Repository: analysiscenter/cardio Branch: master Commit: eb6d1cbe7f11 Files: 75 Total size: 3.0 MB Directory structure: gitextract_p3quxox6/ ├── .gitattributes ├── .gitignore ├── .gitmodules ├── CONTRIBUTING.md ├── LICENSE ├── MANIFEST.in ├── README.md ├── RELEASE.md ├── cardio/ │ ├── __init__.py │ ├── core/ │ │ ├── __init__.py │ │ ├── ecg_batch.py │ │ ├── ecg_batch_tools.py │ │ ├── ecg_dataset.py │ │ ├── kernels.py │ │ └── utils.py │ ├── models/ │ │ ├── __init__.py │ │ ├── dirichlet_model/ │ │ │ ├── __init__.py │ │ │ ├── dirichlet_model.py │ │ │ └── dirichlet_model_training.ipynb │ │ ├── fft_model/ │ │ │ ├── __init__.py │ │ │ ├── fft_model.py │ │ │ └── fft_model_training.ipynb │ │ ├── hmm/ │ │ │ ├── __init__.py │ │ │ ├── hmm.py │ │ │ └── hmmodel_training.ipynb │ │ ├── keras_custom_objects.py │ │ ├── layers.py │ │ └── metrics.py │ ├── pipelines/ │ │ ├── __init__.py │ │ └── pipelines.py │ └── tests/ │ ├── data/ │ │ ├── A00001.hea │ │ ├── A00001.mat │ │ ├── A00002.hea │ │ ├── A00002.mat │ │ ├── A00004.hea │ │ ├── A00004.mat │ │ ├── A00005.hea │ │ ├── A00005.mat │ │ ├── A00008.hea │ │ ├── A00008.mat │ │ ├── A00013.hea │ │ ├── A00013.mat │ │ ├── REFERENCE.csv │ │ ├── sample.dcm │ │ ├── sample.edf │ │ ├── sample.xml │ │ ├── sel100.atr │ │ ├── sel100.hea │ │ └── sel100.pu1 │ └── test_ecgbatch.py ├── docs/ │ ├── Makefile │ ├── api/ │ │ ├── api.rst │ │ ├── core.rst │ │ ├── models.rst │ │ └── pipelines.rst │ ├── conf.py │ ├── index.rst │ ├── make.bat │ ├── modules/ │ │ ├── core.rst │ │ ├── models.rst │ │ ├── modules.rst │ │ └── pipelines.rst │ └── tutorials.rst ├── examples/ │ ├── Getting_started.ipynb │ └── Load_XML.ipynb ├── pylintrc ├── requirements-shippable.txt ├── requirements.txt ├── setup.py ├── shippable.yml └── tutorials/ ├── I.CardIO.ipynb ├── II.Pipelines.ipynb ├── III.Models.ipynb ├── IV.Research.ipynb └── pn2017_data_to_wfdb_format.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitattributes ================================================ # Set the default behavior, in case people don't have core.autocrlf set. * text=auto # Explicitly declare text files you want to always be normalized on checkout. *.py text *.sh text ================================================ FILE: .gitignore ================================================ *.pyc docs/_build/ .cache/ __pycache__/ .ipynb_checkpoints/ ================================================ FILE: .gitmodules ================================================ [submodule "cardio/batchflow"] path = cardio/batchflow url = https://github.com/analysiscenter/dataset.git ================================================ FILE: CONTRIBUTING.md ================================================ - Перед любыми операциями с репозиториями у каждого пользователя должно быть настроено имя и адрес почты: ```bash git config --global user.name "Firstname Lastnameov" git config --global user.email f.lastnameov@analysiscenter.ru ``` Причем email **должен совпадать** с email'ом, который указан в вашем github-аккаунте (в нем может быть несколько email'ов). - В корневом каталоге каждого репозитория должен быть размещен файл README.md с кратким описанием проекта, структуры исходного кода, инструкцией по установке и ссылками на документацию. - Все содержательные файлы рекомендуется размещать в подкаталогах, а в корневом хранить только описательные (README.md, INSTALL.md и т.п.), инсталляционные (setup.py, requirements.txt и т.п.), а также конфигурационные и make-файлы. - Имена файлов должны содержать только латинские буквы. Пробелы в наименованиях файлов не допускаются. - Коммиты в ветку `master` не допускаются. Она должна быть защищена от удаления и изменения истории (Settings - Branches - Protected branches). - Изменения в исходном коде и файлах репозитория рекомендуется производить только в рамках задач (issues). Для каждого изменения исполнитель открывает отдельную ветку с наименованием вида - (например, `i15-dataset` или `i22-HMM`). - В рамках одной задачи можно создавать несколько веток в одном репозитории. - Если у вас нет задачи, имеет смысл ее открыть и явным образом завести в issues. - Коммиты в рабочие ветки рекомендуется делать регулярно, чтобы каждый коммит содержал не слишком объемные, но вместе с тем завершенные и независимые от всего остального изменения в репозитории (лучше закоммитить 3 измененных строки, чем сразу 300). - Коммит должен содержать однострочный англоязычный комментарий (длиной 20-60 символов), отражающий содержание включенных в него изменений исходного кода и файлов. - Более подробное описание изменений следует сохранять в файле HISTORY.md, размещенном в корневом каталоге репозитория. - Выполнив задачу и завершив все изменения, исполнитель открывает pull request на слияние рабочей и продуктивной ветки (например, master). - Перед слиянием рабочая ветка не должна отставать от продуктивной (что можно проверить с помощью `git status`). Для этого следует предварительно синхронизировать рабочую ветку (`git pull`). - После слияния рабочая ветка удаляется. ================================================ FILE: LICENSE ================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "{}" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright {yyyy} {name of copyright owner} Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: MANIFEST.in ================================================ include MANIFEST.in include LICENSE include README.md include setup.py recursive-include cardio * recursive-include docs * recursive-include tutorials * recursive-exclude docs/_build * global-exclude *.pyc *.pyo *.pyd global-exclude *.git global-exclude *.so global-exclude *~ global-exclude \#* global-exclude .DS_Store ================================================ FILE: README.md ================================================ # CardIO `CardIO` is designed to build end-to-end machine learning models for deep research of electrocardiograms. Main features: * load and save signals in various formats: WFDB, DICOM, EDF, XML (Schiller), etc. * resample, crop, flip and filter signals * detect PQ, QT, QRS segments * calculate heart rate and other ECG characteristics * perform complex processing like fourier and wavelet transformations * apply custom functions to the data * recognize heart diseases (e.g. atrial fibrillation) * efficiently work with large datasets that do not even fit into memory * perform end-to-end ECG processing * build, train and test neural networks and other machine learning models For more details see [the documentation and tutorials](https://analysiscenter.github.io/cardio/). ## About CardIO > CardIO is based on [BatchFlow](https://github.com/analysiscenter/batchflow). You might benefit from reading [its documentation](https://analysiscenter.github.io/batchflow). However, it is not required, especially at the beginning. CardIO has three modules: [``core``](https://analysiscenter.github.io/cardio/modules/core.html), [``models``](https://analysiscenter.github.io/cardio/modules/models.html) and [``pipelines``](https://analysiscenter.github.io/cardio/modules/pipelines.html). ``core`` module contains ``EcgBatch`` and ``EcgDataset`` classes. ``EcgBatch`` defines how ECGs are stored and includes actions for ECG processing. These actions might be used to build multi-staged workflows that can also involve machine learning models. ``EcgDataset`` is a class that stores indices of ECGs and generates batches of type ``EcgBatch``. ``models`` module provides several ready to use models for important problems in ECG analysis: * how to detect specific features of ECG like R-peaks, P-wave, T-wave, etc * how to recognize heart diseases from ECG, for example, atrial fibrillation ``pipelines`` module contains predefined workflows to * train a model and perform an inference to detect PQ, QT, QRS segments and calculate heart rate * train a model and perform an inference to find probabilities of heart diseases, in particular, atrial fibrillation ## Basic usage Here is an example of a pipeline that loads ECG signals, makes preprocessing and trains a model for 50 epochs: ```python train_pipeline = ( ds.Pipeline() .init_model("dynamic", DirichletModel, name="dirichlet", config=model_config) .init_variable("loss_history", init_on_each_run=list) .load(components=["signal", "meta"], fmt="wfdb") .load(components="target", fmt="csv", src=LABELS_PATH) .drop_labels(["~"]) .rename_labels({"N": "NO", "O": "NO"}) .flip_signals() .random_resample_signals("normal", loc=300, scale=10) .random_split_signals(2048, {"A": 9, "NO": 3}) .binarize_labels() .train_model("dirichlet", make_data=concatenate_ecg_batch, fetches="loss", save_to=V("loss_history"), mode="a") .run(batch_size=100, shuffle=True, drop_last=True, n_epochs=50) ) ``` ## Installation > `CardIO` module is in the beta stage. Your suggestions and improvements are very welcome. > `CardIO` supports python 3.5 or higher. ### Installation as a python package With [pipenv](https://docs.pipenv.org/): pipenv install git+https://github.com/analysiscenter/cardio.git#egg=cardio With [pip](https://pip.pypa.io/en/stable/): pip3 install git+https://github.com/analysiscenter/cardio.git After that just import `cardio`: ```python import cardio ``` ### Installation as a project repository When cloning repo from GitHub use flag ``--recursive`` to make sure that ``batchflow`` submodule is also cloned. git clone --recursive https://github.com/analysiscenter/cardio.git ## Citing CardIO Please cite CardIO in your publications if it helps your research. [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1156085.svg)](https://doi.org/10.5281/zenodo.1156085) Khudorozhkov R., Illarionov E., Kuvaev A., Podvyaznikov D. CardIO library for deep research of heart signals. 2017. ``` @misc{cardio_2017_1156085, author = {R. Khudorozhkov and E. Illarionov and A. Kuvaev and D. Podvyaznikov}, title = {CardIO library for deep research of heart signals}, year = 2017, doi = {10.5281/zenodo.1156085}, url = {https://doi.org/10.5281/zenodo.1156085} } ``` ================================================ FILE: RELEASE.md ================================================ # Release 0.3.0 ## Major Features and Improvements * `load` method now supports Schiller XML format. * Added channels processing methods: * `EcgBatch.reorder_channels` * `EcgBatch.convert_units` ## Breaking Changes to the API * `Dataset` submodule was updated and renamed to `BatchFlow`. # Release 0.2.0 ## Major Features and Improvements * `load` method now supports new signal formats: * DICOM * EDF * WAV * `meta` component structure has changed - now it always contains a number of predefined keys. * Added channels processing methods: * `EcgBatch.keep_channels` * `EcgBatch.drop_channels` * `EcgBatch.rename_channels` * Added `apply_to_each_channel` method. * Added `standardize` method. * Added complex ECG transformations: * Fourier-based transformations: * `EcgBatch.fft` * `EcgBatch.ifft` * `EcgBatch.rfft` * `EcgBatch.irfft` * `EcgBatch.spectrogram` * Wavelet-based transformations: * `EcgBatch.dwt` * `EcgBatch.idwt` * `EcgBatch.wavedec` * `EcgBatch.waverec` * `EcgBatch.pdwt` * `EcgBatch.cwt` ## Breaking Changes to the API * Changed signature of the following methods: * `EcgBatch.apply_transform` * `EcgBatch.show_ecg` * `EcgBatch.calc_ecg_parameters` * Changed signature of the following pipelines: * `dirichlet_train_pipeline` * `dirichlet_predict_pipeline` * `hmm_preprocessing_pipeline` * `hmm_train_pipeline` * `hmm_predict_pipeline` * `wavelet_transform` method has been deleted. * `update` method has been deleted. * `replace_labels` method has been renamed to `rename_labels`. * `slice_signal` method has been renamed to `slice_signals`. * `ravel` method has been renamed to `unstack_signals`. # Release 0.1.0 Initial release of CardIO. ================================================ FILE: cardio/__init__.py ================================================ """ CardIO package """ from . import batchflow # pylint: disable=wildcard-import from .core import * # pylint: disable=wildcard-import __version__ = '0.3.0' ================================================ FILE: cardio/core/__init__.py ================================================ """ Core CardIO objects """ from .ecg_batch import EcgBatch, add_actions from .ecg_dataset import EcgDataset from . import kernels ================================================ FILE: cardio/core/ecg_batch.py ================================================ """Contains ECG Batch class.""" # pylint: disable=too-many-lines import copy from textwrap import dedent import numpy as np import pandas as pd import scipy import scipy.signal import matplotlib.pyplot as plt import pywt from .. import batchflow as bf from . import kernels from . import ecg_batch_tools as bt from .utils import get_units_conversion_factor, partialmethod, LabelBinarizer ACTIONS_DICT = { "fft": (np.fft.fft, "numpy.fft.fft", "a Discrete Fourier Transform"), "ifft": (np.fft.ifft, "numpy.fft.ifft", "an inverse Discrete Fourier Transform"), "rfft": (np.fft.rfft, "numpy.fft.rfft", "a real-input Discrete Fourier Transform"), "irfft": (np.fft.irfft, "numpy.fft.irfft", "a real-input inverse Discrete Fourier Transform"), "dwt": (pywt.dwt, "pywt.dwt", "a single level Discrete Wavelet Transform"), "idwt": (lambda x, *args, **kwargs: pywt.idwt(*x, *args, **kwargs), "pywt.idwt", "a single level inverse Discrete Wavelet Transform"), "wavedec": (pywt.wavedec, "pywt.wavedec", "a multilevel 1D Discrete Wavelet Transform"), "waverec": (lambda x, *args, **kwargs: pywt.waverec(list(x), *args, **kwargs), "pywt.waverec", "a multilevel 1D Inverse Discrete Wavelet Transform"), "pdwt": (lambda x, part, *args, **kwargs: pywt.downcoef(part, x, *args, **kwargs), "pywt.downcoef", "a partial Discrete Wavelet Transform data decomposition"), "cwt": (lambda x, *args, **kwargs: pywt.cwt(x, *args, **kwargs)[0], "pywt.cwt", "a Continuous Wavelet Transform"), } TEMPLATE_DOCSTRING = """ Compute {description} for each slice of a signal over the axis 0 (typically the channel axis). This method simply wraps ``apply_to_each_channel`` method by setting the ``func`` argument to ``{full_name}``. Parameters ---------- src : str, optional Batch attribute or component name to get the data from. dst : str, optional Batch attribute or component name to put the result in. args : misc Any additional positional arguments to ``{full_name}``. kwargs : misc Any additional named arguments to ``{full_name}``. Returns ------- batch : EcgBatch Transformed batch. Changes ``dst`` attribute or component. """ TEMPLATE_DOCSTRING = dedent(TEMPLATE_DOCSTRING).strip() def add_actions(actions_dict, template_docstring): """Add new actions in ``EcgBatch`` by setting ``func`` argument in ``EcgBatch.apply_to_each_channel`` method to given callables. Parameters ---------- actions_dict : dict A dictionary, containing new methods' names as keys and a callable, its full name and description for each method as values. template_docstring : str A string, that will be formatted for each new method from ``actions_dict`` using ``full_name`` and ``description`` parameters and assigned to its ``__doc__`` attribute. Returns ------- decorator : callable Class decorator. """ def decorator(cls): """Returned decorator.""" for method_name, (func, full_name, description) in actions_dict.items(): docstring = template_docstring.format(full_name=full_name, description=description) method = partialmethod(cls.apply_to_each_channel, func) method.__doc__ = docstring setattr(cls, method_name, method) return cls return decorator @add_actions(ACTIONS_DICT, TEMPLATE_DOCSTRING) # pylint: disable=too-many-public-methods,too-many-instance-attributes class EcgBatch(bf.Batch): """Batch class for ECG signals storing. Contains ECG signals and additional metadata along with various processing methods. Parameters ---------- index : DatasetIndex Unique identifiers of ECGs in the batch. preloaded : tuple, optional Data to put in the batch if given. Defaults to ``None``. unique_labels : 1-D ndarray, optional Array with unique labels in a dataset. Attributes ---------- index : DatasetIndex Unique identifiers of ECGs in the batch. signal : 1-D ndarray Array of 2-D ndarrays with ECG signals in channels first format. annotation : 1-D ndarray Array of dicts with different types of annotations. meta : 1-D ndarray Array of dicts with metadata about signals. target : 1-D ndarray Array with signals' labels. unique_labels : 1-D ndarray Array with unique labels in a dataset. label_binarizer : LabelBinarizer Object for label one-hot encoding. Note ---- Some batch methods take ``index`` as their first argument after ``self``. You should not specify it in your code, it will be passed automatically by ``inbatch_parallel`` decorator. For example, ``resample_signals`` method with ``index`` and ``fs`` arguments should be called as ``batch.resample_signals(fs)``. """ components = "signal", "annotation", "meta", "target" def __init__(self, index, preloaded=None, unique_labels=None): super().__init__(index, preloaded) self.signal = self.array_of_nones self.annotation = self.array_of_dicts self.meta = self.array_of_dicts self.target = self.array_of_nones self._unique_labels = None self._label_binarizer = None self.unique_labels = unique_labels @property def array_of_nones(self): """1-D ndarray: ``NumPy`` array with ``None`` values.""" return np.array([None] * len(self.index)) @property def array_of_dicts(self): """1-D ndarray: ``NumPy`` array with empty ``dict`` values.""" return np.array([{} for _ in range(len(self.index))]) @property def unique_labels(self): """1-D ndarray: Unique labels in a dataset.""" return self._unique_labels @unique_labels.setter def unique_labels(self, val): """Set unique labels value to ``val``. Updates ``self.label_binarizer`` instance. Parameters ---------- val : 1-D ndarray New unique labels. """ self._unique_labels = val if self.unique_labels is None or len(self.unique_labels) == 0: self._label_binarizer = None else: self._label_binarizer = LabelBinarizer().fit(self.unique_labels) @property def label_binarizer(self): """LabelBinarizer: Label binarizer object for unique labels in a dataset.""" return self._label_binarizer def _reraise_exceptions(self, results): """Reraise all exceptions in the ``results`` list. Parameters ---------- results : list Post function computation results. Raises ------ RuntimeError If any paralleled action raised an ``Exception``. """ if bf.any_action_failed(results): all_errors = self.get_errors(results) raise RuntimeError("Cannot assemble the batch", all_errors) @staticmethod def _check_2d(signal): """Check if given signal is 2-D. Parameters ---------- signal : ndarray Signal to check. Raises ------ ValueError If given signal is not two-dimensional. """ if signal.ndim != 2: raise ValueError("Each signal in batch must be 2-D ndarray") # Input/output methods @bf.action def load(self, src=None, fmt=None, components=None, ann_ext=None, *args, **kwargs): """Load given batch components from source. Most of the ``EcgBatch`` actions work under the assumption that both ``signal`` and ``meta`` components are loaded. In case this assumption is not fulfilled, normal operation of the actions is not guaranteed. This method supports loading of signals from WFDB, DICOM, EDF, WAV, XML and Blosc formats. Parameters ---------- src : misc, optional Source to load components from. fmt : str, optional Source format. components : str or array-like, optional Components to load. ann_ext : str, optional Extension of the annotation file. Returns ------- batch : EcgBatch Batch with loaded components. Changes batch data inplace. """ if components is None: components = self.components components = np.unique(components).ravel().tolist() if (fmt == "csv" or fmt is None and isinstance(src, pd.Series)) and components == ['target']: return self._load_labels(src) if fmt in ["wfdb", "dicom", "edf", "wav", "xml"]: unexpected_components = set(components) - set(self.components) if unexpected_components: raise ValueError('Unexpected components: ', unexpected_components) return self._load_data(src=src, fmt=fmt, components=components, ann_ext=ann_ext, *args, **kwargs) return super().load(src=src, fmt=fmt, components=components, **kwargs) @bf.inbatch_parallel(init="indices", post="_assemble_load", target="threads") def _load_data(self, index, src=None, fmt=None, components=None, *args, **kwargs): """Load given components from WFDB, DICOM, EDF, WAV or XML files. Parameters ---------- src : misc, optional Source to load components from. Must be a collection, that can be indexed by indices of a batch. If ``None`` and ``index`` has ``FilesIndex`` type, the path from ``index`` is used. fmt : str, optional Source format. components : iterable, optional Components to load. ann_ext: str, optional Extension of the annotation file. Returns ------- batch : EcgBatch Batch with loaded components. Changes batch data inplace. Raises ------ ValueError If source path is not specified and batch's ``index`` is not a ``FilesIndex``. """ loaders = { "wfdb": bt.load_wfdb, "dicom": bt.load_dicom, "edf": bt.load_edf, "wav": bt.load_wav, "xml": bt.load_xml, } if src is not None: path = src[index] elif isinstance(self.index, bf.FilesIndex): path = self.index.get_fullpath(index) # pylint: disable=no-member else: raise ValueError("Source path is not specified") return loaders[fmt](path, components, *args, **kwargs) def _assemble_load(self, results, *args, **kwargs): """Concatenate results of different workers and update ``self``. Parameters ---------- results : list Workers' results. Returns ------- batch : EcgBatch Assembled batch. Changes components inplace. """ _ = args, kwargs self._reraise_exceptions(results) components = kwargs.get("components", None) if components is None: components = self.components for comp, data in zip(components, zip(*results)): if comp == "signal": data = np.array(data + (None,))[:-1] else: data = np.array(data) setattr(self, comp, data) return self def _load_labels(self, src): """Load labels from a csv file or ``pandas.Series``. Parameters ---------- src : str or Series Path to csv file or ``pandas.Series``. The file should contain two columns: ECG index and label. It shouldn't have a header. Returns ------- batch : EcgBatch Batch with loaded labels. Changes ``self.target`` inplace. Raises ------ TypeError If ``src`` is not a string or ``pandas.Series``. RuntimeError If ``unique_labels`` has not been defined and the batch was not created by a ``Pipeline``. """ if not isinstance(src, (str, pd.Series)): raise TypeError("Unsupported type of source") if isinstance(src, str): src = pd.read_csv(src, header=None, names=["index", "label"], index_col=0)["label"] self.target = src[self.indices].values if self.unique_labels is None: if self.pipeline is None: raise RuntimeError("Batch with undefined unique_labels must be created in a pipeline") ds_indices = self.pipeline.dataset.indices self.unique_labels = np.sort(src[ds_indices].unique()) return self def show_ecg(self, index=None, start=0, end=None, annot=None, subplot_size=(10, 4)): # pylint: disable=too-many-locals, line-too-long """Plot an ECG signal. Optionally highlight QRS complexes along with P and T waves. Each channel is displayed on a separate subplot. Parameters ---------- index : element of ``self.indices``, optional Index of a signal to plot. If undefined, the first ECG in the batch is used. start : int, optional The start point of the displayed part of the signal (in seconds). end : int, optional The end point of the displayed part of the signal (in seconds). annot : str, optional If not ``None``, specifies attribute that stores annotation obtained from ``cardio.models.HMModel``. subplot_size : tuple Width and height of each subplot in inches. Raises ------ ValueError If the chosen signal is not two-dimensional. """ i = 0 if index is None else self.get_pos(None, "signal", index) signal, meta = self.signal[i], self.meta[i] self._check_2d(signal) fs = meta["fs"] num_channels = signal.shape[0] start = np.int(start * fs) end = signal.shape[1] if end is None else np.int(end * fs) figsize = (subplot_size[0], subplot_size[1] * num_channels) _, axes = plt.subplots(num_channels, 1, squeeze=False, figsize=figsize) for channel, (ax,) in enumerate(axes): lead_name = "undefined" if meta["signame"][channel] == "None" else meta["signame"][channel] units = "undefined" if meta["units"][channel] is None else meta["units"][channel] ax.plot((np.arange(start, end) / fs), signal[channel, start:end]) ax.set_title("Lead name: {}".format(lead_name)) ax.set_xlabel("Time (sec)") ax.set_ylabel("Amplitude ({})".format(units)) ax.grid(True, which="major") if annot and hasattr(self, annot): def fill_segments(segment_states, color): """Fill ECG segments with a given color.""" starts, ends = bt.find_intervals_borders(signal_states, segment_states) for start_t, end_t in zip((starts + start) / fs, (ends + start) / fs): for (ax,) in axes: ax.axvspan(start_t, end_t, color=color, alpha=0.3) signal_states = getattr(self, annot)[i][start:end] fill_segments(bt.QRS_STATES, "red") fill_segments(bt.P_STATES, "green") fill_segments(bt.T_STATES, "blue") plt.tight_layout() plt.show() # Batch processing @classmethod def merge(cls, batches, batch_size=None): """Concatenate a list of ``EcgBatch`` instances and split the result into two batches of sizes ``batch_size`` and ``sum(lens of batches) - batch_size`` respectively. Parameters ---------- batches : list List of ``EcgBatch`` instances. batch_size : positive int, optional Length of the first resulting batch. If ``None``, equals the length of the concatenated batch. Returns ------- new_batch : EcgBatch Batch of no more than ``batch_size`` first items from the concatenation of input batches. Contains a deep copy of input batches' data. rest_batch : EcgBatch Batch of the remaining items. Contains a deep copy of input batches' data. Raises ------ ValueError If ``batch_size`` is non-positive or non-integer. """ batches = [batch for batch in batches if batch is not None] if len(batches) == 0: return None, None total_len = np.sum([len(batch) for batch in batches]) if batch_size is None: batch_size = total_len elif not isinstance(batch_size, int) or batch_size < 1: raise ValueError("Batch size must be positive int") indices = np.arange(total_len) data = [] for comp in batches[0].components: data.append(np.concatenate([batch.get(component=comp) for batch in batches])) data = copy.deepcopy(data) new_indices = indices[:batch_size] new_batch = cls(bf.DatasetIndex(new_indices), unique_labels=batches[0].unique_labels) new_batch._data = tuple(comp[:batch_size] for comp in data) # pylint: disable=protected-access, attribute-defined-outside-init, line-too-long if total_len <= batch_size: rest_batch = None else: rest_indices = indices[batch_size:] rest_batch = cls(bf.DatasetIndex(rest_indices), unique_labels=batches[0].unique_labels) rest_batch._data = tuple(comp[batch_size:] for comp in data) # pylint: disable=protected-access, attribute-defined-outside-init, line-too-long return new_batch, rest_batch # Versatile components processing @bf.action def apply_transform(self, func, *args, src="signal", dst="signal", **kwargs): """Apply a function to each item in the batch. Parameters ---------- func : callable A function to apply. Must accept an item of ``src`` as its first argument if ``src`` is not ``None``. src : str, array-like or ``None``, optional The source to get the data from. If ``src`` is ``str``, it is treated as the batch attribute or component name. Defaults to ``signal`` component. dst : str, writeable array-like or ``None``, optional The source to put the result in. If ``dst`` is ``str``, it is treated as the batch attribute or component name. Defaults to ``signal`` component. args : misc Any additional positional arguments to ``func``. kwargs : misc Any additional named arguments to ``func``. Returns ------- batch : EcgBatch Transformed batch. If ``dst`` is ``str``, the corresponding attribute or component is changed inplace. """ if isinstance(dst, str) and not hasattr(self, dst): setattr(self, dst, np.array([None] * len(self.index))) return super().apply_transform(func, *args, src=src, dst=dst, **kwargs) def _init_component(self, *args, **kwargs): """Create and preallocate a new attribute with the name ``dst`` if it does not exist and return batch indices.""" _ = args dst = kwargs.get("dst") if dst is None: raise KeyError("dst argument must be specified") if not hasattr(self, dst): setattr(self, dst, np.array([None] * len(self.index))) return self.indices @bf.action @bf.inbatch_parallel(init="_init_component", src="signal", dst="signal", target="threads") def apply_to_each_channel(self, index, func, *args, src="signal", dst="signal", **kwargs): """Apply a function to each slice of a signal over the axis 0 (typically the channel axis). Parameters ---------- func : callable A function to apply. Must accept a signal as its first argument. src : str, optional Batch attribute or component name to get the data from. Defaults to ``signal`` component. dst : str, optional Batch attribute or component name to put the result in. Defaults to ``signal`` component. args : misc Any additional positional arguments to ``func``. kwargs : misc Any additional named arguments to ``func``. Returns ------- batch : EcgBatch Transformed batch. Changes ``dst`` attribute or component. """ i = self.get_pos(None, src, index) src_data = getattr(self, src)[i] dst_data = np.array([func(slc, *args, **kwargs) for slc in src_data]) getattr(self, dst)[i] = dst_data # Labels processing def _filter_batch(self, keep_mask): """Drop elements from a batch with corresponding ``False`` values in ``keep_mask``. This method creates a new batch and updates only components and ``unique_labels`` attribute. The information stored in other attributes will be lost. Parameters ---------- keep_mask : bool 1-D ndarray Filtering mask. Returns ------- batch : same class as self Filtered batch. Raises ------ SkipBatchException If all batch data was dropped. If the batch is created by a ``pipeline``, its processing will be stopped and the ``pipeline`` will create the next batch. """ indices = self.indices[keep_mask] if len(indices) == 0: raise bf.SkipBatchException("All batch data was dropped") batch = self.__class__(bf.DatasetIndex(indices), unique_labels=self.unique_labels) for component in self.components: setattr(batch, component, getattr(self, component)[keep_mask]) return batch @bf.action def drop_labels(self, drop_list): """Drop elements whose labels are in ``drop_list``. This method creates a new batch and updates only components and ``unique_labels`` attribute. The information stored in other attributes will be lost. Parameters ---------- drop_list : list Labels to be dropped from a batch. Returns ------- batch : EcgBatch Filtered batch. Creates a new ``EcgBatch`` instance. Raises ------ SkipBatchException If all batch data was dropped. If the batch is created by a ``pipeline``, its processing will be stopped and the ``pipeline`` will create the next batch. """ drop_arr = np.asarray(drop_list) self.unique_labels = np.setdiff1d(self.unique_labels, drop_arr) keep_mask = ~np.in1d(self.target, drop_arr) return self._filter_batch(keep_mask) @bf.action def keep_labels(self, keep_list): """Drop elements whose labels are not in ``keep_list``. This method creates a new batch and updates only components and ``unique_labels`` attribute. The information stored in other attributes will be lost. Parameters ---------- keep_list : list Labels to be kept in a batch. Returns ------- batch : EcgBatch Filtered batch. Creates a new ``EcgBatch`` instance. Raises ------ SkipBatchException If all batch data was dropped. If the batch is created by a ``pipeline``, its processing will be stopped and the ``pipeline`` will create the next batch. """ keep_arr = np.asarray(keep_list) self.unique_labels = np.intersect1d(self.unique_labels, keep_arr) keep_mask = np.in1d(self.target, keep_arr) return self._filter_batch(keep_mask) @bf.action def rename_labels(self, rename_dict): """Rename labels with corresponding values from ``rename_dict``. Parameters ---------- rename_dict : dict Dictionary containing ``(old label : new label)`` pairs. Returns ------- batch : EcgBatch Batch with renamed labels. Changes ``self.target`` inplace. """ self.unique_labels = np.array(sorted({rename_dict.get(t, t) for t in self.unique_labels})) self.target = np.array([rename_dict.get(t, t) for t in self.target]) return self @bf.action def binarize_labels(self): """Binarize labels in a batch in a one-vs-all fashion. Returns ------- batch : EcgBatch Batch with binarized labels. Changes ``self.target`` inplace. """ self.target = self.label_binarizer.transform(self.target) return self # Channels processing @bf.inbatch_parallel(init="indices", target="threads") def _filter_channels(self, index, names=None, indices=None, invert_mask=False): """Build and apply a boolean mask for each channel of a signal based on provided channels ``names`` and ``indices``. Mask value for a channel is set to ``True`` if its name or index is contained in ``names`` or ``indices`` respectively. The mask can be inverted before its application if ``invert_mask`` flag is set to ``True``. Parameters ---------- names : str or list or tuple, optional Channels names used to construct the mask. indices : int or list or tuple, optional Channels indices used to construct the mask. invert_mask : bool, optional Specifies whether to invert the mask before its application. Returns ------- batch : EcgBatch Batch with filtered channels. Changes ``self.signal`` and ``self.meta`` inplace. Raises ------ ValueError If both ``names`` and ``indices`` are empty. ValueError If all channels should be dropped. """ i = self.get_pos(None, "signal", index) channels_names = np.asarray(self.meta[i]["signame"]) mask = np.zeros_like(channels_names, dtype=np.bool) if names is None and indices is None: raise ValueError("Both names and indices cannot be empty") if names is not None: names = np.asarray(names) mask |= np.in1d(channels_names, names) if indices is not None: indices = np.asarray(indices) mask |= np.array([i in indices for i in range(len(channels_names))]) if invert_mask: # know pylint bug: https://github.com/PyCQA/pylint/issues/2436 mask = ~mask # pylint: disable=invalid-unary-operand-type if np.sum(mask) == 0: raise ValueError("All channels cannot be dropped") self.signal[i] = self.signal[i][mask] self.meta[i]["signame"] = channels_names[mask] self.meta[i]["units"] = self.meta[i]["units"][mask] @bf.action def drop_channels(self, names=None, indices=None): """Drop channels whose names are in ``names`` or whose indices are in ``indices``. Parameters ---------- names : str or list or tuple, optional Names of channels to be dropped from a batch. indices : int or list or tuple, optional Indices of channels to be dropped from a batch. Returns ------- batch : EcgBatch Batch with dropped channels. Changes ``self.signal`` and ``self.meta`` inplace. Raises ------ ValueError If both ``names`` and ``indices`` are empty. ValueError If all channels should be dropped. """ return self._filter_channels(names, indices, invert_mask=True) @bf.action def keep_channels(self, names=None, indices=None): """Drop channels whose names are not in ``names`` and whose indices are not in ``indices``. Parameters ---------- names : str or list or tuple, optional Names of channels to be kept in a batch. indices : int or list or tuple, optional Indices of channels to be kept in a batch. Returns ------- batch : EcgBatch Batch with dropped channels. Changes ``self.signal`` and ``self.meta`` inplace. Raises ------ ValueError If both ``names`` and ``indices`` are empty. ValueError If all channels should be dropped. """ return self._filter_channels(names, indices, invert_mask=False) @bf.action @bf.inbatch_parallel(init="indices", target="threads") def rename_channels(self, index, rename_dict): """Rename channels with corresponding values from ``rename_dict``. Parameters ---------- rename_dict : dict Dictionary containing ``(old channel name : new channel name)`` pairs. Returns ------- batch : EcgBatch Batch with renamed channels. Changes ``self.meta`` inplace. """ i = self.get_pos(None, "signal", index) old_names = self.meta[i]["signame"] new_names = np.array([rename_dict.get(name, name) for name in old_names], dtype=object) self.meta[i]["signame"] = new_names @bf.action @bf.inbatch_parallel(init="indices", target="threads") def reorder_channels(self, index, new_order): """Change the order of channels in the batch according to the ``new_order``. Parameters ---------- new_order : array_like A list of channel names specifying the order of channels in the transformed batch. Returns ------- batch : EcgBatch Batch with reordered channels. Changes ``self.signal`` and ``self.meta`` inplace. Raises ------ ValueError If unknown lead names are specified. ValueError If all channels should be dropped. """ i = self.get_pos(None, "signal", index) old_order = self.meta[i]["signame"] diff = np.setdiff1d(new_order, old_order) if diff.size > 0: raise ValueError("Unknown lead names: {}".format(", ".join(diff))) if len(new_order) == 0: raise ValueError("All channels cannot be dropped") transform_dict = {k: v for v, k in enumerate(old_order)} indices = [transform_dict[k] for k in new_order] self.signal[i] = self.signal[i][indices] self.meta[i]["signame"] = self.meta[i]["signame"][indices] self.meta[i]["units"] = self.meta[i]["units"][indices] @bf.action @bf.inbatch_parallel(init="indices", target="threads") def convert_units(self, index, new_units): """Convert units of signal's channels to ``new_units``. Parameters ---------- new_units : str, dict or array_like New units of signal's channels. Must be specified in SI format and can be of one of the following types: * ``str`` - defines the same new units for each channel. * ``dict`` - defines the mapping from channel names to new units. Channels, whose names are not in the dictionary, remain unchanged. * ``array_like`` - defines new units for corresponding channels. The length of the sequence in this case must match the number of channels. Returns ------- batch : EcgBatch Batch with converted units. Changes ``self.signal`` and ``self.meta`` inplace. Raises ------ ValueError If ``new_units`` is ``array_like`` and its length doesn't match the number of channels. ValueError If unknown units are used. ValueError If conversion between incompatible units is performed. """ i = self.get_pos(None, "signal", index) old_units = self.meta[i]["units"] channels_names = self.meta[i]["signame"] if isinstance(new_units, str): new_units = [new_units] * len(old_units) elif isinstance(new_units, dict): new_units = [new_units.get(name, unit) for name, unit in zip(channels_names, old_units)] elif len(new_units) != len(old_units): raise ValueError("The length of the new and old units lists must be the same") factors = [get_units_conversion_factor(old, new) for old, new in zip(old_units, new_units)] factors = np.array(factors).reshape(*([-1] + [1] * (self.signal[i].ndim - 1))) self.signal[i] *= factors self.meta[i]["units"] = np.asarray(new_units) # Signal processing @bf.action def convolve_signals(self, kernel, padding_mode="edge", axis=-1, **kwargs): """Convolve signals with given ``kernel``. Parameters ---------- kernel : 1-D array_like Convolution kernel. padding_mode : str or function, optional ``np.pad`` padding mode. axis : int, optional Axis along which signals are sliced. Default value is -1. kwargs : misc Any additional named arguments to ``np.pad``. Returns ------- batch : EcgBatch Convolved batch. Changes ``self.signal`` inplace. Raises ------ ValueError If ``kernel`` is not one-dimensional or has non-numeric ``dtype``. """ for i in range(len(self.signal)): self.signal[i] = bt.convolve_signals(self.signal[i], kernel, padding_mode, axis, **kwargs) return self @bf.action @bf.inbatch_parallel(init="indices", target="threads") def band_pass_signals(self, index, low=None, high=None, axis=-1): """Reject frequencies outside a given range. Parameters ---------- low : positive float, optional High-pass filter cutoff frequency (in Hz). high : positive float, optional Low-pass filter cutoff frequency (in Hz). axis : int, optional Axis along which signals are sliced. Default value is -1. Returns ------- batch : EcgBatch Filtered batch. Changes ``self.signal`` inplace. """ i = self.get_pos(None, "signal", index) self.signal[i] = bt.band_pass_signals(self.signal[i], self.meta[i]["fs"], low, high, axis) @bf.action def drop_short_signals(self, min_length, axis=-1): """Drop short signals from a batch. Parameters ---------- min_length : positive int Minimal signal length. axis : int, optional Axis along which length is calculated. Default value is -1. Returns ------- batch : EcgBatch Filtered batch. Creates a new ``EcgBatch`` instance. """ keep_mask = np.array([sig.shape[axis] >= min_length for sig in self.signal]) return self._filter_batch(keep_mask) @bf.action @bf.inbatch_parallel(init="indices", target="threads") def flip_signals(self, index, window_size=None, threshold=0): """Flip 2-D signals whose R-peaks are directed downwards. Each element of ``self.signal`` must be a 2-D ndarray. Signals are flipped along axis 1 (signal axis). For each subarray of ``window_size`` length skewness is calculated and compared with ``threshold`` to decide whether this subarray should be flipped or not. Then the mode of the result is calculated to make the final decision. Parameters ---------- window_size : int, optional Signal is split into K subarrays of ``window_size`` length. If it is not possible, data in the end of the signal is removed. If ``window_size`` is not given, the whole array is checked without splitting. threshold : float, optional If skewness of a subarray is less than the ``threshold``, it "votes" for flipping the signal. Default value is 0. Returns ------- batch : EcgBatch Batch with flipped signals. Changes ``self.signal`` inplace. Raises ------ ValueError If given signal is not two-dimensional. """ i = self.get_pos(None, "signal", index) self._check_2d(self.signal[i]) sig = bt.band_pass_signals(self.signal[i], self.meta[i]["fs"], low=5, high=50) sig = bt.convolve_signals(sig, kernels.gaussian(11, 3)) if window_size is None: window_size = sig.shape[1] number_of_splits = sig.shape[1] // window_size sig = sig[:, : window_size * number_of_splits] splits = np.split(sig, number_of_splits, axis=-1) votes = [np.where(scipy.stats.skew(subseq, axis=-1) < threshold, -1, 1).reshape(-1, 1) for subseq in splits] mode_of_votes = scipy.stats.mode(votes)[0].reshape(-1, 1) self.signal[i] *= mode_of_votes @bf.action @bf.inbatch_parallel(init="indices", target="threads") def slice_signals(self, index, selection_object): """Perform indexing or slicing of signals in a batch. Allows basic ``NumPy`` indexing and slicing along with advanced indexing. Parameters ---------- selection_object : slice or int or a tuple of slices and ints An object that is used to slice signals. Returns ------- batch : EcgBatch Batch with sliced signals. Changes ``self.signal`` inplace. """ i = self.get_pos(None, "signal", index) self.signal[i] = self.signal[i][selection_object] @staticmethod def _pad_signal(signal, length, pad_value): """Pad signal with ``pad_value`` to the left along axis 1 (signal axis). Parameters ---------- signal : 2-D ndarray Signals to pad. length : positive int Length of padded signal along axis 1. pad_value : float Padding value. Returns ------- signal : 2-D ndarray Padded signals. """ pad_len = length - signal.shape[1] sig = np.pad(signal, ((0, 0), (pad_len, 0)), "constant", constant_values=pad_value) return sig @staticmethod def _get_segmentation_arg(arg, arg_name, target): """Get segmentation step or number of segments for a given signal. Parameters ---------- arg : int or dict Segmentation step or number of segments. arg_name : str Argument name. target : hashable Signal target. Returns ------- arg : positive int Segmentation step or number of segments for given signal. Raises ------ KeyError If ``arg`` dict has no ``target`` key. ValueError If ``arg`` is not int or dict. """ if isinstance(arg, int): return arg if isinstance(arg, dict): arg = arg.get(target) if arg is None: raise KeyError("Undefined {} for target {}".format(arg_name, target)) return arg raise ValueError("Unsupported {} type".format(arg_name)) @staticmethod def _check_segmentation_args(signal, target, length, arg, arg_name): """Check values of segmentation parameters. Parameters ---------- signal : 2-D ndarray Signals to segment. target : hashable Signal target. length : positive int Length of each segment along axis 1. arg : positive int or dict Segmentation step or number of segments. arg_name : str Argument name. Returns ------- arg : positive int Segmentation step or number of segments for given signal. Raises ------ ValueError If: * given signal is not two-dimensional, * ``arg`` is not int or dict, * ``length`` or ``arg`` for a given signal is negative or non-integer. KeyError If ``arg`` dict has no ``target`` key. """ EcgBatch._check_2d(signal) if (length <= 0) or not isinstance(length, int): raise ValueError("Segment length must be positive integer") arg = EcgBatch._get_segmentation_arg(arg, arg_name, target) if (arg <= 0) or not isinstance(arg, int): raise ValueError("{} must be positive integer".format(arg_name)) return arg @bf.action @bf.inbatch_parallel(init="indices", target="threads") def split_signals(self, index, length, step, pad_value=0): """Split 2-D signals along axis 1 (signal axis) with given ``length`` and ``step``. If signal length along axis 1 is less than ``length``, it is padded to the left with ``pad_value``. Notice, that each resulting signal will be a 3-D ndarray of shape ``[n_segments, n_channels, length]``. If you would like to get a number of 2-D signals of shape ``[n_channels, length]`` as a result, you need to apply ``unstack_signals`` method then. Parameters ---------- length : positive int Length of each segment along axis 1. step : positive int or dict Segmentation step. If ``step`` is dict, segmentation step is fetched by signal's target key. pad_value : float, optional Padding value. Defaults to 0. Returns ------- batch : EcgBatch Batch of split signals. Changes ``self.signal`` inplace. Raises ------ ValueError If: * given signal is not two-dimensional, * ``step`` is not int or dict, * ``length`` or ``step`` for a given signal is negative or non-integer. KeyError If ``step`` dict has no signal's target key. """ i = self.get_pos(None, "signal", index) step = self._check_segmentation_args(self.signal[i], self.target[i], length, step, "step size") if self.signal[i].shape[1] < length: tmp_sig = self._pad_signal(self.signal[i], length, pad_value) self.signal[i] = tmp_sig[np.newaxis, ...] else: self.signal[i] = bt.split_signals(self.signal[i], length, step) @bf.action @bf.inbatch_parallel(init="indices", target="threads") def random_split_signals(self, index, length, n_segments, pad_value=0): """Split 2-D signals along axis 1 (signal axis) ``n_segments`` times with random start position and given ``length``. If signal length along axis 1 is less than ``length``, it is padded to the left with ``pad_value``. Notice, that each resulting signal will be a 3-D ndarray of shape ``[n_segments, n_channels, length]``. If you would like to get a number of 2-D signals of shape ``[n_channels, length]`` as a result, you need to apply ``unstack_signals`` method then. Parameters ---------- length : positive int Length of each segment along axis 1. n_segments : positive int or dict Number of segments. If ``n_segments`` is dict, number of segments is fetched by signal's target key. pad_value : float, optional Padding value. Defaults to 0. Returns ------- batch : EcgBatch Batch of split signals. Changes ``self.signal`` inplace. Raises ------ ValueError If: * given signal is not two-dimensional, * ``n_segments`` is not int or dict, * ``length`` or ``n_segments`` for a given signal is negative or non-integer. KeyError If ``n_segments`` dict has no signal's target key. """ i = self.get_pos(None, "signal", index) n_segments = self._check_segmentation_args(self.signal[i], self.target[i], length, n_segments, "number of segments") if self.signal[i].shape[1] < length: tmp_sig = self._pad_signal(self.signal[i], length, pad_value) self.signal[i] = np.tile(tmp_sig, (n_segments, 1, 1)) else: self.signal[i] = bt.random_split_signals(self.signal[i], length, n_segments) @bf.action def unstack_signals(self): """Create a new batch in which each signal's element along axis 0 is considered as a separate signal. This method creates a new batch and updates only components and ``unique_labels`` attribute. Signal's data from non-``signal`` components is duplicated using a deep copy for each of the resulting signals. The information stored in other attributes will be lost. Returns ------- batch : same class as self Batch with split signals and duplicated other components. Examples -------- >>> batch.signal array([array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]])], dtype=object) >>> batch = batch.unstack_signals() >>> batch.signal array([array([0, 1, 2, 3]), array([4, 5, 6, 7]), array([ 8, 9, 10, 11])], dtype=object) """ n_reps = [sig.shape[0] for sig in self.signal] signal = np.array([channel for signal in self.signal for channel in signal] + [None])[:-1] index = bf.DatasetIndex(np.arange(len(signal))) batch = self.__class__(index, unique_labels=self.unique_labels) batch.signal = signal for component_name in set(self.components) - {"signal"}: val = [] component = getattr(self, component_name) is_object_dtype = (component.dtype.kind == "O") for elem, n in zip(component, n_reps): for _ in range(n): val.append(copy.deepcopy(elem)) if is_object_dtype: val = np.array(val + [None])[:-1] else: val = np.array(val) setattr(batch, component_name, val) return batch def _safe_fs_resample(self, index, fs): """Resample 2-D signal along axis 1 (signal axis) to given sampling rate. New sampling rate is guaranteed to be positive float. Parameters ---------- fs : positive float New sampling rate. Raises ------ ValueError If given signal is not two-dimensional. """ i = self.get_pos(None, "signal", index) self._check_2d(self.signal[i]) new_len = max(1, int(fs * self.signal[i].shape[1] / self.meta[i]["fs"])) self.meta[i]["fs"] = fs self.signal[i] = bt.resample_signals(self.signal[i], new_len) @bf.action @bf.inbatch_parallel(init="indices", target="threads") def resample_signals(self, index, fs): """Resample 2-D signals along axis 1 (signal axis) to given sampling rate. Parameters ---------- fs : positive float New sampling rate. Returns ------- batch : EcgBatch Resampled batch. Changes ``self.signal`` and ``self.meta`` inplace. Raises ------ ValueError If given signal is not two-dimensional. ValueError If ``fs`` is negative or non-numeric. """ if fs <= 0: raise ValueError("Sampling rate must be a positive float") self._safe_fs_resample(index, fs) @bf.action @bf.inbatch_parallel(init="indices", target="threads") def random_resample_signals(self, index, distr, **kwargs): """Resample 2-D signals along axis 1 (signal axis) to a new sampling rate, sampled from a given distribution. If new sampling rate is negative, the signal is left unchanged. Parameters ---------- distr : str or callable ``NumPy`` distribution name or a callable to sample from. kwargs : misc Distribution parameters. Returns ------- batch : EcgBatch Resampled batch. Changes ``self.signal`` and ``self.meta`` inplace. Raises ------ ValueError If given signal is not two-dimensional. ValueError If ``distr`` is not a string or a callable. """ if hasattr(np.random, distr): distr_fn = getattr(np.random, distr) fs = distr_fn(**kwargs) elif callable(distr): fs = distr_fn(**kwargs) else: raise ValueError("Unknown type of distribution parameter") if fs <= 0: fs = self[index].meta["fs"] self._safe_fs_resample(index, fs) # Complex ECG processing @bf.action @bf.inbatch_parallel(init="_init_component", src="signal", dst="signal", target="threads") def spectrogram(self, index, *args, src="signal", dst="signal", **kwargs): """Compute a spectrogram for each slice of a signal over the axis 0 (typically the channel axis). This method is a wrapper around ``scipy.signal.spectrogram``, that accepts the same arguments, except the ``fs`` which is substituted automatically from signal's meta. The method returns only the spectrogram itself. Parameters ---------- src : str, optional Batch attribute or component name to get the data from. dst : str, optional Batch attribute or component name to put the result in. args : misc Any additional positional arguments to ``scipy.signal.spectrogram``. kwargs : misc Any additional named arguments to ``scipy.signal.spectrogram``. Returns ------- batch : EcgBatch Transformed batch. Changes ``dst`` attribute or component. """ i = self.get_pos(None, src, index) fs = self.meta[i]["fs"] src_data = getattr(self, src)[i] dst_data = np.array([scipy.signal.spectrogram(slc, fs, *args, **kwargs)[-1] for slc in src_data]) getattr(self, dst)[i] = dst_data @bf.action @bf.inbatch_parallel(init="_init_component", src="signal", dst="signal", target="threads") def standardize(self, index, axis=None, eps=1e-10, *, src="signal", dst="signal"): """Standardize data along specified axes by removing the mean and scaling to unit variance. Parameters ---------- axis : ``None`` or int or tuple of ints, optional Axis or axes along which standardization is performed. The default is to compute for the flattened array. eps: float Small addition to avoid division by zero. src : str, optional Batch attribute or component name to get the data from. dst : str, optional Batch attribute or component name to put the result in. Returns ------- batch : EcgBatch Transformed batch. Changes ``dst`` attribute or component. """ i = self.get_pos(None, src, index) src_data = getattr(self, src)[i] dst_data = ((src_data - np.mean(src_data, axis=axis, keepdims=True)) / np.std(src_data, axis=axis, keepdims=True) + eps) getattr(self, dst)[i] = dst_data @bf.action @bf.inbatch_parallel(init="indices", target="threads") def calc_ecg_parameters(self, index, src=None): """Calculate ECG report parameters and write them to the ``meta`` component. Calculates PQ, QT, QRS intervals along with their borders and the heart rate value based on the annotation and writes them to the ``meta`` component. Parameters ---------- src : str Batch attribute or component name to get the annotation from. Returns ------- batch : EcgBatch Batch with report parameters stored in the ``meta`` component. Raises ------ ValueError If ``src`` is ``None`` or is not an attribute of a batch. """ if not (src and hasattr(self, src)): raise ValueError("Batch does not have an attribute or component {}!".format(src)) i = self.get_pos(None, "signal", index) src_data = getattr(self, src)[i] self.meta[i]["hr"] = bt.calc_hr(self.signal[i], src_data, np.float64(self.meta[i]["fs"]), bt.R_STATE) self.meta[i]["pq"] = bt.calc_pq(src_data, np.float64(self.meta[i]["fs"]), bt.P_STATES, bt.Q_STATE, bt.R_STATE) self.meta[i]["qt"] = bt.calc_qt(src_data, np.float64(self.meta[i]["fs"]), bt.T_STATES, bt.Q_STATE, bt.R_STATE) self.meta[i]["qrs"] = bt.calc_qrs(src_data, np.float64(self.meta[i]["fs"]), bt.S_STATE, bt.Q_STATE, bt.R_STATE) self.meta[i]["qrs_segments"] = np.vstack(bt.find_intervals_borders(src_data, bt.QRS_STATES)) self.meta[i]["p_segments"] = np.vstack(bt.find_intervals_borders(src_data, bt.P_STATES)) self.meta[i]["t_segments"] = np.vstack(bt.find_intervals_borders(src_data, bt.T_STATES)) ================================================ FILE: cardio/core/ecg_batch_tools.py ================================================ """Сontains ECG processing tools.""" import os import struct import datetime from xml.etree import ElementTree import numpy as np from numba import njit from scipy.io import wavfile import pyedflib import wfdb try: import pydicom as dicom except ImportError: import dicom # Constants # This is the predefined keys of the meta component. # Each key is initialized with None. META_KEYS = [ "age", "sex", "timestamp", "comments", "fs", "signame", "units", ] # This is the mapping from inner HMM states to human-understandable # cardiological terms. P_STATES = np.array([14, 15, 16], np.int64) T_STATES = np.array([5, 6, 7, 8, 9, 10], np.int64) QRS_STATES = np.array([0, 1, 2], np.int64) Q_STATE = np.array([0], np.int64) R_STATE = np.array([1], np.int64) S_STATE = np.array([2], np.int64) def check_signames(signame, nsig): """Check that signame is in proper format. Check if signame is a list of values that can be casted to string, othervise generate new signame list with numbers 0 to `nsig`-1 as strings. Parameters ---------- signame : misc Signal names from file. nsig : int Number of signals / channels. Returns ------- signame : list List of string names of signals / channels. """ if isinstance(signame, (tuple, list)) and len(signame) == nsig: signame = [str(name) for name in signame] else: signame = [str(number) for number in range(nsig)] return np.array(signame) def check_units(units, nsig): """Check that units are in proper format. Check if units is a list of values with lenght equal to number of channels. Parameters ---------- units : misc Units from file. nsig : int Number of signals / channels. Returns ------- units : list List of units of signal / channel. """ if not (isinstance(units, (tuple, list)) and len(units) == nsig): units = [None for number in range(nsig)] return np.array(units) def unify_sex(sex): """Maps the sex of a patient into one of the following values: "M", "F" or ``None``. Parameters ---------- sex : str Sex of the patient. Returns ------- sex : str Transformed sex of the patient. """ transform_dict = { "MALE": "M", "M": "M", "FEMALE": "F", "F": "F", } return transform_dict.get(sex) def load_wfdb(path, components, *args, **kwargs): """Load given components from wfdb file. Parameters ---------- path : str Path to .hea file. components : iterable Components to load. ann_ext: str Extension of the annotation file. Returns ------- ecg_data : list List of ecg data components. """ _ = args ann_ext = kwargs.get("ann_ext") path = os.path.splitext(path)[0] record = wfdb.rdrecord(path) signal = record.__dict__.pop("p_signal").T record_meta = record.__dict__ nsig = record_meta["n_sig"] if "annotation" in components and ann_ext is not None: annotation = wfdb.rdann(path, ann_ext) annot = {"annsamp": annotation.sample, "anntype": annotation.symbol} else: annot = {} # Initialize meta with defined keys, load values from record # meta and preprocess to our format. meta = dict(zip(META_KEYS, [None] * len(META_KEYS))) meta.update(record_meta) meta["signame"] = check_signames(meta.pop("sig_name"), nsig) meta["units"] = check_units(meta["units"], nsig) data = {"signal": signal, "annotation": annot, "meta": meta} return [data[comp] for comp in components] def load_dicom(path, components, *args, **kwargs): """ Load given components from DICOM file. Parameters ---------- path : str Path to .hea file. components : iterable Components to load. Returns ------- ecg_data : list List of ecg data components. """ def signal_decoder(record, nsig): """ Helper function to decode signal from binaries when reading from dicom. """ definition = record.WaveformSequence[0].ChannelDefinitionSequence data = record.WaveformSequence[0].WaveformData unpack_fmt = "<{}h".format(int(len(data) / 2)) factor = np.ones(nsig) baseline = np.zeros(nsig) for i in range(nsig): assert definition[i].WaveformBitsStored == 16 channel_sens = definition[i].get("ChannelSensitivity") channel_sens_cf = definition[i].get("ChannelSensitivityCorrectionFactor") if channel_sens is not None and channel_sens_cf is not None: factor[i] = float(channel_sens) * float(channel_sens_cf) channel_bl = definition[i].get("ChannelBaseline") if channel_bl is not None: baseline[i] = float(channel_bl) unpacked_data = struct.unpack(unpack_fmt, data) signals = np.asarray(unpacked_data, dtype=np.float32).reshape(-1, nsig) signals = ((signals + baseline) * factor).T return signals _ = args, kwargs record = dicom.read_file(path) sequence = record.WaveformSequence[0] assert sequence.WaveformSampleInterpretation == 'SS' assert sequence.WaveformBitsAllocated == 16 nsig = sequence.NumberOfWaveformChannels annot = {} meta = dict(zip(META_KEYS, [None] * len(META_KEYS))) if record.PatientAge[-1] == "Y": age = np.int(record.PatientAge[:-1]) else: age = np.int(record.PatientAge[:-1]) / 12.0 meta["age"] = age meta["sex"] = record.PatientSex meta["timestamp"] = record.AcquisitionDateTime meta["comments"] = [section.UnformattedTextValue for section in record.WaveformAnnotationSequence if section.AnnotationGroupNumber == 0] meta["fs"] = sequence.SamplingFrequency meta["signame"] = [section.ChannelSourceSequence[0].CodeMeaning for section in sequence.ChannelDefinitionSequence] meta["units"] = [section.ChannelSensitivityUnitsSequence[0].CodeValue for section in sequence.ChannelDefinitionSequence] meta["signame"] = check_signames(meta["signame"], nsig) meta["units"] = check_units(meta["units"], nsig) signal = signal_decoder(record, nsig) data = {"signal": signal, "annotation": annot, "meta": meta} return [data[comp] for comp in components] def load_edf(path, components, *args, **kwargs): """ Load given components from EDF file. Parameters ---------- path : str Path to .hea file. components : iterable Components to load. Returns ------- ecg_data : list List of ecg data components. """ _ = args, kwargs record = pyedflib.EdfReader(path) annot = {} meta = dict(zip(META_KEYS, [None] * len(META_KEYS))) meta["sex"] = record.getGender() if record.getGender() != '' else None meta["timestamp"] = record.getStartdatetime().strftime("%Y%m%d%H%M%S") nsig = record.signals_in_file if len(np.unique(record.getNSamples())) != 1: raise ValueError("Different signal lenghts are not supported!") if len(np.unique(record.getSampleFrequencies())) == 1: meta["fs"] = record.getSampleFrequencies()[0] else: raise ValueError("Different sampling rates are not supported!") meta["signame"] = record.getSignalLabels() meta["units"] = [record.getSignalHeader(sig)["dimension"] for sig in range(nsig)] meta.update(record.getHeader()) meta["signame"] = check_signames(meta["signame"], nsig) meta["units"] = check_units(meta["units"], nsig) signal = np.array([record.readSignal(i) for i in range(nsig)]) data = {"signal": signal, "annotation": annot, "meta": meta} return [data[comp] for comp in components] def load_wav(path, components, *args, **kwargs): """ Load given components from wav file. Parameters ---------- path : str Path to .hea file. components : iterable Components to load. Returns ------- ecg_data : list List of ecg data components. """ _ = args, kwargs fs, signal = wavfile.read(path) if signal.ndim == 1: nsig = 1 signal = signal.reshape([-1, 1]) elif signal.ndim == 2: nsig = signal.shape[1] else: raise ValueError("Unexpected number of dimensions in signal array: {}".format(signal.ndim)) signal = signal.T annot = {} meta = dict(zip(META_KEYS, [None] * len(META_KEYS))) meta["fs"] = fs meta["signame"] = check_signames(meta["signame"], nsig) meta["units"] = check_units(meta["units"], nsig) data = {"signal": signal, "annotation": annot, "meta": meta} return [data[comp] for comp in components] def load_xml(path, components, xml_type, *args, **kwargs): """Load given components from an XML file. Parameters ---------- path : str A path to an .xml file. components : iterable Components to load. xml_type : str Defines the structure of the file. The following values of the argument are supported: * "schiller" - Schiller XML Returns ------- loaded_data : list A list of loaded ECG data components. """ loaders = { "schiller": load_xml_schiller, } loader = loaders.get(xml_type) if loader is None: err_str = "Unsupported XML type {}. Currently supported XML types: {}" err_msg = err_str.format(xml_type, ", ".join(sorted(loaders.keys()))) raise ValueError(err_msg) return loader(path, components, *args, **kwargs) def load_xml_schiller(path, components, *args, **kwargs): # pylint: disable=too-many-locals """Load given components from a Schiller XML file. Parameters ---------- path : str A path to an .xml file. components : iterable Components to load. Returns ------- loaded_data : list A list of loaded ECG data components. """ _ = args, kwargs root = ElementTree.parse(path).getroot() birthdate = root.find("./patdata/birthdate").text if birthdate is None: age = None else: today = datetime.date.today() birthdate = datetime.datetime.strptime(birthdate, "%Y%m%d") age = today.year - birthdate.year - ((today.month, today.day) < (birthdate.month, birthdate.day)) sex = unify_sex(root.find("./patdata/gender").text) date = root.find("./examdescript/startdatetime/date").text time = root.find("./examdescript/startdatetime/time").text timestamp = datetime.datetime.strptime(date + time, "%Y%m%d%H%M%S%f") ecg_data = root.find("./eventdata/event/wavedata[type='ECG_RHYTHMS']") sig_info = [] for channel in ecg_data.findall("./channel"): sig = [float(val) for val in channel.find("data").text.split(",") if val] name = channel.find("name").text sig_info.append((sig, name)) signal, signame = zip(*sig_info) signal = np.array(signal) signame = np.array(signame) fs = float(ecg_data.find("./resolution/samplerate/value").text) units = ecg_data.find("./resolution/yres/units").text if units == "UV": units = "uV" units = np.array([units] * len(signame)) meta = { "age": age, "sex": sex, "timestamp": timestamp, "comments": None, "fs": fs, "signame": signame, "units": units, } data = { "signal": signal, "annotation": {}, "meta": meta } return [data[comp] for comp in components] @njit(nogil=True) def split_signals(signals, length, step): """Split signals along axis 1 with given ``length`` and ``step``. Parameters ---------- signals : 2-D ndarray Signals to split. length : positive int Length of each segment along axis 1. step : positive int Segmentation step. Returns ------- signals : 3-D ndarray Split signals stacked along new axis with index 0. """ res = np.empty(((signals.shape[1] - length) // step + 1, signals.shape[0], length), dtype=signals.dtype) for i in range(res.shape[0]): res[i, :, :] = signals[:, i * step : i * step + length] return res @njit(nogil=True) def random_split_signals(signals, length, n_segments): """Split signals along axis 1 ``n_segments`` times with random start position and given ``length``. Parameters ---------- signals : 2-D ndarray Signals to split. length : positive int Length of each segment along axis 1. n_segments : positive int Number of segments. Returns ------- signals : 3-D ndarray Split signals stacked along new axis with index 0. """ res = np.empty((n_segments, signals.shape[0], length), dtype=signals.dtype) for i in range(res.shape[0]): ix = np.random.randint(0, signals.shape[1] - length + 1) res[i, :, :] = signals[:, ix : ix + length] return res @njit(nogil=True) def resample_signals(signals, new_length): """Resample signals to new length along axis 1 using linear interpolation. Parameters ---------- signals : 2-D ndarray Signals to resample. new_length : positive int New signals shape along axis 1. Returns ------- signals : 2-D ndarray Resampled signals. """ arg = np.linspace(0, signals.shape[1] - 1, new_length) x_left = arg.astype(np.int32) # pylint: disable=no-member x_right = x_left + 1 x_right[-1] = x_left[-1] alpha = arg - x_left y_left = signals[:, x_left] y_right = signals[:, x_right] return y_left + (y_right - y_left) * alpha def convolve_signals(signals, kernel, padding_mode="edge", axis=-1, **kwargs): """Convolve signals with given ``kernel``. Parameters ---------- signals : ndarray Signals to convolve. kernel : array_like Convolution kernel. padding_mode : str or function ``np.pad`` padding mode. axis : int Axis along which signals are sliced. kwargs : misc Any additional named argments to ``np.pad``. Returns ------- signals : ndarray Convolved signals. Raises ------ ValueError If ``kernel`` is not one-dimensional or has non-numeric ``dtype``. """ kernel = np.asarray(kernel) if len(kernel.shape) == 0: kernel = kernel.ravel() if len(kernel.shape) != 1: raise ValueError("Kernel must be 1-D array") if not np.issubdtype(kernel.dtype, np.number): raise ValueError("Kernel must have numeric dtype") pad = len(kernel) // 2 def conv_func(x): """Convolve padded signal.""" x_pad = np.pad(x, pad, padding_mode, **kwargs) conv = np.convolve(x_pad, kernel, "same") if pad > 0: conv = conv[pad:-pad] return conv signals = np.apply_along_axis(conv_func, arr=signals, axis=axis) return signals def band_pass_signals(signals, freq, low=None, high=None, axis=-1): """Reject frequencies outside given range. Parameters ---------- signals : ndarray Signals to filter. freq : positive float Sampling rate. low : positive float High-pass filter cutoff frequency (Hz). high : positive float Low-pass filter cutoff frequency (Hz). axis : int Axis along which signals are sliced. Returns ------- signals : ndarray Filtered signals. Raises ------ ValueError If ``freq`` is negative or non-numeric. """ if freq <= 0: raise ValueError("Sampling rate must be a positive float") sig_rfft = np.fft.rfft(signals, axis=axis) sig_freq = np.fft.rfftfreq(signals.shape[axis], 1 / freq) mask = np.zeros(len(sig_freq), dtype=bool) if low is not None: mask |= (sig_freq <= low) if high is not None: mask |= (sig_freq >= high) slc = [slice(None)] * signals.ndim slc[axis] = mask sig_rfft[tuple(slc)] = 0 return np.fft.irfft(sig_rfft, n=signals.shape[axis], axis=axis) @njit(nogil=True) def find_intervals_borders(hmm_annotation, inter_val): """Find starts and ends of the intervals. This function finds starts and ends of continuous intervals of values from inter_val in hmm_annotation. Parameters ---------- hmm_annotation : numpy.array Annotation for the signal from hmm_annotation model. inter_val : array_like Values that form interval of interest. Returns ------- starts : 1-D ndarray Indices of the starts of the intervals. ends : 1-D ndarray Indices of the ends of the intervals. """ intervals = np.zeros(hmm_annotation.shape, dtype=np.int8) for val in inter_val: intervals = np.logical_or(intervals, (hmm_annotation == val).astype(np.int8)).astype(np.int8) masque = np.diff(intervals) starts = np.where(masque == 1)[0] + 1 ends = np.where(masque == -1)[0] + 1 if np.any(inter_val == hmm_annotation[:1]): ends = ends[1:] if np.any(inter_val == hmm_annotation[-1:]): starts = starts[:-1] return starts, ends @njit(nogil=True) def find_maxes(signal, starts, ends): """ Find index of the maximum of the segment. Parameters ---------- signal : 2-D ndarray ECG signal. starts : 1-D ndarray Indices of the starts of the intervals. ends : 1-D ndarray Indices of the ens of the intervals. Returns ------- maxes : 1-D ndarray Indices of max values of each interval. Notes ----- Currently works with first lead only. """ maxes = np.empty(starts.shape, dtype=np.float64) for i in range(maxes.shape[0]): maxes[i] = starts[i] + np.argmax(signal[0][starts[i]:ends[i]]) return maxes @njit(nogil=True) def calc_hr(signal, hmm_annotation, fs, r_state=R_STATE): """ Calculate heart rate based on HMM prediction. Parameters ---------- signal : 2-D ndarray ECG signal. hmm_annotation : 1-D ndarray Annotation for the signal from hmm_annotation model. fs : float Sampling rate of the signal. r_state : 1-D ndarray Array with values that represent R peak. Default value is R_STATE, which is a constant of this module. Returns ------- hr_val : float Heart rate in beats per minute. """ starts, ends = find_intervals_borders(hmm_annotation, r_state) # NOTE: Currently works on first lead signal only maxes = find_maxes(signal, starts, ends) diff = maxes[1:] - maxes[:-1] hr_val = (np.median(diff / fs) ** -1) * 60 return hr_val @njit(nogil=True) def calc_pq(hmm_annotation, fs, p_states=P_STATES, q_state=Q_STATE, r_state=R_STATE): """ Calculate PQ based on HMM prediction. Parameters ---------- hmm_annotation : numpy.array Annotation for the signal from hmm_annotation model. fs : float Sampling rate of the signal. p_states : 1-D ndarray Array with values that represent P peak. Default value is P_STATES, which is a constant of this module. q_state : 1-D ndarray Array with values that represent Q peak. Default value is Q_STATE, which is a constant of this module. r_state : 1-D ndarray Array with values that represent R peak. Default value is R_STATE, which is a constant of this module. Returns ------- pq_val : float Duration of PQ interval in seconds. """ p_starts, _ = find_intervals_borders(hmm_annotation, p_states) q_starts, _ = find_intervals_borders(hmm_annotation, q_state) r_starts, _ = find_intervals_borders(hmm_annotation, r_state) p_final = - np.ones(r_starts.shape[0] - 1) q_final = - np.ones(r_starts.shape[0] - 1) maxlen = hmm_annotation.shape[0] if not p_starts.shape[0] * q_starts.shape[0] * r_starts.shape[0]: return 0.00 temp_p = np.zeros(maxlen) temp_p[p_starts] = 1 temp_q = np.zeros(maxlen) temp_q[q_starts] = 1 for i in range(len(r_starts) - 1): low = r_starts[i] high = r_starts[i + 1] inds_p = np.where(temp_p[low:high])[0] + low inds_q = np.where(temp_q[low:high])[0] + low if inds_p.shape[0] == 1 and inds_q.shape[0] == 1: p_final[i] = inds_p[0] q_final[i] = inds_q[0] p_final = p_final[p_final > -1] q_final = q_final[q_final > -1] intervals = q_final - p_final return np.median(intervals) / fs @njit(nogil=True) def calc_qt(hmm_annotation, fs, t_states=T_STATES, q_state=Q_STATE, r_state=R_STATE): """ Calculate QT interval based on HMM prediction. Parameters ---------- hmm_annotation : numpy.array Annotation for the signal from hmm_annotation model. fs : float Sampling rate of the signal. t_states : 1-D ndarray Array with values that represent T peak. Default value is T_STATES, which is a constant of this module. q_state : 1-D ndarray Array with values that represent Q peak. Default value is Q_STATE, which is a constant of this module. r_state : 1-D ndarray Array with values that represent R peak. Default value is R_STATE, which is a constant of this module. Returns ------- qt_val : float Duration of QT interval in seconds. """ _, t_ends = find_intervals_borders(hmm_annotation, t_states) q_starts, _ = find_intervals_borders(hmm_annotation, q_state) r_starts, _ = find_intervals_borders(hmm_annotation, r_state) t_final = - np.ones(r_starts.shape[0] - 1) q_final = - np.ones(r_starts.shape[0] - 1) maxlen = hmm_annotation.shape[0] if not t_ends.shape[0] * q_starts.shape[0] * r_starts.shape[0]: return 0.00 temp_t = np.zeros(maxlen) temp_t[t_ends] = 1 temp_q = np.zeros(maxlen) temp_q[q_starts] = 1 for i in range(len(r_starts) - 1): low = r_starts[i] high = r_starts[i + 1] inds_t = np.where(temp_t[low:high])[0] + low inds_q = np.where(temp_q[low:high])[0] + low if inds_t.shape[0] == 1 and inds_q.shape[0] == 1: t_final[i] = inds_t[0] q_final[i] = inds_q[0] t_final = t_final[t_final > -1][1:] q_final = q_final[q_final > -1][:-1] intervals = t_final - q_final return np.median(intervals) / fs @njit(nogil=True) def calc_qrs(hmm_annotation, fs, s_state=S_STATE, q_state=Q_STATE, r_state=R_STATE): """ Calculate QRS interval based on HMM prediction. Parameters ---------- hmm_annotation : numpy.array Annotation for the signal from hmm_annotation model. fs : float Sampling rate of the signal. s_state : 1-D ndarray Array with values that represent S peak. Default value is S_STATE, which is a constant of this module. q_state : 1-D ndarray Array with values that represent Q peak. Default value is Q_STATE, which is a constant of this module. r_state : 1-D ndarray Array with values that represent R peak. Default value is R_STATE, which is a constant of this module. Returns ------- qrs_val : float Duration of QRS complex in seconds. """ _, s_ends = find_intervals_borders(hmm_annotation, s_state) q_starts, _ = find_intervals_borders(hmm_annotation, q_state) r_starts, _ = find_intervals_borders(hmm_annotation, r_state) s_final = - np.ones(r_starts.shape[0] - 1) q_final = - np.ones(r_starts.shape[0] - 1) maxlen = hmm_annotation.shape[0] if not s_ends.shape[0] * q_starts.shape[0] * r_starts.shape[0]: return 0.00 temp_s = np.zeros(maxlen) temp_s[s_ends] = 1 temp_q = np.zeros(maxlen) temp_q[q_starts] = 1 for i in range(len(r_starts) - 1): low = r_starts[i] high = r_starts[i + 1] inds_s = np.where(temp_s[low:high])[0] + low inds_q = np.where(temp_q[low:high])[0] + low if inds_s.shape[0] == 1 and inds_q.shape[0] == 1: s_final[i] = inds_s[0] q_final[i] = inds_q[0] s_final = s_final[s_final > -1][1:] q_final = q_final[q_final > -1][:-1] intervals = s_final - q_final return np.median(intervals) / fs ================================================ FILE: cardio/core/ecg_dataset.py ================================================ """Contains ECG Dataset class.""" from .. import batchflow as bf from .ecg_batch import EcgBatch class EcgDataset(bf.Dataset): """Dataset that generates batches of ``EcgBatch`` class. Contains indices of ECGs and a specific ``batch_class`` to create and process batches - small subsets of data. Parameters ---------- index : DatasetIndex or None, optional Unique identifiers of ECGs in a dataset. If ``index`` is not given, it is constructed by instantiating ``index_class`` with ``args`` and ``kwargs``. batch_class : type, optional Class of batches, generated by dataset. Must be inherited from ``Batch``. preloaded : tuple, optional Data to put in created batches. Defaults to ``None``. index_class : type, optional Class of built index if ``index`` is not given. Must be inherited from ``DatasetIndex``. args : misc, optional Additional positional argments to ``index_class.__init__``. kwargs : misc, optional Additional named argments to ``index_class.__init__``. """ def __init__(self, index=None, batch_class=EcgBatch, preloaded=None, index_class=bf.FilesIndex, *args, **kwargs): if index is None: index = index_class(*args, **kwargs) super().__init__(index, batch_class, preloaded) ================================================ FILE: cardio/core/kernels.py ================================================ """Contains kernel generation functions.""" import numpy as np def _check_kernel_size(size): """Check if kernel size is a positive integer.""" if not isinstance(size, int) or size < 1: raise ValueError("Kernel size must be a positive integer") def gaussian(size, sigma=None): """Create a 1-D Gaussian kernel. Parameters ---------- size : positive int Kernel size. sigma : positive float, optional Standard deviation of Gaussian distribution. Controls the degree of smoothing. If ``None``, it is set to ``(size + 1) / 6``. Returns ------- kernel : 1-D ndarray Gaussian kernel. Raises ------ ValueError If ``size`` or ``sigma`` is negative or non-numeric. """ _check_kernel_size(size) if sigma is None: sigma = (size + 1) / 6 elif not isinstance(sigma, (int, float)) or sigma <= 0: raise ValueError("Sigma must be a positive integer or float") i = np.arange(size) - (size - 1) / 2 kernel = np.exp(-i**2 / (2 * sigma**2)) return kernel / sum(kernel) ================================================ FILE: cardio/core/utils.py ================================================ """Miscellaneous ECG Batch utils.""" import functools import pint import numpy as np from sklearn.preprocessing import LabelBinarizer as LB UNIT_REGISTRY = pint.UnitRegistry() def get_units_conversion_factor(old_units, new_units): """Return a multiplicative factor to convert a measured quantity from old to new units. Parameters ---------- old_units : str Current units in SI format. new_units : str Target units in SI format. Returns ------- factor : float A factor to convert quantities between units. """ try: # pint exceptions are wrapped with ValueError exceptions because they don't implement __repr__ method factor = UNIT_REGISTRY(old_units).to(new_units).magnitude except Exception as error: raise ValueError(error.__class__.__name__ + ": " + str(error)) return factor def partialmethod(func, *frozen_args, **frozen_kwargs): """Wrap a method with partial application of given positional and keyword arguments. Parameters ---------- func : callable A method to wrap. frozen_args : misc Fixed positional arguments. frozen_kwargs : misc Fixed keyword arguments. Returns ------- method : callable Wrapped method. """ @functools.wraps(func) def method(self, *args, **kwargs): """Wrapped method.""" return func(self, *frozen_args, *args, **frozen_kwargs, **kwargs) return method class LabelBinarizer(LB): """Encode categorical features using a one-hot scheme. Unlike ``sklearn.preprocessing.LabelBinarizer``, each label will be encoded using ``n_classes`` numbers even for binary problems. """ # pylint: disable=invalid-name def transform(self, y): """Transform ``y`` using one-hot encoding. Parameters ---------- y : 1-D ndarray of shape ``[n_samples,]`` Class labels. Returns ------- Y : 2-D ndarray of shape ``[n_samples, n_classes]`` One-hot encoded labels. """ Y = super().transform(y) if len(self.classes_) == 1: Y = 1 - Y if len(self.classes_) == 2: Y = np.hstack((1 - Y, Y)) return Y def inverse_transform(self, Y, threshold=None): """Transform one-hot encoded labels back to class labels. Parameters ---------- Y : 2-D ndarray of shape ``[n_samples, n_classes]`` One-hot encoded labels. threshold : float, optional The threshold used in the binary and multi-label cases. If ``None``, it is assumed to be half way between ``neg_label`` and ``pos_label``. Returns ------- y : 1-D ndarray of shape ``[n_samples,]`` Class labels. """ if len(self.classes_) == 1: y = super().inverse_transform(1 - Y, threshold) elif len(self.classes_) == 2: y = super().inverse_transform(Y[:, 1], threshold) else: y = super().inverse_transform(Y, threshold) return y ================================================ FILE: cardio/models/__init__.py ================================================ """Contains ECG models and custom functions.""" from .dirichlet_model import * # pylint: disable=wildcard-import from .fft_model import * # pylint: disable=wildcard-import from .hmm import * # pylint: disable=wildcard-import from . import metrics ================================================ FILE: cardio/models/dirichlet_model/__init__.py ================================================ """Contains dirichlet model class.""" from .dirichlet_model import DirichletModel, concatenate_ecg_batch ================================================ FILE: cardio/models/dirichlet_model/dirichlet_model.py ================================================ """Contains Dirichlet model class.""" from itertools import zip_longest import numpy as np import tensorflow as tf from ..layers import conv1d_block, resnet1d_block from ...batchflow.models.tf import TFModel #pylint: disable=no-name-in-module, import-error def concatenate_ecg_batch(batch, model, return_targets=True): """Concatenate batch signals and (optionally) targets. Parameters ---------- batch : EcgBatch Batch to concatenate. model : BaseModel A model to get the resulting arguments. return_targets : bool Specifies whether to return concatenated targets. Returns ------- kwargs : dict Named argments for model's train or predict method. Has the following structure: "feed_dict" : dict "signals" : 3-D ndarray Concatenated signals. "targets" : 2-D ndarray, optional Concatenated targets. "split_indices" : 1-D ndarray Split indices to undo the concatenation. """ _ = model x = np.concatenate(batch.signal) split_indices = np.cumsum([item.signal.shape[0] for item in batch])[:-1] res_dict = {"feed_dict": {"signals": x}, "split_indices": split_indices} if return_targets: y = np.concatenate([np.tile(item.target, (item.signal.shape[0], 1)) for item in batch]) res_dict["feed_dict"]["targets"] = y return res_dict class DirichletModelBase(TFModel): """Dirichlet model class. The model predicts Dirichlet distribution parameters from which class probabilities are sampled. Notes ----- **Configuration** Model config must contain the following keys: * input_shape : tuple Input signals's shape without the batch dimension. * class_names : array_like Class names. * loss : ``None`` The model has a predefined loss, so you should leave it ``None``. """ def _build(self, *args, **kwargs): # pylint: disable=too-many-locals """Build Dirichlet model.""" _ = args, kwargs input_shape = self.config["input_shape"] class_names = self.config["class_names"] with self.graph.as_default(): self.store_to_attr("class_names", tf.constant(class_names)) signals = tf.placeholder(tf.float32, shape=(None,) + input_shape, name="signals") self.store_to_attr("signals", signals) signals_channels_last = tf.transpose(signals, perm=[0, 2, 1], name="signals_channels_last") k = 0.001 targets = tf.placeholder(tf.float32, shape=(None, len(class_names)), name="targets") self.store_to_attr("targets", targets) targets_soft = (1 - 2 * k) * targets + k block = conv1d_block("conv", signals_channels_last, is_training=self.is_training, filters=8, kernel_size=5) block_config = [ (8, 3, True), (8, 3, False), (8, 3, True), (8, 3, False), (12, 3, True), (12, 3, False), (12, 3, True), (12, 3, False), (16, 3, True), (16, 3, False), (16, 3, False), (16, 3, True), (16, 3, False), (16, 3, False), (20, 3, True), (20, 3, False), ] for i, (filters, kernel_size, downsample) in enumerate(block_config): block = resnet1d_block("block_" + str(i + 1), block, is_training=self.is_training, filters=filters, kernel_size=kernel_size, downsample=downsample) with tf.variable_scope("global_max_pooling"): # pylint: disable=not-context-manager block = tf.reduce_max(block, axis=1) with tf.variable_scope("dense"): # pylint: disable=not-context-manager dense = tf.layers.dense(block, len(class_names), use_bias=False, name="dense") bnorm = tf.layers.batch_normalization(dense, training=self.is_training, name="batch_norm", fused=True) act = tf.nn.softplus(bnorm, name="activation") parameters = tf.identity(act, name="parameters") self.store_to_attr("parameters", parameters) predictions = tf.identity(act, name="predictions") self.store_to_attr("predictions", predictions) loss = tf.reduce_mean(tf.lbeta(parameters) - tf.reduce_sum((parameters - 1) * tf.log(targets_soft), axis=1), name="loss") tf.losses.add_loss(loss) class DirichletModel(DirichletModelBase): """Dirichlet model with overloaded train and predict methods. * ``train`` method is identical to ``DirichletModelBase.train``, but also accepts ``args`` and ``kwargs``. * ``predict`` method splits the resulting tensor for ``parameters`` fetch using ``split_indices``. It also splits and aggregates results for ``predictions`` fetch to get class probabilities. """ @staticmethod def _get_dirichlet_mixture_stats(alpha): """Get mean and variance vectors of the mixture of Dirichlet distributions with equal weights and given parameters. Parameters ---------- alpha : 2-D ndarray Dirichlet distribution parameters along axis 1 for each mixture component. Returns ------- mean : 1-D ndarray Mean of the mixture. var : 1-D ndarray Variance of the mixture. """ alpha_sum = np.sum(alpha, axis=1)[:, np.newaxis] comp_m1 = alpha / alpha_sum comp_m2 = (alpha * (alpha + 1)) / (alpha_sum * (alpha_sum + 1)) mean = np.mean(comp_m1, axis=0) var = np.mean(comp_m2, axis=0) - mean**2 return mean, var def train(self, fetches=None, feed_dict=None, use_lock=False, *args, **kwargs): """Train the model with the data provided. The only difference between ``DirichletModel.train`` and ``TFModel.train`` is that the former also accepts ``args`` and ``kwargs``. Parameters ---------- fetches : tf.Operation or tf.Tensor or array-like sequence of them Graph element to evaluate in addition to ``train_step``. feed_dict : dict A dictionary that maps graph elements to values. use_lock : bool If ``True``, the whole train step is locked, thus allowing for multithreading. Returns ------- output : same structure as ``fetches`` Calculated values for each element in ``fetches``. """ _ = args, kwargs return super().train(fetches, feed_dict, use_lock) def predict(self, fetches=None, feed_dict=None, split_indices=None): # pylint: disable=arguments-differ """Get predictions on the data provided. Parameters ---------- fetches : tf.Operation or tf.Tensor or array-like sequence of them Graph element to evaluate. If ``fetches`` contains ``parameters`` tensor, the corresponding output is split using ``split_indices``. If ``fetches`` contains ``predictions`` tensor, the corresponding output is split using ``split_indices`` and then aggregated to get class probabilities. feed_dict : dict A dictionary that maps graph elements to values. split_indices : 1-D ndarray Indices used to split ``parameters`` and ``predictions`` tensors. Returns ------- output : same structure as ``fetches`` Calculated values for each element in ``fetches``. """ if isinstance(fetches, (list, tuple)): fetches_list = list(fetches) else: fetches_list = [fetches] output = super().predict(fetches_list, feed_dict) for i, fetch in enumerate(fetches_list): if fetch == "parameters": output[i] = np.split(output[i], split_indices) elif fetch == "predictions": class_names = self.class_names.eval(session=self.session) # pylint: disable=no-member class_names = [c.decode("utf-8") for c in class_names] n_classes = len(class_names) max_var = (n_classes - 1) / n_classes**2 alpha = np.split(output[i], split_indices) targets = feed_dict.get("targets") targets = [] if targets is None else [t[0] for t in np.split(targets, split_indices)] res = [] for a, t in zip_longest(alpha, targets): mean, var = self._get_dirichlet_mixture_stats(a) uncertainty = var[np.argmax(mean)] / max_var predictions_dict = {"target_pred": dict(zip(class_names, mean)), "uncertainty": uncertainty} if t is not None: predictions_dict["target_true"] = dict(zip(class_names, t)) res.append(predictions_dict) output[i] = res if isinstance(fetches, list): pass elif isinstance(fetches, tuple): output = tuple(output) else: output = output[0] return output ================================================ FILE: cardio/models/dirichlet_model/dirichlet_model_training.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Dirichlet model training" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook we will train Dirichlet model for atrial fibrillation detection." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Table of contents" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* [Dataset initialization](#Dataset-initialization)\n", "* [Training pipeline](#Training-pipeline)\n", "* [Saving the model](#Saving-the-model)\n", "* [Testing pipeline](#Testing-pipeline)\n", "* [Predicting pipeline](#Predicting-pipeline)\n", "* [Analyzing the uncertainty](#Analyzing-the-uncertainty)\n", "* [Visualizing predictions](#Visualizing-predictions)\n", " * [Certain prediction](#Certain-prediction)\n", " * [Uncertain prediction](#Uncertain-prediction)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import os\n", "import sys\n", "from functools import partial\n", "\n", "import numpy as np\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from scipy.stats import beta\n", "\n", "sys.path.append(os.path.join(\"..\", \"..\", \"..\"))\n", "import cardio.batchflow as bf\n", "from cardio import EcgDataset\n", "from cardio.batchflow import B, V, F\n", "from cardio.models.dirichlet_model import DirichletModel, concatenate_ecg_batch\n", "from cardio.models.metrics import f1_score, classification_report, confusion_matrix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Seaborn plotting parameters setting:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sns.set(\"talk\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, TensorFlow attempts to allocate almost the entire memory on all of the available GPUs. Executing this instruction makes only the GPU with id 0 visible for TensorFlow in this process." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "env: CUDA_VISIBLE_DEVICES=0\n" ] } ], "source": [ "%env CUDA_VISIBLE_DEVICES=0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dataset initialization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we need to specify paths to ECG signals and their labels:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "SIGNALS_PATH = \"D:\\\\Projects\\\\data\\\\ecg\\\\training2017\\\\\"\n", "SIGNALS_MASK = SIGNALS_PATH + \"*.hea\"\n", "LABELS_PATH = SIGNALS_PATH + \"REFERENCE.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's create an ECG dataset and perform a train/test split:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "eds = EcgDataset(path=SIGNALS_MASK, no_ext=True, sort=True)\n", "eds.split(0.8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training pipeline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dirichlet model builder expects model config to contain input signals' shape and class names:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5, allow_growth=True)\n", "\n", "model_config = {\n", " \"session\": {\"config\": tf.ConfigProto(gpu_options=gpu_options)},\n", " \"input_shape\": F(lambda batch: batch.signal[0].shape[1:]),\n", " \"class_names\": F(lambda batch: batch.label_binarizer.classes_),\n", " \"loss\": None,\n", "}" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "N_EPOCH = 1000\n", "BATCH_SIZE = 256" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Model training pipeline is composed of:\n", "* model initialization with the config defined above\n", "* data loading, preprocessing (e.g. flipping) and augmentation (e.g. resampling)\n", "* train step execution\n", "\n", "Let's create a template pipeline, then link it to our training dataset and run:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "template_train_ppl = (\n", " bf.Pipeline()\n", " .init_model(\"dynamic\", DirichletModel, name=\"dirichlet\", config=model_config)\n", " .init_variable(\"loss_history\", init_on_each_run=list)\n", " .load(components=[\"signal\", \"meta\"], fmt=\"wfdb\")\n", " .load(components=\"target\", fmt=\"csv\", src=LABELS_PATH)\n", " .drop_labels([\"~\"])\n", " .rename_labels({\"N\": \"NO\", \"O\": \"NO\"})\n", " .flip_signals()\n", " .random_resample_signals(\"normal\", loc=300, scale=10)\n", " .random_split_signals(2048, {\"A\": 9, \"NO\": 3})\n", " .binarize_labels()\n", " .train_model(\"dirichlet\", make_data=concatenate_ecg_batch,\n", " fetches=\"loss\", save_to=V(\"loss_history\"), mode=\"a\")\n", " .run(batch_size=BATCH_SIZE, shuffle=True, drop_last=True, n_epochs=N_EPOCH, lazy=True)\n", ")\n", "\n", "train_ppl = (eds.train >> template_train_ppl).run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Training loss is stored in \"loss_history\" pipeline variable. Let's take a look at its plot:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4gAAAEOCAYAAADLxjkiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/w/G2ZMkAT/nCwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_loss = [np.mean(l) for l in np.array_split(train_ppl.get_variable(\"loss_history\"), N_EPOCH)]\n", "\n", "fig = plt.figure(figsize=(15, 4))\n", "plt.plot(train_loss)\n", "plt.xlabel(\"Epochs\")\n", "plt.ylabel(\"Training loss\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, training loss almost reaches a plateau by the end of the training." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Saving the model" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "MODEL_PATH = \"D:\\\\Projects\\\\data\\\\ecg\\\\dirichlet_model\"\n", "train_ppl.save_model(\"dirichlet\", path=MODEL_PATH)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Testing pipeline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Testing pipeline is almost identical to the training one. The differences lie in the absence of signal resampling and the modified segmentation procedure. Notice, that the model is imported from the training pipeline, rather than being constructed from scratch." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "template_test_ppl = (\n", " bf.Pipeline()\n", " .import_model(\"dirichlet\", train_ppl)\n", " .init_variable(\"predictions_list\", init_on_each_run=list)\n", " .load(components=[\"signal\", \"meta\"], fmt=\"wfdb\")\n", " .load(components=\"target\", fmt=\"csv\", src=LABELS_PATH)\n", " .drop_labels([\"~\"])\n", " .rename_labels({\"N\": \"NO\", \"O\": \"NO\"})\n", " .flip_signals()\n", " .split_signals(2048, 2048)\n", " .binarize_labels()\n", " .predict_model(\"dirichlet\", make_data=concatenate_ecg_batch,\n", " fetches=\"predictions\", save_to=V(\"predictions_list\"), mode=\"e\")\n", " .run(batch_size=BATCH_SIZE, shuffle=False, drop_last=False, n_epochs=1, lazy=True)\n", ")\n", "\n", "test_ppl = (eds.test >> template_test_ppl).run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now \"predictions_list\" pipeline variable stores model predictions and true targets for all signals labeled with \"A\", \"O\" and \"N\" in the testing dataset." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will use [F1-score](https://en.wikipedia.org/wiki/F1_score) with macro averaging to measure classification performance:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "predictions = test_ppl.get_variable(\"predictions_list\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.90342902903869682" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f1_score(predictions)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " A 0.85 0.81 0.83 171\n", " NO 0.98 0.98 0.98 1484\n", "\n", "avg / total 0.96 0.96 0.96 1655\n", "\n" ] } ], "source": [ "print(classification_report(predictions))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Also take a look at the more detailed report - the confusion matrix:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "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", "
TrueANOAll
Pred
A13825163
NO3314591492
All17114841655
\n", "
" ], "text/plain": [ "True A NO All\n", "Pred \n", "A 138 25 163\n", "NO 33 1459 1492\n", "All 171 1484 1655" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "confusion_matrix(predictions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The model misclassifies 33 patients with atrial fibrillation and 25 patients with normal and other rhythms. All other patients were classified correctly." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We’ve already obtained good classification performance. Let’s see if we can do even better." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyzing the uncertainty" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition to class probabilities the model returns its uncertainty in the prediction, which varies from 0 (absolutely sure) to 1 (absolutely unsure). You can see the uncertainty histogram on the plot below:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2oAAAEOCAYAAAD40eRuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3X1Y1fX9x/HXOaDAOXSNtDtdiiiKzgT0cGmmUxPLKwVv\n0mp2s81SR7mL6+rGLTWSsnTXNd3iytLatbrStkrQC8tpEtNohtmY5b0uFZ2O3BUhNBAQzvn+/ugH\ni7zhoHwPnwPPxz/I93xv3pzzCs+r7/d8dViWZQkAAAAAYAxnWw8AAAAAAGiKogYAAAAAhqGoAQAA\nAIBhKGoAAAAAYBiKGgAAAAAYJrQtD/7VV/9ty8NfkMPhUNeubn39dZW4ISZaG/mCncgX7ES+YDcy\nBjuZnK9rr73qgss5o/Y9Tue3L6STZwY2IF+wE/mCncgX7EbGYKdgzFcQjQoAAAAAHQNFDQAAAAAM\nQ1EDAAAAAMNQ1AAAAADAMBQ1AAAAADAMRQ0AAAAADENRAwAAAADD+F3USktLNXz4cG3btk2SVFFR\noblz58rj8WjMmDHKzs62bUgAAAAA6EhC/V1x4cKFKi8vb/w+IyNDLpdLhYWFOnz4sGbPnq2+ffsq\nMTHRlkEBAAAAoKPwq6i99dZbioiIULdu3SRJVVVVys/P15YtWxQWFqb4+HilpKQoNze33RS1bbv+\nLZ9ltWibMYk/tGkaAAAAAB1Js0WtuLhYr7/+utauXas777xTknTixAmFhoaqR48ejevFxMQoLy+v\nRQd3OBxyGvYpOafTIUlyOCWnz9GibUNCWrY+Op6GfDV8BVoT+YKdyBfsRsZgp2DM1yWLWn19vX71\nq19p4cKFioqKalx+9uxZhYeHN1k3PDxcNTU1LTp4165uORxmPlmuiLAWb9OlS6QNk6A9iopyt/UI\naMfIF+xEvmA3MgY7BVO+LlnUXn75ZQ0YMECjR49usjwiIkK1tbVNltXU1MjlcrXo4F9/XWXkGbWo\nKLfOVtfK8rVs27KySnuGQrvRkK/y8ir5fC27tBZoDvmCncgX7EbGYCeT83Wxkz2XLGqbNm3SV199\npU2bNkmSKisr9dhjj2nWrFmqq6tTSUmJunfvLunbSyRjY2NbNJRlWfJ6W7RJwFg+tfgzal6vWS86\nzOXzWeQFtiFfsBP5gt3IGOwUTPm6ZFF7//33m3w/duxYZWRk6NZbb9WhQ4e0fPlyPffcc/riiy+0\nceNGvfrqq7YOCwAAAAAdwWVfeLh48WLV19dr9OjRSk9P17x585SQkNCaswEAAABAh+T3v6MmSVu3\nbm38c1RUlLKyslp9IAAAAADo6Ay7lQcAAAAAgKIGAAAAAIahqAEAAACAYShqAAAAAGAYihoAAAAA\nGIaiBgAAAACGoagBAAAAgGEoagAAAABgGIoaAAAAABiGogYAAAAAhqGoAQAAAIBhKGoAAAAAYBiK\nGgAAAAAYhqIGAAAAAIahqAEAAACAYShqAAAAAGAYihoAAAAAGIaiBgAAAACGoagBAAAAgGEoagAA\nAABgGIoaAAAAABiGogYAAAAAhqGoAQAAAIBhKGoAAAAAYBiKGgAAAAAYhqIGAAAAAIahqAEAAACA\nYShqAAAAAGAYihoAAAAAGIaiBgAAAACGoagBAAAAgGEoagAAAABgGIoaAAAAABiGogYAAAAAhqGo\nAQAAAIBhKGoAAAAAYBiKGgAAAAAYhqIGAAAAAIahqAEAAACAYShqAAAAAGAYihoAAAAAGIaiBgAA\nAACG8auobdq0SXfccYcGDx6siRMnKj8/X5JUUVGhuXPnyuPxaMyYMcrOzrZ1WAAAAADoCEKbW6G4\nuFgLFizQa6+9piFDhqiwsFBz5szRRx99pMzMTLlcLhUWFurw4cOaPXu2+vbtq8TExEDMDgAAAADt\nUrNFLSYmRh9//LHcbrfq6+tVWloqt9utzp07Kz8/X1u2bFFYWJji4+OVkpKi3Nxcv4uaw+GQ07CL\nL51OhyTJ4ZScPkeLtg0Jadn66Hga8tXwFWhN5At2Il+wGxmDnYIxX80WNUlyu906efKkxo8fL5/P\np8zMTP3rX/9SaGioevTo0bheTEyM8vLy/D54165uORxmPlmuiLAWb9OlS6QNk6A9iopyt/UIaMfI\nF+xEvmA3MgY7BVO+/CpqktStWzft3r1bRUVFeuSRR/TQQw8pPDy8yTrh4eGqqanx++Bff11l5Bm1\nqCi3zlbXyvK1bNuyskp7hkK70ZCv8vIq+XxWW4+DdoZ8wU7kC3YjY7CTyfm62Mkev4taaOi3qw4f\nPly333679u3bp9ra2ibr1NTUyOVy+T2UZVnyev1ePaAsn+SzWvYier1mvegwl89nkRfYhnzBTuQL\ndiNjsFMw5avZ81kFBQX6+c9/3mRZXV2devbsqbq6OpWUlDQuLy4uVmxsbKsPCQAAAAAdSbNF7Uc/\n+pH27dun3Nxc+Xw+FRQUqKCgQPfcc4+Sk5O1fPlyVVdXa8+ePdq4caNSU1MDMTcAAAAAtFvNFrVr\nr71Wq1at0urVq5WUlKSsrCy99NJL6tOnjxYvXqz6+nqNHj1a6enpmjdvnhISEgIxNwAAAAC0W359\nRi0pKUnr168/b3lUVJSysrJafSgAAAAA6MgMu+ciAAAAAICiBgAAAACGoagBAAAAgGEoagAAAABg\nGIoaAAAAABiGogYAAAAAhqGoAQAAAIBhKGoAAAAAYBiKGgAAAAAYhqIGAAAAAIahqAEAAACAYShq\nAAAAAGAYihoAAAAAGIaiBgAAAACGoagBAAAAgGEoagAAAABgGIoaAAAAABiGogYAAAAAhqGoAQAA\nAIBhKGoAAAAAYBiKGgAAAAAYhqIGAAAAAIahqAEAAACAYShqAAAAAGAYihoAAAAAGIaiBgAAAACG\noagBAAAAgGEoagAAAABgGIoaAAAAABiGogYAAAAAhqGoAQAAAIBhKGoAAAAAYBiKGgAAAAAYhqIG\nAAAAAIahqAEAAACAYShqAAAAAGAYihoAAAAAGIaiBgAAAACGoagBAAAAgGEoagAAAABgGIoaAAAA\nABjGr6JWVFSku+66Sx6PR+PGjdPbb78tSaqoqNDcuXPl8Xg0ZswYZWdn2zosAAAAAHQEoc2tUFFR\noUceeUQZGRmaOHGiDh48qJkzZ6pnz556++235XK5VFhYqMOHD2v27Nnq27evEhMTAzE7AAAAALRL\nzRa1kpISjR49WqmpqZKkgQMHatiwYdq1a5fy8/O1ZcsWhYWFKT4+XikpKcrNzfW7qDkcDjkNu/jS\n6XRIkhxOyelztGjbkJCWrY+OpyFfDV+B1kS+YCfyBbuRMdgpGPPVbFEbMGCAfvvb3zZ+X1FRoaKi\nIsXFxSk0NFQ9evRofCwmJkZ5eXl+H7xrV7ccDjOfLFdEWIu36dIl0oZJ0B5FRbnbegS0Y+QLdiJf\nsBsZg52CKV/NFrXv+u9//6u0tLTGs2qrV69u8nh4eLhqamr83t/XX1cZeUYtKsqts9W1snwt27as\nrNKeodBuNOSrvLxKPp/V1uOgnSFfsBP5gt3IGOxkcr4udrLH76J28uRJpaWlqUePHnrhhRd09OhR\n1dbWNlmnpqZGLpfL76Esy5LX6/fqAWX5JJ/VshfR6zXrRYe5fD6LvMA25At2Il+wGxmDnYIpX36d\nz9q/f7/uvvtujRw5Ui+//LLCw8MVHR2turo6lZSUNK5XXFys2NhY24YFAAAAgI6g2aJWWlqqWbNm\naebMmZo/f76c/3+tYmRkpJKTk7V8+XJVV1drz5492rhxY+NNRwAAAAAAl6fZSx9zcnJUVlamlStX\nauXKlY3Lf/rTn2rx4sVatGiRRo8eLZfLpXnz5ikhIcHWgQEAAACgvWu2qKWlpSktLe2ij2dlZbXq\nQAAAAADQ0Rl2z0UAAAAAAEUNAAAAAAxDUQMAAAAAw1DUAAAAAMAwFDUAAAAAMAxFDQAAAAAMQ1ED\nAAAAAMNQ1AAAAADAMBQ1AAAAADAMRQ0AAAAADENRAwAAAADDUNQAAAAAwDAUNQAAAAAwDEUNAAAA\nAAxDUQMAAAAAw1DUAAAAAMAwFDUAAAAAMAxFDQAAAAAMQ1EDAAAAAMNQ1AAAAADAMBQ1AAAAADAM\nRQ0AAAAADENRAwAAAADDUNQAAAAAwDAUNQAAAAAwDEUNAAAAAAxDUQMAAAAAw1DUAAAAAMAwFDUA\nAAAAMAxFDQAAAAAMQ1EDAAAAAMNQ1AAAAADAMBQ1AAAAADAMRQ0AAAAADENRAwAAAADDUNQAAAAA\nwDAUNQAAAAAwDEUNAAAAAAxDUQMAAAAAw1DUAAAAAMAwFDUAAAAAMEyLitqePXs0cuTIxu8rKio0\nd+5ceTwejRkzRtnZ2a0+IAAAAAB0NKH+rGRZltatW6ff/OY3CgkJaVyekZEhl8ulwsJCHT58WLNn\nz1bfvn2VmJho28AAAAAA0N75dUZt1apVWr16tdLS0hqXVVVVKT8/X+np6QoLC1N8fLxSUlKUm5tr\n27AAAAAA0BH4dUZt2rRpSktL06efftq47MSJEwoNDVWPHj0al8XExCgvL8/vgzscDjkN+5Sc0+mQ\nJDmcktPnaNG2ISEtWx8dT0O+Gr4CrYl8wU7kC3YjY7BTMObLr6J23XXXnbfs7NmzCg8Pb7IsPDxc\nNTU1fh+8a1e3HA4znyxXRFiLt+nSJdKGSdAeRUW523oEtGPkC3YiX7AbGYOdgilffhW1C4mIiFBt\nbW2TZTU1NXK5XH7v4+uvq4w8oxYV5dbZ6lpZvpZtW1ZWac9QaDca8lVeXiWfz2rrcdDOkC/YiXzB\nbmQMdjI5Xxc72XPZRS06Olp1dXUqKSlR9+7dJUnFxcWKjY31ex+WZcnrvdwJ7GX5JJ/VshfR6zXr\nRYe5fD6LvMA25At2Il+wGxmDnYIpX5d9PisyMlLJyclavny5qqurtWfPHm3cuFGpqamtOR8AAAAA\ndDhXdOHh4sWLVV9fr9GjRys9PV3z5s1TQkJCa80GAAAAAB1Siy59HDZsmHbu3Nn4fVRUlLKyslp9\nKAAAAADoyAy7lQcAAAAAgKIGAAAAAIahqAEAAACAYShqAAAAAGAYihoAAAAAGIaiBgAAAACGoagB\nAAAAgGEoagAAAABgGIoaAAAAABiGogYAAAAAhqGoAQAAAIBhKGoAAAAAYBiKGgAAAAAYhqIGAAAA\nAIahqAEAAACAYShqAAAAAGAYihoAAAAAGIaiBgAAAACGoagBAAAAgGEoagAAAABgGIoaAAAAABiG\nogYAAAAAhqGoAQAAAIBhKGoAAAAAYBiKGgAAAAAYhqIGAAAAAIahqAEAAACAYShqAAAAAGAYihoA\nAAAAGIaiBgAAAACGoagBAAAAgGEoagAAAABgGIoaAAAAABgmtK0HaE8+/PzfLd5mTOIPbZgEAAAA\nQDDjjBoAAAAAGIaiBgAAAACGoagBAAAAgGEoagAAAABgGG4m0sa4AQkAAACA7+OMGgAAAAAYhjNq\nQehyzsJJnIkDAAAAgsUVF7UDBw7o6aef1pEjRxQdHa1nnnlGiYmJrTEbWtnlFrxAoEQCAAAA/3NF\nRa22tlZpaWlKS0vTXXfdpQ0bNujhhx9Wfn6+3G53a80IXFAgzyzyWUK0hZbmzulwaNq4OJumAQAA\ngXRFRe2TTz6R0+nUvffeK0maPn263njjDRUUFGjChAmtMiA6hkCe7QvUsS50HKfDIbc7TFVVtfJZ\n1nmPB7LcBep5ML2wBqqEm3xGO5Da4/NtesYDpT2+tvgWGQfaxhUVteLiYvXp06fJspiYGB07dsyv\n7R0Oh5yG3c7E6XRIkhxOyelztPE0aG8czv99vVC+PtpdErBZnI7A5DuQP9PluJzn4XJ+pkA83w6n\n9P6O4zpbXSvLZ/vhLkt7er4bmJ7x1uJwSq6IsIvmqz2+tvhWoDKenHSjpP+9FwNaU0OugilfV1TU\nzp49q4iIiCbLwsPDVVNT49f211wTeSWHt9WdY7l8CAAAINCiovj4DOwTTPm6ovNZERER55Wympoa\nuVyuKxoKAAAAADqyKypqvXv3VnFxcZNlxcXFio2NvaKhAAAAAKAju6KiNnz4cJ07d05r1qxRXV2d\ncnJyVFpaqpEjR7bWfAAAAADQ4Tgs6wK3nmuBQ4cOKTMzU4cPH1Z0dLQyMzP5d9QAAAAA4ApccVED\nAAAAALQuw26ODwAAAACgqAEAAACAYShqAAAAAGAYihoAAAAAGKZDFrUDBw5o+vTpSkxM1OTJk/X5\n559fcL2NGzcqOTlZiYmJ+sUvfqHS0tIAT4pg5W/G1q5dq9tvv11DhgzRtGnTVFRUFOBJEYz8zVeD\nHTt2qH///qqqqgrQhAhm/uarqKhIU6dO1eDBg5WamqodO3YEeFIEI3/zlZ2dreTkZHk8Hv3kJz/R\nvn37AjwpgtmePXsu+c+FBc17fKuDqampsX784x9bf/rTn6xz585Z2dnZ1s0332xVVlY2We/gwYPW\nkCFDrM8//9yqrq62FixYYM2aNauNpkYw8TdjO3bssIYNG2YdOHDA8nq91vr16y2Px2OVlZW10eQI\nBv7mq0F5ebk1ZswYq1+/fhddB2jgb75Onz5tJSUlWe+//77l8/ms9957z/J4PFZ1dXUbTY5g0JL3\nYEOHDrWOHTtmeb1e65VXXrHGjh3bRlMjmPh8Pis7O9vyeDzW0KFDL7hOML3H73Bn1D755BM5nU7d\ne++96tSpk6ZPn65rrrlGBQUFTdZ77733lJycrISEBIWHh+uJJ57Q3/72N3MbN4zhb8ZOnz6thx56\nSAMGDJDT6dTUqVMVEhKiI0eOtNHkCAb+5qtBZmamJkyYEOApEaz8zdeGDRt0yy23aPz48XI4HEpJ\nSdEbb7whp7PDva1AC/ibrxMnTsjn88nr9cqyLDmdToWHh7fR1Agmq1at0urVq5WWlnbRdYLpPX6H\n+41aXFysPn36NFkWExOjY8eONVl27NgxxcbGNn5/9dVX6wc/+IGKi4sDMieCl78ZmzJlimbPnt34\n/T/+8Q9VVVWdty3wXf7mS5LeffddffPNN5oxY0agxkOQ8zdf+/fv1/XXX6+5c+dq2LBhuueee+T1\netW5c+dAjosg42++Ro4cqV69emnixIkaNGiQXnnlFS1btiyQoyJITZs2TRs2bNCgQYMuuk4wvcfv\ncEXt7NmzioiIaLIsPDxcNTU1TZZVV1ef939vIiIiVF1dbfuMCG7+Zuy7jhw5ovT0dKWnp6tLly52\nj4gg5m++SkpKlJWVpSVLlgRyPAQ5f/NVUVGh7OxszZgxQ9u3b9ekSZM0Z84cVVRUBHJcBBl/81Vb\nW6vY2Fjl5OTos88+089+9jP98pe/vOTfo4AkXXfddXI4HJdcJ5je43e4ohYREXHef+g1NTVyuVxN\nll2svH1/PeD7/M1Yg+3bt2vGjBm67777NGfOnECMiCDmT758Pp9+/etf69FHH9X1118f6BERxPz9\n/dW5c2eNGjVKI0eOVKdOnXTffffJ5XJp165dgRwXQcbffK1YsUI33HCDBg0apLCwMM2dO1d1dXUq\nLCwM5Lhop4LpPX6HK2q9e/c+79RmcXFxk1OgktSnT58m65WVlamiooLL0tAsfzMmSevWrVN6eroW\nLVqkRx55JFAjIoj5k6/Tp09r9+7dyszMVFJSkiZNmiRJGj16NHcWxSX5+/srJiZG586da7LM5/PJ\nsizbZ0Tw8jdfJSUlTfLlcDgUEhKikJCQgMyJ9i2Y3uN3uKI2fPhwnTt3TmvWrFFdXZ1ycnJUWlp6\n3i08U1JSlJeXp6KiItXW1up3v/udRo0apauvvrqNJkew8DdjO3bs0DPPPKNXX31VKSkpbTQtgo0/\n+erevbv27NmjoqIiFRUV6d1335UkFRQUKCkpqa1GRxDw9/fX5MmTtX37dn344Yfy+Xxas2aNamtr\nNWzYsDaaHMHA33yNGTNGOTk52r9/v+rr6/X666/L6/XK4/G00eRoT4LpPb7D6oD/++vQoUPKzMzU\n4cOHFR0drczMTCUmJurpp5+WJD377LOSpE2bNikrK0tfffWVkpKStHTpUnXt2rUtR0eQ8CdjDz74\noHbs2HHeddJZWVkaNWpUW4yNIOHv77AGp06dUnJysnbt2iW3290WIyOI+Juv7du3a9myZTpx4oRi\nYmK0aNEiJSQktOXoCAL+5MuyLP3hD3/Q22+/rW+++UYDBgxQRkaG+vXr18bTI1js3LlT6enp2rlz\npyQF7Xv8DlnUAAAAAMBkHe7SRwAAAAAwHUUNAAAAAAxDUQMAAAAAw1DUAAAAAMAwFDUAAAAAMAxF\nDQAAAAAMQ1EDAFxSXFyc4uLi9M9//vO8x/bs2aO4uDg98MADl73/F198UXfffbdf665fv14jRoy4\n7GMF0rlz5/TnP//Z7/UfeOABLVu2zJZ9AwCCD0UNANCsTp06KT8//7zleXl5cjgcbTCR+f7yl7/o\npZde8nv9F198UQ8//LAt+wYABB+KGgCgWUOHDr1gUfvggw+UmJjYBhOZz7KsFq0fFRUlt9tty74B\nAMGHogYAaNa4ceN04MABnT59unHZ4cOHVVlZqSFDhjRZ9/jx40pLS1NSUpKGDx+u5557TrW1tY2P\n7969W9OnT1d8fLxmzpypM2fONNn+6NGjevDBB5WQkKCxY8fqhRdeUF1dXbMz7ty5U3FxcU2OtWzZ\nssbLMnfu3KkRI0Zo3bp1uvXWWxUfH685c+aorKysyT7uvvtuJSQkaPz48dqwYYNfc61fv17Tpk3T\nY489Jo/Ho9dff13z589XaWmp4uLidOrUKVVWViojI0MjRozQwIEDNXbsWL355puN+//upY8vvvii\n0tPTtWTJEg0dOlRJSUl6/vnn5fP5tHPnzib7brj89NSpU437Ki8v10033aT9+/c3+7wBAMxEUQMA\nNOvGG29UXFxck7NqH3zwgcaNGyen839/lZSXl+vee++V2+3WW2+9peXLl2vr1q1aunSpJKmsrEyz\nZs1SYmKicnNzlZycrHfeeadx+9raWs2aNUv9+vVTbm6ulixZovfff1+///3vW+XnKC8vV05Ojlas\nWKE33nhDe/fu1auvvipJOnbsmB566CENGzZMubm5mjNnjhYuXKjdu3f7Nde+ffvUpUsXrVu3TuPH\nj9eCBQvUpUsXbd++Xd26ddPSpUu1f/9+rVq1Sps3b9aUKVO0ZMkSffnllxecdevWraqpqdE777yj\np556Sm+++aY+/PBDDR48uMm+Bw4cqOjoaG3evLlx27y8PPXo0UMDBw5slecNABB4FDUAgF9uu+22\nJkUtLy9P48ePb7LOe++9J6fTqaVLl6pv37665ZZbtGjRIq1du1YVFRXavHmz3G635s+fr969e+v+\n++/XuHHjmmzvcrn05JNPKiYmRjfffLOeeuoprVmzRl6v94p/hvr6ei1YsEADBw7U4MGDNWnSJO3d\nu1eSlJ2drf79++vxxx9XTEyMpk2bpscff1x1dXV+zzV37lz16tVL3bt311VXXSWn06lrr71WISEh\n8ng8ev755zVo0CD17NlTDz/8sLxer44dO3bBWV0ulzIyMhQTE6MpU6aof//+2rt3rzp37nzevlNT\nU7Vp06bGbTdu3KiUlJQrfr4AAG0ntK0HAAAEh3HjxmnlypX65ptvdObMGf3nP//R0KFDVVhY2LjO\n0aNH1b9/f3Xu3LlxmcfjkdfrVXFxsY4cOaJ+/fopJCSk8fFBgwY1nlU6evSoiouLNXjw4MbHLcvS\nuXPn9O9//7tVfo6YmJjGP0dGRqq+vr7x2IMGDWqy7syZMyVJf/3rX5udKzIyUldfffVFjzt16lRt\n3bpV69evV3FxsQ4ePChJFy2gP/zhD9WpU6cLzvp9qampWrFihY4fPy6Xy6WioiI999xzF50FAGA+\nihoAwC/9+/dX9+7dtW3bNn311VcaO3asQkOb/jUSFhZ23nYNRcTr9crhcJx3I4zv7qO+vl4ej+eC\nJeOGG2645HwXuvvkhUrQd8uP9L8bc3x/+Xf5M1d4ePgl53vyySe1c+dOTZ48WXfeeacSExN16623\nXnT9C81zsZuI9OrVS/Hx8dq8ebMiIyN10003qWfPnpecBwBgNi59BAD4bdy4cdq2bZvy8/PPu+xR\nknr37q1Dhw7p3Llzjcs+++wzOZ1O9erVS/369dPBgwebPH7gwIHGP/fp00fHjx9Xt27dFB0drejo\naH355Zdavnx5s3c6bCg2lZWVjctOnjzp98/Wq1evxrNcDR5//HGtWLHisub6bnE8c+aMcnNztWzZ\nMj366KOaMGGCzp49K+ny7uB4oVKampqqbdu2adu2bUpNTW3xPgEAZqGoAQD8dtttt6mgoEBHjx7V\nLbfcct7jqampcjgcmj9/vo4cOaLCwkI9++yzuuOOO9S1a1dNnDhRkrRo0SIdPXpU2dnZTW6CMWnS\nJEnfnn364osv9Pe//10LFy5UaGjoBc/WfVffvn0VHh6ulStX6uTJk1q7dq0+/vhjv3+2GTNmaP/+\n/XrppZd04sQJ5eTkaMuWLRo1atRlzeVyuVRZWamjR48qMjJSbrdbeXl5OnXqlD799FM98cQTkuTX\nHS0vte+GyyEnTJigAwcOaNeuXZowYUKL9wkAMAtFDQDgt8TERLndbo0aNarJ59AauFwu/fGPf1Rp\naanuvPNOzZs3T+PHj2+86+NVV12l1157TcePH9fUqVOVk5Oj+++//7ztz5w5o+nTpys9PV0jRozw\n6/NWkZGRWrJkibZu3aqJEyeqoKBAc+bM8ftnu/HGG7Vy5Url5eUpJSVFr732mpYtW6b4+PjLmuvm\nm29WbGyspkyZooMHD2r58uUqKCjQhAkTlJGRoYkTJyoxMVH79u3ze8YL7bvhjOQ111yjoUOHyuPx\nqGvXri3eJwDALA6LfzUTAIB2ITU1VbNmzdLkyZPbehQAwBXiZiIAAAS5jz76SEVFRSotLb3gZwcB\nAMGHogYAQJBbs2aN9u7dqyVLljR790kAQHDg0kcAAAAAMAw3EwEAAAAAw1DUAAAAAMAwFDUAAAAA\nMAxFDQApcMrIAAAAEklEQVQAAAAMQ1EDAAAAAMP8H2sgip3wq2oKAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "uncertainty = [d[\"uncertainty\"] for d in predictions]\n", "\n", "fig = plt.figure(figsize=(15, 4))\n", "sns.distplot(uncertainty, hist=True, norm_hist=True, kde=False)\n", "plt.xlabel(\"Model uncertainty\")\n", "plt.xlim(-0.05, 1.05)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The figure above shows, that the model is almost always certain in its predictions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compare the metrics for the full testing dataset above with the same metrics for 90% most certain predictions:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "q = 90\n", "thr = np.percentile(uncertainty, q)\n", "certain_predictions = [d for d in predictions if d[\"uncertainty\"] <= thr]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.95671826175423291" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f1_score(certain_predictions)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " A 0.95 0.89 0.92 102\n", " NO 0.99 1.00 0.99 1387\n", "\n", "avg / total 0.99 0.99 0.99 1489\n", "\n" ] } ], "source": [ "print(classification_report(certain_predictions))" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "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", "
TrueANOAll
Pred
A91596
NO1113821393
All10213871489
\n", "
" ], "text/plain": [ "True A NO All\n", "Pred \n", "A 91 5 96\n", "NO 11 1382 1393\n", "All 102 1387 1489" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "confusion_matrix(certain_predictions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can observe a significant increase in precision, recall and F1-score for the atrial fibrillation class. Now only 16 signals were misclassified." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Predicting pipeline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's predict class probabilities for a new, unobserved ECG signal.
