n\nclass UpdateDescriptor(BaseQuery):\n queryable = [\n 'name',\n 'fullName',\n 'describe',\n ]\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n self._return_json = True\n\n def gql(self, *query_vars: str):\n return f'''\n mutation (\n $name: String!\n $with_: UpdateDescriptorInput!\n ) {{\n updateDescriptor (\n name: $name\n with_: $with_\n ) {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n" }, { "path": "xenonpy/mdl/mdl.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\n\nimport os\nimport tarfile\nfrom pathlib import Path\nfrom typing import List, Union\n\nimport pandas as pd\nimport requests\nfrom sklearn.base import BaseEstimator\nfrom tqdm import tqdm\n\nfrom xenonpy.utils import TimedMetaClass\nfrom .base import BaseQuery\nfrom .descriptor import QueryDescriptors, QueryDescriptorsWith, UpdateDescriptor, CreateDescriptor, ListDescriptors, \\\n GetDescriptorDetail\nfrom .method import QueryMethods, QueryMethodsWith, UpdateMethod, CreateMethod, ListMethods, GetMethodDetail\nfrom .model import QueryModelDetails, QueryModelDetailsWith, UploadModel, GetTrainingInfo, GetTrainingEnv, \\\n GetSupplementary, GetModelUrls, GetModelUrl, GetModelDetails, GetModelDetail, ListModelsWithProperty, \\\n ListModelsWithModelset, ListModelsWithMethod, ListModelsWithDescriptor\nfrom .modelset import QueryModelsets, QueryModelsetsWith, UpdateModelset, CreateModelset, ListModelsets, \\\n GetModelsetDetail\nfrom .property import QueryPropertiesWith, QueryProperties, UpdateProperty, CreateProperty, ListProperties, \\\n GetPropertyDetail\n\n__all__ = ['MDL', 'GetVersion', 'QueryModelsetsWith', 'QueryModelsets', 'QueryModelDetailsWith', 'QueryModelDetails',\n 'UpdateModelset', 'UploadModel', 'GetModelsetDetail', 'GetModelDetail', 'GetModelDetails', 'GetModelUrls',\n 'GetModelUrl', 'GetTrainingInfo', 'GetSupplementary', 'GetTrainingEnv', 'ListModelsets',\n 'ListModelsWithDescriptor', 'ListModelsWithMethod', 'ListModelsWithModelset', 'ListModelsWithProperty',\n 'QueryPropertiesWith', 'QueryProperties', 'GetPropertyDetail', 'ListProperties', 'CreateProperty',\n 'CreateModelset', 'UpdateProperty', 'QueryDescriptorsWith', 'QueryDescriptors', 'QueryMethodsWith',\n 'QueryMethods', 'UpdateDescriptor', 'UpdateMethod', 'ListDescriptors', 'ListMethods', 'GetMethodDetail',\n 'GetDescriptorDetail', 'CreateDescriptor', 'CreateMethod']\n\n\nclass GetVersion(BaseQuery):\n queryable = []\n\n def __init__(self, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables={}, api_key=api_key, endpoint=endpoint)\n self._return_json = True\n\n def gql(self, *query_vars: str):\n return '''\n query {\n getVersion \n }\n '''\n\n\nclass MDL(BaseEstimator, metaclass=TimedMetaClass):\n def __init__(self, *, api_key: str = 'anonymous.user.key', endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Access key.\n endpoint:\n Url to XenonPy.MDL api server.\n \"\"\"\n self._endpoint = endpoint\n self._api_key = api_key\n\n @property\n def endpoint(self):\n return self._endpoint\n\n @endpoint.setter\n def endpoint(self, e):\n self._endpoint = e\n\n @property\n def api_key(self):\n return self._api_key\n\n @api_key.setter\n def api_key(self, k):\n \"\"\"\"\"\"\n self._api_key = k\n\n @property\n def version(self):\n return GetVersion()()\n\n def __call__(self, *query: str,\n modelset_has: Union[List[str]] = None,\n property_has: Union[List[str]] = None,\n descriptor_has: Union[List[str]] = None,\n method_has: Union[List[str]] = None,\n lang_has: Union[List[str]] = None,\n regression: bool = None,\n transferred: bool = None,\n deprecated: bool = None,\n succeed: bool = None,\n ) -> Union[QueryModelDetails, QueryModelDetailsWith]:\n \"\"\"\n Query models with specific keywords.\n\n Parameters\n ----------\n query\n Lowercase string for database querying.\n This is a fuzzy searching, any information contains given string will hit.\n modelset_has\n The part of a model set's name.\n For example, ``modelset_has='test`` will hit ``*test*``\n property_has\n A part of the name of property.\n descriptor_has\n A part of the name of descriptor.\n method_has\n A part of the name of training method.\n lang_has\n A part of the name of programming language.\n regression\n If``True``, searching in regression models,\n else, searching in classification models.\n Default is ``True``.\n deprecated\n Model with this mark is deprecated.\n transferred\n If ``True``, searching in transferred models.\n Default is ``False``.\n succeed\n If ``True``, searching in succeed models.\n Default is ``True``.\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n\n if len(query) > 0:\n return QueryModelDetails(dict(query=query), api_key=self.api_key, endpoint=self.endpoint)\n else:\n variables = dict(\n modelset_has=modelset_has,\n property_has=property_has,\n descriptor_has=descriptor_has,\n method_has=method_has,\n lang_has=lang_has,\n regression=regression,\n transferred=transferred,\n deprecated=deprecated,\n succeed=succeed\n )\n variables = {k: v for k, v in variables.items() if v is not None}\n\n return QueryModelDetailsWith(variables, api_key=self.api_key, endpoint=self.endpoint)\n\n def upload_model(self, *,\n modelset_id: int,\n describe: dict,\n training_env: dict = None,\n training_info: dict = None,\n supplementary: dict = None) -> UploadModel:\n \"\"\"\n Upload model to XenonPy.MDL server.\n\n Parameters\n ----------\n modelset_id\n describe\n training_env\n training_info\n supplementary\n\n Returns\n -------\n query\n Querying object.\n\n \"\"\"\n variables = dict(\n model=None,\n id=modelset_id,\n describe=describe,\n training_env=training_env,\n training_info=training_info,\n supplementary=supplementary,\n )\n variables = {k: v for k, v in variables.items() if v is not None}\n\n return UploadModel(variables, api_key=self.api_key, endpoint=self.endpoint)\n\n def get_training_info(self, model_id: int) -> GetTrainingInfo:\n \"\"\"\n Get training information, e.g. ``train_loss``.\n\n Parameters\n ----------\n model_id\n Model id.\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n\n return GetTrainingInfo({'id': model_id}, api_key=self.api_key, endpoint=self.endpoint)\n\n def get_training_env(self, model_id: int) -> GetTrainingEnv:\n \"\"\"\n Get model's training environments.\n\n Parameters\n ----------\n model_id\n Model id.\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n return GetTrainingEnv({'id': model_id}, api_key=self.api_key, endpoint=self.endpoint)\n\n def get_supplementary(self, *, model_id: int) -> GetSupplementary:\n \"\"\"\n Get extract information about the model.\n For regression model this should be *prediction vs observation* data.\n For classification model, this will be the *confusion matrix*.\n\n Parameters\n ----------\n model_id\n Model id.\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n return GetSupplementary({'id': model_id}, api_key=self.api_key, endpoint=self.endpoint)\n\n def get_model_urls(self, *model_ids: int) -> Union[GetModelUrl, GetModelUrls]:\n \"\"\"\n Get download url by model id.\n\n Parameters\n ----------\n model_ids\n Model id.\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n if len(model_ids) == 0:\n raise RuntimeError('input is not non-able')\n if len(model_ids) == 1:\n return GetModelUrl({'id': model_ids[0]}, api_key=self.api_key, endpoint=self.endpoint)\n else:\n return GetModelUrls({'ids': model_ids}, api_key=self.api_key, endpoint=self.endpoint)\n\n def get_model_detail(self, model_id: int) -> GetModelDetail:\n \"\"\"\n Get model detail by model id.\n\n Parameters\n ----------\n model_id\n Model id.\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n return GetModelDetail({'id': model_id}, api_key=self.api_key, endpoint=self.endpoint)\n\n def get_model_details(self, model_ids: List[int]) -> GetModelDetails:\n \"\"\"\n Get multiply model detail by their ids in one querying.\n\n Parameters\n ----------\n model_ids\n Model id.\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n return GetModelDetails({'ids': model_ids}, api_key=self.api_key, endpoint=self.endpoint)\n\n def list_models_with_property(self, name: str) -> ListModelsWithProperty:\n \"\"\"\n List all models relevant with given property.\n\n Parameters\n ----------\n name\n Property name\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n return ListModelsWithProperty({'name': name}, api_key=self.api_key, endpoint=self.endpoint)\n\n def list_models_with_modelset(self, name: str) -> ListModelsWithModelset:\n \"\"\"\n List all models relevant with given modelset.\n\n Parameters\n ----------\n name\n Modelset name\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n return ListModelsWithModelset({'name': name}, api_key=self.api_key, endpoint=self.endpoint)\n\n def list_models_with_method(self, name: str) -> ListModelsWithMethod:\n \"\"\"\n List all models relevant with given method.\n\n Parameters\n ----------\n name\n Method name\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n return ListModelsWithMethod({'name': name}, api_key=self.api_key, endpoint=self.endpoint)\n\n def list_models_with_descriptor(self, name: str) -> ListModelsWithDescriptor:\n \"\"\"\n List all models relevant with given descriptor.\n\n Parameters\n ----------\n name\n Descriptor name\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n return ListModelsWithDescriptor({'name': name}, api_key=self.api_key, endpoint=self.endpoint)\n\n def query_modelsets(self, query: str = None, *,\n name_has: Union[List[str]] = None,\n tag_has: Union[List[str]] = None,\n describe_has: Union[List[str]] = None,\n private: bool = None,\n deprecated: bool = None,\n ) -> Union[QueryModelsets, QueryModelsetsWith]:\n \"\"\"\n Query modelsets with specific keywords.\n\n Parameters\n ----------\n query\n Lowercase string for database querying.\n This is a fuzzy searching, any information contains given string will hit.\n name_has\n The part of a model set's name.\n For example, ``modelset_has='test`` will hit ``*test*``\n tag_has\n A part of the name of property.\n describe_has\n A part of the name of descriptor.\n private\n If``True``, searching in regression models,\n else, searching in classification models.\n Default is ``True``.\n deprecated\n Model with this mark is deprecated.\n deprecated\n If ``True``, searching in transferred models.\n Default is ``False``.\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n\n if query is not None:\n return QueryModelsets(dict(query=query), api_key=self.api_key, endpoint=self.endpoint)\n else:\n variables = dict(\n name_has=name_has,\n tag_has=tag_has,\n describe_has=describe_has,\n private=private,\n deprecated=deprecated,\n )\n variables = {k: v for k, v in variables.items() if v is not None}\n\n return QueryModelsetsWith(variables, api_key=self.api_key, endpoint=self.endpoint)\n\n def update_modelset(self, *,\n modelset_id: int,\n name: str = None,\n describe: str = None,\n sample_code: str = None,\n tags: List[str] = None,\n private: bool = None,\n deprecated: bool = None\n ) -> UpdateModelset:\n \"\"\"\n Update modelset information by modelset id.\n\n Parameters\n ----------\n modelset_id\n name\n describe\n sample_code\n tags\n private\n deprecated\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n with_ = dict(\n name=name,\n describe=describe,\n sample_code=sample_code,\n tags=tags,\n private=private,\n deprecated=deprecated\n )\n with_ = {k: v for k, v in with_.items() if v is not None}\n\n return UpdateModelset({'id': modelset_id, 'with_': with_}, api_key=self.api_key, endpoint=self.endpoint)\n\n def create_modelset(self, *,\n name: str,\n describe: str = None,\n sample_code: str = None,\n tags: List[str] = None,\n private: bool = False\n ):\n \"\"\"\n Create modelset.\n\n Parameters\n ----------\n name\n describe\n sample_code\n tags\n private\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n with_ = dict(\n name=name,\n describe=describe,\n sample_code=sample_code,\n tags=tags,\n private=private\n )\n with_ = {k: v for k, v in with_.items() if v is not None}\n\n return CreateModelset({'with_': with_}, api_key=self.api_key, endpoint=self.endpoint)\n\n def list_modelsets(self) -> ListModelsets:\n \"\"\"\n List all modelsets.\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n return ListModelsets(api_key=self.api_key, endpoint=self.endpoint)\n\n def get_modelset_detail(self, modelset_id: int) -> GetModelsetDetail:\n \"\"\"\n Get modelset detail by modelset id.\n\n Parameters\n ----------\n modelset_id\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n return GetModelsetDetail({'id': modelset_id}, api_key=self.api_key, endpoint=self.endpoint)\n\n def query_descriptors(self, query: str = None, *,\n name_has: Union[List[str]] = None,\n fullname_has: Union[List[str]] = None,\n describe_has: Union[List[str]] = None,\n ) -> Union[QueryDescriptors, QueryDescriptorsWith]:\n \"\"\"\n Query descriptors with specific keywords.\n\n Parameters\n ----------\n query\n Lowercase string for database querying.\n This is a fuzzy searching, any information contains given string will hit.\n name_has\n The part of a model set's name.\n For example, ``modelset_has='test`` will hit ``*test*``\n fullname_has\n Part of the full name.\n describe_has\n Part of the descriptor.\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n\n if query is not None:\n return QueryDescriptors(dict(query=query), api_key=self.api_key, endpoint=self.endpoint)\n else:\n variables = dict(\n name_has=name_has,\n fullName_has=fullname_has,\n describe_has=describe_has,\n )\n variables = {k: v for k, v in variables.items() if v is not None}\n\n return QueryDescriptorsWith(variables, api_key=self.api_key, endpoint=self.endpoint)\n\n def update_descriptor(self, *,\n name: str,\n new_name: str = None,\n describe: str = None,\n fullname: str = None,\n ) -> UpdateDescriptor:\n \"\"\"\n Update descriptor information by name.\n\n Parameters\n ----------\n name\n new_name\n describe\n fullname\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n\n with_ = dict(\n name=new_name,\n describe=describe,\n fullName=fullname,\n )\n with_ = {k: v for k, v in with_.items() if v is not None}\n\n return UpdateDescriptor({'name': name, 'with_': with_}, api_key=self.api_key, endpoint=self.endpoint)\n\n def create_descriptor(self, *,\n name: str,\n describe: str = None,\n fullname: str = None,\n ) -> CreateDescriptor:\n \"\"\"\n Create descriptor.\n\n Parameters\n ----------\n name\n describe\n fullname\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n\n with_ = dict(\n name=name,\n describe=describe,\n fullName=fullname,\n )\n with_ = {k: v for k, v in with_.items() if v is not None}\n\n return CreateDescriptor({'with_': with_}, api_key=self.api_key, endpoint=self.endpoint)\n\n def list_descriptors(self) -> ListDescriptors:\n \"\"\"\n List all descriptor.\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n return ListDescriptors(api_key=self.api_key, endpoint=self.endpoint)\n\n def get_descriptor_detail(self, name: str) -> GetDescriptorDetail:\n \"\"\"\n Get descriptor detail by name.\n\n Parameters\n ----------\n name\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n return GetDescriptorDetail({'name': name}, api_key=self.api_key, endpoint=self.endpoint)\n\n def query_methods(self, query: str = None, *,\n name_has: Union[List[str]] = None,\n fullname_has: Union[List[str]] = None,\n describe_has: Union[List[str]] = None,\n ) -> Union[QueryMethods, QueryMethodsWith]:\n \"\"\"\n Query methods with specific keywords.\n\n Parameters\n ----------\n query\n Lowercase string for database querying.\n This is a fuzzy searching, any information contains given string will hit.\n name_has\n The part of a model set's name.\n For example, ``modelset_has='test`` will hit ``*test*``\n fullname_has\n Part of the full name.\n describe_has\n Part of the descriptor.\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n\n if query is not None:\n return QueryMethods(dict(query=query), api_key=self.api_key, endpoint=self.endpoint)\n else:\n variables = dict(\n name_has=name_has,\n fullName_has=fullname_has,\n describe_has=describe_has,\n )\n variables = {k: v for k, v in variables.items() if v is not None}\n\n return QueryMethodsWith(variables, api_key=self.api_key, endpoint=self.endpoint)\n\n def update_method(self, *,\n name: str,\n new_name: str = None,\n describe: str = None,\n fullname: str = None,\n ) -> UpdateMethod:\n \"\"\"\n Update method information by name.\n\n Parameters\n ----------\n name\n new_name\n describe\n fullname\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n\n with_ = dict(\n name=new_name,\n describe=describe,\n fullName=fullname,\n )\n with_ = {k: v for k, v in with_.items() if v is not None}\n\n return UpdateMethod({'name': name, 'with_': with_}, api_key=self.api_key, endpoint=self.endpoint)\n\n def create_method(self, *,\n name: str,\n describe: str = None,\n fullname: str = None,\n ) -> CreateMethod:\n \"\"\"\n Create method.\n\n Parameters\n ----------\n name\n describe\n fullname\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n\n with_ = dict(\n name=name,\n describe=describe,\n fullName=fullname,\n )\n with_ = {k: v for k, v in with_.items() if v is not None}\n\n return CreateMethod({'with_': with_}, api_key=self.api_key, endpoint=self.endpoint)\n\n def list_methods(self) -> ListMethods:\n \"\"\"\n List all methods.\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n return ListMethods(api_key=self.api_key, endpoint=self.endpoint)\n\n def get_method_detail(self, name: str) -> GetMethodDetail:\n \"\"\"\n Get method detail by name.\n\n Parameters\n ----------\n name\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n return GetMethodDetail({'name': name}, api_key=self.api_key, endpoint=self.endpoint)\n\n def query_properties(self, query: str = None, *,\n name_has: Union[List[str]] = None,\n fullname_has: Union[List[str]] = None,\n describe_has: Union[List[str]] = None,\n symbol_has: Union[List[str]] = None,\n unit_has: Union[List[str]] = None,\n ) -> Union[QueryProperties, QueryPropertiesWith]:\n \"\"\"\n Query properties with specific keywords.\n\n Parameters\n ----------\n query\n Lowercase string for database querying.\n This is a fuzzy searching, any information contains given string will hit.\n name_has\n The part of a model set's name.\n For example, ``modelset_has='test`` will hit ``*test*``\n fullname_has\n Part of the full name.\n describe_has\n Part of the descriptor.\n symbol_has\n Part of the symbol.\n unit_has\n Part of the unit.\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n\n if query is not None:\n return QueryProperties(dict(query=query), api_key=self.api_key, endpoint=self.endpoint)\n else:\n variables = dict(\n name_has=name_has,\n fullName_has=fullname_has,\n describe_has=describe_has,\n symbol_has=symbol_has,\n unit_has=unit_has,\n )\n variables = {k: v for k, v in variables.items() if v is not None}\n\n return QueryPropertiesWith(variables, api_key=self.api_key, endpoint=self.endpoint)\n\n def update_property(self, *,\n name: str,\n new_name: str = None,\n describe: str = None,\n fullname: str = None,\n symbol: str = None,\n unit: str = None,\n ) -> UpdateProperty:\n \"\"\"\n Update property information by name.