\n", "Besides, we will load pretrained model from MODEL_PATH directory instead of importing it from another pipeline:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "SIGNALS_PATH = \"D:\\\\Projects\\\\data\\\\ecg\\\\training2017\\\\\"\n", "MODEL_PATH = \"D:\\\\Projects\\\\data\\\\ecg\\\\dirichlet_model\"\n", "\n", "BATCH_SIZE = 100" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5, allow_growth=True)\n", "\n", "model_config = {\n", " \"session\": {\"config\": tf.ConfigProto(gpu_options=gpu_options)},\n", " \"build\": False,\n", " \"load\": {\"path\": MODEL_PATH},\n", "}" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Restoring parameters from D:\\Projects\\data\\ecg\\dirichlet_model\\model-26000\n" ] } ], "source": [ "template_predict_ppl = (\n", " bf.Pipeline()\n", " .init_model(\"static\", DirichletModel, name=\"dirichlet\", config=model_config)\n", " .init_variable(\"predictions_list\", init_on_each_run=list)\n", " .load(fmt=\"wfdb\", components=[\"signal\", \"meta\"])\n", " .flip_signals()\n", " .split_signals(2048, 2048)\n", " .predict_model(\"dirichlet\", make_data=partial(concatenate_ecg_batch, return_targets=False),\n", " fetches=\"predictions\", save_to=V(\"predictions_list\"), mode=\"e\")\n", " .run(batch_size=BATCH_SIZE, shuffle=False, drop_last=False, n_epochs=1, lazy=True)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to create a dataset with a single ECG in it, then link it to the template predicting pipeline defined above and run it. Model prediction will be stored in the \"predictions_list\" variable." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "signal_name = \"A00001.hea\"\n", "signal_path = SIGNALS_PATH + signal_name\n", "predict_eds = EcgDataset(path=signal_path, no_ext=True, sort=True)\n", "predict_ppl = (predict_eds >> template_predict_ppl).run()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'target_pred': {'A': 0.020024037, 'NO': 0.97997594},\n", " 'uncertainty': 0.0063641071319580078}]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "predict_ppl.get_variable(\"predictions_list\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The length of the resulting list equals the length of the index of the dataset (1 in out case)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizing predictions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's look at the target Dirichlet mixture density for a given signal. The pipeline below stores the signal and Dirichlet distribution parameters in its variables in addition to the predicted class probabilities." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Restoring parameters from D:\\Projects\\data\\ecg\\dirichlet_model\\model-26000\n" ] } ], "source": [ "template_full_predict_ppl = (\n", " bf.Pipeline()\n", " .init_model(\"static\", DirichletModel, name=\"dirichlet\", config=model_config)\n", " .init_variable(\"signals\", init_on_each_run=list)\n", " .init_variable(\"predictions_list\", init_on_each_run=list)\n", " .init_variable(\"parameters_list\", init_on_each_run=list)\n", " .load(fmt=\"wfdb\", components=[\"signal\", \"meta\"])\n", " .update_variable(\"signals\", value=B(\"signal\"))\n", " .flip_signals()\n", " .split_signals(2048, 2048)\n", " .predict_model(\"dirichlet\", make_data=partial(concatenate_ecg_batch, return_targets=False),\n", " fetches=[\"predictions\", \"parameters\"],\n", " save_to=[V(\"predictions_list\"), V(\"parameters_list\")], mode=\"e\")\n", " .run(batch_size=BATCH_SIZE, shuffle=False, drop_last=False, n_epochs=1, lazy=True)\n", ")" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def predict_and_visualize(signal_path):\n", " predict_eds = EcgDataset(path=signal_path, no_ext=True, sort=True)\n", " \n", " full_predict_ppl = (predict_eds >> template_full_predict_ppl).run()\n", " signal = full_predict_ppl.get_variable(\"signals\")[0][0][0][:2000].ravel()\n", " predictions = full_predict_ppl.get_variable(\"predictions_list\")[0]\n", " parameters = full_predict_ppl.get_variable(\"parameters_list\")[0]\n", " \n", " print(predictions)\n", "\n", " x = np.linspace(0.001, 0.999, 1000)\n", " y = np.zeros_like(x)\n", " for alpha in parameters:\n", " y += beta.pdf(x, *alpha)\n", " y /= len(parameters)\n", " \n", " fig, (ax1, ax2) = plt.subplots(1, 2, gridspec_kw={\"width_ratios\": [2.5, 1]}, figsize=(15, 4))\n", "\n", " ax1.plot(signal)\n", "\n", " ax2.plot(x, y)\n", " ax2.fill_between(x, y, alpha=0.3)\n", " ax2.set_ylim(ymin=0)\n", "\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Certain prediction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let’s look at the healthy person’s ECG. The signal is shown on the left plot. Note that it has a clear quasi periodic structure. The right plot shows the pdf of the mixture distributions with atrial fibrillation probability plotted on the horizontal axis. The model is absolutely certain in the absence of AF: almost all the probability density is concentrated around 0." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'target_pred': {'A': 0.016624907, 'NO': 0.98337513}, 'uncertainty': 0.0040795803070068359}\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3kAAAD8CAYAAADKZW0jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhBBCCBGR/w99CXYaiTlNiAAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "predict_and_visualize(SIGNALS_PATH + \"A00150.hea\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Uncertain prediction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now comes an ECG with irregular structure, which may be caused by a disease or some measurement errors. The probability density on the right plot is almost equally concentrated around 0 and 1. This is an example of an uncertain prediction." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'target_pred': {'A': 0.49125454, 'NO': 0.50874543}, 'uncertainty': 0.77499103546142578}\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA24AAAEACAYAAADLMCVkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQuIcBW6EEEIIIYQQEuf+P8CM1PyJK9jBAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "predict_and_visualize(SIGNALS_PATH + \"A01505.hea\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: cardio/models/fft_model/__init__.py ================================================ """Contains fft model class.""" from .fft_model import FFTModel ================================================ FILE: cardio/models/fft_model/fft_model.py ================================================ """ Contains fft_model architecture """ import tensorflow as tf import keras.backend as K from keras.layers import Input, Conv1D, Lambda, \ MaxPooling1D, MaxPooling2D, \ Dense, GlobalMaxPooling2D from keras.layers.core import Dropout from ...batchflow.models.keras import KerasModel #pylint: disable=no-name-in-module, import-error from ..keras_custom_objects import RFFT, Crop, Inception2D class FFTModel(KerasModel):#pylint: disable=too-many-locals ''' FFT inception model. Includes initial convolution layers, then FFT transform, then a series of inception blocks. ''' def _build(self, **kwargs):#pylint: disable=too-many-locals ''' Build model ''' with tf.variable_scope('fft_model'):#pylint: disable=not-context-manager x = Input(kwargs['input_shape']) conv_1 = Conv1D(4, 4, activation='relu')(x) mp_1 = MaxPooling1D()(conv_1) conv_2 = Conv1D(8, 4, activation='relu')(mp_1) mp_2 = MaxPooling1D()(conv_2) conv_3 = Conv1D(16, 4, activation='relu')(mp_2) mp_3 = MaxPooling1D()(conv_3) conv_4 = Conv1D(32, 4, activation='relu')(mp_3) fft_1 = RFFT()(conv_4) crop_1 = Crop(begin=0, size=128)(fft_1) to2d = Lambda(K.expand_dims)(crop_1) incept_1 = Inception2D(4, 4, 3, 3, activation='relu')(to2d) mp2d_1 = MaxPooling2D(pool_size=(4, 2))(incept_1) incept_2 = Inception2D(4, 8, 3, 3, activation='relu')(mp2d_1) mp2d_2 = MaxPooling2D(pool_size=(4, 2))(incept_2) incept_3 = Inception2D(4, 12, 3, 3, activation='relu')(mp2d_2) pool = GlobalMaxPooling2D()(incept_3) fc_1 = Dense(8, kernel_initializer='uniform', activation='relu')(pool) drop = Dropout(0.2)(fc_1) fc_2 = Dense(2, kernel_initializer='uniform', activation='softmax')(drop) return x, fc_2 ================================================ FILE: cardio/models/fft_model/fft_model_training.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# FFT model training\n", "\n", "In this notebook we will train a model based of fast Fourier transform for detection of atrial fibrillation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* [Init dataset](#Init-dataset)\n", "* [Train pipeline](#Train-pipeline)\n", "* [Show loss and metric on train](#Show-loss-and-metric-on-train)\n", "* [Make predictions](#Make-predictions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Init dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For model training we will use the PhysioNet's short single lead ECG recording [database](https://physionet.org/challenge/2017/). To follow the tutorial download the PhysioNet's database." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sys, os\n", "sys.path.append(os.path.join(\"..\", \"..\", \"..\"))\n", "\n", "from cardio import EcgDataset\n", "eds = EcgDataset(path=\"/notebooks/data/ECG/training2017/*.hea\", no_ext=True, sort=True)\n", "eds.split(0.8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train pipeline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To train the model we construct a ```train_pipeline```. Within the pipeline we initialize the ```FFTModel``` model with given model_config, initialize variable to store loss, load and preprocesse signals, declare which components of the batch are ```x``` and ```y``` for the model and train the model." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import numpy as np\n", "import cardio.batchflow as bf\n", "from cardio.batchflow import F, V, B\n", "from cardio.models.fft_model import FFTModel\n", "\n", "model_config = {\n", " \"input_shape\": F(lambda batch: batch.signal[0].shape),\n", " \"loss\": \"binary_crossentropy\",\n", " \"optimizer\": \"adam\"\n", "}\n", "\n", "def make_data(batch, **kwagrs):\n", " return {'x': np.array(list(batch.signal)), 'y': batch.target}\n", " \n", "train_pipeline = (bf.Pipeline()\n", " .init_model(\"dynamic\", FFTModel, name=\"fft_model\", config=model_config)\n", " .init_variable(\"loss_history\", init_on_each_run=list)\n", " .load(fmt=\"wfdb\", components=[\"signal\", \"meta\"])\n", " .load(src=\"/notebooks/data/ECG/training2017/REFERENCE.csv\",\n", " fmt=\"csv\", components=\"target\")\n", " .drop_labels([\"~\"])\n", " .rename_labels({\"N\": \"NO\", \"O\": \"NO\"})\n", " .random_resample_signals(\"normal\", loc=300, scale=10)\n", " .drop_short_signals(4000)\n", " .split_signals(3000, 3000)\n", " .binarize_labels()\n", " .apply_transform(np.transpose, axes=[0, 2, 1], src='signal', dst='signal')\n", " .unstack_signals()\n", " .train_model('fft_model', make_data=make_data,\n", " save_to=V(\"loss_history\"), mode=\"a\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run ```train_pipeline```:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "%env CUDA_VISIBLE_DEVICES=1\n", "\n", "fft_trained = (eds.train >> train_pipeline).run(batch_size=300, shuffle=True,\n", " drop_last=True, n_epochs=250, prefetch=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Show loss and metric on train" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now ```fft_trained``` contains trained model and loss history. Method ```get_variable``` allows to get ```loss_history``` and we can plot it:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "loss = pd.DataFrame(fft_trained.get_variable(\"loss_history\"), columns=[\"loss\"])\n", "loss[\"avr loss\"] = pd.DataFrame(loss).rolling(center=True, window=100).mean()\n", "loss.plot()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Make predictions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For predictions we create ```predict_pipeline```. It differs from ```train_pipeline``` in several things. We import trained model instead of initializing a new one and apply ```predict_model``` instead of ```train_model``` action. Another difference is that we aggregate predictions on signal segments into the final prediction for the whole signal. Function ```make_pivot``` is responsible for it." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def make_pivot(pipeline, variable_name):\n", " cropes = np.array([x[0] for x in pipeline.get_variable(\"shapes\")])\n", " pos = np.vstack([np.pad(np.cumsum(cropes)[:-1], pad_width=(1, 0), mode='constant'), cropes]).T\n", " labels = np.array(pipeline.get_variable(variable_name)) \n", " return np.array([labels[s: s + i].mean(axis=0) for s, i in pos])\n", "\n", "predict_pipeline = (bf.Pipeline()\n", " .import_model(\"fft_model\", fft_trained)\n", " .init_variable(\"true_labels\", init_on_each_run=list)\n", " .init_variable(\"pred_labels\", init_on_each_run=list)\n", " .init_variable(\"shapes\", init_on_each_run=list)\n", " .init_variable(\"pivot_true_labels\", init_on_each_run=list)\n", " .init_variable(\"pivot_pred_labels\", init_on_each_run=list)\n", " .load(fmt=\"wfdb\", components=[\"signal\", \"meta\"])\n", " .load(src=\"/notebooks/data/ECG/training2017/REFERENCE.csv\",\n", " fmt=\"csv\", components=\"target\")\n", " .drop_labels([\"~\"])\n", " .rename_labels({\"N\": \"NO\", \"O\": \"NO\"})\n", " .drop_short_signals(4000)\n", " .split_signals(3000, 3000)\n", " .binarize_labels()\n", " .apply_transform(np.transpose, axes=[0, 2, 1], src='signal', dst='signal')\n", " .update_variable(\"shapes\", F(lambda batch: [x.shape for x in batch.signal]), mode='w')\n", " .unstack_signals()\n", " .update_variable(\"true_labels\", B('target'), mode='w')\n", " .predict_model('fft_model', make_data=make_data, save_to=V(\"pred_labels\"), mode=\"w\")\n", " .update_variable(\"pivot_true_labels\", F(lambda batch: make_pivot(batch.pipeline,\n", " 'true_labels')), mode='e')\n", " .update_variable(\"pivot_pred_labels\", F(lambda batch: make_pivot(batch.pipeline,\n", " 'pred_labels')), mode='e'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run ```predict_pipeline``` on the test part of the dataset:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "res_test = (eds.test >> predict_pipeline).run(batch_size=300, shuffle=False, drop_last=False,\n", " n_epochs=1, prefetch=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Consider predicted values and true labels which are stored in ```pivot_pred_labels``` and ```pivot_true_labels```. We get them through ```get_variable``` method and caclulate several classification metrics:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.878696268304\n" ] } ], "source": [ "from sklearn.metrics import f1_score\n", "\n", "print(f1_score(np.array(res_test.get_variable(\"pivot_true_labels\"))[:, 0],\n", " np.rint(res_test.get_variable(\"pivot_pred_labels\"))[:, 0], average='macro'))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0.0 0.97 0.99 0.98 1443\n", " 1.0 0.88 0.70 0.78 162\n", "\n", "avg / total 0.96 0.96 0.96 1605\n", "\n" ] } ], "source": [ "from sklearn.metrics import classification_report\n", "\n", "print(classification_report(np.array(res_test.get_variable(\"pivot_true_labels\"))[:, 0],\n", " np.rint(res_test.get_variable(\"pivot_pred_labels\"))[:, 0]))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: cardio/models/hmm/__init__.py ================================================ """Contains HMM annotation model class.""" from .hmm import HMModel, prepare_hmm_input ================================================ FILE: cardio/models/hmm/hmm.py ================================================ """ HMModel """ import numpy as np import dill from ...batchflow.models.base import BaseModel #pylint: disable=no-name-in-module, import-error def prepare_hmm_input(batch, model, features, channel_ix): """Concatenate selected channel ``channel_ix`` of the batch attribute ``features``. Parameters ---------- batch : EcgBatch Batch to concatenate. model : BaseModel A model to get the resulting arguments. features : str Specifies batch attribute that contains features for ``HMModel``. channel_ix : int Index of channel, which data should be used in training and predicting. Returns ------- kwargs : dict Named argments for model's train or predict method. Has the following structure: "X" : 2-D ndarray Features concatenated along -1 axis and transposed. "lengths" : list List of lengths of individual feature arrays along -1 axis. """ _ = model hmm_features = getattr(batch, features) x = np.concatenate([features[channel_ix].T for features in hmm_features]) lengths = [features.shape[-1] for features in hmm_features] return {"X": x, "lengths": lengths} class HMModel(BaseModel): """ Hidden Markov Model. This implementation is based on ``hmmlearn`` API. It is supposed that estimators of ``HMModel`` are model classes of ``hmmlearn``. """ def __init__(self, *args, **kwargs): self.estimator = None super().__init__(*args, **kwargs) def build(self, *args, **kwargs): """ Set up estimator as an attribute and make initial settings. Uses estimator from model config variable as estimator. If config contains key ``init_param``, sets up initial values ``means_``, ``covars_``, ``transmat_`` and ``startprob_`` of the estimator as defined in ``init_params``. """ _ = args, kwargs self.estimator = self.get("estimator", self.config) init_params = self.get("init_params", self.config) if init_params is not None: if "m" not in self.estimator.init_params: self.estimator.means_ = init_params["means_"] if "c" not in self.estimator.init_params: self.estimator.covars_ = init_params["covars_"] if "t" not in self.estimator.init_params: self.estimator.transmat_ = init_params["transmat_"] if "s" not in self.estimator.init_params: self.estimator.startprob_ = init_params["startprob_"] def save(self, path, *args, **kwargs): # pylint: disable=arguments-differ """Save ``HMModel`` with ``dill``. Parameters ---------- path : str Path to the file to save model to. """ _ = args, kwargs if self.estimator is not None: with open(path, "wb") as file: dill.dump(self.estimator, file) else: raise ValueError("HMM estimator does not exist. Check your cofig for 'estimator'.") def load(self, path, *args, **kwargs): # pylint: disable=arguments-differ """Load ``HMModel`` from file with ``dill``. Parameters ---------- path : str Path to the model. """ _ = args, kwargs with open(path, "rb") as file: self.estimator = dill.load(file) def train(self, *args, **kwargs): """ Train the model using data provided. Parameters ---------- x : array-like A matrix of observations. Should be of shape (n_samples, n_features). lengths : array-like of integers optional If present, should be of shape (n_sequences, ). Lengths of the individual sequences in ``X``. The sum of these should be ``n_samples``. Notes ----- For more details and other parameters look at the documentation for the estimator used. """ _ = args self.estimator.fit(**kwargs) return list(self.estimator.monitor_.history) def predict(self, *args, **kwargs): """ Make prediction with the data provided. Parameters ---------- X : array-like A matrix of observations. Should be of shape (n_samples, n_features). lengths : array-like of integers optional If present, should be of shape (n_sequences, ). Lengths of the individual sequences in ``X``. The sum of these should be ``n_samples``. Returns ------- output: array Labels for each sample of X. Notes ----- For more details and other parameters look at the documentation for the estimator used. """ _ = args preds = self.estimator.predict(**kwargs) lengths = kwargs.get('lengths') if lengths: output = np.array(np.split(preds, np.cumsum(lengths)[:-1]) + [None])[:-1] else: output = preds return output ================================================ FILE: cardio/models/hmm/hmmodel_training.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# HMModel trainig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook we will train Hidden Markov Model for ECG signal annotation to find P-wave, QRS complex and T-wave using the [QT Database](https://www.physionet.org/physiobank/database/qtdb/) with labeled ECGs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Table of contents" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "* [Setting up](#Setting-up)\n", "* [Helper functions](#Helper-functions)\n", "* [Dataset initialization](#Dataset-initialization)\n", "* [Preprocessing](#Preprocessing)\n", "* [Training](#Training)\n", "* [Prediction](#Prediction)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Setting up" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "%matplotlib inline\n", "\n", "import os\n", "import sys\n", "\n", "from functools import partial\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from hmmlearn import hmm\n", "\n", "sys.path.append(os.path.join(\"..\", \"..\", \"..\"))\n", "import cardio.batchflow as bf\n", "from cardio import EcgBatch\n", "from cardio.batchflow import B, V, F\n", "from cardio.models.hmm import HMModel, prepare_hmm_input\n", "\n", "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Helper functions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here are helper functions to generate data for learning, to get values from pipeline, and to perform calculation of initial parameters of the HMM." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_annsamples(batch):\n", " return [ann[\"annsamp\"] for ann in batch.annotation]\n", "\n", "def get_anntypes(batch):\n", " return [ann[\"anntype\"] for ann in batch.annotation]\n", "\n", "def expand_annotation(annsamp, anntype, length):\n", " \"\"\"Unravel annotation\n", " \"\"\"\n", " begin = -1\n", " end = -1\n", " s = 'none'\n", " states = {'N':0, 'st':1, 't':2, 'iso':3, 'p':4, 'pq':5}\n", " annot_expand = -1 * np.ones(length)\n", "\n", " for j, samp in enumerate(annsamp):\n", " if anntype[j] == '(':\n", " begin = samp\n", " if (end > 0) & (s != 'none'):\n", " if s == 'N':\n", " annot_expand[end:begin] = states['st']\n", " elif s == 't':\n", " annot_expand[end:begin] = states['iso']\n", " elif s == 'p':\n", " annot_expand[end:begin] = states['pq']\n", " elif anntype[j] == ')':\n", " end = samp\n", " if (begin > 0) & (s != 'none'):\n", " annot_expand[begin:end] = states[s]\n", " else:\n", " s = anntype[j]\n", "\n", " return annot_expand\n", "\n", "def prepare_means_covars(hmm_features, clustering, states=[3, 5, 11, 14, 17, 19], num_states=19, num_features=3):\n", " \"\"\"This function is specific to the task and the model configuration, thus contains hardcode.\n", " \"\"\"\n", " means = np.zeros((num_states, num_features))\n", " covariances = np.zeros((num_states, num_features, num_features))\n", " \n", " # Prepearing means and variances\n", " last_state = 0\n", " unique_clusters = len(np.unique(clustering)) - 1 # Excuding value -1, which represents undefined state\n", " for state, cluster in zip(states, np.arange(unique_clusters)):\n", " value = hmm_features[clustering == cluster, :]\n", " means[last_state:state, :] = np.mean(value, axis=0)\n", " covariances[last_state:state, :, :] = value.T.dot(value) / np.sum(clustering == cluster)\n", " last_state = state\n", " \n", " return means, covariances\n", "\n", "def prepare_transmat_startprob():\n", " \"\"\" This function is specific to the task and the model configuration, thus contains hardcode.\n", " \"\"\"\n", " # Transition matrix - each row should add up tp 1\n", " transition_matrix = np.diag(19 * [14/15.0]) + np.diagflat(18 * [1/15.0], 1) + np.diagflat([1/15.0], -18)\n", " \n", " # We suppose that absence of P-peaks is possible\n", " transition_matrix[13,14]=0.9*1/15.0\n", " transition_matrix[13,17]=0.1*1/15.0\n", "\n", " # Initial distribution - should add up to 1\n", " start_probabilities = np.array(19 * [1/np.float(19)])\n", " \n", " return transition_matrix, start_probabilities " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Dataset initialization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we need to specify paths to ECG signals:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "SIGNALS_PATH = \"path_to_QT_database\"\n", "SIGNALS_MASK = os.path.join(SIGNALS_PATH, \"*.hea\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next step is to create dataset and perform train/test split:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "index = bf.FilesIndex(path=SIGNALS_MASK, no_ext=True, sort=True)\n", "dtst = bf.Dataset(index, batch_class=EcgBatch)\n", "dtst.split(0.9)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Preprocessing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to calculate initial parameters for the model to ease the learning process.\n", "To do it, we will define template pipeline that loads data and accumulates the data we need for initial parameters in pipeline variables:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "template_ppl_inits = (\n", " bf.Pipeline()\n", " .init_variable(\"annsamps\", init_on_each_run=list)\n", " .init_variable(\"anntypes\", init_on_each_run=list)\n", " .init_variable(\"hmm_features\", init_on_each_run=list)\n", " .load(fmt='wfdb', components=[\"signal\", \"annotation\", \"meta\"], ann_ext='pu1')\n", " .cwt(src=\"signal\", dst=\"hmm_features\", scales=[4,8,16], wavelet=\"mexh\")\n", " .standardize(axis=-1, src=\"hmm_features\", dst=\"hmm_features\")\n", " .update_variable(\"annsamps\", bf.F(get_annsamples), mode='e')\n", " .update_variable(\"anntypes\", bf.F(get_anntypes), mode='e')\n", " .update_variable(\"hmm_features\", bf.B(\"hmm_features\"), mode='e')\n", " .run(batch_size=20, shuffle=False, drop_last=False, n_epochs=1, lazy=True)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we link the dataset to the template pipeline:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ppl_inits = (dtst >> template_ppl_inits).run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we get values from pipeline variables and perform calculation of the models' initial parameters: means, covariances, transition matrix and initial probabilities." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "lengths = [hmm_features.shape[2] for hmm_features in ppl_inits.get_variable(\"hmm_features\")]\n", "hmm_features = np.concatenate([hmm_features[0,:,:].T for hmm_features in ppl_inits.get_variable(\"hmm_features\")])\n", "anntype = ppl_inits.get_variable(\"anntypes\")\n", "annsamp = ppl_inits.get_variable(\"annsamps\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After this, we expand the annotation with helper function so that each observation has it's own label: 0 for QRS complex, 1 for ST segment, 2 for T-wave, 3 for ISO segment, 4 for P-wave, 5 for PQ segment and -1 for all other observations." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "expanded = np.concatenate([expand_annotation(samp, types, length) for samp, types, length in zip(annsamp, anntype, lengths)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now using unravelled annotation calculate means and covariances for the states of the model:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "means, covariances = prepare_means_covars(hmm_features, expanded, states = [3, 5, 11, 14, 17, 19], num_features = 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And, finally, define matrix of transitions between states and probabilities of the states:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "transition_matrix, start_probabilities = prepare_transmat_startprob()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Training" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In config we first specify that we want model to be build from scratch rather than loaded. Next, pass the desired estimator and initial parameters that we calculated in previous section." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "config_train = {\n", " 'build': True,\n", " 'estimator': hmm.GaussianHMM(n_components=19, n_iter=25, covariance_type=\"full\", random_state=42,\n", " init_params='', verbose=False),\n", " 'init_params': {'means_': means, 'covars_': covariances, 'transmat_': transition_matrix,\n", " 'startprob_': start_probabilities}\n", " }" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Training pipeline consists of the following actions:\n", "* Model initialization (building)\n", "* Data loading\n", "* Preprocessing (e.g. wavelet transformation)\n", "* Train step on current batch data\n", "\n", "Let's create template pipeline:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ppl_train_template = (\n", " bf.Pipeline()\n", " .init_model(\"dynamic\", HMModel, \"HMM\", config=config_train)\n", " .load(fmt='wfdb', components=[\"signal\", \"annotation\", \"meta\"], ann_ext='pu1')\n", " .cwt(src=\"signal\", dst=\"hmm_features\", scales=[4,8,16], wavelet=\"mexh\")\n", " .standardize(axis=-1, src=\"hmm_features\", dst=\"hmm_features\")\n", " .train_model(\"HMM\", make_data=partial(prepare_hmm_input, features=\"hmm_features\", channel_ix=0))\n", " .run(batch_size=20, shuffle=False, drop_last=False, n_epochs=1, lazy=True)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we link this template to the dataset and run:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ppl_train = (dtst >> ppl_train_template).run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From resulting pipeline we can save the trained model to file." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ppl_train.save_model(\"HMM\", path=\"hmmodel.dill\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Prediction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this config we tell that we want to load existing model from specified path:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "config_predict = {\n", " 'build': False,\n", " 'load': {'path': \"hmmodel.dill\"}\n", " }" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Prediction pipeline is somewhat more complex and composed of:\n", "* Model initialization (loading)\n", "* Initialization of pipeline variable\n", "* Data loading\n", "* Preprocesing\n", "* Calculation of ECG parameters, such as HR\n", "* Update of the variable\n", "\n", "Template for this pipeline looks like this:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "template_ppl_predict = (\n", " bf.Pipeline()\n", " .init_model(\"static\", HMModel, \"HMM\", config=config_predict)\n", " .load(fmt=\"wfdb\", components=[\"signal\", \"annotation\", \"meta\"], ann_ext=\"pu1\")\n", " .cwt(src=\"signal\", dst=\"hmm_features\", scales=[4,8,16], wavelet=\"mexh\")\n", " .standardize(axis=-1, src=\"hmm_features\", dst=\"hmm_features\")\n", " .predict_model(\"HMM\", make_data=partial(prepare_hmm_input, features=\"hmm_features\", channel_ix=0),\n", " save_to=bf.B(\"hmm_annotation\"), mode='w')\n", " .calc_ecg_parameters(src=\"hmm_annotation\")\n", " .run(batch_size=20, shuffle=False, drop_last=False, n_epochs=1, lazy=True)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's link pipieline to the dataset:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "ppl_predict = (dtst >> template_ppl_predict)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can generate new batches with ```next_batch()``` method of the pipeline. Let's take a look at the first one:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "batch = ppl_predict.next_batch()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, select fifth and ninth signal from that batch:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "example_1 = 5\n", "example_2 = 9" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can look at the plots with annotated ECG segments and the heart rate, calculated based on this annotation:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Heart rate: 50 bpm\n" ] } ], "source": [ "batch.show_ecg(batch.indices[example_1], 5, 10, \"hmm_annotation\")\n", "print(\"Heart rate: %d bpm\" %batch.meta[example_1][\"hr\"])" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Heart rate: 71 bpm\n" ] } ], "source": [ "batch.show_ecg(batch.indices[example_2], 0, 5, \"hmm_annotation\")\n", "print(\"Heart rate: %d bpm\" %batch.meta[example_2][\"hr\"])" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: cardio/models/keras_custom_objects.py ================================================ """ Contains keras custom objects """ import tensorflow as tf from keras.engine.topology import Layer from keras.layers import Input, Conv2D, Lambda, MaxPooling2D from keras.layers.merge import Concatenate import keras.backend as K class RFFT(Layer): ''' Keras layer for one-dimensional discrete Fourier Transform for real input. Computes rfft transforn on each slice along last dim. Input shape 3D tensor (batch_size, signal_length, nb_channels) Output shape 3D tensor (batch_size, int(signal_length / 2), nb_channels) ''' def rfft(self, x, fft_fn): ''' Computes one-dimensional discrete Fourier Transform on each slice along last dim. Returns amplitude spectrum. Parameters ---------- x : tensor 3D tensor (batch_size, signal_length, nb_channels) fft_fn : function Function that performs fft Returns ------- out : 3D tensor Computed fft(x). 3D tensor (batch_size, signal_length // 2, nb_channels) ''' resh = K.cast(K.map_fn(K.transpose, x), dtype='complex64') spec = K.abs(K.map_fn(fft_fn, resh)) out = K.cast(K.map_fn(K.transpose, spec), dtype='float32') shape = tf.shape(out) new_shape = [shape[0], shape[1] // 2, shape[2]] out_real = tf.slice(out, [0, 0, 0], new_shape) return out_real def call(self, x): ''' Implements Keras call method. ''' return Lambda(self.rfft, arguments={'fft_fn': K.tf.fft})(x) def compute_output_shape(self, input_shape): '''' Implements Keras compute_output_shape method. ''' if input_shape[1] is None: return input_shape return (input_shape[0], input_shape[1] // 2, input_shape[2]) class Crop(Layer): ''' Keras layer returns cropped signal. Input shape 3D tensor (batch_size, signal_length, nb_channels) Output shape 3D tensor (batch_size, size, nb_channels) Parameters ---------- begin : int Begin of the cropped segment size : int Size of the cropped segment Attributes ---------- begin : int Begin of the cropped segment size : int Size of the cropped segment ''' def __init__(self, begin, size, *agrs, **kwargs): self.begin = begin self.size = size super(Crop, self).__init__(*agrs, **kwargs) def call(self, x): ''' Implements Keras call method. ''' return x[:, self.begin: self.begin + self.size, :] def compute_output_shape(self, input_shape): ''' Implements Keras compute_output_shape method. ''' return (input_shape[0], self.size, input_shape[2]) class Inception2D(Layer):#pylint: disable=too-many-instance-attributes ''' Keras layer implements inception block. Input shape 4D tensor (batch_size, width, height, nb_channels) Output shape 4D tensor (batch_size, width, height, 3 * nb_filters + base_dim) Parameters ---------- base_dim : int nb_filters for the first convolution layers. nb_filters : int nb_filters for the second convolution layers. kernel_size_1 : int Kernel_size for the second convolution layer. kernel_size_2 : int Kernel_size for the second convolution layer. activation : string Activation function for each convolution. Default is 'linear'. Attributes ---------- base_dim : int nb_filters for the first convolution layers. nb_filters : int nb_filters for the second convolution layers. kernel_size_1 : int Kernel_size for the second convolution layer. kernel_size_2 : int Kernel_size for the second convolution layer. activation : string Activation function for each convolution. Default is 'linear'. ''' def __init__(self, base_dim, nb_filters,#pylint: disable=too-many-arguments kernel_size_1, kernel_size_2, activation=None, *agrs, **kwargs): self.base_dim = base_dim self.nb_filters = nb_filters self.kernel_size_1 = kernel_size_1 self.kernel_size_2 = kernel_size_2 self.activation = activation if activation is not None else 'linear' self.layers = {} super(Inception2D, self).__init__(*agrs, **kwargs) def build(self, input_shape): ''' Implements Keras build method ''' x = Input(input_shape[1:]) self.layers.update({'conv_1': Conv2D(self.base_dim, (1, 1), activation=self.activation, padding='same')}) _ = self.layers['conv_1'](x) self.layers.update({'conv_2': Conv2D(self.base_dim, (1, 1), activation=self.activation, padding='same')}) out = self.layers['conv_2'](x) self.layers.update({'conv_2a': Conv2D(self.nb_filters, (self.kernel_size_1, self.kernel_size_1), activation=self.activation, padding='same')}) out = self.layers['conv_2a'](out) self.layers.update({'conv_3': Conv2D(self.base_dim, (1, 1), activation=self.activation, padding='same')}) out = self.layers['conv_3'](x) self.layers.update({'conv_3a': Conv2D(self.nb_filters, (self.kernel_size_2, self.kernel_size_2), activation=self.activation, padding='same')}) out = self.layers['conv_3a'](out) self.layers.update({'conv_4': Conv2D(self.nb_filters, (1, 1), activation=self.activation, padding='same')}) _ = self.layers['conv_4'](x) for layer in self.layers.values(): self.trainable_weights.extend(layer.trainable_weights) return super(Inception2D, self).build(input_shape) def call(self, x): ''' Implements Keras call method ''' conv_1 = self.layers['conv_1'](x) conv_2 = self.layers['conv_2'](x) conv_2a = self.layers['conv_2a'](conv_2) conv_3 = self.layers['conv_3'](x) conv_3a = self.layers['conv_3a'](conv_3) pool = MaxPooling2D(strides=(1, 1), padding='same')(x) conv_4 = self.layers['conv_4'](pool) return Concatenate(axis=-1)([conv_1, conv_2a, conv_3a, conv_4]) def compute_output_shape(self, input_shape): ''' Implements Keras compute_output_shape method ''' return (*input_shape[:-1], self.base_dim + 3 * self.nb_filters) ================================================ FILE: cardio/models/layers.py ================================================ """Contains helper functions for tensorflow layers creation.""" import tensorflow as tf def conv1d_block(scope, input_layer, is_training, filters, kernel_size, pool_size=2, pool_stride=2, act_fn=tf.nn.relu): """Create conv1d -> max_pooling1d -> batch_normalization -> activation layer. Parameters ---------- scope : str Variable scope name. input_layer : 3-D Tensor Input tensor with channels_last ordering. is_training : 0-D Tensor with bool dtype True for training phase and False for inference. filters : int The number of filters in the convolution. kernel_size : int The length of the convolution kernel. pool_size : int The size of the pooling window. pool_stride : int The strides of the pooling operation. act_fn : callable Activation function. Returns ------- tensor : Tensor Output tensor. """ with tf.variable_scope(scope): # pylint: disable=not-context-manager conv = tf.layers.conv1d(input_layer, filters, kernel_size, padding="same", data_format="channels_last", use_bias=False, name="conv") pool = tf.layers.max_pooling1d(conv, pool_size, pool_stride, data_format="channels_last", name="pool") bnorm = tf.layers.batch_normalization(pool, training=is_training, name="batch_norm", fused=True) act = act_fn(bnorm, name="activation") return act def resnet1d_block(scope, input_layer, is_training, filters, kernel_size, downsample=False, act_fn=tf.nn.relu): """Create 1-D resnet block with two consequent convolutions and shortcut connection. Parameters ---------- scope : str Variable scope name. input_layer : 3-D Tensor Input tensor with channels_last ordering. is_training : 0-D Tensor with bool dtype True for training phase and False for inference. filters : int The number of filters in both convolutions. kernel_size : int The length of the convolution's kernels. downsample : bool Specifies whether to reduce the spatial dimension by the factor of two. act_fn : callable Activation function. Returns ------- tensor : Tensor Output tensor. """ with tf.variable_scope(scope): # pylint: disable=not-context-manager strides = 2 if downsample else 1 conv = tf.layers.conv1d(input_layer, filters, kernel_size, strides, padding="same", data_format="channels_last", use_bias=False, name="conv_1") bnorm = tf.layers.batch_normalization(conv, training=is_training, name="batch_norm_1", fused=True) act = act_fn(bnorm, name="act_1") conv = tf.layers.conv1d(act, filters, kernel_size, padding="same", data_format="channels_last", use_bias=False, name="conv_2") if downsample: input_layer = tf.layers.max_pooling1d(input_layer, 2, 2, data_format="channels_last", name="pool") if input_layer.get_shape()[-1] != conv.get_shape()[-1]: input_layer = tf.layers.conv1d(input_layer, conv.get_shape()[-1], 1, padding="same", data_format="channels_last", use_bias=False, name="conv_3") conv = conv + input_layer bnorm = tf.layers.batch_normalization(conv, training=is_training, name="batch_norm_2", fused=True) act = act_fn(bnorm, name="act_2") return act ================================================ FILE: cardio/models/metrics.py ================================================ """Contains metric functions.""" import numpy as np import pandas as pd from sklearn import metrics __all__ = ["f1_score", "auc", "classification_report", "confusion_matrix", "calculate_metrics"] def get_class_prob(predictions_dict): """Get true and predicted targets from predictions_dict. Parameters ---------- predictions_dict : dict Dict of model predictions. Must contain "target_true" and "target_pred" keys with corresponding dict values of the form class_label : probability. Returns ------- true_dict : dict True target - value for "target_true" key from the predictions_dict. pred_dict : dict Predicted target - value for "target_pred" key from the predictions_dict. """ true_dict = predictions_dict.get("target_true") pred_dict = predictions_dict.get("target_pred") if true_dict is None or pred_dict is None: raise ValueError("Each element of predictions list must be a dict with target_true and target_pred keys") return true_dict, pred_dict def get_labels(predictions_list): """Get true and predicted class labels from predictions_list. Parameters ---------- predictions_list : list List, containing dicts of model predictions. Each dict must contain "target_true" and "target_pred" keys with corresponding dict values of the form class_label : probability. Returns ------- true_labels : 1-D ndarray True class labels. pred_labels : 1-D ndarray Predicted class labels. """ true_labels = [] pred_labels = [] for predictions_dict in predictions_list: true_dict, pred_dict = get_class_prob(predictions_dict) true_labels.append(max(true_dict, key=true_dict.get)) pred_labels.append(max(pred_dict, key=pred_dict.get)) return np.array(true_labels), np.array(pred_labels) def get_probs(predictions_list): """Get true and predicted class probabilities from predictions_list. Parameters ---------- predictions_list : list List, containing dicts of model predictions. Each dict must contain "target_true" and "target_pred" keys with corresponding dict values of the form class_label : probability. Returns ------- true_probs : 2-D ndarray True class probabilities. pred_probs : 2-D ndarray Predicted class probabilities. """ true_probs = [] pred_probs = [] for predictions_dict in predictions_list: true_dict, pred_dict = get_class_prob(predictions_dict) true_probs.append([true_dict[key] for key in sorted(true_dict.keys())]) pred_probs.append([pred_dict[key] for key in sorted(pred_dict.keys())]) return np.array(true_probs), np.array(pred_probs) def f1_score(predictions_list, labels=None, average="macro", **kwargs): """Compute the F1 score. Parameters ---------- predictions_list : list List, containing dicts of model predictions. Each dict must contain "target_true" and "target_pred" keys with corresponding dict values of the form class_label : probability. average : str or None sklearn.metrics.f1_score average parameter, which determines the type of averaging performed on the data. **kwargs : misc Other sklearn.metrics.f1_score keyword arguments. Returns ------- f1_score : float or array of floats F1 score for each class or weighted average of the F1 scores. """ true_labels, pred_labels = get_labels(predictions_list) if labels is None: labels = sorted(set(true_labels) | set(pred_labels)) return metrics.f1_score(true_labels, pred_labels, labels=labels, average=average, **kwargs) def auc(predictions_list, average="macro", **kwargs): """Compute area under the ROC curve. Parameters ---------- predictions_list : list List, containing dicts of model predictions. Each dict must contain "target_true" and "target_pred" keys with corresponding dict values of the form class_label : probability. average : str or None sklearn.metrics.roc_auc_score average parameter, which determines the type of averaging performed on the data. **kwargs : misc Other sklearn.metrics.roc_auc_score keyword arguments. Returns ------- auc : float or array of floats AUC for each class or weighted average of the AUCs. """ return metrics.roc_auc_score(*get_probs(predictions_list), average=average, **kwargs) def classification_report(predictions_list, **kwargs): """Build a text report showing the main classification metrics. Parameters ---------- predictions_list : list List, containing dicts of model predictions. Each dict must contain "target_true" and "target_pred" keys with corresponding dict values of the form class_label : probability. **kwargs : misc Other sklearn.metrics.classification_report keyword arguments. Returns ------- report : string Text summary of the precision, recall and F1 score for each class. """ return metrics.classification_report(*get_labels(predictions_list), **kwargs) def confusion_matrix(predictions_list, margins=True, **kwargs): """Compute confusion matrix from correct and estimated targets. Parameters ---------- predictions_list : list List, containing dicts of model predictions. Each dict must contain "target_true" and "target_pred" keys with corresponding dict values of the form class_label : probability. margins : bool Specifies whether to add row and column margins with subtotals. **kwargs : misc Other pandas.crosstab keyword arguments. Returns ------- crosstab : DataFrame Classifier confusion matrix. """ true, pred = get_labels(predictions_list) true = pd.Series(true, name="True") pred = pd.Series(pred, name="Pred") return pd.crosstab(pred, true, margins=margins, **kwargs) METRICS_DICT = { "f1_score": f1_score, "auc": auc, "classification_report": classification_report, "confusion_matrix": confusion_matrix, } def calculate_metrics(metrics_list, predictions_list): """Calculate metrics from metrics_list. Parameters ---------- metrics_list : list List of metrics. Each element can be either str with metric name or callable. predictions_list : list List, containing dicts of model predictions. Each dict must contain "target_true" and "target_pred" keys with corresponding dict values of the form class_label : probability. Returns ------- metrics_res : list List containing results for every metric in metrics_list. """ metrics_res = [] for metric in metrics_list: if isinstance(metric, str): metric_fn = METRICS_DICT.get(metric) if metric_fn is None: raise KeyError("Unknown metric name {}".format(metric)) elif callable(metric): metric_fn = metric else: raise ValueError("Unknown metric type") metrics_res.append(metric_fn(predictions_list)) return metrics_res ================================================ FILE: cardio/pipelines/__init__.py ================================================ """Contains ECG pipelines.""" from .pipelines import * # pylint: disable=wildcard-import ================================================ FILE: cardio/pipelines/pipelines.py ================================================ """Contains pipelines.""" from functools import partial import numpy as np import tensorflow as tf from hmmlearn import hmm from .. import batchflow as bf from ..batchflow import F, V from ..models.dirichlet_model import DirichletModel, concatenate_ecg_batch from ..models.hmm import HMModel, prepare_hmm_input def dirichlet_train_pipeline(labels_path, batch_size=256, n_epochs=1000, gpu_options=None, loss_history='loss_history', model_name='dirichlet'): """Train pipeline for Dirichlet model. This pipeline trains Dirichlet model to find propability of atrial fibrillation. It works with dataset that generates batches of class ``EcgBatch``. Parameters ---------- labels_path : str Path to csv file with true labels. batch_size : int Number of samples per gradient update. Default value is 256. n_epochs : int Number of times to iterate over the training data arrays. Default value is 1000. gpu_options : GPUOptions An argument for tf.ConfigProto ``gpu_options`` proto field. Default value is ``None``. loss_history : str Name of pipeline variable to save loss values to. Returns ------- pipeline : Pipeline Output pipeline. """ model_config = { "session": {"config": tf.ConfigProto(gpu_options=gpu_options)}, "input_shape": F(lambda batch: batch.signal[0].shape[1:]), "class_names": F(lambda batch: batch.label_binarizer.classes_), "loss": None, } return (bf.Pipeline() .init_model("dynamic", DirichletModel, name=model_name, config=model_config) .init_variable(loss_history, init_on_each_run=list) .load(components=["signal", "meta"], fmt="wfdb") .load(components="target", fmt="csv", src=labels_path) .drop_labels(["~"]) .rename_labels({"N": "NO", "O": "NO"}) .flip_signals() .random_resample_signals("normal", loc=300, scale=10) .random_split_signals(2048, {"A": 9, "NO": 3}) .binarize_labels() .train_model(model_name, make_data=concatenate_ecg_batch, fetches="loss", save_to=V(loss_history), mode="a") .run(batch_size=batch_size, shuffle=True, drop_last=True, n_epochs=n_epochs, lazy=True)) def dirichlet_predict_pipeline(model_path, batch_size=100, gpu_options=None, predictions='predictions_list', model_name='dirichlet'): """Pipeline for prediction with Dirichlet model. This pipeline finds propability of atrial fibrillation according to Dirichlet model. It works with dataset that generates batches of class ``EcgBatch``. Parameters ---------- model_path : str path to pretrained ``DirichletModel`` batch_size : int Number of samples in batch. Default value is 100. gpu_options : GPUOptions An argument for tf.ConfigProto ``gpu_options`` proto field. Default value is ``None``. predictions: str Name of pipeline variable to save predictions to. Returns ------- pipeline : Pipeline Output pipeline. """ model_config = { "session": {"config": tf.ConfigProto(gpu_options=gpu_options)}, "build": False, "load": {"path": model_path}, } return (bf.Pipeline() .init_model("static", DirichletModel, name=model_name, config=model_config) .init_variable(predictions, init_on_each_run=list) .load(fmt="wfdb", components=["signal", "meta"]) .flip_signals() .split_signals(2048, 2048) .predict_model(model_name, make_data=partial(concatenate_ecg_batch, return_targets=False), fetches="predictions", save_to=V(predictions), mode="e") .run(batch_size=batch_size, shuffle=False, drop_last=False, n_epochs=1, lazy=True)) def hmm_preprocessing_pipeline(batch_size=20, features="hmm_features"): """Preprocessing pipeline for Hidden Markov Model. This pipeline prepares data for ``hmm_train_pipeline``. It works with dataset that generates batches of class ``EcgBatch``. Parameters ---------- batch_size : int Number of samples in batch. Default value is 20. features : str Batch attribute to store calculated features. Returns ------- pipeline : Pipeline Output pipeline. """ def get_annsamples(batch): """Get annsamples from annotation """ return [ann["annsamp"] for ann in batch.annotation] def get_anntypes(batch): """Get anntypes from annotation """ return [ann["anntype"] for ann in batch.annotation] return (bf.Pipeline() .init_variable("annsamps", init_on_each_run=list) .init_variable("anntypes", init_on_each_run=list) .init_variable(features, init_on_each_run=list) .load(fmt='wfdb', components=["signal", "annotation", "meta"], ann_ext='pu1') .cwt(src="signal", dst=features, scales=[4, 8, 16], wavelet="mexh") .standardize(axis=-1, src=features, dst=features) .update_variable("annsamps", bf.F(get_annsamples), mode='e') .update_variable("anntypes", bf.F(get_anntypes), mode='e') .update_variable(features, bf.B(features), mode='e') .run(batch_size=batch_size, shuffle=False, drop_last=False, n_epochs=1, lazy=True)) def hmm_train_pipeline(hmm_preprocessed, batch_size=20, features="hmm_features", channel_ix=0, n_iter=25, random_state=42, model_name='HMM'): """Train pipeline for Hidden Markov Model. This pipeline trains hmm model to isolate QRS, PQ and QT segments. It works with dataset that generates batches of class ``EcgBatch``. Parameters ---------- hmm_preprocessed : Pipeline Pipeline with precomputed hmm features through ``hmm_preprocessing_pipeline`` batch_size : int Number of samples in batch. Default value is 20. features : str Batch attribute to store calculated features. channel_ix : int Index of signal's channel, which should be used in training and predicting. n_iter : int Number of learning iterations for ``HMModel``. random_state: int Random state for ``HMModel``. Returns ------- pipeline : Pipeline Output pipeline. """ def prepare_means_covars(hmm_features, clustering, states=(3, 5, 11, 14, 17, 19), num_states=19, num_features=3): """This function is specific to the task and the model configuration, thus contains hardcode. """ means = np.zeros((num_states, num_features)) covariances = np.zeros((num_states, num_features, num_features)) # Prepearing means and variances last_state = 0 unique_clusters = len(np.unique(clustering)) - 1 # Excuding value -1, which represents undefined state for state, cluster in zip(states, np.arange(unique_clusters)): value = hmm_features[clustering == cluster, :] means[last_state:state, :] = np.mean(value, axis=0) covariances[last_state:state, :, :] = value.T.dot(value) / np.sum(clustering == cluster) last_state = state return means, covariances def prepare_transmat_startprob(): """ This function is specific to the task and the model configuration, thus contains hardcode. """ # Transition matrix - each row should add up tp 1 transition_matrix = np.diag(19 * [14/15.0]) + np.diagflat(18 * [1/15.0], 1) + np.diagflat([1/15.0], -18) # We suppose that absence of P-peaks is possible transition_matrix[13, 14] = 0.9*1/15.0 transition_matrix[13, 17] = 0.1*1/15.0 # Initial distribution - should add up to 1 start_probabilities = np.array(19 * [1/np.float(19)]) return transition_matrix, start_probabilities def expand_annotation(annsamp, anntype, length): """Unravel annotation """ begin = -1 end = -1 s = 'none' states = {'N':0, 'st':1, 't':2, 'iso':3, 'p':4, 'pq':5} annot_expand = -1 * np.ones(length) for j, samp in enumerate(annsamp): if anntype[j] == '(': begin = samp if (end > 0) & (s != 'none'): if s == 'N': annot_expand[end:begin] = states['st'] elif s == 't': annot_expand[end:begin] = states['iso'] elif s == 'p': annot_expand[end:begin] = states['pq'] elif anntype[j] == ')': end = samp if (begin > 0) & (s != 'none'): annot_expand[begin:end] = states[s] else: s = anntype[j] return annot_expand lengths = [features_iter.shape[2] for features_iter in hmm_preprocessed.get_variable(features)] hmm_features = np.concatenate([features_iter[channel_ix, :, :].T for features_iter in hmm_preprocessed.get_variable(features)]) anntype = hmm_preprocessed.get_variable("anntypes") annsamp = hmm_preprocessed.get_variable("annsamps") expanded = np.concatenate([expand_annotation(samp, types, length) for samp, types, length in zip(annsamp, anntype, lengths)]) means, covariances = prepare_means_covars(hmm_features, expanded, states=[3, 5, 11, 14, 17, 19], num_features=3) transition_matrix, start_probabilities = prepare_transmat_startprob() config_train = { 'build': True, 'estimator': hmm.GaussianHMM(n_components=19, n_iter=n_iter, covariance_type="full", random_state=random_state, init_params='', verbose=False), 'init_params': {'means_': means, 'covars_': covariances, 'transmat_': transition_matrix, 'startprob_': start_probabilities} } return (bf.Pipeline() .init_model("dynamic", HMModel, model_name, config=config_train) .load(fmt='wfdb', components=["signal", "annotation", "meta"], ann_ext='pu1') .cwt(src="signal", dst=features, scales=[4, 8, 16], wavelet="mexh") .standardize(axis=-1, src=features, dst=features) .train_model(model_name, make_data=partial(prepare_hmm_input, features=features, channel_ix=channel_ix)) .run(batch_size=batch_size, shuffle=False, drop_last=False, n_epochs=1, lazy=True)) def hmm_predict_pipeline(model_path, batch_size=20, features="hmm_features", channel_ix=0, annot="hmm_annotation", model_name='HMM'): """Prediction pipeline for Hidden Markov Model. This pipeline isolates QRS, PQ and QT segments. It works with dataset that generates batches of class ``EcgBatch``. Parameters ---------- model_path : str Path to pretrained ``HMModel``. batch_size : int Number of samples in batch. Default value is 20. features : str Batch attribute to store calculated features. channel_ix : int Index of channel, which data should be used in training and predicting. annot: str Specifies attribute of batch in which annotation will be stored. Returns ------- pipeline : Pipeline Output pipeline. """ config_predict = { 'build': False, 'load': {'path': model_path} } return (bf.Pipeline() .init_model("static", HMModel, model_name, config=config_predict) .load(fmt="wfdb", components=["signal", "meta"]) .cwt(src="signal", dst=features, scales=[4, 8, 16], wavelet="mexh") .standardize(axis=-1, src=features, dst=features) .predict_model(model_name, make_data=partial(prepare_hmm_input, features=features, channel_ix=channel_ix), save_to=bf.B(annot), mode='w') .calc_ecg_parameters(src=annot) .run(batch_size=batch_size, shuffle=False, drop_last=False, n_epochs=1, lazy=True)) ================================================ FILE: cardio/tests/data/A00001.hea ================================================ A00001 1 300 9000 05:05:15 08/05/2014 A00001.mat 16+24 1000/mV 16 0 -127 0 0 ECG ================================================ FILE: cardio/tests/data/A00002.hea ================================================ A00002 1 300 9000 11:05:25 16/05/2014 A00002.mat 16+24 1000/mV 16 0 128 0 0 ECG ================================================ FILE: cardio/tests/data/A00004.hea ================================================ A00004 1 300 9000 11:05:48 01/05/2014 A00004.mat 16+24 1000/mV 16 0 519 0 0 ECG ================================================ FILE: cardio/tests/data/A00005.hea ================================================ A00005 1 300 18000 08:12:08 23/12/2013 A00005.mat 16+24 1000/mV 16 0 -188 0 0 ECG ================================================ FILE: cardio/tests/data/A00008.hea ================================================ A00008 1 300 18000 06:11:36 24/11/2013 A00008.mat 16+24 1000/mV 16 0 -187 0 0 ECG ================================================ FILE: cardio/tests/data/A00013.hea ================================================ A00013 1 300 9000 10:06:01 10/06/2014 A00013.mat 16+24 1000/mV 16 0 3 0 0 ECG ================================================ FILE: cardio/tests/data/REFERENCE.csv ================================================ A00001,N A00002,N A00004,A A00005,A A00008,O A00013,O ================================================ FILE: cardio/tests/data/sample.xml ================================================ UNDEFINED UNDEFINED 0.0 kg 0.0 cm OFF 19700101 RESTING_ECG Schiller EmbeddedECG AT-10P Schiller 252 Schiller SCM Schiller 1.34 0 END_RECORD 0 ECG_AVERAGES 1 500 1.0 UV P_POS_AMPL 0.11 Q_AMPL -0.12 Q_DUR 24 R_AMPL 0.73 R_DUR 38 S_AMPL -0.11 S_DUR 16 R-_AMPL 0.03 R-_DUR 12 S-_AMPL - S-_DUR - J_AMPL 0.02 ST_SLP 0.04 T_POS_AMPL 0.25 T_NEG_AMPL 0.04 ST_INTEG 0.04 I 35,25,25,30,30,30,30,25,25,25,25,25,20,20,25,25,25,25,25,30,30,30,35,35,30,25,25,35,25,35,25,25,25,25,25,25,25,25,15,15,20,20,20,20,20,20,25,30,30,30,25,15,5,15,15,20,20,25,25,25,25,25,20,15,5,15,15,20,25,30,30,25,25,25,25,25,25,25,30,30,20,20,20,25,25,25,20,20,25,25,20,20,20,20,20,20,20,20,25,25,25,30,35,40,40,40,45,45,45,50,55,60,65,65,70,70,80,80,80,85,85,85,85,100,105,105,110,125,125,125,125,125,120,120,115,105,105,100,90,90,80,60,45,35,30,25,20,20,15,15,15,10,10,10,5,5,5,5,5,10,10,5,5,5,-5,-5,5,10,10,10,5,5,0,0,-10,5,10,10,5,5,5,5,5,5,-10,-20,-30,-50,-75,-90,-105,-110,-125,-125,-125,-80,-25,30,80,150,205,265,350,475,605,675,730,705,605,525,440,345,240,155,80,25,-10,-40,-80,-105,-105,-95,-70,-40,-10,10,20,30,30,30,20,20,20,20,25,25,25,25,20,20,20,20,20,20,20,25,25,25,25,30,30,30,30,30,30,30,30,30,35,40,40,40,40,40,40,40,55,55,45,45,50,50,50,50,55,55,65,75,75,75,75,70,70,70,70,70,80,80,85,85,90,95,100,105,105,105,110,120,120,120,125,125,125,135,140,140,145,155,165,170,175,175,175,180,190,195,205,205,210,215,225,225,230,235,250,250,245,245,245,250,250,245,245,245,240,230,230,230,215,205,205,195,185,185,180,170,165,155,140,125,115,100,100,100,90,80,75,70,70,70,65,55,55,55,50,45,45,40,40,40,40,35,30,30,30,30,30,30,25,25,30,30,30,30,25,20,20,20,20,20,20,25,25,25,25,15,15,15,15,20,20,20,20,25,25,25,25,20,20,15,10,10,10,15,15,15,20,20,20,30,30,20,20,20,20,20,20,30,15,20,30,20,20,15,15,20,30,35,35,35,35,30,20,20,20,15,15,25,25,25,25,15,15,15,15,15,25,25,25,15,25,25,25,30,30,15,15,15,15,15,15,15,15,15,20,20,20,15,15,15,15,25,25,25,25,25,25,25,25,25,25,25,20,20,15,15,15,15,25,15, COMMASEPARATE 500 P_POS_AMPL 0.14 Q_AMPL - Q_DUR - R_AMPL 0.37 R_DUR 62 S_AMPL -0.11 S_DUR 26 R-_AMPL - R-_DUR - S-_AMPL - S-_DUR - J_AMPL - ST_SLP 0.05 T_POS_AMPL 0.16 T_NEG_AMPL 0.05 ST_INTEG 0.05 II 35,35,35,40,40,30,25,25,25,15,30,30,25,25,25,25,30,30,35,35,35,35,35,35,40,40,40,40,40,35,25,25,30,35,35,20,35,35,25,25,30,30,25,25,25,25,30,40,30,30,30,25,25,25,25,25,30,35,35,35,35,35,35,20,30,25,25,25,30,35,35,25,25,25,25,25,30,40,40,30,30,30,45,35,35,30,30,30,30,40,40,35,25,25,25,25,30,30,30,30,30,20,45,60,65,80,85,90,95,105,115,115,125,125,115,125,135,135,135,140,150,160,150,150,150,145,145,160,160,155,145,130,115,115,105,95,90,85,80,70,65,60,55,35,35,20,20,20,20,-5,10,10,10,-15,-5,-5,-5,-5,-10,-10,-10,-15,-15,-10,0,5,5,-15,-15,-15,-15,-25,-15,0,-10,-20,0,0,0,-15,-5,-5,0,10,0,0,15,30,50,60,70,55,55,50,50,35,25,25,50,75,120,195,310,375,270,195,165,165,225,270,285,285,280,230,185,150,100,25,-30,-75,-95,-105,-110,-110,-85,-65,-55,-40,-20,-20,-10,0,20,25,35,35,50,50,40,30,30,30,20,35,35,50,50,45,45,45,45,35,35,40,45,50,50,55,55,55,40,50,60,60,55,55,65,65,60,60,65,65,55,55,70,75,80,80,90,90,90,75,75,80,85,85,80,80,95,95,95,90,90,95,95,105,110,110,110,100,110,110,110,110,115,115,125,130,130,125,125,125,130,135,135,140,145,145,145,145,155,140,150,150,145,145,145,145,150,155,155,145,145,150,150,150,160,150,150,150,150,140,130,130,140,140,130,125,120,110,105,90,90,90,90,85,80,75,70,70,65,65,60,60,50,50,50,50,50,45,45,45,45,35,35,35,35,30,30,25,25,25,30,30,25,20,20,30,20,20,25,25,40,30,25,25,25,20,20,25,30,30,25,25,25,25,25,25,40,30,30,25,25,25,30,35,35,20,35,50,50,45,45,55,55,45,45,55,55,55,55,50,50,40,40,40,55,60,60,60,75,65,50,50,50,50,50,50,50,45,45,50,50,40,40,40,40,40,45,50,60,60,60,55,45,35,45,30,40,50,50,50,50,45,40,40,40,40,40,40,45,45,45,55,55,55,45,55,50,45,45,45,40,40,40,40,40,40,40,70, COMMASEPARATE 500 P_POS_AMPL 0.07 Q_AMPL - Q_DUR - R_AMPL 0.18 R_DUR 22 S_AMPL -0.57 S_DUR 28 R-_AMPL 0.13 R-_DUR 18 S-_AMPL -0.07 S-_DUR 20 J_AMPL -0.02 ST_SLP 0.02 T_POS_AMPL -0.11 T_NEG_AMPL - ST_INTEG 0.02 III 0,10,10,10,10,0,-5,0,0,-10,5,5,5,5,0,0,5,5,10,5,5,5,0,0,10,15,15,5,15,0,0,0,5,10,10,-5,10,10,10,10,10,10,5,5,5,5,5,10,0,0,5,10,20,10,10,5,10,10,10,10,10,10,15,5,25,10,10,5,5,5,5,0,0,0,0,0,5,15,10,0,10,10,25,10,10,5,10,10,5,15,20,15,5,5,5,5,10,10,5,5,5,-10,10,20,25,40,40,45,50,55,60,55,60,60,45,55,55,55,55,55,65,75,65,50,45,40,35,35,35,30,20,5,-5,-5,-10,-10,-15,-15,-10,-20,-15,0,10,0,5,-5,0,0,5,-20,-5,0,0,-25,-10,-10,-10,-10,-15,-20,-20,-20,-20,-15,5,10,0,-25,-25,-25,-20,-30,-15,0,0,-25,-10,-10,-5,-20,-10,-10,-5,5,10,20,45,80,125,150,175,165,180,175,175,115,50,-5,-30,-75,-85,-70,-40,-100,-335,-480,-565,-540,-380,-255,-155,-60,40,75,105,125,110,65,50,30,10,-10,-40,-70,-75,-75,-75,-70,-50,-50,-30,-20,0,5,10,10,25,25,20,10,10,10,0,15,15,25,25,20,20,15,15,5,5,10,15,20,20,25,20,15,0,10,20,20,15,15,10,10,15,15,15,15,5,5,15,20,15,5,15,15,15,5,5,10,15,15,0,0,10,10,5,-5,-10,-10,-10,0,0,-10,-10,-20,-15,-15,-15,-25,-25,-25,-20,-25,-35,-45,-50,-50,-45,-45,-55,-55,-60,-60,-65,-70,-70,-85,-80,-85,-105,-105,-100,-100,-95,-95,-95,-100,-100,-95,-90,-80,-70,-80,-65,-55,-55,-55,-55,-55,-40,-30,-35,-30,-20,-15,-10,-10,-10,-10,0,5,5,5,0,0,0,10,5,5,0,5,5,10,10,5,5,10,15,5,5,5,5,0,5,0,-5,-5,0,0,0,0,0,10,0,0,5,0,15,5,0,10,10,5,5,5,10,10,5,0,0,0,0,5,20,15,20,15,15,10,15,20,15,0,15,20,20,25,25,35,35,25,25,25,40,35,25,30,30,25,25,20,25,25,25,25,40,35,30,30,30,35,35,25,25,20,20,35,35,25,25,25,15,15,20,35,35,35,35,25,15,20,30,15,25,35,35,35,35,30,20,20,20,25,25,25,30,20,20,30,30,30,20,30,25,20,20,20,20,20,25,25,25,25,15,55, COMMASEPARATE 500 P_POS_AMPL -0.12 Q_AMPL - Q_DUR - R_AMPL 0.04 R_DUR 10 S_AMPL -0.45 S_DUR 40 R-_AMPL 0.10 R-_DUR 26 S-_AMPL - S-_DUR - J_AMPL -0.01 ST_SLP -0.04 T_POS_AMPL -0.20 T_NEG_AMPL -0.04 ST_INTEG -0.04 AVR -35,-30,-30,-35,-35,-30,-27,-25,-25,-20,-28,-28,-23,-23,-25,-25,-28,-28,-30,-33,-33,-33,-35,-35,-35,-33,-33,-38,-33,-35,-25,-25,-28,-30,-30,-22,-30,-30,-20,-20,-25,-25,-23,-23,-23,-23,-28,-35,-30,-30,-28,-20,-15,-20,-20,-23,-25,-30,-30,-30,-30,-30,-28,-18,-18,-20,-20,-23,-28,-33,-33,-25,-25,-25,-25,-25,-28,-33,-35,-30,-25,-25,-33,-30,-30,-28,-25,-25,-28,-33,-30,-28,-23,-23,-23,-23,-25,-25,-28,-28,-28,-25,-40,-50,-53,-60,-65,-68,-70,-78,-85,-88,-95,-95,-93,-98,-108,-108,-108,-113,-118,-123,-118,-125,-128,-125,-128,-143,-143,-140,-135,-128,-117,-117,-110,-100,-97,-92,-85,-80,-72,-60,-50,-35,-33,-22,-20,-20,-18,-5,-12,-10,-10,3,0,0,0,0,3,0,0,5,5,3,2,0,-5,3,3,3,5,10,8,0,10,8,-5,-5,-2,5,0,0,-2,-8,5,10,7,10,12,15,17,27,35,37,37,22,0,-27,-65,-112,-162,-230,-330,-425,-437,-435,-447,-435,-415,-397,-362,-315,-260,-193,-133,-88,-45,7,55,90,100,100,90,75,48,28,18,5,-5,-5,-5,-10,-20,-23,-30,-30,-38,-38,-30,-25,-25,-25,-20,-28,-28,-38,-38,-35,-35,-38,-38,-33,-33,-35,-38,-40,-40,-43,-45,-48,-40,-45,-50,-50,-48,-48,-60,-60,-53,-53,-58,-58,-53,-53,-63,-65,-73,-78,-83,-83,-83,-73,-73,-75,-78,-78,-80,-80,-90,-90,-93,-92,-95,-100,-100,-105,-110,-115,-115,-110,-117,-117,-117,-122,-127,-127,-135,-142,-147,-147,-150,-150,-152,-157,-162,-167,-175,-175,-177,-180,-190,-182,-190,-192,-197,-197,-195,-195,-197,-202,-202,-195,-195,-197,-195,-190,-195,-190,-182,-177,-177,-167,-157,-157,-160,-155,-147,-140,-130,-117,-110,-95,-95,-95,-90,-83,-78,-73,-70,-70,-65,-60,-58,-58,-50,-48,-48,-45,-45,-43,-43,-40,-38,-33,-33,-33,-33,-30,-28,-25,-27,-27,-30,-30,-25,-20,-20,-25,-20,-20,-23,-25,-33,-28,-25,-20,-20,-18,-18,-23,-25,-25,-23,-25,-25,-25,-25,-23,-30,-23,-20,-18,-18,-20,-23,-25,-28,-20,-28,-40,-40,-33,-33,-38,-38,-33,-33,-43,-35,-38,-43,-35,-35,-28,-28,-30,-43,-48,-48,-48,-55,-48,-35,-35,-35,-33,-33,-38,-38,-35,-35,-33,-33,-28,-28,-28,-33,-33,-35,-33,-43,-43,-43,-43,-38,-25,-30,-23,-28,-33,-33,-33,-33,-30,-30,-30,-30,-28,-28,-28,-30,-35,-35,-40,-40,-40,-35,-40,-38,-35,-35,-35,-30,-30,-28,-28,-28,-28,-33,-43, COMMASEPARATE 500 P_POS_AMPL 0.06 Q_AMPL -0.15 Q_DUR 24 R_AMPL 0.65 R_DUR 32 S_AMPL -0.07 S_DUR 16 R-_AMPL 0.05 R-_DUR 16 S-_AMPL - S-_DUR - J_AMPL 0.02 ST_SLP 0.01 T_POS_AMPL 0.18 T_NEG_AMPL 0.01 ST_INTEG 0.01 AVL 17,7,7,10,10,15,17,12,12,17,10,10,7,7,12,12,10,10,7,12,12,12,17,17,10,5,5,15,5,17,12,12,10,7,7,15,7,7,2,2,5,5,7,7,7,7,10,10,15,15,10,2,-8,2,2,7,5,7,7,7,7,7,2,5,-10,2,2,7,10,12,12,12,12,12,12,12,10,5,10,15,5,5,-3,7,7,10,5,5,10,5,0,2,7,7,7,7,5,5,10,10,10,20,12,10,7,0,2,0,-3,-3,-3,2,2,2,12,7,12,12,12,15,10,5,10,25,30,32,37,45,45,47,52,60,62,62,62,57,60,57,50,55,47,30,17,17,12,15,10,10,5,18,10,5,5,18,8,8,8,8,10,15,15,13,13,10,-5,-8,2,18,18,18,13,18,8,0,-5,15,10,10,5,13,8,8,5,0,-10,-20,-38,-65,-100,-120,-140,-138,-153,-150,-150,-98,-38,17,55,112,145,167,195,287,470,577,647,622,492,390,297,202,100,40,-13,-50,-60,-53,-65,-67,-57,-42,-15,15,33,43,48,50,40,40,25,20,10,7,7,7,0,0,0,5,5,5,10,2,2,0,0,2,2,7,7,12,12,10,7,5,5,2,7,12,20,15,10,10,12,12,22,22,15,15,17,17,22,22,20,17,25,35,30,30,30,32,32,30,27,27,40,40,37,37,42,50,55,57,57,52,55,65,65,70,70,70,70,80,82,82,82,90,100,107,112,112,110,112,122,125,132,132,137,142,147,155,155,160,177,177,172,172,170,172,172,172,172,170,165,155,150,155,140,130,130,125,120,120,110,100,100,92,80,70,62,55,55,55,45,37,35,32,35,35,32,22,25,25,25,20,20,15,15,17,17,12,7,12,12,12,12,15,10,12,17,17,15,15,12,10,10,5,10,10,7,12,5,10,12,2,2,5,5,7,5,5,7,12,12,12,12,7,0,0,-5,-3,-3,2,0,-3,2,10,2,5,5,-3,-3,-8,-8,-3,-3,2,-13,-8,2,-5,-5,-5,-5,0,2,5,5,5,-3,-3,-5,-5,-5,-10,-10,0,0,2,2,-10,-10,-5,-5,-5,5,5,2,-10,-5,-5,-5,2,7,-3,-8,0,-5,-10,-10,-10,-10,-8,0,0,0,-5,-5,-5,-8,2,2,-3,-3,-3,2,-3,0,2,2,2,0,0,-5,-5,-5,-5,5,-20, COMMASEPARATE 500 P_POS_AMPL 0.10 Q_AMPL - Q_DUR - R_AMPL 0.14 R_DUR 36 S_AMPL -0.20 S_DUR 10 R-_AMPL 0.16 R-_DUR 18 S-_AMPL -0.09 S-_DUR 24 J_AMPL -0.01 ST_SLP 0.03 T_POS_AMPL 0.02 T_NEG_AMPL - ST_INTEG 0.03 AVF 17,22,22,25,25,15,10,12,12,2,17,17,15,15,12,12,17,17,22,20,20,20,17,17,25,27,27,22,27,17,12,12,17,22,22,7,22,22,17,17,20,20,15,15,15,15,17,25,15,15,17,17,22,17,17,15,20,22,22,22,22,22,25,12,27,17,17,15,17,20,20,12,12,12,12,12,17,27,25,15,20,20,35,22,22,17,20,20,17,27,30,25,15,15,15,15,20,20,17,17,17,5,27,40,45,60,62,67,72,80,87,85,92,92,80,90,95,95,95,97,107,117,107,100,97,92,90,97,97,92,82,67,55,55,47,42,37,35,35,25,25,30,32,17,20,7,10,10,12,-12,2,5,5,-20,-7,-7,-7,-7,-12,-15,-15,-17,-17,-12,2,7,2,-20,-20,-20,-17,-27,-15,0,-5,-22,-5,-5,-2,-17,-7,-7,-2,7,5,10,30,55,87,105,122,110,117,112,112,75,37,10,10,0,17,62,135,137,-32,-142,-200,-187,-77,7,65,112,160,152,145,137,105,45,10,-22,-42,-57,-75,-90,-80,-70,-65,-55,-35,-35,-20,-10,10,15,22,22,37,37,30,20,20,20,10,25,25,37,37,32,32,30,30,20,20,25,30,35,35,40,37,35,20,30,40,40,35,35,37,37,37,37,40,40,30,30,42,47,47,42,52,52,52,40,40,45,50,50,40,40,52,52,50,42,40,42,42,52,55,50,50,40,47,47,47,42,45,45,52,52,47,40,37,37,42,45,40,42,42,42,40,37,42,27,35,32,20,20,22,22,27,30,30,22,22,27,30,35,45,35,42,47,47,42,37,37,50,55,47,47,50,47,47,40,40,40,45,45,42,40,35,35,32,37,32,32,25,27,27,30,30,25,25,27,30,20,20,20,20,15,17,12,10,10,15,15,12,10,10,20,10,10,15,12,27,17,12,17,17,12,12,15,20,20,15,12,12,12,12,15,30,22,25,20,20,17,22,27,25,10,25,35,35,35,35,45,45,35,35,40,47,45,40,40,40,32,32,30,40,42,42,42,57,50,40,40,40,42,42,37,37,32,32,42,42,32,32,32,27,27,32,42,47,47,47,40,30,27,37,22,32,42,42,42,42,37,30,30,30,32,32,32,37,32,32,42,42,42,32,42,37,32,32,32,30,30,32,32,32,32,27,62, COMMASEPARATE 500 P_POS_AMPL 0.06 Q_AMPL - Q_DUR - R_AMPL 0.31 R_DUR 40 S_AMPL -0.38 S_DUR 42 R-_AMPL - R-_DUR - S-_AMPL - S-_DUR - J_AMPL 0.07 ST_SLP 0.10 T_POS_AMPL 0.19 T_NEG_AMPL 0.02 ST_INTEG 0.10 V1 35,35,40,40,40,35,35,35,35,35,35,30,30,30,30,20,20,25,35,25,30,30,30,25,25,10,10,10,15,20,30,30,30,25,25,25,20,20,20,20,15,15,15,15,20,20,20,20,20,15,15,15,15,15,25,10,5,5,10,10,10,10,10,10,10,10,5,5,5,5,5,5,5,-5,10,10,10,10,10,5,5,5,5,-5,5,-10,-10,-10,0,-10,-10,-10,-5,-5,0,0,0,0,5,5,10,10,10,10,25,15,25,35,45,45,50,50,50,50,50,45,45,40,40,20,15,15,15,5,5,-10,-15,-25,-30,-40,-40,-40,-45,-50,-50,-40,-40,-40,-35,-35,-25,-15,-15,-15,-15,-20,-20,-25,-25,-25,-20,-20,-15,-15,-15,-15,-15,-15,-15,-20,-25,-25,-20,-20,-20,-20,-20,-15,-10,-10,-10,-15,-15,-15,-15,-15,-15,-10,-5,0,0,0,0,-10,10,10,20,40,60,80,120,170,215,245,270,280,280,300,310,280,280,250,190,105,60,60,-10,-120,-220,-310,-350,-380,-365,-345,-310,-345,-320,-285,-255,-245,-235,-250,-245,-210,-160,-115,-65,-25,20,45,60,70,80,80,80,75,75,75,85,85,90,90,95,95,95,90,90,90,100,90,90,95,95,95,95,95,95,90,90,80,90,100,100,110,110,110,110,110,110,105,100,100,110,110,115,115,115,120,120,120,125,125,125,125,130,130,130,135,135,135,140,145,155,145,145,145,140,140,145,150,155,160,165,165,170,170,170,170,170,170,180,185,185,185,185,175,175,180,185,185,180,180,180,170,170,165,160,160,160,155,155,155,155,145,145,135,130,125,125,115,110,110,105,100,95,95,90,85,85,75,75,75,75,60,60,55,50,50,50,50,50,45,45,40,40,35,25,25,25,25,20,20,25,25,25,25,25,25,25,20,20,25,25,25,25,25,30,30,35,35,35,35,35,30,30,30,30,30,30,30,35,35,40,40,40,40,40,45,45,45,45,45,45,45,45,45,40,40,40,40,45,45,45,45,50,50,45,40,40,30,30,30,35,35,35,35,35,35,35,35,35,35,35,30,30,35,35,35,35,30,30,25,15,15,20,20,20,15,15,15,15,15,15,15,15,15,5,5,5,5,5,5,5,5,5,0,0,5,5,5,5,5,5,0,0,0,0,0,5,5,5,5,5,0,0,-5,-5,-5,0,30, COMMASEPARATE 500 P_POS_AMPL 0.05 Q_AMPL - Q_DUR - R_AMPL 0.86 R_DUR 42 S_AMPL -0.69 S_DUR 32 R-_AMPL - R-_DUR - S-_AMPL - S-_DUR - J_AMPL 0.13 ST_SLP 0.21 T_POS_AMPL 0.69 T_NEG_AMPL 0.07 ST_INTEG 0.21 V2 75,75,75,75,75,70,70,70,75,75,75,70,70,70,80,70,70,70,70,65,65,65,65,65,65,55,55,55,55,55,65,55,55,50,50,50,45,45,55,45,45,45,45,45,45,40,40,40,40,35,35,35,35,35,45,30,30,30,30,30,30,30,30,30,30,25,25,25,25,25,25,20,20,20,25,25,25,25,25,20,20,20,20,20,20,20,20,20,20,20,20,15,15,15,15,15,15,20,20,20,20,25,30,30,40,40,50,55,55,55,60,60,60,60,65,65,65,65,65,55,55,55,60,60,60,60,50,40,40,35,35,30,25,15,15,25,25,20,20,15,15,15,10,5,5,-10,-10,-10,-10,-10,-10,-10,-10,-15,-15,-20,-20,-20,-15,-15,-15,-15,-15,-15,-15,-20,-20,-15,-5,-15,-15,-15,-10,-10,-10,-10,-10,-10,-5,5,5,5,0,0,0,-5,-10,-10,-10,-5,30,75,120,160,210,260,340,455,550,605,670,705,705,715,790,860,805,615,380,185,35,-230,-475,-580,-625,-695,-650,-565,-495,-455,-415,-370,-290,-210,-130,-65,-5,50,90,105,120,130,140,145,150,155,155,155,160,160,160,160,170,170,175,175,180,185,185,185,190,195,200,200,205,205,205,210,210,210,220,220,225,225,235,240,245,245,245,245,250,255,265,270,275,280,285,290,295,295,305,310,315,320,330,335,350,355,360,360,375,385,400,405,420,430,440,450,465,470,480,495,505,515,530,545,560,565,580,590,600,615,625,635,650,655,665,670,680,680,680,685,685,685,685,685,685,685,685,675,670,660,645,635,620,600,580,560,535,505,485,460,435,410,390,365,350,330,310,285,265,250,240,220,210,190,175,165,160,145,140,125,120,110,105,95,85,85,75,70,70,65,65,65,65,60,60,60,60,55,55,55,55,55,60,60,60,60,60,60,65,65,60,60,65,65,70,70,70,70,70,70,75,80,80,80,80,80,80,80,85,85,90,90,90,90,90,90,90,90,90,90,90,90,90,95,95,95,95,90,90,85,85,85,85,85,85,85,85,85,80,75,75,70,70,70,80,70,70,65,65,60,60,60,60,60,55,50,50,50,50,45,45,45,45,40,40,40,40,35,35,35,35,35,30,25,25,30,30,25,25,25,25,25,30,30,30,25,25,25,25,20,20,20,20,15,15,15,15,45, COMMASEPARATE 500 P_POS_AMPL 0.08 Q_AMPL -0.03 Q_DUR 14 R_AMPL 1.70 R_DUR 40 S_AMPL -0.34 S_DUR 26 R-_AMPL - R-_DUR - S-_AMPL - S-_DUR - J_AMPL 0.11 ST_SLP 0.17 T_POS_AMPL 0.69 T_NEG_AMPL 0.08 ST_INTEG 0.17 V3 70,75,75,75,75,65,65,65,70,70,70,65,65,65,65,65,65,65,65,55,65,65,65,55,65,55,55,50,50,50,55,55,55,50,50,50,50,50,50,40,40,45,45,45,45,40,40,40,50,35,40,40,35,35,35,30,30,30,30,30,30,30,30,30,30,25,25,25,25,25,25,25,25,25,30,30,25,25,25,20,20,20,20,20,20,20,20,20,20,20,20,15,15,15,20,20,20,20,20,20,20,25,30,35,45,45,55,55,60,60,65,70,70,70,70,70,75,75,75,75,75,80,90,90,90,90,85,80,80,70,70,60,55,45,45,50,50,45,40,35,35,35,30,20,20,10,5,5,5,0,0,-10,-10,-15,-15,-20,-20,-20,-20,-20,-20,-20,-15,-15,-15,-20,-20,-15,-15,-20,-20,-20,-15,-15,-15,-15,-15,-15,-5,0,0,-10,0,0,10,-5,-10,-20,-20,-20,-20,-30,-20,5,35,60,115,200,305,395,520,660,840,1045,1310,1545,1690,1700,1615,1460,1265,905,500,165,-95,-300,-340,-310,-280,-275,-275,-260,-210,-165,-105,-60,-15,25,60,80,95,105,115,115,120,120,115,115,125,125,125,125,130,130,135,135,140,145,150,150,150,155,160,160,165,165,165,165,170,170,180,180,185,185,195,195,200,200,205,205,210,215,225,225,230,235,240,245,255,255,265,270,280,280,290,295,305,310,320,320,335,345,360,360,375,385,395,400,420,425,440,450,465,470,490,500,515,525,540,550,570,580,600,605,620,625,635,645,655,660,660,670,675,675,680,680,685,685,685,685,680,670,665,650,635,615,600,575,550,525,500,475,450,425,405,380,360,340,320,295,280,260,250,230,215,195,180,170,165,150,145,130,125,115,110,100,90,85,80,75,75,70,70,70,70,65,65,60,60,60,60,55,55,55,60,60,60,60,60,60,65,65,60,60,65,65,65,65,65,65,65,65,70,70,75,75,75,75,75,75,80,80,85,85,85,85,85,85,85,85,85,85,85,85,85,90,90,90,90,85,85,80,80,80,80,80,80,80,80,80,80,75,70,65,65,70,70,70,70,65,65,60,55,55,60,60,55,55,55,50,50,45,45,45,45,45,40,40,40,40,35,35,40,40,35,35,35,35,35,30,30,30,30,20,30,35,35,30,30,30,30,25,25,25,25,20,20,20,20,50, COMMASEPARATE 500 P_POS_AMPL 0.08 Q_AMPL -0.09 Q_DUR 22 R_AMPL 1.78 R_DUR 36 S_AMPL -0.16 S_DUR 24 R-_AMPL - R-_DUR - S-_AMPL - S-_DUR - J_AMPL 0.07 ST_SLP 0.11 T_POS_AMPL 0.53 T_NEG_AMPL 0.06 ST_INTEG 0.11 V4 50,55,55,60,60,55,55,55,60,60,60,55,55,55,55,50,50,50,50,50,50,50,50,50,50,45,45,40,40,40,45,45,45,45,45,45,40,40,40,35,35,35,40,40,40,35,35,35,35,35,40,40,35,35,35,30,25,25,30,30,30,30,30,30,30,30,25,25,25,25,25,25,25,25,25,30,30,30,30,25,25,20,20,20,20,20,20,20,20,20,20,15,15,20,20,20,20,20,20,20,25,30,30,35,40,40,50,55,60,60,65,65,70,70,70,70,70,70,70,70,75,85,95,95,100,100,95,90,90,85,85,80,70,60,60,60,60,60,50,50,50,50,40,35,30,20,15,10,10,10,0,-5,-10,-10,-10,-15,-15,-15,-15,-15,-15,-15,-10,-10,-10,-15,-15,-10,-10,-15,-20,-20,-15,-15,-15,-10,-10,-10,-5,0,0,0,0,5,5,-10,-15,-20,-30,-40,-55,-85,-95,-85,-75,-50,-5,70,155,260,390,545,765,1020,1290,1515,1700,1775,1765,1640,1435,1125,770,380,85,-75,-120,-130,-145,-155,-165,-155,-130,-110,-75,-45,-15,10,40,55,65,75,75,80,80,75,75,75,80,80,80,80,80,80,85,85,90,95,95,95,100,100,100,105,105,105,110,110,110,115,120,120,120,120,125,125,135,140,140,140,140,145,150,150,155,160,165,170,175,175,185,185,185,190,195,205,210,220,220,225,235,240,250,255,265,270,275,280,290,300,305,315,325,330,345,355,365,375,390,395,410,425,435,445,450,460,470,480,485,490,490,500,505,505,515,515,520,525,525,525,525,520,515,510,495,480,470,450,430,410,390,370,350,330,315,300,290,270,255,235,220,205,195,175,165,150,140,135,130,120,115,100,95,85,80,70,65,60,60,55,50,45,45,45,45,45,40,40,40,40,40,35,35,35,35,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,45,45,45,45,45,45,45,45,55,55,55,55,55,55,55,55,50,50,50,50,50,50,55,60,60,60,60,60,55,55,50,50,50,50,50,50,55,55,55,55,50,45,45,50,50,50,50,50,45,45,40,40,40,40,40,40,40,40,35,35,30,30,30,30,30,30,30,25,25,25,30,30,25,25,25,25,25,20,20,25,25,25,25,25,25,25,20,20,20,20,20,15,15,10,10,10,10,35, COMMASEPARATE 500 P_POS_AMPL 0.07 Q_AMPL -0.12 Q_DUR 24 R_AMPL 1.44 R_DUR 36 S_AMPL -0.14 S_DUR 20 R-_AMPL - R-_DUR - S-_AMPL - S-_DUR - J_AMPL 0.04 ST_SLP 0.06 T_POS_AMPL 0.43 T_NEG_AMPL 0.05 ST_INTEG 0.06 V5 55,60,60,60,55,50,45,45,50,50,50,50,50,50,50,50,50,55,55,50,50,50,45,45,45,40,40,40,40,45,50,50,50,45,45,35,35,40,45,45,45,45,45,40,40,35,35,35,35,35,35,35,30,25,25,25,25,30,35,35,30,30,30,30,30,30,25,25,25,25,30,30,25,25,30,30,25,25,25,20,20,20,25,25,25,25,25,25,25,20,20,20,20,20,20,20,25,25,25,25,25,30,30,30,35,35,40,50,50,50,55,55,55,55,55,55,60,65,65,65,70,75,80,85,90,90,90,90,90,85,80,75,70,70,70,70,70,70,65,65,65,60,50,40,30,25,15,10,5,5,0,0,5,5,0,0,0,0,10,-10,-10,-10,-10,-15,-15,-20,-20,-15,-5,-5,-5,-5,5,-10,-10,-15,-15,-15,-10,0,5,5,0,0,0,-20,-35,-45,-55,-65,-80,-105,-120,-120,-115,-90,-45,15,85,160,265,395,585,815,1045,1225,1380,1440,1420,1320,1150,935,695,400,160,40,-25,-65,-105,-140,-140,-130,-105,-95,-65,-40,-20,5,25,35,35,40,40,35,35,35,30,30,45,45,45,45,45,45,45,45,50,50,50,50,55,55,55,55,55,55,55,60,65,65,65,65,70,75,75,75,80,80,80,80,90,90,95,95,95,100,100,105,105,105,115,120,125,130,130,135,145,150,150,155,160,170,170,170,175,185,195,205,215,225,225,235,245,245,250,255,265,270,290,295,310,315,320,325,335,340,355,365,380,385,385,395,400,405,410,410,415,420,420,425,425,425,425,415,400,380,375,365,355,345,335,320,310,290,280,260,245,225,210,195,180,175,165,150,140,125,115,115,110,95,90,75,75,70,65,60,60,55,50,50,45,40,40,35,35,35,30,30,30,30,30,30,30,30,30,30,30,30,25,25,25,25,20,20,25,25,30,30,25,25,30,30,30,30,30,30,25,25,25,25,30,30,30,30,30,25,25,30,30,30,30,30,40,30,35,35,35,35,30,25,25,30,30,40,40,35,35,35,35,35,30,30,30,30,30,30,30,30,30,20,20,20,20,25,25,25,25,15,15,20,20,20,20,20,15,15,15,15,15,15,15,15,25,15,10,5,5,10,10,10,10,15,15,5,5,5,5,5,5,5,5,5,5,0,0,0,0,0,5,35, COMMASEPARATE 500 P_POS_AMPL 0.07 Q_AMPL -0.09 Q_DUR 26 R_AMPL 1.05 R_DUR 40 S_AMPL -0.08 S_DUR 16 R-_AMPL - R-_DUR - S-_AMPL - S-_DUR - J_AMPL 0.02 ST_SLP 0.04 T_POS_AMPL 0.28 T_NEG_AMPL 0.03 ST_INTEG 0.04 V6 25,25,15,15,10,5,5,10,20,20,25,25,25,25,25,25,25,25,25,20,20,20,15,15,15,15,15,25,25,25,25,25,25,25,25,25,30,40,40,40,35,35,30,30,30,25,25,25,25,15,10,10,10,15,20,20,20,20,20,20,20,15,15,15,15,15,15,20,20,25,25,20,20,15,15,20,20,20,20,15,15,20,25,25,25,15,25,25,25,20,20,20,20,20,25,25,25,25,25,25,25,25,25,30,35,35,40,45,45,45,45,45,45,45,45,50,55,65,65,65,70,70,80,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,80,75,65,50,40,25,20,15,15,15,15,10,10,15,15,15,15,15,15,15,10,5,-10,-10,-10,-5,-5,5,15,20,20,15,10,10,0,-5,-5,0,5,15,15,15,5,-5,-5,-5,-15,-20,-25,-35,-45,-60,-80,-95,-95,-95,-85,-55,-15,45,110,195,300,455,625,775,895,1000,1045,1045,985,875,745,610,415,245,150,80,20,-35,-80,-80,-80,-65,-60,-40,-20,5,15,20,20,20,20,20,20,25,25,25,30,40,40,40,40,40,40,40,35,35,35,35,30,30,30,35,35,35,35,45,45,45,45,50,50,50,55,55,55,60,60,60,60,60,65,70,70,70,70,70,75,75,75,85,85,85,85,90,90,95,95,95,90,90,90,105,105,120,120,130,140,150,150,150,150,150,150,155,165,170,175,175,175,185,190,195,200,215,220,225,235,250,255,260,260,260,265,270,270,270,275,275,275,275,270,260,255,250,245,245,245,240,240,240,230,215,195,190,175,165,150,135,125,115,115,110,100,90,80,70,65,60,50,50,45,45,45,45,40,40,35,30,30,30,25,25,25,25,20,15,15,15,15,15,15,10,10,10,10,10,10,5,5,15,15,15,15,20,20,20,20,20,20,20,20,20,20,20,10,5,5,5,5,15,15,15,15,20,20,25,25,25,25,30,30,30,25,25,25,25,25,25,25,25,35,35,40,40,35,35,35,35,35,35,40,40,35,30,30,25,25,25,25,30,30,30,30,30,30,30,30,30,30,30,30,30,30,30,25,25,25,20,20,20,20,30,30,25,25,25,25,25,15,25,25,25,20,20,20,20,20,20,20,20,25,30,30,30,25,25,25,30,75, COMMASEPARATE 500 ECG_STANDARD ECG_RHYTHMS 1 500 1.0 UV I -60,-59,-59,-59,-59,-59,-59,-59,-57,-53,-49,-44,-41,-40,-39,-39,-39,-39,-39,-39,-37,-33,-29,-24,-19,-14,-9,-4,-1,0,0,0,2,6,10,15,20,25,30,35,40,45,50,55,58,59,60,60,62,66,70,75,80,85,90,95,100,105,110,115,120,125,130,135,140,145,150,155,158,159,160,160,163,171,180,190,198,204,210,215,218,219,220,220,218,214,210,205,200,195,190,185,180,175,170,165,158,149,140,130,120,110,100,90,82,76,70,65,60,55,50,45,40,35,30,25,20,15,10,5,-1,-10,-19,-29,-36,-38,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-41,-45,-49,-54,-57,-58,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-57,-53,-49,-44,-41,-40,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-37,-35,-34,-34,-32,-28,-24,-21,-20,-18,-15,-14,-12,-8,-4,0,3,6,9,13,18,19,21,24,25,25,30,37,37,36,39,43,44,45,45,45,47,49,48,46,44,38,33,28,16,0,-13,-24,-34,-44,-52,-58,-62,-63,-65,-66,-63,-60,-58,-55,-54,-54,-52,-50,-51,-53,-54,-59,-67,-70,-69,-69,-69,-71,-73,-75,-78,-82,-82,-75,-71,-71,-73,-70,-63,-54,-44,-35,-50,-100,-110,-130,-155,-170,-185,-190,-205,-205,-185,-140,-85,-30,20,90,125,185,270,395,525,595,650,585,485,405,320,225,140,55,-20,-75,-90,-120,-180,-225,-225,-215,-150,-100,-70,-50,-40,-50,-50,-50,-60,-60,-58,-52,-45,-39,-36,-36,-38,-39,-39,-39,-39,-39,-37,-35,-34,-34,-32,-30,-29,-29,-29,-29,-29,-29,-29,-27,-23,-20,-19,-19,-19,-19,-19,-14,-7,-8,-12,-12,-10,-9,-9,-7,-5,-1,6,12,14,15,13,11,10,10,10,13,18,21,24,26,30,35,42,49,55,62,71,77,79,81,84,85,88,94,98,101,107,116,124,130,133,134,136,142,149,156,160,160,160,162,164,166,170,178,186,187,186,185,187,189,188,186,185,183,178,173,171,165,155,148,143,134,128,124,118,111,104,93,79,66,54,45,42,37,29,21,15,12,11,9,5,3,4,5,5,5,5,5,-9,-29,-36,-38,-39,-39,-39,-39,-37,-33,-29,-24,-21,-20,-19,-19,-19,-19,-19,-19,-21,-25,-29,-34,-37,-38,-39,-39,-36,-28,-19,-9,-6,-11,-20,-29,-36,-38,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-41,-45,-49,-54,-57,-58,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-77,-73,-69,-64,-61,-60,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-57,-53,-49,-44,-41,-40,-39,-39,-39,-39,-39,-39,-41,-45,-49,-54,-57,-58,-59,-59,-59,-59,-59,-44,-24,-17,-15,-13,-9,-4,-1,0,2,4,5,7,9,10,10,8,8,9,13,18,19,21,24,25,25,30,38,43,46,54,61,64,65,65,63,61,59,53,48,44,38,33,28,16,2,-7,-12,-13,-15,-18,-20,-23,-24,-26,-28,-29,-31,-33,-34,-34,-32,-27,-20,-16,-15,-14,-17,-22,-20,-14,-11,-10,-11,-13,-15,-18,-22,-22,-15,-11,-10,-8,-4,0,3,-2,-15,-30,-60,-70,-90,-115,-130,-145,-150,-165,-145,-125,-80,-85,10,100,170,225,245,310,435,565,635,690,645,545,465,380,285,180,115,100,45,10,-60,-120,-145,-145,-135,-70,-80,-70,-50,-40,-30,-10,-10,-20,-20,-19,-17,-15,-14,-16,-21,-28,-34,-37,-38,-39,-39,-36,-30,-24,-19,-14,-11,-10,-9,-9,-9,-9,-9,-9,-7,-3,0,0,0,0,0,0,5,12,11,7,7,9,10,10,12,14,18,24,26,24,20,15,12,11,10,10,13,18,21,24,26,30,35,40,43,44,46,52,57,59,61,64,65,68,74,78,81,87,96,104,110,113,114,115,117,119,121,124,126,130,137,142,146,150,158,166,167,166,165,167,169,168,166,167,169,168,168,169,165,155,148,143,134,128,124,118,111,104,93,79,66,54,45,42,37,29,21,17,17,21,24,21,17,16,15,15,15,15,15,3,-12,-17,-18,-19,-19,-19,-19,-21,-25,-29,-34,-37,-38,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-36,-28,-19,-9,-2,0,0,0,-1,-5,-9,-14,-17,-18,-19,-19,-17,-13,-9,-4,-1,0,0,0,0,0,0,0,0,0,0,0,-1,-5,-9,-14,-17,-18,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-16,-8,1,10,13,8,0,-9,-16,-18,-19,-19,-19,-19,-19,-19,-17,-13,-9,-4,-1,0,0,0,0,0,0,0,-1,-5,-9,-14,-17,-18,-19,-19,-19,-19,-19,-19,-21,-23,-24,-24,-24,-24,-24,-27,-32,-33,-34,-32,-27,-18,-10,-4,0,3,4,6,10,15,20,23,26,29,33,38,38,36,34,30,27,31,39,43,46,54,61,64,66,70,73,76,77,72,67,64,58,53,48,36,20,7,-2,-8,-14,-17,-20,-23,-25,-31,-38,-44,-49,-52,-53,-54,-54,-52,-50,-51,-53,-54,-57,-62,-60,-54,-51,-50,-51,-53,-55,-58,-60,-56,-45,-36,-33,-34,-34,-34,-34,-41,-55,-70,-80,-90,-110,-135,-150,-145,-150,-165,-185,-185,-120,-65,-10,40,150,205,225,310,435,565,615,670,665,565,485,400,285,180,95,20,-35,-50,-80,-120,-125,-125,-95,-70,-60,-30,-30,-20,-10,-10,-10,-20,-40,-38,-32,-25,-19,-16,-16,-18,-19,-21,-25,-29,-34,-36,-35,-34,-34,-31,-25,-19,-14,-11,-10,-9,-9,-9,-7,-3,0,0,0,0,0,0,5,12,11,7,7,9,10,10,12,14,18,26,32,34,35,33,31,30,30,30,33,38,41,44,46,50,55,60,63,64,66,72,77,79,81,84,85,88,94,98,101,107,116,124,130,133,134,136,142,149,156,162,166,170,177,181,180,180,183,188,188,186,185,187,189,188,186,187,189,188,188,189,185,175,168,161,149,138,129,120,112,104,93,79,66,54,45,42,37,29,21,15,12,11,9,3,-2,-3,-4,-4,-4,-4,-4,-16,-31,-36,-38,-37,-33,-29,-24,-21,-20,-19,-19,-21,-25,-29,-34,-37,-38,-39,-39,-39,-39,-39,-39,-41,-45,-49,-54,-57,-58,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-57,-53,-49,-44,-41,-40,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-41,-45,-49,-54,-57,-58,-59,-59,-57,-53,-49,-44,-41,-40,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-41,-43,-44,-44,-44,-44,-44,-41,-36,-35,-34,-32,-28,-24,-21,-20,-18,-15,-14,-12,-8,-4,0,3,6,9,13,18,19,21,24,25,25,30,38,43,46,54,61,64,65,65,63,61,59,53,48,44,38,33,28,16,0,-13,-22,-28,-34,-37,-40,-43,-44,-46,-48,-49,-51,-53,-54,-54,-54,-52,-50,-51,-53,-54,-57,-62,-60,-54,-51,-50,-53,-59,-66,-73,-80,-81,-75,-71,-71,-73,-74,-74,-74,-67,-55,-70,-80,-90,-90,-115,-130,-145,-150,-165,-165,-165,-120,-65,-10,0,70,125,205,290,415,525,575,630,645,545,445,360,265,160,95,20,-35,-70,-100,-140,-165,-145,-135,-130,-100,-70,-50,-40,-30,-30,-30,-40,-40,-39,-37,-35,-34,-34,-36,-38,-39,-39,-39,-39,-39,-36,-30,-24,-19,-14,-11,-10,-9,-11,-15,-19,-24,-27,-27,-23,-20,-19,-19,-19,-19,-19,-14,-7,-8,-12,-12,-10,-9,-9,-7,-5,-1,6,12,14,15,13,11,10,10,10,13,18,21,24,26,30,35,40,43,44,46,52,57,59,61,64,65,68,74,78,81,87,96,104,110,113,114,116,122,129,136,142,146,150,157,162,166,170,178,186,187,186,185,187,189,188,186,184,178,168,158,153,146,135,128,123,114,108,104,100,97,94,88,78,66,54,45,42,37,29,21,15,12,11,9,5,3,4,5,5,5,5,5,-3,-14,-17,-18,-20,-24,-29,-34,-37,-38,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-37,-33,-29,-24,-21,-20,-19,-19,-21,-25,-29,-34,-37,-38,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-41,-45,-49,-54,-57,-58,-59,-59,-57,-53,-49,-44,-41,-40,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-41,-45,-48,-49,-49,-49,-49,-49,-51,-53,-54,-54,-52,-48,-44,-41,-40,-38,-35,-34,-32,-28,-24,-19,-14,-7,0,8,16,19,21,24,25,25,30,38,43,46,54,61,62,59,55,48,43,39,33,28,26,24,23,23,14,0,-13,-22,-30,-39,-47,-55,-61,-63,-65,-68,-69,-71,-73,-74,-74,-74,-72,-70,-71,-73,-74,-77,-82,-80,-74,-71,-70,-71,-73,-75,-78,-82,-82,-75,-71,-71,-73,-74,-74,-74,-74,-75,-90,-80,-90,-90,-115,-130,-165,-170,-185,-205,-205,-180,-125,-70,-20,70,125,165,250,375,505,575,650,645,545,465,380,285,180,95,0,-75,-110,-140,-180,-165,-165,-155,-130,-80,-50,-50,-60,-50,-50,-50,-60,-60,-59,-57,-55,-54,-54,-56,-58,-59,-59,-59,-59,-59,-57,-55,-54,-54,-54,-56,-60,-64,-67,-68,-69,-69,-67,-62,-53,-45,-41,-40,-39,-39,-39,-34,-27,-28,-32,-32,-30,-29,-29,-27,-25,-21,-13,-7,-5,-4,-6,-8,-9,-9,-9,-6,-1,1,2,1,0,0,2,4,5,7,12,17,19,21,24,25,28,34,38,41,47,56,64,70,73,74,76,82,89,96,102,106,110,117,122,126,130,138,146,147,146,145,148,154,158,161,164,163,158,153,151,145,135,128,123,114,108,104,98,91,84,73,59,46,34,25,22,17,9,1,-4,-7,-8,-10,-14,-16,-15,-14,-14,-14,-14,-14,-22,-33,-37,-38,-39,-39,-39,-39,-39,-39,-39,-39,-41,-45,-49,-54,-57,-58,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-77,-73,-69,-64,-59,-54,-49,-44,-41,-40,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-41,-45,-49,-54,-57,-58,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-57,-53,-49,-44,-41,-40,-39,-39,-41,-45,-49,-54,-57,-58,-59,-59,-59,-59,-59,-59,-57,-53,-49,-44,-41,-40,-39,-39,-41,-45,-49,-54,-57,-58,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-77,-75,-74,-74,-72,-68,-64,-61,-60,-58,-55,-54,-52,-48,-44,-39,-34,-27,-20,-11,-3,0,2,4,5,5,10,18,23,26,34,41,44,45,45,43,41,39,33,28,24,18,13,8,-3,-19,-32,-42,-48,-54,-57,-60,-63,-64,-66,-68,-69,-71,-73,-74,-74,-74,-72,-70,-71,-73,-74,-77,-82,-80,-74,-71,-70,-71,-73,-75,-79,-87,-92,-90,-89,-91,-93,-94,-92,-88,-84,-80,-90,-100,-110,-110,-135,-170,-205,-210,-205,-185,-185,-160,-105,-50,40,110,145,185,230,355,505,575,630,625,525,445,360,265,160,95,20,-35,-70,-100,-140,-165,-165,-155,-130,-120,-90,-90,-80,-70,-70,-70,-60,-60,-59,-57,-55,-54,-54,-56,-58,-59,-59,-59,-59,-59,-57,-55,-54,-54,-52,-50,-49,-49,-49,-49,-49,-49,-49,-47,-43,-40,-39,-39,-39,-39,-39,-34,-27,-28,-32,-32,-30,-29,-29,-27,-25,-21,-13,-7,-5,-4,-6,-8,-9,-9,-9,-6,-1,1,4,6,10,15,20,23,24,26,32,37,39,41,44,45,48,54,58,61,67,76,82,84,83,79,78,83,89,96,102,106,110,117,124,131,140,153,164,166,165,165,167,169,168,166,165,163,158,153,151,145,135,128,123,114,108,104,98,91,84,73,59,46,34,25,22,17,9,1,-4,-7,-8,-10,-16,-21,-23,-24,-24,-24,-24,-24,-36,-51,-56,-58,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-77,-73,-69,-64,-61,-60,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-77,-73,-69,-64,-61,-60,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-81,-83,-84,-84,-84,-84,-84,-81,-76,-75,-74,-72,-68,-64,-61,-60,-58,-55,-54,-52,-48,-44,-39,-36,-33,-30,-26,-21,-20,-18,-15,-14,-14,-9,-1,3,6,14,21,24,25,25,23,21,19,13,8,4,-1,-6,-11,-23,-39,-52,-62,-68,-74,-77,-80,-83,-84,-86,-88,-89,-91,-93,-94,-94,-94,-92,-90,-91,-95,-99,-107,-117,-118,-114,-111,-110,-111,-113,-115,-118,-120,-116,-105,-96,-93,-94,-94,-94,-94,-101,-115,-130,-140,-150,-170,-175,-190,-205,-210,-225,-225,-225,-180,-125,-90,-40,50,105,185,290,435,565,675,730,665,545,445,340,245,160,75,-20,-75,-130,-160,-200,-225,-205,-195,-170,-140,-110,-90,-60,-50,-50,-70,-80,-80,-79,-77,-75,-74,-74,-76,-78,-79,-79,-79,-79,-79,-77,-75,-74,-74,-72,-70,-69,-69,-69,-69,-69,-69,-69,-67,-63,-60,-59,-59,-59,-59,-59,-54,-47,-48,-52,-52,-50,-49,-49,-47,-45,-41,-33,-27,-25,-24,-26,-28,-29,-29,-29,-26,-21,-18,-15,-13,-9,-4,2,9,15,22,31,37,39,41,42,39,38,39,40,42,47,56,65,75,83,89,95,102,109,116,122,126,130,137,142,146,150,158,166,167,166,165,167,169,168,166,165,163,158,153,151,145,135,128,123,114,108,104,98,91,84,73,59,46,34,25,22,17,9,1,-4,-7,-8,-10,-16,-21,-23,-24,-24,-24,-24,-24,-42,-67,-75,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-77,-73,-69,-64,-61,-60,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-77,-75,-74,-74,-72,-68,-64,-62,-65,-68,-70,-73,-72,-68,-64,-57,-50,-43,-35,-28,-22,-20,-18,-15,-14,-14,-9,-1,3,6,14,21,24,25,25,23,21,19,13,8,4,-1,-6,-11,-23,-39,-52,-63,-74,-84,-92,-98,-102,-103,-105,-106,-103,-100,-98,-95,-94,-94,-92,-92,-97,-103,-109,-116,-121,-120,-114,-111,-110,-111,-113,-114,-112,-112,-107,-97,-92,-92,-93,-94,-94,-94,-94,-95,-110,-140,-150,-170,-195,-190,-205,-210,-225,-245,-245,-200,-145,-90,-40,30,105,165,250,375,505,575,630,585,485,405,340,265,160,75,0,-55,-110,-140,-160,-185,-205,-195,-170,-140,-110,-90,-80,-50,-50,-70,-80,-100,-98,-92,-85,-79,-76,-76,-78,-79,-79,-79,-79,-79,-77,-75,-74,-74,-72,-70,-69,-69,-69,-69,-69,-69,-69,-67,-63,-60,-59,-59,-59,-59,-59,-54,-47,-48,-52,-52,-50,-49,-49,-47,-45,-41,-31,-21,-15,-9,-7,-8,-9,-9,-11,-11,-11,-13,-14,-12,-8,-4,2,9,15,22,31,37,39,41,44,45,48,54,58,61,67,76,84,90,93,94,96,102,109,116,122,126,130,137,144,151,160,173,184,186,185,185,185,183,178,171,167,164,158,153,149,140,125,113,104,95,88,84,80,77,74,68,58,46,34,25,20,12,0,-13,-22,-27,-28,-30,-36,-41,-43,-44,-44,-44,-44,-44,-56,-71,-76,-78,-77,-73,-69,-64,-61,-60,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-77,-73,-69,-64,-61,-60,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-77,-75,-74,-74,-72,-68,-65,-66,-70,-73,-74,-74,-72,-68,-62,-53,-45,-38,-30,-21,-11,-5,0,3,4,5,8,13,13,11,15,22,24,25,25,23,21,19,13,8,4,-1,-6,-11,-23,-39,-52,-62,-68,-74,-77,-80,-83,-84,-86,-88,-89,-91,-93,-94,-94,-94,-94,-96,-101,-108,-112,-117,-122,-120,-113,-105,-99,-96,-95,-96,-98,-102,-103,-100,-101,-106,-111,-113,-114,-114,-114,-115,-110,-100,-110,-150,-195,-230,-245,-210,-225,-225,-225,-180,-125,-70,-20,50,105,205,310,435,565,655,710,665,565,485,400,305,200,115,20,-35,-70,-120,-160,-205,-205,-195,-170,-140,-110,-90,-80,-70,-70,-90,-100,-80,-81,-83,-85,-89,-92,-95,-98,-99,-99,-99,-99,-99,-96,-90,-84,-79,-74,-71,-70,-69,-71,-75,-79,-84,-87,-87,-83,-80,-79,-79,-79,-79,-79,-74,-67,-68,-72,-72,-70,-69,-69,-67,-65,-61,-51,-41,-35,-29,-27,-28,-29,-29,-29,-26,-21,-18,-15,-13,-9,-4,0,3,4,6,12,17,19,21,24,25,28,34,38,41,47,56,64,70,73,74,76,82,89,96,102,106,110,117,122,126,130,138,146,147,146,145,147,149,148,146,145,143,138,133,131,125,115,108,103,94,88,84,78,71,64,53,39,26,14,5,2,-2,-10,-18,-24,-27,-28,-30,-36,-41,-43,-44,-44,-44,-44,-44,-56,-71,-76,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-77,-73,-69,-64,-61,-60,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-77,-75,-74,-74,-72,-68,-64,-61,-60,-58,-55,-54,-52,-48,-44,-39,-36,-33,-30,-26,-21,-20,-18,-15,-14,-14,-9,-1,3,6,14,21,24,25,25,23,21,19,13,8,4,-1,-8,-17,-33,-54,-71,-81,-88,-94,-96,-95,-93,-89,-87,-88,-89,-91,-93,-94,-94,-94,-92,-90,-91,-93,-94,-97,-102,-100,-94,-91,-90,-91,-93,-95,-98,-102,-102,-95,-91,-91,-93,-94,-94,-94,-101,-115,-130,-120,-130,-150,-155,-170,-185,-190,-205,-205,-225,-180,-125,-90,-40,30,85,165,250,375,505,575,630,605,505,425,340,245,140,55,-40,-95,-130,-160,-200,-225,-225,-215,-190,-180,-150,-90,-80,-70,-70,-70,-80,-80,-79,-77,-75,-74,-74,-76,-78,-79,-79,-79,-79,-79,-77,-75,-74,-74,-72,-70,-69,-69,-69,-69,-69,-69,-69,-67,-63,-60,-59,-59,-59,-59,-59,-54,-47,-48,-52,-52,-50,-49,-49,-47,-45,-41,-33,-27,-25,-24,-26,-28,-29,-29,-29,-26,-21,-18,-15,-13,-9,-4,0,3,4,6,12,17,19,21,24,25,28,34,40,47,57,71,82,89,93,94,96,102,109,116,122,126,130,137,142,146,150,158,166,167,166,165,167,169,168,166,165,163,158,153,151,145,135,128,123,114,108,104,98,91,84,73,58,41,24,10,3,-2,-10,-18,-24,-27,-28,-30,-34,-36,-35,-34,-34,-34,-34,-34,-49,-69,-76,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-99,-99,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-99,-99,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-94,-87,-85,-84,-82,-78,-74,-69,-64,-61,-60,-59,-59,-59,-59,-59,-59,-56,-48,-39,-29,-19,-9,0,10,17,19,20,20,18,14,10,5,-1,-10,-19,-29,-39,-49,-59,-69,-77,-83,-89,-94,-97,-98,-99,-99,-101,-105,-109,-114,-117,-118,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-121,-125,-129,-134,-137,-138,-139,-139,-139,-139,-139,-139,-139,-139,-140,-140,-140,-140,-160,-200,-220,-240,-240,-260,-260,-240,-180,-120,-40,20,80,120,220,340,480,580,640,640,560,480,380,300,160,100,0,-100,-100,-140,-180,-220,-220,-220,-200,-180,-160,-120,-100,-100,-100,-100,-60,-100,-100,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-77,-73,-69,-64,-61,-60,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-57,-53,-49,-44,-37,-28,-19,-9,-1,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100,105,110,115,120,125,130,135,138,139,140,140,140,140,140,140,138,134,130,125,120,115,110,105,98,89,80,70,60,50,40,30,18,4,-10,-24,-36,-43,-49,-54,-57,-58,-59,-59,-61,-65,-69,-74,-79,-84,-87,-88,-89,-89,-89,-89,-92,-97,-98,-99,-101,-105,-109,-114,-117,-118,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-121,-125,-129,-134,-137,-138,-139,-139,-137,-133,-129,-124,-121,-120,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-122,-130,-139,-149,-139,-102,-57,-22,-7,-2,0, COMMASEPARATE 5000 II -420,-419,-419,-419,-419,-419,-419,-419,-419,-419,-419,-419,-417,-413,-409,-404,-401,-400,-399,-399,-399,-399,-399,-399,-397,-393,-389,-384,-381,-380,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-377,-373,-369,-364,-361,-360,-359,-359,-357,-353,-349,-344,-341,-340,-339,-339,-339,-339,-339,-339,-337,-333,-329,-324,-321,-320,-319,-319,-319,-319,-319,-319,-319,-319,-319,-319,-317,-313,-309,-304,-301,-300,-299,-299,-299,-299,-299,-299,-301,-305,-309,-314,-317,-318,-319,-319,-321,-325,-329,-334,-337,-338,-339,-339,-341,-345,-349,-354,-357,-358,-359,-359,-359,-359,-359,-359,-361,-365,-369,-374,-379,-384,-389,-394,-399,-404,-409,-414,-417,-418,-419,-419,-419,-419,-419,-419,-417,-413,-409,-404,-401,-400,-399,-399,-399,-399,-399,-399,-399,-399,-399,-399,-399,-399,-399,-399,-399,-399,-399,-399,-399,-399,-399,-399,-397,-393,-389,-384,-381,-380,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-379,-369,-356,-351,-350,-353,-349,-334,-322,-312,-302,-295,-291,-288,-284,-282,-280,-276,-278,-279,-272,-267,-265,-263,-257,-248,-245,-248,-249,-251,-253,-249,-242,-242,-247,-257,-270,-279,-286,-295,-303,-309,-314,-321,-326,-328,-329,-334,-341,-348,-355,-358,-360,-373,-384,-386,-388,-397,-405,-404,-404,-404,-406,-408,-409,-411,-413,-412,-405,-394,-386,-386,-391,-393,-394,-397,-398,-390,-386,-391,-390,-383,-380,-384,-384,-374,-362,-347,-330,-340,-380,-365,-390,-370,-340,-330,-345,-345,-330,-310,-325,-355,-355,-330,-305,-300,-225,-110,-25,-130,-205,-235,-235,-175,-130,-115,-75,-40,-90,-195,-230,-220,-295,-370,-415,-475,-485,-470,-470,-445,-425,-415,-420,-420,-420,-390,-380,-364,-354,-347,-340,-333,-334,-341,-346,-346,-346,-341,-331,-321,-313,-312,-313,-314,-314,-317,-322,-322,-318,-314,-311,-308,-305,-304,-309,-312,-307,-302,-302,-305,-306,-306,-311,-316,-316,-315,-318,-320,-313,-300,-289,-282,-277,-272,-270,-274,-281,-281,-278,-274,-271,-268,-259,-249,-246,-246,-248,-247,-245,-241,-235,-231,-230,-233,-234,-231,-230,-229,-227,-225,-221,-215,-211,-213,-219,-224,-227,-227,-225,-223,-219,-216,-215,-214,-211,-211,-213,-210,-211,-213,-214,-214,-212,-208,-205,-208,-212,-212,-210,-209,-206,-205,-208,-209,-209,-211,-215,-216,-210,-203,-204,-209,-214,-221,-228,-237,-245,-248,-249,-251,-255,-259,-264,-267,-270,-269,-264,-261,-260,-259,-259,-259,-259,-272,-290,-296,-298,-299,-299,-299,-299,-299,-299,-299,-299,-297,-293,-289,-284,-281,-280,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-277,-273,-269,-264,-261,-260,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-257,-253,-249,-244,-241,-240,-239,-239,-239,-239,-239,-239,-241,-245,-249,-254,-257,-258,-259,-259,-259,-259,-259,-259,-259,-259,-259,-242,-220,-213,-210,-213,-209,-194,-182,-172,-163,-160,-159,-157,-152,-147,-142,-137,-138,-139,-132,-127,-125,-123,-117,-108,-105,-108,-109,-111,-113,-109,-102,-102,-107,-117,-130,-139,-146,-155,-163,-169,-174,-181,-188,-194,-199,-209,-219,-227,-235,-238,-239,-247,-253,-250,-249,-257,-265,-264,-264,-264,-266,-268,-269,-271,-271,-266,-256,-245,-238,-242,-250,-253,-254,-257,-258,-250,-246,-251,-250,-243,-240,-244,-247,-245,-243,-224,-190,-260,-260,-225,-210,-190,-160,-150,-165,-225,-170,-170,-185,-195,-195,-170,-125,-80,-5,110,175,70,-5,-35,-55,-15,30,45,45,40,-10,-55,-90,-160,-275,-330,-335,-335,-345,-330,-330,-325,-305,-295,-240,-220,-220,-230,-220,-204,-194,-187,-180,-173,-174,-181,-186,-188,-192,-192,-187,-180,-173,-172,-173,-174,-174,-177,-182,-180,-173,-164,-156,-150,-146,-145,-149,-152,-147,-142,-142,-143,-140,-136,-136,-138,-137,-135,-138,-142,-138,-130,-124,-121,-116,-111,-110,-114,-121,-121,-118,-115,-116,-118,-114,-107,-105,-106,-108,-105,-99,-91,-80,-73,-70,-73,-74,-71,-70,-69,-67,-65,-61,-55,-51,-51,-53,-54,-52,-48,-45,-43,-39,-36,-35,-34,-31,-31,-33,-30,-31,-33,-34,-34,-32,-28,-25,-28,-32,-32,-30,-29,-26,-25,-28,-29,-29,-32,-40,-46,-45,-41,-43,-49,-54,-61,-68,-77,-85,-88,-89,-91,-95,-98,-99,-97,-95,-94,-96,-98,-99,-99,-99,-99,-99,-112,-130,-136,-138,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-137,-133,-129,-124,-121,-120,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-117,-113,-109,-104,-101,-100,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-79,-79,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-99,-99,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-69,-56,-51,-50,-53,-49,-34,-22,-12,-2,4,10,17,26,32,37,42,37,29,26,22,17,17,22,31,34,31,30,28,28,36,47,52,51,42,29,20,13,4,-3,-9,-12,-15,-18,-19,-21,-30,-39,-47,-55,-58,-59,-67,-73,-70,-69,-77,-85,-84,-84,-84,-86,-88,-89,-91,-91,-86,-76,-65,-58,-62,-70,-73,-75,-83,-89,-86,-85,-91,-90,-83,-79,-79,-77,-70,-64,-57,-50,-60,-80,-65,-10,-30,-20,-10,-25,-25,-10,-10,-5,5,5,10,35,80,155,270,275,170,135,65,65,165,230,245,185,180,130,85,50,0,-55,-90,-135,-175,-185,-190,-150,-125,-125,-115,-100,-60,-40,-30,-60,-44,-34,-27,-20,-13,-14,-21,-26,-28,-32,-32,-27,-20,-13,-12,-13,-14,-14,-17,-22,-22,-18,-14,-11,-8,-5,-4,-9,-12,-7,-2,-2,-2,4,13,18,19,21,24,21,17,21,29,35,38,43,48,49,45,38,38,41,44,43,41,45,52,54,53,51,52,54,58,64,68,69,66,65,68,69,70,72,74,78,84,88,88,86,85,87,91,94,96,100,105,110,115,123,126,125,128,128,128,131,135,142,149,153,151,147,147,149,150,153,154,151,150,150,147,139,133,134,138,136,130,125,120,117,112,109,110,110,108,104,98,89,81,74,68,64,61,60,60,60,60,60,47,29,23,21,22,26,30,35,38,39,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,42,46,50,55,60,65,70,75,78,79,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,82,86,90,95,98,99,100,100,100,100,100,100,100,100,100,100,98,96,95,95,95,95,95,93,91,90,90,87,91,105,117,127,137,144,150,157,166,172,177,182,181,180,187,192,194,196,202,211,214,211,210,208,206,210,217,217,212,202,189,180,175,170,167,166,164,158,151,145,140,130,120,112,104,101,100,92,86,89,90,82,74,75,75,75,73,71,70,68,66,67,72,79,83,78,69,66,65,62,61,69,75,73,79,91,97,94,91,94,96,95,90,80,80,95,110,130,120,130,135,155,150,130,115,125,125,130,155,200,275,390,475,370,255,225,225,285,330,345,345,340,330,285,270,220,85,90,45,25,-5,-30,-30,-25,-5,25,40,60,60,70,100,115,125,132,139,145,140,128,118,113,108,108,113,121,132,137,141,144,145,142,137,137,141,145,148,151,154,155,150,147,152,157,157,156,159,163,163,161,162,164,161,157,161,169,175,178,183,188,189,186,184,188,196,202,202,201,205,210,208,203,196,194,195,198,204,208,209,206,205,208,209,210,212,214,218,224,228,228,226,225,227,231,234,236,240,243,244,245,248,249,251,259,263,264,265,265,267,269,268,261,252,249,250,250,253,256,257,261,265,265,258,253,254,258,256,250,245,238,231,222,214,211,210,208,204,200,195,192,189,188,189,190,190,190,190,190,190,180,167,162,161,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,162,166,170,175,178,179,180,180,182,186,190,195,198,199,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,202,206,210,215,218,219,220,220,220,220,220,220,218,214,210,205,202,201,200,200,202,206,210,215,218,219,220,220,220,220,220,220,220,220,220,220,220,220,220,220,220,220,220,220,220,220,220,220,220,220,220,220,220,220,220,220,222,226,230,235,238,239,240,240,240,240,240,240,240,240,240,240,238,234,231,230,230,230,230,230,230,230,230,230,227,231,245,257,267,277,284,290,297,306,312,317,322,321,320,327,334,340,347,357,369,373,371,370,368,366,370,377,377,372,362,349,340,333,324,316,310,305,298,291,285,280,270,260,250,238,231,225,213,206,209,210,202,194,195,195,195,193,191,190,190,192,197,207,217,222,217,209,206,205,202,201,209,213,208,209,215,213,204,196,195,197,202,210,200,200,215,230,290,300,290,275,275,270,270,235,225,225,270,315,360,415,530,615,570,455,405,405,465,510,505,505,500,430,385,310,260,205,170,125,65,95,90,90,135,155,165,180,200,220,230,220,238,256,272,289,300,293,278,263,254,248,248,253,259,266,267,266,265,265,262,257,257,261,265,268,271,274,275,270,267,272,277,277,276,279,283,283,281,282,284,281,277,281,289,295,298,303,308,309,305,298,298,301,304,303,301,305,312,314,313,311,312,314,318,324,328,329,326,325,328,329,330,332,334,338,344,348,348,346,345,347,351,354,356,360,363,364,365,368,368,366,369,368,366,365,365,367,371,374,371,367,367,369,370,373,374,371,370,370,367,359,353,354,358,356,350,345,338,331,322,314,311,310,308,304,300,295,292,289,286,284,281,280,280,280,280,280,273,264,261,260,260,260,260,260,258,254,250,245,242,241,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,242,246,250,255,258,259,260,260,260,260,260,260,260,260,260,260,260,260,260,260,262,266,270,275,278,279,280,280,278,274,270,265,262,261,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,260,258,254,250,245,242,241,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,240,242,246,250,255,258,259,260,260,260,260,260,260,258,254,250,245,242,241,240,240,240,240,240,240,240,240,240,240,240,240,240,240,237,232,231,230,227,231,245,257,266,272,274,275,278,286,292,297,304,306,310,322,331,334,336,342,351,354,351,350,350,352,361,372,376,372,362,349,340,333,324,316,310,305,298,291,284,275,260,245,233,224,221,220,212,206,209,210,202,194,195,195,195,193,191,190,190,192,197,207,217,222,217,209,205,200,192,186,189,188,178,174,175,168,154,141,140,148,163,180,180,180,195,210,230,260,270,255,295,290,290,255,245,245,290,315,360,415,510,575,470,395,345,325,385,430,465,485,480,430,385,350,300,225,170,145,125,115,90,90,135,155,145,160,180,180,190,200,215,225,232,239,246,245,238,233,231,227,227,232,239,246,247,246,244,240,232,222,219,221,225,228,231,234,235,230,227,232,237,237,236,239,243,243,241,242,244,241,239,246,259,270,277,282,287,289,283,273,268,266,265,263,261,265,272,274,273,271,272,274,278,284,288,289,286,285,288,289,290,292,294,298,304,308,308,306,305,307,311,314,316,320,323,324,325,328,328,326,329,328,326,325,325,327,331,334,331,327,326,324,320,318,316,312,311,310,308,304,303,309,316,315,310,305,297,286,272,259,253,251,249,245,242,241,242,244,245,243,241,240,240,240,240,240,227,209,203,201,200,200,200,200,198,194,190,185,182,181,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,182,186,190,195,198,199,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,200,202,206,210,215,218,219,220,220,220,220,220,220,220,220,220,220,218,214,210,205,202,201,200,200,200,200,200,200,198,194,190,185,182,181,180,180,180,180,180,180,182,186,190,195,198,199,200,200,200,200,200,200,198,194,190,185,182,181,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,182,186,190,195,198,199,200,200,197,189,180,170,162,157,156,155,155,155,155,160,167,169,170,167,171,185,197,207,217,224,230,237,246,252,257,262,261,260,267,272,274,276,282,291,294,291,290,288,286,290,297,297,292,282,269,260,253,244,236,230,225,218,211,205,200,190,180,172,164,161,160,152,146,149,150,142,134,135,135,135,133,131,130,128,126,127,132,139,143,138,129,126,125,122,121,129,133,128,129,136,139,135,132,134,136,135,130,120,120,135,170,210,220,230,195,195,150,150,135,145,145,170,215,280,355,450,495,390,335,325,325,385,430,445,445,440,350,305,270,220,165,110,65,45,55,50,50,75,75,85,120,140,100,110,120,137,151,162,174,185,185,178,173,171,167,167,172,179,186,187,186,185,185,182,177,177,181,185,188,191,194,195,190,187,192,197,197,196,199,203,203,199,196,194,186,179,181,189,195,198,203,208,209,205,198,198,201,204,203,201,205,212,214,213,211,212,214,218,224,228,229,226,225,228,229,230,232,234,238,244,248,248,246,245,247,251,254,256,260,263,264,265,268,268,266,269,268,266,265,265,267,271,274,271,267,267,269,270,273,274,271,270,270,267,259,253,254,260,262,261,260,257,251,242,234,231,230,228,224,220,215,212,209,206,204,201,200,200,200,200,200,180,153,144,141,140,140,140,140,142,146,150,155,158,159,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,158,154,150,145,142,141,140,140,142,146,150,155,158,159,160,160,162,166,170,175,178,179,180,180,180,180,180,180,180,180,180,180,178,174,170,165,162,161,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,158,154,150,145,142,141,140,140,140,140,140,140,142,146,150,155,158,159,160,160,160,160,160,160,160,160,160,160,160,160,160,160,158,154,150,145,142,141,140,140,140,140,140,140,140,140,140,140,140,140,140,140,140,140,140,140,140,140,140,140,140,140,140,140,140,140,140,140,140,143,148,149,150,147,151,165,177,187,197,204,210,217,226,232,239,248,251,255,265,272,274,276,280,285,283,276,272,269,266,270,277,277,272,262,249,240,233,224,216,210,205,198,191,185,180,170,160,152,144,141,140,132,126,129,130,122,114,115,115,115,113,111,110,108,106,107,112,119,123,118,109,106,105,102,101,109,113,108,109,116,119,115,112,114,116,122,130,120,120,135,150,110,160,190,175,155,130,130,135,125,125,150,175,220,295,410,475,390,315,265,265,325,370,405,405,400,370,325,310,260,185,130,45,25,-5,-10,30,55,55,65,140,100,100,70,80,97,111,122,134,145,145,138,133,131,127,127,132,139,146,147,146,145,145,142,137,137,141,145,148,151,154,155,150,148,158,168,173,174,178,183,183,179,176,174,166,159,161,169,175,178,183,188,189,185,178,178,181,184,183,181,185,192,194,193,191,192,194,198,204,208,209,206,205,207,204,200,197,196,199,205,208,208,206,205,207,212,219,226,235,242,244,245,248,248,246,249,248,246,245,245,247,249,248,241,232,229,230,230,233,234,231,230,230,227,219,213,214,220,222,221,220,217,211,202,194,190,185,178,169,161,155,152,149,146,144,141,140,140,140,140,140,133,124,121,120,120,120,120,120,118,114,110,105,100,95,90,85,82,81,80,80,80,80,80,80,80,80,80,80,80,80,80,80,82,86,90,95,98,99,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,98,94,90,85,82,81,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,78,74,70,65,62,61,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,58,54,50,45,42,41,40,40,42,46,50,55,58,59,60,60,58,54,50,45,42,41,40,40,40,40,40,40,40,40,40,40,40,40,35,35,45,52,57,62,64,65,67,71,74,78,83,81,80,87,92,94,96,102,111,114,111,110,108,106,110,117,117,112,102,89,80,73,64,56,50,45,38,31,25,20,10,0,-8,-15,-18,-19,-27,-33,-30,-29,-37,-45,-44,-44,-44,-46,-50,-54,-61,-68,-70,-66,-60,-56,-61,-70,-73,-74,-77,-78,-70,-66,-71,-70,-63,-60,-64,-67,-65,-63,-57,-50,-60,-40,-25,-30,-10,0,10,-5,15,10,10,-5,-55,-55,-30,-5,60,175,250,315,210,175,165,165,185,170,225,225,220,150,105,70,20,-55,-110,-175,-195,-205,-210,-250,-185,-145,-135,-120,-120,-120,-110,-100,-84,-74,-67,-60,-53,-54,-61,-66,-68,-72,-72,-67,-60,-53,-52,-53,-54,-54,-57,-62,-62,-58,-54,-51,-50,-51,-55,-64,-71,-66,-61,-61,-63,-60,-56,-56,-58,-57,-55,-58,-62,-58,-50,-44,-41,-36,-31,-30,-34,-41,-41,-38,-35,-36,-38,-34,-27,-25,-26,-28,-29,-31,-31,-30,-29,-29,-32,-33,-30,-29,-29,-27,-25,-21,-15,-11,-11,-13,-14,-12,-8,-5,-3,1,4,5,5,8,8,6,9,8,6,5,5,7,11,14,11,7,6,4,0,-1,-3,-7,-8,-9,-12,-20,-26,-25,-21,-23,-29,-34,-42,-53,-67,-80,-86,-88,-90,-94,-99,-104,-107,-110,-111,-110,-109,-109,-109,-109,-109,-109,-119,-132,-137,-138,-140,-144,-149,-154,-157,-158,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-161,-165,-169,-174,-177,-178,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-177,-173,-169,-164,-161,-160,-159,-159,-161,-165,-169,-174,-177,-178,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-181,-185,-189,-194,-197,-198,-199,-199,-199,-199,-199,-199,-201,-205,-209,-214,-217,-218,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-217,-213,-209,-204,-201,-200,-199,-199,-199,-199,-199,-199,-201,-205,-209,-214,-217,-218,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-226,-235,-238,-239,-244,-244,-232,-222,-212,-202,-193,-184,-172,-158,-149,-142,-137,-138,-139,-132,-127,-125,-123,-117,-108,-105,-108,-109,-111,-113,-109,-102,-102,-107,-117,-130,-139,-146,-156,-168,-179,-189,-199,-207,-213,-219,-229,-239,-247,-255,-258,-259,-267,-273,-270,-269,-277,-285,-284,-284,-284,-286,-288,-289,-291,-293,-292,-287,-280,-276,-281,-290,-293,-294,-299,-304,-301,-301,-310,-309,-302,-300,-304,-307,-305,-303,-290,-270,-280,-280,-265,-250,-210,-200,-210,-225,-225,-230,-230,-265,-275,-275,-250,-245,-200,-125,30,95,-50,-125,-155,-115,-55,10,25,5,0,-70,-115,-110,-160,-235,-330,-375,-355,-365,-390,-410,-385,-305,-335,-360,-340,-300,-290,-300,-284,-274,-267,-260,-253,-254,-261,-266,-268,-272,-272,-267,-260,-253,-252,-253,-254,-254,-257,-262,-262,-258,-254,-251,-248,-245,-244,-249,-252,-247,-242,-242,-243,-240,-236,-236,-238,-237,-235,-238,-242,-238,-230,-224,-221,-216,-211,-210,-214,-221,-221,-218,-215,-216,-218,-214,-209,-211,-216,-223,-225,-224,-221,-215,-211,-210,-213,-214,-211,-210,-209,-207,-205,-201,-195,-191,-191,-193,-194,-192,-188,-185,-183,-179,-176,-175,-174,-171,-171,-173,-170,-171,-173,-174,-174,-172,-168,-165,-168,-172,-172,-170,-169,-166,-165,-168,-169,-169,-172,-180,-186,-185,-181,-183,-189,-194,-202,-213,-227,-240,-246,-248,-250,-254,-257,-258,-257,-255,-254,-256,-258,-259,-259,-259,-259,-259,-272,-290,-296,-298,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-297,-293,-289,-284,-281,-280,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-281,-285,-289,-294,-297,-298,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-297,-293,-289,-284,-281,-280,-279,-279,-279,-279,-279,-279,-281,-285,-289,-294,-297,-298,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-301,-305,-309,-314,-317,-318,-319,-319,-319,-319,-319,-319,-319,-319,-319,-319,-319,-319,-319,-302,-280,-273,-270,-266,-260,-254,-249,-244,-239,-234,-229,-224,-219,-214,-209,-204,-199,-194,-189,-184,-181,-180,-179,-179,-182,-190,-199,-209,-217,-223,-229,-234,-239,-244,-249,-254,-259,-264,-269,-274,-281,-290,-299,-309,-317,-323,-329,-334,-337,-338,-339,-339,-341,-345,-349,-354,-357,-358,-359,-359,-357,-353,-349,-344,-341,-340,-339,-339,-341,-345,-349,-354,-357,-358,-359,-359,-359,-359,-359,-359,-357,-353,-349,-344,-341,-340,-340,-320,-320,-320,-320,-300,-300,-300,-300,-300,-300,-320,-320,-320,-300,-280,-280,-220,-100,20,20,-100,-180,-180,-160,-80,-60,-20,-60,-100,-180,-200,-220,-280,-320,-360,-420,-440,-480,-480,-460,-440,-380,-380,-380,-340,-340,-340,-340,-326,-321,-320,-319,-319,-319,-319,-319,-317,-313,-309,-304,-301,-300,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-297,-293,-289,-284,-281,-280,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-277,-273,-269,-264,-261,-260,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-257,-253,-249,-244,-241,-240,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-237,-233,-229,-224,-221,-220,-219,-219,-219,-219,-219,-219,-217,-213,-209,-204,-201,-200,-199,-199,-201,-205,-209,-214,-217,-218,-219,-219,-219,-219,-219,-219,-221,-225,-229,-234,-239,-244,-249,-254,-259,-264,-269,-274,-279,-284,-289,-294,-299,-304,-309,-314,-317,-318,-319,-319,-319,-319,-319,-319,-319,-319,-319,-319,-319,-319,-319,-319,-322,-330,-339,-349,-356,-358,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-357,-353,-349,-344,-341,-340,-339,-339,-339,-339,-339,-339,-339,-339,-339,-339,-339,-339,-339,-339,-339,-339,-339,-339,-339,-339,-339,-339,-339,-339,-339,-339,-339,-339,-339,-339,-339,-339,-339,-339,-337,-333,-329,-324,-321,-320,-319,-319,-319,-319,-319,-319,-277,-180,-64,-2,0,0,0, COMMASEPARATE 5000 III -360,-360,-360,-360,-360,-360,-360,-360,-362,-366,-370,-375,-376,-373,-370,-365,-362,-361,-360,-360,-362,-366,-370,-375,-378,-379,-380,-380,-380,-380,-379,-379,-381,-385,-389,-394,-399,-404,-409,-414,-417,-418,-419,-419,-419,-419,-419,-419,-419,-419,-419,-419,-421,-425,-429,-434,-439,-444,-449,-454,-457,-458,-459,-459,-461,-465,-469,-474,-477,-478,-479,-479,-482,-490,-499,-509,-515,-517,-519,-519,-519,-519,-519,-519,-517,-513,-509,-504,-501,-500,-499,-499,-497,-493,-489,-484,-479,-474,-469,-464,-457,-448,-439,-429,-423,-421,-419,-419,-417,-413,-409,-404,-399,-394,-389,-384,-381,-380,-379,-379,-378,-374,-370,-365,-363,-366,-370,-375,-378,-379,-380,-380,-380,-380,-380,-380,-378,-374,-370,-365,-362,-361,-360,-360,-358,-354,-350,-345,-342,-341,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-342,-346,-350,-355,-356,-353,-350,-345,-342,-341,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-340,-332,-321,-317,-316,-321,-321,-310,-301,-292,-284,-280,-277,-276,-276,-278,-280,-279,-284,-288,-285,-285,-284,-284,-281,-273,-270,-278,-286,-288,-289,-288,-285,-286,-292,-302,-315,-326,-335,-343,-349,-353,-352,-354,-354,-344,-329,-321,-317,-314,-311,-306,-302,-311,-321,-321,-322,-334,-345,-346,-349,-350,-352,-356,-359,-360,-360,-358,-346,-327,-316,-317,-322,-324,-323,-324,-323,-312,-304,-309,-315,-312,-309,-311,-314,-311,-308,-303,-295,-290,-280,-255,-260,-215,-170,-145,-155,-140,-125,-125,-185,-270,-325,-350,-395,-425,-410,-380,-420,-655,-800,-885,-820,-660,-535,-435,-300,-180,-145,-175,-155,-130,-175,-190,-190,-250,-270,-320,-370,-375,-375,-375,-370,-370,-370,-330,-320,-306,-302,-302,-301,-297,-298,-303,-307,-307,-307,-302,-292,-284,-278,-278,-279,-282,-284,-288,-293,-293,-289,-285,-282,-279,-278,-281,-289,-293,-288,-283,-283,-286,-292,-299,-303,-304,-304,-305,-309,-311,-306,-295,-288,-288,-289,-286,-285,-287,-292,-291,-288,-284,-284,-286,-280,-273,-272,-276,-283,-289,-294,-296,-297,-302,-307,-312,-315,-315,-315,-317,-321,-323,-322,-322,-327,-337,-349,-357,-361,-363,-367,-372,-375,-376,-375,-374,-373,-375,-379,-380,-389,-399,-401,-400,-397,-395,-394,-396,-398,-397,-393,-387,-379,-376,-373,-364,-357,-354,-349,-344,-334,-321,-315,-313,-307,-300,-294,-291,-290,-290,-286,-280,-276,-274,-276,-278,-279,-274,-267,-265,-265,-264,-264,-264,-264,-263,-261,-260,-260,-260,-260,-260,-260,-262,-266,-270,-275,-276,-273,-270,-265,-262,-261,-260,-260,-258,-254,-250,-245,-242,-241,-240,-240,-243,-251,-260,-270,-273,-268,-259,-250,-243,-241,-240,-240,-238,-234,-230,-225,-222,-221,-220,-220,-220,-220,-220,-220,-218,-214,-210,-205,-202,-201,-200,-200,-200,-200,-200,-200,-200,-200,-200,-200,-198,-194,-190,-185,-182,-181,-180,-180,-180,-180,-180,-180,-180,-180,-180,-180,-180,-180,-180,-180,-182,-186,-190,-195,-198,-199,-200,-200,-200,-200,-200,-200,-200,-200,-200,-200,-200,-200,-200,-200,-198,-194,-190,-185,-184,-187,-190,-195,-198,-199,-200,-200,-202,-206,-210,-215,-216,-213,-210,-205,-202,-201,-200,-200,-200,-200,-200,-198,-196,-196,-195,-200,-200,-190,-181,-172,-165,-164,-164,-164,-161,-157,-152,-145,-146,-148,-145,-145,-144,-144,-141,-133,-130,-138,-147,-154,-159,-163,-163,-166,-172,-182,-193,-200,-205,-208,-211,-213,-212,-214,-216,-210,-201,-202,-207,-214,-220,-220,-219,-224,-229,-224,-221,-228,-234,-231,-230,-230,-234,-241,-249,-255,-256,-252,-239,-223,-218,-228,-239,-243,-243,-244,-243,-232,-224,-229,-235,-232,-230,-236,-243,-245,-246,-222,-175,-230,-200,-155,-120,-75,-30,-5,-15,-60,-25,-45,-105,-110,-205,-270,-295,-305,-250,-200,-260,-495,-640,-725,-700,-560,-435,-335,-240,-140,-125,-155,-135,-170,-215,-210,-190,-190,-210,-260,-250,-255,-255,-255,-210,-210,-210,-210,-200,-185,-177,-172,-166,-157,-153,-153,-152,-151,-154,-153,-148,-144,-143,-148,-154,-160,-163,-167,-173,-171,-164,-155,-147,-141,-139,-142,-149,-152,-147,-142,-142,-143,-145,-148,-147,-145,-144,-144,-148,-152,-150,-144,-142,-145,-142,-135,-130,-129,-133,-132,-128,-125,-129,-136,-135,-131,-131,-136,-143,-145,-142,-135,-126,-125,-127,-132,-135,-135,-135,-137,-141,-143,-142,-142,-147,-155,-163,-167,-166,-163,-162,-162,-160,-160,-161,-164,-168,-173,-179,-180,-189,-199,-201,-200,-197,-195,-194,-196,-198,-199,-199,-197,-194,-194,-193,-184,-177,-175,-174,-174,-169,-159,-154,-153,-147,-140,-134,-131,-130,-130,-126,-120,-116,-115,-116,-118,-119,-115,-113,-114,-114,-114,-114,-114,-114,-115,-118,-119,-120,-120,-120,-120,-120,-118,-114,-110,-105,-102,-101,-100,-100,-100,-100,-100,-100,-100,-100,-100,-100,-100,-100,-100,-100,-101,-105,-110,-115,-119,-120,-119,-119,-118,-114,-110,-105,-102,-101,-100,-100,-100,-100,-100,-100,-100,-100,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-98,-94,-90,-85,-80,-75,-70,-65,-62,-61,-60,-60,-62,-66,-70,-75,-78,-79,-80,-80,-78,-74,-70,-65,-65,-72,-80,-89,-92,-87,-79,-70,-65,-67,-70,-75,-78,-79,-80,-80,-82,-86,-90,-95,-96,-93,-89,-84,-81,-80,-79,-79,-78,-74,-70,-65,-62,-61,-60,-60,-60,-60,-60,-60,-58,-56,-55,-55,-55,-55,-55,-42,-24,-18,-16,-21,-22,-16,-12,-8,-2,1,6,11,16,17,17,19,11,0,-7,-16,-21,-19,-12,1,7,0,-9,-15,-18,-18,-14,-12,-15,-28,-44,-56,-64,-68,-70,-73,-70,-68,-66,-55,-41,-37,-37,-39,-41,-41,-39,-44,-48,-39,-31,-33,-36,-32,-31,-30,-32,-36,-39,-40,-38,-32,-19,-3,2,-8,-19,-23,-24,-30,-34,-28,-25,-35,-45,-47,-46,-45,-43,-36,-30,-16,5,10,0,25,100,105,130,135,125,140,175,175,115,70,15,-30,-115,-125,-70,-40,-160,-395,-480,-605,-600,-400,-255,-155,-100,0,35,65,85,50,25,30,-10,-50,-90,-120,-90,-95,-95,-95,-90,-50,-30,-10,-20,-6,-2,-2,-1,3,2,-3,-7,-7,-7,-3,7,16,22,22,21,17,11,2,-8,-11,-8,-5,-2,1,2,-1,-9,-12,-7,-2,-2,-2,-1,1,7,12,14,15,11,7,9,15,17,12,11,14,14,12,7,8,11,14,10,3,4,8,8,3,-4,-8,-9,-6,-2,-4,-8,-13,-16,-16,-16,-18,-22,-24,-23,-23,-28,-36,-44,-48,-47,-45,-48,-53,-56,-57,-56,-55,-54,-55,-55,-52,-55,-60,-57,-51,-43,-38,-36,-37,-39,-40,-40,-38,-35,-35,-34,-25,-18,-14,-10,-5,5,18,24,26,32,41,51,58,64,68,73,79,83,83,77,70,65,65,66,64,64,64,64,64,64,63,60,59,59,59,59,59,59,59,59,59,59,61,65,69,74,77,78,79,79,79,79,79,79,81,85,89,94,97,98,99,99,99,99,99,99,99,99,99,99,99,99,99,99,101,105,109,114,117,118,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,121,125,129,134,137,138,139,139,137,133,129,124,121,120,119,119,119,119,119,119,121,125,129,134,137,138,139,139,139,139,139,139,139,139,139,139,139,139,139,139,139,139,139,134,127,125,124,119,119,129,138,147,155,159,164,169,174,176,177,179,175,171,174,174,175,175,178,186,189,181,172,165,160,156,156,153,147,137,126,119,116,117,119,122,126,125,123,129,140,143,142,140,138,138,140,135,130,135,138,131,125,128,129,129,127,123,120,119,119,121,129,141,143,132,120,116,118,121,127,142,155,154,154,162,168,167,165,168,170,162,145,150,160,185,200,245,250,275,285,320,315,295,235,190,135,130,85,75,70,100,60,-155,-320,-405,-420,-260,-115,-15,80,180,235,265,305,290,185,230,210,170,130,100,70,45,45,65,70,90,90,110,140,154,162,167,173,179,176,166,157,152,147,147,152,157,162,161,160,158,156,152,146,148,156,164,172,178,181,178,170,166,171,176,176,175,173,170,171,173,174,174,170,166,168,174,176,172,171,174,174,173,173,178,186,192,189,183,184,186,182,173,161,154,152,154,158,156,152,147,144,144,144,142,138,136,137,137,132,124,116,112,113,115,112,107,104,101,98,95,91,87,85,89,85,78,78,79,82,82,79,73,66,65,72,82,95,103,111,126,137,142,144,145,150,158,159,156,157,160,165,168,169,169,173,179,183,185,183,181,180,183,186,186,185,185,185,185,185,183,181,179,179,180,184,189,194,197,198,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,201,205,209,214,217,218,219,219,221,225,229,234,237,238,239,239,239,239,239,239,237,233,229,224,221,220,219,219,221,225,229,234,237,238,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,241,245,249,254,257,258,259,259,259,259,259,259,257,253,249,244,241,240,239,239,241,245,249,254,257,258,259,259,261,265,269,274,277,278,279,279,277,273,269,264,261,260,259,259,259,259,259,259,259,259,259,259,259,259,259,259,261,265,269,274,277,278,279,279,279,279,279,279,279,279,279,279,279,279,279,279,279,279,279,279,281,283,284,284,279,279,289,298,307,315,319,324,329,334,336,336,336,328,320,319,318,321,326,333,344,348,341,332,325,320,316,316,315,313,307,301,297,294,291,288,284,281,275,268,271,280,283,282,280,277,278,280,274,269,274,278,271,265,268,269,269,267,263,260,261,265,271,284,299,302,291,280,276,276,275,276,287,295,290,284,286,284,277,270,269,271,276,285,290,280,305,320,405,430,455,445,460,475,475,415,350,295,290,245,235,250,280,240,65,-120,-245,-240,-80,45,125,220,320,335,385,385,370,345,350,290,230,250,220,170,185,205,225,230,250,270,290,280,297,313,327,343,354,349,336,322,313,307,307,312,316,321,321,320,319,321,322,321,324,329,334,337,338,336,328,315,308,312,316,316,315,313,310,311,313,314,314,310,306,308,314,316,311,310,313,313,311,306,307,310,313,309,302,304,310,313,313,311,310,310,313,317,316,312,307,304,304,304,302,298,296,297,297,292,284,276,272,273,275,272,267,264,261,258,255,251,246,240,239,230,220,218,219,222,223,220,213,206,203,206,212,220,223,226,235,242,244,245,245,250,260,265,266,272,279,285,288,289,289,293,299,303,304,302,300,299,300,300,296,294,294,294,294,294,295,297,298,298,299,299,299,299,297,293,289,284,283,286,289,294,297,298,299,299,299,299,299,299,299,299,299,299,301,305,309,314,317,318,319,319,317,313,309,304,301,300,299,299,299,299,299,299,299,299,299,299,299,299,299,299,299,299,299,299,301,305,309,314,317,318,319,319,319,319,319,319,319,319,319,319,319,319,319,319,319,319,319,319,317,313,309,304,301,300,299,299,301,305,309,314,317,318,319,319,319,319,319,319,317,313,309,304,301,300,299,299,301,305,309,314,315,312,309,304,301,300,299,299,299,299,299,299,299,299,299,299,299,299,299,299,299,299,299,299,299,299,299,299,299,299,299,299,299,299,299,299,299,299,299,299,301,305,309,314,317,318,319,319,319,319,319,319,317,313,309,304,303,306,309,314,317,318,319,319,319,319,319,319,319,319,319,319,314,307,305,304,299,299,309,318,326,330,329,329,330,334,336,336,338,333,330,333,334,334,334,338,346,349,341,332,327,326,327,331,332,327,317,306,299,294,291,288,286,287,285,283,287,294,292,287,281,278,278,280,275,270,275,278,271,265,268,269,269,267,263,260,261,265,271,284,299,302,291,280,275,271,265,261,268,275,270,264,264,259,247,235,232,236,247,260,270,280,305,320,365,430,475,465,500,475,475,415,350,295,250,205,215,230,280,220,-35,-180,-285,-300,-140,-15,105,220,320,335,365,385,370,325,310,310,290,270,220,210,225,245,225,230,250,250,250,260,274,282,287,293,300,301,296,292,290,286,286,291,296,301,301,300,296,290,281,271,268,270,274,277,280,281,278,270,266,271,276,276,275,273,270,271,273,274,274,270,268,273,284,291,290,289,292,293,289,281,277,275,274,269,262,264,268,268,263,256,252,251,254,258,256,252,247,244,244,244,242,238,236,237,237,232,226,222,222,228,233,231,227,224,221,218,215,211,204,195,189,175,162,159,160,162,164,165,163,161,161,161,162,165,165,167,176,182,185,190,195,205,218,224,226,232,238,240,238,234,231,234,240,244,246,248,250,254,261,264,264,264,264,264,264,264,263,260,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,261,265,269,274,277,278,279,279,279,279,279,279,279,279,279,279,279,279,279,279,277,273,269,264,263,266,269,274,277,278,279,279,279,279,279,279,279,279,279,279,277,273,269,264,263,266,269,274,277,278,279,279,277,273,269,264,261,260,259,259,259,259,259,259,261,265,269,274,275,272,269,264,261,260,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,259,261,265,269,274,277,278,279,279,276,268,259,249,243,240,240,239,239,239,239,241,243,244,244,239,239,249,258,267,275,279,284,289,294,296,296,298,294,290,293,293,294,294,297,305,308,300,291,285,280,276,276,273,267,257,246,239,234,231,228,226,226,224,222,228,239,242,242,240,238,238,240,235,230,235,238,231,225,228,229,229,227,223,220,219,221,226,239,256,261,252,240,236,236,235,236,247,253,244,234,232,232,229,226,228,230,236,245,250,260,285,340,385,410,435,405,420,375,375,315,270,235,210,165,175,170,160,60,-175,-340,-405,-340,-160,-15,105,200,280,275,325,345,350,325,310,290,250,250,220,190,185,165,145,170,190,170,190,200,216,228,237,248,259,261,256,252,250,246,246,251,256,261,261,260,257,255,251,246,246,250,254,257,260,261,258,250,246,251,256,256,255,253,250,251,251,248,244,235,228,228,234,236,231,230,233,233,231,226,227,230,233,229,222,223,227,227,222,215,210,205,203,202,197,192,187,184,186,190,192,193,194,196,197,192,183,171,162,158,156,152,147,144,141,138,135,131,126,120,119,110,100,98,99,102,104,105,103,101,102,106,112,120,123,126,135,142,144,145,145,150,162,171,177,187,198,205,208,209,209,213,219,223,224,222,220,219,222,225,224,224,224,224,224,224,222,220,219,219,219,219,219,219,221,225,229,234,237,238,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,237,233,229,224,221,220,219,219,221,225,229,234,237,238,239,239,241,245,249,254,255,252,249,244,241,240,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,239,237,233,229,224,221,220,219,219,219,219,219,219,221,225,229,234,237,238,239,239,239,239,239,239,239,239,239,239,239,239,239,239,237,233,229,224,221,220,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,220,223,223,224,219,219,229,239,252,265,274,283,289,294,296,296,298,294,290,293,294,294,294,295,299,297,285,273,266,260,256,256,253,247,237,226,219,214,211,208,206,206,204,202,208,219,222,223,226,228,233,238,234,229,234,236,225,214,213,210,209,207,203,202,205,209,216,228,240,243,232,220,216,216,215,215,221,225,215,206,208,211,208,206,208,210,216,225,230,260,285,320,305,350,395,385,380,375,375,335,270,215,190,145,115,130,160,100,-115,-260,-365,-320,-160,-35,65,140,240,295,325,365,370,325,290,230,230,190,160,170,165,145,145,190,150,170,150,180,195,203,207,213,221,221,216,212,210,206,206,211,216,221,221,220,217,215,211,206,206,210,214,217,220,221,218,210,207,217,227,232,233,232,230,231,231,228,224,215,208,208,214,216,209,204,203,198,192,186,187,190,195,194,192,198,206,206,201,195,190,185,183,182,177,172,167,164,163,159,152,143,138,138,138,132,124,116,112,113,116,117,117,119,120,118,115,111,104,95,89,75,62,59,60,62,64,65,63,61,62,66,72,80,85,91,105,117,123,124,125,130,140,145,147,152,159,165,168,169,170,173,178,182,183,182,180,179,182,185,184,184,184,184,184,184,189,195,197,198,197,193,189,184,179,174,169,164,161,160,159,159,159,159,159,159,159,159,159,159,159,159,159,159,159,159,159,159,161,165,169,174,177,178,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,177,173,169,164,161,160,159,159,161,165,169,174,177,178,179,179,179,179,179,179,177,173,169,164,161,160,159,159,159,159,159,159,159,159,159,159,159,159,159,159,159,159,159,159,157,153,149,144,141,140,139,139,139,139,139,139,139,139,139,139,139,139,139,139,139,139,139,139,139,139,139,139,139,139,139,139,139,139,139,139,139,139,139,139,137,133,129,124,121,120,119,119,121,125,129,134,137,138,139,139,137,133,129,124,121,120,119,119,119,119,119,119,119,119,117,115,114,114,107,103,110,118,127,135,138,139,139,139,136,131,128,119,110,108,103,99,96,99,107,109,103,97,95,95,95,95,93,87,77,66,59,54,51,48,46,46,44,42,48,59,62,62,60,59,59,61,56,51,56,59,52,46,49,50,50,48,44,42,40,40,42,51,62,64,52,35,26,22,18,18,28,36,32,30,38,46,47,46,49,51,57,65,50,60,85,120,185,230,255,205,240,235,235,175,70,15,-10,-55,-45,-30,-60,-120,-355,-480,-545,-500,-380,-315,-175,-80,20,35,85,105,90,65,50,30,10,-10,-40,-110,-75,-55,-55,-50,-50,-30,-10,-20,-3,9,18,29,39,41,37,33,31,27,27,32,36,37,32,26,20,17,13,7,9,17,25,33,37,36,28,16,8,13,18,18,16,14,11,12,14,15,15,11,7,9,15,17,10,5,4,-1,-7,-13,-12,-9,-6,-10,-17,-16,-12,-12,-17,-24,-29,-34,-35,-36,-41,-46,-51,-54,-54,-54,-57,-61,-63,-62,-62,-67,-75,-83,-87,-86,-84,-87,-92,-95,-98,-101,-105,-109,-114,-120,-121,-130,-140,-142,-141,-138,-136,-135,-137,-139,-139,-139,-138,-134,-134,-132,-123,-117,-115,-114,-114,-109,-99,-94,-93,-87,-81,-79,-81,-85,-88,-86,-80,-76,-75,-77,-79,-80,-75,-69,-66,-65,-65,-65,-65,-65,-63,-61,-61,-60,-61,-65,-70,-75,-78,-79,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-78,-74,-70,-65,-62,-61,-60,-60,-62,-66,-70,-75,-78,-79,-80,-80,-80,-80,-80,-80,-80,-80,-80,-80,-82,-86,-90,-95,-98,-99,-100,-100,-100,-100,-100,-100,-100,-100,-100,-100,-98,-94,-90,-85,-82,-81,-80,-80,-82,-86,-90,-95,-98,-99,-100,-100,-100,-100,-100,-100,-100,-100,-100,-100,-100,-100,-100,-100,-100,-100,-100,-100,-102,-106,-110,-115,-118,-119,-120,-120,-120,-120,-120,-120,-122,-126,-130,-135,-138,-139,-140,-140,-140,-140,-140,-140,-140,-140,-140,-140,-140,-140,-140,-140,-138,-134,-130,-125,-124,-127,-130,-135,-138,-139,-140,-140,-140,-140,-140,-140,-140,-140,-140,-140,-140,-140,-140,-140,-140,-140,-140,-140,-140,-140,-140,-140,-140,-140,-149,-160,-164,-165,-172,-176,-168,-161,-152,-144,-138,-130,-120,-110,-105,-103,-101,-105,-109,-106,-106,-105,-105,-102,-94,-91,-99,-108,-114,-119,-123,-123,-126,-132,-142,-153,-160,-165,-169,-176,-183,-188,-191,-190,-180,-165,-158,-158,-159,-161,-162,-164,-174,-184,-183,-181,-188,-194,-191,-190,-190,-192,-196,-199,-200,-200,-198,-190,-178,-176,-187,-199,-203,-203,-206,-209,-203,-199,-208,-214,-211,-209,-211,-213,-211,-209,-189,-155,-150,-160,-135,-100,-55,-30,-25,-35,-20,-25,-5,-85,-150,-185,-210,-275,-285,-290,-220,-280,-555,-700,-785,-720,-560,-415,-315,-240,-140,-125,-75,-15,-30,-75,-130,-150,-130,-150,-200,-230,-235,-215,-255,-290,-270,-230,-210,-220,-205,-197,-192,-186,-179,-178,-183,-187,-189,-193,-193,-188,-183,-178,-178,-179,-182,-184,-188,-193,-193,-189,-185,-182,-179,-178,-181,-189,-193,-188,-183,-183,-184,-186,-189,-188,-186,-185,-185,-189,-193,-191,-185,-183,-188,-189,-186,-186,-188,-193,-192,-189,-186,-190,-197,-196,-194,-198,-207,-219,-225,-227,-225,-221,-223,-227,-232,-235,-235,-235,-237,-241,-245,-248,-252,-262,-273,-282,-287,-286,-284,-287,-292,-295,-298,-301,-304,-308,-313,-319,-320,-329,-339,-341,-340,-337,-335,-334,-336,-338,-337,-333,-327,-319,-316,-313,-304,-297,-295,-294,-294,-289,-279,-274,-273,-267,-260,-254,-251,-250,-249,-246,-240,-236,-233,-231,-229,-225,-220,-220,-223,-225,-225,-225,-225,-225,-223,-221,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-218,-214,-210,-205,-202,-201,-200,-200,-200,-200,-200,-200,-200,-200,-200,-200,-200,-200,-200,-200,-200,-200,-200,-200,-200,-200,-200,-200,-200,-200,-200,-200,-202,-206,-210,-215,-218,-219,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-218,-214,-210,-205,-202,-201,-200,-200,-198,-194,-190,-185,-184,-187,-190,-195,-198,-199,-200,-200,-202,-206,-210,-215,-218,-219,-220,-220,-218,-214,-210,-205,-202,-201,-200,-200,-200,-200,-200,-200,-202,-206,-210,-215,-218,-219,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-218,-214,-210,-205,-204,-207,-210,-215,-218,-219,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-208,-193,-188,-186,-184,-182,-180,-180,-180,-178,-174,-170,-165,-160,-155,-150,-145,-143,-146,-150,-155,-162,-171,-179,-189,-199,-209,-219,-229,-235,-237,-239,-239,-238,-234,-230,-225,-220,-215,-210,-205,-204,-207,-210,-215,-220,-225,-230,-235,-236,-233,-230,-225,-224,-227,-230,-235,-238,-239,-240,-240,-238,-234,-230,-225,-222,-221,-220,-220,-222,-226,-230,-235,-236,-233,-230,-225,-222,-221,-220,-220,-218,-214,-210,-205,-202,-201,-200,-180,-180,-180,-160,-100,-80,-60,-60,-40,-40,-80,-140,-200,-260,-300,-360,-340,-320,-320,-460,-680,-820,-820,-720,-560,-440,-320,-220,-200,-180,-100,-120,-140,-140,-140,-200,-220,-280,-300,-300,-320,-280,-280,-280,-240,-280,-240,-240,-227,-222,-221,-220,-220,-220,-220,-220,-218,-214,-210,-205,-202,-201,-200,-200,-200,-200,-200,-200,-202,-206,-210,-215,-216,-213,-210,-205,-202,-201,-200,-200,-200,-200,-200,-200,-202,-206,-210,-215,-216,-213,-210,-205,-202,-201,-200,-200,-200,-200,-200,-200,-202,-206,-210,-215,-220,-225,-230,-235,-240,-245,-249,-254,-259,-264,-269,-274,-279,-284,-289,-294,-297,-298,-299,-299,-301,-305,-309,-314,-319,-324,-329,-334,-337,-338,-339,-339,-339,-339,-339,-339,-341,-345,-349,-354,-355,-352,-349,-344,-339,-334,-329,-324,-319,-314,-309,-304,-299,-294,-289,-284,-277,-268,-259,-250,-243,-241,-240,-240,-242,-246,-250,-255,-256,-253,-250,-245,-240,-235,-232,-231,-230,-230,-230,-230,-227,-222,-221,-220,-221,-225,-230,-235,-239,-240,-240,-240,-240,-240,-240,-240,-240,-240,-240,-240,-240,-240,-240,-240,-238,-234,-230,-225,-222,-221,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-220,-218,-214,-210,-205,-202,-201,-200,-200,-202,-206,-210,-215,-218,-219,-220,-220,-218,-214,-210,-205,-202,-201,-200,-200,-197,-189,-180,-170,-138,-78,-7,20,7,2,0, COMMASEPARATE 5000 AVR 240,239,239,239,239,239,239,239,238,236,234,232,229,227,224,222,220,220,219,219,218,216,214,212,208,204,199,194,191,190,190,190,189,187,185,182,180,177,175,172,169,164,160,155,152,151,150,150,148,144,140,135,131,128,125,122,120,117,115,112,109,104,100,95,91,88,85,82,81,80,80,80,78,74,70,65,60,55,50,45,42,41,40,40,41,43,45,47,51,55,60,65,69,72,75,77,82,88,95,102,109,114,120,125,130,135,140,145,149,152,155,157,160,162,165,167,171,175,180,185,190,197,204,212,218,221,224,227,228,229,229,229,229,229,229,229,228,226,224,222,220,220,219,219,220,222,224,227,228,229,229,229,229,229,229,229,229,229,229,229,228,226,224,222,219,217,214,212,210,210,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,209,203,196,193,192,193,189,179,172,166,160,155,153,150,146,143,140,137,136,135,130,125,123,121,117,112,110,109,106,107,109,105,100,99,101,106,113,116,119,124,129,133,138,144,149,156,165,174,183,191,200,205,209,218,224,226,227,230,233,231,230,229,230,230,230,231,233,233,232,231,228,228,230,231,233,235,237,234,234,237,233,227,226,229,227,219,208,196,183,195,240,238,260,263,255,258,268,275,268,248,233,220,193,155,108,88,20,-80,-185,-197,-195,-207,-175,-155,-137,-102,-75,-50,18,108,153,155,208,275,320,350,350,310,285,258,238,228,235,235,235,225,220,211,203,196,190,185,185,190,193,193,193,190,185,179,174,173,174,173,172,173,176,176,174,172,170,169,166,164,165,166,163,161,161,162,160,157,160,164,164,163,164,165,160,153,145,138,133,129,128,131,135,136,134,132,129,125,119,113,110,108,107,103,98,93,87,80,77,77,77,74,73,71,67,64,60,54,48,45,45,46,47,46,42,37,32,28,28,27,25,24,24,20,17,14,14,14,14,11,8,10,13,14,14,16,17,17,22,27,31,34,41,44,43,43,47,53,61,71,81,92,100,103,106,111,117,122,126,128,131,132,131,129,128,127,127,127,127,141,160,166,168,169,169,169,169,168,166,164,162,159,157,154,152,150,150,149,149,150,152,154,157,158,159,159,159,158,154,149,144,143,145,150,154,158,159,159,159,158,156,154,152,150,150,149,149,149,149,149,149,150,152,154,157,158,159,159,159,159,159,159,159,159,159,159,159,160,162,164,167,168,169,169,169,169,169,169,169,169,169,169,169,169,169,169,169,168,166,164,162,160,160,159,159,159,159,159,159,159,159,159,159,159,159,159,159,158,156,154,152,149,147,144,142,140,140,139,139,140,142,144,147,149,152,154,157,158,159,159,159,159,159,159,143,122,115,113,113,109,99,92,86,81,78,77,75,72,69,66,65,65,65,60,55,53,51,47,42,40,39,36,34,34,28,21,19,21,26,34,39,44,51,58,63,68,74,80,89,99,108,116,120,125,128,130,135,139,138,139,143,148,149,149,149,149,148,145,144,143,140,137,134,129,128,131,132,133,135,137,134,134,137,133,127,125,126,126,123,120,113,103,145,160,148,150,153,145,148,158,195,158,148,133,140,93,35,-22,-72,-120,-210,-305,-317,-315,-327,-295,-265,-247,-212,-165,-110,-52,-22,23,75,168,225,240,240,240,200,205,198,178,168,135,115,115,125,120,112,106,101,97,95,98,105,110,113,115,116,113,108,102,98,96,94,93,94,96,95,91,87,83,80,77,74,75,76,74,71,71,72,68,62,63,66,65,63,64,66,63,58,53,49,45,44,45,50,55,55,54,53,52,50,47,42,40,38,37,33,28,24,17,11,7,7,7,4,3,1,-3,-6,-10,-16,-22,-26,-28,-29,-31,-33,-36,-38,-41,-44,-45,-48,-53,-55,-56,-60,-63,-66,-66,-66,-66,-69,-72,-70,-67,-67,-69,-69,-71,-72,-68,-63,-59,-55,-47,-41,-39,-38,-34,-27,-19,-9,1,12,20,23,26,31,37,41,41,38,36,37,40,41,42,42,42,42,42,55,71,77,78,79,79,79,79,80,82,84,87,88,89,89,89,89,89,89,89,89,89,89,89,89,89,89,89,87,81,74,67,62,60,60,60,60,62,64,67,68,69,69,69,67,63,59,54,51,50,50,50,50,50,50,50,50,50,50,50,50,52,54,57,57,56,54,52,50,50,49,49,50,52,54,57,58,59,59,59,58,56,54,52,49,44,39,35,33,36,40,44,49,52,54,57,58,59,59,59,58,56,54,52,49,47,45,42,41,40,40,40,40,42,44,47,48,49,49,49,49,49,49,49,50,51,52,52,52,52,52,48,44,42,42,43,38,26,16,8,1,-4,-7,-12,-18,-24,-29,-33,-32,-29,-29,-30,-27,-26,-28,-31,-31,-31,-34,-35,-37,-45,-54,-58,-58,-56,-51,-48,-45,-38,-32,-27,-23,-19,-15,-8,1,12,21,28,35,38,40,45,49,51,54,61,67,68,69,69,70,70,70,71,72,70,67,64,59,58,61,62,63,68,72,72,73,74,68,60,56,57,56,52,49,49,52,65,80,77,60,82,85,77,87,95,97,97,62,30,2,-25,-92,-142,-190,-290,-355,-367,-375,-367,-365,-365,-357,-322,-235,-180,-113,-53,-8,25,67,105,130,150,140,130,105,78,78,68,55,35,25,25,50,41,33,26,20,14,15,20,23,25,29,31,30,28,24,23,23,22,19,18,18,17,14,12,10,8,6,4,5,6,4,1,1,1,-4,-13,-15,-13,-14,-17,-16,-14,-17,-22,-27,-32,-38,-41,-42,-39,-35,-34,-36,-37,-38,-40,-43,-48,-50,-52,-53,-56,-58,-61,-65,-70,-73,-72,-73,-76,-77,-79,-83,-86,-89,-95,-102,-106,-108,-109,-110,-113,-118,-122,-128,-133,-138,-142,-150,-153,-152,-154,-155,-158,-159,-160,-163,-168,-171,-169,-166,-167,-169,-169,-170,-171,-168,-162,-159,-154,-144,-135,-132,-129,-124,-117,-109,-100,-92,-83,-77,-76,-74,-69,-63,-57,-51,-46,-42,-36,-31,-29,-28,-28,-28,-28,-28,-16,1,6,8,7,3,-1,-6,-9,-10,-11,-11,-10,-8,-6,-3,-2,-1,-1,-1,-1,-1,-1,-1,0,2,4,7,8,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,8,6,4,2,-2,-6,-11,-16,-19,-20,-21,-21,-21,-21,-21,-21,-21,-21,-21,-21,-21,-21,-21,-21,-21,-21,-21,-21,-21,-21,-21,-21,-21,-21,-21,-21,-21,-21,-21,-21,-21,-21,-21,-21,-20,-18,-16,-13,-12,-11,-11,-11,-12,-14,-16,-18,-20,-20,-21,-21,-21,-21,-21,-21,-22,-24,-26,-28,-30,-30,-31,-31,-31,-31,-31,-31,-31,-31,-31,-31,-29,-27,-26,-26,-26,-26,-26,-26,-28,-28,-28,-28,-32,-41,-48,-54,-60,-65,-68,-73,-79,-84,-89,-93,-94,-95,-100,-105,-107,-109,-113,-118,-120,-121,-124,-126,-126,-132,-139,-141,-139,-134,-126,-121,-117,-112,-108,-105,-101,-96,-90,-81,-70,-59,-49,-42,-35,-32,-30,-25,-21,-22,-21,-17,-12,-11,-11,-11,-10,-10,-10,-9,-7,-7,-8,-9,-12,-12,-9,-8,-6,-2,2,2,2,4,-2,-10,-13,-11,-9,-10,-11,-14,-18,-5,0,-3,-10,-8,5,7,7,5,7,17,2,-30,-58,-65,-113,-163,-240,-340,-445,-447,-415,-427,-435,-415,-387,-352,-305,-250,-213,-153,-118,-75,7,25,60,60,70,80,65,47,27,7,-5,-15,-15,-15,-30,-38,-44,-49,-53,-56,-52,-45,-40,-37,-35,-35,-37,-43,-51,-57,-61,-65,-67,-66,-64,-63,-63,-63,-62,-62,-64,-66,-65,-64,-67,-69,-69,-69,-73,-78,-78,-75,-75,-77,-76,-74,-77,-82,-87,-92,-98,-101,-102,-100,-98,-99,-103,-106,-108,-110,-113,-117,-117,-117,-116,-117,-119,-121,-125,-130,-133,-133,-133,-136,-137,-139,-143,-146,-150,-156,-162,-166,-168,-169,-171,-174,-178,-183,-188,-193,-195,-198,-203,-206,-209,-215,-221,-225,-226,-226,-226,-228,-229,-225,-219,-217,-214,-209,-206,-205,-202,-198,-197,-194,-186,-181,-179,-179,-177,-172,-167,-158,-149,-138,-130,-127,-124,-119,-113,-108,-104,-102,-99,-97,-96,-97,-98,-98,-98,-98,-98,-89,-77,-73,-72,-70,-68,-66,-63,-62,-61,-61,-61,-61,-61,-61,-61,-61,-61,-61,-61,-61,-61,-61,-61,-61,-61,-61,-61,-61,-61,-61,-61,-61,-61,-61,-61,-61,-61,-61,-61,-62,-64,-66,-68,-70,-70,-71,-71,-72,-74,-76,-78,-80,-80,-81,-81,-81,-81,-81,-81,-82,-84,-86,-88,-90,-90,-91,-91,-90,-88,-86,-83,-82,-81,-81,-81,-81,-81,-81,-81,-81,-81,-81,-81,-81,-81,-81,-81,-81,-81,-81,-81,-82,-84,-86,-88,-90,-90,-91,-91,-91,-91,-91,-91,-90,-88,-86,-83,-82,-81,-81,-81,-82,-84,-86,-88,-90,-90,-91,-91,-90,-88,-86,-83,-82,-81,-81,-81,-82,-84,-86,-88,-90,-90,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-92,-94,-96,-98,-100,-100,-101,-101,-101,-101,-101,-101,-101,-101,-101,-101,-99,-95,-92,-91,-91,-91,-91,-91,-90,-89,-88,-88,-88,-92,-101,-108,-114,-120,-125,-128,-133,-139,-144,-149,-154,-157,-160,-168,-175,-180,-184,-191,-197,-199,-201,-204,-206,-206,-212,-219,-220,-216,-209,-199,-192,-186,-179,-172,-168,-165,-161,-157,-150,-140,-129,-119,-110,-100,-92,-85,-76,-72,-72,-71,-67,-62,-61,-61,-61,-60,-60,-60,-60,-60,-62,-65,-68,-71,-72,-69,-68,-67,-65,-63,-66,-66,-63,-67,-72,-71,-66,-61,-61,-62,-64,-68,-55,-60,-63,-70,-88,-85,-63,-53,-45,-33,-33,-28,-50,-78,-125,-193,-243,-290,-390,-495,-538,-515,-527,-525,-505,-488,-443,-395,-340,-263,-193,-118,-75,-33,5,20,50,30,20,-5,-43,-53,-53,-65,-75,-85,-85,-80,-90,-100,-109,-118,-123,-119,-110,-102,-98,-95,-95,-97,-101,-106,-107,-106,-106,-105,-101,-97,-95,-97,-98,-100,-102,-106,-111,-113,-113,-116,-119,-119,-119,-123,-128,-128,-125,-125,-127,-126,-124,-127,-132,-137,-143,-148,-152,-153,-150,-145,-145,-146,-148,-149,-150,-153,-157,-158,-157,-156,-157,-159,-162,-166,-170,-173,-173,-173,-176,-177,-179,-183,-186,-190,-196,-202,-206,-208,-209,-211,-214,-218,-223,-228,-233,-235,-238,-243,-245,-246,-250,-253,-256,-256,-256,-256,-260,-264,-265,-264,-266,-266,-264,-263,-263,-258,-253,-249,-245,-237,-231,-229,-228,-224,-217,-209,-199,-189,-178,-170,-167,-164,-159,-153,-148,-144,-142,-140,-136,-134,-133,-133,-133,-133,-133,-133,-126,-116,-112,-111,-111,-111,-111,-111,-110,-108,-106,-103,-101,-98,-96,-93,-92,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-90,-88,-86,-83,-82,-81,-81,-81,-82,-84,-86,-88,-92,-96,-101,-106,-109,-110,-111,-111,-111,-111,-111,-111,-111,-111,-111,-111,-111,-111,-111,-111,-112,-114,-116,-118,-120,-120,-121,-121,-119,-115,-111,-106,-103,-102,-101,-101,-101,-101,-101,-101,-101,-101,-101,-101,-102,-104,-106,-108,-110,-110,-111,-111,-110,-108,-106,-103,-102,-101,-101,-101,-101,-101,-101,-101,-102,-104,-106,-108,-110,-110,-111,-111,-110,-108,-106,-103,-101,-98,-96,-93,-92,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-91,-92,-94,-96,-98,-100,-100,-101,-101,-101,-101,-101,-101,-100,-98,-96,-93,-91,-88,-86,-83,-82,-81,-81,-81,-81,-81,-81,-81,-81,-81,-81,-81,-80,-79,-79,-78,-78,-82,-91,-98,-103,-107,-110,-111,-113,-119,-124,-129,-135,-140,-145,-156,-164,-167,-169,-173,-178,-180,-181,-184,-187,-189,-198,-207,-210,-209,-204,-196,-191,-186,-179,-172,-167,-162,-156,-150,-141,-128,-114,-102,-93,-85,-82,-80,-75,-71,-72,-71,-67,-62,-61,-61,-61,-60,-60,-60,-60,-60,-62,-65,-68,-71,-72,-69,-68,-65,-60,-56,-55,-51,-43,-42,-43,-39,-31,-24,-24,-30,-40,-50,-45,-40,-43,-50,-48,-45,-33,-23,-45,-53,-53,-48,-70,-98,-165,-213,-253,-300,-370,-465,-487,-485,-487,-475,-455,-437,-413,-375,-320,-263,-203,-158,-115,-63,-15,10,20,20,20,15,-23,-33,-33,-45,-55,-55,-65,-70,-78,-84,-89,-93,-96,-95,-90,-87,-86,-84,-84,-87,-91,-96,-97,-96,-96,-95,-92,-87,-85,-86,-88,-90,-91,-94,-96,-95,-94,-97,-99,-99,-99,-103,-108,-108,-105,-105,-107,-106,-105,-110,-117,-125,-132,-138,-141,-143,-139,-133,-130,-129,-128,-129,-130,-133,-138,-140,-142,-143,-146,-149,-151,-155,-160,-163,-163,-163,-166,-167,-169,-173,-176,-180,-186,-192,-195,-195,-194,-193,-195,-199,-203,-208,-213,-215,-218,-223,-226,-229,-235,-241,-245,-246,-245,-246,-249,-252,-250,-247,-246,-244,-239,-236,-234,-229,-223,-219,-216,-209,-206,-207,-207,-203,-197,-189,-178,-166,-153,-142,-138,-134,-129,-123,-119,-117,-117,-117,-115,-111,-109,-108,-108,-108,-108,-108,-96,-79,-74,-72,-71,-71,-71,-71,-69,-65,-61,-56,-53,-52,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-52,-54,-56,-58,-60,-60,-61,-61,-61,-61,-61,-61,-61,-61,-61,-61,-61,-61,-61,-61,-62,-64,-66,-68,-71,-73,-76,-78,-80,-80,-81,-81,-81,-81,-81,-81,-81,-81,-81,-81,-80,-78,-76,-73,-71,-68,-66,-63,-62,-61,-61,-61,-60,-58,-56,-53,-52,-51,-51,-51,-51,-51,-51,-51,-52,-54,-56,-58,-61,-63,-66,-68,-70,-70,-71,-71,-69,-65,-61,-56,-53,-52,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-51,-52,-54,-56,-58,-60,-60,-61,-61,-59,-55,-51,-46,-41,-37,-36,-36,-36,-36,-36,-40,-46,-47,-48,-48,-52,-61,-68,-74,-80,-85,-88,-93,-99,-104,-109,-113,-114,-115,-121,-126,-127,-129,-134,-139,-140,-141,-145,-146,-146,-152,-159,-161,-159,-154,-146,-141,-136,-129,-122,-117,-112,-106,-100,-91,-81,-69,-59,-52,-45,-42,-40,-35,-31,-32,-31,-27,-22,-21,-21,-21,-20,-20,-20,-19,-16,-14,-13,-11,-13,-12,-9,-8,-7,-5,-3,-6,-7,-6,-12,-20,-23,-21,-19,-20,-21,-17,-8,5,10,7,0,-18,-15,-13,7,15,37,37,22,-10,-28,-65,-133,-193,-270,-370,-465,-477,-505,-527,-495,-465,-437,-393,-345,-300,-213,-143,-98,-45,-3,45,80,80,70,60,45,17,7,-13,-35,-45,-15,-15,-20,-29,-37,-44,-50,-56,-55,-50,-47,-46,-44,-44,-47,-51,-56,-57,-56,-57,-58,-57,-54,-54,-56,-58,-60,-61,-64,-66,-65,-64,-67,-69,-69,-69,-73,-78,-78,-74,-72,-72,-69,-65,-67,-72,-77,-83,-88,-92,-93,-90,-85,-85,-86,-88,-89,-90,-94,-99,-101,-102,-104,-107,-112,-117,-123,-130,-133,-133,-133,-135,-134,-134,-136,-137,-140,-146,-152,-157,-161,-164,-168,-173,-178,-183,-188,-193,-195,-198,-203,-205,-206,-210,-213,-216,-216,-216,-216,-219,-222,-220,-217,-216,-216,-214,-213,-213,-208,-203,-199,-195,-187,-181,-179,-179,-177,-173,-167,-158,-149,-138,-130,-127,-124,-119,-113,-108,-104,-102,-100,-95,-92,-89,-88,-88,-88,-88,-88,-69,-43,-35,-32,-31,-31,-31,-31,-32,-34,-36,-38,-40,-40,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-40,-38,-36,-33,-32,-31,-31,-31,-32,-34,-36,-38,-40,-40,-41,-41,-42,-44,-46,-48,-51,-53,-56,-58,-60,-60,-61,-61,-61,-61,-61,-61,-59,-55,-51,-46,-43,-42,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-40,-38,-36,-33,-32,-31,-31,-31,-31,-31,-31,-31,-32,-34,-36,-38,-40,-40,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-41,-40,-38,-36,-33,-32,-31,-31,-31,-31,-31,-31,-31,-31,-31,-31,-31,-31,-31,-31,-31,-31,-31,-31,-31,-31,-31,-31,-31,-31,-31,-31,-31,-31,-31,-31,-31,-31,-33,-37,-38,-38,-38,-42,-51,-58,-61,-65,-67,-69,-73,-79,-84,-91,-99,-104,-110,-119,-125,-127,-129,-133,-136,-135,-134,-136,-136,-136,-142,-149,-151,-149,-144,-136,-131,-126,-119,-112,-107,-102,-96,-90,-81,-71,-59,-49,-39,-30,-25,-21,-15,-12,-12,-12,-10,-7,-9,-10,-11,-10,-10,-9,-6,-2,1,2,1,-2,-2,1,2,3,5,6,1,-1,-1,-6,-12,-14,-11,-9,-10,-11,-14,-18,-5,10,7,10,42,15,7,17,35,57,57,32,10,-18,-55,-103,-163,-230,-330,-425,-447,-445,-447,-425,-405,-387,-373,-335,-280,-223,-163,-128,-75,-23,15,70,90,100,90,55,27,17,7,-45,-25,-15,5,10,0,-10,-19,-28,-35,-35,-30,-27,-26,-24,-24,-27,-31,-36,-37,-36,-37,-38,-37,-34,-34,-36,-38,-40,-41,-44,-46,-45,-45,-50,-55,-57,-58,-62,-68,-68,-64,-62,-62,-59,-55,-57,-62,-67,-74,-81,-87,-90,-89,-85,-85,-86,-87,-86,-85,-86,-89,-91,-93,-94,-97,-102,-107,-113,-120,-123,-123,-123,-126,-125,-124,-126,-127,-130,-136,-142,-146,-148,-149,-151,-154,-161,-168,-176,-182,-185,-188,-193,-196,-199,-205,-211,-215,-216,-215,-216,-217,-216,-210,-202,-198,-197,-194,-193,-192,-186,-178,-172,-166,-157,-151,-149,-150,-150,-148,-144,-138,-129,-118,-110,-105,-99,-89,-78,-70,-64,-62,-60,-55,-52,-49,-48,-48,-48,-48,-48,-39,-27,-23,-21,-22,-24,-26,-28,-29,-27,-26,-23,-20,-15,-11,-6,-3,-2,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-2,-4,-6,-8,-10,-10,-11,-11,-11,-11,-11,-11,-11,-11,-11,-11,-11,-11,-11,-11,-11,-11,-11,-11,-12,-14,-16,-18,-20,-20,-21,-21,-20,-18,-16,-13,-12,-11,-11,-11,-11,-11,-11,-11,-10,-8,-6,-3,-2,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,0,2,4,7,8,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,10,12,14,17,18,19,19,19,18,16,14,12,10,10,9,9,10,12,14,17,18,19,19,19,19,19,19,19,19,19,18,17,17,17,18,16,10,7,6,5,5,4,2,-2,-6,-13,-19,-22,-25,-33,-41,-45,-48,-53,-58,-60,-60,-62,-61,-59,-63,-70,-71,-69,-64,-56,-51,-46,-39,-32,-27,-22,-16,-10,-1,9,21,31,38,44,47,49,55,58,58,58,63,68,68,69,69,70,72,75,81,88,91,91,91,88,87,87,86,85,86,87,84,84,87,85,82,83,87,90,89,88,85,82,85,70,67,90,102,115,117,107,105,107,107,92,90,62,25,-22,-82,-190,-280,-375,-387,-415,-437,-415,-375,-327,-312,-265,-210,-133,-63,-18,25,87,135,190,200,200,190,195,148,118,108,95,95,105,105,90,83,78,76,74,72,74,79,82,83,85,85,83,78,71,68,66,64,62,63,65,66,66,66,67,68,69,69,72,75,72,70,70,71,67,61,62,65,64,62,63,65,62,57,52,46,38,33,30,31,35,35,34,32,31,30,26,21,19,18,16,15,14,14,12,9,6,7,6,3,2,1,-3,-6,-10,-16,-22,-26,-28,-29,-31,-34,-38,-43,-48,-53,-55,-57,-62,-65,-66,-69,-73,-76,-76,-75,-76,-79,-81,-79,-76,-75,-73,-69,-66,-64,-59,-53,-49,-45,-37,-31,-29,-28,-24,-17,-9,2,14,27,38,42,45,50,56,62,66,68,70,74,76,76,77,77,77,77,77,88,102,107,108,110,112,114,117,118,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,120,122,124,127,128,129,129,129,128,126,124,122,120,120,119,119,119,119,119,119,119,119,119,119,120,122,124,127,128,129,129,129,129,129,129,129,129,129,129,129,128,126,124,122,120,120,119,119,120,122,124,127,128,129,129,129,129,129,129,129,129,129,129,129,129,129,129,129,129,129,129,129,130,132,134,137,138,139,139,139,139,139,139,139,140,142,144,147,148,149,149,149,149,149,149,149,149,149,149,149,149,149,149,149,148,146,144,142,139,137,134,132,130,130,129,129,131,135,139,144,147,148,149,149,149,149,149,149,149,149,149,149,149,149,149,149,149,149,152,155,156,157,158,156,148,142,136,130,124,119,112,103,97,91,87,86,85,79,74,73,71,66,61,60,59,55,54,54,48,41,39,41,46,54,59,64,72,80,88,95,104,112,123,137,150,160,168,175,177,177,180,181,179,179,183,188,189,189,189,190,190,190,191,193,193,192,191,188,188,191,192,193,196,200,200,202,206,202,197,196,199,201,200,199,196,193,205,200,198,200,183,185,198,208,215,218,228,223,200,183,145,108,58,-20,-140,-235,-227,-225,-237,-245,-225,-217,-182,-125,-70,8,78,103,145,198,265,300,290,290,290,295,268,198,208,215,205,185,185,190,182,176,171,167,164,165,170,173,174,176,176,173,169,164,163,164,163,162,163,166,166,164,162,160,159,156,154,155,156,153,151,151,151,147,142,142,145,145,143,144,146,143,138,133,127,122,118,117,120,125,125,124,122,121,120,116,112,112,113,114,113,111,109,105,100,97,97,97,94,93,91,87,83,77,69,60,55,52,51,49,46,42,37,32,27,25,22,17,15,14,10,7,4,4,4,4,1,-2,0,3,4,4,6,7,7,12,17,21,25,33,39,41,42,46,53,61,72,86,102,115,122,125,130,136,141,143,143,143,144,146,147,147,147,147,147,147,161,180,186,188,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,188,186,184,182,180,180,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,180,182,184,187,188,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,188,186,184,182,180,180,179,179,180,182,184,187,189,192,194,197,198,199,199,199,198,196,194,192,190,190,189,189,190,192,194,197,198,199,199,199,199,199,199,199,198,196,194,192,190,190,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,189,190,192,194,197,199,202,204,207,208,209,209,209,209,209,209,209,209,209,209,209,209,209,209,198,184,179,177,174,169,164,159,154,150,147,144,142,139,137,134,132,128,121,114,107,100,95,90,85,83,86,90,95,100,105,110,115,120,127,134,142,149,157,164,172,179,187,194,202,207,211,214,217,219,222,224,227,229,232,234,237,238,239,239,239,238,236,234,232,230,230,229,229,230,232,234,237,239,242,244,247,248,249,249,249,248,246,244,242,240,240,240,230,230,230,240,250,260,270,270,280,280,280,250,220,170,130,100,50,-60,-180,-250,-240,-230,-230,-200,-200,-160,-140,-50,0,90,150,160,210,250,290,320,330,340,330,310,280,240,240,240,220,200,220,220,213,210,210,209,209,209,209,209,208,206,204,202,200,200,199,199,199,199,199,199,198,196,194,192,189,187,184,182,180,180,179,179,179,179,179,179,178,176,174,172,169,167,164,162,160,160,159,159,159,159,159,159,158,156,154,152,147,141,134,127,121,118,115,112,110,107,105,102,100,97,95,92,89,84,80,75,71,68,65,62,60,57,55,52,49,44,40,35,32,31,30,30,31,33,35,37,40,42,45,47,50,52,55,57,62,68,75,82,90,97,105,112,121,130,140,149,158,164,169,174,178,181,184,187,189,192,194,197,199,202,203,204,204,204,204,204,206,208,209,209,212,218,224,232,237,238,239,239,239,239,239,239,239,239,239,239,239,239,239,239,238,236,234,232,230,230,229,229,229,229,229,229,229,229,229,229,229,229,229,229,229,229,229,229,230,232,234,237,238,239,239,239,238,236,234,232,230,230,229,229,228,226,224,222,220,220,219,219,221,225,229,234,208,141,61,12,3,1,0, COMMASEPARATE 5000 AVL 150,151,151,151,151,151,151,151,153,157,161,166,168,167,166,163,162,161,161,161,163,167,171,176,180,183,186,188,190,190,190,190,192,196,200,205,210,215,220,225,229,232,235,237,239,239,240,240,241,243,245,247,251,255,260,265,270,275,280,285,289,292,295,297,301,305,310,315,318,319,320,320,323,331,340,350,357,361,365,367,369,369,370,370,368,364,360,355,351,348,345,342,339,334,330,325,319,312,305,297,289,279,270,260,253,249,245,242,239,234,230,225,220,215,210,205,201,198,195,192,189,182,176,168,164,164,166,168,170,170,171,171,171,171,171,171,170,168,166,163,162,161,161,161,159,155,151,146,143,142,141,141,141,141,141,141,141,141,141,141,143,147,151,156,158,157,156,153,152,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,151,148,143,142,141,145,147,143,140,136,133,133,132,132,134,137,140,141,145,149,149,152,152,153,153,149,148,154,162,163,163,164,164,165,169,174,180,187,192,196,198,199,195,194,191,180,165,154,147,140,134,127,122,125,129,128,128,136,143,144,147,148,149,152,155,155,154,152,144,130,123,124,127,128,126,126,124,117,111,114,120,121,119,119,122,124,127,130,130,120,90,73,65,30,0,-20,-17,-32,-40,-30,23,93,148,185,243,275,298,325,408,590,698,768,703,573,470,378,263,160,100,78,40,20,28,5,-17,13,28,85,135,153,163,168,160,160,160,135,130,124,125,129,131,131,131,133,134,134,134,132,127,124,122,122,123,125,127,130,132,132,130,128,127,125,126,129,135,137,135,132,132,134,139,146,148,146,146,148,150,151,150,145,144,147,151,150,150,150,152,151,149,147,149,152,151,149,149,153,159,166,172,176,180,187,192,196,198,200,200,203,208,211,212,215,222,231,240,245,248,250,255,261,266,268,268,267,268,270,273,275,284,293,294,293,291,291,292,292,292,291,288,283,276,274,269,260,253,249,242,236,229,220,213,209,200,190,180,173,168,166,162,155,149,145,144,145,144,140,135,135,135,135,135,135,135,127,116,112,111,111,111,111,111,113,117,121,126,128,127,126,123,122,121,121,121,119,115,111,106,103,102,101,101,104,112,121,131,134,129,120,111,104,102,101,101,100,98,96,93,92,91,91,91,91,91,91,91,89,85,81,76,73,72,71,71,71,71,71,71,71,71,71,71,69,65,61,56,53,52,51,51,51,51,51,51,51,51,51,51,51,51,51,51,53,57,61,66,69,70,71,71,71,71,71,71,71,71,71,71,71,71,71,71,70,68,66,63,64,67,71,76,79,80,81,81,82,84,86,88,88,84,81,76,73,72,71,71,71,71,71,77,86,90,90,94,96,93,90,86,84,84,85,86,85,84,81,77,77,79,79,82,82,83,83,79,78,84,93,99,103,109,112,115,119,124,128,131,132,131,130,129,125,124,122,113,102,98,98,101,103,101,100,101,103,99,97,100,102,99,98,98,101,107,115,120,121,119,111,101,99,107,114,117,116,116,114,107,101,104,110,111,110,114,120,123,125,110,80,100,70,43,15,-20,-50,-70,-67,-52,-60,-40,13,13,108,185,233,265,248,255,347,530,638,708,673,553,450,357,262,160,120,128,90,90,78,45,23,23,38,95,85,93,103,108,90,100,100,95,90,83,80,79,76,71,66,63,59,57,58,57,55,54,57,62,68,73,76,79,82,81,78,73,69,66,66,70,75,76,74,71,71,72,75,80,79,76,76,77,79,81,81,79,80,85,84,80,75,72,73,72,69,68,71,77,78,78,79,83,89,93,93,90,86,89,92,96,98,100,100,103,108,111,112,115,122,130,137,140,140,139,140,141,141,142,144,147,153,158,163,165,174,183,184,183,181,181,182,182,182,183,184,183,181,182,179,170,163,159,154,151,147,139,133,129,120,110,100,93,88,86,82,75,69,66,67,70,72,68,65,65,65,65,65,65,65,59,53,51,51,51,51,51,51,49,45,41,36,33,32,31,31,31,31,31,31,31,31,31,31,31,31,31,31,33,39,46,53,59,60,60,60,59,55,51,46,43,42,41,41,42,44,46,48,50,50,50,50,50,50,50,50,50,50,50,50,49,45,41,36,32,29,26,23,22,21,21,21,22,24,26,28,30,30,31,31,30,28,26,23,25,32,41,50,53,48,40,31,25,25,26,28,30,30,31,31,33,37,41,46,48,47,45,42,41,40,40,40,39,35,31,26,23,22,21,21,21,21,21,21,19,17,16,16,16,16,16,8,-4,-7,-9,-5,-2,-1,1,2,1,1,-1,-3,-3,-1,1,2,7,14,20,27,29,27,23,14,10,15,24,29,32,36,37,38,40,49,58,66,70,70,69,69,64,61,57,46,31,22,18,16,14,12,10,11,12,4,-3,-5,-6,-10,-11,-12,-11,-8,-5,-5,-7,-11,-19,-29,-31,-23,-16,-13,-13,-11,-10,-15,-17,-10,0,6,7,6,5,1,-2,-12,-30,-40,-40,-57,-105,-120,-140,-140,-137,-152,-180,-180,-117,-68,-13,35,132,165,147,175,297,480,547,637,632,482,370,277,192,90,30,-23,-60,-50,-52,-75,-57,-37,-2,25,15,33,33,38,40,20,10,-5,-10,-16,-15,-11,-9,-9,-9,-7,-6,-7,-9,-13,-20,-26,-28,-28,-27,-24,-18,-10,-3,0,-1,-2,-3,-5,-4,-1,5,6,4,1,1,1,3,5,2,-3,-4,-3,-1,1,1,-1,0,7,10,10,10,10,12,11,9,8,11,17,18,18,19,23,29,34,36,35,34,38,42,46,48,50,50,53,58,61,62,65,72,80,87,90,90,90,95,101,106,109,111,112,115,118,117,116,119,124,122,118,114,112,112,112,112,113,114,113,111,112,109,100,93,87,79,71,62,51,44,39,30,19,7,-2,-10,-13,-18,-25,-31,-34,-33,-30,-28,-31,-34,-34,-34,-34,-34,-34,-34,-40,-46,-48,-49,-48,-46,-44,-42,-40,-40,-39,-39,-41,-45,-49,-54,-57,-58,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-80,-82,-84,-87,-87,-86,-84,-82,-80,-80,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-79,-79,-79,-79,-80,-82,-84,-87,-88,-89,-89,-89,-89,-89,-89,-89,-89,-89,-89,-89,-90,-91,-92,-92,-92,-92,-92,-88,-82,-80,-79,-76,-74,-77,-80,-84,-87,-87,-89,-91,-91,-90,-89,-88,-85,-81,-81,-78,-78,-77,-77,-81,-82,-76,-67,-61,-57,-51,-48,-45,-41,-36,-32,-29,-29,-32,-36,-39,-44,-46,-48,-57,-70,-78,-82,-84,-86,-88,-90,-89,-87,-91,-93,-90,-88,-91,-92,-92,-91,-88,-85,-85,-86,-88,-93,-102,-102,-93,-86,-83,-86,-90,-97,-108,-118,-118,-115,-117,-120,-120,-120,-121,-122,-115,-100,-110,-120,-138,-145,-180,-190,-210,-218,-243,-240,-230,-178,-128,-73,-65,-8,25,67,95,177,340,447,517,532,402,280,187,92,-10,-70,-123,-170,-180,-143,-185,-188,-158,-132,-115,-85,-57,-47,-53,-50,-60,-60,-75,-90,-97,-100,-101,-104,-107,-106,-102,-98,-96,-93,-93,-96,-97,-96,-93,-90,-86,-84,-81,-78,-80,-86,-92,-98,-103,-104,-101,-95,-93,-95,-98,-98,-97,-94,-89,-90,-93,-93,-92,-90,-88,-88,-90,-89,-83,-80,-80,-80,-80,-81,-84,-88,-91,-88,-83,-82,-81,-78,-72,-63,-57,-55,-55,-56,-52,-48,-44,-42,-40,-40,-37,-32,-29,-28,-25,-18,-10,-3,0,0,0,5,11,16,20,24,27,33,37,40,40,46,54,54,53,51,52,55,57,60,59,53,43,31,25,17,4,-5,-10,-15,-19,-23,-29,-31,-31,-35,-41,-50,-57,-62,-64,-68,-75,-81,-85,-86,-85,-86,-89,-92,-91,-90,-90,-90,-90,-90,-93,-98,-98,-99,-100,-104,-109,-114,-117,-118,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-120,-122,-124,-127,-128,-129,-129,-129,-130,-132,-134,-137,-138,-139,-139,-139,-139,-139,-139,-139,-137,-133,-129,-124,-121,-120,-119,-119,-121,-125,-129,-134,-137,-138,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-140,-142,-144,-147,-148,-149,-149,-149,-149,-149,-149,-149,-148,-146,-144,-142,-140,-140,-139,-139,-140,-142,-144,-147,-148,-149,-149,-149,-151,-155,-159,-164,-167,-168,-169,-169,-167,-163,-159,-154,-151,-150,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-150,-152,-154,-157,-158,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-160,-162,-164,-164,-164,-164,-164,-164,-166,-168,-169,-169,-166,-164,-167,-170,-174,-177,-177,-179,-181,-181,-180,-178,-175,-168,-160,-156,-151,-151,-153,-155,-160,-162,-156,-147,-141,-137,-131,-128,-127,-127,-126,-127,-127,-128,-129,-130,-129,-129,-126,-123,-129,-140,-148,-152,-155,-158,-163,-168,-168,-166,-170,-173,-170,-168,-171,-172,-172,-171,-168,-165,-166,-169,-173,-181,-191,-191,-183,-176,-173,-174,-174,-176,-183,-189,-186,-180,-179,-178,-175,-172,-172,-173,-175,-180,-190,-180,-198,-205,-260,-280,-310,-308,-323,-340,-340,-298,-238,-183,-155,-88,-55,-43,-15,67,220,347,447,442,312,210,127,32,-70,-120,-193,-230,-240,-243,-265,-228,-198,-203,-175,-125,-118,-128,-143,-140,-150,-160,-175,-170,-178,-185,-191,-199,-204,-203,-197,-191,-186,-183,-183,-186,-187,-188,-188,-187,-187,-189,-191,-193,-196,-199,-202,-203,-203,-199,-191,-180,-175,-176,-178,-178,-177,-174,-169,-170,-173,-173,-172,-170,-168,-168,-170,-169,-162,-159,-159,-159,-159,-157,-158,-160,-161,-158,-152,-152,-154,-156,-157,-156,-154,-153,-154,-155,-152,-148,-144,-142,-140,-140,-137,-132,-129,-128,-125,-118,-110,-103,-100,-100,-100,-95,-89,-84,-80,-76,-73,-67,-62,-57,-55,-46,-37,-36,-37,-39,-38,-33,-28,-23,-20,-22,-27,-34,-36,-41,-50,-57,-61,-66,-69,-73,-81,-87,-91,-100,-110,-120,-127,-132,-134,-138,-145,-151,-154,-155,-154,-155,-157,-158,-156,-154,-154,-154,-154,-154,-159,-165,-168,-168,-169,-169,-169,-169,-168,-166,-164,-162,-162,-166,-169,-174,-177,-178,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-181,-185,-189,-194,-197,-198,-199,-199,-197,-193,-189,-184,-180,-177,-174,-172,-170,-170,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-170,-172,-174,-177,-178,-179,-179,-179,-180,-182,-184,-187,-188,-189,-189,-189,-189,-189,-189,-189,-189,-189,-189,-189,-187,-183,-179,-174,-171,-170,-169,-169,-171,-175,-179,-184,-187,-188,-189,-189,-189,-189,-189,-189,-187,-183,-179,-174,-171,-170,-169,-169,-171,-175,-179,-184,-186,-185,-184,-182,-180,-180,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-180,-182,-184,-187,-188,-189,-189,-189,-189,-189,-189,-189,-188,-186,-184,-182,-182,-186,-189,-194,-197,-198,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-196,-191,-190,-189,-186,-184,-187,-190,-193,-194,-192,-192,-191,-191,-190,-188,-186,-180,-175,-172,-169,-167,-166,-167,-171,-172,-166,-157,-152,-150,-147,-145,-144,-141,-136,-132,-129,-128,-129,-130,-131,-135,-136,-138,-145,-157,-162,-165,-165,-166,-168,-170,-169,-167,-171,-173,-170,-168,-171,-172,-172,-171,-168,-165,-166,-169,-173,-181,-191,-191,-183,-176,-173,-171,-169,-168,-174,-181,-181,-177,-177,-175,-170,-165,-162,-162,-166,-170,-180,-190,-208,-215,-250,-300,-340,-338,-353,-330,-330,-288,-228,-173,-105,-48,-35,-23,-25,67,270,377,457,462,332,230,127,22,-80,-120,-173,-210,-220,-213,-225,-238,-228,-213,-175,-165,-158,-168,-153,-150,-160,-160,-155,-160,-167,-170,-171,-174,-177,-179,-177,-176,-175,-173,-173,-175,-177,-178,-178,-177,-174,-170,-165,-160,-159,-160,-162,-163,-165,-164,-161,-155,-153,-155,-158,-158,-157,-154,-149,-150,-153,-153,-152,-150,-149,-150,-155,-156,-152,-148,-149,-149,-148,-145,-143,-142,-142,-138,-132,-132,-132,-131,-127,-121,-116,-114,-115,-116,-112,-108,-104,-102,-100,-100,-97,-92,-89,-88,-85,-78,-72,-69,-70,-75,-78,-74,-69,-64,-60,-56,-53,-47,-40,-32,-25,-11,1,3,2,1,1,2,2,2,2,1,-2,-6,-7,-11,-21,-27,-31,-38,-44,-51,-60,-67,-71,-80,-90,-97,-102,-105,-105,-109,-116,-122,-125,-128,-129,-132,-139,-143,-144,-144,-144,-144,-144,-144,-150,-156,-158,-159,-159,-159,-159,-159,-160,-162,-164,-167,-168,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-170,-172,-174,-177,-178,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-177,-173,-169,-164,-162,-163,-164,-167,-168,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-168,-166,-164,-162,-162,-166,-169,-174,-177,-178,-179,-179,-178,-176,-174,-172,-170,-170,-169,-169,-169,-169,-169,-169,-170,-172,-174,-177,-176,-173,-169,-164,-161,-160,-159,-159,-160,-162,-164,-167,-168,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-169,-170,-172,-174,-177,-178,-179,-179,-179,-178,-174,-169,-164,-162,-162,-162,-162,-162,-162,-162,-161,-160,-160,-159,-156,-154,-157,-160,-164,-167,-167,-169,-171,-171,-170,-168,-167,-164,-160,-160,-157,-157,-156,-156,-160,-161,-155,-146,-141,-137,-131,-128,-125,-121,-116,-112,-109,-108,-109,-110,-111,-114,-115,-117,-126,-139,-147,-152,-154,-156,-158,-160,-159,-157,-161,-163,-160,-158,-161,-162,-162,-161,-158,-155,-155,-158,-163,-173,-187,-190,-183,-176,-173,-174,-174,-176,-183,-187,-180,-170,-164,-163,-162,-160,-161,-162,-169,-180,-190,-200,-218,-255,-280,-300,-320,-308,-323,-300,-300,-248,-198,-163,-125,-58,-35,7,65,187,370,507,567,502,352,230,117,22,-60,-100,-173,-210,-240,-243,-255,-258,-228,-223,-195,-165,-148,-128,-103,-110,-120,-120,-135,-140,-148,-153,-156,-161,-167,-169,-167,-166,-165,-163,-163,-165,-167,-168,-168,-167,-165,-163,-160,-158,-158,-160,-162,-163,-165,-164,-161,-155,-153,-155,-158,-158,-157,-154,-149,-150,-152,-150,-147,-142,-139,-138,-140,-139,-132,-129,-129,-129,-129,-127,-128,-130,-131,-128,-122,-121,-121,-120,-116,-110,-104,-98,-94,-90,-83,-78,-74,-72,-72,-76,-77,-77,-77,-77,-75,-68,-59,-48,-40,-35,-31,-25,-19,-14,-10,-6,-3,3,8,13,15,24,33,34,33,31,31,32,32,32,31,28,23,16,14,9,0,-7,-11,-16,-19,-23,-32,-40,-47,-57,-70,-80,-87,-92,-94,-98,-105,-111,-114,-115,-114,-115,-119,-123,-124,-124,-124,-124,-124,-124,-132,-144,-147,-149,-149,-149,-149,-149,-150,-152,-154,-157,-158,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-158,-156,-154,-152,-150,-150,-149,-149,-150,-152,-154,-157,-158,-159,-159,-159,-160,-162,-164,-167,-166,-163,-159,-154,-151,-150,-149,-149,-149,-149,-149,-149,-150,-152,-154,-157,-158,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-158,-156,-154,-152,-150,-150,-149,-149,-149,-149,-149,-149,-150,-152,-154,-157,-158,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-158,-156,-154,-152,-150,-150,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-149,-146,-144,-147,-151,-159,-167,-172,-178,-181,-181,-180,-177,-174,-169,-163,-161,-158,-157,-156,-155,-157,-156,-147,-137,-132,-127,-121,-118,-115,-111,-106,-102,-99,-98,-99,-100,-101,-104,-105,-107,-116,-129,-137,-143,-150,-156,-163,-168,-168,-166,-170,-171,-164,-157,-156,-153,-152,-151,-148,-147,-151,-156,-163,-172,-181,-182,-173,-166,-163,-164,-164,-165,-167,-169,-161,-152,-150,-152,-151,-150,-151,-152,-155,-160,-170,-200,-218,-245,-250,-270,-300,-298,-303,-310,-310,-268,-208,-153,-115,-58,-5,17,45,137,310,417,497,452,322,220,137,62,-40,-110,-163,-210,-240,-233,-225,-208,-218,-192,-165,-155,-138,-118,-113,-120,-100,-120,-115,-140,-147,-148,-146,-146,-149,-149,-147,-146,-145,-143,-143,-145,-147,-148,-148,-147,-145,-143,-140,-138,-138,-140,-142,-143,-145,-144,-141,-135,-133,-138,-143,-146,-146,-143,-139,-140,-142,-140,-137,-132,-129,-128,-130,-129,-120,-113,-109,-104,-100,-97,-98,-100,-103,-103,-102,-106,-110,-109,-105,-100,-94,-88,-84,-80,-73,-68,-64,-62,-60,-57,-52,-45,-40,-39,-36,-28,-20,-13,-10,-10,-10,-8,-4,-2,1,4,7,13,20,28,35,49,61,63,62,61,60,59,57,55,52,49,43,36,32,24,10,-2,-10,-15,-19,-23,-30,-34,-37,-42,-51,-60,-67,-72,-75,-81,-89,-98,-103,-105,-104,-105,-109,-113,-114,-114,-114,-114,-114,-114,-123,-133,-137,-138,-137,-133,-129,-124,-120,-117,-114,-112,-111,-113,-114,-117,-118,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-120,-122,-124,-127,-128,-129,-129,-129,-129,-129,-129,-129,-129,-129,-129,-129,-129,-129,-129,-129,-129,-129,-129,-129,-127,-123,-119,-114,-111,-110,-109,-109,-111,-115,-119,-124,-127,-128,-129,-129,-129,-129,-129,-129,-128,-126,-124,-122,-120,-120,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-118,-116,-114,-112,-110,-110,-109,-109,-109,-109,-109,-109,-109,-109,-109,-109,-109,-109,-109,-109,-109,-109,-109,-109,-109,-109,-109,-109,-109,-109,-109,-109,-109,-109,-109,-109,-109,-109,-109,-109,-108,-106,-104,-102,-100,-100,-99,-99,-100,-102,-104,-107,-108,-109,-109,-109,-108,-106,-104,-102,-100,-100,-99,-99,-99,-99,-99,-99,-99,-99,-97,-95,-94,-94,-90,-86,-88,-92,-99,-104,-106,-107,-106,-104,-99,-92,-87,-79,-70,-65,-57,-52,-48,-48,-52,-52,-48,-42,-41,-42,-40,-37,-35,-31,-26,-22,-19,-18,-19,-20,-21,-24,-25,-27,-36,-49,-57,-62,-64,-66,-68,-70,-69,-67,-71,-73,-70,-68,-71,-72,-72,-71,-69,-69,-70,-74,-77,-84,-92,-92,-82,-70,-62,-59,-56,-57,-63,-69,-67,-65,-69,-76,-79,-79,-81,-82,-85,-90,-80,-80,-97,-135,-190,-230,-250,-207,-233,-230,-230,-177,-97,-42,-5,53,75,117,185,277,460,567,627,582,472,400,287,192,90,40,-33,-70,-80,-92,-105,-117,-107,-92,-65,-15,-17,-17,-12,-10,-10,-30,-45,-30,-39,-46,-51,-59,-65,-68,-67,-66,-65,-63,-63,-65,-66,-63,-58,-52,-47,-44,-41,-38,-40,-46,-52,-58,-62,-61,-55,-48,-43,-46,-48,-48,-47,-44,-39,-40,-43,-43,-42,-40,-38,-38,-40,-39,-30,-23,-19,-14,-10,-7,-8,-10,-11,-8,-2,-1,-1,0,4,10,15,19,20,21,27,32,35,38,39,40,43,48,51,52,55,62,70,77,80,80,80,85,91,95,100,103,107,113,118,123,125,134,143,144,143,141,141,142,142,142,142,141,138,134,133,129,119,113,109,104,101,97,89,83,79,70,60,53,48,45,45,42,35,29,26,25,26,25,20,14,12,11,11,11,11,11,4,-5,-7,-9,-9,-7,-4,-2,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,-1,-5,-9,-14,-17,-18,-19,-19,-17,-13,-9,-4,-1,0,1,1,1,1,1,1,1,1,1,1,2,4,6,8,10,10,11,11,11,11,11,11,11,11,11,11,10,8,6,3,2,1,1,1,2,4,6,8,10,10,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,12,14,16,18,20,20,21,21,21,21,21,21,22,24,26,28,30,30,31,31,31,31,31,31,31,31,31,31,31,31,31,31,30,28,26,23,24,27,31,36,39,40,41,41,40,38,36,33,32,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,31,36,43,45,46,50,54,52,50,46,43,42,38,34,31,31,32,33,36,40,40,43,43,44,44,40,39,45,54,59,63,69,72,75,79,84,88,91,92,91,92,94,94,92,87,74,56,44,39,36,34,33,35,41,48,48,47,50,52,49,48,48,49,52,55,55,54,52,47,38,38,47,54,57,56,57,57,53,49,53,60,60,59,59,60,59,58,44,20,10,20,3,-25,-50,-70,-80,-77,-92,-90,-110,-47,13,48,85,153,185,228,235,327,530,638,708,663,533,420,327,242,140,90,18,-40,-50,-42,-35,-37,-47,-32,5,25,43,63,88,110,100,80,65,70,63,60,59,56,53,51,53,54,55,57,57,55,53,52,52,53,55,57,60,62,62,60,58,57,55,56,59,65,67,65,62,62,63,66,71,70,67,67,68,70,72,72,70,71,78,81,81,81,81,83,82,80,79,82,88,89,90,93,99,108,113,115,115,114,118,122,126,128,130,130,133,138,143,148,155,167,178,186,190,190,190,195,201,206,210,214,217,223,228,233,235,244,253,254,253,251,251,252,252,252,251,248,243,236,234,229,220,213,209,204,201,197,189,183,179,170,159,148,138,130,126,122,115,109,105,102,101,98,93,92,94,96,96,96,96,96,87,76,72,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,70,68,66,63,62,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,62,64,66,68,70,70,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,70,68,66,63,62,61,61,61,59,55,51,46,44,45,46,48,50,50,51,51,53,57,61,66,69,70,71,71,69,65,61,56,53,52,51,51,51,51,51,51,53,57,61,66,69,70,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,69,65,61,56,54,55,56,58,60,60,61,61,61,61,61,61,61,61,61,61,61,61,61,57,53,52,51,51,52,53,56,58,59,57,56,53,51,48,46,43,44,49,56,63,72,81,90,100,108,114,120,125,127,126,125,122,119,112,106,98,91,83,76,68,64,62,61,61,62,64,66,68,68,64,61,56,54,55,56,58,60,60,61,61,60,58,56,53,52,51,51,51,52,54,56,58,58,54,51,46,43,42,41,41,40,38,36,33,32,31,30,20,20,20,0,-50,-70,-90,-90,-110,-110,-80,-20,40,110,160,220,230,270,330,470,630,730,730,640,520,410,310,190,150,90,0,10,0,-20,-40,-10,0,40,60,70,100,90,90,90,70,110,70,70,64,62,61,61,61,61,61,61,60,58,56,53,52,51,51,51,51,51,51,51,53,57,61,66,68,67,66,63,62,61,61,61,61,61,61,61,63,67,71,76,78,77,76,73,72,71,71,71,71,71,71,71,73,77,81,86,92,99,106,113,120,125,130,135,140,145,150,155,160,165,170,175,179,182,185,187,191,195,200,205,210,215,220,225,229,232,235,237,239,239,240,240,241,243,245,247,247,243,240,235,230,225,220,215,209,202,195,187,180,172,165,157,148,136,125,113,104,99,96,93,93,94,96,98,98,94,91,86,81,76,73,72,71,71,71,71,68,63,62,61,60,60,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,60,58,56,53,52,51,51,51,51,51,51,51,51,51,51,51,51,51,51,51,51,51,51,51,49,45,41,36,33,32,31,31,33,37,41,46,49,50,51,51,50,48,46,43,42,41,41,41,38,30,21,11,0,-12,-25,-21,-7,-2,0, COMMASEPARATE 5000 AVF -390,-389,-389,-389,-389,-389,-389,-389,-390,-392,-394,-397,-396,-393,-389,-384,-381,-380,-379,-379,-380,-382,-384,-387,-387,-386,-384,-382,-380,-380,-379,-379,-380,-382,-384,-386,-389,-391,-394,-396,-397,-395,-394,-391,-390,-389,-389,-389,-388,-386,-384,-381,-381,-382,-384,-386,-389,-391,-394,-396,-397,-395,-394,-391,-391,-392,-394,-396,-398,-398,-399,-399,-400,-404,-409,-414,-416,-415,-414,-411,-410,-409,-409,-409,-408,-406,-404,-401,-401,-402,-404,-406,-407,-405,-404,-401,-400,-399,-399,-399,-397,-393,-389,-384,-382,-383,-384,-386,-387,-385,-384,-381,-379,-376,-374,-371,-371,-372,-374,-376,-378,-379,-379,-379,-381,-385,-389,-394,-397,-398,-399,-399,-399,-399,-399,-399,-397,-393,-389,-384,-381,-380,-379,-379,-378,-376,-374,-372,-370,-370,-369,-369,-369,-369,-369,-369,-369,-369,-369,-369,-370,-372,-374,-377,-376,-373,-369,-364,-361,-360,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-359,-350,-338,-334,-333,-337,-335,-322,-311,-302,-293,-287,-284,-282,-280,-280,-280,-277,-281,-283,-278,-276,-274,-273,-269,-260,-257,-263,-267,-269,-271,-268,-263,-264,-269,-279,-292,-302,-310,-319,-326,-331,-333,-337,-340,-336,-329,-327,-329,-331,-333,-332,-331,-342,-352,-353,-355,-365,-375,-375,-376,-377,-379,-382,-384,-385,-386,-385,-375,-360,-351,-351,-356,-358,-358,-360,-360,-351,-345,-350,-352,-347,-344,-347,-349,-342,-335,-325,-312,-315,-330,-310,-325,-292,-255,-237,-250,-242,-227,-217,-255,-312,-340,-340,-350,-362,-317,-245,-222,-392,-502,-560,-527,-417,-332,-275,-187,-110,-117,-185,-192,-175,-235,-280,-302,-362,-377,-395,-420,-410,-400,-395,-395,-395,-395,-360,-350,-335,-328,-324,-320,-315,-316,-322,-326,-326,-326,-321,-311,-302,-295,-295,-296,-298,-299,-302,-307,-307,-303,-299,-296,-293,-291,-292,-299,-302,-297,-292,-292,-295,-299,-302,-307,-310,-310,-310,-313,-315,-309,-297,-288,-285,-283,-279,-277,-280,-286,-286,-283,-279,-277,-277,-269,-261,-259,-261,-265,-268,-269,-268,-266,-266,-268,-272,-274,-273,-272,-273,-274,-274,-271,-268,-269,-275,-284,-290,-294,-295,-296,-297,-297,-296,-295,-294,-292,-293,-296,-295,-300,-306,-307,-307,-304,-301,-299,-302,-305,-304,-301,-298,-292,-290,-290,-286,-283,-282,-282,-280,-272,-262,-259,-261,-260,-260,-261,-264,-267,-269,-267,-265,-265,-266,-270,-272,-274,-271,-265,-263,-262,-261,-261,-261,-261,-267,-275,-278,-279,-279,-279,-279,-279,-280,-282,-284,-287,-286,-283,-279,-274,-271,-270,-269,-269,-268,-266,-264,-262,-260,-260,-259,-259,-261,-265,-269,-274,-276,-273,-269,-264,-261,-260,-259,-259,-257,-253,-249,-244,-241,-240,-239,-239,-239,-239,-239,-239,-238,-236,-234,-232,-230,-230,-229,-229,-229,-229,-229,-229,-229,-229,-229,-229,-228,-226,-224,-222,-220,-220,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-220,-222,-224,-227,-228,-229,-229,-229,-229,-229,-229,-229,-229,-229,-229,-229,-229,-229,-229,-229,-227,-223,-219,-214,-212,-213,-214,-217,-218,-219,-219,-219,-221,-225,-229,-234,-236,-235,-234,-232,-230,-230,-229,-229,-229,-229,-229,-220,-208,-204,-202,-206,-204,-192,-181,-172,-164,-162,-161,-160,-156,-152,-147,-141,-142,-143,-138,-136,-134,-133,-129,-120,-117,-123,-128,-132,-136,-136,-132,-134,-139,-149,-161,-169,-175,-181,-187,-191,-193,-197,-202,-202,-200,-205,-213,-220,-227,-229,-229,-235,-241,-237,-235,-242,-249,-247,-247,-247,-250,-254,-259,-263,-263,-259,-247,-234,-228,-235,-244,-248,-248,-250,-250,-241,-235,-240,-242,-237,-235,-240,-245,-245,-244,-223,-182,-245,-230,-190,-165,-132,-95,-77,-90,-142,-97,-107,-145,-152,-200,-220,-210,-192,-127,-45,-42,-212,-322,-380,-377,-287,-202,-145,-97,-50,-67,-105,-112,-165,-245,-270,-262,-262,-277,-295,-290,-290,-280,-275,-225,-215,-215,-220,-210,-194,-185,-179,-173,-165,-163,-167,-169,-169,-173,-172,-167,-162,-158,-160,-163,-167,-168,-172,-177,-175,-168,-159,-151,-145,-142,-143,-149,-152,-147,-142,-142,-143,-142,-142,-141,-141,-140,-139,-143,-147,-144,-137,-133,-133,-129,-123,-120,-121,-127,-126,-123,-120,-122,-127,-124,-119,-118,-121,-125,-125,-120,-113,-103,-99,-98,-102,-104,-103,-102,-103,-104,-104,-101,-98,-99,-103,-108,-110,-109,-105,-103,-102,-99,-98,-98,-99,-99,-102,-106,-105,-110,-116,-117,-117,-114,-111,-109,-112,-115,-115,-114,-113,-110,-109,-110,-106,-103,-103,-107,-110,-107,-100,-98,-101,-100,-100,-101,-104,-107,-109,-107,-105,-105,-106,-107,-107,-107,-104,-104,-106,-106,-106,-106,-106,-106,-113,-124,-127,-129,-129,-129,-129,-129,-128,-126,-124,-122,-120,-120,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-120,-120,-119,-119,-118,-116,-114,-112,-110,-110,-109,-109,-108,-106,-104,-102,-100,-100,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-98,-96,-94,-92,-88,-84,-79,-74,-71,-70,-69,-69,-71,-75,-79,-84,-87,-88,-89,-89,-87,-83,-79,-74,-73,-76,-79,-84,-85,-83,-79,-74,-73,-76,-79,-84,-87,-88,-89,-89,-90,-92,-94,-97,-96,-93,-89,-84,-81,-80,-79,-79,-78,-76,-74,-72,-70,-70,-69,-69,-69,-69,-69,-69,-68,-67,-67,-67,-67,-67,-67,-55,-40,-34,-33,-37,-35,-25,-17,-10,-2,2,8,14,21,24,27,30,24,14,9,3,-2,-1,5,16,20,15,10,6,5,9,16,20,18,7,-7,-18,-25,-32,-36,-41,-41,-41,-42,-37,-31,-33,-38,-43,-48,-49,-49,-55,-60,-54,-50,-55,-60,-58,-57,-57,-59,-62,-64,-65,-64,-59,-47,-34,-28,-35,-44,-48,-49,-56,-61,-57,-55,-63,-67,-65,-62,-62,-60,-53,-47,-36,-22,-25,-40,-20,45,37,55,62,50,57,82,82,55,37,10,-10,-40,-22,42,115,57,-112,-172,-270,-267,-117,-12,45,42,90,82,75,67,25,-15,-30,-72,-112,-137,-155,-120,-110,-110,-105,-95,-55,-35,-20,-40,-25,-18,-14,-10,-5,-6,-12,-16,-17,-19,-17,-10,-2,4,5,4,1,-1,-7,-15,-16,-13,-9,-6,-3,-1,-2,-9,-12,-7,-2,-2,-2,1,7,12,15,17,19,16,12,15,22,26,25,27,31,31,28,22,23,26,29,26,22,24,30,31,28,23,22,22,26,31,32,30,26,24,26,26,26,25,25,27,30,30,26,21,18,20,23,23,21,22,24,27,30,34,35,35,38,36,34,37,42,49,55,58,57,54,53,54,56,59,59,58,62,66,66,64,64,69,78,80,78,78,80,84,85,86,89,91,93,93,90,83,75,69,66,65,62,62,62,62,62,62,55,44,41,40,40,42,44,47,48,49,49,49,50,52,54,57,58,59,59,59,59,59,59,59,60,62,64,67,68,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,71,75,79,84,88,91,94,97,98,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,100,102,104,107,108,109,109,109,108,106,104,102,100,100,99,99,99,99,99,99,101,105,109,114,117,118,119,119,119,119,119,119,119,119,119,119,118,117,117,117,117,117,117,113,109,107,107,103,105,117,127,137,146,151,157,163,170,174,177,180,178,175,180,183,184,185,190,198,201,196,191,186,183,183,186,185,179,169,157,149,145,143,143,144,145,141,137,137,140,136,131,126,121,119,120,113,108,112,114,106,99,101,102,102,100,97,95,93,92,94,100,110,113,105,94,91,91,91,94,105,115,113,116,126,132,130,128,131,133,128,117,115,120,140,155,187,185,202,210,237,232,212,175,157,130,130,120,137,172,245,267,107,-32,-90,-97,12,107,165,212,260,282,275,287,255,135,160,127,97,62,35,20,10,20,45,55,75,75,90,120,134,143,149,156,162,158,147,137,132,127,127,132,139,147,149,150,151,150,147,141,142,148,154,160,164,167,166,160,156,161,166,166,165,166,166,167,167,168,169,165,161,164,171,175,175,177,181,181,179,178,183,191,197,195,192,194,198,195,188,178,174,173,176,181,182,180,176,174,176,176,176,175,175,177,180,180,176,171,168,170,173,173,171,172,172,171,170,169,168,168,174,174,171,171,172,174,175,173,167,159,157,161,166,174,179,184,193,201,203,201,199,202,208,207,203,201,199,198,195,191,190,191,193,193,192,189,186,184,185,187,188,187,187,187,187,187,181,174,170,170,170,172,174,177,178,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,181,185,189,194,197,198,199,199,201,205,209,214,217,218,219,219,219,219,219,219,218,216,214,212,210,210,209,209,210,212,214,217,218,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,221,225,229,234,237,238,239,239,239,239,239,239,237,233,229,224,221,220,219,219,221,225,229,234,237,238,239,239,240,242,244,247,248,249,249,249,248,246,244,242,240,240,239,239,239,239,239,239,239,239,239,239,239,239,239,239,241,245,249,254,257,258,259,259,259,259,259,259,259,259,259,259,258,256,255,254,254,254,254,254,255,256,257,257,253,255,267,277,287,296,301,307,313,320,324,326,329,324,320,323,326,330,336,345,356,360,356,351,346,343,343,346,346,342,334,325,318,313,307,302,297,293,286,279,278,280,276,271,265,257,254,252,243,237,241,244,236,229,231,232,232,230,227,225,225,228,234,245,258,262,254,244,241,240,238,238,248,254,249,246,250,248,240,233,232,234,239,247,245,240,260,275,347,365,372,360,367,372,372,325,287,260,280,280,297,332,405,427,317,167,80,82,192,277,315,362,410,382,385,347,315,275,260,207,147,172,155,130,160,180,195,205,225,245,260,250,267,284,299,316,327,321,307,292,283,277,277,282,287,293,294,293,292,293,292,289,290,295,299,302,304,305,301,292,287,292,296,296,295,296,296,297,297,298,299,295,291,294,301,305,304,306,310,311,308,302,302,305,308,306,301,304,311,313,313,311,311,312,315,320,322,320,316,314,316,316,316,315,315,317,320,320,316,311,308,310,313,313,311,312,312,311,310,309,307,303,304,299,293,291,292,294,297,297,292,286,285,287,291,296,298,298,302,306,305,302,299,302,309,310,308,308,308,308,305,301,300,301,303,303,302,298,296,294,293,292,288,287,287,287,287,287,284,280,279,279,279,279,279,279,277,273,269,264,262,263,264,267,268,269,269,269,269,269,269,269,269,269,269,269,270,272,274,277,278,279,279,279,278,276,274,272,271,273,274,277,278,279,279,279,279,279,279,279,279,279,279,279,279,279,279,279,281,285,289,294,297,298,299,299,298,296,294,292,290,290,289,289,289,289,289,289,289,289,289,289,288,286,284,282,280,280,279,279,280,282,284,287,288,289,289,289,289,289,289,289,288,286,284,282,280,280,279,279,280,282,284,287,286,283,279,274,271,270,269,269,269,269,269,269,269,269,269,269,269,269,269,269,269,269,269,269,269,269,269,269,269,269,269,269,269,269,269,269,269,269,269,269,271,275,279,284,287,288,289,289,289,289,289,289,287,283,279,274,272,273,274,277,278,279,279,279,279,279,279,279,279,279,279,279,275,269,268,267,263,265,277,287,296,301,301,302,304,310,314,316,321,319,320,327,332,334,335,340,348,351,346,341,338,339,344,351,354,349,339,327,319,313,307,302,298,296,291,287,285,284,276,266,257,251,249,250,243,238,242,244,236,229,231,232,232,230,227,225,225,228,234,245,258,262,254,244,240,235,228,223,228,231,224,219,219,213,200,188,186,192,205,220,225,230,250,265,297,345,372,360,397,382,382,335,297,270,270,260,287,322,395,397,217,107,30,12,122,207,285,352,400,382,375,367,335,275,240,227,207,192,155,150,180,200,185,195,215,215,220,230,244,253,259,266,273,273,267,262,260,256,256,261,267,273,274,273,270,265,256,246,243,245,249,252,255,257,256,250,246,251,256,256,255,256,256,257,257,258,259,255,253,259,271,280,283,285,289,291,286,277,272,270,269,266,261,264,270,271,268,263,262,262,266,271,272,270,266,264,266,266,266,265,265,267,270,270,267,264,263,267,272,272,271,272,272,271,270,269,266,260,259,251,244,242,242,244,247,249,247,244,243,242,241,241,240,239,243,246,246,247,249,257,267,269,268,268,267,263,255,246,242,242,244,244,244,244,246,249,253,253,252,252,252,252,252,252,245,234,231,230,229,229,229,229,228,226,224,222,220,220,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,221,225,229,234,237,238,239,239,239,239,239,239,239,239,239,239,239,239,239,239,238,236,234,232,232,236,239,244,247,248,249,249,249,249,249,249,249,249,249,249,247,243,239,234,232,233,234,237,238,239,239,239,237,233,229,224,221,220,219,219,219,219,219,219,221,225,229,234,236,235,234,232,230,230,229,229,228,226,224,222,220,220,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,219,221,225,229,234,237,238,239,239,236,228,219,209,202,198,198,197,197,197,197,200,205,206,207,203,205,217,227,237,246,251,257,263,270,274,276,280,277,275,280,282,284,285,289,298,301,295,290,286,283,283,286,285,279,269,257,249,243,237,232,228,225,221,216,216,219,216,211,206,201,199,200,193,188,192,194,186,179,181,182,182,180,177,175,173,173,176,185,197,202,195,184,181,180,178,178,188,193,186,181,184,185,182,179,181,183,185,187,185,190,210,255,297,315,332,300,307,262,262,225,207,190,190,190,227,262,305,277,107,-2,-40,-7,112,207,275,322,360,312,315,307,285,245,210,177,147,152,135,120,130,120,115,145,165,135,150,160,176,189,199,211,222,223,217,212,210,206,206,211,217,223,224,223,221,220,216,211,211,215,219,222,225,227,226,220,216,221,226,226,225,226,226,227,225,222,219,210,203,204,211,215,214,216,220,221,218,212,212,215,218,216,211,214,219,220,217,213,211,209,210,213,212,210,206,204,207,209,211,212,214,217,220,220,215,208,203,202,203,203,201,202,202,201,200,199,197,193,194,189,183,181,182,184,187,189,187,184,184,187,191,196,198,198,202,206,205,202,199,202,211,216,219,223,227,228,225,221,220,221,223,223,222,218,216,214,214,214,212,212,212,212,212,212,201,186,181,180,179,179,179,179,181,185,189,194,197,198,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,197,193,189,184,181,180,179,179,181,185,189,194,197,198,199,199,201,205,209,214,216,215,214,212,210,210,209,209,209,209,209,209,208,206,204,202,200,200,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,199,197,193,189,184,181,180,179,179,179,179,179,179,181,185,189,194,197,198,199,199,199,199,199,199,199,199,199,199,199,199,199,199,197,193,189,184,181,180,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,179,181,185,186,187,183,185,197,208,219,231,239,246,253,260,264,267,273,272,272,279,283,284,285,287,292,290,280,272,267,263,263,266,265,259,249,237,229,223,217,212,208,205,201,196,196,199,196,191,189,186,187,189,183,177,181,183,173,164,164,162,162,160,157,156,156,157,161,170,179,183,175,164,161,160,158,158,165,169,161,157,162,165,161,159,161,163,169,177,175,190,210,235,207,255,292,280,267,252,252,235,197,170,170,160,167,212,285,287,137,27,-50,-27,82,167,235,272,320,332,325,337,315,255,210,137,127,92,75,100,110,100,105,165,125,135,110,130,146,157,164,173,183,183,177,172,170,166,166,171,177,183,184,183,181,180,176,171,171,175,179,182,185,187,186,180,177,187,197,202,203,205,206,207,205,202,199,190,183,184,191,195,193,193,195,193,188,182,182,185,189,188,186,191,199,200,197,193,191,189,190,193,192,190,186,184,185,181,176,170,167,168,171,170,166,161,158,160,164,168,171,177,181,181,180,179,176,170,169,161,154,152,152,154,156,156,152,146,145,148,151,156,159,161,167,173,175,171,169,172,180,183,184,186,188,188,185,181,180,179,178,175,172,168,166,164,164,164,162,162,162,162,162,162,161,159,159,159,158,156,154,152,148,144,139,134,130,127,124,122,120,120,119,119,119,119,119,119,119,119,119,119,119,119,119,119,121,125,129,134,137,138,139,139,139,139,139,139,139,139,139,139,139,139,139,139,139,139,139,139,138,136,134,132,130,130,129,129,130,132,134,137,138,139,139,139,139,139,139,139,137,133,129,124,121,120,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,117,113,109,104,101,100,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,97,93,89,84,81,80,79,79,81,85,89,94,97,98,99,99,97,93,89,84,81,80,79,79,79,79,79,79,79,79,78,77,77,77,71,69,77,85,92,98,101,102,103,105,105,104,105,100,95,97,97,96,96,100,109,111,107,103,101,100,102,106,105,99,89,77,69,63,57,52,48,45,41,36,36,39,36,31,26,22,20,21,14,9,13,15,7,0,2,3,3,1,-3,-6,-10,-14,-14,-7,1,4,-4,-17,-23,-26,-29,-30,-21,-15,-19,-20,-12,-7,-8,-10,-8,-6,0,7,-5,10,30,45,87,115,132,100,127,122,122,85,7,-20,-20,-30,7,72,95,97,-72,-152,-190,-167,-97,-72,25,72,120,92,95,87,55,5,-30,-72,-92,-107,-125,-180,-130,-100,-95,-85,-85,-75,-60,-60,-43,-32,-24,-15,-7,-6,-12,-16,-18,-22,-22,-17,-12,-8,-10,-13,-17,-18,-22,-27,-26,-20,-14,-9,-6,-7,-13,-24,-31,-26,-21,-21,-23,-23,-22,-22,-22,-21,-20,-23,-27,-24,-17,-13,-15,-15,-13,-15,-20,-27,-26,-23,-20,-23,-27,-25,-19,-18,-21,-26,-29,-32,-33,-33,-35,-37,-41,-43,-42,-41,-43,-44,-44,-41,-38,-39,-43,-48,-50,-49,-46,-46,-47,-47,-47,-48,-50,-50,-53,-57,-56,-61,-67,-68,-68,-65,-62,-60,-63,-66,-66,-67,-69,-67,-68,-69,-65,-63,-63,-67,-70,-67,-60,-58,-61,-60,-61,-66,-74,-82,-87,-87,-85,-85,-87,-90,-93,-95,-93,-89,-87,-87,-87,-87,-87,-87,-91,-96,-99,-99,-100,-104,-109,-114,-117,-118,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-118,-116,-114,-112,-110,-110,-109,-109,-110,-112,-114,-117,-118,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-121,-125,-129,-134,-137,-138,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-137,-133,-129,-124,-121,-120,-119,-119,-121,-125,-129,-134,-137,-138,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-141,-145,-149,-154,-157,-158,-159,-159,-159,-159,-159,-159,-161,-165,-169,-174,-177,-178,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-177,-173,-169,-164,-162,-163,-164,-167,-168,-169,-169,-169,-170,-172,-174,-177,-178,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-187,-197,-201,-202,-208,-210,-200,-191,-182,-173,-165,-157,-146,-134,-127,-122,-119,-121,-124,-119,-116,-115,-114,-109,-101,-98,-103,-108,-112,-116,-116,-112,-114,-119,-129,-141,-149,-155,-162,-172,-181,-188,-195,-198,-196,-192,-193,-198,-203,-208,-210,-211,-220,-228,-226,-225,-232,-239,-237,-237,-237,-239,-242,-244,-245,-246,-245,-238,-229,-226,-234,-244,-248,-248,-252,-256,-252,-250,-259,-261,-256,-254,-257,-260,-258,-256,-239,-212,-215,-220,-200,-175,-132,-115,-117,-130,-122,-127,-117,-175,-212,-230,-230,-260,-242,-207,-95,-92,-302,-412,-470,-417,-307,-202,-145,-117,-70,-97,-95,-62,-95,-155,-230,-262,-242,-257,-295,-320,-310,-260,-295,-325,-305,-265,-250,-260,-244,-235,-229,-223,-216,-216,-222,-226,-228,-232,-232,-227,-221,-215,-215,-216,-218,-219,-222,-227,-227,-223,-219,-216,-213,-211,-212,-219,-222,-217,-212,-212,-213,-213,-212,-212,-212,-211,-210,-213,-217,-214,-207,-203,-204,-202,-198,-198,-201,-207,-206,-203,-200,-203,-207,-205,-201,-204,-211,-221,-225,-225,-223,-218,-217,-218,-222,-224,-223,-222,-223,-224,-225,-224,-223,-226,-232,-237,-240,-239,-236,-236,-237,-237,-237,-238,-239,-239,-242,-246,-245,-250,-256,-257,-257,-254,-251,-249,-252,-255,-254,-251,-248,-242,-240,-240,-236,-233,-233,-237,-240,-237,-230,-228,-231,-230,-231,-233,-239,-245,-247,-247,-245,-245,-245,-244,-243,-240,-237,-238,-240,-242,-242,-242,-242,-242,-247,-255,-258,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-257,-253,-249,-244,-241,-240,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-241,-245,-249,-254,-257,-258,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-257,-253,-249,-244,-241,-240,-239,-239,-238,-236,-234,-232,-232,-236,-239,-244,-247,-248,-249,-249,-250,-252,-254,-257,-258,-259,-259,-259,-258,-256,-254,-252,-250,-250,-249,-249,-249,-249,-249,-249,-250,-252,-254,-257,-258,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-258,-256,-254,-252,-252,-256,-259,-264,-267,-268,-269,-269,-269,-269,-269,-269,-269,-269,-269,-269,-269,-269,-269,-255,-236,-230,-228,-225,-221,-217,-214,-212,-208,-204,-199,-194,-189,-184,-179,-174,-171,-170,-169,-169,-171,-175,-179,-184,-190,-199,-209,-219,-226,-230,-234,-236,-238,-239,-239,-239,-239,-239,-239,-239,-242,-248,-254,-262,-268,-274,-279,-284,-286,-285,-284,-282,-282,-286,-289,-294,-297,-298,-299,-299,-297,-293,-289,-284,-281,-280,-279,-279,-281,-285,-289,-294,-296,-295,-294,-292,-290,-290,-289,-289,-287,-283,-279,-274,-271,-270,-270,-250,-250,-250,-240,-200,-190,-180,-180,-170,-170,-200,-230,-260,-280,-290,-320,-280,-210,-150,-220,-390,-500,-500,-440,-320,-250,-170,-140,-150,-180,-150,-170,-210,-230,-250,-310,-330,-380,-390,-380,-380,-330,-330,-330,-290,-310,-290,-290,-276,-271,-270,-269,-269,-269,-269,-269,-267,-263,-259,-254,-251,-250,-249,-249,-249,-249,-249,-249,-250,-252,-254,-257,-256,-253,-249,-244,-241,-240,-239,-239,-239,-239,-239,-239,-240,-242,-244,-247,-246,-243,-239,-234,-231,-230,-229,-229,-229,-229,-229,-229,-230,-232,-234,-237,-238,-239,-239,-239,-240,-242,-244,-246,-249,-251,-254,-256,-259,-261,-264,-266,-267,-265,-264,-261,-261,-262,-264,-266,-269,-271,-274,-276,-277,-275,-274,-271,-270,-269,-269,-269,-271,-275,-279,-284,-286,-285,-284,-281,-279,-276,-274,-271,-270,-269,-269,-269,-269,-269,-269,-269,-268,-266,-264,-262,-261,-262,-264,-267,-270,-275,-279,-284,-286,-285,-284,-282,-279,-277,-275,-275,-274,-274,-274,-274,-273,-270,-270,-269,-271,-277,-284,-292,-297,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-297,-293,-289,-284,-281,-280,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-278,-276,-274,-272,-270,-270,-269,-269,-270,-272,-274,-277,-278,-279,-279,-279,-277,-273,-269,-264,-261,-260,-259,-259,-258,-254,-249,-244,-207,-129,-35,9,3,1,0, COMMASEPARATE 