\n\n Parameters\n ----------\n name\n new_name\n describe\n fullname\n symbol\n unit\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n with_ = dict(\n name=new_name,\n describe=describe,\n fullName=fullname,\n symbol=symbol,\n priunitvate=unit,\n )\n with_ = {k: v for k, v in with_.items() if v is not None}\n\n return UpdateProperty({'name': name, 'with_': with_}, api_key=self.api_key, endpoint=self.endpoint)\n\n def create_property(self, *,\n name: str,\n describe: str = None,\n fullname: str = None,\n symbol: str = None,\n unit: str = None,\n ) -> CreateProperty:\n \"\"\"\n Create property.\n\n Parameters\n ----------\n name\n describe\n fullname\n symbol\n unit\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n\n with_ = dict(\n name=name,\n describe=describe,\n fullName=fullname,\n symbol=symbol,\n unit=unit\n )\n with_ = {k: v for k, v in with_.items() if v is not None}\n\n return CreateProperty({'with_': with_}, api_key=self.api_key, endpoint=self.endpoint)\n\n def list_properties(self) -> ListProperties:\n \"\"\"\n List all properties.\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n return ListProperties(api_key=self.api_key, endpoint=self.endpoint)\n\n def get_property_detail(self, name: str):\n \"\"\"\n Get property detail by name.\n\n Parameters\n ----------\n name\n\n Returns\n -------\n query\n Querying object.\n \"\"\"\n return GetPropertyDetail({'name': name}, api_key=self.api_key, endpoint=self.endpoint)\n\n def pull(self, *model_ids: Union[int, pd.Series, pd.DataFrame],\n save_to: str = '.') -> pd.DataFrame:\n \"\"\"\n Download model(s) from XenonPy.MDL server.\n\n Parameters\n ----------\n model_ids\n Model ids.\n It can be given by a dataframe.\n In this case, the column with name ``id`` will be used.\n save_to\n Path to save models.\n\n Returns\n -------\n\n \"\"\"\n if len(model_ids) == 0:\n raise RuntimeError('input can not be empty')\n if len(model_ids) == 1:\n if isinstance(model_ids[0], pd.Series):\n model_ids = model_ids[0].tolist()\n\n if isinstance(model_ids[0], pd.DataFrame):\n model_ids = model_ids[0]['id'].tolist()\n\n ret = self.get_model_urls(*model_ids)('id', 'url')\n urls = ret['url'].tolist()\n\n path_list = []\n for url in tqdm(urls):\n path = '/'.join(url.split('/')[4:])\n file = Path(save_to).resolve() / path\n file.parent.mkdir(parents=True, exist_ok=True)\n file = str(file)\n r = requests.get(url=url)\n with open(file, 'wb') as f:\n f.write(r.content)\n path = file[:-7]\n tarfile.open(file).extractall(path=path)\n os.remove(file)\n path_list.append(path)\n\n ret['model'] = path_list\n return ret.drop(columns='url')\n" }, { "path": "xenonpy/mdl/method.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom xenonpy.mdl.base import BaseQuery\n\n\nclass QueryMethodsWith(BaseQuery):\n queryable = [\n 'name',\n 'fullName',\n 'describe',\n ]\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n\n def gql(self, *query_vars: str):\n return f'''\n query (\n $name_has: [String!]\n $fullName_has: [String!]\n $describe_has: [String!]\n ) {{\n queryMethodsWith(\n name_has: $name_has\n fullName_has: $fullName_has\n describe_has: $describe_has\n ) {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n\n\nclass QueryMethods(BaseQuery):\n queryable = [\n 'name',\n 'fullName',\n 'describe',\n ]\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n\n def gql(self, *query_vars: str):\n return f'''\n query ($query: [String!]!) {{\n queryMethods(query: $query) {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n\n\nclass GetMethodDetail(BaseQuery):\n queryable = [\n 'name',\n 'fullName',\n 'describe',\n 'count'\n ]\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n self._return_json = True\n\n def gql(self, *query_vars: str):\n return f'''\n query ($name: String!) {{\n getMethodDetail(name: $name) {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n\n\nclass ListMethods(BaseQuery):\n queryable = [\n 'name',\n 'fullName',\n 'describe',\n ]\n\n def __init__(self, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables={}, api_key=api_key, endpoint=endpoint)\n\n def gql(self, *query_vars: str):\n return f'''\n query {{\n listMethods {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n\n\nclass CreateMethod(BaseQuery):\n queryable = [\n 'name',\n 'fullName',\n 'describe',\n ]\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n self._return_json = True\n\n def gql(self, *query_vars: str):\n return f'''\n mutation ($with_: CreateMethodInput!) {{\n createMethod (with_: $with_) {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n\n\nclass UpdateMethod(BaseQuery):\n queryable = [\n 'name',\n 'fullName',\n 'describe',\n ]\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n self._return_json = True\n\n def gql(self, *query_vars: str):\n return f'''\n mutation (\n $name: String!\n $with_: UpdateMethodInput!\n ) {{\n updateMethod (\n name: $name\n with_: $with_\n ) {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n" }, { "path": "xenonpy/mdl/model.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport pandas as pd\n\nfrom xenonpy.mdl.base import BaseQuery\n\n\nclass QueryModelDetailsWith(BaseQuery):\n common = [\n 'id',\n 'transferred',\n 'succeed',\n 'isRegression',\n 'deprecated',\n 'modelset',\n 'method',\n 'property',\n 'descriptor',\n 'lang',\n\n ]\n\n classification = [\n 'accuracy',\n 'precision',\n 'recall',\n 'f1',\n 'sensitivity',\n 'prevalence',\n 'specificity',\n 'ppv',\n 'npv',\n ]\n\n regression = [\n 'meanAbsError',\n 'maxAbsError',\n 'meanSquareError',\n 'rootMeanSquareError',\n 'r2',\n 'pValue',\n 'spearmanCorr',\n 'pearsonCorr',\n ]\n\n queryable = common + classification + regression\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n\n def gql(self, *query_vars: str):\n reg, cls = [], []\n if 'id' not in query_vars:\n query_vars = query_vars + ('id',)\n for var in query_vars:\n if var in self.common:\n # common.append(var)\n reg.append(var)\n cls.append(var)\n elif var in self.regression:\n reg.append(var)\n elif var in self.classification:\n cls.append(var)\n\n return f'''\n query (\n $modelset_has: [String!]\n $property_has: [String!]\n $descriptor_has: [String!]\n $method_has: [String!]\n $lang_has: [String!]\n $regression: Boolean\n $transferred: Boolean\n $deprecated: Boolean\n $succeed: Boolean\n ) {{\n queryModelDetailsWith(\n modelset_has: $modelset_has\n property_has: $property_has\n descriptor_has: $descriptor_has\n method_has: $method_has\n lang_has: $lang_has\n regression: $regression\n transferred: $transferred\n deprecated: $deprecated\n succeed: $succeed\n ) {{\n ...on Regression {{\n {' '.join(reg)}\n }}\n ...on Classification {{\n {' '.join(cls)}\n }}\n }}\n }}\n '''\n\n\nclass QueryModelDetails(BaseQuery):\n common = [\n 'id',\n 'transferred',\n 'succeed',\n 'isRegression',\n 'deprecated',\n 'modelset',\n 'method',\n 'property',\n 'descriptor',\n 'lang',\n\n ]\n\n classification = [\n 'accuracy',\n 'precision',\n 'recall',\n 'f1',\n 'sensitivity',\n 'prevalence',\n 'specificity',\n 'ppv',\n 'npv',\n ]\n\n regression = [\n 'meanAbsError',\n 'maxAbsError',\n 'meanSquareError',\n 'rootMeanSquareError',\n 'r2',\n 'pValue',\n 'spearmanCorr',\n 'pearsonCorr',\n ]\n queryable = common + classification + regression\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n\n def gql(self, *query_vars: str):\n reg, cls = [], []\n if 'id' not in query_vars:\n query_vars = query_vars + ('id',)\n for var in query_vars:\n if var in self.common:\n reg.append(var)\n cls.append(var)\n elif var in self.regression:\n reg.append(var)\n elif var in self.classification:\n cls.append(var)\n\n return f'''\n query ($query: [String!]!) {{\n queryModelDetails(query: $query) {{\n ...on Regression {{\n {' '.join(reg)}\n }}\n ...on Classification {{\n {' '.join(cls)}\n }}\n }}\n }}\n '''\n\n\nclass GetModelUrls(BaseQuery):\n queryable = [\n 'id',\n 'etag',\n 'url',\n ]\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n\n def gql(self, *query_vars: str):\n return f'''\n query ($ids: [Int!]!) {{\n getModelUrls(ids: $ids) {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n\n\nclass GetModelUrl(BaseQuery):\n queryable = [\n 'id',\n 'etag',\n 'url',\n ]\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n\n def gql(self, *query_vars: str):\n return f'''\n query ($id: Int!) {{\n getModelUrl(id: $id) {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n\n\nclass GetModelDetails(BaseQuery):\n common = [\n 'id',\n 'transferred',\n 'succeed',\n 'isRegression',\n 'deprecated',\n 'modelset',\n 'method',\n 'property',\n 'descriptor',\n 'lang',\n\n ]\n\n classification = [\n 'accuracy',\n 'precision',\n 'recall',\n 'f1',\n 'sensitivity',\n 'prevalence',\n 'specificity',\n 'ppv',\n 'npv',\n ]\n\n regression = [\n 'meanAbsError',\n 'maxAbsError',\n 'meanSquareError',\n 'rootMeanSquareError',\n 'r2',\n 'pValue',\n 'spearmanCorr',\n 'pearsonCorr',\n ]\n queryable = common + classification + regression\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n\n def gql(self, *query_vars: str):\n reg, cls = [], []\n for var in query_vars:\n if var in self.common:\n reg.append(var)\n cls.append(var)\n elif var in self.regression:\n reg.append(var)\n elif var in self.classification:\n cls.append(var)\n\n return f'''\n query ($ids: [Int!]!) {{\n getModelDetails(ids: $ids) {{\n ...on Regression {{\n {' '.join(reg)}\n }}\n ...on Classification {{\n {' '.join(cls)}\n }}\n }}\n }}\n '''\n pass\n\n\nclass GetModelDetail(BaseQuery):\n common = [\n 'id',\n 'transferred',\n 'succeed',\n 'isRegression',\n 'deprecated',\n 'modelset',\n 'method',\n 'property',\n 'descriptor',\n 'lang',\n\n ]\n\n classification = [\n 'accuracy',\n 'precision',\n 'recall',\n 'f1',\n 'sensitivity',\n 'prevalence',\n 'specificity',\n 'ppv',\n 'npv',\n ]\n\n regression = [\n 'meanAbsError',\n 'maxAbsError',\n 'meanSquareError',\n 'rootMeanSquareError',\n 'r2',\n 'pValue',\n 'spearmanCorr',\n 'pearsonCorr',\n ]\n queryable = common + classification + regression\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n\n def gql(self, *query_vars: str):\n reg, cls = [], []\n for var in query_vars:\n if var in self.common:\n reg.append(var)\n cls.append(var)\n elif var in self.regression:\n reg.append(var)\n elif var in self.classification:\n cls.append(var)\n\n return f'''\n query ($id: Int!) {{\n getModelDetail(id: $id) {{\n ...on Regression {{\n {' '.join(reg)}\n }}\n ...on Classification {{\n {' '.join(cls)}\n }}\n }}\n }}\n '''\n pass\n\n\nclass GetTrainingInfo(BaseQuery):\n queryable = []\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n\n @staticmethod\n def _post(ret, return_json):\n return pd.DataFrame(ret)\n\n def gql(self, *query_vars: str):\n return f'''\n query ($id: Int!) {{\n getTrainingInfo(id: $id) \n }}\n '''\n\n\nclass GetTrainingEnv(BaseQuery):\n queryable = []\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n self._return_json = True\n\n def gql(self, *query_vars: str):\n return f'''\n query ($id: Int!) {{\n getTrainingEnv(id: $id) \n }}\n '''\n\n\nclass GetSupplementary(BaseQuery):\n queryable = []\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n self._return_json = True\n\n def gql(self, *query_vars: str):\n return f'''\n query ($id: Int!) {{\n getSupplementary(id: $id) \n }}\n '''\n\n\nclass ListModelsWithProperty(BaseQuery):\n queryable = [\n 'id',\n 'transferred',\n 'succeed',\n 'isRegression',\n 'deprecated',\n 'modelset',\n 'method',\n 'property',\n 'descriptor',\n 'lang'\n ]\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n\n def gql(self, *query_vars: str):\n return f'''\n query ($name: String!) {{\n listModelsWithProperty(name: $name) {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n\n\nclass ListModelsWithModelset(BaseQuery):\n queryable = [\n 'id',\n 'transferred',\n 'succeed',\n 'isRegression',\n 'deprecated',\n 'modelset',\n 'method',\n 'property',\n 'descriptor',\n 'lang'\n ]\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n\n def gql(self, *query_vars: str):\n return f'''\n query ($name: String!) {{\n listModelsWithModelset(name: $name) {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n\n\nclass ListModelsWithMethod(BaseQuery):\n queryable = [\n 'id',\n 'transferred',\n 'succeed',\n 'isRegression',\n 'deprecated',\n 'modelset',\n 'method',\n 'property',\n 'descriptor',\n 'lang'\n ]\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n\n def gql(self, *query_vars: str):\n return f'''\n query ($name: String!) {{\n listModelsWithMethod(name: $name) {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n\n\nclass ListModelsWithDescriptor(BaseQuery):\n queryable = [\n 'id',\n 'transferred',\n 'succeed',\n 'isRegression',\n 'deprecated',\n 'modelset',\n 'method',\n 'property',\n 'descriptor',\n 'lang'\n ]\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n\n def gql(self, *query_vars: str):\n return f'''\n query ($name: String!) {{\n listModelsWithDescriptor(name: $name) {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n\n\nclass UploadModel(BaseQuery):\n queryable = [\n 'id',\n 'etag',\n 'path'\n ]\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n\n def gql(self, *query_vars: str):\n return f\"\"\"\n mutation(\n $id: Int!\n $describe: UploadModelInput!\n $model: Upload!\n $training_env: Json\n $training_info: Json\n $supplementary: Json\n ) {{\n uploadModel(\n modelsetId: $id\n model: $model\n describe: $describe\n training_env: $training_env\n training_info: $training_info\n supplementary: $supplementary\n ) {{\n {' '.join(query_vars)}\n }}\n }}\n \"\"\"\n" }, { "path": "xenonpy/mdl/modelset.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom xenonpy.mdl.base import BaseQuery\n\n\nclass QueryModelsetsWith(BaseQuery):\n queryable = [\n 'id',\n 'name',\n 'describe',\n 'deprecated',\n 'private',\n ]\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n\n def gql(self, *query_vars: str):\n return f'''\n query (\n $name_has: [String!]\n $tag_has: [String!]\n $describe_has: [String!]\n $private: Boolean\n $deprecated: Boolean\n ) {{\n queryModelsetsWith(\n name_has: $name_has\n tag_has: $tag_has\n describe_has: $describe_has\n private: $private\n deprecated: $deprecated\n ) {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n\n\nclass QueryModelsets(BaseQuery):\n queryable = [\n 'id',\n 'name',\n 'describe',\n 'deprecated',\n 'private',\n ]\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n\n def gql(self, *query_vars: str):\n return f'''\n query ($query: [String!]!) {{\n queryModelsets(query: $query) {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n\n\nclass GetModelsetDetail(BaseQuery):\n queryable = [\n 'id',\n 'name',\n 'describe',\n 'deprecated',\n 'private',\n 'contributors',\n 'owner',\n 'sampleCode',\n 'tags',\n 'count'\n ]\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n self._return_json = True\n\n def gql(self, *query_vars: str):\n return f'''\n query ($id: Int!) {{\n getModelsetDetail(id: $id) {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n\n\nclass ListModelsets(BaseQuery):\n queryable = [\n 'id',\n 'name',\n 'describe',\n 'deprecated',\n 'private',\n ]\n\n def __init__(self, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables={}, api_key=api_key, endpoint=endpoint)\n\n def gql(self, *query_vars: str):\n return f'''\n query {{\n listModelsets {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n\n\nclass CreateModelset(BaseQuery):\n queryable = [\n 'id',\n 'name',\n 'describe',\n 'deprecated',\n 'private',\n ]\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n self._return_json = True\n\n def gql(self, *query_vars: str):\n return f'''\n mutation ($with_: CreateModelsetInput!) {{\n createModelset (with_: $with_) {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n\n\nclass UpdateModelset(BaseQuery):\n queryable = [\n 'id',\n 'name',\n 'describe',\n 'deprecated',\n 'private',\n ]\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n self._return_json = True\n\n def gql(self, *query_vars: str):\n return f'''\n mutation (\n $id: Int!\n $with_: UpdateModelsetInput!\n ) {{\n updateModelset (\n id: $id\n with_: $with_\n ) {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n" }, { "path": "xenonpy/mdl/property.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom xenonpy.mdl.base import BaseQuery\n\n\nclass QueryPropertiesWith(BaseQuery):\n queryable = [\n 'name',\n 'fullName',\n 'symbol',\n 'unit',\n 'describe',\n ]\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n\n def gql(self, *query_vars: str):\n return f'''\n query (\n $name_has: [String!]\n $fullName_has: [String!]\n $describe_has: [String!]\n $symbol_has: [String!]\n $unit_has: [String!]\n ) {{\n queryPropertiesWith(\n name_has: $name_has\n fullName_has: $fullName_has\n describe_has: $describe_has\n symbol_has: $symbol_has\n unit_has: $unit_has\n ) {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n\n\nclass QueryProperties(BaseQuery):\n queryable = [\n 'name',\n 'fullName',\n 'symbol',\n 'unit',\n 'describe',\n ]\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n\n def gql(self, *query_vars: str):\n return f'''\n query ($query: [String!]!) {{\n queryProperties(query: $query) {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n\n\nclass GetPropertyDetail(BaseQuery):\n queryable = [\n 'name',\n 'fullName',\n 'symbol',\n 'unit',\n 'describe',\n 'count'\n ]\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n self._return_json = True\n\n def gql(self, *query_vars: str):\n return f'''\n query ($name: String!) {{\n getPropertyDetail(name: $name) {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n\n\nclass ListProperties(BaseQuery):\n queryable = [\n 'name',\n 'fullName',\n 'symbol',\n 'unit',\n 'describe',\n ]\n\n def __init__(self, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables={}, api_key=api_key, endpoint=endpoint)\n\n def gql(self, *query_vars: str):\n return f'''\n query {{\n listProperties {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n\n\nclass CreateProperty(BaseQuery):\n queryable = [\n 'name',\n 'fullName',\n 'symbol',\n 'unit',\n 'describe',\n ]\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n self._return_json = True\n\n def gql(self, *query_vars: str):\n return f'''\n mutation ($with_: CreatePropertyInput!) {{\n createProperty (with_: $with_) {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n\n\nclass UpdateProperty(BaseQuery):\n queryable = [\n 'name',\n 'fullName',\n 'symbol',\n 'unit',\n 'describe',\n ]\n\n def __init__(self, variables, *, api_key: str = 'anonymous.user.key',\n endpoint: str = 'http://xenon.ism.ac.jp/api'):\n \"\"\"\n Access to XenonPy.MDL library.\n\n Parameters\n ----------\n api_key\n Not implement yet.\n \"\"\"\n super().__init__(variables=variables, api_key=api_key, endpoint=endpoint)\n self._return_json = True\n\n def gql(self, *query_vars: str):\n return f'''\n mutation (\n $name: String!\n $with_: UpdatePropertyInput!\n ) {{\n updateProperty (\n name: $name\n with_: $with_\n ) {{\n {' '.join(query_vars)}\n }}\n }}\n '''\n" }, { "path": "xenonpy/model/__init__.py", "content": "# Copyright (c) 2021. TsumiNa. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom .cgcnn import *\nfrom .extern import *\nfrom .sequential import *\n" }, { "path": "xenonpy/model/cgcnn.py", "content": "# Copyright (c) 2021. TsumiNa. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport torch\nfrom torch import nn\n\n__all__ = ['ConvLayer', 'CrystalGraphConvNet']\n\n\nclass ConvLayer(nn.Module):\n \"\"\"\n Convolutional operation on graphs\n \"\"\"\n\n def __init__(self, atom_fea_len, nbr_fea_len):\n \"\"\"\n Initialize ConvLayer.