5000 V1 160,160,160,160,160,160,160,160,160,160,160,160,162,166,170,175,178,179,180,180,180,180,180,180,178,174,170,165,162,161,160,160,160,160,160,160,162,166,170,175,178,179,180,180,182,186,190,195,198,199,200,200,202,206,210,215,218,219,220,220,220,220,220,220,220,220,220,220,220,220,220,220,218,214,210,205,202,201,200,200,198,194,190,185,180,175,170,165,162,161,160,160,160,160,160,160,158,154,150,145,140,135,130,125,122,121,120,120,118,114,110,105,102,101,100,100,100,100,100,100,100,100,100,100,100,100,100,100,98,94,90,85,82,81,80,80,80,80,80,80,82,86,90,95,98,99,100,100,98,94,90,85,82,81,80,80,82,86,90,95,98,99,100,100,102,106,110,115,118,119,120,120,118,114,110,105,102,101,100,100,100,100,100,100,100,100,100,100,98,94,90,85,82,81,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,78,74,70,65,62,61,60,60,60,60,60,60,62,66,70,75,78,79,80,80,78,74,70,65,62,61,60,60,60,60,60,60,60,63,68,69,70,70,70,70,75,78,79,86,95,102,106,109,110,110,110,108,106,104,101,94,83,78,76,72,69,66,62,59,55,48,43,41,37,29,21,19,20,20,22,24,28,36,42,44,46,49,51,54,55,55,57,59,61,64,65,65,65,65,65,63,58,51,47,44,41,40,40,42,47,54,60,63,64,65,65,65,65,67,71,75,78,79,80,77,70,90,90,120,140,160,160,200,230,275,305,330,340,360,380,390,360,360,330,270,185,140,140,90,-20,-140,-250,-290,-340,-325,-285,-190,-225,-220,-185,-135,-125,-115,-130,-165,-150,-100,-75,-25,15,60,85,120,130,137,139,138,136,135,138,143,146,147,146,144,140,135,132,131,134,135,132,132,134,136,140,145,150,152,151,147,146,152,157,162,167,167,164,160,155,150,145,142,144,148,151,154,155,157,159,160,162,164,165,165,167,169,170,172,174,175,177,181,187,187,181,175,168,163,163,166,170,177,186,194,201,207,209,210,210,210,213,219,223,224,225,222,217,217,221,224,223,223,226,227,227,227,224,221,220,218,216,215,215,210,202,192,181,172,167,162,156,152,149,145,140,138,139,140,142,141,137,136,135,130,123,119,115,110,105,100,95,90,87,86,85,85,85,85,85,77,66,62,61,62,66,70,75,78,79,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,82,86,90,95,98,99,100,100,100,100,100,100,100,100,100,100,100,100,100,100,98,94,90,85,82,81,80,80,82,86,90,95,98,99,100,100,100,100,100,100,98,94,90,85,82,81,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,78,74,70,65,60,55,50,45,42,41,40,40,40,40,40,40,40,40,40,40,40,40,40,43,48,49,50,50,50,50,55,58,58,61,65,67,67,69,70,70,72,74,76,79,80,73,63,58,56,52,47,41,32,24,16,9,1,-4,-11,-20,-26,-25,-21,-20,-18,-15,-11,-3,2,4,5,3,3,4,6,10,15,18,21,24,25,25,25,25,25,23,19,16,15,13,9,5,2,2,6,9,10,8,6,5,5,5,5,7,11,15,18,19,20,17,10,30,50,60,60,80,100,140,170,215,245,230,240,280,300,270,320,320,290,230,125,60,20,-70,-180,-260,-310,-330,-360,-365,-385,-390,-385,-360,-325,-315,-305,-295,-290,-245,-190,-140,-75,-25,15,40,65,60,70,90,97,97,96,95,98,103,106,109,111,114,115,113,111,110,113,114,111,112,114,113,109,105,100,93,86,77,71,74,78,83,88,88,84,80,75,70,65,62,64,68,71,74,75,80,90,100,112,122,129,135,142,147,149,151,154,155,157,161,167,169,166,165,163,161,162,166,170,175,180,183,186,189,190,190,190,190,193,199,203,204,205,202,197,197,201,204,203,201,200,197,192,191,190,192,196,197,196,195,195,190,182,172,161,150,142,132,121,112,104,95,85,77,69,60,52,44,38,36,35,28,18,9,0,-6,-8,-9,-9,-11,-13,-14,-14,-14,-14,-14,-14,-16,-18,-19,-19,-19,-19,-19,-19,-17,-13,-9,-4,-1,0,0,0,0,0,0,0,-1,-5,-9,-14,-17,-18,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-21,-25,-29,-34,-37,-38,-39,-39,-37,-33,-29,-24,-21,-20,-19,-19,-21,-25,-29,-34,-37,-38,-39,-39,-39,-39,-39,-39,-41,-45,-49,-54,-57,-58,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-76,-71,-70,-69,-69,-71,-75,-74,-76,-78,-72,-63,-57,-55,-56,-60,-64,-67,-70,-73,-75,-76,-80,-86,-86,-85,-88,-92,-98,-105,-109,-112,-115,-118,-119,-121,-125,-129,-131,-131,-135,-136,-135,-131,-123,-117,-115,-114,-116,-118,-120,-123,-124,-122,-120,-118,-115,-114,-114,-114,-114,-114,-116,-120,-123,-122,-120,-119,-119,-119,-117,-113,-110,-109,-111,-113,-114,-114,-114,-114,-112,-108,-104,-101,-100,-99,-102,-110,-90,-90,-80,-80,-60,-40,0,50,95,125,150,160,160,180,210,180,180,150,90,5,-40,-60,-130,-240,-340,-430,-470,-500,-485,-465,-450,-485,-460,-425,-395,-385,-355,-370,-345,-310,-260,-195,-145,-105,-80,-55,-60,-50,-43,-40,-41,-43,-44,-41,-36,-33,-30,-28,-25,-24,-26,-28,-29,-26,-23,-22,-17,-10,-6,-5,-4,-4,-6,-8,-12,-13,-7,-2,3,8,9,10,10,10,7,0,-8,-10,-9,-7,-5,-4,-2,0,0,2,4,5,5,7,9,10,12,14,15,17,21,27,29,26,25,23,21,22,26,30,35,40,43,46,47,44,40,35,32,34,40,43,44,45,42,37,37,41,44,43,41,40,37,32,29,25,22,21,19,16,15,15,12,7,2,-4,-7,-7,-7,-8,-9,-11,-15,-19,-24,-31,-40,-48,-55,-61,-63,-64,-69,-76,-80,-84,-87,-88,-89,-89,-89,-87,-85,-84,-84,-84,-84,-84,-96,-111,-116,-118,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-117,-113,-109,-104,-101,-100,-99,-99,-99,-99,-99,-99,-101,-105,-109,-114,-117,-118,-119,-119,-117,-113,-109,-104,-101,-100,-99,-99,-101,-105,-109,-114,-117,-118,-119,-119,-121,-125,-129,-134,-137,-138,-139,-139,-139,-139,-139,-139,-141,-145,-149,-154,-157,-158,-159,-159,-157,-153,-149,-144,-141,-140,-139,-139,-139,-139,-139,-139,-141,-145,-149,-154,-157,-158,-159,-159,-159,-159,-159,-159,-161,-165,-169,-174,-177,-178,-179,-179,-177,-173,-169,-164,-161,-160,-159,-159,-159,-159,-159,-159,-159,-159,-159,-161,-163,-164,-164,-166,-168,-169,-164,-161,-160,-153,-144,-137,-133,-130,-129,-129,-129,-131,-133,-135,-138,-145,-156,-161,-163,-167,-172,-178,-185,-189,-192,-195,-198,-199,-201,-205,-209,-211,-211,-215,-216,-215,-211,-203,-197,-195,-194,-196,-198,-200,-203,-204,-202,-200,-198,-195,-194,-194,-194,-194,-194,-196,-200,-203,-202,-200,-199,-199,-199,-197,-193,-190,-189,-191,-193,-194,-194,-194,-194,-192,-188,-184,-181,-180,-179,-182,-190,-170,-170,-160,-140,-120,-80,-40,-10,35,65,90,100,100,120,150,120,120,90,30,-55,-100,-80,-130,-240,-340,-430,-490,-540,-525,-505,-450,-485,-460,-425,-395,-385,-375,-390,-385,-370,-320,-275,-245,-205,-140,-115,-100,-90,-83,-80,-81,-83,-84,-81,-76,-73,-70,-68,-65,-64,-66,-68,-69,-66,-65,-68,-67,-65,-66,-70,-74,-79,-84,-87,-92,-93,-85,-76,-66,-56,-51,-50,-49,-49,-51,-55,-58,-55,-51,-48,-45,-44,-42,-40,-39,-37,-35,-34,-34,-32,-28,-24,-17,-10,-6,-3,1,7,7,1,-4,-11,-16,-16,-13,-9,-2,6,14,21,27,29,30,30,30,33,39,43,44,45,42,37,37,41,44,43,41,40,37,32,29,25,22,21,19,16,15,15,12,7,2,-4,-9,-12,-17,-23,-27,-30,-34,-39,-42,-45,-49,-52,-57,-62,-63,-64,-69,-76,-80,-84,-87,-88,-89,-89,-91,-93,-94,-94,-94,-94,-94,-94,-102,-113,-117,-118,-119,-119,-119,-119,-119,-119,-119,-119,-117,-113,-109,-104,-101,-100,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-101,-105,-109,-114,-117,-118,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-121,-125,-129,-134,-137,-138,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-137,-133,-129,-124,-121,-120,-119,-119,-121,-125,-129,-134,-137,-138,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-141,-145,-149,-154,-157,-158,-159,-159,-157,-153,-149,-144,-141,-140,-139,-139,-139,-139,-139,-139,-136,-131,-130,-129,-129,-129,-129,-124,-121,-120,-113,-104,-97,-93,-90,-89,-89,-89,-91,-93,-95,-98,-105,-116,-120,-118,-117,-117,-120,-128,-135,-143,-150,-156,-158,-160,-164,-167,-165,-161,-160,-158,-155,-151,-143,-137,-135,-134,-136,-138,-140,-143,-144,-142,-140,-138,-135,-134,-134,-134,-134,-134,-136,-140,-143,-142,-140,-139,-139,-139,-137,-133,-130,-129,-131,-133,-135,-139,-144,-149,-151,-148,-144,-141,-140,-139,-142,-150,-130,-130,-100,-60,-40,-20,20,70,115,145,170,180,180,200,210,180,200,170,150,25,-40,-40,-130,-240,-340,-470,-470,-500,-525,-505,-470,-505,-480,-445,-395,-385,-355,-370,-345,-290,-240,-195,-145,-105,-80,-75,-60,-30,-23,-20,-21,-23,-24,-21,-16,-13,-10,-8,-5,-4,-7,-13,-19,-21,-23,-27,-27,-25,-23,-19,-14,-9,-7,-8,-12,-13,-7,-2,3,8,9,10,10,10,8,4,1,4,10,17,24,30,35,38,39,41,44,45,45,47,49,50,52,54,55,57,61,67,69,66,65,63,61,62,66,70,75,80,83,86,89,90,90,90,92,99,110,118,123,124,121,117,117,121,124,123,121,120,117,112,109,105,102,101,99,96,95,95,92,87,82,76,69,61,52,41,32,24,15,5,-1,-5,-9,-12,-17,-22,-23,-24,-29,-36,-40,-44,-47,-48,-49,-49,-51,-53,-54,-54,-54,-54,-54,-54,-62,-73,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-77,-73,-69,-64,-61,-60,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-77,-73,-69,-64,-61,-60,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-101,-105,-109,-114,-117,-118,-119,-119,-117,-113,-110,-109,-109,-109,-109,-109,-102,-93,-90,-89,-89,-89,-89,-84,-81,-80,-73,-64,-57,-53,-50,-49,-47,-43,-40,-38,-37,-38,-45,-56,-61,-63,-67,-72,-77,-82,-85,-88,-92,-97,-98,-100,-106,-113,-115,-116,-118,-117,-115,-111,-103,-97,-95,-94,-96,-98,-100,-103,-104,-102,-100,-98,-95,-94,-94,-94,-96,-100,-106,-115,-121,-121,-120,-119,-117,-113,-107,-98,-92,-90,-91,-93,-94,-94,-94,-94,-92,-88,-84,-81,-81,-85,-93,-105,-90,-90,-80,-40,0,20,60,110,155,165,190,200,200,220,230,220,220,190,130,65,20,0,-70,-180,-280,-390,-430,-460,-445,-445,-410,-445,-420,-385,-335,-325,-335,-350,-325,-290,-240,-195,-125,-85,-40,5,20,10,17,19,18,16,15,18,23,26,29,31,34,35,33,31,30,33,34,31,32,34,35,35,35,35,33,31,27,26,34,43,53,63,68,69,70,70,68,64,61,64,68,71,74,75,77,79,80,82,82,79,75,72,71,70,72,74,75,77,81,87,89,86,85,83,81,82,86,90,95,100,103,106,109,110,110,110,110,113,119,123,124,125,122,117,117,121,124,123,121,120,117,112,109,105,102,101,100,102,106,110,110,107,102,96,89,81,72,61,54,50,45,40,37,34,30,27,22,17,16,15,12,9,10,10,10,10,10,10,8,6,5,5,5,5,5,5,-9,-29,-36,-38,-37,-33,-29,-24,-21,-20,-19,-19,-21,-25,-29,-34,-37,-38,-39,-39,-39,-39,-39,-39,-37,-33,-29,-24,-17,-8,1,10,17,19,20,20,18,14,10,5,2,1,0,0,0,0,0,0,2,6,10,15,18,19,20,20,20,20,20,20,18,14,10,5,2,1,0,0,-1,-5,-9,-14,-17,-18,-19,-19,-21,-25,-29,-34,-37,-38,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-41,-45,-49,-54,-57,-58,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-57,-53,-49,-44,-41,-40,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-41,-45,-49,-54,-57,-58,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-56,-51,-50,-49,-49,-49,-49,-44,-41,-38,-27,-13,-2,4,8,9,10,10,8,6,4,1,-6,-16,-21,-23,-27,-32,-38,-47,-55,-63,-70,-78,-84,-91,-100,-106,-105,-101,-100,-98,-95,-91,-83,-77,-75,-74,-76,-78,-80,-83,-84,-82,-80,-78,-75,-73,-69,-64,-59,-56,-56,-60,-63,-62,-60,-59,-59,-59,-57,-53,-50,-51,-56,-63,-69,-74,-79,-84,-87,-87,-83,-80,-79,-79,-76,-70,-50,-50,-40,-20,0,20,60,130,175,205,230,240,240,240,250,200,200,150,90,5,-20,-20,-90,-200,-260,-350,-390,-420,-385,-345,-310,-365,-340,-305,-295,-285,-275,-310,-305,-270,-220,-175,-125,-85,-40,-15,0,10,17,19,18,16,15,18,23,26,29,31,34,35,33,31,30,33,34,31,32,34,35,35,35,35,33,31,27,26,32,37,42,47,51,55,60,65,67,64,61,64,68,71,74,75,75,73,69,66,65,65,65,67,69,70,72,74,75,77,81,87,89,86,85,83,81,82,86,90,95,100,103,106,109,110,110,110,110,113,119,123,124,125,122,117,117,121,124,123,121,120,117,112,107,99,91,85,80,77,76,75,72,67,62,56,50,47,42,36,32,29,25,20,18,19,20,22,22,22,26,30,28,23,19,15,10,5,0,-4,-9,-12,-13,-14,-14,-14,-14,-14,-22,-33,-37,-38,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-36,-28,-19,-9,-2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-1,-5,-9,-14,-17,-18,-19,-19,-19,-19,-19,-19,-21,-25,-29,-34,-37,-38,-39,-39,-39,-39,-39,-39,-41,-45,-49,-54,-57,-58,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-77,-73,-69,-64,-61,-60,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-76,-71,-70,-69,-69,-69,-69,-64,-61,-60,-53,-44,-37,-33,-30,-29,-29,-29,-31,-33,-35,-38,-45,-56,-61,-63,-67,-72,-78,-87,-95,-103,-110,-116,-118,-120,-124,-127,-125,-121,-120,-118,-115,-111,-103,-97,-95,-94,-96,-98,-100,-104,-109,-112,-115,-116,-115,-114,-114,-112,-108,-104,-101,-101,-103,-102,-100,-99,-99,-99,-97,-93,-90,-89,-91,-93,-94,-94,-94,-94,-92,-88,-84,-81,-80,-79,-82,-90,-70,-90,-100,-80,-40,-20,20,70,115,145,190,200,180,200,210,180,180,170,110,25,-20,-20,-90,-200,-300,-350,-430,-460,-445,-425,-390,-425,-380,-345,-315,-305,-295,-310,-325,-290,-260,-215,-165,-125,-80,-55,-40,-30,-23,-20,-21,-23,-24,-21,-16,-13,-10,-8,-5,-4,-4,-2,1,9,13,11,12,14,15,15,15,15,12,6,-2,-8,-5,-1,3,8,9,10,10,10,8,4,1,4,8,11,14,15,17,19,20,22,24,25,25,27,29,30,32,34,35,37,41,47,49,46,45,43,41,42,46,50,53,54,53,51,50,50,50,50,50,53,59,63,64,65,62,57,57,61,64,63,61,60,57,52,49,45,42,41,40,42,46,50,50,47,42,36,30,27,22,16,12,9,5,0,-3,-5,-9,-12,-17,-22,-23,-24,-29,-36,-40,-44,-47,-48,-49,-49,-51,-53,-54,-54,-54,-54,-54,-54,-62,-73,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-77,-73,-69,-64,-59,-54,-49,-44,-41,-40,-39,-39,-39,-39,-39,-39,-41,-45,-49,-54,-57,-58,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-101,-105,-109,-114,-117,-118,-119,-119,-117,-113,-109,-104,-101,-100,-99,-99,-99,-99,-99,-99,-101,-105,-109,-114,-117,-118,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-116,-111,-110,-109,-109,-109,-107,-98,-90,-84,-74,-64,-57,-53,-50,-49,-49,-49,-51,-53,-55,-58,-65,-76,-81,-83,-87,-92,-98,-107,-115,-123,-130,-136,-140,-146,-155,-163,-164,-161,-160,-158,-155,-151,-143,-137,-135,-134,-136,-138,-140,-143,-144,-142,-140,-138,-135,-134,-132,-128,-124,-119,-117,-120,-123,-122,-120,-119,-119,-119,-117,-113,-110,-109,-112,-118,-124,-129,-132,-133,-132,-128,-124,-121,-120,-119,-122,-130,-110,-110,-100,-80,-40,0,60,110,155,185,210,200,200,200,190,160,160,110,50,-35,-80,-80,-150,-280,-380,-450,-470,-480,-465,-445,-430,-465,-440,-405,-375,-365,-355,-370,-365,-310,-260,-215,-165,-125,-80,-55,-40,-30,-23,-20,-21,-23,-24,-21,-16,-13,-10,-8,-5,-4,-6,-8,-9,-6,-5,-8,-7,-5,-4,-4,-4,-4,-6,-8,-12,-13,-7,-2,3,8,9,10,10,10,8,4,1,4,8,11,14,15,17,19,20,22,22,19,15,12,11,10,12,14,15,17,21,27,31,32,36,39,40,42,46,50,55,60,63,66,69,70,70,70,70,73,79,83,83,79,71,62,59,61,64,63,61,60,57,52,49,45,42,41,39,36,35,35,32,27,22,16,10,7,2,-4,-7,-10,-14,-19,-22,-25,-29,-32,-37,-42,-43,-44,-49,-56,-60,-64,-67,-68,-69,-69,-71,-73,-74,-74,-74,-74,-74,-74,-82,-93,-97,-98,-99,-99,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-79,-79,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-97,-93,-89,-84,-79,-74,-69,-64,-61,-60,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-77,-73,-69,-64,-61,-60,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-77,-73,-69,-64,-61,-60,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-77,-73,-69,-64,-61,-60,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-56,-51,-50,-49,-49,-49,-47,-38,-30,-24,-14,-4,2,6,9,10,10,10,8,6,4,1,-6,-16,-21,-23,-29,-37,-48,-62,-73,-82,-90,-96,-96,-95,-94,-92,-85,-76,-70,-63,-57,-52,-43,-37,-35,-34,-36,-38,-42,-48,-54,-57,-58,-57,-55,-54,-54,-54,-54,-54,-56,-60,-63,-62,-60,-59,-59,-59,-57,-53,-50,-49,-51,-53,-54,-54,-54,-54,-52,-48,-44,-41,-40,-39,-42,-50,-30,-10,0,40,60,40,80,150,215,245,290,300,280,300,350,240,240,210,150,65,60,80,30,-80,-180,-270,-330,-360,-345,-305,-250,-285,-280,-245,-215,-205,-195,-230,-285,-250,-200,-175,-125,-65,-20,5,20,90,95,93,88,81,77,79,83,86,89,91,94,95,95,97,101,109,113,111,112,114,115,115,115,115,112,106,97,91,94,98,103,108,109,110,110,110,108,104,101,104,106,105,103,99,98,99,100,102,106,110,115,122,127,129,131,134,136,142,151,162,167,166,165,163,161,162,166,170,173,174,173,171,170,170,170,170,170,173,179,183,184,185,182,177,177,181,184,183,181,180,177,172,167,159,151,145,140,137,136,135,133,133,133,131,130,132,132,131,130,128,124,120,117,114,110,107,102,97,96,95,90,83,79,75,72,71,70,70,68,66,65,65,65,65,65,65,63,61,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,58,54,50,45,42,41,40,40,42,46,50,55,58,59,60,60,60,60,60,60,60,60,60,60,62,66,70,75,78,79,80,80,78,74,70,65,62,61,60,60,60,60,60,60,60,60,60,60,58,54,50,45,42,41,40,40,40,40,40,40,40,40,40,40,40,40,40,40,38,34,30,25,22,21,20,20,22,26,30,35,38,39,40,40,40,40,40,40,40,40,40,40,40,40,40,40,38,34,30,25,22,21,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,18,14,10,5,2,1,0,0,0,0,0,0,2,6,10,15,18,19,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,25,32,34,35,37,41,45,50,55,58,59,60,60,60,60,60,60,58,54,50,45,38,29,20,10,0,-9,-19,-29,-36,-38,-39,-39,-39,-39,-39,-39,-37,-33,-29,-24,-19,-14,-9,-4,-1,0,0,0,-1,-5,-9,-14,-19,-24,-29,-34,-37,-38,-39,-39,-39,-39,-39,-39,-37,-33,-29,-24,-19,-14,-9,-4,0,5,10,15,18,19,20,20,20,20,20,20,20,27,40,60,60,80,100,120,120,180,240,280,280,280,280,280,300,300,280,280,220,140,60,100,40,-80,-180,-300,-340,-380,-380,-360,-300,-300,-300,-280,-280,-280,-260,-260,-260,-220,-180,-120,-120,-80,0,40,60,80,100,102,106,110,115,118,119,120,120,120,120,120,120,120,120,120,120,118,114,110,105,102,101,100,100,102,106,110,115,118,119,120,120,120,120,120,120,120,120,120,120,122,126,130,135,138,139,140,140,142,146,150,155,158,159,160,160,160,160,160,160,160,160,160,160,162,166,170,175,178,179,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,178,174,170,165,160,155,150,145,142,141,140,140,138,134,130,125,118,109,100,90,82,76,70,65,62,61,60,60,60,60,60,60,60,60,60,60,58,54,51,50,50,50,50,50,40,27,22,21,20,20,20,20,20,20,20,20,22,26,30,35,38,39,40,40,40,40,40,40,40,40,40,40,42,46,50,55,58,59,60,60,58,54,50,45,42,41,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,38,34,30,25,22,21,20,20,20,20,20,20,13,-2,-20,-19,-6,-1,0, COMMASEPARATE 5000 V2 120,125,130,135,138,139,140,140,142,146,150,155,160,165,170,175,180,185,190,195,198,199,200,200,202,206,210,215,220,225,230,235,240,245,250,255,262,271,280,290,298,304,310,315,322,331,340,350,360,370,380,390,402,416,430,445,458,469,480,490,500,510,520,530,540,550,560,570,578,584,590,595,598,599,600,600,600,600,600,600,598,594,590,585,577,564,550,535,518,499,480,460,440,420,400,380,360,340,320,300,280,260,240,220,202,186,170,155,142,131,120,110,100,90,80,70,60,50,40,30,22,16,10,5,-1,-10,-19,-29,-36,-38,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-37,-33,-29,-24,-19,-14,-9,-4,0,5,10,15,18,19,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,18,14,10,5,2,1,0,0,-1,-5,-9,-14,-19,-24,-29,-34,-37,-38,-39,-39,-39,-39,-39,-39,-41,-45,-49,-54,-57,-58,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-81,-85,-89,-92,-93,-94,-94,-94,-94,-89,-82,-80,-79,-77,-73,-70,-66,-61,-56,-50,-46,-45,-43,-40,-39,-39,-37,-35,-34,-34,-34,-37,-43,-49,-52,-55,-58,-59,-62,-70,-76,-80,-83,-85,-89,-96,-101,-100,-94,-91,-88,-85,-84,-84,-86,-90,-93,-99,-106,-108,-109,-109,-109,-109,-109,-111,-113,-115,-118,-119,-117,-115,-114,-114,-114,-114,-114,-116,-118,-117,-112,-110,-113,-114,-112,-110,-109,-109,-109,-109,-107,-102,-97,-95,-96,-98,-100,-100,-105,-110,-110,-110,-105,-70,-5,40,80,150,200,280,395,490,545,610,645,625,615,670,740,685,475,220,25,-125,-390,-615,-720,-725,-795,-750,-665,-575,-535,-495,-470,-410,-330,-250,-205,-145,-90,-30,-15,0,30,38,44,50,53,54,56,59,60,60,63,68,71,74,76,80,83,84,86,90,95,98,101,104,105,107,109,110,113,118,121,124,128,134,140,143,144,145,147,151,157,164,170,175,180,185,190,193,198,204,210,215,222,229,238,248,254,258,264,275,287,297,307,319,330,340,352,362,371,382,394,405,417,431,445,457,467,479,490,502,514,525,537,547,556,564,571,577,579,581,584,585,585,585,585,585,585,582,576,569,558,546,534,518,499,480,458,433,408,384,360,335,312,289,268,249,230,208,186,167,152,137,122,107,91,77,67,57,47,37,27,22,21,20,20,20,20,-6,-41,-53,-57,-58,-59,-59,-59,-57,-53,-49,-44,-41,-40,-39,-39,-39,-39,-39,-39,-37,-33,-29,-24,-21,-20,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-16,-8,1,10,17,19,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,18,14,10,5,0,-4,-9,-14,-19,-24,-29,-34,-37,-38,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-41,-45,-49,-54,-57,-58,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-81,-83,-84,-84,-84,-84,-84,-82,-80,-79,-79,-77,-73,-70,-66,-61,-56,-50,-46,-45,-43,-40,-39,-39,-37,-35,-34,-34,-34,-37,-42,-43,-42,-40,-39,-39,-42,-50,-56,-60,-64,-71,-80,-91,-100,-99,-96,-96,-96,-95,-93,-89,-87,-90,-93,-99,-106,-108,-109,-109,-109,-109,-109,-111,-113,-115,-118,-119,-117,-115,-114,-114,-116,-120,-124,-131,-136,-136,-131,-130,-131,-128,-122,-115,-111,-110,-109,-109,-109,-107,-107,-110,-114,-117,-120,-120,-105,-110,-130,-130,-125,-110,-65,0,40,90,140,240,355,450,505,570,605,605,615,690,740,685,515,280,85,-85,-370,-615,-720,-765,-815,-750,-665,-635,-555,-515,-450,-370,-290,-210,-125,-65,-10,30,45,40,30,38,44,50,53,54,56,59,60,60,63,68,71,74,76,80,83,84,86,90,95,98,101,104,105,107,109,110,113,118,121,124,128,134,140,143,144,145,147,151,157,164,170,175,180,185,190,193,198,204,210,215,222,229,238,248,254,258,264,275,287,297,307,319,330,340,352,362,371,382,394,405,417,431,445,457,467,479,490,502,514,525,537,547,556,564,571,577,579,581,584,585,585,583,579,575,570,563,556,549,538,526,514,498,479,460,438,413,388,364,340,315,292,269,248,229,210,188,166,147,132,117,102,87,71,57,47,37,27,19,13,11,10,10,10,10,10,-13,-44,-54,-57,-58,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-57,-53,-49,-44,-41,-40,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-41,-45,-49,-54,-57,-58,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-99,-99,-99,-99,-101,-105,-109,-114,-117,-118,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-121,-123,-124,-124,-124,-124,-124,-122,-120,-119,-119,-117,-113,-110,-106,-101,-96,-90,-86,-85,-84,-86,-90,-94,-96,-95,-94,-94,-94,-97,-102,-103,-102,-100,-99,-99,-102,-110,-116,-120,-123,-125,-129,-136,-141,-140,-136,-136,-138,-140,-143,-144,-146,-150,-153,-159,-166,-168,-169,-169,-169,-169,-169,-171,-173,-175,-178,-179,-177,-175,-174,-174,-174,-174,-174,-176,-178,-177,-172,-170,-173,-174,-172,-170,-169,-169,-169,-169,-167,-162,-157,-155,-156,-165,-180,-180,-165,-170,-170,-170,-165,-130,-85,-40,0,50,100,180,295,390,445,510,565,565,575,650,700,605,455,220,25,-165,-430,-675,-780,-825,-875,-810,-725,-655,-615,-575,-510,-410,-330,-270,-205,-145,-110,-70,-55,-40,-50,-41,-35,-29,-26,-25,-23,-20,-19,-17,-10,-1,6,12,16,20,23,24,26,30,35,38,41,44,45,47,49,50,53,58,61,64,68,73,74,73,69,66,67,71,77,84,90,95,100,105,110,113,118,124,130,135,142,149,158,168,174,178,184,195,207,217,227,239,250,260,272,282,291,302,314,325,337,351,365,377,387,399,410,422,434,443,451,457,461,465,472,477,479,481,484,485,485,485,485,485,485,482,476,469,458,446,434,418,399,380,358,333,308,284,260,235,212,189,168,149,130,108,86,67,52,37,22,7,-9,-22,-32,-42,-52,-60,-66,-68,-69,-69,-69,-69,-69,-99,-139,-152,-157,-158,-159,-159,-159,-157,-153,-149,-144,-141,-140,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-137,-133,-129,-124,-121,-120,-119,-119,-119,-119,-119,-119,-121,-125,-129,-134,-137,-138,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-141,-145,-149,-154,-157,-158,-159,-159,-159,-159,-159,-159,-161,-165,-169,-174,-177,-178,-179,-179,-179,-179,-179,-179,-181,-185,-189,-194,-197,-198,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-197,-195,-194,-194,-194,-192,-190,-186,-181,-178,-175,-176,-180,-181,-180,-179,-179,-177,-175,-174,-174,-174,-177,-182,-183,-182,-180,-179,-179,-182,-190,-196,-200,-203,-205,-209,-216,-221,-220,-216,-216,-218,-220,-223,-224,-226,-230,-233,-239,-246,-248,-249,-249,-249,-249,-249,-251,-253,-255,-258,-259,-257,-255,-254,-254,-254,-254,-254,-256,-258,-257,-252,-250,-253,-254,-252,-250,-249,-249,-249,-249,-247,-242,-237,-235,-236,-238,-240,-240,-245,-250,-250,-230,-225,-190,-165,-120,-80,-30,20,100,215,330,405,470,505,505,455,530,620,585,395,160,-55,-225,-490,-735,-840,-865,-935,-890,-785,-715,-675,-655,-610,-530,-470,-390,-325,-265,-210,-170,-155,-140,-130,-108,-97,-90,-86,-86,-88,-90,-94,-97,-95,-91,-88,-85,-83,-79,-76,-75,-73,-69,-64,-61,-58,-55,-54,-52,-50,-49,-46,-41,-38,-35,-31,-25,-19,-16,-15,-14,-12,-8,-2,4,10,15,20,25,30,33,38,44,50,55,62,69,78,88,94,98,104,115,127,137,147,159,170,180,192,202,211,222,234,245,257,271,285,297,307,317,324,331,339,346,357,367,376,384,391,397,399,401,404,405,405,405,405,405,405,402,396,389,378,366,354,338,319,300,278,253,228,203,174,145,117,91,69,50,30,10,-8,-22,-32,-43,-57,-72,-88,-102,-112,-122,-132,-140,-146,-148,-149,-149,-149,-149,-149,-172,-203,-214,-217,-218,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-217,-213,-209,-204,-201,-200,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-201,-205,-209,-214,-219,-224,-229,-234,-237,-238,-239,-239,-239,-239,-239,-239,-241,-245,-249,-254,-257,-258,-259,-259,-259,-259,-259,-259,-261,-265,-269,-274,-277,-278,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-281,-285,-289,-294,-297,-298,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-299,-306,-315,-318,-319,-317,-313,-310,-306,-301,-296,-290,-286,-283,-277,-270,-264,-261,-258,-255,-254,-254,-254,-257,-262,-263,-262,-260,-259,-259,-262,-270,-276,-280,-283,-285,-289,-294,-296,-290,-281,-278,-279,-281,-283,-285,-291,-300,-308,-317,-325,-328,-329,-327,-323,-319,-314,-312,-313,-315,-318,-319,-317,-315,-314,-316,-320,-324,-329,-334,-337,-337,-332,-330,-333,-334,-332,-330,-329,-329,-329,-329,-327,-322,-317,-315,-316,-318,-320,-320,-325,-330,-330,-330,-345,-310,-265,-220,-180,-130,-80,0,135,250,305,370,405,405,395,430,540,505,355,120,-135,-245,-450,-755,-880,-925,-995,-970,-885,-815,-775,-695,-650,-570,-490,-410,-345,-285,-230,-190,-195,-180,-170,-161,-155,-149,-146,-145,-143,-140,-139,-139,-136,-131,-128,-127,-128,-129,-131,-133,-132,-128,-124,-119,-112,-105,-99,-94,-91,-90,-86,-81,-78,-75,-71,-66,-65,-66,-70,-73,-72,-68,-62,-53,-44,-34,-24,-16,-10,-6,-1,5,10,15,22,29,38,48,54,58,64,75,87,97,107,119,130,140,152,162,171,182,194,205,217,231,245,257,267,279,290,302,314,325,337,347,356,364,371,377,379,381,384,385,385,385,385,385,385,383,381,379,373,364,353,338,319,300,278,253,228,204,180,155,132,109,88,69,50,28,6,-12,-27,-42,-57,-72,-88,-102,-112,-122,-132,-140,-146,-148,-149,-149,-149,-149,-149,-166,-188,-195,-198,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-197,-193,-189,-184,-181,-180,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-181,-185,-189,-194,-197,-198,-199,-199,-199,-199,-199,-199,-201,-205,-209,-214,-217,-218,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-221,-225,-229,-234,-237,-238,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-237,-233,-230,-226,-221,-216,-210,-206,-203,-197,-190,-184,-181,-178,-175,-174,-174,-174,-177,-182,-183,-182,-180,-179,-179,-182,-190,-196,-200,-203,-205,-209,-216,-221,-220,-216,-215,-213,-210,-208,-205,-206,-210,-213,-219,-226,-228,-229,-229,-229,-229,-229,-231,-233,-235,-238,-239,-237,-235,-234,-234,-234,-234,-234,-236,-238,-237,-232,-230,-233,-234,-232,-230,-229,-229,-229,-229,-227,-222,-217,-215,-216,-218,-220,-220,-225,-230,-230,-230,-225,-190,-145,-100,-60,-10,40,120,235,330,385,450,465,465,495,570,660,605,435,200,-15,-165,-430,-675,-780,-825,-895,-850,-765,-695,-655,-635,-590,-490,-410,-330,-265,-205,-150,-130,-115,-100,-90,-79,-69,-59,-51,-46,-43,-40,-39,-39,-36,-31,-28,-25,-23,-19,-16,-15,-13,-9,-4,0,7,14,20,25,28,29,33,38,41,44,48,53,54,53,49,46,47,51,57,66,75,85,95,103,109,113,118,124,130,135,142,149,158,168,174,176,179,185,192,199,208,219,230,240,252,262,271,282,294,305,317,331,345,357,367,379,390,402,414,425,437,447,456,464,471,477,479,481,484,485,485,487,491,495,500,500,495,488,478,466,454,438,419,401,384,363,343,323,299,275,252,229,208,189,170,147,121,97,77,59,43,28,11,-2,-12,-22,-32,-40,-46,-48,-49,-49,-49,-49,-49,-72,-103,-114,-117,-120,-124,-129,-134,-137,-138,-139,-139,-139,-139,-139,-139,-137,-133,-129,-124,-121,-120,-119,-119,-119,-119,-119,-119,-117,-113,-109,-104,-101,-100,-99,-99,-99,-99,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-79,-79,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-101,-105,-109,-114,-117,-118,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-121,-125,-129,-134,-137,-138,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-141,-145,-149,-154,-157,-158,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-161,-165,-169,-174,-177,-178,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-172,-163,-160,-159,-157,-153,-150,-146,-141,-136,-130,-126,-125,-123,-120,-119,-119,-117,-115,-114,-114,-114,-117,-122,-123,-122,-120,-119,-119,-122,-130,-136,-140,-143,-145,-149,-156,-161,-160,-156,-156,-158,-160,-163,-164,-166,-170,-173,-179,-186,-188,-189,-189,-189,-189,-189,-191,-193,-195,-198,-199,-196,-190,-184,-179,-176,-175,-174,-176,-180,-183,-182,-185,-191,-193,-192,-190,-189,-189,-189,-189,-187,-182,-177,-175,-176,-178,-180,-160,-165,-170,-170,-170,-165,-130,-65,-20,20,70,100,180,275,350,385,450,485,505,555,650,720,625,435,200,25,-125,-390,-595,-700,-765,-875,-830,-745,-675,-635,-595,-570,-490,-390,-310,-245,-185,-130,-90,-75,-60,-50,-39,-29,-19,-11,-6,-3,0,0,0,3,8,11,14,16,20,23,23,21,20,20,20,22,24,25,28,34,40,48,56,60,63,68,74,80,83,84,85,87,91,97,104,110,115,120,125,130,133,138,144,150,155,162,169,178,188,194,198,204,215,227,237,247,259,270,280,292,302,311,322,334,345,357,369,380,387,392,401,410,422,434,446,462,477,491,502,511,517,519,520,518,514,510,507,506,505,505,503,501,499,493,484,473,458,439,420,398,373,348,324,300,275,252,229,208,189,170,148,126,107,92,77,62,47,31,17,7,-2,-12,-22,-32,-37,-38,-39,-39,-39,-39,-66,-101,-113,-117,-118,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-117,-113,-109,-104,-101,-100,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-101,-105,-109,-114,-117,-118,-119,-119,-121,-125,-129,-134,-137,-138,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-141,-145,-149,-154,-157,-158,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-161,-165,-169,-174,-177,-178,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-177,-173,-170,-166,-161,-156,-150,-146,-145,-143,-140,-139,-139,-137,-135,-134,-134,-134,-137,-140,-138,-132,-125,-121,-120,-123,-130,-138,-145,-153,-160,-168,-175,-181,-180,-176,-176,-178,-180,-183,-184,-186,-190,-193,-199,-206,-208,-209,-209,-209,-209,-209,-211,-213,-215,-218,-219,-217,-215,-214,-214,-214,-214,-214,-216,-218,-217,-212,-210,-213,-214,-212,-210,-209,-209,-209,-209,-207,-202,-195,-189,-186,-183,-180,-180,-205,-210,-210,-190,-185,-170,-125,-80,-40,30,80,160,275,370,425,490,525,525,535,610,700,645,435,200,5,-145,-410,-635,-740,-785,-875,-830,-745,-695,-655,-615,-570,-490,-410,-330,-265,-205,-150,-110,-95,-80,-70,-61,-55,-49,-46,-43,-37,-30,-24,-21,-16,-11,-8,-5,-3,1,4,5,7,11,15,18,21,24,25,27,29,30,33,38,41,44,48,54,60,63,64,65,67,71,77,84,90,95,100,105,110,113,118,124,130,135,142,149,158,168,174,178,184,195,207,217,227,239,250,260,272,282,291,302,314,325,337,351,365,377,387,399,410,422,434,445,457,467,476,484,491,497,499,501,504,505,505,505,505,505,505,502,496,489,478,468,459,448,434,418,398,373,348,324,300,275,252,229,208,189,170,148,126,107,92,76,57,37,16,-1,-11,-21,-31,-40,-46,-48,-49,-49,-49,-49,-49,-72,-103,-114,-117,-118,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-117,-113,-109,-104,-101,-100,-99,-99,-99,-99,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-77,-73,-69,-64,-61,-60,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-99,-99,-99,-99,-101,-105,-109,-114,-117,-118,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-121,-125,-129,-134,-137,-138,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-137,-133,-130,-126,-121,-116,-110,-106,-105,-103,-100,-99,-99,-97,-95,-94,-94,-94,-97,-102,-103,-102,-100,-99,-99,-102,-110,-116,-120,-123,-125,-129,-136,-141,-140,-136,-136,-138,-140,-143,-144,-146,-150,-153,-159,-166,-168,-169,-169,-169,-169,-169,-171,-173,-175,-178,-179,-177,-175,-174,-174,-174,-174,-174,-176,-178,-177,-172,-170,-173,-174,-172,-170,-169,-169,-169,-169,-167,-162,-157,-155,-156,-151,-140,-140,-145,-150,-150,-150,-145,-130,-85,-40,0,50,100,180,275,370,405,470,505,545,595,670,720,645,455,220,85,-65,-330,-575,-680,-765,-855,-810,-725,-655,-615,-575,-530,-450,-350,-270,-225,-165,-110,-70,-55,-40,-30,-21,-15,-9,-6,-5,-3,0,0,0,3,8,11,14,16,20,23,24,26,30,35,38,41,44,45,47,49,50,53,58,61,64,68,74,80,83,84,85,87,91,97,104,110,115,120,125,130,133,138,144,150,155,162,169,178,188,194,198,204,215,227,237,247,259,270,280,292,302,311,322,334,345,357,371,385,397,407,419,430,442,454,465,477,487,496,504,511,517,519,520,518,514,510,507,506,505,505,503,501,499,493,484,473,458,439,420,398,373,348,324,300,275,252,229,208,189,170,148,126,107,92,77,62,47,31,17,7,-2,-12,-20,-26,-28,-29,-29,-29,-29,-29,-59,-99,-112,-117,-118,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-117,-113,-109,-104,-101,-100,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-99,-99,-99,-99,-101,-105,-109,-114,-117,-118,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-121,-125,-129,-134,-137,-138,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-137,-133,-130,-126,-121,-116,-110,-106,-105,-103,-100,-99,-99,-97,-95,-94,-94,-94,-97,-102,-103,-102,-98,-94,-89,-87,-92,-97,-100,-103,-105,-109,-116,-121,-120,-116,-116,-118,-120,-123,-124,-126,-130,-133,-139,-146,-148,-149,-149,-149,-149,-149,-151,-153,-155,-158,-159,-157,-155,-154,-154,-154,-154,-154,-156,-158,-157,-152,-150,-153,-155,-158,-160,-164,-167,-168,-169,-167,-162,-157,-155,-156,-158,-160,-160,-145,-150,-150,-150,-145,-110,-45,0,40,90,140,200,315,410,465,530,565,545,535,610,700,645,455,220,25,-125,-390,-635,-740,-745,-815,-790,-705,-635,-615,-575,-550,-470,-390,-290,-225,-165,-110,-70,-55,-40,-30,-8,2,9,13,14,16,19,20,20,23,28,31,34,36,40,43,44,46,50,55,58,61,64,65,67,69,70,73,78,81,84,88,94,100,103,104,105,107,111,117,124,130,135,140,145,150,153,158,164,170,175,182,189,198,208,214,218,224,235,247,256,262,269,275,282,292,302,311,322,334,345,357,371,385,397,407,419,430,442,454,465,477,487,496,505,517,527,534,540,543,544,545,545,545,545,545,542,536,529,518,506,494,478,459,440,418,393,368,344,320,295,272,249,228,209,190,168,146,127,112,97,82,67,51,37,27,17,7,-2,-12,-17,-18,-19,-19,-19,-19,-46,-81,-93,-97,-98,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-79,-79,-79,-79,-77,-73,-69,-64,-61,-60,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-101,-105,-109,-114,-117,-118,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-117,-115,-114,-114,-114,-114,-114,-104,-91,-86,-85,-83,-79,-74,-69,-64,-61,-60,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-99,-99,-99,-99,-101,-105,-109,-114,-117,-118,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-117,-113,-109,-104,-101,-100,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-79,-79,-79,-79,-79,-79,-80,-80,-80,-80,-80,-80,-60,20,60,100,140,200,260,360,480,540,600,640,660,660,700,820,780,620,400,160,20,-220,-500,-640,-680,-740,-740,-660,-580,-540,-520,-460,-400,-320,-240,-160,-120,-60,20,40,60,80,100,113,118,119,120,122,126,130,135,140,145,150,155,158,159,160,160,162,166,170,175,178,179,180,180,182,186,190,195,200,205,210,215,220,225,230,235,240,245,250,255,260,265,270,275,282,291,300,310,318,324,330,335,342,351,360,370,382,396,410,425,438,449,460,470,480,490,500,510,520,530,540,550,562,576,590,605,618,629,640,650,658,664,670,675,678,679,680,680,680,680,680,680,677,669,660,650,637,619,600,580,560,540,520,500,478,454,430,405,382,361,340,320,302,286,270,255,240,225,210,195,180,165,150,135,122,111,100,90,82,76,72,71,70,70,70,70,60,47,42,41,40,40,40,40,40,40,40,40,40,40,40,40,42,46,50,55,58,59,60,60,60,60,60,60,62,66,70,75,78,79,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,53,18,6,2, COMMASEPARATE 5000 V3 100,105,110,115,118,119,120,120,122,126,130,135,140,145,150,155,160,165,170,175,178,179,180,180,180,180,180,180,182,186,190,195,202,211,220,230,238,244,250,255,262,271,280,290,300,310,320,330,340,350,360,370,380,390,400,410,422,436,450,465,478,489,500,510,520,530,540,550,558,564,570,575,582,591,600,610,617,619,620,620,618,614,610,605,597,584,570,555,540,525,510,495,477,454,430,405,382,361,340,320,300,280,260,240,222,206,190,175,162,151,140,130,120,110,100,90,80,70,60,50,42,36,30,25,20,15,10,5,0,-4,-9,-14,-17,-18,-19,-19,-17,-13,-9,-4,-1,0,0,0,0,0,0,0,2,6,10,15,18,19,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,18,14,10,5,0,-4,-9,-14,-17,-18,-19,-19,-21,-25,-29,-34,-37,-38,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-41,-45,-49,-54,-57,-58,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-74,-67,-65,-64,-64,-64,-64,-61,-56,-51,-46,-43,-40,-38,-34,-31,-30,-29,-29,-27,-25,-24,-24,-26,-28,-27,-27,-28,-29,-31,-35,-38,-42,-47,-52,-58,-65,-71,-71,-70,-71,-75,-79,-81,-78,-75,-76,-78,-82,-88,-92,-93,-95,-98,-102,-107,-110,-113,-115,-118,-119,-119,-119,-119,-119,-117,-115,-114,-116,-118,-117,-115,-116,-118,-119,-117,-115,-114,-114,-114,-114,-111,-105,-101,-103,-104,-101,-100,-90,-105,-110,-120,-120,-120,-120,-130,-120,-95,-45,-20,55,140,225,315,440,580,760,945,1210,1445,1590,1580,1455,1300,1125,765,360,45,-195,-400,-440,-410,-380,-375,-375,-360,-310,-265,-225,-180,-135,-95,-60,-40,-25,5,12,16,19,18,16,19,23,24,25,27,29,31,34,36,40,45,48,49,51,55,58,61,64,65,65,67,69,73,78,81,84,88,93,96,99,101,104,106,110,117,122,126,130,135,142,152,162,172,182,191,197,202,209,216,224,231,237,244,255,267,276,284,295,305,313,324,336,347,359,371,382,394,406,415,423,431,440,453,468,483,496,507,517,526,535,545,553,558,563,569,573,576,579,581,584,585,585,583,578,571,562,549,533,516,497,474,450,425,400,375,350,327,304,281,260,240,218,198,179,163,148,131,114,96,82,72,62,52,42,32,27,26,25,25,25,25,3,-25,-34,-37,-38,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-37,-33,-29,-24,-21,-20,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-16,-8,1,10,17,19,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,18,14,10,5,0,-4,-9,-14,-19,-24,-29,-34,-37,-38,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-41,-45,-49,-54,-57,-58,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-72,-63,-60,-59,-57,-53,-49,-42,-37,-32,-27,-23,-20,-18,-14,-11,-10,-11,-15,-18,-20,-23,-24,-24,-22,-17,-12,-10,-9,-11,-15,-18,-22,-27,-32,-38,-45,-51,-51,-50,-51,-55,-59,-62,-63,-65,-71,-76,-81,-88,-92,-93,-95,-98,-102,-107,-110,-113,-115,-118,-119,-119,-119,-119,-119,-119,-121,-125,-131,-136,-136,-135,-136,-136,-133,-127,-120,-116,-115,-114,-114,-111,-105,-101,-103,-104,-101,-100,-90,-105,-110,-140,-140,-140,-140,-150,-140,-115,-85,-60,-5,100,205,315,440,580,760,965,1230,1465,1610,1620,1535,1380,1165,765,340,-15,-275,-460,-480,-450,-460,-415,-395,-360,-310,-265,-205,-140,-95,-55,-20,0,-5,5,-1,-1,0,-1,-3,0,3,4,5,7,9,11,14,16,20,25,28,29,31,35,38,41,44,45,45,47,49,53,58,61,64,68,73,76,79,81,85,92,101,112,121,125,130,135,138,141,142,142,146,152,157,162,169,176,184,191,197,204,215,227,237,249,265,280,292,304,316,327,337,346,352,359,370,382,394,406,419,433,448,463,478,493,508,521,534,545,553,558,561,564,563,561,560,562,564,565,565,563,558,551,542,529,513,496,477,454,430,405,380,355,330,307,284,261,240,220,198,178,159,143,128,111,94,76,62,52,42,32,22,12,7,6,5,5,5,5,-16,-45,-54,-57,-58,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-77,-73,-69,-64,-61,-60,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-101,-105,-109,-114,-117,-118,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-121,-123,-124,-124,-124,-124,-124,-122,-120,-119,-119,-117,-113,-109,-102,-97,-93,-92,-93,-95,-96,-93,-90,-89,-89,-89,-87,-85,-84,-84,-84,-82,-77,-72,-70,-69,-71,-75,-78,-82,-87,-92,-98,-105,-111,-111,-110,-111,-115,-119,-122,-123,-125,-131,-136,-141,-148,-152,-153,-155,-158,-162,-167,-170,-173,-175,-178,-179,-179,-179,-179,-179,-177,-175,-174,-176,-178,-177,-175,-176,-178,-179,-177,-175,-174,-174,-174,-174,-171,-165,-161,-163,-164,-167,-180,-170,-185,-190,-200,-180,-180,-180,-190,-180,-155,-145,-120,-45,40,145,235,380,540,720,925,1190,1405,1530,1540,1455,1280,1045,665,240,-95,-355,-520,-540,-510,-480,-475,-455,-400,-350,-305,-245,-200,-175,-135,-100,-80,-85,-75,-68,-64,-61,-61,-63,-60,-56,-55,-54,-52,-50,-48,-45,-43,-39,-34,-31,-30,-28,-24,-21,-18,-15,-14,-14,-12,-10,-6,-1,1,4,8,13,16,19,21,24,26,30,37,42,46,50,55,60,67,72,77,84,91,97,102,109,116,124,131,137,144,155,167,177,189,205,220,232,244,256,267,279,291,302,314,328,341,354,366,379,393,408,423,434,441,447,451,457,466,474,478,483,489,493,496,499,501,504,505,505,503,498,491,482,469,453,436,417,394,370,345,320,295,270,247,222,196,170,145,120,98,79,63,48,31,14,-3,-17,-27,-37,-47,-55,-61,-63,-64,-64,-64,-64,-64,-96,-138,-152,-157,-158,-159,-159,-159,-157,-153,-149,-144,-141,-140,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-137,-133,-129,-124,-121,-120,-119,-119,-119,-119,-119,-119,-121,-125,-129,-134,-137,-138,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-141,-145,-149,-154,-157,-158,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-161,-165,-169,-174,-177,-178,-179,-179,-179,-179,-179,-179,-181,-185,-189,-194,-197,-198,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-192,-183,-180,-179,-177,-175,-174,-172,-172,-170,-166,-163,-160,-159,-159,-161,-165,-168,-169,-167,-165,-164,-164,-164,-162,-157,-152,-150,-149,-151,-155,-158,-162,-167,-172,-178,-185,-191,-191,-190,-191,-195,-199,-202,-203,-205,-211,-216,-221,-226,-226,-223,-220,-219,-222,-227,-230,-233,-235,-238,-239,-239,-239,-239,-239,-237,-235,-234,-236,-238,-237,-235,-236,-240,-244,-247,-250,-253,-254,-254,-254,-249,-239,-231,-228,-225,-221,-220,-210,-225,-230,-240,-220,-220,-240,-250,-240,-255,-185,-160,-105,-20,105,195,320,440,620,785,1050,1305,1470,1480,1395,1220,1025,665,260,-75,-335,-540,-580,-550,-520,-515,-515,-500,-450,-405,-345,-300,-255,-215,-180,-160,-145,-115,-108,-104,-101,-101,-103,-100,-96,-95,-94,-92,-90,-88,-85,-83,-79,-74,-71,-70,-68,-64,-61,-58,-55,-54,-54,-52,-50,-46,-41,-38,-35,-31,-26,-23,-20,-18,-15,-13,-9,-2,4,11,20,30,38,46,52,57,62,66,67,67,71,77,84,91,97,104,115,127,136,144,155,165,173,184,196,207,219,231,242,254,268,281,294,306,319,333,348,363,378,393,408,421,434,445,453,458,461,464,463,461,460,462,464,465,465,463,458,451,440,423,403,381,359,335,310,285,260,235,210,187,164,141,120,100,78,58,39,23,8,-8,-25,-43,-57,-67,-77,-87,-95,-101,-103,-104,-104,-104,-104,-104,-122,-147,-155,-158,-160,-164,-169,-174,-177,-178,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-177,-173,-169,-164,-161,-160,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-161,-165,-169,-174,-177,-178,-179,-179,-181,-185,-189,-194,-197,-198,-199,-199,-199,-199,-199,-199,-201,-205,-209,-214,-217,-218,-219,-219,-219,-219,-219,-219,-221,-225,-229,-234,-237,-238,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-241,-245,-249,-254,-257,-258,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-257,-253,-249,-242,-237,-232,-227,-223,-220,-218,-214,-211,-210,-209,-209,-207,-205,-204,-204,-204,-202,-197,-192,-190,-189,-191,-195,-198,-202,-207,-212,-218,-225,-231,-231,-230,-231,-235,-239,-242,-243,-245,-251,-256,-260,-263,-262,-258,-257,-258,-262,-267,-270,-273,-275,-278,-279,-279,-279,-279,-281,-283,-285,-289,-294,-297,-297,-295,-296,-298,-299,-297,-295,-294,-294,-294,-294,-291,-285,-281,-283,-284,-281,-280,-270,-285,-290,-300,-300,-320,-320,-310,-300,-295,-265,-240,-165,-80,25,115,240,360,540,725,990,1245,1410,1460,1375,1220,1065,765,280,-95,-355,-620,-680,-650,-600,-595,-575,-540,-470,-425,-365,-320,-275,-235,-200,-200,-185,-175,-168,-164,-161,-161,-163,-160,-156,-155,-154,-152,-150,-148,-145,-143,-139,-134,-131,-130,-128,-124,-121,-118,-115,-114,-114,-112,-110,-106,-101,-98,-95,-91,-86,-83,-80,-78,-75,-73,-69,-62,-57,-53,-49,-44,-39,-32,-27,-22,-15,-8,-2,3,9,16,24,31,37,44,55,67,76,84,95,105,113,124,136,147,159,171,182,194,208,221,234,246,259,273,288,303,316,327,337,346,355,365,373,378,383,389,393,396,399,401,404,405,407,409,408,406,400,388,373,356,337,314,290,265,240,215,190,167,144,121,100,80,58,38,19,3,-13,-34,-56,-78,-95,-106,-116,-126,-135,-141,-143,-144,-144,-144,-144,-144,-162,-187,-195,-198,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-201,-205,-209,-214,-217,-218,-219,-219,-217,-213,-209,-204,-201,-200,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-197,-193,-189,-184,-181,-180,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-181,-185,-189,-194,-199,-204,-209,-214,-217,-218,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-221,-225,-229,-234,-237,-238,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-241,-245,-249,-254,-257,-258,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-261,-265,-269,-274,-277,-278,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-279,-277,-273,-269,-262,-255,-246,-236,-228,-222,-218,-214,-211,-210,-209,-209,-207,-205,-204,-204,-204,-202,-197,-192,-190,-189,-191,-195,-198,-202,-207,-212,-218,-225,-231,-231,-230,-229,-229,-229,-227,-225,-226,-231,-236,-241,-248,-252,-253,-255,-258,-262,-267,-270,-273,-275,-278,-279,-279,-279,-279,-279,-277,-275,-274,-276,-278,-277,-275,-276,-278,-279,-277,-275,-274,-274,-274,-274,-271,-265,-261,-263,-264,-261,-260,-250,-265,-270,-280,-280,-280,-280,-290,-280,-255,-245,-220,-165,-80,25,155,240,360,540,765,1030,1265,1430,1440,1355,1200,1005,645,240,-95,-355,-560,-600,-570,-540,-535,-535,-520,-470,-425,-365,-320,-275,-235,-200,-180,-165,-155,-148,-144,-141,-141,-143,-140,-136,-135,-134,-132,-130,-128,-125,-123,-119,-114,-111,-110,-108,-104,-101,-98,-95,-94,-94,-92,-90,-86,-81,-78,-75,-71,-66,-63,-60,-58,-55,-53,-49,-42,-37,-33,-29,-24,-17,-7,3,13,23,31,37,42,49,56,64,71,75,78,84,91,97,104,115,125,133,144,156,167,179,191,202,214,228,241,254,266,279,293,308,323,336,347,357,366,374,380,383,383,384,390,393,396,399,401,404,405,407,409,408,406,400,388,373,356,339,320,300,280,258,234,210,187,164,141,120,100,78,58,39,23,8,-8,-25,-43,-57,-67,-77,-87,-95,-101,-103,-104,-104,-104,-104,-104,-129,-162,-173,-177,-178,-179,-179,-179,-181,-185,-189,-194,-197,-198,-199,-199,-197,-193,-189,-184,-181,-180,-179,-179,-179,-179,-179,-179,-177,-173,-169,-164,-161,-160,-159,-159,-159,-159,-159,-159,-157,-153,-149,-144,-141,-140,-139,-139,-139,-139,-139,-139,-141,-145,-149,-154,-157,-158,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-161,-165,-169,-174,-177,-178,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-181,-185,-189,-194,-197,-198,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-201,-205,-209,-214,-217,-218,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-221,-223,-224,-224,-224,-224,-224,-222,-220,-219,-219,-217,-213,-209,-202,-197,-192,-187,-183,-180,-178,-174,-171,-170,-169,-169,-167,-165,-164,-164,-164,-162,-157,-152,-150,-149,-151,-155,-158,-162,-167,-172,-178,-185,-191,-191,-190,-191,-195,-199,-202,-203,-205,-211,-216,-221,-228,-232,-233,-235,-238,-242,-247,-250,-253,-255,-258,-259,-257,-253,-249,-244,-239,-236,-235,-236,-238,-237,-235,-236,-240,-244,-247,-250,-253,-254,-254,-254,-251,-245,-241,-243,-250,-256,-260,-230,-245,-230,-240,-240,-240,-240,-250,-240,-215,-185,-180,-125,-40,65,155,280,420,600,845,1110,1325,1450,1460,1375,1220,1025,665,280,-55,-295,-500,-520,-490,-480,-495,-495,-480,-430,-405,-345,-300,-255,-215,-180,-160,-145,-135,-128,-124,-121,-121,-123,-120,-116,-115,-114,-112,-110,-108,-107,-108,-109,-109,-109,-109,-107,-103,-100,-98,-95,-94,-94,-92,-90,-86,-81,-78,-75,-71,-66,-63,-60,-58,-55,-53,-49,-42,-37,-33,-29,-24,-19,-12,-7,-2,4,11,17,22,29,36,44,51,57,64,75,87,96,104,115,125,133,144,156,167,179,191,202,214,228,241,254,266,279,293,308,323,336,347,357,366,375,385,393,398,403,409,413,416,419,421,424,425,425,423,418,411,404,395,383,371,355,333,309,285,260,235,210,187,164,141,120,100,78,58,39,23,6,-14,-36,-58,-75,-86,-96,-106,-115,-121,-123,-124,-124,-124,-124,-124,-149,-182,-193,-197,-198,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-197,-193,-189,-184,-181,-180,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-181,-185,-189,-194,-197,-198,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-201,-205,-209,-214,-217,-218,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-221,-225,-229,-234,-237,-238,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-241,-245,-249,-254,-257,-258,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-259,-257,-253,-249,-242,-237,-232,-227,-223,-220,-218,-214,-211,-210,-209,-209,-207,-205,-204,-204,-202,-197,-187,-177,-172,-170,-171,-175,-178,-182,-187,-192,-200,-211,-221,-226,-228,-230,-234,-239,-241,-238,-235,-236,-238,-242,-248,-252,-253,-255,-258,-262,-267,-270,-273,-275,-278,-279,-279,-279,-279,-279,-277,-275,-274,-276,-278,-277,-275,-276,-278,-279,-277,-275,-274,-274,-274,-274,-271,-265,-261,-263,-264,-261,-260,-250,-265,-270,-280,-280,-280,-280,-290,-280,-255,-225,-200,-145,-60,45,135,280,420,600,805,1070,1305,1450,1440,1355,1220,1025,665,280,-55,-315,-520,-560,-550,-520,-515,-535,-520,-490,-445,-385,-320,-275,-235,-200,-160,-145,-155,-148,-144,-141,-141,-143,-140,-136,-135,-134,-132,-130,-128,-125,-123,-119,-114,-111,-110,-108,-104,-101,-98,-95,-94,-94,-92,-90,-86,-80,-73,-65,-56,-48,-44,-41,-38,-35,-33,-29,-22,-17,-13,-9,-4,0,7,12,17,24,31,37,42,49,56,64,71,77,84,95,107,116,124,135,145,153,164,176,187,199,211,222,234,248,261,274,286,299,313,328,343,356,367,377,386,395,405,413,418,423,429,433,436,439,441,444,445,445,443,438,431,422,409,393,376,357,334,310,285,260,235,210,187,164,141,120,100,78,58,39,23,8,-8,-25,-43,-57,-67,-77,-87,-95,-101,-103,-104,-104,-104,-104,-104,-129,-162,-173,-177,-178,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-177,-173,-169,-164,-161,-160,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-157,-153,-149,-144,-141,-140,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-141,-145,-149,-154,-157,-158,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-161,-165,-169,-174,-177,-178,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-181,-185,-189,-194,-197,-198,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-192,-183,-180,-179,-177,-173,-169,-162,-157,-152,-147,-143,-140,-138,-134,-131,-130,-129,-129,-127,-125,-124,-124,-124,-122,-117,-112,-110,-109,-111,-115,-118,-122,-127,-132,-138,-145,-151,-151,-150,-151,-155,-159,-162,-163,-165,-171,-176,-181,-188,-190,-188,-185,-183,-184,-187,-190,-193,-195,-198,-199,-199,-199,-199,-199,-197,-195,-194,-196,-198,-197,-195,-196,-198,-199,-197,-195,-194,-194,-194,-194,-191,-185,-181,-183,-184,-181,-180,-170,-185,-170,-180,-180,-180,-200,-230,-220,-175,-145,-120,-65,20,105,195,340,480,700,905,1170,1385,1510,1520,1455,1340,1145,785,380,45,-235,-440,-480,-450,-420,-415,-415,-400,-350,-305,-245,-220,-175,-135,-100,-80,-65,-75,-68,-64,-61,-61,-63,-60,-56,-55,-54,-52,-50,-48,-45,-43,-39,-34,-29,-24,-17,-8,-2,1,4,5,5,7,9,13,18,21,24,28,33,36,39,41,44,46,50,57,61,60,60,60,62,67,72,77,84,91,97,102,109,116,124,131,137,144,155,167,176,184,195,205,213,224,236,247,259,271,282,294,308,321,334,346,359,373,388,403,416,427,437,446,454,460,463,463,464,470,473,476,479,481,484,485,487,489,488,486,480,468,453,436,419,400,380,360,338,314,290,267,244,221,200,180,158,138,119,103,88,71,54,36,22,12,2,-7,-15,-21,-23,-24,-24,-24,-24,-24,-49,-82,-93,-97,-98,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-77,-73,-69,-64,-61,-60,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-81,-85,-89,-94,-99,-104,-109,-114,-117,-118,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-121,-125,-129,-134,-137,-138,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-137,-133,-129,-122,-117,-112,-107,-103,-100,-98,-94,-91,-90,-89,-89,-87,-85,-84,-84,-84,-82,-77,-70,-64,-59,-56,-56,-58,-62,-67,-73,-84,-96,-106,-110,-109,-111,-115,-119,-122,-123,-125,-131,-136,-141,-148,-152,-153,-155,-158,-162,-167,-170,-173,-175,-178,-179,-179,-179,-179,-179,-177,-173,-169,-166,-163,-159,-156,-156,-158,-159,-157,-155,-154,-154,-154,-154,-151,-145,-141,-143,-144,-147,-160,-150,-165,-170,-160,-160,-160,-160,-170,-160,-135,-105,-80,-25,60,165,235,360,500,660,865,1130,1385,1530,1540,1455,1300,1125,785,380,65,-195,-400,-460,-430,-400,-415,-415,-420,-370,-325,-265,-220,-175,-135,-100,-80,-65,-35,-28,-24,-21,-21,-23,-20,-16,-15,-14,-12,-10,-8,-5,-3,1,5,8,9,11,15,18,21,24,25,25,27,29,33,38,41,44,48,53,56,59,61,64,66,70,77,82,86,90,95,100,107,112,117,124,131,137,142,149,156,164,171,177,184,195,207,216,224,235,245,253,264,276,287,299,311,322,334,348,361,374,386,399,413,428,443,456,467,477,486,497,511,524,533,541,549,553,556,559,561,564,565,565,563,558,551,544,535,523,511,495,473,449,425,400,375,350,327,304,281,260,240,218,198,179,163,146,125,103,81,64,53,43,33,24,18,16,15,15,15,15,15,-16,-58,-72,-77,-78,-79,-79,-79,-79,-79,-79,-79,-77,-73,-69,-64,-61,-60,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-57,-53,-49,-44,-41,-40,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-41,-45,-49,-54,-57,-58,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-74,-67,-65,-64,-62,-58,-54,-49,-44,-41,-40,-39,-39,-36,-28,-19,-9,-2,0,0,0,0,0,0,0,-3,-10,-19,-29,-36,-38,-39,-39,-41,-45,-49,-54,-57,-58,-59,-59,-61,-65,-69,-74,-79,-84,-89,-94,-97,-98,-99,-99,-99,-99,-99,-99,-101,-105,-109,-114,-117,-118,-119,-119,-117,-113,-109,-104,-101,-100,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-100,-100,-100,-100,-100,-100,-100,-100,-100,-100,-80,-40,20,100,200,280,400,520,700,900,1140,1420,1600,1660,1600,1460,1300,1000,580,240,-60,-300,-400,-400,-380,-380,-380,-360,-340,-280,-240,-160,-140,-100,-40,-40,-20,20,40,40,40,40,40,42,46,50,55,60,65,70,75,78,79,80,80,80,80,80,80,82,86,90,95,100,105,110,115,118,119,120,120,122,126,130,135,140,145,150,155,162,171,180,190,198,204,210,215,220,225,230,235,242,251,260,270,280,290,300,310,320,330,340,350,360,370,380,390,402,416,430,445,458,469,480,490,500,510,520,530,540,550,560,570,577,579,580,580,580,580,580,580,577,569,560,550,538,524,510,495,477,454,430,405,380,355,330,305,282,261,240,220,202,186,170,155,138,119,100,80,63,51,40,30,18,4,-10,-24,-37,-48,-55,-58,-59,-59,-59,-59,-66,-75,-78,-79,-81,-85,-89,-94,-97,-98,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-77,-73,-69,-64,-61,-60,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-49,20,103,98,33,11,4, COMMASEPARATE 5000 V4 120,120,120,120,122,126,130,135,138,139,140,140,142,146,150,155,160,165,170,175,178,179,180,180,180,180,180,180,180,180,180,180,182,186,190,195,202,211,220,230,238,244,250,255,262,271,280,290,298,304,310,315,322,331,340,350,360,370,380,390,400,410,420,430,438,444,450,455,462,471,480,490,498,504,510,515,518,519,520,520,518,514,510,505,500,495,490,485,477,464,450,435,420,405,390,375,360,345,330,315,298,279,260,240,223,211,200,190,180,170,160,150,142,136,130,125,118,109,100,90,82,76,70,65,62,61,60,60,58,54,50,45,42,41,40,40,42,46,50,55,58,59,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,58,54,50,45,42,41,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,38,34,30,25,22,21,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,18,14,10,7,6,5,5,5,5,12,21,24,25,27,29,31,34,33,33,34,36,39,41,44,46,49,50,50,50,50,50,50,50,52,56,57,57,59,58,54,53,54,56,59,56,49,43,41,39,35,27,17,12,11,7,1,-2,-5,-8,-9,-9,-9,-12,-18,-24,-27,-28,-30,-34,-39,-44,-49,-52,-53,-52,-50,-48,-45,-43,-37,-32,-32,-35,-38,-37,-35,-34,-32,-30,-29,-27,-23,-20,-19,-19,-17,-15,-35,-50,-35,-40,-50,-80,-95,-125,-135,-125,-115,-70,15,50,135,220,350,545,765,1020,1290,1535,1720,1795,1765,1620,1415,1105,750,340,45,-75,-160,-170,-165,-175,-185,-195,-170,-150,-115,-85,-55,-30,0,-5,5,35,50,57,57,56,55,57,59,60,58,54,50,47,46,47,51,54,56,62,69,75,80,83,84,86,89,90,92,96,97,94,90,87,86,89,95,98,101,105,112,121,127,131,135,140,143,144,143,143,144,145,147,151,159,170,182,192,199,206,214,221,229,236,244,250,255,262,271,279,286,295,303,313,324,335,345,357,366,372,381,389,396,404,411,420,430,438,444,448,451,454,453,453,454,456,460,463,464,465,463,459,456,452,444,436,425,408,389,370,350,330,310,292,276,262,247,231,214,196,180,167,152,137,124,111,102,96,89,81,72,62,57,56,55,55,55,55,37,12,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,6,10,15,18,19,20,20,20,20,20,20,20,20,20,20,22,26,30,35,38,39,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,38,34,30,25,22,21,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,18,14,10,5,-1,-10,-19,-29,-36,-38,-39,-39,-37,-33,-29,-24,-21,-20,-19,-19,-21,-25,-29,-34,-39,-44,-49,-54,-57,-58,-59,-59,-59,-59,-59,-59,-59,-59,-59,-57,-55,-54,-54,-52,-48,-42,-33,-25,-18,-10,-4,-1,1,4,6,9,8,4,0,-4,-7,-8,-7,-2,6,12,16,19,18,14,11,9,6,4,-1,-10,-16,-18,-19,-19,-22,-27,-28,-29,-32,-38,-44,-51,-58,-64,-67,-68,-72,-78,-84,-87,-88,-90,-93,-94,-94,-94,-94,-94,-94,-96,-100,-106,-111,-111,-110,-111,-113,-112,-107,-100,-96,-93,-90,-89,-87,-83,-80,-79,-79,-77,-75,-75,-90,-95,-120,-130,-140,-155,-185,-175,-165,-155,-130,-85,-10,75,180,310,465,685,960,1230,1455,1640,1715,1705,1580,1355,1005,630,240,-55,-195,-240,-250,-265,-275,-265,-255,-230,-210,-155,-125,-95,-70,-40,-5,5,-5,-16,-18,-20,-23,-24,-22,-20,-19,-19,-19,-19,-17,-15,-13,-9,-6,-5,-3,0,0,2,4,5,7,9,10,12,16,19,20,20,22,24,28,34,38,41,45,52,61,67,71,75,80,83,84,83,83,84,85,87,91,97,104,111,117,121,127,134,141,149,156,164,170,175,182,191,199,206,215,223,233,244,255,265,277,287,297,311,324,335,343,351,360,370,378,384,388,393,399,403,408,411,410,410,408,406,405,403,399,395,387,374,361,347,329,310,290,270,250,230,212,196,182,167,151,135,122,111,102,91,77,64,51,42,36,29,21,12,2,-2,-3,-4,-4,-4,-4,-22,-47,-55,-58,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-77,-73,-69,-64,-61,-60,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-101,-105,-109,-114,-117,-118,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-121,-123,-124,-124,-124,-124,-124,-121,-116,-115,-114,-112,-110,-108,-104,-101,-96,-90,-84,-81,-78,-75,-73,-70,-69,-69,-69,-69,-71,-75,-78,-77,-72,-67,-63,-60,-61,-65,-68,-70,-71,-70,-71,-75,-78,-79,-79,-79,-82,-87,-88,-89,-92,-98,-104,-111,-118,-124,-127,-128,-132,-138,-144,-147,-150,-156,-163,-169,-172,-173,-174,-174,-172,-170,-169,-171,-173,-172,-170,-171,-175,-178,-177,-175,-174,-172,-170,-169,-167,-163,-160,-159,-159,-157,-155,-155,-170,-175,-180,-190,-200,-215,-245,-255,-245,-235,-210,-165,-70,15,120,250,445,665,900,1150,1375,1540,1615,1585,1440,1215,885,530,140,-135,-275,-320,-310,-325,-335,-305,-295,-270,-250,-215,-185,-175,-150,-120,-105,-95,-85,-83,-80,-81,-83,-84,-82,-80,-79,-79,-79,-79,-77,-75,-73,-69,-66,-65,-63,-60,-59,-57,-55,-54,-52,-50,-49,-47,-43,-40,-39,-39,-37,-35,-31,-25,-21,-20,-19,-17,-13,-10,-8,-4,0,5,10,13,18,23,24,26,30,37,44,51,57,61,67,74,81,89,96,104,110,115,122,131,139,146,155,163,173,184,195,205,217,227,237,251,264,275,283,291,300,310,318,324,328,333,339,343,348,353,356,360,363,364,365,363,359,355,347,334,321,307,289,270,250,230,210,190,172,154,136,117,96,75,57,41,27,12,-2,-15,-28,-37,-43,-50,-58,-65,-71,-73,-74,-74,-74,-74,-74,-96,-125,-134,-137,-138,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-137,-133,-129,-124,-121,-120,-119,-119,-121,-125,-129,-134,-137,-138,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-141,-145,-149,-154,-157,-158,-159,-159,-159,-159,-159,-159,-161,-165,-169,-174,-177,-178,-179,-179,-179,-179,-179,-179,-177,-173,-169,-164,-161,-160,-159,-159,-159,-159,-159,-159,-161,-165,-169,-174,-177,-178,-179,-179,-179,-179,-179,-171,-160,-156,-155,-153,-150,-148,-144,-141,-138,-135,-134,-136,-136,-135,-133,-130,-131,-135,-139,-144,-147,-148,-147,-142,-133,-127,-123,-120,-119,-119,-117,-115,-114,-116,-121,-130,-136,-138,-139,-139,-142,-147,-148,-149,-152,-158,-164,-171,-178,-184,-187,-188,-192,-198,-204,-207,-208,-210,-213,-214,-214,-214,-214,-214,-212,-210,-209,-211,-213,-212,-210,-211,-215,-218,-217,-215,-214,-212,-210,-209,-207,-203,-200,-199,-199,-197,-195,-195,-210,-215,-220,-210,-220,-235,-265,-295,-285,-275,-250,-205,-130,-45,60,190,345,545,780,1050,1315,1520,1595,1585,1460,1255,945,590,200,-115,-275,-320,-330,-345,-355,-365,-355,-330,-310,-275,-265,-235,-210,-180,-165,-135,-125,-123,-120,-121,-123,-124,-122,-120,-119,-119,-119,-119,-117,-115,-113,-109,-106,-105,-103,-100,-99,-97,-95,-94,-92,-90,-89,-87,-83,-80,-79,-79,-77,-75,-71,-65,-61,-58,-54,-47,-38,-30,-23,-14,-4,4,10,13,18,21,19,16,15,17,19,21,22,22,27,34,41,50,62,74,85,93,101,110,118,126,135,143,153,164,175,185,197,207,217,231,244,255,263,271,280,290,298,304,308,313,319,323,328,333,336,340,343,344,345,343,339,333,321,304,286,267,244,220,195,172,151,130,112,96,82,67,51,34,16,0,-13,-25,-36,-45,-53,-59,-64,-71,-78,-85,-91,-93,-94,-94,-94,-94,-94,-116,-145,-154,-157,-158,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-161,-165,-169,-174,-177,-178,-179,-179,-177,-173,-169,-164,-161,-160,-159,-159,-159,-159,-159,-159,-157,-153,-149,-144,-141,-140,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-141,-145,-149,-154,-157,-158,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-161,-165,-169,-174,-177,-178,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-181,-185,-189,-194,-197,-198,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-201,-205,-209,-214,-217,-218,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-217,-215,-214,-214,-212,-210,-208,-204,-201,-196,-190,-184,-179,-172,-165,-158,-152,-150,-149,-149,-149,-149,-149,-147,-142,-133,-127,-123,-120,-121,-125,-128,-130,-133,-135,-141,-150,-156,-158,-159,-159,-162,-167,-168,-169,-172,-178,-184,-191,-198,-204,-207,-208,-212,-218,-224,-227,-228,-230,-233,-234,-234,-234,-234,-234,-232,-230,-229,-231,-233,-232,-230,-231,-235,-238,-237,-235,-234,-232,-230,-229,-227,-223,-220,-221,-225,-228,-230,-235,-250,-255,-260,-270,-260,-275,-305,-315,-305,-295,-270,-225,-170,-85,20,150,285,485,740,1030,1275,1460,1575,1565,1460,1295,985,610,200,-135,-335,-400,-410,-405,-415,-405,-395,-350,-330,-295,-265,-235,-210,-200,-185,-175,-165,-163,-160,-161,-163,-164,-162,-160,-159,-159,-159,-159,-157,-155,-153,-149,-146,-145,-143,-140,-139,-137,-135,-134,-132,-130,-129,-127,-123,-120,-119,-119,-117,-115,-111,-105,-101,-100,-99,-97,-93,-90,-88,-84,-79,-72,-63,-55,-46,-38,-35,-33,-29,-22,-15,-8,-2,0,2,4,6,10,17,24,30,37,47,61,74,85,95,103,113,123,129,135,142,149,158,171,184,195,203,211,220,230,238,244,248,253,259,263,268,274,281,290,298,303,304,303,299,295,287,274,261,247,229,210,190,170,150,130,112,96,82,67,51,34,16,0,-13,-27,-42,-55,-68,-77,-83,-90,-98,-107,-117,-122,-123,-124,-124,-124,-124,-142,-167,-175,-178,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-177,-173,-169,-164,-161,-160,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-161,-165,-169,-174,-177,-178,-179,-179,-179,-179,-179,-179,-181,-185,-189,-194,-197,-198,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-201,-205,-209,-214,-217,-218,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-221,-225,-229,-234,-237,-238,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-239,-237,-235,-234,-234,-232,-230,-228,-224,-219,-211,-200,-189,-182,-178,-175,-173,-170,-169,-169,-169,-169,-169,-169,-167,-162,-153,-147,-143,-140,-141,-145,-148,-150,-153,-155,-161,-170,-176,-178,-179,-179,-182,-187,-188,-189,-192,-198,-204,-211,-218,-224,-227,-227,-227,-228,-229,-229,-229,-231,-233,-235,-239,-244,-249,-252,-252,-250,-249,-249,-247,-242,-235,-233,-235,-238,-237,-235,-234,-232,-230,-229,-227,-223,-220,-219,-219,-217,-215,-215,-230,-235,-240,-250,-260,-275,-305,-315,-305,-295,-290,-245,-170,-65,40,150,285,505,760,1030,1255,1460,1535,1525,1400,1195,885,530,160,-135,-295,-340,-350,-365,-375,-405,-395,-350,-330,-295,-265,-235,-210,-180,-165,-155,-165,-163,-160,-161,-163,-164,-162,-160,-159,-159,-159,-159,-157,-153,-147,-138,-130,-126,-123,-120,-119,-117,-115,-114,-112,-110,-109,-107,-103,-100,-99,-99,-97,-95,-91,-85,-81,-80,-79,-77,-73,-72,-73,-74,-74,-72,-68,-65,-61,-55,-49,-42,-33,-24,-16,-8,-2,0,2,4,6,10,17,24,30,35,42,51,59,66,75,83,93,104,115,125,137,147,157,171,184,195,203,211,220,230,238,244,248,251,254,253,253,254,256,260,263,266,270,273,274,275,272,264,256,245,228,209,190,170,150,130,112,96,82,67,51,34,16,0,-13,-27,-42,-55,-68,-75,-78,-80,-83,-89,-97,-102,-103,-104,-104,-104,-104,-129,-162,-173,-177,-178,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-177,-173,-169,-164,-161,-160,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-161,-165,-169,-174,-177,-178,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-181,-185,-189,-194,-197,-198,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-197,-193,-189,-184,-181,-180,-179,-179,-181,-185,-189,-194,-197,-198,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-201,-203,-204,-204,-204,-204,-204,-201,-196,-195,-194,-192,-190,-188,-184,-181,-176,-170,-164,-161,-158,-155,-153,-150,-149,-149,-149,-149,-149,-149,-147,-142,-133,-127,-123,-120,-121,-125,-128,-130,-133,-135,-141,-150,-156,-158,-159,-159,-162,-167,-168,-169,-172,-178,-184,-191,-198,-204,-207,-208,-212,-218,-224,-227,-228,-230,-233,-234,-232,-228,-224,-219,-214,-211,-210,-211,-213,-212,-210,-211,-215,-218,-217,-215,-216,-218,-220,-224,-226,-223,-220,-219,-219,-217,-215,-215,-210,-215,-220,-230,-240,-255,-265,-275,-265,-255,-250,-225,-150,-65,40,190,365,605,860,1110,1315,1460,1535,1525,1400,1195,885,530,160,-115,-235,-280,-310,-345,-355,-385,-375,-350,-330,-295,-265,-235,-210,-180,-185,-175,-165,-149,-142,-142,-143,-144,-142,-140,-139,-139,-139,-139,-137,-135,-133,-129,-126,-125,-123,-120,-119,-117,-115,-114,-112,-110,-109,-107,-103,-100,-99,-99,-97,-95,-91,-85,-81,-80,-79,-77,-73,-70,-68,-64,-59,-54,-49,-46,-41,-36,-35,-33,-29,-22,-15,-8,-2,1,7,14,21,29,36,44,50,55,62,71,79,86,95,103,113,124,135,145,157,167,177,191,204,215,223,231,240,250,258,264,268,273,279,283,288,293,296,300,303,306,310,313,314,313,306,294,281,267,249,230,210,190,170,150,132,116,102,87,71,54,36,20,7,-9,-27,-45,-63,-75,-83,-90,-98,-105,-111,-113,-114,-114,-114,-114,-114,-136,-165,-174,-177,-178,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-177,-173,-169,-164,-161,-160,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-161,-165,-169,-174,-177,-178,-179,-179,-181,-185,-189,-194,-197,-198,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-201,-205,-209,-214,-217,-218,-219,-219,-219,-219,-219,-219,-217,-213,-209,-204,-201,-200,-199,-199,-201,-205,-209,-212,-213,-214,-214,-214,-214,-214,-214,-214,-214,-212,-210,-208,-204,-201,-196,-190,-184,-181,-178,-175,-173,-170,-169,-169,-169,-169,-169,-169,-166,-156,-143,-132,-125,-121,-121,-125,-128,-130,-133,-135,-143,-155,-166,-173,-177,-178,-182,-187,-187,-183,-182,-183,-185,-191,-198,-204,-207,-208,-212,-218,-224,-227,-228,-230,-233,-234,-234,-234,-234,-234,-232,-230,-229,-231,-233,-232,-230,-231,-235,-238,-237,-235,-234,-232,-228,-224,-217,-208,-202,-200,-199,-197,-195,-195,-230,-235,-240,-250,-260,-275,-325,-335,-305,-295,-270,-225,-150,-65,60,190,345,565,820,1090,1315,1500,1575,1565,1460,1255,945,630,200,-95,-255,-300,-330,-345,-355,-365,-355,-350,-330,-295,-265,-235,-210,-180,-145,-135,-145,-143,-140,-141,-143,-144,-142,-140,-139,-139,-139,-139,-137,-133,-127,-118,-110,-106,-103,-100,-99,-99,-101,-105,-108,-109,-109,-107,-103,-99,-94,-89,-82,-77,-72,-65,-61,-61,-65,-68,-69,-69,-67,-63,-59,-52,-43,-35,-26,-18,-15,-13,-9,-2,4,11,17,21,27,34,41,49,56,64,70,75,82,91,99,106,115,123,133,144,155,165,177,187,197,211,224,235,243,251,260,270,278,284,288,293,299,303,308,313,316,320,323,324,325,323,319,315,307,294,281,267,249,230,210,190,170,150,132,116,102,87,71,54,36,20,7,-9,-27,-45,-63,-75,-83,-90,-98,-104,-106,-105,-104,-104,-104,-104,-104,-122,-147,-155,-158,-160,-164,-169,-174,-177,-178,-179,-179,-177,-173,-169,-164,-161,-160,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-157,-153,-149,-144,-141,-140,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-141,-145,-149,-154,-157,-158,-159,-159,-157,-153,-149,-144,-141,-140,-139,-139,-141,-145,-149,-154,-159,-164,-169,-174,-177,-178,-179,-179,-179,-179,-179,-179,-177,-173,-169,-164,-161,-160,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-157,-155,-154,-154,-152,-150,-148,-144,-141,-136,-130,-124,-121,-118,-115,-113,-110,-109,-109,-109,-109,-109,-109,-107,-102,-93,-87,-83,-80,-81,-85,-88,-90,-93,-95,-101,-110,-116,-118,-119,-119,-122,-125,-123,-119,-117,-120,-124,-131,-138,-144,-147,-148,-152,-158,-164,-167,-168,-170,-173,-174,-174,-174,-174,-174,-172,-170,-169,-171,-173,-172,-170,-171,-175,-178,-177,-175,-174,-172,-170,-169,-167,-163,-160,-159,-159,-157,-155,-155,-150,-155,-160,-170,-200,-215,-245,-255,-225,-215,-190,-145,-70,15,120,250,425,665,920,1150,1355,1520,1595,1585,1480,1275,965,630,240,-55,-195,-240,-250,-265,-275,-285,-295,-270,-230,-195,-185,-155,-130,-100,-85,-75,-85,-83,-80,-81,-83,-84,-82,-80,-79,-79,-79,-79,-77,-73,-67,-58,-50,-46,-43,-40,-39,-39,-41,-45,-48,-49,-49,-47,-43,-40,-39,-39,-37,-35,-31,-25,-21,-20,-19,-17,-13,-10,-8,-4,0,5,10,13,18,23,24,26,30,37,44,51,57,61,67,74,81,89,96,104,110,115,122,131,139,146,155,163,173,184,195,205,217,227,237,251,264,275,283,291,300,310,318,324,328,331,334,333,333,334,336,340,343,346,350,353,354,355,352,344,336,325,308,289,270,250,230,210,192,176,162,147,131,114,96,80,67,52,37,24,11,2,-4,-11,-18,-25,-31,-33,-34,-34,-34,-34,-34,-56,-85,-94,-97,-98,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-101,-105,-109,-114,-117,-118,-119,-119,-117,-113,-109,-104,-101,-100,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-101,-105,-109,-114,-117,-118,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-117,-113,-109,-104,-101,-100,-99,-99,-101,-105,-109,-114,-117,-118,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-117,-115,-114,-114,-112,-110,-108,-104,-101,-96,-90,-84,-81,-78,-75,-73,-70,-69,-69,-69,-69,-69,-67,-62,-52,-38,-29,-24,-21,-21,-25,-28,-30,-33,-35,-41,-50,-56,-58,-59,-59,-62,-67,-68,-69,-72,-78,-84,-91,-98,-104,-107,-108,-112,-118,-124,-127,-128,-130,-133,-134,-134,-134,-134,-134,-132,-130,-129,-131,-133,-132,-130,-131,-135,-138,-137,-135,-134,-132,-130,-129,-127,-123,-120,-119,-119,-124,-135,-135,-130,-135,-140,-150,-160,-155,-185,-195,-185,-195,-170,-145,-70,15,120,250,405,625,880,1170,1395,1600,1675,1665,1540,1355,1045,710,320,5,-175,-220,-230,-245,-275,-285,-295,-270,-230,-195,-165,-135,-110,-80,-65,-55,-45,-43,-40,-41,-43,-44,-42,-40,-39,-37,-33,-29,-22,-17,-13,-9,-6,-6,-8,-10,-14,-16,-15,-14,-12,-10,-9,-7,-3,1,5,10,17,22,27,34,38,38,34,31,30,30,32,36,40,45,50,53,58,64,70,77,86,95,103,111,117,121,127,134,141,149,156,164,170,175,182,191,199,205,210,213,218,226,235,245,257,269,283,301,319,333,343,351,360,372,384,395,403,411,419,423,428,431,430,430,428,426,425,423,419,416,412,404,396,385,368,349,330,310,290,270,252,236,222,207,191,174,156,140,127,111,92,74,56,44,36,29,21,12,2,-2,-3,-4,-4,-4,-4,-29,-62,-73,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-77,-73,-69,-64,-61,-60,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-87,-72,-67,-65,-63,-60,-59,-59,-59,-59,-59,-59,-59,-57,-53,-49,-44,-39,-34,-29,-24,-21,-20,-19,-19,-21,-25,-29,-34,-37,-38,-39,-39,-41,-45,-49,-54,-59,-64,-69,-74,-79,-84,-89,-94,-99,-104,-109,-114,-117,-118,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-120,-120,-120,-120,-120,-140,-160,-180,-200,-200,-200,-160,-120,-80,40,120,240,380,580,840,1140,1380,1600,1720,1720,1660,1480,1220,860,520,140,-120,-220,-240,-260,-280,-280,-280,-260,-240,-200,-160,-160,-120,-80,-80,-60,-60,-40,-39,-39,-39,-39,-37,-33,-29,-24,-21,-20,-19,-19,-19,-19,-19,-19,-16,-8,1,10,17,19,20,20,22,26,30,35,38,39,40,40,42,46,50,55,60,65,70,75,80,85,90,95,100,105,110,115,120,125,130,135,142,151,160,170,178,184,190,195,200,205,210,215,222,231,240,250,260,270,280,290,300,310,320,330,340,350,360,370,380,390,400,410,418,424,430,435,438,439,440,440,438,434,430,425,418,409,400,390,378,364,350,335,318,299,280,260,240,220,200,180,162,146,130,115,100,85,70,55,40,25,10,-4,-17,-28,-39,-49,-57,-63,-67,-68,-69,-69,-69,-69,-79,-92,-97,-98,-99,-99,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-42,43,144,131,44,15,5, COMMASEPARATE 5000 V5 240,240,240,240,240,240,240,240,242,246,250,255,258,259,260,260,260,260,260,260,262,266,270,275,278,279,280,280,282,286,290,295,300,305,310,315,320,325,330,335,338,339,340,340,342,346,350,355,360,365,370,375,382,391,400,410,418,424,430,435,442,451,460,470,480,490,500,510,520,530,540,550,558,564,570,575,578,579,580,580,580,580,580,580,578,574,570,565,558,549,540,530,518,504,490,475,460,445,430,415,400,385,370,355,342,331,320,310,300,290,280,270,262,256,250,245,240,235,230,225,220,215,210,205,200,195,190,185,182,181,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,180,182,186,190,195,198,199,200,200,200,200,200,200,198,194,190,185,182,181,180,180,180,180,180,180,180,180,180,180,182,186,190,195,198,199,200,200,198,194,190,185,182,181,180,180,180,180,180,180,180,180,180,180,180,180,180,180,178,174,170,165,162,161,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,160,158,154,150,145,142,141,140,140,140,140,140,140,140,140,140,140,140,148,159,163,164,166,169,170,170,168,166,167,169,170,172,174,175,175,175,175,177,181,184,185,187,191,195,200,205,208,209,210,210,208,204,200,195,192,191,190,190,190,188,186,185,183,178,169,160,152,144,136,130,127,124,121,122,124,123,121,120,120,123,121,114,111,110,108,106,104,101,102,107,112,114,115,118,118,113,109,106,105,107,112,119,123,123,121,120,120,100,85,75,45,15,0,-5,-20,-20,-15,30,75,135,185,260,365,515,705,915,1145,1325,1480,1540,1500,1380,1210,1015,775,480,240,160,95,55,15,-20,-20,-30,-5,5,35,60,80,85,105,115,115,140,137,131,125,118,113,116,122,124,125,125,125,125,125,127,129,130,130,132,134,135,135,135,135,135,137,141,144,145,145,147,151,154,155,157,159,160,160,163,168,171,174,175,177,179,181,184,185,188,194,200,205,208,211,217,224,228,229,230,232,232,231,232,237,246,255,265,275,282,287,296,302,306,310,317,324,335,345,352,357,359,361,367,374,383,394,406,417,422,427,434,440,445,448,451,455,458,461,464,465,465,462,452,437,424,415,405,395,385,373,361,347,332,317,301,284,266,250,235,223,214,203,191,179,166,159,155,147,137,127,119,116,115,115,115,115,115,103,88,83,81,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,78,74,70,65,62,61,60,60,62,66,70,75,78,79,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,78,74,70,65,62,61,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,58,54,50,45,42,41,40,40,40,40,40,40,40,40,40,40,37,32,31,30,30,30,30,33,38,39,40,43,48,49,51,54,55,57,57,54,53,54,55,55,55,55,57,61,62,59,56,55,57,61,65,68,69,70,70,68,66,65,65,67,71,75,80,85,87,86,85,83,78,69,60,52,42,31,20,12,4,-3,-7,-10,-14,-17,-18,-19,-14,-12,-15,-13,-10,-11,-13,-15,-18,-17,-12,-7,-5,-4,-1,-1,-8,-15,-23,-29,-31,-26,-20,-16,-16,-18,-20,-20,-40,-55,-65,-75,-85,-100,-125,-140,-140,-155,-130,-85,15,85,180,285,415,605,835,1065,1185,1320,1380,1360,1280,1110,895,655,360,120,20,-45,-105,-165,-200,-200,-170,-125,-95,-45,-20,0,25,45,55,35,0,0,2,6,9,10,15,22,24,25,25,25,25,23,21,19,15,12,12,14,15,15,15,15,15,17,21,24,25,25,27,31,34,35,37,39,40,40,43,48,51,54,55,57,59,61,64,65,68,74,80,85,88,89,91,94,93,93,96,102,107,109,111,117,126,135,145,155,162,167,176,182,186,190,197,204,215,227,237,247,254,260,267,274,283,294,306,317,322,326,329,330,330,330,332,336,339,341,344,345,345,342,332,317,304,295,285,275,265,253,241,227,212,197,181,164,146,130,115,103,94,83,71,59,46,40,40,37,32,27,24,25,25,25,25,25,25,10,-9,-16,-18,-20,-24,-29,-34,-37,-38,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-41,-45,-49,-54,-57,-58,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-101,-103,-104,-104,-104,-104,-104,-101,-96,-95,-94,-92,-90,-89,-87,-85,-83,-77,-72,-70,-68,-65,-64,-64,-64,-64,-62,-58,-55,-54,-52,-48,-44,-39,-34,-31,-30,-29,-29,-31,-35,-39,-44,-47,-48,-49,-49,-49,-51,-53,-54,-56,-61,-70,-79,-87,-97,-108,-119,-127,-133,-137,-137,-135,-136,-138,-139,-139,-134,-132,-135,-133,-130,-131,-133,-135,-138,-137,-132,-127,-125,-124,-121,-121,-126,-130,-133,-134,-132,-127,-120,-116,-123,-134,-140,-140,-140,-155,-165,-175,-185,-200,-225,-240,-240,-235,-210,-145,-85,-15,60,165,295,485,715,945,1105,1240,1300,1280,1160,990,755,515,220,0,-120,-165,-205,-245,-280,-260,-250,-225,-195,-165,-140,-120,-115,-95,-85,-85,-100,-101,-103,-104,-106,-108,-104,-97,-95,-94,-94,-94,-94,-94,-92,-90,-89,-89,-87,-85,-84,-84,-84,-84,-84,-82,-78,-75,-74,-74,-72,-68,-65,-64,-62,-60,-59,-59,-56,-51,-48,-45,-44,-42,-40,-39,-41,-45,-46,-43,-39,-34,-31,-26,-16,-5,3,9,15,22,27,29,31,37,46,55,65,75,82,87,96,102,106,110,117,124,135,147,157,167,174,180,187,194,203,214,226,237,242,247,254,260,265,267,266,265,263,263,264,265,265,262,252,237,224,215,205,195,185,173,161,147,132,117,101,84,66,50,35,23,14,3,-8,-20,-33,-40,-44,-52,-62,-72,-80,-83,-84,-84,-84,-84,-84,-102,-127,-135,-138,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-141,-145,-149,-154,-157,-158,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-161,-163,-164,-164,-164,-164,-164,-161,-156,-155,-154,-152,-150,-149,-147,-145,-143,-137,-132,-130,-128,-125,-124,-124,-124,-124,-122,-118,-117,-120,-123,-124,-122,-118,-114,-111,-110,-109,-109,-111,-115,-119,-124,-127,-128,-129,-129,-129,-131,-133,-134,-136,-140,-144,-149,-152,-157,-163,-169,-172,-175,-178,-177,-175,-176,-178,-179,-179,-176,-178,-185,-188,-189,-191,-193,-195,-198,-197,-192,-187,-185,-184,-181,-181,-186,-190,-193,-194,-192,-187,-180,-176,-176,-178,-180,-180,-200,-215,-225,-215,-225,-240,-285,-320,-320,-315,-270,-225,-165,-95,-20,85,215,365,595,825,1005,1180,1260,1240,1140,950,735,515,220,-40,-160,-225,-265,-305,-320,-320,-310,-285,-295,-285,-260,-220,-175,-135,-125,-125,-140,-141,-143,-144,-146,-148,-144,-137,-135,-134,-134,-134,-134,-134,-132,-130,-129,-129,-127,-125,-124,-124,-124,-124,-124,-122,-118,-115,-114,-114,-112,-108,-105,-104,-102,-100,-99,-99,-96,-91,-88,-85,-84,-82,-80,-78,-75,-74,-71,-65,-59,-54,-51,-48,-42,-35,-31,-28,-24,-17,-12,-10,-8,-2,6,15,25,35,42,47,56,62,66,70,77,84,95,107,117,127,134,138,141,144,148,156,167,177,182,187,194,200,205,208,211,215,218,221,224,225,225,222,212,197,184,175,165,155,145,133,121,107,92,77,61,44,26,10,-4,-16,-25,-36,-48,-60,-73,-80,-84,-92,-102,-112,-120,-123,-124,-124,-124,-124,-124,-136,-151,-156,-158,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-161,-165,-169,-174,-177,-178,-179,-179,-177,-173,-169,-164,-161,-160,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-161,-165,-169,-174,-177,-178,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-181,-185,-189,-194,-197,-198,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-204,-211,-213,-214,-212,-210,-209,-207,-203,-197,-187,-177,-172,-168,-165,-164,-164,-164,-164,-162,-160,-161,-165,-168,-167,-163,-159,-154,-149,-144,-139,-134,-132,-135,-139,-144,-147,-148,-149,-149,-149,-151,-153,-154,-156,-161,-170,-179,-187,-195,-203,-209,-212,-215,-218,-217,-213,-210,-208,-204,-201,-196,-198,-205,-209,-214,-221,-228,-234,-237,-237,-232,-227,-225,-224,-221,-221,-226,-230,-233,-234,-232,-227,-220,-216,-216,-218,-220,-220,-240,-255,-265,-275,-285,-300,-325,-340,-340,-335,-310,-265,-225,-155,-80,25,135,305,535,765,945,1100,1180,1180,1100,950,735,475,180,-60,-180,-265,-305,-325,-360,-360,-350,-325,-315,-285,-240,-220,-195,-175,-185,-185,-180,-181,-183,-184,-186,-188,-184,-177,-175,-174,-174,-174,-174,-174,-172,-170,-169,-169,-167,-165,-164,-164,-164,-164,-164,-162,-158,-155,-154,-154,-152,-148,-145,-144,-142,-140,-139,-139,-136,-131,-128,-125,-124,-122,-120,-118,-115,-114,-111,-105,-99,-94,-91,-88,-82,-75,-71,-70,-69,-67,-67,-68,-67,-62,-53,-44,-34,-24,-17,-12,-3,2,6,10,17,24,35,47,57,67,74,80,87,94,103,114,126,137,142,146,149,150,150,150,152,156,159,163,169,175,180,180,172,157,144,135,125,115,105,93,81,67,52,37,21,4,-13,-27,-38,-45,-49,-57,-68,-80,-93,-100,-104,-112,-122,-132,-140,-143,-144,-144,-144,-144,-144,-162,-187,-195,-198,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-201,-205,-209,-214,-217,-218,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-221,-225,-229,-234,-237,-238,-239,-239,-237,-233,-229,-224,-221,-220,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-221,-225,-228,-229,-229,-229,-229,-229,-231,-233,-234,-234,-232,-230,-229,-227,-225,-223,-217,-212,-210,-208,-205,-204,-204,-204,-204,-202,-198,-195,-194,-192,-187,-178,-169,-159,-152,-150,-149,-149,-151,-155,-159,-164,-167,-168,-169,-169,-169,-171,-173,-174,-176,-181,-190,-199,-207,-215,-223,-229,-232,-235,-238,-237,-235,-236,-238,-239,-239,-236,-238,-245,-248,-249,-251,-253,-255,-258,-257,-252,-247,-245,-244,-241,-241,-246,-250,-253,-254,-252,-247,-240,-236,-236,-238,-240,-240,-240,-255,-265,-275,-285,-300,-325,-340,-340,-335,-310,-285,-225,-155,-60,45,155,345,575,805,965,1120,1180,1160,1060,890,695,455,180,-60,-200,-265,-285,-325,-360,-380,-370,-325,-315,-285,-260,-260,-235,-195,-185,-185,-180,-181,-183,-184,-186,-188,-184,-177,-175,-174,-174,-174,-174,-174,-172,-170,-169,-169,-167,-165,-164,-164,-164,-164,-164,-162,-158,-155,-154,-154,-152,-148,-145,-144,-142,-140,-139,-139,-136,-131,-128,-125,-124,-122,-120,-118,-115,-114,-111,-106,-105,-104,-106,-106,-101,-95,-91,-88,-84,-77,-72,-70,-68,-62,-53,-44,-34,-24,-17,-12,-3,2,6,10,17,24,35,47,57,67,74,80,87,94,103,114,126,137,142,147,154,160,165,168,171,175,178,181,184,185,185,182,172,157,144,136,130,125,120,112,101,87,72,56,35,13,-8,-27,-43,-55,-64,-76,-88,-100,-113,-120,-124,-132,-142,-150,-154,-154,-154,-154,-154,-154,-154,-169,-189,-196,-198,-199,-199,-199,-199,-201,-205,-209,-214,-217,-218,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-217,-213,-209,-204,-201,-200,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-201,-205,-209,-214,-217,-218,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-221,-225,-229,-234,-237,-238,-239,-239,-237,-233,-229,-224,-221,-220,-219,-219,-221,-225,-229,-234,-237,-238,-239,-239,-237,-233,-229,-224,-221,-220,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-221,-223,-224,-224,-224,-224,-224,-226,-228,-229,-229,-229,-229,-229,-227,-225,-221,-211,-201,-195,-189,-186,-185,-184,-184,-184,-182,-178,-175,-174,-172,-168,-164,-159,-154,-151,-150,-149,-149,-151,-156,-165,-174,-182,-187,-188,-189,-189,-191,-193,-194,-196,-201,-210,-219,-227,-235,-243,-249,-252,-253,-252,-247,-240,-238,-239,-239,-239,-236,-238,-245,-248,-249,-251,-253,-255,-258,-257,-252,-247,-247,-250,-251,-256,-265,-269,-272,-273,-272,-267,-260,-256,-250,-236,-220,-220,-240,-255,-265,-275,-305,-320,-325,-340,-360,-355,-330,-285,-225,-155,-80,25,175,365,615,845,1045,1200,1260,1220,1120,950,715,475,160,-80,-200,-265,-305,-325,-360,-360,-350,-325,-335,-325,-300,-280,-255,-215,-205,-205,-200,-201,-203,-204,-206,-208,-204,-197,-195,-196,-200,-204,-209,-212,-212,-210,-209,-209,-207,-205,-204,-204,-204,-204,-204,-202,-198,-195,-194,-192,-187,-178,-170,-166,-163,-160,-159,-159,-156,-151,-148,-144,-139,-132,-125,-116,-105,-94,-81,-65,-49,-34,-21,-10,2,14,23,29,35,42,47,49,51,57,66,77,91,105,117,126,135,142,146,152,162,174,190,205,217,227,234,241,252,264,278,293,306,317,322,327,334,340,345,348,351,355,358,363,369,375,380,380,372,357,344,335,325,315,305,293,281,267,252,236,215,193,171,152,136,124,115,103,91,79,66,59,55,47,37,27,19,16,15,15,15,15,15,-9,-42,-53,-57,-58,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-101,-105,-109,-114,-117,-118,-119,-119,-119,-119,-119,-119,-117,-113,-109,-104,-101,-100,-99,-99,-101,-105,-109,-114,-117,-118,-119,-119,-119,-119,-119,-119,-119,-112,-103,-100,-99,-99,-101,-105,-106,-105,-103,-97,-92,-90,-88,-85,-84,-84,-84,-84,-82,-78,-75,-74,-72,-68,-64,-59,-54,-51,-50,-49,-49,-51,-55,-59,-64,-67,-68,-69,-69,-69,-71,-73,-74,-76,-81,-90,-99,-107,-115,-123,-129,-132,-135,-138,-137,-135,-136,-138,-139,-139,-136,-138,-147,-153,-159,-166,-171,-175,-178,-177,-170,-161,-155,-149,-142,-142,-147,-150,-153,-154,-152,-147,-140,-136,-136,-138,-140,-140,-200,-215,-225,-195,-205,-200,-225,-240,-240,-235,-230,-185,-125,-55,40,145,275,465,695,925,1125,1280,1320,1300,1220,1050,835,595,300,40,-80,-145,-205,-245,-280,-280,-270,-245,-235,-205,-180,-160,-135,-115,-105,-105,-100,-101,-103,-104,-106,-108,-104,-97,-95,-94,-94,-94,-94,-94,-92,-90,-89,-89,-87,-85,-84,-84,-84,-84,-84,-82,-78,-75,-74,-74,-72,-68,-65,-64,-62,-60,-59,-59,-56,-51,-48,-45,-44,-42,-40,-38,-35,-34,-31,-25,-19,-14,-11,-8,-2,4,8,11,15,22,27,29,31,37,46,55,65,75,82,87,96,102,106,110,117,124,135,147,157,167,174,180,187,194,203,214,226,237,242,247,254,260,265,268,271,275,278,281,284,285,285,280,267,247,229,216,205,195,185,173,161,147,132,117,101,84,66,50,35,23,14,3,-8,-20,-33,-40,-44,-52,-62,-70,-74,-74,-74,-74,-74,-74,-74,-96,-125,-134,-137,-138,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-137,-133,-129,-124,-121,-120,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-121,-125,-129,-134,-137,-138,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-137,-133,-129,-124,-121,-120,-119,-119,-121,-125,-129,-134,-137,-138,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-137,-135,-134,-134,-132,-130,-129,-127,-125,-123,-117,-112,-110,-108,-105,-104,-104,-104,-104,-102,-98,-95,-94,-92,-88,-84,-79,-74,-71,-70,-69,-69,-71,-75,-79,-84,-87,-88,-89,-89,-89,-91,-91,-88,-85,-86,-91,-100,-108,-115,-123,-129,-132,-135,-138,-137,-135,-136,-138,-139,-139,-136,-138,-145,-148,-149,-151,-153,-155,-158,-157,-152,-147,-145,-144,-141,-141,-146,-150,-153,-154,-152,-147,-140,-136,-136,-138,-140,-140,-160,-155,-165,-175,-185,-200,-245,-260,-240,-235,-210,-165,-125,-55,20,125,295,525,755,965,1145,1300,1360,1340,1260,1090,855,615,300,60,-60,-125,-165,-205,-240,-240,-250,-225,-215,-185,-160,-140,-135,-115,-105,-105,-100,-88,-85,-84,-86,-88,-84,-77,-75,-74,-74,-74,-74,-74,-72,-70,-69,-69,-67,-65,-64,-64,-64,-64,-64,-62,-58,-55,-54,-54,-52,-48,-45,-44,-42,-40,-39,-39,-36,-31,-28,-25,-24,-22,-20,-18,-15,-14,-11,-5,0,5,8,9,11,14,13,13,16,22,27,29,31,37,46,55,65,75,82,87,96,102,106,110,117,124,135,148,163,178,189,198,206,214,223,233,241,247,247,249,255,260,265,268,271,275,278,281,284,285,285,282,272,257,244,235,225,215,205,195,187,177,167,156,140,123,106,90,75,63,54,43,31,19,6,-1,-5,-13,-22,-32,-40,-43,-44,-44,-44,-44,-44,-56,-71,-76,-78,-79,-79,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-101,-105,-109,-114,-117,-118,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-117,-113,-109,-104,-101,-100,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-101,-105,-109,-114,-117,-118,-119,-119,-119,-119,-119,-119,-119,-119,-117,-115,-114,-114,-112,-110,-109,-107,-105,-103,-97,-92,-90,-88,-85,-84,-84,-84,-84,-82,-78,-75,-74,-72,-68,-64,-57,-48,-40,-34,-31,-30,-31,-35,-41,-50,-58,-64,-67,-68,-69,-71,-71,-68,-65,-66,-71,-80,-88,-95,-103,-109,-112,-115,-118,-117,-115,-116,-118,-119,-119,-116,-118,-125,-128,-129,-131,-133,-135,-138,-137,-132,-127,-125,-124,-121,-121,-126,-130,-133,-134,-132,-127,-120,-116,-116,-125,-140,-140,-140,-155,-165,-175,-185,-200,-225,-240,-240,-235,-210,-165,-125,-55,20,125,255,445,675,925,1105,1280,1360,1340,1240,1070,855,635,340,60,-60,-125,-165,-205,-260,-280,-270,-245,-215,-185,-160,-140,-115,-95,-105,-105,-80,-81,-83,-84,-86,-88,-84,-77,-75,-74,-74,-74,-74,-74,-72,-70,-69,-69,-67,-65,-64,-64,-64,-64,-64,-62,-58,-55,-54,-54,-52,-48,-45,-44,-42,-40,-39,-39,-36,-31,-28,-25,-24,-22,-20,-18,-15,-14,-11,-5,0,5,8,11,17,24,28,31,35,42,47,49,51,57,66,75,85,95,102,107,116,122,126,130,137,144,155,167,177,187,194,200,207,214,223,234,246,257,262,267,274,280,285,288,291,295,298,303,309,315,320,320,312,297,284,275,265,255,245,233,221,207,192,177,161,144,126,110,95,83,74,61,45,28,11,0,-4,-12,-22,-32,-40,-43,-44,-44,-44,-44,-44,-62,-87,-95,-98,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-79,-79,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-99,-101,-105,-109,-114,-117,-118,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-112,-103,-100,-99,-99,-99,-99,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-77,-73,-69,-64,-61,-60,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-79,-84,-89,-94,-99,-104,-109,-114,-117,-118,-119,-119,-121,-125,-129,-134,-139,-144,-149,-154,-157,-158,-159,-159,-157,-153,-149,-144,-141,-140,-139,-139,-139,-139,-139,-139,-141,-145,-149,-154,-157,-158,-159,-159,-159,-159,-159,-159,-159,-152,-140,-140,-140,-160,-160,-200,-220,-220,-240,-260,-260,-240,-200,-160,-80,20,120,220,400,620,880,1060,1240,1340,1340,1300,1140,960,720,460,160,-20,-120,-160,-220,-260,-260,-260,-260,-240,-220,-180,-180,-140,-120,-120,-100,-100,-100,-99,-99,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-79,-79,-79,-79,-77,-73,-69,-64,-61,-60,-59,-59,-59,-59,-59,-59,-57,-53,-49,-44,-41,-40,-39,-39,-39,-39,-39,-39,-36,-28,-19,-9,-1,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,102,111,120,130,140,150,160,170,178,184,190,195,202,211,220,230,240,250,260,270,278,284,290,295,298,299,300,300,300,300,300,300,298,294,290,285,277,264,250,235,220,205,190,175,160,145,130,115,100,85,70,55,40,25,10,-4,-17,-28,-39,-49,-57,-63,-69,-74,-81,-90,-96,-98,-99,-99,-99,-99,-106,-115,-118,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-102,-63,-17,4,1,0,0, COMMASEPARATE 5000 V6 -60,-59,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-77,-73,-69,-64,-57,-48,-39,-29,-21,-15,-9,-4,0,5,10,15,20,25,30,35,38,39,40,40,40,40,40,40,42,46,50,55,60,65,70,75,80,85,90,95,98,99,100,100,105,117,131,145,158,169,180,190,198,204,210,215,218,219,220,220,218,214,210,205,202,201,200,200,198,194,190,185,178,169,160,150,140,130,120,110,102,96,90,85,78,69,60,50,42,36,30,25,20,15,10,5,-1,-10,-19,-29,-39,-49,-59,-69,-76,-78,-79,-79,-77,-73,-69,-64,-61,-60,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-56,-48,-39,-29,-22,-20,-19,-19,-21,-25,-29,-34,-37,-38,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-37,-33,-29,-24,-21,-20,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-16,-8,1,10,17,19,20,20,20,20,20,20,18,14,10,5,0,-4,-9,-14,-17,-18,-19,-19,-16,-8,1,10,17,19,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,23,28,29,30,32,36,42,49,53,56,60,63,64,65,65,65,65,65,67,71,77,82,84,86,89,93,99,103,104,105,105,105,105,105,105,105,105,105,105,105,105,105,103,99,93,83,71,59,48,41,37,36,35,33,31,32,34,35,35,35,35,35,33,29,21,14,11,12,14,18,26,34,38,38,34,31,27,21,17,19,26,37,47,52,51,44,38,35,-5,-35,-40,-45,-55,-45,-40,-40,-55,-55,-55,-45,-15,25,65,130,215,320,475,625,755,855,960,985,965,905,835,765,630,455,285,190,120,60,-15,-60,-60,-60,-45,-60,-80,-60,5,15,40,40,40,80,53,46,45,45,47,52,57,59,60,60,60,60,58,56,55,55,53,51,50,52,54,55,55,58,63,64,65,67,69,70,72,74,75,77,79,80,80,80,82,86,90,95,100,105,112,119,125,133,141,144,145,147,147,146,144,140,133,126,120,120,123,129,136,142,151,160,167,169,170,170,170,172,177,184,190,193,194,198,204,210,215,223,233,239,248,263,278,289,296,299,301,305,308,309,311,314,315,315,313,308,299,290,280,272,267,264,261,260,257,247,232,219,206,190,173,154,138,126,119,115,108,99,90,80,72,66,59,53,51,52,54,55,55,55,55,55,50,43,41,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,38,34,30,25,22,21,20,20,20,20,20,20,22,26,30,35,38,39,40,40,40,40,40,40,40,40,40,40,42,46,50,55,58,59,60,60,60,60,60,60,60,60,60,60,60,60,60,60,58,54,50,45,42,41,40,40,40,40,40,40,38,34,30,25,22,21,20,20,20,20,20,20,22,26,30,35,38,39,40,40,37,29,20,10,3,1,0,0,2,6,10,15,18,19,20,20,17,9,0,-9,-17,-22,-23,-24,-24,-24,-24,-21,-16,-15,-14,-14,-14,-12,-8,-5,-3,1,4,5,5,5,5,5,5,7,11,17,21,19,16,14,15,20,23,24,26,30,35,40,45,50,55,60,63,64,65,65,65,63,59,53,43,31,19,8,-2,-13,-24,-34,-42,-47,-47,-45,-44,-44,-44,-44,-42,-40,-39,-42,-47,-48,-47,-45,-41,-33,-25,-21,-23,-30,-38,-47,-57,-62,-62,-58,-52,-47,-45,-48,-62,-77,-85,-65,-55,-60,-65,-75,-85,-140,-160,-175,-175,-175,-125,-95,5,65,130,215,320,395,565,695,815,920,985,985,945,835,685,550,335,165,70,0,-60,-115,-140,-140,-120,-105,-80,-60,-40,-35,-25,-60,-80,-80,-80,-51,-36,-26,-20,-14,-7,-2,0,-1,-5,-9,-14,-19,-22,-23,-24,-27,-33,-39,-42,-43,-44,-44,-41,-36,-35,-34,-32,-30,-29,-27,-25,-24,-22,-20,-19,-19,-19,-17,-13,-10,-9,-9,-9,-6,0,5,13,21,24,25,27,27,26,24,20,15,12,11,15,22,29,36,42,51,60,67,69,70,70,70,72,77,84,90,93,94,98,104,110,115,123,133,139,146,157,167,174,176,174,171,170,170,170,172,174,175,175,173,168,161,155,150,147,146,144,141,140,137,127,112,99,88,76,64,50,37,26,19,15,8,0,-9,-19,-26,-28,-30,-31,-30,-28,-25,-24,-24,-24,-24,-24,-36,-51,-56,-58,-60,-64,-69,-74,-77,-78,-79,-79,-77,-73,-69,-64,-61,-60,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-77,-73,-69,-64,-61,-60,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-61,-65,-69,-74,-77,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-99,-99,-99,-99,-101,-103,-104,-104,-104,-104,-104,-101,-96,-95,-94,-94,-94,-92,-88,-85,-83,-79,-76,-75,-74,-74,-74,-74,-74,-72,-68,-62,-57,-55,-53,-50,-46,-40,-36,-35,-34,-34,-34,-34,-34,-34,-34,-34,-34,-34,-34,-34,-34,-36,-40,-46,-56,-68,-80,-91,-100,-108,-114,-119,-124,-127,-127,-125,-123,-119,-114,-109,-106,-106,-110,-118,-125,-128,-127,-125,-121,-113,-105,-101,-101,-105,-108,-112,-118,-122,-122,-118,-112,-107,-105,-108,-115,-121,-125,-125,-135,-140,-145,-155,-165,-180,-200,-195,-195,-215,-205,-175,-135,-75,-10,75,200,355,525,655,755,860,905,885,825,715,585,450,255,85,-10,-80,-140,-195,-200,-180,-180,-145,-140,-140,-120,-95,-105,-120,-120,-120,-120,-119,-117,-115,-114,-112,-107,-102,-100,-98,-94,-89,-84,-82,-83,-84,-84,-86,-88,-89,-87,-85,-84,-84,-81,-76,-75,-74,-72,-70,-69,-67,-65,-64,-62,-60,-59,-59,-59,-57,-53,-50,-49,-49,-49,-47,-45,-44,-41,-36,-35,-34,-32,-30,-28,-25,-24,-26,-28,-29,-24,-17,-10,-3,2,12,26,37,44,48,49,50,52,59,70,80,88,93,98,104,110,113,118,123,124,128,138,148,154,158,159,161,165,170,175,182,189,193,194,193,188,179,170,160,152,147,144,141,140,137,127,112,99,90,82,74,65,57,51,49,50,47,39,30,20,12,6,-1,-6,-10,-13,-14,-14,-14,-14,-14,-14,-29,-49,-56,-58,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-57,-53,-49,-44,-37,-28,-19,-9,-2,0,0,0,-1,-5,-9,-14,-17,-18,-19,-19,-14,-2,11,25,35,38,39,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,42,46,50,55,60,65,70,75,78,79,80,80,78,74,70,65,60,55,50,45,42,41,40,40,43,51,60,70,77,79,80,80,80,80,80,80,82,86,90,95,98,99,100,100,100,100,100,100,98,94,90,85,82,81,80,80,80,80,80,80,80,80,80,80,78,76,75,75,75,75,75,72,67,66,65,65,67,72,81,89,98,111,124,135,142,144,145,145,143,141,140,142,144,145,147,149,151,154,153,149,145,140,135,130,127,126,125,125,125,125,125,125,125,123,119,113,104,96,89,83,79,76,75,75,73,71,72,74,75,75,75,75,75,73,69,61,54,51,52,54,58,66,74,78,78,74,71,67,61,57,57,61,69,78,84,86,82,77,75,75,45,20,15,5,-5,-20,-60,-75,-55,-55,-45,-15,25,85,150,235,340,475,645,795,915,1020,1065,1065,1005,895,765,630,435,225,130,80,40,-15,-60,-60,-60,-45,-40,-20,20,45,55,80,80,80,60,60,62,64,65,67,72,77,79,80,80,80,80,80,82,86,90,92,91,90,92,92,89,85,83,84,85,85,87,87,84,81,79,76,77,79,80,80,80,82,86,87,84,80,75,73,74,75,78,86,95,105,117,126,130,133,134,133,131,130,135,142,149,156,162,171,180,187,189,190,190,190,192,197,204,210,213,213,213,214,215,215,218,223,224,226,232,237,239,238,234,231,230,230,230,232,234,235,235,233,228,224,226,230,237,244,248,251,255,255,247,232,219,208,196,184,170,157,146,139,135,128,119,110,100,90,80,68,58,51,47,46,45,45,45,45,45,37,26,22,21,20,20,20,20,18,14,10,5,2,1,0,0,2,6,10,15,18,19,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,15,3,-10,-24,-34,-37,-38,-39,-36,-28,-19,-9,-1,5,10,15,18,19,20,20,20,20,20,20,20,20,20,20,18,14,10,5,0,-4,-9,-14,-17,-18,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-19,-17,-13,-9,-4,-1,0,0,0,-3,-10,-19,-29,-36,-38,-39,-39,-41,-45,-49,-54,-57,-58,-59,-59,-59,-59,-59,-59,-57,-53,-49,-44,-41,-40,-39,-39,-41,-45,-49,-54,-57,-58,-59,-59,-57,-53,-49,-44,-41,-40,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-39,-44,-51,-53,-54,-54,-54,-52,-48,-44,-37,-28,-20,-16,-15,-14,-14,-14,-14,-12,-8,-4,-2,-5,-8,-9,-6,0,3,6,10,15,20,23,24,25,25,23,19,15,10,7,6,4,0,-6,-16,-28,-40,-51,-58,-62,-63,-62,-60,-58,-52,-45,-39,-34,-29,-26,-25,-26,-30,-39,-51,-58,-62,-63,-60,-53,-45,-41,-41,-45,-48,-52,-58,-62,-62,-58,-52,-47,-45,-48,-55,-61,-65,-65,-95,-100,-105,-115,-105,-120,-140,-155,-175,-175,-165,-135,-95,-55,10,95,200,335,505,655,755,860,905,925,865,795,665,530,355,205,110,20,-40,-95,-160,-160,-160,-125,-120,-80,-60,-35,-5,0,-20,-20,-20,-34,-41,-45,-49,-51,-46,-41,-40,-39,-39,-39,-39,-39,-37,-33,-29,-26,-23,-19,-12,-7,-5,-4,-1,3,4,5,7,9,10,12,14,13,11,9,5,2,1,2,6,9,10,10,10,13,19,25,33,41,44,45,47,49,51,54,55,52,46,40,40,43,49,56,62,71,80,87,89,90,90,90,92,97,104,110,113,114,118,124,130,135,143,153,159,165,172,177,179,180,180,182,186,189,190,192,194,195,195,193,188,181,175,170,167,166,164,161,160,157,147,132,119,110,102,94,85,75,65,58,54,46,34,20,5,-6,-13,-20,-26,-28,-27,-25,-24,-24,-24,-24,-24,-42,-67,-75,-78,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-99,-99,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-81,-85,-89,-94,-99,-104,-109,-114,-117,-118,-119,-119,-119,-119,-119,-119,-117,-113,-109,-104,-99,-94,-89,-84,-81,-80,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-101,-105,-109,-114,-117,-118,-119,-119,-121,-125,-129,-134,-137,-138,-139,-139,-141,-145,-149,-154,-157,-158,-159,-159,-157,-153,-149,-144,-141,-140,-139,-139,-137,-133,-129,-124,-121,-120,-119,-119,-119,-119,-119,-119,-121,-125,-128,-129,-129,-129,-129,-129,-131,-133,-134,-134,-134,-134,-132,-128,-125,-123,-119,-116,-116,-120,-124,-129,-132,-133,-132,-128,-120,-111,-105,-98,-90,-81,-70,-61,-56,-55,-54,-54,-56,-60,-64,-69,-72,-73,-74,-74,-72,-68,-65,-64,-67,-77,-88,-100,-111,-118,-122,-123,-125,-131,-138,-142,-143,-144,-144,-144,-144,-144,-146,-150,-158,-165,-168,-167,-165,-161,-153,-145,-141,-141,-145,-148,-152,-158,-162,-162,-157,-147,-137,-130,-129,-136,-141,-145,-145,-155,-160,-145,-155,-165,-180,-220,-235,-235,-235,-225,-195,-155,-95,-30,55,140,275,445,595,715,820,865,885,825,715,585,450,255,85,-10,-80,-140,-195,-240,-240,-240,-225,-220,-200,-180,-155,-145,-140,-140,-140,-140,-139,-137,-135,-134,-132,-127,-122,-120,-119,-119,-119,-119,-122,-128,-134,-139,-144,-147,-148,-147,-145,-144,-144,-141,-135,-129,-124,-117,-112,-110,-108,-105,-106,-108,-110,-114,-117,-118,-117,-113,-110,-109,-109,-109,-107,-105,-104,-101,-98,-100,-104,-107,-108,-107,-105,-104,-104,-102,-98,-89,-79,-71,-63,-57,-48,-39,-32,-30,-29,-29,-29,-27,-22,-15,-9,-6,-8,-12,-15,-19,-21,-15,-6,0,10,28,48,64,75,78,81,85,88,89,91,94,93,89,83,73,63,56,50,47,47,49,51,55,55,47,32,19,6,-9,-26,-45,-61,-73,-80,-84,-92,-105,-119,-134,-146,-153,-160,-166,-168,-167,-165,-164,-164,-164,-164,-164,-169,-176,-178,-179,-181,-185,-189,-194,-197,-198,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-201,-205,-209,-214,-217,-218,-219,-219,-217,-213,-209,-204,-201,-200,-199,-199,-199,-199,-199,-199,-197,-193,-189,-184,-181,-180,-179,-179,-181,-185,-189,-194,-197,-198,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-197,-193,-189,-184,-181,-180,-179,-179,-181,-185,-189,-194,-199,-204,-209,-214,-217,-218,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-217,-213,-209,-204,-201,-200,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-201,-205,-209,-214,-217,-218,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-219,-217,-215,-214,-214,-214,-214,-212,-208,-205,-203,-199,-196,-195,-194,-194,-194,-194,-194,-192,-188,-182,-177,-175,-173,-170,-166,-160,-156,-155,-154,-154,-154,-154,-156,-160,-164,-169,-172,-173,-174,-174,-174,-176,-180,-186,-196,-208,-220,-231,-238,-242,-243,-244,-244,-242,-237,-230,-226,-225,-224,-224,-224,-226,-230,-238,-247,-253,-257,-260,-259,-252,-245,-241,-241,-245,-248,-252,-258,-262,-262,-258,-250,-241,-235,-233,-230,-226,-225,-225,-235,-240,-265,-275,-305,-320,-340,-355,-335,-335,-325,-315,-275,-235,-170,-85,40,215,405,575,735,840,885,865,785,675,505,370,175,5,-90,-160,-200,-255,-300,-320,-340,-325,-320,-300,-280,-255,-245,-220,-220,-220,-220,-233,-235,-234,-234,-232,-227,-222,-220,-219,-219,-219,-219,-221,-223,-224,-224,-226,-228,-229,-227,-225,-224,-224,-221,-216,-215,-214,-212,-210,-209,-207,-205,-203,-197,-190,-184,-179,-174,-167,-158,-152,-150,-149,-149,-149,-151,-155,-156,-155,-154,-154,-152,-150,-148,-145,-144,-146,-148,-149,-144,-137,-130,-123,-117,-108,-99,-92,-90,-89,-89,-89,-87,-82,-75,-69,-66,-63,-55,-44,-34,-24,-11,3,14,25,37,47,54,58,59,61,65,68,69,71,74,75,75,73,68,61,55,50,47,46,44,41,40,37,27,12,0,-11,-23,-35,-49,-62,-73,-80,-84,-91,-100,-109,-119,-127,-133,-140,-146,-150,-153,-154,-154,-154,-154,-154,-154,-169,-189,-196,-198,-197,-193,-189,-184,-181,-180,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-177,-173,-169,-164,-161,-160,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-157,-153,-149,-144,-141,-140,-139,-139,-141,-145,-149,-154,-157,-158,-159,-159,-159,-159,-159,-159,-157,-153,-149,-144,-141,-140,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-141,-145,-149,-154,-157,-158,-159,-159,-157,-153,-149,-144,-141,-140,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-141,-145,-149,-154,-157,-158,-159,-159,-157,-153,-149,-144,-141,-140,-139,-139,-139,-139,-139,-139,-141,-145,-149,-154,-157,-158,-159,-159,-159,-159,-159,-159,-159,-144,-124,-117,-115,-114,-114,-112,-110,-111,-113,-114,-114,-114,-114,-114,-114,-114,-114,-112,-108,-102,-97,-95,-94,-96,-96,-95,-94,-94,-94,-94,-92,-88,-84,-79,-74,-69,-64,-59,-56,-55,-54,-56,-61,-71,-86,-103,-119,-131,-138,-142,-143,-144,-146,-148,-147,-145,-144,-144,-144,-144,-144,-146,-153,-169,-186,-198,-200,-193,-180,-163,-149,-142,-142,-145,-149,-157,-168,-177,-182,-183,-182,-182,-183,-187,-195,-201,-205,-205,-215,-200,-185,-175,-185,-200,-220,-255,-255,-235,-225,-195,-175,-115,-50,35,140,295,485,635,755,880,925,925,865,735,605,470,275,105,10,-60,-120,-175,-220,-240,-240,-245,-240,-220,-200,-175,-165,-160,-140,-140,-160,-158,-152,-145,-139,-134,-127,-122,-120,-119,-119,-119,-119,-122,-128,-134,-139,-144,-147,-148,-147,-143,-139,-134,-126,-118,-115,-114,-112,-112,-115,-118,-120,-123,-122,-120,-119,-119,-119,-117,-113,-110,-109,-109,-109,-107,-105,-104,-101,-96,-95,-94,-92,-90,-88,-85,-84,-84,-82,-78,-69,-59,-51,-43,-37,-30,-24,-22,-25,-28,-29,-29,-27,-22,-15,-9,-6,-3,4,15,25,33,43,53,59,66,77,87,94,96,94,91,90,90,90,92,94,95,95,93,88,81,75,70,67,66,64,61,60,57,47,32,19,6,-9,-26,-45,-61,-73,-80,-84,-91,-100,-109,-119,-127,-133,-140,-146,-150,-153,-154,-154,-154,-154,-154,-154,-162,-173,-177,-178,-179,-179,-179,-179,-181,-185,-189,-194,-197,-198,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-197,-193,-189,-184,-181,-180,-179,-179,-179,-179,-179,-179,-179,-179,-179,-179,-177,-173,-169,-164,-161,-160,-159,-159,-161,-165,-169,-174,-177,-178,-179,-179,-181,-185,-189,-194,-197,-198,-199,-199,-197,-193,-189,-184,-181,-180,-179,-179,-181,-185,-189,-194,-197,-198,-199,-199,-199,-199,-199,-199,-199,-199,-199,-199,-197,-193,-189,-184,-181,-180,-179,-179,-179,-179,-179,-179,-181,-185,-189,-194,-197,-198,-199,-199,-201,-205,-209,-214,-217,-218,-219,-219,-217,-213,-209,-204,-201,-200,-199,-199,-199,-199,-199,-199,-201,-205,-209,-214,-217,-218,-219,-219,-219,-219,-219,-219,-216,-208,-199,-189,-182,-180,-179,-179,-179,-179,-179,-179,-179,-179,-184,-191,-193,-194,-194,-194,-192,-188,-185,-183,-179,-176,-175,-174,-174,-174,-174,-174,-172,-168,-162,-157,-155,-153,-150,-146,-140,-136,-135,-134,-134,-134,-134,-134,-134,-134,-134,-134,-134,-134,-134,-134,-134,-134,-136,-141,-150,-161,-171,-178,-184,-189,-194,-201,-206,-206,-205,-204,-204,-204,-204,-204,-206,-210,-218,-225,-228,-227,-225,-221,-213,-205,-201,-201,-205,-208,-212,-218,-222,-222,-218,-212,-207,-205,-208,-215,-221,-225,-205,-215,-200,-205,-215,-245,-260,-280,-295,-295,-315,-305,-275,-235,-175,-90,15,140,335,505,635,755,860,905,905,845,735,605,470,255,85,-10,-100,-180,-235,-280,-280,-280,-265,-260,-240,-240,-215,-205,-200,-200,-200,-200,-186,-180,-176,-175,-173,-167,-162,-160,-161,-165,-169,-174,-179,-182,-183,-184,-186,-188,-189,-187,-183,-179,-174,-166,-158,-155,-154,-152,-150,-149,-147,-145,-146,-148,-150,-154,-157,-158,-157,-153,-149,-144,-139,-134,-129,-126,-125,-121,-116,-115,-114,-112,-110,-108,-105,-104,-106,-108,-109,-104,-97,-90,-83,-77,-68,-59,-52,-50,-49,-49,-49,-47,-40,-29,-19,-11,-6,-1,5,10,13,18,23,24,28,38,48,54,58,59,61,65,70,75,82,89,93,94,93,88,81,75,70,67,66,64,61,60,57,47,32,19,6,-9,-26,-45,-61,-73,-80,-84,-91,-100,-109,-119,-127,-133,-140,-146,-150,-153,-154,-154,-154,-154,-154,-154,-156,-158,-159,-159,-159,-159,-159,-159,-161,-165,-169,-174,-177,-178,-179,-179,-179,-179,-179,-179,-177,-173,-169,-164,-161,-160,-159,-159,-159,-159,-159,-159,-161,-165,-169,-174,-177,-178,-179,-179,-177,-173,-169,-164,-161,-160,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-159,-157,-153,-149,-144,-141,-140,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-137,-133,-129,-124,-121,-120,-119,-119,-121,-125,-129,-134,-137,-138,-139,-139,-139,-139,-139,-139,-139,-139,-139,-139,-137,-133,-129,-124,-121,-120,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-119,-116,-108,-99,-89,-82,-80,-79,-79,-81,-85,-89,-94,-97,-98,-99,-99,-99,-99,-99,-99,-97,-93,-89,-84,-81,-80,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-79,-71,-60,-56,-55,-54,-54,-52,-48,-45,-43,-39,-36,-35,-34,-34,-34,-34,-34,-32,-28,-22,-17,-15,-13,-10,-6,0,3,4,5,5,5,5,5,5,5,5,5,7,11,15,20,22,19,13,3,-8,-20,-31,-38,-42,-43,-44,-46,-48,-47,-45,-44,-44,-44,-44,-44,-46,-50,-58,-65,-68,-67,-65,-61,-53,-45,-41,-41,-45,-48,-52,-58,-62,-62,-58,-52,-47,-45,-48,-55,-61,-65,-65,-75,-80,-105,-115,-105,-100,-120,-135,-95,-135,-125,-95,-75,-15,50,135,240,395,585,795,915,1040,1085,1085,1025,915,785,650,455,265,150,80,40,-15,-120,-120,-120,-105,-100,-80,-60,-35,-25,-20,-20,0,20,18,16,14,10,8,13,18,19,21,25,30,35,37,36,35,35,33,31,30,32,34,35,35,38,43,44,45,47,49,50,52,54,55,57,59,60,60,60,62,66,69,70,70,70,73,79,85,93,101,104,105,107,109,111,114,115,112,106,100,100,103,109,116,122,132,146,157,164,168,169,170,172,177,184,190,193,194,198,204,210,215,223,233,239,246,257,267,274,278,279,281,285,290,295,302,309,313,314,313,308,301,295,290,287,286,284,281,280,277,267,252,239,228,216,204,190,177,166,159,155,147,134,120,105,93,86,79,73,69,66,65,65,65,65,65,65,57,46,42,41,39,35,30,25,22,21,20,20,22,26,30,35,38,39,40,40,40,40,40,40,40,40,40,40,38,34,30,25,22,21,20,20,20,20,20,20,22,26,30,35,38,39,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,38,34,30,25,22,21,20,20,22,26,30,35,38,39,40,40,38,34,30,25,22,21,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,18,14,10,5,2,1,0,0,0,0,0,0,2,6,10,15,18,19,20,20,20,20,20,20,18,14,10,5,0,-4,-9,-14,-17,-18,-19,-19,-16,-8,1,10,17,19,20,20,18,14,10,5,2,1,0,0,-1,-5,-9,-14,-19,-24,-29,-34,-37,-38,-39,-39,-37,-33,-29,-24,-17,-8,1,10,13,8,0,-9,-17,-23,-29,-34,-37,-38,-39,-39,-37,-35,-34,-34,-34,-34,-34,-17,4,11,14,16,20,25,30,35,38,39,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,38,34,30,25,22,21,20,20,20,20,20,20,15,3,-10,-24,-36,-43,-49,-54,-57,-58,-59,-59,-57,-53,-49,-44,-41,-40,-39,-39,-39,-39,-39,-39,-41,-45,-49,-54,-57,-58,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-59,-60,-60,-60,-60,-60,-80,-80,-100,-120,-140,-140,-140,-100,-80,0,80,140,240,360,540,720,820,940,1020,1020,1020,940,820,700,540,340,220,140,60,-40,-80,-100,-100,-100,-100,-80,-60,-40,-20,20,-20,20,20,20,20,20,20,20,22,26,30,35,40,45,50,55,58,59,60,60,60,60,60,60,60,60,60,60,60,60,60,60,62,66,70,75,78,79,80,80,80,80,80,80,82,86,90,95,100,105,110,115,120,125,130,135,138,139,140,140,142,146,150,155,162,171,180,190,200,210,220,230,238,244,250,255,260,265,270,275,280,285,290,295,300,305,310,315,320,325,330,335,338,339,340,340,340,340,340,340,340,340,340,340,337,329,320,310,300,290,280,270,260,250,240,230,220,210,200,190,183,181,180,180,178,174,170,165,158,149,140,130,123,121,120,120,120,120,120,120,113,104,101,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,102,106,110,115,118,119,120,120,118,114,110,105,102,101,100,100,102,106,110,115,118,119,120,120,118,114,110,105,102,101,100,100,100,100,100,100,102,106,110,115,118,119,120,120,118,114,110,105,102,101,100,100,100,100,100,100,110,133,161,117,39,13,4, COMMASEPARATE 5000 ECG_STANDARD HR 69 RR 869 P 108 PQ 174 QRS 94 QT 370 QTC 397 P_ON 97 P_OFF 151 QRS_ON 184 QRS_OFF 231 T_OFF 369 AXIS_P 42 AXIS_QRS 0 AXIS_T 3 BP_D - BP_S - R_QRS_ON 353 760 1163 1566 2010 2470 2925 3362 3804 4266 4733 R_QRS_OFF 400 807 1210 1613 2057 2517 2972 3409 3851 4313 4780 R_QRS_TYPE 0 0 0 0 0 0 0 0 0 0 1 R_P_ON 266 673 1076 1479 1923 2383 2838 3275 3717 4179 4646 R_T_OFF 538 945 1348 1751 2195 2655 3110 3547 3989 4451 4918 PACEMAKER MAINS 50 BASELINE 0.05 MYOGRAM OFF HIGHPASS_CUTOFF_FREQ 0.05 LOWPASS_CUTOFF_FREQ 150 MAINS 50 HIGHPASS_CUTOFF_FREQ 0.05 LOWPASS_CUTOFF_FREQ 150 BASELINE_STABILIZER OFF SMOOTHING OFF INTERPRETATION SENSITIVITY LOW AGEGROUP PATDATA TROMBOLYSIS OFF ================================================ FILE: cardio/tests/data/sel100.hea ================================================ sel100 2 250/360 225000 sel100.dat 212 200(0) 11 1024 945 -13873 0 MLII sel100.dat 212 200(0) 11 1024 955 14507 0 V5 # 69 M 1085 1629 x1 # Aldomet, Inderal #Produced by xform from record 100, beginning at 7:00.000 ================================================ FILE: cardio/tests/test_ecgbatch.py ================================================ """Module for testing ecg_batch methods""" import os import random import numpy as np import pytest from cardio import EcgBatch, EcgDataset, batchflow as bf from cardio.core import ecg_batch_tools as bt random.seed(170720143422) @pytest.fixture(scope="module") def setup_module_load(request): """ Fixture to setup module. Performs check for presence of test files, creates initial batch object. """ path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'data') files = ["A00001.hea", "A00001.mat", "A00002.hea", "A00002.mat", "A00004.hea", "A00004.mat", "A00005.hea", "A00005.mat", "A00008.hea", "A00008.mat", "A00013.hea", "A00013.mat", "REFERENCE.csv"] # TODO: make better test for presence of files .hea and # REFERENCE.csv if np.all([os.path.isfile(os.path.join(path, file)) for file in files]): ind = bf.FilesIndex(path=os.path.join(path, 'A*.hea'), no_ext=True, sort=True) else: raise FileNotFoundError("Test files not found in 'tests/data/'!") def teardown_module_load(): """ Teardown module """ request.addfinalizer(teardown_module_load) return ind, path @pytest.fixture() def setup_class_methods(request): """ Fixture to setup class to test EcgBatch methods separately. """ path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'data/') ind = bf.FilesIndex(path=os.path.join(path, 'A*.hea'), no_ext=True, sort=True) batch_loaded = (EcgBatch(ind, unique_labels=["A", "O", "N"]) .load(fmt="wfdb", components=["signal", "annotation", "meta"])) def teardown_class_methods(): """ Teardown class """ request.addfinalizer(teardown_class_methods) return batch_loaded @pytest.fixture(scope="class") def setup_class_dataset(request): """ Fixture to setup class to test EcgBatch methods in pipeline. """ path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'data/') ecg_dataset = EcgDataset(path=os.path.join(path, 'A*.hea'), no_ext=True, sort=True) def teardown_class_dataset(): """ Teardown class """ request.addfinalizer(teardown_class_dataset) return ecg_dataset @pytest.fixture(scope="class") def setup_class_pipeline(request): """ Fixture to setup class to test EcgBatch methods in pipeline. """ path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'data/') target_path = os.path.join(path, "REFERENCE.csv") ecg_pipeline = (EcgDataset(path=os.path.join(path, 'A*.hea'), no_ext=True, sort=True) .p .load(src=None, fmt="wfdb", components=["signal", "annotation", "meta"]) .load(src=target_path, fmt="csv", components=["target"])) def teardown_class_pipeline(): """ Teardown class """ request.addfinalizer(teardown_class_pipeline) return ecg_pipeline class TestEcgBatchLoad(): """ Class for test of load / dump methods. """ def test_load_wfdb(self, setup_module_load): #pylint: disable=redefined-outer-name """ Testing wfdb loader. """ # Arrange ind = setup_module_load[0] batch = EcgBatch(ind) # Act batch = batch.load(fmt="wfdb", components=["signal", "annotation", "meta"]) # Assert assert isinstance(batch.signal, np.ndarray) assert isinstance(batch.meta, np.ndarray) assert isinstance(batch.annotation, np.ndarray) assert batch.signal.shape == (6,) assert batch.annotation.shape == (6,) assert batch.meta.shape == (6,) assert isinstance(batch.signal[0], np.ndarray) assert isinstance(batch.annotation[0], dict) assert isinstance(batch.meta[0], dict) del batch def test_load_wfdb_annotation(self, setup_module_load): #pylint: disable=redefined-outer-name """ Testing wfdb loader for annotation. """ # Arrange path = setup_module_load[1] ind = bf.FilesIndex(path=os.path.join(path, 'sel100.hea'), no_ext=True, sort=True) batch = EcgBatch(ind) # Act batch = batch.load(fmt="wfdb", components=["signal", "annotation", "meta"], ann_ext="pu1") # Assert assert isinstance(batch.signal, np.ndarray) assert isinstance(batch.meta, np.ndarray) assert isinstance(batch.annotation, np.ndarray) assert batch.signal.shape == (1,) assert batch.annotation.shape == (1,) assert batch.meta.shape == (1,) assert isinstance(batch.signal[0], np.ndarray) assert isinstance(batch.annotation[0], dict) assert isinstance(batch.meta[0], dict) assert 'annsamp' in batch.annotation[0] assert 'anntype' in batch.annotation[0] del batch def test_load_dicom(self, setup_module_load): #pylint: disable=redefined-outer-name """ Testing DICOM loader. """ # Arrange path = setup_module_load[1] ind = bf.FilesIndex(path=os.path.join(path, 'sample*.dcm'), no_ext=True, sort=True) batch = EcgBatch(ind) # Act batch = batch.load(fmt="dicom", components=["signal", "annotation", "meta"]) # Assert assert isinstance(batch.signal, np.ndarray) assert isinstance(batch.meta, np.ndarray) assert isinstance(batch.annotation, np.ndarray) assert batch.signal.shape == (1,) assert batch.annotation.shape == (1,) assert batch.meta.shape == (1,) assert isinstance(batch.signal[0], np.ndarray) assert isinstance(batch.annotation[0], dict) assert isinstance(batch.meta[0], dict) del batch def test_load_edf(self, setup_module_load): #pylint: disable=redefined-outer-name """ Testing EDF loader. """ # Arrange path = setup_module_load[1] ind = bf.FilesIndex(path=os.path.join(path, 'sample*.edf'), no_ext=True, sort=True) batch = EcgBatch(ind) # Act batch = batch.load(fmt="edf", components=["signal", "annotation", "meta"]) # Assert assert isinstance(batch.signal, np.ndarray) assert isinstance(batch.meta, np.ndarray) assert isinstance(batch.annotation, np.ndarray) assert batch.signal.shape == (1,) assert batch.annotation.shape == (1,) assert batch.meta.shape == (1,) assert isinstance(batch.signal[0], np.ndarray) assert isinstance(batch.annotation[0], dict) assert isinstance(batch.meta[0], dict) del batch def test_load_wav(self, setup_module_load): #pylint: disable=redefined-outer-name """ Testing EDF loader. """ # Arrange path = setup_module_load[1] ind = bf.FilesIndex(path=os.path.join(path, 'sample*.wav'), no_ext=True, sort=True) batch = EcgBatch(ind) # Act batch = batch.load(fmt="wav", components=["signal", "annotation", "meta"]) # Assert assert isinstance(batch.signal, np.ndarray) assert isinstance(batch.meta, np.ndarray) assert isinstance(batch.annotation, np.ndarray) assert batch.signal.shape == (1,) assert batch.annotation.shape == (1,) assert batch.meta.shape == (1,) assert isinstance(batch.signal[0], np.ndarray) assert isinstance(batch.annotation[0], dict) assert isinstance(batch.meta[0], dict) del batch class TestEcgBatchSingleMethods: """ Class for testing other single methods of EcgBatch class """ @pytest.mark.usefixtures("setup_class_methods") def tets_drop_short_signal(self, setup_class_methods): #pylint: disable=redefined-outer-name """ Testing of drop_short_signals """ # Arrange batch = setup_class_methods # Act batch = batch.drop_short_signals(17000, axis=-1) # Assert assert batch.signal.shape == (2,) assert np.all([(sig.shape[-1] > 17000) for sig in batch.signal]) @pytest.mark.usefixtures("setup_class_methods") def test_split_signals(self, setup_class_methods): #pylint: disable=redefined-outer-name """ Testing split_signals. """ batch = setup_class_methods batch = batch.split_signals(4500, 4499) assert batch.indices.shape == (6,) assert batch.signal[0].shape == (2, 1, 4500) @pytest.mark.usefixtures("setup_class_methods") def test_resample_signals(self, setup_class_methods):#pylint: disable=redefined-outer-name """ Testing resample_signal. """ batch = setup_class_methods batch = batch.resample_signals(150) assert batch.meta[0]['fs'] == 150 assert batch.signal[0].shape == (1, 4500) @pytest.mark.usefixtures("setup_class_methods") def test_band_pass_signals(self, setup_class_methods): #pylint: disable=redefined-outer-name """ Testing band_pass_signals. """ batch = setup_class_methods batch = batch.band_pass_signals(0.5, 5) assert batch.signal.shape == (6,) assert batch.signal[0].shape == (1, 9000) @pytest.mark.usefixtures("setup_class_methods") def test_flip_signals(self, setup_class_methods): #pylint: disable=redefined-outer-name """ Testing flip_signals. """ batch = setup_class_methods batch = batch.flip_signals() assert batch.indices.shape == (6,) @pytest.mark.usefixtures("setup_class_dataset") class TestEcgBatchDataset: """ Class to test EcgBathc load in pipeline """ @pytest.mark.xfail def test_cv_split(self, setup_class_dataset): #pylint: disable=redefined-outer-name """ Testing cv_split. """ ecg_dtst = setup_class_dataset ecg_dtst.cv_split(shares=0.5) assert ecg_dtst.train.indices.shape == (3,) assert ecg_dtst.test.indices.shape == (3,) assert isinstance(ecg_dtst.train, bf.Dataset) assert isinstance(ecg_dtst.test, bf.Dataset) ecg_dtst.cv_split(shares=[0.5, 0.49]) assert ecg_dtst.train.indices.shape == (3,) assert ecg_dtst.test.indices.shape == (2,) assert ecg_dtst.validation.indices.shape == (1,) assert isinstance(ecg_dtst.train, bf.Dataset) assert isinstance(ecg_dtst.test, bf.Dataset) assert isinstance(ecg_dtst.validation, bf.Dataset) def test_pipeline_load(self, setup_class_dataset, setup_module_load): #pylint: disable=redefined-outer-name """ Testing load in pipeline. """ ecg_dtst = setup_class_dataset path = setup_module_load[1] target_path = os.path.join(path, "REFERENCE.csv") ppln = (ecg_dtst.p .load(fmt="wfdb", components=["signal", "annotation", "meta"]) .load(src=target_path, fmt="csv", components=["target"])) batch_size = 2 epochs = ecg_dtst.indices.shape[0] // batch_size assert epochs == 3 for _ in range(epochs): ppln_batch = ppln.next_batch(batch_size, shuffle=False) assert ppln_batch.indices.shape == (batch_size,) assert ppln_batch.signal.shape == (batch_size,) assert np.unique(ppln_batch.target).shape == (1,) first_indice = ppln_batch.indices[0] assert isinstance(ppln_batch[first_indice], bf.components.EcgBatchComponents) #pylint: disable=no-member @pytest.mark.usefixtures("setup_class_pipeline") class TestEcgBatchPipelineMethods: """ Class to test EcgBatch methods in pipeline. """ def test_pipeline_1(self, setup_class_pipeline): #pylint: disable=redefined-outer-name """ Testing typical pipeline. """ ecg_ppln = setup_class_pipeline ppln = (ecg_ppln .drop_labels(["A"]) .flip_signals() .split_signals(4500, 4499) .rename_labels({"A":"A", "N":"NonA", "O":"NonA"})) batch = ppln.next_batch(len(ppln), shuffle=False) assert len(batch) == 4 assert batch.signal[0].shape == (2, 1, 4500) assert np.unique(batch.target)[0] == "NonA" def test_pipeline_2(self, setup_class_pipeline): #pylint: disable=redefined-outer-name """ Testing typical pipeline. """ ecg_ppln = setup_class_pipeline ppln = (ecg_ppln .drop_short_signals(17000, axis=-1) .band_pass_signals(0.5, 50) .resample_signals(150) .binarize_labels()) batch = ppln.next_batch(len(ppln), shuffle=False) assert len(batch) == 2 assert batch.meta[0]["fs"] == 150 assert np.all([(sig.shape[-1] == 9000) for sig in batch.signal]) assert batch.target.shape == (2, 3) def test_get_signal_with_meta(self, setup_module_load): #pylint: disable=redefined-outer-name """ Testing get_signal_meta. """ # Arrange ppln = (bf.Pipeline() .init_variable(name="signal", init_on_each_run=list) .load(fmt='wfdb', components=["signal", "meta"]) .flip_signals() .update_variable("signal", bf.B("signal"), mode='a') .run(batch_size=2, shuffle=False, drop_last=False, n_epochs=1, lazy=True)) dtst = EcgDataset(setup_module_load[0]) # Act ppln_run = (dtst >> ppln).run() signal_var = ppln_run.get_variable("signal") # Assert assert len(signal_var) == 3 assert len(signal_var[0]) == 2 assert signal_var[0][0].shape == (1, 9000) class TestIntervalBatchTools: """ Class for testing batch tools that involved in intervals calculation """ def test_find_interval_borders(self): #pylint: disable=redefined-outer-name """ Testing find_interval_borders. """ # Arrange hmm_annotation = np.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 0, 0, 0, 4, 4, 4, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 0, 0, 0, 1, 1, 1]*2, dtype=np.int64) # Act starts_3, ends_3 = bt.find_intervals_borders(hmm_annotation, np.array([3], dtype=np.int64)) starts_12, ends_12 = bt.find_intervals_borders(hmm_annotation, np.array([1, 2], dtype=np.int64)) # Assert assert np.all(np.equal(starts_3, np.array([6, 25, 46, 65]))) assert np.all(np.equal(ends_3, np.array([9, 30, 49, 70]))) assert np.all(np.equal(starts_12, np.array([18, 37, 58]))) assert np.all(np.equal(ends_12, np.array([25, 46, 65]))) def test_find_maxes(self): #pylint: disable=redefined-outer-name """ Testing find_maxes. """ # Arrange hmm_annotation = np.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 0, 0, 0, 4, 4, 4, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 0, 0, 0, 1, 1, 1]*2, dtype=np.int64) # 9 is the max for interval of 3's # 8 is the max for interval of 1's and 2's signal = np.array([1, 1, 1, 2, 2, 8, 3, 9, 3, 0, 0, 0, 4, 4, 4, 0, 0, 0, 1, 8, 1, 2, 2, 2, 2, 9, 3, 3, 3, 3, 4, 4, 4, 4, 0, 0, 0, 1, 1, 1]*2, dtype=np.float64).reshape(1, -1) # Act starts_3, ends_3 = bt.find_intervals_borders(hmm_annotation, np.array([3], dtype=np.int64)) starts_12, ends_12 = bt.find_intervals_borders(hmm_annotation, np.array([1, 2], dtype=np.int64)) maxes_3 = bt.find_maxes(signal, starts_3, ends_3) maxes_12 = bt.find_maxes(signal, starts_12, ends_12) # Assert assert np.all(np.equal(maxes_3, np.array([7, 25, 47, 65]))) assert np.all(np.equal(maxes_12, np.array([19, 45, 59]))) def test_calc_hr(self): """ Testing calc_hr. """ # Arrange fs = 18.0 R_STATE = np.array([3], dtype=np.int64) # pylint: disable=invalid-name hmm_annotation = np.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 0, 0, 0, 4, 4, 4, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 0, 0, 0, 1, 1, 1]*2, dtype=np.int64) # 9 is the max for interval of 3's # 8 is the max for interval of 1's and 2's signal = np.array([1, 1, 1, 2, 2, 8, 3, 9, 3, 0, 0, 0, 4, 4, 4, 0, 0, 0, 1, 8, 1, 2, 2, 2, 2, 9, 3, 3, 3, 3, 4, 4, 4, 4, 0, 0, 0, 1, 1, 1]*2, dtype=np.float64).reshape(1, -1) # Act hr = bt.calc_hr(signal, hmm_annotation, fs, R_STATE) # pylint: disable=invalid-name # Arrange assert hr == 60 ================================================ FILE: docs/Makefile ================================================ # Minimal makefile for Sphinx documentation # # You can set these variables from the command line. SPHINXOPTS = SPHINXBUILD = python3 -msphinx SPHINXPROJ = CardIO SOURCEDIR = . BUILDDIR = _build # Put it first so that "make" without argument is like "make help". help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) .PHONY: help Makefile # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) ================================================ FILE: docs/api/api.rst ================================================ === API === .. toctree:: :maxdepth: 2 core models pipelines ================================================ FILE: docs/api/core.rst ================================================ ===== Core ===== EcgBatch ======== .. autoclass:: cardio.EcgBatch :show-inheritance: Methods ------- Input/output methods ^^^^^^^^^^^^^^^^^^^^ .. automethod:: cardio.EcgBatch.load .. automethod:: cardio.EcgBatch.dump .. automethod:: cardio.EcgBatch.show_ecg Batch processing ^^^^^^^^^^^^^^^^ .. automethod:: cardio.EcgBatch.deepcopy .. automethod:: cardio.EcgBatch.merge Versatile components processing ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. automethod:: cardio.EcgBatch.apply_transform .. automethod:: cardio.EcgBatch.apply_to_each_channel .. automethod:: cardio.EcgBatch.standardize Labels processing ^^^^^^^^^^^^^^^^^ .. automethod:: cardio.EcgBatch.drop_labels .. automethod:: cardio.EcgBatch.keep_labels .. automethod:: cardio.EcgBatch.rename_labels .. automethod:: cardio.EcgBatch.binarize_labels Channels processing ^^^^^^^^^^^^^^^^^^^ .. automethod:: cardio.EcgBatch.drop_channels .. automethod:: cardio.EcgBatch.keep_channels .. automethod:: cardio.EcgBatch.rename_channels .. automethod:: cardio.EcgBatch.reorder_channels .. automethod:: cardio.EcgBatch.convert_units Signal processing ^^^^^^^^^^^^^^^^^ .. automethod:: cardio.EcgBatch.convolve_signals .. automethod:: cardio.EcgBatch.band_pass_signals .. automethod:: cardio.EcgBatch.drop_short_signals .. automethod:: cardio.EcgBatch.flip_signals .. automethod:: cardio.EcgBatch.slice_signals .. automethod:: cardio.EcgBatch.split_signals .. automethod:: cardio.EcgBatch.random_split_signals .. automethod:: cardio.EcgBatch.unstack_signals .. automethod:: cardio.EcgBatch.resample_signals .. automethod:: cardio.EcgBatch.random_resample_signals Complex ECG processing ^^^^^^^^^^^^^^^^^^^^^^ Fourier-based transformations """"""""""""""""""""""""""""" .. automethod:: cardio.EcgBatch.fft .. automethod:: cardio.EcgBatch.ifft .. automethod:: cardio.EcgBatch.rfft .. automethod:: cardio.EcgBatch.irfft .. automethod:: cardio.EcgBatch.spectrogram Wavelet-based transformations """"""""""""""""""""""""""""" .. automethod:: cardio.EcgBatch.dwt .. automethod:: cardio.EcgBatch.idwt .. automethod:: cardio.EcgBatch.wavedec .. automethod:: cardio.EcgBatch.waverec .. automethod:: cardio.EcgBatch.pdwt .. automethod:: cardio.EcgBatch.cwt Other methods """"""""""""" .. automethod:: cardio.EcgBatch.calc_ecg_parameters EcgDataset ========== .. autoclass:: cardio.EcgDataset :show-inheritance: ================================================ FILE: docs/api/models.rst ================================================ ====== Models ====== -------------- DirichletModel -------------- .. autoclass:: cardio.models.DirichletModel :members: :show-inheritance: ------------------ DirichletModelBase ------------------ .. autoclass:: cardio.models.dirichlet_model.dirichlet_model.DirichletModelBase :members: :show-inheritance: ------- HMModel ------- .. autoclass:: cardio.models.HMModel :show-inheritance: .. automethod:: cardio.models.HMModel.train .. automethod:: cardio.models.HMModel.predict .. automethod:: cardio.models.HMModel.load .. automethod:: cardio.models.HMModel.save --------- FFTModel --------- .. autoclass:: cardio.models.FFTModel :members: :show-inheritance: ================================================ FILE: docs/api/pipelines.rst ================================================ ========= Pipelines ========= dirichlet_train_pipeline ------------------------ .. autofunction:: cardio.pipelines.dirichlet_train_pipeline dirichlet_predict_pipeline -------------------------- .. autofunction:: cardio.pipelines.dirichlet_predict_pipeline hmm_preprocessing_pipeline -------------------------- .. autofunction:: cardio.pipelines.hmm_preprocessing_pipeline hmm_train_pipeline ------------------ .. autofunction:: cardio.pipelines.hmm_train_pipeline hmm_predict_pipeline -------------------- .. autofunction:: cardio.pipelines.hmm_predict_pipeline ================================================ FILE: docs/conf.py ================================================ #!/usr/bin/env python3 # -*- coding: utf-8 -*- # # CardIO documentation build configuration file, created by # hand on Tue Nov 9 12:49:55 2017. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # import os import sys sys.path.insert(0, os.path.join("..")) import cardio extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.doctest', 'sphinx.ext.coverage', 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', 'sphinx.ext.githubpages', 'sphinx.ext.intersphinx', 'sphinx.ext.napoleon', ] napoleon_use_rtype = False templates_path = ['_templates'] source_suffix = ['.rst', '.md'] source_parsers = { } master_doc = 'index' project = 'CardIO' copyright = '2017, Analysis Center' author = 'Analysis Center' # The full version, including alpha/beta/rc tags. release = cardio.__version__ # The short X.Y version. version = '.'.join(release.split('.')[:2]) # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = 'classic' #html_theme = 'bizstyle' html_theme_options = { 'sidebarwidth': 290 } # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # Custom sidebar templates, must be a dictionary that maps document names # to template names. # # This is required for the alabaster theme # refs: http://alabaster.readthedocs.io/en/latest/installation.html#sidebars """ html_theme = 'alabaster' html_sidebars = { '**': [ 'about.html', 'navigation.html', 'relations.html', # needs 'show_related': True theme option to display 'searchbox.html', 'donate.html', ] } """ autoclass_content = 'class' add_module_names = False intersphinx_mapping = { 'python': ('https://docs.python.org/3', None), 'numpy': ('http://docs.scipy.org/doc/numpy/', None), 'scipy': ('http://docs.scipy.org/doc/scipy/reference/', None), 'pandas': ('http://pandas-docs.github.io/pandas-docs-travis/', None), 'batchflow': ('https://analysiscenter.github.io/batchflow/', None), } viewcode_import = True # -- Options for HTMLHelp output ------------------------------------------ # Output file base name for HTML help builder. htmlhelp_basename = 'CardIOdoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'CardIO.tex', 'CardIO Documentation', author, 'manual'), ] # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ (master_doc, 'cardio', 'CardIO Documentation', [author], 1) ] # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'CardIO', 'CardIO Documentation', author, 'CardIO', 'One line description of project.', 'Miscellaneous'), ] #github_doc_root = 'http://github.com/analysiscenter/cardio/' ================================================ FILE: docs/index.rst ================================================ =================================== Welcome to CardIO's documentation! =================================== `CardIO` is designed to build end-to-end machine learning models for deep research of electrocardiograms. Main features: * load and save signals in various formats: WFDB, DICOM, EDF, XML (Schiller), etc. * resample, crop, flip and filter signals * detect PQ, QT, QRS segments * calculate heart rate and other ECG characteristics * perform complex processing like fourier and wavelet transformations * apply custom functions to the data * recognize heart diseases (e.g. atrial fibrillation) * efficiently work with large datasets that do not even fit into memory * perform end-to-end ECG processing * build, train and test neural networks and other machine learning models About CardIO ============ .. note:: CardIO is based on `BatchFlow `_. You might benefit from reading `its documentation `_. However, it is not required, especially at the beginning. CardIO has three modules: :doc:`core <./modules/core>`, :doc:`models <./modules/models>` and :doc:`pipelines <./modules/pipelines>`. ``core`` module contains ``EcgBatch`` and ``EcgDataset`` classes. ``EcgBatch`` defines how ECGs are stored and includes actions for ECG processing. These actions might be used to build multi-staged workflows that can also involve machine learning models. ``EcgDataset`` is a class that stores indices of ECGs and generates batches of type ``EcgBatch``. ``models`` module provides several ready to use models for important problems in ECG analysis: * how to detect specific features of ECG like R-peaks, P-wave, T-wave, etc * how to recognize heart diseases from ECG, for example, atrial fibrillation ``pipelines`` module contains predefined workflows to * train a model and perform an inference to detect PQ, QT, QRS segments and calculate heart rate * train a model and perform an inference to find probabilities of heart diseases, in particular, atrial fibrillation Contents ======== .. toctree:: :maxdepth: 2 :titlesonly: modules/modules tutorials api/api Basic usage =========== Here is an example of a pipeline that loads ECG signals, makes preprocessing and trains a model for 50 epochs: .. code-block:: python train_pipeline = ( bf.Pipeline() .init_model("dynamic", DirichletModel, name="dirichlet", config=model_config) .init_variable("loss_history", init_on_each_run=list) .load(components=["signal", "meta"], fmt="wfdb") .load(components="target", fmt="csv", src=LABELS_PATH) .drop_labels(["~"]) .rename_labels({"N": "NO", "O": "NO"}) .flip_signals() .random_resample_signals("normal", loc=300, scale=10) .random_split_signals(2048, {"A": 9, "NO": 3}) .binarize_labels() .train_model("dirichlet", make_data=concatenate_ecg_batch, fetches="loss", save_to=V("loss_history"), mode="a") .run(batch_size=100, shuffle=True, drop_last=True, n_epochs=50) ) Installation ============ .. note:: `CardIO` module is in the beta stage. Your suggestions and improvements are very welcome. .. note:: `CardIO` supports python 3.5 or higher. Installation as a python package -------------------------------- With `pipenv `_:: pipenv install git+https://github.com/analysiscenter/cardio.git#egg=cardio With `pip `_:: pip3 install git+https://github.com/analysiscenter/cardio.git After that just import `cardio`:: import cardio Installation as a project repository -------------------------------------- When cloning repo from GitHub use flag ``--recursive`` to make sure that ``BatchFlow`` submodule is also cloned:: git clone --recursive https://github.com/analysiscenter/cardio.git Citing CardIO ============== Please cite CardIO in your publications if it helps your research. .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1156085.svg :target: https://doi.org/10.5281/zenodo.1156085 :: Khudorozhkov R., Illarionov E., Kuvaev A., Podvyaznikov D. CardIO library for deep research of heart signals. 2017. :: @misc{cardio_2017_1156085, author = {R. Khudorozhkov and E. Illarionov and A. Kuvaev and D. Podvyaznikov}, title = {CardIO library for deep research of heart signals}, year = 2017, doi = {10.5281/zenodo.1156085}, url = {https://doi.org/10.5281/zenodo.1156085} } ================================================ FILE: docs/make.bat ================================================ @ECHO OFF pushd %~dp0 REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=python -msphinx ) set SOURCEDIR=. set BUILDDIR=_build set SPHINXPROJ=CardIO if "%1" == "" goto help %SPHINXBUILD% >NUL 2>NUL if errorlevel 9009 ( echo. echo.The Sphinx module was not found. Make sure you have Sphinx installed, echo.then set the SPHINXBUILD environment variable to point to the full echo.path of the 'sphinx-build' executable. Alternatively you may add the echo.Sphinx directory to PATH. echo. echo.If you don't have Sphinx installed, grab it from echo.http://sphinx-doc.org/ exit /b 1 ) %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% goto end :help %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% :end popd ================================================ FILE: docs/modules/core.rst ================================================ ==== Core ==== CardIO's core classes are ``EcgBatch`` and ``EcgDataset``. They are responsible for storing of ECGs, batch generation and applying actions to batches. EcgBatch --------- ``EcgBatch`` is a class that defines how to store ECG data and contains actions that can be applied to ECG in preprocessing stage. Attributes of ``EcgBatch``: * ``signal``, stores ECG signals in numpy array * ``annotation``, array of dicts with different types of annotations, e.g. array of R-peaks * ``meta``, array of dicts with metadata about ECG records, e.g. signal frequency * ``target``, array of labels assigned to ECG records * ``unique_labels``, array of all possible target labels in dataset Actions of ``EcgBatch`` allow to: * load ECG records from WFDB, DICOM, EDF, XML (Schiller), WAV or Blosc format * segment, flip and resample signals * filter signals * detect PQ, QT, QRS segments * dump results To learn more about actions refer to the `tutorial `_. EcgDataset ---------- ``EcgDataset`` helps to conveniently create a list of ECG indices and generate batches (small subsets of data) of default type ``EcgBatch``. CardIO generates batches trought a `BatchFlow `_ library. To initialize this process we need to create a sequence of data item ids, e.g. using names of files in specific folder: .. code-block:: python import cardio.batchflow as bf index = bf.FilesIndex(path="../cardio/tests/data/*.hea", no_ext=True, sort=True) Then we specify type of batches we want to generate, e.g. ``EcgBatch``: .. code-block:: python from cardio import EcgBatch eds = bf.Dataset(index, batch_class=EcgBatch) ``EcgDataset`` helps to get the same result in a shorter way: .. code-block:: python from cardio import EcgDataset eds = EcgDataset(path="../cardio/tests/data/*.hea", no_ext=True, sort=True) Now we can call ``EcgDataset.next_batch`` with specified ``batch_size`` argument to generate batches and process them using actions of ``EcgBatch``. API --- See :doc:`Core API <../api/core>` ================================================ FILE: docs/modules/models.rst ================================================ ====== Models ====== This is a place where ECG models live. You can write your own model or exploit provided :doc:`models <../api/models>`. We have two built-in models suited to classify whether ECG signal is normal or pathological, and a model to annotate segments of ECG signal (e.g., P-wave). DirichletModel -------------- This model is used to perform ECG classification. However, instead of predicting class probabilities themselves, the model predicts parameters of the Dirichlet distribution over these probabilities. This is done in order to get model’s confidence in its prediction, which varies from 0 (absolutely sure) to 1 (absolutely unsure). You can read more about the model in this `article `_. The high-level architecture of the network is shown in the figure below. .. image:: dirichlet_model.png .. rubric:: How to use .. code-block :: python import cardio.batchflow as bf from cardio import EcgDataset from cardio.batchflow import F, V from cardio.models.dirichlet_model import DirichletModel, concatenate_ecg_batch eds = EcgDataset(path='./path/to/data/', no_ext=True, sort=True) gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5, allow_growth=True) model_config = { "session": {"config": tf.ConfigProto(gpu_options=gpu_options)}, "input_shape": F(lambda batch: batch.signal[0].shape[1:]), "class_names": F(lambda batch: batch.label_binarizer.classes_), "loss": None, } dirichlet_train_ppl = ( bf.Pipeline() .init_model("dynamic", DirichletModel, name="dirichlet", config=model_config) .init_variable("loss_history", init_on_each_run=list) .load(components=["signal", "meta"], fmt="wfdb") .load(src='./path/to/taret/', fmt="csv", components="target") .drop_labels(["~"]) .rename_labels({"N": "NO", "O": "NO"}) .flip_signals() .random_resample_signals("normal", loc=300, scale=10) .random_split_signals(2048, {"A": 9, "NO": 3}) .binarize_labels() .train_model("dirichlet", make_data=concatenate_ecg_batch, fetches="loss", save_to=V("loss_history"), mode="a") .run(batch_size=100, shuffle=True, drop_last=True, n_epochs=100, lazy=True) ) train_ppl = (eds >> dirichlet_train_ppl).run() HMModel ------- Hidden Markov Model is used to annotate ECG signal. This allows to calculate a number of parameters important for diagnosing. This model allows to detect P and T waves; Q, R, S peaks; PQ and ST segments. It has a total of 19 states, which makes a good compromise on complexity versus performance of the model. There will be 3 states for the ISO, P-wave, and QRS-complex models; 2 states for the PQ and ST segment models; 6 states for the T-wave. The mapping of them to the segments of ECG signal can be found in ``cardio.batch.ecg_batch_tools`` submodule. Also, as you can see in the picture below, we introduce direct transition from last ISO state to first PQ state, thus enabling possibility of absent P-wave. .. image:: hmmodel.png .. rubric:: How to use .. code-block :: python from hmmlearn import hmm import cardio.batchflow as ds from cardio import EcgBatch from cardio.batchflow import B, V, F from cardio.models.hmm import HMModel, prepare_hmm_input model_config = { 'build': True, 'estimator': hmm.GaussianHMM(n_components=19, n_iter=25, covariance_type="full", random_state=42, init_params='mstc', verbose=False), } eds = EcgDataset(path='./path/to/data/', no_ext=True, sort=True) hmm_train_ppl = ( bf.Pipeline() .init_model("dynamic", HMModel, "HMM", config=model_config) .load(fmt='wfdb', components=["signal", "annotation", "meta"], ann_ext='pu1') .cwt(src="signal", dst="hmm_features", scales=[4,8,16], wavelet="mexh") .standardize(axis=-1, src="hmm_features", dst="hmm_features") .train_model("HMM", make_data=partial(prepare_hmm_input, features="hmm_features", channel_ix=0))) .run(batch_size=20, shuffle=False, drop_last=False, n_epochs=1, lazy=True) ) train_ppl = (eds >> hmm_train_ppl).run() FFTModel -------- FFT model learns to classify ECG signals using signal spectrum. At first step it convolves signal with a number of 1D kernels. Then for each channel it applies fast fourier transform. The result is considered as 2D image and is processed with a number of Inception2 blocks to resulting output, which is a predicted class. See below the model architecture: .. image:: fft_model.PNG .. rubric:: How to use We applied this model to arrhythmia prediction from single-lead ECG. Train pipeline we used for the fft model looks as follows: .. code-block :: python import cardio.batchflow as bf from cardio import EcgDataset from cardio.batchflow import F, V from cardio.models.fft_model import FFTModel def make_data(batch, **kwagrs): return {'x': np.array(list(batch.signal)), 'y': batch.target} eds = EcgDataset(path='./path/to/data/', no_ext=True, sort=True) model_config = { "input_shape": F(lambda batch: batch.signal[0].shape), "loss": "binary_crossentropy", "optimizer": "adam" } fft_train_ppl = ( bf.Pipeline() .init_model("dynamic", FFTModel, name="fft_model", config=model_config) .init_variable("loss_history", init_on_each_run=list) .load(fmt="wfdb", components=["signal", "meta"]) .load(src='./path/to/taret/', fmt="csv", components="target") .drop_labels(["~"]) .rename_labels({"N": "NO", "O": "NO"}) .random_resample_signals("normal", loc=300, scale=10) .drop_short_signals(4000) .split_signals(3000, 3000) .binarize_labels() .apply(np.transpose , axes=[0, 2, 1]) .unstack_signals() .train_model('fft_model', make_data=make_data, save_to=V("loss_history"), mode="a") .run(batch_size=100, shuffle=True, drop_last=True, n_epochs=100, prefetch=0, lazy=True) ) train_ppl = (eds >> fft_train_ppl).run() Learn more ------------------------------- To learn more about existing models and building new ones, refer to the `tutorial `_. API --- See :doc:`Models API <../api/models>` ================================================ FILE: docs/modules/modules.rst ================================================ ======== Modules ======== .. toctree:: :maxdepth: 2 core models pipelines ================================================ FILE: docs/modules/pipelines.rst ================================================ ========= Pipelines ========= Module ``pipelines`` contains functions that build pipelines that we used to train models and make predictions. You can use them as-is to train similar models or adjust them in order to get better perfomance. About pipelines --------------- Pre-defined pipelines were designed to make it easier to use our models and make code simpler and more readable. Here is an example: Running these two lines of code .. code-block:: python from cardio.pipelines import dirichlet_train_pipeline pipeline = dirichlet_train_pipeline(labels_path, batch_size=256, n_epochs=1000, gpu_options=gpu_options) is similar to running this .. code-block:: python from cardio import batchflow as bf from cardio.batchflow import F, V from cardio.models import DirichletModel, concatenate_ecg_batch model_config = { "session": {"config": tf.ConfigProto(gpu_options=gpu_options)}, "input_shape": F(lambda batch: batch.signal[0].shape[1:]), "class_names": F(lambda batch: batch.label_binarizer.classes_), "loss": None} pipeline = ( bf.Pipeline() .init_model("dynamic", DirichletModel, name="dirichlet", config=model_config) .init_variable("loss_history", init_on_each_run=list) .load(components=["signal", "meta"], fmt="wfdb") .load(components="target", fmt="csv", src=labels_path) .drop_labels(["~"]) .rename_labels({"N": "NO", "O": "NO"}) .flip_signals() .random_resample_signals("normal", loc=300, scale=10) .random_split_signals(2048, {"A": 9, "NO": 3}) .binarize_labels() .train_model("dirichlet", make_data=concatenate_ecg_batch, fetches="loss", save_to=V("loss_history"), mode="a") .run(batch_size=256, shuffle=True, drop_last=True, n_epochs=1000, lazy=True)) In both cases you obtain ``pipeline``, ready for training ``DirichletModel``. The first example is short but only allows to vary some hyperparameters of the model, while the second example is more flexible in data preprocessing. How to use ---------- Working with pipelines consists of 3 simple steps. First, we import desired pipeline, e.g. ``dirichlet_train_pipeline``: :: from cardio.pipelines import dirichlet_train_pipeline Second, we specify its parameters, e.g. path to a file with labels: :: pipeline = dirichlet_train_pipeline(labels_path='some_path') Third, we pass dataset to the pipeline and run caclulation: :: (dataset >> pipeline).run(batch_size=100, n_epochs=10) Result is typically a trained model or some values stored in pipeline variable (e.g. model predicitons). Available pipelines ------------------- At this moment the module contains following pipelines: * :func:`~cardio.pipelines.dirichlet_train_pipeline` * :func:`~cardio.pipelines.dirichlet_predict_pipeline` * :func:`~cardio.pipelines.hmm_preprocessing_pipeline` * :func:`~cardio.pipelines.hmm_train_pipeline` * :func:`~cardio.pipelines.hmm_predict_pipeline` To learn more about using these pipelines and building new ones see the `tutorial `_. API --- See :doc:`Pipelines API <../api/pipelines>` ================================================ FILE: docs/tutorials.rst ================================================ ========= Tutorials ========= The following tutorials will help you to explore CardIO framework from simple to complex notions. Tutorials are organized as IPython Notebook, so you can run the code cells and see the result. * `CardIO `_ Explains where to get ECG data and how to start using CardIO. You will learn how to create a dataset with ECG's; how to generate batches; which actions you can apply to the data and how those actions affect batch and its components. * `Pipelines `_ Tells you about pipelines, which are also called workflows. You will learn how to build pipelines; how to use pipeline variables; how to add custom actions to the pipelines. Also, you will learn about pipeline algebra and train an atrial fibrillation detection model with built-in pipeline. * `Models `_ Shows how you can train various models with CardIO. You will train built-in CardIO models; build and train simple convolutional neural network using layers and blocks provided in `BatchFlow `_, Tensorflow and Keras; build and train custom logistic regression model. ================================================ FILE: examples/Getting_started.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook briefly shows capabilities of CardIO framework.\n", "\n", "For details refer to other [tutorials](https://github.com/analysiscenter/cardio/tree/master/tutorials) or [CardIO documentation](https://analysiscenter.github.io/cardio/index.html)\n", "You can also find use of article [CardIO framework for deep research of electrocardiograms](https://medium.com/data-analysis-center/cardio-framework-for-deep-research-of-electrocardiograms-2a38a0673b8e), which is closely related to this notebook." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some general imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "env: CUDA_VISIBLE_DEVICES=0\n" ] } ], "source": [ "import os\n", "import sys\n", "sys.path.append(\"..\")\n", "\n", "import cardio.batchflow as bf\n", "from cardio import EcgBatch\n", "from cardio.models.metrics import classification_report\n", "\n", "%env CUDA_VISIBLE_DEVICES=0\n", "import tensorflow as tf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create indices from filenames" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "index = bf.FilesIndex(path=\"../cardio/tests/data/A*.hea\", no_ext=True, sort=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check which indices are in list of indices" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['A00001' 'A00002' 'A00004' 'A00005' 'A00008' 'A00013']\n" ] } ], "source": [ "print(index.indices)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create dataset" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "eds = bf.Dataset(index, batch_class=EcgBatch)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generate batch" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "batch = eds.next_batch(batch_size=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fill batch with data" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "batch_with_data = batch.load(fmt=\"wfdb\", components=[\"signal\", \"meta\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot short segment of ECG with index 'A00001'" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAEYCAYAAABBfQDEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "batch_with_data.show_ecg('A00001', start=10, end=15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For detection of QRS intervals, P-waves and T-waves we need to train model first.\n", "\n", "Download the [QT Database](https://www.physionet.org/physiobank/database/qtdb/) with labeled ECGs. Let ```SIGNALS_PATH``` be the folder where database is saved." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from cardio.pipelines import hmm_preprocessing_pipeline, hmm_train_pipeline\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "SIGNALS_PATH = \"path_to_QT_database\" #set path to QT database\n", "SIGNALS_MASK = os.path.join(SIGNALS_PATH, \"*.hea\")\n", "\n", "index = bf.FilesIndex(path=SIGNALS_MASK, no_ext=True, sort=True)\n", "dtst = bf.Dataset(index, batch_class=EcgBatch)\n", "\n", "pipeline = hmm_preprocessing_pipeline()\n", "ppl_inits = (dtst >> pipeline).run()\n", "\n", "pipeline = hmm_train_pipeline(ppl_inits)\n", "ppl_train = (dtst >> pipeline).run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Save model" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ppl_train.save_model(\"HMM\", path=\"model_dump.dill\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's make prediction with hmm model" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from cardio.pipelines import hmm_predict_pipeline\n", "\n", "batch = (eds >> hmm_predict_pipeline(\"model_dump.dill\", annot=\"hmm_annotation\")).next_batch()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot ECG signal with detected QRS intervals, P-waves and T-waves" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAEYCAYAAABBfQDEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "batch.show_ecg('A00001', start=10, end=15, annot=\"hmm_annotation\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For detection of atrial fibrillation we need to train another model.\n", "\n", "***\n", "**Important note on the data**\n", "\n", "For atrial fibrillation detection we use the data from [PhysioNet CinC Challenge 2017](https://physionet.org/challenge/2017/).\n", "\n", "Due to the updates in ``wfdb`` library, the data from the link above can not be loaded by ``wfdb`` as is. Please, refer to the section *Getting ECG data* in our [tutorial](https://github.com/analysiscenter/cardio/blob/master/tutorials/I.CardIO.ipynb) for instructions to solve this issue.\n", "***\n", "\n", "Let AF_SIGNALS_PATH be the folder where database is saved." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from cardio.pipelines import dirichlet_train_pipeline\n", "\n", "gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.33, allow_growth=True)\n", "\n", "AF_SIGNALS_PATH = \"path_to_PhysioNet_database\" #set path to PhysioNet database\n", "AF_SIGNALS_MASK = os.path.join(AF_SIGNALS_PATH, \"*.hea\")\n", "AF_SIGNALS_REF = os.path.join(AF_SIGNALS_PATH, \"REFERENCE.csv\")\n", "\n", "index = bf.FilesIndex(path=AF_SIGNALS_MASK, no_ext=True, sort=True)\n", "afds = bf.Dataset(index, batch_class=EcgBatch)\n", "\n", "pipeline = dirichlet_train_pipeline(AF_SIGNALS_REF, gpu_options=gpu_options)\n", "train_ppl = (afds >> pipeline).run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Save model" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model_path = \"af_model_dump\"\n", "train_ppl.save_model(\"dirichlet\", path=model_path)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from cardio.pipelines import dirichlet_predict_pipeline\n", "\n", "pipeline = dirichlet_predict_pipeline(model_path, gpu_options=gpu_options)\n", "res = (eds >> pipeline).run()\n", "pred = res.get_variable(\"predictions_list\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get predicted probalilities for atrial fibrillation" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['0.02', '0.02', '0.87', '0.61', '0.03', '0.02']\n" ] } ], "source": [ "print([\"{:.2f}\".format(x[\"target_pred\"][\"A\"]) for x in pred])" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: examples/Load_XML.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sys\n", "\n", "import seaborn as sns\n", "sns.set(\"talk\")\n", "\n", "sys.path.append(\"..\")\n", "import cardio.batchflow as bf\n", "from cardio import EcgDataset" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['sample'], dtype='> template_load_ppl).next_batch(len(eds), shuffle=False)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "batch.show_ecg(subplot_size=(15, 3))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: pylintrc ================================================ [MASTER] extension-pkg-whitelist=numpy init-hook='import sys; sys.path.append(".")' [FORMAT] max-line-length=120 max-attributes=8 max-args=8 max-locals=25 good-names=ax,im,ix,a,b,_,n,s,fs,i,j,k,t,x,y,z,lc,rc [MESSAGE CONTROL] disable=no-value-for-parameter,no-self-use,too-few-public-methods,unsubscriptable-object,no-method-argument,no-member,arguments-differ,len-as-condition,keyword-arg-before-vararg,too-many-locals [TYPECHECK] ignored-modules=numpy, keras [MISCELLANEOUS] notes= ================================================ FILE: requirements-shippable.txt ================================================ pytest>=3.3.0 . ================================================ FILE: requirements.txt ================================================ pytest>=3.3.0 . ================================================ FILE: setup.py ================================================ """ CardIO is a library that works with electrocardiograms. Documentation - https://analysiscenter.github.io/cardio/ """ from setuptools import setup, find_packages import re with open('cardio/__init__.py', 'r') as f: version = re.search(r'^__version__\s*=\s*[\'"]([^\'"]*)[\'"]', f.read(), re.MULTILINE).group(1) with open('docs/index.rst', 'r') as f: long_description = f.read() setup( name='cardio', packages=find_packages(exclude=['tutorials', 'examples', 'docs']), version=version, url='https://github.com/analysiscenter/cardio', license='Apache License 2.0', author='Data Analysis Center team', author_email='cardio@analysiscenter.ru', description='A framework for deep research of electrocardiograms', long_description=long_description, zip_safe=False, platforms='any', install_requires=[ 'numpy>=1.13.1', 'scipy>=0.19.1', 'pandas>=0.21.1', 'scikit-learn==0.19.1', 'numba>=0.35.0', 'pywavelets>=0.5.2', 'matplotlib>=2.1.0', 'dill>=0.2.7.1', 'pydicom>=0.9.9', 'pyedflib>=0.1.11', 'wfdb==2.2.1', 'pint>=0.8.1', ], extras_require={ 'tensorflow': ['tensorflow>=1.4'], 'tensorflow-gpu': ['tensorflow-gpu>=1.4'], 'keras': ['keras>=2.0.0'], 'hmmlearn': ['hmmlearn==0.2.0'] }, classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'Intended Audience :: Science/Research', 'License :: OSI Approved :: Apache Software License', 'Operating System :: OS Independent', 'Programming Language :: Python', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Topic :: Scientific/Engineering' ], ) ================================================ FILE: shippable.yml ================================================ language: none env: global: - DOCKER_ACC=analysiscenter1 - DOCKER_REPO=ds-py3 - TAG="latest" build: pre_ci_boot: image_name: $DOCKER_ACC/$DOCKER_REPO image_tag: $TAG ci: - pip3 install -U pylint - pip3 install -r requirements-shippable.txt - find ./cardio -not -path "./cardio/batchflow/*" -iname "*.py" | xargs -r pylint -rn --rcfile pylintrc - python3 -m pytest -v --ignore=./cardio/batchflow --junitxml=shippable/testresults/pytests.xml integrations: hub: - integrationName: DockerHub analysiscenter.ru type: docker notifications: - integrationName: Slack analysiscenter.ru type: slack recipients: - "#commits" on_success: always on_failure: always ================================================ FILE: tutorials/I.CardIO.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# CardIO framework for deep research of ECG" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### CardIO is based on a very simple and natural approach:\n", "\n", "* Split input dataset into batches to process data batch by batch (because input datasets can be indefinitely large)\n", "* Describe workflow as a sequence of actions (e.g., load data — preprocess data — train model)\n", "* Run the workflow for the whole dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### In this notebook you will learn:\n", "* Where to get ECG data\n", "* How to start using CardIO\n", "* How to apply actions to batch\n", "* How different actions transform batch and its components" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Table of contents" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* [Getting ECG data](#Getting-ECG-data)\n", "* [Indexing of ECGs](#Indexing-of-ECGs)\n", "* [Initialization of dataset](#Initialization-of-dataset)\n", "* [Generating batches](#Generating-batches)\n", "* [Apply actions](#Apply-actions)\n", "* [Actions in CardIO](#Actions-in-CardIO)\n", " * [convolve_signals](#convolve_signals)\n", " * [band_pass_signals](#band_pass_signals)\n", " * [random_split_signals](#random_split_signals)\n", " * [unstack_signals](#unstack_signals)\n", " * [resample_signals](#resample_signals)\n", " * [rfft](#rfft)\n", " * [spectrogram](#spectrogram)\n", " * [cwt](#cwt)\n", " * [apply_transform](#apply_transform)\n", " * [drop_labels](#drop_labels)\n", " * [rename_labels](#rename_labels)\n", " * [rename_channels](#rename_channels)\n", " * [other actions](#other-actions)\n", "* [Summary](#Summary)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting ECG data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this and following tutorials you will work with ECG data through the CardIO framework.\n", "\n", "We suggest loading ECG data from the 2017 PhysioNet/CinC Challenge database of short single lead ECG recording. \n", "\n", "The PhysioNet archive contains 8.528 ECGs in [wfdb](https://www.physionet.org/physiotools/wpg/wpg_35.htm) format. Each ECG has a unique index ranging from \"A00001\" to \"A08528\". According to wbdf format, ECG record consists of several files with the same name but different extensions. We will work with files with ```.hea``` and ```.mat``` extensions. File ```.mat``` contains signal, while ```.hea``` contains meta information about the signal (e.g., sample rate).\n", "\n", "***\n", "### Important note on downloading\n", "First way to get the data is to follow this [link](https://physionet.org/challenge/2017/) and download archive \\\"training2017.zip\\\" or use the [direct](https://physionet.org/challenge/2017/training2017.zip) link.\n", "\n", "Since the release of the challenge wfdb library has changed and now complies with the data standard more strictly. Latest versions of the library can not read the data from this archive because of some discrepancies with the standard. To follow this course of tutorials you can use pn2017_data_to_wfdb_format.py script, which formats the data properly:\n", "```\n", ">>> python3 pn2017_data_to_wfdb.py -p path_to_data\n", "```\n", "\n", "Another way to get the data is to use `dl_database` function from wfdb library. This function downloads the data from this [database](https://physionet.org/physiobank/database/challenge/2017/training/). It may take some time, and the data will be located in several subfolders.\n", "\n", "```python\n", "import wfdb\n", "\n", "wfdb.io.dl_database(db_dir='challenge/2017/training/', \n", " dl_dir='/notebooks/data/ECG/training2017')\n", "```\n", "\n", "If you choose this way, you do not need to process the data with the script mentioned above because records in this database are already formatted properly.\n", "***\n", "\n", "Since the PhysioNet archive was prepared for arrhythmia classification challenge, it also contains REFERENCE.csv file, where each ECG index is labeled with one of four classes:\n", "* Normal rhythm\n", "* AF\n", "* Other rhythms\n", "* Noise\n", "\n", "Read [here](https://physionet.org/challenge/2017/) more about PhysioNet database.\n", "\n", "However, if you do not want to load large dataset now, you can find several ECGs from that database in folder cardio/tests/data (this folder is included in CardIO repository)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Indexing of ECGs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Working with ECG begins with ```FilesIndex```. ```FilesIndex``` contains index and location of each ECG record we want to process. \n", "\n", "Let all ECGs be stored in wfdb format in a folder with path '../cardio/tests/data/' (if you cloned the CardIO repository from [Git](https://github.com/analysiscenter/cardio), this path will contain several examples of ECGs indeed). Let's create a new ```FilesIndex``` with all ECGs from this folder." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sys\n", "import numpy as np\n", "\n", "from matplotlib import pyplot as plt\n", "\n", "sys.path.append('..')\n", "\n", "import cardio.batchflow as bf\n", "index = bf.FilesIndex(path='../cardio/tests/data/A*.hea', no_ext=True, sort=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now each ECG is indexed with its filename without extension, as it is defined by ```no_ext``` argument of ```FilesIndex```. Indices are stored in ```index.indices```:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['A00001' 'A00002' 'A00004' 'A00005' 'A00008' 'A00013']\n" ] } ], "source": [ "print(index.indices)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initialization of Dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have ```index``` that knows which ECGs we want to process. CardIO processes data grouped in batches of class [EcgBatch](https://analysiscenter.github.io/cardio/modiles/core.html). To generate proper batches we create a dataset:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from cardio import EcgBatch\n", "eds = bf.Dataset(index, batch_class=EcgBatch)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the same result can be obtained in a shorter way:\n", "```python\n", "from cardio import EcgDataset\n", "eds = EcgDataset(path='../cardio/tests/data/*.hea', no_ext=True, sort=True)\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generating batches" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's generate a first batch of some size, say 6:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "batch = eds.next_batch(batch_size=6, unique_labels=['A', 'N', 'O'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we pass argument ```unique_labels```, which contains all possible labels that we expect to be in the dataset. We have ECG signals with three different labels that indicate whether the ECG has come from a person with normal rhythm (label \"N\"), a person with atrial fibrillation (\"A\"), or a person with some other abnormal rhythm (\"O\"). \n", "\n", "However, ```batch``` still does not contain any data, it contains only indices and paths to ECGs. To fill it with data we need to apply load action. Next section shows how to apply actions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Apply actions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Any preprocess typically begins with the loading of data. Therefore, we start with an example how to apply action [```load```](https://analysiscenter.github.io/cardio/api/cardio.batch.html#cardio.batch.EcgBatch.load). \n", "\n", "Note that paths to ECGs are already stored in the batch, so simply indicate data format, which is wfdb, and components of the batch we want to load. We load components ```signal``` and ```meta```:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "batch_with_data = batch.load(fmt='wfdb', components=['signal', 'meta'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now ```batch_with_data``` contatains loaded ECGs. Any ECG record can be accessed by its index, e.g., ```batch_with_data['A00001']```. ECG components, signal and meta, can be accessed as ```batch_with_data['A00001'].signal``` and ```batch_with_data['A00001'].meta``` correspondingly." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also load labels for the data using the same action, but with other arguments:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "batch_with_data = batch_with_data.load(src='../cardio/tests/data/REFERENCE.csv', fmt='csv', components='target')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Labels are stored in the ```target``` component. To access e.g. label of signal 'A00001' call ```batch_with_data['A00001'].target```:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'N'" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "batch_with_data['A00001'].target" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's plot signal from ECG with index```'A00001'```:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAEYCAYAAABBfQDEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAIABJREFUeJzsfXmcJEWZ9hNdfc/03MwJzMGNgAIzA8oCreuB7qrr6rro7iq7+qG7urq6fq64q6woH4iL4IUCCiIqNy7XMAPD0HMx931f3TPTd/f03V13VXx/ZEZWZlZmdUZEdmZVdzy/X/+yuirffCMzIiOeeON93yCUUigoKCgoKCgoKCgoaCgLuwAKCgoKCgoKCgoKxQRFkBUUFBQUFBQUFBRMUARZQUFBQUFBQUFBwQRFkBUUFBQUFBQUFBRMUARZQUFBQUFBQUFBwQRFkBUUFBQUFBQUFBRMUARZQUFBoUhACFlECKGEkPKwy6KgoKAwkaEIsoKCgoIHEEJOEkLeG3Y5SgGEkAZCSJwQMmz6e8n0+xRCyP2EkNP6byf0/2eZzrmZELKFEDJCCOnSP/8LIYSEc1cKCgoTCYogKygoKCiMBb5MKZ1s+vswABBCKgG8AeBtAG4CMAXAOwH0AFiun/PvAH4C4EcA5gKYA+CLAK4DUBn0jSgoKEw8KIKsoKCgIAlCyF8SQnYTQvoJIW8RQq4w/fYt3UI6RAg5SAj5mOm3CCHkfwghZwghjQD+YhQ9Jwkh3yCE7CWEDBBCniKEVOu/TSeEvEwI6SaE9OmfzzbJNhBCfqCXb5gQ8hIhZCYh5A+EkEFCyDZCyCLT+RcTQl4nhPQSQo4QQj7p0+P6DIBzAXyMUnqQUpqllHZRSr9PKV1BCJkK4A4A/0IpfZZSOkQ17KKU/h2lNOFTORQUFBRcoQiygoKCggQIIVcCeATAFwDMBPAggBcJIVX6KScAXA9gKoDvAfg9IWSe/tv/AfCXAK4EsBTAJzyo/CQ0y+tiAFcAuEX/vgzAowAWQiOgMQA/t8neDOAfACwAcB6ATbrMDACHANyu39MkAK8D+COA2brcA4SQS/XfP00I2euhrE54L4CVlNJhl9/fCaAKwAuC11dQUFCQhiLICgoKCnK4FcCDlNItlNIMpfQxAAkA1wIApfQZSmmbbil9CsAx6K4E0Mju/ZTSZkppL4C7POj7qX69XgAvAXiHrqeHUvocpTRKKR0CcCeAG22yj1JKT1BKBwC8CuAEpXQ1pTQN4BloRB3QSPtJSumjlNI0pXQXgOcA/I2u64+U0itQGD/VLers7/v69zMBtBeQmwXgjF4mAIBu9e4nhMQIITeMoldBQUFBGipSWkFBQUEOCwF8lhDyr6bvKgHMBwBCyGcAfB3AIv23ydBIIPRzmk1ypzzo6zB9jpr01AK4D5p1ebr+ex0hJEIpzej/d5pkYw7/Tzbd0zWEkH7T7+UAHvdQPoavUEp/7fB9D4B5Dt+bf59FCClnJJlS+i4AIIS0QBl2FBQUAoDqaBQUFBTk0AzgTkrpNNNfLaX0CULIQgAPA/gygJmU0mkA9gNgmRjaAZxjuta5EuX4dwAXAbiGUjoFALO0imR9aAaw1nZPkyml/yxRPobVAD6gu3E4YRM0C/xHfdCloKCgIARFkBUUFBS8o4IQUm36K4dGgL9ICLmGaJhECPkLQkgdgEkAKIBuACCE/COAy0zXexrAVwghZxNCpgP4lkTZ6qBZgfsJITOg+xML4mUAFxJC/oEQUqH/LSOEXCJxTYbHoRHw5/RAwDI9WPDbhJAPUUr7oflqP0AI+QQhpE4/5x3QnqeCgoLCmEMRZAUFBQXvWAGNhLK//6aUbocWbPdzAH0AjkMPnKOUHgRwLzSraCeAywFsNF3vYQCrAOwBsBPA8xJlux9ADYAzADYDWCl6Id2H+f3QgvPaoLl1/BBa8BwIIX9HCDkwymV+bsuDvEO/dgJaoN5haIGAgwC2QnM72aKfcw80t5RvQntundCCH/8DwFui96WgoKDgFYRSGnYZFBQUFBQUFBQUFIoGyoKsoKCgoKCgoKCgYIIiyAoKCgoKCgoKCgomKIKsoKCgoKCgoKCgYIIiyAoKCgoKCgoKCgomTKiNQmbNmkUXLVoUqM6RkRFMmqQyE4UNVQ/hQ9VB+FB1UBxQ9RA+VB0UB8Kohx07dpyhlJ412nmhEmRCyCPQtjTtopRe5vA7AfATAB+CtmPULZTSnfpvnwXwX/qpP9C3dy2IRYsWYfv27X4V3xMaGhpQX18fqE6FfKh6CB+qDsKHqoPigKqH8KHqoDgQRj0QQrzsWBq6i8VvoW2L6oYPArhA/7sVwC8BwJQE/xoAywHcrifZV1BQUFBQUFBQUJBCqASZUroOQG+BUz4K4HdUw2YA0wgh8wB8AMDrlNJeSmkftGTzhYi2goKCgoKCgoKCgicUuw/yAmhbkjK06N+5fZ8HQsit0KzPmDNnDhoaGsakoG4YHh4OXKdCPlQ9hA9VB+FD1UFxQNVD+FB1UBwo5noodoIsDUrpQwAeAoClS5fSoH1dlJ9TcUDVQ/hQdRA+VB0UB1Q9hA9VB8WBYq6HsH2QR0MrgHNM/5+tf+f2vYKCgoKCgoKCgoIUip0gvwjgM0TDtQAGKKXtAFYBeD8hZLoenPd+/TsFBQUFBQUFBQUFKYSd5u0JAPUAZhFCWqBlpqgAAErprwCsgJbi7Ti0NG//qP/WSwj5PoBt+qXuoJQWCvZTUFBQUFBQUFBQ8IRQCTKl9FOj/E4BfMnlt0cAPDIW5VJQUFBQUGDoG0mifSCOS+dPCbsoCgoKAaHYXSwUFBQUFBRCxUd+sQEf+un6sIuhoDDmuP2F/fj3p/eEXYyigCLICp7x1vEzOHlmJOxicOOF3a342a542MVQUFAoUTT3xsIugoJCIHhs0yk8t7Ml7GIUBcZ9mjcF//DpX28BAJy8+y9CLgkfvvrkbin5O146iK6hOH7+6at8KpGCgoKCgoJCMUNZkBUURsEjG5vw8t72sIuhoKAQMrSwGIViQDyVQSar6kNh7KAIsoKCgisUIVBQyEG9DsWDi7+zEv/2lNzqoIJCISiCrDBhkFXWBi48ufU0Ft+2Al2Dyn9bQQEAVA9SXHhpT1vYRVAYx1AEWWHCQA1ufHh+l7Y55Ynu0gvMVAgOG46dQc9wIuxiBAK1oqKgMHGgCLJCIMhkKb79p31oCjELhhrc+FAZ0bqHVCYbckkUihXxVAZ//5stuPXxHWEXJRCM9x5k04keLPrWK+gYUKtGCgqKICsEgqOdQ/jjltP4YogDaRiDWyZL8Zc/W48XdreGoF0OZWUEAJApwYkFpVTKpaZ3JBnqZK5UEE1mAADHOodCLkkwKMFXgQu/33IKALD15PjdmPaF3a1YdudqZMd7ZSpIQxHkEsSbh7vwkZ9vKKkI3qpyrakl0pnQyhBGfxhLZbC/dRD/9af9wSsPGYc7BtHWH07+2C88vgNLvr1CWP59P16Ld/9Pg38FGqdgqzIThWvQErEhN3YPC03wIkSbFI/neI3/+tN+dA8lEE+HXZLC2NvSjztfOahWPkOEyoNcgvjGM3vQM5JE70gSZ9VVhV0chQJgnVssFd7EQBZEUO6m+7Wdx8LIm/3awU4p+Z6RpE8lGd9gQ/dEscaVym2+5961APjfvQhbNRrHBJmtjBX7LX7sgbeQyVJ886aLURER7YUVZKAsyCWIcv1lSWdLzzeUkPBe9DCsP8XeCRcrhuIpbG0av8u84wWMMKp2Pj4QKRG3KhmraqRECDKz5o/nyUqxQxHkEkR5mVZt6UzpvDjFUNJQ+vxiuPESxNee2o1PPrgJvcqSW9RgRGWiWJDHO0rFxUKmeGXsHou8zTJbkiLI4UER5BIEG5RCNMYqeATrhEuxiwvT9+1Y1zAAYCCWCq0MCqODtZCJ0hcVOaeSRqkE5sr1TcV9bwzM0i1iCFtzuBOvHejwu0gTDoogKwSCYhg/w+jzS6MrLj5ESsTKM9Ex0apHxE0rmc6WzGY7embHQC3I20/2cuuTKR5rs8XedNmYKTJZ+affbp8wqRfHEooglzBKaXAqhqKG44NcDHeuoFAY0WQacYFA0lLJ6gAAfSNJ3LXiEEYS4ukLRF7n//vsHiz/f2+URD5xgmD9c9cf68YnfrUJv9nQxCUn0+5Kp8VqUFkswoMiyCWMUnxvwrQkh2JBLsE6siPMwEqFYHDpd1fhPQJp7cJo34tvewWff2w7t9xT25vx4LpGPLezRVi3yO2u3K8tdZeCL6nMq94xEOeeBLT3a5b1I5x5tP1od0HVxi8bTuDe144Iyxd/qxm/UAS5BFGKqZVki+rHLDqUGD0jT2zp1FUxwPBtDbUUEwttArunsT6IBFhTlAKrD/Gn8esa1LbDTqbFLbky73EpdAGiZYynMrj2rjfwref2cckZKdc4Jw8yzzLo3N0/XHkYP1tznFuuFMf58QZFkEsYpfTayAwsw4k0Ft+2Ar/dyLcM52cZhHUGrlFBITjIvFI7TvXiuB6MGQTUQoh38D6rhD7p4A0MYz7PvH62MqRRVDKRzoSzBXeJDCKne6JY9K1XsGocBQcqglzCKKWZpUxJ+6Naqq+H1jWGVgZRlFIdKfiDjcfPYNG3XkFLXzTsogQGEfL58V9uwnt/vNb/wowCKeujjN4SYDqiZTQyLnBagnMp1/j0SRFkQdGvP7UH1971RuAp8ErAMwcAcKhjEADwzHZxF6ZiQ6gEmRByEyHkCCHkOCHkWw6/30cI2a3/HSWE9Jt+y5h+ezHYkocLIwq3hMhXMRRV+SCLIQzDm19tO4x35PmdrQCAt473BKr3r36xEVfe8VqgOkupffvRjmXut1SIjgjYeybqZ81bN1ITFcHUm28c1tx6RpLB7FGdy7ZRGg2nUl8OKMUNzNwQ2lbThJAIgF8AeB+AFgDbCCEvUkoPsnMopV8znf+vAK40XSJGKX1HUOUtRgQ5OMkSDWPGLzBK+RYkFkI/U8p5kIsBss+N0uCX1nVjWuAD2+7m/tFP8hk5H+QJAh98X72idySJq77/On7+6Svxl1fMF1fMAVFfcoMXB9QQqAQHY0XlHdK0DbqygQdblsoktFSIPA/CtCAvB3CcUtpIKU0CeBLARwuc/ykATwRSshJBkO+pfJCdjKxP1sQQXuBS6dyKFbJ1rx7/2KIUn69cijBxWd7++lTPCAB51zIeCN9fwA0hnL482OA+hlJz0xtPk+XQLMgAFgBoNv3fAuAapxMJIQsBLAawxvR1NSFkO4A0gLsppf/rInsrgFsBYM6cOWhoaJAvOQeGh4d91xlPaNHYW7dtQ3tdMHMc80sqcj+nBrUcq9FolFu+O6qZC+KJhNSz3LBhIyZXir++Irq79LKDismHid7eGABg7949yLaJdxXsvnnehVhM071l6za0TBZv4w0NDYZ/pKg8L9o7tPfzyJEjaBgJjtwwFCrzaHXAe78dI1r7zmQywu07KLmWFq1eTpxoREO2eZSznSHSh2T1Jef16zcYsl7eBdZn9g0MBfaM2lq1Z3Ts2DE0JE56lhtO6uQxm+XSebBNc1fo6urkkhtKio9H6bSmc4RzLMpktPrYsHEj6gTGEd5yZrKavk2bNuOsWrE+UGbM4ZXd26U9157eXi7ZseBIfiFMgsyDmwE8Syk1Z7JfSCltJYQsAbCGELKPUnrCLkgpfQjAQwCwdOlSWl9fH0iBGRoaGuC3zqq3VgOJBK6+eikunT/Fs9xIIo2H1zfiS+8+HxURvhculckCq14FAKH72d86ALy1AbW1tdzyzb1RYN2bqKqqEnuWK18BAFx33XWYPqlSWF5E96meEWBdA0DE5MPEr49vAXrO4Ior3o4bLjyL/wK258bzLlRvexOIRrFs2TJcOKdOWPcNN97I3dbN8iJ19nL3HqC1BRdfdDHql53DJfuff9qHnaf78epXr+fW66XMrnUgeL8nuoeB9WsRiUT4n5XoMxaU2zhyEDjZhCVLlqD+xvOEdL7ruuswg7MPKVv9KpDNWvofL+8C6zMnT56M+nrO9iD4jFb37wOaT+PCCy5A/TsXeZbrHUkCa15HWVkZl86B3a3A3t2YPXsO6uuvHF1AR89wAlizGgD/PUbeXAWk09xjUWTNSiCTwTvf9S7MmlzlXaFgXUTe0PRdc821OHdmLZesTP8lKps51Ans3I6ZM2agvn65Z7mx4Eh+IUwXi1YA5tHjbP07J9wMm3sFpbRVPzYCaIDVP3lCgHfp5advHMP9q4/huR38Uaayqzwyvop+LTGFk8VC111aq2Shgz0v2bovtef+hy2ncah9MOxieEYpPV8WyxB0kZkPvGhbLoUla6N/D6iwfrgXirbdoNo8cyMZj769pYIwCfI2ABcQQhYTQiqhkeC8bBSEkIsBTAewyfTddEJIlf55FoDrABy0y45XUEHSxXJVRpP8W8r6RVREruKXr3UoeZBLiUG4IMw7kPZ9lyz9eKi/sQQ1iFHx0zh/slgE54NcSgj6NZHyIxcMnDaC+wLuEcdzuyl2hOZiQSlNE0K+DGAVgAiARyilBwghdwDYTillZPlmAE9Sa890CYAHCSFZaCT/bnP2i4kCXtJabuSqDD4Nix9pecIsgyjGQ+cWJkkMMziUyZcA9wsN46B5c2Hc50EWtqqyFcJgXpawcln7cwFOdSU2SS+FybJXhOqDTCldAWCF7bvv2v7/bwe5twBcPqaFKwHwvjas3Yq8b7IWZDkXC+0YNlkS1BqGUl8RZv8cdgS3iHajyONnnHBFiY3d0hC5X9EVP4ZQ+Aan0qCbgR/tTngyIK+6qPUp5KB20ithCPu0CXS44ZLT0k3zxsh9GINcKpPFHS8d1AJoBDAefODk223p3nsQYG2jlOYCctZHmaV9cb3FjuB9kMOrh6An7aXSB5VIMbmgCHIJwvCFCrBFymoqCt+9EF7gMDuN1w924pGNTbjjpQNS1wllB0JGzsP2QZZTP+5heGuVAkP2YwMXAVHRIL1SIhxBB7zJPBrRzZtkVwJEUUrtACiNrsArFEEuYQT54kgH6fmguxTJTjHsNBZPyfmch+lHHXYWC6El9RK0qoqilFYX/PCP9aMf40UpuHQG3c9lQ+yUgtKcy+QTkEKFPCiCXMKYODvpyen2+zrFrpMhYgRllm4mB+mVC2l5NToVQqlZt4DwylwKz0q0iKVwbwzUduSWD9rFQvVBoUER5BKEXzli+XT6FKQnYA7JWSfk7BPh+CCXbucmk5rPN93SFuTSff4K/sIPS2yQE/0wiRHvo2L3FlQGA6l+VZIhKxcLZ5RIMbmgCHIJI8gXpxgsyPI5baXESw65Qcuf6/DJ+POwZVdJ5H3nxZWOp3RHbijlCaAIZPogUR/koFKnySBoMu9LFgvu8/2Ji/CuT0OpvWPjqdtTBLmEwUtCZN4zeV9QajnyyUqpzl3Hn8twQcZyLg85v8Dccw++zmR0+1kOhcLIkbiJgSD70FJqutmA24FUFgsjO4+cfFBQfVh4UAS5JKG9MYH6IPskL+NiUYopu8Ls3MK0IPu2PXjITshqcCqMoB9P2C4zMtp5ZUvJchh0vciMfaWWT7+EmsG4gyLIJYwgZ7LhbhTCrLBSRQgFfkd3H+kYwnV3r0HTmZFRzzUmJYLaZSwtfrVMeRcLSQu0iPVcP5Zgc+VG4AFLIZMFmRUwbh/kEPo90eebDbzRyzcE0bYb2MTFJ9dCBXEoglzCCDT9S5g+yHKqfSmDsE6fr/fYppNo7Y/hzcNdo+seFxbkcF0swiZkxQ6ZlSEZfUKyfvitSl0jOJe44OGvIWA0SFmQbUdR+aBQKu0g7NWdsYAiyCWIULJYhChvWFJK0CbntxWoujwCAMh4GCGMfLyyBDkEH+Sc7tKWH+8I2spZSm4HDAYh4yx6mPlv+d1BtGNgEyUpFwsx4bA2Cim9Nl9647QbFEEuZfAu2elHEaLpm4uFwLvj10AxHvIgV0S0B5jMeN/8Q9jFQmJAKJbc1SpN3NiilMhC2C5aoj7IoYT3BhgALgK5ID1ZBOxWFKg2cZRKOXmgCHKIeHlvG8779grEUxkheS9WRL8QZrCUb64CoeRB9vt62gWH4ulRz6W5GZEURG6haFwsZPVLyo93lNJyc2guFsI+yAK6fAK/tVvMACK6yuVL2lBZf+uAUCqT9FIpJw8UQQ4R9752FJksRWt/TEg+yOboX5CezEYhcgjHguyve0hMn0wNxVOj69aPwmne2FEkMElQpyHPMpdIXyd4+TCCq8LCRIvol5lk88qGQziokG7RooqT1PAawkRr814xHrfEVgQ5RFRGtMefTHtfLgdypCHYnfTCky8af1aBgvgd3Z1IaW3FmwU5vB5LOm82O4bsYqFMyIURtBtA2P6YUnmQ+bp5U98R/EwrKHeQUK3kgucHvRJZKsSzVIg8DxRBDhGs3wt69h2GLhnxnEVOcqvpEDIi+N2ZprPMxWJ0CzKD8HMrBh9k6TRtCmMJ8b5LMFBKTJ0FMt2ImIcFtRx55cJAUKRMdMLjx0RJ9ArBZXkTs+aHhVJOx+oGRZCLAKIdYZAvjmxnXRRBemHI+1xFOYLs3QdZ2jVFKIuFP5ORsNO0yeRBLhXI1JV43yWmL3wLssSz4vbrFVYlDd77FN0xNFSSWiJtsFT6k7DfzbGAIsghgnUmpRAsIL1hg4yLhU9dRBjL9X5XUSbr3cWCQdyAzCwY/LJ+bfAhP6kJftXAH9kScZ8StSCLquN0U/AbQhZkSSJWCga5oFcSJoIFmaHUiGcptFevUAQ5RMg2JNEXR4QwhZkuK5djU6oIkKVbItJ+d26pjHcXCyNCXFBXMRC80DcKkdAp51YkIytm/RPSpR95LYeiOkt5VzHuW/ahHfFCdOUmaB/kUAzIYTW9kPrhIPuRYoUiyCUIvyL8uXSGKO+Xb1M4GQ3kdNqREXGxEHxwrOihWJB9IgfS8hKrBnKTwuCW8n15Nznlwlg184Nci8UhiOkPNQ8yt7+0GETr05cJuLBuedU8eqTafICy45AfK4JcyggymXuoljyfXrwwluv97jNS+gYhQ4m08dlVd4jPzTdLXwhuMX6pLxXZIMm4rM7wrVQBTlxCuFU2l+YlZaKpDYP2Ybfq5j2fWo5BQUZfkBuq+LfSWzwIlSATQm4ihBwhhBwnhHzL4fdbCCHdhJDd+t/nTb99lhByTP/7bLAl9weyDamU0ryJBnFYZCVtKWFYkP22Apk3h+keShQ811j+FtRlrFSIWFF9cm0I04dYWj4kyw9vvyAXHyAoF4L1zo9c5GKrSGIrfn6snIkaUYIi86LW0XADGEtHn1T3JRqoOY68kMvDUkwIiQD4BYD3AWgBsI0Q8iKl9KDt1KcopV+2yc4AcDuApdDawA5dti+AovsO4c4lwIAV+aArDSKvTknPTH3uTNOZ3AU7BuOYP63GXbVPaSxEbsG/nfR8uYy4fokMHnLWm9KwVIqnaxO1HMo/09IhHNpR5vWlVDDmhNsdhH0KZku8MFYS/HL74kUYKzxACflojyHCtCAvB3CcUtpIKU0CeBLARz3KfgDA65TSXp0Uvw7gpjEq55hD1N8ryPYob8kTt4b4FvAVgjXS/zzIWcycVAkA6ByIj6Jbg/SMXshqJqeSIczsKdoFQhENNOuLHwMw73tdipZDQNayz61NSI+5v+R2IxC2IIsSXe0YxgYj4qsfwU7u5BaxgnthxmMe5NAsyAAWAGg2/d8C4BqH8z5OCLkBwFEAX6OUNrvILnBSQgi5FcCtADBnzhw0NDTIl5wDw8PDrjqHh7Utprdv34He4xHP10yltAwGhw4dQsPQcc9yLS3akvyJEyfQkDntWQ4ATg9mjM8iz/BAmxZUVuh5uGFfpyYbj8Wk6m/btu3oqBOfE65bvw5VEb63f0+XVnaazfrS9vr6Y5gSAXoAbNixHzU9R1zPPdSstZPOzg40NPAvrgwOau3z0JHDaBg5wSV7JpZb3mD3zVP3iUQSALBv3z5UdB3i0m3G5i2b0VgrXucb33oL06v55Lu6tInLkSNH0BBtFNK7dh1/WzNk165DpYusUx3E0rlBlLeNsvadTCa5ZEV19sbz25VXNJ/W2tSJxkY0kBYuWYat27ahnbMPYbxo586dGGrS+nkv78K+Du3ZDg4Oct2recLT0NCASJn3dtTeoY0RjZzP6FCPNj6kUnzt4OhJ1kd1cskd7JEbjwAgGuUbT9hz3bFzJwYavY/XDG82NKCMgz0yQr1nz16QdjGqVqgvGF12Lco52s5hfbzp7u7meq4inCAohEmQveAlAE9QShOEkC8AeAzAe3guQCl9CMBDALB06VJaX1/veyELoaGhAW466/atBwYH8Y6rrsJV5073fM2Kda8BqRQuvOgi1C8717Pc2qEDwKmTOO+881B//RLPcgBwoG0AeGsDALjeTyH07mwB9u5BXd1k1NdfzyUb398O7NqJmpoaId1Y+QoA4Oqrl+LS+VOE5a+//nrUVvK9MqmDncDO7SiLlImV3YZ79qzHvKnVaB05g8lzzkZ9/SWu57ZtOQ0c2Ie5c+eivv7t3Lp+cnAj0N+Piy68CPXLvbczAGjujQJr3wSQay+F3gU7Kje8DiSTeNtll6H+bXO5dAMw6mz58muwaNYkYfl3vvNdmDu1mkv0mbadQEc7LrjwQtRfs1BIr0hbY7I33HADqiucB3CnOhiMp4DVrwHgf7czh7T2XVlZySU7EBPT2dofAxrWcMsBwKbYIaCpEYsWLUZ9/QVcsuzZLlu2FBfP5exDVr0CUODKK6/E0kUzAHh7F4b3tgG7d2Ha1Kmor3+XZ3XpTBZY9SoA4IYbb0RFxDuhX9mzF2hp5n5GlSfOANu2cLeDxg1NwOGDmDNnDurrr/QsV3Fc0wcIjEd6XfKOJ+S1FQCluPLKq3D1Qu/jNdN34431XJMVpu/yyy9H/SVzvOsz6bz++htQU8lJ5pnsDTegqty7bOuWU8CB/TjrrLNQX3+1ZzmecSFohOli0QrgHNP/Z+vfGaCU9lBKWSTSrwFc7VW2lCC7PMULkUC5MIOdcj7IkkF6YbhY+Owrl8lSVETKMHtK1aguFn4FCIrcgW8ZNHwV4q9TAAAgAElEQVTyfReXF/Kr0Q6BLsebZQMM0hMOthPs83zwsQj6fkV9V/3IESzqjy5aVt6+ppS2ms612WBdLMJKbchb3JKOFXJBmAR5G4ALCCGLCSGVAG4G8KL5BELIPNO/HwHA1lpXAXg/IWQ6IWQ6gPfr35Uk+FPqMLniHwgNeQnZYgn4EiKKcipx72tH8LtNJ43/U9ksIhGCuVOq0THojSDzLOs5IQzf65xOf3zfg4QfuwAGuaWxXOCboFyIvsShBT1xEw558hhUGrVSyuwgew0/Ji58+sLpC7jVhh1RPQYIzcWCUpomhHwZGrGNAHiEUnqAEHIHgO2U0hcBfIUQ8hEAaQC9AG7RZXsJId+HRrIB4A5KaW/gNyEJFjwlahURbY8idEmW8MikgCmW904s3Zn4fQPAz9ZoPuafeeciALoFuYxgztRqHGwbLCjL2pX0BisCde9XMFXoFuSQIu3kAnP44I+1Opid9KQmy4LWUeslZMiKmEGDO7cwdf7sSVYw169ogFYp5sMWDzAVHOfF1AnptLQdwUwmKs2bT6CUrgCwwvbdd02fbwNwm4vsIwAeGdMCBoRQdxPyCGnC48MgLO0qIOkiEaarAUM6QxEpK8PMyRVYc6gLlFJX1xPZJS/RiHZNt6Tl13YUvk4YqwZ+EDEpSyXvoChvoeLfIEIMcsvN7ENIFmTu88MjgfzEOhg9svr8uIZ4Fotg9EllMbFch082/E18/IfaSa8IID6LFtMnNuj7ZEEWSvOmfwjBEipjjdF0+ot0NouKCMGsyVWIpTKIpTKu54pa9+wIY2KQ26RE7jphumgESXItspzny01+g7UASm3frd9oWKniuAmHnrCD1yJncbEQdJUIapOIUjAOGTptx8D0CtahmKwEuR5//FgR5GKAaMMKcpDxa6MQMVl/3jxpghuCJdUOzYJMUKtHJseS7gTZN9USriXSqkt4J73QfF05NxAKMpBHVs4PC3IY96vJipFOmb03gsqxH/yGMUJivoBHt4xPr+i231lrA+CUNYkG1F6LGYogFwH4/YTErGtyVrnwiErOkiIHkSKY60aE8PjuYqFnsWCpewpZkH2bWAQk4yQftg+ykE5qPQpdQ0Y/LymSmgSIyQW13OykU65egrPsi2qSsSBnhccW7SjjL80DP1YBhK3rHDUjNVkR0KednwM3tzBJi1qQxxE/VgS5GOBHR8inL3gLsowfcc49QzYbgxzBDYMo2pHOZBEpI6ipGN2CLO2DzI4hWs6liXYIFmQ/sliITpq1z3y6/CCMQaX38uOZSlmhA5Slgn0mdfnMIyy+1TSnuhBcbYxrBCAoM1kRlbNmMRHXxe+HHqJZf4xQMEiPEHI2tPRr1wOYDyAGYD+AVwC8SinvYp6CE8RdJTjPF5QTlXHSHbSs7HUsM2oJVwO/ckOmsxTlZoJcyILs04OTnVgErdsM+WBBGYulvLVT5HxerWG4ggRtFLDqDs4KLKNXdIJrHpVFl8n5U4+KTpQ4BSTlrBj7SZrMZCXnYiG+IiTVf3ITc3FVxQpXgkwIeRTa9s0vA/ghgC4A1QAuBHATgP8khHyLUrouiIKOawgS3SCtMLJExZAX2qRE3PpsvY6cjNhzExAqgEyWojxCci4WBS3I4VlxpYkptR5lrxOkvB+PXTTNlqZfXJYXwum9BEfTrIRJxnguUlbgcCY9XHIyy+SCukU3cxJ/nD5MlAKw6Mq66InAr8kyb3n9WuktJhSyIN9LKd3v8P1+AM/rm3vw7T+rYAHryLhfHMF+XoZ0SAfpScj7t0OPnCVUZrndD1BKkc5qad4YQY4WsCD7oNB8EBGVL4Ks77u0vIRsgMsmWYlBMYyAJz/IHy/YM5KymAtLBmfZt7QF3oBNY2wRpdZ8CDqDkxmilxD2QRa2lnNakE3lk8uDzIcw+pGxRiEf5A/qLhaOoJQmKaXHx6BMEw5BvTgycv5tFMIPv148kU5VdlcqvwIMAc16DAAVHrNYGBuFCOoLjRwiZ6kL34Is8q6wozyZ867Tn0GRF0ETXT/8pcO4X02Wl+jI6xFeiZCYoPHAeM+55cT0+QEe3ZZzA7BYA7a6ELO96Xp5+xG58aYYUYggzwewiRCynhDyL4SQs4Iq1ESBsZNeQD7IMgiTqMjswidbBur6j4C8JNJ6zxeJEEyq1BZ/RhJp1/P9sryL3INvG4WUoFXCD/cQqUwUEuSaF0Hnv5VzB9GOYa0p8JNA+WcrHKfCJybtiy4TiCYKcQuyd/ix7beoP7imM0hZToESgCtBppR+DZoLxX8BuBzAXkLISkLIZwkhdUEVcDwj52IhJs/rx5ezoAhYxWQJckiylutIBNmJlsPPhPZpw4IccJo3CSuqtG5Z+RJtt8EG6XEKOOjlT+8VDFmw6mTXCM6yby0Apy7BreLlUnVRi26vkN1qWtTfVQbiPsjeBf3JYsH7bMyyfLr8kh0vKJjmjWpYSyn9ZwBnA7gPwL8B6AyicBMHghZkUW0CgvJBV2IdqKys5ToCMjIdhqhON6Qzmr9GpMxsQR49zZso/LD6iytnZZC198m223BkZQhDkNY4UdepcHZQYwYCiSsEWKd+9O/8y+RiumXLWkrciqesEgs74nImAdH4Ju1j8JOWYkPBNG8MhJDLoaV7+1sAZwDcNpaFmmjg9zdkcsF0fqIyTrpFIBrx7ksZJDoMTScj9/KeWYYFOUJQXVEGQoBo0t3Fgt1wGETZvyA9Ocg3HfFGE2T6NJmlXDlXEEG5ECzIMhkwGILMYiGc5k1iNUHUPUjUiGG4vYRhQQ5AUCblnqicVB5kiDee8UePC6d5uwAaKb4ZQAbAkwDeTyltDKhsEwainVGQu1jJdkhSQXpSms3XESC45uXKEIkioG0zDQCRsjIQQlBbEUHUw0Yh8sZc8YmBuE77B8HrSK98yOkPCuZiyvgsSikeezE5gurHxEVYUtzdgd8HWX41IahJlmjgZKhBejxZLCzBs4L6JIiq6AoCIN6PlEiX6QmFLMgrATwB4G9d0r0p+AT+5VTtKG6FEWF6Qqp8ERfNselHIbKWDkOEYPuHtG4CKy/TnkNtVXlBC3Kucw6eJMpbrcUG6rzryBVD0O+cHXkHJ3FiI2Opkns3xVZIwtlJT7+GzHxASjYo0mn+LNYWRIk1L5kX9QsPY6tpETlr+kU+hdqzpNz36pe7laiLhZ9xN2HDlSBTSs8z/08ImWI+n1LaO4blmlAQ9TcUnVkKDfqh+nKKW58t15HQrX3ml/fTL4uleSuPaE9iUmXEkw+ytHVHRNan+5a2focgL04yzJ95iY0/gyIvxC3BYnIy7lY5FzN5K7SYLB+E03hKJMWmgiRHNgBNdBVVBkG0XUsdco/X8tZ8Oeszp6wPE9Biw6g+yISQLwD4HoA4rBxryRiWS6EARGfdxukCLVjWf09mlx3fdoQL3nDua2eRMlwstGc4fVIlOgfjo+oOgyT6FdEcjg+xWTocQsQvyylglpV4t4PMxa7JCYkBMLtYiF9DpjkF5YNs0cl7vjGJ4INsffIv5wup8+UaPGIy/uBO1/B2vrgVWKa8uTSK44chewnS+waAyyilZ8a6MBMN4stLYh29jI9QmE2e+jBQAGIvrmyaHj+fm7FRSERLPvP2s6fhqW3NSGeyKI/kJ6TJ+aoHTxL9co2Q9X8OxYIsaBWT2S5ayj1DZhIgbDkUlPOhrDJtQm7CJr4qwCXngwVRxvLIJWcIio2BMhAvs3dJP/oiGZcpmb6AX69Yv1fMKJjmTccJANGxLshEBm+Dyggug4m6ZphlRSGzq5tfG14IDW6WGbU4WRPbQdCqL2VK8wYAF86pQyyVQfdwwlnedhSFGEmUVOqT7lAyeBhHcesNv9+h+bO4Xl4wUZlMC0HIabLyb0SQkwlhNxQ//Ei5267ezwnnQeaTC9Vgw3GujA+yiD5Np7n+OXXJTK58mIAWG7xYkG8D8BYhZAsAYySmlH5lzEo1wcDTOVBKhZfBZHzwpC1xErL+uViIWJDN8iI6+WXcZHMWZG0UmjGpEgDQO5LEvKk1efJsUhIOGRGXNcuLXEbGEptXjgAJkbWdS1iQ+dT6Ex8gSIx44ccmHzIuJUFan3MbhXAGvpk/B0Ry5H2Qx6kFWSI2wAjS490QLKzJsuDkqpjhhSA/CGANgH0AfMgkqWAHzwuXMb0s/LsdMX1cYrqMpAVZcCD1E2IGZHHiAcg9N3vnxrJYRMq0hZ/ptRUAgP5oykVeL4N0gKXIxMInYio7qZEqhdykKFDrjeU6wZGNIPw4/dAHyFtHNVlxiGaU4IWcL7uYRVc4hZmgvC8xDgEYDvwIvJWyIHP3I+Kyov7kxQwvBLmCUvr1sVBOCLkJwE8ARAD8mlJ6t+33rwP4PIA0gG4A/0QpPaX/loFG2gHgNKX0I2NRxiDA0xAzppP5l6XEOj9NVg5SA5vhnhF8mjcraRGwvPOrNGCvJ5YHuUJ3sZhUpb2+brmQcyRTohAIxyVHxoIsm7taFqLpjvwgNiKyUqQvYEuwH2Rexjoa7EYhrN8T1yNeXjEyx4vcpIUTYVqQec71xQdZ5vzgrM8yLpzFCi8E+VVCyK0AXoLVxUIqzRshJALgFwDeB6AFwDZCyIuU0oOm03YBWEopjRJC/hnAPdB28wOAGKX0HTJlKBbwNETz8qD4TnphWAStZeDTrR1DD9IT0GmQXIGy51uQtf+ZD3J1hWZJjqdcCLIxo/en7sZaxkle1q2llLaa9suPWM61g1dWO/LnvxXT6YcFWSqvrLh64awA3HpkLIiCk4hcOxDVx0vGOBUV0D2WcjLtNZcHOZy+gHuibQiMH4bshSB/Sj+at5emkE/zthzAcbYzHyHkSQAfBWAQZErpm6bzNwP4e0mdRQme5pSRGszEzXLyPqWClgL459MkTbaE5P2zODGCzPIgV1dEAAAxV4Is/szN+sNwyZG5jkzuUbtOmUmV1FK+zMDGPaBKtFFROVFy4kcWC+7ANUsBJPSLsdUgVwSErYDCKwJi4qHmQeaQlM2CJAL/gvTE+pEJZUGmlC4eI90LADSb/m8BcE2B8z8H4FXT/9WEkO3Q3C/uppT+r5OQbv2+FQDmzJmDhoYGmTJzY3h42FXn0FAMAHD48BE0RL3t4D2SyrW+5pYWNDR0ey5LZ6eWM/fU6WY0NHRqnwczuP2tOO68rgYL6tyTmhxoz+3YJvIMT55MAgAGBga45ZuaNNmhwSGp+tu7bx8inYe4ZLqjOZP91q1b0TLZS+KXHI43af7B6XSau+zxdK6uGxoasLtLq4M9u3dhqCmCwaT2+94DhzF7+ESefHOrtuDT2dkp9NyGh7X22XSyCQ0NrVyye7vy20uhd8GOrJ6x49ix42hIneLSbX5uu3bvQbIlwiVvHmC279iB3uN88r292nM7deo0Gho6PMsNJXN6t23bhs4p3vVa2+k2tLq8y051cLQvN8HibSfH9PYdj8e4ZI/0iunc49CuvKKrW+v/Ojr43oeUiSHv2bsXpMOLXSkfBw8ewtT+YwC8vQtNep/Z09PDVd7moVxb2LR5M07Ueu+z+vq0ttvW3o6GBu+LxEdOae0gFuNrB21tWh/V09vLJXf0ZC7uQnRMiMfjQrIHDhzE5N6jns7tHMnVxeYtm9HIURdZfbn42PETaMic9izXYdK5Zes2tBUY1+3oMvUjW7ZsxWmO8a5FH2/OcLZXnnEhaLi+6YSQP6OUbijw+xQA5waxDTUh5O8BLAVwo+nrhZTSVkLIEgBrCCH7KKV5LIFS+hCAhwBg6dKltL6+fqyLa0FDQwPcdE7esx4YGsQFF16I+msXerpe30gSeON1AMC8+fNRX3+557I827YTaG/HOeecjfr6SwEA//mnfQBOY2TqQtTfcJ6r7MDuVmDPbgBwvZ9C2Bo/DDSeQN2UKaivv45LdkfyCHDiOCbX1aG+/s+4dWPlKwCAyy67HPWXzuESPdUzAqxrAAAsXbYMF86p45I/WnYCOHIYFeXl3M9tKJ4CVr8GQHvmiQMdwM4dWL50KS5bMBUjiTSwZhXOWbQE9Tfm193q/n3A6dM4a/Yc1NdfyaUbyLXPhQsXob7+Qi7Z1MFOYOd2o+xA4XfBDvL6CiBDcd7556P+z/jm6ObndsXbr8D1F5zFJZ/OZIFV2lz8qquuxjvOmcYl/8sjm4DeXpxz7rmor7/Ys9yZ4QSwZrWm92qtjr3idE8UWKctuC1dtgwXzXVup051UNPYA2zZDID/3T5W1ggcOYSamhou2ZrGHmCrpvPGG2/0nKkhqb8DImX9/antQFcnZs+ezfU+xFMZ4LWVAMT6ENb/XHLJJai/cgEAb+8C6zOnT5+B+vpCtiMrDrUPAhvXAwCuueYaLJw5ybMsa7tz5s5Fff3bPcud3NgEHDrI3Q5ePbMXaGnGtGnTUV9/rWe54+sbgcOasYN7PNLro6q6mk+W1eOll6L+7fM9iTR2DwPr1wIAli+/Botmea+LstdfBTJZLFni3L+74XhXTufSpUtxybwpnmVPnsmNd8uWL8f5syd7lmXjzfQZM1Bfv9yzHM+4EDQKTYU/Tgi5B8BKADugBclVAzgfwLsBLATw7xK6WwGcY/r/bP07Cwgh7wXwnwBupJSafaBb9WMjIaQBwJXQcjaXDNiYwJXFQipIj+nL/63CYaMJi15Jpy8/fErlN40QkXf+LCLPC/sjt281PZqLRW75UtDf03bkQZhBerKp+azy4m0mWF9gcbcQP3an44W9jrzGF8i0KlGXI/98kMXqRTSXMSDguiAol4sT4U1JR3X54Nflxf3geVwsTHJC2sQ3BNM+c8oW+G80yGTJKla4EmRK6dcIITMAfBzA3wCYByAG4BCABwtZlz1iG4ALCCGLoRHjmwF82nwCIeRKaGnmbqKUdpm+nw4gSilNEEJmAbgOWgBfSSEXROXt/K6hOFbuzy3ZivoImfUl0lnL0Q1mgkwp5e8IpQiPuG+TNfCJX97qfihAlvhV5mRdgvTK9SC9SBlBZXkZ4innupPxIXbSzwPeFIS+lkFyUJJN7Sfug+z82ZOsy3W86ZUh5tqRPzhL7Bn7kTZRNHBNWj9nklRRn04pF3xJUs7bDnKTAF45ufEoOJgNWqKGCvEJb6CTK4kxvlhR0JlKz1TxsP7nKyilaULIlwGsgpbm7RFK6QFCyB0AtlNKXwTwIwCTATyjvwAsndslAB4khGSh7QZ4ty37RUnBa6f7xcd3YOfpfpMcrx79aGrCzProlirMSRePxScnI87WZGamMpsv2OXl9fPBbrVnpLPM9PCry8sKZLEQI2o5efsH75AJJnUsAwdkNwqRXTUQDYb1Ywc0QNz6JwKmS24zCwqv1EqmrL6QMXH1wnqDJPSibVd8Es6O4iQwS4GIUJYgfhmA715lV7NE5KzGLF5dZkIvJutXgHYxQCzawCdQSlcAWGH77rumz+91kXsLgHfn2yKH14a4t2XAJifW+ZnFBmN6kI0LyWLISA4S1Hbkk2VEjx+yBLcYXSxYmjcAqKmMIOYyuZHNSyn33E3XEVlxsJWBT7c/FmDRK4gTMefPXkAtsjLkmq+u/MhnzHMFP8rK/3xyn2XeZ17ioMep8q9ESKyAsPOF25Cg4UR+EhCcBZmnPqz3JWqoCGeyLLMCNl7AF5KvMCbw2q7SdouiD5aivqgWKR1NpvN/NMHuYsELmW2PRa0Mdn1C5FzCqicL+/2ySYrZglxTESmQ5s165NcvLm92sRBybfHJrSZMCzL3TpcS75hfOzYGNciJPmMZ61hOkPN0yQmTqKToBFeG0Itu3CHqaiM+aZGbBEvJcVmQ5durnItNkORaziBTjFAEuQjgZXDr14msGVlKse5oN679f2+grT/mQU++vq5BLe5xNBcL2Q5JyiIosa4q20FlJOWNXQAFfOTst511sCBXV0Rcrf+yW02L+tIC8hMqQ1ZIRrLOfLJAixIiMdncZ7kBlVfWDwuymEVOmHAG6BtuvRDn6YLtSCYwWXRSbPggC68UiUPct1cMwgSZVxFh1+AT88OPWEjWQX+pY1SCTAipJYR8hxDysP7/BYSQvxz7ok0ceGlPW5qsOSlrKiIABZ7Yehodg3FsOHbGgx5rh5vJUi21FIBowkqy9rcO4IM/WY/uIe33rKTlRmZ2aXSistZESaInAj87fkbW7S4Wo201Le5iAWF53yZUIu1F2gIsKc+WxjllZSYV1mV1CVkJ9wMunYLPWMa/nNULb7CcWWeQGT9Es9DIZE4QJaziEyXr0bNcCCsJImJSfZGggcPaXsVlRfWOI37syYL8KLQtpt+p/98K4AdjVqIJCC+NeNWBDkyrrcCfXzwbC6bVYPaUKmQpxXBCc41o7ouOeg37DG8gljI6pqjNCvn7zadwqH0Qrx3UsmZkJImm1HK9hCVTOsjONJjKkjVe2Mm5kw/y1JoKDMZTcALTLTyoU8uBC375fofhgyybGlC0vUotb0q0U4ssn6i0fzsvZMifeL2YdYq/0EEtWcttNU0tR6+QdbHgLagvW02LynGUVcbdwekaXiA1QZIg9DJ9drHCC0E+j1J6D4AUAFBKowjSI34cI0dYRz/3VE8UF8+tw4P/cDVe+9oNKCMEWQoc6xwGAPSM5Ltg2GHvcM1uG9GE1Qd5UpUWvzmify87Y2fkToywiOuVWZIF5GbUojIA8Ku1J3D9PW9avjMIsmkZc1pNBfqjbgRZ1B5klRKr79xnmSU3eQuy+GQOCLa9yln+/LEaycjyICNI6K2+7Xy6RetFhnBarsN5vmiwp2UlglPa6KcFyTyvJ1lWcFwQbe9Bb1FtKSdvmj9jBZC3nUtMtKX6arG2U8zwQpCThJAa6O2CEHIeNIuygk/w0om198cwf2oNyiNlmFRVDkKA/lgKHYPa9ql9HggytXW4fTqxqoyUYcS2TM82DhnWXS9k03blyLmMRY4fstZAmY1ZAHH/6btfPZxfFpbmzWZBdvJPB8xLtEJFkPNBDrGX9DOLhax7CQ/k0jOZPnPqlfJfNuSCIeV+BBGJbrBk/8yLoK25IjpzhoxgLLpMX1CZRcznyq6seYFcTmJudQDsK4/BtR3R8hYzvBDk26HtpncOIeQPAN4A8M0xLdUEw2gNMZOl6BxKYN60auO7MkJwpGPQ+H84UTgLBZBvQR6IacRq/rRqxGxZLFjgF0sDF2Y+4JzlR4RcC6vV5CWDzdKGRUa+22DP0OJiUVuJoUTa0Vea2o68kDFA+2Z1Mwk3HOnStv4eVcb5s1fILqmLTgZlLLlyS6NSFaQdOMUs7xWPnERRs4INWjavttN1vEB01S2MTAROm1B508fk+eTMAjyifpA3nj5BLmBOfvIgE+DH3Qex4zgyIY9KkCmlrwP4awC3AHgCwFJKacPYFmtiYbQGdah9EJksxYVz6ozvygjQqWegWDxr0qhZKDQ9xicAQN+IRn7nT6vJsyAPxTXCzPxbrQOaAGkQDFwC5CzIMi88YF+u5EdGouz5ZdGOdhcLSoEhBz9kGas9YFriE5A1Pzc/JkfxVAa3PLoN3/7TPs8y2md+3RYZocmBmKiUH7FFIHirkZwF0LusTFaZ3ADOJ+dGMpp7ozjaOcStn1evTFsQDwwU0yk6KbS3h9GuYyWBgoTVs5QVohZrbj2C15DzQc/X7xUTKkiPEHIV+wOwEEA7gDYA5+rfKfiE0WZ5LIPF8sUzjO+I7gZeV1WO886abPgKF4I9qwHLgbxgWg2iiTTiqYzxcg0nNMI1GNOuK+ozyJAjiiIWOaaYX29Yad4yWYoH154wJhp+MGR2L2Wmt3ZqTQUAOPshG+RFTJ+M5V52YmGXZW114/GeUWWcBsHOwTg+88hWNPd6D2a1f/YK8fy1MsRG3mpo/+wFoplSRDcekslLLkrm3QjH9fe8iffft47nQkJ6edugjKtOzidYkFjzqTP5POckP/3wFvyf320fRZ/YPcquLgES7VWQrMrsdyDnvxxMey1mFNpJ7179WA1gKYA90ILzrgCwHbmsFgqSKNRhbz/Zi++/fBDnzqjFvKk1xvfMiHjh3DpMrnLfLMKih6U50vUNxFIoIzkL8sXfWYn/+4GLcPb0GiO922A8BUqp9E56crNL8RdPtkO0yni/wGsHOnCXyY9YttOglCKtpxIpNzHkabU6QY4VsCAL67QeRWS1z+KdNLsHt0BEJzgNnk9sPY11R7vx4p42fOnd53PL88Ap//ThjkFURMpw3lmTXeX8sI6a9XuFiFUtm6U43j0ceOCbTLCweF5hk36Z1RDO80UzPMhY8FgbFG1D4pbn3HebGkefBFsmsbpw12Ac//KHnbjvb9+Bc2bUFtQnA672au4HOVqA3ERQfKz2IwPGhHCxoJS+m1L6bmiW46sopUsppVcDuBJaqjcFSYwW9ZnJUnziV5sAAB+8fK7lt+qKCADgorl1qK0qx0jCg4uFgwV5ak0Flpw1yTjnR6uO4KtP7sbO0/0AgK1NvVh82wrsae7PKzcPRJedAbN7Br+07JKaqDUmmbGGLPOWvTJifTUpNe+kl/ueEeSBQgRZsMOSsQjIBDc6TWr6XAIRneA0KCXSWn1489XPlweA3206iWMeltSdlo1vun89/vzetQXl/EjPZf/sTdb5OoXwq3Un8P771mF/22Ce3LM7WnDXikMF5TMOI3FzbxS3PLoVAwUmQ05L6wOxFH7x5nHXDXPs5/M8npFEGp16ILRZtlAZ3cBPHnU5Tj0yFmSnLBYtfVEc7xouKCdKjvxMifjYppPYfqoPf9hy2lEmk6WW3WjNGn+zoQn7Wwdc9TlN2r1A9J22nGv6/LM3juGXDScKyrqtCHlZPfPFf51PrKjhJUjvIkqp4fhHKd0P4JKxK9LEg1unwqy4H33HfPz7+y6y/MYa40Vz6jClugIDsaRnvy121kAsjWm1lbjq3OmjlnHt0e5cefXj5x/bjmV3rpNuclgAACAASURBVEbMg/+z01KaV8j48cpuO+xkOe8ajOOuVw+NOiC7lcMLKiLWfEkUmvWsjFh3q5paUwnAeadF2Yk8Exe5jhMB8gqnSQ0fIcnv5FmwacdA3EnAKu0wSLQPxPDdFw7gC7/f4Sr38LpG/ODlg3lWVa+ZTCxuTPo9pDNZ/N2vN2P1wc6CsulM/jPzCqeNMJLpLPa29LtIwJgwH9cnDGad33hmDx5c1+ianxtwTkX2zWf3ouFId0ELolPbeHLrafxo1RG8sLuw3ca+ggYAsWQGjd3u5O+6H64xjBRmnSsPtDveix2iE5dslpomWjIWRE5Zmz8bpRTv/fFafPaRrZ7kzNr2tQzgq0/uQiLt3k/mslh4L+PhjkHHIM/mXm032Sk1zgvjn/jVW7js9lW5L3TBxu5hfP/lg7j9xQOuOt26s/tXH8U9K/MzDjFkHMhqfzSJS76zEm8ccn+nnUhuW38M975+FD8soA9wJtf7Wwdw/T1v4g9bTrnKPbqxCY9vNv+uCbf2x3Dd3WuweRTLvqhLSDHDC0HeSwj5NSGkXv97GMDesS7YRMBoVlW2y90HL5uHynJrVRkEeW4d5kypQipDjbRtbsjarAPRRBq1lRGcM6MWr33tBnzhxiVc5V59qBPdQwkcbHefedvLa77XA20DeOvE6DsApnXmwDtQPL29GU9tazb+531vt5/sxfHO3ODJ1D+8vhEPrm1Ew5EuV1k7eeYdqJzqO0OpJYMFkPNBdrYgW8vNi1z7zF3g5b1to3aUgFxQpzVLkT6oONyfE7qHEhiM56zE1PQ9AIs10A3UYTBs7dMG38Zu9ywad644hF9vaMrLltA1lMuKWYgkOVn+jnQOYePxHnyxADF3k/UKp6xQt794AB/5+UZX8sjSQDLXLqd307zqZIfTKgHLUFKIUDlZuFg/eWa48CqDUxm/8PsdeM+9a40+xoyBaCrPtYfV36H23ErCcNx9VUJknrjjVC+WfHsFDrUz63zuty/9cSfufOVgQXmnoMuheAof/Ml6vFmgzwJMxghdrmckiXgqi9b+GFIOz8iu01zWbzyzBy/sbsPh9vxVl0yWoq0/ZpLTifIoWYP2tQzgpvvX4+H1TSbd2nkn9fbD4mbs2HXa2h7Z3TTr7/aOU30ud2drO6bP968+hgcKWHSdYjG2nexDLJXBz9887i7n0M47PPRd//HsXrxuIt7sKgfatDF65yn3d/J7Lx3EK3tzEz+md3/rAFr7Y3hoXWNB3bm51fhhyF4I8j8COADgq/rfQf07BUmMZiFgg/pZdZV5v/3bn1+IZYum47IFUzFnipb+bbTB3x4oF01mUFupuWpcOKcOn7tuMa6/YBbefvZUAPlWTAO24p7q4Vi6Mcl+4fEd+PTDW0a1rKRtxB4APvST9bjt+cLztG8+uxc/feOYY7nff99aPNDg3kFRqrm33GlaKmblZCSpkFvLkG3QNJd9c2MPPvngJsfMEwx2gkwpsyA7E2RnH9389rXzdN+ogySDfUk6ncniy3/chZsf2uxBNv/zif4MPvbAxlFzdjtZCc3352a5p5Ri2Z2r8ZnfbLV8BwBtA9og6IUgO6X1stenHZZNd/QVFTuB08ruTjKclnH3tmgD2+RqZ6sYpRT7WgaQkpqQ5MvuOq2RhZMuafVYO0xl8t9N1kRPOyzpUqptb++0MsMmeT0FiG6hFK/JtPOz3dzYg9tf2O/oL71OXxnrdWiTB9sH874zJkz9MeO7QpZykSXr53ZqlvATej/D5BLpDF7Z224hh6Pq1I9bm3pxqH2woOsLpdRYxWDXMNehuR276TTrPtalEWOnXV5//PoRvOvuNWjX30uDyJtcoOyZlQCgZyS/DJRqZT+hu4FEk6O7UZlhT3HqhNGCjt3GMKeJa4d+z5Or3EPAspaNlrSjuY9xmqz0jSTx1PZm/NHkYsJ0srFqclXEVacdrORs9c1tvDrdE8V77m1Am/5OmJ/Eva8dwYd/tqHg5KqY4SXNW5xSeh+l9GP6332U0tFHGYVR4eSr+JUnduFLf9wJIDdjnGsKzmN476Vz8MwX34XJVeWYM6UKwOiDvz0bRDSVQU1l7iWdPaUaj3/uGrzn4jkAgCWznAOKKKiFpBQa0HqGE3hpT1teTs9MlqJFn7m77QIYTabxzWf3GIO0uQ862D6IJ7Y2O8q5gQ3+bf0xHO0cxj0rj7ie6zRbN8quF6TQjH7QTpBNn3/6xjFsberFVj07iRMcLcjZfAtyZXkZJlVGPFuQ//qBt/CPj25z1etWZgA4XmAp2pChFB/+2Qb83rSUxwaP3x1MYtfpfuwpsHRvLy/73B/LtZFBF2syG1zNfsY5C5r2XWt/rOCSOOBs9StEggDghOnZsLpgus3+04V8oJ1II3NzmFTpPJhuPN6DD/98A54w+13qwlubenHr77bj5JnCuaOdXErY4M0s53awdsjuJ2c8ork6c5i03bf6GJb+YLXF1cXIzqIzay9ETNNJLXrcNsy59Xfb8dimU0Y/42SQcKoXx/6U9Z0mUlWobZhVeV0Bi5B89yrAahktZFRwmuCxST2LXbHj8U0nsfi2Fca9sCt0mZ5BoUxJ9niaTJaaiF3+83njkDZJZ6lKmT5GmAFtcyw7zK5Ehm5QJDNZg1A7GS6cnpedPBaCU59kXulwe6+dNu1g/UMh0pgp0M7tnxlOOUxIDbcOXadb/Ts9H2bNZ7qSDs8eAB7Z2ITG7hFjQml+Pj9bcxz7Wgew22E16febTzluiFVMKJTFAgBACGmCw6SJUuptPV7BFfYViWgyjRf3tAEAfvjxNDoG4iAEmF1XVfA6s+s0C3LXYOENDlmDZy9NLJnGvCnVeed9/vrFqKksw7ypNfjXJ3bll5taB4UzDrN6hrtfPYxndrTgwjmTdVlNt7lD6RpMYNbk/Ht8eW87nt7e4qDfOgAQh/1NnayMTGznafelNIZ9LfluI3YfqxYX8gA4kDjTG8Qs7oXIkjlTBUPagSADwLTaSscOMxdkl9+xOZFtV3m2hGkiWtFkGrUOpG3Fvg7sswW7MO3RlD6xGMUPOGUyn7Cym32Q+2MpzHZot06WaSYfS2ZAiGbBbemLYuHMSXnnGjIOPhajWZBPnnGwlurCZuvkSCKNs1zeZyfrKLMgu7kdbD+lTbLMBJ1d59GNTXjtYCem11bih5+4wrXsToFvzIWitd+5ruwthz0z80TNqT4e3ahZP5tMbYlZAEd00uk24c5kqWMWC0aoe11czNjEiZ2XsyTnruVk2XeeJGsyI4kMKiIEqQx1bRtff3o3Lp03xfG3QrC/l6yc5j63P5rC9EnWlcVEOoNkOuto7TzVqz1vJ4IJAN95QfO/ZVZ4J/I47EAkh+Ip/GjVkbyxp81Ebp0m7wxMH2t33SZ3pJa+GC4w5f4HgKGEUz8HRE1lc7Ig2w0WgLlfGp0gm4OuWX2YXXqGE2nUVVfkyTlZkNnzcHMF+dXaE5Z2k5to5+59KJ7K60eazuQbMOxW4BEXa7nTPgpMtlefeLpZ2u2rL+wdOdqRK8+JrmEsWzTDct5//e9+AMC1N7n3xWFjVIIMLcUbQzWAvwEww+VcBQ7YO+pDpiW90z1a5PD8qTXGYOUG9qKM6mJhs+KaXSzMmFRVjltvOA8A8Lb5U/Dj14/iZbNvEqwvdyEL8sbjmo9xuz7QMt1ma4TTshmAPF9XJ3LdO5LETAdy7UTCjMFCJ6hO987gFNFMTRMLwDoI2JHnYmEiqWx5NlFgud1uXchSLWjHidROrakwdkW0yrBy51//zHDCcM1xg93S2juS66Db+uM4f3b+CoNTEAjTzzx2egtkpOgajFs6aydrpFvKN6eVCCYfS2WweNYkNHaPoHsoUZAgO2WxMFsns1lq2e4bcLbeOJXdbVK0cn+HZeKWpRoZZJY/t0BYY/tx0ySRlZktj7/V6OznH0tm8N4frzUmr5qsBkZEWl3auH1Syu7VTHCc7rWqvAxDsL472opU1njuTv3BmeEElv5gNRbPytVb1kZU3Fx37O3f7GLGYJ+AnDwz4tiHsDJGk2nMmVKNlr6Y44rGUDyF53e24nlTwidWjq6hOL6xNoovVzThlusW58m6TVzNetoH4nkE+W8f3Izdzf24eG6OVDKdbCXAiaw6uaY4kceoQ32uOtCJ323KXy0yk3m3FR+LPgcS2OLgmuE0GaHUVk6Hd6XQqsRoLhY/WX0M960+mtOnH83W9eF4Gphqlbvt+b3Wvkw/sv7AqS46BuJ5VlX2TM19kNNzcIqPsI+ZbpM5p3c1139pes3vthn2CQmzb5hXzpz6x1LAqASZUmqPyLmfELIDwHfHpkgTB/bANbNF8nTvCLY09eL6C2aNep3qigim1Vagc2g0FwvrMlgsmUFNAZIIAEvOmpw3U6WUWi3IBTof1nExK07OMmEluU7Y0mh1QWDPydyxHOsadiTITsv47P7ZwBdLZZDOZFHuMAGxW0HN+llgklvgGKUUz+1ssX2nHc2W7USBJTY7QaZUt/o6WMun1VY4W5BtR/M1OwfjngmyYUkwERen5exsljoGZrFOms0HClmUPvPIVjSarYv6sS+aRG1lBNFkxlXeKQWbua0vmFaDxu6RgsGszb1Ry70ZBMNE6IYSacP3m8EpmC333KwWZDviqUxeEB6lQOdQHLFUBjMmVaIvmnRcLWEDW59DmdnydXNvzLG+W/ujaO2PWe7NbuXqcXm37WkM2bvVOViYILP0hZYBk1rP7XaYcB/U08k1ObSNLr3fc+tH7Mg69EFmC/KBtgH8xU83OMqy5zMcT+Ps6bVo6Ys5ko6jnU7tQRN+dV8HzsQo/vulg/jE0nPyfFHtOe2drLntAzFcOt9qnWbL2Ic7zO+BJszq2MmCyHyFrTr1SYTpGTnVp51c5yYQubI6va/ltviWrAMJbLFNzgZiKXz3hfxMExTUQnKdLMhnHMid00TJaTwwk2PAPNFxb+sD0VSeC6DRdvRznSYOTun0zH0gg5Nbj9MKMpNlgaRufacjQbatgPVFU95WHvWj+b0o1OcXc97kUX2QzTvqEUKWEkK+CG+WZ4VRYPggZ9ngkiO4/7urDWeGE7jhwtEJMgDMqau2DE6F9I1mQbbD7v6gWZC1Bj+lurzgkmheFDicrcB29Awn8qxX9sEbgOtWr9tO5vv3Mvl2nSBT6mzNzGQptjtENJvJFuAetPDq/o58Wf1ongS5BRU5/cYsyHbLJaATZIcOyO4iYe6QR2srmk52He1otiA7kYKuoQRGkhmU25eI9eNIig26ztbQM8MJHO4Ysty7uc7Pma4l/x92WGYFgO0nHeoM2sQgnaWYr/vyF8qpfP09b1rTeun6zfVmrvdkOouRRBo7TvXhqnOnWXUbVjETQXYYvN2CwVg7PX/2ZFCay+UMABuOncHT25oNq6klUwa0Ou+PJvGOc7QynXAg8M7LzrqrhP7eupFOO9FnddykxwucVVflPBnQ76HbVl7LipIDKXd8RvqkkV3LqV4dc7/qhTXfv9mCbHaXmTXZaqVlz2consaC6TX6dfLbo9M9sPbAyD6Qy9xRqMy5PtNqQTbDzT+Yua8wC7LTu+dIyhgpTxb2s81vB/mk05Egl+UHIQNAn97HzJ9abRgyKKX40h924nO/dY6dyNKcvjLi3Dc5ZTix9+dAvruFo++yfixEkA84ZHZi4y87dziZzksB6bR6wuTME3uvVnK7BdnNml9oJ1bzSoqTfNp2D7nAZub3XFZwFSFZxPF7XrJY3Gv6uwvAVQA+OZaFmigwXCz0/3uGk6iMlKGMACsPdKCmIoIPvG2uq7wZs6dUWV5YJ5hzEWezFDFbkJ4bLpmnLdkt0Zc3NR9k7YVbfNZkVyuTs2VCO5qtIU4d6DG90377OTnSYQzepvOt1pIcnFIL2S1OQL57yLaTvfjFm8cxFE/jSjvhMfmzAu7LVU7E2cimYCL9btkYHt3YlGflpHC3IE+tcfNBth7N1ywUYJjOZNE1FDdZkFkHXdiCwTph+25xlGokNa7frpsfnJk4GLKmABVGSNzSajkFEWYpNSxy86ZpFlS3pXjHXNL6sbU/ZmzQYnYv+sYze/C221ehfSCOD799vlXWsKrGjcwOTn6cXQ51kaXUIDNzdcsva2/ff/kg/v43W/DN5/a6uHZQDCXSSGep4QbjRHTdcmdns9RY8XGbTNgHY9bGTveMoKq8DEtmTcojDe0DMcdyUJMFeeHMWscJ9zaHgFYKip6RBLJUc5fqHcnPBb/FRc5+D2YLsrltL3Foy/FUBkOJNM7Vd2tz8iV1vE/92NIfNdyN7G5xlNK89yBnecy1HTNpOdA2gLeZ8/vadPZHUxhJZjC9tgLJTDZv8u3kmpfr6wq70tndmuwrhE4BxOlM1hKMp8nl+pi6qnJMq600rrHu2Bm8sq/d0WjBZBlhnDe1xrEvdCSP+tFMNs1kOZulRm5lp7J2FwhgdOzLbBZkSq1ZOwCXdmNydZiubwzl5HLlTJC1I9PjOCmmFI+9dTL/e/3Y2h9DjR7c52SIcZucsf5qwbSaPL3m1UwPMZKhwQtB/hzbVY9S+j5K6a0AvG9rVQCEkJsIIUcIIccJId9y+L2KEPKU/vsWQsgi02+36d8fIYR8wI/yBI1c0Jc+40qkMaVG6xwA4JolMxwDoZwwu67acaA1w7xRSFy3mHixIL/7otl48tZrcct1i3R5aswIF8+sxRmHgQkAdut5J6tMGRnsnQSQI7wdA3EjHymzeP2TrrO8jBiyTHddVTmOOhBkSimOdg5h0cxa+w8AtBeXkQ57p/I3v9qEH7+uLandZJucmP1Ztes4z4rZAMQ6M8B5mczNr/R7L+XnOaVZbSMJ5yA9541i7CsGZj/lQm3lgYYTWH7nG8Z9ssv2jCSxYJpuNTN1lEc6htDcGzU6SrtvcpZSy/luFuQjjnWpHftjSZytE2T7oMLQ1h/Dwrw6B+L6c541uQqVkTJXFwtnSxoFpVrO1kv0wJnBeAqpTBa/bDhhBNW+5+LZuHnZuUZGGV01AM038PIFmoOi02Di6C9Pc0vFjNj3R5NIZ7KWwcye25XJ9uuWONbOnVYr3CZVQ/E0KNXcIfpGUpYgzfofvYnfbmzCMZsLQdb0bk2rrUBddYVB6JLpLD7/2HbU/6gBAPDOJTOtOkGN/uDcGbWIpTLGvbf2xxBNpvHGYYfUhDS3rHzR3Dok0tk8y9rG42cwrbbCsjMl6wfNVmyzBdlcH1eeY58k51YTFs+ahNrKiGM/UMgffjiexoxqlgUkp7elL4r/eG5vnix7tqztVFeUGWkLAaDhSDfcQGnOvYIFvLF+J57K4HO/3YaV+zVjjFM/PZLMYOakSlSVl1kMCwy9Notn1nSPAHD29FoLQX50YxM+cP+6vBWsXB+VwrRJFZhs2h3295vz4xrs98jay4JpNZaMNwBwx0sum4A4uIOwZ7z6YCeWfHsFHtGDSp1gNkjZjSWjTfYZ7JZVJ4LMnmnfSArz9f7X3s5b+qIFreSsjZrrYsepPvzLH3bgrlcP48U9bfmrJVRrK2eGk7hswRS97DkdJ8+MoLU/38XIrJMQbdJiv0/z/4l0CbtYAHjW43dcIIREAPwCwAcBXArgU4SQS22nfQ5AH6X0fAD3AfihLnspgJsBvA3ATQAe0K9XUrD7BI8k0phUVW74N15oi+AthMlVEcdlF2d9uRm3F4JMCMG1S2bmAoFMFp9FsyYhmc7iUw9vxuObT1lewD0t/ZhSXY53nTcz75ojNoIcT2Vw7V1v4Lbn9+HFPW14elszKiIEH75iPjbd9h584uqz84Icrjhnai5V3HACp/Xgu5a+GAbjaWN5mYHJD8XTBpFiVpEH157A15/ebZx79vQaXH72VEf5qDHAZPN8hbuG4sby5wcvnwfAmdybr+MFFBSZbNaZINdUIJWhlut1DcXztpo2z+ILZZKw5I6GidyPJI3nxq6VzVJ84P51+MD96wzLsNNzM7cLNwuyk984pVpKwXgqizlTqlFZXuZI7JLpLLqGErjufKtLEoW1rU+rrXC1ILv5/w0l0oinsgbxH4yl8MahLmNHq9/+4zI8cssy1FRGsMgU/KfllaU41RM1CLKT9ds5gCVn+WaTkgNtg9jXOoB0luKbN13kIJO7ZzYRmztVI8gJrwQZ1KirRbNqLemzfr/5FE72RPHfLx1ELJVBnTk3s2niO6myHHXV5YZLwO7mfqw+1IlEOotli6bjr660W9pz/QGzyvYMJ3GiexjX3b0GH/6Z5g/s5LrDSBsLTPv732xBIp3Bw+sa8fWnd+NPu1px09vmWsrK3ocuC0G2+uczzJtajb/Q32NWWDbRnD6pElOqKwyL8+meKD7zyFasOtBR0Co7lEhjuk6Q2X33R5P4sx++aWTtYXVuerRGn3v+7MnG+xtNpvGjVbl0lX991QKrTkoN0rVQf7bs/Xt040m8cbgLO0/3I5ayutvZxyT7CuWaw53Y2tTrYKFnfY32TM6eXmN597/30kEjv/NMU5Bhzo0giem1lZhUFTHKaQ6YXjxrEn7wV5fZ7jHXl86bVo14Kmuszo0k0q4klz3XWMrsv6zJPb9Lq4ffOllWdcGuoQTm6e+XeTzbcaoXz+9y2NVRX0lr7o0afYmdXDoHxTJinTQI8rf/tM+Y1D2zvRl/9sM3HWXZPbJVDvP486mHN2PFvg48tK4RtZURPP65ayyyWUrR2q/1TW+br/VfzILc1h9D/f804Lq71+St4mZN483kqnJMra2wrMok0hnLWMQxDAYOV4JMCLmYEPJxAFMJIX9t+rsFWjYLWSwHcJxS2kgpTQJ4EsBHbed8FMBj+udnAfw50aJUPgrgSUppglLaBOC4fr2Sgp3AjCS01Fm5pWrv6U8qImWjJuPOuVjkrAg1LnkRnWDixxiKpxApIzhb9wvd3NiL7/zvfrz9e6/hgv9cgae3NePlve248tzpxtK4plu/V73zm11XhcFYyiCVz+xowVee2IU9LQOYXluJsjKCeVNrQEiOZLIO9+K5U9A5FEcqk8X771uHG370Jm793XZcf8+bAID6i2Zbyp+zXqeMaPgzwwlQSnHXq4fx/M5cpzZzUmVepgNKKZLpLBLpLGbonTsjPK8d6MCxziF86Ccb8EDDCcyuq8L7LtXySTPCSGmOfEytqbB0zKMhS4EMdbcgA7nO6+ntzVh+5xs40KrnpdTPS+iDRmV5WWEXizyfMu3YO5LE3KnVqKmIGCTzgG4piSYzRqaSK+wE2XTf7Fw7XtzThpf3tuO9l8zJ081kp9VWYM6UKoN8vHX8DH67sQmpTBabGntAKYxNbszyjGjWVEQwvbYyz22AUoquobirL6Z5qRDQOn6z1dCcL9xswabQsgckM1lctmAqCHG2frulFGQWNGY5/7enduNjD7wFADj/rMmGddhJ1iDI+jkJB3cepwE1a3rejOyzuj5qej6XL5iKpQunm+S0RhJNZgxC1TmQQCZLDVerz7xzIR65ZVle6q4szVmQWSBhNJkxsvqc6B7B9NoKfGr5uXn3aViQ9WvuOt2PX69vwp0rDhnv8zc+cBFqTRsksNZtXj1iz+fMcMJilZ8zpRr3fOIKvPrV6w3ZYdOmC3XV5brFneLfntqFdUe78ZUndjladc2kc1qVbkHW25Z9l7Lli02Jokxy5WUEC2dOMgjy67YtyM0ZLJgos66yYOto0tkyax4LzO5s02orMLuu2rC490eT+KffbscnH9yUt0lUznqo6Zw/LUeQzRbom5edg5kmi6WxSjWcxIxJlZgxqQodA3EMxFLoHIyjTg9kvHT+FMPlz1xWtirFCGTvSBLZ7P9n77zD46rO/P8509WrJctykXvv3bjI2EAoCYQWIEsgJGE3m0B62WRTSXZJNo39pRJS2ARCeiB0DBbFGIONe6+4yU22ZNXRlPP745a5M5qRNLJVLL+f59GjKffOPTPn3nve857v+76aL/99M6mwzgOn3M+6Jpx5x7901bg2x7O+zwhzjLbO321Hz3LDz4wYhjaTFYzMMuGotp03luFY3xLi1gff4G9vH4mbHDl/mzNNIfteAPDqLiMO4XN/iRXMsibiFjFjNYTHpQiGo3agq9+xqjJzWAG5CYHHmliVwUnm51oOpR++EB+4mOMINLWN8pYQuQGvMYk0DfSDNU1M/+YLcQVrgilSD/YF2lu/HwtcA+QD73a8Xg985DwcuxxwhnkeBuam2kZrHVZK1QFF5utvJOxbThKUUncDdwOUlpZSVVV1HpreeRoaGlIesyVonGyHjhyhquoUR443E9FQ02AYumcP76aqsf3yjhbVR1oJhiLtfr+mZuMGdeLkSV5eZQzK+/fspKohdalMJ7sPGRfz66+/zo59IQJuTfW+tom+QxHN5/9qXLSLC+vZfjo24LQEg1RVVbFpn/Hds10hDh47xbMvt63OdqI+aH+f6uogra1hqqqq2LyrFZeC6JkjaA1/fqbKXpZ83hwwcn3gPRV/Ee/avZsVLftpCUUJnjmGR8Hb2/bgrz3Q5tjNjfXsXB/fpk2bNnFir3FTKfGHOd0IL7z8GllexcdejB8oxuVF4OhWflSZwcpDjawHVlZVsWVXKz4X+FWY/YeOUlXVViOZ44X6BOfea6tWcex4kJbmaJs+PnTMuPm88PLrVOS5+dN6o58tY6yu7ixVVVW8fdR4XhzQ7K+u6fS1cPToUaqqajhV30xjzXGyPFG27j1IVdUJ/rEnZmx+5s8bATi8M77C4erVb3DEPKdzfYoTp+vijq215t7njN+vVMd0hgE3HDp0iOdfNoIej+zfjS8S4tUd1Xz0Fyd5dn8IDXzdIUlpqY73fu/YuZOGI0af7d6xFRUKcaC6kaqqKrTWBCOw6VSEn24ItsntC7BlyxZO7jf2bzxh3K7e3rwd5zxl98Y17DNfiNTFfo/jx4/zj5eMJED1JamsdwAAIABJREFUR3YTcMP2Pfup8h6lpjnK8++EuG6Uj/UHmxicrTjcEBsoNm/dypkWc3A70HZ5+NDurdw9AVYe8lAYUDy+N3bCbNq8GcsOP7TbyDW6beduqkLvsO9kA/sff5HtNRH+tKutB3n16tUcbzSO6242zs0XXl1Ncwhe2eXwrHoaqT8bM5jDEePec+S4MaCGa4yJwV+fXcmTu1vJ9cHS3JOse+MUzQlLqqtXv8HWGsNIOVN9AIBX33iTvbWxCf+EfE1jzdG4/Va9/jprq40v6j0T8xJaHtWJRS7unhJgy9rVNDXF2m5dDzv3BvEoCGvYsmM3VcED3Le6mb11sePWHNjOWzU7qW812rxz126Om8bttk0b8EWCPLOlgWlff4a6oGbxYA+vHA5z8HQTYwpc7DoT+6y9+/ZRpQ5T1xgkM1sDii0791AVPchf32xiSrGblojGraD5TExS0tjUZLR3XxC/WxM+e5LDZ4x74St74yd7wePx3tL16zdwusVoQ90x4/x9dfUatgVcHKltZliui3fORpk6wE2mN4RZkZgzZ2qpqqri4LFmMjzg8ygONBj3nk0nU0/sW1tbqaqqYvueIAE3nD15lPqWMC+tXMnje4zz7c6JPubn17Bud6xPWoLGfodONZGPGy8uTtS3MvUbzwNwSZli1VGYlXWGfVvjV5pWv7GGqkMhPC7wnzU8vz/626s0hODp/SFG5LnYV9fWedRqHrP6ZDOj8l0cqo/yx6qN+E/u4Nix2OSpqDF+IrFn716qooc4fKqJgmI3ATes276Pleow31htfKcvzgnQHI5Pr7hhw0ZaTedDdovRv6++uZ6Wgx7eqA6zep9xzMGBVpz+5yNHq3n+xRqaQxHqThxhYKbiWJPmD1UbePFgfF8M8zexGSjOUJxq1mzZsgV1bDut4SiLyj28eiTMTx5fxTUjfRT7o1iLAqGGM2x4K36827hxIzXNplPt+C5cCl5et42M07v5+9tNXDrUuPe4leJpczz3uaCx0bi/7j/cgiuiqTtZTW1TiKqqKp7e10pTa8QepwFqG5p73C7rLCkNZK3148DjSqn5WuvVqbbr62itHwQeBJg1a5aurKzs0eNXVVWR6pieV1+AYCuDBg2isnIyP9jyGqVZPvbWnQKtufHyRW1yXabi7dAuIvt3s2TJkqSFMwB8r78ILS0UFxczcepIWPU6s6dNoXJcSdLtE6l+8yBs3cy8+fN5pW4n+WdPs3TBTL6/zlgCHV+WyzfeM5GBuQEW/89Kxg3M4UPXLeYf64/wl12GfMHn81NZWclbwR249+xj9OAB7DvZSNmIUfDWhrjjjSnNprJyCQAvnNnM5tPHqKys5IUzm8k/fowbLp3Db7a+xpdei/eEPXj7TGYOK6Ao28/HXnzKfn3UqFHMml4Oz7/A5HGjeePkPrIKizmbnQXsZMSALAIeN9uqz/LeuWO4tHIkPBfbf/LkKYZO75U1zBs3lB2vH2DC1Jmmh2SNvd1fP7qAKYPz7PzV76zYDXt3sWRJJc+c2kTh6VOU5gXwBjxUVibOCWH4ltfICXhYtSeWYXH+/AU8fXwLdbrB/k0shpxs4McbXsZdOorKecP4/Ttr4XjsBpSTk0Nl5UKOvXkQNm1mwpABrNh+gobCMVwzxVju3nmsnqc3V3PXwuHw7PNxn19WVsa8SyYSfPZZpowbyfHocVx+N4sXz+Vbb78CxBtay5cs5KEKo6T1I2sOMnfuPKp2nYC3tzJ5aCGHTjfHXRO7jtfDc68A8OkbF7Pr9+sYOSCbJzdVUz54CLlD8oANXLVoNlubdrJ310me2Z9cR3z7NZfyvXXP25ODMWPGGisYa97kkjkz2B7cz+4Txm/49Se28uiag6a+LkgyP8bEiRMNz9uq1VTOncrD295iQPkw483thjG+7NKl9vYVkxqp+edWXt9bw4CSUnIG5cO6bbx3+UJ+s+M18ouLqaycyod++xYvHjjBcweMdn7t+pn88a2DNAYjrN5Xw4QJE9h/shF27OLqyyr59Z7X7aIhAFcsmc/ggkzuAlbuPMHje2MR/pMmTTa8dZu2cMWSBXxj9UuUDx1OZeVo7vziU4AxiOdleBk7MCeuouPcufOM46x9m8XTx/HM/s0MHzeZFduOA0a1vvkjivjKjVP47esHWF1tGGQul5vKykp+uOU1CrJ8XLVoFL/asprD3sGsrt7Nu6cOYunS6bEfdkXsupo3bx61W47B1u0snjWV32x5i3ETp3Ji90nYYTgIbl48iabWCH/bHfMIzps/n92r38G7dx+3Xb2UyoUt3Pzz1Rypbeb2ecO4z7EU71q1AloMa8C6Hh4/voHS+tMcrWumtHwolZVjufNZo11XThrIhxcNZ+Yww5N7prEVXnqBUaNGGd7FDZtYunAe0aITfO2JrdQFjfRXP/vIMv729mG++9xO7r91DtebHn+A4RXDWbx4FC3PPk1eho8sX5TissEsWTKeMyue5dpZw/j8FWOJaM1Dr+7n2QOGoZ+RkUFlZSX/PLGR/LM1TBk7lOcO7GTeJYv44+ENQCxrzq1XLuL761bYz6dOm2qUYN68lXnTJ/LIjvWMnzzNyJ6wcg3fvmk2c0cU4laKNftPs/qXhpGUl5fPkiXzOPvai0wfOYBsv5td649QWVnJtqo9QEzWUZ6fYXte3V4vlZWVPHVyIwW1p5g6fgSP793GiMlzeGbFK0wfms/X/mUBSil+f2ANu2sNI9Ln87Jo8RLOPvc008ZWMG9EEY/tjN1Tr71kMj83pS5aaz73ytP2e3PmzOHFmh2MGNDIzZfP4Udvv2RP/haOKuY3H5zNAyt2s2BkEbc9FPtMr89HZWUl39n4KkPzMxg2ULP7RAO1eaNZf2pL7NiXV/KZl5+xn48YMZJFi0Zw9rmnmTqmAldWHYdrmzmZM4IDZzfxlWsm8KGFw2kIhnng7Vjw5JSpU4xVtrc3cdOyOfx262vsDRXw2SUzWPX0dsC4lj5w6VRaXtnLxsN1ZPrcDBw4kOlzxsELK5g2YQz/9YEhjPvKs7Zx/IePzONWs99mThzNk/u2UV6Uy6nDdUycOJFpQwvghRe5YvY4jrTuZ3eLl+GTp7H/2Sq7bbkFxVxx6Qx4MfY9J0+ZYsSFbNvBNcuX8LNtrxLOzMFXPpiwXstHr5ptF//44xeN62bcoDwagmEqKyv5+a7VlGXCxDEDeHq/cb7+ePsaBuQ0xen/Q+5AShupt0lpICulPq+1/i5wm1Lq1sT3tdb3nuOxjwBDHM8Hm68l2+awUsqDkYq7ppP79nkSS003BMMMKczkd3fNMSpgddI4BvCZYdHhqMbrTm4gW+Ur4yQWndAgW1ifagV+lOUF4lLAPX3vQts4f+0LS+1lqkFxejpLThIhy+dm5IBsVmw/wfZjsaCGUSXZ/O5Dc+ICFJWKD+TIy/AycVAuWT43ja0RJpTlsv3YWe6YX8HljuC6RaOLCXjdvLDteNxyeU7AQ3G2n3UHz/DndYeZUJbLU2b7dx+vjytI4Gy7lZvTWsqsbwnHLTO+b9YQZjqWnq22G7+btttekuO3NdOJBENRBuV5cbtUXCaJiNZxBSEsRhRnMbQwk5U7TnD7vGFtih5Yv5uly7N+848/up6vP7Etbqk5cZnNaHdMr1qQ6WN8WS5/WXeIm36xmj0nGvj3ypH8tCq2CpET8LB8Qil1zSEeWXMQjeZgTRM+l6Eh3HS4DmdOX2uZeM2XllGU7eexu+fjUvDUpmo0mq1Hz+LzuBg3MKdN+949dRATynL5zrM7mDmsAJdL8ZsPzuY3qw7w1OZqolrbcohsv5f8TB+nGoI89Oo+W1/4tmNJPcfviZNBGHIi43lehpccv4dH1hy0+/S+BD1kRXEWv/3gHC79XpWdXivgdVGc7bOX44E2OctHl2Tz0B2z2X28nst++IohsWiN4HO78Lpd/OEj89hefdZOQee87oYUxC/JGt/ZOE5+phevW/Hb1/fbekKLh+6YlbTkubUkbl0DH/39OjvLw5P3LLSXW51L5Na9rCUUJeBx25lMLD17sjgEC63hZEMQv8dlywAaW8NU17VQmOXjM5cbE7mqnScS9tP2Mq5SivL8DFtrnLjUbAXpOe8jRvUzD7nBtmkSQ5GobRxb+1lttZbTs/3GeW4FgI0bmEOW38Pt8yu4be4wEtVQmpi0LMOjyAl4qW8JcaYpRGvY0Nh73C48EJdnO9beEFl+t/1eXXOI9QdrmTu80NYCFyWMGdZ5BE6JRdgOIptUnmtP5OeNKORXd8ziZ1V70Rja5VMNQcaX5dASMnSjh8808evXYl7qz14+hkg0livYKcfKz/Qx3JQg/OKVfbRGoty5oMK+7q17WabPTVRjZyQpyfHbAbFgyE0WjIzFFiilePKehazac4r/fmaHLWUqz8+wNcHOfb1uF5+9wtDsXzdtEP/YcDTudz3bHGL8wBxGlmTz4o4TfPKP8Y6axLzI2tnWXD/z/EV859kdPGKWe799njGBzvZ7+M0HZ3PodBNffXxrXNaKwfmGFOvZrcd4dfcp1h+sZeqQfB758Fyy/R5mDy+gtinEB3/zFtGEcy6xXPTkwXk8cMs0irP99nYzhhWw8XCdmXHKTMma4WXG0AL+su4wS8yAWSuH/pTBeW3rAWgjD7ZSRmDoiAHZVO08aQfMWmk3Aa6aPJCnNx+joijLzsdd2xRicEGmfb4eqGnk7YNnuOfS0Xzwkgr2nmzkhp+9zsNbg3yBvkl7QXqWSGQtsC7J37nyFjBaKTVcKeXDCLp7ImGbJ4A7zMc3Ai9pQ8T6BHCLmeViODAaePM8tKlHcaZdAzMgwudmwahivv6eiWl9lnWTS6VD3nb0rD1rM3RpnQ/Ss3BqkPedbGTEgCxbi2u8HxsRBhdk2gb+oPzYTcuZxSLb7+GGmYOJRDW/eNnwFC0aXcx3bphMWV5G3CChUPbvZBmZSimeuGchy8aV8Ni/zmPHfe/ia++Oj/P83Yfm8r2bptrtdhrIS8YMsKsPuV3Kbv/o0hz7ZvHdG6fYulitjchdj0sx1jSQf7/mHXYdr8fndrHqi5fyjWvb9ps9sXC0vSTHnzQqHIwiDD6PK26AtdJvJdMgK6W4dFwJr+89RUso0jY3qfmbW4FI4xwavsQsHsnT/cQCfQoyvdwwo5xQRLPunTPcuaCCe5eNZnJ5Hh+tHMn3b5pqn4tWqlOt4dCZJoozFVPK86lvCccVUnh+23GmDsm39ad2XyhjX6PIhR+P2xUfGIYxqH+0ciQ//5cZ/Oh90wCYVVHI194zwWx7rM+zAx4Ks4wB4VtPbY/7HKtYw9wR8UVC4waYgGE8n2oIcrI+yIKRRfZg2AbTEDtS28ygfENDn+33UB80skIcq4v/3a3vbp2DUa053RikIMu4BrL8HmY5SrU6B8mKoiyumVLGf18/2WyzpqEljEsZutIsv4czTaE2Zdsnl+e10TtGHXpxy0C2jOPRJdm2cQyJ1fsMWsIR/F6XnYkHDMNx2fj4Vapf3D7T1mFqNNV1LQzMC9j9UN8Sprq2mTGl2bx/7jDcLsX8kUVMGZzH4jEDzO9pbOecNFnnc2IRDes+V5Dpc+iljQA0IwtMKE5XfvmE+Aw2yryKNYYGVynICXgpz8/g3ktHAdjtgvj7if0bOQydgAdyMzycbQ7bxXXGO/TDzqIuTqOzMMtnv/eXdYc5draFG2YO5vlPLebP/za/zTHDUU2jaeQMMYP0fl61j9+sOsC4gTlx/aSUYtn4UnweF1rHgkeHF2dRmGUY1x/67dq4bAlzRxSRl9E2APJIbTODCzKoHDOA3ICHP7xpGI8THf1y77JRXDlpIMvHlxpxAKaefEBOIG5c+dO/zicvM35iPKk8jzLz3NVac6S2mfKCjDbfPzGH/7ffO5mfvX+G/bs2tYY5WtfMkMLMNlkcwEhTl4gm1taSHD/zzcnfxkO13DBjMD5HNpClY0vsALeoeV2CcS+y+MCv32TtO2eoKMq0z/+SnIAdpK+J7WeVs77PMc5k+z1cO62cS0YVc/mEUv760fncMnuoecxYes/cgKdNjMbA3ACvfWEp/145qs33DEc1DcEIWT4PSilGl2bTHIrYGXGcv9cP3zeNt768nEyfm5ZQJNYn+QH7+nx2yzGiGpaOKyE/08fMYQV8avkY7p1xPkLauof2JBb/NP8/nGqbc8HUFH8ceA5wA7/WWm9VSn0TWKu1fgL4FfA7pdQe4DSGEY253Z+AbUAY+JjWug/HQibHuvHFgjeMAJeuYBl0obCGJI7nq/73VcdxtZ0QPS0D2RwkapsMz8KIAdlxN4NUxN3szf9WdHRiztzESFr72A7Pz/GzLfbsdeSAbH515+z22+3w4MZm4l7uXjKCte+c5o19p+2AukRunjWE8QNzWbH9OFFt6KJLcvxMGJRLeX4GT5kluMeUZrcxNhK/s2V8DCnMpCQnYHuOEn/DYCiC3+Myb/Yxr3+4nSpGS8eV8NvXD/DPjUfjgk6M48e8e2AENzq599JRzB5eyMOvv8OK7fFBP9axrfQ++Zk+ZlUU8q3rJuHzuLh5lrGQ8897FrbZzzpfotrI5DAgw8UlZmXIV3efZExpNo+sOcjGQ7V88cpxbfb3mB70I2eaKcs1fttED5lV/ONdk8riXreOjdacONtq7ztneBE/WRnzdt+5oIL1B8/wwC3T2V/TSG1TKyu2xzyVGu2YVBne2FBE88FLKrhx5uA2bY4dHzBXWqzzoiw/gy1H6nhyU3XcxOS+6ybZKznO8X3fyUY7CNbi8+8aaxfxsH8nt4sf3zbDriQYihhe82y/MbBl+TxJM1YEvG4WjCpiYG6AqUPyeG7rcTso0utWlCRU0LRWoCzmmenaZg0rYOPhWsKRKDUNrfbEdtqQfFpCEZ795OI2x75i4kAag2E2HKo1JkF1RqW/QfkZ5Pg93PfkNuqaQ7x3eiy0JNPn4YmPL+TPaw/xyi4jCK7B/J4WX756PN97blebDED3LhvNPX9Yz+CCDNsx0RCMkJfhJRyJUtsUCxS+77pJ3Dx7SNz+OO4hp5tayc/w2tfiPctGMyDHz/Uz2p4PA3MDdkCs09DJ8CgyfR6e3XoMt1vhdSumD42tPC0eU8z7Zg1hW/VZO21ZTUMrEwblsnhMMXkZXltrvXRsSZtqpxahcNRcrfNQnp/B1VPK7HtWqkwoLqXQxIqYDCvKtB0vOx2FmT5z2RhmDC2IKwmtzd/o0Okm5o8sQinFkrEl/HPjUVwKhhbGVuZmDitk5rBCvv7EVjSxtHslZqrERz8yt03FSifWpVLfEqauOUS56ZX9xLLRPGCuXBRmxe+f5ffY2YXAqHiotSEPTLy3LhpdzIO3z2pzXK1jbR2QE2CSw+ifNiSvzfZOx9LZlhCZPjdul7K9rhZWlhEnLpexo1X63TrX/2XeML6SpKqgUoqZwwrtAloazQEzKG9oYSYLRxXH7RcMR+PuMT963zQeXn2A9QdraTHTLVo2QuKqjNPj7Pe4GZDjJuB10xyKcPxskPqWMBXFWeSak4EfrTD6xBlI+onlo6mq6ruL/+1JLP5JbGxvg9b6Ped6cK3108DTCa991fG4Bbgpxb7fBr59rm3oTawBJ6qNXKuNreE2ZUc7iyWxSCwBC0mqHelY9HRaBrl5od9lVjQaU2oYt/ddO5GBecmNQzC82/9x5Tj+vv6I7X1oCIbtWfTzn1rM5T98pd1De90uM5WXkW0gMUdxJ5ptepyMG02W301uwMtjd8/nWF1LygEG4g3s+pYQ2QEPfo+bFz+zhPc9+AYbD9W2W7b5UXPp7TerDrDjWD1zhhfag8DJhmAbw9ryILvjPHRGJb3EVFcWc4cXEvC6+PPaw22KeETNU6IlbFS5G22mGJo6OI/ffXguuaZX4siZZttAtgxB49ixioOWZ+dfUnlOHVjNbwxG2H2igSsrjEF6UF6ADYdq+eNbh/jPfxhav5uSGJsZXiPV045j9bahNCRhEBmYl/x3dw5KK7YfZ2xpDgGvm8Wji/nlB2bxkf9bC8Ctc4baqzUVxVm8ujs++4DT65cT8PD4xxayrfpsu8YxGEZG1JRYWF6zMSU5PLWpmnv+sJ5BeQEG5gV4+2BtnBfa6t2oNqQlt8yJN9SSeXosrIlWazhKfTBse5sSy/qCkbcZDE/VG19axt/XHzYMZOJXaD5z2RgyzdSTiWkTpw3JZ9e3ruRHK3ax4VAtW46epSEYtnWJf/voAttbmwxnHx0728L0ofn4PC4+f+U4vmKeF4lyIYh5rq2+cd4z3zt9MO+d3rZv3j11EFdMHMi/P7KOo2a2lcZgmEF5AbT28vKuk/b9bN7wwjb7OycuZxpDcR5Or9vF7fMrkn7HvAxvzEB2LLEHPNjFMp7aVM3MYQVxcje/x813bpzCp/+0gTX7DPnEyYYgxdl+/B43C0cX89SmakYMyGpz73rglmms2X+aR9ccpDUSjTNyfnLbDO5eVIvHrWzPZrLvGtXaYbAG2hQJ+cSy0dyzbLT9HS0sOVZja8Q2vOYOL+SfZr7wVA6VaFTb2WmsiZlTVpEM6zzYYspFrGxJn7psDH948yAn6oPkZaSWKWpilVjHDcxpU8VuQLY/pQTRWv0ryfHHGYqJKxcQu6bRsTR2AD99/0yiUUMmtPdkA1MH5yfZ17iPWJNiawXNmPi6GVfW9njOY2ptlGf3uBRDCjPxuF1kmEYsQDgaby9cN72cmcMKWPTdlcZqZGvEvr4Wju64qm/A6yYYirLJTNk5uTyvTfXXRIlIX6Y96+h7PdaKi5S4tGuhCFqnabA6sJa1kxnIuxPSV2liaaAKMjuvc7ZOcyvJ++LRxpJiqsHByb8uGck7p5t4fqsxY65paLWlF2NKc3jP1EFxmsZECrN8NLVG+MlLe1BKJfXWpGy3NaCi7baXOAzaVEZWIhpLt2gMCAGvmy++axy3/vKNpNpgC0uecP8zRsaP5eNL7RvTUYeH0SIYiuL3uOMkFlFtyGcSS7RaBLxuPnv52DbSAavdYGjtcjO8eEw5SH6GN+58s4wmpeA7N0zh5y/v5VRDK1rHiiO0N5FIxPrd1+yvIRLVTCgybozThubzxr4aVu05RXG2nx/fNp2i7Lafm+n3sOt4PQ3BsD3wJA4ig1JMzKyfbsexejYfqeOr10yw23TZhFJ+cPNUvvvsTjtNk8WQBI+tIdEwUhpmeN1MGJSbdBBs+90NGZOzuMrVU8r44YpdZPs9PPiBWQwpyLQHKgvrPDpa20JzKMLoks7nQrcN5EiUhpZwbDBN2O6n75/RZsUkZnRq+zwBbCOovWNaHsdVe4yAK2vJ2eVSuJLmBsFsV2yF4djZFjsl3e3zhnHL7CF85R9bkk7ErEvN0loPTeJ5S9VWZU5cwNBXOvPO//LV/QzI8bcpdGO01cDSn3b2vun1xMtQrKX5PJ+Kk8NdM6UscVfzuMb+wXCE+pawvay9ZPQAntpUnTRN57XTyplcnsejaw4SMvNYO1cKpw5pa4glorWRO9elINvniZsQvP7FS+PiSpxGqNbavldYKcmsCWI0xVzJOn9O2F7Zzt1jrPPAmkw576O/+9Bcflq1p01Odue+Tq/1wLxAnDfw3mWjuWN+7NwrzvbZzp14OUh8WyuK2sauOMefM42tdlpOMK4RQ27QdlIGRulsTUzu4nQQPPOJxW0kZxaWNzwS1bxzuonBBRm2jfCjW6Zx7x/Wm+lK2/7W1qTAkutZKRJzA15WfHoxy3/wSlwRrLh9vW5aI1HeOnAar1sxqTyPoKNKZWdWnPsS7UksXrYemxrhcRh9tdPMWyycI5ZzxSnCT6Z56gzWiRdKUhAgsRS01praplYCXldas7nE0rFtRP0doDC+czAcYe/JBnsgBfjfW6en3pGY3qlq10mmDs6jIkkQXXvHxTz22gOnjcjgdjy+iTiNh/qWcNxgMbuigLsuGc775w1NtbstcQAjp+biMQPsG/O6d87YHjeLYDhqGx72Z2ij6IS3nd/8uunlyQ1k80SzNIxAUjlISW6A/7lxClOH5DOmNIfrZwzmkvtfQqN5bfcpRiZozjvCav3re2vwuV2MyjfaPm1Ivr20+OhH5tpL9Ylk+tx25gYraGfswBzWfGkZq/ac4ter9pObkfwWZp3X1oTsmqnxBsj1MwYnnWRVFGfxqeVj2Hi4lpd2nDDKLpsJ71Nlh0n+3RVvHzRS1lmeulEl2Tx97yJKc/32hCCP+IHGOoQll0g04NvDOjeq61p4fttx20D5+KWj+ayZgg+MQTzVeRTVhjc3UbvZHpbHcfXeGsYNzOn0vtZ3veXBN+wgNed3uf+GKe3uZ01Ys1MYCUn3Nf///o13OFrXQrbfw0crR/LYW0YKtNkVBUn72boWX91zijf2nebKSZ1bwbpyUhlbzHzkaG3LFoozXDS1xobRm2YNSba7Ychpzd4Txn6WV3bJWMM5kcpRYPVvMByluTVMRicrshrHVPZKQm6GF5dLUWo6Ea6fUR5nHEO8FlVjxBtAbLJpTThSBWoqM9bACsr0ezo3JiUupjnvaWMH5vDALanHFEsCVdvUSobXkAZYDpqpg/P49GVj4rZ/+t5F7DhWzwd+/SZRU2KRl+G17zOfXD6aX7y8L+n90Vr1aw1rdh6rZ3pCEHd7GBM6Q+5SmOWL89YPTawa6sAZk3SwpinOsL5i4kC2ffNd3P/Mdt4/t+0E1Jp0NVsGsuPcGVWSw8ufq7SdRIkEvMZx/7LuMPNGFBEwf9un7l2I26UoSmKQ92U6tHCUUlcDe4H/BX4M7FFKXdndDbsYsLNKoG3d6Ll6kJMF6bUpBYkh3E/HewzEebt+96H067JYs/b1B2sJhqMpDaNklOQYN+h3aprS8mJCbDbdGo7y9JZqrp1WnlLLmwyfx5KvGBpC55Kux+3iq++e0EZLnYo7FwwHDM/DkMKMNkW6xN2DAAAgAElEQVQitNa0RqKmBtn5uqEtba/dxdn+dmfopxtbKeygz2+aNSROv6mU4T1etfdUfEWxTmCdky/tOGEsn5tL/QtHDbA/e+7w1OeA5fVyqVghCDA07dfPGMyT9yxKabRm+txkeN2camilKMtnnz+d4RPLR9vZKVrD8d7YzuJyKepbwrhditmOJfsJg3KTesstrP61qlN19ryC2CT5/1YfAGJe/xtnDmbNl5bZ2yVbMbF+x1Akyu7j9bYMpzNYRXw2Hq5tk8Glo/0g5sVzBvO2ux/xE9acNO6ZljFmSXsyfW5KcwP2BN06N5PtB9ja58QCGan498qRrPj0EiNvuzYmxMOKMsn2qbhzMpW0zopCeMWU/kwfanh/S3MDbPzq5dx1SUXS/ayy0aGIUX47vVgTwJFxBwzv4UufWcL917edtAwryuI/rx7PjKH5RLW2NcmW5CEn4OWvH53PL26fmfR4LmV4OrccqbNlLp1qZ8K1n6iZb4+ohqawprYpZHt0cwNevn/TVP7frTPabF+SG7CrdGptSCycx/vk8jFsv+9dSe9H1uc/vbmao3UtLBmd/BxLhsJY+fvH+qNxAbIdYd0LguEoe082tLmPuF2KL189IamjyTL6H3vzEGv2n27jtBtWlNpRYnmfzzSFuMIhg5w4KI9xA3PTHrt7m864AL8PLNVaV2qtlwBLMco+C+eIs9S0pRPuqoHsvCASqU/QpFpBV/lpGsjvmRorETulvONlukT8HjfBUIQ39tXgUgnVojrAeYGn490yjmss9FafbaElFG1Tbarj/WNLTs5Bo7N8/opYAJrTgB1amGnrEC2s/vN5XHHGsNZ06EEGQzeXSGIUfDoohZ0GrE3gUgdUFMe8FnMdk6HxZTl85ZoJPPaRee0a/FaawIrirLTSEYIxeFo342RL5h3hsz1wEbtkajpYx5xcnpfWvta5veNYvZmKMJ1Uj0abrYA85/XlXNZNtjxqGal3/fYtzraE0ypz7wyWGpvGteXs+f936/S4AbU9rGvoVEMrdc2hduMfEnE5JBYQm8RdPbmMRz8yl1tSnOMZXndc+sx3ddKDrJRiVEm26ZXVrHWsGD3y4blMKMvlfSm8x8b+RrzI/zy3k0tGFcVV98zL9KacIFrfqyUU5fW9NRw5k6yEcXKsZf21B07HVZRrLyj7w4tGMLui0JZj5QY8cffJmcMKU3ody/IyaA5F2HykjmXjkgdLJyNRXpKode2IFw+GqU24n98wc3BKz6y9EonmaG1Lp6V5liPq2S3HcLsU75k2qIM9Yvi9bl7edZLmUIQ7F3Qc92Fh3QsOnGqkqTWS1vXsdil8bhf7zOC+dGwS54q0NaG4kOmMgVyvtd7jeL4Po5qecA5orR0SC0ek/DkayMk0yFbd86fuXci0IcYsv6axtU2Eb0c4o13T9aiB4SVpbI3w9sFaxpTmpGVoOstVlxd0fkCE2JKhFSyXn0I/lQrroq9vCXO6qTVpruD2SHVDHJDt52SC/MXqP7/HFVeSN6o1oUi0Q8/3198zkZIcf9wkIOqUWKRhcEHMSJ1dUdgmo0JHOHNJTyhzeqUVH1o4PM5oToY1gZuQIhClI0rOwUD2e2MTTqcmt7NMN3We6XhUwTjXrGtr5IDstGQdPsfkafGYATz64VhGGOeydbLPbDbz81qZHNLxXFsDKaT3WzubcfXksk5/V0s6YpW7TfeYTq2plTfX7VIsGFmc0shSStn6/3uXjebjS1MHSybdH0PTe7qx1V4NGVKYydOfWMR3bkwuJTH2U9QHw0SiOnVKwSRY48FPVhpDd3ul5ZOx6XAdR+ta2FZ9tuONY421tegj0+gTayVx/ogibp+f/neEro+biaW628M6PVtCUTYfqetULAIYqxRet6I1EqU8PyMtWaM1mXWp9AxOr/nbbDUDGNPxzENsBQJiDpbO4Jy0JOZnvxDpjIG8Vin1tFLqTqXUHcA/gbeUUtcrpa7v5vb1W5wBC/GR8ukNxBY+O81bWwO5qdVY6p1Qlmt4B7QRHFaWhuclkXRn6xBbRtx2tC5to8XIK2o8Hp4kECId0rngIWYs3ffkNrQmbQ9yKq9vfqaPQ6ebeX1PrCSpFdDg97j45rUT7QAkjeFBTpXFwuKyCaW8+eXlcQGP1r5nmlrbpEnrCMswnN6JwJ5E/B633efDutBn1nfo7ECUiGVgJxYP6Ax+x4pM9dnmtD/jhpmDuWP+sE5l+0jEMuzTkTmAcU1aRsOkQblpxQgkGqfD09A+7z8VCwJOJ6jQojjbl9b9ZJiZKmz1vhqmDs5jyZjOL1l7XC6jKh4wpDCDa6eVd7BHDEtiNn9EUdr3P6WwsyS0F4yciDPIb/6IzhtI1nlgBQg/fFfnJXErd57seKMkuJSiNRxl78lG5qchn5swKJe1/7mcRz8yNy3j0fplBuYGeOmzlek11oElZ+rweOY18vOXjTSRizsplVBK2d7mIYXpjbmWM2dYUVantdmAvdqxep9RjXV0Gh5kwE4DC9hVEjuD0ymSboxSX6Qz3yAAHAeWAJXASSADeDdwTbe1rJ8TcVjIUa1psPIcdsEzC+17kI3qVkYEt8ftojkU4UR92/Ri3Y0VUHWqoTVtmQTEUoGlikzuLBPTNLgCCTemdA2XVMuSVn7I2x5aww6zkmDMg+xm3MBcPnO5ESwSNYP0OnvTcbucHgAjZVNU0246umQ4q+91hc+ZFawGd8GbcOeCCq6fUc6ts1MHQLaHZeSmU5HSwppwnmoIcuRMc9Lo9PbIy/DyjWsnJa3I2BHWnaEr53nUvK+UJbm2v3ntRD4xI/l1F47E7kd+j4uyNM4T577paAx3mYZJRysJieRleplleuZvmDk4rcj43AwPNabR+PkrxqUl3fnauycwb0RhWtIwC4XiVH18qsTOYMUMFGX52hTLaA/nRPpLV41LaxLhJB3D2jpips/Nvy4ZmdZxirP9aa2WQGwS99krxp6TtvXOBRVp73PNlLK0PLojio37Z7qOAkt3nO6Krddx/x+YG0jbqeO0T3xpGLrpyhf7Oh3+6lrrD/ZEQy42nDo4TSwvcdfzIMdyoCbSEorgN2fmfo+L7dVn0Tp9qQLAik8vaZM7sbM4jax0AwQBvvGeSdy1cHjaS/1Olo8vSXs27dQePnDLNDsdWlf2d+Jctn/Xj17lwP1XEzQNUmvQt1MEaQhFox16kC2cWmRNzAuQrid0yuA8Nh2uY1g7EdPtcceCCj4wf1jagx8YJcp/cPO0Lh0XYr97Ojd4C6UMb+yru08R1aQVUHquNJkBu10xrsPmwDYgiZfyA/MrqAoeSLpfxHFNjyrJTstDet30cu5/Zge/T1HkJxVTzJR9H03TmAL4+e0z+enKvR3mo07EaSgkZmPoiA9eMpwPXjI8rX1sFJwyPcjF2X5OdbC5hTW5S9cAdF5vXfHqA1SOHZCWYW2tlL1r0sC0DbKuUJIT4MD9V5/TZ8yuKGhTfbUzfHL5mI43cmAFoKYz8QSjouN3n92ZNEdyeziv39Fpyiuc3DpnKP9e2fnr0+N2cdvcoUmLnlyIdGiNmaWc7wEqnNufj0Ih/Z0nNx1l7aEQlUneizOQtbYTyHdF2wuONG/teJDBMJCtfI5d8SB3Rc9p4Uxqnq7+GYwI2cQqcOnyk/e3jVDuCOdgs3RcSdrGnjdF7uJkXlWnBhmcKeo0kUjHEguLuxePIBg2ggqPnGm2y9mmSiyfik8tH8P0IQVxZY7TpSvG8fnAWq4Np0rA2gF+t4s9Jxrwe1x29oCeoMnUAw8r7LqUKN0VGmeVvHRXSP518QjuXFCRdgGA5RNK2XHfu7pUOKA4289Xu2DYOAP6urKq0VUyvG67rH1Rtq/TBrKV8STdSb2TdO/Z188o529vH0mZcz0VliHWFLxwitqW5bUtT90Z0p0A3DZnGCt3nGR5ioqtqRhVks2jH56bsqhLZ0gnQC+RDy8a3qY4U0f813snd/l4fY3OXAH/AA4A/w8jo4X1J3TAExuO8uLBcNL3nGN2NGp4kD0uFSeOTwdn3stEWsIRexByLkeW5vZsypVsv8c+Zm/pk9LRcSWjK8Egqbxxi0YPYE5FIUVZPjvPr6VBtvrJzsEMhKI6aVW0ZIwdmMOPb5tBQaaPqNa8sa+GoYWZaU+KirL93DBzcFpp8foKn758DNdMKePdUzsfNe7EmrROLs/r0epPD9w6ndkVBV1a4bFI10B2yiSSyTPaQynV5d+np6tqzXBMdLoi8+oqVpBUcbYvrRzsS8cO4LIJpdy9aETax7xiomGMpXvNX24acenajVdOKuPuxSP4QpKy8X2Ne80COF0dA9N1ZE0enMcbX1pm3+fTYcGo4rTkNRY3mDmyzyVYLt2Ylf5GZ6yUFq31/2qtV2qtX7b+ur1l/QCv25XSe+XU+GiMnJM5gfSKETixDOtkEotgKGpLLJzLzT05QFhYte0nncOMuDc5n97QLL+HP/3bfBaOLra9hk4NsnE8Y1tbg5ymV0cpI4L9+W3HmdsF7eSFTElOgB/fNqPLsiWL/7iqZwf8pWNL+PO/LejSpGSyqVlMd0l+2fiYZ6unYxN6EucKVE9O+qzKhZeMKk7rHpIT8PLLD8zqkh79BzdP443/WJZ2QKFVVCRd49HtUnzpqvFdkgb1NJaULd1UpxYXQrnkD15SQXl+Blemmb/eSW4Xkwb0FzozcjyglPoa8Dxg56TSWr/dba3qJ3jdiiT2KtGo5slNR2PPzco86RQzSCQmsWhrkAfDEbvCjbWd1616RCeWyNQh+ez7r6u6lAXjXPjekgzGT02eqL4nyPF7eH+KjAaZPo9dKKatB9nYRpulptMd1BWGxAbodJ5ZIZ5UZWD7Ig/cMo0Nh2rTzqc+Z3ghlWMHULXzJPNGXDjfN12s66erK3Vd5QPzK3C7XGkX2zkXsvyeLuXVXzy6mG9dN4nrZ3Q+w8eFxlmzNkC66RsvJCaV57Hqi5ee02f09Djd1+jM1TMZuB24FLDMPW0+F9rB63aRxF7l+W3H+fLft9jPtYYTDUFKzkHy4LWD9Nrqv1pCETsTg2V4FWWlHzV8vuiNi644w3VOOq6vXDOBcBJ9d2fZ/I0rUr6X5XM7PMhG/8UGcOO3sjzIqQL+UmH18cxhBWnr34QLjxEDshnRxYwjP75tBqcbWtstYdsf2PjVy3v8mAGvmw8t7GKAXw+jlOpSesILC+O+mJumVOL7N03tcpD6hcRdlwznVEKO/ouRzpwdNwEjtNatHW4pxOFxu5J6kFdsj09OrrWmpiHIyHNYmuoozVtxttHVPrdhKBfnXNzaonTpzsEt0++hqTVCNKrb9SCHIzoufVtnsOZA6QZeCRcf2X7POctRLgS6oucU+hcfXjScXe8cYWmaGYluSDNryoVKVwJg+yOduRtuAfKBE93cln6Hz63iosMt9p5siHse1bpLZYDjj5VaYtESitiaVqvoRW/oj4XkWLXum0MRe4KTLM1bOI00bxbK9JRcDIbP+ebP/zbfLuAjCEL/YeSAbO6ZHrjoNbZC+3Rm1MwHdiil3iJegyxp3jrA63aRbFX+ZH2QucMLCYajHD7TRHMoQlNrpEsFDWLHMgyhZFksmlojZPrjg/SKssRA7itkmsZrY2vY9iBbQSCWPRzRmqim01ksUh1D6DyzzyG1nSAIgnBh05lR82vd3op+SqLE4mxLiBy/hxP1Qa6aXMaXrhrPdT9ZRY2Zl7grxTMslFL43K6kWSyaWsNkmZHJlmeyNwL0hOTYHuTWCC3heA2yJZGw8lun60G2KuFl+/t+1LUgCIIg9BU6FDQ6U7uZ6d0iwM3d37QLH0NiYWiMn9x0lClff57V+2poDUcpMdMwBbwuqutagK4Vz4g7nseVtFBIU2uETNMIswwmq+yz0PtkmpOXxmDE9iD7EyQWtoGcZv7oRjP4ryvR7IIgCIJwsdKpUVMpNR24DSNgbz/w1+5sVH/B43ahMXIe//CFXQA8v9UI0LOq02R43bbO8Vw8yGDILBI9yJGoJhiO2kbY2WYjvU2OaK/6DFbS+brmEEHbg2zmQTa3sbTl6XqQrapWokEWBEEQhM6TctRUSo0BbjX/TgF/BJTWemkPte2Cx0q9VtPYyl6zzOiOY2cBGGoayJbhCpyTBhmSe5CbbA+iYXBZQWA9XUVPSE2Fmb1k78kGguEoSsU05VYlvVC4axKLppDR/87zTBAEQRCE9mlv1NwBvApco7XeA6CU+tT5OKhSqhDD4K7AKGN9s9b6TMI204CfAbkYso5va63/aL73W2AJUGdufqfWesP5aNv5xDJyvvPsDvu1zYeNJlseZGdFngHnmFnC52mrQa5vMQwky4P4mcvH4ve4WT5ecuL2FQblBcj2e9h9vB6/143f47KlFYkaZHeaEgvLg5wlGmRBEARB6DTtjbbXA9XASqXUL5VSy4it+J4rXwRe1FqPBl40nyfSBHxAaz0ReBfwI6VUvuP9z2mtp5l/fc44hpgH+W9vHwEMY6exNUJhls82WK3gKY/r3Cvbed0uggke5BP1RuIRq/Rscbafr79n4gVRKvNiQSnFqJJsdh1vIBiKxPWNlabN8vx7uxykJx5kQRAEQegsKQ1krfU/tNa3AOOAlcAngRKl1M+UUudaiuha4GHz8cPAdUmOv0trvdt8fBQjD/OAczxuj+J1ePse/9gllOUapaSHFGTYr5fmGa9l+NznXGHO73HbQV4WJ84aAYCWgSz0TcaUZrPreD3BcDSuDK7L9iAbGuS0S02bLmiRWAiCIAhC5+lMFotGrfWjWut3A4OB9cAXzvG4pVrravPxMaDd9X6l1BzAB+x1vPxtpdQmpdQPlVJ90vpz5qydXJ5HvhmEN7gwVsp1UJ5hLEeiSWpSp0lRlo+axvjykJYHuSQncM6fL3QfY0pzqGls5Whdix2gB9hrNpbEwpumxOKaqWWApPUTBEEQhHRIy61k6oQfNP/aRSm1AhiY5K0vJ3ymVkqltA6VUmXA74A7tNaWe/Q/MAxrn9mWLwDfTLH/3cDdAKWlpVRVVXXU9PPGO8cM/W+OD1555WVUazMAobqTdjuOnTaWwIOhyDm3TTUH2XMyzMqVK23P4Vu7W1HA1nWr2XGOHuoLmYaGhh7t+3SpP2GcK1sPnsLvwW7rDvP82Lp9JwA7d2ynqm53pz93hlfzzQUBtq5bfX4b3AX6eh9cDEgf9A2kH3of6YO+QV/uh25bd9VaL0/1nlLquFKqTGtdbRrASctYK6VygaeAL2ut33B8tuV9DiqlfgN8tp122Ab9rFmzdGVlZdrfpavkH6rlJxtWsWB0KZWVs/j9O2vZWnOcaeNHUlk5CoCpja3c/+YLTCrPo7Jy4Tkd72jGQV79+2YqJs9huJkZoersVrIPH2bZpRd38pGqqip6su/TpfBwLQ+8vYqaFs2k8lwqKxcBkLGvBt58g2HDR8L27UydPInKScnmnX2fvt4HFwPSB30D6YfeR/qgb9CX+yG99drzxxPAHebjO4DHEzdQSvmAvwP/p7X+S8J7ZeZ/haFf3tKtre0ik8vzuHGMl29eOwmAqDYc5cWOMs8FWT7+7645/PrO2ed8vDnDjdK4b+6vsV8LhiMEfBKQ19cpzY1JYJwSC0uXbhX8yJS+FARBEIRup7cM5PuBy5RSu4Hl5nOUUrOUUg+Z29wMLAbuVEptMP+mme89opTaDGwGioFv9WzzO4fbpbhmhM82fiydcV5mvB508ZgBFJ1jijeAkQOyyAl42Hykzn6tuTVCwNtb3Sx0lqIsnx2Q59QLW6KYplZDaiEGsiAIgiB0P70S2q61rgGWJXl9LfBh8/Hvgd+n2P/Sbm1gNzFlcB4v7zrZbRkllFIMK8rk0Olm+7WWUJQMSenW5/G4XRRn+zlRHyTfMYGytORWtcUMMZAFQRAEoduR3E89yCeXj2HByGJmDC3otmPkZ/hsYwqgJRyRnMcXCKW5AU7UB+NKjluFQpqCUhFPEARBEHoKWXvvQdwuxfyRRd16jEyfm0aHgdzcGiHgEQP5QsDyHBc6So67bA+ySCwEQRAEoacQA7mfkeX32HpVgJZwVIL0LhCsIM4ShwQnpkEWiYUgCIIg9BRiIPczMn1u25gCI79ywCPdfCEwwAzUHOIoJGN5kK1VgUyRywiCIAhCtyOCxn5Glt9DY9DhQQ6JBvlC4dOXjWVwQSYzh8U06pYGuSEYxud24Umzkp4gCIIgCOkjBnI/I9PnpjkUIRrVuFyKYDgqad4uEIYWZfLZK8bGvWYZyDWNreRKuWhBEARB6BHEcupnWEFczSGzhHU4Gld4QriwsPqutinECLM6oiAIgiAI3YsYyP0MKw2YVXktGIrgFw3yBUu2P7bIM2KAGMiCIAiC0BOI5dTPsDzITUGHB1kkFhcsmf6Y93+4eJAFQRAEoUcQy6mf4ZRYhCNRwlEtEosLmCxHYRBnfmRBEARBELoPMZD7GVbGiuZQhNZIFEAkFhcwbpeyH+cEJEhPEARBEHoCsZz6GRmmgdzSGiEYEgO5P5EbkKQzgiAIgtATiOXUz8hwSCyCYdNAljzI/QLxIAuCIAhCzyAGcj8jM85ANgL1xIN8YWPlQs4RD7IgCIIg9AhiOfUzLA1yU6vDgyxBehc0XrN6XrYYyIIgCILQI4iB3M+wNcihCE2thgc5wyfdfCFzw4zBgHiQBUEQBKGnkBG3n2FrkFsjnGlsBaAgU9KDXcjcd+1EPnv5GFkJEARBEIQeQlyL/YyAJ6ZBrhEDuV/gcbsoyvb3djMEQRAE4aJBDOR+hsulCHhdNIciVNc2AzAwL9DLrRIEQRAEQbhwEIlFPyTD66a5NUJ9S5jCLJ8duCcIgiAIgiB0jBjI/RDLQA5FomT7pYsFQRAEQRDSoVckFkqpQqXUC0qp3eb/ghTbRZRSG8y/JxyvD1dKrVFK7VFK/VEpJSJbBwGvm5ZwlMbWiJ0XWRAEQRAEQegcvaVB/iLwotZ6NPCi+TwZzVrraebfexyvfwf4odZ6FHAG+FD3NvfCwut2EY5EaWoNkyUeZEEQBEEQhLToLQP5WuBh8/HDwHWd3VEppYBLgb90Zf+LAa9HEYpEaQyKB1kQBEEQBCFdlNa65w+qVK3WOt98rIAz1vOE7cLABiAM3K+1/odSqhh4w/Qeo5QaAjyjtZ6U4lh3A3cDlJaWznzssce65TuloqGhgezs7B495n2rm8nwKmpbopRmubhnumSx6I1+EOKRPuh9pA/6BtIPvY/0Qd+gN/ph6dKl67TWszrartvW35VSK4CBSd76svOJ1lorpVJZ6cO01keUUiOAl5RSm4G6dNqhtX4QeBBg1qxZurKyMp3dz5mqqip6+pg/3bEat0tRG2li2KBCKiun9ejx+yK90Q9CPNIHvY/0Qd9A+qH3kT7oG/Tlfug2A1lrvTzVe0qp40qpMq11tVKqDDiR4jOOmP/3KaWqgOnAX4F8pZRHax0GBgNHzvsXuIDxehTBUJSm1giZfpFYCIIgCIIgpENvaZCfAO4wH98BPJ64gVKqQCnlNx8XA5cA27ShCVkJ3Nje/hczXrfL1CCHyfJJkJ4gCIIgCEI69JaBfD9wmVJqN7DcfI5SapZS6iFzm/HAWqXURgyD+H6t9TbzvS8An1ZK7QGKgF/1aOv7OF63i5ZQlGA4SqYYyIIgCIIgCGnRK9aT1roGWJbk9bXAh83HrwOTU+y/D5jTnW28kPG5XdQ1hwDIEomFIAiCIAhCWvSWB1noRrxuRW1zKwAZkuZNEARBEAQhLcRA7odYEgtANMiCIAiCIAhpIgZyP8TriXVrwCseZEEQBEEQhHQQA7kf4nM7DWTpYkEQBEEQhHQQ66kf4nUr+7HfIx5kQRAEQRCEdBADuR/idXiQ/eJBFgRBEARBSAuxnvohTgM5IB5kQRAEQRCEtBADuR/i84gHWRAEQRAEoauI9dQP8bhiGmTJYiEIgiAIgpAeYiD3Q+I0yB7pYkEQBEEQhHQQ66kfInmQBUEQBEEQuo4YyP0QX1yaN+liQRAEQRCEdBDrqR/ilFg49ciCIAiCIAhCx4iB3A9xGshKiYEsCIIgCIKQDmIg90OcBrIgCIIgCIKQHmJJ9UN8HvEaC4IgCIIgdBUxkPsh4kEWBEEQBEHoOmJJ9UPEQBYEQRAEQeg6Ykn1Q8RAFgRBEARB6DpiSfVDfGIgC4IgCIIgdJlesaSUUoVKqReUUrvN/wVJtlmqlNrg+GtRSl1nvvdbpdR+x3vTev5b9F28EqQnCIIgCILQZXrL1fhF4EWt9WjgRfN5HFrrlVrraVrracClQBPwvGOTz1nva6039EirLxBEYiEIgiAIgtB1esuSuhZ42Hz8MHBdB9vfCDyjtW7q1lb1E3ID3t5ugiAIgiAIwgWL0lr3/EGVqtVa55uPFXDGep5i+5eAH2itnzSf/xaYDwQxPdBa62CKfe8G7gYoLS2d+dhjj53Pr9IhDQ0NZGdn9+gxo1pz13NN+N3wi8uyevTYfZXe6AchHumD3kf6oG8g/dD7SB/0DXqjH5YuXbpOaz2ro+26zUBWSq0ABiZ568vAw06DWCl1RmvdRodsvlcGbAIGaa1DjteOAT7gQWCv1vqbHbVp1qxZeu3atWl/l3OhqqqKysrKHj0mwOMbjjBxUB6jSuQGAL3XD0IM6YPeR/qgbyD90PtIH/QNeqMflFKdMpA93dUArfXyVO8ppY4rpcq01tWmsXuinY+6Gfi7ZRybn11tPgwqpX4DfPa8NLofce208t5ugiAIgiAIwgVJb2mQnwDuMB/fATzezra3An9wvmAa1ZY84zpgSze0URAEQRAEQbgI6S0D+X7gMqXUbmC5+Ryl1Cyl1EPWRkqpCmAI8HLC/o8opTYDm4Fi4Fs90GZBEARBEAThIqDbJBbtobWuAZYleX0t8GHH8wNAG62A1vrS7myfIGzFxbsAAAdUSURBVAiCIAiCcPEiCXMFQRAEQRAEwYEYyIIgCIIgCILgQAxkQRAEQRAEQXAgBrIgCIIgCIIgOBADWRAEQRAEQRAc9Eqp6d5CKXUSeKeHD1sMnOrhYwptkX7ofaQPeh/pg76B9EPvI33QN+iNfhimtR7Q0UYXlYHcGyil1nampKHQvUg/9D7SB72P9EHfQPqh95E+6Bv05X4QiYUgCIIgCIIgOBADWRAEQRAEQRAciIHc/TzY2w0QAOmHvoD0Qe8jfdA3kH7ofaQP+gZ9th9EgywIgiAIgiAIDsSDLAiCIAiCIAgOxEAWBEEQBEEQBAdiIHcjSql3KaV2KqX2KKW+2NvtuRhRSh1QSm1WSm1QSq3t7fZcLCilfq2UOqGU2uJ4rVAp9YJSarf5v6A329jfSdEHX1dKHTGvhw1Kqat6s439HaXUEKXUSqXUNqXUVqXUJ8zX5VroQdrpB7keegilVEAp9aZSaqPZB98wXx+ulFpj2kl/VEr5erutFqJB7iaUUm5gF3AZcBh4C7hVa72tVxt2kaGUOgDM0lpLQvgeRCm1GGgA/k9rPcl87bvAaa31/eaEsUBr/YXebGd/JkUffB1o0Fp/rzfbdrGglCoDyrTWbyulcoB1wHXAnci10GO00w83I9dDj6CUUkCW1rpBKeUFXgM+AXwa+JvW+jGl1M+BjVrrn/VmWy3Eg9x9zAH2aK33aa1bgceAa3u5TYLQI2itXwFOJ7x8LfCw+fhhjAFK6CZS9IHQg2itq7XWb5uP64HtQDlyLfQo7fSD0ENogwbzqdf808ClwF/M1/vUtSAGcvdRDhxyPD+MXJC9gQaeV0qtU0rd3duNucgp1VpXm4+PAaW92ZiLmI8rpTaZEgxZ2u8hlFIVwHRgDXIt9BoJ/QByPfQYSim3UmoDcAJ4AdgL1Gqtw+YmfcpOEgNZ6O8s1FrPAK4EPmYuOwu9jDa0XaLv6nl+BowEpgHVwPd7tzkXB0qpbOCvwCe11med78m10HMk6Qe5HnoQrXVEaz0NGIyxyj6ul5vULmIgdx9HgCGO54PN14QeRGt9xPx/Avg7xkUp9A7HTS2gpQk80cvtuejQWh83B6ko8Evkeuh2TL3lX4FHtNZ/M1+Wa6GHSdYPcj30DlrrWmAlMB/IV0p5zLf6lJ0kBnL38RYw2ozQ9AG3AE/0cpsuKpRSWWZABkqpLOByYEv7ewndyBPAHebjO4DHe7EtFyWWUWbyXuR66FbMwKRfAdu11j9wvCXXQg+Sqh/keug5lFIDlFL55uMMjAQG2zEM5RvNzfrUtSBZLLoRM2XMjwA38Gut9bd7uUkXFUqpERheYwAP8Kj0Qc+glPoDUAkUA8eBrwH/AP4EDAXeAW7WWksQWTeRog8qMZaTNXAA+FeHFlY4zyilFgKvApuBqPnylzD0r3It9BDt9MOtyPXQIyilpmAE4bkxnLN/0lp/0xynHwMKgfXAv2itg73X0hhiIAuCIAiCIAiCA5FYCIIgCIIgCIIDMZAFQRAEQRAEwYEYyIIgCIIgCILgQAxkQRAEQRAEQXAgBrIgCIIgCIIgOPB0vIkgCILQnSilioAXzacDgQhw0nzepLVe0A3HnA58XGv9ofP0eR/HaOuvz8fnCYIg9CaS5k0QBKEPoZT6OtCgtf5eNx/nz8C3tNYbz9PnZQKrtNbTz8fnCYIg9CYisRAEQejDKKUazP+VSqmXlVKPK6X2KaXuV0q9Xyn1plJqs1JqpLndAKXUX5VSb5l/lyT5zBxgimUcK6WWKKU2mH/rHRUoP2d+xial1Dcc+3/AfG2jUup3AFrrJuCAUkrK9QqCcMEjEgtBEIQLh6nAeOA0sA94SGs9Ryn1CeAe4JPAA8APtdavKaWGAs+Z+ziZRXxZ3c8CH9Nar1JKZQMtSqnLgdHAHEABTyilFgM1wH8CC7TWp5RShY7PWQssAt48r99aEAShhxEDWRAE4cLhLasUrlJqL/C8+fpmYKn5eDkwQSll7ZOrlMrWWjc4PqeMmMYZYBXwA6XUI8DftNaHTQP5cozyrwDZGAbzVODPWutTAAklkk8A4879awqCIPQuYiALgiBcOAQdj6OO51Fi93MXME9r3dLO5zQDAeuJ1vp+pdRTwFXAKqXUFRhe4//WWv/CuaNS6p52PjdgfrYgCMIFjWiQBUEQ+hfPY8gtAFBKTUuyzXZglGObkVrrzVrr7wBvYXiBnwPuMiUXKKXKlVIlwEvATWbmDRIkFmOIl24IgiBckIiBLAiC0L+4F5hlBtFtA/4tcQOt9Q4gzwrGAz6plNqilNoEhIBntNbPA48Cq5VSm4G/ADla663At4GXlVIbgR84PvoS4IVu+2aCIAg9hKR5EwRBuAhRSn0KqNdaP3SePm868Gmt9e3n4/MEQRB6E/EgC4IgXJz8jHhN87lSDHzlPH6eIAhCryEeZEEQBEEQhP/fjh3TAAAAAAjq39rXEJDCCeMgAwDACGQAABiBDAAAI5ABAGAEMgAATPag5J/p2LKaAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "batch_with_data.show_ecg('A00001')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Any other action can be applied to ```batch_with_data``` in the same way as ```load```. For example, consider action [```flip_signals```](https://analysiscenter.github.io/cardio/api/cardio.batch.html#cardio.batch.EcgBatch.flip_signals). It flips signals whose R-peaks are directed downwards.\n", "\n", "Note that ```flip_signals``` modifies batch inplace, so we create copy of the batch:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "original_batch = batch_with_data.deepcopy()\n", "changed_batch = batch_with_data.deepcopy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then apply ```flip_signals```:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "changed_batch.flip_signals()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can compare results. R-peaks of the signal with index 'A00013' were originally directed downwards:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "original_batch.show_ecg('A00013')\n", "\n", "changed_batch.show_ecg('A00013')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For further analysis we apply ```flip_signals()``` to the ```batch_with_data```:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "batch_with_data = batch_with_data.flip_signals()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Actions in CardIO" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "CardIO contains various actions for ECG processsing. Here are some of them:\n", "\n", "* convolve_signals()\n", "* band_pass_signals()\n", "* random_split_signals()\n", "* unstack_signals()\n", "* resample_signals()\n", "* rfft()\n", "* spectrogram()\n", "* cwt()\n", "* apply_transform()\n", "* drop_labels()\n", "* rename_labels()\n", "* rename_channels()\n", "* ...\n", "\n", "In the following sections we will show how to use them and how these actions affect batch and its components." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [```convolve_signals```](https://analysiscenter.github.io/cardio/api/cardio.batch.html#cardio.batch.EcgBatch.convolve_signals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```convolve_signals()``` action convolves signal with the specified kernel.\n", "\n", "Convolution is a mathematical operation on two functions (or signals) that produces a third function. This is a common operation to remove noise from signals." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$(f*g)[n] = \\sum_{m=-\\infty}^{\\infty} f[m]g[n-m] = \\sum_{m=-\\infty}^{\\infty} f[n-m]g[m]$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's convolve signals in batch with gaussian kernel, which look like this:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/master/.local/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:424: MatplotlibDeprecationWarning: \n", "Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n", " warn_deprecated(\"2.2\", \"Passing one of 'on', 'true', 'off', 'false' as a \"\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import cardio \n", "\n", "kernel = cardio.kernels.gaussian(size=11)\n", "\n", "plt.plot(kernel)\n", "plt.grid(\"on\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "original_batch = batch_with_data.deepcopy()\n", "changed_batch = batch_with_data.deepcopy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we will add some normally distributed noise to the signal and apply ```convolve_signals()```:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "siglen = original_batch[\"A00001\"].signal.shape[1]\n", "\n", "noise = np.random.normal(scale=0.01, size=siglen)\n", "\n", "original_batch[\"A00001\"].signal += noise\n", "changed_batch[\"A00001\"].signal += noise\n", "\n", "changed_batch.convolve_signals(kernel=kernel)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now you can see how this transformation affected the signal:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAEYCAYAAABBfQDEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAIABJREFUeJzsnXl4HNWV9t9TvWizZNmW5X238b6B2QmIQDIwQEIgC2QbMplhCCHJN5kkQ2YmyUwmk8kymZCFkIVAmGwESAIkMTsIvO/7vsi2LNtarV3dXcv9/qiqVnV3datt1a1Sy+f3PHosdbdU11XVt9977nvOISEEGIZhGIZhGIYxUYIeAMMwDMMwDMMMJVggMwzDMAzDMIwDFsgMwzAMwzAM44AFMsMwDMMwDMM4YIHMMAzDMAzDMA5YIDMMwzAMwzCMAxbIDMMwPkJE04lIEFE46LEwDMMw7rBAZhiGsSCiY0R0Y9DjKASIqJaIYkTU7fj6k+P5CiJ6iIhOWM8dsX6ucrzmLiLaQEQ9RNRkfX8/EVEw/yuGYRgTFsgMwzDM+fKAEGKE4+s2ACCiKIDXACwEcBOACgBXAmgFcJn1mn8C8D0A3wYwHsA4APcBuBpA1O//CMMwjBMWyAzDMHlARLcS0XYiaieitUS0xPHcg1aEtIuI9hLRexzPhYjof4iohYiOArhlgOMcI6LPEdFOIuogot8RUbH13Cgi+jMRNRPRWev7yY7frSWir1nj6yaiPxHRGCL6NRF1EtEmIprueP08InqFiNqI6AARvd+j0/VRAFMBvEcIsVcIYQghmoQQ/ymEWElEIwF8FcD9QohnhBBdwmSbEOJDQoi4R+NgGIY5L1ggMwzDDAARLQfwGIB/ADAGwE8APE9ERdZLjgB4G4CRAP4DwK+IaIL13N8DuBXAcgArALw3j0O+H2bkdQaAJQDusR5XADwOYBpMAdoH4Idpv3sXgI8AmARgFoB11u+MBrAPwFes/1MZgFcA/AZAtfV7PyKiBdbzHySinXmM1Y0bAbwohOjO8vyVAIoAPHeef59hGEYqLJAZhmEG5l4APxFCbBBC6EKIJwDEAVwBAEKIp4UQp6xI6e8AHIJlJYApdh8SQtQLIdoA/Hcex/u+9ffaAPwJwDLrOK1CiN8LIXqFEF0A/gvAdWm/+7gQ4ogQogPACwCOCCFeFUJoAJ6GKdQBU7QfE0I8LoTQhBDbAPwewPusY/1GCLEEufm+FVG3v/7TenwMgNM5fq8KQIs1JgCAFfVuJ6I+Irp2gOMyDMNIhbOoGYZhBmYagL8hok85HosCmAgARPRRAJ8FMN16bgRMEQjrNfWO3zuex/HOOL7vdRynFMB3YUaXR1nPlxNRSAihWz83On63z+XnEY7/0+VE1O54Pgzgl3mMz+bTQohHXR5vBTDB5XHn81VEFLZFshDiKgAgopPg4A3DMAHDkxDDMMzA1AP4LyFEpeOrVAjxWyKaBuBnAB4AMEYIUQlgNwC7EsNpAFMcf2vqIMbxTwDmArhcCFEBwI60nk/Vh3oAb6b9n0YIIT4xiPHZvArgrywbhxvrYEbg3+3BsRiGYTyHBTLDMEwqESIqdnyFYQrg+4jocjIpI6JbiKgcQBkAAaAZAIjoYwAWOf7eUwA+TUSTiWgUgAcHMbZymFHgdiIaDctPfJ78GcBFRPQRIopYX5cS0fxB/E2bX8IU4L+3EgEVK1nwX4jor4UQ7TC92j8iovcSUbn1mmUwzyfDMEygsEBmGIZJZSVMEWp//bsQYjPMZLsfAjgL4DCsxDkhxF4A34EZFW0EsBjAGsff+xmAlwDsALAVwB8GMbaHAJQAaAGwHsCL5/uHLA/zO2Em552Caev4JszkORDRh4hozwB/5odpdZC3WH87DjNRbz/MRMBOABth2k42WK/5FkxbyhdgnrdGmMmP/wxg7fn+vxiGYbyAhBBBj4FhGIZhGIZhhgwcQWYYhmEYhmEYByyQGYZhGIZhGMYBC2SGYRiGYRiGccACmWEYhmEYhmEcFFyjkKqqKjF9+nTfj9vT04OyMq4+NFzh6zt84Ws7vOHrO7zh6zu8CeL6btmypUUIMXag10kVyER0E4DvAQgBeFQI8Y2056cCeAJApfWaB4UQK3P9zenTp2Pz5s2SRpyd2tpa1NTU+H5cxh/4+g5f+NoOb/j6Dm/4+g5vgri+RJRPN1N5FgsiCgF4GMDNABYAuJuIFqS97N8APCWEWA6zFuePZI2HYRiGYRiGYfJBpgf5MgCHhRBHhRAJAE8is62oAFBhfT8SZrF6hmEYhmEYhgkMaY1CiOi9AG4SQvyd9fNHAFwuhHjA8ZoJAF4GMApme9EbhRBbXP7WvQDuBYBx48Zd8uSTT0oZcy66u7sxYsQI34/L+ANf3+ELX9vhDV/f4Q1f3+FNENf3+uuv3yKEWDHQ64JO0rsbwC+EEN8hoisB/JKIFgkhDOeLhBA/BfBTAFixYoUIwo/EPqjhDV/f4Qtf2+ENX9/hDV/f4c1Qvr4yLRYNAKY4fp5sPebk4wCeAgAhxDoAxQCqJI6JYRiGYRiGYXIiUyBvAjCHiGYQURRmEt7zaa85AeAGACCi+TAFcrPEMTEMwzAMwzBMTqQJZCGEBuABAC8B2AezWsUeIvoqEb3Letk/Afh7ItoB4LcA7hGyTNEMwzAMwzAMkwdSPchWTeOVaY992fH9XgBXyxwDw1yI7G7owORRJagsjQY9FIZhGIYpOLjVNOM5XTE16CFc0PQmNNz6g9V44Dfbgh4KwzAMwxQkLJAZT3ly4wks/veXcaK1N+ihXLBsPd4OADje1hPwSBiGYRimMGGBzHjK/60zOzie7ugLeCQXLifPmouTCRUlAY+E0Q0BTqtgGIYpPFggM55iC+O4ZgzwSkYWfaoOADBYmAXOsq++jDsfWRv0MBiGYZhzhAUy4ymaYYqyrpgW8EguXHoTpkC2rwUTDEIIdMU0bD3RHvRQGIZhmHOEBTLjKZGQeUt1cqJeYMSsCLKqcxQ/SJq74kEPgWEYhjlPWCAznhJWCABXsgiSPiuC3BPnKH6QnO6IBT0EhmEY5jxhgcx4ikKmQO7sY3EWFL1WBLmbBXKg2JF8hmEYpvBggcx4SkxjcRY0MSuCHFfZYhEkMU5UZRiGKVhYIDOeYm/vs/81OOwqFnG+BoHijCAbnDDJMAxTULBAZjzDMESyvFuCo2eB0etYpHAN3uBwCuSeBO+oMAzDFBIskBnPsO0VAEeQg8SOIAthNqpggsFZC9xetDAMwzCFAQtkxjP6HCIgwQI5MJzRe1VngRwUcUcEmXdUGIZhCgsWyIxnOKNkLAiCQzP6zz0vVIIjpjoXKnwdGIZhCgkWyIxnOLeUExy5DAzNce55oRIcTg8ydzVkGIYpLFggM57hjJIlNPZcBoUzasyRy+BIWTDyQsU3dENgxhf/gl+tPx70UBjGMzTdwO0Pr8EbB5qCHsoFAwtkxjNUnb2vQwFnBJkFcnA4I8h8HfyjqSsGIYBvvrA/6KEwjGc0dcWxvb4dn/3d9qCHcsHAApnxDDtKFgkRR8wCRNMNFEfMtzYLs+BwVnVhi4V/nGo3W3wXR0MBj4RhvKO5Kw6gv1stIx8WyIxn2Fv7ZUVhFsgBohoCpdEwACChsTALCtVx7lV+P/jG6Y4+AEBJhAUyM3xosgWywgLZL1ggM55h2yrKomGOXAaIphsotaJnXMUiOFSuJhIILZaQiIRYSDDDh8ZOc2eE72r/YIHMeIYdNR5RFE5JUGL8RdNFUiDzQiU4nE1a2JPvH31WeT2eg5jhRFfM7MbJbi3/YIHMeIaatFiEWJgFiGoYKLEsFry1HxzOZEmN3w++YSdHss2LGU7Y9zVbkP2DBTLjGarTg8yCIDA0XaA0whaLoNEMI7nNz9fBP+zkSF6kM8MJvq/9hwUy4xlxh8WCozfBIISAZjgtFrwfFxSaLlAc4evgN3HLYsFzkFw03cAv1x9HnGve+4J9X8dVvq/9ggUy4xn2yrYkGuKyVgFhn/cSO0mPRUJgqI6FClss/CNpseBzLpVX9jbiS8/uxv++fDDooVwQ2Pd1XNMhBH+++gELZMYzbDFWEgmlJCgx/mH7Xu3Ipc4TaWDohpEsNcbbov7Rp9pb0QIGz0PSsL2w6+vagh3IBYJ9XxuC66r7BQtkxjNsEVAaNQUyr3L9xy4tZjcK0Q0WZkGh6iKZLJlgi4VvODsYchRZHnZVhViCLRZ+4LyvuUKLP7BAZjzD9lnaUTOOIvuPXbWiOGxv7fM1CArdECixFipssfCPmMOjyQJZHh19KgAgEuayCn6Qcl+zQPYFFsiMZ9ir2iJLIPM2kP+ke5B5kRIcmm4krwNbLPwjJYLMQkIanVYEOaSwjPCD1AgyR+39gO9sxjNU3UA0pCRLW7E48x9biBXzIiVwVF0kI/lssfCPmMaRNj/otCLIcZXFmh8472uuZOEPLJAZz1A1A+EQJSMKLM78x7ZUFIVtDzJfg6DQDWG9H4iTxXwkzhFkX+iMmQK5jwWyL8TZg+w7LJAZz9AMgbBCCCscQQ4KzeAI8lBBNQyEQwpCRHwdfMRpZ2EPsjzsnJM+TtLzhbhmJMtGssXCH1ggM56h6gYiIQUhSyBzYpL/qOll3riKRWDohkBEsSLIXNHFN1S9v/40R5DlYScEs0D2B1U3MKLIqorD97UvsEBmPEM3BEKOCDJHzfynvw4y21yCRtMFQoqCsEJcTcRHNN1AmSUkeCtaHnakni0W/uAUyHxf+4NUgUxENxHRASI6TEQPZnnN+4loLxHtIaLfyBwPIxdVF4iEFIRD7H8NCrsOcpGVHKazMAsMc0eFoCjEkXwfUQ2BMo4gS8e2r2iG4CotPqDpgitE+UxY1h8mohCAhwG8A8BJAJuI6HkhxF7Ha+YA+CKAq4UQZ4moWtZ4GPlohpmkxxHk4EiPIHMnveCwk/TCCvF18BFVN1AaLQLAHmSZOEVxb0LHyBLekJZJQje4rrrPyLyjLwNwWAhxVAiRAPAkgHenvebvATwshDgLAEKIJonjYSSj6WaSXiiZpMdvYr+xJ85ISIFCHMUPElU3EFZMTz5fB//QHB5knoPkoTp2pzhSLx9NF8m66hx88geZAnkSgHrHzyetx5xcBOAiIlpDROuJ6CaJ42Ekoxlmkh5HkINDtc55JEQIKwpfgwCxq7qE2IPsK6pu9Fdx4fMuDWcEmS0W8tEMI9mllu9rf5BmsTiH488BUANgMoC3iGixEKLd+SIiuhfAvQAwbtw41NbW+jxMoLu7O5DjFhJnmmLoiwvs27sHALBh4yacqQgFPKr8GC7Xd3uT2d1qx/ZtAAzUHTuB2tozwQ4qYIK6tglNx6mGk1ATGk6dPoPa2rO+j+FCIP36JjQDPZ3mud6xazeizfsDGtnw5mxHX/L71WvXobpUTrxtuMzNg0EIAVUX6DzbCgDYtXsPytoOBDwqbxjK11emQG4AMMXx82TrMScnAWwQQqgA6ojoIEzBvMn5IiHETwH8FABWrFghampqZI05K7W1tQjiuIXEY0c3An0qli2dDWzbjGXLL8HSKZVBDysvhsv1je0+DWzdissvXYGiresxcdJk1NQsCHpYgRLUtTVe+gtmTp+GPZ2nUFVdiZqa5b6P4ULAeX0NQ0C8uBKTxldjW9NpzJu/ADVLJwY7wGFKdEst0NUDALh4xWWYXT1CynGGy9w8GFTdAF56AdMmTcDGMycxZ+481FwyOehhecJQvr4yLRabAMwhohlEFAVwF4Dn017zLMzoMYioCqbl4qjEMTES0XTDqvvKJcaCwvYFRkMKQiGunhAUhiFgCCQ76fF7wR/sKi4lyTrgfN5loeoieZ7ZYiEX+/yWRPm+9hNpAlkIoQF4AMBLAPYBeEoIsYeIvkpE77Je9hKAViLaC+ANAJ8XQrTKGhMjF00XKVUs+E3sP3YnvbDlBWdhFgz2ebc7S3KraX9Ib5TD9788VN1AWRELZD+w7+vkgoQDH74g1YMshFgJYGXaY192fC8AfNb6YgoczTBQGg73d9LjN7Hv2BOpnRzGi5RgcC5UFG417Rt2FZdiLoclHbOcXhhAggWyZNTkfc0RZD/hwoWMZ2gGR5CDxs5ujoQUhFiYBUZKBDnECxW/SI+08f0vj4RmOFp683mWiT2v2xYLlatY+AILZMYzVF0k674CXIomCPojl2R5kPkaBIGWEslX+Dr4RDLSxl5N6ai6SLb05giyXJIe5AjX9/YTFsiMZ2hWa90wJ+kFhh1ZiCgKwizMAsNpsQhxwxbfSHaSDHMEWTamxYI9yH6QLpA5guwPLJAZzzAtFgp30gsQ23NpV09gYRYMzggyL1T8I5GR7c9zkAwMQ0AzBAtkn7AXekWWt57nE39ggcx4hmYYCCuESIg76QVF0vtqecE5UTIYkgI5xK2m/cS+35NJenzepWBXUSiLmhaLBEc0pWK38i6yd0Z4QeILLJAZz9D0/ta6AK9yg8CO5EQUFmZBkrRY2K2meaHiC+kWC52FmxTsLf7SIhZsfmAv9KJhMwDFCz9/YIHMeIaqC6v+rl1iid/EfqPpAgoBikJcBzlAnJH8kELgt4I/JNLKYfH9LwdVS40gs8VCLvYCJGLtSPF97Q8skBnP0AwzSS8U4ghyUKiGgXDIfFtzBDk4+j3IZsMW9sL6Q0qZQ47cS8MWxKVssfAFe+EXVhREFIWDTz7BApnxDN0q8xZW2IMcFKomELHOf5gn0sBwWiwUhfg6+ER6kirPQXKwBVuyk57GCxGZ2PNHNGwGoHjh5w8skBnPMKOXxFUsAkQzDETCHEEOmmRHQytZ0hB8Hfwg4diKjijEHmRJJD3IbLHwBdURQQ4rCi/8fIIFMuMZdpIeR5CDw27WAoC3mAPEXpiwZ9Bf+i0WHEGWSb/Fgsu8+UH6gpuTIv2BBTLjCUIIlzrI/OHkN3azFoAjyEFif4CFFK5H7Sf2gjASUhAOcf1pWdhlxzgZ0h/sBUg0pCDMVSx8gwUy4wn2GzaicCe9IDEXKbYHmaDz1n4gqEZqJJOFmj8kOILsC7ZgK4ooUIgrFsnG2ZkzzDkNvsECmfEEWwCEHB5k3gbyH1U3EHFaLHgiDQTbfx9SFISIBbJfaCleTa4eIgt7yz9qRepVPs9SUR0LP94Z8Q8WyIwnOBtUsAc5ODTdEUEOsTALiqRnUCHeEvWRpAc5zN5vmaicDOkrzvMdVog93z7BApnxBM2RRKAoBCL2IAeB2e7bjiBzpCEo0pP0DL4OvpCsYmE3ymHhJoX+aiFsZfEDjRfcgcACmfEE1eGRAsxIMr+J/UfVRTJJjzvpBYfqTNIjvg5+0V8HmVuty8SuexwJKYiEFI5oSiYZQQ4rCPFnq2+wQGY8Ibm1qXAFhSDRuJPekCC13JjCEWSfcHo1IyGFyxxKIulBDvNCxA+S97ViWlo4v8cfWCAznpBM0lMc0Uve3vQd1apFDdgRZJ5Ig8D5fuAtUf9QHWXeWLjJI8WDHFKSAo6Rg8qWlkBggcx4gnPCBMxqFpxB7j9mHWTzGigsEALDKdQUrmLhGyleTRYS0nB6kMPc+lg6zrrqkZDCEWSfCOd6kogmA7gLwNsATATQB2A3gL8AeEEIwVeJAdBfsSLM/tdAUXXB12AIYAtiW6hxPWp/UHUDRNygRTZ2o5Aod4r0BdUQiIYUEPF97SdZBTIRPQ5gEoA/A/gmgCYAxQAuAnATgH8logeFEG/5MVBmaOPsFQ+w/zUoVEcEma9BcPSXeevf6hdCgIgCHtnwRtUFIoopJMKKwjYvSaSWeeOIpmxUzUgGPiIhYkuLT+SKIH9HCLHb5fHdAP5ARFEAU+UMiyk0+sta2dFLzrQNAs1IrWLBAjkY+qspUErrdftDjpGDpvcLCV4gysNZVSEc4nwT2WhGf24J39f+kcuDfLNlsXBFCJEQQhyWMCamALFXtCF+EweKpqfWQeZFSjBojiS9pEBmm4V0nDso7I2VR0pnN7ZYSCehG4iG7fuaOxf6RS6BPBHAOiJaRUT3E9FYvwbFFB5aWpIeT5rBkO5B5kVKMPSXeevvLMnXQj6qYweFvbHysD3IEcVsNc0LEbk4Ax88r/tHVoEshPhHmBaKfwOwGMBOInqRiP6GiMr9GiBTGGiOpCTAjiDzpOk3mmEgkuYDFxy59B3dSE0WA7j1uh84q7iwB1keqm4grJhdU7mkp3w0XSASdtgX+Xz7Qs4yb8LkTSHEJwBMBvBdAP8PQKMfg2MKB9XRwQowhQEnEviPlhZBBjhyGQRqmmcQADcL8QHeQfGHTCsLn2eZJPT+wEeErUO+kbPMmw0RLYZZ7u0DAFoAfFHmoJjCQ0+LIIdD/OEUBClVLEL9kctwKMhRXXikesE5guwXqkNIhFhISCO1pb0CTdcCHtHwxhn4CHHE3jdylXmbA1MU3wVAB/AkgHcKIY76NDamgEiWteIqFoHizHbmCHJwaEbqBxrA18EPUiKbHEGWhjNpLMIRZOk472uzhTqfbz/IFUF+EcBvAXwgS7k3hkmiGZlJeuxB9h8z0mB10iOOXAaFpvNCJQgyIm18zqWgaqn11jmiKRfVECn2Ra477Q9ZBbIQYpbzZyKqcL5eCNEmcVxMgeFs8QqwBzkoVMNIqYMMsDALAs0wMhYqfB3k4xQSHEGWR6oHmcuOyUbVDERD/fZFXvj5w4AeZCL6BwD/ASAGwL4qAsBMieNiCgw1rcxbSKFkKSDGH8yKFY5uhta1YJHgPykR5BALZL9wCgmuAy4Ppwc5wgsR6WhGapk3vq/9IZ8kvc8BWCSEaJE9GKZwsSdIZ6MQfhP7i+ro3gZwBDlIUj3ISvIxRi4ZQoK3oqWQSGlpz2XHZKPqAiXR/vKF3LreH3KWebM4AqBX9kCYwkY10pP0CAbX3/UVWyBHQ6nVE1QWCb6jGaK/mgJbLHxDZQ+yL6hpSXo8x8jFrM6SGvjge1s++USQvwhgLRFtABC3HxRCfFraqJiCI9lJT+GoQlBoGZVErPq7vFDxHU03UnZTABbIfqDqRnKBGOFSk9JIr4PM51kuKfXtHda5CJfvlEo+EeSfAHgdwHoAWxxfA0JENxHRASI6TEQP5njdnUQkiGhFPn+XGXq4iTOeNP3FTpQJh7j+btCoemqyGMAC2Q9Sq1iwB1kWqpZaB5kjyHJJL19oP8bIJZ8IckQI8dlz/cNEFALwMIB3ADgJYBMRPS+E2Jv2unIAnwGw4VyPwQwd7A+ilNI/nNnsK/YiJZIWueQObv6jG0ZGJz1+P8jHrOLCVSxkk9ANlEdM+cBJY/Jx3te8I+Uf+USQXyCie4loAhGNtr/y+L3LABwWQhwVQiRgNhp5t8vr/hPAN2FWyWAKFNti4dxW5vevv/RH8VMjDfzh5T9ujULY6iKflE6SlkAWfN49x2llCXHZMelozqohIZ7X/SKfCPLd1r/O9tL5lHmbBKDe8fNJAJc7X0BEFwOYIoT4CxF9PtsfIqJ7AdwLAOPGjUNtbW0ew/aW7u7uQI5bKBw+mgAArH7rTRARWprj6O7RC+acDYfre7rbXKQcOrAftV2HsbfRbP+6YeMmNI68cM1qQVzbltY+6AKora3F3lYdALB5yzZ01V2410EWzuvb0xtDS1MjamtrUX/CnJNef6M2uUhhvKG9sxfFeg9qa2txqj4BVTOkvceGw9w8WHr6YmhqPIPa2rM4Uq8CAFatXoNRxfnEOIc2Q/n6DiiQhRAzZByYiBQA/wvgnjzG8FMAPwWAFStWiJqaGhlDykltbS2COG6hsDG2H+G6o7j++usBAH9p3oFjPS0Fc86Gw/U9cKYLWP0WlixaiJolE2DsbwS2bcbyiy/B0imVQQ8vMIK4tg/vX4uwoqCm5goUH20FNq3H4iVLcdXsKl/HcSHgvL7K6lcxZfI41NQsxl4cBg4dwNVvuxbFnM3kKdHNtZg4fiRqapZju3YQ4ughXHvtdVAkLESGw9w8WOjNlzF9yiTU1CxE06Z6YM9OXHbFlZhUWRL00AbNUL6+WZcfRHRNrl8kogoiWpTjJQ0Apjh+nmw9ZlMOYBGAWiI6BuAKAM9zol5h4txSBrjEUhCk10Hm+rvB4Sw3lkzS461+6XA5LH9IaJkdO/k8y0PVM3MadK4SJZ1cEeQ7iehbAF6EWbWiGUAxgNkArgcwDcA/5fj9TQDmENEMmML4LgAftJ8UQnQASIZTiKgWwOeEEJvP63/CBIrZOax/vRXiBBnf6U+UtCZSrr8bGLohXJL0+DrIRtMNRxUXqxwWCwnPcXqQ7fOtGQaieaU1MeeK5qyKE+KkX7/IKpCFEP9oJePdCeB9ACYA6AOwD8BPhBCrc/1hIYRGRA8AeAlACMBjQog9RPRVAJuFEM979Z9ggkczjJQIclghjpj5jJ0omWw1zdnOgaGmCDWO+PiF2QK5vw4y0F/+kPEO97JjfH/LQAiBhO5soc7zul/k9CALIdoA/Mz6OmeEECsBrEx77MtZXltzPsdghgZqRgRZYUHgM2p6LeoQT6RBYRbxT/tA4wWjdMxyWOb5VojLHMrCuRDhOt9ysc8rVyfyH94PYTxB0/s/mABTnPEb2F9sD7L9wWULBN6K8x/NEMktfo74+INZ0q1/B4W93/JI6AYi4dTObho3rpBCZo+B/k56jFxYIDOekJ6kpxB7kP3GFsLpkR2uv+s/bsli/H6QS3KBGE7zfvNOlqcIITJaegMc0ZRFIhn4SJ1PuJOefFggM56gGakWC/Yg+0/SYpGeHMYCwXd0Q2RUE2GBLJekQFZSk5n4vHuLHamPpkU0eZ6Rg5ZlXuf7Wj4DCmQiKiWiLxHRz6yf5xDRrfKHxhQSmqMMDcBdrIIg2WqaW5IGjqo7LBbEETY/0NI8+FzmUA72QjwS5mRIP+jfGUmvYsH3tWzyiSA/DiAO4Err5wYAX5M2IqYgUR1laADeVg4C22LB9XeDR3cki4VCnCzmB+kefC5zKIeEnm7l4h0SmWTsjPD59o18BPIsIcS3AKgAIIToBcB9O5kUNCM1SU/hTFvfSUZ2ODkscDRdJM8/Z537g5peBzx53jmy6SW2YEsvO8aeWDkkdwbTvfU8n0gnH4GcIKLmdNtOAAAgAElEQVQSAAIAiGgWzIgywyRxNkYAOIIcBFpGJz32IAeFWW4stZqIzkJNKlpGZNOO3Ac2pGFJeqQ+maTH84wU1LT69v2frXxjyyZnHWSLr8DspjeFiH4N4GoA98gcFFN4mK0wUzvpAby97yd2BC1dIPM18B/ngpEXi/7Q32rd2kHhjmNSULXUXIf+Tnp8f8tAzZJbwgsS+QwokIUQrxDRVgBXwLRWfEYI0SJ9ZExBoeoCJZFQ8uekKOA3sW9o7FUbEphlsPoFMtuN/KHfYsQLE5kk0pPGkoKNFyIyUNPLvHF1Ft/IKpCJ6OK0h05b/04loqlCiK3yhsUUGqpuoLy4/3YKcVTBd9Kz+O2APl8Df8nW+YrrUcslWxUXvv+9Jd2DzB57ufQnX3MnPb/JFUH+jvVvMYAVAHbAjCAvAbAZ/VUtGCal9SjAGeRBoGY0CrEiyBzZ8RX7g8sWaCzU/CGR7sHnOUgK6R5ktljIJZG0tHBddb/JmqQnhLheCHE9zMjxxUKIFUKISwAsh1nqjWGSqOmtptn/6jtZC8rzJfAVLUs1BbYbySUjSY+3oqWQIZDZYiGVbB1SuWqIfPKpYjFXCLHL/kEIsRvAfHlDYgoRTTdSI8gsCnzHnjDTI5ec7ewvenKhklaPlxeLUslMZuJImwwSGUl6tmDj8ywDbgAVHPlUsdhJRI8C+JX184cA7JQ3JKYQSbdYhDmD3HdUXSAaUkDE3sAgUdMatigKQSH+QJNN+nnn+18OSQ9y2D7PvBCRSdI6ZCefcic938hHIH8MwCcAfMb6+S0Aj0gbEVOQJNIsFgr7/3xH043k5An0Rxq4g5u/aGkRZMC8FvyBJhcta6McXqR7SaYHmYMhMkmPIPOCxD/yKfMWA/Bd64thXEm3WHD0xn+0tGYt9tY+XwN/SWadK6mLFV6oyCUp3MIcQZZJRqMQS7CxxUIO6WXeOOnXPwYUyERUB6uLnhMhxEwpI2IKErPuq4sHmd/EvqGmLVIUhUC8te876eX2AHOxwh9ocknvOKbwHCSFRLonNsSReplkS4rk8y2ffCwWKxzfFwN4H4DRcobDFCqqbiQjNwBnkAeBposUUQaYkylfA3+xhXA4LWmVr4Nc+reiuVGITFTNroNsR5A5SU8mWTvp8X0tnQGrWAghWh1fDUKIhwDc4sPYmAJC1Y3khAn0Z5Dzm9g/VCO13TdgesFZIPiLm8UiHFL4OkgmPdLGQkIOGVYWuw4ylx2TgpYl+ZQrRMknH4uFs6OeAjOinE/kmblA0A0BQ6QlJXGSnu9oukhJlATMyZQFgr+k16MGzIUKXwe5qEaqtYWTmeTACxF/SVgR+/TkUz7f8slH6H7H8b0GoA7A++UMhylE0iMKAHuQg0AzjJRtfYC39oMgXUAApljmJD252Fv/RaEQABYSskj3IEe47JhUEsmyeub5JiKe130iH4H8cSHEUecDRDRD0niYAiRZF9OlDjK/if3DTJRMjSDzROo//Z30uMybnyTSFur9zYp4699L0ud7jtTLRbUas9gCGTDvbZWT9KSTTye9Z/J8jLlAcdtS7o/e8JvYL9JL7QGmF5yFmb/Ykcz0mtScdS4X+7zz1r9c+s9zqieWWx/LIaHrCCmUvJ8BK/maPcjSyRpBJqJ5ABYCGElEdzieqoBZzYJhADgtFuxBDhLNcK9iwVv7/qImI8hpH2h8GaSi6gaIHB3H7EY53OLbU+zzbAs2u1Okxje4FBJaagI8wLklfpHLYjEXwK0AKgHc5ni8C8DfyxwUU1gktzZd6iDzm9g/EpqRcg0A3toPAs3Fg6xwBFk6cWsHxW61znOQHBK6SDnPgFnJgs+zHBKakWKvALgqjl9kFchCiOcAPEdEVwoh1vk4JqbASNYfdamDzNFL/9AMgaKwW5IeCzM/SW9YYX7PXnDZqJpAkUs3T96K9pb0kp6AFdFki4UUEnqmQObAhz/kslh8QQjxLQAfJKK7058XQnxa6siYgiFb1j7A0Rs/UXUD5cWpb2ne2vcfVc+0WHCypHwSup5q8+I5SApmx04uJ+kX8SwWCw58yCeXxWKf9e9mPwbCFC5qMkkvs1EIiwL/cPOq8da+/7gtGDniIx9VS60DTmR6Y3kO8pb0lvaAea9zQrYcVN19Z5DnE/nkslj8yfr3Cf+GwxQiybI/zjrIxNEbv3H1qinEyTM+k6zqwhFkX1FdtqLDXMXFc+KaW7UcnmdkkdB013md5xP55LJY/AlA1isghHiXlBExBYdrxIw9yL6TcPEGhhTiLH6fseuTRkPsQfaTuGuZQ77/vSahGRkRzUhISe4kMt6SyLYg4flEOrksFv/j2yiYgsbNYsEeZP9xiyDzROo//XWQHVUsuNW0dNRs5bBYuHmKe1UFtnLJwi1JL6wonHzqA7ksFm/a3xNRFMA8mBHlA0KIhA9jYwoEV4tFstU0T5p+4bbFzFv7/mMLYafFIhwixFV+L8jENdufhZvnxF0iyGZnN55nZOCWW2IGPvi+ls2AnfSI6BYARwB8H8APARwmoptlD4wpHOw3akqSHnuQfcdtK4639v3H3lGJcgTZV9ySx7i6gveYFotQymMRReEyb5LIFrHn+1o+uSwWNt8BcL0Q4jAAENEsAH8B8ILMgTGFQ0Kzy1plepBZnPmHWwSNhZn/9NdBTu2kx15YuaiayKziQrxA9Jq4pqM0miodeKdKHvEsydd8vuUzYAQZQJctji2OwuymNyBEdBMRHSCiw0T0oMvznyWivUS0k4heI6JpeY6bGULYEWSnxSJZpJ/fxL4ghIBqdbhyYnoD+Rr4iR1JCylOy5HCXljJxHUjpQ4ywEJCBm4L8UiIOElPElmrs/D5lk4+AnkzEa0konuI6G8A/AnAJiK6g4juyPZLRBQC8DCAmwEsAHA3ES1Ie9k2ACuEEEsAPAPgW+f1v2ACxa1zGBfp9xe73XemN5BbkvpNQjcjmc5WvCGFF4uycUvSC/EC0XPiaqYHOcx1kKWR0I2UDpEAR+z9Ih+BXAygEcB1AGoANAMoAXAbgFtz/N5lAA4LIY5aSX1PAni38wVCiDeEEL3Wj+sBTD6n0TNDAtW2WDgmzTA3CvGVhJZZWgwAQtwowXc03UhJ0AOsrHO2WEjFjGxmnndepHtL1tbHHNGUQnYPMi9IZDOgB1kI8bHz/NuTANQ7fj4J4PIcr/84sviaieheAPcCwLhx41BbW3ueQzp/uru7AzluIbDnhAoA2LRhHQ4VmW9kYYmBI0frUBtqCGxs+VLo17crYZ7vY3VHUGucSD7efjaG9j5R0P+3weL3tT12Ig4YesoxW1ti6Oo2LujrIAv7+nZ296It1JdyjuN9vTjdGOPz7iGd3X1oa06knNPuzj4kdEg5z4U+Nw+Wnr44mhpPo7a2LflYx9kYutThMa8P5es7oEAmohkAPgVguvP1XjYKIaIPA1gBM0qdgRDipwB+CgArVqwQNTU1Xh06b2praxHEcQuBY2vqgL17cd0112BUWTT5eOiVlZgydRpqauYGOLr8KPTre6YjBrz+GhbOm4uay6cmH3+yfgt6W3pQU3NtgKMLFr+v7ctnd6G07UzKMZ9r3I6GeFtB32NDFfv6hte9hikTq1BTszT5XPm2tzB6TClqalYEOMLhBa16BdOmjEdNzeLkY48f3Yj23gRqaq7x/HiFPjcPFvH6i5gxdQpqavodqr86vgl6Rww1NW8LcGTeMJSvbz5VLJ4F8HOY3uNziek3AJji+Hmy9VgKRHQjgH8FcJ0QIn4Of58ZIthbmOkJMtykwj+SFguXOrC8Fecvmm6k+PEBq6MbXwapJFyS9Nir6T1xVc8o88bl9OSRzdLC97V88hHIMSHE98/jb28CMMeKQDcAuAvAB50vIKLlAH4C4CYhRNN5HIMZAiRcylrZP3NtTH9IJNt9p16DEJe58h1NF4iEM68DL1Tk4tZQgau4eI9rZ7cQe5BlYBhmdaLMDpHsrfeDfATy94joKwBeBpCM8Aohtub6JSGERkQPAHgJQAjAY0KIPUT0VQCbhRDPA/g2gBEAnrYyvk94ad1g/EF1qYMMcATZT+wIckZ2uUKcHOYzCd1AJD2CHCLwWlEuWZPHeA7yDFuwcRULf1CNLDuDHHzyhXwE8mIAHwHwdvRbLIT1c06EECsBrEx77MuO72/Me6TMkEUzDIQUSqn7CpiCmSdNf+hv9+2yFceRHV/RdOFSxYJbHsvGrAPudt75/veKRJZ5hi0WcsgW+IiEFK477QP5COT3AZhplWpjmAwSupFhrwDYJ+Un/RYLjqAFjVvLY+5oKBfdENANgWgo1RvL97+3xFVbsKV7kLlxhQyy5ZZEw5QMijDyyKcO8m4AlbIHwhQuboXjASCicHclv8haB5lbHPuOagiEMzyDBIOFmjRssZDh/eZFuqfEdR1ApmCLcDKwFJIRexcPMgtk+eQTQa4EsJ+INiHVg8xeYQaA2Su+KBLKeDwcUtgn5RPJCLLL1icvUvxF0w1E0nZUzGoifB1kkU1IhBQFmqEHMaRhSbYtf24UIodsEWS2WPhDPgL5K9JHwRQ0cU13jSCHQwSVRYEvZI8gc6tpv3GzWHA1EblkExIcufeWeE5PLAdDvMa+r9PnkwhbLHwhn056bzp/JqJrANwN4E3332AuNOJaNosFR5D9InsyB0+kfqPqAsURl2QxtrpIQ2UPvi/krJbD59lz4tk8yLwg8YV8Ish2veIPwkzYqwPwe5mDYgoL04PsZrHgbTe/yCYQwry17zuakVmPV1EIQphlshSXhFZmcGTbQeHqId6STbCFeLdQCtmqE0VCCgxhJqemV49ivCOrQCaii2BGiu8G0ALgdwBICHG9T2NjCoS4pqMo4mKxkBy9EULga3/Zh4Rm4N/ftfCCniiybzGbFgshBKxa44xkVM29zBsA6EJAAV8Hr1GzePA5guwt/QuR1IAI7xbKIZulxZ5fVN1ASMkMTjHekCuCvB/AKgC3CiEOAwAR/aMvo2IKimwWC9nF49840ISfr64DAFw9ewxuWjRB2rGGOtkjDfZEKhANszDzA9UwMqpYhKzGIboh4JLPygyShNWsyK2KC2/9e0dcMxMe0wMiZrUc3iHxmj7VPN8laZOGfZ8ndAPFPKFII1eZtzsAnAbwBhH9jIhuADj0wWRiCmQXi4XkCgq/XHccVSOiqCgO4+W9jdKOUwjEsyRzOIUZ4w+aS2tY+0eOZsqhv4FFZpk3tnl5RzYri70Q5/vbW2IJSyBH0yL21vnne1suWQWyEOJZIcRdAOYBeAPA/wNQTUSPENE7/RogM/SJq+5VLCISy7ydaO1F7cFmfPDyabhmThXWH2mFuICToGyB4JakB/S3LGXko7o0zuGFilxiVqStOOyy9c/3vmckt/wj6Vv+lmDjc+0p2SLItkDmRD25DNgoRAjRI4T4jRDiNgCTAWwD8M/SR8YUDAnNyNjaB+QmiD23vQFCAHddOgVXzByDUx0x1Lf1STlWIaBaW8wZSXqWUJMRadANgc8/vQMffnQD2nu50aaNqrs3CgFYIMvCFsjp9dg5UdhbciVDAhxB9ppsAtn2INvXg5FDPp30kgghzgohfiqEuEHWgJjCI7vFQl4x89cPNGHp5JGYWFmCy2aMBgBsOtYm5ViFQELXEVIoI1ExGdmREGn4885TeHrLSaw+3IKHXj3k+d8vVFTdQDQtSU9JCgj+QJNBzGqBXBxJ30Hhclhe0u9BTm81LW8hfiHTZ1ksiqPuHmS+t+VyTgKZYdzIVcVCRoml1u44tte34/p51QCAOdXlKI2GsPNku+fHKhQSWmZpMcBpsfD+g+uZLScxdXQpbl82Eb/fejIZxbvQ0fTMJL0ICwipxLJuRXMnSS/JGkGWuBC/kMl+X9uWFr63ZcICmRk0MTVbFQs525ubjrVBCOBtc8YCMBNxFk8aie0nOzw/VqGQ1eaiyPngaumOY+2RVty2dALev2IKumIaXtpzxtNjFCqqIVzqUXNSjUySHuSMrWj5HmRNN/Dc9gYcbOySepyhQG+WpDG2WMihT9URViizkx5bLHyBBTIzKIQQ6E1oKI1mWiwiIUVKctiW42cRDStYNKki+djSKZXYd6rzgp0w+lTd9RqEHWXevKT2QDN0Q+DmRRNwxcwxmDiyGM9ua/D0GIWIEMJqNZ1qsfArWXLbibNYuev0sPA664bI+/+RTSBHrEo6MhN4/+flg/jMk9tx5yNrcbZneHvxkwLZZSEC8ALQa/oSRsa5BjhJzy9YIDODIq4ZMARQGs0sqR2WVGJpy/GzWDJpZIrveenkSiR0A/vPdHp+vEKgT3WfSMOSqie8ebAZY8uLsHBiBRSFcNuyiVh1qAVtw1wgDIRmCAjh1opXvoDYdbID7/3xOtz/6634+sp90o7jB42dMVzzzddx5yNrk77XXMS07B5kQF5yZFNXDI+tqcO88eXoiml4eku9lOMMFfpUHcURJSPXwc9qOa/ta8Rjq+suCDtHn6pl+I8Bp0DmBYlMWCAzg8JOInCPXnqfpBdTdexu6MTF00alPL5k8kgAwI4L1GbRl9BcC8Y7Oy55hW4IrDrUjLfNqUp253v30knQDIGVu057dpxCpL/zlXvWucyIz+Nr61AaDeGmhePxi7XH0NBeuFVdHl11FKc7Ythe344Xdw9s3clW5i0sWUj8Ys0xqLqBRz58CRZOrMCr+5qkHGeoYO4WZgZDQj5VaXl2WwM+/sRmfPXPe/H4mmNSjzUU6E3oWSLIdk7D8F8kBAkLZGZQ9KrZBXIkRJ77//ac6kBCN3Dx1FSBPHlUCcaURbGj/sJM1OtT9QxfICCngP/Ok+1o71Vx3UVjk4/Nn1COOdUj8Pz2U54dpxCJq+6dxvxopLD52FlcM7sK/3rLfOiGwAsFuljpjKn47cZ63LpkAsZVFOGVPJoAxVTTg5/exU1mZFM3BJ7ZchI3zKvGjKoyXDO7CttOnEVvQvP8WEOF3ri7YLN3SGQtALtiKh78/U589qntuHLmGCyZPBK/3Xhi2Ne+74lrKCvKXJDYLdUTLJClwgKZGRS9cfPDoMTVYqF4vqW89bgpgC+eVpnyOBFh6ZTKYSeQhcjPP9mXJdIgI0nvrYMtIOpPkgTM83/78knYeKwNR5q7PTtWodEfQXbf6pclINp6EjjR1oulUyoxZXQpZlePwNojrVKOJZtfrjuO7riG+66bhatmVWH90YGbAMVUHcWuSaryqoesP9qKpq443rN8MgDgqtlVUHWBTcfOen6soUJvwj3XwV6IyIogf+nZ3Xhqcz0+euV0/PyeFbh92SQcbenBmc6YlOMNFbrjGkYUuZxvhS0WfsACmRkUdtJGaZZtIK8FwZbjZzF1dCmqy4sznlsyeSQON3ejJz48IjhrDrdg6X+8jDseWTtgCbU+1XCNIMtI0nvrUDOWTBqJ0WXRlMc/cOkUFIUV/Lj2iGfHKjSyWiwkR9jsheGyKebCccnkkdjV0FFwEbbW7jh+tuoo3j6vGosmjcSVM8egpTuBg425F11xTXe1GNmRNhnn/dltDRhRFMYN881ykyumjUJIIWwexvXYe1UdpS4RTdtiIUOwHWjT8ez2U7i/Zjb+/V0LURoNY/lU8z7ffmJ4BUTS6YnrGOEaQZZv2WJYIA+KH795BJ/8zVac7ihcr99g6c3hQY6GFSR0w9MP6V0NHVg6pdL1ucWTRkIIYO/pwk/U0w2Bf/79TsQ0A9tOtOMPW3NXiIip2bxq3raAjak6dp5sxxWzxmQ8VzWiCB+5Yhqe3nIS/7fumCfHKzSSjRSytPyWlaS3rb4dCpnvAQBYMmkkmrviaOyMSzmeDHriGv72F5vQl9Dx+b+aCwC4YqZ5n22syx0N74nr7lvRkhYmMVXHi7vP4KZF45PCvKwojIvGlWP7MNvFctKX0LIEQ+SUkxRC4MkDCUwcWYxPXj87+fj8CWYFo0NNhbVbda4R9u5sFguuYuELLJDPk+317fjGC/vxl52ncdsPVuMjP99wQSYo9am2xcJFIIcUCOGd77IrpqKhvQ/zxpe7Pm+Lg53DIFHvlb2NOHm2D9+/axlmji3DC7tz31vZLBYhj+uT7m7ogKoLXJLmAbf555vn4cb54/Dvz+/BgTPDvy5sOnGro1u6Bzns8UIlnR317bhoXHnyw3SxlbS6q6Fw3gs/efMIdjZ04OEPXpwUQFNGl2BcRdGAtoWeuHupybCkhclr+5rQFddw+7JJKY8vmzIS2+vbYQyDMntu9MSzlJOUlKRXe6AZdR0GPn3DnJTPmOJICBNGFuNYS4+nx5PJa/saMf9LL+Jvf7Ep7/vDtFhkCuQoV7HwBRbI58nKXacRDSl45r4rMX9CBQ42duEzT24rqDesF/TEzYiZ2yrXblzhVW1iuxD/3HHuArm6ohjjK4qxaxh01HtsTR0mjyrBOxaMx9WzqrDtRHvOD5+sSXoelxfbctwUKulVRJLHCyn49nuXIBpW8Kv1xz05ZiGR3WIhbwtaCIEdJ9uxdHL/zsqCCSOhkLmgKQR64hqeWHcc75g/DjcuGJd8nIhw6fTRVnOg7OeuJ+FvpO3PO09hbHkRrkzbSVk2pRJdMQ1Hh+nnQHdcQ3mxS76JhI6dQgg89OpBVJUQ7rh4csbz08eU4VhrYZxnIQT++4X9SOgGXt/fhFf3DZx4CpjvC1eLhaT7uqkrhvf/eB3e9q3X8caBJmw9cRZbjud+7w1nWCCfJxvq2rBsaiVWTB+NX378cvzpU9dACODXGy4sUdDRpwIARpZEMp7z+k2834pIzs0SQQbMyNnOAhEF2djd0IGNdW2456rpCClm8mF3XMua/GY3a3EVyB571Xaf6sSkyhJUjSjK+ppRZVG8fV41XtnbeMFNrNksFlGJXtjjrb1o71WxbGq/QC6JhjC9qgz7CsRu9LtN9ejoU3FfzayM5y6dPhqnO2I5y9b1xHWU5Uge83JhoukGVh9uwQ3zqjPqAS+bYi4ch6vNojOmosJlrpeRDFx7sBk7Tnbg1pkR1y6h06vKcKy117PjyeRAYxcON3XjP961EGPLi/D0lpMD/o5uCPQm3K1DsspGfuW5PdjZ0I6IouBjj2/CHT9aizsfWYebv7cKT20e3jW+3WCBfB4kNAN7T3UkEwUAoLq8GNdeNBYv51GSaDiRSyB7HUE+cKYLI4rCmDyqJOtrlkwaiaPNPeiKqZ4cMwgeX3MMpdEQ3rdiCoD+xKtsCSlxzYCqC9fIjteLlBNtvZhRVTbg666cVYUznTGcPHth+fMTA0SQZXiQbTHmjCADwIIJFQXhx1d1Az9fXYfLpo/OKN8IACumm49tzmGzyFYOKyncPLS27DjZjq6YllLFxWZ29QiURUMFV03ncFP3gLsNQgh09qmoKHYRyB6XMVxzuAX/9sfdmFRZgmsmZV5XAJg+phRtPYnkZ9BQ5oVdZ6AQ8NeLJ+A9yyfhjf1NA3Zd7I6Z9sVc87qXnWNbu+N4ac8Z3HPVDDz7wNX4zA1z8F/vWYSv3b4I4RDhC8/sxGOr6zw7XiHAAvk8ONbaA1UXmD++IuXxa+dU4XhrL06eLYxVbS76Ejre/cPV+NRvt+WMAnb2qYiGFdcMclsgxz16E+8/04WLxo1INqdww/Ze7m4Y+sLAjaauGP604xTed8nk5KJjZlUZyovC2JHFOtJpLQbKc3jVvLoGJ9t6MWV09gWKzaWWqNlYN3wz+t1IWiyydHSTEUHeXt+OkkgIF40bkfL4/AkVOHm2b8gLiKc216OhvQ/31cx0fX7e+AqUF4WxMUd1iJ5Elq1oCZH7Nw+2QCHg6tmZiaohhbBkcmVBRZCbOmO4/eE1uPUHq3O+X3sSOgwBVJTkWIh4sAA81NiFex7fCAD4/t3Lk4vLdKZbC/XjBWCz2FjXhkWTRmJseRFuWTwBmiFQezB3U5n2PlNAjyqNZjwXTeY0eLfg3nqiHYYAbpxfjYriCP7xHRfhQ5dPw4evmIZn778a71gwDl9fuQ97ThX2Du25wAL5PDhklRyaXZ36gWRXVyhUcebkoVcPYsfJDvxpxylsyDFpdvSpqHSJHgP9b2IvipkLIXDgTBfmpi1K0rET9QrFe5nOr9efQEI3cM/VM5KPKQphwcQK7Dnlfl/1Rxoyr0ORh1H8nriG1p4EpowuHfC1F1WXo6I4jM3HLzSB7G6x8DrC5mT90VYsm1KZTAS0WWAluu0fwlHkMx0xfOOF/bhi5mhcP7fa9TUhhXDxtFHYlEu8xXXXDm8RCd7vVYeasWRyJSpdhAsALJtaiX2nO/NqkT0U+PWGE+i2SmPmyhvotBZauSPIg59n/ritAYYAnv3k1bgkS64DAEwbY85Dx4e4zUI3BHaebE/uBC62hPJAXRfbe83zXVnqZl+07msPI8hbT5xFWCEssj5DnYSt3JKKkgi+8cJ+z4451GGBfB4cbOwCUaZAnje+AgoBewt4hXW0uRt3/GgNfvLWUdy+bCKiIQWv5Ugo6OhTXe0VgLe+y6auODr6VMxNi5KlM2ZEESZVlhSkD7k3oeFX64/j7VZnLicLJ47E/jOdrol6XTm24ry0udRbOyNTRg0skBWFsGL66AsughyzqlikeyZleDQBoLkrjv1nunDNnKqM5xZMNAXyUPUhG4bA557eAU0X+MYdS3LuDF05awwONXXjTEdmYwghhJWk597uHvDO2tLRq2JHfTuuvSjTXmEzd1w5NEPgxBAXbjYv7TmDy2eMxvsumYw3DjRlna9z2em8tBBtrGvDsimVGFuePc8B6J+HTrQN7fN8uKkbPQk9KZAVhfD2udV462BzzvmgvS+7QO6vO+3dfLLtxFksnFjhuhtsjiOK+2tmYdWhFqw93OLZcYcyLJDPg8NN3Zg6ujTjRiqJhjBr7Iiskb6hjhACn31qB4629OBf/noevnHnEiybWol1R7PXILXh134AACAASURBVG3tSbhuAQGOCLIH4qw/QS93BBkwV+g7C7CSxeNrjqG1J5FS79Nm4cQKxFQDR10S9bpyRJCjHrYktT/wp+YRQQbM5KojzT1oG8BrN9QRQqAxz45d/XXBUxcrMpLFAGD14WYAwNtcBHJ1eRHGlEWHzHx0rKUHL+w6nZwPHl97DKsPt+BLty5Ibpdno2auKUjfdNmW7tUAIQbIg9C9iea+dagZhjDtdNmYNdZcxBdCR8mTZ3ux/0wXbpw/Du9YMA5dMS3rovZsr/k+dhXIHpUxNAyBfac7sWjiwPN8WVEYVSOKfFmIbDl+Fp99ajv+b92xnMmibmyvN73zzvr9180di66YltU2BwDt1vl226kgIkRDChIezSeabmBHfQeWZynfafPhK6ahurwIj14gXmQWyOfBwcYuzKl2r6SwMMdW+FBn3ZFWbK9vxxf+ah7uvXYWiiMhXDVrDPac6kRHr7uP8XRHHyZUZna1A7yNXtrbxNlqIDtZNrUSx1t7C0qYbTtxFg+9ehB/tXCc67biwknmB4bbvWUnJLpGkD1cpNRbCXf5WCwA4GK721V9Ybfe/ddnd+Pyr7+GR1cdHfC1duv19FqxsjzIv9tUj0mVJVg4MXNb1G6/vvWEv+c/rul4cfdpNDkWFcdaenDrD1bjE7/eig8/ugG/XH8cX1+5DzfOH4e7L5sy4N+cO64cE0YW4xWXJOge1RQJbkLCS4sRALy6rxGjy6I5hcSMsabYP9I89L2xr+83Fxw3zK/G1bOrEFIIa4+4Rwebu8ymM9UVmZFdr6wsDe196EnomDdhYIEMAFNHl0iPIK853IIPP7oBz20/hS8/twdXf+N11Hz7Ddz+8Jq8Iqnb69tRURzGjDH9i8CrZo2BQsBbB7P/vp3El83CGAmRZztS+890oU/VUwoPuFEcCeH25ZPw1sHmYdOxNhcskM+RhGagrqUHc7Js9c+bUIEznbEhnxiTjmEIfO+1Q6guL8Kdl/QXv79y5hgIAax36WSlGwKn22OYVOmetOVlpu3e052YOLIYo8rco9VOlk8pLGHWGVPxwG+2YVxFMb555xLX18waOwLRsOKaINGea+szpEAhjwRyWy9GFIUxymXLz43Fk0cipBC2Hi+8aL7Njvp2/GbDCQDAj988OmAjhF5VRzSkJO99Gxke5G0nzmL90TZ89MppGeXGbC6ZNgpHmnsGzJj3ipbuOD72+Cbc96utuP3hNejoVRHXdHzxD7tAAL5w01xsP9mOLz27GwsnVuC7H1ia01phQ0S48+LJeG1/E+rSagx3J8xz6nZf2gLZtr4MhoRm4I39TXi7S3k3JyOKwhhfUVwQEeRX9jZiRlUZZo4dgbKiMJZMHon1R90jyLZAHlueGRAJedQoxPYTTx8zcKUcAJg2pkyaQO6Mqfjc0zvwoUc3YNKoEqz/4g149bPX4l/+eh7mT6jA6Y4+fPrJ7Un/dja215vdXxXHPVNZGsWSyZVYdag56+81d8cRUijrDm0krHi24N5mJZW6VZFJ56pZY6AZAtuGeZtvgAXyOXO8tQeaITIyxm3sxw81FlYXsUdXH8WGujZ85sY5KSWqlk2tRHFEwbojmQL5TGcMmiEwKUvZNS+39/ec6kx6KgfCFmaF8gZ+apOZxf+9u5ZlTfyJhBTMG1+OHS5dAu0PrjEjslhdrJbfg6W+rReTR5XkJWgA02Ywf0I5thXIQsWN/3n5AEaVRvDt9y5BS3cc63PYjQCro2GOhi1eRTITmoEHf78L4yuKcfflU7O+boW1G+FHFPk7Lx/Aiq+9ivVHW/GRK6ahsSuOW36wCpd+7VWsO9qKf7llPu6vmY3VX7gef7z/KvzhE1e52oKy8dGrpiGiKPhZWiS/O1cE2bLBeZEw98dtJ9EZ0/CupRMHfO2s6rIhH0Gub+vF6sMtuG3JhORjV8wcgx317ehNZIq+5q44omEFFa6NQrzZIbEbf0yvym+XatqYUpzq6ENfwtuEyJiq4++e2IxntzXgvutm4dlPXo2x5UWYXV2Oe6+dhUc+fAl+/OFL0NIdz1n6rDeh4cCZzqT/2Mm1c6qwvb49aaVIp7EzjuryohRh7SSseGex2Hb8LKpGFOUsoWpj/1+Gam6Dl7BAPkdsL+xFWbq52daLAwUkkLccb8M3XzyAmxeNxwcvS/2wLQqHcOn00VjjspW0zfrQXeSyvWv+rjeioC+h42hzNxZkOU46pdEw5o0vLxiB/Nz2U1gyeSQumTY65+uunTMWW46fzbCOtHTHMbIkklF71yYaUjxL0svXXmGzfMoobB+gC+BQZdWhZqw61IJPXj8bty2diNJoaMCW39laHisKIRIizyI+v1hbhwONXfiv9yxyrSpgs2RyJcIKYfNxuQJ5d0MHfvjGYdy0cDye++Q1+M/bF+Gr716IsmgY71w4Ho9/7FLcdalppaiuKMbyqaMyqm4MRHV5Me68ZBKe2XIyxb7RbW3W5YogD7bMoaYbeKT2CBZPGunq905n1tgRONrUPaQb5Tyx9hgIwF2OOf+KmWZ0cIvL/XK6I4ZxFUWuC+SIRzskx1t7UBRWMM4lSu3GggkVEALYd8YbsWYYAm8ebMY9j2/EpmNt+N8PLMODN89zLSG4fOoo3DCvGo+vqXNdUADArpMdMARcBfKNC8bBEGaSpBuNnTFUV2Q/D1EP55Nt9e24eGplXsGPytIoRpVGUFcA5fUGCwvkc+RgYxdCCiUTMdKZVFmCsmgoWQpuKHOwsQt/98Qm/O0vNmNSZQm++V73TPJrZlfhUFN3yocSAPxhawMqisNYmCWy61UE+UBjFwzRX7YqH5ZPNWuRDnVhdqS5G7saOvKKSt20aDx0Q2RMqM1d8ZwZ39FwaNACQQiB+ra+vBP0bC6eVomehJ5sE14o6IbA11fux5TRJfjIldOSfvxVh3J7DntV3VUgA+ZCxYt61DFVx0/fqsM1s6tww/xxOV9bEg1h4aSR2JKjycZg0Q2BLz+3G2PKovjW+5Yka5F/6PJpeOkfr8X/vG8prp9bnffOQy7uu24WNN3Aj9/sjyJ3xM33+BiX7o5JgTxIi8XK3WdwrLUXn7x+Vl7/j5lVZeiKa2jujg/quLI40tyNJ9Ydwx0XT8ZEh0VuxbRRiIQIr7p4vetaejCjyv1zz6tch2OtvZg+pixr1DQd+17z6v7+3DM78DePbcS+0134xh2LB5yXP1EzC2d7VTy1yb3LnJ2Et9RFIC+eNBJTR5fizzvdF91nOmIYl2Ne98pi0daTQF1Lz4AJek6mV5Xh2DBtp+5EqkAmopuI6AARHSaiB12eLyKi31nPbyCi6TLH4wX7z3Rh+pjMChY2ikKYPa58yAsCwxD41G+2YfXhFiyZPBKP3bMiayTq6tlmxMRO6ADM2quv72/C/dfPzhoJ8urDaa+VmJZNiLuxfMqonO2ZhwrPbz8FIuC2PATywokVmDe+HN9/7VBKp8DTHTFU55hIi8KDjyC3dCfQp+qYkscWnJPlBdp694/bGrDvdCc+/1fzkpH5t80Zi+OtvTkbE/Ql3OvxAuZ2vxdb/a/ta0JLdxz/cJ17Y410VkwbhR0n2wd9D/zgtUO47QersSvN5vNI7WFsPdGOf/nr+Tmj2V4wbUwZPnDpFPxibR2etlrftvQZKC8Ou3rw7Ws3mPNuGAI/euMwZlePwDsXjM/rd2ZbO4mHh2CgxDAEvvTsbhRHQvjnm+alPFdWFMZtSybi6S0nUxKzhRCoa+nBzCzVRsIhBZEQIaYO7v4+1tKDqWPyX4RPGFmC+RMq8IdtDYMOhuxu6MAftjbgb6+egU3/eiM+cGl265LNiumjsWLaKPxsVR0SmoGYquOpzfXYZDW1WXO4FdPHlKLKZfFGRLht6QSsPdKK1rSFlKYbONbak0z4dKMorAz6sxXo3wm+eIAEPSczxpRl5AIMR6QJZCIKAXgYwM0AFgC4m4gWpL3s4wDOCiFmA/gugG/KGo9XHDjThXkDlBq7qHrEkBTI+890JhtorDrcggONXfj6exbjlx+/PDmhu7FwYgXmjivH/607ntwyfGx1HUaVRnDPVdOz/p7txewd5KS5q6ED5cW5W0ynY2fjbvM5g/9cMAyBZ7c34MqZYzAux1aaDRHh63csRmNnDPf9agtiqg4hBI40dWfd0QC88SCfaDMnw3P58AJMj+Dosii2St7i9xJNN/DQqwexZPJI3Lq4359pb63niiL3xDVXDzLgzUIFAF7b34jK0giunJnZyc2NS6aNQlwzBtUBq66lB9955SB2NXTgQ4+ux+EmU/gdbe7G9187jFuWmC10/eBLty7A1bOr8PlndqL2QBOa+0TW2tyREIFocBaL1/c3Yf+ZLtxfMyvvyKadizIUPwd+teE41h5pxYM3z3Pdefr7a2eiT9Xx3VcPJh+ra+lBd1zD3BxVhIojIfQNYq63RWGuucyN+66biX2nO3M2OcmHF3afRlghfObGORl1zHPx6RvmoKG9D59/Zgfe86O1+MIzO/G+H6/DrT9YhTcPNuOdC7Mvqm5dMhG6IfDC7tRdwRNtvVB1gdk5zkVxJISYBwvuTcfMBiF2ND4fZlSV4XRHzHPv91BDZgT5MgCHhRBHhRAJAE8CeHfaa94N4Anr+2cA3EBe7MNJoq0ngRNtvQMmi80dX46W7kQyeWoo8OTGE7jpoVW49Qer8UjtEfxiTR2qRhThFkeCRjaICB+7ejr2nu7ES3sa0dDeh1f3NeKuy6ZmjaQDQIn1XGyQb6JNx9pwybRR57RFO6OqDCNLIp74kOtaevDl53a7bjueL6sPteCzT23H8dbeFA/gQFw8dRS+/d6lWHO4FZ/89VacaOtFV1zLWlUFsD3Ig7sGtiA61w8vIsLyKZXJLOlC4C+7TuPk2T586u1zUgTRjKoyTKosyZl53pvQUZbNYhEevMVCNwRqDzSj5qKxeXt47bKBbr7SfFm5y9wGfvq+KxENK7j/11uQ0Ax8+bk9KIoo+MptCzyxUORDaTSMR/9mBaaPKcXXV+7DiU4jo7GODRGZkbZBnPcf1R7G5FElee3y2IwtL0JlaQQHhlgEua6lB19fuQ/XXTQ2I9/EZv6ECnz0iml4Yt0xvLG/CUIIrLWStO0W8m4UR0KDiiAfazVF4Zzqc5tj3rV0Iq6ePQYPvXoQnbHM6lEJzcADv9mKd373TRw4k33B8ubBZlw8bVTWxlfZuPaisfjg5VPx3PZTONPRhx9+cDn+7Zb5/7+9+w6L8kwXP/59hqH3JiogKNh77xGN6YnJpidm09tuyia753fOZpPd7O7Zki0n5WTTszG9bLKJ8SSaZkSNvYsIKiAgTXrvM8/vjxlwEAaGYWQQ7891eQnD8PLMvDPv3O/z3s99k1tWT0SAN3ctGmn3d8cNDSQh0p8vDhZ0uL1tzU9XqRltfDw9XBKgbj5medz2rnx1pb3Nd/ngnkV2/BnpvWjANjEnD5hr7z5a61alVBUQDnSYolFK3QvcCxAVFUVycvIZGrJ9tbW1vLZmEwBelTkkJ+fZva+h0vKi/ef/bWbe8DP5FDumvNHMk5sbGB9mIMBL8ZevLK0ir0zwZNsPmx3aRoRZExto4Jcf72VGlAdaQ4IuIDm56wUGAGbrbPPhoxkkm3OdGnt1kyajuJ7pIU293u9x/mY2p+WRnNy5bFFpg5kj5SYmRxgJ8lbU1tZ2uX2z1vxmSwN5tZq3t+UwZ6gHS2I8qW/VKGCov4GYwK4Dla0FrWRUmrg60YsAr1PBw77iVp7b24TRAIujjQSUHyE5+WiX2+hKOHDrBC/ePlzMemvai0dpFsnJ2V3ev6mhgcLi+j69bzakN+FpgMyDOzney0Ao2NRMRnELX367AX/P/j//bdu3x6tM7CwyMXeoB/HBXQexZq3525YGhgcoPE4eJrk4rcPPY32b2ZVZbPe5LCqvJ0Abuvx5a1MDeYW9fx3byqk2UV7XzBBzWa+2E+Gr+Gr3URJNzr0P/7WtgcQQA3XZB1k5RvHc3lrO/8vXnKgxc8t4Lw7v2c5hp7bsvMtiTbyw31LiK9xk//nwwExmdi7Jyb0/wc2pNrE3t5GbxnmxZfOmXv3uEG8Tu4/mkZzcfeWT/lLZaObvuxsxaM1Vw2vZuHGj3fvO89d85au4481dxAQoqptheIDiROpu8g7beQ+3NpN9oqDL460jdhdZFrrV5B0huSaj/XZ7x2ZbF0Sa2JLRwpPvbuDKxFPVTLTWrEptZlOeZdsPvPkDj8/16XQyV9WkOZRfzzWjPZ16fy4P0STM82GIn4GA8qMkAn9f7IVSkLZ3O2nd/O6koGbWZNbx2VffE+pjIL/WzJ+3NxAXZCD/8G4K0rp+vhtqGqlr0X06nlQ1aVIL6rm6l4+7xBrjrNu0k6IhfYtxHNm/7uL+6M0BWutXgVcBZs2apZOSkvp9DN9+v4GtOV4MC1bcsWJpt7M3i82a5w9+S7ExgqSkaf04SovGFhPPfneM6FBfbpk7gkc+2g+qiVfvXsLQYB/++GUaNY2t/OGqSXYvB3dlyJhKVvxjC9/ntnLBhCiuvWRWj7/jtX4dQ6JjSUoa79RjsZxZ7+Om5bMdqtFoa1/LUZ7//hiz5y/C32YVssmsOf9/kskua8bTo4VrZsSwOEjT1etqS0YpebU7+Ou1UzhZ1cjzGzLYWdRxseLvVkzkymnDefa7Y8SG+XHr/Dh2HS/n1a92ABAaMZT/uX5q+/1ffHkb0SEG1v9iSbcz8N1JAqbtzeOPX6axZGwkK6+w/zqLTN+Kp4eBpKR5Tv0tgDeP7yQxqollSxf3+nc9Y0r59NgOAuImsaSbFr29kVpQRWFlI9NGhHSZ32drw4YNZHiM4Kkd6bSaNd+fMPHiyhldLnBbm1JIXu1enrlhKsumx3T6+TFDFtvXpjF51vwuF4U1JX/N2PgYkpImdvpZWMoPBAd6k5Q0uxePtCNLSanD3H7pwg6Lq3qyqGgfWzLLWLJkSa9nenPK6sj5KpknLhtP0uJRJAGH6nexPr2YqbEh/O7HC7qtC3ymLNGacq8U9mXk8x/XJ9nNf/bf8h2RUUNISuq6xnh3fvVZCt7GPH55fRLBDtb/bvNdZQqf7y9w6jm3p7aplXe25TA8xIcrpgy3m/KRW1ZPWIBXe/WFzJJannhjJ+XNBl65bSaLR/f8Ply0qIU1Bwp4a2s25c0NPHX9zG5bbIft20RQmB9JST1/LnQlZf0x4CjXX7Kkw2xmcnJyl8fm022q2MXGE5X8/paFBPp4sj2rjNc3H2dT3kkeXJpIbJgv//XvFKpCxnDVaelAn+7NAw5w+0Vze5Vq4AoxE2r4/OlNVAaO5EcLR/LAe3sxGlt4/6fn2e0xAPDBid1kl9aTlHSe03/7s31tj3sOU2Icz0GeVNvE77d/R2h0AkkL7c+QO8LR/esOZzJAzgdsWyTFWG/r6j55SikjEAwMjNPt07yd2syB/Hqev2l6j5c2PQyKxaMj23ut97acUV+9sCGDlzdmArA+7STJR0p4aFlie4mu367o/OHtiCkxIfz2igmsOVDA45c6FvD6enr0KcXio10niAryZkp07w9aU2ODMWvL4ou5Nvma2zLLyC6r52fnj6ayvpn3duSyxV+xaGFLpw/BT/bkEehjZMXU4fh4erByXhypBVWE+3tj1ppnvzvKk2tSeea7o1RaF7V8vPsEFfXNxIX7sSAhgo93n+AXF45heIgvh/Kr2JldzuOXjnc6OG5z9YwYrp7ROYg7na+Xkeo+Nq45drK2yw5/jphirUu983hZnwNks1nzl6/TecVaxcBoUPzlmilcM9P+8/DvYy18kZXGhROi+NWl43n4w33c984eHliayLUzY9rfF+V1zfzxyzQSIv1ZMbXrfFrbjoanBwsms6a6sZUQO4GUJcWib5dEdxwvIzbMt1fBMcDM+DBW7y8gr6Kh16X62vIjL550Kpfy2RunsT6tmCVjIt0SHIMlfeLPV08hObm828WB3p7OpVjUNrXy+b58Lp8yvNfBMcDE4cG8uz2X7LJ6uykgvdHYYuKet3azzVqLe/W+fF7+8cxO5R2/OlTE/e/uITrEl3WPLMbH6MFdb+6iodnEh/fOczgQCvTxZOXcOG6eMwKzpsf97OPl0aeGLOkna4gJ9e3VpX5bDy5L5NqXtjLld98Q5ONJVUML4f5ePLwskUeWjwHg3e25PPZpCp/vz+eR5WPaUxg2Hi0hIsCrVwvBXSVxSCDjhgayel8+F0yI4qvUIu5eNLLb4Bgsn619yfkG2Hy0lFA/T7ulWu0J9/fC19OjvbvqYHUmI7ddwGil1EillBdwI7DmtPusAW6zfn0t8L0egIUjU/Kq2Jzfyk+SEhzOQ7tsyjBKa5vZ7EArSldqbDHx3o5clo+P4s6FI0k+UsLs+FB+kpTgku3fvnAkn/50YXsOUk/8vDyodzJA3ppRyuZjpdyxcKRTJxmToy0Hv5T8jouTtmSWYjQo7j1vFL+7chJv3D6bglozj/5rf4e6pTWNLaw7VNgeHAOE+XuxeHQkE4YHMSk6mGdvnM6ixAiCfT35+P75vLRyBqW1TTS1mnnuxuk8sDQBDTz/fQYms2bVlmz8vDy4fnbP7XVdxc/Tw26dTkdUNbSQX9lgtzlOTwJ9PJkxIoSNR+3n7jqi1WTmJ+/t4ZWNWdw8dwSf3D+fWfGh/PLTg3YbYezKLueLrBZumBXLKz+eSXyEP+/dPZcFiRE8t/4YV/zjh/byhY9+tJ+S2iaevn6a3WCgraXz6a8poP0kxF4OY19XnWut2Xm8nDnxji3OszXTevVld86py98nyutZm1LYY7vadSmFTI0JJsZmIVygjydXTY92qLOlu3kbncuN/Xx/PnXNJlbOc3yNgK22K159yf1uk1NWx02vbWdbVhlPXz+V314xgQ1HSngpObPD/bTW/O/6Y4ClbfPbW7N5f0cO2WX1/P26qb2aJWyjlHLoJMjHaHA6YNNaszu7vNdXCW3NGBHKu3fN5SdLErhk0lAeu2QcW365jJ9fOBaDQWEwKP5x83SunDac1IJqbvnnDoqqGmlqNbHxaAmLR0c6vAjT1W5bEM+BvCquemErWmt+PD+ux9/pa843wI7j5cxPCO/141ZKERPqS17FmW3z7W5nbAbZmlP8IPA14AG8obVOVUr9HtittV4D/BN4RymVAZRjCaIHnJc3ZuJrhJ/2IshcOnYIIX6efLY3n6Vjh5zB0XW0el8+5XXN3LkwngWJEfz8wjH4e3n02wKa0/l6eThVxcJs1vxxbRrRIb7dVsroTmSgN8ODfTot1NuaWcbU2JD2tIvzxkRyw1gv3k8v5uvUIi6eZFm4+H16MY0t5m5naQO8jbx7d8fU+gsmRKE51Wr7+lmxfLAzl+/STlJS08Rt8+N6vRCkL/y8nT9JgVNd2Pry4bVkTCR//+YoxTWNDHGwCcDp3t6Ww9epJ3nsknHce94olFK8csssLnt+Mw+9v4+1Dy/uMNPX1Gri8c9SCPNRPLni1CKyQB9P3r5zDqkFVVzx/A+8uTWbSycPY+PREh67ZFy3C2OCfT2JDfNtLz1oq63ld3czyD21pe1ORnEtFfUtzB3VfUOZrowdGkigt5Hd2RX8aHoMdU2t/OjFrZTWNrFkTCTLJ0RR3dBCdUMLM+NC21fe55TVcSCvil9eMq6HvzBw+Xsbqevl619rzbvbcxk3NLC9dX1vjR4SQKC3kb25FVzbzRWOnjS3mrlj1S5Ka5t4/qbp7ZM0WzPLeGtrNj9NSmyvupCSX8Xhwmp+f+VENqQX88x3x/BQigUJ4SSNdU16kz2+Xh6dmhg5am9uBSerm1iU2HMTlu4sSIxgQTfbiAv356lrppBTVsdFz27i8c9SWDw6gsr6Fq6e0T9VWLpyw6xYvj18ku/Ti7ljYXyHk1F7+lo1JK+invzKBu5Z7FyKhCVAtj+DXN3YQqC30W2xhyuc0RxkrfVaYO1pt/3G5utG4LozOYa+0lozYXgQ/k2lvWqL6mU0cMWU4fxr9wlqGlt69bvOMpk1r2zKYnJ0MPMTLLNMXXUA6k9+Xs6lWKzen09qQTXP3TitT6kICxIj+Ca1iBaTGU8PA9WNLaTkVfLA0sQO9zt/hJFd5V488+2x9gB587FSQvw8u+yC1J3TZ7v/cNUk5ieE87/rjxHkY+Sh80c7/Xic4efVt9XOu7PLMRoU03pRJ/N0F00cyt+/Ocoz3x7jySsm9HqfFlU18vS3R1kyJrI9OAYI9vPk+Zumc93L23j4w31Mig7iYF4VK+eO4OvUkxw9WcvPZ3p3edl24vBgksYO4eM9eVTUt+BtNHCjA7VPJw0P7rJkWqm1lmmYf9c50X2dQT5orT/sTMDmYd1/ba2y1x0qorS2icunDGPdoaL22X2Dglc2wVNXT+bGOSP4aNcJDAqumua+4KGvAr2N1HZR3aA7G44Uk1ZYzV/tNE9yhMH6nPe1xOH7O3LIKq1j1e2zWTru1GTLTXNH8M3hk2w4UsxF1hOa93fk4uNp4Mpp0UyKDmbDkRJMaH516fgzHqj49qGqwqot2QT6GLl8as9VlVwhLtyf/7hwLH/4Mo316cUsHh3R5+C8LwwGxWu3ziKvot7hZkw+nh59Op7sPG65mjRnZO+vSAHEhPqxJ6cCrXWn19Y3qZY0n0snD+MfN89weozudlYs0nMnpRQPLE0kWdmvWmHP1TOieWd7DutSivrlkvo3qUUcL63jhZtnDJizNj9PI3W9vLxf39zK374+wpSYYK6Y4nhppa5cNnkYn+zJY/W+fK6bFcuu4+WYNe0nEG08DIpb5sXx5JpUMoprSYj054djpSxMiOhzjqWHQbFi6nCumDIMren3y3h+Xr3fB7Z2Hi9nYnSw07mBAKOjArl1fhxvb8thS0Ypn9w/nyFBPtQ3t3Iov5rpI0LaZ9y3ZpSSVVrHVdOjPUt68QAAHFhJREFU20/w/vuLw7SYzPz+yomdXtvTR4Tyy0vG8Ycv09h4tIQgH2N7reJfXDCGyR6nL3045cbZsXyfXswHO3P50fRoh3JNJw4PYt2hok4nvgWVltmU6JCuZ8i9jX1rFJJWWI230eB0PuuFE6L49eepHCmq4dO9ecSF+/H8TdOprG+hxWwm2NcTheKet3fzq89SKK1t4sNdJ1g6dghDg52b9R8IAryNFNc09nxHGy8lZxId4tvn2s4zRoTyv98fc3qSpLHFxAvJmcwbFdZpBnhxYgQRAV58tjefiyYOpaSmiTUHClgxdTjBvp7MGBHK+3fPxehhYJITazh6y9fTuStVhVUNrDtUxJ0L4/t0jOmtuxaNJMjHk+KaRm5fONLtn5keBkVcuOPvbV9PD5pNZqfXOe08Xk6Qj7Hb2tbdiY/wp7qxlbK65k4LpV9IzsSs4YuDhdyzuLLbq3IDmbSaPoOmxYYwMsKfT/b0PrjuTk1jS6e8QbNZ80JyBvHhfh0W07hbkK+R6gbHgzOtNU98doii6kaeuGxCn4PJJWMimRYbwq8/P8Rn+/L4JvUk/l4eXaYLXDjRUtXg69QiMoprKapubG8O4QpKKbfkuPlZF8+Yneg0VVTVyO6cCpJcUH3idysm8tadcyiuaeSxT1NoNZm59Z87uf6Vbdzz9m7qmlp5YnUKN7++gydWH+KaF7dSUdfMFwcL+DKlkIeWJdr9ALlr0UjeuH0W7941l91PXMCLK2fwyf3ze5ytXzZuCLFhvngYLLW+HdGWh3x6mkW+NUAeFtz14pq+1kE+XFjN2KGBTi/6vXjSMAzKsoh3W1YZV0+PQSlFqL8XQwJ98DZ64GU08PItM1mQEMHfvzlKi8nM/7t4rNNjHggCfIzUNjp+DDqYV8mu7AruWBjfftLmrDkjw9Aatmf1rvRZTWML/3eggHvf2UNJTROPLh/TKYAzehi4Yupwvk8vJq+inttX7URruHvxqQ6LCxIjmDOy9yk5zgjy9eyyDnFP3tuei1lrbp0f7/pBdUMpxfWzY3lw2Wi3X2l1hq+X5bXpbJrFzuPlzI4Pc3oCqK1e9bHTan2X1TZxMK+Se88bRaC3kVVbjju1/YHg7HtVnEWUUtw0J5Y/rU3nkQ/3MTM+jFvmjnD6TNVk1vzm80O8tyOXyEBv/nrtFJaOHUKrycwXBws5lF/N/1w31W2ryrsS7OvVZb6mPe/vzOXTffk8unyMSw7sbZeu7ntnN49+dACw5Ht1dYl/WLAvU2KC2ZBe3P7zRS4MkN3Fz1rKr6HF1KHcnSNW789Ha1zSJU0pxZIxkfznReP4/ReHuejZTWSW1LEoMYLkIyVMfPJrAO49bxSz4kJ58IN9XPnCFspqm5gWG8J9S+yvAVBKsWzcqbJtl0527FKt0cPAFw8uprimkdFRjs2kTI0NQSnLB4xtdZQT5Q2E+HnafY79vJzPGdRak1ZY7XCr465EBnpz0cShrDlQgNGguGZm1/vU18uDd+6aw5GTNQwL9u3XfPkzIcDbSE0vcr9XbcnG30ULaWfHhxHoY2TNgQIumNC5rGBXDuVXcdOr26lpaiXIx8ivL5/Q4XVm6+rpMazaks2iv2zAw6B4/bZZjHHwdexqQb6e1DS2YjJrhz+DGltMvL/Tsqi8t9VVznVtx5m6JlOvr04U1zSSVVrHDX14jY+zzjynFlR1uCK76VgJWluu3lY3tPDlwcL2FMezjQTIZ9iP58Xz3eFiVu8vYPX+AgwKVs7teYXq6ZpbzTz+WQof78njxtmx7D9RyR2rdhHm79W+MGJKTHC/tXt1VKifZ/vipZ4cOFHJ79YcZsmYSB5altjzLzgoMtCbj+9fwIe7cjl2spZHltufVTxvdCQvbcxEA6Mi/R1aLDHQ+VovW9Y1t/YqQK5ubOG1TVnMHxXucNUSR9y+IJ5vDhexPauc62fF8Ndrp/LChgy+Ti3iFxeObS8F99LKGdz/7h5C/bx47sZpZ+wAG+zn2asyXmH+XkyJDubrw0UdZqhTC6oY300bej8vI/VNzgXIpbXNVNS3OH05tM3vr5yEt9HA+eOjun1tK6UY181jOZsE+hipbWrtMlfydCerG/m/AwXcMi+u29JxjvIyGrhxdiz//OE4t8wd0SHQNZs1StFhTCU1Tdzz9m4CfYy8cYel9nt3weak6CCWjo3kh4zS9gkTd2k7kaptbHX4/bQ2pZDyumbucHIh9rmsLSiuaWzpdQrUruOWvPi+TEINCfJhRJgf27PKOly1+D69hHB/LyZHB1M0bggf7jrB7uyKTmmNZwMJkM8wXy8PPrpvHiaz5rZVO3lqbTrnj4vq8QXdajLzyqYsdmWX4+flwa7sCkpqmnj4/NH8/IIxNLaYeCk5k5PVjYQHeNHUYubeJaPcVqbGnlB/L+qbTTS1mjrV67R1KL+KW9/YSWSgN8/eMM3lj8PDoBw6MTlvTCT/2JDBnpwKhy+5D3RBPpa3eU1jK0N6iK9MZk1FfTOtJs1jnx6kvL6Zxy9zrsmLPQaD4s075pBZUsuEYZYg7IGliZ0XTo6PYvfjF+DtaehzzWhXu2ZmDL/5PJVnvztKVUML54+LIq2wusMHxen8vSw5g87MpuSUWVq6jozs24lKZKA3z944vU/bONsEeBvRGuqaTT1eSl+1JRuT1tzZx+YHth4+fzTfpRVzz9u7uXZmLN6eBqobLCkUvl4ezBsVzpBAby6eNIz//uIwFfXNfHL/AofyhpVSrLpjTo/H1/7QFiBXNXSuJ2/P2pQiokN8z8rgyd0Crcf16l6kD7XZebwMX0+PPuemLx8fxdvbsjlZ3UhUkA/NrWaS04u5ZPJQDAbFosQIvDwMJB8tPiv3sQTI/UAphdFD8acfTebCZzbxm88P8eqt9rsNldU28cD7e9meVc7oIQE0tZqZEx/GtbNi2mcIfDw9ePSCMf31EJzWdtCsrG8hKqjrA3hji4l7395NgLeRD+6Z59baqtNHhBAZ6E1JTRPXONCE42wQ6md5PsvrmknoJpX4RHk9K1/fQW65pbalUpYKHGdigY+Pp0d7Lm93nGnQ0B+unxXLe9tzefY7S83ZVVuyActlRXv8rMFZfbOJYN/eBcjZZZZ9Et+LRTzCou31X1HX3G2AnFdRz6otx1kxdTgjwl135SjQx5O37pjD46tTeHd7DhpLCsIya0WKXcfLKa1t5rXNx1EKXlo5s9fvOXcHx3DqRNzRPOSGZhM/ZJRw42zn0w7PZW3PtzOlI3/IKGVWfGifr8rdtiCOVVuP8+72HH5x4Vi2ZJZS09TKBdZUMH9vI9NiQ7rMwa+oa8bbc2CnXUiA3I/iwv159IIxPLUunXUphVzSxYdpc6uZ+9/dw8G8Kp6+fqpDndIGsrbVrcXVTUQFdT1r/tqmLAqqGvnw3nku/WByhqeHgc9+uoDyuuZ+WfndH8KsJxxltd3XKH3qq3RKa5t44jJLSaiFieGD5jK7q/l4evDvny7gwIlK4iP8+cf3GUyKDur2NeNvzQWvb27tdV5vdmkdHgZLcX7RO0OCLMegk9WN3ea5/uGLNJSC/7rY9TWfR4T78c5dc+3+vKKumbWHCkmIDGCenXzjgS6k7USk3rFayBuPltDYYmZ5Fy3fRc8CvE+lWPTGifJ6MkvquNmJVM/TxYX7c/64KN7bkcsDSxN5cUMGUUHeHRa3zx0VxovJmdQ2tXY4QX15Yybvbs/huaSuy2IOBAM7fB+E7l40konDg/jNmlSq6ju+sOuaWvn16kPsyq7gb9ed/cExQGyY5QP9hJ2OOyerG3kxOZNLJg0dMB8MMaF+TnWcGqjCA07NINtzpKiGtSmF3LlwJHcvHsVdi0ZKcNyDAG8jCxMjiA7x5c9XT+4xhcfPZlFNb2WX1RET6ntWLnRxt7Z0tpPVTXbv89WhQr5KLeKhZaN73cbbFUL9vVg5N27AHAOdMdQ6AVJY5VhJvS8OFhDm78U8JxrfiFMpFjW9TLFIttY8XzLGNQvQ71wUT3ldM1e9sIVd2RU8uDSxQ0rc3JHhmMyWTom2fsgoZVJ0MMYBlhZqS462/czoYeAv10yhvK6ZG17dxtZMS73WncfLmf/n9Xy0+wT3L0lghYMtrQe6thmbE+VdB8h/WpuGyax57BLX5rmKU07NINsPEP53/TH8vYzctch1uZeiI9sZ5N7KLqvrVY1UcUqUtXNjUXXXgVtRVSNPrE5l4vAg7j3Pfg656F5UsGUmsLCy5wC5sr6Z9WnFXDJpqNNlC891QdarUNUOLIJfc6CA5U9v5M/r0lj1w3HGDQ0kITLAJeOYPyqcWXGhpBfVsCgxotPM9Iy4EIwGxY7jpwLkirpmDhdWs9CNzVkcISkWbjApOphXbpnJ7784zC2v7+C6mbGsTSlkSJA3q6wrlweLIB9PIgK8OVJU0+lnn+7N4/P9BTyyfLTbUysGM2+jB+H+Xnbbgr69LZsvUwp5eFmiW/O/B7u2Jgi9nUHWWnO8pI5ZcTLT5owQP0+CfT3JKO54DMooruHtbTkkHymhobmVp6+fKzP0feBt9CAy0Jv8yq4nQ9q0mMz85ycHaWo18eP5fb/Mf67y97LULe+pvXdlfTOP/fsgGnhlYxZKwRu3zXZZ3rdSlvKC27PKOG9MZKeqK35eRqbGhrDD2sUTYEtmKVrDwsRwao7bb+TkbhIgu8nyCVHMTwjn0Y/289HuE8SG+fLOXXPdcnnvTJsxIoQ9uR3brdY0tvCntWnMjAvloWX923r5XJQQGUBGSW2n2/fkVPDkmlSWj4/i4X5ugX2uacu/623OYHFNE3XNJqc76J3rlFJMHB5ESv6p9uAtJjO3vbGL4ppGxkQF8tQ1k/tcQk/AyHB/Moo7H2fatJjMPPzBPr45fJInr5ggaVx9oJQiMsCb0h7Wlvxr9wnqmk2s+9li6ptN+Hl5MH6Ya5/3ED8vLp5kf4Hy3JFhvLopi/rmVvy8jPzfgQIiA72ZFhvK5gHcR0ROl93I39vIy7fM5NOfLmDtw4sHZXAMMCs+lJyyekpqTl3i/8eGDEprm3nyigkDqrHJYDU6KoCjRTWdOjD+84es9jrDcqnzzAr1t1wSdXQRU5tM64nNqD6WeDuXzR0ZTmpBNSetaRZfHiwkv7KBl2+ZyZcPL2ZBwsC+1Hu2mBgdxOHC6k7HGbCULn3o/X2sO1TEry+fwB0uLKV3rgoP8KK0m9S5VpOZt7bmMHdkGOOHBTEzLtTlwbEj5o4Kp9Ws2ZNTQVV9CxvSS7hiyvAB/9kvn4huZjAoZowI7XUnnLPJzDhLyshOaw7SnpxyXtuUxXUzYwbVYriBbGFiBDVNrezNrWy/rbnVzIb0Ei6ZNLTXHfZE74X7W3I0y3q4JHq6rBJLDeRRLsoZPBetmDYcBby4IQOtNa9symL0kAC3NtYYjCZHB9PYYiartK7Tz55al85XqZbgWNY6uEZEgLfdALmkpomf/+sA+ZUN3Onm53tmnKXhzY6scv61+wTNJrPdTp4DiXwqijNuakwIYf5erDtUyOIxEfzsw/1Eh/rymysmuHto54xFoyMwGhQbjhS3d09KL6qmocUks2f9xNfLA19PD8p7uCR6uqySOnw8DQyzUyZR9GxkhD83zx3Buzty8fM2klZYzV+vmTLgGiud7drKHKbkVXVoeX0ov4o3thxn5dwREhy7UFSQD/tPVHa6XWvNfe/sJiW/ip8kJXChg23Oz5QAbyOTooPZmllKcU0Tc+LDHKqD724ygyzOOKOHgUsmDWV9WjE/+2AfhVWNPHvD9EE9az7QBPl4Mis+lA3pxe237bPOJk8bIbP4/cW2NbyjUgsswYYEc33zXxePIy7cj5eSMxkR5seV0wdHpaCBJCEygEAfY/vVwjavb87C39vIf56BGtPnsvhwP8rrmjs1Z9mVXcHe3Ep+u2Ii/3XxuAHRiGVxYgR7cyvJq2g4a7rUSoAs+sVNc0bQ0GJiw5ESHl42uj3tQvSfZeOGkF5UQ36lpZrF/hOVDAn0ZngPbc+F64QHePUqxaKxxcSBvEp5v7hAoI8n7989j8cuGcfbd84ZEN3nBhsPg2JBQjg/ZJSitQagqr6FtYeKuGpadK8b5IjuxVmrP+WWdawc8tm+PPy8PPjR9IGTxnDrgjhrWlMkF04c6u7hOEQCZNEvJkUH8/7dc3nuxmk8tCzR3cM5J7W1tk0+YplF3ptbwbTYkAExu3Cu6M0MclFVI39emybdxlxoaLAP9y1JIF4qgpwxixIjyK9s4Lg1D/nzA/k0t5q5YXasm0c2+IwIs7yOs8tO5Xw3tZpYm1LERROHtpeWHAiGBPrw7c+XsOqOOQN+cV4bCZBFv1mQGMGV06LlUrGbJEQGEBPqy4b0Ykprm8gpq2dWvMxM9idHA+TK+mYuf34zb23LYenYSOafxR3WxLll2fgolILV+wtoNZlZtSW7xzbswjltM8g5NjPIG4+UUNXQwpXTJIWorwbO6YUQ4oxSSrF8fBQf7Mwl+Yil3ehgakpzNgj396Kszn5Zpjavbc6itLaZD+6Zx7xRYTLLL84a0SG+nDc6ko925eLr6cHx0jpevmWmu4c1KPl7G4kI8CbbpmrI5/sLCPf3YtEA71J3NpAZZCHOIVfPiKap1cwTq1Pw9FAyq9PPwvy9aWwxd9tuurimkVVbsrl8yjDmJ4RLcCzOOvctGUVxTRN/+SqdBQnhbq+iMJiNGxpIWlE1YGlC9F3aSS6fMkzq2ruAzCALcQ6ZHB3MxOFBpBZUc9HEKHw8ZaFSf4oMtNRCLq5uIj7CyPHSOowGRWyY5VLpKxszeTE5E5NZ8+gFY9w5VCGctiAhgn/dN5+M4lp+NF3S6s6kSdHBvL45i8YWE18dKqKp1cyKaQNncd7ZTAJkIc4hSimev2k6H+zM5e7Fo9w9nHNOtLVbZn5lA0rBpc9txqQ1nz+wkK2ZZfx5XTpJYyN5aNloEqQxiDiLzY4PY3Z8mLuHMejNGxXGyxsz2ZZVxqd784kP92OGlO50CQmQhTjHjIoM4PHLpEmLO8SEWgLkvIp6Nh0roaHFBMDK13dQWd/MxROH8uLKGTLjJoRwyLxR4QT7evLEZ4fIr2zg5xeMkbQsF5EkFSGE6CdDg30wGhQ5ZfV8d/gki0dH8P49c/E2GkgaO4Snb5gqwbEQwmE+nh78x0Vjya9sYHiwD7fNj3f3kAYNmUEWQoh+4ulhYGSEP9+nF5NZUsfNc+NYkBDBtsfOd/fQhBBnqR/Pi2NhQjiRgd7SodaFZAZZCCH60dihgaQX1QCwZEykm0cjhBgMRkUGSHDsYhIgCyFEPzrPGhRHh/iSOEQW4gkhxEAkKRZCCNGPVkwdTlFVI5dOHuruoQghhLBDAmQhhOhHPp4ePHz+aHcPQwghRDckxUIIIYQQQggbEiALIYQQQghhQwJkIYQQQgghbEiALIQQQgghhA0JkIUQQgghhLAhAbIQQgghhBA2JEAWQgghhBDChgTIQgghhBBC2FBaa3ePoVeUUiVAjhv+dARQ6oa/K/qH7N/BS/bt4Cb7d3CT/Tu4uWP/xmmtI3u601kXILuLUmq31nqWu8chzgzZv4OX7NvBTfbv4Cb7d3AbyPtXUiyEEEIIIYSwIQGyEEIIIYQQNiRAdtyr7h6AOKNk/w5esm8HN9m/g5vs38FtwO5fyUEWQgghhBDChswgCyGEEEIIYUMCZCGEEEIIIWxIgNwFpdQbSqlipdQhm9vClFLfKqWOWf8PdecYhXPs7NvrlFKpSimzUmpAlpsRjrGzf/+mlEpXSh1USn2mlApx5xiF8+zs3/+27tv9SqlvlFLD3TlG4byu9q/Nz36hlNJKqQh3jE30jZ337m+VUvnW9+5+pdSl7hzj6SRA7tqbwMWn3fZLYL3WejSw3vq9OPu8Sed9ewi4GtjU76MRrvYmnffvt8AkrfUU4CjwWH8PSrjMm3Tev3/TWk/RWk8DvgB+0++jEq7yJp33L0qpWOBCILe/ByRc5k262LfAM1rradZ/a/t5TN2SALkLWutNQPlpN18JvGX9+i3gqn4dlHCJrvat1jpNa33ETUMSLmRn/36jtW61frsdiOn3gQmXsLN/q22+9Qdk5flZys5nL8AzwH8i+/as1c2+HbAkQHZclNa60Pp1ERDlzsEIIZxyJ7DO3YMQrqWU+qNS6gSwEplBHlSUUlcC+VrrA+4eizgjHrSmSL0x0FJXJUB2grbUxpMzWSHOIkqpx4FW4D13j0W4ltb6ca11LJZ9+6C7xyNcQynlB/wKOekZrF4CEoBpQCHwP+4dTkcSIDvupFJqGID1/2I3j0cI4SCl1O3A5cBKLcXfB7P3gGvcPQjhMgnASOCAUiobS3rUXqXUULeOSriE1vqk1tqktTYDrwFz3D0mWxIgO24NcJv169uAz904FiGEg5RSF2PJX1yhta5393iEaymlRtt8eyWQ7q6xCNfSWqdorYdoreO11vFAHjBDa13k5qEJF2ibdLT6EZYF8wOGdNLrglLqAyAJiABOAk8Cq4F/ASOAHOB6rfVZlXAu7O7bcuB5IBKoBPZrrS9y1xiF8+zs38cAb6DMerftWuv73TJA0Sd29u+lwFjAjOXYfL/WOt9dYxTO62r/aq3/afPzbGCW1rrULQMUTrPz3k3Ckl6hgWzgPpu1Xm4nAbIQQgghhBA2JMVCCCGEEEIIGxIgCyGEEEIIYUMCZCGEEEIIIWxIgCyEEEIIIYQNCZCFEEIIIYSwYXT3AIQQ4lyglAoH1lu/HQqYgBLr9/Va6wVn4G9OBx7UWt/lou09iGWsb7hie0IIMVBJmTchhOhnSqnfArVa67+f4b/zMfAHrfUBF23PD9iitZ7uiu0JIcRAJSkWQgjhZkqpWuv/SUqpjUqpz5VSWUqpp5RSK5VSO5VSKUqpBOv9IpVS/1ZK7bL+W9jFNgOBKW3BsVJqiVJqv/XfPuvPUUr9P+s2Diqlfmfz+7dabzuglHoHwNqJMFspNaBawgohhKtJioUQQgwsU4HxWDo8ZgGva63nKKV+BjwEPAI8Bzyjtf5BKTUC+Nr6O7Zm0bF1638AD2ittyilAoBGpdSFwGhgDqCANUqp87B0HXwCWKC1LlVKhdlsZzewGNjp0kcthBADiATIQggxsOxqa7eqlMoEvrHengIstX69HJiglGr7nSClVIDWutZmO8M4leMMsAV4Win1HvCp1jrPGiBfCOyz3icAS8A8Ffi4raWv1rrcZjvFwLi+P0whhBi4JEAWQoiBpcnma7PN92ZOHbMNwDytdWM322kAfNq+0Vo/pZT6ErgU2KKUugjLrPGftdav2P6iUuqhbrbrY922EEIMWpKDLIQQZ59vsKRbAKCUmtbFfdKARJv7JGitU7TWfwF2YZkF/hq405pygVIqWik1BPgeuM5aeYPTUizG0DF1QwghBh0JkIUQ4uzzMDDLuojuMHD/6XfQWqcDwW2L8YBHlFKHlFIHgRZgndb6G+B9YJtSKgX4BAjUWqcCfwQ2KqUOAE/bbHoh8O0Ze2RCCDEASJk3IYQYpJRSjwI1WuvXXbS96cDPtdY/dsX2hBBioJIZZCGEGLxeomNOc19FAL924faEEGJAkhlkIYQQQgghbMgMshBCCCGEEDYkQBZCCCGEEMKGBMhCCCGEEELYkABZCCGEEEIIGxIgCyGEEEIIYeP/A0UFuu4XzG3nAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "original_batch.show_ecg('A00001', start=10, end=15)\n", "\n", "changed_batch.show_ecg('A00001', start=10, end=15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [```band_pass_signals```](https://analysiscenter.github.io/cardio/api/cardio.batch.html#cardio.batch.EcgBatch.band_pass_signals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A bandpass filter passes frequencies within a certain range and rejects frequencies outside that range. To demonstrate capabilities of ```band_pass_signal()``` action we are going to artificially modify some of the batch's signals.\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "original_batch = batch_with_data.deepcopy()\n", "changed_batch = batch_with_data.deepcopy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, generate some low-frequency noise:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/master/.local/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:424: MatplotlibDeprecationWarning: \n", "Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n", " warn_deprecated(\"2.2\", \"Passing one of 'on', 'true', 'off', 'false' as a \"\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n_sin = 10\n", "noise = np.zeros_like(original_batch['A00013'].signal)\n", "siglen = original_batch[\"A00013\"].signal.shape[1]\n", "t = np.linspace(0, 30, siglen)\n", "for i in range(n_sin):\n", " a = np.random.uniform(0, 0.1)\n", " omega = np.random.uniform(0.1, 0.8)\n", " phi = np.random.uniform(0, 2 * np.pi)\n", " noise += a * np.sin(omega * t + phi)\n", " \n", "fig = plt.figure(figsize=(12, 4))\n", "plt.plot(noise[0])\n", "plt.grid(\"on\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And add this noise to the signal \"A00013\":" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "original_batch['A00013'].signal += noise\n", "changed_batch['A00013'].signal += noise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now apply band-pass filter with low frequency:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "changed_batch.band_pass_signals(low=0.2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And here you can see that the noise was removed:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "original_batch.show_ecg('A00013')\n", "\n", "changed_batch.show_ecg('A00013')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [```random_split_signals```](https://analysiscenter.github.io/cardio/api/cardio.batch.html#cardio.batch.EcgBatch.random_split_signals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```random_split_signals()``` does simple transformation: it splits 2-D signal along axis 1 (typically time axis) into ```n_segments``` with random starting point and defined ```length```." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "original_batch = batch_with_data.deepcopy()\n", "changed_batch = batch_with_data.deepcopy()\n", "\n", "changed_batch.random_split_signals(length=2048, n_segments=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now compare shapes of the initial signal and after the random split. Notice, that resulting array is 3-D: ```[n_segments, n_channels, time]```." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original shape of the signal: (1, 9000)\n", "Shape of the signal after random split: (4, 1, 2048)\n" ] } ], "source": [ "print(\"Original shape of the signal: \", original_batch['A00013'].signal.shape)\n", "print(\"Shape of the signal after random split: \", changed_batch['A00013'].signal.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [```unstack_signals```](https://analysiscenter.github.io/cardio/api/cardio.batch.html#cardio.batch.EcgBatch.unstack_signals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```unstack_signals()``` is useful to maintain batch structure after transformations that change shape of the signal component items, like ```random_split_signals()```.\n", "This action creates a new batch in which each signal's item along axis 0 is considered as a separate signal.\n", "\n", "Here we apply ```random_split_signals()``` to both batches and then apply ```unstack_signals()``` to the changed batch:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "original_batch = batch_with_data.deepcopy()\n", "changed_batch = batch_with_data.deepcopy()\n", "\n", "original_batch.random_split_signals(length=3000, n_segments=4)\n", "\n", "changed_batch.random_split_signals(length=3000, n_segments=4).unstack_signals()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now you can see that nothing happened:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original shape of the signal: (4, 1, 3000)\n", "Shape of the signal after actions: (4, 1, 3000)\n" ] } ], "source": [ "print('Original shape of the signal: ', original_batch['A00013'].signal.shape)\n", "print('Shape of the signal after actions: ', changed_batch['A00013'].signal.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This happened because ```unstack_signals()``` doesn't change existing batch, but returns a new one instead.\n", "\n", "Also, ```unstack_signals()``` changes the ```index``` of the batch. New ```indices``` will be integers from ```0``` to ```N-1```, where N is the sum of the sizes of the first dimension over all signals (3-D) in the batch. \n", "\n", "For example, you have batch with two 3-D signals with shapes ```[3, 12, 1000]``` and ```[2, 12, 1000]```:\n", "``` python\n", ">>> batch.indices\n", "['A00001', 'A00002']\n", ">>> batch['A00001'].signal.shape\n", "[3, 12, 1000]\n", ">>> batch['A00002'].signal.shape\n", "[2, 12, 1000]\n", "```\n", "If you apply ```unstack_signals()``` to the batch you would get a new batch with five 2-D signals and new ```indices```:\n", "``` python\n", ">>> batch = batch.unstack_signals()\n", ">>> batch.indices\n", "[0, 1, 2, 3, 4]\n", ">>> batch[0].signal.shape\n", "[12, 1000]\n", ">>> batch[3].signal.shape\n", "[12, 1000]\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's use ```unstack_signals()``` on our data and see what happens:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "original_batch = batch_with_data.deepcopy()\n", "changed_batch = batch_with_data.deepcopy()\n", "\n", "original_batch.random_split_signals(length=3000, n_segments=4)\n", "\n", "changed_batch = changed_batch.random_split_signals(length=3000, n_segments=4).unstack_signals()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At first, take a glance at the ```signal``` component of the batches:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original shape of `signal` component: (6,)\n", "Shape of `signal` component after unstack: (24,)\n" ] } ], "source": [ "print('Original shape of `signal` component: ', original_batch.signal.shape)\n", "print('Shape of `signal` component after unstack: ', changed_batch.signal.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we compare ```indices``` of the original and changed batches:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Indices of the original batch: ['A00001' 'A00002' 'A00004' 'A00005' 'A00008' 'A00013']\n", "Indices of the batch after unstack: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]\n" ] } ], "source": [ "print('Indices of the original batch: ', original_batch.indices)\n", "print('Indices of the batch after unstack: ', changed_batch.indices)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And, finally, look at the shapes of the signals:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original shape of the signal: (4, 1, 3000)\n", "Shape of the signal after unstacking: (1, 3000)\n" ] } ], "source": [ "print('Original shape of the signal: ', original_batch['A00001'].signal.shape)\n", "print('Shape of the signal after unstacking: ', changed_batch[0].signal.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [```resample_signals```](https://analysiscenter.github.io/cardio/api/cardio.batch.html#cardio.batch.EcgBatch.resample_signals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```resample_signals()``` resamples 2-D signals along time axis (axis 1) to given sampling rate. This action also changes ```signal``` and ```meta``` components." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "original_batch = batch_with_data.deepcopy()\n", "changed_batch = batch_with_data.deepcopy()\n", "\n", "changed_batch.resample_signals(fs=20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you set new sampling rate to a very low value, you can notice changes by eye:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "original_batch.show_ecg('A00001')\n", "\n", "changed_batch.show_ecg('A00001')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And also you can see that signal length has changed:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original shape of the signal: (1, 9000)\n", "Shape of the signal after resampling: (1, 600)\n" ] } ], "source": [ "print('Original shape of the signal: ', original_batch['A00001'].signal.shape)\n", "print('Shape of the signal after resampling: ', changed_batch['A00001'].signal.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "New sampling rate is stored in ```meta``` component under key \"fs\":" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original sampling rate: 300 Hz\n", "Sampling rate after resampling: 20 Hz\n" ] } ], "source": [ "print('Original sampling rate: {} Hz'.format(original_batch['A00001'].meta['fs']))\n", "print('Sampling rate after resampling: {} Hz'.format(changed_batch['A00001'].meta['fs']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [```rfft```](https://analysiscenter.github.io/cardio/api/cardio.batch.html#cardio.batch.EcgBatch.rfft)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This action computes the one-dimensional discrete Fourier Transform (DFT) of a real-valued array by means of an efficient algorithm called the Fast Fourier Transform (FFT). FFT divides a signal into its frequency components. These components are single sinusoidal oscillations at distinct frequencies each with their own amplitude and phase. " ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "changed_batch = batch_with_data.deepcopy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we apply ```rfft()``` to the ```signal``` component and store the result to the atribute ```fft_data```. Note that action has a ```dst``` argument, which defines batch component (or atribute) where the result of the action will be stored." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "changed_batch.rfft(src='signal', dst='fft_data')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And the next step: calculate power spectrum and plot it. To calculate power spectrum we will square the absolute value of the ```rfft()``` result, which is stored in batch attribute defined by ```dst```. \n" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sig_index = changed_batch.get_pos(None, 'signal', index='A00013')\n", "\n", "power_spectrum = np.abs(changed_batch.fft_data[sig_index][0])**2\n", "\n", "fig= plt.figure(figsize=(15, 4))\n", "plt.plot(power_spectrum)\n", "plt.grid(True)\n", "plt.xlabel('Frequency')\n", "plt.ylabel('Amplitude')\n", "plt.title('Power spectrum')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [```spectrogram```](https://analysiscenter.github.io/cardio/api/cardio.batch.html#cardio.batch.EcgBatch.spectrogram)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A spectrogram is a visual representation of the spectrum of frequencies of signal as they vary with time." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll use the ```spectrogram()``` action on ```changed_batch```." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "changed_batch = batch_with_data.deepcopy()\n", "\n", "WIN_SIZE = 16\n", "\n", "changed_batch.spectrogram(nperseg=WIN_SIZE, window=\"hamming\", dst='spectrogram_data')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that ```spectrogram()``` action calculates a spectrogram for each channel, so resulting array for one 2-D signal will have three dimensions: ```[n_channels, frequency, time]```." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "channel = 0\n", "siglen = changed_batch[\"A00013\"].signal.shape[1]\n", "sig_index = changed_batch.get_pos(None, 'signal', index='A00013')\n", "\n", "freqs = np.fft.rfftfreq(WIN_SIZE, 1 / changed_batch.meta[sig_index][\"fs\"])\n", "\n", "_, (ax1, ax2) = plt.subplots(nrows=2, figsize=(15, 8))\n", "ax1.pcolormesh(changed_batch.spectrogram_data[sig_index][channel])\n", "ax1.set_xticks([])\n", "ax1.set_yticklabels(freqs)\n", "ax1.set_ylabel(\"Frequency (Hz)\")\n", "ax1.set_title(\"Spectrogram\")\n", "ax2.plot(changed_batch[\"A00013\"].signal[channel])\n", "ax2.set_xlim(0, siglen)\n", "ax2.set_xlabel(\"Time\")\n", "ax2.set_ylabel(\"Amplitude\")\n", "ax2.set_title(\"Signal A00013\")\n", "ax2.grid(True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [```cwt```](https://analysiscenter.github.io/cardio/api/cardio.batch.html#cardio.batch.EcgBatch.cwt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A continuous wavelet transform (CWT) is used to divide a continuous-time function into wavelets. Unlike Fourier transform, the continuous wavelet transform possesses the ability to construct a time-frequency representation of a signal that offers very good time and frequency localization." ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "changed_batch = batch_with_data.deepcopy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now apply ```cwt()``` with mexican hat wavelet (also known as mexh) and scales from 1 to 30 to the batch:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "min_scale = 1\n", "max_scale = 30\n", "scales = np.arange(min_scale, max_scale+1)\n", "\n", "changed_batch.cwt(scales=scales, wavelet='mexh', src='signal', dst='wavelet')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that result of ```cwt()``` action is a 3-D array: ```[n_channels, n_scales, time]```." ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1, 30, 9000)" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sig_index = changed_batch.get_pos(None, 'signal', index='A00013')\n", "\n", "changed_batch.wavelet[sig_index].shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And here is a visualisation of the result:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "channel = 0\n", "siglen = changed_batch[\"A00013\"].signal.shape[1]\n", "\n", "_, (ax1, ax2) = plt.subplots(nrows=2, figsize=(15, 8))\n", "\n", "ax1.pcolormesh(changed_batch.wavelet[sig_index][channel])\n", "ax1.set_xticks([])\n", "ax1.set_ylabel(\"Scales\")\n", "ax1.set_title(\"Wavelets\")\n", "ax2.plot(changed_batch[\"A00013\"].signal[channel])\n", "ax2.set_ylabel(\"Amplitude\")\n", "ax2.set_title(\"Signal A00013\")\n", "ax2.set_xlim(0, siglen)\n", "ax2.set_xlabel(\"Time\")\n", "ax2.grid(True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [```apply_transform```](https://analysiscenter.github.io/cardio/api/cardio.batch.html#cardio.batch.EcgBatch.apply_transform)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This action applies function given in action's argument to each item in the batch\n", "\n", "For a source ```src``` and a destination ```dst``` ```apply_transform()``` does the following:\n", "\n", "```python\n", "for item in range(len(batch)):\n", " batch.dst[item] = func(batch.src[item], *args, **kwargs)\n", "```\n", "\n", "```src``` and ```dst``` should be a component or an attribute of a batch.\n", "\n", "The main benefit of the ```apply_transform``` action is that it runs in parallel." ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "original_batch = batch_with_data.deepcopy()\n", "changed_batch = batch_with_data.deepcopy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's apply ```np.abs()``` function to the signals and take a look at the result:" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "changed_batch.apply_transform(np.abs)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAEYCAYAAABBfQDEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAIABJREFUeJzsvXmYXEd57/+t7p5F+2pLxrIkgy0b2+AF7zamuSG5JCHhZoFAchOSJ7+Q5TFZSG6uyQ2BQG5ugBBCAgQMgfAkAeIkBGxsyxtqSdZi7etoG42W0Uia0Wj2raeX+v1xzuk+vZ+qt7rqnJ76+JFn67feOudU1XnrrbfeYpxzWCwWi8VisVgsFoeY6QpYLBaLxWKxWCxhwhrIFovFYrFYLBaLD2sgWywWi8VisVgsPqyBbLFYLBaLxWKx+LAGssVisVgsFovF4sMayBaLxWKxWCwWiw9rIFssFotGGGPrGWOcMZYwXReLxWKxVMcayBaLxeLCGDvLGHuH6XpEAcZYijE2wxib8P17xvf3xYyxv2WMnXf/dtr9eaXvM+9jjL3GGJtkjA243/8OY4yZuSqLxWJxsAayxWKxWGR5nHO+0PfvpwCAMdYO4BUAtwN4J4DFAB4CcBXA/e5n/hDA5wF8BsBqAKsA/BaARwC0674Qi8Vi8WMNZIvFYgkAY+xdjLEDjLERxth2xtibfX97wvWQjjPGuhhjP+P7W5wx9teMsUHGWA+An2yg5yxj7I8YY4cYY6OMsX9jjHW6f1vGGPsBY+wKY2zY/X6NTzbFGPsLt34TjLFnGGMrGGP/yhgbY4ztZoyt933+VsbYS4yxIcbYCcbYexXdrl8BsBbAz3DOuzjnec75AOf8k5zz5xhjSwB8AsDvcM7/g3M+zh32c85/iXOeVlQPi8VikcIayBaLxdIAxtjdAL4O4DcBrADwFQBPM8Y63I+cBvBWAEsA/DmAf2GMXef+7TcAvAvA3QDuBfDzAVS+F47n9UYAbwbwq+7vYwC+AWAdHAN0GsAXymTfB+CXAVwP4A0AdrgyywEcA/Ax95oWAHgJwLcAXOvKfYkxdpv7919kjB0KUNdqvAPARs75RI2/PwSgA8D3Jcu3WCyWpmINZIvFYmnMBwF8hXP+Guc8xzn/JoA0gAcBgHP+75zzi66n9N8AnIIbSgDH2P1bznkv53wIwP8LoO/v3PKGADwD4C5Xz1XO+X9yzqc45+MA/i+At5XJfoNzfppzPgrgeQCnOecvc86zAP4djqEOOEb7Wc75NzjnWc75fgD/CeA9rq5vcc7fjPr8netR9/590v39CgCX6sitBDDo1gkA4Hq9Rxhj04yxxxrotVgslqZid1FbLBZLY9YB+ABj7EO+37UDeB0AMMZ+BcCHAax3/7YQjhEI9zO9PrlzAfRd9n0/5dMzH8Dn4HiXl7l/X8QYi3POc+7P/T7Z6So/L/Rd0wOMsRHf3xMA/jlA/Tx+l3P+tSq/vwrguiq/9/99JWMs4RnJnPOHAYAxdgHWeWOxWAxjByGLxWJpTC+A/8s5X+r7N59z/m3G2DoAXwXwOIAVnPOlAI4A8DIxXAJwg6+stYR6/CGAWwA8wDlfDMDztMpkfegFsLnsmhZyzn+bUD+PlwH8dzeMoxo74Hjg361Al8VisSjHGsgWi8VSShtjrNP3LwHHAP4txtgDzGEBY+wnGWOLACwAwAFcAQDG2K8BuMNX3lMAfpcxtoYxtgzAE4S6LYLjBR5hjC2HG08syQ8AbGCM/TJjrM39dx9j7I2EMj3+GY4B/p/uRsCYu1nwTxhjP8E5H4ETq/0lxtjPM8YWuZ+5C879tFgsFqNYA9lisVhKeQ6OEer9+zjnfA+czXZfADAMoBvuxjnOeReAz8LxivYDeBOAbb7yvgrgBQAHAewD8F1C3f4WwDwAgwB2AtgoW5Abw/xjcDbnXYQT1vEpOJvnwBj7JcbY0QbFfKEsD/Jet+w0nI16x+FsBBwDsAtO2Mlr7mc+DScs5Y/h3Ld+OJsf/zeA7bLXZbFYLCpgnHPTdbBYLBaLxWKxWEKD9SBbLBaLxWKxWCw+rIFssVgsFovFYrH4sAayxWKxWCwWi8XiwxrIFovFYrFYLBaLj8gdFLJy5Uq+fv167XonJyexYIHNPhQW7PMID/ZZhAv7PMKFfR7hwj6P8GDqWezdu3eQc35No89FzkBev3499uzZo11vKpVCMpnUrtdSHfs8woN9FuHCPo9wYZ9HuLDPIzyYehaMsSCnmdoQC4vFYrFYLBaLxY81kC0Wi8VisVgsFh/WQLZYLBaLxWKxWHxYA9lisVgsFovFYvFhDWSLxWKxWCwWi8WHNZAtFovFYrFYLBYf1kC2WCwWi8VisVh8WAPZYrFYLBaX3WeHMJvNm66GxWIxjDWQLRaLxWIBcOzSGN7z5R34y+eOma6KxaKdHxy6iMujM6arERqsgWyxWCwWC4DhqVkAwPHLY4ZrYrHoZTabx+Pf2o/3f3Wn6aqEBmsgWyLHwPgMnj49C8656apYLJYWgoEBAPJ2aLHMMTicRn9heMpwTcKDNZDnKO/+4jb82jd2ma6GFH/41EF891QGB3pHTFfFYrG0EDHHPraTb8ucxU4Oi1gDeY5ysHcEm05cMV0NKaZmcwCArGRPHhifwfonnsWes0Mqq2WxWCJOPGY9yJa5Tc42/gLWQLZEjqKXR05+1xnHMP76tjOKamSxWFoB5o4teetBtswxbJOvxBrIlsjBmOflkevRcU/eZnKyWCw+imOL4YpYLBbjWAPZEjliRC8P1cC2WCwWi15GpzP4/765G4MTadNVscwRrIFsiRzeTnNZ+5ZqYFssFotFL9/ZdR4vHxvAVzafNl2VlsS+DiuxBrIlcsTcVisdYuFayHN1M8KrpwaR/MwmzGRypqtisVgsgWDEvSeW+nhp3ixFrIFsiRyeB1lavuBBVlCZCPLnzxzF2atTOD9k811aLNWgjTCWZlBYOTRcj1bFTjwqsQayZc7C5uhb0POgZ3N2RLRY/FgjIbxYD7JFN9ZAtkQOVUtBc9Q+RsxuUrQI8u4vvIq/f+WU6WpoYy5NnvMRW0qzoQDNwd7VSqyBbJFiZ89VPLW712gdqKEWc5XiYQh2SLQE4+CFUXz2pZOmq2FRzEe/dwSv/5PnTFcjEF72ITtsNQd7emQlCdMVsEST9z25EwDw3vtuMFwTM/SPzeCahR2IxaJnpHtVnqubFC0Wi8M/7zxnugqBoYy0+TzH6HQGyxa0K6tPq2HfBpVYD7LFIkjv0BQe+MtX8I+vRvQkvrm0flwDm8HDUp25aSZEwXtYjEEWr+vf/7Abd3/yJQyMzSiulaWVsQayYc4MTtqXtSDUsZwqf9kdZF84eplWkEWKveeG8A8p+Vyohy+M4taPbrTPz1KTuTaFjIB9XHgmMlV9scvp6/1j9pCRWkShDejGGsgGmcnk8Pa/TuEP/u2A6apEEqojlM1VTypxJBwYn8GO01cVVUacn/uHHfjUxuPS8gcvjAAAUieuqKqSpUWIopEwm83j+cOXSF7gKFw2JQbZ25gchQ1+n3/5FNY/8SzSWc2Os/DfGu1YA9kgmVweALDlpH1RR5G5Op78zBe34/1f3Wm6GtIUXpZRtIYsljK+uKkbv/2v+/DKsQHpMqLUF2T8GrEI5b7/p+1O6N5kWq+BHIXJg26sgWyQwoluERqcLC0A0XPeNzKtqCJmsEeNW1qJwQknbODSaLT7ZSNIRnyEUluyCNW11bEGskG89h+FWW2YMB2DPEcDM1qGmE0XZWlAJMOvCHWOUleQucpYhA4ZKcRaa65rFO6NbqyBbBBvhhi1RO1hgfoKi+ArUA2KRkLZdvvxp4/i04QYYjIRWm616GWuNolWN46idDgSMxQvHf47ox9rIBvENkgz2PuuBtnQoH/afhZfImSh8JBdco3Shh2LGSI5eSZt0rN9ISzYI7XDgzWQDWI7gMUIipaPTR80Itt/TC1hWixhJQp9QUUVozDxMbVHIkobNXVhDWST2PYohfV2WChEMbzUYmnIHGnYMvHhUTL+iuEg4rKXRqfxiWe6kHUzZIkQnTukD2sgG8QaekTIeZDVVCNyKHpZmH7nyKo3XW9LeLFtI7zYZ9OYP/2vI/j6tjPYLpGn3t7fSqyBbBDbIImYNtDsAzQK9f7P1fmRpTGyk+eZTC6SJ6PqHMrODE6id2hKn8KIItMEPe/6dATbYBixBrJBrHkVTSLveVZ0AVFdAYlmrS0iyBqp1EnXw3/1Q9z60Y2kMkygsy+//a9TeOunN2nT5yfyY3cDKOnsojqeN5OmGsiMsXcyxk4wxroZY09U+ftaxtgmxth+xtghxthPNLM+YcMLwm/1Tts07H2b09jhXIzzV6cwmxWPTTSFrLHaPTCOWz+6ERuPXFJco8YMTc5q16mCVl8Mi9LlGXsWUbpJmmiagcwYiwP4IoAfB3AbgPczxm4r+9ifAniKc343gPcB+FKz6hNGWn1Qahb0g0Lm+I1vkRhkWebivGp8JoPHPrMJT/znIWFZE/2lf2wGN37kOTx98KKw7L7zIwCAl7rkj16ea0S0K0ugr/d/bWsPeq5MSMvLPBPKc5w7bSA4zfQg3w+gm3PewzmfBfAdAO8u+wwHsNj9fgkA8dEwwtglDdPMRVOpdZC12+Zir/NiErecuiIsa2Ii1HVpDADwH3svCMsW0vgRnjSbY2ODzCToe/v70D0w3oTaVIdk/Gluw+lsDn/x7DH8/Jd3SJdBmZjaVWk1JJpY9vUAen0/XwDwQNlnPg7gRcbYhwAsAPCOagUxxj4I4IMAsGrVKqRSKdV1bcjExIRyvSMzznInz3Mj1wSArNdEvUdGpgEABw8cxGxvXFj+SH8WAHD16qBU/U8NO8bG2NiYsedGYXzcuX/79u3DaI/4/fPYsnUr5iWYdN+g3rvNWzajLSb+JjjelwEA9Pf3R/L5NaLa8xhNOy/b9Oys8DX787Hqul+Hrzh9dHhoSFjnCe/5XhZ/vseHnL49MjpCula/bDPeHX4uXkwDAE6dPInUzBmpMl599VXMS4j1pd/fOAkA+Kd3LpDSKXpPus86z7Wv7wJSKbGJ3tiYM+bt378PqxPTTW/HGTdH2+iUeH9Lp53nuWPHTlwzX8yHOTg4AwA4cuQIOq6InVY6PFMMv9LVz5vdN6g000AOwvsB/BPn/LOMsYcA/DNj7A7OeUmgHOf8SQBPAsC9997Lk8mk9oqmUimo1ts/NgOkXkEsFlNedkM2PgsA8noJ8plcHs8cvIj/cdf1iEkYOP9wYgcwPIS77roLD71hhbD87NHLwP69WLFiJZLJe4XlF50bAl7bgcWLFyOZfERY3jSfO7oNGB3BPffcg7vXLhMvwH32b33rW7GwIyHeNxS1vcceewwdCXED/8qeXuDwIaxavQrJ5F1ydQgx1Z7H4EQa2PQy2trahe97Ls+BF54DQHhmgvATA8De3Vi+fDmSyfuFZAf3XgAOH5R6vp09V4FdO7F06VIkkw8JyQKo2rab8e7w89LwYaD3PG7esAHJB9eJCbv1feTRR7G4s01KVvjaJOW6t/YAx49hzZo1SCZvF5L1j3ljPQeb3o7T2Rzw4kZwiF9n+/aXgXQaDz74IG5YPl9I9lvn9wAD/bj99juQvGO1kOyl0Wkg9UMAcv38B4cu4r/dei3mtwc3K5vdN6g0M8SiD8ANvp/XuL/z8+sAngIAzvkOAJ0AVjaxTqEiqjGcVL696zw+/NRBfHv3eSl56m2bo7e9iLIY5GiepDcXKRyvLXHTKM95ajaLzSfFwzoswVHRDaLUlyjhL7oiD7z7afiwUSEobeBI3yge/9Z+/Ml3D6urUAhopoG8G8DNjLEbGWPtcDbhPV32mfMAfgQAGGNvhGMgz5nRdK7GII9MuUugozNG60GN05qbTy/6yJzE5ef45TE8I7F5zCTF42v16v3Idw/jA1/fhTODk3oVW8SI0GAm9d6UtP5GpzJY/8SzRvp7lCYtaTc7zrkWy2/dNAOZc54F8DiAFwAcg5Ot4ihj7BOMsZ92P/aHAH6DMXYQwLcB/Co37ZbSSOFlZQPqI0bEH5iyPMjRhDrEvPNvt+JD396vqDZ6yct4kAn6eq44hvH4TEZKnvSk9NlRRlHRm+eKs0Z0cnz2qtN+n9zSIyRHaUckWXlRkmx73DElo5RGMghNjUHmnD8H4Lmy3/2Z7/suANEL4lTEHJoLNAW7U3duQ+0+UctUcGZwEj/3D9vx9OOPYM0ysdjEwr3SbDTKeq4pT0bFU41Wy6ATpVeRTL+Vvbz2hJzhp2LCQcrCItGAbdaMSuxJegaJ0qAUKozfN+MVoDHH8yBHtNp4ak8vhiZn8f0D4su9BPuYhOexk/FcA/qdCHPFkzpXEbXjEu4ML5sXNJAVNCPd46unT864VluXsGANZMucg37QiJp6mCbqlzHXjBlvGTMtsYzpGZoyjh7KfS4efStWBjVOPGrk8hzrn3gWX9sqtpSviij1JBMGnGh7NH1gh6l3VKt1W2sgG6RVDK2oItuXI//YChkNiOVE9EZEdQyPu9ZmXmKnnalDFrzsGTkDoYlRap6eh/L/PS+WuxZQZVCF/26pqKIuA07F/ZQpIapjW1ixBrJBgnhmui6O4ZG/+iGGJ2c11ChamBoMIvAuCUi0L2SunqRH8qDNgTcoyQgiLDOrIGcoL1jU+0QjZFdBZO9LFCctrfNeU4c1kA0SpEF+MdWNvpFpbDs92PwKSWDC82B6aT0K3pa6qIpBNv0ciPKtthxYD9PPysS9lsr53IR6BNJLUKwki0UEhjST/VVUtZIYZE0yltpYA9kg+QBxgRk33rAtHs5HZXJglVc9tzepeUS9/tIouu5ITZQ8z6iMqIENR6QsFgThwkalueBq92F6AhUEU6nT5BRq1leG1CoT6Y0a/vYjQzitrjlCkCY16wbvtYfUQI4ysi/SyA8FXgwysRjT9mGkDFSXY5fG8INDBg4dIMlG7z7LMpeuNaqQImg0PV4lad4MZbGwFLFWl0GCNEgvJC1bFpv21J5evOfL25tQKzFM7tY11aFbZSBplesQRpFzUOb+/eo3duHxb8kdMhLl52UkxEK/SmlMHQ6htpDmQvJwRuD6KtGd4tBBpqu26opLUw8KsTSicQfwml22bBv4H//HoSbURxzHiyfWOVR1JVPensh7mZTFIJtFWr+qEAsJmf6xNFmv7jCJqBkXlJe1sUm3gkapYG9iJCCF0KirRn09hvtb1PpsWLEeZIMEacReHtGMod3NYcR4HuMWeRRRDFFQCXWiFqX75xlgMvmF59IqUcGLptkhZmpTl0r9zSYKdfQwlcuYFn4SoRusCWsgGyRIc/ReaOUe5LBgtEsRlct6miI/jCiLQTadTcSo+ki1A9P3SrSnmcpYUDhQRbeBTJBVksUiUq1ZHtExSzqVpKE8yKYms63afqyBbJAgx68WQyyqf1bm0ACVmF5KoiCdG7NFxoLIX0fU668RimfU9ERIJ+bSvEVrHDdRX5OhFaK6afoUhNsQcqVTTrFstVhkayAbJMjZ5wUPcg1DuNbvdWFy5mj6tW1avzQtEoNsmijZjRSDJkKXWUKkno8hWWmdBpSaDkMR0mPjiFsCayAbJEgH8Ixn7yjSijIi+/qSR9UVy4dYtMY9j/p1yJ+OpWqCoHuXOX0Xv/4NflRfmri8ipP0dKPkGGWK/iZ/XiVScfS6T6VTkeZN+12O9vugGVgD2SBBOkBhk16NEAvTs0yjB4XIxocZ0hsavBeM6c2OhqHGmRq7/ggeAShaZVNLtYXNjLr1Gx/Hw9+ZTW18k1OkoAjdG1TD3wS0Yw1kgwTyICPcm/SiDPWgkOiZKaVEfTy0A3pwSPGFEZwER0VfQa8SjyNBNgIxyB5RGHdNZxSRKcN0ncOINZBDTjHEIpweZJMYy4Ns+KZ/7PtHsP6JZ+ULUBaDbDr+fW5BO0yCEoMcrQ1ZHlJZAALsC2kGlGdrwmCMbt8TzGJhcCN3FN/tEVzcqos1kA1SjAus3apYIcQinDHIZjZrhMMwMzUYfHPHOSXl2HzQNEw1wxZ7B1lgfpOeuAdZgVKNyKdrk5QzHINMyWJhKWINZIN4ad7qNWav0eZCelCI0SwWmge9YgFEedMU8iBH+0JkJ0qqXgRRun9RTscoi0z7MHWpKib9OidOUWr7fmQnAqIx6caPDpfS68Xfy8i2JtZANkiQRuUZxnaTnnrkY5AjfNE+yCcSqqmGMagbsUyd8jYXZEkhB4SlHVOrU1HrS1FL8+aN2eLZOiQn4VJSZWVEqFGYXtVtFtZANkiQRuUZyLU26bVmswzGXL52Ei0ymLXGVYhDOexDLs2bijst6IEztb/AiFbzXTJSk/4IxBiZOpGRQhS93s3GGsgGCeRBdltt7U164YjHbXWdJfpNV0AR5EgT0y910/opstrzskYT3S9t022Kgs6qG/Egq4jr1RRrbSwbyhwMpWom1kA2SJAGWQyxCKcH2aSBLh2DSj0ItBCXFlG8GGQ7IpIgnU6nOzyDkJ2BNhHQKwdEs1+a9uBGaSiQCY2K0vV56A/hkvd6R/H+BsEayEYRCbFovRZoLk2b81X+JL2I0zJp3szFB1LL0X/nzBrzoi9dJfdHqhBTrj96EXaTXmNEJ7VGD8LSfYqkpzdK/abJWAPZICIe5LDmQZZRryouixwiIJ3jMhyDAf0Y37mNyZP0pJ6dEo+1oRPqhJe2vU1V4tdMuVTSgSoElGzq0igbkiEwMNKbRedQHmQbnlGJNZAN4tm89YbiooFc4yQ90wZyxHYzAyoMawfdL9GwYXxQNK2fAKXqUkvMBH20Q0ZocpRMI1FKV6XES08vIjBR7XqyEwHxFRDKZNbs3Y3YYZtNJVHvj4yxNQDeB+CtAF4HYBrAEQDPAniec27PPyYQKIuFt0mvVpq3lm2aAZCOb3RjrWRDLEJyyzknekFDch2RJUIeF9IJcSbiHSj1ldPoyBjaX2B6HBcPPTAQJKR7xYagW4kHmSKrd4Eq+vtyalDTQGaMfQPA9QB+AOBTAAYAdALYAOCdAP4PY+wJzvkWHRVtRYK0x4ab9EwbOREyEtThGdhhqIU+uWaVE139FM+q6drrJWrZAHQTtes0WV3SpEluniauR1KupAyZg25MhdBHrQEHpJ4H+bOc8yNVfn8EwHcZY+0A1janWnMDFTHIppHbSOCd5KZfN+C779QYVJo4GWdQIhyKYPwKaJgek6MUs2ciTIJShoq2KecsN9OodHsLqfpN9z1RpA1dyXzGpLzjEjIqSjE9RoSRejHIP+6GWFSFcz7LOe9uQp3mDEEaZMNNekprpAfK4FFaDr0uYdE7m82LL3Oqr4aY/qi9JV3CUG27ASeYTsqhKBS9ulFy1LTpJa0IIGoEGvUgU2QJIRZzJY1eEOoZyK8DsIMxtpUx9juMsWt0VWrOIOJBrhliYTp2TV7W1IBOzoPsflVZ/Q1/+jwe//Z+sXoQH33UBzXqcyRnsSDJynh4CPoIMYJROqDBj8wm2kLf1jw2me6LwvojPnYExWRokEwZXruVir+XkCnKtmaDqGkgc87/AE4IxZ8CeBOAQ4yxjYyxDzDGFumqYCsTKAa54SY9Cb0RNqpVlBM2w3J0OgMAePbQJbF6GExBpLKcqGLKUym3y9x0n5fz3NHiTWViOKPYqPXX2WR7Is1dhKtN8yFLTdIMGdc0he7XFlvGqJvmjTts5pz/NoA1AD4H4PcB9OuoXKuT5407UWGTXo00b6bHc4p6Uwd1UHfcUk4cqsZMJqemIEFMtx0qputP87johRayoECxsFjEG6cg5i9XcAJjvL6CaHamqLk/MhM8gjbKhF9ebagJlAeZMfYmAJ8A8EUAaQAfCSj3TsbYCcZYN2PsiRqfeS9jrIsxdpQx9q2gFW8FhDbpKUzzZnpwU6Xe1GWovn+1MpTorkfUMH35JtNO6cR0PKUsUcpTTvPI6r9OE89TRZ/RVW8lfYYyvhgK4Wo16qV5uxlODuT3AcgB+A6AH+Oc9wQpmDEWh2NQ/yiACwB2M8ae5px3len4CIBHOOfDjLFrpa8kggRpU/kGad6apTdwWYSeYS4G2ax8ORlDx4i36JjWENPhBk4dNOsjbMBRqT/w5zXqqiarPQ8yqUGYiBE3GGKh8SALaTkVYRIapaiEYUxtBvXSvG0E8G0Av1Aj3Vsj7gfQ7RnUjLHvAHg3gC7fZ34DwBc558MAwDkfkNATWYIMMl72ilytNG8S7dK090pdDGxrLN/q9iCrGsxM30b6c6SZQbTTskiqxfWR6qp/6dWUQR/FNG8mMONB1l8GdYyV2hSrwlMuZReQ1bYcNQ1kzvkb/D8zxhb7P885H2pQ9vUAen0/XwDwQNlnNrhlbwMQB/BxzvnG8oIYYx8E8EEAWLVqFVKpVAPV6pmYmFCu99CVLAAgm83WLHsmnQYAjE1MVv3Mtu3bsaxT7MRwf8o46jVt27YdizvEhoGzZ2cBAOfOnUMqJbYxDQDGxqYBAEeOHEH7lePC8scvOJviBgYGpK6/66Lz3EZHR5W0iXNjxRhkkfK2bNmCjoT4EDw+7ty/w4cPo23gmLC8x87XdqJnfky6b1Dv3c6dO7FynljbB4CT553nf+niRaRSV6X1b9+2HUsF+57H1q2vYmG7XL85e/YMUqm+mp+r9jx6Rp02NjubFr7vV6aKEzhR2XG3rx44cADp3nhguSOXnT42PDwkrNPrnwP9/eKyfU7bGBoS1+vHLxukf1yelL/HFy8674gTJ04iNX1GSNZj167d6FsUvC2PpenvEFG5M4X3xnmkUpeFZKempgAABw8exLrO6cC6Tww5/WZysvr7txa9487z5JwLX2c267Tfffv2Y/Js8D4DAIODMwCAo11dWDR8Ukj21LBzrXmeF67zkUGnzuNjY0KyzbCrVFL3qGkAYIz9JoA/BzCD4sSRA3i9Iv03A0jC2QS4hTH2Js75iP9DnPMnATwJAPfeey9PJpMKVIuRSqVQS+++88P42S9tx2t/8iNYtbgzcJn8+ACwdzfi8XjNsmNbXgRmM2jr6Cz9zMZnAQAPPfQwVi8JrhNwcu7ixecBoKbz2Ka6AAAgAElEQVTehrj6H37kYaxc2CEkeiB7Eug+hfXr1iGZvEVY9eeOvAqMjuL2O+5A8vbVwvIDe3qBI4dw7bXXIpm8R1h+9EAfcOgAlixZgmTyYWH5cg70jgDbtwEI+Dzce//oW9+KBR0Nu3AFiw5vBcbGpO+fp/+BBx7AuhUL6vaNevLUtvfAAw/ihuXzhcX7XjsHdB3Bda97HZLJN0nrf+jhh4X6u1/2kUcewbIF7UKiu9PHgZ7TeP2NNyKZvLnm56o9j2W9I8CObWhv7xC+771DU8CWTQDEn9nfHHkVGBvFnXfeiYdvWhlYbvrwJeDAPixfvhzJZLlfpT5D+y4Ahw7i2lWrkEzeLS57+CBWrFiBZPI+IVkAVdt2kP7Rc2UC2Lq5QjaQyquHgAu92LBhA5IPrhOS9ep73/33YcOq4MmproyngU0vA5Dox5L9/3DuFHDqJNatW4tk8lYh2Xm7NwFTU3jznW9Gru9oYN2dPVeBXTuxYMECJJOPBdbXdXEM2LYVjDHh60ykXgCyWdx111144PUrhGT/5dxu4MoAbrvtNiTvfJ2Q7MKzQ8BrOxCLxYTrzE5eAfbswqLFi5FMPhJYTvjdoZkgU8Y/AnAH53w95/xG918Q47gPwA2+n9e4v/NzAcDTnPMM5/wMgJNwDOZI8c3tZwEA208PCskVsyE0zmKhcpOeSiK5LEOss+rlX5Xx5RZLNVplQ6uITrnlbaVVCa7XcBniMeIGY5BlDrKQ1FXM/iKm0/SpdLrDCMMWtqiKIAbyaQBTEmXvBnAzY+xG91jq9wF4uuwz34PjPQZjbCWckItAmwDDRCLm3MZaRmwtvMxt9bpevnCSnspNeq3RmMnxYZK7BFXfv0xWMgaZqDds+aB1EYY80Po36XltXkZWgf4mfz4MshQi15eiVl+XKE0EtI8RhmTDTJD12Y8A2M4Yew1OijcAAOf8d+sJcc6zjLHHAbwAJ77465zzo4yxTwDYwzl/2v3bjzHGuuBkyvhfnHP5oEBDJGLOW6fWcdC1CPLpbCGLRQ0PsuFgfNIAIm2g0gjbyyjjazec88CGu/wmRSmx0EG9DvpJetG5kbSXHz1TjYmT9GQoTCT0a1ZQhL6bZmSTHkWW7AwQfLdTXouEMoxlcInOUChEEAP5KwB+COAwACFXF+f8OQDPlf3uz3zfcwAfdv9FlphrIOeFO1Hjz3tl1spiEcV2qW4cpyXCkT4oRPFNz/uebSbH0R5w452Cvc4GpaOP7jzIUUxfVtAvehBFxLJuUDGXFsyT1WcAUiGdrth0ATpqQixkZCj9pjXfBkEM5DbOeaQN2LASpEnlGuRB1v2irSxMXjQ6afxL4VQLuwz/xCqdzaE9EWw3eat4gmWhDspkrxJNXBq50CCKwSktauQksih2C5PeUSmdEbvLsvUtDPWiMchKJjzRMVhNT8CbRZA38fOMsQ8yxq5jjC33/jW9ZnOARp2Icw7PuVjbQJbQa3pjn6pyDLx8AfUvYP/qwExGYJHGcAyx6Y0Z8jHoDiYPWqMZRPKTYhnj2oRHS9YwKRGWwVhoh9kyohICQ0VXqERxn4ucvFuIVqI2SdNBEA/y+92v/uOlOdSkeWspxBtJfQHPcGqPxzCbyyObyyMRl8u7WqJVaQyyPLKDB93ADVdv9kfPpLO52h8sQ9orEq7LN4ZJbwcpRtGQkUtBp36KcaLEsJFAxZhkesLabIysZBDHWFMrIdrjl+VFQ01DA5lzfqOOikSZZhl63ga9zjbHQM7kOBJiecPDiaKBXLaUojdNVl7tcOAPsRDyIBOhXoXpQVFav6r2FyGDRHe8dEFW2sBQYTDqkVGB6aYk6+E3AdUpKyIvGzpAWqUytJpAmlgSsuSEmZruSMbYo/UEGWOLGWN3qK9SdBFtkwXPYY1G5RlO89odq3i2SjowuY6gDtpu3Wj2JtUvh/IY5MD1iI59ViBKRmUzkTEcdYdlqNCrQr+4LoJs4TvNR1wTJ+2AZi99xPoxNRxLXJ9rMBLakel3exT0Npt6HuSfY4x9GsBGAHsBXAHQCeAmAG8HsA7AHza9hhFAOhtCwBCLeW2OgZzO5QC0CZVRVa/p2FFV5Wge9JqFbAwy2QNsOIaZinSaO2X6FRU0BxC9VaTDPtyvUfJmqQmx0Kc/shsDNb0zlEwqSZv0dAm5oi06FtY0kDnnf+Buxvs5AO8BcB2AaQDHAHyFc/6qniq2Lo0aVa4QYuEYyNVyIZueZarIkapdd8h26fmrI+ZBNmsgSukOwUCqwltHr0R01EUp9pOikypLgRKzGrY9FU1HY8c1kWs+mitFnse8tagbg8w5HwLwVfefRTGNGmTBg1wnxEJKr8Lx1EScHzm9F0298heS34Oc1ulBNhCFrHZyJikXAitddw1okwL9njtTkxjTxqapEIs5k8UCXCwG2f0qvS2W8jwN7RuQ06dVnTboKREs0jRqxJ7hNL9eDLKUYhkh9VDffaa9Pare3TYGWbYsSTn3a9Ri4HUuoavSq0K/Tl3GDHPFY0qzMT2REEV63JFsD+YPfpGQMVznMGIN5BCTc1tsZ6LeJr3oxRObHlzDZlj6DeTpTPPTvBXkDcQgh+zWkzC1lE/ZGR+VuFxT99ZU+7Rp3hpjIlc0+YARGVlDz1F7zHMEsAayShQnIS/EIHse5CqHhUTRQPWgxyBLyhV2GOvVWwv/Yx2fySouvRKTL1K1qvXHB0YV3d6oijKEDROCLvdrROYCAKLXJqUdsgouVOdzlV1RUGPM653pFVcxJAJKIrYCEpSGBjJjbD5j7KOMsa+6P9/MGHtX86sWHWQNvXwDQ60QYtGmOM2b0hhkiThUA57LEnmauPKXmd+DLGQgm74PUjLmLYGCAWVqkyhRVkqfgpcfRa+4HGESG8VdegTMeFb16KmmMwqx1kryeJNkNY8vIRjXm0EQD/I3AKQBPOT+3AfgL5pWozlEUA/yvDoeZCm9SkoxD3WHsdQxtlA/GPgN5LHpjEA9oofpDaKOXBTvXLQM8gr9Gj3IFEx5n0146Wm6aKEHkkrlRSl6CfpkXjGF95LmEDadE4+oEMRAfgPn/NMAMgDAOZ9C63nSjdCoTZXnQa6exULGg6uuNcsUFZU4SF3k3eeciDGMCXiQ6Z54szHMVKjqTW4SJcUg6/YCm4hNJExio/iiV+Nx1DeBooa3kdBoKJtoDuZOkaTfV1mnU1gJYiDPMsbmwb0HjLE3wPEoW4g0zGLhbdIr5EEO30l6MkTdsFJdfy+99dL5bRifEfEgm36S4ph+9mFB6jZw74vEpNj9KneMrLhMLf3iuvWGcJk6MjdqBgY1g4x2WXKoj9hzMZ35RS78zYFy1HSrUTcPssvH4JymdwNj7F8BPALgV5tZqblCYA+y6jRvEce459T9qupd5tVnybw2rR5kKnKGmtnVC4pcWNBdfxP5WGmp2jwjNxrGJmAmjpgmq78TeXWkGcqaPOVKspKQi7AQaWggc85fYoztA/AgnFXJ3+OcDza9ZhFE9RgTLMRCHNMdT5V66nVIvz4V30DvOS+d3y7kQTaN6Q2isngvL6oBpfNFbUKfSoQNE1MhFgRZGipCLMKPuVAfyYlaoR0KK3TkDEWjSt1nQyFjYaamgcwYu6fsV5fcr2sZY2s55/uaV61oIdsJGnXaCgNZWYiFeS8eVVYFYenT3kF6S+a14dzVycByYQsV0Q31pTeXoBmNBryF2jW6eo3FIDtfCSmutSIfYkFZGaDpdvTLSUjaxyR0TyZUTECis2YTjHoe5M+6XzsB3AvgIJzrfzOAPShmtbBI4hlGtbwkXnaDzrohFtELQlb1EjJlIKm+fd5zXjqvDUeEQizMGoim499NxEGW6qe/7LXppRhgBmKQSQajAgNKt+fPvEHV3M8X5BTUUeepkCbDuKScwIYcVq3qdKi5SY9z/nbO+dvheI7v4Zzfyzl/C4C74aR6sxBp1KiyucYeZCm9SkrxyqLMOvXKFeVps13leZDdmdKS+W1CeZDphr6JOMIWHUk14N0706tGQnpNeBqlJc21z6gZJ1HTqT1uvxDGJSOrQL/u623RYT1IFotbOOeHvR8450cAvLF5VZo7NAyx4I1jkKMYB6rqZW1qdi+7s7kW3nNeOq8d05lc1WwlYYSSTUGJfoPeHcD4QowQ1NzfqvTr0RWlJ+OgInuGio2NzdZlejOiaK814SkvlqF3kqhk5aXFYiyCZLE4xBj7GoB/cX/+JQCHmleluUNQD3JnWwyMqUzzZmOQVaDqPhZjkJ3uOD6TxfIF7UrKrkfUvEDK6hAC01ZvHGX0+mkY2olOKJdbOFdC6yREUs7ARjtHVlJO0vIrhuqI48lI9XMJmXJZubCm1uywQTzIvwbgKIDfc/91ub+zlCEc39Tg755B3J6IoT0eU5bFwjgGY2BVqKfKbz55BX/z0snCz/k8R4wBizrbAIidpieDUQ++wnFU+870GuXohhL7bc7BI+e5056PWFHbkNWrW7ZQhiadpLryki9a9MuPMfSHQrtXEqt7CjzWrWYnB0nzNgPgc+4/SxWkB9MGrckzkNviMbQnYkgr2qSnshHTxjszgw8nWgvFDURyBXzg67sAAB/+0Q0AnE168RjD4nmOgRw0DpkeKkKTl9IZAu+th6kUTID+fkt7+RmQVbABUi4jhKEY5MK+iGisUUuP3SSdBFliM9SZxSI8I6SloYHMGDuDKs+Mc/76ptRoDlHMYlH977NuiEVbPIZFHYmqhpPpTAIyqNIvvWxm/A6UkuMcjDEs6vRCLKKTC1kUpZMz44/RrDElJuNi6iQ96dU1mTzICoxr3Xaq6ZhV432pMZRNqoUymvz5ckFazL/mGGRpbfBdL6WQ8BEkBvle3/edAN4DYHlzqjO3aDSgZbKeB5lhyfx2jE7PatGrqyxZUW/QMZXmTPW7hHMg7jOQxwIayFRD38Q7MQzv4TDEy5leUhfSR5E1sBQfgscrjImVOAryz5X+vpC7XtpqpajhR3kmhRhkzX2AspE3bE4nVTSMQeacX/X96+Oc/y2An9RQt5YnaAxyWzyGZfPbMDylxrNo+gVCD5FQcwHSB7wovoE5NwZ5sReDLJDqTQZlWRykBmHzA6nJ6yfpK/sqJGsqXtqMWmlM1Zca9lVShpw0RThkWuro11QBWpgPRa+hVS3TD7ZJBAmx8J+oF4PjUQ7iebY0oFGjyuSLIRbz2xMYmpwSLqPZmIy1apU+meccsRjDwg6nW02mdcUgG/A8qSxL1ovlfjW5HBilO69kw5FGnSY83pXlcG0p9byJvs42ZWRloOKb8GJi42Spfr1BFsX5XWvFWAQxdD/r+z4L4AyA9zanOtFGPJdkfbwQi/Z4DAs64pjO5CRrVqcOGgfyok6t6qroD9cI62SxYGhLOAs6Xnq/Rpi+CtMbRGUJRx0I944gGxWPFmUSQzPC3CV14ouec7G6m0p/VihDOEacODuVEVVidAq+oyX7jYohxqaCNE8QA/nXOec9/l8wxm5sUn0ijaih2TAG2QuxSDDMb09gMl1pIJs2UkibBA0FA1M34qj2MuXcLBZtcadCqk5MbKhfi5bmQTUMqNNC3ffPu169viE1CBtgCoxcUxsSZTCxEdKELhUTAZ3GX6vG1laDEuXTqvcpSB7k/wj4O4tiPAM5EYthfnscU7OqslgUheiDajS9iAB90KQ63m/8yHM40jeKPIfjQY453THoSXpkTzg5RENGpb/tUWPRJeVCMJhTXvLaN+Bo9mT55eQOLZBUqhDhkBKdyhQUIf1cTXuQJSdqqp1fgcpQkI1FF2Hoc82gpgeZMXYrgNsBLGGM/azvT4vhZLOwuMg2jry3Q7bG34tp3hg6EtUPCtG9lKICiiegWjnickS9Cu/ft3adLxwUEosxxGMsMkdNSxGCgTSqm/Qo0CYFZl/2cwHe4F0QqIwI3GM1oQcSThlpXZJy3jeGNl1qD7HwvmmtEOS6IRa3AHgXgKUAfsr3+3EAv9HMSkUV4VQwDRpkNpdHW5yBMYZEPIZsniuJGeY1vpcqi+DNMoWxXKdViDOGLM8jHnMq0xZnyGiKQTaRJq6k7QnGaarQT5FTCSUDiP5DRuRl5XWaeUqq9DrlBG/cxmNWhT2r+p+Pic2i0noMePXp+iI2SGigpoHMOf8+gO8zxh7inO+QKZwx9k4AnwcQB/A1zvlf1fjcz8EJ27iPc75HRlcUadSkMrk82uLOsnvCNaByeY5EvDjwym32CUdjpmdhkJQzYBjWIh5jyOWdEAvAyVjS7CPFTT79UDQ9VUaQ5jvpaaPE/ZuaE+qNj1WwNC0Vv2wgFsUQ8h5ZBUYuYXIpLCclRZNUAWkTsEzbFxeJBPVCLP6Yc/5pAL/IGHt/+d85579br2DGWBzAFwH8KIALAHYzxp7mnHeVfW4RgN8D8JpE/SNNwzRvOV40kF2jOJvnSMR9ZcjoLamDmKejXlmBZZQZKGZQ+bKPxxg453DDj9EejyGbDxqDTNNtIk1cSQwyTT09hpmon4b8C0x3nzMRH0tZ5SlmHpA58EAe/y0Wj+l1QywIxgmt7mLS5NADgqze65QLfaFkjSnXHQVUXG8YqRdiccz9KuvRvR9At5cBgzH2HQDvBtBV9rlPAvgUgP8lqSeyNPJAzbohFkDRg5zNR6fT1KI40EnO6k0bhoo26QFAjDlHTfs9yJls9J9xLUqMCAOTM4pcRTmaVyRNxZiaWC6mZTswgyrjWlzWmznpvHL9Y7eRUB/NclRMLWJEyZgXoV6IxTPu129Kln09gF7fzxcAPOD/gHsIyQ2c82cZYzUNZMbYBwF8EABWrVqFVColWSV5JiYmaurtu5gGAJw8eQqp9NnAZfacdo6OzmQyVcvuvZBGPptDKpXC2bPOKXqpzVuxsL1oVOzbuw9jPfEK2XpcnCh6KFObNxeMbxl27dqNi4uCJEMpcqHPuV/nzp1HKnVZWOeke2DKiRMnkJrqafDpSs6ede775cuXkUoNC8v39DjyQ0PD5LbYd+ECrk7nkZ7OI5VKIZdJo/fiJaRSQw1ld+3ahQsLxe49AEy59++45P3z2LtvH0Z74nX7RjlXp4ttb/PmzYXYaxn27RNv+0Dx+ff29iKVGpDWv2fPHvQLtn2PXbt3o0/w2fUF7DfVnkfXRScDzuTUpHCbPTFUTC8pKjs55bS1rq4uLB4+GViu+4zzjAYHB4V1njkj37892SsDA8J6cz7nxWbfuBqkfxwacJ5PPpcX1nu5fwYAcPbcOaRSl4RkPfYfOIDp88H70qlhuTYxkva9ewSv02v/Mv02k3Hen11dx/CmxTOBdR+/4MiNjY0K1ffIZed55nJZ4evM5px729V1DEtGTgnJDg9PAwBOdZ9GKndeSNYbI7JZ8Tqfcu2TkZERIVmRd4cJ6oVYPIM6kwrO+U9TFDPGYgD+BsCvNvos5/xJAE8CwL333suTySRFtRSpVAq19L4ycgQ4fw4bNtyM5EPrA5d5lHcDp06gra2tatnf7z+AhdPDSCaT6N1xFjh+FA8+/DBWLuwANj4LALj7nrvxlnXLha6le2AceHULAOBtb3tbIYxDCFf/fffdh1tWLxISfXnkMHD+PNauXYtk8lZh1Qv2bwEmxrFhwy1IPrBWWH7f7AngdDdWr16NZPJOYfmjvBs4eQLLli1DMvmgsLx37wBg7dq1YENTGMyNIZlMYtHeFJavXIxk8p6G8vfffx9uulbs3gPAvL0pYHISt9xyC5L3i98/T/8999yDe9Yuq9s3yrkwPAVs3gQAeIzY9u6++x68Zd0yYfHd6eNAz2msWXMDksnbpPW/5S334rbXLZaSve+++7Bhldize3H4MNB7HjesvQHJ5Btrfq7a8xjZ3wccOoCFCxYgmXybkN7OnqvArp0AEPg5e8zfkwKmJvHGN74RybuuDyx3gp0GThzHNSuvQTL5FiGdB7OngO6TUv37QPYk0H0K11x7bf0+WIVMLg+8+DwA4LHH3oZ29+CfIP0j09UP7NuDeDwufI+/e2k/cOki1q1bh2TyFiFZrz3eddddePD1KwKLLTg7BLzmbE0Sqe/A2Ayw6RVhOQB4yW3/Mv02seVFIJPBbbe9EQtHTgXW3b/7PHDkMJYuWYpk8qHA+iYOXQQO7EciUf3dXo/4KxuBXM7pM3cH7zMA8LXu14Crg7jpDW9A8rHXC8mOHnDGiEQiIVznM9vOAMe7sHSp2H0SeXeYoF6IxV8Ty+4DcIPv5zXu7zwWAbgDQMrNyrAawNOMsZ+eKxv18nlvqb66F80fYhGPiZ2yVo/SZW5iWQbzIFPTvFHroSLEIpvLI+8eFAI4MchBN+kZDzWR2ixD06mCMNRBhkK9IxWeoXtzFDE8gxBLWRqDLBfr2uqoiUFWsAmzSZ8vl5N5R3gypk9XFNLXos23XojFZu97xlg7gFvhtNETnPPZAGXvBnCze+peH4D3AfhFX/mjAFb6dKQA/FEUjeNm5ePNZPNVNumVGk+UHe2moG62KORRNnQhKl9mM9kccvmyGORWzoPsw8QhNaXS5uTlrl2F4af3oBBZVMSq6t7wZipuWsWEX7fhqBvtB2dQZFXE/Gt2Xpi2KZpFw/VNxthPAjgN4O8AfAFAN2PsxxvJcc6zAB4H8AKcDX9Pcc6PMsY+wRgjhWeEFdmdrrWoluat3IMst6PdL080Eggd0fRgGYbE+ulMvnCSHhCtPMgyNVD5zE3spA8D1D4vrk+/4adiI6x2o4iwMkfL2uE6DDS2bPmVAfokT2dblm6/ptLZKfA6y62eRH1UrU69EAuPzwJ4O+e8GwAYY28A8CyA5xsJcs6fA/Bc2e/+rMZnkwHq0lI0asxOmjc3i4VrKLdCFguqieJ5wagvX1lUjgXprBNiUUjzVuPERJWEpQWZzspgckynhCZRThKTMjgNeLSMebMoufRImPE+F8vQYzjSoBjXkgZ9oePI6aWleSPIatbXqgTZITPuGccuPXBO07OUIdq+GjXI2WoeZCUhFkUhk3Go8qEpigzcEHggZzI5JwbZtVzaE3HMBgyxiGQMssK7Z2qCVChH45K2X0bnQQmA2eViOYNe3qNqykYg3ScDEz4TIRYmQg9MpCItxiDLY2r1JAyn06okiAd5D2PsOQBPwXlm74Fz6MfPAgDn/LtNrF8koC711np5ZXN5LOhwHlHNEAsZb1KrzBQjfVCEw/hMFgn3OHEAaI+zpnuQTaJ0g6i8hRxJopgb2AS84hsBWcLkXWXbFtLr7cmgFSKlk6KGc15zg3pVWcIKiiyymzaLIULRiPmn6g1DuGIzCGIgdwLoB/A29+crAOYB+Ck47X3OG8gewg2M1x/YZnN5LK1ykh4VpXGglBeJqYGAKq+w3oMTaaxa3FnMYpGIBfcgkzeZ0ZBaxiPqbBVoKy8yCmWESvVS0JnVQcWLXn/8Z8SQ9iBT7pH+d5/0HFyJt1vvvSqGYUXHqG82DQ1kzvmv6ahIKyDaoD1bt5bYbDaP9kKIhfM1Vx5iIVZFV8Zsa1a2xC0rR/REFDcz0NeTrk7OYuXCDnjnZWjJYmHw8av0/phIIWaS4oqTbr36jRo1RrleaGne6Hp1xqyqMBw5F1uSp0wQpW8N9zzBsgXIozvEwthx9CGmoYHspmn7EID1/s9TDwppRcQHGc9bUV0yk+NoS5TGIJdnOKCmczEbg6xft6OX6HlVOBoMT80ik8sbyYNMDlGR8rKZp7Dr38D1F2Qpm/SkvENmPLKyZZCMgwbjal1ZgrGpos6U/MumHR+imKitLp1RDJNQoVeF0yhMBAmx+B6AfwTwDIDWDY4kQPZk1vh7iQc5Xv2gkKgNikB4QhzCcOc4d8IsrlsyD4BYFosoLmspnZwpqEOUUHIAhszyqbRWwiSW8MJV4UmVm8Bw3/eissLqirImQg+Iz1VKllKGtDNFr5z6QvSoi6IdEoQgBvIM5/zvml6TFkDaS1JDLp3NF44rjdfIYkHFRMNWtdlCWl6RYUa9dzHmhNkMjKexZtl8AE6IRdAYZCr0iYrWxc4q+iXlFNVEu1fWkHdTBbqyBziypV+FZDWGKSiTVdWgRXRKb9LzTyI4RHzmtBUUOWQnat7YSErzRhpf5AcJuVzc4jJRIIiB/HnG2McAvAgg7f2Sc76vabWKKMIhFg08yJlcHu3x4gESQJWjpokhFiYwPdskeSJIgqUsnd+OoclZTM3mEHMnQB0iHmTpl5Q5KHGacx0TnlxH1mx4hk6dpE16JW1bVNY1qDR7+ItliJWiwoMsfI+UeMpFrzNaY6zplYhWI4iB/CYAvwzgv6EYYsHdny0+hDtfg1i52aoe5PIQC3FM50GmeMKqFCMupyg9XNBi0tkcOhLxit974TMACpv0vCwWoimQZDBx//0ydP2SLy9V7U/3EighdpoU4yohQy3DRNyzI0TXawqdVTdymxT1WwmV4l5VU+1XgaylSJCDQt4D4PWc87dxzt/u/rPGsQ95T2TJlwpmc0UDua1wkh59+Z1uGPgMbI3LXcrkG9x30XLqsf30IG796EZs6x6s+JuXug9A4aCQtngMnAO5AOn85L045kbPMAzcYfCUaN8EZmr934BKSl5gUrSC3zsq7KmUUVgqq9PTr8KzqnPjpmwZ1I3gpKPSJWQoh4yYWmUKM0EM5CMAlja7InORfMEjVPm3XJ4jl+dojzueRy+LRbnhRH3R6u5Ijrz8C0xlPXTo3Xt2GJyjqoHsTX6A4tKq9zsdccgmsjiUrF6QtMsXEPVNnhTDTyq+0MDStqlgYF5nTG6slmCgEjz8JlqiitU7nSFiVINetN+YWgFR0W0omVTm4kl6SwEcZ4ztRmkMsk3zVobsO6DaQOHlwm1LOC3Oy4NckeZNxoOrKMTAKYsmL4PXB4mOe8KgyUu+1sPLPlItt7E/xML71lspmM3mMb9dqnqhRuWkhr4SYdCTLtVvS79KCWuGavrj0HgAACAASURBVJhQVqh0nzJKi0Gm6zW1wVCXHiVeSk0XWlQjE1OuYkKqR6YgKy8aaoIYyB9rei1aBOGNDt7XKmJpd6NWe/lJeoo9iyaWRqgDumnPs4i897zKJzZAqQc55k69F7lHi49NZ7G0yRayCQOzxIho0WW5ZkExGkl6DdglpmwhyvoGKXxAgXGitVUoWL2RDbHQ6ZSRnuApab8ybdDURNiI2qYT5CS9zf6fGWOPAng/gM3VJeYitE5UTdrLZNBRdlBIxSY9Ax7c0mUy/fqr1UNMv74Qj4l0FgAwk8lV/K3Nv0nPfb5rljn5kC+MTGHtivlk/VXl5MSUoHIAp8Zgq4zFF5fVrE9a0szLj5ZNQu9zUaLX+0YqxZYKj6Ooc4e6fichWRCl3Gc9jZmix0sppzsjhYkTM8NOkBhkMMbuZox9hjF2FsAnARxraq0iiviMuLaF7MWgtlUcFKLgqGnCUqCsTKn+4CEKTUWDgTk24xjIU7OVBnJpFgvPQHaM4gtD03KVE4D8XiUu45l6+mEYyin9jrJ8KheDrACdHlWCS5VkmNf8IYCsEiOXXETTdZlaxqfWVzwPsitnLOZfXkYq1SDhesNMTQ8yY2wDHE/x+wEMAvg3AIxz/nZNdYscssuIVWOQvRCLRFmIRYDsBgE006SJy+SmDRRVIRZBihmfyQCoYSD7Qiy8hBbXLe0EAFwem2lcD+N30izkNG9k/Zoh1JvmVdXvuTaxnO7XR3U8CMvKiyoJvZF9d1H0yGbO0DoRcGtswvDTPb4oaYMt9kqq50E+DifX8bs4549yzv8eQOVbfg6Szubw3X0XKl4cKju850FubxhiIWGg8urfB5andl3vJURcIqdCPg0qgPi460GezmQr/tZWxYPcFo+hPRHD5Gzl51VjwkCktr1aZQnJhWBiQcllTDEaZY5uVoGugyj8stpjtUsimIWXEwHQMgjoRHriY2gSIavfaBgb4d0upd8VlhohWs0ydqlnIP8sgEsANjHGvsoY+xHQTk5sGT7/8il8+KmDeOXYAAD5tpGv05g9j2NnwkvzpjDEQkJGZVmqXlymDewg1PcgF7uTF4MMAAva45hKN56LmljmpBIG41QVuu+jqgwLwrLyouRJDCWWkhK/bGoDKWU81xtioSAMgLgCpEWXlBSMDbIFrcay1hhR2zRqGsic8+9xzt8H4FYAmwD8PoBrGWP/wBj7MV0VDCPnh6YAoMLLJ57FovZgPDw1CwBYvtDJZFDLg0yNAyXLS6CqE1EHPel6CHioCh7kKgZyPBYrnKDns48xvz1R1aBWjZE8yMS2V1KWrBxxBUMFNO+7jNHoIBUTSTKu9bvgjGWEIKyOkMIjTKX8kFLj87ILx3Wo0C/4eYMeZGob1CiqJMwnjDTcpMc5n+Scf4tz/lMA1gDYD+B/N71mISbrpuyKlb1tRDtTPQ/y0IRjIC9zU33FYgwxVtQdFshGkoxOmrhW/eN1NukBxTCLuM9Cnt8ex1SAEItwtYRghKHO6upgZglfd2YHFdepM6WXrE6yPnlR32awaGAmxIKwqkBcyRDduKbieVI85bQJuLzsnPEgV4NzPsw5f5Jz/iPNqlCUmK6SukuEejHIF4adLAbXLeks/C4RjyGTLw+xkBksfLN4jYNNuU4THkyg/gmGqvXXC7FgKGay8A/A7YlYIc1fmDHR9mqVJSZXrIEpKMbbXAjtULGpUH94hr8cUb10aJspBVc/pZ+rX6ecrM72aDKMj7K/iDKJNpV5I4wIGchzlVyeY+fFbGGZ3A0HxpSb49ZrjMOTs4E8fx75OjbQuaFJrF7cic62eOF3iRhDrvwkPalZJo3SzqDfwDa1NF+QL7yA65PLc0y6babaSXqMAW3uJsy4b1Rqi8eQCZCthJoH2gQlLYfoCaWYAyqQ3ZQrWwcVHkpTSBtDGnRV0yslSwqTIOg18mzpY49w7mVD3n1A3BPs6dOd/cJUNydtDgwx1kAOwKvdg/jyoTS+vPk0gKK3b7LMK/i1V8/gA1/fFbjceoPF+auVB0W0xWNVDS1RVGYSINXDkDx1l3vQe+YdEgLUPgHR8yD7Y5Db4qyQ5q+ZmIglD0v8uuqydOijtNvCUrGMXgkZqqwKI5fiPKAuUcsevCGVg5awrF4oQ5tnVU4OUHSdwp7y5r4j6pZBktU8AVdRSAixBnIAPE/xgd4RACh4cSfTld7i3WeHA5ebLxlQS/92bmgK65aXGshObGqpUa5zR281naSXkOwgKyemrIRi/euX44VXLJvfVjN/tZff2p/Foi0eQ7be8kJZPUQpnNRkZDSjeYB5zR8EylB02ZRldGOxtYRDACjIGmC6x7ei3uh4+E2E3qhQFQVbqhBLLNhtCt2NEIVMWWHTHn8fhYcpgTWQAzDmGjpezPGU+7WagSxCnlc3FmYyOVwZT2NtEANZRjGv+q2MuBR0T5r8IOCXa3an9jboLVvQXtNA9jbn+Td8JuIxzAbYjEndeEKF6mWT0qkghtnUYE6PS5T3oOn2RhVkDbRR2nIvRa/85I+yJG9yAkPRo3PSJD2ZlhNTEoMsA+UcsaIoZRWjtSxlayAHYMLNSTs27RrKbpyx93sVqYz8HWpkytGzYmFHyccXdiRKlu1NQY8DVdOZTBlIQV/ABQN5fjtyeV510PRij/1ZLNrjrGZIhp8Lw1PBKlwDE2N4qRdVr5euWAZtgiWvt/r3geUVeHhMHUQhvwlMvo2Y9KIJl6NiU1cEjBOdp/1VLUPSKDeRXYQyD1CzRiou0mqeZGsgByDnLndfHHEyS3ieZG8JXZZaHmTPY714XulJ4E5+3LLcy74ysrk8dp0ZaqjXtJFCNlCp+omxbEENrMGJNABg9WInE0k1L7LnMSrxIMeCxZr/3ncOBKluBTQjgFf9XofucnkTcZCUckyGJpk2THQStXAFQI2HX+cmNiXOCVkPMiEGX1ZOOs0bwbKW8kITGoGKI+WjNlY0whrIAfBslbGZLNLZXCHMwfP0lscZ5QOuc9SKQfY81Ys620o+v6AjUfBaF+R83//V88fx3q/swPHLY3X1mt7oRHnRU3X75ZptYF0ZdwxkL1VfeQ5rBn+IRfH3bYkYMhryXev2YjrytNUHFV6y4mAuY+BTXiJEv44XWiRVb+er7l315fqDf17e6Cs+I32GlKNXvhzTHn5dOkudQuJmuf+LDLoc+8YymigoIzrZuJuPNZAD4O/UF0dmMOMayN5pd+WUn7AXpFx/h/Jy4HYmSh/Pgo76B0hsPnkFQO1DKarpMm0kyckT9dPEA5czMD6DeIzhmkVOqEy1jXexKiEWbTGmJFtJLVR5mihLgNL6DXuQS/XLGUEysoDv3un2bpI6nKznjqCRMAE24aV09Jr13oln3ZDUIykHqOu3QnLyKo1Aar8KVhZNxV43C2sgByDnc/V+67VzhRCLYdeDXN4YJ9PBDhApMTZ8P3hL8V6GA4/57YmKsv1ynmE81UC/or1CblnyL3pTXak4MWluDa6Mp7FyYTva3YlOtVMQPQOZledBbqKB7EHzztF1mprokA6DkNSpAhUvMFO+IZ0GlalwElL4AEGv9BYYA8vxpE16ZV/JFQj0cfp1yiJTRt6QgUrbGBteEo0/YvHHjn5165nCMuXI1Cw45xUNeSKdAdCJRtTqfJ5BHo+Vzl8WdsSrZM7weYPd8mZzjTzI/jo0rGYVebMe4GI5ZtwCBb0N9A+Mp3Htok4k3FzH9WOQi79LxFnojhT3KLkEA21HCQoMTZliSpfgJXQTZL3nJpVn14TnToFO3d5KFfpIeZBFDU5ie5TDjJddXqecbkpNvSZA8gJrHptDMKo3BetBDkA+z8EA/ME7NgBwGuHCjgSyeY6JdLaicZTHCdcut/h9VQ9yrNKDPJ3JlXi0/Xhys9nmNldlL3pp+5ZooJP1l36txZXxNK5Z1FF4jtVCLLzBsPwkvVkdHmTNy8huATT9xNULfxUobVdOL3ViKb+MSQur0e8OoxxHr8JQ1b26oWbE1mfEqZj4iBaRLzwbfYYcdTOi7phy0thWmKTJy7ZYhIU1kIOQ4xwxBvzIG68t/M7beDU8man0IM8Ei0EujQUufu9lzYiXGcgLOxyHvz8O+bf+ZV/he295pdHyvIpcssWyZGQUvQ4MLC2K4HiQfQZyFa+wt8Gz9KCQ5nqQaUurvu+lDGwaSjbpqbp+isdOwUtQTMaR0h1iIXuZFC+wrNfP0WfGw6mqTcrqFA93oE1Oy/WLylL0CslJtkM145SMjBkvsIpTDsNIUw1kxtg7GWMnGGPdjLEnqvz9w4yxLsbYIcbYK4yxdc2sjyz5vGMgr1pcDJu4buk8AM5GvfLO0ChX8eh0BuufeBYvHO0v/C6QB7kjDqByE57nUfbkGhrIdf/aGHWdgDY7p2olD5p1CsjlOa5OuAZy3PMglwr4l1P9s/YwxyAr1Sn1AiCJk+QcWTUvIP2baMzIFspo8udLZQkv64JBRJv86exbsitipj3I4rLyz5W62iSuz/kqEzJD1e3olx9fSF7vFnMhN81AZozFAXwRwI8DuA3A+xljt5V9bD+AeznnbwbwHwA+3az6UMi5BvLKhe2F312/1PUgT81WtOTpTH0DuXeo8oAHfxHFGOTSprqg3fEgl8che2nhvCOwGxpXZCOF5oEmG7gE75IS/QE0X51MI8/hhlh4m/RKnwtD9TzIzlHT1Q8WUYGqF6OR+PUadREqg7AcSPGgkzc4SsaaOrIEvQqMGlmdUuKavfMFWUO2gWxKvNLPiwnLnthGP2RK72QvavZeXrItWKrTTA/y/QC6Oec9nPNZAN8B8G7/BzjnmzjnnrW4E8CaJtZHGi/EgjGG5C3XACh6k0emMhUdvVGatWrp4UoP/PA8yOVp3jwDubR8z2NdiEFusDyv0kiRkzfbiYuTXTUGSzWGJp1nvHxBB9pqeJCB4mzdPxXyPl8tF7LpNDoqQ3J0eoJKyqDIqjK+dBtxhpZe5dXT60tZopa7XfKOA90hN44c4R4r8MiKFkHJ0EC9R+KGtYJxijiBF5ZV0B5azS5vpoF8PYBe388X3N/V4tcBPN/E+kjjhVgAxcM7vHjgYTeThZ9GadYGxtIVv6vqQS5L8zavzQmx8NLMeXh5l71NYJlsoxjk6nqDonKZnIKpGOQg3q10xs1l3RYrZCOpGlfMvINCSj3IQPVNfeU6gx5KU68MEWodbhNYN7EevM5PgcsoDOYS9w7ynUfZyglh45qpSYm4TverlKz8y5p2nySECvocYcryNsUo1xWeocL7S5vg6rlQNWFJuvu5hFC53hazkEOR5o0x9j8B3AvgbTX+/kEAHwSAVatWIZVK6ascgPMX0mDgSKVSeGxJHueuiWP19DkwAAe6TqF/qtSQ6TrZjVT+fM3ydvZUepC3bn0VC9qc4bGr1wmZ2LVzB5Z1Fucwx4ccw3jPvv2lsjt24/KyeMEAO37qFFLZczX1Hxoohmhs3769REcQRtLF6z1w8CBmL8SF5IeHnSO7L166hFSq8dHY5czMzAAAzpw9g1SqT1i+f8CRv3p1SKot9V10JjgTExM15U8NO8/q2NEjBc/Ha3v24Gp38V5d7u/H+KRzL3t6ThfazNmzzvPftHlroU14lHtRXt6UQntc7LWaTjv1P93TgxS7ICQ77cuQcvjIYSQGjtW9D+UculJsezt2iLe9yUxR/5EjR9E5eEJIHgAG3Offf7lf+PnP+K5ftO37607pNwMDA3XrXe15nD3rjDmjo6PC13z8QqbwvajsbMaRPXXqFFKzZwPLXbzktNGhIfE+erl/Rlq235UdHRkRlu0bL46L27fvwIp5TtsO0j+6zzjPZzYzK6x3bMxpF5cuXUYqNRxYzr/SeOToUcy7GrwvybaJrqtF58627duwtCN4/y+0/37xfusNm6e6u7FyRTqw/LnzznO5KtiWetznOTMzI1zXfN57J/QgBbHxeXLSWZDv7e1FKjUgJNvj2iWzs+JtsK/P6a/j4+NCsiLvDhM000DuA3CD7+c17u9KYIy9A8D/AfA2znmlaxUA5/xJAE8CwL333suTyaTyytbjhaFDiA9cgKf3PT/h/P4j217AitVrwCfSwKWLAID2RAzXXn8Dksk31ixv8/hR4OTZkt89+sijWDLf8U737jgLHD2Ktz76CFYu7Ch8ZuHZIWDXDtzxpjcDu3cVfr/h9jfjsZtXgm98DgBww9obkUzeXFN/tqsf2LcHAPDQQw9j9ZLGOZv9DIzPAJteAQDceeedeOSmlULyXz65AxgawnWrr0My+WYhWQDo2PEKMDOD9evXI5ncICz/VN9e4PJlLF+xHMnk/cLyLwwdBnrPY8GChUgm31r1M+3dg8Brr+G+e+5yUrbt3YU777ob961fDmx8FgCwetUqTF+dBEZHcPNNNyH56I0A3Od//CgeeOjhkucPuHHMLxQXWh54+FEsmVd6JHkj2re/DKTTeP2N9dtJNcZmMsDLLwIA7rjjTUje5kxYg/ZJfmIA2LsbAPDgQw/huiXzhPSPTmWAVxz9t99+O5Jvuk5IHgD+vW8fcPkSVq1ahWTyLiHZiXQWePkFAMBdd96JhwXa/sjULPDKSwBo/eaaa69FMnlPzc9Vex67Zo4DPaexePFiJJOPCOkd2NMLHDkEAIGfs0fblheBTAY33XQTko/cGFjuucGDwIULWLZsGZLJB4R0fvfSfuDSRSnZ/3RllyxZimTyISHZE5fHgW1bADht+3p3I3eQ/nGcnQZOHEd7W7vwPf7c0W3A6AhWr16NZPLOwHIzmRzw0kYAwB2Cfal/93ngyGEAYm2irXsQ2P0aAODhhx7GtYuDv3uCtv+quGPuTTfdhIWZc4HrvG2yCzh7BsuXi70rDmZPAd0nMW9ep/DzjL38PJDP48YbX49k8iYh2Xl7U8DkJNasuQHJZPmWr/oc5d3AqRPo6BBvg6+MHAHOn8PCRbXfidUQeXeYoJkG8m4ANzPGboRjGL8PwC/6P8AYuxvAVwC8k3MuNt3RSC7Pq8aiLOxMYCJdGoM8vz3eMMTi8uhMxe84OD7/8incvGphzSwW3oET5XmQp9LZkry5zc5iQS1AdkmwVjnS+pu4HJR2n0FbIlYIS6j3XEoPConV/Hx5lWcbhNPUQ3eMm1MAVdwf50kvQ1iWsMmIHJ5CWD8l3XYFy+K65AA1S/D6j5p2vkolPZAMKaGE2pnJYiEvS9UpnpLOTKwB5d1KSxEnF6sddppmIHPOs4yxxwG8ACAO4Ouc86OMsU8A2MM5fxrAZwAsBPDvbjqU85zzn25WnWTJ5UsNGI+FHQlMpLMlBu/8tnjDTXrnrhazWDDmNCrOgc+9fBIA8Kc/6Xify7NYeAZzueE0kc5iZrb4u4ab9KhZKErKEhZXsMmPKE+OZWv8QvIM1/Z4rBAnXO2AF+8Jl+ZBrh2zXH6/09lgh9LUK0NM2F8OzYgwYqATy1AV/0iLL5R5+ZWWIaaXftOFDTCKLkMbEjmhb5g4xUyFTlGDnjLBVRODLPh5gi4qcqna5A1VJfHh1kAODuf8OQDPlf3uz3zfv6OZ+lWR57y6gdyZwIHzI7joN5A7EnXTvHHOS9K8zW+LY3I2V5I7uehBLvVbe/l0KzzIs7mSjXvlXsXzV6fw8WeO4nO/cBeWzGtTZmDKF0ArhzpQqvJc1xvAvElMR6KY07jC4GXFPJmsZJOe830147e87iQPspSMGa9/UT+9LJKxWOP0y0CyEvpUFUBKEUd6ccoJG9swRBibTHlHZY0TyiY9by+EqMObtjFQgYUsiAlPOQUVvg8msVW0xeziAvYkvQDk8jUM5I5EiXEMOCEW5WnY/JwfmsJ4OosbljvxaV5WjP6xYjlevtyaHuQyA3kinS01kHOl+r+w6RR+eHwAL3U5B5NQBimgdKOYieOKdRi4QeTrUfAgJ2IFj3DQEAsvW8lMpnEWC5kjqZV5yQjlyMqrSfNGX0qUkiX2O9IypuE3mKwxpPtaVbUN+WYqbpwUct+KZrEQ1uSTdYVFD8LQ6f1VgYoTA6V1G1phk0GFdz+MWAM5APUM5HLmtcUxXSfE4vjlcQDAx3/qdvzM3dfjvfc6qZ/7fanfJl35ihjkmBeDXGoUTaazJToz2dJm6uVPHp32dh5TDVQaZA8wyUWkpxN7BnJbPFbT8w/4Qix8L5p57dXT+dXTIwN1cqRLZ4l8yfe0Cc5/7e/DheHKQ3uCyMroVzexk5At+yojK4O0rApvue5QFAOedopeSsiP/NhN16kzHEW6zxFCHQpFSMnob0eeZqeM1jKRrYEcgFoG8toV8yt+t6Ajgak6IRaekbph1SJ87hfuwjWLnCwFl30e5LHpDBgrjUsFih7l8gMkKkIsyryKnqHteaapXsCSQY7wspaF8rIHVBhpjQvwnkF7Ila4/6PTGbzc1V/yueJJesXfFfJdV5loVcYgS3iQFQ3a1GdPOQ5VVn95HTYeuSwtS1FMMVS1b8BRYCEL2xeFrwQjl+CB0/2iV9Inhb30BGRDLAgqqZNzAPje/r6Ge3RUYGRSCcDznVH3hwjLtpZdXCAUeZDDjnOSXuVQcPvrllT8rlGIxeiUYyB7Kd08C2nAbyDPZCu8x0Bx81a5J3IinXVS9rhUGMiFgyccOdljQj1UdQYVng8z8qVfq+EPsfAOCvn8K6dwqUoGE6B0qbKzxoEwQOUgNj6TqfhMUMwcE05dvaA3Pn8VhJeICZND+sqLgheY7henrJwhg96UkeCJymSxkD0YhRYPLAlFJ2Hi43G4bxRbl7TjxwLrpK1SUdDufXaF5NqghMIIYD3IAcjX8CDfdt3iit8t6kxgfKa2B3lsJoMYAxa2O3MTr1h/DPLodKYi/hgoepCzufohFuXL7nG3xaddg6skhpjqxROW1mOg1pV3v249NViyYVJUvt7LtOBBjhc9yOXGMXP/A6qHWMxUMZDLJzfnr4rXn2KqlV6zjLHl+57iRpXSXimp0wNWapDI3zu50BhxmYJeJS97sUJo1yq/vE0KsSDEIMtuepPRVRSUlPPp1JrFQvDztRiYCr7q5ul8tXtQ6NTSYgy9qrE2oIyCznppdAZf29ojppeyZyDEWAM5ADlePQ/yTdcuxA8+9GjJ7xZ1ttX16o1NZ7B4XlshfMIbYPwhFlPpbEUGC6CY3aA8xGLSF2KxoD1eYSB7m8OmqxjIMpDjUAtfJWfnJO2lg8g/vnpGWN67/vNDU1WNWKA0zVui3kl3wiEWpVf/YlnIhggmNnupjEFWUQfRtkyZHKrI4Swr6+k+eGFUqs3LYjKLxY6eq/jm9rNSsro3SFFkixMC0UkIwViVrHBefstE4SZRxxCRqDS/rle7B6XkdEJqv77v/+LZY5L6W8tCtgZyAHJ5XnOmfMf1pWEWizoSSGfzNTdPjU5nsLizePKZ50Ec8G3Sm5rNVfUgF/LjVtuk5xpqi+e1VWRL8OJUvawI1DjOUi8gzQMtg6dzYLx6uEJDed/3k+na3v7a+p2vM5k8fv87B6p+ZjabRyLGEIuxqs8SKPW+lHiQ22p7kMtv3fbTV32bL4NhMgaZauJSvbDlNai2cbK+fjNL8H75V471C4fW+HV/8gddQvK6jXm/HNVQ/djTR6VlhfX6vn/28CUxjyPJ01ipP5jO4vebjoud0+WJiqYEo7w7qI4VjwbHFJTpLOpqdL5BqVzpVxlIqycymg2ObWHFGsgByOU56jkB/SzsdEInJmoYXlcnZ7Fsvs9AdsvtH5spHCs8k8nVjUGu8CD7YpCXzGurMM6LBrIaD7J/YEuduIItJ6+IyVd8I6jf/frUngskAxcoxmWL4L9/Lx+r7sGdzebRnnCeV7XVAA/vKfuN5WIWi8Zp3gDg7OBkgxqXlVH2VYRmZLEYm8ngV76+yzmqt5G8khjkYhmiz1/FC77yBzH5PAf+6N8Pihfg48LwtIBi/S9O2lJxqaxYWdz3f0GtPj2f2ngcL3YF3wDqNUOqXln+a38fjvSNBv58oduIhlgQYvhVbNIDKtOk1sOvUuRdo6L5yrUF5+uxS+O4Mp6u/+HqaqWgTGjDjDWQA1Ari0U1FrixxbVSdJ27OoW1KxYUfvaKnZzNYc0yJzdybQ+y87tyA3hythiDvLizrWKTnnfgxIwrR92k55f/p+1n8Stf3yVWgOSmkjJxAGKz+oK87/tGx3JXw3/9tQysTC5fmNDUC7EoZrEofqbDNayrtiGfuuuXOu3l7FVBA1mRwSO31F9Z1qunBrHl5BV8ZctpIf2y+Iv4zAsnhO4HbRldkQsZwNGLYyTdgxPBX560F6fcsjjFW1iuq1o+8aCyQnrLft57blhAlt4na4V71dZZishKlNeeTMTwU7tRnT30lTp9308J3F8lMbmEfQp7zw3jXX+/laBcUC/BqA8z1kAOQK2T9KrR0ebc0nSVzpTJ5dE3Mo11y4vp4fyew6KBXD2LBWMMbXFWYQBPpnMlIRaNPMh0jwPRi0jV7qu/jIFb4kGUSPnTyJPxwtHLuDQ64/MgN248/nbAGMO8tniNEIui7rVuOzonuFGv6MGXH4BlKTWwnR+8CVyQZ+H/RPfABLkOgBNLHlhWgSEDAE8fvCgu7/teZPm+XBZA3Y3EFbKkSYGsIEFn2c9Chl/hm2IpI1OzePAvX8GB3hGhepwRWNnxHueV8bTwqphX0xeO9uM7u84Hlyt7OKo8tPV1Vv4un+eB+rKq6omkefPrFMo5XzAYZcZYNWOM/2wFUVlhvQo2JYYRayAH4MaVC7B6QbBb5Xn/quWn7RueRi7Psc6XP9kfw7VmmfP7mUwe8Rpex7Z4rKKjjs9kMD2bQ3sihs62WKUHOVNuIBf/pnszil9eRQypTB5gP+Xx3PWYzebxyrH+uvW+Mp7Gb/7zXrzY1Y9214NcKwYZKHqOy9MIzmuvfuCMX3VnWwzXLemU8CALfbxMZJmAoAAAIABJREFUVn6JFKg++HsH23ge96D6//6H3Xj64EXMZHL45X98TdiA8ei5Evz+UT3oHv+1vw97zw0Jyfh15wibCwFns7AOZD3BtKOxS4VkPKP+Enb2XMXlsRl8aVN3A9nSn3sEDGS/7Ee/dySwHFBq2H7vQF9wnXXq0FDW/Ww6m8dnXjgeXBCV48d/7e/DO/5mM7afrr8Jjpd9lUXEg+zXJuqhl4XmjSUY1ypnpS2CNZAD8OmfvxMfuL0j0Gc7Ek78aDXD7ZzrqVrnC7FAFQ/ybC5fM261moGcyXFcGU9jXlsc7YkYZmZz+OqWnkLqOM9g9gxlehaL0p/nuzGzQSHHkfrEPe+jkLhPvjyeux6fffEEfv2be7Czp7Zh03Ol6AkZmZoF0CAGuUoWC8A9kbHKgNx1qbi0zhjD+hULhD3IHlIhEuqiBArfe9fZnmjsaS/Xf/zSGA72jmDrqUF88gddwepQ9rOQIRP4k1Vky4SHJwU32vm0iy6clPc5GaNRCklRlSqlPMg+xlxv+4IqJ6fWk74i4MHz3+Pv7g9u5Dqyxe+DTDKryQFiHlJ/e/ripsahUbV0AsC+804oSqOJarV2yDkX3qQstEnP/64RCrFw6B9L40Pf3i8WxlVFd1DK383awsfKvrYK1kBWjOdBLp9t9g5N4QNurO76Eg/y/9/edcfZVdT779y2d3vLZjd9k5BKSAIkoUPyaFJ8qCCCiAoCoqDwfKiIKKAiTUEUBB4oPQQIJdQUSAFCeu99N9ndbO97d2+d98c5c+6cOTO3hCS7wnw/n3yy99xT5p5p31+PgxFkQK11lBFkAKhq7Uam1438TC9q2npw7wfbcO/72/DZrkZLW9QTYUF68esOhwko3XLHX9Rfib9u1b70tHCAXUBIR4N8wCxL3NwVSnBOPPiJlQx3u0jSfKFiwQq/1yUlyFc9s8L2eWhRVvouFubv/8fC3WhKwxfVcZ9DGjtOsIwKiQQJFTwugiazP3wpkgN+08jze9IKcozZJ4+F3fWdaE+SGUJ8X+laP/gN7IukpwOA5oB6DPMIR2NfaNNj7WwJHFrWjcMhxKUnDDjvwYKdWNXTVJ8bCEdTJihfRGnBj6tgOv7WwttNpbS9dS136fB+2eoTHc90Pp+5oCXLKCPrm+c+r8Cke+ajujX1oNNDdbHoSUeA4C58d0NNekGxJh5btBtL0g2AF8ZRezquVGk9SfHcLxlD1gT5MMPyQRYm09Nc4u3+eX7rb+anCsRdLAC136pP4oMMGO4bmT63lQkDMPwcv8eZnps6Q/jd25vR2p3a5qiCyCkjMZqe9uELTiJ+M/ndnPTSOInPT0eDLKumKEK1yUh9ygFpoRDAcLHoEVQd4jsmAIpyfGjrDh2ShgIAdtQlzxzB43BmQGF/MQKTituAeIrb5bICNWXzIhmG98tOy1dUhXMeXoJLHlua8ByHm8MXSNWWbno68fSa1uQpEpfsbMCo336ITVx2A5YOLBSJpVWk5onFcS1jc1cooZAJxMlTfXsPFm5PL9f34dYgM0tcThINMn9tts+NaIymLAR9kWnFr8c96VjUREIfOjQyxSp/pnSdxILEKo0myygTJ/Sctn2toW2vSYMgpxekd2guFmJ/phUUy1181xzD3aajJ5ySECC+wYZDTIUKGEJ/qvsKO2tvYxfuTjO1Yl+GJsiHGZaLhUSDDAAvXDvNdpxfdPvnxcmtSoPs88g1yNWt3fB73Qm1HNWt3XhxeSUeWbDTOnbWQ4uV56sg0xzK/GXV1xuYs74Gv5+Tnr8d4Fx80s4Jy7V/5b5m/ClF03wqBJnv94X/e5b1d7JcyKm4WDR1ORfZPL8X4ShNK1Kf777WNDV7PDH7bFfjFyp1zdDebWzMokAggzj2PG6CbnNjT3UDE7Vf+xq78NmuRtz44pqUtVh8W5ibTzKiLQoXjWmmYeKvTzdIT9w6+dL2KszfYqQpW1URt9Jc89wqUErx1/k7cOZDi1CrKJ0uf6qBE/64ACf8cUHi68wLK5oCuPa51egMRvD5nkb86LlVSQNzD8UHmVKKyqYu69qDbd3W39Wm9i+ZEoB/bH6mkcoz1Sw7Ynem378GkgkePMQnMC3n57sbk45l/rfurOtIub2yvYNl+Ul2D/bMxTviWlUmZKaTzaglSFHR2IW/zNuBx9PwK0+LIIvPTNFiI17L0sVe/tRynHb/wuTXCg+uN4M+UxHGxWvPeXgJ5m1JLVWhqNX/skAT5MMMVZBeU1cIZ40uwZmjS2zHeb+2LE4ST+hiodggMr0uTBpckLSNotaUUor3NtbgjAcXpmRylwmVXeloH7gbvLCsUpkzWnm9sPz8+o2N6V0vtP+Zz/YhGIkmJfmpbAH8IjqiJMf625vEfUAk334JQXZokEl8I05HG8n/jnQ2VMBO0matOpB2QBH/7v/wrqFpYG1Px8TLEIlSa3NMVavKj59hxdmoaevGdS+swtwttbaS7yJmr6nC3kZntH2q71BsXZ2p3Zm7uRarK5K7CiUL0mvrDuPGF9egLej8zuFikUKb/VZFR/u4a+gMWn6jexoSZx+wB3XStDVSDNsOtuOONzfh4+31aZurUwlInL2mCmc9tNhKzdbYGbI2ehY7kowg2Vx3zHmZakYKUXhKh1Dx1zZ0BA/ZreMfC3dj3f4WfPeZFbjp5bUpXxuNUWw92A5KKT7blbgks8xDia19yTXIsM7bWGVYRZmQqeqbX7y2Huc/8onj+PS/LMZji3bjoXk7UnomYOTdb+4KIRCK4PFFuxOOB/HdNnWGUNvWgxtfXJPcFUsQtKIxim1m7EmyvhW/b+gIYvpfFmPi3fMTXremstlSNPBYursJ1z2/Gm1pKlK+LNAE+TBDFaTX1BlCcbbPcT6vQfa4XVYVtXR9kAHDLD+mLBfv3HwaZowpkZ4jQzASw80z1+FAczc+2JxcYpTN0XQkeJHIpKLNSvT8TWkkuAfkRPeSx5Zi4j3zEl6XinsB0+Qu/83ZtuOqrCQWZBpk4Z3KNGd5mcb4OdSgq3RMf4AzOCwVU73t2dzbX7SjAY2dQUtASoUgO/08I5avd6op//h7FGX7QGm831TEsSccxW2vb8APn13luE9TZ4oEWWj7QfPd3fjSGlz25LKk19t85yWuQa+vPoC5W2rx3l5ne2QEeWddB15JkBbMY5W2t7/XurYgCrKMtexgGhrkiqZAysULxPZurWm3BPvWJOSRv9bvdaG9J4yecBQvLa9UkjeWdo9fx575dB8opVYawGTuC/ydLYKcguJAtp43pjimACGQLBJL2e9U9iq+89RyAPZg4FTwxtoqzN1ci+/9awVmrTqQoK0SDbLlgyyfv7VtPXhvY41t/DOfdjb3VWT1zbXVabuR2dtr//zQvO14YvEePDRvB97beDDl65q7Qnj0452Yu6UW76xXp3gU309XMGr7bcksheJzGzqCSefcjtoOXPrEMjy/rNLx3YvLK/HRtrqUM4x82aAJ8mFG3AfZPmG7QhGryh4PMTI61zzHrTDnewUXi1zuekauJw4uwOjS3JTbHAhFMbrU0HYeipkbSM/NQdR+NaWogdvT0ImV+5odTy8vTj1IBIBtNp83vhQAsL22I7k/cgqrQE84iiyfG2X5fttxeV5rZ3Aeg9/rdghZTn9GYo2ftKo8cX+zXJlNnUG0pNAPopBQnOMU+hI+W3iHexu6rMCiVNx0xC7oDkUtDWGqlgi+DQVcVUtArbmTaS1/9PxqNHeF0nBTsbf+YFtPWnm8+XkTisYcfc6sV7I9lJ+zmV43mgMhnP+3T/CbNzcp5y7T7IkCbV17j2W5OJAkhzT/rvc3B5ISar7FPGau2G/FayR73/xvzfV78ezSCtwyax3ufHuzsrqdbP509ITRE45Z62065CTPz+Zl4jG9bn8LRt/5IZbvbbIdb+gI4vnPK3D9C6sTXg841+NJ98zHKyv340emO4wKMmEhVT9+/r5eN8HWmnbLqtAscQVLdA+Xiwli8rb+8NmVuHnmOtv6sL85YNtjj1QKNjHv/NrKViuHeCKlhNMiELaELz7uyPE84RU0dQVtQpZK4Fq6uxEvr6i0jYQMjwv1KQikiazGTFGXLG+6OM4ihxAP0hehCfJhhuViISymgVAUWT4nQWY+w98/ZRgAjiAnCNLjFwafx2VNOD5Y4msTypDlc2NIUabjHiICoYh1j8YUFjeZ5mFfYxfKb38fLy53SqEiHItApxG0kyxl23//4zNc/tQyB0saWZKDnnA0KcF7b2MNym9/Hw3cgnDOuNKk7bXanQJDDoSjlqDCQ5WhQaVXzvC4bP7Mr68+4MjYQUi8cmM6Gnz+/de2GcTvxD99hJP+/HHSa0WylIpfturZAFDb3hOv9Jhkk3tt1QGbPyxgkCVmsky1+AXfj3l+O0FWvUcVEdxY1Zqye5EscCeZD2+i68XNj1mvpAUYuWv75Rpac3ZMRTjZEiT2eWNn0CKN9WkEAdW0did0YVG19+uTBmJHXYdlaUomCPHXMgvJvC1GoJ+68qWcLNq1d8k0yPF7MME1WeAby1KwXSiz3tDZg7ve2YIFW+uSug7Jvv6N6Y6SSBP9hdJ6cdeeM64U+xq7rDGhIrpPf7LXFth59sNLEItRi0ypNPRszPDWnd+9vRm3vLLe+pxuJqWUwf2Uc8eXYn9zwBJqEwofwnetgZDVxkSBm+J14Si1rREBhcB11TMr8Nu3NtvaVJKbkZLFJtEwYGMvmXVPvEdHTwSUUhxsSz97R1+CJsiHGTIXi3DU0ELI8gXnZHiw/Ddn444LxwEwNB5AYhcLPmUSIcTSIvPE7Pihhdh89/k4ZUQxAGCAoNHk0R2KWpM3FZLBT0KWmm6+uQH9i8vWoYK44Dd1BXHCHxfYFjwZuiTZCvrnZqA7FMVFf/8UxycJ/HlqidE2vnIa0/gzJCLpiTLCLdhah9F3foia1m6ppSBRsRAAjhUmw+uypRX65eyNuPtdezAhQTwH9bI9TSm7WfAb+Y7aDqs/U9EeiQt4Q2cQMUpx4aOf4l+f7Uvh2XbUtfVYcyXRIhyLUfzqjY341Wy7v3ljZ9C6PhCKpqS54H9ChqDNUZGgqhY5Qa5o7Eo5+l8kMs1dIZtmOlnbxXfvSE9nDrGwhDHxR/rn2tcC1bhRaZD5am/pCGbVLd229SWRryr/zQUTygDE57/K0rB2fwt+8+Ym23sSff9V2YFkWvSecMwmhCQNhOUazRQdLAA1XSzaHg9ES+aPzA8LPoUogIQEibdIjCwxrHC8djMRAeS/GVSQiYbOoGWNku0hjZ1B3PvBNvyVCxCnFPjDe1utsaSygvkUcT1zuQCydFImTh9iX58TzTvxd3aHo9bvTDT2xaHdEghZe0Aia5/sjR9ojq8RgXAS4ZD7u39uRkoCbCrChUoo3VLThvLb38d2wSWnrTuM++duxyn3LUxLCdDXoAnyYQbbcOvae6xAAjaRVAU1yvL9lvY3mQbZ63bZIoxdBBYhyxTu73IRa5NLVMwjwBHkQApman4SFmf7kJvhwVLTR6lQ4mctQtzo95kJ4uemGDHLI8tnBLPtSaEammyjEVMUJdrQEmmQH1u4C6FIDCv3NVtaXR4eiQ8yQTw/snhvv8dtaZATLaisXx9btBs/e2Wd8jwe/OuvaetJGmjF0NwVsi3WgDHOG7uNIJ0/vrcVlNKEGj5x061t77HGXiKCrNLS8tpMILW8n3wLvAJBVuWRbTA1cX5BoKpoCqSRqUAwQ3LBN4DaHP+zV9bhoXnbHddf89wqVHJVFBm5lynw+EunlhfZvlMRZDZiw4Jk2MD7jUurPVLc98E2rDNN7gzVrd02ISyQoL/5cVKaZyf0qrHwk5fW4JWV+21aU3HeqYhUq+IdnP+3eIBXMgsX/9oHFhiKg+pWQ7CilOKNNVUplYIe0S8bG6riVSGTC77xJ//hkgm2b1TC26aqNizbE3fr8LpdGJjvt82lRKSTH0/FORmgFNhZ32Fe53xPqjLy87fUWr+vTbH2ppIfPZ10nRnC7RIpBvhxOLDAGIdsvUxIdAVNLq/U6kywRsniXLbUxGNskrns8Jf3z/WjnitWoxJ4Uim2pVKcvW0WtRH339buMF4zfdFFqx/Df4IbhibIhxkuF4HHRfDc5xX478eWoqUrZG0iMhcLEakQZB6ExAP9ZKb900f1AwBMGqLObhEIRa3FsCvJZr9ga50tKMnlIijvl21NoEREfE1lCy5/apljsh1KEnUGv9dtW6gSmUGZKZnXiInvLFGEcaL5zHyJA6GoNF+qMs2b4n68BjnR++F92D9JMam8uEy+uyEebJLo/Z38549x00x7dHtVSzeqO412+jwuPPrxLky4a54yZ6fcxYL5IKtfsIqENnTYCXJKQWBcG8TiIiozL3PfEUlDRVNXyikOZaZyPsBUtVG/u6EGjy/aI7VgnPvIJ/i5KRjFs3k4z+M3x29PGWz7TunTa45pvs+yfG5DgxxSB1ZWNgXw1Cd78T+v2i1Cb62rtgVwpapJ6yf4uavGAiNhvDZYnHfhKMW972+1slUYx2Ip+ZEnK8IhZh8oyPJaRXxufmUd/vf1Dbj9zU22a4hkBbjq5GG24j/JCDI/rET/VpWw+vXHPsMdb9nb4hfW7oTEkeuhomzD6skq4ck07bJ1xUUMAZ25l6iCL5Na3yDXgqqIn1ixM1G/8uOQuWOxtH+JXKv4uVpenIW9DZ3We0msQHAe+5ALnE9WzS8mEHPenVAlRKSife8Mysegai7urO2w1gaZW9U7G2pwzG8/RGN33ybJmiAfAfAkdkddh0W6sjOSJ1TPzTBdLBS+naJJ2EWIpbGUJWy/eOJArL7zHJw3vkz5zO5whDNTqydvVzCC619YbQsccRGDIDOoNAUAcOfbm7FyX7Ntwfe6ScpVvWQYUpRlIxmJUjrJFibxnSXaKBNtGPxvEgP0gORp3sSF0e8xCg1EojGbSwgPQpxWg5QgbKgfbYsXYlC9v1iMSn9/NEaxpdFYCAuzvHh9dRUANVkXAzRtLhZJxp4MLYGwjaCmYlLkN3eRUKgEBOb/KPZTRWOXTauT0Cwt+YrXFMqqOvKkWqZdCkVieGdDDRo7g9ZmFZFpkLm/xVzpKgIm+y1l+X4zt6ra3aHC1GrLgpE2HIj/3kQEmSd94hhXCSRMSOUFcHEVrW7pxtOf7rOlMUs1TV+iLBYLt9c5Ai6HFWVZc/d9RcYD2TJ/8ojUNPwM9oA5+zuXaRxVY1RUFiQyvfMCRmGWXYCREVOZIHXisEIARh5lQP07UyLIUef9VVpPUYPc0BlUBqry45BZatk6mOj98HP1zFElaOwMWQW7Eo97e9+cO77UZjGWjUF+zeKv75+bYdvPVONXRpBn33iK7bNK660iyHsaOq33I/ODf8FModjY/QUc4Y8CNEE+AuA3hpaukKUNGFKUpbrEAtMguxSLgqiddBFiLSAqstQvJwPnH1uKf151Ai6aOMDx/bXPrY67WCTQhsk2EjchGM75vSWSqqslfpyFWb60c/HymDykwOYnmEwDLkIUONoSVBlUkae27rAtElj0AwQSFQpxaukAe0VG3owuIiuNKlYMPEGcOCgfW2riZn7VJqUykwHAnjZj7BiZN4x3JJrXGUQtquFiYZItszRvWAiOAhKPy4Pt3ehvkr5UShrTBBo3lZ+paoweaOm2ba6JtDGy3MV7OdNkWMJseYGF3/xOHVlsO+/11VXYU2+Yfnsk9+FJUW6GxzbuVZU1ZRqnIYVZqGoJJEzNZxH1JMFliczFfHtZXId1nWKNYWSTHys/P3uU7RyWx7qW02qlYnXIz/Qq5/+aymZc+9xqXPPcKtvxISmUgZetCuI+0d4dxoKtdVhTaZ+DjGjZNMgOgux8V6pMIqKy4CcvrcEuIUVaRWMXJtw1D5/uiqf9KhLc6nrCMVz/wmrcwClSZOOEZR9i7Vf5WqdCkGVjVSXsZwhuN1//x2e46pkV0nN5P/lcIaA3FIlhc3WblFzzfXKCKQiwgFHZ+I3FKE6572M8u7TCdlws4y3TdvNZoPj3IArCqvErW7P8XrftvauEDdn4yvC4sLqyxXoHsiwZTMhIL8T76EMT5COM5kDICqYZnkI6MnESOr+3E2ReA+FPkD6GEIILjxuAoiy5jzDTHKuiZAH5AkYIbBrkRJuezD80J8NjIx+qiG1VOqzjh9pdR1TSuWpxEIP0WgNh7Gvswh1vbXJoQlTk5+UV9mInwyT9LPNBBhJlsXBb7d52UJ7Hk4DA405tCs9eUyXVgo4S0gGqfDHXSAgvS2V1oMN4L5VNAUtbsKlankdV1JDUtwctV5IYNRbOSx5birG/m2s7L5HW5UBzt5V2LFmmgYXb67Ca0345XCwU16s272iM2vKstneHpRq6aIzaxrYsaFamoef9dPmvbzt/jO28B+Zux/ubDprXxJ9zoDmAO9/eZNs4CSE2UqMqAiDzERxZkoO69nhuVdm8ZARVDMKbLLh5ySw6n+1qxPSHFtnWClGIVWmQZYrR684YganlhdbnOVwOWtY+VZrJJ793ovV3QZZXKTzxfp4MBARleX7sbw7gyQ12Qsq/F9lql+f32rS57d1hXP/Calz6RDxX9tLdjTj2rnk4/5FPbHNKFPhkZGynJC8wIcShQV5V0YIfv7TGdmz53iZHv4lxJ8FIFAu21mH+1rhlSjav+MqxgNp6pwqs5CHT5qriEUQ9UjASw8Yqey79mtZunPHgQmtOAc69tzMYxcX/+AzXPR8XBEKRGGau2G+zBg0TFCaycXSgxUiBKBYucQgfEi2wqiKn2C89pgvbmspmPDRvu3Vc5rbhdbtsa2MHE8Zi9mI/MiF4SnmhzcLQ1BVCNEZt456N2TRcx3sFmiAfAfBuCs2dIexr6kJ+pjelADY2CVUbNUtAz+AixNIIZqSgTRTzLjOwsZtIAyzVILsIinPiC11X0EjvMmd9tY1gqiLWhxVn2e6r0tSp0kMVZ9sXWZWmURVJ6xe0U93hKP7w7hbMXLEfm4RFU+X/9eDcHTaNQXk/CUFOssiLb8fPaZB5M/yhoKKxC7e9vgE/fXkNqlu7bYuaqO1W+cdVNjo1YQMLMpHr90Dcm04YWoAdte02d4dYjOK11Qds/ZvpdSNkZnhhlpHTH1gkLVKQzMycxxHkaMwYfzJh66/zd9o+fxENMusjprkFgGl//hi3vurMxnLWQ4tw6ROfW5/75/mtNGrsPjKyybud2LWq6qU7wHXhve9vw0vL9+OTXXaXF/76DkWfy9ozdoBdoJIREybMiFq9s4QqojKh50/vb0VFUwB7uXcq/laVlSikMCHLXM8AQ4u8dn8L1u+Xzy+eEBUk0CDXSNYWvsrl8oP269Zz81kl+POxHLIYhEXb6wEYLny8ds8rCOKy/lFrkJ1jaq8QfCXT5opKF5mWU9aOImHt7uiJSIWyVFJJMuUFP0dS1SDL8N7GGkdAsmi9rTHjLFZw6Tef/nQv7nhrk00Qy8nw2OoVyMaRKh2fSJBl71al3RVdX3oiUexvCuDSJ5bh8UV7rLgKmWDudRPb2shcLEbc8QHuEbIpiZgoVPNduL0eI+/4ACPu+MA6xh6ZzMrU29AE+QiA3xiaAyFUNnVJSZMMzM9JRfREKZZfr0RtmPT+SfygE5myZRXDDB/o+D27ghF8vqcJt8xaj0cW7LKOqyq2iZumytypMlUWCoUeVAS/RpGPUdQghyIxy+dLJA6yhe1bJwxyHJNpB5V5kM3FX9SsMg1ybbuRZUJ2T9m+IXt/TGO0dn8rrhZMiaK/tEoIkL2/DI8LIyTj+prThiNGgZ/NXGctwhuqWvGr2Rvxt4/iY4LP0V1oBvqo+l/mh12al4FSUwvFyEh3KIpZq/bjllnr8aqkopc4hlMJ0ovGqJQgl5nZFUSCNGd9DT7b1WgUMekJIxajDpLj97gsgjAg33gPMrLEW2RiCdwOeHRHeA2PPPcqT3RU2nnxmj9+YwImDs63HZMSZHMOiteLa1dXKIIXllWg/Pb3HVphfu6JxXRUvuoqC49qXaxo7MK3/vk5HvnIEJrEtYR3WcvP8iXwT5dpkIH8LLk1cMmOuLAi/u5vHm+sJ3z/rOWsN4wAqoQE0QdZ9k5kygICtSDBa5xl9xOVNnygMyO8MheIDI/LEdQtan23HWyXCswXHmePqQlFYnj+8woM/80HlsuDijiqwjbufmeLVVFRFjDs97ptY4mlfuT3YFlVWLeL2MaSbBypYn/E6rs94Si6Q1HbeqRSaojjuakzhDMfWmR9ZrE/MtLtdbtsBLknHMVuU2hlJdhV4PcEUehi45dqDbIGYKQNq2gM2Px0EyEvCUEWidKgwjjJEFNWySBqkNmCDBhSY0dPWGnCrJVMfpeL2LJzBMJRi8zyvkcHFJkYWJYNBlWQlYogi37XKhcRVaEHkWgEIzFLW8mTqbmbD6JC0obzj3UGP4pEAJBrXq4/c7iU5BrtMvpy4bZ6UCp/juzaqfd+ZKXeYdhlLmw5GR7sFXLniplVVBpU2aaa4XVLBb8zRvVDWZ4fK/Y145+LdwOAtbjyGFwYnxP5mU4ywVsg9ok5f2EIHcxsz955dzhqlXAWx+uyPU2OY+KckW1ete09iMQoxgjuKD86fbjjXMDYMJ/6ZA+ufHo5Jt49X0rufR6XNdeZ/3QidwXArm1JpEEORoHy29/H1pp2S/smauZ4IrXhQKt03vFkKNvnxtUnD8Owonh/EyLXPjHCLWYlEOfFwwt24vdztgCIC9CppMyTnROOxtQEWXhXn/16BgA45oKorePTNeZnem15yXmo3MpUWYv4tF28z/yw4iw88p3JAESCHNc4N1jvSRGAJqYtlAh8qjVWRZD/75O9VnCljIy5iJ0I8eOdETmZIOVxESszBBN0RVemC//+qbRN/zXWXuApFI3hrneMscTyFKsyEmV55Yvuc59XWBn3vH7XAAAgAElEQVRhVAFt/BhmZJ7XcIckjM/jsgsCsjVWJaSKVudwlOKmmWtx9l8Xp33t5mq7RZQJEHINst3FYn9zAOc8vET6HBG837QosLF3xqx7R6q+y+GCJshHGA2dQdS0daesQWY+yKoFcGRJju3z4989wfo7FQ2ySEQmDIprhAqzfAhHKcb9fq54GQBIU3eJiyOlsNIH8QUzVIUWeJIEyP35AMNHSwZH1HYogvqOHscmsK8x4DA/As4NZcXeJmvT4rXe/1y8R/r8/xrb33EslTzIuX4PjukfJ1yqIL1le5vg97pw2jF2QQKQp4gCgGeX7rMJOTvMNEphYTUa1T/HocFRpUYKhKLWJsZw6zmjHP7WWT438jO9eOJ7xrjccMBYlCskgYZDOOFORpD5yGnZ9T6Py4qED4ZjyPC48LePduHdjYYWSHw7Vz693HkPYfy8ubYa84V83CxIchznXvDsNVNx2YlDHPfL8rlxzrhSLNvTZEWty/yXPS5iaRiZBo4neMFIFC8tr7S5lvDkhCczsnENAHM2VFtES7Rk8uOxoimAbzy2FGf/dTHe2xg3DfOkhpEAXiAdXJiJUCTmcJ9ihFE0n+Zk2PtYlsoslSwkMu1pdYJUiCJBHpifCb/XZSu0wrtEMPBzozDLi1AkJvUvl63VhKhLCvO/m9c+873IE2TeVYgJ+ipBQlwPGzqCeHLJHpuAKfP1JUSeJhQw4hcueXwpALkASbhMSmLbGKGXCX9uF7H2CGZNYfffXtuO0x9YqKz2V8D1VW6GR1rxUKXoKchI7mKh8oeWuSjy41wWsGdokDkXi4hR0KitO4yXV1QiGImiUyFkiXtJKBrDwu31NsFKZTUVXSzEKrnsPcna7HUT2xqhrkDp7NehnDJQ3F+ZhZAFLGuC/BXH9oPtoNQZjaoCk1BVWgmekEwtL0QBNwkSaZUYyoSk+/zeyk8omc+wbBNyE6LUPPAbBCPXgwvtpa/Fa1UbpIo4ixvC2soWTLv3Y0y792Pb8cqmLgwpynL4kYnvbBFn/uQ14CKRuv2Csai4/yLH8wF5BhLRjMU2wPiZcheLjVWtGF2aiwKFuVaGDVVtuGnmWuxr7AKl1DKR8mbrLJ8bs39yqkMDX9sWlGrbg5GYbXy897PTcerIfigRctQOLswEIQTHDy3E9WcMx/oDrejhqk/xGJSEIPMm0gqJD7THRSwf9EA4nsvbsmB0BfHg3O1WwR4ZZOTyllnrEY4aZOg3b27ED/9tZCcYPzDP9my/1+UgQX6vG1efMsy2ocgES7fLZWmO8zmCvKehE/d9uA1j7pyLO9/ejFmr4oUlbBUAOaFURWwAtf+mW3D5MQrGdOHOtzdbx/iNV+anPKggE+09EYy44wMbiVPFC8gqTP7+4vEA4sGNSavVwXCxeH/jQfz4xXhwVKXCQgQ41wiXOW54t6GcDI81504ZUYwlv5xuI8glZpyFTEutcncQ14zy4izccOYIVDYHrPWVnxc86XG02ezG/c0BPP95BZbvbYIM4nh8bXUV7v9wO857ZIlFGNU+q4nXmKqWgLJ/VBmUmBVONn7GDcizfmeBICT+72sbEuZ/591X8jK9WMAFBDKCrio8lJ8CQd7fLM8cpBJ6WH/KXMQ8LmIbS3sbunDMbz/EpHvm47dvbcaLyyqVCjHRRYEXWll/qriCaLUU3ctYSlbZ3upxu6z7y3zTGWTuGSVcTJJoRWHvJ2ZpkPu2j4UmyEcQ/XJ8VgqyVFK8AXENssrPzudxWX6vom+ejKyJOGFYIc4dX4oppubN7XZZpJz5gQJyqVS20RNClMTclp+2PYjcDE/CzdztIraUbTzqO3qkRUjERWCW4HfaGYzgkQU7sb22A+XF2Q5CLssC8cClx2FwYaZtQRE3gEQFUWRYKaRJY3muVS4W8cAtw7QvKz6SKEfOwu31mPGXxfjl7I3Y09Dp6KMhhVnIz/Q6fscjH+3EGQ8ucpjHQ5EoijkyzIiXaMYbVBAnvZOHFCIUjWF3fae0X/m+SEaQGzqDDuFqZElOwqDWDzfV4p+L9+BvH+3CL16TlzEX5xBgbKwT7pqHT3Y14pWVBxCKxuDzuDCmLE6Q3YSAEGJt7ANN1ye3i2DKMHse25tnOiscXjF1CAoyjXfH+rY7FMWvZ2+0SqID6uBSvj9VWrZAMKrMCuNVBI0ew1moWpKkX+QDZGu4tWFfY5fUzUh2bNpw4121dYcdKfpGlmTjo1+cZTtWludHVzCKm2auxbwtddYakygVouz95GV6beWcczM8FoEfWJCJYcXZNm0h+/uuOVtw6n124VtWfZSAOOIbMn0eDC3KQigSs1x9eMsaby1g6xoj2RccNwBeN8GO2k7c9c4WtX+tZC0e3i8b4Si11qD2nrBj3SEkPpdVadVOf2CRsuom03SK6wmzwq0W1r/Ft03HhEH5loDKFADf+ufn2F7bnrRwC69BFn2gGdlU+YxnehIT5GV7mpQufapga6YplwmHLhdRWnkAw0Kg8iMWLUW8xpZZphJl+OExe02V7XMkFkNjZ9CWbYTB53ZZAgaLkZBB5orCr6lsPNw0YySA+Htiy5L2Qf4Kg99ABheoBxkPtlkmKsN66QlGJSxGstgGkEI2HPi9bjz9/SkYVZpj3YP5gfE+eKJUSilFVUvAFo3LninTIBdn+7DhQCt+8ep69ISjaOsOIz/LiwuOc+ZhZhhalIWV+5px66x1jsWtoSNoI1+pgFKKl5ZX4tGPd2FfYxfKi7OVhJTHd6YORXGOvQrRUFPAYUQoEdFPBaKW2eFiwflGjynLlZKLVDB7TRXCUWoREQa2sMncQQDg129sBABc9/wq/H7OZrT3RNCP0wywTVQ04/EuM2PKDJeEHbUd0uAVXruZJ0lvyEx/LV0hhCIxG0G+7vTh+Mvlk6yxJzN9s/Rd6/a34M211Y7vEyEYieGVFYb2dkRJNh7/7gm2gBnWf2xzZtpwjxmQIyP8DG/99FScM74UXnOjZptudyjq8HMXfbcnDMrDk9870aadlBUQAeDIWMJDRYKyMjx4cskeVDR2KYVVBt6qcfbDSzBnfTXWVDZje20HThpe7DhfXDuAOClr7wk7SK7H5cIx/e0uZQMK/DbNICOaFY0BpdAq28Rz/R7bffw+t2VdY33HCyFsnL26+gBq2npsZmmVBjlDInyz3L+VTQG0dYeVqcgsImb+d9LwIgzvl23LS3zKCOc7lhG4d24+DYBhXdtwoBUdPRGblo+Brf98kaoZY+xB1CqCzBQIbB6zdfJgWw/WVLZg5T47QWY5ei0NMjeWPthUq6zEycBrkEUNZyAUxdLdjXhrXXpznuHKp5crtdeqdJ1VLd2glCozhMim6CWTBwIwUmvKYmcIEQmyC+s4f3QmJCWqzJcIoUgMt5vrvLi/eNzEEj5FqzOPZNUlGZ8ZYu4LTJBmgvvK2kNr+9GCJshHEEzj5vO4bOQiEVi1vQQFuazBxTY5iyCnwpAd94gPYt5dQ/RLau4KoSccs5mZAYPkiNrJgfl+FGX7sKGqDW+uq8bS3Y1oDYRQkOXFTTNG4q2fniptU2leBrbXduDt9TVYua8Zs9dUYb9JGBo6gjaTPCD3/wXilaiau0I2DdzwflmplSIGUJTlxZaadtw8cy0OtnWjOxTFkKJMK6jrkCrYcWCb0A1njoDHRTBVILD8Ox1TlmtVWOSRTpL1qeX2+7PFT/Y78jO9mLelDvd9sA0fbavHC8sqAdgD5UTND0N/Ljl9eXEWfB4XdtR1SDUrPEkTtUCAYZZt7wnj+D8uABBfZAGjwlQOV/CC54F/vORYG7FKpXgIj2+faAigc01f5LdvOg3nji+1Jd6Pzz3jwQNN4Y2R/uwE44NdyzKbMCtGdzhqab98HhdG9c9xENwLJgzA1yaU2bQ07JT+uRn4/inDrONVLQFEZfWpod7oP9nZgPs/3I5rn1+l3OwZeOEoFInhllnrrVy9srkpc7FgZHRVRYutIiYAW35phgH5fnQFI9b4O2i6Seyoa1e6sckys+QJbYnGqCUEM+LMv2ORhO3h0p/JfF3PHF3i0OYSxDO3vLpqPybdM1/aXiA+jtjanOf3YnRpri2Htxj8ecXUIVKLSK7fi345Pjz68S5c8vhS7GvschSRICBWf/KFhJ68+kTL2giotZVMOGFE69hB+fB5XHho3g5c+sTnjrHE5gDrx/zM+FhaJZDpESXZOEHId88LoKIbUWcwgqueWSEluakoSBKBPYuNH7bO1LR2o607rAwUle3n158xAgPz/ahr63GQ3AuPK8OS22bYxp3X7cIyzrWmoyeM3fUdh1xo64YX1+CjbfUYU5qLsWX2AGSPi1hrz4ACv+O7z3Y1YmtNu9KNhSHLHL9ZGR70z83ArjpDwGJC/dYmZ1GovgRNkI8gmC+U10VSJq+qdGA8Th5RjIsnDsC935wAID7YUskXycAsNS5CLFLOm63EVC5Moj92oD3N050Xj3O4KQwpyrIyJwCGFqGtO4yCTB8yPG4cP7QQMvBuBDe+tAa3vb4Bt7xqaJObAyGbBvGaCT48/f0p0vtcfXK5dQ/ebCkr4KFCYbZR4e+9jQcxc8V+dAYjyPZ5LI1Hui4WItgGceKwIuz+84UOAcqmQS7NTalMOY/Vd56DD35+hrVhOTTI5qIk+x2f/MqI8n/qk72243yqJrb5ixrkLK4PPW6D5K3f3yolqbymSkxnBBjlbJ/5dJ/1mXdTYkQgHoQWb9vlU4c4NF/p4HdfH2/7LLOwsOeyTYSRdzZ9E+UkFy0ujEx3BSPWmpGT4bFINw+ZhpD99rm3nmnLrHGg2alBfvaHU412CmvFzOtOsqUsFPPfylCc4+wzhm9PGew4JnMTYr999poq/H7OFkdRBRFleZkIRmLWWKxp7cFLyyuxdHeTQ6P6mwvGApAHtIka9HAkhlPM6oRiKjvA2WfzttTi5RWViMaoI0f3p7+agbJ8v5Mgc4GAb3N5cmVgvcbmp9dtaNP5Z4la1vsvnai831DBxU+msGHjm5/nGR63TSmisiqwdjKN/ujSHPRLkPefjT+2nvKEd5ngX33DGSMwiBOOzxpdYlsfxRmRzDXoi4A9iymTTjPHzNwttZi5cr/iqrjQxY+7MWW5GFCQiZq2bocP8vQx/TG0OMtmKRLHk5FZ4hMrNV0qEAUNADjtmH4Olx1e0GKZs04cVohffW0MIjGK7/1rBS78+6eWgKoCm9+RaAyjS3OteBje9UsVw9QXcEQJMiHka4SQHYSQ3YSQ2yXfZxBCXjW/X0EIKT+S7TnasGqvp1H+OJGvEoPP48Jj3z3BInxsE0xDgWxtqm4XsfzreB/ml1fsx8Lthm/SA3O3478fM6KYj+UWy/u/dZwjCwXgHPB3vr0Za/e3YnWlulwxAFS3xrUMbFNbt78VqyqaQak9g0dhBlGaiVm2gVUVLTYzm7hJJAKf/P6pT/aiviOILJ/bCmQRI/IPN3j/xZLcDKmvNHO1EbH5nvPRLycD4wfmYc5Np+G1H5/iSGF143TDJ0wslAIYm9V540sdx/kFWkmQBcI9pizX8n0UTXVThxdZpEnUaAFGeqm/fxzPm8wTRuYjLSvVneFxW2OFWRNkeOX6k6XHVeW7+fnByD3LDCJaNxLNRfbO2SbB5t99H8arW7mI856APF0W++0+j8tWibM7HHWkdpphanbF6HMKZ8rDRG4iAFBq9ufYslxcddJQ6/ijV0yWxkPI3HlEjado6RAhjpPbXt9gBRZeMc2eVeTHZxljXLb+isFm4RjFhEH5+PRXM/CdKc7sJKKV7InFe/Dbtzbjzrc32zS582490xLkRO8WQuRCggwsiIm5ckVisbRdzHiIwVLi78nwuBw50Rl4X2yVZZNlaWBEa2xZnnROM8Q1yC7zf/WEGVacbb3jgiwvnr92mu170cVIFcAIGJl7ZJhz02mOGIdEYIT32EH5GFKUia017Xhw7g7HeRvuOg9A3A2CEcZ/XHk8vG4XBhZkYt3+Vry22u4fzCQkfn6I1s9le+K/U1zfVbjt/DF48nsn2I5leF0J3TTYup3lczvcpHhhavyAPLx78+m27xkXiFFgdGkuNlW34e53tiR13+orOGIEmRDiBvA4gAsAjAdwJSFkvHDajwC0UEqPAfAIgAeOVHuOJNb97tzDdi82IRItLiIuNTU/I0rkk1+GH581AmV5flMaN4aBmA/x2udWY8Jd8/AEl+LsOE67MlkijQLqqNdkEeptJun59w/tmuGr/7USQNynFUgciazSFKs2ABn46oChSAzrD7RiQH4mfnn+WBw3KN9RUSxd/OkbExJ+z29KMrPpE1edYJEdHh4XsW3CI0pyMG14kY3s3H7BWPx0+jEA1G45Mk3eQO79ZbiNhS9Z8CJvuuNzEFfcfxGG98u2XDJSydLBjysWxMNejeiD/J2pQ/DBz8/AbefZSzIDhl/lxrvPszSGIlIp380KcIRNIsPIC/v/dDMt39xbz1D+DpbqSBZYleFxO6ocAnITMTOJe1wE+ZleHFPgwi1njwKgdi9hGxs/92sFbVCy6oUs7V84GkP/XGNs3HjWSFwy2Vk8B0jNBUxmSUgFd1w41pY2kYcsiE4kBEzwGFKUJW2nKnvBK4LWkM8EwdxbfOalgwuyUi4Nz8YXm0+hSExqUWB49IrJCe8nuszxpHJAvh9/v/J45btXBaZdzlkJ/ML78bpdmD4mvj5NEwQfdkt274Isdb8PK86y/NNlwXvMrWHykAL0y/HZMhHx8LldmKkQiicMyse4AXHlz3GD7FYEa001JyAjq+XF2fjBKeWW+9mvvzbWdh1bd9l4Y/dh8y7P75G6ZajSovKYy6WjTNV6XF6c7ZgnPredIFfcf5Hte7YX+twulOSq99CibJ/FD443ucEUs8z7oIJMjDbjnpIVGulLOJIa5GkAdlNK91JKQwBmAbhEOOcSAM+bf88GcDaRsYE+jsJsH6YMM9JaAfFFnpm3ZdXGEuH/rj4Rc246LeXzrz6lHHv/fGHKUiRgSPjL7zgbxTkZuNAMnPvasWWO1ETiRsJMyacf0w9juaj+T381A8+YLg9iqUkZ7vvWcQ5Jdoq5iJ44TK5FGsc9L88XHybTyotwo6ktAtQBSH6v27ZQP3DpcQDsxVeYtFyU7SRsQ4uzMG14Ed792em2oDLen1H0bWRgC+WAfD/23XehlNzySOTDuu++C5XBjmLhlXi74u0tT8HVZNIQex963QQPXhY34cpJnTNfM69hPmdcf8e1J5skleVBvXLaEKz/vVzg5Ik/E1CYpn/8wHzb2CWEYPzAPIwdkGfT5g4tysL9l050BAUO75edcipGvr2M4B87MA+PXjEZ/7zqRADAnRePx7xbz7TNEQam5WXaGFkA5rPXTMV3TxqG70wZgrs4lw9RqzuyJBv3fuM4FGf74HO74HYR3HlyJm49ZxQumqgOiGWWoD9+YwLy/B5MHlyAn5hCE0O2zy21JJwzzjjGotsLs3yWb62K/13JaXfF38u7RvDBV7JCLGIMBMNFEwfaPl/M/fZfnj8GeX4PppYX4prTygEAxwvjWxWI9NKPTsJHvzjT5qrC5ubIEud44ds/qMBYKy8b7cODl03EQ9+Wu0CwPl15x9nWMSZkfNfUzI8bkOcQ8O/95gTLVYIXSqaVF+En00di5W/Ptu4pElBeG337BWMxsCDTEsTFd6Fy+3vwsknYd9+FAOL9UsTtfcyqMKw4Cy9ffxL+/M3jrGvZsy4wK+JNkri1MEJVlue32nSspP9ZLvS/Xj5JaVUDgGtOK7fe1wvXTsNvLxxnfed2EZt2+QxuHa24/yJsvud8WzvZHj+4MNO25rHxxd4BAxNuBpvWhXi2Krl1mRf377xoHJ69ZqrlC/4NM7iPd4vgc7RPGJRntYPNVbY2luX5HYGvRdk+3P31Yx1t+PaJg5Hlc1uW1zFluZgwyHj/x/TPwfnHluI6bo7ySo4Xf3QSFt82Hd+ZYigqThlZLM3klY7VuzdAZNHfh+XGhFwG4GuU0uvMz1cDOIlSejN3zmbznCrz8x7znEbhXjcAuAEASktLT5w1a9YRaXMidHZ2IicnNQ1te4iiI0ThdwP/WBfErSdmoCCjb7t790Qo/GZU/Xt7QtjaHEVdF8Wx/dyo7ohhYokbpwz0oH+WC/vaohiQ7bLO57GjOYpRhS7UdFIsPxjBCaVufF4dwZ62GH46KQMlWc730NITQygK5PoI6gMxlOe70Rak2NUSxfv7wjix1I08H8GZg73Y1BBBZXsM00tDjv5o7I6BUqAky4WtTVGsr4+gJwr0yySYXOLG0Dw3uiMUa+oiOL6/B9lmRaX6QAy7WqKYNsADAkOrEYxS/HJJN26anIFPqyPojlB8d6wPxZnO9rcFKbrCxjzK8REbeWfoCFG8vzeMGUM8KM1ObSx8XhNBeZ4LA3OM8zc3RuH3AMcU2MnzzpYoIjFDUzU0zwWfKgCrKgw3AU4d6LFppVfXRlCa7YLXBWS4gUK/C5RSrG+I4mBXDGcM8iLLY2wi25qi2N4cxTdHxTfc3fWdKMnPlmr1IzGKHy8I4MzBHlwxxoeVtRFM6u+x3lEkRrGvLYZRhW4EoxRel6ENmb0zhEgM+PpILxq7YwiEgbFFLmxrjiEUpZjcP7757G6NYlieC11hiu4wMCDH/n47QhSZHmBPawxjipyCRzhGQQD0RICWIMWQXONes7aHcMVYnzVOAGN8t4coppYZz2/qjmFTYxTTh6g14LVdMcQosKQqjMklHowrNtoQjFIs2h/BeeUebGiIYldLDHWBGK47LsORimp+RRhNPTF8e7TP0rqxdosaPrZWUUqx8EAE/TIJeiJAoZ9gdKHburayLYZjCu3vIxSlaO6heHpjEDdOykCRn2BTYxSZHmK9u2iMIkoBn5tg8YEwjuvnRkEGwfzKCGYM8VjrQnuI4t09IZw7zIt+mQQuQlDTGUO2lyAUpSAE6GfOp10tUWxqjOLiEV5sa44i10cwIj/etn1tUbQFKSaVuLHsYBR1XTGU57uQ7SUYlOOy+mhTQwQeF8GYIldCrVp3hGJtXQThGBAIU0wpM9Y2FSIxivkVYcwY6kVjN8XWpijOGOTBwv1hlOe70C/Thd2tUZw+yD4OeiIU4e4u5ObG16o1dREU+gk6QxTBKDCm0I3OMLXmOWBYREJRIMND0B2h1nhYfjCC9fUR3DAxAy5C0BGiaA9SDMpNvKa0BmNYXRvFfw31YH5FBGcN8WBjQxQrayO4aXKG9a6qO2LIyzD6J0qB/lkuBMIU/7cxiPYQRWuQ4n9O9KM0i9jWGUopdrbEMDjXhaXVEZw7zFhjPq0K45gCtzUn6wMx7G6N4dSB8fnLft+augiK/QTFmS743EA4CjT1xDAsz42GQAxzK8K4bLTPehetPTF0RYDSLIK9bTGMLnSjPUQxd18Yk/u78d6eMGYM9aA+QNEWpPjWKC88LmLby3e1RBGIUEwq8SASo1hQGUEgQvHNY7zY0xpDMApM6Bcfh2zeFJrzgs37eRVhDMgmmFjiwe6WKHa0GGOBrYmtPTGsqY/i5AEefF4dwTnm++kIUfx9bQ+G5blw1hAvdjRH0RCI4VujfMgQ1oCm7hjqAxRjilz4vCaCbU0xDMghaAhQXDXOh1d3hFCW5cK55bwVw+jHyvYYartiOGOw8V1FWxQvbQthQLYLPzjWZwXlUcRTQEZjxvjM8hJsb45idKELBMDCAxFMKnFbczcSo5hfGcaZg7zIkex9DDFK8cZOwwJw8Ugv1tZFUOYLOoqfHQ3MmDFjDaVUHsTE4T+CIPOYMmUKXb16terrI4bFixdj+vTpR/25GnLo/ug70H3Rt6D7o29B90ffgu6PvoPe6gtCSEoE+UiqNasB8NEOg81j0nMIIR4A+QDUHvYaGhoaGhoaGhoaRxhHkiCvAjCKEDKcEOIDcAWAd4Rz3gHwA/PvywAspEdKpa2hoaGhoaGhoaGRAg6tPFcKoJRGCCE3A5gHwA3g35TSLYSQPwBYTSl9B8C/ALxICNkNoBkGidbQ0NDQ0NDQ0NDoNRwxggwAlNIPAHwgHPs993cPgG8fyTZoaGhoaGhoaGhopIO+nVpBQ0NDQ0NDQ0ND4yhDE2QNDQ0NDQ0NDQ0NDpoga2hoaGhoaGhoaHDQBFlDQ0NDQ0NDQ0ODwxErFHKkQAhpAFDZC4/uB0BZwETjqEP3R9+B7ou+Bd0ffQu6P/oWdH/0HfRWXwyjlJYkO+k/jiD3Fgghq1OpvKJxdKD7o+9A90Xfgu6PvgXdH30Luj/6Dvp6X2gXCw0NDQ0NDQ0NDQ0OmiBraGhoaGhoaGhocNAEOXX8X283QMMG3R99B7ov+hZ0f/Qt6P7oW9D90XfQp/tC+yBraGhoaGhoaGhocNAaZA0NDQ0NDQ0NDQ0OmiBraGhoaGhoaGhocNAEOQkIIV8jhOwghOwmhNze2+35qoMQUkEI2UQIWU8IWd3b7fmqgRDyb0JIPSFkM3esiBCygBCyy/y/sDfb+FWCoj/uJoRUm3NkPSHkwt5s41cFhJAhhJBFhJCthJAthJBbzON6fvQCEvSHnh+9AEKInxCykhCyweyPe8zjwwkhK0yO9SohxNfbbWXQPsgJQAhxA9gJ4FwAVQBWAbiSUrq1Vxv2FQYhpALAFEqpTvTeCyCEnAmgE8ALlNIJ5rEHATRTSu83hchCSumve7OdXxUo+uNuAJ2U0r/0Ztu+aiCEDAAwgFK6lhCSC2ANgG8A+CH0/DjqSNAfl0PPj6MOQggBkE0p7SSEeAF8BuAWAL8A8CaldBYh5EkAGyilT/RmWxm0BjkxpgHYTSndSykNAZgF4JJebpOGRq+BUvoJgGbh8CUAnjf/fh7GJqRxFKDoD41eAKX0IKV0rfl3B4BtAAZBz49eQYL+0OgFUAOd5kev+Y8C+C8As83jfWp+aIKcGDgRRlYAAAVqSURBVIMAHOA+V0FPsN4GBTCfELKGEHJDbzdGAwBQSik9aP5dC6C0NxujAQC4mRCy0XTB0Cb9owxCSDmA4wGsgJ4fvQ6hPwA9P3oFhBA3IWQ9gHoACwDsAdBKKY2Yp/QpjqUJssZ/Gk6nlJ4A4AIAN5kmZo0+Amr4bGm/rd7FEwBGApgM4CCAv/Zuc75aIITkAHgDwK2U0nb+Oz0/jj4k/aHnRy+BUhqllE4GMBiGhX5sLzcpITRBToxqAEO4z4PNYxq9BEpptfl/PYC3YEwyjd5Fnenvx/z+6nu5PV9pUErrzI0oBuBp6Dly1GD6Vr4B4GVK6ZvmYT0/egmy/tDzo/dBKW0FsAjAKQAKCCEe86s+xbE0QU6MVQBGmVGWPgBXAHinl9v0lQUhJNsMtgAhJBvAeQA2J75K4yjgHQA/MP/+AYA5vdiWrzwYGTPxTeg5clRgBiH9C8A2SunD3Fd6fvQCVP2h50fvgBBSQggpMP/OhJH8YBsMonyZeVqfmh86i0USmClg/gbADeDflNJ7e7lJX1kQQkbA0BoDgAfATN0fRxeEkFcATAfQD0AdgLsAvA3gNQBDAVQCuJxSqgPHjgIU/TEdhvmYAqgA8GPOB1bjCIEQcjqATwFsAhAzD98Bw+9Vz4+jjAT9cSX0/DjqIIRMhBGE54ahnH2NUvoHc1+fBaAIwDoA36OUBnuvpXFogqyhoaGhoaGhoaHBQbtYaGhoaGhoaGhoaHDQBFlDQ0NDQ0NDQ0ODgybIGhoaGhoaGhoaGhw0QdbQ0NDQ0NDQ0NDgoAmyhoaGhoaGhoaGBgdP8lM0NDQ0NL4oCCHFAD42P5YBiAJoMD8HKKWnHoFnHg/gZkrpjw7T/W6G0dZ/H477aWhoaPRV6DRvGhoaGkcZhJC7AXRSSv9yhJ/zOoA/UUo3HKb7ZQFYSik9/nDcT0NDQ6OvQrtYaGhoaPQyCCGd5v/TCSFLCCFzCCF7CSH3E0KuIoSsJIRsIoSMNM8rIYS8QQhZZf47TXLPXAATGTkmhJxFCFlv/lvHVaX8pXmPjYSQe7jrv28e20AIeREAKKUBABWEEF2eV0ND40sN7WKhoaGh0bcwCcA4AM0A9gJ4hlI6jRByC4CfAbgVwKMAHqGUfkYIGQpgnnkNjymwl9G9DcBNlNKlhJAcAD2EkPMAjAIwDQAB8A4h5EwATQDuBHAqpbSREFLE3Wc1gDMArDysv1pDQ0OjD0ETZA0NDY2+hVWs9C0hZA+A+ebxTQBmmH+fA2A8IYRdk0cIyaGUdnL3GYC4jzMALAXwMCHkZQBvUkqrTIJ8HowSrwCQA4MwTwLwOqW0EQCE0sj1AMZ+8Z+poaGh0XehCbKGhoZG30KQ+zvGfY4hvma7AJxMKe1JcJ9uAH72gVJ6PyHkfQAXAlhKCDkfhtb4PkrpU/yFhJCfJbiv37y3hoaGxpcW2gdZQ0ND4z8P82G4WwAACCGTJedsA3AMd85ISukmSukDAFbB0ALPA3Ct6XIBQsggQkh/AAsBfNvMvAHBxWI07K4bGhoaGl86aIKsoaGh8Z+HnwOYYgbRbQVwo3gCpXQ7gHwWjAfgVkLIZkLIRgBhAB9SSucDmAlgGSFkE4DZAHIppVsA3AtgCSFkA4CHuVufBmDBEftlGhoaGn0AOs2bhoaGxpcUhJD/AdBBKX3mMN3veAC/oJRefTjup6GhodFXoTXIGhoaGl9ePAG7T/MXRT8AvzuM99PQ0NDok9AaZA0NDQ0NDQ0NDQ0OWoOsoaGhoaGhoaGhwUETZA0NDQ0NDQ0NDQ0OmiBraGhoaGhoaGhocNAEWUNDQ0NDQ0NDQ4ODJsgaGhoaGhoaGhoaHP4fuL8iRPIOwAUAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "original_batch.show_ecg('A00001')\n", "\n", "changed_batch.show_ecg('A00001')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [```drop_labels```](https://analysiscenter.github.io/cardio/api/cardio.batch.html#cardio.batch.EcgBatch.drop_labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```drop_labels()``` removes those elements from batch, whose labels are listed in the argument ```drop_list```." ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "original_batch = batch_with_data.deepcopy()\n", "changed_batch = batch_with_data.deepcopy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that this action creates a new batch, so we will call it like this:" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "changed_batch = changed_batch.drop_labels([\"O\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can inspect labels in the batches:" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Labels in original batch: ['N' 'N' 'A' 'A' 'O' 'O']\n", "Labels in batch after drop: ['N' 'N' 'A' 'A']\n" ] } ], "source": [ "print('Labels in original batch: ', original_batch.target)\n", "print('Labels in batch after drop: ', changed_batch.target)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As far as this action creates new batch, it changes ```index``` of the batch, removing indices that correspond to signals with undesired label:" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Indices in original batch: ['A00001' 'A00002' 'A00004' 'A00005' 'A00008' 'A00013']\n", "Indices in batch after drop: ['A00001' 'A00002' 'A00004' 'A00005']\n" ] } ], "source": [ "print('Indices in original batch: ', original_batch.indices)\n", "print('Indices in batch after drop: ', changed_batch.indices)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [```rename_labels```](https://analysiscenter.github.io/cardio/api/cardio.batch.html#cardio.batch.EcgBatch.rename_labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```rename_labels()``` simply replaces labels according to the argument ```rename_dict```." ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "original_batch = batch_with_data.deepcopy()\n", "changed_batch = batch_with_data.deepcopy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Suppose, we want to build a classification model to find people with abnormal rhythms using ECG signal. But we have data with three labels: \"N\" (normal rhythm), \"A\" (atrial fibrillation), \"O\" (other abnormal rhythms). So, all we need to do to re-label the data is to combine signals with labels \"A\" and \"O\" under single label. Let's rename labels \"A\" and \"O\" to some other label, say \"AO\":" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "changed_batch = changed_batch.rename_labels(rename_dict={'A':'AO', 'O':'AO'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have only two unique labels in the target component:" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Labels in original batch: ['N' 'N' 'A' 'A' 'O' 'O']\n", "Labels in batch after drop: ['N' 'N' 'AO' 'AO' 'AO' 'AO']\n" ] } ], "source": [ "print('Labels in original batch: ', original_batch.target)\n", "print('Labels in batch after drop: ', changed_batch.target)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And you can see that the property ```unique_labels``` of the batch also has changed:" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Labels in original batch: ['A', 'N', 'O']\n", "Labels in batch after drop: ['AO' 'N']\n" ] } ], "source": [ "print('Labels in original batch: ', original_batch.unique_labels)\n", "print('Labels in batch after drop: ', changed_batch.unique_labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [```rename_channels```](https://analysiscenter.github.io/cardio/api/cardio.batch.html#cardio.batch.EcgBatch.rename_channels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```rename_channels()``` changes names of the channels in ```meta``` component under key \"signame\"." ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "original_batch = batch_with_data.deepcopy()\n", "changed_batch = batch_with_data.deepcopy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to change channel name from \"ECG\" to \"Channel №1\" you need to write this:" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "changed_batch = changed_batch.rename_channels(rename_dict={'ECG':'Channel №1'})" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Signal names in original batch: ['ECG']\n", "Signal names in batch after drop: ['Channel №1']\n" ] } ], "source": [ "print('Signal names in original batch: ', original_batch[\"A00001\"].meta[\"signame\"])\n", "print('Signal names in batch after drop: ', changed_batch[\"A00001\"].meta[\"signame\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that method ```show_ecg()``` now will display new names:" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "original_batch.show_ecg('A00001')\n", "\n", "changed_batch.show_ecg('A00001')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### other actions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can find complete list of available actions in [documentation](https://analysiscenter.github.io/cardio/api/cardio.ecg_batch.html) on EcgBatch." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Summarizing, in Notebook 1 we learned:\n", "* How to get ECG data\n", "* How to create datasets\n", "* How to apply actions\n", "* How actions change batch and its components\n", "\n", "In the next [Notebook 2](https://github.com/analysiscenter/cardio/blob/master/tutorials/II.Pipelines.ipynb) we will combine actions in pipeline." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: tutorials/II.Pipelines.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Build and run pipelines" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In Notebook 2 we learn pipelines. Pipeline is a sequence of actions that we want to apply to dataset. Sometimes it is also called a workflow. It helps to organize your work, combining and giving clear names to action sequences that will be reused many times. For example, when you train a model with batches, you repeat the same process for each batch many times: data loading, preprocessing and passing data to the next model training iteration.\n", "\n", "![img](http://www.hgpresearch.com/wp-content/uploads/2014/01/workflow3.png)\n", "\n", "Instead of maintaining the sequence of actions that can be potentially large enough you simply define a pipeline once and expoit it whenever you need. Moreover, since pipelines only keep a sequence of actions (they itself do not perform any caclulation), it is naturally to expect that we can play with sequences in the same manner as we do it with python lists, for example, contatenate them or repeat several times. Pipelines can do this indeed! It is called pipeline algebra and we consider algebra operations in this tutorial." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Table of contents\n", "* [Build a simple pipeline](#Build-a-simple-pipeline)\n", "* [Pipeline variables](#Pipeline-variables)\n", "* [Add custom function into pipeline](#Add-custom-function-into-pipeline)\n", "* [Pipeline algebra](#Pipeline-algebra)\n", "* [CardIO pipelines](#CardIO-pipelines)\n", " * [Train model to detect atrial fibrillation](#Train-model-to-detect-atrial-fibrillation)\n", " * [Predict atrial fibrillation](#Predict-atrial-fibrillation)\n", "* [Summary](#Summary)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Build a simple pipeline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's start with a very simple pipeline and elaborate it iteratively. In the beginning it will do two actions: load ECG signals and apply filter with 5–40 Hz bandpass. This is typical step in ECG processing. Let's call it ```filter_pipeline```. \n", "\n", "To build a pipeline we initialize it with ```bf.Pipeline()``` statement and append [actions](https://analysiscenter.github.io/cardio/api/cardio.batch.html#methods) from ```EcgBatch```:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sys\n", "sys.path.append(\"..\")\n", "\n", "import cardio.batchflow as bf\n", "\n", "filter_pipeline = (bf.Pipeline()\n", " .load(fmt=\"wfdb\", components=[\"signal\", \"meta\"])\n", " .band_pass_signals(low=5, high=40))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To run the pipeline we need some dataset to be passed in. Let's create an ECG dataset (see [Notebook 1](https://github.com/analysiscenter/cardio/blob/master/tutorials/I.CardIO.ipynb) for dataset): " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import os\n", "from cardio import EcgDataset\n", "\n", "PATH_TO_DATA = \"../cardio/tests/data\" #set path to data\n", "eds = EcgDataset(path=os.path.join(PATH_TO_DATA, \"A*.hea\"), no_ext=True, sort=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can pass dataset ```eds``` into ```filter_pipeline``` and run the calculation:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(eds >> filter_pipeline).run(batch_size=len(eds), n_epochs=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pipeline is completed. Where are loaded and filtered ECG? Answer: they are destroyed. The point is that when we run pipeline batches are generated and __destroyed__ inside the pipeline. It is reasonable if we train a model and do not need to keep batches. However, at the moment we do not train any model and want to see the effect of ```filter_pipeline```. So we slighlty modify the pipeline and initialize a pipeline variable that will keep batches before and after filtering. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pipeline variables\n", "\n", "Sometimes you may need to remember intermediate results within workflows. [Pipeline variable](https://analysiscenter.github.io/batchflow/intro/pipeline.html#pipeline-variables) is a flexile tool that solves this issue. Pipeline variable can be list, array, number or any other data structure. \n", "\n", "Recall that we were going to add a variable in ```filter pipeline``` that will keep batches before and after filtering. So we need a list to which we will append batches. Action ```init_variable('saved_batches', init=list)``` in the beginning of pipeline initilaizes a list with name ```'saved_batches'```. Then somewhere within the pipeline we add action ```update_variable``` and specify which variable will be updated, what is a new value and how to update variable (append, extend or rewrite).\n", "\n", "Below you can find ```filter_pipeline``` with included ```init_variable``` and ```update_variable``` actions. Note that we apply action ```update_variable``` twice. Each action will append batch in its current state to the list ```saved_batches```. Statement ```B()``` says that we want to keep the whole batch. Alternatively, we could specify particular batch component, e.g. ```B('signal')```. Read [here](https://analysiscenter.github.io/batchflow/intro/pipeline.html#updating-a-variable) more about updating of variables." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from cardio.batchflow import B\n", "\n", "filter_pipeline = (bf.Pipeline()\n", " .init_variable('saved_batches', init_on_each_run=list)\n", " .load(fmt=\"wfdb\", components=[\"signal\", \"meta\"])\n", " .update_variable('saved_batches', B(), mode='a')\n", " .band_pass_signals(low=5, high=40)\n", " .update_variable('saved_batches', B(), mode='a'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the modified pipeline:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "filter_pipeline = (eds >> filter_pipeline).run(batch_size=len(eds), n_epochs=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now pipeline variable ```saved_batches``` keeps raw batch (before filtering) and filtered batch. Method ```get_variable``` gets access to the pipeline variable and saved batches:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "raw_batch, filtered_batch = filter_pipeline.get_variable('saved_batches')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The figures below shows raw and filtered ECG signal with index 'A00001':" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "raw_batch.show_ecg('A00001', start=10, end=15)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "filtered_batch.show_ecg('A00001', start=10, end=15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Add custom function into pipeline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Suppose you know some clever method of signal filtering that you want to incorporate into pipeline and that is not in CardIO yet. You can easily include new methods in pipelines using ```apply_transform``` action (see [API](https://analysiscenter.github.io/cardio/api/cardio.batch.html#cardio.batch.EcgBatch.apply_transform) for details). As an example consider ```medfilt``` filter from ```scipy.signal``` module and create ```medfilt_pipeline```. We specify which componet will be the input (```signal```), which component will accept the output (```signal``` again), function for transformation (```medfilt```) and all its argumets (```kernel_size```):" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "from scipy.signal import medfilt\n", "\n", "medfilt_pipeline = (bf.Pipeline()\n", " .init_variable('saved_batches', init_on_each_run=list)\n", " .load(fmt=\"wfdb\", components=[\"signal\", \"meta\"])\n", " .update_variable('saved_batches', B(), mode='a')\n", " .apply_transform(func=medfilt, src='signal', dst='signal', kernel_size=(1, 11))\n", " .update_variable('saved_batches', B(), mode='a'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run pipeline:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "medfilt_pipeline = (eds >> medfilt_pipeline).run(batch_size=len(eds), n_epochs=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compare the same segment before and after applying ```medfilt```:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "raw_batch, filtered_batch = medfilt_pipeline.get_variable('saved_batches')\n", "\n", "raw_batch.show_ecg('A00005', start=10, end=12)\n", "filtered_batch.show_ecg('A00005', start=10, end=12)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the same manner you can add more custom preprocess methods and build very complex pipelines. Moreover, several pipelines can be combined in a single one using algebra of pipelines. Next section shows how it works." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pipeline algebra" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pipeline algebra is a powerful tool to manipulate pipelines. Suppose we have three separate pipelines: one for data loading, one for signal filtering and another one for data augmentation. Although each of them is very simple, let's consider them separately:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "load_pipeline = bf.Pipeline().load(fmt=\"wfdb\", components=[\"signal\", \"meta\"])\n", "\n", "filter_pipeline = bf.Pipeline().band_pass_signals(low=5, high=40)\n", "\n", "augment_pipeline = (bf.Pipeline()\n", " .random_resample_signals(\"normal\", loc=300, scale=10)\n", " .random_split_signals(length=3000, n_segments=5)\n", " .unstack_signals())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first two are already known, while ```augment_pipeline``` is a new one. In general it is a good practice to augment data before model training. In ```augment_pipeline``` we resample signal to random frequency and crop 5 random segment of size 3000." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can build a full workflow (load, filter and augmentation) using pipeline algebra. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "full_pipeline = load_pipeline + filter_pipeline + augment_pipeline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The point is that with pipeline aglebra we can easily rearrage workflows, e.g. build a workflow without signal filtering. We will not drop filtering and rather add some actions to save output batch:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "with bf.Pipeline() as p:\n", " full_pipeline = (p.init_variable('saved_batches', init_on_each_run=list) +\n", " full_pipeline +\n", " p.update_variable('saved_batches', B(), mode='a'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run ```full_pipeline``` and consider output batch:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "batch = (eds >> full_pipeline).run(batch_size=len(eds), n_epochs=1).get_variable('saved_batches')[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot several samples that we have obtained after augmentation. Note that original indices 'A00001', 'A00002' and so on don't make sense anymore and we use natural indexing: 0, 1, 2 and so on. " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "batch.show_ecg(0)\n", "batch.show_ecg(5)\n", "batch.show_ecg(29)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Algebra of pipelines allows to do more complex tricks. We may repeat some pipelines n times (using operation *) and apply pipelines with some probability (using operation @). This is how it may look like:\n", "\n", "```python\n", "with Pipeline() as p:\n", " preprocessing_pipeline = p.first_action() +\n", " p.second_action() +\n", " p.action_we_want_apply_sometimes @ .8 +\n", " p.action_we_want_repeat() * 3 +\n", " p.last_action()\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Read even more about pipelines in [documentation](https://analysiscenter.github.io/batchflow/intro/pipeline.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CardIO pipelines" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With CardIO you may benefit from using built-in pipelines that allow to reproduce the processes of model training and making predictions. At the moment ```cardio.pipelines``` module contains pipelines for [dirichlet_model](https://analysiscenter.github.io/cardio/api/cardio.models.html#dirichletmodel) that learns to predict atrial fibrillation and [hmm_model](https://analysiscenter.github.io/cardio/api/cardio.models.html#hmmodel) that learns to annotate QRS intervals, T-waves and P-waves in ECG signal. \n", "\n", "Under the hood built-in pipelines contain sequence of actions that load data, process it and transform in order to fit the model input. For example consider the ```dirichlet_train_pipeline```:\n", "\n", "```python\n", "(bf.Pipeline()\n", " .init_model(\"dynamic\", DirichletModel, name=\"dirichlet\", config=model_config)\n", " .init_variable(\"loss_history\", init_on_each_run=list)\n", " .load(components=[\"signal\", \"meta\"], fmt=\"wfdb\")\n", " .load(components=\"target\", fmt=\"csv\", src=labels_path)\n", " .drop_labels([\"~\"])\n", " .replace_labels({\"N\": \"NO\", \"O\": \"NO\"})\n", " .flip_signals()\n", " .random_resample_signals(\"normal\", loc=300, scale=10)\n", " .random_split_signals(2048, {\"A\": 9, \"NO\": 3})\n", " .binarize_labels()\n", " .train_model(\"dirichlet\", make_data=concatenate_ecg_batch,\n", " fetches=\"loss\", save_to=V(\"loss_history\"), mode=\"a\")\n", " .run(batch_size=batch_size, shuffle=True, drop_last=True, n_epochs=n_epochs, lazy=True))\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are going to demonstrate how to train the dirichlet model with built-in pipeline and make predictions. However, usage of built-in pipelines assumes that we have an appropriate dataset for model training. As such a dataset we suggest to exploit the PhysioNet database of short single lead ECG recording (see [Notebook 1](https://github.com/analysiscenter/cardio/blob/master/tutorials/I.CardIO.ipynb) for more details). From now on we assume that the dataset as well as the reference file with target labels are located in the same directory and PATH_TO_DATA is a path to that directory." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train model to detect atrial fibrillation\n", "\n", "Let's create a train/test dataset from the PhysioNet database first:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "import os\n", "from cardio import EcgDataset\n", "\n", "PATH_TO_DATA = \"/notebooks/data/ECG/training2017\" #set path to data\n", "pds = EcgDataset(path=os.path.join(PATH_TO_DATA, \"*.hea\"), no_ext=True, sort=True)\n", "pds.split(0.8, shuffle=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the next step we configure the training pipeline. We import function ```dirichlet_train_pipeline``` that creates a pipeline and specify path to the reference file with target labels (see [API](https://analysiscenter.github.io/cardio/api/cardio.pipelines.html#dirichlet-train-pipeline) for more options including GPU configuration):" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "env: CUDA_VISIBLE_DEVICES=0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "from cardio.pipelines import dirichlet_train_pipeline\n", "%env CUDA_VISIBLE_DEVICES=0\n", "\n", "AF_SIGNALS_REF = os.path.join(PATH_TO_DATA, \"REFERENCE.csv\")\n", "pipeline = dirichlet_train_pipeline(AF_SIGNALS_REF, batch_size=256, n_epochs=500)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pass the training part of the dataset and start learning. It may take some time untill we iterate over all epochs:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "trained = (pds.train >> pipeline).run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Save model to use it later for prediction:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "model_path = \"af_model_dump\"\n", "trained.save_model(\"dirichlet\", path=model_path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Predict atrial fibrillation\n", "\n", "To make predictions with dirichlet model we import ```dirichlet_predict_pipeline``` and specify path to trained model:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Restoring parameters from af_model_dump/model-13000\n" ] } ], "source": [ "from cardio.pipelines import dirichlet_predict_pipeline\n", "\n", "pipeline = dirichlet_predict_pipeline(model_path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pass the test part of the dataset for prediction. We will find predictions in pipeline variable ```predictions_list```:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "res = (pds.test >> pipeline).run()\n", "pred = res.get_variable(\"predictions_list\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Show some predicted probabilities and corresponding true labels:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "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", "
AF probTrue label
A084440.06NO
A073610.03NO
A036260.15NO
A043870.54NO
A023340.03NO
A064490.03NO
A052840.07NO
A044180.05NO
A072000.03NO
A082060.03NO
A082470.03NO
A081720.03NO
A030170.47NO
A021420.03NO
A049560.07NO
A002320.03NO
A011990.03NO
A041660.03NO
A070920.93A
A027990.03NO
\n", "