\n\n Parameters\n ----------\n\n atom_fea_len: int\n Number of atom hidden features.\n nbr_fea_len: int\n Number of bond features.\n \"\"\"\n super(ConvLayer, self).__init__()\n self.atom_fea_len = atom_fea_len\n self.nbr_fea_len = nbr_fea_len\n self.fc_full = nn.Linear(2 * self.atom_fea_len + self.nbr_fea_len, 2 * self.atom_fea_len)\n self.sigmoid = nn.Sigmoid()\n self.softplus1 = nn.Softplus()\n self.bn1 = nn.BatchNorm1d(2 * self.atom_fea_len)\n self.bn2 = nn.BatchNorm1d(self.atom_fea_len)\n self.softplus2 = nn.Softplus()\n\n def forward(self, atom_in_fea, nbr_fea, nbr_fea_idx):\n \"\"\"\n Forward pass\n\n N: Total number of atoms in the batch\n M: Max number of neighbors\n\n Parameters\n ----------\n\n atom_in_fea: Variable(torch.Tensor) shape (N, atom_fea_len)\n Atom hidden features before convolution\n nbr_fea: Variable(torch.Tensor) shape (N, M, nbr_fea_len)\n Bond features of each atom's M neighbors\n nbr_fea_idx: torch.LongTensor shape (N, M)\n Indices of M neighbors of each atom\n\n Returns\n -------\n\n atom_out_fea: nn.Variable shape (N, atom_fea_len)\n Atom hidden features after convolution\n\n \"\"\"\n # TODO will there be problems with the index zero padding?\n N, M = nbr_fea_idx.shape\n # convolution\n atom_nbr_fea = atom_in_fea[nbr_fea_idx, :]\n total_nbr_fea = torch.cat(\n [atom_in_fea.unsqueeze(1).expand(N, M, self.atom_fea_len), atom_nbr_fea, nbr_fea],\n dim=2)\n total_gated_fea = self.fc_full(total_nbr_fea)\n total_gated_fea = self.bn1(total_gated_fea.view(-1, self.atom_fea_len * 2)).view(\n N, M, self.atom_fea_len * 2)\n nbr_filter, nbr_core = total_gated_fea.chunk(2, dim=2)\n nbr_filter = self.sigmoid(nbr_filter)\n nbr_core = self.softplus1(nbr_core)\n nbr_sumed = torch.sum(nbr_filter * nbr_core, dim=1)\n nbr_sumed = self.bn2(nbr_sumed)\n out = self.softplus2(atom_in_fea + nbr_sumed)\n return out\n\n\nclass CrystalGraphConvNet(nn.Module):\n \"\"\"\n Create a crystal graph convolutional neural network for predicting total\n material properties.\n\n See Also: [CGCNN]_.\n\n .. [CGCNN] `Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties`__\n __ https://doi.org/10.1103/PhysRevLett.120.145301\n \"\"\"\n\n def __init__(self,\n orig_atom_fea_len,\n nbr_fea_len,\n atom_fea_len=64,\n n_conv=3,\n h_fea_len=128,\n n_h=1,\n classification=False):\n \"\"\"\n Initialize CrystalGraphConvNet.\n\n Parameters\n ----------\n\n orig_atom_fea_len: int\n Number of atom features in the input.\n nbr_fea_len: int\n Number of bond features.\n atom_fea_len: int\n Number of hidden atom features in the convolutional layers\n n_conv: int\n Number of convolutional layers\n h_fea_len: int\n Number of hidden features after pooling\n n_h: int\n Number of hidden layers after pooling\n \"\"\"\n super(CrystalGraphConvNet, self).__init__()\n self.classification = classification\n self.embedding = nn.Linear(orig_atom_fea_len, atom_fea_len)\n self.convs = nn.ModuleList(\n [ConvLayer(atom_fea_len=atom_fea_len, nbr_fea_len=nbr_fea_len) for _ in range(n_conv)])\n self.conv_to_fc = nn.Linear(atom_fea_len, h_fea_len)\n self.conv_to_fc_softplus = nn.Softplus()\n if n_h > 1:\n self.fcs = nn.ModuleList([nn.Linear(h_fea_len, h_fea_len) for _ in range(n_h - 1)])\n self.softpluses = nn.ModuleList([nn.Softplus() for _ in range(n_h - 1)])\n if self.classification:\n self.fc_out = nn.Linear(h_fea_len, 2)\n else:\n self.fc_out = nn.Linear(h_fea_len, 1)\n if self.classification:\n self.logsoftmax = nn.LogSoftmax()\n self.dropout = nn.Dropout()\n\n def forward(self, atom_fea, nbr_fea, nbr_fea_idx, crystal_atom_idx):\n \"\"\"\n Forward pass\n\n N: Total number of atoms in the batch\n M: Max number of neighbors\n N0: Total number of crystals in the batch\n\n Parameters\n ----------\n\n atom_fea: Variable(torch.Tensor) shape (N, orig_atom_fea_len)\n Atom features from atom type\n nbr_fea: Variable(torch.Tensor) shape (N, M, nbr_fea_len)\n Bond features of each atom's M neighbors\n nbr_fea_idx: torch.LongTensor shape (N, M)\n Indices of M neighbors of each atom\n crystal_atom_idx: list of torch.LongTensor of length N0\n Mapping from the crystal idx to atom idx\n\n Returns\n -------\n\n prediction: nn.Variable shape (N, )\n Atom hidden features after convolution\n\n \"\"\"\n atom_fea = self.embedding(atom_fea)\n for conv_func in self.convs:\n atom_fea = conv_func(atom_fea, nbr_fea, nbr_fea_idx)\n crys_fea = self.pooling(atom_fea, crystal_atom_idx)\n crys_fea = self.conv_to_fc(self.conv_to_fc_softplus(crys_fea))\n crys_fea = self.conv_to_fc_softplus(crys_fea)\n if self.classification:\n crys_fea = self.dropout(crys_fea)\n if hasattr(self, 'fcs') and hasattr(self, 'softpluses'):\n for fc, softplus in zip(self.fcs, self.softpluses):\n crys_fea = softplus(fc(crys_fea))\n out = self.fc_out(crys_fea)\n if self.classification:\n out = self.logsoftmax(out)\n return out\n\n @staticmethod\n def pooling(atom_fea, crystal_atom_idx):\n \"\"\"\n Pooling the atom features to crystal features\n\n N: Total number of atoms in the batch\n N0: Total number of crystals in the batch\n\n Parameters\n ----------\n\n atom_fea: Variable(torch.Tensor) shape (N, atom_fea_len)\n Atom feature vectors of the batch\n crystal_atom_idx: list of torch.LongTensor of length N0\n Mapping from the crystal idx to atom idx\n \"\"\"\n assert sum([len(idx_map) for idx_map in crystal_atom_idx]) == \\\n atom_fea.data.shape[0]\n summed_fea = [\n torch.mean(atom_fea[idx_map], dim=0, keepdim=True) for idx_map in crystal_atom_idx\n ]\n return torch.cat(summed_fea, dim=0)\n" }, { "path": "xenonpy/model/extern.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\n\"\"\"\nThis module wrapped external model tool convenient.\n\"\"\"\n" }, { "path": "xenonpy/model/nn/__init__.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom .layer import *\n" }, { "path": "xenonpy/model/nn/layer.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom torch import nn\n\nfrom .wrap import L1\n\n__all__ = ['Layer1d']\n\n\nclass Layer1d(nn.Module):\n \"\"\"\n Base NN layer. This is a wrap around PyTorch.\n See here for details: http://pytorch.org/docs/master/nn.html#\n \"\"\"\n\n def __init__(self, n_in, n_out, *,\n drop_out=0.,\n layer_func=L1.linear(bias=True),\n act_func=nn.ReLU(),\n batch_nor=L1.batch_norm(eps=1e-05, momentum=0.1, affine=True)\n ):\n \"\"\"\n Parameters\n ----------\n n_in: int\n Size of each input sample.\n n_out: int\n Size of each output sample\n drop_out: float\n Probability of an element to be zeroed. Default: 0.5\n layer_func: func\n Layers come with PyTorch.\n act_func: func\n Activation function.\n batch_nor: func\n Normalization layers\n \"\"\"\n super().__init__()\n self.layer = layer_func(n_in, n_out)\n self.batch_nor = None if not batch_nor else batch_nor(n_out)\n self.act_func = None if not act_func else act_func\n self.dropout = None if drop_out == 0. else nn.Dropout(drop_out)\n\n def forward(self, *x):\n _out = self.layer(*x)\n if self.dropout:\n _out = self.dropout(_out)\n if self.batch_nor:\n _out = self.batch_nor(_out)\n if self.act_func:\n _out = self.act_func(_out)\n return _out\n" }, { "path": "xenonpy/model/nn/wrap.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\nfrom functools import partial\n\nimport torch as tc\nfrom torch import nn\n\n__all__ = ['Optim', 'LrScheduler', 'Init', 'L1']\n\n\nclass Optim(object):\n @staticmethod\n def sgd(*args, **kwargs):\n \"\"\"\n Wrapper class for :class:`torch.optim.SGD`.\n http://pytorch.org/docs/0.3.0/optim.html#torch.optim.SGD\n \"\"\"\n return partial(tc.optim.SGD, *args, **kwargs)\n\n @staticmethod\n def ada_delta(*args, **kwargs):\n \"\"\"\n Wrapper class for :class:`torch.optim.Adadelta`.\n http://pytorch.org/docs/0.3.0/optim.html#torch.optim.Adadelta\n \"\"\"\n return partial(tc.optim.Adadelta, *args, **kwargs)\n\n @staticmethod\n def ada_grad(*args, **kwargs):\n \"\"\"\n Wrapper class for :class:`torch.optim.Adagrad`.\n http://pytorch.org/docs/0.3.0/optim.html#torch.optim.Adagrad\n \"\"\"\n return partial(tc.optim.Adagrad, *args, **kwargs)\n\n @staticmethod\n def adam(*args, **kwargs):\n \"\"\"\n Wrapper class for :class:`torch.optim.Adam`.\n http://pytorch.org/docs/0.3.0/optim.html#torch.optim.Adam\n \"\"\"\n return partial(tc.optim.Adam, *args, **kwargs)\n\n @staticmethod\n def sparse_adam(*args, **kwargs):\n \"\"\"\n Wrapper class for :class:`torch.optim.SparseAdam`.\n http://pytorch.org/docs/0.3.0/optim.html#torch.optim.SparseAdam\n \"\"\"\n return partial(tc.optim.SparseAdam, *args, **kwargs)\n\n @staticmethod\n def ada_max(*args, **kwargs):\n \"\"\"\n Wrapper class for :class:`torch.optim.Adamax`.\n http://pytorch.org/docs/0.3.0/optim.html#torch.optim.Adamax\n \"\"\"\n return partial(tc.optim.Adamax, *args, **kwargs)\n\n @staticmethod\n def asgd(*args, **kwargs):\n \"\"\"\n Wrapper class for :class:`torch.optim.ASGD`.\n http://pytorch.org/docs/0.3.0/optim.html#torch.optim.ASGD\n \"\"\"\n return partial(tc.optim.ASGD, *args, **kwargs)\n\n @staticmethod\n def lbfgs(*args, **kwargs):\n \"\"\"\n Wrapper class for :class:`torch.optim.LBFGS`.\n http://pytorch.org/docs/0.3.0/optim.html#torch.optim.LBFGS\n \"\"\"\n return partial(tc.optim.LBFGS, *args, **kwargs)\n\n @staticmethod\n def rms_prop(*args, **kwargs):\n \"\"\"\n Wrapper class for :class:`torch.optim.RMSprop`.\n http://pytorch.org/docs/0.3.0/optim.html#torch.optim.RMSprop\n \"\"\"\n return partial(tc.optim.RMSprop, *args, **kwargs)\n\n @staticmethod\n def r_prop(*args, **kwargs):\n \"\"\"\n Wrapper class for :class:`torch.optim.Rprop`.\n http://pytorch.org/docs/0.3.0/optim.html#torch.optim.Rprop\n \"\"\"\n return partial(tc.optim.Rprop, *args, **kwargs)\n\n\nclass LrScheduler(object):\n @staticmethod\n def lambda_lr(*args, **kwargs):\n \"\"\"\n Wrapper class for :class:`torch.optim.lr_scheduler.LambdaLR`.\n http://pytorch.org/docs/0.3.0/optim.html#torch.optim.lr_scheduler.LambdaLR\n \"\"\"\n return partial(tc.optim.lr_scheduler.LambdaLR, *args, **kwargs)\n\n @staticmethod\n def step_lr(*args, **kwargs):\n \"\"\"\n Wrapper class for :class:`torch.optim.lr_scheduler.StepLR`.\n http://pytorch.org/docs/0.3.0/optim.html#torch.optim.lr_scheduler.StepLR\n \"\"\"\n return partial(tc.optim.lr_scheduler.StepLR, *args, **kwargs)\n\n @staticmethod\n def multi_step_lr(*args, **kwargs):\n \"\"\"\n Wrapper class for :class:`torch.optim.lr_scheduler.MultiStepLR`.\n http://pytorch.org/docs/0.3.0/optim.html#torch.optim.lr_scheduler.MultiStepLR\n \"\"\"\n return partial(tc.optim.lr_scheduler.MultiStepLR, *args, **kwargs)\n\n @staticmethod\n def exponential_lr(*args, **kwargs):\n \"\"\"\n Wrapper class for :class:`torch.optim.lr_scheduler.ExponentialLR`.\n http://pytorch.org/docs/0.3.0/optim.html#torch.optim.lr_scheduler.ExponentialLR\n \"\"\"\n return partial(tc.optim.lr_scheduler.ExponentialLR, *args, **kwargs)\n\n @staticmethod\n def reduce_lr_on_plateau(*args, **kwargs):\n \"\"\"\n Wrapper class for :class:`torch.optim.lr_scheduler.ReduceLROnPlateau`.\n http://pytorch.org/docs/0.3.0/optim.html#torch.optim.lr_scheduler.ReduceLROnPlateau\n \"\"\"\n return partial(tc.optim.lr_scheduler.ReduceLROnPlateau, *args, **kwargs)\n\n\nclass Init(object):\n @staticmethod\n def uniform(*, scale=0.1):\n b = 1 * scale\n a = -b\n return partial(nn.init.uniform, a=a, b=b)\n\n\nclass L1(object):\n @staticmethod\n def conv(*args, **kwargs):\n \"\"\"\n Wrapper class for :class:`torch.nn.Conv1d`.\n http://pytorch.org/docs/0.3.0/optim.html#torch.nn.Conv1d\n \"\"\"\n return partial(nn.Conv1d, *args, **kwargs)\n\n @staticmethod\n def linear(*args, **kwargs):\n \"\"\"\n Wrapper class for :class:`torch.nn.Linear`.\n http://pytorch.org/docs/0.3.0/optim.html#torch.nn.Linear\n \"\"\"\n return partial(nn.Linear, *args, **kwargs)\n\n @staticmethod\n def batch_norm(*args, **kwargs):\n \"\"\"\n Wrapper class for :class:`torch.nn.BatchNorm1d`.\n http://pytorch.org/docs/0.3.0/optim.html#torch.nn.BatchNorm1d\n \"\"\"\n return partial(nn.BatchNorm1d, *args, **kwargs)\n\n @staticmethod\n def instance_norm(*args, **kwargs):\n \"\"\"\n Wrapper class for :class:`torch.nn.InstanceNorm1d`.\n http://pytorch.org/docs/0.3.0/optim.html#torch.nn.InstanceNorm1d\n \"\"\"\n return partial(nn.InstanceNorm1d, *args, **kwargs)\n" }, { "path": "xenonpy/model/sequential.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport math\nfrom typing import Union, Sequence, Callable, Any, Optional\n\nfrom torch import nn\n\n__all__ = ['LinearLayer', 'SequentialLinear']\n\n\nclass LinearLayer(nn.Module):\n \"\"\"\n Base NN layer. This is a wrap around PyTorch.\n See here for details: http://pytorch.org/docs/master/nn.html#\n \"\"\"\n\n def __init__(self,\n in_features: int,\n out_features: int,\n *,\n bias: bool = True,\n dropout: float = 0.,\n activation_func: Callable = nn.ReLU(),\n normalizer: Union[float, None] = .1):\n \"\"\"\n Parameters\n ----------\n in_features:\n Size of each input sample.\n out_features:\n Size of each output sample\n bias:\n If set to ``False``, the layer will not learn an additive bias.\n Default: ``True``\n dropout: float\n Probability of an element to be zeroed. Default: 0.5\n activation_func: func\n Activation function.\n normalizer: func\n Normalization layers\n \"\"\"\n super().__init__()\n self.linear = nn.Linear(in_features, out_features, bias)\n self.dropout = nn.Dropout(dropout)\n self.normalizer = None if not normalizer else nn.BatchNorm1d(out_features, momentum=normalizer)\n self.activation = None if not activation_func else activation_func\n\n def forward(self, x):\n _out = self.linear(x)\n if self.dropout:\n _out = self.dropout(_out)\n if self.normalizer:\n _out = self.normalizer(_out)\n if self.activation:\n _out = self.activation(_out)\n return _out\n\n\nclass SequentialLinear(nn.Module):\n \"\"\"\n Sequential model with linear layers and configurable other hype-parameters.\n e.g. ``dropout``, ``hidden layers``\n\n \"\"\"\n\n def __init__(\n self,\n in_features: int,\n out_features: int,\n bias: bool = True,\n *,\n h_neurons: Union[Sequence[float], Sequence[int]] = (),\n h_bias: Union[bool, Sequence[bool]] = True,\n h_dropouts: Union[float, Sequence[float]] = 0.0,\n h_normalizers: Union[float, None, Sequence[Optional[float]]] = 0.1,\n h_activation_funcs: Union[Callable, None, Sequence[Optional[Callable]]] = nn.ReLU(),\n ):\n \"\"\"\n\n Parameters\n ----------\n in_features\n Size of input.\n out_features\n Size of output.\n bias\n Enable ``bias`` in input layer.\n h_neurons\n Number of neurons in hidden layers.\n Can be a tuple of floats. In that case,\n all these numbers will be used to calculate the neuron numbers.\n e.g. (0.5, 0.4, ...) will be expanded as (in_features * 0.5, in_features * 0.4, ...)\n h_bias\n ``bias`` in hidden layers.\n h_dropouts\n Probabilities of dropout in hidden layers.\n h_normalizers\n Momentum of batched normalizers in hidden layers.\n h_activation_funcs\n Activation functions in hidden layers.\n \"\"\"\n super().__init__()\n self._h_layers = len(h_neurons)\n if self._h_layers > 0:\n if isinstance(h_neurons[0], float):\n tmp = [in_features]\n for i, ratio in enumerate(h_neurons):\n num = math.ceil(in_features * ratio)\n tmp.append(num)\n neurons = tuple(tmp)\n\n elif isinstance(h_neurons[0], int):\n neurons = (in_features,) + tuple(h_neurons)\n else:\n raise RuntimeError('illegal parameter type of ')\n\n activation_funcs = self._check_input(h_activation_funcs)\n normalizers = self._check_input(h_normalizers)\n dropouts = self._check_input(h_dropouts)\n bias = (bias,) + self._check_input(h_bias)\n\n for i in range(self._h_layers):\n setattr(\n self, f'layer_{i}',\n LinearLayer(in_features=neurons[i],\n out_features=neurons[i + 1],\n bias=bias[i],\n dropout=dropouts[i],\n activation_func=activation_funcs[i],\n normalizer=normalizers[i]))\n\n self.output = nn.Linear(neurons[-1], out_features, bias[-1])\n else:\n self.output = nn.Linear(in_features, out_features, bias)\n\n def _check_input(self, i):\n if isinstance(i, Sequence):\n if len(i) != self._h_layers:\n raise RuntimeError(f'number of parameter not consistent with number of layers, '\n f'input is {len(i)} but need to be {self._h_layers}')\n return tuple(i)\n else:\n return tuple([i] * self._h_layers)\n\n def forward(self, x: Any) -> Any:\n for i in range(self._h_layers):\n x = getattr(self, f'layer_{i}')(x)\n return self.output(x)\n" }, { "path": "xenonpy/model/training/__init__.py", "content": "# Copyright (c) 2021. TsumiNa. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom .checker import *\nfrom .clip_grad import *\nfrom .loss import *\nfrom .lr_scheduler import *\nfrom .optimizer import *\nfrom .trainer import *\n" }, { "path": "xenonpy/model/training/base.py", "content": "# Copyright (c) 2021. TsumiNa. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom inspect import signature\nfrom typing import NamedTuple, Tuple, Any, OrderedDict, Union, Dict, Iterable\n\nimport torch\nfrom sklearn.base import BaseEstimator\nfrom torch.optim import Optimizer # noqa\nfrom torch.optim.lr_scheduler import _LRScheduler # noqa\n\nfrom xenonpy.utils import TimedMetaClass, camel_to_snake\n\n__all__ = ['BaseRunner', 'BaseLRScheduler', 'BaseOptimizer', 'BaseExtension']\n\n\n\nclass BaseExtension(object):\n\n def before_proc(self, trainer: 'BaseRunner' = None, is_training: bool = True, *_dependence: 'BaseExtension') -> None:\n pass\n\n def input_proc(self, x_in, y_in, *_dependence: 'BaseExtension') -> Tuple[Any, Any]:\n return x_in, y_in\n\n def step_forward(self, step_info: OrderedDict[Any, int], trainer: 'BaseRunner' = None, is_training: bool = True,\n *_dependence: 'BaseExtension') -> None:\n pass\n\n def output_proc(self, y_pred, y_true, trainer: 'BaseRunner' = None, is_training: bool = True,\n *_dependence: 'BaseExtension') -> Tuple[Any, Any]:\n return y_pred, y_true\n\n def after_proc(self, trainer: 'BaseRunner' = None, is_training: bool = True, *_dependence: 'BaseExtension') -> None:\n pass\n\n def on_reset(self, trainer: 'BaseRunner' = None, is_training: bool = True, *_dependence: 'BaseExtension') -> None:\n pass\n\n def on_checkpoint(self, checkpoint: NamedTuple, trainer: 'BaseRunner' = None, is_training: bool = True,\n *_dependence: 'BaseExtension') -> None:\n pass\n\n\nclass BaseOptimizer(object):\n\n def __init__(self, optimizer, **kwargs):\n self._kwargs = kwargs\n self._optimizer = optimizer\n\n def __call__(self, params: Iterable) -> Optimizer:\n \"\"\"\n\n Parameters\n ----------\n params: Iterable\n iterable of parameters to optimize or dicts defining\n parameter groups\n\n Returns\n -------\n optimizer: Optimizer\n \"\"\"\n return self._optimizer(params, **self._kwargs)\n\n\nclass BaseLRScheduler(object):\n\n def __init__(self, lr_scheduler, **kwargs):\n self._kwargs = kwargs\n self._lr_scheduler = lr_scheduler\n\n def __call__(self, optimizer: Optimizer) -> _LRScheduler:\n \"\"\"\n\n Parameters\n ----------\n optimizer: Optimizer\n Wrapped optimizer.\n\n Returns\n -------\n\n \"\"\"\n return self._lr_scheduler(optimizer, **self._kwargs)\n\n\nclass BaseRunner(BaseEstimator, metaclass=TimedMetaClass):\n T_Extension_Dict = Dict[str, Tuple[BaseExtension, Dict[str, list]]]\n\n def __init__(self, cuda: Union[bool, str, torch.device] = False):\n self._device = self.check_device(cuda)\n self._extensions: BaseRunner.T_Extension_Dict = {}\n\n @property\n def cuda(self):\n return self._device\n\n @staticmethod\n def check_device(cuda: Union[bool, str, torch.device]) -> torch.device:\n if isinstance(cuda, bool):\n if cuda:\n if torch.cuda.is_available():\n return torch.device('cuda')\n else:\n raise RuntimeError('could not use CUDA on this machine')\n else:\n return torch.device('cpu')\n\n if isinstance(cuda, str):\n if 'cuda' in cuda:\n if torch.cuda.is_available():\n return torch.device(cuda)\n else:\n raise RuntimeError('could not use CUDA on this machine')\n elif 'cpu' in cuda:\n return torch.device('cpu')\n else:\n raise RuntimeError(\n 'wrong device identifier'\n 'see also: https://pytorch.org/docs/stable/tensor_attributes.html#torch.torch.device')\n\n if isinstance(cuda, torch.device):\n return cuda\n\n @property\n def device(self):\n return self._device\n\n @device.setter\n def device(self, v):\n self._device = self.check_device(v)\n\n def _make_inject(self, injects, kwargs):\n _kwargs = {k: self._extensions[k][0] for k in injects if k in self._extensions}\n _kwargs.update({k: kwargs[k] for k in injects if k in kwargs})\n return _kwargs\n\n def input_proc(self, x_in, y_in=None, **kwargs):\n for (ext, injects) in self._extensions.values():\n _kwargs = self._make_inject(injects['input_proc'], kwargs)\n x_in, y_in = ext.input_proc(x_in, y_in, **_kwargs)\n return x_in, y_in\n\n def output_proc(self, y_pred, y_true=None, **kwargs):\n for (ext, injects) in self._extensions.values():\n _kwargs = self._make_inject(injects['output_proc'], kwargs)\n y_pred, y_true = ext.output_proc(y_pred, y_true, **_kwargs)\n return y_pred, y_true\n\n def _before_proc(self, **kwargs):\n for (ext, injects) in self._extensions.values():\n _kwargs = self._make_inject(injects['before_proc'], kwargs)\n ext.before_proc(**_kwargs)\n\n def _step_forward(self, **kwargs):\n for (ext, injects) in self._extensions.values():\n _kwargs = self._make_inject(injects['step_forward'], kwargs)\n ext.step_forward(**_kwargs)\n\n def _after_proc(self, **kwargs):\n for (ext, injects) in self._extensions.values():\n _kwargs = self._make_inject(injects['after_proc'], kwargs)\n ext.after_proc(**_kwargs)\n\n def _on_reset(self, **kwargs):\n for (ext, injects) in self._extensions.values():\n _kwargs = self._make_inject(injects['on_reset'], kwargs)\n ext.on_reset(**_kwargs)\n\n def _on_checkpoint(self, **kwargs):\n for (ext, injects) in self._extensions.values():\n _kwargs = self._make_inject(injects['on_checkpoint'], kwargs)\n ext.on_checkpoint(**_kwargs)\n\n def extend(self, *extension: BaseExtension) -> 'BaseRunner':\n \"\"\"\n Add training extensions to trainer.\n\n Parameters\n ----------\n extension: BaseExtension\n Extension.\n\n \"\"\"\n\n def _get_keyword_params(func) -> list:\n sig = signature(func)\n return [p.name for p in sig.parameters.values() if p.kind == p.POSITIONAL_OR_KEYWORD]\n\n # merge exts to named_exts\n for ext in extension:\n name = camel_to_snake(ext.__class__.__name__)\n methods = [\n 'before_proc', 'input_proc', 'step_forward', 'output_proc', 'after_proc', 'on_reset', 'on_checkpoint'\n ]\n dependencies = [_get_keyword_params(getattr(ext, m)) for m in methods]\n dependency_inject = {k: v for k, v in zip(methods, dependencies)}\n\n self._extensions[name] = (ext, dependency_inject)\n\n return self\n\n def remove_extension(self, *extension: str):\n for name in extension:\n if name in self._extensions:\n del self._extensions[name]\n\n def __getitem__(self, item):\n return self._extensions[item][0]\n" }, { "path": "xenonpy/model/training/checker.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom collections import OrderedDict\nfrom collections import defaultdict\nfrom pathlib import Path\nfrom typing import Any, Union, Dict, Callable, Tuple\n\nimport joblib\nimport pandas as pd\nimport torch\nfrom deprecated import deprecated\nfrom torch.nn import Module\n\nfrom xenonpy.model.training.base import BaseRunner\n\n__all__ = ['Checker']\n\n\nclass Checker(object):\n \"\"\"\n Check point.\n \"\"\"\n\n class __SL:\n dump = torch.save\n load = torch.load\n\n def __init__(\n self,\n path: Union[Path, str] = None,\n *,\n increment: bool = False,\n device: Union[bool, str, torch.device] = 'cpu',\n default_handle: Tuple[Callable, str] = (joblib, '.pkl.z'),\n ):\n \"\"\"\n Parameters\n ----------\n path: Union[Path, str]\n Dir path for data access. Can be ``Path`` or ``str``.\n Given a relative path will be resolved to abstract path automatically.\n increment : bool\n Set to ``True`` to prevent the potential risk of overwriting.\n Default ``False``.\n \"\"\"\n if path is None:\n path = Path().cwd().name\n self._path = Path.cwd() / path\n else:\n self._path = Path.cwd() / path\n if increment:\n i = 1\n while Path(f'{path}@{i}').exists():\n i += 1\n self._path = Path.cwd() / f'{path}@{i}'\n self._path.mkdir(parents=True, exist_ok=True)\n # self._path = self._path.resolve()\n self._device = BaseRunner.check_device(device)\n self._handle = default_handle\n\n self._files: Dict[str, str] = defaultdict(str)\n self._make_file_index()\n\n @classmethod\n @deprecated('This method is rotten and will be removed in v1.0.0, use `Checker()` instead')\n def load(cls, model_path):\n return cls(model_path)\n\n @property\n def path(self):\n return str(self._path)\n\n @property\n def files(self):\n return list(self._files.keys())\n\n @property\n def model_name(self):\n \"\"\"\n\n Returns\n -------\n str\n Model name.\n \"\"\"\n return self._path.name\n\n @property\n def model_structure(self):\n structure = self['model_structure']\n return structure\n\n @property\n def training_info(self):\n return self['training_info']\n\n @property\n def describe(self):\n \"\"\"\n Description for this model.\n\n Returns\n -------\n dict\n Description.\n \"\"\"\n return self['describe']\n\n @property\n def model(self):\n \"\"\"\n\n Returns\n -------\n model: :class:`torch.nn.Module`\n A pytorch model.\n \"\"\"\n if (self._path / 'model.pth.m').exists():\n model = torch.load(str(self._path / 'model.pth.m'), map_location=self._device)\n state = self.final_state\n\n if state is not None:\n try:\n model.load_state_dict(state)\n except torch.nn.modules.module.ModuleAttributeError:\n # pytorch 1.6.0 compatability\n for _, m in model.named_modules():\n m._non_persistent_buffers_set = set()\n model.load_state_dict(state)\n return model\n else:\n return model\n return None\n\n @model.setter\n def model(self, model: Module):\n \"\"\"\n Set a model instance.\n\n Parameters\n ----------\n model: :class:`torch.nn.Module`\n Pytorch model instance.\n \"\"\"\n if isinstance(model, Module):\n self(model=model)\n self.init_state = model.state_dict()\n self(model_structure=str(model))\n else:\n raise TypeError(f'except `torch.nn.Module` object but got {type(model)}')\n\n @property\n @deprecated('This property is rotten and will be removed in v1.0.0, use `checker.model` instead')\n def trained_model(self):\n if (self._path / 'trained_model.@1.pkl.z').exists():\n return torch.load(str(self._path / 'trained_model.@1.pkl.z'), map_location=self._device)\n else:\n tmp = self.final_state\n if tmp is not None:\n model: torch.nn.Module = self.model\n model.load_state_dict(tmp)\n return model\n return None\n\n @property\n def model_class(self):\n if (self._path / 'model_class.pkl.z').exists():\n return self['model_class']\n return None\n\n @property\n def model_params(self):\n if (self._path / 'model_params.pkl.z').exists():\n return self['model_params']\n return None\n\n @property\n def init_state(self):\n if (self._path / 'init_state.pth.s').exists():\n return torch.load(str(self._path / 'init_state.pth.s'), map_location=self._device)\n return None\n\n @init_state.setter\n def init_state(self, state: OrderedDict):\n if not isinstance(state, OrderedDict) or not state:\n raise TypeError\n for v in state.values():\n if not isinstance(v, torch.Tensor):\n raise TypeError()\n self((Checker.__SL, '.pth.s'), init_state=state)\n\n @property\n def final_state(self):\n if (self._path / 'final_state.pth.s').exists():\n return torch.load(str(self._path / 'final_state.pth.s'), map_location=self._device)\n return None\n\n @final_state.setter\n def final_state(self, state: OrderedDict):\n if not isinstance(state, OrderedDict) or not state:\n raise TypeError\n for v in state.values():\n if not isinstance(v, torch.Tensor):\n raise TypeError()\n self((Checker.__SL, '.pth.s'), final_state=state)\n\n def _make_file_index(self):\n\n for f in [f for f in self._path.iterdir() if f.match('*.pkl.*') or f.match('*.pd.*') or f.match('*.pth.*')]:\n # select data\n fn = '.'.join(f.name.split('.')[:-2])\n self._files[fn] = str(f)\n\n def _save_data(self, data: Any, filename: str, handle) -> str:\n if isinstance(data, pd.DataFrame):\n file = str(self._path / (filename + '.pd.xz'))\n self._files[filename] = file\n pd.to_pickle(data, file)\n elif isinstance(data, (torch.Tensor, torch.nn.Module)):\n file = str(self._path / (filename + '.pth.m'))\n self._files[filename] = file\n torch.save(data, file)\n else:\n file = str(self._path / (filename + handle[1]))\n self._files[filename] = file\n handle[0].dump(data, file)\n\n return file\n\n def _load_data(self, file: str, handle):\n fp = self._files[file]\n if fp == '':\n return None\n fp_ = Path(fp)\n if not fp_.exists():\n return None\n patten = fp_.name.split('.')[-2]\n if patten == 'pd':\n return pd.read_pickle(fp)\n if patten == 'pth':\n return torch.load(fp, map_location=self._device)\n if patten == 'pkl':\n return joblib.load(fp)\n else:\n return handle.load(fp)\n\n def __getattr__(self, name: str):\n \"\"\"\n Return sub-dataset.\n\n Parameters\n ----------\n name: str\n Dataset name.\n\n Returns\n -------\n self\n \"\"\"\n if name == 'checkpoints':\n sub_set = self.__class__(self._path / name, increment=False, device=self._device)\n setattr(self, f'{name}', sub_set)\n return sub_set\n\n raise AttributeError(f'no such attribute named {name}')\n\n def __getitem__(self, item):\n\n if isinstance(item, str):\n return self._load_data(item, self._handle[0])\n else:\n raise KeyError(f'{item}')\n\n def __call__(self, handle=None, **named_data: Any):\n \"\"\"\n Save data with or without name.\n Data with same name will not be overwritten.\n\n Parameters\n ----------\n handle: Tuple[Callable, str]\n named_data: dict\n Named data as k,v pair.\n\n \"\"\"\n if handle is None:\n handle = self._handle\n\n for k, v in named_data.items():\n self._save_data(v, k, handle)\n\n def set_checkpoint(self, **kwargs):\n self.checkpoints((Checker.__SL, '.pth.s'), **kwargs)\n\n def __repr__(self):\n cont_ls = ['<{}> includes:'.format(self.__class__.__name__)]\n\n for k, v in self._files.items():\n cont_ls.append('\"{}\": {}'.format(k, v))\n\n return '\\n'.join(cont_ls)\n" }, { "path": "xenonpy/model/training/clip_grad.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom torch.nn.utils import clip_grad_norm_, clip_grad_value_\n\n__all__ = ['ClipNorm', 'ClipValue']\n\n\nclass ClipNorm(object):\n\n def __init__(self, max_norm, norm_type=2):\n r\"\"\"Clips gradient norm of an iterable of parameters.\n\n The norm is computed over all gradients together, as if they were\n concatenated into a single vector. Gradients are modified in-place.\n\n Arguments:\n max_norm (float or int): max norm of the gradients\n norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for\n infinity norm.\n\n Returns:\n Total norm of the parameters (viewed as a single vector).\n \"\"\"\n self.norm_type = norm_type\n self.max_norm = max_norm\n\n def __call__(self, params):\n clip_grad_norm_(parameters=params, max_norm=self.max_norm, norm_type=self.norm_type)\n\n\nclass ClipValue(object):\n\n def __init__(self, clip_value):\n r\"\"\"Clips gradient of an iterable of parameters at specified value.\n\n Gradients are modified in-place.\n\n Arguments:\n clip_value (float or int): maximum allowed value of the gradients.\n The gradients are clipped in the range\n :math:`\\left[\\text{-clip\\_value}, \\text{clip\\_value}\\right]`\n \"\"\"\n self.clip_value = clip_value\n\n def __call__(self, params):\n clip_grad_value_(parameters=params, clip_value=self.clip_value)\n" }, { "path": "xenonpy/model/training/dataset/__init__.py", "content": "# Copyright (c) 2021. TsumiNa. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom .array import *\nfrom .cgcnn import *\n" }, { "path": "xenonpy/model/training/dataset/array.py", "content": "# Copyright (c) 2021. TsumiNa. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom typing import Union\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import TensorDataset\nfrom typing import Sequence, Union\n\n__all__ = ['ArrayDataset']\n\n\nclass ArrayDataset(TensorDataset):\n def __init__(self,\n *array: Union[np.ndarray, pd.DataFrame, pd.Series,\n torch.Tensor],\n dtypes: Union[None, Sequence[torch.dtype]] = None):\n if dtypes is None:\n dtypes = [torch.get_default_dtype()] * len(array)\n if len(dtypes) != len(array):\n raise ValueError('length of dtypes not equal to length of array')\n\n array = [\n self._convert(data, dtype) for data, dtype in zip(array, dtypes)\n ]\n super().__init__(*array)\n\n @staticmethod\n def _convert(data, dtype):\n if isinstance(data, torch.Tensor):\n return data\n if isinstance(data, (pd.DataFrame, pd.Series)):\n data = data.values\n if isinstance(data, np.ndarray):\n data = torch.from_numpy(data)\n if not isinstance(data, torch.Tensor):\n raise RuntimeError(\n 'input must be pd.DataFrame, pd.Series, np.ndarray, or torch.Tensor but got %s'\n % data.__class__)\n\n return data.to(dtype)\n" }, { "path": "xenonpy/model/training/dataset/cgcnn.py", "content": "# Copyright (c) 2021. TsumiNa. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom typing import Union\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import Dataset\n\n__all__ = ['CrystalGraphDataset']\n\n\nclass CrystalGraphDataset(Dataset):\n \"\"\"\n\n Parameters\n ----------\n\n root_dir: str\n The path to the root directory of the dataset\n max_num_nbr: int\n The maximum number of neighbors while constructing the crystal graph\n radius: float\n The cutoff radius for searching neighbors\n dmin: float\n The minimum distance for constructing GaussianDistance\n step: float\n The step size for constructing GaussianDistance\n random_seed: int or None\n Random seed for shuffling the dataset\n\n Returns\n -------\n\n atom_fea: torch.Tensor shape (n_i, atom_fea_len)\n nbr_fea: torch.Tensor shape (n_i, M, nbr_fea_len)\n nbr_fea_idx: torch.LongTensor shape (n_i, M)\n target: torch.Tensor shape (1, )\n cif_id: str or int\n \"\"\"\n\n def __init__(self, crystal_features: Union[pd.DataFrame, np.ndarray],\n targets: Union[pd.DataFrame, np.ndarray] = None):\n if isinstance(crystal_features, pd.DataFrame):\n crystal_features = crystal_features.values\n if not isinstance(crystal_features, np.ndarray):\n raise RuntimeError(' must be pd.DataFrame or np.ndarray')\n self.crystal_features = crystal_features\n\n if targets is not None:\n if isinstance(targets, pd.DataFrame):\n targets = targets.values\n if not isinstance(targets, np.ndarray):\n raise RuntimeError(' must be pd.DataFrame, pd.Series or np.ndarray')\n self.targets = targets\n\n def __len__(self):\n return self.crystal_features.shape[0]\n\n def __getitem__(self, idx):\n features = self.crystal_features[idx]\n if self.targets is not None:\n target = self.targets[idx]\n return (features[0], features[1], features[2]), torch.Tensor(target)\n return features[0], features[1], features[2]\n\n @staticmethod\n def collate_fn(dataset_list):\n \"\"\"\n Collate a list of data and return a batch for predicting crystal\n properties.\n\n Parameters\n ----------\n\n dataset_list: list of tuples for each data point.\n (atom_fea, nbr_fea, nbr_fea_idx, target)\n\n atom_fea: torch.Tensor shape (n_i, atom_fea_len)\n nbr_fea: torch.Tensor shape (n_i, M, nbr_fea_len)\n nbr_fea_idx: torch.LongTensor shape (n_i, M)\n target: torch.Tensor shape (1, )\n cif_id: str or int\n\n Returns\n -------\n N = sum(n_i); N0 = sum(i)\n\n batch_atom_fea: torch.Tensor shape (N, orig_atom_fea_len)\n Atom features from atom type\n batch_nbr_fea: torch.Tensor shape (N, M, nbr_fea_len)\n Bond features of each atom's M neighbors\n batch_nbr_fea_idx: torch.LongTensor shape (N, M)\n Indices of M neighbors of each atom\n crystal_atom_idx: list of torch.LongTensor of length N0\n Mapping from the crystal idx to atom idx\n target: torch.Tensor shape (N, 1)\n Target value for prediction\n batch_cif_ids: list\n \"\"\"\n\n def _batch():\n batch_atom_fea.append(atom_fea)\n batch_nbr_fea.append(nbr_fea)\n batch_nbr_fea_idx.append(nbr_fea_idx + base_idx)\n new_idx = torch.LongTensor(np.arange(n_i) + base_idx)\n crystal_atom_idx.append(new_idx)\n\n batch_atom_fea, batch_nbr_fea, batch_nbr_fea_idx = [], [], []\n crystal_atom_idx, batch_target = [], []\n base_idx = 0\n\n if len(dataset_list[0]) == 2:\n for i, ((atom_fea, nbr_fea, nbr_fea_idx), target) in enumerate(dataset_list):\n n_i = atom_fea.shape[0] # number of atoms for this crystal\n _batch()\n base_idx += n_i\n batch_target.append(target)\n return (torch.cat(batch_atom_fea, dim=0),\n torch.cat(batch_nbr_fea, dim=0),\n torch.cat(batch_nbr_fea_idx, dim=0),\n crystal_atom_idx), torch.stack(batch_target, dim=0)\n\n else:\n for i, (atom_fea, nbr_fea, nbr_fea_idx) in enumerate(dataset_list):\n n_i = atom_fea.shape[0] # number of atoms for this crystal\n _batch()\n base_idx += n_i\n return (torch.cat(batch_atom_fea, dim=0),\n torch.cat(batch_nbr_fea, dim=0),\n torch.cat(batch_nbr_fea_idx, dim=0),\n crystal_atom_idx)\n" }, { "path": "xenonpy/model/training/extension/__init__.py", "content": "# Copyright (c) 2021. TsumiNa. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom .persist import *\nfrom .tensor_convert import *\nfrom .validator import *\n" }, { "path": "xenonpy/model/training/extension/persist.py", "content": "# Copyright (c) 2021. TsumiNa. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom copy import deepcopy\nfrom datetime import datetime, timedelta\nfrom pathlib import Path\nfrom platform import version as sys_ver\nfrom sys import version as py_ver\nfrom typing import Union, Callable, Any, OrderedDict\n\nimport numpy as np\nimport torch\n\nfrom xenonpy import __version__\nfrom xenonpy.model.training import Trainer, Checker\nfrom xenonpy.model.training.base import BaseExtension, BaseRunner\n\n__all__ = ['Persist']\n\n\nclass Persist(BaseExtension):\n \"\"\"\n Trainer extension for data persistence\n \"\"\"\n\n def __init__(self,\n path: Union[Path, str] = None,\n *,\n model_class: Callable = None,\n model_params: Union[tuple, dict, any] = None,\n increment: bool = False,\n sync_training_step: bool = False,\n only_best_states: bool = False,\n no_model_saving: bool = False,\n **describe: Any):\n \"\"\"\n\n Parameters\n ----------\n path\n Path for model saving.\n model_class\n A factory function for model reconstructing.\n In most case this is the model class inherits from :class:`torch.nn.Module`\n model_params\n The parameters for model reconstructing.\n This can be anything but in general this is a dict which can be used as kwargs parameters.\n increment\n If ``True``, dir name of path will be decorated with a auto increment number,\n e.g. use ``model_dir@1`` for ``model_dir``.\n sync_training_step\n If ``True``, will save ``trainer.training_info`` at each iteration.\n Default is ``False``, only save ``trainer.training_info`` at each epoch.\n only_best_states\n If ``True``, will only save the models with the best states in terms of each of the criteria.