" ], "text/plain": [ " AF prob True label\n", "A08444 0.06 NO\n", "A07361 0.03 NO\n", "A03626 0.15 NO\n", "A04387 0.54 NO\n", "A02334 0.03 NO\n", "A06449 0.03 NO\n", "A05284 0.07 NO\n", "A04418 0.05 NO\n", "A07200 0.03 NO\n", "A08206 0.03 NO\n", "A08247 0.03 NO\n", "A08172 0.03 NO\n", "A03017 0.47 NO\n", "A02142 0.03 NO\n", "A04956 0.07 NO\n", "A00232 0.03 NO\n", "A01199 0.03 NO\n", "A04166 0.03 NO\n", "A07092 0.93 A\n", "A02799 0.03 NO" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "pd.options.display.float_format = '{:.2f}'.format\n", "\n", "pred_proba = pd.DataFrame([x[\"target_pred\"][\"A\"] for x in pred], \n", " index=pds.test.indices, columns=['AF prob'])\n", "\n", "true_labels = (pd.read_csv(AF_SIGNALS_REF, index_col=0, names=['True label'])\n", " .replace(['N', 'O', '~'], 'NO'))\n", "\n", "df = pd.merge(pred_proba, true_labels, how='left', left_index=True, right_index=True)\n", "df.head(20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In Notebook 2 we learned about pipelines and algebra of pipelines. Now you can:\n", "* construct and run pipelines\n", "* add custom actions\n", "* concatenate several pipelines using algebra of pipelines\n", "* train neural network to predict atrial fibrillation with built-in pipelines.\n", "\n", "In the next [Notebook 3](https://github.com/analysiscenter/cardio/blob/master/tutorials/III.Models.ipynb) we will work with models." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: tutorials/III.Models.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Models for deep research of ECG" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "CardIO framework provides a flexible environment for experiments with deep neural network models and models of other kind. In this tutorial we will start with examples how to use models built-in in CardIO. We will train them to make a segmentation of ECGs and detect heart diseases. Then we will elaborate a custom model using standard blocks and layers provided with CardIO. Thanks to them the process of configuring even a deep neural network is as simple as building a castle from LEGO blocks.\n", "![](http://c.shld.net/rpx/i/s/i/spin/10000351/prod_1876279212?hei=245&wid=245&op_sharpen=1&qlt=85)\n", "Any other models related or not related to neural networks are also welcome using CardIO API for general models, which we consider in the last part of this tutorial. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Table of contents\n", "* [CardIO segmentation models](#CardIO-segmentation-models)\n", " * [Train segmentation model](#Train-segmentation-model)\n", " * [ECG segmentation](#ECG-segmentation)\n", "* [CardIO classification models](#CardIO-classification-models)\n", " * [Train classification model](#Train-classification-model)\n", " * [Predict atrial fibrillation](#Predict-atrial-fibrillation)\n", "* [Common neural network architectures](#Common-neural-network-architectures)\n", "* [Custom neural network models](#Custom-neural-network-models)\n", " * [Layers and blocks from BatchFlow models](#Layers-and-blocks-from-BatchFlow-models)\n", " * [Neural network with Tensorflow](#Neural-network-with-Tensorflow)\n", " * [Neural network with Keras](#Neural-network-with-Keras)\n", "* [General models](#General-models)\n", "* [Summary](#Summary)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# CardIO segmentation models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Analysis of ECG typically starts with isolation of fiducial points within an ECG signal. The figure below (taken from internet) shows various types of that points.\n", "\n", "![](https://archive.cnx.org/resources/c36f8642adfe54b796f9a94593f1be08e6fee4c9/Picture%201.jpg)\n", "\n", "CardIO contains a model designed to automatically isolate QRS intervals, P-waves and T-waves. The model is based on Hidden Markov Model approach (read the [article](https://medium.com/data-analysis-center/annotating-ecg-signals-with-hidden-markov-model-56f8b9abd83a) to see how it works). We consider the model as a \"black box\" without looking under the hood (however, you can always find its description in source [code](https://github.com/analysiscenter/cardio/blob/master/cardio/models/hmm/hmm.py)). First, we will train the model using provided pipeline. Then we will apply the pipeline for segmentation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train segmentation model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To start training we need a database with isolated fiducial points. We suggest to use PhysioNet QT [Database](https://www.physionet.org/physiobank/database/qtdb/), which consists of 105 annotated 15-minute excerpts of two channel ECG. So, download the database to follow the tutorial.\n", "\n", "After downloading the database we make a training dataset as follows:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sys, os\n", "sys.path.append('..')\n", "\n", "from cardio import EcgDataset\n", "\n", "SIGNALS_PATH = \"/notebooks/data/ECG/QT/\" #set path to the QT database\n", "SIGNALS_MASK = os.path.join(SIGNALS_PATH, \"*.hea\")\n", "\n", "dtst = EcgDataset(path=SIGNALS_MASK, no_ext=True, sort=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are two steps in model training. The first one is signal preprocessing described in ```hmm_preprocessing_pipeline``` (see previous [Notebook 2](https://github.com/analysiscenter/cardio/blob/master/tutorials/II.Pipelines.ipynb) for pipelines). Then we apply ```hmm_train_pipeline```. \n", "\n", "We import both pipelines from ```cardio.pipelines``` and organize the process as shown below. Note that pipelines' runtime may take up to several hours. You can reduce the training dataset to speed up the process." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from cardio.pipelines import hmm_preprocessing_pipeline, hmm_train_pipeline\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "pipeline = hmm_preprocessing_pipeline()\n", "ppl_inits = (dtst >> pipeline).run()\n", "\n", "pipeline = hmm_train_pipeline(ppl_inits)\n", "ppl_train = (dtst >> pipeline).run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After both pipelines are completed, don't forget to save the trained model for later use:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ppl_train.save_model(\"HMM\", path=\"model_dump.dll\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ECG segmentation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For segmentation we use ```hmm_predict_pipeline``` defined in ```cardio.pipelines```. Below we create a dataset for segmentation, import ```hmm_predict_pipeline``` and run it:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "from cardio import EcgDataset\n", "from cardio.pipelines import hmm_predict_pipeline\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "eds = EcgDataset(path=\"../cardio/tests/data/A*.hea\", no_ext=True, sort=True)\n", "batch = (eds >> hmm_predict_pipeline(\"model_dump.dll\", annot=\"hmm_annotation\")).next_batch()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now ```res``` contains batch with annotated intervals. Let's visualize e.g. ECG with index 'A00001'. Red color in the figure represents QRS-intervals, green is for P-waves, blue is for T-waves." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "batch.show_ecg(\"A00001\", start=10, end=15, annot=\"hmm_annotation\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "ECG signal with isolated QRS intervals allows to calculate heart rate parameter. In fact, it has been already done in ```hmm_predict_pipeline``` and we can find heart rate in ```meta``` component. For instance, let's check heart rate's value for the same signal 'A00001':" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Heart rate: 79 bpm\n" ] } ], "source": [ "print(\"Heart rate: {0} bpm\".format(int(.5 + batch[\"A00001\"].meta[\"hr\"])))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that in meta there are also available computed median lengths of the PQ interval (key ```\"pq\"```), the QT interval (```\"qt\"```) and the QRS interval (```\"qrs\"```)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# CardIO classification models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "CardIO can predict heart diseases using ECG signals. In the tutorial we will show how to train a model to predict atrial fibrillation. The figure below (taken from internet) schematically shows what differs normal rhythm from atrial fibrillation.\n", "\n", "![](https://i.pinimg.com/564x/00/2b/88/002b88cebc5eda5e80a3046a136fd7a1--atrial-fibrillation-ecg.jpg)\n", "\n", "To train a model we will use the PhysioNet's short single lead ECG recording [database](https://physionet.org/challenge/2017/). To continue with the tutorial download the PhysioNet's database.\n", "\n", "Read the [article](https://medium.com/data-analysis-center/atrial-fibrillation-detection-with-a-deep-probabilistic-model-1239f69eff6c) for description of the model or find its source code in the [repository](https://github.com/analysiscenter/cardio/blob/master/cardio/models/dirichlet_model/dirichlet_model.py). We only note that it's a deep neural network of ResNet-like architecture.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train classification model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we construct a dataset from the PhysioNet database:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import sys, os\n", "sys.path.append(\"..\")\n", "from cardio import EcgDataset\n", "\n", "AF_SIGNALS_PATH = \"/notebooks/data/ECG//training2017\" #set path to the PhysioNet database\n", "AF_SIGNALS_MASK = os.path.join(AF_SIGNALS_PATH, \"*.hea\")\n", "AF_SIGNALS_REF = os.path.join(AF_SIGNALS_PATH, \"REFERENCE.csv\")\n", "\n", "afds = EcgDataset(path=AF_SIGNALS_MASK, no_ext=True, sort=True)\n", "\n", "afds.split(0.8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we import ```dirichlet_train_pipeline``` and run it. The process may take a while, we recommend to ensure that GPU is enabled. You can also set ```n_epochs``` parameter of the pipeline to lower value (default is ```n_epochs```=1000)." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "from cardio.pipelines import dirichlet_train_pipeline\n", "\n", "pipeline = dirichlet_train_pipeline(AF_SIGNALS_REF)\n", "train_ppl = (afds.train >> pipeline).run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check how loss decreases with epochs increasing. We access ```loss_history``` through ```get_variable()``` method:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3oAAAEKCAYAAABE7ieQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd4nNWd9vHvM10jjbpkyUWWC264AG5geg8lgYRUkhBC\nEpYkbHY3yZsXQkjbFFJ385KyISSkAElIwkISwPQOBnds415kW1bv0mj6ef+YYo2aZVvySPL9uS5d\nmnmeZ2aOpMHo1u+c87OMMYiIiIiIiMj4Ycv0AERERERERGR4KeiJiIiIiIiMMwp6IiIiIiIi44yC\nnoiIiIiIyDijoCciIiIiIjLOKOiJiIiIiIiMMwp6IiIiIiIi44yCnoiIiIiIyDijoCciIiIiIjLO\nODI9gKNRXFxsKisrMz0MERERERGRjFi7dm2jMabkSNeNqaBXWVnJmjVrMj0MERERERGRjLAsq2oo\n12nqpoiIiIiIyDijoCciIiIiIjLOKOiJiIiIiIiMMwp6IiIiIiIi44yCnoiIiIiIyDijoCciIiIi\nIjLOKOiJiIiIiIiMMwp6w+jtQ+2srWrJ9DBEREREROQkN6Yapo92V/6/lwHYd9dVGR6JiIiIiIic\nzFTRExERERERGWcU9ERERERERMYZBT0REREREZFxRkFPRERERERknFHQExERERERGWcU9ERERERE\nRMYZBT0REREREZFxRkFPRERERERknFHQGybRmMn0EERERERERAAFvWETisQyPQQRERERERFAQW/Y\nBCPRTA9BREREREQEUNAbNoGwKnoiIiIiIjI6KOgNE1X0RERERERktFDQGyZBrdETEREREZFRQkFv\nmATCquiJiIiIiMjokNGgZ1nWOyzL2m5Z1i7Lsm7L5FiOlyp6IiIiIiIyWmQs6FmWZQd+BlwBzAM+\nZFnWvEyN53gFtRmLiIiIiIiMEpms6C0Ddhlj9hhjQsCfgGsyOJ7jos1YRERERERktMhk0JsEHOhx\n/2DiWBrLsm62LGuNZVlrGhoaTtjgjpbaK4iIiIiIyGgx6jdjMcbcY4xZYoxZUlJSkunhDKhnRc8Y\nk8GRiIiIiIjIyS6TQa8amNLj/uTEsTGp52YsMeU8ERERERHJoEwGvdXAKZZlTbMsywV8EPh7Bsdz\nXHq2V4gq6YmIiIiISAY5MvXCxpiIZVm3Ak8CduA3xpgtmRrP8Uqv6CnoiYiIiIhI5mQs6AEYYx4H\nHs/kGIZLWEFPRERERERGiVG/GctYEY4eDnqauikiIiIiIpmkoDdMwj3CnXKeiIiIiIhkkoLeMNhc\n3UZLVyh1P6akJyIiIiIiGZTRNXrjQSxmuPruV9KPaY2eiIiIiIhkkCp6xynQo1F6UlRBT0RERERE\nMkhB7zh1h/oGvVisnwtFREREREROEAW949Qd7ifoqaInIiIiIiIZpKB3nPqr6Km9goiIiIiIZJKC\n3nHqr6Kngp6IiIiIiGSSgt5x6reip6QnIiIiIiIZpKB3nPz9VPQ0dVNERERERDJJQe84Bfqp6BlV\n9EREREREJIMU9I6TX1M3RURERERklFHQO079bcaiqZsiIiIiIpJJCnrHKaBdN0VEREREZJRR0DtO\nPadu2m0WoIqeiIiIiIhkloLeceo5ddNlj387YyrpiYiIiIhIBinoHaeeffTcTgU9ERERERHJPAW9\n45QW9Bzxb2c0lqnRiIiIiIiIKOgdt55TN90OO6CKnoiIiIiIZJaC3nHy91PRi2kzFhERERERySAF\nvePUs72CKxn0lPNERERERCSDFPSOU7KlAvRYo6epmyIiIiIikkEKesfpdzct49MXzAB6rNFTSU9E\nRERERDJIQW8YOBNVPbVXEBERERGR0UBBbxjYEkHPbsU/R1XRExERERGRDFLQGwYW8YCXyHnajEVE\nRERERDJKQW8YJAOelbihqZsiIiIiIpJJCnrDKLkBp6ZuioiIiIhIJinoDSObKnoiIiIiIjIKKOgN\ng2QnPQU9EREREREZDRT0hsHhNXrxz7FY5sYiIiIiIiKioDeMkhW9qCp6IiIiIiKSQQp6wyC526Y9\nsRtLTJuxiIiIiIhIBmUk6FmW9T7LsrZYlhWzLGtJJsYwEpJr9ZTzREREREQkkzJV0dsMvAd4KUOv\nP6x699HT1E0REREREcmkjAQ9Y8xWY8z2TLz2SLhsXhkA154+EQCjoCciIiIiIhnkyPQAxoOZpTns\nu+sqWrpCgBqmi4iIiIhIZo1Y0LMs6xmgrJ9TdxhjHj2K57kZuBmgoqJimEY3MmyJzVgU9ERERERE\nJJNGLOgZYy4Zpue5B7gHYMmSJaM6QSV33dTMTRERERERySS1VxhGiZynzVhERERERCSjMtVe4d2W\nZR0EzgIesyzryUyMY7glG6bf9cQ26tsDGR6NiIiIiIicrDK16+b/GmMmG2PcxpgJxpjLMzGO4ZYM\negD//ezODI5EREREREROZkcV9Ky47JEazFiXXKMHkO2yZ3AkIiIiIiJyMjti0LMs6/eWZeValuUF\nNgG7LMv6/MgPbezpkfPI9TgzNxARERERETmpDaWit9AY0w5cCzwNTAVuHMlBjVVWj6mbWaroiYiI\niIhIhgwl6Dkty3IA1wCPGmNCQGxkhzX2RRK99DqDEf669mDqeHcoqo1aRERERERkRA0l6N0L7AcK\ngBcty6oAOkd0VONAMBzPwnc+spkv/mUj6/e3APDLl3Zz7c9ezeTQRERERERknDti0DPG/JcxZqIx\n5jJjjAEOABeN/NDGtmAkCkB1azcAgUTwa+wM0tgZyti4RERERERk/BvKZiy3WpaVm7j9S+AN4NyR\nHthYF4zEg10sMYUzuSNnMBwjFI2ljouIiIiIiAy3oUzdvNkY025Z1mXABOBTwPdHdlhj10fPnAoc\nruhFTTLoxc+HorG0zyIiIiIiIsNtKEEvWXq6EviDMWbjEB93UvrPa+dTnudJrdHrXblLHlfQExER\nERGRkTKUwLbRsqzHgauBJyzLyuFw+JN+uB221NTNZEUvHI1/Tga8ZOATEREREREZbo4hXPNxYDGw\nyxjjtyyrGPjEyA5rbHM77IenbibyXDg5ZTOiip6IiIiIiIysIwY9Y0w0Ee7ek2gI/qIx5okRH9kY\n5nba+mzGkgx6yQCYDHwiIiIiIiLDbSi7bn4b+BKwJ/HxfyzL+tZID2wscztsh9fo9Z66GUmv7ImI\niIiIiAy3oUzdfCdwhjEmAmBZ1m+AdcBXRnJgY5nbYccfigA91+glK3rplT0REREREZHhNpSgB+AD\nWnrclkG4HTZa/OlTN299cD2v7W5SRU9EREREREbcUILe94F1lmU9C1jABcCdIzmosa7nGr1kRQ/g\nwTf2k5flBBT0RERERERk5AxlM5b7Lct6HlieOPRVY0z1yA5rbPP02HUz1ivPtXWHgcNTOEVERERE\nRIbbgEHPsqyFvQ7tSnwusiyryBjz1sgNa2xzO/tuxtKbPxTFGENiJ1MREREREZFhM1hF72eDnDPA\necM8lnEj3kcvMXUz1n/Q++yD6/jcRTP5/GWzT+TQRERERETkJDBg0DPGnHsiBzKeuB22w1M3B6jo\nATz45n4FPRERERERGXZH7KMnRy8e9GIYYwas6AG47Pr2i4iIiIjI8FPSGAFupx1j4k3SI9GBg57b\naT+BoxIRERERkZOFgt4IcDvi39ZgJEpgkMboyetERERERESG0xHbK/Sz+yZAG3DAGKMeAf3I97oA\n2NfoJzxYRU9BT0RERERERsBQGqb/GjgN2EK8Yfpc4G3AZ1nWzcaYZ0dwfGPShbNLsNss/rbu4KDX\nuR2auikiIiIiIsNvKCWlfcBiY8xpxphFwGJgB3A58KMRHNuYVZTjZsWMIv751qFBr3M7VdETERER\nEZHhN5SkMbdnc3RjzCZgnjFm1yCPOenNK8+lsTM06DUOm5qli4iIiIjI8BvK1M1tlmXdDfwpcf8D\niWNuIDJiIxvjJhd6+xxz2CwiPdotDLZ+T0RERERE5FgNpaJ3A3AQuC3xcQj4GPGQd/HIDW1sm1KQ\n1eeYz5Oeq4OD7MgpIiIiIiJyrI5Y0TPG+IHvJT56axv2EY0TFf1U9HI8Dlr84dT9UESbloqIiIiI\nyPA7YkXPsqwzLct6wrKsty3L2pH8OBGDG8sm9VPRK8p2p90PKuiJiIiIiMgIGMrUzfuAnwOXAOf2\n+JBBJFsnXDC7JHXsm9ecyifPmcayykIgPei1+kM8v63+xA5SRERERETGpaFsxtJujPnHiI9kHNr6\nzXfgsFuccscTAJTlevjK1fMA+MJDG1m1pyl17S33r2XVnmY2fvUy8rzOjIxXRERERETGh6EEvecs\ny/ou8DAQTB7s2XJB+pflSm+I3rNButtpS9uMZXttBwCdoYiCnoiIiIiIHJehBL1zen0GMMB5wz+c\n8a1ng3SX3ZY2dTPZaKG9O8yk/L7r+0RERERERIZqKLtuDvt6PMuyfgC8EwgBu4GPG2Nah/t1RhuX\n/XDQi1f0egS9RNK74icv88XLZnHrRaec6OGJiIiIiMg4MeBmLJZlfSjx+XP9fRzn6z4NzDfGLAR2\nALcf5/ONCTablbrtdtgJRWIY07dp+g+f0qamIiIiIiJy7Aar6BUkPpcMcs0xMcY81ePuKuC9w/0a\no53bEc/YoWgMt8Peb+ATERERERE5FgMGPWPMzxOf7xzhMdwE/HmEX2PUSQa9YCQR9DI8HhERERER\nGT+OuEbPsqxi4mGssuf1xpibj/C4Z4Cyfk7dYYx5NHHNHUAEeGCQ57kZuBmgoqLiSMMdlR745HK2\n1rSnHXMlK3rJdXpKeiIiIiIiMkyGsuvmo8SnV74CRI9wbYox5pLBzluWdSNwNXCxGWTeojHmHuAe\ngCVLlozJOHT2zGLOnlmcdqxnRe+JTTV0BCNp540xWJaFiIiIiIjI0RpK0Ms2xnxhOF/Usqx3AF8C\nzjfG+IfzuceKZEUvEI7y6QfW9Tnf3q1+eiIiIiIicmwG3HWzhycsy7psmF/3p4APeNqyrA2WZf3P\nMD//qJdsnt7qD/V7vrEr2O9xERERERGRIxlKRe8W4P9aluUn3vfOAowxpvBYX9QYM/NYHzteJKdu\nNneF+z3f1BlixjHud1rfHqDE59bUTxERERGRk9RQKnrFgBPII95qoZgRaLlwsvG64hm7tq273/NN\nncdW0dtR18Gy7zzL71+vOuaxiYiIiIjI2DZgRc+yrFOMMTuBUwe45K2RGdLJYXJBFgDbajsAuPeG\nJWS7HWyrbecb/3ibjkBksIcPqKopvuTxpR0NfGxF5bCMVURERERExpbBpm7eBnwC+Fk/5wxw3oiM\n6CRRnufBbrNSbRey3Q7OmlHEvIm5fOMfb9Me6H9K55HYEzXaqBqwi4iIiIictAZrmP6JxOdzT9xw\nTh4Ou42J+R62Jyp6Pk/8R5Hjjn8+1oqeLbEuLxpT0BMREREROVkNZTMWLMuaA8wDPMljxpgHR2pQ\nJ4spBV4ONMfX6GUnAp7dZpHtstMZPLagZ7fFg15MFT0RERERkZPWETdjsSzrK8Qblv8PcAXw38B7\nR3hcJ4WKQm/qdrbbnrrt8zjpOMLUzfr2AO/9xWs0dKRv2pLMd6roiYiIiIicvIay6+YHgAuBGmPM\nR4FFQPaIjuokMaVH0EtO2QTI8TjSpm62+kPsqu9Me+xvX9vHmqoW/vjm/rTj4WgMUNATERERETmZ\nDWXqZrcxJmpZVsSyLB9QC0wd4XGdFJJBz2ZBlrNnRc9BZzBCXXuAc773HOFoPLTtu+uq1DXJtXi9\nZ2gq6ImIiIiIyFCC3nrLsvKB3wBrgHbgzREd1UliSqLFQrbbkdbc3Odx0tYd5u8bDqVCXm/Jyw2H\nz2862MaLOxoAiBpo6AjiddlT6/9EREREROTkMGgCsOLp4+vGmFbgZ5ZlPQnkGmPWnZDRjXPJil5O\nryDmczs42OLnsU01Az42GQt7VvTe+dNXUrdjMcPSbz9DUbaLtXdeOmxjFhERERGR0W/QNXrGGAM8\n3eP+LoW84VOU7eq34ubzOGj1h9lyqI255bmp45HEtMz3//J1/t9zuwAYaIJmcupmU1eI7lD0qMe2\nubqNlZsHDpoiIiIiIjJ6DWUzlg2WZZ0+4iM5CVmWxZQCb5+KXo7bQXNXiHDU8Klzp3H7FXMAeHln\nI/5QhDf3Nh++eIA2Cj3bK7y8s+Gox3b13a9wy/3K9CIiIiIiY9GAUzcty3IYYyLA6cBqy7J2A13E\nZw0aY8wZJ2iM49rHVlSmrbOD+Bq9pFNKfakdOD/+29W849SytGvDA2y60nMzlqom/3ANV0RERERE\nxoDB1ui9CZwBvOsEjeWkdP3yij7HfJ7DP5YZpdm8XdOWur92f0vatQNNy4wag82CmIlP3xQRERER\nkZPHYFM3LQBjzO7+Pk7Q+E5Kl86bAEBlkRevy4GnR+uFQDg92PlDEfoTisRIFvWau4L9XiMiIiIi\nIuPTYBW9EsuyPj/QSWPMj0dgPEJ8N87N37g8FercjsNBryuYHuz8A1T0el7XrIqeiIiIiMhJZbCg\nZwdyOLyTv5xAOW5HapMWj/Nw4bX3kryBpm4m1/XB8U3dNMak9fgTEREREZHRb7CgV2OM+eYJG4kM\nqOfUzd6SFT3Ta/fNSI9EeDwVvUjM4LQr6ImIiIiIjCVHXKMnmTdo0EtM7wxGYv2e97kdNHceR9CL\nDtSpT0RERERERqvBgt7FJ2wUMqieUzd7605sxjLQFM6yPA8dwQjByNE3TQcIRfsPkCIiIiIiMnoN\nmCCMMc0DnZMTy+M48tTNwABBrizPA0BLV/iYXjusoCciIiIiMuYMVtGTUWKwqZvJSt7qfS39np+U\nnwVAY2eQlZtrafMPHvgi0Rhf//uWHvc1dVNEREREZKxR0BsDBpu66Q9FeXFHA5/74/p+z08uiAe9\nXfWd3HL/Wj76mzcGfa1Ve5r57Wv7UvdV0RMRERERGXsU9MaAQSt64SgbD7QOeH5ygReAqiY/AG8d\nbBv0tRy9dtjUGj0RERERkbFHQW8McDsG/zE9u61+wHOTEhW9quau1LHerRgGk5y6+dhbNbyys3HI\njxMRERERkcxR0BsDBmpYfsncUnxux6AVvWyXg3yvkwPN/tSx2vbAgNcHwumbuiSnbn72wXV85NeD\nT/sUEREREZHRQUFvjDltSj4Az3/xAn56/RmcN6tk0OvdThulPjf7ewS9/U3+Aa/vHfQ0dVNERERE\nZOxxZHoAcnQe/NRymrtCqbV3F88t5bFNNQNe77LbKPV52FHXmTrW2j3wzpvdvYKedt0UERERERl7\nFPTGGK/Lgdd1+Md24exS7DaLaKz/QOZ2xCt6PbX6QwM+f3covYKnXTdFRERERMYeTd0cI06bks8H\nlkzpc7wg28UDn1zOJXMn9Ps4l8NGvteVdqxlkF56vSt6oWiM2AAhUkRERERERidV9MaIRz579oDn\nzpxexOKpBdS2BTj3+8+nnXM5bOS409sztPRT0fOHImyr7eizRi8SNQQi0T7Xi4iIiIjI6KWK3jjh\ntNuYUujtc9xlt5HtTs/zbf1U9G5/eBPv+flrfTZqCUdjdIcU9ERERERExhIFvXHO0SvolfjctPhD\n1HcEuPG+N2noCAKHG6lXt3anPT4cjeFX0BMRERERGVMU9MaZUp+bWy+cmXYsp0fQK8/z0OIP86uX\n9vDC9gYeWnMAAKc93qtvf7Of4hwXz33hfADCUdNn3Z6IiIiIiIxuCnrjzJt3XMIXL5+ddqxn0JuQ\n66HVH6I1MX0z2YvdaY+/FfY3+/E47WS54uv6elf0ItqFU0RERERk1MvIZiyWZf0ncA0QA+qBG40x\nhzIxlpNBz6mbhV4XGw+0phqof3/ldtbua0nbhCXLaU8Fv0g0hj8USZ0LRGLk2PX3ARERERGR0SxT\nv7H/wBiz0BhzGvBP4KsZGsdJoWdFryzPQ31HkDf2NqeOPbutnt0NXan7Wa7DQS8UNWkhMHnbGMML\n2+sHbL3Q1h3us95PREREREROjIwEPWNMe4+72YAatQ2zZz5/Hg9+ajkA2T3aK3x4ecURH+tx2lNr\n9vzBSNrUzWTQe3TDIW68bzV/Wn2g3+e4+u6XOfuu5455/CIiIiIicuwy1kfPsqxvAzcAbcCFmRrH\neDWz1MfMUh+QXtErzfXw4CeXgxVvqVDV5KeyyMu+Hm0Vek7d/NHTOzh1Ym7qXCAcX6N3IDH182BL\nejuGpAPNquaJiIiIiGTKiFX0LMt6xrKszf18XANgjLnDGDMFeAC4dZDnudmyrDWWZa1paGgYqeGO\na7376K2YWcyKGcXc/aHT+cQ50zjnlOK0826HDYfNSt3fcuhwATaYaJ6enLH59Nt1/GVN/1W9nteH\nIjEqb3uMP6yqOq6vRUREREREjmzEgp4x5hJjzPx+Ph7tdekDwHWDPM89xpglxpglJSUlIzXccc3r\nsvd7fOHkfO68eh75WS4AJhdkAbCjrgPLsvp9TLKiFzPxpLezvpO7n9s14Gu3d8c3cmkPxHf5/PFT\n24/hKxARERERkaORkTV6lmWd0uPuNcC2TIzjZDFQaEvKzYpX/JZNKwTAZhv4+mCPzViSDrb4CUX6\nb7vQ1h0PeMHE+SONRUREREREjl+m1ujdZVnWbOLtFaqAWzI0DgGumF/Odx7fxo0rKrn2tElMLfIO\neG0gMRWz5wYtMRPvvzezNKfP9cmg151o0TBIhhQRERERkWGSkaBnjBlwqqaceFMKvey766oBz3/h\n0lk8sqGa3Q1dvLG3mU0H21NTMZP2NXYxszSHYCTKHf+7OXW8PRH0ksGwd0XvS3/dyKHWAPd/cvlw\nfTkiIiIiIie9jO26KSfWQ/9yFj7Psf24//XiU3jnoolc8MMXeGDVfjqDES6Ynb5ecm9jvA/fm3ub\n+evag6njbb2CXu+K3kNrDiIiIiIiIsNLQe8kkVx/d6w8zviGLp3B+BTMHbUdaef3NsWDXjCcvlbv\n8NTNZNDrf+6mMUbr90REREREhomCngzomc+fR67HCcRbLvR0qC2Qdn9vQzzo1XcE044ng15Xao1e\n/2GuvTtCntd5/IMWEREREZHM7LopY8PMUh+luR4AfB4HHmf/b5e8LCf7EhW92vb0ANh76iZALNGE\nr+fOnfUd6Y8TEREREZFjp6AnQ+Kw25hW3HdXTYBFU/KpaQvQHYpSP0DQS07drG7tZvqXH+f8HzzP\n/7y4J3Vd70qgiIiIiIgcOwU9GbLyPE+/x0+bnAfAvqYu6noEPbvN4lBrN5Be0QOoavLzvZWH2yeq\noiciIiIiMnwU9GTIrlpQ3u/x0yrygXiLhbr2w5W506bks6u+EzjcR28gL25v4D/+vIFgJEpjZ5Cf\nPLOTcLT/JuwiIiIiIjI4bcYiQ3bd4sksm1ZIMBIPYN9+7G2e397A/Enxit72ug6qmrr48PIKrl9e\nwUs7Gvneym3Utwf6VPR6e2TDIQAunTeBDQdaueelPUzM9/C+JVNG9osSERERERmHVNGTozKl0MvM\n0hxmluZw9/Vn8I9bz6HU56E4x82Db+ynKxTl8lPLOHViHjNKsgFY9p1n2Zmo7PUneR3AI+uryU/s\nvvnqrsaR/WJERERERMYpBT05ZjluBwsS6/OmFXup7whSnONixYwiAGaUHt68ZV1VS9pje7ZruPm8\n6anba6paCEdM6jZAINx/NdAfirDhQOswfCUiIiIiIuOLgp4Mi6lF8arclQvKcdjjb6vpxdmpENcR\nTF+jNzE/i+uXVwBw7emT+Oe/nsNtV8yhuSuU2sDlYEs3l//XS8y5cyUfumdVn9f88sObuPZnr9Kg\nHTtFRERERNIo6MmwmFYcD3rvWjQxdcyyLG6/Yg457r5LQRdOzuNb18xnw1cvxe2wM39SXuo5Nh9q\nS123va4DgNf3NPWp7G08GL9ue23H8H4xIiIiIiJjnIKeDIv3LZ7MN685lcVTC9KOW5bFlEJv2rEb\nV1Tykw+ejs1mke91pY5XJqqCWw61p13/75ecAkBNW3oLhlxPPECu39/CnY9sTmvt0Jsxhs//eQP3\nvrwnrVH7aBOJxvi3P63n7V7fAxERERGRo6GgJ8OiNNfDDWdVYllWn3O2Xod6rs/rqaJHIJzeY4OW\npZWFABxo9qeFtO5Ehe9HT+/gD6uq+MULuwccX2NniIfXV/Otx7ayfpjX9bX5w5z2zad4Y0/TcT9X\nQ2eQRzcc4rXd2ohGRERERI6dgp6MuGtPm4TP7eAjZ8bX5LkGCHpZLjtlufGm7MnPEF/PB3DDb97k\nly/tAeIVuuqW7iGPobr18LVVTV1H9wUcweZDbbT6w/zo6R19zr22q5G27vCQn6srGA+vncHB+w6K\niIiIiAxGQU9G3KfOm87Gr11GrifeNsFpH/htNzE/HvCy3Q5+8eEz+MstZ1Gedzj03fXENt7/y9dp\n6AzSFYpy6bwJqXMHW/yp27sbOnl2a13q/qEeQa+27cibtwQjUVr9oSF8dRBLVBn9vZrCtwfCXH/v\nG3z2gXVDeh6ArkTA61LQExEREZHjoKAnJ4TNZlGU4wagrEdw6608L16987kdXLGgnKWVhXic9rRr\n3tzbzM+fj0/TfPfpk/jR+xYxtcjLM1vreXjdQWraurniv1/mE79bQyQab+7eM+gNtpYv6TP3r+O0\nbz49pPV8rf54xc4fTN8spiMQD2tvHRz6VNGuRFjsOkKDeRERERGRwfTdDlFkhHzsrKkU57h458KJ\nA16TrN5l97NTZ0+/fW0fEF/XN39SHm/ubaaqyc/nH9qYdt3uhi5ml/mobu0m22WnLM+TCnqBcJT/\n+PMGslx2fvz+01KPafOHeXZbPQCH2gJMSkwdHUiy8tfVq6LXEYgHwNhR7P2SnLo5lIpeJBqjurU7\n1dpCRERERCRJFT05YRx2G9ecNglb791ZekhW+6K9Kmmv3nYRV8wv63N9ZaIlwzmnFPf7fFsSrRqq\nW7qZVJBFWZ6H2kTQ+/PqAzyxuZaH11Wnrm/qDLL8u88cfnx1G0eSrOglQ9pz2+qovO2x1M6Z0aNI\nesnpn13NhMiDAAAgAElEQVTBI1f0/uuZHZz/gxfS1h+KiIiIiICCnowyhdnxdgvJaY9Jk/KzOGWC\nDyCtL1/y9tULy9nyjcs5t1fge213E796aQ+v72liWnE2E3I91LYFMMawLdF/z2W3paZobq/tIBCO\npR6/5VB7n/59vbUmNlvpDEbwhyI8+MZ+AF7c0QD0Da2DOZqK3is74ztzHmz2H+FKERERETnZKOjJ\nqJIMbslpjz3FEpWxBZPy+pyzLItst4M/fGI5lyU2aHHZbfx17UG+/fhWOgIRPrisgvI8DzVtAT59\n/zr2NnYCEIrG2HiwjZauEC8kwlnST57dyZw7V7K1pm9fu0g0xo66Dlp6bNry/LYGIrHDobHnuCG+\nyctgkgGv98Yu/XEn1i727i8oIiIiIqI1ejKqnJFouP6xsyr7nPvYikq21XZw2xWzueTHLw34HN+6\ndj7TirO5ckE51/3iNfK9Li6ZW8oFs0qYNcHHk1vqWLmlFoiHwVA0xrU/e7XP81y5oIzHN8Wve2JT\nDXPLc9PO3/PyHr6/cjsAc8p8BMJRfvPq3lQ7he118aCXrOit3FzDLfev42+fPovFUwtTz/P67iYm\nF2QxpdCbWufXGYzwlUc2UZbr4daLTun360z2I+y52+hoFY0ZYsYMuuOqiIiIiAwf/dYlo0pxjpt9\nd13FhXNK+5wr8bm592NLmFnq44NLp/D7m5b1+xyluR5uv3Iui6bk8+ptF7H6jou567qFWJbFpPws\n7rtxaeraJZUFA47l3adP5uUvXcjpFfmpzVmSjDH8dc3B1H2H3eLqhRNZW9XCrvrOxDWkPsc3folv\nFPOXNQfZVd9JRyDM5uo2PvSrVXz2wXgLBn9it82OQIT7V+3nh0/t4M29zXz10c19dgBNTm89eIR+\ngsnxnCjPb6+nzZ9ekb3uF6+x4q7nTug4RERERE5mCnoyJt113ULOm1VyxOsm5HqwrPTNX6YUerlq\nYTkASysL+3sYAKU+N1MKvVwydwJbDrVT3drNr17aw8rNNXzq92vY09jFB5dOAWBzdXtaxW9irxYS\nT71dR3c4is/jYOWWWi758Yss+sZT/PCpeEVwb2O8iXuyUXp9x+Fefx+853V+/3pVn01XGjvj1xzo\nUdH79St7eeytmtT9lZvjr/VUooI5FENpKTGQxs4gH79vNZ/70/q04xsOtNLQEUy1uxARERGRkaWg\nJyeln3zgNO756GI+tKwi7fjvblrGxq9dxt8+vYJFU/IBOGtGEQCfeWAd3358K7fcv45nttZz8ZxS\nvnHNqQC8a9FE5pb7Us9z/fL0571/VRV5WU5uv2JuapfOmIEXtsfXBHYEIjR1BvH3swlLconf+v2H\n+/EZY2hIhMGqpnjQC0dj/Oip7fz46e2p65JrC9cfaOXXr+xN7UI6kKbOIDO+/Dj/fOvQoNcNJNmv\nsGcVMRQ5HO52nuDqooiIiMjJSmv05KTksNu47NSytNYHP37/Is47pRjLslg89fCUzuTmLxsPHA5a\nv/zoYi6ZOwG7zWLzNy7H7bBh61E57LkGD+JN3m84ayoLJ/fdSGbx1ALWVrXwVnUbnYO0VVhb1UJ7\nIMx1Z0wmHI0RjMTwuR0cbOnGH4qws64TfyjK7oYu9jV28cquRv6+MR7Yalq7+cULu8n3Otnw1csG\nfI0H3thPzMDvX6vi6kH6HQ6kOjGN1OU4/Dek/c1dqdubDrb1WesoIiIiIsNPQU9OavZET793LprI\ne86Y3O81TruNG1dU8vahdm46Zxr7mrq4bN6E1JTQnu0ePn/pLKYWeZlZmgPEG8CHo4auYITPXjiT\nvCxn2mtHY4brzpjMpuo2XtnZOOBumwVeZ6pJfHt3hKKceBuKFTOLeHJLHbvqO1m9rzl1/eOba1Ib\nxQA8sbk28dhENTFmWLmllovmlGKzLL792Nu8d/EU7nt1LwCRWIy7n92Jx2nnU+dNH+J3k9T0Uldi\n05WOQJiddYerePGKYny6qzGG9u4IeV5nn+cRERERkeOjoCcnvd3fuZKBW7jHff1dpw7puT53cXyH\nTGMMeVlOKouyue/jS2nrDjMhN33dXnmeh4Mt3cyflMtZ04v49St7086/49Sy1O6g/3HpLL766BYA\nHt1Qzc76ThZPLeCzF87kyS11bKvt4KE1B5g1IYdozKSFPIBgYvpkzMCybz+TWgP42QtnsLSykN+9\nXsXvXq/CsmBpZQFvH2pnf3M3Oe540Pvk71YTjRnu+3j/G+AkJTeGCUVjBCNRFnz9qdS56SXZ7G44\nXN37w6oqvvroFl7+0oXUdwQ4dWIenkTLCBERERE5Pgp6ctJLVvWGk2VZXLmgjAm5HjxOe1qA+da1\n82kPhDl7RjE/e34Xc8pyuXphearBetInzp3GpIIsrjltIhWF3lTQ21bbQY7bwS8/upj8LCcuu40v\n/fUtAH56/ems39/K7ob00NhTz41efvb8bmB36v6SqQW854zJrN63ia5QlMZOuP3ht3hma3zX0VjM\nsL/ZT2cwwvx++hkmK3p7G7u4+fdrU8fnlPmYV57L63uaUsf+vPoAAM9tq+drf9/C+5dM5vvvXTT4\nN1ZEREREhkRBT2SEfPc9C/s9/pEzp6Zu33PDEgDeu3gyZ04vorq1mxKfmwKvi8JsV9quoC9/6ULq\n2gPcv6qKDyytoDjHDcDHVkzl3lf2cv2yCq6cX86sCT5W72vmQ8squP3hTQB85oIZdAUj/O71KhZP\nLeDqheVsOtjGw+ur08a2fFoRZ00vSjv2xzcPpG6v3FLL5/64nkjM8F8fWMSPntrB/Z9Yzut7mrhi\nflnaJiw9g+uZ04so8bl5eH01ncEIOW4HgXB8PeLKxLTS13YfDoEj5bXdjSytLFQ/PxERERn3rOPZ\nSv1EW7JkiVmzZk2mhyEy6nQEwvg86WvdAuEoc+5cyc3nTefLV85lV30Hl/z4Je69YQmXzJuQqs69\nsL2eX7+6lwPN3fziw2dwxYJyKm97LO25Sn3utEpgf5KbyswszenTu++XH12MMYZb7o/3C7z5vOn8\n/vV9BMKHd+Qsy/Ww6ssXA+APRdha08Ej66u59vSJLJ5aSCAc5X9e3M3SykIqCr1MKfSmvcau+g42\nV7dz7emT+ozt5Z0NPLmllvtX7edjZ03lG9fMH/wbmrC5uo0H39zPTWdPS627FBEREckky7LWGmOW\nHPE6BT2R8as9ECbb5UhNTw1HY/1Wsxo6gtz36l7+/ZJZuBw2Nle3sWpPEw6bxbmzSphckMXsr6wE\nIN/r5Ir5ZayraqU9EKamLZAKgnabxS8+fAY3/2EtVy8s57xTSijL83DOzGIaOoNc89NXsdusPj0B\nARw2i9/ftIy7Vm7jrYOH20CcOjGXxz53Lr99dS9f/8fbABTnuHnttovSdvdc8LUn6QhG+D+Xz8YY\nw0fPqiQvy8mBZj/nfv/5tNfZ8LXL0jbRSYrFDF/9+2beffokFkzKZ/G3nqYjECHX4+A771lAjtvB\njJKcPiFTRERE5ERR0BORYbXhQCtTC73keBypsBgIRwmGY9R1BPjGP7Zw09nTuHjuBLqCEbL7CVLJ\nx7z/l68zp8zHrAk+vvXY1n6vK8v1kOWys7exKxUkJ+S6OaXUxyu7GrlifhkF2S5iMcPW2o609hcA\np1fk854zJnPnI5v7PPdNZ09j1oQcCrJdNHYGuX5ZBYFwjH+8dYgv/fUtSn1ufvGRM7juF69zx5Vz\neXRjNZur21OPf+4L5zO95HCFb3N1G5/743ruuWExtz64nk+eO533Lu5/F9fhYIzhz6sPcMm8Cakp\nvMcrFInR2BlkYn7WsDyfiIiIjAwFPREZtYwxWJaFMYafv7CbU0pzuO/VfWw40Mrfbz2bnfWdXDpv\nArVtAW68702mFedQ4nPzL+dNp6LQyzt+8hI76jrJ9ThoD0Sw2yxmluSQl+WkxOfmnYvK+dyfNqSa\ntV+1sJwPL69gWnE2l/zoRbpC6f0KrzltIqv2NFHXfnh6qs2K71K69iuX8NbBNj7+29Vpj7n9ijnM\nLvMxtzyXD96zKi2QAuy76yoe31TDvPJcKouz2VrTzuu7m5hT7mPJ1EJC0RgxY/j0/WupKPTy3fcs\nTPV1tNssAuEoD6+rZnZZDnXtQRZMyiM3y4nbYWNXfSdX3/0K580q4fc3pe+E+tSWWuaU5VJRdHRV\nx9v+9hZ/Wn2ADV+9lHyvi2jMEIrEyHINz06oDR1BvC77gH8A6KnNH1bbDRERkQEo6InImBKJxmjr\nDlM0hApVOBqjpStEic9NVZOfHI+D4hx3KkACbK/t4NltdVx+ahkzelTfHt1Qzep9zVw6r4zXdjXi\nD0X5w6oqphZ5uWhOKVlOOz9/Ib4TabLBfCgSY9ZXnmDFjCJml/m479V9Q/66fB4HHz1zKve8tIdI\nbOB/b8+bVcLWmnYqCr18/OxK7nxkMy3+cJ/r3A4bVy0s5+F18Y10fvnRxZxRUcD+5i7+vuEQv3u9\nCoDffnwpb9e0U93SzU3nTONQazd/33CIm86Zlmpab4xhX5Of/Cwnp//n0wD828Wn8NkLZ3LdL16j\nurWbBZPyuOmcaRR6XSyY3Hen1d7++OZ+zqgoYHaZL3XMGMO02x9nSmEWL3/pokEfv7WmnSt+8jKX\nzJ3A4qkFfPqCGalzsZjBdhS75IajMX741HZuOKuSSflZRGNmRHbZFREROZHGRNCzLOsLwA+BEmNM\n45GuV9ATkeFmjOGZrfUsnlpAYXa8EX0gHGVbbQdOu8WpE+Phpq49gM/jwOuK7xj60+d2kZvloNUf\nZmllIXPKfdy/qooJuR6efrsOm2XR1BVkV30ngXCMFTOK+N51C9lU3cZXH91MUbabC2aXsKexi6ff\nrgPivRVr2gIAzJ+Uy+lTCvjDqnhwqyj0Uupzs6aqpc/XkKw+DtXFc0pZODmf37y6l7buvmHSabcI\nR/s+ocdpY0ZJvLp62xVzeHTDId61aCKzJviwWfD6niau/9UblPrcPPuF83luWz3zJ+XRHYpy9d2v\nAPDAJ5dz9sxi2vxhVm6p4drTJ+F22FM/i589v4sfPrUj9Zr//NdzmD8pjx11HVz/qzf4j0tP4cPL\np/YZW39e29XI9fe+wecumskNKyq56Icv8NV3njrotNrbH36Lc2aWcNXC8iG9xlAl/1+b/EOEiIjI\nsRr1Qc+yrCnAvcAcYLGCnoiMR3XtATYeaOXiuRNS1SR/KILLbsORWOu4rbad8rws8rKcPLGphjVV\nLXz6ghkU57jZXtvBKaU5qUrWPS/t5m9rq7n1opnUtQeYW57L89vqyXLZeWF7A+V5Hr5w2Wye2VrH\nB5dO4eWdjfzgye3cuKKSsjwPv31tH9trO+gMRphRks11iyezdl88PN50zjS2HGrjzb0trJhRxCMb\nqtM2xgGYlJ/V72Y6eVnOtNDocthSU2d7y/c6iUQNncEIE3LdzC7LZVJ+Fm/sbWJPQ1ef65dWFrC5\nup3uREuO266YwweXTuGel/bw+p4mzppexC0XzOCh1QfYXN3Gu06bSHt3hB88uZ3q1m6WVRZy0dxS\n7npiG6dOzOWW82fgddkp9Xl4ZVcjKzfXcMbUApZWFvKZB9YxtzyX77x7PrVtARZPLaA01zPgz9cY\nw70v7+XM6UV9Kp6v7GykMNvFvIm5fPCe19lV38XPrj+d5b1amByLqqauVJ/OF7bXM6Mkh8kFWQqS\nIiIngbEQ9P4K/CfwKLBEQU9E5PjEYgbLOnLVqK07zHPb6jh/VmmqijmQjkCYl3c2kuWyU5brYW55\nLm/saeLV3U2cP6uEVXuaaO4K0eIPMaMkh8qibP7nxd3kuB185MypPLu1jofXVzMpP4vf3bSUHz21\ng3X7W8hxO+gORTnUFkiFx8JsF81dIW5cUcnSykLC0Rj//ucNlPrcnD+rhCsXlvPbV/el9WicV57L\n1tp2nDYboWhswGqky2HDAoIDhM+BZDntfOTMClYnwnB9e4BTJ+WR63Hy7LY6WntMr71gdgmLKwrY\ncKCVM6YW8OOnd+Cy2/jkudO4+7ldqes+vLyCueW5nFFRQF17gLcOtrFiZhGBcJSvPLKZHLeDG86a\nitfl4MoF5Ty1pZY/rT5AXXuAs2cWM2tCDnc+uoXrzpjM5y6eyVnffQ6Ih/A/3XwmUwq9/G3tQZ7d\nVsfsCblMKcziwtmlvO+Xr7NkagF3Xj2v37WSyTWzc8t9XDRnQtpU6IaOIC67rc/ayXA0xjvvfoUL\n55Tyf98xJ+3codZuspx2Co7wHgM40Ozn/lVVzC7zsbSykJ8+t4s7rp5Lbo+2McnfV3Y3dBEIR5k/\nKT1Y76zrYH+zn4vnTiAaM3QGIse91rOmrZviHLd6b4rIqDKqg55lWdcAFxlj/s2yrH0o6ImIjFuR\naIyuUJS8rPgv3dGYwWbFQ9fBlm5mluYQCEfxOO0EwlGcdht2W3yzng0HWplbnovHeXhTmGe31qWq\npIum5LPpYBv3vbaX/CwXn79sFuuqWghGYry6q5ECr4un3q6lsjib/3PZbB5ac4CqZj/GGPK9LjoC\nEQq8TuaV5/LG3mZcdhtv17TzzkXlLKks5Nev7OWxt2oAcNltLJqSlwoaF84pZV9jF/ub/HSFIkzI\njU+9dTtsqUCZ7CuZ43Zwy/nT+eFTO7AsGOh/vaU+N9GYoakrBMSDZrKSORTleR6mFnlZtae53/OW\nBR6HnfmTcukORynLzcKdmJK7ak8Tb+6NP27R5DwOtnRz5owi7JbFyi21TMh18+HlU6lq8lPqc1Ps\nc/OTZ3bS2BnfgOiOK+fiD0UxGFwOGz99bhdZTju3XjSTJVMLeWlnAwsn51HTFmDV7ibOm1XCvIm5\nNHYE+eJfNnKoLYDNgoWT89lwoJUPLZvC7VfOpaEjyENrDvC3tdWEE2t5ATZ+7TLyspzEYoZnt9Xz\nqd/Hfz94+DMr+O2r+/j7xkO88eWLKfC6cDlstPpDVLd2M7csd9C1ni/vbOArj2zmC5fN5osPbeS0\nKfnce+MScj1O6jsClOS40/6Y8odVVRRnu7hiwdFP993T0Elte4AVM4qP+rHHo2eIHy2e2lLLM1vr\n+N51C0fd2DKtrj1Ac1cotb4605KzSM4chtkJcmwyHvQsy3oGKOvn1B3Al4HLjDFtRwp6lmXdDNwM\nUFFRsbiqqmpExisiItKfVn8Ip92G12XHsiwi0RgG0qo8yZYijZ1BctwOXt7ZiN0Gp08p4PHNNSyr\nLGR6SQ4v7Whgxcwittd2sLexKxVCnttWT2G2ixtXVNLYGeQfG2soznGxrbaDaMzw4eUVlPjctHdH\n+MvaA9gsiw0HWglGoswpy+V9SyZT0xrgj2/up9kfYlpxNl9/16lkOe3c89Iefv78Li6fX8aHllXw\nv+urWb+/Fa/LTqs/RCAco7q1m8kFWUwvyWFbTXtq91iIh8MrF5Tz/LZ6/KEoBV5naqMgu82i1Oem\nLM/D+v3pLU58nnjVdrBNiJLyspz84L0LufkPa9OO26x4NTYQjjGnzEddeyDttbOcdlwOG82JYNxb\ncv1qvtdJRyBCNGYoynZRlOOirTuMw2ZjWnE2NptFZyCMzbJYt78ltebV67ITjsaoKPRSlufh1V1N\nXDZvAvleJ53B+PM9uSW+xvba0yaS73WR73XS3h2hsTNIZZGXmrYAexu7WFxZQFG2ixZ/GLtlke12\n8JtX99LUGeQzF8zEYPC6HJTkuKlpC9DiD5HjdnCorZvmrhDvPn0SpT4P+5u7aOoKsa2mg3NOKWb+\nxDwisRir97XgtFsUeF34PPH+qRv2t5Kf7SLX4+CUUh+t/hD3vrKX/c1+rlk0kZmlOTjsNqIxg9dl\np8TnJhSJUdcewOtysHBKHrvrO4nGDN9buY2zZxZz9cJyHlpzkFKfmw8snYLDZkvtzhuJxtjV0InL\nbkv991BZlE2228G+xi42VbexpLKAx96q4X/XV3PrhTO5YkE59e0Bln3nWQAe+pezOG1KPv5QhHyv\nixe21/PI+mq+ee18AqEoT75dxweWTEnro9ofYwzGkBbqk39QGin17QEM8feNz3P01WRjDGuqWphZ\nkpNWCV/+nWeoaw+y5RuXD2nn4p66Q1HWH2jhrOlFAwboNn+YB96s4sPLph6xCp7cnAxg73evVCjP\nkIwHvQFf0LIWAM8C/sShycAhYJkxpnawx6qiJyIicvSOVMHxhyJ4XY7UtcZARzCC027hsNlwOWx0\nBiMYY/B5nBxs8dMRiDCtOBsAj9POnoZODPHQ1h2Kkud10t4dpiMQYUddB+V5Wexp6OTUiXnMKsvh\niU212G0W3aEoSyoLmF6Swx/f3E91SzefPHca22o7eG1XIwdbuvm3S06hotBLeyDCnoZO9jZ2sau+\nE38oSkcgwooZRVw0p5RgJMafVu9nf5OfC+eU8uquRkp9brbXdeDzOFkwKY91+1voCsYrzF3BCIfa\n4mtOc9wOIlGDzQZXLZzIpoOtvHPRRAC+9c+tBCJR8rOc7Gnswuuyk+N2EIzEWDQ5n0kFWdz78p5U\nQHQ5bORlOWnoCJLtslNRlM2u+g7CUYPTbhEzpELn5IIsNh5sw26zUi1WIB4WAuEoBV4XMWP63YU3\nUxw2Ky3AF2W76ApFAAiE06dH2yxwJKZW95TvddLqD1OU7aI7HMXfo+1NsqJfWZydWrfrddnTrvG6\n7FQUeglHY0RiBptlkeN2kON2kO12sLO+g1Z/mMVTC+gMRrCAtVUtTC7IwuO009gZYsGkw7MFOoMR\nctwO3tzbTInPzawJPho6goSjMSqLs3ljbxNep4PKYi/hqMFhs/B5nOR7ndR3BCnwOnl8U22qwr1s\nWiGzJuSQ5bRT1RT/lddhtzh9SgF2m0UoGqMzEKHFH8Jus/A47by4vYHtdR0U57h416JJTC3ysqOu\ngwfe2A/A2TOL+MDSCtyOw71s2wMRDrb4yctyMjEvi/ZAmLwsJ067jcbOIA+vq2bDgVYm5Wdxxfwy\nSnPdFOe4mVmaQ2cwQkcgws+f38XGg20snlrAv118Cp3BCDVtAeZPzKWxM8TO+g6qmvzUtHXjcth5\nKTF9/r2LJxOKxDh/Vglup42KwvgfNlz2+L8XUwq9qZkcNW3dRGOGKQVe1h9owcJi+fRCojHD24fa\nyc1yYrMsinNcRGKGqiY/jZ1BJhdksb22g3edNhF/MEphjis1LbzU56E9EMZlj/8b5XbE1777QxEi\nMUNHIP5vWF6WE38wysGWblZuqeG6MyZT4HVRkO3iwTf209wV5F2LJvHyrgaqW7q5emH8jyBOuzVq\ng+yoDXp9BqCpmyIiIjLGBcJRLAuaOkMUeF14nPFfxpO/KMZihs5QhByXg0jMEI0Z3A4bNptFU2eQ\nvKx41bErFKEo243HaUtVpCLRGG/XtNMRiDC5IIv8LBdet53N1W2pnXrPqCigoSNIMBIlZuLjmVSQ\nRTgRsLbXdpCb5aQ8z0NlUTbdofgvvgA2G7T6wzR2BnE77JTleTjU2k1Vk5/pJdn4QxGKc9zsb/YT\nDMd412kTqWrq4rlt9Xgcdqpbu8l2OzAGFk7Ow2DoCkbJ9zrZVN1GNGoozHFRUejlrYNtXLmgnGlF\n2fxp9X6213WQ5bTz8bOnsbm6jXX7W/B5HHQFo4mptj6ml+Tw7LZ6mruCVBR6CUUMPo8jHrqddpyJ\n4NMZCNMZjNAZjOJx2ihNtODJ9Tip64hXKXPcdowhHgbrOshKVOqzXXY6ghEqCr20+MPsaehkWnE2\nDptFVZOfqUVeHDYbNe3dOGzx1+sORWnrDlPic9PSFcLnceCw23DYLVx2W6oCXZ7nwZboHXso8fNK\ncjlsxGKGSMxwekU+RdluXt/dSDhqUuE4yxn/mext7LtZFfQN3r2ff6CNsZJ8bgclue5+N8M61ufM\nlP52oe5vurzNglKfh9r29J9Hz8e4HTbet3gK/3nt/BEa7bFT0BMREREROQGSlfDeG2KFo7HUNG9j\nDK3+MJYVn/odCEdxO+1YQFcoklr/GYuZVBXX54lX2pNrmPc1daUqv057vIrl8zgxxlDXHqTY56K9\nO0I4GqM4x01htgsLiBrD7oZOinPcHGrtpqEjmAi+8Sqlz+PkQLOffU1d+DxOSn1udtR1UJTtZmK+\nB38oitthY1dDJ/PKc6lpC2BMvBJe09ZNjsfBwZb45kWRaCxV+U9WYfO8TrKcdg40+ynKceF2xCud\nUWOYV55Le2LtbVt3GJst/j2YWuSN/+Ek28mqPc0UeONTrn0eB+2BMCYxLTsciRGMxAglPme7HTjt\nFl6Xg3A0RmNnMNUeaUqhl7VVLdgtiz2NnUwtymZGSTbtgQhuh42JeVkcbPGzt6mLcMQwozR7yC19\nTqQxE/SOhoKeiIiIiIiczIYa9LRfsIiIiIiIyDijoCciIiIiIjLOKOiJiIiIiIiMMwp6IiIiIiIi\n44yCnoiIiIiIyDijoCciIiIiIjLOKOiJiIiIiIiMMwp6IiIiIiIi48yYaphuWVYDUJXpcfSjGGjM\n9CBk3NL7S0aa3mMykvT+kpGm95iMpNH4/ppqjCk50kVjKuiNVpZlrRlKd3qRY6H3l4w0vcdkJOn9\nJSNN7zEZSWP5/aWpmyIiIiIiIuOMgp6IiIiIiMg4o6A3PO7J9ABkXNP7S0aa3mMykvT+kpGm95iM\npDH7/tIaPRERERERkXFGFT0RERERkf/f3v2H3FnWcRx/f3qmORXmVBjmlBmOYv3Qidj6QcgMs5QW\nJDkxkmVEErmiH67+kSD/KCJtKUL5IyvRYtmS/liNKSVUZqb5s3As08l0s7mVFf7q2x/39ehxuuR5\nnrPnPOc87xcczn1975v7uW64+J7zPfd13Y80Yiz0piDJaUn+kmRzkjWD7o+GU5KjktyS5P4k9yVZ\n3eKHJtmY5MH2Pr/Fk2RtG3d3JzlhsFegYZBkLMmdSX7e2sckua2Nox8l2b/FX9vam9v+RYPst4ZD\nkkBkgMkAAAXHSURBVEOSrEvy5yQPJHm7OUz9kuSz7fPx3iTXJznAHKapSHJ1ku1J7u2JTThnJTm3\nHf9gknMHcS3/j4XeJCUZAy4H3gcsAc5OsmSwvdKQeg74XFUtAZYBn2pjaQ2wqaoWA5taG7oxt7i9\nPgFcMf1d1hBaDTzQ0/4acElVHQs8CZzX4ucBT7b4Je046dV8C9hQVW8EjqMba+YwTVmSI4ELgBOr\n6s3AGLASc5im5nvAaXvEJpSzkhwKXAS8DTgJuGi8OJwpLPQm7yRgc1VtqapngBuAFQPuk4ZQVW2r\nqj+27X/SfUE6km48XdsOuxb4YNteAXy/Or8DDklyxDR3W0MkyULgdODK1g6wHFjXDtlzfI2Pu3XA\nKe146RUlmQe8G7gKoKqeqapdmMPUP3OAuUnmAAcC2zCHaQqq6tfAzj3CE81Z7wU2VtXOqnoS2MjL\ni8eBstCbvCOBR3raW1tMmrQ2xWQpcBuwoKq2tV2PAQvatmNPE3Up8EXgv619GLCrqp5r7d4x9ML4\navt3t+OlvTkG2AFc06YHX5nkIMxh6oOqehT4BvAwXYG3G7gDc5j6b6I5a8bnMgs9aYZIcjDwE+Az\nVfWP3n3VPR7XR+RqwpKcAWyvqjsG3ReNrDnACcAVVbUU+BcvTnkCzGGavDYVbgXdDwqvAw5iht01\n0egZlZxloTd5jwJH9bQXtpg0YUn2oyvyrquqG1v48fHpTO19e4s79jQR7wQ+kOQhuinmy+nWUx3S\npkHBS8fQC+Or7Z8H/H06O6yhsxXYWlW3tfY6usLPHKZ+eA/w16raUVXPAjfS5TVzmPptojlrxucy\nC73Jux1Y3J76tD/dwuCbBtwnDaG2duAq4IGq+mbPrpuA8Sc4nQv8rCf+0fYUqGXA7p6pBtJLVNWX\nqmphVS2iy1M3V9U5wC3Ame2wPcfX+Lg7sx0/9L9qat+pqseAR5K8oYVOAe7HHKb+eBhYluTA9nk5\nPr7MYeq3ieasXwCnJpnf7jyf2mIzhv8wfQqSvJ9u7csYcHVVXTzgLmkIJXkXcCtwDy+uofoy3Tq9\nHwNHA38DPlxVO9sH3WV0U1f+Dayqqj9Me8c1dJKcDHy+qs5I8nq6O3yHAncCH6mqp5McAPyAbq3o\nTmBlVW0ZVJ81HJIcT/ewn/2BLcAquh+TzWGasiRfAc6ie0r1ncDH6dZCmcM0KUmuB04GDgcep3t6\n5nommLOSfIzuOxvAxVV1zXRex6ux0JMkSZKkEePUTUmSJEkaMRZ6kiRJkjRiLPQkSZIkacRY6EmS\nJEnSiLHQkyRJkqQRY6EnSZo1kjyf5K6e15o+nntRknv7dT5JkqZizqA7IEnSNPpPVR0/6E5IkrSv\neUdPkjTrJXkoydeT3JPk90mObfFFSW5OcneSTUmObvEFSX6a5E/t9Y52qrEk301yX5JfJpnbjr8g\nyf3tPDcM6DIlSbOIhZ4kaTaZu8fUzbN69u2uqrcAlwGXtti3gWur6q3AdcDaFl8L/KqqjgNOAO5r\n8cXA5VX1JmAX8KEWXwMsbef55L66OEmSxqWqBt0HSZKmRZKnqurgV4g/BCyvqi1J9gMeq6rDkjwB\nHFFVz7b4tqo6PMkOYGFVPd1zjkXAxqpa3NoXAvtV1VeTbACeAtYD66vqqX18qZKkWc47epIkdWov\n2xPxdM/287y4Fv504HK6u3+3J3GNvCRpn7LQkySpc1bP+2/b9m+AlW37HODWtr0JOB8gyViSeXs7\naZLXAEdV1S3AhcA84GV3FSVJ6id/UZQkzSZzk9zV095QVeP/YmF+krvp7sqd3WKfBq5J8gVgB7Cq\nxVcD30lyHt2du/OBbXv5m2PAD1sxGGBtVe3q2xVJkvQKXKMnSZr12hq9E6vqiUH3RZKkfnDqpiRJ\nkiSNGO/oSZIkSdKI8Y6eJEmSJI0YCz1JkiRJGjEWepIkSZI0Yiz0JEmSJGnEWOhJkiRJ0oix0JMk\nSZKkEfM/UISGfH9yH0IAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "train_loss = train_ppl.get_variable(\"loss_history\")\n", "\n", "fig = plt.figure(figsize=(15, 4))\n", "plt.plot(train_loss)\n", "plt.xlabel(\"Epochs\")\n", "plt.ylabel(\"Training loss\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Save model for later use:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "train_ppl.save_model(\"dirichlet\", path=\"af_model_dump\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Predict atrial fibrillation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We predict atrial fibrillation through ```dirichlet_predict_pipeline```. Below we initialize the pipeline and run it on the test part of the dataset:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Restoring parameters from af_model_dump/model-13000\n" ] } ], "source": [ "from cardio.pipelines import dirichlet_predict_pipeline\n", "\n", "pipeline = dirichlet_predict_pipeline(model_path=\"af_model_dump\")\n", "res = (afds.test >> pipeline).run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now ```res``` contains ```predictions_list```, which we access through ```get_variable()``` method: " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "pred = res.get_variable(\"predictions_list\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Output predicted probabilities:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "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", "
AF probTrue label
A000010.04NO
A000020.03NO
A000030.03NO
A000040.82A
A000050.69A
A000060.13NO
A000070.03NO
A000080.03NO
A000090.95A
A000100.03NO
\n", "
" ], "text/plain": [ " AF prob True label\n", "A00001 0.04 NO\n", "A00002 0.03 NO\n", "A00003 0.03 NO\n", "A00004 0.82 A\n", "A00005 0.69 A\n", "A00006 0.13 NO\n", "A00007 0.03 NO\n", "A00008 0.03 NO\n", "A00009 0.95 A\n", "A00010 0.03 NO" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "pd.options.display.float_format = '{:.2f}'.format\n", "\n", "pred_proba = pd.DataFrame([x[\"target_pred\"][\"A\"] for x in pred], \n", " index=afds.test.indices, columns=['AF prob'])\n", "\n", "true_labels = (pd.read_csv(AF_SIGNALS_REF, index_col=0, names=['True label'])\n", " .replace(['N', 'O', '~'], 'NO'))\n", "\n", "df = pd.merge(pred_proba, true_labels, how='left', left_index=True, right_index=True)\n", "df.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print some classification metrics to quantify the quality of predictions:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 1.00 0.97 0.99 1533\n", " 1 0.81 0.98 0.89 173\n", "\n", "avg / total 0.98 0.98 0.98 1706\n", "\n" ] } ], "source": [ "from sklearn.metrics import classification_report\n", "\n", "print(classification_report((df['True label'] == 'A').astype(int),\n", " (df['AF prob'].astype(float) >= 0.5).astype(int)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We observe that the model is able to detect atrial fibrillarion reasonably well. Note that ```predictions_list``` contains not only predictions, but also confidence in that predictions. Read the [article](https://medium.com/data-analysis-center/atrial-fibrillation-detection-with-a-deep-probabilistic-model-1239f69eff6c) for more details." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Common neural network architectures\n", "\n", "![](http://joelouismarino.github.io/images/blog_images/blog_googlenet_keras/googlenet_components.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Apart from exploiting the ECG-specific models implemented in CardIO, you can also experiment with benchmark models for common segmentation and classification tasks. These are e.g. VGG, ResNet, UNet, DenseNet and several other models. They were carefully implemented in BatchFlow submodule which is a part of CardIO module. It means that you can import desired architecture, e.g. Inception-v1 which is schematically shown in the figure above, with a single line of code:\n", "```python\n", "from cardio.batchflow.models.tf import Inception_v1\n", "```\n", "and immediately start using it. Below we demonstrate how it works. The full list of ready-to-use architectures is given in [documentation](https://analysiscenter.github.io/batchflow/intro/tf_models.html#ready-to-use-models)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import data and model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we construct a dataset from the PhysioNet database:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "import sys, os\n", "sys.path.append(\"..\")\n", "from cardio import EcgDataset\n", "\n", "AF_SIGNALS_PATH = \"/notebooks/data/ECG/training2017\" #set path to PhysioNet database\n", "AF_SIGNALS_MASK = os.path.join(AF_SIGNALS_PATH, \"*.hea\")\n", "AF_SIGNALS_REF = os.path.join(AF_SIGNALS_PATH, \"REFERENCE.csv\")\n", "\n", "afds = EcgDataset(path=AF_SIGNALS_MASK, no_ext=True, sort=True)\n", "\n", "afds.split(0.8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's import ```Inception_v1``` model:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "from cardio.batchflow.models.tf import Inception_v1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Configure model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the next step we configure the model. Let's see into ```model_config```. It states that:\n", "* inputs for the model are signals and labels\n", "* shape of signals will be computed directly from batch\n", "* labels will be transformed from original classes 'A' and 'NO' using one-hot-encoding ('ohe')\n", "* ```signals``` will be considered as feature vector\n", "* we have total 2 classes\n", "* loss is crossentropy\n", "* we use Adam optimizer\n", "* output will be transformed to probabilities (default are logits).\n", "\n", "Note how we set ```shape``` parameter. Since batches will appear only when pipeline will be run, a specific substitution rule is required to obtain batch shape. This rule is given by named expression ```F```, which accepts ```batch``` and returns shape of batch items. Read more about named expressions in [documentation](https://analysiscenter.github.io/batchflow/intro/named_expr.html)." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "from cardio.batchflow import F\n", "\n", "model_config = {\n", " 'inputs': dict(signals={'shape': F(lambda batch: batch.signal[0].shape[1:]), 'data_format': 'channels_first' },\n", " labels={'classes': ['A', 'NO'], 'transform': 'ohe', 'name': 'targets'}),\n", " 'initial_block/inputs': 'signals',\n", " 'body/n_classes': 2,\n", " \"loss\": \"crossentropy\",\n", " \"optimizer\": \"Adam\",\n", " 'output': 'proba'\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we define how to extract signals and labels from batch. We say that ```signals``` is batch component ```signal``` and ```labels``` is batch component ```targets```. For prediction we do not have labels and ```feed_dict``` contains only ```signals```." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "def make_signals(batch):\n", " return np.array([segment for signal in batch.signal for segment in signal])\n", "\n", "def make_targets(batch):\n", " n_reps = [signal.shape[0] for signal in batch.signal]\n", " return np.repeat(batch.target, n_reps, axis=0)\n", "\n", "def make_data(batch, **kwagrs):\n", " if any(elem is None for elem in batch.target):\n", " return {\"feed_dict\": {'signals': make_signals(batch)}}\n", " else:\n", " return {\"feed_dict\": {'signals': make_signals(batch), 'labels': make_targets(batch)}}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train model\n", "\n", "To train the model we construct a ```train_pipeline```. Within the pipeline we initialize the ```Inception_v1``` model with given model_config, initialize variable to store loss, load and preprocess signals, convert signal to spectrogram and train the model." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import cardio.batchflow as bf\n", "from cardio.batchflow import V\n", "import tensorflow as tf\n", "\n", "train_pipeline = (bf.Pipeline()\n", " .init_model(\"dynamic\", Inception_v1, name=\"inception_model\", config=model_config)\n", " .init_variable(\"loss_history\", init_on_each_run=list)\n", " .load(fmt=\"wfdb\", components=[\"signal\", \"meta\"])\n", " .load(src=AF_SIGNALS_REF, fmt=\"csv\", components=\"target\")\n", " .drop_labels([\"~\"])\n", " .rename_labels({\"N\": \"NO\", \"O\": \"NO\"})\n", " .drop_short_signals(4000)\n", " .random_split_signals(3000, 3)\n", " .spectrogram(nperseg=100, noverlap=50)\n", " .train_model(\"inception_model\", make_data=make_data, fetches=\"loss\",\n", " save_to=V(\"loss_history\"), mode=\"a\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run ```train_pipeline```:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [] } ], "source": [ "model_trained = (afds.train >> train_pipeline).run(batch_size=50, n_epochs=100, shuffle=True, drop_last=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now ```model_trained``` contains trained model and loss history. Method ```get_variable``` allows to get ```loss_history``` and we can plot it:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.plot(model_trained.get_variable(\"loss_history\"))\n", "plt.xlabel(\"Iteration\")\n", "plt.ylabel(\"Training loss\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Make predictions\n", "\n", "For predictions we create ```predict_pipeline```. It differs from ```train_pipeline``` in several things. First, we import trained model instead of initializing a new one. Then we do not process signal labels and apply ```predict_model``` instead of ```train_model``` action." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "predict_pipeline = (bf.Pipeline()\n", " .import_model(\"inception_model\", model_trained)\n", " .init_variable(\"predictions_list\", init_on_each_run=list)\n", " .load(fmt=\"wfdb\", components=[\"signal\", \"meta\"])\n", " .random_split_signals(3000, 1)\n", " .spectrogram(nperseg=100, noverlap=50)\n", " .predict_model('inception_model', make_data=make_data,\n", " fetches='proba', save_to=V(\"predictions_list\"), mode=\"e\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run ```predict_pipeline``` on the test part of the dataset:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "res = (afds.test >> predict_pipeline).run(batch_size=len(afds.test), n_epochs=1, shuffle=False, drop_last=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Consider predicted values and true labels. Predictions are stored in ```predictions_list``` and we get them through ```get_variable``` method:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "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", "
AF probTrue label
A000010.00NO
A000020.01NO
A000030.00NO
A000040.90A
A000050.59A
A000060.00NO
A000070.00NO
A000080.00NO
A000090.88A
A000100.00NO
\n", "
" ], "text/plain": [ " AF prob True label\n", "A00001 0.00 NO\n", "A00002 0.01 NO\n", "A00003 0.00 NO\n", "A00004 0.90 A\n", "A00005 0.59 A\n", "A00006 0.00 NO\n", "A00007 0.00 NO\n", "A00008 0.00 NO\n", "A00009 0.88 A\n", "A00010 0.00 NO" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "pd.options.display.float_format = '{:.2f}'.format\n", "\n", "pred_proba = pd.DataFrame([x[0] for x in res.get_variable(\"predictions_list\")], \n", " index=afds.test.indices, columns=['AF prob'])\n", "\n", "true_labels = (pd.read_csv(AF_SIGNALS_REF, index_col=0, names=['True label'])\n", " .replace(['N', 'O', '~'], 'NO'))\n", "\n", "df = pd.merge(pred_proba, true_labels, how='left', left_index=True, right_index=True)\n", "df.head(10)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " NO 0.96 0.99 0.98 1533\n", " A 0.87 0.68 0.76 173\n", "\n", "avg / total 0.96 0.96 0.95 1706\n", "\n" ] } ], "source": [ "from sklearn.metrics import classification_report\n", "\n", "print(classification_report((df['True label'] == 'A').astype(int),\n", " (df['AF prob'].astype(float) >= 0.5).astype(int),\n", " target_names = ['NO', 'A']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We observe that this classification model is not as well as ECG-specific model. However, it is interesting to experiment with other standard architectures and check their ability to predict atrial fibrillation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Custom neural network models\n", "\n", "![](http://www.opennn.net/images/deep_neural_network.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are two types of models that one can build with CardIO: *static* and *dynamic*. *Static* means that all model hyperparameters (input shape, loss function, optimizer etc) are hardcoded once and remain unchanged. However, it would be more convenient if input shape would be automatically computed from batch shape or we could control and vary hyperparameters in pipeline. This means we need a *dynamic* model. Dynamic model receives configuration parameters from pipeline and compiles model according to that parameters. \n", "\n", "In the next section we will build the same neural network model with different frameworks: \n", "* using custom layers and blocks from `batchflow.models`\n", "* TensorFlow\n", "* Keras\n", "\n", "The model architecture will be as simple as several convolutional blocks. Each convolutional block consists of 4x1 convolutional layer, batch normalization, relu activation and 2x1 max-pooling with stride 2. Number of filters will increase as 8, 16, 32, 64 for successive blocks. In the end of the network we apply global max pooling and dense layer with 2 outputs. Below is the diagram with model architecture: \n", "\n", "![](conv_block.PNG)\n", "\n", "This model can be used e.g. for detection of atrial fibrillation. We will run model on PhysioNet's short single lead ECG recording database which we already used in this tutorial. You can recreate it now if neccessary." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "import sys, os\n", "sys.path.append(\"..\")\n", "from cardio import EcgDataset\n", "\n", "AF_SIGNALS_PATH = \"/notebooks/data/ECG/training2017\" #set path to PhysioNet database\n", "AF_SIGNALS_MASK = os.path.join(AF_SIGNALS_PATH, \"*.hea\")\n", "AF_SIGNALS_REF = os.path.join(AF_SIGNALS_PATH, \"REFERENCE.csv\")\n", "\n", "afds = EcgDataset(path=AF_SIGNALS_MASK, no_ext=True, sort=True)\n", "\n", "afds.split(0.8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Moreover, we will use one pipeline for signal preprocess. It looks as follows:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import cardio.batchflow as bf\n", "\n", "preprocess_pipeline = (bf.Pipeline()\n", " .load(fmt=\"wfdb\", components=[\"signal\", \"meta\"])\n", " .load(src=AF_SIGNALS_REF, fmt=\"csv\", components=\"target\")\n", " .drop_labels([\"~\"])\n", " .rename_labels({\"N\": \"NO\", \"O\": \"NO\"})\n", " .drop_short_signals(4000)\n", " .random_split_signals(3000, 3)\n", " .apply_transform(func=np.transpose, src='signal', dst='signal', axes=[0, 2, 1]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This pipeline loads signal and targets, drops noise signals, replaces all non-arrhythmia labels with \"NO\" labels, randomly splits signal to 3 segmets of length 3000 and unstack batch." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Layers and blocks from BatchFlow models\n", "\n", "CardIO includes various standard layers and blocks in its BatchFlow submodule. You can easily construct a very deep neural network concatenating simple elements. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define model architecture\n", "\n", "We are going to build a simple model that consists of convolution blocks, pooling and dense layers. There is no need to define convolution block, instead we import it from standard ```cardio.batchflow.models.tf.layers``` and configure it. Let's consider how to configure convolution block." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```python\n", "from cardio.batchflow.models.tf.layers import conv_block\n", "\n", "outputs = conv_block(inputs, layout='cnap', filters=16, kernel_size=4)\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Layout 'cnap' means that convolution block consists of convolution layer ('c'), batch normalization ('n'), activation ('a') and max pooling ('p'). Alternatively, one could set 'ccnap' to double convolutions, or set 'cap' to exclude batch normalization. Parameter ```filters``` sets number of filters for each 'c'. If you have several 'c' letters in layout, then ```filters``` can be a single value or array of the same length as number of 'c' letters in layout." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using ```conv_block``` we easily build our simple model. Note that ```ConvModel``` is a _dynamic_ model and will receive several hyperparameters from pipeline. For instance, it will receive number of filters for each convolution block." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "from cardio.batchflow.models.tf import TFModel\n", "from cardio.batchflow.models.tf.layers import conv_block, global_max_pooling\n", "\n", "class ConvModel(TFModel):\n", " def body(self, inputs, **kwargs):\n", " conv = inputs\n", " conv = conv_block(conv, 'cnap' * len(kwargs['filters']), kwargs['filters'], kernel_size=4)\n", " flat = global_max_pooling(conv)\n", " logits = tf.layers.dense(flat, self.num_classes('targets'))\n", " return logits" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Configure model\n", "\n", "In the next step we configure the model. Let's see into ```model_config```. It states that:\n", "* inputs for the model are signals and labels\n", "* shape of signals will be computed directly from batch\n", "* labels will be transformed from original classes 'A' and 'NO' using one-hot-encoding ('ohe')\n", "* ```signals``` will be considered as feature vector\n", "* we have total 2 classes\n", "* loss is crossentropy\n", "* we use Adam optimizer\n", "* number of filters for convolutions is [8, 16, 32, 64]\n", "* output will be transformed to probabilities (default are logits)\n", "\n", "Note how we set ```shape``` parameter. Since batches will appear only when pipeline will be run, a specific substitution rule is required to obtain batch shape. This rule is given by named expression ```F```, which accepts ```batch``` and returns shape of batch items. Read more about named expressions in [documentation](https://analysiscenter.github.io/batchflow/intro/named_expr.html)." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "from cardio.batchflow import F\n", "\n", "model_config = {\n", " 'inputs': dict(signals={'shape': F(lambda batch: batch.signal[0].shape[1:])},\n", " labels={'classes': ['A', 'NO'], 'transform': 'ohe', 'name': 'targets'}),\n", " 'initial_block/inputs': 'signals',\n", " 'body/n_classes': 2,\n", " \"loss\": \"crossentropy\",\n", " \"optimizer\": \"Adam\",\n", " 'body/filters': [4, 4, 8, 8, 16, 16, 32, 32],\n", " 'output': 'proba'\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we define how to extract signals and labels from batch. We say that ```signals``` is batch component ```signal``` and ```labels``` is batch component ```targets```. For prediction we do not have labels and ```feed_dict``` contains only ```signals```." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "def make_signals(batch):\n", " return np.array([segment for signal in batch.signal for segment in signal])\n", "\n", "def make_targets(batch):\n", " n_reps = [signal.shape[0] for signal in batch.signal]\n", " return np.repeat(batch.target, n_reps, axis=0)\n", "\n", "def make_data(batch, **kwagrs):\n", " if any(elem is None for elem in batch.target):\n", " return {\"feed_dict\": {'signals': make_signals(batch)}}\n", " else:\n", " return {\"feed_dict\": {'signals': make_signals(batch), 'labels': make_targets(batch)}}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train model\n", "\n", "To train a model we construct ```train_pipeline```. It initializes ```ConvModel``` with given ```model_config```, initializes variable to store loss, preprocesses signals and trains model:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "from cardio.batchflow import Pipeline, V\n", "import tensorflow as tf\n", "\n", "with Pipeline() as p:\n", " train_pipeline = (p.init_model(\"dynamic\", ConvModel, name=\"conv_model\", config=model_config)\n", " +\n", " p.init_variable(\"loss_history\", init_on_each_run=list)\n", " +\n", " preprocess_pipeline\n", " +\n", " p.train_model('conv_model', make_data=make_data, fetches=\"loss\",\n", " save_to=V(\"loss_history\"), mode=\"a\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run ```train_pipeline```:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [] } ], "source": [ "model_trained = (afds.train >> train_pipeline).run(batch_size=256, n_epochs=100, shuffle=True, drop_last=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now ```model_trained``` contains trained model and loss history. Method ```get_variable``` allows to get ```loss_history``` and we can plot it:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.plot(model_trained.get_variable(\"loss_history\"))\n", "plt.xlabel(\"Iteration\")\n", "plt.ylabel(\"Training loss\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Make predictions\n", "\n", "For predictions we create ```predict_pipeline```. It differs from ```train_pipeline``` in several things. First, we import trained model instead of initializing a new one. Then we do not process signal labels and apply ```predict_model``` instead of ```train_model``` action." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "predict_pipeline = (bf.Pipeline()\n", " .import_model(\"conv_model\", model_trained)\n", " .init_variable(\"predictions_list\", init_on_each_run=list)\n", " .load(fmt=\"wfdb\", components=[\"signal\", \"meta\"])\n", " .random_split_signals(3000, 1)\n", " .apply_transform(func=np.transpose, src='signal', dst='signal', axes=[0, 2, 1])\n", " .predict_model('conv_model', make_data=make_data,\n", " fetches='proba', save_to=V(\"predictions_list\"), mode=\"e\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run ```predict_pipeline``` on the test part of the dataset:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "res = (afds.test >> predict_pipeline).run(batch_size=len(afds.test), n_epochs=1, shuffle=False, drop_last=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Consider predicted values and true labels. Predictions are stored in ```predictions_list``` and we get them through ```get_variable``` method:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "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", "
AF probTrue label
A000010.00NO
A000020.00NO
A000030.00NO
A000041.00A
A000050.08A
A000060.00NO
A000070.00NO
A000080.00NO
A000090.93A
A000100.00NO
\n", "
" ], "text/plain": [ " AF prob True label\n", "A00001 0.00 NO\n", "A00002 0.00 NO\n", "A00003 0.00 NO\n", "A00004 1.00 A\n", "A00005 0.08 A\n", "A00006 0.00 NO\n", "A00007 0.00 NO\n", "A00008 0.00 NO\n", "A00009 0.93 A\n", "A00010 0.00 NO" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "pd.options.display.float_format = '{:.2f}'.format\n", "\n", "pred_proba = pd.DataFrame([x[0] for x in res.get_variable(\"predictions_list\")], \n", " index=afds.test.indices, columns=['AF prob'])\n", "\n", "true_labels = (pd.read_csv(AF_SIGNALS_REF, index_col=0, names=['True label'])\n", " .replace(['N', 'O', '~'], 'NO'))\n", "\n", "df = pd.merge(pred_proba, true_labels, how='left', left_index=True, right_index=True)\n", "df.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print some classification metrics to quantify the quality of predictions:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " NO 0.96 0.99 0.97 1533\n", " A 0.84 0.66 0.74 173\n", "\n", "avg / total 0.95 0.95 0.95 1706\n", "\n" ] } ], "source": [ "from sklearn.metrics import classification_report\n", "\n", "print(classification_report((df['True label'] == 'A').astype(int),\n", " (df['AF prob'].astype(float) >= 0.5).astype(int),\n", " target_names = ['NO', 'A']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We observe that our simple ```ConvModel``` is too simple to detect atrial fibrillation reasonably well. Try to increase model depth and number of filters and see how it improves the classification." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Neural network with Tensorflow\n", "\n", "![](https://www.embedded-vision.com/sites/default/files/news/TensorFlowLogo.png?1495115697)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's write convolution block with TensorFlow. It may look as follows:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "\n", "def tf_conv_block(input_layer, nb_filters, is_training):\n", " conv = tf.layers.conv1d(input_layer, nb_filters, 4)\n", " bnorm = tf.layers.batch_normalization(conv, training=is_training, momentum=0.9)\n", " relu = tf.nn.relu(bnorm)\n", " maxp = tf.layers.max_pooling1d(relu, 2, 2)\n", " return maxp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define model\n", "\n", "In general, model is a complex object that includes model architecture, train/predict methods and some others features. However, if you are within TensorFlow, most of features are implemented in class [```TFModel```](https://analysiscenter.github.io/batchflow/intro/tf_models.html#configurationl) from ```batchflow.models.tf```. So, to build a custom model we define a class ```TFConvModel``` that extends ```TFModel``` and specify model architecture within ```_build``` method. Note that we do not hardcode input/output shapes and sequence of filter sizes, instead we will obtain these parameters from pipeline." ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "import sys, os\n", "sys.path.append(os.path.join(\"..\"))\n", "import cardio.batchflow as bf\n", "\n", "from cardio.batchflow.models.tf import TFModel\n", "\n", "class TFConvModel(TFModel):\n", " def _build(self, config=None):\n", " with self.graph.as_default():\n", " x = tf.placeholder(\"float\", (None,) + self.config['input_shape'])\n", " self.store_to_attr(\"x\", x)\n", " y = tf.placeholder(\"float\", (None, self.config['n_classes']))\n", " self.store_to_attr(\"y\", y)\n", " conv = x\n", " for nb_filters in self.config['filters']:\n", " conv = tf_conv_block(conv, nb_filters, self.is_training)\n", " flat = tf.reduce_max(conv, axis=1)\n", " logits = tf.layers.dense(flat, self.config['n_classes'])\n", " output = tf.nn.softmax(logits)\n", " self.store_to_attr(\"output\", output)\n", " if self.config['loss'] == 'binary_crossentropy':\n", " loss = tf.losses.softmax_cross_entropy(onehot_labels=y, logits=logits)\n", " else:\n", " raise KeyError('loss {0} is not implemented'.format(self.config['loss']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Configure model\n", "\n", "```TFConvModel``` is a dynamic model and will receive configuration parameters from pipeline. We collect required configuration parameters in dict with name ```model_config``` that will be passed to pipeline. Note how we set ```input_shape```. Since batches will appear only when pipeline will be run, a specific substitution rule is required to obtain batch shape. This rule is given by named expression ```F```, which accepts ```batch``` and returns shape of batch items. Read more about named expressions in [documentation](https://analysiscenter.github.io/batchflow/intro/named_expr.html)." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "from cardio.batchflow import F\n", "\n", "tf_model_config = {\n", " \"input_shape\": F(lambda batch: batch.signal[0].shape[1:]),\n", " \"n_classes\": 2,\n", " \"loss\": 'binary_crossentropy',\n", " \"optimizer\": \"Adam\",\n", " 'filters': [4, 4, 8, 8, 16, 16, 32, 32]\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next step is function ```make_data```. This function defines what is `x` (features) and what is `y` (targets) for our model. We declare that batch component ```signal``` is `x`, while batch component ```target``` is `y`." ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "def make_signals(batch):\n", " return np.array([segment for signal in batch.signal for segment in signal])\n", "\n", "def make_targets(batch):\n", " n_reps = [signal.shape[0] for signal in batch.signal]\n", " return np.repeat(batch.target, n_reps, axis=0)\n", "\n", "def tf_make_data(batch, **kwagrs):\n", " if any(elem is None for elem in batch.target):\n", " return {\"feed_dict\": {'x': make_signals(batch)}}\n", " else:\n", " return {\"feed_dict\": {'x': make_signals(batch), 'y': make_targets(batch)}}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train pipeline\n", "\n", "Let's combine all together in a single pipeline. In the beginning we initialize model ```TFConvModel``` and indicate configuration parameters. Then we add preprocess pipeline. In the end we add action ```train_model``` and indicate ```make_data``` functions. Output of ```train_model``` (loss on each batch) will be saved to pipeline variable ```loss_history```: " ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "from cardio.batchflow import Pipeline, V\n", "\n", "with Pipeline() as p:\n", " tf_train_pipeline = (p.init_model(\"dynamic\", TFConvModel, name=\"conv_model\", config=tf_model_config)\n", " +\n", " p.init_variable(\"loss_history\", init_on_each_run=list)\n", " +\n", " preprocess_pipeline\n", " +\n", " p.binarize_labels()\n", " +\n", " p.train_model('conv_model', make_data=tf_make_data, fetches=\"loss\",\n", " save_to=V(\"loss_history\"), mode=\"a\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```tf_train_pipeline``` is ready to be run. ```tf_model_trained``` will contain trained model and loss history." ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [] } ], "source": [ "tf_model_trained = (afds.train >> tf_train_pipeline).run(batch_size=256, n_epochs=150, shuffle=True, drop_last=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot loss history:" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.plot(tf_model_trained.get_variable(\"loss_history\")[10:])\n", "plt.xlabel(\"Iteration\")\n", "plt.ylabel(\"Training loss\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Make predictions\n", "\n", "Let's make some predictions. We define ```tf_predict_pipeline``` that consists of several steps. First, we import trained model from ```tf_model_trained``` and define pipeline variable ```predictions_list``` that will store predicted probabilities. Then we load and preprocess signals without targets. Finally, we apply ```predict_model``` action to get predictions." ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "tf_predict_pipeline = (bf.Pipeline()\n", " .import_model(\"conv_model\", tf_model_trained)\n", " .init_variable(\"predictions_list\", init_on_each_run=list)\n", " .load(fmt=\"wfdb\", components=[\"signal\", \"meta\"])\n", " .random_split_signals(3000, 1)\n", " .apply_transform(func=np.transpose, src='signal', dst='signal', axes=[0, 2, 1])\n", " .predict_model('conv_model', make_data=tf_make_data,\n", " fetches='output', save_to=V(\"predictions_list\"), mode=\"e\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run ```tf_predict_pipeline``` on the test part of the dataset:" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "tf_res = (afds.test >> tf_predict_pipeline).run(batch_size=len(afds.test), n_epochs=1, shuffle=False, drop_last=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Consider predicted values and true labels. Predictions are stored in ```predictions_list``` and we get them through ```get_variable``` method:" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "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", "
AF probTrue label
A000010.00NO
A000020.00NO
A000030.00NO
A000040.98A
A000050.32A
A000060.01NO
A000070.02NO
A000080.00NO
A000090.88A
A000100.00NO
\n", "
" ], "text/plain": [ " AF prob True label\n", "A00001 0.00 NO\n", "A00002 0.00 NO\n", "A00003 0.00 NO\n", "A00004 0.98 A\n", "A00005 0.32 A\n", "A00006 0.01 NO\n", "A00007 0.02 NO\n", "A00008 0.00 NO\n", "A00009 0.88 A\n", "A00010 0.00 NO" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "pd.options.display.float_format = '{:.2f}'.format\n", "\n", "pred_proba = pd.DataFrame([x[0] for x in tf_res.get_variable(\"predictions_list\")], \n", " index=afds.test.indices, columns=['AF prob'])\n", "\n", "true_labels = (pd.read_csv(AF_SIGNALS_REF, index_col=0, names=['True label'])\n", " .replace(['N', 'O', '~'], 'NO'))\n", "\n", "df = pd.merge(pred_proba, true_labels, how='left', left_index=True, right_index=True)\n", "df.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print some classification metrics to quantify the quality of predictions:" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " NO 0.97 0.97 0.97 1533\n", " A 0.72 0.77 0.75 173\n", "\n", "avg / total 0.95 0.95 0.95 1706\n", "\n" ] } ], "source": [ "from sklearn.metrics import classification_report\n", "\n", "print(classification_report((df['True label'] == 'A').astype(int),\n", " (df['AF prob'].astype(float) >= 0.5).astype(int),\n", " target_names = ['NO', 'A']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We observe that our simple ```TFConvModel``` is too simple to detect atrial fibrillation reasonably well. Try to increase model depth and number of filters and see how it improves the classification." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Neural network with Keras\n", "\n", "![](https://devblogs.nvidia.com/parallelforall/wp-content/uploads/2017/08/Keras_Logo_358x230.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's define convolutional block from Keras layers:" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "from keras.layers import Conv1D, MaxPooling1D, \\\n", " BatchNormalization, Activation\n", "\n", "def keras_conv_block(input_layer, nb_filters):\n", " conv = Conv1D(nb_filters, 4)(input_layer)\n", " bnorm = BatchNormalization(momentum=0.9)(conv)\n", " relu = Activation('relu')(bnorm)\n", " output = MaxPooling1D()(relu)\n", " return output " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define model architecture\n", "\n", "In general, model is a complex object that includes model architecture, train/predict methods and some others features. However, if you are within Keras, most of features are implemented in class ```KerasModel``` from ``batchflow.models.keras``. So, to build a custom model we define a class ```KerasConvModel``` that extends class ```KerasModel``` and set model architecture within ```_build``` method. Note that ```_build``` is expected to return the first and the last layer of the model. Note also the we do not hardcode input shape and sequence of filter sizes, instead we will obtain these parameters from pipeline." ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "from cardio.batchflow.models.keras import KerasModel\n", "from keras.layers import Input, Flatten, Dense, GlobalMaxPooling1D\n", "\n", "class KerasConvModel(KerasModel):\n", " def _build(self, **kwargs):\n", " x = Input(kwargs['input_shape'])\n", " conv = x\n", " for nb_filters in kwargs['filters']:\n", " conv = keras_conv_block(conv, nb_filters)\n", " flat = GlobalMaxPooling1D()(conv)\n", " output = Dense(2, activation='softmax')(flat)\n", " return x, output" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Configure model\n", "\n", "```KerasConvModel``` is a dynamic model and will receive configuration parameters from pipeline. We collect required configuration parameters in dict with name ```model_config``` that will be passed to pipeline. Note how we set ```input_shape```. Since batches will appear only when pipeline will be run, a specific substitution rule is required to obtain batch shape. This rule is given by named expression ```F```, which accepts ```batch``` and returns shape of batch items. Read more about named expressions in [documentation](https://analysiscenter.github.io/batchflow/intro/named_expr.html)." ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "from cardio.batchflow import F\n", "\n", "model_config = {\n", " \"input_shape\": F(lambda batch: batch.signal[0].shape[1:]),\n", " \"loss\": \"binary_crossentropy\",\n", " \"optimizer\": \"adam\",\n", " 'filters': [4, 4, 8, 8, 16, 16, 32, 32]\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Function ```make_data``` defines what is `x` (features) and what is `y` (tragets) for our model. We declare that batch component ```signal``` is `x`, while batch component ```target``` is `y`." ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "def make_signals(batch):\n", " return np.array([segment for signal in batch.signal for segment in signal])\n", "\n", "def make_targets(batch):\n", " n_reps = [signal.shape[0] for signal in batch.signal]\n", " return np.repeat(batch.target, n_reps, axis=0)\n", "\n", "def keras_make_data(batch, **kwagrs):\n", " if any(elem is None for elem in batch.target):\n", " return {'x': make_signals(batch)}\n", " else:\n", " return {'x': make_signals(batch), 'y': make_targets(batch)}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train pipeline\n", "\n", "Combine all together in a single train pipeline. In the beginning we initialize model ```KerasConvModel``` and indicate configuration parameters. Then we add preprocess pipeline. In the end we add action ```train_model``` and indicate ```make_data``` functions. Output of ```train_model``` (loss on each batch) will be saved to pipeline variable ```loss_history```: " ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "from cardio.batchflow import Pipeline, V\n", "\n", "with Pipeline() as p:\n", " keras_train_pipeline = (p.init_model(\"dynamic\", KerasConvModel, name=\"conv_model\", config=model_config)\n", " +\n", " p.init_variable(\"loss_history\", init_on_each_run=list)\n", " +\n", " preprocess_pipeline\n", " +\n", " p.binarize_labels()\n", " +\n", " p.train_model('conv_model', make_data=keras_make_data, \n", " save_to=V(\"loss_history\"), mode=\"a\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pipeline for training Keras model is ready. Run it:" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [] } ], "source": [ "keras_model_trained = (afds.train >> keras_train_pipeline).run(batch_size=256, n_epochs=150, shuffle=True, drop_last=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot loss history:" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.plot(keras_model_trained.get_variable(\"loss_history\"))\n", "plt.xlabel(\"Iteration\")\n", "plt.ylabel(\"Training loss\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Make predictions\n", "\n", "Making prediction is the same as making prediction with TensorFlow model. We define ```keras_predict_pipeline``` that differs from ```keras_train_pipeline``` in two things. First, we import trained model from ```keras_model_trained instead``` of initializing a new model. Second, train_model action is replaced with predict_model action." ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "keras_predict_pipeline = (bf.Pipeline()\n", " .import_model(\"conv_model\", keras_model_trained)\n", " .init_variable(\"predictions_list\", init_on_each_run=list)\n", " .load(fmt=\"wfdb\", components=[\"signal\", \"meta\"])\n", " .random_split_signals(3000, 1)\n", " .apply_transform(func=np.transpose, src='signal', dst='signal', axes=[0, 2, 1])\n", " .predict_model('conv_model', make_data=keras_make_data,\n", " save_to=V(\"predictions_list\"), mode=\"e\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run ```keras_predict_pipeline``` on the test part of the dataset:" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "keras_res = (afds.test >> keras_predict_pipeline).run(batch_size=len(afds.test), n_epochs=1,\n", " shuffle=False, drop_last=False)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Consider predicted values and true labels. Predictions are stored in ```prediction_list``` and we get them through ```get_variable``` method:" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "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", "
AF probTrue label
A000010.00NO
A000020.00NO
A000030.00NO
A000041.00A
A000050.79A
A000060.00NO
A000070.01NO
A000080.00NO
A000091.00A
A000100.00NO
\n", "
" ], "text/plain": [ " AF prob True label\n", "A00001 0.00 NO\n", "A00002 0.00 NO\n", "A00003 0.00 NO\n", "A00004 1.00 A\n", "A00005 0.79 A\n", "A00006 0.00 NO\n", "A00007 0.01 NO\n", "A00008 0.00 NO\n", "A00009 1.00 A\n", "A00010 0.00 NO" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "pd.options.display.float_format = '{:.2f}'.format\n", "\n", "pred_proba = pd.DataFrame([x[0] for x in keras_res.get_variable(\"predictions_list\")], \n", " index=afds.test.indices, columns=['AF prob'])\n", "\n", "true_labels = (pd.read_csv(AF_SIGNALS_REF, index_col=0, names=['True label'])\n", " .replace(['N', 'O', '~'], 'NO'))\n", "\n", "df = pd.merge(pred_proba, true_labels, how='left', left_index=True, right_index=True)\n", "df.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print some classification metrics to quantify the quality of predictions:" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " NO 0.98 0.96 0.97 1533\n", " A 0.71 0.79 0.75 173\n", "\n", "avg / total 0.95 0.95 0.95 1706\n", "\n" ] } ], "source": [ "from sklearn.metrics import classification_report\n", "\n", "print(classification_report((df['True label'] == 'A').astype(int),\n", " (df['AF prob'].astype(float) >= 0.5).astype(int),\n", " target_names = ['NO', 'A']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We observe that our simple ```ConvModel``` is too simple to detect atrial fibrillation reasonably well. Try to increase model depth and number of filters and see how it improves the classification." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# General models\n", "\n", "![](https://www.add-for.com/wp-content/uploads/2016/11/frameworks.jpg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With CardIO you can implement arbitrary model, train it within pipeline and make inference. Models can be _static_ or _dynamic_. Static model has all its hyperparameters (e.g. data input shape) hardcoded inside the model and they remain unchanged. In contrast, dynamic model receives its parameters from pipeline and thus parameters can be varied." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As an example we will create a logistic regression model using Numpy and separate signals with atrial fibrillation from other signals on a basis of some features. The example, however, will be very rudimentary, since we consider only 6 signals (2 of them have atrial fibrillation) and features are the mean and standard deviation of signal.\n", "\n", "The model will be _dynamic_ and will accept input data of arbitrary shape. So you can generate more signal features and use the same model without any changes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Consider ```gen_features``` function that gets batch of signals as argument and returns computed features (normalized within the batch):" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "def gen_features(x, *args, **kwargs):\n", " _ = args, kwargs\n", " features = np.array([[np.mean(s), np.std(s)] for s in x])\n", " features -= np.mean(features, axis=0) \n", " features /= np.linalg.norm(features, axis=0)\n", " return features" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define model architecture\n", "\n", "A model of logistic regression consists of weights array, train and inference methods. To incorporate custom model into pipeline it should inherit ```BaseModel``` class and its API. So we define a class ```LogisticModel``` that extends ```BaseModel``` and overwrite dummy methods ```build```, ```train``` and ```predict``` from ```BaseModel```. See the realization below. Note that size of weights array and learning rate are configuration parameters of the model and will be passed from pipeline later." ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [], "source": [ "from cardio.batchflow.models import BaseModel\n", "\n", "class LogisticModel(BaseModel):\n", " def __init__(self, *args, **kwargs):\n", " self.weights = np.array([])\n", " self.learning_rate = 0.01\n", " super().__init__(*args, **kwargs)\n", " \n", " def build(self, *args, **kwargs):\n", " self.weights = np.random.rand(self.config['input_shape'])\n", " if 'learning_rate' in self.config:\n", " self.learning_rate = self.config['learning_rate']\n", " \n", " @staticmethod\n", " def sigmoid(x):\n", " return 1. / (1. + np.exp(-x))\n", " \n", " @staticmethod\n", " def sign(x):\n", " sign = np.ones(len(x))\n", " return -1. if x < 0. else 1.\n", " \n", " def train(self, x, y, *args, **kwargs):\n", " logits = np.dot(x, self.weights).reshape((-1, 1))\n", " sign = np.sign(logits)\n", " gradient = np.mean(sign * x * (self.sigmoid(abs(logits)) - sign * y.reshape((-1, 1)) - 0.5 + 0.5 * sign), axis=0)\n", " self.weights -= self.learning_rate * gradient\n", " loss = np.mean(np.maximum(logits, 0) - logits * y.reshape((-1, 1)) + np.log(1 + np.exp(-abs(logits))))\n", " return loss\n", " \n", " def predict(self, x, *args, **kwargs):\n", " scores = np.dot(x, self.weights)\n", " return self.sigmoid(scores)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Configure model\n", "\n", "A size of weights array (which is equal to a number of features for regression + interception term) is a hyperparameter of the ```LogisticModel```. In ```model_config``` we specify that it will be computed directly from batch shape. Thus we can apply the same model to arbitrary number of features." ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "from cardio.batchflow import F\n", "\n", "model_config = {\n", " \"input_shape\": F(lambda batch: batch.signal[0].size + 1),\n", " \"learning_rate\": 1.\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Function ```make_data``` declares what is ```x``` and what is ```y``` for regression. Note that we will use the same ```make_data``` for training and inference and that for inference we have no targets:" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [], "source": [ "def make_data(batch, **kwagrs):\n", " if any(elem is None for elem in batch.target):\n", " return {'x': np.concatenate([np.array(list(batch.signal)), np.ones((len(batch), 1))], axis=1)}\n", " else:\n", " return {'x': np.concatenate([np.array(list(batch.signal)), np.ones((len(batch), 1))], axis=1),\n", " 'y': np.array(list(batch.target))[:, 0]}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train model\n", "\n", "The train pipeline consists of actions that\n", "* initialize ```LogisticModel```\n", "* initialize variable ```loss_history``` that will store loss\n", "* load and preprocess signals\n", "* generate signal features\n", "* train model on batch.\n", "\n", "Below we define ```log_train_pipeline```. Note that it also saves batch to the pipeline variable ```batch```. We will use it later for visualization of features and separating hyperplane." ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import cardio.batchflow as bf\n", "from cardio.batchflow import Pipeline, V, B\n", "\n", "log_train_pipeline = (bf.Pipeline()\n", " .init_model(\"dynamic\", LogisticModel, name=\"log_model\", config=model_config)\n", " .init_variable(\"loss_history\", init_on_each_run=list)\n", " .init_variable(\"batch\", init_on_each_run=list)\n", " .load(fmt=\"wfdb\", components=[\"signal\", \"meta\"])\n", " .load(src=\"../cardio/tests/data/REFERENCE.csv\", fmt=\"csv\", components=\"target\")\n", " .drop_labels([\"~\"])\n", " .rename_labels({\"N\": \"NO\", \"O\": \"NO\"})\n", " .binarize_labels()\n", " .apply_transform_all(func=gen_features, src='signal', dst='signal')\n", " .update_variable('batch', B(), mode='w')\n", " .train_model('log_model', make_data=make_data,\n", " save_to=V(\"loss_history\"), mode=\"a\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run ```log_train_pipeline```:" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "eds = EcgDataset(path=\"../cardio/tests/data/A*.hea\", no_ext=True, sort=True)\n", "log_model_trained = (eds >> log_train_pipeline).run(batch_size=6, n_epochs=100, shuffle=True, drop_last=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now the trained model is stored in ```log_model_trained```. Let's plot the history of training loss:" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.plot(log_model_trained.get_variable(\"loss_history\"))\n", "plt.xlabel(\"Iteration\")\n", "plt.ylabel(\"Training loss\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the loss decreases to zero we can hope that the model separates successfully both classes. Let's plot the signal features and the separating hyperplane. Red color corresponds to signals with atrial fibrillation." ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAEKCAYAAAA1qaOTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njDHGBIQVFGOMMQHx/19Gm/Yu0KNbAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "batch = log_model_trained.get_variable(\"batch\")\n", "signal, target = batch.signal, batch.target\n", "colors = np.array(['b' if t == 0 else 'r' for t in target[:, 0]])\n", "weights = log_model_trained.get_model_by_name(\"log_model\").weights\n", "\n", "order = np.argsort(signal[:, 0])\n", "x = signal[order, 0]\n", "y = signal[order, 1]\n", "\n", "plt.scatter(x, y, c=colors[order])\n", "plt.plot(x, -(x * weights[0] + weights[2]) / weights[1], c='black')\n", "\n", "plt.axes().fill_between(x, -(x * weights[0] + weights[2]) / weights[1], 1.2, color='r', alpha=0.3)\n", "plt.axes().fill_between(x, -0.6, -(x * weights[0] + weights[2]) / weights[1], color='b', alpha=0.3)\n", "plt.xlabel(\"Normalized mean\")\n", "plt.ylabel(\"Normalized std\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We observe that two classes are separated by the model indeed. Hovewer, it makes no sence to rely on these features for practical use. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Make prediction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For inference we create an inference pipeline. It is a bit shorter in comparison to the train pipeline. We import trained model instead of initializing a new one, load and preprocess signals, generate features and make prediction on batch:" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "log_predict_pipeline = (bf.Pipeline()\n", " .import_model(\"log_model\", log_model_trained)\n", " .init_variable(\"predictions_list\", init_on_each_run=list)\n", " .load(fmt=\"wfdb\", components=[\"signal\", \"meta\"])\n", " .apply_transform_all(func=gen_features, src='signal', dst='signal')\n", " .predict_model('log_model', make_data=make_data,\n", " save_to=V(\"predictions_list\"), mode=\"e\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run inference. We exploit the same dataset as for training, so this step is for illustration only:" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "log_res = (eds >> log_predict_pipeline).run(batch_size=len(eds.indices),\n", " n_epochs=1, shuffle=False, drop_last=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Consider predicted values. They are stored in ```predictions_list``` and we get them through ```get_variable``` method:" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "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", "
AF probTrue label
A000010.04NO
A000020.10NO
A000040.93A
A000050.66A
A000080.16NO
A000130.11NO
\n", "
" ], "text/plain": [ " AF prob True label\n", "A00001 0.04 NO\n", "A00002 0.10 NO\n", "A00004 0.93 A\n", "A00005 0.66 A\n", "A00008 0.16 NO\n", "A00013 0.11 NO" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.options.display.float_format = '{:.2f}'.format\n", "\n", "pred_proba = pd.DataFrame([x for x in log_res.get_variable(\"predictions_list\")], \n", " index=eds.indices, columns=['AF prob'])\n", "\n", "true_labels = (pd.read_csv('../cardio/tests/data/REFERENCE.csv', index_col=0, names=['True label'])\n", " .replace(['N', 'O', '~'], 'NO'))\n", "\n", "df = pd.merge(pred_proba, true_labels, how='left', left_index=True, right_index=True)\n", "df.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The point, however, is that regression models substantially rely on custom generated features. It is incredibly difficult to find an appropriate set of features for more or less reliable detection of heart diseases. In contrast, neural networks are able to generate much more complex features that highlight arrhythmia and other diseases effectively." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Summary\n", "\n", "![](https://thumbs.dreamstime.com/t/elecktrocardiogram-ecg-graph-pulse-isolated-white-76413865.jpg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "This is the end of the Notebook 3. Let's summarize what we did:\n", "* we trained built-in CardIO model to isolate fiducial points within ECG signals and detect atrial fibrillation\n", "* built simple convolutional neural network using\n", " * layers and blocks from ``batchflow.models``\n", " * TensorFlow\n", " * Keras\n", "* built logistic regression model and trained it within pipeline.\n", "\n", "Now you have a full range of possibilities to start you own research in ECG.\n", "\n", "See previous [Notebook 1](https://github.com/analysiscenter/cardio/blob/master/tutorials/I.CardIO.ipynb) and [Notebook 2](https://github.com/analysiscenter/cardio/blob/master/tutorials/II.Pipelines.ipynb) on actions and pipelines if you need to refresh these topics.\n", "\n", "In the next [Notebook 4](https://github.com/analysiscenter/cardio/blob/master/tutorials/IV.Research.ipynb) we will run multiple experiments with ``Research`` module of ``BatchFlow``." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: tutorials/IV.Research.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Deep research of electrocardiograms" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In previous tutorials you have learned about batches, pipelines and models. All those concepts are essential components of deep learning research, and CardIO provides convenient implementations of them for all users.\n", "But in order to perform real research, you need to conduct well-described, reproducible experiments and keep track on the results.\n", "\n", "For this task `research` module of `batchflow` comes in handy. In this tutorial we will use `research` to train and test multiple variations of ResNet architecture in the task of atrial fibrillation classification.\n", "\n", "In this tutorial we will cover part of the functionality of `research`, but we strongly recommend to go through [BatchFlow's Research tutorial](https://github.com/analysiscenter/batchflow/blob/master/examples/tutorials/08_research.ipynb) and to take a look at [documentation for Research](https://analysiscenter.github.io/batchflow/intro/research.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Table of contents\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* [Experimental design](#Experimetal-design)\n", " * [Describing model variations](#Describing-model-variations)\n", " * [Creating dataset](#Creating-dataset)\n", " * [Setting up model configuration](#Setting-up-model-configuration)\n", " * [Setting up training parameters](#Setting-up-training-parameters)\n", " * [Defining pipelines](#Defining-pipelines)\n", "* [Running the experiment](#Running-the-experiment)\n", "* [Summary](#Summary)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experimental design" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When you are starting an experiment, the very first thing you should do is to write down the experimental design. Clear and explicit design of experiment makes it easier to reproduce the results, because it helps to make sure that all important conditions are accounted.\n", "\n", "We will now follow this rule and step by step generate experimental desing, which later will be used as configuration for our experiments." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Describing model variations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we shall define which variations of ResNet architecture we are going to test.\n", "`ResNet` class from `batchflow.models` has following configuration parameters:\n", "* number of filters after first convolution\n", "* layout in ResNet blocks\n", "* number of filters in ResNet blocks' convolutions\n", "* number of ResNet blocks between poolings\n", "\n", "We are going to tweak all of them! And, we suppose that you are already familiar with models from `batchflow.models`.\n", "\n", "\n", "Along with custom architectures, let's try some classics, like `ResNet18` and `ResNet34`. To do this, we define \"model\" option of our experiment:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sys\n", "\n", "sys.path.append(\"..\")\n", "\n", "from cardio.batchflow.models.tf import ResNet, ResNet18, ResNet34\n", "from cardio.batchflow.research import Option, Grid, Research\n", "\n", "model_op1 = Option('model', [ResNet18, ResNet34])\n", "model_op2 = Option('model', [ResNet])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, we have defined custom and classic ResNets in different options. Have some patience, in a few moments we will explain this move.\n", "\n", "Next, let's define options for input filters:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "input_filters_op1 = Option('input_filters', [64])\n", "input_filters_op2 = Option('input_filters', [32, 8])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now just follow the same procedure for the rest of the parameters:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "layout_op1 = Option('layout', ['cnacna'])\n", "layout_op2 = Option('layout', ['cna', 'cnacna'])\n", "\n", "blocks_op1= Option('blocks', [[2, 2, 2, 2], [3, 4, 6, 3]])\n", "blocks_op2 = Option('blocks', [[2, 3, 4, 5, 4, 3, 2], [2, 2, 2, 2, 2, 2, 2],\n", " [1, 1, 1, 1, 1, 1, 1]])\n", "\n", "filters_op1 = Option('filters', [[64, 128, 256, 512], [64, 128, 256, 512]])\n", "filters_op2 = Option('filters', [[4, 8, 16, 32, 64, 128, 256], [4, 4, 8, 8, 16, 16, 20]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is just the time to explain why we define two options for each parameter.\n", "\n", "We have defined options for different parameters, but what are we going to do with them now? We will use them to generate a single grid of parameters, that will include all the combinations we want to test. \n", "\n", "Here, take a look at this simple example to get the intuition about grid generation:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Grid([[{'p1': ['1', '2']}, {'p2': ['v1', 'v2']}], [{'p1': ['4']}, {'p2': ['v3']}]])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "op1 = Option('p1', [1, 2])\n", "op2 = Option('p2', ['v1', 'v2'])\n", "op3 = Option('p1', [4])\n", "op4 = Option('p2', ['v3'])\n", "\n", "grid = (op1 * op2 + op3 * op4)\n", "\n", "grid" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can perform multipliction and addition of options. But, as far as `Grid` object is not very intuitive, we can generate all configurations from this grid and take a look at them:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[ConfigAlias({'p1': '1', 'p2': 'v1'}),\n", " ConfigAlias({'p1': '1', 'p2': 'v2'}),\n", " ConfigAlias({'p1': '2', 'p2': 'v1'}),\n", " ConfigAlias({'p1': '2', 'p2': 'v2'}),\n", " ConfigAlias({'p1': '4', 'p2': 'v3'})]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(grid.gen_configs())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This form is much simpler to understand! \n", "\n", "Also, there is a method `Option.product` that implements element-wise multiplication - see an example below." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[ConfigAlias({'p1': '1', 'p2': 'v1'}), ConfigAlias({'p1': '2', 'p2': 'v2'})]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid = Option.product(op1, op2)\n", " \n", "list(grid.gen_configs())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we are ready to generate the grid for our experiment:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "grid = (Option.product(model_op1, blocks_op1, filters_op1) * layout_op1 * input_filters_op1 + \n", " model_op2 * layout_op2 * input_filters_op2 * blocks_op2 * filters_op2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great, we're done with model variations. Now we shall move to the next part - the data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we just follow familiar procedure: setting up paths to the signals and the labels, then creating dataset instance and splitting data into train and test subsets." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "import os\n", "from cardio import batchflow as bf\n", "from cardio import EcgDataset\n", "\n", "PATH = \"/notebooks/data/ECG/training2017\" # Change this path for your data dicrectory\n", "SIGNALS_MASK = os.path.join(PATH, \"A*.hea\")\n", "LABELS_PATH = os.path.join(PATH, \"REFERENCE.csv\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "eds = EcgDataset(path=SIGNALS_MASK, no_ext=True, sort=True)\n", "eds.split(0.8, shuffle=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setting up model configuration" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, here comes the interesting part! When we define `model_config`, we use `C('parameter_name')` for all the parameters we want to vary:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "from cardio.batchflow import F, B, C, V\n", "\n", "model_config = {\n", " 'inputs': dict(signals={'shape': F(lambda batch: batch.signal[0].shape[1:])},\n", " labels={'classes': ['A', 'NO'], 'transform': 'ohe', 'name': 'targets'}),\n", " 'initial_block/inputs': 'signals',\n", " \"loss\": \"ce\",\n", " \"input_block/filters\": C('input_filters'),\n", " \"body/block/layout\": C('layout'),\n", " \"body/filters\": C('filters'),\n", " \"body/num_blocks\": C('blocks'),\n", " \"session/config\": tf.ConfigProto(allow_soft_placement=True),\n", " \"device\": C(\"device\"),\n", " \"optimizer\": \"Adam\",\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Corresponding values from configuration will be inserted instead of those templates." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setting up training parameters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This part is pretty short and simple - just setting up batch size and number of epochs." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "BATCH_SIZE = 32\n", "EPOCHS = 300" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Defining pipelines" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At first we define root pipelines, wich contain only signal processing. Also, we will define a function to flip signals whose R peaks are turned down." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from numba import njit\n", "\n", "@njit(nogil=True)\n", "def center_flip(signal):\n", " \"\"\"\n", " Center signal and flip signals with R peaks turned down.\n", " \"\"\"\n", " return np.random.choice(np.array([1, -1])) * (signal - np.mean(signal))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "root_train = (\n", " bf.Pipeline()\n", " .load(components=[\"signal\", \"meta\"], fmt=\"wfdb\")\n", " .load(components=\"target\", fmt=\"csv\", src=LABELS_PATH)\n", " .drop_labels([\"~\"])\n", " .rename_labels({\"N\": \"NO\", \"O\": \"NO\"})\n", " .apply_to_each_channel(center_flip)\n", " .random_resample_signals(\"normal\", loc=300, scale=10)\n", " .random_split_signals(3000, {\"A\": 6, \"NO\": 2})\n", " .apply_transform(func=np.transpose, src='signal', dst='signal', axes=[0, 2, 1])\n", ").run(BATCH_SIZE, shuffle=True, drop_last=True, n_epochs=None, lazy=True)\n", "\n", "root_test = (\n", " bf.Pipeline()\n", " .load(components=[\"signal\", \"meta\"], fmt=\"wfdb\")\n", " .load(components=\"target\", fmt=\"csv\", src=LABELS_PATH)\n", " .drop_labels([\"~\"])\n", " .rename_labels({\"N\": \"NO\", \"O\": \"NO\"})\n", " .apply_to_each_channel(center_flip)\n", " .split_signals(3000, 3000)\n", " .apply_transform(func=np.transpose, src='signal', dst='signal', axes=[0, 2, 1])\n", ").run(BATCH_SIZE, shuffle=True, drop_last=True, n_epochs=1, lazy=True)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we define main pipelines. Training pipeline is pretty simlpe - we just train the model and save loss to pipeline variable.\n", "Again, we need to define simple function - this one prepares data from batch to be fed into a model." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "def make_data(batch, **kwagrs):\n", " \"\"\"\n", " Prepare data from `signal` and `target` component of batch to be fed into network.\n", " Separates each crop as individual signal and makes a corresponding label.\n", " \"\"\"\n", " n_reps = [signal.shape[0] for signal in batch.signal]\n", " signals = np.array([segment for signal in batch.signal for segment in signal])\n", " targets = np.repeat(batch.target, n_reps, axis=0)\n", " return {\"feed_dict\": {'signals': signals, 'labels': targets}}" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "model_train = (\n", " bf.Pipeline()\n", " .init_variable('loss', init_on_each_run=list)\n", " .init_model('dynamic', C('model'), 'model', config=model_config)\n", " .train_model('model',\n", " make_data=make_data,\n", " fetches=[\"loss\"],\n", " save_to=[V(\"loss\")], mode=\"w\")\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Testing pipeline is a bit more complex. At first, we initialize a variable to store metrics and save number of splits for each signal in batch component `splits`. Then, as usual, import model, make predictions and save predictions and targets into the batch components.\n", "\n", "As far as we make predictions for splits, we need to aggrageate them to obtain prediction for the whole signal. To do so, we will use `aggregate_crop_predictions` and update corresponding batch components.\n", "Then, pipeline method `gather_metrics` accumulates targets and predictions from batch and allows to calculate metrics afterwards." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "def aggregate_crops(arr, splits, agg_func, predictions=True, **kwargs):\n", " \"\"\"\n", " Aggregates predictions or labels from separate crops for whole signal.\n", " If predictions is True, applies softmax function before aggreagation.\n", " \"\"\"\n", " if predictions:\n", " arr -= np.max(arr, axis=1, keepdims=True)\n", " arr_exp = np.exp(arr)\n", " arr = (arr_exp / np.sum(arr_exp, axis=1, keepdims=True))\n", " arr = np.split(arr, np.cumsum(splits)[:-1])\n", " return np.array([agg_func(sig[:, 0]) for sig in arr])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "model_test = (\n", " bf.Pipeline()\n", " .init_variable(\"metrics\", init_on_each_run=None)\n", " .apply_transform(src=\"signal\", dst=\"splits\", func=lambda x: [x.shape[0]])\n", " .import_model(\"model\", C(\"import_from\"))\n", " .predict_model(\"model\", make_data=make_data,\n", " fetches=[\"predictions\", \"targets\"], \n", " save_to=[B('predictions'), B(\"targets\")])\n", " .apply_transform_all(func=aggregate_crops, predictions=True,\n", " src='predictions', dst='predictions', \n", " splits=B('splits'), agg_func=np.mean)\n", " .apply_transform_all(func=aggregate_crops, predictions=False, \n", " src='targets', dst='targets',\n", " splits=B('splits'), agg_func=np.max)\n", " .gather_metrics('class', targets=B('targets'), predictions=B('predictions'),\n", " fmt='proba', save_to=V('metrics'), mode='a')\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Running the experiment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before starting the experiment, we have to set how often to run test pipeline. Calculation of this value is not very strainghtforward because it should be stated in a number of iterations, not epochs." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "TEST_EACH_EPOCH = 20\n", "TRAIN_SIZE = len(eds.train)\n", "ITERATIONS = ((TRAIN_SIZE // BATCH_SIZE) + 1) * EPOCHS\n", "TEST_EXEC_FOR = ITERATIONS // EPOCHS * TEST_EACH_EPOCH\n", "STR_EXEC = '%{}'.format(TEST_EXEC_FOR)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Also, we will set a few constants for research:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "N_REPS = 5\n", "N_BRANCHES = 2\n", "N_WORKERS = 3\n", "GPU = [1, 2, 3, 4, 5, 6]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At the end of run of any pipeline we can execute some function. So let's write two functions to save number of trainable parameters and F1 score: " ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "def get_trainable_variables(iteration, experiment, ppl, model_name=\"model\"):\n", " \"\"\"\n", " Returns a number of trainable variables in the model.\n", " \"\"\"\n", " return experiment[ppl].pipeline.get_model_by_name(model_name).get_number_of_trainable_vars()\n", "\n", "\n", "def calc_metrics(iteration, experiment, ppl, var_name):\n", " \"\"\"\n", " Gets variable with metrics class from the pipeline and\n", " calculates F1 score.\n", " \"\"\"\n", " metrics = experiment[ppl].pipeline.get_variable(var_name)\n", " f1 = metrics.evaluate('f1_score')\n", " return f1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, define the whole research pipeline and run it:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "mr = (\n", " Research()\n", " .pipeline(root_train << eds, model_train,\n", " variables=[\"loss\"], name=\"train\", dump=STR_EXEC)\n", " .pipeline(root_test << eds, model_test,\n", " name=\"test\", execute=STR_EXEC, dump=STR_EXEC,\n", " import_from=\"train\", run=True)\n", " .function(calc_metrics, returns='metrics_f1', name='metrics_f1',\n", " execute=STR_EXEC, dump=STR_EXEC, ppl='test', var_name='metrics')\n", " .function(get_trainable_variables, returns='trainable_variables', \n", " name='trainable_variables', execute=1, dump=1, ppl='train')\n", " .grid(grid)\n", ")" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "mr.run(n_reps=N_REPS, n_iters=ITERATIONS, workers=N_WORKERS, gpu=GPU, \n", " branches=N_BRANCHES, name='ResNet_research', progress_bar=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now in folder ResNet_research you can find the results: loss, saved after each iteration, f1 score on the whole test set calculated each 20 epochs and number of trainable variables in each model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Summary" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook we have learned about research and experiments and now you know how to:\n", "* define research grid\n", "* use metrics in the pipeline\n", "* run research experiments" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: tutorials/pn2017_data_to_wfdb_format.py ================================================ """ Simple script to format PhysioNet/CinC 2017 Challenge data according to wfdb standard.""" import os import argparse import re def check_format(line_values): time_fmt = re.compile(r'\d{2}:\d{2}:\d{2}') new_date_fmt = re.compile(r'\d{2}/\d{2}/\d{4}') old_date_fmt = re.compile(r'\d{4}-\d{2}-\d{2}') if ((time_fmt.fullmatch(line_values[4]) is not None) and (new_date_fmt.fullmatch(line_values[5]) is not None)): which_format = 'new' elif ((time_fmt.fullmatch(line_values[5]) is not None) and (old_date_fmt.fullmatch(line_values[4]) is not None)): which_format = 'old' else: which_format = None return which_format def main(): parser = argparse.ArgumentParser(description='Path to the files and mask') parser.add_argument('-p', '--path', help='Full path to PN2017 files') args = parser.parse_args() all_files = os.listdir(args.path) header_files = [file for file in all_files if file.endswith(".hea")] for header in header_files: path = os.path.join(args.path, header) with open(path, 'r') as f: lines = f.readlines() # We have lines[0]: 'A00005 1 300 18000 2013-12-23 08:12:08 \n' line_values = lines[0].split(' ') if len(line_values) == 7: which_format = check_format(line_values) if which_format == 'old': new_order = [0, 1, 2, 3, 5, 4, 6] line_values[4] = '/'.join(line_values[4].split('-')[::-1]) lines[0] = ' '.join([line_values[i] for i in new_order]) # Now we get lines[0]: 'A00005 1 300 18000 08:12:08 23/12/2013 \n' elif which_format != 'new': error_text = ('Unexpected header format in record {}: `{}`'.format(header, lines[0][:-2])) raise ValueError(error_text) elif len(line_values) < 7: # e.g. in case lines[0]: 'A00005 1 300 18000 2013-12-23 \n' new_order = [0, 1, 2, 3, -1] lines[0] = ' '.join([line_values[i] for i in new_order]) # lines[0]: 'A00005 1 300 18000 \n' else: error_text = ('Unexpected header format in record {}: `{}`'.format(header, lines[0][:-2])) raise ValueError(error_text) with open(path, 'w') as f: f.writelines(lines) if __name__ == '__main__': main()