\n no_model_saving\n Indicate whether to save the connected model and checkpoints.\n describe:\n Any other information to describe this model.\n These information will be saved under model dir by name ``describe.pkl.z``.\n \"\"\"\n self._model_class: Callable = model_class\n self._model_params: Union[list, dict] = model_params\n self.sync_training_step = sync_training_step\n self.only_best_states = only_best_states\n self._increment = increment\n self._describe = describe\n self._describe_ = None\n self._checker: Union[Checker, None] = None\n self._tmp_args: list = []\n self._tmp_kwargs: dict = {}\n self._epoch_count = 0\n self._no_model_saving = no_model_saving\n self.path = path\n\n @property\n def describe(self):\n if self._checker is None:\n raise ValueError('can not access property `describe` before training')\n return self._checker.describe\n\n @property\n def no_model_saving(self):\n return self._no_model_saving\n\n @property\n def path(self):\n if self._checker is None:\n raise ValueError('can not access property `path` before training')\n return str(self._checker.path)\n\n @path.setter\n def path(self, path: Union[Path, str]):\n if self._checker is not None:\n raise ValueError('can not reset property `path` after training')\n self._path = path\n\n @property\n def model_structure(self):\n return self._checker.model_structure\n\n def get_checkpoint(self, id_: str = None):\n if id_ is not None:\n return self._checker.checkpoints[id_]\n return self._checker.checkpoints.files\n\n def __call__(self, handle: Any = None, **kwargs: Any):\n if self._checker is None:\n raise RuntimeError('calling of this method only after the model training')\n self._checker(handle=handle, **kwargs)\n\n def __getitem__(self, item):\n return self._checker[item]\n\n def on_checkpoint(self,\n checkpoint: Trainer.checkpoint_tuple,\n _trainer: BaseRunner = None,\n _is_training: bool = True,\n *_dependence: 'BaseExtension') -> None:\n if self._no_model_saving:\n return None\n if self.only_best_states:\n tmp = checkpoint.id.split('_')\n if tmp[-1] == '1':\n key = '_'.join(tmp[:-1])\n value = deepcopy(checkpoint._asdict())\n self._checker.set_checkpoint(**{key: value})\n else:\n key = checkpoint.id\n value = deepcopy(checkpoint._asdict())\n self._checker.set_checkpoint(**{key: value})\n\n def step_forward(self,\n step_info: OrderedDict[Any, int],\n trainer: Trainer = None,\n _is_training: bool = True,\n *_dependence: BaseExtension) -> None:\n if self.sync_training_step:\n training_info = trainer.training_info\n if training_info is not None:\n self._checker(training_info=training_info)\n else:\n epoch = step_info['i_epoch']\n if epoch > self._epoch_count:\n training_info = trainer.training_info\n if training_info is not None:\n self._epoch_count = epoch\n self._checker(training_info=training_info)\n\n def before_proc(self, trainer: Trainer = None, _is_training: bool = True, *_dependence: BaseExtension) -> None:\n self._checker = Checker(self._path, increment=self._increment)\n if self._model_class is not None:\n self._checker(model_class=self._model_class)\n if self._model_params is not None:\n self._checker(model_params=self._model_params)\n if not self._no_model_saving:\n self._checker.model = trainer.model\n self._describe_ = dict(\n python=py_ver,\n system=sys_ver(),\n numpy=np.__version__,\n torch=torch.__version__,\n xenonpy=__version__,\n device=str(trainer.device),\n start=datetime.now().strftime('%Y/%m/%d %H:%M:%S'),\n finish='N/A',\n time_elapsed='N/A',\n **self._describe,\n )\n self._checker(describe=self._describe_)\n\n def after_proc(self, trainer: Trainer = None, _is_training: bool = True, *_dependence: 'BaseExtension') -> None:\n self._describe_.update(finish=datetime.now().strftime('%Y/%m/%d %H:%M:%S'),\n time_elapsed=str(timedelta(seconds=trainer.timer.elapsed)))\n self._checker.final_state = trainer.model.state_dict()\n self._checker(\n training_info=trainer.training_info,\n describe=self._describe_,\n )\n" }, { "path": "xenonpy/model/training/extension/tensor_convert.py", "content": "# Copyright (c) 2021. TsumiNa. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\nfrom typing import Union, Tuple, Any, Sequence\nfrom scipy.special import softmax\n\nimport numpy as np\nimport pandas as pd\nimport torch\n\nfrom xenonpy.model.training import Trainer\nfrom xenonpy.model.training.base import BaseExtension\n\n__all__ = ['TensorConverter']\n\nT_Data = Union[pd.DataFrame, pd.Series, np.ndarray, torch.Tensor]\n\n\nclass TensorConverter(BaseExtension):\n\n def __init__(self,\n *,\n x_dtype: Union[torch.dtype, Sequence[torch.dtype]] = None,\n y_dtype: Union[torch.dtype, Sequence[torch.dtype]] = None,\n empty_cache: bool = False,\n auto_reshape: bool = True,\n argmax: bool = False,\n probability: bool = False):\n \"\"\"\n Covert tensor like data into :class:`torch.Tensor` automatically.\n \n Parameters\n ----------\n x_dtype\n The :class:`torch.dtype`s of **X** data.\n If ``None``, will convert all data into ``torch.get_default_dtype()`` type.\n Can be a tuple of ``torch.dtype`` when your **X** is a tuple.\n y_dtype\n The :class:`torch.dtype`s of **y** data.\n If ``None``, will convert all data into ``torch.get_default_dtype()`` type.\n Can be a tuple of ``torch.dtype`` when your **y** is a tuple.\n empty_cache\n See Also: https://pytorch.org/docs/stable/cuda.html#torch.cuda.empty_cache\n auto_reshape\n Reshape tensor to (-1, 1) if tensor shape is (n,). Default ``True``.\n argmax\n Apply ``np.argmax(out, 1)`` on the output. This should only be used with classification model.\n Default ``False``. If ``True``, will ignore ``probability`` parameter.\n probability\n Apply ``scipy.special.softmax`` on the output. This should only be used with classification model.\n Default ``False``.\n \"\"\"\n\n self.argmax = argmax\n self.empty_cache = empty_cache\n self.probability = probability\n self.auto_reshape = auto_reshape\n if x_dtype is None:\n self._x_dtype = torch.get_default_dtype()\n else:\n self._x_dtype = x_dtype\n\n if y_dtype is None:\n self._y_dtype = torch.get_default_dtype()\n else:\n self._y_dtype = y_dtype\n\n @property\n def argmax(self):\n return self._argmax\n\n @argmax.setter\n def argmax(self, value):\n self._argmax = value\n\n @property\n def empty_cache(self):\n return self._empty_cache\n\n @empty_cache.setter\n def empty_cache(self, value):\n self._empty_cache = value\n\n @property\n def auto_reshape(self):\n return self._auto_shape\n\n @auto_reshape.setter\n def auto_reshape(self, value):\n self._auto_shape = False if self.argmax or self.probability else value\n\n @property\n def probability(self):\n return self._probability\n\n @probability.setter\n def probability(self, value):\n self._probability = value\n\n def _get_x_dtype(self, i=0):\n if isinstance(self._x_dtype, tuple):\n return self._x_dtype[i]\n return self._x_dtype\n\n def _get_y_dtype(self, i=0):\n if isinstance(self._y_dtype, tuple):\n return self._y_dtype[i]\n return self._y_dtype\n\n def input_proc(self, x_in: Union[Sequence[Union[torch.Tensor, pd.DataFrame, pd.Series, np.ndarray, Any]],\n torch.Tensor, pd.DataFrame, pd.Series, np.ndarray, Any],\n y_in: Union[Sequence[Union[torch.Tensor, pd.DataFrame, pd.Series, np.ndarray,\n Any]], torch.Tensor, pd.DataFrame, pd.Series, np.ndarray,\n Any], trainer: Trainer) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"\n Convert data to :class:`torch.Tensor`.\n\n Parameters\n ----------\n y_in\n x_in\n\n Returns\n -------\n Union[Any, Tuple[Any, Any]]\n\n \"\"\"\n\n def _convert(t, dtype):\n if t is None:\n return t\n\n # if tensor, do nothing\n if isinstance(t, torch.Tensor):\n return t.to(trainer.device, non_blocking=trainer.non_blocking)\n # if pandas, turn to numpy\n if isinstance(t, (pd.DataFrame, pd.Series)):\n t = t.values\n # if numpy, turn to tensor\n if isinstance(t, np.ndarray):\n t = torch.from_numpy(t).to(dtype)\n # return others\n if not isinstance(t, torch.Tensor):\n return t\n # reshape (1,) to (-1, 1)\n if len(t.size()) == 1 and self.auto_reshape:\n t = t.unsqueeze(1)\n return t.to(trainer.device, non_blocking=trainer.non_blocking)\n\n if isinstance(x_in, Sequence):\n x_in = tuple([_convert(t, self._get_x_dtype(i)) for i, t in enumerate(x_in)])\n else:\n x_in = _convert(x_in, self._get_x_dtype())\n\n if isinstance(y_in, Sequence):\n y_in = tuple([_convert(t, self._get_y_dtype(i)) for i, t in enumerate(y_in)])\n else:\n y_in = _convert(y_in, self._get_y_dtype())\n\n return x_in, y_in\n\n def step_forward(self):\n if self._empty_cache:\n torch.cuda.empty_cache()\n\n def output_proc(\n self,\n y_pred: Union[Sequence[Union[torch.Tensor, np.ndarray, Any]], torch.Tensor, Any],\n y_true: Union[Sequence[Union[torch.Tensor, np.ndarray, Any]], torch.Tensor, Any, None],\n is_training: bool,\n ):\n \"\"\"\n Convert :class:`torch.Tensor` to :class:`numpy.ndarray`.\n\n Parameters\n ----------\n y_pred: Union[torch.Tensor, Tuple[torch.Tensor]]\n y_true : Union[torch.Tensor, Tuple[torch.Tensor]]\n is_training: bool\n Specify whether the model in the training mode.\n\n \"\"\"\n\n def _convert(y_, argmax_=False, proba_=False):\n if y_ is None:\n return y_\n else:\n y_ = y_.detach().cpu().numpy()\n if argmax_:\n return np.argmax(y_, 1)\n if proba_:\n return softmax(y_, axis=1)\n return y_\n\n if not is_training:\n if isinstance(y_pred, tuple):\n return tuple([_convert(t, self._argmax, self._probability) for t in y_pred]), _convert(y_true)\n else:\n return _convert(y_pred, self._argmax, self._probability), _convert(y_true)\n else:\n return y_pred, y_true\n" }, { "path": "xenonpy/model/training/extension/validator.py", "content": "# Copyright (c) 2021. TsumiNa. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\nimport numpy as np\n\nfrom collections import OrderedDict\nfrom typing import Callable, Any, Dict, Union\n\nfrom xenonpy.model.utils import regression_metrics, classification_metrics\nfrom xenonpy.model.training import Trainer\nfrom xenonpy.model.training.base import BaseExtension\n\n__all__ = ['Validator']\n\n\nclass Validator(BaseExtension):\n \"\"\"\n Validator extension\n \"\"\"\n\n regress = 'regress'\n classify = 'classify'\n\n def __init__(self,\n metrics_func: Union[str, Callable[[Any, Any], Dict]],\n *,\n each_iteration: bool = True,\n early_stopping: int = None,\n trace_order: int = 1,\n warming_up: int = 0,\n **trace_criteria: Dict[str, float]):\n \"\"\"\n\n Parameters\n ----------\n metrics_func\n Function for metrics calculation.\n If ``str``, should be ``regress`` or ``classify`` which to specify the training types.\n See :py:func:`xenonpy.model.utils.classification_metrics` and\n :py:func:`xenonpy.model.utils.regression_metrics` to know the details.\n you can also give the calculation function yourself.\n each_iteration\n If ``True``, validation will be executed every iteration.\n Otherwise, only validate at each epoch done.\n Default ``True``.\n early_stopping\n Set patience condition of early stopping condition.\n This condition trace criteria setting by ``trace_criteria`` parameter.\n trace_order\n How many ranks of ``trace_criteria`` will be saved as checkpoint.\n Checkpoint name follow the format ``criterion_rank``, e.g. ``mae_1``\n warming_up\n Validator do not set/update checkpoint before ``warming_up`` epochs.\n trace_criteria\n Validation criteria.\n Should follow this formation: ``criterion=target``, e.g ``mae=0, corr=1``.\n The names of criteria must be consistent with the output of``metrics_func``.\n \"\"\"\n if metrics_func == 'regress':\n self.metrics_func = regression_metrics\n elif metrics_func == 'classify':\n self.metrics_func = classification_metrics\n else:\n self.metrics_func = metrics_func\n\n self.warming_up = warming_up\n self.each_iteration = each_iteration\n self.patience = early_stopping + 1 if early_stopping is not None else None\n self._count = early_stopping\n self.order = trace_order\n\n self._epoch_count = 0\n self.trace = {}\n self.trace_order = trace_order\n self.trace_criteria = trace_criteria\n self._set_trace(trace_criteria, trace_order)\n\n self.from_dataset = False\n self.train_loss = np.inf\n\n @property\n def warming_up(self):\n return self._warming_up\n\n @warming_up.setter\n def warming_up(self, val: int):\n if val < 0:\n raise ValueError(\"`warming` up must equal or greater than 0\")\n self._warming_up = val\n\n def _set_trace(self, trace_metrics: dict, trace_order: int):\n for name, target in trace_metrics.items():\n self.trace[name] = (target, [np.inf] * trace_order)\n\n def on_reset(self) -> None:\n self._set_trace(self.trace_criteria, self.trace_order)\n\n def before_proc(self, trainer: Trainer) -> None:\n x_val, y_val = trainer.x_val, trainer.y_val\n val_dataset = trainer.validate_dataset\n\n if x_val is None and y_val is None and val_dataset is not None:\n self.from_dataset = True\n elif x_val is None or y_val is None:\n raise RuntimeError('no data for validation')\n\n def step_forward(self, trainer: Trainer, step_info: OrderedDict) -> None:\n\n def _validate():\n if self.from_dataset:\n y_preds, y_trues = trainer.predict(dataset=trainer.validate_dataset)\n else:\n y_preds, y_trues = trainer.predict(trainer.x_val, trainer.y_val)\n\n train_loss = step_info[trainer.loss_type]\n if train_loss < self.train_loss:\n self.train_loss = train_loss\n self._count = self.patience\n\n metrics = self.metrics_func(y_trues, y_preds)\n for name, (target, current) in self.trace.items():\n if name in metrics:\n score = np.abs(metrics[name] - target)\n if score < current[-1] and step_info['i_epoch'] >= self._warming_up:\n current.append(score)\n current.sort()\n current.pop()\n self._count = self.patience\n if self.order == 1:\n trainer.set_checkpoint(name)\n else:\n index = current.index(score) + 1\n for i in range(self.order, index, -1):\n if f'{name}_{i - 1}' in trainer.checkpoints:\n trainer.checkpoints[f'{name}_{i}'] = trainer.checkpoints[f'{name}_{i - 1}']\n trainer.set_checkpoint(f'{name}_{index}')\n\n if self.patience is not None:\n self._count -= 1\n if self._count == 0:\n trainer.early_stop(\n f'no improvement for {[k for k in self.trace]} since the last {self.patience} iterations, '\n f'finish training at iteration {trainer.total_iterations}')\n\n step_info.update({f'val_{k}': v for k, v in metrics.items()})\n\n if not self.each_iteration:\n epoch = step_info['i_epoch']\n if epoch > self._epoch_count:\n self._epoch_count = epoch\n _validate()\n else:\n _validate()\n" }, { "path": "xenonpy/model/training/loss.py", "content": "# Copyright (c) 2021. TsumiNa. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\n__all__ = [\n 'NLLLoss', 'NLLLoss2d', 'L1Loss', 'MSELoss', 'CrossEntropyLoss', 'CTCLoss', 'PoissonNLLLoss', 'KLDivLoss',\n 'BCELoss', 'BCEWithLogitsLoss', 'MarginRankingLoss', 'HingeEmbeddingLoss', 'MultiLabelMarginLoss', 'SmoothL1Loss',\n 'SoftMarginLoss', 'MultiLabelSoftMarginLoss', 'CosineEmbeddingLoss', 'MultiMarginLoss', 'TripletMarginLoss',\n 'TripletMarginWithDistanceLoss'\n]\n\nfrom torch.nn.modules.loss import *\n" }, { "path": "xenonpy/model/training/lr_scheduler.py", "content": "# Copyright (c) 2021. TsumiNa. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\nfrom torch.optim import lr_scheduler\n\nfrom xenonpy.model.training.base import BaseLRScheduler\n\n__all__ = ['LambdaLR', 'StepLR', 'MultiStepLR', 'ExponentialLR', 'CosineAnnealingLR', 'ReduceLROnPlateau', 'CyclicLR']\n\n\nclass LambdaLR(BaseLRScheduler):\n\n def __init__(self, *, lr_lambda, last_epoch=-1):\n \"\"\"Sets the learning rate of each parameter group to the initial lr\n times a given function. When last_epoch=-1, sets initial lr as lr.\n\n Args:\n lr_lambda (function or list): A function which computes a multiplicative\n factor given an integer parameter epoch, or a list of such\n functions, one for each group in optimizer.param_groups.\n last_epoch (int): The index of last epoch. Default: -1.\n\n Example:\n >>> # Assuming optimizer has two groups.\n >>> lambda1 = lambda epoch: epoch // 30\n >>> lambda2 = lambda epoch: 0.95 ** epoch\n >>> scheduler = LambdaLR(lr_lambda=[lambda1, lambda2])\n >>> scheduler(optimizer)\n >>> for epoch in range(100):\n >>> train(...)\n >>> validate(...)\n >>> scheduler.step(),\n \"\"\"\n super().__init__(lr_scheduler.LambdaLR, lr_lambda=lr_lambda, last_epoch=last_epoch)\n\n\nclass StepLR(BaseLRScheduler):\n\n def __init__(self, *, step_size, gamma=0.1, last_epoch=-1):\n \"\"\"Decays the learning rate of each parameter group by gamma every\n step_size epochs. Notice that such decay can happen simultaneously with\n other changes to the learning rate from outside this scheduler. When\n last_epoch=-1, sets initial lr as lr.\n\n Args:\n step_size (int): Period of learning rate decay.\n gamma (float): Multiplicative factor of learning rate decay.\n Default: 0.1.\n last_epoch (int): The index of last epoch. Default: -1.\n\n Example:\n >>> # Assuming optimizer uses lr = 0.05 for all groups\n >>> # lr = 0.05 if epoch < 30\n >>> # lr = 0.005 if 30 <= epoch < 60\n >>> # lr = 0.0005 if 60 <= epoch < 90\n >>> # ...\n >>> scheduler = StepLR(step_size=30, gamma=0.1)\n >>> scheduler(optimizer)\n >>> for epoch in range(100):\n >>> train(...)\n >>> validate(...)\n >>> scheduler.step()\n \"\"\"\n super().__init__(lr_scheduler.StepLR, step_size=step_size, gamma=gamma, last_epoch=last_epoch)\n\n\nclass MultiStepLR(BaseLRScheduler):\n\n def __init__(self, *, milestones, gamma=0.1, last_epoch=-1):\n \"\"\"Decays the learning rate of each parameter group by gamma once the\n number of epoch reaches one of the milestones. Notice that such decay can\n happen simultaneously with other changes to the learning rate from outside\n this scheduler. When last_epoch=-1, sets initial lr as lr.\n\n Args:\n milestones (list): List of epoch indices. Must be increasing.\n gamma (float): Multiplicative factor of learning rate decay.\n Default: 0.1.\n last_epoch (int): The index of last epoch. Default: -1.\n\n Example:\n >>> # Assuming optimizer uses lr = 0.05 for all groups\n >>> # lr = 0.05 if epoch < 30\n >>> # lr = 0.005 if 30 <= epoch < 80\n >>> # lr = 0.0005 if epoch >= 80\n >>> scheduler = MultiStepLR(milestones=[30,80], gamma=0.1)\n >>> scheduler(optimizer)\n >>> for epoch in range(100):\n >>> train(...)\n >>> validate(...)\n >>> scheduler.step(),\n \"\"\"\n super().__init__(lr_scheduler.MultiStepLR, milestones=milestones, gamma=gamma, last_epoch=last_epoch)\n\n\nclass ExponentialLR(BaseLRScheduler):\n\n def __init__(self, *, gamma, last_epoch=-1):\n \"\"\"Decays the learning rate of each parameter group by gamma every epoch.\n When last_epoch=-1, sets initial lr as lr.\n\n Args:\n gamma (float): Multiplicative factor of learning rate decay.\n last_epoch (int): The index of last epoch. Default: -1.\n \"\"\"\n super().__init__(lr_scheduler.ExponentialLR, gamma=gamma, last_epoch=last_epoch)\n\n\nclass CosineAnnealingLR(BaseLRScheduler):\n\n def __init__(self, *, T_max, eta_min=0, last_epoch=-1):\n r\"\"\"Set the learning rate of each parameter group using a cosine annealing\n schedule, where :math:`\\eta_{max}` is set to the initial lr and\n :math:`T_{cur}` is the number of epochs since the last restart in SGDR:\n\n .. math::\n \\eta_{t+1} = \\eta_{min} + (\\eta_t - \\eta_{min})\\frac{1 +\n \\cos(\\frac{T_{cur+1}}{T_{max}}\\pi)}{1 + \\cos(\\frac{T_{cur}}{T_{max}}\\pi)},\n T_{cur} \\neq (2k+1)T_{max};\\\\\n \\eta_{t+1} = \\eta_{t} + (\\eta_{max} - \\eta_{min})\\frac{1 -\n \\cos(\\frac{1}{T_{max}}\\pi)}{2},\n T_{cur} = (2k+1)T_{max}.\\\\\n\n When last_epoch=-1, sets initial lr as lr. Notice that because the schedule\n is defined recursively, the learning rate can be simultaneously modified\n outside this scheduler by other operators. If the learning rate is set\n solely by this scheduler, the learning rate at each step becomes:\n\n .. math::\n \\eta_t = \\eta_{min} + \\frac{1}{2}(\\eta_{max} - \\eta_{min})(1 +\n \\cos(\\frac{T_{cur}}{T_{max}}\\pi))\n\n It has been proposed in\n `SGDR: Stochastic Gradient Descent with Warm Restarts`_. Note that this only\n implements the cosine annealing part of SGDR, and not the restarts.\n\n Args:\n T_max (int): Maximum number of iterations.\n eta_min (float): Minimum learning rate. Default: 0.\n last_epoch (int): The index of last epoch. Default: -1.\n\n .. _SGDR\\: Stochastic Gradient Descent with Warm Restarts:\n https://arxiv.org/abs/1608.03983\n \"\"\"\n super().__init__(lr_scheduler.CosineAnnealingLR, T_max=T_max, eta_min=eta_min, last_epoch=last_epoch)\n\n\nclass ReduceLROnPlateau(BaseLRScheduler):\n\n def __init__(self,\n *,\n mode='min',\n factor=0.1,\n patience=10,\n verbose=False,\n threshold=1e-4,\n threshold_mode='rel',\n cooldown=0,\n min_lr=0,\n eps=1e-8):\n \"\"\"Reduce learning rate when a metric has stopped improving.\n Models often benefit from reducing the learning rate by a factor\n of 2-10 once learning stagnates. This scheduler reads a metrics\n quantity and if no improvement is seen for a 'patience' number\n of epochs, the learning rate is reduced.\n\n Args:\n mode (str): One of `min`, `max`. In `min` mode, lr will\n be reduced when the quantity monitored has stopped\n decreasing; in `max` mode it will be reduced when the\n quantity monitored has stopped increasing. Default: 'min'.\n factor (float): Factor by which the learning rate will be\n reduced. new_lr = lr * factor. Default: 0.1.\n patience (int): Number of epochs with no improvement after\n which learning rate will be reduced. For example, if\n `patience = 2`, then we will ignore the first 2 epochs\n with no improvement, and will only decrease the LR after the\n 3rd epoch if the loss still hasn't improved then.\n Default: 10.\n verbose (bool): If ``True``, prints a message to stdout for\n each update. Default: ``False``.\n threshold (float): Threshold for measuring the new optimum,\n to only focus on significant changes. Default: 1e-4.\n threshold_mode (str): One of `rel`, `abs`. In `rel` mode,\n dynamic_threshold = best * ( 1 + threshold ) in 'max'\n mode or best * ( 1 - threshold ) in `min` mode.\n In `abs` mode, dynamic_threshold = best + threshold in\n `max` mode or best - threshold in `min` mode. Default: 'rel'.\n cooldown (int): Number of epochs to wait before resuming\n normal operation after lr has been reduced. Default: 0.\n min_lr (float or list): A scalar or a list of scalars. A\n lower bound on the learning rate of all param groups\n or each group respectively. Default: 0.\n eps (float): Minimal decay applied to lr. If the difference\n between new and old lr is smaller than eps, the update is\n ignored. Default: 1e-8.\n\n \"\"\"\n super().__init__(lr_scheduler.ReduceLROnPlateau,\n mode=mode,\n factor=factor,\n patience=patience,\n verbose=verbose,\n threshold=threshold,\n threshold_mode=threshold_mode,\n cooldown=cooldown,\n min_lr=min_lr,\n eps=eps)\n\n\nclass CyclicLR(BaseLRScheduler):\n\n def __init__(self,\n *,\n base_lr,\n max_lr,\n step_size_up=2000,\n step_size_down=None,\n mode='triangular',\n gamma=1.,\n scale_fn=None,\n scale_mode='cycle',\n cycle_momentum=True,\n base_momentum=0.8,\n max_momentum=0.9,\n last_epoch=-1):\n \"\"\"Sets the learning rate of each parameter group according to\n cyclical learning rate policy (CLR). The policy cycles the learning\n rate between two boundaries with a constant frequency, as detailed in\n the paper `Cyclical Learning Rates for Training Neural Networks`_.\n The distance between the two boundaries can be scaled on a per-iteration\n or per-cycle basis.\n\n Cyclical learning rate policy changes the learning rate after every batch.\n `step` should be called after a batch has been used for training.\n\n This class has three built-in policies, as put forth in the paper:\n\n \"triangular\":\n A basic triangular cycle w/ no amplitude scaling.\n \"triangular2\":\n A basic triangular cycle that scales initial amplitude by half each cycle.\n \"exp_range\":\n A cycle that scales initial amplitude by gamma**(cycle iterations) at each\n cycle iteration.\n\n This implementation was adapted from the github repo: `bckenstler/CLR`_\n\n Args:\n optimizer (Optimizer): Wrapped optimizer.\n base_lr (float or list): Initial learning rate which is the\n lower boundary in the cycle for each parameter group.\n max_lr (float or list): Upper learning rate boundaries in the cycle\n for each parameter group. Functionally,\n it defines the cycle amplitude (max_lr - base_lr).\n The lr at any cycle is the sum of base_lr\n and some scaling of the amplitude; therefore\n max_lr may not actually be reached depending on\n scaling function.\n step_size_up (int): Number of training iterations in the\n increasing half of a cycle. Default: 2000\n step_size_down (int): Number of training iterations in the\n decreasing half of a cycle. If step_size_down is None,\n it is set to step_size_up. Default: None\n mode (str): One of {triangular, triangular2, exp_range}.\n Values correspond to policies detailed above.\n If scale_fn is not None, this argument is ignored.\n Default: 'triangular'\n gamma (float): Constant in 'exp_range' scaling function:\n gamma**(cycle iterations)\n Default: 1.0\n scale_fn (function): Custom scaling policy defined by a single\n argument lambda function, where\n 0 <= scale_fn(x) <= 1 for all x >= 0.\n If specified, then 'mode' is ignored.\n Default: None\n scale_mode (str): {'cycle', 'iterations'}.\n Defines whether scale_fn is evaluated on\n cycle number or cycle iterations (training\n iterations since start of cycle).\n Default: 'cycle'\n cycle_momentum (bool): If ``True``, momentum is cycled inversely\n to learning rate between 'base_momentum' and 'max_momentum'.\n Default: True\n base_momentum (float or list): Initial momentum which is the\n lower boundary in the cycle for each parameter group.\n Default: 0.8\n max_momentum (float or list): Upper momentum boundaries in the cycle\n for each parameter group. Functionally,\n it defines the cycle amplitude (max_momentum - base_momentum).\n The momentum at any cycle is the difference of max_momentum\n and some scaling of the amplitude; therefore\n base_momentum may not actually be reached depending on\n scaling function. Default: 0.9\n last_epoch (int): The index of the last batch. This parameter is used when\n resuming a training job. Since `step()` should be invoked after each\n batch instead of after each epoch, this number represents the total\n number of *batches* computed, not the total number of epochs computed.\n When last_epoch=-1, the schedule is started from the beginning.\n Default: -1\n\n .. _Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186\n .. _bckenstler/CLR: https://github.com/bckenstler/CLR\n \"\"\"\n super().__init__(lr_scheduler.CyclicLR,\n base_lr=base_lr,\n max_lr=max_lr,\n step_size_up=step_size_up,\n step_size_down=step_size_down,\n mode=mode,\n gamma=gamma,\n scale_fn=scale_fn,\n scale_mode=scale_mode,\n cycle_momentum=cycle_momentum,\n base_momentum=base_momentum,\n max_momentum=max_momentum,\n last_epoch=last_epoch)\n" }, { "path": "xenonpy/model/training/optimizer.py", "content": "# Copyright (c) 2021. TsumiNa. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom torch import optim\n\nfrom xenonpy.model.training.base import BaseOptimizer\n\n__all__ = ['Adadelta', 'Adagrad', 'Adam', 'Adamax', 'ASGD', 'SGD', 'SparseAdam', 'RMSprop', 'Rprop', 'LBFGS']\n\n\nclass Adadelta(BaseOptimizer):\n\n def __init__(self, *, lr=1.0, rho=0.9, eps=1e-06, weight_decay=0):\n \"\"\"Implements Adadelta algorithm.\n\n It has been proposed in `ADADELTA: An Adaptive Learning Rate Method`__.\n\n Arguments:\n rho (float, optional): coefficient used for computing a running average\n of squared gradients (default: 0.9)\n eps (float, optional): term added to the denominator to improve\n numerical stability (default: 1e-6)\n lr (float, optional): coefficient that scale delta before it is applied\n to the parameters (default: 1.0)\n weight_decay (float, optional): weight decay (L2 penalty) (default: 0)\n\n __ https://arxiv.org/abs/1212.5701\n \"\"\"\n super().__init__(optim.Adadelta, lr=lr, rho=rho, eps=eps, weight_decay=weight_decay)\n\n\nclass Adagrad(BaseOptimizer):\n\n def __init__(self, *, lr=0.01, lr_decay=0, weight_decay=0, initial_accumulator_value=0):\n \"\"\"Implements Adagrad algorithm.\n\n It has been proposed in `Adaptive Subgradient Methods for Online Learning\n and Stochastic Optimization`_.\n\n Arguments:\n lr (float, optional): learning rate (default: 1e-2)\n lr_decay (float, optional): learning rate decay (default: 0)\n weight_decay (float, optional): weight decay (L2 penalty) (default: 0)\n\n .. _Adaptive Subgradient Methods for Online Learning and Stochastic\n Optimization: http://jmlr.org/papers/v12/duchi11a.html\n \"\"\"\n super().__init__(optim.Adagrad,\n lr=lr,\n lr_decay=lr_decay,\n weight_decay=weight_decay,\n initial_accumulator_value=initial_accumulator_value)\n\n\nclass Adam(BaseOptimizer):\n\n def __init__(self, *, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, amsgrad=False):\n r\"\"\"Implements Adam algorithm.\n\n It has been proposed in `Adam: A Method for Stochastic Optimization`_.\n The implementation of the L2 penalty follows changes proposed in `Decoupled Weight Decay Regularization`_.\n\n Arguments:\n lr (float, optional): learning rate (default: 1e-3)\n betas (Tuple[float, float], optional): coefficients used for computing\n running averages of gradient and its square (default: (0.9, 0.999))\n eps (float, optional): term added to the denominator to improve\n numerical stability (default: 1e-8)\n weight_decay (float, optional): weight decay (L2 penalty) (default: 0)\n amsgrad (boolean, optional): whether to use the AMSGrad variant of this\n algorithm from the paper `On the Convergence of Adam and Beyond`_\n (default: False)\n\n .. _Adam\\: A Method for Stochastic Optimization:\n https://arxiv.org/abs/1412.6980\n .. _Decoupled Weight Decay Regularization:\n https://arxiv.org/abs/1711.05101\n .. _On the Convergence of Adam and Beyond:\n https://openreview.net/forum?id=ryQu7f-RZ\n \"\"\"\n\n super().__init__(optim.Adam, lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad)\n\n\nclass AdamW(BaseOptimizer):\n\n def __init__(self, *, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, amsgrad=False):\n r\"\"\"Implements AdamW algorithm.\n\n The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_.\n The AdamW variant was proposed in `Decoupled Weight Decay Regularization`_.\n\n Arguments:\n lr (float, optional): learning rate (default: 1e-3)\n betas (Tuple[float, float], optional): coefficients used for computing\n running averages of gradient and its square (default: (0.9, 0.999))\n eps (float, optional): term added to the denominator to improve\n numerical stability (default: 1e-8)\n weight_decay (float, optional): weight decay (L2 penalty) (default: 0)\n amsgrad (boolean, optional): whether to use the AMSGrad variant of this\n algorithm from the paper `On the Convergence of Adam and Beyond`_\n (default: False)\n\n .. _Adam\\: A Method for Stochastic Optimization:\n https://arxiv.org/abs/1412.6980\n .. _Decoupled Weight Decay Regularization:\n https://arxiv.org/abs/1711.05101\n .. _On the Convergence of Adam and Beyond:\n https://openreview.net/forum?id=ryQu7f-RZ\n \"\"\"\n\n super().__init__(optim.AdamW, lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad)\n\n\nclass SparseAdam(BaseOptimizer):\n\n def __init__(self, *, lr=0.001, betas=(0.9, 0.999), eps=1e-08):\n r\"\"\"Implements lazy version of Adam algorithm suitable for sparse tensors.\n\n In this variant, only moments that show up in the gradient get updated, and\n only those portions of the gradient get applied to the parameters.\n\n Arguments:\n lr (float, optional): learning rate (default: 1e-3)\n betas (Tuple[float, float], optional): coefficients used for computing\n running averages of gradient and its square (default: (0.9, 0.999))\n eps (float, optional): term added to the denominator to improve\n numerical stability (default: 1e-8)\n\n .. _Adam\\: A Method for Stochastic Optimization:\n https://arxiv.org/abs/1412.6980\n \"\"\"\n\n super().__init__(optim.SparseAdam, lr=lr, betas=betas, eps=eps)\n\n\nclass Adamax(BaseOptimizer):\n\n def __init__(self, *, lr=0.002, betas=(0.9, 0.999), eps=1e-08, weight_decay=0):\n \"\"\"Implements Adamax algorithm (a variant of Adam based on infinity norm).\n\n It has been proposed in `Adam: A Method for Stochastic Optimization`__.\n\n Arguments:\n lr (float, optional): learning rate (default: 2e-3)\n betas (Tuple[float, float], optional): coefficients used for computing\n running averages of gradient and its square\n eps (float, optional): term added to the denominator to improve\n numerical stability (default: 1e-8)\n weight_decay (float, optional): weight decay (L2 penalty) (default: 0)\n\n __ https://arxiv.org/abs/1412.6980\n \"\"\"\n\n super().__init__(optim.Adamax, lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)\n\n\nclass ASGD(BaseOptimizer):\n\n def __init__(self, *, lr=0.002, lambd=0.0001, alpha=0.75, t0=1000000.0, weight_decay=0):\n \"\"\"Implements Averaged Stochastic Gradient Descent.\n\n It has been proposed in `Acceleration of stochastic approximation by\n averaging`_.\n\n Arguments:\n lr (float, optional): learning rate (default: 1e-2)\n lambd (float, optional): decay term (default: 1e-4)\n alpha (float, optional): power for eta update (default: 0.75)\n t0 (float, optional): point at which to start averaging (default: 1e6)\n weight_decay (float, optional): weight decay (L2 penalty) (default: 0)\n\n .. _Acceleration of stochastic approximation by averaging:\n http://dl.acm.org/citation.cfm?id=131098\n \"\"\"\n\n super().__init__(optim.ASGD, lr=lr, lambd=lambd, alpha=alpha, t0=t0, weight_decay=weight_decay)\n\n\nclass LBFGS(BaseOptimizer):\n\n def __init__(self,\n *,\n lr=1,\n max_iter=20,\n max_eval=None,\n tolerance_grad=1e-5,\n tolerance_change=1e-9,\n history_size=100,\n line_search_fn=None):\n \"\"\"Implements L-BFGS algorithm.\n\n .. warning::\n This optimizer doesn't support per-parameter options and parameter\n groups (there can be only one).\n\n .. warning::\n Right now all parameters have to be on a single device. This will be\n improved in the future.\n\n .. note::\n This is a very memory intensive optimizer (it requires additional\n ``param_bytes * (history_size + 1)`` bytes). If it doesn't fit in memory\n try reducing the history size, or use a different algorithm.\n\n Arguments:\n lr (float): learning rate (default: 1)\n max_iter (int): maximal number of iterations per optimization step\n (default: 20)\n max_eval (int): maximal number of function evaluations per optimization\n step (default: max_iter * 1.25).\n tolerance_grad (float): termination tolerance on first order optimality\n (default: 1e-5).\n tolerance_change (float): termination tolerance on function\n value/parameter changes (default: 1e-9).\n history_size (int): update history size (default: 100).\n \"\"\"\n\n super().__init__(optim.LBFGS,\n lr=lr,\n max_iter=max_iter,\n max_eval=max_eval,\n tolerance_grad=tolerance_grad,\n tolerance_change=tolerance_change,\n history_size=history_size,\n line_search_fn=line_search_fn)\n\n\nclass RMSprop(BaseOptimizer):\n\n def __init__(self, *, lr=0.01, alpha=0.99, eps=1e-08, weight_decay=0, momentum=0, centered=False):\n \"\"\"Implements RMSprop algorithm.\n\n Proposed by G. Hinton in his\n `course `_.\n\n The centered version first appears in `Generating Sequences\n With Recurrent Neural Networks `_.\n\n Arguments:\n lr (float, optional): learning rate (default: 1e-2)\n momentum (float, optional): momentum factor (default: 0)\n alpha (float, optional): smoothing constant (default: 0.99)\n eps (float, optional): term added to the denominator to improve\n numerical stability (default: 1e-8)\n centered (bool, optional) : if ``True``, compute the centered RMSProp,\n the gradient is normalized by an estimation of its variance\n weight_decay (float, optional): weight decay (L2 penalty) (default: 0)\n\n \"\"\"\n\n super().__init__(optim.RMSprop,\n lr=lr,\n alpha=alpha,\n eps=eps,\n weight_decay=weight_decay,\n momentum=momentum,\n centered=centered)\n\n\nclass Rprop(BaseOptimizer):\n\n def __init__(self, *, lr=0.01, etas=(0.5, 1.2), step_sizes=(1e-06, 50)):\n \"\"\"Implements the resilient backpropagation algorithm.\n\n Arguments:\n lr (float, optional): learning rate (default: 1e-2)\n etas (Tuple[float, float], optional): pair of (etaminus, etaplis), that\n are multiplicative increase and decrease factors\n (default: (0.5, 1.2))\n step_sizes (Tuple[float, float], optional): a pair of minimal and\n maximal allowed step sizes (default: (1e-6, 50))\n \"\"\"\n\n super().__init__(optim.Rprop, lr=lr, etas=etas, step_sizes=step_sizes)\n\n\nclass SGD(BaseOptimizer):\n\n def __init__(self, *, lr=0.01, momentum=0, dampening=0, weight_decay=0, nesterov=False):\n r\"\"\"Implements stochastic gradient descent (optionally with momentum).\n\n Nesterov momentum is based on the formula from\n `On the importance of initialization and momentum in deep learning`__.\n\n Args:\n lr (float): learning rate\n momentum (float, optional): momentum factor (default: 0)\n weight_decay (float, optional): weight decay (L2 penalty) (default: 0)\n dampening (float, optional): dampening for momentum (default: 0)\n nesterov (bool, optional): enables Nesterov momentum (default: False)\n\n Example:\n >>> optimizer = torch.optim.SGD(lr=0.1, momentum=0.9)\n >>> optimizer(model.parameters())\n >>> optimizer.zero_grad()\n >>> loss_fn(model(input), target).backward()\n >>> optimizer.step(),\n\n __ http://www.cs.toronto.edu/%7Ehinton/absps/momentum.pdf\n\n .. note::\n The implementation of SGD with Momentum/Nesterov subtly differs from\n Sutskever et. al. and implementations in some other frameworks.\n\n Considering the specific case of Momentum, the update can be written as\n\n .. math::\n v = \\rho * v + g \\\\\n p = p - lr * v\n\n where p, g, v and :math:`\\rho` denote the parameters, gradient,\n velocity, and momentum respectively.\n\n This is in contrast to Sutskever et. al. and\n other frameworks which employ an update of the form\n\n .. math::\n v = \\rho * v + lr * g \\\\\n p = p - v\n\n The Nesterov version is analogously modified.\n \"\"\"\n\n super().__init__(optim.SGD,\n lr=lr,\n momentum=momentum,\n dampening=dampening,\n weight_decay=weight_decay,\n nesterov=nesterov)\n" }, { "path": "xenonpy/model/training/trainer.py", "content": "# Copyright (c) 2021. TsumiNa. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom collections import OrderedDict, namedtuple\nfrom copy import deepcopy\nfrom pathlib import Path\nfrom typing import Union, Tuple, List, Any, Dict, Callable\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.nn import Module\nfrom torch.optim.lr_scheduler import _LRScheduler, ReduceLROnPlateau\nfrom torch.utils.data import DataLoader\n\nfrom deprecated import deprecated\n\nfrom xenonpy.model.training import ClipValue, ClipNorm, Checker\nfrom xenonpy.model.training.base import BaseOptimizer, BaseLRScheduler, BaseRunner\nfrom xenonpy.utils import camel_to_snake\n\n__all__ = ['Trainer']\n\n\nclass Trainer(BaseRunner):\n checkpoint_tuple = namedtuple('checkpoint', 'id iterations model_state')\n results_tuple = namedtuple('results', 'total_epochs device training_info checkpoints model')\n\n def __init__(\n self,\n *,\n loss_func: torch.nn.Module = None,\n optimizer: BaseOptimizer = None,\n model: Module = None,\n lr_scheduler: BaseLRScheduler = None,\n clip_grad: Union[ClipNorm, ClipValue] = None,\n epochs: int = 200,\n cuda: Union[bool, str, torch.device] = False,\n non_blocking: bool = False,\n ):\n \"\"\"\n NN model trainer.\n\n Parameters\n ----------\n loss_func\n Loss function.\n optimizer\n Optimizer for model parameters tuning.\n model\n Pytorch NN model.\n lr_scheduler\n Learning rate scheduler.\n clip_grad\n Clip grad before each optimize.\n epochs\n Number of iterations.\n cuda\n Set training device(s).\n non_blocking\n When non_blocking is ``True``,\n it tries to convert/move asynchronously with respect to the host if possible.\n \"\"\"\n super().__init__(cuda=cuda)\n # None able\n self._clip_grad = clip_grad\n self._epochs = epochs\n self._non_blocking = non_blocking\n\n # loss function placeholder\n self._loss_func = None\n self._loss_type = None\n\n # model placeholder\n self._model = None\n self._init_states = None\n\n # optimizer placeholder\n self._optim = None\n self._optimizer = None\n self._optimizer_state = None\n\n # lr_scheduler placeholder\n self._scheduler = None\n self._lr_scheduler = None\n\n # init private vars\n self._early_stopping: Tuple[bool, str] = (False, '')\n self._checkpoints: Dict[Union[int, str], Trainer.checkpoint_tuple] = OrderedDict()\n self._training_info: List[OrderedDict] = []\n self._total_its: int = 0 # of total iterations\n self._total_epochs: int = 0 # of total epochs\n\n self._x_val = None\n self._y_val = None\n self._validate_dataset = None\n\n # init\n self.model = model\n self.optimizer = optimizer\n self.lr_scheduler = lr_scheduler\n self.loss_func = loss_func\n\n @property\n def epochs(self):\n return self._epochs\n\n @property\n def non_blocking(self):\n return self._non_blocking\n\n @property\n def loss_type(self):\n return self._loss_type\n\n @property\n def total_epochs(self):\n return self._total_epochs\n\n @property\n def total_iterations(self):\n return self._total_its\n\n @property\n def x_val(self):\n return self._x_val\n\n @property\n def y_val(self):\n return self._y_val\n\n @property\n def validate_dataset(self):\n return self._validate_dataset\n\n @property\n def loss_func(self):\n return self._loss_func\n\n @loss_func.setter\n def loss_func(self, loss_func):\n if loss_func is not None:\n self._loss_func = loss_func\n self._loss_type = 'train_' + camel_to_snake(loss_func.__class__.__name__)\n\n @property\n def training_info(self):\n if len(self._training_info) > 0:\n return pd.DataFrame(data=self._training_info)\n return None\n\n @property\n def device(self):\n return self._device\n\n @device.setter\n def device(self, v):\n self._device = self.check_device(v)\n self.model = None\n\n @property\n def model(self):\n return self._model\n\n @model.setter\n def model(self, model):\n if model is not None:\n if isinstance(model, torch.nn.Module):\n self.reset(to=model)\n else:\n raise TypeError('parameter `m` must be a instance of but got %s' % type(model))\n\n @property\n def optimizer(self):\n return self._optimizer\n\n @optimizer.setter\n def optimizer(self, optimizer):\n if optimizer is not None:\n self._optim = optimizer\n\n if self._optim is not None and self._model is not None:\n self._optimizer = self._optim(self._model.parameters())\n self._optimizer_state = deepcopy(self._optimizer.state_dict())\n\n self.lr_scheduler = None\n\n @property\n def lr_scheduler(self):\n return self._lr_scheduler\n\n @lr_scheduler.setter\n def lr_scheduler(self, scheduler):\n if scheduler is not None:\n self._scheduler = scheduler\n\n if self._scheduler is not None and self._optimizer is not None:\n self._lr_scheduler: Union[_LRScheduler, None] = self._scheduler(self._optimizer)\n\n @property\n def clip_grad(self):\n return self._clip_grad\n\n @clip_grad.setter\n def clip_grad(self, fn):\n self._clip_grad = fn\n\n @property\n def checkpoints(self):\n return self._checkpoints\n\n def get_checkpoint(self, checkpoint: Union[int, str] = None):\n if checkpoint is None:\n return list(self._checkpoints.keys())\n if isinstance(checkpoint, int):\n id_ = f'cp_{checkpoint}'\n return self._checkpoints[id_]\n if isinstance(checkpoint, str):\n return self._checkpoints[checkpoint]\n raise TypeError(f'parameter must be str or int but got {checkpoint.__class__}')\n\n def set_checkpoint(self, id_: str = None):\n if id_ is None:\n id_ = f'cp_{self._total_its}'\n cp = self.checkpoint_tuple(\n id=id_,\n iterations=self._total_its,\n model_state=deepcopy(self._model.state_dict()),\n )\n\n self._checkpoints[id_] = cp\n self._on_checkpoint(checkpoint=cp, trainer=self, is_training=True)\n\n def early_stop(self, msg: str):\n self._early_stopping = (True, msg)\n\n def reset(self, *, to: Union[Module, int, str] = None, remove_checkpoints: bool = True):\n \"\"\"\n Reset trainer.\n This will reset all trainer states and drop all training step information.\n\n Parameters\n ----------\n to: Union[bool, Module]\n Bind trainer to the given model or reset current model to it's initialization states.\n remove_checkpoints\n Set to ``True`` to remove all checkpoints when resetting. Default ``true``.\n \"\"\"\n self._training_info = []\n self._total_its = 0\n self._total_epochs = 0\n self._early_stopping = (False, '')\n\n if isinstance(to, Module):\n self._model = to.to(self._device, non_blocking=self._non_blocking)\n self._init_states = deepcopy(to.state_dict())\n\n self.optimizer = None\n self.lr_scheduler = None\n elif isinstance(to, (int, str)):\n cp = self.get_checkpoint(to)\n self._model.load_state_dict(cp.model_state)\n elif to is None:\n self._model.load_state_dict(self._init_states)\n self._optimizer.load_state_dict(self._optimizer_state)\n else:\n raise TypeError(f'parameter must be torch.nnModule, int, or str but got {type(to)}')\n\n if remove_checkpoints:\n self._checkpoints = OrderedDict()\n\n self._on_reset(trainer=self, is_training=True)\n\n def fit(self,\n x_train: Union[Any, Tuple[Any]] = None,\n y_train: Any = None,\n x_val: Union[Any, Tuple[Any]] = None,\n y_val: Any = None,\n *,\n training_dataset: DataLoader = None,\n validation_dataset: DataLoader = None,\n epochs: int = None,\n checkpoint: Union[bool, int, Callable[[int], Tuple[bool, str]]] = None,\n progress_bar: Union[str, None] = 'auto',\n **model_params):\n \"\"\"\n Train the Neural Network model\n\n Parameters\n ----------\n x_train: Union[torch.Tensor, Tuple[torch.Tensor]]\n Training data. Will be ignored will``training_dataset`` is given.\n y_train: torch.Tensor\n Test data. Will be ignored will``training_dataset`` is given.\n training_dataset: DataLoader\n Torch DataLoader. If given, will only use this as training dataset.\n When loop over this dataset, it should yield a tuple contains ``x_train`` and ``y_train`` in order.\n x_val : Union[Any, Tuple[Any]]\n Data for validation.\n y_val : Any\n Data for validation.\n validation_dataset: DataLoader\n epochs : int\n Epochs. If not ``None``, it will overwrite ``self.epochs`` temporarily.\n checkpoint: Union[bool, int, Callable[[int], bool]]\n If ``True``, will save model states at each step.\n If ``int``, will save model states every `checkpoint` steps.\n If ``Callable``, the function should take current ``total_epochs`` as input return ``bool``.\n progress_bar\n Show progress bar when for training steps. Can be 'auto', 'console', or ``None``.\n See https://pypi.org/project/tqdm/#ipython-jupyter-integration for more details.\n model_params: dict\n Other model parameters.\n \"\"\"\n if epochs is None:\n epochs = self._epochs\n\n prob = self._total_epochs\n if progress_bar is not None:\n if progress_bar == 'auto':\n from tqdm.auto import tqdm\n else:\n from tqdm import tqdm\n\n with tqdm(total=epochs, desc='Training') as pbar:\n for _ in self(x_train=x_train,\n y_train=y_train,\n x_val=x_val,\n y_val=y_val,\n training_dataset=training_dataset,\n validation_dataset=validation_dataset,\n epochs=epochs,\n checkpoint=checkpoint,\n **model_params):\n delta = self._total_epochs - prob\n if delta:\n prob = self._total_epochs\n pbar.update(delta)\n else:\n for _ in self(x_train=x_train,\n y_train=y_train,\n x_val=x_val,\n y_val=y_val,\n training_dataset=training_dataset,\n validation_dataset=validation_dataset,\n epochs=epochs,\n checkpoint=checkpoint,\n **model_params):\n pass\n\n def __call__(self,\n x_train: Union[torch.Tensor, Tuple[torch.Tensor]] = None,\n y_train: Any = None,\n x_val: Union[Any, Tuple[Any]] = None,\n y_val: Any = None,\n *,\n training_dataset: DataLoader = None,\n validation_dataset: DataLoader = None,\n epochs: int = None,\n checkpoint: Union[bool, int, Callable[[int], bool]] = None,\n **model_params):\n \"\"\"\n Train the Neural Network model\n\n Parameters\n ----------\n x_train\n Training data. Will be ignored will ``training_dataset`` is given.\n y_train\n Test data. Will be ignored will ``training_dataset`` is given.\n training_dataset: DataLoader\n Torch DataLoader. If given, will only use this as training dataset.\n When loop over this dataset, it should yield a tuple contains ``x_train`` and ``y_train`` in order.\n x_val : Union[Any, Tuple[Any]]\n Data for validation.\n y_val : Any\n Data for validation.\n validation_dataset : DataLoader\n epochs : int\n Epochs. If not ``None``, it will overwrite ``self.epochs`` temporarily.\n checkpoint: Union[bool, int, Callable[[int], bool]]\n If ``True``, will save model states at each step.\n If ``int``, will save model states every `checkpoint` steps.\n If ``Callable``, the function should take current ``total_epochs`` as input return ``bool``.\n model_params: dict\n Other model parameters.\n\n Yields\n ------\n namedtuple\n\n \"\"\"\n if self._model is None:\n raise RuntimeError(\n 'no model for training, use `trainer.model = ` or `trainer.reset(to=)` to set one')\n if self._loss_func is None:\n raise RuntimeError('no loss function for training, use `trainer.loss_func = ` to set one')\n if self._optimizer is None:\n raise RuntimeError('no optimizer for training, use `trainer.optimizer = ` to set one')\n\n if epochs is None:\n epochs = self._epochs\n\n if training_dataset is not None:\n if y_train is not None or x_train is not None:\n raise RuntimeError('parameter is exclusive of and ')\n else:\n if y_train is None or x_train is None:\n raise RuntimeError('missing parameter or ')\n\n # training step\n def _step(x_, y_, i_b=0):\n\n def closure():\n self._optimizer.zero_grad()\n y_p_ = self._model(*x_, **model_params)\n y_p_, y_t_ = self.output_proc(y_p_, y_, trainer=self, is_training=True)\n loss_ = self._loss_func(y_p_, y_t_)\n loss_.backward()\n\n if self._clip_grad is not None:\n self._clip_grad(self._model.parameters())\n\n return loss_\n\n # make sure running in training mode\n if not self._model.training:\n self._model.train()\n\n train_loss = self._optimizer.step(closure).item()\n\n step_info = OrderedDict(\n total_iters=self._total_its,\n i_epoch=self._total_epochs,\n i_batch=i_b + 1,\n )\n step_info[self._loss_type] = train_loss\n self._total_its += 1\n self._step_forward(step_info=step_info, trainer=self, is_training=True)\n self._training_info.append(step_info)\n\n if self._lr_scheduler is not None:\n if isinstance(self._lr_scheduler, ReduceLROnPlateau):\n self._lr_scheduler.step(train_loss)\n else:\n self._lr_scheduler.step()\n\n return step_info\n\n def _snapshot():\n if checkpoint is not None:\n if isinstance(checkpoint, bool) and checkpoint:\n self.set_checkpoint()\n if isinstance(checkpoint, int):\n if self._total_epochs % checkpoint == 0:\n self.set_checkpoint()\n if callable(checkpoint):\n flag, msg = checkpoint(self._total_epochs)\n if flag:\n self.set_checkpoint(msg)\n\n if validation_dataset is not None:\n if y_val is not None or x_val is not None:\n raise RuntimeError('parameter is exclusive of and ')\n else:\n self._validate_dataset = validation_dataset\n else:\n if y_val is not None and x_val is not None:\n self._x_val, self._y_val = self.input_proc(x_val, y_val, trainer=self, is_training=False)\n\n # before processing\n self._before_proc(trainer=self, is_training=True)\n\n if training_dataset:\n for i_epoch in range(self._total_epochs, epochs + self._total_epochs):\n\n self._total_epochs += 1\n for i_batch, (x_train, y_train) in enumerate(training_dataset):\n x_train, y_train = self.input_proc(x_train, y_train, trainer=self, is_training=True)\n if not isinstance(x_train, tuple):\n x_train = (x_train,)\n yield _step(x_train, y_train, i_batch)\n if self._early_stopping[0]:\n print(f'Early stopping is applied: {self._early_stopping[1]}')\n self._after_proc(trainer=self, is_training=True)\n self._model.eval()\n return\n _snapshot()\n\n else:\n x_train, y_train = self.input_proc(x_train, y_train, trainer=self, is_training=True)\n if not isinstance(x_train, tuple):\n x_train = (x_train,)\n\n for i_epoch in range(self._total_epochs, epochs + self._total_epochs):\n\n self._total_epochs += 1\n yield _step(x_train, y_train)\n if self._early_stopping[0]:\n print(f'Early stopping is applied: {self._early_stopping[1]}.')\n self._after_proc(trainer=self, is_training=True)\n self._model.eval()\n return\n _snapshot()\n\n # after processing\n self._after_proc(trainer=self, is_training=True)\n self._model.eval()\n\n @classmethod\n @deprecated(reason=\"will be removed in v1.0.0, please use `Trainer.from_checker` instead\")\n def load(\n cls,\n from_: Union[str, Path, Checker],\n *,\n loss_func: torch.nn.Module = None,\n optimizer: BaseOptimizer = None,\n lr_scheduler: BaseLRScheduler = None,\n clip_grad: Union[ClipNorm, ClipValue] = None,\n epochs: int = 200,\n cuda: Union[bool, str, torch.device] = False,\n non_blocking: bool = False,\n ) -> 'Trainer':\n return cls.from_checker(from_,\n loss_func=loss_func,\n optimizer=optimizer,\n lr_scheduler=lr_scheduler,\n clip_grad=clip_grad,\n epochs=epochs,\n cuda=cuda,\n non_blocking=non_blocking)\n\n @classmethod\n def from_checker(\n cls,\n checker: Union[str, Path, Checker],\n *,\n loss_func: torch.nn.Module = None,\n optimizer: BaseOptimizer = None,\n lr_scheduler: BaseLRScheduler = None,\n clip_grad: Union[ClipNorm, ClipValue] = None,\n epochs: int = 200,\n cuda: Union[bool, str, torch.device] = False,\n non_blocking: bool = False,\n ) -> 'Trainer':\n \"\"\"\n Load model for local path or :class:`xenonpy.model.training.Checker`.\n\n Parameters\n ----------\n checker\n Path to the model dir or :class:`xenonpy.model.training.Checker` object.\n loss_func\n Loss function.\n optimizer\n Optimizer for model parameters tuning.\n lr_scheduler\n Learning rate scheduler.\n clip_grad\n Clip grad before each optimize.\n epochs\n Number of iterations.\n cuda\n Set training device(s).\n non_blocking\n When non_blocking is ``True``,\n it tries to convert/move asynchronously with respect to the host if possible.\n\n Returns\n -------\n\n \"\"\"\n if isinstance(checker, (str, Path)):\n checker = Checker(checker)\n else:\n checker = checker\n if len(checker.files) == 0:\n raise RuntimeError(f'{checker.path} is not a model dir')\n\n tmp = cls(model=checker.model,\n cuda=cuda,\n loss_func=loss_func,\n optimizer=optimizer,\n lr_scheduler=lr_scheduler,\n clip_grad=clip_grad,\n epochs=epochs,\n non_blocking=non_blocking)\n tmp._training_info = checker.training_info.to_dict(orient='records')\n if Path(checker.path + '/checkpoints').is_dir():\n for k in checker.checkpoints.files:\n tmp._checkpoints[k] = cls.checkpoint_tuple(**checker.checkpoints[k])\n return tmp\n\n def predict(self,\n x_in: Union[Any, Tuple[Any]] = None,\n y_true: Union[Any, Tuple[Any]] = None,\n *,\n dataset: DataLoader = None,\n checkpoint: Union[int, str] = None,\n **model_params) -> Union[Tuple[np.ndarray], np.ndarray]:\n \"\"\"\n Predict from x input.\n This is just a simple wrapper for :meth:`~model.nn.utils.Predictor.predict`.\n\n Parameters\n ----------\n x_in\n Input data for prediction.\n y_true\n True values.\n dataset\n Pytorch :class:`torch.utils.data.DataLoader`.\n checkpoint\n Reset to checkpoint if given.\n model_params: dict\n Model parameters for prediction.\n\n Returns\n -------\n ret\n Predict results. If ``y_true`` is not ``None``, ``y_true`` will be returned in second.\n \"\"\"\n\n def _predict(x_, y_=None):\n x_, y_ = self.input_proc(x_, y_, trainer=self, is_training=False)\n if not isinstance(x_, tuple):\n x_ = (x_,)\n\n if checkpoint:\n cp = self.get_checkpoint(checkpoint)\n model = deepcopy(self._model).to(self.device)\n model.load_state_dict(cp.model_state)\n y_p_ = model(*x_, **model_params)\n else:\n y_p_ = self._model(*x_, **model_params)\n\n return self.output_proc(y_p_, y_, trainer=self, is_training=False)\n\n def _vstack(ls):\n if isinstance(ls[0], np.ndarray):\n return np.concatenate(ls)\n if isinstance(ls[0], torch.Tensor):\n return torch.cat(ls, dim=0)\n return ls\n\n # maker sure eval mode\n self._model.eval()\n\n if x_in is None and y_true is None and dataset is not None:\n y_preds = []\n y_trues = []\n for x_in, y_true in dataset:\n y_pred, y_true = _predict(x_in, y_true)\n y_preds.append(y_pred)\n y_trues.append(y_true)\n return _vstack(y_preds), _vstack(y_trues)\n elif x_in is not None and dataset is None:\n y_preds, y_trues = _predict(x_in, y_true)\n if y_trues is None:\n return y_preds\n return y_preds, y_trues\n else:\n raise RuntimeError('parameters and are mutually exclusive')\n\n def to_namedtuple(self):\n return self.results_tuple(total_epochs=self.total_epochs,\n device=self.device,\n training_info=self.training_info,\n checkpoints={k: deepcopy(v.model_state) for k, v in self._checkpoints.items()},\n model=deepcopy(self._model.cpu()))\n" }, { "path": "xenonpy/model/utils/__init__.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom .metrics import *\n" }, { "path": "xenonpy/model/utils/metrics.py", "content": "# Copyright (c) 2021. TsumiNa. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom collections import OrderedDict\nfrom typing import Union, List, Tuple\n\nimport numpy as np\nimport pandas as pd\nfrom scipy.stats import pearsonr, spearmanr\nfrom sklearn.metrics import mean_absolute_error, r2_score, mean_squared_error, max_error\nfrom sklearn.metrics import f1_score, recall_score, precision_score, accuracy_score\n\n__all__ = ['regression_metrics', 'classification_metrics']\n\n\ndef regression_metrics(y_true: Union[np.ndarray, pd.Series], y_pred: Union[np.ndarray, pd.Series]) -> OrderedDict:\n \"\"\"\n Calculate most common regression scores.\n See Also: https://scikit-learn.org/stable/modules/model_evaluation.html\n \n Parameters\n ----------\n y_true\n True results.\n y_pred\n Predicted results.\n \n Returns\n -------\n OrderedDict\n An :class:`collections.OrderedDict` contains regression scores.\n These scores will be calculated: ``mae``, ``mse``, ``rmse``, ``r2``,\n ``pearsonr``, ``spearmanr``, ``p_value``, and ``max_ae``\n \"\"\"\n if len(y_true.shape) != 1:\n y_true = y_true.flatten()\n if len(y_pred.shape) != 1:\n y_pred = y_pred.flatten()\n\n mask = ~np.isnan(y_pred)\n y_true = y_true[mask]\n y_pred = y_pred[mask]\n\n mae = mean_absolute_error(y_true, y_pred)\n maxae = max_error(y_true, y_pred)\n mse = mean_squared_error(y_true, y_pred)\n rmse = np.sqrt(mse)\n r2 = r2_score(y_true, y_pred)\n pr, p_val = pearsonr(y_true, y_pred)\n sr, _ = spearmanr(y_true, y_pred)\n return OrderedDict(\n mae=mae,\n mse=mse,\n rmse=rmse,\n r2=r2,\n pearsonr=pr,\n spearmanr=sr,\n p_value=p_val,\n max_ae=maxae,\n )\n\n\ndef classification_metrics(\n y_true: Union[np.ndarray, pd.DataFrame, pd.Series],\n y_pred: Union[np.ndarray, pd.Series],\n *,\n average: Union[None, List[str], Tuple[str]] = ('weighted', 'micro', 'macro'),\n labels=None,\n) -> dict:\n \"\"\"\n Calculate most common classification scores.\n See also: https://scikit-learn.org/stable/modules/model_evaluation.html\n \n Parameters\n ----------\n y_true\n True results.\n y_pred\n Predicted results.\n average\n This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned.\n Otherwise, this determines the type of averaging performed on the data:\n\n binary:\n Only report results for the class specified by pos_label. This is applicable only if targets (y_{true,pred})\n are binary.\n micro:\n Calculate metrics globally by counting the total true positives, false negatives and false positives.\n macro:\n Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into\n account.\n weighted:\n Calculate metrics for each label, and find their average weighted by support (the number of true instances\n for each label). This alters ``macro`` to account for label imbalance; it can result in an F-score that is\n not between precision and recall.\n labels\n The set of labels to include when average != ``binary``, and their order if average is None.\n Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority\n negative class, while labels not present in the data will result in 0 components in a macro average.\n For multilabel targets, labels are column indices.\n By default, all labels in y_true and y_pred are used in sorted order.\n\n Returns\n -------\n OrderedDict\n An :class:`collections.OrderedDict` contains classification scores.\n These scores will always contains ``accuracy``, ``f1``, ``precision`` and ``recall``.\n For multilabel targets, based on the selection of the ``average`` parameter, the **weighted**, **micro**,\n and **macro** scores of ``f1`, ``precision``, and ``recall`` will be calculated.\n \"\"\"\n if average is not None and len(average) == 0:\n raise ValueError('need average')\n\n if len(y_true.shape) != 1:\n y_true = np.argmax(y_true, 1)\n if len(y_pred.shape) != 1:\n y_pred = np.argmax(y_pred, 1)\n\n mask = ~np.isnan(y_pred)\n y_true = y_true[mask]\n y_pred = y_pred[mask]\n\n ret = dict(accuracy=accuracy_score(y_true, y_pred))\n\n ret.update(\n f1=f1_score(y_true, y_pred, average=None, labels=labels),\n precision=precision_score(y_true, y_pred, average=None, labels=labels),\n recall=recall_score(y_true, y_pred, average=None, labels=labels),\n )\n\n if 'binary' in average:\n ret.update(\n binary_f1=f1_score(y_true, y_pred, average='binary', labels=labels),\n binary_precision=precision_score(y_true, y_pred, average='binary', labels=labels),\n binary_recall=recall_score(y_true, y_pred, average='binary', labels=labels),\n )\n\n if 'micro' in average:\n ret.update(\n micro_f1=f1_score(y_true, y_pred, average='micro', labels=labels),\n micro_precision=precision_score(y_true, y_pred, average='micro', labels=labels),\n micro_recall=recall_score(y_true, y_pred, average='micro', labels=labels),\n )\n\n if 'macro' in average:\n ret.update(\n macro_f1=f1_score(y_true, y_pred, average='macro', labels=labels),\n macro_precision=precision_score(y_true, y_pred, average='macro', labels=labels),\n macro_recall=recall_score(y_true, y_pred, average='macro', labels=labels),\n )\n \n if 'weighted' in average:\n ret.update(\n weighted_f1=f1_score(y_true, y_pred, average='weighted', labels=labels),\n weighted_precision=precision_score(y_true, y_pred, average='weighted', labels=labels),\n weighted_recall=recall_score(y_true, y_pred, average='weighted', labels=labels),\n )\n\n if 'samples' in average:\n ret.update(\n samples_f1=f1_score(y_true, y_pred, average='samples', labels=labels),\n samples_precision=precision_score(y_true, y_pred, average='samples', labels=labels),\n samples_recall=recall_score(y_true, y_pred, average='samples', labels=labels),\n )\n\n return ret\n" }, { "path": "xenonpy/utils/__init__.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\n\nfrom .parameter_gen import *\nfrom .useful_cls import *\nfrom .useful_func import *\n" }, { "path": "xenonpy/utils/math/__init__.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom .product import Product\n" }, { "path": "xenonpy/utils/math/product.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\n\nimport numpy as np\nfrom numpy import product\n\n\nclass Product(object):\n def __init__(self, *paras, repeat=1):\n if not isinstance(repeat, int):\n raise ValueError('repeat must be int but got {}'.format(\n type(repeat)))\n lens = [len(p) for p in paras]\n if repeat > 1:\n lens = lens * repeat\n size = product(lens)\n acc_list = [np.floor_divide(size, lens[0])]\n for len_ in lens[1:]:\n acc_list.append(np.floor_divide(acc_list[-1], len_))\n\n self.paras = paras * repeat if repeat > 1 else paras\n self.lens = lens\n self.size = size\n self.acc_list = acc_list\n\n def __getitem__(self, index):\n if index > self.size - 1:\n raise IndexError\n ret = [s - 1 for s in self.lens] # from len to index\n remainder = index + 1\n for i, acc in enumerate(self.acc_list):\n quotient, remainder = np.divmod(remainder, acc)\n if remainder == 0:\n ret[i] = quotient - 1\n break\n ret[i] = quotient\n\n return tuple(self.paras[i][j] for i, j in enumerate(ret))\n\n def __len__(self):\n return self.size\n" }, { "path": "xenonpy/utils/parameter_gen.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom collections import OrderedDict\nfrom typing import Union, Dict, Callable, Sequence, Any, Optional\n\nimport numpy as np\nimport pandas as pd\n\n__all__ = ['ParameterGenerator']\n\n\nclass ParameterGenerator(object):\n \"\"\"\n Generator for parameter set generating.\n\n \"\"\"\n\n def __init__(self, seed: Optional[int] = None, **kwargs: Union[Any, Sequence, Callable, Dict]):\n \"\"\"\n\n Parameters\n ----------\n seed\n Numpy random seed.\n kwargs\n Parameter candidate.\n \"\"\"\n if len(kwargs) == 0:\n raise RuntimeError('need parameter candidate')\n\n np.random.seed(seed)\n\n self.tuples = OrderedDict()\n self.funcs = OrderedDict()\n self.dicts = OrderedDict()\n self.others = {}\n\n for k, v in kwargs.items():\n if isinstance(v, (tuple, list, np.ndarray, pd.Series)):\n self.tuples[k] = v\n elif callable(v):\n self.funcs[k] = v\n elif isinstance(v, dict):\n repeat = v['repeat']\n self.dicts[k] = v\n\n if isinstance(repeat, str):\n if repeat in self.tuples:\n self.tuples.move_to_end(repeat, True)\n if repeat in self.dicts:\n self.dicts.move_to_end(repeat, True)\n if repeat in self.funcs:\n self.funcs.move_to_end(repeat, True)\n else:\n self.others[k] = v\n\n def __call__(self, num: int, *, factory=None):\n for _ in range(num):\n tmp = {}\n for k, v in self.tuples.items():\n tmp[k] = self._gen(v)\n\n for k, v in self.funcs.items():\n tmp[k] = v()\n\n for k, v in reversed(self.dicts.items()):\n data = v['data']\n repeat = v['repeat']\n if 'replace' in v:\n replace = v['replace']\n else:\n replace = True\n\n if isinstance(repeat, (tuple, list, np.ndarray, pd.Series)):\n repeat = self._gen(repeat)\n elif isinstance(repeat, str):\n repeat = len(tmp[repeat])\n\n if isinstance(data, (tuple, list, np.ndarray, pd.Series)):\n tmp[k] = self._gen(data, repeat, replace)\n elif callable(data):\n tmp[k] = tuple(data(repeat))\n\n tmp = dict(self.others, **tmp)\n if factory is not None:\n yield tmp, factory(**tmp)\n else:\n yield tmp\n\n @staticmethod\n def _gen(item: Sequence, repeat: int = None, replace: bool = True):\n if repeat is not None:\n idx = np.random.choice(len(item), repeat, replace=replace)\n return tuple([item[i] for i in idx])\n else:\n idx = np.random.choice(len(item))\n return item[idx]\n" }, { "path": "xenonpy/utils/useful_cls.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport time\nimport types\nfrom collections import defaultdict\nfrom datetime import timedelta\nfrom functools import wraps\n\n__all__ = ['Switch', 'TimedMetaClass', 'Timer', 'Singleton']\n\n\nclass Switch(object):\n def __init__(self, value):\n self.value = value\n self.fall = False\n\n def __iter__(self):\n \"\"\"Return the match method once, then stop\"\"\"\n yield self.match\n return\n\n def match(self, *args):\n \"\"\"Indicate whether or not to enter a case suite\"\"\"\n if self.fall or not args:\n return True\n elif self.value in args: # changed for v1.5, see below\n self.fall = True\n return True\n else:\n return False\n\n\nclass Timer(object):\n class _Timer:\n def __init__(self):\n self.start = None\n self.times = []\n\n @property\n def elapsed(self):\n all_ = sum(self.times)\n dt = 0.0\n if self.start:\n dt = time.perf_counter() - self.start\n return all_ + dt\n\n def __repr__(self):\n return f'elapsed: {timedelta(seconds=self.elapsed)} '\n\n def __init__(self, time_func=time.perf_counter):\n self._func = time_func\n self._timers = defaultdict(self._Timer)\n\n def start(self, fn_name='main'):\n if self._timers[fn_name].start is not None:\n raise RuntimeError('Timer <%s> Already started' % fn_name)\n self._timers[fn_name].start = self._func()\n\n def stop(self, fn_name='main'):\n if self._timers[fn_name].start is None:\n raise RuntimeError('Timer <%s> not started' % fn_name)\n elapsed = self._func() - self._timers[fn_name].start\n self._timers[fn_name].times.append(elapsed)\n self._timers[fn_name].start = None\n\n @property\n def elapsed(self):\n if 'main' in self._timers:\n return self._timers['main'].elapsed\n return sum([v.elapsed for v in self._timers.values()])\n\n def __repr__(self):\n tmp = {k: v.elapsed for k, v in self._timers.items()}\n tmp = {k: v for k, v in sorted(tmp.items(), key=lambda t: t[1])}\n return f'Total elapsed: {timedelta(seconds=self.elapsed)} \\n' + \\\n '\\n'.join([f' |- {k}: {timedelta(seconds=v)}' for k, v in\n sorted(tmp.items(), key=lambda t: t[1], reverse=True)])\n\n def __enter__(self):\n self.start()\n return self\n\n def __exit__(self, *args):\n self.stop()\n\n\nclass TimedMetaClass(type):\n \"\"\"\n This metaclass replaces each methods of its classes\n with a new function that is timed\n \"\"\"\n\n @staticmethod\n def _timed(fn):\n if isinstance(fn, (types.FunctionType, types.MethodType)):\n @wraps(fn)\n def fn_(self, *args, **kwargs):\n self._timer.start(fn.__name__)\n try:\n rt = fn(self, *args, **kwargs)\n finally:\n self._timer.stop(fn.__name__)\n return rt\n\n return fn_\n raise TypeError('Need or but got %s' % type(fn))\n\n def __new__(mcs, name, bases, attrs):\n\n if '__init__' in attrs:\n real_init = attrs['__init__']\n\n # we do a deepcopy in case default is mutable\n # but beware, this might not always work\n @wraps(real_init)\n def injected_init(self, *args, **kwargs):\n setattr(self, '_timer', Timer())\n # call the \"real\" __init__ that we hid with our injected one\n real_init(self, *args, **kwargs)\n else:\n def injected_init(self):\n setattr(self, '_timer', Timer())\n # inject it\n attrs['__init__'] = injected_init\n\n for name_, value_ in attrs.items():\n if not name_.startswith(\"__\") and isinstance(value_, (types.FunctionType, types.MethodType)):\n attrs[name_] = TimedMetaClass._timed(value_)\n cls = super(TimedMetaClass, mcs).__new__(mcs, name, bases, attrs)\n cls.timer = property(lambda self: self._timer)\n return cls\n\n\nclass Singleton(type):\n _instances = {}\n\n def __call__(cls, *args, **kwargs):\n if cls not in cls._instances:\n cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)\n return cls._instances[cls]\n" }, { "path": "xenonpy/utils/useful_func.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport re\nfrom contextlib import contextmanager\nfrom os import getenv\nfrom pathlib import Path\n\nfrom ruamel.yaml import YAML\n\nfrom xenonpy._conf import __cfg_root__\nfrom xenonpy._conf import __github_username__, __db_version__\n\n__all__ = ['camel_to_snake', 'get_data_loc', 'get_dataset_url', 'get_sha256', 'absolute_path', 'set_env', 'config']\n\n\n@contextmanager\ndef set_env(**kwargs):\n \"\"\"\n Set temp environment variable with ``with`` statement.\n\n Examples\n --------\n >>> import os\n >>> with set_env(test='test env'):\n >>> print(os.getenv('test'))\n test env\n >>> print(os.getenv('test'))\n None\n\n Parameters\n ----------\n kwargs: dict[str]\n Dict with string value.\n \"\"\"\n import os\n\n tmp = dict()\n for k, v in kwargs.items():\n tmp[k] = os.getenv(k)\n os.environ[k] = v\n yield\n for k, v in tmp.items():\n if not v:\n del os.environ[k]\n else:\n os.environ[k] = v\n\n\ndef config(key=None, **key_vals):\n \"\"\"\n Return config value with key or all config.\n\n Parameters\n ----------\n key: str\n Keys of config item.\n key_vals: dict\n Set item's value by key.\n Returns\n -------\n str\n The value corresponding to the key.\n \"\"\"\n yaml = YAML(typ='safe')\n yaml.indent(mapping=2, sequence=4, offset=2)\n dir_ = Path(__cfg_root__)\n cfg_file = dir_ / 'conf.yml'\n\n # from user local\n with open(str(cfg_file), 'r') as f:\n conf = yaml.load(f)\n\n value = None\n\n # getter\n if key:\n if key in conf:\n value = conf[key]\n else:\n tmp = Path(__file__).parents[1] / 'conf.yml'\n with open(str(tmp)) as f:\n conf_ = yaml.load(f)\n\n if key not in conf_:\n raise RuntimeError('No item(s) named %s in configurations' % key)\n\n value = conf_[key]\n\n # setter\n if key_vals:\n for key, v in key_vals.items():\n conf[key] = v\n with open(str(cfg_file), 'w') as f:\n yaml.dump(conf, f)\n\n return value\n\n\ndef camel_to_snake(text):\n str1 = re.sub('(.)([A-Z][a-z]+)', r'\\1_\\2', text)\n return re.sub('([a-z0-9])([A-Z])', r'\\1_\\2', str1).lower()\n\n\ndef get_dataset_url(name, version=__db_version__):\n \"\"\"\n Return url with the given file name.\n\n Args\n ----\n name: str\n binary file name.\n version: str\n The version of repository.\n See Also: https://github.com/yoshida-lab/dataset/releases\n\n Return\n ------\n str\n binary file url.\n \"\"\"\n return 'https://github.com/{0}/dataset/releases/download/v{1}/{2}.pd.xz'.format(__github_username__, version,\n name)\n\n\ndef get_data_loc(name):\n \"\"\"Return user data location\"\"\"\n\n scheme = ('userdata', 'usermodel')\n if name not in scheme:\n raise ValueError('{} not in {}'.format(name, scheme))\n if getenv(name):\n return str(Path(getenv(name)).expanduser())\n return str(Path(config(name)).expanduser())\n\n\ndef absolute_path(path, ignore_err=True):\n \"\"\"\n Resolve path when path include ``~``, ``parent/here``.\n\n Parameters\n ----------\n path: str\n Path to expand.\n ignore_err: bool\n FileNotFoundError is raised when set to False.\n When True, the path will be created.\n Returns\n -------\n str\n Expanded path.\n \"\"\"\n from sys import version_info\n\n if isinstance(path, str):\n path = Path(path)\n\n if version_info[1] == 5:\n if ignore_err:\n path.expanduser().mkdir(parents=True, exist_ok=True)\n return str(path.expanduser().resolve())\n return str(path.expanduser().resolve(not ignore_err))\n\n\ndef get_sha256(fname):\n \"\"\"\n Calculate file's sha256 value\n\n Parameters\n ----------\n fname: str\n File name.\n\n Returns\n -------\n str\n sha256 value.\n \"\"\"\n from hashlib import sha256\n hasher = sha256()\n with open(fname, \"rb\") as f:\n for chunk in iter(lambda: f.read(65536), b\"\"):\n hasher.update(chunk)\n return hasher.hexdigest()\n" }, { "path": "xenonpy/visualization/__init__.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nfrom .heatmap import DescriptorHeatmap\n" }, { "path": "xenonpy/visualization/heatmap.py", "content": "# Copyright (c) 2021. yoshida-lab. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sb\nfrom typing import Sequence, Union\n\nfrom xenonpy.datatools import Scaler\n\nfrom sklearn.preprocessing import power_transform\nfrom sklearn.base import BaseEstimator\nfrom sklearn.preprocessing import minmax_scale\n\n\nclass DescriptorHeatmap(BaseEstimator):\n \"\"\"\n Heatmap.\n \"\"\"\n\n def __init__(self,\n save=None,\n bc=False,\n cmap='RdBu',\n pivot_kws=None,\n method='average',\n metric='euclidean',\n figsize=None,\n row_cluster=False,\n col_cluster=True,\n row_linkage=None,\n col_linkage=None,\n row_colors=None,\n col_colors=None,\n mask=None,\n **kwargs):\n \"\"\"\n\n Parameters\n ----------\n save\n bc\n pivot_kws\n method\n metric\n figsize\n row_cluster\n col_cluster\n row_linkage\n col_linkage\n row_colors\n col_colors\n mask\n kwargs\n \"\"\"\n self.cmap = cmap\n self.save = save\n self.bc = bc\n self.pivot_kws = pivot_kws\n self.method = method\n self.metric = metric\n self.figsize = figsize\n self.col_cluster = col_cluster\n self.row_linkage = row_linkage\n self.row_cluster = row_cluster\n self.col_linkage = col_linkage\n self.row_colors = row_colors\n self.col_colors = col_colors\n self.mask = mask\n self.kwargs = kwargs\n self.desc = None\n\n def fit(self, desc):\n scaler = Scaler().min_max()\n if self.bc:\n scaler = scaler.yeo_johnson()\n\n self.desc = scaler.fit_transform(desc)\n return self\n\n def draw(self,\n y: Union[Sequence, pd.Series, None] = None,\n name: str = None,\n *,\n return_sorted_idx: bool = False):\n \"\"\"\n Draw figure.\n\n Parameters\n ----------\n y\n Properties values corresponding to samples.\n name\n Property name. If ``name`` is ``None`` and ``y`` is not a ``pandas.Series``.\n No name will be draw in the figure.\n return_sorted_idx\n If ``True``, return sorted column index of descriptor.\n Default ``False``\n\n Returns\n -------\n idx: np.array\n sorted column index if ``return_sorted_idx`` is ``True``\n\n \"\"\"\n heatmap_ax = sb.clustermap(self.desc,\n cmap=self.cmap,\n method=self.method,\n figsize=self.figsize,\n row_cluster=self.row_cluster,\n col_cluster=self.col_cluster,\n **self.kwargs)\n heatmap_ax.cax.set_visible(False)\n heatmap_ax.ax_heatmap.yaxis.set_ticks_position('left')\n heatmap_ax.ax_heatmap.yaxis.set_label_position('left')\n\n if y is None:\n heatmap_ax.ax_col_dendrogram.set_position((0.1, 0.8, 0.9, 0.1))\n heatmap_ax.ax_heatmap.set_position((0.1, 0.2, 0.9, 0.6))\n else:\n heatmap_ax.ax_col_dendrogram.set_position((0.1, 0.8, 0.83, 0.1))\n heatmap_ax.ax_heatmap.set_position((0.1, 0.2, 0.84, 0.6))\n prop_ax = plt.axes([0.95, 0.2, 0.05, 0.6])\n\n # draw prop\n if isinstance(y, pd.Series):\n x_ = y.values\n name_ = y.name\n else:\n x_ = np.asarray(y)\n name_ = ''\n if name is not None:\n name_ = name\n y_ = np.arange(len(x_))[::-1]\n\n prop_ax.plot(x_, y_, lw=4)\n prop_ax.get_yaxis().set_visible(False)\n prop_ax.spines['top'].set_visible(False)\n prop_ax.spines['right'].set_visible(False)\n prop_ax.set_xlabel('{:s}'.format(name_), fontsize='large')\n\n if self.save:\n plt.savefig(**self.save)\n\n if return_sorted_idx:\n try:\n return heatmap_ax.dendrogram_col.reordered_ind\n except AttributeError:\n return np.arange(heatmap_ax.data2d.shape[1])\n" } ]